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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. Hydroclimate Variability : Diagnosis Prediction and Application. Inter-decadal.

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Hydroclimate Variability : Diagnosis Prediction and Application

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  1. 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 Hydroclimate Variability : Diagnosis Prediction and Application

  2. Inter-decadal Climate Decision Analysis: Risk + Values Time Horizon • 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 Data: Historical, Paleo, Scale, Models Weather Hours A Water Resources Management Perspective

  3. 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) Climate Variability

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

  5. 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) Forecast Decision Support System (Evaluate decision strategies Under uncertainty) Application

  6. 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

  7. 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) Research Activities..

  8. 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

  9. 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 Courses

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

  11. The Asymmetric Response to El Nino and La Ninaand a “Green’s Function” of Precipitation Response to SST anomalies

  12. 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

  13. The Time Series and Positive Phase of the Pacific Decadal Oscillation Source: Nathan Mantua, University of Washington

  14. Winter NAO Summer (JJA) PDSI correlations with winter (DJF) NINO3 Rajagopalan et al., 2000

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

  16. 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

  17. Significant Differences in Atlantic Hurricane attributes relative to NINO3 phases Rajagopalan et al., 2000

  18. 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

  19. 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

  20. 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

  21. 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

  22. 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

  23. Existing Salt Model Over-Prediction

  24. 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)

  25. Case Study Area Historic flow from 1906 - 95 Historic salt from 1941 - 95 USGS gauge 09072500 (Colorado River near Glenwood Springs, CO)

  26. Comparison with Observed Historic Salt Prairie et al., 2003

  27. USGS Natural Salt from the Nonparametric Model + Uncertainty

  28. 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

  29. 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

  30. Policy Analysis Future Projections > 750,000 tons salt > 600 mg/L salt concentration

  31. 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

  32. Ensemble Forecast of Spring Streamflows on the Truckee and Carson Rivers

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

  34. 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

  35. 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

  36. Annual Cycle of Flows

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

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

  39. Climate Composites High-Low Flow Vector Winds Sea Surface Temperature

  40. Precipitation Correlation

  41. Geopotential Height Correlation

  42. SST Correlation

  43. Flow - NINO3 / Geopotential HeightRelationship

  44. 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

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