other · Open MIND
This repository contains two Jupyter notebooks implementing a Random Forest–based assessment of drought mitigation potential from bush encroachment control. Hydrological drought was quantified using the Standardized Runoff Index (SRI) derived from ERA5-Land runoff, and agricultural drought using the Standardized Soil Moisture Index (SSI) derived from FLDAS soil moisture (1982–2100). Climate predictors were generated from an ensemble of 10 CMIP6 GDDP models aggregated to monthly means/sums. The workflow integrates Google Earth Engine with Python for data preprocessing, index calculation, and machine-learning analysis to evaluate how changes in vegetation structure influence runoff and soil-moisture–related drought dynamics.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.5281/zenodo.18609861
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
