Presentation

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.

0

5

10

15

20

25

30

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

-5

5

15

25

35

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

deg

C

-5

5

15

25

35

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

deg

C

-5

5

15

25

35

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

deg

C

20 member forecast ensemble

-5

5

15

25

35

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

deg

C

-5

5

15

25

35

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

deg

C

from 1979 SSTsfrom 1980 SSTs

from 1981 SSTs

from 1999 SSTs

from current SSTs

(21 sets)10 member climatology ensembles

Bias: Developing a Correction

10

15

20

25

30

0 0.2 0.4 0.6 0.8 1

percentile (wrt 1979-99)

deg

C

GSM

Observed

July Tavg, for 1 GSM cell

-5

5

15

25

35

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

deg

C

-5

5

15

25

35

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

deg

C

-5

5

15

25

35

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

deg

C

-5

5

15

25

35

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

deg

C

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

-5

5

15

25

35

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

deg

C

bias-corrected

-5

5

15

25

35

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

deg

C

Bias: Spatial Perspectiveexpress as anomaly

-8

-4

0

4

8

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

deg

C

bias-corrected

-5

5

15

25

35

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

deg

C

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

-8

-4

0

4

8

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

deg

C

-8

-4

0

4

8

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

deg

C

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

-5

5

15

25

35

Mon1

Mon2

Mon3

Mon4

Mon5

Mon6

de

g C

Lastly, temporal disaggregation…

VIC-scale monthly forecast

Lastly, temporal disaggregation…

VIC-scale monthly forecast

-5

5

15

25

35

Mon1

Mon2

Mon3

Mon4

Mon5

Mon6

deg

C

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

-5

5

15

25

35

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

deg

C

-5

5

15

25

35

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

deg

C

-5

5

15

25

35

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

deg

C

20 member forecast ensemble

-5

5

15

25

35

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

deg

C

-5

5

15

25

35

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

deg

C

from 1979 SSTsfrom 1980 SSTs

from 1981 SSTs

from 1999 SSTs

from current SSTs

(21 sets)10 member climatology ensembles

GSM climatology: use #2

-5

5

15

25

35

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

deg

C

-5

5

15

25

35

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

deg

C

-5

5

15

25

35

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

deg

C

sample: 21 member climatology ensemble

-5

5

15

25

35

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

deg

C

-5

5

15

25

35

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

deg

C

from 1979 SSTsetc.

from 1999SSTs

10 member climatology ens. (21 sets)

GSM climatology: use #2

-5

5

15

25

35

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

deg

C

sample: 21 member climatology ensemble

-5

5

15

25

35

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

deg

C

-5

5

15

25

35

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

deg

C

-5

5

15

25

35

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

deg

C

-5

5

15

25

35

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

deg

C

from 1979 SSTsetc.

from 1999SSTs

10 member climatology ens. (21 sets)

-5

5

15

25

35

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

deg

C

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

150000

200000

250000

300000

350000

400000

450000

500000

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