1. 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

2. 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

3. 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 , 12 European countries involved. Poland coordinates Hydrological Validation and implementation cluster.

4. 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 Composite image from all Polish radars.
8 radars
989 telemetric posts

5. 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.

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

7. Well informed people do not panic even in extreme conditions

8. 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.

9. 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

10. 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).

11. Hydrological cycle vs. satellite products
Impact studies

12. Precipitation H-SAF work on following products
- Precipitation rate – MW only (3-6h, km), MW+IR (15 min, SEVIRI pixel)
Precipitation phase (3-6h, 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)

13. 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

14. 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

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

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

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

18. 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.

19. 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, Dec.2005,
8 countries of H-SAF consortium represented

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

21. 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

22. 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

23. Lesson learned from operational hydrological models’ analysis
Large variety of models regarding spatial domain – from detailed grids ( m), through HRU based model to catchment based models,
Large variety of requirements regarding temporal domain – from detailed 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,

24. 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.

25. 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)

26. 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

27. 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

28. 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.