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

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 2 nd International Hydrology Prize (1982)

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 Physically based hydrologic modeling of a parking lot Probability, statistics and stochastic hydrology Operational hydrology Climate change Seamless approach to weather water and climate prediction Economics, public policy and Water Resource Systems Hydrometeorological aspects of weather and climate prediction

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

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

Climate Variability and Change and Hydrologic Response

Source: Michael Dettinger

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

Observed and Simulated Changes in Pattern of Annual Runoff Accumulation – NF American River ( )

Observed and Simulated Changes in Flood Frequency – NF American River ( ) “…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

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.

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]

NCEP Global Ensemble Forecasts

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

Precipitation Forecast Correlation Coefficient: Temporal Scale-Dependency NFDC1HUP – January 15 GFS Forecasts Forecast Lead Time to Beginning of Forecast Window (6-hr periods) Forecast Window Width (6-hr periods) Increasing lead time to end of forecast period 1 week 2 weeks Climate Perspective (Y-axis) Weather Perspective (X-axis) We need a seamless approach to weather and climate forecasting to meet the needs of hydrologic ensemble prediction

Hydrologic Ensemble Prediction

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

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

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

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

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

Multiple Scales, Dendritic Basin Structure

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

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?

Atmospheric Forecast PreProcessor to Generate Ensemble Forcing for Hydrologic Models

Ensemble PreProcessor 2 – Step Process Raw Atmospheric Forecasts Estimate Probability Distributions Assign Values to Ensemble Members ESP Input Forcing 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 (Schaake Shuffle) 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

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

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

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)?

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

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: Adjusted ESP Forecast Historical Simulation Raw ESP Forecast

Example PostProcessed Simulations for North Fork American River, CA

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.)

Some Initial Streamflow Hindcast Results 1979 – 2004 at NOAA’s CNRFC

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

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

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?

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 ENSEMBLE analyses 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

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]

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.

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)

Thank You