Hydrology of small field plots used to study phosphorus runoff under simulated rainfall

Use of small plots and rainfall simulators to extrapolate trends in runoff water quality requires careful consideration of hydrologic process represented under such conditions. A modified version of the National Phosphorus Runoff Project (NPRP) protocol was used to assess the hydrology of paired 1 x 2 m plots established on two soils with contrasting hydrologic properties (somewhat poorly drained vs. well drained). Rain simulations (60 mm h(-1)) were conducted to generate 30 min of runoff. For the somewhat poorly drained soil, simulations were conducted in October and May to contrast dry conditions typically targeted by NPRP protocols with wet conditions generally associated with natural runoff. For the well-drained soil, only dry conditions (October) were evaluated. Under dry antecedent moisture conditions, an average of 64 mm of rainfall was applied to the somewhat poorly drained soil to generate 30 min of runoff, as opposed to 96 mm to the well-drained soil. At an extreme, differences in rainfall were equivalent to a 50-yr rainfall-return period. An absence of detectable spatial trends in surface soil moisture suggests uniformity of runoff processes within the plots. No differences in applied rainfall were evident between wet and dry antecedent conditions for the somewhat poorly drained soil. However, significant differences in runoff generation processes were observed in dissolved P concentrations between wet and dry conditions. As natural runoff from the somewhat poorly drained soil is largely under wet antecedent conditions, this study highlights the need for care in interpreting findings from generalized protocols that favor infiltration-excess runoff mechanisms.     

VALIDATION OF AGNPS FOR SMALL WATERSHEDS USING AN INTEGRATED AGNPS/GIS SYSTEM1

ABSTRACT: The AGNPS (AGricultural NonPoint Source) model was evaluated for predicting runoff and sediment delivery from small watersheds of mild topography. Fifty sediment yield events were monitored from two watersheds and five nested subwater-sheds in East Central Illinois throughout the growing season of four years. Half of these events were used to calibrate parameters in the AGNPS model. Average calibrated parameters were used as input for the remaining events to obtain runoff and sediment yield data. These data were used to evaluate the suitability of the AGNPS model for predicting runoff and sediment yield from small, mild-sloped watersheds. An integrated AGNPS/GIS system was used to efficiently create the large number of data input changes necessary to this study. This system is one where the AGNPS model was integrated with the GRASS (Geographic Resources Analysis Support System) GIS (Geographical Information System) to develop a decision support tool to assist with management of runoff and erosion from agricultural watersheds. The integrated system assists with the development of input GIS layers to AGNPS, running the model, and interpretation of the results.     

HYDROLOGIC MODELING OF TWO GLACIATED WATERSHEDS IN NORTHEAST PENNSYLVANIA1

ABSTRACT: A hydrologic modeling study, using the Hydrologic Simulation Program - FORTRAN (HSPF), was conducted in two glaciated watersheds, Purdy Creek and Ariel Creek in northeastern Pennsylvania. Both watersheds have wetlands and poorly drained soils due to low hydraulic conductivity and presence of fragipans. The HSPF model was calibrated in the Purdy Creek watershed and verified in the Ariel Creek watershed for June 1992 to December 1993 period. In Purdy Creek, the total volume of observed stream-flow during the entire simulation period was 13.36 × 10⁶ m³ and the simulated streamflow volume was 13.82 × 10⁶ m³ (5 percent difference). For the verification simulation in Ariel Creek, the difference between the total observed and simulated flow volumes was 17 percent. Simulated peak flow discharges were within two hours of the observed for 30 of 46 peak flow events (discharge greater than 0.1 m³/sec) in Purdy Creek and 27 of 53 events in Ariel Creek. For 22 of the 46 events in Purdy Creek and 24 of 53 in Ariel Creek, the differences between the observed and simulated peak discharge rates were less than 30 percent. These 22 events accounted for 63 percent of total volume of streamflow observed during the selected 46 peak flow events in Purdy Creek. In Ariel Creek, these 24 peak flow events accounted for 62 percent of the total flow observed during all peak flow events. Differences in observed and simulated peak flow rates and volumes (on a percent basis) were greater during the snowmelt runoff events and summer periods than for other times.     

LARGE AREA HYDROLOGIC MODELING AND ASSESSMENT PART II: MODEL APPLICATION1

ABSTRACT: This paper describes the application of a river basin scale hydrologic model (described in Part I) to Richland and Chambers Creeks watershed (RC watershed) in upper Trinity River basin in Texas. The inputs to the model were accumulated from hydro-graphic and geographic databases and maps using a raster-based GIS. Available weather data from 12 weather stations in and around the watershed and stream flow data from two USGS stream gauge station for the period 1965 to 1984 were used in the flow calibration and validation. Sediment calibration was carried out for the period 1988 through 1994 using the 1994 sediment survey data from the Richland-Chambers lake. Sediment validation was conducted on a subwatershed (Mill Creek watershed) situated on Chambers Creek of the RC watershed. The model was evaluated by well established statistical and visual methods and was found to explain at least 84 percent and 65 percent of the variability in the observed stream flow data for the calibration and validation periods, respectively. In addition, the model predicted the accumulated sediment load within 2 percent and 9 percent from the observed data for the RC watershed and Mill Creek watershed, respectively.     

LARGE AREA HYDROLOGIC MODELING AND ASSESSMENT PART I: MODEL DEVELOPMENT1

ABSTRACT: A conceptual, continuous time model called SWAT (Soil and Water Assessment Tool) was developed to assist water resource managers in assessing the impact of management on water supplies and nonpoint source pollution in watersheds and large river basins. The model is currently being utilized in several large area projects by EPA, NOAA, NRCS and others to estimate the off-site impacts of climate and management on water use, non-point source loadings, and pesticide contamination. Model development, operation, limitations, and assumptions are discussed and components of the model are described. In Part II, a GIS input/output interface is presented along with model validation on three basins within the Upper Trinity basin in Texas.     

Development and Testing of a Physically Based Model of Streambank Erosion for Coupling with a Basin‐Scale Hydrologic Model SWAT

A comprehensive streambank erosion model based on excess shear stress has been developed and incorporated in the hydrological model Soil and Water Assessment Tool (SWAT). It takes into account processes such as weathering, vegetative cover, and channel meanders to adjust critical and effective stresses while estimating bank erosion. The streambank erosion model was tested for performance in the Cedar Creek watershed in north‐central Texas where streambank erosion rates are high. A Rapid Geomorphic field assessment (RAP‐M) of the Cedar Creek watershed was done adopting techniques developed by the Natural Resources Conservation Service (NRCS), and the stream segments were categorized into various severity classes. Based on the RAP‐M field assessment, erosion pin sites were established at seven locations within the severely eroding streambanks of the watershed. A Monte Carlo simulation was carried out to assess the sensitivity of different parameters that control streambank erosion such as critical shear stress, erodibility, weathering depth, and weathering duration. The sensitive parameters were adjusted and the model was calibrated based on the bank erosion severity category identified by the RAP‐M field assessment. The average observed erosion rates were in the range 25‐367 mm year^?1. The SWAT model was able to reasonably predict the bank erosion rates within the range of variability observed in the field (R² = 0.90; E = 0.78). Editor's note : This paper is part of the featured series on SWAT Applications for Emerging Hydrologic and Water Quality Challenges. See the February 2017 issue for the introduction and background to the series.     

Approximating SWAT Model Using Artificial Neural Network and Support Vector Machine1

Abstract: With the popularity of complex, physically based hydrologic models, the time consumed for running these models is increasing substantially. Using surrogate models to approximate the computationally intensive models is a promising method to save huge amounts of time for parameter estimation. In this study, two learning machines [Artificial Neural Network (ANN) and support vector machine (SVM)] were evaluated and compared for approximating the Soil and Water Assessment Tool (SWAT) model. These two learning machines were tested in two watersheds (Little River Experimental Watershed in Georgia and Mahatango Creek Experimental Watershed in Pennsylvania). The results show that SVM in general exhibited better generalization ability than ANN. In order to effectively and efficiently apply SVM to approximate SWAT, the effect of cross‐validation schemes, parameter dimensions, and training sample sizes on the performance of SVM was evaluated and discussed. It is suggested that 3‐fold cross‐validation is adequate for training the SVM model, and reducing the parameter dimension through determining the parameter values from field data and the sensitivity analysis is an effective means of improving the performance of SVM. As far as the training sample size, it is difficult to determine the appropriate number of samples for training SVM based on the test results obtained in this study. Simple examples were used to illustrate the potential applicability of combining the SVM model with uncertainty analysis algorithm to save efforts for parameter uncertainty of SWAT. In the future, evaluating the applicability of SVM for approximating SWAT in other watersheds and combining SVM with different parameter uncertainty analysis algorithms and evolutionary optimization algorithms deserve further research.     

GIS‐Based Spatial Precipitation Estimation: A Comparison of Geostatistical Approaches1

Abstract: As one of the primary inputs that drive watershed dynamics, the estimation of spatial variability of precipitation has been shown to be crucial for accurate distributed hydrologic modeling. In this study, a Geographic Information System program, which incorporates Nearest Neighborhood (NN), Inverse Distance Weighted (IDW), Simple Kriging (SK), Ordinary Kriging (OK), Simple Kriging with Local Means (SKlm), and Kriging with External Drift (KED), was developed to facilitate automatic spatial precipitation estimation. Elevation and spatial coordinate information were used as auxiliary variables in SKlm and KED methods. The above spatial interpolation methods were applied in the Luohe watershed with an area of 5,239 km², which is located downstream of the Yellow River basin, for estimating 10 years’ (1991‐2000) daily spatial precipitation using 41 rain gauges. The results obtained in this study show that the spatial precipitation maps estimated by different interpolation methods have similar areal mean precipitation depth, but significantly different values of maximum precipitation, minimum precipitation, and coefficient of variation. The accuracy of the spatial precipitation estimated by different interpolation methods was evaluated using a correlation coefficient, Nash‐Sutcliffe efficiency, and relative mean absolute error. Compared with NN and IDW methods that are widely used in distributed hydrologic modeling systems, the geostatistical methods incorporated in this GIS program can provide more accurate spatial precipitation estimation. Overall, the SKlm_EL_X and KED_EL_X, which incorporate both elevation and spatial coordinate as auxiliary into SKlm and KED, respectively, obtained higher correlation coefficient and Nash‐Sutcliffe efficiency, and lower relative mean absolute error than other methods tested. The GIS program developed in this study can serve as an effective and efficient tool to implement advanced geostatistics methods that incorporate auxiliary information to improve spatial precipitation estimation for hydrologic models.     

AVGWLF‐Based Estimation of Nonpoint Source Nitrogen Loads Generated Within Long Island Sound Subwatersheds1

Abstract: The Generalized Watershed Loading Functions (GWLF) model and its ArcView interface (AVGWLF) were used to estimate and examine the components of the total nitrogen (TN) nonpoint source (NPS) load generated within New York and Connecticut (CT) watersheds surrounding Long Island Sound (LIS, the Sound). The majority of data used as model inputs were generally available from online sources, and the work involved an overall calibration to streamflow and TN data in accordance with generic guidelines recommended in the GWLF manual. The GWLF model performance for three calibration and two validation watersheds in CT was compared with results of a detailed model, Hydrological Simulation Program in Fortran, developed in a previous study. The results of the application illustrate the usefulness of the relatively simpler, less parameter‐intensive GWLF model in performing exploratory loading analysis in preparation for adaptive nutrient management in the LIS watersheds. The presented methodology is valuable for identification of priority watersheds for NPS pollution reduction and also for planning‐level evaluation of best management practices to achieve the desired reductions. It is estimated that ground‐water base flow may be the largest pathway for NPS TN to the Sound, contributing about 54% of the total NPS TN load, a finding with significant implications for LIS total maximum daily load reduction scenarios. In addition to ground water, septic systems are estimated to contribute about 17% of the total load, with the remaining TN load being mostly runoff from urban (17%), agricultural (5%), and low impact (e.g., forest) areas (6%).