Little River Experimental Watershed Research Articles

Designing Watersheds for Integrated Development (DWID): Combining hydrological and economic modeling for optimizing land use change to meet water quality regulations

By combining information on nutrient output from the Soil & Water Assessment Tool (SWAT) and secondary data on local profits from different crop types, we devise a profit maximization problem subject to dynamic water quality constraints, which become gradually more restrictive over time. The solution aims to detect the optimal allocation of land parcels by crop type that maximizes the total net present value of landowner profits throughout the watershed. Over a nine-year time span, our model construct is applied to the Little River Experimental Watershed (LREW) in South Georgia. Water quality constraints involve the landowner adhering to specific permittable limits on numeric nutrient criteria recorded at the watershed outlet under various scenarios, including i) NO3–N constraints, ii) total phosphorus (P) constraints, and iii) concurrent NO3–N and P constraints. In the most extreme case, a reduction in aggregate profits of $24.1 million and $8.1 million was observed for combined NO3– N and P constraints relative to commensurate solo constraints on NO3–N and P, respectively. The Designing Watersheds for Integrated Development (DWID) model could support policymaking for ascertaining trade-offs between economics and water quality channelized through direct and indirect land use change considering environmental regulations in Georgia and beyond.

Little River Experimental Watershed, a keystone in understanding of coastal plain watersheds

AbstractThe US Department of Agriculture‐Agricultural Research Service Southeast Watershed Research Laboratory (SEWRL) initiated a hydrologic research program on the Little River Experimental Watershed (LREW) in 1967. Long‐term (52 years) streamflow data are available for nine sites, including rainfall‐runoff relationships and hydrograph characteristics regularly used in research on interactive effects of climate, vegetation, soils, and land‐use in low‐gradient streams of the US EPA Level III Southeastern Plains ecoregion. A summary of prior research on the LREW illustrates the impact of the watershed on building a regional understanding of hydrology and water quality. Climatic and streamflow data were used to make comparisons of scale across the nine nested LREW watersheds (LRB, LRF, LRI, LRJ, LRK, LRO, LRN, LRM, and LRO3) and two regional watersheds (Alapaha and Little River at Adel). Annual rainfall for the largest LREW, LRB, was 1200 mm while average annual streamflow was 320 mm. Annual rainfall, streamflow, and the ratio between annual streamflow and rainfall (Sratio) were similar (α = 0.05) across LREWs LRB, LRF, LRI, LRJ, LRK, and LRO. While annual rainfall within the 275 ha LRO3 was found to be similar to LRO and LRM (α = 0.05), annual streamflow and Sratio were significantly different (α = 0.05). Comparisons of annual rainfall, streamflow, and Sratio between LRB and the regional watersheds indicated no differences (α = 0.05). Based upon this analysis, most regional watersheds shared similar hydrologic characteristics. LRO3 was an exception, where increases in row crops and decreases in forest coverage resulted in increased streamflow. LREW data have been instrumental in building considerable scientific understanding of flow and transport processes for these stream systems. Continued operation of the LREW hydrologic network will support hydrologic research as well as environmental quality and riparian research programs that address emerging and high priority natural resource and environmental issues.

A New Physically-Based Spatially-Distributed Groundwater Flow Module for SWAT+

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 domain using a water balance equation for each grid cell. The modified SWAT+ modeling code is applied to the Little River Experimental Watershed (LREW) (327 km2) in southern Georgia, USA for demonstration purposes. Using the gwflow module for the LREW increased run-time by 20% compared to the original SWAT+ modeling code. Results from an uncalibrated model are compared against streamflow discharge and groundwater head time series. Although further calibration is required if the LREW model is to be used for scenario analysis, results highlight the capabilities of the new SWAT+ code to simulate both land surface and subsurface hydrological processes and represent the watershed-wide water balance. Using the modified SWAT+ model can provide physically realistic groundwater flow gradients, fluxes, and interactions with streams for modeling studies that assess water supply and conservation practices. This paper also serves as a tutorial on modeling groundwater flow for general watershed modelers.

Riparian land cover and hydrology influence stream dissolved organic matter composition in an agricultural watershed

Dissolved organic matter (DOM) represents an essential component of the carbon cycle and controls biogeochemical and ecological processes in aquatic systems. The composition and reactivity of DOM are determined by the spatial distribution of its sources and its residence time in a watershed. While the effects of agricultural land cover on DOM quality have been reported across spatial and temporal scales, the influence of riparian land cover on stream DOM composition has received little attention. Furthermore, the combined effects of riparian land cover and streamflow rates on DOM composition require investigation. To this end, a multi-year (2016–2018) DOM characterization study was conducted using bi-weekly water samples collected from seven sub-watersheds nested within the Little River Experimental Watershed (LREW) near Tifton, Georgia, USA. DOM optical properties were determined to assess compositional variations using UV–Vis and excitation-emission matrix (EEM) fluorescence spectroscopy coupled with parallel factor (PARAFAC) analysis. PARAFAC analysis indicated that DOM in the LREW was dominated by three humic-like fluorescing components of terrestrial, microbial, and anthropogenic origin and a protein-like component. DOM composition was influenced by riparian land cover and hydrology, and shifted towards recently produced, low molecular weight DOM with low aromaticity as the percentage of agricultural land within riparian wetlands increased. During periods of high discharge and high baseflow, the DOM pool was dominated by recalcitrant and terrestrial-derived material but shifted towards protein-like and microbial-derived with increasing cropland in the riparian area. The results of this two-year study indicate that the replacement of forested riparian buffers with agricultural land can result in altered DOM composition which may affect carbon cycling and downstream water quality in agricultural watersheds.

Water quality and land cover in the Coastal Plain Little River watershed, Georgia, United States

Despite efforts to improve water quality in US watersheds, recent assessments of water quality trends in these watersheds indicate concentrations of nitrogen (N) and phosphorus (P) show minimal changes in the majority of studied streams across the nation. Moreover, results of the US Geological Survey (USGS) National Water-Quality Assessment (NAWQA) indicate that nutrient concentrations in streams and groundwater in basins with significant agriculture or urban development are substantially greater than naturally occurring or background levels. Long-term hydrologic and water quality data from watersheds are a key component to building an understanding that will dictate what and where changes in watershed management need to be made to achieve improvements in water quality. Forty-one years of hydrologic and water quality data from the Little River Experimental Watershed (LREW) were assembled to evaluate trends in streamflow quantity and quality from the LREW and relationships to changes in land cover and management. Concentrations and loads of chloride (Cl), ammonium-N (NH₄-N), nitrate plus nitrite-N (NO₃-N), total kjeldahl N (TKN), total P (TP), and dissolved molybdate reactive P (DMRP) from 1974 to 2014 were determined. In general, concentrations of N and P have remained low and stable, with some increases observed from 1980 to 1999. In contrast, Cl concentration appears to be steadily increasing, possibly due to increased fertilization in the LREW. Flow-weighted mean concentrations were 0.21 mg L⁻¹ y⁻¹ for NO₃-N, 0.08 mg L⁻¹ y⁻¹ for NH₄-N, 1.41 mg L⁻¹ y⁻¹ for TKN, 9.30 mg L⁻¹ y⁻¹ for Cl, 0.15 mg L⁻¹ y⁻¹ for TP, and 0.03 mg L⁻¹ y⁻¹ for DMRP. Average annual loads were 0.64 kg ha⁻¹ y⁻¹ for NO₃-N, 0.25 kg ha⁻¹ y⁻¹ for NH₄-N, 4.47 kg ha⁻¹ y⁻¹ for TKN, 29.54 kg ha⁻¹ y⁻¹ for Cl, 0.46 kg ha⁻¹ y⁻¹ for TP, and 0.10 kg ha⁻¹ y⁻¹ for DMRP. The low nutrient loading in the watershed is attributed to the dense riparian buffers within the LREW, which have been shown to be effective for reducing N loading to the stream. Due to (1) small variations in nutrient concentrations and loads, (2) large variability in precipitation, (3) large variability in streamflow, and (4) small variations in land cover, no clear relationships among changes in land cover and management and nutrient loads were found. Loading rates of N and P, although somewhat influenced by changes in concentration, are largely dictated by changes in streamflow volume in the LREW.

Temporal variations in baseflow for the Little River experimental watershed in South Georgia, USA

Study regionThe Little River Experimental Watershed (LREW) in the Southern Coastal Plain Major Land Resource Area of the U.S.A. (N31°28′54″, W83°35′03″). Study focusSeparation of streamflow hydrographs into rapid stormflow and baseflow can vastly improve our understanding of watershed processes. The two parameter Eckhardt digital filter method for separation of high and low frequency signals was used to estimate the portion of streamflow emanating from soil water and groundwater. The method requires estimation of two fitting parameters, BFImax and alpha (α). The baseflow index (BFI) is the ratio of baseflow volume to total streamflow volume. BFImax was set at 0.80 and α at 0.98. New hydrological insights for the regionBaseflow was found to produce 53% of annual streamflow. Stormflow was found to produce 47% of the annual streamflow. Baseflow was the greatest during the months from December through May (55–57%) and the least during the months from June through November (43–46%). Annual BFI was found to decrease with increasing annual precipitation, indicating that during high precipitation year’s saturation excess driven stormflow increases in the LREW. Hydrograph analysis indicated an average stormflow duration of seven days, typically extended by interflow in this watershed. These observed seasonal patterns can have a significant impact on regional agriculture as well as coastal estuaries that both rely heavily upon streamflow.

High-resolution modeling of the spatial heterogeneity of soil moisture: Applications in network design

The spatial heterogeneity of soil moisture remains a persistent challenge in the design of in situ measurement networks, spatial downscaling of coarse estimates (e.g., satellite retrievals), and hydrologic modeling. To address this challenge, we analyze high-resolution (∼9 m) simulated soil moisture fields over the Little River Experimental Watershed (LREW) in Georgia, USA, to assess the role and interaction of the spatial heterogeneity controls of soil moisture. We calibrate and validate the TOPLATS distributed hydrologic model with high to moderate resolution land and meteorological data sets to provide daily soil moisture fields between 2004 and 2008. The results suggest that topography and soils are the main drivers of spatial heterogeneity over the LREW. We use this analysis to introduce a novel network design method that uses land data sets as proxies of the main drivers of local heterogeneity (topography, land cover, and soil properties) to define unique and representative hydrologic similar units (subsurface, surface, and vegetation) for probe placement. The calibration of the hydrologic model and network design method illustrates how the use of hydrologic similar units in hydrologic modeling could minimize computation and guide efforts toward improved macroscale land surface modeling.

Model for Prioritizing Best Management Practice Implementation: Sediment Load Reduction

Understanding the best way to allocate limited resources is a constant challenge for water quality improvement efforts. The synoptic approach is a tool for geographic prioritization of these efforts. It uses a benefit-cost framework to calculate indices for functional criteria in subunits (watersheds, counties) of a region and then rank the subunits. The synoptic approach was specifically designed to incorporate best professional judgment in cases where information and resources are limited. To date, the synoptic approach has been applied primarily to local or regional wetland restoration prioritization projects. The goal of this work was to develop a synoptic model for prioritizing watersheds within which suites of agricultural best management practices (BMPs) can be implemented to reduce sediment load at the watershed outlets. The model ranks candidate watersheds within an ecoregion or river basin so that BMP implementation within the highest ranked watersheds will result in the most sediment load reduction per conservation dollar invested. The model can be applied anywhere and at many scales provided that the selected suite of BMPs is appropriate for the evaluation area’s biophysical and climatic conditions. The model was specifically developed as a tool for prioritizing BMP implementation efforts in ecoregions containing watersheds associated with the USDA-NRCS conservation effects assessment project (CEAP). This paper presents the testing of the model in the little river experimental watershed (LREW) which is located near Tifton, Georgia, USA and is the CEAP watershed representing the southeastern coastal plain. The application of the model to the LREW demonstrated that the model represents the physical drivers of erosion and sediment loading well. The application also showed that the model is quite responsive to social and economic drivers and is, therefore, best applied at a scale large enough to ensure differences in social and economic drivers across the candidate watersheds. The prioritization model will be used for planning purposes. Its results are visualized as maps which enable resource managers to identify watersheds within which BMP implementation would result in the most water quality improvement per conservation dollar invested.

Multimodel, Multiple Watershed Examination of In-Stream Bacteria Modeling

AbstractExcessive bacteria levels are a major water-quality problem in U.S. water bodies, and this study looked at the use of watershed-scale computer models to predict in-stream bacteria concentrations. The study site was the Little River Experimental Watershed (LREW) in Tifton, Georgia, and fecal coliform fate and transport models were built for four of the LREW subwatersheds over the period January 1996–December 2002. Three models were examined: HSPF, the soil and water assessment tool (SWAT), and the characteristic concentration (CC) model based on the principles of hydrograph separation. It was found that the most sensitive HSPF and SWAT water-quality parameters were associated with the in-stream bacteria processes, whereas parameters relating to terrestrial activity varied from sensitive to completely insensitive. Model performance was evaluated using the Nash–Sutcliffe statistic, and HSPF, SWAT, and the CC model performed comparably while showing distinct performance capabilities.

Assimilation and downscaling of satellite observed soil moisture over the Little River Experimental Watershed in Georgia, USA

A three dimensional Ensemble Kalman filter (3-D EnKF) and a one dimensional EnKF (1-D EnKF) are used in this study to assimilate Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) coarse grid (25km) soil moisture retrievals into the Noah land surface model for fine-scale (1km) surface soil moisture estimation over the Little River Experimental Watershed (LREW), Georgia, USA. For the 1-D EnKF integration, the satellite observations are a priori partitioned to the model fine scale resolution, whereas in the 3-D EnKF integration, the original coarse grid satellite observations are directly used and downscaling is accomplished within the 3-D EnKF update step. In both cases, a first order a priori forecast bias correction is applied. Validation against in situ observations shows that both EnKF algorithms improve the soil moisture estimates, but the 3-D EnKF algorithm better preserves the spatial coherence. It is illustrated how surface soil moisture assimilation affects the deeper layer soil moisture and other water budget variables. Through sensitivity experiments, it is shown that data assimilation accelerates the moisture redistribution compared to the model integrations without assimilation, as surface soil moisture updates are effectively propagated over the entire profile. In the absence of data assimilation, the atmospheric conditions (especially the ratio of evapotranspiration to precipitation) control the model state balancing.

Scale effects on information theory-based measures applied to streamflow patterns in two rural watersheds

Summary Understanding streamflow patterns in space and time is important for improving flood and drought forecasting, water resources management, and predictions of ecological changes. Objectives of this work include (a) to characterize the spatial and temporal patterns of streamflow using information theory-based measures at two thoroughly-monitored agricultural watersheds located in different hydroclimatic zones with similar land use, and (b) to elucidate and quantify temporal and spatial scale effects on those measures. We selected two USDA experimental watersheds to serve as case study examples, including the Little River experimental watershed (LREW) in Tifton, Georgia and the Sleepers River experimental watershed (SREW) in North Danville, Vermont. Both watersheds possess several nested sub-watersheds and more than 30 years of continuous data records of precipitation and streamflow. Information content measures (metric entropy and mean information gain) and complexity measures (effective measure complexity and fluctuation complexity) were computed based on the binary encoding of 5-year streamflow and precipitation time series data. We quantified patterns of streamflow using probabilities of joint or sequential appearances of the binary symbol sequences. Results of our analysis illustrate that information content measures of streamflow time series are much smaller than those for precipitation data, and the streamflow data also exhibit higher complexity, suggesting that the watersheds effectively act as filters of the precipitation information that leads to the observed additional complexity in streamflow measures. Correlation coefficients between the information-theory-based measures and time intervals are close to 0.9, demonstrating the significance of temporal scale effects on streamflow patterns. Moderate spatial scale effects on streamflow patterns are observed with absolute values of correlation coefficients between the measures and sub-watershed area varying from 0.2 to 0.6 in the two watersheds. We conclude that temporal effects must be evaluated and accounted for when the information theory-based methods are used for performance evaluation and comparison of hydrological models.

Remote Sensing of Soil Moisture Using Airborne Hyperspectral Data

Landscape assessment of soil moisture is critical to understanding the hydrological cycle at the regional scale and in broad-scale studies of biophysical processes affected by global climate changes in temperature and precipitation. Traditional efforts to measure soil moisture have been principally restricted to in situ measurements, so remote sensing techniques are often employed. Hyperspectral sensors with finer spatial resolution and narrow band widths may offer an alternative to traditional multispectral analysis of soil moisture, particularly in landscapes with high spatial heterogeneity. This preliminary research evaluates the ability of remotely sensed hyperspectral data to quantify soil moisture for the Little River Experimental Watershed (LREW), Georgia. An airborne hyperspectral instrument with a short-wavelength infrared (SWIR) sensor was flown in 2005 and 2007 and the results were correlated to in situ soil moisture values. A significant statistical correlation (R 2 value above 0.7 for both sampling dates) for the hyperspectral instrument data and the soil moisture probe data at 5.08 cm (2 inches) was determined. While models for the 20.32 cm (8 inches) and 30.48 cm (12 inches) depths were tested, they were not able to estimate soil moisture to the same degree.

An Examination of In-Stream Bacteria Modeling in Agricultural Watersheds

An Examination of In-Stream Bacteria Modeling in Agricultural WatershedsExcessive bacteria levels are a pollutant of concern in U.S. water bodies, and this study looked at the use of watershed computer models to predict in-stream bacteria concentrations. The study site was the Little River Experimental Watershed (LREW) in Tifton, GA, and fecal coliform computer models were built for two of the LREW subwatersheds over the period Jan 1996 - Dec 2002. Three models were...Author(s)Jeffrey J. IudicelloSourceProceedings of the Water Environment FederationSubjectSession 110: Pathways to PathogensDocument typeConference PaperPublisherWater Environment FederationPrint publication date Jan, 2011ISSN1938-6478SICI1938-6478(20110101)2011:8L.7033;1-DOI10.2175/193864711802793290Volume / Issue2011 / 8Content sourceWEFTECFirst / last page(s)7033 - 7056Copyright2011Word count154Subject keywordsHSPFSWATBacteriaFecal ColiformModelingWater Quality

GIS-based spatial precipitation estimation using next generation radar and raingauge data

Precipitation is one important input variable for land surface hydrologic and ecological models. Next Generation Radar (NEXRAD) can provide precipitation products that cover most of the conterminous United States at high resolution (approximately 4 km x 4 km). There are two major issues concerning the application of NEXRAD data: 1) the lack of a NEXRAD geo-processing and geo-referring program and 2) bias correction of NEXRAD estimates. However, in public domain, there is no Geographic Information System (GIS) software that can use geostatistical approaches to calibrate NEXRAD data using raingauge data, and automatically process NEXRAD data for hydrologic and ecological models. In this study, we developed new GIS software for NEXRAD validation and calibration (NEXRAD-VC) using raingauge data. NEXRAD-VC can automatically read in NEXRAD data in NetCDF or XMRG format, transform projection of NEXRAD data to match with raingauge data, apply different geostatistical approaches to calibrate NEXRAD data using raingauge data, evaluate performance of different calibration methods using leave-one-out cross-validation scheme, output spatial precipitation maps in ArcGIS grid format, calculate spatial average precipitation for each spatial modeling unit used by hydrologic and ecological models. The major functions of NEXRAD-VC are illustrated in the Little River Experimental Watershed (LREW) in Georgia using daily precipitation records of fifteen raingauges and NEXRAD products of five years. The validation results show that NEXRAD has a high success rate for detecting rain and no-rain events: 82.8% and 95.6%, respectively. NEXRAD estimates have high correlation with raingauge observations (correlation coefficient of 0.91), but relatively larger relative mean absolute error value of 36%. It is also worth noting that the performance of NEXRAD increases with the decreasing of rainfall variability. Three methods (Bias Adjustment method (BA), Regressing Kriging (RK), and Simple Kriging with varying local means (SKlm)) were employed to calibrate NEXRAD using raingauge data. Overall, SKlm performed the best among these methods. Compared with NEXRAD, SKlm improved the correlation coefficient to 0.96 and the relative mean absolute error to 22.8%, respectively. SKlm also increased the success rate of detection of rain and no-rain events to 87.47% and 96.05%, respectively. Further analysis of the performance of the three calibration methods and NEXRAD for daily spatial precipitation estimation shows that no one method can consistently provide better results than the other methods for each evaluation coefficient for each day. It is suggested that multiple methods be implemented to predict spatial precipitation. The NEXRAD-VC developed in this study can serve as an effective and efficient tool to batch process large amounts of NEXRAD data for hydrologic and ecological modeling.

The evaluation of conservation practice placement in the Little River Experimental Watershed using geographic information systems

The Conservation Effects Assessment Program Watershed Assessment Study is a joint effort between the USDA Natural Resources Conservation Service (NRCS) and the USDA Agricultural Research Service to evaluate the effectiveness of federally funded conservation programs. In response to this initiative, a 26-year history of NRCS conservation practice placement (1980 to 2006) was evaluated for the Little River Experimental Watershed (LREW) in the southeastern coastal plain of Georgia. To accomplish this task, currently available geographic databases were integrated and queried to assess levels of commonly adopted practices and to evaluate factors affecting practice placement. Databases included (1) USDA NRCS Conservation Practice Database for the LREW, (2) USDA NRCS Soil Survey Geographic Database (SSURGO), and (3) 30 m (98 ft) digital elevation maps. Nearly 50% of all cropland fields in the LREW were delineated as having participated in conservation programs. Practices were predominantly used for water quality and erosion control. Sixty to 65% of the fields (77% of land area) implemented soil erosion and/or water quality control practices in high resource concern areas. Results showed that hydrologic group and proximity to a water body, rather than slope class, were the predominant factors in conservation practice placement. Using a subwatershed database having complete field coverage of four LREW subwatersheds (with and without USDA NRCS assistance), geographic information system databases were queried to evaluate the adoption and placement of erosion control practices that were visible in a 2005 digital orthoquad. Forty-seven percent of all fields in the subwatershed database had implemented visible erosion control–specific conservation practices. and implementation was linearly related to slope class ( r 2 = 0.64, p < 0.10). Fields identified as having participated in federally funded conservation programs coincided with high resource concern areas 35% of the time.

Watershed-Scale Fate and Transport of Bacteria

The added dimensionality provided by using multiple models to predict the fate and transport of bacteria at the watershed scale were investigated. Both HSPF and SWAT were applied to the 15.6 km2 catchment K of the Little River Experimental Watershed (LREW) in Georgia. Over the seven-year period from 1996 to 2002, SWAT provided a more accurate description of fecal coliform concentrations, with an NSE of 0.73 compared to 0.33 for HSPF. For this particular watershed, the SWAT process equations are more representative of the watershed-scale fate and transport of bacteria than the HSPF process equations. Based on this comparative analysis, it can be inferred that elevated levels of fecal coliform in the receiving stream are primarily due to in-stream sources. This source characterization could not be achieved by using only the HSPF model, which indicates a much greater contribution from groundwater and terrestrial nonpoint sources. A model-averaging approach in which a weighted average of the HSPF and SWAT predictions are used to predict bacteria concentrations in the receiving stream demonstrates that model weights can be determined such that the NSE of the combined models will be greater than either of the models taken individually. However, in the present case, the marginal improvements in NSE obtained through this integration were small.

Improving Daily Water Yield Estimates in the Little River Watershed: SWAT Adjustments

Researchers are assessing the beneficial effects of conservation practices on water quality with hydrologic models. The assessments depend heavily on accurate simulation of water yield. This study was conducted to improve Soil and Water Assessment Tool (SWAT) hydrologic model daily water yield estimates in the Little River Experimental Watershed (LREW) in south Georgia. The SWAT code was altered to recognize a difference in curve number between growing and dormant seasons, to use an initial abstraction (Ia), of 0.05S rather than 0.2S, and to adjust curve number based on the level of soil saturation in low-lying riparian zones. Refinements were made to two SWAT input parameters, SURLAG and ALPHA_BF, from a previous set of calibration parameters. The combined changes improved the daily Nash-Sutcliffe model efficiency (NSE) from 0.42 to 0.66 for water yield at the outlet of the 16.9 km2 subwatershed K of the LREW for the ten-year period 1995 to 2004. Further calibration of the SURLAG coefficient yielded the largest improvement of five alterations, and changing Ia effected the next largest improvement. Over the ten-year investigation period, the model predicted annual average water yield within 1% of measured streamflow, and deviation between observed and simulated values for stormflow was <2.2%. Annual daily NSEs for each of the ten years were improved; for two years affected by seasonal tropical storm events, NSEs were changed from negative to positive values. The results of this study support the adjustment of the Ia ratio in the runoff curve number and suggest that additional changes to SWAT would improve water yield prediction for southern Coastal Plain locations.

Evaluation of AMSR-E soil moisture results using the in-situ data over the Little River Experimental Watershed, Georgia

An operational global soil moisture data product is currently generated from the observations of the Advanced Microwave Scanning Radiometer (AMSR-E) aboard NASA's Aqua satellite using the retrieval procedure described in Njoku and Chan [Njoku, E.G. and Chan, S.K., 2006. Vegetation and surface roughness effects on AMSR-E land observations, remote sensing environment, 100(2), 190–199]. We have generated another soil moisture dataset from the same AMSR-E observed brightness temperature data using the Land Surface Microwave Emission Model (LSMEM) adopting a different estimation method. This paper focuses on a comparison study of soil moisture estimates from the above two methods. The soil moisture data from current AMSR-E product and LSMEM are compared with the in-situ measured soil moisture datasets over the Little River Experimental Watershed (LREW), Georgia, USA for the year 2003. The comparison study was carried out separately for the AMSR-E daytime and night time overpasses. The LSMEM method performed better than the current operational AMSR-E retrieval algorithm in this study. The differences between the AMSR-E and LSMEM results are mostly due to differences in various simplifications and assumptions made for variables in the radiative transfer equations and the soil and vegetation based physical models and the accuracy of the input surface temperature datasets for the LSMEM forward model approach. This study confirms that remote sensing data have the potential to provide useful hydrologic information, but the accuracy of the geophysical parameters could vary depending on the estimation methods. It cannot be concluded from this study whether the soil moisture estimation by the LSMEM approach will perform better in other geographic, climatic or topographic conditions. Nevertheless, this study sheds light on the effects of different approaches for the estimation of geophysical parameters, which may be useful for current and future satellite missions.

Long‐term water chemistry database, Little River Experimental Watershed, southeast Coastal Plain, United States

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. Monitoring began in 1974 on the entire watershed and four nested subwatersheds, ranging in size from 16.7 to 114.9 km2, and continues until present. Partial records of 7, 10, and 19 years exist for three additional subwatersheds. Suspended solids data are available for all eight subwatersheds for 1974–1978 and 1979–1981, three subwatersheds for 1982–1986, and all eight subwatersheds again beginning in the year 2000. The concentration and associated load data are being published on the LREW database anonymous ftp site (ftp://www.tiftonars.org/).

Stream discharge database, Little River Experimental Watershed, Georgia, United States

The U.S. Department of Agriculture Agricultural Research Service Southeast Watershed Research Laboratory (SEWRL) initiated a hydrologic research program on the Little River Experimental Watershed (LREW) in 1967. Long‐term (up to 37 years) research quality streamflow data are currently available for up to eight flow measurement sites within the Gulf‐Atlantic Coastal Plain physiographic region. The LREW stream discharge research program provides fundamental data for research into hydrologic processes, precipitation‐runoff relationships, hydrograph characteristics, water yield, and interactive effects of climate, vegetation, soils, and land use for low‐gradient Coastal Plain streams. All data are available on the SEWRL anonymous ftp site (ftp://www.tiftonars.org/).