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Advances in seasonal hydrologic prediction. Dennis P. Lettenmaier Department of Civil and Environmental Engineering University of Washington GEOSS Workshop XXXIII: Using Earth Observations for Water Management San Francisco December 18, 2009. Background

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Advances in seasonal hydrologic prediction

Dennis P. Lettenmaier

Department of Civil and Environmental Engineering

University of Washington

GEOSS Workshop XXXIII: Using Earth

Observations for Water Management

San Francisco

December 18, 2009


Talk outline

Background

The University of Washington west-wide seasonal hydrologic forecast system

Current and recent research -- assimilation of satellite data

Is there hydrologically useful skill in climate forecasts?

Concluding thoughts

Talk Outline


1 background the importance of seasonal hydrologic forecasting

Reservoir Storage

Aug

Dec

Apr

Aug

1. Background: The importance of Seasonal Hydrologic Forecasting

water management

hydropower

irrigation

flood control

water supply

fisheries

recreation

navigation

water quality


Application of statistical methods to seasonal hydrologic prediction in the western u s
Application of statistical methods to seasonal hydrologic prediction in the western U.S.

PNW

Snow water content on April 1

SNOTEL Network

McLean, D.A., 1948 Western Snow Conf.

April to August runoff


Overview: ESP Hydrologic prediction strategy prediction in the western U.S.

ESP data flow

The ESP “spider web”


2 the university of washington west wide seasonal hydrologic forecast system
2. The University of Washington west-wide seasonal hydrologic forecast system

6-month ESP streamflow forecasts for western U.S. and Mexico effective 12/7/09


Uw seasonal hydrologic forecast system website
UW Seasonal Hydrologic hydrologic forecast systemForecast System Website


Forecast System Initial State information hydrologic forecast system

Observed SWE

Snowpack

Simulated Initial Condition

Soil Moisture

Simulated Initial Condition


Streamflow forecast details

Flow location maps give access to monthly hydrograph plots, and also to raw forecast data.

Clicking the stream flow forecast map also accesses current basin-averaged conditions

Streamflow Forecast Details



3a current and recent research snow data assimilation
3a: Current and recent research: and also to raw forecast data.Snow data assimilation


MODIS updating of snow covered area and also to raw forecast data.

MODIS Update

local scale weather inputs

Initial Conditions:

soil moisture,

snowpack

Hydrologic model spin up

Hydrologic simulation

Ensemble Forecast:

streamflow, soil moisture, snowpack, runoff

NCDC met. station obs. up to 2-4 months from current

LDAS/other real-time met. forcings for remaining spin-up

End of Month 6 - 12

1-2 years back

25th Day of Month 0

Change in Snowcover as a Result of MODIS Update for April 1, 2004 Forecast

Snowcover before MODIS update

Snowcover after MODIS update


Unadjusted vs adjusted forecast errors, 2001-2003, for reservoir inflow volumes (left plot) and reservoir storage (right)



Wood et al 2005 retrospective assessment results using gsm
Wood et al 2005: Retrospective Assessment: Results using GSM forcings

General finding is that NCEP GSM climate forecasts do not add to skill of ESP forecasts, except…

April GSM forecast with respect to climatology (left) and to ESP (right)


Wood et al 2005 retrospective results for enso years
Wood et al 2005: Retrospective results for ENSO years forcings

Summary: During strong ENSO events, for some river basins (California, Pacific Northwest) runoff forecasts improved with strong-ENSO composite; but Colorado River, upper Rio Grande River basin RO forecasts worsened.

October GSM forecast w.r.t ESP: unconditional (left) and strong-ENSO (right)


Reverse esp vs esp typical results for the western u s
Reverse ESP vs ESP – typical results for the western U.S. forcings

Columbia R. Basin

fcst more impt

ICs more impt

Rio Grande R. Basin


Demeter forecast evaluation

VIC model long-term (1960-99) simulations at ½ degree spatial resolution assumed to be truth

DEMETER reforecasts with ECMWF seasonal forecast model for 6 month lead, forecasts made on Feb 1, May 1, Aug 1, Nov 1 1960-99

9 forecast ensembles on each date

Forecast forcings (precipitation and temperature) downscaled and bias corrected using Wood et al approach (also incorporated in UW West-wide system)

On each forecast date, 9 ensemble members also resampled at random from 1960-99 to form ESP ensemble

Forecast skill evaluated using Cp for unrouted runoff

DEMETER forecast evaluation


Test sites spatial resolution assumed to be truth


Missouri River at Fort Benton spatial resolution assumed to be truth


Snake River at Milne spatial resolution assumed to be truth


Concluding thoughts

Hydrologic prediction skill at S/I lead spatial resolution assumed to be truthtimes comes mostly from initial conditions.

Hence more focus on data assimilation, and its implications for hydrologic forecast skill, needs more attention.

The role of model error in hydrologic predictions needs more focus – how do we best weight land models in multimodel ensemble?

Do hydrologists (and the land data assimilation community) need to expend more effort on hydrologic forecasting?

Concluding thoughts


Streamflow forecast skill, observed streamflow simulated (left panel) and forecasted (right two) using model soil moisture and SWE; MAMJ streamflow conditioned on January 1 model conditions


7 multimodel approaches
7) Multimodel approaches (left panel) and forecasted (right two) using model soil moisture and SWE; MAMJ streamflow conditioned on January 1 model conditions


  • UW Multi-model monitor (left panel) and forecasted (right two) using model soil moisture and SWE; MAMJ streamflow conditioned on January 1 model conditions

  • Same approach as VIC-based SWM

  • Models include VIC, Noah, CLM, Sac


The challenge: Different land schemes have different soil moisture dynamics

Model simulated

soil moisture at cell

(40.25N, 112.25W)


Areas for spatially averaged soil moisture percentiles

NE moisture dynamics

NW

SW

SE

Areas for spatially averaged soil moisture percentiles

Box sizes are 5 x 5 degrees


NW moisture dynamics


SE moisture dynamics


Soil Moisture Percentiles w.r.t. 1920-2003 moisture dynamics

2008-07-01

VIC

CLM

SAC

NOAH

ENSEMBLE

US Drought Monitor


Summary
Summary moisture dynamics

  • West-wide forecast system and SW Monitor are templates for exploration of new forecasting methods

  • Methods perform well in the U.S., where surface obs are relatively abundant.

  • However, ongoing work illustrates the potential for using similar methods in areas where in situ obs are sparse, using e.g. remotely sensed precipitation, and/or weather prediction model analysis fields.

  • New remote sensing data sources (e.g. SWOT) offer tremendous opportunities for extension of these methods to the underdeveloped world.


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