Towards Global Simulation of Irrigation in a Land Surface Model: Multiple Cropping and Rice Paddy in Southeast Asia

NASA Technical Reports Server (NTRS)

Beaudoing, Hiroko Kato; Rodell, Matthew; Ozdogan, Mutlu

2010-01-01

Agricultural land use significantly influences the surface water and energy balances. Effects of irrigation on land surface states and fluxes include repartitioning of latent and sensible heat fluxes, an increase in net radiation, and an increase in soil moisture and runoff. We are working on representing irrigation practices in continental- to global-scale land surface simulation in NASA's Global Land Data Assimilation System (GLDAS). Because agricultural practices across the nations are diverse, and complex, we are attempting to capture the first-order reality of the regional practices before achieving a global implementation. This study focuses on two issues in Southeast Asia: multiple cropping and rice paddy irrigation systems. We first characterize agricultural practices in the region (i.e., crop types, growing seasons, and irrigation) using the Global data set of monthly irrigated and rainfed crop areas around the year 2000 (MIRCA2000) dataset. Rice paddy extent is identified using remote sensing products. Whether irrigated or rainfed, flooded fields need to be represented and treated explicitly. By incorporating these properties and processes into a physically based land surface model, we are able to quantify the impacts on the simulated states and fluxes.

Impact of atmosphere and land surface initial conditions on seasonal forecast of global surface temperature

NASA Astrophysics Data System (ADS)

Materia, Stefano; Borrelli, Andrea; Bellucci, Alessio; Alessandri, Andrea; Di Pietro, Pierluigi; Athanasiadis, Panagiotis; Navarra, Antonio; Gualdi, Silvio

2014-05-01

The impact of land surface and atmosphere initialization on the forecast skill of a seasonal prediction system is investigated, and an effort to disentangle the role played by the individual components to the global predictability is done, via a hierarchy of seasonal forecast experiments performed under different initialization strategies. A realistic atmospheric initial state allows an improved equilibrium between the ocean and overlying atmosphere, mitigating the coupling shock and possibly increasing the model predictive skill in the ocean. In fact, in a few regions characterized by strong air-sea coupling, the atmosphere initial condition affects the forecast skill for several months. In particular, the ENSO region, the eastern tropical Atlantic and the North Pacific benefit significantly from the atmosphere initialization. On mainland, the impact of atmospheric initial conditions is detected in the early phase of the forecast, while the quality of land surface initialization affects the forecast skill in the following lead seasons. The winter forecast in the high latitude plains of Siberia and Canada benefit from the snow initialization, while the impact of soil moisture initial state is particularly effective in the Mediterranean region, in central Asia and Australia. However, initialization through land surface reanalysis does not systematically guarantee an enhancement of the predictive skill: the quality of the forecast is sometimes higher for the non-constrained model. Overall, the introduction of a realistic initialization of land surface and atmosphere substantially increases skill and accuracy. However, further developments in the operating procedure for land surface initialization are required for more accurate seasonal forecasts.

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.

Was There a Significantly Negative Anomaly of Global Land Surface Net Radiation from 2001-2006?

NASA Astrophysics Data System (ADS)

Liang, S.; Jia, A.; Jiang, B.

2016-12-01

Surface net radiation, which characterizes surface energy budget, can be estimated from in-situ measurements, satellite products, model simulations, and reanalysis. Satellite products are usually validated using ground measurements to characterize their uncertainties. The surface net radiation product from the CERES (Clouds and the Earth's Radiant Energy System) has been widely used. After validating it using extensive ground measurements, we also verified that the CERES surface net radiation product is highly accurate. When we evaluated the temporal variations of the averaged global land surface net radiation from the CERES product, we found a significantly negative anomaly starting from 2001, reaching the maximum in 2004, and gradually coming back to normal in 2006. The valley has the magnitude of approximately 3 Wm-2 centered at 2004. After comparing with the high-resolution GLASS (Global LAnd Surface Satellite) net radiation product developed at Beijing Normal University, the CMIP5 model simulations, and the ERA-Interim reanalysis dataset, we concluded that the significant decreasing pattern of land surface net radiation from 2001-2006 is an artifact mainly due to inaccurate longwave net radiation of the CERES surface net radiation product. The current ground measurement networks are not spatially dense enough to capture the false negative anomaly from the CERES product, which calls for more ground measurements.

Mapping the global depth to bedrock for land surface modeling

NASA Astrophysics Data System (ADS)

Shangguan, Wei; Hengl, Tomislav; Mendes de Jesus, Jorge; Yuan, Hua; Dai, Yongjiu

2017-03-01

Depth to bedrock serves as the lower boundary of land surface models, which controls hydrologic and biogeochemical processes. This paper presents a framework for global estimation of depth to bedrock (DTB). Observations were extracted from a global compilation of soil profile data (ca. 1,30,000 locations) and borehole data (ca. 1.6 million locations). Additional pseudo-observations generated by expert knowledge were added to fill in large sampling gaps. The model training points were then overlaid on a stack of 155 covariates including DEM-based hydrological and morphological derivatives, lithologic units, MODIS surface reflectance bands and vegetation indices derived from the MODIS land products. Global spatial prediction models were developed using random forest and Gradient Boosting Tree algorithms. The final predictions were generated at the spatial resolution of 250 m as an ensemble prediction of the two independently fitted models. The 10-fold cross-validation shows that the models explain 59% for absolute DTB and 34% for censored DTB (depths deep than 200 cm are predicted as 200 cm). The model for occurrence of R horizon (bedrock) within 200 cm does a good job. Visual comparisons of predictions in the study areas where more detailed maps of depth to bedrock exist show that there is a general match with spatial patterns from similar local studies. Limitation of the data set and extrapolation in data spare areas should not be ignored in applications. To improve accuracy of spatial prediction, more borehole drilling logs will need to be added to supplement the existing training points in under-represented areas.

The ISLSCP initiative I global datasets: Surface boundary conditions and atmospheric forcings for land-atmosphere studies

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

Sellers, P.J.; Collatz, J.; Koster, R.

1996-09-01

A comprehensive series of global datasets for land-atmosphere models has been collected, formatted to a common grid, and released on a set of CD-ROMs. This paper describes the motivation for and the contents of the dataset. In June of 1992, an interdisciplinary earth science workshop was convened in Columbia, Maryland, to assess progress in land-atmosphere research, specifically in the areas of models, satellite data algorithms, and field experiments. At the workshop, representatives of the land-atmosphere modeling community defined a need for global datasets to prescribe boundary conditions, initialize state variables, and provide near-surface meteorological and radiative forcings for their models.more » The International Satellite Land Surface Climatology Project (ISLSCP), a part of the Global Energy and Water Cycle Experiment, worked with the Distributed Active Archive Center of the National Aeronautics and Space Administration Goddard Space Flight Center to bring the required datasets together in a usable format. The data have since been released on a collection of CD-ROMs. The datasets on the CD-ROMs are grouped under the following headings: vegetation; hydrology and soils; snow, ice, and oceans; radiation and clouds; and near-surface meteorology. All datasets cover the period 1987-88, and all but a few are spatially continuous over the earth`s land surface. All have been mapped to a common 1{degree} x 1{degree} equal-angle grid. The temporal frequency for most of the datasets is monthly. A few of the near-surface meteorological parameters are available both as six-hourly values and as monthly means. 26 refs., 8 figs., 2 tabs.« less

Analytical Retrieval of Global Land Surface Emissivity Maps at AMSR-E passive microwave frequencies

NASA Astrophysics Data System (ADS)

Norouzi, H.; Temimi, M.; Khanbilvardi, R.

2009-12-01

Land emissivity is a crucial boundary condition in Numerical Weather Prediction (NWP) modeling. Land emissivity is also a key indicator of land surface and subsurface properties. The objective of this study, supported by NOAA-NESDIS, is to develop global land emissivity maps using AMSR-E passive microwave measurements along with several ancillary data. The International Satellite Cloud Climatology Project (ISCCP) database has been used to obtain several inputs for the proposed approach such as land surface temperature, cloud mask and atmosphere profile. The Community Radiative Transfer Model (CRTM) has been used to estimate upwelling and downwelling atmospheric contributions. Although it is well known that correction of the atmospheric effect on brightness temperature is required at higher frequencies (over 19 GHz), our preliminary results have shown that a correction at 10.7 GHz is also necessary over specific areas. The proposed approach is based on three main steps. First, all necessary data have been collected and processed. Second, a global cloud free composite of AMSR-E data and corresponding ancillary images is created. Finally, monthly composting of emissivity maps has been performed. AMSR-E frequencies at 6.9, 10.7, 18.7, 36.5 and 89.0 GHz have been used to retrieve the emissivity. Water vapor information obtained from ISCCP (TOVS data) was used to calculate upwelling, downwelling temperatures and atmospheric transmission in order to assess the consistency of those derived from the CRTM model. The frequent land surface temperature (LST) determination (8 times a day) in the ISCCP database has allowed us to assess the diurnal cycle effect on emissivity retrieval. Differences in magnitude and phase between thermal temperature and low frequencies microwave brightness temperature have been noticed. These differences seem to vary in space and time. They also depend on soil texture and thermal inertia. The proposed methodology accounts for these factors and

An enhanced VIIRS aerosol optical thickness (AOT) retrieval algorithm over land using a global surface reflectance ratio database

NASA Astrophysics Data System (ADS)

Zhang, Hai; Kondragunta, Shobha; Laszlo, Istvan; Liu, Hongqing; Remer, Lorraine A.; Huang, Jingfeng; Superczynski, Stephen; Ciren, Pubu

2016-09-01

The Visible/Infrared Imager Radiometer Suite (VIIRS) on board the Suomi National Polar-orbiting Partnership (S-NPP) satellite has been retrieving aerosol optical thickness (AOT), operationally and globally, over ocean and land since shortly after S-NPP launch in 2011. However, the current operational VIIRS AOT retrieval algorithm over land has two limitations in its assumptions for land surfaces: (1) it only retrieves AOT over the dark surfaces and (2) it assumes that the global surface reflectance ratios between VIIRS bands are constants. In this work, we develop a surface reflectance ratio database over land with a spatial resolution 0.1° × 0.1° using 2 years of VIIRS top of atmosphere reflectances. We enhance the current operational VIIRS AOT retrieval algorithm by applying the surface reflectance ratio database in the algorithm. The enhanced algorithm is able to retrieve AOT over both dark and bright surfaces. Over bright surfaces, the VIIRS AOT retrievals from the enhanced algorithm have a correlation of 0.79, mean bias of -0.008, and standard deviation (STD) of error of 0.139 when compared against the ground-based observations at the global AERONET (Aerosol Robotic Network) sites. Over dark surfaces, the VIIRS AOT retrievals using the surface reflectance ratio database improve the root-mean-square error from 0.150 to 0.123. The use of the surface reflectance ratio database also increases the data coverage of more than 20% over dark surfaces. The AOT retrievals over bright surfaces are comparable to MODIS Deep Blue AOT retrievals.

Spatially Complete Global Spectral Surface Albedos: Value-Added Datasets Derived from Terra MODIS Land Products

NASA Technical Reports Server (NTRS)

Moody, Eric G.; King, Michael D.; Platnick, Steven; Schaaf, Crystal B.; Gao, Feng

2004-01-01

Land surface albedo is an important parameter in describing the radiative properties of the earth s surface as it represents the amount of incoming solar radiation that is reflected from the surface. The amount and type of vegetation of the surface dramatically alters the amount of radiation that is reflected; for example, croplands that contain leafy vegetation will reflect radiation very differently than blacktop associated with urban areas. In addition, since vegetation goes through a growth, or phenological, cycle, the amount of radiation that is reflected changes over the course of a year. As a result, albedo is both temporally and spatially dependant upon global location as there is a distribution of vegetated surface types and growing conditions. Land surface albedo is critical for a wide variety of earth system research projects including but not restricted to remote sensing of atmospheric aerosol and cloud properties from space, ground-based analysis of aerosol optical properties from surface-based sun/sky radiometers, biophysically-based land surface modeling of the exchange of energy, water, momentum, and carbon for various land use categories, and surface energy balance studies. These projects require proper representation of the surface albedo s spatial, spectral, and temporal variations, however, these representations are often lacking in datasets prior to the latest generation of land surface albedo products.

High Resolution Land Surface Modeling with the next generation Land Data Assimilation Systems

NASA Astrophysics Data System (ADS)

Kumar, S. V.; Eylander, J.; Peters-Lidard, C.

2005-12-01

Knowledge of land surface processes is important to many real-world applications such as agricultural production, water resources management, and flood predication. The Air Force Weather Agency (AFWA) has provided the USDA and other customers global soil moisture and temperature data for the past 30 years using the agrometeorological data assimilation model (now called AGRMET), merging atmospheric data. Further, accurate initialization of land surface conditions has been shown to greatly influence and improve weather forecast model and seasonal-to-interannual climate predictions. The AFWA AGRMET model exploits real time precipitation observations and analyses, global forecast model and satellite data to generate global estimates of soil moisture, soil temperature and other land surface states at 48km spatial resolution. However, to truly address the land surface initialization and climate prediction problem, and to mitigate the errors introduced by the differences in spatial scales of models, representations of land surface conditions need to be developed at the same fine scales such as that of cloud resolving models. NASA's Goddard Space Flight Center has developed an offline land data assimilation system known as the Land Information System (LIS) capable of modeling land atmosphere interactions at spatial resolutions as fine as 1km. LIS provides a software architecture that integrates the use of the state of the art land surface models, data assimilation techniques, and high performance computing and data management tools. LIS also employs many high resolution surface parameters such as the NASA Earth Observing System (EOS)-era products. In this study we describe the development of a next generation high resolution land surface modeling and data assimilation system, combining the capabilities of LIS and AGRMET. We investigate the influence of high resolution land surface data and observations on the land surface conditions by comparing with the operational AGRMET

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.

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

Generation and Evaluation of a Global Land Surface Phenology Product from Suomi-NPP VIIRS Observations

NASA Astrophysics Data System (ADS)

Zhang, X.; Liu, L.; Yan, D.; Moon, M.; Liu, Y.; Henebry, G. M.; Friedl, M. A.; Schaaf, C.

2017-12-01

Land surface phenology (LSP) datasets have been produced from a variety of coarse spatial resolution satellite observations at both regional and global scales and spanning different time periods since 1982. However, the LSP product generated from NASA's MODerate Resolution Imaging Spectroradiometer (MODIS) data at a spatial resolution of 500m, which is termed Land Cover Dynamics (MCD12Q2), is the only global product operationally produced and freely accessible at annual time steps from 2001. Because MODIS instrument is aging and will be replaced by the Visible Infrared Imaging Radiometer Suite (VIIRS), this research focuses on the generation and evaluation of a global LSP product from Suomi-NPP VIIRS time series observations that provide continuity with the MCD12Q2 product. Specifically, we generate 500m VIIRS global LSP data using daily VIIRS Nadir BRDF (bidirectional reflectance distribution function)-Adjusted reflectances (NBAR) in combination with land surface temperature, snow cover, and land cover type as inputs. The product provides twelve phenological metrics (seven phenological dates and five phenological greenness magnitudes), along with six quality metrics characterizing the confidence and quality associated with phenology retrievals at each pixel. In this paper, we describe the input data and algorithms used to produce this new product, and investigate the impact of VIIRS data time series quality on phenology detections across various climate regimes and ecosystems. As part of our analysis, the VIIRS LSP is evaluated using PhenoCam imagery in North America and Asia, and using higher spatial resolution satellite observations from Landsat 8 over an agricultural area in the central USA. We also explore the impact of high frequency cloud cover on the VIIRS LSP product by comparing with phenology detected from the Advanced Himawari Imager (AHI) onboard Himawari-8. AHI is a new geostationary sensor that observes land surface every 10 minutes, which increases

Evaluation of the global MODIS 30 arc-second spatially and temporally complete snow-free land surface albedo and reflectance anisotropy dataset

NASA Astrophysics Data System (ADS)

Sun, Qingsong; Wang, Zhuosen; Li, Zhan; Erb, Angela; Schaaf, Crystal B.

2017-06-01

Land surface albedo is an essential variable for surface energy and climate modeling as it describes the proportion of incident solar radiant flux that is reflected from the Earth's surface. To capture the temporal variability and spatial heterogeneity of the land surface, satellite remote sensing must be used to monitor albedo accurately at a global scale. However, large data gaps caused by cloud or ephemeral snow have slowed the adoption of satellite albedo products by the climate modeling community. To address the needs of this community, we used a number of temporal and spatial gap-filling strategies to improve the spatial and temporal coverage of the global land surface MODIS BRDF, albedo and NBAR products. A rigorous evaluation of the gap-filled values shows good agreement with original high quality data (RMSE = 0.027 for the NIR band albedo, 0.020 for the red band albedo). This global snow-free and cloud-free MODIS BRDF and albedo dataset (established from 2001 to 2015) offers unique opportunities to monitor and assess the impact of the changes on the Earth's land surface.

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.

Enhancing Global Land Surface Hydrology Estimates from the NASA MERRA Reanalysis Using Precipitation Observations and Model Parameter Adjustments

NASA Technical Reports Server (NTRS)

Reichle, Rolf; Koster, Randal; DeLannoy, Gabrielle; Forman, Barton; Liu, Qing; Mahanama, Sarith; Toure, Ally

2011-01-01

The Modern-Era Retrospective analysis for Research and Applications (MERRA) is a state-of-the-art reanalysis that provides. in addition to atmospheric fields. global estimates of soil moisture, latent heat flux. snow. and runoff for J 979-present. This study introduces a supplemental and improved set of land surface hydrological fields ('MERRA-Land') generated by replaying a revised version of the land component of the MERRA system. Specifically. the MERRA-Land estimates benefit from corrections to the precipitation forcing with the Global Precipitation Climatology Project pentad product (version 2.1) and from revised parameters in the rainfall interception model, changes that effectively correct for known limitations in the MERRA land surface meteorological forcings. The skill (defined as the correlation coefficient of the anomaly time series) in land surface hydrological fields from MERRA and MERRA-Land is assessed here against observations and compared to the skill of the state-of-the-art ERA-Interim reanalysis. MERRA-Land and ERA-Interim root zone soil moisture skills (against in situ observations at 85 US stations) are comparable and significantly greater than that of MERRA. Throughout the northern hemisphere, MERRA and MERRA-Land agree reasonably well with in situ snow depth measurements (from 583 stations) and with snow water equivalent from an independent analysis. Runoff skill (against naturalized stream flow observations from 15 basins in the western US) of MERRA and MERRA-Land is typically higher than that of ERA-Interim. With a few exceptions. the MERRA-Land data appear more accurate than the original MERRA estimates and are thus recommended for those interested in using '\\-tERRA output for land surface hydrological studies.

A novel assessment of the role of land-use and land-cover change in the global carbon cycle, using a new Dynamic Global Vegetation Model version of the CABLE land surface model

NASA Astrophysics Data System (ADS)

Haverd, Vanessa; Smith, Benjamin; Nieradzik, Lars; Briggs, Peter; Canadell, Josep

2017-04-01

In recent decades, terrestrial ecosystems have sequestered around 1.2 PgC y-1, an amount equivalent to 20% of fossil-fuel emissions. This land carbon flux is the net result of the impact of changing climate and CO2 on ecosystem productivity (CO2-climate driven land sink ) and deforestation, harvest and secondary forest regrowth (the land-use change (LUC) flux). The future trajectory of the land carbon flux is highly dependent upon the contributions of these processes to the net flux. However their contributions are highly uncertain, in part because the CO2-climate driven land sink and LUC components are often estimated independently, when in fact they are coupled. We provide a novel assessment of global land carbon fluxes (1800-2015) that integrates land-use effects with the effects of changing climate and CO2 on ecosystem productivity. For this, we use a new land-use enabled Dynamic Global Vegetation Model (DGVM) version of the CABLE land surface model, suitable for use in attributing changes in terrestrial carbon balance, and in predicting changes in vegetation cover and associated effects on land-atmosphere exchange. In this model, land-use-change is driven by prescribed gross land-use transitions and harvest areas, which are converted to changes in land-use area and transfer of carbon between pools (soil, litter, biomass, harvested wood products and cleared wood pools). A novel aspect is the treatment of secondary woody vegetation via the coupling between the land-use module and the POP (Populations Order Physiology) module for woody demography and disturbance-mediated landscape heterogeneity. Land-use transitions to and from secondary forest tiles modify the patch age distribution within secondary-vegetated tiles, in turn affecting biomass accumulation and turnover rates and hence the magnitude of the secondary forest sink. The resulting secondary forest patch age distribution also influences the magnitude of the secondary forest harvest and clearance fluxes

Hyperresolution Global Land Surface Modeling: Meeting a Grand Challenge for Monitoring Earth's Terrestrial Water

NASA Technical Reports Server (NTRS)

Wood, Eric F.; Roundy, Joshua K.; Troy, Tara J.; van Beek, L. P. H.; Bierkens, Marc F. P.; 4 Blyth, Eleanor; de Roo, Ad; Doell. Petra; Ek, Mike; Famiglietti, James;

Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth's terrestrial water

NASA Astrophysics Data System (ADS)

Wood, Eric F.; Roundy, Joshua K.; Troy, Tara J.; van Beek, L. P. H.; Bierkens, Marc F. P.; Blyth, Eleanor; de Roo, Ad; DöLl, Petra; Ek, Mike; Famiglietti, James; Gochis, David; van de Giesen, Nick; Houser, Paul; Jaffé, Peter R.; Kollet, Stefan; Lehner, Bernhard; Lettenmaier, Dennis P.; Peters-Lidard, Christa; Sivapalan, Murugesu; Sheffield, Justin; Wade, Andrew; Whitehead, Paul

2011-05-01

Monitoring Earth's terrestrial water conditions is critically important to many hydrological applications such as global food production; assessing water resources sustainability; and flood, drought, and climate change prediction. These needs have motivated the development of pilot monitoring and prediction systems for terrestrial hydrologic and vegetative states, but to date only at the rather coarse spatial resolutions (˜10-100 km) over continental to global domains. Adequately addressing critical water cycle science questions and applications requires systems that are implemented globally at much higher resolutions, on the order of 1 km, resolutions referred to as hyperresolution in the context of global land surface models. This opinion paper sets forth the needs and benefits for a system that would monitor and predict the Earth's terrestrial water, energy, and biogeochemical cycles. We discuss six major challenges in developing a system: improved representation of surface-subsurface interactions due to fine-scale topography and vegetation; improved representation of land-atmospheric interactions and resulting spatial information on soil moisture and evapotranspiration; inclusion of water quality as part of the biogeochemical cycle; representation of human impacts from water management; utilizing massively parallel computer systems and recent computational advances in solving hyperresolution models that will have up to 109 unknowns; and developing the required in situ and remote sensing global data sets. We deem the development of a global hyperresolution model for monitoring the terrestrial water, energy, and biogeochemical cycles a "grand challenge" to the community, and we call upon the international hydrologic community and the hydrological science support infrastructure to endorse the effort.

Mapping the global depth to bedrock for land surface modelling

NASA Astrophysics Data System (ADS)

Shangguan, W.; Hengl, T.; Yuan, H.; Dai, Y. J.; Zhang, S.

2017-12-01

Depth to bedrock serves as the lower boundary of land surface models, which controls hydrologic and biogeochemical processes. This paper presents a framework for global estimation of Depth to bedrock (DTB). Observations were extracted from a global compilation of soil profile data (ca. 130,000 locations) and borehole data (ca. 1.6 million locations). Additional pseudo-observations generated by expert knowledge were added to fill in large sampling gaps. The model training points were then overlaid on a stack of 155 covariates including DEM-based hydrological and morphological derivatives, lithologic units, MODIS surfacee reflectance bands and vegetation indices derived from the MODIS land products. Global spatial prediction models were developed using random forests and Gradient Boosting Tree algorithms. The final predictions were generated at the spatial resolution of 250m as an ensemble prediction of the two independently fitted models. The 10-fold cross-validation shows that the models explain 59% for absolute DTB and 34% for censored DTB (depths deep than 200 cm are predicted as 200 cm). The model for occurrence of R horizon (bedrock) within 200 cm does a good job. Visual comparisons of predictions in the study areas where more detailed maps of depth to bedrock exist show that there is a general match with spatial patterns from similar local studies. Limitation of the data set and extrapolation in data spare areas should not be ignored in applications. To improve accuracy of spatial prediction, more borehole drilling logs will need to be added to supplement the existing training points in under-represented areas.

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

Land surface energy budget during dry spells: global CMIP5 AMIP simulations vs. satellite observations

NASA Astrophysics Data System (ADS)

Gallego-Elvira, Belen; Taylor, Christopher M.; Harris, Phil P.; Ghent, Darren; Folwell, Sonja S.

2015-04-01

During extended periods without rain (dry spells), the soil can dry out due to vegetation transpiration and soil evaporation. At some point in this drying cycle, land surface conditions change from energy-limited to water-limited evapotranspiration, and this is accompanied by an increase of the ground and overlying air temperatures. Regionally, the characteristics of this transition determine the influence of soil moisture on air temperature and rainfall. Global Climate Models (GCMs) disagree on where and how strongly the surface energy budget is limited by soil moisture. Flux tower observations are improving our understanding of these dry down processes, but typical heterogeneous landscapes are too sparsely sampled to ascertain a representative regional response. Alternatively, satellite observations of land surface temperature (LST) provide indirect information about the surface energy partition at 1km resolution globally. In our study, we analyse how well the dry spell dynamics of LST are represented by GCMs across the globe. We use a spatially and temporally aggregated diagnostic to describe the composite response of LST during surface dry down in rain-free periods in distinct climatic regions. The diagnostic is derived from daytime MODIS-Terra LST observations and bias-corrected meteorological re-analyses, and compared against the outputs of historical climate simulations of seven models running the CMIP5 AMIP experiment. Dry spell events are stratified by antecedent precipitation, land cover type and geographic regions to assess the sensitivity of surface warming rates to soil moisture levels at the onset of a dry spell for different surface and climatic zones. In a number of drought-prone hot spot regions, we find important differences in simulated dry spell behaviour, both between models, and compared to observations. These model biases are likely to compromise seasonal forecasts and future climate projections.

The global signature of post-1900 land ice wastage on vertical land motion

NASA Astrophysics Data System (ADS)

Riva, Riccardo; Frederikse, Thomas; King, Matt; Marzeion, Ben; van den Broeke, Michiel

2017-04-01

The amount of ice stored on land has strongly declined during the 20th century, and melt rates showed a significant acceleration over the last two decades. Land ice wastage is well known to be one of the main drivers of global mean sea-level rise, as widely discussed in the literature and reflected in the last assessment report of the IPCC. A less obvious effect of melting land ice is the response of the solid earth to mass redistribution on its surface, which, in the first approximation, results in land uplift where the load reduces (e.g., close to the meltwater sources) and land subsidence where the load increases (e.g., under the rising oceans). This effect is nowadays well known within the cryospheric and sea level communities. However, what is often not realized is that the solid earth response is a truly global effect: a localized mass change does cause a large deformation signal in its proximity, but also causes a change of the position of every other point on the Earth's surface. The theory of the Earth's elastic response to changing surface loads forms the basis of the 'sea-level equation', which allows sea-level fingerprints of continental mass change to be computed. In this paper, we provide the first dedicated analysis of global vertical land motion driven by land ice wastage. By means of established techniques to compute the solid earth elastic response to surface load changes and the most recent datasets of glacier and ice sheet mass change, we show that land ice loss currently leads to vertical deformation rates of several tenths of mm per year at mid-latitudes, especially over the Northern Hemisphere where most sources are located. In combination with the improved accuracy of space geodetic techniques (e.g., Global Navigation Satellite Systems), this means that the effect of ice melt is non-negligible over a large part of the continents. In particular, we show how deformation rates have been strongly varying through the last century, which implies

Global water balances reconstructed by multi-model offline simulations of land surface models under GSWP3 (Invited)

NASA Astrophysics Data System (ADS)

Oki, T.; KIM, H.; Ferguson, C. R.; Dirmeyer, P.; Seneviratne, S. I.

2013-12-01

As the climate warms, the frequency and severity of flood and drought events is projected to increase. Understanding the role that the land surface will play in reinforcing or diminishing these extremes at regional scales will become critical. In fact, the current development path from atmospheric (GCM) to coupled atmosphere-ocean (AOGCM) to fully-coupled dynamic earth system models (ESMs) has brought new awareness to the climate modeling community of the abundance of uncertainty in land surface parameterizations. One way to test the representativeness of a land surface scheme is to do so in off-line (uncoupled) mode with controlled, high quality meteorological forcing. When multiple land schemes are run in-parallel (with the same forcing data), an inter-comparison of their outputs can provide the basis for model confidence estimates and future model refinements. In 2003, the Global Soil Wetness Project Phase 2 (GSWP2) provided the first global multi-model analysis of land surface state variables and fluxes. It spanned the decade of 1986-1995. While it was state-of-the art at the time, physical schemes have since been enhanced, a number of additional processes and components in the water-energy-eco-systems nexus can now be simulated, , and the availability of global, long-term observationally-based datasets that can be used for forcing and validating models has grown. Today, the data exists to support century-scale off-line experiments. The ongoing follow-on to GSWP2, named GSWP3, capitalizes on these new feasibilities and model functionalities. The project's cornerstone is its century-scale (1901-2010), 3-hourly, 0.5° meteorological forcing dataset that has been dynamically downscaled from the Twentieth Century Reanalysis and bias-corrected using monthly Climate Research Unit (CRU) temperature and Global Precipitation Climatology Centre (GPCC) precipitation data. However, GSWP3 also has an important long-term future climate component that spans the 21st century

A NEW LAND-SURFACE MODEL IN MM5

EPA Science Inventory

There has recently been a general realization that more sophisticated modeling of land-surface processes can be important for mesoscale meteorology models. Land-surface models (LSMs) have long been important components in global-scale climate models because of their more compl...

Recent Progresses in Incorporating Human Land-Water Management into Global Land Surface Models Toward Their Integration into Earth System Models

NASA Technical Reports Server (NTRS)

Pokhrel, Yadu N.; Hanasaki, Naota; Wada, Yoshihide; Kim, Hyungjun

2016-01-01

The global water cycle has been profoundly affected by human land-water management. As the changes in the water cycle on land can affect the functioning of a wide range of biophysical and biogeochemical processes of the Earth system, it is essential to represent human land-water management in Earth system models (ESMs). During the recent past, noteworthy progress has been made in large-scale modeling of human impacts on the water cycle but sufficient advancements have not yet been made in integrating the newly developed schemes into ESMs. This study reviews the progresses made in incorporating human factors in large-scale hydrological models and their integration into ESMs. The study focuses primarily on the recent advancements and existing challenges in incorporating human impacts in global land surface models (LSMs) as a way forward to the development of ESMs with humans as integral components, but a brief review of global hydrological models (GHMs) is also provided. The study begins with the general overview of human impacts on the water cycle. Then, the algorithms currently employed to represent irrigation, reservoir operation, and groundwater pumping are discussed. Next, methodological deficiencies in current modeling approaches and existing challenges are identified. Furthermore, light is shed on the sources of uncertainties associated with model parameterizations, grid resolution, and datasets used for forcing and validation. Finally, representing human land-water management in LSMs is highlighted as an important research direction toward developing integrated models using ESM frameworks for the holistic study of human-water interactions within the Earths system.

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

Characterizing an Integrated Annual Global Measure of the Earth's Maximum Land Surface Temperatures from 2003 to 2012 Reveals Strong Biogeographic Influences

NASA Astrophysics Data System (ADS)

Mildrexler, D. J.; Zhao, M.; Running, S. W.

2014-12-01

Land Surface Temperature (LST) is a good indicator of the surface energy balance because it is determined by interactions and energy fluxes between the atmosphere and the ground. The variability of land surface properties and vegetation densities across the Earth's surface changes these interactions and gives LST a unique biogeographic influence. Natural and human-induced disturbances modify the surface characteristics and alter the expression of LST. This results in a heterogeneous and dynamic thermal environment. Measurements that merge these factors into a single global metric, while maintaining the important biophysical and biogeographical factors of the land surface's thermal environment are needed to better understand integrated temperature changes in the Earth system. Using satellite-based LST we have developed a new global metric that focuses on one critical component of LST that occurs when the relationship between vegetation density and surface temperature is strongly coupled: annual maximum LST (LSTmax). A 10 year evaluation of LSTmax histograms that include every 1-km pixel across the Earth's surface reveals that this integrative measurement is strongly influenced by the biogeographic patterns of the Earth's ecosystems, providing a unique comparative view of the planet every year that can be likened to the Earth's thermal maximum fingerprint. The biogeographical component is controlled by the frequency and distribution of vegetation types across the Earth's land surface and displays a trimodal distribution. The three modes are driven by ice covered polar regions, forests, and hot desert/shrubland environments. In ice covered areas the histograms show that the heat of fusion results in a convergence of surface temperatures around the melting point. The histograms also show low interannual variability reflecting two important global land surface dynamics; 1) only a small fraction of the Earth's surface is disturbed in any given year, and 2) when

Estimating morning changes in land surface temperature from MODIS day/night land surface temperature: Applications for surface energy balance modeling

USDA-ARS?s Scientific Manuscript database

Observations of land surface temperature (LST) are crucial for the monitoring of surface energy fluxes from satellite. Methods that require high temporal resolution LST observations (e.g., from geostationary orbit) can be difficult to apply globally because several geostationary sensors are required...

Global land use change, economic globalization, and the looming land scarcity.

PubMed

Lambin, Eric F; Meyfroidt, Patrick

2011-03-01

A central challenge for sustainability is how to preserve forest ecosystems and the services that they provide us while enhancing food production. This challenge for developing countries confronts the force of economic globalization, which seeks cropland that is shrinking in availability and triggers deforestation. Four mechanisms-the displacement, rebound, cascade, and remittance effects-that are amplified by economic globalization accelerate land conversion. A few developing countries have managed a land use transition over the recent decades that simultaneously increased their forest cover and agricultural production. These countries have relied on various mixes of agricultural intensification, land use zoning, forest protection, increased reliance on imported food and wood products, the creation of off-farm jobs, foreign capital investments, and remittances. Sound policies and innovations can therefore reconcile forest preservation with food production. Globalization can be harnessed to increase land use efficiency rather than leading to uncontrolled land use expansion. To do so, land systems should be understood and modeled as open systems with large flows of goods, people, and capital that connect local land use with global-scale factors.

Global land use change, economic globalization, and the looming land scarcity

PubMed Central

Lambin, Eric F.; Meyfroidt, Patrick

2011-01-01

A central challenge for sustainability is how to preserve forest ecosystems and the services that they provide us while enhancing food production. This challenge for developing countries confronts the force of economic globalization, which seeks cropland that is shrinking in availability and triggers deforestation. Four mechanisms—the displacement, rebound, cascade, and remittance effects—that are amplified by economic globalization accelerate land conversion. A few developing countries have managed a land use transition over the recent decades that simultaneously increased their forest cover and agricultural production. These countries have relied on various mixes of agricultural intensification, land use zoning, forest protection, increased reliance on imported food and wood products, the creation of off-farm jobs, foreign capital investments, and remittances. Sound policies and innovations can therefore reconcile forest preservation with food production. Globalization can be harnessed to increase land use efficiency rather than leading to uncontrolled land use expansion. To do so, land systems should be understood and modeled as open systems with large flows of goods, people, and capital that connect local land use with global-scale factors. PMID:21321211

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

Oscillations in land surface hydrological cycle

NASA Astrophysics Data System (ADS)

Labat, D.

2006-02-01

Hydrological cycle is the perpetual movement of water throughout the various component of the global Earth's system. Focusing on the land surface component of this cycle, the determination of the succession of dry and humid periods is of high importance with respect to water resources management but also with respect to global geochemical cycles. This knowledge requires a specified estimation of recent fluctuations of the land surface cycle at continental and global scales. Our approach leans towards a new estimation of freshwater discharge to oceans from 1875 to 1994 as recently proposed by Labat et al. [Labat, D., Goddéris, Y., Probst, JL, Guyot, JL, 2004. Evidence for global runoff increase related to climate warming. Advances in Water Resources, 631-642]. Wavelet analyses of the annual freshwater discharge time series reveal an intermittent multiannual variability (4- to 8-y, 14- to 16-y and 20- to 25-y fluctuations) and a persistent multidecadal 30- to 40-y variability. Continent by continent, reasonable relationships between land-water cycle oscillations and climate forcing (such as ENSO, NAO or sea surface temperature) are proposed even though if such relationships or correlations remain very complex. The high intermittency of interannual oscillations and the existence of persistent multidecadal fluctuations make prediction difficult for medium-term variability of droughts and high-flows, but lead to a more optimistic diagnostic for long-term fluctuations prediction.

Global Land Information System (GLIS)

USGS Publications Warehouse

,

1992-01-01

The Global Land Information System (GLIS) is an interactive computer system developed by the U.S. Geological Survey (USGS) for scientists seeking sources of information about the Earth's land surfaces. GLIS contains "metadata," that is, descriptive information about data sets. Through GLIS, scientists can evaluate data sets, determine their availability, and place online requests for products. GLIS is more, however, than a mere list of products. It offers online samples of earth science data that may be ordered through the system.

Improving land surface emissivty parameter for land surface models using portable FTIR and remote sensing observation in Taklimakan Desert

NASA Astrophysics Data System (ADS)

Liu, Yongqiang; Mamtimin, Ali; He, Qing

2014-05-01

Because land surface emissivity (ɛ) has not been reliably measured, global climate model (GCM) land surface schemes conventionally set this parameter as simply assumption, for example, 1 as in the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) model, 0.96 for soil and wetland in the Global and Regional Assimilation and Prediction System (GRAPES) Common Land Model (CoLM). This is the so-called emissivity assumption. Accurate broadband emissivity data are needed as model inputs to better simulate the land surface climate. It is demonstrated in this paper that the assumption of the emissivity induces errors in modeling the surface energy budget over Taklimakan Desert where ɛ is far smaller than original value. One feasible solution to this problem is to apply the accurate broadband emissivity into land surface models. The Moderate Resolution Imaging Spectroradiometer (MODIS) instrument has routinely measured spectral emissivities in six thermal infrared bands. The empirical regression equations have been developed in this study to convert these spectral emissivities to broadband emissivity required by land surface models. In order to calibrate the regression equations, using a portable Fourier Transform infrared (FTIR) spectrometer instrument, crossing Taklimakan Desert along with highway from north to south, to measure the accurate broadband emissivity. The observed emissivity data show broadband ɛ around 0.89-0.92. To examine the impact of improved ɛ to radiative energy redistribution, simulation studies were conducted using offline CoLM. The results illustrate that large impacts of surface ɛ occur over desert, with changes up in surface skin temperature, as well as evident changes in sensible heat fluxes. Keywords: Taklimakan Desert, surface broadband emissivity, Fourier Transform infrared spectrometer, MODIS, CoLM

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

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

Land cover mapping of North and Central America—Global Land Cover 2000

USGS Publications Warehouse

Latifovic, Rasim; Zhu, Zhi-Liang

2004-01-01

The Land Cover Map of North and Central America for the year 2000 (GLC 2000-NCA), prepared by NRCan/CCRS and USGS/EROS Data Centre (EDC) as a regional component of the Global Land Cover 2000 project, is the subject of this paper. A new mapping approach for transforming satellite observations acquired by the SPOT4/VGTETATION (VGT) sensor into land cover information is outlined. The procedure includes: (1) conversion of daily data into 10-day composite; (2) post-seasonal correction and refinement of apparent surface reflectance in 10-day composite images; and (3) extraction of land cover information from the composite images. The pre-processing and mosaicking techniques developed and used in this study proved to be very effective in removing cloud contamination, BRDF effects, and noise in Short Wave Infra-Red (SWIR). The GLC 2000-NCA land cover map is provided as a regional product with 28 land cover classes based on modified Federal Geographic Data Committee/Vegetation Classification Standard (FGDC NVCS) classification system, and as part of a global product with 22 land cover classes based on Land Cover Classification System (LCCS) of the Food and Agriculture Organisation. The map was compared on both areal and per-pixel bases over North and Central America to the International Geosphere–Biosphere Programme (IGBP) global land cover classification, the University of Maryland global land cover classification (UMd) and the Moderate Resolution Imaging Spectroradiometer (MODIS) Global land cover classification produced by Boston University (BU). There was good agreement (79%) on the spatial distribution and areal extent of forest between GLC 2000-NCA and the other maps, however, GLC 2000-NCA provides additional information on the spatial distribution of forest types. The GLC 2000-NCA map was produced at the continental level incorporating specific needs of the region.

Mapping 2000 2010 Impervious Surface Change in India Using Global Land Survey Landsat Data

NASA Technical Reports Server (NTRS)

Wang, Panshi; Huang, Chengquan; Brown De Colstoun, Eric C.

2017-01-01

Understanding and monitoring the environmental impacts of global urbanization requires better urban datasets. Continuous field impervious surface change (ISC) mapping using Landsat data is an effective way to quantify spatiotemporal dynamics of urbanization. It is well acknowledged that Landsat-based estimation of impervious surface is subject to seasonal and phenological variations. The overall goal of this paper is to map 200-02010 ISC for India using Global Land Survey datasets and training data only available for 2010. To this end, a method was developed that could transfer the regression tree model developed for mapping 2010 impervious surface to 2000 using an iterative training and prediction (ITP) approach An independent validation dataset was also developed using Google Earth imagery. Based on the reference ISC from the validation dataset, the RMSE of predicted ISC was estimated to be 18.4%. At 95% confidence, the total estimated ISC for India between 2000 and 2010 is 2274.62 +/- 7.84 sq km.

Land–atmosphere feedbacks amplify aridity increase over land under global warming

USGS Publications Warehouse

Berg, Alexis; Findell, Kirsten; Lintner, Benjamin; Giannini, Alessandra; Seneviratne, Sonia I.; van den Hurk, Bart; Lorenz, Ruth; Pitman, Andy; Hagemann, Stefan; Meier, Arndt; Cheruy, Frédérique; Ducharne, Agnès; Malyshev, Sergey; Milly, Paul C. D.

2016-01-01

The response of the terrestrial water cycle to global warming is central to issues including water resources, agriculture and ecosystem health. Recent studies indicate that aridity, defined in terms of atmospheric supply (precipitation, P) and demand (potential evapotranspiration, Ep) of water at the land surface, will increase globally in a warmer world. Recently proposed mechanisms for this response emphasize the driving role of oceanic warming and associated atmospheric processes. Here we show that the aridity response is substantially amplified by land–atmosphere feedbacks associated with the land surface’s response to climate and CO2 change. Using simulations from the Global Land Atmosphere Coupling Experiment (GLACE)-CMIP5 experiment, we show that global aridity is enhanced by the feedbacks of projected soil moisture decrease on land surface temperature, relative humidity and precipitation. The physiological impact of increasing atmospheric CO2 on vegetation exerts a qualitatively similar control on aridity. We reconcile these findings with previously proposed mechanisms by showing that the moist enthalpy change over land is unaffected by the land hydrological response. Thus, although oceanic warming constrains the combined moisture and temperature changes over land, land hydrology modulates the partitioning of this enthalpy increase towards increased aridity.

Magnitude and variability of land evaporation and its components at the global scale

USDA-ARS?s Scientific Manuscript database

A physics-based methodology is applied to estimate global land-surface evaporation from multi-satellite observations. GLEAM (Global Land-surface Evaporation: the Amsterdam Methodology) combines a wide range of remotely sensed observations within a Priestley and Taylor-based framework. Daily actual e...

New Versions of MISR Aerosol and Land Surface Products Available

Atmospheric Science Data Center

2018-02-14

New Versions of MISR Aerosol and Land Surface Products Available Monday, February 12, ... the release of new versions of the MISR Level 2 (L2) Aerosol Product, the MISR L2 Land Surface Product, and the Level 3 (L3) Component Global Aerosol and Land Surface Products.   The new MISR L2 Aerosol Product ...

Towards monitoring land-cover and land-use changes at a global scale: the global land survey 2005

USGS Publications Warehouse

Gutman, G.; Byrnes, Raymond A.; Masek, J.; Covington, S.; Justice, C.; Franks, S.; Headley, Rachel

2008-01-01

Land cover is a critical component of the Earth system, infl uencing land-atmosphere interactions, greenhouse gas fl uxes, ecosystem health, and availability of food, fi ber, and energy for human populations. The recent Integrated Global Observations of Land (IGOL) report calls for the generation of maps documenting global land cover at resolutions between 10m and 30m at least every fi ve years (Townshend et al., in press). Moreover, despite 35 years of Landsat observations, there has not been a unifi ed global analysis of land-cover trends nor has there been a global assessment of land-cover change at Landsat-like resolution. Since the 1990s, the National Aeronautics and Space Administration (NASA) and the U.S. Geological Survey (USGS) have supported development of data sets based on global Landsat observations (Tucker et al., 2004). These land survey data sets, usually referred to as GeoCover ™, provide global, orthorectifi ed, typically cloud-free Landsat imagery centered on the years 1975, 1990, and 2000, with a preference for leaf-on conditions. Collectively, these data sets provided a consistent set of observations to assess land-cover changes at a decadal scale. These data are freely available via the Internet from the USGS Center for Earth Resources Observation and Science (EROS) (see http://earthexplorer.usgs.gov or http://glovis.usgs.gov). This has resulted in unprecedented downloads of data, which are widely used in scientifi c studies of land-cover change (e.g., Boone et al., 2007; Harris et al., 2005; Hilbert, 2006; Huang et al. 2007; Jantz et al., 2005, Kim et al., 2007; Leimgruber, 2005; Masek et al., 2006). NASA and USGS are continuing to support land-cover change research through the development of GLS2005 - an additional global Landsat assessment circa 20051 . Going beyond the earlier initiatives, this data set will establish a baseline for monitoring changes on a 5-year interval and will pave the way toward continuous global land

A new map of global ecological land units—An ecophysiographic stratification approach

USGS Publications Warehouse

Sayre, Roger; Dangermond, Jack; Frye, Charlie; Vaughan, Randy; Aniello, Peter; Breyer, Sean P.; Cribbs, Douglas; Hopkins, Dabney; Nauman, Richard; Derrenbacher, William; Wright, Dawn J.; Brown, Clint; Convis, Charles; Smith, Jonathan H.; Benson, Laurence; Van Sistine, Darren; Warner, Harumi; Cress, Jill Janene; Danielson, Jeffrey J.; Hamann, Sharon L.; Cecere, Thomas; Reddy, Ashwan D.; Burton, Devon; Grosse, Andrea; True, Diane; Metzger, Marc; Hartmann, Jens; Moosdorf, Nils; Durr, Hans; Paganini, Marc; Defourny, Pierre; Arino, Olivier; Maynard, Simone; Anderson, Mark; Comer, Patrick

2014-01-01

In response to the need and an intergovernmental commission for a high resolution and data-derived global ecosystem map, land surface elements of global ecological pattern were characterized in an ecophysiographic stratification of the planet. The stratification produced 3,923 terrestrial ecological land units (ELUs) at a base resolution of 250 meters. The ELUs were derived from data on land surface features in a three step approach. The first step involved acquiring or developing four global raster datalayers representing the primary components of ecosystem structure: bioclimate, landform, lithology, and land cover. These datasets generally represent the most accurate, current, globally comprehensive, and finest spatial and thematic resolution data available for each of the four inputs. The second step involved a spatial combination of the four inputs into a single, new integrated raster dataset where every cell represents a combination of values from the bioclimate, landforms, lithology, and land cover datalayers. This foundational global raster datalayer, called ecological facets (EFs), contains 47,650 unique combinations of the four inputs. The third step involved an aggregation of the EFs into the 3,923 ELUs. This subdivision of the Earth’s surface into relatively fine, ecological land areas is designed to be useful for various types of ecosystem research and management applications, including assessments of climate change impacts to ecosystems, economic and non-economic valuation of ecosystem services, and conservation planning.

The NASA-Goddard Multi-Scale Modeling Framework - Land Information System: Global Land/atmosphere Interaction with Resolved Convection

NASA Technical Reports Server (NTRS)

Mohr, Karen Irene; Tao, Wei-Kuo; Chern, Jiun-Dar; Kumar, Sujay V.; Peters-Lidard, Christa D.

2013-01-01

The present generation of general circulation models (GCM) use parameterized cumulus schemes and run at hydrostatic grid resolutions. To improve the representation of cloud-scale moist processes and landeatmosphere interactions, a global, Multi-scale Modeling Framework (MMF) coupled to the Land Information System (LIS) has been developed at NASA-Goddard Space Flight Center. The MMFeLIS has three components, a finite-volume (fv) GCM (Goddard Earth Observing System Ver. 4, GEOS-4), a 2D cloud-resolving model (Goddard Cumulus Ensemble, GCE), and the LIS, representing the large-scale atmospheric circulation, cloud processes, and land surface processes, respectively. The non-hydrostatic GCE model replaces the single-column cumulus parameterization of fvGCM. The model grid is composed of an array of fvGCM gridcells each with a series of embedded GCE models. A horizontal coupling strategy, GCE4fvGCM4Coupler4LIS, offered significant computational efficiency, with the scalability and I/O capabilities of LIS permitting landeatmosphere interactions at cloud-scale. Global simulations of 2007e2008 and comparisons to observations and reanalysis products were conducted. Using two different versions of the same land surface model but the same initial conditions, divergence in regional, synoptic-scale surface pressure patterns emerged within two weeks. The sensitivity of largescale circulations to land surface model physics revealed significant functional value to using a scalable, multi-model land surface modeling system in global weather and climate prediction.

Land Surface Microwave Emissivity Dynamics: Observations, Analysis and Modeling

NASA Technical Reports Server (NTRS)

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

2014-01-01

Land surface microwave emissivity affects remote sensing of both the atmosphere and the land surface. The dynamical behavior of microwave emissivity over a very diverse sample of land surface types is studied. With seven years of satellite measurements from AMSR-E, we identified various dynamical regimes of the land surface emission. In addition, we used two radiative transfer models (RTMs), the Community Radiative Transfer Model (CRTM) and the Community Microwave Emission Modeling Platform (CMEM), to simulate land surface emissivity dynamics. With both CRTM and CMEM coupled to NASA's Land Information System, global-scale land surface microwave emissivities were simulated for five years, and evaluated against AMSR-E observations. It is found that both models have successes and failures over various types of land surfaces. Among them, the desert shows the most consistent underestimates (by approx. 70-80%), due to limitations of the physical models used, and requires a revision in both systems. Other snow-free surface types exhibit various degrees of success and it is expected that parameter tuning can improve their performances.

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.

The CEOS-Land Surface Imaging Constellation Portal for GEOSS: A resource for land surface imaging system information and data access

USGS Publications Warehouse

Holm, Thomas; Gallo, Kevin P.; Bailey, Bryan

2010-01-01

The Committee on Earth Observation Satellites is an international group that coordinates civil space-borne observations of the Earth, and provides the space component of the Global Earth Observing System of Systems (GEOSS). The CEOS Virtual Constellations concept was implemented in an effort to engage and coordinate disparate Earth observing programs of CEOS member agencies and ultimately facilitate their contribution in supplying the space-based observations required to satisfy the requirements of the GEOSS. The CEOS initially established Study Teams for four prototype constellations that included precipitation, land surface imaging, ocean surface topography, and atmospheric composition. The basic mission of the Land Surface Imaging (LSI) Constellation [1] is to promote the efficient, effective, and comprehensive collection, distribution, and application of space-acquired image data of the global land surface, especially to meet societal needs of the global population, such as those addressed by the nine Group on Earth Observations (GEO) Societal Benefit Areas (SBAs) of agriculture, biodiversity, climate, disasters, ecosystems, energy, health, water, and weather. The LSI Constellation Portal is the result of an effort to address important goals within the LSI Constellation mission and provide resources to assist in planning for future space missions that might further contribute to meeting those goals.

DISAGGREGATION OF GOES LAND SURFACE TEMPERATURES USING SURFACE EMISSIVITY

USDA-ARS?s Scientific Manuscript database

Accurate temporal and spatial estimation of land surface temperatures (LST) is important for modeling the hydrological cycle at field to global scales because LSTs can improve estimates of soil moisture and evapotranspiration. Using remote sensing satellites, accurate LSTs could be routine, but unfo...

Towards a Global Land Subsidence Map

NASA Astrophysics Data System (ADS)

Erkens, G.; Kooi, H.; Sutanudjaja, E.

2017-12-01

Land subsidence is a global problem, but a global land subsidence map is not available yet. Such map is crucial to raise global awareness of land subsidence, as land subsidence causes extensive damage (probably in the order of billions of dollars annually). Insights in the rates of subsidence are particularly relevant for low lying deltas and coastal zones, for which any further loss in elevation is unwanted. With the global land subsidence map relative sea level rise predictions may be improved, contributing to global flood risk calculations. In this contribution, we discuss the approach and progress we have made so far in making a global land subsidence map. The first results will be presented and discussed, and we give an outlook on the work needed to derive a global land subsidence map.

The Met Office Unified Model Global Atmosphere 6.0/6.1 and JULES Global Land 6.0/6.1 configurations

NASA Astrophysics Data System (ADS)

Walters, David; Boutle, Ian; Brooks, Malcolm; Melvin, Thomas; Stratton, Rachel; Vosper, Simon; Wells, Helen; Williams, Keith; Wood, Nigel; Allen, Thomas; Bushell, Andrew; Copsey, Dan; Earnshaw, Paul; Edwards, John; Gross, Markus; Hardiman, Steven; Harris, Chris; Heming, Julian; Klingaman, Nicholas; Levine, Richard; Manners, James; Martin, Gill; Milton, Sean; Mittermaier, Marion; Morcrette, Cyril; Riddick, Thomas; Roberts, Malcolm; Sanchez, Claudio; Selwood, Paul; Stirling, Alison; Smith, Chris; Suri, Dan; Tennant, Warren; Vidale, Pier Luigi; Wilkinson, Jonathan; Willett, Martin; Woolnough, Steve; Xavier, Prince

2017-04-01

We describe Global Atmosphere 6.0 and Global Land 6.0 (GA6.0/GL6.0): the latest science configurations of the Met Office Unified Model and JULES (Joint UK Land Environment Simulator) land surface model developed for use across all timescales. Global Atmosphere 6.0 includes the ENDGame (Even Newer Dynamics for General atmospheric modelling of the environment) dynamical core, which significantly increases mid-latitude variability improving a known model bias. Alongside developments of the model's physical parametrisations, ENDGame also increases variability in the tropics, which leads to an improved representation of tropical cyclones and other tropical phenomena. Further developments of the atmospheric and land surface parametrisations improve other aspects of model performance, including the forecasting of surface weather phenomena. We also describe GA6.1/GL6.1, which includes a small number of long-standing differences from our main trunk configurations that we continue to require for operational global weather prediction. Since July 2014, GA6.1/GL6.1 has been used by the Met Office for operational global numerical weather prediction, whilst GA6.0/GL6.0 was implemented in its remaining global prediction systems over the following year.

Global Assessment of Land Surface Temperature From Geostationary Satellites and Model Estimates

NASA Technical Reports Server (NTRS)

Reichle, Rolf H.; Liu, Q.; Minnis, P.; daSilva, A. M., Jr.; Palikonda, R.; Yost, C. R.

2012-01-01

Land surface (or 'skin') temperature (LST) lies at the heart of the surface energy balance and is a key variable in weather and climate models. In this research we compare two global and independent data sets: (i) LST retrievals from five geostationary satellites generated at the NASA Langley Research Center (LaRC) and (ii) LST estimates from the quasi-operational NASA GEOS-5 global modeling and assimilation system. The objective is to thoroughly understand both data sets and their systematic differences in preparation for the assimilation of the LaRC LST retrievals into GEOS-5. As expected, mean differences (MD) and root-mean-square differences (RMSD) between modeled and retrieved LST vary tremendously by region and time of day. Typical (absolute) MD values range from 1-3 K in Northern Hemisphere mid-latitude regions to near 10 K in regions where modeled clouds are unrealistic, for example in north-eastern Argentina, Uruguay, Paraguay, and southern Brazil. Typically, model estimates of LST are higher than satellite retrievals during the night and lower during the day. RMSD values range from 1-3 K during the night to 2-5 K during the day, but are larger over the 50-120 W longitude band where the LST retrievals are derived from the FY2E platform

Landsat: A global land-observing program

USGS Publications Warehouse

,

2005-01-01

Landsat represents the world’s longest continuously acquired collection of space-based land remote sensing data. The Landsat Project is a joint initiative of the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA) designed to gather Earth resource data from space. NASA developed and launched the spacecrafts, while the USGS handles the operations, maintenance, and management of all ground data reception, processing, archiving, product generation, and distribution.Landsat satellites have been collecting images of the Earth’s surface for more than thirty years. Landsat’s Global Survey Mission is to repeatedly capture images of the Earth’s land mass, coastal boundaries, and coral reefs, and to ensure that sufficient data are acquired to support the observation of changes on the Earth’s land surface and surrounding environment. NASA launched the first Landsat satellite in 1972, and the most recent one, Landsat 7, in 1999. Landsats 5 and 7 continue to capture hundreds of additional images of the Earth’s surface each day. These images provide a valuable resource for people who work

Challenges in Global Land Use/Land Cover Change Modeling

NASA Astrophysics Data System (ADS)

Clarke, K. C.

2011-12-01

For the purposes of projecting and anticipating human-induced land use change at the global scale, much work remains in the systematic mapping and modeling of world-wide land uses and their related dynamics. In particular, research has focused on tropical deforestation, loss of prime agricultural land, loss of wild land and open space, and the spread of urbanization. Fifteen years of experience in modeling land use and land cover change at the regional and city level with the cellular automata model SLEUTH, including cross city and regional comparisons, has led to an ability to comment on the challenges and constraints that apply to global level land use change modeling. Some issues are common to other modeling domains, such as scaling, earth geometry, and model coupling. Others relate to geographical scaling of human activity, while some are issues of data fusion and international interoperability. Grid computing now offers the prospect of global land use change simulation. This presentation summarizes what barriers face global scale land use modeling, but also highlights the benefits of such modeling activity on global change research. An approach to converting land use maps and forecasts into environmental impact measurements is proposed. Using such an approach means that multitemporal mapping, often using remotely sensed sources, and forecasting can also yield results showing the overall and disaggregated status of the environment.

Does surface roughness dominate biophysical forcing of land use and land cover change in the eastern United States?

NASA Astrophysics Data System (ADS)

Burakowski, E. A.; Tawfik, A. B.; Ouimette, A.; Lepine, L. C.; Ollinger, S. V.; Bonan, G. B.; Zarzycki, C. M.; Novick, K. A.

2016-12-01

Changes in land use, land cover, or both promote changes in surface temperature that can amplify or dampen long-term trends driven by natural and anthropogenic climate change by modifying the surface energy budget, primarily through differences in albedo, evapotranspiration, and aerodynamic roughness. Recent advances in variable resolution global models provide the tools necessary to investigate local and global impacts of land use and land cover change by embedding a high-resolution grid over areas of interest in a seamless and computationally efficient manner. Here, we used two eddy covariance tower clusters in the Eastern US (University of New Hampshire UNH and Duke Forest) to validate simulation of surface energy fluxes and properties by the uncoupled Community Land Model (PTCLM4.5) and coupled land-atmosphere Variable-Resolution Community Earth System Model (VR-CESM1.3). Surface energy fluxes and properties are generally well captured by the models for grassland sites, however forested sites tend to underestimate latent heat and overestimate sensible heat flux. Surface roughness emerged as the dominant biophysical forcing factor affecting surface temperature in the eastern United States, generally leading to warmer nighttime temperatures and cooler daytime temperatures. However, the sign and magnitude of the roughness effect on surface temperature was highly sensitive to the calculation of aerodynamic resistance to heat transfer.

The CEOS constellation for land surface imaging

USGS Publications Warehouse

Bailey, G.B.; Berger, Marsha; Jeanjean, H.; Gallo, K.P.

2007-01-01

A constellation of satellites that routinely and frequently images the Earth's land surface in consistently calibrated wavelengths from the visible through the microwave and in spatial detail that ranges from sub-meter to hundreds of meters would offer enormous potential benefits to society. A well-designed and effectively operated land surface imaging satellite constellation could have great positive impact not only on the quality of life for citizens of all nations, but also on mankind's very ability to sustain life as we know it on this planet long into the future. The primary objective of the Committee on Earth Observation Satellites (CEOS) Land Surface Imaging (LSI) Constellation is to define standards (or guidelines) that describe optimal future LSI Constellation capabilities, characteristics, and practices. Standards defined for a LSI Constellation will be based on a thorough understanding of user requirements, and they will address at least three fundamental areas of the systems comprising a Land Surface Imaging Constellation: the space segments, the ground segments, and relevant policies and plans. Studies conducted by the LSI Constellation Study Team also will address current and shorter-term problems and issues facing the land remote sensing community today, such as seeking ways to work more cooperatively in the operation of existing land surface imaging systems and helping to accomplish tangible benefits to society through application of land surface image data acquired by existing systems. 2007 LSI Constellation studies are designed to establish initial international agreements, develop preliminary standards for a mid-resolution land surface imaging constellation, and contribute data to a global forest assessment.

Downwelling Longwave Fluxes at Continental Surfaces-A Comparison of Observations with GCM Simulations and Implications for the Global Land-Surface Radiation Budget.

NASA Astrophysics Data System (ADS)

Garratt, J. R.; Prata, A. J.

1996-03-01

Previous work suggests that general circulation (global climate) models have excess net radiation at land surfaces, apparently due to overestimates in downwelling shortwave flux and underestimates in upwelling long-wave flux. Part of this excess, however, may be compensated for by an underestimate in downwelling longwave flux. Long term observations of the downwelling longwave component at several land stations in Europe, the United States, Australia, and Antarctica suggest that climate models (four are used, as in previous studies) underestimate this flux component on an annual basis by up to 10 W m2, yet with low statistical significance. It is probable that the known underestimate in boundary-layer air temperature contributes to this, as would low model cloudiness and neglect of minor gases such as methane, nitrogen oxide, and the freons. The bias in downwelling longwave flux, together with those found earlier for downwelling shortwave and upwlling long-wave fluxes, are consistent with the model bias found previously for net radiation. All annually averaged fluxes and biases are deduced for global land as a whole.

Impact of fire on global land surface air temperature and energy budget for the 20th century due to changes within ecosystems

NASA Astrophysics Data System (ADS)

Li, Fang; Lawrence, David M.; Bond-Lamberty, Ben

2017-04-01

Fire is a global phenomenon and tightly interacts with the biosphere and climate. This study provides the first quantitative assessment and understanding of fire’s influence on the global annual land surface air temperature and energy budget through its impact on terrestrial ecosystems. Fire impacts are quantified by comparing fire-on and fire-off simulations with the Community Earth System Model (CESM). Results show that, for the 20th century average, fire-induced changes in terrestrial ecosystems significantly increase global land annual mean surface air temperature by 0.18 °C, decrease surface net radiation and latent heat flux by 1.08 W m-2 and 0.99 W m-2, respectively, and have limited influence on sensible heat flux (-0.11 W m-2) and ground heat flux (+0.02 W m-2). Fire impacts are most clearly seen in the tropical savannas. Our analyses suggest that fire increases surface air temperature predominantly by reducing latent heat flux, mainly due to fire-induced damage to the vegetation canopy, and decreases net radiation primarily because fire-induced surface warming significantly increases upward surface longwave radiation. This study provides an integrated estimate of fire and induced changes in ecosystems, climate, and energy budget at a global scale, and emphasizes the importance of a consistent and integrated understanding of fire effects.

Assessment and Enhancement of MERRA Land Surface Hydrology Estimates

NASA Technical Reports Server (NTRS)

Reichle, Rolf H.; Koster, Randal D.; deLannoy, Gabrielle J. M.; Forman, Barton A.; Liu, Qing; Mahanama, Sarith P. P.; Toure, Ally

2012-01-01

The Modern-Era Retrospective analysis for Research and Applications (MERRA) is a state-ofthe-art reanalysis that provides, in addition to atmospheric fields, global estimates of soil moisture, latent heat flux, snow, and runoff for 1979-present. This study introduces a supplemental and improved set of land surface hydrological fields ("MERRA-Land") generated by re-running a revised version of the land component of the MERRA system. Specifically, the MERRA-Land estimates benefit from corrections to the precipitation forcing with the Global Precipitation Climatology Project pentad product (version 2.1) and from revised parameter values in the rainfall interception model, changes that effectively correct for known limitations in the MERRA surface meteorological forcings. The skill (defined as the correlation coefficient of the anomaly time series) in land surface hydrological fields from MERRA and MERRA-Land is assessed here against observations and compared to the skill of the state-of-the-art ERA-Interim (ERA-I) reanalysis. MERRA-Land and ERA-I root zone soil moisture skills (against in situ observations at 85 US stations) are comparable and significantly greater than that of MERRA. Throughout the northern hemisphere, MERRA and MERRA-Land agree reasonably well with in situ snow depth measurements (from 583 stations) and with snow water equivalent from an independent analysis. Runoff skill (against naturalized stream flow observations from 18 US basins) of MERRA and MERRA-Land is typically higher than that of ERA-I. With a few exceptions, the MERRA-Land data appear more accurate than the original MERRA estimates and are thus recommended for those interested in using MERRA output for land surface hydrological studies.

Assessing the Influence of Human Activities on Global Water Resources Using an Advanced Land Surface Model

NASA Astrophysics Data System (ADS)

Pokhrel, Y.; Hanasaki, N.; Koirala, S.; Kanae, S.; Oki, T.

2010-12-01

In order to examine the impact of human intervention on the global hydrological cycle, a Land Surface Model was enhanced with schemes to assess the anthropogenic disturbance on the natural water flow at the global scale. Four different schemes namely; reservoir operation, crop growth, environmental flow, and anthropogenic water withdrawal modules from a state-of-the-art global water resources assessment model called H08 were integrated into an offline version of LSM, Minimal Advance Treatment of Surface Interaction and Runoff (MATSIRO). MATSIRO represents majority of the hydrological processes of water and energy exchange between the land surface and the atmosphere on a physical basis and is designed to be coupled with GCM. The integrated model presented here thus has the capability to simulate both natural and anthropogenic flows of water globally at a spatial resolution of 1°x1°, considering dam operation, domestic, industrial and agricultural water withdrawals and environmental flow requirements. The model can also be coupled with climate models to assess the impact of human activities on the climate system. A simple groundwater scheme was also incorporated and the model can be used to assess the change in water table due to groundwater pumping for irrigation. The model was validated by comparing simulated soil moisture, river discharge and Terrestrial Water Storage Anomaly (TWSA) with observations. The model performs well in simulating TWSA as compared to GRACE observation in different river basins ranging from very wet to very dry. Soil moisture cannot be validated globally because of the lack of validation datasets. For Illinois region, where long term soil moisture observations are available, the model captures the seasonal variation quite well. The simulated global potential irrigation demand is about 1100km3/year, which is within the range of previously published estimates based on various water balance models and LSMs. The model has an advanced option

Simulating global and local surface temperature changes due to Holocene anthropogenic land cover change

NASA Astrophysics Data System (ADS)

He, F.; Vavrus, S. J.; Kutzbach, J. E.; Ruddiman, W. F.; Kaplan, J. O.; Krumhardt, K. M.

2015-12-01

Surface albedo changes from anthropogenic land cover change (ALCC) represent the second-largest negative radiative forcing behind aerosol during the industrial era. Using a new reconstruction of ALCC during the Holocene era by Kaplan et al. [2011], we quantify the local and global temperature response induced by Holocene ALCC in the Community Climate System Model, version 4 (CCSM4). With 1-degree resolution of the CCSM4 slab-ocean model,we find that Holocene ALCC cause a global cooling of 0.17 °C due to the biogeophysical effects of land-atmosphere exchange of momentum, moisture, radiative and heat fluxes. On the global scale, the biogeochemical effects of Holocene ALCC from carbon emissions dominate the biogeophysical effects by causing 0.9 °C global warming. The net effects of Holocene ALCC amount to a global warming of 0.73 °C during the pre-industrial era, which is comparable to the ~0.8 °C warming during industrial times. On local to regional scales, such as parts of Europe, North America and Asia, the biogeophysical effects of Holocene ALCC are significant and comparable to the biogeochemical effect. The lack of ocean dynamics in the 1° CCSM4 slab-ocean simulations could underestimate the climate sensitivity because of the lack of feedbacks from ocean heat transport [Kutzbach et al., 2013; Manabe and Bryan, 1985]. In 1° CCSM4 fully coupled simulations, the climate sensitivity is ~65% larger than the 1° CCSM4 slab-ocean simulations during the Holocene (5.3 °C versus 3.2 °C) [Kutzbach et al., 2013]. With this greater climate sensitivity, the biogeochemical effects of Holocene ALCC could have caused a global warming of ~1.5 °C, and the net biogeophysical and biogeochemical effects of Holocene ALCC could cause a global warming of 1.2 °C during the preindustrial era in our simulations, which is 50% higher than the global warming of ~0.8 °C during industrial times.

New land surface digital elevation model covers the Earth

USGS Publications Warehouse

Gesch, Dean B.; Verdin, Kristine L.; Greenlee, Susan K.

1999-01-01

Land surface elevation around the world is reaching new heights—as far as its description and measurement goes. A new global digital elevation model (DEM) is being cited as a significant improvement in the quality of topographic data available for Earth science studies.Land surface elevation is one of the Earth's most fundamental geophysical properties, but the accuracy and detail with which it has been measured and described globally have been insufficient for many large-area studies. The new model, developed at the U.S. Geological Survey's (USGS) EROS Data Center (EDC), has changed all that.

A Quasi-Global Approach to Improve Day-Time Satellite Surface Soil Moisture Anomalies through the Land Surface Temperature Input

NASA Technical Reports Server (NTRS)

Parinussa, Robert M.; de Jeu, Richard A. M.; van Der Schalie, Robin; Crow, Wade T.; Lei, Fangni; Holmes, Thomas R. H.

2016-01-01

Passive microwave observations from various spaceborne sensors have been linked to the soil moisture of the Earth's surface layer. A new generation of passive microwave sensors are dedicated to retrieving this variable and make observations in the single theoretically optimal L-band frequency (1-2 GHz). Previous generations of passive microwave sensors made observations in a range of higher frequencies, allowing for simultaneous estimation of additional variables required for solving the radiative transfer equation. One of these additional variables is land surface temperature, which plays a unique role in the radiative transfer equation and has an influence on the final quality of retrieved soil moisture anomalies. This study presents an optimization procedure for soil moisture retrievals through a quasi-global precipitation-based verification technique, the so-called Rvalue metric. Various land surface temperature scenarios were evaluated in which biases were added to an existing linear regression, specifically focusing on improving the skills to capture the temporal variability of soil moisture. We focus on the relative quality of the day-time (01:30 pm) observations from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), as these are theoretically most challenging due to the thermal equilibrium theory, and existing studies indicate that larger improvements are possible for these observations compared to their night-time (01:30 am) equivalent. Soil moisture data used in this study were retrieved through the Land Parameter Retrieval Model (LPRM), and in line with theory, both satellite paths show a unique and distinct degradation as a function of vegetation density. Both the ascending (01:30 pm) and descending (01:30 am) paths of the publicly available and widely used AMSR-E LPRM soil moisture products were used for benchmarking purposes. Several scenarios were employed in which the land surface temperature input for the radiative

Next generation of global land cover characterization, mapping, and monitoring

USGS Publications Warehouse

Giri, Chandra; Pengra, Bruce; Long, J.; Loveland, Thomas R.

2013-01-01

Land cover change is increasingly affecting the biophysics, biogeochemistry, and biogeography of the Earth's surface and the atmosphere, with far-reaching consequences to human well-being. However, our scientific understanding of the distribution and dynamics of land cover and land cover change (LCLCC) is limited. Previous global land cover assessments performed using coarse spatial resolution (300 m–1 km) satellite data did not provide enough thematic detail or change information for global change studies and for resource management. High resolution (∼30 m) land cover characterization and monitoring is needed that permits detection of land change at the scale of most human activity and offers the increased flexibility of environmental model parameterization needed for global change studies. However, there are a number of challenges to overcome before producing such data sets including unavailability of consistent global coverage of satellite data, sheer volume of data, unavailability of timely and accurate training and validation data, difficulties in preparing image mosaics, and high performance computing requirements. Integration of remote sensing and information technology is needed for process automation and high-performance computing needs. Recent developments in these areas have created an opportunity for operational high resolution land cover mapping, and monitoring of the world. Here, we report and discuss these advancements and opportunities in producing the next generations of global land cover characterization, mapping, and monitoring at 30-m spatial resolution primarily in the context of United States, Group on Earth Observations Global 30 m land cover initiative (UGLC).

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.

Analysis of global land surface albedo climatology and spatial-temporal variation during 1981-2010 from multiple satellite products

NASA Astrophysics Data System (ADS)

He, Tao; Liang, Shunlin; Song, Dan-Xia

2014-09-01

For several decades, long-term time series data sets of multiple global land surface albedo products have been generated from satellite observations. These data sets have been used as one of the key variables in climate change studies. This study aims to assess the surface albedo climatology and to analyze long-term albedo changes, from nine satellite-based data sets for the period 1981-2010, on a global basis. Results show that climatological surface albedo data sets derived from satellite observations can be used to validate, calibrate, and further improve surface albedo simulations and parameterizations in current climate models. However, the albedo products derived from the International Satellite Cloud Climatology Project and the Global Energy and Water Exchanges Project have large seasonal biases. At latitudes higher than 50°, the maximal difference in winter zonal albedo ranges from 0.1 to 0.4 among the nine satellite data sets. Satellite-based albedo data sets agree relatively well during the summer at high latitudes, with a standard deviation of 0.04 for the 70°-80° zone in both hemispheres. The fine-resolution (0.05°) data sets agree well with each other for all the land cover types in middle to low latitudes; however, large spread was identified for their albedos at middle to high latitudes over land covers with mixed snow and sparse vegetation. By analyzing the time series of satellite-based albedo products over the past three decades, albedo of the Northern Hemisphere was found to be decreasing in July, likely due to the shrinking snow cover. Meanwhile, albedo in January was found to be increasing, likely because of the expansion of snow cover in northern winter. However, to improve the albedo estimation at high latitudes, and ultimately the climate models used for long-term climate change studies, a still better understanding of differences between satellite-based albedo data sets is required.

The global distribution and dynamics of surface soil moisture

NASA Astrophysics Data System (ADS)

McColl, Kaighin A.; Alemohammad, Seyed Hamed; Akbar, Ruzbeh; Konings, Alexandra G.; Yueh, Simon; Entekhabi, Dara

2017-01-01

Surface soil moisture has a direct impact on food security, human health and ecosystem function. It also plays a key role in the climate system, and the development and persistence of extreme weather events such as droughts, floods and heatwaves. However, sparse and uneven observations have made it difficult to quantify the global distribution and dynamics of surface soil moisture. Here we introduce a metric of soil moisture memory and use a full year of global observations from NASA's Soil Moisture Active Passive mission to show that surface soil moisture--a storage believed to make up less than 0.001% of the global freshwater budget by volume, and equivalent to an, on average, 8-mm thin layer of water covering all land surfaces--plays a significant role in the water cycle. Specifically, we find that surface soil moisture retains a median 14% of precipitation falling on land after three days. Furthermore, the retained fraction of the surface soil moisture storage after three days is highest over arid regions, and in regions where drainage to groundwater storage is lowest. We conclude that lower groundwater storage in these regions is due not only to lower precipitation, but also to the complex partitioning of the water cycle by the surface soil moisture storage layer at the land surface.

"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.

A global dataset of crowdsourced land cover and land use reference data.

PubMed

Fritz, Steffen; See, Linda; Perger, Christoph; McCallum, Ian; Schill, Christian; Schepaschenko, Dmitry; Duerauer, Martina; Karner, Mathias; Dresel, Christopher; Laso-Bayas, Juan-Carlos; Lesiv, Myroslava; Moorthy, Inian; Salk, Carl F; Danylo, Olha; Sturn, Tobias; Albrecht, Franziska; You, Liangzhi; Kraxner, Florian; Obersteiner, Michael

2017-06-13

Global land cover is an essential climate variable and a key biophysical driver for earth system models. While remote sensing technology, particularly satellites, have played a key role in providing land cover datasets, large discrepancies have been noted among the available products. Global land use is typically more difficult to map and in many cases cannot be remotely sensed. In-situ or ground-based data and high resolution imagery are thus an important requirement for producing accurate land cover and land use datasets and this is precisely what is lacking. Here we describe the global land cover and land use reference data derived from the Geo-Wiki crowdsourcing platform via four campaigns. These global datasets provide information on human impact, land cover disagreement, wilderness and land cover and land use. Hence, they are relevant for the scientific community that requires reference data for global satellite-derived products, as well as those interested in monitoring global terrestrial ecosystems in general.

A global dataset of crowdsourced land cover and land use reference data

PubMed Central

Fritz, Steffen; See, Linda; Perger, Christoph; McCallum, Ian; Schill, Christian; Schepaschenko, Dmitry; Duerauer, Martina; Karner, Mathias; Dresel, Christopher; Laso-Bayas, Juan-Carlos; Lesiv, Myroslava; Moorthy, Inian; Salk, Carl F.; Danylo, Olha; Sturn, Tobias; Albrecht, Franziska; You, Liangzhi; Kraxner, Florian; Obersteiner, Michael

2017-01-01

Global land cover is an essential climate variable and a key biophysical driver for earth system models. While remote sensing technology, particularly satellites, have played a key role in providing land cover datasets, large discrepancies have been noted among the available products. Global land use is typically more difficult to map and in many cases cannot be remotely sensed. In-situ or ground-based data and high resolution imagery are thus an important requirement for producing accurate land cover and land use datasets and this is precisely what is lacking. Here we describe the global land cover and land use reference data derived from the Geo-Wiki crowdsourcing platform via four campaigns. These global datasets provide information on human impact, land cover disagreement, wilderness and land cover and land use. Hence, they are relevant for the scientific community that requires reference data for global satellite-derived products, as well as those interested in monitoring global terrestrial ecosystems in general. PMID:28608851

Relating Nimbus-7 37 GHz data to global land-surface evaporation, primary productivity and the atmospheric CO2 concentration

NASA Technical Reports Server (NTRS)

Choudhury, B. J.

1988-01-01

Global observations at 37 GHz by the Nimbus-7 SMMR are related to zonal variations of land surface evaporation and primary productivity, as well as to temporal variations of atmospheric CO2 concentration. The temporal variation of CO2 concentration and the zonal variations of evaporation and primary productivity are shown to be highly correlated with the satellite sensor data. The potential usefulness of the 37-GHz data for global biospheric and climate studies is noted.

Impact of fire on global land surface air temperature and energy budget for the 20th century due to changes within ecosystems

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

Li, Fang; Lawrence, David M.; Bond-Lamberty, Ben

Fire is a global phenomenon and tightly interacts with the biosphere and climate. This study provides the first quantitative assessment of fire’s influence on the global land air temperature during the 20th century through its impact on terrestrial ecosystems. We quantify the impact of fire by comparing 20th century fire-on and fire-off simulations with the Community Earth System Model (CESM) as the model platform. Here, results show that fire-induced changes in terrestrial ecosystems increased global land surface air temperature by 0.04 °C. Such changes significantly warmed the tropical savannas and southern Asia mainly by reducing latent heat flux, but cooledmore » Southeast China by enhancing the East Asian winter monsoon. 20% of the early 20th century global land warming can be attributed to fire-induced changes in terrestrial ecosystems, providing a new mechanism for explaining the poorly-understood climate change.« less

Impact of fire on global land surface air temperature and energy budget for the 20th century due to changes within ecosystems

DOE PAGES

Li, Fang; Lawrence, David M.; Bond-Lamberty, Ben

2017-04-03

Fire is a global phenomenon and tightly interacts with the biosphere and climate. This study provides the first quantitative assessment of fire’s influence on the global land air temperature during the 20th century through its impact on terrestrial ecosystems. We quantify the impact of fire by comparing 20th century fire-on and fire-off simulations with the Community Earth System Model (CESM) as the model platform. Here, results show that fire-induced changes in terrestrial ecosystems increased global land surface air temperature by 0.04 °C. Such changes significantly warmed the tropical savannas and southern Asia mainly by reducing latent heat flux, but cooledmore » Southeast China by enhancing the East Asian winter monsoon. 20% of the early 20th century global land warming can be attributed to fire-induced changes in terrestrial ecosystems, providing a new mechanism for explaining the poorly-understood climate change.« less

A Meta-Analysis of Global Urban Land Expansion

PubMed Central

Seto, Karen C.; Fragkias, Michail; Güneralp, Burak; Reilly, Michael K.

2011-01-01

The conversion of Earth's land surface to urban uses is one of the most irreversible human impacts on the global biosphere. It drives the loss of farmland, affects local climate, fragments habitats, and threatens biodiversity. Here we present a meta-analysis of 326 studies that have used remotely sensed images to map urban land conversion. We report a worldwide observed increase in urban land area of 58,000 km2 from 1970 to 2000. India, China, and Africa have experienced the highest rates of urban land expansion, and the largest change in total urban extent has occurred in North America. Across all regions and for all three decades, urban land expansion rates are higher than or equal to urban population growth rates, suggesting that urban growth is becoming more expansive than compact. Annual growth in GDP per capita drives approximately half of the observed urban land expansion in China but only moderately affects urban expansion in India and Africa, where urban land expansion is driven more by urban population growth. In high income countries, rates of urban land expansion are slower and increasingly related to GDP growth. However, in North America, population growth contributes more to urban expansion than it does in Europe. Much of the observed variation in urban expansion was not captured by either population, GDP, or other variables in the model. This suggests that contemporary urban expansion is related to a variety of factors difficult to observe comprehensively at the global level, including international capital flows, the informal economy, land use policy, and generalized transport costs. Using the results from the global model, we develop forecasts for new urban land cover using SRES Scenarios. Our results show that by 2030, global urban land cover will increase between 430,000 km2 and 12,568,000 km2, with an estimate of 1,527,000 km2 more likely. PMID:21876770

Landsat: a global land imaging program

USGS Publications Warehouse

Byrnes, Raymond A.

2012-01-01

Landsat satellites have continuously acquired space-based images of the Earth's land surface, coastal shallows, and coral reefs across four decades. The Landsat Program, a joint effort of the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA), was established to routinely gather land imagery from space. In practice, NASA develops remote-sensing instruments and spacecraft, launches satellites, and validates their performance. The USGS then assumes ownership and operation of the satellites, in addition to managing all ground-data reception, archiving, product generation, and distribution. The result of this program is a visible, long-term record of natural and human-induced changes on the global landscape.

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.

Global land cover mapping using Earth observation satellite data: Recent progresses and challenges

NASA Astrophysics Data System (ADS)

Ban, Yifang; Gong, Peng; Giri, Chandra

2015-05-01

Land cover is an important variable for many studies involving the Earth surface, such as climate, food security, hydrology, soil erosion, atmospheric quality, conservation biology, and plant functioning. Land cover not only changes with human caused land use changes, but also changes with nature. Therefore, the state of land cover is highly dynamic. In winter snow shields underneath various other land cover types in higher latitudes. Floods may persist for a long period in a year over low land areas in the tropical and subtropical regions. Forest maybe burnt or clear cut in a few days and changes to bare land. Within several months, the coverage of crops may vary from bare land to nearly 100% crops and then back to bare land following harvest. The highly dynamic nature of land cover creates a challenge in mapping and monitoring which remains to be adequately addressed. As economic globalization continues to intensify, there is an increasing trend of land cover/land use change, environmental pollution, land degradation, biodiversity loss at the global scale, timely and reliable information on global land cover and its changes is urgently needed to mitigate the negative impact of global environment change.

Land Surface Modeling and Data Assimilation to Support Physical Precipitation Retrievals for GPM

NASA Technical Reports Server (NTRS)

Peters-Lidard, Christa D.; Tian. Yudong; Kumar, Sujay; Geiger, James; Choudhury, Bhaskar

2010-01-01

Objective: The objective of this proposal is to provide a routine land surface modeling and data assimilation capability for GPM in order to provide global land surface states that are necessary to support physical precipitation retrieval algorithms over land. It is well-known that surface emission, particularly over the range of frequencies to be included in GPM, is sensitive to land surface states, including soil properties, vegetation type and greenness, soil moisture, surface temperature, and snow cover, density, and grain size. Therefore, providing a robust capability to routinely provide these critical land states is essential to support GPM-era physical retrieval algorithms over land.

Land cover characterization and land surface parameterization research

USGS Publications Warehouse

Steyaert, Louis T.; Loveland, Thomas R.; Parton, William J.

1997-01-01

The understanding of land surface processes and their parameterization in atmospheric, hydrologic, and ecosystem models has been a dominant research theme over the past decade. For example, many studies have demonstrated the key role of land cover characteristics as controlling factors in determining land surface processes, such as the exchange of water, energy, carbon, and trace gases between the land surface and the lower atmosphere. The requirements for multiresolution land cover characteristics data to support coupled-systems modeling have also been well documented, including the need for data on land cover type, land use, and many seasonally variable land cover characteristics, such as albedo, leaf area index, canopy conductance, surface roughness, and net primary productivity. Recently, the developers of land data have worked more closely with the land surface process modelers in these efforts.

Human Land-Use Practices Lead to Global Long-Term Increases in Photosynthetic Capacity

NASA Technical Reports Server (NTRS)

Mueller, Thomas; Tucker, Compton J.; Dressler, Gunnar; Pinzon, Jorge E.; Leimgruber, Peter; Dubayah, Ralph O.; Hurtt, George C.; Boehning-Gaese, Katrin; Fagan, William F.

2014-01-01

Long-term trends in photosynthetic capacity measured with the satellite-derived Normalized Difference Vegetation Index (NDVI) are usually associated with climate change. Human impacts on the global land surface are typically not accounted for. Here, we provide the first global analysis quantifying the effect of the earth's human footprint on NDVI trends. Globally, more than 20% of the variability in NDVI trends was explained by anthropogenic factors such as land use, nitrogen fertilization, and irrigation. Intensely used land classes, such as villages, showed the greatest rates of increase in NDVI, more than twice than those of forests. These findings reveal that factors beyond climate influence global long-term trends in NDVI and suggest that global climate change models and analyses of primary productivity should incorporate land use effects.

Method and Early Results of Applying the Global Land Data Assimilation System (GLDAS) in the Third Global Reanalysis of NCEP

NASA Astrophysics Data System (ADS)

Meng, J.; Mitchell, K.; Wei, H.; Yang, R.; Kumar, S.; Geiger, J.; Xie, P.

2008-05-01

Over the past several years, the Environmental Modeling Center (EMC) of the National Centers for Environmental Prediction (NCEP) of the U.S. National Weather Service has developed a Global Land Data Assimilation System (GLDAS). For its computational infrastructure, the GLDAS applies the NASA Land Information System (LIS), developed by the Hydrological Science Branch of NASA Goddard Space Flight Center. The land model utilized in the NCEP GLDAS is the NCEP Noah Land Surface Model (Noah LSM). This presentation will 1) describe how the GLDAS component has been included in the development of NCEP's third global reanalysis (with special attention to the input sources of global precipitation), and 2) will present results from the GLDAS component of pilot tests of the new NCEP global reanalysis. Unlike NCEP's past two global reanalysis projects, this new NCEP global reanalysis includes both a global land data assimilation system (GLDAS) and a global ocean data assimilation system (GODAS). The new global reanalysis will span 30-years (1979-2008) and will include a companion realtime operational component. The atmospheric, ocean, and land states of this global reanalysis will provide the initial conditions for NCEP's 3rd- generation global coupled Climate Forecast System (CFS). NCEP is now preparing to launch a 28-year seasonal reforecast project with its new CFS, to provide the reforecast foundation for operational NCEP seasonal climate forecasts using the new CFS. Together, the new global reanalysis and companion CFS reforecasts constitute what NCEP calls the Climate Forecast System Reanalysis and Reforecast (CFSRR) project. Compared to the previous two generations of NCEP global reanalysis, the hallmark of the GLDAS component of CFSRR is GLDAS use of global analyses of observed precipitation to drive the land surface component of the reanalysis (rather than the typical reanalysis approach of using precipitation from the assimilating background atmospheric model

Historical Landsat data comparisons: illustrations of land surface change

USGS Publications Warehouse

Cross, Matthew D.

1990-01-01

This booklet provides an overview of the Landsat program and shows the application of the data to monitor changes occurring on the surface of the Earth. To show changes that have taken place within the last 20 years or less, image pairs were constructed from the Landsat multispectral scanner (MSS) and thematic mapper (TM) sensors. Landsat MSS data provide a historical global record of the land surface from the early 1970's to present. Landsat TM data provide land surface information from the early 1980's to present.

Land Surface Precipitation and Hydrology in MERRA-2

NASA Technical Reports Server (NTRS)

Reichle, R.; Koster, R.; Draper, C.; Liu, Q.; Girotto, M.; Mahanama, S.; De Lannoy, G.; Partyka, G.

2017-01-01

The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), provides global, 1-hourly estimates of land surface conditions for 1980-present at 50-km resolution. Outside of the high latitudes, MERRA-2 uses observations-based precipitation data products to correct the precipitation falling on the land surface. This paper describes the precipitation correction method and evaluates the MERRA-2 land surface precipitation and hydrology. Compared to monthly GPCPv2.2 observations, the corrected MERRA-2 precipitation (M2CORR) is better than the precipitation generated by the atmospheric models within the cyclingMERRA-2 system and the earlier MERRA reanalysis. Compared to 3-hourlyTRMM observations, the M2CORR diurnal cycle has better amplitude but less realistic phasing than MERRA-2 model-generated precipitation. Because correcting the precipitation within the coupled atmosphere-land modeling system allows the MERRA-2 near-surface air temperature and humidity to respond to the improved precipitation forcing, MERRA-2 provides more self-consistent surface meteorological data than were available from the earlier, offline MERRA-Land reanalysis. Overall, MERRA-2 land hydrology estimates are better than those of MERRA-Land and MERRA. A comparison against GRACE satellite observations of terrestrial water storage demonstrates clear improvements in MERRA-2 over MERRA in South America and Africa but also reflects known errors in the observations used to correct the MERRA-2 precipitation. The MERRA-2 and MERRA-Land surface and root zone soil moisture skill vs. in situ measurements is slightly higher than that of ERA-Interim Land and higher than that of MERRA (significantly for surface soil moisture). Snow amounts from MERRA-2 have lower bias and correlate better against reference data than do those of MERRA-Land and MERRA, with MERRA-2 skill roughly matching that of ERA-Interim Land. Seasonal anomaly R values against naturalized stream flow measurements in

Recent Development on the NOAA's Global Surface Temperature Dataset

NASA Astrophysics Data System (ADS)

Zhang, H. M.; Huang, B.; Boyer, T.; Lawrimore, J. H.; Menne, M. J.; Rennie, J.

2016-12-01

Global Surface Temperature (GST) is one of the most widely used indicators for climate trend and extreme analyses. A widely used GST dataset is the NOAA merged land-ocean surface temperature dataset known as NOAAGlobalTemp (formerly MLOST). The NOAAGlobalTemp had recently been updated from version 3.5.4 to version 4. The update includes a significant improvement in the ocean surface component (Extended Reconstructed Sea Surface Temperature or ERSST, from version 3b to version 4) which resulted in an increased temperature trends in recent decades. Since then, advancements in both the ocean component (ERSST) and land component (GHCN-Monthly) have been made, including the inclusion of Argo float SSTs and expanded EOT modes in ERSST, and the use of ISTI databank in GHCN-Monthly. In this presentation, we describe the impact of those improvements on the merged global temperature dataset, in terms of global trends and other aspects.

Development of an Independent Global Land Cover Validation Dataset

NASA Astrophysics Data System (ADS)

Sulla-Menashe, D. J.; Olofsson, P.; Woodcock, C. E.; Holden, C.; Metcalfe, M.; Friedl, M. A.; Stehman, S. V.; Herold, M.; Giri, C.

2012-12-01

Accurate information related to the global distribution and dynamics in global land cover is critical for a large number of global change science questions. A growing number of land cover products have been produced at regional to global scales, but the uncertainty in these products and the relative strengths and weaknesses among available products are poorly characterized. To address this limitation we are compiling a database of high spatial resolution imagery to support international land cover validation studies. Validation sites were selected based on a probability sample, and may therefore be used to estimate statistically defensible accuracy statistics and associated standard errors. Validation site locations were identified using a stratified random design based on 21 strata derived from an intersection of Koppen climate classes and a population density layer. In this way, the two major sources of global variation in land cover (climate and human activity) are explicitly included in the stratification scheme. At each site we are acquiring high spatial resolution (< 1-m) satellite imagery for 5-km x 5-km blocks. The response design uses an object-oriented hierarchical legend that is compatible with the UN FAO Land Cover Classification System. Using this response design, we are classifying each site using a semi-automated algorithm that blends image segmentation with a supervised RandomForest classification algorithm. In the long run, the validation site database is designed to support international efforts to validate land cover products. To illustrate, we use the site database to validate the MODIS Collection 4 Land Cover product, providing a prototype for validating the VIIRS Surface Type Intermediate Product scheduled to start operational production early in 2013. As part of our analysis we evaluate sources of error in coarse resolution products including semantic issues related to the class definitions, mixed pixels, and poor spectral separation between

Land-surface influences on weather and climate

NASA Technical Reports Server (NTRS)

Baer, F.; Mintz, Y.

1984-01-01

Land-surface influences on weather and climate are reviewed. The interrelationship of vegetation, evapotranspiration, atmospheric circulation, and climate is discussed. Global precipitation, soil moisture, the seasonal water cycle, heat transfer, and atmospheric temperature are among the parameters considered in the context of a general biosphere model.

Landsat: A Global Land-Imaging Project

USGS Publications Warehouse

Headley, Rachel

2010-01-01

Across nearly four decades since 1972, Landsat satellites continuously have acquired space-based images of the Earth's land surface, coastal shallows, and coral reefs. The Landsat Program, a joint effort of the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA), was established to routinely gather land imagery from space; consequently, NASA develops remote-sensing instruments and spacecraft, then launches and validates the satellites. The USGS then assumes ownership and operation of the satellites, in addition to managing all ground-data reception, archiving, product generation, and distribution. The result of this program is a visible, long-term record of natural and human-induced changes on the global landscape.

Landsat: A global land-imaging mission

USGS Publications Warehouse

,

2012-01-01

Across four decades since 1972, Landsat satellites have continuously acquired space-based images of the Earth's land surface, coastal shallows, and coral reefs. The Landsat Program, a joint effort of the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA), was established to routinely gather land imagery from space. NASA develops remote-sensing instruments and spacecraft, then launches and validates the performance of the instruments and satellites. The USGS then assumes ownership and operation of the satellites, in addition to managing all ground reception, data archiving, product generation, and distribution. The result of this program is a long-term record of natural and human induced changes on the global landscape.

Global, long-term surface reflectance records from Landsat

USDA-ARS?s Scientific Manuscript database

Global, long-term monitoring of changes in Earth’s land surface requires quantitative comparisons of satellite images acquired under widely varying atmospheric conditions. Although physically based estimates of surface reflectance (SR) ultimately provide the most accurate representation of Earth’s s...

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

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

The Potential Radiative Forcing of Global Land Use and Land Cover Change Activities

NASA Astrophysics Data System (ADS)

Ward, D. S.; Mahowald, N. M.; Kloster, S.

2014-12-01

Given the expected increase in pressure on land resources over the next century, there is a need to understand the total impacts of activities associated with land use and land cover change (LULCC). Here we quantify these impacts using the radiative forcing metric, including forcings from changes in long-lived greenhouse gases, tropospheric ozone, aerosol effects, and land surface albedo. We estimate radiative forcings from the different agents for historical LULCC and for six future projections using simulations from the National Center for Atmospheric Research Community Land Model and Community Atmosphere Models and additional offline analyses. When all forcing agents are considered together we show that 45% (+30%, -20%) of the present-day (2010) anthropogenic radiative forcing can be attributed to LULCC. Changes in the emission of non-CO2 greenhouse gases and aerosols from LULCC enhance the total LULCC radiative forcing by a factor of 2 to 3 with respect to the forcing from CO2 alone. In contrast, the non-CO2 forcings from fossil fuel burning are roughly neutral, due largely to the negative (cooling) impact of aerosols from these sources. We partition the global LULCC radiative forcing into three major sources: direct modification of land cover (e.g. deforestation), agricultural activities, and fire regime changes. Contributions from deforestation and agriculture are roughly equal in the present day, while changes to wildfire activity impose a small negative forcing globally. In 2100, deforestation activities comprise the majority of the LULCC radiative forcing for all projections except one (Representative Concentration Pathway (RCP) 4.5). This suggests that realistic scenarios of future forest area change are essential for projecting the contribution of LULCC to climate change. However, the commonly used RCP land cover change projections all include decreases in global deforestation rates over the next 85 years. To place an upper bound on the potential

TThe role of nitrogen availability in land-atmosphere interactions: a systematic evaluation of carbon-nitrogen coupling in a global land surface model using plot-level nitrogen fertilization experiments

NASA Astrophysics Data System (ADS)

Thomas, R. Q.; Goodale, C. L.; Bonan, G. B.; Mahowald, N. M.; Ricciuto, D. M.; Thornton, P. E.

2010-12-01

Recent research from global land surface models emphasizes the important role of nitrogen cycling on global climate, via its control on the terrestrial carbon balance. Despite the implications of nitrogen cycling on global climate predictions, the research community has not performed a systematic evaluation of nitrogen cycling in global models. Here, we present such an evaluation for one global land model, CLM-CN. In the evaluation we simulated 45 plot-scale nitrogen-fertilization experiments distributed across 33 temperate and boreal forest sites. Model predictions were evaluated against field observations by comparing the vegetation and soil carbon responses to the additional nitrogen. Aggregated across all experiments, the model predicted a larger vegetation carbon response and a smaller soil carbon response than observed; the responses partially offset each other, leading to a slightly larger total ecosystem carbon response than observed. However, the model-observation agreement improved for vegetation carbon when the sites with observed negative carbon responses to nitrogen were excluded, which may be because the model lacks mechanisms whereby nitrogen additions increase tree mortality. Among experiments, younger forests and boreal forests’ vegetation carbon responses were less than predicted and mature forests (> 40 years old) were greater than predicted. Specific to the CLM-CN, this study used a systematic evaluation to identify key areas to focus model development, especially soil carbon- nitrogen interactions and boreal forest nitrogen cycling. Applicable to the modeling community, this study demonstrates a standardized protocol for comparing carbon-nitrogen interactions among global land models.

Modeling large-scale human alteration of land surface hydrology and climate

NASA Astrophysics Data System (ADS)

Pokhrel, Yadu N.; Felfelani, Farshid; Shin, Sanghoon; Yamada, Tomohito J.; Satoh, Yusuke

2017-12-01

Rapidly expanding human activities have profoundly affected various biophysical and biogeochemical processes of the Earth system over a broad range of scales, and freshwater systems are now amongst the most extensively altered ecosystems. In this study, we examine the human-induced changes in land surface water and energy balances and the associated climate impacts using a coupled hydrological-climate model framework which also simulates the impacts of human activities on the water cycle. We present three sets of analyses using the results from two model versions—one with and the other without considering human activities; both versions are run in offline and coupled mode resulting in a series of four experiments in total. First, we examine climate and human-induced changes in regional water balance focusing on the widely debated issue of the desiccation of the Aral Sea in central Asia. Then, we discuss the changes in surface temperature as a result of changes in land surface energy balance due to irrigation over global and regional scales. Finally, we examine the global and regional climate impacts of increased atmospheric water vapor content due to irrigation. Results indicate that the direct anthropogenic alteration of river flow in the Aral Sea basin resulted in the loss of 510 km3 of water during the latter half of the twentieth century which explains about half of the total loss of water from the sea. Results of irrigation-induced changes in surface energy balance suggest a significant surface cooling of up to 3.3 K over 1° grids in highly irrigated areas but a negligible change in land surface temperature when averaged over sufficiently large global regions. Results from the coupled model indicate a substantial change in 2 m air temperature and outgoing longwave radiation due to irrigation, highlighting the non-local (regional and global) implications of irrigation. These results provide important insights on the direct human alteration of land surface

Dryland photoautotrophic soil surface communities endangered by global change

USGS Publications Warehouse

Rodriguez-Caballero, Emilio; Belnap, Jayne; Büdel, Burkhard; Crutzen, Paul J.; Andreae, Meinrat O.; Pöschl, Ulrich; Weber, Bettina

2018-01-01

Photoautotrophic surface communities forming biological soil crusts (biocrusts) are crucial for soil stability as well as water, nutrient and trace gas cycling at regional and global scales. Quantitative information on their global coverage and the environmental factors driving their distribution patterns, however, are not readily available. We use observations and environmental modelling to estimate the global distribution of biocrusts and their response to global change using future projected scenarios. We find that biocrusts currently covering approximately 12% of Earth’s terrestrial surface will decrease by about 25–40% within 65 years due to anthropogenically caused climate change and land-use intensification, responding far more drastically than vascular plants. Our results illustrate that current biocrust occurrence is mainly driven by a combination of precipitation, temperature and land management, and future changes are expected to be affected by land-use and climate change in similar proportion. The predicted loss of biocrusts may substantially reduce the microbial contribution to nitrogen cycling and enhance the emissions of soil dust, which affects the functioning of ecosystems as well as human health and should be considered in the modelling, mitigation and management of global change.

Dryland photoautotrophic soil surface communities endangered by global change

NASA Astrophysics Data System (ADS)

Rodriguez-Caballero, Emilio; Belnap, Jayne; Büdel, Burkhard; Crutzen, Paul J.; Andreae, Meinrat O.; Pöschl, Ulrich; Weber, Bettina

2018-03-01

Photoautotrophic surface communities forming biological soil crusts (biocrusts) are crucial for soil stability as well as water, nutrient and trace gas cycling at regional and global scales. Quantitative information on their global coverage and the environmental factors driving their distribution patterns, however, are not readily available. We use observations and environmental modelling to estimate the global distribution of biocrusts and their response to global change using future projected scenarios. We find that biocrusts currently covering approximately 12% of Earth's terrestrial surface will decrease by about 25-40% within 65 years due to anthropogenically caused climate change and land-use intensification, responding far more drastically than vascular plants. Our results illustrate that current biocrust occurrence is mainly driven by a combination of precipitation, temperature and land management, and future changes are expected to be affected by land-use and climate change in similar proportion. The predicted loss of biocrusts may substantially reduce the microbial contribution to nitrogen cycling and enhance the emissions of soil dust, which affects the functioning of ecosystems as well as human health and should be considered in the modelling, mitigation and management of global change.

Verification of land-atmosphere coupling in forecast models, reanalyses and land surface models using flux site observations.

PubMed

Dirmeyer, Paul A; Chen, Liang; Wu, Jiexia; Shin, Chul-Su; Huang, Bohua; Cash, Benjamin A; Bosilovich, Michael G; Mahanama, Sarith; Koster, Randal D; Santanello, Joseph A; Ek, Michael B; Balsamo, Gianpaolo; Dutra, Emanuel; Lawrence, D M

2018-02-01

We confront four model systems in three configurations (LSM, LSM+GCM, and reanalysis) with global flux tower observations to validate states, surface fluxes, and coupling indices between land and atmosphere. Models clearly under-represent the feedback of surface fluxes on boundary layer properties (the atmospheric leg of land-atmosphere coupling), and may over-represent the connection between soil moisture and surface fluxes (the terrestrial leg). Models generally under-represent spatial and temporal variability relative to observations, which is at least partially an artifact of the differences in spatial scale between model grid boxes and flux tower footprints. All models bias high in near-surface humidity and downward shortwave radiation, struggle to represent precipitation accurately, and show serious problems in reproducing surface albedos. These errors create challenges for models to partition surface energy properly and errors are traceable through the surface energy and water cycles. The spatial distribution of the amplitude and phase of annual cycles (first harmonic) are generally well reproduced, but the biases in means tend to reflect in these amplitudes. Interannual variability is also a challenge for models to reproduce. Our analysis illuminates targets for coupled land-atmosphere model development, as well as the value of long-term globally-distributed observational monitoring.

Atmospheric sensitivity to land surface changes: comparing the impact of albedo, roughness, and evaporative resistance on near-surface air temperature using an idealized land model.

NASA Astrophysics Data System (ADS)

Lague, M. M.; Swann, A. L. S.; Bonan, G. B.

2017-12-01

Past studies have demonstrated how changes in vegetation can impact the atmosphere; however, it is often difficult to identify the exact physical pathway through which vegetation changes drive an atmospheric response. Surface properties (such as vegetation color, or height) control surface energy fluxes, which feed back on the atmosphere on both local and global scales by modifying temperatures, cloud cover, and energy gradients. Understanding how land surface properties influence energy fluxes is crucial for improving our understanding of how vegetation change - past, present, and future - impacts the atmosphere, global climate, and people. We explore the sensitivity of the atmosphere to perturbations of three land surface properties - albedo, roughness, and evaporative resistance - using an idealized land model coupled to an Earth System Model. We derive a relationship telling us how large a change in each surface property is required to drive a local 0.1 K change in 2m air temperature. Using this idealized framework, we are able to separate the influence on the atmosphere of each individual surface property. We demonstrate that the impact of each surface property on the atmosphere is spatially variable - that is, a similar change in vegetation can have different climate impacts if made in different locations. This analysis not only improves our understanding of how the land system can influence climate, but also provides us with a set of theoretical limits on the potential climate impact of arbitrary vegetation change (natural or anthropogenic).

Analysis of Multiple Precipitation Products and Preliminary Assessment of Their Impact on Global Land Data Assimilation System (GLDAS) Land Surface States

NASA Technical Reports Server (NTRS)

Gottschalck, Jon; Meng, Jesse; Rodel, Matt; Houser, paul

2005-01-01

Land surface models (LSMs) are computer programs, similar to weather and climate prediction models, which simulate the stocks and fluxes of water (including soil moisture, snow, evaporation, and runoff) and energy (including the temperature of and sensible heat released from the soil) after they arrive on the land surface as precipitation and sunlight. It is not currently possible to measure all of the variables of interest everywhere on Earth with sufficient accuracy and space-time resolution. Hence LSMs have been developed to integrate the available observations with our understanding of the physical processes involved, using powerful computers, in order to map these stocks and fluxes as they change in time. The maps are used to improve weather forecasts, support water resources and agricultural applications, and study the Earth's water cycle and climate variability. NASA's Global Land Data Assimilation System (GLDAS) project facilitates testing of several different LSMs with a variety of input datasets (e.g., precipitation, plant type). Precipitation is arguably the most important input to LSMs. Many precipitation datasets have been produced using satellite and rain gauge observations and weather forecast models. In this study, seven different global precipitation datasets were evaluated over the United States, where dense rain gauge networks contribute to reliable precipitation maps. We then used the seven datasets as inputs to GLDAS simulations, so that we could diagnose their impacts on output stocks and fluxes of water. In terms of totals, the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) had the closest agreement with the US rain gauge dataset for all seasons except winter. The CMAP precipitation was also the most closely correlated in time with the rain gauge data during spring, fall, and winter, while the satellitebased estimates performed best in summer. The GLDAS simulations revealed that modeled soil moisture is highly

Surface water change as a significant contributor to global evapotranspiration change

NASA Astrophysics Data System (ADS)

Zhan, S.; Song, C.

2017-12-01

Water comprises a critical component of global/regional hydrological and biogeochemical cycles and is essential to all organisms including humans. In the past several decades, climate change has intensified the hydrological cycle, with significant implications for ecosystem services and feedback to regional and global climate. Evapotranspiration (ET) as a linking mechanism between land surface and atmosphere is central to the water cycle and an excellent indicator of the intensity of water cycle. Knowledge of the temporal changes of ET is crucial for accurately estimating global or regional water budgets and better understanding climate and hydrological interactions. While studies have examined changes in global ET, they were conducted using a constant land and surface water (SW) area. However, as many studies have found that global SW is very dynamic and their surface areas have generally been increasing since the 1980s. The conversion from land to water and vice versa significantly changes the local ET since water bodies evaporate at a rate that can be much higher than that of the land. Here, we quantify the global changes in ET caused by such land-water conversion using remotely-sensed SW area and various ET and potential ET products. New SW and lost SW between circa-1985 and circa-2015 were derived from remote sensing and were used to modify the local ET estimates. We found an increase in ET in all continents as consistent with the net increase in SW area. The increasing SW area lead to a global increase in ET by 30.38 ± 5.28 km3/yr. This is a significant contribution when compared to the 92.95 km3/yr/yr increase in ET between 1982-1997 and 103.43 km3/yr/yr decrease between 1998-2008 by Jung et al., (2010) assuming a constant SW. The results enhance our understanding of the water fluxes between the land and atmosphere and supplement land water budget estimates. We conclude that changes in SW lead to a significant change in global ET that cannot be neglected in

A new integrated and homogenized global monthly land surface air temperature dataset for the period since 1900

NASA Astrophysics Data System (ADS)

Xu, Wenhui; Li, Qingxiang; Jones, Phil; Wang, Xiaolan L.; Trewin, Blair; Yang, Su; Zhu, Chen; Zhai, Panmao; Wang, Jinfeng; Vincent, Lucie; Dai, Aiguo; Gao, Yun; Ding, Yihui

2018-04-01

A new dataset of integrated and homogenized monthly surface air temperature over global land for the period since 1900 [China Meteorological Administration global Land Surface Air Temperature (CMA-LSAT)] is developed. In total, 14 sources have been collected and integrated into the newly developed dataset, including three global (CRUTEM4, GHCN, and BEST), three regional and eight national sources. Duplicate stations are identified, and those with the higher priority are chosen or spliced. Then, a consistency test and a climate outlier test are conducted to ensure that each station series is quality controlled. Next, two steps are adopted to assure the homogeneity of the station series: (1) homogenized station series in existing national datasets (by National Meteorological Services) are directly integrated into the dataset without any changes (50% of all stations), and (2) the inhomogeneities are detected and adjusted for in the remaining data series using a penalized maximal t test (50% of all stations). Based on the dataset, we re-assess the temperature changes in global and regional areas compared with GHCN-V3 and CRUTEM4, as well as the temperature changes during the three periods of 1900-2014, 1979-2014 and 1998-2014. The best estimates of warming trends and there 95% confidence ranges for 1900-2014 are approximately 0.102 ± 0.006 °C/decade for the whole year, and 0.104 ± 0.009, 0.112 ± 0.007, 0.090 ± 0.006, and 0.092 ± 0.007 °C/decade for the DJF (December, January, February), MAM, JJA, and SON seasons, respectively. MAM saw the most significant warming trend in both 1900-2014 and 1979-2014. For an even shorter and more recent period (1998-2014), MAM, JJA and SON show similar warming trends, while DJF shows opposite trends. The results show that the ability of CMA-LAST for describing the global temperature changes is similar with other existing products, while there are some differences when describing regional temperature changes.

Food Footprints: Global diet preferences and the land required to sustain them

NASA Astrophysics Data System (ADS)

Cassidy, E. S.; Gerber, J. S.; Foley, J. A.

2011-12-01

Agricultural land occupies approximately 4.9 billion hectares of the earth's surface. The amount of land that is required to feed a person differs globally, however, dependent mainly on diet. Diets dense in grain-fed animal protein require more land than plant-based diets in order to supply the same quantity of calories and protein. As the world's population becomes more affluent, more animal products will be demanded of the food system. In this presentation, I will discuss how diet preferences differ globally and how these preferences translate to the amount of cropland needed to sustain them.

The 1 km AVHRR global land data set: first stages in implementation

USGS Publications Warehouse

Eidenshink, J.C.; Faundeen, J.L.

1994-01-01

The global land 1 km data set project represents an international effort to acquire, archive, process, and distribute 1 km AVHRR data of the entire global land surface in order to meet the needs of the international science community. A network of 26 high resolution picture transmission (HRPT) stations, along with data recorded by the National Oceanic and Atmospheric Administration (NOAA), has been acquiring daily global land coverage since 1 April 1992. A data set of over 30000 AVHRR images has been archived and made available for distribution by the United States Geological Survey, EROS Data Center and the European Space Agency. Under the guidance of the International Geosphere Biosphere programme, processing standards for the AVHRR data have been developed for calibration, atmospheric correction, geometric registration, and the production of global 10-day maximum normalized difference vegetation index (NDVI) composites. The major uses of the composites are related to the study of surface vegetation cover. A prototype 10-day composite was produced for the period of 21–30 June 1992. Production of an 18-month time series of 10-day composites is underway.

Recent decline in the global land evapotranspiration trend due to limited moisture supply.

PubMed

Jung, Martin; Reichstein, Markus; Ciais, Philippe; Seneviratne, Sonia I; Sheffield, Justin; Goulden, Michael L; Bonan, Gordon; Cescatti, Alessandro; Chen, Jiquan; de Jeu, Richard; Dolman, A Johannes; Eugster, Werner; Gerten, Dieter; Gianelle, Damiano; Gobron, Nadine; Heinke, Jens; Kimball, John; Law, Beverly E; Montagnani, Leonardo; Mu, Qiaozhen; Mueller, Brigitte; Oleson, Keith; Papale, Dario; Richardson, Andrew D; Roupsard, Olivier; Running, Steve; Tomelleri, Enrico; Viovy, Nicolas; Weber, Ulrich; Williams, Christopher; Wood, Eric; Zaehle, Sönke; Zhang, Ke

2010-10-21

More than half of the solar energy absorbed by land surfaces is currently used to evaporate water. Climate change is expected to intensify the hydrological cycle and to alter evapotranspiration, with implications for ecosystem services and feedback to regional and global climate. Evapotranspiration changes may already be under way, but direct observational constraints are lacking at the global scale. Until such evidence is available, changes in the water cycle on land−a key diagnostic criterion of the effects of climate change and variability−remain uncertain. Here we provide a data-driven estimate of global land evapotranspiration from 1982 to 2008, compiled using a global monitoring network, meteorological and remote-sensing observations, and a machine-learning algorithm. In addition, we have assessed evapotranspiration variations over the same time period using an ensemble of process-based land-surface models. Our results suggest that global annual evapotranspiration increased on average by 7.1 ± 1.0 millimetres per year per decade from 1982 to 1997. After that, coincident with the last major El Niño event in 1998, the global evapotranspiration increase seems to have ceased until 2008. This change was driven primarily by moisture limitation in the Southern Hemisphere, particularly Africa and Australia. In these regions, microwave satellite observations indicate that soil moisture decreased from 1998 to 2008. Hence, increasing soil-moisture limitations on evapotranspiration largely explain the recent decline of the global land-evapotranspiration trend. Whether the changing behaviour of evapotranspiration is representative of natural climate variability or reflects a more permanent reorganization of the land water cycle is a key question for earth system science.

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.

GLCF: Global Land Survey(GLS)

Science.gov Websites

Services Contact Site Map Go Global Land Survey(GLS) Data Access ESDI Download via Search and Preview Tool gls The Global Land Survey (GLS) collection of Landsat imagery is designed to meet a need from Availability: GLS 1975, 1990, 2000, and 2005 are available to the public for free at the US Geological Survey

Applications of Land Surface Temperature from Microwave Observations

USDA-ARS?s Scientific Manuscript database

Land surface temperature (LST) is a key input for physically-based retrieval algorithms of hydrological states and fluxes. Yet, it remains a poorly constrained parameter for global scale studies. The main two observational methods to remotely measure T are based on thermal infrared (TIR) observation...

Coupled land surface/hydrologic/atmospheric models

NASA Technical Reports Server (NTRS)

Pielke, Roger; Steyaert, Lou; Arritt, Ray; Lahtakia, Mercedes; Smith, Chris; Ziegler, Conrad; Soong, Su Tzai; Avissar, Roni; Wetzel, Peter; Sellers, Piers

1993-01-01

The topics covered include the following: prototype land cover characteristics data base for the conterminous United States; surface evapotranspiration effects on cumulus convection and implications for mesoscale models; the use of complex treatment of surface hydrology and thermodynamics within a mesoscale model and some related issues; initialization of soil-water content for regional-scale atmospheric prediction models; impact of surface properties on dryline and MCS evolution; a numerical simulation of heavy precipitation over the complex topography of California; representing mesoscale fluxes induced by landscape discontinuities in global climate models; emphasizing the role of subgrid-scale heterogeneity in surface-air interaction; and problems with modeling and measuring biosphere-atmosphere exchanges of energy, water, and carbon on large scales.

MEaSUREs Land Surface Temperature from GOES Satellites

NASA Astrophysics Data System (ADS)

Pinker, Rachel T.; Chen, Wen; Ma, Yingtao; Islam, Tanvir; Borbas, Eva; Hain, Chris; Hulley, Glynn; Hook, Simon

2017-04-01

Information on Land Surface Temperature (LST) can be generated from observations made from satellites in low Earth orbit (LEO) such as MODIS and ASTER and by sensors in geostationary Earth orbit (GEO) such as GOES. Under a project titled: "A Unified and Coherent Land Surface Temperature and Emissivity Earth System Data Record for Earth Science" led by Jet Propulsion Laboratory, an effort is underway to develop long term consistent information from both such systems. In this presentation we will describe an effort to derive LST information from GOES satellites. Results will be presented from two approaches: 1) based on regression developed from a wide range of simulations using MODTRAN, SeeBor Version 5.0 global atmospheric profiles and the CAMEL (Combined ASTER and MODIS Emissivity for Land) product based on the standard University of Wisconsin 5 km emissivity values (UWIREMIS) and the ASTER Global Emissivity Database (GED) product; 2) RTTOV radiative transfer model driven with MERRA-2 reanalysis fields. We will present results of evaluation of these two methods against various products, such as MOD11, and ground observations for the five year period of (2004-2008).

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.

2004-01-01

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 Aqua satellite platforms have provided researchers with unprecedented spatial, spectral, and temporal information on the land surface's radiative characteristics. Cloud cover, which cutails 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, climate models, and global change research projects. An ecosystem-dependent temporal interpolation technique is described that has been developed to fill missing or seasonally snow-covered data in the official MOD43B3 albedo product. 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. The resulting snow-free value-added products provide the scientific community with spatially and temporally complete global white- and black-sky surface albedo maps and

Managing the global land resource.

PubMed

Smith, Pete

2018-03-14

With a growing population with changing demands, competition for the global land resource is increasing. We need to feed a projected population of 9-10 billion by 2050, rising to approximately 12 billion by 2100. At the same time, we need to reduce the climate impact of agriculture, forestry and other land use, and we almost certainly need to deliver land-based greenhouse gas removal for additional climate change mitigation. In addition, we need to deliver progress towards meeting the United Nations Sustainable Development Goals, all without compromising the many ecosystem services provided by land and without exceeding planetary boundaries. Managing the land to tackle these pressing issues is a major global challenge. In this perspective paper, I provide a very broad overview of the main challenges, and explore co-benefits, trade-offs and possible solutions. © 2018 The Authors.

Managing the global land resource

PubMed Central

2018-01-01

With a growing population with changing demands, competition for the global land resource is increasing. We need to feed a projected population of 9–10 billion by 2050, rising to approximately 12 billion by 2100. At the same time, we need to reduce the climate impact of agriculture, forestry and other land use, and we almost certainly need to deliver land-based greenhouse gas removal for additional climate change mitigation. In addition, we need to deliver progress towards meeting the United Nations Sustainable Development Goals, all without compromising the many ecosystem services provided by land and without exceeding planetary boundaries. Managing the land to tackle these pressing issues is a major global challenge. In this perspective paper, I provide a very broad overview of the main challenges, and explore co-benefits, trade-offs and possible solutions. PMID:29514961

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

Long term, non-anthropogenic groundwater storage changes simulated by a global land surface model

NASA Astrophysics Data System (ADS)

Li, B.; Rodell, M.; Sheffield, J.; Wood, E. F.

2017-12-01

Groundwater is crucial for meeting agricultural, industrial and municipal water needs, especially in arid, semi-arid and drought impacted regions. Yet, knowledge on groundwater response to climate variability is not well understood due to lack of systematic and continuous in situ measurements. In this study, we investigate global non-anthropogenic groundwater storage variations with a land surface model driven by a 67-year (1948-204) meteorological forcing data set. Model estimates were evaluated using in situ groundwater data from the central and northeastern U.S. and terrestrial water storage derived from the Gravity Recovery and Climate Experiment (GRACE) satellites and found to be reasonable. Empirical orthogonal function (EOF) analysis was employed to examine modes of variability of groundwater storage and their relationship with atmospheric effects such as precipitation and evapotranspiration. The result shows that the leading mode in global groundwater storage reflects the influence of the El Niño Southern Oscillation (ENSO). Consistent with the EOF analysis, global total groundwater storage reflected the low frequency variability of ENSO and decreased significantly over 1948-2014 while global ET and precipitation did not exhibit statistically significant trends. This study suggests that while precipitation and ET are the primary drivers of climate related groundwater variability, changes in other forcing fields than precipitation and temperature are also important because of their influence on ET. We discuss the need to improve model physics and to continuously validate model estimates and forcing data for future studies.

A Modeling Approach to Global Land Surface Monitoring with Low Resolution Satellite Imaging

NASA Technical Reports Server (NTRS)

Hlavka, Christine A.; Dungan, Jennifer; Livingston, Gerry P.; Gore, Warren J. (Technical Monitor)

1998-01-01

The effects of changing land use/land cover on global climate and ecosystems due to greenhouse gas emissions and changing energy and nutrient exchange rates are being addressed by federal programs such as NASA's Mission to Planet Earth (MTPE) and by international efforts such as the International Geosphere-Biosphere Program (IGBP). The quantification of these effects depends on accurate estimates of the global extent of critical land cover types such as fire scars in tropical savannas and ponds in Arctic tundra. To address the requirement for accurate areal estimates, methods for producing regional to global maps with satellite imagery are being developed. The only practical way to produce maps over large regions of the globe is with data of coarse spatial resolution, such as Advanced Very High Resolution Radiometer (AVHRR) weather satellite imagery at 1.1 km resolution or European Remote-Sensing Satellite (ERS) radar imagery at 100 m resolution. The accuracy of pixel counts as areal estimates is in doubt, especially for highly fragmented cover types such as fire scars and ponds. Efforts to improve areal estimates from coarse resolution maps have involved regression of apparent area from coarse data versus that from fine resolution in sample areas, but it has proven difficult to acquire sufficient fine scale data to develop the regression. A method for computing accurate estimates from coarse resolution maps using little or no fine data is therefore needed.

Non-radiative processes dominate land surface signals in the climate system

NASA Astrophysics Data System (ADS)

Bright, R. M.; Davin, E.; O'Halloran, T. L.; Pongratz, J.; Zhao, K.; Cescatti, A.

2016-12-01

Perturbations to the surface energy budget linked to land cover/land management changes (LCMC) are rarely included in land-climate assessments although they have long been recognized as important drivers of local climate change. At local scales, climate forcings from LCMC depend strongly on changes to surface energy redistribution by various non-radiative mechanisms, dampening or even outweighing the local radiative effect of an albedo change. The extent to which these mechanisms are locally relevant for different types of LCMC across the world remains largely unquantified. Here, we combine extensive records of remote sensing and in-situ observations to quantify local forcings for nine common real-world LCMC perturbations, identifying their underlying physical mechanisms and analyzing their spatial patterns at the global scale. We find that throughout the densely populated regions, non-radiative forcings dominate the local surface temperature response in 8 of 9 LCMC scenarios. Further, the observed local response to re-/afforestation is an annual cooling in all regions south of the upper conterminous United States, Western Europe, and Indo-China. Given that the global response to re-/afforestation in these regions is likely a cooling, projects here can be seen as attractive mitigation measures. Our results - gridded to a 1° x 1° resolution - can be directly used to evaluate climate models or compute indicators providing a more comprehensive picture of the trade-offs between local and global climate forcings linked to land sector projects and policies.

A gridded global data set of soil, intact regolith, and sedimentary deposit thicknesses for regional and global land surface modeling

DOE PAGES

Pelletier, Jon D.; Broxton, Patrick D.; Hazenberg, Pieter; ...

2016-01-22

Earth’s terrestrial near-subsurface environment can be divided into relatively porous layers of soil, intact regolith, and sedimentary deposits above unweathered bedrock. Variations in the thicknesses of these layers control the hydrologic and biogeochemical responses of landscapes. Currently, Earth System Models approximate the thickness of these relatively permeable layers above bedrock as uniform globally, despite the fact that their thicknesses vary systematically with topography, climate, and geology. To meet the need for more realistic input data for models, we developed a high-resolution gridded global data set of the average thicknesses of soil, intact regolith, and sedimentary deposits within each 30 arcsecmore » (~ 1 km) pixel using the best available data for topography, climate, and geology as input. Our data set partitions the global land surface into upland hillslope, upland valley bottom, and lowland landscape components and uses models optimized for each landform type to estimate the thicknesses of each subsurface layer. On hillslopes, the data set is calibrated and validated using independent data sets of measured soil thicknesses from the U.S. and Europe and on lowlands using depth to bedrock observations from groundwater wells in the U.S. As a result, we anticipate that the data set will prove useful as an input to regional and global hydrological and ecosystems models.« less

A gridded global data set of soil, intact regolith, and sedimentary deposit thicknesses for regional and global land surface modeling

NASA Astrophysics Data System (ADS)

Pelletier, Jon D.; Broxton, Patrick D.; Hazenberg, Pieter; Zeng, Xubin; Troch, Peter A.; Niu, Guo-Yue; Williams, Zachary; Brunke, Michael A.; Gochis, David

2016-03-01

Earth's terrestrial near-subsurface environment can be divided into relatively porous layers of soil, intact regolith, and sedimentary deposits above unweathered bedrock. Variations in the thicknesses of these layers control the hydrologic and biogeochemical responses of landscapes. Currently, Earth System Models approximate the thickness of these relatively permeable layers above bedrock as uniform globally, despite the fact that their thicknesses vary systematically with topography, climate, and geology. To meet the need for more realistic input data for models, we developed a high-resolution gridded global data set of the average thicknesses of soil, intact regolith, and sedimentary deposits within each 30 arcsec (˜1 km) pixel using the best available data for topography, climate, and geology as input. Our data set partitions the global land surface into upland hillslope, upland valley bottom, and lowland landscape components and uses models optimized for each landform type to estimate the thicknesses of each subsurface layer. On hillslopes, the data set is calibrated and validated using independent data sets of measured soil thicknesses from the U.S. and Europe and on lowlands using depth to bedrock observations from groundwater wells in the U.S. We anticipate that the data set will prove useful as an input to regional and global hydrological and ecosystems models. This article was corrected on 2 FEB 2016. See the end of the full text for details.

The potential of land management to decrease global warming from climate change

NASA Astrophysics Data System (ADS)

Mayer, A.; Hausfather, Z.; Jones, A. D.; Silver, W. L.

2016-12-01

Recent evidence suggests that negative emissions (i.e. sequestration) is critical to slow climate change (IPCC, 2013; Gasser et al, 2015). Agricultural (crop and grazing) lands have the potential to act as a significant carbon sink. These ecosystems cover a significant proportion of the global land surface, and are largely degraded with regard to soil carbon due to previous management practices (Bai et al, 2008). However, few studies have examined the required scale of land management interventions that would be required to make a significant contribution to a portfolio of efforts aimed at limiting anthropogenic influences on global mean temperature. To address this, we modelled the quantitative effect of a range of soil carbon sequestration rates on global temperature to 2100. Results showed that by assuming a baseline emissions scenario outlined in RCP 2.6, the sequestration of an additional 0.7 Pg C per year through improved agricultural land management practices would produce a reduction of 0.1 degrees C from predicted global temperatures by the year 2100. We also compiled previous estimates of global carbon sequestration potential of agricultural soils to compare with our theoretical prediction to determine whether carbon sequestration through existing land management practices has potential to significantly reduce global temperatures. Assuming long-term soil carbon uptake, the combined potential of agricultural land management-based mitigation approaches exceeded 0.25 degrees C warming reduction by the year 2100. However, results were highly sensitive to potential carbon saturation, defined as the maximum threshold for carbon storage in soil. Our results suggest that current land management technologies and available land area exist and could make a measureable impact on warming reduction. Results also highlighted potential carbon saturation as a key gap in knowledge.

Global modeling of land water and energy balances. Part II: Land-characteristic contributions to spatial variability

USGS Publications Warehouse

Milly, P.C.D.; Shmakin, A.B.

2002-01-01

Land water and energy balances vary around the globe because of variations in amount and temporal distribution of water and energy supplies and because of variations in land characteristics. The former control (water and energy supplies) explains much more variance in water and energy balances than the latter (land characteristics). A largely untested hypothesis underlying most global models of land water and energy balance is the assumption that parameter values based on estimated geographic distributions of soil and vegetation characteristics improve the performance of the models relative to the use of globally constant land parameters. This hypothesis is tested here through an evaluation of the improvement in performance of one land model associated with the introduction of geographic information on land characteristics. The capability of the model to reproduce annual runoff ratios of large river basins, with and without information on the global distribution of albedo, rooting depth, and stomatal resistance, is assessed. To allow a fair comparison, the model is calibrated in both cases by adjusting globally constant scale factors for snow-free albedo, non-water-stressed bulk stomatal resistance, and critical root density (which is used to determine effective root-zone depth). The test is made in stand-alone mode, that is, using prescribed radiative and atmospheric forcing. Model performance is evaluated by comparing modeled runoff ratios with observed runoff ratios for a set of basins where precipitation biases have been shown to be minimal. The withholding of information on global variations in these parameters leads to a significant degradation of the capability of the model to simulate the annual runoff ratio. An additional set of optimization experiments, in which the parameters are examined individually, reveals that the stomatal resistance is, by far, the parameter among these three whose spatial variations add the most predictive power to the model in

Global Surface Temperature Change and Uncertainties Since 1861

NASA Technical Reports Server (NTRS)

Shen, Samuel S. P.; Lau, William K. M. (Technical Monitor)

2002-01-01

The objective of this talk is to analyze the warming trend and its uncertainties of the global and hemi-spheric surface temperatures. By the method of statistical optimal averaging scheme, the land surface air temperature and sea surface temperature observational data are used to compute the spatial average annual mean surface air temperature. The optimal averaging method is derived from the minimization of the mean square error between the true and estimated averages and uses the empirical orthogonal functions. The method can accurately estimate the errors of the spatial average due to observational gaps and random measurement errors. In addition, quantified are three independent uncertainty factors: urbanization, change of the in situ observational practices and sea surface temperature data corrections. Based on these uncertainties, the best linear fit to annual global surface temperature gives an increase of 0.61 +/- 0.16 C between 1861 and 2000. This lecture will also touch the topics on the impact of global change on nature and environment. as well as the latest assessment methods for the attributions of global change.

Model evaluation using a community benchmarking system for land surface models

NASA Astrophysics Data System (ADS)

Mu, M.; Hoffman, F. M.; Lawrence, D. M.; Riley, W. J.; Keppel-Aleks, G.; Kluzek, E. B.; Koven, C. D.; Randerson, J. T.

2014-12-01

Evaluation of atmosphere, ocean, sea ice, and land surface models is an important step in identifying deficiencies in Earth system models and developing improved estimates of future change. For the land surface and carbon cycle, the design of an open-source system has been an important objective of the International Land Model Benchmarking (ILAMB) project. Here we evaluated CMIP5 and CLM models using a benchmarking system that enables users to specify models, data sets, and scoring systems so that results can be tailored to specific model intercomparison projects. Our scoring system used information from four different aspects of global datasets, including climatological mean spatial patterns, seasonal cycle dynamics, interannual variability, and long-term trends. Variable-to-variable comparisons enable investigation of the mechanistic underpinnings of model behavior, and allow for some control of biases in model drivers. Graphics modules allow users to evaluate model performance at local, regional, and global scales. Use of modular structures makes it relatively easy for users to add new variables, diagnostic metrics, benchmarking datasets, or model simulations. Diagnostic results are automatically organized into HTML files, so users can conveniently share results with colleagues. We used this system to evaluate atmospheric carbon dioxide, burned area, global biomass and soil carbon stocks, net ecosystem exchange, gross primary production, ecosystem respiration, terrestrial water storage, evapotranspiration, and surface radiation from CMIP5 historical and ESM historical simulations. We found that the multi-model mean often performed better than many of the individual models for most variables. We plan to publicly release a stable version of the software during fall of 2014 that has land surface, carbon cycle, hydrology, radiation and energy cycle components.

Estimating Global Impervious Surface based on Social-economic Data and Satellite Observations

NASA Astrophysics Data System (ADS)

Zeng, Z.; Zhang, K.; Xue, X.; Hong, Y.

2016-12-01

Impervious surface areas around the globe are expanding and significantly altering the surface energy balance, hydrology cycle and ecosystem services. Many studies have underlined the importance of impervious surface, r from hydrological modeling to contaminant transport monitoring and urban development estimation. Therefore accurate estimation of the global impervious surface is important for both physical and social sciences. Given the limited coverage of high spatial resolution imagery and ground survey, using satellite remote sensing and geospatial data to estimate global impervious areas is a practical approach. Based on the previous work of area-weighted imperviousness for north branch of the Chicago River provided by HDR, this study developed a method to determine the percentage of impervious surface using latest global land cover categories from multi-source satellite observations, population density and gross domestic product (GDP) data. Percent impervious surface at 30-meter resolution were mapped. We found that 1.33% of the CONUS (105,814 km2) and 0.475% of the land surface (640,370km2) are impervious surfaces. To test the utility and practicality of the proposed method, National Land Cover Database (NLCD) 2011 percent developed imperviousness for the conterminous United States was used to evaluate our results. The average difference between the derived imperviousness from our method and the NLCD data across CONUS is 1.14%, while difference between our results and the NLCD data are within ±1% over 81.63% of the CONUS. The distribution of global impervious surface map indicates that impervious surfaces are primarily concentrated in China, India, Japan, USA and Europe where are highly populated and/or developed. This study proposes a straightforward way of mapping global imperviousness, which can provide useful information for hydrologic modeling and other applications.

Contributions of projected land use to global radiative forcing ascribed to local sources

NASA Astrophysics Data System (ADS)

Ward, D. S.; Mahowald, N. M.; Kloster, S.

2013-12-01

With global demand for food expected to dramatically increase and put additional pressures on natural lands, there is a need to understand the environmental impacts of land use and land cover change (LULCC). Previous studies have shown that the magnitude and even the sign of the radiative forcing (RF) of biogeophysical effects from LULCC depends on the latitude and forest ecology of the disturbed region. Here we ascribe the contributions to the global RF by land-use related anthropogenic activities to their local sources, organized on a grid of 1.9 degrees latitude by 2.5 degrees longitude. We use RF estimates for the year 2100, using five future LULCC projections, computed from simulations with the National Center for Atmospheric Research Community Land Model and Community Atmosphere Models and additional offline analyses. Our definition of the LULCC RF includes changes to terrestrial carbon storage, methane and nitrous oxide emissions, atmospheric chemistry, aerosol emissions, and surface albedo. We ascribe the RF to gridded locations based on LULCC-related emissions of relevant trace gases and aerosols, including emissions from fires. We find that the largest contributions to the global RF in year 2100 from LULCC originate in the tropics for all future scenarios. In fact, LULCC is the largest tropical source of anthropogenic RF. The LULCC RF in the tropics is dominated by emissions of CO2 from deforestation and methane emissions from livestock and soils. Land surface albedo change is rarely the dominant forcing agent in any of the future LULCC projections, at any location. By combining the five future scenarios we find that deforested area at a specific tropical location can be used to predict the contribution to global RF from LULCC at that location (the relationship does not hold as well in the extratropics). This information could support global efforts like REDD (Reducing Emissions from Deforestation and Forest Degradation), that aim to reduce greenhouse gas

Spatially Complete Surface Albedo Data Sets: Value-Added Products Derived from Terra MODIS Land Products

NASA Technical Reports Server (NTRS)

Moody, Eric G.; King, Michael D.; Platnick, Steven; Schaaf, Crystal B.; Gao, Feng

2004-01-01

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 observations of diffuse bihemispherical (white-sky) and direct beam directional hemispherical (black-sky ) land surface albedo included in the MOD43B3 product from MODIS instruments aboard NASA's Terra and Aqua satellite platforms have provided researchers with unprecedented spatial, spectral, and temporal characteristics. Cloud and seasonal snow cover, however, curtail retrievals to approximately half the global land surfaces on an annual equal-angle basis, precluding MOD43B3 albedo products from direct inclusion in some research projects and production environments.

Internal Physical Features of a Land Surface Model Employing a Tangent Linear Model

NASA Technical Reports Server (NTRS)

Yang, Runhua; Cohn, Stephen E.; daSilva, Arlindo; Joiner, Joanna; Houser, Paul R.

1997-01-01

The Earth's land surface, including its biomass, is an integral part of the Earth's weather and climate system. Land surface heterogeneity, such as the type and amount of vegetative covering., has a profound effect on local weather variability and therefore on regional variations of the global climate. Surface conditions affect local weather and climate through a number of mechanisms. First, they determine the re-distribution of the net radiative energy received at the surface, through the atmosphere, from the sun. A certain fraction of this energy increases the surface ground temperature, another warms the near-surface atmosphere, and the rest evaporates surface water, which in turn creates clouds and causes precipitation. Second, they determine how much rainfall and snowmelt can be stored in the soil and how much instead runs off into waterways. Finally, surface conditions influence the near-surface concentration and distribution of greenhouse gases such as carbon dioxide. The processes through which these mechanisms interact with the atmosphere can be modeled mathematically, to within some degree of uncertainty, on the basis of underlying physical principles. Such a land surface model provides predictive capability for surface variables including ground temperature, surface humidity, and soil moisture and temperature. This information is important for agriculture and industry, as well as for addressing fundamental scientific questions concerning global and local climate change. In this study we apply a methodology known as tangent linear modeling to help us understand more deeply, the behavior of the Mosaic land surface model, a model that has been developed over the past several years at NASA/GSFC. This methodology allows us to examine, directly and quantitatively, the dependence of prediction errors in land surface variables upon different vegetation conditions. The work also highlights the importance of accurate soil moisture information. Although surface

Impact of fire on global land carbon, water, and energy budgets and climate during the 20th century through changing ecosystems

NASA Astrophysics Data System (ADS)

Li, F.; Lawrence, D. M.; Bond-Lamberty, B. P.; Levis, S.

2016-12-01

Fire is an integral Earth system process and the primary form of terrestrial ecosystem disturbance on a global scale. Here we provide the first quantitative assessment and understanding on fire's impact on global land carbon, water, and energy budgets and climate through changing ecosystems. This is done by quantifying the difference between 20th century fire-on and fire-off simulations using the Community Earth System Model (CESM1.2). Results show that fire decreases the net carbon gain of global terrestrial ecosystems by 1.0 Pg C/yr averaged across the 20th century, as a result of biomass and peat burning (1.9 Pg C/yr) partly offset by changing gross primary productivity, respiration, and land-use carbon loss (-0.9 Pg C/yr). In addition, fire's effect on global carbon budget intensifies with time. Fire significantly reduces land evapotranspiration (ET) by 600 km3/yr and increases runoff, but has limited impact on precipitation. The impact on ET and runoff is most clearly seen in the tropical savannas, African rainforest, and some boreal and Southern Asian forests mainly due to fire-induced reduction in the vegetation canopy. It also weakens both the significant upward trend in global land ET prior to the 1950s and the downward trend from 1950 to 1985 by 35%. Fire-induced changes in land ecosystems affects global energy budgets by significantly reducing latent heating and surface net radiation. Fire changes surface radiative budget dominantly by raising surface upward longwave radiation and net longwave radiation. It also increases the global land average surface air temperature (Tas) by 0.04°C, and significantly increases wind speed and decreases surface relative humidity. The fire-induced change in wind speed, Tas, and relative humidity implies a positive feedback loop between fire and climate. Moreover, fire-induced changes in land ecosystems contribute 20% of strong global land warming during 1910-1940, which provides a new mechanism for the early 20th

Global discrimination of land cover types from metrics derived from AVHRR pathfinder data

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

DeFries, R.; Hansen, M.; Townshend, J.

1995-12-01

Global data sets of land cover are a significant requirement for global biogeochemical and climate models. Remotely sensed satellite data is an increasingly attractive source for deriving these data sets due to the resulting internal consistency, reproducibility, and coverage in locations where ground knowledge is sparse. Seasonal changes in the greenness of vegetation, described in remotely sensed data as changes in the normalized difference vegetation index (NDVI) throughout the year, have been the basis for discriminating between cover types in previous attempts to derive land cover from AVHRR data at global and continental scales. This study examines the use ofmore » metrics derived from the NDVI temporal profile, as well as metrics derived from observations in red, infrared, and thermal bands, to improve discrimination between 12 cover types on a global scale. According to separability measures calculated from Bhattacharya distances, average separabilities improved by using 12 of the 16 metrics tested (1.97) compared to separabilities using 12 monthly NDVI values alone (1.88). Overall, the most robust metrics for discriminating between cover types were: mean NDVI, maximum NDVI, NDVI amplitude, AVHRR Band 2 (near-infrared reflectance) and Band 1 (red reflectance) corresponding to the time of maximum NDVI, and maximum land surface temperature. Deciduous and evergreen vegetation can be distinguished by mean NDVI, maximum NDVI, NDVI amplitude, and maximum land surface temperature. Needleleaf and broadleaf vegetation can be distinguished by either mean NDVI and NDVI amplitude or maximum NDVI and NDVI amplitude.« less

Exploration of scaling effects on coarse resolution land surface phenology

USDA-ARS?s Scientific Manuscript database

A great number of land surface phenoloy (LSP) data have been produced from various coarse resolution satellite datasets and detection algorithms across regional and global scales. Unlike field- measured phenological events which are quantitatively defined with clear biophysical meaning, current LSP ...

Revisiting the global surface energy budgets with maximum-entropy-production model of surface heat fluxes

NASA Astrophysics Data System (ADS)

Huang, Shih-Yu; Deng, Yi; Wang, Jingfeng

2017-09-01

The maximum-entropy-production (MEP) model of surface heat fluxes, based on contemporary non-equilibrium thermodynamics, information theory, and atmospheric turbulence theory, is used to re-estimate the global surface heat fluxes. The MEP model predicted surface fluxes automatically balance the surface energy budgets at all time and space scales without the explicit use of near-surface temperature and moisture gradient, wind speed and surface roughness data. The new MEP-based global annual mean fluxes over the land surface, using input data of surface radiation, temperature data from National Aeronautics and Space Administration-Clouds and the Earth's Radiant Energy System (NASA CERES) supplemented by surface specific humidity data from the Modern-Era Retrospective Analysis for Research and Applications (MERRA), agree closely with previous estimates. The new estimate of ocean evaporation, not using the MERRA reanalysis data as model inputs, is lower than previous estimates, while the new estimate of ocean sensible heat flux is higher than previously reported. The MEP model also produces the first global map of ocean surface heat flux that is not available from existing global reanalysis products.

Global land-water nexus: Agricultural land and freshwater use embodied in worldwide supply chains.

PubMed

Chen, B; Han, M Y; Peng, K; Zhou, S L; Shao, L; Wu, X F; Wei, W D; Liu, S Y; Li, Z; Li, J S; Chen, G Q

2018-02-01

As agricultural land and freshwater inextricably interrelate and interact with each other, the conventional water and land policy in "silos" should give way to nexus thinking when formulating the land and water management strategies. This study constructs a systems multi-regional input-output (MRIO) model to expound global land-water nexus by simultaneously tracking agricultural land and freshwater use flows along the global supply chains. Furthermore, land productivity and irrigation water requirements of 160 crops in different regions are investigated to reflect the land-water linkage. Results show that developed economies (e.g., USA and Japan) and major large developing economies (e.g., mainland China and India) are the overriding drivers of agricultural land and freshwater use globally. In general, significant net transfers of these two resources are identified from resource-rich and less-developed economies to resource-poor and more-developed economies. For some crops, blue water productivity is inversely related to land productivity, indicating that irrigation water consumption is sometimes at odds with land use. The results could stimulus international cooperation for sustainable land and freshwater management targeting on original suppliers and final consumers along the global supply chains. Moreover, crop-specific land-water linkage could provide insights for trade-off decisions on minimizing the environmental impacts on local land and water resources. Copyright © 2017 Elsevier B.V. All rights reserved.

Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data

USGS Publications Warehouse

Loveland, Thomas R.; Reed, B.C.; Brown, Jesslyn F.; Ohlen, D.O.; Zhu, Z.; Yang, L.; Merchant, J.W.

2000-01-01

Researchers from the U.S. Geological Survey, University of Nebraska-Lincoln and the European Commission's Joint Research Centre, Ispra, Italy produced a 1 km resolution global land cover characteristics database for use in a wide range of continental-to global-scale environmental studies. This database provides a unique view of the broad patterns of the biogeographical and ecoclimatic diversity of the global land surface, and presents a detailed interpretation of the extent of human development. The project was carried out as an International Geosphere-Biosphere Programme, Data and Information Systems (IGBP-DIS) initiative. The IGBP DISCover global land cover product is an integral component of the global land cover database. DISCover includes 17 general land cover classes defined to meet the needs of IGBP core science projects. A formal accuracy assessment of the DISCover data layer will be completed in 1998. The 1 km global land cover database was developed through a continent-by-continent unsupervised classification of 1 km monthly Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) composites covering 1992-1993. Extensive post-classification stratification was necessary to resolve spectral/temporal confusion between disparate land cover types. The complete global database consists of 961 seasonal land cover regions that capture patterns of land cover, seasonality and relative primary productivity. The seasonal land cover regions were aggregated to produce seven separate land cover data sets used for global environmental modelling and assessment. The data sets include IGBP DISCover, U.S. Geological Survey Anderson System, Simple Biosphere Model, Simple Biosphere Model 2, Biosphere-Atmosphere Transfer Scheme, Olson Ecosystems and Running Global Remote Sensing Land Cover. The database also includes all digital sources that were used in the classification. The complete database can be sourced from the website: http://edcwww.cr.usgs.gov/landdaac/glcc/glcc.html.

Sensitivity of land surface modeling to parameters: An uncertainty quantification method applied to the Community Land Model

NASA Astrophysics Data System (ADS)

Ricciuto, D. M.; Mei, R.; Mao, J.; Hoffman, F. M.; Kumar, J.

2015-12-01

Uncertainties in land parameters could have important impacts on simulated water and energy fluxes and land surface states, which will consequently affect atmospheric and biogeochemical processes. Therefore, quantification of such parameter uncertainties using a land surface model is the first step towards better understanding of predictive uncertainty in Earth system models. In this study, we applied a random-sampling, high-dimensional model representation (RS-HDMR) method to analyze the sensitivity of simulated photosynthesis, surface energy fluxes and surface hydrological components to selected land parameters in version 4.5 of the Community Land Model (CLM4.5). Because of the large computational expense of conducting ensembles of global gridded model simulations, we used the results of a previous cluster analysis to select one thousand representative land grid cells for simulation. Plant functional type (PFT)-specific uniform prior ranges for land parameters were determined using expert opinion and literature survey, and samples were generated with a quasi-Monte Carlo approach-Sobol sequence. Preliminary analysis of 1024 simulations suggested that four PFT-dependent parameters (including slope of the conductance-photosynthesis relationship, specific leaf area at canopy top, leaf C:N ratio and fraction of leaf N in RuBisco) are the dominant sensitive parameters for photosynthesis, surface energy and water fluxes across most PFTs, but with varying importance rankings. On the other hand, for surface ans sub-surface runoff, PFT-independent parameters, such as the depth-dependent decay factors for runoff, play more important roles than the previous four PFT-dependent parameters. Further analysis by conditioning the results on different seasons and years are being conducted to provide guidance on how climate variability and change might affect such sensitivity. This is the first step toward coupled simulations including biogeochemical processes, atmospheric processes

Understanding Decreases in Land Relative Humidity with Global Warming: Conceptual Model and GCM Simulations

NASA Astrophysics Data System (ADS)

Byrne, Michael P.; O'Gorman, Paul A.

2016-12-01

Climate models simulate a strong land-ocean contrast in the response of near-surface relative humidity to global warming: relative humidity tends to increase slightly over oceans but decrease substantially over land. Surface energy balance arguments have been used to understand the response over ocean but are difficult to apply over more complex land surfaces. Here, a conceptual box model is introduced, involving moisture transport between the land and ocean boundary layers and evapotranspiration, to investigate the decreases in land relative humidity as the climate warms. The box model is applied to idealized and full-complexity (CMIP5) general circulation model simulations, and it is found to capture many of the features of the simulated changes in land relative humidity. The box model suggests there is a strong link between fractional changes in specific humidity over land and ocean, and the greater warming over land than ocean then implies a decrease in land relative humidity. Evapotranspiration is of secondary importance for the increase in specific humidity over land, but it matters more for the decrease in relative humidity. Further analysis shows there is a strong feedback between changes in surface-air temperature and relative humidity, and this can amplify the influence on relative humidity of factors such as stomatal conductance and soil moisture.

Improved Hydrology over Peatlands in a Global Land Modeling System

NASA Technical Reports Server (NTRS)

Bechtold, M.; Delannoy, G.; Reichle, R.; Koster, R.; Mahanama, S.; Roose, Dirk

2018-01-01

Peatlands of the Northern Hemisphere represent an important carbon pool that mainly accumulated since the last ice age under permanently wet conditions in specific geological and climatic settings. The carbon balance of peatlands is closely coupled to water table dynamics. Consequently, the future carbon balance over peatlands is strongly dependent on how hydrology in peatlands will react to changing boundary conditions, e.g. due to climate change or regional water level drawdown of connected aquifers or streams. Global land surface modeling over organic-rich regions can provide valuable global-scale insights on where and how peatlands are in transition due to changing boundary conditions. However, the current global land surface models are not able to reproduce typical hydrological dynamics in peatlands well. We implemented specific structural and parametric changes to account for key hydrological characteristics of peatlands into NASA's GEOS-5 Catchment Land Surface Model (CLSM, Koster et al. 2000). The main modifications pertain to the modeling of partial inundation, and the definition of peatland-specific runoff and evapotranspiration schemes. We ran a set of simulations on a high performance cluster using different CLSM configurations and validated the results with a newly compiled global in-situ dataset of water table depths in peatlands. The results demonstrate that an update of soil hydraulic properties for peat soils alone does not improve the performance of CLSM over peatlands. However, structural model changes for peatlands are able to improve the skill metrics for water table depth. The validation results for the water table depth indicate a reduction of the bias from 2.5 to 0.2 m, and an improvement of the temporal correlation coefficient from 0.5 to 0.65, and from 0.4 to 0.55 for the anomalies. Our validation data set includes both bogs (rain-fed) and fens (ground and/or surface water influence) and reveals that the metrics improved less for fens. In

Do Increasingly Globalized Land Systems Promote or Undermine Sustainability?

NASA Astrophysics Data System (ADS)

Munroe, D. K.

2015-12-01

Scholars are now studying land systems in a global context using such concepts as "telecoupling." Research to date has recognized that local land systems may be undermined by globalization, and local people displaced. The land change community emphasizes the ways in which local people make decisions about natural resources given the opportunities and constraints that globalization presents. This talk will present a summary of current land systems science research in agribusiness, global trade and financial institutions, highlighting key ways in which sustainability measures can capture the effects of these actors and activities.

A novel representation of groundwater dynamics in large-scale land surface modelling

NASA Astrophysics Data System (ADS)

Rahman, Mostaquimur; Rosolem, Rafael; Kollet, Stefan

2017-04-01

Land surface processes are connected to groundwater dynamics via shallow soil moisture. For example, groundwater affects evapotranspiration (by influencing the variability of soil moisture) and runoff generation mechanisms. However, contemporary Land Surface Models (LSM) generally consider isolated soil columns and free drainage lower boundary condition for simulating hydrology. This is mainly due to the fact that incorporating detailed groundwater dynamics in LSMs usually requires considerable computing resources, especially for large-scale applications (e.g., continental to global). Yet, these simplifications undermine the potential effect of groundwater dynamics on land surface mass and energy fluxes. In this study, we present a novel approach of representing high-resolution groundwater dynamics in LSMs that is computationally efficient for large-scale applications. This new parameterization is incorporated in the Joint UK Land Environment Simulator (JULES) and tested at the continental-scale.

The role of land surface fluxes in Saudi-KAU AGCM: Temperature climatology over the Arabian Peninsula for the period 1981-2010

NASA Astrophysics Data System (ADS)

Ashfaqur Rahman, M.; Almazroui, Mansour; Nazrul Islam, M.; O'Brien, Enda; Yousef, Ahmed Elsayed

2018-02-01

A new version of the Community Land Model (CLM) was introduced to the Saudi King Abdulaziz University Atmospheric Global Climate Model (Saudi-KAU AGCM) for better land surface component representation, and so to enhance climate simulation. CLM replaced the original land surface model (LSM) in Saudi-KAU AGCM, with the aim of simulating more accurate land surface fluxes globally, but especially over the Arabian Peninsula. To evaluate the performance of Saudi-KAU AGCM, simulations were completed with CLM and LSM for the period 1981-2010. In comparison with LSM, CLM generates surface air temperature values that are closer to National Centre for Environmental Prediction (NCEP) observations. The global annual averages of land surface air temperature are 9.51, 9.52, and 9.57 °C for NCEP, CLM, and LSM respectively, although the same atmospheric radiative and surface forcing from Saudi-KAU AGCM are provided to both LSM and CLM at every time step. The better temperature simulations when using CLM can be attributed to the more comprehensive plant functional type and hierarchical tile approach to the land cover type in CLM, along with better parameterization of upward land surface fluxes compared to LSM. At global scale, CLM exhibits smaller annual and seasonal mean biases of temperature with respect to NCEP data. Moreover, at regional scale, CLM demonstrates reasonable seasonal and annual mean temperature over the Arabian Peninsula as compared to the Climatic Research Unit (CRU) data. Finally, CLM generated better matches to single point-wise observations of surface air temperature and surface fluxes for some case studies.

Shifting relative importance of climatic constraints on land surface phenology

NASA Astrophysics Data System (ADS)

Garonna, Irene; de Jong, Rogier; Stöckli, Reto; Schmid, Bernhard; Schenkel, David; Schimel, David; Schaepman, Michael E.

2018-02-01

Land surface phenology (LSP), the study of seasonal dynamics of vegetated land surfaces from remote sensing, is a key indicator of global change, that both responds to and influences weather and climate. The effects of climatic changes on LSP depend on the relative importance of climatic constraints in specific regions—which are not well understood at global scale. Understanding the climatic constraints that underlie LSP is crucial for explaining climate change effects on global vegetation phenology. We used a combination of modelled and remotely-sensed vegetation activity records to quantify the interplay of three climatic constraints on land surface phenology (namely minimum temperature, moisture availability, and photoperiod), as well as the dynamic nature of these constraints. Our study examined trends and the relative importance of the three constrains at the start and the end of the growing season over eight global environmental zones, for the past three decades. Our analysis revealed widespread shifts in the relative importance of climatic constraints in the temperate and boreal biomes during the 1982-2011 period. These changes in the relative importance of the three climatic constraints, which ranged up to 8% since 1982 levels, varied with latitude and between start and end of the growing season. We found a reduced influence of minimum temperature on start and end of season in all environmental zones considered, with a biome-dependent effect on moisture and photoperiod constraints. For the end of season, we report that the influence of moisture has on average increased for both the temperate and boreal biomes over 8.99 million km2. A shifting relative importance of climatic constraints on LSP has implications both for understanding changes and for improving how they may be modelled at large scales.

Impacts of surface gold mining on land use systems in Western Ghana.

PubMed

Schueler, Vivian; Kuemmerle, Tobias; Schröder, Hilmar

2011-07-01

Land use conflicts are becoming increasingly apparent from local to global scales. Surface gold mining is an extreme source of such a conflict, but mining impacts on local livelihoods often remain unclear. Our goal here was to assess land cover change due to gold surface mining in Western Ghana, one of the world's leading gold mining regions, and to study how these changes affected land use systems. We used Landsat satellite images from 1986-2002 to map land cover change and field interviews with farmers to understand the livelihood implications of mining-related land cover change. Our results showed that surface mining resulted in deforestation (58%), a substantial loss of farmland (45%) within mining concessions, and widespread spill-over effects as relocated farmers expand farmland into forests. This points to rapidly eroding livelihood foundations, suggesting that the environmental and social costs of Ghana's gold boom may be much higher than previously thought.

Quantifying the Effects of Historical Land Cover Conversion Uncertainty on Global Carbon and Climate Estimates

NASA Astrophysics Data System (ADS)

Di Vittorio, A. V.; Mao, J.; Shi, X.; Chini, L.; Hurtt, G.; Collins, W. D.

2018-01-01

Previous studies have examined land use change as a driver of global change, but the translation of land use change into land cover conversion has been largely unconstrained. Here we quantify the effects of land cover conversion uncertainty on the global carbon and climate system using the integrated Earth System Model. Our experiments use identical land use change data and vary land cover conversions to quantify associated uncertainty in carbon and climate estimates. Land cover conversion uncertainty is large, constitutes a 5 ppmv range in estimated atmospheric CO2 in 2004, and generates carbon uncertainty that is equivalent to 80% of the net effects of CO2 and climate and 124% of the effects of nitrogen deposition during 1850-2004. Additionally, land cover uncertainty generates differences in local surface temperature of over 1°C. We conclude that future studies addressing land use, carbon, and climate need to constrain and reduce land cover conversion uncertainties.

Quantifying the Effects of Historical Land Cover Conversion Uncertainty on Global Carbon and Climate Estimates

DOE PAGES

Di Vittorio, A. V.; Mao, J.; Shi, X.; ...

2018-01-03

Previous studies have examined land use change as a driver of global change, but the translation of land use change into land cover conversion has been largely unconstrained. In this paper, we quantify the effects of land cover conversion uncertainty on the global carbon and climate system using the integrated Earth System Model. Our experiments use identical land use change data and vary land cover conversions to quantify associated uncertainty in carbon and climate estimates. Land cover conversion uncertainty is large, constitutes a 5 ppmv range in estimated atmospheric CO 2 in 2004, and generates carbon uncertainty that is equivalentmore » to 80% of the net effects of CO 2 and climate and 124% of the effects of nitrogen deposition during 1850–2004. Additionally, land cover uncertainty generates differences in local surface temperature of over 1°C. Finally, we conclude that future studies addressing land use, carbon, and climate need to constrain and reduce land cover conversion uncertainties.« less

Quantifying the Effects of Historical Land Cover Conversion Uncertainty on Global Carbon and Climate Estimates

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

Di Vittorio, A. V.; Mao, J.; Shi, X.

Previous studies have examined land use change as a driver of global change, but the translation of land use change into land cover conversion has been largely unconstrained. In this paper, we quantify the effects of land cover conversion uncertainty on the global carbon and climate system using the integrated Earth System Model. Our experiments use identical land use change data and vary land cover conversions to quantify associated uncertainty in carbon and climate estimates. Land cover conversion uncertainty is large, constitutes a 5 ppmv range in estimated atmospheric CO 2 in 2004, and generates carbon uncertainty that is equivalentmore » to 80% of the net effects of CO 2 and climate and 124% of the effects of nitrogen deposition during 1850–2004. Additionally, land cover uncertainty generates differences in local surface temperature of over 1°C. Finally, we conclude that future studies addressing land use, carbon, and climate need to constrain and reduce land cover conversion uncertainties.« less

Reconciling Land-Ocean Moisture Transport Variability in Reanalyses with P-ET in Observationally-Driven Land Surface Models

NASA Technical Reports Server (NTRS)

Robertson, Franklin R.; Bosilovich, Michael G.; Roberts, Jason B.

2016-01-01

Vertically integrated atmospheric moisture transport from ocean to land [vertically integrated atmospheric moisture flux convergence (VMFC)] is a dynamic component of the global climate system but remains problematic in atmospheric reanalyses, with current estimates having significant multidecadal global trends differing even in sign. Continual evolution of the global observing system, particularly stepwise improvements in satellite observations, has introduced discrete changes in the ability of data assimilation to correct systematic model biases, manifesting as nonphysical variability. Land surface models (LSMs) forced with observed precipitation P and near-surface meteorology and radiation provide estimates of evapotranspiration (ET). Since variability of atmospheric moisture storage is small on interannual and longer time scales, VMFC equals P minus ET is a good approximation and LSMs can provide an alternative estimate. However, heterogeneous density of rain gauge coverage, especially the sparse coverage over tropical continents, remains a serious concern. Rotated principal component analysis (RPCA) with prefiltering of VMFC to isolate the artificial variability is used to investigate artifacts in five reanalysis systems. This procedure, although ad hoc, enables useful VMFC corrections over global land. The P minus ET estimates from seven different LSMs are evaluated and subsequently used to confirm the efficacy of the RPCA-based adjustments. Global VMFC trends over the period 1979-2012 ranging from 0.07 to minus 0.03 millimeters per day per decade are reduced by the adjustments to 0.016 millimeters per day per decade, much closer to the LSM P minus ET estimate (0.007 millimeters per day per decade). Neither is significant at the 90 percent level. ENSO (El Nino-Southern Oscillation)-related modulation of VMFC and P minus ET remains the largest global interannual signal, with mean LSM and adjusted reanalysis time series correlating at 0.86.

Customer-oriented Data Formats and Services for Global Land Data Assimilation System (GLDAS) Products at the NASA GES DISC

NASA Technical Reports Server (NTRS)

Fang, Hongliang; Beaudoing, Hiroko; Rodell, Matthew; Teng, BIll; Vollmer, Bruce

2008-01-01

The Global Land Data Assimilation System (GLDAS) is generating a series of land surface state (e.g., soil moisture and surface temperature) and flux (e.g., evaporation and sensible heat flux) products simulated by four land surface Models (CLM, Mosaic, Noah and VIC). These products are now accessible at the Hydrology Data and Information Services Center (HDISC), a component of NASA Goddard Earth Sciences Data and Information Services Center (GESDISC).

On the relationship between land surface infrared emissivity and soil moisture

NASA Astrophysics Data System (ADS)

Zhou, Daniel K.; Larar, Allen M.; Liu, Xu

2018-01-01

The relationship between surface infrared (IR) emissivity and soil moisture content has been investigated based on satellite measurements. Surface soil moisture content can be estimated by IR remote sensing, namely using the surface parameters of IR emissivity, temperature, vegetation coverage, and soil texture. It is possible to separate IR emissivity from other parameters affecting surface soil moisture estimation. The main objective of this paper is to examine the correlation between land surface IR emissivity and soil moisture. To this end, we have developed a simple yet effective scheme to estimate volumetric soil moisture (VSM) using IR land surface emissivity retrieved from satellite IR spectral radiance measurements, assuming those other parameters impacting the radiative transfer (e.g., temperature, vegetation coverage, and surface roughness) are known for an acceptable time and space reference location. This scheme is applied to a decade of global IR emissivity data retrieved from MetOp-A infrared atmospheric sounding interferometer measurements. The VSM estimated from these IR emissivity data (denoted as IR-VSM) is used to demonstrate its measurement-to-measurement variations. Representative 0.25-deg spatially-gridded monthly-mean IR-VSM global datasets are then assembled to compare with those routinely provided from satellite microwave (MW) multisensor measurements (denoted as MW-VSM), demonstrating VSM spatial variations as well as seasonal-cycles and interannual variability. Initial positive agreement is shown to exist between IR- and MW-VSM (i.e., R2 = 0.85). IR land surface emissivity contains surface water content information. So, when IR measurements are used to estimate soil moisture, this correlation produces results that correspond with those customarily achievable from MW measurements. A decade-long monthly-gridded emissivity atlas is used to estimate IR-VSM, to demonstrate its seasonal-cycle and interannual variation, which is spatially

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.

Simultaneous inversion of multiple land surface parameters from MODIS optical-thermal observations

NASA Astrophysics Data System (ADS)

Ma, Han; Liang, Shunlin; Xiao, Zhiqiang; Shi, Hanyu

2017-06-01

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 variables usually focus on individual parameters separately even from the same satellite observations, resulting in inconsistent products. Moreover, no efforts have been made to generate global products from integrated observations from the optical to Thermal InfraRed (TIR) spectrum. Particularly, Middle InfraRed (MIR) observations have received little attention due to the complexity of the radiometric signal, which contains both reflected and emitted radiation. In this paper, we propose a unified algorithm for simultaneously retrieving six land surface parameters - Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), land surface albedo, Land Surface Emissivity (LSE), Land Surface Temperature (LST), and Upwelling Longwave radiation (LWUP) by exploiting MODIS visible-to-TIR observations. We incorporate a unified physical radiative transfer model into a data assimilation framework. The MODIS visible-to-TIR time series datasets include the daily surface reflectance product and MIR-to-TIR surface radiance, which are atmospherically corrected from the MODIS data using the Moderate Resolution Transmittance program (MODTRAN, ver. 5.0). LAI was first estimated using a data assimilation method that combines MODIS daily reflectance data and a LAI phenology model, and then the LAI was input to the unified radiative transfer model to simulate spectral surface reflectance and surface emissivity for calculating surface broadband albedo and emissivity, and FAPAR. LST was estimated from the MIR-TIR surface radiance data and the simulated emissivity, using an iterative optimization procedure. Lastly, LWUP was estimated using the LST and surface emissivity. The retrieved six parameters were extensively validated across six representative sites with

Impact of irrigation over India on the land surface fluxes

NASA Astrophysics Data System (ADS)

de Rosnay, P. R.; Polcher, J. P.; Laval, K. L.; Sabre, M. S.

2003-04-01

Irrigation is the main water user in the world with 87 % of the global water consumption being attributed to use on irrigated crop land. There are large spatial variations of the irrigated areas, from 68 % in Asia and 16 % in America, 10 % in Europe and the remaining in Africa and Australia. India is the most important irrigating country in the world with a gross irrigation requirement estimated by the FAO at 457 cubic km by year. The environmental impacts of irrigation are very important: irrigation causes the soil salinization, it affects the water quality and ecology, and increases the incidence of water related diseases. Irrigation is also expected to affect the the land surface energy budget, and thereby the climate system. The work presented here is conducted in the framework of the PROMISE European project. It aims to analyze the sensitivity of the land surface fluxes to the intensive irrigation over Indian peninsula. Numerical experiments are conducted with the land surface scheme ORCHIDEE of the Laboratoire de Meteorologie Dynamique, with a 1 degree spatial resolution. Two 2years simulations, forced by the ISLSCP (1987-88) data sets, are compared, with and without irrigation. The analysis focuses on the effect of land irrigation on the surface fluxes (partition of energy between latent and sensible fluxes), and the river flow.

The global land rush and climate change

NASA Astrophysics Data System (ADS)

Davis, Kyle Frankel; Rulli, Maria Cristina; D'Odorico, Paolo

2015-08-01

Climate change poses a serious global challenge in the face of rapidly increasing human demand for energy and food. A recent phenomenon in which climate change may play an important role is the acquisition of large tracts of land in the developing world by governments and corporations. In the target countries, where land is relatively inexpensive, the potential to increase crop yields is generally high and property rights are often poorly defined. By acquiring land, investors can realize large profits and countries can substantially alter the land and water resources under their control, thereby changing their outlook for meeting future demand. While the drivers, actors, and impacts involved with land deals have received substantial attention in the literature, we propose that climate change plays an important yet underappreciated role, both through its direct effects on agricultural production and through its influence on mitigative or adaptive policy decisions. Drawing from various literature sources as well as a new global database on reported land deals, we trace the evolution of the global land rush and highlight prominent examples in which the role of climate change is evident. We find that climate change—both historical and anticipated—interacts substantially with drivers of land acquisitions, having important implications for the resilience of communities in targeted areas. As a result of this synthesis, we ultimately contend that considerations of climate change should be integrated into future policy decisions relating to the large-scale land acquisitions.

The Implications of Future Food Demand on Global Land Use, Land-Use Change Emissions, and Climate

NASA Astrophysics Data System (ADS)

Calvin, K. V.; Wise, M.; Kyle, P.; Luckow, P.; Clarke, L.; Edmonds, J.; Eom, J.; Kim, S.; Moss, R.; Patel, P.

2011-12-01

In 2005, cropland accounted for approximately 10% of global land area. The amount of cropland needed in the future depends on a number of factors including global population, dietary preferences, and agricultural crop yields. In this paper, we explore the effect of various assumptions about global food demand and agricultural productivity between now and 2100 on global land use, land-use change emissions, and climate using the GCAM model. 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. For this analysis, we look at the effect of alternative socioeconomic pathways, crop yield improvement assumptions, and future meat demand scenarios on the demand for agricultural land. The three socioeconomic pathways explore worlds where global population in 2100 ranges from 6 billion people to 14 billion people. The crop yield improvement assumptions range from a world where yields do not improve beyond today's levels to a world with significantly higher crop productivity. The meat demand scenarios range from a vegetarian world to a world where meat is a dominant source of calories in the global diet. For each of these scenarios, we find that sufficient land exists to feed the global economy. However, rates of deforestation, bioenergy potential, land-use change emissions, and climate change differ across the scenarios. Under less favorable scenarios, deforestation rates, land-use change emissions, and the rate of climate change can be adversely affected.

Estimating Morning Change in Land Surface Temperature from MODIS Day/Night Observations: Applications for Surface Energy Balance Modeling.

PubMed

Hain, Christopher R; Anderson, Martha C

2017-10-16

Observations of land surface temperature (LST) are crucial for the monitoring of surface energy fluxes from satellite. Methods that require high temporal resolution LST observations (e.g., from geostationary orbit) can be difficult to apply globally because several geostationary sensors are required to attain near-global coverage (60°N to 60°S). While these LST observations are available from polar-orbiting sensors, providing global coverage at higher spatial resolutions, the temporal sampling (twice daily observations) can pose significant limitations. For example, the Atmosphere Land Exchange Inverse (ALEXI) surface energy balance model, used for monitoring evapotranspiration and drought, requires an observation of the morning change in LST - a quantity not directly observable from polar-orbiting sensors. Therefore, we have developed and evaluated a data-mining approach to estimate the mid-morning rise in LST from a single sensor (2 observations per day) of LST from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on the Aqua platform. In general, the data-mining approach produced estimates with low relative error (5 to 10%) and statistically significant correlations when compared against geostationary observations. This approach will facilitate global, near real-time applications of ALEXI at higher spatial and temporal coverage from a single sensor than currently achievable with current geostationary datasets.

Effects of explicit convection on global land-atmosphere coupling in the superparameterized CAM

NASA Astrophysics Data System (ADS)

Sun, Jian; Pritchard, Michael S.

2016-09-01

Conventional global climate models are prone to producing unrealistic land-atmosphere coupling signals. Cumulus and convection parameterizations are natural culprits but the effect of bypassing them with explicitly resolved convection on global land-atmosphere coupling dynamics has not been explored systematically. We apply a suite of modern land-atmosphere coupling diagnostics to isolate the effect of cloud Superparameterization in the Community Atmosphere Model (SPCAM) v3.5, focusing on both the terrestrial segment (i.e., soil moisture and surface turbulent fluxes interaction) and atmospheric segment (i.e., surface turbulent fluxes and precipitation interaction) in the water pathway of the land-atmosphere feedback loop. At daily timescales, SPCAM produces stronger uncoupled terrestrial signals (negative sign) over tropical rainforests in wet seasons, reduces the terrestrial coupling strength in the Central Great Plain in American, and reverses the coupling sign (from negative to positive) over India in the boreal summer season—all favorable improvements relative to reanalysis-forced land modeling. Analysis of the triggering feedback strength (TFS) and amplification feedback strength (AFS) shows that SPCAM favorably reproduces the observed geographic patterns of these indices over North America, with the probability of afternoon precipitation enhanced by high evaporative fraction along the eastern United States and Mexico, while conventional CAM does not capture this signal. We introduce a new diagnostic called the Planetary Boundary Layer (PBL) Feedback Strength (PFS), which reveals that SPCAM exhibits a tight connection between the responses of the lifting condensation level, the PBL height, and the rainfall triggering to surface turbulent fluxes; a triggering disconnect is found in CAM.

Global assessment of the economics of land degradation and improvement

NASA Astrophysics Data System (ADS)

Nkonya, Ephraim

2017-04-01

Land degradation—defined by the Millennium Ecosystem Assessment report as the long-term loss of ecosystems services—is a global problem, negatively affecting the livelihoods and food security of billions of people. Intensifying efforts, mobilizing more investments and strengthening the policy commitment for addressing land degradation at the global level needs to be supported by a careful evaluation of the costs and benefits of action versus costs of inaction against land degradation. Consistent with the definition of land degradation, we adopt the Total Economic Value (TEV) approach to determine the costs of land degradation and use remote sensing data and global statistical databases in our analysis. The results show that the annual costs of land degradation due to land use and land cover change (LUCC) are about US231 billion per year or about 0.41 % of the global GDP of US56.49 trillion in 2007. Contrary to past global land degradation assessment studies, land degradation is severe in both tropical and temperate countries. However, the losses from LUCC are especially high in Sub-Saharan Africa, which accounts for 26 % of the total global costs of land degradation due to LUCC. However, the local tangible losses (mainly provisioning services) account only for 46 % of the total cost of land degradation and the rest of the cost is due to the losses of ecosystem services (ES) accruable largely to beneficiaries other than the local land users. These external ES losses include carbon sequestration, biodiversity, genetic information and cultural services. This implies that the global community bears the largest cost of land degradation, which suggests that efforts to address land degradation should be done bearing in mind that the global community,as a whole, incurs larger losses than the local communities experiencing land degradation. The cost of soil fertility mining due to using land degrading management practices on maize, rice and wheat is estimated to be

Using microwave observations to estimate land surface temperature during cloudy conditions

USDA-ARS?s Scientific Manuscript database

Land surface temperature (LST), a key ingredient for physically-based retrieval algorithms of hydrological states and fluxes, remains a poorly constrained parameter for global scale studies. The main two observational methods to remotely measure T are based on thermal infrared (TIR) observations and...

Anticipating land surface change

PubMed Central

Streeter, Richard; Dugmore, Andrew J.

2013-01-01

The interplay of human actions and natural processes over varied spatial and temporal scales can result in abrupt transitions between contrasting land surface states. Understanding these transitions is a key goal of sustainability science because they can represent abrupt losses of natural capital. This paper recognizes flickering between alternate land surface states in advance of threshold change and critical slowing down in advance of both threshold changes and noncritical transformation. The early warning signals we observe are rises in autocorrelation, variance, and skewness within millimeter-resolution thickness measurements of tephra layers deposited in A.D. 2010 and A.D. 2011. These signals reflect changing patterns of surface vegetation, which are known to provide early warning signals of critical transformations. They were observed toward migrating soil erosion fronts, cryoturbation limits, and expanding deflation zones, thus providing potential early warning signals of land surface change. The record of the spatial patterning of vegetation contained in contemporary tephra layers shows how proximity to land surface change could be assessed in the widespread regions affected by shallow layers of volcanic fallout (those that can be subsumed within the existing vegetation cover). This insight shows how we could use tephra layers in the stratigraphic record to identify “near misses,” close encounters with thresholds that did not lead to tipping points, and thus provide additional tools for archaeology, sustainability science, and contemporary land management. PMID:23530230

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.

Influence of land-surface evapotranspiration on the earth's climate

NASA Technical Reports Server (NTRS)

Shukla, J.; Mintz, Y.

1982-01-01

Land-surface evapotranspiration is shown to strongly influence global fields of rainfall, temperature and motion by calculations using a numerical model of the atmosphere, confirming the general belief in the importance of evapotranspiration-producing surface vegetation for the earth's climate. The current version of the Goddard Laboratory atmospheric general circulation model is used in the present experiment, in which conservation equations for mass, momentum, moisture and energy are expressed in finite-difference form for a spherical grid to calculate (1) surface pressure field evolution, and (2) the wind, temperature, and water vapor fields at nine levels between the surface and a 20 km height.

Variability of basin scale water resources indicators derived from global hydrological and land surface models

NASA Astrophysics Data System (ADS)

Werner, Micha; Blyth, Eleanor; Schellekens, Jaap

2016-04-01

Global hydrological and land-surface models are becoming increasingly available, and as the resolution of these improves, as well how hydrological processes are represented, so does their potential. These offer consistent datasets at the global scale, which can be used to establish water balances and derive policy relevant indicators in medium to large basins, including those that are poorly gauged. However, differences in model structure, model parameterisation, and model forcing may result in quite different indicator values being derived, depending on the model used. In this paper we explore indicators developed using four land surface models (LSM) and five global hydrological models (GHM). Results from these models have been made available through the Earth2Observe project, a recent research initiative funded by the European Union 7th Research Framework. All models have a resolution of 0.5 arc degrees, and are forced using the same WATCH-ERA-Interim (WFDEI) meteorological re-analysis data at a daily time step for the 32 year period from 1979 to 2012. We explore three water resources indicators; an aridity index, a simplified water exploitation index; and an indicator that calculates the frequency of occurrence of root zone stress. We compare indicators derived over selected areas/basins in Europe, Colombia, Southern Africa, the Indian Subcontinent and Australia/New Zealand. The hydrological fluxes calculated show quite significant differences between the nine models, despite the common forcing dataset, with these differences reflected in the indicators subsequently derived. The results show that the variability between models is related to the different climates types, with that variability quite logically depending largely on the availability of water. Patterns are also found in the type of models that dominate different parts of the distribution of the indicator values, with LSM models providing lower values, and GHM models providing higher values in some

Evolution of Indian land surface biases in the seasonal hindcasts from the Met Office Global Seasonal Forecasting System GloSea5

NASA Astrophysics Data System (ADS)

Chevuturi, Amulya; Turner, Andrew G.; Woolnoug, Steve J.; Martin, Gill

2017-04-01

In this study we investigate the development of biases over the Indian region in summer hindcasts of the UK Met Office coupled initialised global seasonal forecasting system, GloSea5-GC2. Previous work has demonstrated the rapid evolution of strong monsoon circulation biases over India from seasonal forecasts initialised in early May, together with coupled strong easterly wind biases on the equator. These mean state biases lead to strong precipitation errors during the monsoon over the subcontinent. We analyse a set of three springtime start dates for the 20-year hindcast period (1992-2011) and fifteen total ensemble members for each year. We use comparisons with variety of observations to assess the evolution of the mean state biases over the Indian land surface. All biases within the model develop rapidly, particularly surface heat and radiation flux biases. Strong biases are present within the model climatology from pre-monsoon (May) in the surface heat fluxes over India (higher sensible / lower latent heat fluxes) when compared to observed estimates. The early evolution of such biases prior to onset rains suggests possible problems with the land surface scheme or soil moisture errors. Further analysis of soil moisture over the Indian land surface shows a dry bias present from the beginning of the hindcasts during the pre-monsoon. This lasts until the after the monsoon develops (July) after which there is a wet bias over the region. Soil moisture used for initialization of the model also shows a dry bias when compared against the observed estimates, which may lead to the same in the model. The early dry bias in the model may reduce local moisture availability through surface evaporation and thus may possibly limit precipitation recycling. On this premise, we identify and test the sensitivity of the monsoon in the model against higher soil moisture forcing. We run sensitivity experiments initiated using gridpoint-wise annual soil moisture maxima over the Indian

Climate change impacts on global rainfed agricultural land availability

NASA Astrophysics Data System (ADS)

Zhang, X.; Cai, X.

2010-12-01

Global rainfed agricultural land availability can be subject to significant changes in both magnitude and spatial distribution due to climate change. We assess the possible changes using current and projected climate data from thirteen general circulation models (GCMs) under two emission scenarios, A1B & B1, together with global databases on land, including soil properties and slope. Two ensemble methods with the set of GCMs, Simple Average Method (SAM) and Root Mean Square Error Ensemble Method (RMSEMM), are employed to abate uncertainty involved in global GCM projections for assembling regional climate. Fuzzy logic, which handles land classification in an approximate yet efficient way, is adopted to estimate the land suitability through empirically determined membership functions and fuzzy rules chosen through a learning process based on remote sensed crop land products. Land suitability under five scenarios, which include the present-climate baseline scenario and four projected scenarios, A1B-SAM, A1B-RMSEMM, B1-SAM, and B1-RMSEMM, are assessed for both global and seven important agricultural regions in the world, Africa, China, India, Europe (excluding Russia), Russia, South America, and U.S. It is found that countries at the high latitudes of north hemisphere are more likely to benefit from climate change with respect to agricultural land availability; while countries at mid- and low latitudes may suffer different levels of loss of potential arable land. Expansions of the gross potential arable land are likely to occur in regions at the north high latitudes, including Russia, North China and U.S., while land shrinking can be expected in South America, Africa, India and Europe. Although the greatest potential for agricultural expansion lies in Africa and South America, with current cultivated land accounting for 20% and 13% respectively of the net potential arable land, negative effects from climate change may decline the potential. In summary, climate change

Scale-dependency of the global mean surface temperature trend and its implication for the recent hiatus of global warming.

PubMed

Lin, Yong; Franzke, Christian L E

2015-08-11

Studies of the global mean surface temperature trend are typically conducted at a single (usually annual or decadal) time scale. The used scale does not necessarily correspond to the intrinsic scales of the natural temperature variability. This scale mismatch complicates the separation of externally forced temperature trends from natural temperature fluctuations. The hiatus of global warming since 1999 has been claimed to show that human activities play only a minor role in global warming. Most likely this claim is wrong due to the inadequate consideration of the scale-dependency in the global surface temperature (GST) evolution. Here we show that the variability and trend of the global mean surface temperature anomalies (GSTA) from January 1850 to December 2013, which incorporate both land and sea surface data, is scale-dependent and that the recent hiatus of global warming is mainly related to natural long-term oscillations. These results provide a possible explanation of the recent hiatus of global warming and suggest that the hiatus is only temporary.

Joint variability of global runoff and global sea surface temperatures

USGS Publications Warehouse

McCabe, G.J.; Wolock, D.M.

2008-01-01

Global land surface runoff and sea surface temperatures (SST) are analyzed to identify the primary modes of variability of these hydroclimatic data for the period 1905-2002. A monthly water-balance model first is used with global monthly temperature and precipitation data to compute time series of annual gridded runoff for the analysis period. The annual runoff time series data are combined with gridded annual sea surface temperature data, and the combined dataset is subjected to a principal components analysis (PCA) to identify the primary modes of variability. The first three components from the PCA explain 29% of the total variability in the combined runoff/SST dataset. The first component explains 15% of the total variance and primarily represents long-term trends in the data. The long-term trends in SSTs are evident as warming in all of the oceans. The associated long-term trends in runoff suggest increasing flows for parts of North America, South America, Eurasia, and Australia; decreasing runoff is most notable in western Africa. The second principal component explains 9% of the total variance and reflects variability of the El Ni??o-Southern Oscillation (ENSO) and its associated influence on global annual runoff patterns. The third component explains 5% of the total variance and indicates a response of global annual runoff to variability in North Aflantic SSTs. The association between runoff and North Atlantic SSTs may explain an apparent steplike change in runoff that occurred around 1970 for a number of continental regions.

Derived Land Surface Emissivity From Suomi NPP CrIS

NASA Technical Reports Server (NTRS)

Zhou, Daniel K.; Larar, Allen M.; Liu, Xu

2012-01-01

Presented here is the land surface IR spectral emissivity retrieved from the Cross-track Infrared Sounder (CrIS) measurements. The CrIS is aboard the Suomi National Polar-orbiting Partnership (NPP) satellite launched on October 28, 2011. We describe the retrieval algorithm, demonstrate the surface emissivity retrieved with CrIS measurements, and inter-comparison with the Infrared Atmospheric Sounding Interferometer (IASI) emissivity. We also demonstrate that surface emissivity from satellite measurements can be used in assistance of monitoring global surface climate change, as a long-term measurement of IASI and CrIS will be provided by the series of EUMETSAT MetOp and US Joint Polar Satellite System (JPSS) satellites. Monthly mean surface properties are produced using last 5-year IASI measurements. A temporal variation indicates seasonal diversity and El Nino/La Nina effects not only shown on the water but also on the land. Surface spectral emissivity and skin temperature from current and future operational satellites can be utilized as a means of long-term monitoring of the Earth's environment. CrIS spectral emissivity are retrieved and compared with IASI. The difference is small and could be within expected retrieval error; however it is under investigation.

Validation of the Land-Surface Energy Budget and Planetary Boundary Layer for Several Intensive field Experiments

NASA Technical Reports Server (NTRS)

Bosilovich, Michael G.; Schubert, Siegfried; Molod, Andrea; Houser, Paul R.

1999-01-01

Land-surface processes in a data assimilation system influence the lower troposphere and must be properly represented. With the recent incorporation of the Mosaic Land-surface Model (LSM) into the GEOS Data Assimilation System (DAS), the detailed land-surface processes require strict validation. While global data sources can identify large-scale systematic biases at the monthly timescale, the diurnal cycle is difficult to validate. Moreover, global data sets rarely include variables such as evaporation, sensible heat and soil water. Intensive field experiments, on the other hand, can provide high temporal resolution energy budget and vertical profile data for sufficiently long periods, without global coverage. Here, we evaluate the GEOS DAS against several intensive field experiments. The field experiments are First ISLSCP Field Experiment (FIFE, Kansas, summer 1987), Cabauw (as used in PILPS, Netherlands, summer 1987), Atmospheric Radiation Measurement (ARM, Southern Great Plains, winter and summer 1998) and the Surface Heat Budget of the Arctic Ocean (SHEBA, Arctic ice sheet, winter and summer 1998). The sites provide complete surface energy budget data for periods of at least one year, and some periods of vertical profiles. This comparison provides a detailed validation of the Mosaic LSM within the GEOS DAS for a variety of climatologic and geographic conditions.

Sensitivity of Land Surface Parameters on Thunderstorm Simulation through HRLDAS-WRF Coupling Mode

NASA Astrophysics Data System (ADS)

Kumar, Dinesh; Kumar, Krishan; Mohanty, U. C.; Kisore Osuri, Krishna

2016-07-01

Land surface characteristics play an important role in large scale, regional and mesoscale atmospheric process. Representation of land surface characteristics can be improved through coupling of mesoscale atmospheric models with land surface models. Mesoscale atmospheric models depend on Land Surface Models (LSM) to provide land surface variables such as fluxes of heat, moisture, and momentum for lower boundary layer evolution. Studies have shown that land surface properties such as soil moisture, soil temperature, soil roughness, vegetation cover, have considerable effect on lower boundary layer. Although, the necessity to initialize soil moisture accurately in NWP models is widely acknowledged, monitoring soil moisture at regional and global scale is a very tough task due to high spatial and temporal variability. As a result, the available observation network is unable to provide the required spatial and temporal data for the most part of the globe. Therefore, model for land surface initializations rely on updated land surface properties from LSM. The solution for NWP land-state initialization can be found by combining data assimilation techniques, satellite-derived soil data, and land surface models. Further, it requires an intermediate step to use observed rainfall, satellite derived surface insolation, and meteorological analyses to run an uncoupled (offline) integration of LSM, so that the evolution of modeled soil moisture can be forced by observed forcing conditions. Therefore, for accurate land-state initialization, high resolution land data assimilation system (HRLDAS) is used to provide the essential land surface parameters. Offline-coupling of HRLDAS-WRF has shown much improved results over Delhi, India for four thunder storm events. The evolution of land surface variables particularly soil moisture, soil temperature and surface fluxes have provided more realistic condition. Results have shown that most of domain part became wetter and warmer after

Physically Accurate Soil Freeze-Thaw Processes in a Global Land Surface Scheme

NASA Astrophysics Data System (ADS)

Cuntz, Matthias; Haverd, Vanessa

2018-01-01

The model Soil-Litter-Iso (SLI) calculates coupled heat and water transport in soil. It was recently implemented into the Australian land surface model CABLE, which is the land component of the Australian Community Climate and Earth System Simulator (ACCESS). Here we extended SLI to include accurate freeze-thaw processes in the soil and snow. SLI provides thence an implicit solution of the energy and water balances of soil and snow as a standalone model and within CABLE. The enhanced SLI was tested extensively against theoretical formulations, laboratory experiments, field data, and satellite retrievals. The model performed well for all experiments at wide-ranging temporal and spatial scales. SLI melts snow faster at the end of the cold season compared to observations though because there is no subgrid variability within SLI given by the implicit, coupled solution of energy and water. Combined CABLE-SLI shows very realistic dynamics and extent of permafrost on the Northern hemisphere. It illustrated, however, also the limits of possible comparisons between large-scale land surface models and local permafrost observations. CABLE-SLI exhibits the same patterns of snow depth and snow water equivalent on the Northern hemisphere compared to satellite-derived observations but quantitative comparisons depend largely on the given meteorological input fields. Further extension of CABLE-SLI with depth-dependence of soil carbon will allow realistic projections of the development of permafrost and frozen carbon stocks in a changing climate.

Improving HJ-1B IRS land surface temperature product using ASTER global emissivity database

NASA Astrophysics Data System (ADS)

Li, H.; Hu, T.; Meng, X.; Yongming, D.; Cao, B.; Liu, Q.

2015-12-01

Land surface temperature (LST) is a key parameter for hydrological, meteorological, climatological and environmental studies. Currently many operational LST products have been generated using European and American satellite data, i.e., the Advanced Very High Resolution Radiometer (AVHRR), Advanced Along-Track Scanning Radiometer (AATSR) and Moderate Resolution Imaging Spectroradiometer (MODIS). However, few LST product has been produced using Chinese satellite data. Thus, the objective of this study is to generate reliable LST product using Chinese HJ-1B satellite data. The HJ-1B satellite of China, were launched on September 6, 2008, which are used for disaster and environment monitoring. IRS (Infrared Scanner) is one of the key instruments onboard HJ-1B satellite, it can scan the earth every four days, has four spectral bands ranging from the near-infrared to thermal infrared bands (band 1 0.75 - 1.10μm, band 2 1.55-1.75μm, MIR band 3 3.50 - 3.90μm, band 4 10.5-12.5μm) with 720 km swath. It scans ±29° from nadir and the spatial resolution for band1-3 is 150m and 300m for band4. In this study, a single-channel parametric model (SC-PM) algorithm were used to produce 300m LST product from HJ-1B IRS data. The NCEP atmospheric profiles and a parametric model were used for atmospheric correction. In order to improve the accuracy of the land surface emissivity (LSE), the 1km ASTER Global Emissivity Database (GED) and self-developed 5-day 1km vegetation cover product were used for estimating the LSE based on the Vegetation Cover Method. Two years of HJ-1B IRS LST product in Heihe River basin (Gansu province, China) from June 2012 to June 2014 were generated. The LST products were evaluated against ground observations in an arid area of northwest China during the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) experiment. Four barren surface sites and ten vegetated sites were chosen for the evaluation. The results show that the developed HJ-1B IRS

Exploiting ten years of MERIS data over Land Surfaces

NASA Astrophysics Data System (ADS)

Gobron, Nadine

2012-07-01

Envisat's Medium Resolution Imaging Spectrometer (MERIS) acquires multi-spectral imagery of the Earth in the optical domain over terrestrial surfaces since a decade at global scale. Characteristics of the instrument will be first summarized and then several achievements for terrestrial applications will be presented as scientists have used data or products for characterizing the state of the global system and its variability. The ESA- GlobCover which has developed a service capable of delivering land cover maps on the basis of 300m spectral reflectance measurements will be summarized (Bicheron et al, 2008; Arino et al, 2010). We will highlight recent research results for continental land cover mapping over South America using MERIS data from 2009 and 2010 (Hojas Gascon et al, 2012). The objective and originality of this work was to generate a new land cover using selected MERIS data instead of all available images and supplementary information from associated Level-2 product, i.e. FAPAR, provided at a reduced resolution of 1 km. As the actual two level-2 products provided by ESA correspond to geophysical products such as the FAPAR (Fraction of Synthetically Active Radiation) and the MERIS terrestrial chlorophyll index (MTCI). With this latter, phenology studies as this product is sensitive to canopy chlorophyll content, a vegetation property that is a useful proxy for the canopy physical and chemical alterations associated with phenological change. (Dash and Curran, 2004; Boyd et al, 2011). For exploiting the use of the MGVI, ESA-Joint Research Center (JRC) FAPAR project has been created for producing time-series of geophysical products and will be summarized (Gobron et al, 2006, 2010; Fusco et al, 2003). It uses through various examples such as assessing drought at regional scale (Rossi etl a, 2010) or monitoring the state and changes of land surfaces global scale (Gobron et al, 2012). As the FAPAR value constitutes a state variable in advanced Earth system models

Global land cover mapping: a review and uncertainty analysis

USGS Publications Warehouse

Congalton, Russell G.; Gu, Jianyu; Yadav, Kamini; Thenkabail, Prasad S.; Ozdogan, Mutlu

2014-01-01

Given the advances in remotely sensed imagery and associated technologies, several global land cover maps have been produced in recent times including IGBP DISCover, UMD Land Cover, Global Land Cover 2000 and GlobCover 2009. However, the utility of these maps for specific applications has often been hampered due to considerable amounts of uncertainties and inconsistencies. A thorough review of these global land cover projects including evaluating the sources of error and uncertainty is prudent and enlightening. Therefore, this paper describes our work in which we compared, summarized and conducted an uncertainty analysis of the four global land cover mapping projects using an error budget approach. The results showed that the classification scheme and the validation methodology had the highest error contribution and implementation priority. A comparison of the classification schemes showed that there are many inconsistencies between the definitions of the map classes. This is especially true for the mixed type classes for which thresholds vary for the attributes/discriminators used in the classification process. Examination of these four global mapping projects provided quite a few important lessons for the future global mapping projects including the need for clear and uniform definitions of the classification scheme and an efficient, practical, and valid design of the accuracy assessment.

The EUSTACE project: delivering global, daily information on surface air temperature

NASA Astrophysics Data System (ADS)

Ghent, D.; Rayner, N. A.

2017-12-01

Day-to-day variations in surface air temperature affect society in many ways; however, daily surface air temperature measurements are not available everywhere. A global daily analysis cannot be achieved with measurements made in situ alone, so incorporation of satellite retrievals is needed. To achieve this, in the EUSTACE project (2015-2018, https://www.eustaceproject.eu) we have developed an understanding of the relationships between traditional (land and marine) surface air temperature measurements and retrievals of surface skin temperature from satellite measurements, i.e. Land Surface Temperature, Ice Surface Temperature, Sea Surface Temperature and Lake Surface Water Temperature. Here we discuss the science needed to produce a fully-global daily analysis (or ensemble of analyses) of surface air temperature on the centennial scale, integrating different ground-based and satellite-borne data types. Information contained in the satellite retrievals is used to create globally-complete fields in the past, using statistical models of how surface air temperature varies in a connected way from place to place. This includes developing new "Big Data" analysis methods as the data volumes involved are considerable. We will present recent progress along this road in the EUSTACE project, i.e.: • identifying inhomogeneities in daily surface air temperature measurement series from weather stations and correcting for these over Europe; • estimating surface air temperature over all surfaces of Earth from surface skin temperature retrievals; • using new statistical techniques to provide information on higher spatial and temporal scales than currently available, making optimum use of information in data-rich eras. Information will also be given on how interested users can become involved.

Evaluation of Ten Methods for Initializing a Land Surface Model

NASA Technical Reports Server (NTRS)

Rodell, M.; Houser, P. R.; Berg, A. A.; Famiglietti, J. S.

2005-01-01

Land surface models (LSMs) are computer programs, similar to weather and climate prediction models, which simulate the stocks and fluxes of water (including soil moisture, snow, evaporation, and runoff) and energy (including the temperature of and sensible heat released from the soil) after they arrive on the land surface as precipitation and sunlight. It is not currently possible to measure all of the variables of interest everywhere on Earth with sufficient accuracy and space-time resolution. Hence LSMs have been developed to integrate the available observations with our understanding of the physical processes involved, using powerful computers, in order to map these stocks and fluxes as they change in time. The maps are used to improve weather forecasts, support water resources and agricultural applications, and study the Earth"s water cycle and climate variability. NASA"s Global Land Data Assimilation System (GLDAS) project facilitates testing of several different LSMs with a variety of input datasets (e.g., precipitation, plant type).

Effects of explicit convection on global land-atmosphere coupling in the superparameterized CAM

DOE PAGES

Sun, Jian; Pritchard, Michael S.

2016-07-25

Here, conventional global climate models are prone to producing unrealistic land-atmosphere coupling signals. Cumulus and convection parameterizations are natural culprits but the effect of bypassing them with explicitly resolved convection on global land-atmosphere coupling dynamics has not been explored systematically. We apply a suite of modern land-atmosphere coupling diagnostics to isolate the effect of cloud Superparameterization in the Community Atmosphere Model (SPCAM) v3.5, focusing on both the terrestrial segment (i.e., soil moisture and surface turbulent fluxes interaction) and atmospheric segment (i.e., surface turbulent fluxes and precipitation interaction) in the water pathway of the landatmosphere feedback loop. At daily timescales, SPCAMmore » produces stronger uncoupled terrestrial signals (negative sign) over tropical rainforests in wet seasons, reduces the terrestrial coupling strength in the Central Great Plain in American, and reverses the coupling sign (from negative to positive) over India in the boreal summer season—all favorable improvements relative to reanalysis-forced land modeling. Analysis of the triggering feedback strength (TFS) and amplification feedback strength (AFS) shows that SPCAM favorably reproduces the observed geographic patterns of these indices over North America, with the probability of afternoon precipitation enhanced by high evaporative fraction along the eastern United States and Mexico, while conventional CAM does not capture this signal. We introduce a new diagnostic called the Planetary Boundary Layer (PBL) Feedback Strength (PFS), which reveals that SPCAM exhibits a tight connection between the responses of the lifting condensation level, the PBL height, and the rainfall triggering to surface turbulent fluxes; a triggering disconnect is found in CAM.« less

Effects of explicit convection on global land-atmosphere coupling in the superparameterized CAM

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

Sun, Jian; Pritchard, Michael S.

Here, conventional global climate models are prone to producing unrealistic land-atmosphere coupling signals. Cumulus and convection parameterizations are natural culprits but the effect of bypassing them with explicitly resolved convection on global land-atmosphere coupling dynamics has not been explored systematically. We apply a suite of modern land-atmosphere coupling diagnostics to isolate the effect of cloud Superparameterization in the Community Atmosphere Model (SPCAM) v3.5, focusing on both the terrestrial segment (i.e., soil moisture and surface turbulent fluxes interaction) and atmospheric segment (i.e., surface turbulent fluxes and precipitation interaction) in the water pathway of the landatmosphere feedback loop. At daily timescales, SPCAMmore » produces stronger uncoupled terrestrial signals (negative sign) over tropical rainforests in wet seasons, reduces the terrestrial coupling strength in the Central Great Plain in American, and reverses the coupling sign (from negative to positive) over India in the boreal summer season—all favorable improvements relative to reanalysis-forced land modeling. Analysis of the triggering feedback strength (TFS) and amplification feedback strength (AFS) shows that SPCAM favorably reproduces the observed geographic patterns of these indices over North America, with the probability of afternoon precipitation enhanced by high evaporative fraction along the eastern United States and Mexico, while conventional CAM does not capture this signal. We introduce a new diagnostic called the Planetary Boundary Layer (PBL) Feedback Strength (PFS), which reveals that SPCAM exhibits a tight connection between the responses of the lifting condensation level, the PBL height, and the rainfall triggering to surface turbulent fluxes; a triggering disconnect is found in CAM.« less

[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.

Variations in global land surface phenology: a comparison of satellite optical and passive microwave data

NASA Astrophysics Data System (ADS)

Tong, X.; Tian, F.; Brandt, M.; Zhang, W.; Liu, Y.; Fensholt, R.

2017-12-01

Changes in vegetation phenological events are among the most sensitive biological responses to climate change. In last decades, facilitating by satellite remote sensing techniques, land surface phenology (LSP) have been monitored at global scale using proxy approaches as tracking the temporal change of a satellite-derived vegetation index. However, the existing global assessments of changes in LSP are all established on the basis of leaf phenology using NDVI derived from optical sensors, being responsive to vegetation canopy cover and greenness. Instead, the vegetation optical depth (VOD) parameter from passive microwave sensors, which is sensitive to the aboveground vegetation water content by including as well the woody components in the observations, provides an alternative, independent and comprehensive means for global vegetation phenology monitoring. We used the unique long-term global VOD record available for the period 1992-2012 to monitoring the dynamics of LSP metrics (length of season, start of season and end of season) in comparison with the dynamics of LSP metrics derived from the latest GIMMS NDVI3G V1. We evaluated the differences in the linear trends of LSP metrics between two datasets. Currently, our results suggest that the level of seasonality variation of vegetation water content is less than the vegetation greenness. We found significant phenological changes in vegetation water content in African woodlands, where has been reported with little leaf phenological change regardless of the delays in rainfall onset. Therefore, VOD might allow us to detect temporal shifts in the timing difference of vegetation water storage vs. leaf emergence and to see if some ecophysiological thresholds seem to be reached, that could cause species turnover as climate change-driven alterations to the African monsoon proceed.

Modelling the influence of land-use changes on biophysical and biochemical interactions at regional and global scales.

PubMed

Devaraju, N; Bala, G; Nemani, R

2015-09-01

Land-use changes since the start of the industrial era account for nearly one-third of the cumulative anthropogenic CO2 emissions. In addition to the greenhouse effect of CO2 emissions, changes in land use also affect climate via changes in surface physical properties such as albedo, evapotranspiration and roughness length. Recent modelling studies suggest that these biophysical components may be comparable with biochemical effects. In regard to climate change, the effects of these two distinct processes may counterbalance one another both regionally and, possibly, globally. In this article, through hypothetical large-scale deforestation simulations using a global climate model, we contrast the implications of afforestation on ameliorating or enhancing anthropogenic contributions from previously converted (agricultural) land surfaces. Based on our review of past studies on this subject, we conclude that the sum of both biophysical and biochemical effects should be assessed when large-scale afforestation is used for countering global warming, and the net effect on global mean temperature change depends on the location of deforestation/afforestation. Further, although biochemical effects trigger global climate change, biophysical effects often cause strong local and regional climate change. The implication of the biophysical effects for adaptation and mitigation of climate change in agriculture and agroforestry sectors is discussed. © 2014 John Wiley & Sons Ltd.

Global Land Survey Impervious Mapping Project Web Site

NASA Technical Reports Server (NTRS)

DeColstoun, Eric Brown; Phillips, Jacqueline

2014-01-01

The Global Land Survey Impervious Mapping Project (GLS-IMP) aims to produce the first global maps of impervious cover at the 30m spatial resolution of Landsat. The project uses Global Land Survey (GLS) Landsat data as its base but incorporates training data generated from very high resolution commercial satellite data and using a Hierarchical segmentation program called Hseg. The web site contains general project information, a high level description of the science, examples of input and output data, as well as links to other relevant projects.

The EUSTACE project: delivering global, daily information on surface air temperature

NASA Astrophysics Data System (ADS)

Rayner, Nick

2017-04-01

Day-to-day variations in surface air temperature affect society in many ways; however, daily surface air temperature measurements are not available everywhere. A global daily analysis cannot be achieved with measurements made in situ alone, so incorporation of satellite retrievals is needed. To achieve this, in the EUSTACE project (2015-June 2018, https://www.eustaceproject.eu) we are developing an understanding of the relationships between traditional (land and marine) surface air temperature measurements and retrievals of surface skin temperature from satellite measurements, i.e. Land Surface Temperature, Ice Surface Temperature, Sea Surface Temperature and Lake Surface Water Temperature. Here we discuss the science needed to produce a fully-global daily analysis (or ensemble of analyses) of surface air temperature on the centennial scale, integrating different ground-based and satellite-borne data types. Information contained in the satellite retrievals is used to create globally-complete fields in the past, using statistical models of how surface air temperature varies in a connected way from place to place. As the data volumes involved are considerable, such work needs to include development of new "Big Data" analysis methods. We will present recent progress along this road in the EUSTACE project: 1. providing new, consistent, multi-component estimates of uncertainty in surface skin temperature retrievals from satellites; 2. identifying inhomogeneities in daily surface air temperature measurement series from weather stations and correcting for these over Europe; 3. estimating surface air temperature over all surfaces of Earth from surface skin temperature retrievals; 4. using new statistical techniques to provide information on higher spatial and temporal scales than currently available, making optimum use of information in data-rich eras. Information will also be given on how interested users can become involved.

The EUSTACE project: delivering global, daily information on surface air temperature

NASA Astrophysics Data System (ADS)

Ghent, D.; Rayner, N. A.

2016-12-01

Day-to-day variations in surface air temperature affect society in many ways; however, daily surface air temperature measurements are not available everywhere. A global daily analysis cannot be achieved with measurements made in situ alone, so incorporation of satellite retrievals is needed. To achieve this, in the EUSTACE project (2015-June 2018, https://www.eustaceproject.eu) we are developing an understanding of the relationships between traditional (land and marine) surface air temperature measurements and retrievals of surface skin temperature from satellite measurements, i.e. Land Surface Temperature, Ice Surface Temperature, Sea Surface Temperature and Lake Surface Water Temperature. Here we discuss the science needed to produce a fully-global daily analysis (or ensemble of analyses) of surface air temperature on the centennial scale, integrating different ground-based and satellite-borne data types. Information contained in the satellite retrievals is used to create globally-complete fields in the past, using statistical models of how surface air temperature varies in a connected way from place to place. As the data volumes involved are considerable, such work needs to include development of new "Big Data" analysis methods. We will present recent progress along this road in the EUSTACE project, i.e.: • providing new, consistent, multi-component estimates of uncertainty in surface skin temperature retrievals from satellites; • identifying inhomogeneities in daily surface air temperature measurement series from weather stations and correcting for these over Europe; • estimating surface air temperature over all surfaces of Earth from surface skin temperature retrievals; • using new statistical techniques to provide information on higher spatial and temporal scales than currently available, making optimum use of information in data-rich eras. Information will also be given on how interested users can become involved.

Multisource Estimation of Long-term Global Terrestrial Surface Radiation

NASA Astrophysics Data System (ADS)

Peng, L.; Sheffield, J.

2017-12-01

Land surface net radiation is the essential energy source at the earth's surface. It determines the surface energy budget and its partitioning, drives the hydrological cycle by providing available energy, and offers heat, light, and energy for biological processes. Individual components in net radiation have changed historically due to natural and anthropogenic climate change and land use change. Decadal variations in radiation such as global dimming or brightening have important implications for hydrological and carbon cycles. In order to assess the trends and variability of net radiation and evapotranspiration, there is a need for accurate estimates of long-term terrestrial surface radiation. While large progress in measuring top of atmosphere energy budget has been made, huge discrepancies exist among ground observations, satellite retrievals, and reanalysis fields of surface radiation, due to the lack of observational networks, the difficulty in measuring from space, and the uncertainty in algorithm parameters. To overcome the weakness of single source datasets, we propose a multi-source merging approach to fully utilize and combine multiple datasets of radiation components separately, as they are complementary in space and time. First, we conduct diagnostic analysis of multiple satellite and reanalysis datasets based on in-situ measurements such as Global Energy Balance Archive (GEBA), existing validation studies, and other information such as network density and consistency with other meteorological variables. Then, we calculate the optimal weighted average of multiple datasets by minimizing the variance of error between in-situ measurements and other observations. Finally, we quantify the uncertainties in the estimates of surface net radiation and employ physical constraints based on the surface energy balance to reduce these uncertainties. The final dataset is evaluated in terms of the long-term variability and its attribution to changes in individual

A Unified and Coherent Land Surface Emissivity Earth System Data Record

NASA Astrophysics Data System (ADS)

Knuteson, R. O.; Borbas, E. E.; Hulley, G. C.; Hook, S. J.; Anderson, M. C.; Pinker, R. T.; Hain, C.; Guillevic, P. C.

2014-12-01

Land Surface Temperature and Emissivity (LST&E) data are essential for a wide variety of studies from calculating the evapo-transpiration of plant canopies to retrieving atmospheric water vapor. LST&E products are generated from data acquired by sensors in low Earth orbit (LEO) and by sensors in geostationary Earth orbit (GEO). Although these products represent the same measure, they are produced at different spatial, spectral and temporal resolutions using different algorithms. The different approaches used to retrieve the temperatures and emissivities result in discrepancies and inconsistencies between the different products. NASA has identified a major need to develop long-term, consistent, and calibrated data and products that are valid across multiple missions and satellite sensors. This poster will introduce the land surface emissivity product of the NASA MEASUREs project called A Unified and Coherent Land Surface Temperature and Emissivity (LST&E) Earth System Data Record (ESDR). To develop a unified high spectral resolution emissivity database, the MODIS baseline-fit emissivity database (MODBF) produced at the University of Wisconsin-Madison and the ASTER Global Emissivity Database (ASTER GED) produced at JPL will be merged. The unified Emissivity ESDR will be produced globally at 5km in mean monthly time-steps and for 12 bands from 3.6-14.3 micron and extended to 417 bands using a PC regression approach. The poster will introduce this data product. LST&E is a critical ESDR for a wide variety of studies in particular ecosystem and climate modeling.

Widespread land surface wind decline in the Northern Hemisphere partly attributed to land surface changes

NASA Astrophysics Data System (ADS)

Thepaut, J.; Vautard, R.; Cattiaux, J.; Yiou, P.; Ciais, P.

2010-12-01

pressure gradients, and modeled winds from weather re-analyses do not exhibit any comparable stilling trends than at surface stations. For instance, large-scale circulation changes captured in the most recent European Centre for Medium Range Weather Forecast re-analysis (ERA-interim) can only explain only up to 10-50% of the wind stilling, depending on the region. In addition, a significant amount of the slow-down could originate from a generalized increase in surface roughness, due for instance to forest growth and expansion, and urbanization. This hypothesis, which could explain up to 60% of the decline, is supported by remote sensing observations and theoretical calculations combined with meso-scale model simulations. For future wind power energy resource, the part of wind decline due to land cover changes is easier to cope with than that due to global atmospheric circulation slow down.

Regional-Scale Forcing and Feedbacks from Alternative Scenarios of Global-Scale Land Use Change

NASA Astrophysics Data System (ADS)

Jones, A. D.; Chini, L. P.; Collins, W.; Janetos, A. C.; Mao, J.; Shi, X.; Thomson, A. M.; Torn, M. S.

2011-12-01

Future patterns of land use change depend critically on the degree to which terrestrial carbon management strategies, such as biological carbon sequestration and biofuels, are utilized in order to mitigate global climate change. Furthermore, land use change associated with terrestrial carbon management induces biogeophysical changes to surface energy budgets that perturb climate at regional and possibly global scales, activating different feedback processes depending on the nature and location of the land use change. As a first step in a broader effort to create an integrated earth system model, we examine two scenarios of future anthropogenic activity generated by the Global Change Assessment Model (GCAM) within the full-coupled Community Earth System Model (CESM). Each scenario stabilizes radiative forcing from greenhouse gases and aerosols at 4.5 W/m^2. In the first, stabilization is achieved through a universal carbon tax that values terrestrial carbon equally with fossil carbon, leading to modest afforestation globally and low biofuel utilization. In the second scenario, stabilization is achieved with a tax on fossil fuel and industrial carbon alone. In this case, biofuel utilization increases dramatically and crop area expands to claim approximately 50% of forest cover globally. By design, these scenarios exhibit identical climate forcing from atmospheric constituents. Thus, differences among them can be attributed to the biogeophysical effects of land use change. In addition, we utilize offline radiative transfer and offline land model simulations to identify forcing and feedback mechanisms operating in different regions. We find that boreal deforestation has a strong climatic signature due to significant albedo change coupled with a regional-scale water vapor feedback. Tropical deforestation, on the other hand, has more subtle effects on climate. Globally, the two scenarios yield warming trends over the 21st century that differ by 0.5 degrees Celsius. This

The Development and Validation of a New Land Surface Model for Regional and Global Climate Modeling

NASA Astrophysics Data System (ADS)

Lynch-Stieglitz, Marc

1995-11-01

A new land-surface scheme intended for use in mesoscale and global climate models has been developed and validated. The ground scheme consists of 6 soil layers. Diffusion and a modified tipping bucket model govern heat and water flow respectively. A 3 layer snow model has been incorporated into a modified BEST vegetation scheme. TOPMODEL equations and Digital Elevation Model data are used to generate baseflow which supports lowland saturated zones. Soil moisture heterogeneity represented by saturated lowlands subsequently impacts watershed evapotranspiration, the partitioning of surface fluxes, and the development of the storm hydrograph. Five years of meteorological and hydrological data from the Sleepers river watershed located in the eastern highlands of Vermont where winter snow cover is significant were then used to drive and validate the new scheme. Site validation data were sufficient to evaluate model performance with regard to various aspects of the watershed water balance, including snowpack growth/ablation, the spring snowmelt hydrograph, storm hydrographs, and the seasonal development of watershed evapotranspiration and soil moisture. By including topographic effects, not only are the main spring hydrographs and individual storm hydrographs adequately resolved, but the mechanisms generating runoff are consistent with current views of hydrologic processes. The seasonal movement of the mean water table depth and the saturated area of the watershed are consistent with site data and the overall model hydroclimatology, including the surface fluxes, seems reasonable.

An analysis of IGBP global land-cover characterization process

USGS Publications Warehouse

Loveland, Thomas R.; Zhu, Zhiliang; Ohlen, Donald O.; Brown, Jesslyn F.; Reed, Bradley C.; Yang, Limin

1999-01-01

The international Geosphere Biosphere Programme (IGBP) has called for the development of improved global land-cover data for use in increasingly sophisticated global environmental models. To meet this need, the staff of the U.S. Geological Survey and the University of Nebraska-Lincoln developed and applied a global land-cover characterization methodology using 1992-1993 1-km resolution Advanced Very High Resolution Radiometer (AVHRR) and other spatial data. The methodology, based on unsupervised classification with extensive postclassification refinement, yielded a multi-layer database consisting of eight land-cover data sets, descriptive attributes, and source data. An independent IGBP accuracy assessment reports a global accuracy of 73.5 percent, and continental results vary from 63 percent to 83 percent. Although data quality, methodology, interpreter performance, and logistics affected the results, significant problems were associated with the relationship between AVHRR data and fine-scale, spectrally similar land-cover patterns in complex natural or disturbed landscapes.

Impact of new land boundary conditions from Moderate Resolution Imaging Spectroradiometer (MODIS) data on the climatology of land surface variables

NASA Astrophysics Data System (ADS)

Tian, Y.; Dickinson, R. E.; Zhou, L.; Shaikh, M.

2004-10-01

This paper uses the Community Land Model (CLM2) to investigate the improvements of a new land surface data set, created from multiple high-quality collection 4 Moderate Resolution Imaging Spectroradiometer data of leaf area index (LAI), plant functional type, and vegetation continuous fields, for modeled land surface variables. The previous land surface data in CLM2 underestimate LAI and overestimate the percent cover of grass/crop over most of the global area. For snow-covered regions with abundant solar energy the increased LAI and percent cover of tree/shrub in the new data set decreases the percent cover of surface snow and increases net radiation and thus increases ground and surface (2-m) air temperature, which reduces most of the model cold bias. For snow-free regions the increased LAI and changes in the percent cover from grass/crop to tree or shrub decrease ground and surface air temperature by converting most of the increased net radiation to latent heat flux, which decreases the model warm bias. Furthermore, the new data set greatly decreases ground evaporation and increases canopy evapotranspiration over tropical forests, especially during the wet season, owing to the higher LAI and more trees in the new data set. It makes the simulated ground evaporation and canopy evapotranspiration closer to reality and also reduces the warm biases over tropical regions.

Global land-use change hidden behind nickel consumption.

PubMed

Nakajima, Kenichi; Nansai, Keisuke; Matsubae, Kazuyo; Tomita, Makoto; Takayanagi, Wataru; Nagasaka, Tetsuya

2017-05-15

Economic growth is associated with a rapid rise in the use of natural resources within the economy, and has potential environmental impacts at local and/or global scales. In today's globalized economy, each country has indirect flows supporting its economic activities, and natural resource consumption through supply chains influences environmental impacts far removed from the place of consumption. One way to control environmental impacts associated with consumption of natural resources is to identify the consumption of natural resources and the associated environmental impacts through the global supply chain. In this study, we used a global link input-output model (GLIO, a hybrid multiregional input-output model) to detect the linkages between national nickel consumption and mining-associated global land-use changes. We focused on nickel, whose global demand has risen rapidly in recent years, as a case study. The estimated area of land-use change around the world caused by nickel mining in 2005 was 1.9km 2 , and that induced by Japanese final demand for nickel was 0.38km 2 . Our modeling also revealed that the areas of greatest land-use change associated with nickel mining were concentrated in only a few countries and regions far removed from the place of consumption. For example, 57.7% of the world's land-use changes caused by nickel mining were concentrated in five countries in 2005: Australia, 13.7%; Russia, 12.9%; Indonesia, 12.5%; New Caledonia, 10.4%; and Colombia, 8.2%. The mining-associated land-use change induced by Japanese final demand accounted for 19.5% of the total area affected by land-use change caused by nickel mining. The top three countries accounted for 70.6% (Indonesia: 47.0%, New Caledonia: 16.0%, and Australia: 7.7%), and the top five accounted for 82.4% (the Philippines: 7.5%, and Canada: 4.3%, in addition to the top three countries and regions). Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

GLORI: A GNSS-R Dual Polarization Airborne Instrument for Land Surface Monitoring.

PubMed

Motte, Erwan; Zribi, Mehrez; Fanise, Pascal; Egido, Alejandro; Darrozes, José; Al-Yaari, Amen; Baghdadi, Nicolas; Baup, Frédéric; Dayau, Sylvia; Fieuzal, Remy; Frison, Pierre-Louis; Guyon, Dominique; Wigneron, Jean-Pierre

2016-05-20

Global Navigation Satellite System-Reflectometry (GNSS-R) has emerged as a remote sensing tool, which is complementary to traditional monostatic radars, for the retrieval of geophysical parameters related to surface properties. In the present paper, we describe a new polarimetric GNSS-R system, referred to as the GLObal navigation satellite system Reflectometry Instrument (GLORI), dedicated to the study of land surfaces (soil moisture, vegetation water content, forest biomass) and inland water bodies. This system was installed as a permanent payload on a French ATR42 research aircraft, from which simultaneous measurements can be carried out using other instruments, when required. Following initial laboratory qualifications, two airborne campaigns involving nine flights were performed in 2014 and 2015 in the Southwest of France, over various types of land cover, including agricultural fields and forests. Some of these flights were made concurrently with in situ ground truth campaigns. Various preliminary applications for the characterisation of agricultural and forest areas are presented. Initial analysis of the data shows that the performance of the GLORI instrument is well within specifications, with a cross-polarization isolation better than -15 dB at all elevations above 45°, a relative polarimetric calibration accuracy better than 0.5 dB, and an apparent reflectivity sensitivity better than -30 dB, thus demonstrating its strong potential for the retrieval of land surface characteristics.

GLORI: A GNSS-R Dual Polarization Airborne Instrument for Land Surface Monitoring

PubMed Central

Motte, Erwan; Zribi, Mehrez; Fanise, Pascal; Egido, Alejandro; Darrozes, José; Al-Yaari, Amen; Baghdadi, Nicolas; Baup, Frédéric; Dayau, Sylvia; Fieuzal, Remy; Frison, Pierre-Louis; Guyon, Dominique; Wigneron, Jean-Pierre

2016-01-01

Global Navigation Satellite System-Reflectometry (GNSS-R) has emerged as a remote sensing tool, which is complementary to traditional monostatic radars, for the retrieval of geophysical parameters related to surface properties. In the present paper, we describe a new polarimetric GNSS-R system, referred to as the GLObal navigation satellite system Reflectometry Instrument (GLORI), dedicated to the study of land surfaces (soil moisture, vegetation water content, forest biomass) and inland water bodies. This system was installed as a permanent payload on a French ATR42 research aircraft, from which simultaneous measurements can be carried out using other instruments, when required. Following initial laboratory qualifications, two airborne campaigns involving nine flights were performed in 2014 and 2015 in the Southwest of France, over various types of land cover, including agricultural fields and forests. Some of these flights were made concurrently with in situ ground truth campaigns. Various preliminary applications for the characterisation of agricultural and forest areas are presented. Initial analysis of the data shows that the performance of the GLORI instrument is well within specifications, with a cross-polarization isolation better than −15 dB at all elevations above 45°, a relative polarimetric calibration accuracy better than 0.5 dB, and an apparent reflectivity sensitivity better than −30 dB, thus demonstrating its strong potential for the retrieval of land surface characteristics. PMID:27213393

Report of the panel on the land surface: Process of change, section 5

NASA Technical Reports Server (NTRS)

Adams, John B.; Barron, Eric E.; Bloom, Arthur A.; Breed, Carol; Dohrenwend, J.; Evans, Diane L.; Farr, Thomas T.; Gillespie, Allan R.; Isaks, B. L.; Williams, Richard S.

1991-01-01

The panel defined three main areas of study that are central to the Solid Earth Science (SES) program: climate interactions with the Earth's surface, tectonism as it affects the Earth's surface and climate, and human activities that modify the Earth's surface. Four foci of research are envisioned: process studies with an emphasis on modern processes in transitional areas; integrated studies with an emphasis on long term continental climate change; climate-tectonic interactions; and studies of human activities that modify the Earth's surface, with an emphasis on soil degradation. The panel concluded that there is a clear requirement for global coverage by high resolution stereoscopic images and a pressing need for global topographic data in support of studies of the land surface.

The global potential of bioenergy on abandoned agriculture lands.

PubMed

Campbell, J Elliott; Lobell, David B; Genova, Robert C; Field, Christopher B

2008-08-01

Converting forest lands into bioenergy agriculture could accelerate climate change by emitting carbon stored in forests, while converting food agriculture lands into bioenergy agriculture could threaten food security. Both problems are potentially avoided by using abandoned agriculture lands for bioenergy agriculture. Here we show the global potential for bioenergy on abandoned agriculture lands to be less than 8% of current primary energy demand, based on historical land use data, satellite-derived land cover data, and global ecosystem modeling. The estimated global area of abandoned agriculture is 385-472 million hectares, or 66-110% of the areas reported in previous preliminary assessments. The area-weighted mean production of above-ground biomass is 4.3 tons ha(-1) y(-1), in contrast to estimates of up to 10 tons ha(-1) y(-1) in previous assessments. The energy content of potential biomass grown on 100% of abandoned agriculture lands is less than 10% of primary energy demand for most nations in North America, Europe, and Asia, but it represents many times the energy demand in some African nations where grasslands are relatively productive and current energy demand is low.

Land Use, Climate, and Water Resources-Global Stages of Interaction.

PubMed

Kaushal, Sujay S; Gold, Arthur J; Mayer, Paul M

2017-10-24

Land use and climate change can accelerate the depletion of freshwater resources that support humans and ecosystem services on a global scale. Here, we briefly review studies from around the world, and highlight those in this special issue. We identify stages that characterize increasing interaction between land use and climate change. During the first stage, hydrologic modifications and the built environment amplify overland flow via processes associated with runoff-dominated ecosystems (e.g., soil compaction, impervious surface cover, drainage, and channelization). During the second stage, changes in water storage impact the capacity of ecosystems to buffer extremes in water quantity and quality (e.g., either losses in snowpack, wetlands, and groundwater recharge or gains in water and nutrient storage behind dams in reservoirs). During the third stage, extremes in water quantity and quality contribute to losses in ecosystem services and water security (e.g., clean drinking water, flood mitigation, and habitat availability). During the final stage, management and restoration strategies attempt to regain lost ecosystem structure, function, and services but need to adapt to climate change. By anticipating the increasing interaction between land use and climate change, intervention points can be identified, and management strategies can be adjusted to improve outcomes for realistic expectations. Overall, global water security cannot be adequately restored without considering an increasing interaction between land use and climate change across progressive stages and our ever-increasing human domination of the water cycle from degradation to ecosystem restoration.

Global Impacts of Long-Term Land Cover Changes Within China's Densely Populated Rural Regions

NASA Astrophysics Data System (ADS)

Ellis, E. C.

2006-12-01

Long-term changes in land cover are usually investigated in terms of large-scale change processes such as urban expansion, deforestation and land conversion to agriculture. Yet China's densely populated agricultural regions, which cover more than 2 million square kilometers of Monsoon Asia, have been transformed profoundly over the past fifty years by fine-scale changes in land cover caused by unprecedented changes in population, technology and social conditions. Using a regional sampling and upscaling design coupled with high-resolution landscape change measurements at five field sites, we investigated long-term changes in land cover and ecological processes, circa 1945 to 2002, within and across China's densely populated agricultural regions. As expected, the construction of buildings and roads increased impervious surface area over time, but the total net increase was surprising, being similar in magnitude to the total current extent of China's cities. Agricultural land area declined over the same period, while tree cover increased, by about 10%, driven by tree planting and regrowth around new buildings, the introduction of perennial agriculture, improved forestry, and declines in annual crop cultivation. Though changes in impervious surface areas were closely related to changes in population density, long-term changes in agricultural land and tree cover were unrelated to populated density and required explanation by more complex models with strong regional and biophysical components. Moreover, most of these changes occurred primarily at fine spatial scales (< 30 m), under the threshold for conventional global and regional land cover change measurements. Given that these changes in built structures and vegetation cover have the potential to contribute substantially to regional and global changes in biogeochemistry, hydrology, and land-atmosphere interactions, future investigations of these changes and their impacts across Monsoon Asia would benefit from models

Visualization and Analysis of Multi-scale Land Surface Products via Giovanni Portals

NASA Technical Reports Server (NTRS)

Shen, Suhung; Kempler, Steven J.; Gerasimov, Irina V.

2013-01-01

Large volumes of MODIS land data products at multiple spatial resolutions have been integrated into the Giovanni online analysis system to support studies on land cover and land use changes,focused on the Northern Eurasia and Monsoon Asia regions through the LCLUC program. Giovanni (Goddard Interactive Online Visualization ANd aNalysis Infrastructure) is a Web-based application developed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), providing a simple and intuitive way to visualize, analyze, and access Earth science remotely-sensed and modeled data.Customized Giovanni Web portals (Giovanni-NEESPI andGiovanni-MAIRS) have been created to integrate land, atmospheric,cryospheric, and societal products, enabling researchers to do quick exploration and basic analyses of land surface changes, and their relationships to climate, at global and regional scales. This presentation shows a sample Giovanni portal page, lists selected data products in the system, and illustrates potential analyses with imagesand time-series at global and regional scales, focusing on climatology and anomaly analysis. More information is available at the GES DISCMAIRS data support project portal: http:disc.sci.gsfc.nasa.govmairs.

The role of soil moisture in land surface-atmosphere coupling: climate model sensitivity experiments over India

NASA Astrophysics Data System (ADS)

Williams, Charles; Turner, Andrew

2015-04-01

It is generally acknowledged that anthropogenic land use changes, such as a shift from forested land into irrigated agriculture, may have an impact on regional climate and, in particular, rainfall patterns in both time and space. India provides an excellent example of a country in which widespread land use change has occurred during the last century, as the country tries to meet its growing demand for food. Of primary concern for agriculture is the Indian summer monsoon (ISM), which displays considerable seasonal and subseasonal variability. Although it is evident that changing rainfall variability will have a direct impact on land surface processes (such as soil moisture variability), the reverse impact is less well understood. However, the role of soil moisture in the coupling between the land surface and atmosphere needs to be properly explored before any potential impact of changing soil moisture variability on ISM rainfall can be understood. This paper attempts to address this issue, by conducting a number of sensitivity experiments using a state-of-the-art climate model from the UK Meteorological Office Hadley Centre: HadGEM2. Several experiments are undertaken, with the only difference between them being the extent to which soil moisture is coupled to the atmosphere. Firstly, the land surface is fully coupled to the atmosphere, globally (as in standard model configurations); secondly, the land surface is entirely uncoupled from the atmosphere, again globally, with soil moisture values being prescribed on a daily basis; thirdly, the land surface is uncoupled from the atmosphere over India but fully coupled elsewhere; and lastly, vice versa (i.e. the land surface is coupled to the atmosphere over India but uncoupled elsewhere). Early results from this study suggest certain 'hotspot' regions where the impact of soil moisture coupling/uncoupling may be important, and many of these regions coincide with previous studies. Focusing on the third experiment, i

Surface properties of Mars' polar layered deposits and polar landing sites

USGS Publications Warehouse

Vasavada, Ashwin R.; Williams, Jean-Pierre; Paige, David A.; Herkenhoff, Ken E.; Bridges, Nathan T.; Greeley, Ronald; Murray, Bruce C.; Bass, Deborah S.; McBride, Karen S.

2000-01-01

On December 3, 1999, the Mars Polar Lander and Mars Microprobes will land on the planet's south polar layered deposits near (76°S, 195°W) and conduct the first in situ studies of the planet's polar regions. The scientific goals of these missions address several poorly understood and globally significant issues, such as polar meteorology, the composition and volatile content of the layered deposits, the erosional state and mass balance of their surface, their possible relationship to climate cycles, and the nature of bright and dark aeolian material. Derived thermal inertias of the southern layered deposits are very low (50-100 J m-2 s-1/2 K-1), suggesting that the surface down to a depth of a few centimeters is generally fine grained or porous and free of an appreciable amount of rock or ice. The landing site region is smoother than typical cratered terrain on ∼1 km pixel-1 Viking Orbiter images but contains low-relief texture on ∼5 to 100 m pixel-1 Mariner 9 and Mars Global Surveyor images. The surface of the southern deposits is older than that of the northern deposits and appears to be modified by aeolian erosion or ablation of ground ice.

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.

The impact of climatic and non-climatic factors on land surface temperature in southwestern Romania

NASA Astrophysics Data System (ADS)

Roşca, Cristina Florina; Harpa, Gabriela Victoria; Croitoru, Adina-Eliza; Herbel, Ioana; Imbroane, Alexandru Mircea; Burada, Doina Cristina

2017-11-01

Land surface temperature is one of the most important parameters related to global warming. It depends mainly on soil type, discontinuous vegetation cover, or lack of precipitation. The main purpose of this paper is to investigate the relationship between high LST, synoptic conditions and air masses trajectories, vegetation cover, and soil type in one of the driest region in Romania. In order to calculate the land surface temperature and normalized difference vegetation index, five satellite images of LANDSAT missions 5 and 7, covering a period of 26 years (1986-2011), were selected, all of them collected in the month of June. The areas with low vegetation density were derived from normalized difference vegetation index, while soil types have been extracted from Corine Land Cover database. HYSPLIT application was employed to identify the air masses origin based on their backward trajectories for each of the five study cases. Pearson, logarithmic, and quadratic correlations were used to detect the relationships between land surface temperature and observed ground temperatures, as well as between land surface temperature and normalized difference vegetation index. The most important findings are: strong correlation between land surface temperature derived from satellite images and maximum ground temperature recorded in a weather station located in the area, as well as between areas with land surface temperature equal to or higher than 40.0 °C and those with lack of vegetation; the sandy soils are the most prone to high land surface temperature and lack of vegetation, followed by the chernozems and brown soils; extremely severe drought events may occur in the region.

Evaluation of snow modeling with Noah and Noah-MP land surface models in NCEP GFS/CFS system

NASA Astrophysics Data System (ADS)

Dong, J.; Ek, M. B.; Wei, H.; Meng, J.

2017-12-01

Land surface serves as lower boundary forcing in global forecast system (GFS) and climate forecast system (CFS), simulating interactions between land and the atmosphere. Understanding the underlying land model physics is a key to improving weather and seasonal prediction skills. With the upgrades in land model physics (e.g., release of newer versions of a land model), different land initializations, changes in parameterization schemes used in the land model (e.g., land physical parametrization options), and how the land impact is handled (e.g., physics ensemble approach), it always prompts the necessity that climate prediction experiments need to be re-conducted to examine its impact. The current NASA LIS (version 7) integrates NOAA operational land surface and hydrological models (NCEP's Noah, versions from 2.7.1 to 3.6 and the future Noah-MP), high-resolution satellite and observational data, and land DA tools. The newer versions of the Noah LSM used in operational models have a variety of enhancements compared to older versions, where the Noah-MP allows for different physics parameterization options and the choice could have large impact on physical processes underlying seasonal predictions. These impacts need to be reexamined before implemented into NCEP operational systems. A set of offline numerical experiments driven by the GFS forecast forcing have been conducted to evaluate the impact of snow modeling with daily Global Historical Climatology Network (GHCN).

Multidecadal climate variability of global lands and oceans

USGS Publications Warehouse

McCabe, G.J.; Palecki, M.A.

2006-01-01

Principal components analysis (PCA) and singular value decomposition (SVD) are used to identify the primary modes of decadal and multidecadal variability in annual global Palmer Drought Severity Index (PDSI) values and sea-surface temperature (SSTs). The PDSI and SST data for 1925-2003 were detrended and smoothed (with a 10-year moving average) to isolate the decadal and multidecadal variability. The first two principal components (PCs) of the PDSI PCA explained almost 38% of the decadal and multidecadal variance in the detrended and smoothed global annual PDSI data. The first two PCs of detrended and smoothed global annual SSTs explained nearly 56% of the decadal variability in global SSTs. The PDSI PCs and the SST PCs are directly correlated in a pairwise fashion. The first PDSI and SST PCs reflect variability of the detrended and smoothed annual Pacific Decadal Oscillation (PDO), as well as detrended and smoothed annual Indian Ocean SSTs. The second set of PCs is strongly associated with the Atlantic Multidecadal Oscillation (AMO). The SVD analysis of the cross-covariance of the PDSI and SST data confirmed the close link between the PDSI and SST modes of decadal and multidecadal variation and provided a verification of the PCA results. These findings indicate that the major modes of multidecadal variations in SSTs and land-surface climate conditions are highly interrelated through a small number of spatially complex but slowly varying teleconnections. Therefore, these relations may be adaptable to providing improved baseline conditions for seasonal climate forecasting. Published in 2006 by John Wiley & Sons, Ltd.

A comparative analysis of the Global Land Cover 2000 and MODIS land cover data sets

USGS Publications Warehouse

Giri, C.; Zhu, Z.; Reed, B.

2005-01-01

Accurate and up-to-date global land cover data sets are necessary for various global change research studies including climate change, biodiversity conservation, ecosystem assessment, and environmental modeling. In recent years, substantial advancement has been achieved in generating such data products. Yet, we are far from producing geospatially consistent high-quality data at an operational level. We compared the recently available Global Land Cover 2000 (GLC-2000) and MODerate resolution Imaging Spectrometer (MODIS) global land cover data to evaluate the similarities and differences in methodologies and results, and to identify areas of spatial agreement and disagreement. These two global land cover data sets were prepared using different data sources, classification systems, and methodologies, but using the same spatial resolution (i.e., 1 km) satellite data. Our analysis shows a general agreement at the class aggregate level except for savannas/shrublands, and wetlands. The disagreement, however, increases when comparing detailed land cover classes. Similarly, percent agreement between the two data sets was found to be highly variable among biomes. The identified areas of spatial agreement and disagreement will be useful for both data producers and users. Data producers may use the areas of spatial agreement for training area selection and pay special attention to areas of disagreement for further improvement in future land cover characterization and mapping. Users can conveniently use the findings in the areas of agreement, whereas users might need to verify the informaiton in the areas of disagreement with the help of secondary information. Learning from past experience and building on the existing infrastructure (e.g., regional networks), further research is necessary to (1) reduce ambiguity in land cover definitions, (2) increase availability of improved spatial, spectral, radiometric, and geometric resolution satellite data, and (3) develop advanced

Application of the global Land-Potential Knowledge System (LandPKS) mobile apps to land degradation, restoration and climate change adaptation

USDA-ARS?s Scientific Manuscript database

Combatting land degradation, promoting restoration and adapting to climate change all require an understanding of land potential. A global Land-Potential Knowledge System (LandPKS) is being developed that will address many of these limitations using an open source approach designed to allow anyone w...

Global Surface Net-Radiation at 5 km from MODIS Terra

DOE PAGES

Verma, Manish; Fisher, Joshua; Mallick, Kaniska; ...

2016-09-06

Reliable and fine resolution estimates of surface net-radiation are required for estimating latent and sensible heat fluxes between the land surface and the atmosphere. However, currently, fine resolution estimates of net-radiation are not available and consequently it is challenging to develop multi-year estimates of evapotranspiration at scales that can capture land surface heterogeneity and are relevant for policy and decision-making. We developed and evaluated a global net-radiation product at 5 km and 8-day resolution by combining mutually consistent atmosphere and land data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board Terra. Comparison with net-radiation measurements from 154 globally distributedmore » sites (414 site-years) from the FLUXNET and Surface Radiation budget network (SURFRAD) showed that the net-radiation product agreed well with measurements across seasons and climate types in the extratropics (Wilmott's index ranged from 0.74 for boreal to 0.63 for Mediterranean sites). Mean absolute deviation between the MODIS and measured net-radiation ranged from 38.0 ± 1.8 W.m -2 in boreal to 72.0 ± 4.1 W.m -2 in the tropical climates. The mean bias was small and constituted only 11%, 0.7%, 8.4%, 4.2%, 13.3%, and 5.4% of the mean absolute error in daytime net-radiation in boreal, Mediterranean, temperate-continental, temperate, semi-arid, and tropical climate, respectively. To assess the accuracy of the broader spatiotemporal patterns, we upscaled error-quantified MODIS net-radiation and compared it with the net-radiation estimates from the coarse spatial (1° x 1°) but high temporal resolution gridded net-radiation product from the Clouds and Earth's Radiant Energy System (CERES). Our estimates agreed closely with the net-radiation estimates from the CERES. Difference between the two was less than 10W.m -2 in 94% of the total land area. MODIS net-radiation product will be a valuable resource for the science community studying

Global Surface Net-Radiation at 5 km from MODIS Terra

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

Verma, Manish; Fisher, Joshua; Mallick, Kaniska

Reliable and fine resolution estimates of surface net-radiation are required for estimating latent and sensible heat fluxes between the land surface and the atmosphere. However, currently, fine resolution estimates of net-radiation are not available and consequently it is challenging to develop multi-year estimates of evapotranspiration at scales that can capture land surface heterogeneity and are relevant for policy and decision-making. We developed and evaluated a global net-radiation product at 5 km and 8-day resolution by combining mutually consistent atmosphere and land data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board Terra. Comparison with net-radiation measurements from 154 globally distributedmore » sites (414 site-years) from the FLUXNET and Surface Radiation budget network (SURFRAD) showed that the net-radiation product agreed well with measurements across seasons and climate types in the extratropics (Wilmott's index ranged from 0.74 for boreal to 0.63 for Mediterranean sites). Mean absolute deviation between the MODIS and measured net-radiation ranged from 38.0 ± 1.8 W.m -2 in boreal to 72.0 ± 4.1 W.m -2 in the tropical climates. The mean bias was small and constituted only 11%, 0.7%, 8.4%, 4.2%, 13.3%, and 5.4% of the mean absolute error in daytime net-radiation in boreal, Mediterranean, temperate-continental, temperate, semi-arid, and tropical climate, respectively. To assess the accuracy of the broader spatiotemporal patterns, we upscaled error-quantified MODIS net-radiation and compared it with the net-radiation estimates from the coarse spatial (1° x 1°) but high temporal resolution gridded net-radiation product from the Clouds and Earth's Radiant Energy System (CERES). Our estimates agreed closely with the net-radiation estimates from the CERES. Difference between the two was less than 10W.m -2 in 94% of the total land area. MODIS net-radiation product will be a valuable resource for the science community studying

Upscaling and Downscaling of Land Surface Fluxes with Surface Temperature

NASA Astrophysics Data System (ADS)

Kustas, W. P.; Anderson, M. C.; Hain, C.; Albertson, J. D.; Gao, F.; Yang, Y.

2015-12-01

Land surface temperature (LST) is a key surface boundary condition that is significantly correlated to surface flux partitioning between latent and sensible heat. The spatial and temporal variation in LST is driven by radiation, wind, vegetation cover and roughness as well as soil moisture status in the surface and root zone. Data from airborne and satellite-based platforms provide LST from ~10 km to sub meter resolutions. A land surface scheme called the Two-Source Energy Balance (TSEB) model has been incorporated into a multi-scale regional modeling system ALEXI (Atmosphere Land Exchange Inverse) and a disaggregation scheme (DisALEXI) using higher resolution LST. Results with this modeling system indicates that it can be applied over heterogeneous land surfaces and estimate reliable surface fluxes with minimal in situ information. Consequently, this modeling system allows for scaling energy fluxes from subfield to regional scales in regions with little ground data. In addition, the TSEB scheme has been incorporated into a large Eddy Simulation (LES) model for investigating dynamic interactions between variations in the land surface state reflected in the spatial pattern in LST and the lower atmospheric air properties affecting energy exchange. An overview of research results on scaling of fluxes and interactions with the lower atmosphere from the subfield level to regional scales using the TSEB, ALEX/DisALEX and the LES-TSEB approaches will be presented. Some unresolved issues in the use of LST at different spatial resolutions for estimating surface energy balance and upscaling fluxes, particularly evapotranspiration, will be discussed.

Global surface-based cloud observation for ISCCP

NASA Technical Reports Server (NTRS)

1994-01-01

Visual observations of cloud cover are hindered at night due to inadequate illumination of the clouds. This usually leads to an underestimation of the average cloud cover at night, especially for the amounts of middle and high clouds, in climatologies on surface observations. The diurnal cycles of cloud amounts, if based on all the surface observations, are therefore in error, but they can be obtained more accurately if the nighttime observations are screened to select those made under sufficient moonlight. Ten years of nighttime weather observations from the northern hemisphere in December were classified according to the illuminance of moonlight or twilight on the cloud tops, and a threshold level of illuminance was determined, above which the clouds are apparently detected adequately. This threshold corresponds to light from a full moon at an elevation angle of 6 degrees or from a partial moon at higher elevation, or twilight from the sun less than 9 degrees below the horizon. It permits the use of about 38% of the observations made with the sun below the horizon. The computed diurnal cycles of total cloud cover are altered considerably when this moonlight criterion is imposed. Maximum cloud cover over much of the ocean is now found to be at night or in the morning, whereas computations obtained without benefit of the moonlight criterion, as in our published atlases, showed the time of maximum to be noon or early afternoon in many regions. Cloud cover is greater at night than during the day over the open oceans far from the continents, particularly in summer. However, near noon maxima are still evident in the coastal regions, so that the global annual average oceanic cloud cover is still slightly greater during the day than at night, by 0.3%. Over land, where daytime maxima are still obtained but with reduced amplitude, average cloud cover is 3.3% greater during the daytime. The diurnal cycles of total cloud cover we obtain are compared with those of ISCCP for a

Vegetation controls on the biophysical surface properties at global scale

NASA Astrophysics Data System (ADS)

Forzieri, Giovanni; Cescatti, Alessandro

2016-04-01

Leaf area index (LAI) plays an important role in determining resistances to heat, moisture and momentum exchanges between the land surface and atmosphere. Exploring how variations in LAI may induce changes in the surface energy balance is a key to understanding vegetation-climate interactions and for predicting biophysical climate impacts associated to changes in land cover. To this end, we analyzed remote sensing-observed dynamics in LAI, surface energy fluxes and climate drivers at global scale. We investigated the link between interannual variability of LAI and the components of the surface energy budget under diverse climate gradients. Results show that a 25% increase in annual LAI may induce up to 2% increase in available surface energy, as consequence of higher short wave absorption due to reduced albedos, up to 20% increase and 10% decrease in latent and sensible heat, respectively, leading to a decrease of the Bowen ratio in densely vegetated canopies. Opposite patterns are found for a reduction in LAI of similar magnitude. Such changes are strongly modulated by concurrent year-to-year variations and climatological means of air temperature, precipitation and snow cover as well as by land cover-specific physiological processes. Boreal and semi-arid regions appear to be mostly exposed to large changes in biophysical surface processes induced by interannual fluctuations in LAI. The combination of the emergent patters translates into variations in the long-wave outgoing radiation that reflect the surface warming/cooling associated to LAI changes. These findings provide a deeper understanding of the vegetation control on biophysical surface properties and define a set of observational-based diagnostics of LAI-dependent land surface-atmosphere interactions.

Interannual variations and trends in global land surface phenology derived from enhanced vegetation index during 1982-2010

NASA Astrophysics Data System (ADS)

Zhang, Xiaoyang; Tan, Bin; Yu, Yunyue

2014-05-01

Land surface phenology is widely retrieved from satellite observations at regional and global scales, and its long-term record has been demonstrated to be a valuable tool for reconstructing past climate variations, monitoring the dynamics of terrestrial ecosystems in response to climate impacts, and predicting biological responses to future climate scenarios. This study detected global land surface phenology from the advanced very high resolution radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) data from 1982 to 2010. Based on daily enhanced vegetation index at a spatial resolution of 0.05 degrees, we simulated the seasonal vegetative trajectory for each individual pixel using piecewise logistic models, which was then used to detect the onset of greenness increase (OGI) and the length of vegetation growing season (GSL). Further, both overall interannual variations and pixel-based trends were examined across Koeppen's climate regions for the periods of 1982-1999 and 2000-2010, respectively. The results show that OGI and GSL varied considerably during 1982-2010 across the globe. Generally, the interannual variation could be more than a month in precipitation-controlled tropical and dry climates while it was mainly less than 15 days in temperature-controlled temperate, cold, and polar climates. OGI, overall, shifted early, and GSL was prolonged from 1982 to 2010 in most climate regions in North America and Asia while the consistently significant trends only occurred in cold climate and polar climate in North America. The overall trends in Europe were generally insignificant. Over South America, late OGI was consistent (particularly from 1982 to 1999) while either positive or negative GSL trends in a climate region were mostly reversed between the periods of 1982-1999 and 2000-2010. In the Northern Hemisphere of Africa, OGI trends were mostly insignificant, but prolonged GSL was evident over individual climate regions during the last 3

Translation of Land Surface Model Accuracy and Uncertainty into Coupled Land-Atmosphere Prediction

NASA Technical Reports Server (NTRS)

Santanello, Joseph A.; Kumar, Sujay; Peters-Lidard, Christa D.; Harrison, Kenneth W.; Zhou, Shuija

2012-01-01

Land-atmosphere (L-A) Interactions playa critical role in determining the diurnal evolution of both planetary boundary layer (PBL) and land surface heat and moisture budgets, as well as controlling feedbacks with clouds and precipitation that lead to the persistence of dry and wet regimes. Recent efforts to quantify the strength of L-A coupling in prediction models have produced diagnostics that integrate across both the land and PBL components of the system. In this study, we examine the impact of improved specification of land surface states, anomalies, and fluxes on coupled WRF forecasts during the summers of extreme dry (2006) and wet (2007) land surface conditions in the U.S. Southern Great Plains. The improved land initialization and surface flux parameterizations are obtained through the use of a new optimization and uncertainty estimation module in NASA's Land Information System (US-OPT/UE), whereby parameter sets are calibrated in the Noah land surface model and classified according to a land cover and soil type mapping of the observation sites to the full model domain. The impact of calibrated parameters on the a) spinup of the land surface used as initial conditions, and b) heat and moisture states and fluxes of the coupled WRF Simulations are then assessed in terms of ambient weather and land-atmosphere coupling along with measures of uncertainty propagation into the forecasts. In addition, the sensitivity of this approach to the period of calibration (dry, wet, average) is investigated. Finally, tradeoffs of computational tractability and scientific validity, and the potential for combining this approach with satellite remote sensing data are also discussed.

Estimation of Land Surface Temperature for the Quantitative Analysis of Land Cover of Lower Areas of Sindh to Assess the Impacts of Climate Variability

NASA Astrophysics Data System (ADS)

Qaisar, Maha

2016-07-01

Due to the present land use practices and climate variability, drastic shifts in regional climate and land covers are easily seen and their future reduction and gain are too well predicted. Therefore, there is an increasing need for data on land-cover changes at narrow and broad spatial scales. In this study, a remote sensing-based technique for land-cover-change analysis is applied to the lower Sindh areas for the last decade. Landsat satellite products were analyzed on an alternate yearly basis, from 1990 to 2016. Then Land-cover-change magnitudes were measured and mapped for alternate years. Land Surface Temperature (LST) is one of the critical elements in the natural phenomena of surface energy and water balance at local and global extent. However, LST was computed by using Landsat thermal bands via brightness temperature and a vegetation index. Normalized difference vegetation index (NDVI) was interpreted and maps were achieved. LST reflected NDVI patterns with complexity of vegetation patterns. Along with this, Object Based Image Analysis (OBIA) was done for classifying 5 major classes of water, vegetation, urban, marshy lands and barren lands with significant map layouts. Pakistan Meteorological Department provided the climate data in which rainfall, temperature and air temperature are included. Once the LST and OBIA are performed, overlay analysis was done to correlate the results of LST with OBIA and LST with meteorological data to ascertain the changes in land covers due to increasing centigrade of LST. However, satellite derived LST was also correlated with climate data for environmental analysis and to estimate Land Surface Temperature for assessing the inverse impacts of climate variability. This study's results demonstrate the land-cover changes in Lower Areas of Sindh including the Indus Delta mostly involve variations in land-cover conditions due to inter-annual climatic variability and temporary shifts in seasonality. However it is too concluded

First global WCRP shortwave surface radiation budget dataset

NASA Technical Reports Server (NTRS)

Whitlock, C. H.; Charlock, T. P.; Staylor, W. F.; Pinker, R. T.; Laszlo, I.; Ohmura, A.; Gilgen, H.; Konzelman, T.; DiPasquale, R. C.; Moats, C. D.

1995-01-01

Shortwave radiative fluxes that reach the Earth's surface are key factors that influence atmospheric and oceanic circulations as well as surface climate. Yet, information on these fluxes is meager. Surface site data are generally available from only a limited number of observing stations over land. Much less is known about the large-scale variability of the shortwave radiative fluxes over the oceans, which cover most of the globe. Recognizing the need to produce global-scale fields of such fluxes for use in climate research, the World Climate Research Program has initiated activities that led to the establishment of the Surface Radiation Budget Climatology Project with the ultimate goal to determine various components of the surface radiation budget from satellite data. In this paper, the first global products that resulted from this activity are described. Monthly and daily data on a 280-km grid scale are available. Samples of climate parameters obtainable from the dataset are presented. Emphasis is given to validation and limitations of the results. For most of the globe, satellite estimates have bias values between +/- 20 W/sq m and rms values are around 25 W/sq m. There are specific regions with much larger uncertainties however.

The Effect of Landing Surface on the Plantar Kinetics of Chinese Paratroopers Using Half-Squat Landing

PubMed Central

Li, Yi; Wu, Ji; Zheng, Chao; Huang, Rong Rong; Na, Yuhong; Yang, Fan; Wang, Zengshun; Wu, Di

2013-01-01

The objective of the study was to determine the effect of landing surface on plantar kinetics during a half-squat landing. Twenty male elite paratroopers with formal parachute landing training and over 2 years of parachute jumping experience were recruited. The subjects wore parachuting boots in which pressure sensing insoles were placed. Each subject was instructed to jump off a platform with a height of 60 cm, and land on either a hard or soft surface in a half-squat posture. Outcome measures were maximal plantar pressure, time to maximal plantar pressure (T-MPP), and pressure-time integral (PTI) upon landing on 10 plantar regions. Compared to a soft surface, hard surface produced higher maximal plantar pressure in the 1st to 4th metatarsal and mid-foot regions, but lower maximal plantar pressure in the 5th metatarsal region. Shorter T- MPP was found during hard surface landing in the 1st and 2nd metatarsal and medial rear foot. Landing on a hard surface landing resulted in a lower PTI than a soft surface in the 1stphalangeal region. For Chinese paratroopers, specific foot prosthesis should be designed to protect the1st to 4thmetatarsal region for hard surface landing, and the 1stphalangeal and 5thmetatarsal region for soft surface landing. Key Points Understanding plantar kinetics during the half-squat landing used by Chinese paratroopers can assist in the design of protective footwear. Compared to landing on a soft surface, a hard surface produced higher maximal plantar pressure in the 1st to 4th metatarsal and mid-foot regions, but lower maximal plantar pressure in the 5th metatarsal region. A shorter time to maximal plantar pressure was found during a hard surface landing in the 1st and 2nd metatarsals and medial rear foot. Landing on a hard surface resulted in a lower pressure-time integral than landing on a soft surface in the 1st phalangeal region. For Chinese paratroopers, specific foot prosthesis should be designed to protect the 1st to 4th metatarsal

On the Comparison of the Global Surface Soil Moisture product and Land Surface Modeling

NASA Astrophysics Data System (ADS)

Delorme, B., Jr.; Ottlé, C.; Peylin, P.; Polcher, J.

2016-12-01

Thanks to its large spatio-temporal coverage, the new ESA CCI multi-instruments dataset offers a good opportunity to assess and improve land surface models parametrization. In this study, the ESA CCI surface soil moisture (SSM) combined product (v2.2) has been compared to the simulated top first layers of the ORCHIDEE LSM (the continental part of the IPSL earth system model), in order to evaluate its potential of improvements with data assimilation techniques. The ambition of the work was to develop a comprehensive comparison methodology by analyzing simultaneously the temporal and spatial structures of both datasets. We analyzed the SSM synoptic, seasonal, and inter-annual variations by decomposing the signals into fast and slow components. ORCHIDEE was shown to adequately reproduce the observed SSM dynamics in terms of temporal correlation. However, these correlation scores are supposed to be strongly influenced by SSM seasonal variability and the quality of the model input forcing. Autocorrelation and spectral analyses brought out disagreements in the temporal inertia of the upper soil moisture reservoirs. By linking our results to land cover maps, we found that ORCHIDEE is more dependent on rainfall events compared to the observations in regions with sparse vegetation cover. These diflerences might be due to a wrong partition of rainfall between soil evaporation, transpiration, runofl and drainage in ORCHIDEE. To refine this analysis, a single value decomposition (SVD) of the co-variability between rainfall provided by WFDEI and soil moisture was pursued over Central Europe and South Africa. It showed that spatio-temporal co-varying patterns between ORCHIDEE and rainfall and the ESA-CCI product and rainfall are in relatively good agreement. However, the leading SVD pattern, which exhibits a strong annual cycle and explains the same portion of covariance for both datasets, explains a much larger fraction of variance for ORCHIDEE than for the ESA-CCI product

Using Landsat Thematic Mapper (TM) sensor to detect change in land surface temperature in relation to land use change in Yazd, Iran

NASA Astrophysics Data System (ADS)

Zareie, Sajad; Khosravi, Hassan; Nasiri, Abouzar; Dastorani, Mostafa

2016-11-01

Land surface temperature (LST) is one of the key parameters in the physics of land surface processes from local to global scales, and it is one of the indicators of environmental quality. Evaluation of the surface temperature distribution and its relation to existing land use types are very important to the investigation of the urban microclimate. In arid and semi-arid regions, understanding the role of land use changes in the formation of urban heat islands is necessary for urban planning to control or reduce surface temperature. The internal factors and environmental conditions of Yazd city have important roles in the formation of special thermal conditions in Iran. In this paper, we used the temperature-emissivity separation (TES) algorithm for LST retrieving from the TIRS (Thermal Infrared Sensor) data of the Landsat Thematic Mapper (TM). The root mean square error (RMSE) and coefficient of determination (R2) were used for validation of retrieved LST values. The RMSE of 0.9 and 0.87 °C and R2 of 0.98 and 0.99 were obtained for the 1998 and 2009 images, respectively. Land use types for the city of Yazd were identified and relationships between land use types, land surface temperature and normalized difference vegetation index (NDVI) were analyzed. The Kappa coefficient and overall accuracy were calculated for accuracy assessment of land use classification. The Kappa coefficient values are 0.96 and 0.95 and the overall accuracy values are 0.97 and 0.95 for the 1998 and 2009 classified images, respectively. The results showed an increase of 1.45 °C in the average surface temperature. The results of this study showed that optical and thermal remote sensing methodologies can be used to research urban environmental parameters. Finally, it was found that special thermal conditions in Yazd were formed by land use changes. Increasing the area of asphalt roads, residential, commercial and industrial land use types and decreasing the area of the parks, green spaces and

Analysis of relationships between land surface temperature and land use changes in the Yellow River Delta

NASA Astrophysics Data System (ADS)

Ning, Jicai; Gao, Zhiqiang; Meng, Ran; Xu, Fuxiang; Gao, Meng

2018-06-01

This study analyzed land use and land cover changes and their impact on land surface temperature using Landsat 5 Thematic Mapper and Landsat 8 Operational Land Imager and Thermal Infrared Sensor imagery of the Yellow River Delta. Six Landsat images comprising two time series were used to calculate the land surface temperature and correlated vegetation indices. The Yellow River Delta area has expanded substantially because of the deposited sediment carried from upstream reaches of the river. Between 1986 and 2015, approximately 35% of the land use area of the Yellow River Delta has been transformed into salterns and aquaculture ponds. Overall, land use conversion has occurred primarily from poorly utilized land into highly utilized land. To analyze the variation of land surface temperature, a mono-window algorithm was applied to retrieve the regional land surface temperature. The results showed bilinear correlation between land surface temperature and the vegetation indices (i.e., Normalized Difference Vegetation Index, Adjusted-Normalized Vegetation Index, Soil-Adjusted Vegetation Index, and Modified Soil-Adjusted Vegetation Index). Generally, values of the vegetation indices greater than the inflection point mean the land surface temperature and the vegetation indices are correlated negatively, and vice versa. Land surface temperature in coastal areas is affected considerably by local seawater temperature and weather conditions.

Land surface phenology of Northeast China during 2000-2015: temporal changes and relationships with climate changes.

PubMed

Zhang, Yue; Li, Lin; Wang, Hongbin; Zhang, Yao; Wang, Naijia; Chen, Junpeng

2017-10-01

As an important crop growing area, Northeast China (NEC) plays a vital role in China's food security, which has been severely affected by climate change in recent years. Vegetation phenology in this region is sensitive to climate change, and currently, the relationship between the phenology of NEC and climate change remains unclear. In this study, we used a satellite-derived normalized difference vegetation index (NDVI) to obtain the temporal patterns of the land surface phenology in NEC from 2000 to 2015 and validated the results using ground phenology observations. We then explored the relationships among land surface phenology, temperature, precipitation, and sunshine hours for relevant periods. Our results showed that the NEC experienced great phenological changes in terms of spatial heterogeneity during 2000-2015. The spatial patterns of land surface phenology mainly changed with altitude and land cover type. In most regions of NEC, the start date of land surface phenology had advanced by approximately 1.0 days year -1 , and the length of land surface phenology had been prolonged by approximately 1.0 days year -1 except for the needle-leaf and cropland areas, due to the warm conditions. We found that a distinct inter-annual variation in land surface phenology related to climate variables, even if some areas presented non-significant trends. Land surface phenology was coupled with climate variables and distinct responses at different combinations of temperature, precipitation, sunshine hours, altitude, and anthropogenic influence. These findings suggest that remote sensing and our phenology extracting methods hold great potential for helping to understand how land surface phenology is sensitive to global climate change.

Land-related global habitability science issues

NASA Technical Reports Server (NTRS)

1983-01-01

The scientific investigation of the viewpoint of the biosphere that living organisms and their physical and chemical environment are bound, inseparable parts of one set of closely coupled global processes of the global biogeochemical system, life and life support cycles, is discussed as one of the major scientific challenges of the next decade by building from understanding land processes to interdisciplinary, holistic studies of biospheric dynamics including human impacts.

[The progress in retrieving land surface temperature based on thermal infrared and microwave remote sensing technologies].

PubMed

Zhang, Jia-Hua; Li, Xin; Yao, Feng-Mei; Li, Xian-Hua

2009-08-01

Land surface temperature (LST) is an important parameter in the study on the exchange of substance and energy between land surface and air for the land surface physics process at regional and global scales. Many applications of satellites remotely sensed data must provide exact and quantificational LST, such as drought, high temperature, forest fire, earthquake, hydrology and the vegetation monitor, and the models of global circulation and regional climate also need LST as input parameter. Therefore, the retrieval of LST using remote sensing technology becomes one of the key tasks in quantificational remote sensing study. Normally, in the spectrum bands, the thermal infrared (TIR, 3-15 microm) and microwave bands (1 mm-1 m) are important for retrieval of the LST. In the present paper, firstly, several methods for estimating the LST on the basis of thermal infrared (TIR) remote sensing were synthetically reviewed, i. e., the LST measured with an ground-base infrared thermometer, the LST retrieval from mono-window algorithm (MWA), single-channel algorithm (SCA), split-window techniques (SWT) and multi-channels algorithm(MCA), single-channel & multi-angle algorithm and multi-channels algorithm & multi-angle algorithm, and retrieval method of land surface component temperature using thermal infrared remotely sensed satellite observation. Secondly, the study status of land surface emissivity (epsilon) was presented. Thirdly, in order to retrieve LST for all weather conditions, microwave remotely sensed data, instead of thermal infrared data, have been developed recently, and the LST retrieval method from passive microwave remotely sensed data was also introduced. Finally, the main merits and shortcomings of different kinds of LST retrieval methods were discussed, respectively.

Toward Improved Land Surface Initialization in Support of Regional WRF Forecasts at the Kenya Meteorological Service (KMS)

NASA Technical Reports Server (NTRS)

Case, Jonathan L.; Mungai, John; Sakwa, Vincent; Kabuchanga, Eric; Zavodsky, Bradley T.; Limaye, Ashutosh S.

2014-01-01

SPoRT/SERVIR/RCMRD/KMS Collaboration: Builds off strengths of each organization. SPoRT: Transition of satellite, modeling and verification capabilities; SERVIR-Africa/RCMRD: International capacity-building expertise; KMS: Operational organization with regional weather forecasting expertise in East Africa. Hypothesis: Improved land-surface initialization over Eastern Africa can lead to better temperature, moisture, and ultimately precipitation forecasts in NWP models. KMS currently initializes Weather Research and Forecasting (WRF) model with NCEP/Global Forecast System (GFS) model 0.5-deg initial / boundary condition data. LIS will provide much higher-resolution land-surface data at a scale more representative to regional WRF configuration. Future implementation of real-time NESDIS/VIIRS vegetation fraction to further improve land surface representativeness.

Global climate policy impacts on livestock, land use, livelihoods, and food security.

PubMed

Golub, Alla A; Henderson, Benjamin B; Hertel, Thomas W; Gerber, Pierre J; Rose, Steven K; Sohngen, Brent

2013-12-24

Recent research has shed light on the cost-effective contribution that agriculture can make to global greenhouse gas abatement; however, the resulting impacts on agricultural production, producer livelihoods, and food security remain largely unexplored. This paper provides an integrated assessment of the linkages between land-based climate policies, development, and food security, with a particular emphasis on abatement opportunities and impacts in the livestock sector. Targeting Annex I countries and exempting non-Annex I countries from land-based carbon policies on equity or food security grounds may result in significant leakage rates for livestock production and agriculture as a whole. We find that such leakage can be eliminated by supplying forest carbon sequestration incentives to non-Annex I countries. Furthermore, substantial additional global agricultural abatement can be attained by extending a greenhouse gas emissions tax to non-Annex I agricultural producers, while compensating them for their additional tax expenses. Because of their relatively large emissions intensities and limited abatement possibilities, ruminant meat producers face the greatest market adjustments to land-based climate policies. We also evaluate the impacts of climate policies on livelihoods and food consumption in developing countries. In the absence of non-Annex I abatement policies, these impacts are modest. However, strong income and food consumption impacts surface because of higher food costs after forest carbon sequestration is promoted at a global scale. Food consumption among unskilled labor households falls but rises for the representative farm households, because global agricultural supplies are restricted and farm prices rise sharply in the face of inelastic food demands.

Global climate policy impacts on livestock, land use, livelihoods, and food security

PubMed Central

Golub, Alla A.; Henderson, Benjamin B.; Hertel, Thomas W.; Gerber, Pierre J.; Rose, Steven K.; Sohngen, Brent

2013-01-01

Recent research has shed light on the cost-effective contribution that agriculture can make to global greenhouse gas abatement; however, the resulting impacts on agricultural production, producer livelihoods, and food security remain largely unexplored. This paper provides an integrated assessment of the linkages between land-based climate policies, development, and food security, with a particular emphasis on abatement opportunities and impacts in the livestock sector. Targeting Annex I countries and exempting non-Annex I countries from land-based carbon policies on equity or food security grounds may result in significant leakage rates for livestock production and agriculture as a whole. We find that such leakage can be eliminated by supplying forest carbon sequestration incentives to non-Annex I countries. Furthermore, substantial additional global agricultural abatement can be attained by extending a greenhouse gas emissions tax to non-Annex I agricultural producers, while compensating them for their additional tax expenses. Because of their relatively large emissions intensities and limited abatement possibilities, ruminant meat producers face the greatest market adjustments to land-based climate policies. We also evaluate the impacts of climate policies on livelihoods and food consumption in developing countries. In the absence of non-Annex I abatement policies, these impacts are modest. However, strong income and food consumption impacts surface because of higher food costs after forest carbon sequestration is promoted at a global scale. Food consumption among unskilled labor households falls but rises for the representative farm households, because global agricultural supplies are restricted and farm prices rise sharply in the face of inelastic food demands. PMID:23019587

Updated global soil map for the Weather Research and Forecasting model and soil moisture initialization for the Noah land surface model

NASA Astrophysics Data System (ADS)

DY, C. Y.; Fung, J. C. H.

2016-08-01

A meteorological model requires accurate initial conditions and boundary conditions to obtain realistic numerical weather predictions. The land surface controls the surface heat and moisture exchanges, which can be determined by the physical properties of the soil and soil state variables, subsequently exerting an effect on the boundary layer meteorology. The initial and boundary conditions of soil moisture are currently obtained via National Centers for Environmental Prediction FNL (Final) Operational Global Analysis data, which are collected operationally in 1° by 1° resolutions every 6 h. Another input to the model is the soil map generated by the Food and Agriculture Organization of the United Nations - United Nations Educational, Scientific and Cultural Organization (FAO-UNESCO) soil database, which combines several soil surveys from around the world. Both soil moisture from the FNL analysis data and the default soil map lack accuracy and feature coarse resolutions, particularly for certain areas of China. In this study, we update the global soil map with data from Beijing Normal University in 1 km by 1 km grids and propose an alternative method of soil moisture initialization. Simulations of the Weather Research and Forecasting model show that spinning-up the soil moisture improves near-surface temperature and relative humidity prediction using different types of soil moisture initialization. Explanations of that improvement and improvement of the planetary boundary layer height in performing process analysis are provided.

An approach for the long-term 30-m land surface snow-free albedo retrieval from historic Landsat surface reflectance and MODIS-based a priori anisotropy knowledge

USDA-ARS?s Scientific Manuscript database

Land surface albedo has been recognized by the Global Terrestrial Observing System (GTOS) as an essential climate variable crucial for accurate modeling and monitoring of the Earth’s radiative budget. While global climate studies can leverage albedo datasets from MODIS, VIIRS, and other coarse-reso...

Assessment of MERRA-2 Land Surface Energy Flux Estimates

NASA Technical Reports Server (NTRS)

Draper, Clara; Reichle, Rolf; Koster, Randal

2017-01-01

In MERRA-2, observed precipitation is inserted in place of model-generated precipitation at the land surface. The use of observed precipitation was originally developed for MERRA-Land(a land-only replay of MERRA with model-generated precipitation replaced with observations).Previously shown that the land hydrology in MERRA-2 and MERRA-Land is better than MERRA. We test whether the improved land surface hydrology in MERRA-2 leads to the expected improvements in the land surface energy fluxes and 2 m air temperatures (T2m).

Spatial modeling of agricultural land use change at global scale

NASA Astrophysics Data System (ADS)

Meiyappan, P.; Dalton, M.; O'Neill, B. C.; Jain, A. K.

2014-11-01

Long-term modeling of agricultural land use is central in global scale assessments of climate change, food security, biodiversity, and climate adaptation and mitigation policies. We present a global-scale dynamic land use allocation model and show that it can reproduce the broad spatial features of the past 100 years of evolution of cropland and pastureland patterns. The modeling approach integrates economic theory, observed land use history, and data on both socioeconomic and biophysical determinants of land use change, and estimates relationships using long-term historical data, thereby making it suitable for long-term projections. The underlying economic motivation is maximization of expected profits by hypothesized landowners within each grid cell. The model predicts fractional land use for cropland and pastureland within each grid cell based on socioeconomic and biophysical driving factors that change with time. The model explicitly incorporates the following key features: (1) land use competition, (2) spatial heterogeneity in the nature of driving factors across geographic regions, (3) spatial heterogeneity in the relative importance of driving factors and previous land use patterns in determining land use allocation, and (4) spatial and temporal autocorrelation in land use patterns. We show that land use allocation approaches based solely on previous land use history (but disregarding the impact of driving factors), or those accounting for both land use history and driving factors by mechanistically fitting models for the spatial processes of land use change do not reproduce well long-term historical land use patterns. With an example application to the terrestrial carbon cycle, we show that such inaccuracies in land use allocation can translate into significant implications for global environmental assessments. The modeling approach and its evaluation provide an example that can be useful to the land use, Integrated Assessment, and the Earth system modeling

WRF Simulation over the Eastern Africa by use of Land Surface Initialization

NASA Astrophysics Data System (ADS)

Sakwa, V. N.; Case, J.; Limaye, A. S.; Zavodsky, B.; Kabuchanga, E. S.; Mungai, J.

2014-12-01

The East Africa region experiences severe weather events associated with hazards of varying magnitude. It receives heavy precipitation which leads to wide spread flooding and lack of sufficient rainfall in some parts results into drought. Cases of flooding and drought are two key forecasting challenges for the Kenya Meteorological Service (KMS). The source of heat and moisture depends on the state of the land surface which interacts with the boundary layer of the atmosphere to produce excessive precipitation or lack of it that leads to severe drought. The development and evolution of precipitation systems are affected by heat and moisture fluxes from the land surface within weakly-sheared environments, such as in the tropics and sub-tropics. These heat and moisture fluxes during the day can be strongly influenced by land cover, vegetation, and soil moisture content. Therefore, it is important to represent the land surface state as accurately as possible in numerical weather prediction models. Improved modeling capabilities within the region have the potential to enhance forecast guidance in support of daily operations and high-impact weather over East Africa. KMS currently runs a configuration of the Weather Research and Forecasting (WRF) model in real time to support its daily forecasting operations, invoking the Non-hydrostatic Mesoscale Model (NMM) dynamical core. They make use of the National Oceanic and Atmospheric Administration / National Weather Service Science and Training Resource Center's Environmental Modeling System (EMS) to manage and produce the WRF-NMM model runs on a 7-km regional grid over Eastern Africa.SPoRT and SERVIR provide land surface initialization datasets and model verification tool. The NASA Land Information System (LIS) provide real-time, daily soil initialization data in place of interpolated Global Forecast System soil moisture and temperature data. Model verification is done using the Model Evaluation Tools (MET) package, in order

Land management: data availability and process understanding for global change studies.

PubMed

Erb, Karl-Heinz; Luyssaert, Sebastiaan; Meyfroidt, Patrick; Pongratz, Julia; Don, Axel; Kloster, Silvia; Kuemmerle, Tobias; Fetzel, Tamara; Fuchs, Richard; Herold, Martin; Haberl, Helmut; Jones, Chris D; Marín-Spiotta, Erika; McCallum, Ian; Robertson, Eddy; Seufert, Verena; Fritz, Steffen; Valade, Aude; Wiltshire, Andrew; Dolman, Albertus J

2017-02-01

In the light of daunting global sustainability challenges such as climate change, biodiversity loss and food security, improving our understanding of the complex dynamics of the Earth system is crucial. However, large knowledge gaps related to the effects of land management persist, in particular those human-induced changes in terrestrial ecosystems that do not result in land-cover conversions. Here, we review the current state of knowledge of ten common land management activities for their biogeochemical and biophysical impacts, the level of process understanding and data availability. Our review shows that ca. one-tenth of the ice-free land surface is under intense human management, half under medium and one-fifth under extensive management. Based on our review, we cluster these ten management activities into three groups: (i) management activities for which data sets are available, and for which a good knowledge base exists (cropland harvest and irrigation); (ii) management activities for which sufficient knowledge on biogeochemical and biophysical effects exists but robust global data sets are lacking (forest harvest, tree species selection, grazing and mowing harvest, N fertilization); and (iii) land management practices with severe data gaps concomitant with an unsatisfactory level of process understanding (crop species selection, artificial wetland drainage, tillage and fire management and crop residue management, an element of crop harvest). Although we identify multiple impediments to progress, we conclude that the current status of process understanding and data availability is sufficient to advance with incorporating management in, for example, Earth system or dynamic vegetation models in order to provide a systematic assessment of their role in the Earth system. This review contributes to a strategic prioritization of research efforts across multiple disciplines, including land system research, ecological research and Earth system modelling. © 2016

Surface Properties and Characteristics of Mars Landing Sites from Remote Sensing Data and Ground Truth

NASA Astrophysics Data System (ADS)

Golombek, M. P.; Haldemann, A. F.; Simpson, R. A.; Furgason, R. L.; Putzig, N. E.; Huertas, A.; Arvidson, R. E.; Heet, T.; Bell, J. F.; Mellon, M. T.; McEwen, A. S.

2008-12-01

Surface characteristics at the six sites where spacecraft have successfully landed on Mars can be related favorably to their signatures in remotely sensed data from orbit and from the Earth. Comparisons of the rock abundance, types and coverage of soils (and their physical properties), thermal inertia, albedo, and topographic slope all agree with orbital remote sensing estimates and show that the materials at the landing sites can be used as ground truth for the materials that make up most of the equatorial and mid- to moderately high-latitude regions of Mars. The six landing sites sample two of the three dominant global thermal inertia and albedo units that cover ~80% of the surface of Mars. The Viking, Spirit, Mars Pathfinder, and Phoenix landing sites are representative of the moderate to high thermal inertia and intermediate to high albedo unit that is dominated by crusty, cloddy, blocky or frozen soils (duricrust that may be layered) with various abundances of rocks and bright dust. The Opportunity landing site is representative of the moderate to high thermal inertia and low albedo surface unit that is relatively dust free and composed of dark eolian sand and/or increased abundance of rocks. Rock abundance derived from orbital thermal differencing techniques in the equatorial regions agrees with that determined from rock counts at the surface and varies from ~3-20% at the landing sites. The size-frequency distributions of rocks >1.5 m diameter fully resolvable in HiRISE images of the landing sites follow exponential models developed from lander measurements of smaller rocks and are continuous with these rock distributions indicating both are part of the same population. Interpretation of radar data confirms the presence of load bearing, relatively dense surfaces controlled by the soil type at the landing sites, regional rock populations from diffuse scattering similar to those observed directly at the sites, and root-mean-squared slopes that compare favorably

Developing Land Surface Type Map with Biome Classification Scheme Using Suomi NPP/JPSS VIIRS Data

NASA Astrophysics Data System (ADS)

Zhang, Rui; Huang, Chengquan; Zhan, Xiwu; Jin, Huiran

2016-08-01

Accurate representation of actual terrestrial surface types at regional to global scales is an important element for a wide range of applications, such as land surface parameterization, modeling of biogeochemical cycles, and carbon cycle studies. In this study, in order to meet the requirement of the retrieval of global leaf area index (LAI) and fraction of photosynthetically active radiation absorbed by the vegetation (fPAR) and other studies, a global map generated from Suomi National Polar- orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) surface reflectance data in six major biome classes based on their canopy structures, which include: Grass/Cereal Crops, Shrubs, Broadleaf Crops, Savannas, Broadleaf Forests, and Needleleaf Forests, was created. The primary biome classes were converted from an International Geosphere-Biosphere Program (IGBP) legend global surface type data that was created in previous study, and the separation of two crop types are based on a secondary classification.

Regional and global implications of land-use change and climate change

NASA Astrophysics Data System (ADS)

Stauffer, Heidi Lada

This dissertation has two main components. The first is a longterm regional climate modeling study of the effects of different types of land use changes on Southeast Asian climate under present-day climate conditions and under future projected climate conditions at the end of the 21st Century. The focus of the second component is to estimate daily heat index for projected extreme temperatures at the end of the 21st Century and projecting the number of people affected by those heat conditions. The first component of this study uses a high-resolution regional climate model centered on the Southeast Asian region to compare two land use change scenarios under modern climate and future projected climate conditions. Results from experiments under modern climate conditions indicate that changes in regional climate including widespread surface cooling, increased precipitation, and increased latent heat flux are primarily due to deforestation. As expected from other studies, future climate projections indicate increasing surface temperature and total precipitation. However, the combination of increasing global temperatures and irrigation appears to increase latent heat flux and evapotranspiration, leading to decrease in the surface temperature nearly the same magnitude, increasing both specific humidity and relative humidity. The increasing relative humidity causes low clouds to form, and the net surface solar absorbed flux decreases in response, which further cools the surface. These results imply that deforestation and irrigation have differing complex regional climate responses and the presence of irrigation could mask future surface temperature increases, at least in the short term and reinforce the importance of incorporating land use changes, particularly irrigation, into any studies of future regional climate. The second component of this study uses global daily maximum heat indices derived from future climate future climate simulations for 2098 and projected

Evaluation of MuSyQ land surface albedo based on LAnd surface Parameters VAlidation System (LAPVAS)

NASA Astrophysics Data System (ADS)

Dou, B.; Wen, J.; Xinwen, L.; Zhiming, F.; Wu, S.; Zhang, Y.

2016-12-01

satellite derived Land surface albedo is an essential climate variable which controls the earth energy budget and it can be used in applications such as climate change, hydrology, and numerical weather prediction. However, the accuracy and uncertainty of surface albedo products should be evaluated with a reliable reference truth data prior to applications. A new comprehensive and systemic project of china, called the Remote Sensing Application Network (CRSAN), has been launched recent years. Two subjects of this project is developing a Multi-source data Synergized Quantitative Remote Sensin g Production System ( MuSyQ ) and a Web-based validation system named LAnd surface remote sensing Product VAlidation System (LAPVAS) , which aims to generate a quantitative remote sensing product for ecosystem and environmental monitoring and validate them with a reference validation data and a standard validation system, respectively. Land surface BRDF/albedo is one of product datasets of MuSyQ which has a pentad period with 1km spatial resolution and is derived by Multi-sensor Combined BRDF Inversion ( MCBI ) Model. In this MuSyQ albedo evaluation, a multi-validation strategy is implemented by LAPVAS, including directly and multi-scale validation with field measured albedo and cross validation with MODIS albedo product with different land cover. The results reveal that MuSyQ albedo data with a 5-day temporal resolution is in higher sensibility and accuracy during land cover change period, e.g. snowing. But results without regard to snow or changed land cover, MuSyQ albedo generally is in similar accuracy with MODIS albedo and meet the climate modeling requirement of an absolute accuracy of 0.05.

First global WCRP shortwave surface radiation budget dataset

NASA Technical Reports Server (NTRS)

Whitlock, C. H.; Charlock, T. P.; Staylor, W. F.; Pinker, R. T.; Laszlo, I.; Ohmura, A.; Gilgen, H.; Konzelman, T.; Dipasquale, R. C.; Moats, C. D.

1995-01-01

Shortwave radiative fluxes that reach the earth's surface are key factors that influence atmospheric and oceanic circulations as well as surface climate. Yet, information on these fluxes is meager. Surface site data are generally available from only a limited number of observing stations over land. Much less is known about the large-scale variability of the shortwave radiative fluxes over the oceans, which cover most of the globe. Recognizing the need to produce global-scale fields of such fluxes for use in climate research, the World Climate Research Program has initiated activities that led to the establishment of the Surface Radiation Budget Climatology Project with the ultimate goal to determine various components of the surface radiation budget from satellite data. In this paper, the first global products that resulted from this activity are described. Monthly and daily data on a 280-km grid scale are available. Samples of climate parameters obtainable from the dataset are presented. Emphasis is given to validation and limitations of the results. For most of the globe, satellite estimates have bias values between +/- 20 W/sq m and root mean square (rms) values are around 25 W/sq m. There are specific regions with much larger uncertainties however.

Bridging the Global Precipitation and Soil Moisture Active Passive Missions: Variability of Microwave Surface Emissivity from In situ and Remote Sensing Perspectives

NASA Astrophysics Data System (ADS)

Zheng, Y.; Kirstetter, P.; Hong, Y.; Turk, J.

2016-12-01

The overland precipitation retrievals from satellite passive microwave (PMW) sensors such as the Global Precipitation Mission (GPM) microwave imager (GMI) are impacted by the land surface emissivity. The estimation of PMW emissivity faces challenges because it is highly variable under the influence of surface properties such as soil moisture, surface roughness and vegetation. This study proposes an improved quantitative understanding of the relationship between the emissivity and surface parameters. Surface parameter information is obtained through (i) in-situ measurements from the International Soil Moisture Network and (ii) satellite measurements from the Soil Moisture Active and Passive mission (SMAP) which provides global scale soil moisture estimates. The variation of emissivity is quantified with soil moisture, surface temperature and vegetation at various frequencies/polarization and over different types of land surfaces to sheds light into the processes governing the emission of the land. This analysis is used to estimate the emissivity under rainy conditions. The framework built with in-situ measurements serves as a benchmark for satellite-based analyses, which paves a way toward global scale emissivity estimates using SMAP.

Global surface temperature change analysis based on MODIS data in recent twelve years

NASA Astrophysics Data System (ADS)

Mao, K. B.; Ma, Y.; Tan, X. L.; Shen, X. Y.; Liu, G.; Li, Z. L.; Chen, J. M.; Xia, L.

2017-01-01

Global surface temperature change is one of the most important aspects in global climate change research. In this study, in order to overcome shortcomings of traditional observation methods in meteorology, a new method is proposed to calculate global mean surface temperature based on remote sensing data. We found that (1) the global mean surface temperature was close to 14.35 °C from 2001 to 2012, and the warmest and coldest surface temperatures of the global in the recent twelve years occurred in 2005 and 2008, respectively; (2) the warmest and coldest surface temperatures on the global land surface occurred in 2005 and 2001, respectively, and on the global ocean surface in 2010 and 2008, respectively; and (3) in recent twelve years, although most regions (especially the Southern Hemisphere) are warming, global warming is yet controversial because it is cooling in the central and eastern regions of Pacific Ocean, northern regions of the Atlantic Ocean, northern regions of China, Mongolia, southern regions of Russia, western regions of Canada and America, the eastern and northern regions of Australia, and the southern tip of Africa. The analysis of daily and seasonal temperature change indicates that the temperature change is mainly caused by the variation of orbit of celestial body. A big data model based on orbit position and gravitational-magmatic change of celestial body with the solar or the galactic system should be built and taken into account for climate and ecosystems change at a large spatial-temporal scale.

Integrating global socio-economic influences into a regional land use change model for China

NASA Astrophysics Data System (ADS)

Xu, Xia; Gao, Qiong; Peng, Changhui; Cui, Xuefeng; Liu, Yinghui; Jiang, Li

2014-03-01

With rapid economic development and urbanization, land use in China has experienced huge changes in recent years; and this will probably continue in the future. Land use problems in China are urgent and need further study. Rapid land-use change and economic development make China an ideal region for integrated land use change studies, particularly the examination of multiple factors and global-regional interactions in the context of global economic integration. This paper presents an integrated modeling approach to examine the impact of global socio-economic processes on land use changes at a regional scale. We develop an integrated model system by coupling a simple global socio-economic model (GLOBFOOD) and regional spatial allocation model (CLUE). The model system is illustrated with an application to land use in China. For a given climate change, population growth, and various socio-economic situations, a global socio-economic model simulates the impact of global market and economy on land use, and quantifies changes of different land use types. The land use spatial distribution model decides the type of land use most appropriate in each spatial grid by employing a weighted suitability index, derived from expert knowledge about the ecosystem state and site conditions. A series of model simulations will be conducted and analyzed to demonstrate the ability of the integrated model to link global socioeconomic factors with regional land use changes in China. The results allow an exploration of the future dynamics of land use and landscapes in China.

A Multimodel Global Drought Information System (GDIS) for Near Real-Time Monitoring of Surface Water Conditions (Invited)

NASA Astrophysics Data System (ADS)

Nijssen, B.

2013-12-01

While the absolute magnitude of economic losses associated with weather and climate disasters such as droughts is greatest in the developed world, the relative impact is much larger in the developing world, where agriculture typically constitutes a much larger percentage of the labor force and food insecurity is a major concern. Nonetheless, our ability to monitor and predict the development and occurrence of droughts at a global scale in near real-time is limited and long-term records of soil moisture are essentially non-existent globally The problem is particularly critical given that many of the most damaging droughts occur in parts of the world that are most deficient in terms of in situ precipitation observations. In recent years, a number of near real-time drought monitoring systems have been developed with regional or global extent. While direct observations of key variables such as moisture storage are missing, the evolution of land surface models that are globally applicable provides a means of reconstructing them. The implementation of a multi-model drought monitoring system is described, which provides near real-time estimates of surface moisture storage for the global land areas between 50S and 50N with a time lag of about one day. Near real-time forcings are derived from satellite-based precipitation estimates and modeled air temperatures. The system is distinguished from other operational systems in that it uses multiple land surface models to simulate surface moisture storage, which are then combined to derive a multi-model estimate of drought. Previous work has shown that while land surface models agree in broad context, particularly in terms of soil moisture percentiles, important differences remain, which motivates a multi-model ensemble approach. The system is an extension of similar systems developed by at the University of Washington for the Pacific Northwest and for the United States, but global application of the protocols used in the U

GLEAM version 3: Global Land Evaporation Datasets and Model

NASA Astrophysics Data System (ADS)

Martens, B.; Miralles, D. G.; Lievens, H.; van der Schalie, R.; de Jeu, R.; Fernandez-Prieto, D.; Verhoest, N.

2015-12-01

Terrestrial evaporation links energy, water and carbon cycles over land and is therefore a key variable of the climate system. However, the global-scale magnitude and variability of the flux, and the sensitivity of the underlying physical process to changes in environmental factors, are still poorly understood due to limitations in in situ measurements. As a result, several methods have risen to estimate global patterns of land evaporation from satellite observations. However, these algorithms generally differ in their approach to model evaporation, resulting in large differences in their estimates. One of these methods is GLEAM, the Global Land Evaporation: the Amsterdam Methodology. GLEAM estimates terrestrial evaporation based on daily satellite observations of meteorological variables, vegetation characteristics and soil moisture. Since the publication of the first version of the algorithm (2011), the model has been widely applied to analyse trends in the water cycle and land-atmospheric feedbacks during extreme hydrometeorological events. A third version of the GLEAM global datasets is foreseen by the end of 2015. Given the relevance of having a continuous and reliable record of global-scale evaporation estimates for climate and hydrological research, the establishment of an online data portal to host these data to the public is also foreseen. In this new release of the GLEAM datasets, different components of the model have been updated, with the most significant change being the revision of the data assimilation algorithm. In this presentation, we will highlight the most important changes of the methodology and present three new GLEAM datasets and their validation against in situ observations and an alternative dataset of terrestrial evaporation (ERA-Land). Results of the validation exercise indicate that the magnitude and the spatiotemporal variability of the modelled evaporation agree reasonably well with the estimates of ERA-Land and the in situ

LAnd surface remote sensing Products VAlidation System (LAPVAS) and its preliminary application

NASA Astrophysics Data System (ADS)

Lin, Xingwen; Wen, Jianguang; Tang, Yong; Ma, Mingguo; Dou, Baocheng; Wu, Xiaodan; Meng, Lumin

2014-11-01

The long term record of remote sensing product shows the land surface parameters with spatial and temporal change to support regional and global scientific research widely. Remote sensing product with different sensors and different algorithms is necessary to be validated to ensure the high quality remote sensing product. Investigation about the remote sensing product validation shows that it is a complex processing both the quality of in-situ data requirement and method of precision assessment. A comprehensive validation should be needed with long time series and multiple land surface types. So a system named as land surface remote sensing product is designed in this paper to assess the uncertainty information of the remote sensing products based on a amount of in situ data and the validation techniques. The designed validation system platform consists of three parts: Validation databases Precision analysis subsystem, Inter-external interface of system. These three parts are built by some essential service modules, such as Data-Read service modules, Data-Insert service modules, Data-Associated service modules, Precision-Analysis service modules, Scale-Change service modules and so on. To run the validation system platform, users could order these service modules and choreograph them by the user interactive and then compete the validation tasks of remote sensing products (such as LAI ,ALBEDO ,VI etc.) . Taking SOA-based architecture as the framework of this system. The benefit of this architecture is the good service modules which could be independent of any development environment by standards such as the Web-Service Description Language(WSDL). The standard language: C++ and java will used as the primary programming language to create service modules. One of the key land surface parameter, albedo, is selected as an example of the system application. It is illustrated that the LAPVAS has a good performance to implement the land surface remote sensing product

Spatiotemporal Variations in the Difference between Satellite-observed Land Surface Temperature and Station-based Near-surface Air Temperature

NASA Astrophysics Data System (ADS)

Lian, X.

2016-12-01

There is an increasing demand to integrate land surface temperature (LST) into climate research due to its global coverage, which requires a comprehensive knowledge of its distinctive characteristics compared to near-surface air temperature ( ). Using satellite observations and in-situ station-based datasets, we conducted a global-scale assessment of the spatial, seasonal, and interannual variations in the difference between daytime maximum LST and daytime maximum ( , LST - ) during 2003-2014. Spatially, LST is generally higher than over arid and sparsely vegetated regions in the mid-low latitudes, but LST is lower than in the tropical rainforests due to strong evaporative cooling, and in the high-latitude regions due to snow-induced radiative cooling. Seasonally, is negative in tropical regions throughout the year, while it displays a pronounced seasonality in both the mid-latitudes and boreal regions. The seasonality in the mid-latitudes is a result of the asynchronous responses of LST and to the seasonal cycle of radiation and vegetation abundance, whereas in the boreal regions, seasonality is mainly caused by the change in snow cover. At an interannual scale, only a small proportion of the land surface displays a statistically significant trend (P <0.05) due to the short time span of current measurements. Our study identified substantial spatial heterogeneity and seasonality in , as well as its determinant environmental drivers, and thus provides a useful reference for monitoring near-surface temperature changes using remote sensing, particularly in remote regions.

Global land cover mapping and characterization: present situation and future research priorities

USGS Publications Warehouse

Giri, Chandra

2005-01-01

The availability and accessibility of global land cover data sets plays an important role in many global change studies. The importance of such science‐based information is also reflected in a number of international, regional, and national projects and programs. Recent developments in earth observing satellite technology, information technology, computer hardware and software, and infrastructure development have helped developed better quality land cover data sets. As a result, such data sets are increasingly becoming available, the user‐base is ever widening, application areas have been expanding, and the potential of many other applications are enormous. Yet, we are far from producing high quality global land cover data sets. This paper examines the progress in the development of digital global land cover data, their availability, and current applications. Problems and opportunities are also explained. The overview sets the stage for identifying future research priorities needed for operational land cover assessment and monitoring.

Pre-Launch Performance Assessment of the VIIRS Land Surface Temperature Environmental Data Record

NASA Astrophysics Data System (ADS)

Hauss, B.; Ip, J.; Agravante, H.

2009-12-01

The Visible/Infrared Imager Radiometer Suite (VIIRS) Land Surface Temperature (LST) Environmental Data Record (EDR) provides the surface temperature of land surface including coastal and inland-water pixels at VIIRS moderate resolution (750m) during both day and night. To predict the LST under optimal conditions, the retrieval algorithm utilizes a dual split-window approach with both Short-wave Infrared (SWIR) channels at 3.70 µm (M12) and 4.05 µm (M13), and Long-wave Infrared (LWIR) channels at 10.76 µm (M15) and 12.01 µm (M16) to correct for atmospheric water vapor. Under less optimal conditions, the algorithm uses a fallback split-window approach with M15 and M16 channels. By comparison, the MODIS generalized split-window algorithm only uses the LWIR bands in the retrieval of surface temperature because of the concern for both solar contamination and large emissivity variations in the SWIR bands. In this paper, we assess whether these concerns are real and whether there is an impact on the precision and accuracy of the LST retrieval. The algorithm relies on the VIIRS Cloud Mask IP for identifying cloudy and ocean pixels, the VIIRS Surface Type EDR for identifying the IGBP land cover type for the pixels, and the VIIRS Aerosol Optical Thickness (AOT) IP for excluding pixels with AOT greater than 1.0. In this paper, we will report the pre-launch performance assessment of the LST EDR based on global synthetic data and proxy data from Terra MODIS. Results of both the split-window and dual split-window algorithms will be assessed by comparison either to synthetic "truth" or results of the MODIS retrieval. We will also show that the results of the assessment with proxy data are consistent with those obtained using the global synthetic data.

Global modeling of land water and energy balances. Part I: The land dynamics (LaD) model

USGS Publications Warehouse

Milly, P.C.D.; Shmakin, A.B.

2002-01-01

A simple model of large-scale land (continental) water and energy balances is presented. The model is an extension of an earlier scheme with a record of successful application in climate modeling. The most important changes from the original model include 1) introduction of non-water-stressed stomatal control of transpiration, in order to correct a tendency toward excessive evaporation: 2) conversion from globally constant parameters (with the exception of vegetation-dependent snow-free surface albedo) to more complete vegetation and soil dependence of all parameters, in order to provide more realistic representation of geographic variations in water and energy balances and to enable model-based investigations of land-cover change; 3) introduction of soil sensible heat storage and transport, in order to move toward realistic diurnal-cycle modeling; 4) a groundwater (saturated-zone) storage reservoir, in order to provide more realistic temporal variability of runoff; and 5) a rudimentary runoff-routing scheme for delivery of runoff to the ocean, in order to provide realistic freshwater forcing of the ocean general circulation model component of a global climate model. The new model is tested with forcing from the International Satellite Land Surface Climatology Project Initiative I global dataset and a recently produced observation-based water-balance dataset for major river basins of the world. Model performance is evaluated by comparing computed and observed runoff ratios from many major river basins of the world. Special attention is given to distinguishing between two components of the apparent runoff ratio error: the part due to intrinsic model error and the part due to errors in the assumed precipitation forcing. The pattern of discrepancies between modeled and observed runoff ratios is consistent with results from a companion study of precipitation estimation errors. The new model is tuned by adjustment of a globally constant scale factor for non

Mapping Global Ocean Surface Albedo from Satellite Observations: Models, Algorithms, and Datasets

NASA Astrophysics Data System (ADS)

Li, X.; Fan, X.; Yan, H.; Li, A.; Wang, M.; Qu, Y.

2018-04-01

Ocean surface albedo (OSA) is one of the important parameters in surface radiation budget (SRB). It is usually considered as a controlling factor of the heat exchange among the atmosphere and ocean. The temporal and spatial dynamics of OSA determine the energy absorption of upper level ocean water, and have influences on the oceanic currents, atmospheric circulations, and transportation of material and energy of hydrosphere. Therefore, various parameterizations and models have been developed for describing the dynamics of OSA. However, it has been demonstrated that the currently available OSA datasets cannot full fill the requirement of global climate change studies. In this study, we present a literature review on mapping global OSA from satellite observations. The models (parameterizations, the coupled ocean-atmosphere radiative transfer (COART), and the three component ocean water albedo (TCOWA)), algorithms (the estimation method based on reanalysis data, and the direct-estimation algorithm), and datasets (the cloud, albedo and radiation (CLARA) surface albedo product, dataset derived by the TCOWA model, and the global land surface satellite (GLASS) phase-2 surface broadband albedo product) of OSA have been discussed, separately.

Changes in Land Surface Water Dynamics since the 1990s and Relation to Population Pressure

NASA Technical Reports Server (NTRS)

Prigent, C.; Papa, F.; Aires, F.; Jimenez, C.; Rossow, W. B.; Matthews, E.

2012-01-01

We developed a remote sensing approach based on multi-satellite observations, which provides an unprecedented estimate of monthly distribution and area of land-surface open water over the whole globe. Results for 1993 to 2007 exhibit a large seasonal and inter-annual variability of the inundation extent with an overall decline in global average maximum inundated area of 6% during the fifteen-year period, primarily in tropical and subtropical South America and South Asia. The largest declines of open water are found where large increases in population have occurred over the last two decades, suggesting a global scale effect of human activities on continental surface freshwater: denser population can impact local hydrology by reducing freshwater extent, by draining marshes and wetlands, and by increasing water withdrawals. Citation: Prigent, C., F. Papa, F. Aires, C. Jimenez, W. B. Rossow, and E. Matthews (2012), Changes in land surface water dynamics since the 1990s and relation to population pressure, in section 4, insisting on the potential applications of the wetland dataset.

Improving the Fit of a Land-Surface Model to Data Using its Adjoint

NASA Astrophysics Data System (ADS)

Raoult, N.; Jupp, T. E.; Cox, P. M.; Luke, C.

2015-12-01

Land-surface models (LSMs) are of growing importance in the world of climate prediction. They are crucial components of larger Earth system models that are aimed at understanding the effects of land surface processes on the global carbon cycle. The Joint UK Land Environment Simulator (JULES) is the land-surface model used by the UK Met Office. It has been automatically differentiated using commercial software from FastOpt, resulting in an analytical gradient, or 'adjoint', of the model. Using this adjoint, the adJULES parameter estimation system has been developed, to search for locally optimum parameter sets by calibrating against observations. adJULES presents an opportunity to confront JULES with many different observations, and make improvements to the model parameterisation. In the newest version of adJULES, multiple sites can be used in the calibration, to giving a generic set of parameters that can be generalised over plant functional types. We present an introduction to the adJULES system and its applications to data from a variety of flux tower sites. We show that calculation of the 2nd derivative of JULES allows us to produce posterior probability density functions of the parameters and how knowledge of parameter values is constrained by observations.

REGIONAL AND GLOBAL PATTERNS OF POPULATION, LAND USE AND LAND COVER CHANGE: AN OVERVIEW OF STRESSORS AND IMPACTS

EPA Science Inventory

This paper provides an overview of land use and land cover (LULC) change and regional to global patterns of that change and responses. Human activities now dominate the Earth's global ecosystem and LULC change is one of the most pervasive and influential activities. LULC change a...

Assessing the Effects of Irrigation on Land Surface Processes Utilizing CLM.PF in Los Angeles, California

NASA Astrophysics Data System (ADS)

Reyes, B.; Vahmani, P.; Hogue, T. S.; Maxwell, R. M.

2013-05-01

Irrigation can significantly alter land surface properties including increases in evapotranspiration (ET) and latent heat flux and a decrease in land surface temperatures that have a wide range of effects on the hydrologic cycle. However, most irrigation in land surface modeling studies has generally been limited to large-scale cropland applications while ignoring the, relatively, much smaller use of irrigation in urban areas. Although this assumption may be valid in global studies, as we seek to apply models at higher resolutions and at more local scales, irrigation in urban areas can become a key factor in land-atmosphere interactions. Landscape irrigation can account for large portions of residential urban water use, especially in semi-arid environments (e.g. ~50% in Los Angeles, CA). Previous modeling efforts in urbanized semi-arid regions have shown that disregarding irrigation leads to inaccurate representation of the energy budget. The current research models a 49.5-km2 (19.11-mi2) domain near downtown Los Angeles in the Ballona Creek watershed at a high spatial and temporal resolution using a coupled hydrologic (ParFlow) and land surface model (CLM). Our goals are to (1) provide a sensitivity analysis for urban irrigation parameters including sensitivity to total volume and timing of irrigation, (2) assess the effects of irrigation on varying land cover types on the energy budget, and (3) evaluate if residential water use data is useful in providing estimates for irrigation in land surface modeling. Observed values of land surface parameters from remote sensing products (Land Surface Temperature and ET), water use data from the Los Angeles Department of Water and Power (LADWP), and modeling results from an irrigated version of the NOAH-Urban Canopy Model are being used for comparison and evaluation. Our analysis provides critical information on the degree to which urban irrigation should be represented in high-resolution, semi-arid urban land surface

Assessment of the NASA-USGS Global Land Survey (GLS) Datasets

USGS Publications Warehouse

Gutman, Garik; Huang, Chengquan; Chander, Gyanesh; Noojipady, Praveen; Masek, Jeffery G.

2013-01-01

The Global Land Survey (GLS) datasets are a collection of orthorectified, cloud-minimized Landsat-type satellite images, providing near complete coverage of the global land area decadally since the early 1970s. The global mosaics are centered on 1975, 1990, 2000, 2005, and 2010, and consist of data acquired from four sensors: Enhanced Thematic Mapper Plus, Thematic Mapper, Multispectral Scanner, and Advanced Land Imager. The GLS datasets have been widely used in land-cover and land-use change studies at local, regional, and global scales. This study evaluates the GLS datasets with respect to their spatial coverage, temporal consistency, geodetic accuracy, radiometric calibration consistency, image completeness, extent of cloud contamination, and residual gaps. In general, the three latest GLS datasets are of a better quality than the GLS-1990 and GLS-1975 datasets, with most of the imagery (85%) having cloud cover of less than 10%, the acquisition years clustered much more tightly around their target years, better co-registration relative to GLS-2000, and better radiometric absolute calibration. Probably, the most significant impediment to scientific use of the datasets is the variability of image phenology (i.e., acquisition day of year). This paper provides end-users with an assessment of the quality of the GLS datasets for specific applications, and where possible, suggestions for mitigating their deficiencies.

Improved global simulation of groundwater-ecosystem interactions via tight coupling of a dynamic global ecosystem model and a global hydrological model

NASA Astrophysics Data System (ADS)

Braakhekke, Maarten; Rebel, Karin; Dekker, Stefan; Smith, Benjamin; Sutanudjaja, Edwin; van Beek, Rens; van Kampenhout, Leo; Wassen, Martin

2017-04-01

In up to 30% of the global land surface ecosystems are potentially influenced by the presence of a shallow groundwater table. In these regions upward water flux by capillary rise increases soil moisture availability in the root zone, which has a strong effect on evapotranspiration, vegetation dynamics, and fluxes of carbon and nitrogen. Most global hydrological models and several land surface models simulate groundwater table dynamics and their effects on land surface processes. However, these models typically have relatively simplistic representation of vegetation and do not consider changes in vegetation type and structure. Dynamic global vegetation models (DGVMs), describe land surface from an ecological perspective, combining detailed description of vegetation dynamics and structure, and biogeochemical processes and are thus more appropriate to simulate the ecological and biogeochemical effects of groundwater interactions. However, currently virtually all DGVMs ignore these effects, assuming that water tables are too deep to affect soil moisture in the root zone. We have implemented a tight coupling between the dynamic global ecosystem model LPJ-GUESS and the global hydrological model PCR-GLOBWB, which explicitly simulates groundwater dynamics. This coupled model allows us to explicitly account for groundwater effects on terrestrial ecosystem processes at global scale. Results of global simulations indicate that groundwater strongly influences fluxes of water, carbon and nitrogen, in many regions, adding up to a considerable effect at the global scale.

Estimation of Surface Air Temperature from MODIS 1km Resolution Land Surface Temperature Over Northern China

NASA Technical Reports Server (NTRS)

Shen, Suhung; Leptoukh, Gregory G.; Gerasimov, Irina

2010-01-01

Surface air temperature is a critical variable to describe the energy and water cycle of the Earth-atmosphere system and is a key input element for hydrology and land surface models. It is a very important variable in agricultural applications and climate change studies. This is a preliminary study to examine statistical relationships between ground meteorological station measured surface daily maximum/minimum air temperature and satellite remotely sensed land surface temperature from MODIS over the dry and semiarid regions of northern China. Studies were conducted for both MODIS-Terra and MODIS-Aqua by using year 2009 data. Results indicate that the relationships between surface air temperature and remotely sensed land surface temperature are statistically significant. The relationships between the maximum air temperature and daytime land surface temperature depends significantly on land surface types and vegetation index, but the minimum air temperature and nighttime land surface temperature has little dependence on the surface conditions. Based on linear regression relationship between surface air temperature and MODIS land surface temperature, surface maximum and minimum air temperatures are estimated from 1km MODIS land surface temperature under clear sky conditions. The statistical errors (sigma) of the estimated daily maximum (minimum) air temperature is about 3.8 C(3.7 C).

Land surface Verification Toolkit (LVT)

NASA Technical Reports Server (NTRS)

Kumar, Sujay V.

2017-01-01

LVT is a framework developed to provide an automated, consolidated environment for systematic land surface model evaluation Includes support for a range of in-situ, remote-sensing and other model and reanalysis products. Supports the analysis of outputs from various LIS subsystems, including LIS-DA, LIS-OPT, LIS-UE. Note: The Land Information System Verification Toolkit (LVT) is a NASA software tool designed to enable the evaluation, analysis and comparison of outputs generated by the Land Information System (LIS). The LVT software is released under the terms and conditions of the NASA Open Source Agreement (NOSA) Version 1.1 or later. Land Information System Verification Toolkit (LVT) NOSA.

Land surface dynamics monitoring using microwave passive satellite sensors

NASA Astrophysics Data System (ADS)

Guijarro, Lizbeth Noemi

Soil moisture, surface temperature and vegetation are variables that play an important role in our environment. There is growing demand for accurate estimation of these geophysical parameters for the research of global climate models (GCMs), weather, hydrological and flooding models, and for the application to agricultural assessment, land cover change, and a wide variety of other uses that meet the needs for the study of our environment. The different studies covered in this dissertation evaluate the capabilities and limitations of microwave passive sensors to monitor land surface dynamics. The first study evaluates the 19 GHz channel of the SSM/I instrument with a radiative transfer model and in situ datasets from the Illinois stations and the Oklahoma Mesonet to retrieve land surface temperature and surface soil moisture. The surface temperatures were retrieved with an average error of 5 K and the soil moisture with an average error of 6%. The results show that the 19 GHz channel can be used to qualitatively predict the spatial and temporal variability of surface soil moisture and surface temperature at regional scales. In the second study, in situ observations were compared with sensor observations to evaluate aspects of low and high spatial resolution at multiple frequencies with data collected from the Southern Great Plains Experiment (SGP99). The results showed that the sensitivity to soil moisture at each frequency is a function of wavelength and amount of vegetation. The results confirmed that L-band is more optimal for soil moisture, but each sensor can provide soil moisture information if the vegetation water content is low. The spatial variability of the emissivities reveals that resolution suffers considerably at higher frequencies. The third study evaluates C- and X-bands of the AMSR-E instrument. In situ datasets from the Soil Moisture Experiments (SMEX03) in South Central Georgia were utilized to validate the AMSR-E soil moisture product and to

AATSR: global-change and surface-temperature measurements from Envisat

NASA Astrophysics Data System (ADS)

Llewellyn-Jones, D.; Edwards, M. C.; Mutlow, C. T.; Birks, A. R.; Barton, I. J.; Tait, H.

2001-02-01

The Advanced Along-Track Scanning Radiometer (AATSR) onboard ESA's Envisat spacecraft is designed to meet the challenging task of monitoring and detecting climate change. It builds on the success of its predecessor instruments on the ERS-1 and ERS-2 satellites, and will lead to a 15+ year record of precise and accurate global Sea-Surface Temperature (SST) measurements, thereby making a valuable contribution to the long-term climate record. With its high-accuracy, high-quality imagery and channels in the visible, near-infrared and thermal wavelengths, AATSR data will support many applications in addition to oceanographic and climate research, including a wide range of land-surface, cryosphere and atmospheric studies.

The implementation and validation of improved land-surface hydrology in an atmospheric general circulation model

NASA Technical Reports Server (NTRS)

Johnson, Kevin D.; Entekhabi, Dara; Eagleson, Peter S.

1993-01-01

New land-surface hydrologic parameterizations are implemented into the NASA Goddard Institute for Space Studies (GISS) General Circulation Model (GCM). These parameterizations are: 1) runoff and evapotranspiration functions that include the effects of subgrid-scale spatial variability and use physically based equations of hydrologic flux at the soil surface and 2) a realistic soil moisture diffusion scheme for the movement of water and root sink in the soil column. A one-dimensional climate model with a complete hydrologic cycle is used to screen the basic sensitivities of the hydrological parameterizations before implementation into the full three-dimensional GCM. Results of the final simulation with the GISS GCM and the new land-surface hydrology indicate that the runoff rate, especially in the tropics, is significantly improved. As a result, the remaining components of the heat and moisture balance show similar improvements when compared to observations. The validation of model results is carried from the large global (ocean and land-surface) scale to the zonal, continental, and finally the regional river basin scales.

Ocean to land moisture transport is reflected in sea surface salinity

NASA Astrophysics Data System (ADS)

Schmitt, R. W.; Schanze, J. J.; Li, L.; Ummenhofer, C.

2016-02-01

The ocean has a much larger water cycle than the land, with global ocean evaporation of 13 Sverdrups being 10 times larger than the sum of all river flows. This disparity and the different dynamics of dry surfaces, have led to an unfortunate disconnect between terrestrial hydrologists and oceanographers. Here we show that there is in fact a close coupling between the water cycles of ocean and land. In both cases there is much local recycling of moisture, since it does not travel far in the atmosphere. We argue that the most important water cycle variable is the net export (or import) of water from (to) an area. Over the open ocean this is just evaporation minus precipitation (E-P). The "P vs E" plot is a valuable tool for identifying the source and sink regions of the water cycle. The subtropical high pressure systems are the source regions of the water cycle, with a global net export of 4.5 Sv. The three sinks are the ITCZ in the tropics, the high latitude subpolar lows, and the land, all at about 1.5 Sv, though the subpolar lows do receive more water than the tropics, where high rainfall is maintained by much local recycling. Of course, the signature of E-P in the open ocean is the sea surface salinity (SSS), as only net freshwater fluxes can create salinity variations. With the land receiving 1/3 of the oceanic export, we should expect close coupling between terrestrial rainfall and the salinity of nearby oceans, and SSS variations have indeed been found to be valuable for seasonal rainfall forecasts on land. The remarkable 3-6 month lead of winter-spring SSS over summer rainfall appears to be mediated by the recycling process on land through soil moisture. When soil moisture is high, terrestrial regions can become more oceanic-like, with solar heating energizing evaporation and leading to down-stream propagation of the moisture signal (the "brown ocean" effect). The correlation of high SSS with high rainfall promises to be a very valuable seasonal prediction

Terrestrial Ecosystems - Land Surface Forms of the Conterminous United States

USGS Publications Warehouse

Cress, Jill J.; Sayre, Roger G.; Comer, Patrick; Warner, Harumi

2009-01-01

As part of an effort to map terrestrial ecosystems, the U.S. Geological Survey has generated land surface form classes to be used in creating maps depicting standardized, terrestrial ecosystem models for the conterminous United States, using an ecosystems classification developed by NatureServe . A biophysical stratification approach, developed for South America and now being implemented globally, was used to model the ecosystem distributions. Since land surface forms strongly influence the differentiation and distribution of terrestrial ecosystems, they are one of the key input layers in this biophysical stratification. After extensive investigation into various land surface form mapping methodologies, the decision was made to use the methodology developed by the Missouri Resource Assessment Partnership (MoRAP). MoRAP made modifications to Hammond's land surface form classification, which allowed the use of 30-meter source data and a 1-km2 window for analyzing the data cell and its surrounding cells (neighborhood analysis). While Hammond's methodology was based on three topographic variables, slope, local relief, and profile type, MoRAP's methodology uses only slope and local relief. Using the MoRAP method, slope is classified as gently sloping when more than 50 percent of the area in a 1-km2 neighborhood has slope less than 8 percent, otherwise the area is considered moderately sloping. Local relief, which is the difference between the maximum and minimum elevation in a neighborhood, is classified into five groups: 0-15 m, 16-30 m, 31-90 m, 91-150 m, and >150 m. The land surface form classes are derived by combining slope and local relief to create eight landform classes: flat plains (gently sloping and local relief = 90 m), low hills (not gently sloping and local relief = 150 m). However, in the USGS application of the MoRAP methodology, an additional local relief group was used (> 400 m) to capture additional local topographic variation. As a result, low

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.

Global land-surface primary productivity based upon Nimbus-7 37 GHz data

NASA Technical Reports Server (NTRS)

Choudhury, B. J.

1988-01-01

Accumulation and renewal of organic matter as quantified through net primary productivity (NPP) is considered a very major function of the biosphere, and its estimation is crucial in understanding the carbon cycle. A physically-based model relating NPP to the difference of vertically and horizontally polarized brightness temperatures (Delta T) observed at 37 GHz frequency of the scanning multichannel microwave radiometer on board the Nimbus-7 satellite is used for fitting areally averaged values of NPP and Delta T for five biomes. The land-surface NPP within 80 deg N to 55 deg S is then calculated using the Delta T data and compared with other estimates.

Modelling the angular effects on satellite retrieved LST at global scale using a land surface classification

NASA Astrophysics Data System (ADS)

Ermida, Sofia; DaCamara, Carlos C.; Trigo, Isabel F.; Pires, Ana C.; Ghent, Darren

2017-04-01

Land Surface Temperature (LST) is a key climatological variable and a diagnostic parameter of land surface conditions. Remote sensing constitutes the most effective method to observe LST over large areas and on a regular basis. Although LST estimation from remote sensing instruments operating in the Infrared (IR) is widely used and has been performed for nearly 3 decades, there is still a list of open issues. One of these is the LST dependence on viewing and illumination geometry. This effect introduces significant discrepancies among LST estimations from different sensors, overlapping in space and time, that are not related to uncertainties in the methodologies or input data used. Furthermore, these directional effects deviate LST products from an ideally defined LST, which should represent to the ensemble of directional radiometric temperature of all surface elements within the FOV. Angular effects on LST are here conveniently estimated by means of a kernel model of the surface thermal emission, which describes the angular dependence of LST as a function of viewing and illumination geometry. The model is calibrated using LST data as provided by a wide range of sensors to optimize spatial coverage, namely: 1) a LEO sensor - the Moderate Resolution Imaging Spectroradiometer (MODIS) on-board NASA's TERRA and AQUA; and 2) 3 GEO sensors - the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on-board EUMETSAT's Meteosat Second Generation (MSG), the Japanese Meteorological Imager (JAMI) on-board the Japanese Meteorological Association (JMA) Multifunction Transport SATellite (MTSAT-2), and NASA's Geostationary Operational Environmental Satellites (GOES). As shown in our previous feasibility studies the sampling of illumination and view angles has a high impact on the obtained model parameters. This impact may be mitigated when the sampling size is increased by aggregating pixels with similar surface conditions. Here we propose a methodology where land surface is

Global change pressures on soils from land use and management.

PubMed

Smith, Pete; House, Joanna I; Bustamante, Mercedes; Sobocká, Jaroslava; Harper, Richard; Pan, Genxing; West, Paul C; Clark, Joanna M; Adhya, Tapan; Rumpel, Cornelia; Paustian, Keith; Kuikman, Peter; Cotrufo, M Francesca; Elliott, Jane A; McDowell, Richard; Griffiths, Robert I; Asakawa, Susumu; Bondeau, Alberte; Jain, Atul K; Meersmans, Jeroen; Pugh, Thomas A M

2016-03-01

Soils are subject to varying degrees of direct or indirect human disturbance, constituting a major global change driver. Factoring out natural from direct and indirect human influence is not always straightforward, but some human activities have clear impacts. These include land-use change, land management and land degradation (erosion, compaction, sealing and salinization). The intensity of land use also exerts a great impact on soils, and soils are also subject to indirect impacts arising from human activity, such as acid deposition (sulphur and nitrogen) and heavy metal pollution. In this critical review, we report the state-of-the-art understanding of these global change pressures on soils, identify knowledge gaps and research challenges and highlight actions and policies to minimize adverse environmental impacts arising from these global change drivers. Soils are central to considerations of what constitutes sustainable intensification. Therefore, ensuring that vulnerable and high environmental value soils are considered when protecting important habitats and ecosystems, will help to reduce the pressure on land from global change drivers. To ensure that soils are protected as part of wider environmental efforts, a global soil resilience programme should be considered, to monitor, recover or sustain soil fertility and function, and to enhance the ecosystem services provided by soils. Soils cannot, and should not, be considered in isolation of the ecosystems that they underpin and vice versa. The role of soils in supporting ecosystems and natural capital needs greater recognition. The lasting legacy of the International Year of Soils in 2015 should be to put soils at the centre of policy supporting environmental protection and sustainable development. © 2015 John Wiley & Sons Ltd.

Global Tree Cover and Biomass Carbon on Agricultural Land: The contribution of agroforestry to global and national carbon budgets.

PubMed

Zomer, Robert J; Neufeldt, Henry; Xu, Jianchu; Ahrends, Antje; Bossio, Deborah; Trabucco, Antonio; van Noordwijk, Meine; Wang, Mingcheng

2016-07-20

Agroforestry systems and tree cover on agricultural land make an important contribution to climate change mitigation, but are not systematically accounted for in either global carbon budgets or national carbon accounting. This paper assesses the role of trees on agricultural land and their significance for carbon sequestration at a global level, along with recent change trends. Remote sensing data show that in 2010, 43% of all agricultural land globally had at least 10% tree cover and that this has increased by 2% over the previous ten years. Combining geographically and bioclimatically stratified Intergovernmental Panel on Climate Change (IPCC) Tier 1 default estimates of carbon storage with this tree cover analysis, we estimated 45.3 PgC on agricultural land globally, with trees contributing >75%. Between 2000 and 2010 tree cover increased by 3.7%, resulting in an increase of >2 PgC (or 4.6%) of biomass carbon. On average, globally, biomass carbon increased from 20.4 to 21.4 tC ha(-1). Regional and country-level variation in stocks and trends were mapped and tabulated globally, and for all countries. Brazil, Indonesia, China and India had the largest increases in biomass carbon stored on agricultural land, while Argentina, Myanmar, and Sierra Leone had the largest decreases.

Global Tree Cover and Biomass Carbon on Agricultural Land: The contribution of agroforestry to global and national carbon budgets

PubMed Central

Zomer, Robert J.; Neufeldt, Henry; Xu, Jianchu; Ahrends, Antje; Bossio, Deborah; Trabucco, Antonio; van Noordwijk, Meine; Wang, Mingcheng

2016-01-01

Agroforestry systems and tree cover on agricultural land make an important contribution to climate change mitigation, but are not systematically accounted for in either global carbon budgets or national carbon accounting. This paper assesses the role of trees on agricultural land and their significance for carbon sequestration at a global level, along with recent change trends. Remote sensing data show that in 2010, 43% of all agricultural land globally had at least 10% tree cover and that this has increased by 2% over the previous ten years. Combining geographically and bioclimatically stratified Intergovernmental Panel on Climate Change (IPCC) Tier 1 default estimates of carbon storage with this tree cover analysis, we estimated 45.3 PgC on agricultural land globally, with trees contributing >75%. Between 2000 and 2010 tree cover increased by 3.7%, resulting in an increase of >2 PgC (or 4.6%) of biomass carbon. On average, globally, biomass carbon increased from 20.4 to 21.4 tC ha−1. Regional and country-level variation in stocks and trends were mapped and tabulated globally, and for all countries. Brazil, Indonesia, China and India had the largest increases in biomass carbon stored on agricultural land, while Argentina, Myanmar, and Sierra Leone had the largest decreases. PMID:27435095

CEOS Land Surface Imaging Constellation Mid-Resolution Optical Guidelines

NASA Technical Reports Server (NTRS)

Keyes, Jennifer P.; Killough, B.

2011-01-01

The LSI community of users is large and varied. To reach all these users as well as potential instrument contributors this document has been organized by measurement parameters of interest such as Leaf Area Index and Land Surface Temperature. These measurement parameters and the data presented in this document are drawn from multiple sources, listed at the end of the document, although the two primary ones are "The Space-Based Global Observing System in 2010 (GOS-2010)" that was compiled for the World Meteorological Organization (WMO) by Bizzarro Bizzarri, and the CEOS Missions, Instruments, and Measurements online database (CEOS MIM). For each measurement parameter the following topics will be discussed: (1) measurement description, (2) applications, (3) measurement spectral bands, and (4) example instruments and mission information. The description of each measurement parameter starts with a definition and includes a graphic displaying the relationships to four general land surface imaging user communities: vegetation, water, earth, and geo-hazards, since the LSI community of users is large and varied. The vegetation community uses LSI data to assess factors related to topics such as agriculture, forest management, crop type, chlorophyll, vegetation land cover, and leaf or canopy differences. The water community analyzes snow and lake cover, water properties such as clarity, and body of water delineation. The earth community focuses on minerals, soils, and sediments. The geo-hazards community is designed to address and aid in emergencies such as volcanic eruptions, forest fires, and large-scale damaging weather-related events.

Increasing importance of precipitation variability on global livestock grazing lands

NASA Astrophysics Data System (ADS)

Sloat, Lindsey L.; Gerber, James S.; Samberg, Leah H.; Smith, William K.; Herrero, Mario; Ferreira, Laerte G.; Godde, Cécile M.; West, Paul C.

2018-03-01

Pastures and rangelands underpin global meat and milk production and are a critical resource for millions of people dependent on livestock for food security1,2. Forage growth, which is highly climate dependent3,4, is potentially vulnerable to climate change, although precisely where and to what extent remains relatively unexplored. In this study, we assess climate-based threats to global pastures, with a specific focus on changes in within- and between-year precipitation variability (precipitation concentration index (PCI) and coefficient of variation of precipitation (CVP), respectively). Relating global satellite measures of vegetation greenness (such as the Normalized Difference Vegetation Index; NDVI) to key climatic factors reveals that CVP is a significant, yet often overlooked, constraint on vegetation productivity across global pastures. Using independent stocking data, we found that areas with high CVP support lower livestock densities than less-variable regions. Globally, pastures experience about a 25% greater year-to-year precipitation variation (CVP = 0.27) than the average global land surface area (0.21). Over the past century, CVP has generally increased across pasture areas, although both positive (49% of pasture area) and negative (31% of pasture area) trends exist. We identify regions in which livestock grazing is important for local food access and economies, and discuss the potential for pasture intensification in the context of long-term regional trends in precipitation variability.

Modeling the Acceleration of Global Surface Temperture

NASA Astrophysics Data System (ADS)

Jones, B.

2017-12-01

A mathematical projection focusing on the changing rate of acceleration of Global Surface Temperatures. Using historical trajectory and informed expert near-term prediction, it is possible to extend this further forward drawing a reference arc of acceleration. Presented here is an example of this technique based on data found in the Summary of Findings of A New Estimate of the Average Earth Surface Land Temperature Spanning 1753 to 2011 and that same team's stated prediction to 2050. With this, we can project a curve showing future acceleration: Decade (midpoint) Change in Global Land Temp Degrees C Known Slope Projected Trend 1755 0.000 1955 0.600 0.0030 2005 1.500 0.0051 2045 3.000 0.0375 2095 5.485 0.0497 2145 8.895 0.0682 2195 13.488 0.0919 Observations: Slopes are getting steeper and doing so faster in an "acceleration of the acceleration" or an "arc of acceleration". This is consistent with the non-linear accelerating feedback loops of global warming. Such projected temperatures threaten human civilization and human life. This `thumbnail' projection is consistent with the other long term predictions based on anthropogenic greenhouse gases. This projection is low when compared to those whose forecasts include greenhouse gases released from thawing permafrost and clathrate hydrates. A reference line: This curve should be considered a point of reference. In the near term and absent significant drawdown of greenhouse gases, my "bet" for this AGU session is that future temperatures will generally be above this reference curve. For example, the decade ending 2020 - more than 1.9C and the decade ending 2030 - more than 2.3C - again measured from the 1750 start point. *Caveat: The long term curve and prediction assumes that mankind does not move quickly away from high cost fossil fuels and does not invent, mobilize and take actions drawing down greenhouse gases. Those seeking a comprehensive action plan are directed to drawdown.org

Real Time Land-Surface Hydrologic Modeling Over Continental US

NASA Technical Reports Server (NTRS)

Houser, Paul R.

1998-01-01

The land surface component of the hydrological cycle is fundamental to the overall functioning of the atmospheric and climate processes. Spatially and temporally variable rainfall and available energy, combined with land surface heterogeneity cause complex variations in all processes related to surface hydrology. The characterization of the spatial and temporal variability of water and energy cycles are critical to improve our understanding of land surface-atmosphere interaction and the impact of land surface processes on climate extremes. Because the accurate knowledge of these processes and their variability is important for climate predictions, most Numerical Weather Prediction (NWP) centers have incorporated land surface schemes in their models. However, errors in the NWP forcing accumulate in the surface and energy stores, leading to incorrect surface water and energy partitioning and related processes. This has motivated the NWP to impose ad hoc corrections to the land surface states to prevent this drift. A proposed methodology is to develop Land Data Assimilation schemes (LDAS), which are uncoupled models forced with observations, and not affected by NWP forcing biases. The proposed research is being implemented as a real time operation using an existing Surface Vegetation Atmosphere Transfer Scheme (SVATS) model at a 40 km degree resolution across the United States to evaluate these critical science questions. The model will be forced with real time output from numerical prediction models, satellite data, and radar precipitation measurements. Model parameters will be derived from the existing GIS vegetation and soil coverages. The model results will be aggregated to various scales to assess water and energy balances and these will be validated with various in-situ observations.

GOFC-GOLD :: Global Observation of Forest and Land Cover Dynamics

Science.gov Websites

GTOS HOME OVERVIEW CALENDAR ORGANIZATION LAND TEAM FIRE TEAM NETWORKS WORKING GROUPS PARTNERS DATA availability of observations of forests and land cover at regional and global scales and to produce useful

Benchmarking carbon-nitrogen interactions in Earth System Models to observations: An inter-comparison of nitrogen limitation in global land surface models with carbon and nitrogen cycles (CLM-CN and O-CN)

NASA Astrophysics Data System (ADS)

Thomas, R. Q.; Zaehle, S.; Templer, P. H.; Goodale, C. L.

2011-12-01

Predictions of climate change depend on accurately modeling the feedbacks among the carbon cycle, nitrogen cycle, and climate system. Several global land surface models have shown that nitrogen limitation determines how land carbon fluxes respond to rising CO2, nitrogen deposition, and climate change, thereby influencing predictions of climate change. However, the magnitude of the carbon-nitrogen-climate feedbacks varies considerably by model, leading to critical and timely questions of why they differ and how they compare to field observations. To address these questions, we initiated a model inter-comparison of spatial patterns and drivers of nitrogen limitation. The experiment assessed the regional consequences of sustained nitrogen additions in a set of 25-year global nitrogen fertilization simulations. The model experiments were designed to cover effects from small changes in nitrogen inputs associated with plausible increases in nitrogen deposition to large changes associated with field-based nitrogen fertilization experiments. The analyses of model simulations included assessing the geographically varying degree of nitrogen limitation on plant and soil carbon cycling and the mechanisms underlying model differences. Here, we present results from two global land-surface models (CLM-CN and O-CN) with differing approaches to modeling carbon-nitrogen interactions. The predictions from each model were compared to a set of globally distributed observational data that includes nitrogen fertilization experiments, 15N tracer studies, small catchment nitrogen input-output studies, and syntheses across nitrogen deposition gradients. Together these datasets test many aspects of carbon-nitrogen coupling and are able to differentiate between the two models. Overall, this study is the first to explicitly benchmark carbon and nitrogen interactions in Earth System Models using a range of observations and is a foundation for future inter-comparisons.

Global Flood Response Using Satellite Rainfall Information Coupled with Land Surface and Routing Models

NASA Astrophysics Data System (ADS)

Adler, R. F.; Wu, H.

2016-12-01

The Global Flood Monitoring System (GFMS) (http://flood.umd.edu) has been developed and used in recent years to provide real-time flood detection, streamflow estimates and inundation calculations for most of the globe. The GFMS is driven by satellite-based precipitation, with the accuracy of the flood estimates being primarily dependent on the accuracy of the precipitation analyses and the land surface and routing models used. The routing calculations are done at both 12 km and 1 km resolution. Users of GFMS results include international and national flood response organizations. The devastating floods in October 2015 in South Carolina are analyzed indicating that the GFMS estimated streamflow is accurate and useful indicating significant flooding in the upstream basins. Further downstream the GFMS streamflow underestimates due to the presence of dams which are not accounted for in GFMS. Other examples are given for Yemen and Somalia and for Sri Lanka and southern India. A forecast flood event associated with a typhoon hitting Taiwan is also examined. One-kilometer resolution inundation mapping from GFMS holds the promise of highly useful information for flood disaster response. The algorithm is briefly described and examples are shown for recent cases where inundation estimates available from optical and Synthetic Aperture Radar (SAR) satellite sensors are available. For a case of significant flooding in Texas in May and June along the Brazos River the GFMS calculated streamflow compares favorably with the observed. Available Landsat-based (May 28) and MODIS-based (June 2) inundation analyses from U. of Colorado shows generally good agreement with the GFMS inundation calculation in most of the area where skies were clear and the optical techniques could be applied. The GFMS provides very useful disaster response information on a timely basis. However, there is still significant room for improvement, including improved precipitation information from NASA's Global

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.

Accessing Recent Trend of Land Surface Temperature from Satellite Observations

NASA Technical Reports Server (NTRS)

Shen, Suhung; Leptoukh, Gregory G.; Romanov, Peter

2011-01-01

Land surface temperature (Ts) is an important element to measure the state of terrestrial ecosystems and to study surface energy budgets. In support of the land cover/land use change-related international program MAIRS (Monsoon Asia Integrated Regional Study), we have collected global monthly Ts measured by MODIS since the beginning of the missions. The MODIS Ts time series have approximately 11 years of data from Terra since 2000 and approximately 9 years of data from Aqua since 2002, which makes possible to study the recent climate, such as trend. In this study, monthly climatology from two platforms are calculated and compared with that from AIRS. The spatial patterns of Ts trends are accessed, focusing on the Eurasia region. Furthermore, MODIS Ts trends are compared with those from AIRS and NASA's atmospheric assimilation model, MERRA (Modern Era Retrospective-analysis for Research and Applications). The preliminary results indicate that the recent 8-year Ts trend shows an oscillation-type spatial variation over Eurasia. The pattern is consistent for data from MODIS, AIRS, and MERRA, with the positive center over Eastern Europe, and the negative center over Central Siberia. The calculated climatology and anomaly of MODIS Ts will be integrated into the online visualization system, Giovanni, at NASA GES DISC for easy use by scientists and general public.

LandScan 2016 High-Resolution Global Population Data Set

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

Bright, Edward A; Rose, Amy N; Urban, Marie L

The LandScan data set is a worldwide population database compiled on a 30" x 30" latitude/longitude grid. Census counts (at sub-national level) were apportioned to each grid cell based on likelihood coefficients, which are based on land cover, slope, road proximity, high-resolution imagery, and other data sets. The LandScan data set was developed as part of Oak Ridge National Laboratory (ORNL) Global Population Project for estimating ambient populations at risk.

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.

Improving Frozen Precipitation Density Estimation in Land Surface Modeling

NASA Astrophysics Data System (ADS)

Sparrow, K.; Fall, G. M.

2017-12-01

The Office of Water Prediction (OWP) produces high-value water supply and flood risk planning information through the use of operational land surface modeling. Improvements in diagnosing frozen precipitation density will benefit the NWS's meteorological and hydrological services by refining estimates of a significant and vital input into land surface models. A current common practice for handling the density of snow accumulation in a land surface model is to use a standard 10:1 snow-to-liquid-equivalent ratio (SLR). Our research findings suggest the possibility of a more skillful approach for assessing the spatial variability of precipitation density. We developed a 30-year SLR climatology for the coterminous US from version 3.22 of the Daily Global Historical Climatology Network - Daily (GHCN-D) dataset. Our methods followed the approach described by Baxter (2005) to estimate mean climatological SLR values at GHCN-D sites in the US, Canada, and Mexico for the years 1986-2015. In addition to the Baxter criteria, the following refinements were made: tests were performed to eliminate SLR outliers and frequent reports of SLR = 10, a linear SLR vs. elevation trend was fitted to station SLR mean values to remove the elevation trend from the data, and detrended SLR residuals were interpolated using ordinary kriging with a spherical semivariogram model. The elevation values of each station were based on the GMTED 2010 digital elevation model and the elevation trend in the data was established via linear least squares approximation. The ordinary kriging procedure was used to interpolate the data into gridded climatological SLR estimates for each calendar month at a 0.125 degree resolution. To assess the skill of this climatology, we compared estimates from our SLR climatology with observations from the GHCN-D dataset to consider the potential use of this climatology as a first guess of frozen precipitation density in an operational land surface model. The difference in

Consequences of land use and land cover change

USGS Publications Warehouse

Slonecker, E. Terrence; Barnes, Christopher; Karstensen, Krista; Milheim, Lesley E.; Roig-Silva, Coral M.

2013-01-01

The U.S. Geological Survey (USGS) Climate and Land Use Change Mission Area is one of seven USGS mission areas that focuses on making substantial scientific "...contributions to understanding how Earth systems interact, respond to, and cause global change". Using satellite and other remotely sensed data, USGS scientists monitor patterns of land cover change over space and time at regional, national, and global scales. These data are analyzed to understand the causes and consequences of changing land cover, such as economic impacts, effects on water quality and availability, the spread of invasive species, habitats and biodiversity, carbon fluctuations, and climate variability. USGS scientists are among the leaders in the study of land cover, which is a term that generally refers to the vegetation and artificial structures that cover the land surface. Examples of land cover include forests, grasslands, wetlands, water, crops, and buildings. Land use involves human activities that take place on the land. For example, "grass" is a land cover, whereas pasture and recreational parks are land uses that produce a cover of grass.

Consequences of land-cover misclassification in models of impervious surface

USGS Publications Warehouse

McMahon, G.

2007-01-01

Model estimates of impervious area as a function of landcover area may be biased and imprecise because of errors in the land-cover classification. This investigation of the effects of land-cover misclassification on impervious surface models that use National Land Cover Data (NLCD) evaluates the consequences of adjusting land-cover within a watershed to reflect uncertainty assessment information. Model validation results indicate that using error-matrix information to adjust land-cover values used in impervious surface models does not substantially improve impervious surface predictions. Validation results indicate that the resolution of the landcover data (Level I and Level II) is more important in predicting impervious surface accurately than whether the land-cover data have been adjusted using information in the error matrix. Level I NLCD, adjusted for land-cover misclassification, is preferable to the other land-cover options for use in models of impervious surface. This result is tied to the lower classification error rates for the Level I NLCD. ?? 2007 American Society for Photogrammetry and Remote Sensing.

Global Drought Monitoring and Forecasting based on Satellite Data and Land Surface Modeling

NASA Astrophysics Data System (ADS)

Sheffield, J.; Lobell, D. B.; Wood, E. F.

2010-12-01

Monitoring drought globally is challenging because of the lack of dense in-situ hydrologic data in many regions. In particular, soil moisture measurements are absent in many regions and in real time. This is especially problematic for developing regions such as Africa where water information is arguably most needed, but virtually non-existent on the ground. With the emergence of remote sensing estimates of all components of the water cycle there is now the potential to monitor the full terrestrial water cycle from space to give global coverage and provide the basis for drought monitoring. These estimates include microwave-infrared merged precipitation retrievals, evapotranspiration based on satellite radiation, temperature and vegetation data, gravity recovery measurements of changes in water storage, microwave based retrievals of soil moisture and altimetry based estimates of lake levels and river flows. However, many challenges remain in using these data, especially due to biases in individual satellite retrieved components, their incomplete sampling in time and space, and their failure to provide budget closure in concert. A potential way forward is to use modeling to provide a framework to merge these disparate sources of information to give physically consistent and spatially and temporally continuous estimates of the water cycle and drought. Here we present results from our experimental global water cycle monitor and its African drought monitor counterpart (http://hydrology.princeton.edu/monitor). The system relies heavily on satellite data to drive the Variable Infiltration Capacity (VIC) land surface model to provide near real-time estimates of precipitation, evapotranspiraiton, soil moisture, snow pack and streamflow. Drought is defined in terms of anomalies of soil moisture and other hydrologic variables relative to a long-term (1950-2000) climatology. We present some examples of recent droughts and how they are identified by the system, including

Effect of water table dynamics on land surface hydrologic memory

NASA Astrophysics Data System (ADS)

Lo, Min-Hui; Famiglietti, James S.

2010-11-01

The representation of groundwater dynamics in land surface models has received considerable attention in recent years. Most studies have found that soil moisture increases after adding a groundwater component because of the additional supply of water to the root zone. However, the effect of groundwater on land surface hydrologic memory (persistence) has not been explored thoroughly. In this study we investigate the effect of water table dynamics on National Center for Atmospheric Research Community Land Model hydrologic simulations in terms of land surface hydrologic memory. Unlike soil water or evapotranspiration, results show that land surface hydrologic memory does not always increase after adding a groundwater component. In regions where the water table level is intermediate, land surface hydrologic memory can even decrease, which occurs when soil moisture and capillary rise from groundwater are not in phase with each other. Further, we explore the hypothesis that in addition to atmospheric forcing, groundwater variations may also play an important role in affecting land surface hydrologic memory. Analyses show that feedbacks of groundwater on land surface hydrologic memory can be positive, negative, or neutral, depending on water table dynamics. In regions where the water table is shallow, the damping process of soil moisture variations by groundwater is not significant, and soil moisture variations are mostly controlled by random noise from atmospheric forcing. In contrast, in regions where the water table is very deep, capillary fluxes from groundwater are small, having limited potential to affect soil moisture variations. Therefore, a positive feedback of groundwater to land surface hydrologic memory is observed in a transition zone between deep and shallow water tables, where capillary fluxes act as a buffer by reducing high-frequency soil moisture variations resulting in longer land surface hydrologic memory.

Inclusion of Solar Elevation Angle in Land Surface Albedo Parameterization Over Bare Soil Surface.

PubMed

Zheng, Zhiyuan; Wei, Zhigang; Wen, Zhiping; Dong, Wenjie; Li, Zhenchao; Wen, Xiaohang; Zhu, Xian; Ji, Dong; Chen, Chen; Yan, Dongdong

2017-12-01

Land surface albedo is a significant parameter for maintaining a balance in surface energy. It is also an important parameter of bare soil surface albedo for developing land surface process models that accurately reflect diurnal variation characteristics and the mechanism behind the solar spectral radiation albedo on bare soil surfaces and for understanding the relationships between climate factors and spectral radiation albedo. Using a data set of field observations, we conducted experiments to analyze the variation characteristics of land surface solar spectral radiation and the corresponding albedo over a typical Gobi bare soil underlying surface and to investigate the relationships between the land surface solar spectral radiation albedo, solar elevation angle, and soil moisture. Based on both solar elevation angle and soil moisture measurements simultaneously, we propose a new two-factor parameterization scheme for spectral radiation albedo over bare soil underlying surfaces. The results of numerical simulation experiments show that the new parameterization scheme can more accurately depict the diurnal variation characteristics of bare soil surface albedo than the previous schemes. Solar elevation angle is one of the most important factors for parameterizing bare soil surface albedo and must be considered in the parameterization scheme, especially in arid and semiarid areas with low soil moisture content. This study reveals the characteristics and mechanism of the diurnal variation of bare soil surface solar spectral radiation albedo and is helpful in developing land surface process models, weather models, and climate models.

Preliminary Analysis of the Performance of the Landsat 8/OLI Land Surface Reflectance Product

NASA Technical Reports Server (NTRS)

Vermote, Eric; Justice, Chris; Claverie, Martin; Franch, Belen

2016-01-01

The surface reflectance, i.e., satellite derived top of atmosphere (TOA) reflectance corrected for the temporally, spatially and spectrally varying scattering and absorbing effects of atmospheric gases and aerosols, is needed to monitor the land surface reliably. For this reason, the surface reflectance, and not TOA reflectance, is used to generate the greater majority of global land products, for example, from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors. Even if atmospheric effects are minimized by sensor design, atmospheric effects are still challenging to correct. In particular, the strong impact of aerosols in the visible and near infrared spectral range can be difficult to correct, because they can be highly discrete in space and time (e.g., smoke plumes) and because of the complex scattering and absorbing properties of aerosols that vary spectrally and with aerosol size, shape, chemistry and density.

Inter-Sensor Comparison of Microwave Land Surface Emissivity Products to Improve Precipitation Retrievals

NASA Astrophysics Data System (ADS)

Norouzi, H.; Temimi, M.; Turk, J.; Prigent, C.; Furuzawa, F.; Tian, Y.

2013-12-01

Microwave land surface emissivity acts as the background signal to estimate rain rate, cloud liquid water, and total precipitable water. Therefore, its accuracy can directly affect the uncertainty of such measurements. Over land, unlike over oceans, the microwave emissivity is relatively high and and varies significantly as surface conditions and land cover change. Lack of ground truth measurement of microwave emissivity especially on global scale has made the uncertainty analysis of this parameter very challenging. The present study investigates the consistency among the existing global land emissivity estimates from different microwave sensors. The products are determined from various sensors and frequencies ranging from 7 to 90 GHz. The selected emissivity products in this study are from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) by NOAA - Cooperative remote Sensing and Science and Technology Center (CREST), the Special Sensor Microwave Imager (SSM/I) by The Centre National de la Recherche Scientifique (CNRS) in France, TRMM Microwave Imager (TMI) by Nagoya University, Japan, and WindSat by NASA Jet Propulsion Laboratory (JPL). The emissivity estimates are based on different algorithms and ancillary data sets. This work investigates the difference among these emissivity products from 2003 to 2008 dynamically and spectrally. The similarities and discrepancies of the retrievals are studied at different land cover types. The mean relative difference (MRD) and other statistical parameters are calculated temporally for all five years of the study. Some inherent discrepancies between the selected products can be attributed to the difference in geometry in terms of incident angle, spectral response, and the foot print size which can affect the estimations. The results reveal that in lower frequencies (=<19 GHz) ancillary data especially skin temperature data set is the major source of difference in emissivity retrievals, while in higher frequencies

Impact of Soil Moisture Assimilation on Land Surface Model Spin-Up and Coupled LandAtmosphere Prediction

NASA Technical Reports Server (NTRS)

Santanello, Joseph A., Jr.; Kumar, Sujay V.; Peters-Lidard, Christa D.; Lawston, P.

2016-01-01

Advances in satellite monitoring of the terrestrial water cycle have led to a concerted effort to assimilate soil moisture observations from various platforms into offline land surface models (LSMs). One principal but still open question is that of the ability of land data assimilation (LDA) to improve LSM initial conditions for coupled short-term weather prediction. In this study, the impact of assimilating Advanced Microwave Scanning Radiometer for EOS (AMSR-E) soil moisture retrievals on coupled WRF Model forecasts is examined during the summers of dry (2006) and wet (2007) surface conditions in the southern Great Plains. LDA is carried out using NASAs Land Information System (LIS) and the Noah LSM through an ensemble Kalman filter (EnKF) approach. The impacts of LDA on the 1) soil moisture and soil temperature initial conditions for WRF, 2) land-atmosphere coupling characteristics, and 3) ambient weather of the coupled LIS-WRF simulations are then assessed. Results show that impacts of soil moisture LDA during the spin-up can significantly modify LSM states and fluxes, depending on regime and season. Results also indicate that the use of seasonal cumulative distribution functions (CDFs) is more advantageous compared to the traditional annual CDF bias correction strategies. LDA performs consistently regardless of atmospheric forcing applied, with greater improvements seen when using coarser, global forcing products. Downstream impacts on coupled simulations vary according to the strength of the LDA impact at the initialization, where significant modifications to the soil moisture flux- PBL-ambient weather process chain are observed. Overall, this study demonstrates potential for future, higher-resolution soil moisture assimilation applications in weather and climate research.

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.

Remote sensing of land surface phenology

USGS Publications Warehouse

Meier, G.A.; Brown, Jesslyn F.

2014-01-01

Remote sensing of land-surface phenology is an important method for studying the patterns of plant and animal growth cycles. Phenological events are sensitive to climate variation; therefore phenology data provide important baseline information documenting trends in ecology and detecting the impacts of climate change on multiple scales. The USGS Remote sensing of land surface phenology program produces annually, nine phenology indicator variables at 250 m and 1,000 m resolution for the contiguous U.S. The 12 year archive is available at http://phenology.cr.usgs.gov/index.php.

OCO-2 Column Carbon Dioxide and Biometric Data Jointly Constrain Parameterization and Projection of a Global Land Model

NASA Astrophysics Data System (ADS)

Shi, Z.; Crowell, S.; Luo, Y.; Rayner, P. J.; Moore, B., III

2015-12-01

Uncertainty in predicted carbon-climate feedback largely stems from poor parameterization of global land models. However, calibration of global land models with observations has been extremely challenging at least for two reasons. First we lack global data products from systematical measurements of land surface processes. Second, computational demand is insurmountable for estimation of model parameter due to complexity of global land models. In this project, we will use OCO-2 retrievals of dry air mole fraction XCO2 and solar induced fluorescence (SIF) to independently constrain estimation of net ecosystem exchange (NEE) and gross primary production (GPP). The constrained NEE and GPP will be combined with data products of global standing biomass, soil organic carbon and soil respiration to improve the community land model version 4.5 (CLM4.5). Specifically, we will first develop a high fidelity emulator of CLM4.5 according to the matrix representation of the terrestrial carbon cycle. It has been shown that the emulator fully represents the original model and can be effectively used for data assimilation to constrain parameter estimation. We will focus on calibrating those key model parameters (e.g., maximum carboxylation rate, turnover time and transfer coefficients of soil carbon pools, and temperature sensitivity of respiration) for carbon cycle. The Bayesian Markov chain Monte Carlo method (MCMC) will be used to assimilate the global databases into the high fidelity emulator to constrain the model parameters, which will be incorporated back to the original CLM4.5. The calibrated CLM4.5 will be used to make scenario-based projections. In addition, we will conduct observing system simulation experiments (OSSEs) to evaluate how the sampling frequency and length could affect the model constraining and prediction.

Surface Emissivity Retrieved with Satellite Ultraspectral IR Measurements for Monitoring Global Change

NASA Technical Reports Server (NTRS)

Zhou, Daniel K.; Larar, Allen M.; Liu, Xu; Smith, William L.; Schluessel, Peter

2009-01-01

Surface and atmospheric thermodynamic parameters retrieved with advanced ultraspectral remote sensors aboard Earth observing satellites are critical to general atmospheric and Earth science research, climate monitoring, and weather prediction. Ultraspectral resolution infrared radiance obtained from nadir observations provide atmospheric, surface, and cloud information. Presented here is the global surface IR emissivity retrieved from Infrared Atmospheric Sounding Interferometer (IASI) measurements under "clear-sky" conditions. Fast radiative transfer models, applied to the cloud-free (or clouded) atmosphere, are used for atmospheric profile and surface parameter (or cloud parameter) retrieval. The inversion scheme, dealing with cloudy as well as cloud-free radiances observed with ultraspectral infrared sounders, has been developed to simultaneously retrieve atmospheric thermodynamic and surface (or cloud microphysical) parameters. Rapidly produced surface emissivity is initially evaluated through quality control checks on the retrievals of other impacted atmospheric and surface parameters. Surface emissivity and surface skin temperature from the current and future operational satellites can and will reveal critical information on the Earth s ecosystem and land surface type properties, which can be utilized as part of long-term monitoring for the Earth s environment and global climate change.

Surface Temperature Assimilation in Land Surface Models

NASA Technical Reports Server (NTRS)

Lakshmi, Venkataraman

1997-01-01

This paper examines the utilization of surface temperature as a variable to be assimilated in offline land surface hydrological models. Comparisons between the model computed and satellite observed surface temperatures have been carried out. The assimilation of surface temperature is carried out twice a day (corresponding to the AM and PM overpass of the NOAA10) over the Red- Arkansas basin in the Southwestern United States (31 deg 50 min N - 36 deg N, 94 deg 30 min W - 104 deg 30 min W) for a period of one year (August 1987 to July 1988). The effect of assimilation is to reduce the difference between the surface soil moisture computed for the precipitation and/or shortwave radiation perturbed case and the unperturbed case compared to no assimilation.

Surface Temperature Assimilation in Land Surface Models

NASA Technical Reports Server (NTRS)

Lakshmi, Venkataraman

1999-01-01

This paper examines the utilization of surface temperature as a variable to be assimilated in offline land surface hydrological models. Comparisons between the model computed and satellite observed surface temperatures have been carried out. The assimilation of surface temperature is carried out twice a day (corresponding to the AM and PM overpass of the NOAA10) over the Red-Arkansas basin in the Southwestern United States (31 degs 50 sec N - 36 degrees N, 94 degrees 30 seconds W - 104 degrees 3 seconds W) for a period of one year (August 1987 to July 1988). The effect of assimilation is to reduce the difference between the surface soil moisture computed for the precipitation and/or shortwave radiation perturbed case and the unperturbed case compared to no assimilation.

Aerosol radiative forcing from GEO satellite data over land surfaces

NASA Astrophysics Data System (ADS)

Costa, Maria J.; Silva, Ana M.

2005-10-01

Aerosols direct and indirect effects on the Earth's climate are widely recognized but have yet to be adequately quantified. Difficulties arise due to the very high spatial and temporal variability of aerosols, which is a major cause of uncertainties in radiative forcing studies. The effective monitoring of the global aerosol distribution is only made possible by satellite monitoring and this is the reason why the interest in aerosol observations from satellite passive radiometers is steadily increasing. From the point of view of the study of land surfaces, the atmosphere with its constituents represents an obscurant whose effects should be as much as possible eliminated, being this process sometimes referred to as atmospheric correction. In absence of clouds and using spectral intervals where gas absorption can be avoided to a great extent, only the aerosol effect remains to be corrected. The monitoring of the aerosol particles present in the atmosphere is then crucial to succeed in doing an accurate atmospheric correction, otherwise the surface properties may be inadequately characterised. However, the atmospheric correction over land surfaces turns out to be a difficult task since surface reflection competes with the atmospheric component of the signal. On the other hand, a single mean pre-established aerosol characterisation would not be sufficient for this purpose due to very high spatial and temporal variability of aerosols and their unpredictability, especially what concerns particulary intense "events" such as biomass burning and forest fires, desert dust episodes and volcanic eruptions. In this context, an operational methodology has been developed at the University of Evora - Evora Geophysics Centre (CGE), in the framework of the Satellite Application Facility for Land Surface Analysis - Land SAF, to derive an Aerosol Product from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) data, flying on the Geostationary (GEO) satellite system Meteosat-8

Mapping global surface water inundation dynamics using synergistic information from SMAP, AMSR2 and Landsat

NASA Astrophysics Data System (ADS)

Du, J.; Kimball, J. S.; Galantowicz, J. F.; Kim, S.; Chan, S.; Reichle, R. H.; Jones, L. A.; Watts, J. D.

2017-12-01

A method to monitor global land surface water (fw) inundation dynamics was developed by exploiting the enhanced fw sensitivity of L-band (1.4 GHz) passive microwave observations from the Soil Moisture Active Passive (SMAP) mission. The L-band fw (fwLBand) retrievals were derived using SMAP H-polarization brightness temperature (Tb) observations and predefined L-band reference microwave emissivities for water and land endmembers. Potential soil moisture and vegetation contributions to the microwave signal were represented from overlapping higher frequency Tb observations from AMSR2. The resulting fwLBand global record has high temporal sampling (1-3 days) and 36-km spatial resolution. The fwLBand annual averages corresponded favourably (R=0.84, p<0.001) with a 250-m resolution static global water map (MOD44W) aggregated at the same spatial scale, while capturing significant inundation variations worldwide. The monthly fwLBand averages also showed seasonal inundation changes consistent with river discharge records within six major US river basins. An uncertainty analysis indicated generally reliable fwLBand performance for major land cover areas and under low to moderate vegetation cover, but with lower accuracy for detecting water bodies covered by dense vegetation. Finer resolution (30-m) fwLBand results were obtained for three sub-regions in North America using an empirical downscaling approach and ancillary global Water Occurrence Dataset (WOD) derived from the historical Landsat record. The resulting 30-m fwLBand retrievals showed favourable classification accuracy for water (commission error 31.84%; omission error 28.08%) and land (commission error 0.82%; omission error 0.99%) and seasonal wet and dry periods when compared to independent water maps derived from Landsat-8 imagery. The new fwLBand algorithms and continuing SMAP and AMSR2 operations provide for near real-time, multi-scale monitoring of global surface water inundation dynamics, potentially

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

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

Comparison of Different Global Information Sources Used in Surface Radiative Flux Calculation: Radiative Properties of the Surface

NASA Technical Reports Server (NTRS)

Zhang, Yuanchong; Rossow, William B.; Stackhouse, Paul W., Jr.

2007-01-01

Direct estimates of surface radiative fluxes that resolve regional and weather-scale variabilty over the whole globe with reasonable accuracy have only become possible with the advent of extensive global, mostly satellite, datasets within the past couple of decades. The accuracy of these fluxes, estimated to be about 10-15 W per square meter is largely limited by the accuracy of the input datasets. The leading uncertainties in the surface fluxes are no longer predominantly induced by clouds but are now as much associated with uncertainties in the surface and near-surface atmospheric properties. This study presents a fuller, more quantitative evaluation of the uncertainties for the surface albedo and emissivity and surface skin temperatures by comparing the main available global datasets from the Moderate-Resolution Imaging Spectroradiometer product, the NASA Global Energy and Water Cycle Experiment Surface Radiation Budget project, the European Centre for Medium-Range Weather Forecasts, the National Aeronautics and Space Administration, the National Centers for Environmental Prediction, the International Satellite Cloud Climatology Project (ISCCP), the Laboratoire de Meteorologie Dynamique, NOAA/NASA Pathfinder Advanced Very High Resolution Radiometer project, NOAA Optimum Interpolation Sea Surface Temperature Analysis and the Tropical Rainfall Measuring Mission (TRMM) Microwave Image project. The datasets are, in practice, treated as an ensemble of realizations of the actual climate such that their differences represent an estimate of the uncertainty in their measurements because we do not possess global truth datasets for these quantities. The results are globally representative and may be taken as a generalization of our previous ISCCP-based uncertainty estimates for the input datasets. Surface properties have the primary role in determining the surface upward shortwave (SW) and longwave (LW) flux. From this study, the following conclusions are obtained

Interannual Variations and Trends in Global Land Surface Phenology Derived from Enhanced Vegetation Index During 1982-2010

NASA Technical Reports Server (NTRS)

Zhang, Xiaoyang; Tan, Bin; Yu, Yunyue

2014-01-01

Land swiace phenology is widely retrieved from satellite observations at regional and global scales, and its long-term record has been demonstmted to be a valuable tool for reconstructing past climate variations, monitoring the dynamics of terrestrial ecosystems in response to climate impacts, and predicting biological responses to future climate scenarios. This srudy detected global land surface phenology from the advanced very high resolution radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) data from 1982 to 2010. Based on daily enhanced vegetation index at a spatial resolution of 0.05 degrees, we simulated the seasonal vegetative trajectory for each individual pixel using piecewise logistic models, which was then used to detect the onset of greenness increase (OGI) and the length of vegetation growing season (GSL). Further, both overall interannual variations and pixel-based trends were examIned across Koeppen's climate regions for the periods of 1982-1999 and 2000-2010, respectively. The results show that OGI and OSL varied considerably during 1982-2010 across the globe. Generally, the interarmual variation could be more than a month in precipitation-controlled tropical and dry climates while it was mainly less than 15 days in temperature-controlled temperate, cold, and polar climates. OGI, overall, shifted early, and GSL was prolonged from 1982 to 2010 in most climate regions in North America and Asia while the consistently significant trends only occurred in cold climate and polar climate in North America. The overall trends in Europe were generally insignificant. Over South America, late OGI was consistent (particularly from 1982 to 1999) while either positive or negative OSL trends in a climate region were mostly reversed between the periods of 1982-1999 and 2000-2010. In the Northern Hemisphere of Africa, OGI trends were mostly insignificant, but prolonged GSL was evident over individual climate regions during the last 3

The Continuing Evolution of Land Surface Parameterizations

NASA Technical Reports Server (NTRS)

Koster, Randal; Houser, Paul (Technical Monitor)

2001-01-01

Land surface models (LSMs) play a critical role in the simulation of climate, for they determine the character of a large fraction of the atmosphere's lower boundary. The LSM partitions the net radiative energy at the land surface into sensible heat, latent heat, and energy storage, and it partitions incident precipitation water into evaporation, runoff, and water storage. Numerous modeling experiments and the existing (though very scant) observational evidence suggest that variations in these partitionings can feed back on the atmospheric processes that induce them. This land-atmosphere feedback can in turn have a significant impact on the generation of continental precipitation. For this and other reasons (including the role of the land surface in converting various atmospheric quantities, such as precipitation, into quantities of perhaps higher societal relevance, such as runoff), many modeling groups are placing a high emphasis on improving the treatment of land surface processes in their models. LSMs have evolved substantially from the original bucket model of Manabe et al. This evolution, which is still ongoing, has been documented considerably. The present paper also takes a look at the evolution of LSMs. The perspective here, though, is different - the evolution is considered strictly in terms of the 'balance' between the formulations of evaporation and runoff processes. The paper will argue that a proper balance is currently missing, largely due to difficulties in treating subgrid variability in soil moisture and its impact on the generation of runoff.

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.

Global sea-level rise is recognised, but flooding from anthropogenic land subsidence is ignored around northern Manila Bay, Philippines.

PubMed

Rodolfo, Kelvin S; Siringan, Fernando P

2006-03-01

Land subsidence resulting from excessive extraction of groundwater is particularly acute in East Asian countries. Some Philippine government sectors have begun to recognise that the sea-level rise of one to three millimetres per year due to global warming is a cause of worsening floods around Manila Bay, but are oblivious to, or ignore, the principal reason: excessive groundwater extraction is lowering the land surface by several centimetres to more than a decimetre per year. Such ignorance allows the government to treat flooding as a lesser problem that can be mitigated through large infrastructural projects that are both ineffective and vulnerable to corruption. Money would be better spent on preventing the subsidence by reducing groundwater pumping and moderating population growth and land use, but these approaches are politically and psychologically unacceptable. Even if groundwater use is greatly reduced and enlightened land-use practices are initiated, natural deltaic subsidence and global sea-level rise will continue to aggravate flooding, although at substantially lower rates.

Wetland methane modelling over the Scandinavian Arctic: Performance of current land-surface models

NASA Astrophysics Data System (ADS)

Hayman, Garry; Quiquet, Aurélien; Gedney, Nicola; Clark, Douglas; Friend, Andrew; George, Charles; Prigent, Catherine

2014-05-01

Wetlands are generally accepted as being the largest, but least well quantified, single natural source of CH4, with global emission estimates ranging from 100-231 Tg yr-1 [1] and for which the Boreal and Arctic regions make a significant contribution [2, 3]. The recent review by Melton et al. [4] has provided a summary of the current state of knowledge on the modelling of wetlands and the outcome of the WETCHIMP model intercomparison exercise. Melton et al. found a large variation in the wetland areas and associated methane emissions from the participating models and varying responses to climate change. In this paper, we report results from offline runs of two land surface models over Scandinavia (JULES, the Joint UK Land Environment Simulator [5, 6] and HYBRID8 [7]), using the same driving meteorological dataset (CRU-NCEP) for the period from January 1980 to December 2010. Although the two land surface models are very different, both models have used a TOPMODEL approach to derive the wetland area and have similar parameterisations of the methane wetland emissions. We find that both models give broadly similar results. They underestimate the wetland areas over Northern Scandinavia, compared to remote sensing and map-based datasets of wetlands [8]. This leads to lower predicted methane emissions compared to those observed on the ground and from aircraft [9]. We will present these findings and identify possible reasons for the underprediction. We will show the sensitivity to using the observed wetland areas to improve the methane emission estimates. References [1] Denman, K., et al.,: Couplings Between Changes in the Climate System and Biogeochemistry, In Climate Change 2007: The Physical Science Basis, Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, United Kingdom, 2007; [2] Smith, L. C., et al.: Siberian peatlands a net carbon sink and global methane source since the early

Global land-use and market interactions between climate and bioenergy policies

NASA Astrophysics Data System (ADS)

Golub, A.; Hertel, T. W.; Rose, S. K.

2011-12-01

Over the past few years, interest in bioenergy has boomed with higher oil prices and concerns about energy security, farm incomes, and mitigation of climate change. Large-scale commercial bioenergy production could have far reaching implications for regional and global land use and output markets associated with food, forestry, chemical, and energy sectors, as well as household welfare. Similarly, there is significant interest in international agricultural and forestry based carbon sequestration and greenhouse gas (GHG) mitigation policies, which could also provide revenue to developing countries and farmers in exchange for modifying land management practices. However, bioenergy and climate policies are being formulated largely independent of one another. Understanding the interaction between these potentially competing policy objectives is important for identifying possible constraints that one policy might place on the other, potential complementarities that could be exploited in policy design, and net land-use change and management implications over time. This study develops a new dynamic global computable general equilibrium (CGE) model GDyn-E-AEZ to assess the interaction between biofuels production and climate mitigation policies. The model is built on several existing CGE platforms, including 1) GTAP-AEZ-GHG model (Golub et al., 2009), 2) GTAP-BIO (Birur et al., 2008; Taheripour and Tyner, 2011), and 3) GDyn framework (Ianchovichina and McDougall, 2001) extended to investigate the role of population and per capita income growth, changing consumption patterns, and global economic integration in determining long-run patterns of land-use change. The new model is used to assess the effects of domestic and global bioenergy expansion on future land use, as well as sectoral, regional and global GHG emissions mitigation potential. Do bioenergy programs facilitate or constrain GHG mitigation opportunities? For instance, Golub et al. (2009) estimate substantial GHG

Divergent surface and total soil moisture projections under global warming

USGS Publications Warehouse

Berg, Alexis; Sheffield, Justin; Milly, Paul C.D.

2017-01-01

Land aridity has been projected to increase with global warming. Such projections are mostly based on off-line aridity and drought metrics applied to climate model outputs but also are supported by climate-model projections of decreased surface soil moisture. Here we comprehensively analyze soil moisture projections from the Coupled Model Intercomparison Project phase 5, including surface, total, and layer-by-layer soil moisture. We identify a robust vertical gradient of projected mean soil moisture changes, with more negative changes near the surface. Some regions of the northern middle to high latitudes exhibit negative annual surface changes but positive total changes. We interpret this behavior in the context of seasonal changes in the surface water budget. This vertical pattern implies that the extensive drying predicted by off-line drought metrics, while consistent with the projected decline in surface soil moisture, will tend to overestimate (negatively) changes in total soil water availability.

Future Diet Scenarios and Their Effect on Regional and Global Land Use

NASA Astrophysics Data System (ADS)

Gregg, J. S.; Hvid, A.

2011-12-01