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We assess the ability of a regional weather generator to represent the extremity of heavy precipitation events (HPEs) across spatial and temporal scales. To this end, we implement the multi-site non-stationary Regional Weather Generator (nsRWG) for the area of Germany and generate 100 sets of synthetic daily precipitation data spanning 72 years. The weather extremity index (WEI) and its recent cross-scale modification (xWEI) are applied to quantify the cross-scale extremity of synthetic and observed HPEs and to compare their distributions. The results show that the nsRWG excels in replicating the extremity patterns for almost all 7 durations (ranging from 1 to 7 days) considered. The frequency of small-scale 1-day rainfalls is however slightly overestimated. nsRWG aptly reproduces the potential influential areas of HPEs, whether of short or long duration. It is capable of generating precipitation events mirroring the extremity patterns observed during past disaster-causing HPEs in Germany, while simultaneously accommodating their variations. This study demonstrates the potential of the nsRWG for simulating HPE-related hazard and assessing flood risks. [ABSTRACT FROM AUTHOR]

The major challenge of multiscale measurement methods beyond the point scale is their complex interpretation in the light of landscape heterogeneity. For example, methods like cosmic-ray neutron sensing, remote sensing, or hydrogravimetry are all able to provide an integral value on the water storage, representative for their individual measurement volume. A rigorous assessment of their performance is often hindered by the lack of knowledge about the truth at their corresponding scale, given the high complexity and detail of natural landscapes. In this study we suggest a synthetic virtual landscape that allows for an exact definition of all variables of interest and, consequently, constitutes the so-called 'virtual truth' free of knowledge gaps. Such a landscape can be explored in various 'virtual field campaigns' using 'virtual sensors' that mimic the response and characteristics of actual devices. We use dedicated physically-based models to simulate the signal a sensor would receive. These model outputs termed 'virtual observations' can be explored and also allow the reconstruction of water storage, which can then readily be compared to the 'virtual truth'. Insights from this comparison could help to better understand real measurements and their uncertainties, and to challenge accepted knowledge about signal processing and data interpretation. The 'Virtual Joint Field Campaign' is an open collaborative framework for constructing such landscapes. It comprises data and methods to create and combine different compartments of the landscape (e.g. atmosphere, soil, vegetation). The present study demonstrates virtual observations with Cosmic Ray Neutron Sensing, Hydrogravimetry, and Remote Sensing in three exemplary landscapes. It enables unprecedented opportunities for the systematic assessment of the sensor's strengths and weaknesses and even their combined use. [ABSTRACT FROM AUTHOR]

Spatial counterfactuals are gaining attention to address the lack of robust flood frequency analysis in small catchments. However, the credibility of counterfactual scenarios decreases with the distance rain fields are shifted across space. We limit that distance by a local counterfactual search design, and compare the corresponding scenarios to recently published results from large spatial shifts. We then put all scenarios in context with 200-year return levels, and with flood peaks simulated for the June 2024 flood event in southern Germany. We conclude that local counterfactuals scenarios are transparent and credible, and could complement the anticipation of low probability events. [ABSTRACT FROM AUTHOR]

Counterfactuals are scenarios that describe alternative ways of how an event, in this case an extreme rainfall event, could have unfolded. In this study, we present the results of a counterfactual search for flash flood events in Germany. We used a radar-based precipitation dataset from Germany's national meteorological service (Deutscher Wetterdienst) to identify the 10 most extreme precipitation events in Germany from 2001 to 2022 and then assumed that any of these top 10 events could have happened anywhere in Germany. In other words, the events were shifted around all over Germany. For all resulting positions of the precipitation fields, we simulated the corresponding peak discharge for any affected catchment smaller than 750 km 2. From all the realizations of this simulation experiment, the maximum peak discharge was identified for each catchment. In a case study, we first focused on the devastating flood event in July 2021 in western Germany. We found that a moderate shifting of the event in space could change the event peak flow at the Altenahr gauge by a factor of 2. Compared to the peak flow of 1004 m 3 s -1 caused by the event in its original position, the worst-case counterfactual of that event led to a peak flow of 1311 m 3 s -1. Shifting another event that had occurred just 1 month earlier in eastern Germany over the Ahr River valley even effectuated a simulated peak flow of 1651 m 3 s -1. For all analysed subbasins in Germany, we found that, on average, the highest counterfactual peak exceeded the maximum original peak (between 2001 and 2022) by a factor of 5.3. For 98 % of the basins, the factor was higher than 2. We discuss various limitations of our analysis, which are important to be aware of, namely, the quantification and selection of candidate rainfall events, the hydrological model, and the design of the counterfactual search experiment. Still, we think that these results might help to expand the view of what could happen in the case that certain extreme events occurred elsewhere and thereby reduce the element of surprise in disaster risk management. [ABSTRACT FROM AUTHOR]

Cosmic-ray neutron sensing (CRNS) is becoming increasingly popular for monitoring soil water content (SWC). To retrieve SWC from observed neutron intensities, local measurements of SWC are typically required to calibrate a location-specific parameter, N0 , in the corresponding transfer function. In this study, we develop a generalized conversion function that explicitly takes into account the different factors that govern local neutron intensity. Thus, the parameter N0 becomes location independent, i.e. generally applicable. We demonstrate the feasibility of such a "general calibration function" by analysing 75 CRNS sites from four recently published datasets. Given the choice between the two calibration strategies – local or general – users will wonder which one is preferable. To answer this question, we estimated the resulting uncertainty in the SWC by means of error propagation. While the uncertainty in the local calibration depends on both the local reference SWC itself and its error, the uncertainty in the general calibration is mainly governed by the errors in vegetation biomass and soil bulk density. Our results suggest that a local calibration – generally considered best practice – might often not be the best option. In order to support the decision which calibration strategy – local or general – is actually preferable in the user-specific application context, we provide an interactive online tool that assesses the uncertainty in both options (https://cosmic-sense.github.io/local-or-global , last access: 23 February 2024). [ABSTRACT FROM AUTHOR]

In this study, we investigate how immersive 3D geovisualization can be used in higher education. Based on MacEachren and Kraak's geovisualization cube, we examine the usage of immersive 3D geovisualization and its usefulness in a research-based learning module on flood risk, called GEOSimulator. Results of a survey among participating students reveal benefits, such as better orientation in the study area, higher interactivity with the data, improved discourse among students and enhanced motivation through immersive 3D geovisualization. This suggests that immersive 3D visualization can effectively be used in higher education and that 3D CAVE settings enhance interactive learning between students.

Cosmic-ray neutron sensing (CRNS) is becoming increasingly popular for monitoring soil water content (SWC). To retrieve SWC from observed neutron intensities, local measurements of SWC are typically required to calibrate a locationspecific parameter, N0, in the corresponding transfer function. In this study, we develop a generalized conversion function that explicitly takes into account the different factors that govern local neutron intensity. That way, the parameter N0 becomes location-independent, i.e., generally applicable. We demonstrate the feasibility of such a "general calibration function" by analysing 75 CRNS sites from four recently published datasets. Given the choice between the two calibration strategies - local or general - users will wonder which one is preferable. To answer this question, we estimated the resulting uncertainty of the SWC by means of error propagation. While the uncertainty of the local calibration depends on both the local reference SWC itself and its error, the uncertainty of the general calibration is mainly governed by the errors of vegetation biomass and soil bulk density. An interactive online tool is provided to support the decision decide which calibration strategy - local or general - is preferable in the user-specific application context (https://cosmic-sense.github.io/local-or-global). [ABSTRACT FROM AUTHOR]

Data-driven models have been recently suggested to surrogate computationally expensive hydrodynamic models to map flood hazards. However, most studies focused on developing models for the same area or the same precipitation event. It is thus not obvious how transferable the models are in space. This study evaluates the performance of a convolutional neural network (CNN) based on the U-Net architecture and the random forest (RF) algorithm to predict flood water depth, the models' transferability in space and performance improvement using transfer learning techniques. We used three study areas in Berlin to train, validate and test the models. The results showed that (1) the RF models outperformed the CNN models for predictions within the training domain, presumable at the cost of overfitting; (2) the CNN models had significantly higher potential than the RF models to generalize beyond the training domain; and (3) the CNN models could better benefit from transfer learning technique to boost their performance outside training domains than RF models. [ABSTRACT FROM AUTHOR]