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A process‐based soil erosion model ensemble to assess model uncertainty in climate‐change impact assessments
Author(s) -
Eekhout Joris P.C.,
MillaresValenzuela Agustín,
MartínezSalvador Alberto,
GarcíaLorenzo Rafael,
PérezCutillas Pedro,
ConesaGarcía Carmelo,
Vente Joris
Publication year - 2021
Publication title -
land degradation and development
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.403
H-Index - 81
eISSN - 1099-145X
pISSN - 1085-3278
DOI - 10.1002/ldr.3920
Subject(s) - environmental science , climate change , precipitation , surface runoff , climate model , erosion , wepp , hydrology (agriculture) , soil conservation , climatology , meteorology , geology , agriculture , geography , ecology , paleontology , oceanography , geotechnical engineering , archaeology , biology
The impact of climate change on future soil loss is commonly assessed with soil erosion models, which are suggested to be an important source of uncertainty. Here, we present a novel soil erosion model ensemble to assess model uncertainty in climate‐change impact assessments. The model ensemble consists of five continuous process‐based soil erosion models that run at a daily time step (i.e., DHSVM, HSPF, INCA, MMF, SHETRAN). The models were implemented in the SPHY hydrological model and simulate detachment by raindrop impact, detachment by runoff, and immediate deposition. The soil erosion model ensemble was applied in a semiarid catchment in the southeast of Spain. We applied three future climate scenarios based on global mean temperature rise (+1.5, +2 and +3°C). Data from two contrasting regional climate models were used to assess how an increase and a decrease in projected extreme precipitation affect model uncertainty. Soil loss is projected to increase (up to 95%) and decrease (up to −30%) under climate change, mostly reflecting the change in extreme precipitation. Model uncertainty is found to increase with increasing slope, extreme precipitation and runoff, which reveals some inherent differences in model assumptions among the five models. Moreover, the model uncertainty increases in all climate change scenarios, independent of the projected change in annual precipitation and extreme precipitation. This stresses the importance to consider model uncertainty through model ensembles of climate, hydrology, and soil erosion in climate‐change impact assessments.