1. bookVolume 68 (2020): Issue 2 (June 2020)
Journal Details
License
Format
Journal
eISSN
1338-4333
First Published
28 Mar 2009
Publication timeframe
4 times per year
Languages
English
access type Open Access

Controls on event runoff coefficients and recession coefficients for different runoff generation mechanisms identified by three regression methods

Published Online: 26 May 2020
Volume & Issue: Volume 68 (2020) - Issue 2 (June 2020)
Page range: 155 - 169
Received: 09 May 2019
Accepted: 30 Jan 2020
Journal Details
License
Format
Journal
eISSN
1338-4333
First Published
28 Mar 2009
Publication timeframe
4 times per year
Languages
English
Abstract

The event runoff coefficient (Rc) and the recession coefficient (tc) are of theoretical importance for understanding catchment response and of practical importance in hydrological design. We analyse 57 event periods in the period 2013 to 2015 in the 66 ha Austrian Hydrological Open Air Laboratory (HOAL), where the seven subcatchments are stratified by runoff generation types into wetlands, tile drainage and natural drainage. Three machine learning algorithms (Random forest (RF), Gradient Boost Decision Tree (GBDT) and Support vector machine (SVM)) are used to estimate Rc and tc from 22 event based explanatory variables representing precipitation, soil moisture, groundwater level and season. The model performance of the SVM algorithm in estimating Rc and tc is generally higher than that of the other two methods, measured by the coefficient of determination R2, and the performance for Rc is higher than that for tc. The relative importance of the explanatory variables for the predictions, assessed by a heatmap, suggests that Rc of the tile drainage systems is more strongly controlled by the weather conditions than by the catchment state, while the opposite is true for natural drainage systems. Overall, model performance strongly depends on the runoff generation type.

Keywords

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