Evaluation of different tools for agricultural water assessment at different spatial scales

The purpose of this study is to evaluate different regional tools developed to support irrigation water management, at different spatial scales, to assess their reliability and for identifying possible improvements for a concrete use to support farmers and irrigation consortia. In the first section, a comprehensive sensitivity analysis to quantify the robustness and possible improvements of two agro-hydrological models (CRITERIA-1D and Aquacrop) is conducted at field scale. In the second section, a remote sensing crop classification is evaluated at district-scale, using farmer-reported information as a ground truth classification. In the third section, the two crops data are integrated in two modelling frameworks with different aims: CRITERIA-1D integrated with forecasting weather data and remote sensing classification; Irriframe integrated with observed weather data and farmers crop information. Measured irrigation withdrawals are used to evaluate the two modelling tools. The sensitivity analysis shows the non-representativeness of regional datasets for the specific field application and, on average, models prove to be mostly sensitive to the shallow groundwater level, suggesting a denser piezometers network to better estimate irrigation water needs. The remote sensing crop classification presents a good agreement with land use information, although the identification of several potential irrigated areas not declared by the farmers. A fair correspondence between the estimated and measured seasonal volumes is found, except for the trends, mainly due to the non-consideration by the models of specific agronomical practices. Overall, field-scale models application suggests more specific model settings and calibration, with focus on the capillary rise process simulated by the two models. On the contrary, the seasonal application allows irrigation consortia to meet legal requirements, even exploiting innovative technologies to forecast water use. More user-friendly modelling frameworks and greater collaboration between research and water actors should be also considered to make these tools more usable.