1. Collaborative R&D to support improved Hydroclimate Information used in Short-Term Water Management Eric Rothwell (Reclamation), Levi Brekke (Reclamation), Andy Wood (NCAR), and Jeff Arnold (USACE) Co-Investigators: (NCAR) AJ Newman, K Sampson, TM Hopson, M Clark, (Univ. WA) B Nijssen Reclamation PN – NWS NWRFC Coordination Call, January 15, 2015

2. Research and Development Office (RDO) Science & Technology Program Developing solutions for water & power management –Traditional categories environmental stewardship for water delivery and management water and power infrastructure reliability water operations decision support conserving or expanding water supplies –Priority themes intersecting research categories: advanced water treatment climate change and variability (CCV) invasive quagga mussels renewable energy and energy conservation Sustainable water infrastructure and safety –S&T Portfolio: ~100 to 150 projects, ~8-9M per year recent CCV activity: ~1.5 M/yr; competed and facilitated projects projects led by internal Pis (Regions, TSC) and/or external collaborators

3. More information on S&T’s CCV portfolio http://www.usbr.gov/research/climate/ http://www.usbr.gov/research/climate/ Portfolio Categories 1.Long-term Climate Change Impacts 2.Short-term Climate Variability, from Floods to Droughts (focus today) 3.Data, Tools, and Training Resources 4.Scoping

4. Defining User Needs, Developing Research Strategy Fostering collaborative R&D Developing Training Resources Hosting Workshops on Emerging Topics (e.g., Nonstationarity, Portfolio of Assessemtn Approaches) CCV Priorities Steering: Climate Change and Water Working Group

5. www.ccawwg.us 2013 CCAWWG report: Address User Needs for Short-term information on weather and hydrology

6. S&T Project 2264 Application of a Physically-based Distributed Snowmelt Model in Support of Reservoir Operations and Water Management Objectives ApproachPartners NRCS, BSU Testing the application of a physically-based distributed snow model in an operational forecast setting. Are these modeling techniques appropriate to operational needs and do the deliverable products improve reservoir management outcomes? 1.Application of Isnobal in the Boise River Basin to provide maps of SWE and snowcover energy state 2.Coupling of Isnobal to soil storage or routing model 3.Proof of concept – using the coupled models with short-term weather forecasts to forecast reservoir inflows Graphic … Milestones 1.Spring of 2014 ARS provided weekly SWE maps 2.August 2015 present POC, comparison of historic results, and review of integrating model outputs into operations.

7. S&T Project 9682 Intermediate-range Climate Forecasting to Support Water Supply and Flood Control with a Regionally Focused Mesoscale Model Objectives ApproachPartners USDA-ARS Develop mesoscale weather prediction models, tailored to regional characteristics, to provide hydroclimate forecast data to test accuracy and spatiotemporal coverage. 1.Develop WRF simulation model for a region that includes portions of the PN, Upper Colorado, and Great Plains. 2.Perform historical re-forecasts for a range of years. 3.Quantify and characterize the accuracy of data products from WRF, and how could they enhance run-off forecast products. Graphic Milestones 1.Development of the WRF simulation model for a domain encompassing portions of the headwaters of the PN, UC, and GP Regions. (July 2015) 2.Hold a stakeholder meeting to illicit feed back on initial WRF simulation model (August 2015).

8. S&T Facilitated Research: Airborne Snow Observatory (ASO): Value of Information for supporting Snowmelt Reservoir Operations Objectives ApproachPartners NASA JPL, Reclamation UC, Reclamation TSC ASO technology provides unique wall-to-wall monitoring of basin snowpack and dust conditions. Goal is to assess value of ASO monitoring in the context of Reclamation’s snowmelt management during late Spring to early Summer, focusing on western Colorado. 1.Focus on two reservoir systems and basins (Gunnison, San Juan) 2.Emulate ASO: (a) synthetic “truth” hydrology at high-resolution, (b) simulated snowpack observation using point and ASO schemes 3.Hydrologic Hindcasts: (a) current model + point sensing, (b) improved model + point sensing, (c) improved model + ASO 4.Operations Hindcasts: informed by 3. 5.Assess value of operational effects Graphic Milestones 1.(FY15) Tasks 1-3 Q2, Task 4 Q3, Task 5 Q4 2.Plan to have results meeting and next-steps discussion during Q4 timeframe; include RFCs.

9. S&T Facilitated Research: (FY13-15) The Predictability of Streamflow across the Contiguous United States (FY15-17) Experimental Demonstration and Evaluation of Real-time, Over-the-Loop Streamflow Forecasting Objectives ApproachPartners NCAR, USACE, Reclamation, University of Washington 1.Implement CONUS-wide watershed simulation framework with automated model application and forcing generation. 2.Assemble building blocks for forecasting improvement (described later) 3.Conduct hindcast experiments and operations impact evaluations using these building blocks. 4.Evaluate building blocks in an experimental, real-time forecasting evaluation with forecaster over-the-loop workflow. Milestones Opportunities to engage operators and RFCs to review/discuss: 1.3/15/15: Hindcasts on effects of alternative historical and future forcing generation. 2.6/15/15: Hindcasts on effects of data assimilation and post-processing. 3.9/1/15: (a) Hindcasts on effects of alternative model and calibration approaches, (b) draft real-time system specs Graphic Evaluate work-stage opportunities for improving streamflow forecasts (days to seasons). Assess how these opportunities perform under historical hydroclimate variability (hindcasts) and, subsequently, within a real-time forecasting environment. In the latter, feature forecaster over-the-loop workflow and assess its benefits and disadvantages.

10. More detailed briefing on: (FY13-15) The Predictability of Streamflow across the Contiguous United States (FY15-17) Experimental Demonstration and Evaluation of Real-time, Over-the-Loop Streamflow Forecasting Andy Wood, NCAR

11. Forecast precip / temp Weather and Climate Forecasts River Forecasting System parameters Observed Data Update Model states Models Calibration model outputs Hydrologic Model Analysis Analysis products Outputs Graphics River Forecasts Support W.M. Decisions Motivation: Improve the River Forecasting Process Analysis & Quality Control +

12. Streamflow Prediction System Elements Candidate opportunities for advancement 1) alternative hydrologic model(s), 2) new forcing data/methods (eg, QC) to drive hydrologic modeling 3) new calibration tools to support hydrologic model implementation 4) Improved data assimilation to specify initial watershed conditions for hydrologic forecasts 5) new data and methods to predict future weather and climate 6,7)methods to post-process streamflow forecasts and reduce systematic errors 8) benchmarking / hindcastsing / verification system / ensembles (not shown)

13. Science Questions & Approach Questions: –For different types of forecasts and user needs, what method or data improvement opportunities are most promising? –How do these opportunities fare under historical hydroclimate variability? –How do these opportunities fare in a real-time forecasting environment? To develop answers to above question, we’ve: –Implemented a CONUS-wide watershed simulation framework with automated model application and forcing generation.

14. Opportunity: Create “many basins” platform for forecasting application and evaluation Benefit: Permits efficient study of forecasting elements (model, forcing, data assim, etc.) under a variety of basin and climate conditions Specs: Newman et al. 2014 –Base model: National Weather Service operational Snow-17 and Sacramento-soil moisture accounting model (Snow-17/SAC) … more models to be added –Locations: 670 basins from GAGES-II, Hydro-climatic data network (HCDN)-2009 –Forcings: DAYMET (http://daymet.ornl.gov/), NLDAS, and Maurer et al. (2002) for (a) lumped (Snow-17/SAC apps), (b) hydrologic response unit (from PRISM), and (c) elevation bandhttp://daymet.ornl.gov/ –Calibration: automated Shuffled Complex Evolution (SCE) global optimization routine: 15 years, validation on remaining data for all lump forcing types; areas with seasonal snow, frequent precipitation perform best; high plains, desert SW perform worse Newman, A. J., et al. 2014: “Development of a large-sample watershed-scale hydrometeorological dataset for the contiguous USA: Dataset characteristics and assessment of regional variability in hydrologic model performance,” HESS, in press. CONUS-wide watershed simulation framework

15. Science Questions & Approach Questions: –For different types of forecasts and user needs, what method or data improvement opportunities are most promising? –How do these opportunities fare under historical hydroclimate variability? –How do these opportunities fare in a real-time forecasting environment? To develop answers to above question, we’ve: –Implemented a CONUS-wide watershed simulation framework with automated model application and forcing generation. –Assembled building blocks for forecasting improvement (next slides)

16. Building Blocks: Refine Model Parameters, Initial Conditions Opportunity: Develop ensemble historical CONUS forcing dataset Benefits: supports (1) more robust historical calibration, (2) ensemble- based data assimilation to initialize forecasts Specs: 1/8º grid, informed by 12,000+ stations, 100 ensemble members Example: Example June 1993 precipitation two example members (a-b) ensemble mean (c) ensemble standard deviation (d) Newman, A. J., M. P. Clark, J. Craig, B. Nijssen, A. Wood, E. Gutmann, N. Mizukami, L. Brekke, and J.R. Arnold, 2015: “Gridded Ensemble Precipitation and Temperature Estimates for the Contiguous United States,” in development.

17. Building Blocks: Refine Initial Conditions Opportunity: automated assimilation of observed streamflow (flood forecasts) and snow water equivalent (seasonal forecasts) Benefits: improves initialization of watershed states, replaces manual modifications in forecasting process Specs: apply particle filter (PF) with uncertainty from ensemble forcings Figure: Particle Filter based ensemble DA with 6-hour update cycle automatically adjusts SAC model to correct for model and forcing errors Figure: RMSE of forecasts with DA using PF, EnKF and AEnKF, versus the raw forecast

18. Building Blocks: Estimate Flood Forecast Uncertainty Opportunity: downscaled ensemble met forecasts enable estimation of prediction uncertainty Benefits: supports risk-based approaches for forecast use Specs: use locally-weighted multi-variate regression to downscale GEFS (reforecast) atmospheric predictors to watershed precipitation and temperature Figures: Case study hindcast of 15-day ensemble forecast including 7 days of downscaled GEFS as met forecast (Snow17/SAC model)

19. Building Blocks: Watershed Modeling Opportunity: contrast ability of different modeling approaches to capture hydrologic variability and response Benefits: provides broader array of modeling options for forecasting Specs: baseline is NWS models (Snow17/Sac/UH/etc, lumped); alternatives include gridded VIC, SUMMA in various configurations Figures: Exploring various model configurations and physics (HRU Snow17/SAC, band SUMMA) Snow17-lump Snow17-hru Snow17-band SUMMA-band

20. Building Blocks: Include Climate Forecasts, Post-Processing Opportunity: seasonal climate forecasts can add information to seasonal streamflow predictions Benefits? increased skill benefits water supply forecasts and associated applications Specs: use ESP trace-weighting approaches based on likelihood from principle component regression of predictors including climate system indices and climate forecasts Opportunity: Apply statistical adjustments to raw streamflow forecasts based on past forecast performance or observable error at initiation time Benefits? reduces systematic forecast errors to improve forecast reliability Specs: use linear damping of error at forecast start; other approaches to be added

21. Science Questions & Approach Questions: –For different types of forecasts and user needs, what method or data improvement opportunities are most promising? –How do these opportunities fare under historical hydroclimate variability? –How do these opportunities fare in a real-time forecasting environment? To develop answers to above question, we’ve: –Implemented a CONUS-wide watershed simulation framework with automated model application and forcing generation. –Assembled building blocks for forecasting improvement (next slides) –Initiated hindcast experiments and operations impact evaluations using these method improvement (FY15)

22. Hydrologic Hindcasts Overview Objectives: Evaluate alternative process variations Specify hindcast experiments to address specific questions Inform future real-time system design Forecast Types Flood: run 5-10 years of daily updating, ensemble flood hindcasts with leads 1-7 days, for different process variations. Seasonal: run 30+ years of weekly updating ensemble seasonal hindcasts with lead time 1 year benchmarking

23. Reference: RFC Archived Forecasts Area Model Calib / Spinup Forcing Calib. Param. Future Forcing Data Assim. Post-Process. Hindcasting Process Variations:

24. Flood Forecasts Alternative Historical Forcings? Alternative Future Forcings? Alternative Data Assimilation? Alternative Model or Calibration? Reference: RFC Archived Forecasts HF1 HF2 FF1 FF2 DA1 DA2 MC3 MC2 MC1 AreaHF1HF2FF1FF2DA1DA2MC1MC2MC3 ModelSMA/Snow 17 VICSUMMA band/hru Calib / Spinup Forcing DaymetNewman Ens v0+ best forcing best ens forcing best forcing Calib. Param.SCE MOCOM SCE Future ForcingGEFS control GEFS DS ens v0 GEFS DS ens v1 best GEFS Data Assim.none particle filterEnKFbest DA Post-Process.Linear blend March 15  June 15  September 1 

25. Hindcasting Process Variations: Seasonal Forecasts Alternative Climate Forecasts? Alternative DA or Post- Processing? Reference: RFC Archived Forecasts or ESP CF1 CF2 Alternative Model and Calibration? MC3 MC1 MC2 DP3 DP2 DP1 AreaCF1CF2CF3DP1DP2DP3MC1MC2MC3 ModelSMA/Snow 17 statistical (eg, PCR) SMA/Snow 17 Alt model / calib #1 multi-model Calib / Spinup Forcing best forcing (see HF) mixed obsbest forcing (see HF) as configured Calib. Param.best calibbase calibNAbest calib Alt model / calib #1 as configured Future Forcing ESP + index- based wgts. ESP + CFS- based wgts. pred. clim. fields best clim. forecast as configured Data Assim.none SWE (PF or EnKF) noneSWE (PF or EnKF) best DA + PP combo as configured Post-Process.ST blend NAST blendST blend + regr./analog March 15  June 15  September 1  CF3

26. Case Study Basin Subset 50 watersheds (and growing), chosen for varying hydro-climates & regions, being relatively unimpaired, and supplying reservoir inflows http://www.ral.ucar.edu/staff/wood/case_studies/

27. Basins & Offices: –Basins: See 50 case study watersheds –Offices: at least Reclamation PN & GP; and USACE NWS (Seattle District); aiming to include more … Milestone #1) Late March –NCAR briefing on Flood (HF, FF) and Seasonal (CF); include RFCs –Operators review, react, provide feedback on mgmt relevance Milestone #2) Late June –NCAR briefing on Flood (DA) and Seasonal (DP); include RFCs –Operators review, react, provide feedback on mgmt relevance Milestone #3) Early September –NCAR briefing on Flood (MC) and Seasonal (MC); include RFCs –Operators review, react, provide feedback on mngt relevance –Operators / RFCs provide suggestions on real-time forecasting workflow, products stream, etc. Operators Evaluation (FY15Q2-Q3)

28. Basins & Offices: –Columbia-Snake Headwater Basins, tbd; –Reclamation PN (Boise) and USACE NWS (Seattle District) Emulate how operators… –(1) use forecasts (which are obtained?), (2) plan operations (how do obtained forecasts lend influence?), and (3) operate (roll ahead system states between forecasts) Forecast Process Variants: –Reference –tbd, likely Reference, DA1 (for Flood event hindcasting) and DP3 (for Seasonal event hindcasting) –Focus on set of past difficult events, floods to droughts Why only a set of events? We can’t do full period analysis because we don’t have built models that emulate short-term ops process. Share preliminary findings at Milestone #3) Early September Operations Hindcasting (FY15Q4)

29. Science Questions & Approach Questions: –For different types of forecasts and user needs, what method or data improvement opportunities are most promising? –How do these opportunities fare under historical hydroclimate variability? –How do these opportunities fare in a real-time forecasting environment? To develop answers to above question, we’ve: –Implemented a CONUS-wide watershed simulation framework with automated model application and forcing generation. –Assembled building blocks for forecasting improvement (next slides) –Initiated hindcast experiments and operations impact evaluations using these method improvement (FY15) –Scoped a follow-on, experimental, real-time forecasting evaluation (FY16-17).

30. Hindcasts FY15; Real-Time Forecasting effort FY15Q4 - FY17 Objectives: 1)Test advanced techniques in “forecaster over-the-loop” system. 2)Generate real-time flow forecast products similar to those from the RFCs, as well as other information; display/disseminate on website –daily to subdaily update flood forecasts; monthly to seasonal forecasts –verification (real-time + long-term), trailing forecasts, uncertainty (spread); other water balance variables (forcings, snow/soil moisture), retrospective climatologies, archived hindcasts for past events –real-time reliability less than RFC –transparency on data & methods 3)Interact with operational partners regularly. –feedback on products and guidance on development –gain insight into user decisions, tailoring product formulation –mode of interaction & frequency TBD as project evolves subject to partner interest & availability From Hindcasting to Real-Time Forecasting

31. Key Messages for RFCs Proof-of-concept Research –Aim to assess and evaluate forecasting methods relevant to RFC practices and short-term water management. Two-way Education Opportunity –(1) Reclamation & partners hear from RFCs about how projects and findings resonate with their practices, to what degree –(2) RFCs learn about projects, potentially inform future workflow planning and/or more targeted collaborations w/ Reclamation & partners. Data Sharing during implementation

32. Summary of Upcoming Milestones (opportunities to engage RFCs) 2264: –Spring of 2015 ARS will make weekly SWE maps available –August 2015 present POC, comparison of historic results, and review of integrating model outputs into operations. 9682: –July 2015: Development of the WRF simulation model for a domain encompassing portions of the headwaters of the PN, UC, and GP Regions. –August 2015: Stakeholder meeting to get feedback on initial WRF simulation model. ASO Value of Information –Summer 2015: Results meeting and next-steps discussion NCAR-led effort: Meetings to review –(late March) hindcasts on effects of alternative historical and future forcing generation methods –(mid June) hindcasts on effects of data assimilation and post-processing. –(early September) (a) Hindcasts on effects of alternative model and calibration approaches, (b) draft real-time system specs

33. More Info / Contacts http://www.ral.ucar.edu/projects/hap/flowpredict/ Leads: –Andy Wood (andywood@ucar.edu), Martyn Clark (NCAR, mclark@ucar.edu), Andy Newman (anewman@ucar.edu), Pablo Mendoza (medoza@ucar.edu)andywood@ucar.edu mclark@ucar.eduanewman@ucar.edumedoza@ucar.edu –Jeff Arnold (USACE, Jeffrey.R.Arnold@usace.army.mil)Jeffrey.R.Arnold@usace.army.mil –Levi Brekke (Reclamation, lbrekke@usbr.gov)lbrekke@usbr.gov Collaborators: –University of Washington (Bart Nijssen) –Agencies (e.g. RFCs, USACE & Reclamation field offices) –More welcome!

34. Newman, A. J., M. P. Clark, J. Craig, B. Nijssen, A. Wood, E. Gutmann, N. Mizukami, L. Brekke, and J.R. Arnold, 2014: “Gridded Ensemble Precipitation and Temperature Estimates for the Contiguous United States,” in development. Newman, AJ, MP Clark, K Sampson, AW Wood, LE Hay, A Bock, R Viger, D Blodgett, L Brekke, JR Arnold, T Hopson, and Q Duan, 2014, Development of a large-sample watershed-scale hydrometeorological dataset for the contiguous USA: dataset characteristics and assessment of regional variability in hydrologic model performance, Hydrol. Earth Syst. Sci. Discuss., 11, 5599-5631, doi:10.5194/hessd- 11-5599-2014 (in press) Wood, AW, T Hopson, A Newman, L. Brekke, J. Arnold, M Clark, 2014, quantifying streamflow forecast skill elasticity to initial condition and climate prediction skill. J. Hydromet.(in review) Clark, MP, B Nijssen, JD Lundquist, D Kavetski, DE Rupp, RA Woods, JE Freer, ED Gutmann, AW Wood, LD Brekke, JA. Arnold, DJ Gochis, and RM Rasmussen, 2014, A unified approach to hydrologic modeling: Part 1. Model structure, Wat. Res. Rsrch (submitted) Clark, MP, B Nijssen, JD Lundquist, D Kavetski, DE Rupp, RA Woods, JE Freer, ED Gutmann, AW Wood, DJ Gochis, and RM Rasmussen, DG Tarboton, V Mahat, GN Flerchinger, and DG Marks, 2014, A unified approach to hydrologic modeling: Part 2. Comparison of alternative process representations, Wat. Res. Rsrch (submitted) References

35. EXTRA SLIDES

36. Forecast Element Operational Practice Contrast Impacts of adopting automated technique 1. Hydrologic Model Legacy single-physics, spatially coarse and conceptual models from 1970s-1980s, support forecasting at limited number of river locations Modeling system permitting multiple models and alternative physics portrayals, with spatially distributed multivariate predictions. Pros: Address modeling uncertainty; surpass conceptual model limitations for process representation Cons: Model variations difficult or costly to maintain in single system, and support with training 2. Model Forcings Mean areal averages of station-based precipitation and temperature, spatially and/or temporally disaggregated by radar Probabilistic forcings at varying spatial scales with full meteorological forcing suite provides richer information base Pros: Finer spatial discrimination represents more controls on watershed processes; estimates of watershed condition uncertainty possible Cons: Spatially distributed parameters more difficult to estimate and probabilistic forcings costly to run 3. Model Development – Parameter Estimation Manual calibration oriented toward reproducing daily streamflow; single parameter set Automated calibration (multiple techniques, multivariate focus, multiple parameter sets (e.g., wet/dry) Pros: Represent parameter uncertainty, inform conditional model application (wet/dry), bring consistency and speed to calibration process Cons: Possible loss of skill aspects perceived by forecasters to be important at individual locations; difficulty handling individual station data variations Real-Time Forecasting Scope

37. Forecast Element Operational Practice Contrast Impacts of adopting automated technique 4. Forecasting – Data Assimilation and Initial Basin Condition Estimation Manual adjustment of model states to reflect station snow (SWE) observations and reduce for streamflow (Q) simulation errors Automated assimilation of multiple observed conditions (SWE, Q) to adjust model states via multiple statistical techniques (such as the particle filter) Pros: Supports reproducible updates for efficient, scalable forecast generation, avoids labor-intensive state modification Cons: Performance of automated DA is still less well-understood than other forecast method areas; vulnerable to observed data errors if not caught 5. Forecasting – Estimating Future Weather Manually merged met. forecast grids from models and other NWS weather forecast offices to yield single-value mean areal meteorological forecasts; no use of climate predictions; HEFS 1 and MMEFS 2 Automated downscaling and calibration of GEFS and CFSv2 or NMME ensembles, drawing from larger predictor suite; multiple techniques (e.g., analog, hybrid analog, HEFS). Pros: Automated process allows for rapid real- time updates; ensembles support quantification of forecast uncertainty; reproducibility enables hindcasting and verification, as well as method benchmarking Cons: Nowcast range (1-12 hour) predictions may not integrate as many data sources as RFC forecasters consider, and be less accurate. 6. Forecasting – post-processing of streamflow forecast to reduce errors Manual adjustment of single-value streamflow forecasts based on forecaster intuition and awareness of impact thresholds. Automated application of multiple ensemble streamflow forecast calibration techniques to reduce systematic bias, spread and timing errors. Leverages retrospective simulations and hindcasts. Pros: Reproducible techniques that can be assessed and improved through verification, supporting a quantification of forecast uncertainty. Hindcastable. Cons: Individual events may have regime- related errors that can be perceived by forecasters but are difficult to detect from long- term statistical analysis. Real-Time Forecasting Scope

38. FY15-17 Effort: aiming to kick off the experimental operational forecast system withing year 1 Real-time Forecasting Project Timeline