Predictability and Long Range Forecasting of Colorado Streamflows

1. Predictability and Long Range Forecasting of Colorado Streamflows Jose D. Salas, Chong Fu Department of Civil & Environmental Engineering Colorado State University and Balaji Rajagopalan & Satish Regonda Department of Architectural, Civil & Environmental Engineering University of Colorado Acknowledgment: Colorado Water Resources Research institute

2. And the seven years of plenteousness, that was in the land of Egypt was ended. And the seven years of dearth began to come, according as Joseph had said: and the dearth was in all lands; but in the land of Egypt there was bread. Genesis 41:53

3. 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

4. Diurnal cycle Seasonal cycle Ocean-atmosphere coupled modes (ENSO, NAO, PDO) Thermohaline circulation Milankovich cycle (earth’s orbital and precision) Climate Variability / Predictability • Daily • Annual • Inter-annual to Inter-decadal • Centennial • Millenial

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

6. Global Impacts of ENSO

7. Probabilistic

8. The Perfect Ocean Perfect Ocean for Drought • Hoerling and Kumar (2003)

9. Pacific Ocean and Atmospheric Conditions Key to Western US Hydrologic Variability and Predictability at interannual and interdecadal

10. Colorado (and Western US) Water Resources System Characteristics Flow SNOW • Majority of annual runoff is snowmelt (70%) • Competing demand management: • Conservation and delivery to meet irrigation demands, • hydropower production • environmental releases • Limited Storage capacity • Interannual hydrologic variability For efficient and sustainable water management  skilful forecast of spring (Apr-Jul) streamflows are needed

11. 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

12. Approaches used in the study • Identify potential predictors from large scale land – atmosphere – ocean system for each streamflow series • Reduce the pool of potential predictors based on • statistical techniques • Apply the PCA and Regression techniques and multi- • model ensemble techniques for forecasting at multi-sites. (Regonda et al., 2006, WRR) • Apply the PCA and Regression techniques for • forecasting at single sites • Apply the CCA technique for forecasting at multiple • sites • Test the forecasting models • - fitting • - validation (drop 10% and drop-1)

13. Some Examples • Gunnison River Basin • Streamflow at six locations • Multi-model ensemble forecast technique Regonda et al. (2006)

14. Examples • Five other locations (Yampa, Poudre, San Juan, Arkansas and Rio-Grande) • PCA/regression and CCA techniques

15. River sites used in the study

16. Brief description of the study sites

17. PC1 (basin average) of Gunnison streamflows Correlated with Winter (Nov-Mar) Geopotential Heights

18. PC1 (basin average) of Gunnison streamflows correlated with winter large scale climate variables Meridional Wind Surface Air Temperature Zonal Wind Sea Surface Temperature

19. Winter (Nov – Mar) Vector Wind Composites Wet years Dry years

20. April 1st SWE PC1 April 1st SWE PC1 with Flow PC1 • Deviations from linear relationship (solid circles) • Suggests role of antecedent land conditions?

21. Role of antecedent Land Conditions Years with low snow and proportional high flows Years with high snow and proportional low flows Palmer Drought Severity Index (dry ------wet)

22. Correlation between Apr-Jul flows for the Poudre River and Jan-Mar geopotential heights (700 mb)

23. Correlation between Apr-Jul flows at S. Juan River and previous Oct-Dec geopotential heights (700 mb)

24. Multi-models • December 1st forecast selected 15 models • April 1st forecast selected 6 models • PC1 SWE is present in all models • PDSI is also selected in half of the models

25. Multi-model ensemble forecast (for any year) Use best models (weights are function of goodness of fit) Final ensemble = weighted combination of traces Generate an ensemble of estimated flows (traces) from each model as a function of explained and unexplained model variance Esti. flow 1,1 ------------- Esti. flow 1,100 Esti. flow 2,1 ………….. Esti. flow 2,100 Esti. flow 3,1 …………… Esti. flow 3,100 Esti. flow 1,a Esti. flow 1,b Esti. flow 1,c Esti. flow 1,d Esti. flow 1,e Esti. flow 1,f Esti. flow 2,a Esti. flow 2,b Esti. flow 2,c Esti. flow 3,a Model 1 (0.6) Model 2 (0.3) Model 3 (0.1) Experimental Forecasts

26. BoxPlots Show Probability Distribution of Ensemble Forecast Forecasted spring streamflows = {896,795.65, 936, 1056,891.76,…… } Actual spring streamflows 95th percentile 75th percentile 50th percentile 25th percentile 5th percentile

27. Model Validation for Tomichi River (1949-2002) Jan 1st RPSS: 0.51 Apr 1st RPSS: 0.77

28. Model Validation for Tomichi River (Dry Years) Jan 1st RPSS: 0.32 Apr 1st RPSS: 0.95

29. Model Validation for Tomichi River (Wet Years) Jan 1st RPSS: 0.75 Apr 1st RPSS: 1.00

30. Influence of PDSI • Model 1: PC1 SWE • Model 2: PC1 SWE + PDSI • PDSI shifts ensembles in the right direction

31. Forecast Skill of Spring Flows at Different Lead Times Climate indices + Soil moisture Climate indices + Soil moisture + SWE All Years Dec 1st Jan 1st Feb 1st Mar 1st Apr 1st Wet Years Dry Years Dec 1st Jan 1st Feb 1st Mar 1st Apr 1st Dec 1st Jan 1st Feb 1st Mar 1st Apr 1st

32. Time series of flows, SST, geopotential heights, SWE and PDSI for the San Juan River

33. Relationships between Apr-Jul flows of the San Juan River and potential predictors Flow vs SST Flow vs PDSI Flow vs geopotential height Flow vs SWE

34. San Juan River Validation San Juan River forecast Fitting Validation – 10% dropping

35. Comparison of flow forecasts for fitting and validation (drop 10%) for the SanJuan River fitting Single site validation Multisite

36. Comparison of forecast model performancesR-squares Single site models Multisite models

37. Comparison of forecast model performancesForecast skill scores Single site models Multisite models

38. Summary • Use of large-scale climate information lends long-lead predictability of spring season streamflows in the Colorado River system • Simple statistical methods incorporating climate information provides skilful ensemble streamflow forecast • Skills in the forecast can lead to efficient management and operations of reservoir systems • Aspinall Unit (Regonda, 2006) • Pecos river basin, NM (Grantz, 2006) • Truckee/Carson basins (truckee canal operations), Grantz et al., 2007 • ABCD water utilities (Ben & Subhrendu, AMEC) • Potential use in Climate Change studies and simulation

39. Summary • Partial funding from Colorado Water Research Institute is thankfully acknowledged • http://cadswes.colorado.edu/publications • (PhD thesis) • Regonda, 2006 • Prairie, 2006 • Grantz, 2006 • balajir@colorado.edu