TY - JOUR

T1 - Non-parametric direct mapping of rainfall-runoff relationships : an alternative approach to data analysis and modelling.

AU - Iorgulescu, I.

AU - Beven, Keith J.

PY - 2004

Y1 - 2004

N2 - We present a new approach for the analysis and modeling of catchment rainfall-runoff relationships that uses as predictor variables input history summary variables only. The latter are defined as linear combinations of inputs at a given number of previous time steps. This transforms the dynamic identification problem into a static one. As the identification algorithm we use regression trees, which act as a nonlinear nonparametric model. The original algorithm is adapted to account for serial correlation in variables. The new method is applied to two subcatchments of the U.S. Department of Agriculture Forest Service Andrews Experimental Forest Watershed (Oregon, United States). Simple and interpretable tree models explain more than 80% of the initial deviance of the observations in both calibration and validation. This suggests that the selected variables have a good predictive power and that further modeling attempts using them are warranted. The models show a distinct pattern of the selected explanatory variables. Applications of the method include data quality control, comparative analysis, assessment of hydrological change, and multicriterion evaluation of parametric hydrological models.

AB - We present a new approach for the analysis and modeling of catchment rainfall-runoff relationships that uses as predictor variables input history summary variables only. The latter are defined as linear combinations of inputs at a given number of previous time steps. This transforms the dynamic identification problem into a static one. As the identification algorithm we use regression trees, which act as a nonlinear nonparametric model. The original algorithm is adapted to account for serial correlation in variables. The new method is applied to two subcatchments of the U.S. Department of Agriculture Forest Service Andrews Experimental Forest Watershed (Oregon, United States). Simple and interpretable tree models explain more than 80% of the initial deviance of the observations in both calibration and validation. This suggests that the selected variables have a good predictive power and that further modeling attempts using them are warranted. The models show a distinct pattern of the selected explanatory variables. Applications of the method include data quality control, comparative analysis, assessment of hydrological change, and multicriterion evaluation of parametric hydrological models.

U2 - 10.1029/2004WR003094

DO - 10.1029/2004WR003094

M3 - Journal article

VL - 40

SP - W08403

JO - Water Resources Research

JF - Water Resources Research

SN - 0043-1397

IS - 8

ER -