Development of operational RUSLE soil erosion model in the JRC IMPACT toolbox: assessment and consolidation in Kenya

The report presents the development of an operational Revised Universal Soil Loss Equation (RUSLE) model into the JRC IMPACT Toolbox to support soil erosion modelling activities in Africa. The report first explains the background of the RUSLE model, its parameters and the criteria used to select and...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Simonetti, Dario, Desclée, Baudouin, Clerici, Marco
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:The report presents the development of an operational Revised Universal Soil Loss Equation (RUSLE) model into the JRC IMPACT Toolbox to support soil erosion modelling activities in Africa. The report first explains the background of the RUSLE model, its parameters and the criteria used to select and estimate them. The integration of the RUSLE soil erosion model in IMPACT was challenging given the impressive scientific literature on this topic and the variety of proposed models for the different components. However, based on several tests and cross-analyses, the most reliable formula for each factor (R, K, LS, C and P) have been implemented and assessed. A dedicated module has been implemented to exploit the latest recent global datasets with consolidated parameters and to obtain straightforward and reproducible estimates. Most reliable workflows were selected using directly available EO-based datasets included in the JRC eStation for Climate and Environment services (SRTM, CHIRPS, ESA CCI-LC and ISRIC- AfSIS). A preliminary assessment is performed by comparing its results with reference datasets over Kenya. The limitations and lessons learned are provided as well as some recommendations on the usage. The continuous integration of newly available datasets into the eStation as well as the feedback received from the users, will contribute to consolidate and enrich the proposed RUSLE module.
ISSN:1831-9424