Jerzy Niedbała Institute of Meteorology and Water Management Hydrological Forecasting Office

1. Institute of Meteorology and Water Management, POLAND, Krakow EUMETSAT H-SAF – Satellite Application Facility in Support to Operational Hydrology and Water Management VALIDATION OF SATELLITE PRECIPITATION PRODUCTS WITH USE OF HYDROLOGICAL MODELS – EUMETSAT H-SAF ACTIVITIES Jerzy Niedbała Institute of Meteorology and Water Management Hydrological Forecasting Office Jan Sadoń Institute of Meteorology and Water Management Piotr Struzik Institute of Meteorology and Water Management Satellite Research Department Whole H-SAF Team contributed

2. Presentation outline: 1. EUMETSAT SAFs – already presented by Thomas Heinemann. 2. H-SAF overview – already presented by Bizzaro Bizzari. Remained: 3. IMWM Poland - data sources for hydrological models. 4. Hydrological cycle and information needed for modelling of processes. 5. Hydrological models, thier requirements and possible use of satellite data – outcome of H-SAF (Cluster 4) activities. 6. H-SAF Validation programme (precipitation products): - Conventional validation, - Hydrological impact studies. 7. Solving the problem of spatial and temporal resolution. 8. 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, agrometeorology… • H-SAF activities officially started (15 Sept.2005) – development phase 2005-2010, 12 European countries involved. Poland coordinates Hydrological Validation and implementation cluster.

4. Lightning detection SAFIR Fig. 28 - Composite image from all Polish radars. METEOSAT (7,8,9) NOAA (all) FengYun 1D Ready for Metop 60 Synop 152 Climate 978 Raingauges 196 Snow obs. posts NWP: LM-COSMO, ALADIN IMWM Poland 8 radars 989 telemetric posts

5. Operational Hydrology and modelling • 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. Proper information and warning is highly required Well informed people do not panic even in extreme conditions

8. Development in operational hydrology and resulted demands for better input data • 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 cycle vs. satellite products Impact studies

11. EUMETSAT Satellite Application Facility in Support to Operational Hydrology and Water Management (H-SAF) • H-SAF bottom-up aproach • Requirements driven by operational hydrology needs. • Creation of operational satellite products for: • Better spatialisation of conventional measurements, • To complement ground observations on the areas with sparse ground networks and/or not covered by radars, • Merging satellite products with other data sources, • Redundancy of information - useful in case of disaster situation (damage of measuring posts or data links) • Final assessment of satellite products to be done by Hydrological Impact Studies. • Demonstration and training on satellite products use, in real operational environment of State Hydrological Services

12. End-user feedback Augmented databases Advanced algorithms New instruments Initial databases Baseline algorithms Current instruments Cal/val programme Prototyping Version-1 Version-2 Final Version Operational End-users and Hydrological validation programme Logic of the incremental development scheme 2007 Inter Consortium beta product delivering

13. Belgium, France, Germany, Italy, Poland, Slovakia, Turkey H-SAF activities on satellite products validation Cluster 1 Precipitation products Cluster 2 Soil Moisture products Cluster 3 Snow products Finland Germany Poland Turkey Romania Belgium Germany Hungary Italy Poland Slovakia Turkey Classical Validation. Comparison to ground measurements. Austria France ECMWF Hydrological impact studies At least 24 catchments, 14 operational models

14. The purpose of hydrological validation plan • to establish the plans of the hydrological institutes for performing the impact studies soon after start of the regular products distribution. • The objective of the Hydrological validation programme is to independently assess the benefit of the novel satellite-derived data on practical hydrological applications.

15. Elements of hydrological validation plan • elaborationof the requirements (i.e., what is needed to perform the impact studies), • selection and/or development of the algorithm/modelling tools to perform the impact studies, • describtion of the test sites, their equipment and the experiment planned to be carried out, • Performance of the impact studies on the base of all test sites using all available satellite data, • structure of the Education and Training (E&T) activities.

16. WP - 5000 Hydrological validation Poland (IM WM) WP - 5100 WP - 5200 WP - 5300 WP - 5400 WP - 5500 WP - 5600 Products training Developments Impact study 1 Impact study 2 Impact study 3 Impact study 4 Poland + Several Poland + Several Belgium France Germany I taly WP - 5110 WP - 5210 WP - 5410 WP - 5510 WP - 5310 WP - 5610 Soil moisture Development Grand/Petit Sieg Scheldt river Tanaro river Austria Belgium Morin catchment WP - 5120 WP - 5220 WP - 5420 WP - 5520 WP - 5320 WP - 5620 Sno w Development Beauce Ammer Meuse river Arno river Finland France region catchment WP - 5430 WP - 5130 WP - 5230 WP - 5700 WP - 5530 WP - 5630 Adour - Precipitation Development Impact stu dy 5 Dill Basento river Garonne Italy Germany Poland catchment basin WP - 5240 WP - 5800 WP - 5900 WP - 5710 WP - 5810 Development Impact study 6 Impac t study 7 Sola river Myjava river Italy Slovakia Turkey WP - 5250 WP - 5720 WP - 5820 WP - 5910 Development Skawa river Nitra river Sakarya river Poland WP - 5260 WP - 5930 WP - 5730 WP - 5830 WP - 5920 Upper Development Prosna river Kysuca river Manavgat Slovakia Euphrates WP - 5270 WP - 5950 WP - 5840 WP - 5850 WP - 5940 Development Kırkgöze Hron river Topla river Upper Karasu Turkey basin Structure of the WP-5000 (Hydrovalidation)

17. Catchment characteristics Climatological criterion - classifies rivers depending on the climatological zone the river is located in. Within area covered by EUMETSAT member and cooperating states, three major zones can be distinguished: warm temperate zone (subzones: Mediterranean and marine), cold temperate zone (subzones: continental and subpolar) and mountainous zone, Geographical and high-altitude criterion – describes localization of the river including location in a specific geographical environment. According to this criterion rivers are categorized as for example mountainous rivers, littoral rivers, etc., Catchment management criterion – allows to group the rivers, or more precisely their catchments, regarding predominate spatial management type. (e.g. urban, agricultural or sylvan catchments, etc.), Catchment size criterion – classifies river systems taking into account the total area of their catchments.

18. Hydrological regimes Pluvial, oceanic – occurs in temperate climate. Distribution of precipitation is homogeneous throughout the whole year. The annual river flow is high. In the summer time, due to evaporation, water level in rivers is lower comparing to the winter. Western Europe rivers Pluvial, Mediterranean – maximum water level appears in winter, because of the strongest recharge. Rivers can periodically or even totally dry up in summer. Rivers in Mediterranean Countries Nival – the rivers are frozen through the most part of the year. The highest flows appear in spring due to snowmelt. Minimum water levels are observed in autumn and winter. Rivers in Eastern Europe and Scandinavia, Pluvio-nival – two periods of high water levels are observed: the first in spring, caused by the snow cover melting as well as rainfalls and the second one in summer, caused by rainfalls Rivers in Eastern and partly Central Europe Glacial – the rivers have their headwaters in the glaciated area. Maximum water flows are observed in summer Central Europe rivers.

19. Operational hydrological models and selected testbeds Belgium: SCHEME grid cell conceptual model, 2 test sites Scheldt, Meuse France: SAFRAN-ISBA-MODCOU set of models, 3 test sites Grand/Petit Morin, Beauce, Adour-Garonne, Germany: PRMS, HBV-BfG, MMS/MHMS models, 3 test sites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 Euphrates, Upper Karasu, Kırkgöze , Manavgat, Sakarya, Finland: to be defined.

20. Test catchments location ? • Variety of climatological conditions • Variety of terrain conditions • Variety of land cover • Different hydrological regimes • Catchment size: 242 – 102000 km2 • 902 raingauges, 21 radars • 7 (8) countries • 24 test sites

21. Input data for hydrological models (analysis performed by H-SAF Cluster 4)

22. Lesson learnt • The analysis of the questionnaires from the hydrological modellers do not allow to common definition of the parameters expected from the satellite products at this point. • It is necessary to analyse the available (now or in near future) SAF’s products that could be useful for application in hydrological models inter-SAF activities. • 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 adequate ground measurements), expected from satellite information: soil moisture, snow water content. • We predict constant grow of user requirements during H-SAF development phase due to modernisation of measuring tools and models themselves.

23. General hydrological validationalgorithm (1) Preparation of tool verification - the hydrological model simulated mode - calibration and the verification of hydrological model input data for hydrological models from manual and automatic ground stations and experimental resarch output from hydrological model (simulated hydrograph) hydrological model comparing simulated and observed hydrographs • average square error • average square relative error • maximum relative error • time relative error

24. input data for hydrological models from radar system (now-casting) and meteorological model (forecasting) Hydrological model in operating mode General hydrological validationalgorithm (2) operating mode - starting hydrological model in operating mode input data for hydrological models from manul and automatic ground stations and experimental resarch output from hydrological model (forecasted hydrograph) hydrological model comparing forecasting and observed hydrographs in non-operating time • average square error • average square relative error • maximum relative error • time relative error

25. input data for hydrological models from radar system (now-casting) and meteorological model (forecasting) satellite data General hydrological validationalgorithm (3) Hydrological model in operating mode using satellite data satellite data rainfall and snow operating mode - starting hydrological model in operating mode comparing two forecasted hydrographs (computed on base standard or satellite data) with observed hydrograph in non-operating time output from hydrological model (standard forecasted hydrograph and forecasted hydrograph computed using satellite data) input data for hydrological models from manul and automatic ground stations and experimental resarch hydrological model • average square error • average square relative error • maximum relative error • time relative error soil moisture temperature, rainfall and snow

26. General hydrological validationalgorithm (4) Hydrological validation plan criteria of choice use of the satellite data increases the quality of hydrological forecasting comparing two forecasting hydrographs (computed on base of standard or satellite data) with observed hydrograph in non-operating time statistical analyses NEITHER „YES” NOR „NO” YES NO • average square error • average square relative error • maximum relative error • time relative error we recommend standard data as an input to hydrological forecasting model we recommend satellite data as an input to hydrological forecasting model Further research must be done: when, where and why use of satellite data gives negative results. Satellite data could be used in case when other data are not available Feedback to Clusters 1,2,3

27. Solving the problems with spatial and temporal resolution (satellite products bias ?). • 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.

28. Preprocessing Tasks • Temporal domain - to reach the calculation time step of the hydrological model • Temporal disaggregation of measurements • Spatial domain – data regionalisation/scaling • Spatial interpolation of data (measured or forecasted) to a reference grid or center points of HRUs • Vertical dependence of parameters to be taken into account • Filling of data gaps

29. Preprocessor: Precipitation Forecasted Grid Data Observed Station Data Temporal Disaggregation Temporal Disaggregation Spatial Interpolation (from Station to Reference Grid) Spatial Interpolation (from NWP Grid to Reference Grid) Spatial Interpolation (from Station 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 Satellite data are available in defined time slots (variable for polar sat.) and not in regular grid (resolution depend on viewing angle)

30. Conclusions: • H-SAF is preparing operational structure for hydrology. Without acceptation of products and their quality (at least among EUMETSAT Member and Cooperationg States), this activity will be useless. • Operational structure must include not only products creation but also creation of communication links to the users (GTN-H, WIS ?). • Strong need for closer links between satellite data providers and hydrological users – H-SAF consortium cosists of both. • We do not forsee to re-invent the wheel – large part of activities based on well known and partially tested algorithms and methods. Hydrological component of H-SAF useful not only for internal purposes. • First H-SAF products already available – soil moisture.