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APPLICATION OF A GROUPED RESPONSE UNIT HYDROLOGICAL MODEL TO A NORTHERN WETLAND REGION
Author(s) -
PIETRONIRO ALAIN,
PROWSE TERRY,
HAMLIN LAURENCE,
KOUWEN NICK,
SOULIS RIC
Publication year - 1996
Publication title -
hydrological processes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.222
H-Index - 161
eISSN - 1099-1085
pISSN - 0885-6087
DOI - 10.1002/(sici)1099-1085(199610)10:10<1245::aid-hyp457>3.0.co;2-0
Subject(s) - thematic mapper , terrain , surface runoff , wetland , calibration , hydrology (agriculture) , field (mathematics) , principal component analysis , distributed element model , hydrological modelling , environmental science , thematic map , remote sensing , cartography , computer science , geology , satellite imagery , geography , artificial intelligence , statistics , ecology , climatology , mathematics , physics , geotechnical engineering , quantum mechanics , pure mathematics , biology
Applications of hydrological models to northern wetland‐dominated regions have been limited in the past to a few case studies on small basins employing ‘lumped’ models. Only recently have there been attempts to apply the grouped response unit (GRU) distributed modelling approach using terrain classifications to these same basins. This study summarizes recent efforts in applying such a model. For the purposes of implementing the GRU approach, terrain types that are hydrologically significant and characteristic to the wetland‐dominated regime were successfully discriminated using a principal component analysis and a hybrid unsupervised/supervised classification technique on Landsat–Thematic Mapper imagery. The terrain classifications were then used as input into a distributed hydrological model for calibration and validation using recorded spring runoff events. Preliminary model applications and results are described. Calibration to a historic spring runoff event yielded an r 2 value of 0.86. Model validation, however, yielded much poorer results. The problems of model applicability to this region and limitations of sparse data networks are highlighted. The need for more field research in this type of hydrological regime, and associated improvements to the model parameter set are also identified.