The Soil and Water Assessment Tool (SWAT) model is a continuation of nearly 30 years of modeling efforts conducted by the USDA Agricultural Research Service (ARS). SWAT has gained international acceptance as a robust interdisciplinary watershed modeling tool as evidenced by international SWAT conferences, hundreds of SWAT-related papers presented at numerous other scientific meetings, and dozens of articles published in peer-reviewed journals. The model has also been adopted as part of the U.S. Environmental Protection Agency (USEPA) Better Assessment Science Integrating Point and Nonpoint Sources (BASINS) software package and is being used by many U.S. federal and state agencies, including the USDA within the Conservation Effects Assessment Project (CEAP). At present, over 250 peer-reviewed published articles have been identified that report SWAT applications, reviews of SWAT components, or other research that includes SWAT. Many of these peer-reviewed articles are summarized here according to relevant application categories such as streamflow calibration and related hydrologic analyses, climate change impacts on hydrology, pollutant load assessments, comparisons with other models, and sensitivity analyses and calibration techniques. Strengths and weaknesses of the model are presented, and recommended research needs for SWAT are also provided.

/pdf/the-soil-and-water-assessment-tool-historical-development-4e4d97tdfq.pdf

The Soil and Water Assessment Tool: Historical Development, Applications, and Future Research Directions

The Soil and Water Assessment Tool (SWAT) model is a continuation of nearly 30 years of modeling efforts conducted by the U.S. Department of Agriculture (USDA), Agricultural Research Service. SWAT has gained international acceptance as a robust interdisciplinary watershed modeling tool, as evidenced by international SWAT conferences, hundreds of SWAT-related papers presented at numerous scientific meetings, and dozens of articles published in peer-reviewed journals. The model has also been adopted as part of the U.S. Environmental Protection Agency's BASINS (Better Assessment Science Integrating Point & Nonpoint Sources) software package and is being used by many U.S. federal and state agencies, including the USDA within the Conservation Effects Assessment Project. At present, over 250 peer-reviewed, published articles have been identified that report SWAT applications, reviews of SWAT components, or other research that includes SWAT. Many of these peer-reviewed articles are summarized here according to relevant application categories such as streamflow calibration and related hydrologic analyses, climate change impacts on hydrology, pollutant load assessments, comparisons with other models, and sensitivity analyses and calibration techniques. Strengths and weaknesses of the model are presented, and recommended research needs for SWAT are provided.

Soil and Water Assessment Tool: Historical Development, Applications, and Future Research Directions, The

応用水文 = Applied hydrology

Foreword 1. Introduction 2. Runoff, erosion and delivery to the coastal ocean 3. Temporal variations 4. Human impacts Appendices. Global River Database: Appendix A: North and Central America Appendix B: South America Appendix C: Europe Appendix D: Africa Appendix E: Eurasia Appendix F: Asia Appendix G: Oceania References Index.

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River Discharge to the Coastal Ocean: A Global Synthesis

This section offers suggestions related to helping a student deal with bullying in schools, as well as creating an environment where that individual can easily return to the school community. It also mentions the significance of the method 'Shared Responsibility' in dealing with the situation.

Assessing the Impact

Flood prediction is an important for the design, planning and management of water resources systems. This study presents the use of artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), multiple linear regression (MLR) and multiple nonlinear regression (MNLR) for forecasting maximum daily flow at the outlet of the Khosrow Shirin watershed, located in the Fars Province of Iran. Precipitation data from four meteorological stations were used to develop a multilayer perceptron topology model. Input vectors for simulations included the original precipitation data, an area-weighted average precipitation and antecedent flows with one- and two-day time lags. Performances of the models were evaluated with the RMSE and the R 2. The results showed that the area-weighted precipitation as an input to ANNs and MNLR and the spatially distributed precipitation input to ANFIS and MLR lead to more accurate predictions (e.g., in ANNs up to 2.0 m3 s−1 reduction in RMSE). Overall, the MNLR was shown to be superior (R 2 = 0.81 and RMSE = 0.145 m3 s−1) to ANNs, ANFIS and MLR for prediction of maximum daily flow. Furthermore, models including antecedent flow with one- and two-day time lags significantly improve flow prediction. We conclude that nonlinear regression can be applied as a simple method for predicting the maximum daily flow.

Flood flow forecasting using ANN, ANFIS and regression models

Coupling SWAT and ANN models for enhanced daily streamflow prediction

Identifying critical source areas of nonpoint source pollution with SWAT and GWLF

Land use/cover (LULC) and climate change are two main factors affecting watershed hydrology. In this paper, individual and combined impacts of LULC and climate change on hydrologic processes were analysed applying the model Soil and Water Assessment Tool in a coastal Alabama watershed in USA. Temporally and spatially downscaled Global Circulation Model outputs predict a slight increase in precipitation in the study area, which is also projected to experience substantial urban growth in the future. Changes in flow frequency and volume in the 2030s (2016-2040) compared to a baseline period (1984-2008) at daily, monthly and annual time scales were explored. A redistribution of daily streamflow is projected when either climate or LULC change was considered. High flows are predicted to increase, while low flows are expected to decrease. Combined change effect results in a more noticeable and uneven distribution of daily streamflow. Monthly average streamflow and surface runoff are projected to increase in spring and winter, but especially in fall. LULC change does not have a significant effect on monthly average streamflow, but the change affects partitioning of streamflow, causing higher surface runoff and lower baseflow. The combined effect leads to a dramatic increase in monthly average streamflow with a stronger increasing trend in surface runoff and decreasing trend in baseflow. Copyright (C) 2013 John Wiley & Sons, Ltd.

Latif Kalin

Papers

Flood flow forecasting using ANN, ANFIS and regression models

Coupling SWAT and ANN models for enhanced daily streamflow prediction

Identifying critical source areas of nonpoint source pollution with SWAT and GWLF

Individual and combined effects of land use/cover and climate change on Wolf Bay watershed streamflow in southern Alabama

Modeling effects of changing land use/cover on daily streamflow: An Artificial Neural Network and curve number based hybrid approach