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

Machine learning approaches are increasingly used to extract patterns and insights from the ever-increasing stream of geospatial data, but current approaches may not be optimal when system behaviour is dominated by spatial or temporal context. Here, rather than amending classical machine learning, we argue that these contextual cues should be used as part of deep learning (an approach that is able to extract spatio-temporal features automatically) to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales, for example. The next step will be a hybrid modelling approach, coupling physical process models with the versatility of data-driven machine learning.

/pdf/deep-learning-and-process-understanding-for-data-driven-3so9cx99my.pdf

Deep learning and process understanding for data-driven Earth system science

This is a review of worldwide land degradation problems. Four themes are emphasized: delineating and estimating the magnitude of soil erosion, quantifying erosion and sedimentation impacts on land productivity, establishing quantitative values for erosion-causing parameters, and implementing global and regional soil and water conservation programs. Papers deal with both developing and developed countries and illustrate how erosion control techniques used in developed countries can or cannot be applied in developing countries.

https://gendocs.ru/docs/29/28948/conv_1/file1.pdf

Soil Erosion and Conservation

http://www.hydro.tuwien.ac.at/fileadmin/mediapool-hydro/Publikationen/bloeschl/2004_Merz_JH.pdf

Regionalisation of catchment model parameters

17 18 Lin Qiu 19 Professor, Institute of environmental & municipal engineering, North China Institute of Water 20 Conservancy and Hydroelectric power, ZhengZhou, 450011, P.R.Ch 21 22 Abstract. Developing a hydrological forecasting model based on past records is crucial to 23

J.E. Nash

Papers

River flow forecasting through conceptual models part III - The Ray catchment at Grendon Underwood