SaWaM - Seasonal water resource management in dry areas: Practice transfer of regionalised global information [funded by BMBF]
Dr. Boris Thies
M.Sc Nazli Turini
Amount of daily precipitation in 2017/03/19 in Iran derived from Global Precipitation Measurements missionMethods and tools for the application-oriented transfer of regionalized global information to water management in data-sparse semi-arid regions will be developed and their performance analysed. In a cooperation of seven scientific partners and two companies, the regional focus will be on Sudan, Iran, Brazil, Ecuador/Peru, and West Africa. For hydrological design purposes, our temporal scale covers the past up to the present, while for operational management purposes it will address the upcoming 1-12 months. SaWaM employs models addressing seasonal forecasting, water availability, sedimentation processes, and ecosystem states. Model approaches are extended by satellite-borne methods. An online prototype of a globally applicable decision support system for dry-land water management will be developed through an integrative approach in close cooperation with German business partners and local stakeholders.
Project website: http://www.grow-sawam.org/
Amount of daily precipitation in 2017/03/19 in Iran derived from Global Precipitation Measurements mission
Publications and poster presentations:
- Turini, N.; Thies, B.; Rollenbeck, R.; Fries, A.; Pucha-Cofrep, F.; Orellana-Alvear, J.; Horna, N. & Bendix, J. (2021): Assessment of Satellite-Based Rainfall Products Using a X-Band Rain Radar Network in the Complex Terrain of the Ecuadorian Andes. Atmosphere 12(12), 1678.
- Turini, N.; Thies, B.; Horna, N. & Bendix, J. (2021): Random forest-based rainfall retrieval for Ecuador using GOES-16 and IMERG-V06 data. European Journal of Remote Sensing 54(1), 117-139.
- Turini, N.; Thies, B. & Bendix, J. (2019): Estimating High Spatio-Temporal Resolution Rainfall from MSG1 and GPM IMERG Based on Machine Learning: Case Study of Iran. Remote sensing 11(19), 2307.