Skip to main content
ARS Home » Midwest Area » West Lafayette, Indiana » National Soil Erosion Research Laboratory » Research » Publications at this Location » Publication #330451

Title: Development of a distributed hydrologic model to facilitate watershed management

item LI, SISI - Purdue University
item GITAU, MARGARET - Purdue University
item ENGEL, BERNARD - Purdue University
item ZHANG, LIANG - Chinese Academy Of Sciences
item DU, YUN - Chinese Academy Of Sciences
item WALLACE, CARLINGTON - Purdue University
item Flanagan, Dennis

Submitted to: Hydrological Sciences Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/14/2017
Publication Date: 7/4/2017
Citation: Li, S., Gitau, M., Engel, B.A., Zhang, L., Du, Y., Wallace, C., Flanagan, D.C. 2017. Development of a distributed hydrologic model to facilitate watershed management. Hydrological Sciences Journal. 62:11:1755-1771. doi:10.1080/02626667.2017.1351029.

Interpretive Summary: Computer simulation models are often developed and applied to natural systems, to allow estimation of hydrology (surface runoff, baseflow, etc.) and pollutant losses (sediment, nutrients, pesticides, etc.) under the climatic, soil, topographic, and land use management for a specific location. In this research a new hydrologic model was developed called DHM-WM (Distributed Hydrologic Model for Watershed Management), that is based upon earlier technologies (Curve Number (CN), modified versions of CN, TOPMODEL). The new tool also has a tile drainage component, and utilizes digital elevation model (DEM) data within a geographic information system (GIS). The new model was tested using measured data from 2006 to 2012 for a 43 square kilometer (10,700 acre) watershed located in northeastern Indiana, which is largely agricultural (corn, soybeans, wheat, pasture).The results showed that with or without the tile flow module, the DHM-WM effectively simulated the time series of streamflow at the watershed outlet. As a simple distributed hydrologic model with limited parameters needing calibration, DHM-WM is a promising tool. This research impacts scientists, university faculty members, and students involved in hydrologic and water quality modeling of farms and small watersheds. Improved tools to assess watershed runoff and stream flows allow land managers to more rapidly and confidently assess the effects of different land management systems.

Technical Abstract: Precise and cost-effective watershed management is challenging since nutrient and pollutant mobility varies greatly with source areas as well as flow pathways, which calls for detailed representation of hydrological processes. To facilitate watershed management, a simple yet spatially and temporally distributed hydrologic model (DHM-WM) was developed. The DHM-WM model was based on the Mishra-Singh modified version of the curve number method, with several modifications: (1) The spatial distribution of soil moisture across a watershed was incorporated to simulate variable source areas; (2) The travel time of surface runoff was calculated on a grid cell basis for routing to facilitate tracking non-point source pollutants; and (3) A simple subsurface or tile flow module was included for areas with tile drainage systems. The DHM-WM was applied to a small tile-drained agricultural watershed in northeastern Indiana, United States to evaluate model performance and applicability. The DHM-WM model with the tile flow module was found to be applicable and performed well in the study area, providing balanced water budget and reasonable flow partitioning. The coefficient of determination and Nash-Sutcliffe coefficient on a daily basis were 0.58 and 0.56, respectively, for the calibration period, and 0.63 and 0.62, respectively, for the validation period. Besides, DHM-WM provides detailed information about the source areas of flow components, the travel time and pathways of surface runoff on a grid basis, which is helpful for further nutrient simulation and decision-making. In addition, DHM-WM comprises a small number of parameters which makes the model easily applicable and particularly valuable where data availability is a challenge.