Importance of HydroMeteorological Data Bank for Use in Coupled Models and Disaster Management Using

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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 SORMAN Middle East Technical
University (METU) Department of Civil
Engineering 22 25 May 2004
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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

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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

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Snow studies between 1964-2002

• Snow observations
• Classical methods (snow sticks, snow tubes)

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Recent Studies by DSI and DMI

• In TEFER project, 206 automated meteorological
  stations are under construction
• 3 radar stations are to be operated in western
  regions of Turkey

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Snow studies between 1964-2002

• 2. Snow research and modeling in basin scale
• Basin wide snow studies were initiated by METU,
  Tübitak-Bilten, EIE, DSI and DMI under a
  protocol sponsored by NATO in 1997.

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Snow in Eastern Turkey

• Snowmelt runoff constitutes approximately 60-70
  of yearly total volume in Euphrates (Firat)
  River, where major dams are located in series
  (Keban, Karakaya, Atatürk, Birecik and Karkamis).
• Therefore forecasting the snow potential in
  advance could result in better management of the
  countrys water resources.

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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
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Station Locations in Karasu Basin
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Station Instrumentation
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Güzelyayla Snow-Met Station
Elev 2065 m Lat 40o1219 Long
41o2818
Sensors
Rain Gauge
Snow pillow
Snow Lysimeter
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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
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Güzelyayla Snow-Met StationSnow Pillow
3 meter Diameter Hyphalon Snow Pillow
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Güzelyayla Snow-Met StationSnow Lysimeter

• Snow Lysimeter measures the
• amount
• rate
• duration
• of snow melt

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Snow-Met StationCommunication
Satellite

• Data from snow-met stations are downloaded via
  satellite or GSM where available.

METU Office
Snow-met Station
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Snow-Met Station Processed Data
Lysimeter
Snow Depth
Snow Water Equivalent
Snow data, 2003 water year
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Snow Studies Concentrate on

• Snow cover area monitoring SCA
• Snow water equivalent analysis SWE
• Snow albedo measurements - Albedo

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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)

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Snow Cover Area (SCA)
13 April 1998 Geocoded NOAA Image
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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
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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
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Snow Cover Area (SCA)
13 April 1997
Supervised Class. 13 April 1997
Snow Covered Area
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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.

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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
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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 snow grain size,
depth, age, impurities
Albedometer present at Güzelyayla and Ovacik
Snow-met stations
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Snow Albedo
Daily average snow albedo, 2004 water year
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Snow Albedo
MODIS 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

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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

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Hydrologic Models (SRM)

• Parameters
• Snow runoff coef. (cSn)
• Rain runoff coef. (cRn)
• Degree day factor (a)
• Temp. lapse rate (?)
• Critic temperature (Tcrit)
• Rainy area (RCA)
• Recession coefficient (k)
• Time lag

• Variables
• Snow Covered Area (S)
• Temperature (T)
• Precipitation (P)

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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

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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)

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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
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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
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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.

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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

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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.)
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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
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Model Integration and Outputs
Air temperature Precipitation (rain/snow) Wind Hum
idity 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
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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
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ECMWF DEM Turkey
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MM5 DEM Turkey
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MM5 Land Use Map
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ECMWF Temperature (3 May 2004)
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MM5 Temperature (3 May 2004)
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(No Transcript)
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Read Interpolate Plot (RIP) Air Temperature (3
May 2004)
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Read Interpolate Plot (RIP) Precipitation (3 May
2004)
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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
  (Kirkgöze Basin), where real time ground data
  (snow, meteorological, stream flow) are collected

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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 Kirkgöze Basin

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Global Weather Forecasts - ECMWF
Daily Total Precipitation (mm) of 5 May 2004
Air Temperature (oC) of 5 May 2004 1200
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Hydrological Model (HBV) Runoff Forecast
Observed and calculated runoff hydrographs at
Kirkgöze Basin outlet, DSI 21-01
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Hydrological Model Forecast Results

• R2, Nash efficiency criterion, is used in HBV
  model to show the goodness of fit of the observed
  and calculated values (from -? to 1.0, the
  higher the value the better the model fit).
• where observed runoff,
  average runoff, calculated runoff
• Normal values during HBV model calibrations are
  within the range 0.5-0.9. For this analysis, R2
  is 0.64.

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D. Data Assimilation and Downscaling

• Data collection, analysis and storage
• Quality control
• Physical downscaling of numerical weather
  prediction (ECMWF and/or MM5) model outputs
• Real time forecasting of stream flow with
  hydrological models
• Comparison of model outputs with observations
• Data assimilation and renew

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Users DSI, DMI EIE, KHGM Ministry of Env. and
Forest
Tools Satellites Aircrafts Balloons Meteorolo
gical Weather Stations
Products Snow covered area Snow depth Snow
water equivalent Snow surface temperature Precip
itation Soil moisture
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Downscaling
40 km
1 km
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Temperature and Precipitation Biases
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E. Future research activities for operational
runoff forecast

• Develop/validate hydrological models and coupled
  model sub-components. Improve precipitation
  (snow/rain) and runoff processes related to
  spring snow accumulation/melt
• Conduct experiments to understand the effects of
  terrain data (DEM, land use, soil moisture,
  vegetation)

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• Evaluate the effects of coupled model resolution
  on seasonal and diurnal land-surface atmosphere
  interactions in complex terrain regions.
• Develop techniques for assimilating new Remote
  Sensing products for MODIS / LANDSAT
• Develop and understand cold season precipitation
  including snow and frozen-ground

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• Investigate the effects of climate change
  senarios for mid and long term
• Assess and improve runoff models in coupled form
  to validate streamflow estimates to be used by
  managers / decision makers
• Decrease the effects of flood and drought with
  water resources planning strategies

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THANK YOU