Exploring the hydrological robustness of model-parameter values with alpha shapes

Abstract

[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 shapes and an in-depth posterior performance analysis of the simulations in relation to the observed discharge uncertainty. The 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 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.

Simulating Water Resource Availability under Data Scarcity—A Case Study for the Ferghana Valley (Central Asia)

“…Our straightforward approach to hydrological modeling agrees well with suggestions by [27,28] and takes into account a number of performance criteria (Nash-Sutcliffe efficiency for high and log-transformed flow, and difference in annual water balance), and provides a meaningful representation of hydrological processes, the transformation of behavioral parameter sets in time (validation), and a sensitivity analysis of the model's parameters. We selected the four river catchments with the most complete data and tested hydrological model performance given these aspects.…”

The Plumbing of Land Surface Models: Is Poor Performance a Result of Methodology or Data Quality?

“…While there are advanced methods of multicriteria calibration available (e.g., Guerrero et al 2013;Gupta et al 1999), as well as viable alternatives to performance-based calibration (Schymanski et al 2007), it would seem sensible to also focus on model parsimony, especially in components that are largely underconstrained. These results might also reflect the compensating effect of calibration against streamflow or gridded evapotranspiration products, where model structural and spatial property assumptions form part of the calibration process.…”

Nonparametric catchment clustering using the data depth function

“…It has been used in the field of regional flood frequency analysis (Chebana and Ouarda 2008, Wazneh et al 2013a, Wazneh et al 2013b (Chebana and Ouarda 2011b), regionalization of hydrological model parameters (Bardossy and Singh 2011) and robust estimation of hydrological model parameters (Bárdossy and Singh 2008), defining predictive uncertainty of a model , and in selection of critical events for model calibration (Singh and Bárdossy 2012). For more detailed information about the data depth function and its uses in field of water resources, please refer to Chebana and Ouarda (2011a), Chebana and Ouarda (2011c), Guerrero et al (2013), Krauße and Cullmann (2009) and Singh and Bárdossy (2012).…”