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ARS Home » Midwest Area » West Lafayette, Indiana » National Soil Erosion Research » Research » Publications at this Location » Publication #330415

Research Project: ASSESSING CONSERVATION EFFECTS ON WATER QUANTITY AND QUALITY AT FIELD AND WATERSHED SCALES

Location: National Soil Erosion Research

Title: Comparison of computer models for estimating hydrology and water quality in an agricultural watershed

Author
item Liu, Yaoze - Purdue University
item Li, Sisi - Purdue University
item Wallace, Carlington - Purdue University
item Chaubey, Indrajeet - Purdue University
item Flanagan, Dennis
item Theller, Lawrence - Purdue University
item Engel, Bernard - Purdue University

Submitted to: Water Resources Management
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/26/2017
Publication Date: 5/6/2017
Citation: Liu, Y., Li, S., Wallace, C.W., Chaubey, I., Flanagan, D.C., Theller, L.O., Engel, B.A. 2017. Comparison of computer models for estimating hydrology and water quality in an agricultural watershed. Water Resources Management. 31(11):3641-3665.

Interpretive Summary: A natural resources computer simulation model is a program that is designed to mimic the natural world using mathematical equations. Typically model inputs include at a minimum climate (temperatures and precipitation), topography (slope gradient and lengths), soil (soil texture, organic matter, depth, etc.), and land management (tillage, crops grown, fertilizers and pesticides applied). A number of these types of models have been created, mainly to allow evaluation of the effects of different land management practices on runoff, streamflow, sediment losses, nutrient (Nitrogen and Phosphorus) losses, and pesticide losses. In this study, 6 different models were applied to a 41.5 km2 (10,300 acre) agricultural watershed in northeastern Indiana, and their individual and combined abilities to predict runoff, streamflow, sediment losses, and nutrient losses evaluated. For this particular watershed, the PLOAD (Pollutant Load) model had the best performance in estimating total nitrogen and phosphorus losses in uncalibrated mode. When calibrated with part of the observed data, most of the other models also performed well in estimating runoff, streamflow, and nutrient losses when tested with another independent portion of the observed data. Use of a combination of multiple models also showed some promise as another approach to better simulate this type of agricultural watershed. These results impact scientists, university faculty, and students involved in research related to watershed modeling and assessment of land management practices on water quantity and quality. Also impacted are natural resource managers, action agency staff, private consulting firms, and others involved in application of natural resource models.

Technical Abstract: Various computer models, ranging from simple to complex, have been developed to simulate hydrology and water quality from field to watershed scales. However, many users are uncertain about which model to choose when estimating water quantity and quality conditions in a watershed. This study compared hydrologic/water quality models including Spreadsheet Tool for the Estimation of Pollutant Load (STEPL)-Purdue, Soil and Water Assessment Tool (SWAT), High Impact Targeting (HIT), Long-Term Hydrologic Impact Assessment (L-THIA), Pollutant Load (PLOAD), Spatially and Temporally Distributed Model for Phosphorus Management (STEM-P), Region 5, and ensemble modeling (using STEPL-Purdue, SWAT, L-THIA, PLOAD, and STEM-P). Model capabilities, inputs, and underlying methods to estimate streamflow, surface runoff, baseflow, total nitrogen (TN), total phosphorus (TP), and sediment were examined. Uncalibrated, calibrated, and validated outputs of these models and uncalibrated ensemble modeling in estimating water quantity and quality for a 41.5 km2 agricultural watershed in Northeastern Indiana were explored, and suggestions were provided on the selection and use of models. Models need to be selected carefully based on the simulation objectives, data availability, model characteristics, time constraints, and project budgets.