lumpR 2.0.0: an R package facilitating landscape discretisation for hillslope-based hydrological models

Abstract

Abstract. The characteristics of a landscape pose essential factors for hydrological processes. Therefore, an adequate representation of the landscape of a catchment in hydrological models is vital. However, many of such models exist differing, amongst others, in spatial concept and discretisation. The latter constitutes an essential pre-processing step, for which many different algorithms along with numerous software implementations exist. In that context, existing solutions are often model specific, commercial, or depend on commercial back-end software, and allow only a limited or no workflow automation at all.Consequently, a new package for the scientific software and scripting environment R, called lumpR, was developed. lumpR employs an algorithm for hillslope-based landscape discretisation directed to large-scale application via a hierarchical multi-scale approach. The package addresses existing limitations as it is free and open source, easily extendible to other hydrological models, and the workflow can be fully automated. Moreover, it is user-friendly as the direct coupling to a GIS allows for immediate visual inspection and manual adjustment. Sufficient control is furthermore retained via parameter specification and the option to include expert knowledge. Conversely, completely automatic operation also allows for extensive analysis of aspects related to landscape discretisation.In a case study, the application of the package is presented. A sensitivity analysis of the most important discretisation parameters demonstrates its efficient workflow automation. Considering multiple streamflow metrics, the employed model proved reasonably robust to the discretisation parameters. However, parameters determining the sizes of subbasins and hillslopes proved to be more important than the others, including the number of representative hillslopes, the number of attributes employed for the lumping algorithm, and the number of sub-discretisations of the representative hillslopes.

glmGUI v1.0: an R-based graphical user interface and toolbox for GLM (General Lake Model) simulations

“…As lake-overflow events to the sea were extremely rare in the past century, the catchment area is considered to be a closed basin . The lake watershed is about 12 km 2 with a maximum elevation of 410 m (Pirastru and Niedda, 2013). The only significant tributary inflowing the lake is in the northeast and drains a subcatchment area of 8.1 km 2 .…”

Mesoscale Mapping of Sediment Source Hotspots for Dam Sediment Management in Data-Sparse Semi-Arid Catchments

“…Modeled values of daily sub-catchment sediment yield were obtained by using WASA-SED, a freely available deterministic, process-based, spatially semi-distributed, and time-continuous hydro-sediment logical model, specially developed for semi-arid landscapes [70][71][72][73][74][75][76]. Data pre-processing included: (i) interpolation of reanalyzed of global-gridded daily time-series of air temperature, precipitation, relative humidity, and short-wave radiation for the time period 1950-2018 [60,77] using previously described methods.…”

“…Data pre-processing included: (i) interpolation of reanalyzed of global-gridded daily time-series of air temperature, precipitation, relative humidity, and short-wave radiation for the time period 1950-2018 [60,77] using previously described methods. [78]; (ii) reclassification of land cover map [54] into nine classes; (iii) vegetation parameterization after [51,69,72,[79][80][81][82]; (iv) preparing soil map and soil parameters based on SoilGrids [58,83]; and (v) landscape discretization based on SRTM digital elevation model, vegetation and soil map [75,84]. Unit sedigraph approach was used to calculate sediments, and groundwater below soil zone and snow routine were applied [85].…”

Seasonal drought prediction for semiarid northeast Brazil: what is the added value of a process-based hydrological model?

“…The model was parametrised using the lumpR package for the statistical environment R (Pilz et al, 2017). This included the delineation of catchment and model units, assembly, calculation, and checking of parameters, and the generation of the model's input files.…”

“…Preprocessing schemes in the context of hydrological forecasting usually focus on the improvement of rainfall predictions used as main drivers for hydrological models (e.g. Kelly and Krzysztofowicz, 2000;Reggiani and Weerts, 2008;Verkade et al, 2013). This is partly already included in the downscaling scheme applied to GCM products but may as well be further extended.…”