US CLIVAR ASP Researcher Colloquium Boulder, Co June 13-17, 2011

1. Physical and practical requirements in downscaling for hydrologic assessment and prediction  US CLIVAR ASP Researcher ColloquiumBoulder, Co June 13-17, 2011 Andy Wood NOAA/NWS Colorado Basin River Forecast Center

2. Outline • Hydrologic simulation of extremes • Hydrologic sensitivities • ‘Simple’ Downscaling (in an ideal world) • Typical Downscaling for Hydrologic Assessment • Suggested strategies / priorities

3. Quick Primer on Hydrologic Systems Notable: multi-variateforcings • Notable: • Memory • snowpack • soil layers • Spatial connectivity • - river network

4. CBRFC Watershed Models • RFCs use a snow model and a rainfall-runoff model: • SNOW-17: Temperature index model for simulating snowpack accumulation and melt • Sacramento Soil Moisture Accounting Model: Conceptual hydrologic model used to generate runoff

5. Hydrologic simulation of seasonality Simulation Example Little Cottonwood Creek, Utah For monthly flows: Average Observed 69.3 cfs Average Simulated 67.5 cfs RMSE = 22.83 RMSE/Obs mean = .33 R2 = 0.94

6. Hydrologic models and drought • dry anomalies also simulated well • questions about lack of groundwater in LSMs are valid in some areas From the UW Surface Water Monitor (Wood, 2008)

7. Hydrologic simulation of flooding extremes • hydrology slides Scales that matter in Hydrology http://www.tennessean.com/

8. Nashville, May 1-3, 2010: A univariate event Saturated Water Vapor Stationary front Mid – Mississippi Valley May 1 - 2 Deep moisture advected from Gulf

9. Wasatch Range Creeks: A multivariate event • record snowpack built from months of rain, then cool temperature anomalies • should not be skiable through late July

10. Salt Lake City Watersheds Weber/Provo canal (photo courtesy PRWUA) Little Cottonwood Canyon

11. Modeling scales in hydrologic applications • elevation gradients exert control on weather, even climate • also influences hydrology • determines moisture storage, fate • must account for these effects 5 km 11800’ 5500’ forecast point

12. Modeling scales in hydrologic applications • RFC lumped models recognize this physical consideration – land surface variation • recognize 3 response zones • ignores other variation, e.g, N/S slopes

13. Modeling scales in hydrologic applications • Meteorological forcings are married to hydrologic analysis zones • Captures just enough diurnal/spatial variability to support flood forecasting 5 km 11800’ 5500’ forecast point

14. The making of a hydrologic extreme • Months of prior weather patterns (filling storages – snow, soil) • A terrain-driven pattern of melt, linked by stream network

15. Hydrologic simulation across response range • Note close agreement (obs, sim) across 3 orders of magnitude Dettinger et al., Clim. Cng 2004 Dettinger et al, 2004

16. Outline • Hydrologic simulation of extremes • Hydrologic sensitivities • ‘Simple’ Downscaling (in an ideal world) • Typical Downscaling for Hydrologic Assessment • Suggested strategies / priorities

17. Modeling scales in hydrologic applications • Errors in temperature estimation of just a few degrees can cross important thresholds

18. Sensitivity to Temperature Fraser R nr. Winter Park • in snowmelt regimes, temperature forcing is as sensitive as precipitation

19. Sensitivity to Precipitation • intensity • partitioning between runoff and infiltration • spatial/temporal pattern • synchronization • accuracy • Q = P – E + ΔS • ΔS means biases accumulate • Q = P – E means relative errors in P are magnified in Q

20. Streamflow – Climate Sensitivity: Means • Emigration Canyon – Lower Elevation • -16% flow / degree C win-spr warming • +25% flow / 10% change win-spr precip (C)

21. Patterns over large scales matter • large scale synchronization matters • main-stem river extremes result from effects that accumulate across the basin, so spatial gradients matter (e.g, blue, below freezing, green-red, above)

22. Outline • Hydrologic simulation of extremes • Hydrologic sensitivities • ‘Simple’ Downscaling (in an ideal world) • Typical Downscaling for Hydrologic Assessment • Suggested strategies / priorities

23. A simple downscaling approach Simulated climate past, present, future from GCM (or RCM) interpolation nearest cell hydrology model timestep Hydrologic model that can simulate flow given well constructed meteorology Simulated hydrology past, present, future informed by climate simulation from which to derive period change statistics

24. Hope quickly fades for direct GCM output use No surprise… Prohibitive GCM climatology biases exist even at large scales in time/space from BOR Westwide Study

25. What about RCMs? e.g., NARCCAP

26. On RCMs • Cannot argue that RCMs do not respond to orographic features • Large scale view hides climatology failings • How to interpret projected changes, e.g., increased extremes? F. Dominguez et al. (in preparation, 2011)

27. RCMs still challenged in simulating extremes • relative regional signal is okay • local magnitudes are quite biased in some GCM-RCM combinations • note regional averaging F. Dominguez et al. (in preparation, 2011) 20 year precip 50 year precip

28. Another ‘requirement’ Implication: applications prefer large ensembles of GCM scenarios via a L. Mearns presentation

29. Water applications culture and tough tests

30. Outline • Hydrologic simulation of extremes • Hydrologic sensitivities • ‘Simple’ Downscaling (in an ideal world) • Typical Downscaling for Hydrologic Assessment • Suggested strategies / priorities

31. A simple practical downscaling approach Simulated climate past, present, future from GCM (or RCM) now use coarser resolution ~ monthly, GCM-scale (just reconstruct forcings at required scales) A statistical adjustment scheme Hydrologic model that can simulate flow given well constructed meteorology An observed forcing climatology that works for hydrologic modeling Simulated hydrology past, present, future informed by climate simulation from which to derive period change statistics

32. Prescribed change approach Emigration Canyon projected mean changes for SLC area 2040-2070 versus 1970-2000 112 GCM projections Downscaled via Wood (2004) method - From LLNL CMIP3 112 Projections 1/8o CONUS archive Current climate mean

33. Streamflow – Future Climate Response Emigration Canyon • Let’s take a look at two simple change scenarios • No change in precip • +2, +4 deg C uniform • can also use monthly varying, for given decade • This is the so-called “Delta method” or “perturbation method” Current climate mean

34. Sensitivity of Flow to Projected Temp Changes • Mean annual cycles are well calibrated • Even at +2 degrees, annual cycle diminishes flow • +4 degrees: annual cycle progressively more altered (time, volume) • Emigration Creek (lower elevation) more vulnerable than Little Cottonwood Creek (higher elevation)

35. Expanding correction, adding transient signal Adjustments can go further: * correct whole CDF of output, not just means * apply corrections month-by-month to use time varying GCM output, incorporate GCM sequencing of climate • But this approach may take too much information from GCMs… • GCM sequencing is not always plausible • recent work re-sequences GCM wet/dry periods using paleo spectrum BCSD (Wood et al., 2002, 2004), used in recent DOI western US studies.

36. GCM-based power spectra for Lees Ferry flow • Left Observed • Lower left ECHAM 5 • Lower right NCAR CCSM3.0 • from Ken Nowak, CU

37. For water management uses, tough grading

38. And do these approaches inform about extremes? • Assume met. extreme info is contained in means • hydrologic process still provides non-linear response • Require resampling, scaling, analogue approaches to reconstruct daily meteorology • scalings can blow up (esp. in dry, hence water scarce regions) • Extremes at fine time scales can be poor • depends on underlying distributions of met. variables • high skew or presence of regimes (intermittency) is a problem • pathological results may be rare (but represent the extremes!) • Likely leave information on the table • CMs probably DO have real information about changes in climate parameters (e.g., min temperature, precip. intensity, storm track) • Other approaches exist such as stochastic downscaling, CCA, weather typing • many apps. are univariate or also have trouble reproducing obs. climatology

39. Outline • Hydrologic simulation of extremes • Hydrologic sensitivities • ‘Simple’ Downscaling (in an ideal world) • Typical Downscaling for Hydrologic Assessment • Suggested strategies / priorities

40. Suggested emphases in downscaling for applications • *** Hydrologic extremes often result from complex time/space phenomena *** • Hydrologic applications will rely on statistical downscaling schemes for years • Essential: a high-quality, high-resolution climatology of land surface meteorology (e.g., AOR – sub-daily, < 5 km, multi-variate) • Must move beyond reliance only on familiar fields: P, T • RCMs have valuable role to play, but have challenges to overcome • We will typically need more runs than RCMs can provide • Can we build RCM sensitivities (‘missing from GCMs’) into statistical downscaling approaches? • Extreme value theory can be helpful in shaping downscaling • Multivariate context is needed (space, time, cross-variable) • Perhaps more physical guidance on application (fit without data) • Our applications frameworks must allow for CM climatology error • Avoid an endless chain of corrections…