Motivation and objectives: Particularly in water scarce areas, ecosystem functions, agricultural productivity and thus food security strongly depend on the spatiotemporal variability of hydro-meteorological variables. Under weak technical infrastructure in developing and emerging economies, the quantification of available water resources remains a challenging task. There, usually very limited observation data is available. The overall goal of this WP is the analysis of the performance of both global- and regional-scale hydrometeorological datasets for their praxis-orient applicability in water resources management, and the provision of suitable tools for such analyses. Focus of this WP is on seasonal predictions with lead times of (up to) 12 months.
Methods: Global datasets such as GPCC, GPCP, CRU, and UDEL are used and validated with local observation stations (networks) of the different research regions. Reanalyses (ERA-Interim, MERRA, CFSR) and operational seasonal forecasts (ECMWF-S4, NCAR-CFS) will be dynamically downscaled following a nested approach (WRF) as well as statistical refinement and bias-correction approaches (e.g. Copula functions). An improved quantification of potentially available water resources including an estimation of uncertainty ranges will be done based on statistical assimilation techniques (e.g. Ensemble-Kalman-Filter). For this purpose, special focus is given to the usage of remote sensing products (e.g. SMAP, GRACE/GRACE-FO, MODIS). The development of operational approaches for the combination of historical discharge measurements with data from satellite altimetry (Jason 2 & 3, SARAL/AltiKa, Sentinel 3), and precipitation products (GPM, TRMM) will allow for improved real-time estimations of discharge and precipitation information.
This project was financed by the German Federal Ministry of Education and Research under the financial assistance agreement No 02WGR1421.