Hydroclimate Variability Diagnosis Prediction and Application Balaji Rajagopalan

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Hydroclimate Variability : Diagnosis Prediction and Application Balaji Rajagopalan Department of Civil, Encironmental and

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 T i m e H o r i z

A Water Resources Management Perspective T i m e H o r i z o n Inter-decadal Decision Analysis: Risk + Values Climate • Facility Planning – Reservoir, Treatment Plant Size • Policy + Regulatory Framework – Flood Frequency, Water Rights, 7 Q 10 flow • 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

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)

American River at Fair Oaks - Ann. Max. Flood 100 yr flood estimated from

American River at Fair Oaks - Ann. Max. Flood 100 yr flood estimated from 21 & 51 yr moving windows

Modeling Framework What Drives Year to Year Variability in regional Hydrology? (Floods, Droughts etc.

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)

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 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

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 -

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) Hydraulic. Engineering Pipe Network Design, Pumps, Open Channel flow

Courses • CVEN 3323 (Fall) Hydraulic. Engineering 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

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

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

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

The Time Series and Positive Phase of the Pacific Decadal Oscillation

Winter NAO Summer (JJA) PDSI correlations with winter (DJF) NINO 3

Winter NAO Summer (JJA) PDSI correlations with winter (DJF) NINO 3

American River at Fair Oaks - Ann. Max. Flood 100 yr flood estimated from

American River at Fair Oaks - Ann. Max. Flood 100 yr flood estimated from 21 & 51 yr moving windows

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

Ratio of # days exceeding 50 th & 90 th %, El Nino vs La Nina Ratio of # days exceeding 90 th %, El Nino & La Nina vs Neutral Source: Cayan et al, Journal of Climate, September 1999

Significant Differences in Atlantic Hurricane attributes relative to NINO 3 phases Rajagopalan et al.

Significant Differences in Atlantic Hurricane attributes relative to NINO 3 phases Rajagopalan et al. , 2000

Motivation • Colorado River Basin – arid and semi-arid climates – irrigation demands for

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

Motivation • Salinity Control Forum – Federal Water Pollution Control Act Amendments of 1972 1. Fixed numerical salinity criteria 1. 723 mg/L below Hoover Dam 2. 747 mg/L below Parker Dam 3. 879 mg/L at Imperial Dam 4. review standards on 3 year intervals 2. Develop basin wide plan for salinity control

Salinity Damages and Control Efforts • Damages are presently, aprox. $330 million/year • As

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

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

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 River. Ware

Existing Salt Model Over-Prediction

Existing Salt Model Over-Prediction

Research Objectives • Investigate and improve the models for Simulation of natural salt Variability

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

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

Comparison with Observed Historic Salt Prairie et al. , 2003

USGS Natural Salt from the Nonparametric Model + Uncertainty

USGS Natural Salt from the Nonparametric Model + Uncertainty

CRSS Simulation Model for Future Prediction • Natural flows based on 1906 -1995 •

CRSS Simulation Model for Future Prediction • 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 •

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

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

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

Ensemble Forecast of Spring Streamflows on the Truckee and Carson Rivers

Study Area STAMPEDE WINNEMUCCA LAKE (dry) NEVADA CALIFORNIA PYRAMID LAKE Nixon Reno/Sparks INDEPENDENCE DONNER

Study Area STAMPEDE WINNEMUCCA LAKE (dry) NEVADA CALIFORNIA PYRAMID LAKE Nixon Reno/Sparks INDEPENDENCE DONNER Tahoe City LAKE TAHOE TRUCKEE CANAL Fernley Fallon TRUCKEE RIVER BOCA PROSSER Truckee Stillwater NWR Derby Dam Newlands Project Farad MARTIS Carson City LAHONTAN Ft Churchill CARSON RIVER CARSON LAKE

Motivation • USBR needs good seasonal forecasts on Truckee and Carson Rivers • Forecasts

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

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

Annual Cycle of Flows

Fall Climate Correlations Carson Spring Flow 500 mb Geopotential Height Sea Surface Temperature

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

Winter Climate Correlations Truckee Spring Flow 500 mb Geopotential Height Sea Surface Temperature

Climate Composites High-Low Flow Sea Surface Temperature Vector Winds

Climate Composites High-Low Flow Sea Surface Temperature Vector Winds

Precipitation Correlation

Precipitation Correlation

Geopotential Height Correlation

Geopotential Height Correlation

SST Correlation

SST Correlation

Flow - NINO 3 / Geopotential Height Relationship

Flow - NINO 3 / Geopotential Height Relationship

Hydrologic Forecasting • • Conditional Statistics of Future State, given Current State: Dt :

Hydrologic Forecasting • • Conditional Statistics of Future State, given Current State: Dt : (xt, xt-2 t, …xt-d 1 t, yt-2 t, …yt-d 2 t) 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 Splines Neural Networks

Wet Years: 1994 -1999 1994 1995 19961997 1998 1999 Precipitation 1994 1995 1996 1997

Wet Years: 1994 -1999 1994 1995 19961997 1998 1999 Precipitation 1994 1995 1996 1997 1994 1995 19961997 1998 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 19891990 1991 1992 Precipitation 1987 1988 1989 1990

Dry Years: 1987 -1992 1987 1988 19891990 1991 1992 Precipitation 1987 1988 1989 1990 1987 1988 19891990 1991 1992 Precipitation and Climate • Overprediction w/o Climate (1998, 991) – Might not implement necessary drought precautions in sufficient time

Fall Prediction w/ Climate 1994 1995 19961997 1998 1999 Wet Years 1987 1988 1989

Fall Prediction w/ Climate 1994 1995 19961997 1998 1999 Wet Years 1987 1988 1989 1990 1987 1988 19891990 1991 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

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

Water Balance 1995 Storage 1995 K-NN Ensemble PDF Historical PDF

Truckee-Carson River. Ware Model

Truckee-Carson River. Ware Model

Future Work • Stochastic Model for Timing of the Runoff Disaggregate Spring flows to

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 River. Ware

Initial Study Area: 6 reservoirs in # # # # # # # #

Initial Study Area: 6 reservoirs in # # # # # # # # # # ## # # T $ # Reservatório T 0 - 54 $ T 54 - 148 $ # # $ T # # # $ %U T # # #% U ##$ T# # # $$T T # # Fortaleza % U S # # # Jaguaribe-Metropolitano Hidrossytem # # #% U# T $ # U % # # # ## S # T $ # S # $ T T $ # # S # # # % U #% U # # # # # # T# %U $ # # ## Oros Reservoir # # # # # # N # ## # # # # 1001 - 4725 4726 - 9705 # 9706 - 21909 # 21910 - 48163 # 48164 - 465319 Demanda U 0. 3 % % U 0. 3 - 0. 57 U 0. 57 - 4 % % U 4 - 5. 11 % U 5. 11 - 9. 14 S Nó de Passagem # Link Canais. shp Rios. shp Açudespol. shp Bacia. shp # # # W E # # S 175 - 480 - 1940 T $ População. dbf # # # T $ 148 - 175 Jaguaribe 80% irrigation 20% municipal Mainly in Aug To November Metropolitan 80% Municipal 20% Irrigation Uniform distribution Over the year

Seasonality of rain determined by N-S migration of the ITCZ Rain Start: ITCZ reaches

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

Predictors for Ceara Rainfall/Flow Factors that Affect the ITCZ dynamics – State of Tropical Pacific: El Nino – State of the tropical Atlantic

Oros Annual Flow Forecast from previous July – model fit 1914 -1991, k=30 Correlation

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

Seasonal Cycle Shifts in Annual Cycle of Streamflows

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

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

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)

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/