Urban trees play an important role in enhancing the resilience of cities to climate change, as shading and evapotranspiration reduce ambient temperatures and mitigate the urban heat island effect. However, increasing temperatures and prolonged drought periods threaten the health and survival of urban tree populations, making supplemental irrigation during summer a necessary measure to prevent severe damage. Green infrastructure offers the potential to establish local retention systems, which are capable of capturing the runoff generated by increasingly frequent extreme rainfall events.The water retained in such systems could theoretically be repurposed for the irrigation of urban trees. This study employs an urban hydrological tree model to assess the efficiency of urban water storage in supplying irrigation water for urban trees. The study focused on an area in Berlin-Kreuzberg. According to the Monte Carlo principle, a hypothetical dataset of 30,000 city trees was created, and for each individual tree, the optimal cistern size and catchment area were determined in order to avoid drought stress days. The effectiveness of the cistern parameters determined in this way was then demonstrated using a new, randomly generated dataset, with the number of drought stress days being reduced on average from 5.5 to 0.4 days per year.
This study examines the performance of regionally trained data-driven Long Short-Term Memory (LSTM) networks in comparison to catchment-calibrated conceptual HBV models for rainfall-runoff simulation in Germany. It is based on the Germany-wide CAMELS-DE dataset, which provides hydrometeorological time series and catchment area characteristics for 1,314 selected catchments. The LSTM ensemble achieves a median Nash-Sutcliffe efficiency (NSE) of 0.86, outperforming the HBV model with its median NSE of 0.72 in over 99 % of the catchments. Both models underestimate flood peaks, with the LSTM simulations exhibiting smaller errors and better temporal accuracy. The LSTM achieves high simulation quality, especially in large, topographically heterogeneous, and precipitation-rich catchments. In ungauged catchments, the LSTM model achieves a median NSE of 0.69 through regional training. Even small amounts of training data significantly improve the simulation quality; the median NSE increases to 0.81. Due to its mass-conserving structure, the HBV model is more sensitive to significant mass balance errors in the input data than the LSTM model. A systematic comparison of the two approaches makes it possible to better identify catchments with problematic data quality. The results show that conceptual models like HBV, due to their high interpretability, extrapolation capability and proven practical application, as well as data-driven LSTM models, due to their superior simulation quality and applicability in ungauged basins, have specific strengths and can therefore complement each other optimally in rainfall-runoff simulation.
Increasing periods of drought resulting from climate change pose major challenges for parts of Germany, including the state of Brandenburg. Local authorities, along with other stakeholders, can play a pivotal role in preventing and managing water scarcity. This article, therefore, examines in an explorative manner the activities being taken to avoid and manage water shortages, and the conditions that facilitate or hinder them, based on semi-structured interviews with stakeholders from four small municipalities in Brandenburg. The activities and conditions were analysed in terms of five leverage points: 1) awareness of the urgency and goals; 2) cooperation and responsibilities; 3) existence of, access to, and exchange of knowledge; 4) resources and capacities; and 5) strategies and measures. The results show that awareness of water scarcity varies greatly among respondents, with partly low levels. Communication with other relevant stakeholders is often limited. Various data, information resources and guidance exist. They are mentioned in the article but underutilised up to now. Although some initial individual measures have been implemented, there is still a lack of comprehensive concepts. Key obstacles identified include insufficient human and financial resources, unclear responsibilities, and a perceived lack of urgency. These issues are closely linked, resulting in the issue of water shortages not being addressed on a large scale in the four municipalities. The study highlights the need to strengthen small municipalities in terms of both finances and human resources, the positive local effects of developing climate adaptation plans and the need for both comprehensive coordination and the raising of awareness.
In this study, 1,575 runoff series of the CAMELS-DE database are used to investigate the runoff behaviour for intermittency across Germany. The analysis showed that 16 % of the stations had at least one zero-flow period during the investigation period between 1981 and 2020. 20 stations fell dry on average every one to two years, for 3 to 200 days per year, followed by 38 stations with medium intermittent behaviour with zero-flow days every 2 to 5 years. A larger part of the intermittent stations fell dry rarely: 40 stations every 6 to 10 years and 149 every 10 years or more rarely. The remaining fraction, 84 % of all stations of the CAMELS-DE data set, was perennial during the entire investigation period. An assessment of temporal variability of the annual number of zero-flow days revealed an inhomogeneous change behaviour: for each zero-flow frequency category from frequent to rarely intermittent, different temporal change patterns-increase, decrease or no trend-were found in the dataset. For around 76 stations, an increase of zero flow days was detected, which was statistically significant at 21 stations. 52 of the stations exhibited no significant trend over the entire time period, but a clear increase in the years 2018 to 2020. A moderate correlation was detected between the annual number of zero-flow days and the mean spring and summer temperature, as well as between the potential evapotranspiration and the June SPEI-3 drought index for stations with a highly intermittent regime and increasing trend. For the annual number of zero-flow days of all other intermittent stations and climate metrics, correlation was only weak or not detectable. In order to assess potential association between the classification of the runoff regime into intermittent or perennial regimes and the 67 catchment attributes from the CAMELS-DE dataset, random forest analysis was used. It resulted in no clear picture which attributes may explain the tendency towards intermittent runoff. In contrast, a whole range of not directly discharge-related attributes were identified, which included the frequency of dry days and the areal coverages of aquifer, geochemical rock type, land-use and soil parameters. From those identified attributes, catchments with more permeable soils with higher sand and low clay content tended to be characterized by intermittent rather than perennial runoff.
