Potential variations in low flow hydrological indices associated - PowerPoint PPT Presentation

Potential variations in low flow hydrological indices associated with climate change. André St -Hilaire 1,2 , Anik Daigle 1,2 , Nathalie Thiémonge 3 , Luc Roy 3 , Daniel Caissie 4 , Loubna Benyahya 4 ,Taha Ouarda 1 1. Statistical Hydrology Research Group, INRS-ETE, University of Québec, Canada 2. Canadian Rivers Institute, University of New Brunswick, Canada Hydro- Québec, Montreal, Canada 3. 4. Fisheries and Oceans Canada HydroPredict conference 21 September 2010

Canada: A water rich country… but not without some challenges Instream flow needs must be quantified

Introduction to Hydrologic indices Existing methods for instream flow evalution (Tharme 2003): • Univariate hydrological approaches : Minimum flow requirements: e.g. Tennant (1976) • Hydraulic Approaches: Wetted perimeter • Wetted perimeter, Hydraulic Radius (Reiser et al., 1989). Discharge • Habitat Preferences : • Establish links between habitat variables (depth, velocity, substrate) and fish or invertebrate preferences (Bovee et al. 1986).

Minimum flow approaches may be insufficient  33 %  8 % MAF MAF … And the application of habitat preference models can be costly and difficult

Natural Flow Paradigm: • Flow is the master variable that modulates available habitat. Characteristics of the natural hydrogram should be conserved when managing flows . Water quality Geo- Hydrology Biology morphology Connectivity

Flow Indices • Flow characteristics are described through the use of Hydrologic Indices (HI).  High number of existing indices:  32 (Richter 1996)  171 (Olden & Poff 2003)  201 (Monk et al. 2006).  Are these indices pertinent for Eastern Canada?  Can they be used to quantify changes to low flow conditions (and eventually, habitat changes)?  How will they evolve as a function of climate change?

1. Hydrologic Indices related to low flows Catégories # HIs Examples  Annual min;  24 Monthly Min; Amplitude  Base flow Index .  Q7;  Duration 21 Q30 days/Qmed;  Mean event duration under the 25 th percentile.  # events under the 25th percentile; Frequency 2  # events under 5% of MAF.  Julian day of min annual flow; Occurrence 3  Mean date of the 7 lowest annual flows.  coefficient of variation of monthly min;  Variabilitty 15 coefficient of variation of Q7;  coefficient of variation of dates of the 7 lowest annual flows.

2. Data Base 100 100 a) a) b) Average=4881 km ² Average=35 years 80 80 Frequency (%) Frequency (%) 60 60 Number of 40 40 20 20 stations: 0 0 0-100101-10001001-1000010001-100000>100001 9-2021-3031-4041-5051-60 Range (km2) Range (year) Qc :104 Qc N.-B: 23 Qc N.S.: 26 TNL PEI: 6 NL NB N.&L: 16 IPE NB PEI NE Total: 165 NS Fig.1

Quebec hydrological regions

Example of the spatial distribution of some HI

3. Multivariate analysis • 65 HIs: Space with 65 dimensions. Principal Component Analysis reduces the number of variables by combining indices

Multivariate Analysis: Scree plot 50 • First three principal components:80% of variance 40 % of explained variance • PC1 explains nearly 50% of 30 variance. 20 10 0 0 10 20 30 40 50 60 70 Principal components

3. PC loadings of the 65 indices 1 (blue) = amplitude 2 (light blue) = frequency/timing 3 (yellow) = duration 4 (red) =variation

5. Climate Change scenarios Three Quebec rivers were selected: Romaine, Manic 5, Aux Outardes (1) Climate scenarios (10 for Romaine, 7 for Manic 5 and Outardes 4) were used to provide input to hydrological models (2) SSARR (Streamflow Synthesis and Reservoir Regulation) was used on all three systems. (3) HSAMI (Hydro-Quebec forecasting model, Fortin et al., 2000) was also used on the Romaine River.

5. Romaine River • 13 000 km 2 • 53 years of hydrological data (1956-2008) • Planned Hydroelectric complex • 1550 MW, 4 dams

5. Climate change scenarios used : delta Prec delta Prec Description # delta Tmin delta Tmax delta Tmoy (ratio) (%) MRCC afa (direct and de-biased) 7 3.4 2.7 3.1 1.2 17.9% Echam5 EH5_OM_A2_2 11 3.0 2.4 2.7 1.1 9.8% cgcm3_1_t47 sresa2_3 (direct and de-biased) 22 3.4 2.9 3.2 1.2 17.1% cnrm_cm3 sresb1 run1 35 1.3 1.1 1.2 1.0 1.7% ipsl_cm4 sresa2 run1 51 4.5 4.0 4.2 1.1 10.5% miroc3_2_hires sresa1b run1 53 5.0 4.4 4.7 1.2 15.9% miub_echo_g sresa1b run1 61 3.8 3.3 3.6 1.1 6.4% mri_cgcm2_3_2a sresb1 run2 81 1.6 1.4 1.5 1.1 9.8% • Recent pass: calibration period of hydrological models (1964-1976) • Futur climate (2042-2065)

Example of calculated indices: 10 models Calibration Climate error models bias

Median duration of events under 25th percentile

Mean date of annual minimum

Multivariate assessment of HI: Outardes 4 Scores factoriels des stations par région CP1-CP2 Scores factoriels des stations par région CP1-CP2 10 f1 10 8 -2 p1 9.5 6 9.5 -3 4 9 f3 f4 9 2 -4 f2 f6 p3 CP2 8.5 CP2 0 p5 8.5 -5 o0 c0 p2 f1 f7 -2 p1 f5 8 8 -6 f4 f3 p4 -4 f2 p7 f6 p3 p5 o0 p2 c0 7.5 f7 f5 -7 7.5 -6 p4 p7 -8 p6 7 -8 p6 7 -5 0 5 10 15 3 3.5 4 4.5 5 5.5 6 CP1 CP1 Scores factoriels des stations par région CP1-CP3 Scores factoriels des stations par région CP1-CP3 10 10 f5 3 2 f5 9.5 9.5 2 o0 1 1 o0 9 9 0 f7 0 f7 CP3 f4 CP3 8.5 f2 8.5 -1 f4 f2 f1 -1 -2 p4 f6 p1 p2 p5 8 8 f3 p7 p6 -3 c0 f1 -2 p4 p3 p1 p2 f6 7.5 p5 -4 7.5 f3 p7 p6 -3 c0 -5 p3 7 7 -5 0 5 10 15 3 3.5 4 4.5 5 5.5 6 CP1 CP1

Multivariate assessment of HI: Manic 5 Scores factoriels des stations par région CP1-CP2 Scores factoriels des stations par région CP1-CP2 10 10 o0 4 8 3 9.5 6 9.5 2 f1 o0 4 9 1 9 0 2 f2 f1 CP2 p1 c0 CP2 8.5 f3f4 f5 8.5 -1 0 f2 f7 p1 c0 f6 -2 f3 p5 f4 f5 p3 8 8 f7 p2 f6 -2 p5 -3 p3 p2 -4 7.5 -4 p7 7.5 p7 -5 p4 p6 p4 -6 p6 7 7 -5 0 5 10 15 -2 -1 0 1 2 3 4 5 CP1 CP1 Scores factoriels des stations par région CP1-CP3 Scores factoriels des stations par région CP1-CP3 6 10 o0 6 o0 10 5 5 9.5 9.5 4 4 f5 3 9 f5 9 2 3 f7 1 CP3 CP3 2 8.5 f1 8.5 f7 f2 0 c0 f3 f4 p4 1 -1 p2 f6 p1 8 p6 p5 p7 8 p3 f1 -2 f2 0 c0 f3f4 -3 7.5 p4 7.5 -1 p2 f6 p1 -4 p6 p5 p7 p3 -5 7 7 -2 -1 0 1 2 3 4 5 -5 0 5 10 15 CP1 CP1

Conclusions 1. The NFP is a promising tool for instream flow studies. It needs to be validated against biological data. 2. Simple multivariate analysis can help to reduce the number of indices. 3. Indices can be calculated from hydrological simulations coupled to climate change scenarios 4. Timing and duration of low flow events will likely differ in the future for the 3 rivers. 5. Hydrological model error can be of the same order of magnitude as climate model errors/ biases.

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3. PC scores of stations • Regional differences are identified

4. HI Selection. Criteria: (1) Highest absolute values of loadings on PC1, PC2 et PC3 (2) High separation distance in PC space, orthogonality (3) Each HI category must be represented in the final selection.

4. Selected List HI Définition classe Saturation relative PC 1 PC 2 PC 3 Low flow with return period of 5 QL5 years A -1.00 -0.13 0.18 ML3 Minimum flow in March A -0.01 1.00 -0.19 DL24 Q90/Qmed D -0.86 -0.99 -0.14 Median duration of Q< 25th DL16 percentile D -0.85 0.45 0.04 Number of events under 5% of FL3 MAF F 0.86 -0.17 0.20 MEMINJD Average date of minimum flow T 0.65 0.30 -0.80 MA3 Cv of MAF V 0.77 -0.70 -0.01 CVANNMIN Jul-Sept Cv of summer (Jul-Sept) low flows V 0.57 -0.98 0.87