Satellite-Based Drought Monitoring Using Drought Indices in Salt Lake Meighan, Iran

Drought poses a significant threat to semi-arid regions such as Iran's Meighan Wetland, where limited ground-based data hinders effective monitoring and water resource management. This study addresses the challenge of assessing meteorological drought in data-scarce environments by integrating satellite-derived and reanalysis datasets with ground observations. The objective is to evaluate the spatiotemporal dynamics of drought in the Meighan Wetland from 1998 to 2025 using the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) at 3-, 6-, and 12-month timescales. Monthly precipitation data from the GPM Final Run Version 7 and temperature data from the ERA5-Land product were validated against 10 synoptic stations, with six selected for detailed analysis due to their high accuracy (R > 0.9 for GPM, R² > 0.98 for ERA5-Land). Results identified major drought episodes in 2000, 2008, 2017, and 2021, with SPEI indicating greater severity due to rising evapotranspiration. Correlation between SPI and SPEI was strong (R > 0.87), particularly at longer timescales. Trend analysis using the Mann–Kendall test and Sen’s slope estimator revealed significant drying trends in Arak during late winter and summer. This study demonstrates the efficacy of combining satellite and reanalysis data for robust drought monitoring, offering critical insights for adaptive water resource planning in semi-arid regions.

Institutions

Publication Details

Journal
Journal of Asian Geography
Published
2025-09-28
DOI
https://doi.org/10.36777/jag2025.4.2.6
Primary Topic
Genomics and Phylogenetic Studies
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Satellite-Based Drought Monitoring Using Drought Indices in Salt Lake Meighan, Iran

Journal of Asian Geography
Genomics and Phylogenetic Studies
article

Satellite-Based Drought Monitoring Using Drought Indices in Salt Lake Meighan, Iran

article en

Abstract

Drought poses a significant threat to semi-arid regions such as Iran's Meighan Wetland, where limited ground-based data hinders effective monitoring and water resource management. This study addresses the challenge of assessing meteorological drought in data-scarce environments by integrating satellite-derived and reanalysis datasets with ground observations. The objective is to evaluate the spatiotemporal dynamics of drought in the Meighan Wetland from 1998 to 2025 using the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) at 3-, 6-, and 12-month timescales. Monthly precipitation data from the GPM Final Run Version 7 and temperature data from the ERA5-Land product were validated against 10 synoptic stations, with six selected for detailed analysis due to their high accuracy (R > 0.9 for GPM, R² > 0.98 for ERA5-Land). Results identified major drought episodes in 2000, 2008, 2017, and 2021, with SPEI indicating greater severity due to rising evapotranspiration. Correlation between SPI and SPEI was strong (R > 0.87), particularly at longer timescales. Trend analysis using the Mann–Kendall test and Sen’s slope estimator revealed significant drying trends in Arak during late winter and summer. This study demonstrates the efficacy of combining satellite and reanalysis data for robust drought monitoring, offering critical insights for adaptive water resource planning in semi-arid regions.

Journal of Asian GeographyVol. 4(2)
Universiti Sains Malaysia (MY)
Universiti Sains Malaysia
Clean water and sanitation
Openalex Percentile: Top 49%
Genomics and Phylogenetic Studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.