Climate change threatens the sustainable use of groundwater resources worldwide by affecting future recharge rates. However, assessments of global warming’s impact on groundwater recharge at local scales are lacking. This study provides a continental-scale assessment of groundwater recharge changes in Europe, past, present, and future, at a (5 x 5) km2 resolution under different global warming levels (1.5 K, 2.0 K, and 3.0 K). Utilizing multi-model ensemble simulations from four hydrologic and land-surface models (HMs), our analysis incorporates E-OBS observational forcing data (1970-2015) and five bias-corrected and downscale climate model (GCMs) datasets covering the near-past to future climate conditions (1970-2100). Results reveal a north-south polarization in projected groundwater recharge change: declines over 25-50% in the Mediterranean and increases over 25% in North Scandinavia at high warming levels (2.0-3.0 K). Central Europe shows minimal changes (±5%) with larger uncertainty at lower warming levels. The southeastern Balkan and Mediterranean region exhibited high sensitivity to warming, with changes nearly doubling between 1.5 K and 3.0 K. We identify greater uncertainty from differences among GCMs, though significant uncertainties due to HMs exist in regions like the Mediterranean, Nordic, and Balkan areas. The findings highlight the importance of using multi-model ensembles to assess future groundwater recharge changes in Europe and emphasize the need to mitigate impacts in higher warming scenarios.

Assessing the impact of anthropogenic warming on river high flows is essential for adaptation planning. This study evaluates the potential impacts of climate change on high flows in five major river basins in Germany: the Rhine, Danube, Weser, Elbe and Oder. We used a large multimodel ensemble of 70 bias-adjusted and high-resolution Regional Climate Model (RCM) simulations that downscaled Global Climate Models (GCM). The mesoscale Hydrologic Model (mHM) is forced with the climate simulation data to estimate the 90th percentile and annual maxima for streamflow in a 30-year period under both historical and future climate scenarios (1.5K, 2.0K and 3.0K warming levels). The study also analyzed seasonal variations in streamflow by separating the summer and winter half-year. We identified an overall increase in high flow (15-30\% for the northern Elbe and western Oder basins for the 1.5K warming and $\ge$ 30\% for the 3.0K warming in the summer half-year) and annual maximum flow for most river basins. An exception to this is the robust reduction in the annual maximum and high flow flow ($\le$ 30\% for the 3.0K warming scenario) in the Alpine headwaters region. Significant uncertainty exists in the projections, with GCM selection contributing more to this uncertainty than RCM choice, particularly during the summer and at 3.0K global warming. The provision of this large bias-adjusted climate model ensemble representing a fine river network can further facilitate the provision of reliable local information for the planning of local adaptation measures.

The need to develop and provide integrated observation systems to better understand and manage global and regional environmental change is one of the major challenges facing Earth system science today. In 2008, the German Helmholtz Association took up this challenge and launched the German research infrastructure TERrestrial ENvironmental Observatories (TERENO). The aim of TERENO is the establishment and maintenance of a network of observatories as a basis for an interdisciplinary and long-term research programme to investigate the effects of global environmental change on terrestrial ecosystems and their socio-economic consequences. State-of-the-art methods from the field of environmental monitoring, geophysics, remote sensing, and modelling are used to record and analyze states and fluxes in different environmental disciplines from groundwater through the vadose zone, surface water, and biosphere, up to the lower atmosphere. Over the past 15 years we have collectively gained experience in operating a long-term observing network, thereby overcoming unexpected operational and institutional challenges, exceeding expectations, and facilitating new research. Today, the TERENO network is a key pillar for environmental modelling and forecasting in Germany, an information hub for practitioners and policy stakeholders in agriculture, forestry, and water management at regional to national levels, a nucleus for international collaboration, academic training and scientific outreach, an important anchor for large-scale experiments, and a trigger for methodological innovation and technological progress. This article describes TERENO’s key services and functions, presents the main lessons learned from this 15-year effort, and emphasises the need to continue long-term integrated environmental monitoring programmes in the future.

Global warming is altering soil moisture (SM) droughts in Europe with a strong drying trend projected in the Mediterranean and wetting trends projected in Scandinavia. Central Europe, including Germany, lies in a transitional zone showing weaker and diverging change signals exposing the region to uncertainties. To analyse the projected SM drought changes and associated uncertainties in Germany, we utilize a large multi-model ensemble of 57 bias-adjusted and spatially disaggregated regional climate model simulations to run the hydrologic model mHM at a high spatial resolution (0.015625°, eq. 1.2 km) for Germany over the period 1971-2098. We show that projections of future changes in soil moisture droughts over Germany depend on the emission scenario, the soil depth and the timing during the vegetation growing period. The most robust and widespread increase in soil moisture drought intensities is projected for upper soil layers (0-30 cm) in the late growing season July-September under RCP8.5. There are greater uncertainties in the changes in soil moisture droughts in the early vegetation growing period (April-June). Regional differences are visible, with the strongest Soil moisture Drought Intensities (SDI) increase in the south-west of Germany. In the north-east, we observe divergent trends between the top soils and the total soils (0-200 cm). We find stronger imprints of changes in meteorological drivers controlling the spatial disparities of SM droughts than regional diversity in physio-geographic landscape properties. Our study provides nuanced insights for an important climatic transition zone and is therefore relevant for regions with similar transitions.

This study compares two widely used approaches for modeling soil moisture (SM) infiltration in mesoscale hydrology: the one-dimensional Richards equation (1-D RE), which controls vertical flux exchange but is complex and nonlinear, and the infiltration capacity (IC) scheme, which is simpler and only allows downward SM movement. The challenge in implementing the RE lies in determining effective parameters at the targeted resolution (typically several hundred to thousands of meters), as the RE is inherently nonlinear and developed for much finer scales than those used in typical simulations. To address this, an experiment was conducted using the mHM model equipped with Multiscale Parameter Regionalization (MPR) to parameterize both the RE and IC approaches. The RE parameterization involved the use of three distinct pedo-transfer functions (PTFs). The parameters were estimated across 201 basins in Germany and validated with streamflow data at multiple resolutions, along with SM observations from 46 sites (0-25 cm depth) and 42 sites (25-60 cm and 0-60 cm depths). Results show that mHM-IC and all mHM-RE variants perform comparably well in predicting streamflow. The application of MPR facilitates the transferability of PTF parameters across different scales and areas. Due to its two-way flow mechanism, the mHM-RE variant shows better predictability of SM, especially in deeper soil layers. Although the IC approach frequently leads to saturation in deeper soil layers, it still provides excellent predictability for SM anomalies. This study suggests that appropriate RE parameterization can generate transferable parameters and achieve good results in simulating streamflow and other state variables.

Robust estimation of average soil water content with spatial resolution of a few tens to a few hundreds of meters is essential for evaluating models or data assimilation products. Due to the high spatial variability of soil moisture at the point scale, sufficient coverage of spatial observations is required to estimate a robust field average. If sensors fail over time, averaging the remaining measurements risks the introduction of artificial shifts in the resulting time series. Here, we explore the problem of using incomplete soil moisture observations to estimate spatial averages and propose a correction accounting for temporal persistence of spatial patterns. By transforming, i.e. upscaling, each sensor measurement to the field scale using information from time periods with sufficient coverage, the dependence on full spatial coverage can be decreased. The transformed values allow to build a more robust approximation to the spatial mean, even when spatial coverage becomes sparse. We found that high temporal stability of the sensors does not necessarily guarantee that the transformed time series will provide a good estimate of the mean and therefore recommend the use of robust statistics to derive the field mean, which requires at least three estimates per observation time. The proposed protocol is applicable for observational time series with varying sample size across a given spatial extent, and it can be adopted for other variables exhibiting a temporally stable bias between the individual point observations and field scale average.

FSO is a symbolic regression method that allows for automatic estimation of the structure and parameterization of transfer functions from catchment data. The FSO method transforms the search for an optimal transfer function into a continuous optimization problem using a text generating neural network (variational autoencoder). mHM is a widely applied distributed hydrological model, which uses transfer functions for all its parameters. For this study, we estimate transfer functions for the parameters saturated hydraulic conductivity and field capacity. To avoid the influence of parameter equifinality, the remaining mHM parameter values are optimized simultaneously. The study domain consists of 229 basins, including 7 major basins for Training and 222 smaller basins for validation, distributed across Germany. 5 years of data are used for training und 35 years for validation. By validating the estimated transfer functions in a set of validation basins in a different time period, we can examine the FSO estimated transfer functions influence on model performance, scalability and transferability. We find that transfer functions estimated by FSO lead to a robust performance when being applied in an ungauged setting. The median KGE of the validation basins in the validation time period is 0.73, while the median KGE of the 7 training basins in training time is 0.8. These results look promising, especially since we are only using 5 years of training data, and show the general applicability of FSO for distributed hydrological models.

Parameter estimation is one of the most challenging tasks in large-scale distributed modeling, because of the high dimensionality of the parameter space. Relating model parameters to catchment/landscape characteristics reduces the number of parameters, enhances physical realism, and allows the transfer of hydrological model parameters in time and space. This study presents the first large-scale application of automatic parameter transfer function (TF) estimation for a complex hydrological model. The Function Space Optimization (FSO) method can automatically estimate TF structures and coefficients for distributed models. We apply FSO to the mesoscale Hydrologic Model (mHM, mhm-ufz.org), which is the only available distributed model that includes a priori defined TFs for all its parameters. FSO is used to estimate new TFs for the parameters “saturated hydraulic conductivity” and “field capacity”, which both influence a range of hydrological processes. The setup of mHM from a previous study serves as a benchmark. The estimated TFs resulted in predictions in 222 validation basins with a median NSE of 0.68, showing that even with 5 years of calibration data, high performance in ungauged basins can be achieved. The performance is similar to the benchmark results, showing that the automatic TFs can achieve comparable results to TFs that were developed over years using expert knowledge. In summary, the findings present a step towards automatic TF estimation of model parameters for distributed models.