School of Earth, Environmental, & Marine Sciences Faculty Publications
Document Type
Article
Publication Date
4-10-2026
Abstract
Small reservoirs in semi-arid regions experience substantial evaporative losses but are rarely monitored at daily scales. A multi-reservoir machine learning (ML) framework was developed to estimate daily open-water evaporation. Empirical models (Penman, Penman-Monteith, Priestley-Taylor, Bowen Ratio Energy Budget) andabenchmark combination method (Daily Lake Evaporation Model-DLEM) were compared against ML models. Predictors combined gridded meteorology (gridMET) with reservoir attributes (surface area, average depth, maximum depth, and fetch). ML models (Random Forest-RF, Decision Tree-DT, K-Nearest Neighbor-KNN, and Support Vector Regression-SVR) were trained on four reservoirs using data from 2018 to 2025. Results from ML models were further validated using both DLEM and TexasETNet observations at Delta Lake. On multi-reservoir tests, RF and SVR perform best (R2 = 0.67; RMSE ~ 1.5 mm d− 1; NSE = 0.67). RF aligns most closely with DLEM (R2 = 0.78; RMSE = 1.22 mm d− 1), whereas SVR aligns better with TexasETNet (R2 = 0.33) and shows lowest bias among ML models. Mean annual evaporation from ML and DLEM is 1620–1698 mm yr⁻¹, which exceeds predictions from TexasETNet (1452 mm yr⁻¹) and exhibits narrower interannual variability (± 300–350 mm yr ¹ vs. ±700 mm yr⁻¹). Feature importance and SHAP analyses identified shortwave radiation, temperature (Tmax/Tmin), and ET0 as dominant drivers. The proposed transferable ML framework delivers accurate daily estimates where observations are sparse, while dual validation clarifies differences between process-based and station datasets, supporting reservoir operations and evaporation mitigation planning in under monitored, semi-arid regions.
Recommended Citation
Abdullah, Syed Muhammad Fahad, Chu-Lin Cheng, Jude Benavides, Jungseok Ho, and Rafael M. Almeida. "Applications of machine learning in enhancing evaporation estimation for small reservoirs: a case study in semi-arid South Texas." Modeling Earth Systems and Environment12, no. 3 (2026): 146. https://doi.org/10.1007/s40808-026-02773-0
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Publication Title
Modeling Earth Systems and Environment
DOI
10.1007/s40808-026-02773-0
Included in
Earth Sciences Commons, Environmental Monitoring Commons, Marine Biology Commons, Natural Resources and Conservation Commons, Sustainability Commons, Water Resource Management Commons
Comments
This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material.