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Controls on event runoff coefficients and recession coefficients for different runoff generation mechanisms identified by three regression methods
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Controls on event runoff coefficients and recession coefficients for different runoff generation mechanisms identified by three regression methods
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Journal Article
Controls on event runoff coefficients and recession coefficients for different runoff generation mechanisms identified by three regression methods
2020
Overview
The event runoff coefficient (
) and the recession coefficient (
) 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
and
from 22 event based explanatory variables representing precipitation, soil moisture, groundwater level and season. The model performance of the SVM algorithm in estimating
and
is generally higher than that of the other two methods, measured by the coefficient of determination
, and the performance for
is higher than that for
. The relative importance of the explanatory variables for the predictions, assessed by a heatmap, suggests that
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.
