Selecting best mapping strategies for storm runoff modeling in a mountainous semi‐arid area

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 km2 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.

Assessment and mapping the sensitive areas to desertification in an insular Sahelian mountain region Case study of the Ribeira Seca Watershed, Santiago Island, Cabo Verde

“…In general, rainfall is characterized by high intensities, reaching more than 100 mm h − 1 (Soares, 1985). Daily erosivity can reach values between 200 and 300 KJ mm m −2 h −1 for extreme precipitation (Sanchez-Moreno et al, 2014b). Due to the rugged nature of the islands, the poor rain fed practices and eroded relief, it is not easy to find deep soils.…”

Severe soil erosion during a 3-day exceptional rainfall event: combining modelling and field data for a fallow cereal field

“…The SERT model has the conceptual basis and part of the equations of the Distributed Rainfall–Runoff ( DR2 ) (López‐Vicente and Navas, ) water balance model and the Revised Morgan–Morgan–Finney (Morgan, ) and Modified Morgan–Morgan–Finney (Morgan and Duzant, ) soil erosion models. Another strength of the SERT model is the high number of earth surface processes that can be simulated (15 output maps and values) with 24 inputs (without considering data used for validation) in comparison with other similar tools, such as the open‐source and physically based OpenLISEM (Sanchez‐Moreno et al ., ) and the widely used Soil and Water Assessment Tool (Palazón and Navas, ) models that are highly data demanding. A further strength of the SERT model is that the calibration procedure is not a must to run the model, whereas internal modelling calibration is totally necessary in other models such as the KINEROS‐based modelling tools (Goodrich et al ., ) and the Water and Tillage Erosion Model and Sediment Delivery Model (Alatorre et al ., ).…”

“…() on a typhoon hydrological model in Korea, Sanchez‐Moreno et al . () on the OpenLISEM model in Cape Verde (West Africa), Rodrigues et al . () regarding the Limburg Soil Erosion Model (LISEM) model in two small watersheds in Brazil or the studies by Nunes et al .…”

Design Optimization of Biocrust-Plant Spatial Configuration for Dry Ecosystem Restoration Using Water Redistribution and Erosion Models

“…For identifying the spatial configuration of surface components most efficient in reducing water and nutrient losses and erosion, we applied the spatially distributed runoff and erosion model LISEM (Limburg Soil Erosion Model). This is one of the most widely used and accepted physically distributed models, and its principles are well described in the literature (Jetten and De Roo, 2001;Baartman et al, 2011;Sanchez-Moreno et al, 2013). Moreover, it has been already parameterized to simulate the effects of biocrust, calibrated and validated in a close area densely covered by them (see Rodríguez-Caballero et al, 2015).…”