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The link between soil water content (SWC) and evapotranspiration (ET) is of great importance in ecohydrology, agroecosystem management, and land-atmosphere interaction of earth-system analysis. There is still a need to understand the key processes linking SWC and ET in different vegetated ecosystems, especially at larger scales. Therefore, in this work, we used wavelet coherence analysis to explore the long-term relationship between SWC and ET among the predominant land use types (cropland, evergreen needleleaf forest, mixed forest, open shrubland, wooden tundra, grassland, and mixed tundra) in Europe during the last four decades (1980-2020). To this end, first a principal component analysis was performed among the SWC and ET data from GLDAS, GLEAM, and ERA5-land, and the first component was then used for further analyses when the target variable was in demand. Using the first component, then, we averaged SWC and ET data over the pixels covered by each land use type. Then, for each land-use types, we averaged the data daily for each decade to account for a representative decadal year of daily data. Then, wavelet coherence analysis was conducted between those averaged data of SWC and ET for each land-use type. The results showed a negative correlation between ET and SWC for all land use types, with ET lagging behind SWC, with an average phase shift value of 134 days for grassland (the minimum) and 168 days for mixed tundra (the maximum). Converting the phase shift values to a time lag [lag = n/2 - phase shift for the case phase shift < -n/4 where n represents the period (1/frequency) of the signals] shows (Fig. 1) that ET controls SWC with a lag of 15 days in mixed tundra (the minimum) and 48 days in grassland (the maximum). Moreover, we applied Mann-Kendall trend analysis test and found that the lag between SWC and ET decreases in mixed and wooden tundras with a slope value of -2.4 days/year, while it increases in cropland and grassland with a slope value of 1.6 days/year. Although there is a significant downward trend in evergreen needleleaf forests (with a slope value of -0.3 days/year) and an increasing trend in mixed forests and open shrublands (with a slope value of 0.4 days/year), the lower slope values in these land use types indicate that the change is slower compared to grasslands, croplands, and tundras. Figure 1- Temporal evolution of the lag between soil water content and evapotranspiration in the annual cycle in different land use types in Europe

Soil moisture remote sensing data and hydrological modeling can be combined through data assimilation to provide a comprehensive representation of hydrological states and fluxes. However, uncertainties in modeling and measurements can limit the accuracy of the data assimilation. Therefore, conducting a comprehensive analysis of remotely sensed, modeled, and in-situ soil moisture is crucial to quantitatively assess both systematic and random errors.. Four remotely sensed soil moisture products will be evaluated: the Soil Moisture and Ocean Salinity (SMOS)-L3, the Soil Moisture Active Passive (SMAP)-L3 , the ESA CCI soil moisture, and the Advanced Microwave Scanning Radiometer-2 (AMSR-2). Those are compared to simulations by the Terrestrial System Modeling Platform (TSMP), a scale-consistent, highly modular, massively parallel, fully integrated soil-vegetation-atmosphere modeling system. In-situ reference measurements are obtained by the International Soil Moisture Network (ISMN). Different strategies for assimilation are evaluated: no pre-processing, bias removal in the observations, Savitzky-Golay filtering to remove random noise, range scaling, and cumulative density function (CDF) matching procedure. A comprehensive statistical analysis will be conducted to evaluate the performance of the remotely sensed soil moisture products, including the calculation of key metrics such as the root mean square deviation (RMSD), unbiased RMSD, bias, Pearson correlation coefficient, and Spearman's rank correlation coefficient (Spearman's rho). The analysis will be performed for four sites of TERENO network for dry, wet, and normal years. These resulting reanalysis will offer a potent tool for expanding our knowledge of soil moisture dynamics and assisting in the creation of more efficient land and water management techniques., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

Geodetic measurement techniques provide valuable information for constraining Earth system models towards reality. Assimilating satellite gravity observations measured by GRACE/-FO into hydrological models helps to improve simulations of modeled water storages and fluxes, to overcome deficits resulting from simplifications or processes that are not considered in the model, and to disaggregate GRACE/-FO observations temporally and spatially. However, the best way to assimilate those observations is still a matter of research, often depending on the application. Especially an adequate data assimilation strategy needs to be developed in order to capture extreme events such as heatwaves if the simulations should be of use for European scale water monitoring. Within the Collaborative Research Centre CRC1502, we want to develop a coupled multi-scale, multi-source data assimilation system.In this study, we asses the impact of GRACE/-FO data assimilation over Europe on two different hydrological models: the high-resolution land-surface model CLM3.5 (Community Land Model) at a 12 km and the global conceptual model WGHM (WaterGAP Global Hydrology Model) at a 50 km grid scale. For both models, data assimilation experiments are performed with consistent settings regarding observations and assimilation algorithms. We evaluate simulations of both models by comparing against independent observations such as discharge from river gauges and satellite derived soil moisture. By setting the focus of our analysis on heatwaves, we are able to investigate how these are captured by the models both with and without data assimilation. Finally, we conclude on the consequences of our study for future data assimilation experiments using satellite gravimetry., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

A P-band SAR moisture estimation method is introduced for complex soil permittivity and penetration depth estimation using fully polarimetric P-band SAR signals. This method combines eigen- and model-based decomposition techniques for separation of the total backscattering signal into three scattering components (soil, dihedral, and volume). The incorporation of a soil scattering model allows for the first time the estimation of complex soil permittivity and permittivity-based penetration depth. The proposed method needs no prior assumptions on land cover characteristics and is applicable to a variety of vegetation types. The technique is demonstrated for airborne P-band SAR measurements acquired during the AirMOSS campaign (2012–2015). The estimated complex permittivity agrees well with climate and soil conditions at different monitoring sites. Based on frequency and permittivity, P-band penetration depths vary from 5 cm to 35 cm. This value range is in accordance with previous studies in the literature. Comparison of the results is challenging due to the sparsity of vertical soil in situ sampling. It was found that the disagreement between in situ measurements and SAR-based estimates originates from the discrepancy between the in situ measuring depth of the top-soil layer (0–5 cm) and the median penetration depth of the P-band waves (24.5–27 cm).

Remotely-sensed Evapotranspiration (ET) estimates can effectively contribute to agricultural water stress detection. Fully operational, high temporal, and moderate spatial resolution ET products derived from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor onboard the Meteosat Second Generation (MSG) satellites make it a suitable candidate for water stress monitoring. However, dedicated efforts are still required to develop simple workflows, preferably executable on cloud-based platforms, to evaluate the potentials, exploit the information content, and demonstrate the capability of ‎SEVIRI ET-products for water stress monitoring at larger scales. In this study, an extensive assessment of daily actual and reference ET (SEVIRI-ETa and SEVIRI-‎ET0) products were conducted against in situ measurements collected at 54 Eddy Covariance (EC) sites across Europe ‎distributed in various terrestrial ecosystems (i.e., forest, grassland, cropland, and peatland) between 2011-2018. The evaluated SEVIRI-ET products were then utilized mainly for two purposes: i) providing inputs to run a proposed water stress detection workflow, based on monthly evaporative stress index (ESI) anomaly, and implemented in cloud-based Virtual Earth Laboratory (VLab) platform to monitor one decade (2011 to 2020) of spatio-temporal water stress variations for entire Europe, and ii) investigating the mean terrestrial ecosystems response to water stress. The direct comparison of in situ ET with their corresponding SEVIRI-ET products resulted in a fair agreement in various ecosystems, albeit with expected inter-site variability. At the monthly time step, the mean R2 of 0.78, 0.76, 0.88, 0.85 and RMSE of 0.51, 0.60, 0.41, 0.59 mm day-1 were observed between SEVIRI-ETa and in situ ETa for forest, cropland, peatland and grassland, respectively. Further, mean R2 of 0.95, 0.98, 0.96, 0.97 and RMSE of 0.48, 0.36, 0.31, 0.35 mm day-1 were obtained between SEVIRI-ET0 and in situ ET0 for these land cover types. The SEVIRI-ESI-based monthly water stress workflow implemented on the online VLab platform provides spatio-temporal variations of water stress in Europe for the last decade (i.e., 2011 – 2020) that can be further utilized in scientific research and terrestrial applications. The analysis of various ecosystems' responses to water stress revealed that general water stress effects on vegetated ecosystems are “visible” in the SEVIRI-ESI-based water stress values and anomalies. The results from this study highlight the value, support the potentials, and unlock the full capacity of SEVIRI-ET products and the VLab platform for water stress detection at various terrestrial ecosystems at larger domains.