Conceptual rainfall–runoff models are a valuable tool for predictions in ungauged catchments. However, most of them rely on calibration to determine parameter values. Improving the representation of runoff processes in models is an attractive alternative to calibration. Such an approach requires a straightforward, a priori parameter allocation procedure applicable on a wide range of spatial scales. However, such a procedure has not been developed yet. In this paper, we introduce a process‐based runoff generation module (RGM‐PRO) as a spin‐off of the traditional runoff generation module of the PREVAH hydrological modelling system. RGM‐PRO is able to exploit information from maps of runoff types, which are developed on the basis of field investigations and expert knowledge. It is grid based, and within each grid cell, the process heterogeneity is considered to avoid information loss due to grid resolution. The new module is event based, and initial conditions are assimilated and downscaled from continuous simulations of PREVAH, which are also available for real‐time applications. Four parameter allocation strategies were developed, on the basis of the results of sprinkling experiments on 60‐m2 hillslope plots at several grassland locations in Switzerland, and were tested on five catchments on the Swiss Plateau and Prealps. For the same catchments, simulation results obtained with the best parameter allocation strategy were compared with those obtained with different configurations of the traditional runoff generation module of PREVAH, which was also applied as an event‐based module here. These configurations include a version that avoids calibration, one that transfers calibrated parameters, and one that uses regionalised parameter values. RGM‐PRO simulated heavy events in a more realistic way than the uncalibrated traditional runoff generation module of PREVAH, and, in some instances, it even exceeded the performance of the calibrated traditional one. The use of information on the spatial distribution of runoff types additionally proved to be valuable as a regionalisation technique and showed advantages over the other regionalisation approaches, also in terms of robustness and transferability.

In order to predict streamflow accurately during extended dry periods, we need to understand the spatial variability of low flows and the extent to which it is affected by the spatial organization and drainage of catchment subsurface storage areas. This is especially true in Alpine catchments with widely varying topography, lithology, sediment deposits, and soil properties. Field measurements in the Poschiavino catchment in southern Switzerland, during a winter recession period without recharge, provided a unique opportunity to demonstrate the connections between subsurface storage areas, low flows, and their variability. We measured discharge in four nested sub‐catchments during seven field campaigns in the winter of 2013–2014. We analysed stream water electrical conductivity (EC) and water chemistry to identify the areas contributing to low‐flow discharge and estimated their contributions. Sediment cover type and thickness were mapped using a recently developed tool for geomorphology‐based storage classification of mountainous terrain, to determine the physical properties of the subsurface storage areas contributing to low‐flow discharge. Recession analyses combined with water chemistry data allowed the detection of different drainage timescales and the estimation of storage potential of the unconsolidated (Quaternary) deposits. We found substantial spatial variation in storage depletion between the sub‐catchments, ranging from 54 mm to 200 mm for the four‐month monitoring period. Variability in low‐flow contributions from different catchments and different recession behavior could be related to the differences in the estimated storage potential. For some point sources, we could quantify the contributing area and thus quantify low flows at the hillslope scale. Overall, the low‐flow variability is mostly related to the fraction of precipitation that recharges subsurface storage areas and to the properties influencing their drainage. To capture these processes, we suggest low‐flow geomorphological mapping approaches that consider not only morphometric (shape of the landscape) and geologic (properties of the bedrock) controls but also the water storage potential of debris cover and weathered rock.

Abstract. The identification of landscapes with similar hydrological behaviour is useful for runoff and flood 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 complexities of automatic DRP-mapping approaches affect 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 PREVAH hydrological model, 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. Furthermore, we argue not to use expert knowledge for only model building and constraining, but also in the phase of landscape classification.

A variety of analytical techniques have been used for water determination. These include drying methods and titration methods. In comparison of drying, titration is rapid and can be validated. This article focuses on water determination by Karl Fischer titration.

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.