Drought Monitoring and Prediction Systems at the University of Washington and

Princeton University

Climate Diagnostics and Prediction WorkshopLincoln, NW

October 22, 2008

Dennis P. LettenmaierDepartment of Civil and Environmental Engineering

University of Washington

Outline of this presentation

• Motivation for experimental hydrological prediction systems

• Evolution of the UW and Princeton systems• Current components

– UW west-wide prediction system– UW surface water monitor– Princeton eastern U.S. and CONUS systems– Integration

• Outstanding issues

Motivation for experimental hydrological prediction systems: Traditional “bottom up” hydrologic modeling approach (subbasin by subbasin)

Flood of record

• Principal calibration locations were the Skykomish at Gold Bar and the Snoqualmie at Carnation

Snoqualmie River at Carnation, WA

How important is calibration for seasonal hydrologic prediction?

uncalibrated

uncalibrated bias corrected

calibrated

How important is calibration: ensemble mean (from ESP) vs obs for April-July forecasts on six forecast dates, Gunnison River, CO

From Wood and Lettenmaier (BAMS, 2006):

•Despite the potential benefits of improved hydrologic forecasts, most operational hydrologic prediction at seasonal lead times … are based on methods and data sources that have been in place for almost half a century.

•The skill of western U.S. seasonal streamflow forecasts has generally not improved since the 1960s.

•While forecast accuracy improvements would likely result from observing system densification, the need for long data records in regression-based methods would take decades to realize, and would be complicated by a changing climate.

•We believe that a more promising pathway lies in the development of methods … for assimilating new sources of observational data into land surface energy and water balance models, which can then be forced with modern climate and weather forecasts.

Why do we need an experimental hydrological prediction system?

One reason for the slow progress in hydrologic prediction has been the lack of real-time testing of new prediction models and methods …

The need for a national perspective on hydrologic prediction

• Will help to address emerging water resources operation and planning issues (e.g., nonstationarity)

• Better exploit predictability in weather and climate (which is inherently at progressively larger scales with lead time)

• Make better use of methods, like data assimilation, that can use large scale data sources to improve hydrologic initial conditions

Evolution of the UW and Princeton (near) real-time hydrologic forecast systems

From Wood et al (2002) – development of a hydrologically based statistical downscaling method

GSM Regional Bias:a spatial exampleBias is removed at the monthly GSM-scale from the meteorological forecasts

(so 3rd column ~= 1st column)

Downscaling Test1. Start with GSM-scale

monthly observed met data for 21 years

2. Downscale into a daily VIC-scale time series

3. Force hydrology model to produce streamflow

4. Is observed streamflow reproduced?

Simulations

Forecast Productsstreamflow soil moisture

runoffsnowpack

VIC model spin-upVIC forecast ensemble

climate forecast

information (from GSM)

VIC climatology ensemble

1-2 years back start of month 0 end of month 6

NCDC met. station obs. up to

2-4 months from

current

LDAS/other met.

forcings for remaining

spin-up

data sources

A B C

Model forecasting domain

East Coast hindcast

Approach: 1/8 - 1/4 degree implementation

Pilot scale implementationPacific Northwest

UpdatesDec 28, 2002 ESPJan 15, 2003 ESPFeb 1 ESP, GSM, NSIPPFeb 15 ESPMar 1 ESP, GSM, NSIPPMar 16 ESPApr 1 ESP, GSM, NSIPP<disk crash>

Pilot Forecasts: Initial Conditions

Jan 15, 2003Dec 28, 2002

Feb 1, 2003 Mar 1, 2003 Apr 1, 2003

This past winter, alarmingly low PNW December snowpacks mostly recovered by April, although some locations are still well off their long term averages

Winter 2002/03 forecasts: UW/NRCS comparison

Apr-Sep Streamflow Forecasts Columbia River at the Dalles, OR

50

60

70

80

90

1-Jan 1-Feb 1-Mar 1-Apr 1-Mayforecast date

perc

ent o

f nor

mal

UWNRCSBest Estimate

UW pilot results were comparable to the official streamflow forecasts of the National Resources Conservation Service (NRCS) streamflow forecast group (one location shown).

UW West-wide forecast system – current domain and streamflow forecast points

•~250 forecast points, including ~15 in Mexico

•Forecast models/methods include CPC “official” forecasts, ESP, and stratified ESP

•Forecasts for 6-12 month lead issued twice monthly (winter), monthly otherwise

UW West-wide forecast system soil moisture nowcast (8/6/08)

•Daily updates, 24 hour lag effective ~2 pm Pacific

•Based on ~2000 index stations, adjusted to long-term (1915 – present) climatology

Princeton University drought monitoring and prediction system~weekly nowcast update, eastern U.S. domain

Uses NLDAS forcings

Focus on (soil moisture) drought nowcast and forecast

Forecasts based on Bayesian MME merging of GFS and ESP

UW National Surface Water Monitor•½ degree spatial resolution

•Updates daily (same lag as west-wide system)

•Same index station approach as west-wide system

•Climatology 1915-present

UW Multi-model monitor

•Same approach as VIC-based SWM

•Models include VIC, Noah, CLM, Sac

0

100

Multi-ModelCumulative Probability,

1916-2004

50 800Soil Moisture (mm)

%

Multi-model Ensemble

100

0

Model iCumulative Probability,

1916-2004

50 800Soil Moisture (mm)

%

For each model, re-express current soil moisture as percentile of climatology for this day of year

Model isoil moisture

Model ipercentile

Average all models’ percentiles = 1/N Σ (i=1 to N) percentile i

Multi-ModelpercentileMulti-model ensemble result is

the percentile of the average of model percentiles

This procedure occurs separately for each grid cell

Soil Moisture Percentiles w.r.t. 1920-20032008-07-01

CLM

SAC NOAH

ENSEMBLE

VIC

US Drought Monitor

US Drought Monitor UW Surface Water MonitorMultimodel Ensemble

Jul 1

Aug 5

Sep 2

Agreement: WI drying trend

Agreement: Gulf wetting trend

Disagreement: Dry conditions in N.,S. Carolina?

Agreement: Dry west coast

Soil Moisture Percentiles w.r.t. 1916-20042008-07-01

CLM

SAC NOAH

ENSEMBLE

VIC

US Drought Monitor

Multimodel results with drought monitor color scheme (truncated at 30th percentile)

US Drought Monitor UW multimodel SWM Summer 2008

Jul 1

Aug 5

Sep 2

Ongoing unification of UW and Princeton systems

a) unified nowcast (completed, in testing)b) expansion of multimodel SWM domain

into Mexico (in progress)c) merger of forecast methods (esp.

multimodel Bayesian MME) – plannedd) improved data assimilation – plannede) multiple (land) model forecasts – plannedf) reservoir storage forecasts -- planned

Conclusions and challenges

• Need for national scale hydrological prediction (including streamflow)

• Need for better ways of including a historical perspective (what historical period?) post-data assimilation

• Need for site-specific calibration (MOS-type approaches?) and verification

• Mechanisms for inclusion of local information?