Mapping dominant runoff processes: an evaluation of different approaches using similarity measures and synthetic runoff simulations

Abstract

Abstract. The identification of landscapes with similar hydrological behaviour is useful for runoff predictions in small ungauged catchments. An established method for landscape classification is based on the concept of dominant runoff process (DRP). The various DRP mapping approaches differ with respect to the time and data required for mapping. Manual approaches based on expert knowledge are reliable but time-consuming, whereas automatic GIS-based approaches are easier to implement but rely on simplifications which restrict their application range. To what extent these simplifications are applicable in other catchments is unclear. More information is also needed on how the different complexity of automatic DRP mapping approaches affects hydrological simulations. In this paper, three automatic approaches were used to map two catchments on the Swiss Plateau. The resulting maps were compared to reference maps obtained with manual mapping. Measures of agreement and association, a class comparison and a deviation map were derived. The automatically derived DRP-maps were used in synthetic runoff simulations with an adapted version of the hydrological model PREVAH, and simulation results compared with those from simulations using the reference maps. The DRP-maps derived with the automatic approach with highest complexity and data requirement were the most similar to the reference maps, while those derived with simplified approaches without original soil information differed significantly in terms of both extent and distribution of the DRPs. The runoff simulations derived from the simpler DRP-maps were more uncertain due to inaccuracies in the input data and their coarse resolution, but problems were also linked with the use of topography as a proxy for the storage capacity of soils. The perception of the intensity of the DRP classes also seems to vary among the different authors, and a standardised definition of DRPs is still lacking. We therefore recommend not only using expert knowledge for model building and constraining but also trying to obtain spatially distributed landscape classifications that are as realistic as possible.

Finding behavioral parameterization for a 1-D water balance model by multi-criteria evaluation

“…Together with additional knowledge of the spatial distribution of dominant runoff processes on catchment scale this will facilitate the parameterization of the hydrological model WaSiM-ETH and its subsequent optimization by extending the traditional model evaluation procedure at gaging stations with the search for a best fit of spatial patterns of ETa and runoff processes on catchment scale. A number of automatic mapping approaches for delineation of dominant runoff process exist, which can be used to constrain the uncertainty of hydrological simulations (Antonetti et al, 2016;Behrens et al, 2010). The model RoGeR (Runoff Generation Research) demonstrated its ability to quantify runoff process in high spatial and temporal resolution without the need of parameter calibration (Steinbrich et al, 2016).…”

The impacts of a changing climate on catchment water balance and forest management

“…All these investigations resulted in a classification scheme for identification and mapping dominant processes of discharge generation. These maps are helpful tools to set priorities for a forest management considering "water retention" as forest service (Antonetti, Buss, Scherrer, Margreth, & Zappa, 2015;Schobel, 2008). These maps are helpful tools to set priorities for a forest management considering "water retention" as forest service (Antonetti, Buss, Scherrer, Margreth, & Zappa, 2015;Schobel, 2008).…”

“…Maps for both catchments showing the runoff sensitivity clearly indicate the hot spots of flood generation (Schüler, 2006). These maps are helpful tools to set priorities for a forest management considering "water retention" as forest service (Antonetti, Buss, Scherrer, Margreth, & Zappa, 2015;Schobel, 2008).…”

HESS Opinions: Advocating process modeling and de-emphasizing parameter estimation

“…Other examples of limited calibration (parameter adjustment) and hydrologic reasoning for parameters estimation of physically based distributed models can be found in Feyen et al (2000) using MIKE SHE, Zehe and Blöschl (2004) for parameter adjustments of CAT-FLOW, andBahremand et al (2005, 2007), Liu et al (2003), Liu and De Smedt (2005) with the WetSpa model, and Salvadore (2015) with the WetSpa-Python model. Some recent publications regarding conceptual hydrologic models have also drawn attention to the use of expert knowledge in parameter estimation and constraining parameter calibration; see for example Antonetti et al (2015), Hrachowitz et al (2014), Gharari et al (2014), Hellebrand et al (2011) and Viviroli et al (2009). Overall, the examples mentioned above lend support to the author's conviction that by gaining some understanding about hydrologic processes, and by trying to relate the parameters to observable (or conceptual) watershed characteristics, it is possible to infer reasonable values for the parameters of a hydrological model.…”

“…As a trivial case, consider the parameter Kg m that represents the maximum active groundwater storage (in mm) and controls the amount of evaporation possible from the water table. This parameter has typically been considered to be "insensitive" (see Bahremand and De Smedt, 2008), which makes sense of course if the catchment is mountainous and in an upstream area (e.g., catchment order 2), because logic dictates that since the depth to groundwater is so deep, there will be little or no direct evaporation from the water table. In such a case we can save time by fixing this parameter to a large value and directing our attention to other aspects of the model.…”