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.

Reducing flood risk through improved disaster planning and risk management requires accurate and reliable estimates of flood damages. Models can provide such information by calculating the costs of flooding to exposed assets, such as buildings within a community. Computational or data constraints often lead to the construction of such models from coarse aggregated data, the effect of which is poorly understood. Through the application of a novel spatial segregation framework, we are able to show mathematically that aggregating flood grids through averaging will always introduce a systematic error in a particular direction in partially inundated regions. By applying this framework to a case study we spatially attribute these errors and demonstrate how the exposure of buildings can be an order of magnitude more sensitive to these errors than uninhabited regions. This work provides insight into, and recommendations for, upscaling grids used by flood risk models. Further, we demonstrate a positive dependence of systematic error magnitude on scale coarseness, suggesting coarse models be used with caution and greater attention be paid to issues of scale.

Exploration of SST-Streamflow connection unravels the large scale climate influences that have a potential role in modulating local hydrological components. Most studies exploring this relationship only focus on seasonal or annual scales however, various atmospheric and oceanic phenomena occur at different timescales, which need to be considered. This study investigates the influence of sea surface temperature (SST) on German streamflow, divided into Alpine, Atlantic and Continental streamflow regions, at timescales ranging from sub-seasonal to decadal by integrating wavelet transform and complex network techniques. Wavelet transform is used to decompose the time series into multiple frequency signals, and the spatial connections are identified based on these decomposed signals for the 99 percentile correlation coefficient value by applying network theory. The degree centrality metric is used to evaluate the characteristics of the spatially embedded networks. Our results re-establish known SST regions that have a potential connection with the various streamflow regions of Germany. Spatial patterns that resemble the North Atlantic SST tripole-like pattern is predominant for Alpine streamflow regions at lower timescale. Equatorial Atlantic Mode regions observed for Atlantic streamflow at inter-annual timescale and Vb weather system connected regions in the Mediterranean Sea have appeared for all the streamflow regions of Germany. Besides, continental streamflow regions exhibited combined characteristics of the Alpine and Atlantic streamflow spatial patterns. In addition to the above regions, we also identify the scale specific patterns in the Pacific, Indian and Southern Ocean regions at different timescales ranging from seasonal to decadal scale.

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.