Understanding climate model uncertainty in …...2018/09/19 · Vinod Chilkoti, Tirupati Bolisetti,...

2018 International SWAT ConferenceBrussels, Belgium

Understanding climate model uncertainty in streamflow projection

Vinod Chilkoti, Tirupati Bolisetti, Ram BalachandarDepartment of Civil and Environmental EngineeringUniversity of Windsor, Windsor, Ontario, Canada

Sep 19, 2018

Introduction

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2018 SWAT Conference

• 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

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Model inputs•Climate data•Topography•Soil•Landuse

Climate change impact

Hydrological Model

Climate Model Forcing

Hydrological Model Development

Calibration and Validation

Climate model projections

Bias Corrections

Validated model

Climate change Impacts

assessment

Challenges

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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)

• 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)

No consensus over the cause(s) of uncertaintyImportant to understand the sources of uncertainty

Objectives

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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

Study Area: Magpie River Watershed

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CANADA

• Catchment area 2039 km2

• Length of river – 190 km

SWAT Model Setup

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Topography (DEM) LanduseForest – 70%

Range land – 18%Water – 11%Urban – 01%Delineated subwatersheds

SWAT Model - Input

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• 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

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• 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

SWAT Model Calibration

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Borg Algorithm

SWAT_Edit

SWAT Model

Objective Function evaluation

Parameter generation

Parameter updating in SWAT

Model RunNSE : Nash Sutcliffe EfficiencyRSR : Ratio of root mean square error to

standard deviation of observed dataFDCsign : Flow duration curve bias

Statistical objectives

Hydrological Signature objectives

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−

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1

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)1(n

ii

n

iii

OO

SOMinNSEMinimize

−

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m

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SOMinFDCMinimize

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Objective Functions

1. NSE

2. RSRLow

3. FDCsignature

Results: Model Calibration and Validation


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Statistic Calibration Validation

NSE 0.72 0.81pBIAS 6.7% 2.7%KGE 0.75 0.83p-Factor 0.61 0.73

Validation

Daily simulation Daily simulation

Calibrationsimulated observed flow

Climate Change Projections

• Regional Climate Model (RCM) data is used• Data is extracted from CORDEX (Coordinated Regional

Downscaling Experiment)• CORDEX – North America (NAM) Grid


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Source: http://www.cordex.org/


• Climate projection for two scenario periods Mid-century : 2041 - 2070 End-century : 2071 - 2100

• Multi-model climate ensemble for rcp4.5 scenario used


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Model No

Regional Climate Model (RCM)

Driving General Circulation Model (GCM)

RCM Modeling Agency*

GCMModeling 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 AnalysisSMHI – Swedish Meteorological and Hydrological InstituteDMI – Danish Meteorological Institute

ICHEC – Irish Center for High End ComputingUQAM-Université du Québec à MontréalMPI –Max Planck Institute of Meteorology



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• Climate model data is forced into calibrated hydrological model• Large uncertainty is found in streamflow projection

Average BaselineProjected

Large uncertainty



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• Investigating the cause of streamflow uncertainty

Average BaselineProjected

• Models projecting higher value are always M1, M2 and M3• Climate model ensemble is grouped into two, based on the

driving GCM (boundary conditions)

Climate Model Grouping

• Multi-model climate ensemble for rcp4.5 scenario


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Model No

Regional Climate Model (RCM)

Driving General Circulation Model (GCM)

RCM Modeling Agency

GCMModeling 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


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• Precipitation and temperature data are key inputs for model simulation

Group-1 ModelsGroup-2 ModelsBaseline

• Precipitation projections by the two model groups are not distinct

Climate Model Grouping - Temperatures


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• Temperature projection by different model groups

Minimum Temperature Maximum TemperatureGroup-1 ModelsGroup-2 ModelsBaseline

Differences in the projections by the two model groups are identifiable

Projected Streamflow Comparison


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• Group-1 model projects higher winter and spring temperature compared to Group-2

• This causes higher snow melt and occurring earlier

Comparison of projected streamflow by Group-1 models and Group-2 Models

Group-1Group-2



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• Mann-Whitney test on seasonal streamflow projection

• Results of the two groups are statistically similar only for summer

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

p-value of Mann-Whitney test between projections by the two model groups



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• Change in streamflow w.r.t the baseline is thus variable for the two groups

BaselineProjected

Projection by Group-1 models Projection by Group-2 models

Conclusions


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• 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


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• National Sciences and Engineering Research Council of Canada

• University of Windsor

• Ontario Graduate Scholarship

Partial funding support by the following is gratefully acknowledged

Backup Slides


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• 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

Results: Model Calibration


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Pareto optimal front

• Flow Duration Curve (FDC)

Results: Model Calibration


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simulated observed flow

Volumetric Efficiency

Flow Segment

Exceedance (%)

Calibration

(2003-2008)

Validation

(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

Results: Model Uncertainty


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Observed flow depthSimulated (Pareto optimal)

SurQ - Surface flowGwQ - Ground water flowET - EvapotrasnpirationWY - Water yield

Climate Change Projection


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• PrecipitationAverage BaselineProjected End century scenario

Baseline : 1976 - 2005Mid-century : 2041 - 2070End-century : 2071 - 2100

Climate Change Projection


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• Temperature

Ensemble minimum temperature : End century Average change in minimum Temperature

Average seasonal change : Mid-century Average seasonal change : End-century

Baseline : 1976 - 2005

Mid-century : 2041 - 2070

End-century : 2071 - 2100

Understanding climate model uncertainty in …...2018/09/19 · Vinod Chilkoti, Tirupati Bolisetti,...

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