1. Experimental real-time seasonal hydrologic nowcasting and forecasting in the western U.S. Andy Wood Department of Civil and Environmental Engineering Seminar University of Arizona Department of Hydrology and Water Resources April 27, 2005

2. Outline  Introduction to seasonal forecasting and forecasting system  background  climate forecasts  VIC model spin-up  index station approach  SNOTEL assimilation  Selected results for 2004 and current forecast season  Ongoing work (hydrologic nowcasting & …)

3. The importance of Seasonal Hydrologic Forecasting water management hydropower irrigation flood control water supply fisheries recreation navigation water quality AugDecApr Reservoir Storage Aug

4. Introduction: A definition of useful terms A short poem by someone not primarily known as a poet The Unknown As we know, There are known knowns. There are things we know we know. We also know There are known unknowns. That is to say We know there are some things We know we do not know. But there are also unknown unknowns, The ones we don't know We don't know. Feb. 12, 2002 Department of Defense news briefing Donald Rumsfeld (Disclaimer: the use of this ‘poem’ does not represent a comment on US military policy…)

5. Apr 1 SM Apr 1 SWE Apr-Sep climate Seasonal Forecasting: What do we know and when do we know it? The primary factors determining summer runoff are generally:  SWE at the start of melt (e.g., April 1)  Soil moisture at the start of the forecasted runoff period (e.g, April 1)  April-September Climate the Don Rumsfeld scale

6. Snow water content on April 1 April to August runoff McLean, D.A., 1948 Western Snow Conf. SNOTEL Network Introduction: Hydrologic prediction and the NRCS PNW

7. Technical Advances related to Hydrologic Forecasting 1920s1930s1940s1950s1960s1970s1980s1990s2000s snow survey / graphical forecasts / index methods / i.e., regression computing in water resources aerial snow surveys SNOTEL network ESP method snow cats conceptual hydrologic models

8. Introduction: Hydrologic prediction and ESP NWS River Forecast Center (RFC) approach: rainfall-runoff modeling (i.e., NWS River Forecast System, Anderson, 1973 offspring of Stanford Watershed Model, Crawford & Linsley, 1966) Ensemble Streamflow Prediction (ESP) used for shorter lead predictions; ~ used for longer lead predictions Currently, some western RFCs and NRCS coordinate their seasonal forecasts, using mostly statistical methods. ICs Spin-upForecast obs recently observed meteorological data ensemble of met. data to generate forecast ESP forecast hydrologic state

9. Technical Advances related to Hydrologic Forecasting 1920s1930s1940s1950s1960s1970s1980s1990s2000s snow survey / graphical forecasts / index methods / i.e., regression computing in water resources satellite imagery aerial snow surveys desktop computing SNOTEL network ESP method ENSO / seasonal climate forecasts snow cats Internet / real-time data conceptual hydrologic models physical hydrologic models

10. UW Forecast System Overview

11. Forecast System Overview NCDC met. station obs. up to 2-4 months from current local scale (1/8 degree) weather inputs soil moisture snowpack Hydrologic model spin up SNOTEL Update streamflow, soil moisture, snow water equivalent, runoff 25 th Day, Month 0 1-2 years back LDAS/other real-time met. forcings for spin-up gap Hydrologic forecast simulation Month 6 - 12 INITIAL STATE SNOTEL / MODIS* Update ensemble forecasts ESP traces (40) CPC-based outlook (13) NCEP CFS ensemble (20) NSIPP-1 ensemble (9) * experimental, not yet in real-time product

12. Introduction: UW Experimental Hydrologic Forecasting Soil Moisture Initial Condition Snowpack Initial Condition

13. Introduction: UW Experimental Hydrologic Forecasting VIC model runoff is routed to streamflow gages, and verified against observations

14. Introduction: UW Experimental Hydrologic Forecasting targeted statisticse.g., runoff volumes monthly hydrographs

15. Introduction: UW Experimental Hydrologic Forecasting SWESoil MoistureRunoffPrecipTemp Mar-05 Apr-05 May-05

16. Outline  Introduction to seasonal forecasting and forecasting system  background  climate forecasts  VIC model spin-up  SNOTEL assimilation  Selected results for 2004 and current forecast season  Ongoing work (hydrologic nowcasting & …)

17. Climate Forecasts: Operational Products

18. Climate Forecasts: Use in UW forecast system ESP ENSO/PDO ENSO CPC Official Outlooks Coupled Forecast Model (CFS) CAS OCN SMLR CCA CA NSIPP-1 dynamical model VIC Hydrolog y Model NOAA NASA UW Seasonal Climate Forecast Data Sources

19. Climate Forecasts: Spatial Scale Issues Seattle

20. Climate Forecasts : Bias Issue (prior NCEP model) Sample GSM cell located over Ohio River basin obs prcp GSM prcp obs temp GSM temp JULY Regional Bias: spatial example obs GSM

21. Climate Forecasts: Bias Correction Scheme from COOP observations from GSM climatological runsraw GSM forecast scenario bias-corrected forecast scenario month m

22. Climate Forecasts: CPC Seasonal Outlooks e.g., precipitation

23.  spatial unit for raw forecasts is the Climate Division (102 for U.S.)  CDFs defined by 13 percentile values (0.025 - 0.975) for P and T are given Climate Forecasts: CPC Seasonal Outlook Use

24. Climate Forecasts: CPC Seasonal Outlook Use probabilities => anomalies precipitation

25. VIC initial state: Merging of SNOTEL obs with model SWE The pattern of observed SWE values, which are merged with the forecast initial conditions, is usually in pretty good agreement with the VIC simulated snow state. The PNW currently has very low snowpack, while the Southwest and California have record high snowpacks.

26. VIC initial state: SNOTEL assimilation 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 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. VIC initial state: SNOTEL assimilation April 25, 2004

28. Outline  Introduction to seasonal forecasting and forecasting system  background  climate forecasts  VIC model spin-up  SNOTEL assimilation  Selected results for 2004 and current forecast season  Ongoing work (hydrologic nowcasting & …)  Final comments

29. Results for Winter 2003-04: initial conditions Soil Moisture and Snow Water Equivalent (SWE)

30. Results for Winter 2003-04: initial conditions Soil Moisture and Snow Water Equivalent (SWE)

31. Results for Winter 2003-04: initial conditions Soil Moisture and Snow Water Equivalent (SWE)

32. Results for Winter 2003-04: initial conditions Soil Moisture and Snow Water Equivalent (SWE)

33. Results for Winter 2003-04: initial conditions CPC estimates of seasonal precipitation and temperature very dryhot March

34. Results for Winter 2003-04: initial conditions Soil Moisture and Snow Water Equivalent (SWE)

35. Results for Winter 2003-04: streamflow hydrographs By Fall, slightly low flows were anticipated By winter, moderate deficits were forecasted

36. Results for Winter 2003-04: volume runoff forecasts UPPER HUMBOLDT RIVER BASIN Streamflow Forecasts - May 1, 2003 Forecast Pt============ Chance of Exceeding * =========== Forecast90%70%50% (Most Prob)30%10%30 Yr Avg Period(1000AF) (% AVG.)(1000AF) MARY'S R nr Deeth, Nv APR-JUL12.3 18.7 23 59 27 34 39 MAY-JUL4.5 11.3 16.0 55 21 28 29 LAMOILLE CK nr Lamoille, Nv APR-JUL13.7 17.4 20 67 23 26 30 MAY-JUL11.6 15.4 18.0 64 21 24 28 N F HUMBOLDT R at Devils Gate APR-JUL5.1 11.0 15.0 44 19.0 25 34 MAY-JUL1.7 7.2 11.0 50 14.8 20 22

37. Results for Winter 2003-04: volume runoff forecasts Comparison with NWRFC forecast for Columbia River at the Dalles, OR UW forecasts made on 25 th of each month RFC forecasts made several times monthly: 1 st, mid-month, late (UW’s ESP unconditional and CPC forecasts shown) UW RFC 79% obs

38. Results for Winter 2003-04: volume runoff forecasts Comparison with RFC forecast for Feather River, CA UW forecasts made on 25 th of each month RFC forecasts made on 1 st of month (UW’s ESP unconditional forecasts shown) UW RFC 78% obs

39. Results for Winter 2003-04 : volume forecasts for a sample of PNW locations

41. WY2005, Dec. 1 hydrologic conditions

43. WY2005, Jan. 1 hydrologic conditions

44. WY2005, Feb. 1 hydrologic conditions

45. PNW in crisis?: Headlines from February, 1977

46. Comparison: Columbia R. basin upstream of The Dalles, OR WY2005 WY1977 WY2005 WY1977

47. Results: WY2005, Feb. 1 streamflow forecasts SNOTEL / Env. Canada ASP network is a valuable source of snowpack information. The observed SWE values, which are merged with the forecast initial conditions, were in good agreement with the simulated snow state. 98% 80%85% 1977: 55%

48. WY2005, Mar. 1 hydrologic conditions

49. WY2005, Apr. 1 hydrologic conditions

50. 4/15 ESP forecast: WY2005 Precip, Temp Yakima R. Basin near Parker, WA plots show current + forecast (ESP; min, max and quartiles) against historical 1971-2000 min, max and quartiles

51. Apr-Sep % of avg 3/154/15chg max6157 0.754650 0.504145 0.253942 min31 39 4/15 ESP forecast: WY2005 SM, SWE, RO

52. Ongoing Work: Involvement with Operational Groups A major goal of this work is to transition successful approaches into operational settings. Progress on this front:  Involvement with NRCS National Water and Climate Center (Tom Pagano!)  Involvement with NOAA Climate Prediction Center (CPC)  e.g., related to Drought Outlook  Outreach to PNW water and power community through the UW Climate Impacts Group  e.g., to WA State Executive Water Emergency Committee

53. Outline  Introduction to seasonal forecasting and forecasting system  background  climate forecasts  VIC model spin-up  SNOTEL assimilation  Selected results for 2004 and current forecast season  Ongoing work (hydrologic nowcasting & …)

54. Ongoing work (nowcast & …) snow state update using MODIS multi-model (land-surface in addition to climate) west-wide expansion more forecast points more comprehensive outputs reorganized web-site more verification more involvement with operational groups surface water monitor

55. Ongoing Work: use of remote sensing Trenchant remarks on remote imaging of the land surface Oh my goodness gracious, What you can buy off the Internet In terms of overhead photography! June 9, 2001, following European trip Satellite products with potential for hydrology (short list): MODIS snow-covered area AMSR-E passive microwave-based SWE various microwave based soil moisture products

56. Ongoing work: MODIS snow cover assimilation (Snake R. trial) Snowcover BEFORE update Snowcover AFTER update MODIS update for April 1, 2004 Forecast snow added removed

57. Ongoing work: Rationale for Multi-model forecast framework Single-IC ensemble forecast:  early in seasonal forecast season, climate ensemble spread is large  errors in forecast mainly due to climate forecast errors ensemble member ensemble mean OBS

58. Ongoing work: Rationale for Multi-model forecast framework Single-IC ensemble forecast:  late in seasonal forecast season, climate ensemble is nearly deterministic  errors in forecast mainly due to IC errors ensemble member ensemble mean OBS

59. daily updates 1-2 day lag soil moisture & SWE percentiles ½ degree resolution archive from 1915-current uses ~2130 index stns

60. Ongoing Work: UW SW Monitor trends: 1 week 2 week 1 month

61. Ongoing Work: UW SW Monitor Archive from 1915-current current conditions are a product of the same simulation (same methods, ~same stations) as historical conditions allows comparison of current conditions with historical ones can navigate by month or year

62. Ongoing Work: UW SW Monitor

63. Yakima R. Basin near Parkr, WA

64. Ongoing Work: UW SW Monitor Yakima R. Basin near Parkr, WA why the high soil moisture percentiles? appears to have been relatively cold in last several weeks

65. Ongoing Work: UW SW Monitor soil moisture actually decreasing, but relative to normal conditions at this time, percentile still high

66. Ongoing Work: UW SW Monitor runoff is nearer to normal than soil moisture

67. Final Thought Words of inspiration for researchers everywhere A trained ape can know an awful lot Of what is going on in this world, Just by punching on his mouse For a relatively modest cost! June 9, 2001, following European trip

68. END

69. Framework: Downscaling CPC outlooks downscaling uses Shaake Shuffle (Clark et al., J. of Hydrometeorology, Feb. 2004) to assemble monthly forecast timeseries from CPC percentile values

70. VIC model spinup methods: index stations Example for daily precipitation Index stn pcppcp percentile gridded to 1/8 degree 1/8 degree dense station monthly pcp distribution (N years for each 1/8 degree grid cell) 1/8 degree pcp disagg. to daily using interpolated daily fractions from index stations monthly 0 1

71. Ongoing Work: UW SW Monitor 1920s 1990s

72. Results: CPC-based flow w.r.t. UW obs dataset Answer: YES, with help from bias-correction..........(but) mean std dev

73. Results: CPC-based flow w.r.t. UW obs dataset Additional examples show similar results Mean pretty well reproduced; variability improved mean std dev

74. Results: CPC temp/precip w.r.t. UW obs dataset based on 1960-99

75. Results: CPC temp/precip w.r.t. UW obs dataset based on 1960-99

76. Statistical bias correction can dramatically improve streamflow simulations for use with reservoir models. Natural Flow in the Snake River at Milner