Estimating daily potential evapotranspiration using GNSS-based precipitable water vapor

Area of Science:

• Hydrology
• Meteorology
• Remote Sensing

Background:

• Potential evapotranspiration (PET) is vital for hydrological and energy balance studies.
• The standard Penman-Monteith (PM) model requires extensive meteorological data, often unavailable in regions like Thailand.
• Limited weather station data hinders accurate PET estimation.

Purpose of the Study:

• To develop a Revised Potential Evapotranspiration (RPET) model for daily PET estimation.
• To utilize Global Navigation Satellite System-derived Precipitable Water Vapor (GNSS-PWV) and temperature data as inputs.
• To provide a viable alternative for PET estimation where traditional data is scarce.

Main Methods:

• Developed the RPET model using multiple linear regression analysis.
• Validated the RPET model against the Penman-Monteith (PM) model.
• Compared RPET performance with Global Land Evaporation Amsterdam Model (GLEAM) and ERA5-Land reanalysis data.

Main Results:

• The RPET model demonstrated a strong correlation with the PM model (r=0.85, RMSE=0.97 mm day⁻¹).
• RPET showed superior performance compared to GLEAM and ERA5-Land products.
• The model effectively estimates PET using only GNSS-PWV and temperature data.

Conclusions:

• The RPET model offers a reliable method for daily PET estimation with minimal input data.
• This approach is highly suitable for data-scarce regions, supporting drought assessment and water resource management.
• The integration of GNSS-PWV data enhances hydrological modeling capabilities.