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ARS Home » Midwest Area » Ames, Iowa » National Laboratory for Agriculture and The Environment » Agroecosystems Management Research » Research » Publications at this Location » Publication #304399
Title: Parameterization guidelines and considerations for hydrologic models

Author
item Malone, Robert - Rob
item YAGOW, GENE - Virginia Tech
item Baffaut, Claire
item GITAU, MARGARET - Florida A & M University
item QI, ZHIMING - McGill University - Canada
item AMATYA, DEVENDRA - Us Forest Service (FS)
item PARAJULI, PREM - Mississippi State University
item Bonta, James - Jim
item Green, Timothy

Submitted to: Transactions of the ASABE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/3/2014
Publication Date: 12/1/2015
Citation: Malone, R.W., Yagow, G., Baffaut, C., Gitau, M., Qi, Z., Amatya, D., Parajuli, P., Bonta, J.V., Green, T.R. 2015. Parameterization guidelines and considerations for hydrologic models. Transactions of the ASABE. 58(6):1681-1703.

Interpretive Summary: Parameterization is an important and difficult task that can be defined as imparting knowledge of the physical processes of a system to a model and determining a set of parameter values for a hydrologic or water quality model application. A large amount of literature has been devoted to the use and development of these models over the years; however, few articles have been devoted to developing general parameterization guidelines. Therefore, we developed the following general parameterization guidelines to assist in hydrologic model application: use site specific measured or estimated parameter values where possible; focus parameterization efforts on the most uncertain and sensitive parameters; minimize the number of optimized parameters ("optimized parameters" are parameters that are adjusted to achieve a good fit between model output and field observations such as streamflow or soil water content); constrain parameter values to within justified ranges that conform to accepted literature sources; use multi-criteria to optimize parameter values (multi-criteria refers to more than one model output or target to compare with observed field data); use “soft” data to optimize parameter values (“soft” data are estimated or qualitative knowledge from experimentalists such as estimated evapotranspiration where site specific field measurements are unavailable); and use an initial warm-up period to allow the model output such as simulated soil water content to stabilize before comparing model results with field observations. A few soil and hydrologic parameters common to many models are briefly described along with discussion of measurement and estimation methods and how sensitive the models are to a change in the parameter values. Weather and management inputs such as rainfall and tillage are discussed because they are critical field or watershed information that must be imparted to the model. Several “case studies” briefly illustrate the parameterization guidelines. This research will help model users more consistently parameterize agricultural system models, which will result in more accurate model simulations that are more representative of the field or watershed conditions.

Technical Abstract: Imparting knowledge of the physical processes of a system to a model and determining a set of parameter values for a hydrologic or water quality model application (i.e., parameterization) is an important and difficult task. An exponential increase in literature has been devoted to the use and development of these models over the years; however, few articles have been devoted to developing general parameterization guidelines. Our objective is to develop general parameterization guidelines to assist in hydrologic model application. The following guidelines were extracted from reviewing the special collection of 22 articles (Moriasi et al., 2012) along with other relevant literature: use site specific measured or estimated values where possible; focus parameterization efforts on the most uncertain and sensitive parameters; minimize the number of optimized parameters; constrain parameter values to within justified ranges; use multi-criteria to optimize parameter values; use “soft” data to optimize parameters; and use an initial warm-up period to initialize state variables. A few soil and hydrologic parameters common to many models are briefly described along with discussion of measurement and estimation methods and parameter sensitivity (curve number, manning’s n, soil bulk density and porosity, soil hydraulic conductivity, soil field capacity and wilting point, leaf area index). Weather and management inputs are discussed because they are critical hydrologic system information that must be imparted to the model. Several “case studies” briefly illustrate the parameterization guidelines.