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
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?
NOAA LDAS research into land surface models
UW “Surface Water Monitor”
Detection and Attribution study
talk outline
drought definition practices are evolving…
…and so is land surface modeling
Eric WoodJustin Sheffield
Princeton Univ.
Dan TarpleyNESDIS/ORA
Andy Bailey Dennis LettenmaierUniv. Washington
Wayne HigginsHuug Van den Dool
NCEP/CPC
Ken MitchellDag Lohmann
NCEP/EMC
Univ. MarylandRachel Pinker
Ken CrawfordJeff Basara
Univ. Oklahoma
Alan RobockLifeng Luo
Rutgers Univ.John SchaakeQingyun Duan
NWS/OHD
Tilden MeyersJohn Augustine
NOAA/ARL
Paul HouserBrian Cosgrove
NASA/GSFC
http://ldas.gsfc.nasa.gov
North American Land Data Assimilation System Project
from ken mitchell presentation, march 2002
GCIP
LDAS Soil Wetness Comparison LDAS realtime output example
from ken mitchell presentation, march 2002
most models are in the ballpark on moisture fluxes
correlationsobs obs
Noah Noah
RR RR
ERA40 ERA40
1988 1993
from yun fan / huug vandendool
models give similar, but different answers
spatial Noah VIC LB RR R2 R1 ERA40 temporal
0.82 0.81 0.71 0.59 0.48 0.66 Noah
VIC 0.68 0.80 0.70 0.48 0.40 0.62 VIC
LB 0.77 0.74 0.73 0.56 0.41 0.65 LB
RR 0.59 0.60 0.68 0.54 0.33 0.62 RR
R2 0.46 0.44 0.50 0.48 0.42 0.57 R2
R1 0.43 0.36 0.41 0.32 0.40 0.43 R1
ERA40 0.56 0.48 0.56 0.50 0.47 0.41
Correlations in soil moisture
VIC/Noah are LSMs; LB is leaky bucket; R*/ERA40 are reanalysesfrom yun fan / huug vandendool
NLDAS-era models
1/8-degree resolutionRunoff routing, calibration, validationVegetation: UMD, EROS IGBP, NESDIS greenness, EOS productsSoils: STATSGO, IGBP
snow
LDAS models
sample validation of historic streamflowsimulations
What does an 1/8 degree grid cell look like in real life?
daily updates
1 day lag
soil moisture, SWE, runoff
½ degree resolution
archive: 1915 - now
3-month forecasts
drought indices
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: 1st order Co-op stationdataset inhomogeneities
Surface Water Monitor “Monitoring”
Soil moisture percentiles – agricultural drought
SWE percentiles – hydrologic drought
-- hydropower potential
Surface Water Monitor “Monitoring”
1 month change in soil moisture
1 week change inSWE
Surface Water Monitor “Monitoring”
6-month
Runoff percentiles – hydrological drought
24-month 1-month
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.
Surface Water Monitor “Monitoring -- Indices”
1-monthSPI
1-monthSRI
SPI / SRI
24-monthSPI
24-monthSRI
Surface Water Monitor “Monitoring -- Indices”
SPI / SRI
SRI
Surface Water Monitor Archive (1915-current)
June1934
Aug1993
soilmoist
soilmoist
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
Surface Water Monitor Prediction
Probability of “drought persistence”
median forecastrunoff percentile
lead3 mon
soil moisture runoff
lead3 mon
lead3 mon
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
Washington State ‘Monitor’
Monitoring and Prediction Methods
soil moisture
SWE
WAState
Monitoring and Prediction MethodsWA
State
can use model-basedsystems to estimatetraditional drought indices
work by Shrad Shukla
NOAA PDSIOct 8, 2007
WA State testbed for experimental indices
NOAA PDSIsmoothed SM %-ile
Can we develop alternative, model-based descriptors of drought and stage them reliably for use in state & local actions?
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)
Model Applications: Drought
For More Information
web: http://www.hydro.washington.edu / forecast / monitor /
email: [email protected]
Or Francisco [email protected]
Or read extended abstract from AMS08Talk (Wood, 2008) (13 pages)
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