Hydroclimate Variability : Diagnosis Prediction and Application

Balaji Rajagopalan

Department of Civil, Encironmental and Architectural Engineering

And

Co-operative Institute for Research in Environmental Sciences (CIRES)

University of Colorado

Boulder, CO

Fall 2003

A Water Resources Management Perspective

Time

Horizon

Inter-decadal

Hours Weather

ClimateDecision Analysis: Risk + Values

Data: Historical, Paleo, Scale, Models

• Facility Planning

– Reservoir, Treatment Plant Size

• Policy + Regulatory Framework

– Flood Frequency, Water Rights, 7Q10 flow

• Operational Analysis

– Reservoir Operation, Flood/Drought Preparation

• Emergency Management

– Flood Warning, Drought Response

Climate Variability

• Daily

• Annual

• Inter-annual to Inter-decadal

• Centennial

• Millenial

• Diurnal cycle• Seasonal cycle

• Ocean-atmosphere coupled modes (ENSO, NAO, PDO)

• Thermohaline circulation• Milankovich cycle

(earth’s orbital and precision)

American River at Fair Oaks - Ann. Max. Flood

020,00040,00060,00080,000

100,000120,000140,000160,000180,000

1900 1920 1940 1960 1980 2000

Year

An

n M

ax

Flo

w

100 yr flood estimated from 21 & 51 yr moving windows

What Drives Year to Year Variability in regional

Hydrology?(Floods, Droughts etc.)

Hydroclimate Predictions – Scenario Generation(Nonlinear Time Series Tools, Watershed Modeling)

Decision Support System(Evaluate decision strategiesUnder uncertainty)

Modeling Framework

Forecast

Diagnosis

Application

Research Activities

• Long Term Salinity Modeling on the Colorado River Basin

(USBR, CADSWES)

• Spring Streamflow forecasts on the Truckee / Carson Basin – Applications to Water Management

(USBR Truckee Office, CADSWES)

• Interdecadal Variability of Thailand and Indian Summer Monsoon

• Seasonal Cycle Shifts in Western US Hydroclimatology and Flood Forecasting(NSF, NOAA/WWA)

Research Activities..

• Tools for short term and long term streamflow forecasting and water management Decision Support System

(CIRES/Western Water Assessment, NOAA, USGS)

• Infrastructure Reliability Estimation under Hurricane Hazards

(NSF, Profs. Corotis and Frangopol)

Collaborators

• Edith Zagona, Terry Fulp - CADSWES• Martyn Clark, Subhrendu Gangopadhyay - CIRES • NOAA - Western Water Assessment (WWA)• Katrina Grantz, James Prairie, David Neumann,

Satish Regonda, Yeonsang Hwang, Nkrintra Singhrattna, Somkiat, Apipattanavis, Adam Hobson

Courses• CVEN 3323 (Fall) HydraulicEngineering

Pipe Network Design, Pumps, Open Channel flowHydrology

• CVEN 5333 (Fall) Physical HydrologyHydrologic processes – Precipitation, Infiltration,

Evapotranspiration, Runoff, Flood frequency analysis

• CVEN 5833 (Spring) Advanced Data Analysis Techniques probability density estimation, Monte Carlo, bootstrap, Time series

analysis, Regression analysis

• CVEN 5454 Quantitative MethodsBasic Probability and Statistics; Numerical Methods

• CVEN 6833 (Spring 04) HydroclimatologyLarge scale climate features (El Nino etc.), implications to

regional hydrology, diagnosis from observed data, hydroclimate forecasts, global change

ENSO as a “free” mode of the coupled ocean-atmosphere dynamics in the Tropical Pacific Ocean

The Asymmetric Response to El Nino and La Nina

and a “Green’s Function” of Precipitation Response to

SST anomalies

Positive NAO

•Stonger than usual

•Subtropical High

•Deeper than Normal Icelandic

Low

•Warm and Wet Winters in Europe

•Cold and Dry Winters in N. Canada

•Eastern US – Mild and Wet Winter

The Time Series and Positive Phase of the Pacific Decadal OscillationSource: Nathan Mantua, University of Washington

Summer (JJA) PDSI correlations with winter (DJF) NINO3

Rajagopalan et al., 2000

Winter NAO

American River at Fair Oaks - Ann. Max. Flood

020,00040,00060,00080,000

100,000120,000140,000160,000180,000

1900 1920 1940 1960 1980 2000

Year

An

n M

ax

Flo

w

100 yr flood estimated from 21 & 51 yr moving windows

Source: Cayan et al, Journal of Climate, September 1999

Ratio of # days exceeding 50th & 90th %, El Nino vs La Nina

Ratio of # days exceeding 90th %, El Nino & La Nina vs Neutral

Significant Differences in Atlantic Hurricane attributes relative to NINO3 phases

Rajagopalan et al., 2000

Motivation

Colorado River Basin arid and semi-arid climates

irrigation demands for agriculture

“Law of the River” Mexico Treaty Minute No. 242

Colorado River Basin Salinity Control Act of 1974

Motivation

• Salinity Control Forum

– Federal Water Pollution Control Act Amendments of 1972

Fixed numerical salinity criteria

723 mg/L below Hoover Dam

747 mg/L below Parker Dam

879 mg/L at Imperial Dam

review standards on 3 year intervals

Develop basin wide plan for salinity control

Salinity Damages and Control Efforts

Damages are presently, aprox. $330 million/year

As of 1998 salinity control projects has removed an estimated 634 Ktons of salt from the river

total expenditure through 1998 $426 million

Proposed projects will remove an additional 390 Ktons

projects additional expenditure $170 million

• Additional 453 Ktons of salinity controls needed by 2015

Data taken from Quality of Water, Progress Report 19, 1999 & Progress Report 20,2001

Sources of Salinity• Natural Salt – Water flowing over rocks, sediments,

etc.

(increased Flows increased salinity)

• Anthropogenic – return flows from agriculture, runoff from basins (more development increased salinity)

(hard to quantify)

• Large portion of salinity (roughly 60 ~ 70%) is natural

Existing Colorado River Simulation System (CRSS)

• Includes three interconnected models– salt regression model

• USGS salt model

– stochastic natural flow model• index sequential method

– simulation model of entire Colorado River basin

• implemented in RiverWare

Existing Salt Model Over-Prediction

Research Objectives

Investigate and improve the models for

Simulation of natural salt Variability

(Prairie et al., 2003)

Simulating Natural Hydrologic Variability (Natural Flows) (Prairie et al. 2003)

USGS gauge 09072500

(Colorado River near Glenwood Springs, CO)

• Historic flow from 1906 - 95

• Historic salt from 1941 - 95

Case Study Area

Comparison with Observed Historic Salt

Prairie et al., 2003

USGS Natural Salt from the Nonparametric Model + Uncertainty

CRSS Simulation Model for Future Prediction

saltflow

future agriculture

future exports

future municipal and industrial

synthetic natural flow associated synthetic natural salt mass

simulated future flow simulated future salt mass

USGS stream gauge 09072500

consumptive useirrigatedlands

salt loadings

salt removedwith exports

agricultural

• Natural flows based on 1906-1995

• Natural salt model based on 1941-1995

• Projected depletions 2002-2062

• Constant Ag salt loading of 137,000 tons/year

• Constant salt removal with exports of 100 mg/L/year

Stochastic Planning Runs Projected Future Flow and Salt Mass

• Passing gauge 09072500

• Based on 1906-1995 natural flows

• 1941-1995 monthly salt models

• Simulating 2002 to 2062

Policy Analysis Future Projections

> 750,000 tons salt

> 600 mg/L salt concentration

Future Work

• Extend the Flow and Salt Model to the entire basin

(This is being done currently)

• Improve modeling the “Reservoir effects”

• Assess planning and management strategies in light of Salt projections in the Basin

Ensemble Forecast of Spring Streamflows on the Truckee and Carson Rivers

INDEPENDENCE

DONNERMARTIS

STAMPEDE

BOCA

PROSSER

TRUCKEERIVER

CARSONRIVER

CARSONLAKE

Truckee

CarsonCity

Tahoe City

Nixon

Fernley

DerbyDam

Fallon

WINNEMUCCALAKE (dry)

LAHONTAN

PYRAMID LAKE

NewlandsProject

Stillwater NWR

Reno/Sparks

NE

VA

DA

CA

LIF

OR

NIA

LAKE TAHOE

Study Area

TRUCKEE CANAL

Farad

Ft Churchill

Motivation

• USBR needs good seasonal forecasts on Truckee and Carson Rivers

• Forecasts determine howstorage targets will be met on Lahonton Reservoir to supply Newlands Project

Truckee Canal

Outline of Approach

• Climate DiagnosticsTo identify large scale features correlated to Spring flow in the Truckee and Carson Rivers

• Ensemble ForecastStochastic Models conditioned on climate indicators (Parametric and Nonparametric)

• ApplicationDemonstrate utility of improved forecast to water management

Annual Cycle of Flows

Fall Climate Correlations

500 mb Geopotential Height Sea Surface Temperature

Carson Spring Flow

Winter Climate Correlations

500 mb Geopotential Height Sea Surface Temperature

Truckee Spring Flow

Sea Surface Temperature Vector Winds

High-Low Flow

Climate Composites

Precipitation Correlation

Geopotential Height Correlation

SST Correlation

Flow - NINO3 / Geopotential HeightRelationship

Hydrologic Forecasting

• Conditional Statistics of Future State, given Current State

• Current State: Dt : (xt, xt-, xt-2 , …xt-d1, yt, yt- , yt-2, …yt-d2)

• Future State: xt+T

• Forecast: g(xt+T) = f(Dt)– where g(.) is a function of the future state, e.g., mean or pdf

– and f(.) is a mapping of the dynamics represented by Dt to g(.)

– Challenges• Composition of Dt

• Identify g(.) given Dt and model structure

– For nonlinear f(.) , Nonparametric function estimation methods used• K-nearest neighbor

• Local Regression

• Regression Splines

• Neural Networks

Wet Years: 1994-1999

• Overprediction w/o Climate (1995, 1996)– Might release water for flood control– stuck in spring with

not enough water

• Underprediction w/o Climate (1998)

Precipitation Precipitation and Climate

1994 1995 1996

1994 1995 1996

1994 1995 1996

1994 1995 1996

1997 1998 1999 1997 1998 1999

1997 1998 19991997 1998 1999

Dry Years: 1987-1992

• Overprediction w/o Climate (1998, 991)– Might not implement necessary drought

precautions in sufficient time

Precipitation Precipitation and Climate

1987 1988 1989

1987 1988 1989

1987 1988 1989

1987 1988 1989

1990 1991 1992 1990 1991 1992

1990 1991 19921990 1991 1992

Fall Prediction w/ Climate

• Fall Climate forecast captures whether season will be above or below average

• Results comparable to winter forecast w/o climate

Wet Years Dry Years

1987 1988 1989

1987 1988 1989

1990 1991 1992

1990 1991 1992

1994 1995 1996

1994 1995 1996

1997 1998 1999

1997 1998 1999

Simple Water Balance

• St-1 is the storage at time ‘t-1’, It is the inflow at time ‘t’

and Rt is the release at time ‘t’.• Method to test the utility of the model• Pass Ensemble forecasts (scenarios) for It • Gives water managers a quick look at how much storage

they will have available at the end of the season – to evluate decision strategies

For this demonstration,• Assume St-1=0, Rt= 1/2(avg. Inflowhistorical)

St = St-1 + It - Rt

Water Balance

1995 K-NN Ensemble

PDFHistorical

PDF

1995 Storage

Truckee-Carson RiverWare Model

Future Work

• Stochastic Model for Timing of the RunoffDisaggregate Spring flows to monthly flows.

• Statistical Physical ModelCouple PRMS with stochastic weather generator (conditioned on climate info.)

• Test the utility of these approaches to water management using the USBR operations model in RiverWare

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#S Nó de Passagem

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População.dbf# 1001 - 4725# 4726 - 9705# 9706 - 21909# 21910 - 48163

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Reservatório$T 0 - 54

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$T 480 - 1940

N

EW

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Fortaleza

Initial Study Area: 6 reservoirs in

Jaguaribe-Metropolitano Hidrossytem

Jaguaribe 80% irrigation20% municipalMainly in AugTo November

Metropolitan80% Municipal20% IrrigationUniform distributionOver the year

Oros Reservoir

Seasonality of Oros Inflow

0

50

100

150

200

250

300

350

400

1 2 3 4 5 6 7 8 9 10 11 12

Month

Flo

w (

m^

3/s

)

Mean

Median

Quantile (75)

Quantile (25)

Quantile (90)

Quantile (10)

Seasonality of rain determined by N-S migration of the ITCZ

Rain Start: ITCZ reaches Southernmost (Feb) + January Cold Fronts

Rain End: ITCZ migrates N of Equator (June-July)

Predictors for Ceara Rainfall/Flow

Factors that Affect the ITCZ dynamics– State of Tropical Pacific: El Nino– State of the tropical Atlantic

0

10

20

30

40

50

60

70

80

90

1993 1994 1995 1996 1997 1998 1999 2000

Per90%

Per75%

Per50%

Per25%

Per10%

Obs

Marginal 90%

Marginal 75%

Marginal 50%

Marginal 25%

Marginal 10%

Oros Annual Flow Forecast from previous July

– model fit 1914-1991, k=30 Correlation (Median==Obs)=0.91

Seasonal Cycle Shifts in Annual Cycle of Streamflows

Key Points• Low Frequency Climate Variability (LFV) on interannual to

centenial time scales is a significant part of “natural” variability in the climate system.– A few large-scale climate forcings (“modes”) contribute to MOST of the LFV– ENSO, NAO, PDO– The forcings have large-scale spatial structure and modulate regional climate

• These forcings manifest into LFV in regional hydroclimate variables– Droughts– Floods (mean flows, maximum flows, flood frequency)– Seasonal Temperature and Precipitation and their spells– Storm days

• Implications for – Regional Flood-frequency analyses– Resources planning/management– Hazard management/response strategies– Hydroclimate modeling of watersheds and river basins

Research Directions• Drought Severity

– Longer Records/Tree Rings for diagnosis

– Time Scale for Forecasting? Statistical Properties of Drought ?

• Operational Analyses– Seasonal Supply & Demand

• P, T, Q => Attributes to Forecast ?

• Role of Groundwater ?

• Seasonal Low Flow Attributes

• Low Frequency variations in flood probabilities – Nonstationarity => Risk analysis, Regionalization

– Seasonal Forecast Possibility => Disaster insurance and planning

• Theoretical and Conceptual Models – Predictability => Concepts and Assessment

– Framework: Dynamics of Variability & Mechanisms <= Role of Numerical, Conceptual and Stochastic Models

Publications / References• 2 MS Thesis

http://cadswes.colorado.edu/

(go to publications)

• http://civil.colorado.edu/~balajir

(go to publications)

ASCE Journal of Environmental Engineering,

ASCE Journal of Hydrologic Engineering

Water Resources Research,

AMS Journal of Hydrometeorology,

AMS Journal of Climate

• http://cires.colorado.edu

(go to Wester Water Assessment)

• http://www.cdc.noaa.gov/