1. Understanding and Predicting Interannual Climate Variability : Applications to thailand Summer Monsoon and Truckee/Carson Streamflows Balaji Rajagopalan Nkrintra Singhrattna Katrina Grantz CIVIL, ENVIRONMENTAL AND ARCHITECTURAL ENGINEERING DEPARTMENT UNIVERSITY OF COLORADO AT BOULDER Hydrology Seminar Spring 2004

2. Publications Nkrintra Singhrattna’s MS thesis http://civil.colorado.edu/~singhrat/nkrintra/pa pers/complete.pdf Singhrattna et al. (2003): (under revision) Journal of Climate Singhrattna et al.. (2004) (in review) International Journal of Climatology (http://civil.colorado.edu/~balajir/)http://civil.colorado.edu/~balajir/ Katrina Grantz’s MS thesis http://cadswes.colorado.edu/~grant/papers/Thesis. pdf

3. A Water Resources Management Perspective Time HorizonTime Horizon Inter-decadal Hours Weather Climate Decision 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

4. The Approach Climate Diagnostics Climate Diagnostics Forecasting Model Forecasting Model Decision Support System Decision Support System Forecasting Model stochastic models for ensemble forecasting - conditioned on climate information Climate Diagnostics To identify relevant predictors to streamflow / precipitation Decision Support System (DSS) Couple forecast with DSS to demonstrate utility of forecast

5. Applications 1.THAILAND SUMMER MONSOON 2.TRUCKEE/CARSON SPRING STREAMFLOWS

6. MOTIVATION THAILAND BACKGROUND Location between 5  - 20  N latitudes and 97  -106  E longitudes Population ~ 61.2 million Major occupation: agriculture (50%-60% of national economy) Agriculture depends on precipitation and irrigation that is dependent on precipitation to store in reservoirs as well “Precipitation” is crucial

7. MOTIVATION SEASON OF RAINFALL 80%-90% of annual precipitation occurs during monsoon season (May-Oct) Runoff is stored in reservoirs for use until the next year’s monsoon Variability over inter- annual and decadal time scales –Need to understand this variability

8. DATA DETAILS http://hydro.iis.u- tokyo.ac.jp/GAME-T Thailand Meteorological Dept. Six rainfall stations (r ~ 0.51) Five temperature stations (r ~ 0.50) Atmospheric circulation variables such as SLPs, SSTs and vector winds: NCEP/NCAR Re- analysis (www.cdc.noaa.gov)

9. DATA DETAILS Correlation maps (CMAP and SATs) ensure their consistency Thus, average rainfall ~ “rainfall index” average temperature ~ “temperature index”

10. CLIMATOLOGY Spring (MAM) temperatures set up land-ocean gradient driving the summer monsoon Summer monsoon (rainy season): Aug- Oct (ASO) Little peak in May: Due to Northward movement of ITCZ Enhanced MAM temperatures  Enhanced ASO rainfall  Decreasing monsoon seasonal (ASO) temperatures

11. CLIMATOLOGY ITCZ northward movement: - Cover Thailand in May - Move to China in June - Southward move to cover Thailand again in August AM SON

12. TRENDS Decreasing MAM temperature over decadal (-0.4  C) Decreasing ASO rainfall (-180 mm) Tend to cool land and atmosphere less  Increasing ASO temperature Trends after 1980: Increasing MAM temperature  Increasing ASO rainfall (IPCC 2001 report) Trends are part of global warming trends (IPCC 2001)

13. KEY QUESTION “What drives the interannual and interdecadal variability of Thailand summer monsoon?”

14. Schematic view of sea surface temperature and tropical rainfall in the the equatorial Pacific Ocean during normal, El Niño, and La Niña conditions....

15. Global Impacts of ENSO

16. FIRST INVESTIGATION 21-yr moving window correlation with SOI index: Strong significant correlation only post-1980 Spectral Coherence with SOI index

17. CORRELATION MAPS SST SLP Pre-1980Post-1980

18. COMPOSITE MAPS To understand nonlinear relationship: Composite maps (pre- and post-1980) of high and low rainfall years (3 highest and lowest years) High Low Pre-1980Post-1980

19. RELATIONSHIP WITH CONVECTION PARAMETERS Pre-1980Post-1980 correlation compositeEl Nino-La Nina Pre-1980El Nino-La Nina Post-1980

20. ENSO COMPOSITES Composite maps of SSTs: Strong and eastward anomalies during post-1980 Pre-1980 Post-1980

21. HYPOTHESIS “East Pacific centered ENSO reduces convections in Western Pacific regions (Thailand) while dateline centered ENSO decreases convections in Indian subcontinent” Pre-1980 Post-1980

22. COMPARISON WITH INDIAN MONSOON To show changes in regional impacts of ENSO 21-yr moving window correlation: Indian monsoon lose its correlation with ENSO around post-1980 Thailand monsoon picks up correlation at the same time

23. CASE STUDIES 19972002 SST CMAP

24. SUMMARY Strong relationship between Thailand monsoon and ENSO during post-1980 – when the Indian monsoon shows weakening relationship Descending branch of Walker Cell associated with Eastern Pacific ENSO (post-1980) tend to be over Western pacific (including thailand)  decreased Thailand monsoon rainfall Dateline-centered ENSOs (Pre-1980) tend to suppress convection over the Indian subcontinent

25. Predictor identification Good relation with monsoon rainfall (post-1980) at reasonable lead-time Correlate summer rainfall with large-scale climate variables from prior seasons  identify regions with strong correlations and develop predictor indices

26. CORRELATED WITH STANDARD INDICES Significant correlations at1-2 seasons lead-time

27. CORRELATION MAPS WITH LARGE- SCALE VARIABLES MAMAMJ MJJ SATs

28. CORRELATION MAPS WITH LARGE- SCALE VARIABLES MAMAMJ MJJ SLPs

29. CORRELATION MAPS WITH LARGE- SCALE VARIABLES MJJ AMJMAM SSTs

30. TEMPORAL VARIABILITY OF PREDICTORS Predictors are related to Thailand Monsoon only in the post- 1980 period SST and SLP Predictors are selected for Rainfall Forecasting MAM AMJ MJJ

31. TRADITIONAL MODEL: LINEAR REGRESSION Y = a * SLP + b * SST + e e = residual: normal (Gaussian) distribution with mean = 0, variance =  2 Y assumed normally (Gaussian) distributed Drawbacks: –unable to capture non-Gaussian/nonlinear features –High order fits require large amounts of data –Not portable across data sets

32. NONPARAMETRIC MODEL: local polynomials Y =  (SLPs, SSTs) + e  = local regression (residual: e are saved) Capture any arbitrary: Linear or nonlinear To forecast at any given “x*”, the mean forecast “y*” obtained by local regression (first step) To generate ensemble forecasts: Resample residuals (e) in the neighborhood of “X*” Add residual to mean forecast “y*” Assume a normal distribution “locally” in the neighborhood of “x*” Be able to generate unseen values in historical data y* x* Resample “e” of neighbors E1 E2 E3 E4

33. Local Regression

34. -100-50050 0 200 400 600 Spring Flow vs. Winter Geopotential Height Winter Geopotential Height Anomaly Truckee Spring Volume (kaf) y t * e t * xt*xt* y t * = f(x t *) + e t * Residual Resampling

35. Model Validation & Skill Measure Cross-validation: drop one year from the model and forecast the “unknown” value Compare median of forecasted vs. observed (obtain “r” value) Rank Probability Skill Score Likelihood Skill Score

36. MODEL SKILL ALL YEARSWET YEARSDRY YEARS R = 0.65 llh = 2.09 RPSS = 0.79 llh = 2.85 llh = 1.90 RPSS = 0.98 RPSS = 0.22

37. PDFs PDF obtain exceedence probability for extreme events (wet: >700 mm and dry: <400 mm) show good skill (especially for wet scenarios)

39. Applications TRUCKEE/CARSON SPRING STREAMFLOWS

40. Study Area TRUCKEE CANAL Farad Ft Churchill NEVADA CALIFORNIA Carson Truckee

41. Study Area Prosser Creek Dam Lahontan Reservoir

42. Basin Precipitation NEVADA CALIFORNIA Carson Truckee Average Annual Precipitation

43. Basin Climatology Streamflow in Spring (April, May, June) Precipitation in Winter (November – March) Primarily snowmelt dominated basins

44. Winter Climate Correlations 500mb Geopotential HeightSea Surface Temperature Truckee Spring Flow

45. Climate Indices Use areas of highest correlation to develop indices to be used as predictors in the forecasting model Area averages of geopotential height and SST 500 mb Geopotential HeightSea Surface Temperature

46. Persistence of Climate Patterns Strongest correlation in Winter (Dec-Feb) Correlation statistically significant back to August

47. High Streamflow Years Low Streamflow Years Vector Winds Climate Composites

48. High Streamflow Years Low Streamflow Years Sea Surface Temperature Climate Composites

49. Physical Mechanism L Winds rotate counter- clockwise around area of low pressure bringing warm, moist air to mountains in Western US

50. Forecasting Model Predictors SWE Geopotential Height Sea Surface Temperature

51. Forecasting Results Predictors April 1 st SWE April 1 st SWE Dec-Feb geopotential height Dec-Feb geopotential height 95th 50th 5th April 1 st forecast 95th 50th 5th

52. Forecast Skill Scores April 1 st forecast Median skill scores significantly beat climatology in all year subsets, both Truckee and Carson Truckee slightly better than Carson

54. Model Skills in Water Resources Decision Support System Ensemble Forecasts are passed through a Decision Support System of the Truckee/Carson Basin Ensembles of the decision variables are compared against the “actual” values

55. Seasonal Model Results: 1992 Irrigation Water less than typical– decrease crop size or use drought-resistant crops Truckee Canal smaller diversion-start the season with small diversions (one way canal) Very little Fish Water- releases from Stampede coordinated with Canal diversions Ensemble forecast results Climatology forecast results Observed value results NRCS official forecast results

56. Seasonal Model Results:1993 Irrigation Water more than typical– plenty for irrigation and carryover Truckee Canal larger diversion-start the season at full diversions (limited capacity canal) Plenty Fish Water- FWS may schedule a fish spawning run Ensemble forecast results Climatology forecast results Observed value results NRCS official forecast results

57. Seasonal Model Results: 2003 Irrigation Water pretty average: business as usual Truckee Canal diversions normal: not full capacity, but don’t hold back too much Plenty Fish Water- no releases necessary to augment low flows, may choose a fish spawning run Ensemble forecast results Climatology forecast results Observed value results NRCS official forecast results

58. CONCLUSIONS Interannual/Interdecadal variability of regional hydrology (precipitation, streamflows) is modulated by large-scale ocean-atmospheric features Incorporating Large scale Climate information in regional hydrologic forecasting models (Seasonal streamflows and precipitation) provides significant skill at long lead times Nonparametric methods offer an attractive and flexible alternative to traditional methods. capability to capture any arbitrary relationship data-drive easily portable across sites Significant implications to water (resource) management and planning

59. Future Work Couple ensemble forecasts with RiverWare model Temporal disaggregation Forecast improvements –Joint Truckee/Carson forecast –Objective predictor selection Compare results with physically-based runoff model (e.g. MMS)

60. Acknowledgements Edie Zagona, Martyn Clark, K. Krishna Kumar, Tom Chase Paul Sperry of CIRES and the Innovative Reseach Project Tom Scott of USBR Lahontan Basin Area Office CADSWES IUGG Travel support for Nkrintra Singhrattna