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Selecting best mapping strategies for storm runoff modeling in a mountainous semi‐arid area
Author(s) -
SanchezMoreno Juan Francisco,
Jetten Victor,
Mannaerts Chris M.,
Pina Tavares Jacques
Publication year - 2013
Publication title -
earth surface processes and landforms
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.294
H-Index - 127
eISSN - 1096-9837
pISSN - 0197-9337
DOI - 10.1002/esp.3501
Subject(s) - surface runoff , hydrograph , environmental science , hydrology (agriculture) , soil map , storm , erosion , kriging , arid , watershed , equifinality , drainage basin , geostatistics , soil water , spatial variability , soil science , geology , meteorology , mathematics , geography , cartography , statistics , ecology , paleontology , geotechnical engineering , machine learning , computer science , biology
ABSTRACT Accurate runoff and soil erosion modeling is constrained by data availability, particularly for physically based models such as OpenLISEM that are data demanding, as the processes are calculated on a cell‐by‐cell basis. The first decision when using such models is to select mapping units that best reflect the spatial variability of the soil and hydraulic properties in the catchment. In environments with limited data, available maps are usually generic, with large units that may lump together the values of the soil properties, affecting the spatial patterns of the predictions and output values in the outlet. Conversely, the output results may be equally acceptable, following the principle of equifinality. To studyhow the mapping method selected affects the model outputs, four types of input maps with different degrees of complexity were created: average values allocated to general soil map units (ASG1), average values allocated to detailed map units (ASG2), values interpolated by ordinary kriging (OK) and interpolated by kriging with external drift (KED). The study area was Ribeira Seca, a 90 km 2 catchment located in Santiago Island, Cape Verde (West Africa), a semi‐arid country subject to scarce but extreme rainfall during the short tropical summer monsoon. To evaluate the influence of rainfall on runoff and erosion, two storm events with different intensity and duration were considered. OK and KED inputs produced similar results, with the latter being closer to the observed hydrographs. The highest soil losses were obtained with KED (43 ton ha − 1 for the strongest event). To improve the results of soil loss predictions, higher accurate spatial information on the processes is needed; however, spatial information of input soil properties alone is not enough in complex landscapes. The results demonstrate the importance of selecting the appropriate mapping strategy to obtain reliable runoff and erosion estimates. Copyright © 2013 John Wiley & Sons, Ltd.