Hydroclimate Variability : Diagnosis Prediction and Application
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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
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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
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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)
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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
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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
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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)
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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)
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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
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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
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ENSO as a “free” mode of the coupled ocean-atmosphere dynamics in the Tropical Pacific Ocean
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The Asymmetric Response to El Nino and La Nina
and a “Green’s Function” of Precipitation Response to
SST anomalies
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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
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The Time Series and Positive Phase of the Pacific Decadal OscillationSource: Nathan Mantua, University of Washington
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Summer (JJA) PDSI correlations with winter (DJF) NINO3
Rajagopalan et al., 2000
Winter NAO
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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
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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
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Significant Differences in Atlantic Hurricane attributes relative to NINO3 phases
Rajagopalan et al., 2000
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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
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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
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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
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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
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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
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Existing Salt Model Over-Prediction
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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)
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USGS gauge 09072500
(Colorado River near Glenwood Springs, CO)
• Historic flow from 1906 - 95
• Historic salt from 1941 - 95
Case Study Area
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Comparison with Observed Historic Salt
Prairie et al., 2003
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USGS Natural Salt from the Nonparametric Model + Uncertainty
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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
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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
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Policy Analysis Future Projections
> 750,000 tons salt
> 600 mg/L salt concentration
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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
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Ensemble Forecast of Spring Streamflows on the Truckee and Carson Rivers
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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
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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
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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
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Annual Cycle of Flows
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Fall Climate Correlations
500 mb Geopotential Height Sea Surface Temperature
Carson Spring Flow
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Winter Climate Correlations
500 mb Geopotential Height Sea Surface Temperature
Truckee Spring Flow
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Sea Surface Temperature Vector Winds
High-Low Flow
Climate Composites
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Precipitation Correlation
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Geopotential Height Correlation
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SST Correlation
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Flow - NINO3 / Geopotential HeightRelationship
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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
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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
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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
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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
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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
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Water Balance
1995 K-NN Ensemble
PDFHistorical
1995 Storage
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Truckee-Carson RiverWare Model
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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
Demanda%U 0.3%U 0.3 - 0.57%U 0.57 - 4
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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
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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)
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Predictors for Ceara Rainfall/Flow
Factors that Affect the ITCZ dynamics– State of Tropical Pacific: El Nino– State of the tropical Atlantic
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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
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Seasonal Cycle Shifts in Annual Cycle of Streamflows
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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
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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
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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/