Experimental real-time seasonal hydrologic nowcasting and forecasting in the western U.S. Andy Wood...

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Experimental real-time seasonal hydrologic nowcasting and forecasting in the western U.S.

Andy Wood

Department of Civil and Environmental Engineering

SeminarUniversity of Arizona

Department of Hydrology and Water Resources

April 27, 2005

Outline

Introduction to seasonal forecasting and forecasting systembackgroundclimate forecastsVIC model spin-up

index station approachSNOTEL assimilation

Selected results for 2004 and current forecast season

Ongoing work (hydrologic nowcasting & …)

The importance of Seasonal Hydrologic Forecasting

water management hydropower

irrigationflood controlwater supply

fisheriesrecreationnavigation

water quality

Aug Dec Apr

Introduction: A definition of useful terms

A short poem by someone not primarily known as a poet

The Unknown

As we know,There are known knowns.There are things we know we know.

We also knowThere are known unknowns.That is to sayWe know there are some thingsWe know we do not know.

But there are also unknown unknowns,The ones we don't knowWe don't know.

Feb. 12, 2002 Department of Defense news briefing Donald Rumsfeld

(Disclaimer: the use of this ‘poem’ does not represent a comment on US military policy…)

Apr 1 SM

Apr 1 SWE

Apr-Sep climate

Seasonal Forecasting: What do we know and when do we know it?

The primary factors determining summer runoff are generally: SWE at the start of melt (e.g., April 1) Soil moisture at the start of the forecasted runoff period (e.g, April 1) April-September Climate

April to August runoff

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

SNOTEL Network

Introduction: Hydrologic prediction and the NRCS

Technical Advances related to Hydrologic Forecasting

1920s 1930s 1940s 1950s 1960s 1970s 1980s 1990s 2000s

snow survey / graphical forecasts /

index methods / i.e., regression

computing in water

resources

aerial snow

surveys

SNOTEL network

ESP method

snow cats

conceptualhydrologic

models

Introduction: Hydrologic prediction and ESP

NWS River Forecast Center (RFC) approach:

rainfall-runoff modeling(i.e., NWS River Forecast System,

Anderson, 1973 offspring of Stanford Watershed Model, Crawford & Linsley, 1966)

Ensemble Streamflow Prediction (ESP)

• used for shorter lead predictions;• ~ used for longer lead predictions

Currently, some western RFCs and NRCS coordinate their seasonal forecasts, using mostly statistical methods.

ICsSpin-up Forecast

recently observedmeteorological data

ensemble of met. datato generate forecast

ESP forecast

hydrologicstate

Technical Advances related to Hydrologic Forecasting

1920s 1930s 1940s 1950s 1960s 1970s 1980s 1990s 2000s

snow survey / graphical forecasts /

index methods / i.e., regression

computing in water

resources

satelliteimagery

aerial snow

surveys desktopcomputing

SNOTEL network

ESP method

ENSO / seasonal climate

forecasts

snow catsInternet / real-time

conceptualhydrologic

models

physicalhydrologic

models

UW Forecast System Overview

Forecast System Overview

NCDC met. station obs.

up to 2-4 months from

current

local scale (1/8 degree) weather inputs

soil moisturesnowpack

Hydrologic model spin up

SNOTEL

Update

streamflow, soil moisture, snow water equivalent, runoff

25th Day, Month 01-2 years back

LDAS/other real-time

met. forcings for spin-up

Hydrologic forecast simulation

Month 6 - 12

INITIAL STATE

SNOTEL/ MODIS*Update

ensemble forecasts ESP traces (40) CPC-based outlook (13) NCEP CFS ensemble (20) NSIPP-1 ensemble (9)

* experimental, not yet in real-time product

Introduction: UW Experimental Hydrologic Forecasting

Soil MoistureInitial

Condition

SnowpackInitial Condition

VIC model runoff is routed to streamflow gages, and verified against observations

targeted statistics e.g., runoff volumes

monthly hydrographs

SWE Soil MoistureRunoffPrecip Temp

Mar-05

Apr-05

May-05

Outline

SNOTEL assimilation

Climate Forecasts: Operational Products

Climate Forecasts: Use in UW forecast system

ENSO/PDO

CPC Official Outlooks

Coupled Forecast

Model (CFS)

NSIPP-1 dynamical

VIC Hydrology Model

Seasonal Climate Forecast Data Sources

Climate Forecasts: Spatial Scale Issues

Seattle

Climate Forecasts: Bias Issue (prior NCEP model)

Sample GSM cell located over Ohio River basin

obs prcp GSM prcp

obs temp GSM temp

Regional Bias: spatial example

obsGSM

Climate Forecasts: Bias Correction Scheme

from COOP observations

from GSM climatological runsraw GSM forecast scenario

bias-corrected forecast scenario

month mmonth m

Climate Forecasts: CPC Seasonal Outlooks

e.g., precipitation

spatial unit for raw forecasts is the Climate Division (102 for U.S.)

CDFs defined by 13 percentile values (0.025 - 0.975) for P and T are given

Climate Forecasts: CPC Seasonal Outlook Use

probabilities => anomalies

precipitation

VIC initial state: Merging of SNOTEL obs with model SWE

The pattern of observed SWE values, which are merged with the forecast initial conditions, is usually in pretty good agreement with the VIC simulated snow state.

The PNW currently has very low snowpack, while the Southwest and California have record high snowpacks.

VIC initial state: SNOTEL assimilation

Assimilation Method• weight station OBS’ influence over VIC cell based on distance and

elevation difference• number of stations influencing a given cell depends on specified

influence distances

spatial weighting function

elevationweightingfunction

SNOTEL/ASP

VIC cell

• distances “fit”: OBS weighting increased throughout season

• OBS anomalies applied to VIC long term means, combined with VIC-simulated SWE

• adjustment specific to each VIC snow band

VIC initial state: SNOTEL assimilation

April 25, 2004

Outline

SNOTEL assimilation

Final comments

Results for Winter 2003-04: initial conditionsSoil Moisture and Snow Water Equivalent (SWE)

Results for Winter 2003-04: initial conditionsCPC estimates of seasonal precipitation and temperature

very dry hot

Results for Winter 2003-04: initial conditionsSoil Moisture and Snow Water Equivalent (SWE)

Results for Winter 2003-04: streamflow hydrographs

By Fall, slightly low flows were anticipated

By winter, moderate deficits were forecasted

Results for Winter 2003-04: volume runoff forecasts

UPPER HUMBOLDT RIVER BASIN

Streamflow Forecasts - May 1, 2003

<==== Drier === Future Conditions === Wetter ====>

Forecast Pt ============ Chance of Exceeding * ===========

Forecast 90% 70% 50% (Most Prob) 30% 10% 30 Yr Avg

Period (1000AF) (1000AF) (1000AF) (% AVG.) (1000AF) (1000AF) (1000AF)

MARY'S R nr Deeth, Nv

APR-JUL 12.3 18.7 23 59 27 34 39

MAY-JUL 4.5 11.3 16.0 55 21 28 29

LAMOILLE CK nr Lamoille, Nv

APR-JUL 13.7 17.4 20 67 23 26 30

MAY-JUL 11.6 15.4 18.0 64 21 24 28

N F HUMBOLDT R at Devils Gate

APR-JUL 5.1 11.0 15.0 44 19.0 25 34

MAY-JUL 1.7 7.2 11.0 50 14.8 20 22

Results for Winter 2003-04: volume runoff forecastsComparison with NWRFC forecast for Columbia River at the Dalles, OR

UW forecasts made on 25th of each month

RFC forecasts madeseveral times monthly:1st, mid-month, late

(UW’sESP unconditional and CPC forecasts shown)

UW RFC

79%obs

Results for Winter 2003-04: volume runoff forecastsComparison with RFC forecast for Feather River, CA

UW forecasts made on 25th of each month

RFC forecasts madeon 1st of month

(UW’sESP unconditional forecasts shown)

78%obs

Results for Winter 2003-04: volume forecasts

for a sample of PNW locations

JAN 1, 2004 Summer Runoff Volume Forecasts compared to OBS

OBS %avgRFCUW ESP

Results for Winter 2003-04: volume forecasts

for a sample of PNW locations

APR 1, 2004 Summer Runoff Volume Forecasts compared to OBS

OBS %avgRFCUW ESP

WY2005, Dec. 1 hydrologic conditions

WY2005, Jan. 1 hydrologic conditions

WY2005, Feb. 1 hydrologic conditions

PNW in crisis?: Headlines from February, 1977

Comparison: Columbia R. basin upstream of The Dalles, OR

WY2005

WY1977

WY2005

WY1977

Results: WY2005, Feb. 1 streamflow forecasts

SNOTEL / Env. Canada ASP network is a valuable source of snowpack information.

The observed SWE values, which are merged with the forecast initial conditions, were in good agreement with the simulated snow state.

80%85%1977: 55%

WY2005, Mar. 1 hydrologic conditions

WY2005, Apr. 1 hydrologic conditions

4/15 ESP forecast: WY2005 Precip, Temp

Yakima R. Basin near

Parker, WA

plots show current + forecast(ESP; min, max and quartiles)

against historical 1971-2000min, max and quartiles

Apr-Sep % of avg3/15 4/15 chg

max 61 570.75 46 500.50 41 450.25 39 42min 31 39

4/15 ESP forecast: WY2005 SM, SWE, RO

Ongoing Work: Involvement with Operational Groups

A major goal of this work is to transition successful approaches intooperational settings.

Progress on this front:

Involvement with NRCS National Water and Climate Center (Tom Pagano!)

Involvement with NOAA Climate Prediction Center (CPC) e.g., related to Drought Outlook

Outreach to PNW water and power community through the UW Climate Impacts Group

e.g., to WA State Executive Water Emergency Committee

Outline

SNOTEL assimilation

Ongoing work (nowcast & …)

snow state update using MODIS

multi-model (land-surface in addition to climate)

west-wide expansionmore forecast pointsmore comprehensive outputsreorganized web-sitemore verificationmore involvement with operational groups

surface water monitor

Ongoing Work: use of remote sensing

Trenchant remarks on remote imaging of the land surface

Oh my goodness gracious,What you can buy off the InternetIn terms of overhead photography!

June 9, 2001, following European trip

Satellite products with potential for hydrology (short list):MODIS snow-covered areaAMSR-E passive microwave-based SWEvarious microwave based soil moisture products

Ongoing work: MODIS snow cover assimilation (Snake R. trial)

Snowcover BEFORE update

Snowcover AFTER update

MODIS update for April 1, 2004 Forecast

snowadded

removed

Ongoing work: Rationale for Multi-model forecast framework

Single-IC ensemble forecast:

early in seasonal forecast season, climate ensemble spread is large

errors in forecast mainly due to climate forecast errors

ensemblemember

ensemblemean

Ongoing work: Rationale for Multi-model forecast framework

Single-IC ensemble forecast:

late in seasonal forecast season, climate ensemble is nearly deterministic

errors in forecast mainly due to IC errors

ensemblemember

ensemblemean

dailyupdates

1-2 day lag

soil moisture& SWEpercentiles

½ degreeresolution

archive from1915-current

uses ~2130index stns

Ongoing Work: UW SW Monitor

trends:1 week2 week1 month

Archive from 1915-current

current conditions are a productof the same simulation (samemethods, ~same stations) ashistorical conditions

allows comparison of current conditions with historical ones

can navigate by month or year

Parkr, WA

Ongoing Work:UW SW Monitor

Parkr, WA

why the high soil moisture percentiles?

appears to have been relatively cold in last several weeks

soil moisture actually decreasing, but relative to normal conditions at this time, percentile still high

Ongoing Work:UW SW Monitor

runoff is nearer to normal than soil moisture

Final Thought

Words of inspiration for researchers everywhere

A trained ape can know an awful lotOf what is going on in this world,Just by punching on his mouseFor a relatively modest cost!

June 9, 2001, following European trip

Framework: Downscaling CPC outlooks

downscaling uses Shaake Shuffle (Clark et al., J. of Hydrometeorology, Feb. 2004) to assemble monthly forecast timeseries from CPC percentile values

VIC model spinup methods: index stations

Example for daily precipitation

Index stn pcp pcp percentilegridded to 1/8

degree

1/8 degree dense station monthly pcp distribution(N years for each 1/8 degree grid cell)

1/8 degree pcpdisagg. to dailyusing interpolated daily fractions from index stations

monthly

0 1 0 1

1920s 1990s

Average Flow, Columbia R. at The Dalles, OR

100000

200000

300000

400000

500000

600000

700000

jan feb mar apr may jun jul aug sep oct nov dec

coop avg

coop stdev

raw cpc avg

raw cpc stdev

cpc bc avg

cpc bc stdev

Results: CPC-based flow w.r.t. UW obs dataset

Answer:

YES, with help from bias-correction..........(but)

std dev

Average flow, Sacramento R. (input to Shasta Reservoir)

jan feb mar apr may jun jul aug sep oct nov dec

coop avg

coop stdev

cpc raw avg

cpc raw stdev

cpc bc avg

cpc bc stdev

Results: CPC-based flow w.r.t. UW obs dataset

Additional examples show similar results

Mean pretty well reproduced; variability improved

std dev

Results: CPC temp/precip w.r.t. UW obs dataset

based on 1960-99

Results: CPC temp/precip w.r.t. UW obs dataset

based on 1960-99

OBS VIC biascorr VIC raw

Statistical bias correction can dramatically improve streamflow simulations for use with reservoir models.

Natural Flow in the Snake River at Milner

Experimental real-time seasonal hydrologic nowcasting and forecasting in the western U.S. Andy Wood...

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