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Application of LDAS-era Land Surface Models to Drought Monitoring and Prediction Andy Wood collaborators / contributors Shraddhanand Schukla Kostas Andreadis.

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Presentation on theme: "Application of LDAS-era Land Surface Models to Drought Monitoring and Prediction Andy Wood collaborators / contributors Shraddhanand Schukla Kostas Andreadis."— Presentation transcript:

1 Application of LDAS-era Land Surface Models to Drought Monitoring and Prediction Andy Wood collaborators / contributors Shraddhanand Schukla Kostas Andreadis Dennis Lettenmaier Dept. of Civil and Environmental Engineering Land Surface Hydrology Research Group Drought Monitor Forum Portland, OR October 2007

2 drought definition practices are evolving

3  NOAA LDAS research into land surface models  UW “Surface Water Monitor”  forecasting drought  final comments talk outline

4 NOAA’s Climate Predictions and Projection Program: Parent Program of CPPA (Climate Prediction Program for the Americas) Objectives: to provide climate forecasts to enable regional and national managers to better plan for the impacts of climate variability to provide climate assessments and projections to support policy decisions with objective and accurate climate change information from j. huang, k. mitchell

5 N-LDAS* Collaborators 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 soil moisture 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 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  NOAA LDAS research into land surface models  UW “Surface Water Monitor” & other efforts  forecasting drought  final comments talk outline

14 SW Monitor in a nutshell Background:  merges UW west-wide streamflow forecast system methods with NLDAS modeling advances  “index station” method + VIC implementation (Maurer et al., 2002)  benefits from recent NCDC extension of digital data archives back to 1915 Future Directions:  further development now funded by NOAA TRACS program  test methods for use at NOAA EMC / CPC, with products for NWCC & NDMC  water cycle analysis – current, retrospective, future  “proving ground” for forecasting methods at national scale  staging real-time products based on other UW drought reconstruction work:  Severity-Area-Duration analysis (Andreadis et al. 2005)

15 Nowcast/Forecast System Consistency Issue Retrospective Simulation Daily, 1915 to Near Current “Modern” Simulation (last 5 years) Current Hydrologic State (Nowcast) ASSIMILATION Snow / Soil Moisture / Runoff / ETC new record or “ * ” ?

16 Nowcast/Forecast System Consistency Issue Retrospective Simulation Daily, 1915 to Near Current “Modern” Simulation (last 5 years) Current Hydrologic State (Nowcast) ASSIMILATION Snow / Soil Moisture / Runoff / ETC consistent statistics

17 www.hydro.washington.edu / forecast / monitor /

18 Surface Water Monitor products 1 month change in soil moisture 2 week change in SWE

19 Surface Water Monitor archive (1915-current) June 1934 Aug 1993

20 Drought delineation / S.A.D. index Work of Kostas Andreadis and Liz Clark

21 Washington State ‘Monitor’

22 Monitoring and Prediction Methods soil moisture SWE WA State

23 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

24 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?

25  NOAA LDAS research into land surface models  UW “Surface Water Monitor”  forecasting drought  final comments talk outline

26 drought onset / recovery prediction

27 UW weekly national hydrologic predictions

28 Seasonal predictions and verification of Spring 2007 drought conditions from the Princeton U. VIC/CFS-based uncoupled seasonal forecast system. (Jan ’07 prediction, L. Luo, E. Wood) CPC’s North American Drought Briefing http://www.cpc.ncep.noaa.gov/products/Drought/ other nowcast / forecast efforts Primary Target: http://hydrology.princeton.edu/forecast/

29  NOAA LDAS research into land surface models  UW “Surface Water Monitor”  forecasting drought  final comments talk outline

30 Final Comment How will models (land surface / climate / coupled) become integrated into drought management?  “nowcasting”, forecasting?  retrospective diagnosis?  attribution / detection? LDAS-era models can simulate and will be able to predict land surface variables (e.g., soil moisture) as climate forecasts improve. Many issues need resolving: - will there be a standard or consensus hydrologic product? - a ‘soil moisture deficit’ is not the same as ‘drought’ - what about traditional &/or meteorological indices?

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

32 Initial Condition

33 Verification possibilities? What are the obs for drought? In football, everything is complicated by the presence of the other team. Jean-Paul Sartre modeling observations. paraphrasing

34 SW Monitor Schematic VIC Retrospective Simulation Daily, 1915 to Near Current VIC Real-time Spinup Simulation Hydrologic State NOAA ACIS Prcp Tmax Tmin Coop Stations Index Station Method Gridded Forcing Creation Hydrologic values, anom’s, %-iles w.r.t. retrospective PDF climatology (PDF) of hydrologic values w.r.t. defined period vals, anoms %-iles w.r.t. PDF 1955+ Hydrologic State (-1 Day) 1930s

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