1 Streamflow Forecasting from Weather to Climate Scales Current US Practice, Challenges and Opportunities Helmholtz Environmental Centre Leipzig, Germany,

1 Streamflow Forecasting from Weather to Climate Scales Current US Practice, Challenges and Opportunities Helmholtz Environmental Centre Leipzig, Germany, Oct 13, 2014 Andy Wood NCAR Research Applications Laboratory Boulder, Colorado

Outline Operational Seasonal Forecasting western US objectives processes Predictability Research Opportunities for Improvement

The Arid Lands 3 Many droughts will occur; many seasons in a long series will be fruitless; and it may be doubted whether, on the whole, agriculture will prove remunerative. John Wesley Powell, 1879 Report on the lands of the arid region of the United States Precipitation, 1971-2000 NCAR Boulder, Colorado

Seasonal streamflow prediction is critical 4 One example: Met. Water Dist. of S. California (MWD) MWD gets: 1.25 MAF OR 0.55 MAF from B. Hazencamp, MWD ? +$150M gap Surplus

How are forecasts created?

National Weather Service River Forecast Centers Thirteen River Forecast Centers Established in the 1940s for water supply forecasting Three primary missions: 1. Seasonal Water supply forecasts for water management 2. Daily forecasts for flood, recreation, water management 3. Flash flood warning support

Forecast precip / temp Weather and Climate Forecasts River Forecast Modeling System parameters Observed Data Analysis & Quality Control Calibration model guidance Hydrologic Model Analysis hydrologic expertise & judgment Outputs Graphics River Forecasts Decisions Rules, values, other factors, politics NWS River Forecast Process

MPE Multi-Precip Estimator MPE Multi-Precip Estimator MM Mountain Mapper Daily QC MM Mountain Mapper Daily QC Data Processing Modeling Dissemination GFE Gridded Forecast Editor GFE Gridded Forecast Editor Observed forcings Precipitation + Temperature + Freezing level + ET Process methods vary Interactive Processes Threshold and spatial comparisons Systematic estimation Manual forcing and over-rides Hourly and max/min data (temperature) Quality Control (QC) and processing procedures vary by region and RFC Interactive methods Output qualified for model use Slide by H. Opitz, NWRFC

NationalModelGuidanceNationalModelGuidance National Model guidance available to WFO & RFC WFO & RFC directly share gridded forecasts via GFE exchange Grids at 4km or 2.5km RFCs employ grid and point forcings to hydrologic model Point or gridded data converted to mean areal forcings National Model guidance available to WFO & RFC WFO & RFC directly share gridded forecasts via GFE exchange Grids at 4km or 2.5km RFCs employ grid and point forcings to hydrologic model Point or gridded data converted to mean areal forcings GFE Gridded Forecast Editor National Center Support Forecast Forcings WFO Weather Forecast Office RFC River Forecast Center Slide by H. Opitz, NWRFC

Data Assimilation Modeling Dissemination Sacramento Soil Moisture Accounting Model Data Assimilation Dissemination Observed Hydrology Simulation and Analysis NWS: Streamflow Forecasting is an iterative & interactive process Analysis & Assessment Bias adjustments and error corrections NWS: Streamflow Forecasting is an iterative & interactive process Analysis & Assessment Bias adjustments and error corrections Also: SNOW-17 Temperature index model for simulating snowpack accumulation and melt Slide by H. Opitz, NWRFC

Observed and Simulated not Tracking Result: Improved observed period simulation Runtime Modifications River Forecast Centers MOD capability has been available in the NWS >30 years Generic MOD capability implemented within FEWS Extend capability to other users outside of OHD- core models MOD capability has been available in the NWS >30 years Generic MOD capability implemented within FEWS Extend capability to other users outside of OHD- core models MOD Interface Calibration Climatology Hydrologist render a Run-Time modification to the SACSMA Model and increases the lower zone primary and supplemental states Hydrologist render a Run-Time modification to the SACSMA Model and increases the lower zone primary and supplemental states CHPS Slide by H. Opitz, NWRFC

12 Examples: Nooksack R Typical situation during snowmelt: the simulation goes awry What can a hydrologist deduce from this simulation? As it is, blending simulation and obs gives an unrealistic forecast

13 Examples: Nooksack R One Solution -- Double the snowpack (WECHNG) Other approaches may also have been tried: lower temperatures, raise soil moisture, etc.

14 Examples: Nooksack R The resulting simulation is better, hence the forecast is more confident Flows stay elevated, have diurnal signal of continued melt.

A manual, subjective process 15 WFO+HPC met. forecast Hydrologic simulation and flood forecasting Long-lead forecasting Coordination with USDA/NRCS Meteorological analysis Quality control of station data Quality control of radar and radar parameters Manual elements WFO forecast itself (though based on models) RFC merge with HPC forecast (similar to WFO) Sac./Snow17 model states and parameters Bias-adjustment relative to obs. Flow Input forcings (2 nd chance at adjustment) Model states as adjusted for flood forecasting Choice of models (statistical / ESP ) Blend of models Choice of meteorology: QPF, ENSO, None? Merging with NRCS statistical forecasts means, confidence limits (10-90s) Daily Flood Forecast Water Supply Forecast Process

Water Supply Forecast Methods Statistical Forecasting Statistical Regression Equations Primary NOAA/RFC forecast method from 1940s to mid 1990s. Primary NRCS/NWCC forecast method Historical Relationships between flow, snow, & precipitation (1971-2000) Tied to a fixed runoff period (inflexible) Ensemble Simulation Model Forecasting A component of a continuous conceptual model (NWSRFS) Continuous real time inputs (temperature, precipitation, forecasts) Accounts for soil moisture states (SAC-SMA) - drives runoff efficiency Builds and melts snowpack (Snow-17) output feeds SAC-SMA Flexible run date, forecast period, forecast parameters. Evolving toward ESP as primary forecast tool at NOAA/RFCs

NWS Community Hydrologic Prediction System New forecasting platform at all RFCs New wrapper for old modelsbut... Facilitates research to operations gives more insight into the model used Transition completed in 2011

Sno w Time ESP example -- Feb 1 forecast Future Feb 1 Past Low chance of this level snow or higher High chance of this level snow or higher Medium chance of this level snow or higher The COMET Program ESP forecasts based on (1) initial conditions (e.g. snow pack, base flow, etc) (2) historical meteorological scenarios

Forecasts and Water Management Average contribution to Lake Powell Apr-Jul inflow: Green River34% Colorado River50% San Juan River13% 19 Reclamation Midterm- Probabilistic Model Stakeholder Allocations Graphic from Dr. Katrina Grantz, Bureau of Reclamation CBRFC ensemble flow forecasts for Reclamation water management 19 Forecast impacts, e.g.: if WY12 is wet enough, Lake Mead hits surplus levels, MWD gets water ~ $150M

Water supply forecasting

Runoff forecast for this period Example flow volume prediction increasing watershed moisture reduces prediction spread

Statistical Forecast Equations: Predictor variables must make sense Challenge when few observation sites exist within river basin Challenge when measurement sites are relatively young Fall & Spring precipitation is frequently used (why?) Source: NRCS Sample Equation for April 1: April-July volume Weber @ Oakley = + 3.50 * Apr 1 st Smith & Morehouse (SMMU1) Snow Water Equivalent + 1.66 * Apr 1 st Trial Lake (TRLU1) Snow Water Equivalent + 2.40 * Apr 1 st Chalk Creek #1 (CHCU1) Snow Water Equivalent - 28.27 Trial Lake SNOTEL

What are the sources of seasonal streamflow predictability?

Opportunities for prediction Hydrological Prediction: How well can we estimate the amount of water stored? Accuracy in precipitation estimates Fidelity of hydro model simulations Effectiveness of hydrologic data assimilation methods hydrological predictability Water Cycle (from NASA) Meteorological predictability: How well can we forecast the weather? Opportunities: Which area has most potential for different applications? meteorological predictability

Seasonal predictor importance depends on hydroclimate humid basin uniform rainfall no snow small cycle driven by ET cold basin drier summers deep snow large seasonal cycle April snowmelt dominates May-June runoff

Data & Methods automated system, large number of gages Basin Selection: Used GAGES-II, Hydro-climatic data network (HCDN)-2009 minimal human influence, long period of record large diversity of landscapes, energy- to water-limited ~670 rwatersheds

Assessing the sources of flow forecast skill w=0.5 http://www.ral.ucar.edu/staff/wood/weights/

Snow-Driven Basin in the Western US Wide seasonal variations in influence of different skill sources cold and/or dry forecast period forecast skill depends strongly on initial condition influences warmer wet/snowmelt forecast period forecast skill depends strongly on met. forecast skill IHC: initial Hydrologic Conditions SDF: Seasonal Climate Forecasts

Streamflow Forecast Skill Elasticities The % change in flow forecast skill versus per % change in predictor source skill Can help estimate the benefits of effort to improve forecasts in each area

Opportunities for Improvement of Operational Forecasts

Improved modeling (ie, IHCs) 31 flexible platform

Example: point observations and lumped model zones did not capture difference between these two years 2006 Snowbird SNOTEL trace almost identical to 2010 trace from June 1-10 However, distributed satellite/model analysis indicates south facing slopes were melted out in 2006 much lower runoff as a result 2010 June 1 2006 June 1

From lumped conceptual models 33 SNOW17 SWE Sacramento

To finer resolution physically-based models 34 Distributed modeling of water and energy balance

Improved long-lead flow forecasts via climate prediction Conditioned on IRI seasonal forecast Get the prediction (A:N:B=40:35:25) Divide historical (seasonal) total into 3 tercile categories Bootstrap 40, 35 and 25 sample of historical years from wet, normal and dry categories Apply the ensembles in ESP instead of historical weather sequences (from Balaji Rajagopalan, CU)

43 For seasonal time scales, skill is lower some seasons may have better skill, varying little across leads East R at Almont, Co, precip CFSv2 precipitation forecast skill

37 Short- Range Medium- Range Merging Joining Blending NWS has a new system for incorporating climate forecasts Under development / MMEFS Planned Long- Range Calibrated and seamless short- to long-term forcing input ensembles Day 1-5 ensembles Day 1-14 ensembles Day 15+ ensembles HPC/RFC single-valued forecast GFS/GEFS ens. mean CFS/CFSv2 forecast Other ensembles (SREF, NAEFS) Other info (climate indices)

Is it time to start shifting the forecast paradigm? 38 Advantages of a manual process handles data and model deficiencies in ways that current objective approaches cannot incorporates the knowledge of the expert forecaster in ways current models and procedures may not capable of producing the best forecasts at times Disadvantages limits addition of new techniques that assume objectivity or automation data assimilation, post-processing, verification, ensemblesdata assimilation, post-processing, verification, ensembles labor intensive poor scaling limits opportunity for expanded services cannot quantify uncertainty of an individual forecast cannot quantify skill of forecasting system and set baseline for systematic improvements ie benchmarking the system is partly in the forecaster brain, thus system knowledge can be lost as forecasters retire or move away Such a shift would require broad institutional change training, expertise, center staffing, partner interactions

Questions? Dr. Andy Wood CBRFC Development and Operations Questions? http://en.wikipedia.org/wiki/Lake_Powell [email protected]

April 28, 201040 Precipitation Skill, CD 48 Satish Regonda, OHD

Historical Simulation Q SWE SM Historical DataForecasts PastFuture e.g., SNOW-17 / SAC Fig. M. Clark (NCAR) Streamflow Prediction Challenges: forecast future weather (hours to seasons) SNOW-17 / SAC General State of Practice conduct ensemble simulation at fine-time step (e.g., sub-daily); aggregate to client needs (e.g., daily to seasonal information) adjust results to compensate for known model biases (practice varies)

Assessing regional variations Based on ~420 watersheds, 30 year hindcasts MotivationDevelopment & PerformancePredictabilitySummaryEnsemble Forcing Results are preliminary and subject to change

Flow Forecast Skill Sensitivities A Snowmelt Basin Wide seasonal variations in influence of different skill sources Cold/Warm and Dry/Wet patterns are clear Skill increases in meteorology produce non- linear skill changes in flow Assuming no skill in one source (model accuracy for watershed states, or forecasts)what are the benefits of adding skill in the other source? Results are preliminary and subject to change

Flow Forecast Skill Sensitivities A Rain-Driven Basin IC skill matters most for 1 month forecasts For longer forecasts, met forecast skill dominates Assuming no skill in one source (model accuracy for watershed states, or forecasts)what are the benefits of adding skill in the other source? Results are preliminary and subject to change

47 Atmospheric Pre-Processor: calibration Off line, model joint distribution between single-valued forecast and verifying observation for each lead time X Y Forecast Observed 0 Joint distribution Sample Space PDF of ObservedPDF of Obs. STD Normal NQT Schaake et al. (2007), Wu et al. (2011) Forecast Observed Joint distribution Model Space X Y Correlation (X,Y) Archive of observed-forecast pairs PDF of Forecast PDF of Fcst STD Normal NQT NQT: Normal Quantile Transform

48 Atmospheric Pre-Processor: ensemble generation (1) In real-time, given single-valued forecast, generate ensemble traces from the conditional distribution for each lead time Forecast Observed Joint distribution Model Space x fcst X Y Given single-valued forecast, obtain conditional distribution xixi xnxn Conditional distribution given x fcst Ensemble forecast Probability 0 1 x1x1 Schaake et al. (2007), Wu et al. (2011) Ensemble members for that particular time step x1x1 xnxn NQT -1

Abstract Streamflow Forecasting from Weather to Climate Scales Current US Practice, Challenges and Opportunities Several US agencies are charged with operationally producing seasonal streamflow forecasts, which are a critical input to water management throughout the US, and particularly in the western US, where flood-prone winters and dry summers necessitate careful decision-making over water allocation and use. Water supply forecasts (WSFs) that predict the volume of snowmelt-driven runoff in the spring and early summer, for example, are a critical product informing the regulation of major storage projects such as Lakes Powell and Mead. This presentation focuses on the tradition of water supply forecasting and describes an approach for diagnosing the sources of prediction skill. The talk will highlight strengths and weaknesses of the current practice, which is highly manual and regionalized, and potential opportunities for WSF improvement. The presentation also describes the current practice in flood forecasting, and new developments toward a more centralized modeling and flood prediction paradigm.

Improved Human Resources The forecasting and water management enterprise needs people with a strengths in: watershed science land surface modeling atmospheric and/or climate science probability and statistics objective analysis (time-series, spatial) programming (UNIX, R, Python, traditional, web, database) technical communication systems engineering water resources planning, management and policy 50

Calibration Results Areas with seasonal snow perform best (using NSE benchmark) More arid regions, high plains perform worst Need in-depth examination of poor performing basins Forcing data issues? ~670 basins included in initial calibration

Rain-Driven Basin in the Southeastern US For 6-month forecast, seasonal variation in flow forecast skill sensitivities (met. forecast or IC) is minimal Effectiveness of skillful met. forecasts diminishes if initial conditions are not well-captured

Analysis effort outside the forecast process is critical to the forecast service

Sno w Time Mar 1 forecast Future Mar 1 Past Low chance of this level flow or higher High chance of this level flow or higher Medium chance of this level flow or higher The COMET Program

Sno w Time Apr 1 forecast Future Apr 1 Past Low chance of this level snow or higher High chance of this level snow or higher Medium chance of this level snow or higher The COMET Program

Story line of an extreme year Each hydrologic anomaly has a story line. As water year progresses: past wx/climate (hydrologic initial conditions) are an increasing part of the plot future climate is a diminishing part of the plot extremes often involve pattern persistence but can be more complicated Flow Time Future Feb 1 Past IC signal Wx/Climat e signal e.g., Wood & Lettenmaier, 2008 GRL e.g., storyline: big storm in Feb very wet April cool May/June IC

Both frameworks can be skillful ESP model-based PCR - statistical Yet both have drawbacks raw ESP: fails to account fully for hydrologic uncertainty may have bias overconfident PCR, ie, flow = f(SWE, acc. PCP) cannot provide confidence bounds (heteroskedastic error) in many places small samples used since mid-1990s Ad-hoc combination

Improved post-processing Statistical post-processing of forecasts can correct systematic bias and spread problems (particularly ESP overconfidence and bias). Require long track record of consistent forecasts for training Would need to change the current operational ESP approach I s April 1 Forecast of April-July streamflow volume, North Fork Gunnison (SOMC2) 90 th 50 th 10th

Premise: We can improve short- term water decisions and increase long-term adaptation capacity under climate change by improving monitoring & forecasting Improving how we use this information Report summarizes needs monitoring forecasting information use http://www.ccawwg.us/docs/Short- Term_Water_Management_Decisions_Final_3_Jan_2013.pdf

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1 Streamflow Forecasting from Weather to Climate Scales Current US Practice, Challenges and Opportunities Helmholtz Environmental Centre Leipzig, Germany,