Assessing long-Term hydrological dynamics and water quality using Google Earth Engine: A case study of Ilgln Lake (1985-2024)

Abstract

Monitoring inland water areas is crucial for ecosystem health and water resources management, particularly under impacts of global climate change. Recent advancements in cloud-based platforms like Google Earth Engine (GEE) enable efficient, scalable remote sensing analyses and democratize access to a wide range of data sources. This study leverages the GEE Python API and free and open-source Python libraries (e.g., geemap, scipy, pymannkendall, pingouin) to present a scalable workflow for assessing hydrological and water quality dynamics in shallow lakes. The methodology is demonstrated through a 40-year (1985–2024) case study of Ilgln Lake in Central Anatolia, Trkiye. Based on Landsat 5, 7 and 8 satellite imagery annual water areas, chlorophyll content and turbidity extracted with spectral indices. The climate variables (mean temperature and total precipitation) were extracted from ERA5 (ECMWF Reanalysis Fifth Generation) dataset. The non-parametric Mann-Kendall and Theil Sen’s method was used to investigate trends. The relationship between climate factors and water area/water quality were assessed using Pearson correlation, partial correlation analysis and multiple linear regression. Results revealed Ilgln Lake’s water area significantly declined, and chlorophyll content significantly increased. All code and workflow are publicly available as Jupyter Notebook on GitHub under the open-source MIT license ( https://github.com/earth-obs/lake-gee-hydrology-water-quality). Copyright: