Drought Susceptibility Mapping In Iraq Using GRACE/GRACE-FO, GLDAS, And Machine Learning Algorithms

Abstract

Drought susceptibility mapping is crucial for mitigating water scarcity and its cascading effects. This study utilizes machine learning (ML) and Gravity Recovery and Climate Experiment (GRACE) data to identify drought-prone areas in Iraq. Terrestrial water storage anomaly (TWSA) data derived from GRACE observations was utilized, in conjunction with various meteorological and topographical factors (temperature, precipitation, wind speed, potential evapotranspiration, aridity index, normalized difference vegetation index, groundwater storage, root zone moisture, elevation, and slope) sourced from diverse global datasets, primarily those comprising the Global Land Data Assimilation System (GLDAS). These combined datasets served as input for five machine learning models to identify the drought prone area of Iraq: linear discriminant analysis, classification and regression trees, support vector machine, random forest, and k-nearest neighbor. All models exhibited exceptional performance (>0.9 accuracy, >0.8 kappa values, >0.9 areas under the receiver operating characteristic curve) highlighting the effectiveness of the combined TWSA and multi-factor approach. The analysis identified five drought susceptibility zones across Iraq, ranging from very low to very high. An area of approximately 160,650 km2, primarily concentrated in the northeastern and Mesopotamian plains of central and southern Iraq, was identified as exhibiting high to very high drought susceptibility, highlighting the necessity for targeted mitigation strategies. This study pioneers the use of GRACE-derived TWSA and GLDAS data for drought susceptibility mapping in Iraq, paving the way for data-driven water management interventions.

Department(s)

Geosciences and Geological and Petroleum Engineering

Comments

King Saud University, Grant RSP 2023R425

Keywords and Phrases

Drought; GRACE data; Iraq; Machine learning

International Standard Serial Number (ISSN)

1474-7065

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Elsevier, All rights reserved.

Publication Date

01 Jun 2024

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