Collaborative RD to support improved Hydroclimate Information used

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

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 -9 M per year • recent CCV activity: ~1. 5 M/yr; competed and facilitated projects • projects led by internal Pis (Regions, TSC) and/or external collaborators

More information on S&T’s CCV portfolio 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

CCV Priorities Steering: Climate Change and Water Working Group • • 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)

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

S&T Project 2264 Application of a Physically-based Distributed Snowmelt Model in Support of Reservoir Operations and Water Management Objectives Graphic 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? Approach Partners NRCS, BSU 1. Milestones 2. 3. Application of Isnobal in the Boise River Basin to provide maps of SWE and snowcover energy state Coupling of Isnobal to soil storage or routing model Proof of concept – using the coupled models with short-term weather forecasts to forecast reservoir inflows 1. 2. Spring of 2014 ARS provided weekly SWE maps August 2015 present POC, comparison of historic results, and review of integrating model outputs into operations. • …

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

S&T Facilitated Research: Airborne Snow Observatory (ASO): Value of Information for supporting Snowmelt Reservoir Operations Objectives Graphic 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. Approach 1. 2. 3. 4. 5. Focus on two reservoir systems and basins (Gunnison, San Juan) Emulate ASO: (a) synthetic “truth” hydrology at high-resolution, (b) simulated snowpack observation using point and ASO schemes Hydrologic Hindcasts: (a) current model + point sensing, (b) improved model + point sensing, (c) improved model + ASO Operations Hindcasts: informed by 3. Assess value of operational effects Partners NASA JPL, Reclamation UC, Reclamation TSC Milestones 1. (FY 15) Tasks 1 -3 Q 2, Task 4 Q 3, Task 5 Q 4 2. Plan to have results meeting and next-steps discussion during Q 4 timeframe; include RFCs.

S&T Facilitated Research: (FY 13 -15) The Predictability of Streamflow across the Contiguous United States (FY 15 -17) Experimental Demonstration and Evaluation of Real -time, Over-the-Loop Streamflow Forecasting Objectives 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. Approach 1. 2. 3. 4. Implement CONUS-wide watershed simulation framework with automated model application and forcing generation. Assemble building blocks forecasting improvement (described later) Conduct hindcast experiments and operations impact evaluations using these building blocks. Evaluate building blocks in an experimental, real-time forecasting evaluation with forecaster over-the-loop workflow. Partners NCAR, USACE, Reclamation, University of Washington Milestones Opportunities to engage operators and RFCs to review/discuss: 1. 2. 3. 3/15/15: Hindcasts on effects of alternative historical and future forcing generation. 6/15/15: Hindcasts on effects of data assimilation and post-processing. 9/1/15: (a) Hindcasts on effects of alternative model and calibration approaches, (b) draft real-time system specs

More detailed briefing on: (FY 13 -15) The Predictability of Streamflow across the Contiguous United States (FY 15 -17) Experimental Demonstration and Evaluation of Real-time, Over-the-Loop Streamflow Forecasting Andy Wood, NCAR

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

Candidate opportunities for advancement 1) 2) 3) 4) alternative hydrologic model(s), new forcing data/methods (eg, QC) to drive hydrologic modeling new calibration tools to support hydrologic model implementation 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) Streamflow Prediction System Elements

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.

CONUS-wide watershed simulation framework • • • Opportunity: Create “many basins” platform 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 band 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.

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 forecasting improvement (next slides)

Building Blocks: Refine Model Parameters, Initial Conditions • Opportunity: Develop ensemble historical CONUS forcing dataset • Benefits: supports (1) more robust historical calibration, (2) ensemblebased 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.

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, En. KF and AEn. KF, versus the raw forecast

Building Blocks: Estimate Flood Forecast Uncertainty • Opportunity: downscaled ensemble met forecasts enable estimation of prediction uncertainty • Benefits: supports risk-based approaches 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 (Snow 17/SAC model)

Building Blocks: Watershed Modeling • Opportunity: contrast ability of different modeling approaches to capture hydrologic variability and response • Benefits: provides broader array of modeling options forecasting • Specs: baseline is NWS models (Snow 17/Sac/UH/etc, lumped); alternatives include gridded VIC, SUMMA in various configurations Figures: Exploring various model configurations and physics (HRU Snow 17/SAC, band SUMMA) Snow 17 -lump Snow 17 -hru Snow 17 -band SUMMA-band

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

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 forecasting improvement (next slides) – Initiated hindcast experiments and operations impact evaluations using these method improvement (FY 15)

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

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

Hindcasting Process Variations: FF 1 FF 2 Flood Forecasts Alternative Future Forcings? HF 2 HF 1 Alternative Historical Forcings? Alternative Data Assimilation? HF 1 HF 2 FF 1 FF 2 June 15 DA 1 MC 1 Alternative Model or Calibration? Reference: RFC Archived Forecasts March 15 Area DA 2 DA 1 DA 2 MC 3 September 1 MC 1 MC 2 MC 3 Model SMA/Snow 17 SMA/Snow 17 VIC SUMMA band/hru Calib / Spinup Forcing Daymet Newman Ens v 0+ best forcing best ens forcing best forcing Calib. Param. SCE SCE SCE MOCOM SCE Future Forcing GEFS control GEFS DS ens v 0 GEFS DS ens v 1 best GEFS best GEFS Data Assim. none particle filter En. KF best DA Post-Process. Linear blend Linear blend Linear blend

Hindcasting Process Variations: Seasonal Forecasts CF 3 CF 2 Alternative Climate Forecasts? DP 1 DP 2 Alternative DA or Post. Processing? CF 1 DP 3 MC 2 MC 1 Reference: RFC Archived Forecasts or ESP March 15 MC 3 Alternative Model and Calibration? June 15 September 1 Area CF 1 CF 2 CF 3 DP 1 DP 2 DP 3 MC 1 MC 2 MC 3 Model SMA/Snow 17 statistical (eg, PCR) SMA/Snow 17 Alt model / calib #1 multi-model Calib / Spinup Forcing best forcing (see HF) mixed obs best forcing (see HF) best forcing (see HF) as configured Calib. Param. best calib base calib NA best calib Alt model / calib #1 as configured Future Forcing ESP + indexbased wgts. ESP + CFSbased wgts. pred. clim. fields best clim. forecast as configured Data Assim. none SWE (PF or En. KF) best DA + PP combo as configured Post-Process. ST blend NA ST blend + regr. /analog

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/

Operators Evaluation (FY 15 Q 2 -Q 3) • 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.

Operations Hindcasting (FY 15 Q 4) • 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, DA 1 (for Flood event hindcasting) and DP 3 (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

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 forecasting improvement (next slides) – Initiated hindcast experiments and operations impact evaluations using these method improvement (FY 15) – Scoped a follow-on, experimental, real-time forecasting evaluation (FY 16 -17).

From Hindcasting to Real-Time Forecasting Hindcasts FY 15; Real-Time Forecasting effort FY 15 Q 4 - FY 17 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

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

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

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) – Jeff Arnold (USACE, Jeffrey. R. Arnold@usace. army. mil) – Levi Brekke (Reclamation, lbrekke@usbr. gov) • Collaborators: – University of Washington (Bart Nijssen) – Agencies (e. g. RFCs, USACE & Reclamation field offices) – More welcome!

References • • • 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)

EXTRA SLIDES

Real-Time Forecasting Scope Forecast Element Operational Practice Contrast 1. Hydrologic Model Legacy single-physics, spatially coarse and conceptual models from 1970 s-1980 s, support forecasting at limited number of river locations Modeling system permitting multiple models and alternative physics portrayals, with spatially distributed multivariate predictions. 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 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) Impacts of adopting automated technique 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 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 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 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 CFSv 2 or NMME ensembles, drawing from larger predictor suite; multiple techniques (e. g. , analog, hybrid analog, HEFS). Pros: Automated process allows for rapid realtime 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. 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 regimerelated errors that can be perceived by forecasters but are difficult to detect from longterm statistical analysis. 6. Forecasting – post-processing of streamflow forecast to reduce errors

Real-time Forecasting Project Timeline FY 15 -17 Effort: aiming to kick off the experimental operational forecast system withing year 1