Experimental Real-time Seasonal Hydrologic Forecasting

Andrew WoodDennis Lettenmaier

University of Washington

Arun KumarNCEP/EMC/CMB

presented:

JISAO weekly seminarSeattle, WA Nov 13, 2001

Overview

Research Objective:

To produce monthly to seasonal snowpack, streamflow, runoff & soil moisture forecasts for continental scale river basins

Underlying rationale/motivation:

1.Global numerical weather prediction / climate models (e.g. GSM) take advantage of SST – atmosphere teleconnections

2.Hydrologic models add soil-moisture – streamflow influence (persistence)

Topics Today

1. Approach2. Columbia River basin (summer 2001) application3. East Coast (summer 2000) application4. Related work5. Comments

climate model forecastmeteorological outputs

• ~1.9 degree resolution (T62)• monthly total P, avg T

Use 3 step approach: 1) statistical bias correction 2) downscaling3) hydrologic simulation

General Approach

hydrologic model inputs

streamflow, soil moisture,snowpack,runoff• 1/8-1/4 degree resolution

• daily P, Tmin, Tmax

Models: Global Spectral Model (GSM) ensemble forecasts from NCEP/EMC

• forecast ensembles available near beginning of each month, extend 6 months beginning in following month

• each month:• 210 ensemble members define GSM climatology for

monthly Ptot & Tavg• 20 ensemble members define GSM forecast

Models: VIC Hydrologic Model

domain slide

Example Flow Routing Network

One Way Coupling of GSM and VIC models

a) bias correction: climate model climatology observed climatologyb) spatial interpolation:

GSM (1.8-1.9 deg.) VIC (1/8 deg)c) temporal disaggregation (via resampling of observed patterns):

monthly daily

a. b. c.

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0 1Probability

Te

mp

era

ture

TGSM

TOBS

GSM Regional Bias:a spatial example

Bias is removed at the monthly GSM-scale from the meteorological forecasts

(so 3rd column ~= 1st column)

GSM Regional Bias:

one cell example

For sample cell located over Ohio River basin, biases in monthly Ptot & Tavg are significant!

GSM Regional Bias:

one cell example

Bias: Developing a Correction

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20 member forecast ensemble

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from 1979 SSTsfrom 1980 SSTs

from 1981 SSTs

from 1999 SSTs

from current SSTs

(21 sets)10 member climatology ensembles

Bias: Developing a Correction

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percentile (wrt 1979-99)

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GSM

Observed

July Tavg, for 1 GSM cell

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1979 SSTsetc.

from 1999SSTs

10 member climatology ens.

* for each month, each GSM grid cell and variable

*

Bias: Applying a Correction

Note: we apply correction to both forecast ensembleand climatology ensemble itself, for later use

Bias-Correction: Spatial Perspective

shown1 month,

1 variable (T),1 ens-member

raw GSM output

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bias-corrected

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Bias: Spatial Perspectiveexpress as anomaly

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bias-corrected

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Downscaling: step 1 is interpolation(bias corrected) anomaly anomaly at VIC scale

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Downscaling: step 2 adds spatial VIC-scale variability to smooth anomaly field

mean fields

anomaly

note:month m, m = 1-6ens e, e = 1-20

VIC-scale monthly forecast

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Lastly, temporal disaggregation…

VIC-scale monthly forecast

Lastly, temporal disaggregation…

VIC-scale monthly forecast

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Downscaling Test

1. Start with GSM-scale monthly observed met data for 21 years

2. Downscale into a daily VIC-scale timeseries

3. Force hydrology model to produce streamflow

4. Is observed streamflow reproduced?

GSM forecast and climatology ensembles

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20 member forecast ensemble

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from 1979 SSTsfrom 1980 SSTs

from 1981 SSTs

from 1999 SSTs

from current SSTs

(21 sets)10 member climatology ensembles

GSM climatology: use #2

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sample: 21 member climatology ensemble

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from 1979 SSTsetc.

from 1999SSTs

10 member climatology ens. (21 sets)

GSM climatology: use #2

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Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6

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sample: 21 member climatology ensemble

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from 1979 SSTsetc.

from 1999SSTs

10 member climatology ens. (21 sets)

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20 member forecast ens.

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

Columbia River Application

CRB

Initial Conditions

late-May SWE &water balance

CRB

Initial Conditions

(percentiles)

CRB: May forecastobservedforecast

forecastmedians

CRB: May forecast

hindcast“observed”

forecast

forecast medians

CRB May forecasthindcast “observed”forecast

forecastmedians

CRB May forecast

basin avg. soil moisture

CRB May Forecast

Streamflow

Forecasts of Columbia River Flow @ The Dalles, 2001

0

50000

100000

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Apr May Jun Jul Aug Sep Oct Nov

cfs

Mar fcast

Mar clim

Apr fcast

Apr clim

May fcast

May clim

Hindcast

CRB: sequential streamflow forecasts

hindcast

climatologies

forecasts

ensemble medians

CRBMay Forecast

cumulative flow averages

forecastmedians

East Coast Application

Model forecasting domain

East Coast spin-up period

East Coast spin-up period

East Coast spin-up period

East Coast spin-up period

East Coast hindcast

East Coast hindcast

East Coast hindcast

East Coast hindcast

East Coast

Apr ’00 forecast for May-Jun-Jul

forecast median shown as percentile of climatology ensemble

East Coast

May ’00 forecast for Jun-Jul-Aug

East Coast

Jun ’00 forecast for Jul-Aug-Sep

ENSO extreme pseudo-forecast evaluation

perfect-SST forecasts from Nov. 97

Related Applications

Related: Yakima R. Mesocale Model Downscaling (RCM @ ½ to VIC @ 1/8)

Related:

PCM-based climate change scenarios

Related:

PCM-based climate change scenarios

Related:

PCM-based climate change scenarios

Related:PCM-based climate change scenarios

Summary Comments climate-hydrology forecast model system has potential

can also try other ensemble forecast models/methods can also try other bias-correction/downscaling approaches

critical needs access to quality met data during spinup period ability to demonstrate / assess skill quantitatively

perfect-SST (“AMIP-type”) hindcast ensembles a start, but really need a long term retrospective forecast set

Summary Comments climate-hydrology forecast model system has potential

can also try other ensemble forecast models/methods can also try other bias-correction/downscaling approaches

critical needs access to quality met data during spinup period ability to demonstrate / assess skill quantitatively

perfect-SST (“AMIP-type”) hindcast ensembles a start, but really need a long term retrospective forecast set

2 of me: one for research one for “operations”

END