Pilot6 | EO-based surveillance of POPs pollution
Pilot 5.1 | Improved historical water availability & quality information service
There is plethora of services on the water-relevant sectors which depend on accurate and reliable hydro-climatic information at scales from regional to global. SMHI has developed and setup the Hydrological Predictions for the Environment (HYPE) model to a number of domains to address a number of user (i.e. hydropower, water authorities, emergency response management centres, general public etc.) needs. The model runs operationally at SMHI’s servers for Sweden and the pan-European domain providing open information at different time horizons (i.e. historical records, short range and seasonal forecasts, hydro-climatic projections) to targeted users and the general public.
A recently developed global model setup allows improved water information at different geographic locations, and giving the potential at later state to regionalise the service to other regions. The global HYPE model setup is based on high resolution openly available topography, land cover and river discharge data, including delineation of both lakes and rivers, which makes it better adapted than previous setups for assimilation of EO data representing water level, water extent, evapotranspiration, snow extent and snow water equivalent. An operational application of the global HYPE model is currently in its' final development phase, and will provide on a daily basis, historical and near real-time analyses, as well as medium range and (bi-monthly) seasonal forecasts.
/ Objectives
- Compare and correlate a number of hydro-climatic variables (lake and river water level, snow water storage, evapotranspiration) from the hydrological model and in-situ observations to their corresponding EO-based variables to better understand model setup limitations (linked to location and physiographic characteristics) and assess added-value from EOs.
- Constrain (re-calibrate) the hydrological model parameters using EOs to better describe internal states and improve model reliability in terms of representation of physical processes.
- Combine the water flow and storage information from the model with corresponding in-situ observations and satellite based information with the additional information from the EO-based flood extent mapping to gain an overall improved description of the state of the available water resources.
/ Partners
SMHI, LIST
/ Key Users
SMHI operational water resources analysis and forecasting services with key stakeholders: Swedish Agency for Marine and Water Management (SWAM); Swedish Geological Survey (SGU)
/ Key Datasets
(A) Meteorological forcing data (HydroGFD, Berg et al, 2017) providing daily precipitation, and daily mean, minimum and maximum air temperature at 0.5x0.5 degrees for the period 1960-present (provider: SMHI);
(WAS) World-Wide HYPE model setup which is a multi-basin, watershed-based semi-distributed hydrological model with a global coverage;
(W) EO-based snow water equivalent and snow extent data for the period 1979-present and the regions Northern hemisphere and Europe (provider: ESA GlobSnow, EU FP7 CryoLand, Copernicus Global Land monitoring);
(W) In-situ based river discharge observations given at daily and/or monthly time resolution (provider: SMHI (for Sweden), GRDC (Global Runoff Data Centre; for global data));
(WS) EO-based evapotranspiration, monthly time resolution, 250x250m spatial resolution (provider: MODIS),
(W) In-situ based lake and river water levels, daily time resolution, (provider: SMHI (Sweden), (for example the Danube River basin Authority for Danube));
(W) EO-based lake and river water level, data source: Sentinel-3 altimetry, (SMHI/sub-contractor and/or Corpernicus Global Land Monitoring service)
(W) EO-based flood extent mapping, data source LIST from parallel pilot project.
(WS) EO-based data on land cover, water bodies, glacier, hydrography (ESA CCI Land cover, CCI Glacier; GWD-LR)
Stay in the loop !
Acknowledgment
The e-shape project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement 820852