Water, Weather and Climate in Our Future: What Can We Know?

1. Water, Weather and Climate in Our Future: What Can We Know? John Schaake NOAA’s NWS Office of Hydrologic Development (Retired) AGU Langbein Lecture December 15, 2009

2. Tribute to Walter Langbein • Contributions • stochastic modeling, • quantitative geomorphology • information theory and network design • hydraulics of sediment transport • long term trends in hydrology • USGS • Systems Group • Science Community • AGU - WRR • UNESCO IHD • UA Hydrology Department • Memories Walter receiving 2nd International Hydrology Prize (1982)

3. Physically based hydrologic modeling of a parking lot Economics, public policy and Water Resource Systems Probability, statistics and stochastic hydrology Operational hydrology Climate change Hydrometeorological aspects of weather and climate prediction Seamless approach to weather water and climate prediction My Journey in Hydrology • Education • JHU – Water Resources (and applied math) • Harvard – Design of water resource systems • Academia • U. Florida – Environmental and Systems engineering • MIT – Water Resources • NOAA • Hydrology Lab - NWSRFS • Hydrology Operations • Climate and hydrology • GEWEX • GCIP • GAPP • CPPA • Ensemble prediction • Retirement? • Science infusion • LDAS • MOPEX • DMIP • HEPEX

4. Water, Weather and Climate in our Future • Climate Variability and Change – and Hydrologic Response • Weather and Climate Predictions • Hydrologic Ensemble Prediction • Atmospheric Forecast PreProcessor • Hydrologic Uncertainty PostProcessor • Some Initial Streamflow Hindcast Results

5. What Can We Know? • We need to consider physics to help explain why things happen and to put skill in our predictions • We need to consider uncertainty to make our predictions more useful for making decisions

6. Climate Variability and Changeand Hydrologic Response

7. Source: Michael Dettinger

8. Source: Michael Dettinger

9. Source: Michael Dettinger

10. Warmer Has Observed Climate Change in the American River Basin also been Observed in Streamflow?

11. Observed and Simulated Changes in Pattern of Annual Runoff Accumulation – NF American River (1961-1998)

12. Observed and Simulated Changes in Flood Frequency – NF American River (1961-1998) “…if you're doing an experiment, you should report everything that you think might make it invalid — not only what you think is right about it... Details that could throw doubt on your interpretation must be given, if you know them.” Richard Feynman

13. Research Challenges • How can models and data be used together more effectively to make predictions regarding climate variability and change? • It takes a lot of data to quantify uncertainty. • Making use of models is especially important to better understand non-stationarity and possible future regional effects of anthropogenic climate change.

14. Weather and Climate Predictions Probabilistic forecasts from raw ensembles are not very reliable, due to deficiencies in forecast model, ensemble methods. [Tom Hamill – from a presentation to 2006 NCEP Predictability Meeting, 14 Nov 2006]

15. NCEP Global Ensemble Forecasts

17. Illustration of the temporal scale-dependent skill in GFS ensemble mean winter season precipitation forecasts for the North Fork of the American River Basin.The curve labeled “moving 6hr window” shows how the correlation between 6hr precipitation forecasts and corresponding observations decreases with increasing forecast lead time.The curve labeled “average from t=0” shows the correlations for forecast averages accumulated over windows beginning at t=0 with corresponding observations decreases as the time to the end of the window increases. The remaining curves for similar averages begin at different times after t=0. Fig 5

18. Forecast Window Width (6-hr periods) Climate Perspective (Y-axis) 2 weeks Increasing lead time to end of forecast period 1 week Forecast Lead Time to Beginning of Forecast Window (6-hr periods) Weather Perspective (X-axis) Precipitation Forecast Correlation Coefficient: Temporal Scale-DependencyNFDC1HUP – January 15 GFS Forecasts We need a seamless approach to weather and climate forecasting to meet the needs of hydrologic ensemble prediction

19. Hydrologic Ensemble Prediction

20. Short-term Ensemble Prototype NF American (upper subarea)Example of 5-day Precipitation EnsemblesProduced from RFC single-value precipitation forecasts QPF = 1.5 inches in two successive 6-hr periods, otherwise zero

21. Short-term Ensemble Prototype NF American (upper subarea)Example of 5-day Temperature EnsemblesProduced from MOS temperature forecasts

22. Short-term Ensemble Prototype NF American River Example of 5-day Streamflow EnsemblesProduced from example precipitation and temperature ensembles shown above

23. What is Needed to Make Ensemble Forecasts? Weather and Climate Forecasts Hydrologic Ensemble Prediction System Hydrological Models (& Regulation) Hydrological Models Observations Products and Services

24. Elements of a Hydrologic Ensemble Prediction System Weather and Climate Forecasts Single-value and ensemble forecasts Atmospheric Ensemble Pre-Processor Reliable hydrologic inputs Hydrological Models (& Regulation) Land Data Assimilator Hydrologic Ensemble Processor Hydrological Models Obs Ensemble forecasts Ensemble initial conditions Verification Product Generator and Post-Processor Forecaster Role Reliable hydrologic products Products and Services

25. Multiple Scales, Dendritic Basin Structure

26. Hydrologic Ensemble Prediction Challenges Many Challenges and Sources of Uncertainty • Observation uncertainties and uncertain initial conditions • Future forcing uncertainty, scale dependency, seamless weather to climate prediction, bias correction and downscaling • Upstream regulation • Hydrologic model uncertainties • Need for hydrologic post-processing • HEPEX – International Hydrologic Ensemble Prediction Experiment

27. HEPEX Activities • Workshops • ECMWF, Reading, March, 2004 • NCAR, Boulder, July, 2005 • EC/JRC, Ispra, Italy, June, 2007 • Deltares, Delft, June, 2008 • Meteo-France, Toulouse, June, 2009 • Articles and Journal Special Issues • HESS: HEPEX Special Issue • EOS Article • BAMS Article (Tom Hamill) • ASL Special Issues (2) • Test-bed Projects (20+) • Data Set Development • Future Components of a CHPS? www.hepex.org

28. Atmospheric Forecast PreProcessorto Generate Ensemble Forcing for Hydrologic Models

29. Raw Atmospheric Forecasts Estimate Probability Distributions This step includes downscaling, and correction of bias and spread problems This step assures that members are “consistent” over all basins for the entire forecast period Assign Values to Ensemble Members ESP Input Forcing (Schaake Shuffle) Ensemble PreProcessor2 – Step Process Note: This process requires many years of hindcasts and corresponding analyses of observations to calibrate the procedure and to provide the basis for generating ensemble members using the Schaake Shuffle

30. GFS Precipitation Forecast Verification North Fork American River Raw Forecasts Processed Forecasts Bias Day of Year Day of Year CRPSS Forecast Event Forecast Event

31. Source: Lavers et al, 2009, presentation to 6th GEWEX Conference

32. Research Challenges • How to PreProcess atmosperic forecasts to produce reliable ensemble forcing for hydrology that retains information “sharpness” while also quantifying uncertainty – seamlessly, over all relevant space and time scales ? • How to combine forecast information from multiple atmospheric models and ensemble members from the same model while producing consistent (in space and time) ensemble members (for say the Mississippi river basin)?

33. Raw ESP Streamflow Ensemble Products Hydrologic Post-Processor (Accounts for uncertainty in hydrologic model and in initial conditions) Adjusted ESP Streamflow Ensemble Products Hydrologic Uncertainty Post-Processor(to correct raw ESP bias and spread errors)

34. Hydrologic Post-Processor • The ESP program generates an ensemble of streamflow forecasts that are conditioned on an ensemble of precipitation and temperature forecasts (i.e. ysim|fcst) • These ESP forecasts assume that the initial conditions are known and that the hydrologic model is perfect • Historical simulations of runoff from observed precipitation can be used to represent the uncertainty associated with the fact that the initial conditions are not known exactly and the model is imperfect (i.e. f(yobs|ysim)) • We may be able to neglect the uncertainty in the relationship between yobs and ysim that is caused by the uncertainty in the estimated forcing used to generate ysim during the forecast period if the ensemble forcing is well calibrated. • Then, the pdf of yobs, given the ensemble of precipitation and temperature forecasts can be estimated by the relationship: Raw ESP Forecast Adjusted ESP Forecast Historical Simulation

35. Example PostProcessed Simulations for North Fork American River, CA

36. Research Challenges • How to represent the temporal multi-scale relationship between hydrologic model simulations and the events that actually occur? • How to model known and unknown aspects of upstream regulation (reservoirs, diversions, irrigation, etc.)

37. Some Initial Streamflow Hindcast Results1979 – 2004at NOAA’s CNRFC

38. Forecasts and Simulations for North Fork American River, CA Correlation between observed and predicted cumulative runoff volumes

39. Forecasts and Simulations for Smith River at Crescent City, CA Correlation between observed and predicted cumulative runoff volumes

40. Research Challenges • How can we communicate uncertainty to our users? • How can “users” use the information in hydrologic ensemble forecasts to make decisions? • What hindcast information do users need? • What are the trade-offs from a user perspective of how computational resources are used for weather, climate and hydrologic prediction?

41. Research Challenges • When we analyze observations we usually try to produce a “best” estimate. But non-linear hydrologic systems are very sensitive to uncertainty. Can we produce ENSEMBLEanalyses that represent spacial and temporal scale-dependent analysis uncertainty? • This is especially important in using satellite (and probably radar) observations • Need ensemble analyses to support ensemble data assimilation • How to produce analyses from different data sources that are consistent with climatology for long historical periods

42. Some Messages • All models are imperfect and nothing can be known for sure. • We are beginning to learn how to extract information from models and data, including uncertainty, in a reliable way. • There is no one best model. Several of the best imperfect models may be better than any one. • The scientific foundation for hydrologic prediction requires support from our national science agency (NSF) of research that can be implemented by operational science agencies. • “There cannot be a greater mistake than that of looking superciliously upon practical applications of science. The life and soul of science is its practical application...” [Lord Kelvin]

43. A Road Map for the Science of Hydrologic Prediction Many of these research challenges are discussed in this report that was prepared under the direction of Pedro Restrepo.

44. What Was This Lecture All About? • Some science perspectives of water, weather and climate prediction • How my career led me to believe these perspectives are important • Research opportunities for young people • “Science is a way of trying not to fool yourself. The first principle is that you must not fool yourself, and you are the easiest person to fool.” (Richard Feynman)

45. Thank You