How to improve the representation of hydrological processes in SWAT for a lowland catchment – temporal analysis of parameter sensitivity and model performance

Abstract

Model diagnostic analyses help to improve the understanding of hydrological processes and their representation in hydrological models. A detailed temporal analysis detects periods of poor model performance and model components with potential for model improvements, which cannot be found by analysing the whole discharge time series. In this study, we aim to improve the understanding of hydrological processes by investigating the temporal dynamics of parameter sensitivity and of model performance for the Soil and Water Assessment Tool model applied to the Treene lowland catchment in Northern Germany. The temporal analysis shows that the parameter sensitivity varies temporally with high sensitivity for three groundwater parameters (groundwater time delay, baseflow recession constant and aquifer fraction coefficient) and one evaporation parameter (soil evaporation compensation factor). Whereas the soil evaporation compensation factor dominates in baseflow and resaturation periods, groundwater time delay, baseflow recession constant and aquifer fraction coefficient are dominant in the peak and recession phases. The temporal analysis of model performance identifies three clusters with different model performances, which can be related to different phases of the hydrograph. The lowest performance, when comparing six performance measures, is detected for the baseflow cluster. A spatially distributed analysis for six hydrological stations within the Treene catchment shows similar results for all stations. The linkage of periods with poor model performance to the dominant model components in these phases and with the related hydrological processes shows that the groundwater module has the highest potential for improvement. This temporal diagnostic analysis enhances the understanding of the Soil and Water Assessment Tool model and of the dominant hydrological processes in the lowland catchment. Copyright © 2013 John Wiley & Sons, Ltd.

Regional analysis of parameter sensitivity for simulation of streamflow and hydrological fingerprints

“…This high sensitivity of an evapotranspiration parameter during intermediate-flow conditions coincides with the findings of former studies (e.g. Guse et al, 2014Guse et al, , 2016b.…”

“…Guse et al (2016b) argued that the study of temporal variations in sensitivity is essential to learn about the relation between dominant parameters and governing processes under changing hydrological conditions. The characterisation of temporal dynamics of parameter sensitivity (TEDPAS) has been accomplished in diverse ways (Cloke et al, 2008;Cibin et al, 2010;Herman et al, 2013;Sanadhya et al, 2013;Guse et al, 2014;Pfannerstill et al, 2015;Pianosi and Wagener, 2016). The choice of the temporal resolution is an important factor which clearly influences the way parameters are identified and how inferences on related processes are made (Tang et al, 2007;Massmann and Holzmann, 2012;O'Loughlin et al, 2013).…”

“…If non-sensitive parameters are detected, an indication of model structural deficits (Kirchner, 2006;Gupta et al, 2012), or a lack of the adequate model response target data might be given. Sensitivity analysis is, not just recently, considered as a helpful diagnostic tool to identify structural and performance deficits of hydrological models (McCuen, 1973;Sieber and Uhlenbrook, 2005;Yilmaz et al, 2008;Kavetski and Clark, 2010;Guse et al, 2014;Pfannerstill et al, 2015). showed that a combined analysis of the temporally varying parameter dominance (sensitivity analysis) and model performance (error analysis) can effectively detect structural inadequacies of model components for a specific landscape.…”

“…In recent decades, the method has been used and evaluated in a variety of fields such as hydrogeology (Fontaine et al, 1992), atmospheric sciences (Rodríguez-Camino and Avissar, 1998), geologic nuclear waste disposal modelling (Lu and Mohanty, 2001), food safety risk assessment (Frey and Patil, 2002), or ecologic forestry (Song et al, 2013). A number of studies treat the application of FAST in hydrological modelling Sanadhya et al, 2013;Guse et al, 2014;Pfannerstill et al, 2015;Guse et al, 2016a, b).…”

Sensitivity Analysis of Environmental Models: A Systematic Review with Practical Workflow

“…Sensitivity indices can be computed at different temporal resolutions, therefore obtaining their temporal evolution over the simulation horizon (Wagener and Harvey, 1997;Wagener et al, 2003;Cloke et al, 2008;Reusser and Zehe, 2011;Kelleher et al, 2013;Guse et al, 2014). A similar approach can be applied to the aggregation of spatial patterns into a single output function, whose resolution can be varied in order to capture the space-variability of sensitivities across the model domain (Tang et al, 2007a;van Werkhoven et al, 2008).…”