Financial Support: Climate Change Action Fund (CCAF), EC ALCAN Company, Jonquiere, Quebec

1. Downscaling of GCM Outputs for Flood Frequency Analysis in the Saguenay River System“Desaggregation spatio-temporelle des sorties des GCM pour l’analyse frequentielle des crues dans le bassin du Saguenay” Research Group McMaster University (Dr. Y. Dibike, S. Khan, B. Sawatzky, V. Arnold) Universite Laval (F. Anctil, N. Lauzon) ALCAN (B. Larouche) Financial Support: Climate Change Action Fund (CCAF), EC ALCAN Company, Jonquiere, Quebec

2. Contents Project overview DANN Downscaling Approach Progress Results

3. Project objectives Evaluate stochastic and statistical downscaling methods Develop dynamic neural network downscaling methods Inter-comparison study Hydrologic impact of climate change in the Saguenay watershed Flood regime analysis: Magnitude & Frequency Uncertainty analysis

4. Uncertainty Analysis Simulations Hydrologic Models Downscaling Methods 12 3 WATFLOOD HEC-HMS CEQEAU HBV96 (ANN) SDSM1 LARS-WG2 DANN3 12 3 1 2 3 1 2 3 Flood Regime Analysis Project overview

5. Statistical prediction / estimation Linear regression(Box & Jenkins) Nonlinearregression (Sigmoid, ANN)

6. Artificial Neural Networks (ANNs) Inputs neuron Cell body connection nucleus synapse outputs dendrites axon Simplified natural neurons Artificial neurons

7. Artificial Neuron Neuron i Xj • • Xn-1 Xn Output Wij  G Y=G() bi Win =wijxj + bi Inputs

8. Time Delay Line Si=WiX X(t) wi(0) D X(t-1) G Si D Yi(t) X(t-2) bi D wi(3) X(t-3) Neuron i Delay Line ( order p=3 )

9. N 1 • • • Context units MLP (FNN) & RNN Input Variables Hidden Layer Output layer XQ  XP  •• XN Y XTmx  XTm

10. Context Units MLP --> IDNN / TDNN XQ(t-1) D •• D Hidden Layer MLP ••• Output layer Y(t) XmxT(t) X’(t) D •• RNN --> TDRNN D

11. ANN: Fondamental Elements Data (input selection) Topology (layers & neurons) Structure (Type link) Algorithm Training Degree of importancefor Generalization Degree of difficulty

12. DANN models • IDNN (TDNN) • RNN (Elman) • Jordan RNN • Generalized RNN

13. Study Saguenay-Lac-Saint-Jean (SLSJ) Watershed

14. The Study Area Study… • The Saguenay – Lac-Saint-Jean (SLSJ) hydrologic system in northern Quebec • The total area is about 73,800 km2 • It extends between 70.5o - 74.3o West and between 47.3o - 52.2o North. • Saguenay is a well known flood prone area as many Canadians still remember the year-1996 flood of this river • Only one of the SLSJ sub-basins namely Chute-du-Diableis considered in the first phase of this study

15. Data Collection Study… • Historical (observed) daily meteorological data (such as daily precipitation, maximum and minimum temperature) • ALCAN meteorological network • Environment Canada (METDAT CDROM) • Historical (observed) daily hydrologic data (streamflow and reservoir inflow) • ALCAN hydrometric network • Environment Canada (HYDAT CD-ROM) • Observed daily data of large-scale predictor variables representing the current climate condition (1960 – 2000) • The Reanalysis dataset of the National Centers for Environmental Prediction (NCEP) • GCM output of large-scale predictor variables • The Canadian Climate Impacts and Scenarios (CCIS) project website.

16. GCM Data • The data is extracted from 201-year simulations with the Canadian Global Coupled Model-1 (CGCM1) • Uses the IPCC "IS92a" forcing scenario • The change in greenhouse gases (GHG) forcing corresponds to that observed from 1900 to 1990 and increases at a rate 1% per year thereafter until year 2100. • The direct effect of sulphate aerosols (A) is also included. ** indicates p_, p5 or p8 which represent the variable values near surface, at 500 hPa height or 850 hPa height, respectively.

17. Application: Chute-du-Diable • Chute-du-Diable watershed: 9,700 km2 • Variables to be downscaled (predictands): • daily precipitation & • daily Max and Min temperature • The period between 1961 till 2000 is identified to represent the current climate condition • The future climate change simulations (CGCM1) at the coordinate 50o N latitude and 71o W longitude were extracted for three distinct periods: • the 2020s (2010 and 2039), • the 2050s (2040-2069) and • the 2080s (2070-2099)

18. Downscaling experiment • Case 1: The predictand is observed data from a single station • Chute du Diable • Chute des Passes • Case 2: The predictand is observed data averaged over the basin • From 25 meteorological stations with precipitation and Tmax and Tmin measurements • Model calibration and validation • 30 years (1961-1990) are used for calibration • 10 years of data (1991-2000) are used for validation

19. Selection of predictors • Selecting predictor variables • Very important step • Correlation analysis and scatter plots; DANN sensitivity analysis • Identified variables must be physically sensible Summary of the most relevant large-scale predictor variables identified

20. Model performance criteria • Performance criteria • Precipitation • Mean daily precipitation and daily precipitation variability for each month, • Monthly average dry and wet-spell lengths • Residuals • RMSE, R2, r • Tmax and Tmin • Monthly means and variances • Residuals • RMSE, R2, r

21. Validation results: SDSM, LARS-WG, TDNN1, TDNN3 SDSM LARS-WG TDNN1 TDNN2

22. Residuals SDSM LARS-WG TDNN1 TDNN2

23. Downscaling results for the current and future condition SDSM LARS-WG TDNN1 TDNN2

24. Conclusions • Even though SDSM & LARS-WG models indicate an increasing trend in mean daily temperature, SDSM resulted in a relatively higher increase than that of LARS-WG. The TDNNs indicate a lower increasing trend in mean daily temperature than the SDSM. • SDSM output shows on average an increase in mean daily temperature by about 4.5 oC, while LARS-WG output indicates an average increase in mean daily temperature by about 2.5 oC. • TDNN1 and TDNN2 indicate on average an increase in mean daily temperature by about 3 oC and 3.5 oC respectively. • Both the SDSM and the TDNNs output shows an increasing trend in the daily precipitation and their variability. • LARS-WG results do not show any obvious trend in both the daily precipitation and their variability.

25. Current and Future Work • Application of different hydrologic models (CEQEAU, HEC-HMS and WATFLOOD, HBV96, ANN) for flow simulation in the river basin • Development of a dynamic neural-network based downscaling method -- with an adaptive module to facilitate model transferability • Downscaling the GCM outputs for each of the remaining sub-basins in Saguenay–Lac-Saint-Jean river system • Perform flood frequency analysis in the river system corresponding to the present and predicted future flow regimes • Assess the hydrologic impact of future climate change in the Saguenay river system as a whole.

26. Merci Thanks !