Comparison Study on the Estimation of the Spatial Distribution of Regional Soil Metal(loid)s Pollution Based on Kriging Interpolation and BP Neural Network.

PubMed

Jia, Zhenyi; Zhou, Shenglu; Su, Quanlong; Yi, Haomin; Wang, Junxiao

2017-12-26

Soil pollution by metal(loid)s resulting from rapid economic development is a major concern. Accurately estimating the spatial distribution of soil metal(loid) pollution has great significance in preventing and controlling soil pollution. In this study, 126 topsoil samples were collected in Kunshan City and the geo-accumulation index was selected as a pollution index. We used Kriging interpolation and BP neural network methods to estimate the spatial distribution of arsenic (As) and cadmium (Cd) pollution in the study area. Additionally, we introduced a cross-validation method to measure the errors of the estimation results by the two interpolation methods and discussed the accuracy of the information contained in the estimation results. The conclusions are as follows: data distribution characteristics, spatial variability, and mean square errors (MSE) of the different methods showed large differences. Estimation results from BP neural network models have a higher accuracy, the MSE of As and Cd are 0.0661 and 0.1743, respectively. However, the interpolation results show significant skewed distribution, and spatial autocorrelation is strong. Using Kriging interpolation, the MSE of As and Cd are 0.0804 and 0.2983, respectively. The estimation results have poorer accuracy. Combining the two methods can improve the accuracy of the Kriging interpolation and more comprehensively represent the spatial distribution characteristics of metal(loid)s in regional soil. The study may provide a scientific basis and technical support for the regulation of soil metal(loid) pollution.

Assessing the resolution-dependent utility of tomograms for geostatistics

USGS Publications Warehouse

Day-Lewis, F. D.; Lane, J.W.

2004-01-01

Geophysical tomograms are used increasingly as auxiliary data for geostatistical modeling of aquifer and reservoir properties. The correlation between tomographic estimates and hydrogeologic properties is commonly based on laboratory measurements, co-located measurements at boreholes, or petrophysical models. The inferred correlation is assumed uniform throughout the interwell region; however, tomographic resolution varies spatially due to acquisition geometry, regularization, data error, and the physics underlying the geophysical measurements. Blurring and inversion artifacts are expected in regions traversed by few or only low-angle raypaths. In the context of radar traveltime tomography, we derive analytical models for (1) the variance of tomographic estimates, (2) the spatially variable correlation with a hydrologic parameter of interest, and (3) the spatial covariance of tomographic estimates. Synthetic examples demonstrate that tomograms of qualitative value may have limited utility for geostatistics; moreover, the imprint of regularization may preclude inference of meaningful spatial statistics from tomograms.

Estimating regional plant biodiversity with GIS modelling

Treesearch

Louis R. Iverson; Anantha M. Prasad; Anantha M. Prasad

1998-01-01

We analyzed a statewide species database together with a county-level geographic information system to build a model based on well-surveyed areas to estimate species richness in less surveyed counties. The model involved GIS (Arc/Info) and statistics (S-PLUS), including spatial statistics (S+SpatialStats).

Estimation of crown biomass of Pinus pinaster stands and shrubland above-ground biomass using forest inventory data, remotely sensed imagery and spatial prediction models

Treesearch

H. Viana; J. Aranha; D. Lopes; Warren B. Cohen

2012-01-01

Spatially crown biomass of Pinus pinaster stands and shrubland above-ground biomass (AGB) estimation was carried-out in a region located in Centre-North Portugal, by means of different approaches including forest inventory data, remotely sensed imagery and spatial prediction models. Two cover types (pine stands and shrubland) were inventoried and...

Evaluating the influence of spatial resolution of Landsat predictors on the accuracy of biomass models for large-area estimation across the eastern USA

NASA Astrophysics Data System (ADS)

Deo, Ram K.; Domke, Grant M.; Russell, Matthew B.; Woodall, Christopher W.; Andersen, Hans-Erik

2018-05-01

Aboveground biomass (AGB) estimates for regional-scale forest planning have become cost-effective with the free access to satellite data from sensors such as Landsat and MODIS. However, the accuracy of AGB predictions based on passive optical data depends on spatial resolution and spatial extent of target area as fine resolution (small pixels) data are associated with smaller coverage and longer repeat cycles compared to coarse resolution data. This study evaluated various spatial resolutions of Landsat-derived predictors on the accuracy of regional AGB models at three different sites in the eastern USA: Maine, Pennsylvania-New Jersey, and South Carolina. We combined national forest inventory data with Landsat-derived predictors at spatial resolutions ranging from 30–1000 m to understand the optimal spatial resolution of optical data for large-area (regional) AGB estimation. Ten generic models were developed using the data collected in 2014, 2015 and 2016, and the predictions were evaluated (i) at the county-level against the estimates of the USFS Forest Inventory and Analysis Program which relied on EVALIDator tool and national forest inventory data from the 2009–2013 cycle and (ii) within a large number of strips (~1 km wide) predicted via LiDAR metrics at 30 m spatial resolution. The county-level estimates by the EVALIDator and Landsat models were highly related (R 2 > 0.66), although the R 2 varied significantly across sites and resolution of predictors. The mean and standard deviation of county-level estimates followed increasing and decreasing trends, respectively, with models of coarser resolution. The Landsat-based total AGB estimates were larger than the LiDAR-based total estimates within the strips, however the mean of AGB predictions by LiDAR were mostly within one-standard deviations of the mean predictions obtained from the Landsat-based model at any of the resolutions. We conclude that satellite data at resolutions up to 1000 m provide acceptable accuracy for continental scale analysis of AGB.

GRACE Hydrological estimates for small basins: Evaluating processing approaches on the High Plains Aquifer, USA

NASA Astrophysics Data System (ADS)

Longuevergne, Laurent; Scanlon, Bridget R.; Wilson, Clark R.

2010-11-01

The Gravity Recovery and Climate Experiment (GRACE) satellites provide observations of water storage variation at regional scales. However, when focusing on a region of interest, limited spatial resolution and noise contamination can cause estimation bias and spatial leakage, problems that are exacerbated as the region of interest approaches the GRACE resolution limit of a few hundred km. Reliable estimates of water storage variations in small basins require compromises between competing needs for noise suppression and spatial resolution. The objective of this study was to quantitatively investigate processing methods and their impacts on bias, leakage, GRACE noise reduction, and estimated total error, allowing solution of the trade-offs. Among the methods tested is a recently developed concentration algorithm called spatiospectral localization, which optimizes the basin shape description, taking into account limited spatial resolution. This method is particularly suited to retrieval of basin-scale water storage variations and is effective for small basins. To increase confidence in derived methods, water storage variations were calculated for both CSR (Center for Space Research) and GRGS (Groupe de Recherche de Géodésie Spatiale) GRACE products, which employ different processing strategies. The processing techniques were tested on the intensively monitored High Plains Aquifer (450,000 km2 area), where application of the appropriate optimal processing method allowed retrieval of water storage variations over a portion of the aquifer as small as ˜200,000 km2.

Spatial analysis of instream nitrogen loads and factors controlling nitrogen delivery to streams in the southeastern United States using spatially referenced regression on watershed attributes (SPARROW) and regional classification frameworks

USGS Publications Warehouse

Hoos, A.B.; McMahon, G.

2009-01-01

Understanding how nitrogen transport across the landscape varies with landscape characteristics is important for developing sound nitrogen management policies. We used a spatially referenced regression analysis (SPARROW) to examine landscape characteristics influencing delivery of nitrogen from sources in a watershed to stream channels. Modelled landscape delivery ratio varies widely (by a factor of 4) among watersheds in the southeastern United States - higher in the western part (Tennessee, Alabama, and Mississippi) than in the eastern part, and the average value for the region is lower compared to other parts of the nation. When we model landscape delivery ratio as a continuous function of local-scale landscape characteristics, we estimate a spatial pattern that varies as a function of soil and climate characteristics but exhibits spatial structure in residuals (observed load minus predicted load). The spatial pattern of modelled landscape delivery ratio and the spatial pattern of residuals coincide spatially with Level III ecoregions and also with hydrologic landscape regions. Subsequent incorporation into the model of these frameworks as regional scale variables improves estimation of landscape delivery ratio, evidenced by reduced spatial bias in residuals, and suggests that cross-scale processes affect nitrogen attenuation on the landscape. The model-fitted coefficient values are logically consistent with the hypothesis that broad-scale classifications of hydrologic response help to explain differential rates of nitrogen attenuation, controlling for local-scale landscape characteristics. Negative model coefficients for hydrologic landscape regions where the primary flow path is shallow ground water suggest that a lower fraction of nitrogen mass will be delivered to streams; this relation is reversed for regions where the primary flow path is overland flow.

Spatial analysis of instream nitrogen loads and factors controlling nitrogen delivery to streams in the southeastern United States using spatially referenced regression on watershed attributes (SPARROW) and regional classification frameworks

USGS Publications Warehouse

Hoos, Anne B.; McMahon, Gerard

2009-01-01

Understanding how nitrogen transport across the landscape varies with landscape characteristics is important for developing sound nitrogen management policies. We used a spatially referenced regression analysis (SPARROW) to examine landscape characteristics influencing delivery of nitrogen from sources in a watershed to stream channels. Modelled landscape delivery ratio varies widely (by a factor of 4) among watersheds in the southeastern United States—higher in the western part (Tennessee, Alabama, and Mississippi) than in the eastern part, and the average value for the region is lower compared to other parts of the nation. When we model landscape delivery ratio as a continuous function of local-scale landscape characteristics, we estimate a spatial pattern that varies as a function of soil and climate characteristics but exhibits spatial structure in residuals (observed load minus predicted load). The spatial pattern of modelled landscape delivery ratio and the spatial pattern of residuals coincide spatially with Level III ecoregions and also with hydrologic landscape regions. Subsequent incorporation into the model of these frameworks as regional scale variables improves estimation of landscape delivery ratio, evidenced by reduced spatial bias in residuals, and suggests that cross-scale processes affect nitrogen attenuation on the landscape. The model-fitted coefficient values are logically consistent with the hypothesis that broad-scale classifications of hydrologic response help to explain differential rates of nitrogen attenuation, controlling for local-scale landscape characteristics. Negative model coefficients for hydrologic landscape regions where the primary flow path is shallow ground water suggest that a lower fraction of nitrogen mass will be delivered to streams; this relation is reversed for regions where the primary flow path is overland flow.

[Estimating medicinal yield of Seutellaria baicalensis in Beijing-Tianjin-Hebei region based on 3S technology].

PubMed

Liu, Jin-xinp; Lu, Heng; Zeng, Yan; Yue, Jian-wei; Meng, Fan-yun; Zhang, Yi-guang

2012-09-01

Resources survey of traditional Chinese medicine and reserves estimation are found to be the most important issues for the protection and utilization of traditional Chinese medicine resources, this paper used multi-spatial resolution remote sensing images (RS) , geographic information systems (GIS) and global positioning system (GPS) , to establish Scutellaria resources survey of 3S data platform. Combined with the traditional field survey methods, small-scale habitat types were established based on different skullcap reserve estimation model, which can estimate reserves of the wild Scutellaria in Beijing-Tianjin-Hebei region and improve the estimation accuracy. It can provide an important parameter for the fourth national survey of traditional Chinese medicine resources and traditional Chinese medicine reserves estimates based on 3S technology by multiple spatial scales model.

Estimation of regionalized compositions: A comparison of three methods

USGS Publications Warehouse

Pawlowsky, V.; Olea, R.A.; Davis, J.C.

1995-01-01

A regionalized composition is a random vector function whose components are positive and sum to a constant at every point of the sampling region. Consequently, the components of a regionalized composition are necessarily spatially correlated. This spatial dependence-induced by the constant sum constraint-is a spurious spatial correlation and may lead to misinterpretations of statistical analyses. Furthermore, the cross-covariance matrices of the regionalized composition are singular, as is the coefficient matrix of the cokriging system of equations. Three methods of performing estimation or prediction of a regionalized composition at unsampled points are discussed: (1) the direct approach of estimating each variable separately; (2) the basis method, which is applicable only when a random function is available that can he regarded as the size of the regionalized composition under study; (3) the logratio approach, using the additive-log-ratio transformation proposed by J. Aitchison, which allows statistical analysis of compositional data. We present a brief theoretical review of these three methods and compare them using compositional data from the Lyons West Oil Field in Kansas (USA). It is shown that, although there are no important numerical differences, the direct approach leads to invalid results, whereas the basis method and the additive-log-ratio approach are comparable. ?? 1995 International Association for Mathematical Geology.

Importance of Foliar Nitrogen Concentration to Predict Forest Productivity in the Mid-Atlantic Region

Treesearch

Yude Pan; John Hom; Jennifer Jenkins; Richard Birdsey

2004-01-01

To assess what difference it might make to include spatially defined estimates of foliar nitrogen in the regional application of a forest ecosystem model (PnET-II), we composed model predictions of wood production from extensive ground-based forest inventory analysis data across the Mid-Atlantic region. Spatial variation in foliar N concentration was assigned based on...

Comparison Study on the Estimation of the Spatial Distribution of Regional Soil Metal(loid)s Pollution Based on Kriging Interpolation and BP Neural Network

PubMed Central

Zhou, Shenglu; Su, Quanlong; Yi, Haomin

2017-01-01

Soil pollution by metal(loid)s resulting from rapid economic development is a major concern. Accurately estimating the spatial distribution of soil metal(loid) pollution has great significance in preventing and controlling soil pollution. In this study, 126 topsoil samples were collected in Kunshan City and the geo-accumulation index was selected as a pollution index. We used Kriging interpolation and BP neural network methods to estimate the spatial distribution of arsenic (As) and cadmium (Cd) pollution in the study area. Additionally, we introduced a cross-validation method to measure the errors of the estimation results by the two interpolation methods and discussed the accuracy of the information contained in the estimation results. The conclusions are as follows: data distribution characteristics, spatial variability, and mean square errors (MSE) of the different methods showed large differences. Estimation results from BP neural network models have a higher accuracy, the MSE of As and Cd are 0.0661 and 0.1743, respectively. However, the interpolation results show significant skewed distribution, and spatial autocorrelation is strong. Using Kriging interpolation, the MSE of As and Cd are 0.0804 and 0.2983, respectively. The estimation results have poorer accuracy. Combining the two methods can improve the accuracy of the Kriging interpolation and more comprehensively represent the spatial distribution characteristics of metal(loid)s in regional soil. The study may provide a scientific basis and technical support for the regulation of soil metal(loid) pollution. PMID:29278363

Regional-specific Stochastic Simulation of Spatially-distributed Ground-motion Time Histories using Wavelet Packet Analysis

NASA Astrophysics Data System (ADS)

Huang, D.; Wang, G.

2014-12-01

Stochastic simulation of spatially distributed ground-motion time histories is important for performance-based earthquake design of geographically distributed systems. In this study, we develop a novel technique to stochastically simulate regionalized ground-motion time histories using wavelet packet analysis. First, a transient acceleration time history is characterized by wavelet-packet parameters proposed by Yamamoto and Baker (2013). The wavelet-packet parameters fully characterize ground-motion time histories in terms of energy content, time- frequency-domain characteristics and time-frequency nonstationarity. This study further investigates the spatial cross-correlations of wavelet-packet parameters based on geostatistical analysis of 1500 regionalized ground motion data from eight well-recorded earthquakes in California, Mexico, Japan and Taiwan. The linear model of coregionalization (LMC) is used to develop a permissible spatial cross-correlation model for each parameter group. The geostatistical analysis of ground-motion data from different regions reveals significant dependence of the LMC structure on regional site conditions, which can be characterized by the correlation range of Vs30 in each region. In general, the spatial correlation and cross-correlation of wavelet-packet parameters are stronger if the site condition is more homogeneous. Using the regional-specific spatial cross-correlation model and cokriging technique, wavelet packet parameters at unmeasured locations can be best estimated, and regionalized ground-motion time histories can be synthesized. Case studies and blind tests demonstrated that the simulated ground motions generally agree well with the actual recorded data, if the influence of regional-site conditions is considered. The developed method has great potential to be used in computational-based seismic analysis and loss estimation in a regional scale.

A refined 2010-based VOC emission inventory and its improvement on modeling regional ozone in the Pearl River Delta Region, China.

PubMed

Yin, Shasha; Zheng, Junyu; Lu, Qing; Yuan, Zibing; Huang, Zhijiong; Zhong, Liuju; Lin, Hui

2015-05-01

Accurate and gridded VOC emission inventories are important for improving regional air quality model performance. In this study, a four-level VOC emission source categorization system was proposed. A 2010-based gridded Pearl River Delta (PRD) regional VOC emission inventory was developed with more comprehensive source coverage, latest emission factors, and updated activity data. The total anthropogenic VOC emission was estimated to be about 117.4 × 10(4)t, in which on-road mobile source shared the largest contribution, followed by industrial solvent use and industrial processes sources. Among the industrial solvent use source, furniture manufacturing and shoemaking were major VOC emission contributors. The spatial surrogates of VOC emission were updated for major VOC sources such as industrial sectors and gas stations. Subsector-based temporal characteristics were investigated and their temporal variations were characterized. The impacts of updated VOC emission estimates and spatial surrogates were evaluated by modeling O₃ concentration in the PRD region in the July and October of 2010, respectively. The results indicated that both updated emission estimates and spatial allocations can effectively reduce model bias on O₃ simulation. Further efforts should be made on the refinement of source classification, comprehensive collection of activity data, and spatial-temporal surrogates in order to reduce uncertainty in emission inventory and improve model performance. Copyright © 2015 Elsevier B.V. All rights reserved.

Paleohydrologic techniques used to define the spatial occurrence of floods

USGS Publications Warehouse

Jarrett, R.D.

1990-01-01

Defining the cause and spatial characteristics of floods may be difficult because of limited streamflow and precipitation data. New paleohydrologic techniques that incorporate information from geomorphic, sedimentologic, and botanic studies provide important supplemental information to define homogeneous hydrologic regions. These techniques also help to define the spatial structure of rainstorms and floods and improve regional flood-frequency estimates. The occurrence and the non-occurrence of paleohydrologic evidence of floods, such as flood bars, alluvial fans, and tree scars, provide valuable hydrologic information. The paleohydrologic research to define the spatial characteristics of floods improves the understanding of flood hydrometeorology. This research was used to define the areal extent and contributing drainage area of flash floods in Colorado. Also, paleohydrologic evidence was used to define the spatial boundaries for the Colorado foothills region in terms of the meteorologic cause of flooding and elevation. In general, above 2300 m, peak flows are caused by snowmelt. Below 2300 m, peak flows primarily are caused by rainfall. The foothills region has an upper elevation limit of about 2300 m and a lower elevation limit of about 1500 m. Regional flood-frequency estimates that incorporate the paleohydrologic information indicate that the Big Thompson River flash flood of 1976 had a recurrence interval of approximately 10,000 years. This contrasts markedly with 100 to 300 years determined by using conventional hydrologic analyses. Flood-discharge estimates based on rainfall-runoff methods in the foothills of Colorado result in larger values than those estimated with regional flood-frequency relations, which are based on long-term streamflow data. Preliminary hydrologic and paleohydrologic research indicates that intense rainfall does not occur at higher elevations in other Rocky Mountain states and that the highest elevations for rainfall-producing floods vary by latitude. The study results have implications for floodplain management and design of hydraulic structures in the mountains of Colorado and other Rocky Mountain States. ?? 1990.

Estimating Gross Primary Production in Cropland with High Spatial and Temporal Scale Remote Sensing Data

NASA Astrophysics Data System (ADS)

Lin, S.; Li, J.; Liu, Q.

2018-04-01

Satellite remote sensing data provide spatially continuous and temporally repetitive observations of land surfaces, and they have become increasingly important for monitoring large region of vegetation photosynthetic dynamic. But remote sensing data have their limitation on spatial and temporal scale, for example, higher spatial resolution data as Landsat data have 30-m spatial resolution but 16 days revisit period, while high temporal scale data such as geostationary data have 30-minute imaging period, which has lower spatial resolution (> 1 km). The objective of this study is to investigate whether combining high spatial and temporal resolution remote sensing data can improve the gross primary production (GPP) estimation accuracy in cropland. For this analysis we used three years (from 2010 to 2012) Landsat based NDVI data, MOD13 vegetation index product and Geostationary Operational Environmental Satellite (GOES) geostationary data as input parameters to estimate GPP in a small region cropland of Nebraska, US. Then we validated the remote sensing based GPP with the in-situ measurement carbon flux data. Results showed that: 1) the overall correlation between GOES visible band and in-situ measurement photosynthesis active radiation (PAR) is about 50 % (R2 = 0.52) and the European Center for Medium-Range Weather Forecasts ERA-Interim reanalysis data can explain 64 % of PAR variance (R2 = 0.64); 2) estimating GPP with Landsat 30-m spatial resolution data and ERA daily meteorology data has the highest accuracy(R2 = 0.85, RMSE < 3 gC/m2/day), which has better performance than using MODIS 1-km NDVI/EVI product import; 3) using daily meteorology data as input for GPP estimation in high spatial resolution data would have higher relevance than 8-day and 16-day input. Generally speaking, using the high spatial resolution and high frequency satellite based remote sensing data can improve GPP estimation accuracy in cropland.

Downscaling soil moisture over regions that include multiple coarse-resolution grid cells

USDA-ARS?s Scientific Manuscript database

Many applications require soil moisture estimates over large spatial extents (30-300 km) and at fine-resolutions (10-30 m). Remote-sensing methods can provide soil moisture estimates over very large spatial extents (continental to global) at coarse resolutions (10-40 km), but their output must be d...

Evaluation of dynamically downscaled extreme temperature using a spatially-aggregated generalized extreme value (GEV) model

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

Wang, Jiali; Han, Yuefeng; Stein, Michael L.

2016-02-10

The Weather Research and Forecast (WRF) model downscaling skill in extreme maximum daily temperature is evaluated by using the generalized extreme value (GEV) distribution. While the GEV distribution has been used extensively in climatology and meteorology for estimating probabilities of extreme events, accurately estimating GEV parameters based on data from a single pixel can be difficult, even with fairly long data records. This work proposes a simple method assuming that the shape parameter, the most difficult of the three parameters to estimate, does not vary over a relatively large region. This approach is applied to evaluate 31-year WRF-downscaled extreme maximummore » temperature through comparison with North American Regional Reanalysis (NARR) data. Uncertainty in GEV parameter estimates and the statistical significance in the differences of estimates between WRF and NARR are accounted for by conducting bootstrap resampling. Despite certain biases over parts of the United States, overall, WRF shows good agreement with NARR in the spatial pattern and magnitudes of GEV parameter estimates. Both WRF and NARR show a significant increase in extreme maximum temperature over the southern Great Plains and southeastern United States in January and over the western United States in July. The GEV model shows clear benefits from the regionally constant shape parameter assumption, for example, leading to estimates of the location and scale parameters of the model that show coherent spatial patterns.« less

Effects of finite spatial resolution on quantitative CBF images from dynamic PET

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

Phelps, M.E.; Huang, S.C.; Mahoney, D.K.

1985-05-01

The finite spatial resolution of PET causes the time-activity responses on pixels around the boundaries between gray and white matter regions to contain kinetic components from tissues of different CBF's. CBF values estimated from kinetics of such mixtures are underestimated because of the nonlinear relationship between the time-activity response and the estimated CBF. Computer simulation is used to investigate these effects on phantoms of circular structures and realistic brain slice in terms of object size and quantitative CBF values. The CBF image calculated is compared to the case of having resolution loss alone. Results show that the size of amore » high flow region in the CBF image is decreased while that of a low flow region is increased. For brain phantoms, the qualitative appearance of CBF images is not seriously affected, but the estimated CBF's are underestimated by 11 to 16 percent in local gray matter regions (of size 1 cm/sup 2/) with about 14 percent reduction in global CBF over the whole slice. It is concluded that the combined effect of finite spatial resolution and the nonlinearity in estimating CBF from dynamic PET is quite significant and must be considered in processing and interpreting quantitative CBF images.« less

Stress before and after the 2002 Denali fault earthquake

USGS Publications Warehouse

Wesson, R.L.; Boyd, O.S.

2007-01-01

Spatially averaged, absolute deviatoric stress tensors along the faults ruptured during the 2002 Denali fault earthquake, both before and after the event, are derived, using a new method, from estimates of the orientations of the principal stresses and the stress change associated with the earthquake. Stresses are estimated in three regions along the Denali fault, one of which also includes the Susitna Glacier fault, and one region along the Totschunda fault. Estimates of the spatially averaged shear stress before the earthquake resolved onto the faults that ruptured during the event range from near 1 MPa to near 4 MPa. Shear stresses estimated along the faults in all these regions after the event are near zero (0 ?? 1 MPa). These results suggest that deviatoric stresses averaged over a few tens of km along strike are low, and that the stress drop during the earthquake was complete or nearly so.

Applying spatial analysis techniques to assess the suitability of multipurpose uses of spring water in the Jiaosi Hot Spring Region, Taiwan.

PubMed

Jang, Cheng-Shin; Huang, Han-Chen

2017-07-01

The Jiaosi Hot Spring Region is one of the most famous tourism destinations in Taiwan. The spring water is processed for various uses, including irrigation, aquaculture, swimming, bathing, foot spas, and recreational tourism. Moreover, the multipurpose uses of spring water can be dictated by the temperature of the water. To evaluate the suitability of spring water for these various uses, this study spatially characterized the spring water temperatures of the Jiaosi Hot Spring Region by integrating ordinary kriging (OK), sequential Gaussian simulation (SGS), and Geographic information system (GIS). First, variogram analyses were used to determine the spatial variability of spring water temperatures. Next, OK and SGS were adopted to model the spatial uncertainty and distributions of the spring water temperatures. Finally, the land use (i.e., agriculture, dwelling, public land, and recreation) was determined using GIS and combined with the estimated distributions of the spring water temperatures. A suitable development strategy for the multipurpose uses of spring water is proposed according to the integration of the land use and spring water temperatures. The study results indicate that the integration of OK, SGS, and GIS is capable of characterizing spring water temperatures and the suitability of multipurpose uses of spring water. SGS realizations are more robust than OK estimates for characterizing spring water temperatures compared to observed data. Furthermore, current land use is almost ideal in the Jiaosi Hot Spring Region according to the estimated spatial pattern of spring water temperatures.

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.

Vertical land motion controls regional sea level rise patterns on the United States east coast since 1900

NASA Astrophysics Data System (ADS)

Piecuch, C. G.; Huybers, P. J.; Hay, C.; Mitrovica, J. X.; Little, C. M.; Ponte, R. M.; Tingley, M.

2017-12-01

Understanding observed spatial variations in centennial relative sea level trends on the United States east coast has important scientific and societal applications. Past studies based on models and proxies variously suggest roles for crustal displacement, ocean dynamics, and melting of the Greenland ice sheet. Here we perform joint Bayesian inference on regional relative sea level, vertical land motion, and absolute sea level fields based on tide gauge records and GPS data. Posterior solutions show that regional vertical land motion explains most (80% median estimate) of the spatial variance in the large-scale relative sea level trend field on the east coast over 1900-2016. The posterior estimate for coastal absolute sea level rise is remarkably spatially uniform compared to previous studies, with a spatial average of 1.4-2.3 mm/yr (95% credible interval). Results corroborate glacial isostatic adjustment models and reveal that meaningful long-period, large-scale vertical velocity signals can be extracted from short GPS records.

Evaluating a Local Ensemble Transform Kalman Filter snow cover data assimilation method to estimate SWE within a high-resolution hydrologic modeling framework across Western US mountainous regions

NASA Astrophysics Data System (ADS)

Oaida, C. M.; Andreadis, K.; Reager, J. T., II; Famiglietti, J. S.; Levoe, S.

2017-12-01

Accurately estimating how much snow water equivalent (SWE) is stored in mountainous regions characterized by complex terrain and snowmelt-driven hydrologic cycles is not only greatly desirable, but also a big challenge. Mountain snowpack exhibits high spatial variability across a broad range of spatial and temporal scales due to a multitude of physical and climatic factors, making it difficult to observe or estimate in its entirety. Combing remotely sensed data and high resolution hydrologic modeling through data assimilation (DA) has the potential to provide a spatially and temporally continuous SWE dataset at horizontal scales that capture sub-grid snow spatial variability and are also relevant to stakeholders such as water resource managers. Here, we present the evaluation of a new snow DA approach that uses a Local Ensemble Transform Kalman Filter (LETKF) in tandem with the Variable Infiltration Capacity macro-scale hydrologic model across the Western United States, at a daily temporal resolution, and a horizontal resolution of 1.75 km x 1.75 km. The LETKF is chosen for its relative simplicity, ease of implementation, and computational efficiency and scalability. The modeling/DA system assimilates daily MODIS Snow Covered Area and Grain Size (MODSCAG) fractional snow cover over, and has been developed to efficiently calculate SWE estimates over extended periods of time and covering large regional-scale areas at relatively high spatial resolution, ultimately producing a snow reanalysis-type dataset. Here we focus on the assessment of SWE produced by the DA scheme over several basins in California's Sierra Nevada Mountain range where Airborne Snow Observatory data is available, during the last five water years (2013-2017), which include both one of the driest and one of the wettest years. Comparison against such a spatially distributed SWE observational product provides a greater understanding of the model's ability to estimate SWE and SWE spatial variability, and highlights under which conditions snow cover DA can add value in estimating SWE.

Replacement cost valuation of Northern Pintail (Anas acuta) subsistence harvest in Arctic and sub-Arctic North America

USGS Publications Warehouse

Goldstein, Joshua H.; Thogmartin, Wayne E.; Bagstad, Kenneth J.; Dubovsky, James A.; Mattsson, Brady J.; Semmens, Darius J.; López-Hoffman, Laura; Diffendorfer, James E.

2014-01-01

Migratory species provide economically beneficial ecosystem services to people throughout their range, yet often, information is lacking about the magnitude and spatial distribution of these benefits at regional scales. We conducted a case study for Northern Pintails (hereafter pintail) in which we quantified regional and sub-regional economic values of subsistence harvest to indigenous communities in Arctic and sub-Arctic North America. As a first step, we used the replacement cost method to quantify the cost of replacing pintail subsistence harvest with the most similar commercially available protein (chicken). For an estimated annual subsistence harvest of ˜15,000 pintail, our mean estimate of the total replacement cost was ˜$63,000 yr−1 ($2010 USD), with sub-regional values ranging from \\$263 yr−1 to \\$21,930 yr−1. Our results provide an order-of-magnitude, conservative estimate of one component of the regional ecosystem-service values of pintails, providing perspective on how spatially explicit values can inform migratory species conservation.

Estimation of regional differences in wind erosion sensitivity in Hungary

NASA Astrophysics Data System (ADS)

Mezősi, G.; Blanka, V.; Bata, T.; Kovács, F.; Meyer, B.

2015-01-01

In Hungary, wind erosion is one of the most serious natural hazards. Spatial and temporal variation in the factors that determine the location and intensity of wind erosion damage are not well known, nor are the regional and local sensitivities to erosion. Because of methodological challenges, no multi-factor, regional wind erosion sensitivity map is available for Hungary. The aim of this study was to develop a method to estimate the regional differences in wind erosion sensitivity and exposure in Hungary. Wind erosion sensitivity was modelled using the key factors of soil sensitivity, vegetation cover and wind erodibility as proxies. These factors were first estimated separately by factor sensitivity maps and later combined by fuzzy logic into a regional-scale wind erosion sensitivity map. Large areas were evaluated by using publicly available data sets of remotely sensed vegetation information, soil maps and meteorological data on wind speed. The resulting estimates were verified by field studies and examining the economic losses from wind erosion as compensated by the state insurance company. The spatial resolution of the resulting sensitivity map is suitable for regional applications, as identifying sensitive areas is the foundation for diverse land development control measures and implementing management activities.

sGD: software for estimating spatially explicit indices of genetic diversity.

PubMed

Shirk, A J; Cushman, S A

2011-09-01

Anthropogenic landscape changes have greatly reduced the population size, range and migration rates of many terrestrial species. The small local effective population size of remnant populations favours loss of genetic diversity leading to reduced fitness and adaptive potential, and thus ultimately greater extinction risk. Accurately quantifying genetic diversity is therefore crucial to assessing the viability of small populations. Diversity indices are typically calculated from the multilocus genotypes of all individuals sampled within discretely defined habitat patches or larger regional extents. Importantly, discrete population approaches do not capture the clinal nature of populations genetically isolated by distance or landscape resistance. Here, we introduce spatial Genetic Diversity (sGD), a new spatially explicit tool to estimate genetic diversity based on grouping individuals into potentially overlapping genetic neighbourhoods that match the population structure, whether discrete or clinal. We compared the estimates and patterns of genetic diversity using patch or regional sampling and sGD on both simulated and empirical populations. When the population did not meet the assumptions of an island model, we found that patch and regional sampling generally overestimated local heterozygosity, inbreeding and allelic diversity. Moreover, sGD revealed fine-scale spatial heterogeneity in genetic diversity that was not evident with patch or regional sampling. These advantages should provide a more robust means to evaluate the potential for genetic factors to influence the viability of clinal populations and guide appropriate conservation plans. © 2011 Blackwell Publishing Ltd.

Estimation of Global 1km-grid Terrestrial Carbon Exchange Part II: Evaluations and Applications

NASA Astrophysics Data System (ADS)

Murakami, K.; Sasai, T.; Kato, S.; Niwa, Y.; Saito, M.; Takagi, H.; Matsunaga, T.; Hiraki, K.; Maksyutov, S. S.; Yokota, T.

2015-12-01

Global terrestrial carbon cycle largely depends on a spatial pattern in land cover type, which is heterogeneously-distributed over regional and global scales. Many studies have been trying to reveal distribution of carbon exchanges between terrestrial ecosystems and atmosphere for understanding global carbon cycle dynamics by using terrestrial biosphere models, satellite data, inventory data, and so on. However, most studies remained within several tens of kilometers grid spatial resolution, and the results have not been enough to understand the detailed pattern of carbon exchanges based on ecological community and to evaluate the carbon stocks by forest ecosystems in each countries. Improving the sophistication of spatial resolution is obviously necessary to enhance the accuracy of carbon exchanges. Moreover, the improvement may contribute to global warming awareness, policy makers and other social activities. We show global terrestrial carbon exchanges (net ecosystem production, net primary production, and gross primary production) with 1km-grid resolution. The methodology for these estimations are shown in the 2015 AGU FM poster "Estimation of Global 1km-grid Terrestrial Carbon Exchange Part I: Developing Inputs and Modelling". In this study, we evaluated the carbon exchanges in various regions with other approaches. We used the satellite-driven biosphere model (BEAMS) as our estimations, GOSAT L4A CO2 flux data, NEP retrieved by NICAM and CarbonTracer2013 flux data, for period from Jun 2001 to Dec 2012. The temporal patterns for this period were indicated similar trends between BEAMS, GOSAT, NICAM, and CT2013 in many sub-continental regions. Then, we estimated the terrestrial carbon exchanges in each countries, and could indicated the temporal patterns of the exchanges in large carbon stock regions.Global terrestrial carbon cycle largely depends on a spatial pattern of land cover type, which is heterogeneously-distributed over regional and global scales. Many studies have been trying to reveal distribution of carbon exchanges between terrestrial ecosystems and atmosphere for understanding global carbon cycle dynamics by using terrestrial biosphere models, satellite data, inventory data, and so on. However, most studies remained within several tens of kilometers grid spatial resolution, and the results have not been enough to understand the detailed pattern of carbon exchanges based on ecological community and to evaluate the carbon stocks by forest ecosystems in each countries. Improving the sophistication of spatial resolution is obviously necessary to enhance the accuracy of carbon exchanges. Moreover, the improvement may contribute to global warming awareness, policy makers and other social activities. We show global terrestrial carbon exchanges (net ecosystem production, net primary production, and gross primary production) with 1km-grid resolution. The methodology for these estimations are shown in the 2015 AGU FM poster "Estimation of Global 1km-grid Terrestrial Carbon Exchange Part I: Developing Inputs and Modelling". In this study, we evaluated the carbon exchanges in various regions with other approaches. We used the satellite-driven biosphere model (BEAMS) as our estimations, GOSAT L4A CO2 flux data, NEP retrieved by NICAM and CarbonTracer2013 flux data, for period from Jun 2001 to Dec 2012. The temporal patterns for this period were indicated similar trends between BEAMS, GOSAT, NICAM, and CT2013 in many sub-continental regions. Then, we estimated the terrestrial carbon exchanges in each countries, and could indicated the temporal patterns of the exchanges in large carbon stock regions.

Sampling design optimization for spatial functions

USGS Publications Warehouse

Olea, R.A.

1984-01-01

A new procedure is presented for minimizing the sampling requirements necessary to estimate a mappable spatial function at a specified level of accuracy. The technique is based on universal kriging, an estimation method within the theory of regionalized variables. Neither actual implementation of the sampling nor universal kriging estimations are necessary to make an optimal design. The average standard error and maximum standard error of estimation over the sampling domain are used as global indices of sampling efficiency. The procedure optimally selects those parameters controlling the magnitude of the indices, including the density and spatial pattern of the sample elements and the number of nearest sample elements used in the estimation. As an illustration, the network of observation wells used to monitor the water table in the Equus Beds of Kansas is analyzed and an improved sampling pattern suggested. This example demonstrates the practical utility of the procedure, which can be applied equally well to other spatial sampling problems, as the procedure is not limited by the nature of the spatial function. ?? 1984 Plenum Publishing Corporation.

A Robust and Multi-Weighted Approach to Estimating Topographically Correlated Tropospheric Delays in Radar Interferograms

PubMed Central

Zhu, Bangyan; Li, Jiancheng; Chu, Zhengwei; Tang, Wei; Wang, Bin; Li, Dawei

2016-01-01

Spatial and temporal variations in the vertical stratification of the troposphere introduce significant propagation delays in interferometric synthetic aperture radar (InSAR) observations. Observations of small amplitude surface deformations and regional subsidence rates are plagued by tropospheric delays, and strongly correlated with topographic height variations. Phase-based tropospheric correction techniques assuming a linear relationship between interferometric phase and topography have been exploited and developed, with mixed success. Producing robust estimates of tropospheric phase delay however plays a critical role in increasing the accuracy of InSAR measurements. Meanwhile, few phase-based correction methods account for the spatially variable tropospheric delay over lager study regions. Here, we present a robust and multi-weighted approach to estimate the correlation between phase and topography that is relatively insensitive to confounding processes such as regional subsidence over larger regions as well as under varying tropospheric conditions. An expanded form of robust least squares is introduced to estimate the spatially variable correlation between phase and topography by splitting the interferograms into multiple blocks. Within each block, correlation is robustly estimated from the band-filtered phase and topography. Phase-elevation ratios are multiply- weighted and extrapolated to each persistent scatter (PS) pixel. We applied the proposed method to Envisat ASAR images over the Southern California area, USA, and found that our method mitigated the atmospheric noise better than the conventional phase-based method. The corrected ground surface deformation agreed better with those measured from GPS. PMID:27420066

A Robust and Multi-Weighted Approach to Estimating Topographically Correlated Tropospheric Delays in Radar Interferograms.

PubMed

Zhu, Bangyan; Li, Jiancheng; Chu, Zhengwei; Tang, Wei; Wang, Bin; Li, Dawei

2016-07-12

Spatial and temporal variations in the vertical stratification of the troposphere introduce significant propagation delays in interferometric synthetic aperture radar (InSAR) observations. Observations of small amplitude surface deformations and regional subsidence rates are plagued by tropospheric delays, and strongly correlated with topographic height variations. Phase-based tropospheric correction techniques assuming a linear relationship between interferometric phase and topography have been exploited and developed, with mixed success. Producing robust estimates of tropospheric phase delay however plays a critical role in increasing the accuracy of InSAR measurements. Meanwhile, few phase-based correction methods account for the spatially variable tropospheric delay over lager study regions. Here, we present a robust and multi-weighted approach to estimate the correlation between phase and topography that is relatively insensitive to confounding processes such as regional subsidence over larger regions as well as under varying tropospheric conditions. An expanded form of robust least squares is introduced to estimate the spatially variable correlation between phase and topography by splitting the interferograms into multiple blocks. Within each block, correlation is robustly estimated from the band-filtered phase and topography. Phase-elevation ratios are multiply- weighted and extrapolated to each persistent scatter (PS) pixel. We applied the proposed method to Envisat ASAR images over the Southern California area, USA, and found that our method mitigated the atmospheric noise better than the conventional phase-based method. The corrected ground surface deformation agreed better with those measured from GPS.

Evaluating the Impact of Spatial Resolution of Landsat Predictors on the Accuracy of Biomass Models for Large-area Estimation Across the Eastern USA

NASA Astrophysics Data System (ADS)

Deo, R. K.; Domke, G. M.; Russell, M.; Woodall, C. W.

2017-12-01

Landsat data have been widely used to support strategic forest inventory and management decisions despite the limited success of passive optical remote sensing for accurate estimation of aboveground biomass (AGB). The archive of publicly available Landsat data, available at 30-m spatial resolutions since 1984, has been a valuable resource for cost-effective large-area estimation of AGB to inform national requirements such as for the US national greenhouse gas inventory (NGHGI). In addition, other optical satellite data such as MODIS imagery of wider spatial coverage and higher temporal resolution are enriching the domain of spatial predictors for regional scale mapping of AGB. Because NGHGIs require national scale AGB information and there are tradeoffs in the prediction accuracy versus operational efficiency of Landsat, this study evaluated the impact of various resolutions of Landsat predictors on the accuracy of regional AGB models across three different sites in the eastern USA: Maine, Pennsylvania-New Jersey, and South Carolina. We used recent national forest inventory (NFI) data with numerous Landsat-derived predictors at ten different spatial resolutions ranging from 30 to 1000 m to understand the optimal spatial resolution of the optical data for enhanced spatial inventory of AGB for NGHGI reporting. Ten generic spatial models at different spatial resolutions were developed for all sites and large-area estimates were evaluated (i) at the county-level against the independent designed-based estimates via the US NFI Evalidator tool and (ii) within a large number of strips ( 1 km wide) predicted via LiDAR metrics at a high spatial resolution. The county-level estimates by the Evalidator and Landsat models were statistically equivalent and produced coefficients of determination (R2) above 0.85 that varied with sites and resolution of predictors. The mean and standard deviation of county-level estimates followed increasing and decreasing trends, respectively, with models of decreasing resolutions. The Landsat-based total AGB estimates within the strips against the total AGB obtained using LiDAR metrics did not differ significantly and were within ±15 Mg/ha for each of the sites. We conclude that the optical satellite data at resolutions up to 1000 m provide acceptable accuracy for the US' NGHGI.

Transport of oxygen ions in Er doped La2Mo2O9 oxide ion conductors: Correlation with microscopic length scales

NASA Astrophysics Data System (ADS)

Paul, T.; Ghosh, A.

2018-01-01

We report oxygen ion transport in La2-xErxMo2O9 (0.05 ≤ x ≤ 0.25) oxide ion conductors. We have measured conductivity and dielectric spectra at different temperatures in a wide frequency range. The mean square displacement and spatial extent of non-random sub-diffusive regions are estimated from the conductivity spectra and dielectric spectra, respectively, using linear response theory. The composition dependence of the conductivity is observed to be similar to that of the spatial extent of non-random sub-diffusive regions. The behavior of the composition dependence of the mean square displacement of oxygen ions is opposite to that of the conductivity. The attempt frequency estimated from the analysis of the electric modulus agrees well with that obtained from the Raman spectra analysis. The full Rietveld refinement of X-ray diffraction data of the samples is performed to estimate the distance between different oxygen lattice sites. The results obtained from such analysis confirm the ion hopping within the spatial extent of non-random sub-diffusive regions.

A new framework for estimating return levels using regional frequency analysis

NASA Astrophysics Data System (ADS)

Winter, Hugo; Bernardara, Pietro; Clegg, Georgina

2017-04-01

We propose a new framework for incorporating more spatial and temporal information into the estimation of extreme return levels. Currently, most studies use extreme value models applied to data from a single site; an approach which is inefficient statistically and leads to return level estimates that are less physically realistic. We aim to highlight the benefits that could be obtained by using methodology based upon regional frequency analysis as opposed to classic single site extreme value analysis. This motivates a shift in thinking, which permits the evaluation of local and regional effects and makes use of the wide variety of data that are now available on high temporal and spatial resolutions. The recent winter storms over the UK during the winters of 2013-14 and 2015-16, which have caused wide-ranging disruption and damaged important infrastructure, provide the main motivation for the current work. One of the most impactful natural hazards is flooding, which is often initiated by extreme precipitation. In this presentation, we focus on extreme rainfall, but shall discuss other meteorological variables alongside potentially damaging hazard combinations. To understand the risks posed by extreme precipitation, we need reliable statistical models which can be used to estimate quantities such as the T-year return level, i.e. the level which is expected to be exceeded once every T-years. Extreme value theory provides the main collection of statistical models that can be used to estimate the risks posed by extreme precipitation events. Broadly, at a single site, a statistical model is fitted to exceedances of a high threshold and the model is used to extrapolate to levels beyond the range of the observed data. However, when we have data at many sites over a spatial domain, fitting a separate model for each separate site makes little sense and it would be better if we could incorporate all this information to improve the reliability of return level estimates. Here, we use the regional frequency analysis approach to define homogeneous regions which are affected by the same storms. Extreme value models are then fitted to the data pooled from across a region. We find that this approach leads to more spatially consistent return level estimates with reduced uncertainty bounds.

Comparing Top-down and Bottom-up Estimates of Methane Emissions across Multiple U.S. Basins Provides Insights into National Oil and Gas Emissions and Mitigation Strategies

NASA Astrophysics Data System (ADS)

Hamburg, S.; Alvarez, R.; Lyon, D. R.; Zavala-Araiza, D.

2016-12-01

Several recent studies quantified regional methane emissions in U.S. oil and gas (O&G) basins using top-down approaches such as airborne mass balance measurements. These studies apportioned total methane emissions to O&G based on hydrocarbon ratios or subtracting bottom-up estimates of other sources. In most studies, top-down estimates of O&G methane emissions exceeded bottom-up emission inventories. An exception is the Barnett Shale Coordinated Campaign, which found agreement between aircraft mass balance estimates and a custom emission inventory. Reconciliation of Barnett Shale O&G emissions depended on two key features: 1) matching the spatial domains of top-down and bottom-up estimates, and 2) accounting for fat-tail sources in site-level emission factors. We construct spatially explicit custom emission inventories for domains with top-down O&G emission estimates in eight major U.S. oil and gas production basins using a variety of data sources including a spatially-allocated U.S. EPA Greenhouse Gas Inventory, the EPA Greenhouse Gas Reporting Program, state emission inventories, and recently published measurement studies. A comparison of top-down and our bottom-up estimates of O&G emissions constrains the gap between these approaches and elucidates regional variability in production-normalized loss rates. A comparison of component-level and site-level emission estimates of production sites in the Barnett Shale region - where comprehensive activity data and emissions estimates are available - indicates that abnormal process conditions contribute about 20% of regional O&G emissions. Combining these two analyses provides insights into the relative importance of different equipment, processes, and malfunctions to emissions in each basin. These data allow us to estimate the U.S. O&G supply chain loss rate, recommend mitigation strategies to reduce emissions from existing infrastructure, and discuss how a similar approach can be applied internationally.

Observation-Driven Estimation of the Spatial Variability of 20th Century Sea Level Rise

NASA Astrophysics Data System (ADS)

Hamlington, B. D.; Burgos, A.; Thompson, P. R.; Landerer, F. W.; Piecuch, C. G.; Adhikari, S.; Caron, L.; Reager, J. T.; Ivins, E. R.

2018-03-01

Over the past two decades, sea level measurements made by satellites have given clear indications of both global and regional sea level rise. Numerous studies have sought to leverage the modern satellite record and available historic sea level data provided by tide gauges to estimate past sea level rise, leading to several estimates for the 20th century trend in global mean sea level in the range between 1 and 2 mm/yr. On regional scales, few attempts have been made to estimate trends over the same time period. This is due largely to the inhomogeneity and quality of the tide gauge network through the 20th century, which render commonly used reconstruction techniques inadequate. Here, a new approach is adopted, integrating data from a select set of tide gauges with prior estimates of spatial structure based on historical sea level forcing information from the major contributing processes over the past century. The resulting map of 20th century regional sea level rise is optimized to agree with the tide gauge-measured trends, and provides an indication of the likely contributions of different sources to regional patterns. Of equal importance, this study demonstrates the sensitivities of this regional trend map to current knowledge and uncertainty of the contributing processes.

Evaluation and comparison of methods to estimate irrigation withdrawal for the National Water Census Focus Area Study of the Apalachicola-Chattahoochee-Flint River Basin in southwestern Georgia

USGS Publications Warehouse

Painter, Jaime A.; Torak, Lynn J.; Jones, John W.

2015-09-30

Methods to estimate irrigation withdrawal using nationally available datasets and techniques that are transferable to other agricultural regions were evaluated by the U.S. Geological Survey as part of the Apalachicola-Chattahoochee-Flint (ACF) River Basin focus area study of the National Water Census (ACF–FAS). These methods investigated the spatial, temporal, and quantitative distributions of water withdrawal for irrigation in the southwestern Georgia region of the ACF–FAS, filling a vital need to inform science-based decisions regarding resource management and conservation. The crop– demand method assumed that only enough water is pumped onto a crop to satisfy the deficit between evapotranspiration and precipitation. A second method applied a geostatistical regimen of variography and conditional simulation to monthly metered irrigation withdrawal to estimate irrigation withdrawal where data do not exist. A third method analyzed Landsat satellite imagery using an automated approach to generate monthly estimates of irrigated lands. These methods were evaluated independently and compared collectively with measured water withdrawal information available in the Georgia part of the ACF–FAS, principally in the Chattahoochee-Flint River Basin. An assessment of each method’s contribution to the National Water Census program was also made to identify transfer value of the methods to the national program and other water census studies. None of the three methods evaluated represent a turnkey process to estimate irrigation withdrawal on any spatial (local or regional) or temporal (monthly or annual) extent. Each method requires additional information on agricultural practices during the growing season to complete the withdrawal estimation process. Spatial and temporal limitations inherent in identifying irrigated acres during the growing season, and in designing spatially and temporally representative monitor (meter) networks, can belie the ability of the methods to produce accurate irrigation-withdrawal estimates that can be used to produce dependable and consistent assessments of water availability and use for the National Water Census. Emerging satellite-data products and techniques for data analysis can generate high spatial-resolution estimates of irrigated-acres distributions with near-term temporal frequencies compatible with the needs of the ACF–FAS and the National Water Census.

Groundwater Variability Across Temporal and Spatial Scales in the Central and Northeastern U.S.

NASA Technical Reports Server (NTRS)

Li, Bailing; Rodell, Matthew; Famiglietti, James S.

2015-01-01

Depth-to-water measurements from 181 monitoring wells in unconfined or semi-confined aquifers in nine regions of the central and northeastern U.S. were analyzed. Groundwater storage exhibited strong seasonal variations in all regions, with peaks in spring and lows in autumn, and its interannual variability was nearly unbounded, such that the impacts of droughts, floods, and excessive pumping could persist for many years. We found that the spatial variability of groundwater storage anomalies (deviations from the long term mean) increases as a power function of extent scale (square root of area). That relationship, which is linear on a log-log graph, is common to other hydrological variables but had never before been shown with groundwater data. We describe how the derived power function can be used to determine the number of wells needed to estimate regional mean groundwater storage anomalies with a desired level of accuracy, or to assess uncertainty in regional mean estimates from a set number of observations. We found that the spatial variability of groundwater storage anomalies within a region often increases with the absolute value of the regional mean anomaly, the opposite of the relationship between soil moisture spatial variability and mean. Recharge (drainage from the lowest model soil layer) simulated by the Variable Infiltration Capacity (VIC) model was compatible with observed monthly groundwater storage anomalies and month-to-month changes in groundwater storage.

Logistic regression for southern pine beetle outbreaks with spatial and temporal autocorrelation

Treesearch

M. L. Gumpertz; C.-T. Wu; John M. Pye

2000-01-01

Regional outbreaks of southern pine beetle (Dendroctonus frontalis Zimm.) show marked spatial and temporal patterns. While these patterns are of interest in themselves, we focus on statistical methods for estimating the effects of underlying environmental factors in the presence of spatial and temporal autocorrelation. The most comprehensive available information on...

Modeling spatially-varying landscape change points in species occurrence thresholds

USGS Publications Warehouse

Wagner, Tyler; Midway, Stephen R.

2014-01-01

Predicting species distributions at scales of regions to continents is often necessary, as large-scale phenomena influence the distributions of spatially structured populations. Land use and land cover are important large-scale drivers of species distributions, and landscapes are known to create species occurrence thresholds, where small changes in a landscape characteristic results in abrupt changes in occurrence. The value of the landscape characteristic at which this change occurs is referred to as a change point. We present a hierarchical Bayesian threshold model (HBTM) that allows for estimating spatially varying parameters, including change points. Our model also allows for modeling estimated parameters in an effort to understand large-scale drivers of variability in land use and land cover on species occurrence thresholds. We use range-wide detection/nondetection data for the eastern brook trout (Salvelinus fontinalis), a stream-dwelling salmonid, to illustrate our HBTM for estimating and modeling spatially varying threshold parameters in species occurrence. We parameterized the model for investigating thresholds in landscape predictor variables that are measured as proportions, and which are therefore restricted to values between 0 and 1. Our HBTM estimated spatially varying thresholds in brook trout occurrence for both the proportion agricultural and urban land uses. There was relatively little spatial variation in change point estimates, although there was spatial variability in the overall shape of the threshold response and associated uncertainty. In addition, regional mean stream water temperature was correlated to the change point parameters for the proportion of urban land use, with the change point value increasing with increasing mean stream water temperature. We present a framework for quantify macrosystem variability in spatially varying threshold model parameters in relation to important large-scale drivers such as land use and land cover. Although the model presented is a logistic HBTM, it can easily be extended to accommodate other statistical distributions for modeling species richness or abundance.

Small-Area Estimation of Spatial Access to Care and Its Implications for Policy.

PubMed

Gentili, Monica; Isett, Kim; Serban, Nicoleta; Swann, Julie

2015-10-01

Local or small-area estimates to capture emerging trends across large geographic regions are critical in identifying and addressing community-level health interventions. However, they are often unavailable due to lack of analytic capabilities in compiling and integrating extensive datasets and complementing them with the knowledge about variations in state-level health policies. This study introduces a modeling approach for small-area estimation of spatial access to pediatric primary care that is data "rich" and mathematically rigorous, integrating data and health policy in a systematic way. We illustrate the sensitivity of the model to policy decision making across large geographic regions by performing a systematic comparison of the estimates at the census tract and county levels for Georgia and California. Our results show the proposed approach is able to overcome limitations of other existing models by capturing patient and provider preferences and by incorporating possible changes in health policies. The primary finding is systematic underestimation of spatial access, and inaccurate estimates of disparities across population and across geography at the county level with respect to those at the census tract level with implications on where to focus and which type of interventions to consider.

Understanding and predicting changing use of groundwater with climate and other uncertainties: a Bayesian approach

NASA Astrophysics Data System (ADS)

Costa, F. A. F.; Keir, G.; McIntyre, N.; Bulovic, N.

2015-12-01

Most groundwater supply bores in Australia do not have flow metering equipment and so regional groundwater abstraction rates are not well known. Past estimates of unmetered abstraction for regional numerical groundwater modelling typically have not attempted to quantify the uncertainty inherent in the estimation process in detail. In particular, the spatial properties of errors in the estimates are almost always neglected. Here, we apply Bayesian spatial models to estimate these abstractions at a regional scale, using the state-of-the-art computationally inexpensive approaches of integrated nested Laplace approximation (INLA) and stochastic partial differential equations (SPDE). We examine a case study in the Condamine Alluvium aquifer in southern Queensland, Australia; even in this comparatively data-rich area with extensive groundwater abstraction for agricultural irrigation, approximately 80% of bores do not have reliable metered flow records. Additionally, the metering data in this area are characterised by complicated statistical features, such as zero-valued observations, non-normality, and non-stationarity. While this precludes the use of many classical spatial estimation techniques, such as kriging, our model (using the R-INLA package) is able to accommodate these features. We use a joint model to predict both probability and magnitude of abstraction from bores in space and time, and examine the effect of a range of high-resolution gridded meteorological covariates upon the predictive ability of the model. Deviance Information Criterion (DIC) scores are used to assess a range of potential models, which reward good model fit while penalising excessive model complexity. We conclude that maximum air temperature (as a reasonably effective surrogate for evapotranspiration) is the most significant single predictor of abstraction rate; and that a significant spatial effect exists (represented by the SPDE approximation of a Gaussian random field with a Matérn covariance function). Our final model adopts air temperature, solar exposure, and normalized difference vegetation index (NDVI) as covariates, shows good agreement with previous estimates at a regional scale, and additionally offers rigorous quantification of uncertainty in the estimate.

A Development of Nonstationary Regional Frequency Analysis Model with Large-scale Climate Information: Its Application to Korean Watershed

NASA Astrophysics Data System (ADS)

Kim, Jin-Young; Kwon, Hyun-Han; Kim, Hung-Soo

2015-04-01

The existing regional frequency analysis has disadvantages in that it is difficult to consider geographical characteristics in estimating areal rainfall. In this regard, this study aims to develop a hierarchical Bayesian model based nonstationary regional frequency analysis in that spatial patterns of the design rainfall with geographical information (e.g. latitude, longitude and altitude) are explicitly incorporated. This study assumes that the parameters of Gumbel (or GEV distribution) are a function of geographical characteristics within a general linear regression framework. Posterior distribution of the regression parameters are estimated by Bayesian Markov Chain Monte Carlo (MCMC) method, and the identified functional relationship is used to spatially interpolate the parameters of the distributions by using digital elevation models (DEM) as inputs. The proposed model is applied to derive design rainfalls over the entire Han-river watershed. It was found that the proposed Bayesian regional frequency analysis model showed similar results compared to L-moment based regional frequency analysis. In addition, the model showed an advantage in terms of quantifying uncertainty of the design rainfall and estimating the area rainfall considering geographical information. Finally, comprehensive discussion on design rainfall in the context of nonstationary will be presented. KEYWORDS: Regional frequency analysis, Nonstationary, Spatial information, Bayesian Acknowledgement This research was supported by a grant (14AWMP-B082564-01) from Advanced Water Management Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government.

High-Frequency Subband Compressed Sensing MRI Using Quadruplet Sampling

PubMed Central

Sung, Kyunghyun; Hargreaves, Brian A

2013-01-01

Purpose To presents and validates a new method that formalizes a direct link between k-space and wavelet domains to apply separate undersampling and reconstruction for high- and low-spatial-frequency k-space data. Theory and Methods High- and low-spatial-frequency regions are defined in k-space based on the separation of wavelet subbands, and the conventional compressed sensing (CS) problem is transformed into one of localized k-space estimation. To better exploit wavelet-domain sparsity, CS can be used for high-spatial-frequency regions while parallel imaging can be used for low-spatial-frequency regions. Fourier undersampling is also customized to better accommodate each reconstruction method: random undersampling for CS and regular undersampling for parallel imaging. Results Examples using the proposed method demonstrate successful reconstruction of both low-spatial-frequency content and fine structures in high-resolution 3D breast imaging with a net acceleration of 11 to 12. Conclusion The proposed method improves the reconstruction accuracy of high-spatial-frequency signal content and avoids incoherent artifacts in low-spatial-frequency regions. This new formulation also reduces the reconstruction time due to the smaller problem size. PMID:23280540

Applying spatial analysis techniques to assess the suitability of multipurpose uses of spring water in the Jiaosi Hot Spring Region, Taiwan

NASA Astrophysics Data System (ADS)

Jang, Cheng-Shin

2016-04-01

The Jiaosi Hot Spring Region is located in northeastern Taiwan and is rich in geothermal springs. The geothermal development of the Jiaosi Hot Spring Region dates back to the 18th century and currently, the spring water is processed for various uses, including irrigation, aquaculture, swimming, bathing, foot spas, and recreational tourism. Because of the proximity of the Jiaosi Hot Spring Region to the metropolitan area of Taipei City, the hot spring resources in this region attract millions of tourists annually. Recently, the Taiwan government is paying more attention to surveying the spring water temperatures in the Jiaosi Hot Spring Region because of the severe spring water overexploitation, causing a significant decline in spring water temperatures. Furthermore, the temperature of spring water is a reliable indicator for exploring the occurrence and evolution of springs and strongly affects hydrochemical reactions, components, and magnitudes. The multipurpose uses of spring water can be dictated by the temperature of the water. Therefore, accurately estimating the temperature distribution of the spring water is critical in the Jiaosi Hot Spring Region to facilitate the sustainable development and management of the multipurpose uses of the hot spring resources. To evaluate the suitability of spring water for these various uses, this study spatially characterized the spring water temperatures of the Jiaosi Hot Spring Region by using ordinary kriging (OK), sequential Gaussian simulation (SGS), and geographical information system (GIS). First, variogram analyses were used to determine the spatial variability of spring water temperatures. Next, OK and SGS were adopted to model the spatial distributions and uncertainty of the spring water temperatures. Finally, the land use (i.e., agriculture, dwelling, public land, and recreation) was determined and combined with the estimated distributions of the spring water temperatures using GIS. A suitable development strategy for the multipurpose uses of spring water is proposed according to the integration of the land use and spring water temperatures. The study results indicate that OK, SGS, and GIS are capable of characterizing spring water temperatures and the suitability of multipurpose uses of spring water. SGS realizations are more robust than OK estimates for characterizing spring water temperatures. Furthermore, current land use is almost ideal in the Jiaosi Hot Spring Region according to the estimated spatial pattern of spring water temperatures. Keywords: Hot spring; Temperature; Land use; Ordinary kriging; Sequential Gaussian simulation; Geographical information system

A comparison of regional flood frequency analysis approaches in a simulation framework

NASA Astrophysics Data System (ADS)

Ganora, D.; Laio, F.

2016-07-01

Regional frequency analysis (RFA) is a well-established methodology to provide an estimate of the flood frequency curve at ungauged (or scarcely gauged) sites. Different RFA approaches exist, depending on the way the information is transferred to the site of interest, but it is not clear in the literature if a specific method systematically outperforms the others. The aim of this study is to provide a framework wherein carrying out the intercomparison by building up a virtual environment based on synthetically generated data. The considered regional approaches include: (i) a unique regional curve for the whole region; (ii) a multiple-region model where homogeneous subregions are determined through cluster analysis; (iii) a Region-of-Influence model which defines a homogeneous subregion for each site; (iv) a spatially smooth estimation procedure where the parameters of the regional model vary continuously along the space. Virtual environments are generated considering different patterns of heterogeneity, including step change and smooth variations. If the region is heterogeneous, with the parent distribution changing continuously within the region, the spatially smooth regional approach outperforms the others, with overall errors 10-50% lower than the other methods. In the case of a step-change, the spatially smooth and clustering procedures perform similarly if the heterogeneity is moderate, while clustering procedures work better when the step-change is severe. To extend our findings, an extensive sensitivity analysis has been performed to investigate the effect of sample length, number of virtual stations, return period of the predicted quantile, variability of the scale parameter of the parent distribution, number of predictor variables and different parent distribution. Overall, the spatially smooth approach appears as the most robust approach as its performances are more stable across different patterns of heterogeneity, especially when short records are considered.

a Novel Approach to Veterinary Spatial Epidemiology: Dasymetric Refinement of the Swiss Dog Tumor Registry Data

NASA Astrophysics Data System (ADS)

Boo, G.; Fabrikant, S. I.; Leyk, S.

2015-08-01

In spatial epidemiology, disease incidence and demographic data are commonly summarized within larger regions such as administrative units because of privacy concerns. As a consequence, analyses using these aggregated data are subject to the Modifiable Areal Unit Problem (MAUP) as the geographical manifestation of ecological fallacy. In this study, we create small area disease estimates through dasymetric refinement, and investigate the effects on predictive epidemiological models. We perform a binary dasymetric refinement of municipality-aggregated dog tumor incidence counts in Switzerland for the year 2008 using residential land as a limiting ancillary variable. This refinement is expected to improve the quality of spatial data originally aggregated within arbitrary administrative units by deconstructing them into discontinuous subregions that better reflect the underlying population distribution. To shed light on effects of this refinement, we compare a predictive statistical model that uses unrefined administrative units with one that uses dasymetrically refined spatial units. Model diagnostics and spatial distributions of model residuals are assessed to evaluate the model performances in different regions. In particular, we explore changes in the spatial autocorrelation of the model residuals due to spatial refinement of the enumeration units in a selected mountainous region, where the rugged topography induces great shifts of the analytical units i.e., residential land. Such spatial data quality refinement results in a more realistic estimation of the population distribution within administrative units, and thus, in a more accurate modeling of dog tumor incidence patterns. Our results emphasize the benefits of implementing a dasymetric modeling framework in veterinary spatial epidemiology.

Interactive classification and content-based retrieval of tissue images

NASA Astrophysics Data System (ADS)

Aksoy, Selim; Marchisio, Giovanni B.; Tusk, Carsten; Koperski, Krzysztof

2002-11-01

We describe a system for interactive classification and retrieval of microscopic tissue images. Our system models tissues in pixel, region and image levels. Pixel level features are generated using unsupervised clustering of color and texture values. Region level features include shape information and statistics of pixel level feature values. Image level features include statistics and spatial relationships of regions. To reduce the gap between low-level features and high-level expert knowledge, we define the concept of prototype regions. The system learns the prototype regions in an image collection using model-based clustering and density estimation. Different tissue types are modeled using spatial relationships of these regions. Spatial relationships are represented by fuzzy membership functions. The system automatically selects significant relationships from training data and builds models which can also be updated using user relevance feedback. A Bayesian framework is used to classify tissues based on these models. Preliminary experiments show that the spatial relationship models we developed provide a flexible and powerful framework for classification and retrieval of tissue images.

Simulation of net infiltration and potential recharge using a distributed-parameter watershed model of the Death Valley region, Nevada and California

USGS Publications Warehouse

Hevesi, Joseph A.; Flint, Alan L.; Flint, Lorraine E.

2003-01-01

This report presents the development and application of the distributed-parameter watershed model, INFILv3, for estimating the temporal and spatial distribution of net infiltration and potential recharge in the Death Valley region, Nevada and California. The estimates of net infiltration quantify the downward drainage of water across the lower boundary of the root zone and are used to indicate potential recharge under variable climate conditions and drainage basin characteristics. Spatial variability in recharge in the Death Valley region likely is high owing to large differences in precipitation, potential evapotranspiration, bedrock permeability, soil thickness, vegetation characteristics, and contributions to recharge along active stream channels. The quantity and spatial distribution of recharge representing the effects of variable climatic conditions and drainage basin characteristics on recharge are needed to reduce uncertainty in modeling ground-water flow. The U.S. Geological Survey, in cooperation with the Department of Energy, developed a regional saturated-zone ground-water flow model of the Death Valley regional ground-water flow system to help evaluate the current hydrogeologic system and the potential effects of natural or human-induced changes. Although previous estimates of recharge have been made for most areas of the Death Valley region, including the area defined by the boundary of the Death Valley regional ground-water flow system, the uncertainty of these estimates is high, and the spatial and temporal variability of the recharge in these basins has not been quantified. To estimate the magnitude and distribution of potential recharge in response to variable climate and spatially varying drainage basin characteristics, the INFILv3 model uses a daily water-balance model of the root zone with a primarily deterministic representation of the processes controlling net infiltration and potential recharge. The daily water balance includes precipitation (as either rain or snow), snow accumulation, sublimation, snowmelt, infiltration into the root zone, evapotranspiration, drainage, water content change throughout the root-zone profile (represented as a 6-layered system), runoff (defined as excess rainfall and snowmelt) and surface water run-on (defined as runoff that is routed downstream), and net infiltration (simulated as drainage from the bottom root-zone layer). Potential evapotranspiration is simulated using an hourly solar radiation model to simulate daily net radiation, and daily evapotranspiration is simulated as an empirical function of root zone water content and potential evapotranspiration. The model uses daily climate records of precipitation and air temperature from a regionally distributed network of 132 climate stations and a spatially distributed representation of drainage basin characteristics defined by topography, geology, soils, and vegetation to simulate daily net infiltration at all locations, including stream channels with intermittent streamflow in response to runoff from rain and snowmelt. The temporal distribution of daily, monthly, and annual net infiltration can be used to evaluate the potential effect of future climatic conditions on potential recharge. The INFILv3 model inputs representing drainage basin characteristics were developed using a geographic information system (GIS) to define a set of spatially distributed input parameters uniquely assigned to each grid cell of the INFILv3 model grid. The model grid, which was defined by a digital elevation model (DEM) of the Death Valley region, consists of 1,252,418 model grid cells with a uniform grid cell dimension of 278.5 meters in the north-south and east-west directions. The elevation values from the DEM were used with monthly regression models developed from the daily climate data to estimate the spatial distribution of daily precipitation and air temperature. The elevation values were also used to simulate atmosp

Using spatio-temporal modeling to predict long-term exposure to black smoke at fine spatial and temporal scale

NASA Astrophysics Data System (ADS)

Dadvand, Payam; Rushton, Stephen; Diggle, Peter J.; Goffe, Louis; Rankin, Judith; Pless-Mulloli, Tanja

2011-01-01

Whilst exposure to air pollution is linked to a wide range of adverse health outcomes, assessing levels of this exposure has remained a challenge. This study reports a modeling approach for the estimation of weekly levels of ambient black smoke (BS) at residential postcodes across Northeast England (2055 km 2) over a 12 year period (1985-1996). A two-stage modeling strategy was developed using monitoring data on BS together with a range of covariates including data on traffic, population density, industrial activity, land cover (remote sensing), and meteorology. The first stage separates the temporal trend in BS for the region as a whole from within-region spatial variation and the second stage is a linear model which predicts BS levels at all locations in the region using spatially referenced covariate data as predictors and the regional predicted temporal trend as an offset. Traffic and land cover predictors were included in the final model, which predicted 70% of the spatio-temporal variation in BS across the study region over the study period. This modeling approach appears to provide a robust way of estimating exposure to BS at an inter-urban scale.

Analysis of Extreme Snow Water Equivalent Data in Central New Hampshire

NASA Astrophysics Data System (ADS)

Vuyovich, C.; Skahill, B. E.; Kanney, J. F.; Carr, M.

2017-12-01

Heavy snowfall and snowmelt-related events have been linked to widespread flooding and damages in many regions of the U.S. Design of critical infrastructure in these regions requires spatial estimates of extreme snow water equivalent (SWE). In this study, we develop station specific and spatially explicit estimates of extreme SWE using data from fifteen snow sampling stations maintained by the New Hampshire Department of Environmental Services. The stations are located in the Mascoma, Pemigewasset, Winnipesaukee, Ossipee, Salmon Falls, Lamprey, Sugar, and Isinglass basins in New Hampshire. The average record length for the fifteen stations is approximately fifty-nine years. The spatial analysis of extreme SWE involves application of two Bayesian Hierarchical Modeling methods, one that assumes conditional independence, and another which uses the Smith max-stable process model to account for spatial dependence. We also apply additional max-stable process models, albeit not in a Bayesian framework, that better model the observed dependence among the extreme SWE data. The spatial process modeling leverages readily available and relevant spatially explicit covariate data. The noted additional max-stable process models also used the nonstationary winter North Atlantic Oscillation index, which has been observed to influence snowy weather along the east coast of the United States. We find that, for this data set, SWE return level estimates are consistently higher when derived using methods which account for the observed spatial dependence among the extreme data. This is particularly significant for design scenarios of relevance for critical infrastructure evaluation.

Natal location influences movement and survival of a spatially structured population of snail kites

USGS Publications Warehouse

Martin, J.; Kitchens, W.M.; Hines, J.E.

2007-01-01

Despite the accepted importance of the need to better understand how natal location affects movement decisions and survival of animals, robust estimates of movement and survival in relation to the natal location are lacking. Our study focuses on movement and survival related to the natal location of snail kites in Florida and shows that kites, in addition to exhibiting a high level of site tenacity to breeding regions, also exhibit particular attraction to their natal region. More specifically, we found that estimates of movement from post-dispersal regions were greater toward natal regions than toward non-natal regions (differences were significant for three of four regions). We also found that estimates of natal philopatry were greater than estimates of philopatry to non-natal regions (differences were statistically significant for two of four regions). A previous study indicated an effect of natal region on juvenile survival; in this study, we show an effect of natal region on adult survival. Estimates of adult survival varied among kites that were hatched in different regions. Adults experienced mortality rates characteristic of the region occupied at the time when survival was measured, but because there is a greater probability that kites will return to their natal region than to any other regions, their survival was ultimately influenced by their natal region. In most years, kites hatched in southern regions had greater survival probabilities than did kites hatched in northern regions. However, during a multiregional drought, one of the northern regions served as a refuge from drought, and during this perturbation, survival was greater for birds hatched in the north. Our study shows that natal location may be important in influencing the ecological dynamics of kites but also highlights the importance of considering temporal variation in habitat conditions of spatially structured systems when attempting to evaluate the conservation value of habitats.

Using occupancy estimation to assess the effectiveness of a regional multiple-species conservation plan: bats in the Pacific Northwest

Treesearch

Theodore Weller

2008-01-01

Regional conservation plans are increasingly used to plan for and protect biodiversity at large spatial scales however the means of quantitatively evaluating their effectiveness are rarely specified. Multiple-species approaches, particular those which employ site-occupancy estimation, have been proposed as robust and efficient alternatives for assessing the status of...

Historical and future land use effects on N2O and NO emissions using an ensemble modeling approach: Costa Rica's Caribbean lowlands as an example

USGS Publications Warehouse

Reiners, William A.; Liu, S.; Gerow, K.G.; Keller, M.; Schimel, D.S.

2002-01-01

[1] The humid tropical zone is a major source area for N2O and NO emissions to the atmosphere. Local emission rates vary widely with local conditions, particularly land use practices which swiftly change with expanding settlement and changing market conditions. The combination of wide variation in emission rates and rapidly changing land use make regional estimation and future prediction of biogenic trace gas emission particularly difficult. This study estimates contemporary, historical, and future N2O and NO emissions from 0.5 million ha of northeastern Costa Rica, a well-documented region in the wet tropics undergoing rapid agricultural development. Estimates were derived by linking spatially distributed environmental data with an ecosystem simulation model in an ensemble estimation approach that incorporates the variance and covariance of spatially distributed driving variables. Results include measures of variance for regional emissions. The formation and aging of pastures from forest provided most of the past temporal change in N2O and NO flux in this region; future changes will be controlled by the degree of nitrogen fertilizer application and extent of intensively managed croplands.

Historical and future land use effects on N2O and NO emissions using an ensemble modeling approach: Costa Rica's Caribbean lowlands as an example

NASA Astrophysics Data System (ADS)

Reiners, W. A.; Liu, S.; Gerow, K. G.; Keller, M.; Schimel, D. S.

2002-12-01

The humid tropical zone is a major source area for N2O and NO emissions to the atmosphere. Local emission rates vary widely with local conditions, particularly land use practices which swiftly change with expanding settlement and changing market conditions. The combination of wide variation in emission rates and rapidly changing land use make regional estimation and future prediction of biogenic trace gas emission particularly difficult. This study estimates contemporary, historical, and future N2O and NO emissions from 0.5 million ha of northeastern Costa Rica, a well-documented region in the wet tropics undergoing rapid agricultural development. Estimates were derived by linking spatially distributed environmental data with an ecosystem simulation model in an ensemble estimation approach that incorporates the variance and covariance of spatially distributed driving variables. Results include measures of variance for regional emissions. The formation and aging of pastures from forest provided most of the past temporal change in N2O and NO flux in this region; future changes will be controlled by the degree of nitrogen fertilizer application and extent of intensively managed croplands.

Evaluating land cover influences on model uncertainties—A case study of cropland carbon dynamics in the Mid-Continent Intensive Campaign region

USGS Publications Warehouse

Li, Zhengpeng; Liu, Shuguang; Zhang, Xuesong; West, Tristram O.; Ogle, Stephen M.; Zhou, Naijun

2016-01-01

Quantifying spatial and temporal patterns of carbon sources and sinks and their uncertainties across agriculture-dominated areas remains challenging for understanding regional carbon cycles. Characteristics of local land cover inputs could impact the regional carbon estimates but the effect has not been fully evaluated in the past. Within the North American Carbon Program Mid-Continent Intensive (MCI) Campaign, three models were developed to estimate carbon fluxes on croplands: an inventory-based model, the Environmental Policy Integrated Climate (EPIC) model, and the General Ensemble biogeochemical Modeling System (GEMS) model. They all provided estimates of three major carbon fluxes on cropland: net primary production (NPP), net ecosystem production (NEP), and soil organic carbon (SOC) change. Using data mining and spatial statistics, we studied the spatial distribution of the carbon fluxes uncertainties and the relationships between the uncertainties and the land cover characteristics. Results indicated that uncertainties for all three carbon fluxes were not randomly distributed, but instead formed multiple clusters within the MCI region. We investigated the impacts of three land cover characteristics on the fluxes uncertainties: cropland percentage, cropland richness and cropland diversity. The results indicated that cropland percentage significantly influenced the uncertainties of NPP and NEP, but not on the uncertainties of SOC change. Greater uncertainties of NPP and NEP were found in counties with small cropland percentage than the counties with large cropland percentage. Cropland species richness and diversity also showed negative correlations with the model uncertainties. Our study demonstrated that the land cover characteristics contributed to the uncertainties of regional carbon fluxes estimates. The approaches we used in this study can be applied to other ecosystem models to identify the areas with high uncertainties and where models can be improved to reduce overall uncertainties for regional carbon flux estimates.

Results from OSO-IV - The long term behavior of X-ray emitting regions.

NASA Technical Reports Server (NTRS)

Krieger, A.; Paolini, F.; Vaiana, G. S.; Webb, D.

1972-01-01

Analysis of images of the sun obtained with the aid of a grazing incidence X-ray telescope on board the OSO IV spacecraft in the 2.5 to 12-A waveband nearly continuously from Oct. 27, 1967, to May 12, 1968. The instrument had sufficient spatial resolution (one and four arc minutes) and temporal resolution (5 to 20 min) to estimate the spatial characteristics of X-ray emitting regions and to monitor the temporal behavior of individual active regions. Variations in the absence of flares of as much as a factor of 10 in the X-ray output of individual regions were observed, with typical durations ranging from several hours to several days. The X-ray time variations are related to observations at optical and radio wavelengths. The results are interpreted under the assumption that the X-ray time variations are caused by temperature changes in the coronal portions of active regions. The contribution of radiative losses to the energy budget of the coronal active region is estimated.

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.

Kriging and local polynomial methods for blending satellite-derived and gauge precipitation estimates to support hydrologic early warning systems

USGS Publications Warehouse

Verdin, Andrew; Funk, Christopher C.; Rajagopalan, Balaji; Kleiber, William

2016-01-01

Robust estimates of precipitation in space and time are important for efficient natural resource management and for mitigating natural hazards. This is particularly true in regions with developing infrastructure and regions that are frequently exposed to extreme events. Gauge observations of rainfall are sparse but capture the precipitation process with high fidelity. Due to its high resolution and complete spatial coverage, satellite-derived rainfall data are an attractive alternative in data-sparse regions and are often used to support hydrometeorological early warning systems. Satellite-derived precipitation data, however, tend to underrepresent extreme precipitation events. Thus, it is often desirable to blend spatially extensive satellite-derived rainfall estimates with high-fidelity rain gauge observations to obtain more accurate precipitation estimates. In this research, we use two different methods, namely, ordinary kriging and κ-nearest neighbor local polynomials, to blend rain gauge observations with the Climate Hazards Group Infrared Precipitation satellite-derived precipitation estimates in data-sparse Central America and Colombia. The utility of these methods in producing blended precipitation estimates at pentadal (five-day) and monthly time scales is demonstrated. We find that these blending methods significantly improve the satellite-derived estimates and are competitive in their ability to capture extreme precipitation.

Terrain feature recognition for synthetic aperture radar (SAR) imagery employing spatial attributes of targets

NASA Astrophysics Data System (ADS)

Iisaka, Joji; Sakurai-Amano, Takako

1994-08-01

This paper describes an integrated approach to terrain feature detection and several methods to estimate spatial information from SAR (synthetic aperture radar) imagery. Spatial information of image features as well as spatial association are key elements in terrain feature detection. After applying a small feature preserving despeckling operation, spatial information such as edginess, texture (smoothness), region-likeliness and line-likeness of objects, target sizes, and target shapes were estimated. Then a trapezoid shape fuzzy membership function was assigned to each spatial feature attribute. Fuzzy classification logic was employed to detect terrain features. Terrain features such as urban areas, mountain ridges, lakes and other water bodies as well as vegetated areas were successfully identified from a sub-image of a JERS-1 SAR image. In the course of shape analysis, a quantitative method was developed to classify spatial patterns by expanding a spatial pattern through the use of a series of pattern primitives.

Estimating temporal trend in the presence of spatial complexity: A Bayesian hierarchical model for a wetland plant population undergoing restoration

USGS Publications Warehouse

Rodhouse, T.J.; Irvine, K.M.; Vierling, K.T.; Vierling, L.A.

2011-01-01

Monitoring programs that evaluate restoration and inform adaptive management are important for addressing environmental degradation. These efforts may be well served by spatially explicit hierarchical approaches to modeling because of unavoidable spatial structure inherited from past land use patterns and other factors. We developed Bayesian hierarchical models to estimate trends from annual density counts observed in a spatially structured wetland forb (Camassia quamash [camas]) population following the cessation of grazing and mowing on the study area, and in a separate reference population of camas. The restoration site was bisected by roads and drainage ditches, resulting in distinct subpopulations ("zones") with different land use histories. We modeled this spatial structure by fitting zone-specific intercepts and slopes. We allowed spatial covariance parameters in the model to vary by zone, as in stratified kriging, accommodating anisotropy and improving computation and biological interpretation. Trend estimates provided evidence of a positive effect of passive restoration, and the strength of evidence was influenced by the amount of spatial structure in the model. Allowing trends to vary among zones and accounting for topographic heterogeneity increased precision of trend estimates. Accounting for spatial autocorrelation shifted parameter coefficients in ways that varied among zones depending on strength of statistical shrinkage, autocorrelation and topographic heterogeneity-a phenomenon not widely described. Spatially explicit estimates of trend from hierarchical models will generally be more useful to land managers than pooled regional estimates and provide more realistic assessments of uncertainty. The ability to grapple with historical contingency is an appealing benefit of this approach.

Restoring the spatial resolution of refocus images on 4D light field

NASA Astrophysics Data System (ADS)

Lim, JaeGuyn; Park, ByungKwan; Kang, JooYoung; Lee, SeongDeok

2010-01-01

This paper presents the method for generating a refocus image with restored spatial resolution on a plenoptic camera, which functions controlling the depth of field after capturing one image unlike a traditional camera. It is generally known that the camera captures 4D light field (angular and spatial information of light) within a limited 2D sensor and results in reducing 2D spatial resolution due to inevitable 2D angular data. That's the reason why a refocus image is composed of a low spatial resolution compared with 2D sensor. However, it has recently been known that angular data contain sub-pixel spatial information such that the spatial resolution of 4D light field can be increased. We exploit the fact for improving the spatial resolution of a refocus image. We have experimentally scrutinized that the spatial information is different according to the depth of objects from a camera. So, from the selection of refocused regions (corresponding depth), we use corresponding pre-estimated sub-pixel spatial information for reconstructing spatial resolution of the regions. Meanwhile other regions maintain out-of-focus. Our experimental results show the effect of this proposed method compared to existing method.

Regional flood probabilities

USGS Publications Warehouse

Troutman, Brent M.; Karlinger, Michael R.

2003-01-01

The T‐year annual maximum flood at a site is defined to be that streamflow, that has probability 1/T of being exceeded in any given year, and for a group of sites the corresponding regional flood probability (RFP) is the probability that at least one site will experience a T‐year flood in any given year. The RFP depends on the number of sites of interest and on the spatial correlation of flows among the sites. We present a Monte Carlo method for obtaining the RFP and demonstrate that spatial correlation estimates used in this method may be obtained with rank transformed data and therefore that knowledge of the at‐site peak flow distribution is not necessary. We examine the extent to which the estimates depend on specification of a parametric form for the spatial correlation function, which is known to be nonstationary for peak flows. It is shown in a simulation study that use of a stationary correlation function to compute RFPs yields satisfactory estimates for certain nonstationary processes. Application of asymptotic extreme value theory is examined, and a methodology for separating channel network and rainfall effects on RFPs is suggested. A case study is presented using peak flow data from the state of Washington. For 193 sites in the Puget Sound region it is estimated that a 100‐year flood will occur on the average every 4.5 years.

How will climate change affect spatial planning in agricultural and natural environments? Examples from three Dutch case study regions

NASA Astrophysics Data System (ADS)

Blom-Zandstra, Margaretha; Paulissen, Maurice; Agricola, Herman; Schaap, Ben

2009-11-01

Climate change will place increasing pressure on the functioning of agricultural and natural areas in the Netherlands. Strategies to adapt these areas to stress are likely to require changes in landscape structure and management. In densely populated countries such as the Netherlands, the increased pressure of climate change on agricultural and natural areas will inevitably lead, through the necessity of spatial adaptation measures, to spatial conflicts between the sectors of agriculture and nature. An integrated approach to climate change adaptation may therefore be beneficial in limiting such sectoral conflicts. We explored the conflicting and synergistic properties of different climate adaptation strategies for agricultural and natural environments in the Netherlands. To estimate the feasibility and effectiveness of the strategies, we focussed on three case study regions with contrasting landscape structural, natural and agricultural characteristics. For each region, we estimated the expected climate-related threats and associated trade-offs for arable farming and natural areas for 2040. We describe a number of spatial and integrated adaptation strategies to mitigate these threats. Formulating adaptation strategies requires consultation of different stakeholders and deliberation between different interests. We discuss some trade-offs involved in this decision-making.

Error Estimation in an Optimal Interpolation Scheme for High Spatial and Temporal Resolution SST Analyses

NASA Technical Reports Server (NTRS)

Rigney, Matt; Jedlovec, Gary; LaFontaine, Frank; Shafer, Jaclyn

2010-01-01

Heat and moisture exchange between ocean surface and atmosphere plays an integral role in short-term, regional NWP. Current SST products lack both spatial and temporal resolution to accurately capture small-scale features that affect heat and moisture flux. NASA satellite is used to produce high spatial and temporal resolution SST analysis using an OI technique.

Improving satellite-based post-fire evapotranspiration estimates in semi-arid regions

NASA Astrophysics Data System (ADS)

Poon, P.; Kinoshita, A. M.

2017-12-01

Climate change and anthropogenic factors contribute to the increased frequency, duration, and size of wildfires, which can alter ecosystem and hydrological processes. The loss of vegetation canopy and ground cover reduces interception and alters evapotranspiration (ET) dynamics in riparian areas, which can impact rainfall-runoff partitioning. Previous research evaluated the spatial and temporal trends of ET based on burn severity and observed an annual decrease of 120 mm on average for three years after fire. Building upon these results, this research focuses on the Coyote Fire in San Diego, California (USA), which burned a total of 76 km2 in 2003 to calibrate and improve satellite-based ET estimates in semi-arid regions affected by wildfire. The current work utilizes satellite-based products and techniques such as the Google Earth Engine Application programming interface (API). Various ET models (ie. Operational Simplified Surface Energy Balance Model (SSEBop)) are compared to the latent heat flux from two AmeriFlux eddy covariance towers, Sky Oaks Young (US-SO3), and Old Stand (US-SO2), from 2000 - 2015. The Old Stand tower has a low burn severity and the Young Stand tower has a moderate to high burn severity. Both towers are used to validate spatial ET estimates. Furthermore, variables and indices, such as Enhanced Vegetation Index (EVI), Normalized Difference Moisture Index (NDMI), and the Normalized Burn Ratio (NBR) are utilized to evaluate satellite-based ET through a multivariate statistical analysis at both sites. This point-scale study will able to improve ET estimates in spatially diverse regions. Results from this research will contribute to the development of a post-wildfire ET model for semi-arid regions. Accurate estimates of post-fire ET will provide a better representation of vegetation and hydrologic recovery, which can be used to improve hydrologic models and predictions.

Atmospheric deposition maps for the Rocky Mountains

USGS Publications Warehouse

Nanus, L.; Campbell, D.H.; Ingersoll, G.P.; Clow, D.W.; Mast, M.A.

2003-01-01

Variability in atmospheric deposition across the Rocky Mountains is influenced by elevation, slope, aspect, and precipitation amount and by regional and local sources of air pollution. To improve estimates of deposition in mountainous regions, maps of average annual atmospheric deposition loadings of nitrate, sulfate, and acidity were developed for the Rocky Mountains by using spatial statistics. A parameter-elevation regressions on independent slopes model (PRISM) was incorporated to account for variations in precipitation amount over mountainous regions. Chemical data were obtained from the National Atmospheric Deposition Program/National Trends Network and from annual snowpack surveys conducted by the US Geological Survey and National Park Service, in cooperation with other Federal, State and local agencies. Surface concentration maps were created by ordinary kriging in a geographic information system, using a local trend and mathematical model to estimate the spatial variance. Atmospheric-deposition maps were constructed at 1-km resolution by multiplying surface concentrations from the kriged grid and estimates of precipitation amount from the PRISM model. Maps indicate an increasing spatial trend in concentration and deposition of the modeled constituents, particularly nitrate and sulfate, from north to south throughout the Rocky Mountains and identify hot-spots of atmospheric deposition that result from combined local and regional sources of air pollution. Highest nitrate (2.5-3.0kg/ha N) and sulfate (10.0-12.0kg/ha SO4) deposition is found in northern Colorado.

Using excess 4He to quantify variability in aquitard leakage

NASA Astrophysics Data System (ADS)

Gardner, W. Payton; Harrington, Glenn A.; Smerdon, Brian D.

2012-10-01

SummaryFluid flux through aquitards controls the rate of recharge, discharge, cross-formational fluid flow and contaminant transport in subsurface systems. In this paper, concentrations of 4He are used to investigate the spatial distribution of vertical fluid flux through the regionally extensive Great Artesian Basin aquitard system in northern South Australia. Two vertical profiles of 4He concentration in aquitard pore water, augmented with regional sampling of aquifers above and below the aquitard were used to estimate fluid flux at multiple locations over a large spatial area. 4He concentrations in the shallow aquifer above the Great Artesian Basin range from atmospheric equilibrium to 1000 times enriched over atmosphere. Fluid flux through the aquitard was estimated by fitting observed helium concentrations at each sampling site with a 1-D model of helium transport through the aquitard. Estimated fluid fluxes through the aquitard vary over three orders of magnitude across the study area. In areas of competent aquitard, fluid fluxes are less than 0.003 mm/yr, and mass transport of helium is dominated by molecular diffusion. Preferential discharge zones are clearly identifiable with fluid fluxes up to 3 mm/yr. Our results show that fluid flux through a regionally extensive aquitard can be highly variable at large spatial scales, and that 4He concentrations in aquifers bounding the aquitard system provide a convenient and sensitive method for investigating aquitard flux at the regional scale.

O/H-N/O: the curious case of NGC 4670

NASA Astrophysics Data System (ADS)

Kumari, Nimisha; James, Bethan L.; Irwin, Mike J.; Amorín, Ricardo; Pérez-Montero, Enrique

2018-05-01

We use integral field spectroscopic (IFS) observations from Gemini Multi-Object Spectrograph North (GMOS-N) of a group of four H II regions and the surrounding gas in the central region of the blue compact dwarf (BCD) galaxy NGC 4670. At spatial scales of ˜9 pc, we map the spatial distribution of a variety of physical properties of the ionized gas: internal dust attenuation, kinematics, stellar age, star formation rate, emission-line ratios, and chemical abundances. The region of study is found to be photoionized. Using the robust direct Te method, we estimate metallicity, nitrogen-to-oxygen ratio, and helium abundance of the four H II regions. The same parameters are also mapped for the entire region using the HII-CHI-mistry code. We find that log(N/O) is increased in the region where the Wolf-Rayet bump is detected. The region coincides with the continuum region, around which we detect a slight increase in He abundance. We estimate the number of WC4, WN2-4, and WN7-9 stars from the integrated spectrum of WR bump region. We study the relation between log(N/O) and 12 + log(O/H) using the spatially resolved data of the field of view as well as the integrated data of the H II regions from 10 BCDs. We find an unexpected negative trend between N/O and metallicity. Several scenarios are explored to explain this trend, including nitrogen enrichment, and variations in star formation efficiency via chemical evolution models.

Spatial econometric analysis of factors influencing regional energy efficiency in China.

PubMed

Song, Malin; Chen, Yu; An, Qingxian

2018-05-01

Increased environmental pollution and energy consumption caused by the country's rapid development has raised considerable public concern, and has become the focus of the government and public. This study employs the super-efficiency slack-based model-data envelopment analysis (SBM-DEA) to measure the total factor energy efficiency of 30 provinces in China. The estimation model for the spatial interaction intensity of regional total factor energy efficiency is based on Wilson's maximum entropy model. The model is used to analyze the factors that affect the potential value of total factor energy efficiency using spatial dynamic panel data for 30 provinces during 2000-2014. The study found that there are differences and spatial correlations of energy efficiency among provinces and regions in China. The energy efficiency in the eastern, central, and western regions fluctuated significantly, and was mainly because of significant energy efficiency impacts on influences of industrial structure, energy intensity, and technological progress. This research is of great significance to China's energy efficiency and regional coordinated development.

Identifying change in spatial accumulation of soil salinity in an inland river watershed, China.

PubMed

Wang, Yugang; Deng, Caiyun; Liu, Yan; Niu, Ziru; Li, Yan

2018-04-15

Soil salinity accumulation is strong in arid areas and it has become a serious environmental problem. Knowledge of the process and spatial changes of accumulated salinity in soil can provide an insight into the spatial patterns of soil salinity accumulation. This is especially useful for estimating the spatial transport of soil salinity at the watershed scale. This study aimed to identify spatial patterns of salt accumulation in the top 20cm soils in a typical inland watershed, the Sangong River watershed in arid northwest China, using geostatistics, spatial analysis technology and the Lorenz curve. The results showed that: (1) soil salt content had great spatial variability (coefficient variation >1.0) in both in 1982 and 2015, and about 56% of the studied area experienced transition the degree of soil salt content from one class to another during 1982-2015. (2) Lorenz curves describing the proportions of soil salinity accumulation (SSA) identified that the boundary between soil salinity migration and accumulation regions was 24.3m lower in 2015 than in 1982, suggesting a spatio-temporal inequality in loading of the soil salinity transport region, indicating significant migration of soil salinity from the upstream to the downstream watershed. (3) Regardless of migration or accumulation region, the mean value of SSA per unit area was 0.17kg/m 2 higher in 2015 than 1982 (p<0.01) and the increasing SSA per unit area in irrigated land significantly increased by 0.19kg/m 2 compared with the migration region. Dramatic accumulation of soil salinity in all land use types was clearly increased by 0.29kg/m 2 in this agricultural watershed during the studied period in the arid northwest of China. This study demonstrates the spatial patterns of soil salinity accumulation, which is particularly useful for estimating the spatial transport of soil salinity at the watershed scale. Copyright © 2017 Elsevier B.V. All rights reserved.

Spatial scan statistics for detection of multiple clusters with arbitrary shapes.

PubMed

Lin, Pei-Sheng; Kung, Yi-Hung; Clayton, Murray

2016-12-01

In applying scan statistics for public health research, it would be valuable to develop a detection method for multiple clusters that accommodates spatial correlation and covariate effects in an integrated model. In this article, we connect the concepts of the likelihood ratio (LR) scan statistic and the quasi-likelihood (QL) scan statistic to provide a series of detection procedures sufficiently flexible to apply to clusters of arbitrary shape. First, we use an independent scan model for detection of clusters and then a variogram tool to examine the existence of spatial correlation and regional variation based on residuals of the independent scan model. When the estimate of regional variation is significantly different from zero, a mixed QL estimating equation is developed to estimate coefficients of geographic clusters and covariates. We use the Benjamini-Hochberg procedure (1995) to find a threshold for p-values to address the multiple testing problem. A quasi-deviance criterion is used to regroup the estimated clusters to find geographic clusters with arbitrary shapes. We conduct simulations to compare the performance of the proposed method with other scan statistics. For illustration, the method is applied to enterovirus data from Taiwan. © 2016, The International Biometric Society.

A statistical model of extreme storm rainfall

NASA Astrophysics Data System (ADS)

Smith, James A.; Karr, Alan F.

1990-02-01

A model of storm rainfall is developed for the central Appalachian region of the United States. The model represents the temporal occurrence of major storms and, for a given storm, the spatial distribution of storm rainfall. Spatial inhomogeneities of storm rainfall and temporal inhomogeneities of the storm occurrence process are explicitly represented. The model is used for estimating recurrence intervals of extreme storms. The parameter estimation procedure developed for the model is based on the substitution principle (method of moments) and requires data from a network of rain gages. The model is applied to a 5000 mi2 (12,950 km2) region in the Valley and Ridge Province of Virginia and West Virginia.

Evapotranspiration estimates derived using multi-platform remote sensing in a semiarid region

USDA-ARS?s Scientific Manuscript database

Evapotranspiration (ET) is a key component of the water balance, especially in arid and semiarid regions. The current study takes advantage of spatially-distributed, near real-time information provided by satellite remote sensing to develop a regional scale ET product derived from remotely-sensed ob...

Estimating A Reference Standard Segmentation With Spatially Varying Performance Parameters: Local MAP STAPLE

PubMed Central

Commowick, Olivier; Akhondi-Asl, Alireza; Warfield, Simon K.

2012-01-01

We present a new algorithm, called local MAP STAPLE, to estimate from a set of multi-label segmentations both a reference standard segmentation and spatially varying performance parameters. It is based on a sliding window technique to estimate the segmentation and the segmentation performance parameters for each input segmentation. In order to allow for optimal fusion from the small amount of data in each local region, and to account for the possibility of labels not being observed in a local region of some (or all) input segmentations, we introduce prior probabilities for the local performance parameters through a new Maximum A Posteriori formulation of STAPLE. Further, we propose an expression to compute confidence intervals in the estimated local performance parameters. We carried out several experiments with local MAP STAPLE to characterize its performance and value for local segmentation evaluation. First, with simulated segmentations with known reference standard segmentation and spatially varying performance, we show that local MAP STAPLE performs better than both STAPLE and majority voting. Then we present evaluations with data sets from clinical applications. These experiments demonstrate that spatial adaptivity in segmentation performance is an important property to capture. We compared the local MAP STAPLE segmentations to STAPLE, and to previously published fusion techniques and demonstrate the superiority of local MAP STAPLE over other state-of-the- art algorithms. PMID:22562727

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.

Optimal estimation of spatially variable recharge and transmissivity fields under steady-state groundwater flow. Part 2. Case study

NASA Astrophysics Data System (ADS)

Graham, Wendy D.; Neff, Christina R.

1994-05-01

The first-order analytical solution of the inverse problem for estimating spatially variable recharge and transmissivity under steady-state groundwater flow, developed in Part 1 is applied to the Upper Floridan Aquifer in NE Florida. Parameters characterizing the statistical structure of the log-transmissivity and head fields are estimated from 152 measurements of transmissivity and 146 measurements of hydraulic head available in the study region. Optimal estimates of the recharge, transmissivity and head fields are produced throughout the study region by conditioning on the nearest 10 available transmissivity measurements and the nearest 10 available head measurements. Head observations are shown to provide valuable information for estimating both the transmissivity and the recharge fields. Accurate numerical groundwater model predictions of the aquifer flow system are obtained using the optimal transmissivity and recharge fields as input parameters, and the optimal head field to define boundary conditions. For this case study, both the transmissivity field and the uncertainty of the transmissivity field prediction are poorly estimated, when the effects of random recharge are neglected.

The spatial impact of neighbouring on the exports activities of COMESA countries by using spatial panel models

NASA Astrophysics Data System (ADS)

Hamzalouh, L.; Ismail, M. T.; Rahman, R. A.

2017-09-01

In this paper, spatial panel models were used and the method for selecting the best model amongst the spatial fixed effects model and the spatial random effects model to estimate the fitting model by using the robust Hausman test for analysis of the exports pattern of the Common Market for Eastern and Southern African (COMESA) countries. And examine the effects of the interactions of the economic statistic of explanatory variables on the exports of the COMESA. Results indicated that the spatial Durbin model with fixed effects specification should be tested and considered in most cases of this study. After that, the direct and indirect effects among COMESA regions were assessed, and the role of indirect spatial effects in estimating exports was empirically demonstrated. Regarding originality and research value, and to the best of the authors’ knowledge, this is the first attempt to examine exports between COMESA and its member countries through spatial panel models using XSMLE, which is a new command for spatial analysis using STATA.

Statistical evaluation of a project to estimate fish trajectories through the intakes of Kaplan hydropower turbines

NASA Astrophysics Data System (ADS)

Sutton, Virginia Kay

This paper examines statistical issues associated with estimating paths of juvenile salmon through the intakes of Kaplan turbines. Passive sensors, hydrophones, detecting signals from ultrasonic transmitters implanted in individual fish released into the preturbine region were used to obtain the information to estimate fish paths through the intake. Aim and location of the sensors affects the spatial region in which the transmitters can be detected, and formulas relating this region to sensor aiming directions are derived. Cramer-Rao lower bounds for the variance of estimators of fish location are used to optimize placement of each sensor. Finally, a statistical methodology is developed for analyzing angular data collected from optimally placed sensors.

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.

Effectiveness of conservation easements in agricultural regions.

PubMed

Braza, Mark

2017-08-01

Conservation easements are a standard technique for preventing habitat loss, particularly in agricultural regions with extensive cropland cultivation, yet little is known about their effectiveness. I developed a spatial econometric approach to propensity-score matching and used the approach to estimate the amount of habitat loss prevented by a grassland conservation easement program of the U.S. federal government. I used a spatial autoregressive probit model to predict tract enrollment in the easement program as of 2001 based on tract agricultural suitability, habitat quality, and spatial interactions among neighboring tracts. Using the predicted values from the model, I matched enrolled tracts with similar unenrolled tracts to form a treatment group and a control group. To measure the program's impact on subsequent grassland loss, I estimated cropland cultivation rates for both groups in 2014 with a second spatial probit model. Between 2001 and 2014, approximately 14.9% of control tracts were cultivated and 0.3% of treated tracts were cultivated. Therefore, approximately 14.6% of the protected land would have been cultivated in the absence of the program. My results demonstrate that conservation easements can significantly reduce habitat loss in agricultural regions; however, the enrollment of tracts with low cropland suitability may constrain the amount of habitat loss they prevent. My results also show that spatial econometric models can improve the validity of control groups and thereby strengthen causal inferences about program effectiveness in situations when spatial interactions influence conservation decisions. © 2017 Society for Conservation Biology.

Monitoring survival rates of landbirds at varying spatial scales: An application of the MAPS Program

USGS Publications Warehouse

Rosenberg, D.K.; DeSante, D.F.; Hines, J.E.; Bonney, Rick; Pashley, David N.; Cooper, Robert; Niles, Larry

2000-01-01

Survivorship is a primary demographic parameter affecting population dynamics, and thus trends in species abundance. The Monitoring Avian Productivity and Survivorship (MAPS) program is a cooperative effort designed to monitor landbird demographic parameters. A principle goal of MAPS is to estimate annual survivorship and identify spatial patterns and temporal trends in these rates. We evaluated hypotheses of spatial patterns in survival rates among a collection of neighboring sampling sites, such as within national forests, among biogeographic provinces, and between breeding populations that winter in either Central or South America, and compared these geographic-specific models to a model of a common survival rate among all sampling sites. We used data collected during 1992-1995 from Swainson's Thrush (Cathorus ustulatus) populations in the western region of the United States. We evaluated the ability to detect spatial and temporal patterns of survivorship with simulated data. We found weak evidence of spatial differences in survival rates at the local scale of 'location,' which typically contained 3 mist-netting stations. There was little evidence of differences in survival rates among biogeographic provinces or between populations that winter in either Central or South America. When data were pooled for a regional estimate of survivorship, the percent relative bias due to pooling 'locations' was 12 years of monitoring. Detection of spatial patterns and temporal trends in survival rates from local to regional scales will provide important information for management and future research directed toward conservation of landbirds.

Estimating Biases for Regional Methane Fluxes using Co-emitted Tracers

NASA Astrophysics Data System (ADS)

Bambha, R.; Safta, C.; Michelsen, H. A.; Cui, X.; Jeong, S.; Fischer, M. L.

2017-12-01

Methane is a powerful greenhouse gas, and the development and improvement of emissions models rely on understanding the flux of methane released from anthropogenic sources relative to releases from other sources. Increasing production of shale oil and gas in the mid-latitudes and associated fugitive emissions are suspected to be a dominant contributor to the global methane increase. Landfills, sewage treatment, and other sources may be dominant sources in some parts of the U.S. Large discrepancies between emissions models present a great challenge to reconciling atmospheric measurements with inventory-based estimates for various emissions sectors. Current approaches for measuring regional emissions yield highly uncertain estimates because of the sparsity of measurement sites and the presence of multiple simultaneous sources. Satellites can provide wide spatial coverage at the expense of much lower measurement precision compared to ground-based instruments. Methods for effective assimilation of data from a variety of sources are critically needed to perform regional GHG attribution with existing measurements and to determine how to structure future measurement systems including satellites. We present a hierarchical Bayesian framework to estimate surface methane fluxes based on atmospheric concentration measurements and a Lagrangian transport model (Weather Research and Forecasting and Stochastic Time-Inverted Lagrangian Transport). Structural errors in the transport model are estimated with the help of co-emitted traces species with well defined decay rates. We conduct the analyses at regional scales that are based on similar geographical and meteorological conditions. For regions where data are informative, we further refine flux estimates by emissions sector and infer spatially and temporally varying biases parameterized as spectral random field representations.

The influence of spatial resolution on human health risk co-benefit estimates for global climate policy assessments.

PubMed

Shih, Hsiu-Ching; Crawford-Brown, Douglas; Ma, Hwong-wen

2015-03-15

Assessment of the ability of climate policies to produce desired improvements in public health through co-benefits of air pollution reduction can consume resources in both time and research funds. These resources increase significantly as the spatial resolution of models increases. In addition, the level of spatial detail available in macroeconomic models at the heart of climate policy assessments is much lower than that available in traditional human health risk modeling. It is therefore important to determine whether increasing spatial resolution considerably affects risk-based decisions; which kinds of decisions might be affected; and under what conditions they will be affected. Human health risk co-benefits from carbon emissions reductions that bring about concurrent reductions in Particulate Matter (PM10) emissions is therefore examined here at four levels of spatial resolution (Uniform Nation, Uniform Region, Uniform County/city, Health Risk Assessment) in a case study of Taiwan as one of the geographic regions of a global macroeceonomic model, with results that are representative of small, industrialized nations within that global model. A metric of human health risk mortality (YOLL, years of life lost in life expectancy) is compared under assessments ranging from a "uniform simulation" in which there is no spatial resolution of changes in ambient air concentration under a policy to a "highly spatially resolved simulation" (called here Health Risk Assessment). PM10 is chosen in this study as the indicator of air pollution for which risks are assessed due to its significance as a co-benefit of carbon emissions reductions within climate mitigation policy. For the policy examined, the four estimates of mortality in the entirety of Taiwan are 747 YOLL, 834 YOLL, 984 YOLL and 916 YOLL, under Uniform Taiwan, Uniform Region, Uniform County and Health Risk Assessment respectively; or differences of 18%, 9%, 7% if the HRA methodology is taken as the baseline. While these differences are small compared to uncertainties in health risk assessment more generally, the ranks of different regions and of emissions categories as the focus of regulatory efforts estimated at these four levels of spatial resolution are quite different. The results suggest that issues of risk equity within a nation might be missed by the lower levels of spatial resolution, suggesting that low resolution models are suited to calculating national cost-benefit ratios but not as suited to assessing co-benefits of climate policies reflecting intersubject variability in risk, or in identifying sub-national regions and emissions sectors on which to focus attention (although even here, the errors introduced by low spatial resolution are generally less than 40%). Copyright © 2014 Elsevier Ltd. All rights reserved.

The spatial structure of magnetospheric plasma disturbance estimated by using magnetic data obtained by SWARM satellites.

NASA Astrophysics Data System (ADS)

Yokoyama, Y.; Iyemori, T.; Aoyama, T.

2017-12-01

Field-aligned currents with various spatial scales flow into and out from high-latitude ionosphere. The magnetic fluctuations observed by LEO satellites along their orbits having period longer than a few seconds can be regarded as the manifestations of spatial structure of field aligned currents.This has been confirmed by using the initial orbital characteristics of 3 SWARM-satellites. From spectral analysis, we evaluated the spectral indices of these magnetic fluctuations and investigated their dependence on regions, such as magnetic latitude and MLT and so on. We found that the spectral indices take quite different values between the regions lower than the equatorward boundary of the auroral oval (around 63 degrees' in magnetic latitude) and the regions higher than that. On the other hands, we could not find the clear MLT dependence. In general, the FACs are believed to be generated in the magnetiospheric plasma sheet and boundary layer, and they flow along the field lines conserving their currents.The theory of FAC generation [e.g., Hasegawa and Sato ,1978] indicates that the FACs are strongly connected with magnetospheric plasma disturbances. Although the spectral indices above are these of spatial structures of the FACs over the ionosphere, by using the theoretical equation of FAC generation, we evaluate the spectral indices of magnetospheric plasma disturbance in FAC's generation regions. Furthermore, by projecting the area of fluctuations on the equatorial plane of magnetosphere (i.e. plasma sheet), we can estimate the spatial structure of magnetospheric plasma disturbance. In this presentation, we focus on the characteristics of disturbance in midnight region and discuss the relations to the substorm.

Fine-Scale Mapping by Spatial Risk Distribution Modeling for Regional Malaria Endemicity and Its Implications under the Low-to-Moderate Transmission Setting in Western Cambodia

PubMed Central

Okami, Suguru; Kohtake, Naohiko

2016-01-01

The disease burden of malaria has decreased as malaria elimination efforts progress. The mapping approach that uses spatial risk distribution modeling needs some adjustment and reinvestigation in accordance with situational changes. Here we applied a mathematical modeling approach for standardized morbidity ratio (SMR) calculated by annual parasite incidence using routinely aggregated surveillance reports, environmental data such as remote sensing data, and non-environmental anthropogenic data to create fine-scale spatial risk distribution maps of western Cambodia. Furthermore, we incorporated a combination of containment status indicators into the model to demonstrate spatial heterogeneities of the relationship between containment status and risks. The explanatory model was fitted to estimate the SMR of each area (adjusted Pearson correlation coefficient R2 = 0.774; Akaike information criterion AIC = 149.423). A Bayesian modeling framework was applied to estimate the uncertainty of the model and cross-scale predictions. Fine-scale maps were created by the spatial interpolation of estimated SMRs at each village. Compared with geocoded case data, corresponding predicted values showed conformity [Spearman’s rank correlation r = 0.662 in the inverse distance weighed interpolation and 0.645 in ordinal kriging (95% confidence intervals of 0.414–0.827 and 0.368–0.813, respectively), Welch’s t-test; Not significant]. The proposed approach successfully explained regional malaria risks and fine-scale risk maps were created under low-to-moderate malaria transmission settings where reinvestigations of existing risk modeling approaches were needed. Moreover, different representations of simulated outcomes of containment status indicators for respective areas provided useful insights for tailored interventional planning, considering regional malaria endemicity. PMID:27415623

Merging Station Observations with Large-Scale Gridded Data to Improve Hydrological Predictions over Chile

NASA Astrophysics Data System (ADS)

Peng, L.; Sheffield, J.; Verbist, K. M. J.

2016-12-01

Hydrological predictions at regional-to-global scales are often hampered by the lack of meteorological forcing data. The use of large-scale gridded meteorological data is able to overcome this limitation, but these data are subject to regional biases and unrealistic values at local scale. This is especially challenging in regions such as Chile, where climate exhibits high spatial heterogeneity as a result of long latitude span and dramatic elevation changes. However, regional station-based observational datasets are not fully exploited and have the potential of constraining biases and spatial patterns. This study aims at adjusting precipitation and temperature estimates from the Princeton University global meteorological forcing (PGF) gridded dataset to improve hydrological simulations over Chile, by assimilating 982 gauges from the Dirección General de Aguas (DGA). To merge station data with the gridded dataset, we use a state-space estimation method to produce optimal gridded estimates, considering both the error of the station measurements and the gridded PGF product. The PGF daily precipitation, maximum and minimum temperature at 0.25° spatial resolution are adjusted for the period of 1979-2010. Precipitation and temperature gauges with long and continuous records (>70% temporal coverage) are selected, while the remaining stations are used for validation. The leave-one-out cross validation verifies the robustness of this data assimilation approach. The merged dataset is then used to force the Variable Infiltration Capacity (VIC) hydrological model over Chile at daily time step which are compared to the observations of streamflow. Our initial results show that the station-merged PGF precipitation effectively captures drizzle and the spatial pattern of storms. Overall the merged dataset has significant improvements compared to the original PGF with reduced biases and stronger inter-annual variability. The invariant spatial pattern of errors between the station data and the gridded product opens up the possibility of merging real-time satellite and intermittent gauge observations to produce more accurate real-time hydrological predictions.

Alpine Grassland Soil Organic Carbon Stock and Its Uncertainty in the Three Rivers Source Region of the Tibetan Plateau

PubMed Central

Chang, Xiaofeng; Wang, Shiping; Cui, Shujuan; Zhu, Xiaoxue; Luo, Caiyun; Zhang, Zhenhua; Wilkes, Andreas

2014-01-01

Alpine grassland of the Tibetan Plateau is an important component of global soil organic carbon (SOC) stocks, but insufficient field observations and large spatial heterogeneity leads to great uncertainty in their estimation. In the Three Rivers Source Region (TRSR), alpine grasslands account for more than 75% of the total area. However, the regional carbon (C) stock estimate and their uncertainty have seldom been tested. Here we quantified the regional SOC stock and its uncertainty using 298 soil profiles surveyed from 35 sites across the TRSR during 2006–2008. We showed that the upper soil (0–30 cm depth) in alpine grasslands of the TRSR stores 2.03 Pg C, with a 95% confidence interval ranging from 1.25 to 2.81 Pg C. Alpine meadow soils comprised 73% (i.e. 1.48 Pg C) of the regional SOC estimate, but had the greatest uncertainty at 51%. The statistical power to detect a deviation of 10% uncertainty in grassland C stock was less than 0.50. The required sample size to detect this deviation at a power of 90% was about 6–7 times more than the number of sample sites surveyed. Comparison of our observed SOC density with the corresponding values from the dataset of Yang et al. indicates that these two datasets are comparable. The combined dataset did not reduce the uncertainty in the estimate of the regional grassland soil C stock. This result could be mainly explained by the underrepresentation of sampling sites in large areas with poor accessibility. Further research to improve the regional SOC stock estimate should optimize sampling strategy by considering the number of samples and their spatial distribution. PMID:24819054

Moving across scales: Challenges and opportunities in upscaling carbon fluxes

NASA Astrophysics Data System (ADS)

Naithani, K. J.

2016-12-01

Light use efficiency (LUE) type models are commonly used to upscale terrestrial C fluxes and estimate regional and global C budgets. Model parameters are often estimated for each land cover type (LCT) using flux observations from one or more eddy covariance towers, and then spatially extrapolated by integrating land cover, meteorological, and remotely sensed data. Decisions regarding the type of input data (spatial resolution of land cover data, spatial and temporal length of flux data), representation of landscape structure (land use vs. disturbance regime), and the type of modeling framework (common risk vs. hierarchical) all influence the estimates CO2 fluxes and the associated uncertainties, but are rarely considered together. This work presents a synthesis of past and present efforts for upscaling CO2 fluxes and associated uncertainties in the ChEAS (Chequamegon Ecosystem Atmosphere Study) region in northern Wisconsin and the Upper Peninsula of Michigan. This work highlights two key future research needs. First, the characterization of uncertainties due to all of the abovementioned factors reflects only a (hopefully relevant) subset the overall uncertainties. Second, interactions among these factors are likely critical, but are poorly represented by the tower network at landscape scales. Yet, results indicate significant spatial and temporal heterogeneity of uncertainty in CO2 fluxes which can inform carbon management efforts and prioritize data needs.

Representativeness of regional and global mass-balance measurement networks (Invited)

NASA Astrophysics Data System (ADS)

Cogley, J. G.; Moholdt, G.; Gardner, A. S.

2013-12-01

We showed in a recent publication that regional estimates of glacier mass budgets, obtained by interpolation from in-situ measurements, were markedly more negative than corresponding estimates by satellite gravimetry (GRACE) and satellite altimetry (ICESat) during 2003-2009. Examining the ICESat data in more detail, we found that in-situ records tend to be located in areas where glaciers are thinning more rapidly than as observed in their regional surroundings. Because neither GRACE nor ICESat can provide information for times before 2002-2003, and may not operate without interruption in the future, we explore possible explanations of and remedies for the identified bias in the in-situ network. Sparse spatial sampling, coupled with previously undetected spatial variability of mass balance at scales between the 10-km in-situ scale and the 350-km gravimetric scale, appears to be the leading explanation. Satisfactory remedies are not obvious. Selecting glaciers for in-situ measurement that are more representative will yield only incremental improvements. There appears to be no alternative to mass-balance modelling as a versatile tool for estimation of regional mass balance. However the meteorological data for forcing the surface components of glacier models have coarser resolution than is desirable and are themselves uncertain, especially in the remote regions where much of the glacier ice is found. Measurements of frontal (dynamic) mass changes are still difficult, and modelling of these changes remains underdeveloped in spite of recent advances. Thus research on a broad scale is called for in order to meet the challenge of producing more accurate hindcasts and projections of glacier mass budgets with fine spatial and temporal resolution.

Using Imaging Spectrometry measurements of Ecosystem Composition to constrain Regional Predictions of Carbon, Water and Energy Fluxes

NASA Astrophysics Data System (ADS)

Anderson, C.; Bond-Lamberty, B. P.; Huang, M.; Xu, Y.; Stegen, J.

2016-12-01

Ecosystem composition is a key attribute of terrestrial ecosystems, influencing the fluxes of carbon, water, and energy between the land surface and the atmosphere. The description of current ecosystem composition has traditionally come from relatively few ground-based inventories of the plant canopy, but are spatially limited and do not provide a comprehensive picture of ecosystem composition at regional or global scales. In this analysis, imaging spectrometry measurements, collected as part of the HyspIRI Preparatory Mission, are used to provide spatially-resolved estimates of plant functional type composition providing an important constraint on terrestrial biosphere model predictions of carbon, water and energy fluxes across the heterogeneous landscapes of the Californian Sierras. These landscapes include oak savannas, mid-elevation mixed pines, fir-cedar forests, and high elevation pines. Our results show that imaging spectrometry measurements can be successfully used to estimate regional-scale variation in ecosystem composition and resulting spatial heterogeneity in patterns of carbon, water and energy fluxes and ecosystem dynamics. Simulations at four flux tower sites within the study region yield patterns of seasonal and inter-annual variation in carbon and water fluxes that have comparable accuracy to simulations initialized from ground-based inventory measurements. Finally, results indicate that during the 2012-2015 Californian drought, regional net carbon fluxes fell by 84%, evaporation and transpiration fluxes fell by 53% and 33% respectively, and sensible heat increase by 51%. This study provides a framework for assimilating near-future global satellite imagery estimates of ecosystem composition with terrestrial biosphere models, constraining and improving their predictions of large-scale ecosystem dynamics and functioning.

Using Imaging Spectrometry measurements of Ecosystem Composition to constrain Regional Predictions of Carbon, Water and Energy Fluxes

NASA Astrophysics Data System (ADS)

Antonarakis, A. S.; Bogan, S.; Moorcroft, P. R.

2017-12-01

Ecosystem composition is a key attribute of terrestrial ecosystems, influencing the fluxes of carbon, water, and energy between the land surface and the atmosphere. The description of current ecosystem composition has traditionally come from relatively few ground-based inventories of the plant canopy, but are spatially limited and do not provide a comprehensive picture of ecosystem composition at regional or global scales. In this analysis, imaging spectrometry measurements, collected as part of the HyspIRI Preparatory Mission, are used to provide spatially-resolved estimates of plant functional type composition providing an important constraint on terrestrial biosphere model predictions of carbon, water and energy fluxes across the heterogeneous landscapes of the Californian Sierras. These landscapes include oak savannas, mid-elevation mixed pines, fir-cedar forests, and high elevation pines. Our results show that imaging spectrometry measurements can be successfully used to estimate regional-scale variation in ecosystem composition and resulting spatial heterogeneity in patterns of carbon, water and energy fluxes and ecosystem dynamics. Simulations at four flux tower sites within the study region yield patterns of seasonal and inter-annual variation in carbon and water fluxes that have comparable accuracy to simulations initialized from ground-based inventory measurements. Finally, results indicate that during the 2012-2015 Californian drought, regional net carbon fluxes fell by 84%, evaporation and transpiration fluxes fell by 53% and 33% respectively, and sensible heat increase by 51%. This study provides a framework for assimilating near-future global satellite imagery estimates of ecosystem composition with terrestrial biosphere models, constraining and improving their predictions of large-scale ecosystem dynamics and functioning.

Remote sensing estimation of the total phosphorus concentration in a large lake using band combinations and regional multivariate statistical modeling techniques.

PubMed

Gao, Yongnian; Gao, Junfeng; Yin, Hongbin; Liu, Chuansheng; Xia, Ting; Wang, Jing; Huang, Qi

2015-03-15

Remote sensing has been widely used for ater quality monitoring, but most of these monitoring studies have only focused on a few water quality variables, such as chlorophyll-a, turbidity, and total suspended solids, which have typically been considered optically active variables. Remote sensing presents a challenge in estimating the phosphorus concentration in water. The total phosphorus (TP) in lakes has been estimated from remotely sensed observations, primarily using the simple individual band ratio or their natural logarithm and the statistical regression method based on the field TP data and the spectral reflectance. In this study, we investigated the possibility of establishing a spatial modeling scheme to estimate the TP concentration of a large lake from multi-spectral satellite imagery using band combinations and regional multivariate statistical modeling techniques, and we tested the applicability of the spatial modeling scheme. The results showed that HJ-1A CCD multi-spectral satellite imagery can be used to estimate the TP concentration in a lake. The correlation and regression analysis showed a highly significant positive relationship between the TP concentration and certain remotely sensed combination variables. The proposed modeling scheme had a higher accuracy for the TP concentration estimation in the large lake compared with the traditional individual band ratio method and the whole-lake scale regression-modeling scheme. The TP concentration values showed a clear spatial variability and were high in western Lake Chaohu and relatively low in eastern Lake Chaohu. The northernmost portion, the northeastern coastal zone and the southeastern portion of western Lake Chaohu had the highest TP concentrations, and the other regions had the lowest TP concentration values, except for the coastal zone of eastern Lake Chaohu. These results strongly suggested that the proposed modeling scheme, i.e., the band combinations and the regional multivariate statistical modeling techniques, demonstrated advantages for estimating the TP concentration in a large lake and had a strong potential for universal application for the TP concentration estimation in large lake waters worldwide. Copyright © 2014 Elsevier Ltd. All rights reserved.

The importance of regional models in assessing canine cancer incidences in Switzerland

PubMed Central

Leyk, Stefan; Brunsdon, Christopher; Graf, Ramona; Pospischil, Andreas; Fabrikant, Sara Irina

2018-01-01

Fitting canine cancer incidences through a conventional regression model assumes constant statistical relationships across the study area in estimating the model coefficients. However, it is often more realistic to consider that these relationships may vary over space. Such a condition, known as spatial non-stationarity, implies that the model coefficients need to be estimated locally. In these kinds of local models, the geographic scale, or spatial extent, employed for coefficient estimation may also have a pervasive influence. This is because important variations in the local model coefficients across geographic scales may impact the understanding of local relationships. In this study, we fitted canine cancer incidences across Swiss municipal units through multiple regional models. We computed diagnostic summaries across the different regional models, and contrasted them with the diagnostics of the conventional regression model, using value-by-alpha maps and scalograms. The results of this comparative assessment enabled us to identify variations in the goodness-of-fit and coefficient estimates. We detected spatially non-stationary relationships, in particular, for the variables related to biological risk factors. These variations in the model coefficients were more important at small geographic scales, making a case for the need to model canine cancer incidences locally in contrast to more conventional global approaches. However, we contend that prior to undertaking local modeling efforts, a deeper understanding of the effects of geographic scale is needed to better characterize and identify local model relationships. PMID:29652921

The importance of regional models in assessing canine cancer incidences in Switzerland.

PubMed

Boo, Gianluca; Leyk, Stefan; Brunsdon, Christopher; Graf, Ramona; Pospischil, Andreas; Fabrikant, Sara Irina

2018-01-01

Fitting canine cancer incidences through a conventional regression model assumes constant statistical relationships across the study area in estimating the model coefficients. However, it is often more realistic to consider that these relationships may vary over space. Such a condition, known as spatial non-stationarity, implies that the model coefficients need to be estimated locally. In these kinds of local models, the geographic scale, or spatial extent, employed for coefficient estimation may also have a pervasive influence. This is because important variations in the local model coefficients across geographic scales may impact the understanding of local relationships. In this study, we fitted canine cancer incidences across Swiss municipal units through multiple regional models. We computed diagnostic summaries across the different regional models, and contrasted them with the diagnostics of the conventional regression model, using value-by-alpha maps and scalograms. The results of this comparative assessment enabled us to identify variations in the goodness-of-fit and coefficient estimates. We detected spatially non-stationary relationships, in particular, for the variables related to biological risk factors. These variations in the model coefficients were more important at small geographic scales, making a case for the need to model canine cancer incidences locally in contrast to more conventional global approaches. However, we contend that prior to undertaking local modeling efforts, a deeper understanding of the effects of geographic scale is needed to better characterize and identify local model relationships.

Comparison of estimated and measured sediment yield in the Gualala River

Treesearch

Matthew O’Connor; Jack Lewis; Robert Pennington

2012-01-01

This study compares quantitative erosion rate estimates developed at different spatial and temporal scales. It is motivated by the need to assess potential water quality impacts of a proposed vineyard development project in the Gualala River watershed. Previous erosion rate estimates were developed using sediment source assessment techniques by the North Coast Regional...

Interpolating precipitation and its relation to runoff and non-point source pollution.

PubMed

Chang, Chia-Ling; Lo, Shang-Lien; Yu, Shaw-L

2005-01-01

When rainfall spatially varies, complete rainfall data for each region with different rainfall characteristics are very important. Numerous interpolation methods have been developed for estimating unknown spatial characteristics. However, no interpolation method is suitable for all circumstances. In this study, several methods, including the arithmetic average method, the Thiessen Polygons method, the traditional inverse distance method, and the modified inverse distance method, were used to interpolate precipitation. The modified inverse distance method considers not only horizontal distances but also differences between the elevations of the region with no rainfall records and of its surrounding rainfall stations. The results show that when the spatial variation of rainfall is strong, choosing a suitable interpolation method is very important. If the rainfall is uniform, the precipitation estimated using any interpolation method would be quite close to the actual precipitation. When rainfall is heavy in locations with high elevation, the rainfall changes with the elevation. In this situation, the modified inverse distance method is much more effective than any other method discussed herein for estimating the rainfall input for WinVAST to estimate runoff and non-point source pollution (NPSP). When the spatial variation of rainfall is random, regardless of the interpolation method used to yield rainfall input, the estimation errors of runoff and NPSP are large. Moreover, the relationship between the relative error of the predicted runoff and predicted pollutant loading of SS is high. However, the pollutant concentration is affected by both runoff and pollutant export, so the relationship between the relative error of the predicted runoff and the predicted pollutant concentration of SS may be unstable.

An analysis of simulated and observed storm characteristics

NASA Astrophysics Data System (ADS)

Benestad, R. E.

2010-09-01

A calculus-based cyclone identification (CCI) method has been applied to the most recent re-analysis (ERAINT) from the European Centre for Medium-range Weather Forecasts and results from regional climate model (RCM) simulations. The storm frequency for events with central pressure below a threshold value of 960-990hPa were examined, and the gradient wind from the simulated storm systems were compared with corresponding estimates from the re-analysis. The analysis also yielded estimates for the spatial extent of the storm systems, which was also included in the regional climate model cyclone evaluation. A comparison is presented between a number of RCMs and the ERAINT re-analysis in terms of their description of the gradient winds, number of cyclones, and spatial extent. Furthermore, a comparison between geostrophic wind estimated though triangules of interpolated or station measurements of SLP is presented. Wind still represents one of the more challenging variables to model realistically.

Optimal Scaling of Aftershock Zones using Ground Motion Forecasts

NASA Astrophysics Data System (ADS)

Wilson, John Max; Yoder, Mark R.; Rundle, John B.

2018-02-01

The spatial distribution of aftershocks following major earthquakes has received significant attention due to the shaking hazard these events pose for structures and populations in the affected region. Forecasting the spatial distribution of aftershock events is an important part of the estimation of future seismic hazard. A simple spatial shape for the zone of activity has often been assumed in the form of an ellipse having semimajor axis to semiminor axis ratio of 2.0. However, since an important application of these calculations is the estimation of ground shaking hazard, an effective criterion for forecasting future aftershock impacts is to use ground motion prediction equations (GMPEs) in addition to the more usual approach of using epicentral or hypocentral locations. Based on these ideas, we present an aftershock model that uses self-similarity and scaling relations to constrain parameters as an option for such hazard assessment. We fit the spatial aspect ratio to previous earthquake sequences in the studied regions, and demonstrate the effect of the fitting on the likelihood of post-disaster ground motion forecasts for eighteen recent large earthquakes. We find that the forecasts in most geographic regions studied benefit from this optimization technique, while some are better suited to the use of the a priori aspect ratio.

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.

Spatial distribution of forest aboveground biomass estimated from remote sensing and forest inventory data in New England, USA

Treesearch

Daolan Zheng; Linda S. Heath; Mark J. Ducey

2008-01-01

We combined satellite (Landsat 7 and Moderate Resolution Imaging Spectrometer) and U.S. Department of Agriculture forest inventory and analysis (FIA) data to estimate forest aboveground biomass (AGB) across New England, USA. This is practical for large-scale carbon studies and may reduce uncertainty of AGB estimates. We estimate that total regional forest AGB was 1,867...

Topography-mediated controls on local vegetation phenology estimated from MODIS vegetation index

Treesearch

Taehee Hwang; Conghe Song; James Vose; Lawrence Band

2011-01-01

Forest canopy phenology is an important constraint on annual water and carbon budgets, and responds to regional interannual climate variation. In steep terrain, there are complex spatial variations in phenology due to topographic influences on microclimate, community composition, and available soil moisture. In this study, we investigate spatial patterns of phenology...

The spatial spread of schistosomiasis: A multidimensional network model applied to Saint-Louis region, Senegal

NASA Astrophysics Data System (ADS)

Ciddio, Manuela; Mari, Lorenzo; Sokolow, Susanne H.; De Leo, Giulio A.; Casagrandi, Renato; Gatto, Marino

2017-10-01

Schistosomiasis is a parasitic, water-related disease that is prevalent in tropical and subtropical areas of the world, causing severe and chronic consequences especially among children. Here we study the spatial spread of this disease within a network of connected villages in the endemic region of the Lower Basin of the Senegal River, in Senegal. The analysis is performed by means of a spatially explicit metapopulation model that couples local-scale eco-epidemiological dynamics with spatial mechanisms related to human mobility (estimated from anonymized mobile phone records), snail dispersal and hydrological transport of schistosome larvae along the main water bodies of the region. Results show that the model produces epidemiological patterns consistent with field observations, and point out the key role of spatial connectivity on the spread of the disease. These findings underline the importance of considering different transport pathways in order to elaborate disease control strategies that can be effective within a network of connected populations.

Regional-scale analysis of extreme precipitation from short and fragmented records

NASA Astrophysics Data System (ADS)

Libertino, Andrea; Allamano, Paola; Laio, Francesco; Claps, Pierluigi

2018-02-01

Rain gauge is the oldest and most accurate instrument for rainfall measurement, able to provide long series of reliable data. However, rain gauge records are often plagued by gaps, spatio-temporal discontinuities and inhomogeneities that could affect their suitability for a statistical assessment of the characteristics of extreme rainfall. Furthermore, the need to discard the shorter series for obtaining robust estimates leads to ignore a significant amount of information which can be essential, especially when large return periods estimates are sought. This work describes a robust statistical framework for dealing with uneven and fragmented rainfall records on a regional spatial domain. The proposed technique, named "patched kriging" allows one to exploit all the information available from the recorded series, independently of their length, to provide extreme rainfall estimates in ungauged areas. The methodology involves the sequential application of the ordinary kriging equations, producing a homogeneous dataset of synthetic series with uniform lengths. In this way, the errors inherent to any regional statistical estimation can be easily represented in the spatial domain and, possibly, corrected. Furthermore, the homogeneity of the obtained series, provides robustness toward local artefacts during the parameter-estimation phase. The application to a case study in the north-western Italy demonstrates the potential of the methodology and provides a significant base for discussing its advantages over previous techniques.

Assessment of Global Mercury Deposition through Litterfall.

PubMed

Wang, Xun; Bao, Zhengduo; Lin, Che-Jen; Yuan, Wei; Feng, Xinbin

2016-08-16

There is a large uncertainty in the estimate of global dry deposition of atmospheric mercury (Hg). Hg deposition through litterfall represents an important input to terrestrial forest ecosystems via cumulative uptake of atmospheric Hg (most Hg(0)) to foliage. In this study, we estimate the quantity of global Hg deposition through litterfall using statistical modeling (Monte Carlo simulation) of published data sets of litterfall biomass production, tree density, and Hg concentration in litter samples. On the basis of the model results, the global annual Hg deposition through litterfall is estimated to be 1180 ± 710 Mg yr(-1), more than two times greater than the estimate by GEOS-Chem. Spatial distribution of Hg deposition through litterfall suggests that deposition flux decreases spatially from tropical to temperate and boreal regions. Approximately 70% of global Hg(0) dry deposition occurs in the tropical and subtropical regions. A major source of uncertainty in this study is the heterogeneous geospatial distribution of available data. More observational data in regions (Southeast Asia, Africa, and South America) where few data sets exist will greatly improve the accuracy of the current estimate. Given that the quantity of global Hg deposition via litterfall is typically 2-6 times higher than Hg(0) evasion from forest floor, global forest ecosystems represent a strong Hg(0) sink.

Seismic b-value anomalies prior to the 3rd January 2016, Mw = 6.7 Manipur earthquake of northeast India

NASA Astrophysics Data System (ADS)

Borgohain, Jayanta Madhab; Borah, Kajaljyoti; Biswas, Rajib; Bora, Dipok K.

2018-04-01

Spatial variation of seismic b-value is estimated in the Indo-Myanmar subduction zone of northeast (NE) India using the homogeneous part of earthquake catalogue (1996-2015), recorded by International Seismological Center (ISC), consisting of 895 events of magnitude MW ≥ 3.9. The study region is divided into 1° × 1° square grids and b-values are estimated at each grid by maximum likelihood method. In this study, the b-value varies from 0.75 to 1.54 in the region. Significant variation of low b-value in the respective location may indicate high stress accumulation in that region. Spatial variation reveals intermediate b-value anomalies around the epicenter of the Mw = 6.7 Manipur earthquake which occurred on 3rd January at 23:05 UTC (4 January 2016 at 04:35 IST). The variations of b-values are also estimated with respect to depth. The low b-value associated with the depth range ∼15-55 km, which may imply crustal homogeneity and high stress accumulation in the crust. Since, NE India lies in the seismic zone V of the country; this study can be helpful to understand seismotectonics in the region.

Lower Risk of Cancer in the Areas Inhabited by the German Minority in the Region of Opole, Poland.

PubMed

Chawińska, Ewa; Tukiendorf, Andrzej; Miszczyk, Leszek

2015-01-01

The lower risk of cancer in the areas inhabited by the German minority in the region of Opole, Poland, at the turn of the 1980's and 1990's has been already reported. A reanalysis of the present-day data was conducted. All the cancer cases (at all sites combined) registered within the years 2008-2012 with data collected by the Regional Cancer Registry in Opole were analyzed in this study. To estimate the risk of cancer in different spatial contexts, such as trends, clusters, and levels, modern geostatistical tools were applied. A statistically significant reduction of the cancer risk was reported in administrative units with ≥ 10% of the German minority. Average decreases in relative risk of 13% in men and 16% in women were estimated. The geographical patterns of the estimates are illustrated. The observed differences in the risk of cancer between the ethnic groups (Germans and repatriates) confirm a historical trend of the disease in the region of Opole, Poland. Some genetic, nutritional, or cultural aspects together with economic issues may play a role in the specified spatial disease patterns. © 2015 S. Karger GmbH, Freiburg.

The freight landscape : using secondary data sources to describe metropolitan freight flows.

DOT National Transportation Integrated Search

2010-01-01

Freight flows depend on the spatial organization of freight supply and demand, and on the transportation facilities within the metropolitan area. We use network model data for both the Los Angeles region and San Francisco region, and estimate two set...

An assessment of the feasibility of the use of satellite-only rainfall estimates for the hydrological monitoring in central Italy

NASA Astrophysics Data System (ADS)

Campo, Lorenzo; Caparrini, Francesca

2013-04-01

The need for accurate distributed hydrological modelling has constantly increased in last years for several purposes: agricultural applications, water resources management, hydrological balance at watershed scale, floods forecast. The main input for the hydrological numerical models is rainfall data that present, at the same time, a large availability of measures (in gauged regions, with respect to other micro-meteorological variables) and the most complex spatial patterns. While also in presence of densely gauged watersheds the spatial interpolation of the rainfall is a non-trivial problem, due to the spatial intermittence of the variable (especially at finer temporal scales), ungauged regions need an alternative source of rainfall data in order to perform the hydrological modelling. Such source can be constituted by the satellite-estimated rainfall fields, with reference to both geostationary and polar-orbit platforms. In this work the rainfall product obtained by the Aqua-AIRS sensor were used in order to assess the feasibility of the use of satellite-based rainfall as input for distributed hydrological modelling. The MOBIDIC (MOdello di BIlancio Distribuito e Continuo) model, developed at the Department of civil and Environmental Engineering of the University of Florence and operationally used by Tuscany Region and Umbria Region for flood prediction and management, was used for the experiments. In particular three experiments were carried on: a) hydrological simulation with the use of rain-gauges data, b) simulation with the use of satellite-only rainfall estimates, c) simulation with the combined use of the two sources of data in order to obtain an optimal estimate of the actual rainfall fields. The domain of the study was the central Italy. Several critical events occurred in the area were analyzed. A discussion of the results is provided.

ESTIMATING REGIONAL SPECIES RICHNESS USING A LIMITED NUMBER OF SURVEY UNITS

EPA Science Inventory

The accurate and precise estimation of species richness at large spatial scales using a limited number of survey units is of great significance for ecology and biodiversity conservation. We used the distribution data of native fish and resident breeding bird species compiled for ...

Extrapolating active layer thickness measurements across Arctic polygonal terrain using LiDAR and NDVI data sets.

PubMed

Gangodagamage, Chandana; Rowland, Joel C; Hubbard, Susan S; Brumby, Steven P; Liljedahl, Anna K; Wainwright, Haruko; Wilson, Cathy J; Altmann, Garrett L; Dafflon, Baptiste; Peterson, John; Ulrich, Craig; Tweedie, Craig E; Wullschleger, Stan D

2014-08-01

Landscape attributes that vary with microtopography, such as active layer thickness ( ALT ), are labor intensive and difficult to document effectively through in situ methods at kilometer spatial extents, thus rendering remotely sensed methods desirable. Spatially explicit estimates of ALT can provide critically needed data for parameterization, initialization, and evaluation of Arctic terrestrial models. In this work, we demonstrate a new approach using high-resolution remotely sensed data for estimating centimeter-scale ALT in a 5 km 2 area of ice-wedge polygon terrain in Barrow, Alaska. We use a simple regression-based, machine learning data-fusion algorithm that uses topographic and spectral metrics derived from multisensor data (LiDAR and WorldView-2) to estimate ALT (2 m spatial resolution) across the study area. Comparison of the ALT estimates with ground-based measurements, indicates the accuracy (r 2  = 0.76, RMSE ±4.4 cm) of the approach. While it is generally accepted that broad climatic variability associated with increasing air temperature will govern the regional averages of ALT , consistent with prior studies, our findings using high-resolution LiDAR and WorldView-2 data, show that smaller-scale variability in ALT is controlled by local eco-hydro-geomorphic factors. This work demonstrates a path forward for mapping ALT at high spatial resolution and across sufficiently large regions for improved understanding and predictions of coupled dynamics among permafrost, hydrology, and land-surface processes from readily available remote sensing data.

Regional frequency analysis of extreme rainfall for the Baltimore Metropolitan region based on stochastic storm transposition

NASA Astrophysics Data System (ADS)

Zhou, Z.; Smith, J. A.; Yang, L.; Baeck, M. L.; Wright, D.; Liu, S.

2017-12-01

Regional frequency analyses of extreme rainfall are critical for development of engineering hydrometeorology procedures. In conventional approaches, the assumptions that `design storms' have specified time profiles and are uniform in space are commonly applied but often not appropriate, especially over regions with heterogeneous environments (due to topography, water-land boundaries and land surface properties). In this study, we present regional frequency analyses of extreme rainfall for Baltimore study region combining storm catalogs of rainfall fields derived from weather radar and stochastic storm transposition (SST, developed by Wright et al., 2013). The study region is Dead Run, a small (14.3 km2) urban watershed, in the Baltimore Metropolitan region. Our analyses build on previous empirical and modeling studies showing pronounced spatial heterogeneities in rainfall due to the complex terrain, including the Chesapeake Bay to the east, mountainous terrain to the west and urbanization in this region. We expand the original SST approach by applying a multiplier field that accounts for spatial heterogeneities in extreme rainfall. We also characterize the spatial heterogeneities of extreme rainfall distribution through analyses of rainfall fields in the storm catalogs. We examine the characteristics of regional extreme rainfall and derive intensity-duration-frequency (IDF) curves using the SST approach for heterogeneous regions. Our results highlight the significant heterogeneity of extreme rainfall in this region. Estimates of IDF show the advantages of SST in capturing the space-time structure of extreme rainfall. We also illustrate application of SST analyses for flood frequency analyses using a distributed hydrological model. Reference: Wright, D. B., J. A. Smith, G. Villarini, and M. L. Baeck (2013), Estimating the frequency of extreme rainfall using weather radar and stochastic storm transposition, J. Hydrol., 488, 150-165.

Using High-Resolution Satellite Aerosol Optical Depth To Estimate Daily PM2.5 Geographical Distribution in Mexico City.

PubMed

Just, Allan C; Wright, Robert O; Schwartz, Joel; Coull, Brent A; Baccarelli, Andrea A; Tellez-Rojo, Martha María; Moody, Emily; Wang, Yujie; Lyapustin, Alexei; Kloog, Itai

2015-07-21

Recent advances in estimating fine particle (PM2.5) ambient concentrations use daily satellite measurements of aerosol optical depth (AOD) for spatially and temporally resolved exposure estimates. Mexico City is a dense megacity that differs from other previously modeled regions in several ways: it has bright land surfaces, a distinctive climatological cycle, and an elevated semi-enclosed air basin with a unique planetary boundary layer dynamic. We extend our previous satellite methodology to the Mexico City area, a region with higher PM2.5 than most U.S. and European urban areas. Using a novel 1 km resolution AOD product from the MODIS instrument, we constructed daily predictions across the greater Mexico City area for 2004-2014. We calibrated the association of AOD to PM2.5 daily using municipal ground monitors, land use, and meteorological features. Predictions used spatial and temporal smoothing to estimate AOD when satellite data were missing. Our model performed well, resulting in an out-of-sample cross-validation R(2) of 0.724. Cross-validated root-mean-squared prediction error (RMSPE) of the model was 5.55 μg/m(3). This novel model reconstructs long- and short-term spatially resolved exposure to PM2.5 for epidemiological studies in Mexico City.

Using high-resolution satellite aerosol optical depth to estimate daily PM2.5 geographical distribution in Mexico City

PubMed Central

Just, Allan C.; Wright, Robert O.; Schwartz, Joel; Coull, Brent A.; Baccarelli, Andrea A.; Tellez-Rojo, Martha María; Moody, Emily; Wang, Yujie; Lyapustin, Alexei; Kloog, Itai

2015-01-01

Recent advances in estimating fine particle (PM2.5) ambient concentrations use daily satellite measurements of aerosol optical depth (AOD) for spatially and temporally resolved exposure estimates. Mexico City is a dense megacity that differs from other previously modeled regions in several ways: it has bright land surfaces, a distinctive climatological cycle, and an elevated semi-enclosed air basin with a unique planetary boundary layer dynamic. We extend our previous satellite methodology to the Mexico City area, a region with higher PM2.5 than most US and European urban areas. Using a novel 1 km resolution AOD product from the MODIS instrument, we constructed daily predictions across the greater Mexico City area for 2004–2014. We calibrated the association of AOD to PM2.5 daily using municipal ground monitors, land use, and meteorological features. Predictions used spatial and temporal smoothing to estimate AOD when satellite data were missing. Our model performed well, resulting in an out-of-sample cross validation R2 of 0.724. Cross-validated root mean squared prediction error (RMSPE) of the model was 5.55 μg/m3. This novel model reconstructs long- and short-term spatially resolved exposure to PM2.5 for epidemiological studies in Mexico City. PMID:26061488

Scaling uncertainties in estimating canopy foliar maintenance respiration for black spruce ecosystems in Alaska

USGS Publications Warehouse

Zhang, X.; McGuire, A.D.; Ruess, Roger W.

2006-01-01

A major challenge confronting the scientific community is to understand both patterns of and controls over spatial and temporal variability of carbon exchange between boreal forest ecosystems and the atmosphere. An understanding of the sources of variability of carbon processes at fine scales and how these contribute to uncertainties in estimating carbon fluxes is relevant to representing these processes at coarse scales. To explore some of the challenges and uncertainties in estimating carbon fluxes at fine to coarse scales, we conducted a modeling analysis of canopy foliar maintenance respiration for black spruce ecosystems of Alaska by scaling empirical hourly models of foliar maintenance respiration (Rm) to estimate canopy foliar Rm for individual stands. We used variation in foliar N concentration among stands to develop hourly stand-specific models and then developed an hourly pooled model. An uncertainty analysis identified that the most important parameter affecting estimates of canopy foliar Rm was one that describes R m at 0??C per g N, which explained more than 55% of variance in annual estimates of canopy foliar Rm. The comparison of simulated annual canopy foliar Rm identified significant differences between stand-specific and pooled models for each stand. This result indicates that control over foliar N concentration should be considered in models that estimate canopy foliar Rm of black spruce stands across the landscape. In this study, we also temporally scaled the hourly stand-level models to estimate canopy foliar Rm of black spruce stands using mean monthly temperature data. Comparisons of monthly Rm between the hourly and monthly versions of the models indicated that there was very little difference between the estimates of hourly and monthly models, suggesting that hourly models can be aggregated to use monthly input data with little loss of precision. We conclude that uncertainties in the use of a coarse-scale model for estimating canopy foliar Rm at regional scales depend on uncertainties in representing needle-level respiration and on uncertainties in representing the spatial variability of canopy foliar N across a region. The development of spatial data sets of canopy foliar N represents a major challenge in estimating canopy foliar maintenance respiration at regional scales. ?? Springer 2006.

Crop area estimation using high and medium resolution satellite imagery in areas with complex topography

USGS Publications Warehouse

Husak, G.J.; Marshall, M. T.; Michaelsen, J.; Pedreros, Diego; Funk, Christopher C.; Galu, G.

2008-01-01

Reliable estimates of cropped area (CA) in developing countries with chronic food shortages are essential for emergency relief and the design of appropriate market-based food security programs. Satellite interpretation of CA is an effective alternative to extensive and costly field surveys, which fail to represent the spatial heterogeneity at the country-level. Bias-corrected, texture based classifications show little deviation from actual crop inventories, when estimates derived from aerial photographs or field measurements are used to remove systematic errors in medium resolution estimates. In this paper, we demonstrate a hybrid high-medium resolution technique for Central Ethiopia that combines spatially limited unbiased estimates from IKONOS images, with spatially extensive Landsat ETM+ interpretations, land-cover, and SRTM-based topography. Logistic regression is used to derive the probability of a location being crop. These individual points are then aggregated to produce regional estimates of CA. District-level analysis of Landsat based estimates showed CA totals which supported the estimates of the Bureau of Agriculture and Rural Development. Continued work will evaluate the technique in other parts of Africa, while segmentation algorithms will be evaluated, in order to automate classification of medium resolution imagery for routine CA estimation in the future.

Crop area estimation using high and medium resolution satellite imagery in areas with complex topography

NASA Astrophysics Data System (ADS)

Husak, G. J.; Marshall, M. T.; Michaelsen, J.; Pedreros, D.; Funk, C.; Galu, G.

2008-07-01

Reliable estimates of cropped area (CA) in developing countries with chronic food shortages are essential for emergency relief and the design of appropriate market-based food security programs. Satellite interpretation of CA is an effective alternative to extensive and costly field surveys, which fail to represent the spatial heterogeneity at the country-level. Bias-corrected, texture based classifications show little deviation from actual crop inventories, when estimates derived from aerial photographs or field measurements are used to remove systematic errors in medium resolution estimates. In this paper, we demonstrate a hybrid high-medium resolution technique for Central Ethiopia that combines spatially limited unbiased estimates from IKONOS images, with spatially extensive Landsat ETM+ interpretations, land-cover, and SRTM-based topography. Logistic regression is used to derive the probability of a location being crop. These individual points are then aggregated to produce regional estimates of CA. District-level analysis of Landsat based estimates showed CA totals which supported the estimates of the Bureau of Agriculture and Rural Development. Continued work will evaluate the technique in other parts of Africa, while segmentation algorithms will be evaluated, in order to automate classification of medium resolution imagery for routine CA estimation in the future.

Rapid earthquake hazard and loss assessment for Euro-Mediterranean region

NASA Astrophysics Data System (ADS)

Erdik, Mustafa; Sesetyan, Karin; Demircioglu, Mine; Hancilar, Ufuk; Zulfikar, Can; Cakti, Eser; Kamer, Yaver; Yenidogan, Cem; Tuzun, Cuneyt; Cagnan, Zehra; Harmandar, Ebru

2010-10-01

The almost-real time estimation of ground shaking and losses after a major earthquake in the Euro-Mediterranean region was performed in the framework of the Joint Research Activity 3 (JRA-3) component of the EU FP6 Project entitled "Network of Research Infra-structures for European Seismology, NERIES". This project consists of finding the most likely location of the earthquake source by estimating the fault rupture parameters on the basis of rapid inversion of data from on-line regional broadband stations. It also includes an estimation of the spatial distribution of selected site-specific ground motion parameters at engineering bedrock through region-specific ground motion prediction equations (GMPEs) or physical simulation of ground motion. By using the Earthquake Loss Estimation Routine (ELER) software, the multi-level methodology developed for real time estimation of losses is capable of incorporating regional variability and sources of uncertainty stemming from GMPEs, fault finiteness, site modifications, inventory of physical and social elements subjected to earthquake hazard and the associated vulnerability relationships.

Comprehensive Status and Trends of Nitrogen Loads to Estuaries in the Conterminous United States: Pacific Coast Results

EPA Science Inventory

We applied regional SPARROW (SPAtially Referenced Regressions On Watershed attributes) models to estimate status and trends of potential nitrogen loads to estuaries of the conterminous United States. The original Regional SPARROW models predict average detrended loads by source ...

Preliminary estimates of spatially distributed net infiltration and recharge for the Death Valley region, Nevada-California

USGS Publications Warehouse

Hevesi, J.A.; Flint, A.L.; Flint, L.E.

2002-01-01

A three-dimensional ground-water flow model has been developed to evaluate the Death Valley regional flow system, which includes ground water beneath the Nevada Test Site. Estimates of spatially distributed net infiltration and recharge are needed to define upper boundary conditions. This study presents a preliminary application of a conceptual and numerical model of net infiltration. The model was developed in studies at Yucca Mountain, Nevada, which is located in the approximate center of the Death Valley ground-water flow system. The conceptual model describes the effects of precipitation, runoff, evapotranspiration, and redistribution of water in the shallow unsaturated zone on predicted rates of net infiltration; precipitation and soil depth are the two most significant variables. The conceptual model was tested using a preliminary numerical model based on energy- and water-balance calculations. Daily precipitation for 1980 through 1995, averaging 202 millimeters per year over the 39,556 square kilometers area of the ground-water flow model, was input to the numerical model to simulate net infiltration ranging from zero for a soil thickness greater than 6 meters to over 350 millimeters per year for thin soils at high elevations in the Spring Mountains overlying permeable bedrock. Estimated average net infiltration over the entire ground-water flow model domain is 7.8 millimeters per year.To evaluate the application of the net-infiltration model developed on a local scale at Yucca Mountain, to net-infiltration estimates representing the magnitude and distribution of recharge on a regional scale, the net-infiltration results were compared with recharge estimates obtained using empirical methods. Comparison of model results with previous estimates of basinwide recharge suggests that the net-infiltration estimates obtained using this model may overestimate recharge because of uncertainty in modeled precipitation, bedrock permeability, and soil properties for locations such as the Spring Mountains. Although this model is preliminary and uncalibrated, it provides a first approximation of the spatial distribution of net infiltration for the Death Valley region under current climatic conditions.

Reservoir area of influence and implications for fisheries management

USGS Publications Warehouse

Martin, Dustin R.; Chizinski, Christopher J.; Pope, Kevin L.

2015-01-01

Understanding the spatial area that a reservoir draws anglers from, defined as the reservoir's area of influence, and the potential overlap of that area of influence between reservoirs is important for fishery managers. Our objective was to define the area of influence for reservoirs of the Salt Valley regional fishery in southeastern Nebraska using kernel density estimation. We used angler survey data obtained from in-person interviews at 17 reservoirs during 2009–2012. The area of influence, defined by the 95% kernel density, for reservoirs within the Salt Valley regional fishery varied, indicating that anglers use reservoirs differently across the regional fishery. Areas of influence reveal angler preferences in a regional context, indicating preferred reservoirs with a greater area of influence. Further, differences in areas of influences across time and among reservoirs can be used as an assessment following management changes on an individual reservoir or within a regional fishery. Kernel density estimation provided a clear method for creating spatial maps of areas of influence and provided a two-dimensional view of angler travel, as opposed to the traditional mean travel distance assessment.

Estimating Accuracy of Land-Cover Composition From Two-Stage Clustering Sampling

EPA Science Inventory

Land-cover maps are often used to compute land-cover composition (i.e., the proportion or percent of area covered by each class), for each unit in a spatial partition of the region mapped. We derive design-based estimators of mean deviation (MD), mean absolute deviation (MAD), ...

A power comparison of generalized additive models and the spatial scan statistic in a case-control setting.

PubMed

Young, Robin L; Weinberg, Janice; Vieira, Verónica; Ozonoff, Al; Webster, Thomas F

2010-07-19

A common, important problem in spatial epidemiology is measuring and identifying variation in disease risk across a study region. In application of statistical methods, the problem has two parts. First, spatial variation in risk must be detected across the study region and, second, areas of increased or decreased risk must be correctly identified. The location of such areas may give clues to environmental sources of exposure and disease etiology. One statistical method applicable in spatial epidemiologic settings is a generalized additive model (GAM) which can be applied with a bivariate LOESS smoother to account for geographic location as a possible predictor of disease status. A natural hypothesis when applying this method is whether residential location of subjects is associated with the outcome, i.e. is the smoothing term necessary? Permutation tests are a reasonable hypothesis testing method and provide adequate power under a simple alternative hypothesis. These tests have yet to be compared to other spatial statistics. This research uses simulated point data generated under three alternative hypotheses to evaluate the properties of the permutation methods and compare them to the popular spatial scan statistic in a case-control setting. Case 1 was a single circular cluster centered in a circular study region. The spatial scan statistic had the highest power though the GAM method estimates did not fall far behind. Case 2 was a single point source located at the center of a circular cluster and Case 3 was a line source at the center of the horizontal axis of a square study region. Each had linearly decreasing logodds with distance from the point. The GAM methods outperformed the scan statistic in Cases 2 and 3. Comparing sensitivity, measured as the proportion of the exposure source correctly identified as high or low risk, the GAM methods outperformed the scan statistic in all three Cases. The GAM permutation testing methods provide a regression-based alternative to the spatial scan statistic. Across all hypotheses examined in this research, the GAM methods had competing or greater power estimates and sensitivities exceeding that of the spatial scan statistic.

A power comparison of generalized additive models and the spatial scan statistic in a case-control setting

PubMed Central

2010-01-01

Background A common, important problem in spatial epidemiology is measuring and identifying variation in disease risk across a study region. In application of statistical methods, the problem has two parts. First, spatial variation in risk must be detected across the study region and, second, areas of increased or decreased risk must be correctly identified. The location of such areas may give clues to environmental sources of exposure and disease etiology. One statistical method applicable in spatial epidemiologic settings is a generalized additive model (GAM) which can be applied with a bivariate LOESS smoother to account for geographic location as a possible predictor of disease status. A natural hypothesis when applying this method is whether residential location of subjects is associated with the outcome, i.e. is the smoothing term necessary? Permutation tests are a reasonable hypothesis testing method and provide adequate power under a simple alternative hypothesis. These tests have yet to be compared to other spatial statistics. Results This research uses simulated point data generated under three alternative hypotheses to evaluate the properties of the permutation methods and compare them to the popular spatial scan statistic in a case-control setting. Case 1 was a single circular cluster centered in a circular study region. The spatial scan statistic had the highest power though the GAM method estimates did not fall far behind. Case 2 was a single point source located at the center of a circular cluster and Case 3 was a line source at the center of the horizontal axis of a square study region. Each had linearly decreasing logodds with distance from the point. The GAM methods outperformed the scan statistic in Cases 2 and 3. Comparing sensitivity, measured as the proportion of the exposure source correctly identified as high or low risk, the GAM methods outperformed the scan statistic in all three Cases. Conclusions The GAM permutation testing methods provide a regression-based alternative to the spatial scan statistic. Across all hypotheses examined in this research, the GAM methods had competing or greater power estimates and sensitivities exceeding that of the spatial scan statistic. PMID:20642827

Regional GRACE-based estimates of water mass variations over Australia: validation and interpretation

NASA Astrophysics Data System (ADS)

Seoane, L.; Ramillien, G.; Frappart, F.; Leblanc, M.

2013-04-01

Time series of regional 2°-by-2° GRACE solutions have been computed from 2003 to 2011 with a 10 day resolution by using an energy integral method over Australia [112° E 156° E; 44° S 10° S]. This approach uses the dynamical orbit analysis of GRACE Level 1 measurements, and specially accurate along-track K Band Range Rate (KBRR) residuals (1 μm s-1 level of error) to estimate the total water mass over continental regions. The advantages of regional solutions are a significant reduction of GRACE aliasing errors (i.e. north-south stripes) providing a more accurate estimation of water mass balance for hydrological applications. In this paper, the validation of these regional solutions over Australia is presented as well as their ability to describe water mass change as a reponse of climate forcings such as El Niño. Principal component analysis of GRACE-derived total water storage maps show spatial and temporal patterns that are consistent with independent datasets (e.g. rainfall, climate index and in-situ observations). Regional TWS show higher spatial correlations with in-situ water table measurements over Murray-Darling drainage basin (80-90%), and they offer a better localization of hydrological structures than classical GRACE global solutions (i.e. Level 2 GRGS products and 400 km ICA solutions as a linear combination of GFZ, CSR and JPL GRACE solutions).

Estimating Long Term Surface Soil Moisture in the GCIP Area From Satellite Microwave Observations

NASA Technical Reports Server (NTRS)

Owe, Manfred; deJeu, Vrije; VandeGriend, Adriaan A.

2000-01-01

Soil moisture is an important component of the water and energy balances of the Earth's surface. Furthermore, it has been identified as a parameter of significant potential for improving the accuracy of large-scale land surface-atmosphere interaction models. However, accurate estimates of surface soil moisture are often difficult to make, especially at large spatial scales. Soil moisture is a highly variable land surface parameter, and while point measurements are usually accurate, they are representative only of the immediate site which was sampled. Simple averaging of point values to obtain spatial means often leads to substantial errors. Since remotely sensed observations are already a spatially averaged or areally integrated value, they are ideally suited for measuring land surface parameters, and as such, are a logical input to regional or larger scale land process models. A nine-year database of surface soil moisture is being developed for the Central United States from satellite microwave observations. This region forms much of the GCIP study area, and contains most of the Mississippi, Rio Grande, and Red River drainages. Daytime and nighttime microwave brightness temperatures were observed at a frequency of 6.6 GHz, by the Scanning Multichannel Microwave Radiometer (SMMR), onboard the Nimbus 7 satellite. The life of the SMMR instrument spanned from Nov. 1978 to Aug. 1987. At 6.6 GHz, the instrument provided a spatial resolution of approximately 150 km, and an orbital frequency over any pixel-sized area of about 2 daytime and 2 nighttime passes per week. Ground measurements of surface soil moisture from various locations throughout the study area are used to calibrate the microwave observations. Because ground measurements are usually only single point values, and since the time of satellite coverage does not always coincide with the ground measurements, the soil moisture data were used to calibrate a regional water balance for the top 1, 5, and 10 cm surface layers in order to interpolate daily surface moisture values. Such a climate-based approach is often more appropriate for estimating large-area spatially averaged soil moisture because meteorological data are generally more spatially representative than isolated point measurements of soil moisture. Vegetation radiative transfer characteristics, such as the canopy transmissivity, were estimated from vegetation indices such as the Normalized Difference Vegetation Index (NDVI) and the 37 GHz Microwave Polarization Difference Index (MPDI). Passive microwave remote sensing presents the greatest potential for providing regular spatially representative estimates of surface soil moisture at global scales. Real time estimates should improve weather and climate modelling efforts, while the development of historical data sets will provide necessary information for simulation and validation of long-term climate and global change studies.

A new gridded on-road CO2 emissions inventory for the United States, 1980-2011

NASA Astrophysics Data System (ADS)

Gately, C.; Hutyra, L.; Sue Wing, I.

2013-12-01

On-road transportation is responsible for 28% of all U.S. fossil fuel CO2 emissions. However, mapping vehicle emissions at regional scales is challenging due to data limitations. Existing emission inventories have used spatial proxies such as population and road density to downscale national or state level data, which may introduce errors where the proxy variables and actual emissions are weakly correlated. We have developed a national on-road emissions inventory product based on roadway-level traffic data obtained from the Highway Performance Monitoring System. We produce annual estimates of on-road CO2 emissions at a 1km spatial resolution for the contiguous United States for the years 1980 through 2011. For the year 2011 we also produce an hourly emissions product at the 1km scale using hourly traffic volumes from hundreds of automated traffic counters across the country. National on-road emissions rose at roughly 2% per year from 1980 to 2006, with emissions peaking at 1.71 Tg CO2 in 2007. However, while national emissions have declined 6% since the peak, we observe considerable regional variation in emissions trends post-2007. While many states show stable or declining on-road emissions, several states and metropolitan areas in the Midwest, mountain west and south had emissions increases of 3-10% from 2008 to 2011. Our emissions estimates are consistent with state-reported totals of gasoline and diesel fuel consumption. This is in contrast to on-road CO2 emissions estimated by the Emissions Database of Global Atmospheric Research (EDGAR), which we show to be inconsistent in matching on-road emissions to published fuel consumption at the scale of U.S. states, due to the non-linear relationships between emissions and EDGAR's chosen spatial proxies at these scales. Since our emissions estimates were generated independent of population density and other demographic data, we were able to conduct a panel regression analysis to estimate the relationship between these variables and on-road CO2 at various spatial scales. In the case of Massachusetts we find a non-linear relationship between emissions and population density indicating that increasing density resulted in increased emissions when density is less than 2000 persons-km-2. These results highlight the value of using an emissions inventory with high spatial and temporal resolution. At coarser spatial scales, much of the variation in population density and on-road emissions between towns is lost due to aggregation. The high spatial resolution and broad temporal scope of our CO2 estimates provides a basis for analyses to support emissions monitoring, verification and mitigation policies at regional, state and local scale.

Hydroacoustic estimates of abundance and spatial distribution of pelagic prey fishes in western Lake Superior

USGS Publications Warehouse

Mason, Doran M.; Johnson, Timothy B.; Harvey, Chris J.; Kitchell, James F.; Schram, Stephen T.; Bronte, Charles R.; Hoff, Michael H.; Lozano, Stephen J.; Trebitz, Anett S.; Schreiner, Donald R.; Lamon, E. Conrad; Hrabik, Thomas R.

2005-01-01

Lake herring (Coregonus artedi) and rainbow smelt (Osmerus mordax) are a valuable prey resource for the recovering lake trout (Salvelinus namaycush) in Lake Superior. However, prey biomass may be insufficient to support the current predator demand. In August 1997, we assessed the abundance and spatial distribution of pelagic coregonines and rainbow smelt in western Lake Superior by combining a 120 kHz split beam acoustics system with midwater trawls. Coregonines comprised the majority of the midwater trawl catches and the length distributions for trawl caught fish coincided with estimated sizes of acoustic targets. Overall mean pelagic prey fish biomass was 15.56 kg ha−1 with the greatest fish biomass occurring in the Apostle Islands region (27.98 kg ha−1), followed by the Duluth Minnesota region (20.22 kg ha−1), and with the lowest biomass occurring in the open waters of western Lake Superior (9.46 kg ha−1). Biomass estimates from hydroacoustics were typically 2–134 times greater than estimates derived from spring bottom trawl surveys. Prey fish biomass for Lake Superior is about order of magnitude less than acoustic estimates for Lakes Michigan and Ontario. Discrepancies observed between bioenergetics-based estimates of predator consumption of coregonines and earlier coregonine biomass estimates may be accounted for by our hydroacoustic estimates.

Developing a 30-m grassland productivity estimation map for central Nebraska using 250-m MODIS and 30-m Landsat-8 observations

USGS Publications Warehouse

Gu, Yingxin; Wylie, Bruce K.

2015-01-01

Accurately estimating aboveground vegetation biomass productivity is essential for local ecosystem assessment and best land management practice. Satellite-derived growing season time-integrated Normalized Difference Vegetation Index (GSN) has been used as a proxy for vegetation biomass productivity. A 250-m grassland biomass productivity map for the Greater Platte River Basin had been developed based on the relationship between Moderate Resolution Imaging Spectroradiometer (MODIS) GSN and Soil Survey Geographic (SSURGO) annual grassland productivity. However, the 250-m MODIS grassland biomass productivity map does not capture detailed ecological features (or patterns) and may result in only generalized estimation of the regional total productivity. Developing a high or moderate spatial resolution (e.g., 30-m) productivity map to better understand the regional detailed vegetation condition and ecosystem services is preferred. The 30-m Landsat data provide spatial detail for characterizing human-scale processes and have been successfully used for land cover and land change studies. The main goal of this study is to develop a 30-m grassland biomass productivity estimation map for central Nebraska, leveraging 250-m MODIS GSN and 30-m Landsat data. A rule-based piecewise regression GSN model based on MODIS and Landsat (r = 0.91) was developed, and a 30-m MODIS equivalent GSN map was generated. Finally, a 30-m grassland biomass productivity estimation map, which provides spatially detailed ecological features and conditions for central Nebraska, was produced. The resulting 30-m grassland productivity map was generally supported by the SSURGO biomass production map and will be useful for regional ecosystem study and local land management practices.

Spatial variations in estimated chronic exposure to traffic-related air pollution in working populations: A simulation

PubMed Central

Setton, Eleanor M; Keller, C Peter; Cloutier-Fisher, Denise; Hystad, Perry W

2008-01-01

Background Chronic exposure to traffic-related air pollution is associated with a variety of health impacts in adults and recent studies show that exposure varies spatially, with some residents in a community more exposed than others. A spatial exposure simulation model (SESM) which incorporates six microenvironments (home indoor, work indoor, other indoor, outdoor, in-vehicle to work and in-vehicle other) is described and used to explore spatial variability in estimates of exposure to traffic-related nitrogen dioxide (not including indoor sources) for working people. The study models spatial variability in estimated exposure aggregated at the census tracts level for 382 census tracts in the Greater Vancouver Regional District of British Columbia, Canada. Summary statistics relating to the distributions of the estimated exposures are compared visually through mapping. Observed variations are explored through analyses of model inputs. Results Two sources of spatial variability in exposure to traffic-related nitrogen dioxide were identified. Median estimates of total exposure ranged from 8 μg/m3 to 35 μg/m3 of annual average hourly NO2 for workers in different census tracts in the study area. Exposure estimates are highest where ambient pollution levels are highest. This reflects the regional gradient of pollution in the study area and the relatively high percentage of time spent at home locations. However, for workers within the same census tract, variations were observed in the partial exposure estimates associated with time spent outside the residential census tract. Simulation modeling shows that some workers may have exposures 1.3 times higher than other workers residing in the same census tract because of time spent away from the residential census tract, and that time spent in work census tracts contributes most to the differences in exposure. Exposure estimates associated with the activity of commuting by vehicle to work were negligible, based on the relatively short amount of time spent in this microenvironment compared to other locations. We recognize that this may not be the case for pollutants other than NO2. These results represent the first time spatially disaggregated variations in exposure to traffic-related air pollution within a community have been estimated and reported. Conclusion The results suggest that while time spent in the home indoor microenvironment contributes most to between-census tract variation in estimates of annual average exposures to traffic-related NO2, time spent in the work indoor microenvironment contributes most to within-census tract variation, and time spent in transit by vehicle makes a negligible contribution. The SESM has potential as a policy evaluation tool, given input data that reflect changes in pollution levels or work flow patterns due to traffic demand management and land use development policy. PMID:18638398

Regional air quality impacts of increased natural gas production and use in Texas.

PubMed

Pacsi, Adam P; Alhajeri, Nawaf S; Zavala-Araiza, Daniel; Webster, Mort D; Allen, David T

2013-04-02

Natural gas use in electricity generation in Texas was estimated, for gas prices ranging from $1.89 to $7.74 per MMBTU, using an optimal power flow model. Hourly estimates of electricity generation, for individual electricity generation units, from the model were used to estimate spatially resolved hourly emissions from electricity generation. Emissions from natural gas production activities in the Barnett Shale region were also estimated, with emissions scaled up or down to match demand in electricity generation as natural gas prices changed. As natural gas use increased, emissions decreased from electricity generation and increased from natural gas production. Overall, NOx and SO2 emissions decreased, while VOC emissions increased as natural gas use increased. To assess the effects of these changes in emissions on ozone and particulate matter concentrations, spatially and temporally resolved emissions were used in a month-long photochemical modeling episode. Over the month-long photochemical modeling episode, decreases in natural gas prices typical of those experienced from 2006 to 2012 led to net regional decreases in ozone (0.2-0.7 ppb) and fine particulate matter (PM) (0.1-0.7 μg/m(3)). Changes in PM were predominantly due to changes in regional PM sulfate formation. Changes in regional PM and ozone formation are primarily due to decreases in emissions from electricity generation. Increases in emissions from increased natural gas production were offset by decreasing emissions from electricity generation for all the scenarios considered.

SPATIAL LANDSCAPE INDICES IN A HEDONIC FRAMEWORK: AN ECOLOGICAL ECONOMICS ANALYSIS USING GIS. (R825309)

EPA Science Inventory

Abstract

This paper develops a spatial hedonic model to explain residential values in a region within a 30-mile radius of Washington DC. Hedonic models of housing or land values are commonplace, but are rarely estimated for non-urban problems and never using the type o...

SPATIAL LANDSCAPE INDICES IN A HEDONIC FRAMEWORK: AN ECOLOGICAL ECONOMICS ANALYSIS USING GIS. (R824766)

EPA Science Inventory

Abstract

This paper develops a spatial hedonic model to explain residential values in a region within a 30-mile radius of Washington DC. Hedonic models of housing or land values are commonplace, but are rarely estimated for non-urban problems and never using the type o...

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

Factors influencing reporting and harvest probabilities in North American geese

USGS Publications Warehouse

Zimmerman, G.S.; Moser, T.J.; Kendall, W.L.; Doherty, P.F.; White, Gary C.; Caswell, D.F.

2009-01-01

We assessed variation in reporting probabilities of standard bands among species, populations, harvest locations, and size classes of North American geese to enable estimation of unbiased harvest probabilities. We included reward (US10,20,30,50, or100) and control (0) banded geese from 16 recognized goose populations of 4 species: Canada (Branta canadensis), cackling (B. hutchinsii), Ross's (Chen rossii), and snow geese (C. caerulescens). We incorporated spatially explicit direct recoveries and live recaptures into a multinomial model to estimate reporting, harvest, and band-retention probabilities. We compared various models for estimating harvest probabilities at country (United States vs. Canada), flyway (5 administrative regions), and harvest area (i.e., flyways divided into northern and southern sections) scales. Mean reporting probability of standard bands was 0.73 (95 CI 0.690.77). Point estimates of reporting probabilities for goose populations or spatial units varied from 0.52 to 0.93, but confidence intervals for individual estimates overlapped and model selection indicated that models with species, population, or spatial effects were less parsimonious than those without these effects. Our estimates were similar to recently reported estimates for mallards (Anas platyrhynchos). We provide current harvest probability estimates for these populations using our direct measures of reporting probability, improving the accuracy of previous estimates obtained from recovery probabilities alone. Goose managers and researchers throughout North America can use our reporting probabilities to correct recovery probabilities estimated from standard banding operations for deriving spatially explicit harvest probabilities.

Regional estimation of groundwater arsenic concentrations through systematical dynamic-neural modeling

NASA Astrophysics Data System (ADS)

Chang, Fi-John; Chen, Pin-An; Liu, Chen-Wuing; Liao, Vivian Hsiu-Chuan; Liao, Chung-Min

2013-08-01

Arsenic (As) is an odorless semi-metal that occurs naturally in rock and soil, and As contamination in groundwater resources has become a serious threat to human health. Thus, assessing the spatial and temporal variability of As concentration is highly desirable, particularly in heavily As-contaminated areas. However, various difficulties may be encountered in the regional estimation of As concentration such as cost-intensive field monitoring, scarcity of field data, identification of important factors affecting As, over-fitting or poor estimation accuracy. This study develops a novel systematical dynamic-neural modeling (SDM) for effectively estimating regional As-contaminated water quality by using easily-measured water quality variables. To tackle the difficulties commonly encountered in regional estimation, the SDM comprises of a neural network and four statistical techniques: the Nonlinear Autoregressive with eXogenous input (NARX) network, Gamma test, cross-validation, Bayesian regularization method and indicator kriging (IK). For practical application, this study investigated a heavily As-contaminated area in Taiwan. The backpropagation neural network (BPNN) is adopted for comparison purpose. The results demonstrate that the NARX network (Root mean square error (RMSE): 95.11 μg l-1 for training; 106.13 μg l-1 for validation) outperforms the BPNN (RMSE: 121.54 μg l-1 for training; 143.37 μg l-1 for validation). The constructed SDM can provide reliable estimation (R2 > 0.89) of As concentration at ungauged sites based merely on three easily-measured water quality variables (Alk, Ca2+ and pH). In addition, risk maps under the threshold of the WHO drinking water standard (10 μg l-1) are derived by the IK to visually display the spatial and temporal variation of the As concentration in the whole study area at different time spans. The proposed SDM can be practically applied with satisfaction to the regional estimation in study areas of interest and the estimation of missing, hazardous or costly data to facilitate water resources management.

Spatial and temporal variability of the overall error of National Atmospheric Deposition Program measurements determined by the USGS collocated-sampler program, water years 1989-2001

USGS Publications Warehouse

Wetherbee, G.A.; Latysh, N.E.; Gordon, J.D.

2005-01-01

Data from the U.S. Geological Survey (USGS) collocated-sampler program for the National Atmospheric Deposition Program/National Trends Network (NADP/NTN) are used to estimate the overall error of NADP/NTN measurements. Absolute errors are estimated by comparison of paired measurements from collocated instruments. Spatial and temporal differences in absolute error were identified and are consistent with longitudinal distributions of NADP/NTN measurements and spatial differences in precipitation characteristics. The magnitude of error for calcium, magnesium, ammonium, nitrate, and sulfate concentrations, specific conductance, and sample volume is of minor environmental significance to data users. Data collected after a 1994 sample-handling protocol change are prone to less absolute error than data collected prior to 1994. Absolute errors are smaller during non-winter months than during winter months for selected constituents at sites where frozen precipitation is common. Minimum resolvable differences are estimated for different regions of the USA to aid spatial and temporal watershed analyses.

Regionalized rainfall-runoff model to estimate low flow indices

NASA Astrophysics Data System (ADS)

Garcia, Florine; Folton, Nathalie; Oudin, Ludovic

2016-04-01

Estimating low flow indices is of paramount importance to manage water resources and risk assessments. These indices are derived from river discharges which are measured at gauged stations. However, the lack of observations at ungauged sites bring the necessity of developing methods to estimate these low flow indices from observed discharges in neighboring catchments and from catchment characteristics. Different estimation methods exist. Regression or geostatistical methods performed on the low flow indices are the most common types of methods. Another less common method consists in regionalizing rainfall-runoff model parameters, from catchment characteristics or by spatial proximity, to estimate low flow indices from simulated hydrographs. Irstea developed GR2M-LoiEau, a conceptual monthly rainfall-runoff model, combined with a regionalized model of snow storage and melt. GR2M-LoiEau relies on only two parameters, which are regionalized and mapped throughout France. This model allows to cartography monthly reference low flow indices. The inputs data come from SAFRAN, the distributed mesoscale atmospheric analysis system, which provides daily solid and liquid precipitation and temperature data from everywhere in the French territory. To exploit fully these data and to estimate daily low flow indices, a new version of GR-LoiEau has been developed at a daily time step. The aim of this work is to develop and regionalize a GR-LoiEau model that can provide any daily, monthly or annual estimations of low flow indices, yet keeping only a few parameters, which is a major advantage to regionalize them. This work includes two parts. On the one hand, a daily conceptual rainfall-runoff model is developed with only three parameters in order to simulate daily and monthly low flow indices, mean annual runoff and seasonality. On the other hand, different regionalization methods, based on spatial proximity and similarity, are tested to estimate the model parameters and to simulate low flow indices in ungauged sites. The analysis is carried out on 691 French catchments that are representative of various hydro-meteorological behaviors. The results are validated with a cross-validation procedure and are compared with the ones obtained with GR4J, a conceptual rainfall-runoff model, which already provides daily estimations, but involves four parameters that cannot easily be regionalized.

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.

Spatial and temporal characterization of methane plumes from mobile platforms

NASA Astrophysics Data System (ADS)

O'Brien, A.; Wendt, L.; Miller, D. J.; Lary, D. J.; Zondlo, M. A.

2013-12-01

The spatial and temporal characterization of methane plumes from hydraulic fracturing well sites are presented. Methane measurements from the Marcellus shale region obtained using a commercial instrument on a motor vehicle are discussed. Over 100 well sites in the region were sampled and the methane signature in the vicinity of these wells is presented. Additionally, measurements of methane from our open-path instrument flown aboard the UT Dallas AMR Payload Master 100 remote-controlled, electric aircraft in the Barnett shale region are presented. Using our observations of aircraft surveys near well sites and a gaussian plume dispersion model emission estimates of fugitive methane are presented.

Spatial variation of air quality index and urban driving factors linkages: evidence from Chinese cities.

PubMed

Pu, Haixia; Luo, Kunli; Wang, Pin; Wang, Shaobin; Kang, Shun

2017-02-01

Daily air quality index (AQI) of 161 Chinese cities obtained from the Ministry of Environmental Protection of China in 2015 is conducted. In this study, to better explore spatial distribution and regional characteristic of AQI, global and local spatial autocorrelation is utilized. Pearson's correlation is introduced to determine the influence of single urban indicator on AQI value. Meanwhile, multiple linear stepwise regression is chosen to estimate quantitatively the most influential urban indicators on AQI. The spatial autocorrelation analysis indicates that the AQI value of Chinese 161 cities shows a spatial dependency. Higher AQI is mainly located in north and northwest regions, whereas low AQI is concentrated in the south and the Qinghai-Tibet regions. The low AQI and high AQI values in China both exhibit relative immobility through seasonal variation. The influence degree of three adverse urban driving factors on AQI value is ranked from high to low: coal consumption of manufacturing > building area > coal consumption of the power industry. It is worth noting that the risk of exposed population to poor quality is greater in the northern region than in other regions. The results of the study provide a reference for the formulation of urban policy and improvement of air quality in China.

Long term ice sheet mass change rates and inter-annual variability from GRACE gravimetry.

NASA Astrophysics Data System (ADS)

Harig, C.

2017-12-01

The GRACE time series of gravimetry now stretches 15 years since its launch in 2002. Here we use Slepian functions to estimate the long term ice mass trends of Greenland, Antarctica, and several glaciated regions. The spatial representation shows multi-year to decadal regional shifts in accelerations, in agreement with increases in radar derived ice velocity. Interannual variations in ice mass are of particular interest since they can directly link changes in ice sheets to the drivers of change in the polar ocean and atmosphere. The spatial information retained in Slepian functions provides a tool to determine how this link varies in different regions within an ice sheet. We present GRACE observations of the 2013-2014 slowdown in mass loss of the Greenland ice sheet, which was concentrated in specific parts of the ice sheet and in certain months of the year. We also discuss estimating the relative importance of climate factors that control ice mass balance, as a function of location of the glacier/ice cap as well as the spatial variation within an ice sheet by comparing gravimetry with observations of surface air temperature, ocean temperature, etc. as well as model data from climate reanalysis products.

Development of improved wildfire smoke exposure estimates for health studies in the western U.S.

NASA Astrophysics Data System (ADS)

Ivey, C.; Holmes, H.; Loria Salazar, S. M.; Pierce, A.; Liu, C.

2016-12-01

Wildfire smoke exposure is a significant health concern in the western U.S. because large wildfires have increased in size and frequency over the past four years due to drought conditions. The transport phenomena in complex terrain and timing of the wildfire emissions make the smoke plumes difficult to simulate using conventional air quality models. Monitoring data can be used to estimate exposure metrics, but in rural areas the monitoring networks are too sparse to calculate wildfire exposure metrics for the entire population in a region. Satellite retrievals provide global, spatiotemporal air quality information and are used to track pollution plumes, estimate human exposures, model emissions, and determine sources (i.e., natural versus anthropogenic) in regulatory applications. Particulate matter (PM) exposures can be estimated using columnar aerosol optical depth (AOD), where satellite AOD retrievals serve as a spatial surrogate to simulate surface PM gradients. These exposure models have been successfully used in health effects studies in the eastern U.S. where complex mountainous terrain and surface reflectance do not limit AOD retrival from satellites. Using results from a chemical transport model (CTM) is another effective method to determine spatial gradients of pollutants. However, the CTM does not adequately capture the temporal and spatial distribution of wildfire smoke plumes. By combining the spatiotemporal pollutant fields from both satellite retrievals and CTM results with ground based pollutant observations the spatial wildfire smoke exposure model can be improved. This work will address the challenge of understanding the spatiotemporal distributions of pollutant concentrations to model human exposures of wildfire smoke in regions with complex terrain, where meteorological conditions as well as emission sources significantly influence the spatial distribution of pollutants. The focus will be on developing models to enhance exposure estimates of elevated PM and ozone concentrations from wildfire smoke plumes in the western U.S.

A Spatial Poisson Hurdle Model for Exploring Geographic Variation in Emergency Department Visits

PubMed Central

Neelon, Brian; Ghosh, Pulak; Loebs, Patrick F.

2012-01-01

Summary We develop a spatial Poisson hurdle model to explore geographic variation in emergency department (ED) visits while accounting for zero inflation. The model consists of two components: a Bernoulli component that models the probability of any ED use (i.e., at least one ED visit per year), and a truncated Poisson component that models the number of ED visits given use. Together, these components address both the abundance of zeros and the right-skewed nature of the nonzero counts. The model has a hierarchical structure that incorporates patient- and area-level covariates, as well as spatially correlated random effects for each areal unit. Because regions with high rates of ED use are likely to have high expected counts among users, we model the spatial random effects via a bivariate conditionally autoregressive (CAR) prior, which introduces dependence between the components and provides spatial smoothing and sharing of information across neighboring regions. Using a simulation study, we show that modeling the between-component correlation reduces bias in parameter estimates. We adopt a Bayesian estimation approach, and the model can be fit using standard Bayesian software. We apply the model to a study of patient and neighborhood factors influencing emergency department use in Durham County, North Carolina. PMID:23543242

Deriving Continuous Fields of Tree Cover at 1-m over the Continental United States From the National Agriculture Imagery Program (NAIP) Imagery to Reduce Uncertainties in Forest Carbon Stock Estimation

NASA Astrophysics Data System (ADS)

Ganguly, S.; Basu, S.; Mukhopadhyay, S.; Michaelis, A.; Milesi, C.; Votava, P.; Nemani, R. R.

2013-12-01

An unresolved issue with coarse-to-medium resolution satellite-based forest carbon mapping over regional to continental scales is the high level of uncertainty in above ground biomass (AGB) estimates caused by the absence of forest cover information at a high enough spatial resolution (current spatial resolution is limited to 30-m). To put confidence in existing satellite-derived AGB density estimates, it is imperative to create continuous fields of tree cover at a sufficiently high resolution (e.g. 1-m) such that large uncertainties in forested area are reduced. The proposed work will provide means to reduce uncertainty in present satellite-derived AGB maps and Forest Inventory and Analysis (FIA) based regional estimates. Our primary objective will be to create Very High Resolution (VHR) estimates of tree cover at a spatial resolution of 1-m for the Continental United States using all available National Agriculture Imaging Program (NAIP) color-infrared imagery from 2010 till 2012. We will leverage the existing capabilities of the NASA Earth Exchange (NEX) high performance computing and storage facilities. The proposed 1-m tree cover map can be further aggregated to provide percent tree cover at any medium-to-coarse resolution spatial grid, which will aid in reducing uncertainties in AGB density estimation at the respective grid and overcome current limitations imposed by medium-to-coarse resolution land cover maps. We have implemented a scalable and computationally-efficient parallelized framework for tree-cover delineation - the core components of the algorithm [that] include a feature extraction process, a Statistical Region Merging image segmentation algorithm and a classification algorithm based on Deep Belief Network and a Feedforward Backpropagation Neural Network algorithm. An initial pilot exercise has been performed over the state of California (~11,000 scenes) to create a wall-to-wall 1-m tree cover map and the classification accuracy has been assessed. Results show an improvement in accuracy of tree-cover delineation as compared to existing forest cover maps from NLCD, especially over fragmented, heterogeneous and urban landscapes. Estimates of VHR tree cover will complement and enhance the accuracy of present remote-sensing based AGB modeling approaches and forest inventory based estimates at both national and local scales. A requisite step will be to characterize the inherent uncertainties in tree cover estimates and propagate them to estimate AGB.

Does GPM-based multi-satellite precipitation enhance rainfall estimates over Pakistan and Bolivia arid regions?

NASA Astrophysics Data System (ADS)

Hussain, Y.; Satgé, F.; Bonnet, M. P.; Pillco, R.; Molina, J.; Timouk, F.; Roig, H.; Martinez-Carvajal, H., Sr.; Gulraiz, A.

2016-12-01

Arid regions are sensitive to rainfall variations which are expressed in the form of flooding and droughts. Unfortunately, those regions are poorly monitored and high quality rainfall estimates are still needed. The Global Precipitation Measurement (GPM) mission released two new satellite rainfall products named Integrated Multisatellite Retrievals GPM (IMERG) and Global Satellite Mapping of Precipitation version 6 (GSMaP-v6) bringing the possibility of accurate rainfall monitoring over these countries. This study assessed both products at monthly scale over Pakistan considering dry and wet season over the 4 main climatic zones from 2014 to 2016. With similar climatic conditions, the Altiplano region of Bolivia is considered to quantify the influence of big lakes (Titicaca and Poopó) in rainfall estimates. For comparison, the widely used TRMM-Multisatellite Precipitation Analysis 3B43 (TMPA-3B43) version 7 is also involved in the analysis to observe the potential enhancement in rainfall estimate brought by GPM products. Rainfall estimates derived from 110 rain-gauges are used as reference to compare IMERG, GSMaP-v6 and TMPA-3B43 at the 0.1° and 0.25° spatial resolution. Over both regions, IMERG and GSMaP-v6 capture the spatial pattern of precipitation as well as TMPA-3B43. All products tend to over estimates rainfall over very arid regions. This feature is even more marked during dry season. However, during this season, both reference and estimated rainfall remain very low and do not impact seasonal water budget computation. On a general way, IMERG slightly outperforms TMPA-3B43 and GSMaP-v6 which provides the less accurate rainfall estimate. The TMPA-3B43 rainfall underestimation previously found over Lake Titicaca is still observed in IMERG estimates. However, GSMaP-v6 considerably decreases the underestimation providing the most accurate rainfall estimate over the lake. MOD11C3 Land Surface Temperature (LST) and ASTER Global Emissivity Dataset reveal strong LST and Emissivity anomaly over the lake in comparison with surrounding lands. These anomalies should explain rainfall underestimations tendency over this lake. LST and Emissivity of lake Poopó are closest to surrounding land and the slight observed rainfall overestimation appears to be related to the very arid context of the region.

Remote Sensing of Vegetation Nitrogen Content for Spatially Explicit Carbon and Water Cycle Estimation

NASA Astrophysics Data System (ADS)

Zhang, Y. L.; Miller, J. R.; Chen, J. M.

2009-05-01

Foliage nitrogen concentration is a determinant of photosynthetic capacity of leaves, thereby an important input to ecological models for estimating terrestrial carbon and water budgets. Recently, spectrally continuous airborne hyperspectral remote sensing imagery has proven to be useful for retrieving an important related parameter, total chlorophyll content at both leaf and canopy scales. Thus remote sensing of vegetation biochemical parameters has promising potential for improving the prediction of global carbon and water balance patterns. In this research, we explored the feasibility of estimating leaf nitrogen content using hyperspectral remote sensing data for spatially explicit estimation of carbon and water budgets. Multi-year measurements of leaf biochemical contents of seven major boreal forest species were carried out in northeastern Ontario, Canada. The variation of leaf chlorophyll and nitrogen content in response to various growth conditions, and the relationship between them,were investigated. Despite differences in plant type (deciduous and evergreen), leaf age, stand growth conditions and developmental stages, leaf nitrogen content was strongly correlated with leaf chlorophyll content on a mass basis during the active growing season (r2=0.78). With this general correlation, leaf nitrogen content was estimated from leaf chlorophyll content at an accuracy of RMSE=2.2 mg/g, equivalent to 20.5% of the average measured leaf nitrogen content. Based on this correlation and a hyperspectral remote sensing algorithm for leaf chlorophyll content retrieval, the spatial variation of leaf nitrogen content was inferred from the airborne hyperspectral remote sensing imagery acquired by Compact Airborne Spectrographic Imager (CASI). A process-based ecological model Boreal Ecosystem Productivity Simulator (BEPS) was used for estimating terrestrial carbon and water budgets. In contrast to the scenario with leaf nitrogen content assigned as a constant value without differentiation between and within vegetation types for calculating the photosynthesis rate, we incorporated the spatial distribution of leaf nitrogen content in the model to estimate net primary productivity and evaportranspiration of boreal ecosystem. These regional estimates of carbon and water budgets with and without N mapping are compared, and the importance of this leaf biochemistry information derived from hyperspectral remote sensing in regional mapping of carbon and water fluxes is quantitatively assessed. Keywords: Remote Sensing, Leaf Nitrogen Content, Spatial Distribution, Carbon and Water Budgets, Estimation

RiceAtlas, a spatial database of global rice calendars and production.

PubMed

Laborte, Alice G; Gutierrez, Mary Anne; Balanza, Jane Girly; Saito, Kazuki; Zwart, Sander J; Boschetti, Mirco; Murty, M V R; Villano, Lorena; Aunario, Jorrel Khalil; Reinke, Russell; Koo, Jawoo; Hijmans, Robert J; Nelson, Andrew

2017-05-30

Knowing where, when, and how much rice is planted and harvested is crucial information for understanding the effects of policy, trade, and global and technological change on food security. We developed RiceAtlas, a spatial database on the seasonal distribution of the world's rice production. It consists of data on rice planting and harvesting dates by growing season and estimates of monthly production for all rice-producing countries. Sources used for planting and harvesting dates include global and regional databases, national publications, online reports, and expert knowledge. Monthly production data were estimated based on annual or seasonal production statistics, and planting and harvesting dates. RiceAtlas has 2,725 spatial units. Compared with available global crop calendars, RiceAtlas is nearly ten times more spatially detailed and has nearly seven times more spatial units, with at least two seasons of calendar data, making RiceAtlas the most comprehensive and detailed spatial database on rice calendar and production.

Hybrid Modeling Approach to Estimate Exposures of Hazardous Air Pollutants (HAPs) for the National Air Toxics Assessment (NATA).

PubMed

Scheffe, Richard D; Strum, Madeleine; Phillips, Sharon B; Thurman, James; Eyth, Alison; Fudge, Steve; Morris, Mark; Palma, Ted; Cook, Richard

2016-11-15

A hybrid air quality model has been developed and applied to estimate annual concentrations of 40 hazardous air pollutants (HAPs) across the continental United States (CONUS) to support the 2011 calendar year National Air Toxics Assessment (NATA). By combining a chemical transport model (CTM) with a Gaussian dispersion model, both reactive and nonreactive HAPs are accommodated across local to regional spatial scales, through a multiplicative technique designed to improve mass conservation relative to previous additive methods. The broad scope of multiple pollutants capturing regional to local spatial scale patterns across a vast spatial domain is precedent setting within the air toxics community. The hybrid design exhibits improved performance relative to the stand alone CTM and dispersion model. However, model performance varies widely across pollutant categories and quantifiably definitive performance assessments are hampered by a limited observation base and challenged by the multiple physical and chemical attributes of HAPs. Formaldehyde and acetaldehyde are the dominant HAP concentration and cancer risk drivers, characterized by strong regional signals associated with naturally emitted carbonyl precursors enhanced in urban transport corridors with strong mobile source sector emissions. The multiple pollutant emission characteristics of combustion dominated source sectors creates largely similar concentration patterns across the majority of HAPs. However, reactive carbonyls exhibit significantly less spatial variability relative to nonreactive HAPs across the CONUS.

Characterizing fishing effort and spatial extent of coastal fisheries.

PubMed

Stewart, Kelly R; Lewison, Rebecca L; Dunn, Daniel C; Bjorkland, Rhema H; Kelez, Shaleyla; Halpin, Patrick N; Crowder, Larry B

2010-12-29

Biodiverse coastal zones are often areas of intense fishing pressure due to the high relative density of fishing capacity in these nearshore regions. Although overcapacity is one of the central challenges to fisheries sustainability in coastal zones, accurate estimates of fishing pressure in coastal zones are limited, hampering the assessment of the direct and collateral impacts (e.g., habitat degradation, bycatch) of fishing. We compiled a comprehensive database of fishing effort metrics and the corresponding spatial limits of fisheries and used a spatial analysis program (FEET) to map fishing effort density (measured as boat-meters per km²) in the coastal zones of six ocean regions. We also considered the utility of a number of socioeconomic variables as indicators of fishing pressure at the national level; fishing density increased as a function of population size and decreased as a function of coastline length. Our mapping exercise points to intra and interregional 'hotspots' of coastal fishing pressure. The significant and intuitive relationships we found between fishing density and population size and coastline length may help with coarse regional characterizations of fishing pressure. However, spatially-delimited fishing effort data are needed to accurately map fishing hotspots, i.e., areas of intense fishing activity. We suggest that estimates of fishing effort, not just target catch or yield, serve as a necessary measure of fishing activity, which is a key link to evaluating sustainability and environmental impacts of coastal fisheries.

Cost-effective sampling of ¹³⁷Cs-derived net soil redistribution: part 1--estimating the spatial mean across scales of variation.

PubMed

Li, Y; Chappell, A; Nyamdavaa, B; Yu, H; Davaasuren, D; Zoljargal, K

2015-03-01

The (137)Cs technique for estimating net time-integrated soil redistribution is valuable for understanding the factors controlling soil redistribution by all processes. The literature on this technique is dominated by studies of individual fields and describes its typically time-consuming nature. We contend that the community making these studies has inappropriately assumed that many (137)Cs measurements are required and hence estimates of net soil redistribution can only be made at the field scale. Here, we support future studies of (137)Cs-derived net soil redistribution to apply their often limited resources across scales of variation (field, catchment, region etc.) without compromising the quality of the estimates at any scale. We describe a hybrid, design-based and model-based, stratified random sampling design with composites to estimate the sampling variance and a cost model for fieldwork and laboratory measurements. Geostatistical mapping of net (1954-2012) soil redistribution as a case study on the Chinese Loess Plateau is compared with estimates for several other sampling designs popular in the literature. We demonstrate the cost-effectiveness of the hybrid design for spatial estimation of net soil redistribution. To demonstrate the limitations of current sampling approaches to cut across scales of variation, we extrapolate our estimate of net soil redistribution across the region, show that for the same resources, estimates from many fields could have been provided and would elucidate the cause of differences within and between regional estimates. We recommend that future studies evaluate carefully the sampling design to consider the opportunity to investigate (137)Cs-derived net soil redistribution across scales of variation. Copyright © 2014 Elsevier Ltd. All rights reserved.

Estimating NOx emissions and surface concentrations at high spatial resolution using OMI

NASA Astrophysics Data System (ADS)

Goldberg, D. L.; Lamsal, L. N.; Loughner, C.; Swartz, W. H.; Saide, P. E.; Carmichael, G. R.; Henze, D. K.; Lu, Z.; Streets, D. G.

2017-12-01

In many instances, NOx emissions are not measured at the source. In these cases, remote sensing techniques are extremely useful in quantifying NOx emissions. Using an exponential modified Gaussian (EMG) fitting of oversampled Ozone Monitoring Instrument (OMI) NO2 data, we estimate NOx emissions and lifetimes in regions where these emissions are uncertain. This work also presents a new high-resolution OMI NO2 dataset derived from the NASA retrieval that can be used to estimate surface level concentrations in the eastern United States and South Korea. To better estimate vertical profile shape factors, we use high-resolution model simulations (Community Multi-scale Air Quality (CMAQ) and WRF-Chem) constrained by in situ aircraft observations to re-calculate tropospheric air mass factors and tropospheric NO2 vertical columns during summertime. The correlation between our satellite product and ground NO2 monitors in urban areas has improved dramatically: r2 = 0.60 in new product, r2 = 0.39 in operational product, signifying that this new product is a better indicator of surface concentrations than the operational product. Our work emphasizes the need to use both high-resolution and high-fidelity models in order to re-calculate vertical column data in areas with large spatial heterogeneities in NOx emissions. The methodologies developed in this work can be applied to other world regions and other satellite data sets to produce high-quality region-specific emissions estimates.

Integration of Process Models and Remote Sensing for Estimating Productivity, Soil Moisture, and Energy Fluxes in a Tallgrass Prairie Ecosystem

EPA Science Inventory

We describe a research program aimed at integrating remotely sensed data with an ecosystem model (VELMA) and a soil-vegetation-atmosphere transfer (SVAT) model (SEBS) for generating spatially explicit, regional scale estimates of productivity (biomass) and energy\\mass exchanges i...

Spatial taxation effects on regional coal economic activities

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

Yang, C.W.; Labys, W.C.

1982-01-01

Taxation effects on resource production, consumption and prices are seldom evaluated especially in the field of spatial commodity modeling. The most commonly employed linear programming model has fixed-point estimated demands and capacity constraints; hence it makes taxation effects difficult to be modeled. The second type of resource allocation model, the interregional input-output models does not include a direct and explicit price mechanism. Therefore, it is not suitable for analyzing taxation effects. The third type or spatial commodity model has been econometric in nature. While such an approach has a good deal of flexibility in modeling political and non-economic variables, itmore » treats taxation (or tariff) effects loosely using only dummy variables, and, in many cases, must sacrifice the consistency criterion important for spatial commodity modeling. This leaves model builders only one legitimate choice for analyzing taxation effects: the quadratic programming model which explicitly allows the interplay of regional demand and supply relations via a continuous spatial price constructed by the authors related to the regional demand for and supply of coal from Appalachian markets.« less

Transfer of Satellite Rainfall Uncertainty from Gauged to Ungauged Regions at Regional and Seasonal Timescales

NASA Technical Reports Server (NTRS)

Tang, Ling; Hossain, Faisal; Huffman, George J.

2010-01-01

Hydrologists and other users need to know the uncertainty of the satellite rainfall data sets across the range of time/space scales over the whole domain of the data set. Here, uncertainty' refers to the general concept of the deviation' of an estimate from the reference (or ground truth) where the deviation may be defined in multiple ways. This uncertainty information can provide insight to the user on the realistic limits of utility, such as hydrologic predictability, that can be achieved with these satellite rainfall data sets. However, satellite rainfall uncertainty estimation requires ground validation (GV) precipitation data. On the other hand, satellite data will be most useful over regions that lack GV data, for example developing countries. This paper addresses the open issues for developing an appropriate uncertainty transfer scheme that can routinely estimate various uncertainty metrics across the globe by leveraging a combination of spatially-dense GV data and temporally sparse surrogate (or proxy) GV data, such as the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar and the Global Precipitation Measurement (GPM) mission Dual-Frequency Precipitation Radar. The TRMM Multi-satellite Precipitation Analysis (TMPA) products over the US spanning a record of 6 years are used as a representative example of satellite rainfall. It is shown that there exists a quantifiable spatial structure in the uncertainty of satellite data for spatial interpolation. Probabilistic analysis of sampling offered by the existing constellation of passive microwave sensors indicate that transfer of uncertainty for hydrologic applications may be effective at daily time scales or higher during the GPM era. Finally, a commonly used spatial interpolation technique (kriging), that leverages the spatial correlation of estimation uncertainty, is assessed at climatologic, seasonal, monthly and weekly timescales. It is found that the effectiveness of kriging is sensitive to the type of uncertainty metric, time scale of transfer and the density of GV data within the transfer domain. Transfer accuracy is lowest at weekly timescales with the error doubling from monthly to weekly.However, at very low GV data density (<20% of the domain), the transfer accuracy is too low to show any distinction as a function of the timescale of transfer.

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.

The consequences of landscape change on ecological resources: An assessment of the United States mid-Atlantic region, 1973-1993

USGS Publications Warehouse

Jones, K.B.; Neale, A.C.; Wade, T.G.; Wickham, J.D.; Cross, C.L.; Edmonds, C.M.; Loveland, Thomas R.; Nash, M.S.; Riitters, K.H.; Smith, E.R.

2001-01-01

Spatially explicit identification of changes in ecological conditions over large areas is key to targeting and prioritizing areas for environmental protection and restoration by managers at watershed, basin, and regional scales. A critical limitation to this point has been the development of methods to conduct such broad-scale assessments. Field-based methods have proven to be too costly and too inconsistent in their application to make estimates of ecological conditions over large areas. New spatial data derived from satellite imagery and other sources, the development of statistical models relating landscape composition and pattern to ecological endpoints, and geographic information systems (GIS) make it possible to evaluate ecological conditions at multiple scales over broad geographic regions. In this study, we demonstrate the application of spatially distributed models for bird habitat quality and nitrogen yield to streams to assess the consequences of landcover change across the mid-Atlantic region between the 1970s and 1990s. Moreover, we present a way to evaluate spatial concordance between models related to different environmental endpoints. Results of this study should help environmental managers in the mid-Atlantic region target those areas in need of conservation and protection.

Using rank-order geostatistics for spatial interpolation of highly skewed data in a heavy-metal contaminated site.

PubMed

Juang, K W; Lee, D Y; Ellsworth, T R

2001-01-01

The spatial distribution of a pollutant in contaminated soils is usually highly skewed. As a result, the sample variogram often differs considerably from its regional counterpart and the geostatistical interpolation is hindered. In this study, rank-order geostatistics with standardized rank transformation was used for the spatial interpolation of pollutants with a highly skewed distribution in contaminated soils when commonly used nonlinear methods, such as logarithmic and normal-scored transformations, are not suitable. A real data set of soil Cd concentrations with great variation and high skewness in a contaminated site of Taiwan was used for illustration. The spatial dependence of ranks transformed from Cd concentrations was identified and kriging estimation was readily performed in the standardized-rank space. The estimated standardized rank was back-transformed into the concentration space using the middle point model within a standardized-rank interval of the empirical distribution function (EDF). The spatial distribution of Cd concentrations was then obtained. The probability of Cd concentration being higher than a given cutoff value also can be estimated by using the estimated distribution of standardized ranks. The contour maps of Cd concentrations and the probabilities of Cd concentrations being higher than the cutoff value can be simultaneously used for delineation of hazardous areas of contaminated soils.

Spatially distributed evapotranspiration and recharge estimation for sand regions of Hungary in the context of climate change

NASA Astrophysics Data System (ADS)

Csáki, Péter; Kalicz, Péter; Gribovszki, Zoltán

2016-04-01

Water balance of sand regions of Hungary was analysed using remote-sensing based evapotranspiration (ET) maps (1*1 km spatial resolution) by CREMAP model over the 2000-2008 period. The mean annual (2000-2008) net groundwater recharge (R) estimated as the difference in mean annual precipitation (P) and ET, taking advantage that for sand regions the surface runoff is commonly negligible. For the examined nine-year period (2000-2008) the ET and R were about 90 percent and 10 percent of the P. The mean annual ET and R were analysed in the context of land cover types. A Budyko-model was used in spatially-distributed mode for the climate change impact analysis. The parameters of the Budyko-model (α) was calculated for pixels without surplus water. For the extra-water affected pixels a linear model with β-parameters (actual evapotranspiration / pan-evapotranspiration) was used. These parameter maps can be used for evaluating future ET and R in spatially-distributed mode (1*1 km resolution). By using the two parameter maps (α and β) and data of regional climate models (mean annual temperature and precipitation) evapotranspiration and net groundwater recharge projections have been done for three future periods (2011-2040, 2041-2070, 2071-2100). The expected ET and R changes have been determined relative to a reference period (1981-2010). According to the projections, by the end of the 21th century, ET may increase while in case of R a heavy decrease can be detected for the sand regions of Hungary. This research has been supported by Agroclimate.2 VKSZ_12-1-2013-0034 project. Keywords: evapotranspiration, net groundwater recharge, climate change, Budyko-model

How number line estimation skills relate to neural activations in single digit subtraction problems

PubMed Central

Berteletti, I.; Man, G.; Booth, J.R.

2014-01-01

The Number Line (NL) task requires judging the relative numerical magnitude of a number and estimating its value spatially on a continuous line. Children's skill on this task has been shown to correlate with and predict future mathematical competence. Neurofunctionally, this task has been shown to rely on brain regions involved in numerical processing. However, there is no direct evidence that performance on the NL task is related to brain areas recruited during arithmetical processing and that these areas are domain-specific to numerical processing. In this study, we test whether 8- to 14-year-old's behavioral performance on the NL task is related to fMRI activation during small and large single-digit subtraction problems. Domain-specific areas for numerical processing were independently localized through a numerosity judgment task. Results show a direct relation between NL estimation performance and the amount of the activation in key areas for arithmetical processing. Better NL estimators showed a larger problem size effect than poorer NL estimators in numerical magnitude (i.e., intraparietal sulcus) and visuospatial areas (i.e., posterior superior parietal lobules), marked by less activation for small problems. In addition, the direction of the activation with problem size within the IPS was associated to differences in accuracies for small subtraction problems. This study is the first to show that performance in the NL task, i.e. estimating the spatial position of a number on an interval, correlates with brain activity observed during single-digit subtraction problem in regions thought to be involved numerical magnitude and spatial processes. PMID:25497398

REDRAW-Based Evapotranspiration Estimation in Chongli, North China

NASA Astrophysics Data System (ADS)

Zhang, Z.; Wang, Z.

2017-12-01

Evapotranspiration (ET) is the key component of hydrological cycle and spatial estimates of ET are important elements of atmospheric circulation and hydrologic models. Quantifying the ET over large region is significant for water resources planning, hydrologic water balances, water rights management, and water division. In this study, Evapotranspiration (ET) was estimated using REDRAW model in the Chongli on 2014. REDRAW is a satellite-based balance algorithm with reference dry and wet limits model developed to estimate ET. Remote sensing data obtained from MODIS and meteorological data from China Meteorological Data Sharing Service System were used in ET model. In order to analyze the distribution and time variation of ET over the study region, daily, monthly and yearly ET were calculated for the study area, and ET of different land cover types were calculated. In terms of the monthly ET, the figure was low in winter and high in other seasons, and reaches the maximum value in August, showing a high monthly difference. The ET value of water body was the highest and that of barren or sparse vegetation were the lowest, which accorded with local actual condition. Evaluating spatial temporal distribution of actual ET could assist to understand the water consumption regularity in region and figure out the effect from different land cover, which helped to establish links between land use, water allocation, and water use planning in study region. Due to the groundwater recession in north China, the evaluation of regional total water resources become increasingly essential, and the result of this study can be used to plan the water use. As the Chongli will prepare the ski slopes for Winter Olympics on 2022, accuracy estimation of actual ET can efficiently resolve water conflict and relieve water scarcity.

Parameterizing a Large-scale Water Balance Model in Regions with Sparse Data: The Tigris-Euphrates River Basins as an Example

NASA Astrophysics Data System (ADS)

Flint, A. L.; Flint, L. E.

2010-12-01

The characterization of hydrologic response to current and future climates is of increasing importance to many countries around the world that rely heavily on changing and uncertain water supplies. Large-scale models that can calculate a spatially distributed water balance and elucidate groundwater recharge and surface water flows for large river basins provide a basis of estimates of changes due to future climate projections. Unfortunately many regions in the world have very sparse data for parameterization or calibration of hydrologic models. For this study, the Tigris and Euphrates River basins were used for the development of a regional water balance model at 180-m spatial scale, using the Basin Characterization Model, to estimate historical changes in groundwater recharge and surface water flows in the countries of Turkey, Syria, Iraq, Iran, and Saudi Arabia. Necessary input parameters include precipitation, air temperature, potential evapotranspiration (PET), soil properties and thickness, and estimates of bulk permeability from geologic units. Data necessary for calibration includes snow cover, reservoir volumes (from satellite data and historic, pre-reservoir elevation data) and streamflow measurements. Global datasets for precipitation, air temperature, and PET were available at very large spatial scales (50 km) through the world scale databases, finer scale WorldClim climate data, and required downscaling to fine scales for model input. Soils data were available through world scale soil maps but required parameterization on the basis of textural data to estimate soil hydrologic properties. Soil depth was interpreted from geomorphologic interpretation and maps of quaternary deposits, and geologic materials were categorized from generalized geologic maps of each country. Estimates of bedrock permeability were made on the basis of literature and data on driller’s logs and adjusted during calibration of the model to streamflow measurements where available. Results of historical water balance calculations throughout the Tigris and Euphrates River basins will be shown along with details of processing input data to provide spatial continuity and downscaling. Basic water availability analysis for recharge and runoff is readily available from a determinisitic solar radiation energy balance model and a global potential evapotranspiration model and global estimates of precipitation and air temperature. Future climate estimates can be readily applied to the same water and energy balance models to evaluate future water availability for countries around the globe.

Task relevance modulates the behavioural and neural effects of sensory predictions

PubMed Central

Friston, Karl J.; Nobre, Anna C.

2017-01-01

The brain is thought to generate internal predictions to optimize behaviour. However, it is unclear whether predictions signalling is an automatic brain function or depends on task demands. Here, we manipulated the spatial/temporal predictability of visual targets, and the relevance of spatial/temporal information provided by auditory cues. We used magnetoencephalography (MEG) to measure participants’ brain activity during task performance. Task relevance modulated the influence of predictions on behaviour: spatial/temporal predictability improved spatial/temporal discrimination accuracy, but not vice versa. To explain these effects, we used behavioural responses to estimate subjective predictions under an ideal-observer model. Model-based time-series of predictions and prediction errors (PEs) were associated with dissociable neural responses: predictions correlated with cue-induced beta-band activity in auditory regions and alpha-band activity in visual regions, while stimulus-bound PEs correlated with gamma-band activity in posterior regions. Crucially, task relevance modulated these spectral correlates, suggesting that current goals influence PE and prediction signalling. PMID:29206225

Spatially resolved air-water emissions tradeoffs improve regulatory impact analyses for electricity generation.

PubMed

Gingerich, Daniel B; Sun, Xiaodi; Behrer, A Patrick; Azevedo, Inês L; Mauter, Meagan S

2017-02-21

Coal-fired power plants (CFPPs) generate air, water, and solids emissions that impose substantial human health, environmental, and climate change (HEC) damages. This work demonstrates the importance of accounting for cross-media emissions tradeoffs, plant and regional emissions factors, and spatially variation in the marginal damages of air emissions when performing regulatory impact analyses for electric power generation. As a case study, we assess the benefits and costs of treating wet flue gas desulfurization (FGD) wastewater at US CFPPs using the two best available treatment technology options specified in the 2015 Effluent Limitation Guidelines (ELGs). We perform a life-cycle inventory of electricity and chemical inputs to FGD wastewater treatment processes and quantify the marginal HEC damages of associated air emissions. We combine these spatially resolved damage estimates with Environmental Protection Agency estimates of water quality benefits, fuel-switching benefits, and regulatory compliance costs. We estimate that the ELGs will impose average net costs of $3.01 per cubic meter for chemical precipitation and biological wastewater treatment and $11.26 per cubic meter for zero-liquid discharge wastewater treatment (expected cost-benefit ratios of 1.8 and 1.7, respectively), with damages concentrated in regions containing a high fraction of coal generation or a large chemical manufacturing industry. Findings of net cost for FGD wastewater treatment are robust to uncertainty in auxiliary power source, location of chemical manufacturing, and binding air emissions limits in noncompliant regions, among other variables. Future regulatory design will minimize compliance costs and HEC tradeoffs by regulating air, water, and solids emissions simultaneously and performing regulatory assessments that account for spatial variation in emissions impacts.

Spatially resolved air-water emissions tradeoffs improve regulatory impact analyses for electricity generation

PubMed Central

Gingerich, Daniel B.; Behrer, A. Patrick; Azevedo, Inês L.

2017-01-01

Coal-fired power plants (CFPPs) generate air, water, and solids emissions that impose substantial human health, environmental, and climate change (HEC) damages. This work demonstrates the importance of accounting for cross-media emissions tradeoffs, plant and regional emissions factors, and spatially variation in the marginal damages of air emissions when performing regulatory impact analyses for electric power generation. As a case study, we assess the benefits and costs of treating wet flue gas desulfurization (FGD) wastewater at US CFPPs using the two best available treatment technology options specified in the 2015 Effluent Limitation Guidelines (ELGs). We perform a life-cycle inventory of electricity and chemical inputs to FGD wastewater treatment processes and quantify the marginal HEC damages of associated air emissions. We combine these spatially resolved damage estimates with Environmental Protection Agency estimates of water quality benefits, fuel-switching benefits, and regulatory compliance costs. We estimate that the ELGs will impose average net costs of $3.01 per cubic meter for chemical precipitation and biological wastewater treatment and $11.26 per cubic meter for zero-liquid discharge wastewater treatment (expected cost-benefit ratios of 1.8 and 1.7, respectively), with damages concentrated in regions containing a high fraction of coal generation or a large chemical manufacturing industry. Findings of net cost for FGD wastewater treatment are robust to uncertainty in auxiliary power source, location of chemical manufacturing, and binding air emissions limits in noncompliant regions, among other variables. Future regulatory design will minimize compliance costs and HEC tradeoffs by regulating air, water, and solids emissions simultaneously and performing regulatory assessments that account for spatial variation in emissions impacts. PMID:28167772

Upscaling Ameriflux observations to assess drought impacts on gross primary productivity across the Southwest

NASA Astrophysics Data System (ADS)

Barnes, M.; Moore, D. J.; Scott, R. L.; MacBean, N.; Ponce-Campos, G. E.; Breshears, D. D.

2017-12-01

Both satellite observations and eddy covariance estimates provide crucial information about the Earth's carbon, water and energy cycles. Continuous measurements from flux towers facilitate exploration of the exchange of carbon dioxide, water and energy between the land surface and the atmosphere at fine temporal and spatial scales, while satellite observations can fill in the large spatial gaps of in-situ measurements and provide long-term temporal continuity. The Southwest (Southwest United States and Northwest Mexico) and other semi-arid regions represent a key uncertainty in interannual variability in carbon uptake. Comparisons of existing global upscaled gross primary production (GPP) products with flux tower data at sites across the Southwest show widespread mischaracterization of seasonality in vegetation carbon uptake, resulting in large (up to 200%) errors in annual carbon uptake estimates. Here, remotely sensed and distributed meteorological inputs are used to upscale GPP estimates from 25 Ameriflux towers across the Southwest to the regional scale using a machine learning approach. Our random forest model incorporates two novel features that improve the spatial and temporal variability in GPP. First, we incorporate a multi-scalar drought index at multiple timescales to account for differential seasonality between ecosystem types. Second, our machine learning algorithm was trained on twenty five ecologically diverse sites to optimize both the monthly variability in and the seasonal cycle of GPP. The product and its components will be used to examine drought impacts on terrestrial carbon cycling across the Southwest including the effects of drought seasonality and on carbon uptake. Our spatially and temporally continuous upscaled GPP product drawing from both ground and satellite data over the Southwest region helps us understand linkages between the carbon and water cycles in semi-arid ecosystems and informs predictions of vegetation response to future climate conditions.

Concepts: Integrating population survey data from different spatial scales, sampling methods, and species

USGS Publications Warehouse

Dorazio, Robert; Delampady, Mohan; Dey, Soumen; Gopalaswamy, Arjun M.; Karanth, K. Ullas; Nichols, James D.

2017-01-01

Conservationists and managers are continually under pressure from the public, the media, and political policy makers to provide “tiger numbers,” not just for protected reserves, but also for large spatial scales, including landscapes, regions, states, nations, and even globally. Estimating the abundance of tigers within relatively small areas (e.g., protected reserves) is becoming increasingly tractable (see Chaps. 9 and 10), but doing so for larger spatial scales still presents a formidable challenge. Those who seek “tiger numbers” are often not satisfied by estimates of tiger occupancy alone, regardless of the reliability of the estimates (see Chaps. 4 and 5). As a result, wherever tiger conservation efforts are underway, either substantially or nominally, scientists and managers are frequently asked to provide putative large-scale tiger numbers based either on a total count or on an extrapolation of some sort (see Chaps. 1 and 2).

[MODIS-driven estimation of regional evapotranspiration in Karst area of Southwest China based on the Penman-Monteith-Leuning algorithm.

PubMed

Zhong, Hao Zhe; Xu, Xian Li; Zhang, Rong Fei; Liu, Mei Xian

2018-05-01

Karst area in southwestern China is characterized with complex topography, low soil water capacity, and fragile ecosystem. Accurate estimation of regional evapotranspiration is essential for ecological restoration and water resources management in southwestern China. Based on observed evapotranspiration and meteorological data, this study aimed to estimate spatial upscale evapotranspiration using the MOD15A2 LAI and Penman-Monteith-Leuning (PML) model, within which the stomatal conductance and soil wetness index were optimized by the least-square method. The results showed that the modeled ET well fitted with the observations, with the determination coefficient, Nash efficiency coefficient and RMSE being 0.85, 0.75 and 1.56 mm·d -1 , respectively. The ET exhibited clear seasonality and reached to its maximum in summer, coinciding with vegetation phenology. The annual ET ranged from 534 to 1035 mm·a -1 , with strong spatial heterogeneity which highly related to the precipitation. Evapotranspiration may be affected by precipitation as well as land use types.

[Potentials in the regionalization of health indicators using small-area estimation methods : Exemplary results based on the 2009, 2010 and 2012 GEDA studies].

PubMed

Kroll, Lars Eric; Schumann, Maria; Müters, Stephan; Lampert, Thomas

2017-12-01

Nationwide health surveys can be used to estimate regional differences in health. Using traditional estimation techniques, the spatial depth for these estimates is limited due to the constrained sample size. So far - without special refreshment samples - results have only been available for larger populated federal states of Germany. An alternative is regression-based small-area estimation techniques. These models can generate smaller-scale data, but are also subject to greater statistical uncertainties because of the model assumptions. In the present article, exemplary regionalized results based on the studies "Gesundheit in Deutschland aktuell" (GEDA studies) 2009, 2010 and 2012, are compared to the self-rated health status of the respondents. The aim of the article is to analyze the range of regional estimates in order to assess the usefulness of the techniques for health reporting more adequately. The results show that the estimated prevalence is relatively stable when using different samples. Important determinants of the variation of the estimates are the achieved sample size on the district level and the type of the district (cities vs. rural regions). Overall, the present study shows that small-area modeling of prevalence is associated with additional uncertainties compared to conventional estimates, which should be taken into account when interpreting the corresponding findings.

Potential biases in evapotranspiration estimates from Earth system models due to spatial heterogeneity and lateral moisture redistribution

NASA Astrophysics Data System (ADS)

Rouholahnejad, E.; Kirchner, J. W.

2016-12-01

Evapotranspiration (ET) is a key process in land-climate interactions and affects the dynamics of the atmosphere at local and regional scales. In estimating ET, most earth system models average over considerable sub-grid heterogeneity in land surface properties, precipitation (P), and potential evapotranspiration (PET). This spatial averaging could potentially bias ET estimates, due to the nonlinearities in the underlying relationships. In addition, most earth system models ignore lateral redistribution of water within and between grid cells, which could potentially alter both local and regional ET. Here we present a first attempt to quantify the effects of spatial heterogeneity and lateral redistribution on grid-cell-averaged ET as seen from the atmosphere over heterogeneous landscapes. Using a Budyko framework to express ET as a function of P and PET, we quantify how sub-grid heterogeneity affects average ET at the scale of typical earth system model grid cells. We show that averaging over sub-grid heterogeneity in P and PET, as typical earth system models do, leads to overestimates of average ET. We use a similar approach to quantify how lateral redistribution of water could affect average ET, as seen from the atmosphere. We show that where the aridity index P/PET increases with altitude, gravitationally driven lateral redistribution will increase average ET, implying that models that neglect lateral moisture redistribution will underestimate average ET. In contrast, where the aridity index P/PET decreases with altitude, gravitationally driven lateral redistribution will decrease average ET. This approach yields a simple conceptual framework and mathematical expressions for determining whether, and how much, spatial heterogeneity and lateral redistribution can affect regional ET fluxes as seen from the atmosphere. This analysis provides the basis for quantifying heterogeneity and redistribution effects on ET at regional and continental scales, which will be the focus of future work.

Socio-ecological factors and hand, foot and mouth disease in dry climate regions: a Bayesian spatial approach in Gansu, China

NASA Astrophysics Data System (ADS)

Gou, Faxiang; Liu, Xinfeng; Ren, Xiaowei; Liu, Dongpeng; Liu, Haixia; Wei, Kongfu; Yang, Xiaoting; Cheng, Yao; Zheng, Yunhe; Jiang, Xiaojuan; Li, Juansheng; Meng, Lei; Hu, Wenbiao

2017-01-01

The influence of socio-ecological factors on hand, foot and mouth disease (HFMD) were explored in this study using Bayesian spatial modeling and spatial patterns identified in dry regions of Gansu, China. Notified HFMD cases and socio-ecological data were obtained from the China Information System for Disease Control and Prevention, Gansu Yearbook and Gansu Meteorological Bureau. A Bayesian spatial conditional autoregressive model was used to quantify the effects of socio-ecological factors on the HFMD and explore spatial patterns, with the consideration of its socio-ecological effects. Our non-spatial model suggests temperature (relative risk (RR) 1.15, 95 % CI 1.01-1.31), GDP per capita (RR 1.19, 95 % CI 1.01-1.39) and population density (RR 1.98, 95 % CI 1.19-3.17) to have a significant effect on HFMD transmission. However, after controlling for spatial random effects, only temperature (RR 1.25, 95 % CI 1.04-1.53) showed significant association with HFMD. The spatial model demonstrates temperature to play a major role in the transmission of HFMD in dry regions. Estimated residual variation after taking into account the socio-ecological variables indicated that high incidences of HFMD were mainly clustered in the northwest of Gansu. And, spatial structure showed a unique distribution after taking account of socio-ecological effects.

Peak-flow characteristics of Virginia streams

USGS Publications Warehouse

Austin, Samuel H.; Krstolic, Jennifer L.; Wiegand, Ute

2011-01-01

Peak-flow annual exceedance probabilities, also called probability-percent chance flow estimates, and regional regression equations are provided describing the peak-flow characteristics of Virginia streams. Statistical methods are used to evaluate peak-flow data. Analysis of Virginia peak-flow data collected from 1895 through 2007 is summarized. Methods are provided for estimating unregulated peak flow of gaged and ungaged streams. Station peak-flow characteristics identified by fitting the logarithms of annual peak flows to a Log Pearson Type III frequency distribution yield annual exceedance probabilities of 0.5, 0.4292, 0.2, 0.1, 0.04, 0.02, 0.01, 0.005, and 0.002 for 476 streamgaging stations. Stream basin characteristics computed using spatial data and a geographic information system are used as explanatory variables in regional regression model equations for six physiographic regions to estimate regional annual exceedance probabilities at gaged and ungaged sites. Weighted peak-flow values that combine annual exceedance probabilities computed from gaging station data and from regional regression equations provide improved peak-flow estimates. Text, figures, and lists are provided summarizing selected peak-flow sites, delineated physiographic regions, peak-flow estimates, basin characteristics, regional regression model equations, error estimates, definitions, data sources, and candidate regression model equations. This study supersedes previous studies of peak flows in Virginia.

Variability in modeled cloud feedback tied to differences in the climatological spatial pattern of clouds

NASA Astrophysics Data System (ADS)

Siler, Nicholas; Po-Chedley, Stephen; Bretherton, Christopher S.

2018-02-01

Despite the increasing sophistication of climate models, the amount of surface warming expected from a doubling of atmospheric CO_2 (equilibrium climate sensitivity) remains stubbornly uncertain, in part because of differences in how models simulate the change in global albedo due to clouds (the shortwave cloud feedback). Here, model differences in the shortwave cloud feedback are found to be closely related to the spatial pattern of the cloud contribution to albedo (α) in simulations of the current climate: high-feedback models exhibit lower (higher) α in regions of warm (cool) sea-surface temperatures, and therefore predict a larger reduction in global-mean α as temperatures rise and warm regions expand. The spatial pattern of α is found to be strongly predictive (r=0.84) of a model's global cloud feedback, with satellite observations indicating a most-likely value of 0.58± 0.31 Wm^{-2} K^{-1} (90% confidence). This estimate is higher than the model-average cloud feedback of 0.43 Wm^{-2} K^{-1}, with half the range of uncertainty. The observational constraint on climate sensitivity is weaker but still significant, suggesting a likely value of 3.68 ± 1.30 K (90% confidence), which also favors the upper range of model estimates. These results suggest that uncertainty in model estimates of the global cloud feedback may be substantially reduced by ensuring a realistic distribution of clouds between regions of warm and cool SSTs in simulations of the current climate.

Geographic analysis of species richness and community attributes of forest birds from survey data in the mid-Atlantic integrated assessment region

USGS Publications Warehouse

Cam, E.; Sauer, J.R.; Nichols, J.D.; Hines, J.E.; Flather, C.H.

2000-01-01

Species richness of local communities is a state variable commonly used in community ecology and conservation biology. Investigation of spatial and temporal variations in richness and identification of factors associated with these variations form a basis for specifying management plans, evaluating these plans, and for testing hypotheses of theoretical interest. However, estimation of species richness is not trivial: species can be missed by investigators during sampling sessions. Sampling artifacts can lead to erroneous conclusions on spatial and temporal variation in species richness. Here we use data from the North American Breeding Bird Survey to estimate parameters describing the state of bird communities in the Mid-Atlantic Assessment (MAIA) region: species richness, extinction probability, turnover and relative species richness. We use a recently developed approach to estimation of species richness and related parameters that does not require the assumption that all the species are detected during sampling efforts. The information presented here is intended to visualize the state of bird communities in the MAIA region. We provide information on 1975 and 1990. We also quantified the changes between these years. We summarized and mapped the community attributes at a scale of management interest (watershed units).

Estimating micro area behavioural risk factor prevalence from large population-based surveys: a full Bayesian approach.

PubMed

Seliske, L; Norwood, T A; McLaughlin, J R; Wang, S; Palleschi, C; Holowaty, E

2016-06-07

An important public health goal is to decrease the prevalence of key behavioural risk factors, such as tobacco use and obesity. Survey information is often available at the regional level, but heterogeneity within large geographic regions cannot be assessed. Advanced spatial analysis techniques are demonstrated to produce sensible micro area estimates of behavioural risk factors that enable identification of areas with high prevalence. A spatial Bayesian hierarchical model was used to estimate the micro area prevalence of current smoking and excess bodyweight for the Erie-St. Clair region in southwestern Ontario. Estimates were mapped for male and female respondents of five cycles of the Canadian Community Health Survey (CCHS). The micro areas were 2006 Census Dissemination Areas, with an average population of 400-700 people. Two individual-level models were specified: one controlled for survey cycle and age group (model 1), and one controlled for survey cycle, age group and micro area median household income (model 2). Post-stratification was used to derive micro area behavioural risk factor estimates weighted to the population structure. SaTScan analyses were conducted on the granular, postal-code level CCHS data to corroborate findings of elevated prevalence. Current smoking was elevated in two urban areas for both sexes (Sarnia and Windsor), and an additional small community (Chatham) for males only. Areas of excess bodyweight were prevalent in an urban core (Windsor) among males, but not females. Precision of the posterior post-stratified current smoking estimates was improved in model 2, as indicated by narrower credible intervals and a lower coefficient of variation. For excess bodyweight, both models had similar precision. Aggregation of the micro area estimates to CCHS design-based estimates validated the findings. This is among the first studies to apply a full Bayesian model to complex sample survey data to identify micro areas with variation in risk factor prevalence, accounting for spatial correlation and other covariates. Application of micro area analysis techniques helps define areas for public health planning, and may be informative to surveillance and research modeling of relevant chronic disease outcomes.

Spatial modeling of litter and soil carbon stocks with associated uncertainty on forest land in the conterminous United States

NASA Astrophysics Data System (ADS)

Cao, B.; Domke, G. M.; Russell, M.; McRoberts, R. E.; Walters, B. F.

2017-12-01

Forest ecosystems contribute substantially to carbon (C) storage. The dynamics of litter decomposition, translocation and stabilization into soil layers are essential processes in the functioning of forest ecosystems, as they control the cycling of soil organic matter and the accumulation and release of C to the atmosphere. Therefore, the spatial distributions of litter and soil C stocks are important in greenhouse gas estimation and reporting and inform land management decisions, policy, and climate change mitigation strategies. In this study, we explored the effects of spatial aggregation of climatic, biotic, topographic and soil input data on national estimates of litter and soil C stocks and characterized the spatial distribution of litter and soil C stocks in the conterminous United States. Data from the Forest Inventory and Analysis (FIA) program within the US Forest Service were used with vegetation phenology data estimated from LANDSAT imagery (30 m) and raster data describing relevant environmental parameters (e.g. temperature, precipitation, topographic properties) for the entire conterminous US. Litter and soil C stocks were estimated and mapped through geostatistical analysis and statistical uncertainty bounds on the pixel level predictions were constructed using a Monte Carlo-bootstrap technique, by which credible variance estimates for the C stocks were calculated. The sensitivity of model estimates to spatial aggregation depends on geographic region. Further, using long-term (30-year) climate averages during periods with strong climatic trends results in large differences in litter and soil C stock estimates. In addition, results suggest that local topographic aspect is an important variable in litter and soil C estimation at the continental scale.

Estimates of spatially and temporally resolved constrained black carbon emission over the Indian region using a strategic integrated modelling approach

NASA Astrophysics Data System (ADS)

Verma, S.; Reddy, D. Manigopal; Ghosh, S.; Kumar, D. Bharath; Chowdhury, A. Kundu

2017-10-01

We estimated the latest spatially and temporally resolved gridded constrained black carbon (BC) emissions over the Indian region using a strategic integrated modelling approach. This was done extracting information on initial bottom-up emissions and atmospheric BC concentration from a general circulation model (GCM) simulation in conjunction with the receptor modelling approach. Monthly BC emission (83-364 Gg) obtained from the present study exhibited a spatial and temporal variability with this being the highest (lowest) during February (July). Monthly BC emission flux was considerably high (> 100 kg km- 2) over the entire Indo-Gangetic plain (IGP), east and the west coast during winter months. This was relatively higher over the central and western India than over the IGP during summer months. Annual BC emission rate was 2534 Gg y- 1 with that over the IGP and central India respectively comprising 50% and 40% of the total annual BC emissions over India. A high relative increase was observed in modified BC emissions (more than five times the initial emissions) over the most part of the IGP, east coast, central/northwestern India. The relative predominance of monthly BC emission flux over a region (as depicted from z-score distribution maps) was inferred being consistent with the prevalence of region- and season-specific anthropogenic activity.

Use of MODIS Sensor Images Combined with Reanalysis Products to Retrieve Net Radiation in Amazonia

PubMed Central

de Oliveira, Gabriel; Brunsell, Nathaniel A.; Moraes, Elisabete C.; Bertani, Gabriel; dos Santos, Thiago V.; Shimabukuro, Yosio E.; Aragão, Luiz E. O. C.

2016-01-01

In the Amazon region, the estimation of radiation fluxes through remote sensing techniques is hindered by the lack of ground measurements required as input in the models, as well as the difficulty to obtain cloud-free images. Here, we assess an approach to estimate net radiation (Rn) and its components under all-sky conditions for the Amazon region through the Surface Energy Balance Algorithm for Land (SEBAL) model utilizing only remote sensing and reanalysis data. The study period comprised six years, between January 2001–December 2006, and images from MODIS sensor aboard the Terra satellite and GLDAS reanalysis products were utilized. The estimates were evaluated with flux tower measurements within the Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) project. Comparison between estimates obtained by the proposed method and observations from LBA towers showed errors between 12.5% and 16.4% and 11.3% and 15.9% for instantaneous and daily Rn, respectively. Our approach was adequate to minimize the problem related to strong cloudiness over the region and allowed to map consistently the spatial distribution of net radiation components in Amazonia. We conclude that the integration of reanalysis products and satellite data, eliminating the need for surface measurements as input model, was a useful proposition for the spatialization of the radiation fluxes in the Amazon region, which may serve as input information needed by algorithms that aim to determine evapotranspiration, the most important component of the Amazon hydrological balance. PMID:27347957

Use of MODIS Sensor Images Combined with Reanalysis Products to Retrieve Net Radiation in Amazonia.

PubMed

de Oliveira, Gabriel; Brunsell, Nathaniel A; Moraes, Elisabete C; Bertani, Gabriel; Dos Santos, Thiago V; Shimabukuro, Yosio E; Aragão, Luiz E O C

2016-06-24

In the Amazon region, the estimation of radiation fluxes through remote sensing techniques is hindered by the lack of ground measurements required as input in the models, as well as the difficulty to obtain cloud-free images. Here, we assess an approach to estimate net radiation (Rn) and its components under all-sky conditions for the Amazon region through the Surface Energy Balance Algorithm for Land (SEBAL) model utilizing only remote sensing and reanalysis data. The study period comprised six years, between January 2001-December 2006, and images from MODIS sensor aboard the Terra satellite and GLDAS reanalysis products were utilized. The estimates were evaluated with flux tower measurements within the Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) project. Comparison between estimates obtained by the proposed method and observations from LBA towers showed errors between 12.5% and 16.4% and 11.3% and 15.9% for instantaneous and daily Rn, respectively. Our approach was adequate to minimize the problem related to strong cloudiness over the region and allowed to map consistently the spatial distribution of net radiation components in Amazonia. We conclude that the integration of reanalysis products and satellite data, eliminating the need for surface measurements as input model, was a useful proposition for the spatialization of the radiation fluxes in the Amazon region, which may serve as input information needed by algorithms that aim to determine evapotranspiration, the most important component of the Amazon hydrological balance.

Multivariate Non-Symmetric Stochastic Models for Spatial Dependence Models

NASA Astrophysics Data System (ADS)

Haslauer, C. P.; Bárdossy, A.

2017-12-01

A copula based multivariate framework allows more flexibility to describe different kind of dependences than what is possible using models relying on the confining assumption of symmetric Gaussian models: different quantiles can be modelled with a different degree of dependence; it will be demonstrated how this can be expected given process understanding. maximum likelihood based multivariate quantitative parameter estimation yields stable and reliable results; not only improved results in cross-validation based measures of uncertainty are obtained but also a more realistic spatial structure of uncertainty compared to second order models of dependence; as much information as is available is included in the parameter estimation: incorporation of censored measurements (e.g., below detection limit, or ones that are above the sensitive range of the measurement device) yield to more realistic spatial models; the proportion of true zeros can be jointly estimated with and distinguished from censored measurements which allow estimates about the age of a contaminant in the system; secondary information (categorical and on the rational scale) has been used to improve the estimation of the primary variable; These copula based multivariate statistical techniques are demonstrated based on hydraulic conductivity observations at the Borden (Canada) site, the MADE site (USA), and a large regional groundwater quality data-set in south-west Germany. Fields of spatially distributed K were simulated with identical marginal simulation, identical second order spatial moments, yet substantially differing solute transport characteristics when numerical tracer tests were performed. A statistical methodology is shown that allows the delineation of a boundary layer separating homogenous parts of a spatial data-set. The effects of this boundary layer (macro structure) and the spatial dependence of K (micro structure) on solute transport behaviour is shown.

Tethys – A Python Package for Spatial and Temporal Downscaling of Global Water Withdrawals

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

Li, Xinya; Vernon, Chris R.; Hejazi, Mohamad I.

Downscaling of water withdrawals from regional/national to local scale is a fundamental step and also a common problem when integrating large scale economic and integrated assessment models with high-resolution detailed sectoral models. Tethys, an open-access software written in Python, is developed with statistical downscaling algorithms, to spatially and temporally downscale water withdrawal data to a finer scale. The spatial resolution will be downscaled from region/basin scale to grid (0.5 geographic degree) scale and the temporal resolution will be downscaled from year to month. Tethys is used to produce monthly global gridded water withdrawal products based on estimates from the Globalmore » Change Assessment Model (GCAM).« less

Tethys – A Python Package for Spatial and Temporal Downscaling of Global Water Withdrawals

DOE PAGES

Li, Xinya; Vernon, Chris R.; Hejazi, Mohamad I.; ...

2018-02-09

Downscaling of water withdrawals from regional/national to local scale is a fundamental step and also a common problem when integrating large scale economic and integrated assessment models with high-resolution detailed sectoral models. Tethys, an open-access software written in Python, is developed with statistical downscaling algorithms, to spatially and temporally downscale water withdrawal data to a finer scale. The spatial resolution will be downscaled from region/basin scale to grid (0.5 geographic degree) scale and the temporal resolution will be downscaled from year to month. Tethys is used to produce monthly global gridded water withdrawal products based on estimates from the Globalmore » Change Assessment Model (GCAM).« less

Monthly hydroclimatology of the continental United States

NASA Astrophysics Data System (ADS)

Petersen, Thomas; Devineni, Naresh; Sankarasubramanian, A.

2018-04-01

Physical/semi-empirical models that do not require any calibration are of paramount need for estimating hydrological fluxes for ungauged sites. We develop semi-empirical models for estimating the mean and variance of the monthly streamflow based on Taylor Series approximation of a lumped physically based water balance model. The proposed models require mean and variance of monthly precipitation and potential evapotranspiration, co-variability of precipitation and potential evapotranspiration and regionally calibrated catchment retention sensitivity, atmospheric moisture uptake sensitivity, groundwater-partitioning factor, and the maximum soil moisture holding capacity parameters. Estimates of mean and variance of monthly streamflow using the semi-empirical equations are compared with the observed estimates for 1373 catchments in the continental United States. Analyses show that the proposed models explain the spatial variability in monthly moments for basins in lower elevations. A regionalization of parameters for each water resources region show good agreement between observed moments and model estimated moments during January, February, March and April for mean and all months except May and June for variance. Thus, the proposed relationships could be employed for understanding and estimating the monthly hydroclimatology of ungauged basins using regional parameters.

Improving Evapotranspiration Estimates Using Multi-Platform Remote Sensing

NASA Astrophysics Data System (ADS)

Knipper, Kyle; Hogue, Terri; Franz, Kristie; Scott, Russell

2016-04-01

Understanding the linkages between energy and water cycles through evapotranspiration (ET) is uniquely challenging given its dependence on a range of climatological parameters and surface/atmospheric heterogeneity. A number of methods have been developed to estimate ET either from primarily remote-sensing observations, in-situ measurements, or a combination of the two. However, the scale of many of these methods may be too large to provide needed information about the spatial and temporal variability of ET that can occur over regions with acute or chronic land cover change and precipitation driven fluxes. The current study aims to improve the spatial and temporal variability of ET utilizing only satellite-based observations by incorporating a potential evapotranspiration (PET) methodology with satellite-based down-scaled soil moisture estimates in southern Arizona, USA. Initially, soil moisture estimates from AMSR2 and SMOS are downscaled to 1km through a triangular relationship between MODIS land surface temperature (MYD11A1), vegetation indices (MOD13Q1/MYD13Q1), and brightness temperature. Downscaled soil moisture values are then used to scale PET to actual ET (AET) at a daily, 1km resolution. Derived AET estimates are compared to observed flux tower estimates, the North American Land Data Assimilation System (NLDAS) model output (i.e. Variable Infiltration Capacity (VIC) Macroscale Hydrologic Model, Mosiac Model, and Noah Model simulations), the Operational Simplified Surface Energy Balance Model (SSEBop), and a calibrated empirical ET model created specifically for the region. Preliminary results indicate a strong increase in correlation when incorporating the downscaling technique to original AMSR2 and SMOS soil moisture values, with the added benefit of being able to decipher small scale heterogeneity in soil moisture (riparian versus desert grassland). AET results show strong correlations with relatively low error and bias when compared to flux tower estimates. In addition, AET results show improved bias to those reported by SSEBop, with similar correlations and errors when compared to the empirical ET model. Spatial patterns of estimated AET display patterns representative of the basin's elevation and vegetation characteristics, with improved spatial resolution and temporal heterogeneity when compared to previous models.

Analysis of fractal dimensions of rat bones from film and digital images

NASA Technical Reports Server (NTRS)

Pornprasertsuk, S.; Ludlow, J. B.; Webber, R. L.; Tyndall, D. A.; Yamauchi, M.

2001-01-01

OBJECTIVES: (1) To compare the effect of two different intra-oral image receptors on estimates of fractal dimension; and (2) to determine the variations in fractal dimensions between the femur, tibia and humerus of the rat and between their proximal, middle and distal regions. METHODS: The left femur, tibia and humerus from 24 4-6-month-old Sprague-Dawley rats were radiographed using intra-oral film and a charge-coupled device (CCD). Films were digitized at a pixel density comparable to the CCD using a flat-bed scanner. Square regions of interest were selected from proximal, middle, and distal regions of each bone. Fractal dimensions were estimated from the slope of regression lines fitted to plots of log power against log spatial frequency. RESULTS: The fractal dimensions estimates from digitized films were significantly greater than those produced from the CCD (P=0.0008). Estimated fractal dimensions of three types of bone were not significantly different (P=0.0544); however, the three regions of bones were significantly different (P=0.0239). The fractal dimensions estimated from radiographs of the proximal and distal regions of the bones were lower than comparable estimates obtained from the middle region. CONCLUSIONS: Different types of image receptors significantly affect estimates of fractal dimension. There was no difference in the fractal dimensions of the different bones but the three regions differed significantly.

Estimating and interpreting migration of Amazonian forests using spatially implicit and semi-explicit neutral models.

PubMed

Pos, Edwin; Guevara Andino, Juan Ernesto; Sabatier, Daniel; Molino, Jean-François; Pitman, Nigel; Mogollón, Hugo; Neill, David; Cerón, Carlos; Rivas-Torres, Gonzalo; Di Fiore, Anthony; Thomas, Raquel; Tirado, Milton; Young, Kenneth R; Wang, Ophelia; Sierra, Rodrigo; García-Villacorta, Roosevelt; Zagt, Roderick; Palacios Cuenca, Walter; Aulestia, Milton; Ter Steege, Hans

2017-06-01

With many sophisticated methods available for estimating migration, ecologists face the difficult decision of choosing for their specific line of work. Here we test and compare several methods, performing sanity and robustness tests, applying to large-scale data and discussing the results and interpretation. Five methods were selected to compare for their ability to estimate migration from spatially implicit and semi-explicit simulations based on three large-scale field datasets from South America (Guyana, Suriname, French Guiana and Ecuador). Space was incorporated semi-explicitly by a discrete probability mass function for local recruitment, migration from adjacent plots or from a metacommunity. Most methods were able to accurately estimate migration from spatially implicit simulations. For spatially semi-explicit simulations, estimation was shown to be the additive effect of migration from adjacent plots and the metacommunity. It was only accurate when migration from the metacommunity outweighed that of adjacent plots, discrimination, however, proved to be impossible. We show that migration should be considered more an approximation of the resemblance between communities and the summed regional species pool. Application of migration estimates to simulate field datasets did show reasonably good fits and indicated consistent differences between sets in comparison with earlier studies. We conclude that estimates of migration using these methods are more an approximation of the homogenization among local communities over time rather than a direct measurement of migration and hence have a direct relationship with beta diversity. As betadiversity is the result of many (non)-neutral processes, we have to admit that migration as estimated in a spatial explicit world encompasses not only direct migration but is an ecological aggregate of these processes. The parameter m of neutral models then appears more as an emerging property revealed by neutral theory instead of being an effective mechanistic parameter and spatially implicit models should be rejected as an approximation of forest dynamics.

Estimation of regional surface CO2 fluxes with GOSAT observations using two inverse modeling approaches

NASA Astrophysics Data System (ADS)

Maksyutov, Shamil; Takagi, Hiroshi; Belikov, Dmitry A.; Saeki, Tazu; Zhuravlev, Ruslan; Ganshin, Alexander; Lukyanov, Alexander; Yoshida, Yukio; Oshchepkov, Sergey; Bril, Andrey; Saito, Makoto; Oda, Tomohiro; Valsala, Vinu K.; Saito, Ryu; Andres, Robert J.; Conway, Thomas; Tans, Pieter; Yokota, Tatsuya

2012-11-01

Inverse estimation of surface C02 fluxes is performed with atmospheric transport model using ground-based and GOSAT observations. The NIES-retrieved C02 column mixing (Xc02) and column averaging kernel are provided by GOSAT Level 2 product v. 2.0 and PPDF-DOAS method. Monthly mean C02 fluxes for 64 regions are estimated together with a global mean offset between GOSAT data and ground-based data. We used the fixed-lag Kalman filter to infer monthly fluxes for 42 sub-continental terrestrial regions and 22 oceanic basins. We estimate fluxes and compare results obtained by two inverse modeling approaches. In basic approach adopted in GOSAT Level4 product v. 2.01, we use aggregation of the GOSAT observations into monthly mean over 5x5 degree grids, fluxes are estimated independently for each region, and NIES atmospheric transport model is used for forward simulation. In the alternative method, the model-observation misfit is estimated for each observation separately and fluxes are spatially correlated using EOF analysis of the simulated flux variability similar to geostatistical approach, while transport simulation is enhanced by coupling with a Lagrangian transport model Flexpart. Both methods use using the same set of prior fluxes and region maps. Daily net ecosystem exchange (NEE) is predicted by the Vegetation Integrative Simulator for Trace gases (VISIT) optimized to match seasonal cycle of the atmospheric C02 . Monthly ocean-atmosphere C02 fluxes are produced with an ocean pC02 data assimilation system. Biomass burning fluxes were provided by the Global Fire Emissions Database (GFED); and monthly fossil fuel C02 emissions are estimated with ODIAC inventory. The results of analyzing one year of the GOSAT data suggest that when both GOSAT and ground-based data are used together, fluxes in tropical and other remote regions with lower associated uncertainties are obtained than in the analysis using only ground-based data. With version 2.0 of L2 Xc02 the fluxes appear reasonable for many regions and seasons, however there is a need for improving the L2 bias correction, data filtering and the inverse modeling method to reduce estimated flux anomalies visible in some areas. We also observe that application of spatial flux correlations with EOF­ based approach reduces flux anomalies.

Observations of Electron Vorticity in the Inner Plasmasheet and Its Relationship to Reconnection

NASA Technical Reports Server (NTRS)

Gurgiolo, Chris A.; Goldstein, Melvyn L.; Matthaeus, William H.; Vinas, Adolfo -F.

2011-01-01

Spatial derivatives of the electron moments can be estimated using data from the four Cluster spacecraft. Using spatial derivatives of the velocity we have computed the vorticity in the plasmasheet for several crossings. What we have found is that vorticity appears to be a common feature in the inner plasmasheet. We will show a number of examples. In at least some of the observations the vorticity is well correlated with the passage of Cluster through the ion diffusion region of known reconnection events. That most of the vorticity events observed are reconnection related cannot be dismissed and in fact observations of vorticity may provide a means to locate times when the Cluster spacecraft are magnetically connected to regions where reconnection is taking place. Understanding the role and source of the vorticity should advance our understanding of the dissipation of the turbulence associated with reconnection. In the course of the presentation we will also touch on the methods used to estimate the spatial derivatives as well as the limitations and assumptions involved.

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).

Remote Sensing of Particulate Organic Carbon Pools in the High-Latitude Oceans

NASA Technical Reports Server (NTRS)

Stramski, Dariusz; Stramska, Malgorzata

2005-01-01

The general goal of this project was to characterize spatial distributions at basin scales and variability on monthly to interannual timescales of particulate organic carbon (POC) in the high-latitude oceans. The primary objectives were: (1) To collect in situ data in the north polar waters of the Atlantic and in the Southern Ocean, necessary for the derivation of POC ocean color algorithms for these regions. (2) To derive regional POC algorithms and refine existing regional chlorophyll (Chl) algorithms, to develop understanding of processes that control bio-optical relationships underlying ocean color algorithms for POC and Chl, and to explain bio-optical differentiation between the examined polar regions and within the regions. (3) To determine basin-scale spatial patterns and temporal variability on monthly to interannual scales in satellite-derived estimates of POC and Chl pools in the investigated regions for the period of time covered by SeaWiFS and MODIS missions.

Image Location Estimation by Salient Region Matching.

PubMed

Qian, Xueming; Zhao, Yisi; Han, Junwei

2015-11-01

Nowadays, locations of images have been widely used in many application scenarios for large geo-tagged image corpora. As to images which are not geographically tagged, we estimate their locations with the help of the large geo-tagged image set by content-based image retrieval. In this paper, we exploit spatial information of useful visual words to improve image location estimation (or content-based image retrieval performances). We proposed to generate visual word groups by mean-shift clustering. To improve the retrieval performance, spatial constraint is utilized to code the relative position of visual words. We proposed to generate a position descriptor for each visual word and build fast indexing structure for visual word groups. Experiments show the effectiveness of our proposed approach.

Empirically constrained estimates of Alaskan regional Net Ecosystem Exchange of CO2, 2012-2014

NASA Astrophysics Data System (ADS)

Commane, R.; Lindaas, J.; Benmergui, J. S.; Luus, K. A.; Chang, R. Y. W.; Miller, S. M.; Henderson, J.; Karion, A.; Miller, J. B.; Sweeney, C.; Miller, C. E.; Lin, J. C.; Oechel, W. C.; Zona, D.; Euskirchen, E. S.; Iwata, H.; Ueyama, M.; Harazono, Y.; Veraverbeke, S.; Randerson, J. T.; Daube, B. C.; Pittman, J. V.; Wofsy, S. C.

2015-12-01

We present data-driven estimates of the regional net ecosystem exchange of CO2 across Alaska for three years (2012-2014) derived from CARVE (Carbon in the Arctic Reservoirs Vulnerability Experiment) aircraft measurements. Integrating optimized estimates of annual NEE, we find that the Alaskan region was a small sink of CO2 during 2012 and 2014, but a significant source of CO2 in 2013, even before including emissions from the large forest fire season during 2013. We investigate the drivers of this interannual variability, and the larger spring and fall emissions of CO2 in 2013. To determine the optimized fluxes, we couple the Polar Weather Research and Forecasting (PWRF) model with the Stochastic Time-Inverted Lagrangian Transport (STILT) model, to produce footprints of surface influence that we convolve with a remote-sensing driven model of NEE across Alaska, the Polar Vegetation Photosynthesis and Respiration Model (Polar-VPRM). For each month we calculate a spatially explicit additive flux (ΔF) by minimizing the difference between the measured profiles of the aircraft CO2 data and the modeled profiles, using a framework that combines a uniform correction at regional scales and a Bayesian inversion of residuals at smaller scales. A rigorous estimate of total uncertainty (including atmospheric transport, measurement error, etc.) was made with a combination of maximum likelihood estimation and Monte Carlo error propagation. Our optimized fluxes are consistent with other measurements on multiple spatial scales, including CO2 mixing ratios from the CARVE Tower near Fairbanks and eddy covariance flux towers in both boreal and tundra ecosystems across Alaska. For times outside the aircraft observations (Dec-April) we use the un-optimized polar-VPRM, which has shown good agreement with both tall towers and eddy flux data outside the growing season. This approach allows us to robustly estimate the annual CO2 budget for Alaska and investigate the drivers of both the seasonal cycle and the interannual variability of CO2 for the region.

[Study of spatial stratified sampling strategy of Oncomelania hupensis snail survey based on plant abundance].

PubMed

Xun-Ping, W; An, Z

2017-07-27

Objective To optimize and simplify the survey method of Oncomelania hupensis snails in marshland endemic regions of schistosomiasis, so as to improve the precision, efficiency and economy of the snail survey. Methods A snail sampling strategy (Spatial Sampling Scenario of Oncomelania based on Plant Abundance, SOPA) which took the plant abundance as auxiliary variable was explored and an experimental study in a 50 m×50 m plot in a marshland in the Poyang Lake region was performed. Firstly, the push broom surveyed data was stratified into 5 layers by the plant abundance data; then, the required numbers of optimal sampling points of each layer through Hammond McCullagh equation were calculated; thirdly, every sample point in the line with the Multiple Directional Interpolation (MDI) placement scheme was pinpointed; and finally, the comparison study among the outcomes of the spatial random sampling strategy, the traditional systematic sampling method, the spatial stratified sampling method, Sandwich spatial sampling and inference and SOPA was performed. Results The method (SOPA) proposed in this study had the minimal absolute error of 0.213 8; and the traditional systematic sampling method had the largest estimate, and the absolute error was 0.924 4. Conclusion The snail sampling strategy (SOPA) proposed in this study obtains the higher estimation accuracy than the other four methods.

A comparison of six potential evapotranspiration methods for regional use in the Southeastern United States

Treesearch

Jianbiao Lu; Ge Sun; Steven G. McNulty; Devendra Amatya

2005-01-01

Potential evapotranspiration (PET) is an important index of hydrologic budgets at different spatial scales and is a critical variable for understanding regional biological processes. It is often an important variable in estimating actual evapotranspiration (AET) in rainfall-runoff and ecosystem modeling. However, PET is defined in different ways in the literature and...

Hydropower potential mapping in mountain basins by high-resolution hydrological and GIS analysis

NASA Astrophysics Data System (ADS)

Claps, P.; Gallo, E.; Ganora, D.; Laio, F.; Masoero, A.

2013-12-01

Even in regions with mature hydropower development, needs for stable renewable power sources suggest to revise plans of exploitation of water resources, in compliance to the framework of international and national environmental regulations. This goal requires high-resolution hydrological analysis, that allows to : i) comply with the effects of existing hydropower plants or of other types of water withdrawals; ii) to assist the planner to figure out potential of new plants with still high marginal efficiency; iii) to assist the regulator in the process of comparing projects based on different solutions and different underlying hydrologic estimation methods. Flow duration curves (FDC) are the tool usually adopted to represent water availability and variability for hydropower purposes. They are usually determined in ungauged basins by means of regional statistical analysis. For this study, a 'spatially smooth' regional estimation method (SSEM) has been developed for FDC estimation, with some evolutions from a previous version: i) the method keeps the estimates of mean annual runoff congruent in the confluences by considering only raster-summable explanatory variables; ii) the presence of existing reservoirs and hydropower plants is taken into account by restoring the ';natural' statistics of the curve. The SSEM reconstructs the the FDC in ungauged basins using its L-moments from regressions on geomorphoclimatic descriptors. Relations are obtained on more than 100 gauged basins located in Northwestern Italy. To support the assessment of residual hydropower potential on two specific mountain watersheds the model has been applied extensively (Hi-Res) by mapping the estimated mean flow for each pixel of a DEM-derived river network raster model. 25000 sections were then identified over the network extracted from a 50m-resolution DTM. Spatial algorithms and data management were developed using Free&OpenSource Software (FOSS) (GRASS GIS and PostgreSQL/PostGIS), with the spatial database required to store perimeters and other descriptors needed for the hydrological estimation. Specific efforts have been devoted to spatial representation of the available potential using different flow-(elevation drop) relations for each pixel (along-river path, straight within floating window, in-valley constrained, etc.). This representation expands the information content and the domain of application of the classical hydrodynamic curve ( elevation-drop/ contributing area). Specific and abrupt changes due to existing plants are then clearly represented to provide a complete picture of the available potential for planning and regulation purposes.

A global approach to estimate irrigated areas - a comparison between different data and statistics

NASA Astrophysics Data System (ADS)

Meier, Jonas; Zabel, Florian; Mauser, Wolfram

2018-02-01

Agriculture is the largest global consumer of water. Irrigated areas constitute 40 % of the total area used for agricultural production (FAO, 2014a) Information on their spatial distribution is highly relevant for regional water management and food security. Spatial information on irrigation is highly important for policy and decision makers, who are facing the transition towards more efficient sustainable agriculture. However, the mapping of irrigated areas still represents a challenge for land use classifications, and existing global data sets differ strongly in their results. The following study tests an existing irrigation map based on statistics and extends the irrigated area using ancillary data. The approach processes and analyzes multi-temporal normalized difference vegetation index (NDVI) SPOT-VGT data and agricultural suitability data - both at a spatial resolution of 30 arcsec - incrementally in a multiple decision tree. It covers the period from 1999 to 2012. The results globally show a 18 % larger irrigated area than existing approaches based on statistical data. The largest differences compared to the official national statistics are found in Asia and particularly in China and India. The additional areas are mainly identified within already known irrigated regions where irrigation is more dense than previously estimated. The validation with global and regional products shows the large divergence of existing data sets with respect to size and distribution of irrigated areas caused by spatial resolution, the considered time period and the input data and assumption made.

Characterizing Fishing Effort and Spatial Extent of Coastal Fisheries

PubMed Central

Stewart, Kelly R.; Lewison, Rebecca L.; Dunn, Daniel C.; Bjorkland, Rhema H.; Kelez, Shaleyla; Halpin, Patrick N.; Crowder, Larry B.

2010-01-01

Biodiverse coastal zones are often areas of intense fishing pressure due to the high relative density of fishing capacity in these nearshore regions. Although overcapacity is one of the central challenges to fisheries sustainability in coastal zones, accurate estimates of fishing pressure in coastal zones are limited, hampering the assessment of the direct and collateral impacts (e.g., habitat degradation, bycatch) of fishing. We compiled a comprehensive database of fishing effort metrics and the corresponding spatial limits of fisheries and used a spatial analysis program (FEET) to map fishing effort density (measured as boat-meters per km2) in the coastal zones of six ocean regions. We also considered the utility of a number of socioeconomic variables as indicators of fishing pressure at the national level; fishing density increased as a function of population size and decreased as a function of coastline length. Our mapping exercise points to intra and interregional ‘hotspots’ of coastal fishing pressure. The significant and intuitive relationships we found between fishing density and population size and coastline length may help with coarse regional characterizations of fishing pressure. However, spatially-delimited fishing effort data are needed to accurately map fishing hotspots, i.e., areas of intense fishing activity. We suggest that estimates of fishing effort, not just target catch or yield, serve as a necessary measure of fishing activity, which is a key link to evaluating sustainability and environmental impacts of coastal fisheries. PMID:21206903

A new data set for estimating organic carbon storage to 3 m depth in soils of the northern circumpolar permafrost region

USGS Publications Warehouse

Hugelius, G.; Bockheim, James G.; Camill, P.; Elberling, B.; Grosse, G.; Harden, J.W.; Johnson, Kevin; Jorgenson, T.; Koven, C.D.; Kuhry, P.; Michaelson, G.; Mishra, U.; Palmtag, J.; Ping, C.-L.; O'Donnell, J.; Schirrmeister, L.; Schuur, E.A.G.; Sheng, Y.; Smith, L.C.; Strauss, J.; Yu, Z.

2013-01-01

High-latitude terrestrial ecosystems are key components in the global carbon cycle. The Northern Circumpolar Soil Carbon Database (NCSCD) was developed to quantify stocks of soil organic carbon (SOC) in the northern circumpolar permafrost region (a total area of 18.7 × 106 km2). The NCSCD is a geographical information system (GIS) data set that has been constructed using harmonized regional soil classification maps together with pedon data from the northern permafrost region. Previously, the NCSCD has been used to calculate SOC storage to the reference depths 0–30 cm and 0–100 cm (based on 1778 pedons). It has been shown that soils of the northern circumpolar permafrost region also contain significant quantities of SOC in the 100–300 cm depth range, but there has been no circumpolar compilation of pedon data to quantify this deeper SOC pool and there are no spatially distributed estimates of SOC storage below 100 cm depth in this region. Here we describe the synthesis of an updated pedon data set for SOC storage (kg C m-2) in deep soils of the northern circumpolar permafrost regions, with separate data sets for the 100–200 cm (524 pedons) and 200–300 cm (356 pedons) depth ranges. These pedons have been grouped into the North American and Eurasian sectors and the mean SOC storage for different soil taxa (subdivided into Gelisols including the sub-orders Histels, Turbels, Orthels, permafrost-free Histosols, and permafrost-free mineral soil orders) has been added to the updated NCSCDv2. The updated version of the data set is freely available online in different file formats and spatial resolutions that enable spatially explicit applications in GIS mapping and terrestrial ecosystem models. While this newly compiled data set adds to our knowledge of SOC in the 100–300 cm depth range, it also reveals that large uncertainties remain. Identified data gaps include spatial coverage of deep (> 100 cm) pedons in many regions as well as the spatial extent of areas with thin soils overlying bedrock and the quantity and distribution of massive ground ice. An open access data-portal for the pedon data set and the GIS-data sets is available online at http://bolin.su.se/data/ncscd/.

A new data set for estimating organic carbon storage to 3 m depth in soils of the northern circumpolar permafrost region

DOE PAGES

Hugelius, Gustaf; Bockheim, J. G.; Camill, P.; ...

2013-12-23

High-latitude terrestrial ecosystems are key components in the global carbon cycle. The Northern Circumpolar Soil Carbon Database (NCSCD) was developed to quantify stocks of soil organic carbon (SOC) in the northern circumpolar permafrost region (a total area of 18.7 × 10 6 km 2). The NCSCD is a geographical information system (GIS) data set that has been constructed using harmonized regional soil classification maps together with pedon data from the northern permafrost region. Previously, the NCSCD has been used to calculate SOC storage to the reference depths 0–30 cm and 0–100 cm (based on 1778 pedons). It has been shownmore » that soils of the northern circumpolar permafrost region also contain significant quantities of SOC in the 100–300 cm depth range, but there has been no circumpolar compilation of pedon data to quantify this deeper SOC pool and there are no spatially distributed estimates of SOC storage below 100 cm depth in this region. Here we describe the synthesis of an updated pedon data set for SOC storage (kg C m -2) in deep soils of the northern circumpolar permafrost regions, with separate data sets for the 100–200 cm (524 pedons) and 200–300 cm (356 pedons) depth ranges. These pedons have been grouped into the North American and Eurasian sectors and the mean SOC storage for different soil taxa (subdivided into Gelisols including the sub-orders Histels, Turbels, Orthels, permafrost-free Histosols, and permafrost-free mineral soil orders) has been added to the updated NCSCDv2. The updated version of the data set is freely available online in different file formats and spatial resolutions that enable spatially explicit applications in GIS mapping and terrestrial ecosystem models. While this newly compiled data set adds to our knowledge of SOC in the 100–300 cm depth range, it also reveals that large uncertainties remain. In conclusion, identified data gaps include spatial coverage of deep (> 100 cm) pedons in many regions as well as the spatial extent of areas with thin soils overlying bedrock and the quantity and distribution of massive ground ice.« less

Sex-biased gene flow in spectacled eiders (Anatidae): Inferences from molecular markers with contrasting modes of inheritance

USGS Publications Warehouse

Scribner, Kim T.; Petersen, Margaret R.; Fields, Raymond L.; Talbot, Sandra L.; Pearce, John M.; Chesser, Ronald K.

2001-01-01

Genetic markers that differ in mode of inheritance and rate of evolution (a sex-linked Z-specific microsatellite locus, five biparentally inherited microsatellite loci, and maternally inherited mitochondrial [mtDNA] sequences) were used to evaluate the degree of spatial genetic structuring at macro- and microgeographic scales, among breeding regions and local nesting populations within each region, respectively, for a migratory sea duck species, the spectacled eider (Somateria fisheri). Disjunct and declining breeding populations coupled with sex-specific differences in seasonal migratory patterns and life history provide a series of hypotheses regarding rates and directionality of gene flow among breeding populations from the Indigirka River Delta, Russia, and the North Slope and Yukon-Kuskokwim Delta, Alaska. The degree of differentiation in mtDNA haplotype frequency among breeding regions and populations within regions was high (ϕCT = 0.189, P < 0.01; ϕSC = 0.059, P < 0.01, respectively). Eleven of 17 mtDNA haplotypes were restricted to a single breeding region. Genetic differences among regions were considerably lower for nuclear DNA loci (sex-linked: ϕST = 0.001, P > 0.05; biparentally inherited microsatellites: mean θ = 0.001, P > 0.05) than was observed for mtDNA. Using models explicitly designed for uniparental and biparentally inherited genes, estimates of spatial divergence based on nuclear and mtDNA data together with elements of the species' breeding ecology were used to estimate effective population size and degree of male and female gene flow. Differences in the magnitude and spatial patterns of gene correlations for maternally inherited and nuclear genes revealed that females exhibit greater natal philopatry than do males. Estimates of generational female and male rates of gene flow among breeding regions differed markedly (3.67 × 10−4 and 1.28 × 10−2, respectively). Effective population size for mtDNA was estimated to be at least three times lower than that for biparental genes (30,671 and 101,528, respectively). Large disparities in population sizes among breeding areas greatly reduces the proportion of total genetic variance captured by dispersal, which may accelerate rates of inbreeding (i.e., promote higher coancestries) within populations due to nonrandom pairing of males with females from the same breeding population.

Comparison of data inversion techniques for remotely sensed wide-angle observations of Earth emitted radiation

NASA Technical Reports Server (NTRS)

Green, R. N.

1981-01-01

The shape factor, parameter estimation, and deconvolution data analysis techniques were applied to the same set of Earth emitted radiation measurements to determine the effects of different techniques on the estimated radiation field. All three techniques are defined and their assumptions, advantages, and disadvantages are discussed. Their results are compared globally, zonally, regionally, and on a spatial spectrum basis. The standard deviations of the regional differences in the derived radiant exitance varied from 7.4 W-m/2 to 13.5 W-m/2.

Use of spatial capture–recapture to estimate density of Andean bears in northern Ecuador

USGS Publications Warehouse

Molina, Santiago; Fuller, Angela K.; Morin, Dana J.; Royle, J. Andrew

2017-01-01

The Andean bear (Tremarctos ornatus) is the only extant species of bear in South America and is considered threatened across its range and endangered in Ecuador. Habitat loss and fragmentation is considered a critical threat to the species, and there is a lack of knowledge regarding its distribution and abundance. The species is thought to occur at low densities, making field studies designed to estimate abundance or density challenging. We conducted a pilot camera-trap study to estimate Andean bear density in a recently identified population of Andean bears northwest of Quito, Ecuador, during 2012. We compared 12 candidate spatial capture–recapture models including covariates on encounter probability and density and estimated a density of 7.45 bears/100 km2 within the region. In addition, we estimated that approximately 40 bears used a recently named Andean bear corridor established by the Secretary of Environment, and we produced a density map for this area. Use of a rub-post with vanilla scent attractant allowed us to capture numerous photographs for each event, improving our ability to identify individual bears by unique facial markings. This study provides the first empirically derived density estimate for Andean bears in Ecuador and should provide direction for future landscape-scale studies interested in conservation initiatives requiring spatially explicit estimates of density.

Regional crop gross primary production and yield estimation using fused Landsat-MODIS data

NASA Astrophysics Data System (ADS)

He, M.; Kimball, J. S.; Maneta, M. P.; Maxwell, B. D.; Moreno, A.

2017-12-01

Accurate crop yield assessments using satellite-based remote sensing are of interest for the design of regional policies that promote agricultural resiliency and food security. However, the application of current vegetation productivity algorithms derived from global satellite observations are generally too coarse to capture cropland heterogeneity. Merging information from sensors with reciprocal spatial and temporal resolution can improve the accuracy of these retrievals. In this study, we estimate annual crop yields for seven important crop types -alfalfa, barley, corn, durum wheat, peas, spring wheat and winter wheat over Montana, United States (U.S.) from 2008 to 2015. Yields are estimated as the product of gross primary production (GPP) and a crop-specific harvest index (HI) at 30 m spatial resolution. To calculate GPP we used a modified form of the MOD17 LUE algorithm driven by a 30 m 8-day fused NDVI dataset constructed by blending Landsat (5 or 7) and MODIS Terra reflectance data. The fused 30-m NDVI record shows good consistency with the original Landsat and MODIS data, but provides better spatiotemporal information on cropland vegetation growth. The resulting GPP estimates capture characteristic cropland patterns and seasonal variations, while the estimated annual 30 m crop yield results correspond favorably with county-level crop yield data (r=0.96, p<0.05). The estimated crop yield performance was generally lower, but still favorable in relation to field-scale crop yield surveys (r=0.42, p<0.01). Our methods and results are suitable for operational applications at regional scales.

Leaf Area Index Estimation Using Chinese GF-1 Wide Field View Data in an Agriculture Region.

PubMed

Wei, Xiangqin; Gu, Xingfa; Meng, Qingyan; Yu, Tao; Zhou, Xiang; Wei, Zheng; Jia, Kun; Wang, Chunmei

2017-07-08

Leaf area index (LAI) is an important vegetation parameter that characterizes leaf density and canopy structure, and plays an important role in global change study, land surface process simulation and agriculture monitoring. The wide field view (WFV) sensor on board the Chinese GF-1 satellite can acquire multi-spectral data with decametric spatial resolution, high temporal resolution and wide coverage, which are valuable data sources for dynamic monitoring of LAI. Therefore, an automatic LAI estimation algorithm for GF-1 WFV data was developed based on the radiative transfer model and LAI estimation accuracy of the developed algorithm was assessed in an agriculture region with maize as the dominated crop type. The radiative transfer model was firstly used to simulate the physical relationship between canopy reflectance and LAI under different soil and vegetation conditions, and then the training sample dataset was formed. Then, neural networks (NNs) were used to develop the LAI estimation algorithm using the training sample dataset. Green, red and near-infrared band reflectances of GF-1 WFV data were used as the input variables of the NNs, as well as the corresponding LAI was the output variable. The validation results using field LAI measurements in the agriculture region indicated that the LAI estimation algorithm could achieve satisfactory results (such as R² = 0.818, RMSE = 0.50). In addition, the developed LAI estimation algorithm had potential to operationally generate LAI datasets using GF-1 WFV land surface reflectance data, which could provide high spatial and temporal resolution LAI data for agriculture, ecosystem and environmental management researches.

Evaporation estimation of rift valley lakes: comparison of models.

PubMed

Melesse, Assefa M; Abtew, Wossenu; Dessalegne, Tibebe

2009-01-01

Evapotranspiration (ET) accounts for a substantial amount of the water flux in the arid and semi-arid regions of the World. Accurate estimation of ET has been a challenge for hydrologists, mainly because of the spatiotemporal variability of the environmental and physical parameters governing the latent heat flux. In addition, most available ET models depend on intensive meteorological information for ET estimation. Such data are not available at the desired spatial and temporal scales in less developed and remote parts of the world. This limitation has necessitated the development of simple models that are less data intensive and provide ET estimates with acceptable level of accuracy. Remote sensing approach can also be applied to large areas where meteorological data are not available and field scale data collection is costly, time consuming and difficult. In areas like the Rift Valley regions of Ethiopia, the applicability of the Simple Method (Abtew Method) of lake evaporation estimation and surface energy balance approach using remote sensing was studied. The Simple Method and a remote sensing-based lake evaporation estimates were compared to the Penman, Energy balance, Pan, Radiation and Complementary Relationship Lake Evaporation (CRLE) methods applied in the region. Results indicate a good correspondence of the models outputs to that of the above methods. Comparison of the 1986 and 2000 monthly lake ET from the Landsat images to the Simple and Penman Methods show that the remote sensing and surface energy balance approach is promising for large scale applications to understand the spatial variation of the latent heat flux.

A Framework for Mapping Global Evapotranspiration using 375-m VIIRS LST

NASA Astrophysics Data System (ADS)

Hain, C.; Anderson, M. C.; Schull, M. A.; Neale, C. M. U.

2017-12-01

As the world's water resources come under increasing tension due to dual stressors of climate change and population growth, accurate knowledge of water consumption through evapotranspiration (ET) over a range in spatial scales will be critical in developing adaptation strategies. Remote sensing methods for monitoring consumptive water use are becoming increasingly important, especially in areas of food insecurity. One method to estimate ET from satellite-based methods, the Atmosphere Land Exchange Inverse (ALEXI) model uses the change in morning land surface temperature to estimate the partitioning of sensible/latent heat fluxes which are then used to estimate daily ET. This presentation will outline several recent enhancements to the ALEXI modeling system, with a focus on global ET and drought monitoring. Until recently, ALEXI has been limited to areas with high resolution temporal sampling of geostationary sensors. The use of geostationary sensors makes global mapping a complicated process, especially for real-time applications, as data from as many as five different sensors are required to be ingested and harmonized to create a global mosaic. However, our research team has developed a new and novel method of using twice-daily observations from polar-orbiting sensors such as MODIS and VIIRS to estimate the mid-morning rise in LST that is used to drive the energy balance estimations within ALEXI. This allows the method to be applied globally using a single sensor rather than a global compositing of all available geostationary data. Other advantages of this new method include the higher spatial resolution provided by MODIS and VIIRS and the increased sampling at high latitudes where oblique view angles limit the utility of geostationary sensors. Improvements to the spatial resolution of the thermal infrared wavelengths on the VIIRS instrument, as compared to MODIS (375-m VIIRS vs. 1-km MODIS), allows for a much higher resolution ALEXI product than has been previously available. Therefore, recent developments have been to generate 375-m ALEXI ET products over several pilot regions (e.g. western US and the MENA region). The monitoring of consumptive water use over regions where significant groundwater pumping for irrigation is employed is important to accurately quantify the efficiency of water use in the region.

Towards breaking the spatial resolution barriers: An optical flow and super-resolution approach for sea ice motion estimation

NASA Astrophysics Data System (ADS)

Petrou, Zisis I.; Xian, Yang; Tian, YingLi

2018-04-01

Estimation of sea ice motion at fine scales is important for a number of regional and local level applications, including modeling of sea ice distribution, ocean-atmosphere and climate dynamics, as well as safe navigation and sea operations. In this study, we propose an optical flow and super-resolution approach to accurately estimate motion from remote sensing images at a higher spatial resolution than the original data. First, an external example learning-based super-resolution method is applied on the original images to generate higher resolution versions. Then, an optical flow approach is applied on the higher resolution images, identifying sparse correspondences and interpolating them to extract a dense motion vector field with continuous values and subpixel accuracies. Our proposed approach is successfully evaluated on passive microwave, optical, and Synthetic Aperture Radar data, proving appropriate for multi-sensor applications and different spatial resolutions. The approach estimates motion with similar or higher accuracy than the original data, while increasing the spatial resolution of up to eight times. In addition, the adopted optical flow component outperforms a state-of-the-art pattern matching method. Overall, the proposed approach results in accurate motion vectors with unprecedented spatial resolutions of up to 1.5 km for passive microwave data covering the entire Arctic and 20 m for radar data, and proves promising for numerous scientific and operational applications.

Model intra-comparison of transboundary sulfate loadings over springtime east Asia

NASA Astrophysics Data System (ADS)

Goto, D.; Ohara, T.; Nakajima, T.; Takemura, T.; Kajino, M.; Dai, T.; Matsui, H.; Takami, A.; Hatakeyama, S.; Aoki, K.; Sugimoto, N.; Shimizu, A.

2013-12-01

Over east Asia, a spatial gradient of sulfate aerosols from source to outflow regions has not fully evaluated by simulations. In the present study, we executed a global aerosol-transport model (SPRINTARS) during April 2006 to investigate the spatial gradient of sulfate aerosols using multiple measurements including surface mass concentration, aerosol optical thickness, and vertical profiles of extinction coefficients for spherical particles. We also performed sensitivity experiments to estimate possible uncertainties of sulfate mass loadings caused by macrophysical processes; emission inventory, dynamic core, and spatial resolution. Among the experiments, although a difference in the surface sulfate mass concentrations over east Asia was large, none of the simulations in the present study as well as regional models reproduced the spatial gradient of the surface sulfate from the source over China to the outflow regions in Japan. The sensitivity of different macrophysical factors to the surface sulfate differs from that to sulfate loadings in the column especially in the marine boundary layers (MBL). Therefore, to properly simulate the transboundary air pollution over east Asia is required to use multiple measurements in both the source and outflow regions especially in the MBL during the polluted days.

A tale of two "forests": random forest machine learning AIDS tropical forest carbon mapping.

PubMed

Mascaro, Joseph; Asner, Gregory P; Knapp, David E; Kennedy-Bowdoin, Ty; Martin, Roberta E; Anderson, Christopher; Higgins, Mark; Chadwick, K Dana

2014-01-01

Accurate and spatially-explicit maps of tropical forest carbon stocks are needed to implement carbon offset mechanisms such as REDD+ (Reduced Deforestation and Degradation Plus). The Random Forest machine learning algorithm may aid carbon mapping applications using remotely-sensed data. However, Random Forest has never been compared to traditional and potentially more reliable techniques such as regionally stratified sampling and upscaling, and it has rarely been employed with spatial data. Here, we evaluated the performance of Random Forest in upscaling airborne LiDAR (Light Detection and Ranging)-based carbon estimates compared to the stratification approach over a 16-million hectare focal area of the Western Amazon. We considered two runs of Random Forest, both with and without spatial contextual modeling by including--in the latter case--x, and y position directly in the model. In each case, we set aside 8 million hectares (i.e., half of the focal area) for validation; this rigorous test of Random Forest went above and beyond the internal validation normally compiled by the algorithm (i.e., called "out-of-bag"), which proved insufficient for this spatial application. In this heterogeneous region of Northern Peru, the model with spatial context was the best preforming run of Random Forest, and explained 59% of LiDAR-based carbon estimates within the validation area, compared to 37% for stratification or 43% by Random Forest without spatial context. With the 60% improvement in explained variation, RMSE against validation LiDAR samples improved from 33 to 26 Mg C ha(-1) when using Random Forest with spatial context. Our results suggest that spatial context should be considered when using Random Forest, and that doing so may result in substantially improved carbon stock modeling for purposes of climate change mitigation.

A Tale of Two “Forests”: Random Forest Machine Learning Aids Tropical Forest Carbon Mapping

PubMed Central

Mascaro, Joseph; Asner, Gregory P.; Knapp, David E.; Kennedy-Bowdoin, Ty; Martin, Roberta E.; Anderson, Christopher; Higgins, Mark; Chadwick, K. Dana

2014-01-01

Accurate and spatially-explicit maps of tropical forest carbon stocks are needed to implement carbon offset mechanisms such as REDD+ (Reduced Deforestation and Degradation Plus). The Random Forest machine learning algorithm may aid carbon mapping applications using remotely-sensed data. However, Random Forest has never been compared to traditional and potentially more reliable techniques such as regionally stratified sampling and upscaling, and it has rarely been employed with spatial data. Here, we evaluated the performance of Random Forest in upscaling airborne LiDAR (Light Detection and Ranging)-based carbon estimates compared to the stratification approach over a 16-million hectare focal area of the Western Amazon. We considered two runs of Random Forest, both with and without spatial contextual modeling by including—in the latter case—x, and y position directly in the model. In each case, we set aside 8 million hectares (i.e., half of the focal area) for validation; this rigorous test of Random Forest went above and beyond the internal validation normally compiled by the algorithm (i.e., called “out-of-bag”), which proved insufficient for this spatial application. In this heterogeneous region of Northern Peru, the model with spatial context was the best preforming run of Random Forest, and explained 59% of LiDAR-based carbon estimates within the validation area, compared to 37% for stratification or 43% by Random Forest without spatial context. With the 60% improvement in explained variation, RMSE against validation LiDAR samples improved from 33 to 26 Mg C ha−1 when using Random Forest with spatial context. Our results suggest that spatial context should be considered when using Random Forest, and that doing so may result in substantially improved carbon stock modeling for purposes of climate change mitigation. PMID:24489686

Multi-year Estimates of Methane Fluxes in Alaska from an Atmospheric Inverse Model

NASA Astrophysics Data System (ADS)

Miller, S. M.; Commane, R.; Chang, R. Y. W.; Miller, C. E.; Michalak, A. M.; Dinardo, S. J.; Dlugokencky, E. J.; Hartery, S.; Karion, A.; Lindaas, J.; Sweeney, C.; Wofsy, S. C.

2015-12-01

We estimate methane fluxes across Alaska over a multi-year period using observations from a three-year aircraft campaign, the Carbon Arctic Reservoirs Vulnerability Experiment (CARVE). Existing estimates of methane from Alaska and other Arctic regions disagree in both magnitude and distribution, and before the CARVE campaign, atmospheric observations in the region were sparse. We combine these observations with an atmospheric particle trajectory model and a geostatistical inversion to estimate surface fluxes at the model grid scale. We first use this framework to estimate the spatial distribution of methane fluxes across the state. We find the largest fluxes in the south-east and North Slope regions of Alaska. This distribution is consistent with several estimates of wetland extent but contrasts with the distribution in most existing flux models. These flux models concentrate methane in warmer or more southerly regions of Alaska compared to the estimate presented here. This result suggests a discrepancy in how existing bottom-up models translate wetland area into methane fluxes across the state. We next use the inversion framework to explore inter-annual variability in regional-scale methane fluxes for 2012-2014. We examine the extent to which this variability correlates with weather or other environmental conditions. These results indicate the possible sensitivity of wetland fluxes to near-term variability in climate.

Topological and canonical kriging for design flood prediction in ungauged catchments: an improvement over a traditional regional regression approach?

USGS Publications Warehouse

Archfield, Stacey A.; Pugliese, Alessio; Castellarin, Attilio; Skøien, Jon O.; Kiang, Julie E.

2013-01-01

In the United States, estimation of flood frequency quantiles at ungauged locations has been largely based on regional regression techniques that relate measurable catchment descriptors to flood quantiles. More recently, spatial interpolation techniques of point data have been shown to be effective for predicting streamflow statistics (i.e., flood flows and low-flow indices) in ungauged catchments. Literature reports successful applications of two techniques, canonical kriging, CK (or physiographical-space-based interpolation, PSBI), and topological kriging, TK (or top-kriging). CK performs the spatial interpolation of the streamflow statistic of interest in the two-dimensional space of catchment descriptors. TK predicts the streamflow statistic along river networks taking both the catchment area and nested nature of catchments into account. It is of interest to understand how these spatial interpolation methods compare with generalized least squares (GLS) regression, one of the most common approaches to estimate flood quantiles at ungauged locations. By means of a leave-one-out cross-validation procedure, the performance of CK and TK was compared to GLS regression equations developed for the prediction of 10, 50, 100 and 500 yr floods for 61 streamgauges in the southeast United States. TK substantially outperforms GLS and CK for the study area, particularly for large catchments. The performance of TK over GLS highlights an important distinction between the treatments of spatial correlation when using regression-based or spatial interpolation methods to estimate flood quantiles at ungauged locations. The analysis also shows that coupling TK with CK slightly improves the performance of TK; however, the improvement is marginal when compared to the improvement in performance over GLS.

Determining the spatial variability of wetland soil bulk density, organic matter, and the conversion factor between organic matter and organic carbon across coastal Louisiana, U.S.A.

USGS Publications Warehouse

Wang, Hongqing; Piazza, Sarai C.; Sharp, Leigh A.; Stagg, Camille L.; Couvillion, Brady R.; Steyer, Gregory D.; McGinnis, Thomas E.

2016-01-01

Soil bulk density (BD), soil organic matter (SOM) content, and a conversion factor between SOM and soil organic carbon (SOC) are often used in estimating SOC sequestration and storage. Spatial variability in BD, SOM, and the SOM–SOC conversion factor affects the ability to accurately estimate SOC sequestration, storage, and the benefits (e.g., land building area and vertical accretion) associated with wetland restoration efforts, such as marsh creation and sediment diversions. There are, however, only a few studies that have examined large-scale spatial variability in BD, SOM, and SOM–SOC conversion factors in coastal wetlands. In this study, soil cores, distributed across the entire coastal Louisiana (approximately 14,667 km2) were used to examine the regional-scale spatial variability in BD, SOM, and the SOM–SOC conversion factor. Soil cores for BD and SOM analyses were collected during 2006–09 from 331 spatially well-distributed sites in the Coastwide Reference Monitoring System network. Soil cores for the SOM–SOC conversion factor analysis were collected from 15 sites across coastal Louisiana during 2006–07. Results of a split-plot analysis of variance with incomplete block design indicated that BD and SOM varied significantly at a landscape level, defined by both hydrologic basins and vegetation types. Vertically, BD and SOM varied significantly among different vegetation types. The SOM–SOC conversion factor also varied significantly at the landscape level. This study provides critical information for the assessment of the role of coastal wetlands in large regional carbon budgets and the estimation of carbon credits from coastal restoration.

ACID RAIN MODELING

EPA Science Inventory

This paper provides an overview of existing statistical methodologies for the estimation of site-specific and regional trends in wet deposition. The interaction of atmospheric processes and emissions tend to produce wet deposition data patterns that show large spatial and tempora...

Evaluation of High-Resolution Precipitation Estimates from Satellites during July 2012 Beijing Flood Event Using Dense Rain Gauge Observations

PubMed Central

Chen, Sheng; Liu, Huijuan; You, Yalei; Mullens, Esther; Hu, Junjun; Yuan, Ye; Huang, Mengyu; He, Li; Luo, Yongming; Zeng, Xingji; Tang, Guoqiang; Hong, Yang

2014-01-01

Satellite-based precipitation estimates products, CMORPH and PERSIANN-CCS, were evaluated with a dense rain gauge network over Beijing and adjacent regions for an extremely heavy precipitation event on July 21 2012. CMORPH and PEERSIANN-CSS misplaced the region of greatest rainfall accumulation, and failed to capture the spatial pattern of precipitation, evidenced by a low spatial correlation coefficient (CC). CMORPH overestimated the daily accumulated rainfall by 22.84% while PERSIANN-CCS underestimated by 72.75%. In the rainfall center, both CMORPH and PERSIANN-CCS failed to capture the temporal variation of the rainfall, and underestimated rainfall amounts by 43.43% and 87.26%, respectively. Based on our results, caution should be exercised when using CMORPH and PERSIANN-CCS as input for monitoring and forecasting floods in Beijing urban areas, and the potential for landslides in the mountainous zones west and north of Beijing. PMID:24691358

Evaluation of high-resolution precipitation estimates from satellites during July 2012 Beijing flood event using dense rain gauge observations.

PubMed

Chen, Sheng; Liu, Huijuan; You, Yalei; Mullens, Esther; Hu, Junjun; Yuan, Ye; Huang, Mengyu; He, Li; Luo, Yongming; Zeng, Xingji; Tang, Guoqiang; Hong, Yang

2014-01-01

Satellite-based precipitation estimates products, CMORPH and PERSIANN-CCS, were evaluated with a dense rain gauge network over Beijing and adjacent regions for an extremely heavy precipitation event on July 21 2012. CMORPH and PEERSIANN-CSS misplaced the region of greatest rainfall accumulation, and failed to capture the spatial pattern of precipitation, evidenced by a low spatial correlation coefficient (CC). CMORPH overestimated the daily accumulated rainfall by 22.84% while PERSIANN-CCS underestimated by 72.75%. In the rainfall center, both CMORPH and PERSIANN-CCS failed to capture the temporal variation of the rainfall, and underestimated rainfall amounts by 43.43% and 87.26%, respectively. Based on our results, caution should be exercised when using CMORPH and PERSIANN-CCS as input for monitoring and forecasting floods in Beijing urban areas, and the potential for landslides in the mountainous zones west and north of Beijing.

Estimating Soil Organic Carbon stocks and uncertainties for the National inventory Report - a study case in Southern Belgium

NASA Astrophysics Data System (ADS)

Chartin, Caroline; Stevens, Antoine; Kruger, Inken; Esther, Goidts; Carnol, Monique; van Wesemael, Bas

2016-04-01

As many other countries, Belgium complies with Annex I of the United Nations Framework Convention on Climate Change (UNFCCC). Belgium thus reports its annual greenhouse gas emissions in its national inventory report (NIR), with a distinction between emissions/sequestration in cropland and grassland (EU decision 529/2013). The CO2 fluxes are then based on changes in SOC stocks computed for each of these two types of landuse. These stocks are specified for each of the agricultural regions which correspond to areas with similar agricultural practices (rotations and/or livestock) and yield potentials. For Southern Belgium (Wallonia) consisting of ten agricultural regions, the Soil Monitoring Network (SMN) 'CARBOSOL' has been developed this last decade to survey the state of agricultural soils by quantifying SOC stocks and their evolution in a reasonable number of locations complying with the time and funds allocated. Unfortunately, the 592 points of the CARBOSOL network do not allow a representative and a sound estimation of SOC stocks and its uncertainties for the 20 possible combinations of land use/agricultural regions. Moreover, the SMN CARBIOSOL is based on a legacy database following a convenience scheme sampling strategy rather than a statistical scheme defined by design-based or model-based strategies. Here, we aim to both quantify SOC budgets (i.e., How much?) and spatialize SOC stocks (i.e., Where?) at regional scale (Southern Belgium) based on data from the SMN described above. To this end, we developed a computation procedure based on Digital Soil Mapping techniques and stochastic simulations (Monte-Carlo) allowing the estimation of multiple (10,000) independent spatialized datasets. This procedure accounts for the uncertainties associated to estimations of both i) SOC stock at the pixelscale and ii) parameters of the models. Based on these 10,000 individual realizations of the spatial model, mean SOC stocks and confidence intervals can be then computed at the pixel scale, for selected sub-areas (i.e., the 20 landuse/agricultural region combinations) and for the entire study area.

A Phenology-based Method For Identifying the Planting Fraction of Winter Wheat Using Moderate-resolution Satellite Data

NASA Astrophysics Data System (ADS)

Dong, J.; Liu, W.; Han, W.; Lei, T.; Xia, J.; Yuan, W.

2017-12-01

Winter wheat is a staple food crop for most of the world's population, and the area and spatial distribution of winter wheat are key elements in estimating crop production and ensuring food security. However, winter wheat planting areas contain substantial spatial heterogeneity with mixed pixels for coarse- and moderate-resolution satellite data, leading to significant errors in crop acreage estimation. This study has developed a phenology-based approach using moderate-resolution satellite data to estimate sub-pixel planting fractions of winter wheat. Based on unmanned aerial vehicle (UAV) observations, the unique characteristics of winter wheat with high vegetation index values at the heading stage (May) and low values at the harvest stage (June) were investigated. The differences in vegetation index between heading and harvest stages increased with the planting fraction of winter wheat, and therefore the planting fractions were estimated by comparing the NDVI differences of a given pixel with those of predetermined pure winter wheat and non-winter wheat pixels. This approach was evaluated using aerial images and agricultural statistical data in an intensive agricultural region, Shandong Province in North China. The method explained 60% and 85% of the spatial variation in county- and municipal-level statistical data, respectively. More importantly, the predetermined pure winter wheat and non-winter wheat pixels can be automatically identified using MODIS data according to their NDVI differences, which strengthens the potential to use this method at regional and global scales without any field observations as references.

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.

A spatial econometric analysis of land-use change with land cover trends data: an application to the Pacific Northwest

Treesearch

David J. Lewis; Ralph J. Alig

2014-01-01

This paper develops a plot-level spatial econometric land-use model and estimates it with U.S. Geological Survey Land Cover Trends (LCT) geographic information system panel data for the western halves of the states of Oregon and Washington. The discrete-choice framework we use models plot-scale choices of the three dominant land uses in this region: forest, agriculture...

The Northern Circumpolar Soil Carbon Database: spatially distributed datasets of soil coverage and soil carbon storage in the northern permafrost regions

NASA Astrophysics Data System (ADS)

Hugelius, G.; Tarnocai, C.; Broll, G.; Canadell, J. G.; Kuhry, P.; Swanson, D. K.

2012-08-01

High latitude terrestrial ecosystems are key components in the global carbon (C) cycle. Estimates of global soil organic carbon (SOC), however, do not include updated estimates of SOC storage in permafrost-affected soils or representation of the unique pedogenic processes that affect these soils. The Northern Circumpolar Soil Carbon Database (NCSCD) was developed to quantify the SOC stocks in the circumpolar permafrost region (18.7 × 106 km2). The NCSCD is a polygon-based digital database compiled from harmonized regional soil classification maps in which data on soil order coverage has been linked to pedon data (n = 1647) from the northern permafrost regions to calculate SOC content and mass. In addition, new gridded datasets at different spatial resolutions have been generated to facilitate research applications using the NCSCD (standard raster formats for use in Geographic Information Systems and Network Common Data Form files common for applications in numerical models). This paper describes the compilation of the NCSCD spatial framework, the soil sampling and soil analyses procedures used to derive SOC content in pedons from North America and Eurasia and the formatting of the digital files that are available online. The potential applications and limitations of the NCSCD in spatial analyses are also discussed. The database has the doi:10.5879/ecds/00000001. An open access data-portal with all the described GIS-datasets is available online at: http://dev1.geo.su.se/bbcc/dev/ncscd/.

The Northern Circumpolar Soil Carbon Database: spatially distributed datasets of soil coverage and soil carbon storage in the northern permafrost regions

NASA Astrophysics Data System (ADS)

Hugelius, G.; Tarnocai, C.; Broll, G.; Canadell, J. G.; Kuhry, P.; Swanson, D. K.

2013-01-01

High-latitude terrestrial ecosystems are key components in the global carbon (C) cycle. Estimates of global soil organic carbon (SOC), however, do not include updated estimates of SOC storage in permafrost-affected soils or representation of the unique pedogenic processes that affect these soils. The Northern Circumpolar Soil Carbon Database (NCSCD) was developed to quantify the SOC stocks in the circumpolar permafrost region (18.7 × 106 km2). The NCSCD is a polygon-based digital database compiled from harmonized regional soil classification maps in which data on soil order coverage have been linked to pedon data (n = 1778) from the northern permafrost regions to calculate SOC content and mass. In addition, new gridded datasets at different spatial resolutions have been generated to facilitate research applications using the NCSCD (standard raster formats for use in geographic information systems and Network Common Data Form files common for applications in numerical models). This paper describes the compilation of the NCSCD spatial framework, the soil sampling and soil analytical procedures used to derive SOC content in pedons from North America and Eurasia and the formatting of the digital files that are available online. The potential applications and limitations of the NCSCD in spatial analyses are also discussed. The database has the doi:10.5879/ecds/00000001. An open access data portal with all the described GIS-datasets is available online at: http://www.bbcc.su.se/data/ncscd/.

Estimation of design floods in ungauged catchments using a regional index flood method. A case study of Lake Victoria Basin in Kenya

NASA Astrophysics Data System (ADS)

Nobert, Joel; Mugo, Margaret; Gadain, Hussein

Reliable estimation of flood magnitudes corresponding to required return periods, vital for structural design purposes, is impacted by lack of hydrological data in the study area of Lake Victoria Basin in Kenya. Use of regional information, derived from data at gauged sites and regionalized for use at any location within a homogenous region, would improve the reliability of the design flood estimation. Therefore, the regional index flood method has been applied. Based on data from 14 gauged sites, a delineation of the basin into two homogenous regions was achieved using elevation variation (90-m DEM), spatial annual rainfall pattern and Principal Component Analysis of seasonal rainfall patterns (from 94 rainfall stations). At site annual maximum series were modelled using the Log normal (LN) (3P), Log Logistic Distribution (LLG), Generalized Extreme Value (GEV) and Log Pearson Type 3 (LP3) distributions. The parameters of the distributions were estimated using the method of probability weighted moments. Goodness of fit tests were applied and the GEV was identified as the most appropriate model for each site. Based on the GEV model, flood quantiles were estimated and regional frequency curves derived from the averaged at site growth curves. Using the least squares regression method, relationships were developed between the index flood, which is defined as the Mean Annual Flood (MAF) and catchment characteristics. The relationships indicated area, mean annual rainfall and altitude were the three significant variables that greatly influence the index flood. Thereafter, estimates of flood magnitudes in ungauged catchments within a homogenous region were estimated from the derived equations for index flood and quantiles from the regional curves. These estimates will improve flood risk estimation and to support water management and engineering decisions and actions.

Verification of Agricultural Methane Emission Inventories

NASA Astrophysics Data System (ADS)

Desjardins, R. L.; Pattey, E.; Worth, D. E.; VanderZaag, A.; Mauder, M.; Srinivasan, R.; Worthy, D.; Sweeney, C.; Metzger, S.

2017-12-01

It is estimated that agriculture contributes more than 40% of anthropogenic methane (CH4) emissions in North America. However, these estimates, which are either based on the Intergovernmental Panel on Climate Change (IPCC) methodology or inverse modeling techniques, are poorly validated due to the challenges of separating interspersed CH4 sources within agroecosystems. A flux aircraft, instrumented with a fast-response Picarro CH4 analyzer for the eddy covariance (EC) technique and a sampling system for the relaxed eddy accumulation technique (REA), was flown at an altitude of about 150 m along several 20-km transects over an agricultural region in Eastern Canada. For all flight days, the top-down CH4 flux density measurements were compared to the footprint adjusted bottom-up estimates based on an IPCC Tier II methodology. Information on the animal population, land use type and atmospheric and surface variables were available for each transect. Top-down and bottom-up estimates of CH4 emissions were found to be poorly correlated, and wetlands were the most frequent confounding source of CH4; however, there were other sources such as waste treatment plants and biodigesters. Spatially resolved wavelet covariance estimates of CH4 emissions helped identify the contribution of wetlands to the overall CH4 flux, and the dependence of these emissions on temperature. When wetland contribution in the flux footprint was minimized, top-down and bottom-up estimates agreed to within measurement error. This research demonstrates that although existing aircraft-based technology can be used to verify regional ( 100 km2) agricultural CH4 emissions, it remains challenging due to diverse sources of CH4 present in many regions. The use of wavelet covariance to generate spatially-resolved flux estimates was found to be the best way to separate interspersed sources of CH4.

Estimation of Snow Parameters Based on Passive Microwave Remote Sensing and Meteorological Information

NASA Technical Reports Server (NTRS)

Tsang, Leung; Hwang, Jenq-Neng

1996-01-01

A method to incorporate passive microwave remote sensing measurements within a spatially distributed snow hydrology model to provide estimates of the spatial distribution of Snow Water Equivalent (SWE) as a function of time is implemented. The passive microwave remote sensing measurements are at 25 km resolution. However, in mountain regions the spatial variability of SWE over a 25 km footprint is large due to topographic influences. On the other hand, the snow hydrology model has built-in topographic information and the capability to estimate SWE at a 1 km resolution. In our work, the snow hydrology SWE estimates are updated and corrected using SSM/I passive microwave remote sensing measurements. The method is applied to the Upper Rio Grande River Basin in the mountains of Colorado. The change in prediction of SWE from hydrology modeling with and without updating is compared with measurements from two SNOTEL sites in and near the basin. The results indicate that the method incorporating the remote sensing measurements into the hydrology model is able to more closely estimate the temporal evolution of the measured values of SWE as a function of time.

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.

Temporal trends and spatial distribution of unsafe abortion in Brazil, 1996-2012

PubMed Central

Martins-Melo, Francisco Rogerlândio; Lima, Mauricélia da Silveira; Alencar, Carlos Henrique; Ramos, Alberto Novaes; Carvalho, Francisco Herlânio Costa; Machado, Márcia Maria Tavares; Heukelbach, Jorg

2014-01-01

OBJECTIVE To analyze temporal trends and distribution patterns of unsafe abortion in Brazil. METHODS Ecological study based on records of hospital admissions of women due to abortion in Brazil between 1996 and 2012, obtained from the Hospital Information System of the Ministry of Health. We estimated the number of unsafe abortions stratified by place of residence, using indirect estimate techniques. The following indicators were calculated: ratio of unsafe abortions/100 live births and rate of unsafe abortion/1,000 women of childbearing age. We analyzed temporal trends through polynomial regression and spatial distribution using municipalities as the unit of analysis. RESULTS In the study period, a total of 4,007,327 hospital admissions due to abortions were recorded in Brazil. We estimated a total of 16,905,911 unsafe abortions in the country, with an annual mean of 994,465 abortions (mean unsafe abortion rate: 17.0 abortions/1,000 women of childbearing age; ratio of unsafe abortions: 33.2/100 live births). Unsafe abortion presented a declining trend at national level (R2: 94.0%, p < 0.001), with unequal patterns between regions. There was a significant reduction of unsafe abortion in the Northeast (R2: 93.0%, p < 0.001), Southeast (R2: 92.0%, p < 0.001) and Central-West regions (R2: 64.0%, p < 0.001), whereas the North (R2: 39.0%, p = 0.030) presented an increase, and the South (R2: 22.0%, p = 0.340) remained stable. Spatial analysis identified the presence of clusters of municipalities with high values for unsafe abortion, located mainly in states of the North, Northeast and Southeast Regions. CONCLUSIONS Unsafe abortion remains a public health problem in Brazil, with marked regional differences, mainly concentrated in the socioeconomically disadvantaged regions of the country. Qualification of attention to women’s health, especially to reproductive aspects and attention to pre- and post-abortion processes, are necessary and urgent strategies to be implemented in the country. PMID:25119946

Parameter uncertainty and nonstationarity in regional extreme rainfall frequency analysis in Qu River Basin, East China

NASA Astrophysics Data System (ADS)

Zhu, Q.; Xu, Y. P.; Gu, H.

2014-12-01

Traditionally, regional frequency analysis methods were developed for stationary environmental conditions. Nevertheless, recent studies have identified significant changes in hydrological records, leading to the 'death' of stationarity. Besides, uncertainty in hydrological frequency analysis is persistent. This study aims to investigate the impact of one of the most important uncertainty sources, parameter uncertainty, together with nonstationarity, on design rainfall depth in Qu River Basin, East China. A spatial bootstrap is first proposed to analyze the uncertainty of design rainfall depth estimated by regional frequency analysis based on L-moments and estimated on at-site scale. Meanwhile, a method combining the generalized additive models with 30-year moving window is employed to analyze non-stationarity existed in the extreme rainfall regime. The results show that the uncertainties of design rainfall depth with 100-year return period under stationary conditions estimated by regional spatial bootstrap can reach 15.07% and 12.22% with GEV and PE3 respectively. On at-site scale, the uncertainties can reach 17.18% and 15.44% with GEV and PE3 respectively. In non-stationary conditions, the uncertainties of maximum rainfall depth (corresponding to design rainfall depth) with 0.01 annual exceedance probability (corresponding to 100-year return period) are 23.09% and 13.83% with GEV and PE3 respectively. Comparing the 90% confidence interval, the uncertainty of design rainfall depth resulted from parameter uncertainty is less than that from non-stationarity frequency analysis with GEV, however, slightly larger with PE3. This study indicates that the spatial bootstrap can be successfully applied to analyze the uncertainty of design rainfall depth on both regional and at-site scales. And the non-stationary analysis shows that the differences between non-stationary quantiles and their stationary equivalents are important for decision makes of water resources management and risk management.

Downscaling Satellite Land Surface Temperatures in Urban Regions for Surface Energy Balance Study and Heat Index Development

NASA Astrophysics Data System (ADS)

Norouzi, H.; Bah, A.; Prakash, S.; Nouri, N.; Blake, R.

2017-12-01

A great percentage of the world's population reside in urban areas that are exposed to the threats of global and regional climate changes and associated extreme weather events. Among them, urban heat islands have significant health and economic impacts due to higher thermal gradients of impermeable surfaces in urban regions compared to their surrounding rural areas. Therefore, accurate characterization of the surface energy balance in urban regions are required to predict these extreme events. High spatial resolution Land surface temperature (LST) in the scale of street level in the cities can provide wealth of information to study surface energy balance and eventually providing a reliable heat index. In this study, we estimate high-resolution LST maps using combination of LandSat 8 and infrared based satellite products such as Moderate Resolution Imaging Spectroradiometer (MODIS) and newly launched Geostationary Operational Environmental Satellite-R Series (GOES-R). Landsat 8 provides higher spatial resolution (30 m) estimates of skin temperature every 16 days. However, MODIS and GOES-R have lower spatial resolution (1km and 4km respectively) with much higher temporal resolution. Several statistical downscaling methods were investigated to provide high spatiotemporal LST maps in urban regions. The results reveal that statistical methods such as Principal Component Analysis (PCA) can provide reliable estimations of LST downscaling with 2K accuracy. Other methods also were tried including aggregating (up-scaling) the high-resolution data to a coarse one to examine the limitations and to build the model. Additionally, we deployed flux towers over distinct materials such as concrete, asphalt, and rooftops in New York City to monitor the sensible and latent heat fluxes through eddy covariance method. To account for the incoming and outgoing radiation, a 4-component radiometer is used that can observe both incoming and outgoing longwave and shortwave radiation. This enables us to accurately build the relationship between LST, air temperature, and the heat index in the future.

Spatial distribution of random velocity inhomogeneities in the western part of Nankai subduction zone

NASA Astrophysics Data System (ADS)

Takahashi, T.; Obana, K.; Yamamoto, Y.; Nakanishi, A.; Kodaira, S.; Kaneda, Y.

2011-12-01

In the Nankai trough, there are three seismogenic zones of megathrust earthquakes (Tokai, Tonankai and Nankai earthquakes). Lithospheric structures in and around these seismogenic zones are important for the studies on mutual interactions and synchronization of their fault ruptures. Recent studies on seismic wave scattering at high frequencies (>1Hz) make it possible to estimate 3D distributions of random inhomogeneities (or scattering coefficient) in the lithosphere, and clarified that random inhomogeneity is one of the important medium properties related to microseismicity and damaged structure near the fault zone [Asano & Hasegawa, 2004; Takahashi et al. 2009]. This study estimates the spatial distribution of the power spectral density function (PSDF) of random inhomogeneities the western part of Nankai subduction zone, and examines the relations with crustal velocity structure and seismic activity. Seismic waveform data used in this study are those recorded at seismic stations of Hi-net & F-net operated by NIED, and 160 ocean bottom seismographs (OBSs) deployed at Hyuga-nada region from Dec. 2008 to Jan. 2009. This OBS observation was conducted by JAMSTEC as a part of "Research concerning Interaction Between the Tokai, Tonankai and Nankai Earthquakes" funded by Ministry of Education, Culture, Sports, Science and Technology, Japan. Spatial distribution of random inhomogeneities is estimated by the inversion analysis of the peak delay time of small earthquakes [Takahashi et al. 2009], where the peak delay time is defined as the time lag from the S-wave onset to its maximal amplitude arrival. We assumed the von Karman type functional form for the PSDF. Peak delay times are measured from root mean squared envelopes at 4-8Hz, 8-16Hz and 16-32Hz. Inversion result can be summarized as follows. Random inhomogeneities beneath the Quaternary volcanoes are characterized by strong inhomogeneities at small spatial scale (~ a few hundreds meter) and weak spectral gradient. Those in the Hyuga-nada region are characterized by the strong inhomogeneities at large spatial wavelength and steep spectral gradient. Random inhomogeneities in the Hyuga-nada region are similar with those in the frontal arc high in northern Izu-Bonin arc, which is thought to be a remnant arc that is presently inactive [Takahashi et al. 2011]. This coincidence implies the existence of subducted Kyushu-Palau ridge in this anomaly of random inhomogeneities, which is also suggested by the seismic refraction survey in this region [Nakanishi et al. 2010 AGU Fall Mtg.]. Source rupture areas of large earthquakes (M>6) in Hyuga-nada regions tend to locate around this anomaly of inhomogeneities. We may say that this anomalously inhomogeneous region is a structural factor affecting the seismic activity in Hyuga-nada region.

Risk and resilience in the late glacial: A case study from the western Mediterranean

NASA Astrophysics Data System (ADS)

Barton, C. Michael; Aura Tortosa, J. Emili; Garcia-Puchol, Oreto; Riel-Salvatore, Julien G.; Gauthier, Nicolas; Vadillo Conesa, Margarita; Pothier Bouchard, Geneviève

2018-03-01

The period spanning the Last Glacial Maximum through early Holocene encompasses dramatic and rapid environmental changes that offered both increased risk and new opportunities to human populations of the Mediterranean zone. The regional effects of global climate change varied spatially with latitude, topography, and distance from a shifting coastline; and human adaptations to these changes played out at these regional scales. To better understand the spatial and temporal dynamics of climate change and human social-ecological-technological systems (or SETS) during the transition from full glacial to interglacial, we carried out a meta-analysis of archaeological and paleoenvironmental datasets across the western Mediterranean region. We compiled information on prehistoric technology, land-use, and hunting strategies from 291 archaeological assemblages, recovered from 122 sites extending from southern Spain, through Mediterranean France, to northern and peninsular Italy, as well as 2,386 radiocarbon dates from across this region. We combine these data on human ecological dynamics with paleoenvironmental information derived from global climate models, proxy data, and estimates of coastlines modeled from sea level estimates and digital terrain. The LGM represents an ecologically predictable period for over much of the western Mediterranean, while the remainder of the Pleistocene was increasingly unpredictable, making it a period of increased ecological risk for hunter-gatherers. In response to increasing spatial and temporal uncertainty, hunter-gatherers reorganized different constituents of their SETS, allowing regional populations to adapt to these conditions up to a point. Beyond this threshold, rapid environmental change resulted in significant demographic change in Mediterranean hunter-gatherer populations.

Progress toward improving regional atmospheric inversions using airborne measurements: Results from ACT-America

NASA Astrophysics Data System (ADS)

Davis, K. J.; Baier, B.; Baker, D.; Barkley, Z.; Bell, E.; Bowman, K. W.; Browell, E. V.; Campbell, J.; Chen, H. W.; Choi, Y.; DiGangi, J. P.; Dobler, J. T.; Erxleben, W. H.; Fan, T. F.; Feng, S.; Fried, A.; Gaudet, B. J.; Jacobson, A. R.; Keller, K.; Kooi, S. A.; Lauvaux, T.; Lin, B.; McGill, M. J.; McGregor, D.; Michalak, A.; Obland, M. D.; O'Dell, C.; Pal, S.; Parazoo, N.; Pauly, R.; Randazzo, N. A.; Samaddar, A.; Schuh, A. E.; Sweeney, C.; Wesloh, D.; Williams, C. A.; Zhang, F.; Zhou, Y.

2017-12-01

The Atmospheric Carbon and Transport (ACT) - America mission aims to improve our understanding of transport and fluxes of greenhouse gases (GHGs) via airborne campaigns spanning a range of mid-latitude weather conditions, and thus to improve the accuracy and precision of regional inverse flux estimates of GHGs. ACT-America has conducted three field campaigns with two aircraft across three regions of the eastern United States during summer 2016, winter 2017 and fall 2017. Simulations of atmospheric GHGs have been conducted for a subset of these campaigns. We present progress from these campaigns. Mid-summer observations suggest a net biological source of CO2 to the atmosphere in the Gulf Coast states. These results contradict those terrestrial biosphere models that show net uptake of CO2 in this region in summer. Methane observations downwind of major sources in the MidAtlantic suggest that these sources are represented fairly well by existing emissions inventories. Flux estimation in other regions is underway. Spatially-coherent differences in GHGs extend throughout the depth of the troposphere are observed at frontal boundaries in summer and winter. These spatial structures are captured in global and mesoscale simulations, though the simulated GHG mole fractions are sometimes biased with respect to observations, suggesting potential biases in synoptic transport. Mesoscale simulations overestimate spatial differences in ABL CO2 mole fractions in fair weather conditions as compared to observations and the CarbonTracker global inverse modeling system. ABL depths are simulated fairly well by both mesoscale and global modeling systems, suggesting that either weather-scale flux amplitudes are overestimated by CarbonTracker, or the mesoscale model lacks parameterized transport above the ABL. Measurements of OCS, 14CO2, and CO are being used to attribute CO2 variability to biogenic and anthropogenic processes and to expand the evaluation of GHG simulation systems. Cross-evaluation of OCO-2 and airborne lidar XCO2 observations against in situ measurements is defining the regional precision and accuracy of these observations. These findings are moving us toward improved regional GHG inverse flux estimates via better understanding of prior fluxes, atmospheric transport, and satellite CO2 observations.

Ecosystem CO2 Exchange Across Semiarid Southwestern North America: A Synthesis of Multi-Year Flux Site Observations and its Comparison with Estimates from Terrestrial Biome Models and Remote Sensing

NASA Astrophysics Data System (ADS)

Biederman, J. A.; Scott, R. L.; Goulden, M.; Litvak, M. E.; Kolb, T.; Yepez, E. A.; Garatuza, J.; Oechel, W. C.; Krofcheck, D. J.; Ponce-Campos, G. E.; Bowling, D. R.; Meyers, T. P.; Maurer, G.

2016-12-01

Global carbon cycle studies reveal that semiarid ecosystems dominate the increasing trend and interannual variability of the land CO2 sink. However, the regional terrestrial biome models (TBM) and remote sensing products (RSP) used in large-scale analyses are poorly constrained by ecosystem flux measurements in semiarid regions, which are under-represented in global flux datasets. Here we present eddy covariance measurements from 25 diverse ecosystems in semiarid southwestern North America with ranges in annual precipitation of 100 - 1000 mm, annual temperatures of 2 - 25 °C, and records of 3 - 10 years each (150 site-years in total). We identified seven subregions with unique seasonal dynamics in climate and ecosystem-atmosphere exchange, including net and gross CO2 exchange (photosynthesis and respiration) and evapotranspiration (ET), and we evaluated how well measured dynamics were captured by satellite-based greenness observations of the Enhanced Vegetation Index (EVI). Annual flux integrals were calculated based on site-appropriate ecohydrologic years. Net ecosystem production (NEP) varied between -550 and + 420 g C m-2, highlighting the wide range of regional sink/source function. Annual photosynthesis and respiration were positively related to water availability but were suppressed in warmer years at a given site and at climatically warmer sites, in contrast to positive temperature responses at wetter sites. When precipitation anomalies were spatially coherent across sites (e.g. related to El Niño Southern Oscillation), we found large regional annual anomalies in net and gross CO2 uptake. TBM and RSP were less effective in capturing spatial gradients in mean ET and CO2 exchange across this semiarid region as compared to wetter regions. Measured interannual variability of ET and gross CO2 exchange was 3 - 5 times larger than estimates from TBM or RSP. These results suggest that semiarid regions play an even larger role in regulating interannual variability of the global carbon cycle than currently estimated by models and remote sensing. In on-going work, we expand this spatial-temporal analysis across a broader gradient of water availability using the Fluxnet 2015 dataset.

Distribution of late Pleistocene ice-rich syngenetic permafrost of the Yedoma Suite in east and central Siberia, Russia

USGS Publications Warehouse

Grosse, Guido; Robinson, Joel E.; Bryant, Robin; Taylor, Maxwell D.; Harper, William; DeMasi, Amy; Kyker-Snowman, Emily; Veremeeva, Alexandra; Schirrmeister, Lutz; Harden, Jennifer

2013-01-01

This digital database is the product of collaboration between the U.S. Geological Survey, the Geophysical Institute at the University of Alaska, Fairbanks; the Los Altos Hills Foothill College GeoSpatial Technology Certificate Program; the Alfred Wegener Institute for Polar and Marine Research, Potsdam, Germany; and the Institute of Physical Chemical and Biological Problems in Soil Science of the Russian Academy of Sciences. The primary goal for creating this digital database is to enhance current estimates of soil organic carbon stored in deep permafrost, in particular the late Pleistocene syngenetic ice-rich permafrost deposits of the Yedoma Suite. Previous studies estimated that Yedoma deposits cover about 1 million square kilometers of a large region in central and eastern Siberia, but these estimates generally are based on maps with scales smaller than 1:10,000,000. Taking into account this large area, it was estimated that Yedoma may store as much as 500 petagrams of soil organic carbon, a large part of which is vulnerable to thaw and mobilization from thermokarst and erosion. To refine assessments of the spatial distribution of Yedoma deposits, we digitized 11 Russian Quaternary geologic maps. Our study focused on extracting geologic units interpreted by us as late Pleistocene ice-rich syngenetic Yedoma deposits based on lithology, ground ice conditions, stratigraphy, and geomorphological and spatial association. These Yedoma units then were merged into a single data layer across map tiles. The spatial database provides a useful update of the spatial distribution of this deposit for an approximately 2.32 million square kilometers land area in Siberia that will (1) serve as a core database for future refinements of Yedoma distribution in additional regions, and (2) provide a starting point to revise the size of deep but thaw-vulnerable permafrost carbon pools in the Arctic based on surface geology and the distribution of cryolithofacies types at high spatial resolution. However, we recognize that the extent of Yedoma deposits presented in this database is not complete for a global assessment, because Yedoma deposits also occur in the Taymyr lowlands and Chukotka, and in parts of Alaska and northwestern Canada.

Effects of variations of stage and flux at different frequencies on the estimates using river stage tomography

NASA Astrophysics Data System (ADS)

Wang, Y. L.; Yeh, T. C. J.; Wen, J. C.

2017-12-01

This study is to investigate the ability of river stage tomography to estimate the spatial distribution of hydraulic transmissivity (T), storage coefficient (S), and diffusivity (D) in groundwater basins using information of groundwater level variations induced by periodic variations of stream stage, and infiltrated flux from the stream boundary. In order to accomplish this objective, the sensitivity and correlation of groundwater heads with respect to the hydraulic properties is first conducted to investigate the spatial characteristics of groundwater level in response to the stream variations at different frequencies. Results of the analysis show that the spatial distributions of the sensitivity of heads at an observation well in response to periodic river stage variations are highly correlated despite different frequencies. On the other hand, the spatial patterns of the sensitivity of the observed head to river flux boundaries at different frequencies are different. Specifically, the observed head is highly correlated with T at the region between the stream and observation well when the high-frequency periodic flux is considered. On the other hand, it is highly correlated with T at the region between monitoring well and the boundary opposite to the stream when the low-frequency periodic flux is prescribed to the stream. We also find that the spatial distributions of the sensitivity of observed head to S variation are highly correlated with all frequencies in spite of heads or fluxes stream boundary. Subsequently, the differences of the spatial correlations of the observed heads to the hydraulic properties under the head and flux boundary conditions are further investigated by an inverse model (i.e., successive stochastic linear estimator). This investigation uses noise-free groundwater and stream data of a synthetic aquifer, where aquifer heterogeneity is known exactly. The ability of river stage tomography is then tested with these synthetic data sets to estimate T, S, and D distribution. The results reveal that boundary flux variations with different frequencies contain different information about the aquifer characteristics while the head boundary does not.

Life-history implications of large-scale spatial variation in adult survival of black brant (Branta bernicla nigricans)

USGS Publications Warehouse

Sedinger, James S.; Chelgren, Nathan; Lindberg, Mark S.; Obritchkewitch, Tim; Kirk, Morgan T.; Martin, Philip D.; Anderson, Betty A.; Ward, David H.

2002-01-01

We used capture-recapture methods to estimate adult survival rates for adult female Black Brant (Branta bernicla nigricans; hereafter “brant”) from three colonies in Alaska, two on the Yukon-Kuskokwim Delta, and one on Alaska's Arctic coast. Costs of migration and reproductive effort varied among those colonies, enabling us to examine variation in survival in relation to variation in these other variables. We used the Barker model in program MARK to estimate true annual survival for brant from the three colonies. Models allowing for spatial variation in survival were among the most parsimonious models but were indistinguishable from a model with no spatial variation. Point estimates of annual survival were slightly higher for brant from the Arctic (0.90 ± 0.036) than for brant from either Tutakoke River (0.85 ± 0.004) or Kokechik Bay (0.86 ± 0.011). Thus, our survival estimates do not support a hypothesis that the cost of longer migrations or harvest experienced by brant from the Arctic reduced their annual survival relative to brant from the Yukon-Kuskokwim Delta. Spatial variation in survival provides weak support for life-history theory because brant from the region with lower reproductive investment had slightly higher survival.

Costs and regional impacts of restoration thinning programs on the national forests in eastern Oregon.

Treesearch

Darius M. Adams; Gregory S. Latta

2005-01-01

An intertemporal spatial equilibrium model of the eastern Oregon softwood log market was employed to estimate the market and economic welfare impacts of restoration thinning programs established on national forests in the region. Programs treated only lands with sawtimber thinning volume and varied by the extent of public subsidies for costs, the types of costs that...

Income convergence in a rural, majority African American region

Treesearch

Buddhi Gyawali; Rory Fraser; James Bukenya; John Schelhas

2008-01-01

This paper revisits the issue of income convergence by examining the question of whether poorer Census Block Groups have been catching up with wealthier Census Block Groups over the 1980-2000 period. The dataset consists of 161 Census Block Groups in Alabama’s west-central Black Belt region. Estimates of a spatial lag model provide support for the conditional...

Upscaling and Downscaling of Land Surface Fluxes with Surface Temperature

NASA Astrophysics Data System (ADS)

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

2015-12-01

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

Spatial Distribution of Io's Neutral Oxygen Cloud Observed by Hisaki

NASA Astrophysics Data System (ADS)

Koga, Ryoichi; Tsuchiya, Fuminori; Kagitani, Masato; Sakanoi, Takeshi; Yoneda, Mizuki; Yoshioka, Kazuo; Yoshikawa, Ichiro; Kimura, Tomoki; Murakami, Go; Yamazaki, Atsushi; Smith, H. Todd; Bagenal, Fran

2018-05-01

We report on the spatial distribution of a neutral oxygen cloud surrounding Jupiter's moon Io and along Io's orbit observed by the Hisaki satellite. Atomic oxygen and sulfur in Io's atmosphere escape from the exosphere mainly through atmospheric sputtering. Some of the neutral atoms escape from Io's gravitational sphere and form neutral clouds around Jupiter. The extreme ultraviolet spectrograph called EXCEED (Extreme Ultraviolet Spectroscope for Exospheric Dynamics) installed on the Japan Aerospace Exploration Agency's Hisaki satellite observed the Io plasma torus continuously in 2014-2015, and we derived the spatial distribution of atomic oxygen emissions at 130.4 nm. The results show that Io's oxygen cloud is composed of two regions, namely, a dense region near Io and a diffuse region with a longitudinally homogeneous distribution along Io's orbit. The dense region mainly extends on the leading side of Io and inside of Io's orbit. The emissions spread out to 7.6 Jupiter radii (RJ). Based on Hisaki observations, we estimated the radial distribution of the atomic oxygen number density and oxygen ion source rate. The peak atomic oxygen number density is 80 cm-3, which is spread 1.2 RJ in the north-south direction. We found more oxygen atoms inside Io's orbit than a previous study. We estimated the total oxygen ion source rate to be 410 kg/s, which is consistent with the value derived from a previous study that used a physical chemistry model based on Hisaki observations of ultraviolet emission ions in the Io plasma torus.

High-resolution mapping of anthropogenic heat in China from 1992 to 2010.

PubMed

Yang, Wangming; Chen, Bing; Cui, Xuefeng

2014-04-14

Anthropogenic heat generated by human activity contributes to urban and regional climate warming. Due to the resolution and accuracy of existing anthropogenic heat data, it is difficult to analyze and simulate the corresponding effects. This study exploited a new method to estimate high spatial and temporal resolutions of anthropogenic heat based on long-term data of energy consumption and the US Air Force Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) data from 1992 to 2010 across China. Our results showed that, throughout the entire study period, there are apparent increasing trends in anthropogenic heat in three major metropoli, i.e., the Beijing-Tianjin region, the Yangzi River delta and the Pearl River delta. The annual mean anthropogenic heat fluxes for Beijing, Shanghai and Guangzhou in 2010 were 17 Wm⁻², 19 and 7.8 Wm⁻², respectively. Comparisons with previous studies indicate that DMSP-OLS data could provide a better spatial proxy for estimating anthropogenic heat than population density and our analysis shows better performance at large scales for estimation of anthropogenic heat.

Hydroacoustic estimation of zooplankton biomass at two shoal complexes in the Apostle Islands Region of Lake Superior

USGS Publications Warehouse

Holbrook, B.V.; Hrabik, T.R.; Branstrator, D.K.; Yule, D.L.; Stockwell, J.D.

2006-01-01

Hydroacoustics can be used to assess zooplankton populations, however, backscatter must be scaled to be biologically meaningful. In this study, we used a general model to correlate site-specific hydroacoustic backscatter with zooplankton dry weight biomass estimated from net tows. The relationship between zooplankton dry weight and backscatter was significant (p < 0.001) and explained 76% of the variability in the dry weight data. We applied this regression to hydroacoustic data collected monthly in 2003 and 2004 at two shoals in the Apostle Island Region of Lake Superior. After applying the regression model to convert hydroacoustic backscatter to zooplankton dry weight biomass, we used geostatistics to analyze the mean and variance, and ordinary kriging to create spatial zooplankton distribution maps. The mean zooplankton dry weight biomass estimates from plankton net tows and hydroacoustics were not significantly different (p = 0.19) but the hydroacoustic data had a significantly lower coefficient of variation (p < 0.001). The maps of zooplankton distribution illustrated spatial trends in zooplankton dry weight biomass that were not discernable from the overall means.

Bayesian Spatial Design of Optimal Deep Tubewell Locations in Matlab, Bangladesh.

PubMed

Warren, Joshua L; Perez-Heydrich, Carolina; Yunus, Mohammad

2013-09-01

We introduce a method for statistically identifying the optimal locations of deep tubewells (dtws) to be installed in Matlab, Bangladesh. Dtw installations serve to mitigate exposure to naturally occurring arsenic found at groundwater depths less than 200 meters, a serious environmental health threat for the population of Bangladesh. We introduce an objective function, which incorporates both arsenic level and nearest town population size, to identify optimal locations for dtw placement. Assuming complete knowledge of the arsenic surface, we then demonstrate how minimizing the objective function over a domain favors dtws placed in areas with high arsenic values and close to largely populated regions. Given only a partial realization of the arsenic surface over a domain, we use a Bayesian spatial statistical model to predict the full arsenic surface and estimate the optimal dtw locations. The uncertainty associated with these estimated locations is correctly characterized as well. The new method is applied to a dataset from a village in Matlab and the estimated optimal locations are analyzed along with their respective 95% credible regions.

On the Character and Mitigation of Atmospheric Noise in InSAR Time Series Analysis (Invited)

NASA Astrophysics Data System (ADS)

Barnhart, W. D.; Fielding, E. J.; Fishbein, E.

2013-12-01

Time series analysis of interferometric synthetic aperture radar (InSAR) data, with its broad spatial coverage and ability to image regions that are sometimes very difficult to access, is a powerful tool for characterizing continental surface deformation and its temporal variations. With the impending launch of dedicated SAR missions such as Sentinel-1, ALOS-2, and the planned NASA L-band SAR mission, large volume data sets will allow researchers to further probe ground displacement processes with increased fidelity. Unfortunately, the precision of measurements in individual interferograms is impacted by several sources of noise, notably spatially correlated signals caused by path delays through the stratified and turbulent atmosphere and ionosphere. Spatial and temporal variations in atmospheric water vapor often introduce several to tens of centimeters of apparent deformation in the radar line-of-sight, correlated over short spatial scales (<10 km). Signals resulting from atmospheric path delays are particularly problematic because, like the subsidence and uplift signals associated with tectonic deformation, they are often spatially correlated with topography. In this talk, we provide an overview of the effects of spatially correlated tropospheric noise in individual interferograms and InSAR time series analysis, and we highlight where common assumptions of the temporal and spatial characteristics of tropospheric noise fail. Next, we discuss two classes of methods for mitigating the effects of tropospheric water vapor noise in InSAR time series analysis and single interferograms: noise estimation and characterization with independent observations from multispectral sensors such as MODIS and MERIS; and noise estimation and removal with weather models, multispectral sensor observations, and GPS. Each of these techniques can provide independent assessments of the contribution of water vapor in interferograms, but each technique also suffers from several pitfalls that we outline. The multispectral near-infrared (NIR) sensors provide high spatial resolution (~1 km) estimates of total column tropospheric water vapor by measuring the absorption of reflected solar illumination and provide may excellent estimates of wet delay. The Online Services for Correcting Atmosphere in Radar (OSCAR) project currently provides water vapor products through web services (http://oscar.jpl.nasa.gov). Unfortunately, such sensors require daytime and cloudless observations. Global and regional numerical weather models can provide an additional estimate of both the dry and atmospheric delays with spatial resolution of (3-100 km) and time scales of 1-3 hours, though these models are of lower accuracy than imaging observations and are benefited by independent observations from independent observations of atmospheric water vapor. Despite these issues, the integration of these techniques for InSAR correction and uncertainty estimation may contribute substantially to the reduction and rigorous characterization of uncertainty in InSAR time series analysis - helping to expand the range of tectonic displacements imaged with InSAR, to robustly constrain geophysical models, and to generate a-priori assessments of satellite acquisitions goals.

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

F. Perry

Studies of volcanic risk to the proposed high-level radioactive waste repository at Yucca Mountain have been ongoing for 25 years. These studies are required because three episodes of small-volume, alkalic basaltic volcanism have occurred within 50 km of Yucca Mountain during the Quaternary. Probabilistic hazard estimates for the proposed repository depend on the recurrence rate and spatial distribution of past episodes of volcanism in the region. Several independent research groups have published estimates of the annual probability of a future volcanic disruption of the proposed repository, most of which fall in the range of 10{sup -7} to 10{sup -9} permore » year; similar conclusions were reached. through an extensive expert elicitation sponsored by the Department of Energy in 1995-1996. The estimated probability values are dominated by a regional recurrence rate of 10{sup -5} to 10{sup -6} volcanic events per year (equating to recurrence intervals of several hundred thousand years). The recurrence rate, as well as the spatial density of volcanoes, is low compared to most other basaltic volcanic fields in the western United States, factors that may be related to both the tectonic history of the region and a lithospheric mantle source that is relatively cold and not prone to melting. The link between volcanism and tectonism in the Yucca Mountain region is not well understood beyond a general association between volcanism and regional extension, although areas of locally high extension within the region may control the location of some volcanoes. Recently, new geologic data or hypotheses have emerged that could potentially increase past estimates of the recurrence rate, and thus the probability of repository disruption. These are (1) hypothesized episodes of anomalously high strain rate, (2) hypothesized presence of a regional mantle hotspot, and (3) new aeromagnetic data suggesting as many as twelve previously unrecognized volcanoes buried in alluvial-filled basins near Yucca Mountain.« less

Using remote sensing and GIS techniques to estimate discharge and recharge fluxes for the Death Valley regional groundwater flow system, USA

USGS Publications Warehouse

D'Agnese, F. A.; Faunt, C.C.; Turner, A.K.; ,

1996-01-01

The recharge and discharge components of the Death Valley regional groundwater flow system were defined by techniques that integrated disparate data types to develop a spatially complex representation of near-surface hydrological processes. Image classification methods were applied to multispectral satellite data to produce a vegetation map. The vegetation map was combined with ancillary data in a GIS to delineate different types of wetlands, phreatophytes and wet playa areas. Existing evapotranspiration-rate estimates were used to calculate discharge volumes for these area. An empirical method of groundwater recharge estimation was modified to incorporate data describing soil-moisture conditions, and a recharge potential map was produced. These discharge and recharge maps were readily converted to data arrays for numerical modelling codes. Inverse parameter estimation techniques also used these data to evaluate the reliability and sensitivity of estimated values.The recharge and discharge components of the Death Valley regional groundwater flow system were defined by remote sensing and GIS techniques that integrated disparate data types to develop a spatially complex representation of near-surface hydrological processes. Image classification methods were applied to multispectral satellite data to produce a vegetation map. This map provided a basis for subsequent evapotranspiration and infiltration estimations. The vegetation map was combined with ancillary data in a GIS to delineate different types of wetlands, phreatophytes and wet playa areas. Existing evapotranspiration-rate estimates were then used to calculate discharge volumes for these areas. A previously used empirical method of groundwater recharge estimation was modified by GIS methods to incorporate data describing soil-moisture conditions, and a recharge potential map was produced. These discharge and recharge maps were readily converted to data arrays for numerical modelling codes. Inverse parameter estimation techniques also used these data to evaluate the reliability and sensitivity of estimated values.

Estimating cropland NPP using national crop inventory and MODIS derived crop specific parameters

NASA Astrophysics Data System (ADS)

Bandaru, V.; West, T. O.; Ricciuto, D. M.

2011-12-01

Estimates of cropland net primary production (NPP) are needed as input for estimates of carbon flux and carbon stock changes. Cropland NPP is currently estimated using terrestrial ecosystem models, satellite remote sensing, or inventory data. All three of these methods have benefits and problems. Terrestrial ecosystem models are often better suited for prognostic estimates rather than diagnostic estimates. Satellite-based NPP estimates often underestimate productivity on intensely managed croplands and are also limited to a few broad crop categories. Inventory-based estimates are consistent with nationally collected data on crop yields, but they lack sub-county spatial resolution. Integrating these methods will allow for spatial resolution consistent with current land cover and land use, while also maintaining total biomass quantities recorded in national inventory data. The main objective of this study was to improve cropland NPP estimates by using a modification of the CASA NPP model with individual crop biophysical parameters partly derived from inventory data and MODIS 8day 250m EVI product. The study was conducted for corn and soybean crops in Iowa and Illinois for years 2006 and 2007. We used EVI as a linear function for fPAR, and used crop land cover data (56m spatial resolution) to extract individual crop EVI pixels. First, we separated mixed pixels of both corn and soybean that occur when MODIS 250m pixel contains more than one crop. Second, we substituted mixed EVI pixels with nearest pure pixel values of the same crop within 1km radius. To get more accurate photosynthetic active radiation (PAR), we applied the Mountain Climate Simulator (MTCLIM) algorithm with the use of temperature and precipitation data from the North American Land Data Assimilation System (NLDAS-2) to generate shortwave radiation data. Finally, county specific light use efficiency (LUE) values of each crop for years 2006 to 2007 were determined by application of mean county inventory NPP and EVI-derived APAR into the Monteith equation. Results indicate spatial variability in LUE values across Iowa and Illinois. Northern regions of both Iowa and Illinois have higher LUE values than southern regions. This trend is reflected in NPP estimates. Results also show that corn has higher LUE values than soybean, resulting in higher NPP for corn than for soybean. Current NPP estimates were compared with NPP estimates from MOD17A3 product and with county inventory-based NPP estimates. Results indicate that current NPP estimates closely agree with inventory-based estimates, and that current NPP estimates are higher than those of the MOD17A3 product. It was also found that when mixed pixels were substituted with nearest pure pixels, revised NPP estimates were improved showing better agreement with inventory-based estimates.

Geographic Information for Analysis of Highway Runoff-Quality Data on a National or Regional Scale in the Conterminous United States

USGS Publications Warehouse

Smieszek, Tomas W.; Granato, Gregory E.

2000-01-01

Spatial data are important for interpretation of water-quality information on a regional or national scale. Geographic information systems (GIS) facilitate interpretation and integration of spatial data. The geographic information and data compiled for the conterminous United States during the National Highway Runoff Water-Quality Data and Methodology Synthesis project is described in this document, which also includes information on the structure, file types, and the geographic information in the data files. This 'geodata' directory contains two subdirectories, labeled 'gisdata' and 'gisimage.' The 'gisdata' directory contains ArcInfo coverages, ArcInfo export files, shapefiles (used in ArcView), Spatial Data Transfer Standard Topological Vector Profile format files, and meta files in subdirectories organized by file type. The 'gisimage' directory contains the GIS data in common image-file formats. The spatial geodata includes two rain-zone region maps and a map of national ecosystems originally published by the U.S. Environmental Protection Agency; regional estimates of mean annual streamflow, and water hardness published by the Federal Highway Administration; and mean monthly temperature, mean annual precipitation, and mean monthly snowfall modified from data published by the National Climatic Data Center and made available to the public by the Oregon Climate Service at Oregon State University. These GIS files were compiled for qualitative spatial analysis of available data on a national and(or) regional scale and therefore should be considered as qualitative representations, not precise geographic location information.

Three-dimensional analysis of magnetometer array data

NASA Technical Reports Server (NTRS)

Richmond, A. D.; Baumjohann, W.

1984-01-01

A technique is developed for mapping magnetic variation fields in three dimensions using data from an array of magnetometers, based on the theory of optimal linear estimation. The technique is applied to data from the Scandinavian Magnetometer Array. Estimates of the spatial power spectra for the internal and external magnetic variations are derived, which in turn provide estimates of the spatial autocorrelation functions of the three magnetic variation components. Statistical errors involved in mapping the external and internal fields are quantified and displayed over the mapping region. Examples of field mapping and of separation into external and internal components are presented. A comparison between the three-dimensional field separation and a two-dimensional separation from a single chain of stations shows that significant differences can arise in the inferred internal component.

Spatial cluster detection using dynamic programming.

PubMed

Sverchkov, Yuriy; Jiang, Xia; Cooper, Gregory F

2012-03-25

The task of spatial cluster detection involves finding spatial regions where some property deviates from the norm or the expected value. In a probabilistic setting this task can be expressed as finding a region where some event is significantly more likely than usual. Spatial cluster detection is of interest in fields such as biosurveillance, mining of astronomical data, military surveillance, and analysis of fMRI images. In almost all such applications we are interested both in the question of whether a cluster exists in the data, and if it exists, we are interested in finding the most accurate characterization of the cluster. We present a general dynamic programming algorithm for grid-based spatial cluster detection. The algorithm can be used for both Bayesian maximum a-posteriori (MAP) estimation of the most likely spatial distribution of clusters and Bayesian model averaging over a large space of spatial cluster distributions to compute the posterior probability of an unusual spatial clustering. The algorithm is explained and evaluated in the context of a biosurveillance application, specifically the detection and identification of Influenza outbreaks based on emergency department visits. A relatively simple underlying model is constructed for the purpose of evaluating the algorithm, and the algorithm is evaluated using the model and semi-synthetic test data. When compared to baseline methods, tests indicate that the new algorithm can improve MAP estimates under certain conditions: the greedy algorithm we compared our method to was found to be more sensitive to smaller outbreaks, while as the size of the outbreaks increases, in terms of area affected and proportion of individuals affected, our method overtakes the greedy algorithm in spatial precision and recall. The new algorithm performs on-par with baseline methods in the task of Bayesian model averaging. We conclude that the dynamic programming algorithm performs on-par with other available methods for spatial cluster detection and point to its low computational cost and extendability as advantages in favor of further research and use of the algorithm.

Spatial cluster detection using dynamic programming

PubMed Central

2012-01-01

Background The task of spatial cluster detection involves finding spatial regions where some property deviates from the norm or the expected value. In a probabilistic setting this task can be expressed as finding a region where some event is significantly more likely than usual. Spatial cluster detection is of interest in fields such as biosurveillance, mining of astronomical data, military surveillance, and analysis of fMRI images. In almost all such applications we are interested both in the question of whether a cluster exists in the data, and if it exists, we are interested in finding the most accurate characterization of the cluster. Methods We present a general dynamic programming algorithm for grid-based spatial cluster detection. The algorithm can be used for both Bayesian maximum a-posteriori (MAP) estimation of the most likely spatial distribution of clusters and Bayesian model averaging over a large space of spatial cluster distributions to compute the posterior probability of an unusual spatial clustering. The algorithm is explained and evaluated in the context of a biosurveillance application, specifically the detection and identification of Influenza outbreaks based on emergency department visits. A relatively simple underlying model is constructed for the purpose of evaluating the algorithm, and the algorithm is evaluated using the model and semi-synthetic test data. Results When compared to baseline methods, tests indicate that the new algorithm can improve MAP estimates under certain conditions: the greedy algorithm we compared our method to was found to be more sensitive to smaller outbreaks, while as the size of the outbreaks increases, in terms of area affected and proportion of individuals affected, our method overtakes the greedy algorithm in spatial precision and recall. The new algorithm performs on-par with baseline methods in the task of Bayesian model averaging. Conclusions We conclude that the dynamic programming algorithm performs on-par with other available methods for spatial cluster detection and point to its low computational cost and extendability as advantages in favor of further research and use of the algorithm. PMID:22443103

Exploring the relationship between malaria, rainfall intermittency, and spatial variation in rainfall seasonality

NASA Astrophysics Data System (ADS)

Merkord, C. L.; Wimberly, M. C.; Henebry, G. M.; Senay, G. B.

2014-12-01

Malaria is a major public health problem throughout tropical regions of the world. Successful prevention and treatment of malaria requires an understanding of the environmental factors that affect the life cycle of both the malaria pathogens, protozoan parasites, and its vectors, anopheline mosquitos. Because the egg, larval, and pupal stages of mosquito development occur in aquatic habitats, information about the spatial and temporal distribution of rainfall is critical for modeling malaria risk. Potential sources of hydrological data include satellite-derived rainfall estimates (TRMM and GPM), evapotranspiration derived from a simplified surface energy balance, and estimates of soil moisture and fractional water cover from passive microwave imagery. Previous studies have found links between malaria cases and total monthly or weekly rainfall in areas where both are highly seasonal. However it is far from clear that monthly or weekly summaries are the best metrics to use to explain malaria outbreaks. It is possible that particular temporal or spatial patterns of rainfall result in better mosquito habitat and thus higher malaria risk. We used malaria case data from the Amhara region of Ethiopia and satellite-derived rainfall estimates to explore the relationship between malaria outbreaks and rainfall with the goal of identifying the most useful rainfall metrics for modeling malaria occurrence. First, we explored spatial variation in the seasonal patterns of both rainfall and malaria cases in Amhara. Second, we assessed the relative importance of different metrics of rainfall intermittency, including alternation of wet and dry spells, the strength of intensity fluctuations, and spatial variability in these measures, in determining the length and severity of malaria outbreaks. We also explored the sensitivity of our results to the choice of method for describing rainfall intermittency and the spatial and temporal scale at which metrics were calculated. Results demonstrate that information about the seasonality and intermittency of rainfall has the potential to improve our understanding of malaria epidemiology and improve our ability to forecast malaria outbreaks.

Assessment of spatial distribution of soil loss over the upper basin of Miyun reservoir in China based on RS and GIS techniques.

PubMed

Chen, Tao; Niu, Rui-qing; Wang, Yi; Li, Ping-xiang; Zhang, Liang-pei; Du, Bo

2011-08-01

Soil conservation planning often requires estimates of the spatial distribution of soil erosion at a catchment or regional scale. This paper applied the Revised Universal Soil Loss Equation (RUSLE) to investigate the spatial distribution of annual soil loss over the upper basin of Miyun reservoir in China. Among the soil erosion factors, which are rainfall erosivity (R), soil erodibility (K), slope length (L), slope steepness (S), vegetation cover (C), and support practice factor (P), the vegetative cover or C factor, which represents the effects of vegetation canopy and ground covers in reducing soil loss, has been one of the most difficult to estimate over broad geographic areas. In this paper, the C factor was estimated based on back propagation neural network and the results were compared with the values measured in the field. The correlation coefficient (r) obtained was 0.929. Then the C factor and the other factors were used as the input to RUSLE model. By integrating the six factor maps in geographical information system (GIS) through pixel-based computing, the spatial distribution of soil loss over the upper basin of Miyun reservoir was obtained. The results showed that the annual average soil loss for the upper basin of Miyun reservoir was 9.86 t ha(-1) ya(-1) in 2005, and the area of 46.61 km(2) (0.3%) experiences extremely severe erosion risk, which needs suitable conservation measures to be adopted on a priority basis. The spatial distribution of erosion risk classes was 66.9% very low, 21.89% low, 6.18% moderate, 2.89% severe, and 1.84% very severe. Thus, by using RUSLE in a GIS environment, the spatial distribution of water erosion can be obtained and the regions which susceptible to water erosion and need immediate soil conservation planning and application over the upper watershed of Miyun reservoir in China can be identified.

Regional-scale integration of hydrological and geophysical data using Bayesian sequential simulation: application to field data

NASA Astrophysics Data System (ADS)

Ruggeri, Paolo; Irving, James; Gloaguen, Erwan; Holliger, Klaus

2013-04-01

Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale. However, extending corresponding approaches to the regional scale still represents a major challenge, yet is critically important for the development of groundwater flow and contaminant transport models. To address this issue, we have developed a regional-scale hydrogeophysical data integration technique based on a two-step Bayesian sequential simulation procedure. The objective is to simulate the regional-scale distribution of a hydraulic parameter based on spatially exhaustive, but poorly resolved, measurements of a pertinent geophysical parameter and locally highly resolved, but spatially sparse, measurements of the considered geophysical and hydraulic parameters. To this end, our approach first involves linking the low- and high-resolution geophysical data via a downscaling procedure before relating the downscaled regional-scale geophysical data to the high-resolution hydraulic parameter field. We present the application of this methodology to a pertinent field scenario, where we consider collocated high-resolution measurements of the electrical conductivity, measured using a cone penetrometer testing (CPT) system, and the hydraulic conductivity, estimated from EM flowmeter and slug test measurements, in combination with low-resolution exhaustive electrical conductivity estimates obtained from dipole-dipole ERT meausurements.

Biomass and the Climatic Space from historical to future scenarios of a Seasonally Dry Tropical Forest - Caatinga

NASA Astrophysics Data System (ADS)

Castanho, A. D. D. A.; Coe, M. T.; Maia Andrade, E.; Walker, W.; Baccini, A.; Brando, P. M.; Farina, M.

2017-12-01

The Caatinga found in the semiarid region in northeastern Brazil is the largest continuous seasonally dry tropical forest in South America. The region has for centuries been subject to anthropogenic activities of land conversion, abandonment, and regrowth. The region also has a large spatial variability of edaphic-climatic properties. These effects together contribute to a wide variability of plant physiognomies and biomass concentration. In addition to land use change due to anthropogenic activities the region is exposed in the near and long term to dryer conditions. The main goal of this work was to validate a high spatial resolution (30 m) map of above ground biomass, understand the climatic role in the biomass spatial variability in the present, and the potential threat to vegetation for future climatic shifts. Satellite-derived biomass products are advanced tools that can address spatial changes in forest structure for an extended region. Here we combine a compilation of published field phytosociological observations across the region with a new 30-meter spatial resolution satellite biomass product. Climate data used for this analyses were based on the CRU (Climate Research Unit, UEA) for the historical time period and for the future a mean and 25-75% quantiles of the CMIP Global Climate model estimates for the RCP scenarios of 4.5 and 8.5 W/m2. The high heterogeneity in the biomass and physiognomy distribution across the Caatinga region is mostly explained by the climatic space defined by the precipitation and dryness index. The Caatinga region has historically already been exposed to shift in its climatic properties, driving all the physiognomies, to a dryer climatic space within the last decade. Future climate intensify the observed trends. This study provides a clearer understanding of the spatial distribution of Caatinga vegetation, its biomass, and relationships to climate, which are essential for strategic development planning, preservation of the biome functions, human services, and biodiversity, face future climate scenarios.

Kriging with Unknown Variance Components for Regional Ionospheric Reconstruction.

PubMed

Huang, Ling; Zhang, Hongping; Xu, Peiliang; Geng, Jianghui; Wang, Cheng; Liu, Jingnan

2017-02-27

Ionospheric delay effect is a critical issue that limits the accuracy of precise Global Navigation Satellite System (GNSS) positioning and navigation for single-frequency users, especially in mid- and low-latitude regions where variations in the ionosphere are larger. Kriging spatial interpolation techniques have been recently introduced to model the spatial correlation and variability of ionosphere, which intrinsically assume that the ionosphere field is stochastically stationary but does not take the random observational errors into account. In this paper, by treating the spatial statistical information on ionosphere as prior knowledge and based on Total Electron Content (TEC) semivariogram analysis, we use Kriging techniques to spatially interpolate TEC values. By assuming that the stochastic models of both the ionospheric signals and measurement errors are only known up to some unknown factors, we propose a new Kriging spatial interpolation method with unknown variance components for both the signals of ionosphere and TEC measurements. Variance component estimation has been integrated with Kriging to reconstruct regional ionospheric delays. The method has been applied to data from the Crustal Movement Observation Network of China (CMONOC) and compared with the ordinary Kriging and polynomial interpolations with spherical cap harmonic functions, polynomial functions and low-degree spherical harmonic functions. The statistics of results indicate that the daily ionospheric variations during the experimental period characterized by the proposed approach have good agreement with the other methods, ranging from 10 to 80 TEC Unit (TECU, 1 TECU = 1 × 10 16 electrons/m²) with an overall mean of 28.2 TECU. The proposed method can produce more appropriate estimations whose general TEC level is as smooth as the ordinary Kriging but with a smaller standard deviation around 3 TECU than others. The residual results show that the interpolation precision of the new proposed method is better than the ordinary Kriging and polynomial interpolation by about 1.2 TECU and 0.7 TECU, respectively. The root mean squared error of the proposed new Kriging with variance components is within 1.5 TECU and is smaller than those from other methods under comparison by about 1 TECU. When compared with ionospheric grid points, the mean squared error of the proposed method is within 6 TECU and smaller than Kriging, indicating that the proposed method can produce more accurate ionospheric delays and better estimation accuracy over China regional area.

Kriging with Unknown Variance Components for Regional Ionospheric Reconstruction

PubMed Central

Huang, Ling; Zhang, Hongping; Xu, Peiliang; Geng, Jianghui; Wang, Cheng; Liu, Jingnan

2017-01-01

Ionospheric delay effect is a critical issue that limits the accuracy of precise Global Navigation Satellite System (GNSS) positioning and navigation for single-frequency users, especially in mid- and low-latitude regions where variations in the ionosphere are larger. Kriging spatial interpolation techniques have been recently introduced to model the spatial correlation and variability of ionosphere, which intrinsically assume that the ionosphere field is stochastically stationary but does not take the random observational errors into account. In this paper, by treating the spatial statistical information on ionosphere as prior knowledge and based on Total Electron Content (TEC) semivariogram analysis, we use Kriging techniques to spatially interpolate TEC values. By assuming that the stochastic models of both the ionospheric signals and measurement errors are only known up to some unknown factors, we propose a new Kriging spatial interpolation method with unknown variance components for both the signals of ionosphere and TEC measurements. Variance component estimation has been integrated with Kriging to reconstruct regional ionospheric delays. The method has been applied to data from the Crustal Movement Observation Network of China (CMONOC) and compared with the ordinary Kriging and polynomial interpolations with spherical cap harmonic functions, polynomial functions and low-degree spherical harmonic functions. The statistics of results indicate that the daily ionospheric variations during the experimental period characterized by the proposed approach have good agreement with the other methods, ranging from 10 to 80 TEC Unit (TECU, 1 TECU = 1 × 1016 electrons/m2) with an overall mean of 28.2 TECU. The proposed method can produce more appropriate estimations whose general TEC level is as smooth as the ordinary Kriging but with a smaller standard deviation around 3 TECU than others. The residual results show that the interpolation precision of the new proposed method is better than the ordinary Kriging and polynomial interpolation by about 1.2 TECU and 0.7 TECU, respectively. The root mean squared error of the proposed new Kriging with variance components is within 1.5 TECU and is smaller than those from other methods under comparison by about 1 TECU. When compared with ionospheric grid points, the mean squared error of the proposed method is within 6 TECU and smaller than Kriging, indicating that the proposed method can produce more accurate ionospheric delays and better estimation accuracy over China regional area. PMID:28264424

Increased tree-ring network density reveals more precise estimations of sub-regional hydroclimate variability and climate dynamics in the Midwest, USA

NASA Astrophysics Data System (ADS)

Maxwell, Justin T.; Harley, Grant L.

2017-08-01

Understanding the historic variability in the hydroclimate provides important information on possible extreme dry or wet periods that in turn inform water management plans. Tree rings have long provided historical context of hydroclimate variability of the U.S. However, the tree-ring network used to create these countrywide gridded reconstructions is sparse in certain locations, such as the Midwest. Here, we increase ( n = 20) the spatial resolution of the tree-ring network in southern Indiana and compare a summer (June-August) Palmer Drought Severity Index (PDSI) reconstruction to existing gridded reconstructions of PDSI for this region. We find both droughts and pluvials that were previously unknown that rival the most intense PDSI values during the instrumental period. Additionally, historical drought occurred in Indiana that eclipsed instrumental conditions with regard to severity and duration. During the period 1962-2004 CE, we find that teleconnections of drought conditions through the Atlantic Meridional Overturning Circulation have a strong influence ( r = -0.60, p < 0.01) on secondary tree growth in this region for the late spring-early summer season. These findings highlight the importance of continuing to increase the spatial resolution of the tree-ring network used to infer past climate dynamics to capture the sub-regional spatial variability. Increasing the spatial resolution of the tree-ring network for a given region can better identify sub-regional variability, improve the accuracy of regional tree-ring PDSI reconstructions, and provide better information for climatic teleconnections.

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.

Geostatistical estimation of forest biomass in interior Alaska combining Landsat-derived tree cover, sampled airborne lidar and field observations

NASA Astrophysics Data System (ADS)

Babcock, Chad; Finley, Andrew O.; Andersen, Hans-Erik; Pattison, Robert; Cook, Bruce D.; Morton, Douglas C.; Alonzo, Michael; Nelson, Ross; Gregoire, Timothy; Ene, Liviu; Gobakken, Terje; Næsset, Erik

2018-06-01

The goal of this research was to develop and examine the performance of a geostatistical coregionalization modeling approach for combining field inventory measurements, strip samples of airborne lidar and Landsat-based remote sensing data products to predict aboveground biomass (AGB) in interior Alaska's Tanana Valley. The proposed modeling strategy facilitates pixel-level mapping of AGB density predictions across the entire spatial domain. Additionally, the coregionalization framework allows for statistically sound estimation of total AGB for arbitrary areal units within the study area---a key advance to support diverse management objectives in interior Alaska. This research focuses on appropriate characterization of prediction uncertainty in the form of posterior predictive coverage intervals and standard deviations. Using the framework detailed here, it is possible to quantify estimation uncertainty for any spatial extent, ranging from pixel-level predictions of AGB density to estimates of AGB stocks for the full domain. The lidar-informed coregionalization models consistently outperformed their counterpart lidar-free models in terms of point-level predictive performance and total AGB precision. Additionally, the inclusion of Landsat-derived forest cover as a covariate further improved estimation precision in regions with lower lidar sampling intensity. Our findings also demonstrate that model-based approaches that do not explicitly account for residual spatial dependence can grossly underestimate uncertainty, resulting in falsely precise estimates of AGB. On the other hand, in a geostatistical setting, residual spatial structure can be modeled within a Bayesian hierarchical framework to obtain statistically defensible assessments of uncertainty for AGB estimates.

Temporal and spatial variation in allocating annual traffic activity across an urban region and implications for air quality assessments

PubMed Central

Batterman, Stuart

2015-01-01

Patterns of traffic activity, including changes in the volume and speed of vehicles, vary over time and across urban areas and can substantially affect vehicle emissions of air pollutants. Time-resolved activity at the street scale typically is derived using temporal allocation factors (TAFs) that allow the development of emissions inventories needed to predict concentrations of traffic-related air pollutants. This study examines the spatial and temporal variation of TAFs, and characterizes prediction errors resulting from their use. Methods are presented to estimate TAFs and their spatial and temporal variability and used to analyze total, commercial and non-commercial traffic in the Detroit, Michigan, U.S. metropolitan area. The variability of total volume estimates, quantified by the coefficient of variation (COV) representing the percentage departure from expected hourly volume, was 21, 33, 24 and 33% for weekdays, Saturdays, Sundays and holidays, respectively. Prediction errors mostly resulted from hour-to-hour variability on weekdays and Saturdays, and from day-to-day variability on Sundays and holidays. Spatial variability was limited across the study roads, most of which were large freeways. Commercial traffic had different temporal patterns and greater variability than noncommercial vehicle traffic, e.g., the weekday variability of hourly commercial volume was 28%. The results indicate that TAFs for a metropolitan region can provide reasonably accurate estimates of hourly vehicle volume on major roads. While vehicle volume is only one of many factors that govern on-road emission rates, air quality analyses would be strengthened by incorporating information regarding the uncertainty and variability of traffic activity. PMID:26688671

MODIS EVI-based net primary production in the Sahel 2000-2014

NASA Astrophysics Data System (ADS)

Ardö, Jonas; Tagesson, Torbern; Jamali, Sadegh; Khatir, Abdelrahman

2018-03-01

Africa is facing resource problems due to increasing demand combined with potential climate-induced changes in supply. Here we aim to quantify resources in terms of net primary production (NPP [g C m-2 yr-1]) of vegetation in the Sahel region for 2000-2014. Using time series of the enhanced vegetation index (EVI) from MODIS, NPP was estimated for the Sahel region with a 500 × 500 m spatial resolution and 8-day temporal resolution. The estimates were based on local eddy covariance flux measurements from six sites in the Sahel region and the carbon use efficiency originating from a dynamic vegetation model. No significant NPP change was found for the Sahel as a region but, for sub-regions, significant changes, both increasing and decreasing, were observed. Substantial uncertainties related to NPP estimates and the small availability of evaluation data makes verification difficult. The simplicity of the methodology used, dependent on earth observation only, is considered an advantage.

Using High Spatial Resolution to Improve BOLD fMRI Detection at 3T

PubMed Central

Claise, Béatrice; Jean, Betty

2015-01-01

For different functional magnetic resonance imaging experiments using blood oxygenation level-dependent (BOLD) contrast, the acquisition of T 2*-weighted scans at a high spatial resolution may be advantageous in terms of time-course signal-to-noise ratio and of BOLD sensitivity when the regions are prone to susceptibility artifacts. In this study, we explore this solution by examining how spatial resolution influences activations elicited when appetizing food pictures are viewed. Twenty subjects were imaged at 3 T with two different voxel volumes, 3.4 μl and 27 μl. Despite the diminution of brain coverage, we found that high-resolution acquisition led to a better detection of activations. Though known to suffer to different degrees from susceptibility artifacts, the activations detected by high spatial resolution were notably consistent with those reported in published activation likelihood estimation meta-analyses, corresponding to taste-responsive regions. Furthermore, these regions were found activated bilaterally, in contrast with previous findings. Both the reduction of partial volume effect, which improves BOLD contrast, and the mitigation of susceptibility artifact, which boosts the signal to noise ratio in certain regions, explained the better detection noted with high resolution. The present study provides further evidences that high spatial resolution is a valuable solution for human BOLD fMRI, especially for studying food-related stimuli. PMID:26550990

Global daily reference evapotranspiration modeling and evaluation

USGS Publications Warehouse

Senay, G.B.; Verdin, J.P.; Lietzow, R.; Melesse, Assefa M.

2008-01-01

Accurate and reliable evapotranspiration (ET) datasets are crucial in regional water and energy balance studies. Due to the complex instrumentation requirements, actual ET values are generally estimated from reference ET values by adjustment factors using coefficients for water stress and vegetation conditions, commonly referred to as crop coefficients. Until recently, the modeling of reference ET has been solely based on important weather variables collected from weather stations that are generally located in selected agro-climatic locations. Since 2001, the National Oceanic and Atmospheric Administration’s Global Data Assimilation System (GDAS) has been producing six-hourly climate parameter datasets that are used to calculate daily reference ET for the whole globe at 1-degree spatial resolution. The U.S. Geological Survey Center for Earth Resources Observation and Science has been producing daily reference ET (ETo) since 2001, and it has been used on a variety of operational hydrological models for drought and streamflow monitoring all over the world. With the increasing availability of local station-based reference ET estimates, we evaluated the GDAS-based reference ET estimates using data from the California Irrigation Management Information System (CIMIS). Daily CIMIS reference ET estimates from 85 stations were compared with GDAS-based reference ET at different spatial and temporal scales using five-year daily data from 2002 through 2006. Despite the large difference in spatial scale (point vs. ∼100 km grid cell) between the two datasets, the correlations between station-based ET and GDAS-ET were very high, exceeding 0.97 on a daily basis to more than 0.99 on time scales of more than 10 days. Both the temporal and spatial correspondences in trend/pattern and magnitudes between the two datasets were satisfactory, suggesting the reliability of using GDAS parameter-based reference ET for regional water and energy balance studies in many parts of the world. While the study revealed the potential of GDAS ETo for large-scale hydrological applications, site-specific use of GDAS ETo in complex hydro-climatic regions such as coastal areas and rugged terrain may require the application of bias correction and/or disaggregation of the GDAS ETo using downscaling techniques.

Fast Image Restoration for Spatially Varying Defocus Blur of Imaging Sensor

PubMed Central

Cheong, Hejin; Chae, Eunjung; Lee, Eunsung; Jo, Gwanghyun; Paik, Joonki

2015-01-01

This paper presents a fast adaptive image restoration method for removing spatially varying out-of-focus blur of a general imaging sensor. After estimating the parameters of space-variant point-spread-function (PSF) using the derivative in each uniformly blurred region, the proposed method performs spatially adaptive image restoration by selecting the optimal restoration filter according to the estimated blur parameters. Each restoration filter is implemented in the form of a combination of multiple FIR filters, which guarantees the fast image restoration without the need of iterative or recursive processing. Experimental results show that the proposed method outperforms existing space-invariant restoration methods in the sense of both objective and subjective performance measures. The proposed algorithm can be employed to a wide area of image restoration applications, such as mobile imaging devices, robot vision, and satellite image processing. PMID:25569760

Development of the Vista Methane Emissions Inventory for Southern California: A GIS-Based Approach for Mapping Methane Emissions

NASA Astrophysics Data System (ADS)

Carranza, V.; Frausto-Vicencio, I.; Rafiq, T.; Verhulst, K. R.; Hopkins, F. M.; Rao, P.; Duren, R. M.; Miller, C. E.

2016-12-01

Atmospheric methane (CH4) is the second most prevalent anthropogenic greenhouse gas. Improved estimates of CH4 emissions from cities is essential for carbon cycle science and climate mitigation efforts. Development of spatially-resolved carbon emissions data sets may offer significant advances in understanding and managing carbon emissions from cities. Urban CH4 emissions in particular require spatially resolved emission maps to help resolve uncertainties in the CH4 budget. This study presents a Geographic Information System (GIS)-based approach to mapping CH4 emissions using locations of infrastructure known to handle and emit methane. We constrain the spatial distribution of sources to the facility level for the major CH4 emitting sources in the South Coast Air Basin. GIS spatial modeling was combined with publicly available datasets to determine the distribution of potential CH4 sources. The datasets were processed and validated to ensure accuracy in the location of individual sources. This information was then used to develop the Vista emissions prior, which is a one-year long, spatially-resolved CH4 emissions estimate. Methane emissions were calculated and spatially allocated to produce 1 km x 1 km gridded CH4 emission map spanning the Los Angeles Basin. In future work, the Vista CH4 emissions prior will be compared with existing, coarser-resolution emissions estimates and will be evaluated in inverse modeling studies using atmospheric observations. The Vista CH4 emissions inventory presents the first detailed spatial maps of CH4 sources and emissions estimates in the Los Angeles Basin and is a critical step towards sectoral attribution of CH4 emissions at local to regional scales.

High-Resolution Spatial Distribution and Estimation of Access to Improved Sanitation in Kenya.

PubMed

Jia, Peng; Anderson, John D; Leitner, Michael; Rheingans, Richard

2016-01-01

Access to sanitation facilities is imperative in reducing the risk of multiple adverse health outcomes. A distinct disparity in sanitation exists among different wealth levels in many low-income countries, which may hinder the progress across each of the Millennium Development Goals. The surveyed households in 397 clusters from 2008-2009 Kenya Demographic and Health Surveys were divided into five wealth quintiles based on their national asset scores. A series of spatial analysis methods including excess risk, local spatial autocorrelation, and spatial interpolation were applied to observe disparities in coverage of improved sanitation among different wealth categories. The total number of the population with improved sanitation was estimated by interpolating, time-adjusting, and multiplying the surveyed coverage rates by high-resolution population grids. A comparison was then made with the annual estimates from United Nations Population Division and World Health Organization /United Nations Children's Fund Joint Monitoring Program for Water Supply and Sanitation. The Empirical Bayesian Kriging interpolation produced minimal root mean squared error for all clusters and five quintiles while predicting the raw and spatial coverage rates of improved sanitation. The coverage in southern regions was generally higher than in the north and east, and the coverage in the south decreased from Nairobi in all directions, while Nyanza and North Eastern Province had relatively poor coverage. The general clustering trend of high and low sanitation improvement among surveyed clusters was confirmed after spatial smoothing. There exists an apparent disparity in sanitation among different wealth categories across Kenya and spatially smoothed coverage rates resulted in a closer estimation of the available statistics than raw coverage rates. Future intervention activities need to be tailored for both different wealth categories and nationally where there are areas of greater needs when resources are limited.

Application of a Monte Carlo framework with bootstrapping for quantification of uncertainty in baseline map of carbon emissions from deforestation in Tropical Regions

Treesearch

William Salas; Steve Hagen

2013-01-01

This presentation will provide an overview of an approach for quantifying uncertainty in spatial estimates of carbon emission from land use change. We generate uncertainty bounds around our final emissions estimate using a randomized, Monte Carlo (MC)-style sampling technique. This approach allows us to combine uncertainty from different sources without making...

Reliability-Weighted Integration of Audiovisual Signals Can Be Modulated by Top-down Attention

PubMed Central

Noppeney, Uta

2018-01-01

Abstract Behaviorally, it is well established that human observers integrate signals near-optimally weighted in proportion to their reliabilities as predicted by maximum likelihood estimation. Yet, despite abundant behavioral evidence, it is unclear how the human brain accomplishes this feat. In a spatial ventriloquist paradigm, participants were presented with auditory, visual, and audiovisual signals and reported the location of the auditory or the visual signal. Combining psychophysics, multivariate functional MRI (fMRI) decoding, and models of maximum likelihood estimation (MLE), we characterized the computational operations underlying audiovisual integration at distinct cortical levels. We estimated observers’ behavioral weights by fitting psychometric functions to participants’ localization responses. Likewise, we estimated the neural weights by fitting neurometric functions to spatial locations decoded from regional fMRI activation patterns. Our results demonstrate that low-level auditory and visual areas encode predominantly the spatial location of the signal component of a region’s preferred auditory (or visual) modality. By contrast, intraparietal sulcus forms spatial representations by integrating auditory and visual signals weighted by their reliabilities. Critically, the neural and behavioral weights and the variance of the spatial representations depended not only on the sensory reliabilities as predicted by the MLE model but also on participants’ modality-specific attention and report (i.e., visual vs. auditory). These results suggest that audiovisual integration is not exclusively determined by bottom-up sensory reliabilities. Instead, modality-specific attention and report can flexibly modulate how intraparietal sulcus integrates sensory signals into spatial representations to guide behavioral responses (e.g., localization and orienting). PMID:29527567

The spatiotemporal association of non-prescription retail sales with cases during the 2009 influenza pandemic in Great Britain.

PubMed

Todd, Stacy; Diggle, Peter J; White, Peter J; Fearne, Andrew; Read, Jonathan M

2014-04-29

To assess whether retail sales of non-prescription products can be used for syndromic surveillance and whether it can detect influenza activity at different spatial scales. A secondary objective was to assess whether changes in purchasing behaviour were related to public health advice or levels of media or public interest. The UK. National and regional influenza case estimates and retail sales from a major British supermarket. Weekly, seasonally adjusted sales of over-the-counter symptom remedies and non-pharmaceutical products; recommended as part of the advice offered by public health agencies; were compared with weekly influenza case estimates. Comparisons were made at national and regional spatial resolutions. We also compared sales to national measures of contemporaneous media output and public interest (Internet search volume) related to the pandemic. At a national scale there was no significant correlation between retail sales of symptom remedies and cases for the whole pandemic period in 2009. At the regional scale, a minority of regions showed statistically significant positive correlations between cases and sales of adult 'cold and flu' remedies and cough remedies (3.2%, 5/156, 3.8%, 6/156), but a greater number of regions showed a significant positive correlation between cases and symptomatic remedies for children (35.6%, 55/156). Significant positive correlations between cases and sales of thermometers and antiviral hand gels/wash were seen at both spatial scales (Cor 0.477 (95% CI 0.171 to 0.699); 0.711 (95% CI 0.495 to 0.844)). We found no significant association between retail sales and media reporting or Internet search volume. This study provides evidence that the British public responded appropriately to health messaging about hygiene. Non-prescription retail sales at a national level are not useful for the detection of cases. However, at finer spatial scales, in particular age-groups, retail sales may help augment existing surveillance and merit further study.

Reconstruction of global gridded monthly sectoral water withdrawals for 1971-2010 and analysis of their spatiotemporal patterns

NASA Astrophysics Data System (ADS)

Huang, Zhongwei; Hejazi, Mohamad; Li, Xinya; Tang, Qiuhong; Vernon, Chris; Leng, Guoyong; Liu, Yaling; Döll, Petra; Eisner, Stephanie; Gerten, Dieter; Hanasaki, Naota; Wada, Yoshihide

2018-04-01

Human water withdrawal has increasingly altered the global water cycle in past decades, yet our understanding of its driving forces and patterns is limited. Reported historical estimates of sectoral water withdrawals are often sparse and incomplete, mainly restricted to water withdrawal estimates available at annual and country scales, due to a lack of observations at seasonal and local scales. In this study, through collecting and consolidating various sources of reported data and developing spatial and temporal statistical downscaling algorithms, we reconstruct a global monthly gridded (0.5°) sectoral water withdrawal dataset for the period 1971-2010, which distinguishes six water use sectors, i.e., irrigation, domestic, electricity generation (cooling of thermal power plants), livestock, mining, and manufacturing. Based on the reconstructed dataset, the spatial and temporal patterns of historical water withdrawal are analyzed. Results show that total global water withdrawal has increased significantly during 1971-2010, mainly driven by the increase in irrigation water withdrawal. Regions with high water withdrawal are those densely populated or with large irrigated cropland production, e.g., the United States (US), eastern China, India, and Europe. Seasonally, irrigation water withdrawal in summer for the major crops contributes a large percentage of total annual irrigation water withdrawal in mid- and high-latitude regions, and the dominant season of irrigation water withdrawal is also different across regions. Domestic water withdrawal is mostly characterized by a summer peak, while water withdrawal for electricity generation has a winter peak in high-latitude regions and a summer peak in low-latitude regions. Despite the overall increasing trend, irrigation in the western US and domestic water withdrawal in western Europe exhibit a decreasing trend. Our results highlight the distinct spatial pattern of human water use by sectors at the seasonal and annual timescales. The reconstructed gridded water withdrawal dataset is open access, and can be used for examining issues related to water withdrawals at fine spatial, temporal, and sectoral scales.

The spatiotemporal association of non-prescription retail sales with cases during the 2009 influenza pandemic in Great Britain

PubMed Central

Todd, Stacy; Diggle, Peter J; White, Peter J; Fearne, Andrew; Read, Jonathan M

2014-01-01

Objective To assess whether retail sales of non-prescription products can be used for syndromic surveillance and whether it can detect influenza activity at different spatial scales. A secondary objective was to assess whether changes in purchasing behaviour were related to public health advice or levels of media or public interest. Setting The UK. Participants National and regional influenza case estimates and retail sales from a major British supermarket. Outcome measures Weekly, seasonally adjusted sales of over-the-counter symptom remedies and non-pharmaceutical products; recommended as part of the advice offered by public health agencies; were compared with weekly influenza case estimates. Comparisons were made at national and regional spatial resolutions. We also compared sales to national measures of contemporaneous media output and public interest (Internet search volume) related to the pandemic. Results At a national scale there was no significant correlation between retail sales of symptom remedies and cases for the whole pandemic period in 2009. At the regional scale, a minority of regions showed statistically significant positive correlations between cases and sales of adult ‘cold and flu’ remedies and cough remedies (3.2%, 5/156, 3.8%, 6/156), but a greater number of regions showed a significant positive correlation between cases and symptomatic remedies for children (35.6%, 55/156). Significant positive correlations between cases and sales of thermometers and antiviral hand gels/wash were seen at both spatial scales (Cor 0.477 (95% CI 0.171 to 0.699); 0.711 (95% CI 0.495 to 0.844)). We found no significant association between retail sales and media reporting or Internet search volume. Conclusions This study provides evidence that the British public responded appropriately to health messaging about hygiene. Non-prescription retail sales at a national level are not useful for the detection of cases. However, at finer spatial scales, in particular age-groups, retail sales may help augment existing surveillance and merit further study. PMID:24780494

Energy balance-based distributed modeling of snow and glacier melt runoff for the Hunza river basin in the Pakistan Karakoram Himalayan region

NASA Astrophysics Data System (ADS)

Shrestha, M.; Wang, L.; Koike, T.; Xue, Y.; Hirabayashi, Y.; Ahmad, S.

2012-12-01

A spatially distributed biosphere hydrological model with energy balance-based multilayer snow physics and multilayer glacier model, including debris free and debris covered surface (enhanced WEB-DHM-S) has been developed and applied to the Hunza river basin in the Pakistan Karakoram Himalayan region, where about 34% of the basin area is covered by glaciers. The spatial distribution of seasonal snow and glacier cover, snow and glacier melt runoff along with rainfall-contributed runoff, and glacier mass balances are simulated. The simulations are carried out at hourly time steps and at 1-km spatial resolution for the two hydrological years (2002-2003) with the use of APHRODITE precipitation dataset, observed temperature, and other atmospheric forcing variables from the Global Land Data Assimilation System (GLDAS). The pixel-to-pixel comparisons for the snow-free and snow-covered grids over the region reveal that the simulation agrees well with the Moderate Resolution Imaging Spectroradiometer (MODIS) eight-day maximum snow-cover extent data (MOD10A2) with an accuracy of 83% and a positive bias of 2.8 %. The quantitative evaluation also shows that the model is able to reproduce the river discharge satisfactorily with Nash efficiency of 0.92. It is found that the contribution of rainfall to total streamflow is small (about 10-12%) while the contribution of snow and glacier is considerably large (35-40% for snowmelt and 50-53% for glaciermelt, respectively). The model simulates the state of snow and glaciers at each model grid prognostically and thus can estimate the net annual mass balance. The net mass balance varies from -2 m to +2 m water equivalent. Additionally, the hypsography analysis for the equilibrium line altitude (ELA) suggests that the average ELA in this region is about 5700 m with substantial variation from glacier to glacier and region to region. This study is the first to adopt a distributed biosphere hydrological model with the energy balance- based multilayer snow and glacier module to estimate the spatial distribution of snow/glacier cover and snow and glacier melt runoff for a river basin in the Karakoram Himalayan region.

Large-scale derived flood frequency analysis based on continuous simulation

NASA Astrophysics Data System (ADS)

Dung Nguyen, Viet; Hundecha, Yeshewatesfa; Guse, Björn; Vorogushyn, Sergiy; Merz, Bruno

2016-04-01

There is an increasing need for spatially consistent flood risk assessments at the regional scale (several 100.000 km2), in particular in the insurance industry and for national risk reduction strategies. However, most large-scale flood risk assessments are composed of smaller-scale assessments and show spatial inconsistencies. To overcome this deficit, a large-scale flood model composed of a weather generator and catchments models was developed reflecting the spatially inherent heterogeneity. The weather generator is a multisite and multivariate stochastic model capable of generating synthetic meteorological fields (precipitation, temperature, etc.) at daily resolution for the regional scale. These fields respect the observed autocorrelation, spatial correlation and co-variance between the variables. They are used as input into catchment models. A long-term simulation of this combined system enables to derive very long discharge series at many catchment locations serving as a basic for spatially consistent flood risk estimates at the regional scale. This combined model was set up and validated for major river catchments in Germany. The weather generator was trained by 53-year observation data at 528 stations covering not only the complete Germany but also parts of France, Switzerland, Czech Republic and Australia with the aggregated spatial scale of 443,931 km2. 10.000 years of daily meteorological fields for the study area were generated. Likewise, rainfall-runoff simulations with SWIM were performed for the entire Elbe, Rhine, Weser, Donau and Ems catchments. The validation results illustrate a good performance of the combined system, as the simulated flood magnitudes and frequencies agree well with the observed flood data. Based on continuous simulation this model chain is then used to estimate flood quantiles for the whole Germany including upstream headwater catchments in neighbouring countries. This continuous large scale approach overcomes the several drawbacks reported in traditional approaches for the derived flood frequency analysis and therefore is recommended for large scale flood risk case studies.

ICESat laser altimetry over small mountain glaciers

NASA Astrophysics Data System (ADS)

Treichler, Désirée; Kääb, Andreas

2016-09-01

Using sparsely glaciated southern Norway as a case study, we assess the potential and limitations of ICESat laser altimetry for analysing regional glacier elevation change in rough mountain terrain. Differences between ICESat GLAS elevations and reference elevation data are plotted over time to derive a glacier surface elevation trend for the ICESat acquisition period 2003-2008. We find spatially varying biases between ICESat and three tested digital elevation models (DEMs): the Norwegian national DEM, SRTM DEM, and a high-resolution lidar DEM. For regional glacier elevation change, the spatial inconsistency of reference DEMs - a result of spatio-temporal merging - has the potential to significantly affect or dilute trends. Elevation uncertainties of all three tested DEMs exceed ICESat elevation uncertainty by an order of magnitude, and are thus limiting the accuracy of the method, rather than ICESat uncertainty. ICESat matches glacier size distribution of the study area well and measures small ice patches not commonly monitored in situ. The sample is large enough for spatial and thematic subsetting. Vertical offsets to ICESat elevations vary for different glaciers in southern Norway due to spatially inconsistent reference DEM age. We introduce a per-glacier correction that removes these spatially varying offsets, and considerably increases trend significance. Only after application of this correction do individual campaigns fit observed in situ glacier mass balance. Our correction also has the potential to improve glacier trend significance for other causes of spatially varying vertical offsets, for instance due to radar penetration into ice and snow for the SRTM DEM or as a consequence of mosaicking and merging that is common for national or global DEMs. After correction of reference elevation bias, we find that ICESat provides a robust and realistic estimate of a moderately negative glacier mass balance of around -0.36 ± 0.07 m ice per year. This regional estimate agrees well with the heterogeneous but overall negative in situ glacier mass balance observed in the area.

A novel calibration approach using satellite and visibility observations to estimate fine particulate matter exposures in Southwest Asia and Afghanistan.

PubMed

Masri, Shahir; Garshick, Eric; Coull, Brent A; Koutrakis, Petros

2017-01-01

In order to study effects of ambient particulate matter (PM) it was previously necessary to have access to a comprehensive air monitoring network. However, there are locations in the world where PM levels are above generally accepted exposure standards but lack a monitoring infrastructure. This is true in Iraq and other locations in Southwest Asia and Afghanistan where U.S. and other coalition troops were deployed beginning in 2001. Since aerosol optical depth (AOD), determined by satellite, and visibility are both highly related to atmospheric PM 2.5 (particulate matter with an aerodynamic diameter ≤2.5 μm) concentrations, we employed a novel approach that took advantage of historic airport visibility measurements to calibrate the AOD-visibility relationship and determine visibility spatially and temporally (2006-2007) over an approximately 17,000 km 2 region of Iraq. We obtained daily visibility predictions that were highly associated with satellite-based 1x1 km AOD daily observations (R 2 =0.87). Based on a previously derived calibration between PM 2.5 and visibility, we were able to predict spatially and temporally resolved PM 2.5 concentrations. Variability of PM 2.5 among sites was high, with daily concentrations differing by as much as ~30 μg/m3. This study demonstrates the feasibility of characterizing historic PM 2.5 exposures in Iraq and other locations in Southwest Asia and Afghanistan with similar climate characteristics. This is of utility for epidemiologists seeking to assess the potential health effects related to PM 2.5 exposures among previously deployed military personnel and of the population of the region. This study demonstrates the ability to utilize aerosol optical depth to successfully estimate visibility spatially and temporally in Southwest Asia and Afghanistan. This enables for the estimation of spatially resolved PM 2.5 concentrations in the region. The ability to caracterize PM 2.5 concentrations in Southwest Asia and Afghanistan is highly important for epidemiologists investigating the relationship between chronic exposure to PM 2.5 and respiratory diseases among military personnel deployed to the region. This information will better position policy makers to draft meaningful legislation relating to military health.

AIR MONITOR SITING BY OBJECTIVE

EPA Science Inventory

A method is developed whereby measured pollutant concentrations can be used in conjunction with a mathematical air quality model to estimate the full spatial and temporal concentration distributions of the pollutants over a given region. The method is based on the application of ...

Baseline map of carbon emissions from deforestation in tropical regions.

PubMed

Harris, Nancy L; Brown, Sandra; Hagen, Stephen C; Saatchi, Sassan S; Petrova, Silvia; Salas, William; Hansen, Matthew C; Potapov, Peter V; Lotsch, Alexander

2012-06-22

Policies to reduce emissions from deforestation would benefit from clearly derived, spatially explicit, statistically bounded estimates of carbon emissions. Existing efforts derive carbon impacts of land-use change using broad assumptions, unreliable data, or both. We improve on this approach using satellite observations of gross forest cover loss and a map of forest carbon stocks to estimate gross carbon emissions across tropical regions between 2000 and 2005 as 0.81 petagram of carbon per year, with a 90% prediction interval of 0.57 to 1.22 petagrams of carbon per year. This estimate is 25 to 50% of recently published estimates. By systematically matching areas of forest loss with their carbon stocks before clearing, these results serve as a more accurate benchmark for monitoring global progress on reducing emissions from deforestation.

Baseline Map of Carbon Emissions from Deforestation in Tropical Regions

NASA Astrophysics Data System (ADS)

Harris, Nancy L.; Brown, Sandra; Hagen, Stephen C.; Saatchi, Sassan S.; Petrova, Silvia; Salas, William; Hansen, Matthew C.; Potapov, Peter V.; Lotsch, Alexander

2012-06-01

Policies to reduce emissions from deforestation would benefit from clearly derived, spatially explicit, statistically bounded estimates of carbon emissions. Existing efforts derive carbon impacts of land-use change using broad assumptions, unreliable data, or both. We improve on this approach using satellite observations of gross forest cover loss and a map of forest carbon stocks to estimate gross carbon emissions across tropical regions between 2000 and 2005 as 0.81 petagram of carbon per year, with a 90% prediction interval of 0.57 to 1.22 petagrams of carbon per year. This estimate is 25 to 50% of recently published estimates. By systematically matching areas of forest loss with their carbon stocks before clearing, these results serve as a more accurate benchmark for monitoring global progress on reducing emissions from deforestation.

Structure identification within a transitioning swept-wing boundary layer

NASA Astrophysics Data System (ADS)

Chapman, Keith Lance

1997-08-01

Extensive measurements are made in a transitioning swept-wing boundary layer using hot-film, hot-wire and cross-wire anemometry. The crossflow-dominated flow contains stationary vortices that breakdown near mid-chord. The most amplified vortex wavelength is forced by the use of artificial roughness elements near the leading edge. Two-component velocity and spanwise surface shear-stress correlation measurements are made at two constant chord locations, before and after transition. Streamwise surface shear stresses are also measured through the entire transition region. Correlation techniques are used to identify stationary structures in the laminar regime and coherent structures in the turbulent regime. Basic techniques include observation of the spatial correlations and the spatially distributed auto-spectra. The primary and secondary instability mechanisms are identified in the spectra in all measured fields. The primary mechanism is seen to grow, cause transition and produce large-scale turbulence. The secondary mechanism grows through the entire transition region and produces the small-scale turbulence. Advanced techniques use linear stochastic estimation (LSE) and proper orthogonal decomposition (POD) to identify the spatio-temporal evolutions of structures in the boundary layer. LSE is used to estimate the instantaneous velocity fields using temporal data from just two spatial locations and the spatial correlations. Reference locations are selected using maximum RMS values to provide the best available estimates. POD is used to objectively determine modes characteristic of the measured flow based on energy. The stationary vortices are identified in the first laminar modes of each velocity component and shear component. Experimental evidence suggests that neighboring vortices interact and produce large coherent structures with spanwise periodicity at double the stationary vortex wavelength. An objective transition region detection method is developed using streamwise spatial POD solutions which isolate the growth of the primary and secondary instability mechanisms in the first and second modes, respectively. Temporal evolutions of dominant POD modes in all measured fields are calculated. These scalar POD coefficients contain the integrated characteristics of the entire field, greatly reducing the amount of data to characterize the instantaneous field. These modes may then be used to train future flow control algorithms based on neural networks.

Structure Identification Within a Transitioning Swept-Wing Boundary Layer

NASA Technical Reports Server (NTRS)

Chapman, Keith; Glauser, Mark

1996-01-01

Extensive measurements are made in a transitioning swept-wing boundary layer using hot-film, hot-wire and cross-wire anemometry. The crossflow-dominated flow contains stationary vortices that breakdown near mid-chord. The most amplified vortex wavelength is forced by the use of artificial roughness elements near the leading edge. Two-component velocity and spanwise surface shear-stress correlation measurements are made at two constant chord locations, before and after transition. Streamwise surface shear stresses are also measured through the entire transition region. Correlation techniques are used to identify stationary structures in the laminar regime and coherent structures in the turbulent regime. Basic techniques include observation of the spatial correlations and the spatially distributed auto-spectra. The primary and secondary instability mechanisms are identified in the spectra in all measured fields. The primary mechanism is seen to grow, cause transition and produce large-scale turbulence. The secondary mechanism grows through the entire transition region and produces the small-scale turbulence. Advanced techniques use Linear Stochastic Estimation (LSE) and Proper Orthogonal Decomposition (POD) to identify the spatio-temporal evolutions of structures in the boundary layer. LSE is used to estimate the instantaneous velocity fields using temporal data from just two spatial locations and the spatial correlations. Reference locations are selected using maximum RMS values to provide the best available estimates. POD is used to objectively determine modes characteristic of the measured flow based on energy. The stationary vortices are identified in the first laminar modes of each velocity component and shear component. Experimental evidence suggests that neighboring vortices interact and produce large coherent structures with spanwise periodicity at double the stationary vortex wavelength. An objective transition region detection method is developed using streamwise spatial POD solutions which isolate the growth of the primary and secondary instability mechanisms in the first and second modes, respectively. Temporal evolutions of dominant POD modes in all measured fields are calculated. These scalar POD coefficients contain the integrated characteristics of the entire field, greatly reducing the amount of data to characterize the instantaneous field. These modes may then be used to train future flow control algorithms based on neural networks.

Modeling Soil Organic Carbon Variation Along Climatic and Topographic Trajectories in the Central Andes

NASA Astrophysics Data System (ADS)

Gavilan, C.; Grunwald, S.; Quiroz, R.; Zhu, L.

2015-12-01

The Andes represent the largest and highest mountain range in the tropics. Geological and climatic differentiation favored landscape and soil diversity, resulting in ecosystems adapted to very different climatic patterns. Although several studies support the fact that the Andes are a vast sink of soil organic carbon (SOC) only few have quantified this variable in situ. Estimating the spatial distribution of SOC stocks in data-poor and/or poorly accessible areas, like the Andean region, is challenging due to the lack of recent soil data at high spatial resolution and the wide range of coexistent ecosystems. Thus, the sampling strategy is vital in order to ensure the whole range of environmental covariates (EC) controlling SOC dynamics is represented. This approach allows grasping the variability of the area, which leads to more efficient statistical estimates and improves the modeling process. The objectives of this study were to i) characterize and model the spatial distribution of SOC stocks in the Central Andean region using soil-landscape modeling techniques, and to ii) validate and evaluate the model for predicting SOC content in the area. For that purpose, three representative study areas were identified and a suite of variables including elevation, mean annual temperature, annual precipitation and Normalized Difference Vegetation Index (NDVI), among others, was selected as EC. A stratified random sampling (namely conditioned Latin Hypercube) was implemented and a total of 400 sampling locations were identified. At all sites, four composite topsoil samples (0-30 cm) were collected within a 2 m radius. SOC content was measured using dry combustion and SOC stocks were estimated using bulk density measurements. Regression Kriging was used to map the spatial variation of SOC stocks. The accuracy, fit and bias of SOC models was assessed using a rigorous validation assessment. This study produced the first comprehensive, geospatial SOC stock assessment in this undersampled region that serves as a baseline reference to assess potential impacts of climate and land use change.

Income-related health inequalities across regions in Korea

PubMed Central

2011-01-01

Introduction In addition to economic inequalities, there has been growing concern over socioeconomic inequalities in health across income levels and/or regions. This study measures income-related health inequalities within and between regions and assesses the possibility of convergence of socioeconomic inequalities in health as regional incomes converge. Methods We considered a total of 45,233 subjects (≥ 19 years) drawn from the four waves of the Korean National Health and Nutrition Examination Survey (KNHANES). We considered true health as a latent variable following a lognormal distribution. We obtained ill-health scores by matching self-rated health (SRH) to its distribution and used the Gini Coefficient (GC) and an income-related ill-health Concentration Index (CI) to examine inequalities in income and health, respectively. Results The GC estimates were 0.3763 and 0.0657 for overall and spatial inequalities, respectively. The overall CI was -0.1309, and the spatial CI was -0.0473. The spatial GC and CI estimates were smaller than their counterparts, indicating substantial inequalities in income (from 0.3199 in Daejeon to 0.4233 Chungnam) and income-related health inequalities (from -0.1596 in Jeju and -0.0844 in Ulsan) within regions. The results indicate a positive relationship between the GC and the average ill-health and a negative relationship between the CI and the average ill-health. Those regions with a low level of health tended to show an unequal distribution of income and health. In addition, there was a negative relationship between the GC and the CI, that is, the larger the income inequalities, the larger the health inequalities were. The GC was negatively related to the average regional income, indicating that an increase in a region's average income reduced income inequalities in the region. On the other hand, the CI showed a positive relationship, indicating that an increase in a region's average income reduced health inequalities in the region. Conclusion The results suggest that reducing health inequalities across regions require a more equitable distribution of income and a higher level of average income and that the higher the region's average income, the smaller its health inequalities are. PMID:21967804

Quantification of histone modification ChIP-seq enrichment for data mining and machine learning applications

PubMed Central

2011-01-01

Background The advent of ChIP-seq technology has made the investigation of epigenetic regulatory networks a computationally tractable problem. Several groups have applied statistical computing methods to ChIP-seq datasets to gain insight into the epigenetic regulation of transcription. However, methods for estimating enrichment levels in ChIP-seq data for these computational studies are understudied and variable. Since the conclusions drawn from these data mining and machine learning applications strongly depend on the enrichment level inputs, a comparison of estimation methods with respect to the performance of statistical models should be made. Results Various methods were used to estimate the gene-wise ChIP-seq enrichment levels for 20 histone methylations and the histone variant H2A.Z. The Multivariate Adaptive Regression Splines (MARS) algorithm was applied for each estimation method using the estimation of enrichment levels as predictors and gene expression levels as responses. The methods used to estimate enrichment levels included tag counting and model-based methods that were applied to whole genes and specific gene regions. These methods were also applied to various sizes of estimation windows. The MARS model performance was assessed with the Generalized Cross-Validation Score (GCV). We determined that model-based methods of enrichment estimation that spatially weight enrichment based on average patterns provided an improvement over tag counting methods. Also, methods that included information across the entire gene body provided improvement over methods that focus on a specific sub-region of the gene (e.g., the 5' or 3' region). Conclusion The performance of data mining and machine learning methods when applied to histone modification ChIP-seq data can be improved by using data across the entire gene body, and incorporating the spatial distribution of enrichment. Refinement of enrichment estimation ultimately improved accuracy of model predictions. PMID:21834981

The impact of transport model differences on CO2 surface flux estimates from OCO-2 retrievals of column average CO2

NASA Astrophysics Data System (ADS)

Basu, Sourish; Baker, David F.; Chevallier, Frédéric; Patra, Prabir K.; Liu, Junjie; Miller, John B.

2018-05-01

We estimate the uncertainty of CO2 flux estimates in atmospheric inversions stemming from differences between different global transport models. Using a set of observing system simulation experiments (OSSEs), we estimate this uncertainty as represented by the spread between five different state-of-the-art global transport models (ACTM, LMDZ, GEOS-Chem, PCTM and TM5), for both traditional in situ CO2 inversions and inversions of XCO2 estimates from the Orbiting Carbon Observatory 2 (OCO-2). We find that, in the absence of relative biases between in situ CO2 and OCO-2 XCO2, OCO-2 estimates of terrestrial flux for TRANSCOM-scale land regions can be more robust to transport model differences than corresponding in situ CO2 inversions. This is due to a combination of the increased spatial coverage of OCO-2 samples and the total column nature of OCO-2 estimates. We separate the two effects by constructing hypothetical in situ networks with the coverage of OCO-2 but with only near-surface samples. We also find that the transport-driven uncertainty in fluxes is comparable between well-sampled northern temperate regions and poorly sampled tropical regions. Furthermore, we find that spatiotemporal differences in sampling, such as between OCO-2 land and ocean soundings, coupled with imperfect transport, can produce differences in flux estimates that are larger than flux uncertainties due to transport model differences. This highlights the need for sampling with as complete a spatial and temporal coverage as possible (e.g., using both land and ocean retrievals together for OCO-2) to minimize the impact of selective sampling. Finally, our annual and monthly estimates of transport-driven uncertainties can be used to evaluate the robustness of conclusions drawn from real OCO-2 and in situ CO2 inversions.

Active Longitude and Coronal Mass Ejection Occurrences

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

Gyenge, N.; Kiss, T. S.; Erdélyi, R.

The spatial inhomogeneity of the distribution of coronal mass ejection (CME) occurrences in the solar atmosphere could provide a tool to estimate the longitudinal position of the most probable CME-capable active regions in the Sun. The anomaly in the longitudinal distribution of active regions themselves is often referred to as active longitude (AL). In order to reveal the connection between the AL and CME spatial occurrences, here we investigate the morphological properties of active regions. The first morphological property studied is the separateness parameter, which is able to characterize the probability of the occurrence of an energetic event, such asmore » a solar flare or CME. The second morphological property is the sunspot tilt angle. The tilt angle of sunspot groups allows us to estimate the helicity of active regions. The increased helicity leads to a more complex buildup of the magnetic structure and also can cause CME eruption. We found that the most complex active regions appear near the AL and that the AL itself is associated with the most tilted active regions. Therefore, the number of CME occurrences is higher within the AL. The origin of the fast CMEs is also found to be associated with this region. We concluded that the source of the most probably CME-capable active regions is at the AL. By applying this method, we can potentially forecast a flare and/or CME source several Carrington rotations in advance. This finding also provides new information for solar dynamo modeling.« less

Active Longitude and Coronal Mass Ejection Occurrences

NASA Astrophysics Data System (ADS)

Gyenge, N.; Singh, T.; Kiss, T. S.; Srivastava, A. K.; Erdélyi, R.

2017-03-01

The spatial inhomogeneity of the distribution of coronal mass ejection (CME) occurrences in the solar atmosphere could provide a tool to estimate the longitudinal position of the most probable CME-capable active regions in the Sun. The anomaly in the longitudinal distribution of active regions themselves is often referred to as active longitude (AL). In order to reveal the connection between the AL and CME spatial occurrences, here we investigate the morphological properties of active regions. The first morphological property studied is the separateness parameter, which is able to characterize the probability of the occurrence of an energetic event, such as a solar flare or CME. The second morphological property is the sunspot tilt angle. The tilt angle of sunspot groups allows us to estimate the helicity of active regions. The increased helicity leads to a more complex buildup of the magnetic structure and also can cause CME eruption. We found that the most complex active regions appear near the AL and that the AL itself is associated with the most tilted active regions. Therefore, the number of CME occurrences is higher within the AL. The origin of the fast CMEs is also found to be associated with this region. We concluded that the source of the most probably CME-capable active regions is at the AL. By applying this method, we can potentially forecast a flare and/or CME source several Carrington rotations in advance. This finding also provides new information for solar dynamo modeling.

Comparison of local- to regional-scale estimates of ground-water recharge in Minnesota, USA

USGS Publications Warehouse

Delin, G.N.; Healy, R.W.; Lorenz, D.L.; Nimmo, J.R.

2007-01-01

Regional ground-water recharge estimates for Minnesota were compared to estimates made on the basis of four local- and basin-scale methods. Three local-scale methods (unsaturated-zone water balance, water-table fluctuations (WTF) using three approaches, and age dating of ground water) yielded point estimates of recharge that represent spatial scales from about 1 to about 1000 m2. A fourth method (RORA, a basin-scale analysis of streamflow records using a recession-curve-displacement technique) yielded recharge estimates at a scale of 10–1000s of km2. The RORA basin-scale recharge estimates were regionalized to estimate recharge for the entire State of Minnesota on the basis of a regional regression recharge (RRR) model that also incorporated soil and climate data. Recharge rates estimated by the RRR model compared favorably to the local and basin-scale recharge estimates. RRR estimates at study locations were about 41% less on average than the unsaturated-zone water-balance estimates, ranged from 44% greater to 12% less than estimates that were based on the three WTF approaches, were about 4% less than the age dating of ground-water estimates, and were about 5% greater than the RORA estimates. Of the methods used in this study, the WTF method is the simplest and easiest to apply. Recharge estimates made on the basis of the UZWB method were inconsistent with the results from the other methods. Recharge estimates using the RRR model could be a good source of input for regional ground-water flow models; RRR model results currently are being applied for this purpose in USGS studies elsewhere.

Pan-European household and industrial water demand: regional relevant estimations

NASA Astrophysics Data System (ADS)

Bernhard, Jeroen; Reynaud, Arnaud; de Roo, Ad

2016-04-01

Sustainable water management is of high importance to provide adequate quality and quantity of water to European households, industries and agriculture. Especially since demographic, economic and climate changes are expected to increase competition for water between these sectors in the future. A shortage of water implies a reduction in welfare of households or damage to economic sectors. This socio-economic component should be incorporated into the decision-making process when developing water allocation schemes, requiring detailed water use information and cost/benefit functions. We now present the results of our study which is focused at providing regionally relevant pan-European water demand and cost-benefit estimations for the household and industry sector. We gathered consistent data on water consumption, water prices and other relevant variables at the highest spatial detail available from national statistical offices and other organizational bodies. This database provides the most detailed up to date picture of present water use and water prices across Europe. The use of homogeneous data allowed us to compare regions and analyze spatial patterns. We applied econometric methods to determine the main determinants of water demand and make a monetary valuation of water for both the domestic and industry sector. This monetary valuation is important to allow water allocation based on economic damage estimates. We also attempted to estimate how population growth, as well as socio-economic and climatic changes impact future water demand up to 2050 using a homogeneous method for all countries. European projections for the identified major drivers of water demand were used to simulate future conditions. Subsequently, water demand functions were applied to estimate future water use and potential economic damage caused by water shortages. We present our results while also providing some estimation of the uncertainty of our predictions.

Spatially explicit estimation of heat stress-related impacts of climate change on the milk production of dairy cows in the United Kingdom.

PubMed

Fodor, Nándor; Foskolos, Andreas; Topp, Cairistiona F E; Moorby, Jon M; Pásztor, László; Foyer, Christine H

2018-01-01

Dairy farming is one the most important sectors of United Kingdom (UK) agriculture. It faces major challenges due to climate change, which will have direct impacts on dairy cows as a result of heat stress. In the absence of adaptations, this could potentially lead to considerable milk loss. Using an 11-member climate projection ensemble, as well as an ensemble of 18 milk loss estimation methods, temporal changes in milk production of UK dairy cows were estimated for the 21st century at a 25 km resolution in a spatially-explicit way. While increases in UK temperatures are projected to lead to relatively low average annual milk losses, even for southern UK regions (<180 kg/cow), the 'hottest' 25×25 km grid cell in the hottest year in the 2090s, showed an annual milk loss exceeding 1300 kg/cow. This figure represents approximately 17% of the potential milk production of today's average cow. Despite the potential considerable inter-annual variability of annual milk loss, as well as the large differences between the climate projections, the variety of calculation methods is likely to introduce even greater uncertainty into milk loss estimations. To address this issue, a novel, more biologically-appropriate mechanism of estimating milk loss is proposed that provides more realistic future projections. We conclude that South West England is the region most vulnerable to climate change economically, because it is characterised by a high dairy herd density and therefore potentially high heat stress-related milk loss. In the absence of mitigation measures, estimated heat stress-related annual income loss for this region by the end of this century may reach £13.4M in average years and £33.8M in extreme years.

The estimation of probable maximum precipitation: the case of Catalonia.

PubMed

Casas, M Carmen; Rodríguez, Raül; Nieto, Raquel; Redaño, Angel

2008-12-01

A brief overview of the different techniques used to estimate the probable maximum precipitation (PMP) is presented. As a particular case, the 1-day PMP over Catalonia has been calculated and mapped with a high spatial resolution. For this purpose, the annual maximum daily rainfall series from 145 pluviometric stations of the Instituto Nacional de Meteorología (Spanish Weather Service) in Catalonia have been analyzed. In order to obtain values of PMP, an enveloping frequency factor curve based on the actual rainfall data of stations in the region has been developed. This enveloping curve has been used to estimate 1-day PMP values of all the 145 stations. Applying the Cressman method, the spatial analysis of these values has been achieved. Monthly precipitation climatological data, obtained from the application of Geographic Information Systems techniques, have been used as the initial field for the analysis. The 1-day PMP at 1 km(2) spatial resolution over Catalonia has been objectively determined, varying from 200 to 550 mm. Structures with wavelength longer than approximately 35 km can be identified and, despite their general concordance, the obtained 1-day PMP spatial distribution shows remarkable differences compared to the annual mean precipitation arrangement over Catalonia.

Temporal and spatial characterization of zenith total delay (ZTD) in North Europe

NASA Astrophysics Data System (ADS)

Stoew, B.; Elgered, G.

2003-04-01

The estimates of ZTD are often treated as realizations of random walk stochastic processes. We derive the corresponding process parameters for 34 different locations in North Europe using two measurement techniques - Global Positioning System (GPS) and Water Vapor Radiometer (WVR). GPS-estimated ZTD is an excellent candidate for data assimilation in numerical weather prediction (NWP) models in terms of both spatial and temporal resolution. We characterize the long term behavior of the ZTD as a function of site latitude and height. The spatial characteristics of the ZTD are studied as a function of site separation and season. We investigate the influence of the time-interpolated atmospheric pressure data used for the estimation of zenith wet delay (ZWD) from ZTD. Characterization of extreme atmospheric events can aid the development of an early warning system. We consider two types of extreme meteorological phenomena with regard to their spatial scales. The first type concerns larger regions (including several GPS sites); the extreme weather is characterized by intense precipitation which may result in a flood. The second type is related to local variations in the ZWD/ZTD and can be used for detection/monitoring of passing atmospheric fronts.

Approximation of Confidence Limits on Sample Semivariograms From Single Realizations of Spatially Correlated Random Fields

NASA Astrophysics Data System (ADS)

Shafer, J. M.; Varljen, M. D.

1990-08-01

A fundamental requirement for geostatistical analyses of spatially correlated environmental data is the estimation of the sample semivariogram to characterize spatial correlation. Selecting an underlying theoretical semivariogram based on the sample semivariogram is an extremely important and difficult task that is subject to a great deal of uncertainty. Current standard practice does not involve consideration of the confidence associated with semivariogram estimates, largely because classical statistical theory does not provide the capability to construct confidence limits from single realizations of correlated data, and multiple realizations of environmental fields are not found in nature. The jackknife method is a nonparametric statistical technique for parameter estimation that may be used to estimate the semivariogram. When used in connection with standard confidence procedures, it allows for the calculation of closely approximate confidence limits on the semivariogram from single realizations of spatially correlated data. The accuracy and validity of this technique was verified using a Monte Carlo simulation approach which enabled confidence limits about the semivariogram estimate to be calculated from many synthetically generated realizations of a random field with a known correlation structure. The synthetically derived confidence limits were then compared to jackknife estimates from single realizations with favorable results. Finally, the methodology for applying the jackknife method to a real-world problem and an example of the utility of semivariogram confidence limits were demonstrated by constructing confidence limits on seasonal sample variograms of nitrate-nitrogen concentrations in shallow groundwater in an approximately 12-mi2 (˜30 km2) region in northern Illinois. In this application, the confidence limits on sample semivariograms from different time periods were used to evaluate the significance of temporal change in spatial correlation. This capability is quite important as it can indicate when a spatially optimized monitoring network would need to be reevaluated and thus lead to more robust monitoring strategies.

Spatial-altitudinal and temporal variation of Degree Day Factors (DDFs) in the Upper Indus Basin

NASA Astrophysics Data System (ADS)

Khan, Asif; Attaullah, Haleema; Masud, Tabinda; Khan, Mujahid

2017-04-01

Melt contribution from snow and ice in the Hindukush-Karakoram-Himalayan (HKH) region could account for more than 80% of annual river flows in the Upper Indus Basin (UIB). Increase or decrease in precipitation, energy input and glacier reserves can significantly affect water resources of this region. Therefore improved hydrological modelling and accurate future water resources prediction are vital for food production and hydro-power generation for millions of people living downstream, and are intensively needed. In mountain regions Degree Day Factors (DDFs) significantly vary on spatial and altitudinal basis, and are primary inputs of temperature-based hydrological modelling. However previous studies have used different DDFs as calibration parameters without due attention to the physical meaning of the values employed, and these estimates possess significant variability and uncertainty. This study provides estimates of DDFs for various altitudinal zones in the UIB at sub-basin level. Snow, clean ice and ice with debris cover bear different melt rates (or DDFs), therefore areally-averaged DDFs based on snow, clean and debris-covered ice classes in various altitudinal zones have been estimated for all sub-basins of the UIB. Zonal estimates of DDFs in the current study are significantly different from earlier adopted DDFs, hence suggest a revisit of previous hydrological modelling studies. DDFs presented in current study have been validated by using Snowmelt Runoff Model (SRM) in various sub-basins with good Nash Sutcliffe coefficients (R2 > 0.85) and low volumetric errors (Dv<10%). DDFs and methods provided in the current study can be used in future improved hydrological modelling and to provide accurate predictions of future river flows changes. The methodology used for estimation of DDFs is robust, and can be adopted to produce such estimates in other regions of the, particularly in the nearby other HKH basins.

Modeling Disease Vector Occurrence when Detection Is Imperfect: Infestation of Amazonian Palm Trees by Triatomine Bugs at Three Spatial Scales

PubMed Central

Abad-Franch, Fernando; Ferraz, Gonçalo; Campos, Ciro; Palomeque, Francisco S.; Grijalva, Mario J.; Aguilar, H. Marcelo; Miles, Michael A.

2010-01-01

Background Failure to detect a disease agent or vector where it actually occurs constitutes a serious drawback in epidemiology. In the pervasive situation where no sampling technique is perfect, the explicit analytical treatment of detection failure becomes a key step in the estimation of epidemiological parameters. We illustrate this approach with a study of Attalea palm tree infestation by Rhodnius spp. (Triatominae), the most important vectors of Chagas disease (CD) in northern South America. Methodology/Principal Findings The probability of detecting triatomines in infested palms is estimated by repeatedly sampling each palm. This knowledge is used to derive an unbiased estimate of the biologically relevant probability of palm infestation. We combine maximum-likelihood analysis and information-theoretic model selection to test the relationships between environmental covariates and infestation of 298 Amazonian palm trees over three spatial scales: region within Amazonia, landscape, and individual palm. Palm infestation estimates are high (40–60%) across regions, and well above the observed infestation rate (24%). Detection probability is higher (∼0.55 on average) in the richest-soil region than elsewhere (∼0.08). Infestation estimates are similar in forest and rural areas, but lower in urban landscapes. Finally, individual palm covariates (accumulated organic matter and stem height) explain most of infestation rate variation. Conclusions/Significance Individual palm attributes appear as key drivers of infestation, suggesting that CD surveillance must incorporate local-scale knowledge and that peridomestic palm tree management might help lower transmission risk. Vector populations are probably denser in rich-soil sub-regions, where CD prevalence tends to be higher; this suggests a target for research on broad-scale risk mapping. Landscape-scale effects indicate that palm triatomine populations can endure deforestation in rural areas, but become rarer in heavily disturbed urban settings. Our methodological approach has wide application in infectious disease research; by improving eco-epidemiological parameter estimation, it can also significantly strengthen vector surveillance-control strategies. PMID:20209149

Landscape-scale consequences of differential tree mortality from catastrophic wind disturbance in the Amazon.

PubMed

Rifai, Sami W; Urquiza Muñoz, José D; Negrón-Juárez, Robinson I; Ramírez Arévalo, Fredy R; Tello-Espinoza, Rodil; Vanderwel, Mark C; Lichstein, Jeremy W; Chambers, Jeffrey Q; Bohlman, Stephanie A

2016-10-01

Wind disturbance can create large forest blowdowns, which greatly reduces live biomass and adds uncertainty to the strength of the Amazon carbon sink. Observational studies from within the central Amazon have quantified blowdown size and estimated total mortality but have not determined which trees are most likely to die from a catastrophic wind disturbance. Also, the impact of spatial dependence upon tree mortality from wind disturbance has seldom been quantified, which is important because wind disturbance often kills clusters of trees due to large treefalls killing surrounding neighbors. We examine (1) the causes of differential mortality between adult trees from a 300-ha blowdown event in the Peruvian region of the northwestern Amazon, (2) how accounting for spatial dependence affects mortality predictions, and (3) how incorporating both differential mortality and spatial dependence affect the landscape level estimation of necromass produced from the blowdown. Standard regression and spatial regression models were used to estimate how stem diameter, wood density, elevation, and a satellite-derived disturbance metric influenced the probability of tree death from the blowdown event. The model parameters regarding tree characteristics, topography, and spatial autocorrelation of the field data were then used to determine the consequences of non-random mortality for landscape production of necromass through a simulation model. Tree mortality was highly non-random within the blowdown, where tree mortality rates were highest for trees that were large, had low wood density, and were located at high elevation. Of the differential mortality models, the non-spatial models overpredicted necromass, whereas the spatial model slightly underpredicted necromass. When parameterized from the same field data, the spatial regression model with differential mortality estimated only 7.5% more dead trees across the entire blowdown than the random mortality model, yet it estimated 51% greater necromass. We suggest that predictions of forest carbon loss from wind disturbance are sensitive to not only the underlying spatial dependence of observations, but also the biological differences between individuals that promote differential levels of mortality. © 2016 by the Ecological Society of America.

A diagnostic carbon flux model to monitor the effects of disturbance and interannual variation in climate on regional NEP.

Treesearch

D.P. Turner; W.D. Ritts; J.M. Styles; Z. Yang; W.B. Cohen; B.E. Law; P.E. Thornton

2006-01-01

Net ecosystem production (NEP) was estimated over a 10.9 x 104 km2 forested region in western Oregon USA for 2 yr (2002-2003) using a combination of remote sensing, distributed meteorological data, and a carbon cycle model (CFLUX). High spatial resolution satellite data (Landsat, 30 m) provided information on land cover and...

Improving Quantitative Precipitation Estimation via Data Fusion of High-Resolution Ground-based Radar Network and CMORPH Satellite-based Product

NASA Astrophysics Data System (ADS)

Cifelli, R.; Chen, H.; Chandrasekar, V.; Xie, P.

2015-12-01

A large number of precipitation products at multi-scales have been developed based upon satellite, radar, and/or rain gauge observations. However, how to produce optimal rainfall estimation for a given region is still challenging due to the spatial and temporal sampling difference of different sensors. In this study, we develop a data fusion mechanism to improve regional quantitative precipitation estimation (QPE) by utilizing satellite-based CMORPH product, ground radar measurements, as well as numerical model simulations. The CMORPH global precipitation product is essentially derived based on retrievals from passive microwave measurements and infrared observations onboard satellites (Joyce et al. 2004). The fine spatial-temporal resolution of 0.05o Lat/Lon and 30-min is appropriate for regional hydrologic and climate studies. However, it is inadequate for localized hydrometeorological applications such as urban flash flood forecasting. Via fusion of the Regional CMORPH product and local precipitation sensors, the high-resolution QPE performance can be improved. The area of interest is the Dallas-Fort Worth (DFW) Metroplex, which is the largest land-locked metropolitan area in the U.S. In addition to an NWS dual-polarization S-band WSR-88DP radar (i.e., KFWS radar), DFW hosts the high-resolution dual-polarization X-band radar network developed by the center for Collaborative Adaptive Sensing of the Atmosphere (CASA). This talk will present a general framework of precipitation data fusion based on satellite and ground observations. The detailed prototype architecture of using regional rainfall instruments to improve regional CMORPH precipitation product via multi-scale fusion techniques will also be discussed. Particularly, the temporal and spatial fusion algorithms developed for the DFW Metroplex will be described, which utilizes CMORPH product, S-band WSR-88DP, and X-band CASA radar measurements. In order to investigate the uncertainties associated with each individual product and demonstrate the precipitation data fusion performance, both individual and fused QPE products are evaluated using rainfall measurements from a disdrometer and gauge network.

Estimation of peak discharge quantiles for selected annual exceedance probabilities in northeastern Illinois

USGS Publications Warehouse

Over, Thomas M.; Saito, Riki J.; Veilleux, Andrea G.; Sharpe, Jennifer B.; Soong, David T.; Ishii, Audrey L.

2016-06-28

This report provides two sets of equations for estimating peak discharge quantiles at annual exceedance probabilities (AEPs) of 0.50, 0.20, 0.10, 0.04, 0.02, 0.01, 0.005, and 0.002 (recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years, respectively) for watersheds in Illinois based on annual maximum peak discharge data from 117 watersheds in and near northeastern Illinois. One set of equations was developed through a temporal analysis with a two-step least squares-quantile regression technique that measures the average effect of changes in the urbanization of the watersheds used in the study. The resulting equations can be used to adjust rural peak discharge quantiles for the effect of urbanization, and in this study the equations also were used to adjust the annual maximum peak discharges from the study watersheds to 2010 urbanization conditions.The other set of equations was developed by a spatial analysis. This analysis used generalized least-squares regression to fit the peak discharge quantiles computed from the urbanization-adjusted annual maximum peak discharges from the study watersheds to drainage-basin characteristics. The peak discharge quantiles were computed by using the Expected Moments Algorithm following the removal of potentially influential low floods defined by a multiple Grubbs-Beck test. To improve the quantile estimates, regional skew coefficients were obtained from a newly developed regional skew model in which the skew increases with the urbanized land use fraction. The drainage-basin characteristics used as explanatory variables in the spatial analysis include drainage area, the fraction of developed land, the fraction of land with poorly drained soils or likely water, and the basin slope estimated as the ratio of the basin relief to basin perimeter.This report also provides the following: (1) examples to illustrate the use of the spatial and urbanization-adjustment equations for estimating peak discharge quantiles at ungaged sites and to improve flood-quantile estimates at and near a gaged site; (2) the urbanization-adjusted annual maximum peak discharges and peak discharge quantile estimates at streamgages from 181 watersheds including the 117 study watersheds and 64 additional watersheds in the study region that were originally considered for use in the study but later deemed to be redundant.The urbanization-adjustment equations, spatial regression equations, and peak discharge quantile estimates developed in this study will be made available in the web application StreamStats, which provides automated regression-equation solutions for user-selected stream locations. Figures and tables comparing the observed and urbanization-adjusted annual maximum peak discharge records by streamgage are provided at https://doi.org/10.3133/sir20165050 for download.

Annual survival of Snail Kites in Florida: Radio telemetry versus capture-resighting data

USGS Publications Warehouse

Bennetts, R.E.; Dreitz, V.J.; Kitchens, W.M.; Hines, J.E.; Nichols, J.D.

1999-01-01

We estimated annual survival of Snail Kites (Rostrhamus sociabilis) in Florida using the Kaplan-Meier estimator with data from 271 radio-tagged birds over a three-year period and capture-recapture (resighting) models with data from 1,319 banded birds over a six-year period. We tested the hypothesis that survival differed among three age classes using both data sources. We tested additional hypotheses about spatial and temporal variation using a combination of data from radio telemetry and single- and multistrata capture-recapture models. Results from these data sets were similar in their indications of the sources of variation in survival, but they differed in some parameter estimates. Both data sources indicated that survival was higher for adults than for juveniles, but they did not support delineation of a subadult age class. Our data also indicated that survival differed among years and regions for juveniles but not for adults. Estimates of juvenile survival using radio telemetry data were higher than estimates using capture-recapture models for two of three years (1992 and 1993). Ancillary evidence based on censored birds indicated that some mortality of radio-tagged juveniles went undetected during those years, resulting in biased estimates. Thus, we have greater confidence in our estimates of juvenile survival using capture-recapture models. Precision of estimates reflected the number of parameters estimated and was surprisingly similar between radio telemetry and single-stratum capture-recapture models, given the substantial differences in sample sizes. Not having to estimate resighting probability likely offsets, to some degree, the smaller sample sizes from our radio telemetry data. Precision of capture-recapture models was lower using multistrata models where region-specific parameters were estimated than using single-stratum models, where spatial variation in parameters was not taken into account.

Gaussian Process Regression Model in Spatial Logistic Regression

NASA Astrophysics Data System (ADS)

Sofro, A.; Oktaviarina, A.

2018-01-01

Spatial analysis has developed very quickly in the last decade. One of the favorite approaches is based on the neighbourhood of the region. Unfortunately, there are some limitations such as difficulty in prediction. Therefore, we offer Gaussian process regression (GPR) to accommodate the issue. In this paper, we will focus on spatial modeling with GPR for binomial data with logit link function. The performance of the model will be investigated. We will discuss the inference of how to estimate the parameters and hyper-parameters and to predict as well. Furthermore, simulation studies will be explained in the last section.

Assessment of Wind Datasets for Estimating Offshore Wind Energy along the Central California Coast

NASA Astrophysics Data System (ADS)

Wang, Y. H.; Walter, R. K.; Ruttenberg, B.; White, C.

2017-12-01

Offshore renewable energy along the central California coastline has gained significant interest in recent years. We present a comprehensive analysis of near-surface wind datasets available in this region to facilitate future estimates of wind power generation potential. The analyses are based on local NDBC buoys, satellite-based measurements (QuickSCAT and CCMP V2.0), reanalysis products (NARR and MERRA), and a regional climate model (WRF). There are substantial differences in the diurnal signal during different months among the various products (i.e., satellite-based, reanalysis, and modeled) relative to the local buoys. Moreover, the datasets tended to underestimate wind speed under light wind conditions and overestimate under strong wind conditions. In addition to point-to-point comparisons against local buoys, the spatial variations of bias and error in both the reanalysis products and WRF model data in this region were compared against satellite-based measurements. NARR's bias and root-mean-square-error were generally small in the study domain and decreased with distance from coastlines. Although its smaller spatial resolution is likely to be insufficient to reveal local effects, the small bias and error in near-surface winds, as well as the availability of wind data at the proposed turbine hub heights, suggests that NARR is an ideal candidate for use in offshore wind energy production estimates along the central California coast. The framework utilized here could be applied in other site-specific regions where offshore renewable energy is being considered.

Spatial patterns of frequent floods in Switzerland

NASA Astrophysics Data System (ADS)

Schneeberger, Klaus; Rössler, Ole; Weingartner, Rolf

2017-04-01

Information about the spatial characteristics of high and extreme streamflow is often needed for an accurate analysis of flood risk and effective co-ordination of flood related activities, such as flood defence planning. In this study we analyse the spatial dependence of frequent floods in Switzerland across different scales. Firstly, we determine the average length of high and extreme flow events for 56 runoff time series of Swiss rivers. Secondly, a dependence measure expressing the probability that streamflow peaks are as high as peaks at a conditional site is used to describe and map the spatial extend of joint occurrence of frequent floods across Switzerland. Thirdly, we apply a cluster analysis to identify groups of sites that are likely to react similarly in terms of joint occurrence of high flow events. The results indicate that a time interval with a length of 3 days seems to be most appropriate to characterise the average length of high streamflow events across spatial scales. In the main Swiss basins, high and extreme streamflows were found to be asymptotically independent. In contrast, at the meso-scale distinct flood regions, which react similarly in terms of occurrence of frequent flood, were found. The knowledge about these regions can help to optimise flood defence planning or to estimate regional flood risk properly.

Estimating Landscape Pattern Metrics from a Sample of Land Cover

EPA Science Inventory

Although landscape pattern metrics can be computed directly from wall-to-wall land-cover maps, statistical sampling offers a practical alternative when complete coverage land-cover information is unavailable. Partitioning a region into spatial units (“blocks”) to create a samplin...

Impacts of ozone air pollution and temperature extremes on crop yields: Spatial variability, adaptation and implications for future food security

NASA Astrophysics Data System (ADS)

Tai, Amos P. K.; Val Martin, Maria

2017-11-01

Ozone air pollution and climate change pose major threats to global crop production, with ramifications for future food security. Previous studies of ozone and warming impacts on crops typically do not account for the strong ozone-temperature correlation when interpreting crop-ozone or crop-temperature relationships, or the spatial variability of crop-to-ozone sensitivity arising from varietal and environmental differences, leading to potential biases in their estimated crop losses. Here we develop an empirical model, called the partial derivative-linear regression (PDLR) model, to estimate the spatial variations in the sensitivities of wheat, maize and soybean yields to ozone exposures and temperature extremes in the US and Europe using a composite of multidecadal datasets, fully correcting for ozone-temperature covariation. We find generally larger and more spatially varying sensitivities of all three crops to ozone exposures than are implied by experimentally derived concentration-response functions used in most previous studies. Stronger ozone tolerance is found in regions with high ozone levels and high consumptive crop water use, reflecting the existence of spatial adaptation and effect of water constraints. The spatially varying sensitivities to temperature extremes also indicate stronger heat tolerance in crops grown in warmer regions. The spatial adaptation of crops to ozone and temperature we find can serve as a surrogate for future adaptation. Using the PDLR-derived sensitivities and 2000-2050 ozone and temperature projections by the Community Earth System Model, we estimate that future warming and unmitigated ozone pollution can combine to cause an average decline in US wheat, maize and soybean production by 13%, 43% and 28%, respectively, and a smaller decline for European crops. Aggressive ozone regulation is shown to offset such decline to various extents, especially for wheat. Our findings demonstrate the importance of considering ozone regulation as well as ozone and climate change adaptation (e.g., selecting heat- and ozone-tolerant cultivars, irrigation) as possible strategies to enhance future food security in response to imminent environmental threats.

[Spatial analysis of childhood obesity and overweight in Peru, 2014].

PubMed

Hernández-Vásquez, Akram; Bendezú-Quispe, Guido; Díaz-Seijas, Deysi; Santero, Marilina; Minckas, Nicole; Azañedo, Diego; Antiporta, Daniel A

2016-01-01

To estimate regional prevalence and identify the spatial patterns of the degree of overweight and obesity by districts in under five years children in Peru during 2014. Analysis of the information reported by the Information System Nutritional Status (SIEN) of the number of cases of overweight and obesity in children under five years recorded during 2014. Regional prevalence for overweight and obesity, and their respective confidence intervals to 95% were calculated. Moran index was used to determine patterns of grouping districts with high prevalence of overweight and/or obesity. Data from 1834 districts and 2,318,980 children under five years were analyzed. 158,738 cases (6.84%; CI 95%: 6.81 to 6.87) were overweight, while 56,125 (2.42%; CI 95%: 2.40 to 2.44) obesity. The highest prevalence of overweight were identified in the regions of Tacna (13.9%), Moquegua (11.8%), Callao (10.4%), Lima (10.2%) and Ica (9.3%), and in the same regions for obesity with 5.3%; 4.3%; 4.0%; 4.0% and 3.8% respectively. The spatial analysis found grouping districts of high prevalence in 10% of all districts for both overweight and obesity, identifying 199 districts for overweight (126 urban and 73 rural), and 184 for obesity (136 urban and 48 rural). The highest prevalence of overweight and obesity were identified in the Peruvian coast regions. Moreover, these regions are predominantly exhibited a spatial clustering of districts with high prevalence of overweight and obesity.

Investigation of Ionospheric Spatial Gradients for Gagan Error Correction

NASA Astrophysics Data System (ADS)

Chandra, K. Ravi

In India, Indian Space Research Organization (ISRO) has established with an objective to develop space technology and its application to various national tasks. The national tasks include, establishment of major space systems such as Indian National Satellites (INSAT) for communication, television broadcasting and meteorological services, Indian Remote Sensing Satellites (IRS), etc. Apart from these, to cater to the needs of civil aviation applications, GPS Aided Geo Augmented Navigation (GAGAN) system is being jointly implemented along with Airports Authority of India (AAI) over the Indian region. The most predominant parameter affecting the navigation accuracy of GAGAN is ionospheric delay which is a function of total number of electrons present in one square meter cylindrical cross-sectional area in the line of site direction between the satellite and the user on the earth, i.e. Total Electron Content (TEC). In the equatorial and low latitude regions such as India, TEC is often quite high with large spatial gradients. Carrier phase data from the GAGAN network of Indian TEC Stations is used for estimating and identifying ionospheric spatial gradients inmultiple viewing directions. In this paper amongst the satellite signals arriving in multipledirections,Vertical ionospheric gradients (σVIG) are calculated, inturn spatial ionospheric gradients are identified. In addition, estimated temporal gradients, i.e. rate of TEC Index is also compared. These aspects which contribute to errors can be treated for improved GAGAN system performance.

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.

Climate model uncertainty in impact assessments for agriculture: A multi-ensemble case study on maize in sub-Saharan Africa

NASA Astrophysics Data System (ADS)

Dale, Amy; Fant, Charles; Strzepek, Kenneth; Lickley, Megan; Solomon, Susan

2017-03-01

We present maize production in sub-Saharan Africa as a case study in the exploration of how uncertainties in global climate change, as reflected in projections from a range of climate model ensembles, influence climate impact assessments for agriculture. The crop model AquaCrop-OS (Food and Agriculture Organization of the United Nations) was modified to run on a 2° × 2° grid and coupled to 122 climate model projections from multi-model ensembles for three emission scenarios (Coupled Model Intercomparison Project Phase 3 [CMIP3] SRES A1B and CMIP5 Representative Concentration Pathway [RCP] scenarios 4.5 and 8.5) as well as two "within-model" ensembles (NCAR CCSM3 and ECHAM5/MPI-OM) designed to capture internal variability (i.e., uncertainty due to chaos in the climate system). In spite of high uncertainty, most notably in the high-producing semi-arid zones, we observed robust regional and sub-regional trends across all ensembles. In agreement with previous work, we project widespread yield losses in the Sahel region and Southern Africa, resilience in Central Africa, and sub-regional increases in East Africa and at the southern tip of the continent. Spatial patterns of yield losses corresponded with spatial patterns of aridity increases, which were explicitly evaluated. Internal variability was a major source of uncertainty in both within-model and between-model ensembles and explained the majority of the spatial distribution of uncertainty in yield projections. Projected climate change impacts on maize production in different regions and nations ranged from near-zero or positive (upper quartile estimates) to substantially negative (lower quartile estimates), highlighting a need for risk management strategies that are adaptive and robust to uncertainty.

Spatiotemporal attention operator using isotropic contrast and regional homogeneity

NASA Astrophysics Data System (ADS)

Palenichka, Roman; Lakhssassi, Ahmed; Zaremba, Marek

2011-04-01

A multiscale operator for spatiotemporal isotropic attention is proposed to reliably extract attention points during image sequence analysis. Its consecutive local maxima indicate attention points as the centers of image fragments of variable size with high intensity contrast, region homogeneity, regional shape saliency, and temporal change presence. The scale-adaptive estimation of temporal change (motion) and its aggregation with the regional shape saliency contribute to the accurate determination of attention points in image sequences. Multilocation descriptors of an image sequence are extracted at the attention points in the form of a set of multidimensional descriptor vectors. A fast recursive implementation is also proposed to make the operator's computational complexity independent from the spatial scale size, which is the window size in the spatial averaging filter. Experiments on the accuracy of attention-point detection have proved the operator consistency and its high potential for multiscale feature extraction from image sequences.

Improving Global Models of Remotely Sensed Ocean Chlorophyll Content Using Partial Least Squares and Geographically Weighted Regression

NASA Astrophysics Data System (ADS)

Gholizadeh, H.; Robeson, S. M.

2015-12-01

Empirical models have been widely used to estimate global chlorophyll content from remotely sensed data. Here, we focus on the standard NASA empirical models that use blue-green band ratios. These band ratio ocean color (OC) algorithms are in the form of fourth-order polynomials and the parameters of these polynomials (i.e. coefficients) are estimated from the NASA bio-Optical Marine Algorithm Data set (NOMAD). Most of the points in this data set have been sampled from tropical and temperate regions. However, polynomial coefficients obtained from this data set are used to estimate chlorophyll content in all ocean regions with different properties such as sea-surface temperature, salinity, and downwelling/upwelling patterns. Further, the polynomial terms in these models are highly correlated. In sum, the limitations of these empirical models are as follows: 1) the independent variables within the empirical models, in their current form, are correlated (multicollinear), and 2) current algorithms are global approaches and are based on the spatial stationarity assumption, so they are independent of location. Multicollinearity problem is resolved by using partial least squares (PLS). PLS, which transforms the data into a set of independent components, can be considered as a combined form of principal component regression (PCR) and multiple regression. Geographically weighted regression (GWR) is also used to investigate the validity of spatial stationarity assumption. GWR solves a regression model over each sample point by using the observations within its neighbourhood. PLS results show that the empirical method underestimates chlorophyll content in high latitudes, including the Southern Ocean region, when compared to PLS (see Figure 1). Cluster analysis of GWR coefficients also shows that the spatial stationarity assumption in empirical models is not likely a valid assumption.

ZWD time series analysis derived from NRT data processing. A regional study of PW in Greece.

NASA Astrophysics Data System (ADS)

Pikridas, Christos; Balidakis, Kyriakos; Katsougiannopoulos, Symeon

2015-04-01

ZWD (Zenith Wet/non-hydrostatic Delay) estimates are routinely derived Near Real Time from the new established Analysis Center in the Department of Geodesy and Surveying of Aristotle University of Thessaloniki (DGS/AUT-AC), in the framework of E-GVAP (EUMETNET GNSS water vapour project) since October 2014. This process takes place on an hourly basis and yields, among else, station coordinates and tropospheric parameter estimates for a network of 90+ permanent GNSS (Global Navigation Satellite System) stations. These are distributed at the wider part of Hellenic region. In this study, temporal and spatial variability of ZWD estimates were examined, as well as their relation with coordinate series extracted from both float and fixed solution of the initial phase ambiguities. For this investigation, Bernese GNSS Software v5.2 was used for the acquisition of the 6 month dataset from the aforementioned network. For time series analysis we employed techniques such as the Generalized Lomb-Scargle periodogram and Burg's maximum entropy method due to inefficiencies of the Discrete Fourier Transform application in the test dataset. Through the analysis, interesting results for further geophysical interpretation were drawn. In addition, the spatial and temporal distributions of Precipitable Water vapour (PW) obtained from both ZWD estimates and ERA-Interim reanalysis grids were investigated.

A global estimate of the Earth's magnetic crustal thickness

NASA Astrophysics Data System (ADS)

Vervelidou, Foteini; Thébault, Erwan

2014-05-01

The Earth's lithosphere is considered to be magnetic only down to the Curie isotherm. Therefore the Curie isotherm can, in principle, be estimated by analysis of magnetic data. Here, we propose such an analysis in the spectral domain by means of a newly introduced regional spatial power spectrum. This spectrum is based on the Revised Spherical Cap Harmonic Analysis (R-SCHA) formalism (Thébault et al., 2006). We briefly discuss its properties and its relationship with the Spherical Harmonic spatial power spectrum. This relationship allows us to adapt any theoretical expression of the lithospheric field power spectrum expressed in Spherical Harmonic degrees to the regional formulation. We compared previously published statistical expressions (Jackson, 1994 ; Voorhies et al., 2002) to the recent lithospheric field models derived from the CHAMP and airborne measurements and we finally developed a new statistical form for the power spectrum of the Earth's magnetic lithosphere that we think provides more consistent results. This expression depends on the mean magnetization, the mean crustal thickness and a power law value that describes the amount of spatial correlation of the sources. In this study, we make a combine use of the R-SCHA surface power spectrum and this statistical form. We conduct a series of regional spectral analyses for the entire Earth. For each region, we estimate the R-SCHA surface power spectrum of the NGDC-720 Spherical Harmonic model (Maus, 2010). We then fit each of these observational spectra to the statistical expression of the power spectrum of the Earth's lithosphere. By doing so, we estimate the large wavelengths of the magnetic crustal thickness on a global scale that are not accessible directly from the magnetic measurements due to the masking core field. We then discuss these results and compare them to the results we obtained by conducting a similar spectral analysis, but this time in the cartesian coordinates, by means of a published statistical expression (Maus et al., 1997). We also compare our results to crustal thickness global maps derived by means of additional geophysical data (Purucker et al., 2002).

Laser-diagnostic mapping of temperature and soot statistics in a 2-m diameter turbulent pool fire

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

Kearney, Sean P.; Grasser, Thomas W.

We present spatial profiles of temperature and soot-volume-fraction statistics from a sooting 2-m base diameter turbulent pool fire, burning a 10%-toluene / 90%-methanol fuel mixture. Dual-pump coherent anti-Stokes Raman scattering and laser-induced incandescence are utilized to obtain radial profiles of temperature and soot probability density functions (pdf) as well as estimates of temperature/soot joint statistics at three vertical heights above the surface of the methanol/toluene fuel pool. Results are presented both in the fuel vapor-dome region at ¼ base diameter and in the actively burning region at ½ and ¾ diameters above the fuel surface. The spatial evolution of themore » soot and temperature pdfs is discussed and profiles of the temperature and soot mean and rms statistics are provided. Joint temperature/soot statistics are presented as spatially resolved conditional averages across the fire plume, and in terms of a joint pdf obtained by including measurements from multiple spatial locations.« less

Laser-diagnostic mapping of temperature and soot statistics in a 2-m diameter turbulent pool fire

DOE PAGES

Kearney, Sean P.; Grasser, Thomas W.

2017-08-10

We present spatial profiles of temperature and soot-volume-fraction statistics from a sooting 2-m base diameter turbulent pool fire, burning a 10%-toluene / 90%-methanol fuel mixture. Dual-pump coherent anti-Stokes Raman scattering and laser-induced incandescence are utilized to obtain radial profiles of temperature and soot probability density functions (pdf) as well as estimates of temperature/soot joint statistics at three vertical heights above the surface of the methanol/toluene fuel pool. Results are presented both in the fuel vapor-dome region at ¼ base diameter and in the actively burning region at ½ and ¾ diameters above the fuel surface. The spatial evolution of themore » soot and temperature pdfs is discussed and profiles of the temperature and soot mean and rms statistics are provided. Joint temperature/soot statistics are presented as spatially resolved conditional averages across the fire plume, and in terms of a joint pdf obtained by including measurements from multiple spatial locations.« less

The contribution of glacial isostatic adjustment to projections of sea-level change along the Atlantic and Gulf coasts of North America

NASA Astrophysics Data System (ADS)

Love, R.; Milne, G. A.; Tarasov, L.; Engelhart, S. E.; Hijma, M.; Latychev, K.; Horton, B.; Tornqvist, T. E.

2017-12-01

Using recently compiled and quality-assessed databases of past RSL, including new databases for the United States Gulf Coast and Atlantic Canada, we infer glacial isostatic adjustment (GIA) model parameters to aid in future projections of sea level change. Utilizing the aforementioned RSL databases, we determine those model parameters for 3 different regions which minimizes the misfit of our 1D spherically symmetric model of GIA. From our ensemble of of 363 different viscosity models and 35 different land ice histories we provide uncertainty estimates for future RSL at 13 cities along this coastline. Furthermore, we examine the role of lateral viscosity structure using a 3D finite volume Earth model and find that the influence of lateral structure on RSL is significant, particularly in the early to mid-Holocene. At 13 cities along this coastline, we estimate the GIA contribution to range from a few centimeters (e.g., 3 [-1 to 9] cm Miami) to a few decimeters (e.g., 18 [12-22] cm, Halifax) for the period 2085-2100 relative to 2006-2015 [1σ]. Contributions from ocean steric and dynamic changes as well as those from changes in land ice are also estimated to provide context for the GIA projections at the regional scale. When summing the contributions from all evaluated processes at the 13 cities considered along this coastline, using median or best-estimate values, the GIA signal comprises 5-38% of the total depending on the adopted climate forcing and location. Examining the spatial distribution of other contributors to RSL, we find an approximate net cancellation in their spatial variability. In our results, GIA dominates the net RSL spatial variability north of 35°N, emphasizing the importance of regional scale GIA studies in future sea level projections.

Biomass burning emissions of reactive gases estimated from satellite data analysis and ecosystem modeling for the Brazilian Amazon region

NASA Astrophysics Data System (ADS)

Potter, Christopher; Brooks-Genovese, Vanessa; Klooster, Steven; Torregrosa, Alicia

2002-10-01

To produce a new daily record of trace gas emissions from biomass burning events for the Brazilian Legal Amazon, we have combined satellite advanced very high resolution radiometer (AVHRR) data on fire counts together for the first time with vegetation greenness imagery as inputs to an ecosystem biomass model at 8 km spatial resolution. This analysis goes beyond previous estimates for reactive gas emissions from Amazon fires, owing to a more detailed geographic distribution estimate of vegetation biomass, coupled with daily fire activity for the region (original 1 km resolution), and inclusion of fire effects in extensive areas of the Legal Amazon (defined as the Brazilian states of Acre, Amapá, Amazonas, Maranhao, Mato Grosso, Pará, Rondônia, Roraima, and Tocantins) covered by open woodland, secondary forests, savanna, and pasture vegetation. Results from our emissions model indicate that annual emissions from Amazon deforestation and biomass burning in the early 1990s total to 102 Tg yr-1 carbon monoxide (CO) and 3.5 Tg yr-1 nitrogen oxides (NOx). Peak daily burning emissions, which occurred in early September 1992, were estimated at slightly more than 3 Tg d-1for CO and 0.1 Tg d-1for NOx flux to the atmosphere. Other burning source fluxes of gases with relatively high emission factors are reported, including methane (CH4), nonmethane hydrocarbons (NMHC), and sulfur dioxide (SO2), in addition to total particulate matter (TPM). We estimate the Brazilian Amazon region to be a source of between one fifth and one third for each of these global emission fluxes to the atmosphere. The regional distribution of burning emissions appears to be highest in the Brazilian states of Maranhao and Tocantins, mainly from burning outside of moist forest areas, and in Pará and Mato Grosso, where we identify important contributions from primary forest cutting and burning. These new daily emission estimates of reactive gases from biomass burning fluxes are designed to be used as detailed spatial and temporal inputs to computer models and data analysis of tropospheric chemistry over the tropical region.

Robust geostatistical analysis of spatial data

NASA Astrophysics Data System (ADS)

Papritz, A.; Künsch, H. R.; Schwierz, C.; Stahel, W. A.

2012-04-01

Most of the geostatistical software tools rely on non-robust algorithms. This is unfortunate, because outlying observations are rather the rule than the exception, in particular in environmental data sets. Outlying observations may results from errors (e.g. in data transcription) or from local perturbations in the processes that are responsible for a given pattern of spatial variation. As an example, the spatial distribution of some trace metal in the soils of a region may be distorted by emissions of local anthropogenic sources. Outliers affect the modelling of the large-scale spatial variation, the so-called external drift or trend, the estimation of the spatial dependence of the residual variation and the predictions by kriging. Identifying outliers manually is cumbersome and requires expertise because one needs parameter estimates to decide which observation is a potential outlier. Moreover, inference after the rejection of some observations is problematic. A better approach is to use robust algorithms that prevent automatically that outlying observations have undue influence. Former studies on robust geostatistics focused on robust estimation of the sample variogram and ordinary kriging without external drift. Furthermore, Richardson and Welsh (1995) [2] proposed a robustified version of (restricted) maximum likelihood ([RE]ML) estimation for the variance components of a linear mixed model, which was later used by Marchant and Lark (2007) [1] for robust REML estimation of the variogram. We propose here a novel method for robust REML estimation of the variogram of a Gaussian random field that is possibly contaminated by independent errors from a long-tailed distribution. It is based on robustification of estimating equations for the Gaussian REML estimation. Besides robust estimates of the parameters of the external drift and of the variogram, the method also provides standard errors for the estimated parameters, robustified kriging predictions at both sampled and unsampled locations and kriging variances. The method has been implemented in an R package. Apart from presenting our modelling framework, we shall present selected simulation results by which we explored the properties of the new method. This will be complemented by an analysis of the Tarrawarra soil moisture data set [3].

The use of historical information for regional frequency analysis of extreme skew surge

NASA Astrophysics Data System (ADS)

Frau, Roberto; Andreewsky, Marc; Bernardara, Pietro

2018-03-01

The design of effective coastal protections requires an adequate estimation of the annual occurrence probability of rare events associated with a return period up to 103 years. Regional frequency analysis (RFA) has been proven to be an applicable way to estimate extreme events by sorting regional data into large and spatially distributed datasets. Nowadays, historical data are available to provide new insight on past event estimation. The utilisation of historical information would increase the precision and the reliability of regional extreme's quantile estimation. However, historical data are from significant extreme events that are not recorded by tide gauge. They usually look like isolated data and they are different from continuous data from systematic measurements of tide gauges. This makes the definition of the duration of our observations period complicated. However, the duration of the observation period is crucial for the frequency estimation of extreme occurrences. For this reason, we introduced here the concept of credible duration. The proposed RFA method (hereinafter referenced as FAB, from the name of the authors) allows the use of historical data together with systematic data, which is a result of the use of the credible duration concept.

Lateral Flow of Carbon From U.S. Agricultural Lands: Carbon Uptake, Consumption, and Respiration

NASA Astrophysics Data System (ADS)

Sabesan, A.; West, T. O.; Roddy, A. B.; Marland, G.; Bhaduri, B. L.

2005-12-01

Net carbon exchange between biomass and the atmosphere can be estimated and modeled on a regional basis to understand the effects of land-use change on the carbon cycle and on net CO2 emissions to the atmosphere. However, within ecosystems that are managed to produce commodities for consumption (i.e., agriculture and forest lands), carbon can be transported laterally when crops or timber are harvested, in addition to being transported vertically between plants and the atmosphere. The spatial and temporal domain over which carbon uptake, transport, and release occur has implications for regional carbon studies. For example, carbon may be taken up by crops in one region, but released through human consumption in another region. Estimates of lateral transport and release of carbon may therefore contribute another dimension to bottom-up carbon modeling, and may also be used as input for comparison to top-down atmospheric modeling. Our research to date has focused on the uptake, consumption, and respiration of CO2 associated with agricultural crops and related food commodities. We estimate a net uptake of 495 Tg C on U.S. croplands in 2000. This uptake occurs primarily in the Midwestern U.S. Human respiration of CO2 contributed about 31 Tg C and livestock emitted about 77 Tg C as CO2 and CH4 in 2000. Estimates of CO2 from food wastes in municipal landfills and from human excrement in wastewater treatment plants are currently being developed. The spatial distribution of CO2 uptake and release are mapped, respectively, at the county level and at 1km resolution that is commensurate with Landscan USA population data.

Cluster analysis of quantitative parametric maps from DCE-MRI: application in evaluating heterogeneity of tumor response to antiangiogenic treatment.

PubMed

Longo, Dario Livio; Dastrù, Walter; Consolino, Lorena; Espak, Miklos; Arigoni, Maddalena; Cavallo, Federica; Aime, Silvio

2015-07-01

The objective of this study was to compare a clustering approach to conventional analysis methods for assessing changes in pharmacokinetic parameters obtained from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) during antiangiogenic treatment in a breast cancer model. BALB/c mice bearing established transplantable her2+ tumors were treated with a DNA-based antiangiogenic vaccine or with an empty plasmid (untreated group). DCE-MRI was carried out by administering a dose of 0.05 mmol/kg of Gadocoletic acid trisodium salt, a Gd-based blood pool contrast agent (CA) at 1T. Changes in pharmacokinetic estimates (K(trans) and vp) in a nine-day interval were compared between treated and untreated groups on a voxel-by-voxel analysis. The tumor response to therapy was assessed by a clustering approach and compared with conventional summary statistics, with sub-regions analysis and with histogram analysis. Both the K(trans) and vp estimates, following blood-pool CA injection, showed marked and spatial heterogeneous changes with antiangiogenic treatment. Averaged values for the whole tumor region, as well as from the rim/core sub-regions analysis were unable to assess the antiangiogenic response. Histogram analysis resulted in significant changes only in the vp estimates (p<0.05). The proposed clustering approach depicted marked changes in both the K(trans) and vp estimates, with significant spatial heterogeneity in vp maps in response to treatment (p<0.05), provided that DCE-MRI data are properly clustered in three or four sub-regions. This study demonstrated the value of cluster analysis applied to pharmacokinetic DCE-MRI parametric maps for assessing tumor response to antiangiogenic therapy. Copyright © 2015 Elsevier Inc. All rights reserved.

A method for the calculation of anaerobic oxidation of methane rates across regional scales: an example from the Belt Seas and The Sound (North Sea-Baltic Sea transition)

NASA Astrophysics Data System (ADS)

Mogollón, José M.; Dale, Andrew W.; Jensen, Jørn B.; Schlüter, Michael; Regnier, Pierre

2013-08-01

Estimating the amount of methane in the seafloor globally as well as the flux of methane from sediments toward the ocean-atmosphere system are important considerations in both geological and climate sciences. Nevertheless, global estimates of methane inventories and rates of methane production and consumption through anaerobic oxidation in marine sediments are very poorly constrained. Tools for regionally assessing methane formation and consumption rates would greatly increase our understanding of the spatial heterogeneity of the methane cycle as well as help constrain the global methane budget. In this article, an algorithm for calculating methane consumption rates in the inner shelf is applied to the gas-rich sediments of the Belt Seas and The Sound (North Sea-Baltic Sea transition). It is based on the depth of free gas determined by hydroacoustic techniques and the local methane solubility concentration. Due to the continuous nature of shipboard hydroacoustic measurements, this algorithm captures spatial heterogeneities in methane fluxes better than geochemical analyses of point sources such as observational/sampling stations. The sensibility of the algorithm with respect to the resolution of the free gas depth measurements (2 m vs. 50 cm) is proven of minor importance (a discrepancy of <10%) for a small part of the study area. The algorithm-derived anaerobic methane oxidation rates compare well with previous measured and modeling studies. Finally, regional results reveal that contemporary anaerobic methane oxidation in worldwide inner-shelf sediments may be an order of magnitude lower (ca. 0.24 Tmol year-1) than previous estimates (4.6 Tmol year-1). These algorithms ultimately help improve regional estimates of anaerobic oxidation of methane rates.

Along-the-net reconstruction of hydropower potential with consideration of anthropic alterations

NASA Astrophysics Data System (ADS)

Masoero, A.; Claps, P.; Gallo, E.; Ganora, D.; Laio, F.

2014-09-01

Even in regions with mature hydropower development, requirements for stable renewable power sources suggest revision of plans of exploitation of water resources, while taking care of the environmental regulations. Mean Annual Flow (MAF) is a key parameter when trying to represent water availability for hydropower purposes. MAF is usually determined in ungauged basins by means of regional statistical analysis. For this study a regional estimation method consistent along-the-river network has been developed for MAF estimation; the method uses a multi-regressive approach based on geomorphoclimatic descriptors, and it is applied on 100 gauged basins located in NW Italy. The method has been designed to keep the estimates of mean annual flow congruent at the confluences, by considering only raster-summable explanatory variables. Also, the influence of human alterations in the regional analysis of MAF has been studied: impact due to the presence of existing hydropower plants has been taken into account, restoring the "natural" value of runoff through analytical corrections. To exemplify the representation of the assessment of residual hydropower potential, the model has been applied extensively to two specific mountain watersheds by mapping the estimated mean flow for the basins draining into each pixel of a the DEM-derived river network. Spatial algorithms were developed using the OpenSource Software GRASS GIS and PostgreSQL/PostGIS. Spatial representation of the hydropower potential was obtained using different mean flow vs hydraulic-head relations for each pixel. Final potential indices have been represented and mapped through the Google Earth platform, providing a complete and interactive picture of the available potential, useful for planning and regulation purposes.

R2 TRI facilities with 1999-2011 risk related estimates throughout the census blockgroup

EPA Pesticide Factsheets

This dataset delineates the distribution of estimate risk from the TRI facilities for 1999 - 2011 throughout the census blockgroup of the region using Office of Pollution, Prevention & Toxics (OPPT)'s Risk-Screening Environmental Indicators model (RSEI). The model uses the reported quantities of TRI releases of chemicals to estimate the impacts associated with each type of air release or transfer by every TRI facility.The RSEI was run to generate the estimate risk for each TRI facility in the region. The result from the model is joined to the TRI spatial data. Estimate risk values for each census block group were calculated based on the inverse distance of all the facilities which are within a 50 km radius of the census block group centroid. The estimate risk value for each census block group thus is an aggregated value that takes into account the estimate potential risk of all the facilities within the searching radius (50km).

A Bayesian kriging approach for blending satellite and ground precipitation observations

USGS Publications Warehouse

Verdin, Andrew P.; Rajagopalan, Balaji; Kleiber, William; Funk, Christopher C.

2015-01-01

Drought and flood management practices require accurate estimates of precipitation. Gauge observations, however, are often sparse in regions with complicated terrain, clustered in valleys, and of poor quality. Consequently, the spatial extent of wet events is poorly represented. Satellite-derived precipitation data are an attractive alternative, though they tend to underestimate the magnitude of wet events due to their dependency on retrieval algorithms and the indirect relationship between satellite infrared observations and precipitation intensities. Here we offer a Bayesian kriging approach for blending precipitation gauge data and the Climate Hazards Group Infrared Precipitation satellite-derived precipitation estimates for Central America, Colombia, and Venezuela. First, the gauge observations are modeled as a linear function of satellite-derived estimates and any number of other variables—for this research we include elevation. Prior distributions are defined for all model parameters and the posterior distributions are obtained simultaneously via Markov chain Monte Carlo sampling. The posterior distributions of these parameters are required for spatial estimation, and thus are obtained prior to implementing the spatial kriging model. This functional framework is applied to model parameters obtained by sampling from the posterior distributions, and the residuals of the linear model are subject to a spatial kriging model. Consequently, the posterior distributions and uncertainties of the blended precipitation estimates are obtained. We demonstrate this method by applying it to pentadal and monthly total precipitation fields during 2009. The model's performance and its inherent ability to capture wet events are investigated. We show that this blending method significantly improves upon the satellite-derived estimates and is also competitive in its ability to represent wet events. This procedure also provides a means to estimate a full conditional distribution of the “true” observed precipitation value at each grid cell.

A Discontinuous Galerkin Method for Parabolic Problems with Modified hp-Finite Element Approximation Technique

NASA Technical Reports Server (NTRS)

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

2004-01-01

A recent paper is generalized to a case where the spatial region is taken in R(sup 3). The region is assumed to be a thin body, such as a panel on the wing or fuselage of an aerospace vehicle. The traditional h- as well as hp-finite element methods are applied to the surface defined in the x - y variables, while, through the thickness, the technique of the p-element is employed. Time and spatial discretization scheme based upon an assumption of certain weak singularity of double vertical line u(sub t) double vertical line 2, is used to derive an optimal a priori error estimate for the current method.

The regionalization of national-scale SPARROW models for stream nutrients

USGS Publications Warehouse

Schwarz, Gregory E.; Alexander, Richard B.; Smith, Richard A.; Preston, Stephen D.

2011-01-01

This analysis modifies the parsimonious specification of recently published total nitrogen (TN) and total phosphorus (TP) national-scale SPAtially Referenced Regressions On Watershed attributes models to allow each model coefficient to vary geographically among three major river basins of the conterminous United States. Regionalization of the national models reduces the standard errors in the prediction of TN and TP loads, expressed as a percentage of the predicted load, by about 6 and 7%. We develop and apply a method for combining national-scale and regional-scale information to estimate a hybrid model that imposes cross-region constraints that limit regional variation in model coefficients, effectively reducing the number of free model parameters as compared to a collection of independent regional models. The hybrid TN and TP regional models have improved model fit relative to the respective national models, reducing the standard error in the prediction of loads, expressed as a percentage of load, by about 5 and 4%. Only 19% of the TN hybrid model coefficients and just 2% of the TP hybrid model coefficients show evidence of substantial regional specificity (more than ±100% deviation from the national model estimate). The hybrid models have much greater precision in the estimated coefficients than do the unconstrained regional models, demonstrating the efficacy of pooling information across regions to improve regional models.

Assessing the Impact of Climatic Variability and Change on Maize Production in the Midwestern USA

NASA Astrophysics Data System (ADS)

Andresen, J.; Jain, A. K.; Niyogi, D. S.; Alagarswamy, G.; Biehl, L.; Delamater, P.; Doering, O.; Elias, A.; Elmore, R.; Gramig, B.; Hart, C.; Kellner, O.; Liu, X.; Mohankumar, E.; Prokopy, L. S.; Song, C.; Todey, D.; Widhalm, M.

2013-12-01

Weather and climate remain among the most important uncontrollable factors in agricultural production systems. In this study, three process-based crop simulation models were used to identify the impacts of climate on the production of maize in the Midwestern U.S.A. during the past century. The 12-state region is a key global production area, responsible for more than 80% of U.S. domestic and 25% of total global production. The study is a part of the Useful to Useable (U2U) Project, a USDA NIFA-sponsored project seeking to improve the resilience and profitability of farming operations in the region amid climate variability and change. Three process-based crop simulation models were used in the study: CERES-Maize (DSSAT, Hoogenboom et al., 2012), the Hybrid-Maize model (Yang et al., 2004), and the Integrated Science Assessment Model (ISAM, Song et al., 2013). Model validation was carried out with individual plot and county observations. The models were run with 4 to 50 km spatial resolution gridded weather data for representative soils and cultivars, 1981-2012, to examine spatial and temporal yield variability within the region. We also examined the influence of different crop models and spatial scales on regional scale yield estimation, as well as a yield gap analysis between observed and attainable yields. An additional study was carried out with the CERES-Maize model at 18 individual site locations 1901-2012 to examine longer term historical trends. For all simulations, all input variables were held constant in order to isolate the impacts of climate. In general, the model estimates were in good agreement with observed yields, especially in central sections of the region. Regionally, low precipitation and soil moisture stress were chief limitations to simulated crop yields. The study suggests that at least part of the observed yield increases in the region during recent decades have occurred as the result of wetter, less stressful growing season weather conditions.

Adjusting for sampling variability in sparse data: geostatistical approaches to disease mapping

PubMed Central

2011-01-01

Background Disease maps of crude rates from routinely collected health data indexed at a small geographical resolution pose specific statistical problems due to the sparse nature of the data. Spatial smoothers allow areas to borrow strength from neighboring regions to produce a more stable estimate of the areal value. Geostatistical smoothers are able to quantify the uncertainty in smoothed rate estimates without a high computational burden. In this paper, we introduce a uniform model extension of Bayesian Maximum Entropy (UMBME) and compare its performance to that of Poisson kriging in measures of smoothing strength and estimation accuracy as applied to simulated data and the real data example of HIV infection in North Carolina. The aim is to produce more reliable maps of disease rates in small areas to improve identification of spatial trends at the local level. Results In all data environments, Poisson kriging exhibited greater smoothing strength than UMBME. With the simulated data where the true latent rate of infection was known, Poisson kriging resulted in greater estimation accuracy with data that displayed low spatial autocorrelation, while UMBME provided more accurate estimators with data that displayed higher spatial autocorrelation. With the HIV data, UMBME performed slightly better than Poisson kriging in cross-validatory predictive checks, with both models performing better than the observed data model with no smoothing. Conclusions Smoothing methods have different advantages depending upon both internal model assumptions that affect smoothing strength and external data environments, such as spatial correlation of the observed data. Further model comparisons in different data environments are required to provide public health practitioners with guidelines needed in choosing the most appropriate smoothing method for their particular health dataset. PMID:21978359

Adjusting for sampling variability in sparse data: geostatistical approaches to disease mapping.

PubMed

Hampton, Kristen H; Serre, Marc L; Gesink, Dionne C; Pilcher, Christopher D; Miller, William C

2011-10-06

Disease maps of crude rates from routinely collected health data indexed at a small geographical resolution pose specific statistical problems due to the sparse nature of the data. Spatial smoothers allow areas to borrow strength from neighboring regions to produce a more stable estimate of the areal value. Geostatistical smoothers are able to quantify the uncertainty in smoothed rate estimates without a high computational burden. In this paper, we introduce a uniform model extension of Bayesian Maximum Entropy (UMBME) and compare its performance to that of Poisson kriging in measures of smoothing strength and estimation accuracy as applied to simulated data and the real data example of HIV infection in North Carolina. The aim is to produce more reliable maps of disease rates in small areas to improve identification of spatial trends at the local level. In all data environments, Poisson kriging exhibited greater smoothing strength than UMBME. With the simulated data where the true latent rate of infection was known, Poisson kriging resulted in greater estimation accuracy with data that displayed low spatial autocorrelation, while UMBME provided more accurate estimators with data that displayed higher spatial autocorrelation. With the HIV data, UMBME performed slightly better than Poisson kriging in cross-validatory predictive checks, with both models performing better than the observed data model with no smoothing. Smoothing methods have different advantages depending upon both internal model assumptions that affect smoothing strength and external data environments, such as spatial correlation of the observed data. Further model comparisons in different data environments are required to provide public health practitioners with guidelines needed in choosing the most appropriate smoothing method for their particular health dataset.

Status and Trends of Nitrogen Loads to Estuaries of the Conterminous U.S.

EPA Science Inventory

We applied regional SPARROW (SPAtially Referenced Regressions On Watershed attributes) models to estimate status and trends of potential nitrogen loads to estuaries of the conterminous United States. The original SPARROW models predict average detrended loads by source based on ...

Environmental transmission of Mycobacterium ulcerans drives dynamics of Buruli ulcer in endemic regions of Cameroon

NASA Astrophysics Data System (ADS)

Garchitorena, Andrés; Ngonghala, Calistus N.; Texier, Gaëtan; Landier, Jordi; Eyangoh, Sara; Bonds, Matthew H.; Guégan, Jean-François; Roche, Benjamin

2015-12-01

Buruli Ulcer is a devastating skin disease caused by the pathogen Mycobacterium ulcerans. Emergence and distribution of Buruli ulcer cases is clearly linked to aquatic ecosystems, but the specific route of transmission of M. ulcerans to humans remains unclear. Relying on the most detailed field data in space and time on M. ulcerans and Buruli ulcer available today, we assess the relative contribution of two potential transmission routes -environmental and water bug transmission- to the dynamics of Buruli ulcer in two endemic regions of Cameroon. The temporal dynamics of Buruli ulcer incidence are explained by estimating rates of different routes of transmission in mathematical models. Independently, we also estimate statistical models of the different transmission pathways on the spatial distribution of Buruli ulcer. The results of these two independent approaches are corroborative and suggest that environmental transmission pathways explain the temporal and spatial patterns of Buruli ulcer in our endemic areas better than the water bug transmission.

A Patch Density Recommendation based on Convergence Studies for Vehicle Panel Vibration Response resulting from Excitation by a Diffuse Acoustic Field

NASA Technical Reports Server (NTRS)

Smith, Andrew; LaVerde, Bruce; Jones, Douglas; Towner, Robert; Waldon, James; Hunt, Ron

2013-01-01

Producing fluid structural interaction estimates of panel vibration from an applied pressure field excitation are quite dependent on the spatial correlation of the pressure field. There is a danger of either over estimating a low frequency response or under predicting broad band panel response in the more modally dense bands if the pressure field spatial correlation is not accounted for adequately. It is a useful practice to simulate the spatial correlation of the applied pressure field over a 2d surface using a matrix of small patch area regions on a finite element model (FEM). Use of a fitted function for the spatial correlation between patch centers can result in an error if the choice of patch density is not fine enough to represent the more continuous spatial correlation function throughout the intended frequency range of interest. Several patch density assumptions to approximate the fitted spatial correlation function are first evaluated using both qualitative and quantitative illustrations. The actual response of a typical vehicle panel system FEM is then examined in a convergence study where the patch density assumptions are varied over the same model. The convergence study results illustrate the impacts possible from a poor choice of patch density on the analytical response estimate. The fitted correlation function used in this study represents a diffuse acoustic field (DAF) excitation of the panel to produce vibration response.

Uncertainty in the spatial distribution of tropical forest biomass: a comparison of pan-tropical maps.

PubMed

Mitchard, Edward Ta; Saatchi, Sassan S; Baccini, Alessandro; Asner, Gregory P; Goetz, Scott J; Harris, Nancy L; Brown, Sandra

2013-10-26

Mapping the aboveground biomass of tropical forests is essential both for implementing conservation policy and reducing uncertainties in the global carbon cycle. Two medium resolution (500 m - 1000 m) pantropical maps of vegetation biomass have been recently published, and have been widely used by sub-national and national-level activities in relation to Reducing Emissions from Deforestation and forest Degradation (REDD+). Both maps use similar input data layers, and are driven by the same spaceborne LiDAR dataset providing systematic forest height and canopy structure estimates, but use different ground datasets for calibration and different spatial modelling methodologies. Here, we compare these two maps to each other, to the FAO's Forest Resource Assessment (FRA) 2010 country-level data, and to a high resolution (100 m) biomass map generated for a portion of the Colombian Amazon. We find substantial differences between the two maps, in particular in central Amazonia, the Congo basin, the south of Papua New Guinea, the Miombo woodlands of Africa, and the dry forests and savannas of South America. There is little consistency in the direction of the difference. However, when the maps are aggregated to the country or biome scale there is greater agreement, with differences cancelling out to a certain extent. When comparing country level biomass stocks, the two maps agree with each other to a much greater extent than to the FRA 2010 estimates. In the Colombian Amazon, both pantropical maps estimate higher biomass than the independent high resolution map, but show a similar spatial distribution of this biomass. Biomass mapping has progressed enormously over the past decade, to the stage where we can produce globally consistent maps of aboveground biomass. We show that there are still large uncertainties in these maps, in particular in areas with little field data. However, when used at a regional scale, different maps appear to converge, suggesting we can provide reasonable stock estimates when aggregated over large regions. Therefore we believe the largest uncertainties for REDD+ activities relate to the spatial distribution of biomass and to the spatial pattern of forest cover change, rather than to total globally or nationally summed carbon density.

Spatial variations in mortality in pelagic early life stages of a marine fish (Gadus morhua)

NASA Astrophysics Data System (ADS)

Langangen, Øystein; Stige, Leif C.; Yaragina, Natalia A.; Ottersen, Geir; Vikebø, Frode B.; Stenseth, Nils Chr.

2014-09-01

Mortality of pelagic eggs and larvae of marine fish is often assumed to be constant both in space and time due to lacking information. This may, however, be a gross oversimplification, as early life stages are likely to experience large variations in mortality both in time and space. In this paper we develop a method for estimating the spatial variability in mortality of eggs and larvae. The method relies on survey data and physical-biological particle-drift models to predict the drift of ichthyoplankton. Furthermore, the method was used to estimate the spatially resolved mortality field in the egg and larval stages of Barents Sea cod (Gadus morhua). We analyzed data from the Barents Sea for the period between 1959 and 1993 when there are two surveys available: a spring and a summer survey. An individual-based physical-biological particle-drift model, tailored to the egg and larval stages of Barents Sea cod, was used to predict the drift trajectories from the observed stage-specific distributions in spring to the time of observation in the summer, a drift time of approximately 45 days. We interpreted the spatial patterns in the differences between the predicted and observed abundance distributions in summer as reflecting the spatial patterns in mortality over the drift period. Using the estimated mortality fields, we show that the spatial variations in mortality might have a significant impact on survival to later life stages and we suggest that there may be trade-offs between increased early survival in off shore regions and reduced probability of ending up in the favorable nursing grounds in the Barents Sea. In addition, we show that accounting for the estimated mortality field, improves the correlation between a simulated recruitment index and observation-based indices of juvenile abundance.

Spatially-resolved aircraft-based quantification of methane emissions from the Fayetteville Shale Gas Play

NASA Astrophysics Data System (ADS)

Schwietzke, S.; Petron, G.; Conley, S. A.; Karion, A.; Tans, P. P.; Wolter, S.; King, C. W.; White, A. B.; Coleman, T.; Bianco, L.; Schnell, R. C.

2016-12-01

Confidence in basin scale oil and gas industry related methane (CH4) emission estimates hinges on an in-depth understanding, objective evaluation, and continued improvements of both top-down (e.g. aircraft measurement based) and bottom-up (e.g. emission inventories using facility- and/or component-level measurements) approaches. Systematic discrepancies of CH4 emission estimates between both approaches in the literature have highlighted research gaps. This paper is part of a more comprehensive study to expand and improve this reconciliation effort for a US dry shale gas play. This presentation will focus on refinements of the aircraft mass balance method to reduce the number of potential methodological biases (e.g. data and methodology). The refinements include (i) an in-depth exploration of the definition of upwind conditions and their impact on calculated downwind CH4 enhancements and total CH4 emissions, (ii) taking into account small but non-zero vertical and horizontal wind gradients in the boundary layer, and (iii) characterizing the spatial distribution of CH4 emissions in the study area using aircraft measurements. For the first time to our knowledge, we apply the aircraft mass balance method to calculate spatially resolved total CH4 emissions for 10 km x 60 km sub-regions within the study area. We identify higher-emitting sub-regions and localize repeating emission patterns as well as differences between days. The increased resolution of the top-down calculation will for the first time allow for an in-depth comparison with a spatially and temporally resolved bottom-up emission estimate based on measurements, concurrent activity data and other data sources.

Modeling Reef Fish Biomass, Recovery Potential, and Management Priorities in the Western Indian Ocean.

PubMed

McClanahan, Timothy R; Maina, Joseph M; Graham, Nicholas A J; Jones, Kendall R

2016-01-01

Fish biomass is a primary driver of coral reef ecosystem services and has high sensitivity to human disturbances, particularly fishing. Estimates of fish biomass, their spatial distribution, and recovery potential are important for evaluating reef status and crucial for setting management targets. Here we modeled fish biomass estimates across all reefs of the western Indian Ocean using key variables that predicted the empirical data collected from 337 sites. These variables were used to create biomass and recovery time maps to prioritize spatially explicit conservation actions. The resultant fish biomass map showed high variability ranging from ~15 to 2900 kg/ha, primarily driven by human populations, distance to markets, and fisheries management restrictions. Lastly, we assembled data based on the age of fisheries closures and showed that biomass takes ~ 25 years to recover to typical equilibrium values of ~1200 kg/ha. The recovery times to biomass levels for sustainable fishing yields, maximum diversity, and ecosystem stability or conservation targets once fishing is suspended was modeled to estimate temporal costs of restrictions. The mean time to recovery for the whole region to the conservation target was 8.1(± 3SD) years, while recovery to sustainable fishing thresholds was between 0.5 and 4 years, but with high spatial variation. Recovery prioritization scenario models included one where local governance prioritized recovery of degraded reefs and two that prioritized minimizing recovery time, where countries either operated independently or collaborated. The regional collaboration scenario selected remote areas for conservation with uneven national responsibilities and spatial coverage, which could undermine collaboration. There is the potential to achieve sustainable fisheries within a decade by promoting these pathways according to their social-ecological suitability.

Modeling Reef Fish Biomass, Recovery Potential, and Management Priorities in the Western Indian Ocean

PubMed Central

McClanahan, Timothy R.; Maina, Joseph M.; Graham, Nicholas A. J.; Jones, Kendall R.

2016-01-01

Fish biomass is a primary driver of coral reef ecosystem services and has high sensitivity to human disturbances, particularly fishing. Estimates of fish biomass, their spatial distribution, and recovery potential are important for evaluating reef status and crucial for setting management targets. Here we modeled fish biomass estimates across all reefs of the western Indian Ocean using key variables that predicted the empirical data collected from 337 sites. These variables were used to create biomass and recovery time maps to prioritize spatially explicit conservation actions. The resultant fish biomass map showed high variability ranging from ~15 to 2900 kg/ha, primarily driven by human populations, distance to markets, and fisheries management restrictions. Lastly, we assembled data based on the age of fisheries closures and showed that biomass takes ~ 25 years to recover to typical equilibrium values of ~1200 kg/ha. The recovery times to biomass levels for sustainable fishing yields, maximum diversity, and ecosystem stability or conservation targets once fishing is suspended was modeled to estimate temporal costs of restrictions. The mean time to recovery for the whole region to the conservation target was 8.1(± 3SD) years, while recovery to sustainable fishing thresholds was between 0.5 and 4 years, but with high spatial variation. Recovery prioritization scenario models included one where local governance prioritized recovery of degraded reefs and two that prioritized minimizing recovery time, where countries either operated independently or collaborated. The regional collaboration scenario selected remote areas for conservation with uneven national responsibilities and spatial coverage, which could undermine collaboration. There is the potential to achieve sustainable fisheries within a decade by promoting these pathways according to their social-ecological suitability. PMID:27149673

Estimating regional CO2 and CH4 fluxes using GOSAT XCO2 and XCH4 observations

NASA Astrophysics Data System (ADS)

Fraser, A. C.; Palmer, P. I.; Feng, L.; Parker, R.; Boesch, H.; Cogan, A. J.

2012-12-01

We infer regional monthly surface flux estimates for CO2 and CH4, June 2009-December 2010, from proxy dry-air column-averaged mole fractions of CO2 and CH4 from the Greenhouse gases Observing SATellite (GOSAT) using an ensemble Kalman Filter combined with the GEOS-Chem chemistry transport model. We compare these flux estimates with estimates inferred from in situ surface mole fraction measurements and from combining in situ and GOSAT measurements in order to quantify the added value of GOSAT data above the conventional surface measurement network. We find that the error reduction, a measure of how much the posterior fluxes are being informed by the assimilated data, at least doubles when GOSAT measurements are used versus the surface only inversions, with the exception of regions that are well covered by the surface network at the spatial and temporal resolution of our flux estimation calculation. We have incorporated a new online bias correction scheme to account for GOSAT biases. We report global and regional flux estimates inferred from GOSAT and/or in situ measurements. While the global posterior fluxes from GOSAT and in situ measurements agree, we find significant differences in the regional fluxes, particularly over the tropics. We evaluate the posterior fluxes by comparing them against independent surface mole fraction, column, and aircraft measurements using the GEOS-Chem model as an intermediary.

The advantages, and challenges, in using multiple techniques in the estimation of surface water-groundwater fluxes.

NASA Astrophysics Data System (ADS)

Shanafield, M.; Cook, P. G.

2014-12-01

When estimating surface water-groundwater fluxes, the use of complimentary techniques helps to fill in uncertainties in any individual method, and to potentially gain a better understanding of spatial and temporal variability in a system. It can also be a way of preventing the loss of data during infrequent and unpredictable flow events. For example, much of arid Australia relies on groundwater, which is recharged by streamflow through ephemeral streams during flood events. Three recent surface water/groundwater investigations from arid Australian systems provide good examples of how using multiple field and analysis techniques can help to more fully characterize surface water-groundwater fluxes, but can also result in conflicting values over varying spatial and temporal scales. In the Pilbara region of Western Australia, combining streambed radon measurements, vertical heat transport modeling, and a tracer test helped constrain very low streambed residence times, which are on the order of minutes. Spatial and temporal variability between the methods yielded hyporheic exchange estimates between 10-4 m2 s-1 and 4.2 x 10-2 m2 s-1. In South Australia, three-dimensional heat transport modeling captured heterogeneity within 20 square meters of streambed, identifying areas of sandy soil (flux rates of up to 3 m d-1) and clay (flux rates too slow to be accurately characterized). Streamflow front modeling showed similar flux rates, but averaged over 100 m long stream segments for a 1.6 km reach. Finally, in central Australia, several methods are used to decipher whether any of the flow down a highly ephemeral river contributes to regional groundwater recharge, showing that evaporation and evapotranspiration likely accounts for all of the infiltration into the perched aquifer. Lessons learned from these examples demonstrate the influences of the spatial and temporal variability between techniques on estimated fluxes.

CERES Monthly TOA and SRB Averages (SRBAVG) data in HDF-EOS Grid (CER_SRBAVG_Terra-FM2-MODIS_Edition2C)

NASA Technical Reports Server (NTRS)

Wielicki, Bruce A. (Principal Investigator)

The Monthly TOA/Surface Averages (SRBAVG) product contains a month of space and time averaged Clouds and the Earth's Radiant Energy System (CERES) data for a single scanner instrument. The SRBAVG is also produced for combinations of scanner instruments. The monthly average regional flux is estimated using diurnal models and the 1-degree regional fluxes at the hour of observation from the CERES SFC product. A second set of monthly average fluxes are estimated using concurrent diurnal information from geostationary satellites. These fluxes are given for both clear-sky and total-sky scenes and are spatially averaged from 1-degree regions to 1-degree zonal averages and a global average. For each region, the SRBAVG also contains hourly average fluxes for the month and an overall monthly average. The cloud properties from SFC are column averaged and are included on the SRBAVG. [Location=GLOBAL] [Temporal_Coverage: Start_Date=1998-02-01; Stop_Date=2003-02-28] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Latitude_Resolution=1 degree; Longitude_Resolution=1 degree; Horizontal_Resolution_Range=100 km - < 250 km or approximately 1 degree - < 2.5 degrees; Temporal_Resolution=1 month; Temporal_Resolution_Range=Monthly - < Annual].

CERES Monthly TOA and SRB Averages (SRBAVG) data in HDF-EOS Grid (CER_SRBAVG_TRMM-PFM-VIRS_Edition2B)

NASA Technical Reports Server (NTRS)

Wielicki, Bruce A. (Principal Investigator)

The Monthly TOA/Surface Averages (SRBAVG) product contains a month of space and time averaged Clouds and the Earth's Radiant Energy System (CERES) data for a single scanner instrument. The SRBAVG is also produced for combinations of scanner instruments. The monthly average regional flux is estimated using diurnal models and the 1-degree regional fluxes at the hour of observation from the CERES SFC product. A second set of monthly average fluxes are estimated using concurrent diurnal information from geostationary satellites. These fluxes are given for both clear-sky and total-sky scenes and are spatially averaged from 1-degree regions to 1-degree zonal averages and a global average. For each region, the SRBAVG also contains hourly average fluxes for the month and an overall monthly average. The cloud properties from SFC are column averaged and are included on the SRBAVG. [Location=GLOBAL] [Temporal_Coverage: Start_Date=1998-02-01; Stop_Date=2000-03-31] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Latitude_Resolution=1 degree; Longitude_Resolution=1 degree; Horizontal_Resolution_Range=100 km - < 250 km or approximately 1 degree - < 2.5 degrees; Temporal_Resolution=1 month; Temporal_Resolution_Range=Monthly - < Annual].

CERES Monthly TOA and SRB Averages (SRBAVG) data in HDF-EOS Grid (CER_SRBAVG_Terra-FM1-MODIS_Edition2C)

NASA Technical Reports Server (NTRS)

Wielicki, Bruce A. (Principal Investigator)

The Monthly TOA/Surface Averages (SRBAVG) product contains a month of space and time averaged Clouds and the Earth's Radiant Energy System (CERES) data for a single scanner instrument. The SRBAVG is also produced for combinations of scanner instruments. The monthly average regional flux is estimated using diurnal models and the 1-degree regional fluxes at the hour of observation from the CERES SFC product. A second set of monthly average fluxes are estimated using concurrent diurnal information from geostationary satellites. These fluxes are given for both clear-sky and total-sky scenes and are spatially averaged from 1-degree regions to 1-degree zonal averages and a global average. For each region, the SRBAVG also contains hourly average fluxes for the month and an overall monthly average. The cloud properties from SFC are column averaged and are included on the SRBAVG. [Location=GLOBAL] [Temporal_Coverage: Start_Date=1998-02-01; Stop_Date=2003-02-28] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Latitude_Resolution=1 degree; Longitude_Resolution=1 degree; Horizontal_Resolution_Range=100 km - < 250 km or approximately 1 degree - < 2.5 degrees; Temporal_Resolution=1 month; Temporal_Resolution_Range=Monthly - < Annual].

CERES Monthly TOA and SRB Averages (SRBAVG) data in HDF-EOS Grid (CER_SRBAVG_Terra-FM1-MODIS_Edition2D)

NASA Technical Reports Server (NTRS)

Wielicki, Bruce A. (Principal Investigator)

The Monthly TOA/Surface Averages (SRBAVG) product contains a month of space and time averaged Clouds and the Earth's Radiant Energy System (CERES) data for a single scanner instrument. The SRBAVG is also produced for combinations of scanner instruments. The monthly average regional flux is estimated using diurnal models and the 1-degree regional fluxes at the hour of observation from the CERES SFC product. A second set of monthly average fluxes are estimated using concurrent diurnal information from geostationary satellites. These fluxes are given for both clear-sky and total-sky scenes and are spatially averaged from 1-degree regions to 1-degree zonal averages and a global average. For each region, the SRBAVG also contains hourly average fluxes for the month and an overall monthly average. The cloud properties from SFC are column averaged and are included on the SRBAVG. [Location=GLOBAL] [Temporal_Coverage: Start_Date=1998-02-01; Stop_Date=2004-05-31] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Latitude_Resolution=1 degree; Longitude_Resolution=1 degree; Horizontal_Resolution_Range=100 km - < 250 km or approximately 1 degree - < 2.5 degrees; Temporal_Resolution=1 month; Temporal_Resolution_Range=Monthly - < Annual].

Improved algorithms for estimating Total Alkalinity in Northern Gulf of Mexico

NASA Astrophysics Data System (ADS)

Devkota, M.; Dash, P.

2017-12-01

Ocean Acidification (OA) is one of the serious challenges that have significant impacts on ocean. About 25% of anthropologically generated CO2 is absorbed by the oceans which decreases average ocean pH. This change has critical impacts on marine species, ocean ecology, and associated economics. 35 years of observation concluded that the rate of alteration in OA parameters varies geographically with higher variations in the northern Gulf of Mexico (N-GoM). Several studies have suggested that the Mississippi River affects the carbon dynamics of the N-GoM coastal ecosystem significantly. Total Alkalinity (TA) algorithms developed for major ocean basins produce inaccurate estimations in this region. Hence, a local algorithm to estimate TA is the need for this region, which would incorporate the local effects of oceanographic processes and complex spatial influences. In situ data collected in N-GoM region during the GOMECC-I and II cruises, and GISR Cruises (G-1, 3, 5) from 2007 to 2013 were assimilated and used to calculate the efficiency of the existing TA algorithm that uses Sea Surface Temperature (SST) and Sea Surface Salinity (SSS) as explanatory variables. To improve this algorithm, firstly, statistical analyses were performed to improve the coefficients and the functional form of this algorithm. Then, chlorophyll a (Chl-a) was included as an additional explanatory variable in the multiple linear regression approach in addition to SST and SSS. Based on the average concentration of Chl-a for last 15 years, the N-GoM was divided into two regions, and two separate algorithms were developed for each region. Finally, to address spatial non-stationarity, a Geographically Weighted Regression (GWR) algorithm was developed. The existing TA algorithm resulted considerable algorithm bias with a larger bias in the coastal waters. Chl-a as an additional explanatory variable reduced the bias in the residuals and improved the algorithm efficiency. Chl-a worked as a proxy for addressing the organic pump's pronounced effects in the coastal waters. The GWR algorithm provided a raster surface of the coefficients with even more reliable algorithms to estimate TA with least error. The GWR algorithm addressed the spatial non-stationarity of OA in N-GoM, which apparently was not addressed in the previously developed algorithms.

Integrating Entropy-Based Naïve Bayes and GIS for Spatial Evaluation of Flood Hazard.

PubMed

Liu, Rui; Chen, Yun; Wu, Jianping; Gao, Lei; Barrett, Damian; Xu, Tingbao; Li, Xiaojuan; Li, Linyi; Huang, Chang; Yu, Jia

2017-04-01

Regional flood risk caused by intensive rainfall under extreme climate conditions has increasingly attracted global attention. Mapping and evaluation of flood hazard are vital parts in flood risk assessment. This study develops an integrated framework for estimating spatial likelihood of flood hazard by coupling weighted naïve Bayes (WNB), geographic information system, and remote sensing. The north part of Fitzroy River Basin in Queensland, Australia, was selected as a case study site. The environmental indices, including extreme rainfall, evapotranspiration, net-water index, soil water retention, elevation, slope, drainage proximity, and density, were generated from spatial data representing climate, soil, vegetation, hydrology, and topography. These indices were weighted using the statistics-based entropy method. The weighted indices were input into the WNB-based model to delineate a regional flood risk map that indicates the likelihood of flood occurrence. The resultant map was validated by the maximum inundation extent extracted from moderate resolution imaging spectroradiometer (MODIS) imagery. The evaluation results, including mapping and evaluation of the distribution of flood hazard, are helpful in guiding flood inundation disaster responses for the region. The novel approach presented consists of weighted grid data, image-based sampling and validation, cell-by-cell probability inferring and spatial mapping. It is superior to an existing spatial naive Bayes (NB) method for regional flood hazard assessment. It can also be extended to other likelihood-related environmental hazard studies. © 2016 Society for Risk Analysis.

Development and Application of Version 2.1 of the Fire INventory from NCAR (FINN)

NASA Astrophysics Data System (ADS)

McDonald-Buller, E.; Wiedinmyer, C.; Kimura, Y.

2016-12-01

The Fire INventory from the National Center for Atmospheric Research (FINN) generates global daily emissions estimates of trace gases and particles from open biomass burning, including wildfires, agricultural fires, and prescribed burning. FINN has been widely used for global and regional air quality studies, offering high spatial and temporal resolution necessary for capturing daily variations in emissions and chemistry, consistency across geopolitical boundaries, and chemical speciation profiles for volatile organic compound (VOC) emissions for the GEOS-Chem, SAPRC99, MOZART-4, and Carbon Bond mechanisms. FINN v.1 was first released in 2010 and updated in 2011. FINN v. 1.5 was released in 2014. The work presented here focuses on a collaborative effort between NCAR and the University of Texas at Austin to develop the next generation of the public release of the model, FINN v.2.1, to benefit air quality management and research initiatives within the U.S. and internationally. Specific objectives have included developing a new algorithm for estimating area burned from satellite-derived fire detections, distinguishing major crop types typically found in the U.S., improving the spatial resolution of fuel loading in the United States, and providing flexibility for applying alternative land cover representations from emerging global, U.S. national, and regional land cover products. A case study applies FINN2.1 for regional emission estimates and air quality predictions in Texas during 2012.

Remote sensing based crop type mapping and evapotranspiration estimates at the farm level in arid regions of the globe

NASA Astrophysics Data System (ADS)

Ozdogan, M.; Serrat-Capdevila, A.; Anderson, M. C.

2017-12-01

Despite increasing scarcity of freshwater resources, there is dearth of spatially explicit information on irrigation water consumption through evapotranspiration, particularly in semi-arid and arid geographies. Remote sensing, either alone or in combination with ground surveys, is increasingly being used for irrigation water management by quantifying evaporative losses at the farm level. Increased availability of observations, sophisticated algorithms, and access to cloud-based computing is also helping this effort. This presentation will focus on crop-specific evapotranspiration estimates at the farm level derived from remote sensing in a number of water-scarce regions of the world. The work is part of a larger effort to quantify irrigation water use and improve use efficiencies associated with several World Bank projects. Examples will be drawn from India, where groundwater based irrigation withdrawals are monitored with the help of crop type mapping and evapotranspiration estimates from remote sensing. Another example will be provided from a northern irrigation district in Mexico, where remote sensing is used for detailed water accounting at the farm level. These locations exemplify the success stories in irrigation water management with the help of remote sensing with the hope that spatially disaggregated information on evapotranspiration can be used as inputs for various water management decisions as well as for better water allocation strategies in many other water scarce regions.

A compartmental-spatial system dynamics approach to ground water modeling.

PubMed

Roach, Jesse; Tidwell, Vince

2009-01-01

High-resolution, spatially distributed ground water flow models can prove unsuitable for the rapid, interactive analysis that is increasingly demanded to support a participatory decision environment. To address this shortcoming, we extend the idea of multiple cell (Bear 1979) and compartmental (Campana and Simpson 1984) ground water models developed within the context of spatial system dynamics (Ahmad and Simonovic 2004) for rapid scenario analysis. We term this approach compartmental-spatial system dynamics (CSSD). The goal is to balance spatial aggregation necessary to achieve a real-time integrative and interactive decision environment while maintaining sufficient model complexity to yield a meaningful representation of the regional ground water system. As a test case, a 51-compartment CSSD model was built and calibrated from a 100,0001 cell MODFLOW (McDonald and Harbaugh 1988) model of the Albuquerque Basin in central New Mexico (McAda and Barroll 2002). Seventy-seven percent of historical drawdowns predicted by the MODFLOW model were within 1 m of the corresponding CSSD estimates, and in 80% of the historical model run years the CSSD model estimates of river leakage, reservoir leakage, ground water flow to agricultural drains, and riparian evapotranspiration were within 30% of the corresponding estimates from McAda and Barroll (2002), with improved model agreement during the scenario period. Comparisons of model results demonstrate both advantages and limitations of the CCSD model approach.

Spatial analysis of health risk assessment with arsenic intake of drinking water in the LanYang plain

NASA Astrophysics Data System (ADS)

Chen, C. F.; Liang, C. P.; Jang, C. S.; Chen, J. S.

2016-12-01

Groundwater is one of the most component water resources in Lanyang plain. The groundwater of the Lanyang Plain contains arsenic levels that exceed the current Taiwan Environmental Protection Administration (Taiwan EPA) limit of 10 μg/L. The arsenic of groundwater in some areas of the Lanyang Plain pose great menace for the safe use of groundwater resources. Therefore, poor water quality can adversely impact drinking water uses, leading to human health risks. This study analyzed the potential health risk associated with the ingestion of arsenic-affected groundwater in the arseniasis-endemic Lanyang plain. Geostatistical approach is widely used in spatial variability analysis and distributions of field data with uncertainty. The estimation of spatial distribution of the arsenic contaminant in groundwater is very important in the health risk assessment. This study used indicator kriging (IK) and ordinary kriging (OK) methods to explore the spatial variability of arsenic-polluted parameters. The estimated difference between IK and OK estimates was compared. The extent of arsenic pollution was spatially determined and the Target cancer risk (TR) and dose response were explored when the ingestion of arsenic in groundwater. Thus, a zonal management plan based on safe groundwater use is formulated. The research findings can provide a plan reference of regional water resources supplies for local government administrators and developing groundwater resources in the Lanyang Plain.

Spatial and Temporal Patterns in the Carbon Isotopic Signal of Leaf Wax Aerosols in Continental Air Masses: Linkages with Ecosystem Discrimination

NASA Astrophysics Data System (ADS)

Weber, J.; Conte, M. H.

2006-12-01

Temporal and spatial variations in the concentration and isotopic composition of atmospheric carbon dioxide can be used to estimate the relative magnitudes of the terrestrial and oceanic carbon sinks. An important model parameter is the terrestrial photosynthetic carbon isotopic fractionation of CO2 (Δ), yet estimating Δ over the large spatial scales required by models remains problematic. Epiculticular leaf waxes appear to closely reflect the plant's carbon isotopic discrimination; therefore, the ablated wax aerosols present in well-mixed continental air masses may be used as a proxy to estimate the magnitude of Δ integrated over large (subcontinental) spatial scales. Over the last several years, we have been conducting time-series studies of wax aerosol molecular and isotopic composition at strategically located sites (Maine, northern Alaska, Florida, Bermuda, Barbados) which receive continental air masses passing over major terrestrial biomes (northern temperate/ecotonal boreal forests, tundra, southern US pine/hardwood forests, North American and north African). In this presentation, we describe and contrast patterns of wax aerosol-derived estimates of Δ at these sites. In North American air masses, estimates of Δ range from 14.5-20.5 using the concentration-weighted average δ13C of wax n-acids and from 13.5-19.5 for the wax n-alcohols. Seasonal trends observed in the Florida (southern US) and Bermuda samples (mixed North American air masses) indicate maximum discrimination in early spring and minimum discrimination during the summer dry season. In northern US and high latitude air masses, seasonal trends are less pronounced but in general temporally offset with highest discrimination occurring during late summer. At Barbados, which is dominated by north African air masses passing over regions largely comprised of arid C4 grasslands, estimated Δ for the wax n-acids is significantly lower (14.0-15.5 per mil), consistent with a higher predominance of C4 plants in the aerosol source regions; however, the estimated Δ for the wax n-alcohols is roughly 2 per mil higher indicative of possible different weighting of vegetation sources. Interannual variability is also observed to some extent signifying that the wax aerosol signal of Δ is sensitive to year-to-year differences in environmental forcing (e.g. drought).

Spatial Structure of Above-Ground Biomass Limits Accuracy of Carbon Mapping in Rainforest but Large Scale Forest Inventories Can Help to Overcome.

PubMed

Guitet, Stéphane; Hérault, Bruno; Molto, Quentin; Brunaux, Olivier; Couteron, Pierre

2015-01-01

Precise mapping of above-ground biomass (AGB) is a major challenge for the success of REDD+ processes in tropical rainforest. The usual mapping methods are based on two hypotheses: a large and long-ranged spatial autocorrelation and a strong environment influence at the regional scale. However, there are no studies of the spatial structure of AGB at the landscapes scale to support these assumptions. We studied spatial variation in AGB at various scales using two large forest inventories conducted in French Guiana. The dataset comprised 2507 plots (0.4 to 0.5 ha) of undisturbed rainforest distributed over the whole region. After checking the uncertainties of estimates obtained from these data, we used half of the dataset to develop explicit predictive models including spatial and environmental effects and tested the accuracy of the resulting maps according to their resolution using the rest of the data. Forest inventories provided accurate AGB estimates at the plot scale, for a mean of 325 Mg.ha-1. They revealed high local variability combined with a weak autocorrelation up to distances of no more than10 km. Environmental variables accounted for a minor part of spatial variation. Accuracy of the best model including spatial effects was 90 Mg.ha-1 at plot scale but coarse graining up to 2-km resolution allowed mapping AGB with accuracy lower than 50 Mg.ha-1. Whatever the resolution, no agreement was found with available pan-tropical reference maps at all resolutions. We concluded that the combined weak autocorrelation and weak environmental effect limit AGB maps accuracy in rainforest, and that a trade-off has to be found between spatial resolution and effective accuracy until adequate "wall-to-wall" remote sensing signals provide reliable AGB predictions. Waiting for this, using large forest inventories with low sampling rate (<0.5%) may be an efficient way to increase the global coverage of AGB maps with acceptable accuracy at kilometric resolution.

Rainfall estimation for real time flood monitoring using geostationary meteorological satellite data

NASA Astrophysics Data System (ADS)

Veerakachen, Watcharee; Raksapatcharawong, Mongkol

2015-09-01

Rainfall estimation by geostationary meteorological satellite data provides good spatial and temporal resolutions. This is advantageous for real time flood monitoring and warning systems. However, a rainfall estimation algorithm developed in one region needs to be adjusted for another climatic region. This work proposes computationally-efficient rainfall estimation algorithms based on an Infrared Threshold Rainfall (ITR) method calibrated with regional ground truth. Hourly rain gauge data collected from 70 stations around the Chao-Phraya river basin were used for calibration and validation of the algorithms. The algorithm inputs were derived from FY-2E satellite observations consisting of infrared and water vapor imagery. The results were compared with the Global Satellite Mapping of Precipitation (GSMaP) near real time product (GSMaP_NRT) using the probability of detection (POD), root mean square error (RMSE) and linear correlation coefficient (CC) as performance indices. Comparison with the GSMaP_NRT product for real time monitoring purpose shows that hourly rain estimates from the proposed algorithm with the error adjustment technique (ITR_EA) offers higher POD and approximately the same RMSE and CC with less data latency.

Estimating nitrogen oxides emissions at city scale in China with a nightlight remote sensing model.

PubMed

Jiang, Jianhui; Zhang, Jianying; Zhang, Yangwei; Zhang, Chunlong; Tian, Guangming

2016-02-15

Increasing nitrogen oxides (NOx) emissions over the fast developing regions have been of great concern due to their critical associations with the aggravated haze and climate change. However, little geographically specific data exists for estimating spatio-temporal trends of NOx emissions. In order to quantify the spatial and temporal variations of NOx emissions, a spatially explicit approach based on the continuous satellite observations of artificial nighttime stable lights (NSLs) from the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) was developed to estimate NOx emissions from the largest emission source of fossil fuel combustion. The NSL based model was established with three types of data including satellite data of nighttime stable lights, geographical data of administrative boundaries, and provincial energy consumptions in China, where a significant growth of NOx emission has experienced during three policy stages corresponding to the 9th-11th)Five-Year Plan (FYP, 1995-2010). The estimated national NOx emissions increased by 8.2% per year during the study period, and the total annual NOx emissions in China estimated by the NSL-based model were approximately 4.1%-13.8% higher than the previous estimates. The spatio-temporal variations of NOx emissions at city scale were then evaluated by the Moran's I indices. The global Moran's I indices for measuring spatial agglomerations of China's NOx emission increased by 50.7% during 1995-2010. Although the inland cities have shown larger contribution to the emission growth than the more developed coastal cities since 2005, the High-High clusters of NOx emission located in Beijing-Tianjin-Hebei regions, the Yangtze River Delta, and the Pearl River Delta should still be the major focus of NOx mitigation. Our results indicate that the readily available DMSP/OLS nighttime stable lights based model could be an easily accessible and effective tool for achieving strategic decision making toward NOx reduction. Copyright © 2015 Elsevier B.V. All rights reserved.

Comparison of Weekly Cycle of NO2 Satellite Retrievals and NO(x) Emission Inventories for the Continental United States

NASA Technical Reports Server (NTRS)

Kaynak, B.; Hu, Y.; Martin, R. V.; Sioris, C. E.; Russell, A. G.

2009-01-01

Spatially resolved weekly NO2 variations are obtained from 2003 to 2005 Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) tropospheric NO2 columns for three different types of regions: urban, rural, and rural-point (rural with significant electricity generation unit (EGU) emissions). Regions are compared for magnitudes and weekly profiles. Rural regions do not show any weekly pattern, whereas urban areas show a distinct decrease on the weekends. Rural regions with EGUs show a slight decrease on Sundays. When compared with estimated mobile and stationary nitrogen oxides (NO(x)) emissions from the year 2004 for seven cities, the satellite data have greater variation during weekdays (Monday-Friday). Overall comparisons show that SCIAMACHY derived NO2 correlate well with estimated NO(x) emissions for urban and rural but less for rural-point regions.

Optimal HRF and smoothing parameters for fMRI time series within an autoregressive modeling framework.

PubMed

Galka, Andreas; Siniatchkin, Michael; Stephani, Ulrich; Groening, Kristina; Wolff, Stephan; Bosch-Bayard, Jorge; Ozaki, Tohru

2010-12-01

The analysis of time series obtained by functional magnetic resonance imaging (fMRI) may be approached by fitting predictive parametric models, such as nearest-neighbor autoregressive models with exogeneous input (NNARX). As a part of the modeling procedure, it is possible to apply instantaneous linear transformations to the data. Spatial smoothing, a common preprocessing step, may be interpreted as such a transformation. The autoregressive parameters may be constrained, such that they provide a response behavior that corresponds to the canonical haemodynamic response function (HRF). We present an algorithm for estimating the parameters of the linear transformations and of the HRF within a rigorous maximum-likelihood framework. Using this approach, an optimal amount of both the spatial smoothing and the HRF can be estimated simultaneously for a given fMRI data set. An example from a motor-task experiment is discussed. It is found that, for this data set, weak, but non-zero, spatial smoothing is optimal. Furthermore, it is demonstrated that activated regions can be estimated within the maximum-likelihood framework.

Exploring the role of movement in determining the global distribution of marine biomass using a coupled hydrodynamic - Size-based ecosystem model

NASA Astrophysics Data System (ADS)

Watson, James R.; Stock, Charles A.; Sarmiento, Jorge L.

2015-11-01

Modeling the dynamics of marine populations at a global scale - from phytoplankton to fish - is necessary if we are to quantify how climate change and other broad-scale anthropogenic actions affect the supply of marine-based food. Here, we estimate the abundance and distribution of fish biomass using a simple size-based food web model coupled to simulations of global ocean physics and biogeochemistry. We focus on the spatial distribution of biomass, identifying highly productive regions - shelf seas, western boundary currents and major upwelling zones. In the absence of fishing, we estimate the total ocean fish biomass to be ∼ 2.84 ×109 tonnes, similar to previous estimates. However, this value is sensitive to the choice of parameters, and further, allowing fish to move had a profound impact on the spatial distribution of fish biomass and the structure of marine communities. In particular, when movement is implemented the viable range of large predators is greatly increased, and stunted biomass spectra characterizing large ocean regions in simulations without movement, are replaced with expanded spectra that include large predators. These results highlight the importance of considering movement in global-scale ecological models.

Large-scale estimates of gross primary production on the Qinghai-Tibet plateau based on remote sensing data

NASA Astrophysics Data System (ADS)

Ma, M., II; Yuan, W.; Dong, J.; Zhang, F.; Cai, W.; Li, H.

2017-12-01

Vegetation gross primary production (GPP) is an important variable for the carbon cycle on the Qinghai-Tibetan Plateau (QTP). Based on the measurements from twelve eddy covariance (EC) sites, we validated a light use efficiency model (i.e. EC-LUE) to evaluate the spatial-temporal patterns of GPP and the effect of environmental variables on QTP. The EC-LUE model explained 85.4% of the daily observed GPP variations through all of the twelve EC sites, and characterized very well the seasonal changes of GPP. Annual GPP over the entire QTP ranged from 575 to 703 Tg C, and showed a significantly increasing trend from 1982 to 2013. However, there were large spatial heterogeneities in long-term trends of GPP. Throughout the entire QTP, air temperature TA increase had a greater influence than solar radiation and PREC changes on productivity. Moreover, our results highlight the large uncertainties of previous GPP estimates due to insufficient parameterization and validations. When compared with GPP estimates of the EC-LUE model, most Coupled Model Intercomparison Project (CMIP5) GPP products overestimate the magnitude and increasing trends of regional GPP, which potentially impact the feedback of ecosystems to regional climate changes.

Exceedance probability map: a tool helping the definition of arsenic Natural Background Level (NBL) within the Drainage Basin to the Venice Lagoon (NE Italy)

NASA Astrophysics Data System (ADS)

Dalla Libera, Nico; Fabbri, Paolo; Mason, Leonardo; Piccinini, Leonardo; Pola, Marco

2017-04-01

Arsenic groundwater contamination affects worldwide shallower groundwater bodies. Starting from the actual knowledges around arsenic origin into groundwater, we know that the major part of dissolved arsenic is naturally occurring through the dissolution of As-bearing minerals and ores. Several studies on the shallow aquifers of both the regional Venetian Plain (NE Italy) and the local Drainage Basin to the Venice Lagoon (DBVL) show local high arsenic concentration related to peculiar geochemical conditions, which drive arsenic mobilization. The uncertainty of arsenic spatial distribution makes difficult both the evaluation of the processes involved in arsenic mobilization and the stakeholders' decision about environmental management. Considering the latter aspect, the present study treats the problem of the Natural Background Level (NBL) definition as the threshold discriminating the natural contamination from the anthropogenic pollution. Actually, the UE's Directive 2006/118/EC suggests the procedures and criteria to set up the water quality standards guaranteeing a healthy status and reversing any contamination trends. In addition, the UE's BRIDGE project proposes some criteria, based on the 90th percentile of the contaminant's concentrations dataset, to estimate the NBL. Nevertheless, these methods provides just a statistical NBL for the whole area without considering the spatial variation of the contaminant's concentration. In this sense, we would reinforce the NBL concept using a geostatistical approach, which is able to give some detailed information about the distribution of arsenic concentrations and unveiling zones with high concentrations referred to the Italian drinking water standard (IDWS = 10 µg/liter). Once obtained the spatial information about arsenic distribution, we can apply the 90th percentile methods to estimate some Local NBL referring to every zones with arsenic higher than IDWS. The indicator kriging method was considered because it estimates the spatial distribution of the exceedance probabilities respect some pre-defined thresholds. This approach is largely mentioned in literature to face similar environmental problems. To test the validity of the procedure, we used the dataset from "A.Li.Na" project (founded by the Regional Environmental Agency) that defined regional NBLs of As, Fe, Mn and NH4+ into DBVL's groundwater. Primarily, we defined two thresholds corresponding respectively to the IDWS and the median of the data over the IDWS. These values were decided basing on the dataset's statistical structure and the quality criteria of the GWD 2006/118/EC. Subsequently, we evaluated the spatial distribution of the probability to exceed the defined thresholds using the Indicator kriging. The results highlight different zones with high exceedance probability ranging from 75% to 95% respect both the IDWS and the median value. Considering the geological setting of the DBVL, these probability values correspond with the occurrence of both organic matter and reducing conditions. In conclusion, the spatial prediction of the exceedance probability could be useful to define the areas in which estimate the local NBLs, enhancing the procedure of NBL definition. In that way, the NBL estimation could be more realistic because it considers the spatial distribution of the studied contaminant, distinguishing areas with high natural concentrations from polluted ones.

The Orbiting Carbon Observatory Mission: Watching the Earth Breathe Mapping CO2 from Space

NASA Technical Reports Server (NTRS)

Boain, Ron

2007-01-01

Approach: Collect spatially resolved, high resolution spectroscopic observations of CO2 and O2 absorption in reflected sunlight. Use these data to resolve spatial and temporal variations in the column averaged CO2 dry air mole fraction, X(sub CO2) over the sunlit hemisphere. Employ independent calibration and validation approaches to produce X(sub CO2) estimates with random errors and biases no larger than 1-2 ppm (0.3-0.5%) on regional scales at monthly intervals.

Estimating groundwater extraction in a data-sparse coal seam gas region, Australia

NASA Astrophysics Data System (ADS)

Keir, Greg; Bulovic, Nevenka; McIntyre, Neil

2017-04-01

The semi-arid Surat and Bowen Basins in central Queensland, Australia, are groundwater resources of both national and regional significance. Regional towns, agricultural industries and communities are heavily dependent on the 30 000+ groundwater supply bores for their existence; however groundwater extraction measurements are rare in this area and primarily limited to small irrigation regions. Accordingly, regional groundwater extraction is not well understood, and this may have implications for regional numerical groundwater modelling and impact assessments associated with recent coal seam gas developments. Here we present a novel statistical approach to model regional groundwater extraction that merges flow measurements / estimates with other more commonly available spatial datasets that may be of value, such as climate data, pasture data, surface water availability, etc. A three step modelling approach, combining a property scale magnitude model, a bore scale occurrence model, and a proportional distribution model within properties, is used to estimate bore extraction. We describe the process of model development and selection, and present extraction results on an aquifer-by-aquifer basis suitable for numerical groundwater modelling. Lastly, we conclude with recommendations for future research, particularly related to improvement of attribution of property-scale water demand, and temporal variability in water usage.

Consistency Between Sun-Induced Chlorophyll Fluorescence and Gross Primary Production of Vegetation in North America

NASA Technical Reports Server (NTRS)

Zhang, Yao; Xiao, Xiangming; Jin, Cui; Dong, Jinwei; Zhou, Sha; Wagle, Pradeep; Joiner, Joanna; Guanter, Luis; Zhang, Yongguang; Zhang , Geli;

The effect of spatial resolution on water scarcity estimates in Australia

NASA Astrophysics Data System (ADS)

Gevaert, Anouk; Veldkamp, Ted; van Dijk, Albert; Ward, Philip

2017-04-01

Water scarcity is an important global issue with severe socio-economic consequences, and its occurrence is likely to increase in many regions due to population growth, economic development and climate change. This has prompted a number of global and regional studies to identify areas that are vulnerable to water scarcity and to determine how this vulnerability will change in the future. A drawback of these studies, however, is that they typically have coarse spatial resolutions. Here, we studied the effect of increasing the spatial resolution of water scarcity estimates in Australia, and the Murray-Darling Basin in particular. This was achieved by calculating the water stress index (WSI), an indicator showing the ratio of water use to water availability, at 0.5 and 0.05 degree resolution for the period 1990-2010. Monthly water availability data were based on outputs of the Australian Water Resources Assessment Landscape model (AWRA-L), which was run at both spatial resolutions and at a daily time scale. Water use information was obtained from a monthly 0.5 degree global dataset that distinguishes between water consumption for irrigation, livestock, industrial and domestic uses. The data were downscaled to 0.05 degree by dividing the sectoral water uses over the areas covered by relevant land use types using a high resolution ( 0.5km) land use dataset. The monthly WSIs at high and low resolution were then used to evaluate differences in the patterns of water scarcity frequency and intensity. In this way, we assess to what extent increasing the spatial resolution can improve the identification of vulnerable areas and thereby assist in the development of strategies to lower this vulnerability. The results of this study provide insight into the scalability of water scarcity estimates and the added value of high resolution water scarcity information in water resources management.

A bottom up approach to on-road CO2 emissions estimates: improved spatial accuracy and applications for regional planning.

PubMed

Gately, Conor K; Hutyra, Lucy R; Wing, Ian Sue; Brondfield, Max N

2013-03-05

On-road transportation is responsible for 28% of all U.S. fossil-fuel CO2 emissions. Mapping vehicle emissions at regional scales is challenging due to data limitations. Existing emission inventories use spatial proxies such as population and road density to downscale national or state-level data. Such procedures introduce errors where the proxy variables and actual emissions are weakly correlated, and limit analysis of the relationship between emissions and demographic trends at local scales. We develop an on-road emission inventory product for Massachusetts-based on roadway-level traffic data obtained from the Highway Performance Monitoring System (HPMS). We provide annual estimates of on-road CO2 emissions at a 1 × 1 km grid scale for the years 1980 through 2008. We compared our results with on-road emissions estimates from the Emissions Database for Global Atmospheric Research (EDGAR), with the Vulcan Product, and with estimates derived from state fuel consumption statistics reported by the Federal Highway Administration (FHWA). Our model differs from FHWA estimates by less than 8.5% on average, and is within 5% of Vulcan estimates. We found that EDGAR estimates systematically exceed FHWA by an average of 22.8%. Panel regression analysis of per-mile CO2 emissions on population density at the town scale shows a statistically significant correlation that varies systematically in sign and magnitude as population density increases. Population density has a positive correlation with per-mile CO2 emissions for densities below 2000 persons km(-2), above which increasing density correlates negatively with per-mile emissions.

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

Wolf, Julie; West, Tristram O.; Le Page, Yannick LB

Quantification of biogenic carbon fluxes from agricultural lands is needed to generate comprehensive bottom-up estimates of net carbon exchange for global and regional carbon monitoring. We estimated global agricultural carbon fluxes associated with annual crop net primary production (NPP), harvested biomass, and consumption of biomass by humans and livestock. These estimates were combined for a single estimate of net carbon exchange (NCE) and spatially distributed to 0.05 degree resolution using MODIS satellite land cover data. Global crop NPP in 2011 was estimated at 5.25 ± 0.46 Pg C yr-1, of which 2.05 ± 0.05 Pg C yr-1 was harvested andmore » 0.54 Pg C yr-1 was collected from crop residues for livestock fodder. Total livestock feed intake in 2011 was 2.42 ± 0.21 Pg C yr-1, of which 2.31 ± 0.21 Pg C yr-1 was emitted as CO2, 0.07 ± 0.01 Pg C yr-1 was emitted as CH4, and 0.04 Pg C yr-1 was contained within milk and egg production. Livestock grazed an estimated 1.27 Pg C yr-1 in 2011, which constituted 52.4% of total feed intake. Global human food intake was 0.57 ± 0.03 Pg C yr-1 in 2011, the majority of which is respired as CO2. Completed global cropland carbon budgets accounted for the ultimate use of ca. 80% of harvested biomass. The spatial distribution of these fluxes may be used for global carbon monitoring, estimation of regional uncertainty, and for use as input to Earth system models.« less

Quantifying aggregated uncertainty in Plasmodium falciparum malaria prevalence and populations at risk via efficient space-time geostatistical joint simulation.

PubMed

Gething, Peter W; Patil, Anand P; Hay, Simon I

2010-04-01

Risk maps estimating the spatial distribution of infectious diseases are required to guide public health policy from local to global scales. The advent of model-based geostatistics (MBG) has allowed these maps to be generated in a formal statistical framework, providing robust metrics of map uncertainty that enhances their utility for decision-makers. In many settings, decision-makers require spatially aggregated measures over large regions such as the mean prevalence within a country or administrative region, or national populations living under different levels of risk. Existing MBG mapping approaches provide suitable metrics of local uncertainty--the fidelity of predictions at each mapped pixel--but have not been adapted for measuring uncertainty over large areas, due largely to a series of fundamental computational constraints. Here the authors present a new efficient approximating algorithm that can generate for the first time the necessary joint simulation of prevalence values across the very large prediction spaces needed for global scale mapping. This new approach is implemented in conjunction with an established model for P. falciparum allowing robust estimates of mean prevalence at any specified level of spatial aggregation. The model is used to provide estimates of national populations at risk under three policy-relevant prevalence thresholds, along with accompanying model-based measures of uncertainty. By overcoming previously unchallenged computational barriers, this study illustrates how MBG approaches, already at the forefront of infectious disease mapping, can be extended to provide large-scale aggregate measures appropriate for decision-makers.

The Use of Radar-Based Products for Deriving Extreme Rainfall Frequencies Using Regional Frequency Analysis with Application in South Louisiana

NASA Astrophysics Data System (ADS)

Eldardiry, H. A.; Habib, E. H.

2014-12-01

Radar-based technologies have made spatially and temporally distributed quantitative precipitation estimates (QPE) available in an operational environmental compared to the raingauges. The floods identified through flash flood monitoring and prediction systems are subject to at least three sources of uncertainties: (a) those related to rainfall estimation errors, (b) those due to streamflow prediction errors due to model structural issues, and (c) those due to errors in defining a flood event. The current study focuses on the first source of uncertainty and its effect on deriving important climatological characteristics of extreme rainfall statistics. Examples of such characteristics are rainfall amounts with certain Average Recurrence Intervals (ARI) or Annual Exceedance Probability (AEP), which are highly valuable for hydrologic and civil engineering design purposes. Gauge-based precipitation frequencies estimates (PFE) have been maturely developed and widely used over the last several decades. More recently, there has been a growing interest by the research community to explore the use of radar-based rainfall products for developing PFE and understand the associated uncertainties. This study will use radar-based multi-sensor precipitation estimates (MPE) for 11 years to derive PFE's corresponding to various return periods over a spatial domain that covers the state of Louisiana in southern USA. The PFE estimation approach used in this study is based on fitting generalized extreme value distribution to hydrologic extreme rainfall data based on annual maximum series (AMS). Some of the estimation problems that may arise from fitting GEV distributions at each radar pixel is the large variance and seriously biased quantile estimators. Hence, a regional frequency analysis approach (RFA) is applied. The RFA involves the use of data from different pixels surrounding each pixel within a defined homogenous region. In this study, region of influence approach along with the index flood technique are used in the RFA. A bootstrap technique procedure is carried out to account for the uncertainty in the distribution parameters to construct 90% confidence intervals (i.e., 5% and 95% confidence limits) on AMS-based precipitation frequency curves.

A hierarchical model for estimating the spatial distribution and abundance of animals detected by continuous-time recorders

USGS Publications Warehouse

Dorazio, Robert; Karanth, K. Ullas

2017-01-01

MotivationSeveral spatial capture-recapture (SCR) models have been developed to estimate animal abundance by analyzing the detections of individuals in a spatial array of traps. Most of these models do not use the actual dates and times of detection, even though this information is readily available when using continuous-time recorders, such as microphones or motion-activated cameras. Instead most SCR models either partition the period of trap operation into a set of subjectively chosen discrete intervals and ignore multiple detections of the same individual within each interval, or they simply use the frequency of detections during the period of trap operation and ignore the observed times of detection. Both practices make inefficient use of potentially important information in the data.Model and data analysisWe developed a hierarchical SCR model to estimate the spatial distribution and abundance of animals detected with continuous-time recorders. Our model includes two kinds of point processes: a spatial process to specify the distribution of latent activity centers of individuals within the region of sampling and a temporal process to specify temporal patterns in the detections of individuals. We illustrated this SCR model by analyzing spatial and temporal patterns evident in the camera-trap detections of tigers living in and around the Nagarahole Tiger Reserve in India. We also conducted a simulation study to examine the performance of our model when analyzing data sets of greater complexity than the tiger data.BenefitsOur approach provides three important benefits: First, it exploits all of the information in SCR data obtained using continuous-time recorders. Second, it is sufficiently versatile to allow the effects of both space use and behavior of animals to be specified as functions of covariates that vary over space and time. Third, it allows both the spatial distribution and abundance of individuals to be estimated, effectively providing a species distribution model, even in cases where spatial covariates of abundance are unknown or unavailable. We illustrated these benefits in the analysis of our data, which allowed us to quantify differences between nocturnal and diurnal activities of tigers and to estimate their spatial distribution and abundance across the study area. Our continuous-time SCR model allows an analyst to specify many of the ecological processes thought to be involved in the distribution, movement, and behavior of animals detected in a spatial trapping array of continuous-time recorders. We plan to extend this model to estimate the population dynamics of animals detected during multiple years of SCR surveys.

Trends in Southern Ocean Eddy Kinetic Energy

NASA Astrophysics Data System (ADS)

Chambers, Don

2016-04-01

A recent study by Hogg et al. (JGR, 2015) has demonstrated a 20-year trend in eddy kinetic energy (EKE) computed from satellite altimetry data. However, this estimate is based on an averaging over large spatial areas. In this study, we use the same methods to examine regional EKE trends throughout the Southern Ocean, from 1993-2015. We do find significant positive trends in several areas of the Southern Ocean, mainly in regions with high mean EKE associated with interactions between jets and bathymetry. At the same time, however, there are also regions with significant negative trends. Overall, EKE in the majority of the Southern Ocean has not changed. These results suggest that the estimates of Hogg et al. may have been biased by these regional extremes, and that more work is needed to quantify climatic changes in EKE.

Trends in Southern Ocean Eddy Kinetic Energy

NASA Astrophysics Data System (ADS)

Chambers, D. P.

2016-02-01

A recent study by Hogg et al. (JGR, 2015) has demonstrated a 20-year trend in eddy kinetic energy (EKE) computed from satellite altimetry data. However, this estimate is based on an averaging over large spatial areas. In this study, we use the same methods to examine regional EKE trends throughout the Southern Ocean, from 1993-2015. We do find significant positive trends in several areas of the Southern Ocean, mainly in regions with high mean EKE associated with interactions between jets and bathymetry. At the same time, however, there are also regions with significant negative trends. Overall, EKE in the majority of the Southern Ocean has not changed. These results suggest that the estimates of Hogg et al. may have been biased by these regional extremes, and that more work is needed to quantify climatic changes in EKE.

The Be-WetSpa-Pest modeling approach to simulate human and environmental exposure from pesticide application

NASA Astrophysics Data System (ADS)

Binder, Claudia; Garcia-Santos, Glenda; Andreoli, Romano; Diaz, Jaime; Feola, Giuseppe; Wittensoeldner, Moritz; Yang, Jing

2016-04-01

This study presents an integrative and spatially explicit modeling approach for analyzing human and environmental exposure from pesticide application of smallholders in the potato producing Andean region in Colombia. The modeling approach fulfills the following criteria: (i) it includes environmental and human compartments; (ii) it contains a behavioral decision-making model for estimating the effect of policies on pesticide flows to humans and the environment; (iii) it is spatially explicit; and (iv) it is modular and easily expandable to include additional modules, crops or technologies. The model was calibrated and validated for the Vereda La Hoya and was used to explore the effect of different policy measures in the region. The model has moderate data requirements and can be adapted relatively easy to other regions in developing countries with similar conditions.

Remote Sensing of Spatial Distributions of Greenhouse Gases in the Los Angles Basin

NASA Technical Reports Server (NTRS)

Fu, Dejian; Pongetti, Thomas J.; Sander, Stanley P.; Cheung, Ross; Stutz, Jochen; Park, Chang Hyoun; Li, Qinbin

2011-01-01

The Los Angeles air basin is a significant anthropogenic source of greenhouse gases and pollutants including CO2, CH4, N2O, and CO, contributing significantly to regional and global climate change. Recent legislation in California, the California Global Warming Solutions Act (AB32), established a statewide cap for greenhouse gas emissions for 2020 based on 1990 emissions. Verifying the effectiveness of regional greenhouse gas emissions controls requires high-precision, regional-scale measurement methods combined with models that capture the principal anthropogenic and biogenic sources and sinks. We present a novel approach for monitoring the spatial distributions of greenhouse gases in the Los Angeles basin using high resolution remote sensing spectroscopy. We participated in the CalNex 2010 campaign to provide greenhouse gas distributions for comparison between top-down and bottom-up emission estimates.

Remote Sensing of Spatial Distributions of Greenhouse Gases in the Los Angeles Basin

NASA Technical Reports Server (NTRS)

Fu, Dejian; Sander, Stanley P.; Pongetti, Thomas J.; Cheung, Ross; Stutz, Jochen

2010-01-01

The Los Angeles air basin is a significant anthropogenic source of greenhouse gasses and pollutants including CO2, CH4, N2O, and CO, contributing significantly to regional and global climate change. Recent legislation in California, the California Global Warning Solutions Act (AB32), established a statewide cap for greenhouse gas emissions for 2020 based on 1990 emissions. Verifying the effectiveness of regional greenhouse gas emissions controls requires high-precision, regional-scale measurement methods combined with models that capture the principal anthropogenic and biogenic sources and sinks. We present a novel approach for monitoring the spatial distribution of greenhouse gases in the Los Angeles basin using high resolution remote sensing spectroscopy. We participated in the CalNex 2010 campaign to provide greenhouse gas distributions for comparison between top-down and bottom-up emission estimates.

Reducing Uncertainty in the American Community Survey through Data-Driven Regionalization

PubMed Central

Spielman, Seth E.; Folch, David C.

2015-01-01

The American Community Survey (ACS) is the largest survey of US households and is the principal source for neighborhood scale information about the US population and economy. The ACS is used to allocate billions in federal spending and is a critical input to social scientific research in the US. However, estimates from the ACS can be highly unreliable. For example, in over 72% of census tracts, the estimated number of children under 5 in poverty has a margin of error greater than the estimate. Uncertainty of this magnitude complicates the use of social data in policy making, research, and governance. This article presents a heuristic spatial optimization algorithm that is capable of reducing the margins of error in survey data via the creation of new composite geographies, a process called regionalization. Regionalization is a complex combinatorial problem. Here rather than focusing on the technical aspects of regionalization we demonstrate how to use a purpose built open source regionalization algorithm to process survey data in order to reduce the margins of error to a user-specified threshold. PMID:25723176

Reducing uncertainty in the american community survey through data-driven regionalization.

PubMed

Spielman, Seth E; Folch, David C

2015-01-01

The American Community Survey (ACS) is the largest survey of US households and is the principal source for neighborhood scale information about the US population and economy. The ACS is used to allocate billions in federal spending and is a critical input to social scientific research in the US. However, estimates from the ACS can be highly unreliable. For example, in over 72% of census tracts, the estimated number of children under 5 in poverty has a margin of error greater than the estimate. Uncertainty of this magnitude complicates the use of social data in policy making, research, and governance. This article presents a heuristic spatial optimization algorithm that is capable of reducing the margins of error in survey data via the creation of new composite geographies, a process called regionalization. Regionalization is a complex combinatorial problem. Here rather than focusing on the technical aspects of regionalization we demonstrate how to use a purpose built open source regionalization algorithm to process survey data in order to reduce the margins of error to a user-specified threshold.

Comparison of prognostic and diagnostic surface flux modeling approaches over the Nile River Basin

USDA-ARS?s Scientific Manuscript database

Regional evapotranspiration (ET) can be estimated using diagnostic remote sensing models, generally based on principles of energy balance, or with spatially distributed prognostic models that simultaneously balance both the energy and water budgets over landscapes using predictive equations for land...

“Estimating Regional Background Air Quality using Space/Time Ordinary Kriging to Support Exposure Studies”

EPA Science Inventory

Local-scale dispersion models are increasingly being used to perform exposure assessments. These types of models, while able to characterize local-scale air quality at increasing spatial scale, however, lack the ability to include background concentration in their overall estimat...

Tying Together Methods to Estimate Wetland Connectivity: Tests within the Pipestem, ND

EPA Science Inventory

Surface connectivity of wetlands in the Prairie Pothole Region (PPR) can occur through fill-spill and fill-merge mechanisms, with some wetlands eventually spilling into stream/river systems. These wetland-to-wetland and wetland-to-stream connections vary both spatially and tempor...

The Spatial Distribution of Forest Biomass in the Brazilian Amazon: A Comparison of Estimates

NASA Technical Reports Server (NTRS)

Houghton, R. A.; Lawrence, J. L.; Hackler, J. L.; Brown, S.

2001-01-01

The amount of carbon released to the atmosphere as a result of deforestation is determined, in part, by the amount of carbon held in the biomass of the forests converted to other uses. Uncertainty in forest biomass is responsible for much of the uncertainty in current estimates of the flux of carbon from land-use change. We compared several estimates of forest biomass for the Brazilian Amazon, based on spatial interpolations of direct measurements, relationships to climatic variables, and remote sensing data. We asked three questions. First, do the methods yield similar estimates? Second, do they yield similar spatial patterns of distribution of biomass? And, third, what factors need most attention if we are to predict more accurately the distribution of forest biomass over large areas? Amazonian forests (including dead and below-ground biomass) vary by more than a factor of two, from a low of 39 PgC to a high of 93 PgC. Furthermore, the estimates disagree as to the regions of high and low biomass. The lack of agreement among estimates confirms the need for reliable determination of aboveground biomass over large areas. Potential methods include direct measurement of biomass through forest inventories with improved allometric regression equations, dynamic modeling of forest recovery following observed stand-replacing disturbances (the approach used in this research), and estimation of aboveground biomass from airborne or satellite-based instruments sensitive to the vertical structure plant canopies.

Field significance of performance measures in the context of regional climate model evaluation. Part 1: temperature

NASA Astrophysics Data System (ADS)

Ivanov, Martin; Warrach-Sagi, Kirsten; Wulfmeyer, Volker

2018-04-01

A new approach for rigorous spatial analysis of the downscaling performance of regional climate model (RCM) simulations is introduced. It is based on a multiple comparison of the local tests at the grid cells and is also known as "field" or "global" significance. New performance measures for estimating the added value of downscaled data relative to the large-scale forcing fields are developed. The methodology is exemplarily applied to a standard EURO-CORDEX hindcast simulation with the Weather Research and Forecasting (WRF) model coupled with the land surface model NOAH at 0.11 ∘ grid resolution. Monthly temperature climatology for the 1990-2009 period is analysed for Germany for winter and summer in comparison with high-resolution gridded observations from the German Weather Service. The field significance test controls the proportion of falsely rejected local tests in a meaningful way and is robust to spatial dependence. Hence, the spatial patterns of the statistically significant local tests are also meaningful. We interpret them from a process-oriented perspective. In winter and in most regions in summer, the downscaled distributions are statistically indistinguishable from the observed ones. A systematic cold summer bias occurs in deep river valleys due to overestimated elevations, in coastal areas due probably to enhanced sea breeze circulation, and over large lakes due to the interpolation of water temperatures. Urban areas in concave topography forms have a warm summer bias due to the strong heat islands, not reflected in the observations. WRF-NOAH generates appropriate fine-scale features in the monthly temperature field over regions of complex topography, but over spatially homogeneous areas even small biases can lead to significant deteriorations relative to the driving reanalysis. As the added value of global climate model (GCM)-driven simulations cannot be smaller than this perfect-boundary estimate, this work demonstrates in a rigorous manner the clear additional value of dynamical downscaling over global climate simulations. The evaluation methodology has a broad spectrum of applicability as it is distribution-free, robust to spatial dependence, and accounts for time series structure.

TRMM- and GPM-based precipitation analysis and modelling in the Tropical Andes

NASA Astrophysics Data System (ADS)

Manz, Bastian; Buytaert, Wouter; Zulkafli, Zed; Onof, Christian

2016-04-01

Despite wide-spread applications of satellite-based precipitation products (SPPs) throughout the TRMM-era, the scarcity of ground-based in-situ data (high density gauge networks, rainfall radar) in many hydro-meteorologically important regions, such as tropical mountain environments, has limited our ability to evaluate both SPPs and individual satellite-based sensors as well as accurately model or merge rainfall at high spatial resolutions, particularly with respect to extremes. This has restricted both the understanding of sensor behaviour and performance controls in such regions as well as the accuracy of precipitation estimates and respective hydrological applications ranging from water resources management to early warning systems. Here we report on our recent research into precipitation analysis and modelling using various TRMM and GPM products (2A25, 3B42 and IMERG) in the tropical Andes. In an initial study, 78 high-frequency (10-min) recording gauges in Colombia and Ecuador are used to generate a ground-based validation dataset for evaluation of instantaneous TRMM Precipitation Radar (TPR) overpasses from the 2A25 product. Detection ability, precipitation time-series, empirical distributions and statistical moments are evaluated with respect to regional climatological differences, seasonal behaviour, rainfall types and detection thresholds. Results confirmed previous findings from extra-tropical regions of over-estimation of low rainfall intensities and under-estimation of the highest 10% of rainfall intensities by the TPR. However, in spite of evident regionalised performance differences as a function of local climatological regimes, the TPR provides an accurate estimate of climatological annual and seasonal rainfall means. On this basis, high-resolution (5 km) climatological maps are derived for the entire tropical Andes. The second objective of this work is to improve the local precipitation estimation accuracy and representation of spatial patterns of extreme rainfall probabilities over the region. For this purpose, an ensemble of high-resolution rainfall fields is generated by stochastic simulation using space-time averaged, coarse-scale (daily, 0.25°) satellite-based rainfall inputs (TRMM 3B42/ -RT) and the high-resolution climatological information derived from the TPR as spatial disaggregation proxies. For evaluation and merging, gridded ground-based rainfall fields are generated from gauge data using sequential simulation. Satellite and ground-based ensembles are subsequently merged using an inverse error weighting scheme. The model was tested over a case study in the Colombian Andes with optional coarse-scale bias correction prior to disaggregation and merging. The resulting outputs were assessed in the context of Generalized Extreme Value theory and showed improved estimation of extreme rainfall probabilities compared to the original TMPA inputs. Initial findings using GPM-IMERG inputs are also presented.

Field significance of performance measures in the context of regional climate model evaluation. Part 2: precipitation

NASA Astrophysics Data System (ADS)

Ivanov, Martin; Warrach-Sagi, Kirsten; Wulfmeyer, Volker

2018-04-01

A new approach for rigorous spatial analysis of the downscaling performance of regional climate model (RCM) simulations is introduced. It is based on a multiple comparison of the local tests at the grid cells and is also known as `field' or `global' significance. The block length for the local resampling tests is precisely determined to adequately account for the time series structure. New performance measures for estimating the added value of downscaled data relative to the large-scale forcing fields are developed. The methodology is exemplarily applied to a standard EURO-CORDEX hindcast simulation with the Weather Research and Forecasting (WRF) model coupled with the land surface model NOAH at 0.11 ∘ grid resolution. Daily precipitation climatology for the 1990-2009 period is analysed for Germany for winter and summer in comparison with high-resolution gridded observations from the German Weather Service. The field significance test controls the proportion of falsely rejected local tests in a meaningful way and is robust to spatial dependence. Hence, the spatial patterns of the statistically significant local tests are also meaningful. We interpret them from a process-oriented perspective. While the downscaled precipitation distributions are statistically indistinguishable from the observed ones in most regions in summer, the biases of some distribution characteristics are significant over large areas in winter. WRF-NOAH generates appropriate stationary fine-scale climate features in the daily precipitation field over regions of complex topography in both seasons and appropriate transient fine-scale features almost everywhere in summer. As the added value of global climate model (GCM)-driven simulations cannot be smaller than this perfect-boundary estimate, this work demonstrates in a rigorous manner the clear additional value of dynamical downscaling over global climate simulations. The evaluation methodology has a broad spectrum of applicability as it is distribution-free, robust to spatial dependence, and accounts for time series structure.

Study of Magnetic Field Spatial Variations in the Southern Hemisphere's Low Latitudes due to Different Interplanetary Structures Using the 3-D MHD SWMF/BATSRUS Model

NASA Astrophysics Data System (ADS)

Souza, V. M. C. E. S.; Jauer, P. R.; Alves, L. R.; Padilha, A. L.; Padua, M. B.; Vitorello, I.; Alves, M. V.; Da Silva, L. A.

2017-12-01

Interplanetary structures such as Coronal Mass Ejections (CME), Shocks, Corotating Interaction Regions (CIR) and Magnetic Clouds (MC) interfere directly on Space Weather conditions and can cause severe and intense disturbances in the Earth's magnetic field as measured in space and on the ground. During magnetically disturbed periods characterized by world-wide, abrupt variations of the geomagnetic field, large and intense current systems can be induced and amplified within the Earth even at low latitudes. Such current systems are known as geomagnetically induced currents (GIC) and can cause damage to power transmission lines, transformers and the degradation of pipelines. As part of an effort to estimate GIC intensities throughout the low to equatorial latitudes of the Brazilian territory, we used the 3-D MHD SWMF/BATSRUS code to estimate spatial variations of the geomagnetic field during periods when the magnetosphere is under the influence of CME and MC structures. Specifically, we used the CalcDeltaB tool (Rastatter et al., Space Weather, 2014) to provide a proxy for the spatial variations of the geomagnetic field, with a 1 minute cadence, at 31 virtual magnetometer stations located in the proposed study region. The stations are spatially arranged in a two-dimensional network with each station being 5 degrees apart in latitude and longitude. In a preliminary analysis, we found that prior to the arrival of each interplanetary structure, there is no appreciable variation in the components of the geomagnetic field between the virtual stations. However, when the interplanetary structures reach the magnetosphere, each station perceives the magnetic field variation differently, so that it is not possible to use a single station to represent the magnetic field perturbation throughout the Brazilian region. We discuss the minimum number and spacing between stations to adequately detail the geomagnetic field variations in this region.

Monthly Fossil-Fuel CO2 Emissions: Isomass of Emissions Gridded by One Degree Latitude by One Degree Longitude (1950 - 2007) (V. 2010)

DOE Data Explorer

Andres, R. J. [Carbon Dioxide Information Analysis Center Environmental Sciences Division Oak Ridge National Laboratory Oak Ridge, Tennessee 37831-6290 U.S.A.; Boden, T. A. [Carbon Dioxide Information Analysis Center Environmental Sciences Division Oak Ridge National Laboratory Oak Ridge, Tennessee 37831-6290 U.S.A.; Marland, G. [Research Institute for Environment, Energy, and Economics Appalachian State University Boone, NC 28608-2131 USA

2010-01-01

The basic data provided in these data files are derived from time series of Global, Regional, and National Fossil-Fuel CO2 Emissions (http://cdiac.ess-dive.lbl.gov/trends/emis/overview_2013.html), the references therein, and the methodology described in Andres et al. (2011). The data accessible here take these tabular, national, mass-emissions data, multiply them by stable carbon isotopic signature (del 13C) as described in Andres et al. (2000), and distribute them spatially on a one degree latitude by one degree longitude grid. The within-country spatial distribution is achieved through a fixed population distribution as reported in Andres et al. (1996) for years prior to 1990 and a variable population distribution for later years (Andres et al. 2016). Note that the mass-emissions data used here are based on fossil-fuel consumption estimates as these are more representative of within country emissions than fossil-fuel production estimates (see http://cdiac.ess-dive.lbl.gov/faq.html#Q10 for a description why emission totals based upon consumption differ from those based upon production). The monthly, isotopic (δ 13C) fossil-fuel CO2 emissions estimates from 1950-2013 provided in this database are derived from time series of global, regional, and national fossil-fuel CO2 emissions (Boden et al. 2016), the references therein, and the methodology described in Andres et al. (2011). The data accessible here take these tabular, national, mass-emissions data, multiply them by stable carbon isotopic signatures (δ 13C) as described in Andres et al. (2000), and distribute them spatially on a one degree latitude by one degree longitude grid. The within-country spatial distribution is achieved through a fixed population distribution as reported in Andres et al. (1996). Note that the mass-emissions data used here are based on fossil-fuel consumption estimates as these are more representative of within country emissions than fossil-fuel production estimates (see http://cdiac.ess-dive.lbl.gov/faq.html#Q10 for a description why emission totals based upon consumption differ from those based upon production).

Spatial variability of soil carbon stock in the Urucu river basin, Central Amazon-Brazil.

PubMed

Ceddia, Marcos Bacis; Villela, André Luis Oliveira; Pinheiro, Érika Flávia Machado; Wendroth, Ole

2015-09-01

The Amazon Forest plays a major role in C sequestration and release. However, few regional estimates of soil organic carbon (SOC) stock in this ecoregion exist. One of the barriers to improve SOC estimates is the lack of recent soil data at high spatial resolution, which hampers the application of new methods for mapping SOC stock. The aims of this work were: (i) to quantify SOC stock under undisturbed vegetation for the 0-30 and the 0-100 cm under Amazon Forest; (ii) to correlate the SOC stock with soil mapping units and relief attributes and (iii) to evaluate three geostatistical techniques to generate maps of SOC stock (ordinary, isotopic and heterotopic cokriging). The study site is located in the Central region of Amazon State, Brazil. The soil survey covered the study site that has an area of 80 km(2) and resulted in a 1:10,000 soil map. It consisted of 315 field observations (96 complete soil profiles and 219 boreholes). SOC stock was calculated by summing C stocks by horizon, determined as a product of BD, SOC and the horizon thickness. For each one of the 315 soil observations, relief attributes were derived from a topographic map to understand SOC dynamics. The SOC stocks across 30 and 100 cm soil depth were 3.28 and 7.32 kg C m(-2), respectively, which is, 34 and 16%, lower than other studies. The SOC stock is higher in soils developed in relief forms exhibiting well-drained soils, which are covered by Upland Dense Tropical Rainforest. Only SOC stock in the upper 100 cm exhibited spatial dependence allowing the generation of spatial variability maps based on spatial (co)-regionalization. The CTI was inversely correlated with SOC stock and was the only auxiliary variable feasible to be used in cokriging interpolation. The heterotopic cokriging presented the best performance for mapping SOC stock. Copyright © 2015 Elsevier B.V. All rights reserved.

Using remote sensing and GIS techniques to estimate discharge and recharge. fluxes for the Death Valley regional groundwater flow system, USA

USGS Publications Warehouse

D'Agnese, F. A.; Faunt, C.C.; Keith, Turner A.

1996-01-01

The recharge and discharge components of the Death Valley regional groundwater flow system were defined by remote sensing and GIS techniques that integrated disparate data types to develop a spatially complex representation of near-surface hydrological processes. Image classification methods were applied to multispectral satellite data to produce a vegetation map. This map provided a basis for subsequent evapotranspiration and infiltration estimations. The vegetation map was combined with ancillary data in a GIS to delineate different types of wetlands, phreatophytes and wet playa areas. Existing evapotranspiration-rate estimates were then used to calculate discharge volumes for these areas. A previously used empirical method of groundwater recharge estimation was modified by GIS methods to incorporate data describing soil-moisture conditions, and a recharge potential map was produced. These discharge and recharge maps were readily converted to data arrays for numerical modelling codes. Inverse parameter estimation techniques also used these data to evaluate the reliability and sensitivity of estimated values.

Regional interpretation of water-quality monitoring data

USGS Publications Warehouse

Smith, Richard A.; Schwarz, Gregory E.; Alexander, Richard B.

1997-01-01

We describe a method for using spatially referenced regressions of contaminant transport on watershed attributes (SPARROW) in regional water-quality assessment. The method is designed to reduce the problems of data interpretation caused by sparse sampling, network bias, and basin heterogeneity. The regression equation relates measured transport rates in streams to spatially referenced descriptors of pollution sources and land-surface and stream-channel characteristics. Regression models of total phosphorus (TP) and total nitrogen (TN) transport are constructed for a region defined as the nontidal conterminous United States. Observed TN and TP transport rates are derived from water-quality records for 414 stations in the National Stream Quality Accounting Network. Nutrient sources identified in the equations include point sources, applied fertilizer, livestock waste, nonagricultural land, and atmospheric deposition (TN only). Surface characteristics found to be significant predictors of land-water delivery include soil permeability, stream density, and temperature (TN only). Estimated instream decay coefficients for the two contaminants decrease monotonically with increasing stream size. TP transport is found to be significantly reduced by reservoir retention. Spatial referencing of basin attributes in relation to the stream channel network greatly increases their statistical significance and model accuracy. The method is used to estimate the proportion of watersheds in the conterminous United States (i.e., hydrologic cataloging units) with outflow TP concentrations less than the criterion of 0.1 mg/L, and to classify cataloging units according to local TN yield (kg/km2/yr).

Identifying and Analyzing Uncertainty Structures in the TRMM Microwave Imager Precipitation Product over Tropical Ocean Basins

NASA Technical Reports Server (NTRS)

Liu, Jianbo; Kummerow, Christian D.; Elsaesser, Gregory S.

2016-01-01

Despite continuous improvements in microwave sensors and retrieval algorithms, our understanding of precipitation uncertainty is quite limited, due primarily to inconsistent findings in studies that compare satellite estimates to in situ observations over different parts of the world. This study seeks to characterize the temporal and spatial properties of uncertainty in the Tropical Rainfall Measuring Mission Microwave Imager surface rainfall product over tropical ocean basins. Two uncertainty analysis frameworks are introduced to qualitatively evaluate the properties of uncertainty under a hierarchy of spatiotemporal data resolutions. The first framework (i.e. 'climate method') demonstrates that, apart from random errors and regionally dependent biases, a large component of the overall precipitation uncertainty is manifested in cyclical patterns that are closely related to large-scale atmospheric modes of variability. By estimating the magnitudes of major uncertainty sources independently, the climate method is able to explain 45-88% of the monthly uncertainty variability. The percentage is largely resolution dependent (with the lowest percentage explained associated with a 1 deg x 1 deg spatial/1 month temporal resolution, and highest associated with a 3 deg x 3 deg spatial/3 month temporal resolution). The second framework (i.e. 'weather method') explains regional mean precipitation uncertainty as a summation of uncertainties associated with individual precipitation systems. By further assuming that self-similar recurring precipitation systems yield qualitatively comparable precipitation uncertainties, the weather method can consistently resolve about 50 % of the daily uncertainty variability, with only limited dependence on the regions of interest.

Efficient spatial privacy preserving scheme for sensor network

NASA Astrophysics Data System (ADS)

Debnath, Ashmita; Singaravelu, Pradheepkumar; Verma, Shekhar

2013-03-01

The privacy of sensitive events observed by a wireless sensor networks (WSN) needs to be protected. Adversaries with the knowledge of sensor deployment and network protocols can infer the location of a sensed event by monitoring the communication from the sensors even when the messages are encrypted. Encryption provides confidentiality; however, the context of the event can used to breach the privacy of sensed objects. An adversary can track the trajectory of a moving object or determine the location of the occurrence of a critical event to breach its privacy. In this paper, we propose ring signature to obfuscate the spatial information. Firstly, the extended region of location of an event of interest as estimated from a sensor communication is presented. Then, the increase in this region of spatial uncertainty due to the effect of ring signature is determined. We observe that ring signature can effectively enhance the region of location uncertainty of a sensed event. As the event of interest can be situated anywhere in the enhanced region of uncertainty, its privacy against local or global adversary is ensured. Both analytical and simulation results show that induced delay and throughput are insignificant with negligible impact on the performance of a WSN.

Spatial analysis of relative humidity during ungauged periods in a mountainous region

NASA Astrophysics Data System (ADS)

Um, Myoung-Jin; Kim, Yeonjoo

2017-08-01

Although atmospheric humidity influences environmental and agricultural conditions, thereby influencing plant growth, human health, and air pollution, efforts to develop spatial maps of atmospheric humidity using statistical approaches have thus far been limited. This study therefore aims to develop statistical approaches for inferring the spatial distribution of relative humidity (RH) for a mountainous island, for which data are not uniformly available across the region. A multiple regression analysis based on various mathematical models was used to identify the optimal model for estimating monthly RH by incorporating not only temperature but also location and elevation. Based on the regression analysis, we extended the monthly RH data from weather stations to cover the ungauged periods when no RH observations were available. Then, two different types of station-based data, the observational data and the data extended via the regression model, were used to form grid-based data with a resolution of 100 m. The grid-based data that used the extended station-based data captured the increasing RH trend along an elevation gradient. Furthermore, annual RH values averaged over the regions were examined. Decreasing temporal trends were found in most cases, with magnitudes varying based on the season and region.

Spatial and Temporal Variation in the Effects of Climatic Variables on Dugong Calf Production.

PubMed

Fuentes, Mariana M P B; Delean, Steven; Grayson, Jillian; Lavender, Sally; Logan, Murray; Marsh, Helene

2016-01-01

Knowledge of the relationships between environmental forcing and demographic parameters is important for predicting responses from climatic changes and to manage populations effectively. We explore the relationships between the proportion of sea cows (Dugong dugon) classified as calves and four climatic drivers (rainfall anomaly, Southern Oscillation El Niño Index [SOI], NINO 3.4 sea surface temperature index, and number of tropical cyclones) at a range of spatially distinct locations in Queensland, Australia, a region with relatively high dugong density. Dugong and calf data were obtained from standardized aerial surveys conducted along the study region. A range of lagged versions of each of the focal climatic drivers (1 to 4 years) were included in a global model containing the proportion of calves in each population crossed with each of the lagged versions of the climatic drivers to explore relationships. The relative influence of each predictor was estimated via Gibbs variable selection. The relationships between the proportion of dependent calves and the climatic drivers varied spatially and temporally, with climatic drivers influencing calf counts at sub-regional scales. Thus we recommend that the assessment of and management response to indirect climatic threats on dugongs should also occur at sub-regional scales.

Social networks and trade of services: modelling interregional flows with spatial and network autocorrelation effects

NASA Astrophysics Data System (ADS)

de la Mata, Tamara; Llano, Carlos

2013-07-01

Recent literature on border effect has fostered research on informal barriers to trade and the role played by network dependencies. In relation to social networks, it has been shown that intensity of trade in goods is positively correlated with migration flows between pairs of countries/regions. In this article, we investigate whether such a relation also holds for interregional trade of services. We also consider whether interregional trade flows in services linked with tourism exhibit spatial and/or social network dependence. Conventional empirical gravity models assume the magnitude of bilateral flows between regions is independent of flows to/from regions located nearby in space, or flows to/from regions related through social/cultural/ethic network connections. With this aim, we provide estimates from a set of gravity models showing evidence of statistically significant spatial and network (demographic) dependence in the bilateral flows of the trade of services considered. The analysis has been applied to the Spanish intra- and interregional monetary flows of services from the accommodation, restaurants and travel agencies for the period 2000-2009, using alternative datasets for the migration stocks and definitions of network effects.

Automated measurement of spatial preference in the open field test with transmitted lighting.

PubMed

Kulikov, Alexander V; Tikhonova, Maria A; Kulikov, Victor A

2008-05-30

New modification of the open field was designed to improve automation of the test. The main innovations were: (1) transmitted lighting and (2) estimation of probability to find pixels associated with an animal in the selected region of arena as an objective index of spatial preference. Transmitted (inverted) lighting significantly ameliorated the contrast between an animal and arena and allowed to track white animals with similar efficacy as colored ones. Probability as a measure of preference of selected region was mathematically proved and experimentally verified. A good correlation between probability and classic indices of spatial preference (number of region entries and time spent therein) was shown. The algorithm of calculation of probability to find pixels associated with an animal in the selected region was implemented in the EthoStudio software. Significant interstrain differences in locomotion and the central zone preference (index of anxiety) were shown using the inverted lighting and the EthoStudio software in mice of six inbred strains. The effects of arena shape (circle or square) and a novel object presence in the center of arena on the open field behavior in mice were studied.

Modeling landscape evapotranspiration by integrating land surface phenology and a water balance algorithm

USGS Publications Warehouse

Senay, Gabriel B.

2008-01-01

The main objective of this study is to present an improved modeling technique called Vegetation ET (VegET) that integrates commonly used water balance algorithms with remotely sensed Land Surface Phenology (LSP) parameter to conduct operational vegetation water balance modeling of rainfed systems at the LSP’s spatial scale using readily available global data sets. Evaluation of the VegET model was conducted using Flux Tower data and two-year simulation for the conterminous US. The VegET model is capable of estimating actual evapotranspiration (ETa) of rainfed crops and other vegetation types at the spatial resolution of the LSP on a daily basis, replacing the need to estimate crop- and region-specific crop coefficients.

Estimation of improved resolution soil moisture in vegetated areas using passive AMSR-E data

NASA Astrophysics Data System (ADS)

Moradizadeh, Mina; Saradjian, Mohammad R.

2018-03-01

Microwave remote sensing provides a unique capability for soil parameter retrievals. Therefore, various soil parameters estimation models have been developed using brightness temperature (BT) measured by passive microwave sensors. Due to the low resolution of satellite microwave radiometer data, the main goal of this study is to develop a downscaling approach to improve the spatial resolution of soil moisture estimates with the use of higher resolution visible/infrared sensor data. Accordingly, after the soil parameters have been obtained using Simultaneous Land Parameters Retrieval Model algorithm, the downscaling method has been applied to the soil moisture estimations that have been validated against in situ soil moisture data. Advance Microwave Scanning Radiometer-EOS BT data in Soil Moisture Experiment 2003 region in the south and north of Oklahoma have been used to this end. Results illustrated that the soil moisture variability is effectively captured at 5 km spatial scales without a significant degradation of the accuracy.

Are fractal dimensions of the spatial distribution of mineral deposits meaningful?

USGS Publications Warehouse

Raines, G.L.

2008-01-01

It has been proposed that the spatial distribution of mineral deposits is bifractal. An implication of this property is that the number of deposits in a permissive area is a function of the shape of the area. This is because the fractal density functions of deposits are dependent on the distance from known deposits. A long thin permissive area with most of the deposits in one end, such as the Alaskan porphyry permissive area, has a major portion of the area far from known deposits and consequently a low density of deposits associated with most of the permissive area. On the other hand, a more equi-dimensioned permissive area, such as the Arizona porphyry permissive area, has a more uniform density of deposits. Another implication of the fractal distribution is that the Poisson assumption typically used for estimating deposit numbers is invalid. Based on datasets of mineral deposits classified by type as inputs, the distributions of many different deposit types are found to have characteristically two fractal dimensions over separate non-overlapping spatial scales in the range of 5-1000 km. In particular, one typically observes a local dimension at spatial scales less than 30-60 km, and a regional dimension at larger spatial scales. The deposit type, geologic setting, and sample size influence the fractal dimensions. The consequence of the geologic setting can be diminished by using deposits classified by type. The crossover point between the two fractal domains is proportional to the median size of the deposit type. A plot of the crossover points for porphyry copper deposits from different geologic domains against median deposit sizes defines linear relationships and identifies regions that are significantly underexplored. Plots of the fractal dimension can also be used to define density functions from which the number of undiscovered deposits can be estimated. This density function is only dependent on the distribution of deposits and is independent of the definition of the permissive area. Density functions for porphyry copper deposits appear to be significantly different for regions in the Andes, Mexico, United States, and western Canada. Consequently, depending on which regional density function is used, quite different estimates of numbers of undiscovered deposits can be obtained. These fractal properties suggest that geologic studies based on mapping at scales of 1:24,000 to 1:100,000 may not recognize processes that are important in the formation of mineral deposits at scales larger than the crossover points at 30-60 km. ?? 2008 International Association for Mathematical Geology.

Landsat analysis of tropical forest succession employing a terrain model

NASA Technical Reports Server (NTRS)

Barringer, T. H.; Robinson, V. B.; Coiner, J. C.; Bruce, R. C.

1980-01-01

Landsat multispectral scanner (MSS) data have yielded a dual classification of rain forest and shadow in an analysis of a semi-deciduous forest on Mindonoro Island, Philippines. Both a spatial terrain model, using a fifth side polynomial trend surface analysis for quantitatively estimating the general spatial variation in the data set, and a spectral terrain model, based on the MSS data, have been set up. A discriminant analysis, using both sets of data, has suggested that shadowing effects may be due primarily to local variations in the spectral regions and can therefore be compensated for through the decomposition of the spatial variation in both elevation and MSS data.

Estimating daily PM2.5 and PM10 across the complex geo-climate region of Israel using MAIAC satellite-based AOD data.

PubMed

Kloog, Itai; Sorek-Hamer, Meytar; Lyapustin, Alexei; Coull, Brent; Wang, Yujie; Just, Allan C; Schwartz, Joel; Broday, David M

2015-12-01

Estimates of exposure to PM 2.5 are often derived from geographic characteristics based on land-use regression or from a limited number of fixed ground monitors. Remote sensing advances have integrated these approaches with satellite-based measures of aerosol optical depth (AOD), which is spatially and temporally resolved, allowing greater coverage for PM 2.5 estimations. Israel is situated in a complex geo-climatic region with contrasting geographic and weather patterns, including both dark and bright surfaces within a relatively small area. Our goal was to examine the use of MODIS-based MAIAC data in Israel, and to explore the reliability of predicted PM 2.5 and PM 10 at a high spatiotemporal resolution. We applied a three stage process, including a daily calibration method based on a mixed effects model, to predict ground PM 2.5 and PM 10 over Israel. We later constructed daily predictions across Israel for 2003-2013 using spatial and temporal smoothing, to estimate AOD when satellite data were missing. Good model performance was achieved, with out-of-sample cross validation R 2 values of 0.79 and 0.72 for PM 10 and PM 2.5 , respectively. Model predictions had little bias, with cross-validated slopes (predicted vs. observed) of 0.99 for both the PM 2.5 and PM 10 models. To our knowledge, this is the first study that utilizes high resolution 1km MAIAC AOD retrievals for PM prediction while accounting for geo-climate complexities, such as experienced in Israel. This novel model allowed the reconstruction of long- and short-term spatially resolved exposure to PM 2.5 and PM 10 in Israel, which could be used in the future for epidemiological studies.

Antarctic Mass Loss from GRACE from Space- and Time-Resolved Modeling with Slepian Functions

NASA Astrophysics Data System (ADS)

Simons, F. J.; Harig, C.

2013-12-01

The melting of polar ice sheets is a major contributor to global sea-level rise. Antarctica is of particular interest since most of the mass loss has occurred in West Antarctica, however updated glacial isostatic adjustment (GIA) models and recent mass gains in East Antarctica have reduced the continent-wide integrated decadal trend of mass loss. Here we present a spatially and temporally resolved estimation of the Antarctic ice mass change using Slepian localization functions. With a Slepian basis specifically for Antarctica, the basis functions maximize their energy on the continent and we can project the geopotential fields into a sparse set of orthogonal coefficients. By fitting polynomial functions to the limited basis coefficients we maximize signal-to-noise levels and need not perform smoothing or destriping filters common to other approaches. In addition we determine an empirical noise covariance matrix from the GRACE data to estimate the uncertainty of mass estimation. When applied to large ice sheets, as in our own recent Greenland work, this technique is able to resolve both the overall continental integrated mass trend, as well as the spatial distribution of the mass changes over time. Using CSR-RL05 GRACE data between Jan. 2003 and Jan 2013, we estimate the regional accelerations in mass change for several sub-regions and examine how the spatial pattern of mass has changed. The Amundsen Sea coast of West Antarctica has experienced a large acceleration in mass loss (-26 Gt/yr^2). While mass loss is concentrated near Pine Island and Thwaites glaciers, it has also increased along the coast further towards the Ross ice shelf.

Modeling movement and fidelity of American black ducks

USGS Publications Warehouse

Zimpfer, N.L.; Conroy, M.J.

2006-01-01

Spatial relationships among stocks of breeding waterfowl can be an important component of harvest management. Prediction and optimal harvest management under adaptive harvest management (AHM) requires information on the spatial relationships among breeding populations (fidelity and inter-year exchange), as well as rates of movements from breeding to harvest regions. We used band-recovery data to develop a model to estimate probabilities of movement for American black ducks (Anas rubripes) among 3 Canadian breeding strata and 6 harvest regions (3 in Canada, and 3 in the United States) over the period 1965-1998. Model selection criteria suggested that models containing area-, year-, and age-specific recovery rates with area- and sex-specific movement rates were the best for modeling movement. Movement by males to southern harvest areas was variable depending on the originating area. Males from the western breeding area predominantly moved to the Mississippi Flyway or southern Atlantic Flyway (??ij = 0.353, SE = 0.0187 and ??ij = 0.473, SE = 0.037, respectively), whereas males that originated in the eastern and central breeding strata moved to the northern Atlantic flyway (??ij = 0.842, SE = 0.010 and ??ij = 0.578, SE = 0.0222, respectively). We used combined recoveries and recaptures in Program MARK to estimate fidelity to the 3 Canadian breeding strata. Information criteria identified a model containing sex- and age-specific fidelity for black ducks. Estimates of fidelity were 0.9695 (SE = 0.0249) and 0.9554 (SE = 0.0434) for adult males and females, respectively. Estimates of fidelity for juveniles were slightly lower at 0.9210 (SE = 0.0931) and 0.8870 (SE = 0.0475) for males and females, respectively. These models have application to the development of spatially stratified black duck harvest management models for use in AHM.

Validation of Ground-based Optical Estimates of Auroral Electron Precipitation Energy Deposition

NASA Astrophysics Data System (ADS)

Hampton, D. L.; Grubbs, G. A., II; Conde, M.; Lynch, K. A.; Michell, R.; Zettergren, M. D.; Samara, M.; Ahrns, M. J.

2017-12-01

One of the major energy inputs into the high latitude ionosphere and mesosphere is auroral electron precipitation. Not only does the kinetic energy get deposited, the ensuing ionization in the E and F-region ionosphere modulates parallel and horizontal currents that can dissipate in the form of Joule heating. Global models to simulate these interactions typically use electron precipitation models that produce a poor representation of the spatial and temporal complexity of auroral activity as observed from the ground. This is largely due to these precipitation models being based on averages of multiple satellite overpasses separated by periods much longer than typical auroral feature durations. With the development of regional and continental observing networks (e.g. THEMIS ASI), the possibility of ground-based optical observations producing quantitative estimates of energy deposition with temporal and spatial scales comparable to those known to be exhibited in auroral activity become a real possibility. Like empirical precipitation models based on satellite overpasses such optics-based estimates are subject to assumptions and uncertainties, and therefore require validation. Three recent sounding rocket missions offer such an opportunity. The MICA (2012), GREECE (2014) and Isinglass (2017) missions involved detailed ground based observations of auroral arcs simultaneously with extensive on-board instrumentation. These have afforded an opportunity to examine the results of three optical methods of determining auroral electron energy flux, namely 1) ratio of auroral emissions, 2) green line temperature vs. emission altitude, and 3) parametric estimates using white-light images. We present comparisons from all three methods for all three missions and summarize the temporal and spatial scales and coverage over which each is valid.

Estimating daily PM2.5 and PM10 across the complex geo-climate region of Israel using MAIAC satellite-based AOD data

PubMed Central

Kloog, Itai; Sorek-Hamer, Meytar; Lyapustin, Alexei; Coull, Brent; Wang, Yujie; Just, Allan C.; Schwartz, Joel; Broday, David M.

2017-01-01

Estimates of exposure to PM2.5 are often derived from geographic characteristics based on land-use regression or from a limited number of fixed ground monitors. Remote sensing advances have integrated these approaches with satellite-based measures of aerosol optical depth (AOD), which is spatially and temporally resolved, allowing greater coverage for PM2.5 estimations. Israel is situated in a complex geo-climatic region with contrasting geographic and weather patterns, including both dark and bright surfaces within a relatively small area. Our goal was to examine the use of MODIS-based MAIAC data in Israel, and to explore the reliability of predicted PM2.5 and PM10 at a high spatiotemporal resolution. We applied a three stage process, including a daily calibration method based on a mixed effects model, to predict ground PM2.5 and PM10 over Israel. We later constructed daily predictions across Israel for 2003–2013 using spatial and temporal smoothing, to estimate AOD when satellite data were missing. Good model performance was achieved, with out-of-sample cross validation R2 values of 0.79 and 0.72 for PM10 and PM2.5, respectively. Model predictions had little bias, with cross-validated slopes (predicted vs. observed) of 0.99 for both the PM2.5 and PM10 models. To our knowledge, this is the first study that utilizes high resolution 1km MAIAC AOD retrievals for PM prediction while accounting for geo-climate complexities, such as experienced in Israel. This novel model allowed the reconstruction of long- and short-term spatially resolved exposure to PM2.5 and PM10 in Israel, which could be used in the future for epidemiological studies. PMID:28966551

Monthly Fossil-Fuel CO2 Emissions: Mass of Emissions Gridded by One Degree Latitude by One Degree Longitude - 2016

DOE Data Explorer

Andres, R.J. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Boden, T.A. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Marland, G. [Appalachian State University, Boone, NC (United States)

2016-01-01

The monthly, fossil-fuel CO2 emissions estimates from 1950-2013 provided in this database are derived from time series of global, regional, and national fossil-fuel CO2 emissions (Boden et al. 2016), the references therein, and the methodology described in Andres et al. (2011). The data accessible here take these tabular, national, mass-emissions data and distribute them spatially on a one degree latitude by one degree longitude grid. The within-country spatial distribution is achieved through a fixed population distribution as reported in Andres et al. (1996). Note that the mass-emissions data used here are based on fossil-fuel consumption estimates as these are more representative of within country emissions than fossil-fuel production estimates (see http://cdiac.ess-dive.lbl.gov/faq.html#Q10 for a description why emission totals based upon consumption differ from those based upon production).

Monthly Fossil-Fuel CO2 Emissions: Mass of Emissions Gridded by One Degree Latitude by One Degree Longitude - 2015

DOE Data Explorer

Andres, R.J. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Boden, T.A. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Marland, J. [Appalachian State University, Boone, NC (United States)

2015-01-01

The monthly, fossil-fuel CO2 emissions estimates from 1950-2011 provided in this database are derived from time series of global, regional, and national fossil-fuel CO2 emissions (Boden et al. 2015), the references therein, and the methodology described in Andres et al. (2011). The data accessible here take these tabular, national, mass-emissions data and distribute them spatially on a one degree latitude by one degree longitude grid. The within-country spatial distribution is achieved through a fixed population distribution as reported in Andres et al. (1996). Note that the mass-emissions data used here are based on fossil-fuel consumption estimates as these are more representative of within country emissions than fossil-fuel production estimates (see http://cdiac.ess-dive.lbl.gov/faq.html#Q10 for a description why emission totals based upon consumption differ from those based upon production).

Monthly Fossil-Fuel CO2 Emissions: Mass of Emissions Gridded by One Degree Latitude by One Degree Longitude (1950 - 2010) (V.2010)

DOE Data Explorer

Andres, R. J. [Carbon Dioxide Information Analysis Center Environmental Sciences Division Oak Ridge National Laboratory Oak Ridge, Tennessee 37830-6290 U.S.A.; Boden, T. A. [Carbon Dioxide Information Analysis Center Environmental Sciences Division Oak Ridge National Laboratory Oak Ridge, Tennessee 37830-6290 U.S.A.; Marland, G. [Research Institute for Environment, Energy and Economics Appalachian State University Boone, North Carolina 28608 U.S.A.

2010-01-01

The monthly, fossil-fuel CO2 emissions estimates from 1950-2010 provided in this database are derived from time series of global, regional, and national fossil-fuel CO2 emissions (Boden et al. 2013), the references therein, and the methodology described in Andres et al. (2011). The data accessible here take these tabular, national, mass-emissions data and distribute them spatially on a one degree latitude by one degree longitude grid. The within-country spatial distribution is achieved through a fixed population distribution as reported in Andres et al. (1996). Note that the mass-emissions data used here are based on fossil-fuel consumption estimates as these are more representative of within country emissions than fossil-fuel production estimates (see http://cdiac.ess-dive.lbl.gov/faq.html#Q10 for a description why emission totals based upon consumption differ from those based upon production).

Monthly Fossil-Fuel CO2 Emissions: Mass of Emissions Gridded by One Degree Latitude by One Degree Longitude (V. 2011) (1950 - 2010)

DOE Data Explorer

Andres, R. J. [Carbon Dioxide Information Analysis Center (CDIAC), Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (USA); Boden, Thomas A. [Carbon Dioxide Information Analysis Center (CDIAC), Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (USA_; Marland, G. [Research Institute for Environment, Energy and Economics Appalachian State University Boone, North Carolina 28608 U.S.A.

2011-01-01

The monthly, fossil-fuel CO2 emissions estimates from 1950-2010 provided in this database are derived from time series of global, regional, and national fossil-fuel CO2 emissions (Boden et al. 2013), the references therein, and the methodology described in Andres et al. (2011). The data accessible here take these tabular, national, mass-emissions data and distribute them spatially on a one degree latitude by one degree longitude grid. The within-country spatial distribution is achieved through a fixed population distribution as reported in Andres et al. (1996). Note that the mass-emissions data used here are based on fossil-fuel consumption estimates as these are more representative of within country emissions than fossil-fuel production estimates (see http://cdiac.ess-dive.lbl.gov/faq.html#Q10 for a description why emission totals based upon consumption differ from those based upon production).

Monthly Fossil-Fuel CO2 Emissions: Mass of Emissions Gridded by One Degree Latitude by One Degree Longitude - 2013

DOE Data Explorer

Andres, R. J. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Boden, T.A. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Marland, G. [Appalachain State University, Boone, NC (United States)

1996-01-01

The monthly, fossil-fuel CO2 emissions estimates from 1950-2010 provided in this database are derived from time series of global, regional, and national fossil-fuel CO2 emissions (Boden et al. 2013), the references therein, and the methodology described in Andres et al. (2011). The data accessible here take these tabular, national, mass-emissions data and distribute them spatially on a one degree latitude by one degree longitude grid. The within-country spatial distribution is achieved through a fixed population distribution as reported in Andres et al. (1996). Note that the mass-emissions data used here are based on fossil-fuel consumption estimates as these are more representative of within country emissions than fossil-fuel production estimates (see http://cdiac.ess-dive.lbl.gov/faq.html#Q10 for a description why emission totals based upon consumption differ from those based upon production).

Quantifying spatial scaling patterns and their local and regional correlates in headwater streams: Implications for resilience

USGS Publications Warehouse

Gothe, Emma; Sandin, Leonard; Allen, Craig R.; Angeler, David G.

2014-01-01

The distribution of functional traits within and across spatiotemporal scales has been used to quantify and infer the relative resilience across ecosystems. We use explicit spatial modeling to evaluate within- and cross-scale redundancy in headwater streams, an ecosystem type with a hierarchical and dendritic network structure. We assessed the cross-scale distribution of functional feeding groups of benthic invertebrates in Swedish headwater streams during two seasons. We evaluated functional metrics, i.e., Shannon diversity, richness, and evenness, and the degree of redundancy within and across modeled spatial scales for individual feeding groups. We also estimated the correlates of environmental versus spatial factors of both functional composition and the taxonomic composition of functional groups for each spatial scale identified. Measures of functional diversity and within-scale redundancy of functions were similar during both seasons, but both within- and cross-scale redundancy were low. This apparent low redundancy was partly attributable to a few dominant taxa explaining the spatial models. However, rare taxa with stochastic spatial distributions might provide additional information and should therefore be considered explicitly for complementing future resilience assessments. Otherwise, resilience may be underestimated. Finally, both environmental and spatial factors correlated with the scale-specific functional and taxonomic composition. This finding suggests that resilience in stream networks emerges as a function of not only local conditions but also regional factors such as habitat connectivity and invertebrate dispersal.

An experimental case study to estimate Pre-harvest Wheat Acreage/Production in Hilly and Plain region of Uttarakhand state: Challenges and solutions of problems by using satellite data

NASA Astrophysics Data System (ADS)

Uniyal, D.; Kimothi, M. M.; Bhagya, N.; Ram, R. D.; Patel, N. K.; Dhaundiya, V. K.

2014-11-01

Wheat is an economically important Rabi crop for the state, which is grown on around 26 % of total available agriculture area in the state. There is a variation in productivity of wheat crop in hilly and tarai region. The agricultural productivity is less in hilly region in comparison of tarai region due to terrace cultivation, traditional system of agriculture, small land holdings, variation in physiography, top soil erosion, lack of proper irrigation system etc. Pre-harvest acreage/yield/production estimation of major crops is being done with the help of conventional crop cutting method, which is biased, inaccurate and time consuming. Remote Sensing data with multi-temporal and multi-spectral capabilities has shown new dimension in crop discrimination analysis and acreage/yield/production estimation in recent years. In view of this, Uttarakhand Space Applications Centre (USAC), Dehradun with the collaboration of Space Applications Centre (SAC), ISRO, Ahmedabad and Uttarakhand State Agriculture Department, have developed different techniques for the discrimination of crops and estimation of pre-harvest wheat acreage/yield/production. In the 1st phase, five districts (Dehradun, Almora, Udham Singh Nagar, Pauri Garhwal and Haridwar) with distinct physiography i.e. hilly and plain regions, have been selected for testing and verification of techniques using IRS (Indian Remote Sensing Satellites), LISS-III, LISS-IV satellite data of Rabi season for the year 2008-09 and whole 13 districts of the Uttarakhand state from 2009-14 along with ground data were used for detailed analysis. Five methods have been developed i.e. NDVI (Normalized Differential Vegetation Index), Supervised classification, Spatial modeling, Masking out method and Programming on visual basics methods using multitemporal satellite data of Rabi season along with the collateral and ground data. These methods were used for wheat discriminations and preharvest acreage estimations and subsequently results were compared with Bureau of Estimation Statistics (BES). Out of these five different methods, wheat area that was estimated by spatial modeling and programming on visual basics has been found quite near to Bureau of Estimation Statistics (BES). But for hilly region, maximum fields were going in shadow region, so it was difficult to estimate accurate result, so frequency distribution curve method has been used and frequency range has been decided to discriminate wheat pixels from other pixels in hilly region, digitized those regions and result shows good result. For yield estimation, an algorithm has been developed by using soil characteristics i.e. texture, depth, drainage, temperature, rainfall and historical yield data. To get the production estimation, estimated yield multiplied by acreage of crop per hectare. Result shows deviation for acreage estimation from BES is around 3.28 %, 2.46 %, 3.45 %, 1.56 %, 1.2 % and 1.6 % (estimation not declared till now by state Agriculture dept. For the year 2013-14) estimation and deviation for production estimation is around 4.98 %, 3.66 % 3.21 % , 3.1 % NA and 2.9 % for the consecutive above mentioned years i.e. 2008-09, 2009-10, 2010-11, 2011-12, 2012-13 and 2013-14. The estimated data has been provided to State Agriculture department for their use. To forecast production before harvest facilitate the formulation of workable marketing strategies leading to better export/import of crop in the state, which will help to lead better economic condition of the state. Yield estimation would help agriculture department in assessment of productivity of land for specific crop. Pre-harvest wheat acreage/production estimation, is useful to facilitate the reliable and timely estimates and enable the administrators and planners to take strategic decisions on import-export policy matters and trade negotiations.

Multiple regression and inverse moments improve the characterization of the spatial scaling behavior of daily streamflows in the Southeast United States

USGS Publications Warehouse

Farmer, William H.; Over, Thomas M.; Vogel, Richard M.

2015-01-01

Understanding the spatial structure of daily streamflow is essential for managing freshwater resources, especially in poorly-gaged regions. Spatial scaling assumptions are common in flood frequency prediction (e.g., index-flood method) and the prediction of continuous streamflow at ungaged sites (e.g. drainage-area ratio), with simple scaling by drainage area being the most common assumption. In this study, scaling analyses of daily streamflow from 173 streamgages in the southeastern US resulted in three important findings. First, the use of only positive integer moment orders, as has been done in most previous studies, captures only the probabilistic and spatial scaling behavior of flows above an exceedance probability near the median; negative moment orders (inverse moments) are needed for lower streamflows. Second, assessing scaling by using drainage area alone is shown to result in a high degree of omitted-variable bias, masking the true spatial scaling behavior. Multiple regression is shown to mitigate this bias, controlling for regional heterogeneity of basin attributes, especially those correlated with drainage area. Previous univariate scaling analyses have neglected the scaling of low-flow events and may have produced biased estimates of the spatial scaling exponent. Third, the multiple regression results show that mean flows scale with an exponent of one, low flows scale with spatial scaling exponents greater than one, and high flows scale with exponents less than one. The relationship between scaling exponents and exceedance probabilities may be a fundamental signature of regional streamflow. This signature may improve our understanding of the physical processes generating streamflow at different exceedance probabilities. 

Spatial and Temporal Uncertainty of Crop Yield Aggregations

NASA Technical Reports Server (NTRS)

Porwollik, Vera; Mueller, Christoph; Elliott, Joshua; Chryssanthacopoulos, James; Iizumi, Toshichika; Ray, Deepak K.; Ruane, Alex C.; Arneth, Almut; Balkovic, Juraj; Ciais, Philippe;

Inaccuracies in sediment budgets arising from estimations of tributary sediment inputs: an example from a monitoring network on the southern Colorado plateau

USGS Publications Warehouse

Griffiths, Ronald; Topping, David

2015-01-01

Sediment budgets are an important tool for understanding how riverine ecosystems respond to perturbations. Changes in the quantity and grain-size distribution of sediment within river systems affect the channel morphology and related habitat resources. It is therefore important for resource managers to know if a channel reach is in a state of sediment accumulation, deficit or stasis. Many studies have estimated sediment loads from ungaged tributaries using regional sediment-yield equations or other similar techniques. While these approaches may be valid in regions where rainfall and geology are uniform over large areas, use of sediment-yield equations may lead to poor estimations of sediment loads in semi-arid climates, where rainfall events, contributing geology, and vegetation have large spatial variability.

Spatially Interpolated Disease Prevalence Estimation Using Collateral Indicators of Morbidity and Ecological Risk

PubMed Central

Congdon, Peter

2013-01-01

This paper considers estimation of disease prevalence for small areas (neighbourhoods) when the available observations on prevalence are for an alternative partition of a region, such as service areas. Interpolation to neighbourhoods uses a kernel method extended to take account of two types of collateral information. The first is morbidity and service use data, such as hospital admissions, observed for neighbourhoods. Variations in morbidity and service use are expected to reflect prevalence. The second type of collateral information is ecological risk factors (e.g., pollution indices) that are expected to explain variability in prevalence in service areas, but are typically observed only for neighbourhoods. An application involves estimating neighbourhood asthma prevalence in a London health region involving 562 neighbourhoods and 189 service (primary care) areas. PMID:24129116

Spatially interpolated disease prevalence estimation using collateral indicators of morbidity and ecological risk.

PubMed

Congdon, Peter

2013-10-14

This paper considers estimation of disease prevalence for small areas (neighbourhoods) when the available observations on prevalence are for an alternative partition of a region, such as service areas. Interpolation to neighbourhoods uses a kernel method extended to take account of two types of collateral information. The first is morbidity and service use data, such as hospital admissions, observed for neighbourhoods. Variations in morbidity and service use are expected to reflect prevalence. The second type of collateral information is ecological risk factors (e.g., pollution indices) that are expected to explain variability in prevalence in service areas, but are typically observed only for neighbourhoods. An application involves estimating neighbourhood asthma prevalence in a London health region involving 562 neighbourhoods and 189 service (primary care) areas.

An observational assessment of the influence of mesoscale and submesoscale heterogeneity on ocean biogeochemical reactions

NASA Astrophysics Data System (ADS)

Martin, Adrian P.; Lévy, Marina; van Gennip, Simon; Pardo, Silvia; Srokosz, Meric; Allen, John; Painter, Stuart C.; Pidcock, Roz

2015-09-01

Numerous observations demonstrate that considerable spatial variability exists in components of the marine planktonic ecosystem at the mesoscale and submesoscale (100 km-1 km). The causes and consequences of physical processes at these scales ("eddy advection") influencing biogeochemistry have received much attention. Less studied, the nonlinear nature of most ecological and biogeochemical interactions means that such spatial variability has consequences for regional estimates of processes including primary production and grazing, independent of the physical processes. This effect has been termed "eddy reactions." Models remain our most powerful tools for extrapolating hypotheses for biogeochemistry to global scales and to permit future projections. The spatial resolution of most climate and global biogeochemical models means that processes at the mesoscale and submesoscale are poorly resolved. Modeling work has previously suggested that the neglected eddy reactions may be almost as large as the mean field estimates in some cases. This study seeks to quantify the relative size of eddy and mean reactions observationally, using in situ and satellite data. For primary production, grazing, and zooplankton mortality the eddy reactions are between 7% and 15% of the mean reactions. These should be regarded as preliminary estimates to encourage further observational estimates and not taken as a justification for ignoring eddy reactions. Compared to modeling estimates, there are inconsistencies in the relative magnitude of eddy reactions and in correlations which are a major control on their magnitude. One possibility is that models exhibit much stronger spatial correlations than are found in reality, effectively amplifying the magnitude of eddy reactions.

Ionospheric tomography by gradient-enhanced kriging with STEC measurements and ionosonde characteristics

NASA Astrophysics Data System (ADS)

Minkwitz, David; van den Boogaart, Karl Gerald; Gerzen, Tatjana; Hoque, Mainul; Hernández-Pajares, Manuel

2016-11-01

The estimation of the ionospheric electron density by kriging is based on the optimization of a parametric measurement covariance model. First, the extension of kriging with slant total electron content (STEC) measurements based on a spatial covariance to kriging with a spatial-temporal covariance model, assimilating STEC data of a sliding window, is presented. Secondly, a novel tomography approach by gradient-enhanced kriging (GEK) is developed. Beyond the ingestion of STEC measurements, GEK assimilates ionosonde characteristics, providing peak electron density measurements as well as gradient information. Both approaches deploy the 3-D electron density model NeQuick as a priori information and estimate the covariance parameter vector within a maximum likelihood estimation for the dedicated tomography time stamp. The methods are validated in the European region for two periods covering quiet and active ionospheric conditions. The kriging with spatial and spatial-temporal covariance model is analysed regarding its capability to reproduce STEC, differential STEC and foF2. Therefore, the estimates are compared to the NeQuick model results, the 2-D TEC maps of the International GNSS Service and the DLR's Ionospheric Monitoring and Prediction Center, and in the case of foF2 to two independent ionosonde stations. Moreover, simulated STEC and ionosonde measurements are used to investigate the electron density profiles estimated by the GEK in comparison to a kriging with STEC only. The results indicate a crucial improvement in the initial guess by the developed methods and point out the potential compensation for a bias in the peak height hmF2 by means of GEK.

Estimating under-five mortality in space and time in a developing world context.

PubMed

Wakefield, Jon; Fuglstad, Geir-Arne; Riebler, Andrea; Godwin, Jessica; Wilson, Katie; Clark, Samuel J

2018-01-01

Accurate estimates of the under-five mortality rate in a developing world context are a key barometer of the health of a nation. This paper describes a new model to analyze survey data on mortality in this context. We are interested in both spatial and temporal description, that is wishing to estimate under-five mortality rate across regions and years and to investigate the association between the under-five mortality rate and spatially varying covariate surfaces. We illustrate the methodology by producing yearly estimates for subnational areas in Kenya over the period 1980-2014 using data from the Demographic and Health Surveys, which use stratified cluster sampling. We use a binomial likelihood with fixed effects for the urban/rural strata and random effects for the clustering to account for the complex survey design. Smoothing is carried out using Bayesian hierarchical models with continuous spatial and temporally discrete components. A key component of the model is an offset to adjust for bias due to the effects of HIV epidemics. Substantively, there has been a sharp decline in Kenya in the under-five mortality rate in the period 1980-2014, but large variability in estimated subnational rates remains. A priority for future research is understanding this variability. In exploratory work, we examine whether a variety of spatial covariate surfaces can explain the variability in under-five mortality rate. Temperature, precipitation, a measure of malaria infection prevalence, and a measure of nearness to cities were candidates for inclusion in the covariate model, but the interplay between space, time, and covariates is complex.

Quantitative predictions of streamflow variability in the Susquehanna River Basin

NASA Astrophysics Data System (ADS)

Alexander, R.; Boyer, E. W.; Leonard, L. N.; Duffy, C.; Schwarz, G. E.; Smith, R. A.

2012-12-01

Hydrologic researchers and water managers have increasingly sought an improved understanding of the major processes that control fluxes of water and solutes across diverse environmental settings and large spatial scales. Regional analyses of observed streamflow data have led to advances in our knowledge of relations among land use, climate, and streamflow, with methodologies ranging from statistical assessments of multiple monitoring sites to the regionalization of the parameters of catchment-scale mechanistic simulation models. However, gaps remain in our understanding of the best ways to transfer the knowledge of hydrologic response and governing processes among locations, including methods for regionalizing streamflow measurements and model predictions. We developed an approach to predict variations in streamflow using the SPARROW (SPAtially Referenced Regression On Watershed attributes) modeling infrastructure, with mechanistic functions, mass conservation constraints, and statistical estimation of regional and sub-regional parameters. We used the model to predict discharge in the Susquehanna River Basin (SRB) under varying hydrological regimes that are representative of contemporary flow conditions. The resulting basin-scale water balance describes mean monthly flows in stream reaches throughout the entire SRB (represented at a 1:100,000 scale using the National Hydrologic Data network), with water supply and demand components that are inclusive of a range of hydrologic, climatic, and cultural properties (e.g., precipitation, evapotranspiration, soil and groundwater storage, runoff, baseflow, water use). We compare alternative models of varying complexity that reflect differences in the number and types of explanatory variables and functional expressions as well as spatial and temporal variability in the model parameters. Statistical estimation of the models reveals the levels of complexity that can be uniquely identified, subject to the information content and uncertainties of the hydrologic and climate measurements. Assessment of spatial variations in the model parameters and predictions provides an improved understanding of how much of the hydrologic response to land use, climate, and other properties is unique to specific locations versus more universally observed across catchments of the SRB. This approach advances understanding of water cycle variability at any location throughout the stream network, as a function of both landscape characteristics (e.g., soils, vegetation, land use) and external forcings (e.g., precipitation quantity and frequency). These improvements in predictions of streamflow dynamics will advance the ability to predict spatial and temporal variability in key solutes, such as nutrients, and their delivery to the Chesapeake Bay.

Essays on the Impacts of Geography and Institutions on Access to Energy and Public Infrastructure Services

NASA Astrophysics Data System (ADS)

Archibong, Belinda

While previous literature has emphasized the importance of energy and public infrastructure services for economic development, questions surrounding the implications of unequal spatial distribution in access to these resources remain, particularly in the developing country context. This dissertation provides evidence on the nature, origins and implications of this distribution uniting three strands of research from the development and political economy, regional science and energy economics fields. The dissertation unites three papers on the nature of spatial inequality of access to energy and infrastructure with further implications for conflict risk , the historical institutional and biogeographical determinants of current distribution of access to energy and public infrastructure services and the response of households to fuel price changes over time. Chapter 2 uses a novel survey dataset to provide evidence for spatial clustering of public infrastructure non-functionality at schools by geopolitical zone in Nigeria with further implications for armed conflict risk in the region. Chapter 3 investigates the drivers of the results in chapter 2, exploiting variation in the spatial distribution of precolonial institutions and geography in the region, to provide evidence for the long-term impacts of these factors on current heterogeneity of access to public services. Chapter 4 addresses the policy implications of energy access, providing the first multi-year evidence on firewood demand elasticities in India, using the spatial variation in prices for estimation.

Spatial patterns of mixing in the Solomon Sea

NASA Astrophysics Data System (ADS)

Alberty, M. S.; Sprintall, J.; MacKinnon, J.; Ganachaud, A.; Cravatte, S.; Eldin, G.; Germineaud, C.; Melet, A.

2017-05-01

The Solomon Sea is a marginal sea in the southwest Pacific that connects subtropical and equatorial circulation, constricting transport of South Pacific Subtropical Mode Water and Antarctic Intermediate Water through its deep, narrow channels. Marginal sea topography inhibits internal waves from propagating out and into the open ocean, making these regions hot spots for energy dissipation and mixing. Data from two hydrographic cruises and from Argo profiles are employed to indirectly infer mixing from observations for the first time in the Solomon Sea. Thorpe and finescale methods indirectly estimate the rate of dissipation of kinetic energy (ɛ) and indicate that it is maximum in the surface and thermocline layers and decreases by 2-3 orders of magnitude by 2000 m depth. Estimates of diapycnal diffusivity from the observations and a simple diffusive model agree in magnitude but have different depth structures, likely reflecting the combined influence of both diapycnal mixing and isopycnal stirring. Spatial variability of ɛ is large, spanning at least 2 orders of magnitude within isopycnal layers. Seasonal variability of ɛ reflects regional monsoonal changes in large-scale oceanic and atmospheric conditions with ɛ increased in July and decreased in March. Finally, tide power input and topographic roughness are well correlated with mean spatial patterns of mixing within intermediate and deep isopycnals but are not clearly correlated with thermocline mixing patterns.

Spatial variations of tritium concentrations in groundwater collected in the southern coastal region of Fukushima, Japan, after the nuclear accident.

PubMed

Kashiwaya, Koki; Muto, Yuta; Kubo, Taiki; Ikawa, Reo; Nakaya, Shinji; Koike, Katsuaki; Marui, Atsunao

2017-10-03

Spatial variations in tritium concentrations in groundwater were identified in the southern part of the coastal region in Fukushima Prefecture, Japan. Higher tritium concentrations were measured at wells near the Fukushima Daiichi Nuclear Power Station (F1NPS). Mean tritium concentrations in precipitation in the 5 weeks after the F1NPS accident were estimated to be 433 and 139 TU at a distance of 25 and 50 km, respectively, from the F1NPS. The elevations of tritium concentrations in groundwater were calculated using a simple mixing model of the precipitation and groundwater. By assuming that these precipitation was mixed into groundwater with a background tritium concentration in a hypothetical well, concentrations of 13 and 7 TU at distances of 25 and 50 km from the F1NPS, respectively, were obtained. The calculated concentrations are consistent with those measured at the studied wells. Therefore, the spatial variation in tritium concentrations in groundwater was probably caused by precipitation with high tritium concentrations as a result of the F1NPS accident. However, the highest estimated tritium concentrations in precipitation for the study site were much lower than the WHO limits for drinking water, and the concentrations decreased to almost background level at the wells by mixing with groundwater.

Measurement of turbulent spatial structure and kinetic energy spectrum by exact temporal-to-spatial mapping

NASA Astrophysics Data System (ADS)

Buchhave, Preben; Velte, Clara M.

2017-08-01

We present a method for converting a time record of turbulent velocity measured at a point in a flow to a spatial velocity record consisting of consecutive convection elements. The spatial record allows computation of dynamic statistical moments such as turbulent kinetic wavenumber spectra and spatial structure functions in a way that completely bypasses the need for Taylor's hypothesis. The spatial statistics agree with the classical counterparts, such as the total kinetic energy spectrum, at least for spatial extents up to the Taylor microscale. The requirements for applying the method are access to the instantaneous velocity magnitude, in addition to the desired flow quantity, and a high temporal resolution in comparison to the relevant time scales of the flow. We map, without distortion and bias, notoriously difficult developing turbulent high intensity flows using three main aspects that distinguish these measurements from previous work in the field: (1) The measurements are conducted using laser Doppler anemometry and are therefore not contaminated by directional ambiguity (in contrast to, e.g., frequently employed hot-wire anemometers); (2) the measurement data are extracted using a correctly and transparently functioning processor and are analysed using methods derived from first principles to provide unbiased estimates of the velocity statistics; (3) the exact mapping proposed herein has been applied to the high turbulence intensity flows investigated to avoid the significant distortions caused by Taylor's hypothesis. The method is first confirmed to produce the correct statistics using computer simulations and later applied to measurements in some of the most difficult regions of a round turbulent jet—the non-equilibrium developing region and the outermost parts of the developed jet. The proposed mapping is successfully validated using corresponding directly measured spatial statistics in the fully developed jet, even in the difficult outer regions of the jet where the average convection velocity is negligible and turbulence intensities increase dramatically. The measurements in the developing region reveal interesting features of an incomplete Richardson-Kolmogorov cascade under development.

Accuracy Sampling Design Bias on Coarse Spatial Resolution Land Cover Data in the Great Lakes Region (United States and Canada)

EPA Science Inventory

A number of articles have investigated the impact of sampling design on remotely sensed landcover accuracy estimates. Gong and Howarth (1990) found significant differences for Kappa accuracy values when comparing purepixel sampling, stratified random sampling, and stratified sys...

Microwave Soil Moisture Retrieval Under Trees Using a Modified Tau-Omega Model

USDA-ARS?s Scientific Manuscript database

IPAD is to provide timely and accurate estimates of global crop conditions for use in up-to-date commodity intelligence reports. A crucial requirement of these global crop yield forecasts is the regional characterization of surface and sub-surface soil moisture. However, due to the spatial heterogen...

Invasion complexity at large spatial scales is an emergent property of interactions among landscape characteristics and invader traits

USDA-ARS?s Scientific Manuscript database

Understanding the potential for invasive spread is an important consideration for novel agricultural species that may be translocated or introduced into new regions. However, estimating invasion risks remains a challenging problem, particularly in the context of real, complex landscapes. There is ...

Crop biomass and evapotranspiration estimation using SPOT and Formosat-2 Data

NASA Astrophysics Data System (ADS)

Veloso, Amanda; Demarez, Valérie; Ceschia, Eric; Claverie, Martin

2013-04-01

The use of crop models allows simulating plant development, growth and yield under different environmental and management conditions. When combined with high spatial and temporal resolution remote sensing data, these models provide new perspectives for crop monitoring at regional scale. We propose here an approach to estimate time courses of dry aboveground biomass, yield and evapotranspiration (ETR) for summer (maize, sunflower) and winter crops (wheat) by assimilating Green Area Index (GAI) data, obtained from satellite observations, into a simple crop model. Only high spatial resolution and gap-free satellite time series can provide enough information for efficient crop monitoring applications. The potential of remote sensing data is often limited by cloud cover and/or gaps in observation. Data from different sensor systems need then to be combined. For this work, we employed a unique set of Formosat-2 and SPOT images (164 images) and in-situ measurements, acquired from 2006 to 2010 in southwest France. Among the several land surface biophysical variables accessible from satellite observations, the GAI is the one that has a key role in soil-plant-atmosphere interactions and in biomass accumulation process. Many methods have been developed to relate GAI to optical remote sensing signal. Here, seasonal dynamics of remotely sensed GAI were estimated by applying a method based on the inversion of a radiative transfer model using artificial neural networks. The modelling approach is based on the Simple Algorithm for Yield and Evapotranspiration estimate (SAFYE) model, which couples the FAO-56 model with an agro-meteorological model, based on Monteith's light-use efficiency theory. The SAFYE model is a daily time step crop model that simulates time series of GAI, dry aboveground biomass, grain yield and ETR. Crop and soil model parameters were determined using both in-situ measurements and values found in the literature. Phenological parameters were calibrated by the assimilation of the remotely sensed GAI time series. The calibration process led to accurate spatial estimates of GAI, ETR as well as of biomass and yield over the study area (24 km x 24 km window). The results highlight the interest of using a combined approach (crop model coupled with high spatial and temporal resolution remote sensing data) for the estimation of agronomical variables. At local scale, the model reproduced correctly the biomass production and ETR for summer crops (with relative RMSE of 29% and 35%, respectively). At regional scale, estimated yield and water requirement for irrigation were compared to regional statistics of yield and irrigation inventories provided by the local water agency. Results showed good agreements for inter-annual dynamics of yield estimates. Differences between water requirement for irrigation and actual supply were lower than 10% and inter-annual variability was well represented as well. The work, initially focused on summer crops, is being adapted to winter crops.

Uncertainties in Earthquake Loss Analysis: A Case Study From Southern California

NASA Astrophysics Data System (ADS)

Mahdyiar, M.; Guin, J.

2005-12-01

Probabilistic earthquake hazard and loss analyses play important roles in many areas of risk management, including earthquake related public policy and insurance ratemaking. Rigorous loss estimation for portfolios of properties is difficult since there are various types of uncertainties in all aspects of modeling and analysis. It is the objective of this study to investigate the sensitivity of earthquake loss estimation to uncertainties in regional seismicity, earthquake source parameters, ground motions, and sites' spatial correlation on typical property portfolios in Southern California. Southern California is an attractive region for such a study because it has a large population concentration exposed to significant levels of seismic hazard. During the last decade, there have been several comprehensive studies of most regional faults and seismogenic sources. There have also been detailed studies on regional ground motion attenuations and regional and local site responses to ground motions. This information has been used by engineering seismologists to conduct regional seismic hazard and risk analysis on a routine basis. However, one of the more difficult tasks in such studies is the proper incorporation of uncertainties in the analysis. From the hazard side, there are uncertainties in the magnitudes, rates and mechanisms of the seismic sources and local site conditions and ground motion site amplifications. From the vulnerability side, there are considerable uncertainties in estimating the state of damage of buildings under different earthquake ground motions. From an analytical side, there are challenges in capturing the spatial correlation of ground motions and building damage, and integrating thousands of loss distribution curves with different degrees of correlation. In this paper we propose to address some of these issues by conducting loss analyses of a typical small portfolio in southern California, taking into consideration various source and ground motion uncertainties. The approach is designed to integrate loss distribution functions with different degrees of correlation for portfolio analysis. The analysis is based on USGS 2002 regional seismicity model.

Sample selection and spatial models of housing price indexes, and, A disequilibrium analysis of the U.S. gasoline market using panel data

NASA Astrophysics Data System (ADS)

Hu, Haixin

This dissertation consists of two parts. The first part studies the sample selection and spatial models of housing price index using transaction data on detached single-family houses of two California metropolitan areas from 1990 through 2008. House prices are often spatially correlated due to shared amenities, or when the properties are viewed as close substitutes in a housing submarket. There have been many studies that address spatial correlation in the context of housing markets. However, none has used spatial models to construct housing price indexes at zip code level for the entire time period analyzed in this dissertation to the best of my knowledge. In this paper, I study a first-order autoregressive spatial model with four different weighing matrix schemes. Four sets of housing price indexes are constructed accordingly. Gatzlaff and Haurin (1997, 1998) study the sample selection problem in housing index by using Heckman's two-step method. This method, however, is generally inefficient and can cause multicollinearity problem. Also, it requires data on unsold houses in order to carry out the first-step probit regression. Maximum likelihood (ML) method can be used to estimate a truncated incidental model which allows one to correct for sample selection based on transaction data only. However, convergence problem is very prevalent in practice. In this paper I adopt Lewbel's (2007) sample selection correction method which does not require one to model or estimate the selection model, except for some very general assumptions. I then extend this method to correct for spatial correlation. In the second part, I analyze the U.S. gasoline market with a disequilibrium model that allows lagged-latent variables, endogenous prices, and panel data with fixed effects. Most existing studies (see the survey of Espey, 1998, Energy Economics) of the gasoline market assume equilibrium. In practice, however, prices do not always adjust fast enough to clear the market. Equilibrium assumptions greatly simplify statistical inference, but are very restrictive and can produce conflicting estimates. For example, econometric models of markets that assume equilibrium often produce more elastic demand price elasticity than their disequilibrium counterparts (Holt and Johnson, 1989, Review of Economics and Statistics, Oczkowski, 1998, Economics Letters). The few studies that allow disequilibrium, however, have been limited to macroeconomic time-series data without lagged-latent variables. While time series data allows one to investigate national trends, it cannot be used to identify and analyze regional differences and the role of local markets. Exclusion of the lagged-latent variables is also undesirable because such variables capture adjustment costs and inter-temporal spillovers. Simulation methods offer tractable solutions to dynamic and panel data disequilibrium models (Lee, 1997, Journal of Econometrics), but assume normally distributed errors. This paper compares estimates of price/income elasticity and excess supply/demand across time periods, regions, and model specifications, using both equilibrium and disequilibrium methods. In the equilibrium model, I compare the within group estimator with Anderson and Hsiao's first-difference 2SLS estimator. In the disequilibrium model, I extend Amemiya's 2SLS by using Newey's efficient estimator with optimal instruments.

Estimating wetland methane emissions from the northern high latitudes from 1990 to 2009 using artificial neural networks

NASA Astrophysics Data System (ADS)

Zhu, Xudong; Zhuang, Qianlai; Qin, Zhangcai; Glagolev, Mikhail; Song, Lulu

2013-04-01

Methane (CH4) emissions from wetland ecosystems in nothern high latitudes provide a potentially positive feedback to global climate warming. Large uncertainties still remain in estimating wetland CH4 emisions at regional scales. Here we develop a statistical model of CH4 emissions using an artificial neural network (ANN) approach and field observations of CH4 fluxes. Six explanatory variables (air temperature, precipitation, water table depth, soil organic carbon, soil total porosity, and soil pH) are included in the development of ANN models, which are then extrapolated to the northern high latitudes to estimate monthly CH4 emissions from 1990 to 2009. We estimate that the annual wetland CH4 source from the northern high latitudes (north of 45°N) is 48.7 Tg CH4 yr-1 (1 Tg = 1012 g) with an uncertainty range of 44.0 53.7 Tg CH4 yr-1. The estimated wetland CH4 emissions show a large spatial variability over the northern high latitudes, due to variations in hydrology, climate, and soil conditions. Significant interannual and seasonal variations of wetland CH4 emissions exist in the past 2 decades, and the emissions in this period are most sensitive to variations in water table position. To improve future assessment of wetland CH4 dynamics in this region, research priorities should be directed to better characterizing hydrological processes of wetlands, including temporal dynamics of water table position and spatial dynamics of wetland areas.

Estimating Soil Organic Carbon stocks and uncertainties at the regional scale following a legacy sampling strategy - a case study from southern Belgium

NASA Astrophysics Data System (ADS)

Chartin, Caroline; Krüger, Inken; Goidts, Esther; Carnol, Monique; van Wesemael, Bas

2017-04-01

The quantification and the spatialisation of reliable SOC stocks (Mg C ha-1) and total stock (Tg C) baselines and associated uncertainties are fundamental to detect the gains or losses in SOC, and to locate sensitive areas with low SOC levels. Here, we aim to both quantify and spatialize SOC stocks at regional scale (southern Belgium) based on data from one non-design-based nor model-based sampling scheme. To this end, we developed a computation procedure based on Digital Soil Mapping techniques and stochastic simulations (Monte-Carlo) allowing the estimation of multiple (here, 10,000) independent spatialized datasets. The computation of the prediction uncertainty accounts for the errors associated to the both estimations of i) SOC stock at the pixel-related area scale and ii) parameters of the spatial model. Based on these 10,000 individuals, median SOC stocks and 90% prediction intervals were computed for each pixel, as well as total SOC stocks and their 90% prediction intervals for selected sub-areas and for the entire study area. Hence, a Generalised Additive Model (GAM) explaining 69.3 % of the SOC stock variance was calibrated and then validated (R2 = 0.64). The model overestimated low SOC stock (below 50 Mg C ha-1) and underestimated high SOC stock (especially those above 100 Mg C kg-1). A positive gradient of SOC stock occurred from the northwest to the center of Wallonia with a slight decrease on the southernmost part, correlating to the evolution of precipitation and temperature (along with elevation) and dominant land use. At the catchment scale higher SOC stocks were predicted on valley bottoms, especially for poorly drained soils under grassland. Mean predicted SOC stocks for cropland and grassland in Wallonia were of 26.58 Tg C (SD 1.52) and 43.30 Tg C (2.93), respectively. The procedure developed here allowed to predict realistic spatial patterns of SOC stocks all over agricultural lands of southern Belgium and to produce reliable statistics of total SOC stocks for each of the 20 combinations of land use / agricultural regions of Wallonia. This procedure appears useful to produce soil maps as policy tools in conducting sustainable management at regional and national scales, and to compute statistics which comply with specific requirements of reporting activities.

Observation of spatial and temporal variations in X-ray bright point emergence patterns. [at solar surface

NASA Technical Reports Server (NTRS)

Golub, L.; Krieger, A. S.; Vaiana, G. S.

1976-01-01

Observations of X-ray bright points (XBP) over a six-month interval in 1973 show significant variations in both the number density of XBP as a function of heliographic longitude and in the full-sun average number of XBP from one rotation to the next. The observed increases in XBP emergence are estimated to be equivalent to several large active regions emerging per day for several months. The number of XBP emerging at high latitudes varies in phase with the low-latitude variation and reaches a maximum approximately simultaneous with a major outbreak of active regions. The quantity of magnetic flux emerging in the form of XBP at high latitudes alone is estimated to be as large as the contribution from all active regions.

Reconstruction of global gridded monthly sectoral water withdrawals for 1971–2010 and analysis of their spatiotemporal patterns

DOE PAGES

Huang, Zhongwei; Hejazi, Mohamad; Li, Xinya; ...

2018-04-06

Human water withdrawal has increasingly altered the global water cycle in past decades, yet our understanding of its driving forces and patterns is limited. Reported historical estimates of sectoral water withdrawals are often sparse and incomplete, mainly restricted to water withdrawal estimates available at annual and country scales, due to a lack of observations at seasonal and local scales. In this study, through collecting and consolidating various sources of reported data and developing spatial and temporal statistical downscaling algorithms, we reconstruct a global monthly gridded (0.5°) sectoral water withdrawal dataset for the period 1971–2010, which distinguishes six water use sectors, i.e., irrigation,more » domestic, electricity generation (cooling of thermal power plants), livestock, mining, and manufacturing. Based on the reconstructed dataset, the spatial and temporal patterns of historical water withdrawal are analyzed. Results show that total global water withdrawal has increased significantly during 1971–2010, mainly driven by the increase in irrigation water withdrawal. Regions with high water withdrawal are those densely populated or with large irrigated cropland production, e.g., the United States (US), eastern China, India, and Europe. Seasonally, irrigation water withdrawal in summer for the major crops contributes a large percentage of total annual irrigation water withdrawal in mid- and high-latitude regions, and the dominant season of irrigation water withdrawal is also different across regions. Domestic water withdrawal is mostly characterized by a summer peak, while water withdrawal for electricity generation has a winter peak in high-latitude regions and a summer peak in low-latitude regions. Despite the overall increasing trend, irrigation in the western US and domestic water withdrawal in western Europe exhibit a decreasing trend. Our results highlight the distinct spatial pattern of human water use by sectors at the seasonal and annual timescales. Here, the reconstructed gridded water withdrawal dataset is open access, and can be used for examining issues related to water withdrawals at fine spatial, temporal, and sectoral scales.« less

Reconstruction of global gridded monthly sectoral water withdrawals for 1971–2010 and analysis of their spatiotemporal patterns

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

Huang, Zhongwei; Hejazi, Mohamad; Li, Xinya

Human water withdrawal has increasingly altered the global water cycle in past decades, yet our understanding of its driving forces and patterns is limited. Reported historical estimates of sectoral water withdrawals are often sparse and incomplete, mainly restricted to water withdrawal estimates available at annual and country scales, due to a lack of observations at seasonal and local scales. In this study, through collecting and consolidating various sources of reported data and developing spatial and temporal statistical downscaling algorithms, we reconstruct a global monthly gridded (0.5°) sectoral water withdrawal dataset for the period 1971–2010, which distinguishes six water use sectors, i.e., irrigation,more » domestic, electricity generation (cooling of thermal power plants), livestock, mining, and manufacturing. Based on the reconstructed dataset, the spatial and temporal patterns of historical water withdrawal are analyzed. Results show that total global water withdrawal has increased significantly during 1971–2010, mainly driven by the increase in irrigation water withdrawal. Regions with high water withdrawal are those densely populated or with large irrigated cropland production, e.g., the United States (US), eastern China, India, and Europe. Seasonally, irrigation water withdrawal in summer for the major crops contributes a large percentage of total annual irrigation water withdrawal in mid- and high-latitude regions, and the dominant season of irrigation water withdrawal is also different across regions. Domestic water withdrawal is mostly characterized by a summer peak, while water withdrawal for electricity generation has a winter peak in high-latitude regions and a summer peak in low-latitude regions. Despite the overall increasing trend, irrigation in the western US and domestic water withdrawal in western Europe exhibit a decreasing trend. Our results highlight the distinct spatial pattern of human water use by sectors at the seasonal and annual timescales. Here, the reconstructed gridded water withdrawal dataset is open access, and can be used for examining issues related to water withdrawals at fine spatial, temporal, and sectoral scales.« less

Reconstruction of global gridded monthly sectoral water withdrawals for 1971–2010 and analysis of their spatiotemporal patterns

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

Huang, Zhongwei; Hejazi, Mohamad; Li, Xinya

Human water withdrawal has increasingly altered the global water cycle in past decades, yet our understanding of its driving forces and patterns is limited. Reported historical estimates of sectoral water withdrawals are often sparse and incomplete, mainly restricted to water withdrawal estimates available at annual and country scales, due to a lack of observations at seasonal and local scales. In this study, through collecting and consolidating various sources of reported data and developing spatial and temporal statistical downscaling algorithms, we reconstruct a global monthly gridded (0.5°) sectoral water withdrawal dataset for the period 1971–2010, which distinguishes six water use sectors, i.e., irrigation,more » domestic, electricity generation (cooling of thermal power plants), livestock, mining, and manufacturing. Based on the reconstructed dataset, the spatial and temporal patterns of historical water withdrawal are analyzed. Results show that total global water withdrawal has increased significantly during 1971–2010, mainly driven by the increase in irrigation water withdrawal. Regions with high water withdrawal are those densely populated or with large irrigated cropland production, e.g., the United States (US), eastern China, India, and Europe. Seasonally, irrigation water withdrawal in summer for the major crops contributes a large percentage of total annual irrigation water withdrawal in mid- and high-latitude regions, and the dominant season of irrigation water withdrawal is also different across regions. Domestic water withdrawal is mostly characterized by a summer peak, while water withdrawal for electricity generation has a winter peak in high-latitude regions and a summer peak in low-latitude regions. Despite the overall increasing trend, irrigation in the western US and domestic water withdrawal in western Europe exhibit a decreasing trend. Our results highlight the distinct spatial pattern of human water use by sectors at the seasonal and annual timescales. The reconstructed gridded water withdrawal dataset is open access, and can be used for examining issues related to water withdrawals at fine spatial, temporal, and sectoral scales.« less

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.

The characteristic and influence factors of extinction depth of shallow groundwater on the high-latitude region: a case study on the Sanjiang Plain, northeast China.

PubMed

Wang, Xihua

2018-03-01

Accurate estimation of extinction depth of shallow groundwater (EDSG) and identification of its influence factors are important for sustainable management of groundwater resources, ecological protection, and human health in intensively irrigated region. In this study, the ratio of actual groundwater depth and EDSG (RAE) method was used to understand the spatial variability of EDSG in the Sanjiang Plain, one of China's largest grain production bases and China's largest inland freshwater wetland region. The study showed a large spatial variation of EDSG in the region. Spatially, the sites, which were in the northeast and center had the deepest and the shallowest EDSG, whereby, indicate that it has higher and lower pumping potential capacity. Many factors including climate, soil parameters, vegetation and topography affected the EDSG. We also identified an area of 3.86 × 10 10  m 2 , which accounting for 35.3% of the entire Sanjiang Plain, has exceeded the ESGD by over exploited for years. Knowledge of the variation and influence factors of EDSG for a certain plant system and the current shallow groundwater condition in the higher latitude region can be a key to the development of preventive actions for large quantity pumping groundwater and protection regional and sustainable development of irrigated agriculture.

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.

An operational MODIS aerosol retrieval algorithm at high spatial resolution, and its application over a complex urban region

NASA Astrophysics Data System (ADS)

Wong, Man Sing; Nichol, Janet E.; Lee, Kwon Ho

2011-03-01

Aerosol retrieval algorithms for the MODerate Resolution Imaging Spectroradiometer (MODIS) have been developed to estimate aerosol and microphysical properties of the atmosphere, which help to address aerosol climatic issues at global scale. However, higher spatial resolution aerosol products for urban areas have not been well-researched mainly due to the difficulty of differentiating aerosols from bright surfaces in urban areas. Here, an aerosol retrieval algorithm using the MODIS 500-m resolution bands is described, to retrieve aerosol properties over Hong Kong and the Pearl River Delta region. The rationale of our technique is to first estimate the aerosol reflectances by decomposing the top-of-atmosphere reflectances from surface reflectances and Rayleigh path reflectances. For the determination of surface reflectances, a Minimum Reflectance Technique (MRT) is used, and MRT images are computed for different seasons. For conversion of aerosol reflectance to aerosol optical thickness (AOT), comprehensive Look Up Tables specific to the local region are constructed, which consider aerosol properties and sun-viewing geometry in the radiative transfer calculations. Four local aerosol types, namely coastal urban, polluted urban, dust, and heavy pollution, were derived using cluster analysis on 3 years of AERONET measurements in Hong Kong. The resulting 500 m AOT images were found to be highly correlated with ground measurements from the AERONET (r2 = 0.767) and Microtops II sunphotometers (r2 = 0.760) in Hong Kong. This study further demonstrates the application of the fine resolution AOT images for monitoring inter-urban and intra-urban aerosol distributions and the influence of trans-boundary flows. These applications include characterization of spatial patterns of AOT within the city, and detection of regional biomass burning sources.

Evaluation of Statistical Downscaling Skill at Reproducing Extreme Events

NASA Astrophysics Data System (ADS)

McGinnis, S. A.; Tye, M. R.; Nychka, D. W.; Mearns, L. O.

2015-12-01

Climate model outputs usually have much coarser spatial resolution than is needed by impacts models. Although higher resolution can be achieved using regional climate models for dynamical downscaling, further downscaling is often required. The final resolution gap is often closed with a combination of spatial interpolation and bias correction, which constitutes a form of statistical downscaling. We use this technique to downscale regional climate model data and evaluate its skill in reproducing extreme events. We downscale output from the North American Regional Climate Change Assessment Program (NARCCAP) dataset from its native 50-km spatial resolution to the 4-km resolution of University of Idaho's METDATA gridded surface meterological dataset, which derives from the PRISM and NLDAS-2 observational datasets. We operate on the major variables used in impacts analysis at a daily timescale: daily minimum and maximum temperature, precipitation, humidity, pressure, solar radiation, and winds. To interpolate the data, we use the patch recovery method from the Earth System Modeling Framework (ESMF) regridding package. We then bias correct the data using Kernel Density Distribution Mapping (KDDM), which has been shown to exhibit superior overall performance across multiple metrics. Finally, we evaluate the skill of this technique in reproducing extreme events by comparing raw and downscaled output with meterological station data in different bioclimatic regions according to the the skill scores defined by Perkins et al in 2013 for evaluation of AR4 climate models. We also investigate techniques for improving bias correction of values in the tails of the distributions. These techniques include binned kernel density estimation, logspline kernel density estimation, and transfer functions constructed by fitting the tails with a generalized pareto distribution.

Multiwavelength studies of H II region NGC 2467

NASA Astrophysics Data System (ADS)

Yadav, Ram Kesh; Pandey, A. K.; Sharma, Saurabh; Eswaraiah, C.

2013-06-01

We present the multiwavelength studies of the H II region Sh2-311 to explore the effects of massive stars on low-mass star formation. In this study we have used optical (UBVI) data from ESO 2.2m Wide Field Imager (WFI), Near-Infrared (NIR) (JHKs) data from CTIO 4m Blanco Telescope and archival Spitzer 8μm data. Based on stellar density contours and dust distribution we have divided the complex into three regions i.e., Haffner 19 (H19), Haffner 18 (H18) and NGC 2467. Using the UBVI data we have estimated the basic parameters of these regions. We have constructed the (J - H)/(H - Ks) color-color diagram and a J/(J - H) color-magnitude diagram to identify young stellar objects (YSOs) and to estimate their masses. Spatial distribution of the YSOs indicates that most of them are distributed at the periphery of the H II region and ionizing star may be responsible for the triggering of star formation at the periphery of the H II region.

Estimation and mapping of wet and dry mercury deposition across northeastern North America

USGS Publications Warehouse

Miller, E.K.; Vanarsdale, A.; Keeler, G.J.; Chalmers, A.; Poissant, L.; Kamman, N.C.; Brulotte, R.

2005-01-01

Whereas many ecosystem characteristics and processes influence mercury accumulation in higher trophic-level organisms, the mercury flux from the atmosphere to a lake and its watershed is a likely factor in potential risk to biota. Atmospheric deposition clearly affects mercury accumulation in soils and lake sediments. Thus, knowledge of spatial patterns in atmospheric deposition may provide information for assessing the relative risk for ecosystems to exhibit excessive biotic mercury contamination. Atmospheric mercury concentrations in aerosol, vapor, and liquid phases from four observation networks were used to estimate regional surface concentration fields. Statistical models were developed to relate sparsely measured mercury vapor and aerosol concentrations to the more commonly measured mercury concentration in precipitation. High spatial resolution deposition velocities for different phases (precipitation, cloud droplets, aerosols, and reactive gaseous mercury (RGM)) were computed using inferential models. An empirical model was developed to estimate gaseous elemental mercury (GEM) deposition. Spatial patterns of estimated total mercury deposition were complex. Generally, deposition was higher in the southwest and lower in the northeast. Elevation, land cover, and proximity to urban areas modified the general pattern. The estimated net GEM and RGM fluxes were each greater than or equal to wet deposition in many areas. Mercury assimilation by plant foliage may provide a substantial input of methyl-mercury (MeHg) to ecosystems. ?? 2005 Springer Science+Business Media, Inc.

Building the Foundation for International Conservation Planning for Breeding Ducks across the U.S. and Canadian Border

PubMed Central

Doherty, Kevin E.; Evans, Jeffrey S.; Walker, Johann; Devries, James H.; Howerter, David W.

2015-01-01

We used publically available data on duck breeding distribution and recently compiled geospatial data on upland habitat and environmental conditions to develop a spatially explicit model of breeding duck populations across the entire Prairie Pothole Region (PPR). Our spatial population models were able to identify key areas for duck conservation across the PPR and predict between 62.1 – 79.1% (68.4% avg.) of the variation in duck counts by year from 2002 – 2010. The median difference in observed vs. predicted duck counts at a transect segment level was 4.6 ducks. Our models are the first seamless spatially explicit models of waterfowl abundance across the entire PPR and represent an initial step toward joint conservation planning between Prairie Pothole and Prairie Habitat Joint Ventures. Our work demonstrates that when spatial and temporal variation for highly mobile birds is incorporated into conservation planning it will likely increase the habitat area required to support defined population goals. A major goal of the current North American Waterfowl Management Plan and subsequent action plan is the linking of harvest and habitat management. We contend incorporation of spatial aspects will increase the likelihood of coherent joint harvest and habitat management decisions. Our results show at a minimum, it is possible to produce spatially explicit waterfowl abundance models that when summed across survey strata will produce similar strata level population estimates as the design-based Waterfowl Breeding Pair and Habitat Survey (r2 = 0.977). This is important because these design-based population estimates are currently used to set duck harvest regulations and to set duck population and habitat goals for the North American Waterfowl Management Plan. We hope this effort generates discussion on the important linkages between spatial and temporal variation in population size, and distribution relative to habitat quantity and quality when linking habitat and population goals across this important region. PMID:25714747

Effects of harvesting flowers from shrubs on the persistence and abundance of wild shrub populations at multiple spatial extents.

PubMed

Cabral, Juliano Sarmento; Bond, William J; Midgley, Guy F; Rebelo, Anthony G; Thuiller, Wilfried; Schurr, Frank M

2011-02-01

Wildflower harvesting is an economically important activity of which the ecological effects are poorly understood. We assessed how harvesting of flowers affects shrub persistence and abundance at multiple spatial extents. To this end, we built a process-based model to examine the mean persistence and abundance of wild shrubs whose flowers are subject to harvest (serotinous Proteaceae in the South African Cape Floristic Region). First, we conducted a general sensitivity analysis of how harvesting affects persistence and abundance at nested spatial extents. For most spatial extents and combinations of demographic parameters, persistence and abundance of flowering shrubs decreased abruptly once harvesting rate exceeded a certain threshold. At larger extents, metapopulations supported higher harvesting rates before their persistence and abundance decreased, but persistence and abundance also decreased more abruptly due to harvesting than at smaller extents. This threshold rate of harvest varied with species' dispersal ability, maximum reproductive rate, adult mortality, probability of extirpation or local extinction, strength of Allee effects, and carrying capacity. Moreover, spatial extent interacted with Allee effects and probability of extirpation because both these demographic properties affected the response of local populations to harvesting more strongly than they affected the response of metapopulations. Subsequently, we simulated the effects of harvesting on three Cape Floristic Region Proteaceae species and found that these species reacted differently to harvesting, but their persistence and abundance decreased at low rates of harvest. Our estimates of harvesting rates at maximum sustainable yield differed from those of previous investigations, perhaps because researchers used different estimates of demographic parameters, models of population dynamics, and spatial extent than we did. Good demographic knowledge and careful identification of the spatial extent of interest increases confidence in assessments and monitoring of the effects of harvesting. Our general sensitivity analysis improved understanding of harvesting effects on metapopulation dynamics and allowed qualitative assessment of the probability of extirpation of poorly studied species. ©2010 Society for Conservation Biology.

Soil loss estimation and prioritization of sub-watersheds of Kali River basin, Karnataka, India, using RUSLE and GIS.

PubMed

Markose, Vipin Joseph; Jayappa, K S

2016-04-01

Most of the mountainous regions in tropical humid climatic zone experience severe soil loss due to natural factors. In the absence of measured data, modeling techniques play a crucial role for quantitative estimation of soil loss in such regions. The objective of this research work is to estimate soil loss and prioritize the sub-watersheds of Kali River basin using Revised Universal Soil Loss Equation (RUSLE) model. Various thematic layers of RUSLE factors such as rainfall erosivity (R), soil erodibility (K), topographic factor (LS), crop management factor (C), and support practice factor (P) have been prepared by using multiple spatial and non-spatial data sets. These layers are integrated in geographic information system (GIS) environment and estimated the soil loss. The results show that ∼42 % of the study area falls under low erosion risk and only 6.97 % area suffer from very high erosion risk. Based on the rate of soil loss, 165 sub-watersheds have been prioritized into four categories-very high, high, moderate, and low erosion risk. Anthropogenic activities such as deforestation, construction of dams, and rapid urbanization are the main reasons for high rate of soil loss in the study area. The soil erosion rate and prioritization maps help in implementation of a proper watershed management plan for the river basin.

Where to locate a tree plantation within a low rainfall catchment to minimise impacts on groundwater resources

NASA Astrophysics Data System (ADS)

Dean, J. F.; Webb, J. A.; Jacobsen, G. E.; Chisari, R.; Dresel, P. E.

2014-08-01

Despite the fact that there are many studies that consider the impacts of plantation forestry on water resources, and others that explore the spatial heterogeneity of groundwater recharge in dry regions, there is little marriage of the two subjects in forestry management guidelines and legislation. Here we carry out an in-depth analysis of the groundwater and surface water regime in a low rainfall, high evapotranspiration paired catchment study to examine the impact of reforestation, using water table fluctuations and chloride mass balance methods to estimate groundwater recharge. Recharge estimations using the chloride mass balance method were shown to be more likely representative of groundwater recharge regimes prior to the planting of the trees, and most likely prior to widespread land clearance by European settlers. These estimations were complicated by large amounts of recharge occurring as a result of runoff and streamflow in the lower parts of the catchment. Water table fluctuation method estimations of recharge verified that groundwater recharge occurs predominantly in the lowland areas of the study catchment. This leads to the conclusion that spatial variations in recharge are important considerations for locating tree plantations with respect to conserving water resources for downstream users. For dry regions, this means planting trees in the upland parts of the catchments, as recharge is shown to occur predominantly in the lowland areas.

Cost-effective sampling of (137)Cs-derived net soil redistribution: part 2 - estimating the spatial mean change over time.

PubMed

Chappell, A; Li, Y; Yu, H Q; Zhang, Y Z; Li, X Y

2015-06-01

The caesium-137 ((137)Cs) technique for estimating net, time-integrated soil redistribution by the processes of wind, water and tillage is increasingly being used with repeated sampling to form a baseline to evaluate change over small (years to decades) timeframes. This interest stems from knowledge that since the 1950s soil redistribution has responded dynamically to different phases of land use change and management. Currently, there is no standard approach to detect change in (137)Cs-derived net soil redistribution and thereby identify the driving forces responsible for change. We outline recent advances in space-time sampling in the soil monitoring literature which provide a rigorous statistical and pragmatic approach to estimating the change over time in the spatial mean of environmental properties. We apply the space-time sampling framework, estimate the minimum detectable change of net soil redistribution and consider the information content and cost implications of different sampling designs for a study area in the Chinese Loess Plateau. Three phases (1954-1996, 1954-2012 and 1996-2012) of net soil erosion were detectable and attributed to well-documented historical change in land use and management practices in the study area and across the region. We recommend that the design for space-time sampling is considered carefully alongside cost-effective use of the spatial mean to detect and correctly attribute cause of change over time particularly across spatial scales of variation. Copyright © 2015 Elsevier Ltd. All rights reserved.

A Contemporary Assessment of Lateral Fluxes of Organic Carbon in Inland Waters of the USA and Delivery to Coastal Waters

NASA Astrophysics Data System (ADS)

Boyer, E. W.; Alexander, R. B.; Smith, R. A.; Shih, J.; Schwarz, G. E.

2010-12-01

Organic carbon (OC) is a critical water quality characteristic in surface waters, as it is an important component of the energy balance and food chains in freshwater and estuarine aquatic ecosystems, is significant in the mobilization and transport of contaminants along flow paths, and is associated with the formation of known carcinogens in drinking water supplies. The importance of OC dynamics on water quality has been recognized, but challenges remain in quantitatively addressing processes controlling OC fluxes over broad spatial scales in a hydrological context. Here, we: 1) quantified lateral OC fluxes in rivers, streams, and reservoirs across the nation; 2) partitioned how much organic carbon that is stored in lakes, rivers and streams comes from allochthonous sources (produced in the terrestrial landscape) versus autochthonous sources (produced in-stream by primary production); and 3) estimated the delivery of dissolved and total forms of organic carbon to coastal estuaries and embayments. To accomplish this, we developed national-scale models of organic carbon in U.S. surface waters using the spatially referenced regression on watersheds (SPARROW) technique. This approach uses mechanistic formulations, imposes mass balance constraints, and provides a formal parameter estimation structure to statistically estimate sources and fate of OC in terrestrial and aquatic ecosystems. We make use of a GIS based framework to describe sources of organic matter and characteristics of the landscape that affect its fate and transport, from spatial databases providing characterizations of climate, land cover, primary productivity, topography, soils, geology, and water routing. We calibrated and evaluated the model with statistical estimates of organic carbon loads that were observed at 1,125 monitoring stations across the nation. Our results illustrate spatial patterns and magnitudes OC loadings in rivers and reservoirs, highlighting hot spots and suggesting origins of the OC to each location. Further, our results yield quantitative estimates of aquatic OC fluxes for large water regions and for the nation, providing a refined estimate of the role of surface water fluxes of OC in relationship to regional and national carbon budgets. Finally, we are using our simulations to explore the potential role of climate and other changes in the terrestrial environment on OC fluxes in aquatic systems.

Reliability Correction for Functional Connectivity: Theory and Implementation

PubMed Central

Mueller, Sophia; Wang, Danhong; Fox, Michael D.; Pan, Ruiqi; Lu, Jie; Li, Kuncheng; Sun, Wei; Buckner, Randy L.; Liu, Hesheng

2016-01-01

Network properties can be estimated using functional connectivity MRI (fcMRI). However, regional variation of the fMRI signal causes systematic biases in network estimates including correlation attenuation in regions of low measurement reliability. Here we computed the spatial distribution of fcMRI reliability using longitudinal fcMRI datasets and demonstrated how pre-estimated reliability maps can correct for correlation attenuation. As a test case of reliability-based attenuation correction we estimated properties of the default network, where reliability was significantly lower than average in the medial temporal lobe and higher in the posterior medial cortex, heterogeneity that impacts estimation of the network. Accounting for this bias using attenuation correction revealed that the medial temporal lobe’s contribution to the default network is typically underestimated. To render this approach useful to a greater number of datasets, we demonstrate that test-retest reliability maps derived from repeated runs within a single scanning session can be used as a surrogate for multi-session reliability mapping. Using data segments with different scan lengths between 1 and 30 min, we found that test-retest reliability of connectivity estimates increases with scan length while the spatial distribution of reliability is relatively stable even at short scan lengths. Finally, analyses of tertiary data revealed that reliability distribution is influenced by age, neuropsychiatric status and scanner type, suggesting that reliability correction may be especially important when studying between-group differences. Collectively, these results illustrate that reliability-based attenuation correction is an easily implemented strategy that mitigates certain features of fMRI signal nonuniformity. PMID:26493163

High-Resolution Forest Carbon Monitoring and Modeling: Continued Prototype Development and Deployment Across The Tri-state Area (MD, PA, DE), USA

NASA Astrophysics Data System (ADS)

Hurtt, G. C.; Birdsey, R.; Campbell, E.; Dolan, K. A.; Dubayah, R.; Escobar, V. M.; Finley, A. O.; Flanagan, S.; Huang, W.; Johnson, K.; Lister, A.; ONeil-Dunne, J.; Sepulveda Carlo, E.; Zhao, M.

2017-12-01

Local, national and international programs have increasing need for precise and accurate estimates of forest carbon and structure to support greenhouse gas reduction plans, climate initiatives, and other international climate treaty frameworks. In 2010 Congress directed NASA to initiate research towards the development of Carbon Monitoring Systems (CMS). In response, our team has worked to develop a robust, replicable framework to produce maps of high-resolution carbon stocks and future carbon sequestration potential. High-resolution (30m) maps of carbon stocks and uncertainty were produced by linking national 1m-resolution imagery and existing wall-to-wall airborne lidar to spatially explicit in-situ field observations such as the USFS Forest Inventory and Analysis (FIA) network. These same data, characterizing forest extent and vertical structure, were used to drive a prognostic ecosystem model to predict carbon fluxes and carbon sequestration potential at unprecedented spatial resolution and scale (90m), more than 100,000 times the spatial resolution of standard global models. Through project development, the domain of this research has expanded from two counties in MD (2,181 km2), to the entire state (32,133 km2), to the tri-state region of MD, PA, and DE (157,868 km2), covering forests in four major USDA ecological providences (Eastern Broadleaf, Northeastern Mixed, Outer Coastal Plain, and Central Appalachian). Across the region, we estimate 694 Tg C (14 DE, 113 MD, 567 PA) in above ground biomass, and estimate a carbon sequestration potential more than twice that amount. Empirical biomass products enhance existing approaches though high resolution accounting for trees outside traditional forest maps. Modeling products move beyond traditional MRV, and map future afforestation and reforestation potential for carbon at local actionable spatial scales. These products are relevant to multiple stakeholder needs in the region as discussed through the Tri-sate Working Group, and are actively being used to inform the state of MD's Greenhouse Gas Reduction Act. The approach is scalable, and provides a protoype framework for application in other domains and for future spaceborne lidar missions.

Multiscale Bayesian neural networks for soil water content estimation

NASA Astrophysics Data System (ADS)

Jana, Raghavendra B.; Mohanty, Binayak P.; Springer, Everett P.

2008-08-01

Artificial neural networks (ANN) have been used for some time now to estimate soil hydraulic parameters from other available or more easily measurable soil properties. However, most such uses of ANNs as pedotransfer functions (PTFs) have been at matching spatial scales (1:1) of inputs and outputs. This approach assumes that the outputs are only required at the same scale as the input data. Unfortunately, this is rarely true. Different hydrologic, hydroclimatic, and contaminant transport models require soil hydraulic parameter data at different spatial scales, depending upon their grid sizes. While conventional (deterministic) ANNs have been traditionally used in these studies, the use of Bayesian training of ANNs is a more recent development. In this paper, we develop a Bayesian framework to derive soil water retention function including its uncertainty at the point or local scale using PTFs trained with coarser-scale Soil Survey Geographic (SSURGO)-based soil data. The approach includes an ANN trained with Bayesian techniques as a PTF tool with training and validation data collected across spatial extents (scales) in two different regions in the United States. The two study areas include the Las Cruces Trench site in the Rio Grande basin of New Mexico, and the Southern Great Plains 1997 (SGP97) hydrology experimental region in Oklahoma. Each region-specific Bayesian ANN is trained using soil texture and bulk density data from the SSURGO database (scale 1:24,000), and predictions of the soil water contents at different pressure heads with point scale data (1:1) inputs are made. The resulting outputs are corrected for bias using both linear and nonlinear correction techniques. The results show good agreement between the soil water content values measured at the point scale and those predicted by the Bayesian ANN-based PTFs for both the study sites. Overall, Bayesian ANNs coupled with nonlinear bias correction are found to be very suitable tools for deriving soil hydraulic parameters at the local/fine scale from soil physical properties at coarser-scale and across different spatial extents. This approach could potentially be used for soil hydraulic properties estimation and downscaling.

A Model-Based Approach to Infer Shifts in Regional Fire Regimes Over Time Using Sediment Charcoal Records

NASA Astrophysics Data System (ADS)

Itter, M.; Finley, A. O.; Hooten, M.; Higuera, P. E.; Marlon, J. R.; McLachlan, J. S.; Kelly, R.

2016-12-01

Sediment charcoal records are used in paleoecological analyses to identify individual local fire events and to estimate fire frequency and regional biomass burned at centennial to millenial time scales. Methods to identify local fire events based on sediment charcoal records have been well developed over the past 30 years, however, an integrated statistical framework for fire identification is still lacking. We build upon existing paleoecological methods to develop a hierarchical Bayesian point process model for local fire identification and estimation of fire return intervals. The model is unique in that it combines sediment charcoal records from multiple lakes across a region in a spatially-explicit fashion leading to estimation of a joint, regional fire return interval in addition to lake-specific local fire frequencies. Further, the model estimates a joint regional charcoal deposition rate free from the effects of local fires that can be used as a measure of regional biomass burned over time. Finally, the hierarchical Bayesian approach allows for tractable error propagation such that estimates of fire return intervals reflect the full range of uncertainty in sediment charcoal records. Specific sources of uncertainty addressed include sediment age models, the separation of local versus regional charcoal sources, and generation of a composite charcoal record The model is applied to sediment charcoal records from a dense network of lakes in the Yukon Flats region of Alaska. The multivariate joint modeling approach results in improved estimates of regional charcoal deposition with reduced uncertainty in the identification of individual fire events and local fire return intervals compared to individual lake approaches. Modeled individual-lake fire return intervals range from 100 to 500 years with a regional interval of roughly 200 years. Regional charcoal deposition to the network of lakes is correlated up to 50 kilometers. Finally, the joint regional charcoal deposition rate exhibits changes over time coincident with major climatic and vegetation shifts over the past 10,000 years. Ongoing work will use the regional charcoal deposition rate to estimate changes in biomass burned as a function of climate variability and regional vegetation pattern.

Internal and external potential-field estimation from regional vector data at varying satellite altitude

NASA Astrophysics Data System (ADS)

Plattner, Alain; Simons, Frederik J.

2017-10-01

When modelling satellite data to recover a global planetary magnetic or gravitational potential field, the method of choice remains their analysis in terms of spherical harmonics. When only regional data are available, or when data quality varies strongly with geographic location, the inversion problem becomes severely ill-posed. In those cases, adopting explicitly local methods is to be preferred over adapting global ones (e.g. by regularization). Here, we develop the theory behind a procedure to invert for planetary potential fields from vector observations collected within a spatially bounded region at varying satellite altitude. Our method relies on the construction of spatiospectrally localized bases of functions that mitigate the noise amplification caused by downward continuation (from the satellite altitude to the source) while balancing the conflicting demands for spatial concentration and spectral limitation. The `altitude-cognizant' gradient vector Slepian functions (AC-GVSF) enjoy a noise tolerance under downward continuation that is much improved relative to the `classical' gradient vector Slepian functions (CL-GVSF), which do not factor satellite altitude into their construction. Furthermore, venturing beyond the realm of their first application, published in a preceding paper, in the present article we extend the theory to being able to handle both internal and external potential-field estimation. Solving simultaneously for internal and external fields under the limitation of regional data availability reduces internal-field artefacts introduced by downward-continuing unmodelled external fields, as we show with numerical examples. We explain our solution strategies on the basis of analytic expressions for the behaviour of the estimation bias and variance of models for which signal and noise are uncorrelated, (essentially) space- and band-limited, and spectrally (almost) white. The AC-GVSF are optimal linear combinations of vector spherical harmonics. Their construction is not altogether very computationally demanding when the concentration domains (the regions of spatial concentration) have circular symmetry, for example, on spherical caps or rings—even when the spherical-harmonic bandwidth is large. Data inversion proceeds by solving for the expansion coefficients of truncated function sequences, by least-squares analysis in a reduced-dimensional space. Hence, our method brings high-resolution regional potential-field modelling from incomplete and noisy vector-valued satellite data within reach of contemporary desktop machines.

Variability in Spatially and Temporally Resolved Emissions and Hydrocarbon Source Fingerprints for Oil and Gas Sources in Shale Gas Production Regions.

PubMed

Allen, David T; Cardoso-Saldaña, Felipe J; Kimura, Yosuke

2017-10-17

A gridded inventory for emissions of methane, ethane, propane, and butanes from oil and gas sources in the Barnett Shale production region has been developed. This inventory extends previous spatially resolved inventories of emissions by characterizing the overall variability in emission magnitudes and the composition of emissions at an hourly time resolution. The inventory is divided into continuous and intermittent emission sources. Sources are defined as continuous if hourly averaged emissions are greater than zero in every hour; otherwise, they are classified as intermittent. In the Barnett Shale, intermittent sources accounted for 14-30% of the mean emissions for methane and 10-34% for ethane, leading to spatial and temporal variability in the location of hourly emissions. The combined variability due to intermittent sources and variability in emission factors can lead to wide confidence intervals in the magnitude and composition of time and location-specific emission inventories; therefore, including temporal and spatial variability in emission inventories is important when reconciling inventories and observations. Comparisons of individual aircraft measurement flights conducted in the Barnett Shale region versus the estimated emission rates for each flight from the emission inventory indicate agreement within the expected variability of the emission inventory for all flights for methane and for all but one flight for ethane.

Regional co-location pattern scoping on a street network considering distance decay effects of spatial interaction

PubMed Central

Yu, Wenhao

2017-01-01

Regional co-location scoping intends to identify local regions where spatial features of interest are frequently located together. Most of the previous researches in this domain are conducted on a global scale and they assume that spatial objects are embedded in a 2-D space, but the movement in urban space is actually constrained by the street network. In this paper we refine the scope of co-location patterns to 1-D paths consisting of nodes and segments. Furthermore, since the relations between spatial events are usually inversely proportional to their separation distance, the proposed method introduces the “Distance Decay Effects” to improve the result. Specifically, our approach first subdivides the street edges into continuous small linear segments. Then a value representing the local distribution intensity of events is estimated for each linear segment using the distance-decay function. Each kind of geographic feature can lead to a tessellated network with density attribute, and the generated multiple networks for the pattern of interest will be finally combined into a composite network by calculating the co-location prevalence measure values, which are based on the density variation between different features. Our experiments verify that the proposed approach is effective in urban analysis. PMID:28763496

Uncertainty on shallow landslide hazard assessment: from field data to hazard mapping

NASA Astrophysics Data System (ADS)

Trefolini, Emanuele; Tolo, Silvia; Patelli, Eduardo; Broggi, Matteo; Disperati, Leonardo; Le Tuan, Hai

2015-04-01

Shallow landsliding that involve Hillslope Deposits (HD), the surficial soil that cover the bedrock, is an important process of erosion, transport and deposition of sediment along hillslopes. Despite Shallow landslides generally mobilize relatively small volume of material, they represent the most hazardous factor in mountain regions due to their high velocity and the common absence of warning signs. Moreover, increasing urbanization and likely climate change make shallow landslides a source of widespread risk, therefore the interest of scientific community about this process grown in the last three decades. One of the main aims of research projects involved on this topic, is to perform robust shallow landslides hazard assessment for wide areas (regional assessment), in order to support sustainable spatial planning. Currently, three main methodologies may be implemented to assess regional shallow landslides hazard: expert evaluation, probabilistic (or data mining) methods and physical models based methods. The aim of this work is evaluate the uncertainty of shallow landslides hazard assessment based on physical models taking into account spatial variables such as: geotechnical and hydrogeologic parameters as well as hillslope morphometry. To achieve this goal a wide dataset of geotechnical properties (shear strength, permeability, depth and unit weight) of HD was gathered by integrating field survey, in situ and laboratory tests. This spatial database was collected from a study area of about 350 km2 including different bedrock lithotypes and geomorphological features. The uncertainty associated to each step of the hazard assessment process (e.g. field data collection, regionalization of site specific information and numerical modelling of hillslope stability) was carefully characterized. The most appropriate probability density function (PDF) was chosen for each numerical variable and we assessed the uncertainty propagation on HD strength parameters obtained by empirical relations with geotechnical index properties. Site specific information was regionalized at map scale by (hard and fuzzy) clustering analysis taking into account spatial variables such as: geology, geomorphology and hillslope morphometric variables (longitudinal and transverse curvature, flow accumulation and slope), the latter derived by a DEM with 10 m cell size. In order to map shallow landslide hazard, Monte Carlo simulation was performed for some common physically based models available in literature (eg. SINMAP, SHALSTAB, TRIGRS). Furthermore, a new approach based on the use of Bayesian Network was proposed and validated. Different models, such as Intervals, Convex Models and Fuzzy Sets, were adopted for the modelling of input parameters. Finally, an accuracy assessment was carried out on the resulting maps and the propagation of uncertainty of input parameters into the final shallow landslide hazard estimation was estimated. The outcomes of the analysis are compared and discussed in term of discrepancy among map pixel values and related estimated error. The novelty of the proposed method is on estimation of the confidence of the shallow landslides hazard mapping at regional level. This allows i) to discriminate regions where hazard assessment is robust from areas where more data are necessary to increase the confidence level and ii) to assess the reliability of the procedure used for hazard assessment.

Spatial variation of peat soil properties in the oil-producing region of northeastern Sakhalin

NASA Astrophysics Data System (ADS)

Lipatov, D. N.; Shcheglov, A. I.; Manakhov, D. V.; Zavgorodnyaya, Yu. A.; Rozanova, M. S.; Brekhov, P. T.

2017-07-01

Morphology and properties of medium-deep oligotrophic peat, oligotrophic peat gley, pyrogenic oligotrophic peat gley, and peat gley soils on subshrub-cotton grass-sphagnum bogs and in swampy larch forests of northeastern Sakhalin have been studied. Variation in the thickness and reserves of litters in the studied bog and forest biogeocenoses has been analyzed. The profile distribution and spatial variability of moisture, density, ash, and pHKCl in separate groups of peat soils have been described. The content and spatial variability of petroleum hydrocarbons have been considered in relation to the accumulation of natural bitumoids by peat soils and the technogenic pressing in the oil-producing region. Variation of each parameter at different distances (10, 50, and 1000 m) has been estimated using a hierarchical sampling scheme. The spatial conjugation of soil parameters has been studied by factor analysis using the principal components method and Spearman correlation coefficients. Regression equations have been proposed to describe relationships of ash content with soil density and content of petroleum hydrocarbons in peat horizons.

Detection and Analysis of Complex Patterns of Ice Dynamics in Antarctica from ICESat Laser Altimetry

NASA Astrophysics Data System (ADS)

Babonis, Gregory Scott

There remains much uncertainty in estimating the amount of Antarctic ice mass change, its dynamic component, and its spatial and temporal patterns. This work remedies the limitations of previous studies by generating the first detailed reconstruction of total and dynamic ice thickness and mass changes across Antarctica, from ICESat satellite altimetry observations in 2003-2009 using the Surface Elevation Reconstruction and Change Detection (SERAC) method. Ice sheet thickness changes are calculated with quantified error estimates for each time when ICESat flew over a ground-track crossover region, at approximately 110,000 locations across the Antarctic Ice Sheet. The time series are partitioned into changes due to surficial processes and ice dynamics. The new results markedly improve the spatial and temporal resolution of surface elevation, volume, and mass change rates for the AIS, and can be sampled at annual temporal resolutions. The results indicate a complex spatiotemporal pattern of dynamic mass loss in Antarctica, especially along individual outlet glaciers, and allow for the quantification of the annual contribution of Antarctic ice loss to sea level rise. Over 5000 individual locations exhibit either strong dynamic ice thickness change patterns, accounting for approximately 500 unique spatial clusters that identify regions likely influenced by subglacial hydrology. The spatial distribution and temporal behavior of these regions reveal the complexity and short-time scale variability in the subglacial hydrological system. From the 500 unique spatial clusters, over 370 represent newly identified, and not previously published, potential subglacial water bodies indicating an active subglacial hydrological system over a much larger region than previously observed. These numerous new observations of dynamic changes provide more than simply a larger set of data. Examination of both regional and local scale dynamic change patterns across Antarctica shows newly discovered connections between the geology and ice sheet dynamics of Antarctica, particularly along the boundary between East and West Antarctica in the Pagano Shear Zone. Additionally, increased dynamic activity is shown to concentrate in regions of Antarctica most likely to experience catastrophic failure and collapse in the future. Further quantification of mass and volume changes demonstrates that the methods described within allow for a true reconciliation between different satellite methods of measuring ice sheet mass and volume balance, and show that Antarctica is losing enough mass between 2003 and 2009 to raise global sea levels 0.1 mm/yr during that time. Additionally, analysis of local patterns of dynamic ice thickness changes shows that there is continued or increased ice loss, since before the ICESat mission period, in many of the coastal sectors of Antarctica.

The role of spatial aggregation in forensic entomology.

PubMed

Fiene, Justin G; Sword, Gregory A; Van Laerhoven, Sherah L; Tarone, Aaron M

2014-01-01

A central concept in forensic entomology is that arthropod succession on carrion is predictable and can be used to estimate the postmortem interval (PMI) of human remains. However, most studies have reported significant variation in successional patterns, particularly among replicate carcasses, which has complicated estimates of PMIs. Several forensic entomology researchers have proposed that further integration of ecological and evolutionary theory in forensic entomology could help advance the application of succession data for producing PMI estimates. The purpose of this essay is to draw attention to the role of spatial aggregation of arthropods among carrion resources as a potentially important aspect to consider for understanding and predicting the assembly of arthropods on carrion over time. We review ecological literature related to spatial aggregation of arthropods among patchy and ephemeral resources, such as carrion, and when possible integrate these results with published forensic literature. We show that spatial aggregation of arthropods across resources is commonly reported and has been used to provide fundamental insight for understanding regional and local patterns of arthropod diversity and coexistence. Moreover, two suggestions are made for conducting future research. First, because intraspecific aggregation affects species frequency distributions across carcasses, data from replicate carcasses should not be combined, but rather statistically quantified to generate occurrence probabilities. Second, we identify a need for studies that tease apart the degree to which community assembly on carrion is spatially versus temporally structured, which will aid in developing mechanistic hypotheses on the ecological factors shaping community assembly on carcasses.

Using LiDAR and quickbird data to model plant production and quantify uncertainties associated with wetland detection and land cover generalizations

USGS Publications Warehouse

Cook, B.D.; Bolstad, P.V.; Naesset, E.; Anderson, R. Scott; Garrigues, S.; Morisette, J.T.; Nickeson, J.; Davis, K.J.

2009-01-01

Spatiotemporal data from satellite remote sensing and surface meteorology networks have made it possible to continuously monitor global plant production, and to identify global trends associated with land cover/use and climate change. Gross primary production (GPP) and net primary production (NPP) are routinely derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard satellites Terra and Aqua, and estimates generally agree with independent measurements at validation sites across the globe. However, the accuracy of GPP and NPP estimates in some regions may be limited by the quality of model input variables and heterogeneity at fine spatial scales. We developed new methods for deriving model inputs (i.e., land cover, leaf area, and photosynthetically active radiation absorbed by plant canopies) from airborne laser altimetry (LiDAR) and Quickbird multispectral data at resolutions ranging from about 30??m to 1??km. In addition, LiDAR-derived biomass was used as a means for computing carbon-use efficiency. Spatial variables were used with temporal data from ground-based monitoring stations to compute a six-year GPP and NPP time series for a 3600??ha study site in the Great Lakes region of North America. Model results compared favorably with independent observations from a 400??m flux tower and a process-based ecosystem model (BIOME-BGC), but only after removing vapor pressure deficit as a constraint on photosynthesis from the MODIS global algorithm. Fine-resolution inputs captured more of the spatial variability, but estimates were similar to coarse-resolution data when integrated across the entire landscape. Failure to account for wetlands had little impact on landscape-scale estimates, because vegetation structure, composition, and conversion efficiencies were similar to upland plant communities. Plant productivity estimates were noticeably improved using LiDAR-derived variables, while uncertainties associated with land cover generalizations and wetlands in this largely forested landscape were considered less important.

Assessment of Rainfall Estimates Using a Standard Z-R Relationship and the Probability Matching Method Applied to Composite Radar Data in Central Florida

NASA Technical Reports Server (NTRS)

Crosson, William L.; Duchon, Claude E.; Raghavan, Ravikumar; Goodman, Steven J.

1996-01-01

Precipitation estimates from radar systems are a crucial component of many hydrometeorological applications, from flash flood forecasting to regional water budget studies. For analyses on large spatial scales and long timescales, it is frequently necessary to use composite reflectivities from a network of radar systems. Such composite products are useful for regional or national studies, but introduce a set of difficulties not encountered when using single radars. For instance, each contributing radar has its own calibration and scanning characteristics, but radar identification may not be retained in the compositing procedure. As a result, range effects on signal return cannot be taken into account. This paper assesses the accuracy with which composite radar imagery can be used to estimate precipitation in the convective environment of Florida during the summer of 1991. Results using Z = 30OR(sup 1.4) (WSR-88D default Z-R relationship) are compared with those obtained using the probability matching method (PMM). Rainfall derived from the power law Z-R was found to he highly biased (+90%-l10%) compared to rain gauge measurements for various temporal and spatial integrations. Application of a 36.5-dBZ reflectivity threshold (determined via the PMM) was found to improve the performance of the power law Z-R, reducing the biases substantially to 20%-33%. Correlations between precipitation estimates obtained with either Z-R relationship and mean gauge values are much higher for areal averages than for point locations. Precipitation estimates from the PMM are an improvement over those obtained using the power law in that biases and root-mean-square errors are much lower. The minimum timescale for application of the PMM with the composite radar dataset was found to be several days for area-average precipitation. The minimum spatial scale is harder to quantify, although it is concluded that it is less than 350 sq km. Implications relevant to the WSR-88D system are discussed.

Spatially explicit estimation of heat stress-related impacts of climate change on the milk production of dairy cows in the United Kingdom

PubMed Central

Topp, Cairistiona F. E.; Moorby, Jon M.; Pásztor, László; Foyer, Christine H.

2018-01-01

Dairy farming is one the most important sectors of United Kingdom (UK) agriculture. It faces major challenges due to climate change, which will have direct impacts on dairy cows as a result of heat stress. In the absence of adaptations, this could potentially lead to considerable milk loss. Using an 11-member climate projection ensemble, as well as an ensemble of 18 milk loss estimation methods, temporal changes in milk production of UK dairy cows were estimated for the 21st century at a 25 km resolution in a spatially-explicit way. While increases in UK temperatures are projected to lead to relatively low average annual milk losses, even for southern UK regions (<180 kg/cow), the ‘hottest’ 25×25 km grid cell in the hottest year in the 2090s, showed an annual milk loss exceeding 1300 kg/cow. This figure represents approximately 17% of the potential milk production of today’s average cow. Despite the potential considerable inter-annual variability of annual milk loss, as well as the large differences between the climate projections, the variety of calculation methods is likely to introduce even greater uncertainty into milk loss estimations. To address this issue, a novel, more biologically-appropriate mechanism of estimating milk loss is proposed that provides more realistic future projections. We conclude that South West England is the region most vulnerable to climate change economically, because it is characterised by a high dairy herd density and therefore potentially high heat stress-related milk loss. In the absence of mitigation measures, estimated heat stress-related annual income loss for this region by the end of this century may reach £13.4M in average years and £33.8M in extreme years. PMID:29738581

Spatial variations of the 3 micron emission features within UV-excited nebulae - Photochemical evolution of interstellar polycyclic aromatic hydrocarbons

NASA Technical Reports Server (NTRS)

Geballe, T. R.; Tielens, A. G. G. M.; Allamandola, L. J.; Moorhouse, A.; Brand, P. W. J. L.

1989-01-01

Spectra at 3 microns have been obtained at several positions in the Orion Bar region and in the nebula surrounding HD 44179. Weak emission features at 3.40, 3.46, 3.51, and 3.57 microns are prominent in the Orion Bar region. The 3.40- and 3.51-micron features increase in intensity relative to the dominant 3.29-micron feature. The spectrum obtained in the Red Rectangle region 5 arcsecs north of HD 44179 are similar to those in the Orion Bar, with a weak, broad 3.40-micron feature at the position of HD 44179. The spatial behavior of the weak emission features is explained in terms of hot bands of the CH stretch and overtones, and combination bands of other fundamental vibrations in simple PAHs. Based on the susceptibility of PAHs to destruction by the far UV fields in both regions, PAH sizes are estimated at 20-50 carbon atoms.

Hot emission model for mobile sources: application to the metropolitan region of the city of Santiago, Chile.

PubMed

Corvalán, Roberto M; Osses, Mauricio; Urrutia, Cristian M

2002-02-01

Depending on the final application, several methodologies for traffic emission estimation have been developed. Emission estimation based on total miles traveled or other average factors is a sufficient approach only for extended areas such as national or worldwide areas. For road emission control and strategies design, microscale analysis based on real-world emission estimations is often required. This involves actual driving behavior and emission factors of the local vehicle fleet under study. This paper reports on a microscale model for hot road emissions and its application to the metropolitan region of the city of Santiago, Chile. The methodology considers the street-by-street hot emission estimation with its temporal and spatial distribution. The input data come from experimental emission factors based on local driving patterns and traffic surveys of traffic flows for different vehicle categories. The methodology developed is able to estimate hourly hot road CO, total unburned hydrocarbons (THCs), particulate matter (PM), and NO(x) emissions for predefined day types and vehicle categories.

Environmental drivers of spatial variation in whole-tree transpiration in an aspen-dominated upland-to-wetland forest gradient

NASA Astrophysics Data System (ADS)

Loranty, Michael M.; Mackay, D. Scott; Ewers, Brent E.; Adelman, Jonathan D.; Kruger, Eric L.

2008-02-01

Assumed representative center-of-stand measurements are typical inputs to models that scale forest transpiration to stand and regional extents. These inputs do not consider gradients in transpiration at stand boundaries or along moisture gradients and therefore potentially bias the large-scale estimates. We measured half-hourly sap flux (JS) for 173 trees in a spatially explicit cyclic sampling design across a topographically controlled gradient between a forested wetland and upland forest in northern Wisconsin. Our analyses focused on three dominant species in the site: quaking aspen (Populus tremuloides Michx), speckled alder (Alnus incana (DuRoi) Spreng), and white cedar (Thuja occidentalis L.). Sapwood area (AS) was used to scale JS to whole tree transpiration (EC). Because spatial patterns imply underlying processes, geostatistical analyses were employed to quantify patterns of spatial autocorrelation across the site. A simple Jarvis type model parameterized using a Monte Carlo sampling approach was used to simulate EC (EC-SIM). EC-SIM was compared with observed EC(EC-OBS) and found to reproduce both the temporal trends and spatial variance of canopy transpiration. EC-SIM was then used to examine spatial autocorrelation as a function of environmental drivers. We found no spatial autocorrelation in JS across the gradient from forested wetland to forested upland. EC was spatially autocorrelated and this was attributed to spatial variation in AS which suggests species spatial patterns are important for understanding spatial estimates of transpiration. However, the range of autocorrelation in EC-SIM decreased linearly with increasing vapor pressure deficit, implying that consideration of spatial variation in the sensitivity of canopy stomatal conductance to D is also key to accurately scaling up transpiration in space.

Estimating crop net primary production using inventory data and MODIS-derived parameters

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

Bandaru, Varaprasad; West, Tristram O.; Ricciuto, Daniel M.

2013-06-03

National estimates of spatially-resolved cropland net primary production (NPP) are needed for diagnostic and prognostic modeling of carbon sources, sinks, and net carbon flux. Cropland NPP estimates that correspond with existing cropland cover maps are needed to drive biogeochemical models at the local scale and over national and continental extents. Existing satellite-based NPP products tend to underestimate NPP on croplands. A new Agricultural Inventory-based Light Use Efficiency (AgI-LUE) framework was developed to estimate individual crop biophysical parameters for use in estimating crop-specific NPP. The method is documented here and evaluated for corn and soybean crops in Iowa and Illinois inmore » years 2006 and 2007. The method includes a crop-specific enhanced vegetation index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), shortwave radiation data estimated using Mountain Climate Simulator (MTCLIM) algorithm and crop-specific LUE per county. The combined aforementioned variables were used to generate spatially-resolved, crop-specific NPP that correspond to the Cropland Data Layer (CDL) land cover product. The modeling framework represented well the gradient of NPP across Iowa and Illinois, and also well represented the difference in NPP between years 2006 and 2007. Average corn and soybean NPP from AgI-LUE was 980 g C m-2 yr-1 and 420 g C m-2 yr-1, respectively. This was 2.4 and 1.1 times higher, respectively, for corn and soybean compared to the MOD17A3 NPP product. Estimated gross primary productivity (GPP) derived from AgI-LUE were in close agreement with eddy flux tower estimates. The combination of new inputs and improved datasets enabled the development of spatially explicit and reliable NPP estimates for individual crops over large regional extents.« less

Fleeing to Fault Zones: Incorporating Syrian Refugees into Earthquake Risk Analysis along the East Anatolian and Dead Sea Rift Fault Zones

NASA Astrophysics Data System (ADS)

Wilson, B.; Paradise, T. R.

2016-12-01

The influx of millions of Syrian refugees into Turkey has rapidly changed the population distribution along the Dead Sea Rift and East Anatolian Fault zones. In contrast to other countries in the Middle East where refugees are accommodated in camp environments, the majority of displaced individuals in Turkey are integrated into cities, towns, and villages—placing stress on urban settings and increasing potential exposure to strong shaking. Yet, displaced populations are not traditionally captured in data sources used in earthquake risk analysis or loss estimations. Accordingly, we present a district-level analysis assessing the spatial overlap of earthquake hazards and refugee locations in southeastern Turkey to determine how migration patterns are altering seismic risk in the region. Using migration estimates from the U.S. Humanitarian Information Unit, we create three district-level population scenarios that combine official population statistics, refugee camp populations, and low, median, and high bounds for integrated refugee populations. We perform probabilistic seismic hazard analysis alongside these population scenarios to map spatial variations in seismic risk between 2011 and late 2015. Our results show a significant relative southward increase of seismic risk for this period due to refugee migration. Additionally, we calculate earthquake fatalities for simulated earthquakes using a semi-empirical loss estimation technique to determine degree of under-estimation resulting from forgoing migration data in loss modeling. We find that including refugee populations increased casualties by 11-12% using median population estimates, and upwards of 20% using high population estimates. These results communicate the ongoing importance of placing environmental hazards in their appropriate regional and temporal context which unites physical, political, cultural, and socio-economic landscapes. Keywords: Earthquakes, Hazards, Loss-Estimation, Syrian Crisis, Migration, Refugees

Evaluating the utility of the medium-spatial resolution Landsat 8 multispectral sensor in quantifying aboveground biomass in uMgeni catchment, South Africa

NASA Astrophysics Data System (ADS)

Dube, Timothy; Mutanga, Onisimo

2015-03-01

Aboveground biomass estimation is critical in understanding forest contribution to regional carbon cycles. Despite the successful application of high spatial and spectral resolution sensors in aboveground biomass (AGB) estimation, there are challenges related to high acquisition costs, small area coverage, multicollinearity and limited availability. These challenges hamper the successful regional scale AGB quantification. The aim of this study was to assess the utility of the newly-launched medium-resolution multispectral Landsat 8 Operational Land Imager (OLI) dataset with a large swath width, in quantifying AGB in a forest plantation. We applied different sets of spectral analysis (test I: spectral bands; test II: spectral vegetation indices and test III: spectral bands + spectral vegetation indices) in testing the utility of Landsat 8 OLI using two non-parametric algorithms: stochastic gradient boosting and the random forest ensembles. The results of the study show that the medium-resolution multispectral Landsat 8 OLI dataset provides better AGB estimates for Eucalyptus dunii, Eucalyptus grandis and Pinus taeda especially when using the extracted spectral information together with the derived spectral vegetation indices. We also noted that incorporating the optimal subset of the most important selected medium-resolution multispectral Landsat 8 OLI bands improved AGB accuracies. We compared medium-resolution multispectral Landsat 8 OLI AGB estimates with Landsat 7 ETM + estimates and the latter yielded lower estimation accuracies. Overall, this study demonstrates the invaluable potential and strength of applying the relatively affordable and readily available newly-launched medium-resolution Landsat 8 OLI dataset, with a large swath width (185-km) in precisely estimating AGB. This strength of the Landsat OLI dataset is crucial especially in sub-Saharan Africa where high-resolution remote sensing data availability remains a challenge.

Biogenic carbon fluxes from global agricultural production and consumption

NASA Astrophysics Data System (ADS)

Wolf, Julie; West, Tristram O.; Le Page, Yannick; Kyle, G. Page; Zhang, Xuesong; Collatz, G. James; Imhoff, Marc L.

2015-10-01

Quantification of biogenic carbon fluxes from agricultural lands is needed to generate comprehensive bottom-up estimates of net carbon exchange for global and regional carbon monitoring. We estimated global agricultural carbon fluxes associated with annual crop net primary production (NPP), harvested biomass, and consumption of biomass by humans and livestock. These estimates were combined for a single estimate of net carbon exchange and spatially distributed to 0.05° resolution using Moderate Resolution Imaging Spectroradiometer satellite land cover data. Global crop NPP in 2011 was estimated at 5.25 ± 0.46 Pg C yr-1, of which 2.05 ± 0.05 Pg C yr-1 was harvested and 0.54 Pg C yr-1 was collected from crop residues for livestock fodder. Total livestock feed intake in 2011 was 2.42 ± 0.21 Pg C yr-1, of which 2.31 ± 0.21 Pg C yr-1 was emitted as CO2, 0.07 ± 0.01 Pg C yr-1 was emitted as CH4, and 0.04 Pg C yr-1 was contained within milk and egg production. Livestock grazed an estimated 1.27 Pg C yr-1 in 2011, which constituted 52.4% of total feed intake. Global human food intake was 0.57 ± 0.03 Pg C yr-1 in 2011, the majority of which was respired as CO2. Completed global cropland carbon budgets accounted for the ultimate use of approximately 80% of harvested biomass. The spatial distribution of these fluxes may be used for global carbon monitoring, estimation of regional uncertainty, and for use as input to Earth system models.

Syzygies, Pluricanonical Maps, and the Birational Geometry of Varieties of Maximal Albanese Dimension

NASA Astrophysics Data System (ADS)

Tesfagiorgis, Kibrewossen B.

Satellite Precipitation Estimates (SPEs) may be the only available source of information for operational hydrologic and flash flood prediction due to spatial limitations of radar and gauge products in mountainous regions. The present work develops an approach to seamlessly blend satellite, available radar, climatological and gauge precipitation products to fill gaps in ground-based radar precipitation field. To mix different precipitation products, the error of any of the products relative to each other should be removed. For bias correction, the study uses a new ensemble-based method which aims to estimate spatially varying multiplicative biases in SPEs using a radar-gauge precipitation product. Bias factors were calculated for a randomly selected sample of rainy pixels in the study area. Spatial fields of estimated bias were generated taking into account spatial variation and random errors in the sampled values. In addition to biases, sometimes there is also spatial error between the radar and satellite precipitation estimates; one of them has to be geometrically corrected with reference to the other. A set of corresponding raining points between SPE and radar products are selected to apply linear registration using a regularized least square technique to minimize the dislocation error in SPEs with respect to available radar products. A weighted Successive Correction Method (SCM) is used to make the merging between error corrected satellite and radar precipitation estimates. In addition to SCM, we use a combination of SCM and Bayesian spatial method for merging the rain gauges and climatological precipitation sources with radar and SPEs. We demonstrated the method using two satellite-based, CPC Morphing (CMORPH) and Hydro-Estimator (HE), two radar-gauge based, Stage-II and ST-IV, a climatological product PRISM and rain gauge dataset for several rain events from 2006 to 2008 over different geographical locations of the United States. Results show that: (a) the method of ensembles helped reduce biases in SPEs significantly; (b) the SCM method in combination with the Bayesian spatial model produced a precipitation product in good agreement with independent measurements .The study implies that using the available radar pixels surrounding the gap area, rain gauge, PRISM and satellite products, a radar like product is achievable over radar gap areas that benefits the operational meteorology and hydrology community.

Anurans in a Subarctic Tundra Landscape Near Cape Churchill, Manitoba

USGS Publications Warehouse

Reiter, M.E.; Boal, C.W.; Andersen, D.E.

2008-01-01

Distribution, abundance, and habitat relationships of anurans inhabiting subarctic regions are poorly understood, and anuran monitoring protocols developed for temperate regions may not be applicable across large roadless areas of northern landscapes. In addition, arctic and subarctic regions of North America are predicted to experience changes in climate and, in some areas, are experiencing habitat alteration due to high rates of herbivory by breeding and migrating waterfowl. To better understand subarctic anuran abundance, distribution, and habitat associations, we conducted anuran calling surveys in the Cape Churchill region of Wapusk National Park, Manitoba, Canada, in 2004 and 2005. We conducted surveys along ~l-km transects distributed across three landscape types (coastal tundra, interior sedge meadow-tundra, and boreal forest-tundra interface) to estimate densities and probabilities of detection of Boreal Chorus Frogs (Pseudacris maculata) and Wood Frogs (Lithobates sylvaticus). We detected a Wood Frog or Boreal Chorus Frog on 22 (87%) of 26 transects surveyed, but probability of detection varied between years and species and among landscape types. Estimated densities of both species increased from the coastal zone inland toward the boreal forest edge. Our results suggest anurans occur across all three landscape types in our study area, but that species-specific spatial patterns exist in their abundances. Considerations for both spatial and temporal variation in abundance and detection probability need to be incorporated into surveys and monitoring programs for subarctic anurans.

A Deep Chandra ACIS Study of NGC 4151. II. The Innermost Emission Line Region and Strong Evidence for Radio Jet-NLR Cloud Collision

NASA Astrophysics Data System (ADS)

Wang, Junfeng; Fabbiano, Giuseppina; Elvis, Martin; Risaliti, Guido; Mundell, Carole G.; Karovska, Margarita; Zezas, Andreas

2011-07-01

We have studied the X-ray emission within the inner ~150 pc radius of NGC 4151 by constructing high spatial resolution emission line images of blended O VII, O VIII, and Ne IX. These maps show extended structures that are spatially correlated with the radio outflow and optical [O III] emission. We find strong evidence for jet-gas cloud interaction, including morphological correspondences with regions of X-ray enhancement, peaks of near-infrared [Fe II] emission, and optical clouds. In these regions, moreover, we find evidence of elevated Ne IX/O VII ratios; the X-ray emission of these regions also exceeds that expected from nuclear photoionization. Spectral fitting reveals the presence of a collisionally ionized component. The thermal energy of the hot gas suggests that >~ 0.1% of the estimated jet power is deposited into the host interstellar medium through interaction between the radio jet and the dense medium of the circumnuclear region. We find possible pressure equilibrium between the collisionally ionized hot gas and the photoionized line-emitting cool clouds. We also obtain constraints on the extended iron and silicon fluorescent emission. Both lines are spatially unresolved. The upper limit on the contribution of an extended emission region to the Fe Kα emission is <~ 5% of the total, in disagreement with a previous claim that 65% of the Fe Kα emission originates in the extended narrow line region.

Estimating the permanent loss of groundwater storage in the southern San Joaquin Valley, California

NASA Astrophysics Data System (ADS)

Smith, R. G.; Knight, R.; Chen, J.; Reeves, J. A.; Zebker, H. A.; Farr, T.; Liu, Z.

2017-03-01

In the San Joaquin Valley, California, recent droughts starting in 2007 have increased the pumping of groundwater, leading to widespread subsidence. In the southern portion of the San Joaquin Valley, vertical subsidence as high as 85 cm has been observed between June 2007 and December 2010 using Interferometric Synthetic Aperture Radar (InSAR). This study seeks to map regions where inelastic (not recoverable) deformation occurred during the study period, resulting in permanent compaction and loss of groundwater storage. We estimated the amount of permanent compaction by incorporating multiple data sets: the total deformation derived from InSAR, estimated skeletal-specific storage and hydraulic parameters, geologic information, and measured water levels during our study period. We used two approaches, one that we consider to provide an estimate of the lowest possible amount of inelastic deformation, and one that provides a more reasonable estimate. These two approaches resulted in a spatial distribution of values for the percentage of the total deformation that was inelastic, with the former estimating a spatially averaged value of 54%, and the latter a spatially averaged value of 98%. The former corresponds to the permanent loss of 4.14 × 108 m3 of groundwater storage, or roughly 5% of the volume of groundwater used over the study time period; the latter corresponds to the loss of 7.48 × 108 m3 of groundwater storage, or roughly 9% of the volume of groundwater used. This study demonstrates that a data-driven approach can be used effectively to estimate the permanent loss of groundwater storage.

Downscaling Soil Moisture in the Southern Great Plains Through a Calibrated Multifractal Model for Land Surface Modeling Applications

NASA Technical Reports Server (NTRS)

Mascaro, Giuseppe; Vivoni, Enrique R.; Deidda, Roberto

2010-01-01

Accounting for small-scale spatial heterogeneity of soil moisture (theta) is required to enhance the predictive skill of land surface models. In this paper, we present the results of the development, calibration, and performance evaluation of a downscaling model based on multifractal theory using aircraft!based (800 m) theta estimates collected during the southern Great Plains experiment in 1997 (SGP97).We first demonstrate the presence of scale invariance and multifractality in theta fields of nine square domains of size 25.6 x 25.6 sq km, approximately a satellite footprint. Then, we estimate the downscaling model parameters and evaluate the model performance using a set of different calibration approaches. Results reveal that small-scale theta distributions are adequately reproduced across the entire region when coarse predictors include a dynamic component (i.e., the spatial mean soil moisture ) and a stationary contribution accounting for static features (i.e., topography, soil texture, vegetation). For wet conditions, we found similar multifractal properties of soil moisture across all domains, which we ascribe to the signature of rainfall spatial variability. For drier states, the theta fields in the northern domains are more intermittent than in southern domains, likely because of differences in the distribution of vegetation coverage. Through our analyses, we propose a regional downscaling relation for coarse, satellite-based soil moisture estimates, based on ancillary information (static and dynamic landscape features), which can be used in the study area to characterize statistical properties of small-scale theta distribution required by land surface models and data assimilation systems.

The assessment of Global Precipitation Measurement estimates over the Indian subcontinent

NASA Astrophysics Data System (ADS)

Murali Krishna, U. V.; Das, Subrata Kumar; Deshpande, Sachin M.; Doiphode, S. L.; Pandithurai, G.

2017-08-01

Accurate and real-time precipitation estimation is a challenging task for current and future spaceborne measurements, which is essential to understand the global hydrological cycle. Recently, the Global Precipitation Measurement (GPM) satellites were launched as a next-generation rainfall mission for observing the global precipitation characteristics. The purpose of the GPM is to enhance the spatiotemporal resolution of global precipitation. The main objective of the present study is to assess the rainfall products from the GPM, especially the Integrated Multi-satellitE Retrievals for the GPM (IMERG) data by comparing with the ground-based observations. The multitemporal scale evaluations of rainfall involving subdaily, diurnal, monthly, and seasonal scales were performed over the Indian subcontinent. The comparison shows that the IMERG performed better than the Tropical Rainfall Measuring Mission (TRMM)-3B42, although both rainfall products underestimated the observed rainfall compared to the ground-based measurements. The analyses also reveal that the TRMM-3B42 and IMERG data sets are able to represent the large-scale monsoon rainfall spatial features but are having region-specific biases. The IMERG shows significant improvement in low rainfall estimates compared to the TRMM-3B42 for selected regions. In the spatial distribution, the IMERG shows higher rain rates compared to the TRMM-3B42, due to its enhanced spatial and temporal resolutions. Apart from this, the characteristics of raindrop size distribution (DSD) obtained from the GPM mission dual-frequency precipitation radar is assessed over the complex mountain terrain site in the Western Ghats, India, using the DSD measured by a Joss-Waldvogel disdrometer.

Rainwater content estimated using polarimetric radar parameters in the Heihe River Basin

NASA Astrophysics Data System (ADS)

Zhao, Guo; Chu, Rongzhong; Zhang, Tong; Jia, Wei

2013-02-01

The rainwater content of cold and arid regions has strong spatial and temporal heterogeneity. Representing rainwater content at high resolution can help us understand the characteristics of inland river basin water cycles and improve the prediction accuracy of hydrological models. Data were used from the Watershed Allied Telemetry Experimental Research (WATER) project of the Heihe River Basin, which is the second largest inland river basin in the arid regions of northwest China. We used raindrop size distributions to improve the rain water content estimation of meteorological radar and to obtain accurate rain water content data in this area. Subsequently, four estimation methods applied in the polarimetric radar were tested. The results of a non-linear regression method show that M(KDP, ZH, ZDR) has the highest accuracy for measuring rain water content. Finally, the formula for measuring the spatial rain water content was applied to a polarimetric radar with an X-band (714XDP). The influence of raindrop size distribution (DSD) on the formula M(KDP, ZH, ZDR) is lowest sensitivity, and it can be explained as follows. On the one hand, the horizontal and vertical front reflection cross sections of the radar are different, so KDP is proportional to the 3rd power of the raindrop diameter. On the other hand, the rear cross section of the radar is proportional to the sixth power of the raindrop diameter. The rainfall's spatial water content M is proportional to the 3rd power of the raindrop diameter, so the influence of the drop size distribution (DSD) on KDP is much smaller than that of ZH.

Estimation of Regional Net CO2 Exchange over the Southern Great Plains

NASA Astrophysics Data System (ADS)

Biraud, S. C.; Riley, W. J.; Fischer, M. L.; Torn, M. S.; Cooley, H. S.

2004-12-01

Estimating spatially distributed ecosystem CO2 exchange is an important component of the North American Carbon Program. We describe here a methodology to estimate Net Ecosystem Exchange (NEE) over the Southern Great Plains, using: (1) data from the Department Of Energy's Atmospheric Radiation Measurement (ARM) sites in Oklahoma and Kansas; (2) meteorological forcing data from the Mesonet facilities; (3) soil and vegetation types from 1 km resolution USGS databases; (4) vegetation status (e.g., LAI) from 1 km satellite measurements of surface reflectance (MODIS); (5) a tested land-surface model; and (6) a coupled land-surface and meteorological model (MM5/ISOLSM). This framework allows us to simulate regional surface fluxes in addition to ABL and free troposphere concentrations of CO2 at a continental scale with fine-scale nested grids centered on the ARM central facility. We use the offline land-surface and coupled models to estimate regional NEE, and compare predictions to measurements from the 9 Extended Facility sites with eddy correlation measurements. Site level comparisons to portable ECOR measurements in several crop types are also presented. Our approach also allows us to extend bottom-up estimates to periods and areas where meteorological forcing data are unavailable.