Floods are among the most frequent and deadliest natural disasters, and the magnitude and frequency of floods is expected to increase. Therefore, the effects of different flood risk management options need to be evaluated. In this study, afforestation, permeable concrete implementation, and the use of dry and wet retention reservoirs were tested as possible options for urban flood risk reduction in a case study involving the Glinščica river catchment (Slovenia). Additionally, the effect of dry and wet reservoirs was investigated at a larger (catchment) scale. Results showed that in the case of afforestation and permeable concrete, large areas are required to achieve notable peak discharge reduction (from a catchment scale point of view). The costs related to the implementation of such measures could be relatively high, and may become even higher than the potential benefits related to the multifunctionality and multi-purpose opportunities of such measures. On the other hand, dry and wet retention reservoirs could provide more significant peak discharge reductions; if appropriate locations are available, such reservoirs could be implemented at acceptable costs for decision makers. However, the results of this study show that reservoir effects quickly reduce with scale. This means that while these measures can have significant local effects, they may have only a minor impact at larger scales. We found that this was also the case for the afforestation and permeable concrete.

As could be seen in recent years, ensuring the water supply-demand balance is a topic of increasing concern to supply companies facing the threat of increased demand scenarios resulting from long-term effects due to climate change. Especially demand peaks of multiple hours during the day or persisting demand peaks of several days caused by prolongued dry periods and more heat days throughout the summer force water suppliers to more efficiently control and manage their resources. Being able to take proactive and informed decisions through reliable short-term probabilistic forecasts is therefore crucial in this context.This research proposes two probabilistic deep learning architectures based on long short-term memory (LSTM) networks to forecast hourly water demand up to 10 days in advance. Both models processes different temporal sequences of data, including past observations of water demand and regressors as well as future regressors with different time lengths. The models encode long-term historic information of the water demand and features, including historic meteorological information, and simultaneously incorporate short-term future information on calender- and weather features using statistically optimized point forecasts (DWD MOSMIX) of the latter. Through implementing the models in an autoregressive manner, the output is fed back into itself at each step and predictions are made conditioned on the previous one to account for correct path dependency between consecutive hours. This way the model produces multi-step-ahead forecasts of variable length by using future information together with the historic context.In a case study of central Germany, the performance of the proposed deep learning models was compared to a Lasso estimated high-dimensional time series model and a conventional AR(p) model. Results indicate the potential of the proposed approach of using weather forecasts in short-term water demand prediction especially for lead times larger than 24 hours.

As could be seen in recent years, the impact of climate change is already detectable in water demand patterns and results in new challenges for the water supply sector. Demand peaks caused by changing climate conditions such as longer dry periods force water suppliers to a more efficient control and management of their assets and water resources to avert supply shortages. Especially demand peaks of multiple hours during the day or persisting demand peaks of several days and weeks threaten the supply demand-balance. By utilizing accurate forecasts of the expected water demand, suppliers are enabled to better prepare their assets for such extreme conditions.To adapt to the consequences of changing hydro-climatic and demand conditions, this research proposes a water demand forecasting model to predict such extreme demand conditions caused by climate change for the short- to mid-term range. Here, a special emphasis is put on modelling the impact of weather variables on the water consumption caused by climate change. Those effects are complex, non-linear and multidimensional in nature and therefore challenging to model. Focusing on the practical usage, the forecasting model is appropriate for real-time application providing accurate forecasts coupled with a high interpretability. This allows the quantification of the ongoing effects of climate change and enables a better consideration of the underlying uncertainty.Our case study uses real data on district level from two regions in West and Central Germany. To appropriately account for the practical need of varying forecast schemes, historical demand and weather data are used at quarter-hourly, hourly as well as daily resolution.Multiple linear, non-linear and stacked models tailored to the forecasting purpose and the varying horizons are implemented with a clear focus on interpretability and forecasting accuracy. To model the underlying uncertainty, complete probablistic forecasts are proposed. Model assessment takes place by utilizing appropriate metrics as the MAE, CRPS or energy score.

Ensuring water supply-demand balance has become increasingly vital for supply companies due to climate change, demographic shifts and unforeseen future changes. Specifically, efficient management of water resources is necessary during prolonged dry spells and hotter summer days when demand for water peaks. By providing precise water demand forecasts, suppliers can proactively manage their facilities and prevent shortages or the depletion of the available sources. Since water demand is influenced by various factors, including weather conditions and human activities, it is essential to distinguish their impacts. This study analyses the effect of weather variables through comprehensive statistical analysis, revealing intricate, multidimensional effects that are difficult to model. In addition to historical district- level data from West and Central Germany, we use historical weather variables and calendar data at an hourly resolution. We present a non-linear deep learning model, compared against a Lasso-estimated time series model and a classical ARðpÞ model, which is designed to operate across various lead times (24-240 hours). By prioritising performance, accuracy and interpretability, these models are well suited for real-time application and offer comprehensive probabilistic forecasts. The assessment of the model is carried out by utilising appropriate metrics for point and probabilistic forecasts. By assessing the predictors’ impact, we measure the continuing effects of climate change on water usage.

List of Contributors was published in AI in Business and Economics on page 257.

Short-term water demand forecasting is critical to enable optimal system operation. For practical purposes, the accuracy of the forecast and the adaptability to changing conditions are paramount. Therefore, for the Battle of Water Demand Forecasting (BWDF), we propose a precise and highly flexible forecasting methodology to allow an excellent adaptation to District Meter Areas (DMA)-specific characteristics. The proposed method consists of data cleaning and pre-processing, the training of individual forecast models and finally of combining the individual forecasts by the smoothed Bernstein Online Aggregation (BOA) algorithm. The ensemble of individual forecasting models includes simple time series, high-dimensional linear, and highly non-linear models such as neural networks.