sensitivity of california integrated forecast and

04/23/2013 GEORGAKAKOS 1

Sensitivity of California Integrated

Forecast and Reservoir Management

to Projected Climate Change

Konstantine (Kosta) Georgakakos(*), Nicholas E. Graham

HYDROLOGIC RESEARCH CENTER

Aris P. Georgakakos and Huaming Yao

GEORGIA INSTITUTE OF TECHNOLOGY

(*) Also Adjunct Professor with Scripps Institution of Oceanography, UCSD


PRESENTATION FOCUS

Reservoir Management in Northern California under

climatic variability and change

A system of reservoirs modulates the climatic and weather variability in order to

produce downstream benefits:

- flood damage mitigation

- hydroelectric power production

- water conservation for municipal, industrial and agricultural supply

- ecosystem benefits

- others

Reservoir effectiveness is substantially influenced by

- climatic variability and trends

- demand variability and trends

- changing water markets

Important target of reservoir management is to

- maximize water use efficiency


ELEMENTS OF CURRENT RESERVOIR MANAGEMENT

Simulation runs with

- historical data and statistics

- a detailed numerical description of the system

to get a set of operating rules (guide rules) on which to base

operational management

(E.G., No precip. forecasts are used for management, only observed precip.)

Willis and co-authors

(2011) San Francisco Estuary a

And watershed Science, 9(2)

Oroville Rule Curve

Max allowed storage


ELEMENTS OF CURRENT RESERVOIR MANAGEMENT

Planning involves several stakeholders and several objectives

as well as some coordination among reservoir sites

No two systems are the same and generalization is difficult

in reservoir management (climate, demands, system structure)

Climate/weather predictions must be translated

to system decision variables to be useful for management

Surface Temperature

Wind

etc.

Reservoir Pool

Energy Balance

& Release Level

Water

Temperature

Climate/weather prediction Decision Variable Index

E.G., cold water species fisheries management (Huang and others, 2011, JAWRA, 47(4))


TWO ISSUES TO DISCUSS

The Integrated Forecast and Management Project (INFORM)

for Northern California (prototype demonstration project)

Assessing adaptive reservoir management versus current

management through simulation experiments

http://www.hrc-lab.org/projects (follow link to INFORM)

Georgakakos and co-authors (2011a-b) Journal of Hydrology (on line)

http://www.sciencedirect.com/science/article/pii/S0022169411002939

http://www.sciencedirect.com/science/article/pii/S0022169411003015


123.5 123 122.5 122 121.5 121 120.5

38.5

39

39.5

40

40.5

41

41.5

0 500 1000 1500 2000 2500 3000 3500 4000

Elevation (meters)

Degrees West Longitude

De

gre

es

No

rth

La

titu

de

Major Resevoirs in Nothern California

TrinityShasta

Orovil le

Folsom

Trinity River

Pit River

Sacramento River

Feather River

N. Fork Ame rican RiverOroville

Folsom

Trinity Shasta

VISION

Improve water resources

management in Northern

California using climate,

hydrologic, and decision

science

CHALLENGE

INFORM Region

New Bullards Bar

1 TAF = 1.2 mill m^3


Sponsors:

CALFED Bay Delta Authority

California Energy Commission

National Oceanic and Atmospheric Administration

(CPO and NWS/OHD)

Members of Oversight and Implementation Committee:

California Department of Water Resources

California-Nevada River Forecast Center

Sacramento Area Flood Control Agency

U.S. Army Corps of Engineers

U.S. Bureau of Reclamation

National Centers of Environmental Prediction (NCEP)

GIT

HRC

SPONSORS-COLLABORATORS

Implement an integrated forecast-management system for the Northern California reservoirs using real-time data and operational forecast models

(Aspects of actual system to be represented were selected in collaboration with Agencies)

Perform tests with actual data and with management input

Demonstrate the utility of climate and hydrologic forecasts for water resources management in Northern California for several years


INFORM GOALS AND OBJECTIVES

ACTUAL VIRTUAL

Real Time Input

Measurable

Benefits

Other

Benefits Other

Benefits

Risk-based

Tradeoffs

ACTUAL VIRTUAL

Real Time Input

System

Characteristics

Measurable

Benefits

Other

Benefits

Operation

Rules

Measurable

Benefits


ATM FORECASTS

Adjustments

HYDROLOGIC MODEL

Ensemble Forecast

DECISION MODEL RESERVOIR

MANAGERS

Release Schedule

ASSESSMENT SYSTEM

Management Benefits Water Supply, Flood Control

Hydropower, Env. Protection, etc.

ATM DATA

NWS-CNRFC

FORECASTERS

Forecast component

Management component

San Joaquin River

San Luis

Clair Engle Lake

Trinity Power Plant

Lewiston

Lewiston

JF Carr

Whiskeytown

Shasta

Keswick

ShastaSpring Cr

Keswick

Oroville

Thermalito

Folsom

Natoma

New Melones

Tulloch

Goodwin

Oroville

Folsom

Nimbus

Melones

Tracy

Pumping

Banks

Pumping

San Joaquin River

Am

erican R

iver

Feath

er

Riv

er

Sacram

ento River

Trin

ity Rive

r

Cle

ar C

reek

Yuba River

Bear River

Delta-Mendota Canal

California Aqueduct

O’Neill

Forebay

To Dos Amigos PP

To Mendota Pool

Sacramento San Joaquin

River DeltaReservoir/

Lake

Power Plant

Pumping Plant

River Node

Reservoir/

Lake

Power Plant

Pumping Plant

Reservoir/

Lake

Power Plant

Pumping Plant

River Node

ISV

IFT

IES,IMC,IYB,ITI

DDLT,DBS,DCCWD,DNBA

DDM

DFDM

DDA

DSF

DSB

Black Butte

New Bullards Bar

INFORM SYSTEM COMPONENTS


Integration with operational agency data, forecasts and models

NCEP(GFS&CFS) and CNRFC(NWSRFS&CHPS)

GFS (0 - 16Days/6hourly) CFS (0 - 35 days/6hourly) CFS (1- 9 months/monthly)

WRF-ARW ICRM PROBABILISTIC

DOWNSCALING

HYDROLOGIC

MODEL

HYDROLOGIC

MODEL

HYDROLOGIC

MODEL

DECISION MODEL

3D ATM 3D ATM T,PREC

T, PREC, PET T, PREC, PET T, PREC, PET

Qinflow Qinflow Qinflow

SM

SN

Historical

Data

FORECAST ELEMENTS

SM

SN

Water Distribution

Flow Regulation

Hydro Plant Operation

Emergency Response

Monthly Decisions • Releases/Energy

Target Conditions

• State Variables

Planning Tradeoffs • Water Supply/Allocation • Energy Generation • Carry-over Storage • Env.-Ecosystem Management

Development Tradeoffs • Urban/Industrial • Agriculture • Power System • Socio-economic & Ecological Sustainability

Operational Tradeoffs • Flood Management • Water Distribution • Energy Generation • Env.-Ecosystem Management

Benefit/Impact Functions • Water Supply • Energy • Flood Damage • Env.-Ecosystem

Scenario/Policy Assessment

Monthly / Several Decades

Actual Hydrologic

Conditions

Actual Demands

Climate-Hydrologic

Forecasts

Demand Forecasts • Water • Food • Energy • Env.-Ecosystem

Climate-Hydrologic Forecasts

Demand Forecasts • Water Supply • Power Load/Tariffs • Flood Damage • Env.-Ecosystem Targets

Inflow Scenarios

Development/Demand

Scenarios • Water/Energy • Water/Benefit Sharing • Environmental Sustainability

Daily Decisions • Releases/Energy

Target Conditions

• State Variables

Benefit/Impact Functions • Water Supply • Energy • Flood Damage • Env.-Ecosystem

Near Real Time Decision Support

Hourly / 1 Day

Mid/Short Range Decision Support

Daily, 6-Hourly, or Hourly / 1 Month

Long Range Decision Support

Weekly, 10-Day or Monthly / 1-2 Years

Infrastructure Develpmnt.

Water Sharing Compacts

Sustainability Targets

Management Policy

INFORM DSS ELEMENTS

Multiple Objectives, Time Scales, & Decision Makers

Managem

ent

Agencie

s/D

ecis

ions

Pla

nnin

g A

gencie

s/D

ecis

ions

Opera

tional Pla

nnin

g a

nd M

anagem

ent

Off

-lin

e

Ass

ess

ments


Forecasted Inflow Mean Comparison

1727

6988

5108

3139

2511

9184

5174

3578

2009

7195

4067

3482

1802

4805

35883334

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

Trinity Shasta Oroville Folsom

Infl

ow

(cfs

)

Historical

F2006

F2007

F2008

Mean Inflow Forecast Comparison (9 Months)

(2006, 2007, 2008)

2006 (Wet); 2007 (Average); 2008 (Dry)


13 04/23/2013 GEORGAKAKOS

FORECAST UTILITY DEMONSTRATION

CLIMATE INFORMATION USED

CLIMATE MODEL: NCAR CCSM3.0 (COUPLED MODEL) SCENARIO: A1B MIDDLE LEVEL SCENARIO DECLINING EMISSIONS AFTER 2050 MAX CO2 CONCENTRATON OF ~715 PPM AT 2100 RESOLUTION: ~120KM HORIZONTAL, 26 VERTICAL LAYERS AND 6HRS VARIABLES USED: 3-D ATMOSPHERIC VARIABLES

TWO INPUT SETS: 1970 -2019 AND 2050 - 2099

Good Large Scale Precipitation Correspondence of Historical 1950-1999 run with NCEP Reanalysis 1948-1997 for West Coast

DYNAMICAL DOWNSCALING COMPONENTS FOR MEAN AREAL PRECIPITATION AND MEAN AREAL TEMPERATURE

OROGRAPHIC PRECIPITATION GRIDDED MODEL WINDFLOW FROM QUASI STEADY STATE POTENTIAL THEORY FLOW WATER SUBSTANCE SOURCE/ADVECTION MODEL WITH KESSLER MICROPHYSICS 10X10 SQKM SPATIAL AND 6HOURLY TEMPORAL RESOLUTION SURFACE TEMPERATURE GRIDDED MODEL INTERPOLATION/ADJUSTMENT OF CCSM3.0 LOW LEVEL TEMPERATURE OVER TERRAIN SURFACE ENERGY BALANCE MODEL (OROGRAPHIC AND SNOW/SOIL MODEL COUPLING) 10X10 SQKM SPATIAL AND 6HOURLY TEMPORAL RESOLUTION GIS-BASED SYSTEM FOR CATCHMENT DELINEATION AND PRODUCTION OF MAP AND MAT IN N. CALIFORNIA

Reference: HRC-GWRI: http://www.energy.ca.gov/pier/project_reports/CEC-500-2006-109.html

SNOW – SOIL – CHANNEL MODELS

Reference: HRC-GWRI: http://www.energy.ca.gov/pier/project_reports/CEC-500-2006-109.html

ADAPTATION OF NWS OPERATIONAL SNOW ACCUMULATION AND ABLATION MODEL ADAPTATION OF NWS OPERATIONAL SOIL WATER ACCOUNTING MODEL

KINEMATIC ROUTING THROUGH RIVER NETWORK FOR ALL BASINS

Operational Inform Domain

Oroville Subcatchments

Oroville- Historical ESP Reliability Diagrams

SELECTED RESULTS FROM HISTORICAL AND FUTURE RUNS

Precipitation Difference (mm/6hrs) – Feb Temperature Difference (oC) – Feb 1800Z

Winter is warmer and wetter (higher elevations) in future than historical runs

Simulated Flows: Black line: Future Red Line : Historical

Earlier and Higher Future Flows For Shasta and Oroville


Current Policy Adaptive Policy

Focuses on current month Optimizes over the next 9 months

Deterministic Risk based

Adjusts demand targets twice a year Re-optimizes every month

Follows COA in extra water allocation Finds optimal allocation strategy each t

CURRENT AND ADAPTIVE MANAGEMENT POLICIES

For more details see: Georgakakos, A., et al. 2012, JH 412-413, 34-46


Current versus Adaptive Management Policies under a Changing Climate

DSS vs. Current Policy

4.92.3

22.1

-2.0 -0.9

0.0

-19.3

0.6

57.8

-0.8

29.9

-35.6

-50

-30

-10

10

30

50

70

Pre

ce

nt

(%)

Historical

Future

Avg. Spillage

Avg. WS Min. WS

Avg. EnergyFirm Energy

X2 Location

CLIMATE CHANGE STUDY RESULTS

System Water Deliveries, Historical Period

0

2000

4000

6000

8000

10000

12000

14000

0 10 20 30 40 50 60 70 80 90 100

Exceedance of Probability(%)

TA

F/Y

ea

r

His/DSS

His/CurrentPolicy

System Water Deliveries, Future Period

0

2000

4000

6000

8000

10000

12000

14000

0 10 20 30 40 50 60 70 80 90 100

Exceedance of Probability(%)

TA

F/Y

ea

r

Future/DSS

Future/CurrentPolicy


Future A1B scenario portents intensifying water stresses (due to seasonal inflow

shifts and higher inflow variability) and higher vulnerability to extreme droughts.

Adaptive, risk based, reservoir regulation strategies are self tuning to the changing

climate, deliver more robust performance than current management practices,

and can considerably mitigate the negative impacts of increased water stresses.

Effective implementation of adaptive, risk based, reservoir regulation strategies

require

• more flexible laws and policy statutes (COA, heuristic rules, etc.),

• a new level of institutional cooperation for water resources management, and

• capacity building of agency personnel in modern decision support methods

CONCLUSIONS AND PROSPECT


Thank You

HRC

K.P. Georgakakos, PI, Hydroclimatology

N.E. Graham, Co-PI, Climate Science & Prediction

T.M. Carpenter, Hydrometeorological Forecasting

M. Murphy, J. Wang, and F.-Y. Cheng, Mesoscale

Meteorological Modeling

E. Shamir, Hydrologic Modeling

C. Spencer and J. Sperfslage, Computer Science

GWRI

A.P. Georgakakos, Co-PI, Decision Science

Huaming Yao, Hydropower

Martin Kistenmacher, Uncertainty Mgt

Dongha Kim, Routing/Temperature Models

INFORM Contributing Scientists/Engineers

sensitivity of california integrated forecast and

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