Seasonal Water Resources Management for Semiarid Areas: Bias-corrected and spatially disaggregated seasonal forecasts for the Rio São Francisco Basin (Brazil)
Project: Seasonal Water Resources Management for Semiarid Areas: Regionalized Global Data and Transfer to Practise - GRoW-SaWaM (BMBF): The SaWaM-Project, which is funded by the German Federal Ministry of Education and Research (BMBF) within the "Water as a global Resource (GRoW)“ initiative, aims at the development of methods and products for improving the water management in semi-arid regions. The methodological core of the project is a model chain, where global hydrometeorological information is first adapted towards five different study regions in Brazil (Rio São Francisco Basin), Iran (Karun Basin), Sudan and Ethiopia (Tekeze-Atbara and Blue Nile Basins), Ecuador and Peru (Catamayo-Chira Basin) and West-Africa (Niger and Volta Basins). Special focus is put on the application of seasonal hydrometeorological forecasts, which give information about the precipitation or temperature to be expected during the coming months. The regionalized information is then used as driving data for hydrological and ecosystem models, which allow for the description of water-management-related parameters and aspects both in the past, but also for the coming months. Further information can be found at http://grow-sawam.org/. Summary: This dataset group contains the regionalised seasonal forecasts for the SaWaM study domain D02 (Rio São Francisco, Brazil). The data is based on the latest seasonal forecast product SEAS5 from the European Centre for Medium Range Weather Forecast (ECMWF), which has been Bias-Corrected and Spatially Disaggregated (BCSD) towards the ERA5-Land high-resolution replay of the land component of ECMWF's ERA5 climate reanalysis. It hence provides a temporally and spatially consistent set of land surface variables for driving e.g. hydrological models or assessing the regional forecast skill of seasonal forecasts. Currently, the dataset group contains daily and monthly ensemble (re)forecasts during the period 1981 to 2019. In particular, each forecast with 25 (before 2017) and 51 (since 2017) ensemble members contains daily and monthly forecasts for precipitation, maximum, minimum, and average temperature as well as radiation from the issue date for the next 215 days.
Lizenz:
Creative Commons Attribution 4.0 International (CC BY 4.0)
