Lysimeter-validated gene expression programming models for evapotranspiration estimation in semi-arid Syria
Year: 2026
Type: article
Abstract: Accurate estimation of evapotranspiration is essential for irrigation scheduling and water-balance assessment, yet physically based methods often require complete meteorological inputs that are not consistently available in data-scarce regions. This study developed locally adapted, closed-form evapotranspiration equations using Gene Expression Programming (GEP) and evaluated their performance against lysimeter measurements in the central region of Syria. The dataset comprised 421 records from Al-Mukhtariyah (1996–2005) and Tizin (2004–2005) research stations, including daily maximum and minimum air temperature, relative humidity, wind speed, and sunshine duration, together with decadal (10-day) actual evapotranspiration measured by non-weighing drainage lysimeters. Three conventional equations (Blaney–Criddle, Ivanov, and Penman equations) were used as benchmarks, and three GEP models were generated in GeneXproTools V5 using combinations of the available climatic predictors. Results showed that all GEP models outperformed the conventional equations in both training and testing, with Model I providing the highest accuracy and the most stable statistics relative to lysimeter observations, including improved reproduction of peak and low evapotranspiration values. Sensitivity analysis for Model I indicated dominant contributions from mean temperature (45.5%) and sunshine duration (29.2%), followed by wind speed (13.0%) and relative humidity (12.3%). Overall, the proposed GEP equations offer accurate and interpretable alternatives for evapotranspiration estimation under limited or variable data availability, supporting practical irrigation management in arid and semi-arid settings.
Source: DYSONA – Applied Science
Institutions: Homs University, Al Wataniya Private University
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7203558347