The hydrological model Soil Water Assessment Tool (SWAT) is widely used in water resources management worldwide. It is also used to simulate catchment hydrology in high‐mountainous regions where glaciers play an important role. However, SWAT considers glaciers in a simplistic way. Although some efforts were done to overcome this limitation, there is no official version available that considers glaciers adequately. This strongly impairs its applicability in glacierized catchments. In this technical note, we propose a novel version of the traditional SWAT, called SWAT‐GL, which introduces (1) a mass balance module and (2) a glacier evolution routine to represent dynamic glacier changes. Mass balance calculations are based on a conceptual degree‐day approach, similar to the snow routine implemented in SWAT. Glacier evolution is realized using the delta‐h (∆h) parameterization, which requires a minimum of data and is thus suitable in data‐scarce regions. The approach allows users to simulate spatially distributed glacier changes. Annual mass balance changes are translated to distributed ice thickness changes depending on the glacier elevation. We demonstrate how SWAT‐GL is technically integrated into SWAT and how glaciers are merged with the existing spatial units. Model code and test data are freely accessible to promote further model development efforts and a wide application. Ultimately, SWAT‐GL aims to make SWAT easily applicable in glacierized catchments without the need of additional tools.

The present study analyzes the projected changes of extreme climate indices over Central Asia using regional climate model (RCM) simulations from the Coordinated Regional Climate Downscaling Experiment (CORDEX) - Coordinated Output for Regional Evaluations (CORE). The extreme indices are based on precipitation and temperature and are inspected for present (1981–2005) and future periods - near- (2031–2055) and far-future (2071–2095) - to assess the long-term climate change under the representative concentration pathway RCP8.5. Projected changes are analyzed for three different model ensembles. These ensembles are based on CORDEX-Central Asia (ENS_CAS, four ensemble members) and CORDEX-East Asia (ENS_EAS, six ensemble members), and a combination of both (ENS, ten ensemble members) for our study area centered over high mountain Asia, called Central East Asia (CEAS). For precipitation indices, an increase of consecutive dry days (CDD) in ENS_EAS and a slight to moderate decrease in northern parts in ENS_CAS during near-future is observed. Consecutive wet days (CWD), very heavy precipitation events (R20mm), maximum one-day precipitation (RX1day), and very wet days (R95p) are projected to increase in most areas. All indices show a further intensification towards the end of the century over large parts of the domain, e.g., + 7.8% / +5.6 days for CDD, + 96.6% / +0.26 days for R20mm, and + 19.7% for RX1day as median of ENS over CEAS. For temperature indices, the ensembles project a strong increase over the high mountain regions and southern parts for consecutive summer days (CSU, + 108.5% / +38.3 days), heat wave duration index (HWDI, + 1379.1% / +91.37 days), and the percentage of very hot days (TX90p, + 391.1% / +34.54 days). Accordingly, the number of consecutive frost days (CFD, -43.7% / -25.2 days) and the percentage of very cold days (TX10p, -83.4% / -8.13 days) are projected to decrease. The first-time usage of CORDEX-CORE and the larger ensemble size by considering simulations from overlapping domains increase the robustness of the findings from earlier studies. However, some discrepancies in the projected changes prevail among the different RCMs being part of the two CORDEX-domains and in specific landscapes like complex mountainous or lake areas. These uncertainties may be tackled by further model development with improved land-surface processes and potentially higher spatial resolution.

Evapotranspiration (ET) is pivotal in the terrestrial water cycle in subhumid and tropical regions. In the water cycle, the contribution of plant transpiration can be distinctively more significant than soil evaporation. The seasonal dynamics of plant phenology, commonly represented as the vegetation attribute leaf area index (LAI), closely correlates with actual ET (AET). Hence, addressing the reciprocal LAI–AET interaction is essential for practitioners and researchers to comprehensively quantify the hydrological processes in water resources management, particularly in the perennially vegetated regions of West Africa. However, due to a lack of field measurements, evaluation of the LAI–AET interaction still remains challenging. Hence, our study aims to improve the understanding of the role of the LAI in AET estimation by investigating characteristic regions of West Africa. We set up ecohydrological models (using the Soil and Water Assessment Tool for the tropics – SWAT-T) for two homogeneous land cover types (forest and grassland) to guarantee the representativeness of field measurements for the LAI and AET. We apply different potential ET methods (the Hargreaves; Penman–Monteith – PET-PM; and Priestley–Taylor methods) to evaluate the LAI–AET interaction in SWAT-T. Further, the elementary effects method quantifies the parameter sensitivity for 27 relevant LAI–AET parameters. The comprehensive parameter set is then optimized using the shuffled complex evolution algorithm. Finally, we apply a benchmarking test to assess the performance of SWAT-T with respect to the simulation of AET and to determine the relevance of detailed LAI modeling. The results show that SWAT-T is capable of accurately predicting the LAI and AET at the footprint scale. While all three PET methods facilitate adequate modeling of the LAI and AET, the PET-PM technique outperforms the other methods for AET, independent of the land cover type. Moreover, the benchmarking highlights that, if it only accounts for the LAI but disregards AET data, an optimization process's prediction of AET still yields an adequate performance with SWAT-T for all PET methods and land cover types. Our findings demonstrate that the significance of detailed LAI modeling for the AET estimation is more pronounced in the forested than in the grassland region.

Soil water availability is an essential prerequisite for vegetation functioning. Vegetation takes up water from varying soil depths depending on the characteristics of its 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 characterize vegetation functioning through the near-infrared reflectance of vegetation (NIRv) and compare its relation to (i) near-surface soil moisture from the ESA's Climate Change Initiative (CCI) and (ii) total water storage from the Gravity Recovery and Climate Experiment (GRACE) mission at a monthly timescale during the growing season. The relationships are quantified through partial correlations to mitigate the influence of confounding factors such as energy- and other water-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 that of 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. At the same time, we note that this comparison is hampered by different noise levels in these satellite data streams. In line with this, an attribution analysis that examines the relative importance of soil water storage for vegetation reveals that they are controlled by (i) water availability influenced by the climate and (ii) vegetation type reflecting adaptation of the 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.

The recently released SWAT-GL aims to overcome multiple limitations of the traditional hydrological model SWAT (Soil Water Assessment Tool) in glaciated mountainous catchments. SWAT-GL intends to increase the applicability of SWAT in these catchments and to reduce misapplication when glaciers have a significant role in the catchment hydrology. It thereby relies on a mass balance module, based on a degree-day approach similar to SWAT's snow melt module, extended by a glacier evolution component that is based on the delta-h (Δh) parameterization. The latter is a mass-conserving approach that enables the spatial distribution of ice thickness changes and thus dynamic glacier retreat. However, the extended SWAT version has not yet been comprehensively benchmarked. Hence, our paper aims to evaluate SWAT-GL with four different benchmark glaciers, which are part of the United States Geological Survey Benchmark Glacier Project. The benchmarking considers a comprehensive evaluation procedure, where SWAT-GL is optimized on glacier mass balance and hypsometry as well as snow cover. Snow cover is included to consider snow–glacier feedbacks. In addition, a sensitivity analysis using elementary effects (or the Morris method) is performed to give a detailed picture of the importance of the introduced glacier processes, as well as the relevance of the interactions with the already-existing snow routine. We intentionally did not include discharge in the optimization procedure to fully demonstrate the capabilities of SWAT-GL in terms of glacier and snow processes. Results demonstrate that SWAT-GL is able to represent the characteristics of contrasting glaciated catchments, which underlines SWAT-GL's applicability and transferability. We could further show its strong (non-linear) interactions with the existing snow routine, suggesting a simultaneous calibration of the snow components. While snow and glacier processes were adequately represented in the catchments, discharge was not necessarily represented sufficiently when excluded from the optimization procedure. However, SWAT-GL has been shown to be easily capable of reproducing discharge when used in a stand-alone optimization, although this may come at the expense of model consistency. Lastly, SWAT-GL significantly outperformed a standard SWAT model used for benchmarking purposes in high mountain environments.