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Monitoring reservoir storage using remote sensing and large language models.

Ioannis N Daliakopoulos¹

¹Department of Agriculture, Hellenic Mediterranean University, Heraklion, 71410, Greece; Institute of Energy, Environment & Climate Change, Hellenic Mediterranean University, Heraklion, 71410, Greece.

Journal of Environmental Management

|March 26, 2026

Summary

This summary is machine-generated.

This study validates synthetic-aperture radar (SAR) reservoir monitoring using large language models (LLMs) to extract storage data from online media. This approach provides reliable water storage records for data-scarce regions.

Keywords:

Aposelemis Data fusion Google earth engine Large language models Sentinel-1 Synthetic-aperture radar

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Area of Science:

Hydrology and Remote Sensing
Artificial Intelligence in Environmental Monitoring

Background:

Surface-water reservoir monitoring is crucial but often hampered by limited gauging data.
Existing remote sensing (RS) methods require validation against reliable storage records, which are frequently unavailable.

Purpose of the Study:

To develop and validate a novel framework for reservoir storage estimation using synthetic-aperture radar (SAR) and large language models (LLMs).
To overcome limitations in data scarcity for reservoir monitoring in regions with sparse or non-public gauging stations.

Main Methods:

Processing Sentinel-1 SAR imagery in Google Earth Engine to delineate water extent.
Extracting quantitative reservoir storage data and dates from online media using a Custom Search Engine (CSE) and LLM (GPT-4o-mini), followed by human curation.
Translating mapped water area to storage volume using a design area-storage relationship and validating SAR parameters (T, r) against curated LLM-derived storage time series.

Main Results:

Achieved high performance with Kling-Gupta Efficiency (KGE) of 0.96-0.98 and Root Mean Square Error (RMSE) of 1.5-2.0 hm³ for a 10-year storage time series reconstruction.
Successfully reconstructed the storage of Aposelemis Dam, a critical water supply in Crete, using 82 curated date-storage pairs.
Demonstrated limited bias in residual analysis, though sensitivity to SAR parameter selection and reference data quality was noted.

Conclusions:

Combining SAR data with LLM-assisted, human-guided validation offers a scalable solution for reservoir monitoring in data-scarce regions.
The framework provides a pathway to enhance the reliability and timeliness of reservoir storage records globally.
Further research should address uncertainties related to SAR threshold selection and the heterogeneity of online media reporting.