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Global warming impacts the hydrological cycle, affecting the seasonality and timing of extreme precipitation. Understanding historical changes in extreme precipitation occurrence is crucial for assessing their impacts. This study uses relative entropy to analyze historical changes in seasonality and timing of extreme daily precipitation occurrences on the global domain for 63 years of fifth generation of the European Reanalysis reanalysis data. Our analysis reveals distinct regional patterns of change. During the second half of the 20th century, Africa and Asia experienced high clustering of precipitation extremes. Over the past 60 years, clustering increased in Africa while becoming more spread out in Asia. North America and Australia had initially lower clustering and showed slight increases over time. Extreme events in extra‐tropical land regions mainly occurred in summer, with modest shifts in timing. These findings have implications for risk assessments of natural hazard like flash floods and landslides, emphasizing the necessity for region‐specific adaptation strategies. Global warming is changing how and when heavy rain and extreme weather events happen. It is important to understand these changes for planning and preparing for floods and other water‐related problems. In this study, we looked at long records of rainstorms to see how they have changed over time. We found that different regions have experienced different changes. During the second half of the 20th century, Africa and Asia were regions with the strongest seasonality. Over the 60 years we analyzed, seasonal clustering overall increased in Africa, while in Asia they became more spread out throughout the year. In Europe, North America and Australia, rainstorms were more spread out throughout the year but became slightly more concentrated. Most of the heavy rainstorms outside the tropics happened in summer, with a small shift in timing. These findings show how various regions have experienced different changes, which is important to take into account when planning and preparing for floods. Global assessment of changes (1959–2021) in seasonality and timing of extreme daily precipitation occurrences using relative entropyPrecipitation extremes became more clustered in Africa and less clustered in AsiaShifts in timing of extreme precipitation by only a few days for most regions Global assessment of changes (1959–2021) in seasonality and timing of extreme daily precipitation occurrences using relative entropy Precipitation extremes became more clustered in Africa and less clustered in Asia Shifts in timing of extreme precipitation by only a few days for most regions

Distributed temperature sensing has proven a useful technique for geoscientists to obtain spatially distributed temperature data. When studies require high‐resolution temperature data in three spatial dimensions, current practices to enhance the spatial resolution do not suffice. For example, double‐diffusive phenomena induce sharp and small‐scale temperature patterns in water bodies subject to thermohaline gradients. This article presents a novel approach for a 3‐D dense distributed temperature sensing setup, the design of which can be customized to the required spatial resolution in each dimension. Temperature is measured along fiber‐optic cables that can be arranged as needed. In this case, we built a dense cage of very thin (1.6 mm) cables to ensure that interference with flow patterns was minimal. Application in water bodies with double‐diffusion‐induced sharp temperature gradients shows that the setup is well able to capture small‐scale temperature patterns and even detects small unsuspected seeps and potential salt‐fingers. However, the potential effect of the setup on the flow patterns requires further study. A fiber‐optic DTS setup was designed to measure detailed temperature profiles in three dimensionsThe setup avoids the application of continuing coils for high‐resolution temperature sensingDetailed temperature signatures in thermohaline stratified water bodies are captured

The suggestions by Hutton et al. might not be enough to guarantee reproducible computational hydrology. Archiving software code and research data alone will not be enough. We add to the suggestion of Hutton et al. that hydrologists not only document their (computer) work, but that hydrologists use the latest best practices in designing research software, most notably the use of containers and open interfaces. To make sure hydrologists know of these best practices, we urge close collaboration with Research Software Engineers (RSEs). Storing software code and data is not enough to guarantee reproducibilityWe suggest to use container technology to guarantee reproducibilityWork closely together with Research Software Engineers (RSEs)

Quantification of human impact on water, sediment, and nutrient fluxes at the global scale demands characterization of reservoirs with an accuracy that is presently unavailable. This letter presents a new method, based on virtual dam placement, to make accurate estimations of area‐volume relationships of large reservoirs, using solely readily available elevation data. The new method is based on regional similarity of area‐volume relationships. The essence of the method is that virtual reservoirs are created in the vicinity of an existing reservoir to derive area‐volume relationships for the existing reservoir. The derived area‐volume relationships reproduced in situ bathymetric data well. An intercomparison for twelve reservoirs resulted in an average R2= 0.93. This is a significant improvement on estimates using the best existing global regression model, which gives R2= 0.54 for the same set of reservoirs. Humans have significantly altered the global water, sediment, and nutrient cycle by constructing large dams. However, it's difficult to quantify the impact of dams on a global scale without characterizing reservoirs with an accuracy that is presently unavailable. Here, we present a new method, based on virtual dam placement, to make accurate estimations of the relationship between reservoir surface area and reservoir volume. The only data required is an elevation map derived from satellite data. With this method, we create virtual reservoirs in the vicinity of an existing reservoir to derive relations for the existing reservoir. The derived relations closely matched field observations taken with boats measuring water depths in 12 existing reservoirs. The new method opens the way to accurate reservoir characterization at global scale for better management of the Earth's valuable resources. A method is presented to approximate area‐volume relationships for large reservoirsThe method makes use of similarity of area‐volume relationships within regionsThe method requires only freely available, satellite‐based digital elevation models (DEMs)