1. EO-HYDRO Progress Meeting – Milan, 9 November 2006 Benefit/Cost analysis for SWE estimation based on the EQeau approach Karem Chokmani Service utility review, Canada Pierre Vincent

2. EO-HYDRO Progress Meeting – Milan, 9 November 2006 2 Benefit/cost analysis outline Introduction Objective The EQeau model Benefit/cost analysis methodology Uncertainties in SWE estimated using EQeau approach Uncertainties in SWE estimated using conventional approach Benefit/cost ratio calculation Summary

3. EO-HYDRO Progress Meeting – Milan, 9 November 2006 3 Introduction Snow water equivalent (SWE) is a key parameter in hydroelectric production forecast Forecasting models use catchments SWE mean values in order to estimate water contributions resulting from spring snow melting These values are calculated by interpolating local SWE measurements Expensive and time consuming process

4. EO-HYDRO Progress Meeting – Milan, 9 November 2006 4 Introduction EQeau model: semi-empirical model to estimate SWE using C-band SAR data (Bernier et Fortin, 1998) Tested in a pre-operational mode (2000-2002) over the La Grande river basin (in collaboration with Hydro-Quebec)

5. EO-HYDRO Progress Meeting – Milan, 9 November 2006 5 Objective Assessment of the benefit/cost ratio related to the use of the EQeau model for SWE estimation compared to the conventional method currently employed by Hydro-Quebec

6. EO-HYDRO Progress Meeting – Milan, 9 November 2006 6 The EQeau model The increase in the backscattering signal, between a winter SAR image and late fall snow-free SAR image, could be associated with a gradual increase of the soil temperature resulting from snow thermal resistance (STR) effect (insulating capacity). STR is related to SWE via snow depth and density. The backscattering signal ratio (BR) can be related to STR and by the way to SWE

7. EO-HYDRO Progress Meeting – Milan, 9 November 2006 7 The EQeau model Frost penetration depends on the snow thermal resistance (STR) which is related to SWE via snow depth and density. ε of a frozen soil is smaller than that of a wet soil (radiation penetrates deeper into frozen soil). The more STR is low (shallow depth and/or high density), the more the soil temperature will decrease, with a corresponding reduction of ε and of the backscattering signal (BS). The increase in the BS, between a winter SAR image and late fall snow-free SAR image, could be associated with a gradual increase of the soil temperature resulting from the STR effect. The backscattering signal ratio (BR) can be related to STR Bernier and Fortin (1998) established a consistent relationship between BR and STR

8. EO-HYDRO Progress Meeting – Milan, 9 November 2006 8 The EQeau model Range application: 80 mm <SWE< 490 mm Application conditions: –BS is originating from the snow/soil interface: sparse vegetation cover; dry snow pack –The soil type should be sensitive to freezing conditions (containing a low percentage of coarse materiel); –The reference image soil should be frozen; –For the winter images, the air temperature << 0ºC for long time period, as to insure that the snow thermal resistances impact is optimal.

9. EO-HYDRO Progress Meeting – Milan, 9 November 2006 9 The EQeau model SWE = STRx xK Local snow density Land Cover Snow density Fall SAR imageWinter SAR imageBR image SWE STR = mxBR + b & EQeau 2002 version

10. EO-HYDRO Progress Meeting – Milan, 9 November 2006 10 The EQeau model In 2004, EQeau was modified in order to improve its performances –A new pre-processing chain with a pixel size of 375 meters (optimized size) –Use of either ENVISAT ASAR Wide Swath or ScanSAR Narrow Radarsat-1 data

11. EO-HYDRO Progress Meeting – Milan, 9 November 2006 11 The EQeau model –The modification of EQeau algorithm parameters and the optimization of the EQeau model –The correlation between SAR signal and STR is still low at 22%

12. EO-HYDRO Progress Meeting – Milan, 9 November 2006 12 The EQeau model –A new method to interpolate snow density values taking into account altitude and latitude AltitudeLocal snow density Linear regression + Inverse distance interpolation of regression residues Interpolated snow density Cross-validation Linear regression

13. EO-HYDRO Progress Meeting – Milan, 9 November 2006 13 The EQeau model Modified approach explains 78% of SWE variability with an error of 29 mm 6% increase in accuracy is related to the input of SAR data into the EQeau model The impact of a such improvement needs to be specified Cross-validation

14. EO-HYDRO Progress Meeting – Milan, 9 November 2006 14 Benefit/Cost analysis methodology Field measurements (Snow lines) Uncertainty? Benefit / Cost Ratio Martin et al. study GAIN OR LOSS OF ACCURACY Extrapolation to one sub-watershed SWE Uncertainty? Integrated SWE at the sub-watershed level Uncertainty? EQeau model SWE at the pixel scale Uncertainty?

15. EO-HYDRO Progress Meeting – Milan, 9 November 2006 15 Methodology Benefit/cost ratio resulting from the improvement of the SWE estimation accuracy related to the use RADARSAT-1 imagery (Martin et al., 1999) –3 acquisition modes were evaluated (ScanSAR, Wide and Standard) –Investments in R & D, project exploitation costs and incomes resulting from an increase in forecasts accuracy were taken into account –The study involved the period 1994-2007 (results remain valid) –2 initial reservoir levels Linear relation between error reduction and B/C ratio Martin et al. study

16. EO-HYDRO Progress Meeting – Milan, 9 November 2006 16 Uncertainties in SWE estimated using EQeau approach Total error for SWE values estimated by EQeau for a pixel size of 375 m x 375 m (E T ) is the resultant of two components: –E p : error resulting from uncertainty on the model parameters (random error) 38% -26%; –E A : model fitting error : –Var( ) is error variance ( 28.5 mm, random error) and b is the bias ( 4.5 mm, systematic error) EPEP EAEA EQeau model SWE at the pixel scale Uncertainty?

17. EO-HYDRO Progress Meeting – Milan, 9 November 2006 17 Uncertainties in SWE estimated using EQeau approach When averaging the SWE values at the sub-basin level, the random error component is cancelled: The final error associated with SWE estimation at this level from the EQeau method is quite low 4.5 mm representing only 2% for mean SWE values of 250 mm. Integrated SWE at the sub-watershed level Uncertainty?

18. EO-HYDRO Progress Meeting – Milan, 9 November 2006 18 Uncertainties in SWE estimated using conventional approach Local SWE measurements are interpolated over a regular grid (10x10 km 2 ) using inverse- distance interpolation Interpolated values are averaged over the sub-basin level –Starting from field measurements error (6.5 mm) and using Monte Carlo simulation, we find E p 2.5 mm –Interpolation technique error (E A ) is 30 mm and the mean bias is 1.4 mm The SWE estimation error at the sub-basin level is represented by the bias which varies according to the SWE value Field measurements (Snow lines) Uncertainty? Extrapolation to one sub-watershed SWE Uncertainty?

19. EO-HYDRO Progress Meeting – Milan, 9 November 2006 19 Benefit/cost ratio calculation Benefit / Cost Ratio Martin et al. study GAIN OR LOSS OF ACCURACY

20. EO-HYDRO Progress Meeting – Milan, 9 November 2006 20 Summary Benefit/Cost (B\C) ratio of less than 1 is obtained with the EQeau Model when the (SWE) estimation is close to the field measurements average value –B/C ratio is unfavourable with EQeau in spatially homogenous snow conditions In spatially variable snow conditions (high SWE variance), the B/C ratio is very favourable with EQeau –La Grande River watershed: huge basin (200 000 km 2 ) where, snow conditions are not homogenous and vary spatially from East to West.

21. EO-HYDRO Progress Meeting – Milan, 9 November 2006 Thank you