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
Inter-decadal
Decision Analysis: Risk + Values
T
• Facility Planning
i
– Reservoir, Treatment Plant Size
m
e
• Policy + Regulatory Framework
Climate
– Flood Frequency, Water Rights, 7Q10 flow
H
o
r
i
z
o
n
• Operational Analysis
– Reservoir Operation, Flood/Drought Preparation
• Emergency Management
– Flood Warning, Drought Response
Data: Historical, Paleo, Scale, Models
Hours
Weather
Climate Variability
• Daily
• Annual
• Diurnal cycle
• Seasonal cycle
• Inter-annual to Interdecadal
• Ocean-atmosphere
coupled modes (ENSO,
NAO, PDO)
• Centennial
• Millenial
• Thermohaline circulation
• Milankovich cycle (earth’s
orbital and precision)
Ann Max Flow
American River at Fair Oaks - Ann. Max. Flood
180,000
160,000
140,000
120,000
100,000
80,000
60,000
40,000
20,000
0
1900
1920
1940
1960
Year
100 yr flood estimated from
21 & 51 yr moving windows
1980
2000
Modeling Framework
What Drives Year to Year
Variability in regional
Hydrology?
(Floods, Droughts etc.)
Diagnosis
Hydroclimate Predictions – Scenario Generation
(Nonlinear Time Series Tools, Watershed Modeling)
Decision Support System
(Evaluate decision strategies
Under uncertainty)
Forecast
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 flow
Hydrology
• CVEN 5333 (Fall)
Physical Hydrology
Hydrologic 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 Methods
Basic Probability and Statistics; Numerical Methods
• CVEN 6833 (Spring 04)
Hydroclimatology
Large 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
oceanatmosphere
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 Oscillation
Winter NAO
Summer (JJA) PDSI
correlations with
winter (DJF) NINO3
Ann Max Flow
American River at Fair Oaks - Ann. Max. Flood
180,000
160,000
140,000
120,000
100,000
80,000
60,000
40,000
20,000
0
1900
1920
1940
1960
Year
100 yr flood estimated from
21 & 51 yr moving windows
1980
2000
Ratio of # days exceeding 50th
& 90th %, El Nino vs La Nina
Ratio of # days exceeding 90th
%, El Nino & La Nina vs Neutral
Source: Cayan et al, Journal of Climate, September 1999
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)
Case Study Area
• Historic flow from 1906 - 95
• Historic salt from 1941 - 95
USGS gauge 09072500
(Colorado River near Glenwood Springs, CO)
Comparison with
Observed Historic Salt
Prairie et al., 2003
USGS Natural Salt from the Nonparametric
Model + Uncertainty
CRSS Simulation Model
for Future Prediction
synthetic natural flow
flow
associated synthetic natural salt mass
• Natural flows based on
1906-1995
salt
• Natural salt model based
on 1941-1995
future agriculture
consumptive use
irrigated
lands
agricultural
salt loadings
salt removed
with exports
future exports
future municipal and industrial
• Projected depletions
2002-2062
• Constant Ag salt loading
of 137,000 tons/year
• Constant salt removal
with exports of 100
mg/L/year
USGS stream gauge 09072500
simulated future flow
simulated future salt mass
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
Study Area
WINNEMUCCA
LAKE (dry)
NEVADA
CALIFORNIA
PYRAMID
LAKE
Nixon
Stillwater NWR
Derby
Dam
STAMPEDE
Reno/Sparks
INDEPENDENCE
DONNER
Fernley
Newlands
Project
Farad
MARTIS
Carson
City
Ft Churchill
Tahoe City
LAKE TAHOE
Fallon
TRUCKEE
RIVER
BOCA
PROSSER
Truckee
TRUCKEE
CANAL
CARSON
RIVER
LAHONTAN
CARSON
LAKE
Motivation
•
USBR needs good seasonal forecasts on Truckee and
Carson Rivers
•
Forecasts determine how
storage targets will be met
on Lahonton Reservoir to
supply Newlands Project
Truckee Canal
Outline of Approach
• Climate Diagnostics
To identify large scale features correlated to Spring flow in the
Truckee and Carson Rivers
• Ensemble Forecast
Stochastic Models conditioned on climate indicators (Parametric and
Nonparametric)
• Application
Demonstrate utility of improved forecast to water management
Annual Cycle of Flows
Fall Climate Correlations
Carson Spring Flow
500 mb Geopotential Height
Sea Surface Temperature
Winter Climate Correlations
Truckee Spring Flow
500 mb Geopotential Height
Sea Surface Temperature
Climate Composites
High-Low Flow
Sea Surface Temperature
Vector Winds
Precipitation Correlation
Geopotential Height Correlation
SST Correlation
Flow - NINO3 / Geopotential Height
Relationship
Hydrologic Forecasting
•
•
•
•
Conditional Statistics of Future State, given Current State
Current State: Dt : (xt, xt-t, xt-2 t, …xt-d1t, yt, yt- t, yt-2t, …yt-d2t)
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
1994
1995
1996
1997
1998
1999
1994
1995
1996
1997
1998
1999
Precipitation
1994
1995
1996
1994
1995
1996
1997
1997
1998
1998
1999
1999
Precipitation and Climate
• Overprediction w/o Climate (1995, 1996)
– Might release water for flood control– stuck in spring with
not enough water
• Underprediction w/o Climate (1998)
Dry Years: 1987-1992
1987
1988
1989
1990
1991
1992
1987
1988
1989
1990
1991
1992
1987
1988
1989
1987
1988
1989
Precipitation
1990
1991
1990
Precipitation and Climate
• Overprediction w/o Climate (1998, 991)
– Might not implement necessary drought
precautions in sufficient time
1991
1992
1992
Fall Prediction w/ Climate
1994
1995
1996
1997
1998
1999
1994
1995
1996
1997
1998
1999
Wet Years
1987
1988
1989
1987
1988
1989
1990
1990
1991
1991
1992
1992
Dry Years
• Fall Climate forecast captures whether season will be
above or below average
• Results comparable to winter forecast w/o climate
Simple Water Balance
St = St-1 + It - Rt
• 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)
Water Balance
1995 Storage
1995 K-NN
Ensemble
PDF
Historical
PDF
Truckee-Carson RiverWare Model
Future Work
• Stochastic Model for
Timing of the Runoff
Disaggregate Spring flows to monthly flows.
• Statistical Physical Model
Couple 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
Initial Study Area: 6 reservoirs in
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1001 -4725
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# 9706 -21909
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Demanda
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%
U 0.3- 0.57
%
U 0.57- 4
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%
U 4- 5.11
%
U 5.11- 9.14
S NódePassagem
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Rios.shp
Açudespol.shp
Bacia.shp
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Jaguaribe
80% irrigation
20% municipal
Mainly in Aug
To November
Metropolitan
80% Municipal
20% Irrigation
Uniform distribution
Over the year
Seasonality of Oros Inflow
Flow (m^3/s)
400
Mean
350
Median
300
Quantile (75)
Quantile (25)
250
Quantile (90)
200
Quantile (10)
150
100
50
0
1
2
3
4
5
6
7
8
9
10
11
12
Month
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
90
Marginal 90%
80
70
Per90%
Per75%
60
Per50%
Marginal 75%
50
Per25%
Per10%
40
Obs
30
Marginal 50%
20
10
Marginal 25%
Marginal 10%
0
1993
1994
1995
1996
1997
1998
1999
2000
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/