evaluating regional watershed sensitivity to climate ... · grass shrub/sage/chaparral bare rural...

Evaluating Regional Watershed Sensitivity to Climate Change: Future Runoff and

Sediment Variability in Southern California

Dr. Terri Hogue Sonya Lopez, Ph.D. Candidate University of California—Los Angeles

Hydrology & Water Resources

Hydrology and Water Resources at UCLA

Managing an Uncertain Future

GCM change in temperature simulations from IPCC AR4 Synthesis Report (2007)

How will this climate variability affect southern California?


Watershed Response

● How will regional watersheds respond to future

climate variability?

● What level of cahnge can we expect for runoff,

sediment, other water quality parameters?

● How will varying watershed characteristics (e.g.

land use patterns) mitigate response to future

climate?

● How will downstream ecosystems respond to altered

inputs (flow and sediment)?


Climate Models

Solution:

Reduce simulation uncertainty (downscale) to make water quantity and quality predictions at resolutions appropriate for predictions and management decisions

Hydro model

What are they? GCMs use CO2 emission scenarios to general circulation variables How are they useful? Identify overall trends for large spatial regions Resolution: 2.5 – 10

Issue? Fail to capture climatology at a resolution necessary for regional or watershed scale analysis


Research Approach

Phase I: Develop Framework for Regional Assessment of Climate Change (Lopez et al., 2011, in

review)

Phase II: Statistical Downscaling GCM Simulations for Southern California

Phase III: Investigation of Climate Change Impacts on Southern California Watersheds using Hydrologic Models

Phase IV: Water Quality and Quantity Comparison of the Quasi-Synthetic Framework and SD Approach


Phase I: Framework for Regional Assessment

Goal: Develop quasi-synthetic framework to perform a quick regional

assessment of flow and sediment changes due to climate variability.

Hypothesis: By developing archetypal or ―representative‖ that (1) emulate

observed hydrologic behavior and (2) have observed physiological features we will ascertain impacts to water resources.

Motivation: • Efficient regional assessment for water resource managers • Can be used to evaluate land-use influence • Can help understand impact on downstream ecosystems. This work is performed in collaboration with Southern California Coastal Water Research Project (SCCWRP)

Lopez et al. 2011


Region I (n=4)

Region II (n=2)

Region III (n=5)

Coastal Watersheds


Regional Land Cover

RI (veg) RII (urban) RIII (mix)0

20

40

60

80

100

Pe

rce

nt A

rea

[%

]

Agriculture

Forest

Grass

Shrub/Sage/Chaparral

Bare

Rural

Urban Development

Water

Wetland

90%

39%

75%

RI (veg) RII (urban) RIII (mix)0

20

40

60

80

100P

erc

en

t A

rea

[%

]

Agriculture

Forest

Grass

Shrub/Sage/Chaparral

Bare

Rural

Urban Development

Water

Wetland

Region I

Ventura County

―Vegetated‖

Region II Los Angeles

County ―Urbanized‖

Region III San Diego

County ―Mixed‖


Design “3” Regional Archetypal Watersheds


Climate Scenarios

1960 1970 1980 1990 20000

20

40

60

80(a) = 33.35

2= 177.85

Region I: Santa Maria

An

nu

al P

recip

. [c

m]

1960 1970 1980 1990 20000

20

40

60

80(b) = 31.83

2= 208.44

Region II: Los Angeles

An

nu

al P

recip

. [c

m]

1960 1970 1980 1990 20000

20

40

60

80(c) = 25.10

2= 108.03

Region III: San Diego

An

nu

al P

recip

. [c

m]

1960 1970 1980 1990 2000

55

60

65(d) = 56.8375

2= 1.2348

Me

an

An

nu

al T

em

p [o

C]

Year

1960 1970 1980 1990 2000

55

60

65

(e) = 62.653

2= 1.4496

Year

Me

an

An

nu

al T

em

p [o

C]

Annual P & T Trendline Mean

1960 1970 1980 1990 2000

55

60

65

(f) = 63.6315

2= 1.8339

Me

an

An

nu

al T

em

p [o

C]

Year

GCM change in temperature simulations from IPCC AR4 Synthesis Report (2007)

Temperature - Regression

using historical data

• 0.5 to 2°C in California within the first 30 years of the 21st century

(California Action Team, 2009)

0.5 to 3°C

Precipitation - Increase variability: 5, 10, 25, 50%


Temperature Precipitation [%] #

[°C] 0 5 10 25 50 Scenarios

0.5 5

1 5

2 5

3 5

Regression 1

Total 21

Climate Matrix


Hydrologic Model: EPA HSPF

• Conceptual-based, lumped parameter model

• Hydrology parameters required – 20 Pervious – 6 Impervious

• Operates on watershed scale

• Required HSPF inputs:

precipitation and potential evaporation (or inputs of Temperature for internal calculations of PE)

Environmental Protection Agency—Hydrologic Simulation Program Fortran

Parameter feasibility obtained from previous studies and moderate adjustments to parameters


Model Verification: Annual Runoff

• Annual response best at lower flows for ―Archetypes‖

• Difficulty capturing high flows in vegetated and mixed

archetypes

• Best model representation in urban system

Runoff ratio - Q

ROP

0.11

0.12

I

obs

RO

RO

0.53

0.58

II

obs

RO

RO

0.17

0.13

III

obs

RO

RO


Model Verification: Seasonal Patterns

• Seasonal Response generally captured by ―Archetypes‖

• Larger Variability in vegetated and mixed hydrographs

• Best model representation in urban system


Results: Storm Volume Changes

Liters 35 yr Event

RI

1x1014

0% 1.0x1014

+23% 1.2x1014

RII

4x1014

0% 4.0x1014

+16% 4.6x1014

RIII

1x1014

-1% 9.9x1014

+32% 1.32x1014

Uncertainty bounds relative wide for all systems

• More for vegetated and mixed

Recurrence intervals for total storm volume

Change to dryer years (more frequent)

Change to wetter years (less frequent)

Liters 2 yr Event

RI

2x101

3

-7% 1.9x1013

+6% 2.1x1013

RII

1x101

4

-5% 9.5x1013

+3% 9.7x1013

RIII

3x101

3

-5% 2.8x1013

+11% 3.3x1013

Large deviations in the wetter years


Results: Peak Flow Changes

Recurrence intervals for Peak Flow (Qpk)

Uncertainty bounds wide for all systems during extreme storm events

Infrequent storm events with a higher recurrence interval will be more extreme

cms 35 yr Event

RI

46

-4% 44.2

+92% 88.3

RII

1817

+5% 1907.9

+104% 3706.7

RIII

280

+6% 296.8

+120% 616.0

Change to dryer years (more frequent)

Change to wetter years (less frequent)

cms 2 yr Event

RI

22

-5% 20.9

+17% 25.7

RII

590

-8% 542.8

+32% 778.8

RIII

121

-5% 115.0

+25% 151.3

Large deviations in the wetter years


Veg.

Urban

Mixed

Annual Monthly

More vegetation — reduced flow due to temperature increases

Loss primarily occurs during dry periods


Results: Annual Storm Sediments

Enhanced Sediment Flux Wash-off during intense storms

Recurrence interval changes – Precipitation Variability & Temperature Inc


Phase II: Statistical Downscaling of GCMs

Enhanced Canonical Correlation Analysis

(Lopez and Hogue)

Identifies spatial/temporal patterns

Multiple Linear Regression Analysis (Zepeda, in progress)

Integrates multiple variables into predicting

Observed Precipitation Temperature

4 GCMs identified

21 GCM variables

Step 1: Extract P & T obs Step 2: GCMs

Step 3: Statistical Downscaling methods

Testing multiple predictor/predictand relationships & consider land use and topography


“Predictand” Observations

47 Precipitation sites

29 Temperature sites Historical trend analysis (Zepeda, in progress)

Data Period

Historical: 1961-2000

Counties: Santa Barbara, Ventura, Los Angeles, Orange, San Diego

Mean Annual Precip [1961 – 2000]

Mean Annual Temp [1961 – 2000]

2.8° x 2.8° Grid


Temperature Results: Los Angeles

Daily Temperature (1961-2000)

Model Statistic GCM ECCA

CNRM-CM3 RMSE 5.22 2.95

% BIAS 1.08 0.00

R2 0.69 0.86

GFDL-CM2.0 RMSE 4.53 2.95

% BIAS 0.53 -0.01

R2 0.67 0.86


% BIAS 0.84 -0.01

R2 0.70 0.86

MRI-CGCM2-3.2a RMSE 4.54 2.94

% BIAS 0.85 0.00

R2 0.74 0.86

01/00 04/09 07/18 10/26282

284

286

288

290

292

294

296

298

Me

an

Da

ily T

[K

]

Pre-ECCA

OBS

CNRM-CM3

GFDL-CM2.0

GFDL-CM2.1

MRI-CGCM2-3.2A

01/00 04/09 07/18 10/26282

284

286

288

290

292

294

296

298

Me

an

Da

ily T

[K

]

Post-ECCA

01/00 04/09 07/18 10/26282

284

286

288

290

292

294

296

298

Me

an

Da

ily T

[K

]

Pre-ECCA

OBS CNRM-CM3 GFDL-CM2.0 GFDL-CM2.1 MRI-CGCM2-3.2A

01/00 04/09 07/18 10/26282

284

286

288

290

292

294

296

298

Me

an

Da

ily T

[K

]

Post-ECCA

RMSE = Root Mean squared Error measure of the differences between predicted and observed

%BIAS = Percent Bias Oversimulation or undersimulation indicator

R2 = Correlation measures strength of linear relationship


01/00 04/09 07/18 10/260

0.5

1

1.5

2

2.5

Me

an

Da

ily P

recip

[cm

]

Pre-ECCA

OBS

CNRM-CM3

GFDL-CM2.0

GFDL-CM2.1

MRI-CGCM2-3.2A

01/00 04/09 07/18 10/260

0.5

1

1.5

2

2.5

Me

an

Da

ily P

recip

[cm

]

Post-ECCA

OBS

CNRM-CM3

GFDL-CM2.0

GFDL-CM2.1

MRI-CGCM2-3.2A

Precipitation Results: Los Angeles

Daily Precipitation (1961-2000)

Model Statistic GCM ECCA

CNRM-CM3 RMSE 0.91 0.99

% BIAS 251.66 12.59

R2 0.04 0.17


% BIAS 258.14 27.01

R2 0.06 0.27


% BIAS 245.21 16.81

R2 0.03 0.24

MRI-CGCM2-3.2a RMSE 1.14 1.04

% BIAS 278.97 25.42

R2 0.03 0.22

01/00 04/09 07/18 10/260

0.5

1

1.5

2

2.5

Me

an

Da

ily P

recip

[cm

]

Pre-ECCA

OBS

CNRM-CM3

GFDL-CM2.0

GFDL-CM2.1

MRI-CGCM2-3.2A

01/00 04/09 07/18 10/260

0.5

1

1.5

2

2.5

Me

an

Da

ily P

recip

[cm

]

Post-ECCA

OBS

CNRM-CM3

GFDL-CM2.0

GFDL-CM2.1

MRI-CGCM2-3.2A

Bias

RMSE = Root Mean squared Error measure of the differences between predicted and observed

%BIAS = Percent Bias Oversimulation or undersimulation indicator

R2 = Correlation measures strength of linear relationship


Concluding Remarks Key Results Temperature increase: • Greatest impact on vegetated systems during the low flow season • Causes minimal change to storm volume and peak discharge • Causes increase in sediments during low flow periods

Precipitation variability: • Causes increase in peak storm discharge in all systems

More significant in urbanized systems Produces a shift in recurrence intervals

Precipitation variability and temperature increase: • Causes SIGNIFICANT increase in storm sediments in urban systems

Enhanced scour and wash-off from pockets of pervious surfaces Benefits of Archetypal Method User-defined regional classification Minimal geographic and met data required Potential application to ungauged systems

Ongoing Work • Refinement of precipitation and temperature time-series using IPCC

simulations and statistical downscaling Use to drive a diverse set of regional watersheds (specific systems) Compare two approaches


Acknowledgements SCCWRP, NSF GRFP, NSF UCLA SEE-LA GK-12, NSF CAREER

Questions ??

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