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. 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. Climate Variability / Predictability 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)

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. 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 Colorado (and Western US) Water Resources System Characteristics SNOW Flow For efficient and sustainable water management  skilful forecast of spring (Apr-Jul) streamflows are needed

11. What Drives Year to Year Variability in regional Hydrology? (Floods, Droughts etc.) Hydroclimate Predictions – Scenario Generation (Nonlinear Time Series Tools, Watershed Modeling) Decision Support System (Evaluate decision strategies Under uncertainty) Modeling Framework Forecast Diagnosis 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 Surface Air Temperature Zonal Wind Sea Surface Temperature Meridional Wind

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

20. Deviations from linear relationship (solid circles) Suggests role of antecedent land conditions? PC1 Flows Vs. PC1 SWE Feb.Mar.Apr. Correlation Coefficient 0.670.720.82 April 1 st SWE PC1 with Flow PC1 April 1 st SWE PC1

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

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 1 st forecast selected 15 models April 1 st forecast selected 6 models PC1 SWE is present in all models PDSI is also selected in half of the models

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

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

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

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

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

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

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

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 geopotential height Flow vs PDSI Flow vs SWE

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

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

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

37. Comparison of forecast model performances Forecast 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