Application of LDAS-era Land Surface Models to Drought

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

drought definition practices are evolving

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

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

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

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

most models are in the ballpark on soil moisture 1993 1988 correlations obs Noah RR RR ERA 40 from yun fan / huug vandendool

models give similar, but different answers correlations spatial Noah VIC LB RR R 2 R 1 0. 82 0. 81 0. 71 0. 59 0. 48 0. 80 0. 70 0. 48 0. 40 0. 62 VIC 0. 73 0. 56 0. 41 0. 65 LB 0. 54 0. 33 0. 62 RR 0. 42 0. 57 R 2 0. 43 R 1 VIC 0. 68 LB 0. 77 0. 74 RR 0. 59 0. 60 0. 68 R 2 0. 46 0. 44 0. 50 0. 48 R 1 0. 43 0. 36 0. 41 0. 32 0. 40 ERA 40 0. 56 0. 48 0. 56 0. 50 0. 47 ERA 40 0. 66 temporal Noah 0. 41 VIC/Noah are LSMs; LB is leaky bucket; R*/ERA 40 are reanalyses from yun fan / huug vandendool

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

LDAS models sample validation of historic streamflow simulations

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

talk outline q NOAA LDAS research into land surface models q UW “Surface Water Monitor” & other efforts q forecasting drought q final comments

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

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

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

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

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

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

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

Washington State ‘Monitor’

Monitoring and Prediction Methods soil moisture SWE WA State

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

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? NOAA PDSI smoothed SM %-ile

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

drought onset / recovery prediction

UW weekly national hydrologic predictions

other nowcast / forecast efforts 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) http: //hydrology. princeton. edu/forecast/ Primary Target: CPC’s North American Drought Briefing http: //www. cpc. ncep. noaa. gov/products/Drought/

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

Final Comment 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? How will models (land surface / climate / coupled) become integrated into drought management? q “nowcasting”, forecasting? q retrospective diagnosis? q attribution / detection?

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

Initial Condition

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

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