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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 ŞORMAN Middle East Technical University (METU) Dep artment of Civil Engineering 22 – 25 May 2004. Introduction.

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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 ŞORMAN

Middle East Technical University (METU)

Department of Civil Engineering

22 – 25 May 2004



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


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
snow studies between 1964 2002
Snow studies between 1964-2002
  • Snow observations

Classical methods (snow sticks, snow tubes)

recent studies by ds and dm
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
snow studies between 1964 20026
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.

snow in eastern turkey
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.
automated snow meteorological snow met stations

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

g zelyayla snow met station
Güzelyayla Snow-Met Station

Elev: 2065 mLat: 40o12`19`` Long: 41o28`18``


Rain Gauge

Snow pillow

Snow Lysimeter

g zelyayla snow met station sensors
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

g zelyayla snow met station snow pillow
Güzelyayla Snow-Met StationSnow Pillow

3 meter Diameter Hyphalon Snow Pillow

g zelyayla snow met station snow lysimeter
Güzelyayla Snow-Met StationSnow Lysimeter
  • Snow Lysimeter measures the
  • amount
  • rate
  • duration
  • of snow melt
snow met station communication
Snow-Met StationCommunication


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

METU Office

Snow-met Station

snow met station processed data
Snow-Met Station Processed Data

Snow Depth


Snow Water Equivalent

Snow data,2003 water year

snow studies concentrate on
Snow Studies Concentrate on
  • Snow cover area monitoring – SCA
  • Snow water equivalent analysis – SWE
  • Snow albedo measurements - Albedo
snow cover area sca
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)

Snow Cover Area (SCA)

13 April 1998 Geocoded NOAA Image

snow cover area sca20
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

snow cover area sca21
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


Snow Cover Area (SCA)

13 April 1997

Supervised Class.

13 April 1997

Snow Covered Area


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.
snow water equivalent swe
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

snow albedo
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

snow albedo26
Snow Albedo

Daily average snow albedo, 2004 water year

snow albedo27

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
b1 hydrological models
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

hydrologic models srm

Qn+1 = [cSn . an (Tn + Tn) Sn + cRn . Pn] (A.10000/86400) (1-kn+1) + Qn kn+1

Flow Recession

Snow melt


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)
hydrologic models hbv
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

hydrologic models snobal
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)

near real time forecasts
Near Real Time Forecasts



SSM/I(passive mw)

MODIS (optic)

Web site

Modem-Satellite Phone



Hydrologic models


Runoff Stations







GRIB format

Grided Binary





Remote Sensing




High spatial elevation model


(9x9km) [1.2GB]

Snow Covered





Non hydro static

Atm. Model

Grid Distributed


P/ T


Grid Data





Model Variables




Model Parameters

Integration of Real Time Atmospheric and Hydrological Models for Runoff Forecasts in Turkey

results conclusions from hydrological model studies
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.
results conclusions from hydrological model studies35
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
b2 atmospher ic hydrological model coupling
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.)

model input flow
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)



  • Point
  • Meteorologic observations
  • Hydrometric flow observations

Quality Check

Hydrological Model

model integration and outputs
Model Integration and Outputs

Air temperature

Precipitation (rain/snow)



Air Pressure


Atmospheric Model


Forecast / Analysis data


Snow water equivalent

Snow depth

Snow covered area

Snow temperature

Melt rate


Energy flux

Hydrological Model

(Operational /


State and Diagnostic Data

physical downscaling of thermodynamic variables
Physical Downscaling of Thermodynamic Variables

Thermodynamic Variables

(Pressure, Temperature, Humudity)


Elevation greater than

Model elevation?



Extrapolate temperature and virtual

Temperature to DEM elevation;

Compute pressure via hydrostatic


Interpolate pressure, temperature

and virtual temperature

to DEM elevation

Derive relative

humudity from

temperature and


c analysis of the early 2004 flood event
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
hydrological runoff forecasting
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

global weather forecasts ecmwf

Global Weather Forecasts - ECMWF

Daily Total Precipitation (mm) of 5 May 2004

Air Temperature (oC) of 5 May 2004 12:00

hydrological model hbv runoff forecast

Hydrological Model (HBV) Runoff Forecast

Observed and calculated runoff hydrographs at Kırkgöze Basin outlet, DSİ 21-01

hydrological model forecast results

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.

d data assimilation and downscaling
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




Ministry of

Env. and Forest









Snow covered area

Snow depth

Snow water equivalent

Snow surface temperature


Soil moisture


40 km

1 km

e future research activities for operational runoff forecast
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)
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
Investigate the effects of climate change senarios for mid and long term
  • Assess and improve runoff models in coupled form to validate streamflowestimates to be used by managers / decision makers
  • Decrease the effects of flood and drought with water resources planning strategies