7 datasets found

OpenLISEM is an open-source hydrological model suited for the simulation of floods, flash floods and erosion events. The following sections provide an overview of the results from the OpenLISEM model used in the exposure mapping A 30x30m flood map (maximum flood height) for the BuPuSa region was developed for several points on the intensity-frequency-duration curve. This curve represents the extreme value analysis (EVA) for the rainfall across the BuPuSa area. Based on 50 years of historic rainfall data from TAMSAT the EVA is developed for a 1000 year period. From this different rainfall intensities area taken which are referred to at the return period. The statistical possibility of a certain rainfall intensity to happen once in X many years. Flood maps were developed for the following return periods: 1/2, 1/10, 1/50, 1/100 and 1/1000. In addition to 5 different return periods, two different scenarios were modeled. A short high intensity rainfall event that typically causes flash floods, and a longer term lower intensity rainfall event that typically causes fluvial (river) floods. These events were represented by respectively a 6h rainfall event and a 14 day rainfall event. As a result 10 different flood maps were developed.

The CRIDA approach provides a crucial framework to enable water managers and policy makers to assess the impact of climate uncertainty and change on their water resources and work towards effective adaptation strategies. This multi-step process embraces a participatory, bottom-up approach to identify water security hazards, and is sensitive to indigenous and gender-related water vulnerabilities. By engaging local communities in the design of the analysis, the information provided by scientific modeling and climate analysis can be tailored and thus provide more useful answers to the challenges they are facing. They are also providing a more informed starting point to assess the different options for adaptation, and design robust adaptation pathways, in line with the local needs.

This publication has laid out a strategic framework to integrate Open Science into hydrology, illustrating its true potential to enhance research transparency, collaboration, and accessibility within water management practices. The six pillars — open data, open source, open publishing, open infrastructure, open education, and open participation — constitute the structure of the Open Hydrology framework designed to promote transparency and reproducibility.

This publication reviews the current state-of-the-art of AI and Machine Learning (ML) applications within water management, introducing some of the main concepts and providing the reader with a general understanding of different technologies and concepts. Further, it features examples of the most influential applications of AI within water management and highlights the ethical challenges when streamlining AI for water resources management.

These datasets and maps present the landslides observed and landslide susceptibility of the Chimanimani and Chipinge Districts of Zimbabwe. The maps provide a qualitative assessment of the likelihood of a landslide occurrence in an area depend on the terrain condition, and knowledge of prior landslides in the region. These maps have been prepared with the recording of 20 000 landslides, that have been identified in the region, particularly those as a result of the Cyclone Idai that occurred in 2019.

These datasets and maps present the exposure to landslides (return period in years) for the Chimanimani and Chipinge Districts of Zimbabwe. The maps provide a qualitative assessment of the likelihood of a landslide impact to to the terrain conditions, knowledge of prior landslides in the region and landslide susceptibility. These maps have been prepared with the recording of 20 000 landslides, that have been identified in the region, particularly those as a result of the Cyclone Idai that occurred in 2019.