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, nonpoint 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.

Large Area Hydrologic Modeling and Assessment Part i: Model Development

http://abe.ufl.edu/Faculty/jjones/ABE_5646/Xtra%20files/The%20DSSAT%20Cropping%20System%20Model.pdf

The DSSAT cropping system model

An overview of APSIM, a model designed for farming systems simulation

https://www.eawag.ch/fileadmin/Domain1/Abteilungen/siam/software/swat/thur.pdf

Modelling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT

The State of Texas has initiated the development of a Total Maximum Daily Load program in the Bosque River Watershed, where point and nonpoint sources of pollution are a concern. Soil Water Assessment Tool (SWAT) was validated for flow, sediment, and nutrients in the watershed to evaluate alternative management scenarios and estimate their effects in controlling pollution. This paper discusses the calibration and validation at two locations, Hico and Valley Mills, along the North Bosque River. Calibration for flow was performed from 1960 through 1998. Sediment and nutrient calibration was done from 1993 through 1997 at Hico and from 1996 through 1997 at Valley Mills. Model validation was performed for 1998. Time series plots and statistical measures were used to verify model predictions. Predicted values generally matched well with the observed values during calibration and validation (R2≥ 0.6 and Nash-Suttcliffe Efficiency ≥ 0.5, in most instances) except for some underprediction of nitrogen during calibration at both locations and sediment and organic nutrients during validation at Valley Mills. This study showed that SWAT was able to predict flow, sediment, and nutrients successfully and can be used to study the effects of alternative management scenarios.

Validation of the SWAT Model on a Large River Basin With Point and Nonpoint Sources

PrefaceDuring the last 20 years rapid progress has been made in the simulation of agricultural processes. A number of models are now available to simulate processes such as weather, hydrology, nutrient cycling and movement, tillage, soil erosion, soil temperature, and crop growth and development. Many of these models are quite restricted in purpose, simulating only discrete processes such as denitrification, leaching of NO3, soil temperature, or the movement of water in the soil. Others integrate several of these processes. During the last few years more comprehensive agricultural simulation models have begun to appear. These models simulate a number of processes and predict their interacting effects on crop growth and yield. As more and more processes are simulated, model development and testing require the expertise of an increased number of scientific disciplines and more teamwork and organization. Modeling comprehensive agricultural systems is rapidly becoming a team effort involving scientists around the world and demanding well-integrated networks to exchange both experimental data and software.This book provides the documentation, testing, and software of CERES-Maize, a quite comprehensive model of maize (Zea mays L.) growth and development. Two versions of the model are provided. The standard version considers the independent and interacting effects of genotype, weather, and hydrology. The nitrogen version considers those factors as well as nitrogen nutrition. The development of the Crop-Environment Resource Synthesis (CERES) Maize model was coordinated by Dr. J. T. Ritchie at the Grassland, Soil and Water Research Laboratory of the United States Department of Agriculture, Agricultural Research Service (USDA-ARS). Dr. Ritchie was responsible for the conceptual development o f the model as well as many of the subroutines and much of the detailed FORTRAN code. However, he was aided by a large, informal network of experimental scientists and modelers from throughout the world. These cooperators are acknowledged in the text, but it is appropriate to point out here that their ideas, data, and constructive suggestions were indispensable.Shortly before completion and final testing of the CERES-Maize model, Dr. Ritchie left ARS for a position at Michigan State University. Final modifications and testing were completed by the editors.

CERES-Maize: a simulation model of maize growth and development

A mathematical model called EPIC (Erosion-Productivity Impact Calculator) was developed to determine the relationship between soil erosion and soil productivity throughout the U.S. EPIC continuously simulates the processes involved simultaneously and realistically, using a daily time step and readily available inputs. Since erosion can be relatively slow process, EPIC is capable of simulating hundreds of years if necessary. EPIC is generally applicable, computationally efficient, and capable of computing the effects of management changes on outputs. The model must be comprehensive to define the erosion-productivity relationship adequately. EPIC is composed of physically-based components for simulating erosion, plant growth, and related processes and economic components for assessing the cost of erosion, determining optimal management strategies, etc. The EPIC components include weather simulation, hydrology, erosion-sedimentation, nutrient cycling, plant growth, tillage, soil temperature, economics, and plant environment control. Typical results are presented for 15 of the 163 tests performed in the continental U.S. and Hawaii. These results generally indicate that EPIC is capable of simulating erosion and crop growth realistically.

A Modeling Approach to Determining the Relationship Between Erosion and Soil Productivity

EPIC: An operational model for evaluation of agricultural sustainability

A method for estimating the direct and climatic effects of rising atmospheric carbon dioxide on growth and yield of crops: Part II—Sensitivity analysis at three sites in the Midwestern USA

Paul T. Dyke

Papers

CERES-Maize: a simulation model of maize growth and development

A Modeling Approach to Determining the Relationship Between Erosion and Soil Productivity

EPIC: An operational model for evaluation of agricultural sustainability

A method for estimating the direct and climatic effects of rising atmospheric carbon dioxide on growth and yield of crops: Part II—Sensitivity analysis at three sites in the Midwestern USA

Preparing the erosion productivity impact calculator (EPIC) model to simulate crop response to climate change and the direct effects of CO2