Exploring the hydrological robustness of model‐parameter values with alpha shapes. Issue 10 (16th October 2013) - Guerrero, José‐Luis; Westerberg, Ida K.; Halldin, Sven; Lundin, Lars‐Christer; Xu, Chong‐Yu
<x xml:space="preserve">Abstract</x> [1] Estimation of parameter values in hydrological models has gradually moved from subjective, trial‐and‐error methods into objective estimation methods. Translation of nature's complexity to bit operations is an uncertain process as a result of data errors, epistemic gaps, computational deficiencies, and other limitations, and relies on calibration to fit model output to observed data. The robustness of the calibrated parameter values to these types of uncertainties is therefore an important concern. In this study, we investigated how the hydrological robustness of the model‐parameter values varied within the geometric structure of the behavioral (well‐performing) parameter space with a depth function based on α shapes and an in‐depth posterior performance analysis of the simulations in relation to the observed discharge uncertainty. The α shape depth is a nonconvex measure that may provide an accurate and tight delimitation of the geometric structure of the behavioral space for both unimodal and multimodal parameter‐value distributions. WASMOD, a parsimonious rainfall‐runoff model, was applied to six Honduran and one UK catchment, with differing data quality and hydrological characteristics. Model evaluation was done with two performance measures, the Nash‐Sutcliffe efficiency and one based on flow‐duration curves. Deep parameter vectors were in general found to be more hydrologically robust than shallow ones in the analyses we performed; model‐performance values increased with depth, deviations to the observed data for the high‐flow aspects of the hydrograph generally decreased with increasing depth, deep parameter vectors generally transferred in time with maintained high performance values, and the model had a low sensitivity to small changes in the parameter values. The tight delimitation of the behavioral space provided by the α shapes depth function showed a potential to improve the efficiency of calibration techniques that require further exploration. For computational reasons only a three‐parameter model could be used, which limited the applicability of this depth measure and the conclusions drawn in this paper, especially concerning hydrological robustness at low flows.