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Predicting event response in a nested catchment with generalized linear models and a distributed watershed model

  • This study focuses on the prediction of event-based runoff coefficients (an important descriptor of flood events) for nested catchments up to an area of 50?km(2) in the Eastern Ore Mountains. The four main objectives of the study are (i) the prediction of runoff coefficients with the statistical method of generalized linear models, (ii) the comparison of the results of the linear models with estimates of a distributed conceptual model, (iii) the comparison of the dynamics of observed soil moisture and simulated saturation deficit of the hydrological model and (iv) the analysis of the relationship between runoff coefficient and observed and simulated wetness. Different predictor variables were selected to describe the runoff coefficient and were differentiated into variables describing the catchment’s antecedent wetness and meteorological forcing. The best statistical model was estimated in a stepwise approach on the basis of hierarchical partitioning, an exhaustive search algorithm and model validation with jackknifing. We thenThis study focuses on the prediction of event-based runoff coefficients (an important descriptor of flood events) for nested catchments up to an area of 50?km(2) in the Eastern Ore Mountains. The four main objectives of the study are (i) the prediction of runoff coefficients with the statistical method of generalized linear models, (ii) the comparison of the results of the linear models with estimates of a distributed conceptual model, (iii) the comparison of the dynamics of observed soil moisture and simulated saturation deficit of the hydrological model and (iv) the analysis of the relationship between runoff coefficient and observed and simulated wetness. Different predictor variables were selected to describe the runoff coefficient and were differentiated into variables describing the catchment’s antecedent wetness and meteorological forcing. The best statistical model was estimated in a stepwise approach on the basis of hierarchical partitioning, an exhaustive search algorithm and model validation with jackknifing. We then applied the rainfall runoff model WaSiM ETH to predict the runoff processes for the two larger catchments. Locally measured small-scale soil moisture (acquired at a scale of four to five magnitudes smaller than the catchment) was identified as one of the key predictor variables for the estimation of the runoff coefficient with the general linear model. It was found that the relationship betweenobserved and simulated (using WaSiM ETH) wetness is strongly hysteretic. The runoff coefficients derived from the rainfall runoff simulations systematically underestimate the observed values. Copyright (C) 2012 John Wiley & Sons, Ltd.‚Ķshow moreshow less

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Metadaten
Author:T. Graeff, E. Zehe, T. Blume, T. Francke, B. Schroeder
DOI:https://doi.org/10.1002/hyp.8463
ISSN:0885-6087 (print)
ISSN:1099-1085 (online)
Parent Title (English):HYDROLOGICAL PROCESSES
Publisher:WILEY-BLACKWELL
Place of publication:HOBOKEN
Document Type:Review
Language:English
Year of first Publication:2012
Year of Completion:2012
Release Date:2017/03/26
Tag:GLM; antecedent wetness; nested catchment; runoff coefficient; soil moisture
Volume:26
Issue:24
Pagenumber:21
First Page:3749
Last Page:3769
Funder:German Ministry of Education and Research (BMBF) as part of RIMAX (Risikomanagement extremer Hochwasserereignisse); University of Potsdam; Potsdam Graduate School; Technische Universitat Munchen; OPAQUE (operational discharge and flooding predictions in head catchments) project [0330713D]