Drought and Environmental Transformations of Lake Urmia: An Integrated Analysis Using Machine Learning and GIS
DOI:
https://doi.org/10.14738/aivp.125.17718Keywords:
Urmia Lake, Watershed, Drought, Climate changes, Remote Sensing, Agriculture.Abstract
In the late 1990s, Lake Urmia, located in northwestern Iran, was the largest saltwater lake in the Middle East. However, it has experienced severe shrinkage over the years, culminating in its division into two separate sections by 2008, with long-term consequences still uncertain. This study examines the changes in Lake Urmia from 2010 to 2020, focusing on various drought indicators such as Temperature Condition Index (TCI), Normalized Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), Palmer Drought Severity Index (PDSI), and Standardized Precipitation Index (SPI). We monitored evolving drought patterns in the lake's watershed and applied machine learning techniques to classify agricultural lands within the basin, assessing their transformations over time. Additionally, we analyzed the distribution of the UV Aerosol Index to understand dust storm occurrences caused by the exposed lakebed due to prolonged droughts.
Our study presents statistical findings from modeling the interactions among these indicators, aiming to deepen our understanding of the drought impacts on Lake Urmia and its surrounding area. The research highlights the urgent need for measures to preserve the region’s ecological balance. Given Lake Urmia’s significance to neighboring countries and its historical and ecological value, international cooperation is essential. We employed Geographic Information Systems (GIS) and Google Earth Engine to enhance the clarity of our findings, providing a comprehensive view of the environmental changes. Our analysis of key indicators—VCI, PDSI, TCI, and SPI—reveals significant relationships that offer insights into the interplay between vegetation health and climatic conditions, which are crucial for effective resource management.
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