Importance of Hydro-Meteorological Data Bank for Use in Coupled Models and Disaster Management Using New Techniq

1. Importance of Hydro-Meteorological Data Bank for Use in Coupled Models and Disaster Management Using New Techniques (RS/GIS) in Turkey Prof. Dr. A. Ünal ŞORMAN Middle East Technical University (METU) Department of Civil Engineering 22 – 25 May 2004

2. Introduction Speech can be divided into 5 main topics: A. Importance of snow and data collection B. Hydrological models and coupling with atmospheric circulation models C. Flood forecasting from early snowmelt/rainfall in 2004 (a case study in Turkey) D. Scaling and meteorological data assimilation E. Future research activities for operational runoff forecast

3. A. Importance of Snow and Data Collection • Snow is an important resource of water • Determination of SWE is important to forecast the volume of spring melt • Ground truth is the main data source in investigating the snow covered areas • Reflectance values from the snow surface should be watched during the snow melt period

4. Snow studies between 1964-2002 • Snow observations Classical methods (snow sticks, snow tubes)

5. Recent Studies by DSİ and DMİ • In TEFER project, 206 automated meteorological stations are under construction • 3 radar stations are to be operated in western regions of Turkey

6. Snow studies between 1964-2002 2. Snow research and modeling in basin scale Basin wide snow studies were initiated by METU, Tübitak-Bilten, EİE, DSİ and DMİ under a protocol sponsored by NATOin 1997.

7. Snow in Eastern Turkey • Snowmelt runoff constitutes approximately 60-70% of yearly total volume in Euphrates (Fırat)River, where major dams are located in series (Keban, Karakaya, Atatürk, Birecik and Karkamış). • Therefore forecasting the snow potential in advance could result in better management of the country’s water resources.

8. Karasu Basin Automated Snow & Meteorological (Snow-Met) Stations Because of high snow potential, Karasu Basin in the Upper Euphrates is selected as a pilot basin for snow studies

9. Station Locations in Karasu Basin

10. Station Instrumentation

11. Güzelyayla Snow-Met Station Elev: 2065 mLat: 40o12`19`` Long: 41o28`18`` Sensors Rain Gauge Snow pillow Snow Lysimeter

12. Güzelyayla Snow-Met Station Sensors Ultra Sonic Depth Sensor Temperature and Relative Humidity Sensor Inmarsat Antenna Wind Speed and Direction Sensor Solar Radiation Sensor Net Radiometer

13. Güzelyayla Snow-Met StationSnow Pillow 3 meter Diameter Hyphalon Snow Pillow

14. Güzelyayla Snow-Met StationSnow Lysimeter • Snow Lysimeter measures the • amount • rate • duration • of snow melt

15. Snow-Met StationCommunication Satellite • Data from snow-met stations are downloaded via satellite or GSM where available. METU Office Snow-met Station

16. Snow-Met Station Processed Data Snow Depth Lysimeter Snow Water Equivalent Snow data,2003 water year

17. Snow Studies Concentrate on • Snow cover area monitoring – SCA • Snow water equivalent analysis – SWE • Snow albedo measurements - Albedo

18. Snow Cover Area (SCA) • National Oceanic and Atmospheric Administration (NOAA) Temporal Resolution: 2 or 3 times a day Spatial Resolution: 1.1 km • Supervised Classification • Unsupervised Classification • Threshold (Theta Algorithm)

19. Snow Cover Area (SCA) 13 April 1998 Geocoded NOAA Image

20. Snow Cover Area (SCA) • Special Sensor Microwave/Imager (SSM/I) Temporal Resolution: 1 or 2 times a day Spatial Resolution: 30 km Modified Grody/Basist Algorithm, 3 April 1997

21. Snow Cover Area (SCA) • Moderate Resolution Imaging Spectroradiometer (MODIS) Temporal Resolution: 1 or 2 times a day Spatial Resolution: 0.5 km 5 April 2004

22. Snow Cover Area (SCA) 13 April 1997 Supervised Class. 13 April 1997 Snow Covered Area

23. Snow Water Equivalent (SWE) • Snow Water Equivalent is the actual amount of water stored in the basin which will turn into runoff once snow melt occurs.

24. Snow Water Equivalent (SWE) • Snow pillows are used to measure continuous SWE at a point • SWE data are randomly checked by snow tube measurements done by state organizations near snow-met stations Station SWE, 2003 Water Year

25. Snow Albedo • Albedo is a very critical parameter in snow as it determines the amount of absorbed solar energy (major energy for snowmelt) for melting process to take place, “Energy Budget”. • Dry fresh snow albedo ~ 0.80-0.90 • Wet dirty snow albedo~ 0.20-0.30 Snow albedo is a function of snowgrain size, depth, age, impurities… Albedometer present at Güzelyayla and Ovacık Snow-met stations

26. Snow Albedo Daily average snow albedo, 2004 water year

27. MODIS Albedo Snow Albedo • Daily and 16-day albedo values from MODIS Aqua/Terra satellite are analyzed • Snow albedo variation is significant especially during snow ablation stage. Therefore, temporal variation as well as spatial variation is important • Snow albedo is used in energy balance models and modified temperature index models in hydrologic modeling

28. B1. Hydrological Models • SRM (Snowmelt Runoff Model) Switzerland-USA, Temperature Index Model • HBV (Hydrologiska By-rans avdeling for Vattenbalans) Sweden-Norway, Temperature Index Model • SNOBAL (Snow Balance) USA, Point Two Layer Energy Balance

29. Qn+1 = [cSn . an (Tn + Tn) Sn + cRn . Pn] (A.10000/86400) (1-kn+1) + Qn kn+1 Flow Recession Snow melt Rainfall Hydrologic Models (SRM) • Parameters • Snow runoff coef. (cSn) • Rain runoff coef. (cRn) • Degree day factor (a) • Temp. lapse rate (γ) • Critic temperature (Tcrit) • Rainy area (RCA) • Recession coefficient (k) • Time lag • Variables • Snow Covered Area (S) • Temperature (T) • Precipitation (P)

30. Hydrologic Models (HBV) Model Structure • Snow routine Critical Temp, Degree day, Rain/Snow correction coeff. • Soil Moisture Field Capacity, Pot. Evap. • Upper Zone Quick recession coeff. • Lower Zone Slow recession coeff., Percolation

31. Hydrologic Models (SNOBAL) Q = Rnet + H + LE + G + M Q: net energy change in snowpack(W/m2) Rnet: net radiation (W/m2) H: sensible heat flux (W/m2) LE: latent heat flux (W/m2) G: ground heat (W/m2) M: advection(W/m2)

32. Near Real Time Forecasts NOAA (optic) SSM/I(passive mw) MODIS (optic) Web site Modem-Satellite Phone METU cd/ftp Hydrologic models DSI Runoff Stations GSM ftp ECMWF DMI MM5

33. GRIB format Grided Binary Boundary Conditions ECMWF (40x40km) Remote Sensing NOAA/AVHRR MODIS GIS High spatial elevation model MM5 (9x9km) [1.2GB] Snow Covered Area Basin Characteristics NCAR Non hydro static Atm. Model Grid Distributed SCA P/ T Forecasted Grid Data Format Conversion Hydrological Models Model Variables (Temp.,Prec.) Forecasted Runoff Model Parameters Integration of Real Time Atmospheric and Hydrological Models for Runoff Forecasts in Turkey

34. Results & Conclusions from hydrological model studies • Formation of a common digital data banks Format conventions and parameter selections Enabling research oriented data sharing • Installation of new hydro meteorological stations and quality increment by optimization • Use of RS and GIS in basin model studies. Related software, hardware and satellite selection.

35. Results & Conclusions from hydrological model studies • Simulation and forecast studies by Lumped/Distributed (full/semi) models in {daily, monthly and yearly basis} • Providing the cooperation between universities and governmental organizations • Selection of projects having national priorities

36. B2. Atmospheric – Hydrological Model Coupling Elements of Hydrologic Cycle State and Diagnostic Parameters (Snow water equivalent, depth, snow surface temperature, Elements of net energy, melt speed, Stream flow, etc.)

37. Model Input Flow • Grid • Atmospheric Weather Prediction • (Analysis or Forecast) • NOAA / AVHRR Images • (1100 m resolution) • (Snow covered area, cloud, land) • MODIS Images • (500 m Resolution) • (Snow covered area, albedo) Geophysical Maps (Digital elevation Model, Land use, soil type, vegetal cover) Physical Downscaling • Point • Meteorologic observations • Hydrometric flow observations Quality Check Hydrological Model

38. Model Integration and Outputs Air temperature Precipitation (rain/snow) Wind Humidity Air Pressure Cloud Atmospheric Model (Forecast/Analysis) Forecast / Analysis data Integration Snow water equivalent Snow depth Snow covered area Snow temperature Melt rate Flow Energy flux Hydrological Model (Operational / Research) State and Diagnostic Data

39. Physical Downscaling of Thermodynamic Variables Thermodynamic Variables (Pressure, Temperature, Humudity) DEM Elevation greater than Model elevation? Yes No Extrapolate temperature and virtual Temperature to DEM elevation; Compute pressure via hydrostatic relation Interpolate pressure, temperature and virtual temperature to DEM elevation Derive relative humudity from temperature and pressure

40. ECMWF DEMTurkey

41. MM5 DEM Turkey

42. MM5 Land Use Map

43. ECMWF Temperature (3 May 2004)

44. MM5 Temperature (3 May 2004)

46. Read Interpolate Plot (RIP) Air Temperature (3 May 2004)

47. Read Interpolate Plot (RIP) Precipitation (3 May 2004)

48. C. Analysis of the early 2004 flood event • An unexpected snowmelt event has occurred during late February and early March of 2004 in the eastern and southern parts of Turkey • An analysis of the flood event is simulated using +1 day weather forecast data in a hydrological model to forecast runoff in Upper Karasu Basin (Kırkgöze Basin), where real time ground data (snow, meteorological, stream flow) are collected

49. Hydrological Runoff Forecasting • HBV Model (Temperature Index Model) • Input data into HBV model from global weather forecasts (ECMWF) Daily total precipitation Daily average air temperature • Forecast simulations during the period of 28 February - 7 March 2004 in Kırkgöze Basin

50. Global Weather Forecasts - ECMWF Daily Total Precipitation (mm) of 5 May 2004 Air Temperature (oC) of 5 May 2004 12:00