2018 International SWAT Conference Brussels, Belgium Understanding climate model uncertainty in streamflow projection Vinod Chilkoti, Tirupati Bolisetti, Ram Balachandar Department of Civil and Environmental Engineering University of Windsor, Windsor, Ontario, Canada
Introduction 2 Changing climate poses a crucial threat to the seasonal distribution of water availability Hydrological models forced with climate model data to project the future streamflow
Climate Impact Assessment Modeling Chain 3 Model inputs Climate data Topography Soil Landuse Climate model projections Bias Corrections Climate Model Forcing Hydrological Model Development Calibration and Validation Validated model Hydrological Model Climate change Impacts assessment Climate change impact
Challenges 4 Suit of uncertainties inherent in the modeling chain is a major cause of concern Climate models GCM (Graham et al. 2007 ) RCM (Bosshard et al. 2013, Chen et al. 2011a) No consensus over the cause(s) of uncertainty Important to understand the sources of uncertainty Downscaling method (Chen et al. 2011b) Hydrologic Model Input (Renard et al. 2011) Model Structure (Ludwig et al. 2009, Poulin et al. 2011) Model parameters (Wilby 2005, Bastola et al. 2011) Observed (output) data (mostly considered sacred)
Objectives 5 Major objectives of this research are to investigate the Effects of climate model uncertainty on streamflow projection Role of climate model ensemble members in the projection uncertainty
6 Study Area: Magpie River Watershed CANADA Catchment area 2039 km 2 Length of river 190 km
SWAT Model Setup 7 Topography (DEM) Delineated subwatersheds Landuse Forest 70% Range land 18% Water 11% Urban 01%
SWAT Model - Input 8 Climate Data Long term data available only at one station (Wawa A) Gridded climate data is used (Ref: Hutchinson et al., Hopkinson et al.,) Flow data at Wawa is used for calibration and validation Magpie River
SWAT Model Calibration 9 13 model parameters are calibrated 4- surface water parameters (CN2, CH_K2,SOL_AWC & ESCO) 3-ground water parameters (RCHRG_DP, GW_REVAP, ALPHA_BF) 6-snow parameters (SFTMP, SMTMP, SMFMX, SMFMN, TIMP & SNOCOVMX) Model Calibration Calibrated SWAT model using multi-objective optimization framework Borg algorithm Falls under class of evolutionary algorithms relatively newer algorithm
10 SWAT Model Calibration Borg Algorithm SWAT_Ed it Parameter updating in SWAT SWAT Model Parameter generation Objective Functions 1. NSE 2. RSR Low 3. FDC signature Minimize(1 NSE) = Min Minimize( FDC sign n i= 1 n ) = Min i= 1 ( O S ) m i 2 2 ( ) O O i i i= 1 m i= 1 O i i O S i Model Run Objective Function evaluation NSE : Nash Sutcliffe Efficiency RSR : Ratio of root mean square error to standard deviation of observed data FDC sign : Flow duration curve bias Statistical objectives Hydrological Signature objectives
11 Results: Model Calibration and Validation Calibration Validation simulated observed flow Daily simulation Daily simulation Statistic Calibration Validation NSE 0.72 0.81 pbias 6.7% 2.7% KGE 0.75 0.83 p-factor 0.61 0.73
Climate Change Projections 12 Regional Climate Model (RCM) data is used Data is extracted from CORDEX (Coordinated Regional Downscaling Experiment) CORDEX North America (NAM) Grid Source: http://www.cordex.org/
Climate Change Projections 13 Climate projection for two scenario periods Mid-century : 2041-2070 End-century : 2071-2100 Multi-model climate ensemble for rcp4.5 scenario used Model No Regional Climate Model (RCM) RCM Modeling Agency* Driving General Circulation Model (GCM) GCM Modeling Agency* M1 CanRCM4 CCCma CanESM2 CCCma M2 RCA4 SMHI CanESM2 CCCma M3 CRCM5 UQAM CanESM2 CCCma M4 RCA4 SMHI EC-EARTH ICHEC M5 HIRHAM5 DMI EC-EARTH ICHEC M6 CRCM5 UQAM MPI-ESM-LR MPI-M * CCCma- Canadian Center for Climate Modeling and Analysis SMHI Swedish Meteorological and Hydrological Institute DMI Danish Meteorological Institute ICHEC Irish Center for High End Computing UQAM-Université du Québec à Montréal MPI Max Planck Institute of Meteorology
Climate Change Projections 14 Climate model data is forced into calibrated hydrological model Large uncertainty is found in streamflow projection Average Baseline Projected Large uncertainty
15 Climate Change Projections Investigating the cause of streamflow uncertainty Average Baseline Projected Models projecting higher value are always M1, M2 and M3 Climate model ensemble is grouped into two, based on the driving GCM (boundary conditions)
16 Climate Model Grouping Multi-model climate ensemble for rcp4.5 scenario Model No Regional Climate Model (RCM) Modeling RCM Agency Driving General Circulation Model (GCM) Modeling GCM Agency M1 CanRCM4 CCCma CanESM2 CCCma M2 RCA4 SMHI CanESM2 CCCma M3 CRCM5 UQAM CanESM2 CCCma M4 RCA4 SMHI EC-EARTH ICHEC M5 HIRHAM5 DMI EC-EARTH ICHEC M6 CRCM5 UQAM MPI-ESM-LR MPI-M Group-1 Group-2
Climate Model Grouping - Precipitation 17 Precipitation and temperature data are key inputs for model simulation Group-1 Models Group-2 Models Baseline Precipitation projections by the two model groups are not distinct
Climate Model Grouping - Temperatures 18 Temperature projection by different model groups Minimum Temperature Group-1 Models Group-2 Models Baseline Maximum Temperature Differences in the projections by the two model groups are identifiable
Projected Streamflow Comparison 19 Group-1 model projects higher winter and spring temperature compared to Group-2 This causes higher snow melt and occurring earlier Group-1 Group-2 Comparison of projected streamflow by Group-1 models and Group-2 Models
20 Projected Streamflow Comparison Mann-Whitney test on seasonal streamflow projection p-value of Mann-Whitney test between projections by the two model groups Winter Spring Summer Autumn Mid century 2.2 x 10-16 4.2 x 10-4 0.92 9.8 x 10-7 End century 2.2 x 10-16 2.2 x 10-16 0.34 2.4 x 10-3 Results of the two groups are statistically similar only for summer
Projected Streamflow Comparison 21 Change in streamflow w.r.t the baseline is thus variable for the two groups Baseline Projected Projection by Group-1 models Projection by Group-2 models
Conclusions 22 Reasons for high uncertainty due to climate models has been investigated Uncertainty is prevalent in the scenario streamflow projection Uncertainty due to climate model ensemble has been highlighted Driving GCM is the major cause of uncertainty The presented idea needs to be affirmed using more number of climate models in other watersheds
Acknowledgments 23 Partial funding support by the following is gratefully acknowledged National Sciences and Engineering Research Council of Canada University of Windsor Ontario Graduate Scholarship
24 Backup Slides
Results: Model Calibration 25 Borg-SWAT optimization Calibration period : 2003 to 2008 Validation period : 2009 to 2012 22 optimal parameter sets are obtained Parameters are equally likely simulator of the model Pareto optimal front
26 Results: Model Calibration Flow Duration Curve (FDC) simulated observed flow Volumetric Efficiency Flow Segment Exceedance (%) Calibration Validation (2003-2008) (2009-2012) Monthly Daily Monthly Daily Peak 0-1 0.95 0.7 0.95 0.67 High 1 20 0.69 0.6 0.69 0.57 Mid 20 70 0.65 0.59 0.62 0.56 Low 70-100 0.5 0.3 0.51 0.41
27 Results: Model Uncertainty Observed flow depth Simulated (Pareto optimal) SurQ - Surface flow GwQ - Ground water flow ET - Evapotrasnpiration WY - Water yield
Climate Change Projection 28 Precipitation Average Baseline Projected End century scenario Baseline : 1976-2005 Mid-century : 2041-2070 End-century : 2071-2100
Climate Change Projection 29 Temperature Baseline : 1976-2005 Mid-century : 2041-2070 End-century : 2071-2100 Ensemble minimum temperature : End century Average change in minimum Temperature Average seasonal change : Mid-century Average seasonal change : End-century