Assessing the Impact of Agricultural Practices and Urban Expansion on Drought Dynamics Using a Multi-Drought Index Application Implemented in Google Earth Engine: A Case Study of the Oum Er-Rbia Watershed, Morocco-Reference-Cited by-同舟云学术

Assessing the Impact of Agricultural Practices and Urban Expansion on Drought Dynamics Using a Multi-Drought Index Application Implemented in Google Earth Engine: A Case Study of the Oum Er-Rbia Watershed, Morocco

Published:2024-09-12 Issue:18 Volume:16 Page:3398
ISSN:2072-4292
Container-title:Remote Sensing
language:en
Short-container-title:Remote Sensing

Author:

Serbouti Imane¹,Chenal Jérôme¹²^ORCID,Pradhan Biswajeet³^ORCID,Diop El Bachir¹,Azmi Rida¹^ORCID,Abdem Seyid Abdellahi Ebnou¹,Adraoui Meriem¹^ORCID,Hlal Mohammed¹^ORCID,Bounabi Mariem¹^ORCID

Affiliation:

1. Center of Urban Systems (CUS), Mohammed VI Polytechnic University (UM6P), Ben Guerir 43150, Morocco

2. Urban and Regional Planning Community (CEAT), Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland

3. Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Civil and Environmental Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia

Abstract

Drought monitoring is a critical environmental challenge, particularly in regions where irrigated agricultural intensification and urban expansion pressure water resources. This study assesses the impact of these activities on drought dynamics in Morocco’s Oum Er-Rbia (OER) watershed from 2002 to 2022, using the newly developed Watershed Integrated Multi-Drought Index (WIMDI), through Google Earth Engine (GEE). WIMDI integrates several drought indices, including SMCI, ESI, VCI, TVDI, SWI, PCI, and SVI, via a localized weighted averaging model (LOWA). Statistical validation against various drought-type indices including SPI, SDI, SEDI, and SMCI showed WIMDI’s strong correlations (r-values up to 0.805) and lower RMSE, indicating superior accuracy. Spatiotemporal validation against aggregated drought indices such as VHI, VDSI, and SDCI, along with time-series analysis, confirmed WIMDI’s robustness in capturing drought variability across the OER watershed. These results highlight WIMDI’s potential as a reliable tool for effective drought monitoring and management across diverse ecosystems and climates.

Funder

University of Technology Sydney

Publisher

MDPI AG

Link

https://www.mdpi.com/2072-4292/16/18/3398/pdf

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