## 2 Materials and Methods
Soil matric flow is modeled by the Richards equation. Hydraulic properties are derived according to Clapp and Hornberger [41] in BROOK90, while in LWF-BROOK90, a Mualem-van Genuchten parameterization [42] is also possible. The model operates with input about climate (temperature (min. and max. ), precipitation, relative humidity, solar radiation, wind speed,) in daily time steps, leaf phenology, leaf area index, stem area index, stand height, fine root distribution, and soil hydraulic parameters. To obtain complete daily climate time series from 1961 to 2019 for model input, we used in the first step the Deutscher Wetterdienst (DWD) station data [43] and interpolated it to the plots by means of ordinary kriging (precipitation) or regression kriging (all other meteorological variables) as described by [PERSON] et al. [44]. Then, in the second step, the measured time series from 1996 to 2019 were homogenized and gap filled with the interpolated time series [44]. In a final step, the interpolated time series was adjusted to the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Site** & **1101** & **1202** & **1203** & **1204** & **1205** & **1302** & **1303** \\ \hline Species & pine/oak & pine & pine & pine & pine & beech & pine \\ Age 1  & 149/74 & 94 & 114 & 114 & 94 & 95 & 88 \\ Top height 2 [m] & 25.5 & 30.3 & 27.6 & 26.7 & 29.4 & 27.0 & 25.4 \\ Basal area 2 [m\({}^{2}\)] & 22.9 & 49.8 & 37.7 & 32.8 & 30.9 & 30.4 & 31.9 \\  & (34.9) & (53.8) & (37.8) & (33.0) & (32.7) & & \\ Stems per ha 2 [m\({}^{2}\)] & 147 & 525 & 338 & 422 & 316 & 656 & 556 \\  & (563) & (1034) & (381) & (513) & (806) & 656 & 556 \\  & \(\mathrm{G}\mathrm{V}\mathrm{\SIUnitSymbolDegree}\) & \(\mathrm{S}\mathrm{\SIUnitSymbolDegree}\) & \(\mathrm{S}\mathrm{\SIUnitSymbolDegree}\) & \(\mathrm{S}\mathrm{\SIUnitSymbolDegree}\) & \(\mathrm{S}\mathrm{\SIUnitSymbolDegree}\) & \(\mathrm{S}\mathrm{\SIUnitSymbolDegree}\) \\ \multirow{2}{*}{ \begin{tabular}{c} Soil type \\ \end{tabular}  } & \(\mathrm{B}\mathrm{r}\mathrm{u}\mathrm{r}\mathrm  
homogenized time series with means of quantile mapping [45], and both time series were merged. Climate scenario data from the Euro-Cordex project with a spatial resolution of 0.11\({}^{\circ}\) and a daily time resolution from 2010 to 2100 was used [21]. These data are based on CMIP5-climate scenarios, which were downscaled by regional climate models. We worked with two combinations of global and regional circulation models, MPI-ESM-REMO (MPI/REMO) and EC-Earth-RCA4 (ECE/RCA4), and two RCPs (2.6, 8.5). The model behavior was already examined for Germany and revealed a larger temperature increase for ECE/RCA4 compared to MPI/REMO under both RCPs, while the latter shows an increase of precipitation, which is more pronounced under the RCP8.5 [46]. The climate scenario time series were bias-adjusted to the historical time series by means of quantile mapping [45]. Leaf Phenology was estimated by the model of [PERSON] [47] and [PERSON] [35], whose parameterization was optimized for northeast Germany. Stand height was calculated as the top height from measured tree heights. To estimate the tree height development from 1961 to 2019, the height development from digital yield tables [48] was adjusted to the measured heights. Leaf area index (LAI) was modeled by using the tree data of consecutive forest inventories in combination with allometric functions for individual tree biomass (see [PERSON] et al. [49] for more details), and specific leaf area (SLA) values from the database of life-history traits of Northwest European flora (LEDA) [50]. The stem area index (SAI) was estimated with diameter at breast height (DBH) based functions [51]. The development of LAI and SAI from 1961 to 2019 was estimated by applying the same functions to the yield tables, and by adjusting that LAI and SAI time series to the values estimated with the forest inventory data. The LAI of ground vegetation was estimated by determining the leaf biomass with vegetation surveys in combination with the Phytocalc model [52; 53] and plant allometry [54].