Enhancing regional Flood Early Warning Systems using High-Resolution GRACE/GRACE-FO total water storage (TWS) and wetness index data

High resolution gravity field data sets provide valuable information about the wetness state of a catchment, which is a useful indicator in flood early warning systems.

The HiGrav Project aims to increase the temporal and spatial resolution of GRACE/GRACE-Follow-On (FO) data and derived terrestrial water storage anomalies (TWSAs) and wetness index data. Further, the project will assess the utility of this enhanced information basis for flood forecasting in terms of forecast accuracy and flood early warning reliability.

We will explore if downscaled GRACE/GRACE-FO data sets are useful to improve flood forecasting and warning at a regional scale (areas of around 10.000 km²). For this purpose, the semi-distributed, process-based hydrological model PANTA-RHEI will be used. The model is used operationally by the Flood Forecasting and Warning Centre in Lower Saxony (NLWKN - HWVZ). An extended PANTA RHEI model will be developed to dynamically assimilate high-resolution GRACE/GRACE-FO TWS data for regional flood forecasting by associating these data with model parameters and state variables that represent catchment water storage in terms of e.g. soil moisture and snow using machine learning. The performance of the extended model will be assessed in the Aller-Leine-Oker, Ilmenau, Hase, Wümme, Hunte, Vechte, Große Aue river basins in Lower Saxony with catchment areas between 3.000 and 15.000 km² using hindcast simulations in the period from 2017 to 2025 which includes significant flood events.

 With this contribution, we aim to present our scientific motivation, discuss the methodological framework, ideas and anticipated outcomes.