On the gap between crop and land surface models: comparing irrigation and other land surface estimates from AquaCrop and Noah-MP over the Po Valley

Abstract

Land surface and crop models both simulate irrigation, but they differ in their approaches, primarily because they were originally developed for distinct purposes and scales. Through an example case study in a highly irrigated region, this research helps to better understand the gap between these models and the complexity of irrigation modeling. More specifically, irrigation was estimated over the Po Valley (Italy) at a 1 km2 spatial resolution using (i) a crop model, AquaCrop, and (ii) a land surface model, Noah-MP. Both models were run with sprinkler irrigation using a similar setup within NASA's Land Information System, i.e. forced with the same meteorology and constrained by the same soil texture and generic crop parameterization. Irrigation estimates were evaluated at the pixel and basin scale, using in situ reference data. In addition, surface soil moisture (SSM), vegetation, and evapotranspiration (ET) estimates were compared with satellite retrievals. Noah-MP has on average higher annual irrigation rates (434 mmyr-1) than AquaCrop (268 mmyr-1), mainly because Noah-MP simulates more irrigation water losses (not consumed by transpiration) via runoff, interception, and soil evaporative losses, whereas AquaCrop only accounts for soil evaporative losses. When adding representative application water losses to irrigation estimates from AquaCrop, and conveyance water losses to the estimates from both models, the irrigation estimates from both models fall within reported ranges of 500-600 mmyr-1. For the field-based evaluation, Noah-MP presents large irrigation events (>100 mm per event) and less interannual variability than AquaCrop. Two-week averaged SSM estimates from both models agree well with downscaled estimates from the Soil Moisture Active Passive (SMAP) mission, with spatially averaged unbiased root mean square differences of 0.05 and 0.04 m3 m-3 for AquaCrop and Noah-MP, respectively. Both models show limitations in terms of vegetation and ET modeling, mainly due to simplistic vegetation modules and suboptimal parameterization in both models. The results highlight the complexity of irrigation modeling due to its anthropogenic nature, and also show the need for better observations to validate and guide model estimates: reference irrigation data are sparse and satellite retrievals under irrigated conditions are quite uncertain.