Setting up a hydrological model of Alberta: Data discrimination analyses prior to calibration. (December 2015)

• Record Type: Journal Article
• Authors: Faramarzi, Monireh; Srinivasan, Raghavan; Iravani, Majid; Bladon, Kevin D.; Abbaspour, Karim C.; Zehnder, Alexander J.B.; Goss, Greg G.
• Is Part Of: Environmental modelling & software. Volume 74(2015:Dec.)
• Page Start: 48
• Page End: 65
• Publication Date: 2015-12
• Subjects: SWAT -- Watershed modeling -- Climate data -- Hydrological processes -- Geo-spatial data; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2015.09.006
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Failure to setup a large-scale hydrological model correctly may not allow proper calibration and uncertainty analyses, leading to inaccurate model prediction. To build a model with accurate accounting of hydrological processes, a data discrimination procedure was applied in this study. The framework uses a hydrological model of Alberta built with the Soil and Water Assessment Tool (SWAT) program. The model was used to quantify the causes and extents of biases in predictions due to different types of input data. Data types represented different sources of errors, including input data (e.g., climate), conceptual model (e.g., potholes, glaciers), and control structure (e.g., reservoirs, dams). The results showed that accounting for these measures leads to a better physical accounting of hydrological processes, significantly improving the overall model performance. The procedure used in this study helps to avoid unnecessary and arbitrary adjustment of parameters to compensate for the errors in the model structure. Highlights: The Soil and Water Assessment Tool was used to setup a hydrological model of Alberta. A data discrimination procedure was developed to test the accuracy of the model. Various data representing diverse hydro-climatic, geo-spatial conditions were tested. An accurate representation of the processes will avoid improper model calibration.