## 2 Materials and Methods
Then the leaf biomass was multiplied with SLA values from the LEDA database [50]. Root distribution was estimated by modeling the fine root density according to a statistical model based on physical soil properties, soil organic matter, depths, and slope [55] and then fitting the depths distribution with the beta-model [56; 57]. In LWF-BROOK90, the soil hydraulic parameterization depends on Mualem-van Genuchten parameters. These were derived from measured water retention curves for distinct soil horizons. Moreover, measured gravel content was available for the complete soil profiles down to a varying depth depending on the monitoring site. The data for deeper soil layers down to a depth of 4 m were derived either, if soil texture was available, by soil texture-based Mualem-van Genuchten values [58], or by assigning the values of the deepest soil layer that contains data. The tortuosity parameter \(l\) was set to 0.5. We used default parameter values to run LWF-BROOK90 using the R-interface _brook90r_[59]. Exceptions were canopy and leaf parameters for which we used tree-specific values (stomatal conductance [60], winter LAI fraction [35], albedo, and leaf size [55], Table S1). We considered the canopies to be closed. Soil water flows were set to free drainage, no bypass-flow, and constant source area flow parameters and infiltration depth between all sites. We simulated soil hydrology of the organic layer and the mineral soil down to a depth of 4 m. The soil column was intersected in thinner layers starting with 1 cm in the organic layer and topsoil to increasing thickness up to 20 cm in the bottom layers. The macropore infiltration parameter _infexp_ was calibrated for each site. Therefore, _infexp_ was varied stepwise between 0.01 and 1. Then the model output was compared with measurements. We used measurements of volumetric soil water content. The historical time series 1961-2019 was modeled twice--one time with stand development as described above, and the second time with constant LAI, SAI, and height, which were derived from the year 2019. The future climate scenarios also run with these stand data. The first run was used for validating model performance with soil water content and interception values. The second run was used to examine the impact of climate change on water budgets. We used constant stand parameters to estimate the change between the historical time period and the scenarios without interactions to stand development. In addition to LWF-Brook90, according to Food and Agriculture Organisation of the United Nations (FAO) we calculated grass reference evapotranspiration (PET [61]) and climate water balance (CWB). From the variables obtained from the model output, we further calculated indicators for soil water supply and drought stress. We calculated plant-available soil water down to a depth of 200 cm (SW200) as  
\[SW200=\sum_{i=1}^{n}(min\left(\theta_{i},\theta_{f_{ci}}\right)-\theta_{wp_{i} })\times(1-c_{i})\times d_{i}, \tag{1}\]  
where \(\theta_{i}\) = volumetric water content, \(\theta_{fc_{i}}\), \(\theta_{wp_{i}}\) = volumetric water contents at field capacity (\(-6.3\) kPa) and wilting point (\(-1583\) kPa), \(c_{i}\) = coarse fragment content, \(d_{i}\)= layer thickness, and \(n\) = lowest layer in 200 cm depths, for layer \(i=1\) to layer \(n\). To indicate soil-moisture and drought-stress for the forests, we used the transpiration index, which is the ratio of actual to potential transpiration [62] and ranges between 0 and 1. We followed [PERSON] et al. [63] and defined days with a transpiration index below 0.7 as dry days (TI). Our statistical evaluation is based on annual values. To evaluate the changes in water budgets within the time period 1961-2019 and to assess the future changes in water budgets, we used two approaches. First, the period 1961-1990 was compared with 1991-2019, and the latter was compared with 2071-2100 by means of ANOVA and \(t\)-test.