We present a novel non-stationary Regional Weather Generator (nsRWG) based on an auto-regressive process and marginal distributions conditioned on climate variables. We use large-scale circulation patterns as a latent variable and regional daily mean temperature as a covariate for marginal precipitation distributions to account for dynamic and thermodynamic changes in the atmosphere, respectively. Circulation patterns are classified using ERA5 reanalysis mean sea level pressure fields. We set up nsRWG for the Central European region using data from the E-OBS dataset, covering major river basins in Germany and riparian countries. nsRWG is meticulously evaluated, showing good results in reproducing at-site and spatial characteristics of precipitation and temperature. Using time series of circulation patterns and the regional daily mean temperature derived from General Circulation Models (GCMs), we inform nsRWG about the projected future climate. In this approach, we utilize GCM output variables, such as pressure and temperature, which are typically more accurately simulated by GCMs than precipitation. In an exemplary application, nsRWG statistically downscales precipitation from nine CMIP6 GCMs generating a long synthetic but spatially and temporally consistent weather series. The results suggest an increase in extreme precipitation over the German basins, aligning with previous regional analyses. nsRWG offers a key benefit for hydrological impact studies by providing long-term (thousands of years) consistent synthetic weather data indispensable for the robust estimation of probability changes of hydrologic extremes such as floods.

The need for explicitly considering equity in adaptation planning is increasingly being recognized. However, quantitative evaluations of adaptation options often adopt an aggregated perspective, while disaggregation of results is important to learn about who benefits when and where. A typical example is adaptation of rice agriculture in the Vietnam Mekong Delta. In the past two decades, efforts focused on flood protection have mainly benefitted large-scale farmers while harming small-scale farmers. To investigate the distributional consequences of adaptation policies in the Vietnam Mekong Delta, we assess both aggregate efficiency and equity indicators, as well as disaggregated impacts in terms of district-level farmers profitability. Doing so requires an adequate representation of the co-evolutionary dynamics between the human and environmental systems which influence farmers profitability. We develop a spatially-explicit integrated assessment model that couples inundation and sedimentation dynamics, soil fertility and nutrient dynamics, and behavioral land-use change and farmers profitability calculation. We find that inter-district inequality responds in a non-linear way to climatic and socio-economic changes and choices of adaptation policies. Distinctive inequality patterns emerge from even slightly different combinations of policies and realizations of uncertain futures. We also find that there is no simple ranking of alternative adaptation policies, so one should make trade-offs based on the agreed preferences. Accounting for equity implies exploring the distribution of outcomes over different actor groups over a range of uncertain futures. Only by accounting for multisectoral dynamics can planners anticipate the equity consequences of adaptation options and prepare additional measures to aid the worse-off actors.

Understanding groundwater behavior is essential for water resources management in alluvial deltas. This study investigated the trends of groundwater levels (GWLs), the memory effect of alluvial aquifers, and the response times between surface water and groundwater across the Vietnamese Mekong Delta (VMD). 88 time series of GWL between 1996 and 2017 were collected at 27 national stations. Trend analysis, auto- and cross-correlation, and time-series decomposition were applied within a moving window approach to examine nonstationary behavior. Our study revealed high ratios of the seasonal component in shallow aquifers, and dominating ratios of the trend component in deep aquifers. These findings indicate an effective connection between the Holocene aquifer and surface water, and a high potential for shallow groundwater recharge. On the other hand, low-permeable aquicludes separating the aquifers behave as low-pass filters that reduce the high‐frequency signals in the GWL variations, and limit the recharge to the deep groundwater. Declining GWLs (0.01-0.55 m/year) were detected for all aquifers throughout the 22 years of observation, indicating that the groundwater system is currently not fully recharged. Stronger declining trends were detected for deep groundwater. While the slight decline of GWLs in the Holocene aquifer (0.01-0.11 m/year) is likely caused by natural conditions, the significant decline in the Pliocene and Miocene aquifers (0.30-0.55 m/year) is attributed to the overexploitation of groundwater. The time-variant trend analysis indicates that the decrease of GWL accelerated continuously. The groundwater memory effect varies according to the geographical location, being shorter in shallow aquifers and flood-prone areas and longer in deep aquifers and coastal areas. Variation of the response time between the river and alluvial aquifers is controlled by groundwater depth, seasonal variability, and the location with shorter response times for shallow groundwater, during the flood season, and in flood-prone areas. Our findings are not only essential for groundwater resource management in the VMD, but they also characterize general mechanisms of aquifer systems in alluvial settings.