1. Dennis P. Lettenmaier Department of Civil and Environmental Engineering University of Washington CSIRO Land and Water Seminar Series September 28, 2009 Experimental seasonal hydrologic forecast system for the western U.S.

2. Outline 1) Overview 2) Two systems: – Surface Water Monitor (1/2 degree, mostly nowcast (some drought forecast), multimodel – West-wide forecast system (1/8 degree, VIC only, nowcast and streamflow forecasts) 3)The role of calibration 4)Data assimilation 5) Hydrologic predictability – Technological development and transfer – Nation-wide and International collaboration 6) Ongoing applications and related work

3. From Wood and Lettenmaier (BAMS, 2006): Despite the potential benefits of improved hydrologic forecasts, most operational hydrologic prediction at seasonal lead times … are based on methods and data sources that have been in place for almost half a century. The skill of western U.S. seasonal streamflow forecasts has generally not improved since the 1960s. While forecast accuracy improvements would likely result from observing system densification, the need for long data records in regression-based methods would take decades to realize, and would be complicated by a changing climate. We believe that a more promising pathway lies in the development of methods … for assimilating new sources of observational data into land surface energy and water balance models, which can then be forced with modern climate and weather forecasts. Why do we need an experimental hydrological prediction system?

4. One reason for the slow progress in hydrologic prediction has been the lack of real-time testing of new prediction models and methods …

5. The need for a national perspective on hydrologic prediction Will help to address emerging water resources operation and planning issues (e.g., nonstationarity) Better exploit predictability in weather and climate (which is inherently at progressively larger scales with lead time) Utilize macroscale models (which are amenable to to-down application at large continental and sub- continental scales) Make better use of methods, like data assimilation, that can use large scale data sources to improve hydrologic initial conditions

7. Bias correction is a key step in linking large scale atmospheric forecast models with macroscale hydrologic forecasting From Wood et al (2002) – development of a hydrologically based statistical downscaling method

8. Downscaling Test 1.Start with GSM-scale monthly observed met data for 21 years 2.Downscale into a daily VIC-scale time series 3.Force hydrology model to produce streamflow 4.Is observed streamflow reproduced?

9. Simulations Forecast Products streamflow soil moisture runoff snowpack VIC model spin-up VIC forecast ensemble climate forecast information (from GSM) VIC climatology ensemble 1-2 years back start of month 0end of month 6 NCDC met. station obs. up to 2-4 months from current LDAS/other met. forcings for remaining spin-up data sources A B C

10. University of Washington/Princeton University North American Hydrological Systems (NAHS) Low resolution (1/2 degree) SWM High resolution (1/8 degree) WSHFS NAHS Monitoring Forecast USMEX In progress (09/2009) done 1/8 NCAST ESP CPC CFS ½ NCAST Multi-model Drought ESP Drought CPC Drought Multi-model

11. Surface Water Monitor Drought Monitoring – Daily nowcast of Soil moisture and Runoff percentiles at ½ deg spatial resolution. – Multimodel based ensembles for the CONUS and Mexico Drought Prediction – Forecast of Soil Moisture (SM) and 3 months cumulative Runoff (RO) percentile at ½ deg spatial resolution out to 1 to 3 months. http://www.hydro.washington.edu/forecast/monitor/index.shtml

12. Drought Monitoring (Daily Process Flow) Previous day’s meteorological observations from index stations, gridded to 0.5 degree All models use same input forcings, different formats Model results expressed as percentiles of historical output Average Percentiles Compute Percentiles Make Plots

13. Multimodel based SM percentile Nowcast (2009-09-23) (http://www.hydro.washington.edu/forecast/monitor/curr/conus.mexico/main_sm.multimodel.shtml) Models used:  VIC 4.0.6  Noah 2.8  Sacramento/Snow-17 (SAC)  CLM 3.5 VIC NOAH_2.8 SAC CLM Multimodel

14. Forecasts based on: -Ensemble Streamflow Prediction (ESP) Climatological (1950-2004) weather ensembles ENSO years only -Climate Prediction Center (CPC) weather forecasts Climatological weather ensembles adjusted by CPC predicted monthly anomalies. ( Work in progress ) Drought Prediction http://www.hydro.washington.edu/forecast/monitor/outlook/index.shtml

15. Initial Hydrologic Conditions on 2009-09-18 Climatological ESP based 1 month lead SM percentile forecast CPC forecast-based 1 month lead SM percentile forecast ENSO subset ESP based 1 month lead SM percentile forecast

16. Initial Hydrologic Conditions on 2009-09-18 Climatological ESP based 3 month lead RO percentile forecast CPC forecast-based 3 month lead RO percentile forecast ENSO subset ESP based 3 month lead RO percentile forecast

17. Use of SWM in United States Drought Monitor SWM SM and RO percentile nowcast is used by USDM for drought monitoring http://www.drought.gov/portal/server.pt/communit y/drought_indicators/223/soil_moisture/278

18. Use of SWM in Drought Outlook SWM ESP based forecast is one of the tools consulted in the monthly drought monitor briefings to provide drought outlook http://www.drought.gov/portal/server.pt/community/forecasti ng/209/soil_moisture/338

19. University of Washington/Princeton University West-wide Seasonal Hydrological Forecast System (WSHFS) http://www.hydro.washington.edu/forecast/westwide/ Sub-continental Mid-Resolution

20. University of WashingtonUniversity of Washington and IMTA (Mexico) NCAST (nowcast)forecast The WSHFS

21. The WSHFS monitoring and forecast Quasi-real-time daily representation of current Hydrological Conditions: NCAST – Currently covers all US, Mexico, and the upper Columbia basin in Canada NCAST downloads meteorological data from ACIS (NCDC-COOP stations) in the US, Environment Canada, and Servicio Meteorológico Nacional (or NARR and NLDAS) in Mexico Over 2300 stations are used by Index-station method to generate the forcings at 1/8 degree (>800 in Mexico and 10 in the Upper Columbia River Basin in Canada) Assimilates SNOTEL data (NCAST and Forecast) ESP-and CPC-based Streamflow forecast on more than 200 stations over most of the US (and ESP-based on more than 20 over México)

22. University of WashingtonUniversity of Washington and Princeton University NCAST Evolution

23. University of Washington/Princeton University North American Hydrological Systems (NAHS) Low resolution (1/2 degree) SWM High resolution (1/8 degree) WSHFS NAHS Monitoring Forecast USMEX In progress (09/2009) done 1/8 NCAST ESP CPC CFS ½ NCAST Multi-model Drought ESP Drought CPC Drought Multi-model

24. WSHFS (current operations) ESP CPC

25. Forecast System Initial State information Soil Moisture Simulated Initial Condition Snowpack Simulated Initial Condition Observed SWE

26. Assimilation Method weight station OBS’ influence over VIC cell based on distance and elevation difference number of stations influencing a given cell depends on specified influence distances spatial weighting function elevation weighting function SNOTEL/ASP VIC cell Forecast System Initial State Snow Adjustment distances “fit”: OBS weighting increased throughout season OBS anomalies applied to VIC long term means, combined with VIC- simulated SWE adjustment specific to each VIC snow band

27. Streamflow Forecast Details Flow location maps give access to monthly hydrograph plots, and also to raw forecast data. Clicking the stream flow forecast map also accesses current basin-averaged conditions

28. Streamflow Forecast Results: Westwide at a Glance

29. Winter 2006-07: seasonal volume forecast for APR-SEP OBS Forecasts made on 1 st of Month

30. Is calibration necessary? Approach: Use percentile mapping bias correction on uncalibrated forecasts, compare Cp (1 – forecast MSE/unconditional variance) for calibrated and uncalibrated (using N-LDAS parameters) for a range of forecast dates and lead times at 8 forecast sites throughout the western U.S. Result: Bias corrected uncalibrated forecasts did nearly as well at most sites, and better at some Conclusion: Perhaps calibration (the Achilles Heel of dynamic hydrologic forecasting methods) isn’t really necessary Details: Shi et al, JH< 2009

33. What are the sources of skill in seasonal hydrological forecasts?

34. Results of previous seasonal hydrologic predictability studies for continental U.S. 1.Wood et al, “A retrospective assessment of NCEP climate model-based ensemble hydrologic forecasting in the western United States”, JGR, 2005 2.Wood, “An ensemble-based framework for characterizing sources of uncertainty in hydrologic prediction” 3.Work in progress, DEMETER forecasts over continental U.S.

35. Wood et al 2005: Retrospective Assessment: Results using GSM General finding is that NCEP GSM climate forecasts do not add to skill of ESP forecasts, except… April GSM forecast with respect to climatology (left) and to ESP (right)

36. Wood et al 2005: Retrospective results for ENSO years October GSM forecast w.r.t ESP: unconditional (left) and strong-ENSO (right) Summary: During strong ENSO events, for some river basins (California, Pacific Northwest) runoff forecasts improved with strong-ENSO composite; but Colorado River, upper Rio Grande River basin RO forecasts worsened.

37. Wood and Lettenmaier (GRL, 2008) Reverse ESP

38. Reverse ESP vs ESP – typical results for the western U.S. Columbia R. Basin Rio Grande R. Basin ICs more impt fcst more impt

39. DEMETER forecast evaluation VIC model long-term (1960-99) simulations at ½ degree spatial resolution assumed to be truth DEMETER reforecasts with ECMWF seasonal forecast model for 6 month lead, forecasts made on Feb 1, May 1, Aug 1, Nov 1 1960-99 9 forecast ensembles on each date Forecast forcings (precipitation and temperature) downscaled and bias corrected using Wood et al approach (also incorporated in UW West-wide system) On each forecast date, 9 ensemble members also resampled at random from 1960-99 to form ESP ensemble Forecast skill evaluated using Cp for unrouted runoff

40. Test sites

41. Missouri River at Fort Benton

42. Snake River at Milne

43. Summary There isn’t much prediction skill in dynamical global forecast models at lead times more than ~ 1 month, except possibly in ENSO non-neutral years Most hydrological forecast skill therefore derives from knowledge of initial conditions Some climate forecast skill can nonetheless be derived from categorical forecasts (e.g., stratified ESP forecasts)

44. Applications and Research Climate Impacts Group – Quarterly Forecast for the PNW – Annual Forecast for users in the states of WA, OR, and ID Wildfire Forecast (UC Merced and USDA Forest Service) CLIMAS (University of Arizona and Mexican Institutions) Drought Predictability over Mexico using WSHFS and applying ESP and CFS techniques An average of 1.2 requests of data per month, including forcings and forecast consults US and Mexican agencies and Institutes consult or are in process to use some of the tools (Instituto Mexicano de Tecnologia del Agua [IMTA], Comision Nacional del Agua [CNA])

45. Applications CLIMAS Wildfire Forecast Climate Impacts Group

46. Long-term Historical Observed Atmos. Forcing Realtime Atmos. Forcing VIC Long-term Hydrological States VIC Realtime Hydrological States Soil Moisture Percentiles (SMI) ESPs, CFSs, and Nowcast RMSE OBS (NCAST) and Forecast (ESP and CFS) 1971-2000 2007 Initializations Mar, May, Jul, Sep, Nov Mexico, North Central, Northwest, andSouth Modelling-based Atmos. Forcing + Long-term VIC CFS-Long-term Hydrological States 1971-2000 ESPCFS NCAST Border Drought Predictability Assessment

47. Initial Conditions March April May June ObservationsForecasts ESPCFS Ensemble Performance (soil moisture Percentiles) 2007

48. Snow data assimilation: Remote Sensing applications http://www.hydro.washington.edu/forecast/rsda/ Regional High Resolution

49. Aqua Terra MODIS Snow Data UW Hydrological Forecast System Snowpack Initial Condition Streamflow forecast Remotely Sensed Snow for Streamflow Forecast

50. Snow cover areal (SCA) SCA: snow cover percentage of total area in the Feather River basin, California. SCA (%) Ablation period Feather River Oroville Reservoir

51. Predicted Streamflow at Oroville Reservoir Overestimated (without MODIS) Improved (with MODIS) outlier Year: 2000-2008

52. Retrospective streamflow forecasts Mean Absolute Error (MAE) Two-week forecasts Seasonal forecasts Year: 2000-2008 Seasonal forecast: streamflow forecasts from forecast date through July 31 Two-week forecast: two-week lead-time streamflow forecasts Inclusion of MODIS data reduced forecast errors in 70% of the two- week forecasts and 85% of the seasonal forecasts in the ablation period.

53. UW Remote Sensing Data Assimilation & Seasonal Climate Forecasts Daily updating over the Western U.S. UWSDAS Website: http://www.hydro.washington.edu/forecast/rsda/ UWSDAS leverages heavily from the University of Washington’s west-wide hydrologic forecast system.

54. Backward extension in time – pre-MODIS Greatest obstacle to inclusion of satellite data in statistical methods is short data records: - MODIS data goes to 1999 only -AVHRR data considered “meteorological” and not well archived - NESDIS maintains a SCA record dating back to 1966, but its resolution is too coarse. - NOHRSC has 1 km SCA records from the late 1980s, but data are sporadic and generated by evolving set of algorithms. The Terra satellite (top), launched in Dec 1999, and the Aqua satellite, launched in May 2002.

55. Land Long-Term Data Record Recent NASA project to produce a prototype climate data record from AVHRR and MODIS instruments: Pedelty J, Devadiga S, Masuoka E, Brown M, Pinzon J, Tucker C, Roy D, Ju J, Vermote E, Prince S, Nagol J, Justice C, Schaaf C, Liu J, Privette J, Pinheiro A (2007) Generating a long-term land data record from the AVHRR and MODIS instruments. 2007 IGARSS, July 23–27, Barcelona 0.05° (~4 km) AVHRR data (1981–1999); will include year of AVHRR from 2000s for calibration w/ MODIS

56. Snowcode Estimates daily snow cover, including periods of cloudy conditions by temporal filtering and interpolation: Zhao H, Fernandes R (2009) Daily snow cover estimation from Advanced Very High Resolution Radiometer Polar Pathfinder data over Northern Hemisphere land surfaces during 1982–2004. J Geophys Res 114(D05113):1–14 Ongoing effort to apply snowcode to LTDR data, and extract fractional snow cover to match higher resolution of MODIS data.

57. Summary Evaluations show that MODIS snow product has the greatest misclassified fractions during the ablation period and at the beginning of the snow accumulation period. VIC application with and without MODIS SCA updating showed that the greatest improvements occurred during the snow ablation season, with little to no improvement otherwise. Inclusion of MODIS data reduced forecast errors in 70% of the two-week forecasts and 85% of the seasonal forecasts in the ablation period.

58. Statistical-dynamical Seasonal Runoff Forecast Regional High Resolution

59. Can model-simulated snow states and MODIS SCA data be adapted to the regression-based forecasting system used by California’s Department of Water Resources? Study focused on 14 watersheds located in the (blue (green) Sacramento River (blue), San Joaquin River (green), and Tulare Lake (red) (red) hydrologic regions, which together are responsible for ~60% of the state’s runoff. Upper Sacramento Feather Yuba American Cosumnes Mokelumne Stanislaus Tuolomne Merced San Joaquin Kings Kaweah Tule Kern Hybrid statistical-dynamical Seasonal Runoff Forecast

60. Potential Benefits Improve forecast skill by enhancing the spatial representation of snow states. Alleviate pressure to conduct labor-intensive snow surveys. Provide the ability to generate forecasts late in the melt season, when even high-elevation snow sensors are laid bare, but forecasts are nonetheless valuable. MODIS image on Jun 3, 2006… snow indicated in white, clouds indicated in blue. Feather American Tuolomne Merced

61. CA DWR’s forecasts of Apr-Jul runoff rely on manual measurements of SWE at snow courses in each watershed (left, for the Feather). Problems can occur late in the year when snow remains at high elevations, but not at snow course Possible approach is to regress seasonal flow on VIC-simulated SWE (which includes high elevations where snow remains) rather than observed SWE (right). Currently 1/8°; higher resolution (1/16°) in progress. Hybrid statistical-dynamical seasonal runoff forecasts – Feather River, CA

62. Preliminary Results… Sacramento River Feather (1781 taf) +100 Yuba (1005 taf) American (1240 taf) U. Sacramento (2494 taf) 0 -100FMMJJA Skill compared in plots of 10th and 90th percentiles of resulting residuals, provided as a percentageof mean annual flow (shown in parentheses). Blue = model-based,black = ground-based. Blue = model-based, black = ground-based.FMMJJAFMMJJAFMMJJA

63. Preliminary Results… San Joaquin River Merced (632 taf) +100 San Joaquin (1254 taf) Tuolomne (1220 taf) 0 -100FMMJJA Skill compared in plots of 10th and 90th percentiles of resulting residuals, provided as a percentageof mean annual flow (shown in parentheses). Green = model-based,black = ground-based. Green = model-based, black = ground-based.FMMJJAFMMJJA

64. Concluding thoughts Macroscale hydrological models offer a viable top-down approach to seasonal hydrological forecasting over large domains, and to the overlapping problem of drought nowcasting and forecasting Most of the realizable skill in seasonal hydrological forecasting comes from knowledge of hydrologic initial conditions (not seasonal climate forecast skill) Hence, more work on operational hydrologic data assimilation is in order Remote sensing can provide on piece of the puzzle, however lack of long-term, consistent data records remains a challenge