In increasingly expanding cities, roofs are still largely unused areas to counteract the negative impacts of urbanization on the water balance and to reduce flooding. To estimate the effect of green roofs as a sustainable low impact development (LID) technique on the building scale, different approaches to predict the runoff are carried out. In hydrological modelling, representing vegetation feedback on evapotranspiration (ET) is still considered challenging. In this research article, the focus is on improving the representation of the coupled soil–vegetation system of green roofs. Relevant data to calibrate and validate model representations were obtained from an existing field campaign comprising several green roof test plots with different characteristics. A coupled model, utilizing both the Penman–Monteith equation to estimate ET and the software EPA stormwater management model (SWMM) to calculate the runoff, was set up. Through the application of an automatic calibration procedure, we demonstrate that this coupled modelling approach (Kling–Gupta efficiency KGE = 0.88) outperforms the standard ET representation in EPA SWMM (KGE = −0.35), whilst providing a consistent and robust parameter set across all green roof configurations. Moreover, through a global sensitivity analysis, the impact of changes in model parameters was quantified in order to aid modelers in simplifying their parameterization of EPA SWMM. Finally, an improved model using the Penman–Monteith equation and various recommendations are presented.

<p>Due to climate change, an assessment of future changes in hydrological systems is necessary for appropriate planning, particularly for water management. So far, especially flood and low flow events have been studied. But groundwater levels are also influenced by extended dry periods and seasonal shifts in precipitation, as groundwater recharge is directly related to precipitation and evaporation. In the study area of Lower Saxony (Germany), groundwater is an important resource for drinking water and supplies about 86 % of the demand (MU 2021). Therefore, knowledge about possible changes is of highest importance for a possible need for action.</p> <p>In this study, climate characterising indices are used. Based on the assumed relationship between groundwater levels and meteorological indices, a simplified statistical approach should be used. Therefore, multiple linear regression models were set up for groundwater level estimation. Local models were set up for 734 groundwater monitoring wells in Lower Saxony, Germany. In order to take the persistence of the meteorological indices into account, moving averages and time lags were also included. Using a split validation procedure, which could be carried out for 114 stations with sufficient time series lengths, shows a good performance of the models. In order to make statements about future changes, the models were applied using climate model data based on the RCP8.5 scenario. Analyses for the reference period show that the groundwater levels can be sufficiently estimated. Slight changes were detected for the near future (2021-2050) and for the far future (2071-2100). For the majority of the measuring stations, decreases in mean annual low groundwater levels and slightly decreases in mean annual high groundwater levels are observed for both future periods. The number of low-level months does not change, while the number of high-level months increases slightly. In addition, a delayed timing of the annual extremes can be detected.</p>