slide1 n.
Skip this Video
Loading SlideShow in 5 Seconds..
Andy Wood Senior Scientist (Hydrology), 3TIER™, Inc. Affiliate Professor, U. of Washington Dept. of  Civil & Environ PowerPoint Presentation
Download Presentation
Andy Wood Senior Scientist (Hydrology), 3TIER™, Inc. Affiliate Professor, U. of Washington Dept. of  Civil & Environ

Loading in 2 Seconds...

  share
play fullscreen
1 / 30
Download Presentation

Andy Wood Senior Scientist (Hydrology), 3TIER™, Inc. Affiliate Professor, U. of Washington Dept. of  Civil & Environ - PowerPoint PPT Presentation

tale
111 Views
Download Presentation

Andy Wood Senior Scientist (Hydrology), 3TIER™, Inc. Affiliate Professor, U. of Washington Dept. of  Civil & Environ

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

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

  4. drought definition practices are evolving…

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

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

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

  8. models give similar, but different answers Correlations in soil moisture VIC/Noah are LSMs; LB is leaky bucket; R*/ERA40 are reanalyses from yun fan / huug vandendool

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

  10. LDAS models sample validation of historic streamflow simulations

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

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

  13. example: 1st order Co-op station dataset inhomogeneities 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)

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

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

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

  17. 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.

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

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

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

  21. 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

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

  23. Surface Water Monitor Applications • 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)

  24. Washington State ‘Monitor’

  25. WA State Monitoring and Prediction Methods soil moisture SWE

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

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

  28. 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)

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

  30. 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