1 RTI-USU Discussion Virtual, June 3, 2015 Science to support water resource operations and management Andy Wood and Martyn Clark NCAR Research Applications Laboratory Hydrometeorological Applications Program

Key reports 2

User needs provide agency motivation 3 Other Categories

Streamflow Prediction System Elements Candidate opportunities for forecast improvement 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)

Watershed Modeling Dataset Goal: framework for calibrating and running watershed models CONUS-wide – including for short range and seasonal ensemble forecasting Basin Selection – Used GAGES-II, Hydro-climatic data network (HCDN)-2009 Initial Data & Models, Calibration Approach – Forcing via Daymet ( – NWS operational Snow-17 and Sacramento-soil moisture accounting model (Snow-17/SAC) – Shuffled complex evolution (SCE) global optimization routine Andy Newman

Hydrologic modeling Revealing impacts of model choice Propositions: 1.Most hydrologic modelers share a common understanding of how the dominant fluxes of water and energy affect the time evolution of thermodynamic and hydrologic states ▫The collective understanding of the connectivity of state variables and fluxes allows us to formulate general conservation equations in different sub- domains ▫The conservation equations are scale-invariant 2.Differences among models relate to a)the spatial discretization of the model domain; b)the approaches used to parameterize individual fluxes (including model parameter values); and c)the methods used to solve the governing model equations. General schematic of the terrestrial water cycle, showing dominant fluxes of water and energy Given these propositions, it is possible to develop a unifying model framework For example, by defining a single set of conservation equations, with the capability to use different spatial discretizations (e.g., multi-scale grids, HRUs; connected or disconnected), different flux parameterizations and model parameters, and different time stepping schemes Clark et al. (WRR 2011); Clark et al. (WRR 2015a; 2015b)

soil aquifer soil aquifer soil c) Column organization a) GRUs b) HRUs i) lumpii) grid iii) polygon

The unified approach to hydrologic modeling (SUMMA) Martyn Clark

Pragmatic Model Architectures & Physics what are appropriate/tractable scales & complexity to capture variability? Snow17- Sacramento SUMMA- Sacramento One Lump HRUs Elevation Bands Crystal River

Diagnosis of Model States – eg, SWE June 20 th, 1983 Near start of rise to peak Snow17-HRU too much in lower elevations Snow17-lump too little in higher elevations SUMMA-band too little in higher elevations Snow17-band probably about right given flow performance SUMMA

High Flow Year Snow17-lump Snow17-hru Snow17-band SUMMA-band June 20

Clark & Slater, 2006 – JHM 1) Estimate probability of precipitation (POP), amount and error at each grid cell 2) Synthesize ensembles from POP, amount & error station observations ____ generate spatial correlation structure & uncertainty Example: Precip over the Colorado Headwaters DA Datasets -- Creating Met. Forcing Uncertainty Andy Newman, NCAR The interpolation of station obs to gridded fields can generate many equally valid realizations (analyses) Most existing datasets just provide a single realization.

Example CONUS Precipitation & Temperature ~12,000 stations used for analysis target is 1/8 o grid (~12 km), all CONUS land pixels 100-member forcing ensemble

Example CONUS Precipitation Figure 4. Example monthly precipitation totals from two ensemble members (a-b), along with the monthly ensemble mean (c) and standard deviation (d). April 2008 example Estimating this uncertainty is valuable for: More robust model calibration Input to data assimilation techniques, which require specification of model uncertainty

Example Application Snowmelt dominated basin in Colorado Rockies Example water year daily temperature (a) Snow water equivalent accumulation (b) Simple temperature index model (optimized for Daymet (green)) Ensemble Hydrometorology Dataset

operational example of automated DA 16 Alternatives to manual spinup: ensemble initializations (particle filter) system by Amy Sansone, Matt Wiley, 3TIER slide from DOH Mtg talk, 2012

Example: Flood forecasting 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)

Real-time demonstration and evaluation In case study basins, demonstrate and evaluate experimental, automated days-to-seasons flow forecasts using: met and flow data quality control and various real-time forcing generation approaches ensemble meteorological forecasts and downscaling techniques variations in model physics and architecture automated, objective model calibration data assimilation flow forecast post-processing hindcasting and verification Partner with USACE/Recl. field office personnel for evaluation and to guide product development Bart Nijssen, U. Washington, is a collaborator

Initial case study set for real-time prediction demo chosen for varying hydroclimates, being relatively unimpaired, and feeding reservoir inflows -- subset of nation-wide model dataset

Thank You 20 &

Harnessing Seasonal Climate Forecasts Opportunity: seasonal climate forecasts can add information to seasonal streamflow predictions Benefits? increased skill benefits water supply forecasts and associated applications Specs: use ensemble trace-weighting approaches based on likelihood from regression of predictors e.g., climate system variables or climate forecasts

Hydrologic Hindcasting Objectives: Evaluate alternative process variations Specify hindcast experiments to address specific questions Inform future real-time system design Forecast Types Flood: 5-10 year hindcast daily updating leads 1-7 days Seasonal: 30+ year hindcast weekly updating lead time 1 year