Dissecting the multi-scale spatial relationship of earthworm assemblages with soil environmental variability.

PubMed

Jiménez, Juan J; Decaëns, Thibaud; Lavelle, Patrick; Rossi, Jean-Pierre

2014-12-05

Studying the drivers and determinants of species, population and community spatial patterns is central to ecology. The observed structure of community assemblages is the result of deterministic abiotic (environmental constraints) and biotic factors (positive and negative species interactions), as well as stochastic colonization events (historical contingency). We analyzed the role of multi-scale spatial component of soil environmental variability in structuring earthworm assemblages in a gallery forest from the Colombian "Llanos". We aimed to disentangle the spatial scales at which species assemblages are structured and determine whether these scales matched those expressed by soil environmental variables. We also tested the hypothesis of the "single tree effect" by exploring the spatial relationships between root-related variables and soil nutrient and physical variables in structuring earthworm assemblages. Multivariate ordination techniques and spatially explicit tools were used, namely cross-correlograms, Principal Coordinates of Neighbor Matrices (PCNM) and variation partitioning analyses. The relationship between the spatial organization of earthworm assemblages and soil environmental parameters revealed explicitly multi-scale responses. The soil environmental variables that explained nested population structures across the multi-spatial scale gradient differed for earthworms and assemblages at the very-fine- (<10 m) to medium-scale (10-20 m). The root traits were correlated with areas of high soil nutrient contents at a depth of 0-5 cm. Information on the scales of PCNM variables was obtained using variogram modeling. Based on the size of the plot, the PCNM variables were arbitrarily allocated to medium (>30 m), fine (10-20 m) and very fine scales (<10 m). Variation partitioning analysis revealed that the soil environmental variability explained from less than 1% to as much as 48% of the observed earthworm spatial variation. A large proportion of the spatial variation did not depend on the soil environmental variability for certain species. This finding could indicate the influence of contagious biotic interactions, stochastic factors, or unmeasured relevant soil environmental variables.

Effects of Topography-driven Micro-climatology on Evaporation

NASA Astrophysics Data System (ADS)

Adams, D. D.; Boll, J.; Wagenbrenner, N. S.

2017-12-01

The effects of spatial-temporal variation of climatic conditions on evaporation in micro-climates are not well defined. Current spatially-based remote sensing and modeling for evaporation is limited for high resolutions and complex topographies. We investigated the effect of topography-driven micro-climatology on evaporation supported by field measurements and modeling. Fourteen anemometers and thermometers were installed in intersecting transects over the complex topography of the Cook Agronomy Farm, Pullman, WA. WindNinja was used to create 2-D vector maps based on recorded observations for wind. Spatial analysis of vector maps using ArcGIS was performed for analysis of wind patterns and variation. Based on field measurements, wind speed and direction show consequential variability based on hill-slope location in this complex topography. Wind speed and wind direction varied up to threefold and more than 45 degrees, respectively for a given time interval. The use of existing wind models enables prediction of wind variability over the landscape and subsequently topography-driven evaporation patterns relative to wind. The magnitude of the spatial-temporal variability of wind therefore resulted in variable evaporation rates over the landscape. These variations may contribute to uneven crop development patterns observed during the late growth stages of the agricultural crops at the study location. Use of hill-slope location indexes and appropriate methods for estimating actual evaporation support development of methodologies to better define topography-driven heterogeneity in evaporation. The cumulative effects of spatially-variable climatic factors on evaporation are important to quantify the localized water balance and inform precision farming practices.

A dynamic aerodynamic resistance approach to calculate high resolution sensible heat fluxes in urban areas

NASA Astrophysics Data System (ADS)

Crawford, Ben; Grimmond, Sue; Kent, Christoph; Gabey, Andrew; Ward, Helen; Sun, Ting; Morrison, William

2017-04-01

Remotely sensed data from satellites have potential to enable high-resolution, automated calculation of urban surface energy balance terms and inform decisions about urban adaptations to environmental change. However, aerodynamic resistance methods to estimate sensible heat flux (QH) in cities using satellite-derived observations of surface temperature are difficult in part due to spatial and temporal variability of the thermal aerodynamic resistance term (rah). In this work, we extend an empirical function to estimate rah using observational data from several cities with a broad range of surface vegetation land cover properties. We then use this function to calculate spatially and temporally variable rah in London based on high-resolution (100 m) land cover datasets and in situ meteorological observations. In order to calculate high-resolution QH based on satellite-observed land surface temperatures, we also develop and employ novel methods to i) apply source area-weighted averaging of surface and meteorological variables across the study spatial domain, ii) calculate spatially variable, high-resolution meteorological variables (wind speed, friction velocity, and Obukhov length), iii) incorporate spatially interpolated urban air temperatures from a distributed sensor network, and iv) apply a modified Monte Carlo approach to assess uncertainties with our results, methods, and input variables. Modeled QH using the aerodynamic resistance method is then compared to in situ observations in central London from a unique network of scintillometers and eddy-covariance measurements.

Hyperspectral and multispectral data fusion based on linear-quadratic nonnegative matrix factorization

NASA Astrophysics Data System (ADS)

Benhalouche, Fatima Zohra; Karoui, Moussa Sofiane; Deville, Yannick; Ouamri, Abdelaziz

2017-04-01

This paper proposes three multisharpening approaches to enhance the spatial resolution of urban hyperspectral remote sensing images. These approaches, related to linear-quadratic spectral unmixing techniques, use a linear-quadratic nonnegative matrix factorization (NMF) multiplicative algorithm. These methods begin by unmixing the observable high-spectral/low-spatial resolution hyperspectral and high-spatial/low-spectral resolution multispectral images. The obtained high-spectral/high-spatial resolution features are then recombined, according to the linear-quadratic mixing model, to obtain an unobservable multisharpened high-spectral/high-spatial resolution hyperspectral image. In the first designed approach, hyperspectral and multispectral variables are independently optimized, once they have been coherently initialized. These variables are alternately updated in the second designed approach. In the third approach, the considered hyperspectral and multispectral variables are jointly updated. Experiments, using synthetic and real data, are conducted to assess the efficiency, in spatial and spectral domains, of the designed approaches and of linear NMF-based approaches from the literature. Experimental results show that the designed methods globally yield very satisfactory spectral and spatial fidelities for the multisharpened hyperspectral data. They also prove that these methods significantly outperform the used literature approaches.

Collocation mismatch uncertainties in satellite aerosol retrieval validation

NASA Astrophysics Data System (ADS)

Virtanen, Timo H.; Kolmonen, Pekka; Sogacheva, Larisa; Rodríguez, Edith; Saponaro, Giulia; de Leeuw, Gerrit

2018-02-01

Satellite-based aerosol products are routinely validated against ground-based reference data, usually obtained from sun photometer networks such as AERONET (AEROsol RObotic NETwork). In a typical validation exercise a spatial sample of the instantaneous satellite data is compared against a temporal sample of the point-like ground-based data. The observations do not correspond to exactly the same column of the atmosphere at the same time, and the representativeness of the reference data depends on the spatiotemporal variability of the aerosol properties in the samples. The associated uncertainty is known as the collocation mismatch uncertainty (CMU). The validation results depend on the sampling parameters. While small samples involve less variability, they are more sensitive to the inevitable noise in the measurement data. In this paper we study systematically the effect of the sampling parameters in the validation of AATSR (Advanced Along-Track Scanning Radiometer) aerosol optical depth (AOD) product against AERONET data and the associated collocation mismatch uncertainty. To this end, we study the spatial AOD variability in the satellite data, compare it against the corresponding values obtained from densely located AERONET sites, and assess the possible reasons for observed differences. We find that the spatial AOD variability in the satellite data is approximately 2 times larger than in the ground-based data, and the spatial variability correlates only weakly with that of AERONET for short distances. We interpreted that only half of the variability in the satellite data is due to the natural variability in the AOD, and the rest is noise due to retrieval errors. However, for larger distances (˜ 0.5°) the correlation is improved as the noise is averaged out, and the day-to-day changes in regional AOD variability are well captured. Furthermore, we assess the usefulness of the spatial variability of the satellite AOD data as an estimate of CMU by comparing the retrieval errors to the total uncertainty estimates including the CMU in the validation. We find that accounting for CMU increases the fraction of consistent observations.

The need for spatially explicit quantification of benefits in invasive-species management.

PubMed

Januchowski-Hartley, Stephanie R; Adams, Vanessa M; Hermoso, Virgilio

2018-04-01

Worldwide, invasive species are a leading driver of environmental change across terrestrial, marine, and freshwater environments and cost billions of dollars annually in ecological damages and economic losses. Resources limit invasive-species control, and planning processes are needed to identify cost-effective solutions. Thus, studies are increasingly considering spatially variable natural and socioeconomic assets (e.g., species persistence, recreational fishing) when planning the allocation of actions for invasive-species management. There is a need to improve understanding of how such assets are considered in invasive-species management. We reviewed over 1600 studies focused on management of invasive species, including flora and fauna. Eighty-four of these studies were included in our final analysis because they focused on the prioritization of actions for invasive species management. Forty-five percent (n = 38) of these studies were based on spatial optimization methods, and 35% (n = 13) accounted for spatially variable assets. Across all 84 optimization studies considered, 27% (n = 23) explicitly accounted for spatially variable assets. Based on our findings, we further explored the potential costs and benefits to invasive species management when spatially variable assets are explicitly considered or not. To include spatially variable assets in decision-making processes that guide invasive-species management there is a need to quantify environmental responses to invasive species and to enhance understanding of potential impacts of invasive species on different natural or socioeconomic assets. We suggest these gaps could be filled by systematic reviews, quantifying invasive species impacts on native species at different periods, and broadening sources and enhancing sharing of knowledge. © 2017 Society for Conservation Biology.

Spatial Models for Prediction and Early Warning of Aedes aegypti Proliferation from Data on Climate Change and Variability in Cuba.

PubMed

Ortiz, Paulo L; Rivero, Alina; Linares, Yzenia; Pérez, Alina; Vázquez, Juan R

2015-04-01

Climate variability, the primary expression of climate change, is one of the most important environmental problems affecting human health, particularly vector-borne diseases. Despite research efforts worldwide, there are few studies addressing the use of information on climate variability for prevention and early warning of vector-borne infectious diseases. Show the utility of climate information for vector surveillance by developing spatial models using an entomological indicator and information on predicted climate variability in Cuba to provide early warning of danger of increased risk of dengue transmission. An ecological study was carried out using retrospective and prospective analyses of time series combined with spatial statistics. Several entomological and climatic indicators were considered using complex Bultó indices -1 and -2. Moran's I spatial autocorrelation coefficient specified for a matrix of neighbors with a radius of 20 km, was used to identify the spatial structure. Spatial structure simulation was based on simultaneous autoregressive and conditional autoregressive models; agreement between predicted and observed values for number of Aedes aegypti foci was determined by the concordance index Di and skill factor Bi. Spatial and temporal distributions of populations of Aedes aegypti were obtained. Models for describing, simulating and predicting spatial patterns of Aedes aegypti populations associated with climate variability patterns were put forward. The ranges of climate variability affecting Aedes aegypti populations were identified. Forecast maps were generated for the municipal level. Using the Bultó indices of climate variability, it is possible to construct spatial models for predicting increased Aedes aegypti populations in Cuba. At 20 x 20 km resolution, the models are able to provide warning of potential changes in vector populations in rainy and dry seasons and by month, thus demonstrating the usefulness of climate information for epidemiological surveillance.

A Study on the Effects of Spatial Scale on Snow Process in Hyper-Resolution Hydrological Modelling over Mountainous Areas

NASA Astrophysics Data System (ADS)

Garousi Nejad, I.; He, S.; Tang, Q.; Ogden, F. L.; Steinke, R. C.; Frazier, N.; Tarboton, D. G.; Ohara, N.; Lin, H.

2017-12-01

Spatial scale is one of the main considerations in hydrological modeling of snowmelt in mountainous areas. The size of model elements controls the degree to which variability can be explicitly represented versus what needs to be parameterized using effective properties such as averages or other subgrid variability parameterizations that may degrade the quality of model simulations. For snowmelt modeling terrain parameters such as slope, aspect, vegetation and elevation play an important role in the timing and quantity of snowmelt that serves as an input to hydrologic runoff generation processes. In general, higher resolution enhances the accuracy of the simulation since fine meshes represent and preserve the spatial variability of atmospheric and surface characteristics better than coarse resolution. However, this increases computational cost and there may be a scale beyond which the model response does not improve due to diminishing sensitivity to variability and irreducible uncertainty associated with the spatial interpolation of inputs. This paper examines the influence of spatial resolution on the snowmelt process using simulations of and data from the Animas River watershed, an alpine mountainous area in Colorado, USA, using an unstructured distributed physically based hydrological model developed for a parallel computing environment, ADHydro. Five spatial resolutions (30 m, 100 m, 250 m, 500 m, and 1 km) were used to investigate the variations in hydrologic response. This study demonstrated the importance of choosing the appropriate spatial scale in the implementation of ADHydro to obtain a balance between representing spatial variability and the computational cost. According to the results, variation in the input variables and parameters due to using different spatial resolution resulted in changes in the obtained hydrological variables, especially snowmelt, both at the basin-scale and distributed across the model mesh.

Numerical investigation of aggregated fuel spatial pattern impacts on fire behavior

DOE PAGES

Parsons, Russell A.; Linn, Rodman Ray; Pimont, Francois; ...

2017-06-18

Here, landscape heterogeneity shapes species distributions, interactions, and fluctuations. Historically, in dry forest ecosystems, low canopy cover and heterogeneous fuel patterns often moderated disturbances like fire. Over the last century, however, increases in canopy cover and more homogeneous patterns have contributed to altered fire regimes with higher fire severity. Fire management strategies emphasize increasing within-stand heterogeneity with aggregated fuel patterns to alter potential fire behavior. Yet, little is known about how such patterns may affect fire behavior, or how sensitive fire behavior changes from fuel patterns are to winds and canopy cover. Here, we used a physics-based fire behavior model,more » FIRETEC, to explore the impacts of spatially aggregated fuel patterns on the mean and variability of stand-level fire behavior, and to test sensitivity of these effects to wind and canopy cover. Qualitative and quantitative approaches suggest that spatial fuel patterns can significantly affect fire behavior. Based on our results we propose three hypotheses: (1) aggregated spatial fuel patterns primarily affect fire behavior by increasing variability; (2) this variability should increase with spatial scale of aggregation; and (3) fire behavior sensitivity to spatial pattern effects should be more pronounced under moderate wind and fuel conditions.« less

Numerical investigation of aggregated fuel spatial pattern impacts on fire behavior

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

Parsons, Russell A.; Linn, Rodman Ray; Pimont, Francois

Here, landscape heterogeneity shapes species distributions, interactions, and fluctuations. Historically, in dry forest ecosystems, low canopy cover and heterogeneous fuel patterns often moderated disturbances like fire. Over the last century, however, increases in canopy cover and more homogeneous patterns have contributed to altered fire regimes with higher fire severity. Fire management strategies emphasize increasing within-stand heterogeneity with aggregated fuel patterns to alter potential fire behavior. Yet, little is known about how such patterns may affect fire behavior, or how sensitive fire behavior changes from fuel patterns are to winds and canopy cover. Here, we used a physics-based fire behavior model,more » FIRETEC, to explore the impacts of spatially aggregated fuel patterns on the mean and variability of stand-level fire behavior, and to test sensitivity of these effects to wind and canopy cover. Qualitative and quantitative approaches suggest that spatial fuel patterns can significantly affect fire behavior. Based on our results we propose three hypotheses: (1) aggregated spatial fuel patterns primarily affect fire behavior by increasing variability; (2) this variability should increase with spatial scale of aggregation; and (3) fire behavior sensitivity to spatial pattern effects should be more pronounced under moderate wind and fuel conditions.« less

Exploring changes in the spatial distribution of stream baseflow generation during a seasonal recession

USGS Publications Warehouse

Payn, R.A.; Gooseff, M.N.; McGlynn, B.L.; Bencala, K.E.; Wondzell, S.M.

2012-01-01

Relating watershed structure to streamflow generation is a primary focus of hydrology. However, comparisons of longitudinal variability in stream discharge with adjacent valley structure have been rare, resulting in poor understanding of the distribution of the hydrologic mechanisms that cause variability in streamflow generation along valleys. This study explores detailed surveys of stream base flow across a gauged, 23 km2 mountain watershed. Research objectives were (1) to relate spatial variability in base flow to fundamental elements of watershed structure, primarily topographic contributing area, and (2) to assess temporal changes in the spatial patterns of those relationships during a seasonal base flow recession. We analyzed spatiotemporal variability in base flow using (1) summer hydrographs at the study watershed outlet and 5 subwatershed outlets and (2) longitudinal series of discharge measurements every ~100 m along the streams of the 3 largest subwatersheds (1200 to 2600 m in valley length), repeated 2 to 3 times during base flow recession. Reaches within valley segments of 300 to 1200 m in length tended to demonstrate similar streamflow generation characteristics. Locations of transitions between these segments were consistent throughout the recession, and tended to be collocated with abrupt longitudinal transitions in valley slope or hillslope-riparian characteristics. Both within and among subwatersheds, correlation between the spatial distributions of streamflow and topographic contributing area decreased during the recession, suggesting a general decrease in the influence of topography on stream base flow contributions. As topographic controls on base flow evidently decreased, multiple aspects of subsurface structure were likely to have gained influence.

Modelling Geomechanical Heterogeneity of Rock Masses Using Direct and Indirect Geostatistical Conditional Simulation Methods

NASA Astrophysics Data System (ADS)

Eivazy, Hesameddin; Esmaieli, Kamran; Jean, Raynald

2017-12-01

An accurate characterization and modelling of rock mass geomechanical heterogeneity can lead to more efficient mine planning and design. Using deterministic approaches and random field methods for modelling rock mass heterogeneity is known to be limited in simulating the spatial variation and spatial pattern of the geomechanical properties. Although the applications of geostatistical techniques have demonstrated improvements in modelling the heterogeneity of geomechanical properties, geostatistical estimation methods such as Kriging result in estimates of geomechanical variables that are not fully representative of field observations. This paper reports on the development of 3D models for spatial variability of rock mass geomechanical properties using geostatistical conditional simulation method based on sequential Gaussian simulation. A methodology to simulate the heterogeneity of rock mass quality based on the rock mass rating is proposed and applied to a large open-pit mine in Canada. Using geomechanical core logging data collected from the mine site, a direct and an indirect approach were used to model the spatial variability of rock mass quality. The results of the two modelling approaches were validated against collected field data. The study aims to quantify the risks of pit slope failure and provides a measure of uncertainties in spatial variability of rock mass properties in different areas of the pit.

Spatial analysis and land use regression of VOCs and NO(2) from school-based urban air monitoring in Detroit/Dearborn, USA.

PubMed

Mukerjee, Shaibal; Smith, Luther A; Johnson, Mary M; Neas, Lucas M; Stallings, Casson A

2009-08-01

Passive ambient air sampling for nitrogen dioxide (NO(2)) and volatile organic compounds (VOCs) was conducted at 25 school and two compliance sites in Detroit and Dearborn, Michigan, USA during the summer of 2005. Geographic Information System (GIS) data were calculated at each of 116 schools. The 25 selected schools were monitored to assess and model intra-urban gradients of air pollutants to evaluate impact of traffic and urban emissions on pollutant levels. Schools were chosen to be statistically representative of urban land use variables such as distance to major roadways, traffic intensity around the schools, distance to nearest point sources, population density, and distance to nearest border crossing. Two approaches were used to investigate spatial variability. First, Kruskal-Wallis analyses and pairwise comparisons on data from the schools examined coarse spatial differences based on city section and distance from heavily trafficked roads. Secondly, spatial variation on a finer scale and as a response to multiple factors was evaluated through land use regression (LUR) models via multiple linear regression. For weeklong exposures, VOCs did not exhibit spatial variability by city section or distance from major roads; NO(2) was significantly elevated in a section dominated by traffic and industrial influence versus a residential section. Somewhat in contrast to coarse spatial analyses, LUR results revealed spatial gradients in NO(2) and selected VOCs across the area. The process used to select spatially representative sites for air sampling and the results of coarse and fine spatial variability of air pollutants provide insights that may guide future air quality studies in assessing intra-urban gradients.

Throughfall and its spatial variability beneath xerophytic shrub canopies within water-limited arid desert ecosystems

NASA Astrophysics Data System (ADS)

Zhang, Ya-feng; Wang, Xin-ping; Hu, Rui; Pan, Yan-xia

2016-08-01

Throughfall is known to be a critical component of the hydrological and biogeochemical cycles of forested ecosystems with inherently temporal and spatial variability. Yet little is understood concerning the throughfall variability of shrubs and the associated controlling factors in arid desert ecosystems. Here we systematically investigated the variability of throughfall of two morphological distinct xerophytic shrubs (Caragana korshinskii and Artemisia ordosica) within a re-vegetated arid desert ecosystem, and evaluated the effects of shrub structure and rainfall characteristics on throughfall based on heavily gauged throughfall measurements at the event scale. We found that morphological differences were not sufficient to generate significant difference (P < 0.05) in throughfall between two studied shrub species under the same rainfall and meteorological conditions in our study area, with a throughfall percentage of 69.7% for C. korshinskii and 64.3% for A. ordosica. We also observed a highly variable patchy pattern of throughfall beneath individual shrub canopies, but the spatial patterns appeared to be stable among rainfall events based on time stability analysis. Throughfall linearly increased with the increasing distance from the shrub base for both shrubs, and radial direction beneath shrub canopies had a pronounced impact on throughfall. Throughfall variability, expressed as the coefficient of variation (CV) of throughfall, tended to decline with the increase in rainfall amount, intensity and duration, and stabilized passing a certain threshold. Our findings highlight the great variability of throughfall beneath the canopies of xerophytic shrubs and the time stability of throughfall pattern among rainfall events. The spatially heterogeneous and temporally stable throughfall is expected to generate a dynamic patchy distribution of soil moisture beneath shrub canopies within arid desert ecosystems.

The impact of sedimentary anisotropy on solute mixing in stacked scour-pool structures

NASA Astrophysics Data System (ADS)

Bennett, Jeremy P.; Haslauer, Claus P.; Cirpka, Olaf A.

2017-04-01

The spatial variability of hydraulic conductivity is known to have a strong impact on solute spreading and mixing. In most investigations, its local anisotropy has been neglected. Recent studies have shown that spatially varying orientation in sedimentary anisotropy can lead to twisting flow enhancing transverse mixing, but most of these studies used geologically implausible geometries. We use an object-based approach to generate stacked scour-pool structures with either isotropic or anisotropic filling which are typically reported in glacial outwash deposits. We analyze how spatially variable isotropic conductivity and variation of internal anisotropy in these features impacts transverse plume deformation and both longitudinal and transverse spreading and mixing. In five test cases, either the scalar values of conductivity or the spatial orientation of its anisotropy is varied between the scour-pool structures. Based on 100 random configurations, we compare the variability of velocity components, stretching and folding metrics, advective travel-time distributions, one and two-particle statistics in advective-dispersive transport, and the flux-related dilution indices for steady state advective-dispersive transport among the five test cases. Variation in the orientation of internal anisotropy causes strong variability in the lateral velocity components, which leads to deformation in transverse directions and enhances transverse mixing, whereas it hardly affects the variability of the longitudinal velocity component and thus longitudinal spreading and mixing. The latter is controlled by the spatial variability in the scalar values of hydraulic conductivity. Our results demonstrate that sedimentary anisotropy is important for transverse mixing, whereas it may be neglected when considering longitudinal spreading and mixing.

Assessing performance and seasonal bias of pollen-based climate reconstructions in a perfect model world

NASA Astrophysics Data System (ADS)

Trachsel, M.; Rehfeld, K.; Telford, R.; Laepple, T.

2017-12-01

Reconstructions of summer, winter or annual mean temperatures based on the species composition of bio-indicators such as pollen are routinely used in climate model-proxy data comparison studies. Most reconstruction algorithms exploit the joint distribution of modern spatial climate and species distribution for the development of the reconstructions. They rely on the space-for-time substitution and the specific assumption that environmental variables other than those reconstructed are not important or that their relationship with the reconstructed variable(s) should be the same in the past as in the modern spatial calibration dataset. Here we test the implications of this "correlative uniformitarianism" assumption on climate reconstructions in an ideal model world, in which climate and vegetation are known at all times. The alternate reality is a climate simulation of the last 6000 years with dynamic vegetation. Transient changes of plant functional types are considered as surrogate pollen counts and allow us to establish, apply and evaluate transfer functions in the modeled world. We find that the transfer function cross validation r2 is of limited use to identify reconstructible climate variables, as it only relies on the modern spatial climate-vegetation relationship. However, ordination approaches that assess the amount of fossil vegetation variance explained by the reconstructions are promising. We show that correlations between climate variables in the modern climate-vegetation relationship are systematically extended into the reconstructions. Summer temperatures, the most prominent driving variable for modeled vegetation change in the Northern Hemisphere, are accurately reconstructed. However, the amplitude of the model winter and mean annual temperature cooling between the mid-Holocene and present day is overestimated and similar to the summer trend in magnitude. This effect occurs because temporal changes of a dominant climate variable are imprinted on a less important variable, leading to reconstructions biased towards the dominant variable's trends. Our results, although based on a model vegetation that is inevitably simpler than reality, indicate that reconstructions of multiple climate variables based on modern spatial bio-indicator datasets should be treated with caution.

Variability of hazardous air pollutants in an urban area

NASA Astrophysics Data System (ADS)

Spicer, Chester W.; Buxton, Bruce E.; Holdren, Michael W.; Smith, Deborah L.; Kelly, Thomas J.; Rust, Steven W.; Pate, Alan D.; Sverdrup, George M.; Chuang, Jane C.

The variability of hazardous air pollutants (HAPs) is an important factor in determining human exposure to such chemicals, and in designing HAP measurement programs. This study has investigated the factors which contribute to HAP variability in an urban area. Six measurement sites separated by up to 12 km collected data with 3 h time resolution to examine spatial variability within neighborhoods and between neighborhoods. The measurements were made in Columbus, OH. The 3 h results also were used to study temporal variability, and duplicate samples collected at each site were used to determine the component of variability attributable to the measurement process. Hourly samples collected over 10 days at one site provided further insight into the temporal resolution needed to capture short-term peak concentrations. Measurements at the 6 spatial sites focused on 78 chemicals. Twenty-three of these species were found in at least 95% of the 3 h samples, and 39 chemicals were present at least 60% of the time. The relative standard deviations for most of these 39 frequently detected chemicals was 1.0 or lower. Variability was segmented into temporal, spatial, and measurement components. Temporal variation was the major contributor to HAP variability for 19 of the 39 frequently detected compounds, based on the 3 h data. Measurement imprecision contributed less than 25% for most of the volatile organic species, but 30% or more of the variability for carbonyl compounds, trace elements, and particle-bound extractable organic mass. Interestingly, the spatial component contributed less than 20% of the total variability for all the chemicals except sulfur. Based on the data with hourly resolution, peak to median ratios (hourly peak to 24 h median) averaged between 2 and 4 for most of the volatile organic compounds, but there were two species with peak to median ratios of about 10.

Spatial variability of excess mortality during prolonged dust events in a high-density city: a time-stratified spatial regression approach.

PubMed

Wong, Man Sing; Ho, Hung Chak; Yang, Lin; Shi, Wenzhong; Yang, Jinxin; Chan, Ta-Chien

2017-07-24

Dust events have long been recognized to be associated with a higher mortality risk. However, no study has investigated how prolonged dust events affect the spatial variability of mortality across districts in a downwind city. In this study, we applied a spatial regression approach to estimate the district-level mortality during two extreme dust events in Hong Kong. We compared spatial and non-spatial models to evaluate the ability of each regression to estimate mortality. We also compared prolonged dust events with non-dust events to determine the influences of community factors on mortality across the city. The density of a built environment (estimated by the sky view factor) had positive association with excess mortality in each district, while socioeconomic deprivation contributed by lower income and lower education induced higher mortality impact in each territory planning unit during a prolonged dust event. Based on the model comparison, spatial error modelling with the 1st order of queen contiguity consistently outperformed other models. The high-risk areas with higher increase in mortality were located in an urban high-density environment with higher socioeconomic deprivation. Our model design shows the ability to predict spatial variability of mortality risk during an extreme weather event that is not able to be estimated based on traditional time-series analysis or ecological studies. Our spatial protocol can be used for public health surveillance, sustainable planning and disaster preparation when relevant data are available.

Geoelectrical characterisation of basement aquifers: the case of Iberekodo, southwestern Nigeria

NASA Astrophysics Data System (ADS)

Aizebeokhai, Ahzegbobor P.; Oyeyemi, Kehinde D.

2018-03-01

Basement aquifers, which occur within the weathered and fractured zones of crystalline bedrocks, are important groundwater resources in tropical and subtropical regions. The development of basement aquifers is complex owing to their high spatial variability. Geophysical techniques are used to obtain information about the hydrologic characteristics of the weathered and fractured zones of the crystalline basement rocks, which relates to the occurrence of groundwater in the zones. The spatial distributions of these hydrologic characteristics are then used to map the spatial variability of the basement aquifers. Thus, knowledge of the spatial variability of basement aquifers is useful in siting wells and boreholes for optimal and perennial yield. Geoelectrical resistivity is one of the most widely used geophysical methods for assessing the spatial variability of the weathered and fractured zones in groundwater exploration efforts in basement complex terrains. The presented study focuses on combining vertical electrical sounding with two-dimensional (2D) geoelectrical resistivity imaging to characterise the weathered and fractured zones in a crystalline basement complex terrain in southwestern Nigeria. The basement aquifer was delineated, and the nature, extent and spatial variability of the delineated basement aquifer were assessed based on the spatial variability of the weathered and fractured zones. The study shows that a multiple-gradient array for 2D resistivity imaging is sensitive to vertical and near-surface stratigraphic features, which have hydrological implications. The integration of resistivity sounding with 2D geoelectrical resistivity imaging is efficient and enhances near-surface characterisation in basement complex terrain.

Similar Processes but Different Environmental Filters for Soil Bacterial and Fungal Community Composition Turnover on a Broad Spatial Scale

PubMed Central

Chemidlin Prévost-Bouré, Nicolas; Dequiedt, Samuel; Thioulouse, Jean; Lelièvre, Mélanie; Saby, Nicolas P. A.; Jolivet, Claudy; Arrouays, Dominique; Plassart, Pierre; Lemanceau, Philippe; Ranjard, Lionel

2014-01-01

Spatial scaling of microorganisms has been demonstrated over the last decade. However, the processes and environmental filters shaping soil microbial community structure on a broad spatial scale still need to be refined and ranked. Here, we compared bacterial and fungal community composition turnovers through a biogeographical approach on the same soil sampling design at a broad spatial scale (area range: 13300 to 31000 km2): i) to examine their spatial structuring; ii) to investigate the relative importance of environmental selection and spatial autocorrelation in determining their community composition turnover; and iii) to identify and rank the relevant environmental filters and scales involved in their spatial variations. Molecular fingerprinting of soil bacterial and fungal communities was performed on 413 soils from four French regions of contrasting environmental heterogeneity (Landes

Similar processes but different environmental filters for soil bacterial and fungal community composition turnover on a broad spatial scale.

PubMed

Chemidlin Prévost-Bouré, Nicolas; Dequiedt, Samuel; Thioulouse, Jean; Lelièvre, Mélanie; Saby, Nicolas P A; Jolivet, Claudy; Arrouays, Dominique; Plassart, Pierre; Lemanceau, Philippe; Ranjard, Lionel

2014-01-01

Spatial scaling of microorganisms has been demonstrated over the last decade. However, the processes and environmental filters shaping soil microbial community structure on a broad spatial scale still need to be refined and ranked. Here, we compared bacterial and fungal community composition turnovers through a biogeographical approach on the same soil sampling design at a broad spatial scale (area range: 13300 to 31000 km2): i) to examine their spatial structuring; ii) to investigate the relative importance of environmental selection and spatial autocorrelation in determining their community composition turnover; and iii) to identify and rank the relevant environmental filters and scales involved in their spatial variations. Molecular fingerprinting of soil bacterial and fungal communities was performed on 413 soils from four French regions of contrasting environmental heterogeneity (Landes

Predicting the potential distribution of the amphibian pathogen Batrachochytrium dendrobatidis in East and Southeast Asia.

PubMed

Moriguchi, Sachiko; Tominaga, Atsushi; Irwin, Kelly J; Freake, Michael J; Suzuki, Kazutaka; Goka, Koichi

2015-04-08

Batrachochytrium dendrobatidis (Bd) is the pathogen responsible for chytridiomycosis, a disease that is associated with a worldwide amphibian population decline. In this study, we predicted the potential distribution of Bd in East and Southeast Asia based on limited occurrence data. Our goal was to design an effective survey area where efforts to detect the pathogen can be focused. We generated ecological niche models using the maximum-entropy approach, with alleviation of multicollinearity and spatial autocorrelation. We applied eigenvector-based spatial filters as independent variables, in addition to environmental variables, to resolve spatial autocorrelation, and compared the model's accuracy and the degree of spatial autocorrelation with those of a model estimated using only environmental variables. We were able to identify areas of high suitability for Bd with accuracy. Among the environmental variables, factors related to temperature and precipitation were more effective in predicting the potential distribution of Bd than factors related to land use and cover type. Our study successfully predicted the potential distribution of Bd in East and Southeast Asia. This information should now be used to prioritize survey areas and generate a surveillance program to detect the pathogen.

Sampling design for spatially distributed hydrogeologic and environmental processes

USGS Publications Warehouse

Christakos, G.; Olea, R.A.

1992-01-01

A methodology for the design of sampling networks over space is proposed. The methodology is based on spatial random field representations of nonhomogeneous natural processes, and on optimal spatial estimation techniques. One of the most important results of random field theory for physical sciences is its rationalization of correlations in spatial variability of natural processes. This correlation is extremely important both for interpreting spatially distributed observations and for predictive performance. The extent of site sampling and the types of data to be collected will depend on the relationship of subsurface variability to predictive uncertainty. While hypothesis formulation and initial identification of spatial variability characteristics are based on scientific understanding (such as knowledge of the physics of the underlying phenomena, geological interpretations, intuition and experience), the support offered by field data is statistically modelled. This model is not limited by the geometric nature of sampling and covers a wide range in subsurface uncertainties. A factorization scheme of the sampling error variance is derived, which possesses certain atttactive properties allowing significant savings in computations. By means of this scheme, a practical sampling design procedure providing suitable indices of the sampling error variance is established. These indices can be used by way of multiobjective decision criteria to obtain the best sampling strategy. Neither the actual implementation of the in-situ sampling nor the solution of the large spatial estimation systems of equations are necessary. The required values of the accuracy parameters involved in the network design are derived using reference charts (readily available for various combinations of data configurations and spatial variability parameters) and certain simple yet accurate analytical formulas. Insight is gained by applying the proposed sampling procedure to realistic examples related to sampling problems in two dimensions. ?? 1992.

Geographical Gradients in Argentinean Terrestrial Mammal Species Richness and Their Environmental Correlates

PubMed Central

Márquez, Ana L.; Real, Raimundo; Kin, Marta S.; Guerrero, José Carlos; Galván, Betina; Barbosa, A. Márcia; Olivero, Jesús; Palomo, L. Javier; Vargas, J. Mario; Justo, Enrique

2012-01-01

We analysed the main geographical trends of terrestrial mammal species richness (SR) in Argentina, assessing how broad-scale environmental variation (defined by climatic and topographic variables) and the spatial form of the country (defined by spatial filters based on spatial eigenvector mapping (SEVM)) influence the kinds and the numbers of mammal species along these geographical trends. We also evaluated if there are pure geographical trends not accounted for by the environmental or spatial factors. The environmental variables and spatial filters that simultaneously correlated with the geographical variables and SR were considered potential causes of the geographic trends. We performed partial correlations between SR and the geographical variables, maintaining the selected explanatory variables statistically constant, to determine if SR was fully explained by them or if a significant residual geographic pattern remained. All groups and subgroups presented a latitudinal gradient not attributable to the spatial form of the country. Most of these trends were not explained by climate. We used a variation partitioning procedure to quantify the pure geographic trend (PGT) that remained unaccounted for. The PGT was larger for latitudinal than for longitudinal gradients. This suggests that historical or purely geographical causes may also be relevant drivers of these geographical gradients in mammal diversity. PMID:23028254

EO based Agro-ecosystem approach for climate change adaptation in enhancing the crop production efficiency in the Indo-gangetic plains of India

NASA Astrophysics Data System (ADS)

Pandey, Suraj

This study develops a spatial mapping of agro-ecological zones based on earth observation model using MODIS regional dataset as a tool to guide key areas of cropping system and targeting to climate change strategies. This tool applies to the Indo-gangetic Plains of north India to target the domains of bio-physical characteristics and socio-economics with respect to changing climate in the region. It derive on secondary data for spatially-explicit variables at the state/district level, which serve as indicators of climate variability based on sustainable livelihood approach, natural, social and human. The study details the methodology used and generates the spatial climate risk maps for composite indicators of livelihood and vulnerability index in the region.

Impact of land-use on groundwater quality: GIS-based study from an alluvial aquifer in the western Ganges basin

NASA Astrophysics Data System (ADS)

Khan, Arina; Khan, Haris Hasan; Umar, Rashid

2017-12-01

In this study, groundwater quality of an alluvial aquifer in the western Ganges basin is assessed using a GIS-based groundwater quality index (GQI) concept that uses groundwater quality data from field survey and laboratory analysis. Groundwater samples were collected from 42 wells during pre-monsoon and post-monsoon periods of 2012 and analysed for pH, EC, TDS, Anions (Cl, SO4, NO3), and Cations (Ca, Mg, Na). To generate the index, several parameters were selected based on WHO recommendations. The spatially variable grids of each parameter were modified by normalizing with the WHO standards and finally integrated into a GQI grid. The mean GQI values for both the season suggest good groundwater quality. However, spatial variations exist and are represented by GQI map of both seasons. This spatial variability was compared with the existing land-use, prepared using high-resolution satellite imagery available in Google earth. The GQI grids were compared to the land-use map using an innovative GIS-based method. Results indicate that the spatial variability of groundwater quality in the region is not fully controlled by the land-use pattern. This probably reflects the diffuse nature of land-use classes, especially settlements and plantations.

Calibration of a distributed hydrologic model for six European catchments using remote sensing data

NASA Astrophysics Data System (ADS)

Stisen, S.; Demirel, M. C.; Mendiguren González, G.; Kumar, R.; Rakovec, O.; Samaniego, L. E.

2017-12-01

While observed streamflow has been the single reference for most conventional hydrologic model calibration exercises, the availability of spatially distributed remote sensing observations provide new possibilities for multi-variable calibration assessing both spatial and temporal variability of different hydrologic processes. In this study, we first identify the key transfer parameters of the mesoscale Hydrologic Model (mHM) controlling both the discharge and the spatial distribution of actual evapotranspiration (AET) across six central European catchments (Elbe, Main, Meuse, Moselle, Neckar and Vienne). These catchments are selected based on their limited topographical and climatic variability which enables to evaluate the effect of spatial parameterization on the simulated evapotranspiration patterns. We develop a European scale remote sensing based actual evapotranspiration dataset at a 1 km grid scale driven primarily by land surface temperature observations from MODIS using the TSEB approach. Using the observed AET maps we analyze the potential benefits of incorporating spatial patterns from MODIS data to calibrate the mHM model. This model allows calibrating one-basin-at-a-time or all-basins-together using its unique structure and multi-parameter regionalization approach. Results will indicate any tradeoffs between spatial pattern and discharge simulation during model calibration and through validation against independent internal discharge locations. Moreover, added value on internal water balances will be analyzed.

Identifying public water facilities with low spatial variability of disinfection by-products for epidemiological investigations

PubMed Central

Hinckley, A; Bachand, A; Nuckols, J; Reif, J

2005-01-01

Background and Aims: Epidemiological studies of disinfection by-products (DBPs) and reproductive outcomes have been hampered by misclassification of exposure. In most epidemiological studies conducted to date, all persons living within the boundaries of a water distribution system have been assigned a common exposure value based on facility-wide averages of trihalomethane (THM) concentrations. Since THMs do not develop uniformly throughout a distribution system, assignment of facility-wide averages may be inappropriate. One approach to mitigate this potential for misclassification is to select communities for epidemiological investigations that are served by distribution systems with consistently low spatial variability of THMs. Methods and Results: A feasibility study was conducted to develop methods for community selection using the Information Collection Rule (ICR) database, assembled by the US Environmental Protection Agency. The ICR database contains quarterly DBP concentrations collected between 1997 and 1998 from the distribution systems of 198 public water facilities with minimum service populations of 100 000 persons. Facilities with low spatial variation of THMs were identified using two methods; 33 facilities were found with low spatial variability based on one or both methods. Because brominated THMs may be important predictors of risk for adverse reproductive outcomes, sites were categorised into three exposure profiles according to proportion of brominated THM species and average TTHM concentration. The correlation between THMs and haloacetic acids (HAAs) in these facilities was evaluated to see whether selection by total trihalomethanes (TTHMs) corresponds to low spatial variability for HAAs. TTHMs were only moderately correlated with HAAs (r = 0.623). Conclusions: Results provide a simple method for a priori selection of sites with low spatial variability from state or national public water facility datasets as a means to reduce exposure misclassification in epidemiological studies of DBPs. PMID:15961627

Spatial Heterogeneity of Leaf Area Index (LAI) and Its Temporal Course on Arable Land: Combining Field Measurements, Remote Sensing and Simulation in a Comprehensive Data Analysis Approach (CDAA).

PubMed

Reichenau, Tim G; Korres, Wolfgang; Montzka, Carsten; Fiener, Peter; Wilken, Florian; Stadler, Anja; Waldhoff, Guido; Schneider, Karl

2016-01-01

The ratio of leaf area to ground area (leaf area index, LAI) is an important state variable in ecosystem studies since it influences fluxes of matter and energy between the land surface and the atmosphere. As a basis for generating temporally continuous and spatially distributed datasets of LAI, the current study contributes an analysis of its spatial variability and spatial structure. Soil-vegetation-atmosphere fluxes of water, carbon and energy are nonlinearly related to LAI. Therefore, its spatial heterogeneity, i.e., the combination of spatial variability and structure, has an effect on simulations of these fluxes. To assess LAI spatial heterogeneity, we apply a Comprehensive Data Analysis Approach that combines data from remote sensing (5 m resolution) and simulation (150 m resolution) with field measurements and a detailed land use map. Test area is the arable land in the fertile loess plain of the Rur catchment on the Germany-Belgium-Netherlands border. LAI from remote sensing and simulation compares well with field measurements. Based on the simulation results, we describe characteristic crop-specific temporal patterns of LAI spatial variability. By means of these patterns, we explain the complex multimodal frequency distributions of LAI in the remote sensing data. In the test area, variability between agricultural fields is higher than within fields. Therefore, spatial resolutions less than the 5 m of the remote sensing scenes are sufficient to infer LAI spatial variability. Frequency distributions from the simulation agree better with the multimodal distributions from remote sensing than normal distributions do. The spatial structure of LAI in the test area is dominated by a short distance referring to field sizes. Longer distances that refer to soil and weather can only be derived from remote sensing data. Therefore, simulations alone are not sufficient to characterize LAI spatial structure. It can be concluded that a comprehensive picture of LAI spatial heterogeneity and its temporal course can contribute to the development of an approach to create spatially distributed and temporally continuous datasets of LAI.

Spatial Heterogeneity of Leaf Area Index (LAI) and Its Temporal Course on Arable Land: Combining Field Measurements, Remote Sensing and Simulation in a Comprehensive Data Analysis Approach (CDAA)

PubMed Central

Korres, Wolfgang; Montzka, Carsten; Fiener, Peter; Wilken, Florian; Stadler, Anja; Waldhoff, Guido; Schneider, Karl

2016-01-01

The ratio of leaf area to ground area (leaf area index, LAI) is an important state variable in ecosystem studies since it influences fluxes of matter and energy between the land surface and the atmosphere. As a basis for generating temporally continuous and spatially distributed datasets of LAI, the current study contributes an analysis of its spatial variability and spatial structure. Soil-vegetation-atmosphere fluxes of water, carbon and energy are nonlinearly related to LAI. Therefore, its spatial heterogeneity, i.e., the combination of spatial variability and structure, has an effect on simulations of these fluxes. To assess LAI spatial heterogeneity, we apply a Comprehensive Data Analysis Approach that combines data from remote sensing (5 m resolution) and simulation (150 m resolution) with field measurements and a detailed land use map. Test area is the arable land in the fertile loess plain of the Rur catchment on the Germany-Belgium-Netherlands border. LAI from remote sensing and simulation compares well with field measurements. Based on the simulation results, we describe characteristic crop-specific temporal patterns of LAI spatial variability. By means of these patterns, we explain the complex multimodal frequency distributions of LAI in the remote sensing data. In the test area, variability between agricultural fields is higher than within fields. Therefore, spatial resolutions less than the 5 m of the remote sensing scenes are sufficient to infer LAI spatial variability. Frequency distributions from the simulation agree better with the multimodal distributions from remote sensing than normal distributions do. The spatial structure of LAI in the test area is dominated by a short distance referring to field sizes. Longer distances that refer to soil and weather can only be derived from remote sensing data. Therefore, simulations alone are not sufficient to characterize LAI spatial structure. It can be concluded that a comprehensive picture of LAI spatial heterogeneity and its temporal course can contribute to the development of an approach to create spatially distributed and temporally continuous datasets of LAI. PMID:27391858

A multi-scale comparison of trait linkages to environmental and spatial variables in fish communities across a large freshwater lake.

PubMed

Strecker, Angela L; Casselman, John M; Fortin, Marie-Josée; Jackson, Donald A; Ridgway, Mark S; Abrams, Peter A; Shuter, Brian J

2011-07-01

Species present in communities are affected by the prevailing environmental conditions, and the traits that these species display may be sensitive indicators of community responses to environmental change. However, interpretation of community responses may be confounded by environmental variation at different spatial scales. Using a hierarchical approach, we assessed the spatial and temporal variation of traits in coastal fish communities in Lake Huron over a 5-year time period (2001-2005) in response to biotic and abiotic environmental factors. The association of environmental and spatial variables with trophic, life-history, and thermal traits at two spatial scales (regional basin-scale, local site-scale) was quantified using multivariate statistics and variation partitioning. We defined these two scales (regional, local) on which to measure variation and then applied this measurement framework identically in all 5 study years. With this framework, we found that there was no change in the spatial scales of fish community traits over the course of the study, although there were small inter-annual shifts in the importance of regional basin- and local site-scale variables in determining community trait composition (e.g., life-history, trophic, and thermal). The overriding effects of regional-scale variables may be related to inter-annual variation in average summer temperature. Additionally, drivers of fish community traits were highly variable among study years, with some years dominated by environmental variation and others dominated by spatially structured variation. The influence of spatial factors on trait composition was dynamic, which suggests that spatial patterns in fish communities over large landscapes are transient. Air temperature and vegetation were significant variables in most years, underscoring the importance of future climate change and shoreline development as drivers of fish community structure. Overall, a trait-based hierarchical framework may be a useful conservation tool, as it highlights the multi-scaled interactive effect of variables over a large landscape.

Spatial Variability of the Topsoil Organic Carbon in the Moso Bamboo Forests of Southern China in Association with Soil Properties

PubMed Central

Zhang, Houxi; Zhuang, Shunyao; Qian, Haiyan; Wang, Feng; Ji, Haibao

2015-01-01

Understanding the spatial variability of soil organic carbon (SOC) must be enhanced to improve sampling design and to develop soil management strategies in terrestrial ecosystems. Moso bamboo (Phyllostachys pubescens Mazel ex Houz.) forests have a high SOC storage potential; however, they also vary significantly spatially. This study investigated the spatial variability of SOC (0-20 cm) in association with other soil properties and with spatial variables in the Moso bamboo forests of Jian’ou City, which is a typical bamboo hometown in China. 209 soil samples were collected from Moso bamboo stands and then analyzed for SOC, bulk density (BD), pH, cation exchange capacity (CEC), and gravel content (GC) based on spatial distribution. The spatial variability of SOC was then examined using geostatistics. A Kriging map was produced through ordinary interpolation and required sample numbers were calculated by classical and Kriging methods. An aggregated boosted tree (ABT) analysis was also conducted. A semivariogram analysis indicated that ln(SOC) was best fitted with an exponential model and that it exhibited moderate spatial dependence, with a nugget/sill ratio of 0.462. SOC was significantly and linearly correlated with BD (r = −0.373**), pH (r = −0.429**), GC (r = −0.163*), CEC (r = 0.263**), and elevation (r = 0.192**). Moreover, the Kriging method requires fewer samples than the classical method given an expected standard error level as per a variance analysis. ABT analysis indicated that the physicochemical variables of soil affected SOC variation more significantly than spatial variables did, thus suggesting that the SOC in Moso bamboo forests can be strongly influenced by management practices. Thus, this study provides valuable information in relation to sampling strategy and insight into the potential of adjustments in agronomic measure, such as in fertilization for Moso bamboo production. PMID:25789615

A SPATIAL ANALYSIS OF THE FINE ROOT BIOMASS FROM STAND DATA IN THE PACIFIC NORTHWEST

EPA Science Inventory

High spatial variability of fine roots in natural forest stands makes accurate estimates of stand-level fine root biomass difficult and expensive to obtain by standard coring methods. This study uses aboveground tree metrics and spatial relationships to improve core-based estima...

Utility of computer simulations in landscape genetics

Treesearch

Bryan K. Epperson; Brad H. McRae; Kim Scribner; Samuel A. Cushman; Michael S. Rosenberg; Marie-Josee Fortin; Patrick M. A. James; Melanie Murphy; Stephanie Manel; Pierre Legendre; Mark R. T. Dale

2010-01-01

Population genetics theory is primarily based on mathematical models in which spatial complexity and temporal variability are largely ignored. In contrast, the field of landscape genetics expressly focuses on how population genetic processes are affected by complex spatial and temporal environmental heterogeneity. It is spatially explicit and relates patterns to...

Relevance of anisotropy and spatial variability of gas diffusivity for soil-gas transport

NASA Astrophysics Data System (ADS)

Schack-Kirchner, Helmer; Kühne, Anke; Lang, Friederike

2017-04-01

Models of soil gas transport generally do not consider neither direction dependence of gas diffusivity, nor its small-scale variability. However, in a recent study, we could provide evidence for anisotropy favouring vertical gas diffusion in natural soils. We hypothesize that gas transport models based on gas diffusion data measured with soil rings are strongly influenced by both, anisotropy and spatial variability and the use of averaged diffusivities could be misleading. To test this we used a 2-dimensional model of soil gas transport to under compacted wheel tracks to model the soil-air oxygen distribution in the soil. The model was parametrized with data obtained from soil-ring measurements with its central tendency and variability. The model includes vertical parameter variability as well as variation perpendicular to the elongated wheel track. Different parametrization types have been tested: [i)]Averaged values for wheel track and undisturbed. em [ii)]Random distribution of soil cells with normally distributed variability within the strata. em [iii)]Random distributed soil cells with uniformly distributed variability within the strata. All three types of small-scale variability has been tested for [j)] isotropic gas diffusivity and em [jj)]reduced horizontal gas diffusivity (constant factor), yielding in total six models. As expected the different parametrizations had an important influence to the aeration state under wheel tracks with the strongest oxygen depletion in case of uniformly distributed variability and anisotropy towards higher vertical diffusivity. The simple simulation approach clearly showed the relevance of anisotropy and spatial variability in case of identical central tendency measures of gas diffusivity. However, until now it did not consider spatial dependency of variability, that could even aggravate effects. To consider anisotropy and spatial variability in gas transport models we recommend a) to measure soil-gas transport parameters spatially explicit including different directions and b) to use random-field stochastic models to assess the possible effects for gas-exchange models.

General constraints on sampling wildlife on FIA plots

USGS Publications Warehouse

Bailey, L.L.; Sauer, J.R.; Nichols, J.D.; Geissler, P.H.; McRoberts, Ronald E.; Reams, Gregory A.; Van Deusen, Paul C.; McWilliams, William H.; Cieszewski, Chris J.

2005-01-01

This paper reviews the constraints to sampling wildlife populations at FIA points. Wildlife sampling programs must have well-defined goals and provide information adequate to meet those goals. Investigators should choose a State variable based on information needs and the spatial sampling scale. We discuss estimation-based methods for three State variables: species richness, abundance, and patch occupancy. All methods incorporate two essential sources of variation: detectability estimation and spatial variation. FIA sampling imposes specific space and time criteria that may need to be adjusted to meet local wildlife objectives.

Evaluating spatial and temporal variability in growth and mortality for recreational fisheries with limited catch data

USGS Publications Warehouse

Li, Yan; Wagner, Tyler; Jiao, Yan; Lorantas, Robert M.; Murphy, Cheryl

2018-01-01

Understanding the spatial and temporal variability in life-history traits among populations is essential for the management of recreational fisheries. However, valuable freshwater recreational fish species often suffer from a lack of catch information. In this study, we demonstrated the use of an approach to estimate the spatial and temporal variability in growth and mortality in the absence of catch data and apply the method to riverine smallmouth bass (Micropterus dolomieu) populations in Pennsylvania, USA. Our approach included a growth analysis and a length-based analysis that estimates mortality. Using a hierarchical Bayesian approach, we examined spatial variability in growth and mortality by assuming parameters vary spatially but remain constant over time and temporal variability by assuming parameters vary spatially and temporally. The estimated growth and mortality of smallmouth bass showed substantial variability over time and across rivers. We explored the relationships of the estimated growth and mortality with spring water temperature and spring flow. Growth rate was likely to be positively correlated with these two factors, while young mortality was likely to be positively correlated with spring flow. The spatially and temporally varying growth and mortality suggest that smallmouth bass populations across rivers may respond differently to management plans and disturbance such as environmental contamination and land-use change. The analytical approach can be extended to other freshwater recreational species that also lack of catch data. The approach could also be useful in developing population assessments with erroneous catch data or be used as a model sensitivity scenario to verify traditional models even when catch data are available.

Impact of rainfall spatial variability on Flash Flood Forecasting

NASA Astrophysics Data System (ADS)

Douinot, Audrey; Roux, Hélène; Garambois, Pierre-André; Larnier, Kevin

2014-05-01

According to the United States National Hazard Statistics database, flooding and flash flooding have caused the largest number of deaths of any weather-related phenomenon over the last 30 years (Flash Flood Guidance Improvement Team, 2003). Like the storms that cause them, flash floods are very variable and non-linear phenomena in time and space, with the result that understanding and anticipating flash flood genesis is far from straightforward. In the U.S., the Flash Flood Guidance (FFG) estimates the average number of inches of rainfall for given durations required to produce flash flooding in the indicated county. In Europe, flash flood often occurred on small catchments (approximately 100 km2) and it has been shown that the spatial variability of rainfall has a great impact on the catchment response (Le Lay and Saulnier, 2007). Therefore, in this study, based on the Flash flood Guidance method, rainfall spatial variability information is introduced in the threshold estimation. As for FFG, the threshold is the number of millimeters of rainfall required to produce a discharge higher than the discharge corresponding to the first level (yellow) warning of the French flood warning service (SCHAPI: Service Central d'Hydrométéorologie et d'Appui à la Prévision des Inondations). The indexes δ1 and δ2 of Zoccatelli et al. (2010), based on the spatial moments of catchment rainfall, are used to characterize the rainfall spatial distribution. Rainfall spatial variability impacts on warning threshold and on hydrological processes are then studied. The spatially distributed hydrological model MARINE (Roux et al., 2011), dedicated to flash flood prediction is forced with synthetic rainfall patterns of different spatial distributions. This allows the determination of a warning threshold diagram: knowing the spatial distribution of the rainfall forecast and therefore the 2 indexes δ1 and δ2, the threshold value is read on the diagram. A warning threshold diagram is built for each studied catchment. The proposed methodology is applied on three Mediterranean catchments often submitted to flash floods. The new forecasting method as well as the Flash Flood Guidance method (uniform rainfall threshold) are tested on 25 flash floods events that had occurred on those catchments. Results show a significant impact of rainfall spatial variability. Indeed, it appears that the uniform rainfall threshold (FFG threshold) always overestimates the observed rainfall threshold. The difference between the FFG threshold and the proposed threshold ranges from 8% to 30%. The proposed methodology allows the calculation of a threshold more representative of the observed one. However, results strongly depend on the related event duration and on the catchment properties. For instance, the impact of the rainfall spatial variability seems to be correlated with the catchment size. According to these results, it seems to be interesting to introduce information on the catchment properties in the threshold calculation. Flash Flood Guidance Improvement Team, 2003. River Forecast Center (RFC) Development Management Team. Final Report. Office of Hydrologic Development (OHD), Silver Spring, Mary-land. Le Lay, M. and Saulnier, G.-M., 2007. Exploring the signature of climate and landscape spatial variabilities in flash flood events: Case of the 8-9 September 2002 Cévennes-Vivarais catastrophic event. Geophysical Research Letters, 34(L13401), doi:10.1029/2007GL029746. Roux, H., Labat, D., Garambois, P.-A., Maubourguet, M.-M., Chorda, J. and Dartus, D., 2011. A physically-based parsimonious hydrological model for flash floods in Mediterranean catchments. Nat. Hazards Earth Syst. Sci. J1 - NHESS, 11(9), 2567-2582. Zoccatelli, D., Borga, M., Zanon, F., Antonescu, B. and Stancalie, G., 2010. Which rainfall spatial information for flash flood response modelling? A numerical investigation based on data from the Carpathian range, Romania. Journal of Hydrology, 394(1-2), 148-161.

Controls on the variability of net infiltration to desert sandstone

USGS Publications Warehouse

Heilweil, Victor M.; McKinney, Tim S.; Zhdanov, Michael S.; Watt, Dennis E.

2007-01-01

As populations grow in arid climates and desert bedrock aquifers are increasingly targeted for future development, understanding and quantifying the spatial variability of net infiltration becomes critically important for accurately inventorying water resources and mapping contamination vulnerability. This paper presents a conceptual model of net infiltration to desert sandstone and then develops an empirical equation for its spatial quantification at the watershed scale using linear least squares inversion methods for evaluating controlling parameters (independent variables) based on estimated net infiltration rates (dependent variables). Net infiltration rates used for this regression analysis were calculated from environmental tracers in boreholes and more than 3000 linear meters of vadose zone excavations in an upland basin in southwestern Utah underlain by Navajo sandstone. Soil coarseness, distance to upgradient outcrop, and topographic slope were shown to be the primary physical parameters controlling the spatial variability of net infiltration. Although the method should be transferable to other desert sandstone settings for determining the relative spatial distribution of net infiltration, further study is needed to evaluate the effects of other potential parameters such as slope aspect, outcrop parameters, and climate on absolute net infiltration rates.

Spatial correlation of probabilistic earthquake ground motion and loss

USGS Publications Warehouse

Wesson, R.L.; Perkins, D.M.

2001-01-01

Spatial correlation of annual earthquake ground motions and losses can be used to estimate the variance of annual losses to a portfolio of properties exposed to earthquakes A direct method is described for the calculations of the spatial correlation of earthquake ground motions and losses. Calculations for the direct method can be carried out using either numerical quadrature or a discrete, matrix-based approach. Numerical results for this method are compared with those calculated from a simple Monte Carlo simulation. Spatial correlation of ground motion and loss is induced by the systematic attenuation of ground motion with distance from the source, by common site conditions, and by the finite length of fault ruptures. Spatial correlation is also strongly dependent on the partitioning of the variability, given an event, into interevent and intraevent components. Intraevent variability reduces the spatial correlation of losses. Interevent variability increases spatial correlation of losses. The higher the spatial correlation, the larger the variance in losses to a port-folio, and the more likely extreme values become. This result underscores the importance of accurately determining the relative magnitudes of intraevent and interevent variability in ground-motion studies, because of the strong impact in estimating earthquake losses to a portfolio. The direct method offers an alternative to simulation for calculating the variance of losses to a portfolio, which may reduce the amount of calculation required.

Analysis of spatial correlation in predictive models of forest variables that use LiDAR auxiliary information

Treesearch

F. Mauro; Vicente J. Monleon; H. Temesgen; L.A. Ruiz

2017-01-01

Accounting for spatial correlation of LiDAR model errors can improve the precision of model-based estimators. To estimate spatial correlation, sample designs that provide close observations are needed, but their implementation might be prohibitively expensive. To quantify the gains obtained by accounting for the spatial correlation of model errors, we examined (

Spatial drought reconstructions for central High Asia based on tree rings

NASA Astrophysics Data System (ADS)

Fang, Keyan; Davi, Nicole; Gou, Xiaohua; Chen, Fahu; Cook, Edward; Li, Jinbao; D'Arrigo, Rosanne

2010-11-01

Spatial reconstructions of drought for central High Asia based on a tree-ring network are presented. Drought patterns for central High Asia are classified into western and eastern modes of variability. Tree-ring based reconstructions of the Palmer drought severity index (PDSI) are presented for both the western central High Asia drought mode (1587-2005), and for the eastern central High Asia mode (1660-2005). Both reconstructions, generated using a principal component regression method, show an increased variability in recent decades. The wettest epoch for both reconstructions occurred from the 1940s to the 1950s. The most extreme reconstructed drought for western central High Asia was from the 1640s to the 1650s, coinciding with the collapse of the Chinese Ming Dynasty. The eastern central High Asia reconstruction has shown a distinct tendency towards drier conditions since the 1980s. Our spatial reconstructions agree well with previous reconstructions that fall within each mode, while there is no significant correlation between the two spatial reconstructions.

Technique for ship/wake detection

DOEpatents

Roskovensky, John K [Albuquerque, NM

2012-05-01

An automated ship detection technique includes accessing data associated with an image of a portion of Earth. The data includes reflectance values. A first portion of pixels within the image are masked with a cloud and land mask based on spectral flatness of the reflectance values associated with the pixels. A given pixel selected from the first portion of pixels is unmasked when a threshold number of localized pixels surrounding the given pixel are not masked by the cloud and land mask. A spatial variability image is generated based on spatial derivatives of the reflectance values of the pixels which remain unmasked by the cloud and land mask. The spatial variability image is thresholded to identify one or more regions within the image as possible ship detection regions.

Risk assessment of groundwater level variability using variable Kriging methods

NASA Astrophysics Data System (ADS)

Spanoudaki, Katerina; Kampanis, Nikolaos A.

2015-04-01

Assessment of the water table level spatial variability in aquifers provides useful information regarding optimal groundwater management. This information becomes more important in basins where the water table level has fallen significantly. The spatial variability of the water table level in this work is estimated based on hydraulic head measured during the wet period of the hydrological year 2007-2008, in a sparsely monitored basin in Crete, Greece, which is of high socioeconomic and agricultural interest. Three Kriging-based methodologies are elaborated in Matlab environment to estimate the spatial variability of the water table level in the basin. The first methodology is based on the Ordinary Kriging approach, the second involves auxiliary information from a Digital Elevation Model in terms of Residual Kriging and the third methodology calculates the probability of the groundwater level to fall below a predefined minimum value that could cause significant problems in groundwater resources availability, by means of Indicator Kriging. The Box-Cox methodology is applied to normalize both the data and the residuals for improved prediction results. In addition, various classical variogram models are applied to determine the spatial dependence of the measurements. The Matérn model proves to be the optimal, which in combination with Kriging methodologies provides the most accurate cross validation estimations. Groundwater level and probability maps are constructed to examine the spatial variability of the groundwater level in the basin and the associated risk that certain locations exhibit regarding a predefined minimum value that has been set for the sustainability of the basin's groundwater resources. Acknowledgement The work presented in this paper has been funded by the Greek State Scholarships Foundation (IKY), Fellowships of Excellence for Postdoctoral Studies (Siemens Program), 'A simulation-optimization model for assessing the best practices for the protection of surface water and groundwater in the coastal zone', (2013 - 2015). Varouchakis, E. A. and D. T. Hristopulos (2013). "Improvement of groundwater level prediction in sparsely gauged basins using physical laws and local geographic features as auxiliary variables." Advances in Water Resources 52: 34-49. Kitanidis, P. K. (1997). Introduction to geostatistics, Cambridge: University Press.

Optimization techniques for integrating spatial data

USGS Publications Warehouse

Herzfeld, U.C.; Merriam, D.F.

1995-01-01

Two optimization techniques ta predict a spatial variable from any number of related spatial variables are presented. The applicability of the two different methods for petroleum-resource assessment is tested in a mature oil province of the Midcontinent (USA). The information on petroleum productivity, usually not directly accessible, is related indirectly to geological, geophysical, petrographical, and other observable data. This paper presents two approaches based on construction of a multivariate spatial model from the available data to determine a relationship for prediction. In the first approach, the variables are combined into a spatial model by an algebraic map-comparison/integration technique. Optimal weights for the map comparison function are determined by the Nelder-Mead downhill simplex algorithm in multidimensions. Geologic knowledge is necessary to provide a first guess of weights to start the automatization, because the solution is not unique. In the second approach, active set optimization for linear prediction of the target under positivity constraints is applied. Here, the procedure seems to select one variable from each data type (structure, isopachous, and petrophysical) eliminating data redundancy. Automating the determination of optimum combinations of different variables by applying optimization techniques is a valuable extension of the algebraic map-comparison/integration approach to analyzing spatial data. Because of the capability of handling multivariate data sets and partial retention of geographical information, the approaches can be useful in mineral-resource exploration. ?? 1995 International Association for Mathematical Geology.

Hybrid Optimal Design of the Eco-Hydrological Wireless Sensor Network in the Middle Reach of the Heihe River Basin, China

PubMed Central

Kang, Jian; Li, Xin; Jin, Rui; Ge, Yong; Wang, Jinfeng; Wang, Jianghao

2014-01-01

The eco-hydrological wireless sensor network (EHWSN) in the middle reaches of the Heihe River Basin in China is designed to capture the spatial and temporal variability and to estimate the ground truth for validating the remote sensing productions. However, there is no available prior information about a target variable. To meet both requirements, a hybrid model-based sampling method without any spatial autocorrelation assumptions is developed to optimize the distribution of EHWSN nodes based on geostatistics. This hybrid model incorporates two sub-criteria: one for the variogram modeling to represent the variability, another for improving the spatial prediction to evaluate remote sensing productions. The reasonability of the optimized EHWSN is validated from representativeness, the variogram modeling and the spatial accuracy through using 15 types of simulation fields generated with the unconditional geostatistical stochastic simulation. The sampling design shows good representativeness; variograms estimated by samples have less than 3% mean error relative to true variograms. Then, fields at multiple scales are predicted. As the scale increases, estimated fields have higher similarities to simulation fields at block sizes exceeding 240 m. The validations prove that this hybrid sampling method is effective for both objectives when we do not know the characteristics of an optimized variables. PMID:25317762

Quantifying Spatial and Seasonal Variability in Atmospheric Ammonia with In Situ and Space-Based Observations

NASA Technical Reports Server (NTRS)

Pinder, Robert W.; Walker, John T.; Bash, Jesse O.; Cady-Pereira, Karen E.; Henze, Daven K.; Luo, Mingzhao; Osterman, Gregory B.; Shepard, Mark W.

2011-01-01

Ammonia plays an important role in many biogeochemical processes, yet atmospheric mixing ratios are not well known. Recently, methods have been developed for retrieving NH3 from space-based observations, but they have not been compared to in situ measurements. We have conducted a field campaign combining co-located surface measurements and satellite special observations from the Tropospheric Emission Spectrometer (TES). Our study includes 25 surface monitoring sites spanning 350 km across eastern North Carolina, a region with large seasonal and spatial variability in NH3. From the TES spectra, we retrieve a NH3 representative volume mixing ratio (RVMR), and we restrict our analysis to times when the region of the atmosphere observed by TES is representative of the surface measurement. We find that the TES NH3 RVMR qualitatively captures the seasonal and spatial variability found in eastern North Carolina. Both surface measurements and TES NH3 show a strong correspondence with the number of livestock facilities within 10 km of the observation. Furthermore, we find that TES H3 RVMR captures the month-to-month variability present in the surface observations. The high correspondence with in situ measurements and vast spatial coverage make TES NH3 RVMR a valuable tool for understanding regional and global NH3 fluxes.

Hybrid optimal design of the eco-hydrological wireless sensor network in the middle reach of the Heihe River Basin, China.

PubMed

Kang, Jian; Li, Xin; Jin, Rui; Ge, Yong; Wang, Jinfeng; Wang, Jianghao

2014-10-14

The eco-hydrological wireless sensor network (EHWSN) in the middle reaches of the Heihe River Basin in China is designed to capture the spatial and temporal variability and to estimate the ground truth for validating the remote sensing productions. However, there is no available prior information about a target variable. To meet both requirements, a hybrid model-based sampling method without any spatial autocorrelation assumptions is developed to optimize the distribution of EHWSN nodes based on geostatistics. This hybrid model incorporates two sub-criteria: one for the variogram modeling to represent the variability, another for improving the spatial prediction to evaluate remote sensing productions. The reasonability of the optimized EHWSN is validated from representativeness, the variogram modeling and the spatial accuracy through using 15 types of simulation fields generated with the unconditional geostatistical stochastic simulation. The sampling design shows good representativeness; variograms estimated by samples have less than 3% mean error relative to true variograms. Then, fields at multiple scales are predicted. As the scale increases, estimated fields have higher similarities to simulation fields at block sizes exceeding 240 m. The validations prove that this hybrid sampling method is effective for both objectives when we do not know the characteristics of an optimized variables.

A COMPARISON OF ILLUMINATION GEOMETRY-BASED METHODS FOR TOPOGRAPHIC CORRECTION OF QUICKBIRD IMAGES OF AN UNDULANT AREA

USDA-ARS?s Scientific Manuscript database

The high spatial resolution of QuickBird satellite images makes it possible to show spatial variability at fine details. However, the effect of topography-induced illumination variations become more evident, even in moderately sloped areas. Based on a high resolution (1 m) digital elevation model ge...

Assessing performance and seasonal bias of pollen-based climate reconstructions in a perfect model world

NASA Astrophysics Data System (ADS)

Rehfeld, Kira; Trachsel, Mathias; Telford, Richard J.; Laepple, Thomas

2016-12-01

Reconstructions of summer, winter or annual mean temperatures based on the species composition of bio-indicators such as pollen, foraminifera or chironomids are routinely used in climate model-proxy data comparison studies. Most reconstruction algorithms exploit the joint distribution of modern spatial climate and species distribution for the development of the reconstructions. They rely on the space-for-time substitution and the specific assumption that environmental variables other than those reconstructed are not important or that their relationship with the reconstructed variable(s) should be the same in the past as in the modern spatial calibration dataset. Here we test the implications of this "correlative uniformitarianism" assumption on climate reconstructions in an ideal model world, in which climate and vegetation are known at all times. The alternate reality is a climate simulation of the last 6000 years with dynamic vegetation. Transient changes of plant functional types are considered as surrogate pollen counts and allow us to establish, apply and evaluate transfer functions in the modeled world. We find that in our model experiments the transfer function cross validation r2 is of limited use to identify reconstructible climate variables, as it only relies on the modern spatial climate-vegetation relationship. However, ordination approaches that assess the amount of fossil vegetation variance explained by the reconstructions are promising. We furthermore show that correlations between climate variables in the modern climate-vegetation relationship are systematically extended into the reconstructions. Summer temperatures, the most prominent driving variable for modeled vegetation change in the Northern Hemisphere, are accurately reconstructed. However, the amplitude of the model winter and mean annual temperature cooling between the mid-Holocene and present day is overestimated and similar to the summer trend in magnitude. This effect occurs because temporal changes of a dominant climate variable, such as summer temperatures in the model's Arctic, are imprinted on a less important variable, leading to reconstructions biased towards the dominant variable's trends. Our results, although based on a model vegetation that is inevitably simpler than reality, indicate that reconstructions of multiple climate variables based on modern spatial bio-indicator datasets should be treated with caution. Expert knowledge on the ecophysiological drivers of the proxies, as well as statistical methods that go beyond the cross validation on modern calibration datasets, are crucial to avoid misinterpretation.

Global patterns and climate drivers of water-use efficiency in terrestrial ecosystems deduced from satellite-based datasets and carbon cycle models

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

Sun, Yan; Piao, Shilong; Huang, Mengtian

Our aim is to investigate how ecosystem water-use efficiency (WUE) varies spatially under different climate conditions, and how spatial variations in WUE differ from those of transpiration-based water-use efficiency (WUE t) and transpiration-based inherent water-use efficiency (IWUE t). LocationGlobal terrestrial ecosystems. We investigated spatial patterns of WUE using two datasets of gross primary productivity (GPP) and evapotranspiration (ET) and four biosphere model estimates of GPP and ET. Spatial relationships between WUE and climate variables were further explored through regression analyses. Global WUE estimated by two satellite-based datasets is 1.9 ± 0.1 and 1.8 ± 0.6g C m -2mm -1 lowermore » than the simulations from four process-based models (2.0 ± 0.3g C m -2mm -1) but comparable within the uncertainty of both approaches. In both satellite-based datasets and process models, precipitation is more strongly associated with spatial gradients of WUE for temperate and tropical regions, but temperature dominates north of 50 degrees N. WUE also increases with increasing solar radiation at high latitudes. The values of WUE from datasets and process-based models are systematically higher in wet regions (with higher GPP) than in dry regions. WUE t shows a lower precipitation sensitivity than WUE, which is contrary to leaf- and plant-level observations. IWUE t, the product of WUE t and water vapour deficit, is found to be rather conservative with spatially increasing precipitation, in agreement with leaf- and plant-level measurements. In conclusion, WUE, WUE t and IWUE t produce different spatial relationships with climate variables. In dry ecosystems, water losses from evaporation from bare soil, uncorrelated with productivity, tend to make WUE lower than in wetter regions. Yet canopy conductance is intrinsically efficient in those ecosystems and maintains a higher IWUEt. This suggests that the responses of each component flux of evapotranspiration should be analysed separately when investigating regional gradients in WUE, its temporal variability and its trends.« less

Global patterns and climate drivers of water-use efficiency in terrestrial ecosystems deduced from satellite-based datasets and carbon cycle models

DOE PAGES

Sun, Yan; Piao, Shilong; Huang, Mengtian; ...

2015-12-23

Our aim is to investigate how ecosystem water-use efficiency (WUE) varies spatially under different climate conditions, and how spatial variations in WUE differ from those of transpiration-based water-use efficiency (WUE t) and transpiration-based inherent water-use efficiency (IWUE t). LocationGlobal terrestrial ecosystems. We investigated spatial patterns of WUE using two datasets of gross primary productivity (GPP) and evapotranspiration (ET) and four biosphere model estimates of GPP and ET. Spatial relationships between WUE and climate variables were further explored through regression analyses. Global WUE estimated by two satellite-based datasets is 1.9 ± 0.1 and 1.8 ± 0.6g C m -2mm -1 lowermore » than the simulations from four process-based models (2.0 ± 0.3g C m -2mm -1) but comparable within the uncertainty of both approaches. In both satellite-based datasets and process models, precipitation is more strongly associated with spatial gradients of WUE for temperate and tropical regions, but temperature dominates north of 50 degrees N. WUE also increases with increasing solar radiation at high latitudes. The values of WUE from datasets and process-based models are systematically higher in wet regions (with higher GPP) than in dry regions. WUE t shows a lower precipitation sensitivity than WUE, which is contrary to leaf- and plant-level observations. IWUE t, the product of WUE t and water vapour deficit, is found to be rather conservative with spatially increasing precipitation, in agreement with leaf- and plant-level measurements. In conclusion, WUE, WUE t and IWUE t produce different spatial relationships with climate variables. In dry ecosystems, water losses from evaporation from bare soil, uncorrelated with productivity, tend to make WUE lower than in wetter regions. Yet canopy conductance is intrinsically efficient in those ecosystems and maintains a higher IWUEt. This suggests that the responses of each component flux of evapotranspiration should be analysed separately when investigating regional gradients in WUE, its temporal variability and its trends.« less

Minimizing Spatial Variability of Healthcare Spatial Accessibility-The Case of a Dengue Fever Outbreak.

PubMed

Chu, Hone-Jay; Lin, Bo-Cheng; Yu, Ming-Run; Chan, Ta-Chien

2016-12-13

Outbreaks of infectious diseases or multi-casualty incidents have the potential to generate a large number of patients. It is a challenge for the healthcare system when demand for care suddenly surges. Traditionally, valuation of heath care spatial accessibility was based on static supply and demand information. In this study, we proposed an optimal model with the three-step floating catchment area (3SFCA) to account for the supply to minimize variability in spatial accessibility. We used empirical dengue fever outbreak data in Tainan City, Taiwan in 2015 to demonstrate the dynamic change in spatial accessibility based on the epidemic trend. The x and y coordinates of dengue-infected patients with precision loss were provided publicly by the Tainan City government, and were used as our model's demand. The spatial accessibility of heath care during the dengue outbreak from August to October 2015 was analyzed spatially and temporally by producing accessibility maps, and conducting capacity change analysis. This study also utilized the particle swarm optimization (PSO) model to decrease the spatial variation in accessibility and shortage areas of healthcare resources as the epidemic went on. The proposed method in this study can help decision makers reallocate healthcare resources spatially when the ratios of demand and supply surge too quickly and form clusters in some locations.

[Spatial differentiation and impact factors of Yutian Oasis's soil surface salt based on GWR model].

PubMed

Yuan, Yu Yun; Wahap, Halik; Guan, Jing Yun; Lu, Long Hui; Zhang, Qin Qin

2016-10-01

In this paper, topsoil salinity data gathered from 24 sampling sites in the Yutian Oasis were used, nine different kinds of environmental variables closely related to soil salinity were selec-ted as influencing factors, then, the spatial distribution characteristics of topsoil salinity and spatial heterogeneity of influencing factors were analyzed by combining the spatial autocorrelation with traditional regression analysis and geographically weighted regression model. Results showed that the topsoil salinity in Yutian Oasis was not of random distribution but had strong spatial dependence, and the spatial autocorrelation index for topsoil salinity was 0.479. Groundwater salinity, groundwater depth, elevation and temperature were the main factors influencing topsoil salt accumulation in arid land oases and they were spatially heterogeneous. The nine selected environmental variables except soil pH had significant influences on topsoil salinity with spatial disparity. GWR model was superior to the OLS model on interpretation and estimation of spatial non-stationary data, also had a remarkable advantage in visualization of modeling parameters.

SimAlba: A Spatial Microsimulation Approach to the Analysis of Health Inequalities

PubMed Central

Campbell, Malcolm; Ballas, Dimitris

2016-01-01

This paper presents applied geographical research based on a spatial microsimulation model, SimAlba, aimed at estimating geographically sensitive health variables in Scotland. SimAlba has been developed in order to answer a variety of “what-if” policy questions pertaining to health policy in Scotland. Using the SimAlba model, it is possible to simulate the distributions of previously unknown variables at the small area level such as smoking, alcohol consumption, mental well-being, and obesity. The SimAlba microdataset has been created by combining Scottish Health Survey and Census data using a deterministic reweighting spatial microsimulation algorithm developed for this purpose. The paper presents SimAlba outputs for Scotland’s largest city, Glasgow, and examines the spatial distribution of the simulated variables for small geographical areas in Glasgow as well as the effects on individuals of different policy scenario outcomes. In simulating previously unknown spatial data, a wealth of new perspectives can be examined and explored. This paper explores a small set of those potential avenues of research and shows the power of spatial microsimulation modeling in an urban context. PMID:27818989

Quantitative analysis of spatial variability of geotechnical parameters

NASA Astrophysics Data System (ADS)

Fang, Xing

2018-04-01

Geotechnical parameters are the basic parameters of geotechnical engineering design, while the geotechnical parameters have strong regional characteristics. At the same time, the spatial variability of geotechnical parameters has been recognized. It is gradually introduced into the reliability analysis of geotechnical engineering. Based on the statistical theory of geostatistical spatial information, the spatial variability of geotechnical parameters is quantitatively analyzed. At the same time, the evaluation of geotechnical parameters and the correlation coefficient between geotechnical parameters are calculated. A residential district of Tianjin Survey Institute was selected as the research object. There are 68 boreholes in this area and 9 layers of mechanical stratification. The parameters are water content, natural gravity, void ratio, liquid limit, plasticity index, liquidity index, compressibility coefficient, compressive modulus, internal friction angle, cohesion and SP index. According to the principle of statistical correlation, the correlation coefficient of geotechnical parameters is calculated. According to the correlation coefficient, the law of geotechnical parameters is obtained.

Spatial variability of intake fractions for Canadian emission scenarios: a comparison between three resolution scales.

PubMed

Manneh, Rima; Margni, Manuele; Deschênes, Louise

2010-06-01

Spatially differentiated intake fractions (iFs) linked to Canadian emissions of toxic organic chemicals were developed using the multimedia and multipathways fate and exposure model IMPACT 2002. The fate and exposure of chemicals released to the Canadian environment were modeled with a single regional mass-balance model and three models that provided multiple mass-balance regions within Canada. These three models were based on the Canadian subwatersheds (172 zones), ecozones (15 zones), and provinces (13 zones). Releases of 32 organic chemicals into water and air were considered. This was done in order to (i) assess and compare the spatial variability of iFs within and across the three levels of regionalization and (ii) compare the spatial iFs to nonspatial ones. Results showed that iFs calculated using the subwatershed resolution presented a higher spatial variability (up to 10 orders of magnitude for emissions into water) than the ones based on the ecozones and provinces, implying that higher spatial resolution could potentially reduce uncertainty in iFs and, therefore, increase the discriminating power when assessing and comparing toxic releases for known emission locations. Results also indicated that, for an unknown emission location, a model with high spatial resolution such as the subwatershed model could significantly improve the accuracy of a generic iF. Population weighted iFs span up to 3 orders of magnitude compared to nonspatial iFs calculated by the one-box model. Less significant differences were observed when comparing spatial versus nonspatial iFs from the ecozones and provinces, respectively.

Assessment of flood susceptible areas using spatially explicit, probabilistic multi-criteria decision analysis

NASA Astrophysics Data System (ADS)

Tang, Zhongqian; Zhang, Hua; Yi, Shanzhen; Xiao, Yangfan

2018-03-01

GIS-based multi-criteria decision analysis (MCDA) is increasingly used to support flood risk assessment. However, conventional GIS-MCDA methods fail to adequately represent spatial variability and are accompanied with considerable uncertainty. It is, thus, important to incorporate spatial variability and uncertainty into GIS-based decision analysis procedures. This research develops a spatially explicit, probabilistic GIS-MCDA approach for the delineation of potentially flood susceptible areas. The approach integrates the probabilistic and the local ordered weighted averaging (OWA) methods via Monte Carlo simulation, to take into account the uncertainty related to criteria weights, spatial heterogeneity of preferences and the risk attitude of the analyst. The approach is applied to a pilot study for the Gucheng County, central China, heavily affected by the hazardous 2012 flood. A GIS database of six geomorphological and hydrometeorological factors for the evaluation of susceptibility was created. Moreover, uncertainty and sensitivity analysis were performed to investigate the robustness of the model. The results indicate that the ensemble method improves the robustness of the model outcomes with respect to variation in criteria weights and identifies which criteria weights are most responsible for the variability of model outcomes. Therefore, the proposed approach is an improvement over the conventional deterministic method and can provides a more rational, objective and unbiased tool for flood susceptibility evaluation.

Effect of land use on the spatial variability of organic matter and nutrient status in an Oxisol

NASA Astrophysics Data System (ADS)

Paz-Ferreiro, Jorge; Alves, Marlene Cristina; Vidal Vázquez, Eva

2013-04-01

Heterogeneity is now considered as an inherent soil property. Spatial variability of soil attributes in natural landscapes results mainly from soil formation factors. In cultivated soils much heterogeneity can additionally occur as a result of land use, agricultural systems and management practices. Organic matter content (OMC) and nutrients associated to soil exchange complex are key attribute in the maintenance of a high quality soil. Neglecting spatial heterogeneity in soil OMC and nutrient status at the field scale might result in reduced yield and in environmental damage. We analyzed the impact of land use on the pattern of spatial variability of OMC and soil macronutrients at the stand scale. The study was conducted in São Paulo state, Brazil. Land uses were pasture, mango orchard and corn field. Soil samples were taken at 0-10 cm and 10-20 cm depth in 84 points, within 100 m x 100 m plots. Texture, pH, OMC, cation exchange capacity (CEC), exchangeable cations (Ca, Mg, K, H, Al) and resin extractable phosphorus were analyzed.. Statistical variability was found to be higher in parameters defining the soil nutrient status (resin extractable P, K, Ca and Mg) than in general soil properties (OMC, CEC, base saturation and pH). Geostatistical analysis showed contrasting patterns of spatial dependence for the different soil uses, sampling depths and studied properties. Most of the studied data sets collected at two different depths exhibited spatial dependence at the sampled scale and their semivariograms were modeled by a nugget effect plus a structure. The pattern of soil spatial variability was found to be different between the three study soil uses and at the two sampling depths, as far as model type, nugget effect or ranges of spatial dependence were concerned. Both statistical and geostatistical results pointed out the importance of OMC as a driver responsible for the spatial variability of soil nutrient status.

Empirical Assessment of Spatial Prediction Methods for Location Cost Adjustment Factors

PubMed Central

Migliaccio, Giovanni C.; Guindani, Michele; D'Incognito, Maria; Zhang, Linlin

2014-01-01

In the feasibility stage, the correct prediction of construction costs ensures that budget requirements are met from the start of a project's lifecycle. A very common approach for performing quick-order-of-magnitude estimates is based on using Location Cost Adjustment Factors (LCAFs) that compute historically based costs by project location. Nowadays, numerous LCAF datasets are commercially available in North America, but, obviously, they do not include all locations. Hence, LCAFs for un-sampled locations need to be inferred through spatial interpolation or prediction methods. Currently, practitioners tend to select the value for a location using only one variable, namely the nearest linear-distance between two sites. However, construction costs could be affected by socio-economic variables as suggested by macroeconomic theories. Using a commonly used set of LCAFs, the City Cost Indexes (CCI) by RSMeans, and the socio-economic variables included in the ESRI Community Sourcebook, this article provides several contributions to the body of knowledge. First, the accuracy of various spatial prediction methods in estimating LCAF values for un-sampled locations was evaluated and assessed in respect to spatial interpolation methods. Two Regression-based prediction models were selected, a Global Regression Analysis and a Geographically-weighted regression analysis (GWR). Once these models were compared against interpolation methods, the results showed that GWR is the most appropriate way to model CCI as a function of multiple covariates. The outcome of GWR, for each covariate, was studied for all the 48 states in the contiguous US. As a direct consequence of spatial non-stationarity, it was possible to discuss the influence of each single covariate differently from state to state. In addition, the article includes a first attempt to determine if the observed variability in cost index values could be, at least partially explained by independent socio-economic variables. PMID:25018582

Submesoscale Sea Surface Temperature Variability from UAV and Satellite Measurements

NASA Astrophysics Data System (ADS)

Castro, S. L.; Emery, W. J.; Tandy, W., Jr.; Good, W. S.

2017-12-01

Technological advances in spatial resolution of observations have revealed the importance of short-lived ocean processes with scales of O(1km). These submesoscale processes play an important role for the transfer of energy from the meso- to small scales and for generating significant spatial and temporal intermittency in the upper ocean, critical for the mixing of the oceanic boundary layer. Submesoscales have been observed in sea surface temperatures (SST) from satellites. Satellite SST measurements are spatial averages over the footprint of the satellite. When the variance of the SST distribution within the footprint is small, the average value is representative of the SST over the whole pixel. If the variance is large, the spatial heterogeneity is a source of uncertainty in satellite derived SSTs. Here we show evidence that the submesoscale variability in SSTs at spatial scales of 1km is responsible for the spatial variability within satellite footprints. Previous studies of the spatial variability in SST, using ship-based radiometric data suggested that variability at scales smaller than 1 km is significant and affects the uncertainty of satellite-derived skin SSTs. We examine data collected by a calibrated thermal infrared radiometer, the Ball Experimental Sea Surface Temperature (BESST), flown on a UAV over the Arctic Ocean and compare them with coincident measurements from the MODIS spaceborne radiometer to assess the spatial variability of SST within 1 km pixels. By taking the standard deviation of all the BESST measurements within individual MODIS pixels we show that significant spatial variability exists within the footprints. The distribution of the surface variability measured by BESST shows a peak value of O(0.1K) with 95% of the pixels showing σ < 0.45K. More importantly, high-variability pixels are located at density fronts in the marginal ice zone, which are a primary source of submesoscale intermittency near the surface in the Arctic Ocean. Wavenumber spectra of the BESST SSTs indicate a spectral slope of -2, consistent with the presence of submesoscale processes. Furthermore, not only is the BESST wavenumber spectra able to match the MODIS SST spectra well, but also extends the spectral slope of -2 by 2 decades relative to MODIS, from wavelengths of 8km to 0.08km.

Integrating Ecosystem Carbon Dynamics into State-and-Transition Simulation Models of Land Use/Land Cover Change

NASA Astrophysics Data System (ADS)

Sleeter, B. M.; Daniel, C.; Frid, L.; Fortin, M. J.

2016-12-01

State-and-transition simulation models (STSMs) provide a general approach for incorporating uncertainty into forecasts of landscape change. Using a Monte Carlo approach, STSMs generate spatially-explicit projections of the state of a landscape based upon probabilistic transitions defined between states. While STSMs are based on the basic principles of Markov chains, they have additional properties that make them applicable to a wide range of questions and types of landscapes. A current limitation of STSMs is that they are only able to track the fate of discrete state variables, such as land use/land cover (LULC) classes. There are some landscape modelling questions, however, for which continuous state variables - for example carbon biomass - are also required. Here we present a new approach for integrating continuous state variables into spatially-explicit STSMs. Specifically we allow any number of continuous state variables to be defined for each spatial cell in our simulations; the value of each continuous variable is then simulated forward in discrete time as a stochastic process based upon defined rates of change between variables. These rates can be defined as a function of the realized states and transitions of each cell in the STSM, thus providing a connection between the continuous variables and the dynamics of the landscape. We demonstrate this new approach by (1) developing a simple IPCC Tier 3 compliant model of ecosystem carbon biomass, where the continuous state variables are defined as terrestrial carbon biomass pools and the rates of change as carbon fluxes between pools, and (2) integrating this carbon model with an existing LULC change model for the state of Hawaii, USA.

Proposal for a Spatial Organization Model in Soil Science (The Example of the European Communities Soil Map).

ERIC Educational Resources Information Center

King, D.; And Others

1994-01-01

Discusses the computational problems of automating paper-based spatial information. A new relational structure for soil science information based on the main conceptual concepts used during conventional cartographic work is proposed. This model is a computerized framework for coherent description of the geographical variability of soils, combined…

Exploring the spatial variability of soil properties in an Alfisol Catena

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

Rosemary, F.; Vitharana, U. W. A.; Indraratne, S. P.

Detailed digital soil maps showing the spatial heterogeneity of soil properties consistent with the landscape are required for site-specific management of plant nutrients, land use planning and process-based environmental modeling. We characterized the short-scale spatial heterogeneity of soil properties in an Alfisol catena in a tropical landscape of Sri Lanka. The impact of different land-uses (paddy, vegetable and un-cultivated) was examined to assess the impact of anthropogenic activities on the variability of soil properties at the catenary level. Conditioned Latin hypercube sampling was used to collect 58 geo-referenced topsoil samples (0–30 cm) from the study area. Soil samples were analyzedmore » for pH, electrical conductivity (EC), organic carbon (OC), cation exchange capacity (CEC) and texture. The spatial correlation between soil properties was analyzed by computing crossvariograms and subsequent fitting of theoretical model. Spatial distribution maps were developed using ordinary kriging. The range of soil properties, pH: 4.3–7.9; EC: 0.01–0.18 dS m –1 ; OC: 0.1–1.37%; CEC: 0.44– 11.51 cmol (+) kg –1 ; clay: 1.5–25% and sand: 59.1–84.4% and their coefficient of variations indicated a large variability in the study area. Electrical conductivity and pH showed a strong spatial correlation which was reflected by the cross-variogram close to the hull of the perfect correlation. Moreover, cross-variograms calculated for EC and Clay, CEC and OC, CEC and clay and CEC and pH indicated weak positive spatial correlation between these properties. Relative nugget effect (RNE) calculated from variograms showed strongly structured spatial variability for pH, EC and sand content (RNE < 25%) while CEC, organic carbon and clay content showed moderately structured spatial variability (25% < RNE < 75%). Spatial dependencies for examined soil properties ranged from 48 to 984 m. The mixed effects model fitting followed by Tukey's post-hoc test showed significant effect of land use on the spatial variability of EC. Our study revealed a structured variability of topsoil properties in the selected tropical Alfisol catena. Except for EC, observed variability was not modified by the land uses. Investigated soil properties showed distinct spatial structures at different scales and magnitudes of strength. Our results will be useful for digital soil mapping, site specific management of soil properties, developing appropriate land use plans and quantifying anthropogenic impacts on the soil system.« less

Method and system for spatially variable rate application of agricultural chemicals based on remotely sensed vegetation data

NASA Technical Reports Server (NTRS)

Lewis, Mark David (Inventor); Seal, Michael R. (Inventor); Hood, Kenneth Brown (Inventor); Johnson, James William (Inventor)

2007-01-01

Remotely sensed spectral image data are used to develop a Vegetation Index file which represents spatial variations of actual crop vigor throughout a field that is under cultivation. The latter information is processed to place it in a format that can be used by farm personnel to correlate and calibrate it with actually observed crop conditions existing at control points within the field. Based on the results, farm personnel formulate a prescription request, which is forwarded via email or FTP to a central processing site, where the prescription is prepared. The latter is returned via email or FTP to on-side farm personnel, who can load it into a controller on a spray rig that directly applies inputs to the field at a spatially variable rate.

Method and apparatus for spatially variable rate application of agricultural chemicals based on remotely sensed vegetation data

NASA Technical Reports Server (NTRS)

Hood, Kenneth Brown (Inventor); Johnson, James William (Inventor); Seal, Michael R. (Inventor); Lewis, Mark David (Inventor)

2004-01-01

Remotely sensed spectral image data are used to develop a Vegetation Index file which represents spatial variations of actual crop vigor throughout a field that is under cultivation. The latter information is processed to place it in a format that can be used by farm personnel to correlate and calibrate it with actually observed crop conditions existing at control points within the field. Based on the results, farm personnel formulate a prescription request, which is forwarded via email or FTP to a central processing site, where the prescription is prepared. The latter is returned via email or FTP to on-side farm personnel, who can load it into a controller on a spray rig that directly applies inputs to the field at a spatially variable rate.

Monitoring and identification of spatiotemporal landscape changes in multiple remote sensing images by using a stratified conditional Latin hypercube sampling approach and geostatistical simulation.

PubMed

Lin, Yu-Pin; Chu, Hone-Jay; Huang, Yu-Long; Tang, Chia-Hsi; Rouhani, Shahrokh

2011-06-01

This study develops a stratified conditional Latin hypercube sampling (scLHS) approach for multiple, remotely sensed, normalized difference vegetation index (NDVI) images. The objective is to sample, monitor, and delineate spatiotemporal landscape changes, including spatial heterogeneity and variability, in a given area. The scLHS approach, which is based on the variance quadtree technique (VQT) and the conditional Latin hypercube sampling (cLHS) method, selects samples in order to delineate landscape changes from multiple NDVI images. The images are then mapped for calibration and validation by using sequential Gaussian simulation (SGS) with the scLHS selected samples. Spatial statistical results indicate that in terms of their statistical distribution, spatial distribution, and spatial variation, the statistics and variograms of the scLHS samples resemble those of multiple NDVI images more closely than those of cLHS and VQT samples. Moreover, the accuracy of simulated NDVI images based on SGS with scLHS samples is significantly better than that of simulated NDVI images based on SGS with cLHS samples and VQT samples, respectively. However, the proposed approach efficiently monitors the spatial characteristics of landscape changes, including the statistics, spatial variability, and heterogeneity of NDVI images. In addition, SGS with the scLHS samples effectively reproduces spatial patterns and landscape changes in multiple NDVI images.

Geographical distribution patterns of iodine in drinking-water and its associations with geological factors in Shandong Province, China.

PubMed

Gao, Jie; Zhang, Zhijie; Hu, Yi; Bian, Jianchao; Jiang, Wen; Wang, Xiaoming; Sun, Liqian; Jiang, Qingwu

2014-05-19

County-based spatial distribution characteristics and the related geological factors for iodine in drinking-water were studied in Shandong Province (China). Spatial autocorrelation analysis and spatial scan statistic were applied to analyze the spatial characteristics. Generalized linear models (GLMs) and geographically weighted regression (GWR) studies were conducted to explore the relationship between water iodine level and its related geological factors. The spatial distribution of iodine in drinking-water was significantly heterogeneous in Shandong Province (Moran's I = 0.52, Z = 7.4, p < 0.001). Two clusters for high iodine in drinking-water were identified in the south-western and north-western parts of Shandong Province by the purely spatial scan statistic approach. Both GLMs and GWR indicated a significantly global association between iodine in drinking-water and geological factors. Furthermore, GWR showed obviously spatial variability across the study region. Soil type and distance to Yellow River were statistically significant at most areas of Shandong Province, confirming the hypothesis that the Yellow River causes iodine deposits in Shandong Province. Our results suggested that the more effective regional monitoring plan and water improvement strategies should be strengthened targeting at the cluster areas based on the characteristics of geological factors and the spatial variability of local relationships between iodine in drinking-water and geological factors.

Spatial Scaling of Environmental Variables Improves Species-Habitat Models of Fishes in a Small, Sand-Bed Lowland River

PubMed Central

Radinger, Johannes; Wolter, Christian; Kail, Jochem

2015-01-01

Habitat suitability and the distinct mobility of species depict fundamental keys for explaining and understanding the distribution of river fishes. In recent years, comprehensive data on river hydromorphology has been mapped at spatial scales down to 100 m, potentially serving high resolution species-habitat models, e.g., for fish. However, the relative importance of specific hydromorphological and in-stream habitat variables and their spatial scales of influence is poorly understood. Applying boosted regression trees, we developed species-habitat models for 13 fish species in a sand-bed lowland river based on river morphological and in-stream habitat data. First, we calculated mean values for the predictor variables in five distance classes (from the sampling site up to 4000 m up- and downstream) to identify the spatial scale that best predicts the presence of fish species. Second, we compared the suitability of measured variables and assessment scores related to natural reference conditions. Third, we identified variables which best explained the presence of fish species. The mean model quality (AUC = 0.78, area under the receiver operating characteristic curve) significantly increased when information on the habitat conditions up- and downstream of a sampling site (maximum AUC at 2500 m distance class, +0.049) and topological variables (e.g., stream order) were included (AUC = +0.014). Both measured and assessed variables were similarly well suited to predict species’ presence. Stream order variables and measured cross section features (e.g., width, depth, velocity) were best-suited predictors. In addition, measured channel-bed characteristics (e.g., substrate types) and assessed longitudinal channel features (e.g., naturalness of river planform) were also good predictors. These findings demonstrate (i) the applicability of high resolution river morphological and instream-habitat data (measured and assessed variables) to predict fish presence, (ii) the importance of considering habitat at spatial scales larger than the sampling site, and (iii) that the importance of (river morphological) habitat characteristics differs depending on the spatial scale. PMID:26569119

Partitioning the impacts of spatial and climatological rainfall variability in urban drainage modeling

NASA Astrophysics Data System (ADS)

Peleg, Nadav; Blumensaat, Frank; Molnar, Peter; Fatichi, Simone; Burlando, Paolo

2017-03-01

The performance of urban drainage systems is typically examined using hydrological and hydrodynamic models where rainfall input is uniformly distributed, i.e., derived from a single or very few rain gauges. When models are fed with a single uniformly distributed rainfall realization, the response of the urban drainage system to the rainfall variability remains unexplored. The goal of this study was to understand how climate variability and spatial rainfall variability, jointly or individually considered, affect the response of a calibrated hydrodynamic urban drainage model. A stochastic spatially distributed rainfall generator (STREAP - Space-Time Realizations of Areal Precipitation) was used to simulate many realizations of rainfall for a 30-year period, accounting for both climate variability and spatial rainfall variability. The generated rainfall ensemble was used as input into a calibrated hydrodynamic model (EPA SWMM - the US EPA's Storm Water Management Model) to simulate surface runoff and channel flow in a small urban catchment in the city of Lucerne, Switzerland. The variability of peak flows in response to rainfall of different return periods was evaluated at three different locations in the urban drainage network and partitioned among its sources. The main contribution to the total flow variability was found to originate from the natural climate variability (on average over 74 %). In addition, the relative contribution of the spatial rainfall variability to the total flow variability was found to increase with longer return periods. This suggests that while the use of spatially distributed rainfall data can supply valuable information for sewer network design (typically based on rainfall with return periods from 5 to 15 years), there is a more pronounced relevance when conducting flood risk assessments for larger return periods. The results show the importance of using multiple distributed rainfall realizations in urban hydrology studies to capture the total flow variability in the response of the urban drainage systems to heavy rainfall events.

Climate-based archetypes for the environmental fate assessment of chemicals.

PubMed

Ciuffo, Biagio; Sala, Serenella

2013-11-15

Emissions of chemicals have been on the rise for years, and their impacts are greatly influenced by spatial differentiation. Chemicals are usually emitted locally but their impact can be felt both locally and globally, due to their chemical properties and persistence. The variability of environmental parameters in the emission compartment may affect the chemicals' fate and the exposure at different orders of magnitude. The assessment of the environmental fate of chemicals and the inherent spatial differentiation requires the use of multimedia models at various levels of complexity (from a simple box model to complex computational and high-spatial-resolution models). The objective of these models is to support ecological and human health risk assessment, by reducing the uncertainty of chemical impact assessments. The parameterisation of spatially resolved multimedia models is usually based on scenarios of evaluative environments, or on geographical resolutions related to administrative boundaries (e.g. countries/continents) or landscape areas (e.g. watersheds, eco-regions). The choice of the most appropriate scale and scenario is important from a management perspective, as a balance should be reached between a simplified approach and computationally intensive multimedia models. In this paper, which aims to go beyond the more traditional approach based on scale/resolution (cell, country, and basin), we propose and assess climate-based archetypes for the impact assessment of chemicals released in air. We define the archetypes based on the main drivers of spatial variability, which we systematically identify by adopting global sensitivity analysis techniques. A case study that uses the high resolution multimedia model MAPPE (Multimedia Assessment of Pollutant Pathways in the Environment) is presented. Results of the analysis showed that suitable archetypes should be both climate- and chemical-specific, as different chemicals (or groups of them) have different traits that influence their spatial variability. This hypothesis was tested by comparing the variability of the output of MAPPE for four different climatic zones on four different continents for four different chemicals (which represent different combinations of physical and chemical properties). Results showed the high suitability of climate-based archetypes in assessing the impacts of chemicals released in air. However, further research work is still necessary to test these findings. Copyright © 2013 Elsevier Ltd. All rights reserved.

Sharpening method of satellite thermal image based on the geographical statistical model

NASA Astrophysics Data System (ADS)

Qi, Pengcheng; Hu, Shixiong; Zhang, Haijun; Guo, Guangmeng

2016-04-01

To improve the effectiveness of thermal sharpening in mountainous regions, paying more attention to the laws of land surface energy balance, a thermal sharpening method based on the geographical statistical model (GSM) is proposed. Explanatory variables were selected from the processes of land surface energy budget and thermal infrared electromagnetic radiation transmission, then high spatial resolution (57 m) raster layers were generated for these variables through spatially simulating or using other raster data as proxies. Based on this, the local adaptation statistical relationship between brightness temperature (BT) and the explanatory variables, i.e., the GSM, was built at 1026-m resolution using the method of multivariate adaptive regression splines. Finally, the GSM was applied to the high-resolution (57-m) explanatory variables; thus, the high-resolution (57-m) BT image was obtained. This method produced a sharpening result with low error and good visual effect. The method can avoid the blind choice of explanatory variables and remove the dependence on synchronous imagery at visible and near-infrared bands. The influences of the explanatory variable combination, sampling method, and the residual error correction on sharpening results were analyzed deliberately, and their influence mechanisms are reported herein.

General Constraints on Sampling Wildlife on FIA Plots

Treesearch

Larissa L. Bailey; John R. Sauer; James D. Nichols; Paul H. Geissler

2005-01-01

This paper reviews the constraints to sampling wildlife populations at FIA points. Wildlife sampling programs must have well-defined goals and provide information adequate to meet those goals. Investigators should choose a State variable based on information needs and the spatial sampling scale. We discuss estimation-based methods for three State variables: species...

Spatial variability of climate change impacts on yield of rice and wheat in the Indian Ganga Basin.

PubMed

Mishra, Ashok; Singh, R; Raghuwanshi, N S; Chatterjee, C; Froebrich, Jochen

2013-12-01

Indian Ganga Basin (IGB), one of the most densely populated areas in the world, is facing a significant threat to food grain production, besides increased yield gap between actual and potential production, due to climate change. We have analyzed the spatial variability of climate change impacts on rice and wheat yields at three different locations representing the upper, middle and lower IGB. The DSSAT model is used to simulate the effects of climate variability and climate change on rice and wheat yields by analyzing: (i) spatial crop yield response to current climate, and (ii) impact of a changing climate as projected by two regional climate models, REMO and HadRM3, based on SRES A1B emission scenarios for the period 2011-2040. Results for current climate demonstrate a significant gap between actual and potential yield for upper, middle and lower IGB stations. The analysis based on RCM projections shows that during 2011-2040, the largest reduction in rice and wheat yields will occur in the upper IGB (reduction of potential rice and wheat yield respectively by 43.2% and 20.9% by REMO, and 24.8% and 17.2% by HadRM3). In the lower IGB, however, contrasting results are obtained, with HadRM3 based projections showing an increase in the potential rice and wheat yields, whereas, REMO based projections show decreased potential yields. We discuss the influence of agro-climatic factors; variation in temperature, length of maturity period and leaf area index which are responsible for modeled spatial variability in crop yield response within the IGB. Copyright © 2013 Elsevier B.V. All rights reserved.

Spatial and Temporal Dynamics in Air Pollution Exposure Assessment

PubMed Central

Dias, Daniela; Tchepel, Oxana

2018-01-01

Analyzing individual exposure in urban areas offers several challenges where both the individual’s activities and air pollution levels demonstrate a large degree of spatial and temporal dynamics. This review article discusses the concepts, key elements, current developments in assessing personal exposure to urban air pollution (seventy-two studies reviewed) and respective advantages and disadvantages. A new conceptual structure to organize personal exposure assessment methods is proposed according to two classification criteria: (i) spatial-temporal variations of individuals’ activities (point-fixed or trajectory based) and (ii) characterization of air quality (variable or uniform). This review suggests that the spatial and temporal variability of urban air pollution levels in combination with indoor exposures and individual’s time-activity patterns are key elements of personal exposure assessment. In the literature review, the majority of revised studies (44 studies) indicate that the trajectory based with variable air quality approach provides a promising framework for tackling the important question of inter- and intra-variability of individual exposure. However, future quantitative comparison between the different approaches should be performed, and the selection of the most appropriate approach for exposure quantification should take into account the purpose of the health study. This review provides a structured basis for the intercomparing of different methodologies and to make their advantages and limitations more transparent in addressing specific research objectives. PMID:29558426

Monthly and spatially resolved black carbon emission inventory of India: uncertainty analysis

NASA Astrophysics Data System (ADS)

Paliwal, Umed; Sharma, Mukesh; Burkhart, John F.

2016-10-01

Black carbon (BC) emissions from India for the year 2011 are estimated to be 901.11 ± 151.56 Gg yr-1 based on a new ground-up, GIS-based inventory. The grid-based, spatially resolved emission inventory includes, in addition to conventional sources, emissions from kerosene lamps, forest fires, diesel-powered irrigation pumps and electricity generators at mobile towers. The emissions have been estimated at district level and were spatially distributed onto grids at a resolution of 40 × 40 km2. The uncertainty in emissions has been estimated using a Monte Carlo simulation by considering the variability in activity data and emission factors. Monthly variation of BC emissions has also been estimated to account for the seasonal variability. To the total BC emissions, domestic fuels contributed most significantly (47 %), followed by industry (22 %), transport (17 %), open burning (12 %) and others (2 %). The spatial and seasonal resolution of the inventory will be useful for modeling BC transport in the atmosphere for air quality, global warming and other process-level studies that require greater temporal resolution than traditional inventories.

Added-values of high spatiotemporal remote sensing data in crop yield estimation

NASA Astrophysics Data System (ADS)

Gao, F.; Anderson, M. C.

2017-12-01

Timely and accurate estimation of crop yield before harvest is critical for food market and administrative planning. Remote sensing derived parameters have been used for estimating crop yield by using either empirical or crop growth models. The uses of remote sensing vegetation index (VI) in crop yield modeling have been typically evaluated at regional and country scales using coarse spatial resolution (a few hundred to kilo-meters) data or assessed over a small region at field level using moderate resolution spatial resolution data (10-100m). Both data sources have shown great potential in capturing spatial and temporal variability in crop yield. However, the added value of data with both high spatial and temporal resolution data has not been evaluated due to the lack of such data source with routine, global coverage. In recent years, more moderate resolution data have become freely available and data fusion approaches that combine data acquired from different spatial and temporal resolutions have been developed. These make the monitoring crop condition and estimating crop yield at field scale become possible. Here we investigate the added value of the high spatial and temporal VI for describing variability of crop yield. The explanatory ability of crop yield based on high spatial and temporal resolution remote sensing data was evaluated in a rain-fed agricultural area in the U.S. Corn Belt. Results show that the fused Landsat-MODIS (high spatial and temporal) VI explains yield variability better than single data source (Landsat or MODIS alone), with EVI2 performing slightly better than NDVI. The maximum VI describes yield variability better than cumulative VI. Even though VI is effective in explaining yield variability within season, the inter-annual variability is more complex and need additional information (e.g. weather, water use and management). Our findings augment the importance of high spatiotemporal remote sensing data and supports new moderate resolution satellite missions for agricultural applications.

Disentangling environmental and spatial effects on phylogenetic structure of angiosperm tree communities in China.

PubMed

Qian, Hong; Chen, Shengbin; Zhang, Jin-Long

2017-07-17

Niche-based and neutrality-based theories are two major classes of theories explaining the assembly mechanisms of local communities. Both theories have been frequently used to explain species diversity and composition in local communities but their relative importance remains unclear. Here, we analyzed 57 assemblages of angiosperm trees in 0.1-ha forest plots across China to examine the effects of environmental heterogeneity (relevant to niche-based processes) and spatial contingency (relevant to neutrality-based processes) on phylogenetic structure of angiosperm tree assemblages distributed across a wide range of environment and space. Phylogenetic structure was quantified with six phylogenetic metrics (i.e., phylogenetic diversity, mean pairwise distance, mean nearest taxon distance, and the standardized effect sizes of these three metrics), which emphasize on different depths of evolutionary histories and account for different degrees of species richness effects. Our results showed that the variation in phylogenetic metrics explained independently by environmental variables was on average much greater than that explained independently by spatial structure, and the vast majority of the variation in phylogenetic metrics was explained by spatially structured environmental variables. We conclude that niche-based processes have played a more important role than neutrality-based processes in driving phylogenetic structure of angiosperm tree species in forest communities in China.

Strategies for minimizing sample size for use in airborne LiDAR-based forest inventory

USGS Publications Warehouse

Junttila, Virpi; Finley, Andrew O.; Bradford, John B.; Kauranne, Tuomo

2013-01-01

Recently airborne Light Detection And Ranging (LiDAR) has emerged as a highly accurate remote sensing modality to be used in operational scale forest inventories. Inventories conducted with the help of LiDAR are most often model-based, i.e. they use variables derived from LiDAR point clouds as the predictive variables that are to be calibrated using field plots. The measurement of the necessary field plots is a time-consuming and statistically sensitive process. Because of this, current practice often presumes hundreds of plots to be collected. But since these plots are only used to calibrate regression models, it should be possible to minimize the number of plots needed by carefully selecting the plots to be measured. In the current study, we compare several systematic and random methods for calibration plot selection, with the specific aim that they be used in LiDAR based regression models for forest parameters, especially above-ground biomass. The primary criteria compared are based on both spatial representativity as well as on their coverage of the variability of the forest features measured. In the former case, it is important also to take into account spatial auto-correlation between the plots. The results indicate that choosing the plots in a way that ensures ample coverage of both spatial and feature space variability improves the performance of the corresponding models, and that adequate coverage of the variability in the feature space is the most important condition that should be met by the set of plots collected.

Spatial generalised linear mixed models based on distances.

PubMed

Melo, Oscar O; Mateu, Jorge; Melo, Carlos E

2016-10-01

Risk models derived from environmental data have been widely shown to be effective in delineating geographical areas of risk because they are intuitively easy to understand. We present a new method based on distances, which allows the modelling of continuous and non-continuous random variables through distance-based spatial generalised linear mixed models. The parameters are estimated using Markov chain Monte Carlo maximum likelihood, which is a feasible and a useful technique. The proposed method depends on a detrending step built from continuous or categorical explanatory variables, or a mixture among them, by using an appropriate Euclidean distance. The method is illustrated through the analysis of the variation in the prevalence of Loa loa among a sample of village residents in Cameroon, where the explanatory variables included elevation, together with maximum normalised-difference vegetation index and the standard deviation of normalised-difference vegetation index calculated from repeated satellite scans over time. © The Author(s) 2013.

Detecting high spatial variability of ice shelf basal mass balance, Roi Baudouin Ice Shelf, Antarctica

NASA Astrophysics Data System (ADS)

Berger, Sophie; Drews, Reinhard; Helm, Veit; Sun, Sainan; Pattyn, Frank

2017-11-01

Ice shelves control the dynamic mass loss of ice sheets through buttressing and their integrity depends on the spatial variability of their basal mass balance (BMB), i.e. the difference between refreezing and melting. Here, we present an improved technique - based on satellite observations - to capture the small-scale variability in the BMB of ice shelves. As a case study, we apply the methodology to the Roi Baudouin Ice Shelf, Dronning Maud Land, East Antarctica, and derive its yearly averaged BMB at 10 m horizontal gridding. We use mass conservation in a Lagrangian framework based on high-resolution surface velocities, atmospheric-model surface mass balance and hydrostatic ice-thickness fields (derived from TanDEM-X surface elevation). Spatial derivatives are implemented using the total-variation differentiation, which preserves abrupt changes in flow velocities and their spatial gradients. Such changes may reflect a dynamic response to localized basal melting and should be included in the mass budget. Our BMB field exhibits much spatial detail and ranges from -14.7 to 8.6 m a-1 ice equivalent. Highest melt rates are found close to the grounding line where the pressure melting point is high, and the ice shelf slope is steep. The BMB field agrees well with on-site measurements from phase-sensitive radar, although independent radar profiling indicates unresolved spatial variations in firn density. We show that an elliptical surface depression (10 m deep and with an extent of 0.7 km × 1.3 km) lowers by 0.5 to 1.4 m a-1, which we tentatively attribute to a transient adaptation to hydrostatic equilibrium. We find evidence for elevated melting beneath ice shelf channels (with melting being concentrated on the channel's flanks). However, farther downstream from the grounding line, the majority of ice shelf channels advect passively (i.e. no melting nor refreezing) toward the ice shelf front. Although the absolute, satellite-based BMB values remain uncertain, we have high confidence in the spatial variability on sub-kilometre scales. This study highlights expected challenges for a full coupling between ice and ocean models.

Incorporating twitter-based human activity information in spatial analysis of crashes in urban areas.

PubMed

Bao, Jie; Liu, Pan; Yu, Hao; Xu, Chengcheng

2017-09-01

The primary objective of this study was to investigate how to incorporate human activity information in spatial analysis of crashes in urban areas using Twitter check-in data. This study used the data collected from the City of Los Angeles in the United States to illustrate the procedure. The following five types of data were collected: crash data, human activity data, traditional traffic exposure variables, road network attributes and social-demographic data. A web crawler by Python was developed to collect the venue type information from the Twitter check-in data automatically. The human activities were classified into seven categories by the obtained venue types. The collected data were aggregated into 896 Traffic Analysis Zones (TAZ). Geographically weighted regression (GWR) models were developed to establish a relationship between the crash counts reported in a TAZ and various contributing factors. Comparative analyses were conducted to compare the performance of GWR models which considered traditional traffic exposure variables only, Twitter-based human activity variables only, and both traditional traffic exposure and Twitter-based human activity variables. The model specification results suggested that human activity variables significantly affected the crash counts in a TAZ. The results of comparative analyses suggested that the models which considered both traditional traffic exposure and human activity variables had the best goodness-of-fit in terms of the highest R 2 and lowest AICc values. The finding seems to confirm the benefits of incorporating human activity information in spatial analysis of crashes using Twitter check-in data. Copyright © 2017 Elsevier Ltd. All rights reserved.

Rupture Propagation for Stochastic Fault Models

NASA Astrophysics Data System (ADS)

Favreau, P.; Lavallee, D.; Archuleta, R.

2003-12-01

The inversion of strong motion data of large earhquakes give the spatial distribution of pre-stress on the ruptured faults and it can be partially reproduced by stochastic models, but a fundamental question remains: how rupture propagates, constrained by the presence of spatial heterogeneity? For this purpose we investigate how the underlying random variables, that control the pre-stress spatial variability, condition the propagation of the rupture. Two stochastic models of prestress distributions are considered, respectively based on Cauchy and Gaussian random variables. The parameters of the two stochastic models have values corresponding to the slip distribution of the 1979 Imperial Valley earthquake. We use a finite difference code to simulate the spontaneous propagation of shear rupture on a flat fault in a 3D continuum elastic body. The friction law is the slip dependent friction law. The simulations show that the propagation of the rupture front is more complex, incoherent or snake-like for a prestress distribution based on Cauchy random variables. This may be related to the presence of a higher number of asperities in this case. These simulations suggest that directivity is stronger in the Cauchy scenario, compared to the smoother rupture of the Gauss scenario.

Environmental and biological characteristics of Atlantic bluefin tuna and albacore spawning habitats based on their egg distributions

NASA Astrophysics Data System (ADS)

Reglero, Patricia; Santos, Maria; Balbín, Rosa; Laíz-Carrión, Raul; Alvarez-Berastegui, Diego; Ciannelli, Lorenzo; Jiménez, Elisa; Alemany, Francisco

2017-06-01

Tuna spawning habitats are traditionally characterized using data sets of larvae or gonads from mature adults and concurrent environmental variables. Data on egg distributions have never previously been used since molecular analyses are mandatory to identify tuna eggs to species level. However, in this study we use molecularly derived egg distribution data, in addition to larval data, to characterize hydrographic and biological drivers of the spatial distribution of eggs and larvae of bluefin Thunnus thynnus and albacore tuna Thunnus alalunga in the Balearic Sea, a main spawning area of these species in the Mediterranean. The effects of the hydrography, characterized by salinity, temperature and geostrophic velocity, on the spatial distributions of the eggs and larvae are investigated. Three biological variables are used to describe the productivity in the area: chlorophyll a in the mixed layer, chlorophyll a in the deep chlorophyll maximum and mesozooplankton biomass in the mixed layer. Our results point to the importance of salinity fronts and temperatures above a minimum threshold in shaping the egg and larval distribution of both species. The spatial distribution of the biotic variables was very scattered, and they did not emerge as significant variables in the presence-absence models. However, they became significant when modeling egg and larval abundances. The lack of correlation between the three biotic variables challenges the use of chlorophyll a to describe trophic scenarios for the larvae and suggests that the spatial distribution of resources is not persistent in time. The different patterns in relation to biotic variables across species and stages found in this and other studies indicate a still elusive understanding of the link between trophic levels involving tuna early larval stages. Our ability to improve short-term forecasting and long-term predictions of climate effects on the egg and larval distributions is discussed based on the consistency of the environmentally driven spatial patterns for the two species.

Measuring high-density built environment for public health research: Uncertainty with respect to data, indicator design and spatial scale.

PubMed

Sun, Guibo; Webster, Chris; Ni, Michael Y; Zhang, Xiaohu

2018-05-07

Uncertainty with respect to built environment (BE) data collection, measure conceptualization and spatial scales is evident in urban health research, but most findings are from relatively lowdensity contexts. We selected Hong Kong, an iconic high-density city, as the study area as limited research has been conducted on uncertainty in such areas. We used geocoded home addresses (n=5732) from a large population-based cohort in Hong Kong to extract BE measures for the participants' place of residence based on an internationally recognized BE framework. Variability of the measures was mapped and Spearman's rank correlation calculated to assess how well the relationships among indicators are preserved across variables and spatial scales. We found extreme variations and uncertainties for the 180 measures collected using comprehensive data and advanced geographic information systems modelling techniques. We highlight the implications of methodological selection and spatial scales of the measures. The results suggest that more robust information regarding urban health research in high-density city would emerge if greater consideration were given to BE data, design methods and spatial scales of the BE measures.

Comparing cropland net primary production estimates from inventory, a satellite-based model, and a process-based model in the Midwest of the United States

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

Li, Zhengpeng; Liu, Shuguang; Tan, Zhengxi

2014-04-01

Accurately quantifying the spatial and temporal variability of net primary production (NPP) for croplands is essential to understand regional cropland carbon dynamics. We compared three NPP estimates for croplands in the Midwestern United States: inventory-based estimates using crop yield data from the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS); estimates from the satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) NPP product; and estimates from the General Ensemble biogeochemical Modeling System (GEMS) process-based model. The three methods estimated mean NPP in the range of 469–687 g C m -2 yr -1 and total NPP in the range of 318–490more » Tg C yr -1 for croplands in the Midwest in 2007 and 2008. The NPP estimates from crop yield data and the GEMS model showed the mean NPP for croplands was over 650 g C m -2 yr -1 while the MODIS NPP product estimated the mean NPP was less than 500 g C m -2 yr -1. MODIS NPP also showed very different spatial variability of the cropland NPP from the other two methods. We found these differences were mainly caused by the difference in the land cover data and the crop specific information used in the methods. Our study demonstrated that the detailed mapping of the temporal and spatial change of crop species is critical for estimating the spatial and temporal variability of cropland NPP. Finally, we suggest that high resolution land cover data with species–specific crop information should be used in satellite-based and process-based models to improve carbon estimates for croplands.« less

Comparing cropland net primary production estimates from inventory, a satellite-based model, and a process-based model in the Midwest of the United States

USGS Publications Warehouse

Li, Zhengpeng; Liu, Shuguang; Tan, Zhengxi; Bliss, Norman B.; Young, Claudia J.; West, Tristram O.; Ogle, Stephen M.

2014-01-01

Accurately quantifying the spatial and temporal variability of net primary production (NPP) for croplands is essential to understand regional cropland carbon dynamics. We compared three NPP estimates for croplands in the Midwestern United States: inventory-based estimates using crop yield data from the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS); estimates from the satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) NPP product; and estimates from the General Ensemble biogeochemical Modeling System (GEMS) process-based model. The three methods estimated mean NPP in the range of 469–687 g C m−2 yr−1and total NPP in the range of 318–490 Tg C yr−1 for croplands in the Midwest in 2007 and 2008. The NPP estimates from crop yield data and the GEMS model showed the mean NPP for croplands was over 650 g C m−2 yr−1 while the MODIS NPP product estimated the mean NPP was less than 500 g C m−2 yr−1. MODIS NPP also showed very different spatial variability of the cropland NPP from the other two methods. We found these differences were mainly caused by the difference in the land cover data and the crop specific information used in the methods. Our study demonstrated that the detailed mapping of the temporal and spatial change of crop species is critical for estimating the spatial and temporal variability of cropland NPP. We suggest that high resolution land cover data with species–specific crop information should be used in satellite-based and process-based models to improve carbon estimates for croplands.

Estimates of reservoir methane emissions based on a spatially balanced probabilistic-survey

EPA Science Inventory

Global estimates of methane (CH4) emissions from reservoirs are poorly constrained, partly due to the challenges of accounting for intra-reservoir spatial variability. Reservoir-scale emission rates are often estimated by extrapolating from measurement made at a few locations; h...

Optimizing landslide susceptibility zonation: Effects of DEM spatial resolution and slope unit delineation on logistic regression models

NASA Astrophysics Data System (ADS)

Schlögel, R.; Marchesini, I.; Alvioli, M.; Reichenbach, P.; Rossi, M.; Malet, J.-P.

2018-01-01

We perform landslide susceptibility zonation with slope units using three digital elevation models (DEMs) of varying spatial resolution of the Ubaye Valley (South French Alps). In so doing, we applied a recently developed algorithm automating slope unit delineation, given a number of parameters, in order to optimize simultaneously the partitioning of the terrain and the performance of a logistic regression susceptibility model. The method allowed us to obtain optimal slope units for each available DEM spatial resolution. For each resolution, we studied the susceptibility model performance by analyzing in detail the relevance of the conditioning variables. The analysis is based on landslide morphology data, considering either the whole landslide or only the source area outline as inputs. The procedure allowed us to select the most useful information, in terms of DEM spatial resolution, thematic variables and landslide inventory, in order to obtain the most reliable slope unit-based landslide susceptibility assessment.

A GIS approach to conducting biogeochemical research in wetlands

NASA Technical Reports Server (NTRS)

Brannon, David P.; Irish, Gary J.

1985-01-01

A project was initiated to develop an environmental data base to address spatial aspects of both biogeochemical cycling and resource management in wetlands. Specific goals are to make regional methane flux estimates and site specific water level predictions based on man controlled water releases within a wetland study area. The project will contribute to the understanding of the Earth's biosphere through its examination of the spatial variability of methane emissions. Although wetlands are thought to be one of the primary sources for release of methane to the atmosphere, little is known about the spatial variability of methane flux. Only through a spatial analysis of methane flux rates and the environmental factors which influence such rates can reliable regional and global methane emissions be calculated. Data will be correlated and studied from Landsat 4 instruments, from a ground survey of water level recorders, precipitation recorders, evaporation pans, and supplemental gauges, and from flood gate water release; and regional methane flux estimates will be made.

Exploring the Specifications of Spatial Adjacencies and Weights in Bayesian Spatial Modeling with Intrinsic Conditional Autoregressive Priors in a Small-area Study of Fall Injuries

PubMed Central

Law, Jane

2016-01-01

Intrinsic conditional autoregressive modeling in a Bayeisan hierarchical framework has been increasingly applied in small-area ecological studies. This study explores the specifications of spatial structure in this Bayesian framework in two aspects: adjacency, i.e., the set of neighbor(s) for each area; and (spatial) weight for each pair of neighbors. Our analysis was based on a small-area study of falling injuries among people age 65 and older in Ontario, Canada, that was aimed to estimate risks and identify risk factors of such falls. In the case study, we observed incorrect adjacencies information caused by deficiencies in the digital map itself. Further, when equal weights was replaced by weights based on a variable of expected count, the range of estimated risks increased, the number of areas with probability of estimated risk greater than one at different probability thresholds increased, and model fit improved. More importantly, significance of a risk factor diminished. Further research to thoroughly investigate different methods of variable weights; quantify the influence of specifications of spatial weights; and develop strategies for better defining spatial structure of a map in small-area analysis in Bayesian hierarchical spatial modeling is recommended. PMID:29546147

Separating the effects of environment and space on tree species distribution: from population to community.

PubMed

Lin, Guojun; Stralberg, Diana; Gong, Guiquan; Huang, Zhongliang; Ye, Wanhui; Wu, Linfang

2013-01-01

Quantifying the relative contributions of environmental conditions and spatial factors to species distribution can help improve our understanding of the processes that drive diversity patterns. In this study, based on tree inventory, topography and soil data from a 20-ha stem-mapped permanent forest plot in Guangdong Province, China, we evaluated the influence of different ecological processes at different spatial scales using canonical redundancy analysis (RDA) at the community level and multiple linear regression at the species level. At the community level, the proportion of explained variation in species distribution increased with grid-cell sizes, primarily due to a monotonic increase in the explanatory power of environmental variables. At the species level, neither environmental nor spatial factors were important determinants of overstory species' distributions at small cell sizes. However, purely spatial variables explained most of the variation in the distributions of understory species at fine and intermediate cell sizes. Midstory species showed patterns that were intermediate between those of overstory and understory species. At the 20-m cell size, the influence of spatial factors was stronger for more dispersal-limited species, suggesting that much of the spatial structuring in this community can be explained by dispersal limitation. Comparing environmental factors, soil variables had higher explanatory power than did topography for species distribution. However, both topographic and edaphic variables were highly spatial structured. Our results suggested that dispersal limitation has an important influence on fine-intermediate scale (from several to tens of meters) species distribution, while environmental variability facilitates species distribution at intermediate (from ten to tens of meters) and broad (from tens to hundreds of meters) scales.

Soil nutrient-landscape relationships in a lowland tropical rainforest in Panama

USGS Publications Warehouse

Barthold, F.K.; Stallard, R.F.; Elsenbeer, H.

2008-01-01

Soils play a crucial role in biogeochemical cycles as spatially distributed sources and sinks of nutrients. Any spatial patterns depend on soil forming processes, our understanding of which is still limited, especially in regards to tropical rainforests. The objective of our study was to investigate the effects of landscape properties, with an emphasis on the geometry of the land surface, on the spatial heterogeneity of soil chemical properties, and to test the suitability of soil-landscape modeling as an appropriate technique to predict the spatial variability of exchangeable K and Mg in a humid tropical forest in Panama. We used a design-based, stratified sampling scheme to collect soil samples at 108 sites on Barro Colorado Island, Panama. Stratifying variables are lithology, vegetation and topography. Topographic variables were generated from high-resolution digital elevation models with a grid size of 5 m. We took samples from five depths down to 1 m, and analyzed for total and exchangeable K and Mg. We used simple explorative data analysis techniques to elucidate the importance of lithology for soil total and exchangeable K and Mg. Classification and Regression Trees (CART) were adopted to investigate importance of topography, lithology and vegetation for the spatial distribution of exchangeable K and Mg and with the intention to develop models that regionalize the point observations using digital terrain data as explanatory variables. Our results suggest that topography and vegetation do not control the spatial distribution of the selected soil chemical properties at a landscape scale and lithology is important to some degree. Exchangeable K is distributed equally across the study area indicating that other than landscape processes, e.g. biogeochemical processes, are responsible for its spatial distribution. Lithology contributes to the spatial variation of exchangeable Mg but controlling variables could not be detected. The spatial variation of soil total K and Mg is mainly influenced by lithology. ?? 2007 Elsevier B.V. All rights reserved.

One perspective on spatial variability in geologic mapping

USGS Publications Warehouse

Markewich, H.W.; Cooper, S.C.

1991-01-01

This paper discusses some of the differences between geologic mapping and soil mapping, and how the resultant maps are interpreted. The role of spatial variability in geologic mapping is addressed only indirectly because in geologic mapping there have been few attempts at quantification of spatial differences. This is largely because geologic maps deal with temporal as well as spatial variability and consider time, age, and origin, as well as composition and geometry. Both soil scientists and geologists use spatial variability to delineate mappable units; however, the classification systems from which these mappable units are defined differ greatly. Mappable soil units are derived from systematic, well-defined, highly structured sets of taxonomic criteria; whereas mappable geologic units are based on a more arbitrary heirarchy of categories that integrate many features without strict values or definitions. Soil taxonomy is a sorting tool used to reduce heterogeneity between soil units. Thus at the series level, soils in any one series are relatively homogeneous because their range of properties is small and well-defined. Soil maps show the distribution of soils on the land surface. Within a map area, soils, which are often less than 2 m thick, show a direct correlation to topography and to active surface processes as well as to parent material.

Spatial-temporal and cancer risk assessment of selected hazardous air pollutants in Seattle.

PubMed

Wu, Chang-fu; Liu, L-J Sally; Cullen, Alison; Westberg, Hal; Williamson, John

2011-01-01

In the Seattle Air Toxics Monitoring Pilot Program, we measured 15 hazardous air pollutants (HAPs) at 6 sites for more than a year between 2000 and 2002. Spatial-temporal variations were evaluated with random-effects models and principal component analyses. The potential health risks were further estimated based on the monitored data, with the incorporation of the bootstrapping technique for the uncertainty analysis. It is found that the temporal variability was generally higher than the spatial variability for most air toxics. The highest temporal variability was observed for tetrachloroethylene (70% temporal vs. 34% spatial variability). Nevertheless, most air toxics still exhibited significant spatial variations, even after accounting for the temporal effects. These results suggest that it would require operating multiple air toxics monitoring sites over a significant period of time with proper monitoring frequency to better evaluate population exposure to HAPs. The median values of the estimated inhalation cancer risks ranged between 4.3 × 10⁻⁵ and 6.0 × 10⁻⁵, with the 5th and 95th percentile levels exceeding the 1 in a million level. VOCs as a whole contributed over 80% of the risk among the HAPs measured and arsenic contributed most substantially to the overall risk associated with metals. Copyright © 2010 Elsevier Ltd. All rights reserved.

Landscape genomics of Sphaeralcea ambigua in the Mojave Desert: a multivariate, spatially-explicit approach to guide ecological restoration

USGS Publications Warehouse

Shryock, Daniel F.; Havrilla, Caroline A.; DeFalco, Lesley; Esque, Todd C.; Custer, Nathan; Wood, Troy E.

2015-01-01

Local adaptation influences plant species’ responses to climate change and their performance in ecological restoration. Fine-scale physiological or phenological adaptations that direct demographic processes may drive intraspecific variability when baseline environmental conditions change. Landscape genomics characterize adaptive differentiation by identifying environmental drivers of adaptive genetic variability and mapping the associated landscape patterns. We applied such an approach to Sphaeralcea ambigua, an important restoration plant in the arid southwestern United States, by analyzing variation at 153 amplified fragment length polymorphism loci in the context of environmental gradients separating 47 Mojave Desert populations. We identified 37 potentially adaptive loci through a combination of genome scan approaches. We then used a generalized dissimilarity model (GDM) to relate variability in potentially adaptive loci with spatial gradients in temperature, precipitation, and topography. We identified non-linear thresholds in loci frequencies driven by summer maximum temperature and water stress, along with continuous variation corresponding to temperature seasonality. Two GDM-based approaches for mapping predicted patterns of local adaptation are compared. Additionally, we assess uncertainty in spatial interpolations through a novel spatial bootstrapping approach. Our study presents robust, accessible methods for deriving spatially-explicit models of adaptive genetic variability in non-model species that will inform climate change modelling and ecological restoration.

PID temperature controller in pig nursery: spatial characterization of thermal environment

NASA Astrophysics Data System (ADS)

de Souza Granja Barros, Juliana; Rossi, Luiz Antonio; Menezes de Souza, Zigomar

2018-05-01

The use of enhanced technologies of temperature control can improve the thermal conditions in environments of livestock facilities. The objective of this study was to evaluate the spatial distribution of the thermal environment variables in a pig nursery with a heating system with two temperature control technologies based on the geostatistical analysis. The following systems were evaluated: overhead electrical resistance with Proportional, Integral, and Derivative (PID) controller and overhead electrical resistance with a thermostat. We evaluated the climatic variables: dry bulb temperature (Tbs), air relative humidity (RH), temperature and humidity index (THI), and enthalpy in the winter, at 7:00, 12:00, and 18:00 h. The spatial distribution of these variables was mapped by kriging. The results showed that the resistance heating system with PID controllers improved the thermal comfort conditions in the pig nursery in the coldest hours, maintaining the spatial distribution of the air temperature more homogeneous in the pen. During the hottest weather, neither system provided comfort.

PID temperature controller in pig nursery: spatial characterization of thermal environment

NASA Astrophysics Data System (ADS)

de Souza Granja Barros, Juliana; Rossi, Luiz Antonio; Menezes de Souza, Zigomar

2017-11-01

The use of enhanced technologies of temperature control can improve the thermal conditions in environments of livestock facilities. The objective of this study was to evaluate the spatial distribution of the thermal environment variables in a pig nursery with a heating system with two temperature control technologies based on the geostatistical analysis. The following systems were evaluated: overhead electrical resistance with Proportional, Integral, and Derivative (PID) controller and overhead electrical resistance with a thermostat. We evaluated the climatic variables: dry bulb temperature (Tbs), air relative humidity (RH), temperature and humidity index (THI), and enthalpy in the winter, at 7:00, 12:00, and 18:00 h. The spatial distribution of these variables was mapped by kriging. The results showed that the resistance heating system with PID controllers improved the thermal comfort conditions in the pig nursery in the coldest hours, maintaining the spatial distribution of the air temperature more homogeneous in the pen. During the hottest weather, neither system provided comfort.

Hierarchical Bayesian spatial models for multispecies conservation planning and monitoring.

PubMed

Carroll, Carlos; Johnson, Devin S; Dunk, Jeffrey R; Zielinski, William J

2010-12-01

Biologists who develop and apply habitat models are often familiar with the statistical challenges posed by their data's spatial structure but are unsure of whether the use of complex spatial models will increase the utility of model results in planning. We compared the relative performance of nonspatial and hierarchical Bayesian spatial models for three vertebrate and invertebrate taxa of conservation concern (Church's sideband snails [Monadenia churchi], red tree voles [Arborimus longicaudus], and Pacific fishers [Martes pennanti pacifica]) that provide examples of a range of distributional extents and dispersal abilities. We used presence-absence data derived from regional monitoring programs to develop models with both landscape and site-level environmental covariates. We used Markov chain Monte Carlo algorithms and a conditional autoregressive or intrinsic conditional autoregressive model framework to fit spatial models. The fit of Bayesian spatial models was between 35 and 55% better than the fit of nonspatial analogue models. Bayesian spatial models outperformed analogous models developed with maximum entropy (Maxent) methods. Although the best spatial and nonspatial models included similar environmental variables, spatial models provided estimates of residual spatial effects that suggested how ecological processes might structure distribution patterns. Spatial models built from presence-absence data improved fit most for localized endemic species with ranges constrained by poorly known biogeographic factors and for widely distributed species suspected to be strongly affected by unmeasured environmental variables or population processes. By treating spatial effects as a variable of interest rather than a nuisance, hierarchical Bayesian spatial models, especially when they are based on a common broad-scale spatial lattice (here the national Forest Inventory and Analysis grid of 24 km(2) hexagons), can increase the relevance of habitat models to multispecies conservation planning. Journal compilation © 2010 Society for Conservation Biology. No claim to original US government works.

Geographical Distribution Patterns of Iodine in Drinking-Water and Its Associations with Geological Factors in Shandong Province, China

PubMed Central

Gao, Jie; Zhang, Zhijie; Hu, Yi; Bian, Jianchao; Jiang, Wen; Wang, Xiaoming; Sun, Liqian; Jiang, Qingwu

2014-01-01

County-based spatial distribution characteristics and the related geological factors for iodine in drinking-water were studied in Shandong Province (China). Spatial autocorrelation analysis and spatial scan statistic were applied to analyze the spatial characteristics. Generalized linear models (GLMs) and geographically weighted regression (GWR) studies were conducted to explore the relationship between water iodine level and its related geological factors. The spatial distribution of iodine in drinking-water was significantly heterogeneous in Shandong Province (Moran’s I = 0.52, Z = 7.4, p < 0.001). Two clusters for high iodine in drinking-water were identified in the south-western and north-western parts of Shandong Province by the purely spatial scan statistic approach. Both GLMs and GWR indicated a significantly global association between iodine in drinking-water and geological factors. Furthermore, GWR showed obviously spatial variability across the study region. Soil type and distance to Yellow River were statistically significant at most areas of Shandong Province, confirming the hypothesis that the Yellow River causes iodine deposits in Shandong Province. Our results suggested that the more effective regional monitoring plan and water improvement strategies should be strengthened targeting at the cluster areas based on the characteristics of geological factors and the spatial variability of local relationships between iodine in drinking-water and geological factors. PMID:24852390

Atmospheric Parameter Climatologies from AIRS: Monitoring Short-, and Longer-Term Climate Variabilities and 'Trends'

NASA Technical Reports Server (NTRS)

Molnar, Gyula; Susskind, Joel

2008-01-01

The AIRS instrument is currently the best space-based tool to simultaneously monitor the vertical distribution of key climatically important atmospheric parameters as well as surface properties, and has provided high quality data for more than 5 years. AIRS analysis results produced at the GODDARD/DAAC, based on Versions 4 & 5 of the AIRS retrieval algorithm, are currently available for public use. Here, first we present an assessment of interrelationships of anomalies (proxies of climate variability based on 5 full years, since Sept. 2002) of various climate parameters at different spatial scales. We also present AIRS-retrievals-based global, regional and 1x1 degree grid-scale "trend"-analyses of important atmospheric parameters for this 5-year period. Note that here "trend" simply means the linear fit to the anomaly (relative the mean seasonal cycle) time series of various parameters at the above-mentioned spatial scales, and we present these to illustrate the usefulness of continuing AIRS-based climate observations. Preliminary validation efforts, in terms of intercomparisons of interannual variabilities with other available satellite data analysis results, will also be addressed. For example, we show that the outgoing longwave radiation (OLR) interannual spatial variabilities from the available state-of-the-art CERES measurements and from the AIRS computations are in remarkably good agreement. Version 6 of the AIRS retrieval scheme (currently under development) promises to further improve bias agreements for the absolute values by implementing a more accurate radiative transfer model for the OLR computations and by improving surface emissivity retrievals.

Spatial ability in secondary school students: intra-sex differences based on self-selection for physical education.

PubMed

Tlauka, Michael; Williams, Jennifer; Williamson, Paul

2008-08-01

Past research has demonstrated consistent sex differences with men typically outperforming women on tests of spatial ability. However, less is known about intra-sex effects. In the present study, two groups of female students (physical education and non-physical education secondary students) and two corresponding groups of male students explored a large-scale virtual shopping centre. In a battery of tasks, spatial knowledge of the shopping centre as well as mental rotation ability were tested. Additional variables considered were circulating testosterone levels, the ratio of 2D:4D digit length, and computer experience. The results revealed both sex and intra-sex differences in spatial ability. Variables related to virtual navigation and computer ability and experience were found to be the most powerful predictors of group membership. Our results suggest that in female and male secondary students, participation in physical education and spatial skill are related.

Crime Modeling using Spatial Regression Approach

NASA Astrophysics Data System (ADS)

Saleh Ahmar, Ansari; Adiatma; Kasim Aidid, M.

2018-01-01

Act of criminality in Indonesia increased both variety and quantity every year. As murder, rape, assault, vandalism, theft, fraud, fencing, and other cases that make people feel unsafe. Risk of society exposed to crime is the number of reported cases in the police institution. The higher of the number of reporter to the police institution then the number of crime in the region is increasing. In this research, modeling criminality in South Sulawesi, Indonesia with the dependent variable used is the society exposed to the risk of crime. Modelling done by area approach is the using Spatial Autoregressive (SAR) and Spatial Error Model (SEM) methods. The independent variable used is the population density, the number of poor population, GDP per capita, unemployment and the human development index (HDI). Based on the analysis using spatial regression can be shown that there are no dependencies spatial both lag or errors in South Sulawesi.

City scale pollen concentration variability

NASA Astrophysics Data System (ADS)

van der Molen, Michiel; van Vliet, Arnold; Krol, Maarten

2016-04-01

Pollen are emitted in the atmosphere both in the country-side and in cities. Yet the majority of the population is exposed to pollen in cities. Allergic reactions may be induced by short-term exposure to pollen. This raises the question how variable pollen concentration in cities are in temporally and spatially, and how much of the pollen in cities are actually produced in the urban region itself. We built a high resolution (1 × 1 km) pollen dispersion model based on WRF-Chem to study a city's pollen budget and the spatial and temporal variability in concentration. It shows that the concentrations are highly variable, as a result of source distribution, wind direction and boundary layer mixing, as well as the release rate as a function of temperature, turbulence intensity and humidity. Hay Fever Forecasts based on such high resolution emission and physical dispersion modelling surpass traditional hay fever warning methods based on temperature sum methods. The model gives new insights in concentration variability, personal and community level exposure and prevention. The model will be developped into a new forecast tool to serve allergic people to minimize their exposure and reduce nuisance, coast of medication and sick leave. This is an innovative approach in hay fever warning systems.

Evaluation of fine soil moisture data from the IFloodS (NASA GPM) Ground Validation campaign using a fully-distributed ecohydrological model

NASA Astrophysics Data System (ADS)

Bastola, S.; Dialynas, Y. G.; Arnone, E.; Bras, R. L.

2014-12-01

The spatial variability of soil, vegetation, topography, and precipitation controls hydrological processes, consequently resulting in high spatio-temporal variability of most of the hydrological variables, such as soil moisture. Limitation in existing measuring system to characterize this spatial variability, and its importance in various application have resulted in a need of reconciling spatially distributed soil moisture evolution model and corresponding measurements. Fully distributed ecohydrological model simulates soil moisture at high resolution soil moisture. This is relevant for range of environmental studies e.g., flood forecasting. They can also be used to evaluate the value of space born soil moisture data, by assimilating them into hydrological models. In this study, fine resolution soil moisture data simulated by a physically-based distributed hydrological model, tRIBS-VEGGIE, is compared with soil moisture data collected during the field campaign in Turkey river basin, Iowa. The soil moisture series at the 2 and 4 inch depth exhibited a more rapid response to rainfall as compared to bottom 8 and 20 inch ones. The spatial variability in two distinct land surfaces of Turkey River, IA, reflects the control of vegetation, topography and soil texture in the characterization of spatial variability. The comparison of observed and simulated soil moisture at various depth showed that model was able to capture the dynamics of soil moisture at a number of gauging stations. Discrepancies are large in some of the gauging stations, which are characterized by rugged terrain and represented, in the model, through large computational units.

Habitat models to predict wetland bird occupancy influenced by scale, anthropogenic disturbance, and imperfect detection

USGS Publications Warehouse

Glisson, Wesley J.; Conway, Courtney J.; Nadeau, Christopher P.; Borgmann, Kathi L.

2017-01-01

Understanding species–habitat relationships for endangered species is critical for their conservation. However, many studies have limited value for conservation because they fail to account for habitat associations at multiple spatial scales, anthropogenic variables, and imperfect detection. We addressed these three limitations by developing models for an endangered wetland bird, Yuma Ridgway's rail (Rallus obsoletus yumanensis), that examined how the spatial scale of environmental variables, inclusion of anthropogenic disturbance variables, and accounting for imperfect detection in validation data influenced model performance. These models identified associations between environmental variables and occupancy. We used bird survey and spatial environmental data at 2473 locations throughout the species' U.S. range to create and validate occupancy models and produce predictive maps of occupancy. We compared habitat-based models at three spatial scales (100, 224, and 500 m radii buffers) with and without anthropogenic disturbance variables using validation data adjusted for imperfect detection and an unadjusted validation dataset that ignored imperfect detection. The inclusion of anthropogenic disturbance variables improved the performance of habitat models at all three spatial scales, and the 224-m-scale model performed best. All models exhibited greater predictive ability when imperfect detection was incorporated into validation data. Yuma Ridgway's rail occupancy was negatively associated with ephemeral and slow-moving riverine features and high-intensity anthropogenic development, and positively associated with emergent vegetation, agriculture, and low-intensity development. Our modeling approach accounts for common limitations in modeling species–habitat relationships and creating predictive maps of occupancy probability and, therefore, provides a useful framework for other species.

Spatial and temporal variability in rates of landsliding in seismically active mountain ranges

NASA Astrophysics Data System (ADS)

Parker, R.; Petley, D.; Rosser, N.; Densmore, A.; Gunasekera, R.; Brain, M.

2012-04-01

Where earthquake and precipitation driven disasters occur in steep, mountainous regions, landslides often account for a large proportion of the associated damage and losses. This research addresses spatial and temporal variability in rates of landslide occurrence in seismically active mountain ranges as a step towards developing better regional scale prediction of losses in such events. In the first part of this paper we attempt to explain reductively the variability in spatial rates of landslide occurrence, using data from five major earthquakes. This is achieved by fitting a regression-based conditional probability model to spatial probabilities of landslide occurrence, using as predictor variables proxies for spatial patterns of seismic ground motion and modelled hillslope stability. A combined model for all earthquakes performs well in hindcasting spatial probabilities of landslide occurrence as a function of readily-attainable spatial variables. We present validation of the model and demonstrate the extent to which it may be applied globally to derive landslide probabilities for future earthquakes. In part two we examine the temporal behaviour of rates of landslide occurrence. This is achieved through numerical modelling to simulate the behaviour of a hypothetical landscape. The model landscape is composed of hillslopes that continually weaken, fail and reset in response to temporally-discrete forcing events that represent earthquakes. Hillslopes with different geometries require different amounts of weakening to fail, such that they fail and reset at different temporal rates. Our results suggest that probabilities of landslide occurrence are not temporally constant, but rather vary with time, irrespective of changes in forcing event magnitudes or environmental conditions. Various parameters influencing the magnitude and temporal patterns of this variability are identified, highlighting areas where future research is needed. This model has important implications for landslide hazard and risk analysis in mountain areas as existing techniques usually assume that susceptibility to failure does not change with time.

The role of visualization in learning from computer-based images

NASA Astrophysics Data System (ADS)

Piburn, Michael D.; Reynolds, Stephen J.; McAuliffe, Carla; Leedy, Debra E.; Birk, James P.; Johnson, Julia K.

2005-05-01

Among the sciences, the practice of geology is especially visual. To assess the role of spatial ability in learning geology, we designed an experiment using: (1) web-based versions of spatial visualization tests, (2) a geospatial test, and (3) multimedia instructional modules built around QuickTime Virtual Reality movies. Students in control and experimental sections were administered measures of spatial orientation and visualization, as well as a content-based geospatial examination. All subjects improved significantly in their scores on spatial visualization and the geospatial examination. There was no change in their scores on spatial orientation. A three-way analysis of variance, with the geospatial examination as the dependent variable, revealed significant main effects favoring the experimental group and a significant interaction between treatment and gender. These results demonstrate that spatial ability can be improved through instruction, that learning of geological content will improve as a result, and that differences in performance between the genders can be eliminated.

Exploring objective climate classification for the Himalayan arc and adjacent regions using gridded data sources

NASA Astrophysics Data System (ADS)

Forsythe, N.; Blenkinsop, S.; Fowler, H. J.

2015-05-01

A three-step climate classification was applied to a spatial domain covering the Himalayan arc and adjacent plains regions using input data from four global meteorological reanalyses. Input variables were selected based on an understanding of the climatic drivers of regional water resource variability and crop yields. Principal component analysis (PCA) of those variables and k-means clustering on the PCA outputs revealed a reanalysis ensemble consensus for eight macro-climate zones. Spatial statistics of input variables for each zone revealed consistent, distinct climatologies. This climate classification approach has potential for enhancing assessment of climatic influences on water resources and food security as well as for characterising the skill and bias of gridded data sets, both meteorological reanalyses and climate models, for reproducing subregional climatologies. Through their spatial descriptors (area, geographic centroid, elevation mean range), climate classifications also provide metrics, beyond simple changes in individual variables, with which to assess the magnitude of projected climate change. Such sophisticated metrics are of particular interest for regions, including mountainous areas, where natural and anthropogenic systems are expected to be sensitive to incremental climate shifts.

Spatial variability in plankton biomass and hydrographic variables along an axial transect in Chesapeake Bay

NASA Astrophysics Data System (ADS)

Zhang, X.; Roman, M.; Kimmel, D.; McGilliard, C.; Boicourt, W.

2006-05-01

High-resolution, axial sampling surveys were conducted in Chesapeake Bay during April, July, and October from 1996 to 2000 using a towed sampling device equipped with sensors for depth, temperature, conductivity, oxygen, fluorescence, and an optical plankton counter (OPC). The results suggest that the axial distribution and variability of hydrographic and biological parameters in Chesapeake Bay were primarily influenced by the source and magnitude of freshwater input. Bay-wide spatial trends in the water column-averaged values of salinity were linear functions of distance from the main source of freshwater, the Susquehanna River, at the head of the bay. However, spatial trends in the water column-averaged values of temperature, dissolved oxygen, chlorophyll-a and zooplankton biomass were nonlinear along the axis of the bay. Autocorrelation analysis and the residuals of linear and quadratic regressions between each variable and latitude were used to quantify the patch sizes for each axial transect. The patch sizes of each variable depended on whether the data were detrended, and the detrending techniques applied. However, the patch size of each variable was generally larger using the original data compared to the detrended data. The patch sizes of salinity were larger than those for dissolved oxygen, chlorophyll-a and zooplankton biomass, suggesting that more localized processes influence the production and consumption of plankton. This high-resolution quantification of the zooplankton spatial variability and patch size can be used for more realistic assessments of the zooplankton forage base for larval fish species.

Effects of input uncertainty on cross-scale crop modeling

NASA Astrophysics Data System (ADS)

Waha, Katharina; Huth, Neil; Carberry, Peter

2014-05-01

The quality of data on climate, soils and agricultural management in the tropics is in general low or data is scarce leading to uncertainty in process-based modeling of cropping systems. Process-based crop models are common tools for simulating crop yields and crop production in climate change impact studies, studies on mitigation and adaptation options or food security studies. Crop modelers are concerned about input data accuracy as this, together with an adequate representation of plant physiology processes and choice of model parameters, are the key factors for a reliable simulation. For example, assuming an error in measurements of air temperature, radiation and precipitation of ± 0.2°C, ± 2 % and ± 3 % respectively, Fodor & Kovacs (2005) estimate that this translates into an uncertainty of 5-7 % in yield and biomass simulations. In our study we seek to answer the following questions: (1) are there important uncertainties in the spatial variability of simulated crop yields on the grid-cell level displayed on maps, (2) are there important uncertainties in the temporal variability of simulated crop yields on the aggregated, national level displayed in time-series, and (3) how does the accuracy of different soil, climate and management information influence the simulated crop yields in two crop models designed for use at different spatial scales? The study will help to determine whether more detailed information improves the simulations and to advise model users on the uncertainty related to input data. We analyse the performance of the point-scale crop model APSIM (Keating et al., 2003) and the global scale crop model LPJmL (Bondeau et al., 2007) with different climate information (monthly and daily) and soil conditions (global soil map and African soil map) under different agricultural management (uniform and variable sowing dates) for the low-input maize-growing areas in Burkina Faso/West Africa. We test the models' response to different levels of input data from very little to very detailed information, and compare the models' abilities to represent the spatial variability and temporal variability in crop yields. We display the uncertainty in crop yield simulations from different input data and crop models in Taylor diagrams which are a graphical summary of the similarity between simulations and observations (Taylor, 2001). The observed spatial variability can be represented well from both models (R=0.6-0.8) but APSIM predicts higher spatial variability than LPJmL due to its sensitivity to soil parameters. Simulations with the same crop model, climate and sowing dates have similar statistics and therefore similar skill to reproduce the observed spatial variability. Soil data is less important for the skill of a crop model to reproduce the observed spatial variability. However, the uncertainty in simulated spatial variability from the two crop models is larger than from input data settings and APSIM is more sensitive to input data then LPJmL. Even with a detailed, point-scale crop model and detailed input data it is difficult to capture the complexity and diversity in maize cropping systems.

[Spatial variability of surface soil nutrients in the landslide area of Beichuan County, South- west China, after 5 · 12 Wenchuan Earthquake].

PubMed

Mai, Ji-shan; Zhao, Ting-ning; Zheng, Jiang-kun; Shi, Chang-qing

2015-12-01

Based on grid sampling and laboratory analysis, spatial variability of surface soil nutrients was analyzed with GS⁺ and other statistics methods on the landslide area of Fenghuang Mountain, Leigu Town, Beichuan County. The results showed that except for high variability of available phosphorus, other soil nutrients exhibited moderate variability. The ratios of nugget to sill of the soil available phosphorus and soil organic carbon were 27.9% and 28.8%, respectively, showing moderate spatial correlation, while the ratios of nugget to sill of the total nitrogen (20.0%), total phosphorus (24.3%), total potassium (11.1%), available nitrogen (11.2%), and available potassium (22.7%) suggested strong spatial correlation. The total phosphorus had the maximum range (1232.7 m), followed by available nitrogen (541.27 m), total nitrogen (468.35 m), total potassium (136.0 m), available potassium (128.7 m), available phosphorus (116.6 m), and soil organic carbon (93.5 m). Soil nutrients had no significant variation with the increase of altitude, but gradually increased from the landslide area, the transition area, to the little-impacted area. The total and available phosphorus contents of the landslide area decreased by 10.3% and 79.7% compared to that of the little-impacted area, respectively. The soil nutrient contents in the transition area accounted for 31.1%-87.2% of that of the little-impacted area, with the nant reason for the spatial variability of surface soil nutrients.

Secure quantum key distribution using continuous variables of single photons.

PubMed

Zhang, Lijian; Silberhorn, Christine; Walmsley, Ian A

2008-03-21

We analyze the distribution of secure keys using quantum cryptography based on the continuous variable degree of freedom of entangled photon pairs. We derive the information capacity of a scheme based on the spatial entanglement of photons from a realistic source, and show that the standard measures of security known for quadrature-based continuous variable quantum cryptography (CV-QKD) are inadequate. A specific simple eavesdropping attack is analyzed to illuminate how secret information may be distilled well beyond the bounds of the usual CV-QKD measures.

Analysis of sensitivity and uncertainty in an individual-based model of a threatened wildlife species

Treesearch

Bruce G. Marcot; Peter H. Singleton; Nathan H. Schumaker

2015-01-01

Sensitivity analysis—determination of how prediction variables affect response variables—of individual-based models (IBMs) are few but important to the interpretation of model output. We present sensitivity analysis of a spatially explicit IBM (HexSim) of a threatened species, the Northern Spotted Owl (NSO; Strix occidentalis caurina) in Washington...

Spatial Variability of Soil-Water Storage in the Southern Sierra Critical Zone Observatory: Measurement and Prediction

NASA Astrophysics Data System (ADS)

Oroza, C.; Bales, R. C.; Zheng, Z.; Glaser, S. D.

2017-12-01

Predicting the spatial distribution of soil moisture in mountain environments is confounded by multiple factors, including complex topography, spatial variably of soil texture, sub-surface flow paths, and snow-soil interactions. While remote-sensing tools such as passive-microwave monitoring can measure spatial variability of soil moisture, they only capture near-surface soil layers. Large-scale sensor networks are increasingly providing soil-moisture measurements at high temporal resolution across a broader range of depths than are accessible from remote sensing. It may be possible to combine these in-situ measurements with high-resolution LIDAR topography and canopy cover to estimate the spatial distribution of soil moisture at high spatial resolution at multiple depths. We study the feasibility of this approach using six years (2009-2014) of daily volumetric water content measurements at 10-, 30-, and 60-cm depths from the Southern Sierra Critical Zone Observatory. A non-parametric, multivariate regression algorithm, Random Forest, was used to predict the spatial distribution of depth-integrated soil-water storage, based on the in-situ measurements and a combination of node attributes (topographic wetness, northness, elevation, soil texture, and location with respect to canopy cover). We observe predictable patterns of predictor accuracy and independent variable ranking during the six-year study period. Predictor accuracy is highest during the snow-cover and early recession periods but declines during the dry period. Soil texture has consistently high feature importance. Other landscape attributes exhibit seasonal trends: northness peaks during the wet-up period, and elevation and topographic-wetness index peak during the recession and dry period, respectively.

Spatial Variability of Grapevine Bud Burst Percentage and Its Association with Soil Properties at Field Scale

PubMed Central

Li, Tao; Hao, Xinmei; Kang, Shaozhong

2016-01-01

There is a growing interest in precision viticulture with the development of global positioning system and geographical information system technologies. Limited information is available on spatial variation of bud behavior and its possible association with soil properties. The objective of this study was to investigate spatial variability of bud burst percentage and its association with soil properties based on 2-year experiments at a vineyard of arid northwest China. Geostatistical approach was used to describe the spatial variation in bud burst percentage within the vineyard. Partial least square regressions (PLSRs) of bud burst percentage with soil properties were used to evaluate the contribution of soil properties to overall spatial variability in bud burst percentage for the high, medium and low bud burst percentage groups. Within the vineyard, the coefficient of variation (CV) of bud burst percentage was 20% and 15% for 2012 and 2013 respectively. Bud burst percentage within the vineyard showed moderate spatial variability, and the overall spatial pattern of bud burst percentage was similar between the two years. Soil properties alone explained 31% and 37% of the total spatial variation respectively for the low group of 2012 and 2013, and 16% and 24% for the high group of 2012 and 2013 respectively. For the low group, the fraction of variations explained by soil properties was found similar between the two years, while there was substantial difference for the high group. The findings are expected to lay a good foundation for developing remedy measures in the areas with low bud burst percentage, thus in turn improving the overall grape yield and quality. PMID:27798692

Spatial Variability of Snowpack Properties On Small Slopes

NASA Astrophysics Data System (ADS)

Pielmeier, C.; Kronholm, K.; Schneebeli, M.; Schweizer, J.

The spatial variability of alpine snowpacks is created by a variety of parameters like deposition, wind erosion, sublimation, melting, temperature, radiation and metamor- phism of the snow. Spatial variability is thought to strongly control the avalanche initi- ation and failure propagation processes. Local snowpack measurements are currently the basis for avalanche warning services and there exist contradicting hypotheses about the spatial continuity of avalanche active snow layers and interfaces. Very little about the spatial variability of the snowpack is known so far, therefore we have devel- oped a systematic and objective method to measure the spatial variability of snowpack properties, layering and its relation to stability. For a complete coverage, the analysis of the spatial variability has to entail all scales from mm to km. In this study the small to medium scale spatial variability is investigated, i.e. the range from centimeters to tenths of meters. During the winter 2000/2001 we took systematic measurements in lines and grids on a flat snow test field with grid distances from 5 cm to 0.5 m. Fur- thermore, we measured systematic grids with grid distances between 0.5 m and 2 m in undisturbed flat fields and on small slopes above the tree line at the Choerbschhorn, in the region of Davos, Switzerland. On 13 days we measured the spatial pattern of the snowpack stratigraphy with more than 110 snow micro penetrometer measure- ments at slopes and flat fields. Within this measuring grid we placed 1 rutschblock and 12 stuffblock tests to measure the stability of the snowpack. With the large num- ber of measurements we are able to use geostatistical methods to analyse the spatial variability of the snowpack. Typical correlation lengths are calculated from semivari- ograms. Discerning the systematic trends from random spatial variability is analysed using statistical models. Scale dependencies are shown and recurring scaling patterns are outlined. The importance of the small and medium scale spatial variability for the larger (kilometer) scale spatial variability as well as for the avalanche formation are discussed. Finally, an outlook on spatial models for the snowpack variability is given.

The complex roles of space and environment in structuring functional, taxonomic and phylogenetic beta diversity of frogs in the Atlantic Forest

PubMed Central

Luiz, Amom Mendes; Sawaya, Ricardo J.

2018-01-01

Ecological communities are complex entities that can be maintained and structured by niche-based processes such as environmental conditions, and spatial processes such as dispersal. Thus, diversity patterns may be shaped simultaneously at different spatial scales by very distinct processes. Herein we assess whether and how functional, taxonomic, and phylogenetic beta diversities of frog tadpoles are explained by environmental and/or spatial predictors. We implemented a distance–based redundancy analysis to explore variation in components of beta diversity explained by pure environmental and pure spatial predictors, as well as their interactions, at both fine and broad spatial scales. Our results indicated important but complex roles of spatial and environmental predictors in structuring phylogenetic, taxonomic and functional beta diversities. The pure fine-scales spatial fraction was more important in structuring all beta diversity components, especially to functional and taxonomical spatial turnover. Environmental variables such as canopy cover and vegetation structure were important predictors of all components, but especially to functional and taxonomic beta diversity. We emphasize that distinct factors related to environment and space are affecting distinct components of beta diversity in different ways. Although weaker, phylogenetic beta diversity, which is structured more on biogeographical scales, and thus can be represented by spatially structured processes, was more related to broad spatial processes than other components. However, selected fine-scale spatial predictors denoted negative autocorrelation, which may be revealing the existence of differences in unmeasured habitat variables among samples. Although overall important, local environmental-based processes explained better functional and taxonomic beta diversity, as these diversity components carry an important ecological value. We highlight the importance of assessing different components of diversity patterns at different scales by spatially explicit models in order to improve our understanding of community structure and help to unravel the complex nature of biodiversity. PMID:29672575

Spatial Variability of Wet Troposphere Delays Over Inland Water Bodies

NASA Astrophysics Data System (ADS)

Mehran, Ali; Clark, Elizabeth A.; Lettenmaier, Dennis P.

2017-11-01

Satellite radar altimetry has enabled the study of water levels in large lakes and reservoirs at a global scale. The upcoming Surface Water and Ocean Topography (SWOT) satellite mission (scheduled launch 2020) will simultaneously measure water surface extent and elevation at an unprecedented accuracy and resolution. However, SWOT retrieval accuracy will be affected by a number of factors, including wet tropospheric delay—the delay in the signal's passage through the atmosphere due to atmospheric water content. In past applications, the wet tropospheric delay over large inland water bodies has been corrected using atmospheric moisture profiles based on atmospheric reanalysis data at relatively coarse (tens to hundreds of kilometers) spatial resolution. These products cannot resolve subgrid variations in wet tropospheric delays at the spatial resolutions (of 1 km and finer) that SWOT is intended to resolve. We calculate zenith wet tropospheric delays (ZWDs) and their spatial variability from Weather Research and Forecasting (WRF) numerical weather prediction model simulations at 2.33 km spatial resolution over the southwestern U.S., with attention in particular to Sam Rayburn, Ray Hubbard, and Elephant Butte Reservoirs which have width and length dimensions that are of order or larger than the WRF spatial resolution. We find that spatiotemporal variability of ZWD over the inland reservoirs depends on climatic conditions at the reservoir location, as well as distance from ocean, elevation, and surface area of the reservoir, but that the magnitude of subgrid variability (relative to analysis and reanalysis products) is generally less than 10 mm.

Estimating neighborhood variability with a binary comparison matrix.

USGS Publications Warehouse

Murphy, D.L.

1985-01-01

A technique which utilizes a binary comparison matrix has been developed to implement a neighborhood function for a raster format data base. The technique assigns an index value to the center pixel of 3- by 3-pixel neighborhoods. The binary comparison matrix provides additional information not found in two other neighborhood variability statistics; the function is sensitive to both the number of classes within the neighborhood and the frequency of pixel occurrence in each of the classes. Application of the function to a spatial data base from the Kenai National Wildlife Refuge, Alaska, demonstrates 1) the numerical distribution of the index values, and 2) the spatial patterns exhibited by the numerical values. -Author

Local-scale spatial modelling for interpolating climatic temperature variables to predict agricultural plant suitability

NASA Astrophysics Data System (ADS)

Webb, Mathew A.; Hall, Andrew; Kidd, Darren; Minansy, Budiman

2016-05-01

Assessment of local spatial climatic variability is important in the planning of planting locations for horticultural crops. This study investigated three regression-based calibration methods (i.e. traditional versus two optimized methods) to relate short-term 12-month data series from 170 temperature loggers and 4 weather station sites with data series from nearby long-term Australian Bureau of Meteorology climate stations. The techniques trialled to interpolate climatic temperature variables, such as frost risk, growing degree days (GDDs) and chill hours, were regression kriging (RK), regression trees (RTs) and random forests (RFs). All three calibration methods produced accurate results, with the RK-based calibration method delivering the most accurate validation measures: coefficients of determination ( R 2) of 0.92, 0.97 and 0.95 and root-mean-square errors of 1.30, 0.80 and 1.31 °C, for daily minimum, daily maximum and hourly temperatures, respectively. Compared with the traditional method of calibration using direct linear regression between short-term and long-term stations, the RK-based calibration method improved R 2 and reduced root-mean-square error (RMSE) by at least 5 % and 0.47 °C for daily minimum temperature, 1 % and 0.23 °C for daily maximum temperature and 3 % and 0.33 °C for hourly temperature. Spatial modelling indicated insignificant differences between the interpolation methods, with the RK technique tending to be the slightly better method due to the high degree of spatial autocorrelation between logger sites.

Effect of Variable Spatial Scales on USLE-GIS Computations

NASA Astrophysics Data System (ADS)

Patil, R. J.; Sharma, S. K.

2017-12-01

Use of appropriate spatial scale is very important in Universal Soil Loss Equation (USLE) based spatially distributed soil erosion modelling. This study aimed at assessment of annual rates of soil erosion at different spatial scales/grid sizes and analysing how changes in spatial scales affect USLE-GIS computations using simulation and statistical variabilities. Efforts have been made in this study to recommend an optimum spatial scale for further USLE-GIS computations for management and planning in the study area. The present research study was conducted in Shakkar River watershed, situated in Narsinghpur and Chhindwara districts of Madhya Pradesh, India. Remote Sensing and GIS techniques were integrated with Universal Soil Loss Equation (USLE) to predict spatial distribution of soil erosion in the study area at four different spatial scales viz; 30 m, 50 m, 100 m, and 200 m. Rainfall data, soil map, digital elevation model (DEM) and an executable C++ program, and satellite image of the area were used for preparation of the thematic maps for various USLE factors. Annual rates of soil erosion were estimated for 15 years (1992 to 2006) at four different grid sizes. The statistical analysis of four estimated datasets showed that sediment loss dataset at 30 m spatial scale has a minimum standard deviation (2.16), variance (4.68), percent deviation from observed values (2.68 - 18.91 %), and highest coefficient of determination (R2 = 0.874) among all the four datasets. Thus, it is recommended to adopt this spatial scale for USLE-GIS computations in the study area due to its minimum statistical variability and better agreement with the observed sediment loss data. This study also indicates large scope for use of finer spatial scales in spatially distributed soil erosion modelling.

Variability in Soil Properties at Different Spatial Scales (1 m to 1 km) in a Deciduous Forest Ecosystem

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

Garten Jr, Charles T; Kang, S.; Brice, Deanne Jane

2007-01-01

The purpose of this research was to test the hypothesis that variability in 11 soil properties, related to soil texture and soil C and N, would increase from small (1 m) to large (1 km) spatial scales in a temperate, mixed-hardwood forest ecosystem in east Tennessee, USA. The results were somewhat surprising and indicated that a fundamental assumption in geospatial analysis, namely that variability increases with increasing spatial scale, did not apply for at least five of the 11 soil properties measured over a 0.5-km2 area. Composite mineral soil samples (15 cm deep) were collected at 1, 5, 10, 50,more » 250, and 500 m distances from a center point along transects in a north, south, east, and westerly direction. A null hypothesis of equal variance at different spatial scales was rejected (P{le}0.05) for mineral soil C concentration, silt content, and the C-to-N ratios in particulate organic matter (POM), mineral-associated organic matter (MOM), and whole surface soil. Results from different tests of spatial variation, based on coefficients of variation or a Mantel test, led to similar conclusions about measurement variability and geographic distance for eight of the 11 variables examined. Measurements of mineral soil C and N concentrations, C concentrations in MOM, extractable soil NH{sub 4}-N, and clay contents were just as variable at smaller scales (1-10 m) as they were at larger scales (50-500 m). On the other hand, measurement variation in mineral soil C-to-N ratios, MOM C-to-N ratios, and the fraction of soil C in POM clearly increased from smaller to larger spatial scales. With the exception of extractable soil NH4-N, measured soil properties in the forest ecosystem could be estimated (with 95% confidence) to within 15% of their true mean with a relatively modest number of sampling points (n{le}25). For some variables, scaling up variation from smaller to larger spatial domains within the ecosystem could be relatively easy because small-scale variation may be indicative of variation at larger scales.« less

The relationship between observational scale and explained variance in benthic communities

PubMed Central

Flood, Roger D.; Frisk, Michael G.; Garza, Corey D.; Lopez, Glenn R.; Maher, Nicole P.

2018-01-01

This study addresses the impact of spatial scale on explaining variance in benthic communities. In particular, the analysis estimated the fraction of community variation that occurred at a spatial scale smaller than the sampling interval (i.e., the geographic distance between samples). This estimate is important because it sets a limit on the amount of community variation that can be explained based on the spatial configuration of a study area and sampling design. Six benthic data sets were examined that consisted of faunal abundances, common environmental variables (water depth, grain size, and surficial percent cover), and sonar backscatter treated as a habitat proxy (categorical acoustic provinces). Redundancy analysis was coupled with spatial variograms generated by multiscale ordination to quantify the explained and residual variance at different spatial scales and within and between acoustic provinces. The amount of community variation below the sampling interval of the surveys (< 100 m) was estimated to be 36–59% of the total. Once adjusted for this small-scale variation, > 71% of the remaining variance was explained by the environmental and province variables. Furthermore, these variables effectively explained the spatial structure present in the infaunal community. Overall, no scale problems remained to compromise inferences, and unexplained infaunal community variation had no apparent spatial structure within the observational scale of the surveys (> 100 m), although small-scale gradients (< 100 m) below the observational scale may be present. PMID:29324746

The propagation of inventory-based positional errors into statistical landslide susceptibility models

NASA Astrophysics Data System (ADS)

Steger, Stefan; Brenning, Alexander; Bell, Rainer; Glade, Thomas

2016-12-01

There is unanimous agreement that a precise spatial representation of past landslide occurrences is a prerequisite to produce high quality statistical landslide susceptibility models. Even though perfectly accurate landslide inventories rarely exist, investigations of how landslide inventory-based errors propagate into subsequent statistical landslide susceptibility models are scarce. The main objective of this research was to systematically examine whether and how inventory-based positional inaccuracies of different magnitudes influence modelled relationships, validation results, variable importance and the visual appearance of landslide susceptibility maps. The study was conducted for a landslide-prone site located in the districts of Amstetten and Waidhofen an der Ybbs, eastern Austria, where an earth-slide point inventory was available. The methodological approach comprised an artificial introduction of inventory-based positional errors into the present landslide data set and an in-depth evaluation of subsequent modelling results. Positional errors were introduced by artificially changing the original landslide position by a mean distance of 5, 10, 20, 50 and 120 m. The resulting differently precise response variables were separately used to train logistic regression models. Odds ratios of predictor variables provided insights into modelled relationships. Cross-validation and spatial cross-validation enabled an assessment of predictive performances and permutation-based variable importance. All analyses were additionally carried out with synthetically generated data sets to further verify the findings under rather controlled conditions. The results revealed that an increasing positional inventory-based error was generally related to increasing distortions of modelling and validation results. However, the findings also highlighted that interdependencies between inventory-based spatial inaccuracies and statistical landslide susceptibility models are complex. The systematic comparisons of 12 models provided valuable evidence that the respective error-propagation was not only determined by the degree of positional inaccuracy inherent in the landslide data, but also by the spatial representation of landslides and the environment, landslide magnitude, the characteristics of the study area, the selected classification method and an interplay of predictors within multiple variable models. Based on the results, we deduced that a direct propagation of minor to moderate inventory-based positional errors into modelling results can be partly counteracted by adapting the modelling design (e.g. generalization of input data, opting for strongly generalizing classifiers). Since positional errors within landslide inventories are common and subsequent modelling and validation results are likely to be distorted, the potential existence of inventory-based positional inaccuracies should always be considered when assessing landslide susceptibility by means of empirical models.

Investigatigating inter-/intra-annual variability of surface hydrology at northern high latitude from spaceborne measurements

NASA Astrophysics Data System (ADS)

Kang, K.; Duguay, C. R.

2014-12-01

Lakes encompass a large part of the surface cover in the northern boreal and tundra areas of northern Canada and are therefore a significant component of the terrestrial hydrological system. To understand the hydrologic cycle over subarctic and arctic landscapes, estimating surface parameters such as surface net radiation, soil moisture, and surface albedo is important. Although ground-based field measurements provide a good temporal resolution, these data provide a limited spatial representation and are often restricted to the summer period (from June to August), and few surface-based stations are located in high-latitude regions. In this respect, spaceborne remote sensing provides the means to monitor surface hydrology and to estimate components of the surface energy balance with reasonable spatial and temporal resolutions required for hydrological investigations, as well as for providing more spatially representative lake-relevant information than available from in situ measurements. The primary objective of this study is to quantify the sources of temporal and spatial variability in surface albedo over subarctic wetland from satellite derived albedo measurements in the Hudson Bay Lowlands near Churchill, Manitoba. The spatial variability in albedo within each land-cover type is investigated through optical satellite imagery from Landsat-5 Thematic Mapper, Landsat-7 Enhanced Thematic Mapper Plus, and Landsat-8 Operational Land Imager obtained in different seasons from spring into fall (April and October) over a 30-year period (1984-2013). These data allowed for an examination of the spatial variability of surface albedo under relatively dry and wet summer conditions (i.e. 1984, 1998 versus 1991, 2005). A detailed analysis of Landsat-derived surface albedo (ranging from 0.09 to 0.15) conducted in the Churchill region for August is inversely related to surface water fraction calculated from Landsat images. Preliminary analysis of surface albedo observed between July and August are 0.10 to 0.15, and vary due to differences in meteorological parameters such as rainfall, surface moisture and surface air temperature. Overall, spaceborne optical data are an invaluable source for investigating changes and variability in surface albedo in relation to surface hydrology over subarctic regions.

A Dasymetric-Based Monte Carlo Simulation Approach to the Probabilistic Analysis of Spatial Variables

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

Morton, April M; Piburn, Jesse O; McManamay, Ryan A

2017-01-01

Monte Carlo simulation is a popular numerical experimentation technique used in a range of scientific fields to obtain the statistics of unknown random output variables. Despite its widespread applicability, it can be difficult to infer required input probability distributions when they are related to population counts unknown at desired spatial resolutions. To overcome this challenge, we propose a framework that uses a dasymetric model to infer the probability distributions needed for a specific class of Monte Carlo simulations which depend on population counts.

Spatial structure and scaling of macropores in hydrological process at small catchment scale

NASA Astrophysics Data System (ADS)

Silasari, Rasmiaditya; Broer, Martine; Blöschl, Günter

2013-04-01

During rainfall events, the formation of overland flow can occur under the circumstances of saturation excess and/or infiltration excess. These conditions are affected by the soil moisture state which represents the soil water content in micropores and macropores. Macropores act as pathway for the preferential flows and have been widely studied locally. However, very little is known about their spatial structure and conductivity of macropores and other flow characteristic at the catchment scale. This study will analyze these characteristics to better understand its importance in hydrological processes. The research will be conducted in Petzenkirchen Hydrological Open Air Laboratory (HOAL), a 64 ha catchment located 100 km west of Vienna. The land use is divided between arable land (87%), pasture (5%), forest (6%) and paved surfaces (2%). Video cameras will be installed on an agricultural field to monitor the overland flow pattern during rainfall events. A wireless soil moisture network is also installed within the monitored area. These field data will be combined to analyze the soil moisture state and the responding surface runoff occurrence. The variability of the macropores spatial structure of the observed area (field scale) then will be assessed based on the topography and soil data. Soil characteristics will be supported with laboratory experiments on soil matrix flow to obtain proper definitions of the spatial structure of macropores and its variability. A coupled physically based distributed model of surface and subsurface flow will be used to simulate the variability of macropores spatial structure and its effect on the flow behaviour. This model will be validated by simulating the observed rainfall events. Upscaling from field scale to catchment scale will be done to understand the effect of macropores variability on larger scales by applying spatial stochastic methods. The first phase in this study is the installation and monitoring configuration of video cameras and soil moisture monitoring equipment to obtain the initial data of overland flow occurrence and soil moisture state relationships.

Invertebrate Metacommunity Structure and Dynamics in an Andean Glacial Stream Network Facing Climate Change

PubMed Central

Cauvy-Fraunié, Sophie; Espinosa, Rodrigo; Andino, Patricio; Jacobsen, Dean; Dangles, Olivier

2015-01-01

Under the ongoing climate change, understanding the mechanisms structuring the spatial distribution of aquatic species in glacial stream networks is of critical importance to predict the response of aquatic biodiversity in the face of glacier melting. In this study, we propose to use metacommunity theory as a conceptual framework to better understand how river network structure influences the spatial organization of aquatic communities in glacierized catchments. At 51 stream sites in an Andean glacierized catchment (Ecuador), we sampled benthic macroinvertebrates, measured physico-chemical and food resource conditions, and calculated geographical, altitudinal and glaciality distances among all sites. Using partial redundancy analysis, we partitioned community variation to evaluate the relative strength of environmental conditions (e.g., glaciality, food resource) vs. spatial processes (e.g., overland, watercourse, and downstream directional dispersal) in organizing the aquatic metacommunity. Results revealed that both environmental and spatial variables significantly explained community variation among sites. Among all environmental variables, the glacial influence component best explained community variation. Overland spatial variables based on geographical and altitudinal distances significantly affected community variation. Watercourse spatial variables based on glaciality distances had a unique significant effect on community variation. Within alpine catchment, glacial meltwater affects macroinvertebrate metacommunity structure in many ways. Indeed, the harsh environmental conditions characterizing glacial influence not only constitute the primary environmental filter but also, limit water-borne macroinvertebrate dispersal. Therefore, glacier runoff acts as an aquatic dispersal barrier, isolating species in headwater streams, and preventing non-adapted species to colonize throughout the entire stream network. Under a scenario of glacier runoff decrease, we expect a reduction in both environmental filtering and dispersal limitation, inducing a taxonomic homogenization of the aquatic fauna in glacierized catchments as well as the extinction of specialized species in headwater groundwater and glacier-fed streams, and consequently an irreversible reduction in regional diversity. PMID:26308853

Stochastical analysis of surfactant-enhanced remediation of denser-than-water nonaqueous phase liquid (DNAPL)-contaminated soils.

PubMed

Zhang, Renduo; Wood, A Lynn; Enfield, Carl G; Jeong, Seung-Woo

2003-01-01

Stochastical analysis was performed to assess the effect of soil spatial variability and heterogeneity on the recovery of denser-than-water nonaqueous phase liquids (DNAPL) during the process of surfactant-enhanced remediation. UTCHEM, a three-dimensional, multicomponent, multiphase, compositional model, was used to simulate water flow and chemical transport processes in heterogeneous soils. Soil spatial variability and heterogeneity were accounted for by considering the soil permeability as a spatial random variable and a geostatistical method was used to generate random distributions of the permeability. The randomly generated permeability fields were incorporated into UTCHEM to simulate DNAPL transport in heterogeneous media and stochastical analysis was conducted based on the simulated results. From the analysis, an exponential relationship between average DNAPL recovery and soil heterogeneity (defined as the standard deviation of log of permeability) was established with a coefficient of determination (r2) of 0.991, which indicated that DNAPL recovery decreased exponentially with increasing soil heterogeneity. Temporal and spatial distributions of relative saturations in the water phase, DNAPL, and microemulsion in heterogeneous soils were compared with those in homogeneous soils and related to soil heterogeneity. Cleanup time and uncertainty to determine DNAPL distributions in heterogeneous soils were also quantified. The study would provide useful information to design strategies for the characterization and remediation of nonaqueous phase liquid-contaminated soils with spatial variability and heterogeneity.

Indian Summer Monsoon Rainfall: Implications of Contrasting Trends in the Spatial Variability of Means and Extremes

PubMed Central

Ghosh, Subimal; Vittal, H.; Sharma, Tarul; Karmakar, Subhankar; Kasiviswanathan, K. S.; Dhanesh, Y.; Sudheer, K. P.; Gunthe, S. S.

2016-01-01

India’s agricultural output, economy, and societal well-being are strappingly dependent on the stability of summer monsoon rainfall, its variability and extremes. Spatial aggregate of intensity and frequency of extreme rainfall events over Central India are significantly increasing, while at local scale they are spatially non-uniform with increasing spatial variability. The reasons behind such increase in spatial variability of extremes are poorly understood and the trends in mean monsoon rainfall have been greatly overlooked. Here, by using multi-decadal gridded daily rainfall data over entire India, we show that the trend in spatial variability of mean monsoon rainfall is decreasing as exactly opposite to that of extremes. The spatial variability of extremes is attributed to the spatial variability of the convective rainfall component. Contrarily, the decrease in spatial variability of the mean rainfall over India poses a pertinent research question on the applicability of large scale inter-basin water transfer by river inter-linking to address the spatial variability of available water in India. We found a significant decrease in the monsoon rainfall over major water surplus river basins in India. Hydrological simulations using a Variable Infiltration Capacity (VIC) model also revealed that the water yield in surplus river basins is decreasing but it is increasing in deficit basins. These findings contradict the traditional notion of dry areas becoming drier and wet areas becoming wetter in response to climate change in India. This result also calls for a re-evaluation of planning for river inter-linking to supply water from surplus to deficit river basins. PMID:27463092

Indian Summer Monsoon Rainfall: Implications of Contrasting Trends in the Spatial Variability of Means and Extremes.

PubMed

Ghosh, Subimal; Vittal, H; Sharma, Tarul; Karmakar, Subhankar; Kasiviswanathan, K S; Dhanesh, Y; Sudheer, K P; Gunthe, S S

2016-01-01

India's agricultural output, economy, and societal well-being are strappingly dependent on the stability of summer monsoon rainfall, its variability and extremes. Spatial aggregate of intensity and frequency of extreme rainfall events over Central India are significantly increasing, while at local scale they are spatially non-uniform with increasing spatial variability. The reasons behind such increase in spatial variability of extremes are poorly understood and the trends in mean monsoon rainfall have been greatly overlooked. Here, by using multi-decadal gridded daily rainfall data over entire India, we show that the trend in spatial variability of mean monsoon rainfall is decreasing as exactly opposite to that of extremes. The spatial variability of extremes is attributed to the spatial variability of the convective rainfall component. Contrarily, the decrease in spatial variability of the mean rainfall over India poses a pertinent research question on the applicability of large scale inter-basin water transfer by river inter-linking to address the spatial variability of available water in India. We found a significant decrease in the monsoon rainfall over major water surplus river basins in India. Hydrological simulations using a Variable Infiltration Capacity (VIC) model also revealed that the water yield in surplus river basins is decreasing but it is increasing in deficit basins. These findings contradict the traditional notion of dry areas becoming drier and wet areas becoming wetter in response to climate change in India. This result also calls for a re-evaluation of planning for river inter-linking to supply water from surplus to deficit river basins.

Improving spatial prediction of Schistosoma haematobium prevalence in southern Ghana through new remote sensors and local water access profiles.

PubMed

Kulinkina, Alexandra V; Walz, Yvonne; Koch, Magaly; Biritwum, Nana-Kwadwo; Utzinger, Jürg; Naumova, Elena N

2018-06-04

Schistosomiasis is a water-related neglected tropical disease. In many endemic low- and middle-income countries, insufficient surveillance and reporting lead to poor characterization of the demographic and geographic distribution of schistosomiasis cases. Hence, modeling is relied upon to predict areas of high transmission and to inform control strategies. We hypothesized that utilizing remotely sensed (RS) environmental data in combination with water, sanitation, and hygiene (WASH) variables could improve on the current predictive modeling approaches. Schistosoma haematobium prevalence data, collected from 73 rural Ghanaian schools, were used in a random forest model to investigate the predictive capacity of 15 environmental variables derived from RS data (Landsat 8, Sentinel-2, and Global Digital Elevation Model) with fine spatial resolution (10-30 m). Five methods of variable extraction were tested to determine the spatial linkage between school-based prevalence and the environmental conditions of potential transmission sites, including applying the models to known human water contact locations. Lastly, measures of local water access and groundwater quality were incorporated into RS-based models to assess the relative importance of environmental and WASH variables. Predictive models based on environmental characterization of specific locations where people contact surface water bodies offered some improvement as compared to the traditional approach based on environmental characterization of locations where prevalence is measured. A water index (MNDWI) and topographic variables (elevation and slope) were important environmental risk factors, while overall, groundwater iron concentration predominated in the combined model that included WASH variables. The study helps to understand localized drivers of schistosomiasis transmission. Specifically, unsatisfactory water quality in boreholes perpetuates reliance of surface water bodies, indirectly increasing schistosomiasis risk and resulting in rapid reinfection (up to 40% prevalence six months following preventive chemotherapy). Considering WASH-related risk factors in schistosomiasis prediction can help shift the focus of control strategies from treating symptoms to reducing exposure.

Spatial and Temporal Evaluation of Soil Erosion with RUSLE: A Case Study in an Olive Orchard Microcathment in Spain

EPA Science Inventory

Soil loss is commonly estimated using the Revised Universal Soil Loss Equation (RUSLE). Since RUSLE is an empirically based soil loss model derived from surveys on plots, the high spatial and temporal variability of erosion in Mediterranean environments and scale effects provoke...

Spatial and Temporal Evaluation of Soil Erosion with RUSLE: A case Study in an Olive Orchard Microcathment in Spain

EPA Science Inventory

Soil loss is commonly estimated using the Revised Universal Soil Loss Equation (RUSLE). Since RUSLE is an empirically based soil loss model derived from surveys on plots, the high spatial and temporal variability of erosion in Mediterranean environments and scale effects provo...

Spatial heterogeneity of leaf area index across scales from simulation and remote sensing

NASA Astrophysics Data System (ADS)

Reichenau, Tim G.; Korres, Wolfgang; Montzka, Carsten; Schneider, Karl

2016-04-01

Leaf area index (LAI, single sided leaf area per ground area) influences mass and energy exchange of vegetated surfaces. Therefore LAI is an input variable for many land surface schemes of coupled large scale models, which do not simulate LAI. Since these models typically run on rather coarse resolution grids, LAI is often inferred from coarse resolution remote sensing. However, especially in agriculturally used areas, a grid cell of these products often covers more than a single land-use. In that case, the given LAI does not apply to any single land-use. Therefore, the overall spatial heterogeneity in these datasets differs from that on resolutions high enough to distinguish areas with differing land-use. Detailed process-based plant growth models simulate LAI for separate plant functional types or specific species. However, limited availability of observations causes reduced spatial heterogeneity of model input data (soil, weather, land-use). Since LAI is strongly heterogeneous in space and time and since processes depend on LAI in a nonlinear way, a correct representation of LAI spatial heterogeneity is also desirable on coarse resolutions. The current study assesses this issue by comparing the spatial heterogeneity of LAI from remote sensing (RapidEye) and process-based simulations (DANUBIA simulation system) across scales. Spatial heterogeneity is assessed by analyzing LAI frequency distributions (spatial variability) and semivariograms (spatial structure). Test case is the arable land in the fertile loess plain of the Rur catchment near the Germany-Netherlands border.

A comparative analysis reveals weak relationships between ecological factors and beta diversity of stream insect metacommunities at two spatial levels.

PubMed

Heino, Jani; Melo, Adriano S; Bini, Luis Mauricio; Altermatt, Florian; Al-Shami, Salman A; Angeler, David G; Bonada, Núria; Brand, Cecilia; Callisto, Marcos; Cottenie, Karl; Dangles, Olivier; Dudgeon, David; Encalada, Andrea; Göthe, Emma; Grönroos, Mira; Hamada, Neusa; Jacobsen, Dean; Landeiro, Victor L; Ligeiro, Raphael; Martins, Renato T; Miserendino, María Laura; Md Rawi, Che Salmah; Rodrigues, Marciel E; Roque, Fabio de Oliveira; Sandin, Leonard; Schmera, Denes; Sgarbi, Luciano F; Simaika, John P; Siqueira, Tadeu; Thompson, Ross M; Townsend, Colin R

2015-03-01

The hypotheses that beta diversity should increase with decreasing latitude and increase with spatial extent of a region have rarely been tested based on a comparative analysis of multiple datasets, and no such study has focused on stream insects. We first assessed how well variability in beta diversity of stream insect metacommunities is predicted by insect group, latitude, spatial extent, altitudinal range, and dataset properties across multiple drainage basins throughout the world. Second, we assessed the relative roles of environmental and spatial factors in driving variation in assemblage composition within each drainage basin. Our analyses were based on a dataset of 95 stream insect metacommunities from 31 drainage basins distributed around the world. We used dissimilarity-based indices to quantify beta diversity for each metacommunity and, subsequently, regressed beta diversity on insect group, latitude, spatial extent, altitudinal range, and dataset properties (e.g., number of sites and percentage of presences). Within each metacommunity, we used a combination of spatial eigenfunction analyses and partial redundancy analysis to partition variation in assemblage structure into environmental, shared, spatial, and unexplained fractions. We found that dataset properties were more important predictors of beta diversity than ecological and geographical factors across multiple drainage basins. In the within-basin analyses, environmental and spatial variables were generally poor predictors of variation in assemblage composition. Our results revealed deviation from general biodiversity patterns because beta diversity did not show the expected decreasing trend with latitude. Our results also call for reconsideration of just how predictable stream assemblages are along ecological gradients, with implications for environmental assessment and conservation decisions. Our findings may also be applicable to other dynamic systems where predictability is low.

Effect of climate data on simulated carbon and nitrogen balances for Europe

NASA Astrophysics Data System (ADS)

Blanke, Jan Hendrik; Lindeskog, Mats; Lindström, Johan; Lehsten, Veiko

2016-05-01

In this study, we systematically assess the spatial variability in carbon and nitrogen balance simulations related to the choice of global circulation models (GCMs), representative concentration pathways (RCPs), spatial resolutions, and the downscaling methods used as calculated with LPJ-GUESS. We employed a complete factorial design and performed 24 simulations for Europe with different climate input data sets and different combinations of these four factors. Our results reveal that the variability in simulated output in Europe is moderate with 35.6%-93.5% of the total variability being common among all combinations of factors. The spatial resolution is the most important factor among the examined factors, explaining 1.5%-10.7% of the total variability followed by GCMs (0.3%-7.6%), RCPs (0%-6.3%), and downscaling methods (0.1%-4.6%). The higher-order interactions effect that captures nonlinear relations between the factors and random effects is pronounced and accounts for 1.6%-45.8% to the total variability. The most distinct hot spots of variability include the mountain ranges in North Scandinavia and the Alps, and the Iberian Peninsula. Based on our findings, we advise to conduct the application of models such as LPJ-GUESS at a reasonably high spatial resolution which is supported by the model structure. There is no notable gain in simulations of ecosystem carbon and nitrogen stocks and fluxes from using regionally downscaled climate in preference to bias-corrected, bilinearly interpolated CMIP5 projections.

Do walleye pollock exhibit flexibility in where or when they spawn based on variability in water temperature?

NASA Astrophysics Data System (ADS)

Bacheler, Nathan M.; Ciannelli, Lorenzo; Bailey, Kevin M.; Bartolino, Valerio

2012-06-01

Environmental variability is increasingly recognized as a primary determinant of year-class strength of marine fishes by directly or indirectly influencing egg and larval development, growth, and survival. Here we examined the role of annual water temperature variability in determining when and where walleye pollock (Theragra chalcogramma) spawn in the eastern Bering Sea. Walleye pollock spawning was examined using both long-term ichthyoplankton data (N=19 years), as well as with historical spatially explicit, foreign-reported, commercial catch data occurring during the primary walleye pollock spawning season (February-May) each year (N=22 years in total). We constructed variable-coefficient generalized additive models (GAMs) to relate the spatially explicit egg or adult catch-per-unit-effort (CPUE) to predictor variables including spawning stock biomass, season, position, and water temperature. The adjusted R2 value was 63.1% for the egg CPUE model and 35.5% for the adult CPUE model. Both egg and adult GAMs suggest that spawning progresses seasonally from Bogoslof Island in February and March to Outer Domain waters between the Pribilof and Unimak Islands by May. Most importantly, walleye pollock egg and adult CPUE was predicted to generally increase throughout the study area as mean annual water temperature increased. These results suggest low interannual variability in the spatial and temporal dynamics of walleye pollock spawning regardless of changes in environmental conditions, at least at the spatial scale examined in this study and within the time frame of decades.

Accounting for autocorrelation in multi-drug resistant tuberculosis predictors using a set of parsimonious orthogonal eigenvectors aggregated in geographic space.

PubMed

Jacob, Benjamin J; Krapp, Fiorella; Ponce, Mario; Gottuzzo, Eduardo; Griffith, Daniel A; Novak, Robert J

2010-05-01

Spatial autocorrelation is problematic for classical hierarchical cluster detection tests commonly used in multi-drug resistant tuberculosis (MDR-TB) analyses as considerable random error can occur. Therefore, when MDRTB clusters are spatially autocorrelated the assumption that the clusters are independently random is invalid. In this research, a product moment correlation coefficient (i.e., the Moran's coefficient) was used to quantify local spatial variation in multiple clinical and environmental predictor variables sampled in San Juan de Lurigancho, Lima, Peru. Initially, QuickBird 0.61 m data, encompassing visible bands and the near infra-red bands, were selected to synthesize images of land cover attributes of the study site. Data of residential addresses of individual patients with smear-positive MDR-TB were geocoded, prevalence rates calculated and then digitally overlaid onto the satellite data within a 2 km buffer of 31 georeferenced health centers, using a 10 m2 grid-based algorithm. Geographical information system (GIS)-gridded measurements of each health center were generated based on preliminary base maps of the georeferenced data aggregated to block groups and census tracts within each buffered area. A three-dimensional model of the study site was constructed based on a digital elevation model (DEM) to determine terrain covariates associated with the sampled MDR-TB covariates. Pearson's correlation was used to evaluate the linear relationship between the DEM and the sampled MDR-TB data. A SAS/GIS(R) module was then used to calculate univariate statistics and to perform linear and non-linear regression analyses using the sampled predictor variables. The estimates generated from a global autocorrelation analyses were then spatially decomposed into empirical orthogonal bases using a negative binomial regression with a non-homogeneous mean. Results of the DEM analyses indicated a statistically non-significant, linear relationship between georeferenced health centers and the sampled covariate elevation. The data exhibited positive spatial autocorrelation and the decomposition of Moran's coefficient into uncorrelated, orthogonal map pattern components revealed global spatial heterogeneities necessary to capture latent autocorrelation in the MDR-TB model. It was thus shown that Poisson regression analyses and spatial eigenvector mapping can elucidate the mechanics of MDR-TB transmission by prioritizing clinical and environmental-sampled predictor variables for identifying high risk populations.

Capability of Hyperspectral data in Spatial Variability Distribution of Chlorophyll and Water Stress in Rice Agriculture System

NASA Astrophysics Data System (ADS)

Moharana, S.; Dutta, S.

2016-12-01

Abstract : The mapping and analysis of spatial variability within the field is a challenging task. However, field variability of a single vegetation cover does not give satisfactory results mainly due to low spectral resolution and non-availability of remote sensing data. From the NASA Earth Observing-1 (EO-1) satellite data, spatial distribution of biophysical parameters like chlorophyll and relative water content in a rice agriculture system is carried out in the present study. Hyperion L1R product composed of 242 spectral bands with 30m spatial resolution was acquired for Assam, India. This high dimensional data is allowed for pre-processing to get an atmospherically corrected imagery. Moreover, ground based hyperspectral measurements are collected from experimental rice fields from the study site using hand held ASD spectroradiometer (350-1050 nm). Published indices specifically designed for chlorophyll (OASVI, mSR, and MTCI indices) and water content (WI and WBI indices) are selected based on stastical performance of the in-situ hyperspectral data. Index models are established for the respective biophysical parameters and observed that the aforementioned indices followed different linear and nonlinear relationships which are completely different from the published indices. By employing the presently developed relationships, spatial variation of total chlorophyll and water stress are mapped for a rice agriculture system from Hyperion imagery. The findings showed that, the variation of chlorophyll and water content ranged from 1.77-10.61mg/g and 40-90% respectively for the studied rice agriculture system. The spatial distribution of these parameters resulted from presently developed index models are well captured from Hyperion imagery and they have good agreement with observed field based chlorophyll (1.14-7.26 mg/g) and water content (60-95%) of paddy crop. This study can be useful in providing essential information to assess the paddy field heterogeneity in an agriculture system. Keywords: Paddy crop, vegetation index, hyperspectral data, chlorophyll, water content

Basin-scale heterogeneity in Antarctic precipitation and its impact on surface mass variability

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

Fyke, Jeremy; Lenaerts, Jan T. M.; Wang, Hailong

Annually averaged precipitation in the form of snow, the dominant term of the Antarctic Ice Sheet surface mass balance, displays large spatial and temporal variability. Here we present an analysis of spatial patterns of regional Antarctic precipitation variability and their impact on integrated Antarctic surface mass balance variability simulated as part of a preindustrial 1800-year global, fully coupled Community Earth System Model simulation. Correlation and composite analyses based on this output allow for a robust exploration of Antarctic precipitation variability. We identify statistically significant relationships between precipitation patterns across Antarctica that are corroborated by climate reanalyses, regional modeling and icemore » core records. These patterns are driven by variability in large-scale atmospheric moisture transport, which itself is characterized by decadal- to centennial-scale oscillations around the long-term mean. We suggest that this heterogeneity in Antarctic precipitation variability has a dampening effect on overall Antarctic surface mass balance variability, with implications for regulation of Antarctic-sourced sea level variability, detection of an emergent anthropogenic signal in Antarctic mass trends and identification of Antarctic mass loss accelerations.« less

Basin-scale heterogeneity in Antarctic precipitation and its impact on surface mass variability

DOE PAGES

Fyke, Jeremy; Lenaerts, Jan T. M.; Wang, Hailong

2017-11-15

Annually averaged precipitation in the form of snow, the dominant term of the Antarctic Ice Sheet surface mass balance, displays large spatial and temporal variability. Here we present an analysis of spatial patterns of regional Antarctic precipitation variability and their impact on integrated Antarctic surface mass balance variability simulated as part of a preindustrial 1800-year global, fully coupled Community Earth System Model simulation. Correlation and composite analyses based on this output allow for a robust exploration of Antarctic precipitation variability. We identify statistically significant relationships between precipitation patterns across Antarctica that are corroborated by climate reanalyses, regional modeling and icemore » core records. These patterns are driven by variability in large-scale atmospheric moisture transport, which itself is characterized by decadal- to centennial-scale oscillations around the long-term mean. We suggest that this heterogeneity in Antarctic precipitation variability has a dampening effect on overall Antarctic surface mass balance variability, with implications for regulation of Antarctic-sourced sea level variability, detection of an emergent anthropogenic signal in Antarctic mass trends and identification of Antarctic mass loss accelerations.« less

Effects of Spatial Variability of Soil Properties on the Triggering of Rainfall-Induced Shallow Landslides

NASA Astrophysics Data System (ADS)

Fan, Linfeng; Lehmann, Peter; Or, Dani

2015-04-01

Naturally-occurring spatial variations in soil properties (e.g., soil depth, moisture, and texture) affect key hydrological processes and potentially the mechanical response of soil to hydromechanical loading (relative to the commonly-assumed uniform soil mantle). We quantified the effects of soil spatial variability on the triggering of rainfall-induced shallow landslides at the hillslope- and catchment-scales, using a physically-based landslide triggering model that considers interacting soil columns with mechanical strength thresholds (represented by the Fiber Bundle Model). The spatial variations in soil properties are represented as Gaussian random distributions and the level of variation is characterized by the coefficient of variation and correlation lengths of soil properties (i.e., soil depth, soil texture and initial water content in this study). The impacts of these spatial variations on landslide triggering characteristics were measured by comparing the times to triggering and landslide volumes for heterogeneous soil properties and homogeneous cases. Results at hillslope scale indicate that for spatial variations of an individual property (without cross correlation), the increasing of coefficient of variation introduces weak spots where mechanical damage is accelerated and leads to earlier onset of landslide triggering and smaller volumes. Increasing spatial correlation length of soil texture and initial water content also induces early landslide triggering and small released volumes due to the transition of failure mode from brittle to ductile failure. In contrast, increasing spatial correlation length of soil depth "reduces" local steepness and postpones landslide triggering. Cross-correlated soil properties generally promote landslide initiation, but depending on the internal structure of spatial distribution of each soil property, landslide triggering may be reduced. The effects of cross-correlation between initial water content and soil texture were investigated in detail at the catchment scale by incorporating correlations of both variables with topography. Results indicate that the internal structure of the spatial distribution of each soil property together with their interplays determine the overall performance of the coupled spatial variability. This study emphasizes the importance of both the randomness and spatial structure of soil properties on landslide triggering and characteristics.

Snowpack spatial and temporal variability assessment using SMP high-resolution penetrometer

NASA Astrophysics Data System (ADS)

Komarov, Anton; Seliverstov, Yuriy; Sokratov, Sergey; Grebennikov, Pavel

2017-04-01

This research is focused on study of spatial and temporal variability of structure and characteristics of snowpack, quick identification of layers based on hardness and dispersion values received from snow micro penetrometer (SMP). We also discuss the detection of weak layers and definition of their parameters in non-alpine terrain. As long as it is the first SMP tool available in Russia, our intent is to test it in different climate and weather conditions. During two separate snowpack studies in plain and mountain landscapes, we derived density and grain size profiles by comparing snow density and grain size from snowpits and SMP measurements. The first case study was MSU meteorological observatory test site in Moscow. SMP data was obtained by 6 consecutive measurements along 10 m transects with a horizontal resolution of approximately 50 cm. The detailed description of snowpack structure, density, grain size, air and snow temperature was also performed. By comparing this information, the detailed scheme of snowpack evolution was created. The second case study was in Khibiny mountains. One 10-meter-long transect was made. SMP, density, grain size and snow temperature data was obtained with horizontal resolution of approximately 50 cm. The high-definition profile of snowpack density variation was acquired using received data. The analysis of data reveals high spatial and temporal variability in snow density and layer structure in both horizontal and vertical dimensions. It indicates that the spatial variability is exhibiting similar spatial patterns as surface topology. This suggests a strong influence from such factors as wind and liquid water pressure on the temporal and spatial evolution of snow structure. It was also defined, that spatial variation of snowpack characteristics is substantial even within homogeneous plain landscape, while in high-latitude mountain regions it grows significantly.

Modelling spatial and temporal vegetation variability with the Climate Constrained Vegetation Index: evidence of CO2 fertilisation and of water stress in continental interiors

NASA Astrophysics Data System (ADS)

Los, S. O.

2015-06-01

A model was developed to simulate spatial, seasonal and interannual variations in vegetation in response to temperature, precipitation and atmospheric CO2 concentrations; the model addresses shortcomings in current implementations. The model uses the minimum of 12 temperature and precipitation constraint functions to simulate NDVI. Functions vary based on the Köppen-Trewartha climate classification to take adaptations of vegetation to climate into account. The simulated NDVI, referred to as the climate constrained vegetation index (CCVI), captured the spatial variability (0.82 < r <0.87), seasonal variability (median r = 0.83) and interannual variability (median global r = 0.24) in NDVI. The CCVI simulated the effects of adverse climate on vegetation during the 1984 drought in the Sahel and during dust bowls of the 1930s and 1950s in the Great Plains in North America. A global CO2 fertilisation effect was found in NDVI data, similar in magnitude to that of earlier estimates (8 % for the 20th century). This effect increased linearly with simple ratio, a transformation of the NDVI. Three CCVI scenarios, based on climate simulations using the representative concentration pathway RCP4.5, showed a greater sensitivity of vegetation towards precipitation in Northern Hemisphere mid latitudes than is currently implemented in climate models. This higher sensitivity is of importance to assess the impact of climate variability on vegetation, in particular on agricultural productivity.

Variation in soil carbon dioxide efflux at two spatial scales in a topographically complex boreal forest

USGS Publications Warehouse

Kelsey, Katharine C.; Wickland, Kimberly P.; Striegl, Robert G.; Neff, Jason C.

2012-01-01

Carbon dynamics of high-latitude regions are an important and highly uncertain component of global carbon budgets, and efforts to constrain estimates of soil-atmosphere carbon exchange in these regions are contingent on accurate representations of spatial and temporal variability in carbon fluxes. This study explores spatial and temporal variability in soilatmosphere carbon dynamics at both fine and coarse spatial scales in a high-elevation, permafrost-dominated boreal black spruce forest. We evaluate the importance of landscape-level investigations of soil-atmosphere carbon dynamics by characterizing seasonal trends in soil-atmosphere carbon exchange, describing soil temperature-moisture-respiration relations, and quantifying temporal and spatial variability at two spatial scales: the plot scale (0–5 m) and the landscape scale (500–1000 m). Plot-scale spatial variability (average variation on a given measurement day) in soil CO2 efflux ranged from a coefficient of variation (CV) of 0.25 to 0.69, and plot-scale temporal variability (average variation of plots across measurement days) in efflux ranged from a CV of 0.19 to 0.36. Landscape-scale spatial and temporal variability in efflux was represented by a CV of 0.40 and 0.31, respectively, indicating that plot-scale spatial variability in soil respiration is as great as landscape-scale spatial variability at this site. While soil respiration was related to soil temperature at both the plot- and landscape scale, landscape-level descriptions of soil moisture were necessary to define soil respiration-moisture relations. Soil moisture variability was also integral to explaining temporal variability in soil respiration. Our results have important implications for research efforts in high-latitude regions where remote study sites make landscape-scale field campaigns challenging.

Precision Viticulture : is it relevant to manage the vineyard according to the within field spatial variability of the environment ?

NASA Astrophysics Data System (ADS)

Tisseyre, Bruno

2015-04-01

For more than 15 years, research projects are conducted in the precision viticulture (PV) area around the world. These research projects have provided new insights into the within-field variability in viticulture. Indeed, access to high spatial resolution data (remote sensing, embedded sensors, etc.) changes the knowledge we have of the fields in viticulture. In particular, the field which was until now considered as a homogeneous management unit, presents actually a high spatial variability in terms of yield, vigour an quality. This knowledge will lead (and is already causing) changes on how to manage the vineyard and the quality of the harvest at the within field scale. From the experimental results obtained in various countries of the world, the goal of the presentation is to provide figures on: - the spatial variability of the main parameters (yield, vigor, quality), and how this variability is organized spatially, - the temporal stability of the observed spatial variability and the potential link with environmental parameters like soil, topography, soil water availability, etc. - information sources available at a high spatial resolution conventionally used in precision agriculture likely to highlight this spatial variability (multi-spectral images, soil electrical conductivity, etc.) and the limitations that these information sources are likely to present in viticulture. Several strategies are currently being developed to take into account the within field variability in viticulture. They are based on the development of specific equipments, sensors, actuators and site specific strategies with the aim of adapting the vineyard operations at the within-field level. These strategies will be presented briefly in two ways : - Site specific operations (fertilization, pruning, thinning, irrigation, etc.) in order to counteract the effects of the environment and to obtain a final product with a controlled and consistent wine quality, - Differential harvesting with the objective to take advantage of the observed spatial variability to produce different quality of wines. These later approach tends to produce very different quality wines which will be blended to control the final quality and/or marketed differently. These applications show that the environment and its spatial variability can be valued with the goal of controlling the final quality of the wine produced. Technologies to characterize the spatial variability of vine fields are currently in rapid evolution. They will significantly impact production methods and management strategies of the vineyard. In its last part, the presentation will summarize the technologies likely to impact the knowledge and the vineyard management either at the field level, at the vineyard level or at the regional level. A brief overview of the needs in terms of information processing will be also performed. A reflection on the difficulties that might limit the adoption of precision viticulture technologies (PV) will be done. Indeed, although very informative, PV entails high costs of information acquisition and data processing. Cost is one of the major obstacles to the dissemination of these tools and services to the majority of wine producers. In this context, the pooling of investments is a choke point to make the VP accessible to the highest number of growers. Thus, to be adopted, the VP will necessarily satisfy the operational requirements at the field level, but also throughout the whole production area (at the regional level). This working scale raises new scientific questions to be addressed.

[Spatial variability of soil nutrients based on geostatistics combined with GIS--a case study in Zunghua City of Hebei Province].

PubMed

Guo, X; Fu, B; Ma, K; Chen, L

2000-08-01

Geostatistics combined with GIS was applied to analyze the spatial variability of soil nutrients in topsoil (0-20 cm) in Zunghua City of Hebei Province. GIS can integrate attribute data with geographical data of system variables, which makes the application of geostatistics technique for large spatial scale more convenient. Soil nutrient data in this study included available N (alkaline hydrolyzing nitrogen), total N, available K, available P and organic matter. The results showed that the semivariograms of soil nutrients were best described by spherical model, except for that of available K, which was best fitted by complex structure of exponential model and linear with sill model. The spatial variability of available K was mainly produced by structural factor, while that of available N, total N, available P and organic matter was primarily caused by random factor. However, their spatial heterogeneity degree was different: the degree of total N and organic matter was higher, and that of available P and available N was lower. The results also indicated that the spatial correlation of the five tested soil nutrients at this large scale was moderately dependent. The ranges of available N and available P were almost same, which were 5 km and 5.5 km, respectively. The range of total N was up to 18 km, and that of organic matter was 8.5 km. For available K, the spatial variability scale primarily expressed exponential model between 0-3.5 km, but linear with sill model between 3.5-25.5 km. In addition, five soil nutrients exhibited different isotropic ranges. Available N and available P were isotropic through the whole research range (0-28 km). The isotropic range of available K was 0-8 km, and that of total N and organic matter was 0-10 km.

Self-similarity in nature

NASA Astrophysics Data System (ADS)

Timashev, S. F.

2000-02-01

A general phenomenological approach to the analysis of experimental temporal, spatial and energetic series for extracting truly physical non-model parameters ("passport data") is presented, which may be used to characterize and distinguish the evolution as well as the spatial and energetic structure of any open nonlinear dissipative system. This methodology is based on a postulate concerning the crucial information contained in the sequences of non-regularities of the measured dynamic variable (temporal, spatial, energetic). In accordance with this approach, multi-parametric formulas for dynamic variable power spectra as well as for structural functions of different orders are identical for every spatial-temporal-energetic level of the system under consideration. In effect, this entails the introduction of a new kind of self-similarity in Nature. An algorithm has been developed for obtaining as many "passport data" as are necessary for the characterization of a dynamic system. Applications of this approach in the analysis of various experimental series (temporal, spatial, energetic) demonstrate its potential for defining adequate phenomenological parameters of different dynamic processes and structures.

Evidence and mapping of extinction debts for global forest-dwelling reptiles, amphibians and mammals

NASA Astrophysics Data System (ADS)

Chen, Youhua; Peng, Shushi

2017-03-01

Evidence of extinction debts for the global distributions of forest-dwelling reptiles, mammals and amphibians was tested and the debt magnitude was estimated and mapped. By using different correlation tests and variable importance analysis, the results showed that spatial richness patterns for the three forest-dwelling terrestrial vertebrate groups had significant and stronger correlations with past forest cover area and other variables in the 1500 s, implying the evidence for extinction debts. Moreover, it was likely that the extinction debts have been partially paid, given that their global richness patterns were also significantly correlated with contemporary forest variables in the 2000 s (but the absolute magnitudes of the correlation coefficients were usually smaller than those calculated for historical forest variables). By utilizing species-area relationships, spatial extinction-debt magnitudes for the three vertebrate groups at the global scale were estimated and the hotspots of extinction debts were identified. These high-debt hotspots were generally situated in areas that did not spatially overlap with hotspots of species richness or high extinction-risk areas based on IUCN threatened status to a large extent. This spatial mismatch pattern suggested that necessary conservation efforts should be directed toward high-debt areas that are still overlooked.

Evidence and mapping of extinction debts for global forest-dwelling reptiles, amphibians and mammals.

PubMed

Chen, Youhua; Peng, Shushi

2017-03-16

Evidence of extinction debts for the global distributions of forest-dwelling reptiles, mammals and amphibians was tested and the debt magnitude was estimated and mapped. By using different correlation tests and variable importance analysis, the results showed that spatial richness patterns for the three forest-dwelling terrestrial vertebrate groups had significant and stronger correlations with past forest cover area and other variables in the 1500 s, implying the evidence for extinction debts. Moreover, it was likely that the extinction debts have been partially paid, given that their global richness patterns were also significantly correlated with contemporary forest variables in the 2000 s (but the absolute magnitudes of the correlation coefficients were usually smaller than those calculated for historical forest variables). By utilizing species-area relationships, spatial extinction-debt magnitudes for the three vertebrate groups at the global scale were estimated and the hotspots of extinction debts were identified. These high-debt hotspots were generally situated in areas that did not spatially overlap with hotspots of species richness or high extinction-risk areas based on IUCN threatened status to a large extent. This spatial mismatch pattern suggested that necessary conservation efforts should be directed toward high-debt areas that are still overlooked.

Spatial statistics of hydrography and water chemistry in a eutrophic boreal lake based on sounding and water samples.

PubMed

Leppäranta, Matti; Lewis, John E; Heini, Anniina; Arvola, Lauri

2018-06-04

Spatial variability, an essential characteristic of lake ecosystems, has often been neglected in field research and monitoring. In this study, we apply spatial statistical methods for the key physics and chemistry variables and chlorophyll a over eight sampling dates in two consecutive years in a large (area 103 km 2 ) eutrophic boreal lake in southern Finland. In the four summer sampling dates, the water body was vertically and horizontally heterogenic except with color and DOC, in the two winter ice-covered dates DO was vertically stratified, while in the two autumn dates, no significant spatial differences in any of the measured variables were found. Chlorophyll a concentration was one order of magnitude lower under the ice cover than in open water. The Moran statistic for spatial correlation was significant for chlorophyll a and NO 2 +NO 3 -N in all summer situations and for dissolved oxygen and pH in three cases. In summer, the mass centers of the chemicals were within 1.5 km from the geometric center of the lake, and the 2nd moment radius ranged in 3.7-4.1 km respective to 3.9 km for the homogeneous situation. The lateral length scales of the studied variables were 1.5-2.5 km, about 1 km longer in the surface layer. The detected spatial "noise" strongly suggests that besides vertical variation also the horizontal variation in eutrophic lakes, in particular, should be considered when the ecosystems are monitored.

Moving forward socio-economically focused models of deforestation.

PubMed

Dezécache, Camille; Salles, Jean-Michel; Vieilledent, Ghislain; Hérault, Bruno

2017-09-01

Whilst high-resolution spatial variables contribute to a good fit of spatially explicit deforestation models, socio-economic processes are often beyond the scope of these models. Such a low level of interest in the socio-economic dimension of deforestation limits the relevancy of these models for decision-making and may be the cause of their failure to accurately predict observed deforestation trends in the medium term. This study aims to propose a flexible methodology for taking into account multiple drivers of deforestation in tropical forested areas, where the intensity of deforestation is explicitly predicted based on socio-economic variables. By coupling a model of deforestation location based on spatial environmental variables with several sub-models of deforestation intensity based on socio-economic variables, we were able to create a map of predicted deforestation over the period 2001-2014 in French Guiana. This map was compared to a reference map for accuracy assessment, not only at the pixel scale but also over cells ranging from 1 to approximately 600 sq. km. Highly significant relationships were explicitly established between deforestation intensity and several socio-economic variables: population growth, the amount of agricultural subsidies, gold and wood production. Such a precise characterization of socio-economic processes allows to avoid overestimation biases in high deforestation areas, suggesting a better integration of socio-economic processes in the models. Whilst considering deforestation as a purely geographical process contributes to the creation of conservative models unable to effectively assess changes in the socio-economic and political contexts influencing deforestation trends, this explicit characterization of the socio-economic dimension of deforestation is critical for the creation of deforestation scenarios in REDD+ projects. © 2017 John Wiley & Sons Ltd.

Automated retrieval of forest structure variables based on multi-scale texture analysis of VHR satellite imagery

NASA Astrophysics Data System (ADS)

Beguet, Benoit; Guyon, Dominique; Boukir, Samia; Chehata, Nesrine

2014-10-01

The main goal of this study is to design a method to describe the structure of forest stands from Very High Resolution satellite imagery, relying on some typical variables such as crown diameter, tree height, trunk diameter, tree density and tree spacing. The emphasis is placed on the automatization of the process of identification of the most relevant image features for the forest structure retrieval task, exploiting both spectral and spatial information. Our approach is based on linear regressions between the forest structure variables to be estimated and various spectral and Haralick's texture features. The main drawback of this well-known texture representation is the underlying parameters which are extremely difficult to set due to the spatial complexity of the forest structure. To tackle this major issue, an automated feature selection process is proposed which is based on statistical modeling, exploring a wide range of parameter values. It provides texture measures of diverse spatial parameters hence implicitly inducing a multi-scale texture analysis. A new feature selection technique, we called Random PRiF, is proposed. It relies on random sampling in feature space, carefully addresses the multicollinearity issue in multiple-linear regression while ensuring accurate prediction of forest variables. Our automated forest variable estimation scheme was tested on Quickbird and Pléiades panchromatic and multispectral images, acquired at different periods on the maritime pine stands of two sites in South-Western France. It outperforms two well-established variable subset selection techniques. It has been successfully applied to identify the best texture features in modeling the five considered forest structure variables. The RMSE of all predicted forest variables is improved by combining multispectral and panchromatic texture features, with various parameterizations, highlighting the potential of a multi-resolution approach for retrieving forest structure variables from VHR satellite images. Thus an average prediction error of ˜ 1.1 m is expected on crown diameter, ˜ 0.9 m on tree spacing, ˜ 3 m on height and ˜ 0.06 m on diameter at breast height.

Continental-scale temperature covariance in proxy reconstructions and climate models

NASA Astrophysics Data System (ADS)

Hartl-Meier, Claudia; Büntgen, Ulf; Smerdon, Jason; Zorita, Eduardo; Krusic, Paul; Ljungqvist, Fredrik; Schneider, Lea; Esper, Jan

2017-04-01

Inter-continental temperature variability over the past millennium has been reported to be more coherent in climate model simulations than in multi-proxy-based reconstructions, a finding that undermines the representation of spatial variability in either of these approaches. We assess the covariance of summer temperatures among Northern Hemisphere continents by comparing tree-ring based temperature reconstructions with state-of-the-art climate model simulations over the past millennium. We find inter-continental temperature covariance to be larger in tree-ring-only reconstructions compared to those derived from multi-proxy networks, thus enhancing the agreement between proxy- and model-based spatial representations. A detailed comparison of simulated temperatures, however, reveals substantial spread among the models. Over the past millennium, inter-continental temperature correlations are driven by the cooling after major volcanic eruptions in 1257, 1452, 1601, and 1815. The coherence of these synchronizing events appears to be elevated in several climate simulations relative to their own covariance baselines and the proxy reconstructions, suggesting these models overestimate the amplitude of cooling in response to volcanic forcing at large spatial scales.

Modeling Spatial and Temporal Variability in Ammonia Emissions from Agricultural Fertilization

NASA Astrophysics Data System (ADS)

Balasubramanian, S.; Koloutsou-Vakakis, S.; Rood, M. J.

2013-12-01

Ammonia (NH3), is an important component of the reactive nitrogen cycle and a precursor to formation of atmospheric particulate matter (PM). Predicting regional PM concentrations and deposition of nitrogen species to ecosystems requires representative emission inventories. Emission inventories have traditionally been developed using top down approaches and more recently from data assimilation based on satellite and ground based ambient concentrations and wet deposition data. The National Emission Inventory (NEI) indicates agricultural fertilization as the predominant contributor (56%) to NH3 emissions in Midwest USA, in 2002. However, due to limited understanding of the complex interactions between fertilizer usage, farm practices, soil and meteorological conditions and absence of detailed statistical data, such emission estimates are currently based on generic emission factors, time-averaged temporal factors and coarse spatial resolution. Given the significance of this source, our study focuses on developing an improved NH3 emission inventory for agricultural fertilization at finer spatial and temporal scales for air quality modeling studies. Firstly, a high-spatial resolution 4 km x 4 km NH3 emission inventory for agricultural fertilization has been developed for Illinois by modifying spatial allocation of emissions based on combining crop-specific fertilization rates with cropland distribution in the Sparse Matrix Operator Kernel Emissions model. Net emission estimates of our method are within 2% of NEI, since both methods are constrained by fertilizer sales data. However, we identified localized crop-specific NH3 emission hotspots at sub-county resolutions absent in NEI. Secondly, we have adopted the use of the DeNitrification-DeComposition (DNDC) Biogeochemistry model to simulate the physical and chemical processes that control volatilization of nitrogen as NH3 to the atmosphere after fertilizer application and resolve the variability at the hourly scale. Representative temporal factors are being developed to capture crop-specific NH3 emission variability by combining knowledge of local crop management practices with high resolution cropland and soil maps. This improved spatially and temporally dependent NH3 emission inventory for agricultural fertilization is being prepared as a direct input to a state of the art air quality model to evaluate the effects of agricultural fertilization on regional air quality and atmospheric deposition of reactive nitrogen species.

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

Treesearch

Jeffrey D. Kline; Ralph J. Alig

2001-01-01

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

Spatial heterogeneity and air pollution removal by an urban forest

Treesearch

Francisco J. Escobedo; David J. Nowak

2009-01-01

Estimates of air pollution removal by the urban forest have mostly been based on mean values of forest structure variables for an entire city. However, the urban forest is not uniformly distributed across a city because of biophysical and social factors. Consequently, air pollution removal function by urban vegetation should vary because of this spatial heterogeneity....

Dispersal Ability Determines the Role of Environmental, Spatial and Temporal Drivers of Metacommunity Structure

PubMed Central

Padial, André A.; Ceschin, Fernanda; Declerck, Steven A. J.; De Meester, Luc; Bonecker, Cláudia C.; Lansac-Tôha, Fabio A.; Rodrigues, Liliana; Rodrigues, Luzia C.; Train, Sueli; Velho, Luiz F. M.; Bini, Luis M.

2014-01-01

Recently, community ecologists are focusing on the relative importance of local environmental factors and proxies to dispersal limitation to explain spatial variation in community structure. Albeit less explored, temporal processes may also be important in explaining species composition variation in metacommunities occupying dynamic systems. We aimed to evaluate the relative role of environmental, spatial and temporal variables on the metacommunity structure of different organism groups in the Upper Paraná River floodplain (Brazil). We used data on macrophytes, fish, benthic macroinvertebrates, zooplankton, periphyton, and phytoplankton collected in up to 36 habitats during a total of eight sampling campaigns over two years. According to variation partitioning results, the importance of predictors varied among biological groups. Spatial predictors were particularly important for organisms with comparatively lower dispersal ability, such as aquatic macrophytes and fish. On the other hand, environmental predictors were particularly important for organisms with high dispersal ability, such as microalgae, indicating the importance of species sorting processes in shaping the community structure of these organisms. The importance of watercourse distances increased when spatial variables were the main predictors of metacommunity structure. The contribution of temporal predictors was low. Our results emphasize the strength of a trait-based analysis and of better defining spatial variables. More importantly, they supported the view that “all-or- nothing” interpretations on the mechanisms structuring metacommunities are rather the exception than the rule. PMID:25340577

The gravity of pollination: integrating at-site features into spatial analysis of contemporary pollen movement.

PubMed

DiLeo, Michelle F; Siu, Jenna C; Rhodes, Matthew K; López-Villalobos, Adriana; Redwine, Angela; Ksiazek, Kelly; Dyer, Rodney J

2014-08-01

Pollen-mediated gene flow is a major driver of spatial genetic structure in plant populations. Both individual plant characteristics and site-specific features of the landscape can modify the perceived attractiveness of plants to their pollinators and thus play an important role in shaping spatial genetic variation. Most studies of landscape-level genetic connectivity in plants have focused on the effects of interindividual distance using spatial and increasingly ecological separation, yet have not incorporated individual plant characteristics or other at-site ecological variables. Using spatially explicit simulations, we first tested the extent to which the inclusion of at-site variables influencing local pollination success improved the statistical characterization of genetic connectivity based upon examination of pollen pool genetic structure. The addition of at-site characteristics provided better models than those that only considered interindividual spatial distance (e.g. IBD). Models parameterized using conditional genetic covariance (e.g. population graphs) also outperformed those assuming panmixia. In a natural population of Cornus florida L. (Cornaceae), we showed that the addition of at-site characteristics (clumping of primary canopy opening above each maternal tree and maternal tree floral output) provided significantly better models describing gene flow than models including only between-site spatial (IBD) and ecological (isolation by resistance) variables. Overall, our results show that including interindividual and local ecological variation greatly aids in characterizing landscape-level measures of contemporary gene flow. © 2014 John Wiley & Sons Ltd.

Modeling spatiotemporal covariance for magnetoencephalography or electroencephalography source analysis.

PubMed

Plis, Sergey M; George, J S; Jun, S C; Paré-Blagoev, J; Ranken, D M; Wood, C C; Schmidt, D M

2007-01-01

We propose a new model to approximate spatiotemporal noise covariance for use in neural electromagnetic source analysis, which better captures temporal variability in background activity. As with other existing formalisms, our model employs a Kronecker product of matrices representing temporal and spatial covariance. In our model, spatial components are allowed to have differing temporal covariances. Variability is represented as a series of Kronecker products of spatial component covariances and corresponding temporal covariances. Unlike previous attempts to model covariance through a sum of Kronecker products, our model is designed to have a computationally manageable inverse. Despite increased descriptive power, inversion of the model is fast, making it useful in source analysis. We have explored two versions of the model. One is estimated based on the assumption that spatial components of background noise have uncorrelated time courses. Another version, which gives closer approximation, is based on the assumption that time courses are statistically independent. The accuracy of the structural approximation is compared to an existing model, based on a single Kronecker product, using both Frobenius norm of the difference between spatiotemporal sample covariance and a model, and scatter plots. Performance of ours and previous models is compared in source analysis of a large number of single dipole problems with simulated time courses and with background from authentic magnetoencephalography data.

High-resolution spatial databases of monthly climate variables (1961-2010) over a complex terrain region in southwestern China

NASA Astrophysics Data System (ADS)

Wu, Wei; Xu, An-Ding; Liu, Hong-Bin

2015-01-01

Climate data in gridded format are critical for understanding climate change and its impact on eco-environment. The aim of the current study is to develop spatial databases for three climate variables (maximum, minimum temperatures, and relative humidity) over a large region with complex topography in southwestern China. Five widely used approaches including inverse distance weighting, ordinary kriging, universal kriging, co-kriging, and thin-plate smoothing spline were tested. Root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) showed that thin-plate smoothing spline with latitude, longitude, and elevation outperformed other models. Average RMSE, MAE, and MAPE of the best models were 1.16 °C, 0.74 °C, and 7.38 % for maximum temperature; 0.826 °C, 0.58 °C, and 6.41 % for minimum temperature; and 3.44, 2.28, and 3.21 % for relative humidity, respectively. Spatial datasets of annual and monthly climate variables with 1-km resolution covering the period 1961-2010 were then obtained using the best performance methods. Comparative study showed that the current outcomes were in well agreement with public datasets. Based on the gridded datasets, changes in temperature variables were investigated across the study area. Future study might be needed to capture the uncertainty induced by environmental conditions through remote sensing and knowledge-based methods.

Compensatory Water Effects Link Yearly Global Land CO2 Sink Changes to Temperature

NASA Technical Reports Server (NTRS)

Jung, Martin; Reichstein, Markus; Tramontana, Gianluca; Viovy, Nicolas; Schwalm, Christopher R.; Wang, Ying-Ping; Weber, Ulrich; Weber, Ulrich; Zaehle, Soenke; Zeng, Ning;

Model simulations and proxy-based reconstructions for the European region in the past millennium (Invited)

NASA Astrophysics Data System (ADS)

Zorita, E.

2009-12-01

One of the objectives when comparing simulations of past climates to proxy-based climate reconstructions is to asses the skill of climate models to simulate climate change. This comparison may accomplished at large spatial scales, for instance the evolution of simulated and reconstructed Northern Hemisphere annual temperature, or at regional or point scales. In both approaches a 'fair' comparison has to take into account different aspects that affect the inevitable uncertainties and biases in the simulations and in the reconstructions. These efforts face a trade-off: climate models are believed to be more skillful at large hemispheric scales, but climate reconstructions are these scales are burdened by the spatial distribution of available proxies and by methodological issues surrounding the statistical method used to translate the proxy information into large-spatial averages. Furthermore, the internal climatic noise at large hemispheric scales is low, so that the sampling uncertainty tends to be also low. On the other hand, the skill of climate models at regional scales is limited by the coarse spatial resolution, which hinders a faithful representation of aspects important for the regional climate. At small spatial scales, the reconstruction of past climate probably faces less methodological problems if information from different proxies is available. The internal climatic variability at regional scales is, however, high. In this contribution some examples of the different issues faced when comparing simulation and reconstructions at small spatial scales in the past millennium are discussed. These examples comprise reconstructions from dendrochronological data and from historical documentary data in Europe and climate simulations with global and regional models. These examples indicate that the centennial climate variations can offer a reasonable target to assess the skill of global climate models and of proxy-based reconstructions, even at small spatial scales. However, as the focus shifts towards higher frequency variability, decadal or multidecadal, the need for larger simulation ensembles becomes more evident. Nevertheless,the comparison at these time scales may expose some lines of research on the origin of multidecadal regional climate variability.

Testing for entanglement with periodic coarse graining

NASA Astrophysics Data System (ADS)

Tasca, D. S.; Rudnicki, Łukasz; Aspden, R. S.; Padgett, M. J.; Souto Ribeiro, P. H.; Walborn, S. P.

2018-04-01

Continuous-variable systems find valuable applications in quantum information processing. To deal with an infinite-dimensional Hilbert space, one in general has to handle large numbers of discretized measurements in tasks such as entanglement detection. Here we employ the continuous transverse spatial variables of photon pairs to experimentally demonstrate entanglement criteria based on a periodic structure of coarse-grained measurements. The periodization of the measurements allows an efficient evaluation of entanglement using spatial masks acting as mode analyzers over the entire transverse field distribution of the photons and without the need to reconstruct the probability densities of the conjugate continuous variables. Our experimental results demonstrate the utility of the derived criteria with a success rate in entanglement detection of ˜60 % relative to 7344 studied cases.

Validating crash locations for quantitative spatial analysis: a GIS-based approach.

PubMed

Loo, Becky P Y

2006-09-01

In this paper, the spatial variables of the crash database in Hong Kong from 1993 to 2004 are validated. The proposed spatial data validation system makes use of three databases (the crash, road network and district board databases) and relies on GIS to carry out most of the validation steps so that the human resource required for manually checking the accuracy of the spatial data can be enormously reduced. With the GIS-based spatial data validation system, it was found that about 65-80% of the police crash records from 1993 to 2004 had correct road names and district board information. In 2004, the police crash database contained about 12.7% mistakes for road names and 9.7% mistakes for district boards. The situation was broadly comparable to the United Kingdom. However, the results also suggest that safety researchers should carefully validate spatial data in the crash database before scientific analysis.

Fine-scale habitat modeling of a top marine predator: do prey data improve predictive capacity?

PubMed

Torres, Leigh G; Read, Andrew J; Halpin, Patrick

2008-10-01

Predators and prey assort themselves relative to each other, the availability of resources and refuges, and the temporal and spatial scale of their interaction. Predictive models of predator distributions often rely on these relationships by incorporating data on environmental variability and prey availability to determine predator habitat selection patterns. This approach to predictive modeling holds true in marine systems where observations of predators are logistically difficult, emphasizing the need for accurate models. In this paper, we ask whether including prey distribution data in fine-scale predictive models of bottlenose dolphin (Tursiops truncatus) habitat selection in Florida Bay, Florida, U.S.A., improves predictive capacity. Environmental characteristics are often used as predictor variables in habitat models of top marine predators with the assumption that they act as proxies of prey distribution. We examine the validity of this assumption by comparing the response of dolphin distribution and fish catch rates to the same environmental variables. Next, the predictive capacities of four models, with and without prey distribution data, are tested to determine whether dolphin habitat selection can be predicted without recourse to describing the distribution of their prey. The final analysis determines the accuracy of predictive maps of dolphin distribution produced by modeling areas of high fish catch based on significant environmental characteristics. We use spatial analysis and independent data sets to train and test the models. Our results indicate that, due to high habitat heterogeneity and the spatial variability of prey patches, fine-scale models of dolphin habitat selection in coastal habitats will be more successful if environmental variables are used as predictor variables of predator distributions rather than relying on prey data as explanatory variables. However, predictive modeling of prey distribution as the response variable based on environmental variability did produce high predictive performance of dolphin habitat selection, particularly foraging habitat.

Usefulness of AIRS-Derived OLR, Temperature, Water Vapor and Cloudiness Anomaly Trends for GCM Validation

NASA Technical Reports Server (NTRS)

Molnar, Gyula I.; Susskind, Joel; Iredell, Lena F.

2010-01-01

Mainly due to their global nature, satellite observations can provide a very useful basis for GCM validations. In particular, satellite sounders such as AIRS provide 3-D spatial information (most useful for GCMs), so the question arises: can we use AIRS datasets for climate variability assessments? We show that the recent (September 2002 February 2010) CERES-observed negative trend in OLR of approx.-0.1 W/sq m/yr averaged over the globe is found in the AIRS OLR data as well. Most importantly, even minute details (down to 1 x 1 degree GCM-scale resolution) of spatial and temporal anomalies and trends of OLR as observed by CERES and computed based on AIRS-retrieved surface and atmospheric geophysical parameters over this time period are essentially the same. The correspondence can be seen even in the very large spatial variations of these trends with local values ranging from -2.6 W/sq m/yr to +3.0 W/sq m/yr in the tropics, for example. This essentially perfect agreement of OLR anomalies and trends derived from observations by two different instruments, in totally independent and different manners, implies that both sets of results must be highly accurate, and indirectly validates the anomalies and trends of other AIRS derived products as well. These products show that global and regional anomalies and trends of OLR, water vapor and cloud cover over the last 7+ years are strongly influenced by EI-Nino-La Nina cycles . We have created climate parameter anomaly datasets using AIRS retrievals which can be compared directly with coupled GCM climate variability assessments. Moreover, interrelationships of these anomalies and trends should also be similar between the observed and GCM-generated datasets, and, in cases of discrepancies, GCM parameterizations could be improved based on the relationships observed in the data. First, we assess spatial "trends" of variability of climatic parameter anomalies [since anomalies relative to the seasonal cycle are good proxies of climate variability] at the common 1x1 degree GCM grid-scale by creating spatial anomaly "trends" based on the first 7+ years of AIRS Version 5 Leve13 data. We suggest that modelers should compare these with their (coupled) GCM's performance covering the same period. We evaluate temporal variability and interrelations of climatic anomalies on global to regional e.g., deep Tropical Hovmoller diagrams, El-Nino-related variability scales, and show the effects of El-Nino-La Nina activity on tropical anomalies and trends of water vapor cloud cover and OLR. For GCMs to be trusted highly for long-term climate change predictions, they should be able to reproduce findings similar to these. In summary, the AIRS-based climate variability analyses provide high quality, informative and physically plausible interrelationships among OLR, temperature, humidity and cloud cover both on the spatial and temporal scales. GCM validations can use these results even directly, e. g., by creating 1x1 degree trendmaps for the same period in coupled climate simulations.

Impact of spatial variability and sampling design on model performance

NASA Astrophysics Data System (ADS)

Schrape, Charlotte; Schneider, Anne-Kathrin; Schröder, Boris; van Schaik, Loes

2017-04-01

Many environmental physical and chemical parameters as well as species distributions display a spatial variability at different scales. In case measurements are very costly in labour time or money a choice has to be made between a high sampling resolution at small scales and a low spatial cover of the study area or a lower sampling resolution at the small scales resulting in local data uncertainties with a better spatial cover of the whole area. This dilemma is often faced in the design of field sampling campaigns for large scale studies. When the gathered field data are subsequently used for modelling purposes the choice of sampling design and resulting data quality influence the model performance criteria. We studied this influence with a virtual model study based on a large dataset of field information on spatial variation of earthworms at different scales. Therefore we built a virtual map of anecic earthworm distributions over the Weiherbach catchment (Baden-Württemberg in Germany). First of all the field scale abundance of earthworms was estimated using a catchment scale model based on 65 field measurements. Subsequently the high small scale variability was added using semi-variograms, based on five fields with a total of 430 measurements divided in a spatially nested sampling design over these fields, to estimate the nugget, range and standard deviation of measurements within the fields. With the produced maps, we performed virtual samplings of one up to 50 random points per field. We then used these data to rebuild the catchment scale models of anecic earthworm abundance with the same model parameters as in the work by Palm et al. (2013). The results of the models show clearly that a large part of the non-explained deviance of the models is due to the very high small scale variability in earthworm abundance: the models based on single virtual sampling points on average obtain an explained deviance of 0.20 and a correlation coefficient of 0.64. With increasing sampling points per field, we averaged the measured abundance of the sampling within each field to obtain a more representative value of the field average. Doubling the samplings per field strongly improved the model performance criteria (explained deviance 0.38 and correlation coefficient 0.73). With 50 sampling points per field the performance criteria were 0.91 and 0.97 respectively for explained deviance and correlation coefficient. The relationship between number of samplings and performance criteria can be described with a saturation curve. Beyond five samples per field the model improvement becomes rather small. With this contribution we wish to discuss the impact of data variability at sampling scale on model performance and the implications for sampling design and assessment of model results as well as ecological inferences.

The role of environmental variables in structuring landscape-scale species distributions in seafloor habitats.

PubMed

Kraan, Casper; Aarts, Geert; Van der Meer, Jaap; Piersma, Theunis

2010-06-01

Ongoing statistical sophistication allows a shift from describing species' spatial distributions toward statistically disentangling the possible roles of environmental variables in shaping species distributions. Based on a landscape-scale benthic survey in the Dutch Wadden Sea, we show the merits of spatially explicit generalized estimating equations (GEE). The intertidal macrozoobenthic species, Macoma balthica, Cerastoderma edule, Marenzelleria viridis, Scoloplos armiger, Corophium volutator, and Urothoe poseidonis served as test cases, with median grain-size and inundation time as typical environmental explanatory variables. GEEs outperformed spatially naive generalized linear models (GLMs), and removed much residual spatial structure, indicating the importance of median grain-size and inundation time in shaping landscape-scale species distributions in the intertidal. GEE regression coefficients were smaller than those attained with GLM, and GEE standard errors were larger. The best fitting GEE for each species was used to predict species' density in relation to median grain-size and inundation time. Although no drastic changes were noted compared to previous work that described habitat suitability for benthic fauna in the Wadden Sea, our predictions provided more detailed and unbiased estimates of the determinants of species-environment relationships. We conclude that spatial GEEs offer the necessary methodological advances to further steps toward linking pattern to process.

Beyond the SCS-CN method: A theoretical framework for spatially lumped rainfall-runoff response

NASA Astrophysics Data System (ADS)

Bartlett, M. S.; Parolari, A. J.; McDonnell, J. J.; Porporato, A.

2016-06-01

Since its introduction in 1954, the Soil Conservation Service curve number (SCS-CN) method has become the standard tool, in practice, for estimating an event-based rainfall-runoff response. However, because of its empirical origins, the SCS-CN method is restricted to certain geographic regions and land use types. Moreover, it does not describe the spatial variability of runoff. To move beyond these limitations, we present a new theoretical framework for spatially lumped, event-based rainfall-runoff modeling. In this framework, we describe the spatially lumped runoff model as a point description of runoff that is upscaled to a watershed area based on probability distributions that are representative of watershed heterogeneities. The framework accommodates different runoff concepts and distributions of heterogeneities, and in doing so, it provides an implicit spatial description of runoff variability. Heterogeneity in storage capacity and soil moisture are the basis for upscaling a point runoff response and linking ecohydrological processes to runoff modeling. For the framework, we consider two different runoff responses for fractions of the watershed area: "prethreshold" and "threshold-excess" runoff. These occur before and after infiltration exceeds a storage capacity threshold. Our application of the framework results in a new model (called SCS-CNx) that extends the SCS-CN method with the prethreshold and threshold-excess runoff mechanisms and an implicit spatial description of runoff. We show proof of concept in four forested watersheds and further that the resulting model may better represent geographic regions and site types that previously have been beyond the scope of the traditional SCS-CN method.

Forest Stand Canopy Structure Attribute Estimation from High Resolution Digital Airborne Imagery

Treesearch

Demetrios Gatziolis

2006-01-01

A study of forest stand canopy variable assessment using digital, airborne, multispectral imagery is presented. Variable estimation involves stem density, canopy closure, and mean crown diameter, and it is based on quantification of spatial autocorrelation among pixel digital numbers (DN) using variogram analysis and an alternative, non-parametric approach known as...

Variability of wildland fire emissions across the contiguous United States

Treesearch

YongQiang Liu

2004-01-01

This study analyzes spatial and temporal variability of emissions from wildland fires across the contiguous US. The emissions are estimates based on a recently constructed dataset of historical fire records collected by multiple US governlnental agencies. Both wildfire and prescribed fires have the highest emissions over the Pacific coastal states. Prescribed fire...

Spatial variability of detrended soil plow layer penetrometer resistance transect in a sugarcane field

NASA Astrophysics Data System (ADS)

Pérez, Luis D.; Cumbrera, Ramiro; Mato, Juan; Millán, Humberto; Tarquis, Ana M.

2015-04-01

Spatial variability of soil properties is relevant for identifying those zones with physical degradation. In this sense, one has to face the problem of identifying the origin and distribution of spatial variability patterns (Brouder et al., 2001; Millán et al., 2012). The objective of the present work was to quantify the spatial structure of soil penetrometer resistance (PR) collected from a transect data consisted of 221 points equidistant. In each sampling, readings were obtained from 0 cm till 70 cm of depth, with an interval of 5 cm (Pérez, 2012). The study was conducted on a Vertisol (Typic Hapludert) dedicated to sugarcane (Saccharum officinarum L.) production during the last sixty years (Pérez et al., 2010). Recently, scaling approach has been applied on the determination of the scaling data properties (Tarquis et al., 2008; Millán et al., 2012; Pérez, 2012). We focus in the Hurst analysis to characterize the data variability for each depth. Previously a detrended analysis was conducted in order to better study de intrinsic variability of the series. The Hurst exponent (H) for each depth was estimated showing a characteristic pattern and differentiating PR evolution in depth. References Brouder, S., Hofmann, B., Reetz, H.F., 2001. Evaluating spatial variability of soil parameters for input management. Better Crops 85, 8-11. Millán, H; AM Tarquís, Luís D. Pérez, Juan Mato, Mario González-Posada, 2012. Spatial variability patterns of some Vertisol properties at a field scale using standardized data. Soil and Tillage Research, 120, 76-84. Pérez, Luís D. 2012. Influencia de la maquinaria agrícola sobre la variabilidad espacial de la compactación del suelo. Aplicación de la metodología geoestadística-fractal. PhD thesis, UPM (In Spanish). Pérez, Luís D., Humberto Millán, Mario González-Posada 2010. Spatial complexity of soil plow layer penetrometer resistance as influenced by sugarcane harvesting: A prefractal approach. Soil and Tillage Research, 110(1), 77-86. Tarquis, A.M., N. Bird, M.C. Cartagena, A. Whitmore and Y. Pachepsky, 2008. Multiscale entropy-based analyses of soil transect data. Vadose Zone Journal, 7(2), 563-569.

Mapping Spatial Variability of Soil Salinity in a Coastal Paddy Field Based on Electromagnetic Sensors

PubMed Central

Guo, Yan; Huang, Jingyi; Shi, Zhou; Li, Hongyi

2015-01-01

In coastal China, there is an urgent need to increase land area for agricultural production and urban development, where there is a rapid growing population. One solution is land reclamation from coastal tidelands, but soil salinization is problematic. As such, it is very important to characterize and map the within-field variability of soil salinity in space and time. Conventional methods are often time-consuming, expensive, labor-intensive, and unpractical. Fortunately, proximal sensing has become an important technology in characterizing within-field spatial variability. In this study, we employed the EM38 to study spatial variability of soil salinity in a coastal paddy field. Significant correlation relationship between ECa and EC1:5 (i.e. r >0.9) allowed us to use EM38 data to characterize the spatial variability of soil salinity. Geostatistical methods were used to determine the horizontal spatio-temporal variability of soil salinity over three consecutive years. The study found that the distribution of salinity was heterogeneous and the leaching of salts was more significant in the edges of the study field. By inverting the EM38 data using a Quasi-3D inversion algorithm, the vertical spatio-temporal variability of soil salinity was determined and the leaching of salts over time was easily identified. The methodology of this study can be used as guidance for researchers interested in understanding soil salinity development as well as land managers aiming for effective soil salinity monitoring and management practices. In order to better characterize the variations in soil salinity to a deeper soil profile, the deeper mode of EM38 (i.e., EM38v) as well as other EMI instruments (e.g. DUALEM-421) can be incorporated to conduct Quasi-3D inversions for deeper soil profiles. PMID:26020969

Inter-annual variability of carbon fluxes in temperate forest ecosystems: effects of biotic and abiotic factors

NASA Astrophysics Data System (ADS)

Chen, M.; Keenan, T. F.; Hufkens, K.; Munger, J. W.; Bohrer, G.; Brzostek, E. R.; Richardson, A. D.

2014-12-01

Carbon dynamics in terrestrial ecosystems are influenced by both abiotic and biotic factors. Abiotic factors, such as variation in meteorological conditions, directly drive biophysical and biogeochemical processes; biotic factors, referring to the inherent properties of the ecosystem components, reflect the internal regulating effects including temporal dynamics and memory. The magnitude of the effect of abiotic and biotic factors on forest ecosystem carbon exchange has been suggested to vary at different time scales. In this study, we design and conduct a model-data fusion experiment to investigate the role and relative importance of the biotic and abiotic factors for inter-annual variability of the net ecosystem CO2 exchange (NEE) of temperate deciduous forest ecosystems in the Northeastern US. A process-based model (FöBAAR) is parameterized at four eddy-covariance sites using all available flux and biometric measurements. We conducted a "transplant" modeling experiment, that is, cross- site and parameter simulations with different combinations of site meteorology and parameters. Using wavelet analysis and variance partitioning techniques, analysis of model predictions identifies both spatial variant and spatially invariant parameters. Variability of NEE was primarily modulated by gross primary productivity (GPP), with relative contributions varying from hourly to yearly time scales. The inter-annual variability of GPP and NEE is more regulated by meteorological forcing, but spatial variability in certain model parameters (biotic response) has more substantial effects on the inter-annual variability of ecosystem respiration (Reco) through the effects on carbon pools. Both the biotic and abiotic factors play significant roles in modulating the spatial and temporal variability in terrestrial carbon cycling in the region. Together, our study quantifies the relative importance of both, and calls for better understanding of them to better predict regional CO2 exchanges.

Mapping spatial variability of soil salinity in a coastal paddy field based on electromagnetic sensors.

PubMed

Guo, Yan; Huang, Jingyi; Shi, Zhou; Li, Hongyi

2015-01-01

In coastal China, there is an urgent need to increase land area for agricultural production and urban development, where there is a rapid growing population. One solution is land reclamation from coastal tidelands, but soil salinization is problematic. As such, it is very important to characterize and map the within-field variability of soil salinity in space and time. Conventional methods are often time-consuming, expensive, labor-intensive, and unpractical. Fortunately, proximal sensing has become an important technology in characterizing within-field spatial variability. In this study, we employed the EM38 to study spatial variability of soil salinity in a coastal paddy field. Significant correlation relationship between ECa and EC1:5 (i.e. r >0.9) allowed us to use EM38 data to characterize the spatial variability of soil salinity. Geostatistical methods were used to determine the horizontal spatio-temporal variability of soil salinity over three consecutive years. The study found that the distribution of salinity was heterogeneous and the leaching of salts was more significant in the edges of the study field. By inverting the EM38 data using a Quasi-3D inversion algorithm, the vertical spatio-temporal variability of soil salinity was determined and the leaching of salts over time was easily identified. The methodology of this study can be used as guidance for researchers interested in understanding soil salinity development as well as land managers aiming for effective soil salinity monitoring and management practices. In order to better characterize the variations in soil salinity to a deeper soil profile, the deeper mode of EM38 (i.e., EM38v) as well as other EMI instruments (e.g. DUALEM-421) can be incorporated to conduct Quasi-3D inversions for deeper soil profiles.

A computed tomography-based spatial normalization for the analysis of [18F] fluorodeoxyglucose positron emission tomography of the brain.

PubMed

Cho, Hanna; Kim, Jin Su; Choi, Jae Yong; Ryu, Young Hoon; Lyoo, Chul Hyoung

2014-01-01

We developed a new computed tomography (CT)-based spatial normalization method and CT template to demonstrate its usefulness in spatial normalization of positron emission tomography (PET) images with [(18)F] fluorodeoxyglucose (FDG) PET studies in healthy controls. Seventy healthy controls underwent brain CT scan (120 KeV, 180 mAs, and 3 mm of thickness) and [(18)F] FDG PET scans using a PET/CT scanner. T1-weighted magnetic resonance (MR) images were acquired for all subjects. By averaging skull-stripped and spatially-normalized MR and CT images, we created skull-stripped MR and CT templates for spatial normalization. The skull-stripped MR and CT images were spatially normalized to each structural template. PET images were spatially normalized by applying spatial transformation parameters to normalize skull-stripped MR and CT images. A conventional perfusion PET template was used for PET-based spatial normalization. Regional standardized uptake values (SUV) measured by overlaying the template volume of interest (VOI) were compared to those measured with FreeSurfer-generated VOI (FSVOI). All three spatial normalization methods underestimated regional SUV values by 0.3-20% compared to those measured with FSVOI. The CT-based method showed slightly greater underestimation bias. Regional SUV values derived from all three spatial normalization methods were correlated significantly (p < 0.0001) with those measured with FSVOI. CT-based spatial normalization may be an alternative method for structure-based spatial normalization of [(18)F] FDG PET when MR imaging is unavailable. Therefore, it is useful for PET/CT studies with various radiotracers whose uptake is expected to be limited to specific brain regions or highly variable within study population.

Applying multibeam sonar and mathematical modeling for mapping seabed substrate and biota of offshore shallows

NASA Astrophysics Data System (ADS)

Herkül, Kristjan; Peterson, Anneliis; Paekivi, Sander

2017-06-01

Both basic science and marine spatial planning are in a need of high resolution spatially continuous data on seabed habitats and biota. As conventional point-wise sampling is unable to cover large spatial extents in high detail, it must be supplemented with remote sensing and modeling in order to fulfill the scientific and management needs. The combined use of in situ sampling, sonar scanning, and mathematical modeling is becoming the main method for mapping both abiotic and biotic seabed features. Further development and testing of the methods in varying locations and environmental settings is essential for moving towards unified and generally accepted methodology. To fill the relevant research gap in the Baltic Sea, we used multibeam sonar and mathematical modeling methods - generalized additive models (GAM) and random forest (RF) - together with underwater video to map seabed substrate and epibenthos of offshore shallows. In addition to testing the general applicability of the proposed complex of techniques, the predictive power of different sonar-based variables and modeling algorithms were tested. Mean depth, followed by mean backscatter, were the most influential variables in most of the models. Generally, mean values of sonar-based variables had higher predictive power than their standard deviations. The predictive accuracy of RF was higher than that of GAM. To conclude, we found the method to be feasible and with predictive accuracy similar to previous studies of sonar-based mapping.

Industrial implementation of spatial variability control by real-time SPC

NASA Astrophysics Data System (ADS)

Roule, O.; Pasqualini, F.; Borde, M.

2016-10-01

Advanced technology nodes require more and more information to get the wafer process well setup. The critical dimension of components decreases following Moore's law. At the same time, the intra-wafer dispersion linked to the spatial non-uniformity of tool's processes is not capable to decrease in the same proportions. APC systems (Advanced Process Control) are being developed in waferfab to automatically adjust and tune wafer processing, based on a lot of process context information. It can generate and monitor complex intrawafer process profile corrections between different process steps. It leads us to put under control the spatial variability, in real time by our SPC system (Statistical Process Control). This paper will outline the architecture of an integrated process control system for shape monitoring in 3D, implemented in waferfab.

Sex modifies the relationship between age and gait: a population-based study of older adults.

PubMed

Callisaya, Michele L; Blizzard, Leigh; Schmidt, Michael D; McGinley, Jennifer L; Srikanth, Velandai K

2008-02-01

Adequate mobility is essential to maintain an independent and active lifestyle. The aim of this cross-sectional study is to examine the associations of age with temporal and spatial gait variables in a population-based sample of older people, and whether these associations are modified by sex. Men and women aged 60-86 years were randomly selected from the Southern Tasmanian electoral roll (n = 223). Gait speed, step length, cadence, step width, and double-support phase were recorded with a GAITRite walkway. Regression analysis was used to model the relationship between age, sex, and gait variables. For men, after adjusting for height and weight, age was linearly associated with all gait variables (p <.05) except cadence (p =.11). For women, all variables demonstrated a curvilinear association, with age-related change in these variables commencing during the 7th decade. Significant interactions were found between age and sex for speed (p =.04), cadence (p =.01), and double-support phase (p =.03). Associations were observed between age and a broad range of temporal and spatial gait variables in this study. These associations differed by sex, suggesting that the aging process may affect gait in men and women differently. These results provide a basis for further research into sex differences and mechanisms underlying gait changes with advancing age.

Ensemble-Based Parameter Estimation in a Coupled GCM Using the Adaptive Spatial Average Method

DOE PAGES

Liu, Y.; Liu, Z.; Zhang, S.; ...

2014-05-29

Ensemble-based parameter estimation for a climate model is emerging as an important topic in climate research. And for a complex system such as a coupled ocean–atmosphere general circulation model, the sensitivity and response of a model variable to a model parameter could vary spatially and temporally. An adaptive spatial average (ASA) algorithm is proposed to increase the efficiency of parameter estimation. Refined from a previous spatial average method, the ASA uses the ensemble spread as the criterion for selecting “good” values from the spatially varying posterior estimated parameter values; these good values are then averaged to give the final globalmore » uniform posterior parameter. In comparison with existing methods, the ASA parameter estimation has a superior performance: faster convergence and enhanced signal-to-noise ratio.« less

Spatial analysis of factors influencing long-term stress in the grizzly bear (Ursus arctos) population of Alberta, Canada.

PubMed

Bourbonnais, Mathieu L; Nelson, Trisalyn A; Cattet, Marc R L; Darimont, Chris T; Stenhouse, Gordon B

2013-01-01

Non-invasive measures for assessing long-term stress in free ranging mammals are an increasingly important approach for understanding physiological responses to landscape conditions. Using a spatially and temporally expansive dataset of hair cortisol concentrations (HCC) generated from a threatened grizzly bear (Ursus arctos) population in Alberta, Canada, we quantified how variables representing habitat conditions and anthropogenic disturbance impact long-term stress in grizzly bears. We characterized spatial variability in male and female HCC point data using kernel density estimation and quantified variable influence on spatial patterns of male and female HCC stress surfaces using random forests. Separate models were developed for regions inside and outside of parks and protected areas to account for substantial differences in anthropogenic activity and disturbance within the study area. Variance explained in the random forest models ranged from 55.34% to 74.96% for males and 58.15% to 68.46% for females. Predicted HCC levels were higher for females compared to males. Generally, high spatially continuous female HCC levels were associated with parks and protected areas while low-to-moderate levels were associated with increased anthropogenic disturbance. In contrast, male HCC levels were low in parks and protected areas and low-to-moderate in areas with increased anthropogenic disturbance. Spatial variability in gender-specific HCC levels reveal that the type and intensity of external stressors are not uniform across the landscape and that male and female grizzly bears may be exposed to, or perceive, potential stressors differently. We suggest observed spatial patterns of long-term stress may be the result of the availability and distribution of foods related to disturbance features, potential sexual segregation in available habitat selection, and may not be influenced by sources of mortality which represent acute traumas. In this wildlife system and others, conservation and management efforts can benefit by understanding spatial- and gender-based stress responses to landscape conditions.

Spatial Analysis of Factors Influencing Long-Term Stress in the Grizzly Bear (Ursus arctos) Population of Alberta, Canada

PubMed Central

Bourbonnais, Mathieu L.; Nelson, Trisalyn A.; Cattet, Marc R. L.; Darimont, Chris T.; Stenhouse, Gordon B.

2013-01-01

Non-invasive measures for assessing long-term stress in free ranging mammals are an increasingly important approach for understanding physiological responses to landscape conditions. Using a spatially and temporally expansive dataset of hair cortisol concentrations (HCC) generated from a threatened grizzly bear (Ursus arctos) population in Alberta, Canada, we quantified how variables representing habitat conditions and anthropogenic disturbance impact long-term stress in grizzly bears. We characterized spatial variability in male and female HCC point data using kernel density estimation and quantified variable influence on spatial patterns of male and female HCC stress surfaces using random forests. Separate models were developed for regions inside and outside of parks and protected areas to account for substantial differences in anthropogenic activity and disturbance within the study area. Variance explained in the random forest models ranged from 55.34% to 74.96% for males and 58.15% to 68.46% for females. Predicted HCC levels were higher for females compared to males. Generally, high spatially continuous female HCC levels were associated with parks and protected areas while low-to-moderate levels were associated with increased anthropogenic disturbance. In contrast, male HCC levels were low in parks and protected areas and low-to-moderate in areas with increased anthropogenic disturbance. Spatial variability in gender-specific HCC levels reveal that the type and intensity of external stressors are not uniform across the landscape and that male and female grizzly bears may be exposed to, or perceive, potential stressors differently. We suggest observed spatial patterns of long-term stress may be the result of the availability and distribution of foods related to disturbance features, potential sexual segregation in available habitat selection, and may not be influenced by sources of mortality which represent acute traumas. In this wildlife system and others, conservation and management efforts can benefit by understanding spatial- and gender-based stress responses to landscape conditions. PMID:24386273

Longterm and spatial variability of Aerosol optical properties measured by sky radiometer in Japan sites

NASA Astrophysics Data System (ADS)

Aoki, K.

2016-12-01

Aerosols and cloud play an important role in the climate change. We started the long-term monitoring of aerosol and cloud optical properties since 1990's by using sky radiometer (POM-01, 02; Prede Co. Ltd., Japan). We provide the information, in this presentation, on the aerosol optical properties with respect to their temporal and spatial variability in Japan site (ex. Sapporo, Toyama, Kasuga and etc). The global distributions of aerosols have been derived from earth observation satellite and have been simulated in numerical models, which assume optical parameters. However, these distributions are difficult to derive because of variability in time and space. Therefore, Aerosol optical properties were investigated using the measurements from ground-based and ship-borne sky radiometer. The sky radiometer is an automatic instrument that takes observations only in daytime under the clear sky conditions. Observation of diffuse solar intensity interval was made every ten or five minutes by once. The aerosol optical properties were computed using the SKYRAD.pack version 4.2. The obtained Aerosol optical properties (Aerosol optical thickness, Ångström exponent, Single scattering albedo, and etc.) and size distribution volume clearly showed spatial and temporal variability in Japan area. In this study, we present the temporal and spatial variability of Aerosol optical properties at several Japan sites, applied to validation of satellite and numerical models. This project is validation satellite of GCOM-C, JAXA. The GCOM-C satellite scheduled to be launched in early 2017.

A geostatistical analysis of small-scale spatial variability in bacterial abundance and community structure in salt marsh creek bank sediments

NASA Technical Reports Server (NTRS)

Franklin, Rima B.; Blum, Linda K.; McComb, Alison C.; Mills, Aaron L.

2002-01-01

Small-scale variations in bacterial abundance and community structure were examined in salt marsh sediments from Virginia's eastern shore. Samples were collected at 5 cm intervals (horizontally) along a 50 cm elevation gradient, over a 215 cm horizontal transect. For each sample, bacterial abundance was determined using acridine orange direct counts and community structure was analyzed using randomly amplified polymorphic DNA fingerprinting of whole-community DNA extracts. A geostatistical analysis was used to determine the degree of spatial autocorrelation among the samples, for each variable and each direction (horizontal and vertical). The proportion of variance in bacterial abundance that could be accounted for by the spatial model was quite high (vertical: 60%, horizontal: 73%); significant autocorrelation was found among samples separated by 25 cm in the vertical direction and up to 115 cm horizontally. In contrast, most of the variability in community structure was not accounted for by simply considering the spatial separation of samples (vertical: 11%, horizontal: 22%), and must reflect variability from other parameters (e.g., variation at other spatial scales, experimental error, or environmental heterogeneity). Microbial community patch size based upon overall similarity in community structure varied between 17 cm (vertical) and 35 cm (horizontal). Overall, variability due to horizontal position (distance from the creek bank) was much smaller than that due to vertical position (elevation) for both community properties assayed. This suggests that processes more correlated with elevation (e.g., drainage and redox potential) vary at a smaller scale (therefore producing smaller patch sizes) than processes controlled by distance from the creek bank. c2002 Federation of European Microbiological Societies. Published by Elsevier Science B.V. All rights reserved.

Multilevel discretized random field models with 'spin' correlations for the simulation of environmental spatial data

NASA Astrophysics Data System (ADS)

Žukovič, Milan; Hristopulos, Dionissios T.

2009-02-01

A current problem of practical significance is how to analyze large, spatially distributed, environmental data sets. The problem is more challenging for variables that follow non-Gaussian distributions. We show by means of numerical simulations that the spatial correlations between variables can be captured by interactions between 'spins'. The spins represent multilevel discretizations of environmental variables with respect to a number of pre-defined thresholds. The spatial dependence between the 'spins' is imposed by means of short-range interactions. We present two approaches, inspired by the Ising and Potts models, that generate conditional simulations of spatially distributed variables from samples with missing data. Currently, the sampling and simulation points are assumed to be at the nodes of a regular grid. The conditional simulations of the 'spin system' are forced to respect locally the sample values and the system statistics globally. The second constraint is enforced by minimizing a cost function representing the deviation between normalized correlation energies of the simulated and the sample distributions. In the approach based on the Nc-state Potts model, each point is assigned to one of Nc classes. The interactions involve all the points simultaneously. In the Ising model approach, a sequential simulation scheme is used: the discretization at each simulation level is binomial (i.e., ± 1). Information propagates from lower to higher levels as the simulation proceeds. We compare the two approaches in terms of their ability to reproduce the target statistics (e.g., the histogram and the variogram of the sample distribution), to predict data at unsampled locations, as well as in terms of their computational complexity. The comparison is based on a non-Gaussian data set (derived from a digital elevation model of the Walker Lake area, Nevada, USA). We discuss the impact of relevant simulation parameters, such as the domain size, the number of discretization levels, and the initial conditions.

Linking 3D spatial models of fuels and fire: Effects of spatial heterogeneity on fire behavior

Treesearch

Russell A. Parsons; William E. Mell; Peter McCauley

2011-01-01

Crownfire endangers fire fighters and can have severe ecological consequences. Prediction of fire behavior in tree crowns is essential to informed decisions in fire management. Current methods used in fire management do not address variability in crown fuels. New mechanistic physics-based fire models address convective heat transfer with computational fluid dynamics (...

Selecting Optimal Random Forest Predictive Models: A Case Study on Predicting the Spatial Distribution of Seabed Hardness

PubMed Central

Li, Jin; Tran, Maggie; Siwabessy, Justy

2016-01-01

Spatially continuous predictions of seabed hardness are important baseline environmental information for sustainable management of Australia’s marine jurisdiction. Seabed hardness is often inferred from multibeam backscatter data with unknown accuracy and can be inferred from underwater video footage at limited locations. In this study, we classified the seabed into four classes based on two new seabed hardness classification schemes (i.e., hard90 and hard70). We developed optimal predictive models to predict seabed hardness using random forest (RF) based on the point data of hardness classes and spatially continuous multibeam data. Five feature selection (FS) methods that are variable importance (VI), averaged variable importance (AVI), knowledge informed AVI (KIAVI), Boruta and regularized RF (RRF) were tested based on predictive accuracy. Effects of highly correlated, important and unimportant predictors on the accuracy of RF predictive models were examined. Finally, spatial predictions generated using the most accurate models were visually examined and analysed. This study confirmed that: 1) hard90 and hard70 are effective seabed hardness classification schemes; 2) seabed hardness of four classes can be predicted with a high degree of accuracy; 3) the typical approach used to pre-select predictive variables by excluding highly correlated variables needs to be re-examined; 4) the identification of the important and unimportant predictors provides useful guidelines for further improving predictive models; 5) FS methods select the most accurate predictive model(s) instead of the most parsimonious ones, and AVI and Boruta are recommended for future studies; and 6) RF is an effective modelling method with high predictive accuracy for multi-level categorical data and can be applied to ‘small p and large n’ problems in environmental sciences. Additionally, automated computational programs for AVI need to be developed to increase its computational efficiency and caution should be taken when applying filter FS methods in selecting predictive models. PMID:26890307

Selecting Optimal Random Forest Predictive Models: A Case Study on Predicting the Spatial Distribution of Seabed Hardness.

PubMed

Li, Jin; Tran, Maggie; Siwabessy, Justy

2016-01-01

Spatially continuous predictions of seabed hardness are important baseline environmental information for sustainable management of Australia's marine jurisdiction. Seabed hardness is often inferred from multibeam backscatter data with unknown accuracy and can be inferred from underwater video footage at limited locations. In this study, we classified the seabed into four classes based on two new seabed hardness classification schemes (i.e., hard90 and hard70). We developed optimal predictive models to predict seabed hardness using random forest (RF) based on the point data of hardness classes and spatially continuous multibeam data. Five feature selection (FS) methods that are variable importance (VI), averaged variable importance (AVI), knowledge informed AVI (KIAVI), Boruta and regularized RF (RRF) were tested based on predictive accuracy. Effects of highly correlated, important and unimportant predictors on the accuracy of RF predictive models were examined. Finally, spatial predictions generated using the most accurate models were visually examined and analysed. This study confirmed that: 1) hard90 and hard70 are effective seabed hardness classification schemes; 2) seabed hardness of four classes can be predicted with a high degree of accuracy; 3) the typical approach used to pre-select predictive variables by excluding highly correlated variables needs to be re-examined; 4) the identification of the important and unimportant predictors provides useful guidelines for further improving predictive models; 5) FS methods select the most accurate predictive model(s) instead of the most parsimonious ones, and AVI and Boruta are recommended for future studies; and 6) RF is an effective modelling method with high predictive accuracy for multi-level categorical data and can be applied to 'small p and large n' problems in environmental sciences. Additionally, automated computational programs for AVI need to be developed to increase its computational efficiency and caution should be taken when applying filter FS methods in selecting predictive models.

Mapping snow depth return levels: smooth spatial modeling versus station interpolation

NASA Astrophysics Data System (ADS)

Blanchet, J.; Lehning, M.

2010-12-01

For adequate risk management in mountainous countries, hazard maps for extreme snow events are needed. This requires the computation of spatial estimates of return levels. In this article we use recent developments in extreme value theory and compare two main approaches for mapping snow depth return levels from in situ measurements. The first one is based on the spatial interpolation of pointwise extremal distributions (the so-called Generalized Extreme Value distribution, GEV henceforth) computed at station locations. The second one is new and based on the direct estimation of a spatially smooth GEV distribution with the joint use of all stations. We compare and validate the different approaches for modeling annual maximum snow depth measured at 100 sites in Switzerland during winters 1965-1966 to 2007-2008. The results show a better performance of the smooth GEV distribution fitting, in particular where the station network is sparser. Smooth return level maps can be computed from the fitted model without any further interpolation. Their regional variability can be revealed by removing the altitudinal dependent covariates in the model. We show how return levels and their regional variability are linked to the main climatological patterns of Switzerland.

From stage to age in variable environments: life expectancy and survivorship.

PubMed

Tuljapurkar, Shripad; Horvitz, Carol C

2006-06-01

Stage-based demographic data are now available on many species of plants and some animals, and they often display temporal and spatial variability. We provide exact formulas to compute age-specific life expectancy and survivorship from stage-based data for three models of temporal variability: cycles, serially independent random variation, and a Markov chain. These models provide a comprehensive description of patterns of temporal variation. Our formulas describe the effects of cohort (birth) environmental condition on mortality at all ages, and of the effects on survivorship of environmental variability experienced over the course of life. This paper complements existing methods for time-invariant stage-based data, and adds to the information on population growth and dynamics available from stochastic demography.

How does spatial variability of climate affect catchment streamflow predictions?

EPA Science Inventory

Spatial variability of climate can negatively affect catchment streamflow predictions if it is not explicitly accounted for in hydrologic models. In this paper, we examine the changes in streamflow predictability when a hydrologic model is run with spatially variable (distribute...

Spatial representation and cognitive modulation of response variability in the lateral intraparietal area priority map.

PubMed

Falkner, Annegret L; Goldberg, Michael E; Krishna, B Suresh

2013-10-09

The lateral intraparietal area (LIP) in the macaque contains a priority-based representation of the visual scene. We previously showed that the mean spike rate of LIP neurons is strongly influenced by spatially wide-ranging surround suppression in a manner that effectively sharpens the priority map. Reducing response variability can also improve the precision of LIP's priority map. We show that when a monkey plans a visually guided delayed saccade with an intervening distractor, variability (measured by the Fano factor) decreases both for neurons representing the saccade goal and for neurons representing the broad spatial surround. The reduction in Fano factor is maximal for neurons representing the saccade goal and steadily decreases for neurons representing more distant locations. LIP Fano factor changes are behaviorally significant: increasing expected reward leads to lower variability for the LIP representation of both the target and distractor locations, and trials with shorter latency saccades are associated with lower Fano factors in neurons representing the surround. Thus, the LIP Fano factor reflects both stimulus and behavioral engagement. Quantitative modeling shows that the interaction between mean spike count and target-receptive field (RF) distance in the surround during the predistractor epoch is multiplicative: the Fano factor increases more steeply with mean spike count further away from the RF. A negative-binomial model for LIP spike counts captures these findings quantitatively, suggests underlying mechanisms based on trial-by-trial variations in mean spike rate or burst-firing patterns, and potentially provides a principled framework to account simultaneously for the previously observed unsystematic relationships between spike rate and variability in different brain areas.

Variability, trends, and drivers of regional fluctuations in Australian fire activity

NASA Astrophysics Data System (ADS)

Earl, Nick; Simmonds, Ian

2017-07-01

Throughout the world fire regimes are determined by climate, vegetation, and anthropogenic factors, and they have great spatial and temporal variability. The availability of high-quality satellite data has revolutionized fire monitoring, allowing for a more consistent and comprehensive evaluation of temporal and spatial patterns. Here we utilize a satellite based "active fire" (AF) product to statistically analyze 2001-2015 variability and trends in Australian fire activity and link this to precipitation and large-scale atmospheric structures (namely, the El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD)) known to have potential for predicting fire activity in different regions. It is found that Australian fire activity is decreasing (during summer (December-February)) or stable, with high temporal and spatial variability. Eastern New South Wales (NSW) has the strongest decreasing trend (to the 1% confidence level), especially during the winter (JJA) season. Other significantly decreasing areas are Victoria/NSW, Tasmania, and South-east Queensland. These decreasing fire regions are relatively highly populated, so we suggest that the declining trends are due to improved fire management, reducing the size and duration of bush fires. Almost half of all Australian AFs occur during spring (September-November). We show that there is considerable potential throughout Australia for a skillful forecast for future season fire activity based on current and previous precipitation activity, ENSO phase, and to a lesser degree, the IOD phase. This is highly variable, depending on location, e.g., the IOD phase is for more indicative of fire activity in southwest Western Australia than for Queensland.

Unsupervised Unmixing of Hyperspectral Images Accounting for Endmember Variability.

PubMed

Halimi, Abderrahim; Dobigeon, Nicolas; Tourneret, Jean-Yves

2015-12-01

This paper presents an unsupervised Bayesian algorithm for hyperspectral image unmixing, accounting for endmember variability. The pixels are modeled by a linear combination of endmembers weighted by their corresponding abundances. However, the endmembers are assumed random to consider their variability in the image. An additive noise is also considered in the proposed model, generalizing the normal compositional model. The proposed algorithm exploits the whole image to benefit from both spectral and spatial information. It estimates both the mean and the covariance matrix of each endmember in the image. This allows the behavior of each material to be analyzed and its variability to be quantified in the scene. A spatial segmentation is also obtained based on the estimated abundances. In order to estimate the parameters associated with the proposed Bayesian model, we propose to use a Hamiltonian Monte Carlo algorithm. The performance of the resulting unmixing strategy is evaluated through simulations conducted on both synthetic and real data.

Subwavelength grating enabled on-chip ultra-compact optical true time delay line

PubMed Central

Wang, Junjia; Ashrafi, Reza; Adams, Rhys; Glesk, Ivan; Gasulla, Ivana; Capmany, José; Chen, Lawrence R.

2016-01-01

An optical true time delay line (OTTDL) is a basic photonic building block that enables many microwave photonic and optical processing operations. The conventional design for an integrated OTTDL that is based on spatial diversity uses a length-variable waveguide array to create the optical time delays, which can introduce complexities in the integrated circuit design. Here we report the first ever demonstration of an integrated index-variable OTTDL that exploits spatial diversity in an equal length waveguide array. The approach uses subwavelength grating waveguides in silicon-on-insulator (SOI), which enables the realization of OTTDLs having a simple geometry and that occupy a compact chip area. Moreover, compared to conventional wavelength-variable delay lines with a few THz operation bandwidth, our index-variable OTTDL has an extremely broad operation bandwidth practically exceeding several tens of THz, which supports operation for various input optical signals with broad ranges of central wavelength and bandwidth. PMID:27457024

Subwavelength grating enabled on-chip ultra-compact optical true time delay line.

PubMed

Wang, Junjia; Ashrafi, Reza; Adams, Rhys; Glesk, Ivan; Gasulla, Ivana; Capmany, José; Chen, Lawrence R

2016-07-26

An optical true time delay line (OTTDL) is a basic photonic building block that enables many microwave photonic and optical processing operations. The conventional design for an integrated OTTDL that is based on spatial diversity uses a length-variable waveguide array to create the optical time delays, which can introduce complexities in the integrated circuit design. Here we report the first ever demonstration of an integrated index-variable OTTDL that exploits spatial diversity in an equal length waveguide array. The approach uses subwavelength grating waveguides in silicon-on-insulator (SOI), which enables the realization of OTTDLs having a simple geometry and that occupy a compact chip area. Moreover, compared to conventional wavelength-variable delay lines with a few THz operation bandwidth, our index-variable OTTDL has an extremely broad operation bandwidth practically exceeding several tens of THz, which supports operation for various input optical signals with broad ranges of central wavelength and bandwidth.

Patterns and scaling properties of surface soil moisture in an agricultural landscape: An ecohydrological modeling study

NASA Astrophysics Data System (ADS)

Korres, W.; Reichenau, T. G.; Schneider, K.

2013-08-01

Soil moisture is a key variable in hydrology, meteorology and agriculture. Soil moisture, and surface soil moisture in particular, is highly variable in space and time. Its spatial and temporal patterns in agricultural landscapes are affected by multiple natural (precipitation, soil, topography, etc.) and agro-economic (soil management, fertilization, etc.) factors, making it difficult to identify unequivocal cause and effect relationships between soil moisture and its driving variables. The goal of this study is to characterize and analyze the spatial and temporal patterns of surface soil moisture (top 20 cm) in an intensively used agricultural landscape (1100 km2 northern part of the Rur catchment, Western Germany) and to determine the dominant factors and underlying processes controlling these patterns. A second goal is to analyze the scaling behavior of surface soil moisture patterns in order to investigate how spatial scale affects spatial patterns. To achieve these goals, a dynamically coupled, process-based and spatially distributed ecohydrological model was used to analyze the key processes as well as their interactions and feedbacks. The model was validated for two growing seasons for the three main crops in the investigation area: Winter wheat, sugar beet, and maize. This yielded RMSE values for surface soil moisture between 1.8 and 7.8 vol.% and average RMSE values for all three crops of 0.27 kg m-2 for total aboveground biomass and 0.93 for green LAI. Large deviations of measured and modeled soil moisture can be explained by a change of the infiltration properties towards the end of the growing season, especially in maize fields. The validated model was used to generate daily surface soil moisture maps, serving as a basis for an autocorrelation analysis of spatial patterns and scale. Outside of the growing season, surface soil moisture patterns at all spatial scales depend mainly upon soil properties. Within the main growing season, larger scale patterns that are induced by soil properties are superimposed by the small scale land use pattern and the resulting small scale variability of evapotranspiration. However, this influence decreases at larger spatial scales. Most precipitation events cause temporarily higher surface soil moisture autocorrelation lengths at all spatial scales for a short time even beyond the autocorrelation lengths induced by soil properties. The relation of daily spatial variance to the spatial scale of the analysis fits a power law scaling function, with negative values of the scaling exponent, indicating a decrease in spatial variability with increasing spatial resolution. High evapotranspiration rates cause an increase in the small scale soil moisture variability, thus leading to large negative values of the scaling exponent. Utilizing a multiple regression analysis, we found that 53% of the variance of the scaling exponent can be explained by a combination of an independent LAI parameter and the antecedent precipitation.

Estimation of the temperature spatial variability in confined spaces based on thermal imaging

NASA Astrophysics Data System (ADS)

Augustyn, Grzegorz; Jurasz, Jakub; Jurczyk, Krzysztof; Korbiel, Tomasz; Mikulik, Jerzy; Pawlik, Marcin; Rumin, Rafał

2017-11-01

In developed countries the salaries of office workers are several times higher than the total cost of maintaining and operating the building. Therefore even a small improvement in human work productivity and performance as a result of enhancing the quality of their work environment may lead to a meaningful economic benefits. The air temperature is the most commonly used indicator in assessing the indoor environment quality. What is more, it is well known that thermal comfort has the biggest impact on employees performance and their ability to work efficiently. In majority of office buildings, indoor temperature is managed by heating, ventilation and air conditioning (HVAC) appliances. However the way how they are currently managed and controlled leads to the nonhomogeneous distribution of temperature in certain space. An approach to determining the spatial variability of temperature in confined spaces was introduced based on thermal imaging temperature measurements. The conducted research and obtained results enabled positive verification of the method and creation of surface plot illustrating the temperature variability.

Disentangling the drivers of taxonomic and phylogenetic beta diversities in disturbed and undisturbed subtropical forests

NASA Astrophysics Data System (ADS)

Liu, Jinliang; Qian, Hong; Jin, Yi; Wu, Chuping; Chen, Jianhua; Yu, Shuquan; Wei, Xinliang; Jin, Xiaofeng; Liu, Jiajia; Yu, Mingjian

2016-10-01

Understanding the relative importance of dispersal limitation and environmental filtering processes in structuring the beta diversities of subtropical forests in human disturbed landscapes is still limited. Here we used taxonomic (TBD) and phylogenetic (PBD), including terminal PBD (PBDt) and basal PBD (PBDb), beta diversity indices to quantify the taxonomic and phylogenetic turnovers at different depths of evolutionary history in disturbed and undisturbed subtropical forests. Multiple linear regression model and distance-based redundancy analysis were used to disentangle the relative importance of environmental and spatial variables. Environmental variables were significantly correlated with TBD and PBDt metrics. Temperature and precipitation were major environmental drivers of beta diversity patterns, which explained 7-27% of the variance in TBD and PBDt, whereas the spatial variables independently explained less than 1% of the variation for all forests. The relative importance of environmental and spatial variables differed between disturbed and undisturbed forests (e.g., when Bray-Curtis was used as a beta diversity metric, environmental variable had a significant effect on beta diversity for disturbed forests but had no effect on undisturbed forests). We conclude that environmental filtering plays a more important role than geographical limitation and disturbance history in driving taxonomic and terminal phylogenetic beta diversity.

Disentangling the drivers of taxonomic and phylogenetic beta diversities in disturbed and undisturbed subtropical forests

PubMed Central

Liu, Jinliang; Qian, Hong; Jin, Yi; Wu, Chuping; Chen, Jianhua; Yu, Shuquan; Wei, Xinliang; Jin, Xiaofeng; Liu, Jiajia; Yu, Mingjian

2016-01-01

Understanding the relative importance of dispersal limitation and environmental filtering processes in structuring the beta diversities of subtropical forests in human disturbed landscapes is still limited. Here we used taxonomic (TBD) and phylogenetic (PBD), including terminal PBD (PBDt) and basal PBD (PBDb), beta diversity indices to quantify the taxonomic and phylogenetic turnovers at different depths of evolutionary history in disturbed and undisturbed subtropical forests. Multiple linear regression model and distance-based redundancy analysis were used to disentangle the relative importance of environmental and spatial variables. Environmental variables were significantly correlated with TBD and PBDt metrics. Temperature and precipitation were major environmental drivers of beta diversity patterns, which explained 7–27% of the variance in TBD and PBDt, whereas the spatial variables independently explained less than 1% of the variation for all forests. The relative importance of environmental and spatial variables differed between disturbed and undisturbed forests (e.g., when Bray-Curtis was used as a beta diversity metric, environmental variable had a significant effect on beta diversity for disturbed forests but had no effect on undisturbed forests). We conclude that environmental filtering plays a more important role than geographical limitation and disturbance history in driving taxonomic and terminal phylogenetic beta diversity. PMID:27775021

Towards a More Biologically-meaningful Climate Characterization: Variability in Space and Time at Multiple Scales

NASA Astrophysics Data System (ADS)

Christianson, D. S.; Kaufman, C. G.; Kueppers, L. M.; Harte, J.

2013-12-01

Sampling limitations and current modeling capacity justify the common use of mean temperature values in summaries of historical climate and future projections. However, a monthly mean temperature representing a 1-km2 area on the landscape is often unable to capture the climate complexity driving organismal and ecological processes. Estimates of variability in addition to mean values are more biologically meaningful and have been shown to improve projections of range shifts for certain species. Historical analyses of variance and extreme events at coarse spatial scales, as well as coarse-scale projections, show increasing temporal variability in temperature with warmer means. Few studies have considered how spatial variance changes with warming, and analysis for both temporal and spatial variability across scales is lacking. It is unclear how the spatial variability of fine-scale conditions relevant to plant and animal individuals may change given warmer coarse-scale mean values. A change in spatial variability will affect the availability of suitable habitat on the landscape and thus, will influence future species ranges. By characterizing variability across both temporal and spatial scales, we can account for potential bias in species range projections that use coarse climate data and enable improvements to current models. In this study, we use temperature data at multiple spatial and temporal scales to characterize spatial and temporal variability under a warmer climate, i.e., increased mean temperatures. Observational data from the Sierra Nevada (California, USA), experimental climate manipulation data from the eastern and western slopes of the Rocky Mountains (Colorado, USA), projected CMIP5 data for California (USA) and observed PRISM data (USA) allow us to compare characteristics of a mean-variance relationship across spatial scales ranging from sub-meter2 to 10,000 km2 and across temporal scales ranging from hours to decades. Preliminary spatial analysis at fine-spatial scales (sub-meter to 10-meter) shows greater temperature variability with warmer mean temperatures. This is inconsistent with the inherent assumption made in current species distribution models that fine-scale variability is static, implying that current projections of future species ranges may be biased -- the direction and magnitude requiring further study. While we focus our findings on the cross-scaling characteristics of temporal and spatial variability, we also compare the mean-variance relationship between 1) experimental climate manipulations and observed conditions and 2) temporal versus spatial variance, i.e., variability in a time-series at one location vs. variability across a landscape at a single time. The former informs the rich debate concerning the ability to experimentally mimic a warmer future. The latter informs space-for-time study design and analyses, as well as species persistence via a combined spatiotemporal probability of suitable future habitat.

Testing the use of standardised indices and GRACE satellite data to estimate the European 2015 groundwater drought in near-real time

NASA Astrophysics Data System (ADS)

Van Loon, Anne F.; Kumar, Rohini; Mishra, Vimal

2017-04-01

In 2015, central and eastern Europe were affected by a severe drought. This event has recently been studied from meteorological and streamflow perspective, but no analysis of the groundwater situation has been performed. One of the reasons is that real-time groundwater level observations often are not available. In this study, we evaluate two alternative approaches to quantify the 2015 groundwater drought over two regions in southern Germany and eastern Netherlands. The first approach is based on spatially explicit relationships between meteorological conditions and historic groundwater level observations. The second approach uses the Gravity Recovery Climate Experiment (GRACE) terrestrial water storage (TWS) and groundwater anomalies derived from GRACE-TWS and (near-)surface storage simulations by the Global Land Data Assimilation System (GLDAS) models. We combined the monthly groundwater observations from 2040 wells to establish the spatially varying optimal accumulation period between the Standardised Groundwater Index (SGI) and the Standardized Precipitation Evapotranspiration Index (SPEI) at a 0.25° gridded scale. The resulting optimal accumulation periods range between 1 and more than 24 months, indicating strong spatial differences in groundwater response time to meteorological input over the region. Based on the estimated optimal accumulation periods and available meteorological time series, we reconstructed the groundwater anomalies up to 2015 and found that in Germany a uniform severe groundwater drought persisted for several months during this year, whereas the Netherlands appeared to have relatively high groundwater levels. The differences between this event and the 2003 European benchmark drought are striking. The 2003 groundwater drought was less uniformly pronounced, both in the Netherlands and Germany. This is because slowly responding wells (the ones with optimal accumulation periods of more than 12 months) still were above average from the wet year of 2002, which experienced severe flooding in central Europe. GRACE-TWS and GRACE-based groundwater anomalies did not capture the spatial variability of the 2003 and 2015 drought events satisfactorily. GRACE-TWS did show that both 2003 and 2015 were relatively dry, but the differences between Germany and the Netherlands in 2015 and the spatially variable groundwater drought pattern in 2003 were not captured. This could be associated with the coarse spatial scale of GRACE. The simulated groundwater anomalies based on GRACE-TWS deviated considerably from the GRACE-TWS signal and from observed groundwater anomalies. The uncertainty in the GRACE-based groundwater anomalies mainly results from uncertainties in the simulation of soil moisture by the different GLDAS models. The GRACE-based groundwater anomalies are therefore not suitable for use in real-time groundwater drought monitoring in our case study regions. The alternative approach based on the spatially variable relationship between meteorological conditions and groundwater levels is more suitable to quantify groundwater drought in near real-time. Compared to the meteorological drought and streamflow drought (described in previous studies), the groundwater drought of 2015 had a more pronounced spatial variability in its response to meteorological conditions, with some areas primarily influenced by short-term meteorological deficits and others influenced by meteorological deficits accumulated over the preceding 2 years or more. In drought management, this information is very useful and our approach to quantify groundwater drought can be used until real-time groundwater observations become readily available.

A Spatial Framework to Map Heat Health Risks at Multiple Scales.

PubMed

Ho, Hung Chak; Knudby, Anders; Huang, Wei

2015-12-18

In the last few decades extreme heat events have led to substantial excess mortality, most dramatically in Central Europe in 2003, in Russia in 2010, and even in typically cool locations such as Vancouver, Canada, in 2009. Heat-related morbidity and mortality is expected to increase over the coming centuries as the result of climate-driven global increases in the severity and frequency of extreme heat events. Spatial information on heat exposure and population vulnerability may be combined to map the areas of highest risk and focus mitigation efforts there. However, a mismatch in spatial resolution between heat exposure and vulnerability data can cause spatial scale issues such as the Modifiable Areal Unit Problem (MAUP). We used a raster-based model to integrate heat exposure and vulnerability data in a multi-criteria decision analysis, and compared it to the traditional vector-based model. We then used the Getis-Ord G(i) index to generate spatially smoothed heat risk hotspot maps from fine to coarse spatial scales. The raster-based model allowed production of maps at spatial resolution, more description of local-scale heat risk variability, and identification of heat-risk areas not identified with the vector-based approach. Spatial smoothing with the Getis-Ord G(i) index produced heat risk hotspots from local to regional spatial scale. The approach is a framework for reducing spatial scale issues in future heat risk mapping, and for identifying heat risk hotspots at spatial scales ranging from the block-level to the municipality level.

Spatio-Temporal Variability of Groundwater Storage in India

NASA Technical Reports Server (NTRS)

Bhanja, Soumendra; Rodell, Matthew; Li, Bailing; Mukherjee, Abhijit

2016-01-01

Groundwater level measurements from 3907 monitoring wells, distributed within 22 major river basins of India, are assessed to characterize their spatial and temporal variability. Ground water storage (GWS) anomalies (relative to the long-term mean) exhibit strong seasonality, with annual maxima observed during the monsoon season and minima during pre-monsoon season. Spatial variability of GWS anomalies increases with the extent of measurements, following the power law relationship, i.e., log-(spatial variability) is linearly dependent on log-(spatial extent).In addition, the impact of well spacing on spatial variability and the power law relationship is investigated. We found that the mean GWS anomaly sampled at a 0.25 degree grid scale closes to unweighted average over all wells. The absolute error corresponding to each basin grows with increasing scale, i.e., from 0.25 degree to 1 degree. It was observed that small changes in extent could create very large changes in spatial variability at large grid scales. Spatial variability of GWS anomaly has been found to vary with climatic conditions. To our knowledge, this is the first study of the effects of well spacing on groundwater spatial variability. The results may be useful for interpreting large scale groundwater variations from unevenly spaced or sparse groundwater well observations or for siting and prioritizing wells in a network for groundwater management. The output of this study could be used to maintain a cost effective groundwater monitoring network in the study region and the approach can also be used in other parts of the globe.

Spatio-temporal variability of groundwater storage in India.

PubMed

Bhanja, Soumendra N; Rodell, Matthew; Li, Bailing; Mukherjee, Abhijit

2017-01-01

Groundwater level measurements from 3907 monitoring wells, distributed within 22 major river basins of India, are assessed to characterize their spatial and temporal variability. Groundwater storage (GWS) anomalies (relative to the long-term mean) exhibit strong seasonality, with annual maxima observed during the monsoon season and minima during pre-monsoon season. Spatial variability of GWS anomalies increases with the extent of measurements, following the power law relationship, i.e., log-(spatial variability) is linearly dependent on log-(spatial extent). In addition, the impact of well spacing on spatial variability and the power law relationship is investigated. We found that the mean GWS anomaly sampled at a 0.25 degree grid scale closes to unweighted average over all wells. The absolute error corresponding to each basin grows with increasing scale, i.e., from 0.25 degree to 1 degree. It was observed that small changes in extent could create very large changes in spatial variability at large grid scales. Spatial variability of GWS anomaly has been found to vary with climatic conditions. To our knowledge, this is the first study of the effects of well spacing on groundwater spatial variability. The results may be useful for interpreting large scale groundwater variations from unevenly spaced or sparse groundwater well observations or for siting and prioritizing wells in a network for groundwater management. The output of this study could be used to maintain a cost effective groundwater monitoring network in the study region and the approach can also be used in other parts of the globe.

Spatial Variance in Resting fMRI Networks of Schizophrenia Patients: An Independent Vector Analysis

PubMed Central

Gopal, Shruti; Miller, Robyn L.; Michael, Andrew; Adali, Tulay; Cetin, Mustafa; Rachakonda, Srinivas; Bustillo, Juan R.; Cahill, Nathan; Baum, Stefi A.; Calhoun, Vince D.

2016-01-01

Spatial variability in resting functional MRI (fMRI) brain networks has not been well studied in schizophrenia, a disease known for both neurodevelopmental and widespread anatomic changes. Motivated by abundant evidence of neuroanatomical variability from previous studies of schizophrenia, we draw upon a relatively new approach called independent vector analysis (IVA) to assess this variability in resting fMRI networks. IVA is a blind-source separation algorithm, which segregates fMRI data into temporally coherent but spatially independent networks and has been shown to be especially good at capturing spatial variability among subjects in the extracted networks. We introduce several new ways to quantify differences in variability of IVA-derived networks between schizophrenia patients (SZs = 82) and healthy controls (HCs = 89). Voxelwise amplitude analyses showed significant group differences in the spatial maps of auditory cortex, the basal ganglia, the sensorimotor network, and visual cortex. Tests for differences (HC-SZ) in the spatial variability maps suggest, that at rest, SZs exhibit more activity within externally focused sensory and integrative network and less activity in the default mode network thought to be related to internal reflection. Additionally, tests for difference of variance between groups further emphasize that SZs exhibit greater network variability. These results, consistent with our prediction of increased spatial variability within SZs, enhance our understanding of the disease and suggest that it is not just the amplitude of connectivity that is different in schizophrenia, but also the consistency in spatial connectivity patterns across subjects. PMID:26106217

Spatial variability of E. coli in an urban salt-wedge estuary.

PubMed

Jovanovic, Dusan; Coleman, Rhys; Deletic, Ana; McCarthy, David

2017-01-15

This study investigated the spatial variability of a common faecal indicator organism, Escherichia coli, in an urban salt-wedge estuary in Melbourne, Australia. Data were collected through comprehensive depth profiling in the water column at four sites and included measurements of temperature, salinity, pH, dissolved oxygen, turbidity, and E. coli concentrations. Vertical variability of E. coli was closely related to the salt-wedge dynamics; in the presence of a salt-wedge, there was a significant decrease in E. coli concentrations with depth. Transverse variability was low and was most likely dwarfed by the analytical uncertainties of E. coli measurements. Longitudinal variability was also low, potentially reflecting minimal die-off, settling, and additional inputs entering along the estuary. These results were supported by a simple mixing model that predicted E. coli concentrations based on salinity measurements. Additionally, an assessment of a sentinel monitoring station suggested routine monitoring locations may produce conservative estimates of E. coli concentrations in stratified estuaries. Copyright © 2016 Elsevier Ltd. All rights reserved.

Metacommunity composition of web-spiders in a fragmented neotropical forest: relative importance of environmental and spatial effects.

PubMed

Baldissera, Ronei; Rodrigues, Everton N L; Hartz, Sandra M

2012-01-01

The distribution of beta diversity is shaped by factors linked to environmental and spatial control. The relative importance of both processes in structuring spider metacommunities has not yet been investigated in the Atlantic Forest. The variance explained by purely environmental, spatially structured environmental, and purely spatial components was compared for a metacommunity of web spiders. The study was carried out in 16 patches of Atlantic Forest in southern Brazil. Field work was done in one landscape mosaic representing a slight gradient of urbanization. Environmental variables encompassed plot- and patch-level measurements and a climatic matrix, while principal coordinates of neighbor matrices (PCNMs) acted as spatial variables. A forward selection procedure was carried out to select environmental and spatial variables influencing web-spider beta diversity. Variation partitioning was used to estimate the contribution of pure environmental and pure spatial effects and their shared influence on beta-diversity patterns, and to estimate the relative importance of selected environmental variables. Three environmental variables (bush density, land use in the surroundings of patches, and shape of patches) and two spatial variables were selected by forward selection procedures. Variation partitioning revealed that 15% of the variation of beta diversity was explained by a combination of environmental and PCNM variables. Most of this variation (12%) corresponded to pure environmental and spatially environmental structure. The data indicated that (1) spatial legacy was not important in explaining the web-spider beta diversity; (2) environmental predictors explained a significant portion of the variation in web-spider composition; (3) one-third of environmental variation was due to a spatial structure that jointly explains variation in species distributions. We were able to detect important factors related to matrix management influencing the web-spider beta-diversity patterns, which are probably linked to historical deforestation events.

Analysis of watershed topography effects on summer precipitation variability in the southwestern United States

NASA Astrophysics Data System (ADS)

Sohoulande Djebou, Dagbegnon C.; Singh, Vijay P.; Frauenfeld, Oliver W.

2014-04-01

With climate change, precipitation variability is projected to increase. The present study investigates the potential interactions between watershed characteristics and precipitation variability. The watershed is considered as a functional unit that may impact seasonal precipitation. The study uses historical precipitation data from 370 meteorological stations over the last five decades, and digital elevation data from regional watersheds in the southwestern United States. This domain is part of the North American Monsoon region, and the summer period (June-July-August, JJA) was considered. Based on an initial analysis for 1895-2011, the JJA precipitation accounts, on average, for 22-43% of the total annual precipitation, with higher percentages in the arid part of the region. The unique contribution of this research is that entropy theory is used to address precipitation variability in time and space. An entropy-based disorder index was computed for each station's precipitation record. The JJA total precipitation and number of precipitation events were considered in the analysis. The precipitation variability potentially induced by watershed topography was investigated using spatial regionalization combining principal component and cluster analysis. It was found that the disorder in precipitation total and number of events tended to be higher in arid regions. The spatial pattern showed that the entropy-based variability in precipitation amount and number of events gradually increased from east to west in the southwestern United States. Regarding the watershed topography influence on summer precipitation patterns, hilly relief has a stabilizing effect on seasonal precipitation variability in time and space. The results show the necessity to include watershed topography in global and regional climate model parameterizations.

Unsupervised classification of multivariate geostatistical data: Two algorithms

NASA Astrophysics Data System (ADS)

Romary, Thomas; Ors, Fabien; Rivoirard, Jacques; Deraisme, Jacques

2015-12-01

With the increasing development of remote sensing platforms and the evolution of sampling facilities in mining and oil industry, spatial datasets are becoming increasingly large, inform a growing number of variables and cover wider and wider areas. Therefore, it is often necessary to split the domain of study to account for radically different behaviors of the natural phenomenon over the domain and to simplify the subsequent modeling step. The definition of these areas can be seen as a problem of unsupervised classification, or clustering, where we try to divide the domain into homogeneous domains with respect to the values taken by the variables in hand. The application of classical clustering methods, designed for independent observations, does not ensure the spatial coherence of the resulting classes. Image segmentation methods, based on e.g. Markov random fields, are not adapted to irregularly sampled data. Other existing approaches, based on mixtures of Gaussian random functions estimated via the expectation-maximization algorithm, are limited to reasonable sample sizes and a small number of variables. In this work, we propose two algorithms based on adaptations of classical algorithms to multivariate geostatistical data. Both algorithms are model free and can handle large volumes of multivariate, irregularly spaced data. The first one proceeds by agglomerative hierarchical clustering. The spatial coherence is ensured by a proximity condition imposed for two clusters to merge. This proximity condition relies on a graph organizing the data in the coordinates space. The hierarchical algorithm can then be seen as a graph-partitioning algorithm. Following this interpretation, a spatial version of the spectral clustering algorithm is also proposed. The performances of both algorithms are assessed on toy examples and a mining dataset.

Shifts of environmental and phytoplankton variables in a regulated river: A spatial-driven analysis.

PubMed

Sabater-Liesa, Laia; Ginebreda, Antoni; Barceló, Damià

2018-06-18

The longitudinal structure of the environmental and phytoplankton variables was investigated in the Ebro River (NE Spain), which is heavily affected by water abstraction and regulation. A first exploration indicated that the phytoplankton community did not resist the impact of reservoirs and barely recovered downstream of them. The spatial analysis showed that the responses of the phytoplankton and environmental variables were not uniform. The two set of variables revealed spatial variability discontinuities and river fragmentation upstream and downstream from the reservoirs. Reservoirs caused the replacement of spatially heterogeneous habitats by homogeneous spatially distributed water bodies, these new environmental conditions downstream benefiting the opportunist and cosmopolitan algal taxa. The application of a spatial auto-regression model to algal biomass (chlorophyll-a) permitted to capture the relevance and contribution of extra-local influences in the river ecosystem. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Regulation of the demographic structure in isomorphic biphasic life cycles at the spatial fine scale.

PubMed

Vieira, Vasco Manuel Nobre de Carvalho da Silva; Mateus, Marcos Duarte

2014-01-01

Isomorphic biphasic algal life cycles often occur in the environment at ploidy abundance ratios (Haploid:Diploid) different from 1. Its spatial variability occurs within populations related to intertidal height and hydrodynamic stress, possibly reflecting the niche partitioning driven by their diverging adaptation to the environment argued necessary for their prevalence (evolutionary stability). Demographic models based in matrix algebra were developed to investigate which vital rates may efficiently generate an H:D variability at a fine spatial resolution. It was also taken into account time variation and type of life strategy. Ploidy dissimilarities in fecundity rates set an H:D spatial structure miss-fitting the ploidy fitness ratio. The same happened with ploidy dissimilarities in ramet growth whenever reproductive output dominated the population demography. Only through ploidy dissimilarities in looping rates (stasis, breakage and clonal growth) did the life cycle respond to a spatially heterogeneous environment efficiently creating a niche partition. Marginal locations were more sensitive than central locations. Related results have been obtained experimentally and numerically for widely different life cycles from the plant and animal kingdoms. Spore dispersal smoothed the effects of ploidy dissimilarities in fertility and enhanced the effects of ploidy dissimilarities looping rates. Ploidy dissimilarities in spore dispersal could also create the necessary niche partition, both over the space and time dimensions, even in spatial homogeneous environments and without the need for conditional differentiation of the ramets. Fine scale spatial variability may be the key for the prevalence of isomorphic biphasic life cycles, which has been neglected so far.

Regulation of the Demographic Structure in Isomorphic Biphasic Life Cycles at the Spatial Fine Scale

PubMed Central

Vieira, Vasco Manuel Nobre de Carvalho da Silva; Mateus, Marcos Duarte

2014-01-01

Isomorphic biphasic algal life cycles often occur in the environment at ploidy abundance ratios (Haploid:Diploid) different from 1. Its spatial variability occurs within populations related to intertidal height and hydrodynamic stress, possibly reflecting the niche partitioning driven by their diverging adaptation to the environment argued necessary for their prevalence (evolutionary stability). Demographic models based in matrix algebra were developed to investigate which vital rates may efficiently generate an H:D variability at a fine spatial resolution. It was also taken into account time variation and type of life strategy. Ploidy dissimilarities in fecundity rates set an H:D spatial structure miss-fitting the ploidy fitness ratio. The same happened with ploidy dissimilarities in ramet growth whenever reproductive output dominated the population demography. Only through ploidy dissimilarities in looping rates (stasis, breakage and clonal growth) did the life cycle respond to a spatially heterogeneous environment efficiently creating a niche partition. Marginal locations were more sensitive than central locations. Related results have been obtained experimentally and numerically for widely different life cycles from the plant and animal kingdoms. Spore dispersal smoothed the effects of ploidy dissimilarities in fertility and enhanced the effects of ploidy dissimilarities looping rates. Ploidy dissimilarities in spore dispersal could also create the necessary niche partition, both over the space and time dimensions, even in spatial homogeneous environments and without the need for conditional differentiation of the ramets. Fine scale spatial variability may be the key for the prevalence of isomorphic biphasic life cycles, which has been neglected so far. PMID:24658603

The Canadian Hydrological Model (CHM): A multi-scale, variable-complexity hydrological model for cold regions

NASA Astrophysics Data System (ADS)

Marsh, C.; Pomeroy, J. W.; Wheater, H. S.

2016-12-01

There is a need for hydrological land surface schemes that can link to atmospheric models, provide hydrological prediction at multiple scales and guide the development of multiple objective water predictive systems. Distributed raster-based models suffer from an overrepresentation of topography, leading to wasted computational effort that increases uncertainty due to greater numbers of parameters and initial conditions. The Canadian Hydrological Model (CHM) is a modular, multiphysics, spatially distributed modelling framework designed for representing hydrological processes, including those that operate in cold-regions. Unstructured meshes permit variable spatial resolution, allowing coarse resolutions at low spatial variability and fine resolutions as required. Model uncertainty is reduced by lessening the necessary computational elements relative to high-resolution rasters. CHM uses a novel multi-objective approach for unstructured triangular mesh generation that fulfills hydrologically important constraints (e.g., basin boundaries, water bodies, soil classification, land cover, elevation, and slope/aspect). This provides an efficient spatial representation of parameters and initial conditions, as well as well-formed and well-graded triangles that are suitable for numerical discretization. CHM uses high-quality open source libraries and high performance computing paradigms to provide a framework that allows for integrating current state-of-the-art process algorithms. The impact of changes to model structure, including individual algorithms, parameters, initial conditions, driving meteorology, and spatial/temporal discretization can be easily tested. Initial testing of CHM compared spatial scales and model complexity for a spring melt period at a sub-arctic mountain basin. The meshing algorithm reduced the total number of computational elements and preserved the spatial heterogeneity of predictions.

SPATIAL AND TEMPORAL VARIABILITY AND DRIVERS OF NET ECOSYSTEM METABOLISM IN WESTERN GULF OF MEXICO ESTUARIES

EPA Science Inventory

Net ecosystem metabolism (NEM) is becoming a commonly used ecological indicator of estuarine ecosystem metabolic rates. Estuarine ecosystem processes are spatially and temporally variable, but the corresponding variability in NEM has not been properly assessed. Spatial and temp...

Impacts of cattle grazing on spatio-temporal variability of soil moisture and above-ground live plant biomass in mixed grasslands

NASA Astrophysics Data System (ADS)

Virk, Ravinder

Areas with relatively high spatial heterogeneity generally have more biodiversity than spatially homogeneous areas due to increased potential habitat. Management practices such as controlled grazing also affect the biodiversity in grasslands, but the nature of this impact is not well understood. Therefore this thesis studies the impacts of variation in grazing on soil moisture and biomass heterogeneity. These are not only important in terms of management of protected grasslands, but also for designing an effective grazing system from a livestock management point of view. This research is a part of the cattle grazing experiment underway in Grasslands National Park (GNP) of Canada since 2006, as part of the adaptive management process for restoring ecological integrity of the northern mixed-grass prairie region. An experimental approach using field measurements and remote sensing (Landsat) was combined with modelling (CENTURY) to examine and predict the impacts of grazing intensity on the spatial heterogeneity and patterns of above-ground live plant biomass (ALB) in experimental pastures in a mixed grassland ecosystem. The field-based research quantified the temporal patterns and spatial variability in both soil moisture (SM) and ALB, and the influence of local intra-seasonal weather variability and slope location on the spatio-temporal variability of SM and ALB at field plot scales. Significant impacts of intra-seasonal weather variability, slope position and grazing pressure on SM and ALB across a range of scales (plot and local (within pasture)) were found. Grazing intensity significantly affected the ALB even after controlling for the effect of slope position. Satellite-based analysis extended the scale of interest to full pastures and the surrounding region to assess the effects of grazing intensity on the spatio-temporal pattern of ALB in mixed grasslands. Overall, low to moderate grazing intensity showed increase in ALB heterogeneity whereas no change in ALB heterogeneity over time was observed for heavy grazing intensity. All grazing intensities showed decrease in spatial range (patch size) over time indicating that grazing is a patchy process. The study demonstrates that cattle grazing with variable intensity can maintain and change the spatial patterns of vegetation in the studied region. Using a modelling approach, the relative degrees to which grazing intensity and soil properties affect grassland productivity and carbon dynamics at longer time-periods were investigated. Both grass productivity and carbon dynamics are sensitive to variability in soil texture and grazing intensity. Moderate grazing is predicted to be the best option in terms of maintaining sufficient heterogeneity to support species diversity, as well as for carbon management in the mixed grassland ecosystem.

Post-fire reconstructions of fire intensity from fire severity data: quantifying the role of spatial variability of fire intensity on forest dynamics

NASA Astrophysics Data System (ADS)

Baker, Patrick; Oborne, Lisa

2015-04-01

Large, high-intensity fires have direct and long-lasting effects on forest ecosystems and present a serious threat to human life and property. However, even within the most catastrophic fires there is important variability in local-scale intensity that has important ramifications for forest mortality and regeneration. Quantifying this variability is difficult due to the rarity of catastrophic fire events, the extreme conditions at the time of the fires, and their large spatial extent. Instead fire severity is typically measured or estimated from observed patterns of vegetation mortality; however, differences in species- and size-specific responses to fires often makes fire severity a poor proxy for fire intensity. We developed a statistical method using simple, plot-based measurements of individual tree mortality to simultaneously estimate plot-level fire intensity and species-specific mortality patterns as a function of tree size. We applied our approach to an area of forest burned in the catastrophic Black Saturday fires that occurred near Melbourne, Australia, in February 2009. Despite being the most devastating fire in the past 70 years and our plots being located in the area that experienced some of the most intense fires in the 350,000 ha fire complex, we found that the estimated fire intensity was highly variable at multiple spatial scales. All eight tree species in our study differed in their susceptibility to fire-induced mortality, particularly among the largest size classes. We also found that seedling height and species richness of the post-fire seedling communities were both positively correlated with fire intensity. Spatial variability in disturbance intensity has important, but poorly understood, consequences for the short- and long-term dynamics of forests in the wake of catastrophic wildfires. Our study provides a tool to estimate fire intensity after a fire has passed, allowing new opportunities for linking spatial variability in fire intensity to forest ecosystem dynamics.

Can Process Understanding Help Elucidate The Structure Of The Critical Zone? Comparing Process-Based Soil Formation Models With Digital Soil Mapping.

NASA Astrophysics Data System (ADS)

Vanwalleghem, T.; Román, A.; Peña, A.; Laguna, A.; Giráldez, J. V.

2017-12-01

There is a need for better understanding the processes influencing soil formation and the resulting distribution of soil properties in the critical zone. Soil properties can exhibit strong spatial variation, even at the small catchment scale. Especially soil carbon pools in semi-arid, mountainous areas are highly uncertain because bulk density and stoniness are very heterogeneous and rarely measured explicitly. In this study, we explore the spatial variability in key soil properties (soil carbon stocks, stoniness, bulk density and soil depth) as a function of processes shaping the critical zone (weathering, erosion, soil water fluxes and vegetation patterns). We also compare the potential of traditional digital soil mapping versus a mechanistic soil formation model (MILESD) for predicting these key soil properties. Soil core samples were collected from 67 locations at 6 depths. Total soil organic carbon stocks were 4.38 kg m-2. Solar radiation proved to be the key variable controlling soil carbon distribution. Stone content was mostly controlled by slope, indicating the importance of erosion. Spatial distribution of bulk density was found to be highly random. Finally, total carbon stocks were predicted using a random forest model whose main covariates were solar radiation and NDVI. The model predicts carbon stocks that are double as high on north versus south-facing slopes. However, validation showed that these covariates only explained 25% of the variation in the dataset. Apparently, present-day landscape and vegetation properties are not sufficient to fully explain variability in the soil carbon stocks in this complex terrain under natural vegetation. This is attributed to a high spatial variability in bulk density and stoniness, key variables controlling carbon stocks. Similar results were obtained with the mechanistic soil formation model MILESD, suggesting that more complex models might be needed to further explore this high spatial variability.

HESS Opinions: The need for process-based evaluation of large-domain hyper-resolution models

NASA Astrophysics Data System (ADS)

Melsen, Lieke A.; Teuling, Adriaan J.; Torfs, Paul J. J. F.; Uijlenhoet, Remko; Mizukami, Naoki; Clark, Martyn P.

2016-03-01

A meta-analysis on 192 peer-reviewed articles reporting on applications of the variable infiltration capacity (VIC) model in a distributed way reveals that the spatial resolution at which the model is applied has increased over the years, while the calibration and validation time interval has remained unchanged. We argue that the calibration and validation time interval should keep pace with the increase in spatial resolution in order to resolve the processes that are relevant at the applied spatial resolution. We identified six time concepts in hydrological models, which all impact the model results and conclusions. Process-based model evaluation is particularly relevant when models are applied at hyper-resolution, where stakeholders expect credible results both at a high spatial and temporal resolution.

HESS Opinions: The need for process-based evaluation of large-domain hyper-resolution models

NASA Astrophysics Data System (ADS)

Melsen, L. A.; Teuling, A. J.; Torfs, P. J. J. F.; Uijlenhoet, R.; Mizukami, N.; Clark, M. P.

2015-12-01

A meta-analysis on 192 peer-reviewed articles reporting applications of the Variable Infiltration Capacity (VIC) model in a distributed way reveals that the spatial resolution at which the model is applied has increased over the years, while the calibration and validation time interval has remained unchanged. We argue that the calibration and validation time interval should keep pace with the increase in spatial resolution in order to resolve the processes that are relevant at the applied spatial resolution. We identified six time concepts in hydrological models, which all impact the model results and conclusions. Process-based model evaluation is particularly relevant when models are applied at hyper-resolution, where stakeholders expect credible results both at a high spatial and temporal resolution.

Adaptation of a pattern-scaling approach for assessment of local (village/valley) scale water resources and related vulnerabilities in the Upper Indus Basin

NASA Astrophysics Data System (ADS)

Forsythe, Nathan; Kilsby, Chris G.; Fowler, Hayley J.; Archer, David R.

2010-05-01

The water resources of the Upper Indus Basin (UIB) are of the utmost importance to the economic wellbeing of Pakistan. The irrigated agriculture made possible by Indus river runoff underpins the food security for Pakistan's nearly 200 million people. Contributions from hydropower account for more than one fifth of peak installed electrical generating capacity in a country where widespread, prolonged load-shedding handicaps business activity and industrial development. Pakistan's further socio-economic development thus depends largely on optimisation of its precious water resources. Confident, accurate seasonal predictions of water resource availability coupled with sound understanding of interannual variability are urgent insights needed by development planners and infrastructure managers at all levels. This study focuses on the challenge of providing meaningful quantitative information at the village/valley scale in the upper reaches of the UIB. Proceeding by progressive reductions in scale, the typology of the observed UIB hydrological regimes -- glacial, nival and pluvial -- are examined with special emphasis on interannual variability for individual seasons. Variations in discharge (runoff) are compared to observations of climate parameters (temperature, precipitation) and available spatial data (elevation, snow cover and snow-water-equivalent). The first scale presented is composed of the large-scale, long-record gauged UIB tributary basins. The Pakistan Water and Power Development Authority (WAPDA) has maintained these stations for several decades in order to monitor seasonal flows and accumulate data for design of further infrastructure. Data from basins defined by five gauging stations on the Indus, Hunza, Gilgit and Astore rivers are examined. The second scale presented is a set of smaller gauged headwater catchments with short records. These gauges were installed by WAPDA and its partners amongst the international development agencies to assess potential sites for medium-scale infrastructure projects. These catchments are placed in their context within the hydrological regime classification using the spatial data and (remote sensing) observations as well as river gauging measurements. The study assesses the degree of similarity with the larger basins of the same hydrological regime. This assessment focuses on the measured response to observed climate variable anomalies. The smallest scale considered is comprised of a number of case studies at the ungauged village/valley scale. These examples are based on the delineation of areas to which specific communities (villages) have customary (riparian) water rights. These examples were suggested by non-governmental organisations working on grassroots economic development initiatives and small-scale infrastructure projects in the region. The direct observations available for these subcatchments are limited to spatial data (elevation, snow parameters). The challenge at this level is to accurately extrapolate areal values (precipitation, temperature, runoff) from point observations at the basin scale. The study assesses both the degree of similarity in the distribution of spatial parameters to the larger gauged basins and the interannual variability (spatial heterogeneity) of remotely-sensed snow cover and snow-water-equivalent at this subcatchment scale. Based upon the characterisation of spatial and interannual variability at these three spatial scales, the challenges facing local water resource managers and infrastructure operators are enumerated. Local vulnerabilities include, but are not limited to, varying thresholds in irrigation water requirements based on crop-type, minimum base flows for micro-hydropower generation during winter (high load) months and relatively small but growing demand for domestic water usage. In conclusion the study posits potential strategies for managing interannual variability and potential emerging trends. Suggested strategies are guided by the principles of low-risk adaptation, participative decision making and local capacity building.

Spatial patterning in PM2.5 constituents under an inversion-focused sampling design across an urban area of complex terrain

PubMed Central

Tunno, Brett J; Dalton, Rebecca; Michanowicz, Drew R; Shmool, Jessie L C; Kinnee, Ellen; Tripathy, Sheila; Cambal, Leah; Clougherty, Jane E

2016-01-01

Health effects of fine particulate matter (PM2.5) vary by chemical composition, and composition can help to identify key PM2.5 sources across urban areas. Further, this intra-urban spatial variation in concentrations and composition may vary with meteorological conditions (e.g., mixing height). Accordingly, we hypothesized that spatial sampling during atmospheric inversions would help to better identify localized source effects, and reveal more distinct spatial patterns in key constituents. We designed a 2-year monitoring campaign to capture fine-scale intra-urban variability in PM2.5 composition across Pittsburgh, PA, and compared both spatial patterns and source effects during “frequent inversion” hours vs 24-h weeklong averages. Using spatially distributed programmable monitors, and a geographic information systems (GIS)-based design, we collected PM2.5 samples across 37 sampling locations per year to capture variation in local pollution sources (e.g., proximity to industry, traffic density) and terrain (e.g., elevation). We used inductively coupled plasma mass spectrometry (ICP-MS) to determine elemental composition, and unconstrained factor analysis to identify source suites by sampling scheme and season. We examined spatial patterning in source factors using land use regression (LUR), wherein GIS-based source indicators served to corroborate factor interpretations. Under both summer sampling regimes, and for winter inversion-focused sampling, we identified six source factors, characterized by tracers associated with brake and tire wear, steel-making, soil and road dust, coal, diesel exhaust, and vehicular emissions. For winter 24-h samples, four factors suggested traffic/fuel oil, traffic emissions, coal/industry, and steel-making sources. In LURs, as hypothesized, GIS-based source terms better explained spatial variability in inversion-focused samples, including a greater contribution from roadway, steel, and coal-related sources. Factor analysis produced source-related constituent suites under both sampling designs, though factors were more distinct under inversion-focused sampling. PMID:26507005

Performance analysis of MIMO wireless optical communication system with Q-ary PPM over correlated log-normal fading channel

NASA Astrophysics Data System (ADS)

Wang, Huiqin; Wang, Xue; Lynette, Kibe; Cao, Minghua

2018-06-01

The performance of multiple-input multiple-output wireless optical communication systems that adopt Q-ary pulse position modulation over spatial correlated log-normal fading channel is analyzed in terms of its un-coded bit error rate and ergodic channel capacity. The analysis is based on the Wilkinson's method which approximates the distribution of a sum of correlated log-normal random variables to a log-normal random variable. The analytical and simulation results corroborate the increment of correlation coefficients among sub-channels lead to system performance degradation. Moreover, the receiver diversity has better performance in resistance of spatial correlation caused channel fading.

Ground-based measurement of surface temperature and thermal emissivity

NASA Technical Reports Server (NTRS)

Owe, M.; Van De Griend, A. A.

1994-01-01

Motorized cable systems for transporting infrared thermometers have been used successfully during several international field campaigns. Systems may be configured with as many as four thermal sensors up to 9 m above the surface, and traverse a 30 m transect. Ground and canopy temperatures are important for solving the surface energy balance. The spatial variability of surface temperature is often great, so that averaged point measurements result in highly inaccurate areal estimates. The cable systems are ideal for quantifying both temporal and spatial variabilities. Thermal emissivity is also necessary for deriving the absolute physical temperature, and measurements may be made with a portable measuring box.

A new Downscaling Approach for SMAP, SMOS and ASCAT by predicting sub-grid Soil Moisture Variability based on Soil Texture

NASA Astrophysics Data System (ADS)

Montzka, C.; Rötzer, K.; Bogena, H. R.; Vereecken, H.

2017-12-01

Improving the coarse spatial resolution of global soil moisture products from SMOS, SMAP and ASCAT is currently an up-to-date topic. Soil texture heterogeneity is known to be one of the main sources of soil moisture spatial variability. A method has been developed that predicts the soil moisture standard deviation as a function of the mean soil moisture based on soil texture information. It is a closed-form expression using stochastic analysis of 1D unsaturated gravitational flow in an infinitely long vertical profile based on the Mualem-van Genuchten model and first-order Taylor expansions. With the recent development of high resolution maps of basic soil properties such as soil texture and bulk density, relevant information to estimate soil moisture variability within a satellite product grid cell is available. Here, we predict for each SMOS, SMAP and ASCAT grid cell the sub-grid soil moisture variability based on the SoilGrids1km data set. We provide a look-up table that indicates the soil moisture standard deviation for any given soil moisture mean. The resulting data set provides important information for downscaling coarse soil moisture observations of the SMOS, SMAP and ASCAT missions. Downscaling SMAP data by a field capacity proxy indicates adequate accuracy of the sub-grid soil moisture patterns.

A spatial hazard model for cluster detection on continuous indicators of disease: application to somatic cell score.

PubMed

Gay, Emilie; Senoussi, Rachid; Barnouin, Jacques

2007-01-01

Methods for spatial cluster detection dealing with diseases quantified by continuous variables are few, whereas several diseases are better approached by continuous indicators. For example, subclinical mastitis of the dairy cow is evaluated using a continuous marker of udder inflammation, the somatic cell score (SCS). Consequently, this study proposed to analyze spatialized risk and cluster components of herd SCS through a new method based on a spatial hazard model. The dataset included annual SCS for 34 142 French dairy herds for the year 2000, and important SCS risk factors: mean parity, percentage of winter and spring calvings, and herd size. The model allowed the simultaneous estimation of the effects of known risk factors and of potential spatial clusters on SCS, and the mapping of the estimated clusters and their range. Mean parity and winter and spring calvings were significantly associated with subclinical mastitis risk. The model with the presence of 3 clusters was highly significant, and the 3 clusters were attractive, i.e. closeness to cluster center increased the occurrence of high SCS. The three localizations were the following: close to the city of Troyes in the northeast of France; around the city of Limoges in the center-west; and in the southwest close to the city of Tarbes. The semi-parametric method based on spatial hazard modeling applies to continuous variables, and takes account of both risk factors and potential heterogeneity of the background population. This tool allows a quantitative detection but assumes a spatially specified form for clusters.

Spatial pattern analysis of Cu, Zn and Ni and their interpretation in the Campania region (Italy)

NASA Astrophysics Data System (ADS)

Petrik, Attila; Albanese, Stefano; Jordan, Gyozo; Rolandi, Roberto; De Vivo, Benedetto

2017-04-01

The uniquely abundant Campanian topsoil dataset enabled us to perform a spatial pattern analysis on 3 potentially toxic elements of Cu, Zn and Ni. This study is focusing on revealing the spatial texture and distribution of these elements by spatial point pattern and image processing analysis such as lineament density and spatial variability index calculation. The application of these methods on geochemical data provides a new and efficient tool to understand the spatial variation of concentrations and their background/baseline values. The determination and quantification of spatial variability is crucial to understand how fast the change in concentration is in a certain area and what processes might govern the variation. The spatial variability index calculation and image processing analysis including lineament density enables us to delineate homogenous areas and analyse them with respect to lithology and land use. Identification of spatial outliers and their patterns were also investigated by local spatial autocorrelation and image processing analysis including the determination of local minima and maxima points and singularity index analysis. The spatial variability of Cu and Zn reveals the highest zone (Cu: 0.5 MAD, Zn: 0.8-0.9 MAD, Median Deviation Index) along the coast between Campi Flegrei and the Sorrento Peninsula with the vast majority of statistically identified outliers and high-high spatial clustered points. The background/baseline maps of Cu and Zn reveals a moderate to high variability (Cu: 0.3 MAD, Zn: 0.4-0.5 MAD) NW-SE oriented zone including disrupted patches from Bisaccia to Mignano following the alluvial plains of Appenine's rivers. This zone has high abundance of anomaly concentrations identified using singularity analysis and it also has a high density of lineaments. The spatial variability of Ni shows the highest variability zone (0.6-0.7 MAD) around Campi Flegrei where the majority of low outliers are concentrated. The variability of background/baseline map of Ni reveals a shift to the east in case of highest variability zones coinciding with limestone outcrops. The high segmented area between Mignano and Bisaccia partially follows the alluvial plains of Appenine's rivers which seem to be playing a crucial role in the distribution and redistribution pattern of Cu, Zn and Ni in Campania. The high spatial variability zones of the later elements are located in topsoils on volcanoclastic rocks and are mostly related to cultivation and urbanised areas.

Macromolecular Crowding Induces Spatial Correlations That Control Gene Expression Bursting Patterns.

PubMed

Norred, S Elizabeth; Caveney, Patrick M; Chauhan, Gaurav; Collier, Lauren K; Collier, C Patrick; Abel, Steven M; Simpson, Michael L

2018-05-18

Recent superresolution microscopy studies in E. coli demonstrate that the cytoplasm has highly variable local concentrations where macromolecular crowding plays a central role in establishing membrane-less compartmentalization. This spatial inhomogeneity significantly influences molecular transport and association processes central to gene expression. Yet, little is known about how macromolecular crowding influences gene expression bursting-the episodic process where mRNA and proteins are produced in bursts. Here, we simultaneously measured mRNA and protein reporters in cell-free systems, showing that macromolecular crowding decoupled the well-known relationship between fluctuations in the protein population (noise) and mRNA population statistics. Crowded environments led to a 10-fold increase in protein noise even though there were only modest changes in the mRNA population and fluctuations. Instead, cell-like macromolecular crowding created an inhomogeneous spatial distribution of mRNA ("spatial noise") that led to large variability in the protein production burst size. As a result, the mRNA spatial noise created large temporal fluctuations in the protein population. These results highlight the interplay between macromolecular crowding, spatial inhomogeneities, and the resulting dynamics of gene expression, and provide insights into using these organizational principles in both cell-based and cell-free synthetic biology.

Interannual rainfall variability and SOM-based circulation classification

NASA Astrophysics Data System (ADS)

Wolski, Piotr; Jack, Christopher; Tadross, Mark; van Aardenne, Lisa; Lennard, Christopher

2018-01-01

Self-Organizing Maps (SOM) based classifications of synoptic circulation patterns are increasingly being used to interpret large-scale drivers of local climate variability, and as part of statistical downscaling methodologies. These applications rely on a basic premise of synoptic climatology, i.e. that local weather is conditioned by the large-scale circulation. While it is clear that this relationship holds in principle, the implications of its implementation through SOM-based classification, particularly at interannual and longer time scales, are not well recognized. Here we use a SOM to understand the interannual synoptic drivers of climate variability at two locations in the winter and summer rainfall regimes of South Africa. We quantify the portion of variance in seasonal rainfall totals that is explained by year to year differences in the synoptic circulation, as schematized by a SOM. We furthermore test how different spatial domain sizes and synoptic variables affect the ability of the SOM to capture the dominant synoptic drivers of interannual rainfall variability. Additionally, we identify systematic synoptic forcing that is not captured by the SOM classification. The results indicate that the frequency of synoptic states, as schematized by a relatively disaggregated SOM (7 × 9) of prognostic atmospheric variables, including specific humidity, air temperature and geostrophic winds, captures only 20-45% of interannual local rainfall variability, and that the residual variance contains a strong systematic component. Utilising a multivariate linear regression framework demonstrates that this residual variance can largely be explained using synoptic variables over a particular location; even though they are used in the development of the SOM their influence, however, diminishes with the size of the SOM spatial domain. The influence of the SOM domain size, the choice of SOM atmospheric variables and grid-point explanatory variables on the levels of explained variance, is consistent with the general understanding of the dominant processes and atmospheric variables that affect rainfall variability at a particular location.

Spatial variability in land-atmosphere coupling strength at the ARM Southern Great Plains site under different cloud regimes

NASA Astrophysics Data System (ADS)

Tang, Q.; Xie, S.; Zhang, Y.

2016-12-01

The paucity of land/soil observations is a long-standing limitation for land-atmosphere (LA) coupling studies, in particular for estimating the spatial variability in the coupling strengths. Spatially dense atmospheric radiation measurement (ARM) sites deployed at the U.S. Southern Great Plains (SGP) covers a wide range of vegetation, surface, and soil types, and thus allow us to observe the spatial patterns of LA coupling. The upcoming "super site" at SGP will facilitate these studies at even finer scales. While many previous studies have focused only on the observations from the central facility (CF) site or the domain mean from multiple sites, in the present work we examine the robustness of many key surface and land observations (e.g., radiation, turbulence fluxes, soil moisture, etc.) at extended sites besides the CF site for a decade. The coupling strengths are estimated with temporal covariations between important variables. We subsample the data to different categories based on different cloud regimes (e.g., clear sky, shallow cumulus, and deep cumulus. These cloud regimes are strongly impacted by local factors. The spatial variability of coupling strengths at different ARM sites is assessed with respect to dominant drivers (i.e., vegetation, land type, etc.). The results of this study will provide insights for improving the representation of LA coupling in climate models by providing observational constraints to parameterizations, e.g., shallow convective schemes. This work is performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-ABS-698523

Spatial Uncertainty Modeling of Fuzzy Information in Images for Pattern Classification

PubMed Central

Pham, Tuan D.

2014-01-01

The modeling of the spatial distribution of image properties is important for many pattern recognition problems in science and engineering. Mathematical methods are needed to quantify the variability of this spatial distribution based on which a decision of classification can be made in an optimal sense. However, image properties are often subject to uncertainty due to both incomplete and imprecise information. This paper presents an integrated approach for estimating the spatial uncertainty of vagueness in images using the theory of geostatistics and the calculus of probability measures of fuzzy events. Such a model for the quantification of spatial uncertainty is utilized as a new image feature extraction method, based on which classifiers can be trained to perform the task of pattern recognition. Applications of the proposed algorithm to the classification of various types of image data suggest the usefulness of the proposed uncertainty modeling technique for texture feature extraction. PMID:25157744

Optimization of Decision-Making for Spatial Sampling in the North China Plain, Based on Remote-Sensing a Priori Knowledge

NASA Astrophysics Data System (ADS)

Feng, J.; Bai, L.; Liu, S.; Su, X.; Hu, H.

2012-07-01

In this paper, the MODIS remote sensing data, featured with low-cost, high-timely and moderate/low spatial resolutions, in the North China Plain (NCP) as a study region were firstly used to carry out mixed-pixel spectral decomposition to extract an useful regionalized indicator parameter (RIP) (i.e., an available ratio, that is, fraction/percentage, of winter wheat planting area in each pixel as a regionalized indicator variable (RIV) of spatial sampling) from the initial selected indicators. Then, the RIV values were spatially analyzed, and the spatial structure characteristics (i.e., spatial correlation and variation) of the NCP were achieved, which were further processed to obtain the scalefitting, valid a priori knowledge or information of spatial sampling. Subsequently, founded upon an idea of rationally integrating probability-based and model-based sampling techniques and effectively utilizing the obtained a priori knowledge or information, the spatial sampling models and design schemes and their optimization and optimal selection were developed, as is a scientific basis of improving and optimizing the existing spatial sampling schemes of large-scale cropland remote sensing monitoring. Additionally, by the adaptive analysis and decision strategy the optimal local spatial prediction and gridded system of extrapolation results were able to excellently implement an adaptive report pattern of spatial sampling in accordance with report-covering units in order to satisfy the actual needs of sampling surveys.

Estimating recharge rates with analytic element models and parameter estimation

USGS Publications Warehouse

Dripps, W.R.; Hunt, R.J.; Anderson, M.P.

2006-01-01

Quantifying the spatial and temporal distribution of recharge is usually a prerequisite for effective ground water flow modeling. In this study, an analytic element (AE) code (GFLOW) was used with a nonlinear parameter estimation code (UCODE) to quantify the spatial and temporal distribution of recharge using measured base flows as calibration targets. The ease and flexibility of AE model construction and evaluation make this approach well suited for recharge estimation. An AE flow model of an undeveloped watershed in northern Wisconsin was optimized to match median annual base flows at four stream gages for 1996 to 2000 to demonstrate the approach. Initial optimizations that assumed a constant distributed recharge rate provided good matches (within 5%) to most of the annual base flow estimates, but discrepancies of >12% at certain gages suggested that a single value of recharge for the entire watershed is inappropriate. Subsequent optimizations that allowed for spatially distributed recharge zones based on the distribution of vegetation types improved the fit and confirmed that vegetation can influence spatial recharge variability in this watershed. Temporally, the annual recharge values varied >2.5-fold between 1996 and 2000 during which there was an observed 1.7-fold difference in annual precipitation, underscoring the influence of nonclimatic factors on interannual recharge variability for regional flow modeling. The final recharge values compared favorably with more labor-intensive field measurements of recharge and results from studies, supporting the utility of using linked AE-parameter estimation codes for recharge estimation. Copyright ?? 2005 The Author(s).

Effects of rainfall spatial variability and intermittency on shallow landslide triggering patterns at a catchment scale

NASA Astrophysics Data System (ADS)

von Ruette, J.; Lehmann, P.; Or, D.

2014-10-01

The occurrence of shallow landslides is often associated with intense and prolonged rainfall events, where infiltrating water reduces soil strength and may lead to abrupt mass release. Despite general understanding of the role of rainfall water in slope stability, the prediction of rainfall-induced landslides remains a challenge due to natural heterogeneity that affect hydrologic loading patterns and the largely unobservable internal progressive failures. An often overlooked and potentially important factor is the role of rainfall variability in space and time on landslide triggering that is often obscured by coarse information (e.g., hourly radar data at spatial resolution of a few kilometers). To quantify potential effects of rainfall variability on failure dynamics, spatial patterns, landslide numbers and volumes, we employed a physically based "Catchment-scale Hydromechanical Landslide Triggering" (CHLT) model for a study area where a summer storm in 2002 triggered 51 shallow landslides. In numerical experiments based on the CHLT model, we applied the measured rainfall amount of 53 mm in different artificial spatiotemporal rainfall patterns, resulting in between 30 and 100 landslides and total released soil volumes between 3000 and 60,000 m3 for the various scenarios. Results indicate that low intensity rainfall below soil's infiltration capacity resulted in the largest mechanical perturbation. This study illustrates how small-scale rainfall variability that is often overlooked by present operational rainfall data may play a key role in shaping landslide patterns.

Simulating maize yield and biomass with spatial variability of soil field capacity

USDA-ARS?s Scientific Manuscript database

Spatial variability in field soil water and other properties is a challenge for system modelers who use only representative values for model inputs, rather than their distributions. In this study, we compared simulation results from a calibrated model with spatial variability of soil field capacity ...

Spatial Variability of Sources and Mixing State of Atmospheric Particles in a Metropolitan Area.

PubMed

Ye, Qing; Gu, Peishi; Li, Hugh Z; Robinson, Ellis S; Lipsky, Eric; Kaltsonoudis, Christos; Lee, Alex K Y; Apte, Joshua S; Robinson, Allen L; Sullivan, Ryan C; Presto, Albert A; Donahue, Neil M

2018-05-30

Characterizing intracity variations of atmospheric particulate matter has mostly relied on fixed-site monitoring and quantifying variability in terms of different bulk aerosol species. In this study, we performed ground-based mobile measurements using a single-particle mass spectrometer to study spatial patterns of source-specific particles and the evolution of particle mixing state in 21 areas in the metropolitan area of Pittsburgh, PA. We selected sampling areas based on traffic density and restaurant density with each area ranging from 0.2 to 2 km 2 . Organics dominate particle composition in all of the areas we sampled while the sources of organics differ. The contribution of particles from traffic and restaurant cooking varies greatly on the neighborhood scale. We also investigate how primary and aged components in particles mix across the urban scale. Lastly we quantify and map the particle mixing state for all areas we sampled and discuss the overall pattern of mixing state evolution and its implications. We find that in the upwind and downwind of the urban areas, particles are more internally mixed while in the city center, particle mixing state shows large spatial heterogeneity that is mostly driven by emissions. This study is to our knowledge, the first study to perform fine spatial scale mapping of particle mixing state using ground-based mobile measurement and single-particle mass spectrometry.

Bayesian spatial prediction of the site index in the study of the Missouri Ozark Forest Ecosystem Project

Treesearch

Xiaoqian Sun; Zhuoqiong He; John Kabrick

2008-01-01

This paper presents a Bayesian spatial method for analysing the site index data from the Missouri Ozark Forest Ecosystem Project (MOFEP). Based on ecological background and availability, we select three variables, the aspect class, the soil depth and the land type association as covariates for analysis. To allow great flexibility of the smoothness of the random field,...

Discontinuous Galerkin Finite Element Method for Parabolic Problems

NASA Technical Reports Server (NTRS)

Kaneko, Hideaki; Bey, Kim S.; Hou, Gene J. W.

2004-01-01

In this paper, we develop a time and its corresponding spatial discretization scheme, based upon the assumption of a certain weak singularity of parallel ut(t) parallel Lz(omega) = parallel ut parallel2, for the discontinuous Galerkin finite element method for one-dimensional parabolic problems. Optimal convergence rates in both time and spatial variables are obtained. A discussion of automatic time-step control method is also included.

Geographic dimensions of heat-related mortality in seven U.S. cities.

PubMed

Hondula, David M; Davis, Robert E; Saha, Michael V; Wegner, Carleigh R; Veazey, Lindsay M

2015-04-01

Spatially targeted interventions may help protect the public when extreme heat occurs. Health outcome data are increasingly being used to map intra-urban variability in heat-health risks, but there has been little effort to compare patterns and risk factors between cities. We sought to identify places within large metropolitan areas where the mortality rate is highest on hot summer days and determine if characteristics of high-risk areas are consistent from one city to another. A Poisson regression model was adapted to quantify temperature-mortality relationships at the postal code scale based on 2.1 million records of daily all-cause mortality counts from seven U.S. cities. Multivariate spatial regression models were then used to determine the demographic and environmental variables most closely associated with intra-city variability in risk. Significant mortality increases on extreme heat days were confined to 12-44% of postal codes comprising each city. Places with greater risk had more developed land, young, elderly, and minority residents, and lower income and educational attainment, but the key explanatory variables varied from one city to another. Regression models accounted for 14-34% of the spatial variability in heat-related mortality. The results emphasize the need for public health plans for heat to be locally tailored and not assume that pre-identified vulnerability indicators are universally applicable. As known risk factors accounted for no more than one third of the spatial variability in heat-health outcomes, consideration of health outcome data is important in efforts to identify and protect residents of the places where the heat-related health risks are the highest. Copyright © 2015 Elsevier Inc. All rights reserved.

The spatial variable glacier mass loss over the southeast Tibet Plateau and the climate cause analyses

NASA Astrophysics Data System (ADS)

Ke, L.; Ding, X.; Song, C.; Sheng, Y.

2016-12-01

Temperate glaciers can be highly sensitive to global climate change due to relatively humid and warm local climate. Numerous temperate glaciers are distributed in the southeastern Tibet Plateau (SETP) and their changes are still poorly represented. Based on a latest glacier inventory and ICESat altimetry measurements, we examine the spatial heterogeneity of glacier change in the SETP (including the central and eastern Nyainqêntanglha ranges) and further analyze its relation with climate change by using station-based and gridded meteorological data. Our results show that SETP glaciers experienced drastic surface lowering at about -0.84±0.26 m a-1 on average over 2003-2008. Debris-covered ice thinned at an average rate of -1.13±0.32 m a-1, in comparison with -0.92±0.17 m a-1 over the debris-free ice areas. The thinning rate is the strongest in the southeastern sub-region (up to -1.24 m a-1 ) and moderate ( -0.45 m a-1 ) in the central and northwestern parts, which is in general agreement with the pattern of surface mass changes based on the GRACE gravimetry observation. Long-term climate data at weather stations show that, in comparison with the period of 1992-2002, mean temperature increased by 0.46 °C - 0.59 °C in the recent decade (2003-2013); while the change of summer precipitation exhibited remarkably spatial variability, following a southeast-northwest contrasting pattern (decreasing by over 10% in the southeast, to stable level in the central region, and increment up to 10% in the northwest). This spatially variable precipitation change is consistent with results from CN05 grid data and ERA re-analysis data, and agrees well with the spatial pattern of glacier surface elevation changes. The results suggest that overall negative glacier mass balances in SETP are governed by temperature rising, while the different precipitation change could contribute to inconsistent glacier thinning rates. The spatial pattern of precipitation decrease and mass loss might be tele-connected with the dynamics of the Indian summer monsoon.

Synoptic Storms in the North Atlantic in the Atmospheric Reanalysis and Scatterometer-Based Wind Products

NASA Astrophysics Data System (ADS)

Dukhovskoy, D. S.; Bourassa, M. A.

2016-12-01

The study compares and analyses the characteristics of synoptic storms in the Subpolar North Atlantic over the time period from 2000 through 2009 derived from reanalysis data sets and scatterometer-based gridded wind products. The analysis is performed for ocean 10-m winds derived from the following wind data sets: NCEP/DOE AMIP-II reanalysis (NCEPR2), NCAR/CFSR, Arctic System Reanalysis (ASR) version 1, Cross-Calibrated Multi-Platform (CCMP) wind product versions 1.1 and recently released version 2.0 prepared by the Remote Sensing Systems, and QuikSCAT. A cyclone tracking algorithm employed in this study for storm identification is based on average vorticity fields derived from the wind data. The study discusses storm characteristics such as storm counts, trajectories, intensity, integrated kinetic energy, spatial scale. Interannal variability of these characteristics in the data sets is compared. The analyses demonstrates general agreement among the wind data products on the characteristics of the storms, their spatial distribution and trajectories. On average, the NCEPR2 storms are more energetic mostly due to large spatial scales and stronger winds. There is noticeable interannual variability in the storm characteristics, yet no obvious trend in storms is observed in the data sets.

Spatio-temporal representativeness of ground-based downward solar radiation measurements

NASA Astrophysics Data System (ADS)

Schwarz, Matthias; Wild, Martin; Folini, Doris

2017-04-01

Surface solar radiation (SSR) is most directly observed with ground based pyranometer measurements. Besides measurement uncertainties, which arise from the pyranometer instrument itself, also errors attributed to the limited spatial representativeness of observations from single sites for their large-scale surrounding have to be taken into account when using such measurements for energy balance studies. In this study the spatial representativeness of 157 homogeneous European downward surface solar radiation time series from the Global Energy Balance Archive (GEBA) and the Baseline Surface Radiation Network (BSRN) were examined for the period 1983-2015 by using the high resolution (0.05°) surface solar radiation data set from the Satellite Application Facility on Climate Monitoring (CM-SAF SARAH) as a proxy for the spatiotemporal variability of SSR. By correlating deseasonalized monthly SSR time series form surface observations against single collocated satellite derived SSR time series, a mean spatial correlation pattern was calculated and validated against purely observational based patterns. Generally decreasing correlations with increasing distance from station, with high correlations (R2 = 0.7) in proximity to the observational sites (±0.5°), was found. When correlating surface observations against time series from spatially averaged satellite derived SSR data (and thereby simulating coarser and coarser grids), very high correspondence between sites and the collocated pixels has been found for pixel sizes up to several degrees. Moreover, special focus was put on the quantification of errors which arise in conjunction to spatial sampling when estimating the temporal variability and trends for a larger region from a single surface observation site. For 15-year trends on a 1° grid, errors due to spatial sampling in the order of half of the measurement uncertainty for monthly mean values were found.

Modelling space of spread Dengue Hemorrhagic Fever (DHF) in Central Java use spatial durbin model

NASA Astrophysics Data System (ADS)

Ispriyanti, Dwi; Prahutama, Alan; Taryono, Arkadina PN

2018-05-01

Dengue Hemorrhagic Fever is one of the major public health problems in Indonesia. From year to year, DHF causes Extraordinary Event in most parts of Indonesia, especially Central Java. Central Java consists of 35 districts or cities where each region is close to each other. Spatial regression is an analysis that suspects the influence of independent variables on the dependent variables with the influences of the region inside. In spatial regression modeling, there are spatial autoregressive model (SAR), spatial error model (SEM) and spatial autoregressive moving average (SARMA). Spatial Durbin model is the development of SAR where the dependent and independent variable have spatial influence. In this research dependent variable used is number of DHF sufferers. The independent variables observed are population density, number of hospitals, residents and health centers, and mean years of schooling. From the multiple regression model test, the variables that significantly affect the spread of DHF disease are the population and mean years of schooling. By using queen contiguity and rook contiguity, the best model produced is the SDM model with queen contiguity because it has the smallest AIC value of 494,12. Factors that generally affect the spread of DHF in Central Java Province are the number of population and the average length of school.

Reward-based plasticity of spatial priority maps: Exploiting inter-subject variability to probe the underlying neurobiology.

PubMed

Della Libera, Chiara; Calletti, Riccardo; Eštočinová, Jana; Chelazzi, Leonardo; Santandrea, Elisa

2017-04-01

Recent evidence indicates that the attentional priority of objects and locations is altered by the controlled delivery of reward, reflecting reward-based attentional learning. Here, we take an approach hinging on intersubject variability to probe the neurobiological bases of the reward-driven plasticity of spatial priority maps. Specifically, we ask whether an individual's susceptibility to the reward-based treatment can be accounted for by specific predictors, notably personality traits that are linked to reward processing (along with more general personality traits), but also gender. Using a visual search protocol, we show that when different target locations are associated with unequal reward probability, different priorities are acquired by the more rewarded relative to the less rewarded locations. However, while males exhibit the expected pattern of results, with greater priority for locations associated with higher reward, females show an opposite trend. Critically, both the extent and the direction of reward-based adjustments are further predicted by personality traits indexing reward sensitivity, indicating that not only male and female brains are differentially sensitive to reward, but also that specific personality traits further contribute to shaping their learning-dependent attentional plasticity. These results contribute to a better understanding of the neurobiology underlying reward-dependent attentional learning and cross-subject variability in this domain.

An Investigation on the Spatial Variability of Manning Roughness Coefficients in Continental-scale River Routing Simulations

NASA Astrophysics Data System (ADS)

Luo, X.; Hong, Y.; Lei, X.; Leung, L. R.; Li, H. Y.; Getirana, A.

2017-12-01

As one essential component of the Earth system modeling, the continental-scale river routing computation plays an important role in applications of Earth system models, such as evaluating the impacts of the global change on water resources and flood hazards. The streamflow timing, which depends on the modeled flow velocities, can be an important aspect of the model results. River flow velocities have been estimated by using the Manning's equation where the Manning roughness coefficient is a key and sensitive parameter. In some early continental-scale studies, the Manning coefficient was determined with simplified methods, such as using a constant value for the entire basin. However, large spatial variability is expected in the Manning coefficients for the numerous channels composing the river network in distributed continental-scale hydrologic modeling. In the application of a continental-scale river routing model in the Amazon Basin, we use spatially varying Manning coefficients dependent on channel sizes and attempt to represent the dominant spatial variability of Manning coefficients. Based on the comparisons of simulation results with in situ streamflow records and remotely sensed river stages, we investigate the comparatively optimal Manning coefficients and explicitly demonstrate the advantages of using spatially varying Manning coefficients. The understanding obtained in this study could be helpful in the modeling of surface hydrology at regional to continental scales.

Environmental factors prevail over dispersal constraints in determining the distribution and assembly of Trichoptera species in mountain lakes.

PubMed

de Mendoza, Guillermo; Ventura, Marc; Catalan, Jordi

2015-07-01

Aiming to elucidate whether large-scale dispersal factors or environmental species sorting prevail in determining patterns of Trichoptera species composition in mountain lakes, we analyzed the distribution and assembly of the most common Trichoptera (Plectrocnemia laetabilis, Polycentropus flavomaculatus, Drusus rectus, Annitella pyrenaea, and Mystacides azurea) in the mountain lakes of the Pyrenees (Spain, France, Andorra) based on a survey of 82 lakes covering the geographical and environmental extremes of the lake district. Spatial autocorrelation in species composition was determined using Moran's eigenvector maps (MEM). Redundancy analysis (RDA) was applied to explore the influence of MEM variables and in-lake, and catchment environmental variables on Trichoptera assemblages. Variance partitioning analysis (partial RDA) revealed the fraction of species composition variation that could be attributed uniquely to either environmental variability or MEM variables. Finally, the distribution of individual species was analyzed in relation to specific environmental factors using binomial generalized linear models (GLM). Trichoptera assemblages showed spatial structure. However, the most relevant environmental variables in the RDA (i.e., temperature and woody vegetation in-lake catchments) were also related with spatial variables (i.e., altitude and longitude). Partial RDA revealed that the fraction of variation in species composition that was uniquely explained by environmental variability was larger than that uniquely explained by MEM variables. GLM results showed that the distribution of species with longitudinal bias is related to specific environmental factors with geographical trend. The environmental dependence found agrees with the particular traits of each species. We conclude that Trichoptera species distribution and composition in the lakes of the Pyrenees are governed predominantly by local environmental factors, rather than by dispersal constraints. For boreal lakes, with similar environmental conditions, a strong role of dispersal capacity has been suggested. Further investigation should address the role of spatial scaling, namely absolute geographical distances constraining dispersal and steepness of environmental gradients at short distances.

Environmental factors prevail over dispersal constraints in determining the distribution and assembly of Trichoptera species in mountain lakes

PubMed Central

de Mendoza, Guillermo; Ventura, Marc; Catalan, Jordi

2015-01-01

Aiming to elucidate whether large-scale dispersal factors or environmental species sorting prevail in determining patterns of Trichoptera species composition in mountain lakes, we analyzed the distribution and assembly of the most common Trichoptera (Plectrocnemia laetabilis, Polycentropus flavomaculatus, Drusus rectus, Annitella pyrenaea, and Mystacides azurea) in the mountain lakes of the Pyrenees (Spain, France, Andorra) based on a survey of 82 lakes covering the geographical and environmental extremes of the lake district. Spatial autocorrelation in species composition was determined using Moran’s eigenvector maps (MEM). Redundancy analysis (RDA) was applied to explore the influence of MEM variables and in-lake, and catchment environmental variables on Trichoptera assemblages. Variance partitioning analysis (partial RDA) revealed the fraction of species composition variation that could be attributed uniquely to either environmental variability or MEM variables. Finally, the distribution of individual species was analyzed in relation to specific environmental factors using binomial generalized linear models (GLM). Trichoptera assemblages showed spatial structure. However, the most relevant environmental variables in the RDA (i.e., temperature and woody vegetation in-lake catchments) were also related with spatial variables (i.e., altitude and longitude). Partial RDA revealed that the fraction of variation in species composition that was uniquely explained by environmental variability was larger than that uniquely explained by MEM variables. GLM results showed that the distribution of species with longitudinal bias is related to specific environmental factors with geographical trend. The environmental dependence found agrees with the particular traits of each species. We conclude that Trichoptera species distribution and composition in the lakes of the Pyrenees are governed predominantly by local environmental factors, rather than by dispersal constraints. For boreal lakes, with similar environmental conditions, a strong role of dispersal capacity has been suggested. Further investigation should address the role of spatial scaling, namely absolute geographical distances constraining dispersal and steepness of environmental gradients at short distances. PMID:26257867

[Drivers of human-caused fire occurrence and its variation trend under climate change in the Great Xing'an Mountains, Northeast China].

PubMed

Li, Shun; Wu, Zhi Wei; Liang, Yu; He, Hong Shi

2017-01-01

The Great Xing'an Mountains are an important boreal forest region in China with high frequency of fire occurrences. With climate change, this region may have a substantial change in fire frequency. Building the relationship between spatial pattern of human-caused fire occurrence and its influencing factors, and predicting the spatial patterns of human-caused fires under climate change scenarios are important for fire management and carbon balance in boreal forests. We employed a spatial point pattern model to explore the relationship between the spatial pattern of human-caused fire occurrence and its influencing factors based on a database of historical fire records (1967-2006) in the Great Xing'an Mountains. The fire occurrence time was used as dependent variable. Nine abiotic (annual temperature and precipitation, elevation, aspect, and slope), biotic (vegetation type), and human factors (distance to the nearest road, road density, and distance to the nearest settlement) were selected as explanatory variables. We substituted the climate scenario data (RCP 2.6 and RCP 8.5) for the current climate data to predict the future spatial patterns of human-caused fire occurrence in 2050. Our results showed that the point pattern progress (PPP) model was an effective tool to predict the future relationship between fire occurrence and its spatial covariates. The climatic variables might significantly affect human-caused fire occurrence, while vegetation type, elevation and human variables were important predictors of human-caused fire occurrence. The human-caused fire occurrence probability was expected to increase in the south of the area, and the north and the area along the main roads would also become areas with high human-caused fire occurrence. The human-caused fire occurrence would increase by 72.2% under the RCP 2.6 scenario and by 166.7% under the RCP 8.5 scenario in 2050. Under climate change scenarios, the spatial patterns of human-caused fires were mainly influenced by the climate and human factors.

Species richness and biomass explain spatial turnover in ecosystem functioning across tropical and temperate ecosystems.

PubMed

Barnes, Andrew D; Weigelt, Patrick; Jochum, Malte; Ott, David; Hodapp, Dorothee; Haneda, Noor Farikhah; Brose, Ulrich

2016-05-19

Predicting ecosystem functioning at large spatial scales rests on our ability to scale up from local plots to landscapes, but this is highly contingent on our understanding of how functioning varies through space. Such an understanding has been hampered by a strong experimental focus of biodiversity-ecosystem functioning research restricted to small spatial scales. To address this limitation, we investigate the drivers of spatial variation in multitrophic energy flux-a measure of ecosystem functioning in complex communities-at the landscape scale. We use a structural equation modelling framework based on distance matrices to test how spatial and environmental distances drive variation in community energy flux via four mechanisms: species composition, species richness, niche complementarity and biomass. We found that in both a tropical and a temperate study region, geographical and environmental distance indirectly influence species richness and biomass, with clear evidence that these are the dominant mechanisms explaining variability in community energy flux over spatial and environmental gradients. Our results reveal that species composition and trait variability may become redundant in predicting ecosystem functioning at the landscape scale. Instead, we demonstrate that species richness and total biomass may best predict rates of ecosystem functioning at larger spatial scales. © 2016 The Author(s).

What Do They Have in Common? Drivers of Streamflow Spatial Correlation and Prediction of Flow Regimes in Ungauged Locations

NASA Astrophysics Data System (ADS)

Betterle, A.; Radny, D.; Schirmer, M.; Botter, G.

2017-12-01

The spatial correlation of daily streamflows represents a statistical index encapsulating the similarity between hydrographs at two arbitrary catchment outlets. In this work, a process-based analytical framework is utilized to investigate the hydrological drivers of streamflow spatial correlation through an extensive application to 78 pairs of stream gauges belonging to 13 unregulated catchments in the eastern United States. The analysis provides insight on how the observed heterogeneity of the physical processes that control flow dynamics ultimately affect streamflow correlation and spatial patterns of flow regimes. Despite the variability of recession properties across the study catchments, the impact of heterogeneous drainage rates on the streamflow spatial correlation is overwhelmed by the spatial variability of frequency and intensity of effective rainfall events. Overall, model performances are satisfactory, with root mean square errors between modeled and observed streamflow spatial correlation below 10% in most cases. We also propose a method for estimating streamflow correlation in the absence of discharge data, which proves useful to predict streamflow regimes in ungauged areas. The method consists in setting a minimum threshold on the modeled flow correlation to individuate hydrologically similar sites. Catchment outlets that are most correlated (ρ>0.9) are found to be characterized by analogous streamflow distributions across a broad range of flow regimes.

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

NASA Astrophysics Data System (ADS)

Moglen, G. E.; Kim, S.

2005-12-01

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

Tigers on trails: occupancy modeling for cluster sampling.

PubMed

Hines, J E; Nichols, J D; Royle, J A; MacKenzie, D I; Gopalaswamy, A M; Kumar, N Samba; Karanth, K U

2010-07-01

Occupancy modeling focuses on inference about the distribution of organisms over space, using temporal or spatial replication to allow inference about the detection process. Inference based on spatial replication strictly requires that replicates be selected randomly and with replacement, but the importance of these design requirements is not well understood. This paper focuses on an increasingly popular sampling design based on spatial replicates that are not selected randomly and that are expected to exhibit Markovian dependence. We develop two new occupancy models for data collected under this sort of design, one based on an underlying Markov model for spatial dependence and the other based on a trap response model with Markovian detections. We then simulated data under the model for Markovian spatial dependence and fit the data to standard occupancy models and to the two new models. Bias of occupancy estimates was substantial for the standard models, smaller for the new trap response model, and negligible for the new spatial process model. We also fit these models to data from a large-scale tiger occupancy survey recently conducted in Karnataka State, southwestern India. In addition to providing evidence of a positive relationship between tiger occupancy and habitat, model selection statistics and estimates strongly supported the use of the model with Markovian spatial dependence. This new model provides another tool for the decomposition of the detection process, which is sometimes needed for proper estimation and which may also permit interesting biological inferences. In addition to designs employing spatial replication, we note the likely existence of temporal Markovian dependence in many designs using temporal replication. The models developed here will be useful either directly, or with minor extensions, for these designs as well. We believe that these new models represent important additions to the suite of modeling tools now available for occupancy estimation in conservation monitoring. More generally, this work represents a contribution to the topic of cluster sampling for situations in which there is a need for specific modeling (e.g., reflecting dependence) for the distribution of the variable(s) of interest among subunits.

Natural habitats matter: Determinants of spatial pattern in the composition of animal assemblages of the Czech Republic

NASA Astrophysics Data System (ADS)

Divíšek, Jan; Zelený, David; Culek, Martin; Št'astný, Karel

2014-08-01

Studies that explore species-environment relationships at a broad scale are usually limited by the availability of sufficient habitat description, which is often too coarse to differentiate natural habitat patches. Therefore, it is not well understood how the distribution of natural habitats affects broad-scale patterns in the distribution of animal species. In this study, we evaluate the role of field-mapped natural habitats, land-cover types derived from remote sensing and climate on the composition of assemblages of five distinct animal groups, namely non-volant mammals, birds, reptiles, amphibians and butterflies native to the Czech Republic. First, we used variation partitioning based on redundancy analysis to evaluate the extent to which the environmental variables and their spatial structure might underlie the observed spatial patterns in the composition of animal assemblages. Second, we partitioned variations explained by climate, natural habitats and land-cover to compare their relative importance. Finally, we tested the independent effects of each variable in order to evaluate the significance of their contributions to the environmental model. Our results showed that spatial patterns in the composition of assemblages of almost all the considered animal groups may be ascribed mostly to variations in the environment. Although the shared effects of climatic variables, natural habitats and land-cover types explained the largest proportion of variation in each animal group, the variation explained purely by natural habitats was always higher than the variation explained purely by climate or land-cover. We conclude that most spatial variation in the composition of assemblages of almost all animal groups probably arises from biological processes operating within a spatially structured environment and suggest that natural habitats are important to explain observed patterns because they often perform better than habitat descriptions based on remote sensing. This underlines the value of using appropriate habitat data, for which high-resolution and large-area field-mapping projects are necessary.

Spatial patterns of throughfall isotopic composition at the event and seasonal timescales

Treesearch

Scott T. Allen; Richard F. Keim; Jeffrey J. McDonnell

2015-01-01

Spatial variability of throughfall isotopic composition in forests is indicative of complex processes occurring in the canopy and remains insufficiently understood to properly characterize precipitation inputs to the catchment water balance. Here we investigate variability of throughfall isotopic composition with the objectives: (1) to quantify the spatial variability...

Long-term integrating samplers for indoor air and sub slab soil gas at VI sites

EPA Science Inventory

Vapor intrusion (VI) site assessments are plagued by substantial spatial and temporal variability that makes exposure and risk assessment difficult. Most risk-based decision making for volatile organic compound (VOC) exposure in the indoor environment is based on health benchmark...

Inventory implications of using sampling variances in estimation of growth model coefficients

Treesearch

Albert R. Stage; William R. Wykoff

2000-01-01

Variables based on stand densities or stocking have sampling errors that depend on the relation of tree size to plot size and on the spatial structure of the population, ignoring the sampling errors of such variables, which include most measures of competition used in both distance-dependent and distance-independent growth models, can bias the predictions obtained from...

Spatial and temporal variability in the R-5 infiltration data set: Déjà vu and rainfall-runoff simulations

NASA Astrophysics Data System (ADS)

Loague, Keith; Kyriakidis, Phaedon C.

1997-12-01

This paper is a continuation of the event-based rainfall-runoff model evaluation study reported by Loague and Freeze [1985[. Here we reevaluate the performance of a quasi-physically based rainfall-runoff model for three large events from the well-known R-5 catchment. Five different statistical criteria are used to quantitatively judge model performance. Temporal variability in the large R-5 infiltration data set [Loague and Gander, 1990] is filtered by working in terms of permeability. The transformed data set is reanalyzed via geostatistical methods to model the spatial distribution of permeability across the R-5 catchment. We present new estimates of the spatial distribution of infiltration that are in turn used in our rainfall-runoff simulations with the Horton rainfall-runoff model. The new rainfall-runoff simulations, complicated by reinfiltration impacts at the smaller scales of characterization, indicate that the near-surface hydrologic response of the R-5 catchment is most probably dominated by a combination of the Horton and Dunne overland flow mechanisms.

A spatial model for a stream networks of Citarik River with the environmental variables: potential of hydrogen (PH) and temperature

NASA Astrophysics Data System (ADS)

Bachrudin, A.; Mohamed, N. B.; Supian, S.; Sukono; Hidayat, Y.

2018-03-01

Application of existing geostatistical theory of stream networks provides a number of interesting and challenging problems. Most of statistical tools in the traditional geostatistics have been based on a Euclidean distance such as autocovariance functions, but for stream data is not permissible since it deals with a stream distance. To overcome this autocovariance developed a model based on the distance the flow with using convolution kernel approach (moving average construction). Spatial model for a stream networks is widely used to monitor environmental on a river networks. In a case study of a river in province of West Java, the objective of this paper is to analyze a capability of a predictive on two environmental variables, potential of hydrogen (PH) and temperature using ordinary kriging. Several the empirical results show: (1) The best fit of autocovariance functions for temperature and potential hydrogen (ph) of Citarik River is linear which also yields the smallest root mean squared prediction error (RMSPE), (2) the spatial correlation values between the locations on upstream and on downstream of Citarik river exhibit decreasingly

Characterization of the spatial distribution of porosity in the eogenetic karst Miami Limestone using ground penetrating radar

NASA Astrophysics Data System (ADS)

Mount, G. J.; Comas, X.; Wright, W. J.; McClellan, M. D.

2014-12-01

Hydrogeologic characterization of karst limestone aquifers is difficult due to the variability in the spatial distribution of porosity and dissolution features. Typical methods for aquifer investigation, such as drilling and pump testing, are limited by the scale or spatial extent of the measurement. Hydrogeophysical techniques such as ground penetrating radar (GPR) can provide indirect measurements of aquifer properties and be expanded spatially beyond typical point measures. This investigation used a multiscale approach to identify and quantify porosity distribution in the Miami Limestone, the lithostratigraphic unit that composes the uppermost portions of the Biscayne Aquifer in Miami Dade County, Florida. At the meter scale, laboratory measures of porosity and dielectric permittivity were made on blocks of Miami Limestone using zero offset GPR, laboratory and digital image techniques. Results show good correspondence between GPR and analytical porosity estimates and show variability between 22 and 66 %. GPR measurements at the field scale 10-1000 m investigated the bulk porosity of the limestone based on the assumption that a directly measured water table would remain at a consistent depth in the GPR reflection record. Porosity variability determined from the changes in the depth to water table resulted in porosity values that ranged from 33 to 61 %, with the greatest porosity variability being attributed to the presence of dissolution features. At the larger field scales, 100 - 1000 m, fitting of hyperbolic diffractions in GPR common offsets determined the vertical and horizontal variability of porosity in the saturated subsurface. Results indicate that porosity can vary between 23 and 41 %, and delineate potential areas of enhanced recharge or groundwater / surface water interactions. This study shows porosity variability in the Miami Limestone can range from 22 to 66 % within 1.5 m distances, with areas of high macroporosity or karst dissolution features occupying the higher end of the range. Spatial variability in porosity distribution may affect ground water recharge, allowing zones of high porosity and thus enhanced infiltration to concentrate contaminants into the aquifer and may play a role in small and regional scale aquifer models.

Strategies for satellite-based monitoring of CO2 from distributed area and point sources

NASA Astrophysics Data System (ADS)

Schwandner, Florian M.; Miller, Charles E.; Duren, Riley M.; Natraj, Vijay; Eldering, Annmarie; Gunson, Michael R.; Crisp, David

2014-05-01

Atmospheric CO2 budgets are controlled by the strengths, as well as the spatial and temporal variabilities of CO2 sources and sinks. Natural CO2 sources and sinks are dominated by the vast areas of the oceans and the terrestrial biosphere. In contrast, anthropogenic and geogenic CO2 sources are dominated by distributed area and point sources, which may constitute as much as 70% of anthropogenic (e.g., Duren & Miller, 2012), and over 80% of geogenic emissions (Burton et al., 2013). Comprehensive assessments of CO2 budgets necessitate robust and highly accurate satellite remote sensing strategies that address the competing and often conflicting requirements for sampling over disparate space and time scales. Spatial variability: The spatial distribution of anthropogenic sources is dominated by patterns of production, storage, transport and use. In contrast, geogenic variability is almost entirely controlled by endogenic geological processes, except where surface gas permeability is modulated by soil moisture. Satellite remote sensing solutions will thus have to vary greatly in spatial coverage and resolution to address distributed area sources and point sources alike. Temporal variability: While biogenic sources are dominated by diurnal and seasonal patterns, anthropogenic sources fluctuate over a greater variety of time scales from diurnal, weekly and seasonal cycles, driven by both economic and climatic factors. Geogenic sources typically vary in time scales of days to months (geogenic sources sensu stricto are not fossil fuels but volcanoes, hydrothermal and metamorphic sources). Current ground-based monitoring networks for anthropogenic and geogenic sources record data on minute- to weekly temporal scales. Satellite remote sensing solutions would have to capture temporal variability through revisit frequency or point-and-stare strategies. Space-based remote sensing offers the potential of global coverage by a single sensor. However, no single combination of orbit and sensor provides the full range of temporal sampling needed to characterize distributed area and point source emissions. For instance, point source emission patterns will vary with source strength, wind speed and direction. Because wind speed, direction and other environmental factors change rapidly, short term variabilities should be sampled. For detailed target selection and pointing verification, important lessons have already been learned and strategies devised during JAXA's GOSAT mission (Schwandner et al, 2013). The fact that competing spatial and temporal requirements drive satellite remote sensing sampling strategies dictates a systematic, multi-factor consideration of potential solutions. Factors to consider include vista, revisit frequency, integration times, spatial resolution, and spatial coverage. No single satellite-based remote sensing solution can address this problem for all scales. It is therefore of paramount importance for the international community to develop and maintain a constellation of atmospheric CO2 monitoring satellites that complement each other in their temporal and spatial observation capabilities: Polar sun-synchronous orbits (fixed local solar time, no diurnal information) with agile pointing allow global sampling of known distributed area and point sources like megacities, power plants and volcanoes with daily to weekly temporal revisits and moderate to high spatial resolution. Extensive targeting of distributed area and point sources comes at the expense of reduced mapping or spatial coverage, and the important contextual information that comes with large-scale contiguous spatial sampling. Polar sun-synchronous orbits with push-broom swath-mapping but limited pointing agility may allow mapping of individual source plumes and their spatial variability, but will depend on fortuitous environmental conditions during the observing period. These solutions typically have longer times between revisits, limiting their ability to resolve temporal variations. Geostationary and non-sun-synchronous low-Earth-orbits (precessing local solar time, diurnal information possible) with agile pointing have the potential to provide, comprehensive mapping of distributed area sources such as megacities with longer stare times and multiple revisits per day, at the expense of global access and spatial coverage. An ad hoc CO2 remote sensing constellation is emerging. NASA's OCO-2 satellite (launch July 2014) joins JAXA's GOSAT satellite in orbit. These will be followed by GOSAT-2 and NASA's OCO-3 on the International Space Station as early as 2017. Additional polar orbiting satellites (e.g., CarbonSat, under consideration at ESA) and geostationary platforms may also become available. However, the individual assets have been designed with independent science goals and requirements, and limited consideration of coordinated observing strategies. Every effort must be made to maximize the science return from this constellation. We discuss the opportunities to exploit the complementary spatial and temporal coverage provided by these assets as well as the crucial gaps in the capabilities of this constellation. References Burton, M.R., Sawyer, G.M., and Granieri, D. (2013). Deep carbon emissions from volcanoes. Rev. Mineral. Geochem. 75: 323-354. Duren, R.M., Miller, C.E. (2012). Measuring the carbon emissions of megacities. Nature Climate Change 2, 560-562. Schwandner, F.M., Oda, T., Duren, R., Carn, S.A., Maksyutov, S., Crisp, D., Miller, C.E. (2013). Scientific Opportunities from Target-Mode Capabilities of GOSAT-2. NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena CA, White Paper, 6p., March 2013.

Validation of spatial variability in downscaling results from the VALUE perfect predictor experiment

NASA Astrophysics Data System (ADS)

Widmann, Martin; Bedia, Joaquin; Gutiérrez, Jose Manuel; Maraun, Douglas; Huth, Radan; Fischer, Andreas; Keller, Denise; Hertig, Elke; Vrac, Mathieu; Wibig, Joanna; Pagé, Christian; Cardoso, Rita M.; Soares, Pedro MM; Bosshard, Thomas; Casado, Maria Jesus; Ramos, Petra

2016-04-01

VALUE is an open European network to validate and compare downscaling methods for climate change research. Within VALUE a systematic validation framework to enable the assessment and comparison of both dynamical and statistical downscaling methods has been developed. In the first validation experiment the downscaling methods are validated in a setup with perfect predictors taken from the ERA-interim reanalysis for the period 1997 - 2008. This allows to investigate the isolated skill of downscaling methods without further error contributions from the large-scale predictors. One aspect of the validation is the representation of spatial variability. As part of the VALUE validation we have compared various properties of the spatial variability of downscaled daily temperature and precipitation with the corresponding properties in observations. We have used two test validation datasets, one European-wide set of 86 stations, and one higher-density network of 50 stations in Germany. Here we present results based on three approaches, namely the analysis of i.) correlation matrices, ii.) pairwise joint threshold exceedances, and iii.) regions of similar variability. We summarise the information contained in correlation matrices by calculating the dependence of the correlations on distance and deriving decorrelation lengths, as well as by determining the independent degrees of freedom. Probabilities for joint threshold exceedances and (where appropriate) non-exceedances are calculated for various user-relevant thresholds related for instance to extreme precipitation or frost and heat days. The dependence of these probabilities on distance is again characterised by calculating typical length scales that separate dependent from independent exceedances. Regionalisation is based on rotated Principal Component Analysis. The results indicate which downscaling methods are preferable if the dependency of variability at different locations is relevant for the user.

Beyond the SCS curve number: A new stochastic spatial runoff approach

NASA Astrophysics Data System (ADS)

Bartlett, M. S., Jr.; Parolari, A.; McDonnell, J.; Porporato, A. M.

2015-12-01

The Soil Conservation Service curve number (SCS-CN) method is the standard approach in practice for predicting a storm event runoff response. It is popular because its low parametric complexity and ease of use. However, the SCS-CN method does not describe the spatial variability of runoff and is restricted to certain geographic regions and land use types. Here we present a general theory for extending the SCS-CN method. Our new theory accommodates different event based models derived from alternative rainfall-runoff mechanisms or distributions of watershed variables, which are the basis of different semi-distributed models such as VIC, PDM, and TOPMODEL. We introduce a parsimonious but flexible description where runoff is initiated by a pure threshold, i.e., saturation excess, that is complemented by fill and spill runoff behavior from areas of partial saturation. To facilitate event based runoff prediction, we derive simple equations for the fraction of the runoff source areas, the probability density function (PDF) describing runoff variability, and the corresponding average runoff value (a runoff curve analogous to the SCS-CN). The benefit of the theory is that it unites the SCS-CN method, VIC, PDM, and TOPMODEL as the same model type but with different assumptions for the spatial distribution of variables and the runoff mechanism. The new multiple runoff mechanism description for the SCS-CN enables runoff prediction in geographic regions and site runoff types previously misrepresented by the traditional SCS-CN method. In addition, we show that the VIC, PDM, and TOPMODEL runoff curves may be more suitable than the SCS-CN for different conditions. Lastly, we explore predictions of sediment and nutrient transport by applying the PDF describing runoff variability within our new framework.

Recent changes in county-level corn yield variability in the United States from observations and crop models

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

Leng, Guoyong

The United States is responsible for 35% and 60% of global corn supply and exports. Enhanced supply stability through a reduction in the year-to-year variability of US corn yield would greatly benefit global food security. Important in this regard is to understand how corn yield variability has evolved geographically in the history and how it relates to climatic and non-climatic factors. Results showed that year-to-year variation of US corn yield has decreased significantly during 1980-2010, mainly in Midwest Corn Belt, Nebraska and western arid regions. Despite the country-scale decreasing variability, corn yield variability exhibited an increasing trend in South Dakota,more » Texas and Southeast growing regions, indicating the importance of considering spatial scales in estimating yield variability. The observed pattern is partly reproduced by process-based crop models, simulating larger areas experiencing increasing variability and underestimating the magnitude of decreasing variability. And 3 out of 11 models even produced a differing sign of change from observations. Hence, statistical model which produces closer agreement with observations is used to explore the contribution of climatic and non-climatic factors to the changes in yield variability. It is found that climate variability dominate the change trends of corn yield variability in the Midwest Corn Belt, while the ability of climate variability in controlling yield variability is low in southeastern and western arid regions. Irrigation has largely reduced the corn yield variability in regions (e.g. Nebraska) where separate estimates of irrigated and rain-fed corn yield exist, demonstrating the importance of non-climatic factors in governing the changes in corn yield variability. The results highlight the distinct spatial patterns of corn yield variability change as well as its influencing factors at the county scale. I also caution the use of process-based crop models, which have substantially underestimated the change trend of corn yield variability, in projecting its future changes.« less

Verification of Weather Running Estimate-Nowcast (WRE-N) Forecasts Using a Spatial-Categorical Method

DTIC Science & Technology

2017-07-01

forecasts and observations on a common grid, which enables the application a number of different spatial verification methods that reveal various...forecasts of continuous meteorological variables using categorical and object-based methods . White Sands Missile Range (NM): Army Research Laboratory (US... Research version of the Weather Research and Forecasting Model adapted for generating short-range nowcasts and gridded observations produced by the

Can we improve streamflow simulation by using higher resolution rainfall information?

NASA Astrophysics Data System (ADS)

Lobligeois, Florent; Andréassian, Vazken; Perrin, Charles

2013-04-01

The catchment response to rainfall is the interplay between space-time variability of precipitation, catchment characteristics and antecedent hydrological conditions. Precipitation dominates the high frequency hydrological response, and its simulation is thus dependent on the way rainfall is represented. One of the characteristics which distinguishes distributed from lumped models is their ability to represent explicitly the spatial variability of precipitation and catchment characteristics. The sensitivity of runoff hydrographs to the spatial variability of forcing data has been a major concern of researchers over the last three decades. However, although the literature on the relationship between spatial rainfall and runoff response is abundant, results are contrasted and sometimes contradictory. Several studies concluded that including information on rainfall spatial distribution improves discharge simulation (e.g. Ajami et al., 2004, among others) whereas other studies showed the lack of significant improvement in simulations with better information on rainfall spatial pattern (e.g. Andréassian et al., 2004, among others). The difficulties to reach a clear consensus is mainly due to the fact that each modeling study is implemented only on a few catchments whereas the impact of the spatial distribution of rainfall on runoff is known to be catchment and event characteristics-dependent. Many studies are virtual experiments and only compare flow simulations, which makes it difficult to reach conclusions transposable to real-life case studies. Moreover, the hydrological rainfall-runoff models differ between the studies and the parameterization strategies sometimes tend to advantage the distributed approach (or the lumped one). Recently, Météo-France developed a rainfall reanalysis over the whole French territory at the 1-kilometer resolution and the hourly time step over a 10-year period combining radar data and raingauge measurements: weather radar data were corrected and adjusted with both hourly and daily raingauge data. Based on this new high resolution product, we propose a framework to evaluate the improvements in streamflow simulation by using higher resolution rainfall information. Semi-distributed modelling is performed for different spatial resolution of precipitation forcing: from lumped to semi-distributed simulations. Here we do not work on synthetic (simulated) streamflow, but with actual measurements, on a large set of 181 French catchments representing a variety of size and climate. The rainfall-runoff model is re-calibrated for each resolution of rainfall spatial distribution over a 5-year sub-period and evaluated on the complementary sub-period in validation mode. The results are analysed by catchment classes based on catchment area and for various types of rainfall events based on the spatial variability of precipitation. References Ajami, N. K., Gupta, H. V, Wagener, T. & Sorooshian, S. (2004) Calibration of a semi-distributed hydrologic model for streamflow estimation along a river system. Journal of Hydrology 298(1-4), 112-135. Andréassian, V., Oddos, A., Michel, C., Anctil, F., Perrin, C. & Loumagne, C. (2004) Impact of spatial aggregation of inputs and parameters on the efficiency of rainfall-runoff models: A theoretical study using chimera watersheds. Water Resources Research 40(5), 1-9.

Comparison of climate envelope models developed using expert-selected variables versus statistical selection

USGS Publications Warehouse

Brandt, Laura A.; Benscoter, Allison; Harvey, Rebecca G.; Speroterra, Carolina; Bucklin, David N.; Romañach, Stephanie; Watling, James I.; Mazzotti, Frank J.

2017-01-01

Climate envelope models are widely used to describe potential future distribution of species under different climate change scenarios. It is broadly recognized that there are both strengths and limitations to using climate envelope models and that outcomes are sensitive to initial assumptions, inputs, and modeling methods Selection of predictor variables, a central step in modeling, is one of the areas where different techniques can yield varying results. Selection of climate variables to use as predictors is often done using statistical approaches that develop correlations between occurrences and climate data. These approaches have received criticism in that they rely on the statistical properties of the data rather than directly incorporating biological information about species responses to temperature and precipitation. We evaluated and compared models and prediction maps for 15 threatened or endangered species in Florida based on two variable selection techniques: expert opinion and a statistical method. We compared model performance between these two approaches for contemporary predictions, and the spatial correlation, spatial overlap and area predicted for contemporary and future climate predictions. In general, experts identified more variables as being important than the statistical method and there was low overlap in the variable sets (<40%) between the two methods Despite these differences in variable sets (expert versus statistical), models had high performance metrics (>0.9 for area under the curve (AUC) and >0.7 for true skill statistic (TSS). Spatial overlap, which compares the spatial configuration between maps constructed using the different variable selection techniques, was only moderate overall (about 60%), with a great deal of variability across species. Difference in spatial overlap was even greater under future climate projections, indicating additional divergence of model outputs from different variable selection techniques. Our work is in agreement with other studies which have found that for broad-scale species distribution modeling, using statistical methods of variable selection is a useful first step, especially when there is a need to model a large number of species or expert knowledge of the species is limited. Expert input can then be used to refine models that seem unrealistic or for species that experts believe are particularly sensitive to change. It also emphasizes the importance of using multiple models to reduce uncertainty and improve map outputs for conservation planning. Where outputs overlap or show the same direction of change there is greater certainty in the predictions. Areas of disagreement can be used for learning by asking why the models do not agree, and may highlight areas where additional on-the-ground data collection could improve the models.

Learning a common dictionary for subject-transfer decoding with resting calibration.

PubMed

Morioka, Hiroshi; Kanemura, Atsunori; Hirayama, Jun-ichiro; Shikauchi, Manabu; Ogawa, Takeshi; Ikeda, Shigeyuki; Kawanabe, Motoaki; Ishii, Shin

2015-05-01

Brain signals measured over a series of experiments have inherent variability because of different physical and mental conditions among multiple subjects and sessions. Such variability complicates the analysis of data from multiple subjects and sessions in a consistent way, and degrades the performance of subject-transfer decoding in a brain-machine interface (BMI). To accommodate the variability in brain signals, we propose 1) a method for extracting spatial bases (or a dictionary) shared by multiple subjects, by employing a signal-processing technique of dictionary learning modified to compensate for variations between subjects and sessions, and 2) an approach to subject-transfer decoding that uses the resting-state activity of a previously unseen target subject as calibration data for compensating for variations, eliminating the need for a standard calibration based on task sessions. Applying our methodology to a dataset of electroencephalography (EEG) recordings during a selective visual-spatial attention task from multiple subjects and sessions, where the variability compensation was essential for reducing the redundancy of the dictionary, we found that the extracted common brain activities were reasonable in the light of neuroscience knowledge. The applicability to subject-transfer decoding was confirmed by improved performance over existing decoding methods. These results suggest that analyzing multisubject brain activities on common bases by the proposed method enables information sharing across subjects with low-burden resting calibration, and is effective for practical use of BMI in variable environments. Copyright © 2015 Elsevier Inc. All rights reserved.

Empirical spatial econometric modelling of small scale neighbourhood

NASA Astrophysics Data System (ADS)

Gerkman, Linda

2012-07-01

The aim of the paper is to model small scale neighbourhood in a house price model by implementing the newest methodology in spatial econometrics. A common problem when modelling house prices is that in practice it is seldom possible to obtain all the desired variables. Especially variables capturing the small scale neighbourhood conditions are hard to find. If there are important explanatory variables missing from the model, the omitted variables are spatially autocorrelated and they are correlated with the explanatory variables included in the model, it can be shown that a spatial Durbin model is motivated. In the empirical application on new house price data from Helsinki in Finland, we find the motivation for a spatial Durbin model, we estimate the model and interpret the estimates for the summary measures of impacts. By the analysis we show that the model structure makes it possible to model and find small scale neighbourhood effects, when we know that they exist, but we are lacking proper variables to measure them.

Using JournalMap to link spatial information with ecological site descriptions

USDA-ARS?s Scientific Manuscript database

JournalMap is a scientific literature search engine that empowers you to find relevant research based on location and biophysical variables as well as traditional keyword searches. All publications are geotagged based on reported location information and plotted on a world map showing where the rese...

Quantifying Spatial and Seasonal Variability in Atmospheric Ammonia with In Situ and Space-Based Observations

EPA Science Inventory

Ammonia plays an important role in many biogeochemical processes, yet atmospheric mixing ratios arc not well known. Recently, methods have been developed for retrieving NH₃ from space-based observations, but they have not been compared to in situ measurements. We have ...

Variogram-based feature extraction for neural network recognition of logos

NASA Astrophysics Data System (ADS)

Pham, Tuan D.

2003-03-01

This paper presents a new approach for extracting spatial features of images based on the theory of regionalized variables. These features can be effectively used for automatic recognition of logo images using neural networks. Experimental results on a public-domain logo database show the effectiveness of the proposed approach.

On the use of variable coherence in inverse scattering problems

NASA Astrophysics Data System (ADS)

Baleine, Erwan

Even though most of the properties of optical fields, such as wavelength, polarization, wavefront curvature or angular spectrum, have been commonly manipulated in a variety of remote sensing procedures, controlling the degree of coherence of light did not find wide applications until recently. Since the emergence of optical coherence tomography, a growing number of scattering techniques have relied on temporal coherence gating which provides efficient target selectivity in a way achieved only by bulky short pulse measurements. The spatial counterpart of temporal coherence, however, has barely been exploited in sensing applications. This dissertation examines, in different scattering regimes, a variety of inverse scattering problems based on variable spatial coherence gating. Within the framework of the radiative transfer theory, this dissertation demonstrates that the short range correlation properties of a medium under test can be recovered by varying the size of the coherence volume of an illuminating beam. Nonetheless, the radiative transfer formalism does not account for long range correlations and current methods for retrieving the correlation function of the complex susceptibility require cumbersome cross-spectral density measurements. Instead, a variable coherence tomographic procedure is proposed where spatial coherence gating is used to probe the structural properties of single scattering media over an extended volume and with a very simple detection system. Enhanced backscattering is a coherent phenomenon that survives strong multiple scattering. The variable coherence tomography approach is extended in this context to diffusive media and it is demonstrated that specific photon trajectories can be selected in order to achieve depth-resolved sensing. Probing the scattering properties of shallow and deeper layers is of considerable interest in biological applications such as diagnosis of skin related diseases. The spatial coherence properties of an illuminating field can be manipulated over dimensions much larger than the wavelength thus providing a large effective sensing area. This is a practical advantage over many near-field microscopic techniques, which offer a spatial resolution beyond the classical diffraction limit but, at the expense of scanning a probe over a large area of a sample which is time consuming, and, sometimes, practically impossible. Taking advantage of the large field of view accessible when using the spatial coherence gating, this dissertation introduces the principle of variable coherence scattering microscopy. In this approach, a subwavelength resolution is achieved from simple far-zone intensity measurements by shaping the degree of spatial coherence of an evanescent field. Furthermore, tomographic techniques based on spatial coherence gating are especially attractive because they rely on simple detection schemes which, in principle, do not require any optical elements such as lenses. To demonstrate this capability, a correlated lensless imaging method is proposed and implemented, where both amplitude and phase information of an object are obtained by varying the degree of spatial coherence of the incident beam. Finally, it should be noted that the idea of using the spatial coherence properties of fields in a tomographic procedure is applicable to any type of electromagnetic radiation. Operating on principles of statistical optics, these sensing procedures can become alternatives for various target detection schemes, cutting-edge microscopies or x-ray imaging methods.

Newtonian nudging for a Richards equation-based distributed hydrological model

NASA Astrophysics Data System (ADS)

Paniconi, Claudio; Marrocu, Marino; Putti, Mario; Verbunt, Mark

The objective of data assimilation is to provide physically consistent estimates of spatially distributed environmental variables. In this study a relatively simple data assimilation method has been implemented in a relatively complex hydrological model. The data assimilation technique is Newtonian relaxation or nudging, in which model variables are driven towards observations by a forcing term added to the model equations. The forcing term is proportional to the difference between simulation and observation (relaxation component) and contains four-dimensional weighting functions that can incorporate prior knowledge about the spatial and temporal variability and characteristic scales of the state variable(s) being assimilated. The numerical model couples a three-dimensional finite element Richards equation solver for variably saturated porous media and a finite difference diffusion wave approximation based on digital elevation data for surface water dynamics. We describe the implementation of the data assimilation algorithm for the coupled model and report on the numerical and hydrological performance of the resulting assimilation scheme. Nudging is shown to be successful in improving the hydrological simulation results, and it introduces little computational cost, in terms of CPU and other numerical aspects of the model's behavior, in some cases even improving numerical performance compared to model runs without nudging. We also examine the sensitivity of the model to nudging term parameters including the spatio-temporal influence coefficients in the weighting functions. Overall the nudging algorithm is quite flexible, for instance in dealing with concurrent observation datasets, gridded or scattered data, and different state variables, and the implementation presented here can be readily extended to any of these features not already incorporated. Moreover the nudging code and tests can serve as a basis for implementation of more sophisticated data assimilation techniques in a Richards equation-based hydrological model.

Local short-term variability in solar irradiance

NASA Astrophysics Data System (ADS)

Lohmann, Gerald M.; Monahan, Adam H.; Heinemann, Detlev

2016-05-01

Characterizing spatiotemporal irradiance variability is important for the successful grid integration of increasing numbers of photovoltaic (PV) power systems. Using 1 Hz data recorded by as many as 99 pyranometers during the HD(CP)2 Observational Prototype Experiment (HOPE), we analyze field variability of clear-sky index k* (i.e., irradiance normalized to clear-sky conditions) and sub-minute k* increments (i.e., changes over specified intervals of time) for distances between tens of meters and about 10 km. By means of a simple classification scheme based on k* statistics, we identify overcast, clear, and mixed sky conditions, and demonstrate that the last of these is the most potentially problematic in terms of short-term PV power fluctuations. Under mixed conditions, the probability of relatively strong k* increments of ±0.5 is approximately twice as high compared to increment statistics computed without conditioning by sky type. Additionally, spatial autocorrelation structures of k* increment fields differ considerably between sky types. While the profiles for overcast and clear skies mostly resemble the predictions of a simple model published by , this is not the case for mixed conditions. As a proxy for the smoothing effects of distributed PV, we finally show that spatial averaging mitigates variability in k* less effectively than variability in k* increments, for a spatial sensor density of 2 km-2.

Water supply, demand, and quality indicators for assessing the spatial distribution of water resource vulnerability in the Columbia River Basin

USGS Publications Warehouse

Chang, Heejun; Jung, Il-Won; Strecker, Angela L.; Wise, Daniel; Lafrenz, Martin; Shandas, Vivek; ,; Yeakley, Alan; Pan, Yangdong; Johnson, Gunnar; Psaris, Mike

2013-01-01

We investigated water resource vulnerability in the US portion of the Columbia River basin (CRB) using multiple indicators representing water supply, water demand, and water quality. Based on the US county scale, spatial analysis was conducted using various biophysical and socio-economic indicators that control water vulnerability. Water supply vulnerability and water demand vulnerability exhibited a similar spatial clustering of hotspots in areas where agricultural lands and variability of precipitation were high but dam storage capacity was low. The hotspots of water quality vulnerability were clustered around the main stem of the Columbia River where major population and agricultural centres are located. This multiple equal weight indicator approach confirmed that different drivers were associated with different vulnerability maps in the sub-basins of the CRB. Water quality variables are more important than water supply and water demand variables in the Willamette River basin, whereas water supply and demand variables are more important than water quality variables in the Upper Snake and Upper Columbia River basins. This result suggests that current water resources management and practices drive much of the vulnerability within the study area. The analysis suggests the need for increased coordination of water management across multiple levels of water governance to reduce water resource vulnerability in the CRB and a potentially different weighting scheme that explicitly takes into account the input of various water stakeholders.

SPAGETTA: a Multi-Purpose Gridded Stochastic Weather Generator

NASA Astrophysics Data System (ADS)

Dubrovsky, M.; Huth, R.; Rotach, M. W.; Dabhi, H.

2017-12-01

SPAGETTA is a new multisite/gridded multivariate parametric stochastic weather generator (WG). Site-specific precipitation occurrence and amount are modelled by Markov chain and Gamma distribution, the non-precipitation variables are modelled by an autoregressive (AR) model conditioned on precipitation occurrence, and the spatial coherence of all variables is modelled following the Wilks' (2009) approach. SPAGETTA may be run in two modes. Mode 1: it is run as a classical WG, which is calibrated using weather series from multiple sites, and only then it may produce arbitrarily long synthetic series mimicking the spatial and temporal structure of the calibration data. To generate the weather series representing the future climate, the WG parameters are modified according to the climate change scenario, typically derived from GCM or RCM simulations. Mode 2: the user provides only basic information (not necessarily to be realistic) on the temporal and spatial auto-correlation structure of the weather variables and their mean annual cycle; the generator itself derives the parameters of the underlying AR model, which produces the multi-site weather series. Optionally, the user may add the spatially varying trend, which is superimposed to the synthetic series. The contribution consists of following parts: (a) Model of the WG. (b) Validation of WG in terms of the spatial temperature and precipitation characteristics, including characteristics of spatial hot/cold/dry/wet spells. (c) Results of the climate change impact experiment, in which the WG parameters representing the spatial and temporal variability are modified using the climate change scenarios and the effect on the above spatial validation indices is analysed. In this experiment, the WG is calibrated using the E-OBS gridded daily weather data for several European regions, and the climate change scenarios are derived from the selected RCM simulations (CORDEX database). (d) The second mode of operation will be demonstrated by results obtained while developing the methodology for assessing collective significance of trends in multi-site weather series. The performance of the proposed test statistics is assessed based on large number of realisations of synthetic series produced by WG assuming a given statistical structure and trend of the weather series.

Understanding the Complexity of Temperature Dynamics in Xinjiang, China, from Multitemporal Scale and Spatial Perspectives

PubMed Central

Chen, Yaning; Li, Weihong; Liu, Zuhan; Wei, Chunmeng; Tang, Jie

2013-01-01

Based on the observed data from 51 meteorological stations during the period from 1958 to 2012 in Xinjiang, China, we investigated the complexity of temperature dynamics from the temporal and spatial perspectives by using a comprehensive approach including the correlation dimension (CD), classical statistics, and geostatistics. The main conclusions are as follows (1) The integer CD values indicate that the temperature dynamics are a complex and chaotic system, which is sensitive to the initial conditions. (2) The complexity of temperature dynamics decreases along with the increase of temporal scale. To describe the temperature dynamics, at least 3 independent variables are needed at daily scale, whereas at least 2 independent variables are needed at monthly, seasonal, and annual scales. (3) The spatial patterns of CD values at different temporal scales indicate that the complex temperature dynamics are derived from the complex landform. PMID:23843732

Spatial Durbin model analysis macroeconomic loss due to natural disasters

NASA Astrophysics Data System (ADS)

Kusrini, D. E.; Mukhtasor

2015-03-01

Magnitude of the damage and losses caused by natural disasters is huge for Indonesia, therefore this study aimed to analyze the effects of natural disasters for macroeconomic losses that occurred in 115 cities/districts across Java during 2012. Based on the results of previous studies it is suspected that it contains effects of spatial dependencies in this case, so that the completion of this case is performed using a regression approach to the area, namely Analysis of Spatial Durbin Model (SDM). The obtained significant predictor variable is population, and predictor variable with a significant weighting is the number of occurrences of disasters, i.e., disasters in the region which have an impact on other neighboring regions. Moran's I index value using the weighted Queen Contiguity also showed significant results, meaning that the incidence of disasters in the region will decrease the value of GDP in other.

Using geomorphological variables to predict the spatial distribution of plant species in agricultural drainage networks

PubMed Central

Bailly, Jean-Stéphane; Vinatier, Fabrice

2018-01-01

To optimize ecosystem services provided by agricultural drainage networks (ditches) in headwater catchments, we need to manage the spatial distribution of plant species living in these networks. Geomorphological variables have been shown to be important predictors of plant distribution in other ecosystems because they control the water regime, the sediment deposition rates and the sun exposure in the ditches. Whether such variables may be used to predict plant distribution in agricultural drainage networks is unknown. We collected presence and absence data for 10 herbaceous plant species in a subset of a network of drainage ditches (35 km long) within a Mediterranean agricultural catchment. We simulated their spatial distribution with GLM and Maxent model using geomorphological variables and distance to natural lands and roads. Models were validated using k-fold cross-validation. We then compared the mean Area Under the Curve (AUC) values obtained for each model and other metrics issued from the confusion matrices between observed and predicted variables. Based on the results of all metrics, the models were efficient at predicting the distribution of seven species out of ten, confirming the relevance of geomorphological variables and distance to natural lands and roads to explain the occurrence of plant species in this Mediterranean catchment. In particular, the importance of the landscape geomorphological variables, ie the importance of the geomorphological features encompassing a broad environment around the ditch, has been highlighted. This suggests that agro-ecological measures for managing ecosystem services provided by ditch plants should focus on the control of the hydrological and sedimentological connectivity at the catchment scale. For example, the density of the ditch network could be modified or the spatial distribution of vegetative filter strips used for sediment trapping could be optimized. In addition, the vegetative filter strips could constitute new seed bank sources for species that are affected by the distance to natural lands and roads. PMID:29360857

Using geomorphological variables to predict the spatial distribution of plant species in agricultural drainage networks.

PubMed

Rudi, Gabrielle; Bailly, Jean-Stéphane; Vinatier, Fabrice

2018-01-01

To optimize ecosystem services provided by agricultural drainage networks (ditches) in headwater catchments, we need to manage the spatial distribution of plant species living in these networks. Geomorphological variables have been shown to be important predictors of plant distribution in other ecosystems because they control the water regime, the sediment deposition rates and the sun exposure in the ditches. Whether such variables may be used to predict plant distribution in agricultural drainage networks is unknown. We collected presence and absence data for 10 herbaceous plant species in a subset of a network of drainage ditches (35 km long) within a Mediterranean agricultural catchment. We simulated their spatial distribution with GLM and Maxent model using geomorphological variables and distance to natural lands and roads. Models were validated using k-fold cross-validation. We then compared the mean Area Under the Curve (AUC) values obtained for each model and other metrics issued from the confusion matrices between observed and predicted variables. Based on the results of all metrics, the models were efficient at predicting the distribution of seven species out of ten, confirming the relevance of geomorphological variables and distance to natural lands and roads to explain the occurrence of plant species in this Mediterranean catchment. In particular, the importance of the landscape geomorphological variables, ie the importance of the geomorphological features encompassing a broad environment around the ditch, has been highlighted. This suggests that agro-ecological measures for managing ecosystem services provided by ditch plants should focus on the control of the hydrological and sedimentological connectivity at the catchment scale. For example, the density of the ditch network could be modified or the spatial distribution of vegetative filter strips used for sediment trapping could be optimized. In addition, the vegetative filter strips could constitute new seed bank sources for species that are affected by the distance to natural lands and roads.

Spatial variability of harmful algal blooms in Milford Lake, Kansas, July and August 2015

USGS Publications Warehouse

Foster, Guy M.; Graham, Jennifer L.; Stiles, Tom C.; Boyer, Marvin G.; King, Lindsey R.; Loftin, Keith A.

2017-01-09

Cyanobacterial harmful algal blooms (CyanoHABs) tend to be spatially variable vertically in the water column and horizontally across the lake surface because of in-lake and weather-driven processes and can vary by orders of magnitude in concentration across relatively short distances (meters or less). Extreme spatial variability in cyanobacteria and associated compounds poses unique challenges to collecting representative samples for scientific study and public-health protection. The objective of this study was to assess the spatial variability of cyanobacteria and microcystin in Milford Lake, Kansas, using data collected on July 27 and August 31, 2015. Spatially dense near-surface data were collected by the U.S. Geological Survey, nearshore data were collected by the Kansas Department of Health and Environment, and open-water data were collected by U.S. Army Corps of Engineers. CyanoHABs are known to be spatially variable, but that variability is rarely quantified. A better understanding of the spatial variability of cyanobacteria and microcystin will inform sampling and management strategies for Milford Lake and for other lakes with CyanoHAB issues throughout the Nation.The CyanoHABs in Milford Lake during July and August 2015 displayed the extreme spatial variability characteristic of cyanobacterial blooms. The phytoplankton community was almost exclusively cyanobacteria (greater than 90 percent) during July and August. Cyanobacteria (measured directly by cell counts and indirectly by regression-estimated chlorophyll) and microcystin (measured directly by enzyme-linked immunosorbent assay [ELISA] and indirectly by regression estimates) concentrations varied by orders of magnitude throughout the lake. During July and August 2015, cyanobacteria and microcystin concentrations decreased in the downlake (towards the outlet) direction.Nearshore and open-water surface grabs were collected and analyzed for microcystin as part of this study. Samples were collected in the uplake (Zone C), midlake (Zone B), and downlake (Zone A) parts of the lake. Overall, no consistent pattern was indicated as to which sample location (nearshore or open water) had the highest microcystin concentrations. In July, the maximum microcystin concentration observed in each zone was detected at a nearshore site, and in August, maximum microcystin concentrations in each zone were detected at an open-water site.The Kansas Department of Health and Environment uses two guidance levels (a watch and a warning level) to issue recreational public-health advisories for CyanoHABs in Kansas lakes. The levels are based on concentrations of microcystin and numbers of cyanobacteria. In July and August, discrete water-quality samples were predominantly indicative of warning status in Zone C, watch status in Zone B, and no advisories in Zone A. Regression-estimated microcystin concentrations, which provided more thorough coverage of Milford Lake (n=683–720) than discrete samples (n=21–24), generally indicated the same overall pattern. Regardless of the individual agencies sampling approach, the overall public-health advisory status of each zone in Milford Lake was similar according to the Kansas Department of Health and Environment guidance levels.

Determinants of single family residential water use across scales in four western US cities.

PubMed

Chang, Heejun; Bonnette, Matthew Ryan; Stoker, Philip; Crow-Miller, Britt; Wentz, Elizabeth

2017-10-15

A growing body of literature examines urban water sustainability with increasing evidence that locally-based physical and social spatial interactions contribute to water use. These studies however are based on single-city analysis and often fail to consider whether these interactions occur more generally. We examine a multi-city comparison using a common set of spatially-explicit water, socioeconomic, and biophysical data. We investigate the relative importance of variables for explaining the variations of single family residential (SFR) water uses at Census Block Group (CBG) and Census Tract (CT) scales in four representative western US cities - Austin, Phoenix, Portland, and Salt Lake City, - which cover a wide range of climate and development density. We used both ordinary least squares regression and spatial error regression models to identify the influence of spatial dependence on water use patterns. Our results show that older downtown areas show lower water use than newer suburban areas in all four cities. Tax assessed value and building age are the main determinants of SFR water use across the four cities regardless of the scale. Impervious surface area becomes an important variable for summer water use in all cities, and it is important in all seasons for arid environments such as Phoenix. CT level analysis shows better model predictability than CBG analysis. In all cities, seasons, and spatial scales, spatial error regression models better explain the variations of SFR water use. Such a spatially-varying relationship of urban water consumption provides additional evidence for the need to integrate urban land use planning and municipal water planning. Copyright © 2017 Elsevier B.V. All rights reserved.

Universal Quantum Computing with Measurement-Induced Continuous-Variable Gate Sequence in a Loop-Based Architecture.

PubMed

Takeda, Shuntaro; Furusawa, Akira

2017-09-22

We propose a scalable scheme for optical quantum computing using measurement-induced continuous-variable quantum gates in a loop-based architecture. Here, time-bin-encoded quantum information in a single spatial mode is deterministically processed in a nested loop by an electrically programmable gate sequence. This architecture can process any input state and an arbitrary number of modes with almost minimum resources, and offers a universal gate set for both qubits and continuous variables. Furthermore, quantum computing can be performed fault tolerantly by a known scheme for encoding a qubit in an infinite-dimensional Hilbert space of a single light mode.

Universal Quantum Computing with Measurement-Induced Continuous-Variable Gate Sequence in a Loop-Based Architecture

NASA Astrophysics Data System (ADS)

Takeda, Shuntaro; Furusawa, Akira

2017-09-01

We propose a scalable scheme for optical quantum computing using measurement-induced continuous-variable quantum gates in a loop-based architecture. Here, time-bin-encoded quantum information in a single spatial mode is deterministically processed in a nested loop by an electrically programmable gate sequence. This architecture can process any input state and an arbitrary number of modes with almost minimum resources, and offers a universal gate set for both qubits and continuous variables. Furthermore, quantum computing can be performed fault tolerantly by a known scheme for encoding a qubit in an infinite-dimensional Hilbert space of a single light mode.

A novel spatial performance metric for robust pattern optimization of distributed hydrological models

NASA Astrophysics Data System (ADS)

Stisen, S.; Demirel, C.; Koch, J.

2017-12-01

Evaluation of performance is an integral part of model development and calibration as well as it is of paramount importance when communicating modelling results to stakeholders and the scientific community. There exists a comprehensive and well tested toolbox of metrics to assess temporal model performance in the hydrological modelling community. On the contrary, the experience to evaluate spatial performance is not corresponding to the grand availability of spatial observations readily available and to the sophisticate model codes simulating the spatial variability of complex hydrological processes. This study aims at making a contribution towards advancing spatial pattern oriented model evaluation for distributed hydrological models. This is achieved by introducing a novel spatial performance metric which provides robust pattern performance during model calibration. The promoted SPAtial EFficiency (spaef) metric reflects three equally weighted components: correlation, coefficient of variation and histogram overlap. This multi-component approach is necessary in order to adequately compare spatial patterns. spaef, its three components individually and two alternative spatial performance metrics, i.e. connectivity analysis and fractions skill score, are tested in a spatial pattern oriented model calibration of a catchment model in Denmark. The calibration is constrained by a remote sensing based spatial pattern of evapotranspiration and discharge timeseries at two stations. Our results stress that stand-alone metrics tend to fail to provide holistic pattern information to the optimizer which underlines the importance of multi-component metrics. The three spaef components are independent which allows them to complement each other in a meaningful way. This study promotes the use of bias insensitive metrics which allow comparing variables which are related but may differ in unit in order to optimally exploit spatial observations made available by remote sensing platforms. We see great potential of spaef across environmental disciplines dealing with spatially distributed modelling.

Spatial Variability of Dissolved Organic Carbon in Headwater Wetlands in Central Pennsylvania

NASA Astrophysics Data System (ADS)

Reichert-Eberhardt, A. J.; Wardrop, D.; Boyer, E. W.

2011-12-01

Dissolved organic carbon (DOC) is known to be of an important factor in many microbially mediated biochemical processes, such as denitrification, that occur in wetlands. The spatial variability of DOC within a wetland could impact the microbes that fuel these processes, which in turn can affect the ecosystem services provided by wetlands. However, the amount of spatial variability of DOC in wetlands is generally unknown. Furthermore, it is unknown how disturbance to wetlands can affect spatial variability of DOC. Previous research in central Pennsylvania headwater wetland soils has shown that wetlands with increased human disturbance had decreased heterogeneity in soil biochemistry. To address groundwater chemical variability 20 monitoring wells were installed in a random pattern in a 400 meter squared plot in a low-disturbance headwater wetland and a high-disturbance headwater wetland in central Pennsylvania. Water samples from these wells will be analyzed for DOC, dissolved inorganic carbon, nitrate, ammonia, and sulfate concentrations, as well as pH, conductivity, and temperature on a seasonal basis. It is hypothesized that there will be greater spatial variability of groundwater chemistry in the low disturbance wetland than the high disturbance wetland. This poster will present the initial data concerning DOC spatial variability in both the low and high impact headwater wetlands.

The use of process models to inform and improve statistical models of nitrate occurrence, Great Miami River Basin, southwestern Ohio

USGS Publications Warehouse

Walter, Donald A.; Starn, J. Jeffrey

2013-01-01

Statistical models of nitrate occurrence in the glacial aquifer system of the northern United States, developed by the U.S. Geological Survey, use observed relations between nitrate concentrations and sets of explanatory variables—representing well-construction, environmental, and source characteristics— to predict the probability that nitrate, as nitrogen, will exceed a threshold concentration. However, the models do not explicitly account for the processes that control the transport of nitrogen from surface sources to a pumped well and use area-weighted mean spatial variables computed from within a circular buffer around the well as a simplified source-area conceptualization. The use of models that explicitly represent physical-transport processes can inform and, potentially, improve these statistical models. Specifically, groundwater-flow models simulate advective transport—predominant in many surficial aquifers— and can contribute to the refinement of the statistical models by (1) providing for improved, physically based representations of a source area to a well, and (2) allowing for more detailed estimates of environmental variables. A source area to a well, known as a contributing recharge area, represents the area at the water table that contributes recharge to a pumped well; a well pumped at a volumetric rate equal to the amount of recharge through a circular buffer will result in a contributing recharge area that is the same size as the buffer but has a shape that is a function of the hydrologic setting. These volume-equivalent contributing recharge areas will approximate circular buffers in areas of relatively flat hydraulic gradients, such as near groundwater divides, but in areas with steep hydraulic gradients will be elongated in the upgradient direction and agree less with the corresponding circular buffers. The degree to which process-model-estimated contributing recharge areas, which simulate advective transport and therefore account for local hydrologic settings, would inform and improve the development of statistical models can be implicitly estimated by evaluating the differences between explanatory variables estimated from the contributing recharge areas and the circular buffers used to develop existing statistical models. The larger the difference in estimated variables, the more likely that statistical models would be changed, and presumably improved, if explanatory variables estimated from contributing recharge areas were used in model development. Comparing model predictions from the two sets of estimated variables would further quantify—albeit implicitly—how an improved, physically based estimate of explanatory variables would be reflected in model predictions. Differences between the two sets of estimated explanatory variables and resultant model predictions vary spatially; greater differences are associated with areas of steep hydraulic gradients. A direct comparison, however, would require the development of a separate set of statistical models using explanatory variables from contributing recharge areas. Area-weighted means of three environmental variables—silt content, alfisol content, and depth to water from the U.S. Department of Agriculture State Soil Geographic (STATSGO) data—and one nitrogen-source variable (fertilizer-application rate from county data mapped to Enhanced National Land Cover Data 1992 (NLCDe 92) agricultural land use) can vary substantially between circular buffers and volume-equivalent contributing recharge areas and among contributing recharge areas for different sets of well variables. The differences in estimated explanatory variables are a function of the same factors affecting the contributing recharge areas as well as the spatial resolution and local distribution of the underlying spatial data. As a result, differences in estimated variables between circular buffers and contributing recharge areas are complex and site specific as evidenced by differences in estimated variables for circular buffers and contributing recharge areas of existing public-supply and network wells in the Great Miami River Basin. Large differences in areaweighted mean environmental variables are observed at the basin scale, determined by using the network of uniformly spaced hypothetical wells; the differences have a spatial pattern that generally is similar to spatial patterns in the underlying STATSGO data. Generally, the largest differences were observed for area-weighted nitrogen-application rate from county and national land-use data; the basin-scale differences ranged from -1,600 (indicating a larger value from within the volume-equivalent contributing recharge area) to 1,900 kilograms per year (kg/yr); the range in the underlying spatial data was from 0 to 2,200 kg/yr. Silt content, alfisol content, and nitrogen-application rate are defined by the underlying spatial data and are external to the groundwater system; however, depth to water is an environmental variable that can be estimated in more detail and, presumably, in a more physically based manner using a groundwater-flow model than using the spatial data. Model-calculated depths to water within circular buffers in the Great Miami River Basin differed substantially from values derived from the spatial data and had a much larger range. Differences in estimates of area-weighted spatial variables result in corresponding differences in predictions of nitrate occurrence in the aquifer. In addition to the factors affecting contributing recharge areas and estimated explanatory variables, differences in predictions also are a function of the specific set of explanatory variables used and the fitted slope coefficients in a given model. For models that predicted the probability of exceeding 1 and 4 milligrams per liter as nitrogen (mg/L as N), predicted probabilities using variables estimated from circular buffers and contributing recharge areas generally were correlated but differed significantly at the local and basin scale. The scale and distribution of prediction differences can be explained by the underlying differences in the estimated variables and the relative weight of the variables in the statistical models. Differences in predictions of exceeding 1 mg/L as N, which only includes environmental variables, generally correlated with the underlying differences in STATSGO data, whereas differences in exceeding 4 mg/L as N were more spatially extensive because that model included environmental and nitrogen-source variables. Using depths to water from within circular buffers derived from the spatial data and depths to water within the circular buffers calculated from the groundwater-flow model, restricted to the same range, resulted in large differences in predicted probabilities. The differences in estimated explanatory variables between contributing recharge areas and circular buffers indicate incorporation of physically based contributing recharge area likely would result in a different set of explanatory variables and an improved set of statistical models. The use of a groundwater-flow model to improve representations of source areas or to provide more-detailed estimates of specific explanatory variables includes a number of limitations and technical considerations. An assumption in these analyses is that (1) there is a state of mass balance between recharge and pumping, and (2) transport to a pumped well is under a steady state flow field. Comparison of volumeequivalent contributing recharge areas under steady-state and transient transport conditions at a location in the southeastern part of the basin shows the steady-state contributing recharge area is a reasonable approximation of the transient contributing recharge area after between 10 and 20 years of pumping. The first assumption is a more important consideration for this analysis. A gradient effect refers to a condition where simulated pumping from a well is less than recharge through the corresponding contributing recharge area. This generally takes place in areas with steep hydraulic gradients, such as near discharge locations, and can be mitigated using a finer model discretization. A boundary effect refers to a condition where recharge through the contributing recharge area is less than pumping. This indicates other sources of water to the simulated well and could reflect a real hydrologic process. In the Great Miami River Basin, large gradient and boundary effects—defined as the balance between pumping and recharge being less than half—occurred in 5 and 14 percent of the basin, respectively. The agreement between circular buffers and volume-equivalent contributing recharge areas, differences in estimated variables, and the effect on statisticalmodel predictions between the population of wells with a balance between pumping and recharge within 10 percent and the population of all wells were similar. This indicated process-model limitations did not affect the overall findings in the Great Miami River Basin; however, this would be model specific, and prudent use of a process model needs to entail a limitations analysis and, if necessary, alterations to the model.

Optimized algorithm for the spatial nonuniformity correction of an imaging system based on a charge-coupled device color camera.

PubMed

de Lasarte, Marta; Pujol, Jaume; Arjona, Montserrat; Vilaseca, Meritxell

2007-01-10

We present an optimized linear algorithm for the spatial nonuniformity correction of a CCD color camera's imaging system and the experimental methodology developed for its implementation. We assess the influence of the algorithm's variables on the quality of the correction, that is, the dark image, the base correction image, and the reference level, and the range of application of the correction using a uniform radiance field provided by an integrator cube. The best spatial nonuniformity correction is achieved by having a nonzero dark image, by using an image with a mean digital level placed in the linear response range of the camera as the base correction image and taking the mean digital level of the image as the reference digital level. The response of the CCD color camera's imaging system to the uniform radiance field shows a high level of spatial uniformity after the optimized algorithm has been applied, which also allows us to achieve a high-quality spatial nonuniformity correction of captured images under different exposure conditions.

Influence of spatial variability of hydraulic characteristics of soils on surface parameters obtained from remote sensing data in infrared and microwaves

NASA Technical Reports Server (NTRS)

Brunet, Y.; Vauclin, M.

1985-01-01

The correct interpretation of thermal and hydraulic soil parameters infrared from remotely sensed data (thermal infrared, microwaves) implies a good understanding of the causes of their temporal and spatial variability. Given this necessity, the sensitivity of the surface variables (temperature, moisture) to the spatial variability of hydraulic soil properties is tested with a numerical model of heat and mass transfer between bare soil and atmosphere. The spatial variability of hydraulic soil properties is taken into account in terms of the scaling factor. For a given soil, the knowledge of its frequency distribution allows a stochastic use of the model. The results are treated statistically, and the part of the variability of soil surface parameters due to that of soil hydraulic properties is evaluated quantitatively.

Spatial Assessment of the Association between Long-Term Exposure to Environmental Factors and the Occurrence of Amyotrophic Lateral Sclerosis in Catalonia, Spain: A Population-Based Nested Case-Control Study.

PubMed

Povedano, Mònica; Saez, Marc; Martínez-Matos, Juan-Antonio; Barceló, Maria Antònia

2018-05-31

It is believed that an interaction between genetic and non-genetic factors may be involved in the development of amyotrophic lateral sclerosis (ALS). With the exception of exposure to agricultural chemicals like pesticides, evidence of an association between environmental risk factors and ALS is inconsistent. Our objective here was to investigate the association between long-term exposure to environmental factors and the occurrence of ALS in Catalonia, Spain, and to provide evidence that spatial clusters of ALS related to these environmental factors exist. We carried out a nested case-control study constructed from a retrospective population-based cohort, covering the entire region. Environmental variables were the explanatory variables of interest. We controlled for both observed and unobserved confounders. We have found some spatial clusters of ALS. The results from the multivariate model suggest that these clusters could be related to some of the environmental variables, in particular agricultural chemicals. In addition, in high-risk clusters, besides corresponding to agricultural areas, key road infrastructures with a high density of traffic are also located. Our results indicate that some environmental factors, in particular those associated with exposure to pesticides and air pollutants as a result of urban traffic, could be associated with the occurrence of ALS. © 2018 S. Karger AG, Basel.

An efficient deterministic-probabilistic approach to modeling regional groundwater flow: 2. Application to Owens Valley, California

USGS Publications Warehouse

Guymon, Gary L.; Yen, Chung-Cheng

1990-01-01

The applicability of a deterministic-probabilistic model for predicting water tables in southern Owens Valley, California, is evaluated. The model is based on a two-layer deterministic model that is cascaded with a two-point probability model. To reduce the potentially large number of uncertain variables in the deterministic model, lumping of uncertain variables was evaluated by sensitivity analysis to reduce the total number of uncertain variables to three variables: hydraulic conductivity, storage coefficient or specific yield, and source-sink function. Results demonstrate that lumping of uncertain parameters reduces computational effort while providing sufficient precision for the case studied. Simulated spatial coefficients of variation for water table temporal position in most of the basin is small, which suggests that deterministic models can predict water tables in these areas with good precision. However, in several important areas where pumping occurs or the geology is complex, the simulated spatial coefficients of variation are over estimated by the two-point probability method.

An efficient deterministic-probabilistic approach to modeling regional groundwater flow: 2. Application to Owens Valley, California

NASA Astrophysics Data System (ADS)

Guymon, Gary L.; Yen, Chung-Cheng

1990-07-01

The applicability of a deterministic-probabilistic model for predicting water tables in southern Owens Valley, California, is evaluated. The model is based on a two-layer deterministic model that is cascaded with a two-point probability model. To reduce the potentially large number of uncertain variables in the deterministic model, lumping of uncertain variables was evaluated by sensitivity analysis to reduce the total number of uncertain variables to three variables: hydraulic conductivity, storage coefficient or specific yield, and source-sink function. Results demonstrate that lumping of uncertain parameters reduces computational effort while providing sufficient precision for the case studied. Simulated spatial coefficients of variation for water table temporal position in most of the basin is small, which suggests that deterministic models can predict water tables in these areas with good precision. However, in several important areas where pumping occurs or the geology is complex, the simulated spatial coefficients of variation are over estimated by the two-point probability method.

Spatial and temporal variability of guinea grass (Megathyrsus maximus) fuel loads and moisture on Oahu, Hawaii

Treesearch

Lisa M. Ellsworth; Creighton M. Litton; Andrew D. Taylor; J. Boone Kauffman

2013-01-01

Frequent wildfires in tropical landscapes dominated by non-native invasive grasses threaten surrounding ecosystems and developed areas. To better manage fire, accurate estimates of the spatial and temporal variability in fuels are urgently needed. We quantified the spatial variability in live and dead fine fuel loads and moistures at four guinea grass (...

Correlation Based Target Location and Identification

DTIC Science & Technology

1992-12-01

Research Daugman (7) cites research on the mammalian visual nervous system (retina, lateral geniculate , and primary visual cortex) as motivation for...brains, they can still sort slides into natural categories such as people, trees, and bodies of water, a capability that humans do easily. As such...critical neurobiological variables of a given neuron’s orientation and spatial frequency preference, the tuning bandwidths for these variables, the

Evaluating shrub-associated spatial patterns of soil properties in a shrub-steppe ecosystem using multiple-variable geostatistics

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

Halvorson, J.J.; Smith, J.L.; Bolton, H. Jr.

1995-09-01

Geostatistics are often calculated for a single variable at a time, even though many natural phenomena are functions of several variables. The objective of this work was to demonstrate a nonparametric approach for assessing the spatial characteristics of multiple-variable phenomena. Specifically, we analyzed the spatial characteristics of resource islands in the soil under big sagebrush (Artemisia tridentala Nutt.), a dominant shrub in the intermountain western USA. For our example, we defined resource islands as a function of six soil variables representing concentrations of soil resources, populations of microorganisms, and soil microbial physiological variables. By collectively evaluating the indicator transformations ofmore » these individual variables, we created a new data set, termed a multiple-variable indicator transform or MVIT. Alternate MVITs were obtained by varying the selection criteria. Each MVIT was analyzed with variography to characterize spatial continuity, and with indicator kriging to predict the combined probability of their occurrence at unsampled locations in the landscape. Simple graphical analysis and variography demonstrated spatial dependence for all individual soil variables. Maps derived from ordinary kriging of MVITs suggested that the combined probabilities for encountering zones of above-median resources were greatest near big sagebrush. 51 refs., 5 figs., 1 tab.« less

Floodplain complexity and surface metrics: influences of scale and geomorphology

USGS Publications Warehouse

Scown, Murray W.; Thoms, Martin C.; DeJager, Nathan R.

2015-01-01

Many studies of fluvial geomorphology and landscape ecology examine a single river or landscape, thus lack generality, making it difficult to develop a general understanding of the linkages between landscape patterns and larger-scale driving variables. We examined the spatial complexity of eight floodplain surfaces in widely different geographic settings and determined how patterns measured at different scales relate to different environmental drivers. Floodplain surface complexity is defined as having highly variable surface conditions that are also highly organised in space. These two components of floodplain surface complexity were measured across multiple sampling scales from LiDAR-derived DEMs. The surface character and variability of each floodplain were measured using four surface metrics; namely, standard deviation, skewness, coefficient of variation, and standard deviation of curvature from a series of moving window analyses ranging from 50 to 1000 m in radius. The spatial organisation of each floodplain surface was measured using spatial correlograms of the four surface metrics. Surface character, variability, and spatial organisation differed among the eight floodplains; and random, fragmented, highly patchy, and simple gradient spatial patterns were exhibited, depending upon the metric and window size. Differences in surface character and variability among the floodplains became statistically stronger with increasing sampling scale (window size), as did their associations with environmental variables. Sediment yield was consistently associated with differences in surface character and variability, as were flow discharge and variability at smaller sampling scales. Floodplain width was associated with differences in the spatial organization of surface conditions at smaller sampling scales, while valley slope was weakly associated with differences in spatial organisation at larger scales. A comparison of floodplain landscape patterns measured at different scales would improve our understanding of the role that different environmental variables play at different scales and in different geomorphic settings.

Human impact on sediment fluxes within the Blue Nile and Atbara River basins

NASA Astrophysics Data System (ADS)

Balthazar, Vincent; Vanacker, Veerle; Girma, Atkilt; Poesen, Jean; Golla, Semunesh

2013-01-01

A regional assessment of the spatial variability in sediment yields allows filling the gap between detailed, process-based understanding of erosion at field scale and empirical sediment flux models at global scale. In this paper, we focus on the intrabasin variability in sediment yield within the Blue Nile and Atbara basins as biophysical and anthropogenic factors are presumably acting together to accelerate soil erosion. The Blue Nile and Atbara River systems are characterized by an important spatial variability in sediment fluxes, with area-specific sediment yield (SSY) values ranging between 4 and 4935 t/km2/y. Statistical analyses show that 41% of the observed variation in SSY can be explained by remote sensing proxy data of surface vegetation cover, rainfall intensity, mean annual temperature, and human impact. The comparison of a locally adapted regression model with global predictive sediment flux models indicates that global flux models such as the ART and BQART models are less suited to capture the spatial variability in area-specific sediment yields (SSY), but they are very efficient to predict absolute sediment yields (SY). We developed a modified version of the BQART model that estimates the human influence on sediment yield based on a high resolution composite measure of local human impact (human footprint index) instead of countrywide estimates of GNP/capita. Our modified version of the BQART is able to explain 80% of the observed variation in SY for the Blue Nile and Atbara basins and thereby performs only slightly less than locally adapted regression models.

Landscape effects on mallard habitat selection at multiple spatial scales during the non-breeding period

USGS Publications Warehouse

Beatty, William S.; Webb, Elisabeth B.; Kesler, Dylan C.; Raedeke, Andrew H.; Naylor, Luke W.; Humburg, Dale D.

2014-01-01

Previous studies that evaluated effects of landscape-scale habitat heterogeneity on migratory waterbird distributions were spatially limited and temporally restricted to one major life-history phase. However, effects of landscape-scale habitat heterogeneity on long-distance migratory waterbirds can be studied across the annual cycle using new technologies, including global positioning system satellite transmitters. We used Bayesian discrete choice models to examine the influence of local habitats and landscape composition on habitat selection by a generalist dabbling duck, the mallard (Anas platyrhynchos), in the midcontinent of North America during the non-breeding period. Using a previously published empirical movement metric, we separated the non-breeding period into three seasons, including autumn migration, winter, and spring migration. We defined spatial scales based on movement patterns such that movements >0.25 and <30.00 km were classified as local scale and movements >30.00 km were classified as relocation scale. Habitat selection at the local scale was generally influenced by local and landscape-level variables across all seasons. Variables in top models at the local scale included proximities to cropland, emergent wetland, open water, and woody wetland. Similarly, variables associated with area of cropland, emergent wetland, open water, and woody wetland were also included at the local scale. At the relocation scale, mallards selected resource units based on more generalized variables, including proximity to wetlands and total wetland area. Our results emphasize the role of landscape composition in waterbird habitat selection and provide further support for local wetland landscapes to be considered functional units of waterbird conservation and management.

Spatial variability versus parameter uncertainty in freshwater fate and exposure factors of chemicals.

PubMed

Nijhof, Carl O P; Huijbregts, Mark A J; Golsteijn, Laura; van Zelm, Rosalie

2016-04-01

We compared the influence of spatial variability in environmental characteristics and the uncertainty in measured substance properties of seven chemicals on freshwater fate factors (FFs), representing the residence time in the freshwater environment, and on exposure factors (XFs), representing the dissolved fraction of a chemical. The influence of spatial variability was quantified using the SimpleBox model in which Europe was divided in 100 × 100 km regions, nested in a regional (300 × 300 km) and supra-regional (500 × 500 km) scale. Uncertainty in substance properties was quantified by means of probabilistic modelling. Spatial variability and parameter uncertainty were expressed by the ratio k of the 95%ile and 5%ile of the FF and XF. Our analysis shows that spatial variability ranges in FFs of persistent chemicals that partition predominantly into one environmental compartment was up to 2 orders of magnitude larger compared to uncertainty. For the other (less persistent) chemicals, uncertainty in the FF was up to 1 order of magnitude larger than spatial variability. Variability and uncertainty in freshwater XFs of the seven chemicals was negligible (k < 1.5). We found that, depending on the chemical and emission scenario, accounting for region-specific environmental characteristics in multimedia fate modelling, as well as accounting for parameter uncertainty, can have a significant influence on freshwater fate factor predictions. Therefore, we conclude that it is important that fate factors should not only account for parameter uncertainty, but for spatial variability as well, as this further increases the reliability of ecotoxicological impacts in LCA. Copyright © 2016 Elsevier Ltd. All rights reserved.

Climatological Downscaling and Evaluation of AGRMET Precipitation Analyses Over the Continental U.S.

NASA Astrophysics Data System (ADS)

Garcia, M.; Peters-Lidard, C. D.; Eylander, J. B.; Daly, C.; Tian, Y.; Zeng, J.

2007-05-01

The spatially distributed application of a land surface model (LSM) over a region of interest requires the application of similarly distributed precipitation fields that can be derived from various sources, including surface gauge networks, surface-based radar, and orbital platforms. The spatial variability of precipitation influences the spatial organization of soil temperature and moisture states and, consequently, the spatial variability of land- atmosphere fluxes. The accuracy of spatially-distributed precipitation fields can contribute significantly to the uncertainty of model-based hydrological states and fluxes at the land surface. Collaborations between the Air Force Weather Agency (AFWA), NASA, and Oregon State University have led to improvements in the processing of meteorological forcing inputs for the NASA-GSFC Land Information System (LIS; Kumar et al. 2006), a sophisticated framework for LSM operation and model coupling experiments. Efforts at AFWA toward the production of surface hydrometeorological products are currently in transition from the legacy Agricultural Meteorology modeling system (AGRMET) to use of the LIS framework and procedures. Recent enhancements to meteorological input processing for application to land surface models in LIS include the assimilation of climate-based information for the spatial interpolation and downscaling of precipitation fields. Climatological information included in the LIS-based downscaling procedure for North America is provided by a monthly high-resolution PRISM (Daly et al. 1994, 2002; Daly 2006) dataset based on a 30-year analysis period. The combination of these sources and methods attempts to address the strengths and weaknesses of available legacy products, objective interpolation methods, and the PRISM knowledge-based methodology. All of these efforts are oriented on an operational need for timely estimation of spatial precipitation fields at adequate spatial resolution for customer dissemination and near-real-time simulations in regions of interest. This work focuses on value added to the AGRMET precipitation product by the inclusion of high-quality climatological information on a monthly time scale. The AGRMET method uses microwave-based satellite precipitation estimates from various polar-orbiting platforms (NOAA POES and DMSP), infrared-based estimates from geostationary platforms (GOES, METEOSAT, etc.), related cloud analysis products, and surface gauge observations in a complex and hierarchical blending process. Results from processing of the legacy AGRMET precipitation products over the U.S. using LIS-based methods for downscaling, both with and without climatological factors, are evaluated against high-resolution monthly analyses using the PRISM knowledge- based method (Daly et al. 2002). It is demonstrated that the incorporation of climatological information in a downscaling procedure can significantly enhance the accuracy, and potential utility, of AFWA precipitation products for military and civilian customer applications.

Sulfur dioxide in the Venus Atmosphere: II. Spatial and temporal variability

NASA Astrophysics Data System (ADS)

Vandaele, A. C.; Korablev, O.; Belyaev, D.; Chamberlain, S.; Evdokimova, D.; Encrenaz, Th.; Esposito, L.; Jessup, K. L.; Lefèvre, F.; Limaye, S.; Mahieux, A.; Marcq, E.; Mills, F. P.; Montmessin, F.; Parkinson, C. D.; Robert, S.; Roman, T.; Sandor, B.; Stolzenbach, A.; Wilson, C.; Wilquet, V.

2017-10-01

The vertical distribution of sulfur species in the Venus atmosphere has been investigated and discussed in Part I of this series of papers dealing with the variability of SO2 on Venus. In this second part, we focus our attention on the spatial (horizontal) and temporal variability exhibited by SO2. Appropriate data sets - SPICAV/UV nadir observations from Venus Express, ground-based ALMA and TEXES, as well as UV observation on the Hubble Space Telescope - have been considered for this analysis. High variability both on short-term and short-scale are observed. The long-term trend observed by these instruments shows a succession of rapid increases followed by slow decreases in the SO2 abundance at the cloud top level, implying that the transport of air from lower altitudes plays an important role. The origins of the larger amplitude short-scale, short-term variability observed at the cloud tops are not yet known but are likely also connected to variations in vertical transport of SO2 and possibly to variations in the abundance and production and loss of H2O, H2SO4, and Sx.

Organic carbon stock modelling for the quantification of the carbon sinks in terrestrial ecosystems

NASA Astrophysics Data System (ADS)

Durante, Pilar; Algeet, Nur; Oyonarte, Cecilio

2017-04-01

Given the recent environmental policies derived from the serious threats caused by global change, practical measures to decrease net CO2 emissions have to be put in place. Regarding this, carbon sequestration is a major measure to reduce atmospheric CO2 concentrations within a short and medium term, where terrestrial ecosystems play a basic role as carbon sinks. Development of tools for quantification, assessment and management of organic carbon in ecosystems at different scales and management scenarios, it is essential to achieve these commitments. The aim of this study is to establish a methodological framework for the modeling of this tool, applied to a sustainable land use planning and management at spatial and temporal scale. The methodology for carbon stock estimation in ecosystems is based on merger techniques between carbon stored in soils and aerial biomass. For this purpose, both spatial variability map of soil organic carbon (SOC) and algorithms for calculation of forest species biomass will be created. For the modelling of the SOC spatial distribution at different map scales, it is necessary to fit in and screen the available information of soil database legacy. Subsequently, SOC modelling will be based on the SCORPAN model, a quantitative model use to assess the correlation among soil-forming factors measured at the same site location. These factors will be selected from both static (terrain morphometric variables) and dynamic variables (climatic variables and vegetation indexes -NDVI-), providing to the model the spatio-temporal characteristic. After the predictive model, spatial inference techniques will be used to achieve the final map and to extrapolate the data to unavailable information areas (automated random forest regression kriging). The estimated uncertainty will be calculated to assess the model performance at different scale approaches. Organic carbon modelling of aerial biomass will be estimate using LiDAR (Light Detection And Ranging) algorithms. The available LiDAR databases will be used. LiDAR statistics (which describe the LiDAR cloud point data to calculate forest stand parameters) will be correlated with different canopy cover variables. The regression models applied to the total area will produce a continuous geo-information map to each canopy variable. The CO2 estimation will be calculated by dry-mass conversion factors for each forest species (C kg-CO2 kg equivalent). The result is the organic carbon modelling at spatio-temporal scale with different levels of uncertainty associated to the predictive models and diverse detailed scales. However, one of the main expected problems is due to the heterogeneous spatial distribution of the soil information, which influences on the prediction of the models at different spatial scales and, consequently, at SOC map scale. Besides this, the variability and mixture of the forest species of the aerial biomass decrease the accuracy assessment of the organic carbon.

Macromolecular Crowding Induces Spatial Correlations That Control Gene Expression Bursting Patterns

DOE PAGES

Norred, Sarah Elizabeth; Caveney, Patrick M.; Chauhan, Gaurav; ...

2018-04-24

Recent superresolution microscopy studies in E. coli demonstrate that the cytoplasm has highly variable local concentrations where macromolecular crowding plays a central role in establishing membrane-less compartmentalization. This spatial inhomogeneity significantly influences molecular transport and association processes central to gene expression. Yet, little is known about how macromolecular crowding influences gene expression bursting—the episodic process where mRNA and proteins are produced in bursts. Here, we simultaneously measured mRNA and protein reporters in cell-free systems, showing that macromolecular crowding decoupled the well-known relationship between fluctuations in the protein population (noise) and mRNA population statistics. Crowded environments led to a 10-fold increasemore » in protein noise even though there were only modest changes in the mRNA population and fluctuations. Instead, cell-like macromolecular crowding created an inhomogeneous spatial distribution of mRNA (“spatial noise”) that led to large variability in the protein production burst size. As a result, the mRNA spatial noise created large temporal fluctuations in the protein population. Furthermore, these results highlight the interplay between macromolecular crowding, spatial inhomogeneities, and the resulting dynamics of gene expression, and provide insights into using these organizational principles in both cell-based and cell-free synthetic biology.« less

Macromolecular Crowding Induces Spatial Correlations That Control Gene Expression Bursting Patterns

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

Norred, Sarah Elizabeth; Caveney, Patrick M.; Chauhan, Gaurav

Recent superresolution microscopy studies in E. coli demonstrate that the cytoplasm has highly variable local concentrations where macromolecular crowding plays a central role in establishing membrane-less compartmentalization. This spatial inhomogeneity significantly influences molecular transport and association processes central to gene expression. Yet, little is known about how macromolecular crowding influences gene expression bursting—the episodic process where mRNA and proteins are produced in bursts. Here, we simultaneously measured mRNA and protein reporters in cell-free systems, showing that macromolecular crowding decoupled the well-known relationship between fluctuations in the protein population (noise) and mRNA population statistics. Crowded environments led to a 10-fold increasemore » in protein noise even though there were only modest changes in the mRNA population and fluctuations. Instead, cell-like macromolecular crowding created an inhomogeneous spatial distribution of mRNA (“spatial noise”) that led to large variability in the protein production burst size. As a result, the mRNA spatial noise created large temporal fluctuations in the protein population. Furthermore, these results highlight the interplay between macromolecular crowding, spatial inhomogeneities, and the resulting dynamics of gene expression, and provide insights into using these organizational principles in both cell-based and cell-free synthetic biology.« less

The test-retest reliability and minimal detectable change of spatial and temporal gait variability during usual over-ground walking for younger and older adults.

PubMed

Almarwani, Maha; Perera, Subashan; VanSwearingen, Jessie M; Sparto, Patrick J; Brach, Jennifer S

2016-02-01

Gait variability is a marker of gait performance and future mobility status in older adults. Reliability of gait variability has been examined mainly in community dwelling older adults who are likely to fluctuate over time. The purpose of this study was to compare test-retest reliability and determine minimal detectable change (MDC) of spatial and temporal gait variability in younger and older adults. Forty younger (mean age=26.6 ± 6.0 years) and 46 older adults (mean age=78.1 ± 6.2 years) were included in the study. Gait characteristics were measured twice, approximately 1 week apart, using a computerized walkway (GaitMat II). Participants completed 4 passes on the GaitMat II at their self-selected walking speed. Test-retest reliability was calculated using Intra-class correlation coefficients (ICCs(2,1)), 95% limits of agreement (95% LoA) in conjunction with Bland-Altman plots, relative limits of agreement (LoA%) and standard error of measurement (SEM). The MDC at 90% and 95% level were also calculated. ICCs of gait variability ranged 0.26-0.65 in younger and 0.28-0.74 in older adults. The LoA% and SEM were consistently higher (i.e. less reliable) for all gait variables in older compared to younger adults except SEM for step width. The MDC was consistently larger for all gait variables in older compared to younger adults except step width. ICCs were of limited utility due to restricted ranges in younger adults. Based on absolute reliability measures and MDC, younger had greater test-retest reliability and smaller MDC of spatial and temporal gait variability compared to older adults. Copyright © 2015 Elsevier B.V. All rights reserved.

[Evaluation on environmental quality of heavy metals in soils and vegetables based on geostatistics and GIS].

PubMed

Xie, Zheng-miao; Li, Jing; Wang, Bi-ling; Chen, Jian-jun

2006-10-01

Contents of heavy metals (Pb, Zn, Cd, Cu) in soils and vegetables from Dongguan town in Shangyu city, China were studied using geostatistical analysis and GIS technique to evaluate environmental quality. Based on the evaluation criteria, the distribution of the spatial variability of heavy metals in soil-vegetable system was mapped and analyzed. The results showed that the distribution of soil heavy metals in a large number of soil samples in Dongguan town was asymmetric. The contents of Zn and Cu were lower than those of Cd and Pb. The concentrations distribution of Pb, Zn, Cd and Cu in soils and vegetables were different in spatial variability. There was a close relationship between total and available contents of heavy metals in soil. The contents of Pb and Cd in green vegetables were higher than those of Zn and Cu and exceeded the national sanitation standards for vegetables.

Study on temporal and spatial variations of urban land use based on land change data

NASA Astrophysics Data System (ADS)

Jiang, Ping; Liu, Yanfang; Fan, Min; Zhang, Yang

2009-10-01

With the rapid development of urbanization, demands of urban land increase in succession, hence, to analyze temporal and spatial variations of urban land use becomes more and more important. In this paper, the principle of trend surface analysis and formula of urban land sprawl index ( ULSI) are expatiated at first, and then based on land change data of Jiayu county, the author fits quadratic trend surface by choosing urban land area as dependent variable and urbanization and GDP as independent variables from 1996 to 2006, draws isoline of trend surface and residual values; and then urban land sprawl indexes of towns are calculated on the basis of urban land area of 1996 and 2006 and distribution map of ULSI is plotted. After analyzing those results, we can conclude that there is consanguineous relationship between urban land area and urbanization, economic level etc.

A Bayesian hierarchical model with spatial variable selection: the effect of weather on insurance claims

PubMed Central

Scheel, Ida; Ferkingstad, Egil; Frigessi, Arnoldo; Haug, Ola; Hinnerichsen, Mikkel; Meze-Hausken, Elisabeth

2013-01-01

Climate change will affect the insurance industry. We develop a Bayesian hierarchical statistical approach to explain and predict insurance losses due to weather events at a local geographic scale. The number of weather-related insurance claims is modelled by combining generalized linear models with spatially smoothed variable selection. Using Gibbs sampling and reversible jump Markov chain Monte Carlo methods, this model is fitted on daily weather and insurance data from each of the 319 municipalities which constitute southern and central Norway for the period 1997–2006. Precise out-of-sample predictions validate the model. Our results show interesting regional patterns in the effect of different weather covariates. In addition to being useful for insurance pricing, our model can be used for short-term predictions based on weather forecasts and for long-term predictions based on downscaled climate models. PMID:23396890

Spatial parameters of walking gait and footedness.

PubMed

Zverev, Y P

2006-01-01

The present study was undertaken to assess whether footedness has effects on selected spatial and angular parameters of able-bodied gait by evaluating footprints of young adults. A total of 112 males and 93 females were selected from among students and staff members of the University of Malawi using a simple random sampling method. Footedness of subjects was assessed by the Waterloo Footedness Questionnaire Revised. Gait at natural speed was recorded using the footprint method. The following spatial parameters of gait were derived from the inked footprint sequences of subjects: step and stride lengths, gait angle and base of gait. The anthropometric measurements taken were weight, height, leg and foot length, foot breadth, shoulder width, and hip and waist circumferences. The prevalence of right-, left- and mix-footedness in the whole sample of young Malawian adults was 81%, 8.3% and 10.7%, respectively. One-way analysis of variance did not reveal a statistically significant difference between footedness categories in the mean values of anthropometric measurements (p > 0.05 for all variables). Gender differences in step and stride length values were not statistically significant. Correction of these variables for stature did not change the trend. Males had significantly broader steps than females. Normalized values of base of gait had similar gender difference. The group means of step length and normalized step length of the right and left feet were similar, for males and females. There was a significant side difference in the gait angle in both gender groups of volunteers with higher mean values on the left side compared to the right one (t = 2.64, p < 0.05 for males, and t = 2.78, p < 0.05 for females). One-way analysis of variance did not demonstrate significant difference between footedness categories in the mean values of step length, gait angle, bilateral differences in step length and gait angle, stride length, gait base and normalized gait variables of male and female volunteers (p > 0.05 for all variables). The present study demonstrated that footedness does not affect spatial and angular parameters of walking gait.

Advances in satellite remote sensing of environmental variables for epidemiological applications.

PubMed

Goetz, S J; Prince, S D; Small, J

2000-01-01

Earth-observing satellites have provided an unprecedented view of the land surface but have been exploited relatively little for the measurement of environmental variables of particular relevance to epidemiology. Recent advances in techniques to recover continuous fields of air temperature, humidity, and vapour pressure deficit from remotely sensed observations have significant potential for disease vector monitoring and related epidemiological applications. We report on the development of techniques to map environmental variables with relevance to the prediction of the relative abundance of disease vectors and intermediate hosts. Improvements to current methods of obtaining information on vegetation properties, canopy and surface temperature and soil moisture over large areas are also discussed. Algorithms used to measure these variables incorporate visible, near-infrared and thermal infrared radiation observations derived from time series of satellite-based sensors, focused here primarily but not exclusively on the Advanced Very High Resolution Radiometer (AVHRR) instruments. The variables compare favourably with surface measurements over a broad array of conditions at several study sites, and maps of retrieved variables captured patterns of spatial variability comparable to, and locally more accurate than, spatially interpolated meteorological observations. Application of multi-temporal maps of these variables are discussed in relation to current epidemiological research on the distribution and abundance of some common disease vectors.

Functional cerebral distance and the effect of emotional music on spatial rotation scores in undergraduate women and men.

PubMed

Bertsch, Sharon; Knee, H Donald; Webb, Jeffrey L

2011-02-01

The influence of listening to music on subsequent spatial rotation scores has a controversial history. The effect is unreliable, seeming to depend on several as yet unexplored factors. Using a large sample (167 women, 160 men; M age = 18.9 yr.), two related variables were investigated: participants' sex and the emotion conveyed by the music. Participants listened to 90 sec. of music that portrayed emotions of approach (happiness), or withdrawal (anger), or heard no music at all. They then performed a two-dimensional spatial rotation task. No significant difference was found in spatial rotation scores between groups exposed to music and those who were not. However, a significant interaction was found based on the sex of the participants and the emotion portrayed in the music they heard. Women's scores increased (relative to a no-music condition) only after hearing withdrawal-based music, while men's scores increased only after listening to the approach-based music. These changes were explained using the theory of functional cerebral distance.

Spatial analysis of macro-level bicycle crashes using the class of conditional autoregressive models.

PubMed

Saha, Dibakar; Alluri, Priyanka; Gan, Albert; Wu, Wanyang

2018-02-21

The objective of this study was to investigate the relationship between bicycle crash frequency and their contributing factors at the census block group level in Florida, USA. Crashes aggregated over the census block groups tend to be clustered (i.e., spatially dependent) rather than randomly distributed. To account for the effect of spatial dependence across the census block groups, the class of conditional autoregressive (CAR) models were employed within the hierarchical Bayesian framework. Based on four years (2011-2014) of crash data, total and fatal-and-severe injury bicycle crash frequencies were modeled as a function of a large number of variables representing demographic and socio-economic characteristics, roadway infrastructure and traffic characteristics, and bicycle activity characteristics. This study explored and compared the performance of two CAR models, namely the Besag's model and the Leroux's model, in crash prediction. The Besag's models, which differ from the Leroux's models by the structure of how spatial autocorrelation are specified in the models, were found to fit the data better. A 95% Bayesian credible interval was selected to identify the variables that had credible impact on bicycle crashes. A total of 21 variables were found to be credible in the total crash model, while 18 variables were found to be credible in the fatal-and-severe injury crash model. Population, daily vehicle miles traveled, age cohorts, household automobile ownership, density of urban roads by functional class, bicycle trip miles, and bicycle trip intensity had positive effects in both the total and fatal-and-severe crash models. Educational attainment variables, truck percentage, and density of rural roads by functional class were found to be negatively associated with both total and fatal-and-severe bicycle crash frequencies. Published by Elsevier Ltd.

Global Gradients of Coral Exposure to Environmental Stresses and Implications for Local Management

PubMed Central

Maina, Joseph; McClanahan, Tim R.; Venus, Valentijn; Ateweberhan, Mebrahtu; Madin, Joshua

2011-01-01

Background The decline of coral reefs globally underscores the need for a spatial assessment of their exposure to multiple environmental stressors to estimate vulnerability and evaluate potential counter-measures. Methodology/Principal Findings This study combined global spatial gradients of coral exposure to radiation stress factors (temperature, UV light and doldrums), stress-reinforcing factors (sedimentation and eutrophication), and stress-reducing factors (temperature variability and tidal amplitude) to produce a global map of coral exposure and identify areas where exposure depends on factors that can be locally managed. A systems analytical approach was used to define interactions between radiation stress variables, stress reinforcing variables and stress reducing variables. Fuzzy logic and spatial ordinations were employed to quantify coral exposure to these stressors. Globally, corals are exposed to radiation and reinforcing stress, albeit with high spatial variability within regions. Based on ordination of exposure grades, regions group into two clusters. The first cluster was composed of severely exposed regions with high radiation and low reducing stress scores (South East Asia, Micronesia, Eastern Pacific and the central Indian Ocean) or alternatively high reinforcing stress scores (the Middle East and the Western Australia). The second cluster was composed of moderately to highly exposed regions with moderate to high scores in both radiation and reducing factors (Caribbean, Great Barrier Reef (GBR), Central Pacific, Polynesia and the western Indian Ocean) where the GBR was strongly associated with reinforcing stress. Conclusions/Significance Despite radiation stress being the most dominant stressor, the exposure of coral reefs could be reduced by locally managing chronic human impacts that act to reinforce radiation stress. Future research and management efforts should focus on incorporating the factors that mitigate the effect of coral stressors until long-term carbon reductions are achieved through global negotiations. PMID:21860667

Geostatistics for spatial genetic structures: study of wild populations of perennial ryegrass.

PubMed

Monestiez, P; Goulard, M; Charmet, G

1994-04-01

Methods based on geostatistics were applied to quantitative traits of agricultural interest measured on a collection of 547 wild populations of perennial ryegrass in France. The mathematical background of these methods, which resembles spatial autocorrelation analysis, is briefly described. When a single variable is studied, the spatial structure analysis is similar to spatial autocorrelation analysis, and a spatial prediction method, called "kriging", gives a filtered map of the spatial pattern over all the sampled area. When complex interactions of agronomic traits with different evaluation sites define a multivariate structure for the spatial analysis, geostatistical methods allow the spatial variations to be broken down into two main spatial structures with ranges of 120 km and 300 km, respectively. The predicted maps that corresponded to each range were interpreted as a result of the isolation-by-distance model and as a consequence of selection by environmental factors. Practical collecting methodology for breeders may be derived from such spatial structures.

Use of portable exposure meters for comparing mobile phone base station radiation in different types of areas in the cities of Basel and Amsterdam.

PubMed

Urbinello, Damiano; Huss, Anke; Beekhuizen, Johan; Vermeulen, Roel; Röösli, Martin

2014-01-15

Radiofrequency electromagnetic fields (RF-EMF) are highly variable and differ considerably within as well as between areas. Exposure assessment studies characterizing spatial and temporal variation are limited so far. Our objective was to evaluate sources of data variability and the repeatability of daily measurements using portable exposure meters (PEMs). Data were collected at 12 days between November 2010 and January 2011 with PEMs in four different types of urban areas in the cities of Basel (BSL) and Amsterdam (AMS). Exposure from mobile phone base stations ranged from 0.30 to 0.53 V/m in downtown and business areas and in residential areas from 0.09 to 0.41 V/m. Analysis of variance (ANOVA) demonstrated that measurements from various days were highly reproducible (measurement duration of approximately 30 min) with only 0.6% of the variance of all measurements from mobile phone base station radiation being explained by the measurement day and only 0.2% by the measurement time (morning, noon, afternoon), whereas type of area (30%) and city (50%) explained most of the data variability. We conclude that mobile monitoring of exposure from mobile phone base station radiation with PEMs is useful due to the high repeatability of mobile phone base station exposure levels, despite the high spatial variation. © 2013.

Dynamics of land change in India: a fine-scale spatial analysis

NASA Astrophysics Data System (ADS)

Meiyappan, P.; Roy, P. S.; Sharma, Y.; Jain, A. K.; Ramachandran, R.; Joshi, P. K.

2015-12-01

Land is scarce in India: India occupies 2.4% of worlds land area, but supports over 1/6th of worlds human and livestock population. This high population to land ratio, combined with socioeconomic development and increasing consumption has placed tremendous pressure on India's land resources for food, feed, and fuel. In this talk, we present contemporary (1985 to 2005) spatial estimates of land change in India using national-level analysis of Landsat imageries. Further, we investigate the causes of the spatial patterns of change using two complementary lines of evidence. First, we use statistical models estimated at macro-scale to understand the spatial relationships between land change patterns and their concomitant drivers. This analysis using our newly compiled extensive socioeconomic database at village level (~630,000 units), is 100x higher in spatial resolution compared to existing datasets, and covers over 200 variables. The detailed socioeconomic data enabled the fine-scale spatial analysis with Landsat data. Second, we synthesized information from over 130 survey based case studies on land use drivers in India to complement our macro-scale analysis. The case studies are especially useful to identify unobserved variables (e.g. farmer's attitude towards risk). Ours is the most detailed analysis of contemporary land change in India, both in terms of national extent, and the use of detailed spatial information on land change, socioeconomic factors, and synthesis of case studies.

Effects of soil spatial variability at the hillslope and catchment scales on characteristics of rainfall-induced landslides

NASA Astrophysics Data System (ADS)

Fan, Linfeng; Lehmann, Peter; Or, Dani

2016-03-01

Spatial variations in soil properties affect key hydrological processes, yet their role in soil mechanical response to hydro-mechanical loading is rarely considered. This study aims to fill this gap by systematically quantifying effects of spatial variations in soil type and initial water content on rapid rainfall-induced shallow landslide predictions at the hillslope- and catchment-scales. We employed a physically-based landslide triggering model that considers mechanical interactions among soil columns governed by strength thresholds. At the hillslope scale, we found that the emergence of weak regions induced by spatial variations of soil type and initial water content resulted in early triggering of landslides with smaller volumes of released mass relative to a homogeneous slope. At the catchment scale, initial water content was linked to a topographic wetness index, whereas soil type varied deterministically with soil depth considering spatially correlated stochastic components. Results indicate that a strong spatial organization of initial water content delays landslide triggering, whereas spatially linked soil type with soil depth promoted landslide initiation. Increasing the standard deviation and correlation length of the stochastic component of soil type increases landslide volume and hastens onset of landslides. The study illustrates that for similar external boundary conditions and mean soil properties, landslide characteristics vary significantly with soil variability, hence it must be considered for improved landslide model predictions.

An Ambulatory Method of Identifying Anterior Cruciate Ligament Reconstructed Gait Patterns

PubMed Central

Patterson, Matthew R.; Delahunt, Eamonn; Sweeney, Kevin T.; Caulfield, Brian

2014-01-01

The use of inertial sensors to characterize pathological gait has traditionally been based on the calculation of temporal and spatial gait variables from inertial sensor data. This approach has proved successful in the identification of gait deviations in populations where substantial differences from normal gait patterns exist; such as in Parkinsonian gait. However, it is not currently clear if this approach could identify more subtle gait deviations, such as those associated with musculoskeletal injury. This study investigates whether additional analysis of inertial sensor data, based on quantification of gyroscope features of interest, would provide further discriminant capability in this regard. The tested cohort consisted of a group of anterior cruciate ligament reconstructed (ACL-R) females and a group of non-injured female controls, each performed ten walking trials. Gait performance was measured simultaneously using inertial sensors and an optoelectronic marker based system. The ACL-R group displayed kinematic and kinetic deviations from the control group, but no temporal or spatial deviations. This study demonstrates that quantification of gyroscope features can successfully identify changes associated with ACL-R gait, which was not possible using spatial or temporal variables. This finding may also have a role in other clinical applications where small gait deviations exist. PMID:24451464

Partitioning the factors of spatial variation in regeneration density of shade-tolerant tree species.

PubMed

Gravel, Dominique; Beaudet, Marilou; Messier, Christian

2008-10-01

Understanding coexistence of highly shade-tolerant tree species is a longstanding challenge for forest ecologists. A conceptual model for the coexistence of sugar maple (Acer saccharum) and American beech (Fagus grandibfolia) has been proposed, based on a low-light survival/high-light growth trade-off, which interacts with soil fertility and small-scale spatiotemporal variation in the environment. In this study, we first tested whether the spatial distribution of seedlings and saplings can be predicted by the spatiotemporal variability of light availability and soil fertility, and second, the manner in which the process of environmental filtering changes with regeneration size. We evaluate the support for this hypothesis relative to the one for a neutral model, i.e., for seed rain density predicted from the distribution of adult trees. To do so, we performed intensive sampling over 86 quadrats (5 x 5 m) in a 0.24-ha plot in a mature maple-beech community in Quebec, Canada. Maple and beech abundance, soil characteristics, light availability, and growth history (used as a proxy for spatiotemporal variation in light availability) were finely measured to model variation in sapling composition across different size classes. Results indicate that the variables selected to model species distribution do effectively change with size, but not as predicted by the conceptual model. Our results show that variability in the environment is not sufficient to differentiate these species' distributions in space. Although species differ in their spatial distribution in the small size classes, they tend to correlate at the larger size class in which recruitment occurs. Overall, the results are not supportive of a model of coexistence based on small-scale variations in the environment. We propose that, at the scale of a local stand, the lack of fit of the model could result from the high similarity of species in the range of environmental conditions encountered, and we suggest that coexistence would be stable only at larger spatial scales at which variability in the environment is greater.

How does complex terrain influence responses of carbon and water cycle processes to climate variability and climate change? (Invited)

NASA Astrophysics Data System (ADS)

Bond, B. J.; Peterson, K.; McKane, R.; Lajtha, K.; Quandt, D. J.; Allen, S. T.; Sell, S.; Daly, C.; Harmon, M. E.; Johnson, S. L.; Spies, T.; Sollins, P.; Abdelnour, A. G.; Stieglitz, M.

2010-12-01

We are pursuing the ambitious goal of understanding how complex terrain influences the responses of carbon and water cycle processes to climate variability and climate change. Our studies take place in H.J. Andrews Experimental Forest, an LTER (Long Term Ecological Research) site situated in Oregon’s central-western Cascade Range. Decades of long-term measurements and intensive research have revealed influences of topography on vegetation patterns, disturbance history, and hydrology. More recent research has shown surprising interactions between microclimates and synoptic weather patterns due to cold air drainage and pooling in mountain valleys. Using these data and insights, in addition to a recent LiDAR (Light Detection and Ranging) reconnaissance and a small sensor network, we are employing process-based models, including “SPA” (Soil-Plant-Atmosphere, developed by Mathew Williams of the University of Edinburgh), and “VELMA” (Visualizing Ecosystems for Land Management Alternatives, developed by Marc Stieglitz and colleagues of the Georgia Institute of Technology) to focus on two important features of mountainous landscapes: heterogeneity (both spatial and temporal) and connectivity (atmosphere-canopy-hillslope-stream). Our research questions include: 1) Do fine-scale spatial and temporal heterogeneity result in emergent properties at the basin scale, and if so, what are they? 2) How does connectivity across ecosystem components affect system responses to climate variability and change? Initial results show that for environmental drivers that elicit non-linear ecosystem responses on the plot scale, such as solar radiation, soil depth and soil water content, fine-scale spatial heterogeneity may produce unexpected emergent properties at larger scales. The results from such modeling experiments are necessarily a function of the supporting algorithms. However, comparisons based on models such as SPA and VELMA that operate at much different spatial scales (plots vs. hillslopes) and levels of biophysical organization (individual plants vs. aggregate plant biomass) can help us to understand how and why mountainous ecosystems may have distinctive responses to climate variability and climate change.

Seasonal variability shapes resilience of small-scale fisheries in Baja California Sur, Mexico.

PubMed

Pellowe, Kara E; Leslie, Heather M

2017-01-01

Small-scale fisheries are an important source of food and livelihoods to coastal communities around the world. Understanding the seasonality of fisheries catch and composition is crucial to fisheries management, particularly in the context of changing environmental and socioeconomic conditions. While seasonal variability directly impacts the lives of fishers, most fisheries studies focus on longer-term change. Here we examine seasonal variability in the small-scale fisheries of Baja California Sur, Mexico based on 13 years of government fisheries data. We investigate how four fisheries indicators with direct relevance to ecological resilience-magnitude and variance of landed fish biomass, taxon richness and the proportion of top-trophic-level taxa in total catch-vary within and among years and at multiple spatial scales. We find that these resilience indicators vary both seasonally and spatially. These results highlight the value of finer-scale monitoring and management, particularly for data-poor fisheries.

Seasonal variability shapes resilience of small-scale fisheries in Baja California Sur, Mexico

PubMed Central

Leslie, Heather M.

2017-01-01

Small-scale fisheries are an important source of food and livelihoods to coastal communities around the world. Understanding the seasonality of fisheries catch and composition is crucial to fisheries management, particularly in the context of changing environmental and socioeconomic conditions. While seasonal variability directly impacts the lives of fishers, most fisheries studies focus on longer-term change. Here we examine seasonal variability in the small-scale fisheries of Baja California Sur, Mexico based on 13 years of government fisheries data. We investigate how four fisheries indicators with direct relevance to ecological resilience–magnitude and variance of landed fish biomass, taxon richness and the proportion of top-trophic-level taxa in total catch–vary within and among years and at multiple spatial scales. We find that these resilience indicators vary both seasonally and spatially. These results highlight the value of finer-scale monitoring and management, particularly for data-poor fisheries. PMID:28783740

A Tracking Analyst for large 3D spatiotemporal data from multiple sources (case study: Tracking volcanic eruptions in the atmosphere)

NASA Astrophysics Data System (ADS)

Gad, Mohamed A.; Elshehaly, Mai H.; Gračanin, Denis; Elmongui, Hicham G.

2018-02-01

This research presents a novel Trajectory-based Tracking Analyst (TTA) that can track and link spatiotemporally variable data from multiple sources. The proposed technique uses trajectory information to determine the positions of time-enabled and spatially variable scatter data at any given time through a combination of along trajectory adjustment and spatial interpolation. The TTA is applied in this research to track large spatiotemporal data of volcanic eruptions (acquired using multi-sensors) in the unsteady flow field of the atmosphere. The TTA enables tracking injections into the atmospheric flow field, the reconstruction of the spatiotemporally variable data at any desired time, and the spatiotemporal join of attribute data from multiple sources. In addition, we were able to create a smooth animation of the volcanic ash plume at interactive rates. The initial results indicate that the TTA can be applied to a wide range of multiple-source data.

Interaction Between Spatial and Feature Attention in Posterior Parietal Cortex

PubMed Central

Ibos, Guilhem; Freedman, David J.

2016-01-01

Summary Lateral intraparietal (LIP) neurons encode a vast array of sensory and cognitive variables. Recently, we proposed that the flexibility of feature representations in LIP reflect the bottom-up integration of sensory signals, modulated by feature-based attention (FBA), from upstream feature-selective cortical neurons. Moreover, LIP activity is also strongly modulated by the position of space-based attention (SBA). However, the mechanisms by which SBA and FBA interact to facilitate the representation of task-relevant spatial and non-spatial features in LIP remain unclear. We recorded from LIP neurons during performance of a task which required monkeys to detect specific conjunctions of color, motion-direction, and stimulus position. Here we show that FBA and SBA potentiate each other’s effect in a manner consistent with attention gating the flow of visual information along the cortical visual pathway. Our results suggest that linear bottom-up integrative mechanisms allow LIP neurons to emphasize task-relevant spatial and non-spatial features. PMID:27499082

Interaction between Spatial and Feature Attention in Posterior Parietal Cortex.

PubMed

Ibos, Guilhem; Freedman, David J

2016-08-17

Lateral intraparietal (LIP) neurons encode a vast array of sensory and cognitive variables. Recently, we proposed that the flexibility of feature representations in LIP reflect the bottom-up integration of sensory signals, modulated by feature-based attention (FBA), from upstream feature-selective cortical neurons. Moreover, LIP activity is also strongly modulated by the position of space-based attention (SBA). However, the mechanisms by which SBA and FBA interact to facilitate the representation of task-relevant spatial and non-spatial features in LIP remain unclear. We recorded from LIP neurons during performance of a task that required monkeys to detect specific conjunctions of color, motion direction, and stimulus position. Here we show that FBA and SBA potentiate each other's effect in a manner consistent with attention gating the flow of visual information along the cortical visual pathway. Our results suggest that linear bottom-up integrative mechanisms allow LIP neurons to emphasize task-relevant spatial and non-spatial features. Copyright © 2016 Elsevier Inc. All rights reserved.

Modeling the impacts of phenological and inter-annual changes in landscape metrics on local biodiversity of agricultural lands of Eastern Ontario using multi-spatial and multi-temporal remote sensing data

NASA Astrophysics Data System (ADS)

Alavi-Shoushtari, N.; King, D.

2017-12-01

Agricultural landscapes are highly variable ecosystems and are home to many local farmland species. Seasonal, phenological and inter-annual agricultural landscape dynamics have potential to affect the richness and abundance of farmland species. Remote sensing provides data and techniques which enable monitoring landscape changes in multiple temporal and spatial scales. MODIS high temporal resolution remote sensing images enable detection of seasonal and phenological trends, while Landsat higher spatial resolution images, with its long term archive enables inter-annual trend analysis over several decades. The objective of this study to use multi-spatial and multi-temporal remote sensing data to model the response of farmland species to landscape metrics. The study area is the predominantly agricultural region of eastern Ontario. 92 sample landscapes were selected within this region using a protocol designed to maximize variance in composition and configuration heterogeneity while controlling for amount of forest and spatial autocorrelation. Two sample landscape extents (1×1km and 3×3km) were selected to analyze the impacts of spatial scale on biodiversity response. Gamma diversity index data for four taxa groups (birds, butterflies, plants, and beetles) were collected during the summers of 2011 and 2012 within the cropped area of each landscape. To extract the seasonal and phenological metrics a 2000-2012 MODIS NDVI time-series was used, while a 1985-2012 Landsat time-series was used to model the inter-annual trends of change in the sample landscapes. The results of statistical modeling showed significant relationships between farmland biodiversity for several taxa and the phenological and inter-annual variables. The following general results were obtained: 1) Among the taxa groups, plant and beetles diversity was most significantly correlated with the phenological variables; 2) Those phenological variables which are associated with the variability in the start of season date across the sample landscapes and the variability in the corresponding NDVI values at that date showed the strongest correlation with the biodiversity indices; 3) The significance of the models improved when using 3×3km site extent both for MODIS and Landsat based models due most likely to the larger sample size over 3x3km.

Optimization of Variable-Depth Liner Configurations for Increased Broadband Noise Reduction

NASA Technical Reports Server (NTRS)

Jones, M. G.; Watson, W. R.; Nark, D. M.; Schiller, N. H.; Born, J. C.

2016-01-01

This paper employs three acoustic propagation codes to explore variable-depth liner configurations for the NASA Langley Grazing Flow Impedance Tube (GFIT). The initial study demonstrates that a variable impedance can acceptably be treated as a uniform impedance if the spatial extent over which this variable impedance occurs is less than one-third of a wavelength of the incident sound. A constrained optimization study is used to design a variable-depth liner and to select an optimization metric. It also provides insight regarding how much attenuation can be achieved with variable-depth liners. Another optimization study is used to design a liner with much finer chamber depth resolution for the Mach 0.0 and 0.3 test conditions. Two liners are designed based on spatial rearrangement of chambers from this liner to determine whether the order is critical. Propagation code predictions suggest this is not the case. Both liners are fabricated via additive manufacturing and tested in the GFIT for the Mach 0.0 condition. Predicted and measured attenuations compare favorably across the full frequency range. These results clearly suggest that the chambers can be arranged in any order, thus offering the potential for innovative liner designs to minimize depth and weight.

Can APEX Represent In-Field Spatial Variability and Simulate Its Effects On Crop Yields?

USDA-ARS?s Scientific Manuscript database

Precision agriculture, from variable rate nitrogen application to precision irrigation, promises improved management of resources by considering the spatial variability of topography and soil properties. Hydrologic models need to simulate the effects of this variability if they are to inform about t...

Interferometric Synthetic Aperture Radar to capture spatial variability of local land-based subsidence

NASA Astrophysics Data System (ADS)

Bekaert, D. P.; Hamlington, B.; Buzzanga, B. A.; Jones, C. E.

2017-12-01

The rate of relative sea level rise results from a combination of land subsidence and rising seas associated with global warming on long timescales and exacerbated by shifts in ocean dynamics on shorter timescales. An understanding of the current-day magnitude of each component is needed to create accurate projections of future relative sea level rise upon which to base planning efforts. Current day land-based subsidence rates derived from GPS often lack the spatial resolution to capture the local spatial variability needed when assessing the impact of relative sea-level rise. Interferometric Synthetic Aperture Radar (InSAR) is an attractive technique that has the potential to provide a measurement every 20-30m when good signal coherence is maintained. In practice, coastal regions are challenging for InSAR due to variable vegetation cover and soil moisture, which can be in part mitigated by applying advanced time-series InSAR techniques. After applying time-series InSAR, derived rates need to be combined with GPS to tie relative subsidence rates into a geodetic reference frame. Given the need to make projections of relative sea-level rise it is particularly important to propagate all uncertainties during the different processing stages. Here we provide results from ALOS and Sentinel-1 over Hampton Roads area in the Chesapeake Bay region, which is experiencing one of the highest rates of relative sea level rise on the Atlantic coast of the United States. Although the current derived subsidence rates have large uncertainties, it is expected that this will improve with the decadal observations from Sentinel-1.

Nitrogen isotopic baselines and implications for estimating foraging habitat and trophic position of yellowfin tuna in the Indian and Pacific Oceans

NASA Astrophysics Data System (ADS)

Lorrain, Anne; Graham, Brittany S.; Popp, Brian N.; Allain, Valérie; Olson, Robert J.; Hunt, Brian P. V.; Potier, Michel; Fry, Brian; Galván-Magaña, Felipe; Menkes, Christophe E. R.; Kaehler, Sven; Ménard, Frédéric

2015-03-01

Assessment of isotopic compositions at the base of food webs is a prerequisite for using stable isotope analysis to assess foraging locations and trophic positions of marine organisms. Our study represents a unique application of stable-isotope analyses across multiple trophic levels (primary producer, primary consumer and tertiary consumer) and over a large spatial scale in two pelagic marine ecosystems. We found that δ15N values of particulate organic matter (POM), barnacles and phenylalanine from the muscle tissue of yellowfin tuna all showed similar spatial patterns. This consistency suggests that isotopic analysis of any of these can provide a reasonable proxy for isotopic variability at the base of the food web. Secondly, variations in the δ15N values of yellowfin tuna bulk-muscle tissues paralleled the spatial trends observed in all of these isotopic baseline proxies. Variation in isotopic composition at the base of the food web, rather than differences in tuna diet, explained the 11‰ variability observed in the bulk-tissue δ15N values of yellowfin tuna. Evaluating the trophic position of yellowfin tuna using amino-acid isotopic compositions across the western Indian and equatorial Pacific Oceans strongly suggests these tuna occupy similar trophic positions, albeit absolute trophic positions estimated by this method were lower than expected. This study reinforces the importance of considering isotopic baseline variability for diet studies, and provides new insights into methods that can be applied to generate nitrogen isoscapes for worldwide comparisons of top predators in marine ecosystems.

Spatially Resolved Measurements of CO2 and CH4 Concentration and Gas-Exchange Velocity Highly Influence Carbon-Emission Estimates of Reservoirs

PubMed Central

2017-01-01

The magnitude of diffusive carbon dioxide (CO2) and methane (CH4) emission from man-made reservoirs is uncertain because the spatial variability generally is not well-represented. Here, we examine the spatial variability and its drivers for partial pressure, gas-exchange velocity (k), and diffusive flux of CO2 and CH4 in three tropical reservoirs using spatially resolved measurements of both gas concentrations and k. We observed high spatial variability in CO2 and CH4 concentrations and flux within all three reservoirs, with river inflow areas generally displaying elevated CH4 concentrations. Conversely, areas close to the dam are generally characterized by low concentrations and are therefore not likely to be representative for the whole system. A large share (44–83%) of the within-reservoir variability of gas concentration was explained by dissolved oxygen, pH, chlorophyll, water depth, and within-reservoir location. High spatial variability in k was observed, and kCH4 was persistently higher (on average, 2.5 times more) than kCO2. Not accounting for the within-reservoir variability in concentrations and k may lead to up to 80% underestimation of whole-system diffusive emission of CO2 and CH4. Our findings provide valuable information on how to develop field-sampling strategies to reliably capture the spatial heterogeneity of diffusive carbon fluxes from reservoirs. PMID:29257874

Spatial EPR entanglement in atomic vapor quantum memory

NASA Astrophysics Data System (ADS)

Parniak, Michal; Dabrowski, Michal; Wasilewski, Wojciech

Spatially-structured quantum states of light are staring to play a key role in modern quantum science with the rapid development of single-photon sensitive cameras. In particular, spatial degree of freedom holds a promise to enhance continous-variable quantum memories. Here we present the first demonstration of spatial entanglement between an atomic spin-wave and a photon measured with an I-sCMOS camera. The system is realized in a warm atomic vapor quantum memory based on rubidium atoms immersed in inert buffer gas. In the experiment we create and characterize a 12-dimensional entangled state exhibiting quantum correlations between a photon and an atomic ensemble in position and momentum bases. This state allows us to demonstrate the Einstein-Podolsky-Rosen paradox in its original version, with an unprecedented delay time of 6 μs between generation of entanglement and detection of the atomic state.

Gbm.auto: A software tool to simplify spatial modelling and Marine Protected Area planning

PubMed Central

Officer, Rick; Clarke, Maurice; Reid, David G.; Brophy, Deirdre

2017-01-01

Boosted Regression Trees. Excellent for data-poor spatial management but hard to use Marine resource managers and scientists often advocate spatial approaches to manage data-poor species. Existing spatial prediction and management techniques are either insufficiently robust, struggle with sparse input data, or make suboptimal use of multiple explanatory variables. Boosted Regression Trees feature excellent performance and are well suited to modelling the distribution of data-limited species, but are extremely complicated and time-consuming to learn and use, hindering access for a wide potential user base and therefore limiting uptake and usage. BRTs automated and simplified for accessible general use with rich feature set We have built a software suite in R which integrates pre-existing functions with new tailor-made functions to automate the processing and predictive mapping of species abundance data: by automating and greatly simplifying Boosted Regression Tree spatial modelling, the gbm.auto R package suite makes this powerful statistical modelling technique more accessible to potential users in the ecological and modelling communities. The package and its documentation allow the user to generate maps of predicted abundance, visualise the representativeness of those abundance maps and to plot the relative influence of explanatory variables and their relationship to the response variables. Databases of the processed model objects and a report explaining all the steps taken within the model are also generated. The package includes a previously unavailable Decision Support Tool which combines estimated escapement biomass (the percentage of an exploited population which must be retained each year to conserve it) with the predicted abundance maps to generate maps showing the location and size of habitat that should be protected to conserve the target stocks (candidate MPAs), based on stakeholder priorities, such as the minimisation of fishing effort displacement. Gbm.auto for management in various settings By bridging the gap between advanced statistical methods for species distribution modelling and conservation science, management and policy, these tools can allow improved spatial abundance predictions, and therefore better management, decision-making, and conservation. Although this package was built to support spatial management of a data-limited marine elasmobranch fishery, it should be equally applicable to spatial abundance modelling, area protection, and stakeholder engagement in various scenarios. PMID:29216310

Quantifying spatial and temporal variability in atmospheric ammonia with in situ and space-based observations--article

EPA Science Inventory

Ammonia plays an important role in many biogeochemical processes, yet atmospheric mixing ratios are not well known. Recently, methods have been developed for retrieving NH3 from space-based observations, but they have not been compared to in situ measurements. We have conducted a...

Logical recoding of S-R rules can reverse the effects of spatial S-R correspondence.

PubMed

Wühr, Peter; Biebl, Rupert

2009-02-01

Two experiments investigated competing explanations for the reversal of spatial stimulus-response (S-R) correspondence effects (i.e., Simon effects) with an incompatible S-R mapping on the relevant, nonspatial dimension. Competing explanations were based on generalized S-R rules (logical-recoding account) or referred to display-control arrangement correspondence or to S-S congruity. In Experiment 1, compatible responses to finger-name stimuli presented at left/right locations produced normal Simon effects, whereas incompatible responses to finger-name stimuli produced an inverted Simon effect. This finding supports the logical-recoding account. In Experiment 2, spatial S-R correspondence and color S-R correspondence were varied independently, and main effects of these variables were observed. The lack of an interaction between these variables, however, disconfirms a prediction of the display-control arrangement correspondence account. Together, the results provide converging evidence for the logical-recoding account. This account claims that participants derive generalized response selection rules (e.g., the identity or reversal rule) from specific S-R rules and inadvertently apply the generalized rules to the irrelevant (spatial) S-R dimension when selecting their response.

Spatial distribution estimation of malaria in northern China and its scenarios in 2020, 2030, 2040 and 2050.

PubMed

Song, Yongze; Ge, Yong; Wang, Jinfeng; Ren, Zhoupeng; Liao, Yilan; Peng, Junhuan

2016-07-07

Malaria is one of the most severe parasitic diseases in the world. Spatial distribution estimation of malaria and its future scenarios are important issues for malaria control and elimination. Furthermore, sophisticated nonlinear relationships for prediction between malaria incidence and potential variables have not been well constructed in previous research. This study aims to estimate these nonlinear relationships and predict future malaria scenarios in northern China. Nonlinear relationships between malaria incidence and predictor variables were constructed using a genetic programming (GP) method, to predict the spatial distributions of malaria under climate change scenarios. For this, the examples of monthly average malaria incidence were used in each county of northern China from 2004 to 2010. Among the five variables at county level, precipitation rate and temperature are used for projections, while elevation, water density index, and gross domestic product are held at their present-day values. Average malaria incidence was 0.107 ‰ per annum in northern China, with incidence characteristics in significant spatial clustering. A GP-based model fit the relationships with average relative error (ARE) = 8.127 % for training data (R(2) = 0.825) and 17.102 % for test data (R(2) = 0.532). The fitness of GP results are significantly improved compared with those by generalized additive models (GAM) and linear regressions. With the future precipitation rate and temperature conditions in Special Report on Emission Scenarios (SRES) family B1, A1B and A2 scenarios, spatial distributions and changes in malaria incidences in 2020, 2030, 2040 and 2050 were predicted and mapped. The GP method increases the precision of predicting the spatial distribution of malaria incidence. With the assumption of varied precipitation rate and temperature, and other variables controlled, the relationships between incidence and the varied variables appear sophisticated nonlinearity and spatially differentiation. Using the future fluctuated precipitation and the increased temperature, median malaria incidence in 2020, 2030, 2040 and 2050 would significantly increase that it might increase 19 to 29 % in 2020, but currently China is in the malaria elimination phase, indicating that the effective strategies and actions had been taken. While the mean incidences will not increase even reduce due to the incidence reduction in high-risk regions but the simultaneous expansion of the high-risk areas.

Spatial variability of the Black Sea surface temperature from high resolution modeling and satellite measurements

NASA Astrophysics Data System (ADS)

Mizyuk, Artem; Senderov, Maxim; Korotaev, Gennady

2016-04-01

Large number of numerical ocean models were implemented for the Black Sea basin during last two decades. They reproduce rather similar structure of synoptical variability of the circulation. Since 00-s numerical studies of the mesoscale structure are carried out using high performance computing (HPC). With the growing capacity of computing resources it is now possible to reconstruct the Black Sea currents with spatial resolution of several hundreds meters. However, how realistic these results can be? In the proposed study an attempt is made to understand which spatial scales are reproduced by ocean model in the Black Sea. Simulations are made using parallel version of NEMO (Nucleus for European Modelling of the Ocean). A two regional configurations with spatial resolutions 5 km and 2.5 km are described. Comparison of the SST from simulations with two spatial resolutions shows rather qualitative difference of the spatial structures. Results of high resolution simulation are compared also with satellite observations and observation-based products from Copernicus using spatial correlation and spectral analysis. Spatial scales of correlations functions for simulated and observed SST are rather close and differs much from satellite SST reanalysis. Evolution of spectral density for modelled SST and reanalysis showed agreed time periods of small scales intensification. Using of the spectral analysis for satellite measurements is complicated due to gaps. The research leading to this results has received funding from Russian Science Foundation (project № 15-17-20020)

Temporal changes of spatial soil moisture patterns: controlling factors explained with a multidisciplinary approach

NASA Astrophysics Data System (ADS)

Martini, Edoardo; Wollschläger, Ute; Kögler, Simon; Behrens, Thorsten; Dietrich, Peter; Reinstorf, Frido; Schmidt, Karsten; Weiler, Markus; Werban, Ulrike; Zacharias, Steffen

2016-04-01

Characterizing the spatial patterns of soil moisture is critical for hydrological and meteorological models, as soil moisture is a key variable that controls matter and energy fluxes and soil-vegetation-atmosphere exchange processes. Deriving detailed process understanding at the hillslope scale is not trivial, because of the temporal variability of local soil moisture dynamics. Nevertheless, it remains a challenge to provide adequate information on the temporal variability of soil moisture and its controlling factors. Recent advances in wireless sensor technology allow monitoring of soil moisture dynamics with high temporal resolution at varying scales. In addition, mobile geophysical methods such as electromagnetic induction (EMI) have been widely used for mapping soil water content at the field scale with high spatial resolution, as being related to soil apparent electrical conductivity (ECa). The objective of this study was to characterize the spatial and temporal pattern of soil moisture at the hillslope scale and to infer the controlling hydrological processes, integrating well established and innovative sensing techniques, as well as new statistical methods. We combined soil hydrological and pedological expertise with geophysical measurements and methods from digital soil mapping for designing a wireless soil moisture monitoring network. For a hillslope site within the Schäfertal catchment (Central Germany), soil water dynamics were observed during 14 months, and soil ECa was mapped on seven occasions whithin this period of time using an EM38-DD device. Using the Spearman rank correlation coefficient, we described the temporal persistence of a dry and a wet characteristic state of soil moisture as well as the switching mechanisms, inferring the local properties that control the observed spatial patterns and the hydrological processes driving the transitions. Based on this, we evaluated the use of EMI for mapping the spatial pattern of soil moisture under different hydrologic conditions and the factors controlling the temporal variability of the ECa-soil moisture relationship. The approach provided valuable insight into the time-varying contribution of local and nonlocal factors to the characteristic spatial patterns of soil moisture and the transition mechanisms. The spatial organization of soil moisture was controlled by different processes in different soil horizons, and the topsoil's moisture did not mirror processes that take place within the soil profile. Results show that, for the Schäfertal hillslope site which is presumed to be representative for non-intensively managed soils with moderate clay content, local soil properties (e.g., soil texture and porosity) are the major control on the spatial pattern of ECa. In contrast, the ECa-soil moisture relationship is small and varies over time indicating that ECa is not a good proxy for soil moisture estimation at the investigated site.Occasionally observed stronger correlations between ECa and soil moisture may be explained by background dependencies of ECa to other state variables such as pore water electrical conductivity. The results will help to improve conceptual understanding for hydrological model studies at similar or smaller scales, and to transfer observation concepts and process understanding to larger or less instrumented sites, as well as to constrain the use of EMI-based ECa data for hydrological applications.

The evolution of mapping habitat for northern spotted owls (Strix occidentalis caurina): A comparison of photo-interpreted, Landsat-based, and lidar-based habitat maps

USGS Publications Warehouse

Ackers, Steven H.; Davis, Raymond J.; Olsen, K.; Dugger, Catherine

2015-01-01

Wildlife habitat mapping has evolved at a rapid pace over the last few decades. Beginning with simple, often subjective, hand-drawn maps, habitat mapping now involves complex species distribution models (SDMs) using mapped predictor variables derived from remotely sensed data. For species that inhabit large geographic areas, remote sensing technology is often essential for producing range wide maps. Habitat monitoring for northern spotted owls (Strix occidentalis caurina), whose geographic covers about 23 million ha, is based on SDMs that use Landsat Thematic Mapper imagery to create forest vegetation data layers using gradient nearest neighbor (GNN) methods. Vegetation data layers derived from GNN are modeled relationships between forest inventory plot data, climate and topographic data, and the spectral signatures acquired by the satellite. When used as predictor variables for SDMs, there is some transference of the GNN modeling error to the final habitat map.Recent increases in the use of light detection and ranging (lidar) data, coupled with the need to produce spatially accurate and detailed forest vegetation maps have spurred interest in its use for SDMs and habitat mapping. Instead of modeling predictor variables from remotely sensed spectral data, lidar provides direct measurements of vegetation height for use in SDMs. We expect a SDM habitat map produced from directly measured predictor variables to be more accurate than one produced from modeled predictors.We used maximum entropy (Maxent) SDM modeling software to compare predictive performance and estimates of habitat area between Landsat-based and lidar-based northern spotted owl SDMs and habitat maps. We explored the differences and similarities between these maps, and to a pre-existing aerial photo-interpreted habitat map produced by local wildlife biologists. The lidar-based map had the highest predictive performance based on 10 bootstrapped replicate models (AUC = 0.809 ± 0.011), but the performance of the Landsat-based map was within acceptable limits (AUC = 0.717 ± 0.021). As is common with photo-interpreted maps, there was no accuracy assessment available for comparison. The photo-interpreted map produced the highest and lowest estimates of habitat area, depending on which habitat classes were included (nesting, roosting, and foraging habitat = 9962 ha, nesting habitat only = 6036 ha). The Landsat-based map produced an estimate of habitat area that was within this range (95% CI: 6679–9592 ha), while the lidar-based map produced an area estimate similar to what was interpreted by local wildlife biologists as nesting (i.e., high quality) habitat using aerial imagery (95% CI: 5453–7216). Confidence intervals of habitat area estimates from the SDMs based on Landsat and lidar overlapped.We concluded that both Landsat- and lidar-based SDMs produced reasonable maps and area estimates for northern spotted owl habitat within the study area. The lidar-based map was more precise and spatially similar to what local wildlife biologists considered spotted owl nesting habitat. The Landsat-based map provided a less precise spatial representation of habitat within the relatively small geographic confines of the study area, but habitat area estimates were similar to both the photo-interpreted and lidar-based maps.Photo-interpreted maps are time consuming to produce, subjective in nature, and difficult to replicate. SDMs provide a framework for efficiently producing habitat maps that can be replicated as habitat conditions change over time, provided that comparable remotely sensed data are available. When the SDM uses predictor variables extracted from lidar data, it can produce a habitat map that is both accurate and useful at large and small spatial scales. In comparison, SDMs using Landsat-based data are more appropriate for large scale analyses of amounts and general spatial patterns of habitat at regional scales.

The spatial and temporal variability of groundwater recharge in a forested basin in northern Wisconsin

USGS Publications Warehouse

Dripps, W.R.; Bradbury, K.R.

2010-01-01

Recharge varies spatially and temporally as it depends on a wide variety of factors (e.g. vegetation, precipitation, climate, topography, geology, and soil type), making it one of the most difficult, complex, and uncertain hydrologic parameters to quantify. Despite its inherent variability, groundwater modellers, planners, and policy makers often ignore recharge variability and assume a single average recharge value for an entire watershed. Relatively few attempts have been made to quantify or incorporate spatial and temporal recharge variability into water resource planning or groundwater modelling efforts. In this study, a simple, daily soil-water balance model was developed and used to estimate the spatial and temporal distribution of groundwater recharge of the Trout Lake basin of northern Wisconsin for 1996-2000 as a means to quantify recharge variability. For the 5 years of study, annual recharge varied spatially by as much as 18 cm across the basin; vegetation was the predominant control on this variability. Recharge also varied temporally with a threefold annual difference over the 5-year period. Intra-annually, recharge was limited to a few isolated events each year and exhibited a distinct seasonal pattern. The results suggest that ignoring recharge variability may not only be inappropriate, but also, depending on the application, may invalidate model results and predictions for regional and local water budget calculations, water resource management, nutrient cycling, and contaminant transport studies. Recharge is spatially and temporally variable, and should be modelled as such. Copyright ?? 2009 John Wiley & Sons, Ltd.

Spatial and temporal variability of throughfall and soil moisture in a deciduous forest in the low mountain ranges (Hesse, Germany)

NASA Astrophysics Data System (ADS)

Chifflard, Peter; Weishaupt, Philipp; Reiss, Martin

2017-04-01

Spatial and temporal patterns of throughfall can affect the heterogeneity of ecological, biogeochemical and hydrological processes at a forest floor and further the underlying soil. Previous research suggests different factors controlling the spatial and temporal patterns of throughfall, but most studies focus on coniferous forest, where the vegetation coverage is more or less constant over time. In deciduous forests the leaf area index varies due to the leaf fall in autumn which implicates a specific spatial and temporal variability of throughfall and furthermore of the soil moisture. Therefore, in the present study, the measurements of throughfall and soil moisture in a deciduous forest in the low mountain ranges focused especially on the period of leaf fall. The aims of this study were: 1) to detect the spatial and temporal variability of both the throughfall and the soil moisture, 2) to examine the temporal stability of the spatial patterns of the throughfall and soil moisture and 3) relate the soil moisture patterns to the throughfall patterns and further to the canopy characteristics. The study was carried out in a small catchment on middle Hesse (Germany) which is covered by beech forest. Annual mean air temperature is 9.4°C (48.9˚F) and annual mean precipitation is 650 mm. Base materials for soil genesis is greywacke and clay shale from Devonian deposits. The soil type at the study plot is a shallow cambisol. The study plot covers an area of about 150 m2 where 77 throughfall samplers where installed. The throughfall and the soil moisture (FDR-method, 20 cm depth) was measured immediately after every rainfall event at the 77 measurement points. During the period of October to December 2015 altogether 7 events were investigated. The geostatistical method kriging was used to interpolate between the measurements points to visualize the spatial patterns of each investigated parameter. Time-stability-plots were applied to examine temporal scatters of each investigated parameter. The spearmen and pearson correlation coefficients were applied to detect the relationship between the different investigated parameters. First results show that the spatial variability of throughfall decreases if the total amount of the throughfall increases. The soil moisture shows a similar behavior. It`s spatial variability decreases if higher soil moisture values were measured. Concerning the temporal stability of throughfall it can be shown that it is very high during the leaf-free period, although the rainfall events have different total througfall amounts. The soil moisture patterns consists of a low temporal stability and additionally only during one event a significant correlations between throughfall and soil moisture patterns exists. This implies that other factors than the throughfall patterns control the spatial patterns of soil moisture.

Three dimensional simulation of spatial and temporal variability of stratospheric hydrogen chloride

NASA Technical Reports Server (NTRS)

Kaye, Jack A.; Rood, Richard B.; Jackman, Charles H.; Allen, Dale J.; Larson, Edmund M.

1989-01-01

Spatial and temporal variability of atmospheric HCl columns are calculated for January 1979 using a three-dimensional chemistry-transport model designed to provide the best possible representation of stratospheric transport. Large spatial and temporal variability of the HCl columns is shown to be correlated with lower stratospheric potential vorticity and thus to be of dynamical origin. Systematic longitudinal structure is correlated with planetary wave structure. These results can help place spatially and temporally isolated column and profile measurements in a regional and/or global perspective.

The Application of FIA-based Data to Wildlife Habitat Modeling: A Comparative Study

Treesearch

Thomas C., Jr. Edwards; Gretchen G. Moisen; Tracey S. Frescino; Randall J. Schultz

2005-01-01

We evaluated the capability of two types of models, one based on spatially explicit variables derived from FIA data and one using so-called traditional habitat evaluation methods, for predicting the presence of cavity-nesting bird habitat in Fishlake National Forest, Utah. Both models performed equally well, in measures of predictive accuracy, with the FIA-based model...

Spatial versus Day-To-Day Within-Lake Variability in Tropical Floodplain Lake CH4 Emissions – Developing Optimized Approaches to Representative Flux Measurements

PubMed Central

Peixoto, Roberta B.; Machado-Silva, Fausto; Marotta, Humberto; Enrich-Prast, Alex; Bastviken, David

2015-01-01

Inland waters (lakes, rivers and reservoirs) are now understood to contribute large amounts of methane (CH4) to the atmosphere. However, fluxes are poorly constrained and there is a need for improved knowledge on spatiotemporal variability and on ways of optimizing sampling efforts to yield representative emission estimates for different types of aquatic ecosystems. Low-latitude floodplain lakes and wetlands are among the most high-emitting environments, and here we provide a detailed investigation of spatial and day-to-day variability in a shallow floodplain lake in the Pantanal in Brazil over a five-day period. CH4 flux was dominated by frequent and ubiquitous ebullition. A strong but predictable spatial variability (decreasing flux with increasing distance to the shore or to littoral vegetation) was found, and this pattern can be addressed by sampling along transects from the shore to the center. Although no distinct day-to-day variability were found, a significant increase in flux was identified from measurement day 1 to measurement day 5, which was likely attributable to a simultaneous increase in temperature. Our study demonstrates that representative emission assessments requires consideration of spatial variability, but also that spatial variability patterns are predictable for lakes of this type and may therefore be addressed through limited sampling efforts if designed properly (e.g., fewer chambers may be used if organized along transects). Such optimized assessments of spatial variability are beneficial by allowing more of the available sampling resources to focus on assessing temporal variability, thereby improving overall flux assessments. PMID:25860229

Spatial variability in intertidal macroalgal assemblages on the North Portuguese coast: consistence between species and functional group approaches

NASA Astrophysics Data System (ADS)

Veiga, P.; Rubal, M.; Vieira, R.; Arenas, F.; Sousa-Pinto, I.

2013-03-01

Natural assemblages are variable in space and time; therefore, quantification of their variability is imperative to identify relevant scales for investigating natural or anthropogenic processes shaping these assemblages. We studied the variability of intertidal macroalgal assemblages on the North Portuguese coast, considering three spatial scales (from metres to 10 s of kilometres) following a hierarchical design. We tested the hypotheses that (1) spatial pattern will be invariant at all the studied scales and (2) spatial variability of macroalgal assemblages obtained by using species will be consistent with that obtained using functional groups. This was done considering as univariate variables: total biomass and number of taxa as well as biomass of the most important species and functional groups and as multivariate variables the structure of macroalgal assemblages, both considering species and functional groups. Most of the univariate results confirmed the first hypothesis except for the total number of taxa and foliose macroalgae that showed significant variability at the scale of site and area, respectively. In contrast, when multivariate patterns were examined, the first hypothesis was rejected except at the scale of 10 s of kilometres. Both uni- and multivariate results indicated that variation was larger at the smallest scale, and thus, small-scale processes seem to have more effect on spatial variability patterns. Macroalgal assemblages, both considering species and functional groups as surrogate, showed consistent spatial patterns, and therefore, the second hypothesis was confirmed. Consequently, functional groups may be considered a reliable biological surrogate to study changes on macroalgal assemblages at least along the investigated Portuguese coastline.

Tannat grape composition responses to spatial variability of temperature in an Uruguay's coastal wine region

NASA Astrophysics Data System (ADS)

Fourment, Mercedes; Ferrer, Milka; González-Neves, Gustavo; Barbeau, Gérard; Bonnardot, Valérie; Quénol, Hervé

2017-09-01

Spatial variability of temperature was studied in relation to the berry basic composition and secondary compounds of the Tannat cultivar at harvest from vineyards located in Canelones and Montevideo, the most important wine region of Uruguay. Monitoring of berries and recording of temperature were performed in 10 commercial vineyards of Tannat situated in the southern coastal wine region of the country for three vintages (2012, 2013, and 2014). Results from a multivariate correlation analysis between berry composition and temperature over the three vintages showed that (1) Tannat responses to spatial variability of temperature were different over the vintages, (2) correlations between secondary metabolites and temperature were higher than those between primary metabolites, and (3) correlation values between berry composition and climate variables increased when ripening occurred under dry conditions (below average rainfall). For a particular studied vintage (2013), temperatures explained 82.5% of the spatial variability of the berry composition. Daily thermal amplitude was found to be the most important spatial mode of variability with lower values recorded at plots nearest to the sea and more exposed to La Plata River. The highest levels in secondary compounds were found in berries issued from plots situated as far as 18.3 km from La Plata River. The increasing knowledge of temperature spatial variability and its impact on grape berry composition contributes to providing possible issues to adapt grapevine to climate change.

Spatial variability in acoustic backscatter as an indicator of tissue homogenate production in pulsed cavitational ultrasound therapy.

PubMed

Parsons, Jessica E; Cain, Charles A; Fowlkes, J Brian

2007-03-01

Spatial variability in acoustic backscatter is investigated as a potential feedback metric for assessment of lesion morphology during cavitation-mediated mechanical tissue disruption ("histotripsy"). A 750-kHz annular array was aligned confocally with a 4.5 MHz passive backscatter receiver during ex vivo insonation of porcine myocardium. Various exposure conditions were used to elicit a range of damage morphologies and backscatter characteristics [pulse duration = 14 micros, pulse repetition frequency (PRF) = 0.07-3.1 kHz, average I(SPPA) = 22-44 kW/cm2]. Variability in backscatter spatial localization was quantified by tracking the lag required to achieve peak correlation between sequential RF A-lines received. Mean spatial variability was observed to be significantly higher when damage morphology consisted of mechanically disrupted tissue homogenate versus mechanically intact coagulation necrosis (2.35 +/- 1.59 mm versus 0.067 +/- 0.054 mm, p < 0.025). Statistics from these variability distributions were used as the basis for selecting a threshold variability level to identify the onset of homogenate formation via an abrupt, sustained increase in spatially dynamic backscatter activity. Specific indices indicative of the state of the homogenization process were quantified as a function of acoustic input conditions. The prevalence of backscatter spatial variability was observed to scale with the amount of homogenate produced for various PRFs and acoustic intensities.

Impacts of Spatial Fidelity Violations in the Forward Signal Model on DOAS-based Greenhouse Gas Retrievals: a Preliminary Analysis for OCO-2 (and Other Missions)

NASA Technical Reports Server (NTRS)

Davis, Anthony B.; Frakenbert, Christian

2012-01-01

Success in three aspects of OCO-2 mission is threatened by unaccounted spa,al variability effects, all involving atmospheric scattering: 1. Low/moderately opaque clouds can escape the prescreening by mimicking a brighter surface. 2. Prescreening does not account for long-range radia,ve impact (adjacency effect) of nearby clouds. Need for extended cloud masking? 3. Oblique looks in target mode are highly exposed to surface adjacency and aerosol variability effects.We'll be covering all three bases!

Predictor variable resolution governs modeled soil types

USDA-ARS?s Scientific Manuscript database

Soil mapping identifies different soil types by compressing a unique suite of spatial patterns and processes across multiple spatial scales. It can be quite difficult to quantify spatial patterns of soil properties with remotely sensed predictor variables. More specifically, matching the right scale...

Optimal Interpolation scheme to generate reference crop evapotranspiration

NASA Astrophysics Data System (ADS)

Tomas-Burguera, Miquel; Beguería, Santiago; Vicente-Serrano, Sergio; Maneta, Marco

2018-05-01

We used an Optimal Interpolation (OI) scheme to generate a reference crop evapotranspiration (ETo) grid, forcing meteorological variables, and their respective error variance in the Iberian Peninsula for the period 1989-2011. To perform the OI we used observational data from the Spanish Meteorological Agency (AEMET) and outputs from a physically-based climate model. To compute ETo we used five OI schemes to generate grids for the five observed climate variables necessary to compute ETo using the FAO-recommended form of the Penman-Monteith equation (FAO-PM). The granularity of the resulting grids are less sensitive to variations in the density and distribution of the observational network than those generated by other interpolation methods. This is because our implementation of the OI method uses a physically-based climate model as prior background information about the spatial distribution of the climatic variables, which is critical for under-observed regions. This provides temporal consistency in the spatial variability of the climatic fields. We also show that increases in the density and improvements in the distribution of the observational network reduces substantially the uncertainty of the climatic and ETo estimates. Finally, a sensitivity analysis of observational uncertainties and network densification suggests the existence of a trade-off between quantity and quality of observations.

Boosted Regression Tree Models to Explain Watershed ...

EPA Pesticide Factsheets

Boosted regression tree (BRT) models were developed to quantify the nonlinear relationships between landscape variables and nutrient concentrations in a mesoscale mixed land cover watershed during base-flow conditions. Factors that affect instream biological components, based on the Index of Biotic Integrity (IBI), were also analyzed. Seasonal BRT models at two spatial scales (watershed and riparian buffered area [RBA]) for nitrite-nitrate (NO2-NO3), total Kjeldahl nitrogen, and total phosphorus (TP) and annual models for the IBI score were developed. Two primary factors — location within the watershed (i.e., geographic position, stream order, and distance to a downstream confluence) and percentage of urban land cover (both scales) — emerged as important predictor variables. Latitude and longitude interacted with other factors to explain the variability in summer NO2-NO3 concentrations and IBI scores. BRT results also suggested that location might be associated with indicators of sources (e.g., land cover), runoff potential (e.g., soil and topographic factors), and processes not easily represented by spatial data indicators. Runoff indicators (e.g., Hydrological Soil Group D and Topographic Wetness Indices) explained a substantial portion of the variability in nutrient concentrations as did point sources for TP in the summer months. The results from our BRT approach can help prioritize areas for nutrient management in mixed-use and heavily impacted watershed

Comparison of bottom-track to global positioning system referenced discharges measured using an acoustic Doppler current profiler

USGS Publications Warehouse

Wagner, Chad R.; Mueller, David S.

2011-01-01

A negative bias in discharge measurements made with an acoustic Doppler current profiler (ADCP) can be caused by the movement of sediment on or near the streambed. The integration of a global positioning system (GPS) to track the movement of the ADCP can be used to avoid the systematic negative bias associated with a moving streambed. More than 500 discharge transects from 63 discharge measurements with GPS data were collected at sites throughout the US, Canada, and New Zealand with no moving bed to compare GPS and bottom-track-referenced discharges. Although the data indicated some statistical bias depending on site conditions and type of GPS data used, these biases were typically about 0.5% or less. An assessment of differential correction sources was limited by a lack of data collected in a range of different correction sources and different GPS receivers at the same sites. Despite this limitation, the data indicate that the use of Wide Area Augmentation System (WAAS) corrected positional data is acceptable for discharge measurements using GGA as the boat-velocity reference. The discharge data based on GPS-referenced boat velocities from the VTG data string, which does not require differential correction, were comparable to the discharges based on GPS-referenced boat velocities from the differentially-corrected GGA data string. Spatial variability of measure discharges referenced to GGA, VTG and bottom-tracking is higher near the channel banks. The spatial variability of VTG-referenced discharges is correlated with the spatial distribution of maximum Horizontal Dilution of Precision (HDOP) values and the spatial variability of GGA-referenced discharges is correlated with proximity to channel banks.

VEMAP Phase 2 bioclimatic database. I. Gridded historical (20th century) climate for modeling ecosystem dynamics across the conterminous USA

USGS Publications Warehouse

Kittel, T.G.F.; Rosenbloom, N.A.; Royle, J. Andrew; Daly, Christopher; Gibson, W.P.; Fisher, H.H.; Thornton, P.; Yates, D.N.; Aulenbach, S.; Kaufman, C.; McKeown, R.; Bachelet, D.; Schimel, D.S.; Neilson, R.; Lenihan, J.; Drapek, R.; Ojima, D.S.; Parton, W.J.; Melillo, J.M.; Kicklighter, D.W.; Tian, H.; McGuire, A.D.; Sykes, M.T.; Smith, B.; Cowling, S.; Hickler, T.; Prentice, I.C.; Running, S.; Hibbard, K.A.; Post, W.M.; King, A.W.; Smith, T.; Rizzo, B.; Woodward, F.I.

2004-01-01

Analysis and simulation of biospheric responses to historical forcing require surface climate data that capture those aspects of climate that control ecological processes, including key spatial gradients and modes of temporal variability. We developed a multivariate, gridded historical climate dataset for the conterminous USA as a common input database for the Vegetation/Ecosystem Modeling and Analysis Project (VEMAP), a biogeochemical and dynamic vegetation model intercomparison. The dataset covers the period 1895-1993 on a 0.5?? latitude/longitude grid. Climate is represented at both monthly and daily timesteps. Variables are: precipitation, mininimum and maximum temperature, total incident solar radiation, daylight-period irradiance, vapor pressure, and daylight-period relative humidity. The dataset was derived from US Historical Climate Network (HCN), cooperative network, and snowpack telemetry (SNOTEL) monthly precipitation and mean minimum and maximum temperature station data. We employed techniques that rely on geostatistical and physical relationships to create the temporally and spatially complete dataset. We developed a local kriging prediction model to infill discontinuous and limited-length station records based on spatial autocorrelation structure of climate anomalies. A spatial interpolation model (PRISM) that accounts for physiographic controls was used to grid the infilled monthly station data. We implemented a stochastic weather generator (modified WGEN) to disaggregate the gridded monthly series to dailies. Radiation and humidity variables were estimated from the dailies using a physically-based empirical surface climate model (MTCLIM3). Derived datasets include a 100 yr model spin-up climate and a historical Palmer Drought Severity Index (PDSI) dataset. The VEMAP dataset exhibits statistically significant trends in temperature, precipitation, solar radiation, vapor pressure, and PDSI for US National Assessment regions. The historical climate and companion datasets are available online at data archive centers. ?? Inter-Research 2004.

Spatial variability of surface fuels in treated and untreated ponderosa pine forests of the southern Rocky Mountains

Treesearch

Emma Vakili; Chad M. Hoffman; Robert E. Keane; Wade T. Tinkham; Yvette Dickinson

2016-01-01

There is growing consensus that spatial variability in fuel loading at scales down to 0.5 m may govern fire behaviour and effects. However, there remains a lack of understanding of how fuels vary through space in wildland settings. This study quantifies surface fuel loading and its spatial variability in ponderosa pine sites before and after fuels treatment in the...

Temporal and spatial variability of wind resources in the United States as derived from the Climate Forecast System Reanalysis

Treesearch

Lejiang Yu; Shiyuan Zhong; Xindi Bian; Warren E. Heilman

2015-01-01

This study examines the spatial and temporal variability of wind speed at 80m above ground (the average hub height of most modern wind turbines) in the contiguous United States using Climate Forecast System Reanalysis (CFSR) data from 1979 to 2011. The mean 80-m wind exhibits strong seasonality and large spatial variability, with higher (lower) wind speeds in the...

Making Energy-Water Nexus Scenarios more Fit-for-Purpose through Better Characterization of Extremes

NASA Astrophysics Data System (ADS)

Yetman, G.; Levy, M. A.; Chen, R. S.; Schnarr, E.

2017-12-01

Often quantitative scenarios of future trends exhibit less variability than the historic data upon which the models that generate them are based. The problem of dampened variability, which typically also entails dampened extremes, manifests both temporally and spatially. As a result, risk assessments that rely on such scenarios are in danger of producing misleading results. This danger is pronounced in nexus issues, because of the multiple dimensions of change that are relevant. We illustrate the above problem by developing alternative joint distributions of the probability of drought and of human population totals, across U.S. counties over the period 2010-2030. For the dampened-extremes case we use drought frequencies derived from climate models used in the U.S. National Climate Assessment and the Environmental Protection Agency's population and land use projections contained in its Integrated Climate and Land Use Scenarios (ICLUS). For the elevated extremes case we use an alternative spatial drought frequency estimate based on tree-ring data, covering a 555-year period (Ho et al 2017); and we introduce greater temporal and spatial extremes in the ICLUS socioeconomic projections so that they conform to observed extremes in the historical U.S. spatial census data 1790-present (National Historical Geographic Information System). We use spatial and temporal coincidence of high population and extreme drought as a proxy for energy-water nexus risk. We compare the representation of risk in the dampened-extreme and elevated-extreme scenario analysis. We identify areas of the country where using more realistic portrayals of extremes makes the biggest difference in estimate risk and suggest implications for future risk assessments. References: Michelle Ho, Upmanu Lall, Xun Sun, Edward R. Cook. 2017. Multiscale temporal variability and regional patterns in 555 years of conterminous U.S. streamflow. Water Resources Research. . doi: 10.1002/2016WR019632

Utilizing NASA DISCOVER-AQ Data to Examine Spatial Gradients in Complex Emission Environments

NASA Astrophysics Data System (ADS)

Buzanowicz, M. E.; Moore, W.; Crawford, J. H.; Schroeder, J.

2017-12-01

Although many regulations have been enacted with the goal of improving air quality, many parts of the US are still classified as `non-attainment areas' because they frequently violate federal air quality standards. Adequately monitoring the spatial distribution of pollutants both within and outside of non-attainment areas has been an ongoing challenge for regulators. Observations of near-surface pollution from space-based platforms would provide an unprecedented view of the spatial distribution of pollution, but this goal has not yet been realized due to fundamental limitations of satellites, specifically because the footprint size of satellite measurements may not be sufficiently small enough to capture true gradients in pollution, and rather represents an average over a large area. NASA's DISCOVER-AQ was a multi-year field campaign aimed at improving our understanding of the role that remote sensing, including satellite-based remote sensing, could play in air quality monitoring systems. DISCOVER-AQ data will be utilized to create a metric to examine spatial gradients and how satellites can capture those gradients in areas with complex emission environments. Examining horizontal variability within a vertical column is critical to understanding mixing within the atmosphere. Aircraft spirals conducted during DISCOVER-AQ were divided into octants, and averages of a given a species were calculated, with certain points receiving a flag. These flags were determined by calculating gradients between subsequent octants. Initial calculations have shown that over areas with large point source emissions, such as Platteville and Denver-La Casa in Colorado, and Essex, Maryland, satellite retrievals may not adequately capture spatial variability in the atmosphere, thus complicating satellite inversion techniques and limiting our ability to understand human exposure on sub-grid scales. Further calculations at other locations and for other trace gases are necessary to determine the effects of vertical variability within the atmosphere.

Random field theory to interpret the spatial variability of lacustrine soils

NASA Astrophysics Data System (ADS)

Russo, Savino; Vessia, Giovanna

2015-04-01

The lacustrine soils are quaternary soils, dated from Pleistocene to Holocene periods, generated in low-energy depositional environments and characterized by soil mixture of clays, sands and silts with alternations of finer and coarser grain size layers. They are often met at shallow depth filling several tens of meters of tectonic or erosive basins typically placed in internal Appenine areas. The lacustrine deposits are often locally interbedded by detritic soils resulting from the failure of surrounding reliefs. Their heterogeneous lithology is associated with high spatial variability of physical and mechanical properties both along horizontal and vertical directions. The deterministic approach is still commonly adopted to accomplish the mechanical characterization of these heterogeneous soils where undisturbed sampling is practically not feasible (if the incoherent fraction is prevalent) or not spatially representative (if the cohesive fraction prevails). The deterministic approach consists on performing in situ tests, like Standard Penetration Tests (SPT) or Cone Penetration Tests (CPT) and deriving design parameters through "expert judgment" interpretation of the measure profiles. These readings of tip and lateral resistances (Rp and RL respectively) are almost continuous but highly variable in soil classification according to Schmertmann (1978). Thus, neglecting the spatial variability cannot be the best strategy to estimated spatial representative values of physical and mechanical parameters of lacustrine soils to be used for engineering applications. Hereafter, a method to draw the spatial variability structure of the aforementioned measure profiles is presented. It is based on the theory of the Random Fields (Vanmarcke 1984) applied to vertical readings of Rp measures from mechanical CPTs. The proposed method relies on the application of the regression analysis, by which the spatial mean trend and fluctuations about this trend are derived. Moreover, the scale of fluctuation is calculated to measure the maximum length beyond which profiles of measures are independent. The spatial mean trend can be used to identify "quasi-homogeneous" soil layers where the standard deviation and the scale of fluctuation can be calculated. In this study, five Rp profiles performed in the lacustrine deposits of the high River Pescara Valley have been analyzed. There, silty clay deposits with thickness ranging from a few meters to about 60m, and locally rich in sands and peats, are investigated. In this study, vertical trends of Rp profiles have been derived to be converted into design parameter mean trends. Furthermore, the variability structure derived from Rp readings can be propagated to design parameters to calculate the "characteristic values" requested by the European building codes. References Schmertmann J.H. 1978. Guidelines for Cone Penetration Test, Performance and Design. Report No. FHWA-TS-78-209, U.S. Department of Transportation, Washington, D.C., pp. 145. Vanmarcke E.H. 1984. Random Fields, analysis and synthesis. Cambridge (USA): MIT Press.

Landscape genetic approaches to guide native plant restoration in the Mojave Desert

USGS Publications Warehouse

Shryock, Daniel F.; Havrilla, Caroline A.; DeFalco, Lesley; Esque, Todd C.; Custer, Nathan; Wood, Troy E.

2016-01-01

Restoring dryland ecosystems is a global challenge due to synergistic drivers of disturbance coupled with unpredictable environmental conditions. Dryland plant species have evolved complex life-history strategies to cope with fluctuating resources and climatic extremes. Although rarely quantified, local adaptation is likely widespread among these species and potentially influences restoration outcomes. The common practice of reintroducing propagules to restore dryland ecosystems, often across large spatial scales, compels evaluation of adaptive divergence within these species. Such evaluations are critical to understanding the consequences of large-scale manipulation of gene flow and to predicting success of restoration efforts. However, genetic information for species of interest can be difficult and expensive to obtain through traditional common garden experiments. Recent advances in landscape genetics offer marker-based approaches for identifying environmental drivers of adaptive genetic variability in non-model species, but tools are still needed to link these approaches with practical aspects of ecological restoration. Here, we combine spatially-explicit landscape genetics models with flexible visualization tools to demonstrate how cost-effective evaluations of adaptive genetic divergence can facilitate implementation of different seed sourcing strategies in ecological restoration. We apply these methods to Amplified Fragment Length Polymorphism (AFLP) markers genotyped in two Mojave Desert shrub species of high restoration importance: the long-lived, wind-pollinated gymnosperm Ephedra nevadensis, and the short-lived, insect-pollinated angiosperm Sphaeralcea ambigua. Mean annual temperature was identified as an important driver of adaptive genetic divergence for both species. Ephedra showed stronger adaptive divergence with respect to precipitation variability, while temperature variability and precipitation averages explained a larger fraction of adaptive divergence in Sphaeralcea. We describe multivariate statistical approaches for interpolating spatial patterns of adaptive divergence while accounting for potential bias due to neutral genetic structure. Through a spatial bootstrapping procedure, we also visualize patterns in the magnitude of model uncertainty. Finally, we introduce an interactive, distance-based mapping approach that explicitly links marker-based models of adaptive divergence with local or admixture seed sourcing strategies, promoting effective native plant restoration.

Integrating satellite remote sensing data and field data to predict rangeland structural indicators at the continental scale

NASA Astrophysics Data System (ADS)

Zhang, J.; Okin, G.

2016-12-01

Rangelands provide a variety of important ecosystem goods and services across drylands globally. They are also the most important emitters of dust across the globe. Field data collection based on points does not represent spatially continuous information about surface variables and, given the vast size of the world's rangelands, cannot cover even a small fraction of their area. Remote sensing is potentially a labor- and time-saving method to observe important rangeland vegetation variables at both temporal and spatial scales. Information on vegetation cover, bare gap size, and plant height provide key rangeland vegetation variables in arid and semiarid rangelands, in part because they strongly impact dust emission and determine wildlife habitat characteristics. This study reports on relationships between remote sensing in the reflected solar spectrum and field measures related to these three variables, and shows how these relationships can be extended to produce spatially and temporally continuous datasets coupled with quantitative estimates of error. Field data for this study included over 3,800 Assessment, Inventory, and Monitoring (AIM) measurements on Bureau of Land Management (BLM) lands throughout the western US. Remote sensing data were derived from MODIS nadir BRDF-adjusted reflectance (NBAR) and Landsat 8 OLI surface reflectance. Normalized bare gap size, total foliar cover, herbaceous cover and herbaceous height exhibit the greatest predictability from remote sensing variables with physically-reasonable relationships between remote sensing variables and field measures. Data fields produced using these relationships across the western US exhibit good agreement with independent high-resolution imagery.

Uncertainty in the profitability of fertilizer management based on various sampling designs.

NASA Astrophysics Data System (ADS)

Muhammed, Shibu; Ben, Marchant; Webster, Richard; Milne, Alice; Dailey, Gordon; Whitmore, Andrew

2016-04-01

Many farmers sample their soil to measure the concentrations of plant nutrients, including phosphorus (P), so as to decide how much fertilizer to apply. Now that fertilizer can be applied at variable rates, farmers want to know whether maps of nutrient concentration made from grid samples or from field subdivisions (zones within their fields) are merited: do such maps lead to greater profit than would a single measurement on a bulked sample for each field when all costs are taken into account? We have examined the merits of grid-based and zone-based sampling strategies over single field-based averages using continuous spatial data on wheat yields at harvest in six fields in southern England and simulated concentrations of P in the soil. Features of the spatial variation in the yields provide predictions about which sampling scheme is likely to be most cost effective, but there is uncertainty associated with these predictions that must be communicated to farmers. Where variograms of the yield have large variances and long effective ranges, grid-sampling and mapping nutrients are likely to be cost-effective. Where effective ranges are short, sampling must be dense to reveal the spatial variation and may be expensive. In these circumstances variable-rate application of fertilizer is likely to be impracticable and almost certainly not cost-effective. We have explored several methods for communicating these results and found that the most effective method was using probability maps that show the likelihood of grid-based and zone-based sampling being more profitable that a field-based estimate.

Variability of isotope and major ion chemistry in the Allequash Basin, Wisconsin

USGS Publications Warehouse

Walker, John F.; Hunt, Randall J.; Bullen, Thomas D.; Krabbenhoft, David P.; Kendall, Carol

2003-01-01

As part of ongoing research conducted at one of the U.S. Geological Survey's Water, Energy, and Biogeochem-ical Budgets sites, work was undertaken to describe the spatial and temporal variability of stream and ground water isotopic composition and cation chemistry in the Trout Lake watershed, to relate the variability to the watershed flow system, and to identify the linkages of geochemical evolution and source of water in the watershed. The results are based on periodic sampling of sites at two scales along Allequash Creek, a small headwater stream in northern Wisconsin. Based on this sampling, there are distinct water isotopic and geochemical differences observed at a smaller hillslope scale and the larger Allequash Creek scale. The variability was larger than expected for this simple watershed, and is likely to be seen in more complex basins. Based on evidence from multiple isotopes and stream chemistry, the flow system arises from three main source waters (terrestrial-, lake-, or wetland-derived recharge) that can be identified along any flowpath using water isotopes together with geochemical characteristics such as iron concentrations. The ground water chemistry demonstrates considerable spatial variability that depends mainly on the flow-path length and water mobility through the aquifer. Calcium concentrations increase with increasing flowpath length, whereas strontium isotope ratios increase with increasing extent of stagnation in either the unsaturated or saturated zones as waters move from source to sink. The flowpath distribution we identify provides important constraints on the calibration of ground water flow models such as that undertaken by Pint et al. (this issue).

Spatial Variability of Plant Available Water, Soil Organic Carbon, and Microbial Biomass under Divergent Land Uses: A Comparison among Regression-Kriging, Cokriging, and Regression-Cokriging

NASA Astrophysics Data System (ADS)

Kiani, M.; Hernandez Ramirez, G.; Quideau, S.

2016-12-01

Improved knowledge about the spatial variability of plant available water (PAW), soil organic carbon (SOC), and microbial biomass carbon (MBC) as affected by land-use systems can underpin the identification and inventory of beneficial ecosystem good and services in both agricultural and wild lands. Little research has been done that addresses the spatial patterns of PAW, SOC, and MBC under different land use types at a field scale. Therefore, we collected 56 soil samples (5-10 cm depth increment), using a nested cyclic sampling design within both a native grassland (NG) site and an irrigated cultivated (IC) site located near Brooks, Alberta. Using classical statistical and geostatistical methods, we characterized the spatial heterogeneities of PAW, SOC, and MBC under NG and IC using several geostatistical methods such as ordinary kriging (OK), regression-kriging (RK), cokriging (COK), and regression-cokriging (RCOK). Converting the native grassland to irrigated cultivated land altered soil pore distribution by reducing macroporosity which led to lower saturated water content and half hydraulic conductivity in IC compared to NG. This conversion also decreased the relative abundance of gram-negative bacteria, while increasing both the proportion of gram-positive bacteria and MBC concentration. At both studied sites, the best fitted spatial model was Gaussian based on lower RSS and higher R2 as criteria. The IC had stronger degree of spatial dependence and longer range of spatial auto-correlation revealing a homogenization of the spatial variability of soil properties as a result of intensive, recurrent agricultural activities. Comparison of OK, RK, COK, and RCOK approaches indicated that cokriging method had the best performance demonstrating a profound improvement in the accuracy of spatial estimations of PAW, SOC, and MBC. It seems that the combination of terrain covariates such as elevation and depth-to-water with kriging techniques offers more capability for incorporating explicit ancillary information in predictive soil mapping. Overall, identification of spatial patterns of soil properties in agricultural lands gives a bird's eye view to land owners to implement and improve management practices which lead to more sustainable production.

Accuracy and Spatial Variability in GPS Surveying for Landslide Mapping on Road Inventories at a Semi-Detailed Scale: the Case in Colombia

NASA Astrophysics Data System (ADS)

Murillo Feo, C. A.; Martnez Martinez, L. J.; Correa Muñoz, N. A.

2016-06-01

The accuracy of locating attributes on topographic surfaces when, using GPS in mountainous areas, is affected by obstacles to wave propagation. As part of this research on the semi-automatic detection of landslides, we evaluate the accuracy and spatial distribution of the horizontal error in GPS positioning in the tertiary road network of six municipalities located in mountainous areas in the department of Cauca, Colombia, using geo-referencing with GPS mapping equipment and static-fast and pseudo-kinematic methods. We obtained quality parameters for the GPS surveys with differential correction, using a post-processing method. The consolidated database underwent exploratory analyses to determine the statistical distribution, a multivariate analysis to establish relationships and partnerships between the variables, and an analysis of the spatial variability and calculus of accuracy, considering the effect of non-Gaussian distribution errors. The evaluation of the internal validity of the data provide metrics with a confidence level of 95% between 1.24 and 2.45 m in the static-fast mode and between 0.86 and 4.2 m in the pseudo-kinematic mode. The external validity had an absolute error of 4.69 m, indicating that this descriptor is more critical than precision. Based on the ASPRS standard, the scale obtained with the evaluated equipment was in the order of 1:20000, a level of detail expected in the landslide-mapping project. Modelling the spatial variability of the horizontal errors from the empirical semi-variogram analysis showed predictions errors close to the external validity of the devices.

Groundwater Quality: Analysis of Its Temporal and Spatial Variability in a Karst Aquifer.

PubMed

Pacheco Castro, Roger; Pacheco Ávila, Julia; Ye, Ming; Cabrera Sansores, Armando

2018-01-01

This study develops an approach based on hierarchical cluster analysis for investigating the spatial and temporal variation of water quality governing processes. The water quality data used in this study were collected in the karst aquifer of Yucatan, Mexico, the only source of drinking water for a population of nearly two million people. Hierarchical cluster analysis was applied to the quality data of all the sampling periods lumped together. This was motivated by the observation that, if water quality does not vary significantly in time, two samples from the same sampling site will belong to the same cluster. The resulting distribution maps of clusters and box-plots of the major chemical components reveal the spatial and temporal variability of groundwater quality. Principal component analysis was used to verify the results of cluster analysis and to derive the variables that explained most of the variation of the groundwater quality data. Results of this work increase the knowledge about how precipitation and human contamination impact groundwater quality in Yucatan. Spatial variability of groundwater quality in the study area is caused by: a) seawater intrusion and groundwater rich in sulfates at the west and in the coast, b) water rock interactions and the average annual precipitation at the middle and east zones respectively, and c) human contamination present in two localized zones. Changes in the amount and distribution of precipitation cause temporal variation by diluting groundwater in the aquifer. This approach allows to analyze the variation of groundwater quality controlling processes efficiently and simultaneously. © 2017, National Ground Water Association.

Making riverscapes real

NASA Astrophysics Data System (ADS)

Carbonneau, Patrice; Fonstad, Mark A.; Marcus, W. Andrew; Dugdale, Stephen J.

2012-01-01

The structure and function of rivers have long been characterized either by: (1) qualitative models such as the River Continuum Concept or Serial Discontinuity Concept which paint broad descriptive portraits of how river habitats and communities vary, or (2) quantitative models, such as downstream hydraulic geometry, which rely on a limited number of measurements spread widely throughout a river basin. In contrast, authors such as Fausch et al. (2002) and Wiens (2002) proposed applying existing quantitative, spatially comprehensive ecology and landscape ecology methods to rivers. This new framework for river sciences which preserves variability and spatial relationships is called a riverine landscape or a 'riverscape'. Application of this riverscape concept requires information on the spatial distribution of organism-scale habitats throughout entire river systems. This article examines the ways in which recent technical and methodological developments can allow us to quantitatively implement and realize the riverscape concept. Using 3-cm true color aerial photos and 5-m resolution elevation data from the River Tromie, Scotland, we apply the newly developed Fluvial Information System which integrates a suite of cutting edge, high resolution, remote sensing methods in a spatially explicit framework. This new integrated approach allows for the extraction of primary fluvial variables such as width, depth, particle size, and elevation. From these first-order variables, we derive second-order geomorphic and hydraulic variables including velocity, stream power, Froude number and shear stress. Channel slope can be approximated from available topographic data. Based on these first and second-order variables, we produce riverscape metrics that begin to explore how geomorphic structures may influence river habitats, including connectivity, patchiness of habitat, and habitat distributions. The results show a complex interplay of geomorphic variable and habitat patchiness that is not predicted by existing fluvial theory. Riverscapes, thus, challenge the existing understanding of how rivers structure themselves and will force development of new paradigms.

Biopsy variability of lymphocytic infiltration in breast cancer subtypes and the ImmunoSkew score

NASA Astrophysics Data System (ADS)

Khan, Adnan Mujahid; Yuan, Yinyin

2016-11-01

The number of tumour biopsies required for a good representation of tumours has been controversial. An important factor to consider is intra-tumour heterogeneity, which can vary among cancer types and subtypes. Immune cells in particular often display complex infiltrative patterns, however, there is a lack of quantitative understanding of the spatial heterogeneity of immune cells and how this fundamental biological nature of human tumours influences biopsy variability and treatment resistance. We systematically investigate biopsy variability for the lymphocytic infiltrate in 998 breast tumours using a novel virtual biopsy method. Across all breast cancers, we observe a nonlinear increase in concordance between the biopsy and whole-tumour score of lymphocytic infiltrate with increasing number of biopsies, yet little improvement is gained with more than four biopsies. Interestingly, biopsy variability of lymphocytic infiltrate differs considerably among breast cancer subtypes, with the human epidermal growth factor receptor 2-positive (HER2+) subtype having the highest variability. We subsequently identify a quantitative measure of spatial variability that predicts disease-specific survival in HER2+ subtype independent of standard clinical variables (node status, tumour size and grade). Our study demonstrates how systematic methods provide new insights that can influence future study design based on a quantitative knowledge of tumour heterogeneity.

Spatial patterns of distribution and abundance of Harrisia portoricensis, an endangered Caribbean cactus

Treesearch

J. Rojas-Sandoval; E. J. Melendez-Ackerman; NO-VALUE

2013-01-01

Aims The spatial distribution of biotic and abiotic factors may play a dominant role in determining the distribution and abundance of plants in arid and semiarid environments. In this study, we evaluated how spatial patterns of microhabitat variables and the degree of spatial dependence of these variables influence the distribution and abundance of the endangered...

Solving Large-scale Spatial Optimization Problems in Water Resources Management through Spatial Evolutionary Algorithms

NASA Astrophysics Data System (ADS)

Wang, J.; Cai, X.

2007-12-01

A water resources system can be defined as a large-scale spatial system, within which distributed ecological system interacts with the stream network and ground water system. Water resources management, the causative factors and hence the solutions to be developed have a significant spatial dimension. This motivates a modeling analysis of water resources management within a spatial analytical framework, where data is usually geo- referenced and in the form of a map. One of the important functions of Geographic information systems (GIS) is to identify spatial patterns of environmental variables. The role of spatial patterns in water resources management has been well established in the literature particularly regarding how to design better spatial patterns for satisfying the designated objectives of water resources management. Evolutionary algorithms (EA) have been demonstrated to be successful in solving complex optimization models for water resources management due to its flexibility to incorporate complex simulation models in the optimal search procedure. The idea of combining GIS and EA motivates the development and application of spatial evolutionary algorithms (SEA). SEA assimilates spatial information into EA, and even changes the representation and operators of EA. In an EA used for water resources management, the mathematical optimization model should be modified to account the spatial patterns; however, spatial patterns are usually implicit, and it is difficult to impose appropriate patterns to spatial data. Also it is difficult to express complex spatial patterns by explicit constraints included in the EA. The GIS can help identify the spatial linkages and correlations based on the spatial knowledge of the problem. These linkages are incorporated in the fitness function for the preference of the compatible vegetation distribution. Unlike a regular GA for spatial models, the SEA employs a special hierarchical hyper-population and spatial genetic operators to represent spatial variables in a more efficient way. The hyper-population consists of a set of populations, which correspond to the spatial distributions of the individual agents (organisms). Furthermore spatial crossover and mutation operators are designed in accordance with the tree representation and then applied to both organisms and populations. This study applies the SEA to a specific problem of water resources management- maximizing the riparian vegetation coverage in accordance with the distributed groundwater system in an arid region. The vegetation coverage is impacted greatly by the nonlinear feedbacks and interactions between vegetation and groundwater and the spatial variability of groundwater. The SEA is applied to search for an optimal vegetation configuration compatible to the groundwater flow. The results from this example demonstrate the effectiveness of the SEA. Extension of the algorithm for other water resources management problems is discussed.

UAV-Based Estimation of Carbon Exports from Heterogeneous Soil Landscapes—A Case Study from the CarboZALF Experimental Area

PubMed Central

Wehrhan, Marc; Rauneker, Philipp; Sommer, Michael

2016-01-01

The advantages of remote sensing using Unmanned Aerial Vehicles (UAVs) are a high spatial resolution of images, temporal flexibility and narrow-band spectral data from different wavelengths domains. This enables the detection of spatio-temporal dynamics of environmental variables, like plant-related carbon dynamics in agricultural landscapes. In this paper, we quantify spatial patterns of fresh phytomass and related carbon (C) export using imagery captured by a 12-band multispectral camera mounted on the fixed wing UAV Carolo P360. The study was performed in 2014 at the experimental area CarboZALF-D in NE Germany. From radiometrically corrected and calibrated images of lucerne (Medicago sativa), the performance of four commonly used vegetation indices (VIs) was tested using band combinations of six near-infrared bands. The highest correlation between ground-based measurements of fresh phytomass of lucerne and VIs was obtained for the Enhanced Vegetation Index (EVI) using near-infrared band b899. The resulting map was transformed into dry phytomass and finally upscaled to total C export by harvest. The observed spatial variability at field- and plot-scale could be attributed to small-scale soil heterogeneity in part. PMID:26907284

UAV-Based Estimation of Carbon Exports from Heterogeneous Soil Landscapes--A Case Study from the CarboZALF Experimental Area.

PubMed

Wehrhan, Marc; Rauneker, Philipp; Sommer, Michael

2016-02-19

The advantages of remote sensing using Unmanned Aerial Vehicles (UAVs) are a high spatial resolution of images, temporal flexibility and narrow-band spectral data from different wavelengths domains. This enables the detection of spatio-temporal dynamics of environmental variables, like plant-related carbon dynamics in agricultural landscapes. In this paper, we quantify spatial patterns of fresh phytomass and related carbon (C) export using imagery captured by a 12-band multispectral camera mounted on the fixed wing UAV Carolo P360. The study was performed in 2014 at the experimental area CarboZALF-D in NE Germany. From radiometrically corrected and calibrated images of lucerne (Medicago sativa), the performance of four commonly used vegetation indices (VIs) was tested using band combinations of six near-infrared bands. The highest correlation between ground-based measurements of fresh phytomass of lucerne and VIs was obtained for the Enhanced Vegetation Index (EVI) using near-infrared band b899. The resulting map was transformed into dry phytomass and finally upscaled to total C export by harvest. The observed spatial variability at field- and plot-scale could be attributed to small-scale soil heterogeneity in part.

A Skilful Marine Sclerochronological Network Based Reconstruction of North Atlantic Subpolar Gyre Dynamics

NASA Astrophysics Data System (ADS)

Reynolds, D.; Hall, I. R.; Slater, S. M.; Scourse, J. D.; Wanamaker, A. D.; Halloran, P. R.; Garry, F. K.

2017-12-01

Spatial network analyses of precisely dated, and annually resolved, tree-ring proxy records have facilitated robust reconstructions of past atmospheric climate variability and the associated mechanisms and forcings that drive it. In contrast, a lack of similarly dated marine archives has constrained the use of such techniques in the marine realm, despite the potential for developing a more robust understanding of the role basin scale ocean dynamics play in the global climate system. Here we show that a spatial network of marine molluscan sclerochronological oxygen isotope (δ18Oshell) series spanning the North Atlantic region provides a skilful reconstruction of basin scale North Atlantic sea surface temperatures (SSTs). Our analyses demonstrate that the composite marine series (referred to as δ18Oproxy_PC1) is significantly sensitive to inter-annual variability in North Atlantic SSTs (R=-0.61 P<0.01) and surface air temperatures (SATs; R=-0.67, P<0.01) over the 20th century. Subpolar gyre (SPG) SSTs dominates variability in the δ18Oproxy_PC1 series at sub-centennial frequencies (R=-0.51, P<0.01). Comparison of the δ18Oproxy_PC1 series against variability in the strength of the European Slope Current and maximum North Atlantic meridional overturning circulation derived from numeric climate models (CMIP5), indicates that variability in the SPG region, associated with the strength of the surface currents of the North Atlantic, are playing a significant role in shaping the multi-decadal scale SST variability over the industrial era. These analyses demonstrate that spatial networks developed from sclerochronological archives can provide powerful baseline archives of past ocean variability that can facilitate the development of a quantitative understanding for the role the oceans play in the global climate systems and constraining uncertainties in numeric climate models.

Spatial variability of soil salinity in coastal saline soil at different scales in the Yellow River Delta, China.

PubMed

Wang, Zhuoran; Zhao, Gengxing; Gao, Mingxiu; Chang, Chunyan

2017-02-01

The objectives of this study were to explore the spatial variability of soil salinity in coastal saline soil at macro, meso and micro scales in the Yellow River delta, China. Soil electrical conductivities (ECs) were measured at 0-15, 15-30, 30-45 and 45-60 cm soil depths at 49 sampling sites during November 9 to 11, 2013. Soil salinity was converted from soil ECs based on laboratory analyses. Our results indicated that at the macro scale, soil salinity was high with strong variability in each soil layer, and the content increased and the variability weakened with increasing soil depth. From east to west in the region, the farther away from the sea, the lower the soil salinity was. The degrees of soil salinization in three deeper soil layers are 1.14, 1.24 and 1.40 times higher than that in the surface soil. At the meso scale, the sequence of soil salinity in different topographies, soil texture and vegetation decreased, respectively, as follows: depression >flatland >hillock >batture; sandy loam >light loam >medium loam >heavy loam >clay; bare land >suaeda salsa >reed >cogongrass >cotton >paddy >winter wheat. At the micro scale, soil salinity changed with elevation in natural micro-topography and with anthropogenic activities in cultivated land. As the study area narrowed down to different scales, the spatial variability of soil salinity weakened gradually in cultivated land and salt wasteland except the bare land.

A neuroanatomical model of space-based and object-centered processing in spatial neglect.

PubMed

Pedrazzini, Elena; Schnider, Armin; Ptak, Radek

2017-11-01

Visual attention can be deployed in space-based or object-centered reference frames. Right-hemisphere damage may lead to distinct deficits of space- or object-based processing, and such dissociations are thought to underlie the heterogeneous nature of spatial neglect. Previous studies have suggested that object-centered processing deficits (such as in copying, reading or line bisection) result from damage to retro-rolandic regions while impaired spatial exploration reflects damage to more anterior regions. However, this evidence is based on small samples and heterogeneous tasks. Here, we tested a theoretical model of neglect that takes in account the space- and object-based processing and relates them to neuroanatomical predictors. One hundred and one right-hemisphere-damaged patients were examined with classic neuropsychological tests and structural brain imaging. Relations between neglect measures and damage to the temporal-parietal junction, intraparietal cortex, insula and middle frontal gyrus were examined with two structural equation models by assuming that object-centered processing (involved in line bisection and single-word reading) and space-based processing (involved in cancelation tasks) either represented a unique latent variable or two distinct variables. Of these two models the latter had better explanatory power. Damage to the intraparietal sulcus was a significant predictor of object-centered, but not space-based processing, while damage to the temporal-parietal junction predicted space-based, but not object-centered processing. Space-based processing and object-centered processing were strongly intercorrelated, indicating that they rely on similar, albeit partly dissociated processes. These findings indicate that object-centered and space-based deficits in neglect are partly independent and result from superior parietal and inferior parietal damage, respectively.

Electrochemical state and internal variables estimation using a reduced-order physics-based model of a lithium-ion cell and an extended Kalman filter

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

Stetzel, KD; Aldrich, LL; Trimboli, MS

2015-03-15

This paper addresses the problem of estimating the present value of electrochemical internal variables in a lithium-ion cell in real time, using readily available measurements of cell voltage, current, and temperature. The variables that can be estimated include any desired set of reaction flux and solid and electrolyte potentials and concentrations at any set of one-dimensional spatial locations, in addition to more standard quantities such as state of charge. The method uses an extended Kalman filter along with a one-dimensional physics-based reduced-order model of cell dynamics. Simulations show excellent and robust predictions having dependable error bounds for most internal variables.more » (C) 2014 Elsevier B.V. All rights reserved.« less

Delineation of marsh types and marsh-type change in coastal Louisiana for 2007 and 2013

USGS Publications Warehouse

Hartley, Stephen B.; Couvillion, Brady R.; Enwright, Nicholas M.

2017-05-30

The Bureau of Ocean Energy Management researchers often require detailed information regarding emergent marsh vegetation types (such as fresh, intermediate, brackish, and saline) for modeling habitat capacities and mitigation. In response, the U.S. Geological Survey in cooperation with the Bureau of Ocean Energy Management produced a detailed change classification of emergent marsh vegetation types in coastal Louisiana from 2007 and 2013. This study incorporates two existing vegetation surveys and independent variables such as Landsat Thematic Mapper multispectral satellite imagery, high-resolution airborne imagery from 2007 and 2013, bare-earth digital elevation models based on airborne light detection and ranging, alternative contemporary land-cover classifications, and other spatially explicit variables. An image classification based on image objects was created from 2007 and 2013 National Agriculture Imagery Program color-infrared aerial photography. The final products consisted of two 10-meter raster datasets. Each image object from the 2007 and 2013 spatial datasets was assigned a vegetation classification by using a simple majority filter. In addition to those spatial datasets, we also conducted a change analysis between the datasets to produce a 10-meter change raster product. This analysis identified how much change has taken place and where change has occurred. The spatial data products show dynamic areas where marsh loss is occurring or where marsh type is changing. This information can be used to assist and advance conservation efforts for priority natural resources.

Leveraging organismal biology to forecast the effects of climate change.

PubMed

Buckley, Lauren B; Cannistra, Anthony F; John, Aji

2018-04-26

Despite the pressing need for accurate forecasts of ecological and evolutionary responses to environmental change, commonly used modelling approaches exhibit mixed performance because they omit many important aspects of how organisms respond to spatially and temporally variable environments. Integrating models based on organismal phenotypes at the physiological, performance and fitness levels can improve model performance. We summarize current limitations of environmental data and models and discuss potential remedies. The paper reviews emerging techniques for sensing environments at fine spatial and temporal scales, accounting for environmental extremes, and capturing how organisms experience the environment. Intertidal mussel data illustrate biologically important aspects of environmental variability. We then discuss key challenges in translating environmental conditions into organismal performance including accounting for the varied timescales of physiological processes, for responses to environmental fluctuations including the onset of stress and other thresholds, and for how environmental sensitivities vary across lifecycles. We call for the creation of phenotypic databases to parameterize forecasting models and advocate for improved sharing of model code and data for model testing. We conclude with challenges in organismal biology that must be solved to improve forecasts over the next decade.acclimation, biophysical models, ecological forecasting, extremes, microclimate, spatial and temporal variability.

A method for estimating spatially variable seepage and hydrualic conductivity in channels with very mild slopes

USGS Publications Warehouse

Shanafield, Margaret; Niswonger, Richard G.; Prudic, David E.; Pohll, Greg; Susfalk, Richard; Panday, Sorab

2014-01-01

Infiltration along ephemeral channels plays an important role in groundwater recharge in arid regions. A model is presented for estimating spatial variability of seepage due to streambed heterogeneity along channels based on measurements of streamflow-front velocities in initially dry channels. The diffusion-wave approximation to the Saint-Venant equations, coupled with Philip's equation for infiltration, is connected to the groundwater model MODFLOW and is calibrated by adjusting the saturated hydraulic conductivity of the channel bed. The model is applied to portions of two large water delivery canals, which serve as proxies for natural ephemeral streams. Estimated seepage rates compare well with previously published values. Possible sources of error stem from uncertainty in Manning's roughness coefficients, soil hydraulic properties and channel geometry. Model performance would be most improved through more frequent longitudinal estimates of channel geometry and thalweg elevation, and with measurements of stream stage over time to constrain wave timing and shape. This model is a potentially valuable tool for estimating spatial variability in longitudinal seepage along intermittent and ephemeral channels over a wide range of bed slopes and the influence of seepage rates on groundwater levels.

Spatial and temporal variability of microgeographic genetic structure in white-tailed deer

USGS Publications Warehouse

Scribner, Kim T.; Smith, Michael H.; Chesser, Ronald K.

1997-01-01

Techniques are described that define contiguous genetic subpopulations of white-tailed deer (Odocoileus virginianus) based on the spatial dispersion of 4,749 individuals that possessed discrete character values (alleles or genotypes) during each of 6 years (1974-1979). White-tailed deer were not uniformly distributed in space, but exhibited considerable spatial genetic structuring. Significant non-random clusters of individuals were documented during each year based on specific alleles and genotypes at the Sdh locus. Considerable temporal variation was observed in the position and genetic composition of specific clusters, which reflected changes in allele frequency in small geographic areas. The position of clusters did not consistently correspond with traditional management boundaries based on major discontinuities in habitat (swamp versus upland) and hunt compartments that were defined by roads and streams. Spatio-temporal stability of observed genetic contiguous clusters was interpreted relative to method and intensity of harvest, movements, and breeding ecology.

Modeling Above-Ground Biomass Across Multiple Circum-Arctic Tundra Sites Using High Spatial Resolution Remote Sensing

NASA Astrophysics Data System (ADS)

Räsänen, Aleksi; Juutinen, Sari; Aurela, Mika; Virtanen, Tarmo

2017-04-01

Biomass is one of the central bio-geophysical variables in Earth observation for tracking plant productivity, and flow of carbon, nutrients, and water. Most of the satellite based biomass mapping exercises in Arctic environments have been performed by using rather coarse spatial resolution data, e.g. Landsat and AVHRR which have spatial resolutions of 30 m and >1 km, respectively. While the coarse resolution images have high temporal resolution, they are incapable of capturing the fragmented nature of tundra environment and fine-scale changes in vegetation and carbon exchange patterns. Very high spatial resolution (VHSR, spatial resolution 0.5-2 m) satellite images have the potential to detect environmental variables with an ecologically sound spatial resolution. The usage of VHSR images has, nevertheless, been modest so far in biomass modeling in the Arctic. Our objectives were to use VHSR for predicting above ground biomass in tundra landscapes, evaluate whether a common predictive model can be applied across circum-Arctic tundra and peatland sites having different types of vegetation, and produce knowledge on distribution of plant functional types (PFT) in these sites. Such model development is dependent on ground-based surveys of vegetation with the same spatial resolution and extent with the VHSR images. In this study, we conducted ground-based surveys of vegetation composition and biomass in four different arctic tundra or peatland areas located in Russia, Canada, and Finland. First, we sorted species into PFTs and developed PFT-specific models to predict biomass on the basis of non-destructive measurements (cover, height). Second, we predicted overall biomass on landscape scale by combinations of single bands and vegetation indices of very high resolution satellite images (QuickBird or WorldView-2 images of the eight sites). We compared area-specific empirical regression models and common models that were applied across all sites. We found that NDVI was usually the highest scoring spectral indices in explaining biomass distribution with good explanatory power. Furthermore, models which had more than one explanatory variable had higher explanatory power than models with a single index. The dissimilarity between common and site-specific model estimates was, however, high and data indicates that variation in vegetation properties and its impact on spectral reflectance needs to be acknowledged. Our work produced knowledge on above-ground biomass distribution and contribution of PFTs across circum-Arctic low-growth landscapes and will contribute to developing space-borne vegetation monitoring schemes utilizing VHSR satellite images.

Monte Carlo-based assessment of the trade-off between spatial resolution, field-of-view and scattered radiation in the variable resolution X-ray CT scanner.

PubMed

Arabi, Hossein; Kamali Asl, Ali Reza; Ay, Mohammad Reza; Zaidi, Habib

2015-07-01

The purpose of this work is to evaluate the impact of optimization of magnification on performance parameters of the variable resolution X-ray (VRX) CT scanner. A realistic model based on an actual VRX CT scanner was implemented in the GATE Monte Carlo simulation platform. To evaluate the influence of system magnification, spatial resolution, field-of-view (FOV) and scatter-to-primary ratio of the scanner were estimated for both fixed and optimum object magnification at each detector rotation angle. Comparison and inference between these performance parameters were performed angle by angle to determine appropriate object position at each opening half angle. Optimization of magnification resulted in a trade-off between spatial resolution and FOV of the scanner at opening half angles of 90°-12°, where the spatial resolution increased up to 50% and the scatter-to-primary ratio decreased from 4.8% to 3.8% at a detector angle of about 90° for the same FOV and X-ray energy spectrum. The disadvantage of magnification optimization at these angles is the significant reduction of the FOV (up to 50%). Moreover, magnification optimization was definitely beneficial for opening half angles below 12° improving the spatial resolution from 7.5 cy/mm to 20 cy/mm. Meanwhile, the FOV increased by more than 50% at these angles. It can be concluded that optimization of magnification is essential for opening half angles below 12°. For opening half angles between 90° and 12°, the VRX CT scanner magnification should be set according to the desired spatial resolution and FOV. Copyright © 2015 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

Comparison of spatial association approaches for landscape mapping of soil organic carbon stocks

NASA Astrophysics Data System (ADS)

Miller, B. A.; Koszinski, S.; Wehrhan, M.; Sommer, M.

2015-03-01

The distribution of soil organic carbon (SOC) can be variable at small analysis scales, but consideration of its role in regional and global issues demands the mapping of large extents. There are many different strategies for mapping SOC, among which is to model the variables needed to calculate the SOC stock indirectly or to model the SOC stock directly. The purpose of this research is to compare direct and indirect approaches to mapping SOC stocks from rule-based, multiple linear regression models applied at the landscape scale via spatial association. The final products for both strategies are high-resolution maps of SOC stocks (kg m-2), covering an area of 122 km2, with accompanying maps of estimated error. For the direct modelling approach, the estimated error map was based on the internal error estimations from the model rules. For the indirect approach, the estimated error map was produced by spatially combining the error estimates of component models via standard error propagation equations. We compared these two strategies for mapping SOC stocks on the basis of the qualities of the resulting maps as well as the magnitude and distribution of the estimated error. The direct approach produced a map with less spatial variation than the map produced by the indirect approach. The increased spatial variation represented by the indirect approach improved R2 values for the topsoil and subsoil stocks. Although the indirect approach had a lower mean estimated error for the topsoil stock, the mean estimated error for the total SOC stock (topsoil + subsoil) was lower for the direct approach. For these reasons, we recommend the direct approach to modelling SOC stocks be considered a more conservative estimate of the SOC stocks' spatial distribution.

Comparison of spatial association approaches for landscape mapping of soil organic carbon stocks

NASA Astrophysics Data System (ADS)

Miller, B. A.; Koszinski, S.; Wehrhan, M.; Sommer, M.

2014-11-01

The distribution of soil organic carbon (SOC) can be variable at small analysis scales, but consideration of its role in regional and global issues demands the mapping of large extents. There are many different strategies for mapping SOC, among which are to model the variables needed to calculate the SOC stock indirectly or to model the SOC stock directly. The purpose of this research is to compare direct and indirect approaches to mapping SOC stocks from rule-based, multiple linear regression models applied at the landscape scale via spatial association. The final products for both strategies are high-resolution maps of SOC stocks (kg m-2), covering an area of 122 km2, with accompanying maps of estimated error. For the direct modelling approach, the estimated error map was based on the internal error estimations from the model rules. For the indirect approach, the estimated error map was produced by spatially combining the error estimates of component models via standard error propagation equations. We compared these two strategies for mapping SOC stocks on the basis of the qualities of the resulting maps as well as the magnitude and distribution of the estimated error. The direct approach produced a map with less spatial variation than the map produced by the indirect approach. The increased spatial variation represented by the indirect approach improved R2 values for the topsoil and subsoil stocks. Although the indirect approach had a lower mean estimated error for the topsoil stock, the mean estimated error for the total SOC stock (topsoil + subsoil) was lower for the direct approach. For these reasons, we recommend the direct approach to modelling SOC stocks be considered a more conservative estimate of the SOC stocks' spatial distribution.

Multivariate spatial analysis of a heavy rain event in a densely populated delta city

NASA Astrophysics Data System (ADS)

Gaitan, Santiago; ten Veldhuis, Marie-claire; Bruni, Guenda; van de Giesen, Nick

2014-05-01

Delta cities account for half of the world's population and host key infrastructure and services for the global economic growth. Due to the characteristic geography of delta areas, these cities face high vulnerability to extreme weather and pluvial flooding risks, that are expected to increase as climate change drives heavier rain events. Besides, delta cities are subjected to fast urban densification processes that progressively make them more vulnerable to pluvial flooding. Delta cities need to be adapted to better cope with this threat. The mechanism leading to damage after heavy rains is not completely understood. For instance, current research has shown that rain intensities and volumes can only partially explain the occurrence and localization of rain-related insurance claims (Spekkers et al., 2013). The goal of this paper is to provide further insights into spatial characteristics of the urban environment that can significantly be linked to pluvial-related flooding impacts. To that end, a study-case has been selected: on October 12 to 14 2013, a heavy rain event triggered pluvial floods in Rotterdam, a densely populated city which is undergoing multiple climate adaptation efforts and is located in the Meuse river Delta. While the average yearly precipitation in this city is around 800 mm, local rain gauge measurements ranged from aprox. 60 to 130 mm just during these three days. More than 600 citizens' telephonic complaints reported impacts related to rainfall. The registry of those complaints, which comprises around 300 calls made to the municipality and another 300 to the fire brigade, was made available for research. Other accessible information about this city includes a series of rainfall measurements with up to 1 min time-step at 7 different locations around the city, ground-based radar rainfall data (1 Km^2 spatial resolution and 5 min time-step), a digital elevation model (50 cm of horizontal resolution), a model of overland-flow paths, cadastral maps describing individual location and types of buildings, and maps on categorical socioeconomic statistics (1 Ha of spatial resolution). On the basis of the quality and availability of the mentioned information, spatial and temporal units of analysis will be discussed and defined. Aggregation of single occurrences for binary variables will be performed, while simple interpolations or averages will be used in case of continuous or categorical data. To determine spatial clustering within each variable, Nearest Neighbor Distance and Spatial Autocorrelation tests will be carried out. When appropriate, the Getis-Ord Gi* test will be used to identify single variable clusters. Finally, with the purpose of inferring possible associations between the available spatially distributed variables, a Mantel test will be applied to variables with a probed non-random spatial pattern. The results of this paper will allow to determine if the environmental characteristics described by the available data can provide additional explanation of the variability of rain-related damage in a delta city which is willing to become climate-proof.

The calibration analysis of soil infiltration formula in farmland scale

NASA Astrophysics Data System (ADS)

Qian, Tao; Han, Na Na; Chang, Shuan Ling

2018-06-01

Soil infiltration characteristic is an important basis of farmland scale parameter estimation. Based on 12 groups of double-loop infiltration tests conducted in the test field of tianjin agricultural university west campus. Based on the calibration theory and the combination of statistics, the calibration analysis of phillips formula was carried out and the spatial variation characteristics of the calibration factor were analyzed. Results show that in study area based on the soil stability infiltration rate A calculate calibration factor αA calibration effect is best, that is suitable for the area formula of calibration infiltration and αA variation coefficient is 0.3234, with A certain degree of spatial variability.

The spatial pattern of suicide in the US in relation to deprivation, fragmentation and rurality.

PubMed

Congdon, Peter

2011-01-01

Analysis of geographical patterns of suicide and psychiatric morbidity has demonstrated the impact of latent ecological variables (such as deprivation, rurality). Such latent variables may be derived by conventional multivariate techniques from sets of observed indices (for example, by principal components), by composite variable methods or by methods which explicitly consider the spatial framework of areas and, in particular, the spatial clustering of latent risks and outcomes. This article considers a latent random variable approach to explaining geographical contrasts in suicide in the US; and it develops a spatial structural equation model incorporating deprivation, social fragmentation and rurality. The approach allows for such latent spatial constructs to be correlated both within and between areas. Potential effects of area ethnic mix are also included. The model is applied to male and female suicide deaths over 2002–06 in 3142 US counties.

Satellite-derived potential evapotranspiration for distributed hydrologic runoff modeling

NASA Astrophysics Data System (ADS)

Spies, R. R.; Franz, K. J.; Bowman, A.; Hogue, T. S.; Kim, J.

2012-12-01

Distributed models have the ability of incorporating spatially variable data, especially high resolution forcing inputs such as precipitation, temperature and evapotranspiration in hydrologic modeling. Use of distributed hydrologic models for operational streamflow prediction has been partially hindered by a lack of readily available, spatially explicit input observations. Potential evapotranspiration (PET), for example, is currently accounted for through PET input grids that are based on monthly climatological values. The goal of this study is to assess the use of satellite-based PET estimates that represent the temporal and spatial variability, as input to the National Weather Service (NWS) Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM). Daily PET grids are generated for six watersheds in the upper Mississippi River basin using a method that applies only MODIS satellite-based observations and the Priestly Taylor formula (MODIS-PET). The use of MODIS-PET grids will be tested against the use of the current climatological PET grids for simulating basin discharge. Gridded surface temperature forcing data are derived by applying the inverse distance weighting spatial prediction method to point-based station observations from the Automated Surface Observing System (ASOS) and Automated Weather Observing System (AWOS). Precipitation data are obtained from the Climate Prediction Center's (CPC) Climatology-Calibrated Precipitation Analysis (CCPA). A-priori gridded parameters for the Sacramento Soil Moisture Accounting Model (SAC-SMA), Snow-17 model, and routing model are initially obtained from the Office of Hydrologic Development and further calibrated using an automated approach. The potential of the MODIS-PET to be used in an operational distributed modeling system will be assessed with the long-term goal of promoting research to operations transfers and advancing the science of hydrologic forecasting.

A probabilistic approach to quantifying spatial patterns of flow regimes and network-scale connectivity

NASA Astrophysics Data System (ADS)

Garbin, Silvia; Alessi Celegon, Elisa; Fanton, Pietro; Botter, Gianluca

2017-04-01

The temporal variability of river flow regime is a key feature structuring and controlling fluvial ecological communities and ecosystem processes. In particular, streamflow variability induced by climate/landscape heterogeneities or other anthropogenic factors significantly affects the connectivity between streams with notable implication for river fragmentation. Hydrologic connectivity is a fundamental property that guarantees species persistence and ecosystem integrity in riverine systems. In riverine landscapes, most ecological transitions are flow-dependent and the structure of flow regimes may affect ecological functions of endemic biota (i.e., fish spawning or grazing of invertebrate species). Therefore, minimum flow thresholds must be guaranteed to support specific ecosystem services, like fish migration, aquatic biodiversity and habitat suitability. In this contribution, we present a probabilistic approach aiming at a spatially-explicit, quantitative assessment of hydrologic connectivity at the network-scale as derived from river flow variability. Dynamics of daily streamflows are estimated based on catchment-scale climatic and morphological features, integrating a stochastic, physically based approach that accounts for the stochasticity of rainfall with a water balance model and a geomorphic recession flow model. The non-exceedance probability of ecologically meaningful flow thresholds is used to evaluate the fragmentation of individual stream reaches, and the ensuing network-scale connectivity metrics. A multi-dimensional Poisson Process for the stochastic generation of rainfall is used to evaluate the impact of climate signature on reach-scale and catchment-scale connectivity. The analysis shows that streamflow patterns and network-scale connectivity are influenced by the topology of the river network and the spatial variability of climatic properties (rainfall, evapotranspiration). The framework offers a robust basis for the prediction of the impact of land-use/land-cover changes and river regulation on network-scale connectivity.

Geomorphology Drives Amphibian Beta Diversity in Atlantic Forest Lowlands of Southeastern Brazil

PubMed Central

Luiz, Amom Mendes; Leão-Pires, Thiago Augusto; Sawaya, Ricardo J.

2016-01-01

Beta diversity patterns are the outcome of multiple processes operating at different scales. Amphibian assemblages seem to be affected by contemporary climate and dispersal-based processes. However, historical processes involved in present patterns of beta diversity remain poorly understood. We assess and disentangle geomorphological, climatic and spatial drivers of amphibian beta diversity in coastal lowlands of the Atlantic Forest, southeastern Brazil. We tested the hypothesis that geomorphological factors are more important in structuring anuran beta diversity than climatic and spatial factors. We obtained species composition via field survey (N = 766 individuals), museum specimens (N = 9,730) and literature records (N = 4,763). Sampling area was divided in four spatially explicit geomorphological units, representing historical predictors. Climatic descriptors were represented by the first two axis of a Principal Component Analysis. Spatial predictors in different spatial scales were described by Moran Eigenvector Maps. Redundancy Analysis was implemented to partition the explained variation of species composition by geomorphological, climatic and spatial predictors. Moreover, spatial autocorrelation analyses were used to test neutral theory predictions. Beta diversity was spatially structured in broader scales. Shared fraction between climatic and geomorphological variables was an important predictor of species composition (13%), as well as broad scale spatial predictors (13%). However, geomorphological variables alone were the most important predictor of beta diversity (42%). Historical factors related to geomorphology must have played a crucial role in structuring amphibian beta diversity. The complex relationships between geomorphological history and climatic gradients generated by the Serra do Mar Precambrian basements were also important. We highlight the importance of combining spatially explicit historical and contemporary predictors for understanding and disentangling major drivers of beta diversity patterns. PMID:27171522

Geomorphology Drives Amphibian Beta Diversity in Atlantic Forest Lowlands of Southeastern Brazil.

PubMed

Luiz, Amom Mendes; Leão-Pires, Thiago Augusto; Sawaya, Ricardo J

2016-01-01

Beta diversity patterns are the outcome of multiple processes operating at different scales. Amphibian assemblages seem to be affected by contemporary climate and dispersal-based processes. However, historical processes involved in present patterns of beta diversity remain poorly understood. We assess and disentangle geomorphological, climatic and spatial drivers of amphibian beta diversity in coastal lowlands of the Atlantic Forest, southeastern Brazil. We tested the hypothesis that geomorphological factors are more important in structuring anuran beta diversity than climatic and spatial factors. We obtained species composition via field survey (N = 766 individuals), museum specimens (N = 9,730) and literature records (N = 4,763). Sampling area was divided in four spatially explicit geomorphological units, representing historical predictors. Climatic descriptors were represented by the first two axis of a Principal Component Analysis. Spatial predictors in different spatial scales were described by Moran Eigenvector Maps. Redundancy Analysis was implemented to partition the explained variation of species composition by geomorphological, climatic and spatial predictors. Moreover, spatial autocorrelation analyses were used to test neutral theory predictions. Beta diversity was spatially structured in broader scales. Shared fraction between climatic and geomorphological variables was an important predictor of species composition (13%), as well as broad scale spatial predictors (13%). However, geomorphological variables alone were the most important predictor of beta diversity (42%). Historical factors related to geomorphology must have played a crucial role in structuring amphibian beta diversity. The complex relationships between geomorphological history and climatic gradients generated by the Serra do Mar Precambrian basements were also important. We highlight the importance of combining spatially explicit historical and contemporary predictors for understanding and disentangling major drivers of beta diversity patterns.

An Updating System for the Gridded Population Database of China Based on Remote Sensing, GIS and Spatial Database Technologies.

PubMed

Yang, Xiaohuan; Huang, Yaohuan; Dong, Pinliang; Jiang, Dong; Liu, Honghui

2009-01-01

The spatial distribution of population is closely related to land use and land cover (LULC) patterns on both regional and global scales. Population can be redistributed onto geo-referenced square grids according to this relation. In the past decades, various approaches to monitoring LULC using remote sensing and Geographic Information Systems (GIS) have been developed, which makes it possible for efficient updating of geo-referenced population data. A Spatial Population Updating System (SPUS) is developed for updating the gridded population database of China based on remote sensing, GIS and spatial database technologies, with a spatial resolution of 1 km by 1 km. The SPUS can process standard Moderate Resolution Imaging Spectroradiometer (MODIS L1B) data integrated with a Pattern Decomposition Method (PDM) and an LULC-Conversion Model to obtain patterns of land use and land cover, and provide input parameters for a Population Spatialization Model (PSM). The PSM embedded in SPUS is used for generating 1 km by 1 km gridded population data in each population distribution region based on natural and socio-economic variables. Validation results from finer township-level census data of Yishui County suggest that the gridded population database produced by the SPUS is reliable.

Geostatistical interpolation model selection based on ArcGIS and spatio-temporal variability analysis of groundwater level in piedmont plains, northwest China.

PubMed

Xiao, Yong; Gu, Xiaomin; Yin, Shiyang; Shao, Jingli; Cui, Yali; Zhang, Qiulan; Niu, Yong

2016-01-01

Based on the geo-statistical theory and ArcGIS geo-statistical module, datas of 30 groundwater level observation wells were used to estimate the decline of groundwater level in Beijing piedmont. Seven different interpolation methods (inverse distance weighted interpolation, global polynomial interpolation, local polynomial interpolation, tension spline interpolation, ordinary Kriging interpolation, simple Kriging interpolation and universal Kriging interpolation) were used for interpolating groundwater level between 2001 and 2013. Cross-validation, absolute error and coefficient of determination (R(2)) was applied to evaluate the accuracy of different methods. The result shows that simple Kriging method gave the best fit. The analysis of spatial and temporal variability suggest that the nugget effects from 2001 to 2013 were increasing, which means the spatial correlation weakened gradually under the influence of human activities. The spatial variability in the middle areas of the alluvial-proluvial fan is relatively higher than area in top and bottom. Since the changes of the land use, groundwater level also has a temporal variation, the average decline rate of groundwater level between 2007 and 2013 increases compared with 2001-2006. Urban development and population growth cause over-exploitation of residential and industrial areas. The decline rate of the groundwater level in residential, industrial and river areas is relatively high, while the decreasing of farmland area and development of water-saving irrigation reduce the quantity of water using by agriculture and decline rate of groundwater level in agricultural area is not significant.

Environmental condition assessment of US military installations using GIS based spatial multi-criteria decision analysis.

PubMed

Singer, Steve; Wang, Guangxing; Howard, Heidi; Anderson, Alan

2012-08-01

Environment functions in various aspects including soil and water conservation, biodiversity and habitats, and landscape aesthetics. Comprehensive assessment of environmental condition is thus a great challenge. The issues include how to assess individual environmental components such as landscape aesthetics and integrate them into an indicator that can comprehensively quantify environmental condition. In this study, a geographic information systems based spatial multi-criteria decision analysis was used to integrate environmental variables and create the indicator. This approach was applied to Fort Riley Military installation in which land condition and its dynamics due to military training activities were assessed. The indicator was derived by integrating soil erosion, water quality, landscape fragmentation, landscape aesthetics, and noise based on the weights from the experts by assessing and ranking the environmental variables in terms of their importance. The results showed that landscape level indicator well quantified the overall environmental condition and its dynamics, while the indicator at level of patch that is defined as a homogeneous area that is different from its surroundings detailed the spatiotemporal variability of environmental condition. The environmental condition was mostly determined by soil erosion, then landscape fragmentation, water quality, landscape aesthetics, and noise. Overall, environmental condition at both landscape and patch levels greatly varied depending on the degree of ground and canopy disturbance and their spatial patterns due to military training activities and being related to slope. It was also determined the environment itself could be recovered quickly once military training was halt or reduced. Thus, this study provided an effective tool for the army land managers to monitor environmental dynamics and plan military training activities. Its limitation lies at that the obtained values of the indicator vary and are subjective to the experts' knowledge and experience. Thus, further advancing this approach is needed by developing a scientific method to derive the weights of environmental variables.

Estimating Prediction Uncertainty from Geographical Information System Raster Processing: A User's Manual for the Raster Error Propagation Tool (REPTool)

USGS Publications Warehouse

Gurdak, Jason J.; Qi, Sharon L.; Geisler, Michael L.

2009-01-01

The U.S. Geological Survey Raster Error Propagation Tool (REPTool) is a custom tool for use with the Environmental System Research Institute (ESRI) ArcGIS Desktop application to estimate error propagation and prediction uncertainty in raster processing operations and geospatial modeling. REPTool is designed to introduce concepts of error and uncertainty in geospatial data and modeling and provide users of ArcGIS Desktop a geoprocessing tool and methodology to consider how error affects geospatial model output. Similar to other geoprocessing tools available in ArcGIS Desktop, REPTool can be run from a dialog window, from the ArcMap command line, or from a Python script. REPTool consists of public-domain, Python-based packages that implement Latin Hypercube Sampling within a probabilistic framework to track error propagation in geospatial models and quantitatively estimate the uncertainty of the model output. Users may specify error for each input raster or model coefficient represented in the geospatial model. The error for the input rasters may be specified as either spatially invariant or spatially variable across the spatial domain. Users may specify model output as a distribution of uncertainty for each raster cell. REPTool uses the Relative Variance Contribution method to quantify the relative error contribution from the two primary components in the geospatial model - errors in the model input data and coefficients of the model variables. REPTool is appropriate for many types of geospatial processing operations, modeling applications, and related research questions, including applications that consider spatially invariant or spatially variable error in geospatial data.

Spectral and spatial variability of undisturbed and disturbed grass under different view and illumination directions

NASA Astrophysics Data System (ADS)

Borel-Donohue, Christoph C.; Shivers, Sarah Wells; Conover, Damon

2017-05-01

It is well known that disturbed grass covered surfaces show variability with view and illumination conditions. A good example is a grass field in a soccer stadium that shows stripes indicating in which direction the grass was mowed. These spatial variations are due to a complex interplay of spectral characteristics of grass blades, density, their length and orientations. Viewing a grass surface from nadir or near horizontal directions results in observing different components. Views from a vertical direction show more variations due to reflections from the randomly oriented grass blades and their shadows. Views from near horizontal show a mixture of reflected and transmitted light from grass blades. An experiment was performed on a mowed grass surface which had paths of simulated heavy foot traffic laid down in different directions. High spatial resolution hyperspectral data cubes were taken by an imaging spectrometer covering the visible through near infrared over a period of time covering several hours. Ground truth grass reflectance spectra with a hand held spectrometer were obtained of undisturbed and disturbed areas. Close range images were taken of selected areas with a hand held camera which were then used to reconstruct the 3D geometry of the grass using structure-from-motion algorithms. Computer graphics rendering using raytracing of reconstructed and procedurally created grass surfaces were used to compute BRDF models. In this paper, we discuss differences between observed and simulated spectral and spatial variability. Based on the measurements and/or simulations, we derive simple spectral index methods to detect spatial disturbances and apply scattering models.

Variability of the raindrop size distribution at small spatial scales

NASA Astrophysics Data System (ADS)

Berne, A.; Jaffrain, J.

2010-12-01

Because of the interactions between atmospheric turbulence and cloud microphysics, the raindrop size distribution (DSD) is strongly variable in space and time. The spatial variability of the DSD at small spatial scales (below a few km) is not well documented and not well understood, mainly because of a lack of adequate measurements at the appropriate resolutions. A network of 16 disdrometers (Parsivels) has been designed and set up over EPFL campus in Lausanne, Switzerland. This network covers a typical operational weather radar pixel of 1x1 km2. The question of the significance of the variability of the DSD at such small scales is relevant for radar remote sensing of rainfall because the DSD is often assumed to be uniform within a radar sample volume and because the Z-R relationships used to convert the measured radar reflectivity Z into rain rate R are usually derived from point measurements. Thanks to the number of disdrometers, it was possible to quantify the spatial variability of the DSD at the radar pixel scale and to show that it can be significant. In this contribution, we show that the variability of the total drop concentration, of the median volume diameter and of the rain rate are significant, taking into account the sampling uncertainty associated with disdrometer measurements. The influence of this variability on the Z-R relationship can be non-negligible. Finally, the spatial structure of the DSD is quantified using a geostatistical tool, the variogram, and indicates high spatial correlation within a radar pixel.

Lutzomyia longipalpis Presence and Abundance Distribution at Different Micro-spatial Scales in an Urban Scenario

PubMed Central

Santini, María Soledad; Utgés, María Eugenia; Berrozpe, Pablo; Manteca Acosta, Mariana; Casas, Natalia; Heuer, Paola; Salomón, O. Daniel

2015-01-01

The principal objective of this study was to assess a modeling approach to Lu. longipalpis distribution in an urban scenario, discriminating micro-scale landscape variables at microhabitat and macrohabitat scales and the presence from the abundance of the vector. For this objective, we studied vectors and domestic reservoirs and evaluated different environmental variables simultaneously, so we constructed a set of 13 models to account for micro-habitats, macro-habitats and mixed-habitats. We captured a total of 853 sandflies, of which 98.35% were Lu. longipalpis. We sampled a total of 197 dogs; 177 of which were associated with households where insects were sampled. Positive rK39 dogs represented 16.75% of the total, of which 47% were asymptomatic. Distance to the border of the city and high to medium density vegetation cover ended to be the explanatory variables, all positive, for the presence of sandflies in the city. All variables in the abundance model ended to be explanatory, trees around the trap, distance to the stream and its quadratic, being the last one the only one with negative coefficient indicating that the maximum abundance was associated with medium values of distance to the stream. The spatial distribution of dogs infected with L. infantum showed a heterogeneous pattern throughout the city; however, we could not confirm an association of the distribution with the variables assessed. In relation to Lu. longipalpis distribution, the strategy to discriminate the micro-spatial scales at which the environmental variables were recorded allowed us to associate presence with macrohabitat variables and abundance with microhabitat and macrohabitat variables. Based on the variables associated with Lu. longipalpis, the model will be validated in other cities and environmental surveillance, and control interventions will be proposed and evaluated in the microscale level and integrated with socio-cultural approaches and programmatic and village (mesoscale) strategies. PMID:26274318

Lutzomyia longipalpis Presence and Abundance Distribution at Different Micro-spatial Scales in an Urban Scenario.

PubMed

Santini, María Soledad; Utgés, María Eugenia; Berrozpe, Pablo; Manteca Acosta, Mariana; Casas, Natalia; Heuer, Paola; Salomón, O Daniel

2015-01-01

The principal objective of this study was to assess a modeling approach to Lu. longipalpis distribution in an urban scenario, discriminating micro-scale landscape variables at microhabitat and macrohabitat scales and the presence from the abundance of the vector. For this objective, we studied vectors and domestic reservoirs and evaluated different environmental variables simultaneously, so we constructed a set of 13 models to account for micro-habitats, macro-habitats and mixed-habitats. We captured a total of 853 sandflies, of which 98.35% were Lu. longipalpis. We sampled a total of 197 dogs; 177 of which were associated with households where insects were sampled. Positive rK39 dogs represented 16.75% of the total, of which 47% were asymptomatic. Distance to the border of the city and high to medium density vegetation cover ended to be the explanatory variables, all positive, for the presence of sandflies in the city. All variables in the abundance model ended to be explanatory, trees around the trap, distance to the stream and its quadratic, being the last one the only one with negative coefficient indicating that the maximum abundance was associated with medium values of distance to the stream. The spatial distribution of dogs infected with L. infantum showed a heterogeneous pattern throughout the city; however, we could not confirm an association of the distribution with the variables assessed. In relation to Lu. longipalpis distribution, the strategy to discriminate the micro-spatial scales at which the environmental variables were recorded allowed us to associate presence with macrohabitat variables and abundance with microhabitat and macrohabitat variables. Based on the variables associated with Lu. longipalpis, the model will be validated in other cities and environmental surveillance, and control interventions will be proposed and evaluated in the microscale level and integrated with socio-cultural approaches and programmatic and village (mesoscale) strategies.

A new approach in space-time analysis of multivariate hydrological data: Application to Brazil's Nordeste region rainfall

NASA Astrophysics Data System (ADS)

Sicard, Emeline; Sabatier, Robert; Niel, HéLèNe; Cadier, Eric

2002-12-01

The objective of this paper is to implement an original method for spatial and multivariate data, combining a method of three-way array analysis (STATIS) with geostatistical tools. The variables of interest are the monthly amounts of rainfall in the Nordeste region of Brazil, recorded from 1937 to 1975. The principle of the technique is the calculation of a linear combination of the initial variables, containing a large part of the initial variability and taking into account the spatial dependencies. It is a promising method that is able to analyze triple variability: spatial, seasonal, and interannual. In our case, the first component obtained discriminates a group of rain gauges, corresponding approximately to the Agreste, from all the others. The monthly variables of July and August strongly influence this separation. Furthermore, an annual study brings out the stability of the spatial structure of components calculated for each year.

Regional risk assessment for contaminated sites part 2: ranking of potentially contaminated sites.

PubMed

Pizzol, Lisa; Critto, Andrea; Agostini, Paola; Marcomini, Antonio

2011-11-01

Environmental risks are traditionally assessed and presented in non spatial ways although the heterogeneity of the contaminants spatial distributions, the spatial positions and relations between receptors and stressors, as well as the spatial distribution of the variables involved in the risk assessment, strongly influence exposure estimations and hence risks. Taking into account spatial variability is increasingly being recognized as a further and essential step in sound exposure and risk assessment. To address this issue an innovative methodology which integrates spatial analysis and a relative risk approach was developed. The purpose of this methodology is to prioritize sites at regional scale where a preliminary site investigation may be required. The methodology aimed at supporting the inventory of contaminated sites was implemented within the spatial decision support sYstem for Regional rIsk Assessment of DEgraded land, SYRIADE, and was applied to the case-study of the Upper Silesia region (Poland). The developed methodology and tool are both flexible and easy to adapt to different regional contexts, allowing the user to introduce the regional relevant parameters identified on the basis of user expertise and regional data availability. Moreover, the used GIS functionalities, integrated with mathematical approaches, allow to take into consideration, all at once, the multiplicity of sources and impacted receptors within the region of concern, to assess the risks posed by all contaminated sites in the region and, finally, to provide a risk-based ranking of the potentially contaminated sites. Copyright © 2011. Published by Elsevier Ltd.

Ecosystem variability along the estuarine salinity gradient: Examples from long-term study of San Francisco Bay

USGS Publications Warehouse

Cloern, James E.; Jassby, Alan D.; Schraga, Tara; Kress, Erica S.; Martin, Charles A.

2017-01-01

The salinity gradient of estuaries plays a unique and fundamental role in structuring spatial patterns of physical properties, biota, and biogeochemical processes. We use variability along the salinity gradient of San Francisco Bay to illustrate some lessons about the diversity of spatial structures in estuaries and their variability over time. Spatial patterns of dissolved constituents (e.g., silicate) can be linear or nonlinear, depending on the relative importance of river-ocean mixing and internal sinks (diatom uptake). Particles have different spatial patterns because they accumulate in estuarine turbidity maxima formed by the combination of sinking and estuarine circulation. Some constituents have weak or no mean spatial structure along the salinity gradient, reflecting spatially distributed sources along the estuary (nitrate) or atmospheric exchanges that buffer spatial variability of ecosystem metabolism (dissolved oxygen). The density difference between freshwater and seawater establishes stratification in estuaries stronger than the thermal stratification of lakes and oceans. Stratification is strongest around the center of the salinity gradient and when river discharge is high. Spatial distributions of motile organisms are shaped by species-specific adaptations to different salinity ranges (shrimp) and by behavioral responses to environmental variability (northern anchovy). Estuarine spatial patterns change over time scales of events (intrusions of upwelled ocean water), seasons (river inflow), years (annual weather anomalies), and between eras separated by ecosystem disturbances (a species introduction). Each of these lessons is a piece in the puzzle of how estuarine ecosystems are structured and how they differ from the river and ocean ecosystems they bridge.

Spatial interpolation schemes of daily precipitation for hydrologic modeling

USGS Publications Warehouse

Hwang, Y.; Clark, M.R.; Rajagopalan, B.; Leavesley, G.

2012-01-01

Distributed hydrologic models typically require spatial estimates of precipitation interpolated from sparsely located observational points to the specific grid points. We compare and contrast the performance of regression-based statistical methods for the spatial estimation of precipitation in two hydrologically different basins and confirmed that widely used regression-based estimation schemes fail to describe the realistic spatial variability of daily precipitation field. The methods assessed are: (1) inverse distance weighted average; (2) multiple linear regression (MLR); (3) climatological MLR; and (4) locally weighted polynomial regression (LWP). In order to improve the performance of the interpolations, the authors propose a two-step regression technique for effective daily precipitation estimation. In this simple two-step estimation process, precipitation occurrence is first generated via a logistic regression model before estimate the amount of precipitation separately on wet days. This process generated the precipitation occurrence, amount, and spatial correlation effectively. A distributed hydrologic model (PRMS) was used for the impact analysis in daily time step simulation. Multiple simulations suggested noticeable differences between the input alternatives generated by three different interpolation schemes. Differences are shown in overall simulation error against the observations, degree of explained variability, and seasonal volumes. Simulated streamflows also showed different characteristics in mean, maximum, minimum, and peak flows. Given the same parameter optimization technique, LWP input showed least streamflow error in Alapaha basin and CMLR input showed least error (still very close to LWP) in Animas basin. All of the two-step interpolation inputs resulted in lower streamflow error compared to the directly interpolated inputs. ?? 2011 Springer-Verlag.

EXPOSURE MONITORING COMPONENT FOR DETROIT CHILDREN'S HEALTH STUDY ( DCHS )

EPA Science Inventory

Conventional, regulatory-based air monitoring is expensive and, thus, conducted at one or few locations in a city. This provides limited info on intra-urban variability and spatial distribution of air pollution. Research-oriented urban network monitoring has progressed with inc...

An alternative way to evaluate chemistry-transport model variability

NASA Astrophysics Data System (ADS)

Menut, Laurent; Mailler, Sylvain; Bessagnet, Bertrand; Siour, Guillaume; Colette, Augustin; Couvidat, Florian; Meleux, Frédérik

2017-03-01

A simple and complementary model evaluation technique for regional chemistry transport is discussed. The methodology is based on the concept that we can learn about model performance by comparing the simulation results with observational data available for time periods other than the period originally targeted. First, the statistical indicators selected in this study (spatial and temporal correlations) are computed for a given time period, using colocated observation and simulation data in time and space. Second, the same indicators are used to calculate scores for several other years while conserving the spatial locations and Julian days of the year. The difference between the results provides useful insights on the model capability to reproduce the observed day-to-day and spatial variability. In order to synthesize the large amount of results, a new indicator is proposed, designed to compare several error statistics between all the years of validation and to quantify whether the period and area being studied were well captured by the model for the correct reasons.

Discrete capacity limits and neuroanatomical correlates of visual short-term memory for objects and spatial locations.

PubMed

Konstantinou, Nikos; Constantinidou, Fofi; Kanai, Ryota

2017-02-01

Working memory is responsible for keeping information in mind when it is no longer in view, linking perception with higher cognitive functions. Despite such crucial role, short-term maintenance of visual information is severely limited. Research suggests that capacity limits in visual short-term memory (VSTM) are correlated with sustained activity in distinct brain areas. Here, we investigated whether variability in the structure of the brain is reflected in individual differences of behavioral capacity estimates for spatial and object VSTM. Behavioral capacity estimates were calculated separately for spatial and object information using a novel adaptive staircase procedure and were found to be unrelated, supporting domain-specific VSTM capacity limits. Voxel-based morphometry (VBM) analyses revealed dissociable neuroanatomical correlates of spatial versus object VSTM. Interindividual variability in spatial VSTM was reflected in the gray matter density of the inferior parietal lobule. In contrast, object VSTM was reflected in the gray matter density of the left insula. These dissociable findings highlight the importance of considering domain-specific estimates of VSTM capacity and point to the crucial brain regions that limit VSTM capacity for different types of visual information. Hum Brain Mapp 38:767-778, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Eigenvector Spatial Filtering Regression Modeling of Ground PM2.5 Concentrations Using Remotely Sensed Data.

PubMed

Zhang, Jingyi; Li, Bin; Chen, Yumin; Chen, Meijie; Fang, Tao; Liu, Yongfeng

2018-06-11

This paper proposes a regression model using the Eigenvector Spatial Filtering (ESF) method to estimate ground PM 2.5 concentrations. Covariates are derived from remotely sensed data including aerosol optical depth, normal differential vegetation index, surface temperature, air pressure, relative humidity, height of planetary boundary layer and digital elevation model. In addition, cultural variables such as factory densities and road densities are also used in the model. With the Yangtze River Delta region as the study area, we constructed ESF-based Regression (ESFR) models at different time scales, using data for the period between December 2015 and November 2016. We found that the ESFR models effectively filtered spatial autocorrelation in the OLS residuals and resulted in increases in the goodness-of-fit metrics as well as reductions in residual standard errors and cross-validation errors, compared to the classic OLS models. The annual ESFR model explained 70% of the variability in PM 2.5 concentrations, 16.7% more than the non-spatial OLS model. With the ESFR models, we performed detail analyses on the spatial and temporal distributions of PM 2.5 concentrations in the study area. The model predictions are lower than ground observations but match the general trend. The experiment shows that ESFR provides a promising approach to PM 2.5 analysis and prediction.

Fine-Scale Spatial Variability of Pedestrian-Level Particulate Matters in Compact Urban Commercial Districts in Hong Kong

PubMed Central

Ng, Edward

2017-01-01

Particulate matters (PM) at the pedestrian level significantly raises the health impacts in the compact urban environment of Hong Kong. A detailed investigation of the fine-scale spatial variation of pedestrian-level PM is necessary to assess the health risk to pedestrians in the outdoor environment. However, the collection of PM data is difficult in the compact urban environment of Hong Kong due to the limited amount of roadside monitoring stations and the complicated urban context. In this study, we measured the fine-scale spatial variability of the PM in three of the most representative commercial districts of Hong Kong using a backpack outdoor environmental measuring unit. Based on the measurement data, 13 types of geospatial interpolation methods were examined for the spatial mapping of PM2.5 and PM10 with a group of building geometrical covariates. Geostatistical modelling was adopted as the basis of spatial interpolation of the PM. The results show that the original cokriging with the exponential kernel function provides the best performance in the PM mapping. Using the fine-scale building geometrical features as covariates slightly improves the interpolation performance. The study results also imply that the fine-scale, localized pollution emission sources heavily influence pedestrian exposure to PM. PMID:28869527

Exploring Spatial Variability in the Relationship between Long Term Limiting Illness and Area Level Deprivation at the City Level Using Geographically Weighted Regression

PubMed Central

Morrissey, Karyn

2015-01-01

Ecological influences on health outcomes are associated with the spatial stratification of health. However, the majority of studies that seek to understand these ecological influences utilise aspatial methods. Geographically weighted regression (GWR) is a spatial statistics tool that expands standard regression by allowing for spatial variance in parameters. This study contributes to the urban health literature, by employing GWR to uncover geographic variation in Limiting Long Term Illness (LLTI) and area level effects at the small area level in a relatively small, urban environment. Using GWR it was found that each of the three contextual covariates, area level deprivation scores, the percentage of the population aged 75 years plus and the percentage of residences of white ethnicity for each LSOA exhibited a non-stationary relationship with LLTI across space. Multicollinearity among the predictor variables was found not to be a problem. Within an international policy context, this research indicates that even at the city level, a “one-size fits all” policy strategy is not the most appropriate approach to address health outcomes. City “wide” health polices need to be spatially adaptive, based on the contextual characteristics of each area. PMID:29546118

Neural network configuration and efficiency underlies individual differences in spatial orientation ability.

PubMed

Arnold, Aiden E G F; Protzner, Andrea B; Bray, Signe; Levy, Richard M; Iaria, Giuseppe

2014-02-01

Spatial orientation is a complex cognitive process requiring the integration of information processed in a distributed system of brain regions. Current models on the neural basis of spatial orientation are based primarily on the functional role of single brain regions, with limited understanding of how interaction among these brain regions relates to behavior. In this study, we investigated two sources of variability in the neural networks that support spatial orientation--network configuration and efficiency--and assessed whether variability in these topological properties relates to individual differences in orientation accuracy. Participants with higher accuracy were shown to express greater activity in the right supramarginal gyrus, the right precentral cortex, and the left hippocampus, over and above a core network engaged by the whole group. Additionally, high-performing individuals had increased levels of global efficiency within a resting-state network composed of brain regions engaged during orientation and increased levels of node centrality in the right supramarginal gyrus, the right primary motor cortex, and the left hippocampus. These results indicate that individual differences in the configuration of task-related networks and their efficiency measured at rest relate to the ability to spatially orient. Our findings advance systems neuroscience models of orientation and navigation by providing insight into the role of functional integration in shaping orientation behavior.

Spatial heterogeneity of within-stream methane concentrations

NASA Astrophysics Data System (ADS)

Crawford, John T.; Loken, Luke C.; West, William E.; Crary, Benjamin; Spawn, Seth A.; Gubbins, Nicholas; Jones, Stuart E.; Striegl, Robert G.; Stanley, Emily H.

2017-05-01

Streams, rivers, and other freshwater features may be significant sources of CH4 to the atmosphere. However, high spatial and temporal variabilities hinder our ability to understand the underlying processes of CH4 production and delivery to streams and also challenge the use of scaling approaches across large areas. We studied a stream having high geomorphic variability to assess the underlying scale of CH4 spatial variability and to examine whether the physical structure of a stream can explain the variation in surface CH4. A combination of high-resolution CH4 mapping, a survey of groundwater CH4 concentrations, quantitative analysis of methanogen DNA, and sediment CH4 production potentials illustrates the spatial and geomorphic controls on CH4 emissions to the atmosphere. We observed significant spatial clustering with high CH4 concentrations in organic-rich stream reaches and lake transitions. These sites were also enriched in the methane-producing mcrA gene and had highest CH4 production rates in the laboratory. In contrast, mineral-rich reaches had significantly lower concentrations and had lesser abundances of mcrA. Strong relationships between CH4 and the physical structure of this aquatic system, along with high spatial variability, suggest that future investigations will benefit from viewing streams as landscapes, as opposed to ecosystems simply embedded in larger terrestrial mosaics. In light of such high spatial variability, we recommend that future workers evaluate stream networks first by using similar spatial tools in order to build effective sampling programs.

The macro-structural variability of the human neocortex.

PubMed

Kruggel, Frithjof

2018-05-15

The human neocortex shows a considerable individual structural variability. While primary gyri and sulci are found in all normally developed brains and bear clear-cut gross structural descriptions, secondary structures are highly variable and not present in all brains. The blend of common and individual structures poses challenges when comparing structural and functional results from quantitative neuroimaging studies across individuals, and sets limits on the precision of location information much above the spatial resolution of current neuroimaging methods. This work aimed at quantifying structural variability on the neocortex, and at assessing the spatial relationship between regions common to all brains and their individual structural variants. Based on structural MRI data provided as the "900 Subjects Release" of the Human Connectome Project, a data-driven analytic approach was employed here from which the definition of seven cortical "communities" emerged. Apparently, these communities comprise common regions of structural features, while the individual variability is confined within a community. Similarities between the community structure and the state of the brain development at gestation week 32 lead suggest that communities are segregated early. Subdividing the neocortex into communities is suggested as anatomically more meaningful than the traditional lobar structure. Copyright © 2018 Elsevier Inc. All rights reserved.

Examining Impulse-Variability in Kicking.

PubMed

Chappell, Andrew; Molina, Sergio L; McKibben, Jonathon; Stodden, David F

2016-07-01

This study examined variability in kicking speed and spatial accuracy to test the impulse-variability theory prediction of an inverted-U function and the speed-accuracy trade-off. Twenty-eight 18- to 25-year-old adults kicked a playground ball at various percentages (50-100%) of their maximum speed at a wall target. Speed variability and spatial error were analyzed using repeated-measures ANOVA with built-in polynomial contrasts. Results indicated a significant inverse linear trajectory for speed variability (p < .001, η2= .345) where 50% and 60% maximum speed had significantly higher variability than the 100% condition. A significant quadratic fit was found for spatial error scores of mean radial error (p < .0001, η2 = .474) and subject-centroid radial error (p < .0001, η2 = .453). Findings suggest variability and accuracy of multijoint, ballistic skill performance may not follow the general principles of impulse-variability theory or the speed-accuracy trade-off.

Spatial analysis of agro-ecological data: Detection of spatial patterns combining three different methodical approaches

NASA Astrophysics Data System (ADS)

Heuer, A.; Casper, M. C.; Vohland, M.

2009-04-01

Processes in natural systems and the resulting patterns occur in ecological space and time. To study natural structures and to understand the functional processes it is necessary to identify the relevant spatial and temporal space at which these all occur; or with other words to isolate spatial and temporal patterns. In this contribution we will concentrate on the spatial aspects of agro-ecological data analysis. Data were derived from two agricultural plots, each of about 5 hectares, in the area of Newel, located in Western Palatinate, Germany. The plots had been conventionally cultivated with a crop rotation of winter rape, winter wheat and spring barley. Data about physical and chemical soil properties, vegetation and topography were i) collected by measurements in the field during three vegetation periods (2005-2008) and/or ii) derived from hyperspectral image data, acquired by a HyMap airborne imaging sensor (2005). To detect spatial variability within the plots, we applied three different approaches that examine and describe relationships among data. First, we used variography to get an overview of the data. A comparison of the experimental variograms facilitated to distinguish variables, which seemed to occur in related or dissimilar spatial space. Second, based on data available in raster-format basic cell statistics were conducted, using a geographic information system. Here we could make advantage of the powerful classification and visualization tool, which supported the spatial distribution of patterns. Third, we used an approach that is being used for visualization of complex highly dimensional environmental data, the Kohonen self-organizing map. The self-organizing map (SOM) uses multidimensional data that gets further reduced in dimensionality (2-D) to detect similarities in data sets and correlation between single variables. One of SOM's advantages is its powerful visualization capability. The combination of the three approaches leads to comprehensive and reasonable results, which will be presented in detail. It can be concluded, that the chosen strategy made it possible to complement preliminary findings, to validate the results of a single approach and to clearly delineate spatial patterns.

Relating remotely sensed optical variability to marine benthic biodiversity.

PubMed

Herkül, Kristjan; Kotta, Jonne; Kutser, Tiit; Vahtmäe, Ele

2013-01-01

Biodiversity is important in maintaining ecosystem viability, and the availability of adequate biodiversity data is a prerequisite for the sustainable management of natural resources. As such, there is a clear need to map biodiversity at high spatial resolutions across large areas. Airborne and spaceborne optical remote sensing is a potential tool to provide such biodiversity data. The spectral variation hypothesis (SVH) predicts a positive correlation between spectral variability (SV) of a remotely sensed image and biodiversity. The SVH has only been tested on a few terrestrial plant communities. Our study is the first attempt to apply the SVH in the marine environment using hyperspectral imagery recorded by Compact Airborne Spectrographic Imager (CASI). All coverage-based diversity measures of benthic macrophytes and invertebrates showed low but statistically significant positive correlations with SV whereas the relationship between biomass-based diversity measures and SV were weak or lacking. The observed relationships did not vary with spatial scale. SV had the highest independent effect among predictor variables in the statistical models of coverage-derived total benthic species richness and Shannon index. Thus, the relevance of SVH in marine benthic habitats was proved and this forms a prerequisite for the future use of SV in benthic biodiversity assessments.

Multivariate Analysis and Modeling of Sediment Pollution Using Neural Network Models and Geostatistics

NASA Astrophysics Data System (ADS)

Golay, Jean; Kanevski, Mikhaïl

2013-04-01

The present research deals with the exploration and modeling of a complex dataset of 200 measurement points of sediment pollution by heavy metals in Lake Geneva. The fundamental idea was to use multivariate Artificial Neural Networks (ANN) along with geostatistical models and tools in order to improve the accuracy and the interpretability of data modeling. The results obtained with ANN were compared to those of traditional geostatistical algorithms like ordinary (co)kriging and (co)kriging with an external drift. Exploratory data analysis highlighted a great variety of relationships (i.e. linear, non-linear, independence) between the 11 variables of the dataset (i.e. Cadmium, Mercury, Zinc, Copper, Titanium, Chromium, Vanadium and Nickel as well as the spatial coordinates of the measurement points and their depth). Then, exploratory spatial data analysis (i.e. anisotropic variography, local spatial correlations and moving window statistics) was carried out. It was shown that the different phenomena to be modeled were characterized by high spatial anisotropies, complex spatial correlation structures and heteroscedasticity. A feature selection procedure based on General Regression Neural Networks (GRNN) was also applied to create subsets of variables enabling to improve the predictions during the modeling phase. The basic modeling was conducted using a Multilayer Perceptron (MLP) which is a workhorse of ANN. MLP models are robust and highly flexible tools which can incorporate in a nonlinear manner different kind of high-dimensional information. In the present research, the input layer was made of either two (spatial coordinates) or three neurons (when depth as auxiliary information could possibly capture an underlying trend) and the output layer was composed of one (univariate MLP) to eight neurons corresponding to the heavy metals of the dataset (multivariate MLP). MLP models with three input neurons can be referred to as Artificial Neural Networks with EXternal drift (ANNEX). Moreover, the exact number of output neurons and the selection of the corresponding variables were based on the subsets created during the exploratory phase. Concerning hidden layers, no restriction were made and multiple architectures were tested. For each MLP model, the quality of the modeling procedure was assessed by variograms: if the variogram of the residuals demonstrates pure nugget effect and if the level of the nugget exactly corresponds to the nugget value of the theoretical variogram of the corresponding variable, all the structured information has been correctly extracted without overfitting. Finally, it is worth mentioning that simple MLP models are not always able to remove all the spatial correlation structure from the data. In that case, Neural Network Residual Kriging (NNRK) can be carried out and risk assessment can be conducted with Neural Network Residual Simulations (NNRS). Finally, the results of the ANNEX models were compared to those of ordinary (co)kriging and (co)kriging with an external drift. It was shown that the ANNEX models performed better than traditional geostatistical algorithms when the relationship between the variable of interest and the auxiliary predictor was not linear. References Kanevski, M. and Maignan, M. (2004). Analysis and Modelling of Spatial Environmental Data. Lausanne: EPFL Press.

Characterizing grazing disturbance in semiarid ecosystems across broad spatial scales using multiple indices.

USGS Publications Warehouse

Beever, Erik A.; Tausch, Robin J.; Brussard, Peter F.

2003-01-01

Although management and conservation strategies continue to move toward broader spatial scales and consideration of many taxonomic groups simultaneously, researchers have struggled to characterize responses to disturbance at these scales. Most studies of disturbance by feral grazers investigate effects on only one or two ecosystem elements across small spatial scales, limiting their applicability to ecosystem-level management. To address this inadequacy, in 1997 and 1998 we examined disturbance created by feral horses (Equus caballus) in nine mountain ranges of the western Great Basin, USA, using plants, small mammals, ants, and soil compaction as indicators. Nine horse-occupied and 10 horse-removed sites were stratified into high- and low-elevation groups, and all sites at each elevation had similar vegetation type, aspect, slope gradient, and recent (≥15-yr) fire and livestock-grazing histories. Using reciprocal averaging and TWINSPAN analyses, we compared relationships among sites using five data sets: abiotic variables, percent cover by plant species, an index of abundance by plant species, 10 disturbance-sensitive response variables, and grass and shrub species considered “key” indicators by land managers. Although reciprocal averaging and TWINSPAN analyses of percent cover, abiotic variables, and key species suggested relationships between sites influenced largely by biogeography (i.e., mountain range), disturbance-sensitive variables clearly segregated horse-occupied and horse-removed sites. These analyses suggest that the influence of feral horses on many Great Basin ecosystem attributes is not being detected by monitoring only palatable plant species. We recommend development of an expanded monitoring strategy based not only on established vegetation measurements investigating forage consumption, but also including disturbance-sensitive variables (e.g., soil surface hardness, abundance of ant mounds) that more completely reflect the suite of effects that a large-bodied grazer may impose on mountain ecosystems, independent of vegetation differences. By providing a broader-based mechanism for detection of adverse effects, this strategy would provide management agencies with defensible data in a sociopolitical arena that has been embroiled in conflict for several decades.

Using Moran's I and GIS to study spatial pattern of forest litter carbon density in typical subtropical region, China

NASA Astrophysics Data System (ADS)

Fu, W. J.; Jiang, P. K.; Zhou, G. M.; Zhao, K. L.

2013-12-01

The spatial variation of forest litter carbon (FLC) density in the typical subtropical forests in southeast China was investigated using Moran's I, geostatistics and a geographical information system (GIS). A total of 839 forest litter samples were collected based on a 12 km (South-North) × 6 km (East-West) grid system in Zhejiang Province. Forest litter carbon density values were very variable, ranging from 10.2 kg ha-1 to 8841.3 kg ha-1, with an average of 1786.7 kg ha-1. The aboveground biomass had the strongest positive correlation with FLC density, followed by forest age and elevation. Global Moran's I revealed that FLC density had significant positive spatial autocorrelation. Clear spatial patterns were observed using Local Moran's I. A spherical model was chosen to fit the experimental semivariogram. The moderate "nugget-to-sill" (0.536) value revealed that both natural and anthropogenic factors played a key role in spatial heterogeneity of FLC density. High FLC density values were mainly distributed in northwestern and western part of Zhejiang province, which were related to adopting long-term policy of forest conservation in these areas. While Hang-Jia-Hu (HJH) Plain, Jin-Qu (JQ) basin and coastal areas had low FLC density due to low forest coverage and intensive management of economic forests. These spatial patterns in distribution map were in line with the spatial-cluster map described by local Moran's I. Therefore, Moran's I, combined with geostatistics and GIS could be used to study spatial patterns of environmental variables related to forest ecosystem.

Spatial Distribution of a Large Herbivore Community at Waterholes: An Assessment of Its Stability over Years in Hwange National Park, Zimbabwe.

PubMed

Chamaillé-Jammes, Simon; Charbonnel, Anaïs; Dray, Stéphane; Madzikanda, Hillary; Fritz, Hervé

2016-01-01

The spatial structuring of populations or communities is an important driver of their functioning and their influence on ecosystems. Identifying the (in)stability of the spatial structure of populations is a first step towards understanding the underlying causes of these structures. Here we studied the relative importance of spatial vs. interannual variability in explaining the patterns of abundance of a large herbivore community (8 species) at waterholes in Hwange National Park (Zimbabwe). We analyzed census data collected over 13 years using multivariate methods. Our results showed that variability in the census data was mostly explained by the spatial structure of the community, as some waterholes had consistently greater herbivore abundance than others. Some temporal variability probably linked to Park-scale migration dependent on annual rainfall was noticeable, however. Once this was accounted for, little temporal variability remained to be explained, suggesting that other factors affecting herbivore abundance over time had a negligible effect at the scale of the study. The extent of spatial and temporal variability in census data was also measured for each species. This study could help in projecting the consequences of surface water management, and more generally presents a methodological framework to simultaneously address the relative importance of spatial vs. temporal effects in driving the distribution of organisms across landscapes.

Spatial Distribution of a Large Herbivore Community at Waterholes: An Assessment of Its Stability over Years in Hwange National Park, Zimbabwe

PubMed Central

Chamaillé-Jammes, Simon; Charbonnel, Anaïs; Dray, Stéphane; Madzikanda, Hillary; Fritz, Hervé

2016-01-01

The spatial structuring of populations or communities is an important driver of their functioning and their influence on ecosystems. Identifying the (in)stability of the spatial structure of populations is a first step towards understanding the underlying causes of these structures. Here we studied the relative importance of spatial vs. interannual variability in explaining the patterns of abundance of a large herbivore community (8 species) at waterholes in Hwange National Park (Zimbabwe). We analyzed census data collected over 13 years using multivariate methods. Our results showed that variability in the census data was mostly explained by the spatial structure of the community, as some waterholes had consistently greater herbivore abundance than others. Some temporal variability probably linked to Park-scale migration dependent on annual rainfall was noticeable, however. Once this was accounted for, little temporal variability remained to be explained, suggesting that other factors affecting herbivore abundance over time had a negligible effect at the scale of the study. The extent of spatial and temporal variability in census data was also measured for each species. This study could help in projecting the consequences of surface water management, and more generally presents a methodological framework to simultaneously address the relative importance of spatial vs. temporal effects in driving the distribution of organisms across landscapes. PMID:27074044

The Impact of Soil Sampling Errors on Variable Rate Fertilization

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

R. L. Hoskinson; R C. Rope; L G. Blackwood

2004-07-01

Variable rate fertilization of an agricultural field is done taking into account spatial variability in the soil’s characteristics. Most often, spatial variability in the soil’s fertility is the primary characteristic used to determine the differences in fertilizers applied from one point to the next. For several years the Idaho National Engineering and Environmental Laboratory (INEEL) has been developing a Decision Support System for Agriculture (DSS4Ag) to determine the economically optimum recipe of various fertilizers to apply at each site in a field, based on existing soil fertility at the site, predicted yield of the crop that would result (and amore » predicted harvest-time market price), and the current costs and compositions of the fertilizers to be applied. Typically, soil is sampled at selected points within a field, the soil samples are analyzed in a lab, and the lab-measured soil fertility of the point samples is used for spatial interpolation, in some statistical manner, to determine the soil fertility at all other points in the field. Then a decision tool determines the fertilizers to apply at each point. Our research was conducted to measure the impact on the variable rate fertilization recipe caused by variability in the measurement of the soil’s fertility at the sampling points. The variability could be laboratory analytical errors or errors from variation in the sample collection method. The results show that for many of the fertility parameters, laboratory measurement error variance exceeds the estimated variability of the fertility measure across grid locations. These errors resulted in DSS4Ag fertilizer recipe recommended application rates that differed by up to 138 pounds of urea per acre, with half the field differing by more than 57 pounds of urea per acre. For potash the difference in application rate was up to 895 pounds per acre and over half the field differed by more than 242 pounds of potash per acre. Urea and potash differences accounted for almost 87% of the cost difference. The sum of these differences could result in a $34 per acre cost difference for the fertilization. Because of these differences, better analysis or better sampling methods may need to be done, or more samples collected, to ensure that the soil measurements are truly representative of the field’s spatial variability.« less

Estimating 1970-99 average annual groundwater recharge in Wisconsin using streamflow data

USGS Publications Warehouse

Gebert, Warren A.; Walker, John F.; Kennedy, James L.

2011-01-01

Average annual recharge in Wisconsin for the period 1970-99 was estimated using streamflow data from U.S. Geological Survey continuous-record streamflow-gaging stations and partial-record sites. Partial-record sites have discharge measurements collected during low-flow conditions. The average annual base flow of a stream divided by the drainage area is a good approximation of the recharge rate; therefore, once average annual base flow is determined recharge can be calculated. Estimates of recharge for nearly 72 percent of the surface area of the State are provided. The results illustrate substantial spatial variability of recharge across the State, ranging from less than 1 inch to more than 12 inches per year. The average basin size for partial-record sites (50 square miles) was less than the average basin size for the gaging stations (305 square miles). Including results for smaller basins reveals a spatial variability that otherwise would be smoothed out using only estimates for larger basins. An error analysis indicates that the techniques used provide base flow estimates with standard errors ranging from 5.4 to 14 percent.

GIS Based Distributed Runoff Predictions in Variable Source Area Watersheds Employing the SCS-Curve Number

NASA Astrophysics Data System (ADS)

Steenhuis, T. S.; Mendoza, G.; Lyon, S. W.; Gerard Marchant, P.; Walter, M. T.; Schneiderman, E.

2003-04-01

Because the traditional Soil Conservation Service Curve Number (SCS-CN) approach continues to be ubiquitously used in GIS-BASED water quality models, new application methods are needed that are consistent with variable source area (VSA) hydrological processes in the landscape. We developed within an integrated GIS modeling environment a distributed approach for applying the traditional SCS-CN equation to watersheds where VSA hydrology is a dominant process. Spatial representation of hydrologic processes is important for watershed planning because restricting potentially polluting activities from runoff source areas is fundamental to controlling non-point source pollution. The methodology presented here uses the traditional SCS-CN method to predict runoff volume and spatial extent of saturated areas and uses a topographic index to distribute runoff source areas through watersheds. The resulting distributed CN-VSA method was incorporated in an existing GWLF water quality model and applied to sub-watersheds of the Delaware basin in the Catskill Mountains region of New York State. We found that the distributed CN-VSA approach provided a physically-based method that gives realistic results for watersheds with VSA hydrology.

Spatial patterns of development drive water use

USGS Publications Warehouse

Sanchez, G.M.; Smith, J.W.; Terando, Adam J.; Sun, G.; Meentemeyer, R.K.

2018-01-01

Water availability is becoming more uncertain as human populations grow, cities expand into rural regions and the climate changes. In this study, we examine the functional relationship between water use and the spatial patterns of developed land across the rapidly growing region of the southeastern United States. We quantified the spatial pattern of developed land within census tract boundaries, including multiple metrics of density and configuration. Through non‐spatial and spatial regression approaches we examined relationships and spatial dependencies between the spatial pattern metrics, socio‐economic and environmental variables and two water use variables: a) domestic water use, and b) total development‐related water use (a combination of public supply, domestic self‐supply and industrial self‐supply). Metrics describing the spatial patterns of development had the highest measure of relative importance (accounting for 53% of model's explanatory power), explaining significantly more variance in water use compared to socio‐economic or environmental variables commonly used to estimate water use. Integrating metrics characterizing the spatial pattern of development into water use models is likely to increase their utility and could facilitate water‐efficient land use planning.

Spatial Patterns of Development Drive Water Use

NASA Astrophysics Data System (ADS)

Sanchez, G. M.; Smith, J. W.; Terando, A.; Sun, G.; Meentemeyer, R. K.

2018-03-01

Water availability is becoming more uncertain as human populations grow, cities expand into rural regions and the climate changes. In this study, we examine the functional relationship between water use and the spatial patterns of developed land across the rapidly growing region of the southeastern United States. We quantified the spatial pattern of developed land within census tract boundaries, including multiple metrics of density and configuration. Through non-spatial and spatial regression approaches we examined relationships and spatial dependencies between the spatial pattern metrics, socio-economic and environmental variables and two water use variables: a) domestic water use, and b) total development-related water use (a combination of public supply, domestic self-supply and industrial self-supply). Metrics describing the spatial patterns of development had the highest measure of relative importance (accounting for 53% of model's explanatory power), explaining significantly more variance in water use compared to socio-economic or environmental variables commonly used to estimate water use. Integrating metrics characterizing the spatial pattern of development into water use models is likely to increase their utility and could facilitate water-efficient land use planning.

Measuring spatial variability in soil characteristics

DOEpatents

Hoskinson, Reed L.; Svoboda, John M.; Sawyer, J. Wayne; Hess, John R.; Hess, J. Richard

2002-01-01

The present invention provides systems and methods for measuring a load force associated with pulling a farm implement through soil that is used to generate a spatially variable map that represents the spatial variability of the physical characteristics of the soil. An instrumented hitch pin configured to measure a load force is provided that measures the load force generated by a farm implement when the farm implement is connected with a tractor and pulled through or across soil. Each time a load force is measured, a global positioning system identifies the location of the measurement. This data is stored and analyzed to generate a spatially variable map of the soil. This map is representative of the physical characteristics of the soil, which are inferred from the magnitude of the load force.

Determination of riverbank erosion probability using Locally Weighted Logistic Regression

NASA Astrophysics Data System (ADS)

Ioannidou, Elena; Flori, Aikaterini; Varouchakis, Emmanouil A.; Giannakis, Georgios; Vozinaki, Anthi Eirini K.; Karatzas, George P.; Nikolaidis, Nikolaos

2015-04-01

Riverbank erosion is a natural geomorphologic process that affects the fluvial environment. The most important issue concerning riverbank erosion is the identification of the vulnerable locations. An alternative to the usual hydrodynamic models to predict vulnerable locations is to quantify the probability of erosion occurrence. This can be achieved by identifying the underlying relations between riverbank erosion and the geomorphological or hydrological variables that prevent or stimulate erosion. Thus, riverbank erosion can be determined by a regression model using independent variables that are considered to affect the erosion process. The impact of such variables may vary spatially, therefore, a non-stationary regression model is preferred instead of a stationary equivalent. Locally Weighted Regression (LWR) is proposed as a suitable choice. This method can be extended to predict the binary presence or absence of erosion based on a series of independent local variables by using the logistic regression model. It is referred to as Locally Weighted Logistic Regression (LWLR). Logistic regression is a type of regression analysis used for predicting the outcome of a categorical dependent variable (e.g. binary response) based on one or more predictor variables. The method can be combined with LWR to assign weights to local independent variables of the dependent one. LWR allows model parameters to vary over space in order to reflect spatial heterogeneity. The probabilities of the possible outcomes are modelled as a function of the independent variables using a logistic function. Logistic regression measures the relationship between a categorical dependent variable and, usually, one or several continuous independent variables by converting the dependent variable to probability scores. Then, a logistic regression is formed, which predicts success or failure of a given binary variable (e.g. erosion presence or absence) for any value of the independent variables. The erosion occurrence probability can be calculated in conjunction with the model deviance regarding the independent variables tested. The most straightforward measure for goodness of fit is the G statistic. It is a simple and effective way to study and evaluate the Logistic Regression model efficiency and the reliability of each independent variable. The developed statistical model is applied to the Koiliaris River Basin on the island of Crete, Greece. Two datasets of river bank slope, river cross-section width and indications of erosion were available for the analysis (12 and 8 locations). Two different types of spatial dependence functions, exponential and tricubic, were examined to determine the local spatial dependence of the independent variables at the measurement locations. The results show a significant improvement when the tricubic function is applied as the erosion probability is accurately predicted at all eight validation locations. Results for the model deviance show that cross-section width is more important than bank slope in the estimation of erosion probability along the Koiliaris riverbanks. The proposed statistical model is a useful tool that quantifies the erosion probability along the riverbanks and can be used to assist managing erosion and flooding events. Acknowledgements This work is part of an on-going THALES project (CYBERSENSORS - High Frequency Monitoring System for Integrated Water Resources Management of Rivers). The project has been co-financed by the European Union (European Social Fund - ESF) and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF) - Research Funding Program: THALES. Investing in knowledge society through the European Social Fund.

Spatiotemporal estimation of historical PM2.5 concentrations using PM10, meteorological variables, and spatial effect

NASA Astrophysics Data System (ADS)

Li, Lianfa; Wu, Anna H.; Cheng, Iona; Chen, Jiu-Chiuan; Wu, Jun

2017-10-01

Monitoring of fine particulate matter with diameter <2.5 μm (PM2.5) started from 1999 in the US and even later in many other countries. The lack of historical PM2.5 data limits epidemiological studies of long-term exposure of PM2.5 and health outcomes such as cancer. In this study, we aimed to design a flexible approach to reliably estimate historical PM2.5 concentrations by incorporating spatial effect and the measurements of existing co-pollutants such as particulate matter with diameter <10 μm (PM10) and meteorological variables. Monitoring data of PM10, PM2.5, and meteorological variables covering the entire state of California were obtained from 1999 through 2013. We developed a spatiotemporal model that quantified non-linear associations between PM2.5 concentrations and the following predictor variables: spatiotemporal factors (PM10 and meteorological variables), spatial factors (land-use patterns, traffic, elevation, distance to shorelines, and spatial autocorrelation), and season. Our model accounted for regional-(county) scale spatial autocorrelation, using spatial weight matrix, and local-scale spatiotemporal variability, using local covariates in additive non-linear model. The spatiotemporal model was evaluated, using leaving-one-site-month-out cross validation. Our final daily model had an R2 of 0.81, with PM10, meteorological variables, and spatial autocorrelation, explaining 55%, 10%, and 10% of the variance in PM2.5 concentrations, respectively. The model had a cross-validation R2 of 0.83 for monthly PM2.5 concentrations (N = 8170) and 0.79 for daily PM2.5 concentrations (N = 51,421) with few extreme values in prediction. Further, the incorporation of spatial effects reduced bias in predictions. Our approach achieved a cross validation R2 of 0.61 for the daily model when PM10 was replaced by total suspended particulate. Our model can robustly estimate historical PM2.5 concentrations in California when PM2.5 measurements were not available.

Dynamic optimization of open-loop input signals for ramp-up current profiles in tokamak plasmas

NASA Astrophysics Data System (ADS)

Ren, Zhigang; Xu, Chao; Lin, Qun; Loxton, Ryan; Teo, Kok Lay

2016-03-01

Establishing a good current spatial profile in tokamak fusion reactors is crucial to effective steady-state operation. The evolution of the current spatial profile is related to the evolution of the poloidal magnetic flux, which can be modeled in the normalized cylindrical coordinates using a parabolic partial differential equation (PDE) called the magnetic diffusion equation. In this paper, we consider the dynamic optimization problem of attaining the best possible current spatial profile during the ramp-up phase of the tokamak. We first use the Galerkin method to obtain a finite-dimensional ordinary differential equation (ODE) model based on the original magnetic diffusion PDE. Then, we combine the control parameterization method with a novel time-scaling transformation to obtain an approximate optimal parameter selection problem, which can be solved using gradient-based optimization techniques such as sequential quadratic programming (SQP). This control parameterization approach involves approximating the tokamak input signals by piecewise-linear functions whose slopes and break-points are decision variables to be optimized. We show that the gradient of the objective function with respect to the decision variables can be computed by solving an auxiliary dynamic system governing the state sensitivity matrix. Finally, we conclude the paper with simulation results for an example problem based on experimental data from the DIII-D tokamak in San Diego, California.

Temporal and spatial variability in North Carolina piedmont stream temperature

Treesearch

J.L. Boggs; G. Sun; S.G. McNulty; W. Swartley; Treasure E.; W. Summer

2009-01-01

Understanding temporal and spatial patterns of in-stream temperature can provide useful information to managing future impacts of climate change on these systems. This study will compare temporal patterns and spatial variability of headwater in-stream temperature in six catchments in the piedmont of North Carolina in two different geological regions, Carolina slate...

Investigation of the marked and long-standing spatial inhomogeneity of the Hungarian suicide rate: a spatial regression approach.

PubMed

Balint, Lajos; Dome, Peter; Daroczi, Gergely; Gonda, Xenia; Rihmer, Zoltan

2014-02-01

In the last century Hungary had astonishingly high suicide rates characterized by marked regional within-country inequalities, a spatial pattern which has been quite stable over time. To explain the above phenomenon at the level of micro-regions (n=175) in the period between 2005 and 2011. Our dependent variable was the age and gender standardized mortality ratio (SMR) for suicide while explanatory variables were factors which are supposed to influence suicide risk, such as measures of religious and political integration, travel time accessibility of psychiatric services, alcohol consumption, unemployment and disability pensionery. When applying the ordinary least squared regression model, the residuals were found to be spatially autocorrelated, which indicates the violation of the assumption on the independence of error terms and - accordingly - the necessity of application of a spatial autoregressive (SAR) model to handle this problem. According to our calculations the SARlag model was a better way (versus the SARerr model) of addressing the problem of spatial autocorrelation, furthermore its substantive meaning is more convenient. SMR was significantly associated with the "political integration" variable in a negative and with "lack of religious integration" and "disability pensionery" variables in a positive manner. Associations were not significant for the remaining explanatory variables. Several important psychiatric variables were not available at the level of micro-regions. We conducted our analysis on aggregate data. Our results may draw attention to the relevance and abiding validity of the classic Durkheimian suicide risk factors - such as lack of social integration - apropos of the spatial pattern of Hungarian suicides. © 2013 Published by Elsevier B.V.

Combining Individual-Based Modeling and Food Microenvironment Descriptions To Predict the Growth of Listeria monocytogenes on Smear Soft Cheese

PubMed Central

Ferrier, Rachel; Hezard, Bernard; Lintz, Adrienne; Stahl, Valérie

2013-01-01

An individual-based modeling (IBM) approach was developed to describe the behavior of a few Listeria monocytogenes cells contaminating smear soft cheese surface. The IBM approach consisted of assessing the stochastic individual behaviors of cells on cheese surfaces and knowing the characteristics of their surrounding microenvironments. We used a microelectrode for pH measurements and micro-osmolality to assess the water activity of cheese microsamples. These measurements revealed a high variability of microscale pH compared to that of macroscale pH. A model describing the increase in pH from approximately 5.0 to more than 7.0 during ripening was developed. The spatial variability of the cheese surface characterized by an increasing pH with radius and higher pH on crests compared to that of hollows on cheese rind was also modeled. The microscale water activity ranged from approximately 0.96 to 0.98 and was stable during ripening. The spatial variability on cheese surfaces was low compared to between-cheese variability. Models describing the microscale variability of cheese characteristics were combined with the IBM approach to simulate the stochastic growth of L. monocytogenes on cheese, and these simulations were compared to bacterial counts obtained from irradiated cheeses artificially contaminated at different ripening stages. The simulated variability of L. monocytogenes counts with the IBM/microenvironmental approach was consistent with the observed one. Contrasting situations corresponding to no growth or highly contaminated foods could be deduced from these models. Moreover, the IBM approach was more effective than the traditional population/macroenvironmental approach to describe the actual bacterial behavior variability. PMID:23872572

Is temperature the main cause of dengue rise in non-endemic countries? The case of Argentina

PubMed Central

2012-01-01

Background Dengue cases have increased during the last decades, particularly in non-endemic areas, and Argentina was no exception in the southern transmission fringe. Although temperature rise has been blamed for this, human population growth, increased travel and inefficient vector control may also be implicated. The relative contribution of geographic, demographic and climatic of variables on the occurrence of dengue cases was evaluated. Methods According to dengue history in the country, the study was divided in two decades, a first decade corresponding to the reemergence of the disease and the second including several epidemics. Annual dengue risk was modeled by a temperature-based mechanistic model as annual days of possible transmission. The spatial distribution of dengue occurrence was modeled as a function of the output of the mechanistic model, climatic, geographic and demographic variables for both decades. Results According to the temperature-based model dengue risk increased between the two decades, and epidemics of the last decade coincided with high annual risk. Dengue spatial occurrence was best modeled by a combination of climatic, demographic and geographic variables and province as a grouping factor. It was positively associated with days of possible transmission, human population number, population fall and distance to water bodies. When considered separately, the classification performance of demographic variables was higher than that of climatic and geographic variables. Conclusions Temperature, though useful to estimate annual transmission risk, does not fully describe the distribution of dengue occurrence at the country scale. Indeed, when taken separately, climatic variables performed worse than geographic or demographic variables. A combination of the three types was best for this task. PMID:22768874

Monitoring snow cover variability (2000-2014) in the Hengduan Mountains based on cloud-removed MODIS products with an adaptive spatio-temporal weighted method

NASA Astrophysics Data System (ADS)

Li, Xinghua; Fu, Wenxuan; Shen, Huanfeng; Huang, Chunlin; Zhang, Liangpei

2017-08-01

Monitoring the variability of snow cover is necessary and meaningful because snow cover is closely connected with climate and ecological change. In this work, 500 m resolution MODIS daily snow cover products from 2000 to 2014 were adopted to analyze the status in Hengduan Mountains. In order to solve the spatial discontinuity caused by clouds in the products, we propose an adaptive spatio-temporal weighted method (ASTWM), which is based on the initial result of a Terra and Aqua combination. This novel method simultaneously considers the temporal and spatial correlations of the snow cover. The simulated experiments indicate that ASTWM removes clouds completely, with a robust overall accuracy (OA) of above 93% under different cloud fractions. The spatio-temporal variability of snow cover in the Hengduan Mountains was investigated with two indices: snow cover days (SCD) and snow fraction. The results reveal that the annual SCD gradually increases and the coefficient of variation (CV) decreases with elevation. The pixel-wise trends of SCD first rise and then drop in most areas. Moreover, intense intra-annual variability of the snow fraction occurs from October to March, during which time there is abundant snow cover. The inter-annual variability, which mainly occurs in high elevation areas, shows an increasing trend before 2004/2005 and a decreasing trend after 2004/2005. In addition, the snow fraction responds to the two climate factors of air temperature and precipitation. For the intra-annual variability, when the air temperature and precipitation decrease, the snow cover increases. Besides, precipitation plays a more important role in the inter-annual variability of snow cover than temperature.

Spatial patterns in community response to aircraft noise associated with non-noise factors

NASA Astrophysics Data System (ADS)

Hall, F. L.; Taylor, S. M.; Birnie, S. E.

1980-08-01

Non-noise aspects of airport operations may affect individuals' responses to aircraft noise. Fear of crashes, other forms of pollution, and proximity to the flight path are three such non-noise aspects which have spatial patterns that are closely related to the pattern of noise contours around an airport. If these variables affect response to aircraft noise, they may therefore confound attempts to understand relationships between noise level and community response. Analyses based on data from 673 individuals around Toronto International Airport suggest that these factors do affect annoyance responses, but do not affect reported activity interference. Hence it may prove fruitful, in aggregate analyses of community response data, to control for these variables in order to better understand the noise-annoyance relationships.

Structural Variability of Tropospheric Growth Factors Transforming Mid-latitude Cyclones to Severe Storms over the North Atlantic

NASA Astrophysics Data System (ADS)

Wild, Simon; Befort, Daniel J.; Leckebusch, Gregor C.

2015-04-01

The development of European surface wind storms out of normal mid-latitude cyclones is substantially influenced by upstream tropospheric growth factors over the Northern Atlantic. The main factors include divergence and vorticity advection in the upper troposphere, latent heat release and the presence of instabilities of short baroclinic waves of suitable wave lengths. In this study we examine a subset of these potential growth factors and their related influences on the transformation of extra-tropical cyclones into severe damage prone surface storm systems. Previous studies have shown links between specific growth factors and surface wind storms related to extreme cyclones. In our study we investigate in further detail spatial and temporal variability patterns of these upstream processes at different vertical levels of the troposphere. The analyses will comprise of the three growth factors baroclinicity, latent heat release and upper tropospheric divergence. Our definition of surface wind storms is based on the Storm Severity Index (SSI) alongside a wind tracking algorithm identifying areas of exceedances of the local 98th percentile of the 10m wind speed. We also make use of a well-established extra-tropical cyclone identification and tracking algorithm. These cyclone tracks form the base for a composite analysis of the aforementioned growth factors using ERA-Interim Reanalysis from 1979 - 2014 for the extended winter season (ONDJFM). Our composite analysis corroborates previous similar studies but extends them by using an impact based algorithm for the identification of strong wind systems. Based on this composite analysis we further identify variability patterns for each growth factor most important for the transformation of a cyclone into a surface wind storm. We thus also address the question whether the link between storm intensity and related growth factor anomaly taking into account its spatial variability is stable and can be quantified. While the robustness of our preliminary results is generally dependent on the growth factor investigated, some examples include i) the overall availability of latent heat seems to be less important than its spatial structure around the cyclone core and ii) the variability of upper-tropospheric baroclinicity appears to be highest north of the surface position of the cyclone, especially for those that transform into a surface storm.

The effect of short-range spatial variability on soil sampling uncertainty.

PubMed

Van der Perk, Marcel; de Zorzi, Paolo; Barbizzi, Sabrina; Belli, Maria; Fajgelj, Ales; Sansone, Umberto; Jeran, Zvonka; Jaćimović, Radojko

2008-11-01

This paper aims to quantify the soil sampling uncertainty arising from the short-range spatial variability of elemental concentrations in the topsoils of agricultural, semi-natural, and contaminated environments. For the agricultural site, the relative standard sampling uncertainty ranges between 1% and 5.5%. For the semi-natural area, the sampling uncertainties are 2-4 times larger than in the agricultural area. The contaminated site exhibited significant short-range spatial variability in elemental composition, which resulted in sampling uncertainties of 20-30%.

Ground and satellite based assessment of meteorological droughts: The Coello river basin case study

NASA Astrophysics Data System (ADS)

Cruz-Roa, A. F.; Olaya-Marín, E. J.; Barrios, M. I.

2017-10-01

The spatial distribution of droughts is a key factor for designing water management policies at basin scale in arid and semi-arid regions. Ground hydro-meteorological data in neo-tropical areas are scarce; therefore, the merging of ground and satellite datasets is a promissory approach for improving our understanding of water distribution. This paper compares three monthly rainfall interpolation methods for drought evaluation. The ordinary kriging technique based on ground data, and cokriging with elevation as auxiliary variable were compared against cokriging using the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA). Twenty rain gauge stations and the 3B42V7 version of the TMPA research dataset were considered. Comparisons were made over the Coello river basin (Colombia) at 3″ spatial resolution covering a period of eight years (1998-2005). The best spatial rainfall estimation was found for cokriging using ground data and elevation. The spatial support of TMPA dataset is very coarse for a merged interpolation with ground data, this spatial scales discrepancy highlight the need to consider scaling rules in the interpolation process.

Fourier transform infrared spectroscopy microscopic imaging classification based on spatial-spectral features

NASA Astrophysics Data System (ADS)

Liu, Lian; Yang, Xiukun; Zhong, Mingliang; Liu, Yao; Jing, Xiaojun; Yang, Qin

2018-04-01

The discrete fractional Brownian incremental random (DFBIR) field is used to describe the irregular, random, and highly complex shapes of natural objects such as coastlines and biological tissues, for which traditional Euclidean geometry cannot be used. In this paper, an anisotropic variable window (AVW) directional operator based on the DFBIR field model is proposed for extracting spatial characteristics of Fourier transform infrared spectroscopy (FTIR) microscopic imaging. Probabilistic principal component analysis first extracts spectral features, and then the spatial features of the proposed AVW directional operator are combined with the former to construct a spatial-spectral structure, which increases feature-related information and helps a support vector machine classifier to obtain more efficient distribution-related information. Compared to Haralick’s grey-level co-occurrence matrix, Gabor filters, and local binary patterns (e.g. uniform LBPs, rotation-invariant LBPs, uniform rotation-invariant LBPs), experiments on three FTIR spectroscopy microscopic imaging datasets show that the proposed AVW directional operator is more advantageous in terms of classification accuracy, particularly for low-dimensional spaces of spatial characteristics.

Assessment of the spatial variability in tall wheatgrass forage using LANDSAT 8 satellite imagery to delineate potential management zones.

PubMed

Cicore, Pablo; Serrano, João; Shahidian, Shakib; Sousa, Adelia; Costa, José Luis; da Silva, José Rafael Marques

2016-09-01

Little information is available on the degree of within-field variability of potential production of Tall wheatgrass (Thinopyrum ponticum) forage under unirrigated conditions. The aim of this study was to characterize the spatial variability of the accumulated biomass (AB) without nutritional limitations through vegetation indexes, and then use this information to determine potential management zones. A 27-×-27-m grid cell size was chosen and 84 biomass sampling areas (BSA), each 2 m(2) in size, were georeferenced. Nitrogen and phosphorus fertilizers were applied after an initial cut at 3 cm height. At 500 °C day, the AB from each sampling area, was collected and evaluated. The spatial variability of AB was estimated more accurately using the Normalized Difference Vegetation Index (NDVI), calculated from LANDSAT 8 images obtained on 24 November 2014 (NDVInov) and 10 December 2014 (NDVIdec) because the potential AB was highly associated with NDVInov and NDVIdec (r (2)  = 0.85 and 0.83, respectively). These models between the potential AB data and NDVI were evaluated by root mean squared error (RMSE) and relative root mean squared error (RRMSE). This last coefficient was 12 and 15 % for NDVInov and NDVIdec, respectively. Potential AB and NDVI spatial correlation were quantified with semivariograms. The spatial dependence of AB was low. Six classes of NDVI were analyzed for comparison, and two management zones (MZ) were established with them. In order to evaluate if the NDVI method allows us to delimit MZ with different attainable yields, the AB estimated for these MZ were compared through an ANOVA test. The potential AB had significant differences among MZ. Based on these findings, it can be concluded that NDVI obtained from LANDSAT 8 images can be reliably used for creating MZ in soils under permanent pastures dominated by Tall wheatgrass.

Towards integrated assessment of the northern Adriatic Sea sediment budget using remote sensing

NASA Astrophysics Data System (ADS)

Taramelli, A.; Filipponi, F.; Valentini, E.; Zucca, F.; Gutierrez, O. Q.; Liberti, L.; Cordella, M.

2014-12-01

Understanding the factors influencing sediment fluxes is a key issue to interpret the evolution of coastal sedimentation under natural and human impact and relevant for the natural resources management. Despite river plumes represent one of the major gain in sedimentary budget of littoral cells, knowledge of factors influencing complex behavior of coastal plumes, like river discharge characteristics, wind stress and hydro-climatic variables, has not been yet fully investigated. Use of Earth Observation data allows the identification of spatial and temporal variations of suspended sediments related to river runoff, seafloor erosion, sediment transport and deposition processes. Objective of the study is to investigate sediment fluxes in northern Adriatic Sea by linking suspended sediment patterns of coastal plumes to hydrologic and climatic forcing regulating the sedimentary cell budget and geomorphological evolution in coastal systems and continental shelf waters. Analysis of Total Suspended Matter (TSM) product, derived from 2002-2012 MERIS time series, was done to map changes in spatial and temporal dimension of suspended sediments, focusing on turbid plume waters and intense wind stress conditions. From the generated multi temporal TSM maps, dispersal patterns of major freshwater runoff plumes in northern Adriatic Sea were evaluated through spatial variability of coastal plumes shape and extent. Additionally, sediment supply from river distributary mouths was estimated from TSM and correlated with river discharge rates, wind field and wave field through time. Spatial based methodology has been developed to identify events of wave-generated resuspension of sediments, which cause variation in water column turbidity, occurring during intense wind stress and extreme metocean conditions, especially in the winter period. The identified resuspension events were qualitatively described and compared with to hydro-climatic variables. The identification of spatial and temporal pattern variability highlighted the presence of seasonal sediment dynamics linked to the seasonal cycle in river discharge and wind stress. Results suggest that sediment fluxes generate geomorphological variations in northern Adriatic Sea, which are mainly controlled by river discharge rates and modulated by the winds.

The role of storm scale, position and movement in controlling urban flood response

NASA Astrophysics Data System (ADS)

ten Veldhuis, Marie-claire; Zhou, Zhengzheng; Yang, Long; Liu, Shuguang; Smith, James

2018-01-01

The impact of spatial and temporal variability of rainfall on hydrological response remains poorly understood, in particular in urban catchments due to their strong variability in land use, a high degree of imperviousness and the presence of stormwater infrastructure. In this study, we analyze the effect of storm scale, position and movement in relation to basin scale and flow-path network structure on urban hydrological response. A catalog of 279 peak events was extracted from a high-quality observational dataset covering 15 years of flow observations and radar rainfall data for five (semi)urbanized basins ranging from 7.0 to 111.1 km2 in size. Results showed that the largest peak flows in the event catalog were associated with storm core scales exceeding basin scale, for all except the largest basin. Spatial scale of flood-producing storm events in the smaller basins fell into two groups: storms of large spatial scales exceeding basin size or small, concentrated events, with storm core much smaller than basin size. For the majority of events, spatial rainfall variability was strongly smoothed by the flow-path network, increasingly so for larger basin size. Correlation analysis showed that position of the storm in relation to the flow-path network was significantly correlated with peak flow in the smallest and in the two more urbanized basins. Analysis of storm movement relative to the flow-path network showed that direction of storm movement, upstream or downstream relative to the flow-path network, had little influence on hydrological response. Slow-moving storms tend to be associated with higher peak flows and longer lag times. Unexpectedly, position of the storm relative to impervious cover within the basins had little effect on flow peaks. These findings show the importance of observation-based analysis in validating and improving our understanding of interactions between the spatial distribution of rainfall and catchment variability.

Using Moran's I and GIS to study the spatial pattern of forest litter carbon density in a subtropical region of southeastern China

NASA Astrophysics Data System (ADS)

Fu, W. J.; Jiang, P. K.; Zhou, G. M.; Zhao, K. L.

2014-04-01

Spatial pattern information of carbon density in forest ecosystem including forest litter carbon (FLC) plays an important role in evaluating carbon sequestration potentials. The spatial variation of FLC density in the typical subtropical forests in southeastern China was investigated using Moran's I, geostatistics and a geographical information system (GIS). A total of 839 forest litter samples were collected based on a 12 km (south-north) × 6 km (east-west) grid system in Zhejiang province. Forest litter carbon density values were very variable, ranging from 10.2 kg ha-1 to 8841.3 kg ha-1, with an average of 1786.7 kg ha-1. The aboveground biomass had the strongest positive correlation with FLC density, followed by forest age and elevation. Global Moran's I revealed that FLC density had significant positive spatial autocorrelation. Clear spatial patterns were observed using local Moran's I. A spherical model was chosen to fit the experimental semivariogram. The moderate "nugget-to-sill" (0.536) value revealed that both natural and anthropogenic factors played a key role in spatial heterogeneity of FLC density. High FLC density values were mainly distributed in northwestern and western part of Zhejiang province, which were related to adopting long-term policy of forest conservation in these areas, while Hang-Jia-Hu (HJH) Plain, Jin-Qu (JQ) Basin and coastal areas had low FLC density due to low forest coverage and intensive management of economic forests. These spatial patterns were in line with the spatial-cluster map described by local Moran's I. Therefore, Moran's I, combined with geostatistics and GIS, could be used to study spatial patterns of environmental variables related to forest ecosystem.

[Spatial variation characteristics of surface soil water content, bulk density and saturated hydraulic conductivity on Karst slopes].

PubMed

Zhang, Chuan; Chen, Hong-Song; Zhang, Wei; Nie, Yun-Peng; Ye, Ying-Ying; Wang, Ke-Lin

2014-06-01

Surface soil water-physical properties play a decisive role in the dynamics of deep soil water. Knowledge of their spatial variation is helpful in understanding the processes of rainfall infiltration and runoff generation, which will contribute to the reasonable utilization of soil water resources in mountainous areas. Based on a grid sampling scheme (10 m x 10 m) and geostatistical methods, this paper aimed to study the spatial variability of surface (0-10 cm) soil water content, soil bulk density and saturated hydraulic conductivity on a typical shrub slope (90 m x 120 m, projected length) in Karst area of northwest Guangxi, southwest China. The results showed that the surface soil water content, bulk density and saturated hydraulic conductivity had different spatial dependence and spatial structure. Sample variogram of the soil water content was fitted well by Gaussian models with the nugget effect, while soil bulk density and saturated hydraulic conductivity were fitted well by exponential models with the nugget effect. Variability of soil water content showed strong spatial dependence, while the soil bulk density and saturated hydraulic conductivity showed moderate spatial dependence. The spatial ranges of the soil water content and saturated hydraulic conductivity were small, while that of the soil bulk density was much bigger. In general, the soil water content increased with the increase of altitude while it was opposite for the soil bulk densi- ty. However, the soil saturated hydraulic conductivity had a random distribution of large amounts of small patches, showing high spatial heterogeneity. Soil water content negatively (P < 0.01) correlated with the bulk density and saturated hydraulic conductivity, while there was no significant correlation between the soil bulk density and saturated hydraulic conductivity.

PP-SWAT: A phython-based computing software for efficient multiobjective callibration of SWAT

USDA-ARS?s Scientific Manuscript database

With enhanced data availability, distributed watershed models for large areas with high spatial and temporal resolution are increasingly used to understand water budgets and examine effects of human activities and climate change/variability on water resources. Developing parallel computing software...

Sensor-based precision fertilization for field crops

USDA-ARS?s Scientific Manuscript database

From the development of the first viable variable-rate fertilizer systems in the upper Midwest USA, precision agriculture is now approaching three decades old. Early precision fertilization practice relied on laboratory analysis of soil samples collected on a spatial pattern to define the nutrient-s...

Neighborhood social capital and crime victimization: comparison of spatial regression analysis and hierarchical regression analysis.

PubMed

Takagi, Daisuke; Ikeda, Ken'ichi; Kawachi, Ichiro

2012-11-01

Crime is an important determinant of public health outcomes, including quality of life, mental well-being, and health behavior. A body of research has documented the association between community social capital and crime victimization. The association between social capital and crime victimization has been examined at multiple levels of spatial aggregation, ranging from entire countries, to states, metropolitan areas, counties, and neighborhoods. In multilevel analysis, the spatial boundaries at level 2 are most often drawn from administrative boundaries (e.g., Census tracts in the U.S.). One problem with adopting administrative definitions of neighborhoods is that it ignores spatial spillover. We conducted a study of social capital and crime victimization in one ward of Tokyo city, using a spatial Durbin model with an inverse-distance weighting matrix that assigned each respondent a unique level of "exposure" to social capital based on all other residents' perceptions. The study is based on a postal questionnaire sent to 20-69 years old residents of Arakawa Ward, Tokyo. The response rate was 43.7%. We examined the contextual influence of generalized trust, perceptions of reciprocity, two types of social network variables, as well as two principal components of social capital (constructed from the above four variables). Our outcome measure was self-reported crime victimization in the last five years. In the spatial Durbin model, we found that neighborhood generalized trust, reciprocity, supportive networks and two principal components of social capital were each inversely associated with crime victimization. By contrast, a multilevel regression performed with the same data (using administrative neighborhood boundaries) found generally null associations between neighborhood social capital and crime. Spatial regression methods may be more appropriate for investigating the contextual influence of social capital in homogeneous cultural settings such as Japan. Copyright © 2012 Elsevier Ltd. All rights reserved.

Patterns of distribution, abundance, and change over time in a subarctic marine bird community

NASA Astrophysics Data System (ADS)

Cushing, Daniel A.; Roby, Daniel D.; Irons, David B.

2018-01-01

Over recent decades, marine ecosystems of Prince William Sound (PWS), Alaska, have experienced concurrent effects of natural and anthropogenic perturbations, including variability in the climate system of the northeastern Pacific Ocean. We documented spatial and temporal patterns of variability in the summer marine bird community in relation to habitat and climate variability using boat-based surveys of marine birds conducted during the period 1989-2012. We hypothesized that a major factor structuring marine bird communities in PWS would be proximity to the shoreline, which is theorized to relate to aspects of food web structure. We also hypothesized that shifts in physical ecosystem drivers differentially affected nearshore-benthic and pelagic components of PWS food webs. We evaluated support for our hypotheses using an approach centered on community-level patterns of spatial and temporal variability. We found that an environmental gradient related to water depth and distance from shore was the dominant factor spatially structuring the marine bird community. Responses of marine birds to this onshore-offshore environmental gradient were related to dietary specialization, and separated marine bird taxa by prey type. The primary form of temporal variability over the study period was monotonic increases or decreases in abundance for 11 of 18 evaluated genera of marine birds; 8 genera had declined, whereas 3 had increased. The greatest declines occurred in genera associated with habitats that were deeper and farther from shore. Furthermore, most of the genera that declined primarily fed on pelagic prey resources, such as forage fish and mesozooplankton, and few were directly affected by the 1989 Exxon Valdez oil spill. Our observations of synchronous declines are indicative of a shift in pelagic components of PWS food webs. This pattern was correlated with climate variability at time-scales of several years to a decade.

Patterns of distribution, abundance, and change over time in a subarctic marine bird community

USGS Publications Warehouse

Cushing, Daniel; Roby, Daniel D.; Irons, David B.

2017-01-01

Over recent decades, marine ecosystems of Prince William Sound (PWS), Alaska, have experienced concurrent effects of natural and anthropogenic perturbations, including variability in the climate system of the northeastern Pacific Ocean. We documented spatial and temporal patterns of variability in the summer marine bird community in relation to habitat and climate variability using boat-based surveys of marine birds conducted during the period 1989–2012. We hypothesized that a major factor structuring marine bird communities in PWS would be proximity to the shoreline, which is theorized to relate to aspects of food web structure. We also hypothesized that shifts in physical ecosystem drivers differentially affected nearshore-benthic and pelagic components of PWS food webs. We evaluated support for our hypotheses using an approach centered on community-level patterns of spatial and temporal variability. We found that an environmental gradient related to water depth and distance from shore was the dominant factor spatially structuring the marine bird community. Responses of marine birds to this onshore-offshore environmental gradient were related to dietary specialization, and separated marine bird taxa by prey type. The primary form of temporal variability over the study period was monotonic increases or decreases in abundance for 11 of 18 evaluated genera of marine birds; 8 genera had declined, whereas 3 had increased. The greatest declines occurred in genera associated with habitats that were deeper and farther from shore. Furthermore, most of the genera that declined primarily fed on pelagic prey resources, such as forage fish and mesozooplankton, and few were directly affected by the 1989 Exxon Valdez oil spill. Our observations of synchronous declines are indicative of a shift in pelagic components of PWS food webs. This pattern was correlated with climate variability at time-scales of several years to a decade.

Use of multiple functional traits of protozoa for bioassessment of marine pollution.

PubMed

Zhong, Xiaoxiao; Xu, Guangjian; Xu, Henglong

2017-06-30

Ecological parameters based on multiply functional traits have many advantages for monitoring programs by reducing "signal to noise" ratios of observed species data. To identify potential indicators for bioassessment of marine pollution in function space, the functional patterns of protozoan communities and relationships with environmental changes were studied in coastal waters of the Yellow Sea during a 1-year period. The results showed that: (1) the spatial variability in functional trait distributions of the protozoa was significantly associated with changes in environmental variables, especially chemical oxygen demand (COD) and nutrients on spatial scale; (2) the functional traits, especially food resources and feeding type, were significantly correlated with COD and nutrients; and (3) the functional diversity indices were generally related to nutrients or COD. Based on the results, we suggest that the functional traits and diversity indices of protozoan communities may be used as more effective indicators for bioassessment of marine pollution. Copyright © 2017 Elsevier Ltd. All rights reserved.

Spatial Analysis of Ambient PM2.5 Exposure and Bladder Cancer Mortality in Taiwan

PubMed Central

Yeh, Hsin-Ling; Hsu, Shang-Wei; Chang, Yu-Chia; Chan, Ta-Chien; Tsou, Hui-Chen; Chang, Yen-Chen; Chiang, Po-Huang

2017-01-01

Fine particulate matter (PM2.5) is an air pollutant that is receiving intense regulatory attention in Taiwan. In previous studies, the effect of air pollution on bladder cancer has been explored. This study was conducted to elucidate the effect of atmospheric PM2.5 and other local risk factors on bladder cancer mortality based on available 13-year mortality data. Geographically weighted regression (GWR) was applied to estimate and interpret the spatial variability of the relationships between bladder cancer mortality and ambient PM2.5 concentrations, and other variables were covariates used to adjust for the effect of PM2.5. After applying a GWR model, the concentration of ambient PM2.5 showed a positive correlation with bladder cancer mortality in males in northern Taiwan and females in most of the townships in Taiwan. This is the first time PM2.5 has been identified as a risk factor for bladder cancer based on the statistical evidence provided by GWR analysis. PMID:28489042

Spatialized Application of Remotely Sensed Data Assimilation Methods for Farmland Drought Monitoring Using Two Different Crop Models

NASA Astrophysics Data System (ADS)

Silvestro, Paolo Cosmo; Casa, Raffaele; Pignatti, Stefano; Castaldi, Fabio; Yang, Hao; Guijun, Yang

2016-08-01

The aim of this work was to develop a tool to evaluate the effect of water stress on yield losses at the farmland and regional scale, by assimilating remotely sensed biophysical variables into crop growth models. Biophysical variables were retrieved from HJ1A, HJ1B and Landsat 8 images, using an algorithm based on the training of artificial neural networks on PROSAIL.For the assimilation, two crop models of differing degree of complexity were used: Aquacrop and SAFY. For Aquacrop, an optimization procedure to reduce the difference between the remotely sensed and simulated CC was developed. For the modified version of SAFY, the assimilation procedure was based on the Ensemble Kalman Filter.These procedures were tested in a spatialized application, by using data collected in the rural area of Yangling (Shaanxi Province) between 2013 and 2015Results were validated by utilizing yield data both from ground measurements and statistical survey.

Characterization of soil spatial variability for site-specific management using soil electrical conductivity and other remotely sensed data

NASA Astrophysics Data System (ADS)

Bang, Jisu

Field-scale characterization of soil spatial variability using remote sensing technology has potential for achieving the successful implementation of site-specific management (SSM). The objectives of this study were to: (i) examine the spatial relationships between apparent soil electrical conductivity (EC a) and soil chemical and physical properties to determine if EC a could be useful to characterize soil properties related to crop productivity in the Coastal Plain and Piedmont of North Carolina; (ii) evaluate the effects of in-situ soil moisture variation on ECa mapping as a basis for characterization of soil spatial variability and as a data layer in cluster analysis as a means of delineating sampling zones; (iii) evaluate clustering approaches using different variable sets for management zone delineation to characterize spatial variability in soil nutrient levels and crop yields. Studies were conducted in two fields in the Piedmont and three fields in the Coastal Plain of North Carolina. Spatial measurements of ECa via electromagnetic induction (EMI) were compared with soil chemical parameters (extractable P, K, and micronutrients; pH, cation exchange capacity [CEC], humic matter or soil organic matter; and physical parameters (percentage sand, silt, and clay; and plant-available water [PAW] content; bulk density; cone index; saturated hydraulic conductivity [Ksat] in one of the coastal plain fields) using correlation analysis across fields. We also collected ECa measurements in one coastal plain field on four days with significantly different naturally occurring soil moisture conditions measured in five increments to 0.75 m using profiling time-domain reflectometry probes to evaluate the temporal variability of ECa associated with changes in in-situ soil moisture content. Nonhierarchical k-means cluster analysis using sensor-based field attributes including vertical ECa, near-infrared (NIR) radiance of bare-soil from an aerial color infrared (CIR) image, elevation, slope, and their combinations was performed to delineate management zones. The strengths and signs of the correlations between ECa and measured soil properties varied among fields. Few strong direct correlations were found between ECa and the soil chemical and physical properties studied (r2 < 0.50), but correlations improved considerably when zone mean ECa and zone means of selected soil properties among ECa zones were compared. The results suggested that field-scale ECa survey is not able to directly predict soil nutrient levels at any specific location, but could delimit distinct zones of soil condition among which soil nutrient levels differ, providing an effective basis for soil sampling on a zone basis. (Abstract shortened by UMI.)

Efficiently enforcing artisanal fisheries to protect estuarine biodiversity.

PubMed

Duarte de Paula Costa, Micheli; Mills, Morena; Richardson, Anthony J; Fuller, Richard A; Muelbert, José H; Possingham, Hugh P

2018-06-26

Artisanal fisheries support millions of livelihoods worldwide, yet ineffective enforcement can allow for continued environmental degradation due to overexploitation. Here, we use spatial planning to design an enforcement strategy for a pre-existing spatial closure for artisanal fisheries considering climate variability, existing seasonal fishing closures, representative conservation targets and enforcement costs. We calculated enforcement cost in three ways, based on different assumptions about who could be responsible for monitoring the fishery. We applied this approach in the Patos Lagoon estuary (Brazil), where we found three important results. First, spatial priorities for enforcement were similar under different climate scenarios. Second, we found that the cost and percentage of area enforced varied among scenarios tested by the conservation planning analysis, with only a modest increase in budget needed to incorporate climate variability. Third, we found that spatial priorities for enforcement depend on whether enforcement is carried out by a central authority or by the community itself. Here, we demonstrated a method that can be used to efficiently design enforcement plans, resulting in the conservation of biodiversity and estuarine resources. Also, cost of enforcement can be potentially reduced when fishers are empowered to enforce management within their fishing grounds. © 2018 by the Ecological Society of America.

Prospects and pitfalls of occupational hazard mapping: 'between these lines there be dragons'.

PubMed

Koehler, Kirsten A; Volckens, John

2011-10-01

Hazard data mapping is a promising new technique that can enhance the process of occupational exposure assessment and risk communication. Hazard maps have the potential to improve worker health by providing key input for the design of hazard intervention and control strategies. Hazard maps are developed with aid from direct-reading instruments, which can collect highly spatially and temporally resolved data in a relatively short period of time. However, quantifying spatial-temporal variability in the occupational environment is not a straightforward process, and our lack of understanding of how to ascertain and model spatial and temporal variability is a limiting factor in the use and interpretation of workplace hazard maps. We provide an example of how sources of and exposures to workplace hazards may be mischaracterized in a hazard map due to a lack of completeness and representativeness of collected measurement data. Based on this example, we believe that a major priority for research in this emerging area should focus on the development of a statistical framework to quantify uncertainty in spatially and temporally varying data. In conjunction with this need is one for the development of guidelines and procedures for the proper sampling, generation, and evaluation of workplace hazard maps.

The geostatistical approach for structural and stratigraphic framework analysis of offshore NW Bonaparte Basin, Australia

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

Wahid, Ali, E-mail: ali.wahid@live.com; Salim, Ahmed Mohamed Ahmed, E-mail: mohamed.salim@petronas.com.my; Yusoff, Wan Ismail Wan, E-mail: wanismail-wanyusoff@petronas.com.my

2016-02-01

Geostatistics or statistical approach is based on the studies of temporal and spatial trend, which depend upon spatial relationships to model known information of variable(s) at unsampled locations. The statistical technique known as kriging was used for petrophycial and facies analysis, which help to assume spatial relationship to model the geological continuity between the known data and the unknown to produce a single best guess of the unknown. Kriging is also known as optimal interpolation technique, which facilitate to generate best linear unbiased estimation of each horizon. The idea is to construct a numerical model of the lithofacies and rockmore » properties that honor available data and further integrate with interpreting seismic sections, techtonostratigraphy chart with sea level curve (short term) and regional tectonics of the study area to find the structural and stratigraphic growth history of the NW Bonaparte Basin. By using kriging technique the models were built which help to estimate different parameters like horizons, facies, and porosities in the study area. The variograms were used to determine for identification of spatial relationship between data which help to find the depositional history of the North West (NW) Bonaparte Basin.« less

Spatial modelling of landscape aesthetic potential in urban-rural fringes.

PubMed

Sahraoui, Yohan; Clauzel, Céline; Foltête, Jean-Christophe

2016-10-01

The aesthetic potential of landscape has to be modelled to provide tools for land-use planning. This involves identifying landscape attributes and revealing individuals' landscape preferences. Landscape aesthetic judgments of individuals (n = 1420) were studied by means of a photo-based survey. A set of landscape visibility metrics was created to measure landscape composition and configuration in each photograph using spatial data. These metrics were used as explanatory variables in multiple linear regressions to explain aesthetic judgments. We demonstrate that landscape aesthetic judgments may be synthesized in three consensus groups. The statistical results obtained show that landscape visibility metrics have good explanatory power. Ultimately, we propose a spatial modelling of landscape aesthetic potential based on these results combined with systematic computation of visibility metrics. Copyright © 2016 Elsevier Ltd. All rights reserved.

Assessment of the Suitability of High Resolution Numerical Weather Model Outputs for Hydrological Modelling in Mountainous Cold Regions

NASA Astrophysics Data System (ADS)

Rasouli, K.; Pomeroy, J. W.; Hayashi, M.; Fang, X.; Gutmann, E. D.; Li, Y.

2017-12-01

The hydrology of mountainous cold regions has a large spatial variability that is driven both by climate variability and near-surface process variability associated with complex terrain and patterns of vegetation, soils, and hydrogeology. There is a need to downscale large-scale atmospheric circulations towards the fine scales that cold regions hydrological processes operate at to assess their spatial variability in complex terrain and quantify uncertainties by comparison to field observations. In this research, three high resolution numerical weather prediction models, namely, the Intermediate Complexity Atmosphere Research (ICAR), Weather Research and Forecasting (WRF), and Global Environmental Multiscale (GEM) models are used to represent spatial and temporal patterns of atmospheric conditions appropriate for hydrological modelling. An area covering high mountains and foothills of the Canadian Rockies was selected to assess and compare high resolution ICAR (1 km × 1 km), WRF (4 km × 4 km), and GEM (2.5 km × 2.5 km) model outputs with station-based meteorological measurements. ICAR with very low computational cost was run with different initial and boundary conditions and with finer spatial resolution, which allowed an assessment of modelling uncertainty and scaling that was difficult with WRF. Results show that ICAR, when compared with WRF and GEM, performs very well in precipitation and air temperature modelling in the Canadian Rockies, while all three models show a fair performance in simulating wind and humidity fields. Representation of local-scale atmospheric dynamics leading to realistic fields of temperature and precipitation by ICAR, WRF, and GEM makes these models suitable for high resolution cold regions hydrological predictions in complex terrain, which is a key factor in estimating water security in western Canada.

Regional co-variability of spatial and temporal soil moisture-precipitation coupling in North Africa: an observational perspective

NASA Astrophysics Data System (ADS)

Petrova, Irina Y.; van Heerwaarden, Chiel C.; Hohenegger, Cathy; Guichard, Françoise

2018-06-01

The magnitude and sign of soil moisture-precipitation coupling (SMPC) is investigated using a probability-based approach and 10 years of daily microwave satellite data across North Africa at a 1° horizontal scale. Specifically, the co-existence and co-variability of spatial (i.e. using soil moisture gradients) and temporal (i.e. using soil moisture anomaly) soil moisture effects on afternoon rainfall is explored. The analysis shows that in the semi-arid environment of the Sahel, the negative spatial and the negative temporal coupling relationships do not only co-exist, but are also dependent on one another. Hence, if afternoon rain falls over temporally drier soils, it is likely to be surrounded by a wetter environment. Two regions are identified as SMPC hot spots. These are the south-western part of the domain (7-15° N, 10° W-7° E), with the most robust negative SMPC signal, and the South Sudanese region (5-13° N, 24-34° E). The sign and significance of the coupling in the latter region is found to be largely modulated by the presence of wetlands and is susceptible to the number of long-lived propagating convective systems. The presence of wetlands and an irrigated land area is found to account for about 30 % of strong and significant spatial SMPC in the North African domain. This study provides the first insight into regional variability of SMPC in North Africa, and supports the potential relevance of mechanisms associated with enhanced sensible heat flux and mesoscale variability in surface soil moisture for deep convection development.

Analysis of biophysical and anthropogenic variables and their relation to the regional spatial variation of aboveground biomass illustrated for North and East Kalimantan, Borneo.

PubMed

Van der Laan, Carina; Verweij, Pita A; Quiñones, Marcela J; Faaij, André Pc

2014-12-01

Land use and land cover change occurring in tropical forest landscapes contributes substantially to carbon emissions. Better insights into the spatial variation of aboveground biomass is therefore needed. By means of multiple statistical tests, including geographically weighted regression, we analysed the effects of eight variables on the regional spatial variation of aboveground biomass. North and East Kalimantan were selected as the case study region; the third largest carbon emitting Indonesian provinces. Strong positive relationships were found between aboveground biomass and the tested variables; altitude, slope, land allocation zoning, soil type, and distance to the nearest fire, road, river and city. Furthermore, the results suggest that the regional spatial variation of aboveground biomass can be largely attributed to altitude, distance to nearest fire and land allocation zoning. Our study showed that in this landscape, aboveground biomass could not be explained by one single variable; the variables were interrelated, with altitude as the dominant variable. Spatial analyses should therefore integrate a variety of biophysical and anthropogenic variables to provide a better understanding of spatial variation in aboveground biomass. Efforts to minimise carbon emissions should incorporate the identified factors, by 1) the maintenance of lands with high AGB or carbon stocks, namely in the identified zones at the higher altitudes; and 2) regeneration or sustainable utilisation of lands with low AGB or carbon stocks, dependent on the regeneration capacity of the vegetation. Low aboveground biomass densities can be found in the lowlands in burned areas, and in non-forest zones and production forests.

Black Sea spectral bio-optical models based on satellite data and their applications for assessment of spatial and temporal variability in waters transparency, chlorophyll a content and primary production

NASA Astrophysics Data System (ADS)

Churilova, T.; Suslin, V.

2012-04-01

Satellite observations of ocean color provide a unique opportunity in oceanography to assess productivity of the sea on different spatial and temporal scales. However it has been shown that the standard SeaWiFS algorithm generally overestimates summer chlorophyll concentration and underestimates pigment content during spring phytoplankton bloom in comparison with in situ measurements. It is required to develop regional algorithms which are based on biooptical characteristics typical for the Sea and consequently could be used for correct transformation of spectral features of water-leaving radiance to chlorophyll a concentrations (Chl), light absorption features of suspended and dissolved organic matter (CDM), downwelling light attenuation coefficient/euphotic zone depth (PAR1%) and rate of primary synthesis of organic substances (PP). The numerous measurements of light absorption spectra of phytoplankton, non-algal particles and coloured dissolved organic matter carried out since 1996 in different seasons and regions of the Black Sea allowed to make a parameterization of the light absorption by all optically active components. Taking into account regional peculiarities of the biooptical parameters, their difference between seasons, shallow and deep-waters, their depth-dependent variability within photosynthetic zone regional spectral models for estimation of chlorophyll a concentration (Chl Model), colored dissolved and suspended organic matter absorption (CDM Model), downwelling irradiance (PAR Model) and primary production (PP Model) have been developed based on satellite data. Test of validation of models showed appropriate accuracy of the models. The developed models have been applied for estimation of spatial/temporal variability of chlorophyll a, dissolved organic matter concentrations, waters transparency, euphotic zone depth and primary production based on SeaWiFS data. Two weeks averaged maps of spatial distribution of these parameters have been composed for period from 1998 to 2009 (most of them presented on site http://blackseacolor.com/browser3.html). Comparative analysis of long-term series (since 1998) of these parameters with subsurface water temperature (SST) and solar radiance of the sea surface (PAR-0m) revealed the key factors determining the seasonal and inter-annual variations of Chl, PAR1%, CDM, PP. The seasonal dynamics of these parameters were more pronounced compared with inter-annual variability. The later was related to climate effect. In deep-waters region relatively lower SST during cold winters were forcing more intensive winter-spring phytoplankton bloom. In north-western shelf inter-annual variability in river (Danube) run off, which was related to climate change as well, determined year-to-year changing in Chl, CDM, PAR1%, and PP.

The Effects of Fine-scale Soil Moisture and Canopy Heterogeneities on Energy and Soil Water Fluxes in a Temperate Mixed Deciduous Forest

NASA Astrophysics Data System (ADS)

He, L.; Ivanov, V. Y.; Bohrer, G.; Maurer, K.; Vogel, C. S.; Moghaddam, M.

2011-12-01

Vegetation is heterogeneous at different scales, influencing spatially variable energy and water exchanges between land-surface and atmosphere. Current land surface parameterizations of large-scale models consider spatial variability at a scale of a few kilometers and treat vegetation cover as aggregated patches with uniform properties. However, the coupling mechanisms between fine-scale soil moisture, vegetation, and energy fluxes such as evapotranspiration are strongly nonlinear; the aggregation of surface variations may produce biased energy fluxes. This study aims to improve the understanding of the scale impact in atmosphere-biosphere-hydrosphere interactions, which affects predictive capabilities of land surface models. The study uses a high-resolution, physically-based ecohydrological model tRIBS + VEGGIE as a data integration tool to upscale the heterogeneity of canopy distribution resolved at a few meters to the watershed scale. The study was carried out for a spatially heterogeneous, temperate mixed forest environment of Northern Michigan located near the University of Michigan Biological Station (UMBS). Energy and soil water dynamics were simulated at the tree-canopy resolution in the horizontal plane for a small domain (~2 sq. km) located within a footprint of the AmeriFlux tower. A variety of observational data were used to constrain and confirm the model, including a 3-m profile continuous soil moisture dataset and energy flux data (measured at the AmeriFlux tower footprint). A scenario with a spatially uniform canopy, corresponding to the commonly used 'big-leaf' scheme in land surface parameterizations was used to infer the effects of coarse-scale averaging. To gain insights on how heterogeneous canopy and soil moisture interact and contribute to the domain-averaged transpiration, several scenarios of tree-scale leaf area and soil moisture spatial variability were designed. Specifically, for the same mean states, the scenarios of variability of canopy biomass account for the spatial distribution of photosynthesis (and thus the stomatal resistance), the aerodynamic and leaf boundary layer resistances as well as the differential radiation forcing due to tall tree exposure and lateral shading of short trees. The numerical experiments show that by transpiring spatially varying amounts of water, heterogeneous canopies adjust the spatial soil water state to the scaled inverse of the canopy biomass regardless of the initial moisture state. Such a spatial distribution can be further wiped out because of the differential water stress. The aggregation of canopy-scale atmosphere-biosphere-hydrosphere interactions demonstrates non-linear relationship between soil moisture and evapotranspiration, influencing domain-averaged energy fluxes.

Flume experimental evaluation of the effect of rill flow path tortuosity on rill roughness based on the Manning–Strickler equation

USDA-ARS?s Scientific Manuscript database

Numerous soil erosion models compute concentrated flow hydraulics based on the Manning–Strickler equation (v = kSt R2/3 I1/2) even though the range of the application on rill flow is unclear. Unconfined rill morphologies generate local friction effects and consequently spatially variable rill roughn...

A comparison of recharge rates in aquifers of the United States based on groundwater-age data

USGS Publications Warehouse

McMahon, P.B.; Plummer, Niel; Böhlke, J.K.; Shapiro, S.D.; Hinkle, S.R.

2011-01-01

An overview is presented of existing groundwater-age data and their implications for assessing rates and timescales of recharge in selected unconfined aquifer systems of the United States. Apparent age distributions in aquifers determined from chlorofluorocarbon, sulfur hexafluoride, tritium/helium-3, and radiocarbon measurements from 565 wells in 45 networks were used to calculate groundwater recharge rates. Timescales of recharge were defined by 1,873 distributed tritium measurements and 102 radiocarbon measurements from 27 well networks. Recharge rates ranged from < 10 to 1,200 mm/yr in selected aquifers on the basis of measured vertical age distributions and assuming exponential age gradients. On a regional basis, recharge rates based on tracers of young groundwater exhibited a significant inverse correlation with mean annual air temperature and a significant positive correlation with mean annual precipitation. Comparison of recharge derived from groundwater ages with recharge derived from stream base-flow evaluation showed similar overall patterns but substantial local differences. Results from this compilation demonstrate that age-based recharge estimates can provide useful insights into spatial and temporal variability in recharge at a national scale and factors controlling that variability. Local age-based recharge estimates provide empirical data and process information that are needed for testing and improving more spatially complete model-based methods.

Systems, methods, and software for determining spatially variable distributions of the dielectric properties of a heterogeneous material

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

Farrington, Stephen P.

Systems, methods, and software for measuring the spatially variable relative dielectric permittivity of materials along a linear or otherwise configured sensor element, and more specifically the spatial variability of soil moisture in one dimension as inferred from the dielectric profile of the soil matrix surrounding a linear sensor element. Various methods provided herein combine advances in the processing of time domain reflectometry data with innovations in physical sensing apparatuses. These advancements enable high temporal (and thus spatial) resolution of electrical reflectance continuously along an insulated waveguide that is permanently emplaced in contact with adjacent soils. The spatially resolved reflectance ismore » directly related to impedance changes along the waveguide that are dominated by electrical permittivity contrast due to variations in soil moisture. Various methods described herein are thus able to monitor soil moisture in profile with high spatial resolution.« less

Use of generalized linear models and digital data in a forest inventory of Northern Utah

USGS Publications Warehouse

Moisen, Gretchen G.; Edwards, Thomas C.

1999-01-01

Forest inventories, like those conducted by the Forest Service's Forest Inventory and Analysis Program (FIA) in the Rocky Mountain Region, are under increased pressure to produce better information at reduced costs. Here we describe our efforts in Utah to merge satellite-based information with forest inventory data for the purposes of reducing the costs of estimates of forest population totals and providing spatial depiction of forest resources. We illustrate how generalized linear models can be used to construct approximately unbiased and efficient estimates of population totals while providing a mechanism for prediction in space for mapping of forest structure. We model forest type and timber volume of five tree species groups as functions of a variety of predictor variables in the northern Utah mountains. Predictor variables include elevation, aspect, slope, geographic coordinates, as well as vegetation cover types based on satellite data from both the Advanced Very High Resolution Radiometer (AVHRR) and Thematic Mapper (TM) platforms. We examine the relative precision of estimates of area by forest type and mean cubic-foot volumes under six different models, including the traditional double sampling for stratification strategy. Only very small gains in precision were realized through the use of expensive photointerpreted or TM-based data for stratification, while models based on topography and spatial coordinates alone were competitive. We also compare the predictive capability of the models through various map accuracy measures. The models including the TM-based vegetation performed best overall, while topography and spatial coordinates alone provided substantial information at very low cost.

Coastal Vulnerability and risk assessment of infrastructures, natural and cultural heritage sites in Greece.

NASA Astrophysics Data System (ADS)

Alexandrakis, George; Kampanis, Nikolaos

2016-04-01

The majority of human activities are concentrated around coastal areas, making coastline retreat, a significant threat to coastal infrastructure, thus increasing protection cost and investment revenue losses. In this study the management of coastal areas in terms of protecting coastal infrastructures, cultural and environmental heritage sites, through risk assessment analysis is been made. The scope is to provide data for spatial planning for future developments in the coastal zone and the protection of existing ones. Also to determine the impact of coastal changes related to the loss of natural resources, agricultural land and beaches. The analysis is based on a multidisciplinary approach, combining environmental, spatial and economic data. This can be implemented by integrating the assessment of vulnerability of coasts, the spatial distribution and structural elements of coastal infrastructure (transport, tourism, and energy) and financial data by region, in a spatial database. The approach is based on coastal vulnerability estimations, considering sea level rise, land loss, extreme events, safety, adaptability and resilience of infrastructure and natural sites. It is based on coupling of environmental indicators and econometric models to determine the socio-economic impact in coastal infrastructure, cultural and environmental heritage sites. The indicators include variables like the coastal geomorphology; coastal slope; relative sea-level rise rate; shoreline erosion/accretion rate; mean tidal range and mean wave height. The anthropogenic factors include variables like settlements, sites of cultural heritage, transport networks, land uses, significance of infrastructure (e.g. military, power plans) and economic activities. The analysis in performed by a GIS application. The forcing variables are determined with the use of sub-indices related to coastal geomorphology, climate and wave variables and the socioeconomics of the coastal zone. The Greek coastline in considered as a case study, where the majority of the coastline appears to be undergoing erosion, with approximately 25% of the Aegean coastline, consisting mainly of beach zones and low-lying coastal (including deltaic) plains. In terms of economic activates coastal tourism is most effected, as beach zones are very high vulnerable to erosion. Also, small ports in remote islands are also found to be highly vulnerable. Acknowledgments This work was implemented within the framework of "Post-Doctoral Excellence Scholarship. State Scholarships Foundation, Greece IKY- Siemens Action"

Groundwater level responses to precipitation variability in Mediterranean insular aquifers

NASA Astrophysics Data System (ADS)

Lorenzo-Lacruz, Jorge; Garcia, Celso; Morán-Tejeda, Enrique

2017-09-01

Groundwater is one of the largest and most important sources of fresh water on many regions under Mediterranean climate conditions, which are exposed to large precipitation variability that includes frequent meteorological drought episodes, and present high evapotranspiration rates and water demand during the dry season. The dependence on groundwater increases in those areas with predominant permeable lithologies, contributing to aquifer recharge and the abundance of ephemeral streams. The increasing pressure of tourism on water resources in many Mediterranean coastal areas, and uncertainty related to future precipitation and water availability, make it urgent to understand the spatio-temporal response of groundwater bodies to precipitation variability, if sustainable use of the resource is to be achieved. We present an assessment of the response of aquifers to precipitation variability based on correlations between the Standardized Precipitation Index (SPI) at various time scales and the Standardized Groundwater Index (SGI) across a Mediterranean island. We detected three main responses of aquifers to accumulated precipitation anomalies: (i) at short time scales of the SPI (<6 months); (ii) at medium time scales (6-24 months); and at long time scales (>24 months). The differing responses were mainly explained by differences in lithology and the percentage of highly permeable rock strata in the aquifer recharge areas. We also identified differences in the months and seasons when aquifer storages are more dependent on precipitation; these were related to climate seasonality and the degree of aquifer exploitation or underground water extraction. The recharge of some aquifers, especially in mountainous areas, is related to precipitation variability within a limited spatial extent, whereas for aquifers located in the plains, precipitation variability influence much larger areas; the topography and geological structure of the island explain these differences. Results indicate large spatial variability in the response of aquifers to precipitation in a very small area, highlighting the importance of having high spatial resolution hydro-climatic databases available to enable full understanding of the effects of climate variability on scarce water resources.

Modeling the Spatial Dynamics of Regional Land Use: The CLUE-S Model

NASA Astrophysics Data System (ADS)

Verburg, Peter H.; Soepboer, Welmoed; Veldkamp, A.; Limpiada, Ramil; Espaldon, Victoria; Mastura, Sharifah S. A.

2002-09-01

Land-use change models are important tools for integrated environmental management. Through scenario analysis they can help to identify near-future critical locations in the face of environmental change. A dynamic, spatially explicit, land-use change model is presented for the regional scale: CLUE-S. The model is specifically developed for the analysis of land use in small regions (e.g., a watershed or province) at a fine spatial resolution. The model structure is based on systems theory to allow the integrated analysis of land-use change in relation to socio-economic and biophysical driving factors. The model explicitly addresses the hierarchical organization of land use systems, spatial connectivity between locations and stability. Stability is incorporated by a set of variables that define the relative elasticity of the actual land-use type to conversion. The user can specify these settings based on expert knowledge or survey data. Two applications of the model in the Philippines and Malaysia are used to illustrate the functioning of the model and its validation.

Modeling the spatial dynamics of regional land use: the CLUE-S model.

PubMed

Verburg, Peter H; Soepboer, Welmoed; Veldkamp, A; Limpiada, Ramil; Espaldon, Victoria; Mastura, Sharifah S A

2002-09-01

Land-use change models are important tools for integrated environmental management. Through scenario analysis they can help to identify near-future critical locations in the face of environmental change. A dynamic, spatially explicit, land-use change model is presented for the regional scale: CLUE-S. The model is specifically developed for the analysis of land use in small regions (e.g., a watershed or province) at a fine spatial resolution. The model structure is based on systems theory to allow the integrated analysis of land-use change in relation to socio-economic and biophysical driving factors. The model explicitly addresses the hierarchical organization of land use systems, spatial connectivity between locations and stability. Stability is incorporated by a set of variables that define the relative elasticity of the actual land-use type to conversion. The user can specify these settings based on expert knowledge or survey data. Two applications of the model in the Philippines and Malaysia are used to illustrate the functioning of the model and its validation.

Development of a microscale land use regression model for predicting NO2 concentrations at a heavy trafficked suburban area in Auckland, NZ.

PubMed

Weissert, L F; Salmond, J A; Miskell, G; Alavi-Shoshtari, M; Williams, D E

2018-04-01

Land use regression (LUR) analysis has become a key method to explain air pollutant concentrations at unmeasured sites at city or country scales, but little is known about the applicability of LUR at microscales. We present a microscale LUR model developed for a heavy trafficked section of road in Auckland, New Zealand. We also test the within-city transferability of LUR models developed at different spatial scales (local scale and city scale). Nitrogen dioxide (NO 2 ) was measured during summer at 40 sites and a LUR model was developed based on standard criteria. The results showed that LUR models are able to capture the microscale variability with the model explaining 66% of the variability in NO 2 concentrations. Predictor variables identified at this scale were street width, distance to major road, presence of awnings and number of bus stops, with the latter three also being important determinants at the local scale. This highlights the importance of street and building configurations for individual exposure at the street level. However, within-city transferability was limited with the number of bus stops being the only significant predictor variable at all spatial scales and locations tested, indicating the strong influence of diesel emissions related to bus traffic. These findings show that air quality monitoring is necessary at a high spatial density within cities in capturing small-scale variability in NO 2 concentrations at the street level and assessing individual exposure to traffic related air pollutants. Copyright © 2017. Published by Elsevier B.V.

ISM DUST GRAINS AND N-BAND SPECTRAL VARIABILITY IN THE SPATIALLY RESOLVED SUBARCSECOND BINARY UY Aur

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

Skemer, Andrew J.; Close, Laird M.; Hinz, Philip M.

2010-03-10

The 10 {mu}m silicate feature is an essential diagnostic of dust-grain growth and planet formation in young circumstellar disks. The Spitzer Space Telescope has revolutionized the study of this feature, but due to its small (85 cm) aperture, it cannot spatially resolve small/medium-separation binaries ({approx}<3''; {approx}< 420 AU) at the distances of the nearest star-forming regions ({approx}140 pc). Large, 6-10 m ground-based telescopes with mid-infrared instruments can resolve these systems. In this paper, we spatially resolve the 0.''88 binary, UY Aur, with MMTAO/BLINC-MIRAC4 mid-infrared spectroscopy. We then compare our spectra to Spitzer/IRS (unresolved) spectroscopy, and resolved images from IRTF/MIRAC2, Keck/OSCIR,more » and Gemini/Michelle, which were taken over the past decade. We find that UY Aur A has extremely pristine, interstellar medium (ISM)-like grains and that UY Aur B has an unusually shaped silicate feature, which is probably the result of blended emission and absorption from foreground extinction in its disk. We also find evidence for variability in both UY Aur A and UY Aur B by comparing synthetic photometry from our spectra with resolved imaging from previous epochs. The photometric variability of UY Aur A could be an indication that the silicate emission itself is variable, as was recently found in EX Lupi. Otherwise, the thermal continuum is variable, and either the ISM-like dust has never evolved, or it is being replenished, perhaps by UY Aur's circumbinary disk.« less

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

PubMed Central

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

2013-01-01

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

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

PubMed

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

2013-01-01

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

In vivo wide-field multispectral dosimeter for use in ALA-PpIX based photodynamic therapy of skin

NASA Astrophysics Data System (ADS)

LaRochelle, Ethan P. M.; Davis, Scott C.; de Souza, Ana Luiza Ribeiro; Pogue, Brian W.

2017-02-01

Photodynamic therapy (PDT) for Actinic Kertoses (AK) using aminoluvelinic acid (ALA) is an FDA-approved treatment, which is generally effective, yet response rates vary. The origin of the variability is not well characterized, but may be related to inter-patient variability in the production of protoporphyrin IX (PpIX). While fiber-based point probe systems provide a method for measuring PpIX production, these measurements have demonstrated large spatial and inter-operator variability. Thus, in an effort to improve patient-specific dosimetry and treatment it is important to develop a robust system that accounts for spatial variability and reduces the chance of operator errors. To address this need, a wide-field multispectral imaging system was developed that is capable of quantifying maps of PpIX in both liquid phantoms and in vivo experiments, focusing on high sensitivity light signals. The system uses both red and blue excitation to elicit a fluorescent response at varying skin depths. A ten-position filter wheel with bandpass filters ranging from 635nm to 710nm are used to capture images along the emission band. A linear least-square spectral fitting algorithm provides the ability to decouple background autofluorescence from PpIX fluorescence, which has improved the system sensitivity by an order of magnitude, detecting nanomolar PpIX concentrations in liquid phantoms in the presence of 2% whole blood and 2% intralipid.

Spring onset variations and long-term trends from new hemispheric-scale products and remote sensing

NASA Astrophysics Data System (ADS)

Dye, D. G.; Li, X.; Ault, T.; Zurita-Milla, R.; Schwartz, M. D.

2015-12-01

Spring onset is commonly characterized by plant phenophase changes among a variety of biophysical transitions and has important implications for natural and man-managed ecosystems. Here, we present a new integrated analysis of variability in gridded Northern Hemisphere spring onset metrics. We developed a set of hemispheric temperature-based spring indices spanning 1920-2013. As these were derived solely from meteorological data, they are used as a benchmark for isolating the climate system's role in modulating spring "green up" estimated from the annual cycle of normalized difference vegetation index (NDVI). Spatial patterns of interannual variations, teleconnections, and long-term trends were also analyzed in all metrics. At mid-to-high latitudes, all indices exhibit larger variability at interannual to decadal time scales than at spatial scales of a few kilometers. Trends of spring onset vary across space and time. However, compared to long-term trend, interannual to decadal variability generally accounts for a larger portion of the total variance in spring onset timing. Therefore, spring onset trends identified from short existing records may be aliased by decadal climate variations due to their limited temporal depth, even when these records span the entire satellite era. Based on our findings, we also demonstrated that our indices have skill in representing ecosystem-level spring phenology and may have important implications in understanding relationships between phenology, atmosphere dynamics and climate variability.

The concurrent multiplicative-additive approach for gauge-radar/satellite multisensor precipitation estimates

NASA Astrophysics Data System (ADS)

Garcia-Pintado, J.; Barberá, G. G.; Erena Arrabal, M.; Castillo, V. M.

2010-12-01

Objective analysis schemes (OAS), also called ``succesive correction methods'' or ``observation nudging'', have been proposed for multisensor precipitation estimation combining remote sensing data (meteorological radar or satellite) with data from ground-based raingauge networks. However, opposite to the more complex geostatistical approaches, the OAS techniques for this use are not optimized. On the other hand, geostatistical techniques ideally require, at the least, modelling the covariance from the rain gauge data at every time step evaluated, which commonly cannot be soundly done. Here, we propose a new procedure (concurrent multiplicative-additive objective analysis scheme [CMA-OAS]) for operational rainfall estimation using rain gauges and meteorological radar, which does not require explicit modelling of spatial covariances. On the basis of a concurrent multiplicative-additive (CMA) decomposition of the spatially nonuniform radar bias, within-storm variability of rainfall and fractional coverage of rainfall are taken into account. Thus both spatially nonuniform radar bias, given that rainfall is detected, and bias in radar detection of rainfall are handled. The interpolation procedure of CMA-OAS is built on the OAS, whose purpose is to estimate a filtered spatial field of the variable of interest through a successive correction of residuals resulting from a Gaussian kernel smoother applied on spatial samples. The CMA-OAS, first, poses an optimization problem at each gauge-radar support point to obtain both a local multiplicative-additive radar bias decomposition and a regionalization parameter. Second, local biases and regionalization parameters are integrated into an OAS to estimate the multisensor rainfall at the ground level. The approach considers radar estimates as background a priori information (first guess), so that nudging to observations (gauges) may be relaxed smoothly to the first guess, and the relaxation shape is obtained from the sequential optimization. The procedure is suited to relatively sparse rain gauge networks. To show the procedure, six storms are analyzed at hourly steps over 10,663 km2. Results generally indicated an improved quality with respect to other methods evaluated: a standard mean-field bias adjustment, an OAS spatially variable adjustment with multiplicative factors, ordinary cokriging, and kriging with external drift. In theory, it could be equally applicable to gauge-satellite estimates and other hydrometeorological variables.

Snowpack spatial variability: Towards understanding its effect on remote sensing measurements and snow slope stability

NASA Astrophysics Data System (ADS)

Marshall, Hans-Peter

The distribution of water in the snow-covered areas of the world is an important climate change indicator, and it is a vital component of the water cycle. At local and regional scales, the snow water equivalent (SWE), the amount of liquid water a given area of the snowpack represents, is very important for water resource management, flood forecasting, and prediction of available hydropower energy. Measurements from only a few automatic weather stations, such as the SNOTEL network, or sparse manual snowpack measurements are typically extrapolated for estimating SWE over an entire basin. Widespread spatial variability in the distribution of SWE and snowpack stratigraphy at local scales causes large errors in these basin estimates. Remote sensing measurements offer a promising alternative, due to their large spatial coverage and high temporal resolution. Although snow cover extent can currently be estimated from remote sensing data, accurately quantifying SWE from remote sensing measurements has remained difficult, due to a high sensitivity to variations in grain size and stratigraphy. In alpine snowpacks, the large degree of spatial variability of snowpack properties and geometry, caused by topographic, vegetative, and microclimatic effects, also makes prediction of snow avalanches very difficult. Ground-based radar and penetrometer measurements can quickly and accurately characterize snowpack properties and SWE in the field. A portable lightweight radar was developed, and allows a real-time estimate of SWE to within 10%, as well as measurements of depths of all major density transitions within the snowpack. New analysis techniques developed in this thesis allow accurate estimates of mechanical properties and an index of grain size to be retrieved from the SnowMicroPenetrometer. These two tools together allow rapid characterization of the snowpack's geometry, mechanical properties, and SWE, and are used to guide a finite element model to study the stress distribution on a slope. The ability to accurately characterize snowpack properties at much higher resolutions and spatial extent than previously possible will hopefully help lead to a more complete understanding of spatial variability, its effect on remote sensing measurements and snow slope stability, and result in improvements in avalanche prediction and accuracy of SWE estimates from space.