Output

    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.

Occupied for the results of science case 5.

Although reservoir operations have strong impacts on local to regional surface water hydrology, many land-surface and hydrological models simply ignore reservoir operations altogether. For those land-surface and hydrological models that do include reservoir operations, operations often rely on generalized rules that may not be suitable for all reservoirs. Therefore, the aim of science case 3 is to investigate the added benefit of EO products for simulating reservoir operations. To investigate the added benefit of EO product, we compared EO-based reservoir storage estimates of GloLakes with discharge observations up and downstream of reservoirs in the Tugela river basin in South Africa, the primary region of interest. GloLakes provides valuable information on reservoir storage dynamics, which allows for estimating potential reservoir impacts on discharge. However, our results show that the reservoirs in the Tugela river basin have an insubstantial impact on the surface hydrology. No substantial discharge alternations were identified from the discharge observations, and the variability in reservoir capacity and storage was relatively small compared to the annual discharge. Our science case continued to the Italian Po river basin, the secondary region of interest. Unfortunately, no EO-based reservoir storage estimates are available in this region, as the spatial coverage of GloLakes is limited and does not cover the region’s major reservoir. Therefore, we relied on hydrological simulations, both with and without reservoir operations, of surface hydrology to estimate the impact of reservoir operation in the Po river basin. Our results indicate that larger reservoirs in the Po river basin have an insubstantial impact on discharge, whereas smaller reservoirs can strongly affect discharge locally. Due to the limited impacts of reservoirs in the Tugela basin and the lack of EO-based observations in the Po, our science case could not conclusively investigate the added benefit of EO products for simulating reservoir operations. Nevertheless, the potential of EO-based reservoir storage estimates are substantial. Not only will such EO-based products allow for correcting hydrological simulations without reservoirs, but they would also allow better approximation of reservoir operation rules, tailored to individual reservoirs. Therefore, further investment in EO-based reservoir storage estimates, especially in highly managed river basins, is needed to improve the state-of-the-art in hydrological modelling. Figure 1: Simulated multi-year monthly median discharge (m3 s-1) with and without reservoirs for the Miorina reservoir. Colors indicate the hydrological models whereas linetypes indicates simulations with and without reservoirs. The shaded area indicates the 25th to 75th percentile range. Although high-resolution satellite, hydrological, and land surface models are advancing, accurately understanding the water cycle in catchments remains challenging due to uncertainties in data accuracy, spatial variability, and the complex interactions between surface and subsurface processes. This science case leverages multiple state-of-the-art satellite datasets and hydrological/land surface models. We evaluated the water cycle closure for the Po River Catchment using 84 different combinations of precipitation, actual evapotranspiration (ET), and runoff. This assessment incorporated Earth-observed precipitation (CNR-IRPI combined product) and evapotranspiration (MODIS Terra), selected based on the round-robin evaluation conducted in WP4, along with outputs from six models—mHM, PCR-GLOBWB, CLM, TETIS, Wflow, and GEOframe—within WP5-Experiment 2. For the Rhine River Catchment, the evaluation included 40 combinations, based on the outputs of four hydrological models: mHM, PCR-GLOBWB, CLM, and Wflow_sbm. Similarly, for the Tugela River Catchment, 40 combinations were evaluated using outputs from the models: mHM, PCR-GLOBWB, TETIS, and Wflow_sbm. Fig. 1 shows the scheme of different combination over this study. The heatmaps in Fig. 2 present the primary results, showing annual water cycle residuals for various combinations of precipitation, ET, and runoff across the different catchments. Lighter shades of red or blue indicate smaller residuals, meaning a better closure of the water budget in each catchment.

Work Package 0 Management and Synthesis Activities (Status: In progress): WP0 is in charge of the coordination of the project and the communication of between project partners and with ESA delegates.  Work Package 1 Consolidation of a Reference EO dataset (Status: Done): Water cycle variables studied during the project and the hosting partner ( sources of figure extraction)    The first step of the project is to catalogue, retrieve and prepare a set of reference Earth Observations (EO) dataset. This dataset gathers remotely-sensed products monitoring essential variables from the continental water cycle at high-resolution that are soil moisture, evapotranspiration, precipitation, snow, surface water, ground and total water storage, irrigation. This Work Package (WP) is a group effort as several partners from the consortium implemented on this dataset, each partner being leader of a given variable from the above list while CSGROUP managed the overall WP. The purpose was then to organize a collection of EO products and also extract and prepare them for each of the study regions and the study period. This EO reference dataset will then be studied and compared through the Round Robin study in WP4. Work Package 2 Consolidation of a Reference LSM dataset (Status: Done): 4dHydro working package 2 aims to deliver and benchmark a (tier 1) hydrological reference dataset. This dataset comprises of existing LSM/HM simulations from previous studies and covers a wide range of LSM/HM models and simulations. Despite this diversity, simulations in the reference dataset are uniformly formatted following our storage protocol (see the working package 2 User Manual under 4dhydro.eu/outputs). This uniformity allows for seamless comparisons across simulations in our benchmark. The benchmark comprehensively validates the dataset’s simulations using observations from discharge gauges, evapotranspiration towers, soil moisture stations, and satellite-based total water storage anomaly measurements. comparison of simulated and observed discharge Work Package 3  Community Open Science Data & Code sharing Tool (Status: In progress): WP3 is developing an Open Science Data and Code sharing Tool offering easy access to the EO and non EO datasets, code and model results. This team is responsible for managing the Open Science Catalogue. Work Package 4 Round Robin Experiment (Status: Done): Revision date: March 14, 2024 WP4 has the goal of cross-comparing state-of-art, high resolution Earth Observation datasets in the domains of: soil moisture, precipitation, evapotranspiration, and snow depth and extent. The output of the activity feeds into the subsequent EO-model integration project phase by defining strengths and weaknesses of the observational products with respect to the most comprehensive available reference data. Advanced statistical methods are employed to provide uncertainty information and objective skill criteria for the candidate datasets, as well as defining their spatial scale representativeness (figure below). Overview of the multi-scale analysis of the SMAP NSIDC 1km soil moisture product (Lakshmi & Fang, 2023) over the various domains, against the 0.1° resolution ERA5 Land (E5L, Muñoz-Sabater et al., 2021), the 0.0125° mesoscale Hydrologic Model (mHM, Samaniego et al., 2010), and the ISMN point measurements (Dorigo et al., 2021). The coloured points show the temporal correlation against ISMN   Dorigo, W., Himmelbauer, I., Aberer, D., Schremmer, L., Petrakovic, I., Zappa, L., Preimesberger, W., Xaver, A., Annor, F., Ardö, J., Baldocchi, D., Bitelli, M., Blöschl, G., Bogena, H., Brocca, L., Calvet, J.-C., Camarero, J. J., Capello, G., Choi, M., Cosh, M. C., van de Giesen, N., Hajdu, I., Ikonen, J., Jensen, K. H., Kanniah, K. D., de Kat, I., Kirchengast, G., Kumar Rai, P., Kyrouac, J., Larson, K., Liu, S., Loew, A., Moghaddam, M., Martínez Fernández, J., Mattar Bader, C., Morbidelli, R., Musial, J. P., Osenga, E., Palecki, M. A., Pellarin, T., Petropoulos, G. P., Pfeil, I., Powers, J., Robock, A., Rüdiger, C., Rummel, U., Strobel, M., Su, Z., Sullivan, R., Tagesson, T., Varlagin, A., Vreugdenhil, M., Walker, J., Wen, J., Wenger, F., Wigneron, J. P., Woods, M., Yang, K., Zeng, Y., Zhang, X., Zreda, M., Dietrich, S., Gruber, A., van Oevelen, P., Wagner, W., Scipal, K., Drusch, M., and Sabia, R.: The International Soil Moisture Network: serving Earth system science for over a decade, Hydrol. Earth Syst. Sci., 25, 5749–5804, https://doi.org/10.5194/hess-25-5749-2021, 2021. Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021.  Lakshmi, V. and B. Fang. 2023. SMAP-Derived 1-km Downscaled Surface Soil Moisture Product, Version 1. [Indicate subset used]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/U8QZ2AXE5V7B. Accessed: 09-09-2023.  Samaniego, L., Kumar, R. & Attinger, S. Multiscale parameter regionalization of a grid-based hydrologic model at the mesoscale. Water Resour. Res. 46, https://doi.org/10.1029/2008WR007327 (2010). Revision date: May 5, 2025  The deliverable has been finalized, and the outcomes of this work package have been submitted as a manuscript for publication. Work Package 5 Benchmarking and Data-assimilation experiment (Status: In progress): Revision date: March 14, 2024 4DHydro working package 5 builds on earlier simulations analysed in WP2, and it further aims at adjusting the source code of the LSMs and HMs to be able to run multiple high-resolution simulations. Compared with earlier WP2 simulations, we will employ unified meteorological forcing data sets here. The hydrological simulations will be performed at different spatial resolutions (i.e. 1km and 5km, as shown here) and evaluated over multivariate characteristics with hierarchically increasing complexity. Different spatial scales and EO scenarios with and without in-situ observations will be tested across three domains of interest and ultimately evaluated in the European domain at 1km. The discharge simulation using mHM model at high spatial resolution (0.015625 degree) The discharge simulation using mHM model at low spatial resolution (0.125 degree). Revision date: September 28, 2024 The fifth work package (WP5) of the 4DHydro project focuses on conducting benchmarking exercises to evaluate model performance before and after integrating high-resolution Earth Observation (EO) data. The ultimate goal is to generate improved terrestrial Essential Climate Variables (tECVs) datasets for specific study areas at a