May 2025

    Occupied for the results of science case 2.

Occupied for the results of science case 1.

The Po river basin is characterized by considerable variability of the landscape and climate and, more importantly, by a strong human impact on the water cycle. Currently, we have only large-scale and rough estimates of the total water use in the Po basin, which may be representative of other basins in the world. By using the high-resolution model simulations and through their integration with high-resolution EO products the purpose of this science case is to answer the key question: How do LSMs and water balance improve when using EO information on irrigation? To account for irrigation, a high-resolution regional dataset for the Po was used, developed as result of the ESA Irrigation+ project. It employs a soil moisture (SM) based inversion approach1 from EO data to produce an irrigation dataset with a 1km resolution and a weekly aggregation from January 2016 to December 2021. Using this irrigation dataset added to EMO1 as precipitation input, four (4) LSM/HMs models: TETIS, mesoscale Hydrologic Model (mHM)5, PCRaster Global Water Balance (PCR-GLOBWB) and Community Land Model (CLM), were run at two spatial resolutions: 5 km and 1 km. The water balance components and models’ performances were compared across three modelling experiments: The first WP5 experiments 20 and 21 at 5km and 1km resolution respectively, used only EMO1 precipitation as input and models were calibrated against discharge measurements. This setup is referred to in this document as SC40. The second experiment, named SC41, uses the irrigation data added to EMO1 as rainfall input without model calibration. And the third experiment, SC42, corresponds to the calibration of experiment SC41 against a naturalized discharge dataset, estimated as the sum of observed flow series and irrigation water abstractions10 at several stations on the Po river. The results indicate that all the variables considered, evapotranspiration, discharge, surface soil moisture and total water storage are sensitive to the inclusion of irrigation. In experiment SC41, changes are concentrated in the irrigated areas, whereas in SC42, the effects are distributed over the whole basin. Focusing on the water balance, the inclusion of irrigation in SC41 leads to an increase in evapotranspiration compared to baseline at both spatial resolutions. In contrast, SC42 shows a decrease in evapotranspiration, possibly due to an overestimation in the baseline. After applying the post-process removal of abstractions, all models in SC41 show a decrease in discharge, except for PCR-GLOBWB. These inconsistencies are partly due to the lack of calibration, which was addressed in the SC42 experiment. Results from SC42 demonstrate an overall improvement in the representation of basin-scale fluxes and storage after calibration. Performance also improves on the SC42, as shown by higher Kling-Gupta Efficiency (KGE) values at both daily and monthly timescales. These results suggest that the incorporation of EO-based irrigation products can improve hydrological simulations at both fine (1 km) and coarse (5 km) resolution over the Po basin. Such improvements may have implications for other basins affected by irrigation practices, although further research is needed to better understand the differences between model results.