Quantifying evaporation depth from on-farm storages using water-level observations

Abstract

Evaporation from on-farm storages represents a potentially significant yet poorly quantified component of water loss in agricultural systems. The performance of commonly used open-water evaporation models, many of which were developed for large lakes and reservoirs, on small (many <100 ha) and highly managed storages remains poorly understood due to limited observational data for validation. To address this gap, this study collates in situ water-level records from thirteen monitored storages spanning a major agricultural catchment in Australia and evaluates nine evaporation models, including Penman-based variants, area-dependent mass transfer models, and a remote-sensing-based hybrid model. The ensemble mean and ensemble median derived from the individual models are also included to assess whether ensemble estimates provide more accurate and robust evaporation estimates than individual model outputs. Water-level-derived evaporation is used as an observational benchmark across these storages, while dynamic water surface area is estimated from Sentinel-2 imagery to support model evaluation. Results show substantial differences among individual models. The ensemble approaches generally ranked among the leading methods across the monitored storages and assessment metrics (Pearson correlation, bias, and RMSE). Sensitivity test using alternative benchmark-screening configurations produced similar model rankings. Despite the influence of non-evaporative signals in water-level observations such as seepage, these data offer a valuable and previously unavailable constraint for evaluating evaporation at the storage scale. These findings establish a practical and transferable framework for quantifying evaporation depth from small water bodies. The resulting depth estimates can subsequently be combined with independently derived water-surface areas when volumetric evaporation losses are required for storage-scale or regional water accounting.