Applications of macroscale hydrologic models for water cycle monitoring and streamflow prediction...

Applications of macroscale hydrologic models for water cycle monitoring and streamflow prediction

Andy Wood Civil and Environmental Engineering

Seminar

Civil and Environmental EngineeringUniversity of Washington

April 6, 2006

Outline

Macroscale hydrologic modeling

Seasonal forecasting systemoverviewsample results

Hydrologic monitoring systemoverviewsample results

Interactions, Current and Future

Macroscale Hydrologic Modeling

Much of hydrologic theory and practice has been developed at point or bench scale, and at catchment scales.

Early hydrologic modelsapplied over sub-catchmentscales, and this is still themost common applicationtoday.

These models strike abalance between physicaland calibrated parameterinputs

Mastin & Vaccaro, 2002, USGS Open File Report 02-404

Macroscale Hydrologic Modeling

Macroscale hydrologicmodels grew out of aneed for improvedsurface representationsin general circulationmodels (GCMs)

Tend to be “flat earth”models

Also contain a mix ofmeasured and calibration parameterinputs

The Land-Surface Water Budget

E P

W Q

(Near Surface) Water Balance Equation

dt

dWEPQ

Almost all hydrologic models have a water budget, e.g.,

Q: runoff (which may become streamflow)P: precipitationE: evaporationW: water storage

There is an enormous range in complexity in how the terms are calculated

VIC Hydrologic Model

•Developed over last 12 years

•Includes an optional energy budget closure at each time step

•Multiple vegetation classes in each cell

•Sub-grid elevation band definition (for snow)

•Subgrid infiltration/runoff variability

•Operates at 1 hour and longer timesteps

What does a 1/8 degree pixel look like in real life?

Precipitation - the best measured

U.S. Station density: 1 per 700 km2

Ameriflux (flux towers) measure E, since mid 1990’s

Water Budget: Q = P – E – dW/dt

Source: A. Robock, Rutgers U.

Top 1-m soil moisture measurementsSnow water equivalent obs.

Runoff (Streamflow) Observations: Calibration

•Streamflow in the U.S. measured at roughly 7,000 active gauging stations.

•Most stations represent regulated flow conditions

Source: U.S.G.S.

Outline





The importance of Seasonal Hydrologic Forecasting

water management hydropower

irrigationflood controlwater supply

fisheriesrecreationnavigation

water quality

Aug Dec Apr

Res

ervo

ir S

tora

ge

Aug

How does one make a forecast of river flow?

Naïve forecast (“climatology”) – simply use historical averages

Persistence (or anti-persistence)

(Multiple) Regression ForecastTraditional Predictors:

snowpack (SWE), accumulated precipitation, current or past river flow, measured over the drainage basinMore advanced predictors:

ENSO state indicators (Nino3.4, SOI)

Predictand: daily, monthly or seasonal streamflow at some lead time in the future.

Model-based approaches

Sn

ow w

ater

con

ten

t on

Ap

ril

1

April to August runoff

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

SNOTEL Network

Introduction: Hydrologic prediction and the NRCS

PNW

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

obs

recently observedmeteorological data

ensemble of met. datato generate forecast

ESP forecast

hydrologicstate

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

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

data

conceptualhydrologic

models

physicalhydrologic

models

UW Forecast System Website

UW Forecast Approach 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

gap

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

Forecast System Initial State information

Soil MoistureSimulated Initial

Condition

SnowpackSimulated Initial Condition

Observed SWE

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

Forecast System Initial State Snow Adjustment

• 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

Streamflow Forecast Results: Westwide at a Glance

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

Streamflow Forecast Details

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

Streamflow Forecast Results: Spatial

SWE Soil MoistureRunoffPrecip Temp

Apr-06

May-06

Jun-06

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 forecasts

for a sample of PNW locations

OCT 1, 2003 Summer Runoff Volume Forecasts compared to OBS

50

60

70

80

90

100

110

MIC

AA

LIB

BY

HH

OR

S

JLA

KE

LG

RA

N

DW

OR

S

DA

LL

E

pe

rce

nt

of

av

era

ge

OBS %avgRFCUW ESP



NOV 1, 2003 Summer Runoff Volume Forecasts compared to OBS

50

60

70

80

90

100

110

MIC

AA

LIB

BY

HH

OR

S

JLA

KE

LG

RA

N

DW

OR

S

DA

LL

E

pe

rce

nt

of

av

era

ge

OBS %avgRFCUW ESP



JAN 1, 2004 Summer Runoff Volume Forecasts compared to OBS

50

60

70

80

90

100

110

MIC

AA

LIB

BY

HH

OR

S

JLA

KE

LG

RA

N

DW

OR

S

DA

LL

E

pe

rce

nt

of

av

era

ge

OBS %avgRFCUW ESP



APR 1, 2004 Summer Runoff Volume Forecasts compared to OBS

50

60

70

80

90

100

110

MIC

AA

LIB

BY

HH

OR

S

JLA

KE

LG

RA

N

DW

OR

S

DA

LL

E

pe

rce

nt

of

av

era

ge

OBS %avgRFCUW ESP

Scientific and Engineering Aspects

The forecast system involves many engineering challenges

There are also important science questions:

Can climate forecast advances be harnessed to advance hydrologic forecasting?

Can remote sensing advances yield enough information to improve hydrologic simulation?

What role should data assimilation have in hydrologic prediction?

How can model-based systems help us understand hydrologic (and climate) variability?

How well can our models account for uncertainty?

Testbed work: Using Climate Forecasts

Testbed work: MODIS snow cover assimilation

Snowcover BEFORE update

Snowcover AFTER update

MODIS update for April 1, 2004 Forecast

snowadded

removed

Outline





½ degree VIC implementation

Free running since last June

Uses data feed from NOAA ACIS server

“Browsable” Archive, 1915-present

UW Real-time Daily NowcastSM, SWE

(RO)

We are currently migrating the daily update methodsto the west-wide forecast system (1/8 degree)

The challenge of changing observing systems

1920s 1990s

Meteorological stations that still report in real time today

Surface Water Monitor Archive

July 2002: the western U.S. drought centers on Colorado

March 1997: La Nina conditions bring the highest

recorded snowfall to the PNW

Surface Water Monitor Archive

August 1993: the highest recorded flow on the Mississippi R.

March 2002: Virginia experiences severe drought, many well failures

Current exploration that combines the two projects

modes of variability

Absolute Correlations with Apr-Sep Avg FlowColumbia R. at The Dalles, OR

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

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

Co

rre

lati

on

Co

eff

icie

nt

NS-DIFF

NS-SUM

LOCAL

Nino3.4

PC1&2

Absolute Correlations with Apr-Jul Avg FlowFeather R. at Oroville Dam, CA

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

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

Cor

rela

tion

Coe

ffic

ient

can we use the modes of variability to predict summer streamflow?

Outline





“User” Interactions associated with these research applications

UW HydrologicForecast and Nowcast

Systems

U. Arizona / USBRforecast study, Lower

Colorado basin

NWS Hydrologic Ensemble Prediction

Experiment

3TIER Environmental Forecast Group

NRCS National Water and Climate

Center

NOAA Climate Prediction Center’s US Drought Outlook

Miscellaneous:Seattle City Light,energy traders,

hydropower utilities,NOAA regional climate offices

UW Rick Palmer Group Puget

Sound region flow forecasts

UW Climate Impacts Group (CIG)

Annual Water Outlook meetings

NOAA National Centers for

Environmental Prediction (NCEP) testbed activities

Columbia River Inter-tribal Fish

Commission

Klamath R. Basin Bureau of

Reclamation

UCI / California Dept of Water

Resources

WA State Dept of Ecology & Yakima R. Basin

Bureau of Reclamation

newUS Drought Monitor

Princeton University Hydrologic Forecast

System

Current Activities: Pilot basin operational efforts

Two basin-focused water resources forecast efforts just beginning operational streamflow and hydrologic forecasting in the Yakima

R. Basin NOAA-supported (SARP program, 2 years, starts in April) collaborate with USBR; target reservoir operations & water

allocations for irrigation, and also state-level drought planning

operational streamflow and hydrologic forecasting in the Klamath R. basin and in California (initially the Feather R. basin). NASA-supported (CAN decision support, 3 years, with UCI) collaborate with USBR, CADWR incorporate MODIS snowcover in initial state estimation,

and use satellite based crop ET as well.

Current Activities: Pilot basin operational efforts

For both projects, model resolution

in target basins is increasing to 1/16

degree lat-lon resolution

The Yakima R. Basin, within WA State domain

for SARP project

Acknowledgements

Dennis Lettenmaier, UW

Phil Pasteris, Tom Pagano, Tom Perkins (NRCS Nat. Water & Climate Center)

Graduate Students, Post-Docs and Research StaffAli AkandaGeorge TaylorNiklas ChristensenKathy DevlinTed Bohn, Nathalie Voisin, Darren WiltonKaiyun LiAlan HamletEd Maurer

Other Advice: Rick Palmer, Steve Burges, Anne Steinemann

Major Federal Funding Sources: NASA, NOAA

Questions?

Websites:

www.hydro.washington.edu / forecast / westwide /

www.hydro.washington.edu / forecast / monitor /

Email:

Andy Wood: [email protected]

How does one make a forecast of river flow?

Climate Forecasts: Bias Issue (prior NCEP model)

Sample GSM cell located over Ohio River basin

obs prcp GSM prcp

obs temp GSM temp

JULY

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

Validation with Illinois Soil Moisture

19 observing stations are compared to the 17 1/8º modeled grid cells that contain the observation points.

Persistence

Moisture Level

Moisture Flux

Variability

Applications of macroscale hydrologic models for water cycle monitoring and streamflow prediction...

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