Research project OUTLAST - 2022 to 2025
OUTLAST - Development of an operational, multi-sectoral global drought hazard forecasting system
Objective of the research project OUTLAST is to develop the first global, multi-sectoral and operational drought forecasting system for quantifying drought hazards in 1) water supply, 2) riverine ecosystems, 3) non-agricultural land ecosystems, 4) rainfed agriculture, and 5) irrigated agriculture and to implement the system as a component of the Global Hydrological Status and Outlook System (HydroSOS) of the World Meteorological Organization (WMO). Together with pilot users in the project regions of Lake Victoria Basin (Burundi, Kenya, Rwanda, Tanzania and Uganda) and West and Central Asia (e.g. Afghanistan, Armenia, Azerbaijan, Iran, Iraq, Lebanon, Oman, Syria, Tajikistan, Turkey, Uzbekistan), the value of these global-scale forecasts for the next six months for data-poor, transboundary watersheds will be tested. Through co-design, appropriate drought indicators will be defined and the web portal and pilot applications of these global forecasts for drought management and water governance will be developed.
Subproject at the Division Agronomy
The subproject at the University of Göttingen will contribute to the overall objective by (i) coordinating the project network, (ii) developing operational seasonal drought forecasts for irrigated and rainfed agriculture and integrating the forecasting system into the overall multi-sectoral forecasting system, and (iii) systematically evaluating the multi-sectoral forecasting system on a global scale as well as on a regional scale in cooperation with the regional pilot users.
Project coordination includes the overall coordination and activities of the sub-projects, monitoring of project progress, ensuring efficient and targeted communication between project partners as well as with pilot users, project funders (reporting), and the public. In addition to the kickoff workshop, 4 project workshops are planned, of which the validation workshop and the results and transfer workshop will be organized by the subproject at University of Göttingen.
The simulation of drought hazards in irrigated and rainfed agriculture will be based on preliminary work performed in the research project GlobeDrought by applying the Global Crop Water Model (GCWM). This model will be further developed to an operational forecasting system to provide, in monthly time steps, ensemble forecasts for the upcoming 6 months. Tools will be developed for an automated processing of model outputs to calculate otimized drought hazard indicators developed within the OUTLAST project together with regional pilot users.
In collaboration with the project partners at Karlsruhe Institute of Technology and University of Frankfurt am Main the predictive quality of the global-scale drought forecasts will be systematically evaluated with respect to (i) the type of drought studied (meteorological droughts, soil moisture droughts, hydrological droughts), (ii) the length of the forecast period, (iii) seasonal differences, and (iv) regional differences in forecast quality. In addition, a comparison is made with results from other drought information systems. The correlation of simulated drought hazards with reforted drought impacts, such as anomalies in flow or crop yields, will be tested.
Persons in charge:
Neda Abbasi
Malte Weller
Prof. Dr. Stefan Siebert
Project duration:
1. September 2022 bis 31. August 2025
Funding:
BMBF (Funding measure Water as a Global Resource, GRoW)
Website:
https://outlast-project.net/
Project partners funded by BMBF:
Goethe-Universität Frankfurt am Main, Institute of Physical Geography (IPG)
Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research (IMK-IFU)
German Federal Institute of Hydrology Koblenz, International Centre for Water Resources and Global Change (ICWRGC)
Project partners without BMBF funding:
World Meteorological Organization (WMO)
Lake Victoria Basin Commission (LVBC)
UNESCO Regional Centre on Urban Water Management Tehran, Iran (RCUWM)
