Quantitative evaluation of the risk induced by dominant geomorphological processes on different land uses, based on GIS spatial analysis models

NASA Astrophysics Data System (ADS)

Ştefan, Bilaşco; Sanda, Roşca; Ioan, Fodorean; Iuliu, Vescan; Sorin, Filip; Dănuţ, Petrea

2017-12-01

Maramureş Land is mostly characterized by agricultural and forestry land use due to its specific configuration of topography and its specific pedoclimatic conditions. Taking into consideration the trend of the last century from the perspective of land management, a decrease in the surface of agricultural lands to the advantage of built-up and grass lands, as well as an accelerated decrease in the forest cover due to uncontrolled and irrational forest exploitation, has become obvious. The field analysis performed on the territory of Maramureş Land has highlighted a high frequency of two geomorphologic processes — landslides and soil erosion — which have a major negative impact on land use due to their rate of occurrence. The main aim of the present study is the GIS modeling of the two geomorphologic processes, determining a state of vulnerability (the USLE model for soil erosion and a quantitative model based on the morphometric characteristics of the territory, derived from the HG. 447/2003) and their integration in a complex model of cumulated vulnerability identification. The modeling of the risk exposure was performed using a quantitative approach based on models and equations of spatial analysis, which were developed with modeled raster data structures and primary vector data, through a matrix highlighting the correspondence between vulnerability and land use classes. The quantitative analysis of the risk was performed by taking into consideration the exposure classes as modeled databases and the land price as a primary alphanumeric database using spatial analysis techniques for each class by means of the attribute table. The spatial results highlight the territories with a high risk to present geomorphologic processes that have a high degree of occurrence and represent a useful tool in the process of spatial planning.

Quantitative evaluation of the risk induced by dominant geomorphological processes on different land uses, based on GIS spatial analysis models

NASA Astrophysics Data System (ADS)

Ştefan, Bilaşco; Sanda, Roşca; Ioan, Fodorean; Iuliu, Vescan; Sorin, Filip; Dănuţ, Petrea

2018-06-01

Maramureş Land is mostly characterized by agricultural and forestry land use due to its specific configuration of topography and its specific pedoclimatic conditions. Taking into consideration the trend of the last century from the perspective of land management, a decrease in the surface of agricultural lands to the advantage of built-up and grass lands, as well as an accelerated decrease in the forest cover due to uncontrolled and irrational forest exploitation, has become obvious. The field analysis performed on the territory of Maramureş Land has highlighted a high frequency of two geomorphologic processes — landslides and soil erosion — which have a major negative impact on land use due to their rate of occurrence. The main aim of the present study is the GIS modeling of the two geomorphologic processes, determining a state of vulnerability (the USLE model for soil erosion and a quantitative model based on the morphometric characteristics of the territory, derived from the HG. 447/2003) and their integration in a complex model of cumulated vulnerability identification. The modeling of the risk exposure was performed using a quantitative approach based on models and equations of spatial analysis, which were developed with modeled raster data structures and primary vector data, through a matrix highlighting the correspondence between vulnerability and land use classes. The quantitative analysis of the risk was performed by taking into consideration the exposure classes as modeled databases and the land price as a primary alphanumeric database using spatial analysis techniques for each class by means of the attribute table. The spatial results highlight the territories with a high risk to present geomorphologic processes that have a high degree of occurrence and represent a useful tool in the process of spatial planning.

Model-based synthesis of locally contingent responses to global market signals

NASA Astrophysics Data System (ADS)

Magliocca, N. R.

2015-12-01

Rural livelihoods and the land systems on which they depend are increasingly influenced by distant markets through economic globalization. Place-based analyses of land and livelihood system sustainability must then consider both proximate and distant influences on local decision-making. Thus, advancing land change theory in the context of economic globalization calls for a systematic understanding of the general processes as well as local contingencies shaping local responses to global signals. Synthesis of insights from place-based case studies of land and livelihood change is a path forward for developing such systematic knowledge. This paper introduces a model-based synthesis approach to investigating the influence of local socio-environmental and agent-level factors in mediating land-use and livelihood responses to changing global market signals. A generalized agent-based modeling framework is applied to six case-study sites that differ in environmental conditions, market access and influence, and livelihood settings. The largest modeled land conversions and livelihood transitions to market-oriented production occurred in sties with relatively productive agricultural land and/or with limited livelihood options. Experimental shifts in the distributions of agents' risk tolerances generally acted to attenuate or amplify responses to changes in global market signals. Importantly, however, responses of agents at different points in the risk tolerance distribution varied widely, with the wealth gap growing wider between agents with higher or lower risk tolerance. These results demonstrate model-based synthesis is a promising approach to overcome many of the challenges of current synthesis methods in land change science, and to identify generalized as well as locally contingent responses to global market signals.

The Evaluation of Land Ecological Safety of Chengchao Iron Mine Based on PSR and MEM

NASA Astrophysics Data System (ADS)

Jin, Xiangdong; Chen, Yong

2018-01-01

Land ecological security is of vital importance to local security and sustainable development of mining activities. The study has analyzed the potential causal chains between the land ecological security of Iron Mine mining environment, mine resource and the social-economic background. On the base of Pressure-State-Response model, the paper set up a matter element evaluation model of land ecological security, and applies it in Chengchao iron mine. The evaluation result proves to be effective in land ecological evaluation.

Effects of land use data on dry deposition in a regional photochemical model for eastern Texas.

PubMed

McDonald-Buller, E; Wiedinmyer, C; Kimura, Y; Allen, D

2001-08-01

Land use data are among the inputs used to determine dry deposition velocities for photochemical grid models such as the Comprehensive Air Quality Model with extensions (CAMx) that is currently used for attainment demonstrations and air quality planning by the state of Texas. The sensitivity of dry deposition and O3 mixing ratios to land use classification was investigated by comparing predictions based on default U.S. Geological Survey (USGS) land use data to predictions based on recently compiled land use data that were collected to improve biogenic emissions estimates. Dry deposition of O3 decreased throughout much of eastern Texas, especially in urban areas, with the new land use data. Predicted 1-hr averaged O3 mixing ratios with the new land use data were as much as 11 ppbv greater and 6 ppbv less than predictions based on USGS land use data during the late afternoon. In addition, the area with peak O3 mixing ratios in excess of 100 ppbv increased significantly in urban areas when deposition velocities were calculated based on the new land use data. Finally, more detailed data on land use within urban areas resulted in peak changes in O3 mixing ratios of approximately 2 ppbv. These results indicate the importance of establishing accurate, internally consistent land use data for photochemical modeling in urban areas in Texas. They also indicate the need for field validation of deposition rates in areas experiencing changing land use patterns, such as during urban reforestation programs or residential and commercial development.

An integrated approach to modeling changes in land use, land cover, and disturbance and their impact on ecosystem carbon dynamics: a case study in the Sierra Nevada Mountains of California

USGS Publications Warehouse

Sleeter, Benjamin M.; Liu, Jinxun; Daniel, Colin; Frid, Leonardo; Zhu, Zhiliang

2015-01-01

Increased land-use intensity (e.g. clearing of forests for cultivation, urbanization), often results in the loss of ecosystem carbon storage, while changes in productivity resulting from climate change may either help offset or exacerbate losses. However, there are large uncertainties in how land and climate systems will evolve and interact to shape future ecosystem carbon dynamics. To address this we developed the Land Use and Carbon Scenario Simulator (LUCAS) to track changes in land use, land cover, land management, and disturbance, and their impact on ecosystem carbon storage and flux within a scenario-based framework. We have combined a state-and-transition simulation model (STSM) of land change with a stock and flow model of carbon dynamics. Land-change projections downscaled from the Intergovernmental Panel on Climate Change’s (IPCC) Special Report on Emission Scenarios (SRES) were used to drive changes within the STSM, while the Integrated Biosphere Simulator (IBIS) ecosystem model was used to derive input parameters for the carbon stock and flow model. The model was applied to the Sierra Nevada Mountains ecoregion in California, USA, a region prone to large wildfires and a forestry sector projected to intensify over the next century. Three scenario simulations were conducted, including a calibration scenario, a climate-change scenario, and an integrated climate- and land-change scenario. Based on results from the calibration scenario, the LUCAS age-structured carbon accounting model was able to accurately reproduce results obtained from the process-based biogeochemical model. Under the climate-only scenario, the ecoregion was projected to be a reliable net sink of carbon, however, when land use and disturbance were introduced, the ecoregion switched to become a net source. This research demonstrates how an integrated approach to carbon accounting can be used to evaluate various drivers of ecosystem carbon change in a robust, yet transparent modeling environment.

Use of a land-use-based emissions inventory in delineating clean-air zones

Treesearch

Victor S. Fahrer; Howard A. Peters

1977-01-01

Use of a land-use-based emissions inventory from which air-pollution estimates can be projected was studied. First the methodology used to establish a land-use-based emission inventory is described. Then this inventory is used as input in a simple model that delineates clean air and buffer zones. The model is applied to the town of Burlington, Massachusetts....

Land Data Assimilation of Satellite-Based Soil Moisture Products Using the Land Information System Over the NLDAS Domain

NASA Technical Reports Server (NTRS)

Mocko, David M.; Kumar, S. V.; Peters-Lidard, C. D.; Tian, Y.

2011-01-01

This presentation will include results from data assimilation simulations using the NASA-developed Land Information System (LIS). Using the ensemble Kalman filter in LIS, two satellite-based soil moisture products from the AMSR-E instrument were assimilated, one a NASA-based product and the other from the Land Parameter Retrieval Model (LPRM). The domain and land-surface forcing data from these simulations were from the North American Land Data Assimilation System Phase-2, over the period 2002-2008. The Noah land-surface model, version 3.2, was used during the simulations. Changes to estimates of land surface states, such as soil moisture, as well as changes to simulated runoff/streamflow will be presented. Comparisons over the NLDAS domain will also be made to two global reference evapotranspiration (ET) products, one an interpolated product based on FLUXNET tower data and the other a satellite- based algorithm from the MODIS instrument. Results of an improvement metric show that assimilating the LPRM product improved simulated ET estimates while the NASA-based soil moisture product did not.

Modeling the effect of land use change on hydrology of a forested watershed in coastal South Carolina.

Treesearch

Zhaohua Dai; Devendra M. Amatya; Ge Sun; Changsheng Li; Carl C. Trettin; Harbin Li

2009-01-01

Since hydrology is one of main factors controlling wetland functions, hydrologic models are useful for evaluating the effects of land use change on we land ecosystems. We evaluated two process-based hydrologic models with...

[A review on research of land surface water and heat fluxes].

PubMed

Sun, Rui; Liu, Changming

2003-03-01

Many field experiments were done, and soil-vegetation-atmosphere transfer(SVAT) models were stablished to estimate land surface heat fluxes. In this paper, the processes of experimental research on land surface water and heat fluxes are reviewed, and three kinds of SVAT model(single layer model, two layer model and multi-layer model) are analyzed. Remote sensing data are widely used to estimate land surface heat fluxes. Based on remote sensing and energy balance equation, different models such as simplified model, single layer model, extra resistance model, crop water stress index model and two source resistance model are developed to estimate land surface heat fluxes and evapotranspiration. These models are also analyzed in this paper.

An integrated GIS-based interval-probabilistic programming model for land-use planning management under uncertainty--a case study at Suzhou, China.

PubMed

Lu, Shasha; Zhou, Min; Guan, Xingliang; Tao, Lizao

2015-03-01

A large number of mathematical models have been developed for supporting optimization of land-use allocation; however, few of them simultaneously consider land suitability (e.g., physical features and spatial information) and various uncertainties existing in many factors (e.g., land availabilities, land demands, land-use patterns, and ecological requirements). This paper incorporates geographic information system (GIS) technology into interval-probabilistic programming (IPP) for land-use planning management (IPP-LUPM). GIS is utilized to assemble data for the aggregated land-use alternatives, and IPP is developed for tackling uncertainties presented as discrete intervals and probability distribution. Based on GIS, the suitability maps of different land users are provided by the outcomes of land suitability assessment and spatial analysis. The maximum area of every type of land use obtained from the suitability maps, as well as various objectives/constraints (i.e., land supply, land demand of socioeconomic development, future development strategies, and environmental capacity), is used as input data for the optimization of land-use areas with IPP-LUPM model. The proposed model not only considers the outcomes of land suitability evaluation (i.e., topography, ground conditions, hydrology, and spatial location) but also involves economic factors, food security, and eco-environmental constraints, which can effectively reflect various interrelations among different aspects in a land-use planning management system. The case study results at Suzhou, China, demonstrate that the model can help to examine the reliability of satisfying (or risk of violating) system constraints under uncertainty. Moreover, it may identify the quantitative relationship between land suitability and system benefits. Willingness to arrange the land areas based on the condition of highly suitable land will not only reduce the potential conflicts on the environmental system but also lead to a lower economic benefit. However, a strong desire to develop lower suitable land areas will bring not only a higher economic benefit but also higher risks of violating environmental and ecological constraints. The land manager should make decisions through trade-offs between economic objectives and environmental/ecological objectives.

Incorporating Land-Use Mapping Uncertainty in Remote Sensing Based Calibration of Land-Use Change Models

NASA Astrophysics Data System (ADS)

Cockx, K.; Van de Voorde, T.; Canters, F.; Poelmans, L.; Uljee, I.; Engelen, G.; de Jong, K.; Karssenberg, D.; van der Kwast, J.

2013-05-01

Building urban growth models typically involves a process of historic calibration based on historic time series of land-use maps, usually obtained from satellite imagery. Both the remote sensing data analysis to infer land use and the subsequent modelling of land-use change are subject to uncertainties, which may have an impact on the accuracy of future land-use predictions. Our research aims to quantify and reduce these uncertainties by means of a particle filter data assimilation approach that incorporates uncertainty in land-use mapping and land-use model parameter assessment into the calibration process. This paper focuses on part of this work, more in particular the modelling of uncertainties associated with the impervious surface cover estimation and urban land-use classification adopted in the land-use mapping approach. Both stages are submitted to a Monte Carlo simulation to assess their relative contribution to and their combined impact on the uncertainty in the derived land-use maps. The approach was applied on the central part of the Flanders region (Belgium), using a time-series of Landsat/SPOT-HRV data covering the years 1987, 1996, 2005 and 2012. Although the most likely land-use map obtained from the simulation is very similar to the original classification, it is shown that the errors related to the impervious surface sub-pixel fraction estimation have a strong impact on the land-use map's uncertainty. Hence, incorporating uncertainty in the land-use change model calibration through particle filter data assimilation is proposed to address the uncertainty observed in the derived land-use maps and to reduce uncertainty in future land-use predictions.

A GIS-based hedonic price model for agricultural land

NASA Astrophysics Data System (ADS)

Demetriou, Demetris

2015-06-01

Land consolidation is a very effective land management planning approach that aims towards rural/agricultural sustainable development. Land reallocation which involves land tenure restructuring is the most important, complex and time consuming component of land consolidation. Land reallocation relies on land valuation since its fundamental principle provides that after consolidation, each landowner shall be granted a property of an aggregate value that is approximately the same as the value of the property owned prior to consolidation. Therefore, land value is the crucial factor for the land reallocation process and hence for the success and acceptance of the final land consolidation plan. Land valuation is a process of assigning values to all parcels (and its contents) and it is usually carried out by an ad-hoc committee. However, the process faces some problems such as it is time consuming hence costly, outcomes may present inconsistency since it is carried out manually and empirically without employing systematic analytical tools and in particular spatial analysis tools and techniques such as statistical/mathematical. A solution to these problems can be the employment of mass appraisal land valuation methods using automated valuation models (AVM) based on international standards. In this context, this paper presents a spatial based linear hedonic price model which has been developed and tested in a case study land consolidation area in Cyprus. Results showed that the AVM is capable to produce acceptable in terms of accuracy and reliability land values and to reduce time hence cost required by around 80%.

A Land System representation for global assessments and land-use modeling.

PubMed

van Asselen, Sanneke; Verburg, Peter H

2012-10-01

Current global scale land-change models used for integrated assessments and climate modeling are based on classifications of land cover. However, land-use management intensity and livestock keeping are also important aspects of land use, and are an integrated part of land systems. This article aims to classify, map, and to characterize Land Systems (LS) at a global scale and analyze the spatial determinants of these systems. Besides proposing such a classification, the article tests if global assessments can be based on globally uniform allocation rules. Land cover, livestock, and agricultural intensity data are used to map LS using a hierarchical classification method. Logistic regressions are used to analyze variation in spatial determinants of LS. The analysis of the spatial determinants of LS indicates strong associations between LS and a range of socioeconomic and biophysical indicators of human-environment interactions. The set of identified spatial determinants of a LS differs among regions and scales, especially for (mosaic) cropland systems, grassland systems with livestock, and settlements. (Semi-)Natural LS have more similar spatial determinants across regions and scales. Using LS in global models is expected to result in a more accurate representation of land use capturing important aspects of land systems and land architecture: the variation in land cover and the link between land-use intensity and landscape composition. Because the set of most important spatial determinants of LS varies among regions and scales, land-change models that include the human drivers of land change are best parameterized at sub-global level, where similar biophysical, socioeconomic and cultural conditions prevail in the specific regions. © 2012 Blackwell Publishing Ltd.

Land-use change in oil palm dominated tropical landscapes-An agent-based model to explore ecological and socio-economic trade-offs.

PubMed

Dislich, Claudia; Hettig, Elisabeth; Salecker, Jan; Heinonen, Johannes; Lay, Jann; Meyer, Katrin M; Wiegand, Kerstin; Tarigan, Suria

2018-01-01

Land-use changes have dramatically transformed tropical landscapes. We describe an ecological-economic land-use change model as an integrated, exploratory tool used to analyze how tropical land-use change affects ecological and socio-economic functions. The model analysis seeks to determine what kind of landscape mosaic can improve the ensemble of ecosystem functioning, biodiversity, and economic benefit based on the synergies and trade-offs that we have to account for. More specifically, (1) how do specific ecosystem functions, such as carbon storage, and economic functions, such as household consumption, relate to each other? (2) How do external factors, such as the output prices of crops, affect these relationships? (3) How do these relationships change when production inefficiency differs between smallholder farmers and learning is incorporated? We initialize the ecological-economic model with artificially generated land-use maps parameterized to our study region. The economic sub-model simulates smallholder land-use management decisions based on a profit maximization assumption. Each household determines factor inputs for all household fields and decides on land-use change based on available wealth. The ecological sub-model includes a simple account of carbon sequestration in above-ground and below-ground vegetation. We demonstrate model capabilities with results on household consumption and carbon sequestration from different output price and farming efficiency scenarios. The overall results reveal complex interactions between the economic and ecological spheres. For instance, model scenarios with heterogeneous crop-specific household productivity reveal a comparatively high inertia of land-use change. Our model analysis even shows such an increased temporal stability in landscape composition and carbon stocks of the agricultural area under dynamic price trends. These findings underline the utility of ecological-economic models, such as ours, to act as exploratory tools which can advance our understanding of the mechanisms underlying the trade-offs and synergies of ecological and economic functions in tropical landscapes.

Land-use change in oil palm dominated tropical landscapes—An agent-based model to explore ecological and socio-economic trade-offs

PubMed Central

Dislich, Claudia; Hettig, Elisabeth; Heinonen, Johannes; Lay, Jann; Meyer, Katrin M.; Wiegand, Kerstin; Tarigan, Suria

2018-01-01

Land-use changes have dramatically transformed tropical landscapes. We describe an ecological-economic land-use change model as an integrated, exploratory tool used to analyze how tropical land-use change affects ecological and socio-economic functions. The model analysis seeks to determine what kind of landscape mosaic can improve the ensemble of ecosystem functioning, biodiversity, and economic benefit based on the synergies and trade-offs that we have to account for. More specifically, (1) how do specific ecosystem functions, such as carbon storage, and economic functions, such as household consumption, relate to each other? (2) How do external factors, such as the output prices of crops, affect these relationships? (3) How do these relationships change when production inefficiency differs between smallholder farmers and learning is incorporated? We initialize the ecological-economic model with artificially generated land-use maps parameterized to our study region. The economic sub-model simulates smallholder land-use management decisions based on a profit maximization assumption. Each household determines factor inputs for all household fields and decides on land-use change based on available wealth. The ecological sub-model includes a simple account of carbon sequestration in above-ground and below-ground vegetation. We demonstrate model capabilities with results on household consumption and carbon sequestration from different output price and farming efficiency scenarios. The overall results reveal complex interactions between the economic and ecological spheres. For instance, model scenarios with heterogeneous crop-specific household productivity reveal a comparatively high inertia of land-use change. Our model analysis even shows such an increased temporal stability in landscape composition and carbon stocks of the agricultural area under dynamic price trends. These findings underline the utility of ecological-economic models, such as ours, to act as exploratory tools which can advance our understanding of the mechanisms underlying the trade-offs and synergies of ecological and economic functions in tropical landscapes. PMID:29351290

Comparison of two perturbation methods to estimate the land surface modeling uncertainty

NASA Astrophysics Data System (ADS)

Su, H.; Houser, P.; Tian, Y.; Kumar, S.; Geiger, J.; Belvedere, D.

2007-12-01

In land surface modeling, it is almost impossible to simulate the land surface processes without any error because the earth system is highly complex and the physics of the land processes has not yet been understood sufficiently. In most cases, people want to know not only the model output but also the uncertainty in the modeling, to estimate how reliable the modeling is. Ensemble perturbation is an effective way to estimate the uncertainty in land surface modeling, since land surface models are highly nonlinear which makes the analytical approach not applicable in this estimation. The ideal perturbation noise is zero mean Gaussian distribution, however, this requirement can't be satisfied if the perturbed variables in land surface model have physical boundaries because part of the perturbation noises has to be removed to feed the land surface models properly. Two different perturbation methods are employed in our study to investigate their impact on quantifying land surface modeling uncertainty base on the Land Information System (LIS) framework developed by NASA/GSFC land team. One perturbation method is the built-in algorithm named "STATIC" in LIS version 5; the other is a new perturbation algorithm which was recently developed to minimize the overall bias in the perturbation by incorporating additional information from the whole time series for the perturbed variable. The statistical properties of the perturbation noise generated by the two different algorithms are investigated thoroughly by using a large ensemble size on a NASA supercomputer and then the corresponding uncertainty estimates based on the two perturbation methods are compared. Their further impacts on data assimilation are also discussed. Finally, an optimal perturbation method is suggested.

Modelling Participatory Geographic Information System for Customary Land Conflict Resolution

NASA Astrophysics Data System (ADS)

Gyamera, E. A.; Arko-Adjei, A.; Duncan, E. E.; Kuma, J. S. Y.

2017-11-01

Since land contributes to about 73 % of most countries Gross Domestic Product (GDP), attention on land rights have tremendously increased globally. Conflicts over land have therefore become part of the major problems associated with land administration. However, the conventional mechanisms for land conflict resolution do not provide satisfactory result to disputants due to various factors. This study sought to develop a Framework of using Participatory Geographic Information System (PGIS) for customary land conflict resolution. The framework was modelled using Unified Modelling Language (UML). The PGIS framework, called butterfly model, consists of three units namely, Social Unit (SU), Technical Unit (TU) and Decision Making Unit (DMU). The name butterfly model for land conflict resolution was adopted for the framework based on its features and properties. The framework has therefore been recommended to be adopted for land conflict resolution in customary areas.

Modeling the Land Use/Cover Change in an Arid Region Oasis City Constrained by Water Resource and Environmental Policy Change using Cellular Automata Model

NASA Astrophysics Data System (ADS)

Hu, X.; Li, X.; Lu, L.

2017-12-01

Land use/cover change (LUCC) is an important subject in the research of global environmental change and sustainable development, while spatial simulation on land use/cover change is one of the key content of LUCC and is also difficult due to the complexity of the system. The cellular automata (CA) model had an irreplaceable role in simulating of land use/cover change process due to the powerful spatial computing power. However, the majority of current CA land use/cover models were binary-state model that could not provide more general information about the overall spatial pattern of land use/cover change. Here, a multi-state logistic-regression-based Markov cellular automata (MLRMCA) model and a multi-state artificial-neural-network-based Markov cellular automata (MANNMCA) model were developed and were used to simulate complex land use/cover evolutionary process in an arid region oasis city constrained by water resource and environmental policy change, the Zhangye city during the period of 1990-2010. The results indicated that the MANNMCA model was superior to MLRMCA model in simulated accuracy. These indicated that by combining the artificial neural network with CA could more effectively capture the complex relationships between the land use/cover change and a set of spatial variables. Although the MLRMCA model were also some advantages, the MANNMCA model was more appropriate for simulating complex land use/cover dynamics. The two proposed models were effective and reliable, and could reflect the spatial evolution of regional land use/cover changes. These have also potential implications for the impact assessment of water resources, ecological restoration, and the sustainable urban development in arid areas.

Land cover change or land-use intensification: simulating land system change with a global-scale land change model.

PubMed

van Asselen, Sanneke; Verburg, Peter H

2013-12-01

Land-use change is both a cause and consequence of many biophysical and socioeconomic changes. The CLUMondo model provides an innovative approach for global land-use change modeling to support integrated assessments. Demands for goods and services are, in the model, supplied by a variety of land systems that are characterized by their land cover mosaic, the agricultural management intensity, and livestock. Land system changes are simulated by the model, driven by regional demand for goods and influenced by local factors that either constrain or promote land system conversion. A characteristic of the new model is the endogenous simulation of intensification of agricultural management versus expansion of arable land, and urban versus rural settlements expansion based on land availability in the neighborhood of the location. Model results for the OECD Environmental Outlook scenario show that allocation of increased agricultural production by either management intensification or area expansion varies both among and within world regions, providing useful insight into the land sparing versus land sharing debate. The land system approach allows the inclusion of different types of demand for goods and services from the land system as a driving factor of land system change. Simulation results are compared to observed changes over the 1970-2000 period and projections of other global and regional land change models. © 2013 John Wiley & Sons Ltd.

Modifying a dynamic global vegetation model for simulating large spatial scale land surface water balances

NASA Astrophysics Data System (ADS)

Tang, G.; Bartlein, P. J.

2012-08-01

Satellite-based data, such as vegetation type and fractional vegetation cover, are widely used in hydrologic models to prescribe the vegetation state in a study region. Dynamic global vegetation models (DGVM) simulate land surface hydrology. Incorporation of satellite-based data into a DGVM may enhance a model's ability to simulate land surface hydrology by reducing the task of model parameterization and providing distributed information on land characteristics. The objectives of this study are to (i) modify a DGVM for simulating land surface water balances; (ii) evaluate the modified model in simulating actual evapotranspiration (ET), soil moisture, and surface runoff at regional or watershed scales; and (iii) gain insight into the ability of both the original and modified model to simulate large spatial scale land surface hydrology. To achieve these objectives, we introduce the "LPJ-hydrology" (LH) model which incorporates satellite-based data into the Lund-Potsdam-Jena (LPJ) DGVM. To evaluate the model we ran LH using historical (1981-2006) climate data and satellite-based land covers at 2.5 arc-min grid cells for the conterminous US and for the entire world using coarser climate and land cover data. We evaluated the simulated ET, soil moisture, and surface runoff using a set of observed or simulated data at different spatial scales. Our results demonstrate that spatial patterns of LH-simulated annual ET and surface runoff are in accordance with previously published data for the US; LH-modeled monthly stream flow for 12 major rivers in the US was consistent with observed values respectively during the years 1981-2006 (R2 > 0.46, p < 0.01; Nash-Sutcliffe Coefficient > 0.52). The modeled mean annual discharges for 10 major rivers worldwide also agreed well (differences < 15%) with observed values for these rivers. Compared to a degree-day method for snowmelt computation, the addition of the solar radiation effect on snowmelt enabled LH to better simulate monthly stream flow in winter and early spring for rivers located at mid-to-high latitudes. In addition, LH-modeled monthly soil moisture for the state of Illinois (US) agreed well (R2 = 0.79, p < 0.01) with observed data for the years 1984-2001. Overall, this study justifies both the feasibility of incorporating satellite-based land covers into a DGVM and the reliability of LH to simulate land-surface water balances. To better estimate surface/river runoff at mid-to-high latitudes, we recommended that LPJ-DGVM considers the effects of solar radiation on snowmelt.

The development and application of a decision support system for land management in the Lake Tahoe Basin—The Land Use Simulation Model

USGS Publications Warehouse

Forney, William M.; Oldham, I. Benson; Crescenti, Neil

2013-01-01

This report describes and applies the Land Use Simulation Model (LUSM), the final modeling product for the long-term decision support project funded by the Southern Nevada Public Land Management Act and developed by the U.S. Geological Survey’s Western Geographic Science Center for the Lake Tahoe Basin. Within the context of the natural-resource management and anthropogenic issues of the basin and in an effort to advance land-use and land-cover change science, this report addresses the problem of developing the LUSM as a decision support system. It includes consideration of land-use modeling theory, fire modeling and disturbance in the wildland-urban interface, historical land-use change and its relation to active land management, hydrologic modeling and the impact of urbanization as related to the Lahontan Regional Water Quality Control Board’s recently developed Total Maximum Daily Load report for the basin, and biodiversity in urbanizing areas. The LUSM strives to inform land-management decisions in a complex regulatory environment by simulating parcel-based, land-use transitions with a stochastic, spatially constrained, agent-based model. The tool is intended to be useful for multiple purposes, including the multiagency Pathway 2007 regional planning effort, the Tahoe Regional Planning Agency (TRPA) Regional Plan Update, and complementary research endeavors and natural-resource-management efforts. The LUSM is an Internet-based, scenario-generation decision support tool for allocating retired and developed parcels over the next 20 years. Because USGS staff worked closely with TRPA staff and their “Code of Ordinances” and analyzed datasets of historical management and land-use practices, this report accomplishes the task of providing reasonable default values for a baseline scenario that can be used in the LUSM. One result from the baseline scenario for the model suggests that all vacant parcels could be allocated within 12 years. Results also include: assessment of model functionality, brief descriptions of the 7 basic output tables, assessment of the rate of change in land-use allocation pools over time, locations and amounts of the spatially explicit probabilities of land-use transitions by real estate commodity, and analysis of the state change from today’s existing land cover to potential land uses in the future. Assumptions and limitations of the model are presented. This report concludes with suggested next steps to support the continued utility of the LUSM and additional research avenues.

Design and landing dynamic analysis of reusable landing leg for a near-space manned capsule

NASA Astrophysics Data System (ADS)

Yue, Shuai; Nie, Hong; Zhang, Ming; Wei, Xiaohui; Gan, Shengyong

2018-06-01

To improve the landing performance of a near-space manned capsule under various landing conditions, a novel landing system is designed that employs double chamber and single chamber dampers in the primary and auxiliary struts, respectively. A dynamic model of the landing system is established, and the damper parameters are determined by employing the design method. A single-leg drop test with different initial pitch angles is then conducted to compare and validate the simulation model. Based on the validated simulation model, seven critical landing conditions regarding nine crucial landing responses are found by combining the radial basis function (RBF) surrogate model and adaptive simulated annealing (ASA) optimization method. Subsequently, the adaptability of the landing system under critical landing conditions is analyzed. The results show that the simulation effectively results match the test results, which validates the accuracy of the dynamic model. In addition, all of the crucial responses under their corresponding critical landing conditions satisfy the design specifications, demonstrating the feasibility of the landing system.

Modelling and validation land-atmospheric heat fluxes by using classical surface parameters over the Tibetan Plateau

NASA Astrophysics Data System (ADS)

Ma, W.; Ma, Y.; Hu, Z.; Zhong, L.

2017-12-01

In this study, a land-atmosphere model was initialized by ingesting AMSR-E products, and the results were compared with the default model configuration and with in situ long-term CAMP/Tibet observations. Firstly our field observation sites will be introduced based on ITPCAS (Institute of Tibetan Plateau Research, Chinese Academy of Sciences). Then, a land-atmosphere model was initialized by ingesting AMSR-E products, and the results were compared with the default model configuration and with in situ long-term CAMP/Tibet observations. The differences between the AMSR-E initialized model runs with the default model configuration and in situ data showed an apparent inconsistency in the model-simulated land surface heat fluxes. The results showed that the soil moisture was sensitive to the specific model configuration. To evaluate and verify the model stability, a long-term modeling study with AMSR-E soil moisture data ingestion was performed. Based on test simulations, AMSR-E data were assimilated into an atmospheric model for July and August 2007. The results showed that the land surface fluxes agreed well with both the in situ data and the results of the default model configuration. Therefore, the simulation can be used to retrieve land surface heat fluxes from an atmospheric model over the Tibetan Plateau.

Land Suitability Modeling using a Geographic Socio-Environmental Niche-Based Approach: A Case Study from Northeastern Thailand

PubMed Central

Heumann, Benjamin W.; Walsh, Stephen J.; Verdery, Ashton M.; McDaniel, Phillip M.; Rindfuss, Ronald R.

2012-01-01

Understanding the pattern-process relations of land use/land cover change is an important area of research that provides key insights into human-environment interactions. The suitability or likelihood of occurrence of land use such as agricultural crop types across a human-managed landscape is a central consideration. Recent advances in niche-based, geographic species distribution modeling (SDM) offer a novel approach to understanding land suitability and land use decisions. SDM links species presence-location data with geospatial information and uses machine learning algorithms to develop non-linear and discontinuous species-environment relationships. Here, we apply the MaxEnt (Maximum Entropy) model for land suitability modeling by adapting niche theory to a human-managed landscape. In this article, we use data from an agricultural district in Northeastern Thailand as a case study for examining the relationships between the natural, built, and social environments and the likelihood of crop choice for the commonly grown crops that occur in the Nang Rong District – cassava, heavy rice, and jasmine rice, as well as an emerging crop, fruit trees. Our results indicate that while the natural environment (e.g., elevation and soils) is often the dominant factor in crop likelihood, the likelihood is also influenced by household characteristics, such as household assets and conditions of the neighborhood or built environment. Furthermore, the shape of the land use-environment curves illustrates the non-continuous and non-linear nature of these relationships. This approach demonstrates a novel method of understanding non-linear relationships between land and people. The article concludes with a proposed method for integrating the niche-based rules of land use allocation into a dynamic land use model that can address both allocation and quantity of agricultural crops. PMID:24187378

Degradation dynamics and bioavailability of land-based dissolved organic nitrogen in the Bohai Sea: Linking experiment with modeling.

PubMed

Li, Keqiang; Ma, Yunpeng; Dai, Aiquan; Wang, Xiulin

2017-11-30

Dissolved organic nitrogen (DON) is the major nitrogen form in the Bohai Sea. Land-based DON is released into the nitrogen pool and degraded by planktonic microbiota in coastal ocean. In this study, we evaluated the degradation of land-based DON, particularly its dynamics and bioavailability, in coastal water by linking experiment and modeling. Results showed that the degradation rate constant of DON from sewage treatment plant was significantly faster than those of other land-based sources (P<0.05). DON was classified into three categories based on dynamics and bioavailability. The supply of dissolved inorganic nitrogen (DIN) pool from the DON pool of Liao River, Hai River, and Yellow River was explored using a 3D hydrodynamic multi-DON biogeochemical model in the Bohai Sea. In the model, large amounts of DIN were supplied from DON of Liao River than the other rivers because of prolonged flushing time in Liaodong Bay. Copyright © 2017 Elsevier Ltd. All rights reserved.

Data model for the collaboration between land administration systems and agricultural land parcel identification systems.

PubMed

Inan, Halil Ibrahim; Sagris, Valentina; Devos, Wim; Milenov, Pavel; van Oosterom, Peter; Zevenbergen, Jaap

2010-12-01

The Common Agricultural Policy (CAP) of the European Union (EU) has dramatically changed after 1992, and from then on the CAP focused on the management of direct income subsidies instead of production-based subsidies. For this focus, Member States (MS) are expected to establish Integrated Administration and Control System (IACS), including a Land Parcel Identification System (LPIS) as the spatial part of IACS. Different MS have chosen different solutions for their LPIS. Currently, some MS based their IACS/LPIS on data from their Land Administration Systems (LAS), and many others use purpose built special systems for their IACS/LPIS. The issue with these different IACS/LPIS is that they do not have standardized structures; rather, each represents a unique design in each MS, both in the case of LAS based or special systems. In this study, we aim at designing a core data model for those IACS/LPIS based on LAS. For this purpose, we make use of the ongoing standardization initiatives for LAS (Land Administration Domain Model: LADM) and IACS/LPIS (LPIS Core Model: LCM). The data model we propose in this study implies the collaboration between LADM and LCM and includes some extensions. Some basic issues with the collaboration model are discussed within this study: registration of farmers, land use rights and farming limitations, geometry/topology, temporal data management etc. For further explanation of the model structure, sample instance level diagrams illustrating some typical situations are also included. Copyright © 2010 Elsevier Ltd. All rights reserved.

Simulation of urban land surface temperature based on sub-pixel land cover in a coastal city

NASA Astrophysics Data System (ADS)

Zhao, Xiaofeng; Deng, Lei; Feng, Huihui; Zhao, Yanchuang

2014-11-01

The sub-pixel urban land cover has been proved to have obvious correlations with land surface temperature (LST). Yet these relationships have seldom been used to simulate LST. In this study we provided a new approach of urban LST simulation based on sub-pixel land cover modeling. Landsat TM/ETM+ images of Xiamen city, China on both the January of 2002 and 2007 were used to acquire land cover and then extract the transformation rule using logistic regression. The transformation possibility was taken as its percent in the same pixel after normalization. And cellular automata were used to acquire simulated sub-pixel land cover on 2007 and 2017. On the other hand, the correlations between retrieved LST and sub-pixel land cover achieved by spectral mixture analysis in 2002 were examined and a regression model was built. Then the regression model was used on simulated 2007 land cover to model the LST of 2007. Finally the LST of 2017 was simulated for urban planning and management. The results showed that our method is useful in LST simulation. Although the simulation accuracy is not quite satisfactory, it provides an important idea and a good start in the modeling of urban LST.

Land-use threats and protected areas: a scenario-based, landscape level approach

USGS Publications Warehouse

Wilson, Tamara S.; Sleeter, Benjamin M.; Sleeter, Rachel R.; Soulard, Christopher E.

2014-01-01

Anthropogenic land use will likely present a greater challenge to biodiversity than climate change this century in the Pacific Northwest, USA. Even if species are equipped with the adaptive capacity to migrate in the face of a changing climate, they will likely encounter a human-dominated landscape as a major dispersal obstacle. Our goal was to identify, at the ecoregion-level, protected areas in close proximity to lands with a higher likelihood of future land-use conversion. Using a state-and-transition simulation model, we modeled spatially explicit (1 km2) land use from 2000 to 2100 under seven alternative land-use and emission scenarios for ecoregions in the Pacific Northwest. We analyzed scenario-based land-use conversion threats from logging, agriculture, and development near existing protected areas. A conversion threat index (CTI) was created to identify ecoregions with highest projected land-use conversion potential within closest proximity to existing protected areas. Our analysis indicated nearly 22% of land area in the Coast Range, over 16% of land area in the Puget Lowland, and nearly 11% of the Cascades had very high CTI values. Broader regional-scale land-use change is projected to impact nearly 40% of the Coast Range, 30% of the Puget Lowland, and 24% of the Cascades (i.e., two highest CTI classes). A landscape level, scenario-based approach to modeling future land use helps identify ecoregions with existing protected areas at greater risk from regional land-use threats and can help prioritize future conservation efforts.

Using exploratory regression to identify optimal driving factors for cellular automaton modeling of land use change.

PubMed

Feng, Yongjiu; Tong, Xiaohua

2017-09-22

Defining transition rules is an important issue in cellular automaton (CA)-based land use modeling because these models incorporate highly correlated driving factors. Multicollinearity among correlated driving factors may produce negative effects that must be eliminated from the modeling. Using exploratory regression under pre-defined criteria, we identified all possible combinations of factors from the candidate factors affecting land use change. Three combinations that incorporate five driving factors meeting pre-defined criteria were assessed. With the selected combinations of factors, three logistic regression-based CA models were built to simulate dynamic land use change in Shanghai, China, from 2000 to 2015. For comparative purposes, a CA model with all candidate factors was also applied to simulate the land use change. Simulations using three CA models with multicollinearity eliminated performed better (with accuracy improvements about 3.6%) than the model incorporating all candidate factors. Our results showed that not all candidate factors are necessary for accurate CA modeling and the simulations were not sensitive to changes in statistically non-significant driving factors. We conclude that exploratory regression is an effective method to search for the optimal combinations of driving factors, leading to better land use change models that are devoid of multicollinearity. We suggest identification of dominant factors and elimination of multicollinearity before building land change models, making it possible to simulate more realistic outcomes.

A data-based conservation planning tool for Florida panthers

USGS Publications Warehouse

Murrow, Jennifer L.; Thatcher, Cindy A.; Van Manen, Frank T.; Clark, Joseph D.

2013-01-01

Habitat loss and fragmentation are the greatest threats to the endangered Florida panther (Puma concolor coryi). We developed a data-based habitat model and user-friendly interface so that land managers can objectively evaluate Florida panther habitat. We used a geographic information system (GIS) and the Mahalanobis distance statistic (D2) to develop a model based on broad-scale landscape characteristics associated with panther home ranges. Variables in our model were Euclidean distance to natural land cover, road density, distance to major roads, human density, amount of natural land cover, amount of semi-natural land cover, amount of permanent or semi-permanent flooded area–open water, and a cost–distance variable. We then developed a Florida Panther Habitat Estimator tool, which automates and replicates the GIS processes used to apply the statistical habitat model. The estimator can be used by persons with moderate GIS skills to quantify effects of land-use changes on panther habitat at local and landscape scales. Example applications of the tool are presented.

MODIS land cover uncertainty in regional climate simulations

NASA Astrophysics Data System (ADS)

Li, Xue; Messina, Joseph P.; Moore, Nathan J.; Fan, Peilei; Shortridge, Ashton M.

2017-12-01

MODIS land cover datasets are used extensively across the climate modeling community, but inherent uncertainties and associated propagating impacts are rarely discussed. This paper modeled uncertainties embedded within the annual MODIS Land Cover Type (MCD12Q1) products and propagated these uncertainties through the Regional Atmospheric Modeling System (RAMS). First, land cover uncertainties were modeled using pixel-based trajectory analyses from a time series of MCD12Q1 for Urumqi, China. Second, alternative land cover maps were produced based on these categorical uncertainties and passed into RAMS. Finally, simulations from RAMS were analyzed temporally and spatially to reveal impacts. Our study found that MCD12Q1 struggles to discriminate between grasslands and croplands or grasslands and barren in this study area. Such categorical uncertainties have significant impacts on regional climate model outputs. All climate variables examined demonstrated impact across the various regions, with latent heat flux affected most with a magnitude of 4.32 W/m2 in domain average. Impacted areas were spatially connected to locations of greater land cover uncertainty. Both biophysical characteristics and soil moisture settings in regard to land cover types contribute to the variations among simulations. These results indicate that formal land cover uncertainty analysis should be included in MCD12Q1-fed climate modeling as a routine procedure.

ICLUS v1.3 Population Projections

EPA Pesticide Factsheets

Climate and land-use change are major components of global environmental change with feedbacks between these components. The consequences of these interactions show that land use may exacerbate or alleviate climate change effects. Based on these findings it is important to use land-use scenarios that are consistent with the specific assumptions underlying climate-change scenarios. The Integrated Climate and Land-Use Scenarios (ICLUS) project developed land-use outputs that are based on a downscaled version of the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) social, economic, and demographic storylines. ICLUS outputs are derived from a pair of models. A demographic model generates county-level population estimates that are distributed by a spatial allocation model (SERGoM v3) as housing density across the landscape. Land-use outputs were developed for the four main SRES storylines and a baseline (base case). The model is run for the conterminous USA and output is available for each scenario by decade to 2100. In addition to housing density at a 1 hectare spatial resolution, this project also generated estimates of impervious surface at a resolution of 1 square kilometer. This shapefile holds population data for all counties of the conterminous USA for all decades (2010-2100) and SRES population growth scenarios (A1, A2, B1, B2), as well as a 'base case' (BC) scenario, for use in the Integrated Climate and Land Use

Coupling Cellular Automata Land Use Change with Distributed Hydrologic Models

NASA Astrophysics Data System (ADS)

Shu, L.; Duffy, C.

2017-12-01

There has been extensive research on LUC modeling with broad applications to simulating urban growth and changing demographic patterns across multiple scales. The importance of land conversion is a critical issue in watershed scale studies and is generally not treated in most watershed modeling approaches. In this study we apply spatially explicit hydrologic and landuse change models and the Conestoga Watershed in Lancaster County, Pennsylvania. The Penn State Integrated Hydrologic Model (PIHM) partitions the water balance in space and time over the urban catchment, the coupled Cellular Automata Land Use Change model (CALUC) dynamically simulates the evolution of land use classes based on physical measures associated with population change and land use demand factors. The CALUC model is based on iteratively applying discrete rules to each individual spatial cell. The essence the CA modeling involves calculation of the Transition Potential (TP) for conversion of a grid cell from one land use class to another. This potential includes five factors: random perturbation, suitability, accessibility, neighborhood effect, inertia effects and zonal factors. In spite of simplicity, this CALUC model has been shown to be very effective for simulating LUC leading to the emergence of complex spatial patterns. The components of TP are derived from present land use data for landuse reanalysis and for realistic future land use scenarios. For the CALUC we use early-settlement (circa 1790) initial land class values and final or present-day (2010) land classes to calibrate the model. CALUC- PIHM dynamically simulates the hydrologic response of conversion from pre-settlement to present landuse. The simulations highlight the capability and value of dynamic coupling of catchment hydrology with land use change over long time periods. Analysis of the simulation uses various metrics such as the distributed water balance, flow duration curves, etc. to show how deforestation, urbanization and agricultural land development interact for the period 1790- present.

Effects of Topography-based Subgrid Structures on Land Surface Modeling

NASA Astrophysics Data System (ADS)

Tesfa, T. K.; Ruby, L.; Brunke, M.; Thornton, P. E.; Zeng, X.; Ghan, S. J.

2017-12-01

Topography has major control on land surface processes through its influence on atmospheric forcing, soil and vegetation properties, network topology and drainage area. Consequently, accurate climate and land surface simulations in mountainous regions cannot be achieved without considering the effects of topographic spatial heterogeneity. To test a computationally less expensive hyper-resolution land surface modeling approach, we developed topography-based landunits within a hierarchical subgrid spatial structure to improve representation of land surface processes in the ACME Land Model (ALM) with minimal increase in computational demand, while improving the ability to capture the spatial heterogeneity of atmospheric forcing and land cover influenced by topography. This study focuses on evaluation of the impacts of the new spatial structures on modeling land surface processes. As a first step, we compare ALM simulations with and without subgrid topography and driven by grid cell mean atmospheric forcing to isolate the impacts of the subgrid topography on the simulated land surface states and fluxes. Recognizing that subgrid topography also has important effects on atmospheric processes that control temperature, radiation, and precipitation, methods are being developed to downscale atmospheric forcings. Hence in the second step, the impacts of the subgrid topographic structure on land surface modeling will be evaluated by including spatial downscaling of the atmospheric forcings. Preliminary results on the atmospheric downscaling and the effects of the new spatial structures on the ALM simulations will be presented.

Impacts of incorporating land exchanges between forestry and agriculture in sector models.

Treesearch

Ralph J. Alig; Darius M. Adams; Bruce A. McCarl

1998-01-01

The forest and agriculture sectors are linked by having a portion of their land bases suitable for use in either sector. A substantial part of the southern land base is suitable for either forestry or agriculture use, with most of forestation on U.S. agriculture land in the South. We examine how land exchanges between forestry and agriculture are influenced by specific...

Integrated modelling of anthropogenic land-use and land-cover change on the global scale

NASA Astrophysics Data System (ADS)

Schaldach, R.; Koch, J.; Alcamo, J.

2009-04-01

In many cases land-use activities go hand in hand with substantial modifications of the physical and biological cover of the Earth's surface, resulting in direct effects on energy and matter fluxes between terrestrial ecosystems and the atmosphere. For instance, the conversion of forest to cropland is changing climate relevant surface parameters (e.g. albedo) as well as evapotranspiration processes and carbon flows. In turn, human land-use decisions are also influenced by environmental processes. Changing temperature and precipitation patterns for example are important determinants for location and intensity of agriculture. Due to these close linkages, processes of land-use and related land-cover change should be considered as important components in the construction of Earth System models. A major challenge in modelling land-use change on the global scale is the integration of socio-economic aspects and human decision making with environmental processes. One of the few global approaches that integrates functional components to represent both anthropogenic and environmental aspects of land-use change, is the LandSHIFT model. It simulates the spatial and temporal dynamics of the human land-use activities settlement, cultivation of food crops and grazing management, which compete for the available land resources. The rational of the model is to regionalize the demands for area intensive commodities (e.g. crop production) and services (e.g. space for housing) from the country-level to a global grid with the spatial resolution of 5 arc-minutes. The modelled land-use decisions within the agricultural sector are influenced by changing climate and the resulting effects on biomass productivity. Currently, this causal chain is modelled by integrating results from the process-based vegetation model LPJmL model for changing crop yields and net primary productivity of grazing land. Model output of LandSHIFT is a time series of grid maps with land-use/land-cover information that can serve as basis for further impact analysis. An exemplary simulation study with LandSHIFT is presented, based on scenario assumptions from the UNEP Global Environmental Outlook 4. Time horizon of the analysis is the year 2050. Changes of future food production on country level are computed by the agro-economy model IMPACT as a function of demography, economic development and global trade pattern. Together with scenario assumptions on climatic change and population growth, this data serves as model input to compute the changing land-use und land-cover. The continental and global scale model results are then analysed with respect to changes in the spatial pattern of natural vegetation as well as the resulting effects on evapotranspiration processes and land surface parameters. Furthermore, possible linkages of LandSHIFT to the different components of Earth System models (e.g. climate and natural vegetation) are discussed.

Land Use, climate change and BIOdiversity in cultural landscapes (LUBIO): Assessing feedbacks and promoting land-use strategies towards a viable future

NASA Astrophysics Data System (ADS)

Dullinger, Iwona; Bohner, Andreas; Dullinger, Stefan; Essl, Franz; Gaube, Veronika; Haberl, Helmut; Mayer, Andreas; Plutzar, Christoph; Remesch, Alexander

2016-04-01

Land-use and climate change are important, pervasive drivers of global environmental change and pose major threats to global biodiversity. Research to date has mostly focused either on land-use change or on climate change, but rarely on the interactions between both drivers, even though it is expected that systemic feedbacks between changes in climate and land use will have important effects on biodiversity. In particular, climate change will not only alter the pool of plant and animal species capable of thriving in a specific area, it will also force land owners to reconsider their land use decisions. Such changes in land-use practices may have major additional effects on local and regional species composition and abundance. In LUBIO, we will explore the anticipated systemic feedbacks between (1) climate change, (2) land owner's decisions on land use, (3) land-use change, and (4) changes in biodiversity patterns during the coming decades in a regional context which integrates a broad range of land use practices and intensity gradients. To achieve this goal, an integrated socioecological model will be designed and implemented, consisting of three principal components: (1) an agent based model (ABM) that simulates decisions of important actors, (2) a spatially explicit GIS model that translates these decisions into changes in land cover and land use patterns, and (3) a species distribution model (SDM) that calculates changes in biodiversity patterns following from both changes in climate and the land use decisions as simulated in the ABM. Upon integration of these three components, the coupled socioecological model will be used to generate scenarios of future land-use decisions of landowners under climate change and, eventually, the combined effects of climate and land use changes on biodiversity. Model development of the ABM will be supported by a participatory process intended to collect regional and expert knowledge through a series of expert interviews, a series of transdisciplinary participatory modelling workshops, and a questionnaire-based survey targeted at regional farmers. Beside the integrated socioecological model a catalogue of recommended actions will be developed in order to distribute the insights of the research to the most relevant regional stakeholder groups.

Simulation of land use change in the three gorges reservoir area based on CART-CA

NASA Astrophysics Data System (ADS)

Yuan, Min

2018-05-01

This study proposes a new method to simulate spatiotemporal complex multiple land uses by using classification and regression tree algorithm (CART) based CA model. In this model, we use classification and regression tree algorithm to calculate land class conversion probability, and combine neighborhood factor, random factor to extract cellular transformation rules. The overall Kappa coefficient is 0.8014 and the overall accuracy is 0.8821 in the land dynamic simulation results of the three gorges reservoir area from 2000 to 2010, and the simulation results are satisfactory.

The impacts of land use change on malaria vector abundance in a water-limited, highland region of Ethiopia.

PubMed

Stryker, Jody J; Bomblies, Arne

2012-12-01

Changes in land use and climate are expected to alter the risk of malaria transmission in areas where rainfall limits vector abundance. We use a coupled hydrology-entomology model to investigate the effects of land use change on hydrological processes impacting mosquito abundance in a highland village of Ethiopia. Land use affects partitioning of rainfall into infiltration and runoff that reaches small-scale topographic depressions, which constitute the primary breeding habitat of Anopheles arabiensis mosquitoes. A physically based hydrology model isolates hydrological mechanisms by which land use impacts pool formation and persistence, and an agent-based entomology model evaluates the response of mosquito populations. This approach reproduced observed interannual variability in mosquito abundance between the 2009 and 2010 wet seasons. Several scenarios of land cover were then evaluated using the calibrated, field-validated model. Model results show variation in pool persistence and depth, as well as in mosquito abundance, due to land use changes alone. The model showed particular sensitivity to surface roughness, but also to root zone uptake. Scenarios in which land use was modified from agriculture to forest generally resulted in lowest mosquito abundance predictions; classification of the entire domain as rainforest produced a 34% decrease in abundance compared to 2010 results. This study also showed that in addition to vegetation type, spatial proximity of land use change to habitat locations has an impact on mosquito abundance. This modeling approach can be applied to assess impacts of climate and land use conditions that fall outside of the range of previously observed variability.

The Impacts of Land Use Change on Malaria Vector Abundance in a Water-Limited Highland Region of Ethiopia

NASA Astrophysics Data System (ADS)

Stryker, J.; Bomblies, A.

2012-12-01

Changes in land use and climate are expected to alter risk of malaria transmission in areas where rainfall limits vector abundance. We use a coupled hydrology-entomology model to investigate the effects of land use change on hydrological processes impacting mosquito abundance in a highland village of Ethiopia. Land use affects partitioning of rainfall into infiltration and runoff that reaches small-scale topographic depressions, which constitute the primary breeding habitat of Anopheles arabiensis mosquitoes. A physically-based hydrology model isolates hydrological mechanisms by which land use impacts pool formation and persistence, and an agent-based entomology model evaluates the response of mosquito populations. This approach reproduced observed interannual variability in mosquito abundance between the 2009 and 2010 wet seasons. Several scenarios of land cover were then evaluated using the calibrated, field-validated model. Model results show variation in pool persistence and depth, as well as in mosquito abundance, due to land use changes alone. The model showed particular sensitivity to surface roughness, but also to root zone uptake. Scenarios in which land use was modified from agriculture to forest generally resulted in lowest mosquito abundance predictions; classification of the entire domain as rainforest produced a 34% decrease in abundance compared to 2010 results. This study also showed that in addition to vegetation type, spatial proximity of land use change to habitat locations has an impact on mosquito abundance. This modeling approach can be applied to assess impacts of climate and land use conditions that fall outside of the range of previously observed variability.

Generation of High Resolution Land Surface Parameters in the Community Land Model

NASA Astrophysics Data System (ADS)

Ke, Y.; Coleman, A. M.; Wigmosta, M. S.; Leung, L.; Huang, M.; Li, H.

2010-12-01

The Community Land Model (CLM) is the land surface model used for the Community Atmosphere Model (CAM) and the Community Climate System Model (CCSM). It examines the physical, chemical, and biological processes across a variety of spatial and temporal scales. Currently, efforts are being made to improve the spatial resolution of the CLM, in part, to represent finer scale hydrologic characteristics. Current land surface parameters of CLM4.0, in particular plant functional types (PFT) and leaf area index (LAI), are generated from MODIS and calculated at a 0.05 degree resolution. These MODIS-derived land surface parameters have also been aggregated to coarser resolutions (e.g., 0.5, 1.0 degrees). To evaluate the response of CLM across various spatial scales, higher spatial resolution land surface parameters need to be generated. In this study we examine the use of Landsat TM/ETM+ imagery and data fusion techniques for generating land surface parameters at a 1km resolution within the Pacific Northwest United States. . Land cover types and PFTs are classified based on Landsat multi-season spectral information, DEM, National Land Cover Database (NLCD) and the USDA-NASS Crop Data Layer (CDL). For each PFT, relationships between MOD15A2 high quality LAI values, Landsat-based vegetation indices, climate variables, terrain, and laser-altimeter derived vegetation height are used to generate monthly LAI values at a 30m resolution. The high-resolution PFT and LAI data are aggregated to create a 1km model grid resolution. An evaluation and comparison of CLM land surface response at both fine and moderate scale is presented.

Connecting Satellite Observations with Water Cycle Variables Through Land Data Assimilation: Examples Using the NASA GEOS-5 LDAS

NASA Technical Reports Server (NTRS)

Reichle, Rolf H.; De Lannoy, Gabrielle J. M.; Forman, Barton A.; Draper, Clara S.; Liu, Qing

2013-01-01

A land data assimilation system (LDAS) can merge satellite observations (or retrievals) of land surface hydrological conditions, including soil moisture, snow, and terrestrial water storage (TWS), into a numerical model of land surface processes. In theory, the output from such a system is superior to estimates based on the observations or the model alone, thereby enhancing our ability to understand, monitor, and predict key elements of the terrestrial water cycle. In practice, however, satellite observations do not correspond directly to the water cycle variables of interest. The present paper addresses various aspects of this seeming mismatch using examples drawn from recent research with the ensemble-based NASA GEOS-5 LDAS. These aspects include (1) the assimilation of coarse-scale observations into higher-resolution land surface models, (2) the partitioning of satellite observations (such as TWS retrievals) into their constituent water cycle components, (3) the forward modeling of microwave brightness temperatures over land for radiance-based soil moisture and snow assimilation, and (4) the selection of the most relevant types of observations for the analysis of a specific water cycle variable that is not observed (such as root zone soil moisture). The solution to these challenges involves the careful construction of an observation operator that maps from the land surface model variables of interest to the space of the assimilated observations.

Case studies, cross-site comparisons, and the challenge of generalization: comparing agent-based models of land-use change in frontier regions

PubMed Central

Parker, Dawn C.; Entwisle, Barbara; Rindfuss, Ronald R.; Vanwey, Leah K.; Manson, Steven M.; Moran, Emilio; An, Li; Deadman, Peter; Evans, Tom P.; Linderman, Marc; Rizi, S. Mohammad Mussavi; Malanson, George

2009-01-01

Cross-site comparisons of case studies have been identified as an important priority by the land-use science community. From an empirical perspective, such comparisons potentially allow generalizations that may contribute to production of global-scale land-use and land-cover change projections. From a theoretical perspective, such comparisons can inform development of a theory of land-use science by identifying potential hypotheses and supporting or refuting evidence. This paper undertakes a structured comparison of four case studies of land-use change in frontier regions that follow an agent-based modeling approach. Our hypothesis is that each case study represents a particular manifestation of a common process. Given differences in initial conditions among sites and the time at which the process is observed, actual mechanisms and outcomes are anticipated to differ substantially between sites. Our goal is to reveal both commonalities and differences among research sites, model implementations, and ultimately, conclusions derived from the modeling process. PMID:19960107

Case studies, cross-site comparisons, and the challenge of generalization: comparing agent-based models of land-use change in frontier regions.

PubMed

Parker, Dawn C; Entwisle, Barbara; Rindfuss, Ronald R; Vanwey, Leah K; Manson, Steven M; Moran, Emilio; An, Li; Deadman, Peter; Evans, Tom P; Linderman, Marc; Rizi, S Mohammad Mussavi; Malanson, George

2008-01-01

Cross-site comparisons of case studies have been identified as an important priority by the land-use science community. From an empirical perspective, such comparisons potentially allow generalizations that may contribute to production of global-scale land-use and land-cover change projections. From a theoretical perspective, such comparisons can inform development of a theory of land-use science by identifying potential hypotheses and supporting or refuting evidence. This paper undertakes a structured comparison of four case studies of land-use change in frontier regions that follow an agent-based modeling approach. Our hypothesis is that each case study represents a particular manifestation of a common process. Given differences in initial conditions among sites and the time at which the process is observed, actual mechanisms and outcomes are anticipated to differ substantially between sites. Our goal is to reveal both commonalities and differences among research sites, model implementations, and ultimately, conclusions derived from the modeling process.

Advances in land modeling of KIAPS based on the Noah Land Surface Model

NASA Astrophysics Data System (ADS)

Koo, Myung-Seo; Baek, Sunghye; Seol, Kyung-Hee; Cho, Kyoungmi

2017-08-01

As of 2013, the Noah Land Surface Model (LSM) version 2.7.1 was implemented in a new global model being developed at the Korea Institute of Atmospheric Prediction Systems (KIAPS). This land surface scheme is further refined in two aspects, by adding new physical processes and by updating surface input parameters. Thus, the treatment of glacier land, sea ice, and snow cover are addressed more realistically. Inconsistencies in the amount of absorbed solar flux at ground level by the land surface and radiative processes are rectified. In addition, new parameters are available by using 1-km land cover data, which had usually not been possible at a global scale. Land surface albedo/emissivity climatology is newly created using Moderate-Resolution Imaging Spectroradiometer (MODIS) satellitebased data and adjusted parameterization. These updates have been applied to the KIAPS-developed model and generally provide a positive impact on near-surface weather forecasting.

The effects of changing land cover on streamflow simulation in Puerto Rico

USGS Publications Warehouse

Van Beusekom, Ashley E.; Hay, Lauren E.; Viger, Roland; Gould, William A.; Collazo, Jaime; Henareh Khalyani, Azad

2014-01-01

This study quantitatively explores whether land cover changes have a substantive impact on simulated streamflow within the tropical island setting of Puerto Rico. The Precipitation Runoff Modeling System (PRMS) was used to compare streamflow simulations based on five static parameterizations of land cover with those based on dynamically varying parameters derived from four land cover scenes for the period 1953-2012. The PRMS simulations based on static land cover illustrated consistent differences in simulated streamflow across the island. It was determined that the scale of the analysis makes a difference: large regions with localized areas that have undergone dramatic land cover change may show negligible difference in total streamflow, but streamflow simulations using dynamic land cover parameters for a highly altered subwatershed clearly demonstrate the effects of changing land cover on simulated streamflow. Incorporating dynamic parameterization in these highly altered watersheds can reduce the predictive uncertainty in simulations of streamflow using PRMS. Hydrologic models that do not consider the projected changes in land cover may be inadequate for water resource management planning for future conditions.

Optimal allocation model of construction land based on two-level system optimization theory

NASA Astrophysics Data System (ADS)

Liu, Min; Liu, Yanfang; Xia, Yuping; Lei, Qihong

2007-06-01

The allocation of construction land is an important task in land-use planning. Whether implementation of planning decisions is a success or not, usually depends on a reasonable and scientific distribution method. Considering the constitution of land-use planning system and planning process in China, multiple levels and multiple objective decision problems is its essence. Also, planning quantity decomposition is a two-level system optimization problem and an optimal resource allocation decision problem between a decision-maker in the topper and a number of parallel decision-makers in the lower. According the characteristics of the decision-making process of two-level decision-making system, this paper develops an optimal allocation model of construction land based on two-level linear planning. In order to verify the rationality and the validity of our model, Baoan district of Shenzhen City has been taken as a test case. Under the assistance of the allocation model, construction land is allocated to ten townships of Baoan district. The result obtained from our model is compared to that of traditional method, and results show that our model is reasonable and usable. In the end, the paper points out the shortcomings of the model and further research directions.

A model-based analysis of a display for helicopter landing approach. [control theoretical model of human pilot

NASA Technical Reports Server (NTRS)

Hess, R. A.; Wheat, L. W.

1975-01-01

A control theoretic model of the human pilot was used to analyze a baseline electronic cockpit display in a helicopter landing approach task. The head down display was created on a stroke written cathode ray tube and the vehicle was a UH-1H helicopter. The landing approach task consisted of maintaining prescribed groundspeed and glideslope in the presence of random vertical and horizontal turbulence. The pilot model was also used to generate and evaluate display quickening laws designed to improve pilot vehicle performance. A simple fixed base simulation provided comparative tracking data.

A Resource-Based Modelling Framework to Assess Habitat Suitability for Steppe Birds in Semiarid Mediterranean Agricultural Systems

PubMed Central

Cardador, Laura; De Cáceres, Miquel; Bota, Gerard; Giralt, David; Casas, Fabián; Arroyo, Beatriz; Mougeot, François; Cantero-Martínez, Carlos; Moncunill, Judit; Butler, Simon J.; Brotons, Lluís

2014-01-01

European agriculture is undergoing widespread changes that are likely to have profound impacts on farmland biodiversity. The development of tools that allow an assessment of the potential biodiversity effects of different land-use alternatives before changes occur is fundamental to guiding management decisions. In this study, we develop a resource-based model framework to estimate habitat suitability for target species, according to simple information on species’ key resource requirements (diet, foraging habitat and nesting site), and examine whether it can be used to link land-use and local species’ distribution. We take as a study case four steppe bird species in a lowland area of the north-eastern Iberian Peninsula. We also compare the performance of our resource-based approach to that obtained through habitat-based models relating species’ occurrence and land-cover variables. Further, we use our resource-based approach to predict the effects that change in farming systems can have on farmland bird habitat suitability and compare these predictions with those obtained using the habitat-based models. Habitat suitability estimates generated by our resource-based models performed similarly (and better for one study species) than habitat based-models when predicting current species distribution. Moderate prediction success was achieved for three out of four species considered by resource-based models and for two of four by habitat-based models. Although, there is potential for improving the performance of resource-based models, they provide a structure for using available knowledge of the functional links between agricultural practices, provision of key resources and the response of organisms to predict potential effects of changing land-uses in a variety of context or the impacts of changes such as altered management practices that are not easily incorporated into habitat-based models. PMID:24667825

Research Advances on Radiation Transfer Modeling and Inversion for Multi-scale Land Surface Remote Sensing

NASA Astrophysics Data System (ADS)

Liu, Q.; Li, J.; Du, Y.; Wen, J.; Zhong, B.; Wang, K.

2011-12-01

As the remote sensing data accumulating, it is a challenge and significant issue how to generate high accurate and consistent land surface parameter product from the multi source remote observation and the radiation transfer modeling and inversion methodology are the theoretical bases. In this paper, recent research advances and unresolved issues are presented. At first, after a general overview, recent research advances on multi-scale remote sensing radiation transfer modeling are presented, including leaf spectrum model, vegetation canopy BRDF models, directional thermal infrared emission models, rugged mountains area radiation models, and kernel driven models etc. Then, new methodologies on land surface parameters inversion based on multi-source remote sensing data are proposed, taking the land surface Albedo, leaf area index, temperature/emissivity, and surface net radiation as examples. A new synthetic land surface parameter quantitative remote sensing product generation system is suggested and the software system prototype will be demonstrated. At last, multi-scale field experiment campaigns, such as the field campaigns in Gansu and Beijing, China are introduced briefly. The ground based, tower based, and airborne multi-angular measurement system have been built to measure the directional reflectance, emission and scattering characteristics from visible, near infrared, thermal infrared and microwave bands for model validation and calibration. The remote sensing pixel scale "true value" measurement strategy have been designed to gain the ground "true value" of LST, ALBEDO, LAI, soil moisture and ET etc. at 1-km2 for remote sensing product validation.

Automatic Hazard Detection for Landers

NASA Technical Reports Server (NTRS)

Huertas, Andres; Cheng, Yang; Matthies, Larry H.

2008-01-01

Unmanned planetary landers to date have landed 'blind'; that is, without the benefit of onboard landing hazard detection and avoidance systems. This constrains landing site selection to very benign terrain,which in turn constrains the scientific agenda of missions. The state of the art Entry, Descent, and Landing (EDL) technology can land a spacecraft on Mars somewhere within a 20-100km landing ellipse.Landing ellipses are very likely to contain hazards such as craters, discontinuities, steep slopes, and large rocks, than can cause mission-fatal damage. We briefly review sensor options for landing hazard detection and identify a perception approach based on stereo vision and shadow analysis that addresses the broadest set of missions. Our approach fuses stereo vision and monocular shadow-based rock detection to maximize spacecraft safety. We summarize performance models for slope estimation and rock detection within this approach and validate those models experimentally. Instantiating our model of rock detection reliability for Mars predicts that this approach can reduce the probability of failed landing by at least a factor of 4 in any given terrain. We also describe a rock detector/mapper applied to large-high-resolution images from the Mars Reconnaissance Orbiter (MRO) for landing site characterization and selection for Mars missions.

Model of land cover change prediction in West Java using cellular automata-Markov chain (CA-MC)

NASA Astrophysics Data System (ADS)

Virtriana, Riantini; Sumarto, Irawan; Deliar, Albertus; Pasaribu, Udjianna S.; Taufik, Moh.

2015-04-01

Land is a fundamental factor that closely related to economic growth and supports the needs of human life. Land-use activity is a major issue and challenge for country planners. The cause of change in land use type activity may be due to socio economic development or due to changes in the environment or may be due to both. In an effort to understand the phenomenon of land cover changes, can be approached through land cover change modelling. Based on the facts and data contained, West Java has a high economic activity that will have an impact on land cover change. CA-MC is a model that used to determine the statistical change probabilistic for each of land cover type from land cover data at different time periods. CA-MC is able to provide the output of land cover type that should occurred. Results from a CA-MC modelling in predicting land cover changes showed an accuracy rate of 95.42%.

A comparison between conventional and LANDSAT based hydrologic modeling: The Four Mile Run case study

NASA Technical Reports Server (NTRS)

Ragan, R. M.; Jackson, T. J.; Fitch, W. N.; Shubinski, R. P.

1976-01-01

Models designed to support the hydrologic studies associated with urban water resources planning require input parameters that are defined in terms of land cover. Estimating the land cover is a difficult and expensive task when drainage areas larger than a few sq. km are involved. Conventional and LANDSAT based methods for estimating the land cover based input parameters required by hydrologic planning models were compared in a case study of the 50.5 sq. km (19.5 sq. mi) Four Mile Run Watershed in Virginia. Results of the study indicate that the LANDSAT based approach is highly cost effective for planning model studies. The conventional approach to define inputs was based on 1:3600 aerial photos, required 110 man-days and a total cost of $14,000. The LANDSAT based approach required 6.9 man-days and cost $2,350. The conventional and LANDSAT based models gave similar results relative to discharges and estimated annual damages expected from no flood control, channelization, and detention storage alternatives.

Exploring an Ecologically Sustainable Scheme for Landscape Restoration of Abandoned Mine Land: Scenario-Based Simulation Integrated Linear Programming and CLUE-S Model

PubMed Central

Zhang, Liping; Zhang, Shiwen; Huang, Yajie; Cao, Meng; Huang, Yuanfang; Zhang, Hongyan

2016-01-01

Understanding abandoned mine land (AML) changes during land reclamation is crucial for reusing damaged land resources and formulating sound ecological restoration policies. This study combines the linear programming (LP) model and the CLUE-S model to simulate land-use dynamics in the Mentougou District (Beijing, China) from 2007 to 2020 under three reclamation scenarios, that is, the planning scenario based on the general land-use plan in study area (scenario 1), maximal comprehensive benefits (scenario 2), and maximal ecosystem service value (scenario 3). Nine landscape-scale graph metrics were then selected to describe the landscape characteristics. The results show that the coupled model presented can simulate the dynamics of AML effectively and the spatially explicit transformations of AML were different. New cultivated land dominates in scenario 1, while construction land and forest land account for major percentages in scenarios 2 and 3, respectively. Scenario 3 has an advantage in most of the selected indices as the patches combined most closely. To conclude, reclaiming AML by transformation into more forest can reduce the variability and maintain the stability of the landscape ecological system in study area. These findings contribute to better mapping AML dynamics and providing policy support for the management of AML. PMID:27023575

Exploring an Ecologically Sustainable Scheme for Landscape Restoration of Abandoned Mine Land: Scenario-Based Simulation Integrated Linear Programming and CLUE-S Model.

PubMed

Zhang, Liping; Zhang, Shiwen; Huang, Yajie; Cao, Meng; Huang, Yuanfang; Zhang, Hongyan

2016-03-24

Understanding abandoned mine land (AML) changes during land reclamation is crucial for reusing damaged land resources and formulating sound ecological restoration policies. This study combines the linear programming (LP) model and the CLUE-S model to simulate land-use dynamics in the Mentougou District (Beijing, China) from 2007 to 2020 under three reclamation scenarios, that is, the planning scenario based on the general land-use plan in study area (scenario 1), maximal comprehensive benefits (scenario 2), and maximal ecosystem service value (scenario 3). Nine landscape-scale graph metrics were then selected to describe the landscape characteristics. The results show that the coupled model presented can simulate the dynamics of AML effectively and the spatially explicit transformations of AML were different. New cultivated land dominates in scenario 1, while construction land and forest land account for major percentages in scenarios 2 and 3, respectively. Scenario 3 has an advantage in most of the selected indices as the patches combined most closely. To conclude, reclaiming AML by transformation into more forest can reduce the variability and maintain the stability of the landscape ecological system in study area. These findings contribute to better mapping AML dynamics and providing policy support for the management of AML.

Modifying a dynamic global vegetation model for simulating large spatial scale land surface water balance

NASA Astrophysics Data System (ADS)

Tang, G.; Bartlein, P. J.

2012-01-01

Water balance models of simple structure are easier to grasp and more clearly connect cause and effect than models of complex structure. Such models are essential for studying large spatial scale land surface water balance in the context of climate and land cover change, both natural and anthropogenic. This study aims to (i) develop a large spatial scale water balance model by modifying a dynamic global vegetation model (DGVM), and (ii) test the model's performance in simulating actual evapotranspiration (ET), soil moisture and surface runoff for the coterminous United States (US). Toward these ends, we first introduced development of the "LPJ-Hydrology" (LH) model by incorporating satellite-based land covers into the Lund-Potsdam-Jena (LPJ) DGVM instead of dynamically simulating them. We then ran LH using historical (1982-2006) climate data and satellite-based land covers at 2.5 arc-min grid cells. The simulated ET, soil moisture and surface runoff were compared to existing sets of observed or simulated data for the US. The results indicated that LH captures well the variation of monthly actual ET (R2 = 0.61, p < 0.01) in the Everglades of Florida over the years 1996-2001. The modeled monthly soil moisture for Illinois of the US agrees well (R2 = 0.79, p < 0.01) with the observed over the years 1984-2001. The modeled monthly stream flow for most 12 major rivers in the US is consistent R2 > 0.46, p < 0.01; Nash-Sutcliffe Coefficients >0.52) with observed values over the years 1982-2006, respectively. The modeled spatial patterns of annual ET and surface runoff are in accordance with previously published data. Compared to its predecessor, LH simulates better monthly stream flow in winter and early spring by incorporating effects of solar radiation on snowmelt. Overall, this study proves the feasibility of incorporating satellite-based land-covers into a DGVM for simulating large spatial scale land surface water balance. LH developed in this study should be a useful tool for studying effects of climate and land cover change on land surface hydrology at large spatial scales.

Site selection model for new metro stations based on land use

NASA Astrophysics Data System (ADS)

Zhang, Nan; Chen, Xuewu

2015-12-01

Since the construction of metro system generally lags behind the development of urban land use, sites of metro stations should adapt to their surrounding situations, which was rarely discussed by previous research on station layout. This paper proposes a new site selection model to find the best location for a metro station, establishing the indicator system based on land use and combining AHP with entropy weight method to obtain the schemes' ranking. The feasibility and efficiency of this model has been validated by evaluating Nanjing Shengtai Road station and other potential sites.

The potential for land use change to reduce flood risk in mid-sized catchments in the Myjava region of Slovakia

NASA Astrophysics Data System (ADS)

Rončák, Peter; Lisovszki, Evelin; Szolgay, Ján; Hlavčová, Kamila; Kohnová, Silvia; Csoma, Rózsa; Poórová, Jana

2017-06-01

The effects of land use management practices on surface runoff are evident on a local scale, but evidence of their impact on the scale of a watershed is limited. This study focuses on an analysis of the impact of land use changes on the flood regime in the Myjava River basin, which is located in Western Slovakia. The Myjava River basin has an area of 641.32 km2 and is typified by the formation of fast runoff processes, intensive soil erosion, and muddy floods. The main factors responsible for these problems with flooding and soil erosion are the basin's location, geology, pedology, agricultural land use, and cropping practices. The GIS-based, spatially distributed WetSpa rainfall-runoff model was used to simulate mean daily discharges in the outlet of the basin as well as the individual components of the water balance. The model was calibrated based on the period between 1997 and 2012 with outstanding results (an NS coefficient of 0.702). Various components of runoff (e.g., surface, interflow and groundwater) and several elements of the hydrological balance (evapotranspiration and soil moisture) were simulated under various land use scenarios. Six land use scenarios (`crop', `grass', `forest', `slope', `elevation' and `optimal') were developed. The first three scenarios exhibited the ability of the WetSpa model to simulate runoff under changed land use conditions and enabled a better adjustment of the land use parameters of the model. Three other "more realistic" land use scenarios, which were based on the distribution of land use classes (arable land, grass and forest) regarding permissible slopes in the catchment, confirmed the possibility of reducing surface runoff and maximum discharges with applicable changes in land use and land management. These scenarios represent practical, realistic and realizable land use management solutions and they could be economically implemented to mitigate soil erosion processes and enhance the flood protection measures in the Myjava River basin.

Interfacing geographic information systems and remote sensing for rural land-use analysis

NASA Technical Reports Server (NTRS)

Nellis, M. Duane; Lulla, Kamlesh; Jensen, John

1990-01-01

Recent advances in computer-based geographic information systems (GISs) are briefly reviewed, with an emphasis on the incorporation of remote-sensing data in GISs for rural applications. Topics addressed include sampling procedures for rural land-use analyses; GIS-based mapping of agricultural land use and productivity; remote sensing of land use and agricultural, forest, rangeland, and water resources; monitoring the dynamics of irrigation agriculture; GIS methods for detecting changes in land use over time; and the development of land-use modeling strategies.

An interval chance-constrained fuzzy modeling approach for supporting land-use planning and eco-environment planning at a watershed level.

PubMed

Ou, Guoliang; Tan, Shukui; Zhou, Min; Lu, Shasha; Tao, Yinghui; Zhang, Zuo; Zhang, Lu; Yan, Danping; Guan, Xingliang; Wu, Gang

2017-12-15

An interval chance-constrained fuzzy land-use allocation (ICCF-LUA) model is proposed in this study to support solving land resource management problem associated with various environmental and ecological constraints at a watershed level. The ICCF-LUA model is based on the ICCF (interval chance-constrained fuzzy) model which is coupled with interval mathematical model, chance-constrained programming model and fuzzy linear programming model and can be used to deal with uncertainties expressed as intervals, probabilities and fuzzy sets. Therefore, the ICCF-LUA model can reflect the tradeoff between decision makers and land stakeholders, the tradeoff between the economical benefits and eco-environmental demands. The ICCF-LUA model has been applied to the land-use allocation of Wujiang watershed, Guizhou Province, China. The results indicate that under highly land suitable conditions, optimized area of cultivated land, forest land, grass land, construction land, water land, unused land and landfill in Wujiang watershed will be [5015, 5648] hm 2 , [7841, 7965] hm 2 , [1980, 2056] hm 2 , [914, 1423] hm 2 , [70, 90] hm 2 , [50, 70] hm 2 and [3.2, 4.3] hm 2 , the corresponding system economic benefit will be between 6831 and 7219 billion yuan. Consequently, the ICCF-LUA model can effectively support optimized land-use allocation problem in various complicated conditions which include uncertainties, risks, economic objective and eco-environmental constraints. Copyright © 2017 Elsevier Ltd. All rights reserved.

Assessing uncertainties in land cover projections.

PubMed

Alexander, Peter; Prestele, Reinhard; Verburg, Peter H; Arneth, Almut; Baranzelli, Claudia; Batista E Silva, Filipe; Brown, Calum; Butler, Adam; Calvin, Katherine; Dendoncker, Nicolas; Doelman, Jonathan C; Dunford, Robert; Engström, Kerstin; Eitelberg, David; Fujimori, Shinichiro; Harrison, Paula A; Hasegawa, Tomoko; Havlik, Petr; Holzhauer, Sascha; Humpenöder, Florian; Jacobs-Crisioni, Chris; Jain, Atul K; Krisztin, Tamás; Kyle, Page; Lavalle, Carlo; Lenton, Tim; Liu, Jiayi; Meiyappan, Prasanth; Popp, Alexander; Powell, Tom; Sands, Ronald D; Schaldach, Rüdiger; Stehfest, Elke; Steinbuks, Jevgenijs; Tabeau, Andrzej; van Meijl, Hans; Wise, Marshall A; Rounsevell, Mark D A

2017-02-01

Understanding uncertainties in land cover projections is critical to investigating land-based climate mitigation policies, assessing the potential of climate adaptation strategies and quantifying the impacts of land cover change on the climate system. Here, we identify and quantify uncertainties in global and European land cover projections over a diverse range of model types and scenarios, extending the analysis beyond the agro-economic models included in previous comparisons. The results from 75 simulations over 18 models are analysed and show a large range in land cover area projections, with the highest variability occurring in future cropland areas. We demonstrate systematic differences in land cover areas associated with the characteristics of the modelling approach, which is at least as great as the differences attributed to the scenario variations. The results lead us to conclude that a higher degree of uncertainty exists in land use projections than currently included in climate or earth system projections. To account for land use uncertainty, it is recommended to use a diverse set of models and approaches when assessing the potential impacts of land cover change on future climate. Additionally, further work is needed to better understand the assumptions driving land use model results and reveal the causes of uncertainty in more depth, to help reduce model uncertainty and improve the projections of land cover. © 2016 John Wiley & Sons Ltd.

[An operational remote sensing algorithm of land surface evapotranspiration based on NOAA PAL dataset].

PubMed

Hou, Ying-Yu; He, Yan-Bo; Wang, Jian-Lin; Tian, Guo-Liang

2009-10-01

Based on the time series 10-day composite NOAA Pathfinder AVHRR Land (PAL) dataset (8 km x 8 km), and by using land surface energy balance equation and "VI-Ts" (vegetation index-land surface temperature) method, a new algorithm of land surface evapotranspiration (ET) was constructed. This new algorithm did not need the support from meteorological observation data, and all of its parameters and variables were directly inversed or derived from remote sensing data. A widely accepted ET model of remote sensing, i. e., SEBS model, was chosen to validate the new algorithm. The validation test showed that both the ET and its seasonal variation trend estimated by SEBS model and our new algorithm accorded well, suggesting that the ET estimated from the new algorithm was reliable, being able to reflect the actual land surface ET. The new ET algorithm of remote sensing was practical and operational, which offered a new approach to study the spatiotemporal variation of ET in continental scale and global scale based on the long-term time series satellite remote sensing images.

Exploring Land Use and Land Cover Change and Feedbacks in the Global Change Assessment Model

NASA Astrophysics Data System (ADS)

Chen, M.; Vernon, C. R.; Huang, M.; Calvin, K. V.; Le Page, Y.; Kraucunas, I.

2017-12-01

Land Use and Land Cover Change (LULCC) is a major driver of global and regional environmental change. Projections of land use change are thus an essential component in Integrated Assessment Models (IAMs) to study feedbacks between transformation of energy systems and land productivity under the context of climate change. However, the spatial scale of IAMs, e.g., the Global Change Assessment Model (GCAM), is typically larger than the scale of terrestrial processes in the human-Earth system, LULCC downscaling therefore becomes a critical linkage among these multi-scale and multi-sector processes. Parametric uncertainties in LULCC downscaling algorithms, however, have been under explored, especially in the context of how such uncertainties could propagate to affect energy systems in a changing climate. In this study, we use a LULCC downscaling model, Demeter, to downscale GCAM-based future land use scenarios into fine spatial scales, and explore the sensitivity of downscaled land allocations to key parameters. Land productivity estimates (e.g., biomass production and crop yield) based on the downscaled LULCC scenarios are then fed to GCAM to evaluate how energy systems might change due to altered water and carbon cycle dynamics and their interactions with the human system, , which would in turn affect future land use projections. We demonstrate that uncertainties in LULCC downscaling can result in significant differences in simulated scenarios, indicating the importance of quantifying parametric uncertainties in LULCC downscaling models for integrated assessment studies.

Importance of land use update during the calibration period and simulation of water balance response to land use change in the upper Rio das Mortes Catchment (Cerrado Biome, Central-Western Brazil)

NASA Astrophysics Data System (ADS)

Lamparter, Gabriele; Kovacs, Kristof; Nobrega, Rodolfo; Gerold, Gerhard

2015-04-01

Changes in the hydrological balance and following degradation of the water ecosystem services due to large scale land use changes are reported from agricultural frontiers all over the world. Traditionally, hydrological models including vegetation and land use as a part of the hydrological cycle use a fixed distribution of land use for the calibration period. We believe that a meaningful calibration - especially when investigating the effects of land use change on hydrology - demands the inclusion of land use change during the calibration period into the calibration procedure. The SWAT (Soil and Water Assessment Tool) model is a process-based, semi-distributed model calculating the different components of the water balance. The model bases on the definition of hydrological response units (HRUs) which are based on soil, vegetation and slope distribution. It specifically emphasises the role of land use and land management on the water balance. The Central-Western region of Brazil is one of the leading agricultural frontiers, which experienced rapid and radical deforestation and agricultural intensification in the last 40 years (from natural Cerrado savannah to cattle grazing to intensive corn and soya cropland). The land use history of the upper Rio das Mortes catchment (with 17500 km²) is reasonably well documented since the 1970th. At the same time there are almost continuous climate and runoff data available for the period between 1988 and 2011. Therefore, the work presented here shows the model calibration and validation of the SWAT model with the land use update function for three different periods (1988 to 1998, 1998 to 2007 and 2007 to 2011) in comparison with the same calibration periods using a steady state land use distribution. The use of the land use update function allows a clearer identification which changes in the discharge are due to climatic variability and which are due to changes in the vegetation cover. With land use update included into the calibration procedure, the impact of land use change on overall modelled runoff was more pronounced. For example, the accordance of modelled peak discharge improved for the period from 1988 to 1998 (with a decrease of primary Cerrado from 60 to 30 %) with the use of the land use update function compared to the steady state calibration. The effect for the following two periods 1998 to 2007 and 2007 to 2011 (with a decrease of primary Cerrado from 30 to 24 % and 24 to 19 % respectively) show only a small improvement of the model fit.

GCAM 3.0 Agriculture and Land Use: Data Sources and Methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kyle, G. Page; Luckow, Patrick; Calvin, Katherine V.

This report presents the data processing methods used in the GCAM 3.0 agriculture and land use component, starting from all source data used, and detailing all calculations and assumptions made in generating the model inputs. The report starts with a brief introduction to modeling of agriculture and land use in GCAM 3.0, and then provides documentation of the data and methods used for generating the base-year dataset and future scenario parameters assumed in the model input files. Specifically, the report addresses primary commodity production, secondary (animal) commodity production, disposition of commodities, land allocation, land carbon contents, and land values.

Sinte Gleska University Reclaims Land from Loneliness.

ERIC Educational Resources Information Center

Crazy Bull, Cheryl

2000-01-01

Sinte Gleska University's (SGU) model for community development includes transformation of an old boarding school site, community-based collaborations in gardening and nutrition, and a bison restoration project. Tribal members learn to work with the land in harmony with tribal stewardship models as well as Western land use and agricultural…

Evaluating Global Land-use Change Scenario: Carbon Emission in RCP Scenarios and its Effects on Climate Response

NASA Astrophysics Data System (ADS)

Kato, E.; Kawamiya, M.

2011-12-01

In CMIP5 experiments, new emissions scenarios for GCMs and Earth System Models (ESMs) have been constructed as Representative Concentration Pathways (RCPs) by a community effort of Integrated Assessment Modeling (IAM) groups. In RCP scenarios, regional land-use scenarios have been depicted based on the socio-economic assumption of IAMs, and also downscaled spatially explicit land-use maps from the regional scenarios are prepared. In the land-use harmonization project, integrated gridded land-use transition data for the past and future time period has been developed from the reconstruction based on HYDE 3 agricultural data and FAO wood harvest data, and the future land-use scenarios from IAMs. These gridded land-use dataset are used as a forcing of some ESMs participating to the CMIP5 experiments, to assess the biogeochemical and biogeophysical effects of land-use and land cover change in the climate change simulation. In this study, global net CO2 emissions from land-use change for RCP scenarios are evaluated with an offline terrestrial biogeochemical model, VISIT (Vegetation Integrative SImulation Tool). Also the emissions are evaluated with coupled ESM, MIROC-ESM following the LUCID-CMIP5 protocol to see the effect of land-use and land cover change on climate response. Using the model output, consistency of the land-use change CO2 emission scenarios provided by RCPs are evaluated in terms of effect of CO2 fertilization, climate change, and land-use transition itself including the effect of biomass crops production with CCS. We find that a land-use scenario with decreased agricultural land-use intensity such as RCP 6.0 shows possibility of further absorption of CO2 through the climate-carbon feedback, and cooling effect through both biogeochemical and biogeophysical effects.

Land-use impacts on water resources and protected areas: applications of state-and-transition simulation modeling of future scenarios

NASA Astrophysics Data System (ADS)

Wilson, T. S.; Sleeter, B. M.; Sherba, J.; Cameron, D.

2014-12-01

Human land use will increasingly contribute to habitat losses and water shortages in California, given future population projections and associated demand for agricultural land. Understanding how land-use change may impact future water use and where existing protected areas may be threatened by land-use conversion will be important if effective, sustainable management approaches are to be implemented. We used a state-and-transition simulation modeling (STSM) framework to simulate spatially-explicit (1 km2) historical (1992-2010) and future (2011-2060) land-use change for 52 California counties within the Mediterranean California ecoregion. Historical land use change estimates were derived from the Farmland Mapping and Monitoring Program (FMMP) dataset and attributed with county-level agricultural water-use data from the California Department of Water Resources (CDWR). Six future alternative land-use scenarios were developed and modeled using the historical land-use change estimates and land-use projections based on the Intergovernmental Panel on Climate Change's (IPCC) Special Report on Emission Scenarios (SRES) A2 and B1 scenarios. Resulting spatial land-use scenario outputs were combined based on scenario agreement and a land conversion threat index developed to evaluate vulnerability of existing protected areas. Modeled scenario output of county-level agricultural water use data were also summarized, enabling examination of alternative water use futures. We present results of two separate applications of STSM of land-use change, demonstrating the utility of STSM in analyzing land-use related impacts on water resources as well as potential threats to existing protected land. Exploring a range of alternative, yet plausible, land-use change impacts will help to better inform resource management and mitigation strategies.

Development of a Landforms Model for Puerto Rico and its Application for Land Cover Change Analysis

Treesearch

Sebastian Martinuzzi; William A. Gould; Olga M. Ramos Gonzalez; Brook E. Edwards

2007-01-01

Comprehensive analysis of land morphology is essential to supporting a wide range environmental studies. We developed a landforms model that identifies eleven landform units for Puerto Rico based on parameters of land position and slope. The model is capable of extracting operational information in a simple way and is adaptable to different environments and objectives...

Advancing land surface model development with satellite-based Earth observations

NASA Astrophysics Data System (ADS)

Orth, Rene; Dutra, Emanuel; Trigo, Isabel F.; Balsamo, Gianpaolo

2017-04-01

The land surface forms an essential part of the climate system. It interacts with the atmosphere through the exchange of water and energy and hence influences weather and climate, as well as their predictability. Correspondingly, the land surface model (LSM) is an essential part of any weather forecasting system. LSMs rely on partly poorly constrained parameters, due to sparse land surface observations. With the use of newly available land surface temperature observations, we show in this study that novel satellite-derived datasets help to improve LSM configuration, and hence can contribute to improved weather predictability. We use the Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL) and validate it comprehensively against an array of Earth observation reference datasets, including the new land surface temperature product. This reveals satisfactory model performance in terms of hydrology, but poor performance in terms of land surface temperature. This is due to inconsistencies of process representations in the model as identified from an analysis of perturbed parameter simulations. We show that HTESSEL can be more robustly calibrated with multiple instead of single reference datasets as this mitigates the impact of the structural inconsistencies. Finally, performing coupled global weather forecasts we find that a more robust calibration of HTESSEL also contributes to improved weather forecast skills. In summary, new satellite-based Earth observations are shown to enhance the multi-dataset calibration of LSMs, thereby improving the representation of insufficiently captured processes, advancing weather predictability and understanding of climate system feedbacks. Orth, R., E. Dutra, I. F. Trigo, and G. Balsamo (2016): Advancing land surface model development with satellite-based Earth observations. Hydrol. Earth Syst. Sci. Discuss., doi:10.5194/hess-2016-628

Climate and land use controls over terrestrial water use efficiency in monsoon Asia.

Treesearch

Hanqin Tian; Chaoqun Lu; Guangsheng Chen; Xiaofeng Xu; Mingliang Liu; et al

2011-01-01

Much concern has been raised regarding how and to what extent climate change and intensive human activities have altered water use efficiency (WUE, amount of carbon uptake per unit of water use) in monsoon Asia. By using a process-based ecosystem model [dynamic land ecosystem model (DLEM)], we examined effects of climate change, land use/cover change, and land...

Land Use Change on Household Farms in the Ecuadorian Amazon: Design and Implementation of an Agent-Based Model.

PubMed

Mena, Carlos F; Walsh, Stephen J; Frizzelle, Brian G; Xiaozheng, Yao; Malanson, George P

2011-01-01

This paper describes the design and implementation of an Agent-Based Model (ABM) used to simulate land use change on household farms in the Northern Ecuadorian Amazon (NEA). The ABM simulates decision-making processes at the household level that is examined through a longitudinal, socio-economic and demographic survey that was conducted in 1990 and 1999. Geographic Information Systems (GIS) are used to establish spatial relationships between farms and their environment, while classified Landsat Thematic Mapper (TM) imagery is used to set initial land use/land cover conditions for the spatial simulation, assess from-to land use/land cover change patterns, and describe trajectories of land use change at the farm and landscape levels. Results from prior studies in the NEA provide insights into the key social and ecological variables, describe human behavioral functions, and examine population-environment interactions that are linked to deforestation and agricultural extensification, population migration, and demographic change. Within the architecture of the model, agents are classified as active or passive. The model comprises four modules, i.e., initialization, demography, agriculture, and migration that operate individually, but are linked through key household processes. The main outputs of the model include a spatially-explicit representation of the land use/land cover on survey and non-survey farms and at the landscape level for each annual time-step, as well as simulated socio-economic and demographic characteristics of households and communities. The work describes the design and implementation of the model and how population-environment interactions can be addressed in a frontier setting. The paper contributes to land change science by examining important pattern-process relations, advocating a spatial modeling approach that is capable of synthesizing fundamental relationships at the farm level, and links people and environment in complex ways.

Large-Eddy Atmosphere-Land-Surface Modelling over Heterogeneous Surfaces: Model Development and Comparison with Measurements

NASA Astrophysics Data System (ADS)

Shao, Yaping; Liu, Shaofeng; Schween, Jan H.; Crewell, Susanne

2013-08-01

A model is developed for the large-eddy simulation (LES) of heterogeneous atmosphere and land-surface processes. This couples a LES model with a land-surface scheme. New developments are made to the land-surface scheme to ensure the adequate representation of atmosphere-land-surface transfers on the large-eddy scale. These include, (1) a multi-layer canopy scheme; (2) a method for flux estimates consistent with the large-eddy subgrid closure; and (3) an appropriate soil-layer configuration. The model is then applied to a heterogeneous region with 60-m horizontal resolution and the results are compared with ground-based and airborne measurements. The simulated sensible and latent heat fluxes are found to agree well with the eddy-correlation measurements. Good agreement is also found in the modelled and observed net radiation, ground heat flux, soil temperature and moisture. Based on the model results, we study the patterns of the sensible and latent heat fluxes, how such patterns come into existence, and how large eddies propagate and destroy land-surface signals in the atmosphere. Near the surface, the flux and land-use patterns are found to be closely correlated. In the lower boundary layer, small eddies bearing land-surface signals organize and develop into larger eddies, which carry the signals to considerably higher levels. As a result, the instantaneous flux patterns appear to be unrelated to the land-use patterns, but on average, the correlation between them is significant and persistent up to about 650 m. For a given land-surface type, the scatter of the fluxes amounts to several hundred W { m }^{-2}, due to (1) large-eddy randomness; (2) rapid large-eddy and surface feedback; and (3) local advection related to surface heterogeneity.

Surrogate Based Uni/Multi-Objective Optimization and Distribution Estimation Methods

NASA Astrophysics Data System (ADS)

Gong, W.; Duan, Q.; Huo, X.

2017-12-01

Parameter calibration has been demonstrated as an effective way to improve the performance of dynamic models, such as hydrological models, land surface models, weather and climate models etc. Traditional optimization algorithms usually cost a huge number of model evaluations, making dynamic model calibration very difficult, or even computationally prohibitive. With the help of a serious of recently developed adaptive surrogate-modelling based optimization methods: uni-objective optimization method ASMO, multi-objective optimization method MO-ASMO, and probability distribution estimation method ASMO-PODE, the number of model evaluations can be significantly reduced to several hundreds, making it possible to calibrate very expensive dynamic models, such as regional high resolution land surface models, weather forecast models such as WRF, and intermediate complexity earth system models such as LOVECLIM. This presentation provides a brief introduction to the common framework of adaptive surrogate-based optimization algorithms of ASMO, MO-ASMO and ASMO-PODE, a case study of Common Land Model (CoLM) calibration in Heihe river basin in Northwest China, and an outlook of the potential applications of the surrogate-based optimization methods.

Characteristics and Impact of Imperviousness From a GIS-based Hydrological Perspective

NASA Astrophysics Data System (ADS)

Moglen, G. E.; Kim, S.

2005-12-01

With the concern that imperviousness can be differently quantified depending on data sources and methods, this study assessed imperviousness estimates using two different data sources: land use and land cover. Year 2000 land use developed by the Maryland Department of Planning was utilized to estimate imperviousness by assigning imperviousness coefficients to unique land use categories. These estimates were compared with imperviousness estimates based on satellite-derived land cover from the 2001 National Land Cover Dataset. Our study developed the relationships between these two estimates in the form of regression equations to convert imperviousness derived from one data source to the other. The regression equations are considered reliable, based on goodness-of-fit measures. Furthermore, this study examined how quantitatively different imperviousness estimates affect the prediction of hydrological response both in the flow regime and in the thermal regime. We assessed the relationships between indicators of hydrological response and imperviousness-descriptors. As indicators of flow variability, coefficient of variance, lag-one autocorrelation, and mean daily flow change were calculated based on measured mean daily stream flow from the water year 1997 to 2003. For thermal variability, indicators such as percent-days of surge, degree-day, and mean daily temperature difference were calculated base on measured stream temperature over several basins in Maryland. To describe imperviousness through the hydrological process, GIS-based spatially distributed hydrological models were developed based on a water-balance method and the SCS-CN method. Imperviousness estimates from land use and land cover were used as predictors in these models to examine the effect of imperviousness using different data sources on the prediction of hydrological response. Indicators of hydrological response were also regressed on aggregate imperviousness. This allowed for identifying if hydrological response is more sensitive to spatially distributed imperviousness or aggregate (lumped) imperviousness. The regressions between indicators of hydrological response and imperviousness-descriptors were evaluated by examining goodness-of-fit measures such as explained variance or relative standard error. The results show that imperviousness estimates using land use are better predictors of flow variability and thermal variability than imperviousness estimates using land cover. Also, this study reveals that flow variability is more sensitive to spatially distributed models than lumped models, while thermal variability is equally responsive to both models. The findings from this study can be further examined from a policy perspective with regard to policies that are based on a threshold concept for imperviousness impacts on the ecological and hydrological system.

Predicting future land cover change and its impact on streamflow and sediment load in a trans-boundary river basin

NASA Astrophysics Data System (ADS)

Wang, Jie; Wang, Hao; Ning, Shaowei; Hiroshi, Ishidaira

2018-06-01

Sediment load can provide very important perspective on erosion of river basin. The changes of human-induced vegetation cover, such as deforestation or afforestation, affect sediment yield process of a catchment. We have already evaluated that climate change and land cover change changed the historical streamflow and sediment yield, and land cover change is the main factor in Red river basin. But future streamflow and sediment yield changes under potential future land cover change scenario still have not been evaluated. For this purpose, future scenario of land cover change is developed based on historical land cover changes and land change model (LCM). In addition, future leaf area index (LAI) is simulated by ecological model (Biome-BGC) based on future land cover scenario. Then future scenarios of land cover change and LAI are used to drive hydrological model and new sediment rating curve. The results of this research provide information that decision-makers need in order to promote water resources planning efforts. Besides that, this study also contributes a basic framework for assessing climate change impacts on streamflow and sediment yield that can be applied in the other basins around the world.

What are the effects of Agro-Ecological Zones and land use region boundaries on land resource projection using the Global Change Assessment Model?

DOE Office of Scientific and Technical Information (OSTI.GOV)

Di Vittorio, Alan V.; Kyle, Page; Collins, William D.

Understanding potential impacts of climate change is complicated by spatially mismatched land representations between gridded datasets and models, and land use models with larger regions defined by geopolitical and/or biophysical criteria. Here in this study, we quantify the sensitivity of Global Change Assessment Model (GCAM) outputs to the delineation of Agro-Ecological Zones (AEZs), which are normally based on historical (1961–1990) climate. We reconstruct GCAM's land regions using projected (2071–2100) climate, and find large differences in estimated future land use that correspond with differences in agricultural commodity prices and production volumes. Importantly, historically delineated AEZs experience spatially heterogeneous climate impacts overmore » time, and do not necessarily provide more homogenous initial land productivity than projected AEZs. Finally, we conclude that non-climatic criteria for land use region delineation are likely preferable for modeling land use change in the context of climate change, and that uncertainty associated with land delineation needs to be quantified.« less

What are the effects of Agro-Ecological Zones and land use region boundaries on land resource projection using the Global Change Assessment Model?

DOE PAGES

Di Vittorio, Alan V.; Kyle, Page; Collins, William D.

2016-09-03

Understanding potential impacts of climate change is complicated by spatially mismatched land representations between gridded datasets and models, and land use models with larger regions defined by geopolitical and/or biophysical criteria. Here in this study, we quantify the sensitivity of Global Change Assessment Model (GCAM) outputs to the delineation of Agro-Ecological Zones (AEZs), which are normally based on historical (1961–1990) climate. We reconstruct GCAM's land regions using projected (2071–2100) climate, and find large differences in estimated future land use that correspond with differences in agricultural commodity prices and production volumes. Importantly, historically delineated AEZs experience spatially heterogeneous climate impacts overmore » time, and do not necessarily provide more homogenous initial land productivity than projected AEZs. Finally, we conclude that non-climatic criteria for land use region delineation are likely preferable for modeling land use change in the context of climate change, and that uncertainty associated with land delineation needs to be quantified.« less

Optimal land use/land cover classification using remote sensing imagery for hydrological modeling in a Himalayan watershed

NASA Astrophysics Data System (ADS)

Saran, Sameer; Sterk, Geert; Kumar, Suresh

2009-10-01

Land use/land cover is an important watershed surface characteristic that affects surface runoff and erosion. Many of the available hydrological models divide the watershed into Hydrological Response Units (HRU), which are spatial units with expected similar hydrological behaviours. The division into HRU's requires good-quality spatial data on land use/land cover. This paper presents different approaches to attain an optimal land use/land cover map based on remote sensing imagery for a Himalayan watershed in northern India. First digital classifications using maximum likelihood classifier (MLC) and a decision tree classifier were applied. The results obtained from the decision tree were better and even improved after post classification sorting. But the obtained land use/land cover map was not sufficient for the delineation of HRUs, since the agricultural land use/land cover class did not discriminate between the two major crops in the area i.e. paddy and maize. Subsequently the digital classification on fused data (ASAR and ASTER) were attempted to map land use/land cover classes with emphasis to delineate the paddy and maize crops but the supervised classification over fused datasets did not provide the desired accuracy and proper delineation of paddy and maize crops. Eventually, we adopted a visual classification approach on fused data. This second step with detailed classification system resulted into better classification accuracy within the 'agricultural land' class which will be further combined with topography and soil type to derive HRU's for physically-based hydrological modeling.

Development and calibration of the statewide land use-transport model

DOT National Transportation Integrated Search

1999-02-12

The TRANUS package has been used to develop an integrated land use-transport model at the statewide level. It is based upon a specific modeling approach described by de la Barra (1989,1995). For the prototype statewide model developed during this pro...

Sensitivity of WRF-Chem model to land surface schemes: Assessment in a severe dust outbreak episode in the Central Mediterranean (Apulia Region)

NASA Astrophysics Data System (ADS)

Rizza, Umberto; Miglietta, Mario Marcello; Mangia, Cristina; Ielpo, Pierina; Morichetti, Mauro; Iachini, Chiara; Virgili, Simone; Passerini, Giorgio

2018-03-01

The Weather Research and Forecasting model with online coupled chemistry (WRF-Chem) is applied to simulate a severe Saharan dust outbreak event that took place over Southern Italy in March 2016. Numerical experiments have been performed applying a physics-based dust emission model, with soil properties generated from three different Land Surface Models, namely Noah, RUC and Noah-MP. The model performance in reproducing the severe desert dust outbreak is analysed using an observational dataset of aerosol and desert dust features that includes optical properties from satellite and ground-based sun-photometers, and in-situ particulate matter mass concentration (PM) data. The results reveal that the combination of the dust emission model with the RUC Land Surface Model significantly over-predicts the emitted mineral dust; on the other side, the combination with Noah or Noah-MP Land Surface Model (LSM) gives better results, especially for the daily averaged PM10.

Land use allocation model considering climate change impact

NASA Astrophysics Data System (ADS)

Lee, D. K.; Yoon, E. J.; Song, Y. I.

2017-12-01

In Korea, climate change adaptation plans are being developed for each administrative district based on impact assessments constructed in various fields. This climate change impact assessments are superimposed on the actual space, which causes problems in land use allocation because the spatial distribution of individual impacts may be different each other. This implies that trade-offs between climate change impacts can occur depending on the composition of land use. Moreover, the actual space is complexly intertwined with various factors such as required area, legal regulations, and socioeconomic values, so land use allocation in consideration of climate change can be very difficult problem to solve (Liu et al. 2012; Porta et al. 2013).Optimization techniques can generate a sufficiently good alternatives for land use allocation at the strategic level if only the fitness function of relationship between impact and land use composition are derived. It has also been noted that land use optimization model is more effective than the scenario-based prediction model in achieving the objectives for problem solving (Zhang et al. 2014). Therefore in this study, we developed a quantitative tool, MOGA (Multi Objective Genetic Algorithm), which can generate a comprehensive land use allocations considering various climate change impacts, and apply it to the Gangwon-do in Korea. Genetic Algorithms (GAs) are the most popular optimization technique to address multi-objective in land use allocation. Also, it allows for immediate feedback to stake holders because it can run a number of experiments with different parameter values. And it is expected that land use decision makers and planners can formulate a detailed spatial plan or perform additional analysis based on the result of optimization model. Acknowledgments: This work was supported by the Korea Ministry of Environment (MOE) as "Climate Change Correspondence Program (Project number: 2014001310006)"

Geographically explicit urban land use change scenarios for Mega cities: a case study in Tokyo

NASA Astrophysics Data System (ADS)

Yamagata, Y.; Bagan, H.; Seya, H.; Nakamichi, K.

2010-12-01

In preparation for the IPCC 5th assessment report, the international modeling community is developing four Representative Concentration Paths employing the scenarios developed by four different Integrated Assessment Models. These RCPs will be employed as an input to climate models, such as Earth System Models. In these days, the importance of assessment of not only global but also local (city/zone level) impacts of global change has gradually been recognized, thereby downscaling climate models are one of the urgent problems to be solved. Needless to say, reliable downscaling requires spatially high resolution land use change scenarios. So far, there has been proposed a lot of methods for constructing land use change scenarios with considering economic behavior of human, such as agent-based model (e.g., Parker et al., 2001), and land use transport (LUT) model (e.g., Anas and Liu, 2007). The latter approach in particular has widely been applied to actual urban/transport policy; hence modeling the interaction between them is very important for creating reliable land use change scenarios. However, the LUT models are usually built based on the zones of cities/municipalities whose spatial resolutions are too low to derive sensible parameters of the climate models. Moreover, almost all of the works which attempt to build spatially high resolution LUT model employs very small regions as the study area. The objective of this research is deriving various input parameters to climate models such as population density, fractional green vegetation cover, and anthropogenic heat emission with spatially high resolution land use change scenarios constructed with LUT model. The study area of this research is Tokyo metropolitan area, which is the largest urban area in the world (United Nations., 2010). Firstly, this study employs very high ground resolution zones composed of micro districts around 1km2. Secondly, the research attempt to combine remote sensing techniques and LUT models to derive future distribution of fractional green vegetation cover. The study has created two extreme land-use scenarios: urban concentration (compact city) and dispersion scenarios in order to show possible range of future land use change, and derives the input parameters for the climate models. The authors are planning to open the scenarios and derived parameters to relate researches. Anas, A. and Y. Liu. (2007). A Regional Economy, Land Use, and Transportation Model (REULU-TRAN): Formulation, Algorithm Design, and Testing. Journal of Regional Science, 47, 415-455. Parker, D.C., T. Berger, S.M. Manson, Editors (2001). Agent-Based Models of Land-Use and Land-Cover Change. LUCC Report Series No. 6, (Accessed: 27 AUG. 2009; http://www.globallandproject.org/Documents/LUCC_No_6.pdf) United Nations. (2010). World urbanization prospects: City population.

Modeling vulnerability of groundwater to pollution under future scenarios of climate change and biofuels-related land use change: a case study in North Dakota, USA.

PubMed

Li, Ruopu; Merchant, James W

2013-03-01

Modeling groundwater vulnerability to pollution is critical for implementing programs to protect groundwater quality. Most groundwater vulnerability modeling has been based on current hydrogeology and land use conditions. However, groundwater vulnerability is strongly dependent on factors such as depth-to-water, recharge and land use conditions that may change in response to future changes in climate and/or socio-economic conditions. In this research, a modeling framework, which employs three sets of models linked within a geographic information system (GIS) environment, was used to evaluate groundwater pollution risks under future climate and land use changes in North Dakota. The results showed that areas with high vulnerability will expand northward and/or northwestward in Eastern North Dakota under different scenarios. GIS-based models that account for future changes in climate and land use can help decision-makers identify potential future threats to groundwater quality and take early steps to protect this critical resource. Copyright © 2013 Elsevier B.V. All rights reserved.

Parametric design and analysis on the landing gear of a planet lander using the response surface method

NASA Astrophysics Data System (ADS)

Zheng, Guang; Nie, Hong; Luo, Min; Chen, Jinbao; Man, Jianfeng; Chen, Chuanzhi; Lee, Heow Pueh

2018-07-01

The purpose of this paper is to obtain the design parameter-landing response relation for designing the configuration of the landing gear in a planet lander quickly. To achieve this, parametric studies on the landing gear are carried out using the response surface method (RSM), based on a single landing gear landing model validated by experimental results. According to the design of experiment (DOE) results of the landing model, the RS (response surface)-functions of the three crucial landing responses are obtained, and the sensitivity analysis (SA) of the corresponding parameters is performed. Also, two multi-objective optimizations designs on the landing gear are carried out. The analysis results show that the RS (response surface)-model performs well for the landing response design process, with a minimum fitting accuracy of 98.99%. The most sensitive parameters for the three landing response are the design size of the buffers, struts friction and the diameter of the bending beam. Moreover, the good agreement between the simulated model and RS-model results are obtained in two optimized designs, which show that the RS-model coupled with the FE (finite element)-method is an efficient method to obtain the design configuration of the landing gear.

Exploring the Mechanisms of Ecological Land Change Based on the Spatial Autoregressive Model: A Case Study of the Poyang Lake Eco-Economic Zone, China

PubMed Central

Xie, Hualin; Liu, Zhifei; Wang, Peng; Liu, Guiying; Lu, Fucai

2013-01-01

Ecological land is one of the key resources and conditions for the survival of humans because it can provide ecosystem services and is particularly important to public health and safety. It is extremely valuable for effective ecological management to explore the evolution mechanisms of ecological land. Based on spatial statistical analyses, we explored the spatial disparities and primary potential drivers of ecological land change in the Poyang Lake Eco-economic Zone of China. The results demonstrated that the global Moran’s I value is 0.1646 during the 1990 to 2005 time period and indicated significant positive spatial correlation (p < 0.05). The results also imply that the clustering trend of ecological land changes weakened in the study area. Some potential driving forces were identified by applying the spatial autoregressive model in this study. The results demonstrated that the higher economic development level and industrialization rate were the main drivers for the faster change of ecological land in the study area. This study also tested the superiority of the spatial autoregressive model to study the mechanisms of ecological land change by comparing it with the traditional linear regressive model. PMID:24384778

Impact of Land Cover Characterization and Properties on Snow Albedo in Climate Models

NASA Astrophysics Data System (ADS)

Wang, L.; Bartlett, P. A.; Chan, E.; Montesano, P.

2017-12-01

The simulation of winter albedo in boreal and northern environments has been a particular challenge for land surface modellers. Assessments of output from CMIP3 and CMIP5 climate models have revealed that many simulations are characterized by overestimation of albedo in the boreal forest. Recent studies suggest that inaccurate representation of vegetation distribution, improper simulation of leaf area index, and poor treatment of canopy-snow processes are the primary causes of albedo errors. While several land cover datasets are commonly used to derive plant functional types (PFT) for use in climate models, new land cover and vegetation datasets with higher spatial resolution have become available in recent years. In this study, we compare the spatial distribution of the dominant PFTs and canopy cover fractions based on different land cover datasets, and present results from offline simulations of the latest version Canadian Land Surface Scheme (CLASS) over the northern Hemisphere land. We discuss the impact of land cover representation and surface properties on winter albedo simulations in climate models.

Land cover impacts on stream nutrients and fecal coliform in the lower Piedmont of West Georgia

NASA Astrophysics Data System (ADS)

Schoonover, Jon E.; Lockaby, B. Graeme

2006-12-01

SummaryAs urbanization infiltrates into rural areas, stream water quality is expected to decline as a result from increased impervious surface and greater sources for pollutants. Consequently, West Georgia's water quality is threatened by extensive development as well as other land uses such as livestock grazing and silvicultural activity. Maintenance of stream water quality, as land development occurs, is critical for the protection of drinking water and biotic integrity. A 2-phase, watershed-scale study was established to develop relationships among land cover and water quality within western Georgia. During phase 1, nutrient and fecal coliform data were collected within 18 mixed land use watersheds, ranging in size from 500 to 2500 ha. Regression models were developed that related land cover to stream water nutrient and fecal coliform concentrations. Nutrient and fecal coliform concentrations within watersheds having >24% impervious surface (IS) were often higher than those in nonurban watersheds (i.e., <5% IS) during both base flow (N: 1.64 mg/L versus 0.61 mg/L, and FC: 430 versus 120 MPN/100 ml) and storm flow (N: 1.93 mg/L versus 0.36 mg/L, and FC: 1600 versus 167 MPN/100 ml). Fecal coliform bacteria in urbanized areas consistently exceeded the US EPA's review criterion for recreational waters during both base flow and to a greater extent storm flow. During phase 2, regression models were tested based on data from six newly chosen watersheds with similar land use/cover patterns. Lastly, theoretical watersheds, based on land use percentages, were created to illustrate trends in water quality impairment as land development occurs. The models developed from this research could be used to forecast water quality changes under various land use scenarios in the developing Piedmont region of the US.

Refining multi-model projections of temperature extremes by evaluation against land-atmosphere coupling diagnostics

NASA Astrophysics Data System (ADS)

Sippel, Sebastian; Zscheischler, Jakob; Mahecha, Miguel D.; Orth, Rene; Reichstein, Markus; Vogel, Martha; Seneviratne, Sonia I.

2017-05-01

The Earth's land surface and the atmosphere are strongly interlinked through the exchange of energy and matter. This coupled behaviour causes various land-atmosphere feedbacks, and an insufficient understanding of these feedbacks contributes to uncertain global climate model projections. For example, a crucial role of the land surface in exacerbating summer heat waves in midlatitude regions has been identified empirically for high-impact heat waves, but individual climate models differ widely in their respective representation of land-atmosphere coupling. Here, we compile an ensemble of 54 combinations of observations-based temperature (T) and evapotranspiration (ET) benchmarking datasets and investigate coincidences of T anomalies with ET anomalies as a proxy for land-atmosphere interactions during periods of anomalously warm temperatures. First, we demonstrate that a large fraction of state-of-the-art climate models from the Coupled Model Intercomparison Project (CMIP5) archive produces systematically too frequent coincidences of high T anomalies with negative ET anomalies in midlatitude regions during the warm season and in several tropical regions year-round. These coincidences (high T, low ET) are closely related to the representation of temperature variability and extremes across the multi-model ensemble. Second, we derive a land-coupling constraint based on the spread of the T-ET datasets and consequently retain only a subset of CMIP5 models that produce a land-coupling behaviour that is compatible with these benchmark estimates. The constrained multi-model simulations exhibit more realistic temperature extremes of reduced magnitude in present climate in regions where models show substantial spread in T-ET coupling, i.e. biases in the model ensemble are consistently reduced. Also the multi-model simulations for the coming decades display decreased absolute temperature extremes in the constrained ensemble. On the other hand, the differences between projected and present-day climate extremes are affected to a lesser extent by the applied constraint, i.e. projected changes are reduced locally by around 0.5 to 1 °C - but this remains a local effect in regions that are highly sensitive to land-atmosphere coupling. In summary, our approach offers a physically consistent, diagnostic-based avenue to evaluate multi-model ensembles and subsequently reduce model biases in simulated and projected extreme temperatures.

Simulating land use changes in the Upper Narew catchment using the RegCM model

NASA Astrophysics Data System (ADS)

Liszewska, Malgorzata; Osuch, Marzena; Romanowicz, Renata

2010-05-01

Catchment hydrology is influenced by climate forcing in the form of precipitation, temperature, evapotranspiration and human interactions such as land use and water management practices. The difficulty in separating different causes of change in a hydrological regime results from the complexity of interactions between those three factors and catchment responses and the uncertainty and scarcity of available observations. This paper describes an application of a regional climate model to simulate the variability in precipitation, temperature, evaporation and discharge under different land use parameterizations, using the Upper Narew catchment (north-east Poland) as a case study. We use RegCM3 model, developed at the International Centre for Theoretical Physics, Trieste, Italy. The model's dynamic core is based on the hydrostatic version of the NCAR/PSU Mesoscale Model version 5 (primitive equations, hydrostatic, compressible, sigma-vertical coordinate). The physical input includes radiation transfer, large-scale and convective precipitation, Planetary Boundary Layer, biosphere. The RegCM3 model has options to interface with a variety of re-analyses and GCM boundary conditions, and can thus be used for scenario assessments. The variability of hydrological conditions in response to regional climate model projections is modeled using an integrated Data Based Mechanistic (DBM) rainfall-flow/flow-routing model of the Upper River Narew catchment. The modelling tool developed is formulated in the MATLAB-SIMULINK language. The basic system structure includes rainfall-flow and flow routing modules, based on a Stochastic Transfer Function (STF) approach combined with a nonlinear transformation of rainfall into effective rainfall. We analyse the signal resulting from modified land use in a given region. 10 month-long runs have been performed from February to November for the period of 1991-2000 based on the NCEP re-analyses. The land use data have been taken from the GLCC dataset and the Corine Land Cover programme (http://dataservice.eea.europa.eu/, GIOS, Poland). Simulations taking into account land use modifications in the catchment are compared with the reference simulations under no change in land use in the region. In the second part of the paper we discuss the application of the RegCM3 model in two climate change scenarios (SRES A2 and B1). The study is a contribution to the LUWR programme (http://luwr.igf.edu.pl).

Evaluating land-use change scenarios for the Puget Sound Basin, Washington, within the ecosystem recovery target model-based framework

USGS Publications Warehouse

Villarreal, Miguel; Labiosa, Bill; Aiello, Danielle

2017-05-23

The Puget Sound Basin, Washington, has experienced rapid urban growth in recent decades, with varying impacts to local ecosystems and natural resources. To plan for future growth, land managers often use scenarios to assess how the pattern and volume of growth may affect natural resources. Using three different land-management scenarios for the years 2000–2060, we assessed various spatial patterns of urban growth relative to maps depicting a model-based characterization of the ecological integrity and recent development pressure of individual land parcels. The three scenarios depict future trajectories of land-use change under alternative management strategies—status quo, managed growth, and unconstrained growth. The resulting analysis offers a preliminary assessment of how future growth patterns in the Puget Sound Basin may impact land targeted for conservation and how short-term metrics of land-development pressure compare to longer term growth projections.

The Development of Cadastral Domain Model Oriented at Unified Real Estate Registration of China Based on Ontology

NASA Astrophysics Data System (ADS)

Li, M.; Zhu, X.; Shen, C.; Chen, D.; Guo, W.

2012-07-01

With the certain regulation of unified real estate registration taken by the Property Law and the step-by-step advance of simultaneous development in urban and rural in China, it is the premise and foundation to clearly specify property rights and their relations in promoting the integrated management of urban and rural land. This paper aims at developing a cadastral domain model oriented at unified real estate registration of China from the perspective of legal and spatial, which set up the foundation for unified real estate registration, and facilitates the effective interchange of cadastral information and the administration of land use. The legal cadastral model is provided based on the analysis of gap between current model and the demand of unified real estate registration, which implies the restrictions between different rights. Then the new cadastral domain model is constructed based on the legal cadastral domain model and CCDM (van Oosterom et al., 2006), which integrate real estate rights of urban land and rural land. Finally, the model is validated by a prototype system. The results show that the model is applicable for unified real estate registration in China.

High-resolution Continental Scale Land Surface Model incorporating Land-water Management in United States

NASA Astrophysics Data System (ADS)

Shin, S.; Pokhrel, Y. N.

2016-12-01

Land surface models have been used to assess water resources sustainability under changing Earth environment and increasing human water needs. Overwhelming observational records indicate that human activities have ubiquitous and pertinent effects on the hydrologic cycle; however, they have been crudely represented in large scale land surface models. In this study, we enhance an integrated continental-scale land hydrology model named Leaf-Hydro-Flood to better represent land-water management. The model is implemented at high resolution (5km grids) over the continental US. Surface water and groundwater are withdrawn based on actual practices. Newly added irrigation, water diversion, and dam operation schemes allow better simulations of stream flows, evapotranspiration, and infiltration. Results of various hydrologic fluxes and stores from two sets of simulation (one with and the other without human activities) are compared over a range of river basin and aquifer scales. The improved simulations of land hydrology have potential to build consistent modeling framework for human-water-climate interactions.

Alternative Zoning Scenarios for Regional Sustainable Land Use Controls in China: A Knowledge-Based Multiobjective Optimisation Model

PubMed Central

Xia, Yin; Liu, Dianfeng; Liu, Yaolin; He, Jianhua; Hong, Xiaofeng

2014-01-01

Alternative land use zoning scenarios provide guidance for sustainable land use controls. This study focused on an ecologically vulnerable catchment on the Loess Plateau in China, proposed a novel land use zoning model, and generated alternative zoning solutions to satisfy the various requirements of land use stakeholders and managers. This model combined multiple zoning objectives, i.e., maximum zoning suitability, maximum planning compatibility and maximum spatial compactness, with land use constraints by using goal programming technique, and employed a modified simulated annealing algorithm to search for the optimal zoning solutions. The land use zoning knowledge was incorporated into the initialisation operator and neighbourhood selection strategy of the simulated annealing algorithm to improve its efficiency. The case study indicates that the model is both effective and robust. Five optimal zoning scenarios of the study area were helpful for satisfying the requirements of land use controls in loess hilly regions, e.g., land use intensification, agricultural protection and environmental conservation. PMID:25170679

A Simplified Land Model (SLM) for use in cloud-resolving models: Formulation and evaluation

NASA Astrophysics Data System (ADS)

Lee, Jungmin M.; Khairoutdinov, Marat

2015-09-01

A Simplified Land Model (SLM) that uses a minimalist set of parameters with a single-layer vegetation and multilevel soil structure has been developed distinguishing canopy and undercanopy energy budgets. The primary motivation has been to design a land model for use in the System for Atmospheric Modeling (SAM) cloud-resolving model to study land-atmosphere interactions with a sufficient level of realism. SLM uses simplified expressions for the transport of heat, moisture, momentum, and radiation in soil-vegetation system. The SLM performance has been evaluated over several land surface types using summertime tower observations of micrometeorological and biophysical data from three AmeriFlux sites, which include grassland, cropland, and deciduous-broadleaf forest. In general, the SLM captures the observed diurnal cycle of surface energy budget and soil temperature reasonably well, although reproducing the evolution of soil moisture, especially after rain events, has been challenging. The SLM coupled to SAM has been applied to the case of summertime shallow cumulus convection over land based on the Atmospheric Radiation Measurements (ARM) Southern Great Plain (SGP) observations. The simulated surface latent and sensible heat fluxes as well as the evolution of thermodynamic profiles in convective boundary layer agree well with the estimates based on the observations. Sensitivity of atmospheric boundary layer development to the soil moisture and different land cover types has been also examined.

Quantifying and Modelling Long Term Sediment Dynamics in Catchments in Western Europe

NASA Astrophysics Data System (ADS)

Notebaert, B.; De Brue, H.; Verstraeten, G.; Broothaerts, N.

2015-12-01

Quantification of sediment dynamics allows to get insight in driving forces and internal dynamics of the sediment cascade system. A useful tool to achieve this is the sediment budget approach, which encompasses the quantification of different sinks and sources. A Holocene time-differentiated sediment budget has been constructed for the Belgian Dijle River catchment (720 km²), based on a large set of field data. The results show how soil erosion is driven by land use changes over longer timescales. Sediment redistribution and the relative importance of the different sinks also vary over time, mainly as a result of changing land use and related landscape connectivity. However, the coarse temporal resolution typically associated with Holocene studies complicates the understanding of sub-millennial scale processes. In a second step, the field-based sediment budget was combined with a modeling approach using Watem/Sedem, a spatially distributed model that simulates soil erosion and colluvial deposition. After validation of the model calibration against the sediment budget, the model was used in a sensitivity analysis. Results confirm the overwhelming influence of human land use on both soil erosion and landscape connectivity, whereas the climatic impact is comparatively small. In addition to catchment-wide simulations, the model also served to test the relative importance of lynchets and dry valleys in different environments. Finally, the geomorphic model was used to simulate past land use, taking into account equifinality. For this purpose, a large series of hypothetical time-independent land use maps of the Dijle catchment were modeled based on a multi-objective allocation algorithm, and applied in Watem/Sedem. Modeled soil erosion and sediment deposition outcomes for each scenario were subsequently compared with the field-based record, taking into account uncertainties. As such, the model allows to evaluate and select realistic land use scenarios for the Holocene.

Research priorities in land use and land-cover change for the Earth system and integrated assessment modelling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hibbard, Kathleen A.; Janetos, Anthony C.; Van Vuuren, Detlef

2010-11-15

This special issue has highlighted recent and innovative methods and results that integrate observations and AQ3 modelling analyses of regional to global aspect of biophysical and biogeochemical interactions of land-cover change with the climate system. Both the Earth System and the Integrated Assessment modeling communities recognize the importance of an accurate representation of land use and land-cover change to understand and quantify the interactions and feedbacks with the climate and socio-economic systems, respectively. To date, cooperation between these communities has been limited. Based on common interests, this work discusses research priorities in representing land use and land-cover change for improvedmore » collaboration across modelling, observing and measurement communities. Major research topics in land use and land-cover change are those that help us better understand (1) the interaction of land use and land cover with the climate system (e.g. carbon cycle feedbacks), (2) the provision of goods and ecosystem services by terrestrial (natural and anthropogenic) land-cover types (e.g. food production), (3) land use and management decisions and (4) opportunities and limitations for managing climate change (for both mitigation and adaptation strategies).« less

Climate Change and Socio-Hydrological Dynamics: Adaptations and Feedbacks

NASA Astrophysics Data System (ADS)

Woyessa, Yali E.; Welderufael, Worku A.

2012-10-01

A functioning ecological system results in ecosystem goods and services which are of direct value to human beings. Ecosystem services are the conditions and processes which sustain and fulfil human life, and maintain biodiversity and the production of ecosystem goods. However, human actions affect ecological systems and the services they provide through various activities, such as land use, water use, pollution and climate change. Climate change is perhaps one of the most important sustainable development challenges that threatens to undo many of the development efforts being made to reach the targets set for the Millennium Development Goals. Understanding the provision of ecosystem services and how they change under different scenarios of climate and biophysical conditions could assist in bringing the issue of ecosystem services into decision making process. Similarly, the impacts of land use change on ecosystems and biodiversity have received considerable attention from ecologists and hydrologists alike. Land use change in a catchment can impact on water supply by altering hydrological processes, such as infiltration, groundwater recharge, base flow and direct runoff. In the past a variety of models were used for predicting landuse changes. Recently, the focus has shifted away from using mathematically oriented models to agent-based modeling (ABM) approach to simulate land use scenarios. The agent-based perspective, with regard to land-use cover change, is centered on the general nature and rules of land-use decision making by individuals. A conceptual framework is developed to investigate the possibility of incorporating the human dimension of land use decision and climate change model into a hydrological model in order to assess the impact of future land use scenario and climate change on the ecological system in general and water resources in particular.

Mars Exploration Rovers Landing Dispersion Analysis

NASA Technical Reports Server (NTRS)

Knocke, Philip C.; Wawrzyniak, Geoffrey G.; Kennedy, Brian M.; Desai, Prasun N.; Parker, TImothy J.; Golombek, Matthew P.; Duxbury, Thomas C.; Kass, David M.

2004-01-01

Landing dispersion estimates for the Mars Exploration Rover missions were key elements in the site targeting process and in the evaluation of landing risk. This paper addresses the process and results of the landing dispersion analyses performed for both Spirit and Opportunity. The several contributors to landing dispersions (navigation and atmospheric uncertainties, spacecraft modeling, winds, and margins) are discussed, as are the analysis tools used. JPL's MarsLS program, a MATLAB-based landing dispersion visualization and statistical analysis tool, was used to calculate the probability of landing within hazardous areas. By convolving this with the probability of landing within flight system limits (in-spec landing) for each hazard area, a single overall measure of landing risk was calculated for each landing ellipse. In-spec probability contours were also generated, allowing a more synoptic view of site risks, illustrating the sensitivity to changes in landing location, and quantifying the possible consequences of anomalies such as incomplete maneuvers. Data and products required to support these analyses are described, including the landing footprints calculated by NASA Langley's POST program and JPL's AEPL program, cartographically registered base maps and hazard maps, and flight system estimates of in-spec landing probabilities for each hazard terrain type. Various factors encountered during operations, including evolving navigation estimates and changing atmospheric models, are discussed and final landing points are compared with approach estimates.

"Global warming, continental drying? Interpreting projected aridity changes over land under climate change"

NASA Astrophysics Data System (ADS)

Berg, Alexis

2017-04-01

In recent years, a number of studies have suggested that, as climate warms, the land surface will globally become more arid. Such results usually rely on drought or aridity diagnostics, such as the Palmer Drought Severity Index or the Aridity Index (ratio of precipitation over potential evapotranspiration, PET), applied to climate model projections of surface climate. From a global perspective, the projected widespread drying of the land surface is generally interpreted as the result of the dominant, ubiquitous warming-induced PET increase, which overwhelms the slight overall precipitation increase projected over land. However, several lines of evidence, based on (paleo)observations and climate model projections, raise questions regarding this interpretation of terrestrial climate change. In this talk, I will review elements of the literature supporting these different perspectives, and will present recent results based on CMIP5 climate model projections regarding changes in aridity over land that shed some light on this discussion. Central to the interpretation of projected land aridity changes is the understanding of projected PET trends over land and their link with changes in other variables of the terrestrial water cycle (ET, soil moisture) and surface climate in the context of the coupled land-atmosphere system.

Global consequences of afforestation and bioenergy cultivation on ecosystem service indicators

NASA Astrophysics Data System (ADS)

Krause, Andreas; Pugh, Thomas A. M.; Bayer, Anita D.; Doelman, Jonathan C.; Humpenöder, Florian; Anthoni, Peter; Olin, Stefan; Bodirsky, Benjamin L.; Popp, Alexander; Stehfest, Elke; Arneth, Almut

2017-11-01

Land management for carbon storage is discussed as being indispensable for climate change mitigation because of its large potential to remove carbon dioxide from the atmosphere, and to avoid further emissions from deforestation. However, the acceptance and feasibility of land-based mitigation projects depends on potential side effects on other important ecosystem functions and their services. Here, we use projections of future land use and land cover for different land-based mitigation options from two land-use models (IMAGE and MAgPIE) and evaluate their effects with a global dynamic vegetation model (LPJ-GUESS). In the land-use models, carbon removal was achieved either via growth of bioenergy crops combined with carbon capture and storage, via avoided deforestation and afforestation, or via a combination of both. We compare these scenarios to a reference scenario without land-based mitigation and analyse the LPJ-GUESS simulations with the aim of assessing synergies and trade-offs across a range of ecosystem service indicators: carbon storage, surface albedo, evapotranspiration, water runoff, crop production, nitrogen loss, and emissions of biogenic volatile organic compounds. In our mitigation simulations cumulative carbon storage by year 2099 ranged between 55 and 89 GtC. Other ecosystem service indicators were influenced heterogeneously both positively and negatively, with large variability across regions and land-use scenarios. Avoided deforestation and afforestation led to an increase in evapotranspiration and enhanced emissions of biogenic volatile organic compounds, and to a decrease in albedo, runoff, and nitrogen loss. Crop production could also decrease in the afforestation scenarios as a result of reduced crop area, especially for MAgPIE land-use patterns, if assumed increases in crop yields cannot be realized. Bioenergy-based climate change mitigation was projected to affect less area globally than in the forest expansion scenarios, and resulted in less pronounced changes in most ecosystem service indicators than forest-based mitigation, but included a possible decrease in nitrogen loss, crop production, and biogenic volatile organic compounds emissions.

Simple rules can guide whether land- or ocean-based conservation will best benefit marine ecosystems.

PubMed

Saunders, Megan I; Bode, Michael; Atkinson, Scott; Klein, Carissa J; Metaxas, Anna; Beher, Jutta; Beger, Maria; Mills, Morena; Giakoumi, Sylvaine; Tulloch, Vivitskaia; Possingham, Hugh P

2017-09-01

Coastal marine ecosystems can be managed by actions undertaken both on the land and in the ocean. Quantifying and comparing the costs and benefits of actions in both realms is therefore necessary for efficient management. Here, we quantify the link between terrestrial sediment runoff and a downstream coastal marine ecosystem and contrast the cost-effectiveness of marine- and land-based conservation actions. We use a dynamic land- and sea-scape model to determine whether limited funds should be directed to 1 of 4 alternative conservation actions-protection on land, protection in the ocean, restoration on land, or restoration in the ocean-to maximise the extent of light-dependent marine benthic habitats across decadal timescales. We apply the model to a case study for a seagrass meadow in Australia. We find that marine restoration is the most cost-effective action over decadal timescales in this system, based on a conservative estimate of the rate at which seagrass can expand into a new habitat. The optimal decision will vary in different social-ecological contexts, but some basic information can guide optimal investments to counteract land- and ocean-based stressors: (1) marine restoration should be prioritised if the rates of marine ecosystem decline and expansion are similar and low; (2) marine protection should take precedence if the rate of marine ecosystem decline is high or if the adjacent catchment is relatively intact and has a low rate of vegetation decline; (3) land-based actions are optimal when the ratio of marine ecosystem expansion to decline is greater than 1:1.4, with terrestrial restoration typically the most cost-effective action; and (4) land protection should be prioritised if the catchment is relatively intact but the rate of vegetation decline is high. These rules of thumb illustrate how cost-effective conservation outcomes for connected land-ocean systems can proceed without complex modelling.

Implication of remotely sensed data to incorporate land cover effect into a linear reservoir-based rainfall-runoff model

USDA-ARS?s Scientific Manuscript database

This study investigates the effect of land use on the Geomorphological Cascade of unequal Linear Reservoirs (GCUR) model. We use the Normalized Difference Vegetation Index (NDVI) derived from remotely sensed data as a measure of land use. Our approach has two important aspects: (i) it considers the ...

Using 3d Bim Model for the Value-Based Land Share Calculations

NASA Astrophysics Data System (ADS)

Çelik Şimşek, N.; Uzun, B.

2017-11-01

According to the Turkish condominium ownership system, 3D physical buildings and its condominium units are registered to the condominium ownership books via 2D survey plans. Currently, 2D representations of the 3D physical objects, causes inaccurate and deficient implementations for the determination of the land shares. Condominium ownership and easement right are established with a clear indication of land shares (condominium ownership law, article no. 3). So, the land share of each condominium unit have to be determined including the value differences among the condominium units. However the main problem is that, land share has often been determined with area based over the project before construction of the building. The objective of this study is proposing a new approach in terms of value-based land share calculations of the condominium units that subject to condominium ownership. So, the current approaches and its failure that have taken into account in determining the land shares are examined. And factors that affect the values of the condominium units are determined according to the legal decisions. This study shows that 3D BIM models can provide important approaches for the valuation problems in the determination of the land shares.

Calibration to improve forward model simulation of microwave emissivity at GPM frequencies over the U.S. Southern Great Plains

PubMed Central

Harrison, Kenneth W.; Tian, Yudong; Peters-Lidard, Christa D.; Ringerud, Sarah; Kumar, Sujay V.

2018-01-01

Better estimation of land surface microwave emissivity promises to improve over-land precipitation retrievals in the GPM era. Forward models of land microwave emissivity are available but have suffered from poor parameter specification and limited testing. Here, forward models are calibrated and the accompanying change in predictive power is evaluated. With inputs (e.g., soil moisture) from the Noah land surface model and applying MODIS LAI data, two microwave emissivity models are tested, the Community Radiative Transfer Model (CRTM) and Community Microwave Emission Model (CMEM). The calibration is conducted with the NASA Land Information System (LIS) parameter estimation subsystem using AMSR-E based emissivity retrievals for the calibration dataset. The extent of agreement between the modeled and retrieved estimates is evaluated using the AMSR-E retrievals for a separate 7-year validation period. Results indicate that calibration can significantly improve the agreement, simulating emissivity with an across-channel average root-mean-square-difference (RMSD) of about 0.013, or about 20% lower than if relying on daily estimates based on climatology. The results also indicate that calibration of the microwave emissivity model alone, as was done in prior studies, results in as much as 12% higher across-channel average RMSD, as compared to joint calibration of the land surface and microwave emissivity models. It remains as future work to assess the extent to which the improvements in emissivity estimation translate into improvements in precipitation retrieval accuracy. PMID:29795962

The Lake Tahoe Basin Land Use Simulation Model

USGS Publications Warehouse

Forney, William M.; Oldham, I. Benson

2011-01-01

This U.S. Geological Survey Open-File Report describes the final modeling product for the Tahoe Decision Support System project for the Lake Tahoe Basin funded by the Southern Nevada Public Land Management Act and the U.S. Geological Survey's Geographic Analysis and Monitoring Program. This research was conducted by the U.S. Geological Survey Western Geographic Science Center. The purpose of this report is to describe the basic elements of the novel Lake Tahoe Basin Land Use Simulation Model, publish samples of the data inputs, basic outputs of the model, and the details of the Python code. The results of this report include a basic description of the Land Use Simulation Model, descriptions and summary statistics of model inputs, two figures showing the graphical user interface from the web-based tool, samples of the two input files, seven tables of basic output results from the web-based tool and descriptions of their parameters, and the fully functional Python code.

Multi-scale, multi-model assessment of projected land allocation

NASA Astrophysics Data System (ADS)

Vernon, C. R.; Huang, M.; Chen, M.; Calvin, K. V.; Le Page, Y.; Kraucunas, I.

2017-12-01

Effects of land use and land cover change (LULCC) on climate are generally classified into two scale-dependent processes: biophysical and biogeochemical. An extensive amount of research has been conducted related to the impact of each process under alternative climate change futures. However, these studies are generally focused on the impacts of a single process and fail to bridge the gap between sector-driven scale dependencies and any associated dynamics. Studies have been conducted to better understand the relationship of these processes but their respective scale has not adequately captured overall interdependencies between land surface changes and changes in other human-earth systems (e.g., energy, water, economic, etc.). There has also been considerable uncertainty surrounding land use land cover downscaling approaches due to scale dependencies. Demeter, a land use land cover downscaling and change detection model, was created to address this science gap. Demeter is an open-source model written in Python that downscales zonal land allocation projections to the gridded resolution of a user-selected spatial base layer (e.g., MODIS, NLCD, EIA CCI, etc.). Demeter was designed to be fully extensible to allow for module inheritance and replacement for custom research needs, such as flexible IO design to facilitate the coupling of Earth system models (e.g., the Accelerated Climate Modeling for Energy (ACME) and the Community Earth System Model (CESM)) to integrated assessment models (e.g., the Global Change Assessment Model (GCAM)). In this study, we first assessed the sensitivity of downscaled LULCC scenarios at multiple resolutions from Demeter to its parameters by comparing them to historical LULC change data. "Optimal" values of key parameters for each region were identified and used to downscale GCAM-based future scenarios consistent with those in the Land Use Model Intercomparison Project (LUMIP). Demeter-downscaled land use scenarios were then compared to the default LUMIP scenarios to illustrate the uncertainties in projected LULC as a result of difference in downscaling algorithms. Our results show that such uncertainties could propagate to other components in ACME and CESM and lead to significant differences in simulated water and biogeochemical cycles.

Monitoring and Predicting Land-use Changes and the Hydrology of the Urbanized Paochiao Watershed in Taiwan Using Remote Sensing Data, Urban Growth Models and a Hydrological Model.

PubMed

Lin, Yu-Pin; Lin, Yun-Bin; Wang, Yen-Tan; Hong, Nien-Ming

2008-02-04

Monitoring and simulating urban sprawl and its effects on land-use patterns andhydrological processes in urbanized watersheds are essential in land-use and waterresourceplanning and management. This study applies a novel framework to the urbangrowth model Slope, Land use, Excluded land, Urban extent, Transportation, andHillshading (SLEUTH) and land-use change with the Conversion of Land use and itsEffects (CLUE-s) model using historical SPOT images to predict urban sprawl in thePaochiao watershed in Taipei County, Taiwan. The historical and predicted land-use datawas input into Patch Analyst to obtain landscape metrics. This data was also input to theGeneralized Watershed Loading Function (GWLF) model to analyze the effects of futureurban sprawl on the land-use patterns and watershed hydrology. The landscape metrics ofthe historical SPOT images show that land-use patterns changed between 1990-2000. TheSLEUTH model accurately simulated historical land-use patterns and urban sprawl in thePaochiao watershed, and simulated future clustered land-use patterns (2001-2025). TheCLUE-s model also simulated land-use patterns for the same period and yielded historical trends in the metrics of land-use patterns. The land-use patterns predicted by the SLEUTHand CLUE-s models show the significant impact urban sprawl will have on land-usepatterns in the Paochiao watershed. The historical and predicted land-use patterns in thewatershed tended to fragment, had regular shapes and interspersion patterns, but wererelatively less isolated in 2001-2025 and less interspersed from 2005-2025 compared withland-use pattern in 1990. During the study, the variability and magnitude of hydrologicalcomponents based on the historical and predicted land-use patterns were cumulativelyaffected by urban sprawl in the watershed; specifically, surface runoff increasedsignificantly by 22.0% and baseflow decreased by 18.0% during 1990-2025. The proposedapproach is an effective means of enhancing land-use monitoring and management ofurbanized watersheds.

Research Advances on Radiation Transfer Modeling and Inversion for Multi-Scale Land Surface Remote Sensing

NASA Astrophysics Data System (ADS)

Liu, Q.

2011-09-01

At first, research advances on radiation transfer modeling on multi-scale remote sensing data are presented: after a general overview of remote sensing radiation transfer modeling, several recent research advances are presented, including leaf spectrum model (dPROS-PECT), vegetation canopy BRDF models, directional thermal infrared emission models(TRGM, SLEC), rugged mountains area radiation models, and kernel driven models etc. Then, new methodologies on land surface parameters inversion based on multi-source remote sensing data are proposed. The land surface Albedo, leaf area index, temperature/emissivity, and surface net radiation etc. are taken as examples. A new synthetic land surface parameter quantitative remote sensing product generation system is designed and the software system prototype will be demonstrated. At last, multi-scale field experiment campaigns, such as the field campaigns in Gansu and Beijing, China will be introduced briefly. The ground based, tower based, and airborne multi-angular measurement system have been built to measure the directional reflectance, emission and scattering characteristics from visible, near infrared, thermal infrared and microwave bands for model validation and calibration. The remote sensing pixel scale "true value" measurement strategy have been designed to gain the ground "true value" of LST, ALBEDO, LAI, soil moisture and ET etc. at 1-km2 for remote sensing product validation.

A web-based land cover classification system based on ontology model of different classification systems

NASA Astrophysics Data System (ADS)

Lin, Y.; Chen, X.

2016-12-01

Land cover classification systems used in remote sensing image data have been developed to meet the needs for depicting land covers in scientific investigations and policy decisions. However, accuracy assessments of a spate of data sets demonstrate that compared with the real physiognomy, each of the thematic map of specific land cover classification system contains some unavoidable flaws and unintended deviation. This work proposes a web-based land cover classification system, an integrated prototype, based on an ontology model of various classification systems, each of which is assigned the same weight in the final determination of land cover type. Ontology, a formal explication of specific concepts and relations, is employed in this prototype to build up the connections among different systems to resolve the naming conflicts. The process is initialized by measuring semantic similarity between terminologies in the systems and the search key to produce certain set of satisfied classifications, and carries on through searching the predefined relations in concepts of all classification systems to generate classification maps with user-specified land cover type highlighted, based on probability calculated by votes from data sets with different classification system adopted. The present system is verified and validated by comparing the classification results with those most common systems. Due to full consideration and meaningful expression of each classification system using ontology and the convenience that the web brings with itself, this system, as a preliminary model, proposes a flexible and extensible architecture for classification system integration and data fusion, thereby providing a strong foundation for the future work.

The effects of changing land cover on streamflow simulation in Puerto Rico

Treesearch

A.E. Van Beusekom; L.E. Hay; R.J. Viger; W.A. Gould; J.A. Collazo; A. Henareh Khalyani

2014-01-01

This study quantitatively explores whether land cover changes have a substantive impact on simulated streamflow within the tropical island setting of Puerto Rico. The Precipitation Runoff Modeling System (PRMS) was used to compare streamflow simulations based on five static parameterizations of land cover with those based on dynamically varying parameters derived from...

Harmonizing estimates of forest land area from national-level forest inventory and satellite imagery

Treesearch

Bonnie Ruefenacht; Mark D. Nelson; Mark Finco

2009-01-01

Estimates of forest land area are derived both from national-level forest inventories and satellite image-based map products. These estimates can differ substantially within subregional extents (e.g., states or provinces) primarily due to differences in definitions of forest land between inventory- and image-based approaches. We present a geospatial modeling approach...

Spatial and Temporal Dynamics and Value of Nature-Based Recreation, Estimated via Social Media.

PubMed

Sonter, Laura J; Watson, Keri B; Wood, Spencer A; Ricketts, Taylor H

2016-01-01

Conserved lands provide multiple ecosystem services, including opportunities for nature-based recreation. Managing this service requires understanding the landscape attributes underpinning its provision, and how changes in land management affect its contribution to human wellbeing over time. However, evidence from both spatially explicit and temporally dynamic analyses is scarce, often due to data limitations. In this study, we investigated nature-based recreation within conserved lands in Vermont, USA. We used geotagged photographs uploaded to the photo-sharing website Flickr to quantify visits by in-state and out-of-state visitors, and we multiplied visits by mean trip expenditures to show that conserved lands contributed US $1.8 billion (US $0.18-20.2 at 95% confidence) to Vermont's tourism industry between 2007 and 2014. We found eight landscape attributes explained the pattern of visits to conserved lands; visits were higher in larger conserved lands, with less forest cover, greater trail density and more opportunities for snow sports. Some of these attributes differed from those found in other locations, but all aligned with our understanding of recreation in Vermont. We also found that using temporally static models to inform conservation decisions may have perverse outcomes for nature-based recreation. For example, static models suggest conserved land with less forest cover receive more visits, but temporally dynamic models suggest clearing forests decreases, rather than increases, visits to these sites. Our results illustrate the importance of understanding both the spatial and temporal dynamics of ecosystem services for conservation decision-making.

Building factorial regression models to explain and predict nitrate concentrations in groundwater under agricultural land

NASA Astrophysics Data System (ADS)

Stigter, T. Y.; Ribeiro, L.; Dill, A. M. M. Carvalho

2008-07-01

SummaryFactorial regression models, based on correspondence analysis, are built to explain the high nitrate concentrations in groundwater beneath an agricultural area in the south of Portugal, exceeding 300 mg/l, as a function of chemical variables, electrical conductivity (EC), land use and hydrogeological setting. Two important advantages of the proposed methodology are that qualitative parameters can be involved in the regression analysis and that multicollinearity is avoided. Regression is performed on eigenvectors extracted from the data similarity matrix, the first of which clearly reveals the impact of agricultural practices and hydrogeological setting on the groundwater chemistry of the study area. Significant correlation exists between response variable NO3- and explanatory variables Ca 2+, Cl -, SO42-, depth to water, aquifer media and land use. Substituting Cl - by the EC results in the most accurate regression model for nitrate, when disregarding the four largest outliers (model A). When built solely on land use and hydrogeological setting, the regression model (model B) is less accurate but more interesting from a practical viewpoint, as it is based on easily obtainable data and can be used to predict nitrate concentrations in groundwater in other areas with similar conditions. This is particularly useful for conservative contaminants, where risk and vulnerability assessment methods, based on assumed rather than established correlations, generally produce erroneous results. Another purpose of the models can be to predict the future evolution of nitrate concentrations under influence of changes in land use or fertilization practices, which occur in compliance with policies such as the Nitrates Directive. Model B predicts a 40% decrease in nitrate concentrations in groundwater of the study area, when horticulture is replaced by other land use with much lower fertilization and irrigation rates.

A land-use and land-cover modeling strategy to support a national assessment of carbon stocks and fluxes

USGS Publications Warehouse

Sohl, Terry L.; Sleeter, Benjamin M.; Zhu, Zhiliang; Sayler, Kristi L.; Bennett, Stacie; Bouchard, Michelle; Reker, Ryan R.; Hawbaker, Todd J.; Wein, Anne M.; Liu, Shuguang; Kanengieter, Ronald L.; Acevedo, William

2012-01-01

Changes in land use, land cover, disturbance regimes, and land management have considerable influence on carbon and greenhouse gas (GHG) fluxes within ecosystems. Through targeted land-use and land-management activities, ecosystems can be managed to enhance carbon sequestration and mitigate fluxes of other GHGs. National-scale, comprehensive analyses of carbon sequestration potential by ecosystem are needed, with a consistent, nationally applicable land-use and land-cover (LULC) modeling framework a key component of such analyses. The U.S. Geological Survey has initiated a project to analyze current and projected future GHG fluxes by ecosystem and quantify potential mitigation strategies. We have developed a unique LULC modeling framework to support this work. Downscaled scenarios consistent with IPCC Special Report on Emissions Scenarios (SRES) were constructed for U.S. ecoregions, and the FORE-SCE model was used to spatially map the scenarios. Results for a prototype demonstrate our ability to model LULC change and inform a biogeochemical modeling framework for analysis of subsequent GHG fluxes. The methodology was then successfully used to model LULC change for four IPCC SRES scenarios for an ecoregion in the Great Plains. The scenario-based LULC projections are now being used to analyze potential GHG impacts of LULC change across the U.S.

Understanding the Dynamics of Socio-Hydrological Environment: a Conceptual Framework

NASA Astrophysics Data System (ADS)

Woyessa, Y.; Welderufael, W.; Edossa, D.

2011-12-01

Human actions affect ecological systems and the services they provide through various activities, such as land use, water use, pollution and climate change. Climate change is perhaps one of the most important sustainable development challenges that threaten to undo many of the development efforts being made to reach the targets set for the Millennium Development Goals. Understanding the change of ecosystems under different scenarios of climate and biophysical conditions could assist in bringing the issue of ecosystem services into decision making process. Similarly, the impacts of land use change on ecosystems and biodiversity have received considerable attention from ecologists and hydrologists alike. Land use change in a catchment can impact on water supply by altering hydrological processes, such as infiltration, groundwater recharge, base flow and direct runoff. In the past a variety of models were used for predicting land-use changes. Recently the focus has shifted away from using mathematically oriented models to agent-based modelling (ABM) approach to simulate land use scenarios. A conceptual framework is being developed which integrates climate change scenarios and the human dimension of land use decision into a hydrological model in order to assess its impacts on the socio-hydrological dynamics of a river basin. The following figures present the framework for the analysis and modelling of the socio-hydrological dynamics. Keywords: climate change, land use, river basin

Incorporating GIS and remote sensing for census population disaggregation

NASA Astrophysics Data System (ADS)

Wu, Shuo-Sheng'derek'

Census data are the primary source of demographic data for a variety of researches and applications. For confidentiality issues and administrative purposes, census data are usually released to the public by aggregated areal units. In the United States, the smallest census unit is census blocks. Due to data aggregation, users of census data may have problems in visualizing population distribution within census blocks and estimating population counts for areas not coinciding with census block boundaries. The main purpose of this study is to develop methodology for estimating sub-block areal populations and assessing the estimation errors. The City of Austin, Texas was used as a case study area. Based on tax parcel boundaries and parcel attributes derived from ancillary GIS and remote sensing data, detailed urban land use classes were first classified using a per-field approach. After that, statistical models by land use classes were built to infer population density from other predictor variables, including four census demographic statistics (the Hispanic percentage, the married percentage, the unemployment rate, and per capita income) and three physical variables derived from remote sensing images and building footprints vector data (a landscape heterogeneity statistics, a building pattern statistics, and a building volume statistics). In addition to statistical models, deterministic models were proposed to directly infer populations from building volumes and three housing statistics, including the average space per housing unit, the housing unit occupancy rate, and the average household size. After population models were derived or proposed, how well the models predict populations for another set of sample blocks was assessed. The results show that deterministic models were more accurate than statistical models. Further, by simulating the base unit for modeling from aggregating blocks, I assessed how well the deterministic models estimate sub-unit-level populations. I also assessed the aggregation effects and the resealing effects on sub-unit estimates. Lastly, from another set of mixed-land-use sample blocks, a mixed-land-use model was derived and compared with a residential-land-use model. The results of per-field land use classification are satisfactory with a Kappa accuracy statistics of 0.747. Model Assessments by land use show that population estimates for multi-family land use areas have higher errors than those for single-family land use areas, and population estimates for mixed land use areas have higher errors than those for residential land use areas. The assessments of sub-unit estimates using a simulation approach indicate that smaller areas show higher estimation errors, estimation errors do not relate to the base unit size, and resealing improves all levels of sub-unit estimates.

Defining, Measuring, and Incentivizing Sustainable Land Use to Meet Human Needs

NASA Astrophysics Data System (ADS)

Nicholas, K. A.; Brady, M. V.; Olin, S.; Ekroos, J.; Hall, M.; Seaquist, J. W.; Lehsten, V.; Smith, H.

2016-12-01

Land is a natural capital that supports the flow of an enormous amount of ecosystem services critical to human welfare. Sustainable land use, which we define as land use that meets both current and future human needs for ecosystem services, is essential to meet global goals for climate mitigation and sustainable development, while maintaining natural capital. However, it is not clear what governance is needed to achieve sustainable land use under multiple goals (as defined by the values of relevant decision-makers and land managers), particularly under climate change. Here we develop a conceptual model for examining the interactions and tradeoffs among multiple goals, as well as their spatial interactions (teleconnections), in research developed using Design Thinking principles. We have selected five metrics for provisioning (food production, and fiber production for wood and energy), regulating and maintenance (climate mitigation and biodiversity conservation), and cultural (heritage) ecosystem services. Using the case of Sweden, we estimate indicators for these metrics using a combination of existing data synthesis and process-based simulation modeling. We also develop and analyze new indicators (e.g., combining data on land use, bird conservation status, and habitat specificity to make a predictive model of bird diversity changes on agricultural or forested land). Our results highlight both expected tradeoffs (e.g., between food production and biodiversity conservation) as well as unexpected opportunities for synergies under different land management scenarios and strategies. Our model also provides a practical way to make decision-maker values explicit by comparing both quantity and preferences for bundles of ecosystem services under various scenarios. We hope our model will help in considering competing interests and shaping economic incentives and governance structures to meet national targets in support of global goals for sustainable management of land-based ecosystem services.

HYDROLOGIC MODEL UNCERTAINTY ASSOCIATED WITH SIMULATING FUTURE LAND-COVER/USE SCENARIOS: A RETROSPECTIVE ANALYSIS

EPA Science Inventory

GIS-based hydrologic modeling offers a convenient means of assessing the impacts associated with land-cover/use change for environmental planning efforts. Alternative future scenarios can be used as input to hydrologic models and compared with existing conditions to evaluate pot...

Land use and climate change impacts on runoff and soil erosion at the hillslope scale in the Brazilian Cerrado.

PubMed

Anache, Jamil A A; Flanagan, Dennis C; Srivastava, Anurag; Wendland, Edson C

2018-05-01

Land use and climate change can influence runoff and soil erosion, threatening soil and water conservation in the Cerrado biome in Brazil. The adoption of a process-based model was necessary due to the lack of long-term observed data. Our goals were to calibrate the WEPP (Water Erosion Prediction Project) model for different land uses under subtropical conditions in the Cerrado biome; predict runoff and soil erosion for these different land uses; and simulate runoff and soil erosion considering climate change. We performed the model calibration using a 5-year dataset (2012-2016) of observed runoff and soil loss in four different land uses (wooded Cerrado, tilled fallow without plant cover, pasture, and sugarcane) in experimental plots. Selected soil and management parameters were optimized for each land use during the WEPP model calibration with the existing field data. The simulations were conducted using the calibrated WEPP model components with a 100-year climate dataset created with CLIGEN (weather generator) based on regional climate statistics. We obtained downscaled General Circulation Model (GCM) projections, and runoff and soil loss were predicted with WEPP using future climate scenarios for 2030, 2060, and 2090 considering different Representative Concentration Pathways (RCPs). The WEPP model had an acceptable performance for the subtropical conditions. Land use can influence runoff and soil loss rates in a significant way. Potential climate changes, which indicate the increase of rainfall intensities and depths, may increase the variability and rates of runoff and soil erosion. However, projected climate changes did not significantly affect the runoff and soil erosion for the four analyzed land uses at our location. Finally, the runoff behavior was distinct for each land use, but for soil loss we found similarities between pasture and wooded Cerrado, suggesting that the soil may attain a sustainable level when the land management follows conservation principles. Copyright © 2017 Elsevier B.V. All rights reserved.

Land Use Change on Household Farms in the Ecuadorian Amazon: Design and Implementation of an Agent-Based Model

PubMed Central

Mena, Carlos F.; Walsh, Stephen J.; Frizzelle, Brian G.; Xiaozheng, Yao; Malanson, George P.

2010-01-01

This paper describes the design and implementation of an Agent-Based Model (ABM) used to simulate land use change on household farms in the Northern Ecuadorian Amazon (NEA). The ABM simulates decision-making processes at the household level that is examined through a longitudinal, socio-economic and demographic survey that was conducted in 1990 and 1999. Geographic Information Systems (GIS) are used to establish spatial relationships between farms and their environment, while classified Landsat Thematic Mapper (TM) imagery is used to set initial land use/land cover conditions for the spatial simulation, assess from-to land use/land cover change patterns, and describe trajectories of land use change at the farm and landscape levels. Results from prior studies in the NEA provide insights into the key social and ecological variables, describe human behavioral functions, and examine population-environment interactions that are linked to deforestation and agricultural extensification, population migration, and demographic change. Within the architecture of the model, agents are classified as active or passive. The model comprises four modules, i.e., initialization, demography, agriculture, and migration that operate individually, but are linked through key household processes. The main outputs of the model include a spatially-explicit representation of the land use/land cover on survey and non-survey farms and at the landscape level for each annual time-step, as well as simulated socio-economic and demographic characteristics of households and communities. The work describes the design and implementation of the model and how population-environment interactions can be addressed in a frontier setting. The paper contributes to land change science by examining important pattern-process relations, advocating a spatial modeling approach that is capable of synthesizing fundamental relationships at the farm level, and links people and environment in complex ways. PMID:24436501

Estimating ecosystem carbon change in the Conterminous United States based on 40 years of land-use change and disturbance

NASA Astrophysics Data System (ADS)

Sleeter, B. M.; Rayfield, B.; Liu, J.; Sherba, J.; Daniel, C.; Frid, L.; Wilson, T. S.; Zhu, Z.

2016-12-01

Since 1970, the combined changes in land use, land management, climate, and natural disturbances have dramatically altered land cover in the United States, resulting in the potential for significant changes in terrestrial carbon storage and flux between ecosystems and the atmosphere. Processes including urbanization, agricultural expansion and contraction, and forest management have had impacts - both positive and negative - on the amount of natural vegetation, the age structure of forests, and the amount of impervious cover. Anthropogenic change coupled with climate-driven changes in natural disturbance regimes, particularly the frequency and severity of wildfire, together determine the spatio-temporal patterns of land change and contribute to changing ecosystem carbon dynamics. Quantifying this effect and its associated uncertainties is fundamental to developing a rigorous and transparent carbon monitoring and assessment programs. However, large-scale systematic inventories of historical land change and their associated uncertainties are sparse. To address this need, we present a newly developed modeling framework, the Land Use and Carbon Scenario Simulator (LUCAS). The LUCAS model integrates readily available high quality, empirical land-change data into a stochastic space-time simulation model representing land change feedbacks on carbon cycling in terrestrial ecosystems. We applied the LUCAS model to estimate regional scale changes in carbon storage, atmospheric flux, and net biome production in 84 ecological regions of the conterminous United States for the period 1970-2015. The model was parameterized using a newly available set of high resolution (30 m) land-change data, compiled from Landsat remote sensing imagery, including estimates of uncertainty. Carbon flux parameters for each ecological region were derived from the IBIS dynamic global vegetation model with full carbon cycle accounting. This paper presents our initial findings describing regional and temporal changes and variability in carbon storage and flux resulting from land use change and disturbance between 1973 and 2015. Additionally, based on stochastic simulations we quantify and present key sources of uncertainty in the estimation of terrestrial ecosystem carbon dynamics.

Scenarios of land use change for agriculture: the role of Land Evaluation in improving model simulation

NASA Astrophysics Data System (ADS)

Mereu, V.; Santini, M.; Dettori, G.; Muresu, P.; Spano, D.; Duce, P.

2009-12-01

Integrated scenarios of future climate and land use represent a useful input for impact studies about global changes. In particular, improving future land use simulations is essential for the agricultural sector, which is influenced by both biogeophysical constraints and human needs. Often land use change models are mainly based on statistical relationships between known land use distribution and biophysical or socio-economic factors, neglecting the necessary consideration of physical constraints that interact in making lands more or less capable for agriculture and suitable for supporting specific crops. In this study, a well developed land use change model (CLUE@CMCC) was suited for the Mediterranean basin case study, focusing on croplands. Several climate scenarios and future demands for croplands were combined to drive the model, while the same climate scenarios were used to more reliably allocate crops in the most suitable areas on the basis of Land Evaluation techniques. The probability for each map unit to sustain a specific crop, usually related to location characteristics, elasticity to conversion and competition among land use types, now includes specific crop-favoring location characteristics. Results, besides improving the consistency of the land use change model to allocate land for the future, can have the main feedback to suggest feasibility or reasonable thresholds to adjust land use demands during dynamic simulations.

An international land-biosphere model benchmarking activity for the IPCC Fifth Assessment Report (AR5)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hoffman, Forrest M; Randerson, James T; Thornton, Peter E

2009-12-01

The need to capture important climate feedbacks in general circulation models (GCMs) has resulted in efforts to include atmospheric chemistry and land and ocean biogeochemistry into the next generation of production climate models, called Earth System Models (ESMs). While many terrestrial and ocean carbon models have been coupled to GCMs, recent work has shown that such models can yield a wide range of results (Friedlingstein et al., 2006). This work suggests that a more rigorous set of global offline and partially coupled experiments, along with detailed analyses of processes and comparisons with measurements, are needed. The Carbon-Land Model Intercomparison Projectmore » (C-LAMP) was designed to meet this need by providing a simulation protocol and model performance metrics based upon comparisons against best-available satellite- and ground-based measurements (Hoffman et al., 2007). Recently, a similar effort in Europe, called the International Land Model Benchmark (ILAMB) Project, was begun to assess the performance of European land surface models. These two projects will now serve as prototypes for a proposed international land-biosphere model benchmarking activity for those models participating in the IPCC Fifth Assessment Report (AR5). Initially used for model validation for terrestrial biogeochemistry models in the NCAR Community Land Model (CLM), C-LAMP incorporates a simulation protocol for both offline and partially coupled simulations using a prescribed historical trajectory of atmospheric CO2 concentrations. Models are confronted with data through comparisons against AmeriFlux site measurements, MODIS satellite observations, NOAA Globalview flask records, TRANSCOM inversions, and Free Air CO2 Enrichment (FACE) site measurements. Both sets of experiments have been performed using two different terrestrial biogeochemistry modules coupled to the CLM version 3 in the Community Climate System Model version 3 (CCSM3): the CASA model of Fung, et al., and the carbon-nitrogen (CN) model of Thornton. Comparisons of the CLM3 offline results against observational datasets have been performed and are described in Randerson et al. (2009). CLM version 4 has been evaluated using C-LAMP, showing improvement in many of the metrics. Efforts are now underway to initiate a Nitrogen-Land Model Intercomparison Project (N-LAMP) to better constrain the effects of the nitrogen cycle in biosphere models. Presented will be new results from C-LAMP for CLM4, initial N-LAMP developments, and the proposed land-biosphere model benchmarking activity.« less

Assessing the Impact of Land Use and Land Cover Change on Global Water Resources

NASA Astrophysics Data System (ADS)

Batra, N.; Yang, Y. E.; Choi, H. I.; Islam, A.; Charlotte, D. F.; Cai, X.; Kumar, P.

2007-12-01

Land use and land cover changes (LULCC) significantly modify the hydrological regime of the watersheds, affecting water resources and environment from regional to global scale. This study seeks to advance and integrate water and energy cycle observation, scientific understanding, and human impacts to assess future water availability. To achieve the research objective, we integrate and interpret past and current space based and in situ observations into a global hydrologic model (GHM). GHM is developed with enhanced spatial and temporal resolution, physical complexity, hydrologic theory and processes to quantify the impact of LULCC on physical variables: surface runoff, subsurface flow, groundwater, infiltration, ET, soil moisture, etc. Coupled with the common land model (CLM), a 3-dimensional volume averaged soil-moisture transport (VAST) model is expanded to incorporate the lateral flow and subgrid heterogeneity. The model consists of 11 soil-hydrology layers to predict lateral as well as vertical moisture flux transport based on Richard's equations. The primary surface boundary conditions (SBCs) include surface elevation and its derivatives, land cover category, sand and clay fraction profiles, bedrock depth and fractional vegetation cover. A consistent global GIS-based dataset is constructed for the SBCs of the model from existing observational datasets comprising of various resolutions, map projections and data formats. Global ECMWF data at 6-hour time steps for the period 1971 through 2000 is processed to get the forcing data which includes incoming longwave and shortwave radiation, precipitation, air temperature, pressure, wind components, boundary layer height and specific humidity. Land use land cover data, generated using IPCC scenarios for every 10 years from 2000 to 2100 is used for future assessment on water resources. Alterations due to LULCC on surface water balance components: ET, groundwater recharge and runoff are then addressed in the study. Land use change disrupts the hydrological cycle through increasing the water yield at some places leading to floods while diminishing, or even eliminating the low flow at other places.

AUTOMATED GEOSPATIAL WATERSHED ASSESSMENT (AGWA): A GIS-BASED HYDROLOGIC MODELING TOOL FOR LANDSCAPE ASSESSMENT AND WATERSHED MANAGEMENT

EPA Science Inventory

The assessment of land use and land cover is an extremely important activity for contemporary land management. A large body of current literature suggests that human land-use practice is the most important factor influencing natural resource management and environmental condition...

Model Meets Data: Challenges and Opportunities to Implement Land Management in Earth System Models

NASA Astrophysics Data System (ADS)

Pongratz, J.; Dolman, A. J.; Don, A.; Erb, K. H.; Fuchs, R.; Herold, M.; Jones, C.; Luyssaert, S.; Kuemmerle, T.; Meyfroidt, P.

2016-12-01

Land-based demand for food and fibre is projected to increase in the future. In light of global sustainability challenges only part of this increase will be met by expansion of land use into relatively untouched regions. Additional demand will have to be fulfilled by intensification and other adjustments in management of land that already is under agricultural and forestry use. Such land management today occurs on about half of the ice-free land surface, as compared to only about one quarter that has undergone a change in land cover. As the number of studies revealing substantial biogeophysical and biogeochemical effects of land management is increasing, moving beyond land cover change towards including land management has become a key focus for Earth system modeling. However, a basis for prioritizing land management activities for implementation in models is lacking. We lay this basis for prioritization in a collaborative project across the disciplines of Earth system modeling, land system science, and Earth observation. We first assess the status and plans of implementing land management in Earth system and dynamic global vegetation models. A clear trend towards higher complexity of land use representation is visible. We then assess five criteria for prioritizing the implementation of land management activities: (1) spatial extent, (2) evidence for substantial effects on the Earth system, (3) process understanding, (4) possibility to link the management activity to existing concepts and structures of models, (5) availability of data required as model input. While the first three criteria have been assessed by an earlier study for ten common management activities, we review strategies for implementation in models and the availability of required datasets. We can thus evaluate the management activities for their performance in terms of importance for the Earth system, possibility of technical implementation in models, and data availability. This synthesis reveals some "low-hanging" fruits for model implementation, but also challenges for the assessment of land management effects by modeling. The identified gaps can guide prioritization within the data community from the Earth system and Earth system modeling perspective.

Model meets data: Challenges and opportunities to implement land management in Earth System Models

NASA Astrophysics Data System (ADS)

Pongratz, Julia; Dolman, Han; Don, Axel; Erb, Karl-Heinz; Fuchs, Richard; Herold, Martin; Jones, Chris; Luyssaert, Sebastiaan; Kuemmerle, Tobias; Meyfroidt, Patrick; Naudts, Kim

2017-04-01

Land-based demand for food and fibre is projected to increase in the future. In light of global sustainability challenges only part of this increase will be met by expansion of land use into relatively untouched regions. Additional demand will have to be fulfilled by intensification and other adjustments in management of land that already is under agricultural and forestry use. Such land management today occurs on about half of the ice-free land surface, as compared to only about one quarter that has undergone a change in land cover. As the number of studies revealing substantial biogeophysical and biogeochemical effects of land management is increasing, moving beyond land cover change towards including land management has become a key focus for Earth system modeling. However, a basis for prioritizing land management activities for implementation in models is lacking. We lay this basis for prioritization in a collaborative project across the disciplines of Earth system modeling, land system science, and Earth observation. We first assess the status and plans of implementing land management in Earth system and dynamic global vegetation models. A clear trend towards higher complexity of land use representation is visible. We then assess five criteria for prioritizing the implementation of land management activities: (1) spatial extent, (2) evidence for substantial effects on the Earth system, (3) process understanding, (4) possibility to link the management activity to existing concepts and structures of models, (5) availability of data required as model input. While the first three criteria have been assessed by an earlier study for ten common management activities, we review strategies for implementation in models and the availability of required datasets. We can thus evaluate the management activities for their performance in terms of importance for the Earth system, possibility of technical implementation in models, and data availability. This synthesis reveals some "low-hanging" fruits for model implementation, but also challenges for the assessment of land management effects by modeling. The identified gaps can guide prioritization within the data community from the Earth system and Earth system modeling perspective.

Simulating the conversion of rural settlements to town land based on multi-agent systems and cellular automata.

PubMed

Liu, Yaolin; Kong, Xuesong; Liu, Yanfang; Chen, Yiyun

2013-01-01

Rapid urbanization in China has triggered the conversion of land from rural to urban use, particularly the conversion of rural settlements to town land. This conversion is the result of the joint effects of the geographic environment and agents involving the government, investors, and farmers. To understand the dynamic interaction dominated by agents and to predict the future landscape of town expansion, a small town land-planning model is proposed based on the integration of multi-agent systems (MAS) and cellular automata (CA). The MAS-CA model links the decision-making behaviors of agents with the neighbor effect of CA. The interaction rules are projected by analyzing the preference conflicts among agents. To better illustrate the effects of the geographic environment, neighborhood, and agent behavior, a comparative analysis between the CA and MAS-CA models in three different towns is presented, revealing interesting patterns in terms of quantity, spatial characteristics, and the coordinating process. The simulation of rural settlements conversion to town land through modeling agent decision and human-environment interaction is very useful for understanding the mechanisms of rural-urban land-use change in developing countries. This process can assist town planners in formulating appropriate development plans.

Estimation and mapping of above-ground biomass of mangrove forests and their replacement land uses in the Philippines using Sentinel imagery

NASA Astrophysics Data System (ADS)

Castillo, Jose Alan A.; Apan, Armando A.; Maraseni, Tek N.; Salmo, Severino G.

2017-12-01

The recent launch of the Sentinel-1 (SAR) and Sentinel-2 (multispectral) missions offers a new opportunity for land-based biomass mapping and monitoring especially in the tropics where deforestation is highest. Yet, unlike in agriculture and inland land uses, the use of Sentinel imagery has not been evaluated for biomass retrieval in mangrove forest and the non-forest land uses that replaced mangroves. In this study, we evaluated the ability of Sentinel imagery for the retrieval and predictive mapping of above-ground biomass of mangroves and their replacement land uses. We used Sentinel SAR and multispectral imagery to develop biomass prediction models through the conventional linear regression and novel Machine Learning algorithms. We developed models each from SAR raw polarisation backscatter data, multispectral bands, vegetation indices, and canopy biophysical variables. The results show that the model based on biophysical variable Leaf Area Index (LAI) derived from Sentinel-2 was more accurate in predicting the overall above-ground biomass. In contrast, the model which utilised optical bands had the lowest accuracy. However, the SAR-based model was more accurate in predicting the biomass in the usually deficient to low vegetation cover non-forest replacement land uses such as abandoned aquaculture pond, cleared mangrove and abandoned salt pond. These models had 0.82-0.83 correlation/agreement of observed and predicted value, and root mean square error of 27.8-28.5 Mg ha-1. Among the Sentinel-2 multispectral bands, the red and red edge bands (bands 4, 5 and 7), combined with elevation data, were the best variable set combination for biomass prediction. The red edge-based Inverted Red-Edge Chlorophyll Index had the highest prediction accuracy among the vegetation indices. Overall, Sentinel-1 SAR and Sentinel-2 multispectral imagery can provide satisfactory results in the retrieval and predictive mapping of the above-ground biomass of mangroves and the replacement non-forest land uses, especially with the inclusion of elevation data. The study demonstrates encouraging results in biomass mapping of mangroves and other coastal land uses in the tropics using the freely accessible and relatively high-resolution Sentinel imagery.

The dynamic and indirect spatial effects of neighborhood conditions on land value, spatial panel dynamic econometrics model

NASA Astrophysics Data System (ADS)

Fitriani, Rahma; Sumarminingsih, Eni; Astutik, Suci

2017-05-01

Land value is the product of past decision of its use leading to its value, as well as the value of the surrounded land. It is also affected by the local characteristic and the spillover development demand of the previous time period. The effect of each factor on land value will have dynamic and spatial virtues. Thus, a spatial panel dynamic model is used to estimate the particular effects. The model will be useful for predicting the future land value or the effect of implemented policy on land value. The objective of this paper is to derive the dynamic and indirect spatial marginal effects of the land characteristic and the spillover development demand on land value. Each effect is the partial derivative of the expected land value based on the spatial dynamic model with respect to each variable, by considering different time period and different location. The results indicate that the instant change of local or neighborhood characteristics on land value affect the local and the immediate neighborhood land value. However, the longer the change take place, the effect will spread further, not only on the immediate neighborhood.

A two stream radiative transfer model for scaling solar induced fluorescence from leaf to canopy

NASA Astrophysics Data System (ADS)

Quaife, T. L.

2017-12-01

Solar induced fluorescence (SIF) is becoming widely used as a proxy for gross primary productivity (GPP), in particular with the advent of its measurement by Earth Observation satellites such as OCO and GOSAT. A major attraction of SIF is that it is independent of the assumptions embedded in light use efficiency based GPP products derived from satellite missions such as MODIS. The assumptions in such products are likely not compatible with any given land surface model and hence comparing the two is problematic. On the other hand to compare land surface model predictions of GPP to satellite based SIF data requires either (a) translation of SIF into estimates of GPP, or (b) direct predictions of SIF from the land surface model itself. The former typically relies on empirical relationships, whereas the latter can make direct use of our physiological understanding of the link between photosynthesis and fluorescence at the leaf scale and is therefore preferable. Here I derive a two stream model for fluorescence that is capable of translating between leaf scale models of SIF and the canopy leaving radiance taking into account all levels of photon scattering. Other such models have been developed previously but the model described here is physically consistent with the Sellers' two stream radiative transfer scheme which is widely used in modern land surface models. Consequently any model that already employs the Sellers's scheme can use the new model without requiring modification. This includes, for example, JULES, the land surface model of the new UK Earth System Model (UKESM) and CLM, the US Community Land Model (part of the NCAR Earth System Model). The new canopy SIF model is extremely computationally efficient and can be applied to vertically inhomogeneous canopies.

Optimization of Land Use Suitability for Agriculture Using Integrated Geospatial Model and Genetic Algorithms

NASA Astrophysics Data System (ADS)

Mansor, S. B.; Pormanafi, S.; Mahmud, A. R. B.; Pirasteh, S.

2012-08-01

In this study, a geospatial model for land use allocation was developed from the view of simulating the biological autonomous adaptability to environment and the infrastructural preference. The model was developed based on multi-agent genetic algorithm. The model was customized to accommodate the constraint set for the study area, namely the resource saving and environmental-friendly. The model was then applied to solve the practical multi-objective spatial optimization allocation problems of land use in the core region of Menderjan Basin in Iran. The first task was to study the dominant crops and economic suitability evaluation of land. Second task was to determine the fitness function for the genetic algorithms. The third objective was to optimize the land use map using economical benefits. The results has indicated that the proposed model has much better performance for solving complex multi-objective spatial optimization allocation problems and it is a promising method for generating land use alternatives for further consideration in spatial decision-making.

Open Land-Use Map: A Regional Land-Use Mapping Strategy for Incorporating OpenStreetMap with Earth Observations

NASA Astrophysics Data System (ADS)

Yang, D.; Fu, C. S.; Binford, M. W.

2017-12-01

The southeastern United States has high landscape heterogeneity, withheavily managed forestlands, highly developed agriculture lands, and multiple metropolitan areas. Human activities are transforming and altering land patterns and structures in both negative and positive manners. A land-use map for at the greater scale is a heavy computation task but is critical to most landowners, researchers, and decision makers, enabling them to make informed decisions for varying objectives. There are two major difficulties in generating the classification maps at the regional scale: the necessity of large training point sets and the expensive computation cost-in terms of both money and time-in classifier modeling. Volunteered Geographic Information (VGI) opens a new era in mapping and visualizing our world, where the platform is open for collecting valuable georeferenced information by volunteer citizens, and the data is freely available to the public. As one of the most well-known VGI initiatives, OpenStreetMap (OSM) contributes not only road network distribution, but also the potential for using this data to justify land cover and land use classifications. Google Earth Engine (GEE) is a platform designed for cloud-based mapping with a robust and fast computing power. Most large scale and national mapping approaches confuse "land cover" and "land-use", or build up the land-use database based on modeled land cover datasets. Unlike most other large-scale approaches, we distinguish and differentiate land-use from land cover. By focusing our prime objective of mapping land-use and management practices, a robust regional land-use mapping approach is developed by incorporating the OpenstreepMap dataset into Earth observation remote sensing imageries instead of the often-used land cover base maps.

Effects of Land-Use Changes and Ground-Water Withdrawals on Stream Base Flow, Pocono Creek Watershed, Monroe County, Pennsylvania

USGS Publications Warehouse

Sloto, Ronald A.

2008-01-01

The Pocono Creek watershed drains 46.5 square miles in eastern Monroe County, Pa. Between 2000 and 2020, the population of Monroe County is expected to increase by 70 percent, which will result in substantial changes in land-use patterns. An evaluation of the effect of reduced recharge from land-use changes and additional ground-water withdrawals on stream base flow was done by the U.S. Geological Survey (USGS) in cooperation with the U.S. Environmental Protection Agency (USEPA) and the Delaware River Basin Commission as part of the USEPA?s Framework for Sustainable Watershed Management Initiative. Two models were used. A Soil and Water Assessment Tool (SWAT) model developed by the USEPA provided areal recharge values for 2000 land use and projected full buildout land use. The USGS MODFLOW-2000 ground-water-flow model was used to estimate the effect of reduced recharge from changes in land use and additional ground-water withdrawals on stream base flow. This report describes the ground-water-flow-model simulations. The Pocono Creek watershed is underlain by sedimentary rock of Devonian age, which is overlain by a veneer of glacial deposits. All water-supply wells are cased into and derive water from the bedrock. In the ground-water-flow model, the surficial geologic units were grouped into six categories: (1) moraine deposits, (2) stratified drift, (3) lake deposits, (4) outwash, (5) swamp deposits, and (6) undifferentiated deposits. The unconsolidated surficial deposits are not used as a source of water. The ground-water and surface-water systems are well connected in the Pocono Creek watershed. Base flow measured on October 13, 2004, at 27 sites for model calibration showed that streams gained water between all sites measured except in the lower reach of Pocono Creek. The ground-water-flow model included the entire Pocono Creek watershed. Horizontally, the modeled area was divided into a 53 by 155 cell grid with 6,060 active cells. Vertically, the modeled area was discretized into four layers. Layers 1 and 2 represented the unconsolidated surficial deposits where they are present and bedrock where the surficial deposits are absent. Layer 3 represented shallow bedrock and was 200 ft (feet) thick. Layer 4 represented deep bedrock and was 300 ft thick. A total of 873 cells representing streams were assigned to layer 1. Recharge rates for model calibration were provided by the USEPA SWAT model for 2000 land-use conditions. Recharge rates for 2000 for the 29 subwatersheds in the SWAT model ranged from 6.11 to 22.66 inches per year. Because the ground-water-flow model was calibrated to base-flow data collected on October 13, 2004, the 2000 recharge rates were multiplied by 1.18 so the volume of recharge was equal to the volume of streamflow measured at the mouth of Pocono Creek. During model calibration, adjustments were made to aquifer hydraulic conductivity and streambed conductance. Simulated base flows and hydraulic heads were compared to measured base flows and hydraulic heads using the root mean squared error (RMSE) between measured and simulated values. The RMSE of the calibrated model for base flow was 4.7 cubic feet per second for 27 locations, and the RMSE for hydraulic heads for 15 locations was 35 ft. The USEPA SWAT model was used to provide areal recharge values for 2000 and full buildout land-use conditions. The change in recharge ranged from an increase of 37.8 percent to a decrease of 60.8 percent. The ground-water-flow model was used to simulate base flow for 2000 and full buildout land-use conditions using steady-state simulations. The decrease in simulated base flow ranged from 3.8 to 63 percent at the streamflow-measurement sites. Simulated base flow at streamflow-gaging station Pocono Creek above Wigwam Run near Stroudsburg, Pa. (01441495), decreased 25 percent. This is in general agreement with the SWAT model, which estimated a 30.6-percent loss in base flow at the streamflow-gaging station.

Towards A Synthesis Of Land Dynamics And Hydrological Processes Across Central Asia

NASA Astrophysics Data System (ADS)

Sokolik, I. N.; Tatarskii, V.; Shiklomanov, A. I.; Henebry, G. M.; de Beurs, K.; Laruelle, M.

2016-12-01

We present results from an ongoing project that aims to synthesize land dynamics, hydrological processes, and socio-economic changes across the five countries of Central Asia. We have developed a fully coupled model that takes into account the reconstructed land cover and land use dynamics to simulate dust emissions. A comparable model has been developed to model smoke emissions from wildfires. Both models incorporate land dynamics explicitly. We also present a characterization of land surface change based on a suite of MODIS products including vegetation indices, evapotranspiration, land surface temperature, and albedo. These results are connected with ongoing land privatization reforms that different across the region. We also present a regional analysis of water resources, including the significant impact of using surface water for irrigation in an arid landscape. We applied the University of New Hampshire hydrological model to understand the consequences of changes in climate, water, and land use on regional hydrological processes and water use. Water security and its dynamic have been estimated through an analysis of multiple indices and variables characterizing the water availability and water use. The economic consequences of the water privatization processes will be presented.

Simple rules can guide whether land- or ocean-based conservation will best benefit marine ecosystems

PubMed Central

Bode, Michael; Atkinson, Scott; Klein, Carissa J.; Metaxas, Anna; Beher, Jutta; Beger, Maria; Mills, Morena; Giakoumi, Sylvaine; Tulloch, Vivitskaia; Possingham, Hugh P.

2017-01-01

Coastal marine ecosystems can be managed by actions undertaken both on the land and in the ocean. Quantifying and comparing the costs and benefits of actions in both realms is therefore necessary for efficient management. Here, we quantify the link between terrestrial sediment runoff and a downstream coastal marine ecosystem and contrast the cost-effectiveness of marine- and land-based conservation actions. We use a dynamic land- and sea-scape model to determine whether limited funds should be directed to 1 of 4 alternative conservation actions—protection on land, protection in the ocean, restoration on land, or restoration in the ocean—to maximise the extent of light-dependent marine benthic habitats across decadal timescales. We apply the model to a case study for a seagrass meadow in Australia. We find that marine restoration is the most cost-effective action over decadal timescales in this system, based on a conservative estimate of the rate at which seagrass can expand into a new habitat. The optimal decision will vary in different social–ecological contexts, but some basic information can guide optimal investments to counteract land- and ocean-based stressors: (1) marine restoration should be prioritised if the rates of marine ecosystem decline and expansion are similar and low; (2) marine protection should take precedence if the rate of marine ecosystem decline is high or if the adjacent catchment is relatively intact and has a low rate of vegetation decline; (3) land-based actions are optimal when the ratio of marine ecosystem expansion to decline is greater than 1:1.4, with terrestrial restoration typically the most cost-effective action; and (4) land protection should be prioritised if the catchment is relatively intact but the rate of vegetation decline is high. These rules of thumb illustrate how cost-effective conservation outcomes for connected land–ocean systems can proceed without complex modelling. PMID:28877168

Estimating Antarctica land topography from GRACE gravity and ICESat altimetry data

NASA Astrophysics Data System (ADS)

Wu, I.; Chao, B. F.; Chen, Y.

2009-12-01

We propose a new method combining GRACE (Gravity Recovery and Climate Experiment) gravity and ICESat (Ice, Cloud, and land Elevation Satellite) altimetry data to estimate the land topography for Antarctica. Antarctica is the fifth-largest continent in the world and about 98% of Antarctica is covered by ice, where in-situ measurements are difficult. Some experimental airborne radar and ground-based radar data have revealed very limited land topography beneath heavy ice sheet. To estimate the land topography for the full coverage of Antarctica, we combine GRACE data that indicate the mass distribution, with data of ICESat laser altimetry that provide high-resolution mapping of ice topography. Our approach is actually based on some geological constraints: assuming uniform densities of the land and ice considering the Airy-type isostasy. In the beginning we construct an initial model for the ice thickness and land topography based on the BEDMAP ice thickness and ICESat data. Thereafter we forward compute the model’s gravity field and compare with the GRACE observed data. Our initial model undergoes the adjustments to improve the fit between modeled results and the observed data. Final examination is done by comparing our results with previous but sparse observations of ice thickness to reconfirm the reliability of our results. As the gravitational inversion problem is non-unique, our estimating result is just one of all possibilities constrained by available data in optimal way.

An investigation into the vertical axis control power requirements for landing VTOL type aircraft onboard nonaviation ships in various sea states

NASA Technical Reports Server (NTRS)

Stevens, M. E.; Roskam, J.

1985-01-01

The problem of determining the vertical axis control requirements for landing a VTOL aircraft on a moving ship deck in various sea states is examined. Both a fixed-base piloted simulation and a nonpiloted simulation were used to determine the landing performance as influenced by thrust-to-weight ratio, vertical damping, and engine lags. The piloted simulation was run using a fixed-based simulator at Ames Research center. Simplified versions of an existing AV-8A Harrier model and an existing head-up display format were used. The ship model used was that of a DD963 class destroyer. Simplified linear models of the pilot, aircraft, ship motion, and ship air-wake turbulence were developed for the nonpiloted simulation. A unique aspect of the nonpiloted simulation was the development of a model of the piloting strategy used for shipboard landing. This model was refined during the piloted simulation until it provided a reasonably good representation of observed pilot behavior.

Modelling land cover change in the Ganga basin

NASA Astrophysics Data System (ADS)

Moulds, S.; Tsarouchi, G.; Mijic, A.; Buytaert, W.

2013-12-01

Over recent decades the green revolution in India has driven substantial environmental change. Modelling experiments have identified northern India as a 'hot spot' of land-atmosphere coupling strength during the boreal summer. However, there is a wide range of sensitivity of atmospheric variables to soil moisture between individual climate models. The lack of a comprehensive land cover change dataset to force climate models has been identified as a major contributor to model uncertainty. In this work a time series dataset of land cover change between 1970 and 2010 is constructed for northern India to improve the quantification of regional hydrometeorological feedbacks. The MODIS instrument on board the Aqua and Terra satellites provides near-continuous remotely sensed datasets from 2000 to the present day. However, the quality of satellite products before 2000 is poor. To complete the dataset MODIS images are extrapolated back in time using the Conversion of Land Use and its Effects at small regional extent (CLUE-s) modelling framework. Non-spatial estimates of land cover area from national agriculture and forest statistics, available on a state-wise, annual basis, are used as a direct model input. Land cover change is allocated spatially as a function of biophysical and socioeconomic drivers identified using logistic regression. This dataset will provide an essential input to a high resolution, physically based land surface model to generate the lower boundary condition to assess the impact of land cover change on regional climate.

Optimal land use/cover classification using remote sensing imagery for hydrological modelling in a Himalayan watershed

NASA Astrophysics Data System (ADS)

Saran, Sameer; Sterk, Geert; Kumar, Suresh

2007-10-01

Land use/cover is an important watershed surface characteristic that affects surface runoff and erosion. Many of the available hydrological models divide the watershed into Hydrological Response Units (HRU), which are spatial units with expected similar hydrological behaviours. The division into HRU's requires good-quality spatial data on land use/cover. This paper presents different approaches to attain an optimal land use/cover map based on remote sensing imagery for a Himalayan watershed in northern India. First digital classifications using maximum likelihood classifier (MLC) and a decision tree classifier were applied. The results obtained from the decision tree were better and even improved after post classification sorting. But the obtained land use/cover map was not sufficient for the delineation of HRUs, since the agricultural land use/cover class did not discriminate between the two major crops in the area i.e. paddy and maize. Therefore we adopted a visual classification approach using optical data alone and also fused with ENVISAT ASAR data. This second step with detailed classification system resulted into better classification accuracy within the 'agricultural land' class which will be further combined with topography and soil type to derive HRU's for physically-based hydrological modelling.

Estimating European soil organic carbon mitigation potential in a global integrated land use model

NASA Astrophysics Data System (ADS)

Frank, Stefan; Böttcher, Hannes; Schneider, Uwe; Schmid, Erwin; Havlík, Petr

2013-04-01

Several studies have shown the dynamic interaction between soil organic carbon (SOC) sequestration rates, soil management decisions and SOC levels. Management practices such as reduced and no-tillage, improved residue management and crop rotations as well as the conversion of marginal cropland to native vegetation or conversion of cultivated land to permanent grassland offer the potential to increase SOC content. Even though dynamic interactions are widely acknowledged in literature, they have not been implemented in most existing land use decision models. A major obstacle is the high data and computing requirements for an explicit representation of alternative land use sequences since a model has to be able to track all different management decision paths. To our knowledge no study accounted so far for SOC dynamics explicitly in a global integrated land use model. To overcome these conceptual difficulties described above we apply an approach capable of accounting for SOC dynamics in GLOBIOM (Global Biosphere Management Model), a global recursive dynamic partial equilibrium bottom-up model integrating the agricultural, bioenergy and forestry sectors. GLOBIOM represents all major land based sectors and therefore is able to account for direct and indirect effects of land use change as well as leakage effects (e.g. through trade) implicitly. Together with the detailed representation of technologies (e.g. tillage and fertilizer management systems), these characteristics make the model a highly valuable tool for assessing European SOC emissions and mitigation potential. Demand and international trade are represented in this version of the model at the level of 27 EU member states and 23 aggregated world regions outside Europe. Changes in the demand on the one side, and profitability of the different land based activities on the other side, are the major determinants of land use change in GLOBIOM. In this paper we estimate SOC emissions from cropland for the EU until 2050 explicitly considering SOC dynamics due to land use and land management in a global integrated land use model. Moreover, we calculate the EU SOC mitigation potential taking into account leakage effects outside Europe as well as related feed backs from other sectors. In sensitivity analysis, we disaggregate the SOC mitigation potential i.e. we quantify the impact of different management systems and crop rotations to identify most promising mitigation strategies.

Scalability of grid- and subbasin-based land surface modeling approaches for hydrologic simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tesfa, Teklu K.; Ruby Leung, L.; Huang, Maoyi

2014-03-27

This paper investigates the relative merits of grid- and subbasin-based land surface modeling approaches for hydrologic simulations, with a focus on their scalability (i.e., abilities to perform consistently across a range of spatial resolutions) in simulating runoff generation. Simulations produced by the grid- and subbasin-based configurations of the Community Land Model (CLM) are compared at four spatial resolutions (0.125o, 0.25o, 0.5o and 1o) over the topographically diverse region of the U.S. Pacific Northwest. Using the 0.125o resolution simulation as the “reference”, statistical skill metrics are calculated and compared across simulations at 0.25o, 0.5o and 1o spatial resolutions of each modelingmore » approach at basin and topographic region levels. Results suggest significant scalability advantage for the subbasin-based approach compared to the grid-based approach for runoff generation. Basin level annual average relative errors of surface runoff at 0.25o, 0.5o, and 1o compared to 0.125o are 3%, 4%, and 6% for the subbasin-based configuration and 4%, 7%, and 11% for the grid-based configuration, respectively. The scalability advantages of the subbasin-based approach are more pronounced during winter/spring and over mountainous regions. The source of runoff scalability is found to be related to the scalability of major meteorological and land surface parameters of runoff generation. More specifically, the subbasin-based approach is more consistent across spatial scales than the grid-based approach in snowfall/rainfall partitioning, which is related to air temperature and surface elevation. Scalability of a topographic parameter used in the runoff parameterization also contributes to improved scalability of the rain driven saturated surface runoff component, particularly during winter. Hence this study demonstrates the importance of spatial structure for multi-scale modeling of hydrological processes, with implications to surface heat fluxes in coupled land-atmosphere modeling.« less

Potential for Woody Bioenergy Crops Grown on Marginal Lands in the US Midwest to Reduce Carbon Emissions

NASA Astrophysics Data System (ADS)

Sahajpal, R.; Hurtt, G. C.; Fisk, J. P.; Izaurralde, R. C.; Zhang, X.

2012-12-01

While cellulosic biofuels are widely considered to be a low carbon energy source for the future, a comprehensive assessment of the environmental sustainability of existing and future biofuel systems is needed to assess their utility in meeting US energy and food needs without exacerbating environmental harm. To assess the carbon emission reduction potential of cellulosic biofuels, we need to identify lands that are initially not storing large quantities of carbon in soil and vegetation but are capable of producing abundant biomass with limited management inputs, and accurately model forest production rates and associated input requirements. Here we present modeled results for carbon emission reduction potential and cellulosic ethanol production of woody bioenergy crops replacing existing native prairie vegetation grown on marginal lands in the US Midwest. Marginal lands are selected based on soil properties describing use limitation, and are extracted from the SSURGO (Soil Survey Geographic) database. Yield estimates for existing native prairie vegetation on marginal lands modeled using the process-based field-scale model EPIC (Environmental Policy Integrated Climate) amount to ~ 6.7±2.0 Mg ha-1. To model woody bioenergy crops, the individual-based terrestrial ecosystem model ED (Ecosystem Demography) is initialized with the soil organic carbon stocks estimated at the end of the EPIC simulation. Four woody bioenergy crops: willow, southern pine, eucalyptus and poplar are parameterized in ED. Sensitivity analysis of model parameters and drivers is conducted to explore the range of carbon emission reduction possible with variation in woody bioenergy crop types, spatial and temporal resolution. We hypothesize that growing cellulosic crops on these marginal lands can provide significant water quality, biodiversity and GHG emissions mitigation benefits, without accruing additional carbon costs from the displacement of food and feed production.

Simple Process-Based Simulators for Generating Spatial Patterns of Habitat Loss and Fragmentation: A Review and Introduction to the G-RaFFe Model

PubMed Central

Pe'er, Guy; Zurita, Gustavo A.; Schober, Lucia; Bellocq, Maria I.; Strer, Maximilian; Müller, Michael; Pütz, Sandro

2013-01-01

Landscape simulators are widely applied in landscape ecology for generating landscape patterns. These models can be divided into two categories: pattern-based models that generate spatial patterns irrespective of the processes that shape them, and process-based models that attempt to generate patterns based on the processes that shape them. The latter often tend toward complexity in an attempt to obtain high predictive precision, but are rarely used for generic or theoretical purposes. Here we show that a simple process-based simulator can generate a variety of spatial patterns including realistic ones, typifying landscapes fragmented by anthropogenic activities. The model “G-RaFFe” generates roads and fields to reproduce the processes in which forests are converted into arable lands. For a selected level of habitat cover, three factors dominate its outcomes: the number of roads (accessibility), maximum field size (accounting for land ownership patterns), and maximum field disconnection (which enables field to be detached from roads). We compared the performance of G-RaFFe to three other models: Simmap (neutral model), Qrule (fractal-based) and Dinamica EGO (with 4 model versions differing in complexity). A PCA-based analysis indicated G-RaFFe and Dinamica version 4 (most complex) to perform best in matching realistic spatial patterns, but an alternative analysis which considers model variability identified G-RaFFe and Qrule as performing best. We also found model performance to be affected by habitat cover and the actual land-uses, the latter reflecting on land ownership patterns. We suggest that simple process-based generators such as G-RaFFe can be used to generate spatial patterns as templates for theoretical analyses, as well as for gaining better understanding of the relation between spatial processes and patterns. We suggest caution in applying neutral or fractal-based approaches, since spatial patterns that typify anthropogenic landscapes are often non-fractal in nature. PMID:23724108

Simple process-based simulators for generating spatial patterns of habitat loss and fragmentation: a review and introduction to the G-RaFFe model.

PubMed

Pe'er, Guy; Zurita, Gustavo A; Schober, Lucia; Bellocq, Maria I; Strer, Maximilian; Müller, Michael; Pütz, Sandro

2013-01-01

Landscape simulators are widely applied in landscape ecology for generating landscape patterns. These models can be divided into two categories: pattern-based models that generate spatial patterns irrespective of the processes that shape them, and process-based models that attempt to generate patterns based on the processes that shape them. The latter often tend toward complexity in an attempt to obtain high predictive precision, but are rarely used for generic or theoretical purposes. Here we show that a simple process-based simulator can generate a variety of spatial patterns including realistic ones, typifying landscapes fragmented by anthropogenic activities. The model "G-RaFFe" generates roads and fields to reproduce the processes in which forests are converted into arable lands. For a selected level of habitat cover, three factors dominate its outcomes: the number of roads (accessibility), maximum field size (accounting for land ownership patterns), and maximum field disconnection (which enables field to be detached from roads). We compared the performance of G-RaFFe to three other models: Simmap (neutral model), Qrule (fractal-based) and Dinamica EGO (with 4 model versions differing in complexity). A PCA-based analysis indicated G-RaFFe and Dinamica version 4 (most complex) to perform best in matching realistic spatial patterns, but an alternative analysis which considers model variability identified G-RaFFe and Qrule as performing best. We also found model performance to be affected by habitat cover and the actual land-uses, the latter reflecting on land ownership patterns. We suggest that simple process-based generators such as G-RaFFe can be used to generate spatial patterns as templates for theoretical analyses, as well as for gaining better understanding of the relation between spatial processes and patterns. We suggest caution in applying neutral or fractal-based approaches, since spatial patterns that typify anthropogenic landscapes are often non-fractal in nature.

A multi-model framework for simulating wildlife population response to land-use and climate change

USGS Publications Warehouse

McRae, B.H.; Schumaker, N.H.; McKane, R.B.; Busing, R.T.; Solomon, A.M.; Burdick, C.A.

2008-01-01

Reliable assessments of how human activities will affect wildlife populations are essential for making scientifically defensible resource management decisions. A principle challenge of predicting effects of proposed management, development, or conservation actions is the need to incorporate multiple biotic and abiotic factors, including land-use and climate change, that interact to affect wildlife habitat and populations through time. Here we demonstrate how models of land-use, climate change, and other dynamic factors can be integrated into a coherent framework for predicting wildlife population trends. Our framework starts with land-use and climate change models developed for a region of interest. Vegetation changes through time under alternative future scenarios are predicted using an individual-based plant community model. These predictions are combined with spatially explicit animal habitat models to map changes in the distribution and quality of wildlife habitat expected under the various scenarios. Animal population responses to habitat changes and other factors are then projected using a flexible, individual-based animal population model. As an example application, we simulated animal population trends under three future land-use scenarios and four climate change scenarios in the Cascade Range of western Oregon. We chose two birds with contrasting habitat preferences for our simulations: winter wrens (Troglodytes troglodytes), which are most abundant in mature conifer forests, and song sparrows (Melospiza melodia), which prefer more open, shrubby habitats. We used climate and land-use predictions from previously published studies, as well as previously published predictions of vegetation responses using FORCLIM, an individual-based forest dynamics simulator. Vegetation predictions were integrated with other factors in PATCH, a spatially explicit, individual-based animal population simulator. Through incorporating effects of landscape history and limited dispersal, our framework predicted population changes that typically exceeded those expected based on changes in mean habitat suitability alone. Although land-use had greater impacts on habitat quality than did climate change in our simulations, we found that small changes in vital rates resulting from climate change or other stressors can have large consequences for population trajectories. The ability to integrate bottom-up demographic processes like these with top-down constraints imposed by climate and land-use in a dynamic modeling environment is a key advantage of our approach. The resulting framework should allow researchers to synthesize existing empirical evidence, and to explore complex interactions that are difficult or impossible to capture through piecemeal modeling approaches. ?? 2008 Elsevier B.V.

Assessing Climatic Impacts due to Land Use Change over Southeast Asian Maritime Continent base on Mesoscale Model Simulations

NASA Astrophysics Data System (ADS)

Feng, N.; Christopher, S. A.; Nair, U. S.

2014-12-01

Due to increasing urbanization, deforestation, and agriculture, land use change over Southeast Asia has dramatically risen during the last decades. Large areas of peat swamp forests over the Southeast Asian Maritime Continent region (10°S~20°N and 90°E~135°E) have been cleared for agricultural purposes. The Center for Remote Imaging, Sensing and Processing (CRISP) Moderate Resolution Imaging Spectroradiometer (MODIS) derived land cover classification data show that changes in land use are dominated by conversion of peat swamp forests to oil palm plantation, open lowland or lowland mosaic categories. Nested grid simulations based on Weather Research Forecasting Version 3.6 modelling system (WRFV3.6) over the central region of the Sarawak coast are used to investigate the climatic impacts of land use change over Maritime Continent. Numerical simulations were conducted for August of 2009 for satellite derived land cover scenarios for years 2000 and 2010. The variations in cloud formation, precipitation, and regional radiative and non-radiative parameters on climate results from land use change have been assessed based on numerical simulation results. Modelling studies demonstrate that land use change such as extensive deforestation processes can produce a negative radiative forcing due to the surface albedo increase and evapotranspiration decrease, while also largely caused reduced rainfall and cloud formation, and enhanced shortwave radiative forcing and temperature over the study area. Land use and land cover changes, similar to the domain in this study, has also occurred over other regions in Southeast Asia including Indonesia and could also impact cloud and precipitation formation in these regions.

Evaluation of historical land cover, land use, and land-use change emissions in the GCAM integrated assessment model

NASA Astrophysics Data System (ADS)

Calvin, K. V.; Wise, M.; Kyle, P.; Janetos, A. C.; Zhou, Y.

2012-12-01

Integrated Assessment Models (IAMs) are often used as science-based decision-support tools for evaluating the consequences of climate and energy policies, and their use in this framework is likely to increase in the future. However, quantitative evaluation of these models has been somewhat limited for a variety of reasons, including data availability, data quality, and the inherent challenges in projections of societal values and decision-making. In this analysis, we identify and confront methodological challenges involved in evaluating the agriculture and land use component of the Global Change Assessment Model (GCAM). GCAM is a global integrated assessment model, linking submodules of the regionally disaggregated global economy, energy system, agriculture and land-use, terrestrial carbon cycle, oceans and climate. GCAM simulates supply, demand, and prices for energy and agricultural goods from 2005 to 2100 in 5-year increments. In each time period, the model computes the allocation of land across a variety of land cover types in 151 different regions, assuming that farmers maximize profits and that food demand is relatively inelastic. GCAM then calculates both emissions from land-use practices, and long-term changes in carbon stocks in different land uses, thus providing simulation information that can be compared to observed historical data. In this work, we compare GCAM results, both in recent historic and future time periods, to historical data sets. We focus on land use, land cover, land-use change emissions, and albedo.

Extreme Rock Distributions on Mars and Implications for Landing Safety

NASA Technical Reports Server (NTRS)

Golombek, M. P.

2001-01-01

Prior to the landing of Mars Pathfinder, the size-frequency distribution of rocks from the two Viking landing sites and Earth analog surfaces was used to derive a size-frequency model, for nomimal rock distributions on Mars. This work, coupled with extensive testing of the Pathfinder airbag landing system, allowed an estimate of what total rock abundances derived from thermal differencing techniques could be considered safe for landing. Predictions based on this model proved largely correct at predicting the size-frequency distribution of rocks at the Mars Pathfinder site and the fraction of potentially hazardous rocks. In this abstract, extreme rock distributions observed in Mars Orbiter Camera (MOC) images are compared with those observed at the three landing sites and model distributions as an additional constraint on potentially hazardous surfaces on Mars.

Human-environment interactions and sustainable urban development: Spatial modeling and landscape prediction the case of Nang Rong town, Thailand

NASA Astrophysics Data System (ADS)

Varnakovida, Pariwate

It is now well-recognized that, at local, regional, and global scales, land use changes are significantly altering land cover, perhaps at an accelerating pace. Further, the world's scientific community is increasingly recognizing what, in retrospect, should have been obvious, that human behavior and agency is a critical driver of Land Cover and Land Use Change. In this research, using recently developed computer modeling procedures and a rich case study, I develop spatially-explicit model-based simulations of LULCC scenarios within the rubric of sustainability science for Nang Rong town, Thailand. The research draws heavily on recent work in geography and complexity theory. A series of scenarios were built to explore different development trajectories based upon empirically observed relationships. The development models incorporate a) history and spatial pattern of village settlement; b) road development and changing geographic accessibility; c) population; d) biophysical characteristics and e) social drivers. This research uses multi-temporal and spatially-explicit data, analytic results, and dynamic modeling approaches combined with to describe, explain, and explore LULCC as the consequences of different production theories for rural, small town urbanization in the South East Asian context. Two Agent Based models were built: 1) Settlement model and 2) Land-use model. The Settlement model suggests that new development will emerge along the existing road network especially along the major highway and in close proximity to the urban center. If the population doubles in 2021, the settlement process may inhibit development along some corridors creating low density sprawl. The Land-use model under the urban expansion scenario suggests that new settlements will occur in close proximity to the town center and roads; even though, the area is suitable for rice farming or located on a flood plain. The Land-use model under the cash-crop expansion scenario captures that new agriculture will occur on the flood plain and other areas suitable for rice farming. The Land-use model under the King's Theory scenario suggests that agriculture agents occupied more disperse lands than the cash-crops scenario. In addition, the King's Theory scenario provided more access to water surface than other scenarios and was the most sustainable development plan. These products offer a better understanding of the urban growth and LULCC at a regional scale and will potentially guide more systematic and effective resource management and policy decisions. Although this research focuses on a specific site, the methods employed are applicable to other rural regions with similar characteristics.

Root traits explain observed tundra vegetation nitrogen uptake patterns: Implications for trait-based land models: Tundra N Uptake Model-Data Comparison

DOE PAGES

Zhu, Qing; Iversen, Colleen M.; Riley, William J.; ...

2016-12-23

Ongoing climate warming will likely perturb vertical distributions of nitrogen availability in tundra soils through enhancing nitrogen mineralization and releasing previously inaccessible nitrogen from frozen permafrost soil. But, arctic tundra responses to such changes are uncertain, because of a lack of vertically explicit nitrogen tracer experiments and untested hypotheses of root nitrogen uptake under the stress of microbial competition implemented in land models. We conducted a vertically explicit 15N tracer experiment for three dominant tundra species to quantify plant N uptake profiles. Then we applied a nutrient competition model (N-COM), which is being integrated into the ACME Land Model, tomore » explain the observations. Observations using an 15N tracer showed that plant N uptake profiles were not consistently related to root biomass density profiles, which challenges the prevailing hypothesis that root density always exerts first-order control on N uptake. By considering essential root traits (e.g., biomass distribution and nutrient uptake kinetics) with an appropriate plant-microbe nutrient competition framework, our model reasonably reproduced the observed patterns of plant N uptake. Additionally, we show that previously applied nutrient competition hypotheses in Earth System Land Models fail to explain the diverse plant N uptake profiles we observed. These results cast doubt on current climate-scale model predictions of arctic plant responses to elevated nitrogen supply under a changing climate and highlight the importance of considering essential root traits in large-scale land models. Finally, we provided suggestions and a short synthesis of data availability for future trait-based land model development.« less

Root traits explain observed tundra vegetation nitrogen uptake patterns: Implications for trait-based land models: Tundra N Uptake Model-Data Comparison

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhu, Qing; Iversen, Colleen M.; Riley, William J.

Ongoing climate warming will likely perturb vertical distributions of nitrogen availability in tundra soils through enhancing nitrogen mineralization and releasing previously inaccessible nitrogen from frozen permafrost soil. But, arctic tundra responses to such changes are uncertain, because of a lack of vertically explicit nitrogen tracer experiments and untested hypotheses of root nitrogen uptake under the stress of microbial competition implemented in land models. We conducted a vertically explicit 15N tracer experiment for three dominant tundra species to quantify plant N uptake profiles. Then we applied a nutrient competition model (N-COM), which is being integrated into the ACME Land Model, tomore » explain the observations. Observations using an 15N tracer showed that plant N uptake profiles were not consistently related to root biomass density profiles, which challenges the prevailing hypothesis that root density always exerts first-order control on N uptake. By considering essential root traits (e.g., biomass distribution and nutrient uptake kinetics) with an appropriate plant-microbe nutrient competition framework, our model reasonably reproduced the observed patterns of plant N uptake. Additionally, we show that previously applied nutrient competition hypotheses in Earth System Land Models fail to explain the diverse plant N uptake profiles we observed. These results cast doubt on current climate-scale model predictions of arctic plant responses to elevated nitrogen supply under a changing climate and highlight the importance of considering essential root traits in large-scale land models. Finally, we provided suggestions and a short synthesis of data availability for future trait-based land model development.« less

Impact of land cover change on the environmental hydrology characteristics in Kelantan river basin, Malaysia

NASA Astrophysics Data System (ADS)

Saadatkhah, Nader; Mansor, Shattri; Khuzaimah, Zailani; Asmat, Arnis; Adnan, Noraizam; Adam, Siti Noradzah

2016-09-01

Changing the land cover/ land use has serious environmental impacts affecting the ecosystem in Malaysia. The impact of land cover changes on the environmental functions such as surface water, loss water, and soil moisture is considered in this paper on the Kelantan river basin. The study area at the east coast of the peninsular Malaysia has suffered significant land cover changes in the recent years. The current research tried to assess the impact of land cover changes in the study area focused on the surface water, loss water, and soil moisture from different land use classes and the potential impact of land cover changes on the ecosystem of Kelantan river basin. To simulate the impact of land cover changes on the environmental hydrology characteristics, a deterministic regional modeling were employed in this study based on five approaches, i.e. (1) Land cover classification based on Landsat images; (2) assessment of land cover changes during last three decades; (3) Calculation the rate of water Loss/ Infiltration; (4) Assessment of hydrological and mechanical effects of the land cover changes on the surface water; and (5) evaluation the impact of land cover changes on the ecosystem of the study area. Assessment of land cover impact on the environmental hydrology was computed with the improved transient rainfall infiltration and grid based regional model (Improved-TRIGRS) based on the transient infiltration, and subsequently changes in the surface water, due to precipitation events. The results showed the direct increased in surface water from development area, agricultural area, and grassland regions compared with surface water from other land covered areas in the study area. The urban areas or lower planting density areas tend to increase for surface water during the monsoon seasons, whereas the inter flow from forested and secondary jungle areas contributes to the normal surface water.

A sea-land segmentation algorithm based on multi-feature fusion for a large-field remote sensing image

NASA Astrophysics Data System (ADS)

Li, Jing; Xie, Weixin; Pei, Jihong

2018-03-01

Sea-land segmentation is one of the key technologies of sea target detection in remote sensing images. At present, the existing algorithms have the problems of low accuracy, low universality and poor automatic performance. This paper puts forward a sea-land segmentation algorithm based on multi-feature fusion for a large-field remote sensing image removing island. Firstly, the coastline data is extracted and all of land area is labeled by using the geographic information in large-field remote sensing image. Secondly, three features (local entropy, local texture and local gradient mean) is extracted in the sea-land border area, and the three features combine a 3D feature vector. And then the MultiGaussian model is adopted to describe 3D feature vectors of sea background in the edge of the coastline. Based on this multi-gaussian sea background model, the sea pixels and land pixels near coastline are classified more precise. Finally, the coarse segmentation result and the fine segmentation result are fused to obtain the accurate sea-land segmentation. Comparing and analyzing the experimental results by subjective vision, it shows that the proposed method has high segmentation accuracy, wide applicability and strong anti-disturbance ability.

Simulating the hydrologic impacts of land cover and climate changes in a semi-arid watershed

EPA Pesticide Factsheets

Changes in climate and land cover are among the principal variables affecting watershed hydrology.This paper uses a cell-based model to examine the hydrologic impacts of climate and land-cover changes in thesemi-arid Lower Virgin River (LVR) watershed located upstream of Lake Mead, Nevada, USA. The cell-basedmodel is developed by considering direct runoff based on the Soil Conservation Service - Curve Number (SCSCN)method and surplus runoff based on the Thornthwaite water balance theory. After calibration and validation,the model is used to predict LVR discharge under future climate and land-cover changes. The hydrologicsimulation results reveal climate change as the dominant factor and land-cover change as a secondary factor inregulating future river discharge. The combined effects of climate and land-cover changes will slightly increaseriver discharge in summer but substantially decrease discharge in winter. This impact on water resources deservesattention in climate change adaptation planning.This dataset is associated with the following publication:Chen, H., S. Tong, H. Yang, and J. Yang. Simulating the hydrologic impacts of land cover and climate changes in a semi-arid watershed. Hydrological Sciences Journal. IAHS LIMITED, Oxford, UK, 60(10): 1739-1758, (2015).

Integrating statistical and process-based models to produce probabilistic landslide hazard at regional scale

NASA Astrophysics Data System (ADS)

Strauch, R. L.; Istanbulluoglu, E.

2017-12-01

We develop a landslide hazard modeling approach that integrates a data-driven statistical model and a probabilistic process-based shallow landslide model for mapping probability of landslide initiation, transport, and deposition at regional scales. The empirical model integrates the influence of seven site attribute (SA) classes: elevation, slope, curvature, aspect, land use-land cover, lithology, and topographic wetness index, on over 1,600 observed landslides using a frequency ratio (FR) approach. A susceptibility index is calculated by adding FRs for each SA on a grid-cell basis. Using landslide observations we relate susceptibility index to an empirically-derived probability of landslide impact. This probability is combined with results from a physically-based model to produce an integrated probabilistic map. Slope was key in landslide initiation while deposition was linked to lithology and elevation. Vegetation transition from forest to alpine vegetation and barren land cover with lower root cohesion leads to higher frequency of initiation. Aspect effects are likely linked to differences in root cohesion and moisture controlled by solar insulation and snow. We demonstrate the model in the North Cascades of Washington, USA and identify locations of high and low probability of landslide impacts that can be used by land managers in their design, planning, and maintenance.

The South Florida Ecosystem Portfolio Model - A Map-Based Multicriteria Ecological, Economic, and Community Land-Use Planning Tool

USGS Publications Warehouse

Labiosa, William B.; Bernknopf, Richard; Hearn, Paul; Hogan, Dianna; Strong, David; Pearlstine, Leonard; Mathie, Amy M.; Wein, Anne M.; Gillen, Kevin; Wachter, Susan

2009-01-01

The South Florida Ecosystem Portfolio Model (EPM) prototype is a regional land-use planning Web tool that integrates ecological, economic, and social information and values of relevance to decision-makers and stakeholders. The EPM uses a multicriteria evaluation framework that builds on geographic information system-based (GIS) analysis and spatially-explicit models that characterize important ecological, economic, and societal endpoints and consequences that are sensitive to regional land-use/land-cover (LULC) change. The EPM uses both economics (monetized) and multiattribute utility (nonmonetized) approaches to valuing these endpoints and consequences. This hybrid approach represents a methodological middle ground between rigorous economic and ecological/ environmental scientific approaches. The EPM sacrifices some degree of economic- and ecological-forecasting precision to gain methodological transparency, spatial explicitness, and transferability, while maintaining credibility. After all, even small steps in the direction of including ecosystem services evaluation are an improvement over current land-use planning practice (Boyd and Wainger, 2003). There are many participants involved in land-use decision-making in South Florida, including local, regional, State, and Federal agencies, developers, environmental groups, agricultural groups, and other stakeholders (South Florida Regional Planning Council, 2003, 2004). The EPM's multicriteria evaluation framework is designed to cut across the objectives and knowledge bases of all of these participants. This approach places fundamental importance on social equity and stakeholder participation in land-use decision-making, but makes no attempt to determine normative socially 'optimal' land-use plans. The EPM is thus a map-based set of evaluation tools for planners and stakeholders to use in their deliberations of what is 'best', considering a balancing of disparate interests within a regional perspective. Although issues of regional ecological sustainability can be explored with the EPM (for example, changes in biodiversity potential and regional habitat fragmentation), it does not attempt to define or evaluate long-term ecological sustainability as such. Instead, the EPM is intended to provide transparent first-order indications of the direction of ecological, economic, and community change, not to make detailed predictions of ecological, economic, and social outcomes. In short, the EPM is an attempt to widen the perspectives of its users by integrating natural and social scientific information in a framework that recognizes the diversity of values at stake in South Florida land-use planning. For terrestrial ecosystems, land-cover change is one of the most important direct drivers of changes in ecosystem services (Hassan and others, 2005). More specifically, the fragmentation of habitat from expanding low-density development across landscapes appears to be a major driver of terrestrial species decline and the impairment of terrestrial ecosystem integrity, in some cases causing irreversible impairment from a land-use planning perspective (Brody, 2008; Peck, 1998). Many resource managers and land-use planners have come to realize that evaluating land-use conversions on a parcel-by-parcel basis leads to a fragmented and narrow view of the regional effects of natural land-cover loss to development (Marsh and Lallas, 1995). The EPM is an attempt to integrate important aspects of the coupled natural-system/human-system view from a regional planning perspective. The EPM evaluates proposed land-use changes, both conversion and intensification, in terms of relevant ecological, economic, and social criteria that combine information about probable land-use outcomes, based on ecological and environmental models, as well as value judgments, as expressed in user-modifiable preference models. Based on on-going meetings and interviews with stakeholders and potential tool users we foc

Modelling Hydrology and Erosion in a Changing Socio-Economic Environment

NASA Astrophysics Data System (ADS)

Kirkby, M. J.; van Delden, H.; Hahn, B. M.; Irvine, B. J.

2009-12-01

Although forecasting systems have a limited time horizon due to the impact of unforeseen events, a rationally based model is able to provide some insights into likely short term behaviour, taking account of the dynamic interactions between climate, physical processes and land use decisions. The biophysical model (PESERA) takes land use decisions as inputs, together with climatic data or scenarios, topography and soils, to generate estimates of runoff, soil erosion, crop or natural vegetation growth and physical suitability, primarily based on crop yields. Estimates are made at a spatial resolution of 100 - 1000 m according to the area involved, the former for a catchment of say 1000 km2, the latter for a continental region. The generic dynamic land-use (change) model (METRONAMICA) has been fully integrated with PESERA to create the ‘Integrated Assessment Model’ (IAM) within the DESURVEY European project. The IAM takes biophysical performance and other spatial characteristics as an input and combines them with socio-economic data to determine potential suitability for each possible residential, commercial, agricultural etc use. The model then allocates each land use type and detailed agricultural class to the locations with the highest potential, based on neighbouring and historic choices as well as short-term economic advantage to estimate probabilities of change to alternative uses. Suitability, accessibility and zoning regulations are included in the decision process to provide the locational characteristics. Change of land use over time is thus determined within a cellular automaton model that generates rational spatial patterns of land use choice. The IAM is being applied at country scale in southern Europe and to smaller regions in North Africa. Although, in the long term, climate change is likely to dominate physical and economic impacts, in the shorter term over which this type of model can most reliably be used, the significance of land use change is much stronger.

Classification of Land Cover and Land Use Based on Convolutional Neural Networks

NASA Astrophysics Data System (ADS)

Yang, Chun; Rottensteiner, Franz; Heipke, Christian

2018-04-01

Land cover describes the physical material of the earth's surface, whereas land use describes the socio-economic function of a piece of land. Land use information is typically collected in geospatial databases. As such databases become outdated quickly, an automatic update process is required. This paper presents a new approach to determine land cover and to classify land use objects based on convolutional neural networks (CNN). The input data are aerial images and derived data such as digital surface models. Firstly, we apply a CNN to determine the land cover for each pixel of the input image. We compare different CNN structures, all of them based on an encoder-decoder structure for obtaining dense class predictions. Secondly, we propose a new CNN-based methodology for the prediction of the land use label of objects from a geospatial database. In this context, we present a strategy for generating image patches of identical size from the input data, which are classified by a CNN. Again, we compare different CNN architectures. Our experiments show that an overall accuracy of up to 85.7 % and 77.4 % can be achieved for land cover and land use, respectively. The classification of land cover has a positive contribution to the classification of the land use classification.

Characterizing Satellite Rainfall Errors based on Land Use and Land Cover and Tracing Error Source in Hydrologic Model Simulation

NASA Astrophysics Data System (ADS)

Gebregiorgis, A. S.; Peters-Lidard, C. D.; Tian, Y.; Hossain, F.

2011-12-01

Hydrologic modeling has benefited from operational production of high resolution satellite rainfall products. The global coverage, near-real time availability, spatial and temporal sampling resolutions have advanced the application of physically based semi-distributed and distributed hydrologic models for wide range of environmental decision making processes. Despite these successes, the existence of uncertainties due to indirect way of satellite rainfall estimates and hydrologic models themselves remain a challenge in making meaningful and more evocative predictions. This study comprises breaking down of total satellite rainfall error into three independent components (hit bias, missed precipitation and false alarm), characterizing them as function of land use and land cover (LULC), and tracing back the source of simulated soil moisture and runoff error in physically based distributed hydrologic model. Here, we asked "on what way the three independent total bias components, hit bias, missed, and false precipitation, affect the estimation of soil moisture and runoff in physically based hydrologic models?" To understand the clear picture of the outlined question above, we implemented a systematic approach by characterizing and decomposing the total satellite rainfall error as a function of land use and land cover in Mississippi basin. This will help us to understand the major source of soil moisture and runoff errors in hydrologic model simulation and trace back the information to algorithm development and sensor type which ultimately helps to improve algorithms better and will improve application and data assimilation in future for GPM. For forest and woodland and human land use system, the soil moisture was mainly dictated by the total bias for 3B42-RT, CMORPH, and PERSIANN products. On the other side, runoff error was largely dominated by hit bias than the total bias. This difference occurred due to the presence of missed precipitation which is a major contributor to the total bias both during the summer and winter seasons. Missed precipitation, most likely light rain and rain over snow cover, has significant effect on soil moisture and are less capable of producing runoff that results runoff dependency on the hit bias only.

Predicting storm runoff from different land-use classes using a geographical information system-based distributed model

NASA Astrophysics Data System (ADS)

Liu, Y. B.; Gebremeskel, S.; de Smedt, F.; Hoffmann, L.; Pfister, L.

2006-02-01

A method is presented to evaluate the storm runoff contributions from different land-use class areas within a river basin using the geographical information system-based hydrological model WetSpa. The modelling is based on division of the catchment into a grid mesh. Each cell has a unique response function independent of the functioning of other cells. Summation of the flow responses from the cells with the same land-use type results in the storm runoff contribution from these areas. The model was applied on the Steinsel catchment in the Alzette river basin, Grand Duchy of Luxembourg, with 52 months of meteo-hydrological measurements. The simulation results show that the direct runoff from urban areas is dominant for a flood event compared with runoff from other land-use areas in this catchment, and this tends to increase for small floods and for the dry-season floods, whereas the interflow from forested, pasture and agricultural field areas contributes to recession flow. It is demonstrated that the relative contribution from urban areas decreases with flow coefficient, that cropland relative contribution is nearly constant, and that the relative contribution from grassland and woodland increases with flow coefficient with regard to their percentage of land-use class areas within the study catchment.

An area-level model of vehicle-pedestrian injury collisions with implications for land use and transportation planning.

PubMed

Wier, Megan; Weintraub, June; Humphreys, Elizabeth H; Seto, Edmund; Bhatia, Rajiv

2009-01-01

There is growing awareness among urban planning, public health, and transportation professionals that design decisions and investments that promote walking can be beneficial for human and ecological health. Planners need practical tools to consider the impact of development on pedestrian safety, a key requirement for the promotion of walking. Simple bivariate models have been used to predict changes in vehicle-pedestrian injury collisions based on changes in traffic volume. We describe the development of a multivariate, area-level regression model of vehicle-pedestrian injury collisions based on environmental and population data in 176 San Francisco, California census tracts. Predictor variables examined included street, land use, and population characteristics, including commute behaviors. The final model explained approximately 72% of the systematic variation in census-tract vehicle-pedestrian injury collisions and included measures of traffic volume, arterial streets without transit, land area, proportion of land area zoned for neighborhood commercial and residential-neighborhood commercial uses, employee and resident populations, proportion of people living in poverty and proportion aged 65 and older. We have begun to apply this model to predict area-level change in vehicle-pedestrian injury collisions associated with land use development and transportation planning decisions.

A land-use and land-cover modeling strategy to support a national assessment of carbon stocks and fluxes

USGS Publications Warehouse

Sohl, Terry L.; Sleeter, Benjamin M.; Zhu, Zhi-Liang; Sayler, Kristi L.; Bennett, Stacie; Bouchard, Michelle; Reker, Ryan R.; Hawbaker, Todd; Wein, Anne; Liu, Shu-Guang; Kanengleter, Ronald; Acevedo, William

2012-01-01

Changes in land use, land cover, disturbance regimes, and land management have considerable influence on carbon and greenhouse gas (GHG) fluxes within ecosystems. Through targeted land-use and landmanagement activities, ecosystems can be managed to enhance carbon sequestration and mitigate fluxes of other GHGs. National-scale, comprehensive analyses of carbon sequestration potential by ecosystem are needed, with a consistent, nationally applicable land-use and land-cover (LULC) modeling framework a key component of such analyses. The U.S. Geological Survey has initiated a project to analyze current and projected future GHG fluxes by ecosystem and quantify potential mitigation strategies. We have developed a unique LULC modeling framework to support this work. Downscaled scenarios consistent with IPCC Special Report on Emissions Scenarios (SRES) were constructed for U.S. ecoregions, and the FORE-SCE model was used to spatially map the scenarios. Results for a prototype demonstrate our ability to model LULC change and inform a biogeochemical modeling framework for analysis of subsequent GHG fluxes. The methodology was then successfully used to model LULC change for four IPCC SRES scenarios for an ecoregion in the Great Plains. The scenario-based LULC projections are now being used to analyze potential GHG impacts of LULC change across the U.S.

Quantitative Analysis of Intra Urban Growth Modeling using socio economic agents by combining cellular automata model with agent based model

NASA Astrophysics Data System (ADS)

Singh, V. K.; Jha, A. K.; Gupta, K.; Srivastav, S. K.

2017-12-01

Recent studies indicate that there is a significant improvement in the urban land use dynamics through modeling at finer spatial resolutions. Geo-computational models such as cellular automata and agent based model have given evident proof regarding the quantification of the urban growth pattern with urban boundary. In recent studies, socio- economic factors such as demography, education rate, household density, parcel price of the current year, distance to road, school, hospital, commercial centers and police station are considered to the major factors influencing the Land Use Land Cover (LULC) pattern of the city. These factors have unidirectional approach to land use pattern which makes it difficult to analyze the spatial aspects of model results both quantitatively and qualitatively. In this study, cellular automata model is combined with generic model known as Agent Based Model to evaluate the impact of socio economic factors on land use pattern. For this purpose, Dehradun an Indian city is selected as a case study. Socio economic factors were collected from field survey, Census of India, Directorate of economic census, Uttarakhand, India. A 3X3 simulating window is used to consider the impact on LULC. Cellular automata model results are examined for the identification of hot spot areas within the urban area and agent based model will be using logistic based regression approach where it will identify the correlation between each factor on LULC and classify the available area into low density, medium density, high density residential or commercial area. In the modeling phase, transition rule, neighborhood effect, cell change factors are used to improve the representation of built-up classes. Significant improvement is observed in the built-up classes from 84 % to 89 %. However after incorporating agent based model with cellular automata model the accuracy improved from 89 % to 94 % in 3 classes of urban i.e. low density, medium density and commercial classes. Sensitivity study of the model indicated that southern and south-west part of the city have shown improvement and small patches of growth are also observed in the north western part of the city.The study highlights the growing importance of socio economic factors and geo-computational modeling approach on changing LULC of newly growing cities of modern India.

Simulation of ground-water flow and rainfall runoff with emphasis on the effects of land cover, Whittlesey Creek, Bayfield County, Wisconsin, 1999-2001

USGS Publications Warehouse

Lenz, Bernard N.; Saad, David A.; Fitzpatrick, Faith A.

2003-01-01

The effects of land cover on flooding and base-flow characteristics of Whittlesey Creek, Bayfield County, Wis., were examined in a study that involved ground-water-flow and rainfall-runoff modeling. Field data were collected during 1999-2001 for synoptic base flow, streambed head and temperature, precipitation, continuous streamflow and stream stage, and other physical characteristics. Well logs provided data for potentiometric-surface altitudes and stratigraphic descriptions. Geologic, soil, hydrography, altitude, and historical land-cover data were compiled into a geographic information system and used in two ground-water-flow models (GFLOW and MODFLOW) and a rainfall-runoff model (SWAT). A deep ground-water system intersects Whittlesey Creek near the confluence with the North Fork, producing a steady base flow of 17?18 cubic feet per second. Upstream from the confluence, the creek has little or no base flow; flow is from surface runoff and a small amount of perched ground water. Most of the base flow to Whittlesey Creek originates as recharge through the permeable sands in the center of the Bayfield Peninsula to the northwest of the surface-water-contributing basin. Based on simulations, model-wide changes in recharge caused a proportional change in simulated base flow for Whittlesey Creek. Changing the simulated amount of recharge by 25 to 50 percent in only the ground-water-contributing area results in relatively small changes in base flow to Whittlesey Creek (about 2?11 percent). Simulated changes in land cover within the Whittlesey Creek surface-water-contributing basin would have minimal effects on base flow and average annual runoff, but flood peaks (based on daily mean flows on peak-flow days) could be affected. Based on the simulations, changing the basin land cover to a reforested condition results in a reduction in flood peaks of about 12 to 14 percent for up to a 100-yr flood. Changing the basin land cover to 25 percent urban land or returning basin land cover to the intensive row-crop agriculture of the 1920s results in flood peaks increasing by as much as 18 percent. The SWAT model is limited to a daily time step, which is adequate for describing the surface-water/ground-water interaction and percentage changes. It may not, however, be adequate in describing peak flow because the instantaneous peak flow in Whittlesey Creek during a flood can be more than twice the magnitude of the daily mean flow during that same flood. In addition, the storage and infiltration capacities of wetlands in the basin are not fully understood and need further study.

Prescription of land-surface boundary conditions in GISS GCM 2: A simple method based on high-resolution vegetation data bases

NASA Technical Reports Server (NTRS)

Matthews, E.

1984-01-01

A simple method was developed for improved prescription of seasonal surface characteristics and parameterization of land-surface processes in climate models. This method, developed for the Goddard Institute for Space Studies General Circulation Model II (GISS GCM II), maintains the spatial variability of fine-resolution land-cover data while restricting to 8 the number of vegetation types handled in the model. This was achieved by: redefining the large number of vegetation classes in the 1 deg x 1 deg resolution Matthews (1983) vegetation data base as percentages of 8 simple types; deriving roughness length, field capacity, masking depth and seasonal, spectral reflectivity for the 8 types; and aggregating these surface features from the 1 deg x 1 deg resolution to coarser model resolutions, e.g., 8 deg latitude x 10 deg longitude or 4 deg latitude x 5 deg longitude.

Spatial and Temporal Dynamics and Value of Nature-Based Recreation, Estimated via Social Media

PubMed Central

Watson, Keri B.; Wood, Spencer A.; Ricketts, Taylor H.

2016-01-01

Conserved lands provide multiple ecosystem services, including opportunities for nature-based recreation. Managing this service requires understanding the landscape attributes underpinning its provision, and how changes in land management affect its contribution to human wellbeing over time. However, evidence from both spatially explicit and temporally dynamic analyses is scarce, often due to data limitations. In this study, we investigated nature-based recreation within conserved lands in Vermont, USA. We used geotagged photographs uploaded to the photo-sharing website Flickr to quantify visits by in-state and out-of-state visitors, and we multiplied visits by mean trip expenditures to show that conserved lands contributed US $1.8 billion (US $0.18–20.2 at 95% confidence) to Vermont’s tourism industry between 2007 and 2014. We found eight landscape attributes explained the pattern of visits to conserved lands; visits were higher in larger conserved lands, with less forest cover, greater trail density and more opportunities for snow sports. Some of these attributes differed from those found in other locations, but all aligned with our understanding of recreation in Vermont. We also found that using temporally static models to inform conservation decisions may have perverse outcomes for nature-based recreation. For example, static models suggest conserved land with less forest cover receive more visits, but temporally dynamic models suggest clearing forests decreases, rather than increases, visits to these sites. Our results illustrate the importance of understanding both the spatial and temporal dynamics of ecosystem services for conservation decision-making. PMID:27611325

Large ensemble and large-domain hydrologic modeling: Insights from SUMMA applications in the Columbia River Basin

NASA Astrophysics Data System (ADS)

Ou, G.; Nijssen, B.; Nearing, G. S.; Newman, A. J.; Mizukami, N.; Clark, M. P.

2016-12-01

The Structure for Unifying Multiple Modeling Alternatives (SUMMA) provides a unifying modeling framework for process-based hydrologic modeling by defining a general set of conservation equations for mass and energy, with the capability to incorporate multiple choices for spatial discretizations and flux parameterizations. In this study, we provide a first demonstration of large-scale hydrologic simulations using SUMMA through an application to the Columbia River Basin (CRB) in the northwestern United States and Canada for a multi-decadal simulation period. The CRB is discretized into 11,723 hydrologic response units (HRUs) according to the United States Geologic Service Geospatial Fabric. The soil parameters are derived from the Natural Resources Conservation Service Soil Survey Geographic (SSURGO) Database. The land cover parameters are based on the National Land Cover Database from the year 2001 created by the Multi-Resolution Land Characteristics (MRLC) Consortium. The forcing data, including hourly air pressure, temperature, specific humidity, wind speed, precipitation, shortwave and longwave radiations, are based on Phase 2 of the North American Land Data Assimilation System (NLDAS-2) and averaged for each HRU. The simulation results are compared to simulations with the Variable Infiltration Capacity (VIC) model and the Precipitation Runoff Modeling System (PRMS). We are particularly interested in SUMMA's capability to mimic model behaviors of the other two models through the selection of appropriate model parameterizations in SUMMA.

Modelling land use/cover changes with markov-cellular automata in Komering Watershed, South Sumatera

NASA Astrophysics Data System (ADS)

Kusratmoko, E.; Albertus, S. D. Y.; Supriatna

2017-01-01

This research has a purpose to study and develop a model that can representing and simulating spatial distribution pattern of land use change in Komering watershed. The Komering watershed is one of nine sub Musi river basin and is located in the southern part of Sumatra island that has an area of 8060,62 km2. Land use change simulations, achieved through Markov-cellular automata (CA) methodologies. Slope, elevation, distance from road, distance from river, distance from capital sub-district, distance from settlement area area were driving factors that used in this research. Land use prediction result in 2030 also shows decrease of forest acreage up to -3.37%, agricultural land decreased up to -2.13%, and open land decreased up to -0.13%. On the other hand settlement area increased up to 0.07%, and plantation land increased up to 5.56%. Based on the predictive result, land use unconformity percentage to RTRW in Komering watershed is 18.62 % and land use conformity is 58.27%. Based on the results of the scenario, where forest in protected areas and agriculture land are maintained, shows increase the land use conformity amounted to 60.41 % and reduce unconformity that occur in Komering watershed to 17.23 %.

Modeling the Behaviour of an Advanced Material Based Smart Landing Gear System for Aerospace Vehicles

DOE Office of Scientific and Technical Information (OSTI.GOV)

Varughese, Byji; Dayananda, G. N.; Rao, M. Subba

2008-07-29

The last two decades have seen a substantial rise in the use of advanced materials such as polymer composites for aerospace structural applications. In more recent years there has been a concerted effort to integrate materials, which mimic biological functions (referred to as smart materials) with polymeric composites. Prominent among smart materials are shape memory alloys, which possess both actuating and sensory functions that can be realized simultaneously. The proper characterization and modeling of advanced and smart materials holds the key to the design and development of efficient smart devices/systems. This paper focuses on the material characterization; modeling and validationmore » of the model in relation to the development of a Shape Memory Alloy (SMA) based smart landing gear (with high energy dissipation features) for a semi rigid radio controlled airship (RC-blimp). The Super Elastic (SE) SMA element is configured in such a way that it is forced into a tensile mode of high elastic deformation. The smart landing gear comprises of a landing beam, an arch and a super elastic Nickel-Titanium (Ni-Ti) SMA element. The landing gear is primarily made of polymer carbon composites, which possess high specific stiffness and high specific strength compared to conventional materials, and are therefore ideally suited for the design and development of an efficient skid landing gear system with good energy dissipation characteristics. The development of the smart landing gear in relation to a conventional metal landing gear design is also dealt with.« less

Assessing the vulnerability of human and biological communities to changing ecosystem services using a GIS-based multi-criteria decision support tool

USGS Publications Warehouse

Villarreal, Miguel; Norman, Laura M.; Labiosa, William B.

2012-01-01

In this paper we describe an application of a GIS-based multi-criteria decision support web tool that models and evaluates relative changes in ecosystem services to policy and land management decisions. The Santa Cruz Watershed Ecosystem Portfolio (SCWEPM) was designed to provide credible forecasts of responses to ecosystem drivers and stressors and to illustrate the role of land use decisions on spatial and temporal distributions of ecosystem services within a binational (U.S. and Mexico) watershed. We present two SCWEPM sub-models that when analyzed together address bidirectional relationships between social and ecological vulnerability and ecosystem services. The first model employs the Modified Socio-Environmental Vulnerability Index (M-SEVI), which assesses community vulnerability using information from U.S. and Mexico censuses on education, access to resources, migratory status, housing situation, and number of dependents. The second, relating land cover change to biodiversity (provisioning services), models changes in the distribution of terrestrial vertebrate habitat based on multitemporal vegetation and land cover maps, wildlife habitat relationships, and changes in land use/land cover patterns. When assessed concurrently, the models exposed some unexpected relationships between vulnerable communities and ecosystem services provisioning. For instance, the most species-rich habitat type in the watershed, Desert Riparian Forest, increased over time in areas occupied by the most vulnerable populations and declined in areas with less vulnerable populations. This type of information can be used to identify ecological conservation and restoration targets that enhance the livelihoods of people in vulnerable communities and promote biodiversity and ecosystem health.

Advancing Land-Sea Conservation Planning: Integrating Modelling of Catchments, Land-Use Change, and River Plumes to Prioritise Catchment Management and Protection.

PubMed

Álvarez-Romero, Jorge G; Pressey, Robert L; Ban, Natalie C; Brodie, Jon

2015-01-01

Human-induced changes to river loads of nutrients and sediments pose a significant threat to marine ecosystems. Ongoing land-use change can further increase these loads, and amplify the impacts of land-based threats on vulnerable marine ecosystems. Consequently, there is a need to assess these threats and prioritise actions to mitigate their impacts. A key question regarding prioritisation is whether actions in catchments to maintain coastal-marine water quality can be spatially congruent with actions for other management objectives, such as conserving terrestrial biodiversity. In selected catchments draining into the Gulf of California, Mexico, we employed Land Change Modeller to assess the vulnerability of areas with native vegetation to conversion into crops, pasture, and urban areas. We then used SedNet, a catchment modelling tool, to map the sources and estimate pollutant loads delivered to the Gulf by these catchments. Following these analyses, we used modelled river plumes to identify marine areas likely influenced by land-based pollutants. Finally, we prioritised areas for catchment management based on objectives for conservation of terrestrial biodiversity and objectives for water quality that recognised links between pollutant sources and affected marine areas. Our objectives for coastal-marine water quality were to reduce sediment and nutrient discharges from anthropic areas, and minimise future increases in coastal sedimentation and eutrophication. Our objectives for protection of terrestrial biodiversity covered species of vertebrates. We used Marxan, a conservation planning tool, to prioritise interventions and explore spatial differences in priorities for both objectives. Notable differences in the distributions of land values for terrestrial biodiversity and coastal-marine water quality indicated the likely need for trade-offs between catchment management objectives. However, there were priority areas that contributed to both sets of objectives. Our study demonstrates a practical approach to integrating models of catchments, land-use change, and river plumes with conservation planning software to inform prioritisation of catchment management.

Advancing Land-Sea Conservation Planning: Integrating Modelling of Catchments, Land-Use Change, and River Plumes to Prioritise Catchment Management and Protection

PubMed Central

Álvarez-Romero, Jorge G.; Pressey, Robert L.; Ban, Natalie C.; Brodie, Jon

2015-01-01

Human-induced changes to river loads of nutrients and sediments pose a significant threat to marine ecosystems. Ongoing land-use change can further increase these loads, and amplify the impacts of land-based threats on vulnerable marine ecosystems. Consequently, there is a need to assess these threats and prioritise actions to mitigate their impacts. A key question regarding prioritisation is whether actions in catchments to maintain coastal-marine water quality can be spatially congruent with actions for other management objectives, such as conserving terrestrial biodiversity. In selected catchments draining into the Gulf of California, Mexico, we employed Land Change Modeller to assess the vulnerability of areas with native vegetation to conversion into crops, pasture, and urban areas. We then used SedNet, a catchment modelling tool, to map the sources and estimate pollutant loads delivered to the Gulf by these catchments. Following these analyses, we used modelled river plumes to identify marine areas likely influenced by land-based pollutants. Finally, we prioritised areas for catchment management based on objectives for conservation of terrestrial biodiversity and objectives for water quality that recognised links between pollutant sources and affected marine areas. Our objectives for coastal-marine water quality were to reduce sediment and nutrient discharges from anthropic areas, and minimise future increases in coastal sedimentation and eutrophication. Our objectives for protection of terrestrial biodiversity covered species of vertebrates. We used Marxan, a conservation planning tool, to prioritise interventions and explore spatial differences in priorities for both objectives. Notable differences in the distributions of land values for terrestrial biodiversity and coastal-marine water quality indicated the likely need for trade-offs between catchment management objectives. However, there were priority areas that contributed to both sets of objectives. Our study demonstrates a practical approach to integrating models of catchments, land-use change, and river plumes with conservation planning software to inform prioritisation of catchment management. PMID:26714166

Mapping and quantifying ecosystem services: analysis of trade-offs and synergies at the different spatial scales

NASA Astrophysics Data System (ADS)

Tomczyk, Aleksandra; Ewertowski, Marek

2014-05-01

The importance of conserving the natural environment has been known for a long time. It can be fulfilled by designation of protected areas as well as proper management of broader landscapes. During past two decades, conservation has shifted from a predominantly species- and habitat-focus to a more holistic "ecosystem approach" with an emphasis on "ecosystem services", which underpin the benefits which society can obtain (directly or indirectly) from ecosystems. This study aims to investigate and compare existing land use prioritization models and to develop new GIS-based frameworks for analysis for different spatial scales. Research were carried out in several conservation areas in UK and Poland. Main focus was on regulating (including regulation of soil erosion and landslide susceptibility) and recreation services. A new GIS-based model was developed which enabled to analysis of this services. Different spatial scales, ranging from whole conservation areas to single catchments were chosen for mapping and quantifying. Based on different scenarios three sets of ecosystem services were calculated. Data contained specific land-cover/land-use resulting from the different strategy of the natural conservation for each of the study sites. Modelling was carried out based on the trends identified on the basis of past changes in land-use/land-cover (based on analysis of time-series satellite images), and the probability of a particular class of land-use/land-cover for the chosen scenario. Comparison between results revealed ecosystem service tradeoffs (when the obtaining of one service results in the reducing of another service) and synergies (when multiple services can be provides simultaneously). Results of the study shows where (and under which condition): (1) conservation areas can accommodate more visitors and in the same time provide regulation of soil erosion and protection against landslide developments, (2) further development of recreation services will lead to inevitable degradation of environment. Based on these results several further activities were proposed: from changing of conservation strategy for some part of the areas to changing of the land cover/land use.

Land Surface Verification Toolkit (LVT) - A Generalized Framework for Land Surface Model Evaluation

NASA Technical Reports Server (NTRS)

Kumar, Sujay V.; Peters-Lidard, Christa D.; Santanello, Joseph; Harrison, Ken; Liu, Yuqiong; Shaw, Michael

2011-01-01

Model evaluation and verification are key in improving the usage and applicability of simulation models for real-world applications. In this article, the development and capabilities of a formal system for land surface model evaluation called the Land surface Verification Toolkit (LVT) is described. LVT is designed to provide an integrated environment for systematic land model evaluation and facilitates a range of verification approaches and analysis capabilities. LVT operates across multiple temporal and spatial scales and employs a large suite of in-situ, remotely sensed and other model and reanalysis datasets in their native formats. In addition to the traditional accuracy-based measures, LVT also includes uncertainty and ensemble diagnostics, information theory measures, spatial similarity metrics and scale decomposition techniques that provide novel ways for performing diagnostic model evaluations. Though LVT was originally designed to support the land surface modeling and data assimilation framework known as the Land Information System (LIS), it also supports hydrological data products from other, non-LIS environments. In addition, the analysis of diagnostics from various computational subsystems of LIS including data assimilation, optimization and uncertainty estimation are supported within LVT. Together, LIS and LVT provide a robust end-to-end environment for enabling the concepts of model data fusion for hydrological applications. The evolving capabilities of LVT framework are expected to facilitate rapid model evaluation efforts and aid the definition and refinement of formal evaluation procedures for the land surface modeling community.

Land Ecological Security Evaluation of Underground Iron Mine Based on PSR Model

NASA Astrophysics Data System (ADS)

Xiao, Xiao; Chen, Yong; Ruan, Jinghua; Hong, Qiang; Gan, Yong

2018-01-01

Iron ore mine provides an important strategic resource to the national economy while it also causes many serious ecological problems to the environment. The study summed up the characteristics of ecological environment problems of underground iron mine. Considering the mining process of underground iron mine, we analysis connections between mining production, resource, environment and economical background. The paper proposed a land ecological security evaluation system and method of underground iron mine based on Pressure-State-Response model. Our application in Chengchao iron mine proves its efficiency and promising guide on land ecological security evaluation.

On approaches to analyze the sensitivity of simulated hydrologic fluxes to model parameters in the community land model

DOE PAGES

Bao, Jie; Hou, Zhangshuan; Huang, Maoyi; ...

2015-12-04

Here, effective sensitivity analysis approaches are needed to identify important parameters or factors and their uncertainties in complex Earth system models composed of multi-phase multi-component phenomena and multiple biogeophysical-biogeochemical processes. In this study, the impacts of 10 hydrologic parameters in the Community Land Model on simulations of runoff and latent heat flux are evaluated using data from a watershed. Different metrics, including residual statistics, the Nash-Sutcliffe coefficient, and log mean square error, are used as alternative measures of the deviations between the simulated and field observed values. Four sensitivity analysis (SA) approaches, including analysis of variance based on the generalizedmore » linear model, generalized cross validation based on the multivariate adaptive regression splines model, standardized regression coefficients based on a linear regression model, and analysis of variance based on support vector machine, are investigated. Results suggest that these approaches show consistent measurement of the impacts of major hydrologic parameters on response variables, but with differences in the relative contributions, particularly for the secondary parameters. The convergence behaviors of the SA with respect to the number of sampling points are also examined with different combinations of input parameter sets and output response variables and their alternative metrics. This study helps identify the optimal SA approach, provides guidance for the calibration of the Community Land Model parameters to improve the model simulations of land surface fluxes, and approximates the magnitudes to be adjusted in the parameter values during parametric model optimization.« less

Future water demand in California under a broad range of land use scenarios

NASA Astrophysics Data System (ADS)

Wilson, T. S.; Sleeter, B. M.; Cameron, D. R.

2016-12-01

California continues to be gripped by the most severe drought on record. Most general circulation models agree the state will continue to warm this century and research suggests persistent, long-term droughts may become the new normal, exacerbating an already uncertain water supply future. Population increases and agricultural intensification will likely stress existing, highly variable inter-annual water supplies even further in coming decades. Using the Land Use and Carbon Scenario Simulator (LUCAS) model, we explore a wide range of potential water demand futures from 2012 to 2062 based on 8 alternative, spatially-explicit (1 km) land use scenarios and land-use related water demand. Scenarios include low and high rates for urbanization, agricultural expansion, and agricultural contraction as well as lowest and highest rates for the combined suite of anthropogenic land uses. Land change values were sampled from county-level historical (1991-2012) land change data and county-level average water use data for urban areas (i.e. municipal and industrial) and annual and perennial cropland. We modeled 100 Monte Carlo simulations for each scenario to better characterize and capture model uncertainty and a range of potential future outcomes. Results show water demand in Mediterranean California was lowest in the low anthropogenic change scenario, dropping an average 2.7 million acre feet (MAF) by 2062. The highest water demand was seen in the high urbanization (+3.2 MAF), high agricultural expansion (+4.1 MAF), and the high anthropogenic (+4.3 MAF) scenarios. Results provide water managers and policy makers with information on diverging land use and water use futures, based on observed land change and water use trends, helping better inform land and resource management decisions.

Advances in Land Data Assimilation at the NASA Goddard Space Flight Center

NASA Technical Reports Server (NTRS)

Reichle, Rolf

2009-01-01

Research in land surface data assimilation has grown rapidly over the last decade. In this presentation we provide a brief overview of key research contributions by the NASA Goddard Space Flight Center (GSFC). The GSFC contributions to land assimilation primarily include the continued development and application of the Land Information System (US) and the ensemble Kalman filter (EnKF). In particular, we have developed a method to generate perturbation fields that are correlated in space, time, and across variables and that permit the flexible modeling of errors in land surface models and observations, along with an adaptive filtering approach that estimates observation and model error input parameters. A percentile-based scaling method that addresses soil moisture biases in model and observational estimates opened the path to the successful application of land data assimilation to satellite retrievals of surface soil moisture. Assimilation of AMSR-E surface soil moisture retrievals into the NASA Catchment model provided superior surface and root zone assimilation products (when validated against in situ measurements and compared to the model estimates or satellite observations alone). The multi-model capabilities of US were used to investigate the role of subsurface physics in the assimilation of surface soil moisture observations. Results indicate that the potential of surface soil moisture assimilation to improve root zone information is higher when the surface to root zone coupling is stronger. Building on this experience, GSFC leads the development of the Level 4 Surface and Root-Zone Soil Moisture (L4_SM) product for the planned NASA Soil-Moisture-Active-Passive (SMAP) mission. A key milestone was the design and execution of an Observing System Simulation Experiment that quantified the contribution of soil moisture retrievals to land data assimilation products as a function of retrieval and land model skill and yielded an estimate of the error budget for the SMAP L4_SM product. Terrestrial water storage observations from GRACE satellite system were also successfully assimilated into the NASA Catchment model and provided improved estimates of groundwater variability when compared to the model estimates alone. Moreover, satellite-based land surface temperature (LST) observations from the ISCCP archive were assimilated using a bias estimation module that was specifically designed for LST assimilation. As with soil moisture, LST assimilation provides modest yet statistically significant improvements when compared to the model or satellite observations alone. To achieve the improvement, however, the LST assimilation algorithm must be adapted to the specific formulation of LST in the land model. An improved method for the assimilation of snow cover observations was also developed. Finally, the coupling of LIS to the mesoscale Weather Research and Forecasting (WRF) model enabled investigations into how the sensitivity of land-atmosphere interactions to the specific choice of planetary boundary layer scheme and land surface model varies across surface moisture regimes, and how it can be quantified and evaluated against observations. The on-going development and integration of land assimilation modules into the Land Information System will enable the use of GSFC software with a variety of land models and make it accessible to the research community.

Comparing crop growth and carbon budgets simulated across AmeriFlux agricultural sites using the community land model (CLM)

USDA-ARS?s Scientific Manuscript database

Improving process-based crop models is needed to achieve high fidelity forecasts of regional energy, water, and carbon exchange. However, most state-of-the-art Land Surface Models (LSMs) assessed in the fifth phase of the Coupled Model Inter-comparison project (CMIP5) simulated crops as simple C3 or...

Effects of endogenous factors on regional land-use carbon emissions based on the Grossman decomposition model: a case study of Zhejiang Province, China.

PubMed

Wu, Cifang; Li, Guan; Yue, Wenze; Lu, Rucheng; Lu, Zhangwei; You, Heyuan

2015-02-01

The impact of land-use change on greenhouse gas emissions has become a core issue in current studies on global change and carbon cycle. However, a comprehensive evaluation of the effects of land-use changes on carbon emissions is very necessary. This paper attempted to apply the Grossman decomposition model to estimate the scale, structural, and management effects of land-use carbon emissions based on final energy consumption by establishing the relationship between the types of land use and carbon emissions in energy consumption. It was shown that land-use carbon emissions increase from 169.5624 million tons in 2000 to 637.0984 million tons in 2010, with an annual average growth rate of 14.15%. Meanwhile, land-use carbon intensity increased from 17.59 t/ha in 2000 to 64.42 t/ha in 2010, with an average annual growth rate of 13.86%. The results indicated that rapid industrialization and urbanization in Zhejiang Province promptly increased urban land and industrial land, which consequently affected land-use extensive emissions. The structural and management effects did not mitigate land-use carbon emissions. By contrast, both factors evidently affected the growth of carbon emissions because of the rigid demands of energy-intensive land-use types and the absence of land management. Results called for the policy implications of optimizing land-use structures and strengthening land-use management.

Effects of Endogenous Factors on Regional Land-Use Carbon Emissions Based on the Grossman Decomposition Model: A Case Study of Zhejiang Province, China

NASA Astrophysics Data System (ADS)

Wu, Cifang; Li, Guan; Yue, Wenze; Lu, Rucheng; Lu, Zhangwei; You, Heyuan

2015-02-01

The impact of land-use change on greenhouse gas emissions has become a core issue in current studies on global change and carbon cycle. However, a comprehensive evaluation of the effects of land-use changes on carbon emissions is very necessary. This paper attempted to apply the Grossman decomposition model to estimate the scale, structural, and management effects of land-use carbon emissions based on final energy consumption by establishing the relationship between the types of land use and carbon emissions in energy consumption. It was shown that land-use carbon emissions increase from 169.5624 million tons in 2000 to 637.0984 million tons in 2010, with an annual average growth rate of 14.15 %. Meanwhile, land-use carbon intensity increased from 17.59 t/ha in 2000 to 64.42 t/ha in 2010, with an average annual growth rate of 13.86 %. The results indicated that rapid industrialization and urbanization in Zhejiang Province promptly increased urban land and industrial land, which consequently affected land-use extensive emissions. The structural and management effects did not mitigate land-use carbon emissions. By contrast, both factors evidently affected the growth of carbon emissions because of the rigid demands of energy-intensive land-use types and the absence of land management. Results called for the policy implications of optimizing land-use structures and strengthening land-use management.

Characteristics of organic soil in black spruce forests: implications for the application of land surface and ecosystem models in cold regions

Treesearch

Shuhua Yi; Kristen Manies; Jennifer Harden; David McGuire

2009-01-01

Soil organic layers (OL) play an important role in land-atmosphere exchanges of water, energy and carbon in cold environments. The proper implementation of OL in land surface and ecosystem models is important for predicting dynamic responses to climate warming. Based on the analysis of OL samples of black spruce (Picea mariana), we recommend that...

Simulating the Conversion of Rural Settlements to Town Land Based on Multi-Agent Systems and Cellular Automata

PubMed Central

Liu, Yaolin; Kong, Xuesong; Liu, Yanfang; Chen, Yiyun

2013-01-01

Rapid urbanization in China has triggered the conversion of land from rural to urban use, particularly the conversion of rural settlements to town land. This conversion is the result of the joint effects of the geographic environment and agents involving the government, investors, and farmers. To understand the dynamic interaction dominated by agents and to predict the future landscape of town expansion, a small town land-planning model is proposed based on the integration of multi-agent systems (MAS) and cellular automata (CA). The MAS-CA model links the decision-making behaviors of agents with the neighbor effect of CA. The interaction rules are projected by analyzing the preference conflicts among agents. To better illustrate the effects of the geographic environment, neighborhood, and agent behavior, a comparative analysis between the CA and MAS-CA models in three different towns is presented, revealing interesting patterns in terms of quantity, spatial characteristics, and the coordinating process. The simulation of rural settlements conversion to town land through modeling agent decision and human-environment interaction is very useful for understanding the mechanisms of rural-urban land-use change in developing countries. This process can assist town planners in formulating appropriate development plans. PMID:24244472

Global albedo change and radiative cooling from anthropogenic land-cover change, 1700 to 2005 based on MODIS, land-use harmonization and radiative kernels

USDA-ARS?s Scientific Manuscript database

Widespread anthropogenic land-cover change over the last five centuries has influenced the global climate system through both biogeochemical and biophysical processes. Models indicate that warming from carbon emissions associated with land cover conversion have been partially offset if not outweigh...

Model studies of crosswind landing-gear configurations for STOL aircraft

NASA Technical Reports Server (NTRS)

Stubbs, S. M.; Byrdsong, T. A.

1973-01-01

A dynamic model was used to directly compare four different crosswind landing gear mechanisms. The model was landed as a free body onto a laterally sloping runway used to simulate a crosswind side force. A radio control system was used for steering to oppose the side force as the model rolled to a stop. The configuration in which the landing gears are alined by the pilot and locked in the direction of motion prior to touchdown gave the smoothest runout behavior with the vehicle maintaining its crab angle throughout the landing roll. Nose wheel steering was confirmed to be better than steering with nose and main gears differentially or together. Testing is continuing to obtain quantitative data to establish an experimental data base for validation of an analytical program that will be capable of predicting full scale results.

Land use as a Driver of Patterns of Rodenticide Exposure in Modeled Kit Fox Populations

EPA Science Inventory

Although rodenticides are increasingly regulated, they nonetheless cause poisonings in many non-target wildlife species. Second-generation anticoagualant rodenticide use is common in agricultural and residential lands. Here, we use an individual-based population model to assess t...

A Thermal-based Two-Source Energy Balance Model for Estimating Evapotranspiration over Complex Canopies

USDA-ARS?s Scientific Manuscript database

Land surface temperature (LST) provides valuable information for quantifying root-zone water availability, evapotranspiration (ET) and crop condition as well as providing useful information for constraining prognostic land surface models. This presentation describes a robust but relatively simple LS...

Exploring Agricultural Livelihood Transitions with an Agent-Based Virtual Laboratory: Global Forces to Local Decision-Making

PubMed Central

Magliocca, Nicholas R.; Brown, Daniel G.; Ellis, Erle C.

2013-01-01

Rural populations are undergoing rapid changes in both their livelihoods and land uses, with associated impacts on ecosystems, global biogeochemistry, and climate change. A primary challenge is, thus, to explain these shifts in terms of the actors and processes operating within a variety of land systems in order to understand how land users might respond locally to future changes in broader-scale environmental and economic conditions. Using ‘induced intensification’ theory as a benchmark, we develop a generalized agent-based model to investigate mechanistic explanations of relationships between agricultural intensity and population density, environmental suitability, and market influence. Land-use and livelihood decisions modeled from basic micro-economic theories generated spatial and temporal patterns of agricultural intensification consistent with predictions of induced intensification theory. Further, agent actions in response to conditions beyond those described by induced intensification theory were explored, revealing that interactions among environmental constraints, population pressure, and market influence may produce transitions to multiple livelihood regimes of varying market integration. The result is new hypotheses that could modify and enrich understanding of the classic relationship between agricultural intensity and population density. The strength of this agent-based model and the experimental results is the generalized form of the decision-making processes underlying land-use and livelihood transitions, creating the prospect of a virtual laboratory for systematically generating hypotheses of how agent decisions and interactions relate to observed land-use and livelihood patterns across diverse land systems. PMID:24039892

High-resolution climate and land surface interactions modeling over Belgium: current state and decennial scale projections

NASA Astrophysics Data System (ADS)

Jacquemin, Ingrid; Henrot, Alexandra-Jane; Beckers, Veronique; Berckmans, Julie; Debusscher, Bos; Dury, Marie; Minet, Julien; Hamdi, Rafiq; Dendoncker, Nicolas; Tychon, Bernard; Hambuckers, Alain; François, Louis

2016-04-01

The interactions between land surface and climate are complex. Climate changes can affect ecosystem structure and functions, by altering photosynthesis and productivity or inducing thermal and hydric stresses on plant species. These changes then impact socio-economic systems, through e.g., lower farming or forestry incomes. Ultimately, it can lead to permanent changes in land use structure, especially when associated with other non-climatic factors, such as urbanization pressure. These interactions and changes have feedbacks on the climate systems, in terms of changing: (1) surface properties (albedo, roughness, evapotranspiration, etc.) and (2) greenhouse gas emissions (mainly CO2, CH4, N2O). In the framework of the MASC project (« Modelling and Assessing Surface Change impacts on Belgian and Western European climate »), we aim at improving regional climate model projections at the decennial scale over Belgium and Western Europe by combining high-resolution models of climate, land surface dynamics and socio-economic processes. The land surface dynamics (LSD) module is composed of a dynamic vegetation model (CARAIB) calculating the productivity and growth of natural and managed vegetation, and an agent-based model (CRAFTY), determining the shifts in land use and land cover. This up-scaled LSD module is made consistent with the surface scheme of the regional climate model (RCM: ALARO) to allow simulations of the RCM with a fully dynamic land surface for the recent past and the period 2000-2030. In this contribution, we analyze the results of the first simulations performed with the CARAIB dynamic vegetation model over Belgium at a resolution of 1km. This analysis is performed at the species level, using a set of 17 species for natural vegetation (trees and grasses) and 10 crops, especially designed to represent the Belgian vegetation. The CARAIB model is forced with surface atmospheric variables derived from the monthly global CRU climatology or ALARO outputs (from a 4 km resolution simulation) for the recent past and the decennial projections. Evidently, these simulations lead to a first analysis of the impact of climate change on carbon stocks (e.g., biomass, soil carbon) and fluxes (e.g., gross and net primary productivities (GPP and NPP) and net ecosystem production (NEP)). The surface scheme is based on two land use/land cover databases, ECOPLAN for the Flemish region and, for the Walloon region, the COS-Wallonia database and the Belgian agricultural statistics for agricultural land. Land use and land cover are fixed through time (reference year: 2007) in these simulations, but a first attempt of coupling between CARAIB and CRAFTY will be made to establish dynamic land use change scenarios for the next decades. A simulation with variable land use would allow an analysis of land use change impacts not only on crop yields and the land carbon budget, but also on climate relevant parameters, such as surface albedo, roughness length and evapotranspiration towards a coupling with the RCM.

Land Surface Model Biases and their Impacts on the Assimilation of Snow-related Observations

NASA Astrophysics Data System (ADS)

Arsenault, K. R.; Kumar, S.; Hunter, S. M.; Aman, R.; Houser, P. R.; Toll, D.; Engman, T.; Nigro, J.

2007-12-01

Some recent snow modeling studies have employed a wide range of assimilation methods to incorporate snow cover or other snow-related observations into different hydrological or land surface models. These methods often include taking both model and observation biases into account throughout the model integration. This study focuses more on diagnosing the model biases and presenting their subsequent impacts on assimilating snow observations and modeled snowmelt processes. In this study, the land surface model, the Community Land Model (CLM), is used within the Land Information System (LIS) modeling framework to show how such biases impact the assimilation of MODIS snow cover observations. Alternative in-situ and satellite-based observations are used to help guide the CLM LSM in better predicting snowpack conditions and more realistic timing of snowmelt for a western US mountainous region. Also, MODIS snow cover observation biases will be discussed, and validation results will be provided. The issues faced with inserting or assimilating MODIS snow cover at moderate spatial resolutions (like 1km or less) will be addressed, and the impacts on CLM will be presented.

Macro-level safety analysis of pedestrian crashes in Shanghai, China.

PubMed

Wang, Xuesong; Yang, Junguang; Lee, Chris; Ji, Zhuoran; You, Shikai

2016-11-01

Pedestrian safety has become one of the most important issues in the field of traffic safety. This study aims at investigating the association between pedestrian crash frequency and various predictor variables including roadway, socio-economic, and land-use features. The relationships were modeled using the data from 263 Traffic Analysis Zones (TAZs) within the urban area of Shanghai - the largest city in China. Since spatial correlation exists among the zonal-level data, Bayesian Conditional Autoregressive (CAR) models with seven different spatial weight features (i.e. (a) 0-1 first order, adjacency-based, (b) common boundary-length-based, (c) geometric centroid-distance-based, (d) crash-weighted centroid-distance-based, (e) land use type, adjacency-based, (f) land use intensity, adjacency-based, and (g) geometric centroid-distance-order) were developed to characterize the spatial correlations among TAZs. Model results indicated that the geometric centroid-distance-order spatial weight feature, which was introduced in macro-level safety analysis for the first time, outperformed all the other spatial weight features. Population was used as the surrogate for pedestrian exposure, and had a positive effect on pedestrian crashes. Other significant factors included length of major arterials, length of minor arterials, road density, average intersection spacing, percentage of 3-legged intersections, and area of TAZ. Pedestrian crashes were higher in TAZs with medium land use intensity than in TAZs with low and high land use intensity. Thus, higher priority should be given to TAZs with medium land use intensity to improve pedestrian safety. Overall, these findings can help transportation planners and managers understand the characteristics of pedestrian crashes and improve pedestrian safety. Copyright © 2016 Elsevier Ltd. All rights reserved.

Preliminary Analysis of the efficacy of Artificial neural Network (ANN) and Cellular Automaton (CA) based Land Use Models in Urban Land-Use Planning

NASA Astrophysics Data System (ADS)

Harun, R.

2013-05-01

This research provides an opportunity of collaboration between urban planners and modellers by providing a clear theoretical foundations on the two most widely used urban land use models, and assessing the effectiveness of applying the models in urban planning context. Understanding urban land cover change is an essential element for sustainable urban development as it affects ecological functioning in urban ecosystem. Rapid urbanization due to growing inclination of people to settle in urban areas has increased the complexities in predicting that at what shape and size cities will grow. The dynamic changes in the spatial pattern of urban landscapes has exposed the policy makers and environmental scientists to great challenge. But geographic science has grown in symmetry to the advancements in computer science. Models and tools are developed to support urban planning by analyzing the causes and consequences of land use changes and project the future. Of all the different types of land use models available in recent days, it has been found by researchers that the most frequently used models are Cellular Automaton (CA) and Artificial Neural Networks (ANN) models. But studies have demonstrated that the existing land use models have not been able to meet the needs of planners and policy makers. There are two primary causes identified behind this prologue. First, there is inadequate understanding of the fundamental theories and application of the models in urban planning context i.e., there is a gap in communication between modellers and urban planners. Second, the existing models exclude many key drivers in the process of simplification of the complex urban system that guide urban spatial pattern. Thus the models end up being effective in assessing the impacts of certain land use policies, but cannot contribute in new policy formulation. This paper is an attempt to increase the knowledge base of planners on the most frequently used land use model and also assess the relative effectiveness of the two models, ANN and CA, in urban planning. The questions that are addressed in this research are: a) What makes ANN models different from CA models?; b) Which model has higher accuracy in predicting future urban land use change?; and c) Are the models effective enough in guiding urban land use policies and strategies? The models that are used for this research are Multilayer Perceptron (MLP) and CA model, available in IDRISI Taiga. Since, the objective is to perform a comparative analysis and draw general inferences irrespective of specific urban policies, the availability of data was given more emphasis over the selection of particular location. Urban area in Massachusetts was chosen to conduct the study due to data availability. Extensive literature review was performed to understand the functionality of the two models. The models were applied to predict future changes and the accuracy assessment was performed using standard matrix. Inferences were drawn about the applicability of the models in urban planning context along with recommendations. This research will not only develop understanding of land use models among urban planners, but also will create an environment of coupled research between planners and modellers.

The Joint UK Land Environment Simulator (JULES), model description - Part 2: Carbon fluxes and vegetation dynamics

NASA Astrophysics Data System (ADS)

Clark, D. B.; Mercado, L. M.; Sitch, S.; Jones, C. D.; Gedney, N.; Best, M. J.; Pryor, M.; Rooney, G. G.; Essery, R. L. H.; Blyth, E.; Boucher, O.; Harding, R. J.; Huntingford, C.; Cox, P. M.

2011-09-01

The Joint UK Land Environment Simulator (JULES) is a process-based model that simulates the fluxes of carbon, water, energy and momentum between the land surface and the atmosphere. Many studies have demonstrated the important role of the land surface in the functioning of the Earth System. Different versions of JULES have been employed to quantify the effects on the land carbon sink of climate change, increasing atmospheric carbon dioxide concentrations, changing atmospheric aerosols and tropospheric ozone, and the response of methane emissions from wetlands to climate change. This paper describes the consolidation of these advances in the modelling of carbon fluxes and stores, in both the vegetation and soil, in version 2.2 of JULES. Features include a multi-layer canopy scheme for light interception, including a sunfleck penetration scheme, a coupled scheme of leaf photosynthesis and stomatal conductance, representation of the effects of ozone on leaf physiology, and a description of methane emissions from wetlands. JULES represents the carbon allocation, growth and population dynamics of five plant functional types. The turnover of carbon from living plant tissues is fed into a 4-pool soil carbon model. The process-based descriptions of key ecological processes and trace gas fluxes in JULES mean that this community model is well-suited for use in carbon cycle, climate change and impacts studies, either in standalone mode or as the land component of a coupled Earth system model.

A global assessment of gross and net land change dynamics for current conditions and future scenarios

NASA Astrophysics Data System (ADS)

Fuchs, Richard; Prestele, Reinhard; Verburg, Peter H.

2018-05-01

The consideration of gross land changes, meaning all area gains and losses within a pixel or administrative unit (e.g. country), plays an essential role in the estimation of total land changes. Gross land changes affect the magnitude of total land changes, which feeds back to the attribution of biogeochemical and biophysical processes related to climate change in Earth system models. Global empirical studies on gross land changes are currently lacking. Whilst the relevance of gross changes for global change has been indicated in the literature, it is not accounted for in future land change scenarios. In this study, we extract gross and net land change dynamics from large-scale and high-resolution (30-100 m) remote sensing products to create a new global gross and net change dataset. Subsequently, we developed an approach to integrate our empirically derived gross and net changes with the results of future simulation models by accounting for the gross and net change addressed by the land use model and the gross and net change that is below the resolution of modelling. Based on our empirical data, we found that gross land change within 0.5° grid cells was substantially larger than net changes in all parts of the world. As 0.5° grid cells are a standard resolution of Earth system models, this leads to an underestimation of the amount of change. This finding contradicts earlier studies, which assumed gross land changes to appear in shifting cultivation areas only. Applied in a future scenario, the consideration of gross land changes led to approximately 50 % more land changes globally compared to a net land change representation. Gross land changes were most important in heterogeneous land systems with multiple land uses (e.g. shifting cultivation, smallholder farming, and agro-forestry systems). Moreover, the importance of gross changes decreased over time due to further polarization and intensification of land use. Our results serve as an empirical database for land change dynamics that can be applied in Earth system models and integrated assessment models.

Land-use and carbon cycle responses to moderate climate change: implications for land-based mitigation?

PubMed

Humpenöder, Florian; Popp, Alexander; Stevanovic, Miodrag; Müller, Christoph; Bodirsky, Benjamin Leon; Bonsch, Markus; Dietrich, Jan Philipp; Lotze-Campen, Hermann; Weindl, Isabelle; Biewald, Anne; Rolinski, Susanne

2015-06-02

Climate change has impacts on agricultural yields, which could alter cropland requirements and hence deforestation rates. Thus, land-use responses to climate change might influence terrestrial carbon stocks. Moreover, climate change could alter the carbon storage capacity of the terrestrial biosphere and hence the land-based mitigation potential. We use a global spatially explicit economic land-use optimization model to (a) estimate the mitigation potential of a climate policy that provides economic incentives for carbon stock conservation and enhancement, (b) simulate land-use and carbon cycle responses to moderate climate change (RCP2.6), and (c) investigate the combined effects throughout the 21st century. The climate policy immediately stops deforestation and strongly increases afforestation, resulting in a global mitigation potential of 191 GtC in 2100. Climate change increases terrestrial carbon stocks not only directly through enhanced carbon sequestration (62 GtC by 2100) but also indirectly through less deforestation due to higher crop yields (16 GtC by 2100). However, such beneficial climate impacts increase the potential of the climate policy only marginally, as the potential is already large under static climatic conditions. In the broader picture, this study highlights the importance of land-use dynamics for modeling carbon cycle responses to climate change in integrated assessment modeling.

Sensing Urban Land-Use Patterns by Integrating Google Tensorflow and Scene-Classification Models

NASA Astrophysics Data System (ADS)

Yao, Y.; Liang, H.; Li, X.; Zhang, J.; He, J.

2017-09-01

With the rapid progress of China's urbanization, research on the automatic detection of land-use patterns in Chinese cities is of substantial importance. Deep learning is an effective method to extract image features. To take advantage of the deep-learning method in detecting urban land-use patterns, we applied a transfer-learning-based remote-sensing image approach to extract and classify features. Using the Google Tensorflow framework, a powerful convolution neural network (CNN) library was created. First, the transferred model was previously trained on ImageNet, one of the largest object-image data sets, to fully develop the model's ability to generate feature vectors of standard remote-sensing land-cover data sets (UC Merced and WHU-SIRI). Then, a random-forest-based classifier was constructed and trained on these generated vectors to classify the actual urban land-use pattern on the scale of traffic analysis zones (TAZs). To avoid the multi-scale effect of remote-sensing imagery, a large random patch (LRP) method was used. The proposed method could efficiently obtain acceptable accuracy (OA = 0.794, Kappa = 0.737) for the study area. In addition, the results show that the proposed method can effectively overcome the multi-scale effect that occurs in urban land-use classification at the irregular land-parcel level. The proposed method can help planners monitor dynamic urban land use and evaluate the impact of urban-planning schemes.

Use of multi-temporal SPOT-5 satellite images for land degradation assessment in Cameron Highlands, Malaysia using Geospatial techniques

NASA Astrophysics Data System (ADS)

Nampak, Haleh; Pradhan, Biswajeet

2016-07-01

Soil erosion is the common land degradation problem worldwide because of its economic and environmental impacts. Therefore, land-use change detection has become one of the major concern to geomorphologists, environmentalists, and land use planners due to its impact on natural ecosystems. The objective of this paper is to evaluate the relationship between land use/cover changes and land degradation in the Cameron highlands (Malaysia) through multi-temporal remotely sensed satellite images and ancillary data. Land clearing in the study area has resulted increased soil erosion due to rainfall events. Also unsustainable development and agriculture, mismanagement and lacking policies contribute to increasing soil erosion rates. The LULC distribution of the study area was mapped for 2005, 2010, and 2015 through SPOT-5 satellite imagery data which were classified based on object-based classification. A soil erosion model was also used within a GIS in order to study the susceptibility of the areas affected by changes to overland flow and rain splash erosion. The model consists of four parameters, namely soil erodibility, slope, vegetation cover and overland flow. The results of this research will be used in the selection of the areas that require mitigation processes which will reduce their degrading potential. Key words: Land degradation, Geospatial, LULC change, Soil erosion modelling, Cameron highlands.

A spatial model of land use change for western Oregon and western Washington.

Treesearch

Jeffrey D. Kline; Ralph J. Alig

2001-01-01

We developed an empirical model describing the probability that forests and farmland in western Oregon and western Washington were developed for residential, commercial, or industrial uses during a 30-year period, as a function of spatial socioeconomic variables, ownership, and geographic and physical land characteristics. The empirical model is based on a conceptual...

Optimal land use management for soil erosion control by using an interval-parameter fuzzy two-stage stochastic programming approach.

PubMed

Han, Jing-Cheng; Huang, Guo-He; Zhang, Hua; Li, Zhong

2013-09-01

Soil erosion is one of the most serious environmental and public health problems, and such land degradation can be effectively mitigated through performing land use transitions across a watershed. Optimal land use management can thus provide a way to reduce soil erosion while achieving the maximum net benefit. However, optimized land use allocation schemes are not always successful since uncertainties pertaining to soil erosion control are not well presented. This study applied an interval-parameter fuzzy two-stage stochastic programming approach to generate optimal land use planning strategies for soil erosion control based on an inexact optimization framework, in which various uncertainties were reflected. The modeling approach can incorporate predefined soil erosion control policies, and address inherent system uncertainties expressed as discrete intervals, fuzzy sets, and probability distributions. The developed model was demonstrated through a case study in the Xiangxi River watershed, China's Three Gorges Reservoir region. Land use transformations were employed as decision variables, and based on these, the land use change dynamics were yielded for a 15-year planning horizon. Finally, the maximum net economic benefit with an interval value of [1.197, 6.311] × 10(9) $ was obtained as well as corresponding land use allocations in the three planning periods. Also, the resulting soil erosion amount was found to be decreased and controlled at a tolerable level over the watershed. Thus, results confirm that the developed model is a useful tool for implementing land use management as not only does it allow local decision makers to optimize land use allocation, but can also help to answer how to accomplish land use changes.

Optimal Land Use Management for Soil Erosion Control by Using an Interval-Parameter Fuzzy Two-Stage Stochastic Programming Approach

NASA Astrophysics Data System (ADS)

Han, Jing-Cheng; Huang, Guo-He; Zhang, Hua; Li, Zhong

2013-09-01

Soil erosion is one of the most serious environmental and public health problems, and such land degradation can be effectively mitigated through performing land use transitions across a watershed. Optimal land use management can thus provide a way to reduce soil erosion while achieving the maximum net benefit. However, optimized land use allocation schemes are not always successful since uncertainties pertaining to soil erosion control are not well presented. This study applied an interval-parameter fuzzy two-stage stochastic programming approach to generate optimal land use planning strategies for soil erosion control based on an inexact optimization framework, in which various uncertainties were reflected. The modeling approach can incorporate predefined soil erosion control policies, and address inherent system uncertainties expressed as discrete intervals, fuzzy sets, and probability distributions. The developed model was demonstrated through a case study in the Xiangxi River watershed, China's Three Gorges Reservoir region. Land use transformations were employed as decision variables, and based on these, the land use change dynamics were yielded for a 15-year planning horizon. Finally, the maximum net economic benefit with an interval value of [1.197, 6.311] × 109 was obtained as well as corresponding land use allocations in the three planning periods. Also, the resulting soil erosion amount was found to be decreased and controlled at a tolerable level over the watershed. Thus, results confirm that the developed model is a useful tool for implementing land use management as not only does it allow local decision makers to optimize land use allocation, but can also help to answer how to accomplish land use changes.

A thermal-based remote sensing modeling system for estimating daily evapotranspiration from field to global scales

USDA-ARS?s Scientific Manuscript database

Thermal-infrared (TIR) remote sensing of land surface temperature (LST) provides valuable information for quantifying root-zone water availability, evapotranspiration (ET) and crop condition as well as providing useful information for constraining prognostic land surface models. This presentation d...

Update of aircraft profile data for the Integrated Noise Model computer program, vol. 2 : appendix A aircraft takeoff and landing profiles

DOT National Transportation Integrated Search

1992-03-01

This report provides aircraft takeoff and landing profiles, aircraft aerodynamic performance coefficients and engine performance coefficients for the aircraft data base (Database 9) in the Integrated Noise Model (INM) computer program. Flight profile...

Weighing the relative potential impacts of climate change and land-use change on an endangered bird.

PubMed

Bancroft, Betsy A; Lawler, Joshua J; Schumaker, Nathan H

2016-07-01

Climate change and land-use change are projected to be the two greatest drivers of biodiversity loss over the coming century. Land-use change has resulted in extensive habitat loss for many species. Likewise, climate change has affected many species resulting in range shifts, changes in phenology, and altered interactions. We used a spatially explicit, individual-based model to explore the effects of land-use change and climate change on a population of the endangered Red-cockaded Woodpecker (RCW; Picoides borealis). We modeled the effects of land-use change using multiple scenarios representing different spatial arrangements of new training areas for troops across Fort Benning. We used projected climate-driven changes in habitat and changes in reproductive output to explore the potential effects of climate change. We summarized potential changes in habitat based on the output of the dynamic vegetation model LPJ-GUESS, run for multiple climate change scenarios through the year 2100. We projected potential changes in reproduction based on an empirical relationship between spring precipitation and the mean number of successful fledglings produced per nest attempt. As modeled in our study, climate change had virtually no effect on the RCW population. Conversely, simulated effects of land-use change resulted in the loss of up to 28 breeding pairs by 2100. However, the simulated impacts of development depended on where the development occurred and could be completely avoided if the new training areas were placed in poor-quality habitat. Our results demonstrate the flexibility inherent in many systems that allows seemingly incompatible human land uses, such as development, and conservation actions to exist side by side.

Modelling Nitrogen Oxides in Los Angeles Using a Hybrid Dispersion/Land Use Regression Model

NASA Astrophysics Data System (ADS)

Wilton, Darren C.

The goal of this dissertation is to develop models capable of predicting long term annual average NOx concentrations in urban areas. Predictions from simple meteorological dispersion models and seasonal proxies for NO2 oxidation were included as covariates in a land use regression (LUR) model for NOx in Los Angeles, CA. The NO x measurements were obtained from a comprehensive measurement campaign that is part of the Multi-Ethnic Study of Atherosclerosis Air Pollution Study (MESA Air). Simple land use regression models were initially developed using a suite of GIS-derived land use variables developed from various buffer sizes (R²=0.15). Caline3, a simple steady-state Gaussian line source model, was initially incorporated into the land-use regression framework. The addition of this spatio-temporally varying Caline3 covariate improved the simple LUR model predictions. The extent of improvement was much more pronounced for models based solely on the summer measurements (simple LUR: R²=0.45; Caline3/LUR: R²=0.70), than it was for models based on all seasons (R²=0.20). We then used a Lagrangian dispersion model to convert static land use covariates for population density, commercial/industrial area into spatially and temporally varying covariates. The inclusion of these covariates resulted in significant improvement in model prediction (R²=0.57). In addition to the dispersion model covariates described above, a two-week average value of daily peak-hour ozone was included as a surrogate of the oxidation of NO2 during the different sampling periods. This additional covariate further improved overall model performance for all models. The best model by 10-fold cross validation (R²=0.73) contained the Caline3 prediction, a static covariate for length of A3 roads within 50 meters, the Calpuff-adjusted covariates derived from both population density and industrial/commercial land area, and the ozone covariate. This model was tested against annual average NOx concentrations from an independent data set from the EPA's Air Quality System (AQS) and MESA Air fixed site monitors, and performed very well (R²=0.82).

Towards extensive spatio-temporal reconstructions of North American land cover: a comparison of state-of-the-art pollen-vegetation models

NASA Astrophysics Data System (ADS)

Dawson, A.; Trachsel, M.; Goring, S. J.; Paciorek, C. J.; McLachlan, J. S.; Jackson, S. T.; Williams, J. W.

2017-12-01

Pollen records have been extensively used to reconstruct past changes in vegetation and study the underlying processes. However, developing the statistical techniques needed to accurately represent both data and process uncertainties is a formidable challenge. Recent advances in paleoecoinformatics (e.g. the Neotoma Paleoecology Database and the European Pollen Database), Bayesian age-depth models, and process-based pollen-vegetation models, and Bayesian hierarchical modeling have pushed paleovegetation reconstructions forward to a point where multiple sources of uncertainty can be incorporated into reconstructions, which in turn enables new hypotheses to be asked and more rigorous integration of paleovegetation data with earth system models and terrestrial ecosystem models. Several kinds of pollen-vegetation models have been developed, notably LOVE/REVEALS, STEPPS, and classical transfer functions such as the modern analog technique. LOVE/REVEALS has been adopted as the standard method for the LandCover6k effort to develop quantitative reconstructions of land cover for the Holocene, while STEPPS has been developed recently as part of the PalEON project and applied to reconstruct with uncertainty shifts in forest composition in New England and the upper Midwest during the late Holocene. Each PVM has different assumptions and structure and uses different input data, but few comparisons among approaches yet exist. Here, we present new reconstructions of land cover change in northern North America during the Holocene based on LOVE/REVEALS and data drawn from the Neotoma database and compare STEPPS-based reconstructions to those from LOVE/REVEALS. These parallel developments with LOVE/REVEALS provide an opportunity to compare and contrast models, and to begin to generate continental scale reconstructions, with explicit uncertainties, that can provide a base for interdisciplinary research within the biogeosciences. We show how STEPPS provides an important benchmark for past land-cover reconstruction, and how the LandCover 6k effort in North America advances our understanding of the past by allowing cross-continent comparisons using standardized methods and quantifying the impact of humans in the early Anthropocene.

Disagreement between Hydrological and Land Surface models on the water budgets in the Arctic: why is this and which of them is right?

NASA Astrophysics Data System (ADS)

Blyth, E.; Martinez-de la Torre, A.; Ellis, R.; Robinson, E.

2017-12-01

The fresh-water budget of the Artic region has a diverse range of impacts: the ecosystems of the region, ocean circulation response to Arctic freshwater, methane emissions through changing wetland extent as well as the available fresh water for human consumption. But there are many processes that control the budget including a seasonal snow packs building and thawing, freezing soils and permafrost, extensive organic soils and large wetland systems. All these processes interact to create a complex hydrological system. In this study we examine a suite of 10 models that bring all those processes together in a 25 year reanalysis of the global water budget. We assess their performance in the Arctic region. There are two approaches to modelling fresh-water flows at large scales, referred to here as `Hydrological' and `Land Surface' models. While both approaches include a physically based model of the water stores and fluxes, the Land Surface models links the water flows to an energy-based model for processes such as snow melt and soil freezing. This study will analyse the impact of that basic difference on the regional patterns of evapotranspiration, runoff generation and terrestrial water storage. For the evapotranspiration, the Hydrological models tend to have a bigger spatial range in the model bias (difference to observations), implying greater errors compared to the Land-Surface models. For instance, some regions such as Eastern Siberia have consistently lower Evaporation in the Hydrological models than the Land Surface models. For the Runoff however, the results are the other way round with a slightly higher spatial range in bias for the Land Surface models implying greater errors than the Hydrological models. A simple analysis would suggest that Hydrological models are designed to get the runoff right, while Land Surface models designed to get the evapotranspiration right. Tracing the source of the difference suggests that the difference comes from the treatment of snow and evapotranspiration. The study reveals that expertise in the role of snow on runoff generation and evapotranspiration in Hydrological and Land Surface could be combined to improve the representation of the fresh water flows in the Arctic in both approaches. Improved observations are essential to make these modelling advances possible.

Land Surface Data Assimilation

NASA Astrophysics Data System (ADS)

Houser, P. R.

2012-12-01

Information about land surface water, energy and carbon conditions is of critical importance to real-world applications such as agricultural production, water resource management, flood prediction, water supply, weather and climate forecasting, and environmental preservation. While ground-based observational networks are improving, the only practical way to observe these land surface states on continental to global scales is via satellites. Remote sensing can make spatially comprehensive measurements of various components of the terrestrial system, but it cannot provide information on the entire system (e.g. evaporation), and the observations represent only an instant in time. Land surface process models may be used to predict temporal and spatial terrestrial dynamics, but these predictions are often poor, due to model initialization, parameter and forcing, and physics errors. Therefore, an attractive prospect is to combine the strengths of land surface models and observations (and minimize the weaknesses) to provide a superior terrestrial state estimate. This is the goal of land surface data assimilation. Data Assimilation combines observations into a dynamical model, using the model's equations to provide time continuity and coupling between the estimated fields. Land surface data assimilation aims to utilize both our land surface process knowledge, as embodied in a land surface model, and information that can be gained from observations. Both model predictions and observations are imperfect and we wish to use both synergistically to obtain a more accurate result. Moreover, both contain different kinds of information, that when used together, provide an accuracy level that cannot be obtained individually. Model biases can be mitigated using a complementary calibration and parameterization process. Limited point measurements are often used to calibrate the model(s) and validate the assimilation results. This presentation will provide a brief background on land surface observation, modeling and data assimilation, followed by a discussion of various hydrologic data assimilation challenges, and finally conclude with several land surface data assimilation case studies.

INTEGRATING LANDSCAPE ASSESSMENT AND HYDROLOGIC MODELING FOR LAND COVER CHANGE ANALYSIS

EPA Science Inventory

This study is based on the assumption that land cover change and rainfall spatial variability affect the r-ainfall-runoff relationships on the watershed. Hydrologic response is an integrated indicator of watershed condition, and changes in land cover may affect the overall health...

Exploring agent-level calculations of risk and returns in relation to observed land-use changes in the US Great Plains, 1870–1940

PubMed Central

Sylvester, Kenneth M.; Brown, Daniel G.; Leonard, Susan H.; Merchant, Emily; Hutchins, Meghan

2015-01-01

Land-use change in the U.S. Great Plains since agricultural settlement in the second half of the nineteenth century has been well documented. While aggregate historical trends are easily tracked, the decision-making of individual farmers is difficult to reconstruct. We use an agent-based model to tell the history of the settlement of the West by simulating farm-level agricultural decision making based on historical data about prices, yields, farming costs, and environmental conditions. The empirical setting for the model is the period between 1875 and 1940 in two townships in Kansas, one in the shortgrass region and the other in the mixed grass region. Annual historical data on yields and prices determine profitability of various land uses and thereby inform decision-making, in conjunction with the farmer’s previous experience and randomly assigned levels of risk aversion. Results illustrating the level of agreement between model output and unique and detailed household-level records of historical land use and farm size suggest that economic behavior and natural endowments account for land change processes to some degree, but are incomplete. Discrepancies are examined to identify missing processes through model experiments, in which we adjust input and output prices, crop yields, agent memory, and risk aversion. These analyses demonstrate how agent-based modeling can be a useful laboratory for thinking about social and economic behavior in the past. PMID:25729323

Modeling Land Use/Cover Changes in an African Rural Landscape

NASA Astrophysics Data System (ADS)

Kamusoko, C.; Aniya, M.

2006-12-01

Land use/cover changes are analyzed in the Bindura district of Zimbabwe, Africa through the integration of data from a time series of Landsat imagery (1973, 1989 and 2000), a household survey and GIS coverages. We employed a hybrid supervised/unsupervised classification approach to generate land use/cover maps from which landscape metrics were calculated. Population and other household variables were derived from a sample of surveyed villages, while road accessibility and slope were obtained from topographic maps and digital elevation model, respectively. Markov-cellular automata modeling approach that incorporates Markov chain analysis, cellular automata and multi-criteria evaluation (MCE) / multi-objective allocation (MOLA) procedures was used to simulate land use/cover changes. A GIS-based MCE technique computed transition potential maps, whereas transition areas were derived from the 1973-2000 land use/cover maps using the Markov chain analysis. A 5 x 5 cellular automata filter was used to develop a spatially explicit contiguity- weighting factor to change the cells based on its previous state and those of its neighbors, while MOLA resolved land use/cover class allocation conflicts. The kappa index of agreement was used for model validation. Observed trends in land use/cover changes indicate that deforestation and the encroachment of cultivation in woodland areas is a continuous trend in the study area. This suggests that economic activities driven by agricultural expansion were the main causes of landscape fragmentation, leading to landscape degradation. Rigorous calibration of transition potential maps done by a MCE algorithm and Markovian transition probabilities produced accurate inputs for the simulation of land use/cover changes. Overall standard kappa index of agreement ranged from 0.73 to 0.83, which is sufficient for simulating land use/cover changes in the study area. Land use/cover simulations under the 1989 and 2000 scenario indicated further landscape degradation in the rural areas of the Bindura district. Keywords: Zimbabwe, land use/cover changes, landscape fragmentation, GIS, land use/cover change modeling, multi-criteria evaluation/multi-objective allocation procedures, Markov-cellular automata

The Joint UK Land Environment Simulator (JULES), Model description - Part 2: Carbon fluxes and vegetation

NASA Astrophysics Data System (ADS)

Clark, D. B.; Mercado, L. M.; Sitch, S.; Jones, C. D.; Gedney, N.; Best, M. J.; Pryor, M.; Rooney, G. G.; Essery, R. L. H.; Blyth, E.; Boucher, O.; Harding, R. J.; Cox, P. M.

2011-03-01

The Joint UK Land Environment Simulator (JULES) is a process-based model that simulates the fluxes of carbon, water, energy and momentum between the land surface and the atmosphere. Past studies with JULES have demonstrated the important role of the land surface in the Earth System. Different versions of JULES have been employed to quantify the effects on the land carbon sink of separately changing atmospheric aerosols and tropospheric ozone, and the response of methane emissions from wetlands to climate change. There was a need to consolidate these and other advances into a single model code so as to be able to study interactions in a consistent manner. This paper describes the consolidation of these advances into the modelling of carbon fluxes and stores, in the vegetation and soil, in version 2.2 of JULES. Features include a multi-layer canopy scheme for light interception, including a sunfleck penetration scheme, a coupled scheme of leaf photosynthesis and stomatal conductance, representation of the effects of ozone on leaf physiology, and a description of methane emissions from wetlands. JULES represents the carbon allocation, growth and population dynamics of five plant functional types. The turnover of carbon from living plant tissues is fed into a 4-pool soil carbon model. The process-based descriptions of key ecological processes and trace gas fluxes in JULES mean that this community model is well-suited for use in carbon cycle, climate change and impacts studies, either in standalone mode or as the land component of a coupled Earth system model.

Landcover Classification Using Deep Fully Convolutional Neural Networks

NASA Astrophysics Data System (ADS)

Wang, J.; Li, X.; Zhou, S.; Tang, J.

2017-12-01

Land cover classification has always been an essential application in remote sensing. Certain image features are needed for land cover classification whether it is based on pixel or object-based methods. Different from other machine learning methods, deep learning model not only extracts useful information from multiple bands/attributes, but also learns spatial characteristics. In recent years, deep learning methods have been developed rapidly and widely applied in image recognition, semantic understanding, and other application domains. However, there are limited studies applying deep learning methods in land cover classification. In this research, we used fully convolutional networks (FCN) as the deep learning model to classify land covers. The National Land Cover Database (NLCD) within the state of Kansas was used as training dataset and Landsat images were classified using the trained FCN model. We also applied an image segmentation method to improve the original results from the FCN model. In addition, the pros and cons between deep learning and several machine learning methods were compared and explored. Our research indicates: (1) FCN is an effective classification model with an overall accuracy of 75%; (2) image segmentation improves the classification results with better match of spatial patterns; (3) FCN has an excellent ability of learning which can attains higher accuracy and better spatial patterns compared with several machine learning methods.

The challenges associated with applying global models in heterogeneous landscapes: A case study using MOD17 GPP estimates in Hawaii

NASA Astrophysics Data System (ADS)

Kimball, H.; Selmants, P. C.; Running, S. W.; Moreno, A.; Giardina, C. P.

2016-12-01

In this study we evaluate the influence of spatial data product accuracy and resolution on the application of global models for smaller scale heterogeneous landscapes. In particular, we assess the influence of locally specific land cover and high-resolution climate data products on estimates of Gross Primary Production (GPP) for the Hawaiian Islands using the MOD17 model. The MOD17 GPP algorithm uses a measure of the fraction of absorbed photosynthetically active radiation from the National Aeronautics and Space Administration's Earth Observation System. This direct measurement is combined with global land cover (500-m resolution) and climate models ( 1/2-degree resolution) to estimate GPP. We first compared the alignment between the global land cover model used in MOD17 with a Hawaii specific land cover data product. We found that there was a 51.6% overall agreement between the two land cover products. We then compared four MOD17 GPP models: A global model that used the global land cover and low-resolution global climate data products, a model produced using the Hawaii specific land cover and low-resolution global climate data products, a model with global land cover and high-resolution climate data products, and finally, a model using both Hawaii specific land cover and high-resolution climate data products. We found that including either the Hawaii specific land cover or the high-resolution Hawaii climate data products with MOD17 reduced overall estimates of GPP by 8%. When both were used, GPP estimates were reduced by 16%. The reduction associated with land cover is explained by a reduction of the total area designated as evergreen broad leaf forest and an increase in the area designated as barren or sparsely vegetated in the Hawaii land cover product as compared to the global product. The climate based reduction is explained primarily by the spatial resolution and distribution of solar radiation in the Hawaiian Islands. This study highlights the importance of accuracy and resolution when applying global models to highly variable landscapes and provides an estimate of the influence of land cover and climate data products on estimates of GPP using MOD17.

Landscape determinants and remote sensing of anopheline mosquito larval habitats in the western Kenya highlands.

PubMed

Mushinzimana, Emmanuel; Munga, Stephen; Minakawa, Noboru; Li, Li; Feng, Chen-Chieng; Bian, Ling; Kitron, Uriel; Schmidt, Cindy; Beck, Louisa; Zhou, Guofa; Githeko, Andrew K; Yan, Guiyun

2006-02-16

In the past two decades the east African highlands have experienced several major malaria epidemics. Currently there is a renewed interest in exploring the possibility of anopheline larval control through environmental management or larvicide as an additional means of reducing malaria transmission in Africa. This study examined the landscape determinants of anopheline mosquito larval habitats and usefulness of remote sensing in identifying these habitats in western Kenya highlands. Panchromatic aerial photos, Ikonos and Landsat Thematic Mapper 7 satellite images were acquired for a study area in Kakamega, western Kenya. Supervised classification of land-use and land-cover and visual identification of aquatic habitats were conducted. Ground survey of all aquatic habitats was conducted in the dry and rainy seasons in 2003. All habitats positive for anopheline larvae were identified. The retrieved data from the remote sensors were compared to the ground results on aquatic habitats and land-use. The probability of finding aquatic habitats and habitats with Anopheles larvae were modelled based on the digital elevation model and land-use types. The misclassification rate of land-cover types was 10.8% based on Ikonos imagery, 22.6% for panchromatic aerial photos and 39.2% for Landsat TM 7 imagery. The Ikonos image identified 40.6% of aquatic habitats, aerial photos identified 10.6%, and Landsate TM 7 image identified 0%. Computer models based on topographic features and land-cover information obtained from the Ikonos image yielded a misclassification rate of 20.3-22.7% for aquatic habitats, and 18.1-25.1% for anopheline-positive larval habitats. One-metre spatial resolution Ikonos images combined with computer modelling based on topographic land-cover features are useful tools for identification of anopheline larval habitats, and they can be used to assist to malaria vector control in western Kenya highlands.

Downscaling Global Land Cover Projections from an Integrated Assessment Model for Use in Regional Analyses: Results and Evaluation for the US from 2005 to 2095

DOE Office of Scientific and Technical Information (OSTI.GOV)

West, Tristram O.; Le Page, Yannick LB; Huang, Maoyi

2014-06-05

Projections of land cover change generated from Integrated Assessment Models (IAM) and other economic-based models can be applied for analyses of environmental impacts at subregional and landscape scales. For those IAM and economic models that project land use at the sub-continental or regional scale, these projections must be downscaled and spatially distributed prior to use in climate or ecosystem models. Downscaling efforts to date have been conducted at the national extent with relatively high spatial resolution (30m) and at the global extent with relatively coarse spatial resolution (0.5 degree).

Sensitivity of global and regional terrestrial carbon storage to the direct CO2 effect and climate change based on the CMIP5 model intercomparison.

PubMed

Peng, Jing; Dan, Li; Huang, Mei

2014-01-01

Global and regional land carbon storage has been significantly affected by increasing atmospheric CO2 concentration and climate change. Based on fully coupled climate-carbon-cycle simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5), we investigate sensitivities of land carbon storage to rising atmospheric CO2 concentration and climate change over the world and 21 regions during the 130 years. Overall, the simulations suggest that consistently spatial positive effects of the increasing CO2 concentrations on land carbon storage are expressed with a multi-model averaged value of 1.04 PgC per ppm. The stronger positive values are mainly located in the broad areas of temperate and tropical forest, especially in Amazon basin and western Africa. However, large heterogeneity distributed for sensitivities of land carbon storage to climate change. Climate change causes decrease in land carbon storage in most tropics and the Southern Hemisphere. In these regions, decrease in soil moisture (MRSO) and enhanced drought somewhat contribute to such a decrease accompanied with rising temperature. Conversely, an increase in land carbon storage has been observed in high latitude and altitude regions (e.g., northern Asia and Tibet). The model simulations also suggest that global negative impacts of climate change on land carbon storage are predominantly attributed to decrease in land carbon storage in tropics. Although current warming can lead to an increase in land storage of high latitudes of Northern Hemisphere due to elevated vegetation growth, a risk of exacerbated future climate change may be induced due to release of carbon from tropics.

Sensitivity of Global and Regional Terrestrial Carbon Storage to the Direct CO2 Effect and Climate Change Based on the CMIP5 Model Intercomparison

PubMed Central

Peng, Jing; Dan, Li; Huang, Mei

2014-01-01

Global and regional land carbon storage has been significantly affected by increasing atmospheric CO2 concentration and climate change. Based on fully coupled climate-carbon-cycle simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5), we investigate sensitivities of land carbon storage to rising atmospheric CO2 concentration and climate change over the world and 21 regions during the 130 years. Overall, the simulations suggest that consistently spatial positive effects of the increasing CO2 concentrations on land carbon storage are expressed with a multi-model averaged value of 1.04PgC per ppm. The stronger positive values are mainly located in the broad areas of temperate and tropical forest, especially in Amazon basin and western Africa. However, large heterogeneity distributed for sensitivities of land carbon storage to climate change. Climate change causes decrease in land carbon storage in most tropics and the Southern Hemisphere. In these regions, decrease in soil moisture (MRSO) and enhanced drought somewhat contribute to such a decrease accompanied with rising temperature. Conversely, an increase in land carbon storage has been observed in high latitude and altitude regions (e.g., northern Asia and Tibet). The model simulations also suggest that global negative impacts of climate change on land carbon storage are predominantly attributed to decrease in land carbon storage in tropics. Although current warming can lead to an increase in land storage of high latitudes of Northern Hemisphere due to elevated vegetation growth, a risk of exacerbated future climate change may be induced due to release of carbon from tropics. PMID:24748331

Simulation on Change Law of Runoff, Sediment and Non-point Source Nitrogen and Phosphorus Discharge under Different Land uses Based on SWAT Model: A Case Study of Er hai Lake Small Watershed

NASA Astrophysics Data System (ADS)

Tong, Xiao Xia; Lai Cui, Yuan; Chen, Man Yu; Hu, Bo; Xu, Wen Sheng

2018-05-01

The Er yuan watershed of Er hai district is chosen as the research area, the law of runoff and sediment and non-point source nitrogen and phosphorus discharges under different land uses during 2001 to 2014 are simulated based on SWAT model. Results of simulation indicate that the order of total runoff yield of different land use type from high to low is grassland, paddy fields, dry land. Specifically, the order of surface runoff yield from high to low is paddy fields, dry land, grassland, the order of lateral runoff yield from high to low is paddy fields, dry land, grassland, the order of groundwater runoff yield from high to low is grassland, paddy fields, dry land. The orders of sediment and nitrogen and phosphorus yield per unit area of different land use type are the same, grassland> paddy fields> dry land. It can be seen, nitrogen and phosphorus discharges from paddy fields and dry land are the main sources of agricultural non-point pollution of the irrigated area. Therefore, reasonable field management measures which can decrease the discharge of nitrogen and phosphorus of paddy fields and dry land are the key to agricultural non-point source pollution prevention and control.

Pairing FLUXNET sites to validate model representations of land-use/land-cover change

NASA Astrophysics Data System (ADS)

Chen, Liang; Dirmeyer, Paul A.; Guo, Zhichang; Schultz, Natalie M.

2018-01-01

Land surface energy and water fluxes play an important role in land-atmosphere interactions, especially for the climatic feedback effects driven by land-use/land-cover change (LULCC). These have long been documented in model-based studies, but the performance of land surface models in representing LULCC-induced responses has not been investigated well. In this study, measurements from proximate paired (open versus forest) flux tower sites are used to represent observed deforestation-induced changes in surface fluxes, which are compared with simulations from the Community Land Model (CLM) and the Noah Multi-Parameterization (Noah-MP) land model. Point-scale simulations suggest the CLM can represent the observed diurnal and seasonal changes in net radiation (Rnet) and ground heat flux (G), but difficulties remain in the energy partitioning between latent (LE) and sensible (H) heat flux. The CLM does not capture the observed decreased daytime LE, and overestimates the increased H during summer. These deficiencies are mainly associated with models' greater biases over forest land-cover types and the parameterization of soil evaporation. Global gridded simulations with the CLM show uncertainties in the estimation of LE and H at the grid level for regional and global simulations. Noah-MP exhibits a similar ability to simulate the surface flux changes, but with larger biases in H, G, and Rnet change during late winter and early spring, which are related to a deficiency in estimating albedo. Differences in meteorological conditions between paired sites is not a factor in these results. Attention needs to be devoted to improving the representation of surface heat flux processes in land models to increase confidence in LULCC simulations.

QUANTIFYING AN UNCERTAIN FUTURE: HYDROLOGIC MODEL PERFORMANCE FOR A SERIES OF REALIZED "FUTURE" CONDITIONS

EPA Science Inventory

GIS-based hydrologic modeling offers a convenient means of assessing the impacts associated with land-cover/use change for environmental planning efforts. Future scenarios can be developed through a combination of modifications to the land-cover/use maps used to parameterize hydr...

Progress in remote sensing of global land surface heat fluxes and evaporations with a turbulent heat exchange parameterization method

NASA Astrophysics Data System (ADS)

Chen, Xuelong; Su, Bob

2017-04-01

Remote sensing has provided us an opportunity to observe Earth land surface with a much higher resolution than any of GCM simulation. Due to scarcity of information for land surface physical parameters, up-to-date GCMs still have large uncertainties in the coupled land surface process modeling. One critical issue is a large amount of parameters used in their land surface models. Thus remote sensing of land surface spectral information can be used to provide information on these parameters or assimilated to decrease the model uncertainties. Satellite imager could observe the Earth land surface with optical, thermal and microwave bands. Some basic Earth land surface status (land surface temperature, canopy height, canopy leaf area index, soil moisture etc.) has been produced with remote sensing technique, which already help scientists understanding Earth land and atmosphere interaction more precisely. However, there are some challenges when applying remote sensing variables to calculate global land-air heat and water exchange fluxes. Firstly, a global turbulent exchange parameterization scheme needs to be developed and verified, especially for global momentum and heat roughness length calculation with remote sensing information. Secondly, a compromise needs to be innovated to overcome the spatial-temporal gaps in remote sensing variables to make the remote sensing based land surface fluxes applicable for GCM model verification or comparison. A flux network data library (more 200 flux towers) was collected to verify the designed method. Important progress in remote sensing of global land flux and evaporation will be presented and its benefits for GCM models will also be discussed. Some in-situ studies on the Tibetan Plateau and problems of land surface process simulation will also be discussed.

Case analyses and numerical simulation of soil thermal impacts on land surface energy budget based on an off-line land surface model

NASA Astrophysics Data System (ADS)

Guo, W. D.; Sun, S. F.; Qian, Y. F.

2002-05-01

The statistical relationship between soil thermal anomaly and short-term climate change is presented based on a typical case study. Furthermore, possible physical mechanisms behind the relationship are revealed through using an off-line land surface model with a reasonable soil thermal forcing at the bottom of the soil layer. In the first experiment, the given heat flux is 5 W m(-2) at the bottom of the soil layer (in depth of 6.3 m) for 3 months, while only a positive ground temperature anomaly of 0.06degreesC can be found compared to the control run. The anomaly, however, could reach 0.65degreesC if the soil thermal conductivity was one order of magnitude larger. It could be even as large as 0.81degreesC assuming the heat flux at bottom is 10 W m(-2). Meanwhile, an increase of about 10 W m(-2) was detected both for heat flux in soil and sensible heat on land surface, which is not neglectable to the short-term climate change. The results show that considerable response in land surface energy budget could be expected when the soil thermal forcing reaches a certain spatial-temporal scale. Therefore, land surface models should not ignore the upward heat flux from the bottom of the soil layer, Moreover, integration for a longer period of time and coupled land-atmosphere model are also necessary for the better understanding of this issues.

From plot to regional scales: Effect of land use and soil type on soil erosion in the southern Amazon

NASA Astrophysics Data System (ADS)

Schindewolf, Marcus; Schultze, Nico; Amorim, Ricardo S. S.; Schmidt, Jürgen

2015-04-01

The corridor along the Brazilian Highway 163 in the Southern Amazon is affected by radical changes in land use patterns. In order to enable a model based assessment of erosion risks on different land use and soil types a transportable disc type rainfall simulator is applied to identify the most important infiltration and erosion parameters of the EROSION 3D model. Since particle detachment highly depends on experimental plot length, a combined runoff supply is used for the virtually extension of the plot length to more than 20 m. Simulations were conducted on the most common regional land use, soil management and soil types for dry and wet runs. The experiments are characterized by high final infiltration rates (0.3 - 2.5 mm*min^-1), low sediment concentrations (0.2-6.5 g*L^-1) and accordingly low soil loss rates (0.002-50 Kg*m^-2), strongly related to land use, applied management and soil type. Ploughed pastures and clear cuts reveal highest soil losses whereas croplands are less affected. Due to higher aggregate stabilities Ferrasols are less endangered than Acrisols. Derived model parameters are plausible, comparable to existing data bases and reproduce the effects of land use and soil management on soil loss. Thus it is possible to apply the EROSION 3D soil loss model in Southern Amazonia for erosion risk assessment and scenario simulation under changing climate and land use conditions.

Land-use and land-cover scenarios and spatial modeling at the regional scale

USGS Publications Warehouse

Sohl, Terry L.; Sleeter, Benjamin M.

2012-01-01

Land-use and land-cover (LULC) change has altered a large part of the earth's surface. Scenarios of potential future LULC change are required in order to better manage potential impacts on biodiversity, carbon fluxes, climate change, hydrology, and many other ecological processes. The U.S. Geological Survey is analyzing potential future LULC change in the United States, using an approach based on scenario construction and spatially explicit modeling. Similar modeling techniques are being used to produce historical LULC maps from 1940 to present. With the combination of backcast and forecast LULC data, the USGS is providing consistent LULC data for historical, current, and future time frames to support a variety of research applications.

Emerging ecological datasets with application for modeling North American dust emissions

USDA-ARS?s Scientific Manuscript database

In 2011 the US Bureau of Land Management (BLM) established the Assessment, Inventory and Monitoring (AIM) program to monitor the condition of BLM land and to provide data to support evidence-based management of multi-use public lands. The monitoring program shares core data collection methods with t...

Application of the Auto-Tuned Land Assimilation System (ATLAS) to ASCAT and SMOS soil moisture retrieval products

USDA-ARS?s Scientific Manuscript database

Land data assimilations are typically based on highly uncertain assumptions regarding the statistical structure of observation and modeling errors. Left uncorrected, poor assumptions can degrade the quality of analysis products generated by land data assimilation systems. Recently, Crow and van de...

Research on application of intelligent computation based LUCC model in urbanization process

NASA Astrophysics Data System (ADS)

Chen, Zemin

2007-06-01

Global change study is an interdisciplinary and comprehensive research activity with international cooperation, arising in 1980s, with the largest scopes. The interaction between land use and cover change, as a research field with the crossing of natural science and social science, has become one of core subjects of global change study as well as the front edge and hot point of it. It is necessary to develop research on land use and cover change in urbanization process and build an analog model of urbanization to carry out description, simulation and analysis on dynamic behaviors in urban development change as well as to understand basic characteristics and rules of urbanization process. This has positive practical and theoretical significance for formulating urban and regional sustainable development strategy. The effect of urbanization on land use and cover change is mainly embodied in the change of quantity structure and space structure of urban space, and LUCC model in urbanization process has been an important research subject of urban geography and urban planning. In this paper, based upon previous research achievements, the writer systematically analyzes the research on land use/cover change in urbanization process with the theories of complexity science research and intelligent computation; builds a model for simulating and forecasting dynamic evolution of urban land use and cover change, on the basis of cellular automation model of complexity science research method and multi-agent theory; expands Markov model, traditional CA model and Agent model, introduces complexity science research theory and intelligent computation theory into LUCC research model to build intelligent computation-based LUCC model for analog research on land use and cover change in urbanization research, and performs case research. The concrete contents are as follows: 1. Complexity of LUCC research in urbanization process. Analyze urbanization process in combination with the contents of complexity science research and the conception of complexity feature to reveal the complexity features of LUCC research in urbanization process. Urban space system is a complex economic and cultural phenomenon as well as a social process, is the comprehensive characterization of urban society, economy and culture, and is a complex space system formed by society, economy and nature. It has dissipative structure characteristics, such as opening, dynamics, self-organization, non-balance etc. Traditional model cannot simulate these social, economic and natural driving forces of LUCC including main feedback relation from LUCC to driving force. 2. Establishment of Markov extended model of LUCC analog research in urbanization process. Firstly, use traditional LUCC research model to compute change speed of regional land use through calculating dynamic degree, exploitation degree and consumption degree of land use; use the theory of fuzzy set to rewrite the traditional Markov model, establish structure transfer matrix of land use, forecast and analyze dynamic change and development trend of land use, and present noticeable problems and corresponding measures in urbanization process according to research results. 3. Application of intelligent computation research and complexity science research method in LUCC analog model in urbanization process. On the basis of detailed elaboration of the theory and the model of LUCC research in urbanization process, analyze the problems of existing model used in LUCC research (namely, difficult to resolve many complexity phenomena in complex urban space system), discuss possible structure realization forms of LUCC analog research in combination with the theories of intelligent computation and complexity science research. Perform application analysis on BP artificial neural network and genetic algorithms of intelligent computation and CA model and MAS technology of complexity science research, discuss their theoretical origins and their own characteristics in detail, elaborate the feasibility of them in LUCC analog research, and bring forward improvement methods and measures on existing problems of this kind of model. 4. Establishment of LUCC analog model in urbanization process based on theories of intelligent computation and complexity science. Based on the research on abovementioned BP artificial neural network, genetic algorithms, CA model and multi-agent technology, put forward improvement methods and application assumption towards their expansion on geography, build LUCC analog model in urbanization process based on CA model and Agent model, realize the combination of learning mechanism of BP artificial neural network and fuzzy logic reasoning, express the regulation with explicit formula, and amend the initial regulation through self study; optimize network structure of LUCC analog model and methods and procedures of model parameters with genetic algorithms. In this paper, I introduce research theory and methods of complexity science into LUCC analog research and presents LUCC analog model based upon CA model and MAS theory. Meanwhile, I carry out corresponding expansion on traditional Markov model and introduce the theory of fuzzy set into data screening and parameter amendment of improved model to improve the accuracy and feasibility of Markov model in the research on land use/cover change.

Incorporating JULES into NASA's Land Information System (LIS) and Investigations of Land-Atmosphere Coupling

NASA Technical Reports Server (NTRS)

Santanello, Joseph

2011-01-01

NASA's Land Information System (LIS; lis.gsfc.nasa.gov) is a flexible land surface modeling and data assimilation framework developed over the past decade with the goal of integrating satellite- and ground-based observational data products and advanced land surface modeling techniques to produce optimal fields of land surface states and fluxes. LIS features a high performance and flexible design, and operates on an ensemble of land surface models for extension over user-specified regional or global domains. The extensible interfaces of LIS allow the incorporation of new domains, land surface models (LSMs), land surface parameters, meteorological inputs, data assimilation and optimization algorithms. In addition, LIS has also been demonstrated for parameter estimation and uncertainty estimation, and has been coupled to the Weather Research and Forecasting (WRF) mesoscale model. A visiting fellowship is currently underway to implement JULES into LIS and to undertake some fundamental science on the feedbacks between the land surface and the atmosphere. An overview of the LIS system, features, and sample results will be presented in an effort to engage the community in the potential advantages of LIS-JULES for a range of applications. Ongoing efforts to develop a framework for diagnosing land-atmosphere coupling will also be presented using the suite of LSM and PBL schemes available in LIS and WRF along with observations from the U. S .. Southern Great Plains. This methodology provides a potential pathway to study factors controlling local land-atmosphere coupling (LoCo) using the LIS-WRF system, which will serve as a testbed for future experiments to evaluate coupling diagnostics within the community.

A stochastic Forest Fire Model for future land cover scenarios assessment

NASA Astrophysics Data System (ADS)

D'Andrea, M.; Fiorucci, P.; Holmes, T. P.

2010-10-01

Land cover is affected by many factors including economic development, climate and natural disturbances such as wildfires. The ability to evaluate how fire regimes may alter future vegetation, and how future vegetation may alter fire regimes, would assist forest managers in planning management actions to be carried out in the face of anticipated socio-economic and climatic change. In this paper, we present a method for calibrating a cellular automata wildfire regime simulation model with actual data on land cover and wildfire size-frequency. The method is based on the observation that many forest fire regimes, in different forest types and regions, exhibit power law frequency-area distributions. The standard Drossel-Schwabl cellular automata Forest Fire Model (DS-FFM) produces simulations which reproduce this observed pattern. However, the standard model is simplistic in that it considers land cover to be binary - each cell either contains a tree or it is empty - and the model overestimates the frequency of large fires relative to actual landscapes. Our new model, the Modified Forest Fire Model (MFFM), addresses this limitation by incorporating information on actual land use and differentiating among various types of flammable vegetation. The MFFM simulation model was tested on forest types with Mediterranean and sub-tropical fire regimes. The results showed that the MFFM was able to reproduce structural fire regime parameters for these two regions. Further, the model was used to forecast future land cover. Future research will extend this model to refine the forecasts of future land cover and fire regime scenarios under climate, land use and socio-economic change.

Satellite Data Inform Forecasts of Crop Growth

NASA Technical Reports Server (NTRS)

2015-01-01

During a Stennis Space Center-led program called Ag 20/20, an engineering contractor developed models for using NASA satellite data to predict crop yield. The model was eventually sold to Genscape Inc., based in Louisville, Kentucky, which has commercialized it as LandViewer. Sold under a subscription model, LandViewer software provides predictions of corn production to ethanol plants and grain traders.

Modeling hydrology and in-stream transport on drained forested lands in coastal Carolinas, U.S.A.

Treesearch

Devendra Amatya

2005-01-01

This study summarizes the successional development and testing of forest hydrologic models based on DRAINMOD that predicts the hydrology of low-gradient poorly drained watersheds as affected by land management and climatic variation. The field scale (DRAINLOB) and watershed-scale in-stream routing (DRAINWAT) models were successfully tested with water table and outflow...

Supporting the operational use of process based hydrological models and NASA Earth Observations for use in land management and post-fire remediation through a Rapid Response Erosion Database (RRED).

NASA Astrophysics Data System (ADS)

Miller, M. E.; Elliot, W.; Billmire, M.; Robichaud, P. R.; Banach, D. M.

2017-12-01

We have built a Rapid Response Erosion Database (RRED, http://rred.mtri.org/rred/) for the continental United States to allow land managers to access properly formatted spatial model inputs for the Water Erosion Prediction Project (WEPP). Spatially-explicit process-based models like WEPP require spatial inputs that include digital elevation models (DEMs), soil, climate and land cover. The online database delivers either a 10m or 30m USGS DEM, land cover derived from the Landfire project, and soil data derived from SSURGO and STATSGO datasets. The spatial layers are projected into UTM coordinates and pre-registered for modeling. WEPP soil parameter files are also created along with linkage files to match both spatial land cover and soils data with the appropriate WEPP parameter files. Our goal is to make process-based models more accessible by preparing spatial inputs ahead of time allowing modelers to focus on addressing scenarios of concern. The database provides comprehensive support for post-fire hydrological modeling by allowing users to upload spatial soil burn severity maps, and within moments returns spatial model inputs. Rapid response is critical following natural disasters. After moderate and high severity wildfires, flooding, erosion, and debris flows are a major threat to life, property and municipal water supplies. Mitigation measures must be rapidly implemented if they are to be effective, but they are expensive and cannot be applied everywhere. Fire, runoff, and erosion risks also are highly heterogeneous in space, creating an urgent need for rapid, spatially-explicit assessment. The database has been used to help assess and plan remediation on over a dozen wildfires in the Western US. Future plans include expanding spatial coverage, improving model input data and supporting additional models. Our goal is to facilitate the use of the best possible datasets and models to support the conservation of soil and water.

Decision analysis and risk models for land development affecting infrastructure systems.

PubMed

Thekdi, Shital A; Lambert, James H

2012-07-01

Coordination and layering of models to identify risks in complex systems such as large-scale infrastructure of energy, water, and transportation is of current interest across application domains. Such infrastructures are increasingly vulnerable to adjacent commercial and residential land development. Land development can compromise the performance of essential infrastructure systems and increase the costs of maintaining or increasing performance. A risk-informed approach to this topic would be useful to avoid surprise, regret, and the need for costly remedies. This article develops a layering and coordination of models for risk management of land development affecting infrastructure systems. The layers are: system identification, expert elicitation, predictive modeling, comparison of investment alternatives, and implications of current decisions for future options. The modeling layers share a focus on observable factors that most contribute to volatility of land development and land use. The relevant data and expert evidence include current and forecasted growth in population and employment, conservation and preservation rules, land topography and geometries, real estate assessments, market and economic conditions, and other factors. The approach integrates to a decision framework of strategic considerations based on assessing risk, cost, and opportunity in order to prioritize needs and potential remedies that mitigate impacts of land development to the infrastructure systems. The approach is demonstrated for a 5,700-mile multimodal transportation system adjacent to 60,000 tracts of potential land development. © 2011 Society for Risk Analysis.

Modeling Future Land Use Scenarios in South Korea: Applying the IPCC Special Report on Emissions Scenarios and the SLEUTH Model on a Local Scale

NASA Astrophysics Data System (ADS)

Han, Haejin; Hwang, YunSeop; Ha, Sung Ryong; Kim, Byung Sik

2015-05-01

This study developed three scenarios of future land use/land cover on a local level for the Kyung-An River Basin and its vicinity in South Korea at a 30-m resolution based on the two scenario families of the Intergovernmental Panel on Climate Change (IPCC) Special Report Emissions Scenarios (SRES): A2 and B1, as well as a business-as-usual scenario. The IPCC SRES A2 and B1 were used to define future local development patterns and associated land use change. We quantified the population-driven demand for urban land use for each qualitative storyline and allocated the urban demand in geographic space using the SLEUTH model. The model results demonstrate the possible land use/land cover change scenarios for the years from 2000 to 2070 by examining the broad narrative of each SRES within the context of a local setting, such as the Kyoungan River Basin, constructing narratives of local development shifts and modeling a set of `best guess' approximations of the future land use conditions in the study area. This study found substantial differences in demands and patterns of land use changes among the scenarios, indicating compact development patterns under the SRES B1 compared to the rapid and dispersed development under the SRES A2.

Modeling future land use scenarios in South Korea: applying the IPCC special report on emissions scenarios and the SLEUTH model on a local scale.

PubMed

Han, Haejin; Hwang, YunSeop; Ha, Sung Ryong; Kim, Byung Sik

2015-05-01

This study developed three scenarios of future land use/land cover on a local level for the Kyung-An River Basin and its vicinity in South Korea at a 30-m resolution based on the two scenario families of the Intergovernmental Panel on Climate Change (IPCC) Special Report Emissions Scenarios (SRES): A2 and B1, as well as a business-as-usual scenario. The IPCC SRES A2 and B1 were used to define future local development patterns and associated land use change. We quantified the population-driven demand for urban land use for each qualitative storyline and allocated the urban demand in geographic space using the SLEUTH model. The model results demonstrate the possible land use/land cover change scenarios for the years from 2000 to 2070 by examining the broad narrative of each SRES within the context of a local setting, such as the Kyoungan River Basin, constructing narratives of local development shifts and modeling a set of 'best guess' approximations of the future land use conditions in the study area. This study found substantial differences in demands and patterns of land use changes among the scenarios, indicating compact development patterns under the SRES B1 compared to the rapid and dispersed development under the SRES A2.

Simulating Land-Use Change using an Agent-Based Land Transaction Model

NASA Astrophysics Data System (ADS)

Bakker, M. M.; van Dijk, J.; Alam, S. J.

2013-12-01

In the densely populated cultural landscapes of Europe, the vast majority of all land is owned by private parties, be it farmers (the majority), nature organizations, property developers, or citizens. Therewith, the vast majority of all land-use change arises from land transactions between different owner types: successful farms expand at the expense of less successful farms, and meanwhile property developers, individual citizens, and nature organizations also actively purchase land. These land transactions are driven by specific properties of the land, by governmental policies, and by the (economic) motives of both buyers and sellers. Climate/global change can affect these drivers at various scales: at the local scale changes in hydrology can make certain land less or more desirable; at the global scale the agricultural markets will affect motives of farmers to buy or sell land; while at intermediate (e.g. provincial) scales property developers and nature conservationists may be encouraged or discouraged to purchase land. The cumulative result of all these transactions becomes manifest in changing land-use patterns, and consequent environmental responses. Within the project Climate Adaptation for Rural Areas an agent-based land-use model was developed that explores the future response of individual land users to climate change, within the context of wider global change (i.e. policy and market change). It simulates the exchange of land among farmers and between farmers and nature organizations and property developers, for a specific case study area in the east of the Netherlands. Results show that local impacts of climate change can result in a relative stagnation in the land market in waterlogged areas. Furthermore, the increase in dairying at the expense of arable cultivation - as has been observed in the area in the past - is slowing down as arable produce shows a favourable trend in the agricultural world market. Furthermore, budgets for nature managers are obviously an important driver for nature expansion, but without a strict zoning plan imposed by a government, it is difficult to achieve a continuous, defragmented nature area. Lastly, the model suggests that with time the trend in ever-increasing farm sizes is gradually levelling out. The decision rules that determine the behaviours of the individual agents in the model (selling land, buying land, or none of the two) are calibrated on historical census records, using multi-nominal logistic regression. Because estimating who will sell and who will buy can only be done with a limited certainty, our model reproduces the volatility / uncertainty in who will do what and when. This makes that each specific future scenario can have numerous realizations of reality. Our stakeholders (including, besides policy makers, also local farmers and nature organizations) indicate that this aspect of the model strongly contributes to its credibility. Nevertheless, within different scenarios certain (spatial) trends are distinguishable, so that the model is -besides credible - also useful for exploring future trends.

A simple hydrologically based model of land surface water and energy fluxes for general circulation models

NASA Technical Reports Server (NTRS)

Liang, XU; Lettenmaier, Dennis P.; Wood, Eric F.; Burges, Stephen J.

1994-01-01

A generalization of the single soil layer variable infiltration capacity (VIC) land surface hydrological model previously implemented in the Geophysical Fluid Dynamics Laboratory (GFDL) general circulation model (GCM) is described. The new model is comprised of a two-layer characterization of the soil column, and uses an aerodynamic representation of the latent and sensible heat fluxes at the land surface. The infiltration algorithm for the upper layer is essentially the same as for the single layer VIC model, while the lower layer drainage formulation is of the form previously implemented in the Max-Planck-Institut GCM. The model partitions the area of interest (e.g., grid cell) into multiple land surface cover types; for each land cover type the fraction of roots in the upper and lower zone is specified. Evapotranspiration consists of three components: canopy evaporation, evaporation from bare soils, and transpiration, which is represented using a canopy and architectural resistance formulation. Once the latent heat flux has been computed, the surface energy balance is iterated to solve for the land surface temperature at each time step. The model was tested using long-term hydrologic and climatological data for Kings Creek, Kansas to estimate and validate the hydrological parameters, and surface flux data from three First International Satellite Land Surface Climatology Project Field Experiment (FIFE) intensive field campaigns in the summer-fall of 1987 to validate the surface energy fluxes.

A Generalized Deforestation and Land-Use Change Scenario Generator for Use in Climate Modelling Studies

PubMed Central

Tompkins, Adrian Mark; Caporaso, Luca; Biondi, Riccardo; Bell, Jean Pierre

2015-01-01

A new deforestation and land-use change scenario generator model (FOREST-SAGE) is presented that is designed to interface directly with dynamic vegetation models used in latest generation earth system models. The model requires a regional-scale scenario for aggregate land-use change that may be time-dependent, provided by observational studies or by regional land-use change/economic models for future projections. These land-use categories of the observations/economic model are first translated into equivalent plant function types used by the particular vegetation model, and then FOREST-SAGE disaggregates the regional-scale scenario to the local grid-scale of the earth system model using a set of risk-rules based on factors such as proximity to transport networks, distance weighted population density, forest fragmentation and presence of protected areas and logging concessions. These rules presently focus on the conversion of forest to agriculture and pasture use, but could be generalized to other land use change conversions. After introducing the model, an evaluation of its performance is shown for the land-cover changes that have occurred in the Central African Basin from 2001–2010 using retrievals from MODerate Resolution Imaging Spectroradiometer Vegetation Continuous Field data. The model is able to broadly reproduce the spatial patterns of forest cover change observed by MODIS, and the use of the local-scale risk factors enables FOREST-SAGE to improve land use change patterns considerably relative to benchmark scenarios used in the latest Coupled Model Intercomparison Project integrations. The uncertainty to the various risk factors is investigated using an ensemble of investigations, and it is shown that the model is sensitive to the population density, forest fragmentation and reforestation factors specified. PMID:26394392

A zone-based approach to identifying urban land uses using nationally-available data

NASA Astrophysics Data System (ADS)

Falcone, James A.

Accurate identification of urban land use is essential for many applications in environmental study, ecological assessment, and urban planning, among other fields. However, because physical surfaces of land cover types are not necessarily related to their use and economic function, differentiating among thematically-detailed urban land uses (single-family residential, multi-family residential, commercial, industrial, etc.) using remotely-sensed imagery is a challenging task, particularly over large areas. Because the process requires an interpretation of tone/color, size, shape, pattern, and neighborhood association elements within a scene, it has traditionally been accomplished via manual interpretation of aerial photography or high-resolution satellite imagery. Although success has been achieved for localized areas using various automated techniques based on high-spatial or high-spectral resolution data, few detailed (Anderson Level II equivalent or greater) urban land use mapping products have successfully been created via automated means for broad (multi-county or larger) areas, and no such product exists today for the United States. In this study I argue that by employing a zone-based approach it is feasible to map thematically-detailed urban land use classes over large areas using appropriate combinations of non-image based predictor data which are nationally and publicly available. The approach presented here uses U.S. Census block groups as the basic unit of geography, and predicts the percent of each of ten land use types---nine of them urban---for each block group based on a number of data sources, to include census data, nationally-available point locations of features from the USGS Geographic Names Information System, historical land cover, and metrics which characterize spatial pattern, context (e.g. distance to city centers or other features), and measures of spatial autocorrelation. The method was demonstrated over a four-county area surrounding the city of Boston. A generalized version of the method (six land use classes) was also developed and cross-validated among additional geographic settings: Atlanta, Los Angeles, and Providence. The results suggest that even with the thematically-detailed ten-class structure, it is feasible to map most urban land uses with reasonable accuracy at the block group scale, and results improve with class aggregation. When classified by predicted majority land use, 79% of block groups correctly matched the actual majority land use with the ten-class models. Six-class models typically performed well for the geographic area they were developed from, however models had mixed performance when transported to other geographic settings. Contextual variables, which characterized a block group's spatial relationship to city centers, transportation routes, and other amenities, were consistently strong predictors of most land uses, a result which corresponds to classic urban land use theory. The method and metrics derived here provide a prototype for mapping urban land uses from readily-available data over broader geographic areas than is generally practiced today using current image-based solutions.

On land-use modeling: A treatise of satellite imagery data and misclassification error

NASA Astrophysics Data System (ADS)

Sandler, Austin M.

Recent availability of satellite-based land-use data sets, including data sets with contiguous spatial coverage over large areas, relatively long temporal coverage, and fine-scale land cover classifications, is providing new opportunities for land-use research. However, care must be used when working with these datasets due to misclassification error, which causes inconsistent parameter estimates in the discrete choice models typically used to model land-use. I therefore adapt the empirical correction methods developed for other contexts (e.g., epidemiology) so that they can be applied to land-use modeling. I then use a Monte Carlo simulation, and an empirical application using actual satellite imagery data from the Northern Great Plains, to compare the results of a traditional model ignoring misclassification to those from models accounting for misclassification. Results from both the simulation and application indicate that ignoring misclassification will lead to biased results. Even seemingly insignificant levels of misclassification error (e.g., 1%) result in biased parameter estimates, which alter marginal effects enough to affect policy inference. At the levels of misclassification typical in current satellite imagery datasets (e.g., as high as 35%), ignoring misclassification can lead to systematically erroneous land-use probabilities and substantially biased marginal effects. The correction methods I propose, however, generate consistent parameter estimates and therefore consistent estimates of marginal effects and predicted land-use probabilities.

Assessing the impacts of land use on downstream water quality using a hydrologically sensitive area concept.

PubMed

Giri, Subhasis; Qiu, Zeyuan; Zhang, Zhen

2018-05-01

Understanding the relationship between land use and water quality is essential to improve water quality through carefully managing landscape change. This study applies a linear mixed model at both watershed and hydrologically sensitive areas (HSAs) scales to assess such a relationship in 28 northcentral New Jersey watersheds located in a rapidly urbanizing region in the United States. Two models differ in terms of the geographic scope used to derive land use matrices that quantify land use conditions. The land use matrices at the watershed and HSAs scales represent the land use conditions in these watersheds and their HSAs, respectively. HSAs are the hydrological "hotspots" in a watershed that are prone to runoff generation during storm events. HSAs are derived using a soil topographic index (STI) that predicts hydrological sensitivity of a landscape based on a variable source area hydrology concept. The water quality indicators in these models are total nitrogen (TN), total phosphorus (TP) and total suspended solids (TSS) concentrations in streams observed at the watershed outlets. The modeling results suggest that presence of low density urban land, agricultural land and wetlands elevate while forest decreases TN, TP and/or TSS concentrations in streams. The watershed scale model tends to emphasize the role of agricultural lands in water quality degradation while the HSA scale model highlights the role of forest in water quality improvement. This study supports the hypothesis that even though HSAs are relatively smaller area compared to watershed, still the land uses within HSAs have similar impacts on downstream water quality as the land uses in entire watersheds, since both models have negligible differences in model evaluation parameters. Inclusion of HSAs brings an interesting perspective to understand the dynamic relationships between land use and water quality. Copyright © 2018 Elsevier Ltd. All rights reserved.

A new concept for simulation of vegetated land surface dynamics - Part 1: The event driven phenology model

NASA Astrophysics Data System (ADS)

Kovalskyy, V.; Henebry, G. M.

2012-01-01

Phenologies of the vegetated land surface are being used increasingly for diagnosis and prognosis of climate change consequences. Current prospective and retrospective phenological models stand far apart in their approaches to the subject. We report on an exploratory attempt to implement a phenological model based on a new event driven concept which has both diagnostic and prognostic capabilities in the same modeling framework. This Event Driven Phenological Model (EDPM) is shown to simulate land surface phenologies and phenophase transition dates in agricultural landscapes based on assimilation of weather data and land surface observations from spaceborne sensors. The model enables growing season phenologies to develop in response to changing environmental conditions and disturbance events. It also has the ability to ingest remotely sensed data to adjust its output to improve representation of the modeled variable. We describe the model and report results of initial testing of the EDPM using Level 2 flux tower records from the Ameriflux sites at Mead, Nebraska, USA, and at Bondville, Illinois, USA. Simulating the dynamics of normalized difference vegetation index based on flux tower data, the predictions by the EDPM show good agreement (RMSE < 0.08; r2 > 0.8) for maize and soybean during several growing seasons at different locations. This study presents the EDPM used in the companion paper (Kovalskyy and Henebry, 2011) in a coupling scheme to estimate daily actual evapotranspiration over multiple growing seasons.

A new concept for simulation of vegetated land surface dynamics - Part 1: The event driven phenology model

NASA Astrophysics Data System (ADS)

Kovalskyy, V.; Henebry, G. M.

2011-05-01

Phenologies of the vegetated land surface are being used increasingly for diagnosis and prognosis of climate change consequences. Current prospective and retrospective phenological models stand far apart in their approaches to the subject. We report on an exploratory attempt to implement a phenological model based on a new event driven concept which has both diagnostic and prognostic capabilities in the same modeling framework. This Event Driven Phenological Model (EDPM) is shown to simulate land surface phenologies and phenophase transition dates in agricultural landscapes based on assimilation of weather data and land surface observations from spaceborne sensors. The model enables growing season phenologies to develop in response to changing environmental conditions and disturbance events. It also has the ability to ingest remotely sensed data to adjust its output to improve representation of the modeled variable. We describe the model and report results of initial testing of the EDPM using Level 2 flux tower records from the Ameriflux sites at Mead, Nebraska, USA, and at Bondville, Illinois, USA. Simulating the dynamics of normalized difference vegetation index based on flux tower data, the predictions by the EDPM show good agreement (RMSE < 0.08; r2>0.8) for maize and soybean during several growing seasons at different locations. This study presents the EDPM used in the companion paper (Kovalskyy and Henebry, 2011) in a coupling scheme to estimate daily actual evapotranspiration over multiple growing seasons.

Coupled hydrologic and land use change models for decision making on land and water resources in the Upper Blue Nile basin

NASA Astrophysics Data System (ADS)

Yalew, Seleshi; van der Zaag, Pieter; Mul, Marloes; Uhlenbrook, Stefan; Teferi, Ermias; van Griensven, Ann; van der Kwast, Johannes

2013-04-01

Hydrology of a basin, alongside climate change, is well documented to impact and to be impacted by land use/land cover change processes. The need to understand the impacts of hydrology on land use change and vice- versa cannot be overstated especially in basins such as the Upper Blue Nile in Ethiopia, where the vast majority of farmers depend on rain-fed agriculture. A slight fluctuation in rainy seasons or an increase or decrease in magnitude of precipitation can easily trigger drought or flooding. On the other hand, ever growing population and emerging economic development, among others, is likely to continually alter land use/land cover change, thereby affecting hydrological processes. With the intention of identifying and analyzing interactions and future scenarios of the hydrology and land use/land cover, we carried out a case study on a meso-scale catchment, in the Upper Blue Nile basin. A land use model using SITE (SImulation of Terrestrial Environments) was built for analyzing land use trends from aerial land cover photographs of 1957 and simulate until 2009 based on socio-economic as well as biophysical factors. Major land use drivers in the catchment were identified and used as input to the land use model. Separate land use maps were produced using Landsat images of 1972, 1986, 1994 and 2009 for historical calibration of the land use model. By the same token, a hydrological model for the same catchment was built using the SWAT (Soil and Water Assessment Tool) model. After calibration of the two independent models, they were loosely coupled for analyzing the changes in either of the models and impacts on the other. Among other details, the coupled model performed better in identifying limiting factors from both the hydrology as well as from the land use perspectives. For instance, the simulation of the uncoupled land use model alone (without inputs from SWAT on the water budget of each land use parcel) continually considered a land use type such as a wet land/marsh land, simply as a wetland until the simulation period finishes. The wetland or the marsh land, which is not crop friendly in the location, does not get allocated to any other land use such as for certain crop types or settlement, because the land use model cannot tell how much water is added to or drained from each parcel every season. However, the simulation feedback from the coupled hydrological model shows that certain wetland/marsh land parcels, in fact, hold less and less water or even dry up during the simulation period, thereby putting themselves as a good candidate to be picked by the land use model in a next time step and to be allocated to other land use types. The same way, a measure in the land use aspect, which considers socio-economic as well as biophysical driving forces of in the catchment, shows changes in runoff and sedimentation levels in SWAT model outputs. The results of a future scenario considering the continuing population growth projects that about 35% of the wetland dries up and gets converted to cultivation by 2020. This study emphasizes the importance of identifying possible impacts of the future hydrology on other components of the socio-environmental systems and contrariwise during environmental decision making, especially in areas where a relatively small change may have large impacts (such flood and/or drought prone basins as the Nile). The study also demonstrates a sound methodology for assessing the impact of land use change on hydrology and vice-versa by dynamically exchanging data through feedback mechanisms (coupling socio-environmental and hydrological models) which lead to a better understanding of socio-environmental problems. Keywords: Coupling, socio-environment, Nile, land use models, hydrological models

New Estimates of Land Use Intensity of Potential Bioethanol Production in the U.S.A.

NASA Astrophysics Data System (ADS)

Kheshgi, H. S.; Song, Y.; Torkamani, S.; Jain, A. K.

2016-12-01

We estimate potential bioethanol land use intensity (the inverse of potential bioethanol yield per hectare) across the United States by modeling crop yields and conversion to bioethanol (via a fermentation pathway), based on crop field studies and conversion technology analyses. We apply the process-based land surface model, the Integrated Science Assessment model (ISAM), to estimate the potential yield of four crops - corn, Miscanthus, and two variants of switchgrass (Cave-in-Rock and Alamo) - across the U.S.A. landscape for the 14-year period from 1999 through 2012, for the case with fertilizer application but without irrigation. We estimate bioethanol yield based on recent experience for corn bioethanol production from corn kernel, and current cellulosic bioethanol process design specifications under the assumption of the maximum practical harvest fraction for the energy grasses (Miscanthus and switchgrasses) and a moderate (30%) harvest fraction of corn stover. We find that each of four crops included has regions where that crop is estimated to have the lowest land use intensity (highest potential bioethanol yield per hectare). We find that minimizing potential land use intensity by including both corn and the energy grasses only improves incrementally to that of corn (using both harvested kernel and stover for bioethanol). Bioethanol land use intensity is one fundamental factor influencing the desirability of biofuels, but is not the only one; others factors include economics, competition with food production and land use, water and climate, nitrogen runoff, life-cycle emissions, and the pace of crop and technology improvement into the future.

Retrieval of land parameters by multi-sensor information using the Earth Observation Land Data Assimilation System

NASA Astrophysics Data System (ADS)

Chernetskiy, Maxim; Gobron, Nadine; Gomez-Dans, Jose; Disney, Mathias

2016-07-01

Upcoming satellite constellations will substantially increase the amount of Earth Observation (EO) data, and presents us with the challenge of consistently using all these available information to infer the state of the land surface, parameterised through Essential Climate Variables (ECVs). A promising approach to this problem is the use of physically based models that describe the processes that generate the images, using e.g. radiative transfer (RT) theory. However, these models need to be inverted to infer the land surface parameters from the observations, and there is often not enough information in the EO data to satisfactorily achieve this. Data assimilation (DA) approaches supplement the EO data with prior information in the form of models or prior parameter distributions, and have the potential for solving the inversion problem. These methods however are computationally expensive. In this study, we show the use of fast surrogate models of the RT codes (emulators) based on Gaussian Processes (Gomez-Dans et al, 2016) embedded with the Earth Observation Land Data Assimilation System (EO-LDAS) framework (Lewis et al 2012) in order to estimate the surface of the land surface from a heterogeneous set of optical observations. The study uses time series of moderate spatial resolution observations from MODIS (250 m), MERIS (300 m) and MISR (275 m) over one site to infer the temporal evolution of a number of land surface parameters (and associated uncertainties) related to vegetation: leaf area index (LAI), leaf chlorophyll content, etc. These parameter estimates are then used as input to an RT model (semidiscrete or PROSAIL, for example) to calculate fluxes such as broad band albedo or fAPAR. The study demonstrates that blending different sensors in a consistent way using physical models results in a rich and coherent set of land surface parameters retrieved, with quantified uncertainties. The use of RT models also allows for the consistent prediction of fluxes, with a simple mechanism for propagating the uncertainty in the land surface parameters to the flux estimates.

Modeling the effect of terraces on land degradation in tropical upland agricultural area

NASA Astrophysics Data System (ADS)

Christanto, N.; Shrestha, D. P.; Jetten, V. G.; Setiawan, A.

2012-04-01

Java, the most populated Island in Indonesia, in the pas view decades suffer land degradation do to extreme weather, population pressure and landuse/cover change. The study area, Serayu sub-catchment, as part of Serayu catchment is one of the representative example of Indonesia region facing land use change and land degradation problem. The study attempted to simulate the effect of terraces on land degradation (Soil erosion and landslide hazard) in Serayu sub-catchment using deterministic modeling by means of PCRaster® simulation. The effect of the terraces on tropical upland agricultural area is less studied. This paper will discuss about the effect of terraces on land degradation assessment. Detail Dem is extremely difficult to obtain in developing country like Indonesia. Therefore, an artificial DEM which give an impression of the terraces was built. Topographical maps, Ikonos Image and average of height distribution based on field measurement were used to build the artificial DEM. The result is used in STARWARS model as an input. In combine with Erosion model and PROBSTAB, soil erosion and landslide hazard were quantified. The models were run in two different environment based on the: 1) normal DEM 2.) Artificial DEM (with terraces impression). The result is compared. The result shows that the models run in an artificial DEM give a significant increase on the probability of failure by 20.5%. In the other hand, the erosion rate has fall by 11.32% as compared to the normal DEM. The result of hydrological sensitivity analysis shows that soil depth was the most sensitive parameter. For the slope stability modeling, the most sensitive parameter was slope followed by friction angle and cohesion. The erosion modeling, the model was sensitive to the vegetation cover, soil erodibility followed by BD and KSat. Model validations were applied to assess the accuracy of the models. However, the results of dynamic modeling are ideal for land degradation assessment. Dynamic modeling software such as PC Raster® which is open source and free are reliable alternative to other commercial software

What are the effects of Agro-Ecological Zones and land use region boundaries on land resource projection using the Global Change Assessment Model?

DOE Office of Scientific and Technical Information (OSTI.GOV)

Di Vittorio, Alan V.; Kyle, Page; Collins, William D.

Understanding the potential impacts of climate change is complicated by mismatched spatial representations between gridded Earth System Models (ESMs) and Integrated Assessment Models (IAMs), whose regions are typically larger and defined by geopolitical and biophysical criteria. In this study we address uncertainty stemming from the construction of land use regions in an IAM, the Global Change Assessment Model (GCAM), whose regions are currently based on historical climatic conditions (1961-1990). We re-define GCAM’s regions according to projected climatic conditions (2070-2099), and investigate how this changes model outcomes for land use, agriculture, and forestry. By 2100, we find potentially large differences inmore » projected global and regional area of biomass energy crops, fodder crops, harvested forest, and intensive pasture. These land area differences correspond with changes in agricultural commodity prices and production. These results have broader implications for understanding policy scenarios and potential impacts, and for evaluating and comparing IAM and ESM simulations.« less

A multitemporal (1979-2009) land-use/land-cover dataset of the binational Santa Cruz Watershed

USGS Publications Warehouse

2011-01-01

Trends derived from multitemporal land-cover data can be used to make informed land management decisions and to help managers model future change scenarios. We developed a multitemporal land-use/land-cover dataset for the binational Santa Cruz watershed of southern Arizona, United States, and northern Sonora, Mexico by creating a series of land-cover maps at decadal intervals (1979, 1989, 1999, and 2009) using Landsat Multispectral Scanner and Thematic Mapper data and a classification and regression tree classifier. The classification model exploited phenological changes of different land-cover spectral signatures through the use of biseasonal imagery collected during the (dry) early summer and (wet) late summer following rains from the North American monsoon. Landsat images were corrected to remove atmospheric influences, and the data were converted from raw digital numbers to surface reflectance values. The 14-class land-cover classification scheme is based on the 2001 National Land Cover Database with a focus on "Developed" land-use classes and riverine "Forest" and "Wetlands" cover classes required for specific watershed models. The classification procedure included the creation of several image-derived and topographic variables, including digital elevation model derivatives, image variance, and multitemporal Kauth-Thomas transformations. The accuracy of the land-cover maps was assessed using a random-stratified sampling design, reference aerial photography, and digital imagery. This showed high accuracy results, with kappa values (the statistical measure of agreement between map and reference data) ranging from 0.80 to 0.85.

Coupled carbon-nitrogen land surface modelling for UK agricultural landscapes using JULES and JULES-ECOSSE-FUN (JEF)

NASA Astrophysics Data System (ADS)

Comyn-Platt, Edward; Clark, Douglas; Blyth, Eleanor

2016-04-01

The UK is required to provide accurate estimates of the UK greenhouse gas (GHG; CO2, CH4 and N2O) emissions for the UNFCCC (United Nations Framework Convention on Climate Change). Process based land surface models (LSMs), such as the Joint UK Land Environment Simulator (JULES), attempt to provide such estimates based on environmental (e.g. land use and soil type) and meteorological conditions. The standard release of JULES focusses on the water and carbon cycles, however, it has long been suggested that a coupled carbon-nitrogen scheme could enhance simulations. This is of particular importance when estimating agricultural emission inventories where the carbon cycle is effectively managed via the human application of nitrogen based fertilizers. JULES-ECOSSE-FUN (JEF) links JULES with the Estimation of Carbon in Organic Soils - Sequestration and Emission (ECOSSE) model and the Fixation and Uptake of Nitrogen (FUN) model as a means of simulating C:N coupling. This work presents simulations from the standard release of JULES and the most recent incarnation of the JEF coupled system at the point and field scale. Various configurations of JULES and JEF were calibrated and fine-tuned based on comparisons with observations from three UK field campaigns (Crichton, Harwood Forest and Brattleby) specifically chosen to represent the managed vegetation types that cover the UK. The campaigns included flux tower and chamber measurements of CO2, CH4 and N2O amongst other meteorological parameters and records of land management such as application of fertilizer and harvest date at the agricultural sites. Based on the results of these comparisons, JULES and/or JEF will be used to provide simulations on the regional and national scales in order to provide improved estimates of the total UK emission inventory.

An integrated multi-criteria scenario evaluation web tool for participatory land-use planning in urbanized areas: The Ecosystem Portfolio Model

USGS Publications Warehouse

Labiosa, Bill; Forney, William M.; Hearn,, Paul P.; Hogan, Dianna M.; Strong, David R.; Swain, Eric D.; Esnard, Ann-Margaret; Mitsova-Boneva, D.; Bernknopf, R.; Pearlstine, Leonard; Gladwin, Hugh

2013-01-01

Land-use land-cover change is one of the most important and direct drivers of changes in ecosystem functions and services. Given the complexity of the decision-making, there is a need for Internet-based decision support systems with scenario evaluation capabilities to help planners, resource managers and communities visualize, compare and consider trade-offs among the many values at stake in land use planning. This article presents details on an Ecosystem Portfolio Model (EPM) prototype that integrates ecological, socio-economic information and associated values of relevance to decision-makers and stakeholders. The EPM uses a multi-criteria scenario evaluation framework, Geographic Information Systems (GIS) analysis and spatially-explicit land-use/land-cover change-sensitive models to characterize changes in important land-cover related ecosystem values related to ecosystem services and functions, land parcel prices, and community quality-of-life (QoL) metrics. Parameters in the underlying models can be modified through the interface, allowing users in a facilitated group setting to explore simultaneously issues of scientific uncertainty and divergence in the preferences of stakeholders. One application of the South Florida EPM prototype reported in this article shows the modeled changes (which are significant) in aggregate ecological value, landscape patterns and fragmentation, biodiversity potential and ecological restoration potential for current land uses compared to the 2050 land-use scenario. Ongoing refinements to EPM, and future work especially in regard to modifiable sea level rise scenarios are also discussed.

Uncertainties in the land-use flux resulting from land-use change reconstructions and gross land transitions

NASA Astrophysics Data System (ADS)

Bayer, Anita D.; Lindeskog, Mats; Pugh, Thomas A. M.; Anthoni, Peter M.; Fuchs, Richard; Arneth, Almut

2017-02-01

Land-use and land-cover (LUC) changes are a key uncertainty when attributing changes in measured atmospheric CO2 concentration to its sinks and sources and must also be much better understood to determine the possibilities for land-based climate change mitigation, especially in the light of human demand on other land-based resources. On the spatial scale typically used in terrestrial ecosystem models (0.5 or 1°) changes in LUC over time periods of a few years or more can include bidirectional changes on the sub-grid level, such as the parallel expansion and abandonment of agricultural land (e.g. in shifting cultivation) or cropland-grassland conversion (and vice versa). These complex changes between classes within a grid cell have often been neglected in previous studies, and only net changes of land between natural vegetation cover, cropland and pastures accounted for, mainly because of a lack of reliable high-resolution historical information on gross land transitions, in combination with technical limitations within the models themselves. In the present study we applied a state-of-the-art dynamic global vegetation model with a detailed representation of croplands and carbon-nitrogen dynamics to quantify the uncertainty in terrestrial ecosystem carbon stocks and fluxes arising from the choice between net and gross representations of LUC. We used three frequently applied global, one recent global and one recent European LUC datasets, two of which resolve gross land transitions, either in Europe or in certain tropical regions. When considering only net changes, land-use-transition uncertainties (expressed as 1 standard deviation around decadal means of four models) in global carbon emissions from LUC (ELUC) are ±0.19, ±0.66 and ±0.47 Pg C a-1 in the 1980s, 1990s and 2000s, respectively, or between 14 and 39 % of mean ELUC. Carbon stocks at the end of the 20th century vary by ±11 Pg C for vegetation and ±37 Pg C for soil C due to the choice of LUC reconstruction, i.e. around 3 % of the respective C pools. Accounting for sub-grid (gross) land conversions significantly increased the effect of LUC on global and European carbon stocks and fluxes, most noticeably enhancing global cumulative ELUC by 33 Pg C (1750-2014) and entailing a significant reduction in carbon stored in vegetation, although the effect on soil C stocks was limited. Simulations demonstrated that assessments of historical carbon stocks and fluxes are highly uncertain due to the choice of LUC reconstruction and that the consideration of different contrasting LUC reconstructions is needed to account for this uncertainty. The analysis of gross, in addition to net, land-use changes showed that the full complexity of gross land-use changes is required in order to accurately predict the magnitude of LUC change emissions. This introduces technical challenges to process-based models and relies on extensive information regarding historical land-use transitions.

Three-dimensional numerical modeling of land subsidence in Shanghai, China

NASA Astrophysics Data System (ADS)

Ye, Shujun; Luo, Yue; Wu, Jichun; Yan, Xuexin; Wang, Hanmei; Jiao, Xun; Teatini, Pietro

2016-05-01

Shanghai, in China, has experienced two periods of rapid land subsidence mainly caused by groundwater exploitation related to economic and population growth. The first period occurred during 1956-1965 and was characterized by an average land subsidence rate of 83 mm/yr, and the second period occurred during 1990-1998 with an average subsidence rate of 16 mm/yr. Owing to the establishment of monitoring networks for groundwater levels and land subsidence, a valuable dataset has been collected since the 1960s and used to develop regional land subsidence models applied to manage groundwater resources and mitigate land subsidence. The previous geomechanical modeling approaches to simulate land subsidence were based on one-dimensional (1D) vertical stress and deformation. In this study, a numerical model of land subsidence is developed to simulate explicitly coupled three-dimensional (3D) groundwater flow and 3D aquifer-system displacements in downtown Shanghai from 30 December 1979 to 30 December 1995. The model is calibrated using piezometric, geodetic-leveling, and borehole extensometer measurements made during the 16-year simulation period. The 3D model satisfactorily reproduces the measured piezometric and deformation observations. For the first time, the capability exists to provide some preliminary estimations on the horizontal displacement field associated with the well-known land subsidence in Shanghai and for which no measurements are available. The simulated horizontal displacements peak at 11 mm, i.e. less than 10 % of the simulated maximum land subsidence, and seems too small to seriously damage infrastructure such as the subways (metro lines) in the center area of Shanghai.

Modeling Temporal and Spatial Flows of Ecosystem Services in Chittenden County, VT

NASA Astrophysics Data System (ADS)

Voigt, B. G.; Bagstad, K.; Johnson, G.; Villa, F.

2010-12-01

This paper documents the integration of ARIES (ARtificial Intelligence for Ecosystem Services) with the land use change model UrbanSim to explore the impacts of current and future land use patterns on flood protection and water provision services in Chittenden County, VT. ARIES, an open source modeling platform, is particularly well-suited for measuring, mapping, and modeling the temporal and spatial flows of ecosystem services across the landscape, linking the areas of provision (sources) with human beneficiaries (users) through a spatially explicit agent-based modeling approach. UrbanSim is an open source agent-based land use model designed to facilitate a wide-range of scenarios based on user-specified behavioral assumptions, zoning regulations, and demographic, economic, and infrastructure (e.g. transportation, water, sewer, etc.) parameters. Ecosystem services travel through time and space and are susceptible to disruption and destruction from both natural and anthropogenic perturbations. The conversion of forested or agricultural land to urbanizing uses is replete with a long history of hydrologic impairment, habitat fragmentation, and the degradation of sensitive landscapes. Development decisions are predicated on the presence of landscape characteristics that meet the needs of developers and satisfy the desires of consumers, with minimal consideration of access to or effect on the provision of ecosystem services. The County houses nearly 25% of the state’s population and several employment centers that draw labor from throughout the region. Additionally, the County is expected to maintain modest residential and employment growth over the next 30 years, and will continue to serve as the state’s population and employment center. Expected future growth is likely to adversely affect the remaining farm and forest land in the County in the absence of policies to support sustainable development. We demonstrate how ARIES can be used to quantify changes in ecosystem service provision based on the outcomes of alternative land use change model scenarios. Stakeholder workshops were hosted to develop scenarios relevant to planning for future growth in the County, including alternative zoning regulations, road network improvements, and a range of future population projections. The results of the land use change simulations were passed to ARIES to model flood protection and water provision services for each of the alternative scenarios. We present Bayesian models of the ecosystem services as individual source, sink, and use components coupled with models of temporal and spatial flows of services across the landscape. Specific beneficiaries include homeowners, farmers, and other business property owners. The location choice decisions of residential and non-residential agents under the alternative scenarios resulted in varying access to ecosystem services depending on development density, habitat fragmentation, and the degree of hydrological impairment, among other factors. Modeled outputs include maps depicting flow paths (linking sources to beneficiaries), changes in land use, hotspot locations that are critical to sustain the flow of services across the landscape, and the demand for and supply of the modeled services.

Forest management applications of Landsat data in a geographic information system

NASA Technical Reports Server (NTRS)

Maw, K. D.; Brass, J. A.

1982-01-01

The utility of land-cover data resulting from Landsat MSS classification can be greatly enhanced by use in combination with ancillary data. A demonstration forest management applications data base was constructed for Santa Cruz County, California, to demonstrate geographic information system applications of classified Landsat data. The data base contained detailed soils, digital terrain, land ownership, jurisdictional boundaries, fire events, and generalized land-use data, all registered to a UTM grid base. Applications models were developed from problems typical of fire management and reforestation planning.

Mine Land Reclamation and Eco-Reconstruction in Shanxi Province I: Mine Land Reclamation Model

PubMed Central

Bing-yuan, Hao; Li-xun, Kang

2014-01-01

Coal resource is the main primary energy in our country, while Shanxi Province is the most important province in resource. Therefore Shanxi is an energy base for our country and has a great significance in energy strategy. However because of the heavy development of the coal resource, the ecological environment is worsening and the farmland is reducing continuously in Shanxi Province. How to resolve the contradiction between coal resource exploitation and environmental protection has become the imperative. Thus the concept of “green mining industry” is arousing more and more attention. In this assay, we will talk about the basic mode of land reclamation in mine area, the engineering study of mine land reclamation, the comprehensive model study of mine land reclamation, and the design and model of ecological agricultural reclamation in mining subsidence. PMID:25050398

Exploring the Influence of Topography on Belowground C Processes Using a Coupled Hydrologic-Biogeochemical Model

NASA Astrophysics Data System (ADS)

Shi, Y.; Davis, K. J.; Eissenstat, D. M.; Kaye, J. P.; Duffy, C.; Yu, X.; He, Y.

2014-12-01

Belowground carbon processes are affected by soil moisture and soil temperature, but current biogeochemical models are 1-D and cannot resolve topographically driven hill-slope soil moisture patterns, and cannot simulate the nonlinear effects of soil moisture on carbon processes. Coupling spatially-distributed physically-based hydrologic models with biogeochemical models may yield significant improvements in the representation of topographic influence on belowground C processes. We will couple the Flux-PIHM model to the Biome-BGC (BBGC) model. Flux-PIHM is a coupled physically-based land surface hydrologic model, which incorporates a land-surface scheme into the Penn State Integrated Hydrologic Model (PIHM). The land surface scheme is adapted from the Noah land surface model. Because PIHM is capable of simulating lateral water flow and deep groundwater, Flux-PIHM is able to represent the link between groundwater and the surface energy balance, as well as the land surface heterogeneities caused by topography. The coupled Flux-PIHM-BBGC model will be tested at the Susquehanna/Shale Hills critical zone observatory (SSHCZO). The abundant observations, including eddy covariance fluxes, soil moisture, groundwater level, sap flux, stream discharge, litterfall, leaf area index, above ground carbon stock, and soil carbon efflux, make SSHCZO an ideal test bed for the coupled model. In the coupled model, each Flux-PIHM model grid will couple a BBGC cell. Flux-PIHM will provide BBGC with soil moisture and soil temperature information, while BBGC provides Flux-PIHM with leaf area index. Preliminary results show that when Biome- BGC is driven by PIHM simulated soil moisture pattern, the simulated soil carbon is clearly impacted by topography.

The GEWEX LandFlux project: Evaluation of model evaporation using tower-based and globally gridded forcing data

DOE Office of Scientific and Technical Information (OSTI.GOV)

McCabe, M. F.; Ershadi, A.; Jimenez, C.

Determining the spatial distribution and temporal development of evaporation at regional and global scales is required to improve our understanding of the coupled water and energy cycles and to better monitor any changes in observed trends and variability of linked hydrological processes. With recent international efforts guiding the development of long-term and globally distributed flux estimates, continued product assessments are required to inform upon the selection of suitable model structures and also to establish the appropriateness of these multi-model simulations for global application. In support of the objectives of the Global Energy and Water Cycle Exchanges (GEWEX) LandFlux project, fourmore » commonly used evaporation models are evaluated against data from tower-based eddy-covariance observations, distributed across a range of biomes and climate zones. The selected schemes include the Surface Energy Balance System (SEBS) approach, the Priestley–Taylor Jet Propulsion Laboratory (PT-JPL) model, the Penman–Monteith-based Mu model (PM-Mu) and the Global Land Evaporation Amsterdam Model (GLEAM). Here we seek to examine the fidelity of global evaporation simulations by examining the multi-model response to varying sources of forcing data. To do this, we perform parallel and collocated model simulations using tower-based data together with a global-scale grid-based forcing product. Through quantifying the multi-model response to high-quality tower data, a better understanding of the subsequent model response to the coarse-scale globally gridded data that underlies the LandFlux product can be obtained, while also providing a relative evaluation and assessment of model performance. Using surface flux observations from 45 globally distributed eddy-covariance stations as independent metrics of performance, the tower-based analysis indicated that PT-JPL provided the highest overall statistical performance (0.72; 61 W m –2; 0.65), followed closely by GLEAM (0.68; 64 W m –2; 0.62), with values in parentheses representing the R 2, RMSD and Nash–Sutcliffe efficiency (NSE), respectively. PM-Mu (0.51; 78 W m –2; 0.45) tended to underestimate fluxes, while SEBS (0.72; 101 W m –2; 0.24) overestimated values relative to observations. A focused analysis across specific biome types and climate zones showed considerable variability in the performance of all models, with no single model consistently able to outperform any other. Results also indicated that the global gridded data tended to reduce the performance for all of the studied models when compared to the tower data, likely a response to scale mismatch and issues related to forcing quality. Rather than relying on any single model simulation, the spatial and temporal variability at both the tower- and grid-scale highlighted the potential benefits of developing an ensemble or blended evaporation product for global-scale LandFlux applications. Hence, challenges related to the robust assessment of the LandFlux product are also discussed.« less

The GEWEX LandFlux project: Evaluation of model evaporation using tower-based and globally gridded forcing data

DOE PAGES

McCabe, M. F.; Ershadi, A.; Jimenez, C.; ...

2016-01-26

Determining the spatial distribution and temporal development of evaporation at regional and global scales is required to improve our understanding of the coupled water and energy cycles and to better monitor any changes in observed trends and variability of linked hydrological processes. With recent international efforts guiding the development of long-term and globally distributed flux estimates, continued product assessments are required to inform upon the selection of suitable model structures and also to establish the appropriateness of these multi-model simulations for global application. In support of the objectives of the Global Energy and Water Cycle Exchanges (GEWEX) LandFlux project, fourmore » commonly used evaporation models are evaluated against data from tower-based eddy-covariance observations, distributed across a range of biomes and climate zones. The selected schemes include the Surface Energy Balance System (SEBS) approach, the Priestley–Taylor Jet Propulsion Laboratory (PT-JPL) model, the Penman–Monteith-based Mu model (PM-Mu) and the Global Land Evaporation Amsterdam Model (GLEAM). Here we seek to examine the fidelity of global evaporation simulations by examining the multi-model response to varying sources of forcing data. To do this, we perform parallel and collocated model simulations using tower-based data together with a global-scale grid-based forcing product. Through quantifying the multi-model response to high-quality tower data, a better understanding of the subsequent model response to the coarse-scale globally gridded data that underlies the LandFlux product can be obtained, while also providing a relative evaluation and assessment of model performance. Using surface flux observations from 45 globally distributed eddy-covariance stations as independent metrics of performance, the tower-based analysis indicated that PT-JPL provided the highest overall statistical performance (0.72; 61 W m –2; 0.65), followed closely by GLEAM (0.68; 64 W m –2; 0.62), with values in parentheses representing the R 2, RMSD and Nash–Sutcliffe efficiency (NSE), respectively. PM-Mu (0.51; 78 W m –2; 0.45) tended to underestimate fluxes, while SEBS (0.72; 101 W m –2; 0.24) overestimated values relative to observations. A focused analysis across specific biome types and climate zones showed considerable variability in the performance of all models, with no single model consistently able to outperform any other. Results also indicated that the global gridded data tended to reduce the performance for all of the studied models when compared to the tower data, likely a response to scale mismatch and issues related to forcing quality. Rather than relying on any single model simulation, the spatial and temporal variability at both the tower- and grid-scale highlighted the potential benefits of developing an ensemble or blended evaporation product for global-scale LandFlux applications. Hence, challenges related to the robust assessment of the LandFlux product are also discussed.« less

Numerical Model Sensitivity to Heterogeneous Satellite Derived Vegetation Roughness

NASA Technical Reports Server (NTRS)

Jasinski, Michael; Eastman, Joseph; Borak, Jordan

2011-01-01

The sensitivity of a mesoscale weather prediction model to a 1 km satellite-based vegetation roughness initialization is investigated for a domain within the south central United States. Three different roughness databases are employed: i) a control or standard lookup table roughness that is a function only of land cover type, ii) a spatially heterogeneous roughness database, specific to the domain, that was previously derived using a physically based procedure and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery, and iii) a MODIS climatologic roughness database that like (i) is a function only of land cover type, but possesses domain specific mean values from (ii). The model used is the Weather Research and Forecast Model (WRF) coupled to the Community Land Model within the Land Information System (LIS). For each simulation, a statistical comparison is made between modeled results and ground observations within a domain including Oklahoma, Eastern Arkansas, and Northwest Louisiana during a 4-day period within IHOP 2002. Sensitivity analysis compares the impact the three roughness initializations on time-series temperature, precipitation probability of detection (POD), average wind speed, boundary layer height, and turbulent kinetic energy (TKE). Overall, the results indicate that, for the current investigation, replacement of the standard look-up table values with the satellite-derived values statistically improves model performance for most observed variables. Such natural roughness heterogeneity enhances the surface wind speed, PBL height and TKE production up to 10 percent, with a lesser effect over grassland, and greater effect over mixed land cover domains.

Virtual Observation System for Earth System Model: An Application to ACME Land Model Simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Dali; Yuan, Fengming; Hernandez, Benjamin

Investigating and evaluating physical-chemical-biological processes within an Earth system model (EMS) can be very challenging due to the complexity of both model design and software implementation. A virtual observation system (VOS) is presented to enable interactive observation of these processes during system simulation. Based on advance computing technologies, such as compiler-based software analysis, automatic code instrumentation, and high-performance data transport, the VOS provides run-time observation capability, in-situ data analytics for Earth system model simulation, model behavior adjustment opportunities through simulation steering. A VOS for a terrestrial land model simulation within the Accelerated Climate Modeling for Energy model is also presentedmore » to demonstrate the implementation details and system innovations.« less

Virtual Observation System for Earth System Model: An Application to ACME Land Model Simulations

DOE PAGES

Wang, Dali; Yuan, Fengming; Hernandez, Benjamin; ...

2017-01-01

Investigating and evaluating physical-chemical-biological processes within an Earth system model (EMS) can be very challenging due to the complexity of both model design and software implementation. A virtual observation system (VOS) is presented to enable interactive observation of these processes during system simulation. Based on advance computing technologies, such as compiler-based software analysis, automatic code instrumentation, and high-performance data transport, the VOS provides run-time observation capability, in-situ data analytics for Earth system model simulation, model behavior adjustment opportunities through simulation steering. A VOS for a terrestrial land model simulation within the Accelerated Climate Modeling for Energy model is also presentedmore » to demonstrate the implementation details and system innovations.« less

Changing crops in response to climate: virtual Nang Rong, Thailand in an agent based simulation

PubMed Central

Malanson, George P.; Verdery, Ashton M.; Walsh, Stephen J.; Sawangdee, Yothin; Heumann, Benjamin W.; McDaniel, Philip M.; Frizzelle, Brian G.; Williams, Nathalie E.; Yao, Xiaozheng; Entwisle, Barbara; Rindfuss, Ronald R.

2014-01-01

The effects of extended climatic variability on agricultural land use were explored for the type of system found in villages of northeastern Thailand. An agent based model developed for the Nang Rong district was used to simulate land allotted to jasmine rice, heavy rice, cassava, and sugar cane. The land use choices in the model depended on likely economic outcomes, but included elements of bounded rationality in dependence on household demography. The socioeconomic dynamics are endogenous in the system, and climate changes were added as exogenous drivers. Villages changed their agricultural effort in many different ways. Most villages reduced the amount of land under cultivation, primarily with reduction in jasmine rice, but others did not. The variation in responses to climate change indicates potential sensitivity to initial conditions and path dependence for this type of system. The differences between our virtual villages and the real villages of the region indicate effects of bounded rationality and limits on model applications. PMID:25061240

Simulating the hydrologic impacts of land-cover and climate changes in a semi-arid watershed

EPA Science Inventory

Changes in climate and land cover are principal variables affecting watershed hydrology. This paper uses a cell-based model to examine the hydrologic impacts of climate and land cover changes in the semi-arid Lower Virgin River (LVR) watershed located upstream of Lake Mead, Nevad...

Economic factors influencing land use changes in the South-Central United States

Treesearch

Ralph J. Alig; Fred C. White; Brian C. Murray

1988-01-01

Econometric models of land use change were estimated for two physiographic regions in the South-Central United States. Results are consistent-with the economic hierarchy of land use, with population and personal income being significant explanatory variables. Findings regarding the importance of relative agricultural and forestry market-based incomes in influencing...

LAND COVER CHANGE AND LARGE SCALE HYDROLOGIC MODELING OF THE SAN PEDRO RIVER AND CATSKILL/DELAWARE BASINS

EPA Science Inventory

This study is based on the assumption that land cover change and rainfall spatial variability affect the r-ainfall-runoff relationships on the watershed. Hydrologic response is an integrated indicator of watershed condition, and changes in land cover may affect the overall health...

Development of An Empirical Water Quality Model for Stormwater Based on Watershed Land Use in Puget Sound

DTIC Science & Technology

2007-03-29

Development of An Empirical Water Quality Model for Stormwater Based on Watershed Land Use in Puget Sound Valerie I. Cullinan, Christopher W. May...Systems Center, Bremerton, WA) Introduction The Sinclair and Dyes Inlet watershed is located on the west side of Puget Sound in Kitsap County...Washington, U.S.A. (Figure 1). The Puget Sound Naval Shipyard (PSNS), U.S Environmental Protection Agency (USEPA), the Washington State Department of

Estimating Evapotranspiration with Land Data Assimilation Systems

NASA Technical Reports Server (NTRS)

Peters-Lidard, C. D.; Kumar, S. V.; Mocko, D. M.; Tian, Y.

2011-01-01

Advancements in both land surface models (LSM) and land surface data assimilation, especially over the last decade, have substantially advanced the ability of land data assimilation systems (LDAS) to estimate evapotranspiration (ET). This article provides a historical perspective on international LSM intercomparison efforts and the development of LDAS systems, both of which have improved LSM ET skill. In addition, an assessment of ET estimates for current LDAS systems is provided along with current research that demonstrates improvement in LSM ET estimates due to assimilating satellite-based soil moisture products. Using the Ensemble Kalman Filter in the Land Information System, we assimilate both NASA and Land Parameter Retrieval Model (LPRM) soil moisture products into the Noah LSM Version 3.2 with the North American LDAS phase 2 (NLDAS-2) forcing to mimic the NLDAS-2 configuration. Through comparisons with two global reference ET products, one based on interpolated flux tower data and one from a new satellite ET algorithm, over the NLDAS2 domain, we demonstrate improvement in ET estimates only when assimilating the LPRM soil moisture product.

Seasonal monitoring of soil erosion at regional scale: An application of the G2 model in Crete focusing on agricultural land uses

NASA Astrophysics Data System (ADS)

Panagos, Panagos; Christos, Karydas; Cristiano, Ballabio; Ioannis, Gitas

2014-04-01

A new soil erosion model, namely G2, was applied in the island of Crete with a focus on agricultural land uses, including potential grazing lands. The G2 model was developed within the Geoland2 project as an agro-environmental service in the framework of the Global Monitoring for Environment and Security (GMES, now Copernicus) initiative. The G2 model takes advantage of the empirical background of the Universal Soil Loss Equation (USLE) and the Gavrilovic model, together with readily available time series of vegetation layers and 10-min rainfall intensity data to produce monthly time-step erosion risk maps at 300 m cell size. The innovations of the G2 model include the implementation of land-use influence parameters based on empirical data and the introduction of a corrective term in the estimation of the topographic influence factor. The mean annual erosion rate in Crete was found to be 8.123 t ha-1. The season from October to January (the rainy season in Crete) was found to be the most critical, accounting for 80% of the annual erosion in the island. Seasonal erosion figures proved to be crucial for the identification of erosion hotspots and of risky land uses. In Crete, high annual erosion figures were detected in natural grasslands and shrublands (14.023 t ha-1), mainly due to the intensification of livestock grazing during the past decades. The G2 model allows for the integrated spatio-temporal monitoring of soil erosion per land-use type based on moderate data input requirements and existing datasets.

Urban Expansion Modeling Approach Based on Multi-Agent System and Cellular Automata

NASA Astrophysics Data System (ADS)

Zeng, Y. N.; Yu, M. M.; Li, S. N.

2018-04-01

Urban expansion is a land-use change process that transforms non-urban land into urban land. This process results in the loss of natural vegetation and increase in impervious surfaces. Urban expansion also alters the hydrologic cycling, atmospheric circulation, and nutrient cycling processes and generates enormous environmental and social impacts. Urban expansion monitoring and modeling are crucial to understanding urban expansion process, mechanism, and its environmental impacts, and predicting urban expansion in future scenarios. Therefore, it is important to study urban expansion monitoring and modeling approaches. We proposed to simulate urban expansion by combining CA and MAS model. The proposed urban expansion model based on MSA and CA was applied to a case study area of Changsha-Zhuzhou-Xiangtan urban agglomeration, China. The results show that this model can capture urban expansion with good adaptability. The Kappa coefficient of the simulation results is 0.75, which indicated that the combination of MAS and CA offered the better simulation result.

Simulating the hydrological impacts of inter-annual and seasonal variability in land use land cover change on streamflow

NASA Astrophysics Data System (ADS)

Taxak, A. K.; Ojha, C. S. P.

2017-12-01

Land use and land cover (LULC) changes within a watershed are recognised as an important factor affecting hydrological processes and water resources. LULC changes continuously not only in long term but also on the inter-annual and season level. Changes in LULC affects the interception, storage and moisture. A widely used approach in rainfall-runoff modelling through Land surface models (LSM)/ hydrological models is to keep LULC same throughout the model running period. In long term simulations where land use change take place during the run period, using a single LULC does not represent a true picture of ground conditions could result in stationarity of model responses. The present work presents a case study in which changes in LULC are incorporated by using multiple LULC layers. LULC for the study period were created using imageries from Landsat series, Sentinal, EO-1 ALI. Distributed, physically based Variable Infiltration Capacity (VIC) model was modified to allow inclusion of LULC as a time varying variable just like climate. The Narayani basin was simulated with LULC, leaf area index (LAI), albedo and climate data for 1992-2015. The results showed that the model simulation with varied parametrization approach has a large improvement over the conventional fixed parametrization approach in terms of long-term water balance. The proposed modelling approach could improve hydrological modelling for applications like land cover change studies, water budget studies etc.

Modelling strategies to predict the multi-scale effects of rural land management change

NASA Astrophysics Data System (ADS)

Bulygina, N.; Ballard, C. E.; Jackson, B. M.; McIntyre, N.; Marshall, M.; Reynolds, B.; Wheater, H. S.

2011-12-01

Changes to the rural landscape due to agricultural land management are ubiquitous, yet predicting the multi-scale effects of land management change on hydrological response remains an important scientific challenge. Much empirical research has been of little generic value due to inadequate design and funding of monitoring programmes, while the modelling issues challenge the capability of data-based, conceptual and physics-based modelling approaches. In this paper we report on a major UK research programme, motivated by a national need to quantify effects of agricultural intensification on flood risk. Working with a consortium of farmers in upland Wales, a multi-scale experimental programme (from experimental plots to 2nd order catchments) was developed to address issues of upland agricultural intensification. This provided data support for a multi-scale modelling programme, in which highly detailed physics-based models were conditioned on the experimental data and used to explore effects of potential field-scale interventions. A meta-modelling strategy was developed to represent detailed modelling in a computationally-efficient manner for catchment-scale simulation; this allowed catchment-scale quantification of potential management options. For more general application to data-sparse areas, alternative approaches were needed. Physics-based models were developed for a range of upland management problems, including the restoration of drained peatlands, afforestation, and changing grazing practices. Their performance was explored using literature and surrogate data; although subject to high levels of uncertainty, important insights were obtained, of practical relevance to management decisions. In parallel, regionalised conceptual modelling was used to explore the potential of indices of catchment response, conditioned on readily-available catchment characteristics, to represent ungauged catchments subject to land management change. Although based in part on speculative relationships, significant predictive power was derived from this approach. Finally, using a formal Bayesian procedure, these different sources of information were combined with local flow data in a catchment-scale conceptual model application , i.e. using small-scale physical properties, regionalised signatures of flow and available flow measurements.

Rice production model based on the concept of ecological footprint

NASA Astrophysics Data System (ADS)

Faiz, S. A.; Wicaksono, A. D.; Dinanti, D.

2017-06-01

Pursuant to what had been stated in Region Spatial Planning (RTRW) of Malang Regency for period 2010-2030, Malang Regency was considered as the center of agricultural development, including districts bordered with Malang City. To protect the region functioning as the provider of rice production, then the policy of sustainable food farming-land (LP2B) was made which its implementation aims to protect rice-land. In the existing condition, LP2B system was not maximally executed, and it caused a limited extend of rice-land to deliver rice production output. One cause related with the development of settlements and industries due to the effect of Malang City that converted land-function. Location of research focused on 30 villages with direct border with Malang City. Review was conducted to develop a model of relation between farming production output and ecological footprint variables. These variables include rice-land area (X1), built land percentage (X2), and number of farmers (X3). Analysis technique was regression. Result of regression indicated that the model of rice production output Y=-207,983 + 10.246X1. Rice-land area (X1) was the most influential independent variable. It was concluded that of villages directly bordered with Malang City, there were 11 villages with higher production potential because their rice production yield was more than 1,000 tons/year, while 12 villages were threatened with low production output because its rice production yield only attained 500 tons/year. Based on the model and the spatial direction of RTRW, it can be said that the direction for the farming development policy must be redesigned to maintain rice-land area on the regions on which agricultural activity was still dominant. Because rice-land area was the most influential factor to farming production. Therefore, the wider the rice-land is, the higher rice production output is on each village.

Design of an Agent-Based Model to Examine Population-Environment Interactions in Nang Rong District, Thailand.

PubMed

Walsh, Stephen J; Malanson, George P; Entwisle, Barbara; Rindfuss, Ronald R; Mucha, Peter J; Heumann, Benjamin W; McDaniel, Philip M; Frizzelle, Brian G; Verdery, Ashton M; Williams, Nathalie; Xiaozheng, Yao; Ding, Deng

2013-05-01

The design of an Agent-Based Model (ABM) is described that integrates Social and Land Use Modules to examine population-environment interactions in a former agricultural frontier in Northeastern Thailand. The ABM is used to assess household income and wealth derived from agricultural production of lowland, rain-fed paddy rice and upland field crops in Nang Rong District as well as remittances returned to the household from family migrants who are engaged in off-farm employment in urban destinations. The ABM is supported by a longitudinal social survey of nearly 10,000 households, a deep satellite image time-series of land use change trajectories, multi-thematic social and ecological data organized within a GIS, and a suite of software modules that integrate data derived from an agricultural cropping system model (DSSAT - Decision Support for Agrotechnology Transfer) and a land suitability model (MAXENT - Maximum Entropy), in addition to multi-dimensional demographic survey data of individuals and households. The primary modules of the ABM are the Initialization Module, Migration Module, Assets Module, Land Suitability Module, Crop Yield Module, Fertilizer Module, and the Land Use Change Decision Module. The architecture of the ABM is described relative to module function and connectivity through uni-directional or bi-directional links. In general, the Social Modules simulate changes in human population and social networks, as well as changes in population migration and household assets, whereas the Land Use Modules simulate changes in land use types, land suitability, and crop yields. We emphasize the description of the Land Use Modules - the algorithms and interactions between the modules are described relative to the project goals of assessing household income and wealth relative to shifts in land use patterns, household demographics, population migration, social networks, and agricultural activities that collectively occur within a marginalized environment that is subjected to a suite of endogenous and exogenous dynamics.

Design of an Agent-Based Model to Examine Population-Environment Interactions in Nang Rong District, Thailand

PubMed Central

Walsh, Stephen J.; Malanson, George P.; Entwisle, Barbara; Rindfuss, Ronald R.; Mucha, Peter J.; Heumann, Benjamin W.; McDaniel, Philip M.; Frizzelle, Brian G.; Verdery, Ashton M.; Williams, Nathalie; Xiaozheng, Yao; Ding, Deng

2013-01-01

The design of an Agent-Based Model (ABM) is described that integrates Social and Land Use Modules to examine population-environment interactions in a former agricultural frontier in Northeastern Thailand. The ABM is used to assess household income and wealth derived from agricultural production of lowland, rain-fed paddy rice and upland field crops in Nang Rong District as well as remittances returned to the household from family migrants who are engaged in off-farm employment in urban destinations. The ABM is supported by a longitudinal social survey of nearly 10,000 households, a deep satellite image time-series of land use change trajectories, multi-thematic social and ecological data organized within a GIS, and a suite of software modules that integrate data derived from an agricultural cropping system model (DSSAT – Decision Support for Agrotechnology Transfer) and a land suitability model (MAXENT – Maximum Entropy), in addition to multi-dimensional demographic survey data of individuals and households. The primary modules of the ABM are the Initialization Module, Migration Module, Assets Module, Land Suitability Module, Crop Yield Module, Fertilizer Module, and the Land Use Change Decision Module. The architecture of the ABM is described relative to module function and connectivity through uni-directional or bi-directional links. In general, the Social Modules simulate changes in human population and social networks, as well as changes in population migration and household assets, whereas the Land Use Modules simulate changes in land use types, land suitability, and crop yields. We emphasize the description of the Land Use Modules – the algorithms and interactions between the modules are described relative to the project goals of assessing household income and wealth relative to shifts in land use patterns, household demographics, population migration, social networks, and agricultural activities that collectively occur within a marginalized environment that is subjected to a suite of endogenous and exogenous dynamics. PMID:24277975

Estimation of Multiple Parameters over Vegetated Surfaces by Integrating Optical-Thermal Remote Sensing Observations

NASA Astrophysics Data System (ADS)

Ma, H.

2016-12-01

Land surface parameters from remote sensing observations are critical in monitoring and modeling of global climate change and biogeochemical cycles. Current methods for estimating land surface parameters are generally parameter-specific algorithms and are based on instantaneous physical models, which result in spatial, temporal and physical inconsistencies in current global products. Besides, optical and Thermal Infrared (TIR) remote sensing observations are usually separated to use based on different models , and the Middle InfraRed (MIR) observations have received little attention due to the complexity of the radiometric signal that mixes both reflected and emitted fluxes. In this paper, we proposed a unified algorithm for simultaneously retrieving a total of seven land surface parameters, including Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), land surface albedo, Land Surface Temperature (LST), surface emissivity, downward and upward longwave radiation, by exploiting remote sensing observations from visible to TIR domain based on a common physical Radiative Transfer (RT) model and a data assimilation framework. The coupled PROSPECT-VISIR and 4SAIL RT model were used for canopy reflectance modeling. At first, LAI was estimated using a data assimilation method that combines MODIS daily reflectance observation and a phenology model. The estimated LAI values were then input into the RT model to simulate surface spectral emissivity and surface albedo. Besides, the background albedo and the transmittance of solar radiation, and the canopy albedo were also calculated to produce FAPAR. Once the spectral emissivity of seven MODIS MIR to TIR bands were retrieved, LST can be estimated from the atmospheric corrected surface radiance by exploiting an optimization method. At last, the upward longwave radiation were estimated using the retrieved LST, broadband emissivity (converted from spectral emissivity) and the downward longwave radiation (modeled by MODTRAN). These seven parameters were validated over several representative sites with different biome type, and compared with MODIS and GLASS product. Results showed that this unified inversion algorithm can retrieve temporally complete and physical consistent land surface parameters with high accuracy.

Methods used to parameterize the spatially-explicit components of a state-and-transition simulation model

USGS Publications Warehouse

Sleeter, Rachel; Acevedo, William; Soulard, Christopher E.; Sleeter, Benjamin M.

2015-01-01

Spatially-explicit state-and-transition simulation models of land use and land cover (LULC) increase our ability to assess regional landscape characteristics and associated carbon dynamics across multiple scenarios. By characterizing appropriate spatial attributes such as forest age and land-use distribution, a state-and-transition model can more effectively simulate the pattern and spread of LULC changes. This manuscript describes the methods and input parameters of the Land Use and Carbon Scenario Simulator (LUCAS), a customized state-and-transition simulation model utilized to assess the relative impacts of LULC on carbon stocks for the conterminous U.S. The methods and input parameters are spatially explicit and describe initial conditions (strata, state classes and forest age), spatial multipliers, and carbon stock density. Initial conditions were derived from harmonization of multi-temporal data characterizing changes in land use as well as land cover. Harmonization combines numerous national-level datasets through a cell-based data fusion process to generate maps of primary LULC categories. Forest age was parameterized using data from the North American Carbon Program and spatially-explicit maps showing the locations of past disturbances (i.e. wildfire and harvest). Spatial multipliers were developed to spatially constrain the location of future LULC transitions. Based on distance-decay theory, maps were generated to guide the placement of changes related to forest harvest, agricultural intensification/extensification, and urbanization. We analyze the spatially-explicit input parameters with a sensitivity analysis, by showing how LUCAS responds to variations in the model input. This manuscript uses Mediterranean California as a regional subset to highlight local to regional aspects of land change, which demonstrates the utility of LUCAS at many scales and applications.

Developing a top-down land-use management procedure for fish habitat enhancement

NASA Astrophysics Data System (ADS)

Chiang, Li-Chi; Lin, Yu-Pin; Wu, Chen-Huan

2013-04-01

Land-use change can influence stream ecosystem and alter instream physical, chemical and biological habitat. For example, urbanization usually contributes to increasing sediment loadings to streams and inappropriate agricultural management results in degradation of stream water quality. Watershed model is an effective way to forecast the watershed response to different land-use change scenarios. We developed a top-down approach from the watershed scale to the microscale by combining the habitat model, land-use change model and watershed hydrological model. This approach can assist land-use planner to make optimal decisions with fish habitat enhancement. The study was conducted in Datuan Stream, located in Tamsui District, New Taipei City and the target species is monk goby (Sicyopterus japonicus). The spatially explicit land-use change model, CLUE-s was first applied to project several future land-use scenarios and the Soil and Water Assessment Tool (SWAT) was then applied to simulate streamflow for different land-use scenarios. The simulated streamflow were used as input data for simulating river habitat, where Habitat Suitability Analysis is one of the most important processes. The relationship between target species and multiple environmental factors of habitat was first developed using the Habitat suitability index (HSI). In this study, we used fish presence probabilities for each velocity and water depth to establish different HSI functions under 4 flow conditions (slack, riffle, pool and run) using genetic programming (GP). The physical habitat model, River 2D, was then applied to simulate the river section and calculate weighted usable area (WUA). Based on the WUA results for different land-use scenarios, we further evaluated the relationships between WUA and land-use/landscape patterns using a spatial pattern analysis program, Fragstats. The results showed that by using the habitat model for classified flows, the habitat suitability curve which reflects different activities of fish (ex: spawning, preying) is more practical. Moreover, the proposed land-use management procedure can be useful for future land-use planning with fish habitat conservation.

Modelling land use change in the Ganga basin

NASA Astrophysics Data System (ADS)

Moulds, Simon; Mijic, Ana; Buytaert, Wouter

2014-05-01

Over recent decades the green revolution in India has driven substantial environmental change. Modelling experiments have identified northern India as a "hot spot" of land-atmosphere coupling strength during the boreal summer. However, there is a wide range of sensitivity of atmospheric variables to soil moisture between individual climate models. The lack of a comprehensive land use change dataset to force climate models has been identified as a major contributor to model uncertainty. This work aims to construct a monthly time series dataset of land use change for the period 1966 to 2007 for northern India to improve the quantification of regional hydrometeorological feedbacks. The Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on board the Aqua and Terra satellites provides near-continuous remotely sensed datasets from 2000 to the present day. However, the quality and availability of satellite products before 2000 is poor. To complete the dataset MODIS images are extrapolated back in time using the Conversion of Land Use and its Effects at Small regional extent (CLUE-S) modelling framework, recoded in the R programming language to overcome limitations of the original interface. Non-spatial estimates of land use area published by the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) for the study period, available on an annual, district-wise basis, are used as a direct model input. Land use change is allocated spatially as a function of biophysical and socioeconomic drivers identified using logistic regression. The dataset will provide an essential input to a high-resolution, physically-based land-surface model to generate the lower boundary condition to assess the impact of land use change on regional climate.

Environmental application of remote sensing methods to coastal zone land use and marine resource management. Appendix F: User's guide for advection, convection prototype. [southeastern Virginia

NASA Technical Reports Server (NTRS)

1972-01-01

A user's manual is provided for the environmental computer model proposed for the Richmond-Cape Henry Environmental Laboratory (RICHEL) application project for coastal zone land use investigations and marine resources management. The model was developed around the hydrologic cycle and includes two data bases consisting of climate and land use variables. The main program is described, along with control parameters to be set and pertinent subroutines.

New and Improved GLDAS and NLDAS Data Sets and Data Services at HDISC/NASA

NASA Technical Reports Server (NTRS)

Rui, Hualan; Beaudoing, Hiroko Kato; Mocko, David M.; Rodell, Matthew; Teng, William L.; Vollmer. Bruce

2010-01-01

Terrestrial hydrological variables are important in global hydrology, climate, and carbon cycle studies. Generating global fields of these variables, however, is still a challenge. The goal of a land data assimilation system (LDAS)is to ingest satellite-and ground-based observational data products, using advanced land surface modeling and data assimilation techniques, in order to generate optimal fields of land surface states and fluxes data and, thereby, facilitate hydrology and climate modeling, research, and forecast.

Rates and potentials of soil organic carbon sequestration in agricultural lands in Japan: an assessment using a process-based model and spatially-explicit land-use change inventories

NASA Astrophysics Data System (ADS)

Yagasaki, Y.; Shirato, Y.

2013-11-01

In order to develop a system to estimate a country-scale soil organic carbon stock change (SCSC) in agricultural lands in Japan that enables to take account effect of land-use changes, climate, different agricultural activity and nature of soils, a spatially-explicit model simulation system using Rothamsted Carbon Model (RothC) integrated with spatial and temporal inventories was developed. Future scenarios on agricultural activity and land-use change were prepared, in addition to future climate projections by global climate models, with purposely selecting rather exaggerated and contrasting set of scenarios to assess system's sensitivity as well as to better factor out direct human influence in the SCSC accounting. Simulation was run from year 1970 to 2008, and to year 2020, with historical inventories and future scenarios involving target set in agricultural policy, respectively, and subsequently until year 2100 with no temporal changes in land-use and agricultural activity but with varying climate to investigate course of SCSC. Results of the country-scale SCSC simulation have indicated that conversion of paddy fields to croplands occurred during past decades, as well as a large conversion of agricultural fields to settlements or other lands that have occurred in historical period and would continue in future, could act as main factors causing greater loss of soil organic carbon (SOC) at country-scale, with reduction organic carbon input to soils and enhancement of SOC decomposition by transition of soil environment to aerobic conditions, respectively. Scenario analysis indicated that an option to increase organic carbon input to soils with intensified rotation with suppressing conversion of agricultural lands to other land-use types could achieve reduction of CO2 emission due to SCSC in the same level as that of another option to let agricultural fields be abandoned. These results emphasize that land-use changes, especially conversion of the agricultural lands to other land-use types by abandoning or urbanization accompanied by substantial changes in the rate of organic carbon input to soils, could cause a greater or comparable influence on country-scale SCSC compared with changes in management of agricultural lands. A net-net based accounting on SCSC showed potential influence of variations in future climate on SCSC, that highlighted importance of application of process-based model for estimation of this quantity. Whereas a baseline-based accounting on SCSC was shown to have robustness over variations in future climate and effectiveness to factor out direct human-induced influence on SCSC. Validation of the system's function to estimate SCSC in agricultural lands, by comparing simulation output with data from nation-wide stationary monitoring conducted during year 1979-1998, suggested that the system has an acceptable levels of validity, though only for limited range of conditions at current stage. In addition to uncertainties in estimation of the rate of organic carbon input to soils in different land-use types at large-scale, time course of SOC sequestration, supposition on land-use change pattern in future, as well as feasibility of agricultural policy planning are considered as important factors that need to be taken account in estimation on a potential of country-scale SCSC.

Monitoring and Assessment of Military Installation Land Condition under Training Disturbance Using Remote Sensing

NASA Astrophysics Data System (ADS)

Rijal, Santosh

Various military training activities are conducted in more than 11.3 million hectares of land (> 5,500 training sites) in the United States (U.S.). These training activities directly and indirectly degrade the land. Land degradation can impede continuous military training. In order to sustain long term training missions and Army combat readiness, the environmental conditions of the military installations need to be carefully monitored and assessed. Furthermore, the National Environmental Policy Act of 1969 (NEPA) and the U.S. Army Regulation 200-2 require the DoD to minimize the environmental impacts of training and document the environmental consequences of their actions. To achieve these objectives, the Department of Army initiated an Integrated Training Area Management (ITAM) program to manage training lands through assessing their environmental requirements and establishing policies and procedures to achieve optimum, sustainable use of training lands. One of the programs under ITAM, Range and Training Land Assessment (RTLA) was established to collect field-based data for monitoring installation's environmental condition. Due to high cost and inefficiencies involved in the collection of field data, the RTLA program was stopped in several military installations. Therefore, there has been a strong need to develop an efficient and low cost remote sensing based methodology for assessing and monitoring land conditions of military installations. It is also important to make a long-term assessment of installation land condition for understanding cumulative impacts of continuous military training activities. Additionally, it is unclear that compared to non-military land condition, to what extent military training activities have led to the degradation of land condition for military installations. The first paper of this dissertation developed a soil erosion relevant and image derived cover factor (ICF) based on linear spectral mixture (LSM) analysis to assess and monitor the land condition of military land and compare it with non-military land. The results from this study can provide FR land managers with the information of the spatial variation and temporal trend of land condition in FR. Fort Riley land managers can also use this method for monitoring their land condition at a very low cost. This method can thus be applied to other military installations as well as non-military lands. Furthermore, one of the most significant environmental problems in military installations of the U.S. is the formation of gullies due to the intensive use of military vehicle. However, to our knowledge, no remote sensing based method has been developed and used to assess the detection of gullies in military installations. In the second paper of this dissertation, light detection and ranging (LiDAR) derived digital elevation model (DEM) of 2010 and WorldView-2 images of 2010 were used to quantify the gullies in FR. This method can be easily applied to assess gullies in non-military installations. On the other hand, modeling the land condition of military installation is critical to understand the spatial and temporal pattern of military training induced disturbance and land recovery. In the third paper, it was assumed that the military training induced disturbance was spatially auto-correlated and thus four regression models including i) linear stepwise regression (LSR) ii) logistic regression (LR), iii) geographically weighted linear regression (GWR), and iv) geographically weighted logistic regression (GWLR) were developed and compared using remote sensing image derived spectral variables for years 1990, 1997, 1998, 1999, and 2001. It was found that the spatial distribution of the military training disturbance was well demonstrated by all the regression models with higher intensities of military training disturbance in the northwest and central west parts of the installation. Compared to other regression models, GWR accurately estimated the land condition of FR. This result provided the applicability of using local variability based regression model to accurately predict land condition. Different plant communities of military installations respond differently to military training induced disturbance. The information of the spatial distribution of plant species in military installations is important to gain insight of the resilient capacity of the land following disturbances. For the purpose, in the fourth paper, hyperspectral in-situ data were collected from FR and KPBS in the summer of 2015 using a hyperspectral instrument. Principal component analysis (PCA) and band relative importance (BRI) were used to identify relative importance of each of the spectral bands. The results from this study provided useful information about the optimal wavelengths that help distinguish different plant species of FR and can be easily used with high resolution hyperspectral images for mapping the spatial distribution of the plant species. This information will be helpful for the sustainable management of the tallgrass prairie ecosystem. (Abstract shortened by ProQuest.).

Simulation of the spatial stresses due to territorial land development on Yellow River Delta Nature Reserve using a GIS-based assessment model.

PubMed

Zhang, Baolei; Zhang, Qiaoyun; Feng, Qingyu; Cui, Bohao; Zhang, Shumin

2017-07-01

This study aimed at assessing the stresses from land development in or around Yellow River Delta Nature Reserve (YRDNR) and identifying the impacted areas. Major land development types (reservoirs, pond, aquafarm, salt pan, road, residential land, industry land, farming land, and fishing land) in or around the YRDNR from 1995 to 2014 were identified using spatial data sets derived from remote sensing imageries. The spatial stresses were simulated by considering disturbance due to land development activities and accessibility of disturbance using a geographic information system based model. The stresses were then used to identify the impacted area by land development (IALD). The results indicated that main increasing land development types in the study area from 1995 to 2014 were salt pan and construction land. The 98.2% of expanded land development area and 93.7% of increased pump number showed a good control of reserve function zone on land development spread. The spatial stress values and percentages of IALD increased from 1995 to 2014, and IALD percentage exceeded 50% for both parts of YRDNR in 2014. The results of this study also provided the information that detailed planning of the YRDNR (2014-2020) could decrease the spatial stress and IALD percentage of the whole YRDNR on the condition that the area of land development activities increased by 24.4 km 2 from 2014 to 2020. Effective measures should be taken to protect such areas from being further disturbed in order to achieve the goal of a more effective conservation of the YRDNR, and attention should be paid to the disordered land development activities in or around the natural reserves.

Land Covers Classification Based on Random Forest Method Using Features from Full-Waveform LIDAR Data

NASA Astrophysics Data System (ADS)

Ma, L.; Zhou, M.; Li, C.

2017-09-01

In this study, a Random Forest (RF) based land covers classification method is presented to predict the types of land covers in Miyun area. The returned full-waveforms which were acquired by a LiteMapper 5600 airborne LiDAR system were processed, including waveform filtering, waveform decomposition and features extraction. The commonly used features that were distance, intensity, Full Width at Half Maximum (FWHM), skewness and kurtosis were extracted. These waveform features were used as attributes of training data for generating the RF prediction model. The RF prediction model was applied to predict the types of land covers in Miyun area as trees, buildings, farmland and ground. The classification results of these four types of land covers were obtained according to the ground truth information acquired from CCD image data of the same region. The RF classification results were compared with that of SVM method and show better results. The RF classification accuracy reached 89.73% and the classification Kappa was 0.8631.

A linked land-sea modeling framework to inform ridge-to-reef management in high oceanic islands.

PubMed

Delevaux, Jade M S; Whittier, Robert; Stamoulis, Kostantinos A; Bremer, Leah L; Jupiter, Stacy; Friedlander, Alan M; Poti, Matthew; Guannel, Greg; Kurashima, Natalie; Winter, Kawika B; Toonen, Robert; Conklin, Eric; Wiggins, Chad; Knudby, Anders; Goodell, Whitney; Burnett, Kimberly; Yee, Susan; Htun, Hla; Oleson, Kirsten L L; Wiegner, Tracy; Ticktin, Tamara

2018-01-01

Declining natural resources have led to a cultural renaissance across the Pacific that seeks to revive customary ridge-to-reef management approaches to protect freshwater and restore abundant coral reef fisheries. Effective ridge-to-reef management requires improved understanding of land-sea linkages and decision-support tools to simultaneously evaluate the effects of terrestrial and marine drivers on coral reefs, mediated by anthropogenic activities. Although a few applications have linked the effects of land cover to coral reefs, these are too coarse in resolution to inform watershed-scale management for Pacific Islands. To address this gap, we developed a novel linked land-sea modeling framework based on local data, which coupled groundwater and coral reef models at fine spatial resolution, to determine the effects of terrestrial drivers (groundwater and nutrients), mediated by human activities (land cover/use), and marine drivers (waves, geography, and habitat) on coral reefs. We applied this framework in two 'ridge-to-reef' systems (Hā'ena and Ka'ūpūlehu) subject to different natural disturbance regimes, located in the Hawaiian Archipelago. Our results indicated that coral reefs in Ka'ūpūlehu are coral-dominated with many grazers and scrapers due to low rainfall and wave power. While coral reefs in Hā'ena are dominated by crustose coralline algae with many grazers and less scrapers due to high rainfall and wave power. In general, Ka'ūpūlehu is more vulnerable to land-based nutrients and coral bleaching than Hā'ena due to high coral cover and limited dilution and mixing from low rainfall and wave power. However, the shallow and wave sheltered back-reef areas of Hā'ena, which support high coral cover and act as nursery habitat for fishes, are also vulnerable to land-based nutrients and coral bleaching. Anthropogenic sources of nutrients located upstream from these vulnerable areas are relevant locations for nutrient mitigation, such as cesspool upgrades. In this study, we located coral reefs vulnerable to land-based nutrients and linked them to priority areas to manage sources of human-derived nutrients, thereby demonstrating how this framework can inform place-based ridge-to-reef management.

Assessing the effects of the Great Eastern China urbanization on the East Asian summer monsoon by coupling an urban canopy model with a Regional Climate Model

NASA Astrophysics Data System (ADS)

Liu, Z.; Xue, Y.; Liu, S.; Oleson, K. W.

2012-12-01

The urbanization causes one of the most significant land cover changes. Especially over the eastern China from Beijing to Shanghai, the great urbanization occurs during the past half century.It modifies the physical characteristics of land surface, including land surface albedo, surface roughness length and aerodynamicresistanceand thermodynamic conduction over land. All of these play very important role in regional climate change. Afteremploying several WRF/Urban models to tests land use and land cover change(LUCC) caused by urbanization in East Asia, we decided to introducea urban canopy submodule,the Community Land surface Model urban scheme(CLMU)to the WRF and coupled with the WRF-SSiB3 regional climate model. The CLMU and SSIB share the similar principal to treat the surface energy and water balances and aerodynamic resistance between land and atmosphere. In the urban module, the energy balances on the five surface conditions are considered separately: building roof, sun side building wall, shade side building wall, pervious land surface and impervious road. The surface turbulence calculation is based on Monin-Obukhov similarity theory. We have made further improvements for the urban module. Over each surface condition, a method to calculate sky view factor (SVF) is developed based on the physically process while most urban models simply provide an empirical value for SVF. Our approach along with other improvement in short and long wave radiation transfer improves the accuracy of long-wave and shortwave radiation processing over urban surface. The force-restore approximation is employed to calculate the temperature of each outer surfaces of building. The inner side temperature is used as the restore term and was assigned as a tuning constant. Based on the nature of the force-restore method and our tests, we decide to employ the air mean temperature of last 72 hours as a restore term, which substantially improve the surface energy balance. We evaluate the ability of the newly coupled model by two runs: one without and one with the urban canopy module. The coupled model is integrated from March through September, covering a summer monsoon season. The preliminary results show more significant urban heat island (UHI) effect over urban areas with the urban canopy model. The existence of the UHIs enhances the convection in lower atmosphere, affects the water vapor transportation and precipitation of the surrounding area, consistent with the phenomena that occur in urban areas. We further test the effect of urbanization on the monsoon by introducing two maps, one with and one without urbanization and the effect of the urbanization on the monsoon evolution and low level circulation will be discussed in the presentation.

Interactions between natural-occurring landscape conditions and land use influencing the abundance of riverine smallmouth bass, micropterus dolomieu

USGS Publications Warehouse

Brewer, S.K.; Rabeni, C.F.

2011-01-01

This study examined how interactions between natural landscape features and land use influenced the abundance of smallmouth bass, Micropterus dolomieu, in Missouri, USA, streams. Stream segments were placed into one of four groups based on natural-occurring watershed characteristics (soil texture and soil permeability) predicted to relate to smallmouth bass abundance. Within each group, stream segments were assigned forest (n = 3), pasture (n = 3), or urban (n = 3) designations based on the percentages of land use within each watershed. Analyses of variance indicated smallmouth bass densities differed between land use and natural conditions. Decision tree models indicated abundance was highest in forested stream segments and lowest in urban stream segments, regardless of group designation. Land use explained the most variation in decision tree models, but in-channel features of temperature, flow, and sediment also contributed significantly. These results are unique and indicate the importance of natural-occurring watershed conditions in defining the potential of populations and how finer-scale filters interact with land use to further alter population potential. Smallmouth bass has differing vulnerabilities to land-use attributes, and the better the natural watershed conditions are for population success, the more resilient these populations will be when land conversion occurs.

GIS-based integrated assessment and decision support system for land use planning in consideration of carbon sequestration benefits

NASA Astrophysics Data System (ADS)

Wang, Jun; Chen, J. M.; Li, Manchun; Ju, Weimin

2007-06-01

As the major eligible land use activities in the Clean Development Mechanism (CDM), afforestation and reforestation offer opportunities and potential economic benefits for developing countries to participate in carbon-trade in the potential international carbon (C) sink markets. However, the design and selection of appropriate afforestation and reforestation locations in CDM are complex processes which need integrated assessment (IA) of C sequestration (CS) potential, environmental effects, and socio-economic impacts. This paper promotes the consideration of CS benefits in local land use planning and presents a GIS-based integrated assessment and spatial decision support system (IA-SDSS) to support decision-making on 'where' and 'how' to afforest. It integrates an Integrated Terrestrial Ecosystem Carbon Model (InTEC) and a GIS platform for modeling regional long-term CS potential and assessment of geo-referenced land use criteria including CS consequence, and produces ranking of plantation schemes with different tree species using the Analytic hierarchy process (AHP) method. Three land use scenarios are investigated: (i) traditional land use planning criteria without C benefits, (ii) land use for CS with low C price, and (iii) land use for CS with high price. Different scenarios and consequences will influence the weights of tree-species selection in the AHP decision process.

A QUANTITATIVE ASSESSMENT OF A COMBINED SPECTRAL AND GIS RULE-BASED LAND-COVER CLASSIFICATION IN THE NEUSE RIVER BASIN OF NORTH CAROLINA

EPA Science Inventory

The 14,582 km2 Neuse River Basin in North Carolina was characterized based on a user defined land-cover (LC) classification system developed specifically to support spatially explicit, non-point source nitrogen allocation modeling studies. Data processing incorporated both spect...

Land-based Colleges Offer Science Students a Sense of Place.

ERIC Educational Resources Information Center

Ambler, Marjane

1998-01-01

Describes the mission of land-based tribal colleges and universities to create educational math and science models, overcome obstacles in teaching, and keep native tribal students focused on their sense of place and culture. Asserts that faculty strive to make science important to students and encourage them to dream of positively influencing…

Testing the Joint UK Land Environment Simulator (JULES) for flood forecasting

NASA Astrophysics Data System (ADS)

Batelis, Stamatios-Christos; Rosolem, Rafael; Han, Dawei; Rahman, Mostaquimur

2017-04-01

Land Surface Models (LSM) are based on physics principles and simulate the exchanges of energy, water and biogeochemical cycles between the land surface and lower atmosphere. Such models are typically applied for climate studies or effects of land use changes but as the resolution of LSMs and supporting observations are continuously increasing, its representation of hydrological processes need to be addressed adequately. For example, changes in climate and land use can alter the hydrology of a region, for instance, by altering its flooding regime. LSMs can be a powerful tool because of their ability to spatially represent a region with much finer resolution. However, despite such advantages, its performance has not been extensively assessed for flood forecasting simply because its representation of typical hydrological processes, such as overland flow and river routing, are still either ignored or roughly represented. In this study, we initially test the Joint UK Land Environment Simulator (JULES) as a flood forecast tool focusing on its river routing scheme. In particular, JULES river routing parameterization is based on the Rapid Flow Model (RFM) which relies on six prescribed parameters (two surface and two subsurface wave celerities, and two return flow fractions). Although this routing scheme is simple, the prescription of its six default parameters is still too generalized. Our aim is to understand the importance of each RFM parameter in a series of JULES simulations at a number of catchments in the UK for the 2006-2015 period. This is carried out, for instance, by making a number of assumptions of parameter behaviour (e.g., spatially uniform versus varying and/or temporally constant or time-varying parameters within each catchment). Hourly rainfall radar in combination with the CHESS (Climate, Hydrological and Ecological research Support System) meteorological daily data both at 1 km2 resolution are used. The evaluation of the model is based on hourly runoff data provided by the National River Flood Archive using a number of model performance metrics. We use a calibrated conceptually-based lumped model, more typically applied in flood studies, as a benchmark for our analysis.

Impact of Land Use/Land Cover Conditions on WRF Model Evaluation for Heat Island Assessment

NASA Astrophysics Data System (ADS)

Bhati, S.; Mohan, M.

2017-12-01

Urban heat island effect has been assessed using Weather Research and Forecasting model (WRF v3.5) focusing on air temperature and surface skin temperature in the sub-tropical urban Indian megacity of Delhi. Impact of urbanization related changes in land use/land cover (LULC) on model outputs has been analyzed. Four simulations have been carried out with different types of LULC data viz. (1) USGS , (2) MODIS, (3) user-modified USGS and (4) user modified land use data coupled with urban canopy model (UCM) for incorporation of canopy features. Heat island intensities have been estimated based on these simulations and subsequently compared with those derived from in-situ and satellite observations. There is a significant improvement in model performance with modification of LULC and inclusion of UCM. Overall, RMSEs for near surface temperature improved from 6.3°C to 3.9°C and index of agreement for mean urban heat island intensities (UHI) improved from 0.4 to 0.7 with modified land use coupled with UCM. In general, model is able to capture the magnitude of UHI as well as high UHI zones well. The study highlights the importance of appropriate and updated representation of landuse-landcover and urban canopies for improving predictive capabilities of the mesoscale models.

Analysis of the geomorphology surrounding the Chang'e-3 landing site

NASA Astrophysics Data System (ADS)

Li, Chun-Lai; Mu, Ling-Li; Zou, Xiao-Duan; Liu, Jian-Jun; Ren, Xin; Zeng, Xing-Guo; Yang, Yi-Man; Zhang, Zhou-Bin; Liu, Yu-Xuan; Zuo, Wei; Li, Han

2014-12-01

Chang'e-3 (CE-3) landed on the Mare Imbrium basin in the east part of Sinus Iridum (19.51°W, 44.12°N), which was China's first soft landing on the Moon and it started collecting data on the lunar surface environment. To better understand the environment of this region, this paper utilizes the available high-resolution topography data, image data and geological data to carry out a detailed analysis and research on the area surrounding the landing site (Sinus Iridum and 45 km×70 km of the landing area) as well as on the topography, landform, geology and lunar dust of the area surrounding the landing site. A general topographic analysis of the surrounding area is based on a digital elevation model and digital elevation model data acquired by Chang'e-2 that have high resolution; the geology analysis is based on lunar geological data published by USGS; the study on topographic factors and distribution of craters and rocks in the surrounding area covering 4 km×4 km or even smaller is based on images from the CE-3 landing camera and images from the topographic camera; an analysis is done of the effect of the CE-3 engine plume on the lunar surface by comparing images before and after the landing using data from the landing camera. A comprehensive analysis of the results shows that the landing site and its surrounding area are identified as typical lunar mare with flat topography. They are suitable for maneuvers by the rover, and are rich in geological phenomena and scientific targets, making it an ideal site for exploration.

Mapping irrigated lands at 250-m scale by merging MODIS data and National Agricultural Statistics

USGS Publications Warehouse

Pervez, Md Shahriar; Brown, Jesslyn F.

2010-01-01

Accurate geospatial information on the extent of irrigated land improves our understanding of agricultural water use, local land surface processes, conservation or depletion of water resources, and components of the hydrologic budget. We have developed a method in a geospatial modeling framework that assimilates irrigation statistics with remotely sensed parameters describing vegetation growth conditions in areas with agricultural land cover to spatially identify irrigated lands at 250-m cell size across the conterminous United States for 2002. The geospatial model result, known as the Moderate Resolution Imaging Spectroradiometer (MODIS) Irrigated Agriculture Dataset (MIrAD-US), identified irrigated lands with reasonable accuracy in California and semiarid Great Plains states with overall accuracies of 92% and 75% and kappa statistics of 0.75 and 0.51, respectively. A quantitative accuracy assessment of MIrAD-US for the eastern region has not yet been conducted, and qualitative assessment shows that model improvements are needed for the humid eastern regions where the distinction in annual peak NDVI between irrigated and non-irrigated crops is minimal and county sizes are relatively small. This modeling approach enables consistent mapping of irrigated lands based upon USDA irrigation statistics and should lead to better understanding of spatial trends in irrigated lands across the conterminous United States. An improved version of the model with revised datasets is planned and will employ 2007 USDA irrigation statistics.

Evaluating Impacts of climate and land use changes on streamflow using SWAT and land use models based CESM1-CAM5 Climate scenarios

NASA Astrophysics Data System (ADS)

Lin, Tzu Ping; Lin, Yu Pin; Lien, Wan Yu

2015-04-01

Climate change projects have various levels of impacts on hydrological cycles around the world. The impact of climate change and uncertainty of climate projections from general circulation models (GCMs) from the Coupled Model Intercomparison Project (CMIP5) which has been just be released in Taiwan, 2014. Since the streamflow run into ocean directly due to the steep terrain and the rainfall difference between wet and dry seasons is apparent; as a result, the allocation water resource reasonable is very challenge in Taiwan, particularly under climate change. The purpose of this study was to evaluate the impacts of climate and land use changes on a small watershed in Taiwan. The AR5 General Circulation Models(GCM) output data was adopted in this study and was downscaled from the monthly to the daily weather data as the input data of hydrological model such as Soil and Water Assessment Tool (SWAT) model in this study. The spatially explicit land uses change model, the Conservation of Land Use and its Effects at Small regional extent (CLUE-s), was applied to simulate land use scenarios in 2020-2039. Combined climate and land use change scenarios were adopted as input data of the hydrological model, the SWAT model, to estimate the future streamflows. With the increasing precipitation, increasing urban area and decreasing agricultural and grass land, the annual streamflow in the most of twenty-three subbasins were also increased. Besides, due to the increasing rainfall in wet season and decreasing rainfall in dry season, the difference of streamflow between wet season and dry season are also increased. This result indicates a more stringent challenge on the water resource management in future. Therefore, impacts on water resource caused by climate change and land use change should be considered in water resource planning for the Datuan river watershed. Keywords: SWAT, GCM, CLUE-s, streamflow, climate change, land use change

Effects of Land-use/Land-cover and Climate Changes on Water Quantity and Quality in Sub-basins near Major US Cities in the Great Lakes Region

NASA Astrophysics Data System (ADS)

Murphy, L.; Al-Hamdan, M. Z.; Crosson, W. L.; Barik, M.

2017-12-01

Land-cover change over time to urbanized, less permeable surfaces, leads to reduced water infiltration at the location of water input while simultaneously transporting sediments, nutrients and contaminants farther downstream. With an abundance of agricultural fields bordering the greater urban areas of Milwaukee, Detroit, and Chicago, water and nutrient transport is vital to the farming industry, wetlands, and communities that rely on water availability. Two USGS stream gages each located within a sub-basin near each of these Great Lakes Region cities were examined, one with primarily urban land-cover between 1992 and 2011, and one with primarily agriculture land-cover. ArcSWAT, a watershed model and soil and water assessment tool used in extension with ArcGIS, was used to develop hydrologic models that vary the land-covers to simulate surface runoff during a model run period from 2004 to 2008. Model inputs that include a digital elevation model (DEM), Landsat-derived land-use/land-cover (LULC) satellite images from 1992, 2001, and 2011, soil classification, and meteorological data were used to determine the effect of different land-covers on the water runoff, nutrients and sediments. The models were then calibrated and validated to USGS stream gage data measurements over time. Additionally, the watershed model was run based on meteorological data from an IPCC CMIP5 high emissions climate change scenario for 2050. Model outputs from the different LCLU scenarios were statistically evaluated and results showed that water runoff, nutrients and sediments were impacted by LULC change in four out of the six sub-basins. In the 2050 climate scenario, only one out of the six sub-basin's water quantity and quality was affected. These results contribute to the importance of developing hydrologic models as the dependence on the Great Lakes as a freshwater resource competes with the expansion of urbanization leading to the movement of runoff, nutrients, and sediments off the land.

Increasing efficiency of CO2 uptake by combined land-ocean sink

NASA Astrophysics Data System (ADS)

van Marle, M.; van Wees, D.; Houghton, R. A.; Nassikas, A.; van der Werf, G.

2017-12-01

Carbon-climate feedbacks are one of the key uncertainties in predicting future climate change. Such a feedback could originate from carbon sinks losing their efficiency, for example due to saturation of the CO2 fertilization effect or ocean warming. An indirect approach to estimate how the combined land and ocean sink responds to climate change and growing fossil fuel emissions is based on assessing the trends in the airborne fraction of CO2 emissions from fossil fuel and land use change. One key limitation with this approach has been the large uncertainty in quantifying land use change emissions. We have re-assessed those emissions in a more data-driven approach by combining estimates coming from a bookkeeping model with visibility-based land use change emissions available for the Arc of Deforestation and Equatorial Asia, two key regions with large land use change emissions. The advantage of the visibility-based dataset is that the emissions are observation-based and this dataset provides more detailed information about interannual variability than previous estimates. Based on our estimates we provide evidence that land use and land cover change emissions have increased more rapidly than previously thought, implying that the airborne fraction has decreased since the start of CO2 measurements in 1959. This finding is surprising because it means that the combined land and ocean sink has become more efficient while the opposite is expected.

A Comparison of Methods for a Priori Bias Correction in Soil Moisture Data Assimilation

NASA Technical Reports Server (NTRS)

Kumar, Sujay V.; Reichle, Rolf H.; Harrison, Kenneth W.; Peters-Lidard, Christa D.; Yatheendradas, Soni; Santanello, Joseph A.

2011-01-01

Data assimilation is being increasingly used to merge remotely sensed land surface variables such as soil moisture, snow and skin temperature with estimates from land models. Its success, however, depends on unbiased model predictions and unbiased observations. Here, a suite of continental-scale, synthetic soil moisture assimilation experiments is used to compare two approaches that address typical biases in soil moisture prior to data assimilation: (i) parameter estimation to calibrate the land model to the climatology of the soil moisture observations, and (ii) scaling of the observations to the model s soil moisture climatology. To enable this research, an optimization infrastructure was added to the NASA Land Information System (LIS) that includes gradient-based optimization methods and global, heuristic search algorithms. The land model calibration eliminates the bias but does not necessarily result in more realistic model parameters. Nevertheless, the experiments confirm that model calibration yields assimilation estimates of surface and root zone soil moisture that are as skillful as those obtained through scaling of the observations to the model s climatology. Analysis of innovation diagnostics underlines the importance of addressing bias in soil moisture assimilation and confirms that both approaches adequately address the issue.

A watershed model to integrate EO data

NASA Astrophysics Data System (ADS)

Jauch, Eduardo; Chambel-Leitao, Pedro; Carina, Almeida; Brito, David; Cherif, Ines; Alexandridis, Thomas; Neves, Ramiro

2013-04-01

MOHID LAND is a open source watershed model developed by MARETEC and is part of the MOHID Framework. It integrates four mediums (or compartments): porous media, surface, rivers and atmosphere. The movement of water between these mediums are based on mass and momentum balance equations. The atmosphere medium is not explicity simulated. Instead, it's used as boundary condition to the model through meteorological properties: precipitation, solar radiation, wind speed/direction, relative humidity and air temperature. The surface medium includes the overland runoff and vegetation growth processes and is simulated using a 2D grid. The porous media includes both the unsaturated (soil) and saturated zones (aquifer) and is simulated using a 3D grid. The river flow is simulated through a 1D drainage network. All these mediums are linked through evapotranspiration and flow exchanges (infiltration, river-soil growndwater flow, surface-river overland flow). Besides the water movement, it is also possible to simulate water quality processes and solute/sediment transport. Model setup include the definition of the geometry and the properties of each one of its compartments. After the setup of the model, the only continuous input data that MOHID LAND requires are the atmosphere properties (boundary conditions) that can be provided as timeseries or spacial data. MOHID LAND has been adapted the last 4 years under FP7 and ESA projects to integrate Earth Observation (EO) data, both variable in time and in space. EO data can be used to calibrate/validate or as input/assimilation data to the model. The currently EO data used include LULC (Land Use Land Cover) maps, LAI (Leaf Area Index) maps, EVTP (Evapotranspiration) maps and SWC (Soil Water Content) maps. Model results are improved by the EO data, but the advantage of this integration is that the model can still run without the EO data. This means that model do not stop due to unavailability of EO data and can run on a forecast mode. The LCLU maps are coupled with a database that transforms land use into model properties through lookup tables. The LAI maps, usually based on NDVI satellite images, can be used directly as input to the model. When the vegetation growth is being simulated, the use of a LAI distributed in space improve the model results, by improving, for example, the estimated evapotranspiration, the estimated values of biomass, the nutrient uptake, etc. MOHID LAND calculates a Reference Evapotranspiration (rEVTP), based on the meteorological properties. The Actual Evapotranspiration (aEVTP) is then computed based on vegetation transpiration, soil evaporation and the available water in soil. Alternatively, EO derived maps of EVTP can be used as input to the model, in the place of the rEVTP, or even in the place of the aEVTP, both being provided as boundary condition. The same can be done with SWC maps, that can be used to initialize the model soil water content. The integration of EO data with MOHID LAND was tested and is being continuously developed and applied for support farmers and to help water managers to improve the water management.

A Prototype Physical Database for Passive Microwave Retrievals of Precipitation over the US Southern Great Plains

NASA Technical Reports Server (NTRS)

Ringerud, S.; Kummerow, C. D.; Peters-Lidard, C. D.

2015-01-01

An accurate understanding of the instantaneous, dynamic land surface emissivity is necessary for a physically based, multi-channel passive microwave precipitation retrieval scheme over land. In an effort to assess the feasibility of the physical approach for land surfaces, a semi-empirical emissivity model is applied for calculation of the surface component in a test area of the US Southern Great Plains. A physical emissivity model, using land surface model data as input, is used to calculate emissivity at the 10GHz frequency, combining contributions from the underlying soil and vegetation layers, including the dielectric and roughness effects of each medium. An empirical technique is then applied, based upon a robust set of observed channel covariances, extending the emissivity calculations to all channels. For calculation of the hydrometeor contribution, reflectivity profiles from the Tropical Rainfall Measurement Mission Precipitation Radar (TRMM PR) are utilized along with coincident brightness temperatures (Tbs) from the TRMM Microwave Imager (TMI), and cloud-resolving model profiles. Ice profiles are modified to be consistent with the higher frequency microwave Tbs. Resulting modeled top of the atmosphere Tbs show correlations to observations of 0.9, biases of 1K or less, root-mean-square errors on the order of 5K, and improved agreement over the use of climatological emissivity values. The synthesis of these models and data sets leads to the creation of a simple prototype Tb database that includes both dynamic surface and atmospheric information physically consistent with the land surface model, emissivity model, and atmospheric information.

A comprehensive evaluation of input data-induced uncertainty in nonpoint source pollution modeling

NASA Astrophysics Data System (ADS)

Chen, L.; Gong, Y.; Shen, Z.

2015-11-01

Watershed models have been used extensively for quantifying nonpoint source (NPS) pollution, but few studies have been conducted on the error-transitivity from different input data sets to NPS modeling. In this paper, the effects of four input data, including rainfall, digital elevation models (DEMs), land use maps, and the amount of fertilizer, on NPS simulation were quantified and compared. A systematic input-induced uncertainty was investigated using watershed model for phosphorus load prediction. Based on the results, the rain gauge density resulted in the largest model uncertainty, followed by DEMs, whereas land use and fertilizer amount exhibited limited impacts. The mean coefficient of variation for errors in single rain gauges-, multiple gauges-, ASTER GDEM-, NFGIS DEM-, land use-, and fertilizer amount information was 0.390, 0.274, 0.186, 0.073, 0.033 and 0.005, respectively. The use of specific input information, such as key gauges, is also highlighted to achieve the required model accuracy. In this sense, these results provide valuable information to other model-based studies for the control of prediction uncertainty.

Vertical Landing Aerodynamics of Reusable Rocket Vehicle

NASA Astrophysics Data System (ADS)

Nonaka, Satoshi; Nishida, Hiroyuki; Kato, Hiroyuki; Ogawa, Hiroyuki; Inatani, Yoshifumi

The aerodynamic characteristics of a vertical landing rocket are affected by its engine plume in the landing phase. The influences of interaction of the engine plume with the freestream around the vehicle on the aerodynamic characteristics are studied experimentally aiming to realize safe landing of the vertical landing rocket. The aerodynamic forces and surface pressure distributions are measured using a scaled model of a reusable rocket vehicle in low-speed wind tunnels. The flow field around the vehicle model is visualized using the particle image velocimetry (PIV) method. Results show that the aerodynamic characteristics, such as the drag force and pitching moment, are strongly affected by the change in the base pressure distributions and reattachment of a separation flow around the vehicle.

Optimization of land use of agricultural farms in Sumedang regency by using linear programming models

NASA Astrophysics Data System (ADS)

Zenis, F. M.; Supian, S.; Lesmana, E.

2018-03-01

Land is one of the most important assets for farmers in Sumedang Regency. Therefore, agricultural land should be used optimally. This study aims to obtain the optimal land use composition in order to obtain maximum income. The optimization method used in this research is Linear Programming Models. Based on the results of the analysis, the composition of land use for rice area of 135.314 hectares, corn area of 11.798 hectares, soy area of 2.290 hectares, and peanuts of 2.818 hectares with the value of farmers income of IDR 2.682.020.000.000,-/year. The results of this analysis can be used as a consideration in decisions making about cropping patterns by farmers.

[Assessment of land use environmental impacts in urban built-up area: a case study in main built-up area of Nanchang City].

PubMed

Chen, Wen-Bo; Liu, Shi-Yu; Yu, Dun; Zou, Qiu-Ming

2009-07-01

Based on the relevant studies of land use environmental impacts and the characteristics of urban land use, a conceptual model on the assessment of land use environmental impacts in urban built-up area was established. This model grouped the land use environmental impacts in built-up area into four basic processes, i. e., detailization, abstractization, matching, and evaluation. A case study was conducted in the main built-up area of Nanchang City, with noise, smell, dust, and hazard as the impact factors. In the test area, noise had a widespread impact, its impacting area accounting for 59% of the total, smell and dust impacts centralized in the east and south parts, while hazard impact was centralized in the southeast part, an industrial area. This assessment model of four basic processes was practical, and could provide basis for the decision-making of urban land use management and planning.

Earth observation data based rapid flood-extent modelling for tsunami-devastated coastal areas

NASA Astrophysics Data System (ADS)

Hese, Sören; Heyer, Thomas

2016-04-01

Earth observation (EO)-based mapping and analysis of natural hazards plays a critical role in various aspects of post-disaster aid management. Spatial very high-resolution Earth observation data provide important information for managing post-tsunami activities on devastated land and monitoring re-cultivation and reconstruction. The automatic and fast use of high-resolution EO data for rapid mapping is, however, complicated by high spectral variability in densely populated urban areas and unpredictable textural and spectral land-surface changes. The present paper presents the results of the SENDAI project, which developed an automatic post-tsunami flood-extent modelling concept using RapidEye multispectral satellite data and ASTER Global Digital Elevation Model Version 2 (GDEM V2) data of the eastern coast of Japan (captured after the Tohoku earthquake). In this paper, the authors developed both a bathtub-modelling approach and a cost-distance approach, and integrated the roughness parameters of different land-use types to increase the accuracy of flood-extent modelling. Overall, the accuracy of the developed models reached 87-92%, depending on the analysed test site. The flood-modelling approach was explained and results were compared with published approaches. We came to the conclusion that the cost-factor-based approach reaches accuracy comparable to published results from hydrological modelling. However the proposed cost-factor approach is based on a much simpler dataset, which is available globally.

Atmospheric Constraints on Landing Site Selection

NASA Astrophysics Data System (ADS)

Kass, David M.; Schofield, J. T.

2001-01-01

The Martian atmosphere is a significant part of the environment that the Mars Exploration Rovers (MER) will encounter. As such, it imposes important constraints on where the rovers can and cannot land. Unfortunately, as there are no meteorological instruments on the rovers, there is little atmospheric science that can be accomplished, and no scientific preference for landing sites. The atmosphere constrains landing site selection in two main areas, the entry descent and landing (EDL) process and the survivability of the rovers on the surface. EDL is influenced by the density profile and boundary layer winds (up to altitudes of 5 to 10 km). Surface survivability involves atmospheric dust, temperatures and winds. During EDL, the atmosphere is used to slow the lander down, both ballistically and on the parachute. This limits the maximum elevation of the landing site to -1.3 km below the MOLA reference aeroid. The landers need to encounter a sufficiently dense atmosphere to be able to stop, and the deeper the landing site, the more column integrated atmosphere the lander can pass through before reaching the surface. The current limit was determined both by a desire to be able to reach the hematite region and by a set of atmosphere models we developed for EDL simulations. These are based on Thermal Emission Spectrometer (TES) atmospheric profile measurements, Ames Mars General Circulation Model (MGCM) results, and the 1-D Ames GCM radiative/convective model by J. Murphy. The latter is used for the near surface diurnal cycle. The current version of our model encompasses representative latitude bands, but we intend to make specific models for the final candidate landing sites to insure that they fall within the general envelope. The second constraint imposed on potential landing sites through the EDL process is the near surface wind. The wind in the lower approximately 5 km determines the horizontal velocity that the landers have when they land. Due to the mechanics of the landing process, the total velocity (including both the horizontal and vertical components) determines whether or not the landers are successful. Unfortunately, the landing system has no easy way to nullify any horizontal velocity imparted by the wind, so the landing sites selected need to have as little wind as possible. In addition to the mean wind velocity, the landing system is sensitive to vertical wind shear in the lowest kilometer or so. Wind shear can deflect the retro rockets (RADs) from their nominal vertical orientation producing unwanted horizontal spacecraft velocities. Both mean velocity and wind shear are dominated by the the local topography and other surface properties (in particular albedo and thermal inertia which control the surface temperature). This is seen even in simplified 2-D mesoscale models. The effects in a fully 3-D model are expected to he even more topographically dependent. In particular there is potential for wind channeling in canyons and other terrain features. Boundary layer winds and wind shear are currently being modeled based on terrestrial data and boundary layer scaling laws modified for Martian conditions. We hope to supplement this with mesoscale model results (from several sources) once the number of landing sites is reduced to a manageable number.

Adaptation of Land-Use Demands to the Impact of Climate Change on the Hydrological Processes of an Urbanized Watershed

PubMed Central

Lin, Yu-Pin; Hong, Nien-Ming; Chiang, Li-Chi; Liu, Yen-Lan; Chu, Hone-Jay

2012-01-01

The adaptation of land-use patterns is an essential aspect of minimizing the inevitable impact of climate change at regional and local scales; for example, adapting watershed land-use patterns to mitigate the impact of climate change on a region’s hydrology. The objective of this study is to simulate and assess a region’s ability to adapt to hydrological changes by modifying land-use patterns in the Wu-Du watershed in northern Taiwan. A hydrological GWLF (Generalized Watershed Loading Functions) model is used to simulate three hydrological components, namely, runoff, groundwater and streamflow, based on various land-use scenarios under six global climate models. The land-use allocations are simulated by the CLUE-s model for the various development scenarios. The simulation results show that runoff and streamflow are strongly related to the precipitation levels predicted by different global climate models for the wet and dry seasons, but groundwater cycles are more related to land-use. The effects of climate change on groundwater and runoff can be mitigated by modifying current land-use patterns; and slowing the rate of urbanization would also reduce the impact of climate change on hydrological components. Thus, land-use adaptation on a local/regional scale provides an alternative way to reduce the impacts of global climate change on local hydrology. PMID:23202833

Using satellite-based estimates of evapotranspiration and groundwater changes to determine anthropogenic water fluxes in land surface models

USDA-ARS?s Scientific Manuscript database

Irrigation is a widely used water management practice that is often poorly parameterized in land surface and climate models. Previous studies have addressed this issue via use of irrigation area, applied water inventory data, or soil moisture content. These approaches have a variety of drawbacks i...

Landing System Development- Design and Test Prediction of a Lander Leg Using Nonlinear Analysis

NASA Astrophysics Data System (ADS)

Destefanis, Stefano; Buchwald, Robert; Pellegrino, Pasquale; Schroder, Silvio

2014-06-01

Several mission studies have been performed focusing on a soft and precision landing using landing legs. Examples for such missions are Mars Sample Return scenarios (MSR), Lunar landing scenarios (MoonNEXT, Lunar Lander) and small body sample return studies (Marco Polo, MMSR, Phootprint). Such missions foresee a soft landing on the planet surface for delivering payload in a controlled manner and limiting the landing loads.To ensure a successful final landing phase, a landing system is needed, capable of absorbing the residual velocities (vertical, horizontal and angular) at touch- down, and insuring a controlled attitude after landing. Such requirements can be fulfilled by using landing legs with adequate damping.The Landing System Development (LSD) study, currently in its phase 2, foresees the design, analysis, verification, manufacturing and testing of a representative landing leg breadboard based on the Phase B design of the ESA Lunar Lander. Drop tests of a single leg will be performed both on rigid and soft ground, at several impact angles. The activity is covered under ESA contract with TAS-I as Prime Contractor, responsible for analysis and verification, Astrium GmbH for design and test and QinetiQ Space for manufacturing. Drop tests will be performed at the Institute of Space Systems of the German Aerospace Center (DLR-RY) in Bremen.This paper presents an overview of the analytical simulations (test predictions and design verification) performed, comparing the results produced by Astrium made multi body model (rigid bodies, nonlinearities accounted for in mechanical joints and force definitions, based on development tests) and TAS-I made nonlinear explicit model (fully deformable bodies).

Hydrography synthesis using LANDSAT remote sensing and the SCS models

NASA Technical Reports Server (NTRS)

Ragan, R. M.; Jackson, T. J.

1976-01-01

The land cover requirements of the Soil Conservation Service (SCS) Model used for hydrograph synthesis in urban areas were modified to be LANDSAT compatible. The Curve Numbers obtained with these alternate land cover categories compare well with those obtained in published example problems using the conventional categories. Emergency spillway hydrographs and synthetic flood frequency flows computed for a 21.1 sq. mi. test area showed excellent agreement between the conventional aerial photo-based and the Landsat-based SCS approaches.

Understanding land surface evapotranspiration with satellite multispectral measurements

NASA Technical Reports Server (NTRS)

Menenti, M.

1993-01-01

Quantitative use of remote multispectral measurements to study and map land surface evapotranspiration has been a challenging issue for the past 20 years. Past work is reviewed against process physics. A simple two-layer combination-type model is used which is applicable to both vegetation and bare soil. The theoretic analysis is done to show which land surface properties are implicitly defined by such evaporation models and to assess whether they are measurable as a matter of principle. Conceptual implications of the spatial correlation of land surface properties, as observed by means of remote multispectral measurements, are illustrated with results of work done in arid zones. A normalization of spatial variability of land surface evaporation is proposed by defining a location-dependent potential evaporation and surface temperature range. Examples of the application of remote based estimates of evaporation to hydrological modeling studies in Egypt and Argentina are presented.

Assessment of soil erosion risk in Komering watershed, South Sumatera, using SWAT model

NASA Astrophysics Data System (ADS)

Salsabilla, A.; Kusratmoko, E.

2017-07-01

Changes in land use watershed led to environmental degradation. Estimated loss of soil erosion is often difficult due to some factors such as topography, land use, climate and human activities. This study aims to predict soil erosion hazard and sediment yield using the Soil and Water Assessment Tools (SWAT) hydrological model. The SWAT was chosen because it can simulate the model with limited data. The study area is Komering watershed (806,001 Ha) in South Sumatera Province. There are two factors land management intervention: 1) land with agriculture, and 2) land with cultivation. These factors selected in accordance with the regulations of spatial plan area. Application of the SWAT demonstrated that the model can predict surface runoff, soil erosion loss and sediment yield. The erosion risk for each watershed can be classified and predicted its changes based on the scenarios which arranged. In this paper, we also discussed the relationship between the distribution of erosion risk and watershed's characteristics in a spatial perspective.

Development of Land Segmentation, Stream-Reach Network, and Watersheds in Support of Hydrological Simulation Program-Fortran (HSPF) Modeling, Chesapeake Bay Watershed, and Adjacent Parts of Maryland, Delaware, and Virginia

USGS Publications Warehouse

Martucci, Sarah K.; Krstolic, Jennifer L.; Raffensperger, Jeff P.; Hopkins, Katherine J.

2006-01-01

The U.S. Geological Survey, U.S. Environmental Protection Agency Chesapeake Bay Program Office, Interstate Commission on the Potomac River Basin, Maryland Department of the Environment, Virginia Department of Conservation and Recreation, Virginia Department of Environmental Quality, and the University of Maryland Center for Environmental Science are collaborating on the Chesapeake Bay Regional Watershed Model, using Hydrological Simulation Program - FORTRAN to simulate streamflow and concentrations and loads of nutrients and sediment to Chesapeake Bay. The model will be used to provide information for resource managers. In order to establish a framework for model simulation, digital spatial datasets were created defining the discretization of the model region (including the Chesapeake Bay watershed, as well as the adjacent parts of Maryland, Delaware, and Virginia outside the watershed) into land segments, a stream-reach network, and associated watersheds. Land segmentation was based on county boundaries represented by a 1:100,000-scale digital dataset. Fifty of the 254 counties and incorporated cities in the model region were divided on the basis of physiography and topography, producing a total of 309 land segments. The stream-reach network for the Chesapeake Bay watershed part of the model region was based on the U.S. Geological Survey Chesapeake Bay SPARROW (SPAtially Referenced Regressions On Watershed attributes) model stream-reach network. Because that network was created only for the Chesapeake Bay watershed, the rest of the model region uses a 1:500,000-scale stream-reach network. Streams with mean annual streamflow of less than 100 cubic feet per second were excluded based on attributes from the dataset. Additional changes were made to enhance the data and to allow for inclusion of stream reaches with monitoring data that were not part of the original network. Thirty-meter-resolution Digital Elevation Model data were used to delineate watersheds for each stream reach. State watershed boundaries replaced the Digital Elevation Model-derived watersheds where coincident. After a number of corrections, the watersheds were coded to indicate major and minor basin, mean annual streamflow, and each watershed's unique identifier as well as that of the downstream watershed. Land segments and watersheds were intersected to create land-watershed segments for the model.

Land Suitability Assessment in the Catchment Area of Four Southwestern Atlantic Coastal Lagoons: Multicriteria and Optimization Modeling

NASA Astrophysics Data System (ADS)

Rodriguez-Gallego, Lorena; Achkar, Marcel; Conde, Daniel

2012-07-01

In the present study, a land suitability assessment was conducted in the basin of four Uruguayan coastal lagoons (Southwestern Atlantic) to analyze the productive development while minimizing eutrophication, biodiversity loss and conflicts among different land uses. Suitable land for agriculture, forest, livestock ranching, tourism and conservation sectors were initially established based on a multi-attribute model developed using a geographic information system. Experts were consulted to determine the requirements for each land use sector and the incompatibilities among land use types. The current and potential conflicts among incompatible land use sectors were analyzed by overlapping land suitability maps. We subsequently applied a multi-objective model where land (pixels) with similar suitability was clustered into "land suitability groups", using a two-phase cluster analysis and the Akaike Information Criterion. Finally, a linear programming optimization procedure was applied to allocate land use sectors into land suitable groups, maximizing total suitability and minimizing interference among sectors. Results indicated that current land use overlapped by 4.7 % with suitable land of other incompatible sectors. However, the suitable land of incompatible sectors overlapped in 20.3 % of the study area, indicating a high potential for the occurrence of future conflict. The highest competition was between agriculture and conservation, followed by forest and agriculture. We explored scenarios where livestock ranching and tourism intensified, and found that interference with conservation and agriculture notably increased. This methodology allowed us to analyze current and potential land use conflicts and to contribute to the strategic planning of the study area.

Land suitability assessment in the catchment area of four Southwestern Atlantic coastal lagoons: multicriteria and optimization modeling.

PubMed

Rodriguez-Gallego, Lorena; Achkar, Marcel; Conde, Daniel

2012-07-01

In the present study, a land suitability assessment was conducted in the basin of four Uruguayan coastal lagoons (Southwestern Atlantic) to analyze the productive development while minimizing eutrophication, biodiversity loss and conflicts among different land uses. Suitable land for agriculture, forest, livestock ranching, tourism and conservation sectors were initially established based on a multi-attribute model developed using a geographic information system. Experts were consulted to determine the requirements for each land use sector and the incompatibilities among land use types. The current and potential conflicts among incompatible land use sectors were analyzed by overlapping land suitability maps. We subsequently applied a multi-objective model where land (pixels) with similar suitability was clustered into "land suitability groups", using a two-phase cluster analysis and the Akaike Information Criterion. Finally, a linear programming optimization procedure was applied to allocate land use sectors into land suitable groups, maximizing total suitability and minimizing interference among sectors. Results indicated that current land use overlapped by 4.7 % with suitable land of other incompatible sectors. However, the suitable land of incompatible sectors overlapped in 20.3 % of the study area, indicating a high potential for the occurrence of future conflict. The highest competition was between agriculture and conservation, followed by forest and agriculture. We explored scenarios where livestock ranching and tourism intensified, and found that interference with conservation and agriculture notably increased. This methodology allowed us to analyze current and potential land use conflicts and to contribute to the strategic planning of the study area.

Incorporating shrub and snag specific LiDAR data into GAP wildlife models

Treesearch

Teresa J Lorenz; Kerri T Vierling; Jody Vogeler; Jeffrey Lonneker; Jocelyn Aycrigg

2015-01-01

The U.S. Geological Survey’s Gap Analysis Program (hereafter, GAP) is a nationally based program that uses land cover, vertebrate distributions, and land ownership to identify locations where gaps in conservation coverage exist, and GAP products are commonly used by government agencies, nongovernmental organizations, and private citizens. The GAP land-cover...

A spatial modeling framework to evaluate domestic biofuel-induced potential land use changed and emissions

USGS Publications Warehouse

Elliot, Joshua; Sharma, Bhavna; Best, Neil; Glotter, Michael; Dunn, Jennifer B.; Foster, Ian; Miguez, Fernando; Mueller, Steffen; Wang, Michael

2014-01-01

We present a novel bottom-up approach to estimate biofuel-induced land-use change (LUC) and resulting CO2 emissions in the U.S. from 2010 to 2022, based on a consistent methodology across four essential components: land availability, land suitability, LUC decision-making, and induced CO2 emissions. Using highresolution geospatial data and modeling, we construct probabilistic assessments of county-, state-, and national-level LUC and emissions for macroeconomic scenarios. We use the Cropland Data Layer and the Protected Areas Database to characterize availability of land for biofuel crop cultivation, and the CERES-Maize and BioCro biophysical crop growth models to estimate the suitability (yield potential) of available lands for biofuel crops. For LUC decisionmaking, we use a county-level stochastic partial-equilibrium modeling framework and consider five scenarios involving annual ethanol production scaling to 15, 22, and 29 BG, respectively, in 2022, with corn providing feedstock for the first 15 BG and the remainder coming from one of two dedicated energy crops. Finally, we derive high-resolution above-ground carbon factors from the National Biomass and Carbon Data set to estimate emissions from each LUC pathway. Based on these inputs, we obtain estimates for average total LUC emissions of 6.1, 2.2, 1.0, 2.2, and 2.4 gCO2e/MJ for Corn-15 Billion gallons (BG), Miscanthus × giganteus (MxG)-7 BG, Switchgrass (SG)-7 BG, MxG-14 BG, and SG-14 BG scenarios, respectively.

Spatial heterogeneity of leaf area index across scales from simulation and remote sensing

NASA Astrophysics Data System (ADS)

Reichenau, Tim G.; Korres, Wolfgang; Montzka, Carsten; Schneider, Karl

2016-04-01

Leaf area index (LAI, single sided leaf area per ground area) influences mass and energy exchange of vegetated surfaces. Therefore LAI is an input variable for many land surface schemes of coupled large scale models, which do not simulate LAI. Since these models typically run on rather coarse resolution grids, LAI is often inferred from coarse resolution remote sensing. However, especially in agriculturally used areas, a grid cell of these products often covers more than a single land-use. In that case, the given LAI does not apply to any single land-use. Therefore, the overall spatial heterogeneity in these datasets differs from that on resolutions high enough to distinguish areas with differing land-use. Detailed process-based plant growth models simulate LAI for separate plant functional types or specific species. However, limited availability of observations causes reduced spatial heterogeneity of model input data (soil, weather, land-use). Since LAI is strongly heterogeneous in space and time and since processes depend on LAI in a nonlinear way, a correct representation of LAI spatial heterogeneity is also desirable on coarse resolutions. The current study assesses this issue by comparing the spatial heterogeneity of LAI from remote sensing (RapidEye) and process-based simulations (DANUBIA simulation system) across scales. Spatial heterogeneity is assessed by analyzing LAI frequency distributions (spatial variability) and semivariograms (spatial structure). Test case is the arable land in the fertile loess plain of the Rur catchment near the Germany-Netherlands border.

Towards Improved High-Resolution Land Surface Hydrologic Reanalysis Using a Physically-Based Hydrologic Model and Data Assimilation

NASA Astrophysics Data System (ADS)

Shi, Y.; Davis, K. J.; Zhang, F.; Duffy, C.; Yu, X.

2014-12-01

A coupled physically based land surface hydrologic model, Flux-PIHM, has been developed by incorporating a land surface scheme into the Penn State Integrated Hydrologic Model (PIHM). The land surface scheme is adapted from the Noah land surface model. Flux-PIHM has been implemented and manually calibrated at the Shale Hills watershed (0.08 km2) in central Pennsylvania. Model predictions of discharge, point soil moisture, point water table depth, sensible and latent heat fluxes, and soil temperature show good agreement with observations. When calibrated only using discharge, and soil moisture and water table depth at one point, Flux-PIHM is able to resolve the observed 101 m scale soil moisture pattern at the Shale Hills watershed when an appropriate map of soil hydraulic properties is provided. A Flux-PIHM data assimilation system has been developed by incorporating EnKF for model parameter and state estimation. Both synthetic and real data assimilation experiments have been performed at the Shale Hills watershed. Synthetic experiment results show that the data assimilation system is able to simultaneously provide accurate estimates of multiple parameters. In the real data experiment, the EnKF estimated parameters and manually calibrated parameters yield similar model performances, but the EnKF method significantly decreases the time and labor required for calibration. The data requirements for accurate Flux-PIHM parameter estimation via data assimilation using synthetic observations have been tested. Results show that by assimilating only in situ outlet discharge, soil water content at one point, and the land surface temperature averaged over the whole watershed, the data assimilation system can provide an accurate representation of watershed hydrology. Observations of these key variables are available with national and even global spatial coverage (e.g., MODIS surface temperature, SMAP soil moisture, and the USGS gauging stations). National atmospheric reanalysis products, soil databases and land cover databases (e.g., NLDAS-2, SSURGO, NLCD) can provide high resolution forcing and input data. Therefore the Flux-PIHM data assimilation system could be readily expanded to other watersheds to provide regional scale land surface and hydrologic reanalysis with high spatial temporal resolution.

Modeling the influence of long term human-induced land use conversion on sediment fluxes and carbon dynamics at the catchment scale

NASA Astrophysics Data System (ADS)

Bouchoms, Samuel; Van Oost, Kristof; Vanacker, Veerle

2014-05-01

Over the past 20 years, there has been increasing evidence of the strong impact of human activities on the landscape, specifically on soil erosion due to the removal of natural vegetation cover for agricultural and urban purposes. The results question the widespread hypothesis of a steady state landscape since it appears that the balance between soil production and erosion may be broken altering the interactions between chemical, physical and biological processes in both soil and landscape system. Yet, the relationship between this accelerated erosion and the carbon dynamics at the landscape scale remains an important area of investigation. Recent attempts to combine geomorphic models, soil redistribution and carbon dynamic has proved themselves valuable in term of supporting the importance of lateral fluxes as a crucial control of carbon dynamic at the landscape scale. We use the SPEROS LT model, a modified version of SPEROS-C which includes dynamic land use and soil physical properties, to assess the impact of historical land use conversion on sediment and carbon fluxes in the Dijle catchment. This particular location has experienced a significant human impact since the Roman period, undergoing heavy deforestation and expansion of agricultural lands followed by a period of abandonment. The last 400 to 500 years saw a dramatic increase in the intensity of land use conversion associated to population growth leading to forest cleaning and urbanization. Our main objective is to validate the combined geomorphic and soil carbon turnover process descriptions of the model. Historical land use proportions are based on existing literature estimations and spatial assignation of the land conversion relies on simple allocation rules based on criteria such as slope or soil texture. Land use scenarios are constructed for the last 2000 years. We confront the model results with observations and perform a sensitivity analysis. The results indicate that the general trends in sediment production and deposition, as well as soil carbon storage are well predicted by the model. We discuss the key-parameters of the model and the implications of past erosion-deposition for the future C budget of the Dijle catchment.

Flood projections within the Niger River Basin under future land use and climate change.

PubMed

Aich, Valentin; Liersch, Stefan; Vetter, Tobias; Fournet, Samuel; Andersson, Jafet C M; Calmanti, Sandro; van Weert, Frank H A; Hattermann, Fred F; Paton, Eva N

2016-08-15

This study assesses future flood risk in the Niger River Basin (NRB), for the first time considering the simultaneous effects of both projected climate change and land use changes. For this purpose, an ecohydrological process-based model (SWIM) was set up and validated for past climate and land use dynamics of the entire NRB. Model runs for future flood risks were conducted with an ensemble of 18 climate models, 13 of them dynamically downscaled from the CORDEX Africa project and five statistically downscaled Earth System Models. Two climate and two land use change scenarios were used to cover a broad range of potential developments in the region. Two flood indicators (annual 90th percentile and the 20-year return flood) were used to assess the future flood risk for the Upper, Middle and Lower Niger as well as the Benue. The modeling results generally show increases of flood magnitudes when comparing a scenario period in the near future (2021-2050) with a base period (1976-2005). Land use effects are more uncertain, but trends and relative changes for the different catchments of the NRB seem robust. The dry areas of the Sahelian and Sudanian regions of the basin show a particularly high sensitivity to climatic and land use changes, with an alarming increase of flood magnitudes in parts. A scenario with continuing transformation of natural vegetation into agricultural land and urbanization intensifies the flood risk in all parts of the NRB, while a "regreening" scenario can reduce flood magnitudes to some extent. Yet, land use change effects were smaller when compared to the effects of climate change. In the face of an already existing adaptation deficit to catastrophic flooding in the region, the authors argue for a mix of adaptation and mitigation efforts in order to reduce the flood risk in the NRB. Copyright © 2016 Elsevier B.V. All rights reserved.

Reconciling estimates of the contemporary North American carbon balance among terrestrial biosphere models, atmospheric inversions, and a new approach for estimating net ecosystem exchange from inventory-based data

USGS Publications Warehouse

Hayes, Daniel J.; Turner, David P.; Stinson, Graham; McGuire, A. David; Wei, Yaxing; West, Tristram O.; Heath, Linda S.; de Jong, Bernardus; McConkey, Brian G.; Birdsey, Richard A.; Kurz, Werner A.; Jacobson, Andrew R.; Huntzinger, Deborah N.; Pan, Yude; Post, W. Mac; Cook, Robert B.

2012-01-01

We develop an approach for estimating net ecosystem exchange (NEE) using inventory-based information over North America (NA) for a recent 7-year period (ca. 2000–2006). The approach notably retains information on the spatial distribution of NEE, or the vertical exchange between land and atmosphere of all non-fossil fuel sources and sinks of CO2, while accounting for lateral transfers of forest and crop products as well as their eventual emissions. The total NEE estimate of a -327 ± 252 TgC yr-1 sink for NA was driven primarily by CO2 uptake in the Forest Lands sector (-248 TgC yr-1), largely in the Northwest and Southeast regions of the US, and in the Crop Lands sector (-297 TgC yr-1), predominantly in the Midwest US states. These sinks are counteracted by the carbon source estimated for the Other Lands sector (+218 TgC yr-1), where much of the forest and crop products are assumed to be returned to the atmosphere (through livestock and human consumption). The ecosystems of Mexico are estimated to be a small net source (+18 TgC yr-1) due to land use change between 1993 and 2002. We compare these inventory-based estimates with results from a suite of terrestrial biosphere and atmospheric inversion models, where the mean continental-scale NEE estimate for each ensemble is -511 TgC yr-1 and -931 TgC yr-1, respectively. In the modeling approaches, all sectors, including Other Lands, were generally estimated to be a carbon sink, driven in part by assumed CO2 fertilization and/or lack of consideration of carbon sources from disturbances and product emissions. Additional fluxes not measured by the inventories, although highly uncertain, could add an additional -239 TgC yr-1 to the inventory-based NA sink estimate, thus suggesting some convergence with the modeling approaches.

Data needs and data bases for climate studies

NASA Technical Reports Server (NTRS)

Matthews, Elaine

1986-01-01

Two complementary global digital data bases of vegetation and land use, compiled at 1 deg resolution from published sources for use in climate studies, are discussed. The data bases were implemented, in several individually tailored formulations, in a series of climate related applications including: land-surface prescriptions in three-dimensional general circulation models, global biogeochemical cycles (CO2, methane), critical-area mapping for satellite monitoring of land-cover change, and large-scale remote sensing of surface reflectance. The climate applications are discussed with reference to data needs, and data availability from traditional and remote sensing sources.

A Prototype Land Information Sensor Web: Design, Implementation and Implication for the SMAP Mission

NASA Astrophysics Data System (ADS)

Su, H.; Houser, P.; Tian, Y.; Geiger, J. K.; Kumar, S. V.; Gates, L.

2009-12-01

Land Surface Model (LSM) predictions are regular in time and space, but these predictions are influenced by errors in model structure, input variables, parameters and inadequate treatment of sub-grid scale spatial variability. Consequently, LSM predictions are significantly improved through observation constraints made in a data assimilation framework. Several multi-sensor satellites are currently operating which provide multiple global observations of the land surface, and its related near-atmospheric properties. However, these observations are not optimal for addressing current and future land surface environmental problems. To meet future earth system science challenges, NASA will develop constellations of smart satellites in sensor web configurations which provide timely on-demand data and analysis to users, and can be reconfigured based on the changing needs of science and available technology. A sensor web is more than a collection of satellite sensors. That means a sensor web is a system composed of multiple platforms interconnected by a communication network for the purpose of performing specific observations and processing data required to support specific science goals. Sensor webs can eclipse the value of disparate sensor components by reducing response time and increasing scientific value, especially when the two-way interaction between the model and the sensor web is enabled. The study of a prototype Land Information Sensor Web (LISW) is sponsored by NASA, trying to integrate the Land Information System (LIS) in a sensor web framework which allows for optimal 2-way information flow that enhances land surface modeling using sensor web observations, and in turn allows sensor web reconfiguration to minimize overall system uncertainty. This prototype is based on a simulated interactive sensor web, which is then used to exercise and optimize the sensor web modeling interfaces. The Land Information Sensor Web Service-Oriented Architecture (LISW-SOA) has been developed and it is the very first sensor web framework developed especially for the land surface studies. Synthetic experiments based on the LISW-SOA and the virtual sensor web provide a controlled environment in which to examine the end-to-end performance of the prototype, the impact of various sensor web design trade-offs and the eventual value of sensor webs for a particular prediction or decision support. In this paper, the design, implementation of the LISW-SOA and the implication for the Soil Moisture Active and Passive (SMAP) mission is presented. Particular attention is focused on examining the relationship between the economic investment on a sensor web (space and air borne, ground based) and the accuracy of the model predicted soil moisture, which can be achieved by using such sensor observations. The Study of Virtual Land Information Sensor Web (LISW) is expected to provide some necessary a priori knowledge for designing and deploying the next generation Global Earth Observing System of systems (GEOSS).

Modeling land use change impacts on water resources in a tropical West African catchment (Dano, Burkina Faso)

NASA Astrophysics Data System (ADS)

Yira, Y.; Diekkrüger, B.; Steup, G.; Bossa, A. Y.

2016-06-01

This study investigates the impacts of land use change on water resources in the Dano catchment, Burkina Faso, using a physically based hydrological simulation model and land use scenarios. Land use dynamic in the catchment was assessed through the analysis of four land use maps corresponding to the land use status in 1990, 2000, 2007, and 2013. A reclassification procedure levels out differences between the classification schemes of the four maps. The land use maps were used to build five land use scenarios corresponding to different levels of land use change in the catchment. Water balance was simulated by applying the Water flow and balance Simulation Model (WaSiM) using observed discharge, soil moisture, and groundwater level for model calibration and validation. Model statistical quality measures (R2, NSE and KGE) achieved during calibration and validation ranged between 0.6 and 0.9 for total discharge, soil moisture, and groundwater level, indicating a good agreement between observed and simulated variables. After a successful multivariate validation the model was applied to the land use scenarios. The land use assessment exhibited a decrease of savannah at an annual rate of 2% since 1990. Conversely, cropland and urban areas have increased. Since urban areas occupy only 3% of the catchment it can be assumed that savannah was mainly converted to cropland. The conversion rate of savannah was lower than the annual population growth of 3%. A clear increase in total discharge (+17%) and decrease in evapotranspiration (-5%) was observed following land use change in the catchment. A strong relationship was established between savannah degradation, cropland expansion, discharge increase and reduction of evapotranspiration. The increase in total discharge is related to high peak flow, suggesting (i) an increase in water resources that are not available for plant growth and human consumption and (ii) an alteration of flood risk for both the population within and downstream of the catchment.

The Significance of Land Cover Delineation on Soil Erosion Assessment.

PubMed

Efthimiou, Nikolaos; Psomiadis, Emmanouil

2018-04-25

The study aims to evaluate the significance of land cover delineation on soil erosion assessment. To that end, RUSLE (Revised Universal Soil Loss Equation) was implemented at the Upper Acheloos River catchment, Western Central Greece, annually and multi-annually for the period 1965-92. The model estimates soil erosion as the linear product of six factors (R, K, LS, C, and P) considering the catchment's climatic, pedological, topographic, land cover, and anthropogenic characteristics, respectively. The C factor was estimated using six alternative land use delineations of different resolution, namely the CORINE Land Cover (CLC) project (2000, 2012 versions) (1:100,000), a land use map conducted by the Greek National Agricultural Research Foundation (NAGREF) (1:20,000), a land use map conducted by the Greek Payment and Control Agency for Guidance and Guarantee Community Aid (PCAGGCA) (1:5,000), and the Landsat 8 16-day Normalized Difference Vegetation Index (NDVI) dataset (30 m/pixel) (two approximations) based on remote sensing data (satellite image acquired on 07/09/2016) (1:40,000). Since all other factors remain unchanged per each RUSLE application, the differences among the yielded results are attributed to the C factor (thus the land cover pattern) variations. Validation was made considering the convergence between simulated (modeled) and observed sediment yield. The latter was estimated based on field measurements conducted by the Greek PPC (Public Power Corporation). The model performed best at both time scales using the Landsat 8 (Eq. 13) dataset, characterized by a detailed resolution and a satisfactory categorization, allowing the identification of the most susceptible to erosion areas.

The influence of agroforestry and other land-use types on the persistence of a Sumatran tiger (Panthera tigris sumatrae) population: an individual-based model approach.

PubMed

Imron, Muhammad Ali; Herzog, Sven; Berger, Uta

2011-08-01

The importance of preserving both protected areas and their surrounding landscapes as one of the major conservation strategies for tigers has received attention over recent decades. However, the mechanism of how land-use surrounding protected areas affects the dynamics of tiger populations is poorly understood. We developed Panthera Population Persistence (PPP)--an individual-based model--to investigate the potential mechanism of the Sumatran tiger population dynamics in a protected area and under different land-use scenarios surrounding the reserve. We tested three main landscape compositions (single, combined and real land-uses of Tesso-Nilo National Park and its surrounding area) on the probability of and time to extinction of the Sumatran tiger over 20 years in Central Sumatra. The model successfully explains the mechanisms behind the population response of tigers under different habitat landscape compositions. Feeding and mating behaviours of tigers are key factors, which determined population persistence in a heterogeneous landscape. All single land-use scenarios resulted in tiger extinction but had a different probability of extinction within 20 years. If tropical forest was combined with other land-use types, the probability of extinction was smaller. The presence of agroforesty and logging concessions adjacent to protected areas encouraged the survival of tiger populations. However, with the real land-use scenario of Tesso-Nilo National Park, tigers could not survive for more than 10 years. Promoting the practice of agroforestry systems surrounding the park is probably the most reasonable way to steer land-use surrounding the Tesso-Nilo National Park to support tiger conservation.

Nutrient cycle benchmarks for earth system land model

NASA Astrophysics Data System (ADS)

Zhu, Q.; Riley, W. J.; Tang, J.; Zhao, L.

2017-12-01

Projecting future biosphere-climate feedbacks using Earth system models (ESMs) relies heavily on robust modeling of land surface carbon dynamics. More importantly, soil nutrient (particularly, nitrogen (N) and phosphorus (P)) dynamics strongly modulate carbon dynamics, such as plant sequestration of atmospheric CO2. Prevailing ESM land models all consider nitrogen as a potentially limiting nutrient, and several consider phosphorus. However, including nutrient cycle processes in ESM land models potentially introduces large uncertainties that could be identified and addressed by improved observational constraints. We describe the development of two nutrient cycle benchmarks for ESM land models: (1) nutrient partitioning between plants and soil microbes inferred from 15N and 33P tracers studies and (2) nutrient limitation effects on carbon cycle informed by long-term fertilization experiments. We used these benchmarks to evaluate critical hypotheses regarding nutrient cycling and their representation in ESMs. We found that a mechanistic representation of plant-microbe nutrient competition based on relevant functional traits best reproduced observed plant-microbe nutrient partitioning. We also found that for multiple-nutrient models (i.e., N and P), application of Liebig's law of the minimum is often inaccurate. Rather, the Multiple Nutrient Limitation (MNL) concept better reproduces observed carbon-nutrient interactions.

A Dynamic Simulation Model of Land-Use, Population, and Rural Livelihoods in the Central Rift Valley of Ethiopia

NASA Astrophysics Data System (ADS)

Garedew, Efrem; Sandewall, Mats; Soderberg, Ulf

2012-01-01

The dynamic interactions between society and land resources have to be taken into account when planning and managing natural resources. A computer model, using STELLA software, was developed through active participation of purposively selected farm households from different wealth groups, age groups and gender within a rural community and some members of Kebelle council. The aim of the modeling was to study the perceived changes in land-use, population and livelihoods over the next 30 years and to improve our understanding of the interactions among them. The modeling output is characterized by rapid population growth, declining farm size and household incomes, deteriorating woody vegetation cover and worsening land degradation if current conditions remain. However, through integrated intervention strategies (including forest increase, micro-finance, family planning, health and education) the woody vegetation cover is likely to increase in the landscape, population growth is likely to slow down and households' income is likely to improve. A validation assessment of the simulation model based on historical data on land-use and population from 1973 to 2006 showed that the model is relatively robust. We conclude that as a supporting tool, the simulation model can contribute to the decision making process.

Forest cover change prediction using hybrid methodology of geoinformatics and Markov chain model: A case study on sub-Himalayan town Gangtok, India

NASA Astrophysics Data System (ADS)

Mukhopadhyay, Anirban; Mondal, Arun; Mukherjee, Sandip; Khatua, Dipam; Ghosh, Subhajit; Mitra, Debasish; Ghosh, Tuhin

2014-08-01

In the Himalayan states of India, with increasing population and activities, large areas of forested land are being converted into other land-use features. There is a definite cause and effect relationship between changing practice for development and changes in land use. So, an estimation of land use dynamics and a futuristic trend pattern is essential. A combination of geospatial and statistical techniques were applied to assess the present and future land use/land cover scenario of Gangtok, the subHimalayan capital of Sikkim. Multi-temporal satellite imageries of the Landsat series were used to map the changes in land use of Gangtok from 1990 to 2010. Only three major land use classes (built-up area and bare land, step cultivated area, and forest) were considered as the most dynamic land use practices of Gangtok. The conventional supervised classification, and spectral indices-based thresholding using NDVI (Normalized Difference Vegetation Index) and SAVI (Soil Adjusted Vegetation Index) were applied along with the accuracy assessments. Markov modelling was applied for prediction of land use/land cover change and was validated. SAVI provides the most accurate estimate, i.e., the difference between predicted and actual data is minimal. Finally, a combination of Markov modelling and SAVI was used to predict the probable land-use scenario in Gangtok in 2020 AD, which indicted that more forest areas will be converted for step cultivation by the year 2020.

The benefits of GIS to land use planning

NASA Astrophysics Data System (ADS)

Strielko, Irina; Pereira, Paulo

2014-05-01

The development of information technologies has significantly changed the approach to land use and spatial planning, management of natural resources. GIS considerably simplifies territorial planning operating analyzing necessary data concerning their spatial relationship that allows carrying out complex assessment of the situation and creates a basis for adoption of more exact and scientifically reasonable decisions in the course of land use. To assess the current land use situation and the possibility of modeling possible future changes associated with complex of adopted measures GIS allows the integration of diverse spatial data, for example, data about soils, climate, vegetation, and other and also to visualize available information in the form of maps, graphs or charts, 3D models. For the purposes of land use GIS allow using data of remote sensing, which allows to make monitoring of anthropogenic influence in a particular area and estimate scales and rates of degradation of green cover, flora and fauna. Assessment of land use can be made in complex or componentwise, indicating the test sites depending on the goals. GIS make it easy to model spatial distribution of various types of pollution of stationary and mobile sources in soil, atmosphere and the hydrological network. Based on results of the analysis made by GIS choose the optimal solutions of land use that provide the minimum impact on environment, make optimal decisions of conflict associated with land use and control of their using. One of the major advantages of using GIS is possibility of the complex analysis in concrete existential aspect. Analytical opportunities of GIS define conditionality of spatial distribution of objects and interrelation communication between them. For a variety of land management objectives analysis method is chosen based on the parameters of the problem and parameters of use of its results.

Simulation of Regionally Ecological Land Based on a Cellular Automation Model: A Case Study of Beijing, China

PubMed Central

Xie, Hualin; Kung, Chih-Chun; Zhang, Yanting; Li, Xiubin

2012-01-01

Ecological land is like the “liver” of a city and is very useful to public health. Ecological land change is a spatially dynamic non-linear process under the interaction between natural and anthropogenic factors at different scales. In this study, by setting up natural development scenario, object orientation scenario and ecosystem priority scenario, a Cellular Automation (CA) model has been established to simulate the evolution pattern of ecological land in Beijing in the year 2020. Under the natural development scenario, most of ecological land will be replaced by construction land and crop land. But under the scenarios of object orientation and ecosystem priority, the ecological land area will increase, especially under the scenario of ecosystem priority. When considering the factors such as total area of ecological land, loss of key ecological land and spatial patterns of land use, the scenarios from priority to inferiority are ecosystem priority, object orientation and natural development, so future land management policies in Beijing should be focused on conversion of cropland to forest, wetland protection and prohibition of exploitation of natural protection zones, water source areas and forest parks to maintain the safety of the regional ecosystem. PMID:23066410

Simulation of regionally ecological land based on a cellular automation model: a case study of Beijing, China.

PubMed

Xie, Hualin; Kung, Chih-Chun; Zhang, Yanting; Li, Xiubin

2012-08-01

Ecological land is like the "liver" of a city and is very useful to public health. Ecological land change is a spatially dynamic non-linear process under the interaction between natural and anthropogenic factors at different scales. In this study, by setting up natural development scenario, object orientation scenario and ecosystem priority scenario, a Cellular Automation (CA) model has been established to simulate the evolution pattern of ecological land in Beijing in the year 2020. Under the natural development scenario, most of ecological land will be replaced by construction land and crop land. But under the scenarios of object orientation and ecosystem priority, the ecological land area will increase, especially under the scenario of ecosystem priority. When considering the factors such as total area of ecological land, loss of key ecological land and spatial patterns of land use, the scenarios from priority to inferiority are ecosystem priority, object orientation and natural development, so future land management policies in Beijing should be focused on conversion of cropland to forest, wetland protection and prohibition of exploitation of natural protection zones, water source areas and forest parks to maintain the safety of the regional ecosystem.

Projecting Future Land Use Changes in West Africa Driven by Climate and Socioeconomic Factors: Uncertainties and Implications for Adaptation

NASA Astrophysics Data System (ADS)

Wang, G.; Ahmed, K. F.; You, L.

2015-12-01

Land use changes constitute an important regional climate change forcing in West Africa, a region of strong land-atmosphere coupling. At the same time, climate change can be an important driver for land use, although its importance relative to the impact of socio-economic factors may vary significant from region to region. This study compares the contributions of climate change and socioeconomic development to potential future changes of agricultural land use in West Africa and examines various sources of uncertainty using a land use projection model (LandPro) that accounts for the impact of socioeconomic drivers on the demand side and the impact of climate-induced crop yield changes on the supply side. Future crop yield changes were simulated by a process-based crop model driven with future climate projections from a regional climate model, and future changes of food demand is projected using a model for policy analysis of agricultural commodities and trade. The impact of human decision-making on land use was explicitly considered through multiple "what-if" scenarios to examine the range of uncertainties in projecting future land use. Without agricultural intensification, the climate-induced decrease of crop yield together with increase of food demand are found to cause a significant increase in agricultural land use at the expense of forest and grassland by the mid-century, and the resulting land use land cover changes are found to feed back to the regional climate in a way that exacerbates the negative impact of climate on crop yield. Analysis of results from multiple decision-making scenarios suggests that human adaptation characterized by science-informed decision making to minimize land use could be very effective in many parts of the region.

Spatio-temporal Assessment Of The Land Use Implications Of Solar PV And Bioenergy Deployment In The UK TM Energy Model

NASA Astrophysics Data System (ADS)

Sobral Mourao, Z.; Konadu, D. D.; Skelton, S.; Lupton, R.

2015-12-01

The UK TIMES model (UKTM) succeeds the UK MARKAL as the underlying model of the UK Department of Energy and Climate Change (DECC) for long term energy system planning and policy development. It generates energy system pathways which achieve the 80% greenhouse gas (GHG) emissions reduction target by 2050, stipulated in the UK Climate Change Act (2008), at the least possible cost. Some of these pathways prescribe large-scale deployment of solar PV and indigenously sourced bioenergy, which are land intensive and could result in significant land use transitions; but would this create competition and stress for UK land use? To answer the above question, this study uses an integrated spatio-temporal modelling approach, ForeseerTM, which characterises the interdependencies between the energy and land systems by evaluating the land required under each pathways for solar PV and bioenergy, based on scenarios of a range of PV conversion efficiencies, and energy crop yield projections. The outcome is compared with availability of suitable locations for solar PV and sustainable limits of agricultural land appropriation for bioenergy production to assess potential stresses and competition with other land use services. Preliminary results show UKTM pathways could pose significant impact on the UK land use system. Bioenergy deployment could potentially compete with other land services by taking up a significant part of the available UK agricultural land thus competing directly with food production, most notably livestock production. For pathways with significant solar PV deployment, direct competition would not be focussed on the high quality land used for food crop production but rather for land used for livestock production and other ecosystem services.

[Dynamic evolution of landscape spatial pattern in Taihu Lake basin, China].

PubMed

Wang, Fang; Xie, Xiao Ping; Chen, Zhi Cong

2017-11-01

Based on the land-use satellite image datasets of 2000, 2010 and 2015, the landscape index, dynamic change model, landscape transfer matrix and CLUE-S model were integrated to analyze the dynamic evolution of the landscape spatial pattern of Taihu Lake basin. The results showed that the landscape type of the basin was dominated by cultivated land and construction land, and the degree of landscape fragmentation was strengthened from 2000 to 2015, and the distribution showed a uniform trend. From the point of transfer dynamic change, the cultivated land and construction land changed significantly, which was reduced by 6761 km 2 (2.1%) and increased by 6615.33 km 2 (8.4%), respectively. From the landscape transfer, it could be seen that the main change direction of the cultivated land reduction was the construction land, and the cultivated land with 7866.30 km 2 was converted into construction land, accounting for 91.6% of the cultivated land change, and the contribution to the construction land was 96.5%. The trend of dynamic changes of cultivated and construction land in the counties and cities was the same as that of the whole Taihu Lake basin. For Shanghai Central Urban, as well as Pudong District, Lin'an City, Baoshan District, Minhang District, Jiading District and Changzhou City, the area of the cultivated land and construction land changed more prominently. However, compared with the CLUE-S model for the landscape pattern change in 2030, the change of cultivated and construction lands would be the largest in the natural development scenario. Under the ecological protection scenario, the area of grassland would increase and the dynamic degree would reach 54.5%. Under the situation of cultivated land protection, the conversion of cultivated land to construction land would be decreased.

An integrated modeling framework of socio-economic, biophysical, and hydrological processes in Midwest landscapes: Remote sensing data, agro-hydrological model, and agent-based model

NASA Astrophysics Data System (ADS)

Ding, Deng

Intensive human-environment interactions are taking place in Midwestern agricultural systems. An integrated modeling framework is suitable for predicting dynamics of key variables of the socio-economic, biophysical, hydrological processes as well as exploring the potential transitions of system states in response to changes of the driving factors. The purpose of this dissertation is to address issues concerning the interacting processes and consequent changes in land use, water balance, and water quality using an integrated modeling framework. This dissertation is composed of three studies in the same agricultural watershed, the Clear Creek watershed in East-Central Iowa. In the first study, a parsimonious hydrologic model, the Threshold-Exceedance-Lagrangian Model (TELM), is further developed into RS-TELM (Remote Sensing TELM) to integrate remote sensing vegetation data for estimating evapotranspiration. The goodness of fit of RS-TELM is comparable to a well-calibrated SWAT (Soil and Water Assessment Tool) and even slightly superior in capturing intra-seasonal variability of stream flow. The integration of RS LAI (Leaf Area Index) data improves the model's performance especially over the agriculture dominated landscapes. The input of rainfall datasets with spatially explicit information plays a critical role in increasing the model's goodness of fit. In the second study, an agent-based model is developed to simulate farmers' decisions on crop type and fertilizer application in response to commodity and biofuel crop prices. The comparison between simulated crop land percentage and crop rotations with satellite-based land cover data suggest that farmers may be underestimating the effects that continuous corn production has on yields (yield drag). The simulation results given alternative market scenarios based on a survey of agricultural land owners and operators in the Clear Creek Watershed show that, farmers see cellulosic biofuel feedstock production in the form of perennial grasses or corn stover as a more risky enterprise than their current crop production systems, likely because of market and production risks and lock in effects. As a result farmers do not follow a simple farm-profit maximization rule. In the third study, the consequent water quantity and quality change of the potential land use transitions given alternative biofuel crop market scenarios is explored in a case study in the Clear Creek watershed. A computer program is developed to implement the loose-coupling strategy to couple an agent-based land use model with SWAT. The simulation results show that watershed-scale water quantity (water yield and runoff) and quality variables (sediment and nutrient loads) decrease in values as switchgrass price increases. However, negligence of farmers risk aversions towards biofuel crop adoption would cause overestimation of the impacts of switchgrass price on water quantity and quality.

Quantifying the indirect impacts of climate on agriculture: an inter-method comparison

DOE PAGES

Calvin, Kate; Fisher-Vanden, Karen

2017-10-27

Climate change and increases in CO2 concentration affect the productivity of land, with implications for land use, land cover, and agricultural production. Much of the literature on the effect of climate on agriculture has focused on linking projections of changes in climate to process-based or statistical crop models. However, the changes in productivity have broader economic implications that cannot be quantified in crop models alone. How important are these socio-economic feedbacks to a comprehensive assessment of the impacts of climate change on agriculture? In this paper, we attempt to measure the importance of these interaction effects through an inter-method comparisonmore » between process models, statistical models, and integrated assessment model (IAMs). We find the impacts on crop yields vary widely between these three modeling approaches. Yield impacts generated by the IAMs are 20%-40% higher than the yield impacts generated by process-based or statistical crop models, with indirect climate effects adjusting yields by between - 12% and + 15% (e.g. input substitution and crop switching). The remaining effects are due to technological change.« less

Quantifying the indirect impacts of climate on agriculture: an inter-method comparison

NASA Astrophysics Data System (ADS)

Calvin, Kate; Fisher-Vanden, Karen

2017-11-01

Climate change and increases in CO2 concentration affect the productivity of land, with implications for land use, land cover, and agricultural production. Much of the literature on the effect of climate on agriculture has focused on linking projections of changes in climate to process-based or statistical crop models. However, the changes in productivity have broader economic implications that cannot be quantified in crop models alone. How important are these socio-economic feedbacks to a comprehensive assessment of the impacts of climate change on agriculture? In this paper, we attempt to measure the importance of these interaction effects through an inter-method comparison between process models, statistical models, and integrated assessment model (IAMs). We find the impacts on crop yields vary widely between these three modeling approaches. Yield impacts generated by the IAMs are 20%-40% higher than the yield impacts generated by process-based or statistical crop models, with indirect climate effects adjusting yields by between -12% and +15% (e.g. input substitution and crop switching). The remaining effects are due to technological change.

Quantifying the indirect impacts of climate on agriculture: an inter-method comparison

DOE Office of Scientific and Technical Information (OSTI.GOV)

Calvin, Kate; Fisher-Vanden, Karen

Climate change and increases in CO2 concentration affect the productivity of land, with implications for land use, land cover, and agricultural production. Much of the literature on the effect of climate on agriculture has focused on linking projections of changes in climate to process-based or statistical crop models. However, the changes in productivity have broader economic implications that cannot be quantified in crop models alone. How important are these socio-economic feedbacks to a comprehensive assessment of the impacts of climate change on agriculture? In this paper, we attempt to measure the importance of these interaction effects through an inter-method comparisonmore » between process models, statistical models, and integrated assessment model (IAMs). We find the impacts on crop yields vary widely between these three modeling approaches. Yield impacts generated by the IAMs are 20%-40% higher than the yield impacts generated by process-based or statistical crop models, with indirect climate effects adjusting yields by between - 12% and + 15% (e.g. input substitution and crop switching). The remaining effects are due to technological change.« less

Global Precipitation Responses to Land Hydrological Processes

NASA Astrophysics Data System (ADS)

Lo, M.; Famiglietti, J. S.

2012-12-01

Several studies have established that soil moisture increases after adding a groundwater component in land surface models due to the additional supply of subsurface water. However, impacts of groundwater on the spatial-temporal variability of precipitation have received little attention. Through the coupled groundwater-land-atmosphere model (NCAR Community Atmosphere Model + Community Land Model) simulations, this study explores how groundwater representation in the model alters the precipitation spatiotemporal distributions. Results indicate that the effect of groundwater on the amount of precipitation is not globally homogeneous. Lower tropospheric water vapor increases due to the presence of groundwater in the model. The increased water vapor destabilizes the atmosphere and enhances the vertical upward velocity and precipitation in tropical convective regions. Precipitation, therefore, is inhibited in the descending branch of convection. As a result, an asymmetric dipole is produced over tropical land regions along the equator during the summer. This is analogous to the "rich-get-richer" mechanism proposed by previous studies. Moreover, groundwater also increased short-term (seasonal) and long-term (interannual) memory of precipitation for some regions with suitable groundwater table depth and found to be a function of water table depth. Based on the spatial distributions of the one-month-lag autocorrelation coefficients as well as Hurst coefficients, air-land interaction can occur from short (several months) to long (several years) time scales. This study indicates the importance of land hydrological processes in the climate system and the necessity of including the subsurface processes in the global climate models.

Effects of Land Use Land Cover (LULC) and Climate on Simulation of Phosphorus loading in the Southeast United States Region

NASA Astrophysics Data System (ADS)

Jima, T. G.; Roberts, A.

2013-12-01

Quality of coastal and freshwater resources in the Southeastern United States is threatened due to Eutrophication as a result of excessive nutrients, and phosphorus is acknowledged as one of the major limiting nutrients. In areas with much non-point source (NPS) pollution, land use land cover and climate have been found to have significant impact on water quality. Landscape metrics applied in catchment and riparian stream based nutrient export models are known to significantly improve nutrient prediction. The regional SPARROW (Spatially Referenced Regression On Watershed attributes), which predicts Total Phosphorus has been developed by the Southeastern United States regions USGS, as part of the National Water Quality Assessment (NAWQA) program and the model accuracy was found to be 67%. However, landscape composition and configuration metrics which play a significant role in the source, transport and delivery of the nutrient have not been incorporated in the model. Including these matrices in the models parameterization will improve the models accuracy and improve decision making process for mitigating and managing NPS phosphorus in the region. The National Land Cover Data 2001 raster data will be used (since the base line is 2002) for the region (with 8321 watersheds ) with fragstats 4.1 and ArcGIS Desktop 10.1 for the analysis of landscape matrices, buffers and creating map layers. The result will be imported to the Southeast SPARROW model and will be analyzed. Resulting statistical significance and model accuracy will be assessed and predictions for those areas with no water quality monitoring station will be made.

Aircraft wing structural design optimization based on automated finite element modelling and ground structure approach

NASA Astrophysics Data System (ADS)

Yang, Weizhu; Yue, Zhufeng; Li, Lei; Wang, Peiyan

2016-01-01

An optimization procedure combining an automated finite element modelling (AFEM) technique with a ground structure approach (GSA) is proposed for structural layout and sizing design of aircraft wings. The AFEM technique, based on CATIA VBA scripting and PCL programming, is used to generate models automatically considering the arrangement of inner systems. GSA is used for local structural topology optimization. The design procedure is applied to a high-aspect-ratio wing. The arrangement of the integral fuel tank, landing gear and control surfaces is considered. For the landing gear region, a non-conventional initial structural layout is adopted. The positions of components, the number of ribs and local topology in the wing box and landing gear region are optimized to obtain a minimum structural weight. Constraints include tank volume, strength, buckling and aeroelastic parameters. The results show that the combined approach leads to a greater weight saving, i.e. 26.5%, compared with three additional optimizations based on individual design approaches.

New and Improved GLDAS Data Sets and Data Services at NASA GES DISC

NASA Technical Reports Server (NTRS)

Rui, Hualan; Beaudoing, Hiroko; Teng, William; Vollmer, Bruce; Rodell, Matthew; Lei, Guang-Dih

2012-01-01

The goal of a Land Data Assimilation System (LDAS) is to ingest satellite- and ground-based observational data products, using advanced land surface modeling and data assimilation techniques, in order to generate optimal fields of land surface states and fluxes data and, thereby, facilitate hydrology and climate modeling, research, and forecast. With the motivation of creating more climatologically consistent data sets, NASA GSFC's Hydrological Sciences Laboratory has generated more than 60 years (Jan. 1948-- Dec. 2008) of Global LDAS Version 2 (GLDAS-2) data, by using the Princeton Forcing Data Set and upgraded versions of Land Surface Models (LSMs). GLDAS data and data services are provided at NASA GES DISC Hydrology Data and Information Services Center (HDISC), in collaboration with HSL and LDAS.

Bus Lifecycle Cost Model for Federal Land Management Agencies.

DOT National Transportation Integrated Search

2011-09-30

The Bus Lifecycle Cost Model is a spreadsheet-based planning tool that estimates capital, operating, and maintenance costs for various bus types over the full lifecycle of the vehicle. The model is based on a number of operating characteristics, incl...

Distributed Hydrologic Modeling of Semiarid Basins in Arizona: A Platform for Land Cover and Climate Change Assessments

NASA Astrophysics Data System (ADS)

Hawkins, G. A.; Vivoni, E. R.

2011-12-01

Watershed management is challenged by rising concerns over climate change and its potential to interact with land cover alterations to impact regional water supplies and hydrologic processes. The inability to conduct experimental manipulations that address climate and land cover change at watershed scales limits the capacity of water managers to make decisions to protect future supplies. As a result, spatially-explicit, physically-based models possess value for predicting the possible consequences on watershed hydrology. In this study, we apply a distributed watershed model, the Triangulated Irregular Network (TIN)-based Real-time Integrated Basin Simulator (tRIBS), to the Beaver Creek basin in Arizona. This sub-basin of the Verde River is representative of the regional topography, land cover, soils distribution and availability of hydrologic data in forested regions of northern Arizona. As such, it can serve as a demonstration study in the broader region to illustrate the utility of distributed models for change assessment studies. Through a model application to summertime conditions, we compare the hydrologic response from three sources of meteorological input: (1) an available network of ground-based stations, (2) weather radar rainfall estimates, and (3) the North American Land Data Assimilation System (NLDAS). Comparisons focus on analysis of spatiotemporal distributions of precipitation, soil moisture, runoff generation, evapotranspiration and recharge from the root zone at high resolution for an assessment of sustainable water supplies for agricultural and domestic purposes. We also present a preliminary analysis of the impact of vegetation change arising from historical treatments in the Beaver Creek to inform the hydrologic consequences in the form of soil moisture and evapotranspiration patterns with differing degrees of proposed forest thinning. Our results are discussed in the context of improved hydrologic predictions for sustainability and decision-making under the uncertainties induced by combined climate and land cover change.

Upper Blue Nile basin water budget from a multi-model perspective

NASA Astrophysics Data System (ADS)

Jung, Hahn Chul; Getirana, Augusto; Policelli, Frederick; McNally, Amy; Arsenault, Kristi R.; Kumar, Sujay; Tadesse, Tsegaye; Peters-Lidard, Christa D.

2017-12-01

Improved understanding of the water balance in the Blue Nile is of critical importance because of increasingly frequent hydroclimatic extremes under a changing climate. The intercomparison and evaluation of multiple land surface models (LSMs) associated with different meteorological forcing and precipitation datasets can offer a moderate range of water budget variable estimates. In this context, two LSMs, Noah version 3.3 (Noah3.3) and Catchment LSM version Fortuna 2.5 (CLSMF2.5) coupled with the Hydrological Modeling and Analysis Platform (HyMAP) river routing scheme are used to produce hydrological estimates over the region. The two LSMs were forced with different combinations of two reanalysis-based meteorological datasets from the Modern-Era Retrospective analysis for Research and Applications datasets (i.e., MERRA-Land and MERRA-2) and three observation-based precipitation datasets, generating a total of 16 experiments. Modeled evapotranspiration (ET), streamflow, and terrestrial water storage estimates were evaluated against the Atmosphere-Land Exchange Inverse (ALEXI) ET, in-situ streamflow observations, and NASA Gravity Recovery and Climate Experiment (GRACE) products, respectively. Results show that CLSMF2.5 provided better representation of the water budget variables than Noah3.3 in terms of Nash-Sutcliffe coefficient when considering all meteorological forcing datasets and precipitation datasets. The model experiments forced with observation-based products, the Climate Hazards group Infrared Precipitation with Stations (CHIRPS) and the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA), outperform those run with MERRA-Land and MERRA-2 precipitation. The results presented in this paper would suggest that the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System incorporate CLSMF2.5 and HyMAP routing scheme to better represent the water balance in this region.

REDD+ and climate smart agriculture in landscapes: A case study in Vietnam using companion modelling.

PubMed

Salvini, G; Ligtenberg, A; van Paassen, A; Bregt, A K; Avitabile, V; Herold, M

2016-05-01

Finding land use strategies that merge land-based climate change mitigation measures and adaptation strategies is still an open issue in climate discourse. This article explores synergies and trade-offs between REDD+, a scheme that focuses mainly on mitigation through forest conservation, with "Climate Smart Agriculture", an approach that emphasizes adaptive agriculture. We introduce a framework for ex-ante assessment of the impact of land management policies and interventions and for quantifying their impacts on land-based mitigation and adaptation goals. The framework includes a companion modelling (ComMod) process informed by interviews with policymakers, local experts and local farmers. The ComMod process consists of a Role-Playing Game with local farmers and an Agent Based Model. The game provided a participatory means to develop policy and climate change scenarios. These scenarios were then used as inputs to the Agent Based Model, a spatially explicit model to simulate landscape dynamics and the associated carbon emissions over decades. We applied the framework using as case study a community in central Vietnam, characterized by deforestation for subsistence agriculture and cultivation of acacias as a cash crop. The main findings show that the framework is useful in guiding consideration of local stakeholders' goals, needs and constraints. Additionally the framework provided beneficial information to policymakers, pointing to ways that policies might be re-designed to make them better tailored to local circumstances and therefore more effective in addressing synergistically climate change mitigation and adaptation objectives. Copyright © 2015 Elsevier Ltd. All rights reserved.

Reconstructing the spatial pattern of historical forest land in China in the past 300 years

NASA Astrophysics Data System (ADS)

Yang, Xuhong; Jin, Xiaobin; Xiang, Xiaomin; Fan, Yeting; Shan, Wei; Zhou, Yinkang

2018-06-01

The reconstruction of the historical forest spatial distribution is of a great significance to understanding land surface cover in historical periods as well as its climate and ecological effects. Based on the maximum scope of historical forest land before human intervention, the characteristics of human behaviors in farmland reclamation and deforestation for heating and timber, we create a spatial evolution model to reconstruct the spatial pattern of historical forest land. The model integrates the land suitability for reclamation, the difficulty of deforestation, the attractiveness of timber trading markets and the abundance of forest resources to calibrate the potential scope of historical forest land with the rationale that the higher the probability of deforestation for reclamation and wood, the greater the likelihood that the forest land will be deforested. Compared to the satellite-based forest land distribution in 2000, about 78.5% of our reconstructed historical forest grids are of the absolute error between 25% and -25% while as many as 95.85% of those grids are of the absolute error between 50% and -50%, which indirectly validates the feasibility of our reconstructed model. Then, we simulate the spatial distribution of forest land in China in 1661, 1724, 1820, 1887, 1933 and 1952 with the grid resolution of 1 km × 1 km. Our result shows that (1) the reconstructed historical forest land in China in the past 300 years concentrates in DaXingAnLing, XiaoXingAnLing, ChangBaiShan, HengDuanShan, DaBaShan, WuYiShan, DaBieShan, XueFengShang and etc.; (2) in terms of the spatial evolution, historical forest land shrank gradually in LiaoHe plains, SongHuaJiang-NenJiang plains and SanJiang plains of eastnorth of China, Sichuan basins and YunNan-GuiZhou Plateaus; and (3) these observations are consistent to the proceeding of agriculture reclamation in China in past 300 years towards Northeast China and Southwest China.

The Evaluation of Land Use Status in Mountainous Counties of Southwest China Based on Comprehensive Evaluation Models: A Case Study of Baoxing County, Sichuan Province

NASA Astrophysics Data System (ADS)

Cao, Mengtian; Shen, Jundi; Chen, Zhehua

2018-06-01

In mountainous areas of Southwest China, the land resources are scarce, and the ecological environment is fragile, so it is particularly important to carry out the evaluation of land use status for the sustainability of land development. Taking Baoxing County in Sichuan Province, the typical mountainous county in Southwest China, as an instance, this study refers to the existing research frameworks to establish the evaluation system of land use status. Meanwhile, the comprehensive evaluation models are used to evaluate land use status. As indicated from the results, in Baoxing County, the comprehensive evaluation score of the overall status of land use, the evaluation score of the development degree of land, the evaluation score of the intensive management degree of land and the evaluation score of the comprehensive benefits of land were 83.5, 108.24, 72.25 and 80.77, respectively. Land use status is generally at the relatively rational use stage, and the main problems are the lack of land investment and the low mechanization level of agricultural production. It is suggested to increase the financial investment in land and enhance the intensive degree and comprehensive benefits of land in the future.

Impact of Uncertainties in Meteorological Forcing Data and Land Surface Parameters on Global Estimates of Terrestrial Water Balance Components

NASA Astrophysics Data System (ADS)

Nasonova, O. N.; Gusev, Ye. M.; Kovalev, Ye. E.

2009-04-01

Global estimates of the components of terrestrial water balance depend on a technique of estimation and on the global observational data sets used for this purpose. Land surface modelling is an up-to-date and powerful tool for such estimates. However, the results of modelling are affected by the quality of both a model and input information (including meteorological forcing data and model parameters). The latter is based on available global data sets containing meteorological data, land-use information, and soil and vegetation characteristics. Now there are a lot of global data sets, which differ in spatial and temporal resolution, as well as in accuracy and reliability. Evidently, uncertainties in global data sets will influence the results of model simulations, but to which extent? The present work is an attempt to investigate this issue. The work is based on the land surface model SWAP (Soil Water - Atmosphere - Plants) and global 1-degree data sets on meteorological forcing data and the land surface parameters, provided within the framework of the Second Global Soil Wetness Project (GSWP-2). The 3-hourly near-surface meteorological data (for the period from 1 July 1982 to 31 December 1995) are based on reanalyses and gridded observational data used in the International Satellite Land-Surface Climatology Project (ISLSCP) Initiative II. Following the GSWP-2 strategy, we used a number of alternative global forcing data sets to perform different sensitivity experiments (with six alternative versions of precipitation, four versions of radiation, two pure reanalysis products and two fully hybridized products of meteorological data). To reveal the influence of model parameters on simulations, in addition to GSWP-2 parameter data sets, we produced two alternative global data sets with soil parameters on the basis of their relationships with the content of clay and sand in a soil. After this the sensitivity experiments with three different sets of parameters were performed. As a result, 16 variants of global annual estimates of water balance components were obtained. Application of alternative data sets on radiation, precipitation, and soil parameters allowed us to reveal the influence of uncertainties in input data on global estimates of water balance components.

Complex land use and cover trajectories in the northern Choco bioregion of Colombia

NASA Astrophysics Data System (ADS)

Santos, Carolina

The Choco bioregion in Northwestern Colombia is a lowland rain forest and hotspot of biodiversity. Significant land use and cover change (LUCC) is occurring throughout the region driven by global markets, illicit drug production, and civil unrest. The dominant land cover conversion is from primary forest to African Palm plantations, mediated and modified by complex combinations of social and biophysical drivers. This research combined a remote sensing based methodology to monitor LUCC in the region with an analytical approach for evaluating the possible trajectories of LUCC in a complex biological, socio-economical, and political environment. Synoptic LUCC models were developed using textural classification derived from Synthetic Aperture Radar (SAR) images for the period 1995 to 2010. LUCC models along with empirical social and spatial biophysical drivers were used to project historical land use trajectories. DINAMICA EGO a complex systems based spatial analytical framework was adopted as the platform to model land use change. The RADAR backscatter was able to capture areas were forest has been converted to African Oil Palm Plantations. However, an in depth characterization of the LUC dynamics was problematic given the spectral and spatial limitations of the sensor combined with the lack of ground data. The results of the LUC model suggest that under the current socio-political conditions African oil palm plantations will continue to expand toward forested areas into the territories traditionally inhabited by Afro-Colombians and Indigenous populations. Insecure land tenure appears as a main driver of the transformation in close association with the conditions created by the armed conflict, and the drug traffic. The rate of the transformation appears to slow down in the period after 2007. However, according to the model by 2020 most of the area inhabited by ethnic groups will be transform to AOP. This study contributes towards the understanding of land use change in the context of social conflict. Although, it is recognized that conflicts impact the land--use and land-cover, few studies have address the linkages between particular circumstances and events in the conflict and the consequences for the land. As resource conflicts spread around the globe a better understanding of how it impacts the land and the people is paramount.

Lunar lander stage requirements based on the Civil Needs Data Base

NASA Technical Reports Server (NTRS)

Mulqueen, John A.

1992-01-01

This paper examines the lunar lander stages that will be necessary for the future exploration and development of the Moon. Lunar lander stage sizing is discussed based on the projected lunar payloads listed in the Civil Needs Data Base. Factors that will influence the lander stage design are identified and discussed. Some of these factors are (1) lunar orbiting and lunar surface lander bases; (2) implications of direct landing trajectories and landing from a parking orbit; (3) implications of landing site and parking orbit; (4) implications of landing site and parking orbit selection; (5) the use of expendable and reusable lander stages; and (6) the descent/ascent trajectories. Data relating the lunar lander stage design requirements to each of the above factors and others are presented in parametric form. These data will provide useful design data that will be applicable to future mission model modifications and design studies.

Drivers and rates of stock assessments in the United States

PubMed Central

Thorson, James T.; Melnychuk, Michael C.; Methot, Richard; Blackhart, Kristan

2018-01-01

Fisheries management is most effective when based on scientific estimates of sustainable fishing rates. While some simple approaches allow estimation of harvest limits, more data-intensive stock assessments are generally required to evaluate the stock’s biomass and fishing rates relative to sustainable levels. Here we evaluate how stock characteristics relate to the rate of new assessments in the United States. Using a statistical model based on time-to-event analysis and 569 coastal marine fish and invertebrate stocks landed in commercial fisheries, we quantify the impact of region, habitat, life-history, and economic factors on the annual probability of being assessed. Although the majority of landings come from assessed stocks in all regions, less than half of the regionally-landed species currently have been assessed. As expected, our time-to-event model identified landed tonnage and ex-vessel price as the dominant factors determining increased rates of new assessments. However, we also found that after controlling for landings and price, there has been a consistent bias towards assessing larger-bodied species. A number of vulnerable groups such as rockfishes (Scorpaeniformes) and groundsharks (Carcharhiniformes) have a relatively high annual probability of being assessed after controlling for their relatively small tonnage and low price. Due to relatively low landed tonnage and price of species that are currently unassessed, our model suggests that the number of assessed stocks will increase more slowly in future decades. PMID:29750789

Crowd-Sourcing Management Activity Data to Drive GHG Emission Inventories in the Land Use Sector

NASA Astrophysics Data System (ADS)

Paustian, K.; Herrick, J.

2015-12-01

Greenhouse gas (GHG) emissions from the land use sector constitute the largest source category for many countries in Africa. Enhancing C sequestration and reducing GHG emissions on managed lands in Africa has to potential to attract C financing to support adoption of more sustainable land management practices that, in addition to GHG mitigation, can provide co-benefits of more productive and climate-resilient agroecosystems. However, robust systems to measure and monitor C sequestration/GHG reductions are currently a significant barrier to attracting more C financing to land use-related mitigation efforts.Anthropogenic GHG emissions are driven by a variety of environmental factors, including climate and soil attributes, as well as human-activities in the form of land use and management practices. GHG emission inventories typically use empirical or process-based models of emission rates that are driven by environmental and management variables. While a lack of field-based flux and C stock measurements are a limiting factor for GHG estimation, we argue that an even greater limitation may be availabiity of data on the management activities that influence flux rates, particularly in developing countries in Africa. In most developed countries there is a well-developed infrastructure of agricultural statistics and practice surveys that can be used to drive model-based GHG emission estimations. However, this infrastructure is largely lacking in developing countries in Africa. While some activity data (e.g. land cover change) can be derived from remote sensing, many key data (e.g., N fertilizer practices, residue management, manuring) require input from the farmers themselves. The explosive growth in cellular technology, even in many of the poorest parts of Africa, suggests the potential for a new crowd-sourcing approach and direct engagement with farmers to 'leap-frog' the land resource information model of developed countries. Among the many benefits of this approach would be high resolution management data to support GHG inventories at multiple scales. We present an overall conceptual model for this approach and examples from on-going projects in Africa employing direct farmer engagement, cellular technology and apps to develop this information resource.

Land-use change may exacerbate climate change impacts on water resources in the Ganges basin

NASA Astrophysics Data System (ADS)

Tsarouchi, Gina; Buytaert, Wouter

2018-02-01

Quantifying how land-use change and climate change affect water resources is a challenge in hydrological science. This work aims to quantify how future projections of land-use and climate change might affect the hydrological response of the Upper Ganges river basin in northern India, which experiences monsoon flooding almost every year. Three different sets of modelling experiments were run using the Joint UK Land Environment Simulator (JULES) land surface model (LSM) and covering the period 2000-2035: in the first set, only climate change is taken into account, and JULES was driven by the CMIP5 (Coupled Model Intercomparison Project Phase 5) outputs of 21 models, under two representative concentration pathways (RCP4.5 and RCP8.5), whilst land use was held fixed at the year 2010. In the second set, only land-use change is taken into account, and JULES was driven by a time series of 15 future land-use pathways, based on Landsat satellite imagery and the Markov chain simulation, whilst the meteorological boundary conditions were held fixed at years 2000-2005. In the third set, both climate change and land-use change were taken into consideration, as the CMIP5 model outputs were used in conjunction with the 15 future land-use pathways to force JULES. Variations in hydrological variables (stream flow, evapotranspiration and soil moisture) are calculated during the simulation period. Significant changes in the near-future (years 2030-2035) hydrologic fluxes arise under future land-cover and climate change scenarios pointing towards a severe increase in high extremes of flow: the multi-model mean of the 95th percentile of streamflow (Q5) is projected to increase by 63 % under the combined land-use and climate change high emissions scenario (RCP8.5). The changes in all examined hydrological components are greater in the combined land-use and climate change experiment. Results are further presented in a water resources context, aiming to address potential implications of climate change and land-use change from a water demand perspective. We conclude that future water demands in the Upper Ganges region for winter months may not be met.

Comparing alternative tree canopy cover estimates derived from digital aerial photography and field-based assessments

Treesearch

Tracey S. Frescino; Gretchen G. Moisen

2012-01-01

A spatially-explicit representation of live tree canopy cover, such as the National Land Cover Dataset (NLCD) percent tree canopy cover layer, is a valuable tool for many applications, such as defining forest land, delineating wildlife habitat, estimating carbon, and modeling fire risk and behavior. These layers are generated by predictive models wherein their accuracy...

Toiling Together for Social Cohesion: International Influences on the Development of Teacher Education in the United States

ERIC Educational Resources Information Center

Ramsey, Paul J.

2014-01-01

This article examines the ways in which the very idea of teacher education in the United States was transplanted from foreign lands. Teacher education, particularly normal school training, was based on a model imported from despotic Prussia, a model that was popularised by French and American visitors to the northeastern German land. Although…

Predicting bee community responses to land-use changes: Effects of geographic and taxonomic biases.

PubMed

De Palma, Adriana; Abrahamczyk, Stefan; Aizen, Marcelo A; Albrecht, Matthias; Basset, Yves; Bates, Adam; Blake, Robin J; Boutin, Céline; Bugter, Rob; Connop, Stuart; Cruz-López, Leopoldo; Cunningham, Saul A; Darvill, Ben; Diekötter, Tim; Dorn, Silvia; Downing, Nicola; Entling, Martin H; Farwig, Nina; Felicioli, Antonio; Fonte, Steven J; Fowler, Robert; Franzén, Markus; Goulson, Dave; Grass, Ingo; Hanley, Mick E; Hendrix, Stephen D; Herrmann, Farina; Herzog, Felix; Holzschuh, Andrea; Jauker, Birgit; Kessler, Michael; Knight, M E; Kruess, Andreas; Lavelle, Patrick; Le Féon, Violette; Lentini, Pia; Malone, Louise A; Marshall, Jon; Pachón, Eliana Martínez; McFrederick, Quinn S; Morales, Carolina L; Mudri-Stojnic, Sonja; Nates-Parra, Guiomar; Nilsson, Sven G; Öckinger, Erik; Osgathorpe, Lynne; Parra-H, Alejandro; Peres, Carlos A; Persson, Anna S; Petanidou, Theodora; Poveda, Katja; Power, Eileen F; Quaranta, Marino; Quintero, Carolina; Rader, Romina; Richards, Miriam H; Roulston, T'ai; Rousseau, Laurent; Sadler, Jonathan P; Samnegård, Ulrika; Schellhorn, Nancy A; Schüepp, Christof; Schweiger, Oliver; Smith-Pardo, Allan H; Steffan-Dewenter, Ingolf; Stout, Jane C; Tonietto, Rebecca K; Tscharntke, Teja; Tylianakis, Jason M; Verboven, Hans A F; Vergara, Carlos H; Verhulst, Jort; Westphal, Catrin; Yoon, Hyung Joo; Purvis, Andy

2016-08-11

Land-use change and intensification threaten bee populations worldwide, imperilling pollination services. Global models are needed to better characterise, project, and mitigate bees' responses to these human impacts. The available data are, however, geographically and taxonomically unrepresentative; most data are from North America and Western Europe, overrepresenting bumblebees and raising concerns that model results may not be generalizable to other regions and taxa. To assess whether the geographic and taxonomic biases of data could undermine effectiveness of models for conservation policy, we have collated from the published literature a global dataset of bee diversity at sites facing land-use change and intensification, and assess whether bee responses to these pressures vary across 11 regions (Western, Northern, Eastern and Southern Europe; North, Central and South America; Australia and New Zealand; South East Asia; Middle and Southern Africa) and between bumblebees and other bees. Our analyses highlight strong regionally-based responses of total abundance, species richness and Simpson's diversity to land use, caused by variation in the sensitivity of species and potentially in the nature of threats. These results suggest that global extrapolation of models based on geographically and taxonomically restricted data may underestimate the true uncertainty, increasing the risk of ecological surprises.

Simulating the Effects of Irrigation over the U.S. in a Land Surface Model Based on Satellite Derived Agricultural Data

NASA Technical Reports Server (NTRS)

Ozdogan, Mutlu; Rodell, Matthew; Beaudoing, Hiroko Kato; Toll, David L.

2009-01-01

A novel method is introduced for integrating satellite derived irrigation data and high-resolution crop type information into a land surface model (LSM). The objective is to improve the simulation of land surface states and fluxes through better representation of agricultural land use. Ultimately, this scheme could enable numerical weather prediction (NWP) models to capture land-atmosphere feedbacks in managed lands more accurately and thus improve forecast skill. Here we show that application of the new irrigation scheme over the continental US significantly influences the surface water and energy balances by modulating the partitioning of water between the surface and the atmosphere. In our experiment, irrigation caused a 12% increase in evapotranspiration (QLE) and an equivalent reduction in the sensible heat flux (QH) averaged over all irrigated areas in the continental US during the 2003 growing season. Local effects were more extreme: irrigation shifted more than 100 W/m from QH to QLE in many locations in California, eastern Idaho, southern Washington, and southern Colorado during peak crop growth. In these cases, the changes in ground heat flux (QG), net radiation (RNET), evapotranspiration (ET), runoff (R), and soil moisture (SM) were more than 3 W/m(sup 2), 20 W/m(sup 2), 5 mm/day, 0.3 mm/day, and 100 mm, respectively. These results are highly relevant to continental- to global-scale water and energy cycle studies that, to date, have struggled to quantify the effects of agricultural management practices such as irrigation. Based on the results presented here, we expect that better representation of managed lands will lead to improved weather and climate forecasting skill when the new irrigation scheme is incorporated into NWP models such as NOAA's Global Forecast System (GFS).

The influence of global sea surface temperature variability on the large-scale land surface temperature

NASA Astrophysics Data System (ADS)

Tyrrell, Nicholas L.; Dommenget, Dietmar; Frauen, Claudia; Wales, Scott; Rezny, Mike

2015-04-01

In global warming scenarios, global land surface temperatures () warm with greater amplitude than sea surface temperatures (SSTs), leading to a land/sea warming contrast even in equilibrium. Similarly, the interannual variability of is larger than the covariant interannual SST variability, leading to a land/sea contrast in natural variability. This work investigates the land/sea contrast in natural variability based on global observations, coupled general circulation model simulations and idealised atmospheric general circulation model simulations with different SST forcings. The land/sea temperature contrast in interannual variability is found to exist in observations and models to a varying extent in global, tropical and extra-tropical bands. There is agreement between models and observations in the tropics but not the extra-tropics. Causality in the land-sea relationship is explored with modelling experiments forced with prescribed SSTs, where an amplification of the imposed SST variability is seen over land. The amplification of to tropical SST anomalies is due to the enhanced upper level atmospheric warming that corresponds with tropical moist convection over oceans leading to upper level temperature variations that are larger in amplitude than the source SST anomalies. This mechanism is similar to that proposed for explaining the equilibrium global warming land/sea warming contrast. The link of the to the dominant mode of tropical and global interannual climate variability, the El Niño Southern Oscillation (ENSO), is found to be an indirect and delayed connection. ENSO SST variability affects the oceans outside the tropical Pacific, which in turn leads to a further, amplified and delayed response of.

Agent-based modeling of deforestation in southern Yucatán, Mexico, and reforestation in the Midwest United States

PubMed Central

Manson, Steven M.; Evans, Tom

2007-01-01

We combine mixed-methods research with integrated agent-based modeling to understand land change and economic decision making in the United States and Mexico. This work demonstrates how sustainability science benefits from combining integrated agent-based modeling (which blends methods from the social, ecological, and information sciences) and mixed-methods research (which interleaves multiple approaches ranging from qualitative field research to quantitative laboratory experiments and interpretation of remotely sensed imagery). We test assumptions of utility-maximizing behavior in household-level landscape management in south-central Indiana, linking parcel data, land cover derived from aerial photography, and findings from laboratory experiments. We examine the role of uncertainty and limited information, preferences, differential demographic attributes, and past experience and future time horizons. We also use evolutionary programming to represent bounded rationality in agriculturalist households in the southern Yucatán of Mexico. This approach captures realistic rule of thumb strategies while identifying social and environmental factors in a manner similar to econometric models. These case studies highlight the role of computational models of decision making in land-change contexts and advance our understanding of decision making in general. PMID:18093928

Modelling Seasonally Freezing Ground Conditions

DTIC Science & Technology

1989-05-01

used as the ’snow input’ in the larger hydrological models, e.g. Pangburn (1987). The most advanced index model is Anderson’s (1973) model. This bases...source as the soils) is shown in figures 32 and 33. Table 10 shows the percentage areas of Hydrologic Soil Groups, Land Use and Slope Distribution for...C") z c~cu CYa) 65 table 10: Percentage areas of Hydrologic Soil Grouos, Land Use and Slope Distribution over W3 (?Pn!ke e: al., 1978) Parameter

Effects of future climate conditions on terrestrial export from coastal southern California

NASA Astrophysics Data System (ADS)

Feng, D.; Zhao, Y.; Raoufi, R.; Beighley, E.; Melack, J. M.

2015-12-01

The Santa Barbara Coastal - Long Term Ecological Research Project (SBC-LTER) is focused on investigating the relative importance of land and ocean processes in structuring giant kelp forest ecosystems. Understanding how current and future climate conditions influence terrestrial export is a central theme for the project. Here we combine the Hillslope River Routing (HRR) model and daily precipitation and temperature downscaled using statistical downscaling based on localized constructed Analogs (LOCA) to estimate recent streamflow dynamics (2000 to 2014) and future conditions (2015 to 2100). The HRR model covers the SBC-LTER watersheds from just west of the Ventura River to Point Conception; a land area of roughly 800 km2 with 179 watersheds ranging from 0.1 to 123 km2. The downscaled climate conditions have a spatial resolution of 6 km by 6 km. Here, we use the Penman-Monteith method with the Food and Agriculture Organization of the United Nations (FAO) limited climate data approximations and land surface conditions (albedo, leaf area index, land cover) measured from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites to estimate potential evapotranspiration (PET). The HRR model is calibrated for the period 2000 to 2014 using USGS and LTER streamflow. An automated calibration technique is used. For future climate scenarios, we use mean 8-day land cover conditions. Future streamflow, ET and soil moisture statistics are presented and based on downscaled P and T from ten climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5).

A Monte Carlo approach to the inverse problem of diffuse pollution risk in agricultural catchments

NASA Astrophysics Data System (ADS)

Milledge, D.; Lane, S. N.; Heathwaite, A. L.; Reaney, S.

2012-04-01

The hydrological and biogeochemical processes that operate in catchments influence the ecological quality of freshwater systems through delivery of fine sediment, nutrients and organic matter. As an alternative to the, often complex, reductionist models we outline a - data-driven - approach based on 'inverse modelling'. We invert SCIMAP, a parsimonious risk based model that has an explicit treatment of hydrological connectivity, and use a Bayesian approach to determine the risk that must be assigned to different land uses in a catchment in order to explain the spatial patterns of measured in-stream nutrient concentrations. First, we apply the model to a set of eleven UK catchments to show that: 1) some land use generates a consistently high or low risk of diffuse nitrate (N) and Phosphate (P) pollution; but 2) the risks associated with different land uses vary both between catchments and between P and N delivery; and 3) that the dominant sources of P and N risk in the catchment are often a function of the spatial configuration of land uses. These results suggest that on a case by case basis, inverse modelling may be used to help prioritise the focus of interventions to reduce diffuse pollution risk for freshwater ecosystems. However, a key uncertainty in this approach is the extent to which it can recover the 'true' risks associated with a land cover given error in both the input parameters and equifinality in model outcomes. We test this using a set of synthetic scenarios in which the true risks can be pre-assigned then compared with those recovered from the inverse model. We use these scenarios to identify the number of simulations and observations required to optimize recovery of the true weights, then explore the conditions under which the inverse model becomes equifinal (hampering recovery of the true weights) We find that this is strongly dependent on the covariance in land covers between subcatchments, introducing the possibility that instream sampling could be designed or subsampled to maximize identifiability of the risks associated with a given land cover.

User Generated Spatial Content Sources for Land Use/Land Cover Validation Purposes: Suitability Analysis and Integration Model

NASA Astrophysics Data System (ADS)

Estima, Jacinto Paulo Simoes

Traditional geographic information has been produced by mapping agencies and corporations, using high skilled people as well as expensive precision equipment and procedures, in a very costly approach. The production of land use and land cover databases are just one example of such traditional approach. On the other side, The amount of Geographic Information created and shared by citizens through the Web has been increasing exponentially during the last decade, resulting from the emergence and popularization of technologies such as the Web 2.0, cloud computing, GPS, smart phones, among others. Such comprehensive amount of free geographic data might have valuable information to extract and thus opening great possibilities to improve significantly the production of land use and land cover databases. In this thesis we explored the feasibility of using geographic data from different user generated spatial content initiatives in the process of land use and land cover database production. Data from Panoramio, Flickr and OpenStreetMap were explored in terms of their spatial and temporal distribution, and their distribution over the different land use and land cover classes. We then proposed a conceptual model to integrate data from suitable user generated spatial content initiatives based on identified dissimilarities among a comprehensive list of initiatives. Finally we developed a prototype implementing the proposed integration model, which was then validated by using the prototype to solve four identified use cases. We concluded that data from user generated spatial content initiatives has great value but should be integrated to increase their potential. The possibility of integrating data from such initiatives in an integration model was proved. Using the developed prototype, the relevance of the integration model was also demonstrated for different use cases. None None None

High Performance Hydrometeorological Modeling, Land Data Assimilation and Parameter Estimation with the Land Information System at NASA/GSFC

NASA Astrophysics Data System (ADS)

Peters-Lidard, C. D.; Kumar, S. V.; Santanello, J. A.; Tian, Y.; Rodell, M.; Mocko, D.; Reichle, R.

2008-12-01

The Land Information System (LIS; http://lis.gsfc.nasa.gov; Kumar et al., 2006; Peters-Lidard et al., 2007) is a flexible land surface modeling framework that has been developed with the goal of integrating satellite- and ground-based observational data products and advanced land surface modeling techniques to produce optimal fields of land surface states and fluxes. The LIS software was the co-winner of NASA's 2005 Software of the Year award. LIS facilitates the integration of observations from Earth-observing systems and predictions and forecasts from Earth System and Earth science models into the decision-making processes of partnering agency and national organizations. Due to its flexible software design, LIS can serve both as a Problem Solving Environment (PSE) for hydrologic research to enable accurate global water and energy cycle predictions, and as a Decision Support System (DSS) to generate useful information for application areas including disaster management, water resources management, agricultural management, numerical weather prediction, air quality and military mobility assessment. LIS has evolved from two earlier efforts - North American Land Data Assimilation System (NLDAS; Mitchell et al. 2004) and Global Land Data Assimilation System (GLDAS; Rodell et al. 2004) that focused primarily on improving numerical weather prediction skills by improving the characterization of the land surface conditions. Both of these systems, now use specific configurations of the LIS software in their current implementations. LIS not only consolidates the capabilities of these two systems, but also enables a much larger variety of configurations with respect to horizontal spatial resolution, input datasets and choice of land surface model through 'plugins'. In addition to these capabilities, LIS has also been demonstrated for parameter estimation (Peters-Lidard et al., 2008; Santanello et al., 2007) and data assimilation (Kumar et al., 2008). Examples and case studies demonstrating the capabilities and impacts of LIS for hydrometeorological modeling, land data assimilation and parameter estimation will be presented.

Distributed Modelling of Stormflow Generation: Assessing the Effect of Ground Cover

NASA Astrophysics Data System (ADS)

Jarihani, B.; Sidle, R. C.; Roth, C. H.; Bartley, R.; Wilkinson, S. N.

2017-12-01

Understanding the effects of grazing management and land cover changes on surface hydrology is important for water resources and land management. A distributed hydrological modelling platform, wflow, (that was developed as part of Deltares's OpenStreams project) is used to assess the effect of land management practices on runoff generation processes. The model was applied to Weany Creek, a small catchment (13.6 km2) of the Burdekin Basin, North Australia, which is being studied to understand sources of sediment and nutrients to the Great Barrier Reef. Satellite and drone-based ground cover data, high resolution topography from LiDAR, soil properties, and distributed rainfall data were used to parameterise the model. Wflow was used to predict total runoff, peak runoff, time of rise, and lag time for several events of varying magnitudes and antecedent moisture conditions. A nested approach was employed to calibrate the model by using recorded flow hydrographs at three scales: (1) a hillslope sub-catchment: (2) a gullied sub-catchment; and the 13.6 km2 catchment outlet. Model performance was evaluated by comparing observed and predicted stormflow hydrograph attributes using the Nash Sutcliffe efficiency metric. By using a nested approach, spatiotemporal patterns of overland flow occurrence across the catchment can also be evaluated. The results show that a process-based distributed model can be calibrated to simulate spatial and temporal patterns of runoff generation processes, to help identify dominant processes which may be addressed by land management to improve rainfall retention. The model will be used to assess the effects of ground cover changes due to management practices in grazed lands on storm runoff.

Impact of Land Model Depth on Long Term Climate Variability and Change.

NASA Astrophysics Data System (ADS)

Gonzalez-Rouco, J. F.; García-Bustamante, E.; Hagemann, S.; Lorentz, S.; Jungclaus, J.; de Vrese, P.; Melo, C.; Navarro, J.; Steinert, N.

2017-12-01

The available evidence indicates that the simulation of subsurface thermodynamics in current General Circulation Models (GCMs) is not accurate enough due to the land-surface model imposing a zero heat flux boundary condition that is too close to the surface. Shallow land model components distort the amplitude and phase of the heat propagation in the subsurface with implications for energy storage and land-air interactions. Off line land surface model experiments forced with GCM climate change simulations and comparison with borehole temperature profiles indicate there is a large reduction of the energy storage of the soil using the typical shallow land models included in most GCMs. However, the impact of increasing the depth of the soil model in `on-line' GCM simulations of climate variability or climate change has not yet been systematically explored. The JSBACH land surface model has been used in stand alone mode, driven by outputs of the MPIESM to assess the impacts of progressively increasing the depth of the soil model. In a first stage, preindustrial control simulations are developed increasing the lower depth of the zero flux bottom boundary condition placed for temperature at the base of the fifth model layer (9.83 m) down to 294.6 m (layer 9), thus allowing for the bottom layers to reach equilibrium. Starting from piControl conditions, historical and scenario simulations have been performed since 1850 yr. The impact of increasing depths on the subsurface layer temperatures is analysed as well as the amounts of energy involved. This is done also considering permafrost processes (freezing and thawing). An evaluation on the influence of deepening the bottom boundary on the simulation of low frequency variability and temperature trends is provided.

Hyperspectral Analysis of Soil Total Nitrogen in Subsided Land Using the Local Correlation Maximization-Complementary Superiority (LCMCS) Method.

PubMed

Lin, Lixin; Wang, Yunjia; Teng, Jiyao; Xi, Xiuxiu

2015-07-23

The measurement of soil total nitrogen (TN) by hyperspectral remote sensing provides an important tool for soil restoration programs in areas with subsided land caused by the extraction of natural resources. This study used the local correlation maximization-complementary superiority method (LCMCS) to establish TN prediction models by considering the relationship between spectral reflectance (measured by an ASD FieldSpec 3 spectroradiometer) and TN based on spectral reflectance curves of soil samples collected from subsided land which is determined by synthetic aperture radar interferometry (InSAR) technology. Based on the 1655 selected effective bands of the optimal spectrum (OSP) of the first derivate differential of reciprocal logarithm ([log{1/R}]'), (correlation coefficients, p < 0.01), the optimal model of LCMCS method was obtained to determine the final model, which produced lower prediction errors (root mean square error of validation [RMSEV] = 0.89, mean relative error of validation [MREV] = 5.93%) when compared with models built by the local correlation maximization (LCM), complementary superiority (CS) and partial least squares regression (PLS) methods. The predictive effect of LCMCS model was optional in Cangzhou, Renqiu and Fengfeng District. Results indicate that the LCMCS method has great potential to monitor TN in subsided lands caused by the extraction of natural resources including groundwater, oil and coal.

Mapping large-area landscape suitability for honey bees to assess the influence of land-use change on sustainability of national pollination services.

PubMed

Gallant, Alisa L; Euliss, Ned H; Browning, Zac

2014-01-01

Pollination is a critical ecosystem service affected by various drivers of land-use change, such as policies and programs aimed at land resources, market values for crop commodities, local land-management decisions, and shifts in climate. The United States is the world's most active market for pollination services by honey bees, and the Northern Great Plains provide the majority of bee colonies used to meet the Nation's annual pollination needs. Legislation requiring increased production of biofuel crops, increasing commodity prices for crops of little nutritional value for bees in the Northern Great Plains, and reductions in government programs aimed at promoting land conservation are converging to alter the regional landscape in ways that challenge beekeepers to provide adequate numbers of hives for national pollination services. We developed a spatially explicit model that identifies sites with the potential to support large apiaries based on local-scale land-cover requirements for honey bees. We produced maps of potential apiary locations for North Dakota, a leading producer of honey, based on land-cover maps representing (1) an annual time series compiled from existing operational products and (2) a realistic scenario of land change. We found that existing land-cover products lack sufficient local accuracy to monitor actual changes in landscape suitability for honey bees, but our model proved informative for evaluating effects on suitability under scenarios of land change. The scenario we implemented was aligned with current drivers of land-use change in the Northern Great Plains and highlighted the importance of conservation lands in landscapes intensively and extensively managed for crops.

Mapping large-area landscape suitability for honey bees to assess the influence of land-use change on sustainability of national pollination services

USGS Publications Warehouse

Gallant, Alisa L.; Euliss, Ned H.; Browning, Zac

2014-01-01

Pollination is a critical ecosystem service affected by various drivers of land-use change, such as policies and programs aimed at land resources, market values for crop commodities, local land-management decisions, and shifts in climate. The United States is the world's most active market for pollination services by honey bees, and the Northern Great Plains provide the majority of bee colonies used to meet the Nation's annual pollination needs. Legislation requiring increased production of biofuel crops, increasing commodity prices for crops of little nutritional value for bees in the Northern Great Plains, and reductions in government programs aimed at promoting land conservation are converging to alter the regional landscape in ways that challenge beekeepers to provide adequate numbers of hives for national pollination services. We developed a spatially explicit model that identifies sites with the potential to support large apiaries based on local-scale land-cover requirements for honey bees. We produced maps of potential apiary locations for North Dakota, a leading producer of honey, based on land-cover maps representing (1) an annual time series compiled from existing operational products and (2) a realistic scenario of land change. We found that existing land-cover products lack sufficient local accuracy to monitor actual changes in landscape suitability for honey bees, but our model proved informative for evaluating effects on suitability under scenarios of land change. The scenario we implemented was aligned with current drivers of land-use change in the Northern Great Plains and highlighted the importance of conservation lands in landscapes intensively and extensively managed for crops.

Mapping Large-Area Landscape Suitability for Honey Bees to Assess the Influence of Land-Use Change on Sustainability of National Pollination Services

PubMed Central

Gallant, Alisa L.; Euliss, Ned H.; Browning, Zac

2014-01-01

Pollination is a critical ecosystem service affected by various drivers of land-use change, such as policies and programs aimed at land resources, market values for crop commodities, local land-management decisions, and shifts in climate. The United States is the world's most active market for pollination services by honey bees, and the Northern Great Plains provide the majority of bee colonies used to meet the Nation's annual pollination needs. Legislation requiring increased production of biofuel crops, increasing commodity prices for crops of little nutritional value for bees in the Northern Great Plains, and reductions in government programs aimed at promoting land conservation are converging to alter the regional landscape in ways that challenge beekeepers to provide adequate numbers of hives for national pollination services. We developed a spatially explicit model that identifies sites with the potential to support large apiaries based on local-scale land-cover requirements for honey bees. We produced maps of potential apiary locations for North Dakota, a leading producer of honey, based on land-cover maps representing (1) an annual time series compiled from existing operational products and (2) a realistic scenario of land change. We found that existing land-cover products lack sufficient local accuracy to monitor actual changes in landscape suitability for honey bees, but our model proved informative for evaluating effects on suitability under scenarios of land change. The scenario we implemented was aligned with current drivers of land-use change in the Northern Great Plains and highlighted the importance of conservation lands in landscapes intensively and extensively managed for crops. PMID:24919181

[Process of land use transition and its impact on regional ecological quality in the Middle Reaches of Heihe River, China].

PubMed

Wang, Fu Hong; Zhao, Rui Feng; Zhang, Li Hua; Li, Hong Wei

2017-12-01

Land use transition is one of the main drivers of regional ecosystem change in arid area, which directly affects human well-being. Based on the satellite images of 1987, 2001 and 2016, the change detection assessment model and ecological response model were used to analyze the process of land use transition and response of ecological quality during 1987-2016 in the ecologically fragile middle reaches of the Heihe River. The results showed that the land use change was significant during 1987-2016 and the total change increased significantly, as well as the continuous increase of the cultivated land and construction land. There was a strong tendency of transform from grassland to cultivated land, while the tendency of transforming unused land to other land classes was not strong under a random process of gain or loss. During 1987-2016, the ecological quality of the study area displayed a decreasing trend as a whole and the ecological land decreased by 2.8%. The land use transition with the greatest impact on the ecological environment degradation was the transition of the grassland to the cultivated land and unused land. Therefore, in order to promote the sustainable use of regional land resources and to improve the regional ecological quality, it is necessary to allocate the proportion of production land and ecological land according to the regional water resources.

Investigate the plant biomass response to climate warming in permafrost ecosystem using matrix-based data assimilation

NASA Astrophysics Data System (ADS)

Lu, X.; Du, Z.; Schuur, E.; Luo, Y.

2017-12-01

Permafrost is one of the most vulnerable regions on the earth with over 40% world soil C represented in this region. Future climate warming potentially has a great impact on this region. On one hand, rising temperature accelerates permafrost soil thaw and release more C from land. On the other hand, warming may also increase the plant growing season length and therefore negatively feedback to climate change by increasing annual land C uptake. However, whether permafrost vegetation biomass change in response to warming can sequester more C has not been well understood. Manipulated air warming experiments reported that air warming has very limited impacts on grass land productivity and biomass growth in permafrost region [Mauritz et al., 2017]. It is hard to reveal the mechanisms behind the limited air warming response directly from experiment data. We employ a vegetation C cycle matrix model based on Community land model 4.5 (CLM4.5) and data assimilation technique to investigate how much do phenology and physiology processes contribute to the response respectively. Our results indicate phenology contributes the most in response to warming. The shift of vegetation parameter distributions after 2012 indicate vegetation acclimation may explain the modest response in plant biomass to air warming. The results suggest future model development need to take vegetation acclimation more seriously. The novel matrix-based model allows data assimilation to be conducted more efficiently. It provides more functional understanding of the models as well as the mechanism behind experiment data.

Application of Physics Based Distributed Hydrologic Models to Assess Anthropologic Land Disturbance in Watersheds

NASA Astrophysics Data System (ADS)

Downer, C. W.; Ogden, F. L.; Byrd, A. R.

2008-12-01

The Department of Defense (DoD) manages approximately 200,000 km2 of land within the United States on military installations and flood control and river improvement projects. The Watershed Systems Group (WSG) within the Coastal and Hydraulics Laboratory of the Engineer Research and Development Center (ERDC) supports the US Army and the US Army Corps of Engineers in both military and civil operations through the development, modification and application of surface and sub-surface hydrologic models. The US Army has a long history of land management and the development of analytical tools to assist with the management of US Army lands. The US Army has invested heavily in the distributed hydrologic model GSSHA and its predecessor CASC2D. These tools have been applied at numerous military and civil sites to analyze the effects of landscape alteration on hydrologic response and related consequences, changes in erosion and sediment transport, along with associated contaminants. Examples include: impacts of military training and land management activities, impact of changing land use (urbanization or environmental restoration), as well as impacts of management practices employed to abate problems, i.e. Best Management Practices (BMPs). Traditional models such as HSPF and SWAT, are largely conceptual in nature. GSSHA attempts to simulate the physical processes actually occurring in the watershed allowing the user to explicitly simulate changing parameter values in response to changes in land use, land cover, elevation, etc. Issues of scale raise questions: How do we best include fine-scale land use or management features in models of large watersheds? Do these features have to be represented explicitly through physical processes in the watershed domain? Can a point model, physical or empirical, suffice? Can these features be lumped into coarsely resolved numerical grids or sub-watersheds? In this presentation we will discuss the US Army's distributed hydrologic models in terms of how they simulate the relevant processes and present multiple applications of the models used for analyzing land management and land use change. Using these applications as a basis we will discuss issues related to the analysis of anthropogenic alterations in the landscape.

Pairing top-down and bottom-up approaches to analyze catchment scale management of water quality and quantity

NASA Astrophysics Data System (ADS)

Lovette, J. P.; Duncan, J. M.; Band, L. E.

2016-12-01

Watershed management requires information on the hydrologic impacts of local to regional land use, land cover and infrastructure conditions. Management of runoff volumes, storm flows, and water quality can benefit from large scale, "top-down" screening tools, using readily available information, as well as more detailed, "bottom-up" process-based models that explicitly track local runoff production and routing from sources to receiving water bodies. Regional scale data, available nationwide through the NHD+, and top-down models based on aggregated catchment information provide useful tools for estimating regional patterns of peak flows, volumes and nutrient loads at the catchment level. Management impacts can be estimated with these models, but have limited ability to resolve impacts beyond simple changes to land cover proportions. Alternatively, distributed process-based models provide more flexibility in modeling management impacts by resolving spatial patterns of nutrient source, runoff generation, and uptake. This bottom-up approach can incorporate explicit patterns of land cover, drainage connectivity, and vegetation extent, but are typically applied over smaller areas. Here, we first model peak flood flows and nitrogen loads across North Carolina's 70,000 NHD+ catchments using USGS regional streamflow regression equations and the SPARROW model. We also estimate management impact by altering aggregated sources in each of these models. To address the missing spatial implications of the top-down approach, we further explore the demand for riparian buffers as a management strategy, simulating the accumulation of nutrient sources along flow paths and the potential mitigation of these sources through forested buffers. We use the Regional Hydro-Ecological Simulation System (RHESSys) to model changes across several basins in North Carolina's Piedmont and Blue Ridge regions, ranging in size from 15 - 1,130 km2. The two approaches provide a complementary set of tools for large area screening, followed by smaller, more process based assessment and design tools.

Integrating macro and micro scale approaches in the agent-based modeling of residential dynamics

NASA Astrophysics Data System (ADS)

Saeedi, Sara

2018-06-01

With the advancement of computational modeling and simulation (M&S) methods as well as data collection technologies, urban dynamics modeling substantially improved over the last several decades. The complex urban dynamics processes are most effectively modeled not at the macro-scale, but following a bottom-up approach, by simulating the decisions of individual entities, or residents. Agent-based modeling (ABM) provides the key to a dynamic M&S framework that is able to integrate socioeconomic with environmental models, and to operate at both micro and macro geographical scales. In this study, a multi-agent system is proposed to simulate residential dynamics by considering spatiotemporal land use changes. In the proposed ABM, macro-scale land use change prediction is modeled by Artificial Neural Network (ANN) and deployed as the agent environment and micro-scale residential dynamics behaviors autonomously implemented by household agents. These two levels of simulation interacted and jointly promoted urbanization process in an urban area of Tehran city in Iran. The model simulates the behavior of individual households in finding ideal locations to dwell. The household agents are divided into three main groups based on their income rank and they are further classified into different categories based on a number of attributes. These attributes determine the households' preferences for finding new dwellings and change with time. The ABM environment is represented by a land-use map in which the properties of the land parcels change dynamically over the simulation time. The outputs of this model are a set of maps showing the pattern of different groups of households in the city. These patterns can be used by city planners to find optimum locations for building new residential units or adding new services to the city. The simulation results show that combining macro- and micro-level simulation can give full play to the potential of the ABM to understand the driving mechanism of urbanization and provide decision-making support for urban management.

Modeling evapotranspiration over China's landmass from 1979-2012 using three surface models

NASA Astrophysics Data System (ADS)

Sun, Shaobo; Chen, Baozhang; Zhang, Huifang; Lin, Xiaofeng

2017-04-01

Land surface models (LSMs) are useful tools to estimate land evapotranspiration at a grid scale and for a long-term applications. Here, the Community Land Model 4.0 (CLM4.0), Dynamic Land Model (DLM) and Variable Infiltration Capacity (VIC) model were driven with observation-based forcing data sets, and a multiple LSM ensemble-averaged evapotranspiration (ET) product (LSMs-ET) was developed and its spatial-temporal variations were analyzed for the China landmass over the period 1979-2012. Evaluations against measurements from nine flux towers at site scale and surface water budget based ET at regional scale showed that the LSMs-ET had good performance in most areas of China's landmass. The inter-comparisons between the ET estimates and the independent ET products from remote sensing and upscaling methods suggested that there were a fairly consistent patterns between each data sets. The LSMs-ET produced a mean annual ET of 351.24±10.7 mm yr-1 over 1979-2012, and its spatial-temporal variation analyses showed that (i) there was an overall significant ET increasing trend, with a value of 0.72 mm yr-1 (p < 0.01); (ii) 36.01% of Chinese land had significant increasing trends, ranging from 1 to 9 mm yr-1, while only 6.41% of the area showed significant decreasing trends, ranging from -6.28 to -0.08 mm yr-1. Analyses of ET variations in each climate region clearly showed that the Tibetan Plateau areas were the main contributors to the overall increasing ET trends of China.

Detecting ecosystem performance anomalies for land management in the upper colorado river basin using satellite observations, climate data, and ecosystem models

USGS Publications Warehouse

Gu, Yingxin; Wylie, B.K.

2010-01-01

This study identifies areas with ecosystem performance anomalies (EPA) within the Upper Colorado River Basin (UCRB) during 2005-2007 using satellite observations, climate data, and ecosystem models. The final EPA maps with 250-m spatial resolution were categorized as normal performance, underperformance, and overperformance (observed performance relative to weather-based predictions) at the 90% level of confidence. The EPA maps were validated using "percentage of bare soil" ground observations. The validation results at locations with comparable site potential showed that regions identified as persistently underperforming (overperforming) tended to have a higher (lower) percentage of bare soil, suggesting that our preliminary EPA maps are reliable and agree with ground-based observations. The 3-year (2005-2007) persistent EPA map from this study provides the first quantitative evaluation of ecosystem performance anomalies within the UCRB and will help the Bureau of Land Management (BLM) identify potentially degraded lands. Results from this study can be used as a prototype by BLM and other land managers for making optimal land management decisions. ?? 2010 by the authors.

Detecting Ecosystem Performance Anomalies for Land Management in the Upper Colorado River Basin Using Satellite Observations, Climate Data, and Ecosystem Models

USGS Publications Warehouse

Gu, Yingxin; Wylie, Bruce K.

2010-01-01

This study identifies areas with ecosystem performance anomalies (EPA) within the Upper Colorado River Basin (UCRB) during 2005–2007 using satellite observations, climate data, and ecosystem models. The final EPA maps with 250-m spatial resolution were categorized as normal performance, underperformance, and overperformance (observed performance relative to weather-based predictions) at the 90% level of confidence. The EPA maps were validated using “percentage of bare soil” ground observations. The validation results at locations with comparable site potential showed that regions identified as persistently underperforming (overperforming) tended to have a higher (lower) percentage of bare soil, suggesting that our preliminary EPA maps are reliable and agree with ground-based observations. The 3-year (2005–2007) persistent EPA map from this study provides the first quantitative evaluation of ecosystem performance anomalies within the UCRB and will help the Bureau of Land Management (BLM) identify potentially degraded lands. Results from this study can be used as a prototype by BLM and other land managers for making optimal land management decisions.

Constraints and Approach for Selecting the Mars Surveyor '01 Landing Site

NASA Technical Reports Server (NTRS)

Golombek, M.; Bridges, N.; Gilmore, M.; Haldemann, A.; Parker, T.; Saunders, R.; Spencer, D.; Smith, J.; Weitz, C.

1999-01-01

There are many similarities between the Mars Surveyor '01 (MS '01) landing site selection process and that of Mars Pathfinder. The selection process includes two parallel activities in which engineers define and refine the capabilities of the spacecraft through design, testing and modeling and scientists define a set of landing site constraints based on the spacecraft design and landing scenario. As for Pathfinder, the safety of the site is without question the single most important factor, for the simple reason that failure to land safely yields no science and exposes the mission and program to considerable risk. The selection process must be thorough and defensible and capable of surviving multiple withering reviews similar to the Pathfinder decision. On Pathfinder, this was accomplished by attempting to understand the surface properties of sites using available remote sensing data sets and models based on them. Science objectives are factored into the selection process only after the safety of the site is validated. Finally, as for Pathfinder, the selection process is being done in an open environment with multiple opportunities for community involvement including open workshops, with education and outreach opportunities.

Constraints, Approach and Present Status for Selecting the Mars Surveyor 2001 Landing Site

NASA Technical Reports Server (NTRS)

Golombek, M.; Anderson, F.; Bridges, N.; Briggs, G.; Gilmore, M.; Gulick, V.; Haldemann, A.; Parker, T.; Saunders, R.; Spencer, D.;

National forest economic clusters: a new model for assessing national-forest-based natural resources products and services.

Treesearch

Thomas D. Rojas

2007-01-01

National forest lands encompass numerous rural and urban communities. Some national-forest-based communities lie embedded within national forests, and others reside just outside the official boundaries of national forests. The urban and rural communities within or near national forest lands include a wide variety of historical traditions and cultural values that affect...

Incorporating GIS data into an agent-based model to support planning policy making for the development of creative industries

NASA Astrophysics Data System (ADS)

Liu, Helin; Silva, Elisabete A.; Wang, Qian

2016-07-01

This paper presents an extension to the agent-based model "Creative Industries Development-Urban Spatial Structure Transformation" by incorporating GIS data. Three agent classes, creative firms, creative workers and urban government, are considered in the model, and the spatial environment represents a set of GIS data layers (i.e. road network, key housing areas, land use). With the goal to facilitate urban policy makers to draw up policies locally and optimise the land use assignment in order to support the development of creative industries, the improved model exhibited its capacity to assist the policy makers conducting experiments and simulating different policy scenarios to see the corresponding dynamics of the spatial distributions of creative firms and creative workers across time within a city/district. The spatiotemporal graphs and maps record the simulation results and can be used as a reference by the policy makers to adjust land use plans adaptively at different stages of the creative industries' development process.

Modeling Land Use Change Impacts on Water Resources in a Tropical West African Catchment (dano, Burkina Faso)

NASA Astrophysics Data System (ADS)

Yira, Y.; Diekkrüger, B.; Steup, G.; Bossa, A. Y.

2015-12-01

This study investigates the impacts of land use change on water resources in the Dano catchment, Burkina Faso, using a physically based hydrological simulation model and land use scenarios. Land use dynamic in the catchment was assessed through the analysis of four land use maps corresponding to the land use status in 1990, 2000, 2007 and 2013. A reclassification procedure of the maps permitted to assess the major land use changes in the catchment from 1990 to 2013. The land use maps were used to build five land use scenarios corresponding to different levels of land use change in the catchment. Water balance was simulated by applying the Water flow and balance Simulation Model (WaSiM) using observed discharge, soil moisture, and groundwater level for model calibration and validation. Model statistical quality measures (R2, NSE and KGE) achieved during the calibration and the validation ranged between 0.9 and 0.6 for total discharge, soil moisture, and groundwater level, indicating satisfying to good agreements between observed and simulated variables. After a successful multi-criteria validation the model was run with the land use scenarios. The land use assessment exhibited a decrease of savannah at an annual rate of 2% since 1990. Conversely, cropland and urban areas have increased. Since urban areas occupy only 3% of the catchment in 2013 it can be assumed that savannah was mainly converted to cropland. The increase in cropland area results from the population growth and the farming system in the catchment. A clear increase in total discharge (+17%) and decrease in evapotranspiration (-5%) was observed following land use change in the catchment. A strong relationship was established between savannah degradation, cropland expansion, discharge increase and reduction of evapotranspiration. The increase in total discharge is related to high discharge and peak flow, suggesting (i) an increase in water resources that is not available for plant growth and the population of the catchment and (ii) an alteration of flood risk for both the population within and downstream of the catchment.

Land Use Zoning at the County Level Based on a Multi-Objective Particle Swarm Optimization Algorithm: A Case Study from Yicheng, China

PubMed Central

Liu, Yaolin; Wang, Hua; Ji, Yingli; Liu, Zhongqiu; Zhao, Xiang

2012-01-01

Comprehensive land-use planning (CLUP) at the county level in China must include land-use zoning. This is specifically stipulated by the China Land Management Law and aims to achieve strict control on the usages of land. The land-use zoning problem is treated as a multi-objective optimization problem (MOOP) in this article, which is different from the traditional treatment. A particle swarm optimization (PSO) based model is applied to the problem and is developed to maximize the attribute differences between land-use zones, the spatial compactness, the degree of spatial harmony and the ecological benefits of the land-use zones. This is subject to some constraints such as: the quantity limitations for varying land-use zones, regulations assigning land units to a certain land-use zone, and the stipulation of a minimum parcel area in a land-use zoning map. In addition, a crossover and mutation operator from a genetic algorithm is adopted to avoid the prematurity of PSO. The results obtained for Yicheng, a county in central China, using different objective weighting schemes, are compared and suggest that: (1) the fundamental demand for attribute difference between land-use zones leads to a mass of fragmentary land-use zones; (2) the spatial pattern of land-use zones is remarkably optimized when a weight is given to the sub-objectives of spatial compactness and the degree of spatial harmony, simultaneously, with a reduction of attribute difference between land-use zones; (3) when a weight is given to the sub-objective of ecological benefits of the land-use zones, the ecological benefits get a slight increase also at the expense of a reduction in attribute difference between land-use zones; (4) the pursuit of spatial harmony or spatial compactness may have a negative effect on each other; (5) an increase in the ecological benefits may improve the spatial compactness and spatial harmony of the land-use zones; (6) adjusting the weights assigned to each sub-objective can generate a corresponding optimal solution, with a different quantity structure and spatial pattern to satisfy the preference of the different decision makers; (7) the model proposed in this paper is capable of handling the land-use zoning problem, and the crossover and mutation operator can improve the performance of the model, but, nevertheless, leads to increased time consumption. PMID:23066398

Impact of LUCC on streamflow based on the SWAT model over the Wei River basin on the Loess Plateau in China

NASA Astrophysics Data System (ADS)

Wang, Hong; Sun, Fubao; Xia, Jun; Liu, Wenbin

2017-04-01

Under the Grain for Green Project in China, vegetation recovery construction has been widely implemented on the Loess Plateau for the purpose of soil and water conservation. Now it is becoming controversial whether the recovery construction involving vegetation, particularly forest, is reducing the streamflow in the rivers of the Yellow River basin. In this study, we chose the Wei River, the largest branch of the Yellow River, with revegetated construction area as the study area. To do that, we apply the widely used Soil and Water Assessment Tool (SWAT) model for the upper and middle reaches of the Wei River basin. The SWAT model was forced with daily observed meteorological forcings (1960-2009) calibrated against daily streamflow for 1960-1969, validated for the period of 1970-1979, and used for analysis for 1980-2009. To investigate the impact of LUCC (land use and land cover change) on the streamflow, we firstly use two observed land use maps from 1980 and 2005 that are based on national land survey statistics merged with satellite observations. We found that the mean streamflow generated by using the 2005 land use map decreased in comparison with that using the 1980 one, with the same meteorological forcings. Of particular interest here is that the streamflow decreased on agricultural land but increased in forest areas. More specifically, the surface runoff, soil flow, and baseflow all decreased on agricultural land, while the soil flow and baseflow of forest areas increased. To investigate that, we then designed five scenarios: (S1) the present land use (1980) and (S2) 10 %, (S3) 20 %, (S4) 40 %, and (S5) 100 % of agricultural land that was converted into mixed forest. We found that the streamflow consistently increased with agricultural land converted into forest by about 7.4 mm per 10 %. Our modeling results suggest that forest recovery construction has a positive impact on both soil flow and baseflow by compensating for reduced surface runoff, which leads to a slight increase in the streamflow in the Wei River with the mixed landscapes on the Loess Plateau that include earth-rock mountain area.

Application of a Three-Dimensional Water Quality Model as a Decision Support Tool for the Management of Land-Use Changes in the Catchment of an Oligotrophic Lake

NASA Astrophysics Data System (ADS)

Trolle, Dennis; Spigel, Bob; Hamilton, David P.; Norton, Ned; Sutherland, Donna; Plew, David; Allan, Mathew G.

2014-09-01

While expansion of agricultural land area and intensification of agricultural practices through irrigation and fertilizer use can bring many benefits to communities, intensifying land use also causes more contaminants, such as nutrients and pesticides, to enter rivers, lakes, and groundwater. For lakes such as Benmore in the Waitaki catchment, South Island, New Zealand, an area which is currently undergoing agricultural intensification, this could potentially lead to marked degradation of water clarity as well as effects on ecological, recreational, commercial, and tourism values. We undertook a modeling study to demonstrate science-based options for consideration of agricultural intensification in the catchment of Lake Benmore. Based on model simulations of a range of potential future nutrient loadings, it is clear that different areas within Lake Benmore may respond differently to increased nutrient loadings. A western arm (Ahuriri) could be most severely affected by land-use changes and associated increases in nutrient loadings. Lake-wide annual averages of an eutrophication indicator, the trophic level index (TLI) were derived from simulated chlorophyll a, total nitrogen, and total phosphorus concentrations. Results suggest that the lake will shift from oligotrophic (TLI = 2-3) to eutrophic (TLI = 4-5) as external loadings are increased eightfold over current baseline loads, corresponding to the potential land-use intensification in the catchment. This study provides a basis for use of model results in a decision-making process by outlining the environmental consequences of a series of land-use management options, and quantifying nutrient load limits needed to achieve defined trophic state objectives.

Development of a 3D Underground Cadastral System with Indoor Mapping for As-Built BIM: The Case Study of Gangnam Subway Station in Korea.

PubMed

Kim, Sangmin; Kim, Jeonghyun; Jung, Jaehoon; Heo, Joon

2015-12-09

The cadastral system provides land ownership information by registering and representing land boundaries on a map. The current cadastral system in Korea, however, focuses mainly on the management of 2D land-surface boundaries. It is not yet possible to provide efficient or reliable land administration, as this 2D system cannot support or manage land information on 3D properties (including architectures and civil infrastructures) for both above-ground and underground facilities. A geometrical model of the 3D parcel, therefore, is required for registration of 3D properties. This paper, considering the role of the cadastral system, proposes a framework for a 3D underground cadastral system that can register various types of 3D underground properties using indoor mapping for as-built Building Information Modeling (BIM). The implementation consists of four phases: (1) geometric modeling of a real underground infrastructure using terrestrial laser scanning data; (2) implementation of as-built BIM based on geometric modeling results; (3) accuracy assessment for created as-built BIM using reference points acquired by total station; and (4) creation of three types of 3D underground cadastral map to represent underground properties. The experimental results, based on indoor mapping for as-built BIM, show that the proposed framework for a 3D underground cadastral system is able to register the rights, responsibilities, and restrictions corresponding to the 3D underground properties. In this way, clearly identifying the underground physical situation enables more reliable and effective decision-making in all aspects of the national land administration system.

Development of a ground hydrology model suitable for global climate modeling using soil morphology and vegetation cover, and an evaluation of remotely sensed information

NASA Technical Reports Server (NTRS)

Zobler, L.; Lewis, R.

1988-01-01

The long-term purpose was to contribute to scientific understanding of the role of the planet's land surfaces in modulating the flows of energy and matter which influence the climate, and to quantify and monitor human-induced changes to the land environment that may affect global climate. Highlights of the effort include the following: production of geo-coded, digitized World Soil Data file for use with the Goddard Institute for Space Studies (GISS) climate model; contribution to the development of a numerical physically-based model of ground hydrology; and assessment of the utility of remote sensing for providing data on hydrologically significant land surface variables.

Multiscale Spatial Assessment of Determinant Factors of Land Use Change: Study at Urban Area of Yogyakarta

NASA Astrophysics Data System (ADS)

Susilo, Bowo

2017-12-01

Studies of land use change have been undertaken by different researchers using various methods. Among those methods, modelling is widely utilized. Modelling land use change required several components remarked as model variables. Those represent any conditions or factors which considered relevant or have some degree of correlation to the changes of land use. Variables which have significant correlation to land use change are referred as determinant factors or driving forces. Those factors as well as changes of land use are distributed across space and therefore referred as spatial determinant factors. The main objective of the research was to examine land use change and its determinant factors. Area and location of land use change were analysed based on three different years of land use maps, which are 1993, 2000 and 2007. Spatial and temporal analysis were performed which emphasize to the influence of scale to both of analysis’s. Urban area of Yogyakarta was selected as study area. Study area covered three different districts (kabupaten), involving 20 sub districts and totally consists of 74 villages. Result of this study shows that during 14 years periods (1993 to 2007), there were about 1,460 hectares of land use change had been taken place. Dominant type of land use change is agricultural to residential. The uses of different spatial and temporal scale in analysis were able to reveal different factors related to land use change. In general, factors influencing the quantities of land use change in the study area were population growth and the availability of land. The use of data with different spatial resolution can reveal the presence of various factors associated with the location of the change. Locations of land use change were influenced or determined by accessibility factors.

A Coupled Natural-Human Modeling of the Land Loss Probability in the Mississippi River Delta

NASA Astrophysics Data System (ADS)

Cai, H.; Lam, N.; Zou, L.

2017-12-01

The Mississippi River Delta (MRD) is one of the most environmentally threatened areas in the United States. The area has been suffering substantial land loss during the past decades. Land loss in the MRD has been a subject of intense research by many researchers from multiple disciplines, aiming at mitigating the land loss process and its potential damage. A majority of land loss projections were derived solely from the natural processes, such as sea level rise, regional subsidence, and reduced sediment flows. However, sufficient evidence has shown that land loss in the MRD also relates to human-induced factors such as land fragmentation, neighborhood effects, urbanization, energy industrialization, and marine transportation. How to incorporate both natural and human factors into the land loss modeling stays a huge challenge. Using a coupled-natural and human (CNH) approach can help uncover the complex mechanism of land loss in the MRD, and provide more accurate spatiotemporal projection of land loss patterns and probability. This study uses quantitative approaches to investigate the relationships between land loss and a wide range of socio-ecological variables in the MRD. A model of land loss probability based on selected socio-ecological variables and its neighborhood effects will be derived through variogram and regression analyses. Then, we will simulate the land loss probability and patterns under different scenarios such as sea-level rise, changes in storm frequency and strength, and changes in population to evaluate the sustainability of the MRD. The outcome of this study will be a layer of pixels with information on the probability of land-water conversion. Knowledge gained from this study will provide valuable insights into the optimal mitigation strategies of land loss prevention and restoration and help build long-term sustainability in the Mississippi River Delta.

Evaluation of eco-physiological indicators in Northeast Asia dryland regions based on MODIS products and ecological models

NASA Astrophysics Data System (ADS)

Kang, W.

2017-12-01

Ecosystem carbon-energy-water circles have significant effect on function and structure and vice verse. Based on these circles mechanism, some eco-physiological indicators, like Transpiration (T), gross primary productivity (GPP), light use efficiency (LUE) and water use efficiency (WUE), are commonly applied to assess terrestrial ecosystem function and structure dynamics. The ecosystem weakened function and simple structure in Northeast dryland regions resulted from land degradation or desertification, which could be demonstrated by above-mentioned indicators. In this study, based on MODIS atmosphere (MYD07, MYD04, MYD06 data) and land products (MYD13A2 NDVI, MYD11A1 LST, MYD15A2 LAI and land cover data), we first retrieved transpiration and LUE via Penman-Monteith Model and modified Vegetation Photosynthesis Model (VPM), respectively; and then evaluated dynamics of these eco-physiological indicators (Tair, VPD, T, LUE, GPP and WUE) and some hotspots were found for next land degradation assessment. The results showed: (1) LUE and WUE are lower in barren or sparsely vegetated area and grasslands than in forest and croplands. (2) Whereas, all indicators presented higher variability in grassland area, particularly in east Mongolia. (3) GPP and transpiration have larger variability than other indicators due to fraction of absorbed Photosynthetically active radiation (FPAR). These eco-physiological indicators are expected to continue to change under future climate change and to help to assess land degradation from ecosystem energy-water-carbon perspectives.

Weather Research and Forecasting Model Wind Sensitivity Study at Edwards Air Force Base, CA

NASA Technical Reports Server (NTRS)

Watson, Leela R.; Bauman, William H., III

2008-01-01

NASA prefers to land the space shuttle at Kennedy Space Center (KSC). When weather conditions violate Flight Rules at KSC, NASA will usually divert the shuttle landing to Edwards Air Force Base (EAFB) in Southern California. But forecasting surface winds at EAFB is a challenge for the Spaceflight Meteorology Group (SMG) forecasters due to the complex terrain that surrounds EAFB, One particular phenomena identified by SMG is that makes it difficult to forecast the EAFB surface winds is called "wind cycling". This occurs when wind speeds and directions oscillate among towers near the EAFB runway leading to a challenging deorbit bum forecast for shuttle landings. The large-scale numerical weather prediction models cannot properly resolve the wind field due to their coarse horizontal resolutions, so a properly tuned high-resolution mesoscale model is needed. The Weather Research and Forecasting (WRF) model meets this requirement. The AMU assessed the different WRF model options to determine which configuration best predicted surface wind speed and direction at EAFB, To do so, the AMU compared the WRF model performance using two hot start initializations with the Advanced Research WRF and Non-hydrostatic Mesoscale Model dynamical cores and compared model performance while varying the physics options.

Assessment of environmental impacts following alternative agricultural policy scenarios.

PubMed

Bárlund, I; Lehtonen, H; Tattari, S

2005-01-01

Abstract Finnish agriculture is likely to undergo major changes in the near and intermediate future. The ifuture policy context can be examined at a general level by strategic scenario building. Computer-based modelling in combination with agricultural policy scenarios can in turn create a basis for the assessments of changes in environmental quality following possible changes in Finnish agriculture. The analysis of economic consequences is based on the DREMFIA model, which is applied to study effects of various agricultural policies on land use, animal production, and farmers' income. The model is suitable for an impact analysis covering an extended time span--here up to the year 2015. The changes in land use, obtained with the DREMFIA model assuming rational economic behaviour, form the basis when evaluating environmental impacts of different agricultural policies. The environmental impact assessment is performed using the field scale nutrient transport model ICECREAM. The modelled variables are nitrogen and phosphorus losses in surface runoff and percolation. In this paper the modelling strategy will be presented and highlighted using two case study catchments with varying environmental conditions and land use as an example. In addition, the paper identifies issues arising when connecting policy scenarios with impact modelling.

A linked land-sea modeling framework to inform ridge-to-reef management in high oceanic islands

PubMed Central

Whittier, Robert; Stamoulis, Kostantinos A.; Bremer, Leah L.; Jupiter, Stacy; Friedlander, Alan M.; Poti, Matthew; Guannel, Greg; Kurashima, Natalie; Winter, Kawika B.; Toonen, Robert; Conklin, Eric; Wiggins, Chad; Knudby, Anders; Goodell, Whitney; Burnett, Kimberly; Yee, Susan; Htun, Hla; Oleson, Kirsten L. L.; Wiegner, Tracy; Ticktin, Tamara

2018-01-01

Declining natural resources have led to a cultural renaissance across the Pacific that seeks to revive customary ridge-to-reef management approaches to protect freshwater and restore abundant coral reef fisheries. Effective ridge-to-reef management requires improved understanding of land-sea linkages and decision-support tools to simultaneously evaluate the effects of terrestrial and marine drivers on coral reefs, mediated by anthropogenic activities. Although a few applications have linked the effects of land cover to coral reefs, these are too coarse in resolution to inform watershed-scale management for Pacific Islands. To address this gap, we developed a novel linked land-sea modeling framework based on local data, which coupled groundwater and coral reef models at fine spatial resolution, to determine the effects of terrestrial drivers (groundwater and nutrients), mediated by human activities (land cover/use), and marine drivers (waves, geography, and habitat) on coral reefs. We applied this framework in two ‘ridge-to-reef’ systems (Hā‘ena and Ka‘ūpūlehu) subject to different natural disturbance regimes, located in the Hawaiian Archipelago. Our results indicated that coral reefs in Ka‘ūpūlehu are coral-dominated with many grazers and scrapers due to low rainfall and wave power. While coral reefs in Hā‘ena are dominated by crustose coralline algae with many grazers and less scrapers due to high rainfall and wave power. In general, Ka‘ūpūlehu is more vulnerable to land-based nutrients and coral bleaching than Hā‘ena due to high coral cover and limited dilution and mixing from low rainfall and wave power. However, the shallow and wave sheltered back-reef areas of Hā‘ena, which support high coral cover and act as nursery habitat for fishes, are also vulnerable to land-based nutrients and coral bleaching. Anthropogenic sources of nutrients located upstream from these vulnerable areas are relevant locations for nutrient mitigation, such as cesspool upgrades. In this study, we located coral reefs vulnerable to land-based nutrients and linked them to priority areas to manage sources of human-derived nutrients, thereby demonstrating how this framework can inform place-based ridge-to-reef management. PMID:29538392

Assessing the influence of land use land cover pattern, socio economic factors and air quality status to predict morbidity on the basis of logistic based regression model

NASA Astrophysics Data System (ADS)

Dixit, A.; Singh, V. K.

2017-12-01

Recent studies conducted by World Health Organisation (WHO) estimated that 92 % of the total world population are living in places where the air quality level has exceeded the WHO standard limit for air quality. This is due to the change in Land Use Land Cover (LULC) pattern, socio economic drivers and anthropogenic heat emission caused by manmade activity. Thereby, many prevalent human respiratory diseases such as lung cancer, chronic obstructive pulmonary disease and emphysema have increased in recent times. In this study, a quantitative relationship is developed between land use (built-up land, water bodies, and vegetation), socio economic drivers and air quality parameters using logistic based regression model over 7 different cities of India for the winter season of 2012 to 2016. Different LULC, socio economic, industrial emission sources, meteorological condition and air quality level from the monitoring stations are taken to estimate the influence on morbidity of each city. Results of correlation are analyzed between land use variables and monthly concentration of pollutants. These values range from 0.63 to 0.76. Similarly, the correlation value between land use variable with socio economic and morbidity ranges from 0.57 to 0.73. The performance of model is improved from 67 % to 79 % in estimating morbidity for the year 2015 and 2016 due to the better availability of observed data.The study highlights the growing importance of incorporating socio-economic drivers with air quality data for evaluating morbidity rate for each city in comparison to just change in quantitative analysis of air quality.

Spatially Complete Global Surface Albedos Derived from Terra/MODIS Data

NASA Technical Reports Server (NTRS)

King, Michael D.; Moody, Eric G.; Schaaf, Crystal B.; Platnick, Steven

2006-01-01

Spectral land surface albedo is an important parameter for describing the radiative properties of the Earth. Accordingly it reflects the consequences of natural and human interactions, such as anthropogenic, meteorological, and phenological effects, on global and local climatological trends. Consequently, albedos are integral parts in a variety of research areas, such as general circulation models (GCMs), energy balance studies, modeling of land use and land use change, and biophysical, oceanographic, and meteorological studies. , Over five years of land surface anisotropy, diffuse bihemispherical (white-sky) albedo and direct beam directional hemispherical (black-sky) albedo from observations acquired by the MODIS instruments aboard NASA s Terra and Aqua satellite platforms have provided researchers with unprecedented spatial, spectral, and temporal information on the land surface s radiative characteristics. However, roughly 30% of the global land surface, on an annual equal-angle basis, is obscured due to persistent and transient cloud cover, while another 207% is obscured due to ephemeral and seasonal snow effects. This precludes the MOD43B3 albedo products from being directly used in some remote sensing and ground-based applications, climate models, and global change research projects. To provide researchers with the requisite spatially complete global snow-free land surface albedo dataset, an ecosystem-dependent temporal interpolation technique was developed to fill missing or lower quality data and snow covered values from the official MOD43B3 dataset with geophysically realistic values. The method imposes pixel-level and local regional ecosystem-dependent phenological behavior onto retrieved pixel temporal data in such a way as to maintain pixel-level spatial and spectral detail and integrity. The phenological curves are derived from statistics based on the MODIS MOD12Q1 IGBP land cover classification product geolocated with the MOD43B3 data.

"Detecting people"

NASA Astrophysics Data System (ADS)

Arneth, A.; Pugh, T.; Krause, A.; Bayer, A.; Lindeskog, M.

2015-12-01

Land-use change (LUC) is known to significantly affect biogeochemical cycles as well as surface energy partitioning - with important implications, ranging from understanding present-day measurements, to simulations of climate change and impacts on ecosystems, to assessments of the mitigation potential of land-based mitigation policies on ecosystems. When connecting observations of surface-atmosphere interactions and modelling at different scales, two important issues in this context are: legacy effects (e.g., to what degree and for how long does past LUC at a given location affect vegetation structure, CO2 fluxes and carbon pools), and sub-grid variability of the land-use change per se (e.g., whether bi-directional information about changes are taken into consideration). Both are important when bridging between scales (in time and in space) to enhance long-term observation networks. This contribution to the session will be very much from a process-based modelling perspective. Using a second generation dynamic global vegetation model we will show how different land-use histories impact vegetation and soil recovery (carbon pool-size, fluxes) differently, depending on the type of previous land-use, its length, and on the type of biome. We also study the difference between "gross" and "net" LUC accounting for simulated carbon cycling. Two important aspects, considering the session's objectives, are: 1) When establishing and developing observation networks, land-use history is key information for the interpretation of measured fluxes and needs to be collected and made available;, 2) Observation networks that "operate" solely in the natural science domain need to increasingly seek cooperation with socio-economic observations (such as land-use change, land management) in order to gain better understanding of coupled socio-ecological systems.

Spatially Complete Global Surface Albedos Derived from Terra/MODIS Data

NASA Technical Reports Server (NTRS)

King, Michael D.; Moody, Eric G.; Platnick, Steven; Schaaf, Crystal B.

2005-01-01

Spectral land surface albedo is an important parameter for describing the radiative properties of the Earth. Accordingly it reflects the consequences of natural and human interactions, such as anthropogenic, meteorological, and phenological effects, on global and local climatological trends. Consequently, albedos are integral parts in a variety of research areas, such as general circulation models (GCMs), energy balance studies, modeling of land use and land use change, and biophysical, oceanographic, and meteorological studies. Recent production of land surface anisotropy, diffuse bihemispherical (white-sky) albedo and direct beam directional hemispherical (black-sky) albedo from observations acquired by the MODIS instruments aboard NASA's Terra and &la satellite platforms have provided researchers with unprecedented spatial, spectral, and temporal information on the land surface's radiative characteristics. Cloud cover, which curtails retrievals, and the presence of ephemeral and seasonal snow limit the snow-free data to approximately half the global land surfaces on an annual equal-angle basis. This precludes the MOD43B3 albedo products from being used in some remote sensing and ground-based applications, &mate models, and global change research projects.

Global Land Carbon Uptake from Trait Distributions

NASA Astrophysics Data System (ADS)

Butler, E. E.; Datta, A.; Flores-Moreno, H.; Fazayeli, F.; Chen, M.; Wythers, K. R.; Banerjee, A.; Atkin, O. K.; Kattge, J.; Reich, P. B.

2016-12-01

Historically, functional diversity in land surface models has been represented through a range of plant functional types (PFTs), each of which has a single value for all of its functional traits. Here we expand the diversity of the land surface by using a distribution of trait values for each PFT. The data for these trait distributions is from a sub-set of the global database of plant traits, TRY, and this analysis uses three leaf traits: mass based nitrogen and phosphorus content and specific leaf area, which influence both photosynthesis and respiration. The data are extrapolated into continuous surfaces through two methodologies. The first, a categorical method, classifies the species observed in TRY into satellite estimates of their plant functional type abundances - analogous to how traits are currently assigned to PFTs in land surface models. Second, a Bayesian spatial method which additionally estimates how the distribution of a trait changes in accord with both climate and soil covariates. These two methods produce distinct patterns of diversity which are incorporated into a land surface model to estimate how the range of trait values affects the global land carbon budget.

Quantifying the impact of land use change on hydrological responses in the Upper Ganga Basin, India

NASA Astrophysics Data System (ADS)

Tsarouchi, Georgia-Marina; Mijic, Ana; Moulds, Simon; Chawla, Ila; Mujumdar, Pradeep; Buytaert, Wouter

2013-04-01

Quantifying how changes in land use affect the hydrological response at the river basin scale is a challenge in hydrological science and especially in the tropics where many regions are considered data sparse. Earlier work by the authors developed and used high-resolution, reconstructed land cover maps for northern India, based on satellite imagery and historic land-use maps for the years 1984, 1998 and 2010. Large-scale land use changes and their effects on landscape patterns can impact water supply in a watershed by altering hydrological processes such as evaporation, infiltration, surface runoff, groundwater discharge and stream flow. Three land use scenarios were tested to explore the sensitivity of the catchment's response to land use changes: (a) historic land use of 1984 with integrated evolution to 2010; (b) land use of 2010 remaining stable; and (c) hypothetical future projection of land use for 2030. The future scenario was produced with Markov chain analysis and generation of transition probability matrices, indicating transition potentials from one land use class to another. The study used socio-economic (population density), geographic (distances to roads and rivers, and location of protected areas) and biophysical drivers (suitability of soil for agricultural production, slope, aspect, and elevation). The distributed version of the land surface model JULES was integrated at a resolution of 0.01° for the years 1984 to 2030. Based on a sensitivity analysis, the most sensitive parameters were identified. Then, the model was calibrated against measured daily stream flow data. The impact of land use changes was investigated by calculating annual variations in hydrological components, differences in annual stream flow and surface runoff during the simulation period. The land use changes correspond to significant differences on the long-term hydrologic fluxes for each scenario. Once analysed from a future water resources perspective, the results will be beneficial in constructing decision support tools for regional land-use planning and management.

Emerging ecological datasets with application for modeling North American dust emissions

NASA Astrophysics Data System (ADS)

McCord, S.; Stauffer, N. G.; Garman, S.; Webb, N.

2017-12-01

In 2011 the US Bureau of Land Management (BLM) established the Assessment, Inventory and Monitoring (AIM) program to monitor the condition of BLM land and to provide data to support evidence-based management of multi-use public lands. The monitoring program shares core data collection methods with the Natural Resources Conservation Service's (NRCS) National Resources Inventory (NRI), implemented on private lands nationally. Combined, the two programs have sampled >30,000 locations since 2003 to provide vegetation composition, vegetation canopy height, the size distribution of inter-canopy gaps, soil texture and crusting information on rangelands and pasture lands across North America. The BLM implements AIM on more than 247.3 million acres of land across the western US, encompassing major dust source regions of the Chihuahuan, Sonoran, Mojave and Great Basin deserts, the Colorado Plateau, and potential high-latitude dust sources in Alaska. The AIM data are publicly available and can be used to support modeling of land surface and boundary-layer processes, including dust emission. While understanding US dust source regions and emission processes has been of national interest since the 1930s Dust Bowl, most attention has been directed to the croplands of the Great Plains and emission hot spots like Owens Lake, California. The magnitude, spatial extent and temporal dynamics of dust emissions from western dust source areas remain highly uncertain. Here, we use ensemble modeling with empirical and physically-based dust emission schemes applied to AIM monitoring data to assess regional-scale patterns of aeolian sediment mass fluxes and dust emissions. The analysis enables connections to be made between dust emission rates at source and other indicators of ecosystem function at the landscape scale. Emerging ecological datasets like AIM provide new opportunities to evaluate aeolian sediment transport responses to land surface conditions, potential interactions with disturbances (e.g., fire) and ecological change (e.g., invasive species), and the impacts of anthropogenic land use and land cover change.

Coupling integrated assessment and earth system models: concepts and an application to land use change

NASA Astrophysics Data System (ADS)

O'Neill, B. C.; Lawrence, P.; Ren, X.

2016-12-01

Collaboration between the integrated assessment modeling (IAM) and earth system modeling (ESM) communities is increasing, driven by a growing interest in research questions that require analysis integrating both social and natural science components. This collaboration often takes the form of integrating their respective models. There are a number of approaches available to implement this integration, ranging from one-way linkages to full two-way coupling, as well as approaches that retain a single modeling framework but improve the representation of processes from the other framework. We discuss the pros and cons of these different approaches and the conditions under which a two-way coupling of IAMs and ESMs would be favored over a one-way linkage. We propose a criterion that is necessary and sufficient to motivate two-way coupling: A human process must have an effect on an earth system process that is large enough to cause a change in the original human process that is substantial compared to other uncertainties in the problem being investigated. We then illustrate a test of this criterion for land use-climate interactions based on work using the Community Earth System Model (CESM) and land use scenarios from the Representative Concentration Pathways (RCPs), in which we find that the land use effect on regional climate is unlikely to meet the criterion. We then show an example of implementing a one-way linkage of land use and agriculture between an IAM, the integrated Population-Economy-Technology-Science (iPETS) model, and CESM that produces fully consistent outcomes between iPETS and the CESM land surface model. We use the linked system to model the influence of climate change on crop yields, agricultural land use, crop prices and food consumption under two alternative future climate scenarios. This application demonstrates the ability to link an IAM to a global land surface and climate model in a computationally efficient manner.

Land-based approach to evaluate sustainable land management and adaptive capacity of ecosystems/lands

NASA Astrophysics Data System (ADS)

Kust, German; Andreeva, Olga

2015-04-01

A number of new concepts and paradigms appeared during last decades, such as sustainable land management (SLM), climate change (CC) adaptation, environmental services, ecosystem health, and others. All of these initiatives still not having the common scientific platform although some agreements in terminology were reached, schemes of links and feedback loops created, and some models developed. Nevertheless, in spite of all these scientific achievements, the land related issues are still not in the focus of CC adaptation and mitigation. The last did not grow much beyond the "greenhouse gases" (GHG) concept, which makes land degradation as the "forgotten side of climate change" The possible decision to integrate concepts of climate and desertification/land degradation could be consideration of the "GHG" approach providing global solution, and "land" approach providing local solution covering other "locally manifesting" issues of global importance (biodiversity conservation, food security, disasters and risks, etc.) to serve as a central concept among those. SLM concept is a land-based approach, which includes the concepts of both ecosystem-based approach (EbA) and community-based approach (CbA). SLM can serve as in integral CC adaptation strategy, being based on the statement "the more healthy and resilient the system is, the less vulnerable and more adaptive it will be to any external changes and forces, including climate" The biggest scientific issue is the methods to evaluate the SLM and results of the SLM investments. We suggest using the approach based on the understanding of the balance or equilibrium of the land and nature components as the major sign of the sustainable system. Prom this point of view it is easier to understand the state of the ecosystem stress, size of the "health", range of adaptive capacity, drivers of degradation and SLM nature, as well as the extended land use, and the concept of environmental land management as the improved SLM approach. A number of case studies justify the schemes developed to explain this approach.

An individual-based modelling approach to estimate landscape connectivity for bighorn sheep (Ovis canadensis).

PubMed

Allen, Corrie H; Parrott, Lael; Kyle, Catherine

2016-01-01

Background. Preserving connectivity, or the ability of a landscape to support species movement, is among the most commonly recommended strategies to reduce the negative effects of climate change and human land use development on species. Connectivity analyses have traditionally used a corridor-based approach and rely heavily on least cost path modeling and circuit theory to delineate corridors. Individual-based models are gaining popularity as a potentially more ecologically realistic method of estimating landscape connectivity. However, this remains a relatively unexplored approach. We sought to explore the utility of a simple, individual-based model as a land-use management support tool in identifying and implementing landscape connectivity. Methods. We created an individual-based model of bighorn sheep (Ovis canadensis) that simulates a bighorn sheep traversing a landscape by following simple movement rules. The model was calibrated for bighorn sheep in the Okanagan Valley, British Columbia, Canada, a region containing isolated herds that are vital to conservation of the species in its northern range. Simulations were run to determine baseline connectivity between subpopulations in the study area. We then applied the model to explore two land management scenarios on simulated connectivity: restoring natural fire regimes and identifying appropriate sites for interventions that would increase road permeability for bighorn sheep. Results. This model suggests there are no continuous areas of good habitat between current subpopulations of sheep in the study area; however, a series of stepping-stones or circuitous routes could facilitate movement between subpopulations and into currently unoccupied, yet suitable, bighorn habitat. Restoring natural fire regimes or mimicking fire with prescribed burns and tree removal could considerably increase bighorn connectivity in this area. Moreover, several key road crossing sites that could benefit from wildlife overpasses were identified. Discussion. By linking individual-scale movement rules to landscape-scale outcomes, our individual-based model of bighorn sheep allows for the exploration of how on-the-ground management or conservation scenarios may increase functional connectivity for the species in the study area. More generally, this study highlights the usefulness of individual-based models to identify how a species makes broad use of a landscape for movement. Application of this approach can provide effective quantitative support for decision makers seeking to incorporate wildlife conservation and connectivity into land use planning.

An individual-based modelling approach to estimate landscape connectivity for bighorn sheep (Ovis canadensis)

PubMed Central

Allen, Corrie H.; Kyle, Catherine

2016-01-01

Background. Preserving connectivity, or the ability of a landscape to support species movement, is among the most commonly recommended strategies to reduce the negative effects of climate change and human land use development on species. Connectivity analyses have traditionally used a corridor-based approach and rely heavily on least cost path modeling and circuit theory to delineate corridors. Individual-based models are gaining popularity as a potentially more ecologically realistic method of estimating landscape connectivity. However, this remains a relatively unexplored approach. We sought to explore the utility of a simple, individual-based model as a land-use management support tool in identifying and implementing landscape connectivity. Methods. We created an individual-based model of bighorn sheep (Ovis canadensis) that simulates a bighorn sheep traversing a landscape by following simple movement rules. The model was calibrated for bighorn sheep in the Okanagan Valley, British Columbia, Canada, a region containing isolated herds that are vital to conservation of the species in its northern range. Simulations were run to determine baseline connectivity between subpopulations in the study area. We then applied the model to explore two land management scenarios on simulated connectivity: restoring natural fire regimes and identifying appropriate sites for interventions that would increase road permeability for bighorn sheep. Results. This model suggests there are no continuous areas of good habitat between current subpopulations of sheep in the study area; however, a series of stepping-stones or circuitous routes could facilitate movement between subpopulations and into currently unoccupied, yet suitable, bighorn habitat. Restoring natural fire regimes or mimicking fire with prescribed burns and tree removal could considerably increase bighorn connectivity in this area. Moreover, several key road crossing sites that could benefit from wildlife overpasses were identified. Discussion. By linking individual-scale movement rules to landscape-scale outcomes, our individual-based model of bighorn sheep allows for the exploration of how on-the-ground management or conservation scenarios may increase functional connectivity for the species in the study area. More generally, this study highlights the usefulness of individual-based models to identify how a species makes broad use of a landscape for movement. Application of this approach can provide effective quantitative support for decision makers seeking to incorporate wildlife conservation and connectivity into land use planning. PMID:27168997

The variability of runoff and soil erosion in the Brazilian Cerrado biome due to the potential land use and climate changes

NASA Astrophysics Data System (ADS)

Alexandre Ayach Anache, Jamil; Wendland, Edson; Malacarne Pinheiro Rosalem, Lívia; Srivastava, Anurag; Flanagan, Dennis

2017-04-01

Changes in land use and climate can influence runoff and soil loss, threatening soil and water conservation in the Cerrado biome in Brazil. Due to the lack of long term observed data for runoff and soil erosion in Brazil, the adoption of a process-based model was necessary, representing the variability of both variables in a continuous simulation approach. Thus, we aimed to calibrate WEPP (Water Erosion Prediction Project) model for different land uses (undisturbed Cerrado, fallow, pasture, and sugarcane) under subtropical conditions inside the Cerrado biome; predict runoff and soil erosion for these different land uses; and simulate runoff and soil erosion considering climate change scenarios. We performed the model calibration using a 4-year dataset of observed runoff and soil loss in four different land uses (undisturbed Cerrado, fallow, pasture, and sugarcane). The WEPP model components (climate, topography, soil, and management) were calibrated according to field data. However, soil and management were optimized according to each land use using a parameter estimation tool. The observations were conducted between 2012 and 2015 in experimental plots (5 m width, 20 m length, 9% slope gradient, 3 replicates per treatment). The simulations were done using the calibrated WEPP model components, but changing the 4-year observed climate file by a 100-year dataset created with CLIGEN (weather generator) based on regional climate statistics. Afterwards, using MarkSim DSSAT Weather File Generator, runoff and soil loss were simulated using future climate scenarios for 2030, 2060, and 2090. To analyze the data, we used non-parametric statistics as data do not follow normal distribution. The results show that WEPP model had an acceptable performance for the considered conditions. In addition, both land use and climate can influence on runoff and soil loss rates. Potential climate changes which consider the increase of rainfall intensities and depths in the studied region may increase the variability and rates for runoff and soil erosion. However, the climate did not change the differences and similarities between the rates of the four analyzed land uses. The runoff behavior is distinct for all land uses, but for soil loss we found similarities between pasture and undisturbed Cerrado, suggesting that soil sustainability could be reached when the management follows conservation principles.

Decision-support systems for natural-hazards and land-management issues

USGS Publications Warehouse

Dinitz, Laura; Forney, William; Byrd, Kristin

2012-01-01

Scientists at the USGS Western Geographic Science Center are developing decision-support systems (DSSs) for natural-hazards and land-management issues. DSSs are interactive computer-based tools that use data and models to help identify and solve problems. These systems can provide crucial support to policymakers, planners, and communities for making better decisions about long-term natural hazards mitigation and land-use planning.

Comparing long-term geomorphic model outcomes with sediment archives highlights the need for high-resolution Holocene land cover reconstructions

NASA Astrophysics Data System (ADS)

De Brue, Hanne; Verstraeten, Gert

2013-04-01

During the last decade, several global land cover reconstructions have been produced that enable to quantify human impact on the landscape since the introduction of agriculture. Application of these land cover maps in geomorphic models potentially allows to estimate the anthropogenic impact on sediment fluxes and thus to reconstruct changes in landscape morphology through time. However, current land cover reconstructions face some drawbacks. First of all, their low spatial resolution (i.e. 5 arc-minutes at best) questions their use in geomorphic models, as sub-catchment vegetation patterns play an important role in sediment dynamics. Existing global land cover reconstructions also do not differentiate the typology of human impact (cropland, grazing land, disturbed forests), although the susceptibility of different anthropogenic land uses towards erosion varies greatly. Finally, the various land cover reconstructions differ significantly regarding the estimated intensity of human impact for the preindustrial period. In this study, we assessed the performance of a spatially distributed erosion and sediment redistribution model that operates at high resolution (100 m) to the quality and spatial resolution of input land cover maps. This was done through a comparison of two sets of model runs. Firstly, low-resolution land cover (expressed as percentage of non-natural vegetation) maps were resampled to a spatial resolution of 100 m without differentiation of non-natural vegetation types. For the second set of model runs, estimated non-natural vegetation was differentiated in areas of cropland and grassland, and spatially allocated to a high-resolution grid (100 m) using a logistic model that relates contemporary land cover classes to slope, soil characteristics, landforms and distance to rivers. For both land cover maps, different scenarios for the ratio between cropland and grassland were simulated. Analyses were performed for several time periods throughout the Holocene, for the Scheldt River Basin (19,000 km2) in Belgium and northern France. Results indicate that low-resolution land cover information, regardless of the considered cropland/grassland ratio, leads to largely overestimated sediment fluxes when compared to field-based sediment budgets. Allocation of land cover to a higher spatial resolution yields far better results. Variations in model outcomes are related to differences in landscape connectivity between allocated and non-allocated land cover. These results point towards the need for higher-resolution land cover maps that incorporate the patchiness of vegetation at relevant scales regarding geomorphic processes. Also, model results with allocated and non-allocated land cover maps differ greatly for different cropland/grassland ratios. This indicates that there is not only a need for land cover reconstructions at high spatial resolution, but also that differentiation between cropland and grassland is essential for accurate geomorphic modeling. Further improvements in land cover reconstructions are thus needed before reliable quantitative estimates of anthropogenic impact on soil profiles and sediment redistribution can be simulated at continental scales. Detailed historic sediment budgets can provide an important tool not only for validating but also for reconstructing land cover histories.

Meteorological Effects of Land Cover Changes in Hungary during the 20th Century

NASA Astrophysics Data System (ADS)

Drüszler, Á.; Vig, P.; Csirmaz, K.

2012-04-01

Geological, paleontological and geomorphologic studies show that the Earth's climate has always been changing since it came into existence. The climate change itself is self-evident. Therefore the far more serious question is how much does mankind strengthen or weaken these changes beyond the natural fluctuation and changes of climate. The aim of the present study was to restore the historical land cover changes and to simulate the meteorological consequences of these changes. Two different land cover maps for Hungary were created in vector data format using GIS technology. The land cover map for 1900 was reconstructed based on statistical data and two different historical maps: the derived map of the 3rd Military Mapping Survey of the Austro-Hungarian Empire and the Synoptic Forestry Map of the Kingdom of Hungary. The land cover map for 2000 was derived from the CORINE land cover database. Significant land cover changes were found in Hungary during the 20th century according to the examinations of these maps and statistical databases. The MM5 non-hydrostatic dynamic model was used to further evaluate the meteorological effects of these changes. The lower boundary conditions for this mesoscale model were generated for two selected time periods (for 1900 and 2000) based on the reconstructed maps. The dynamic model has been run with the same detailed meteorological conditions of selected days from 2006 and 2007, but with modified lower boundary conditions. The set of the 26 selected initial conditions represents the whole set of the macrosynoptic situations for Hungary. In this way, 2×26 "forecasts" were made with 48 hours of integration. The effects of land cover changes under different weather situations were further weighted by the long-term (1961-1990) mean frequency of the corresponding macrosynoptic types, to assume the climatic effects from these stratified averages. The detailed evaluation of the model results were made for three different meteorological variables (temperature, dew point and precipitation).

Exploring new topography-based subgrid spatial structures for improving land surface modeling

DOE PAGES

Tesfa, Teklu K.; Leung, Lai-Yung Ruby

2017-02-22

Topography plays an important role in land surface processes through its influence on atmospheric forcing, soil and vegetation properties, and river network topology and drainage area. Land surface models with a spatial structure that captures spatial heterogeneity, which is directly affected by topography, may improve the representation of land surface processes. Previous studies found that land surface modeling, using subbasins instead of structured grids as computational units, improves the scalability of simulated runoff and streamflow processes. In this study, new land surface spatial structures are explored by further dividing subbasins into subgrid structures based on topographic properties, including surface elevation,more » slope and aspect. Two methods (local and global) of watershed discretization are applied to derive two types of subgrid structures (geo-located and non-geo-located) over the topographically diverse Columbia River basin in the northwestern United States. In the global method, a fixed elevation classification scheme is used to discretize subbasins. The local method utilizes concepts of hypsometric analysis to discretize each subbasin, using different elevation ranges that also naturally account for slope variations. The relative merits of the two methods and subgrid structures are investigated for their ability to capture topographic heterogeneity and the implications of this on representations of atmospheric forcing and land cover spatial patterns. Results showed that the local method reduces the standard deviation (SD) of subgrid surface elevation in the study domain by 17 to 19 % compared to the global method, highlighting the relative advantages of the local method for capturing subgrid topographic variations. The comparison between the two types of subgrid structures showed that the non-geo-located subgrid structures are more consistent across different area threshold values than the geo-located subgrid structures. Altogether the local method and non-geo-located subgrid structures effectively and robustly capture topographic, climatic and vegetation variability, which is important for land surface modeling.« less

Exploring new topography-based subgrid spatial structures for improving land surface modeling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tesfa, Teklu K.; Leung, Lai-Yung Ruby

Topography plays an important role in land surface processes through its influence on atmospheric forcing, soil and vegetation properties, and river network topology and drainage area. Land surface models with a spatial structure that captures spatial heterogeneity, which is directly affected by topography, may improve the representation of land surface processes. Previous studies found that land surface modeling, using subbasins instead of structured grids as computational units, improves the scalability of simulated runoff and streamflow processes. In this study, new land surface spatial structures are explored by further dividing subbasins into subgrid structures based on topographic properties, including surface elevation,more » slope and aspect. Two methods (local and global) of watershed discretization are applied to derive two types of subgrid structures (geo-located and non-geo-located) over the topographically diverse Columbia River basin in the northwestern United States. In the global method, a fixed elevation classification scheme is used to discretize subbasins. The local method utilizes concepts of hypsometric analysis to discretize each subbasin, using different elevation ranges that also naturally account for slope variations. The relative merits of the two methods and subgrid structures are investigated for their ability to capture topographic heterogeneity and the implications of this on representations of atmospheric forcing and land cover spatial patterns. Results showed that the local method reduces the standard deviation (SD) of subgrid surface elevation in the study domain by 17 to 19 % compared to the global method, highlighting the relative advantages of the local method for capturing subgrid topographic variations. The comparison between the two types of subgrid structures showed that the non-geo-located subgrid structures are more consistent across different area threshold values than the geo-located subgrid structures. Altogether the local method and non-geo-located subgrid structures effectively and robustly capture topographic, climatic and vegetation variability, which is important for land surface modeling.« less

Forecasting future phosphorus export to the Laurentian Great Lakes from land-derived nutrient inputs

NASA Astrophysics Data System (ADS)

LaBeau, M. B.; Robertson, D. M.; Mayer, A. S.; Pijanowski, B. C.

2011-12-01

Anthropogenic use of the land through agricultural and urban activities has significantly increased phosphorus loading to rivers that flow to the Great Lakes. Phosphorus (P) is a critical element in the eutrophication of the freshwater ecosystems, most notably the Great Lakes. To better understand factors influencing phosphorus delivery to aquatic systems and thus their potential harmful effects to lake ecosystems, models that predict P export should incorporate account for changing changes in anthropogenic activities. Land-derived P from high yielding sources, such as agriculture and urban areas, affect eutrophication at various scales (e.g. specific bays to all of Lake Erie). SPARROW (SPAtially Referenced Regression On Watershed attributes) is a spatially explicit watershed model that has been used to understand linkages between land-derived sources and nutrient transport to the Great Lakes. The Great Lakes region is expected to experience a doubling of urbanized areas along with a ten percent increase in agricultural use over the next 40 years, which is likely to increase P loading. To determine how these changes will impact P loading, SPARROW have been developed that relate changes in land use to changes in nutrient sources, including relationships between row crop acreage and fertilizer intensity and urban land use and point source intensity. We used land use projections from the Land Transformation Model, a, spatially explicit, neural-net based land change model. Land use patterns from current to 2040 were used as input into HydroSPARROW, a forecasting tool that enables SPARROW to simulate the effects of various land-use and climate scenarios. Consequently, this work is focusing on understanding the effects of how specific agriculture and urbanization activities affect P loading in the watersheds of the Laurentian Great Lakes to potentially find strategies to reduce the extent and severity of future eutrophication.

Predicting Plant Diversity Patterns in Madagascar: Understanding the Effects of Climate and Land Cover Change in a Biodiversity Hotspot

PubMed Central

Brown, Kerry A.; Parks, Katherine E.; Bethell, Colin A.; Johnson, Steig E.; Mulligan, Mark

2015-01-01

Climate and land cover change are driving a major reorganization of terrestrial biotic communities in tropical ecosystems. In an effort to understand how biodiversity patterns in the tropics will respond to individual and combined effects of these two drivers of environmental change, we use species distribution models (SDMs) calibrated for recent climate and land cover variables and projected to future scenarios to predict changes in diversity patterns in Madagascar. We collected occurrence records for 828 plant genera and 2186 plant species. We developed three scenarios, (i.e., climate only, land cover only and combined climate-land cover) based on recent and future climate and land cover variables. We used this modelling framework to investigate how the impacts of changes to climate and land cover influenced biodiversity across ecoregions and elevation bands. There were large-scale climate- and land cover-driven changes in plant biodiversity across Madagascar, including both losses and gains in diversity. The sharpest declines in biodiversity were projected for the eastern escarpment and high elevation ecosystems. Sharp declines in diversity were driven by the combined climate-land cover scenarios; however, there were subtle, region-specific differences in model outputs for each scenario, where certain regions experienced relatively higher species loss under climate or land cover only models. We strongly caution that predicted future gains in plant diversity will depend on the development and maintenance of dispersal pathways that connect current and future suitable habitats. The forecast for Madagascar’s plant diversity in the face of future environmental change is worrying: regional diversity will continue to decrease in response to the combined effects of climate and land cover change, with habitats such as ericoid thickets and eastern lowland and sub-humid forests particularly vulnerable into the future. PMID:25856241

Predicting plant diversity patterns in Madagascar: understanding the effects of climate and land cover change in a biodiversity hotspot.

PubMed

Brown, Kerry A; Parks, Katherine E; Bethell, Colin A; Johnson, Steig E; Mulligan, Mark

2015-01-01

Climate and land cover change are driving a major reorganization of terrestrial biotic communities in tropical ecosystems. In an effort to understand how biodiversity patterns in the tropics will respond to individual and combined effects of these two drivers of environmental change, we use species distribution models (SDMs) calibrated for recent climate and land cover variables and projected to future scenarios to predict changes in diversity patterns in Madagascar. We collected occurrence records for 828 plant genera and 2186 plant species. We developed three scenarios, (i.e., climate only, land cover only and combined climate-land cover) based on recent and future climate and land cover variables. We used this modelling framework to investigate how the impacts of changes to climate and land cover influenced biodiversity across ecoregions and elevation bands. There were large-scale climate- and land cover-driven changes in plant biodiversity across Madagascar, including both losses and gains in diversity. The sharpest declines in biodiversity were projected for the eastern escarpment and high elevation ecosystems. Sharp declines in diversity were driven by the combined climate-land cover scenarios; however, there were subtle, region-specific differences in model outputs for each scenario, where certain regions experienced relatively higher species loss under climate or land cover only models. We strongly caution that predicted future gains in plant diversity will depend on the development and maintenance of dispersal pathways that connect current and future suitable habitats. The forecast for Madagascar's plant diversity in the face of future environmental change is worrying: regional diversity will continue to decrease in response to the combined effects of climate and land cover change, with habitats such as ericoid thickets and eastern lowland and sub-humid forests particularly vulnerable into the future.

Importance of scale, land cover, and weather on the abundance of bird species in a managed forest

USGS Publications Warehouse

Grinde, Alexis R.; Hiemi, Gerald J.; Sturtevant, Brian R.; Panci, Hannah; Thogmartin, Wayne E.; Wolter, Peter

2017-01-01

Climate change and habitat loss are projected to be the two greatest drivers of biodiversity loss over the coming century. While public lands have the potential to increase regional resilience of bird populations to these threats, long-term data are necessary to document species responses to changes in climate and habitat to better understand population vulnerabilities. We used generalized linear mixed models to determine the importance of stand-level characteristics, multi-scale land cover, and annual weather factors to the abundance of 61 bird species over a 20-year time frame in Chippewa National Forest, Minnesota, USA. Of the 61 species modeled, we were able to build final models with R-squared values that ranged from 26% to 69% for 37 species; the remaining 24 species models had issues with convergence or low explanatory power (R-squared < 20%). Models for the 37 species show that stand-level characteristics, land cover factors, and annual weather effects on species abundance were species-specific and varied within guilds. Forty-one percent of the final species models included stand-level characteristics, 92% included land cover variables at the 200 m scale, 51% included land cover variables at the 500 m scale, 46% included land cover variables at the 1000 m scale, and 38% included weather variables in best models. Three species models (8%) included significant weather and land cover interaction terms. Overall, models indicated that aboveground tree biomass and land cover variables drove changes in the majority of species. Of those species models including weather variables, more included annual variation in precipitation or drought than temperature. Annual weather variability was significantly more likely to impact abundance of species associated with deciduous forests and bird species that are considered climate sensitive. The long-term data and models we developed are particularly suited to informing science-based adaptive forest management plans that incorporate climate sensitivity, aim to conserve large areas of forest habitat, and maintain an historical mosaic of cover types for conserving a diverse and abundant avian assemblage.

[Analysis on nitrogen and phosphorus loading of non-point sources in Shiqiao river watershed based on L-THIA model].

PubMed

Li, Kai; Zeng, Fan-Tang; Fang, Huai-Yang; Lin, Shu

2013-11-01

Based on the Long-term Hydrological Impact Assessment (L-THIA) model, the effect of land use and rainfall change on nitrogen and phosphorus loading of non-point sources in Shiqiao river watershed was analyzed. The parameters in L-THIA model were revised according to the data recorded in the scene of runoff plots, which were set up in the watershed. The results showed that the distribution of areas with high pollution load was mainly concentrated in agricultural land and urban land. Agricultural land was the biggest contributor to nitrogen and phosphorus load. From 1995 to 2010, the load of major pollutants, namely TN and TP, showed an obviously increasing trend with increase rates of 17.91% and 25.30%, respectively. With the urbanization in the watershed, urban land increased rapidly and its area proportion reached 43.94%. The contribution of urban land to nitrogen and phosphorus load was over 40% in 2010. This was the main reason why pollution load still increased obviously while the agricultural land decreased greatly in the past 15 years. The rainfall occurred in the watershed was mainly concentrated in the flood season, so the nitrogen and phosphorus load of the flood season was far higher than that of the non-flood season and the proportion accounting for the whole year was over 85%. Pearson regression analysis between pollution load and the frequency of different patterns of rainfall demonstrated that rainfall exceeding 20 mm in a day was the main rainfall type causing non-point source pollution.

Sensitivity Analysis in Agent-Based Models of Socio-Ecological Systems: An Example in Agricultural Land Conservation for Lake Water Quality Improvement

NASA Astrophysics Data System (ADS)

Ligmann-Zielinska, A.; Kramer, D. B.; Spence Cheruvelil, K.; Soranno, P.

2012-12-01

Socio-ecological systems are dynamic and nonlinear. To account for this complexity, we employ agent-based models (ABMs) to study macro-scale phenomena resulting from micro-scale interactions among system components. Because ABMs typically have many parameters, it is challenging to identify which parameters contribute to the emerging macro-scale patterns. In this paper, we address the following question: What is the extent of participation in agricultural land conservation programs given heterogeneous landscape, economic, social, and individual decision making criteria in complex lakesheds? To answer this question, we: [1] built an ABM for our model system; [2] simulated land use change resulting from agent decision making, [3] estimated the uncertainty of the model output, decomposed it and apportioned it to each of the parameters in the model. Our model system is a freshwater socio-ecological system - that of farmland and lake water quality within a region containing a large number of lakes and high proportions of agricultural lands. Our study focuses on examining how agricultural land conversion from active to fallow reduces freshwater nutrient loading and improves water quality. Consequently, our ABM is composed of farmer agents who make decisions related to participation in a government-sponsored Conservation Reserve Program (CRP) managed by the Farm Service Agency (FSA). We also include an FSA agent, who selects enrollment offers made by farmers and announces the signup results leading to land use change. The model is executed in a Monte Carlo simulation framework to generate a distribution of maps of fallow lands that are used for calculating nutrient loading to lakes. What follows is a variance-based sensitivity analysis of the results. We compute sensitivity indices for individual parameters and their combinations, allowing for identification of the most influential as well as the insignificant inputs. In the case study, we observe that farmland conservation is first and foremost driven by the FSA signup choices. Environmental criteria used in FSA offer selection play a secondary role in farmland-to-fallow-land conversion. Farmer decision making is mainly influenced by the willingness to reduce the potential annual rental payments. As the case study demonstrates, our approach leads to ABM simplification without the loss of outcome variability. It also shows how to represent the magnitude of ABM complexity and isolate the effects of the interconnected explanatory variables on the simulated emergent phenomena. More importantly, the results of our research indicate that some of the parameters exert influence on model outcomes only if analyzed in combination with other parameters. Without evaluating the interaction effects among inputs, we risk losing important functional relationships among ABM components and, consequently, we potentially reduce its explanatory power.

Sustainability of integrated land and water resources management in the face of climate and land use changes

NASA Astrophysics Data System (ADS)

Setegn, Shimelis

2017-04-01

Sustainable development integrates economic development, social development, and environmental protection. Land and Water resources are under severe pressure from increasing populations, fast development, deforestation, intensification of agriculture and the degrading environment in many part of the world. The demand for adequate and safe supplies of water is becoming crucial especially in the overpopulated urban centers of the Caribbean islands. Moreover, population growth coupled with environmental degradation and possible adverse impacts of land use and climate change are major factors limiting freshwater resource availability. The main objective of this study is to develop a hydrological model and analyze the spatiotemporal variability of hydrological processes in the Caribbean islands of Puerto Rico and Jamaica. Physically based eco-hydrological model was developed and calibrated in the Rio Grande Manati and Wag water watershed. Spatial distribution of annual hydrological processes, water balance components for wet and dry years, and annual hydrological water balance of the watershed are discussed. The impact of land use and climate change are addressed in the watersheds. Appropriate nature based adaptation strategies were evaluated. The study will present a good understanding of advantages and disadvantages of nature-based solutions for adapting climate change, hydro-meteorological risks and other extreme hydrological events.

Using the FORE-SCE model to project land-cover change in the southeastern United States

USGS Publications Warehouse

Sohl, Terry; Sayler, Kristi L.

2008-01-01

A wide variety of ecological applications require spatially explicit current and projected land-use and land-cover data. The southeastern United States has experienced massive land-use change since European settlement and continues to experience extremely high rates of forest cutting, significant urban development, and changes in agricultural land use. Forest-cover patterns and structure are projected to change dramatically in the southeastern United States in the next 50 years due to population growth and demand for wood products [Wear, D.N., Greis, J.G. (Eds.), 2002. Southern Forest Resource Assessment. General Technical Report SRS-53. U.S. Department of Agriculture, Forest Service, Southern Research Station, Asheville, NC, 635 pp]. Along with our climate partners, we are examining the potential effects of southeastern U.S. land-cover change on regional climate. The U.S. Geological Survey (USGS) Land Cover Trends project is analyzing contemporary (1973-2000) land-cover change in the conterminous United States, providing ecoregion-by-ecoregion estimates of the rates of change, descriptive transition matrices, and changes in landscape metrics. The FORecasting SCEnarios of future land-cover (FORE-SCE) model used Land Cover Trends data and theoretical, statistical, and deterministic modeling techniques to project future land-cover change through 2050 for the southeastern United States. Prescriptions for future proportions of land cover for this application were provided by ecoregion-based extrapolations of historical change. Logistic regression was used to develop relationships between suspected drivers of land-cover change and land cover, resulting in the development of probability-of-occurrence surfaces for each unique land-cover type. Forest stand age was initially established with Forest Inventory and Analysis (FIA) data and tracked through model iterations. The spatial allocation procedure placed patches of new land cover on the landscape until the scenario prescriptions were met, using measured Land Cover Trends data to guide patch characteristics and the probability surfaces to guide placement. The approach provides an efficient method for extrapolating historical land-cover trends and is amenable to the incorporation of more detailed and focused studies for the establishment of scenario prescriptions.

A dynamic simulation model of land-use, population, and rural livelihoods in the Central Rift Valley of Ethiopia.

PubMed

Garedew, Efrem; Sandewall, Mats; Soderberg, Ulf

2012-01-01

The dynamic interactions between society and land resources have to be taken into account when planning and managing natural resources. A computer model, using STELLA software, was developed through active participation of purposively selected farm households from different wealth groups, age groups and gender within a rural community and some members of Kebelle council. The aim of the modeling was to study the perceived changes in land-use, population and livelihoods over the next 30 years and to improve our understanding of the interactions among them. The modeling output is characterized by rapid population growth, declining farm size and household incomes, deteriorating woody vegetation cover and worsening land degradation if current conditions remain. However, through integrated intervention strategies (including forest increase, micro-finance, family planning, health and education) the woody vegetation cover is likely to increase in the landscape, population growth is likely to slow down and households' income is likely to improve. A validation assessment of the simulation model based on historical data on land-use and population from 1973 to 2006 showed that the model is relatively robust. We conclude that as a supporting tool, the simulation model can contribute to the decision making process.

Evaluation of ecosystem service based on scenario simulation of land use in Yunnan Province

NASA Astrophysics Data System (ADS)

Zhang, Hong; Liao, Xiaoli; Zhai, Tianlin

2018-04-01

Climate change and rapid urbanization are important factors restricting future land use. Situational analysis, as an important foundation for the optimization of land use, needs to focus on the impact of climate factors and socio-economic factors. In this paper, the Markov model and the DLS (Simulation of Land System Dynamics) model are combined for the first time, and the land use pattern in 2020 is simulated based on the data of land use in 2000 and 2010 as well as the climate, soil, topography and socio-economic factors of Yunnan Province. In his paper, we took Yunnan Province as the case study area, and selected 12 driving factors by logistic regression method, then the land use demands and layout of Yunnan Province in 2020 has been forecasted and simulated under business as usual (BAU) scenario and farmland protection (FP) scenario and the changes in ecosystem service value has been calculated. The result shows that: (1) after the regression analysis and ROC (Relative Operating Characteristics) test, the 12 factors selected in this paper have a strong ability to explain the land use change in Yunnan Province. (2) Under the two scenarios, the significant reduction of arable land area is a common feature of land use change in Yunnan Province in the future, and its main land use type will be construction land. However, under FP scenario, the current situation where construction land encroach on arable land will be improved. Compared with the change from 2000 to 2010, the trend of arable land, forest land, water area, construction land and unused land will be the same under the two scenarios, whereas the change trend of grassland was opposite. (3) From 2000 to 2020, the value of ecosystem services in Yunnan Province is on the rise, but the ecosystem service value under FP scenario is higher than that of the ecosystem services under BAU scenario. In general, land use in 2020 in Yunnan Province continues the pattern of 2010, but there are also significant spatial differences. Under the BAU scenario, the construction land is mainly in the south of Lijiang City and the northeastern part of Kunming. Under the FP scenario, the new construction land is concentrated near the Lashi dam in northern Yunnan Province, and the high-quality arable land in the valley will be better protected. The research results can provide reference for the optimization of land use pattern in Yunnan Province, and provide scientific basis for land use management and planning. Based on the value of ecosystem services, we should implement the policy of strict protection of arable land, both to ensure food supply and promote the healthy development of ecological environment.

Time-varying parameter models for catchments with land use change: the importance of model structure

NASA Astrophysics Data System (ADS)

Pathiraja, Sahani; Anghileri, Daniela; Burlando, Paolo; Sharma, Ashish; Marshall, Lucy; Moradkhani, Hamid

2018-05-01

Rapid population and economic growth in Southeast Asia has been accompanied by extensive land use change with consequent impacts on catchment hydrology. Modeling methodologies capable of handling changing land use conditions are therefore becoming ever more important and are receiving increasing attention from hydrologists. A recently developed data-assimilation-based framework that allows model parameters to vary through time in response to signals of change in observations is considered for a medium-sized catchment (2880 km2) in northern Vietnam experiencing substantial but gradual land cover change. We investigate the efficacy of the method as well as the importance of the chosen model structure in ensuring the success of a time-varying parameter method. The method was used with two lumped daily conceptual models (HBV and HyMOD) that gave good-quality streamflow predictions during pre-change conditions. Although both time-varying parameter models gave improved streamflow predictions under changed conditions compared to the time-invariant parameter model, persistent biases for low flows were apparent in the HyMOD case. It was found that HyMOD was not suited to representing the modified baseflow conditions, resulting in extreme and unrealistic time-varying parameter estimates. This work shows that the chosen model can be critical for ensuring the time-varying parameter framework successfully models streamflow under changing land cover conditions. It can also be used to determine whether land cover changes (and not just meteorological factors) contribute to the observed hydrologic changes in retrospective studies where the lack of a paired control catchment precludes such an assessment.

Eco-hydrologic model cascades: Simulating land use and climate change impacts on hydrology, hydraulics and habitats for fish and macroinvertebrates.

PubMed

Guse, Björn; Kail, Jochem; Radinger, Johannes; Schröder, Maria; Kiesel, Jens; Hering, Daniel; Wolter, Christian; Fohrer, Nicola

2015-11-15

Climate and land use changes affect the hydro- and biosphere at different spatial scales. These changes alter hydrological processes at the catchment scale, which impact hydrodynamics and habitat conditions for biota at the river reach scale. In order to investigate the impact of large-scale changes on biota, a cascade of models at different scales is required. Using scenario simulations, the impact of climate and land use change can be compared along the model cascade. Such a cascade of consecutively coupled models was applied in this study. Discharge and water quality are predicted with a hydrological model at the catchment scale. The hydraulic flow conditions are predicted by hydrodynamic models. The habitat suitability under these hydraulic and water quality conditions is assessed based on habitat models for fish and macroinvertebrates. This modelling cascade was applied to predict and compare the impacts of climate- and land use changes at different scales to finally assess their effects on fish and macroinvertebrates. Model simulations revealed that magnitude and direction of change differed along the modelling cascade. Whilst the hydrological model predicted a relevant decrease of discharge due to climate change, the hydraulic conditions changed less. Generally, the habitat suitability for fish decreased but this was strongly species-specific and suitability even increased for some species. In contrast to climate change, the effect of land use change on discharge was negligible. However, land use change had a stronger impact on the modelled nitrate concentrations affecting the abundances of macroinvertebrates. The scenario simulations for the two organism groups illustrated that direction and intensity of changes in habitat suitability are highly species-dependent. Thus, a joined model analysis of different organism groups combined with the results of hydrological and hydrodynamic models is recommended to assess the impact of climate and land use changes on river ecosystems. Copyright © 2015 Elsevier B.V. All rights reserved.

Towards a community Earth System Model

NASA Astrophysics Data System (ADS)

Blackmon, M.

2003-04-01

The Community Climate System Model, version 2 (CCSM2), was released in June 2002. CCSM2 has several new components and features, which I will discuss briefly. I will also show a few results from a multi-century equilibrium run with this model, emphasizing the improvements over the earlier simulation using the original CSM. A few flaws and inadequacies in CCSM2 have been identified. I will also discuss briefly work underway to improve the model and present results, if available. CCSM2, with improvements, will be the basis for the development of a Community Earth System Model (CESM). The highest priority for expansion of the model involves incorporation of biogeosciences into the coupled model system, with emphasis given to the carbon, nitrogen and iron cycles. The overall goal of the biogeosciences project within CESM is to understand the regulation of planetary energetics, planetary ecology, and planetary metabolism through exchanges of energy, momentum, and materials among atmosphere, land, and ocean, and the response of the climate system through these processes to changes in land cover and land use. In particular, this research addresses how biogeochemical coupling of carbon, nitrogen, and iron cycles affects climate and how human perturbations of these cycles alter climate. To accomplish these goals, the Community Land Model, the land component of CCSM2, is being developed to include river routing, carbon and nitrogen cycles, emissions of mineral aerosols and biogenic volatile organic compounds, dry deposition of various gases, and vegetation dynamics. The carbon and nitrogen cycles are being implemented using parameterizations developed as part of a state-of-the-art ecosystem biogeochemistry model. The primary goal of this research is to provide an accurate net flux of CO2 between the land and the atmosphere so that CESM can be used to study the dynamics of the coupled climate-carbon system. Emissions of biogenic volatile organic compounds are also based on a state-of-the-art emissions model and depend on plant type, leaf area index, photosynthetically active radiation, and leaf temperature. Dust emissions and deposition are being developed to implement a fully coupled dust cycle in CCSM, including the radiative effects of dust and carbon feedbacks related to fertilization of ocean and terrestrial ecosystems. Dust mobilization depends on surface wind speed, soil moisture, plant cover, and soil texture. Dust dry deposition processes include sedimentation and turbulent mix-out. A major research focus is how natural and human-mediated changes in land cover and ecosystem functions alter surface energy fluxes, the hydrological cycle, and biogeochemical cycles. Human land uses include conversion of natural vegetation to cropland, soil degradation, and urbanization. Climate feedbacks associated with natural changes in land cover are being assessed by developing and implementing a model of natural vegetation dynamics for use with the Community Land Model. Development of a marine ecosystem model is also underway. The ecosystem model is based on the global, mixed-layer marine ecosystem model of Moore et al., which includes parameterizations for such things as iron limitation and scavenging, zooplankton grazing, nitrogen fixation, calcification, and ballast-based remineralization. A series of experiments is being planned to assess the coupling of the ecology to the biogeochemistry, to adequately tune some of the model parameters that are poorly constrained by data, to explore new parameterizations and processes (e.g., riverine and atmospheric inputs of nutrients), and to conduct uncoupled application studies (e.g., deliberate carbon sequestration, retrospective historical simulations, iron-dust deposition response). Longer term plans include investigating biogeochemical processes in the coastal zone and how to incorporate these processes into a global ocean model, either through subgrid-scale parameterizations or model nesting. A Whole Atmosphere Community Climate Model(WACCM) is being developed. The vertical extent of the model is 150 km at present, but extension to 500 km is eventually expected. Interactive chemistry is being incorporated. This model will be used as the atmospheric component of CESM for some experiments. One expected application is the study of solar variability and its impact on climate variability in the troposphere and at the atmosphere, ocean, land interface. Preliminary results using some of these model components will be shown. A timeline for development and use of the models will be given.

[Urban greenbelt eco-service value of Hangzhou City under effects of land use change: an evaluation with CITYgreen model].

PubMed

Zhang, Kan; Zhang, Jianying; Chen, Yingxu; Zhu, Yinmei

2006-10-01

Based on the Landset TM information of land use/cover change and greenbelt distribution in Hangzhou city in 1994 and 2004, and by using CITYgreen model, this paper estimated the eco-service value of urban greenbelt in the city under the effects of land use change and economic development. The results showed that in the 10 years from 1994 to 2004, the greenbelt area in the city decreased by 20. 4% , while its eco-service value increased by 168 million yuan. The annual increment of greenbelt eco-service value and GDP was 111.92% and 5. 32% , respectively. Suitable adjustment of land use pattern in the city harmonized the relationships between urban economic development and urban eco-function, and achieved higher eco-service efficiency of land utilization.

Statistical modeling of urban air temperature distributions under different synoptic conditions

NASA Astrophysics Data System (ADS)

Beck, Christoph; Breitner, Susanne; Cyrys, Josef; Hald, Cornelius; Hartz, Uwe; Jacobeit, Jucundus; Richter, Katja; Schneider, Alexandra; Wolf, Kathrin

2015-04-01

Within urban areas air temperature may vary distinctly between different locations. These intra-urban air temperature variations partly reach magnitudes that are relevant with respect to human thermal comfort. Therefore and furthermore taking into account potential interrelations with other health related environmental factors (e.g. air quality) it is important to estimate spatial patterns of intra-urban air temperature distributions that may be incorporated into urban planning processes. In this contribution we present an approach to estimate spatial temperature distributions in the urban area of Augsburg (Germany) by means of statistical modeling. At 36 locations in the urban area of Augsburg air temperatures are measured with high temporal resolution (4 min.) since December 2012. These 36 locations represent different typical urban land use characteristics in terms of varying percentage coverages of different land cover categories (e.g. impervious, built-up, vegetated). Percentage coverages of these land cover categories have been extracted from different sources (Open Street Map, European Urban Atlas, Urban Morphological Zones) for regular grids of varying size (50, 100, 200 meter horizonal resolution) for the urban area of Augsburg. It is well known from numerous studies that land use characteristics have a distinct influence on air temperature and as well other climatic variables at a certain location. Therefore air temperatures at the 36 locations are modeled utilizing land use characteristics (percentage coverages of land cover categories) as predictor variables in Stepwise Multiple Regression models and in Random Forest based model approaches. After model evaluation via cross-validation appropriate statistical models are applied to gridded land use data to derive spatial urban air temperature distributions. Varying models are tested and applied for different seasons and times of the day and also for different synoptic conditions (e.g. clear and calm situations, cloudy and windy situations). Based on hourly air temperature data from our measurements in the urban area of Augsburg distinct temperature differences between locations with different urban land use characteristics are revealed. Under clear and calm weather conditions differences between mean hourly air temperatures reach values around 8°C. Whereas during cloudy and windy weather maximum differences in mean hourly air temperatures do not exceed 5°C. Differences appear usually slightly more pronounced in summer than in winter. First results from the application of statistical modeling approaches reveal promising skill of the models in terms of explained variances reaching up to 60% in leave-one-out cross-validation experiments. The contribution depicts the methodology of our approach and presents and discusses first results.

Land-use impacts on water resources and protected areas: applications of state-and-transition simulation modeling of future scenarios

USGS Publications Warehouse

Wilson, Tamara; Sleeter, Benjamin M.; Sherba, Jason T.; Dick Cameron,

2015-01-01

Human land use will increasingly contribute to habitat loss and water shortages in California, given future population projections and associated land-use demand. Understanding how land-use change may impact future water use and where existing protected areas may be threatened by land-use conversion will be important if effective, sustainable management approaches are to be implemented. We used a state-and-transition simulation modeling (STSM) framework to simulate spatially-explicit (1 km2) historical (1992-2010) and future (2011-2060) land-use change for 52 California counties within Mediterranean California ecoregions. Historical land use and land cover (LULC) change estimates were derived from the Farmland Mapping and Monitoring Program dataset and attributed with county-level agricultural water-use data from the California Department of Water Resources. Five future alternative land-use scenarios were developed and modeled using the historical land-use change estimates and land-use projections based on the Intergovernmental Panel on Climate Change's Special Report on Emission Scenarios A2 and B1 scenarios. Spatial land-use transition outputs across scenarios were combined to reveal scenario agreement and a land conversion threat index was developed to evaluate vulnerability of existing protected areas to proximal land conversion. By 2060, highest LULC conversion threats were projected to impact nearly 10,500 km2 of land area within 10 km of a protected area boundary and over 18,000 km2 of land area within essential habitat connectivity areas. Agricultural water use declined across all scenarios perpetuating historical drought-related land use from 2008-2010 and trends of annual cropland conversion into perennial woody crops. STSM is useful in analyzing land-use related impacts on water resource use as well as potential threats to existing protected land. Exploring a range of alternative, yet plausible, LULC change impacts will help to better inform resource management and mitigation strategies.

Spatial and temporal diversification of crops dynamics in soil erosion modelling. A case study in the arable land of the upper Enziwigger River, Switzerland.

NASA Astrophysics Data System (ADS)

Borrelli, Pasquale; Meusburger, Katrin; Panagos, Panos; Ballabio, Cristiano; Alewell, Christine

2017-04-01

Accelerated soil erosion by water is a widespread phenomenon that affects several Mediterranean and Alpine landscapes causing on-site and off-site environmental impacts. Recognized in the EU Thematic Strategy for Soil Protection as one of the major threats to European soils (COM(2006)231), accelerated soil erosion is a major concern in landscape management and conservation planning (UN SDG 2.4). Agriculture and associated land-use change is the primary cause of accelerated soil erosion. This, because the soil displacement by water erosion mainly occurs when bare-sloped soil surfaces are exposed to the effect of rainfall and overland flow. The Revised Universal Soil Loss Equation (RUSLE) and other RUSLE-based models (which account for more than 90% of current worldwide modelling applications) describe the effect of the vegetation in the so called cover and management factor (C). The C-factor is generally the most challenging modelling component to compute over large study sites. To run a GIS-based RUSLE modelling for a study site greater than few hectares, the use of a simplified approach to assess the C-factor is inevitably necessary. In most of the cases, the C-factor values are assigned to the different land-use classes according to i) the C-values proposed in the literature, and ii) through land-use classifications based on vegetation indices (VI). In previous national (Land Use Policy, 50, 408-421, 2016) and pan-European (Environmental Science & Policy, 54, 438-447, 2015) studies, we computed regional C-values through weighted average operations combining crop statistics with remote sensing and GIS modelling techniques. Here, we present the preliminary results of an object-oriented change detection approach that we are testing to acquire spatial as well temporal crops dynamics at field-scale level in complex agricultural systems.

Urban surface energy fluxes based on remotely-sensed data and micrometeorological measurements over the Kansai area, Japan

NASA Astrophysics Data System (ADS)

Sukeyasu, T.; Ueyama, M.; Ando, T.; Kosugi, Y.; Kominami, Y.

2017-12-01

The urban heat island is associated with land cover changes and increases in anthropogenic heat fluxes. Clear understanding of the surface energy budget at urban area is the most important for evaluating the urban heat island. In this study, we develop a model based on remotely-sensed data for the Kansai area in Japan and clarify temporal transitions and spatial distributions of the surface energy flux from 2000 to 2016. The model calculated the surface energy fluxes based on various satellite and GIS products. The model used land surface temperature, surface emissivity, air temperature, albedo, downward shortwave radiation and land cover/use type from the moderate resolution imaging spectroradiometer (MODIS) under cloud free skies from 2000 to 2016 over the Kansai area in Japan (34 to 35 ° N, 135 to 136 ° E). Net radiation was estimated by a radiation budget of upward/downward shortwave and longwave radiation. Sensible heat flux was estimated by a bulk aerodynamic method. Anthropogenic heat flux was estimated by the inventory data. Latent heat flux was examined with residues of the energy budget and parameterization of bulk transfer coefficients. We validated the model using observed fluxes from five eddy-covariance measurement sites: three urban sites and two forested sites. The estimated net radiation roughly agreed with the observations, but the sensible heat flux were underestimated. Based on the modeled spatial distributions of the fluxes, the daytime net radiation in the forested area was larger than those in the urban area, owing to higher albedo and land surface temperatures in the urban area than the forested area. The estimated anthropogenic heat flux was high in the summer and winter periods due to increases in energy-requirements.

Deriving global parameter estimates for the Noah land surface model using FLUXNET and machine learning

NASA Astrophysics Data System (ADS)

Chaney, Nathaniel W.; Herman, Jonathan D.; Ek, Michael B.; Wood, Eric F.

2016-11-01

With their origins in numerical weather prediction and climate modeling, land surface models aim to accurately partition the surface energy balance. An overlooked challenge in these schemes is the role of model parameter uncertainty, particularly at unmonitored sites. This study provides global parameter estimates for the Noah land surface model using 85 eddy covariance sites in the global FLUXNET network. The at-site parameters are first calibrated using a Latin Hypercube-based ensemble of the most sensitive parameters, determined by the Sobol method, to be the minimum stomatal resistance (rs,min), the Zilitinkevich empirical constant (Czil), and the bare soil evaporation exponent (fxexp). Calibration leads to an increase in the mean Kling-Gupta Efficiency performance metric from 0.54 to 0.71. These calibrated parameter sets are then related to local environmental characteristics using the Extra-Trees machine learning algorithm. The fitted Extra-Trees model is used to map the optimal parameter sets over the globe at a 5 km spatial resolution. The leave-one-out cross validation of the mapped parameters using the Noah land surface model suggests that there is the potential to skillfully relate calibrated model parameter sets to local environmental characteristics. The results demonstrate the potential to use FLUXNET to tune the parameterizations of surface fluxes in land surface models and to provide improved parameter estimates over the globe.

Changes in catchment hydrology in relation to vegetation recovery: a comparative modelling experiment

NASA Astrophysics Data System (ADS)

Lana-Renault, Noemí; Karssenberg, Derek; Latron, Jérôme; Serrano, Mā Pilar; Regüés, David; Bierkens, Marc F. P.

2010-05-01

Mediterranean mountains have been largely affected by land abandonment and subsequent vegetation recovery, with a general expansion of shrubs and forests. Such a large scale land-cover change has modified the hydrological behavior of these areas, with significant impact on runoff production. Forecasting the trend of water resources under future re-vegetation scenarios is of paramount importance in Mediterranean basins, where water management relies on runoff generated in these areas. With this purpose, a modelling experiment was designed based on the information collected in two neighbouring research catchments with a different history of land use in the central Spanish Pyrenees. One (2.84 km2) is an abandoned agricultural catchment subjected to plant colonization and at present mainly covered by shrubs. The other (0.92 km2) is a catchment covered by dense natural forest, representative of undisturbed environments. Here we present the results of the analysis of the hydrological differences between the two catchments, and a description of the approach and results of the modelling experiment. In a statistical analysis of the field data, significant differences were observed in the streamflow response of the two catchments. The forested catchment recorded fewer floods per year compared to the old agricultural catchment, and its hydrological response was characterised by a marked seasonality, with autumn and spring as the only high flow periods. Stormflow was generally higher in the old agricultural catchment, especially for low to intermediate size events; only for large events the stormflow in the forested catchment was sometimes greater. Under drier conditions, the relative differences in the stormflow between the two catchments tended to increase whereas under wet conditions they tended to be similar. The forested catchment always reacted more slowly to rainfall, with lower peakflows (generally one order of magnitude lower) and longer recession limbs. The modelling experiment aims at separating the effect of land cover from other differences (e.g. catchment area, morphology) between the two catchments. This approach allows us to make general statements on effects of land cover, required for future predictions for larger areas. In our modelling experiment, a process-based distributed hydrological model is used for the two catchments. First, we calibrate the model using data from the two catchments until a single set of parameters valid for both is found. With this set of parameters and considering a given meteorological driver (due to their proximity, it can be considered the same for both catchments), runoff at the outlet of each catchment is simulated. Land cover is then swapped between catchments and a new runoff simulation is performed for each "swapped" catchment, using the same set of parameters and the same meteorological driver. The effects of the land cover change are determined by analysing the differences between the first and the "swapped" simulations. This study is based on an analysis of the hydrological differences of two catchments with different history of land use, and a comparative modelling experiment applied to them. Following this approach, we attempt to advance our understanding of the effects of land-use/land-cover changes in catchment hydrology and, ultimately, anticipate their hydrological consequences under a future re-vegetation scenario.

The impacts of land reclamation on suspended-sediment dynamics in Jiaozhou Bay, Qingdao, China

NASA Astrophysics Data System (ADS)

Gao, Guan Dong; Wang, Xiao Hua; Bao, Xian Wen; Song, Dehai; Lin, Xiao Pei; Qiao, Lu Lu

2018-06-01

A three-dimensional, high-resolution tidal model coupled with the UNSW sediment model (UNSW-Sed) based on Finite Volume Coastal Ocean Model (FVCOM) was set up to study the suspended-sediment dynamics and its change in Jiaozhou Bay (JZB) due to land reclamation over the period 1935 to 2008. During the past decades, a large amount of tidal flats were lost due to land reclamation. Other than modulating the tides, the tidal flats are a primary source for sediment resuspensions, leading to turbidity maxima nearshore. The tidal dynamics are dominant in controlling the suspended-sediment dynamics in JZB and have experienced significant changes with the loss of tidal flats due to the land reclamation. The sediment model coupled with the tide model was used to investigate the changes in suspended-sediment dynamics due to the land reclamation from 1935 to 2008, including suspended-sediment concentrations (SSC) and the horizontal suspended-sediment fluxes. This model can predict the general patterns of the spatial and temporal variation of SSC. The model was applied to investigate how the net transport of suspended sediments between JZB and its adjacent sea areas changed with land reclamation: in 1935 the net movement of suspended sediments was from JZB to the adjacent sea (erosion for JZB), primarily caused by horizontal advection associated with a horizontal gradient in the SSC; This seaward transport (erosion for JZB) had gradually declined from 1935 to 2008. If land reclamation on a large scale is continued in future, the net transport between JZB and the adjacent sea would turn landward and JZB would switch from erosion to siltation due to the impact of land reclamation on the horizontal advection of suspended sediments. We also evaluate the primary physical mechanisms including advection of suspended sediments, settling lag and tidal asymmetry, which control the suspended-sediment dynamics with the process of land reclamation.

Comparative economic performance and carbon footprint of two farming models for producing atlantic salmon (salmo salar): Land-based closed containment system in freshwater and open pen in seawater

USDA-ARS?s Scientific Manuscript database

Ocean net pen production of Atlantic salmon is approaching 2 million metric tons (MT) annually and has proven to be cost- and energy- efficient. Recently, with technology improvements, freshwater aquaculture of Atlantic salmon from eggs to harvestable size of 4 -5 kg in land-based closed containmen...

Combination of poroelasticity theory and constant strain rate test in modelling land subsidence due to groundwater extraction

NASA Astrophysics Data System (ADS)

Pham, Tien Hung; Rühaak, Wolfram; Sass, Ingo

2017-04-01

Extensive groundwater extraction leads to a drawdown of the ground water table. Consequently, soil effective stress increases and can cause land subsidence. Analysis of land subsidence generally requires a numerical model based on poroelasticity theory, which was first proposed by Biot (1941). In the review of regional land subsidence accompanying groundwater extraction, Galloway and Burbey (2011) stated that more research and application is needed in coupling of stress-dependent land subsidence process. In geotechnical field, the constant rate of strain tests (CRS) was first introduced in 1969 (Smith and Wahls 1969) and was standardized in 1982 through the designation D4186-82 by American Society for Testing and Materials. From the reading values of CRS tests, the stress-dependent parameters of poroelasticity model can be calculated. So far, there is no research to link poroelasticity theory with CRS tests in modelling land subsidence due to groundwater extraction. One dimensional CRS tests using conventional compression cell and three dimension CRS tests using Rowe cell were performed. The tests were also modelled by using finite element method with mixed elements. Back analysis technique is used to find the suitable values of hydraulic conductivity and bulk modulus that depend on the stress or void ratio. Finally, the obtained results are used in land subsidence models. Biot, M. A. (1941). "General theory of three-dimensional consolidation." Journal of applied physics 12(2): 155-164. Galloway, D. L. and T. J. Burbey (2011). "Review: Regional land subsidence accompanying groundwater extraction." Hydrogeology Journal 19(8): 1459-1486. Smith, R. E. and H. E. Wahls (1969). "Consolidation under constant rates of strain." Journal of Soil Mechanics & Foundations Div.

Impacts of Potential Changes in Land Use, Climate, and Water Use on Water Availability, Coastal Carolinas Region, Southeastern United States

NASA Astrophysics Data System (ADS)

Gurley, L. N.; Garcia, A. M.

2017-12-01

Sustainable growth in coastal areas with rapidly increasing populations, such as the coastal regions of North and South Carolina, relies on an understanding of the current state of coastal natural resources coupled with the ability to assess future impacts of changing coastal communities and resources. Changes in climate, water use, population, and land use (e.g. urbanization) will place additional stress on societal and ecological systems that are already competing for water resources. The potential effects of these stressors on water availability are not fully known. To meet societal and ecological needs, water resources management and planning efforts require estimates of likely impacts of population growth, land-use, and climate. Two Soil and Water Assessment (SWAT) hydrologic models were developed to help address the challenges that water managers face in the Carolinas: the (1) Cape Fear and (2) Pee Dee drainage basins. SWAT is a basin-scale, process-based watershed model with the capability of simulating water-management scenarios. Model areas were divided into two square mile sub-basins to evaluate ecological response at headwater streams. The sub-basins were subsequently divided into smaller, discrete hydrologic response units based on land use, slope, and soil type. Monthly and annual water-use data were used for 2000 to 2014 and included estimates of municipal, industrial, agricultural, and commercial water use. Models were calibrated for 2000 to 2014 and potential future streamflows were estimated through 2060 based on a suite of scenarios that integrated land use change projections, climate projections and water-use forecasts. The approaches and new techniques developed as part of this research could be applied to other coastal areas that face similar current and future water availability demands.

Retrieval and Mapping of Soil Texture Based on Land Surface Diurnal Temperature Range Data from MODIS

PubMed Central

Wang, De-Cai; Zhang, Gan-Lin; Zhao, Ming-Song; Pan, Xian-Zhang; Zhao, Yu-Guo; Li, De-Cheng; Macmillan, Bob

2015-01-01

Numerous studies have investigated the direct retrieval of soil properties, including soil texture, using remotely sensed images. However, few have considered how soil properties influence dynamic changes in remote images or how soil processes affect the characteristics of the spectrum. This study investigated a new method for mapping regional soil texture based on the hypothesis that the rate of change of land surface temperature is related to soil texture, given the assumption of similar starting soil moisture conditions. The study area was a typical flat area in the Yangtze-Huai River Plain, East China. We used the widely available land surface temperature product of MODIS as the main data source. We analyzed the relationships between the content of different particle soil size fractions at the soil surface and land surface day temperature, night temperature and diurnal temperature range (DTR) during three selected time periods. These periods occurred after rainfalls and between the previous harvest and the subsequent autumn sowing in 2004, 2007 and 2008. Then, linear regression models were developed between the land surface DTR and sand (> 0.05 mm), clay (< 0.001 mm) and physical clay (< 0.01 mm) contents. The models for each day were used to estimate soil texture. The spatial distribution of soil texture from the studied area was mapped based on the model with the minimum RMSE. A validation dataset produced error estimates for the predicted maps of sand, clay and physical clay, expressed as RMSE of 10.69%, 4.57%, and 12.99%, respectively. The absolute error of the predictions is largely influenced by variations in land cover. Additionally, the maps produced by the models illustrate the natural spatial continuity of soil texture. This study demonstrates the potential for digitally mapping regional soil texture variations in flat areas using readily available MODIS data. PMID:26090852

Retrieval and Mapping of Soil Texture Based on Land Surface Diurnal Temperature Range Data from MODIS.

PubMed

Wang, De-Cai; Zhang, Gan-Lin; Zhao, Ming-Song; Pan, Xian-Zhang; Zhao, Yu-Guo; Li, De-Cheng; Macmillan, Bob

2015-01-01

Numerous studies have investigated the direct retrieval of soil properties, including soil texture, using remotely sensed images. However, few have considered how soil properties influence dynamic changes in remote images or how soil processes affect the characteristics of the spectrum. This study investigated a new method for mapping regional soil texture based on the hypothesis that the rate of change of land surface temperature is related to soil texture, given the assumption of similar starting soil moisture conditions. The study area was a typical flat area in the Yangtze-Huai River Plain, East China. We used the widely available land surface temperature product of MODIS as the main data source. We analyzed the relationships between the content of different particle soil size fractions at the soil surface and land surface day temperature, night temperature and diurnal temperature range (DTR) during three selected time periods. These periods occurred after rainfalls and between the previous harvest and the subsequent autumn sowing in 2004, 2007 and 2008. Then, linear regression models were developed between the land surface DTR and sand (> 0.05 mm), clay (< 0.001 mm) and physical clay (< 0.01 mm) contents. The models for each day were used to estimate soil texture. The spatial distribution of soil texture from the studied area was mapped based on the model with the minimum RMSE. A validation dataset produced error estimates for the predicted maps of sand, clay and physical clay, expressed as RMSE of 10.69%, 4.57%, and 12.99%, respectively. The absolute error of the predictions is largely influenced by variations in land cover. Additionally, the maps produced by the models illustrate the natural spatial continuity of soil texture. This study demonstrates the potential for digitally mapping regional soil texture variations in flat areas using readily available MODIS data.

Uncertainty in solid precipitation and snow depth prediction for Siberia using the Noah and Noah-MP land surface models

NASA Astrophysics Data System (ADS)

Suzuki, Kazuyoshi; Zupanski, Milija

2018-01-01

In this study, we investigate the uncertainties associated with land surface processes in an ensemble predication context. Specifically, we compare the uncertainties produced by a coupled atmosphere-land modeling system with two different land surface models, the Noah- MP land surface model (LSM) and the Noah LSM, by using the Maximum Likelihood Ensemble Filter (MLEF) data assimilation system as a platform for ensemble prediction. We carried out 24-hour prediction simulations in Siberia with 32 ensemble members beginning at 00:00 UTC on 5 March 2013. We then compared the model prediction uncertainty of snow depth and solid precipitation with observation-based research products and evaluated the standard deviation of the ensemble spread. The prediction skill and ensemble spread exhibited high positive correlation for both LSMs, indicating a realistic uncertainty estimation. The inclusion of a multiple snowlayer model in the Noah-MP LSM was beneficial for reducing the uncertainties of snow depth and snow depth change compared to the Noah LSM, but the uncertainty in daily solid precipitation showed minimal difference between the two LSMs. The impact of LSM choice in reducing temperature uncertainty was limited to surface layers of the atmosphere. In summary, we found that the more sophisticated Noah-MP LSM reduces uncertainties associated with land surface processes compared to the Noah LSM. Thus, using prediction models with improved skill implies improved predictability and greater certainty of prediction.

Impacts of land use and population density on seasonal surface water quality using a modified geographically weighted regression.

PubMed

Chen, Qiang; Mei, Kun; Dahlgren, Randy A; Wang, Ting; Gong, Jian; Zhang, Minghua

2016-12-01

As an important regulator of pollutants in overland flow and interflow, land use has become an essential research component for determining the relationships between surface water quality and pollution sources. This study investigated the use of ordinary least squares (OLS) and geographically weighted regression (GWR) models to identify the impact of land use and population density on surface water quality in the Wen-Rui Tang River watershed of eastern China. A manual variable excluding-selecting method was explored to resolve multicollinearity issues. Standard regression coefficient analysis coupled with cluster analysis was introduced to determine which variable had the greatest influence on water quality. Results showed that: (1) Impact of land use on water quality varied with spatial and seasonal scales. Both positive and negative effects for certain land-use indicators were found in different subcatchments. (2) Urban land was the dominant factor influencing N, P and chemical oxygen demand (COD) in highly urbanized regions, but the relationship was weak as the pollutants were mainly from point sources. Agricultural land was the primary factor influencing N and P in suburban and rural areas; the relationship was strong as the pollutants were mainly from agricultural surface runoff. Subcatchments located in suburban areas were identified with urban land as the primary influencing factor during the wet season while agricultural land was identified as a more prevalent influencing factor during the dry season. (3) Adjusted R 2 values in OLS models using the manual variable excluding-selecting method averaged 14.3% higher than using stepwise multiple linear regressions. However, the corresponding GWR models had adjusted R 2 ~59.2% higher than the optimal OLS models, confirming that GWR models demonstrated better prediction accuracy. Based on our findings, water resource protection policies should consider site-specific land-use conditions within each watershed to optimize mitigation strategies for contrasting land-use characteristics and seasonal variations. Copyright © 2016 Elsevier B.V. All rights reserved.

Retrieval of land surface temperature (LST) from landsat TM6 and TIRS data by single channel radiative transfer algorithm using satellite and ground-based inputs

NASA Astrophysics Data System (ADS)

Chatterjee, R. S.; Singh, Narendra; Thapa, Shailaja; Sharma, Dravneeta; Kumar, Dheeraj

2017-06-01

The present study proposes land surface temperature (LST) retrieval from satellite-based thermal IR data by single channel radiative transfer algorithm using atmospheric correction parameters derived from satellite-based and in-situ data and land surface emissivity (LSE) derived by a hybrid LSE model. For example, atmospheric transmittance (τ) was derived from Terra MODIS spectral radiance in atmospheric window and absorption bands, whereas the atmospheric path radiance and sky radiance were estimated using satellite- and ground-based in-situ solar radiation, geographic location and observation conditions. The hybrid LSE model which is coupled with ground-based emissivity measurements is more versatile than the previous LSE models and yields improved emissivity values by knowledge-based approach. It uses NDVI-based and NDVI Threshold method (NDVITHM) based algorithms and field-measured emissivity values. The model is applicable for dense vegetation cover, mixed vegetation cover, bare earth including coal mining related land surface classes. The study was conducted in a coalfield of India badly affected by coal fire for decades. In a coal fire affected coalfield, LST would provide precise temperature difference between thermally anomalous coal fire pixels and background pixels to facilitate coal fire detection and monitoring. The derived LST products of the present study were compared with radiant temperature images across some of the prominent coal fire locations in the study area by graphical means and by some standard mathematical dispersion coefficients such as coefficient of variation, coefficient of quartile deviation, coefficient of quartile deviation for 3rd quartile vs. maximum temperature, coefficient of mean deviation (about median) indicating significant increase in the temperature difference among the pixels. The average temperature slope between adjacent pixels, which increases the potential of coal fire pixel detection from background pixels, is significantly larger in the derived LST products than the corresponding radiant temperature images.

Performance assessment of geospatial simulation models of land-use change--a landscape metric-based approach.

PubMed

Sakieh, Yousef; Salmanmahiny, Abdolrassoul

2016-03-01

Performance evaluation is a critical step when developing land-use and cover change (LUCC) models. The present study proposes a spatially explicit model performance evaluation method, adopting a landscape metric-based approach. To quantify GEOMOD model performance, a set of composition- and configuration-based landscape metrics including number of patches, edge density, mean Euclidean nearest neighbor distance, largest patch index, class area, landscape shape index, and splitting index were employed. The model takes advantage of three decision rules including neighborhood effect, persistence of change direction, and urbanization suitability values. According to the results, while class area, largest patch index, and splitting indices demonstrated insignificant differences between spatial pattern of ground truth and simulated layers, there was a considerable inconsistency between simulation results and real dataset in terms of the remaining metrics. Specifically, simulation outputs were simplistic and the model tended to underestimate number of developed patches by producing a more compact landscape. Landscape-metric-based performance evaluation produces more detailed information (compared to conventional indices such as the Kappa index and overall accuracy) on the model's behavior in replicating spatial heterogeneity features of a landscape such as frequency, fragmentation, isolation, and density. Finally, as the main characteristic of the proposed method, landscape metrics employ the maximum potential of observed and simulated layers for a performance evaluation procedure, provide a basis for more robust interpretation of a calibration process, and also deepen modeler insight into the main strengths and pitfalls of a specific land-use change model when simulating a spatiotemporal phenomenon.

Spatial landuse planning using land evaluation and dynamic system to define sustainable area of paddy field: Case study in Karawang Regency, West Java, Indonesia

NASA Astrophysics Data System (ADS)

Widiatmaka, Widiatmaka; Ambarwulan, Wiwin; Firmansyah, Irman; Munibah, Khursatul; Santoso, Paulus B. K.

2015-04-01

Indonesia is the country with the 4th largest population in the worlds; the population reached more than 237 million people. With rice as the staple food for more than 95 percent of the population, there is an important role of paddy field in Indonesian food security. Actually, paddy field in Java has produced 52,6% of the total rice production in Indonesia, showing the very high dependence of Indonesia on food production from paddy fields in Java island. Karawang Regency is one of the regions in West Java Province that contribute to the national food supply, due to its high soil fertility and its high extent of paddy field. Dynamics of land use change in this region are high because of its proximity to urban area; this dynamics has led to paddy field conversion to industry and residential landuse, which in turn change the regional rice production capacity. Decreasing paddy field landuse in this region could be serve as an example case of the general phenomena which occurred in Javanese rice production region. The objective of this study were: (i) to identify the suitable area for paddy field, (ii) to modelize the decreasing of paddy field in socio-economic context of the region, and (iii) to plan the spatial priority area of paddy field protection according to model prediction. A land evaluation for paddy was completed after a soil survey, while IKONOS imagery was analyzed to delineate paddy fields. Dynamic system model of paddy field land use is built, and then based on the model built, the land area of paddy field untill 2040 in some scenarios was developped. The research results showed that the land suitability class for paddy fields in Karawang Regency ranged from very suitable (S1) to marginally suitable (S3), with various land characteristics as limiting factors. The model predicts that if the situation of paddy field land use change continues in its business as usual path, paddy field area that would exist in the region in 2040 will stay half of the recent area. Based on the model, the scenario were developed for the protection of priority area. With such scenario, paddy field remains close to the value predicted oficially. Spatial information then can play a role by presenting the scenario spatially. Combining spatial information with land suitability, priority areas of paddy field protection can be delineated. Policies that followed also then be compiled, including the location of protection. Key-words: Land evaluation, food security, spatial information

Modeling the Impacts of Global Climate and Regional Land Use Change on Regional Climate, Air Quality and Public Health in the New York Metropolitan Region

NASA Astrophysics Data System (ADS)

Rosenthal, J. E.; Knowlton, K. M.; Kinney, P. L.

2002-12-01

There is an imminent need to downscale the global climate models used by international consortiums like the IPCC (Intergovernmental Panel on Climate Change) to predict the future regional impacts of climate change. To meet this need, a "place-based" climate model that makes specific regional projections about future environmental conditions local inhabitants could face is being created by the Mailman School of Public Health at Columbia University, in collaboration with other researchers and universities, for New York City and the 31 surrounding counties. This presentation describes the design and initial results of this modeling study, aimed at simulating the effects of global climate change and regional land use change on climate and air quality over the northeastern United States in order to project the associated public health impacts in the region. Heat waves and elevated concentrations of ozone and fine particles are significant current public health stressors in the New York metropolitan area. The New York Climate and Health Project is linking human dimension and natural sciences models to assess the potential for future public health impacts from heat stress and air quality, and yield improved tools for assessing climate change impacts. The model will be applied to the NY metropolitan east coast region. The following questions will be addressed: 1. What changes in the frequency and severity of extreme heat events are likely to occur over the next 80 years due to a range of possible scenarios of land use and land cover (LU/LC) and climate change in the region? 2. How might the frequency and severity of episodic concentrations of ozone (O3) and airborne particulate matter smaller than 2.5 æm in diameter (PM2.5) change over the next 80 years due to a range of possible scenarios of land use and climate change in the metropolitan region? 3. What is the range of possible human health impacts of these changes in the region? 4. How might projected future human exposures and responses to heat stress and air quality differ as a function of socio-economic status and race/ethnicity across the region? The model systems used for this study are the Goddard Institute for Space Studies (GISS) Global Atmosphere-Ocean Model; the Regional Atmospheric Modeling System (RAMS) and PennState/NCAR MM5 mesoscale meteorological models; the SLEUTH land use model; the Sparse Matrix Operator Kernel Emissions Modeling System (SMOKE); the Community Multiscale Air Quality (CMAQ) and Comprehensive Air Quality Model with Extensions (CAMx) models for simulating regional air quality; and exposure-risk coefficients for assessing population health impacts based on exposure to extreme heat, fine particulates (PM2.5) and ozone. Two different IPCC global emission scenarios and two different regional land use growth scenarios are considered in the simulations, spanning a range of possible futures. In addition to base simulations for selected time periods in the decade 1990 - 2000, the integrated model is used to simulate future scenarios in the 2020s, 2050s, and 2080s. Predictions from both the meteorological models and the air quality models are compared against available observations for the simulations in the 1990s to establish baseline model performance. A series of sensitivity tests will address whether changes in meteorology due to global climate change, changes in regional land use, or changes in emissions have the largest impact on predicted ozone and particulate matter concentrations.

Comprehensive evaluation of land quality basing on 3S technology and farmers' survey: A case study in Crisscross Region of Wind-drift Sand Regions along the Great Wall and Loess Plateau

NASA Astrophysics Data System (ADS)

Zhang, Yan-yu; Wang, Jing; Shi, Yan-xi; Li, Yu-huan; Lv, Chun-yan

2005-10-01

The Crisscross Region of Wind-drift Sand Regions along the Great Wall and Loess Plateau locates in southern Ordos Plateau and northern Chinese Loess Plateau, where wind erosion and water erosion coexist and specified environmental and socio-economic factors, especially human activities induce serious land degradation. However, there are only a few studies provide an overall assessment consequences. Integrated land quality assessment considering impacts of soil, topography, vegetation, environmental hazards, social-economic factors and land managements are imperative to the regional sustainable land managements. A pilot study was made in Hengshan County (Shanxi Province) with the objective of developing comprehensive land quality evaluation model integrating data from farmers' survey and Remote Sensing. Surveys were carried out in 107 households of study area in 2003 and 2004 to get farmers' perceptions of land quality and to collect correlative information. It was found out that farmers evaluated land quality by slope, water availability, soil texture, yields, amount of fertilizer, crop performance, sandy erosion degree and water erosion degree. Scientists' indicators which emphasize on getting information by RS technology were introduced to reflecting above indicators information for the sake of developing a rapid, efficient and local-fitted land quality assessment model including social-economic, environmental and anthropogenic factors. Data from satellite and surveys were integrated with socio-economic statistic data using geographical information system (GIS) and three indexes, namely Production Press Index (PPI), Land State Index (LSI) and Farmer Behavior Index (FBI) were proposed to measure different aspects of land quality. A model was further derived from the three indexes to explore the overall land quality of the study area. Results suggest that local land prevalently had a poor quality. This paper shows that whilst the model was competent for its work in the study area and evaluation results would supply beneficial information for management decisions.

Status and future transition of rapid urbanizing landscape in central Western Ghats - CA based approach

NASA Astrophysics Data System (ADS)

Bharath, S..; Rajan, K. S.; Ramachandra, T. V.

2014-11-01

The land use changes in forested landscape are highly complex and dynamic, affected by the natural, socio-economic, cultural, political and other factors. The remote sensing (RS) and geographical information system (GIS) techniques coupled with multi-criteria evaluation functions such as Markov-cellular automata (CA-Markov) model helps in analysing intensity, extent and future forecasting of human activities affecting the terrestrial biosphere. Karwar taluk of Central Western Ghats in Karnataka state, India has seen rapid transitions in its forest cover due to various anthropogenic activities, primarily driven by major industrial activities. A study based on Landsat and IRS derived data along with CA-Markov method has helped in characterizing the patterns and trends of land use changes over a period of 2004-2013, expected transitions was predicted for a set of scenarios through 2013-2022. The analysis reveals the loss of pristine forest cover from 75.51% to 67.36% (1973 to 2013) and increase in agriculture land as well as built-up area of 8.65% (2013), causing impact on local flora and fauna. The other factors driving these changes are the aggregated level of demand for land, local and regional effects of land use activities such as deforestation, improper practices in expansion of agriculture and infrastructure development, deteriorating natural resources availability. The spatio temporal models helped in visualizing on-going changes apart from prediction of likely changes. The CA-Markov based analysis provides us insights into the localized changes impacting these regions and can be useful in developing appropriate mitigation management approaches based on the modelled future impacts. This necessitates immediate measures for minimizing the future impacts.

Hotspots of uncertainty in land-use and land-cover change projections: a global-scale model comparison.

PubMed

Prestele, Reinhard; Alexander, Peter; Rounsevell, Mark D A; Arneth, Almut; Calvin, Katherine; Doelman, Jonathan; Eitelberg, David A; Engström, Kerstin; Fujimori, Shinichiro; Hasegawa, Tomoko; Havlik, Petr; Humpenöder, Florian; Jain, Atul K; Krisztin, Tamás; Kyle, Page; Meiyappan, Prasanth; Popp, Alexander; Sands, Ronald D; Schaldach, Rüdiger; Schüngel, Jan; Stehfest, Elke; Tabeau, Andrzej; Van Meijl, Hans; Van Vliet, Jasper; Verburg, Peter H

2016-12-01

Model-based global projections of future land-use and land-cover (LULC) change are frequently used in environmental assessments to study the impact of LULC change on environmental services and to provide decision support for policy. These projections are characterized by a high uncertainty in terms of quantity and allocation of projected changes, which can severely impact the results of environmental assessments. In this study, we identify hotspots of uncertainty, based on 43 simulations from 11 global-scale LULC change models representing a wide range of assumptions of future biophysical and socioeconomic conditions. We attribute components of uncertainty to input data, model structure, scenario storyline and a residual term, based on a regression analysis and analysis of variance. From this diverse set of models and scenarios, we find that the uncertainty varies, depending on the region and the LULC type under consideration. Hotspots of uncertainty appear mainly at the edges of globally important biomes (e.g., boreal and tropical forests). Our results indicate that an important source of uncertainty in forest and pasture areas originates from different input data applied in the models. Cropland, in contrast, is more consistent among the starting conditions, while variation in the projections gradually increases over time due to diverse scenario assumptions and different modeling approaches. Comparisons at the grid cell level indicate that disagreement is mainly related to LULC type definitions and the individual model allocation schemes. We conclude that improving the quality and consistency of observational data utilized in the modeling process and improving the allocation mechanisms of LULC change models remain important challenges. Current LULC representation in environmental assessments might miss the uncertainty arising from the diversity of LULC change modeling approaches, and many studies ignore the uncertainty in LULC projections in assessments of LULC change impacts on climate, water resources or biodiversity. © 2016 The Authors. Global Change Biology Published by John Wiley & Sons Ltd.

Making direct use of canopy profiles in vegetation - atmosphere coupling

NASA Astrophysics Data System (ADS)

Ryder, James; Polcher, Jan; Peylin, Philippe; Ottlé, Catherine; Chen, Yiying; van Gorsel, Eva; Haverd, Vanessa; McGrath, Matthew; Naudts, Kim; Otto, Juliane; Valade, Aude; Luyssaert, Sebastiaan

2015-04-01

Most coupled land-surface regional models use the 'big-leaf' approach for simulating the sensible and latent heat fluxes of different vegetation types. However, there has been a progression in the types of questions being asked of these models, such as the consequences of land-use change or the behaviour of BVOCs and aerosol. In addition, recent years has seen growth in the availability of in-canopy datasets across a broaded range of species, with which to calibrate these simulations. Hence, there is now an argument for transferring some of the techniques and processes previously used in local, site-based land surface models to the land surface components of models which operate on a regional or even global scale. We describe here the development and evaluation of a vertical canopy energy budget model (Ryder, J et al., 2014) that can be coupled to an atmospheric model such as LMDz. Significantly, the model preserves the implicit coupling of the land-surface to atmosphere interface, which means that run-time efficiences are preserved. This is acheived by means of an interface based on the approach of Polcher et al. (1998) and Best et al. (2004), but newly developed for a canopy column. The model makes use of techniques from site-based models, such as the calculation of vertical turbulence statistics using a second-order closure model (Massman & Weil, 1999), and the distribution of long-wave and short-wave radiation over the profile, the latter using an innovate multilayer albedo scheme (McGrath et al., in prep.). Complete profiles of atmospheric temperature and specific humidity are now calculated, in order to simulate sensible and latent heat fluxes, as well as the leaf temperature at each level in the model. The model is shown to perform stably, and reproduces well flux measurements at an initial test site, across a time period of several days, or over the course of a year. Further applications of the model might be to simulate mixed canopies, the light-stimulated emission of chemical species, or the ecological consequences of changes to temperature profile as a results of changes to stand structure. References: Best, M. J., Beljaars, A. C. M., Polcher, J., & Viterbo, P. (2004). A proposed structure for coupling tiled surfaces with the planetary boundary layer. Journal of Hydrometeorology, 5, 1271-1278. Massman, W. J., & Weil, J. C., 1999. 'An analytical one-dimensional second-order closure model of turbulence statistics and the lagrangian time scale within and above plant canopies of arbitrary structure', Boundary-Layer Meteorology, 91, 81-107. Mcgrath, M. J., Pinty, B., Ryder, J., Otto, J., & Luyssaert, S., in prep. A multilevel canopy radiative transfer scheme based on a domain-averaged structure factor Polcher, J., McAvaney, B., Viterbo, P., Gaertner, M., Hahmann, A., Mahfouf, J.-F., Noilhan, J., Phillips, T.J., Pitman, A. J., Schlosser, C. A., Schulz, J-P, Timbal, B., Verseghy, D. L., Xue, Y. (1998). A proposal for a general interface between land surface schemes and general circulation models. Global and Planetary Change, 19, 261-276. Ryder, J., J. Polcher, P. Peylin, C. Ottlé, Y. Chen, E. Van Gorsel, V. Haverd, M. J. McGrath, K.Naudts, J. Otto, A. Valade, and S. Luyssaert, 2014. 'A multi-layer land surface energy budget model for implicit coupling with global atmospheric simulations', Geosci. Model Dev. Discuss. 7, 8649-8701

Global models underestimate large decadal declining and rising water storage trends relative to GRACE satellite data

PubMed Central

Scanlon, Bridget R.; Zhang, Zizhan; Save, Himanshu; Sun, Alexander Y.; van Beek, Ludovicus P. H.; Wiese, David N.; Reedy, Robert C.; Longuevergne, Laurent; Döll, Petra; Bierkens, Marc F. P.

2018-01-01

Assessing reliability of global models is critical because of increasing reliance on these models to address past and projected future climate and human stresses on global water resources. Here, we evaluate model reliability based on a comprehensive comparison of decadal trends (2002–2014) in land water storage from seven global models (WGHM, PCR-GLOBWB, GLDAS NOAH, MOSAIC, VIC, CLM, and CLSM) to trends from three Gravity Recovery and Climate Experiment (GRACE) satellite solutions in 186 river basins (∼60% of global land area). Medians of modeled basin water storage trends greatly underestimate GRACE-derived large decreasing (≤−0.5 km3/y) and increasing (≥0.5 km3/y) trends. Decreasing trends from GRACE are mostly related to human use (irrigation) and climate variations, whereas increasing trends reflect climate variations. For example, in the Amazon, GRACE estimates a large increasing trend of ∼43 km3/y, whereas most models estimate decreasing trends (−71 to 11 km3/y). Land water storage trends, summed over all basins, are positive for GRACE (∼71–82 km3/y) but negative for models (−450 to −12 km3/y), contributing opposing trends to global mean sea level change. Impacts of climate forcing on decadal land water storage trends exceed those of modeled human intervention by about a factor of 2. The model-GRACE comparison highlights potential areas of future model development, particularly simulated water storage. The inability of models to capture large decadal water storage trends based on GRACE indicates that model projections of climate and human-induced water storage changes may be underestimated. PMID:29358394

Global models underestimate large decadal declining and rising water storage trends relative to GRACE satellite data.

PubMed

Scanlon, Bridget R; Zhang, Zizhan; Save, Himanshu; Sun, Alexander Y; Müller Schmied, Hannes; van Beek, Ludovicus P H; Wiese, David N; Wada, Yoshihide; Long, Di; Reedy, Robert C; Longuevergne, Laurent; Döll, Petra; Bierkens, Marc F P

2018-02-06

Assessing reliability of global models is critical because of increasing reliance on these models to address past and projected future climate and human stresses on global water resources. Here, we evaluate model reliability based on a comprehensive comparison of decadal trends (2002-2014) in land water storage from seven global models (WGHM, PCR-GLOBWB, GLDAS NOAH, MOSAIC, VIC, CLM, and CLSM) to trends from three Gravity Recovery and Climate Experiment (GRACE) satellite solutions in 186 river basins (∼60% of global land area). Medians of modeled basin water storage trends greatly underestimate GRACE-derived large decreasing (≤-0.5 km 3 /y) and increasing (≥0.5 km 3 /y) trends. Decreasing trends from GRACE are mostly related to human use (irrigation) and climate variations, whereas increasing trends reflect climate variations. For example, in the Amazon, GRACE estimates a large increasing trend of ∼43 km 3 /y, whereas most models estimate decreasing trends (-71 to 11 km 3 /y). Land water storage trends, summed over all basins, are positive for GRACE (∼71-82 km 3 /y) but negative for models (-450 to -12 km 3 /y), contributing opposing trends to global mean sea level change. Impacts of climate forcing on decadal land water storage trends exceed those of modeled human intervention by about a factor of 2. The model-GRACE comparison highlights potential areas of future model development, particularly simulated water storage. The inability of models to capture large decadal water storage trends based on GRACE indicates that model projections of climate and human-induced water storage changes may be underestimated. Copyright © 2018 the Author(s). Published by PNAS.

Simulation of the dissolved nitrogen and phosphorus loads in different land uses in the Three Gorges Reservoir Region--based on the improved export coefficient model.

PubMed

Wang, Jinliang; Shao, Jing'an; Wang, Dan; Ni, Jiupai; Xie, Deti

2015-11-01

Nonpoint source pollution is one of the primary causes of eutrophication of water bodies. The concentrations and loads of dissolved pollutants have a direct bearing on the environmental quality of receiving water bodies. Based on the Johnes export coefficient model, a pollutant production coefficient was established by introducing the topographical index and measurements of annual rainfall. A pollutant interception coefficient was constructed by considering the width and slope of present vegetation. These two coefficients were then used as the weighting factors to modify the existing export coefficients of various land uses. A modified export coefficient model was created to estimate the dissolved nitrogen and phosphorus loads in different land uses in the Three Gorges Reservoir Region (TGRR) in 1990, 1995, 2000, 2005, and 2010. The results show that the new land use export coefficient was established by the modification of the production pollution coefficient and interception pollution coefficient. This modification changed the single numerical structure of the original land use export coefficient and takes into consideration temporal and spatial differentiation features. The modified export coefficient retained the change structure of the original single land use export coefficient, and also demonstrated that the land use export coefficient was not only impacted by the change of land use itself, but was also influenced by other objective conditions, such as the characteristics of the underlying surface, amount of rainfall, and the overall presence of vegetation. In the five analyzed years, the simulation values of the dissolved nitrogen and phosphorus loads in paddy fields increased after applying the modification in calculation. The dissolved nitrogen and phosphorus loads in dry land comprised the largest proportions of the TGRR's totals. After modification, the dry land values showed an initial increase and then a decrease over time, but the increments were much smaller than those of the paddy field. The dissolved nitrogen and phosphorus loads in the woodland and meadow decreased after modification. The dissolved nitrogen and phosphorus loads in the building lot were the lowest but showed an increase with the progression of time. These results demonstrate that the modified export coefficient model significantly improves the accuracy of dissolved pollutant load simulation for different land uses in the TGRR, especially the accuracy of dissolved nitrogen load simulation.

Development of management information system for land in mine area based on MapInfo

NASA Astrophysics Data System (ADS)

Wang, Shi-Dong; Liu, Chuang-Hua; Wang, Xin-Chuang; Pan, Yan-Yu

2008-10-01

MapInfo is current a popular GIS software. This paper introduces characters of MapInfo and GIS second development methods offered by MapInfo, which include three ones based on MapBasic, OLE automation, and MapX control usage respectively. Taking development of land management information system in mine area for example, in the paper, the method of developing GIS applications based on MapX has been discussed, as well as development of land management information system in mine area has been introduced in detail, including development environment, overall design, design and realization of every function module, and simple application of system, etc. The system uses MapX 5.0 and Visual Basic 6.0 as development platform, takes SQL Server 2005 as back-end database, and adopts Matlab 6.5 to calculate number in back-end. On the basis of integrated design, the system develops eight modules including start-up, layer control, spatial query, spatial analysis, data editing, application model, document management, results output. The system can be used in mine area for cadastral management, land use structure optimization, land reclamation, land evaluation, analysis and forecasting for land in mine area and environmental disruption, thematic mapping, and so on.

A global model of carbon-nutrient interactions

NASA Technical Reports Server (NTRS)

Moore, Berrien, III; Gildea, Patricia; Vorosmarty, Charles; Mellilo, Jerry M.; Peterson, Bruce J.

1985-01-01

The global biogeochemical model presented has two primary objectives. First, it characterizes natural elemental cycles and their linkages for the four elements significant to Earth's biota: C, N, S, and P. Second, it describes changes in these cycles due to human activity. Global nutrient cycles were studied within the drainage basins of several major world rivers on each continent. The initial study region was the Mississippi drainage basin, concentrating on carbon and nitrogen. The model first establishes the nutrient budgets of the undisturbed ecosystems in a study region. It then uses a data set of land use histories for that region to document the changes in these budgets due to land uses. Nutrient movement was followed over time (1800 to 1980) for 30 ecosystems and 10 land use categories. A geographically referenced ecological information system (GREIS) was developed to manage the digital global data bases of 0.5 x 0.5 grid cells needed to run the model: potential vegetation, drainage basins, precipitation, runoff, contemporary land cover, and FAO soil maps of the world. The results show the contributions of land use categories to river nutrient loads on a continental scale; shifts in nutrient cycling patterns from closed, steady state systems to mobile transient or open, steady state systems; soil organic matter depletion patterns in U.S. agricultural lands; changing nutrient ratios due to land use changes; and the effect of using heavy fertilizer on aquatic systems.

Excellent approach to modeling urban expansion by fuzzy cellular automata: agent base model

NASA Astrophysics Data System (ADS)

Khajavigodellou, Yousef; Alesheikh, Ali A.; Mohammed, Abdulrazak A. S.; Chapi, Kamran

2014-09-01

Recently, the interaction between humans and their environment is the one of important challenges in the world. Landuse/ cover change (LUCC) is a complex process that includes actors and factors at different social and spatial levels. The complexity and dynamics of urban systems make the applicable practice of urban modeling very difficult. With the increased computational power and the greater availability of spatial data, micro-simulation such as the agent based and cellular automata simulation methods, has been developed by geographers, planners, and scholars, and it has shown great potential for representing and simulating the complexity of the dynamic processes involved in urban growth and land use change. This paper presents Fuzzy Cellular Automata in Geospatial Information System and remote Sensing to simulated and predicted urban expansion pattern. These FCA-based dynamic spatial urban models provide an improved ability to forecast and assess future urban growth and to create planning scenarios, allowing us to explore the potential impacts of simulations that correspond to urban planning and management policies. A fuzzy inference guided cellular automata approach. Semantic or linguistic knowledge on Land use change is expressed as fuzzy rules, based on which fuzzy inference is applied to determine the urban development potential for each pixel. The model integrates an ABM (agent-based model) and FCA (Fuzzy Cellular Automata) to investigate a complex decision-making process and future urban dynamic processes. Based on this model rapid development and green land protection under the influences of the behaviors and decision modes of regional authority agents, real estate developer agents, resident agents and non- resident agents and their interactions have been applied to predict the future development patterns of the Erbil metropolitan region.

Comparing the Performance of Three Land Models in Global C Cycle Simulations: A Detailed Structural Analysis: Structural Analysis of Land Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rafique, Rashid; Xia, Jianyang; Hararuk, Oleksandra

Land models are valuable tools to understand the dynamics of global carbon (C) cycle. Various models have been developed and used for predictions of future C dynamics but uncertainties still exist. Diagnosing the models’ behaviors in terms of structures can help to narrow down the uncertainties in prediction of C dynamics. In this study three widely used land surface models, namely CSIRO’s Atmosphere Biosphere Land Exchange (CABLE) with 9 C pools, Community Land Model (version 3.5) combined with Carnegie-Ames-Stanford Approach (CLM-CASA) with 12 C pools and Community Land Model (version 4) (CLM4) with 26 C pools were driven by themore » observed meteorological forcing. The simulated C storage and residence time were used for analysis. The C storage and residence time were computed globally for all individual soil and plant pools, as well as net primary productivity (NPP) and its allocation to different plant components’ based on these models. Remotely sensed NPP and statistically derived HWSD, and GLC2000 datasets were used as a reference to evaluate the performance of these models. Results showed that CABLE exhibited better agreement with referenced C storage and residence time for plant and soil pools, as compared with CLM-CASA and CLM4. CABLE had longer bulk residence time for soil C pools and stored more C in roots, whereas, CLM-CASA and CLM4 stored more C in woody pools due to differential NPP allocation. Overall, these results indicate that the differences in C storage and residence times in three models are largely due to the differences in their fundamental structures (number of C pools), NPP allocation and C transfer rates. Our results have implications in model development and provide a general framework to explain the bias/uncertainties in simulation of C storage and residence times from the perspectives of model structures.« less

Quantifying differences in land use emission estimates implied by definition discrepancies

NASA Astrophysics Data System (ADS)

Stocker, B. D.; Joos, F.

2015-11-01

The quantification of CO2 emissions from anthropogenic land use and land use change (eLUC) is essential to understand the drivers of the atmospheric CO2 increase and to inform climate change mitigation policy. Reported values in synthesis reports are commonly derived from different approaches (observation-driven bookkeeping and process-modelling) but recent work has emphasized that inconsistencies between methods may imply substantial differences in eLUC estimates. However, a consistent quantification is lacking and no concise modelling protocol for the separation of primary and secondary components of eLUC has been established. Here, we review differences of eLUC quantification methods and apply an Earth System Model (ESM) of Intermediate Complexity to quantify them. We find that the magnitude of effects due to merely conceptual differences between ESM and offline vegetation model-based quantifications is ~ 20 % for today. Under a future business-as-usual scenario, differences tend to increase further due to slowing land conversion rates and an increasing impact of altered environmental conditions on land-atmosphere fluxes. We establish how coupled Earth System Models may be applied to separate secondary component fluxes of eLUC arising from the replacement of potential C sinks/sources and the land use feedback and show that secondary fluxes derived from offline vegetation models are conceptually and quantitatively not identical to either, nor their sum. Therefore, we argue that synthesis studies should resort to the "least common denominator" of different methods, following the bookkeeping approach where only primary land use emissions are quantified under the assumption of constant environmental boundary conditions.

Modelling the Relationship Between Land Surface Temperature and Landscape Patterns of Land Use Land Cover Classification Using Multi Linear Regression Models

NASA Astrophysics Data System (ADS)

Bernales, A. M.; Antolihao, J. A.; Samonte, C.; Campomanes, F.; Rojas, R. J.; dela Serna, A. M.; Silapan, J.

2016-06-01

The threat of the ailments related to urbanization like heat stress is very prevalent. There are a lot of things that can be done to lessen the effect of urbanization to the surface temperature of the area like using green roofs or planting trees in the area. So land use really matters in both increasing and decreasing surface temperature. It is known that there is a relationship between land use land cover (LULC) and land surface temperature (LST). Quantifying this relationship in terms of a mathematical model is very important so as to provide a way to predict LST based on the LULC alone. This study aims to examine the relationship between LST and LULC as well as to create a model that can predict LST using class-level spatial metrics from LULC. LST was derived from a Landsat 8 image and LULC classification was derived from LiDAR and Orthophoto datasets. Class-level spatial metrics were created in FRAGSTATS with the LULC and LST as inputs and these metrics were analysed using a statistical framework. Multi linear regression was done to create models that would predict LST for each class and it was found that the spatial metric "Effective mesh size" was a top predictor for LST in 6 out of 7 classes. The model created can still be refined by adding a temporal aspect by analysing the LST of another farming period (for rural areas) and looking for common predictors between LSTs of these two different farming periods.

Satellite land use acquisition and applications to hydrologic planning models

NASA Technical Reports Server (NTRS)

Algazi, V. R.; Suk, M.

1977-01-01

A developing operational procedure for use by the Corps of Engineers in the acquisition of land use information for hydrologic planning purposes was described. The operational conditions preclude the use of dedicated, interactive image processing facilities. Given the constraints, an approach to land use classification based on clustering seems promising and was explored in detail. The procedure is outlined and examples of application to two watersheds given.

Modeling and predicting urban growth pattern of the Tokyo metropolitan area based on cellular automata

NASA Astrophysics Data System (ADS)

Zhao, Yaolong; Zhao, Junsan; Murayama, Yuji

2008-10-01

The period of high economic growth in Japan which began in the latter half of the 1950s led to a massive migration of population from rural regions to the Tokyo metropolitan area. This phenomenon brought about rapid urban growth and urban structure changes in this area. Purpose of this study is to establish a constrained CA (Cellular Automata) model with GIS (Geographical Information Systems) to simulate urban growth pattern in the Tokyo metropolitan area towards predicting urban form and landscape for the near future. Urban land-use is classified into multi-categories for interpreting the effect of interaction among land-use categories in the spatial process of urban growth. Driving factors of urban growth pattern, such as land condition, railway network, land-use zoning, random perturbation, and neighborhood interaction and so forth, are explored and integrated into this model. These driving factors are calibrated based on exploratory spatial data analysis (ESDA), spatial statistics, logistic regression, and "trial and error" approach. The simulation is assessed at both macro and micro classification levels in three ways: visual approach; fractal dimension; and spatial metrics. Results indicate that this model provides an effective prototype to simulate and predict urban growth pattern of the Tokyo metropolitan area.

Predicting bee community responses to land-use changes: Effects of geographic and taxonomic biases

PubMed Central

De Palma, Adriana; Abrahamczyk, Stefan; Aizen, Marcelo A.; Albrecht, Matthias; Basset, Yves; Bates, Adam; Blake, Robin J.; Boutin, Céline; Bugter, Rob; Connop, Stuart; Cruz-López, Leopoldo; Cunningham, Saul A.; Darvill, Ben; Diekötter, Tim; Dorn, Silvia; Downing, Nicola; Entling, Martin H.; Farwig, Nina; Felicioli, Antonio; Fonte, Steven J.; Fowler, Robert; Franzén, Markus; Goulson, Dave; Grass, Ingo; Hanley, Mick E.; Hendrix, Stephen D.; Herrmann, Farina; Herzog, Felix; Holzschuh, Andrea; Jauker, Birgit; Kessler, Michael; Knight, M. E.; Kruess, Andreas; Lavelle, Patrick; Le Féon, Violette; Lentini, Pia; Malone, Louise A.; Marshall, Jon; Pachón, Eliana Martínez; McFrederick, Quinn S.; Morales, Carolina L.; Mudri-Stojnic, Sonja; Nates-Parra, Guiomar; Nilsson, Sven G.; Öckinger, Erik; Osgathorpe, Lynne; Parra-H, Alejandro; Peres, Carlos A.; Persson, Anna S.; Petanidou, Theodora; Poveda, Katja; Power, Eileen F.; Quaranta, Marino; Quintero, Carolina; Rader, Romina; Richards, Miriam H.; Roulston, T’ai; Rousseau, Laurent; Sadler, Jonathan P.; Samnegård, Ulrika; Schellhorn, Nancy A.; Schüepp, Christof; Schweiger, Oliver; Smith-Pardo, Allan H.; Steffan-Dewenter, Ingolf; Stout, Jane C.; Tonietto, Rebecca K.; Tscharntke, Teja; Tylianakis, Jason M.; Verboven, Hans A. F.; Vergara, Carlos H.; Verhulst, Jort; Westphal, Catrin; Yoon, Hyung Joo; Purvis, Andy

2016-01-01

Land-use change and intensification threaten bee populations worldwide, imperilling pollination services. Global models are needed to better characterise, project, and mitigate bees' responses to these human impacts. The available data are, however, geographically and taxonomically unrepresentative; most data are from North America and Western Europe, overrepresenting bumblebees and raising concerns that model results may not be generalizable to other regions and taxa. To assess whether the geographic and taxonomic biases of data could undermine effectiveness of models for conservation policy, we have collated from the published literature a global dataset of bee diversity at sites facing land-use change and intensification, and assess whether bee responses to these pressures vary across 11 regions (Western, Northern, Eastern and Southern Europe; North, Central and South America; Australia and New Zealand; South East Asia; Middle and Southern Africa) and between bumblebees and other bees. Our analyses highlight strong regionally-based responses of total abundance, species richness and Simpson's diversity to land use, caused by variation in the sensitivity of species and potentially in the nature of threats. These results suggest that global extrapolation of models based on geographically and taxonomically restricted data may underestimate the true uncertainty, increasing the risk of ecological surprises. PMID:27509831

Land use change modeling through scenario-based cellular automata Markov: improving spatial forecasting.

PubMed

Jahanishakib, Fatemeh; Mirkarimi, Seyed Hamed; Salmanmahiny, Abdolrassoul; Poodat, Fatemeh

2018-05-08

Efficient land use management requires awareness of past changes, present actions, and plans for future developments. Part of these requirements is achieved using scenarios that describe a future situation and the course of changes. This research aims to link scenario results with spatially explicit and quantitative forecasting of land use development. To develop land use scenarios, SMIC PROB-EXPERT and MORPHOL methods were used. It revealed eight scenarios as the most probable. To apply the scenarios, we considered population growth rate and used a cellular automata-Markov chain (CA-MC) model to implement the quantified changes described by each scenario. For each scenario, a set of landscape metrics was used to assess the ecological integrity of land use classes in terms of fragmentation and structural connectivity. The approach enabled us to develop spatial scenarios of land use change and detect their differences for choosing the most integrated landscape pattern in terms of landscape metrics. Finally, the comparison between paired forecasted scenarios based on landscape metrics indicates that scenarios 1-1, 2-2, 3-2, and 4-1 have a more suitable integrity. The proposed methodology for developing spatial scenarios helps executive managers to create scenarios with many repetitions and customize spatial patterns in real world applications and policies.

Climate Simulations based on a different-grid nested and coupled model

NASA Astrophysics Data System (ADS)

Li, Dan; Ji, Jinjun; Li, Yinpeng

2002-05-01

An atmosphere-vegetation interaction model (A VIM) has been coupled with a nine-layer General Cir-culation Model (GCM) of Institute of Atmospheic Physics/State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (IAP/LASG), which is rhomboidally truncated at zonal wave number 15, to simulate global climatic mean states. A VIM is a model having inter-feedback between land surface processes and eco-physiological processes on land. As the first step to couple land with atmosphere completely, the physiological processes are fixed and only the physical part (generally named the SVAT (soil-vegetation-atmosphere-transfer scheme) model) of AVIM is nested into IAP/LASG L9R15 GCM. The ocean part of GCM is prescribed and its monthly sea surface temperature (SST) is the climatic mean value. With respect to the low resolution of GCM, i.e., each grid cell having lon-gitude 7.5° and latitude 4.5°, the vegetation is given a high resolution of 1.5° by 1.5° to nest and couple the fine grid cells of land with the coarse grid cells of atmosphere. The coupling model has been integrated for 15 years and its last ten-year mean of outputs was chosen for analysis. Compared with observed data and NCEP reanalysis, the coupled model simulates the main characteris-tics of global atmospheric circulation and the fields of temperature and moisture. In particular, the simu-lated precipitation and surface air temperature have sound results. The work creates a solid base on coupling climate models with the biosphere.

Describing Ecosystem Complexity through Integrated Catchment Modeling

NASA Astrophysics Data System (ADS)

Shope, C. L.; Tenhunen, J. D.; Peiffer, S.

2011-12-01

Land use and climate change have been implicated in reduced ecosystem services (ie: high quality water yield, biodiversity, and agricultural yield. The prediction of ecosystem services expected under future land use decisions and changing climate conditions has become increasingly important. Complex policy and management decisions require the integration of physical, economic, and social data over several scales to assess effects on water resources and ecology. Field-based meteorology, hydrology, soil physics, plant production, solute and sediment transport, economic, and social behavior data were measured in a South Korean catchment. A variety of models are being used to simulate plot and field scale experiments within the catchment. Results from each of the local-scale models provide identification of sensitive, local-scale parameters which are then used as inputs into a large-scale watershed model. We used the spatially distributed SWAT model to synthesize the experimental field data throughout the catchment. The approach of our study was that the range in local-scale model parameter results can be used to define the sensitivity and uncertainty in the large-scale watershed model. Further, this example shows how research can be structured for scientific results describing complex ecosystems and landscapes where cross-disciplinary linkages benefit the end result. The field-based and modeling framework described is being used to develop scenarios to examine spatial and temporal changes in land use practices and climatic effects on water quantity, water quality, and sediment transport. Development of accurate modeling scenarios requires understanding the social relationship between individual and policy driven land management practices and the value of sustainable resources to all shareholders.

Impacts of Changing Climatic Drivers and Land use features on Future Stormwater Runoff in the Northwest Florida Basin: A Large-Scale Hydrologic Modeling Assessment

NASA Astrophysics Data System (ADS)

Khan, M.; Abdul-Aziz, O. I.

2017-12-01

Potential changes in climatic drivers and land cover features can significantly influence the stormwater budget in the Northwest Florida Basin. We investigated the hydro-climatic and land use sensitivities of stormwater runoff by developing a large-scale process-based rainfall-runoff model for the large basin by using the EPA Storm Water Management Model (SWMM 5.1). Climatic and hydrologic variables, as well as land use/cover features were incorporated into the model to account for the key processes of coastal hydrology and its dynamic interactions with groundwater and sea levels. We calibrated and validated the model by historical daily streamflow observations during 2009-2012 at four major rivers in the basin. Downscaled climatic drivers (precipitation, temperature, solar radiation) projected by twenty GCMs-RCMs under CMIP5, along with the projected future land use/cover features were also incorporated into the model. The basin storm runoff was then simulated for the historical (2000s = 1976-2005) and two future periods (2050s = 2030-2059, and 2080s = 2070-2099). Comparative evaluation of the historical and future scenarios leads to important guidelines for stormwater management in Northwest Florida and similar regions under a changing climate and environment.

Weak Hydrological Sensitivity to Temperature Change over Land, Independent of Climate Forcing

NASA Technical Reports Server (NTRS)

Samset, B. H.; Myhre, G.; Forster, P. M.; Hodnebrog, O.; Andrews, T.; Boucher, O.; Faluvegi, G.; Flaeschner, D.; Kasoar, M.; Kharin, V.;

Coupled impacts of climate and land use change across a river-lake continuum: insights from an integrated assessment model of Lake Champlain’s Missisquoi Basin, 2000-2040

NASA Astrophysics Data System (ADS)

Zia, Asim; Bomblies, Arne; Schroth, Andrew W.; Koliba, Christopher; Isles, Peter D. F.; Tsai, Yushiou; Mohammed, Ibrahim N.; Bucini, Gabriela; Clemins, Patrick J.; Turnbull, Scott; Rodgers, Morgan; Hamed, Ahmed; Beckage, Brian; Winter, Jonathan; Adair, Carol; Galford, Gillian L.; Rizzo, Donna; Van Houten, Judith

2016-11-01

Global climate change (GCC) is projected to bring higher-intensity precipitation and higher-variability temperature regimes to the Northeastern United States. The interactive effects of GCC with anthropogenic land use and land cover changes (LULCCs) are unknown for watershed level hydrological dynamics and nutrient fluxes to freshwater lakes. Increased nutrient fluxes can promote harmful algal blooms, also exacerbated by warmer water temperatures due to GCC. To address the complex interactions of climate, land and humans, we developed a cascading integrated assessment model to test the impacts of GCC and LULCC on the hydrological regime, water temperature, water quality, bloom duration and severity through 2040 in transnational Lake Champlain’s Missisquoi Bay. Temperature and precipitation inputs were statistically downscaled from four global circulation models (GCMs) for three Representative Concentration Pathways. An agent-based model was used to generate four LULCC scenarios. Combined climate and LULCC scenarios drove a distributed hydrological model to estimate river discharge and nutrient input to the lake. Lake nutrient dynamics were simulated with a 3D hydrodynamic-biogeochemical model. We find accelerated GCC could drastically limit land management options to maintain water quality, but the nature and severity of this impact varies dramatically by GCM and GCC scenario.

Strong and nonlinear effects of fragmentation on ecosystem service provision at multiple scales

NASA Astrophysics Data System (ADS)

Mitchell, Matthew G. E.; Bennett, Elena M.; Gonzalez, Andrew

2015-09-01

Human actions, such as converting natural land cover to agricultural or urban land, result in the loss and fragmentation of natural habitat, with important consequences for the provision of ecosystem services. Such habitat loss is especially important for services that are supplied by fragments of natural land cover and that depend on flows of organisms, matter, or people across the landscape to produce benefits, such as pollination, pest regulation, recreation and cultural services. However, our quantitative knowledge about precisely how different patterns of landscape fragmentation might affect the provision of these types of services is limited. We used a simple, spatially explicit model to evaluate the potential impact of natural land cover loss and fragmentation on the provision of hypothetical ecosystem services. Based on current literature, we assumed that fragments of natural land cover provide ecosystem services to the area surrounding them in a distance-dependent manner such that ecosystem service flow depended on proximity to fragments. We modeled seven different patterns of natural land cover loss across landscapes that varied in the overall level of landscape fragmentation. Our model predicts that natural land cover loss will have strong and unimodal effects on ecosystem service provision, with clear thresholds indicating rapid loss of service provision beyond critical levels of natural land cover loss. It also predicts the presence of a tradeoff between maximizing ecosystem service provision and conserving natural land cover, and a mismatch between ecosystem service provision at landscape versus finer spatial scales. Importantly, the pattern of landscape fragmentation mitigated or intensified these tradeoffs and mismatches. Our model suggests that managing patterns of natural land cover loss and fragmentation could help influence the provision of multiple ecosystem services and manage tradeoffs and synergies between services across different human-dominated landscapes.

Boosted Regression Tree Models to Explain Watershed Nutrient Concentrations and Biological Condition

EPA Science Inventory

Boosted regression tree (BRT) models were developed to quantify the nonlinear relationships between landscape variables and nutrient concentrations in a mesoscale mixed land cover watershed during base-flow conditions. Factors that affect instream biological components, based on ...

Implementation of 5-layer thermal diffusion scheme in weather research and forecasting model with Intel Many Integrated Cores

NASA Astrophysics Data System (ADS)

Huang, Melin; Huang, Bormin; Huang, Allen H.

2014-10-01

For weather forecasting and research, the Weather Research and Forecasting (WRF) model has been developed, consisting of several components such as dynamic solvers and physical simulation modules. WRF includes several Land- Surface Models (LSMs). The LSMs use atmospheric information, the radiative and precipitation forcing from the surface layer scheme, the radiation scheme, and the microphysics/convective scheme all together with the land's state variables and land-surface properties, to provide heat and moisture fluxes over land and sea-ice points. The WRF 5-layer thermal diffusion simulation is an LSM based on the MM5 5-layer soil temperature model with an energy budget that includes radiation, sensible, and latent heat flux. The WRF LSMs are very suitable for massively parallel computation as there are no interactions among horizontal grid points. The features, efficient parallelization and vectorization essentials, of Intel Many Integrated Core (MIC) architecture allow us to optimize this WRF 5-layer thermal diffusion scheme. In this work, we present the results of the computing performance on this scheme with Intel MIC architecture. Our results show that the MIC-based optimization improved the performance of the first version of multi-threaded code on Xeon Phi 5110P by a factor of 2.1x. Accordingly, the same CPU-based optimizations improved the performance on Intel Xeon E5- 2603 by a factor of 1.6x as compared to the first version of multi-threaded code.

Revolutionary land use change in the 21st century: Is (rangeland) science relevant?

USGS Publications Warehouse

Herrick, J.E.; Brown, J.R.; Bestelmeyer, B.T.; Andrews, S.S.; Baldi, G.; Davies, J.; Duniway, M.; Havstad, K.M.; Karl, J.W.; Karlen, D.L.; Peters, Debra P.C.; Quinton, J.N.; Riginos, C.; Shaver, P.L.; Steinaker, D.; Twomlow, S.

2012-01-01

Rapidly increasing demand for food, fiber, and fuel together with new technologies and the mobility of global capital are driving revolutionary changes in land use throughout the world. Efforts to increase land productivity include conversion of millions of hectares of rangelands to crop production, including many marginal lands with low resistance and resilience to degradation. Sustaining the productivity of these lands requires careful land use planning and innovative management systems. Historically, this responsibility has been left to agronomists and others with expertise in crop production. In this article, we argue that the revolutionary land use changes necessary to support national and global food security potentially make rangeland science more relevant now than ever. Maintaining and increasing relevance will require a revolutionary change in range science from a discipline that focuses on a particular land use or land cover to one that addresses the challenge of managing all lands that, at one time, were considered to be marginal for crop production. We propose four strategies to increase the relevance of rangeland science to global land management: 1) expand our awareness and understanding of local to global economic, social, and technological trends in order to anticipate and identify drivers and patterns of conversion; 2) emphasize empirical studies and modeling that anticipate the biophysical (ecosystem services) and societal consequences of large-scale changes in land cover and use; 3) significantly increase communication and collaboration with the disciplines and sectors of society currently responsible for managing the new land uses; and 4) develop and adopt a dynamic and flexible resilience-based land classification system and data-supported conceptual models (e.g., state-and-transition models) that represent all lands, regardless of use and the consequences of land conversion to various uses instead of changes in state or condition that are focused on a single land use.

Quality Evaluation of Land-Cover Classification Using Convolutional Neural Network

NASA Astrophysics Data System (ADS)

Dang, Y.; Zhang, J.; Zhao, Y.; Luo, F.; Ma, W.; Yu, F.

2018-04-01

Land-cover classification is one of the most important products of earth observation, which focuses mainly on profiling the physical characters of the land surface with temporal and distribution attributes and contains the information of both natural and man-made coverage elements, such as vegetation, soil, glaciers, rivers, lakes, marsh wetlands and various man-made structures. In recent years, the amount of high-resolution remote sensing data has increased sharply. Accordingly, the volume of land-cover classification products increases, as well as the need to evaluate such frequently updated products that is a big challenge. Conventionally, the automatic quality evaluation of land-cover classification is made through pixel-based classifying algorithms, which lead to a much trickier task and consequently hard to keep peace with the required updating frequency. In this paper, we propose a novel quality evaluation approach for evaluating the land-cover classification by a scene classification method Convolutional Neural Network (CNN) model. By learning from remote sensing data, those randomly generated kernels that serve as filter matrixes evolved to some operators that has similar functions to man-crafted operators, like Sobel operator or Canny operator, and there are other kernels learned by the CNN model that are much more complex and can't be understood as existing filters. The method using CNN approach as the core algorithm serves quality-evaluation tasks well since it calculates a bunch of outputs which directly represent the image's membership grade to certain classes. An automatic quality evaluation approach for the land-cover DLG-DOM coupling data (DLG for Digital Line Graphic, DOM for Digital Orthophoto Map) will be introduced in this paper. The CNN model as an robustness method for image evaluation, then brought out the idea of an automatic quality evaluation approach for land-cover classification. Based on this experiment, new ideas of quality evaluation of DLG-DOM coupling land-cover classification or other kinds of labelled remote sensing data can be further studied.

Hyperspectral Analysis of Soil Total Nitrogen in Subsided Land Using the Local Correlation Maximization-Complementary Superiority (LCMCS) Method

PubMed Central

Lin, Lixin; Wang, Yunjia; Teng, Jiyao; Xi, Xiuxiu

2015-01-01

The measurement of soil total nitrogen (TN) by hyperspectral remote sensing provides an important tool for soil restoration programs in areas with subsided land caused by the extraction of natural resources. This study used the local correlation maximization-complementary superiority method (LCMCS) to establish TN prediction models by considering the relationship between spectral reflectance (measured by an ASD FieldSpec 3 spectroradiometer) and TN based on spectral reflectance curves of soil samples collected from subsided land which is determined by synthetic aperture radar interferometry (InSAR) technology. Based on the 1655 selected effective bands of the optimal spectrum (OSP) of the first derivate differential of reciprocal logarithm ([log{1/R}]′), (correlation coefficients, p < 0.01), the optimal model of LCMCS method was obtained to determine the final model, which produced lower prediction errors (root mean square error of validation [RMSEV] = 0.89, mean relative error of validation [MREV] = 5.93%) when compared with models built by the local correlation maximization (LCM), complementary superiority (CS) and partial least squares regression (PLS) methods. The predictive effect of LCMCS model was optional in Cangzhou, Renqiu and Fengfeng District. Results indicate that the LCMCS method has great potential to monitor TN in subsided lands caused by the extraction of natural resources including groundwater, oil and coal. PMID:26213935

Systemic change increases forecast uncertainty of land use change models

NASA Astrophysics Data System (ADS)

Verstegen, J. A.; Karssenberg, D.; van der Hilst, F.; Faaij, A.

2013-12-01

Cellular Automaton (CA) models of land use change are based on the assumption that the relationship between land use change and its explanatory processes is stationary. This means that model structure and parameterization are usually kept constant over time, ignoring potential systemic changes in this relationship resulting from societal changes, thereby overlooking a source of uncertainty. Evaluation of the stationarity of the relationship between land use and a set of spatial attributes has been done by others (e.g., Bakker and Veldkamp, 2012). These studies, however, use logistic regression, separate from the land use change model. Therefore, they do not gain information on how to implement the spatial attributes into the model. In addition, they often compare observations for only two points in time and do not check whether the change is statistically significant. To overcome these restrictions, we assimilate a time series of observations of real land use into a land use change CA (Verstegen et al., 2012), using a Bayesian data assimilation technique, the particle filter. The particle filter was used to update the prior knowledge about the parameterization and model structure, i.e. the selection and relative importance of the drivers of location of land use change. In a case study of sugar cane expansion in Brazil, optimal model structure and parameterization were determined for each point in time for which observations were available (all years from 2004 to 2012). A systemic change, i.e. a statistically significant deviation in model structure, was detected for the period 2006 to 2008. In this period the influence on the location of sugar cane expansion of the driver sugar cane in the neighborhood doubled, while the influence of slope and potential yield decreased by 75% and 25% respectively. Allowing these systemic changes to occur in our CA in the future (up to 2022) resulted in an increase in model forecast uncertainty by a factor two compared to the assumption of a stationary system. This means that the assumption of a constant model structure is not adequate and largely underestimates uncertainty in the forecast. Non-stationarity in land use change projections is challenging to model, because it is difficult to determine when the system will change and how. We believe that, in sight of these findings, land use change modelers should be more aware, and communicate more clearly, that what they try to project is at the limits, and perhaps beyond the limits, of what is still projectable. References Bakker, M., Veldkamp, A., 2012. Changing relationships between land use and environmental characteristics and their consequences for spatially explicit land-use change prediction. Journal of Land Use Science 7, 407-424. Verstegen, J.A., Karssenberg, D., van der Hilst, F., Faaij, A.P.C., 2012. Spatio-Temporal Uncertainty in Spatial Decision Support Systems: a Case Study of Changing Land Availability for Bioenergy Crops in Mozambique. Computers , Environment and Urban Systems 36, 30-42.

Hydrologic models for land-atmosphere retrospective studies of the use of LANDSAT and AVHRR data

NASA Technical Reports Server (NTRS)

Duchon, Claude E.; Williams, T. H. Lee; Nicks, Arlin D.

1988-01-01

The use of a Geographic Information System (GIS) and LANDSAT analysis in conjunction with the Simulator for Water Resources on a Rural Basin (SWRRB) hydrologic model to examine the water balance on the Little Washita River basin is discussed. LANDSAT analysis was used to divide the basin into eight non-contiguous land covers or subareas: rangeland, grazed range, winter wheat, alfalfa/pasture, bare soil, water, woodland, and impervious land (roads, quarry). The use of a geographic information system allowed for the calculation of SWRRB model parameters in each subarea. Four data sets were constructed in order to compare SWRRB estimates of hydrologic processes using two methods of maximum LAI and two methods of watershed subdivision. Maximum LAI was determined from a continental scale map, which provided a value of 4.5 for the entire basin, and from its association with the type of land-cover (eight values). The two methods of watershed subdivision were determined according to drainage subbasin (four) and the eight land-covers. These data sets were used with the SWRRB model to obtain daily hydrologic estimates for 1985. The results of the one year analysis lead to the conclusion that the greater homogeneity of a land-cover subdivision provides better water yield estimates than those based on a drainage properties subdivision.