Some river basins are considered to be very similar because they have a similar background such as a transboundary, facing threats of human activities. But we still lack understanding of differences under their general similarities. Therefore, we proposed a framework based on a Bayesian network to group watersheds based on similarity levels and compare the causal and systematic differences within the group. We applied it to the Amu and Syr Darya River basin and discussed its universality.
Sara Top, Lola Kotova, Lesley De Cruz, Svetlana Aniskevich, Leonid Bobylev, Rozemien De Troch, Natalia Gnatiuk, Anne Gobin, Rafiq Hamdi, Arne Kriegsmann, Armelle Reca Remedio, Abdulla Sakalli, Hans Van De Vyver, Bert Van Schaeybroeck, Viesturs Zandersons, Philippe De Maeyer, Piet Termonia, and Steven Caluwaerts
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-368,https://doi.org/10.5194/gmd-2019-368, 2020
Revised manuscript accepted for GMD
Detailed climate data is needed to assess the impact of climate change on human and natural systems. The model performance of two high resolution regional climate models, ALARO-0 and REMO2015, was investigated over Central Asia, a vulnerable region where detailed climate information is scarce. Both models are able to reproduce the observed spatial patterns for temperature and precipitation, making their produced climate data suitable for climate impact studies over the Central Asia region.
Adequate flood damage assessments can help to minimize damage costs in the SIDS. Data availability is, however, a major issue in these areas. In order to determine the minimal data necessary for an adequate result, a sensitivity analysis was performed on the input data. This has shown that population density, in combination with an average number of people per household, is a good parameter to determine building damage. Furthermore, a complete road dataset is visually indispensable.