Abstract. Soil water availability is an essential prerequisite for vegetation functioning. Vegetation takes up water from varying soil depths depending on the characteristics of their rooting system and soil moisture availability across depth. The depth of vegetation water uptake is largely unknown across large spatial scales as a consequence of sparse ground measurements. At the same time, emerging satellite-derived observations of vegetation functioning, surface soil moisture and terrestrial water storage, present an opportunity to assess the depth of vegetation water uptake globally. In this study, we characterise vegetation functioning through the Near-Infrared Reflectance of Vegetation (NIRv), and compare its relation to (i) near-surface soil moisture from ESA-CCI and (ii) total water storage from GRACE at the monthly time scale during the growing season. The relationships are quantified through partial correlations to mitigate the influence of confounding factors such as energy-related variables. We find that vegetation functioning is generally more strongly related to near-surface soil moisture, particularly in semi-arid regions and areas with low tree cover. In contrast, in regions with high tree cover and in arid regions, the correlation with terrestrial water storage is comparable to or even higher than with near-surface soil moisture, indicating that trees can and do make use of their deeper rooting systems to access deeper soil moisture, similar to vegetation in arid regions. In line with this, an attribution analysis that examines the relative importance of these soil water storages for vegetation reveals that they are controlled by (i) water availability influenced by the climate and (ii) vegetation type reflecting adaptation of ecosystems to local water resources. Next to variations in space, the vegetation water uptake depth also varies in time. During dry periods, the relative importance of terrestrial water storage increases, highlighting the relevance of deeper water resources during rain-scarce periods. Overall, the synergistic exploitation of state-of-the-art satellite data products to disentangle the relevance of near-surface vs. terrestrial water storage for vegetation functioning can inform the representation of vegetation-water interactions in land surface models to support more accurate climate change projections.

Due to the remote location and the extreme climate, monitoring stations in Arctic rivers such as Lena in Siberia have been decreasing through time. Every year, after a long harsh winter, the accumulated snow on the Lena watershed melts, leading to the major annual spring flood event causing heavy transport of sediments, organic carbon, and trace metals, both into as well as within the delta. This study aims to analyze the hydrodynamic processes of the spring flood taking place every year in the Lena Delta. Thus, a combination of remote sensing techniques and hydrodynamic modeling methodologies is used to overcome limitations caused by missing ground-truth data. As a test site for this feasibility study, the outlet of the Lena River to its delta was selected. Lena Delta is an extensive wetland spanning from northeast Siberia into the Arctic Ocean. Spaceborne Synthetic Aperture Radar (SAR) data of the TerraSAR-X/TanDEM-X satellite mission served as input for the hydrodynamic modeling software HEC-RAS. The model resulted in inundation areas, flood depths, and flow velocities. The model accuracy assessed by comparing the multi-temporal modeled inundation areas with the satellite-derived inundation areas ranged between 65 and 95%, with kappa coefficients ranging between 0.78 and 0.97, showing moderate to almost perfect levels of agreement between the two inundation boundaries. Modeling results of high flow discharges show a better agreement with the satellite-derived inundation areas compared to that of lower flow discharges. Overall, the remote-sensing-based hydrodynamic modeling succeeded in indicating the increase and decrease in the inundation areas, flood depths, and flow velocities during the annual flood events.

<p><span>In this study we investigated the impacts of climate change on a large nivo-glacial river basin (Naryn Basin) in </span><span>Central Asia (Kyrgyzstan) using two different families of General Circulation Models (GCMs). Hence, we use </span><span>the widely used ISIMIP2 (Inter-Sectoral Impact Model Intercomparison Project) data which is based on the </span><span>GCMs of the 5th stage of the Coupled Model Intercomparison Project (CMIP5), as well as the newly derived </span><span>ISIMIP3 data, which uses the latest GCM data from phase 6 of CMIP (CMIP6) to drive a hydrological model </span><span>(Soil Water Assessment Tool - SWAT). As both sources of forcing (ISIMIP2 & ISIMIP3) show considerable </span><span>differences in multiple aspects such as used GCM family, projections, bias-adjustment technique and reference </span><span>dataset, we evaluate and compare the individual projected changes of both generations on different variables </span><span>of the hydrological cycle, such as snowmelt, evapotranspiration and soil moisture. </span><span>In order to quantify the uncertainty contribution of different components along </span><span>the modelling chain we perform a sensitivity analysis using an ANOVA (Analysis of Variance) approach. </span><span>Hereby, it is intended to reveal which source (CMIP phase, GCMs, scenario) can be attributed the largest </span><span>contribution. Results show that significant differences in the impact assessment can occur depending on the</span><br><span>CMIP generation. It is also shown that the CMIP phase has a high contribution to the total uncertainty </span><span>estimates. However, in a next step special ephasize is put on the improvement of nivo-glacial processes, which will be performed by an improvement of the hydrological model SWAT, by integrating a glacier module which accounts for not only for glacier mass balance changes but also considers glacier recession and to a limited degree  potential advance.</span></p>