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

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: awood@3tiergroup.com

Or Francisco Munozfmunoz@hydro.washington

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