How to Constrain Multi‐Objective Calibrations of the SWAT Model Using Water Balance Components

Accurate discharge simulation is one of the most common objectives of hydrological modeling studies. However, a good simulation of discharge is not necessarily the result of a realistic simulation of hydrological processes within the catchment. We propose an evaluation framework that considers both discharge and water balance components as evaluation criteria for calibration of the Soil and Water Assessment Tool (SWAT). In this study, we integrated average annual values of surface runoff, groundwater flow, and evapotranspiration in the model evaluation procedure to constrain the selection of good model runs for the Little River Experimental Watershed in Georgia, United States. For evaluating water balance and discharge dynamics, the Nash‐Sutcliffe efficiency (NSE) and percent bias (PBIAS) were used. In addition, the ratio of root mean square error and standard deviation of measured data (RSR) was calculated for individual segments of the flow duration curve to identify the best model runs in terms of discharge magnitude. Our results indicate that good statistics for discharge do not guarantee realistic simulations of individual water balance components. Therefore, we recommend constraining the ranges of water balance components to achieve a more realistic simulation of the entire hydrological system, even if tradeoffs between good statistics for discharge simulations and reasonable amounts of the water balance components are unavoidable. 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.     

VIC模型与SWAT模型在中小流域径流模拟中的对比研究

结合GIS与RS技术的分布式水文模型已成为当今水文界研究的重点。从气象与水文水资源学科交叉的角度对分布式水文模型VIC模型与SWAT模型进行研究,并将其应用于白莲河流域,以此探讨该模型在中小流域的适用性。模拟结果表明,VIC模型与SWAT模型在白莲河流域率定期与检验期的模拟效果相差很小。SWAT模型的效率系数与相关系数略高一些,SWAT模型的模拟效果比较平均,每年相差不大;但VIC模型在1995年和1999年模拟效果明显好于其它年份,尤其在2002年,VIC模型模拟的洪峰与实测的相差较大,从而影响总体的效率系数偏低,而SWAT模型模拟的更接近实测值。研究结果表明两种模型对于我国中小流域的径流模拟具有一定的适用性.     

Development of a Quantitative Pasture Phosphorus Management Tool Using the SWAT Model1

Abstract: Assessment tools to evaluate phosphorus loss from agricultural lands allow conservation planners to evaluate the impact of management decisions on water quality. Available tools to predict phosphorus loss from agricultural fields are either: (1) qualitative indices with limited applicability to address offsite water quality standards, or (2) models which are prohibitively complex for application by most conservation planners. The purpose of this research was to develop a simple interface for a comprehensive hydrologic/water quality model to allow its usage by farmers and conservation planners. The Pasture Phosphorus Management (PPM) Calculator was developed to predict average annual phosphorus (P) losses from pastures under a variety of field conditions and management options. PPM Calculator is a vastly simplified interface for the Soil and Water Assessment Tool (SWAT) model that requires no knowledge of SWAT by the user. PPM Calculator was validated using 33 months of data on four pasture fields in northwestern Arkansas. This tool has been extensively applied in the Lake Eucha/Spavinaw Basin in northeastern Oklahoma and northwestern Arkansas. PPM Calculator allows conservation planners to take advantage of the predictive capacity of a comprehensive hydrologic water quality model typically reserved for use by hydrologists and engineers. This research demonstrates the applicability of existing water quality models in the development of user friendly P management tools.     

Impacts of Climate Change on Hydrology and Water Resources in the Boise and Spokane River Basins1

Jin, Xin and Venkataramana Sridhar, 2012. Impacts of Climate Change on Hydrology and Water Resources in the Boise and Spokane River Basins. Journal of the American Water Resources Association (JAWRA) 48(2): 197‐220. DOI: 10.1111/j.1752‐1688.2011.00605.x Abstract: In the Pacific Northwest, warming climate has resulted in a lengthened growing season, declining snowpack, and earlier timing of spring runoff. This study characterizes the impact of climate change in two basins in Idaho, the Spokane River and the Boise River basins. We simulated the basin‐scale hydrology by coupling the downscaled precipitation and temperature outputs from a suite of global climate models and the Soil and Water Assessment Tool (SWAT), between 2010 and 2060 and assess the impacts of climate change on water resources in the region. For the Boise River basin, changes in precipitation ranged from ?3.8 to 36%. Changes in temperature were expected to be between 0.02 and 3.9°C. In the Spokane River region, changes in precipitation were expected to be between ?6.7 and 17.9%. Changes in temperature appeared between 0.1 and 3.5°C over a period of the next five decades between 2010 and 2060. Without bias‐correcting the simulated streamflow, in the Boise River basin, change in peak flows (March through June) was projected to range from ?58 to +106 m³/s and, for the Spokane River basin, the range was expected to be from ?198 to +88 m³/s. Both the basins exhibited substantial variability in precipitation, evapotranspiration, and recharge estimates, and this knowledge of possible hydrologic impacts at the watershed scale can help the stakeholders with possible options in their decision‐making process.     

Optimal Selection of Priority Development Areas Considering Tradeoffs Between Hydrology and Development Configuration

Mixed-integer linear programs are proposed for siting development and conservation areas in watersheds, addressing economic objectives (development perimeter and proximity) and ecological objectives. Links between watershed hydrology and ecology need not be well defined. Parameters for the linear programs are obtained from linearization of the SWAT hydrologic model.     

COMPARISON OF PROCESS‐BASED AND ARTIFICIAL NEURAL NETWORK APPROACHES FOR STREAMFLOW MODELING IN AN AGRICULTURAL WATERSHED1

ABSTRACT: The performance of the Soil and Water Assessment Tool (SWAT) and artificial neural network (ANN) models in simulating hydrologic response was assessed in an agricultural watershed in southeastern Pennsylvania. All of the performance evaluation measures including Nash‐Sutcliffe coefficient of efficiency (E) and coefficient of determination (R²) suggest that the ANN monthly predictions were closer to the observed flows than the monthly predictions from the SWAT model. More specifically, monthly streamflow E and R² were 0.54 and 0.57, respectively, for the SWAT model calibration period, and 0.71 and 0.75, respectively, for the ANN model training period. For the validation period, these values were ?0.17 and 0.34 for the SWAT and 0.43 and 0.45 for the ANN model. SWAT model performance was affected by snowmelt events during winter months and by the model's inability to adequately simulate base flows. Even though this and other studies using ANN models suggest that these models provide a viable alternative approach for hydrologic and water quality modeling, ANN models in their current form are not spatially distributed watershed modeling systems. However, considering the promising performance of the simple ANN model, this study suggests that the ANN approach warrants further development to explicitly address the spatial distribution of hydrologic/water quality processes within watersheds.     

EFFECTS OF STATSGO AND SSURGO AS INPUTS ON SWAT MODEL'S SNOWMELT SIMULATION1

ABSTRACT: Soil data comprise a basic input of SWAT (Soil and Water Assessment Tool) for a watershed application. For watersheds where site specific soil data are unavailable, the two U.S. Department of Agriculture (USDA) soil databases, the State Soil Geographic (STATSGO) and Soil Survey Geographic (SSURGO) databases, may be the best alternatives. Although it has been noted that SWAT models using the STATSGO and SSURGO data may give different simulation results for water, sediment, and agricultural chemical yields, information is scarce on the effects of using these two databases in predicting streamflows that are predominantly generated from melting snow in spring. The objective of this study was to assess the effects of using STATSGO versus SSURGO as an input for the SWAT model's simulation of the streamflows in the upper 45 percent of the Elm River watershed in eastern North Dakota. Designating the model as SWAT‐STATSGO when the STATSGO data were used and SWAT‐SSURGO when the SSURGO data were used, SWAT‐STATSGO and SWAT‐SSURGO were separately calibrated and validated using the observed daily streamflows. The results indicated that SWAT‐SSURGO provided an overall better prediction of the discharges than SWAT‐STATSGO, although both did a good and comparable job of predicting the high streamflows. However, SWAT‐STATSGO predicted the low streamflows more accurately and had a slightly better performance during the validation period. In addition, the discrepancies between the discharges predicted by these two SWAT models tended to be larger at upstream locations than at those farther downstream within the study area.     

WATER QUALITY MODELING OF ALTERNATIVE AGRICULTURAL SCENARIOS IN THE U.S. CORN BELT1

ABSTRACT: Simulated water quality resulting from three alternative future land‐use scenarios for two agricultural watersheds in central Iowa was compared to water quality under current and historic land use/land cover to explore both the potential water quality impact of perpetuating current trends and potential benefits of major changes in agricultural practices in the U.S. Corn Belt. The Soil Water Assessment Tool (SWAT) was applied to evaluate the effect of management practices on surface water discharge and annual loads of sediment and nitrate in these watersheds. The agricultural practices comprising Scenario 1, which assumes perpetuation of current trends (conversion to conservation tillage, increase in farm size and land in production, use of currently‐employed Best Management Practices (BMPs)) result in simulated increased export of nitrate and decreased export of sediment relative to the present. However, simulations indicate that the substantial changes in agricultural practices envisioned in Scenarios 2 and 3 (conversion to conservation tillage, strip intercropping, rotational grazing, conservation set‐asides and greatly extended use of best management practices (BMPs) such as riparian buffers, engineered wetlands, grassed waterways, filter strips and field borders) could potentially reduce current loadings of sediment by 37 to 67 percent and nutrients by 54 to 75 percent. Results from the study indicate that major improvements in water quality in these agricultural watersheds could be achieved if such environmentally‐targeted agricultural practices were employed. Traditional approaches to water quality improvement through application of traditional BMPs will result in little or no change in nutrient export and minor decreases in sediment export from Corn Belt watersheds.     

Water Supply Planning Considering Uncertainties in Future Water Demand and Climate: A Case Study in an Illinois Watershed

Ensuring an adequate, reliable, clean, and affordable water supply for citizens and industries requires informed, long-range water supply planning, which is critically important for water security. A balance between water supply and demand must be considered for a long-term plan. However, water demand projections are often highly uncertain. Climate change could impact the hydrologic processes, and consequently, threaten water supply. Thus, understanding the uncertainties in future water demand and climate is critical for developing a sound water supply plan. In Illinois, regional water supply planning attempts to explore the impacts of future water demand and climate on water supply using scenario analyses and hydrologic modeling. This study is aimed at developing a water supply planning framework that considers both future water demand and climate change impacts. This framework is based on the Soil and Water Assessment Tool to simulate the watershed hydrology and conduct scenario analyses that consider the uncertainties in both future water demand and climate as well as their impacts on water supply. The framework was applied to water supply planning efforts in the Kankakee River watershed. The Kankakee River watershed model was calibrated and validated to observed streamflow records at four long-term United States Geological Survey streamflow gages. Because of the many model parameters involved, the calibration process was automated and was followed by a manual refinement, resulting in good model performance. Long-range water demand projections were prepared by the Illinois State Water Survey. Six future water demand scenarios were established based on a suite of assumptions. Climate scenarios were obtained from the Coupled Model Intercomparison Projection Phase 5 datasets. Three representative concentration pathways (RCPs), RCP2.6, RCP4.5, and RCP8.5, are used in the study. The scenario simulation results demonstrated that climate change appears to have a greater impact on water availability in the study area than water demand. The framework developed in this study can also be used to explore the impacts of uncertainties of water demand and climate on water supply and can be extended to other regions and watersheds.     

SWAT模型在茫溪河流域非点源污染研究中的应用

本研究采用美国农业部农业研究所开发的SWAT模型(Soil and Water Assessment Tool)对茫溪河流域非点源污染进行了模拟研究,构建了适用于茫溪河流域的非点源污染模型,对流域的非点源污染进行了模拟和分析。模拟结果显示:茫溪河流域污染特征以非点源污染为主;东茫溪河水系的泥沙、氮、磷等负荷均大于西茫溪河水系;单位泥沙负荷最大的区域为流域东北部、东部和东南部一带的深丘地区,是茫溪河流域的主要泥沙输出区;氮、磷负荷最大的区域为茫溪河中下游一带的平坝、浅丘地区,是茫溪河流域的主要氮、磷输出区;研究区内泥沙、营养物质的输出具有很强的时间规律,有机氮、有机磷的输出在雨季(6月~9月)会出现高峰,因此雨季是流域内非点源污染源输出的重点时段。