Evapotranspiration (ET) and biomass data are critical inputs for the economic models used to forecast the impacts of water management interventions. In water-scarce basins where ground-based data is limited, these forecasts rely heavily on remote sensing (RS) products. This study conducts a comparative analysis to quantify how the choice of RS input data influences the ex-ante performance assessment of water pricing policy simulations for Lebanon’s upper Litani River Basin driven by two distinct RS datasets: FAO WaPOR V3 and a hybrid single source surface energy balance (HSEB) model coupled with the Global Yield and Evaporation Mapper in Earth Engine (GYMEE). Results indicate that while biomass estimates correlate strongly, WaPOR V3 consistently estimates higher yields (up to 66%). Conversely, HSEB estimates significantly higher Actual ET than WaPOR V3 (34% to 66%), reversing trends observed in previous WaPOR versions. When integrated into a microeconomic ensemble, these biophysical discrepancies lead to fundamentally divergent policy performance forecasts. Models informed by HSEB predict a highly elastic response to pricing, suggesting significant water savings and a shift to rainfed crops. In contrast, WaPOR V3 inputs drive a nearly inelastic response, forecasting minimal water savings and higher initial farmer profits. Consequently, maximum tariff revenue estimates differ by 9% solely based on the RS input chosen. These findings demonstrate that policy performance assessments are highly sensitive to the underlying water use data. The study highlights that in the absence of ground validation, relying on a single RS product can lead to unquantified uncertainty in policy design, underscoring the need for sensitivity analyzes in hydro-economic modeling.
Jaafar et al. (Wed,) studied this question.