ALBERT

Beschreibung: A portion of globally generated surface and groundwater resources evaporates from wetlands, waterbodies and irrigated areas. This secondary evaporation of “blue” water directly affects the remaining water resources available for ecosystems and human use. At the global scale, a lack of detailed water balance studies and direct observations limits our understanding of the magnitude and spatial and temporal distribution of secondary evaporation. Here, we propose a methodology to assimilate satellite-derived information into the landscape hydrological model W3 at an unprecedented 0.05∘, or ca. 5 km resolution globally. The assimilated data are all derived from MODIS observations, including surface water extent, surface albedo, vegetation cover, leaf area index, canopy conductance and land surface temperature (LST). The information from these products is imparted on the model in a simple but efficient manner, through a combination of direct insertion of the surface water extent, an evaporation flux adjustment based on LST and parameter nudging for the other observations. The resulting water balance estimates were evaluated against river basin discharge records and the water balance of closed basins and demonstrably improved water balance estimates compared to ignoring secondary evaporation (e.g., bias improved from +38 to +2 mm yr−1). The evaporation estimates derived from assimilation were combined with global mapping of irrigation crops to derive a minimum estimate of irrigation water requirements (I0), representative of optimal irrigation efficiency. Our I0 estimates were lower than published country-level estimates of irrigation water use produced by alternative estimation methods, for reasons that are discussed. We estimate that 16 % of globally generated water resources evaporate before reaching the oceans, enhancing total terrestrial evaporation by 6.1×1012 m3 yr−1 or 8.8 %. Of this volume, 5 % is evaporated from irrigation areas, 58 % from terrestrial waterbodies and 37 % from other surfaces. Model-data assimilation at even higher spatial resolutions can achieve a further reduction in uncertainty but will require more accurate and detailed mapping of surface water dynamics and areas equipped for irrigation.