Institute of Meteorology and Water Management, POLAND Land SAF and H-SAF products as complimentary source of information for operational hydrology Jerzy Niedbała, Jan Sadoń, Piotr Struzik

Presentation outline: Introduction - IMWM data sources for hydrological models. Hydrological cycle and information needed for modelling of processes. Hydrological models, thier requirements and possible use of satellite data – outcome of H-SAF (Cluster 4) activities. SAF products for operational hydrology: - Land SAF products, - proposed H-SAF products. 5. Solving the problem of spatial and temporal resolution. 6. Conclusions

Institute of Meteorology and Water Management, Kraków, Poland Hydrological Forecasting Office Satellite Research Department EUMETSAT Satellite Application Facility in Support to Operational Hydrology and Water Management (H-SAF) Activities: Operational hydrology – forecasting and warning Operational receiving, processing and distribution of satellite products to the users in IMWM network. Research and implementation of satellite products in meteorology, hydrology and agrometeorology. H-SAF activities just officially started (15 Sept.2005) – development phase 2005-2010, 12 European countries involved. Poland coordinates Hydrological Validation and implementation cluster.

Lightning detection SAFIR METEOSAT (7,8) NOAA (all) FengYun 1D Ready for Metop 60 Synop 152 Climate 978 Raingauges 196 Snow obs. posts NWP: DWD, ALADIN IMWM Poland Fig. 28 - Composite image from all Polish radars. 8 radars 989 telemetric posts

Main activities of operational hydrology is closely related with use of forecasting hydrological models, which convert meteorological inputs, hydrological inputs and parameters characterising cachment to discharges in streams and rivers. Use of such a models is also part of flood warning systems, which determine risk of flood according to forecasted hydrograms. Generally most of hydrological models are deterministic, based on physical equations describing water fluxes and energy exchange. Many models used in operational practise are still conceptual, semiempirical or empirical. Well-designed, distributed network that measure temperature, precipitation (rainfall and snowfall), snowpack, soil moisture, vegetation properties, radiation, wind, evaporative flux and humidity contribute to the quality of hydrological forecasts.

Hydrological processes are frequently rapid and dangerous Development of flash flood as a result of severe storm (Switzerland)

Well informed people do not panic even in extreme conditions

Development in operational hydrology and resulted demands Classic hydrological models have been optimised for use with point observations (such as precipitation and streamflow) and were inadequate for extension to data assimilation, which is distributed in space. We observe consequent exchange of hydrological models used operationally from models, which can accept only point data to the models based on gridded information. An improved higher resolution of observed data will decrease the uncertainty of hydrological model predictions. Remote sensing data should bring new type of information (both qualitative and quantitative) accepted by hydrological models.

Space structure of model levels. Preprocessing of data is required to fit input variables to model resolution. - grid based A model structure based on regular fine grids, matching the resolution of the other spatial data, is much to be preferred. based on HRU’s (Hydrological Response Units) Data are spatially aggregated giving mean values for Hydrological Responce Units (HRU). lumped subcatchment is another alternative frequently used in operational hydrology. Different scales of data processing in typical levels of hydrological model Satellite information (depending of instrument used) has frequently not adequate spatial and temporal resolutions – down/up scaling and merging with other data sources is required

Hydrological model of subcachment consists of several modelled processes characteristic for most of continuous hydrological models: · Model for snow accumulation and melting (snow layer) · Potential evapotranspiration model (vegetation and surface layer) · Generation of runoff (soil layer) · Transformation of runoff (stream layer) Further modelling concern channel routing and reservoir control. Results are sensitive to: space distribution of meteorological input data (e.g. precipitation), space distribution of state variables (e.g. soil moisture), space distribution of parameters (e.g. soil depth).

Hydrological cycle vs. satellite products Impact studies

Precipitation H-SAF work on following products - Precipitation rate – MW only (3-6h, 10-15 km), MW+IR (15 min, SEVIRI pixel) Precipitation phase (3-6h, 10-15 km) Accumulated precipitation (3h, 10 km) Satellite data used during development phase: Meteosat (MVIRI, SEVIRI), DMSP (SSM/I, SSMIS), NOAA + MetOp (AMSU-A, AMSU-B/MHS), EOS/Aqua (AMSR-E, AMSU-A, HSB), TRMM (TMI, PR, LIS) Satellite data used during operational phase: Meteosat (SEVIRI), NPOESS (CMIS, ATMS), Further satellites of the GPM (all equipped at least with a MW radiometer, one also with radar)

Soil Moisture LAND SAF: Soil moisture SEVIRI pixel, daily (not operational yet) Satellite information: SEVIRI (pixel resolution), Problem with cloudiness, wind speed, high latitudes H-SAF future products: Soil moisture in the surface layer Soil moisture in the roots region Satellite information: Metop/ASCAT (25 km/36h), NPOESS/CIMS (40 km/6h), Problem: spatial resolution

Snow Land SAF Snow cover – SEVIRI, daily (not operational yet) H-SAF: Snow recognition (SR) – 2/5 km, 6h Snow effective coverage (SCA) – 5/10 km, 6h Snow status (wet or dry) – 5 km, 6h Snow Water Equivalent (SWE) – 10km, 6h Development: NOAA (AVHRR), MetOp (AVHRR, ASCAT), Meteosat (SEVIRI), EOS-Terra/Aqua (MODIS), DMSP (SSM/I, SSMIS), EOS-Aqua (AMSR-E), QuickSCAT (SeaWinds) Operations: MetOp (AVHRR, ASCAT), Meteosat (SEVIRI), NPOESS (VIIRS, CMIS), MW radiometers of the GPM constellation

Vegetation properties Land SAF: LAI (Leaf Area Index) FVC (Fractional Vegetation Cover) Products not yet operational Needed by hydrology for proper interception modelling

Additional useful Land SAF products for operational hydrology Evapotranspiration Radiation products – DSSF, DSLF (operational) Albedo

Land SAF products for hydrology - summary Products required by most of operational hydrological models Products required by certain models

H-SAF Cluster 4 leaded by Poland Partners: Austria, Belgium, Finland, France, Germany, Italy, Romania, Slovakia, Turkey, ECMWF Fig. 27 – Concept of the hydrological validation Cluster and its relation to Clusters 1-3.

Hydrological models from 7 countries (14 models) were analysed, focusing on impact studies with use of H-SAF satellite products H-SAF Cluster 4 meeting in Kraków, 19-20 Dec.2005, 8 countries of H-SAF consortium represented

Countries involved in impact studies / validation of satellite products with use of hydrological models

Operational hydrological models and test sites selected for validation experiments: Belgium: SCHEME grid cell conceptual model, 2 test sites: Scheldt, Meuse river catchments France: SAFRAN-ISBA-MODCOU set of models, 3 test sites: Grand Morin, petit Morin, Beauce area, Germany: PRMS, HBV-BfG, MMS/MHMS models, 1 test site Sieg river Italy: ARTU’, NASH, DRiFT models, 3 test sites: Arno, Basento, Tanaro rivers, Poland: SH system (SMA, conceptual), 3 test sites: Soła, Skawa, Prosna, Slovakia: MIKE11-NAM, Hron rainfall-runoff, 5 test sites: Myjava, Kysuca, Nitra, Hron, Topľa. Turkey: Snowmelt Runoff Model SRM, HBV models, 5 test sites: Upper Eufratus, Upper Karasu, Kırkgöze Basin, SAKARYA, WESTERN MEDITERRANEAN BASIN, Finland: TBD

Required input data and parameters for selected hydrological models Required spatial resolution Required temporal resolution Precipitation Regular grid 500 m, 1 km, 7 km, 8 km HRU, subcatchment 10 min, 15 min, 1h, 3h, 6h, daily Snawfall Regular grid 1 km, 8 km, HRU 6 h - daily Air temperature 8 km, 25 km 10 min, 1h, daily Soil moisture   Snow covered area Catchment daily Solar radiation 8 km, 50 km 1h, 6h Snow water equivalent Long wave radiation 8 km 1h Sun duration 50 km 6h Albedo Land use, vegetation type 250 m, 1km 10 days, season Wind speed 10 min, 1h Humidity 1h, 6 h

Lesson learned from operational hydrological models’ analysis Large variety of models regarding spatial domain – from detailed grids (100-500 m), through HRU based model to catchment based models, Large variety of requirements regarding temporal domain – from detailed 10-15 min data to daily means, Most important inputs: precipitation (including snow), temperature, radiation components (mainly solar). Less frequently used parameters: detailed radiation budget (short/long wave), evapotranspiration, vegetation type and actual status. Data needed (lack of adquate ground measurements), expected from satellite information: soil moisture, snow water content,

Solving the problem of spatial and temporal resolution. 1. H-SAF additional tasks: up/down scaling methods and algorithms, Merging sateliite products with ground observations, Adapting satellite products to hydrological models inputs (interfaces). 2. Tools included in hydrological models (also considered): Data processors, Embedded GIS tools.

Why Pre-Processing of meteorological data ? Stations are sparsely, not uniformly distributed Measurements are taken at different time steps Measurements mostly NOT taken at the locations (center points) of a HRU Observations at a certain station may be affected by systematic errors (e.g. temperatures are too high due to radiation) Variation of meteorological variables (temperature, snow/rain, precipitation) depends on the topography Satellite data are available in defined time slots (variable for polar sat.) and not regular grid (resolution depend on viewing angle)

Preprocessing Tasks Temporal domain - to reach the calculation time step of the hydrological model Temporal disaggregation of measurements Spatial domain – data regionalisation Spatial interpolation of data (measured or forecasted) to a reference grid or center points of HRUs Take vertical dependence of parameters to be interpolated into account Filling of data gaps

Preprocessor: Temperature, Precipitation Precipitation, Temperature Observed Station Data Forecasted Grid Data Temporal Disaggregation Temporal Disaggregation Spatial Interpolation (from Station to Reference Grid) Spatial Interpolation (from Station to Reference Grid) Spatial Interpolation (from NWP Grid to Reference Grid) Spatial Interpolation (from NWP Grid to Reference Grid) Spatial Aggregation from Reference Grid to HRU Spatial Aggregation from Reference Grid to HRU Spatial Aggregation from Reference Grid to HRU Spatial Aggregation from Reference Grid to HRU Observed station data transformed into mean HRU values Forecasted grid data transformed into mean HRU values

Conclusions Hydrological community will benefit from many SAFs, mainly: H-SAF, Land SAF, NWC-SAF. Other SAFs will benefit from H-SAF eg. NWP-SAF. Actual and proposed SAF products are not much overlapping in field of hydrology. In case of potentially similar products, methodology and satellite data used are different (eg. Soil moisture). Activities planned in frame of H_SAF (impact studies) may take into account also products from the other SAFs, specially from Land SAF.