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 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.

How can expert knowledge increase the realism of conceptual hydrological models? A case study in the Swiss Pre-Alps

“…The number of output classes of the 15 process map by Gharari et al (2011) differs from that of the process maps used in Antonetti et al (2017) for the identification of plausible parameter ranges. However, by comparing the landscape classes and runoff types on two catchments on the Swiss Plateau using similarity measures, Antonetti et al (2016) found out that the most similar pairs were wetland-RT1, hillslope-RT3, and plateau-RT5. The same initial ranges of these runoff types were therefore assigned to the corresponding landscape class accordingly.…”

“…Scherrer, 1997). The third mapping approach we used is based on the experimentalist approach introduced by Schmocker-Fackel et al (2007) and Margreth (2010), which has already been used 10 in, for instance, Antonetti et al (2016) and Antonetti et al (2017). Basically, the approach consists of the following steps.…”

“…15 Both approaches have strengths and weaknesses when implementing expert knowledge in process-based hydrological modelling. Bottom-up mapping approaches are often considered to require much data (Hümann and Müller, 2013;Müller et al, 2009), whereas top-down classification approaches are considered too coarse to detect the spatial distribution of processes with enough accuracy (Antonetti et al, 2016). Top-down models and parameterisations may be too simplistic and, therefore, require calibration (e.g.…”

Identifying Dominant Runoff Processes at a Regional Scale – A GIS - Based Approach

“…DRP classifications may be manual or automatic. While manual approaches are based on extensive field investigations and interpretation and upscaling of the results based on expert knowledge, however automatic methods on the other hand generally rely on GIS and on algorithms based on simplifications of expert knowledge (Antonetti et al, 2016). Automatic approaches differ in the type of data requirement.…”

“…Automatic approaches differ in the type of data requirement. Some rely solely on topographic information (Gharari et al, 2011) while others employ all available information (Schmocker-Fackel et al, 2007) The result of both manual and automated approaches is a DRP-map which represent the spatial distribution of the hydrological behaviour of the soil during prolonged rainfall events (Müller et al, 2009 (Antonetti et al, 2016), where the numbers from 1 to 3 represent the delay in their reaction to rainfall, with 1 representing an almost immediate reaction, 2 a slightly delayed one, and 3 a strong delayed one (Müller et al, 2009;Scherrer and Naef, 2003;Schmocker-Fackel et al, 2007).…”