Experimental Real-time Seasonal Hydrologic Forecasting

Andrew WoodDennis P. Lettenmaier

University of Washington

presented:AMS Conference on Applied Climatology, 2002

Portland, OR May 2002

Project 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

1. Approach2. Columbia River basin (summer 2001) results3. Ongoing Work4. Comments

climate model forecastmeteorological outputs

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

Use 3 steps: 1) statistical bias correction 2) downscaling and disaggregation3) hydrologic simulation

General Approach

hydrologic model inputs

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

• daily P, Tmin, Tmax

Models: 1. 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: 2. VIC Hydrologic Model

domain slide

Flow Routing Network

One Way Coupling of GSM and VIC models

a) bias correction: climate model climatology observed climatology

b) 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

Bias Example:

JFM precipitation from Parallel Climate Model (DOE)

climate model vs. “observed” distributions at climate model scale (T42)

Dealing with bias using a climatology-based correction

Note: we apply correction to both forecast ensemble and climatology ensemble itself (to use as a baseline)

Downscaling: add spatial VIC-scale variability

observed mean fields

(1/8-1/4 degree)

monthly GSManomaly (T62)

VIC-scale monthly forecast

interpolated to VIC scale

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

Lastly, temporal disaggregation…

for each VIC-scale monthly forecast value, e.g.:

-5

5

15

25

35

Mon1

Mon2

Mon3

Mon4

Mon5

Mon6

deg

C

-5

5

15

25

35

Mon1

Mon2

Mon3

Mon4

Mon5

Mon6

de

g C

Simulations

start of month 0 end of month 6

Forecast Productsstreamflow soil moisture

runoffsnowpack

VIC model spin-upVIC forecast ensemble

climate forecast

information (from GSM)

VIC climatology ensemble

1-2 years back

NCDC met. station obs. up to

2-4 months from

current

LDAS/other met.

forcings for remaining

spin-up

data sources

Columbia River Basin Application

Initial Conditions

late-May SWE &water balance

Initial Conditions

late-May SWE &water balance(percentiles)

May climate forecastobservedforecast

forecastmedians

May snowpack forecast

hindcast“observed”

forecast

forecast medians

May runoff & soil moisture forecasthindcast “observed”forecast

forecastmedians

May streamflow forecast

Ongoing Work: Assessment and Expansion

Tercile Prediction “Hit Rate”

e.g., GSM Ensemble “Forecast” Average, January

(based on retrospectiveperfect-SST ensemble forecasts)

Masked for local significance

U.S. West-wide Hydrologic Forecasting

Summary Comments

climate-hydrology model forecasting method has potential hydrologic persistence was most important in the CRB

example

bias-correction of climate model outputs (using a climate model hindcast climatology) is critical

access to quality met data for hydrologic model initialization is also essential