1. Applications of macroscale land surface modeling: (1) drought monitoring and prediction; and (2) detection and attribution of climate change effects on western US hydrology Andy Wood Senior Scientist (Hydrology), 3TIER™, Inc. Affiliate Professor, U. of Washington Dept. of Civil & Environmental Engineering awood@3tiergroup.com

2. 3TIER was founded in 1999. HQ in Seattle ~55 FTEs (~20 PhD in atmos sci, hydrology, math, rem. sensing, power engineering) ~1800 CPUs Panama, India offices Founded and run by scientists and engineers to put academic research into practice Focused on the renewable energy sector  5,000+ MW wind energy forecasting  3,500 MW hydropower forecasting  Extensive international wind resource assessment  Solar assessment & forecasting  Global hydropower assessment?

3.  NOAA LDAS research into land surface models  UW “Surface Water Monitor”  Detection and Attribution study talk outline

4. drought definition practices are evolving…

5. …and so is land surface modeling Eric Wood Justin Sheffield Princeton Univ. Dan Tarpley NESDIS/ORA Andy Bailey Dennis Lettenmaier Univ. Washington Wayne Higgins Huug Van den Dool NCEP/CPC Ken Mitchell Dag Lohmann NCEP/EMC Univ. Maryland Rachel Pinker Ken Crawford Jeff Basara Univ. Oklahoma Alan Robock Lifeng Luo Rutgers Univ. John Schaake Qingyun Duan NWS/OHD Tilden Meyers John Augustine NOAA/ARL Paul Houser Brian Cosgrove NASA/GSFC http://ldas.gsfc.nasa.gov North American Land Data Assimilation System Project from ken mitchell presentation, march 2002 GCIP

6. LDAS Goals 1)Provide land-state initial conditions (soil moist, snowpack) for: a) realtime coupled model forecasts of weather / seasonal climate b) retrospective land-memory predictability studies 2) Improve LSM physics by sharing methodologies / data sources 3) Identify causes of the spread in magnitudes of surface water fluxes and surface water storage typically seen in LSM intercomparisons 4) Compare land states of the uncoupled LDAS with traditional coupled land/atmosphere 4DDA 5) Demonstrate how to assimilate land-state related satellite retrievals (e.g., snowpack, skin temperature, soil moisture) from ken mitchell presentation, march 2002

7. LDAS Soil Wetness Comparison LDAS realtime output example from ken mitchell presentation, march 2002

8. most models are in the ballpark on moisture fluxes correlations obs Noah RR ERA40 1988 1993 from yun fan / huug vandendool

9. models give similar, but different answers spatialNoahVICLBRRR2R1ERA40temporal 0.820.810.710.590.480.66Noah VIC 0.680.800.700.480.400.62 VIC LB 0.770.740.730.560.410.65 LB RR 0.590.600.680.540.330.62 RR R2 0.460.440.500.480.420.57 R2 R1 0.430.360.410.320.400.43 R1 ERA40 0.560.480.560.500.470.41 Correlations in soil moisture VIC/Noah are LSMs; LB is leaky bucket; R*/ERA40 are reanalyses from yun fan / huug vandendool

10. NLDAS-era models 1/8-degree resolution Runoff routing, calibration, validation Vegetation: UMD, EROS IGBP, NESDIS greenness, EOS products Soils: STATSGO, IGBP snow

11. LDAS models sample validation of historic streamflow simulations

12. What does an 1/8 degree grid cell look like in real life?

13. daily updates 1 day lag soil moisture, SWE, runoff ½ degree resolution archive: 1915 - now 3-month forecasts drought indices

14. Surface Water Monitor Goals  Serve as a manageable test-bed for development of hydrologic products for resource management, e.g., energy, water, hazard (drought, flood)  Provide real-time estimate of surface moisture AND a long statistically consistent historical retrospective (unlike most existing nowcast systems) example: 1 st order Co-op station dataset inhomogeneities

15. Surface Water Monitor “Monitoring” Soil moisture percentiles – agricultural drought SWE percentiles – hydrologic drought -- hydropower potential

16. Surface Water Monitor “Monitoring” 1 month change in soil moisture 1 week change in SWE

17. Surface Water Monitor “Monitoring” 6-month Runoff percentiles – hydrological drought 24-month 1-month

18. Surface Water Monitor “Monitoring -- Indices” Standardized RUNOFF Index (SRI)?  mirrors Std PRECIP Index (SPI)  made possible by modeled runoff  described in: - Shukla, S. and A.W. Wood, Use of a standardized runoff index for characterizing hydrologic drought, GRL (in press); - Mo, K., JHM (in review).  computed DAILY, using rolling climatology, at ½ degree.

19. Surface Water Monitor “Monitoring -- Indices” 1-month SPI 1-month SRI SPI / SRI 24-month SPI 24-month SRI

20. Surface Water Monitor “Monitoring -- Indices” SPI / SRI SRI

21. Surface Water Monitor Archive (1915-current) June 1934 Aug 1993 soil moist soil moist

22. Surface Water Monitor Prediction  Each week, initialize ensemble hydrologic (3-mon) forecasts  Climate forecasts now derived from climatological ESP and ENSO-subset ESP  Working with CPC to add other climate forecasts – e.g., CPC outlooks, EOT

23. Surface Water Monitor Prediction Probability of “drought persistence” median forecast runoff percentile lead 3 mon soil moisture runoff lead 3 mon lead 3 mon

24. SW Monitor products have been used as input to:  NOAA CPC Drought Outlook  NOAA CPC North American Drought Briefing http://www.cpc.ncep.noaa.gov/products/Drought/  National Drought Mitigation Center Drought Monitor  NRCS National Water and Climate Center Weekly Report  Various research applications:  Fire season prediction in Florida  Electric utility storm damage prediction (S. Quiring, TAMU) Surface Water Monitor Applications

25. Washington State ‘Monitor’

26. Monitoring and Prediction Methods soil moisture SWE WA State

27. Monitoring and Prediction Methods WA State can use model-based systems to estimate traditional drought indices work by Shrad Shukla NOAA PDSI Oct 8, 2007

28. WA State testbed for experimental indices NOAA PDSI smoothed SM %-ile Can we develop alternative, model-based descriptors of drought and stage them reliably for use in state & local actions?

29. Final Comments (Part 1)  The SW Monitor is now using LDAS-era science to monitor and predict drought-relevant land surface variables.  SW Monitor products are providing information to national scale drought monitoring and prediction efforts, as well as to varied research efforts.  Such systems could form an objective monitoring & prediction track to parallel the drought-focused subjective-consensus approaches we now have: i.e., decision support.  How will models (land surface / climate / coupled) be integrated into drought management? There is no model variable named “drought”. Ongoing/future efforts:  incorporating multiple models into SW Monitor (at UW)  transitioning SW Monitor methods / product ideas to NCEP (EMC/CPC)  global version? (possibly w/ 3TIER Inc., Seattle)

30. Model Applications: Drought For More Information web: http://www.hydro.washington.edu / forecast / monitor / email: awood@3tiergroup.comawood@3tiergroup.com Or Francisco Munoz fmunoz@hydro.washington Or read extended abstract from AMS08 Talk (Wood, 2008) (13 pages)

31. Acknowledgments NOAA CDEP, CPPA, SARP, TRACS Feedback from: Doug Lecomte (CPC) Kelly Redmond (DRI) Victor Murphy (SRCC) Mark Svoboda (NDMC) David Sathiaraj (SRCC/ACIS) Tom Pagano & Phil Pasteris (NWCC) In house: Ali Akanda, George Thomas Kostas Andreadis, Shrad Shukla