N onpoint source (NPS) pollution is considered one of the most serious forms of contamination thr... more N onpoint source (NPS) pollution is considered one of the most serious forms of contamination threatening the nation's water resources, and agriculture accounts for up to two-thirds of this pollution (Long, 1991). Consequently, emphasis has been placed on development of best management practices, or BMPs, that limit movement of pollutants from agricultural areas. While on-site BMPs reduce pollutant transport from agricultural sources in many cases, on-site BMPs are not adequate to meet national water quality goals in all situations (Clausen and Means, 1989). For example, in humid climatic regions, high-intensity rainfall events can result in storm runoff and erosion with significant pollutant transport off-site and subsequent adverse downstream environmental impacts. Vegetative buffers have received considerable attention as a means of reducing transport of NPS pollutants in runoff from agricultural areas. Riparian forests are a type of vegetative buffer that has shown potential for reducing agricultural NPS pollutants reaching stream drainage networks and, depending on local geology, regional groundwater supplies. Early research on the quality of streamflow from agricultural watersheds in the Gulf-Atlantic Coastal Plain indicated the magnitude of the role of existing, mature riparian forests in reducing transport of agricultural NPS pollutants (Asmussen et al., 1979; Sheridan et al., 1982; Yates and Sheridan, 1983). Subsequent research at several locations in the Coastal Plain provided an understanding of the physical, biological, and chemical processes that remove NPS pollutants from surface runoff and subsurface flows, as well as the potential for management of agricultural NPS pollution by riparian forest buffers
A water quality sampling program was initiated in 1974 by the U.S. Department of Agriculture Agri... more A water quality sampling program was initiated in 1974 by the U.S. Department of Agriculture Agricultural Research Service on the 334 km2 Little River Experimental Watershed (LREW) near Tifton in south Georgia to monitor the effects of changing land use and agricultural practices over time and to support development of simulation models capable of predicting future impacts of agricultural land use and management changes. Stream samples were taken on a weekly or more frequent basis and were analyzed for chloride, ammonium nitrogen, nitrate plus nitrite nitrogen, total kjeldahl nitrogen, total phosphorus, and dissolved molybdate reactive phosphorus.
Abstract: Long-term stream water quality data may provide opportunities to study the effec-tivene... more Abstract: Long-term stream water quality data may provide opportunities to study the effec-tiveness of conservation practices. The first three decades of data for the Little River in south-western Georgia were analyzed for trends as part of the Conservation Effects Assessment Project. ...
The authors are solely responsible for the content of this technical presentation. The technical ... more The authors are solely responsible for the content of this technical presentation. The technical presentation does not necessarily reflect the official position of the American Society of Agricultural Engineers (ASAE), and its printing and distribution does not constitute an endorsement of views which may be expressed. Technical presentations are not subject to the formal peer review process by ASAE editorial committees; therefore, they are not to be presented as refereed publications. Citation of this work should state that it is from an ASAE meeting paper. EXAMPLE: Author's Last Name, Initials. Title of Presentation. ASAE Meeting Paper No. xx-xxxx. St. Joseph, Mich.: ASAE. For information about securing permission to reprint or reproduce a technical presentation, please contact ASAE at hq@asae.org or 616.429.0300
Standards for allowable total maximum daily loads (TMDLs) of sediment are being assigned to selec... more Standards for allowable total maximum daily loads (TMDLs) of sediment are being assigned to selected streams and rivers across the US. Estimates of total daily loads (TDLs) of sediment are required for comparison with TMDLs for those streams which are deemed to have impaired water quality due to sediment, and are required for formulating plans to improve these streams. A TMDL is defined as a daily stream loading rate (e.g. kg day-1). However, actual stream sediment data are collected in terms of concentrations (e.g. mg L-1), thus requiring estimates of streamflow to make the necessary rate and load conversions. The USDA-ARS, Southeast Watershed Research Laboratory (SEWRL) in Tifton, GA has collected over 30 years of hydrologic and climatic data from the 334 km2 Little River Watershed (LRW). The LRW is typical of heavily vegetated, slow-moving stream systems of the southeastern Coastal Plain. Field studies quantifying dissolved and suspended loads in LRW streamflow have been conducte...
The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L.4_SM) product provi... more The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L.4_SM) product provides global, 3-hourly, 9-km resolution estimates of surface (0-5 cm) and root zone (0-100 cm) soil moisture with a mean latency of~2.5 days. The underlying L4_SM algorithm assimilates SMAP radiometer brightness temperature (Tb) observations into the NASA Catchment land surface model using a spatially distributed ensemble Kalman filter. In Version 4 of the L4_SM modeling system the upward recharge of surface soil moisture from below under nonequilibrium conditions was reduced, resulting in less bias and improved dynamic range of L4_SM surface soil moisture compared to earlier versions. This change and additional technical modifications to the system reduce the mean and standard deviation of the observation-minus-forecast Tb residuals and overall soil moisture analysis increments while maintaining the skill of the L4_SM soil moisture estimates versus independent in situ measurements; the average, biasadjusted root-mean-square error in Version 4 is 0.039 m 3 /m 3 for surface and 0.026 m 3 /m 3 for root zone soil moisture. Moreover, the coverage of assimilated SMAP observations in Version 4 is near global owing to the use of additional satellite Tb records for algorithm calibration. L4_SM soil moisture uncertainty estimates are biased low (by 0.01-0.02 m 3 /m 3) against actual errors (computed versus in situ measurements). L4_SM runoff estimates, an additional product of the L4_SM algorithm, are biased low (by 35 mm/year) against streamflow measurements. Compared to Version 3, bias in Version 4 is reduced by 46% for surface soil moisture uncertainty estimates and by 33% for runoff estimates. Plain Language Summary Soil moisture is important because of its impact on the land surface water, energy, and nutrient cycles. The low-frequency microwave observations collected by the NASA Soil Moisture Active Passive (SMAP) satellite are suitable for estimating soil moisture globally. Their sensitivity, however, is limited to the top few centimeters of the soil, and observations are only available every other day depending on location. The SMAP Level-4 Soil Moisture (L4_SM) data product addresses these limitations by merging the satellite observations into a numerical model of the land surface water and energy balance while considering the uncertainty of the observations and model estimates. The resulting L4_SM data product is publicly disseminated within~2.5 days from the time of observation and provides global estimates of surface and deeper-layer (or "root zone") soil moisture at 3-hourly temporal and 9-km spatial resolution. This study presents an overview of recent updates in the L4_SM algorithm and an assessment of the quality of the resulting Version-4 soil moisture estimates. The algorithm updates reduce bias in L4_SM surface soil moisture and runoff estimates compared to previous versions while otherwise maintaining the product skill, which meets the accuracy requirement specified prior to SMAP's launch.
Andrew S. Mehring 1 , Dustin W. Kemp 1 , David D. Bosch 2 , Richard Lowrance 2 ... is often cited... more Andrew S. Mehring 1 , Dustin W. Kemp 1 , David D. Bosch 2 , Richard Lowrance 2 ... is often cited as a factor behind low oxygen levels observed in forested blackwater rivers. ... portion of oxygen demand generated by decomposing organic matter in the Little River in southern ...
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
A soil moisture (SM) disaggregation algorithm based on thermal inertia (TI) theory was implemente... more A soil moisture (SM) disaggregation algorithm based on thermal inertia (TI) theory was implemented to downscale the soil moisture active passive (SMAP) enhanced product (SPL2SMP_E) from 9 to 1 km over the continental United States. The algorithm applies land surface temperature and normalized difference vegetation index from moderate resolution imaging spectroradiometer (MODIS) at higher spatial resolution to estimate relative soil wetness within a coarse SMAP grid-this MODISderived relative wetness is then used to produce the downscaled SMAP SM. Results from the algorithm were evaluated in terms of their spatio-temporal coverage and accuracy using in situ measurements from SMAP core validation sites (CVS), the U.S. Department of Agriculture Soil Climate Analysis Network (SCAN), and the Manuscript
ABSTRACT Cloudy conditions reduce the utility of optical imagery for crop monitoring. New constel... more ABSTRACT Cloudy conditions reduce the utility of optical imagery for crop monitoring. New constellations of satellites – including the RADARSAT Constellation Mission (RCM) and Sentinel-1A/B, both available under free and open data policies – can be used to create stacks of dense seasonal C-band Synthetic Aperture Radar (SAR) data. Yet to date, the contribution of SAR imagery to operational crop mapping is often limited to that of a gap-filler, compensating for optical data obscured by clouds. The Joint Experiment for Crop Assessment and Monitoring (JECAM) SAR Inter-Comparison Experiment is a multi-year, multi-partner project focused on evaluating methods for SAR-based crop classification. Stacks of dense time-series SAR imagery, from RADARSAT-2 and Sentinel-1 satellites, were acquired for 10 sites located in six countries. Decision Tree (DT) and Random Forest (RF) classification methodologies were applied to these SAR data-stacks, as well as to data-stacks of optical only, and optimized SAR/optical data combinations. For the dense time-series SAR stacks, overall classification accuracies above 85% and 80% were obtained for 6 of 10 and 8 of 10 sites, respectively. For maize, the SAR-only data delivered user’s and producer’s accuracies greater than 90% for half the sites. For soya bean, accuracies greater than 80% were reported for 5 of 9 sites and classification accuracies were greater than 80% for wheat on half the sites. Classification results were influenced by the mix and number of agriculture classes present at each site, the available SAR imagery, as well as the training and validation data sets for individual crop types. These results have important operational implications for regions of the world dominated by cloudy conditions and the lack of adequate amounts of optical imagery to support satellite-based crop monitoring.
The Little River Experimental Watershed (LREW), in the headwaters of the Suwannee River, is one o... more The Little River Experimental Watershed (LREW), in the headwaters of the Suwannee River, is one of the 13 CSREES CEAP watersheds. LREW consists of 8 nested watersheds totaling 334 km2. We have 32 years of hydrologic and water quality data and for this project also developed GIS data sets that describe land use, crops, and conservation practices in LREW for most of the 32-year record period. Our project established a series of hypothesis about the physical and socioeconomic linkages between conservation practice adoption and water quality in LREW. We then developed a series of objectives with which to test our hypotheses. The most important question answered by this project was: Are water quality changes discernible over the last 32 years of changes in conservation practices? This paper will provide a summary of the results obtained from the project. The results from this project will allows us to determine cause and effect relationships between conservation practices and water quali...
NASA’s Soil Moisture Active Passive (SMAP) mission has been validating its soil moisture (SM) pro... more NASA’s Soil Moisture Active Passive (SMAP) mission has been validating its soil moisture (SM) products since the start of data production on March 31, 2015. Prior to launch, the mission defined a set of criteria for core validation sites (CVS) that enable the testing of the key mission SM accuracy requirement (unbiased root-mean-square error <0.04 m3/m3). The validation approach also includes other (“sparse network”) in situ SM measurements, satellite SM products, model-based SM products, and field experiments. Over the past six years, the SMAP SM products have been analyzed with respect to these reference data, and the analysis approaches themselves have been scrutinized in an effort to best understand the products’ performance. Validation of the most recent SMAP Level 2 and 3 SM retrieval products (R17000) shows that the L-band (1.4 GHz) radiometer-based SM record continues to meet mission requirements. The products are generally consistent with SM retrievals from the ESA Soil ...
Watershed models are used worldwide to assist with water and nutrient management under conditions... more Watershed models are used worldwide to assist with water and nutrient management under conditions of changing climate, land use, and population. Of these models, the Soil and Water Assessment Tool (SWAT) and SWAT+ are the most widely used, although their performance in groundwater-driven watersheds can sometimes be poor due to a simplistic representation of groundwater processes. The purpose of this paper is to introduce a new physically-based spatially-distributed groundwater flow module called gwflow for the SWAT+ watershed model. The module is embedded in the SWAT+ modeling code and is intended to replace the current SWAT+ aquifer module. The model accounts for recharge from SWAT+ Hydrologic Response Units (HRUs), lateral flow within the aquifer, Evapotranspiration (ET) from shallow groundwater, groundwater pumping, groundwater–surface water interactions through the streambed, and saturation excess flow. Groundwater head and groundwater storage are solved throughout the watershed...
... Corresponding author: David D. Bosch, USDAARS, Southeast Watershed Research Laboratory, PO B... more ... Corresponding author: David D. Bosch, USDAARS, Southeast Watershed Research Laboratory, PO Box 946, Tifton, GA 31793; phone: 2293863899; fax ... Agrichemical leaching is a potential problem in produc-tion areas of southern Georgia where sandy soils predomi-nate. ...
This study compares different methods to extract soil moisture information through the assimilati... more This study compares different methods to extract soil moisture information through the assimilation of Soil Moisture Active Passive (SMAP) observations. Neural network (NN) and physically-based SMAP soil moisture retrievals were assimilated into the National Aeronautics and Space Administration (NASA) Catchment model over the contiguous United States for April 2015 to March 2017. By construction, the NN retrievals are consistent with the global climatology of the Catchment model soil moisture. Assimilating the NN retrievals without further bias correction improved the surface and root zone correlations against in situ measurements from 14 SMAP core validation sites (CVS) by 0.12 and 0.16, respectively, over the model-only skill, and reduced the surface and root zone unbiased root-mean-square error (ubRMSE) by 0.005 m 3 m −3 and 0.001 m 3 m −3 , respectively. The assimilation reduced the average absolute surface bias against the CVS measurements by 0.009 m 3 m −3 , but increased the root zone bias by 0.014 m 3 m −3. Assimilating the NN retrievals after a localized bias correction yielded slightly lower surface correlation and ubRMSE improvements, but generally the skill differences were small. The assimilation of the physically-based SMAP Level-2 passive soil moisture retrievals using a global bias correction yielded similar skill improvements, as did the direct assimilation of locally bias-corrected SMAP brightness temperatures within the SMAP Level-4 soil moisture algorithm. The results show that global bias correction methods may be able to extract more independent information from SMAP observations compared to local bias correction methods, but without accurate quality control and observation error characterization they are also more vulnerable to adverse effects from retrieval errors related to uncertainties in the retrieval inputs and algorithm. Furthermore, the results show that using global bias correction approaches without a simultaneous re-calibration of the land model processes can lead to skill degradation in other land surface variables.
N onpoint source (NPS) pollution is considered one of the most serious forms of contamination thr... more N onpoint source (NPS) pollution is considered one of the most serious forms of contamination threatening the nation's water resources, and agriculture accounts for up to two-thirds of this pollution (Long, 1991). Consequently, emphasis has been placed on development of best management practices, or BMPs, that limit movement of pollutants from agricultural areas. While on-site BMPs reduce pollutant transport from agricultural sources in many cases, on-site BMPs are not adequate to meet national water quality goals in all situations (Clausen and Means, 1989). For example, in humid climatic regions, high-intensity rainfall events can result in storm runoff and erosion with significant pollutant transport off-site and subsequent adverse downstream environmental impacts. Vegetative buffers have received considerable attention as a means of reducing transport of NPS pollutants in runoff from agricultural areas. Riparian forests are a type of vegetative buffer that has shown potential for reducing agricultural NPS pollutants reaching stream drainage networks and, depending on local geology, regional groundwater supplies. Early research on the quality of streamflow from agricultural watersheds in the Gulf-Atlantic Coastal Plain indicated the magnitude of the role of existing, mature riparian forests in reducing transport of agricultural NPS pollutants (Asmussen et al., 1979; Sheridan et al., 1982; Yates and Sheridan, 1983). Subsequent research at several locations in the Coastal Plain provided an understanding of the physical, biological, and chemical processes that remove NPS pollutants from surface runoff and subsurface flows, as well as the potential for management of agricultural NPS pollution by riparian forest buffers
A water quality sampling program was initiated in 1974 by the U.S. Department of Agriculture Agri... more A water quality sampling program was initiated in 1974 by the U.S. Department of Agriculture Agricultural Research Service on the 334 km2 Little River Experimental Watershed (LREW) near Tifton in south Georgia to monitor the effects of changing land use and agricultural practices over time and to support development of simulation models capable of predicting future impacts of agricultural land use and management changes. Stream samples were taken on a weekly or more frequent basis and were analyzed for chloride, ammonium nitrogen, nitrate plus nitrite nitrogen, total kjeldahl nitrogen, total phosphorus, and dissolved molybdate reactive phosphorus.
Abstract: Long-term stream water quality data may provide opportunities to study the effec-tivene... more Abstract: Long-term stream water quality data may provide opportunities to study the effec-tiveness of conservation practices. The first three decades of data for the Little River in south-western Georgia were analyzed for trends as part of the Conservation Effects Assessment Project. ...
The authors are solely responsible for the content of this technical presentation. The technical ... more The authors are solely responsible for the content of this technical presentation. The technical presentation does not necessarily reflect the official position of the American Society of Agricultural Engineers (ASAE), and its printing and distribution does not constitute an endorsement of views which may be expressed. Technical presentations are not subject to the formal peer review process by ASAE editorial committees; therefore, they are not to be presented as refereed publications. Citation of this work should state that it is from an ASAE meeting paper. EXAMPLE: Author's Last Name, Initials. Title of Presentation. ASAE Meeting Paper No. xx-xxxx. St. Joseph, Mich.: ASAE. For information about securing permission to reprint or reproduce a technical presentation, please contact ASAE at hq@asae.org or 616.429.0300
Standards for allowable total maximum daily loads (TMDLs) of sediment are being assigned to selec... more Standards for allowable total maximum daily loads (TMDLs) of sediment are being assigned to selected streams and rivers across the US. Estimates of total daily loads (TDLs) of sediment are required for comparison with TMDLs for those streams which are deemed to have impaired water quality due to sediment, and are required for formulating plans to improve these streams. A TMDL is defined as a daily stream loading rate (e.g. kg day-1). However, actual stream sediment data are collected in terms of concentrations (e.g. mg L-1), thus requiring estimates of streamflow to make the necessary rate and load conversions. The USDA-ARS, Southeast Watershed Research Laboratory (SEWRL) in Tifton, GA has collected over 30 years of hydrologic and climatic data from the 334 km2 Little River Watershed (LRW). The LRW is typical of heavily vegetated, slow-moving stream systems of the southeastern Coastal Plain. Field studies quantifying dissolved and suspended loads in LRW streamflow have been conducte...
The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L.4_SM) product provi... more The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L.4_SM) product provides global, 3-hourly, 9-km resolution estimates of surface (0-5 cm) and root zone (0-100 cm) soil moisture with a mean latency of~2.5 days. The underlying L4_SM algorithm assimilates SMAP radiometer brightness temperature (Tb) observations into the NASA Catchment land surface model using a spatially distributed ensemble Kalman filter. In Version 4 of the L4_SM modeling system the upward recharge of surface soil moisture from below under nonequilibrium conditions was reduced, resulting in less bias and improved dynamic range of L4_SM surface soil moisture compared to earlier versions. This change and additional technical modifications to the system reduce the mean and standard deviation of the observation-minus-forecast Tb residuals and overall soil moisture analysis increments while maintaining the skill of the L4_SM soil moisture estimates versus independent in situ measurements; the average, biasadjusted root-mean-square error in Version 4 is 0.039 m 3 /m 3 for surface and 0.026 m 3 /m 3 for root zone soil moisture. Moreover, the coverage of assimilated SMAP observations in Version 4 is near global owing to the use of additional satellite Tb records for algorithm calibration. L4_SM soil moisture uncertainty estimates are biased low (by 0.01-0.02 m 3 /m 3) against actual errors (computed versus in situ measurements). L4_SM runoff estimates, an additional product of the L4_SM algorithm, are biased low (by 35 mm/year) against streamflow measurements. Compared to Version 3, bias in Version 4 is reduced by 46% for surface soil moisture uncertainty estimates and by 33% for runoff estimates. Plain Language Summary Soil moisture is important because of its impact on the land surface water, energy, and nutrient cycles. The low-frequency microwave observations collected by the NASA Soil Moisture Active Passive (SMAP) satellite are suitable for estimating soil moisture globally. Their sensitivity, however, is limited to the top few centimeters of the soil, and observations are only available every other day depending on location. The SMAP Level-4 Soil Moisture (L4_SM) data product addresses these limitations by merging the satellite observations into a numerical model of the land surface water and energy balance while considering the uncertainty of the observations and model estimates. The resulting L4_SM data product is publicly disseminated within~2.5 days from the time of observation and provides global estimates of surface and deeper-layer (or "root zone") soil moisture at 3-hourly temporal and 9-km spatial resolution. This study presents an overview of recent updates in the L4_SM algorithm and an assessment of the quality of the resulting Version-4 soil moisture estimates. The algorithm updates reduce bias in L4_SM surface soil moisture and runoff estimates compared to previous versions while otherwise maintaining the product skill, which meets the accuracy requirement specified prior to SMAP's launch.
Andrew S. Mehring 1 , Dustin W. Kemp 1 , David D. Bosch 2 , Richard Lowrance 2 ... is often cited... more Andrew S. Mehring 1 , Dustin W. Kemp 1 , David D. Bosch 2 , Richard Lowrance 2 ... is often cited as a factor behind low oxygen levels observed in forested blackwater rivers. ... portion of oxygen demand generated by decomposing organic matter in the Little River in southern ...
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
A soil moisture (SM) disaggregation algorithm based on thermal inertia (TI) theory was implemente... more A soil moisture (SM) disaggregation algorithm based on thermal inertia (TI) theory was implemented to downscale the soil moisture active passive (SMAP) enhanced product (SPL2SMP_E) from 9 to 1 km over the continental United States. The algorithm applies land surface temperature and normalized difference vegetation index from moderate resolution imaging spectroradiometer (MODIS) at higher spatial resolution to estimate relative soil wetness within a coarse SMAP grid-this MODISderived relative wetness is then used to produce the downscaled SMAP SM. Results from the algorithm were evaluated in terms of their spatio-temporal coverage and accuracy using in situ measurements from SMAP core validation sites (CVS), the U.S. Department of Agriculture Soil Climate Analysis Network (SCAN), and the Manuscript
ABSTRACT Cloudy conditions reduce the utility of optical imagery for crop monitoring. New constel... more ABSTRACT Cloudy conditions reduce the utility of optical imagery for crop monitoring. New constellations of satellites – including the RADARSAT Constellation Mission (RCM) and Sentinel-1A/B, both available under free and open data policies – can be used to create stacks of dense seasonal C-band Synthetic Aperture Radar (SAR) data. Yet to date, the contribution of SAR imagery to operational crop mapping is often limited to that of a gap-filler, compensating for optical data obscured by clouds. The Joint Experiment for Crop Assessment and Monitoring (JECAM) SAR Inter-Comparison Experiment is a multi-year, multi-partner project focused on evaluating methods for SAR-based crop classification. Stacks of dense time-series SAR imagery, from RADARSAT-2 and Sentinel-1 satellites, were acquired for 10 sites located in six countries. Decision Tree (DT) and Random Forest (RF) classification methodologies were applied to these SAR data-stacks, as well as to data-stacks of optical only, and optimized SAR/optical data combinations. For the dense time-series SAR stacks, overall classification accuracies above 85% and 80% were obtained for 6 of 10 and 8 of 10 sites, respectively. For maize, the SAR-only data delivered user’s and producer’s accuracies greater than 90% for half the sites. For soya bean, accuracies greater than 80% were reported for 5 of 9 sites and classification accuracies were greater than 80% for wheat on half the sites. Classification results were influenced by the mix and number of agriculture classes present at each site, the available SAR imagery, as well as the training and validation data sets for individual crop types. These results have important operational implications for regions of the world dominated by cloudy conditions and the lack of adequate amounts of optical imagery to support satellite-based crop monitoring.
The Little River Experimental Watershed (LREW), in the headwaters of the Suwannee River, is one o... more The Little River Experimental Watershed (LREW), in the headwaters of the Suwannee River, is one of the 13 CSREES CEAP watersheds. LREW consists of 8 nested watersheds totaling 334 km2. We have 32 years of hydrologic and water quality data and for this project also developed GIS data sets that describe land use, crops, and conservation practices in LREW for most of the 32-year record period. Our project established a series of hypothesis about the physical and socioeconomic linkages between conservation practice adoption and water quality in LREW. We then developed a series of objectives with which to test our hypotheses. The most important question answered by this project was: Are water quality changes discernible over the last 32 years of changes in conservation practices? This paper will provide a summary of the results obtained from the project. The results from this project will allows us to determine cause and effect relationships between conservation practices and water quali...
NASA’s Soil Moisture Active Passive (SMAP) mission has been validating its soil moisture (SM) pro... more NASA’s Soil Moisture Active Passive (SMAP) mission has been validating its soil moisture (SM) products since the start of data production on March 31, 2015. Prior to launch, the mission defined a set of criteria for core validation sites (CVS) that enable the testing of the key mission SM accuracy requirement (unbiased root-mean-square error <0.04 m3/m3). The validation approach also includes other (“sparse network”) in situ SM measurements, satellite SM products, model-based SM products, and field experiments. Over the past six years, the SMAP SM products have been analyzed with respect to these reference data, and the analysis approaches themselves have been scrutinized in an effort to best understand the products’ performance. Validation of the most recent SMAP Level 2 and 3 SM retrieval products (R17000) shows that the L-band (1.4 GHz) radiometer-based SM record continues to meet mission requirements. The products are generally consistent with SM retrievals from the ESA Soil ...
Watershed models are used worldwide to assist with water and nutrient management under conditions... more Watershed models are used worldwide to assist with water and nutrient management under conditions of changing climate, land use, and population. Of these models, the Soil and Water Assessment Tool (SWAT) and SWAT+ are the most widely used, although their performance in groundwater-driven watersheds can sometimes be poor due to a simplistic representation of groundwater processes. The purpose of this paper is to introduce a new physically-based spatially-distributed groundwater flow module called gwflow for the SWAT+ watershed model. The module is embedded in the SWAT+ modeling code and is intended to replace the current SWAT+ aquifer module. The model accounts for recharge from SWAT+ Hydrologic Response Units (HRUs), lateral flow within the aquifer, Evapotranspiration (ET) from shallow groundwater, groundwater pumping, groundwater–surface water interactions through the streambed, and saturation excess flow. Groundwater head and groundwater storage are solved throughout the watershed...
... Corresponding author: David D. Bosch, USDAARS, Southeast Watershed Research Laboratory, PO B... more ... Corresponding author: David D. Bosch, USDAARS, Southeast Watershed Research Laboratory, PO Box 946, Tifton, GA 31793; phone: 2293863899; fax ... Agrichemical leaching is a potential problem in produc-tion areas of southern Georgia where sandy soils predomi-nate. ...
This study compares different methods to extract soil moisture information through the assimilati... more This study compares different methods to extract soil moisture information through the assimilation of Soil Moisture Active Passive (SMAP) observations. Neural network (NN) and physically-based SMAP soil moisture retrievals were assimilated into the National Aeronautics and Space Administration (NASA) Catchment model over the contiguous United States for April 2015 to March 2017. By construction, the NN retrievals are consistent with the global climatology of the Catchment model soil moisture. Assimilating the NN retrievals without further bias correction improved the surface and root zone correlations against in situ measurements from 14 SMAP core validation sites (CVS) by 0.12 and 0.16, respectively, over the model-only skill, and reduced the surface and root zone unbiased root-mean-square error (ubRMSE) by 0.005 m 3 m −3 and 0.001 m 3 m −3 , respectively. The assimilation reduced the average absolute surface bias against the CVS measurements by 0.009 m 3 m −3 , but increased the root zone bias by 0.014 m 3 m −3. Assimilating the NN retrievals after a localized bias correction yielded slightly lower surface correlation and ubRMSE improvements, but generally the skill differences were small. The assimilation of the physically-based SMAP Level-2 passive soil moisture retrievals using a global bias correction yielded similar skill improvements, as did the direct assimilation of locally bias-corrected SMAP brightness temperatures within the SMAP Level-4 soil moisture algorithm. The results show that global bias correction methods may be able to extract more independent information from SMAP observations compared to local bias correction methods, but without accurate quality control and observation error characterization they are also more vulnerable to adverse effects from retrieval errors related to uncertainties in the retrieval inputs and algorithm. Furthermore, the results show that using global bias correction approaches without a simultaneous re-calibration of the land model processes can lead to skill degradation in other land surface variables.
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Papers by David Bosch