The importance of topography-controlled sub-grid process heterogeneity and semi-quantitative prior constraints in distributed hydrological models

Abstract

Abstract. Heterogeneity of landscape features like terrain, soil, and vegetation properties affects the partitioning of water and energy. However, it remains unclear to what extent an explicit representation of this heterogeneity at the sub-grid scale of distributed hydrological models can improve the hydrological consistency and the robustness of such models. In this study, hydrological process complexity arising from subgrid topography heterogeneity was incorporated into the distributed mesoscale Hydrologic Model (mHM). Seven study catchments across Europe were used to test whether (1) the incorporation of additional sub-grid variability on the basis of landscape-derived response units improves model internal dynamics, (2) the application of semi-quantitative, expertknowledge-based model constraints reduces model uncertainty, and whether (3) the combined use of sub-grid response units and model constraints improves the spatial transferability of the model. Unconstrained and constrained versions of both the original mHM and mHMtopo, which allows for topography-based sub-grid heterogeneity, were calibrated for each catchment individually following a multi-objective calibration strategy. In addition, four of the study catchments were simultaneously calibrated and their feasible parameter sets were transferred to the remaining three receiver catchments. In a post-calibration evaluation procedure the probabilities of model and transferability improvement, when accounting for sub-grid variability and/or applying expert-knowledge-based model constraints, were assessed on the basis of a set of hydrological signatures. In terms of the Euclidian distance to the optimal model, used as an overall measure of model performance with respect to the individual signatures, the model improvement achieved by introducing sub-grid heterogeneity to mHM in mHMtopo was on average 13 %. The addition of semi-quantitative constraints to mHM and mHMtopo resulted in improvements of 13 and 19 %, respectively, compared to the base case of the unconstrained mHM. Most significant improvements in signature representations were, in particular, achieved for low flow statistics. The application of prior semi-quantitative constraints further improved the partitioning between runoff and evaporative fluxes. In addition, it was shown that suitable semi-quantitative prior constraints in combination with the transfer-function-based regularization approach of mHM can be beneficial for spatial model transferability as the Euclidian distances for the signatures improved on average by 2 %. The effect of semi-quantitative prior constraints combined with topography-guided sub-grid heterogeneity on transferability showed a more variable picture of improvements and deteriorations, but most improvements were observed for low flow statistics.

Impact of model structure on flow simulation and hydrological realism: from a lumped to a semi-distributed approach

“…Such experiments have been helpful for exploring model simulation performance of lumped (e.g., Duan et al, 2006;Breuer et al, 2009), semi-distributed (e.g., Duan et al, 2006;Holländer et al, 2009) and distributed (e.g., Henderson-Sellers et al, 1993;Reed et al, 2004;Holländer et al, 2009;Smith et al, 2012;Nijzink and Savenije, 2016) models in a consistent way using the same input data. To go beyond specific analyses and provide general conclusions, multi-catchment experiments have been proposed by several authors (e.g., Perrin et al, 2001;Gupta et al, 2014) and are now used extensively.…”

HESS Opinions: The complementary merits of competing modelling philosophies in hydrology

“…Butts et al, 2004;Kollet and Maxwell, 2006;Zehe et al, 2006;Kumar et al, 2013), and thus do not necessarily describe the system at a higher spatial resolution than so-called semi-distributed models, as the applied grid cells can often be larger than sub-catchments and/or hydrological response units (e.g. Nijzink et al, 2016a).…”

“…based on hydrological response units or related concepts (Beven and Kirkby, 1979;Knudsen et al, 1986;Flügel, 1995;Winter, 2001;Seibert et al, 2003;Uhlenbrook et al, 2004, 2010Schmocker-Fackel et al, 2007Gharari et al, 2011;Zehe et al, 2014;Haghnegahdar et al, 2015), with an equilibrated balance between process heterogeneity and information/data availability and tested and evaluated against multivariate observed response dynamics, and conceptual models have been shown to be versatile enough to identify and represent the dominant hydrological processes and their heterogeneity in a catchment (e.g. Boyle et al, 2001;Fenicia et al, 2008a, b;Winsemius et al, 2008;Kumar et al, 2013;Hrachowitz et al, 2014;Nijzink et al, 2016a) within limited uncertainty.…”

“…hydrological response units), resembling simplified continuum formulations (e.g. Weiler and McDonnell, 2004;Nijzink et al, 2016a).…”

“…The red dots indicate the two endpoints along the resolutioncomplexity continuum. Models: 1: unit hydrograph (Sherman, 1932); 2: HBV (Bergström, 1992); 3: SUPERFLEX ; 4: FLEX-Topo 5: mhM (Samaniego et al, 2010); 6: mhM-topo (Nijzink et al, 2016a); 7: SWAT (Arnold et al, 1998); 8: NWS-Sacramento (Burnash, 1995); 9: GR4J (Perrin et al, 2003); 10: HYPE (Lindström et al, 2010); 11: VIC (Liang et al, 1994); 12: TOPMODEL (Beven and Kirkby, 1979); 13: CRHM; 14: TAC D (Uhlenbrook et al, 2004); 15: WASIM-ETH (Schulla and Kasper, 1998); 16: DHSVM (Wigmosta et al, 1994); 17: MIKE-SHE (Refsgaard and Storm, 1996); 18: PARFLOW ; 19: CATFLOW (Zehe et al, 2001); 20: HYDRUS-3D (Šimůnek et al, 2008); 21: CATHY (Camporese et al, 2010); 22: HydroGeoSphere (Jones et al, 2006); 23: PIHM (Qu and Duffy, 2007). and unambiguous terminology may be one of the reasons for many misunderstandings between the different model communities. We therefore think that a somewhat more rigorous model taxonomy needs to be the first step to clarify these misunderstandings and to pave the way for increased convergence of the individual modelling strategies.…”