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An experimental seasonal hydrologic forecast system for the western U.S. Dennis P. Lettenmaier Department of Civil and Environmental Engineering University of Washington for Cincinnati Earth Systems Science Seminar Series and Advanced Environmental Seminar Series

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an experimental seasonal hydrologic forecast system for the western u s
An experimental seasonal hydrologic forecast system for the western U.S.

Dennis P. Lettenmaier

Department of Civil and Environmental Engineering

University of Washington

for

Cincinnati Earth Systems Science Seminar Series

and

Advanced Environmental Seminar Series

University of Cincinatti

Apr 30, 2004

outline of this talk
Outline of this talk
  • Introduction: seasonal hydrologic forecasting historical development; current operational methods
  • Modeling framework and implementation for long-lead seasonal streamflow forecasting
  • Estimating the hydrologic initial conditions
  • Approaches based on ensemble climate prediction
  • Data assimilation – example using the MODIS snow cover product
  • Westwide streamflow forecast system – experience in winters 2002-3 and 2003-4
  • Conclusions and unsolved problems
introduction seasonal hydrologic forecasts
Reservoir Storage

Aug

Dec

Apr

Aug

Introduction: Seasonal Hydrologic Forecasts

water management

hydropower

irrigation

flood control

water supply

fisheries

recreation

navigation

water quality

introduction hydrologic prediction long history
Introduction:Hydrologic prediction – long history

Snow water content on April 1

  • should add my personal pics of -
    • snow sampling
    • snotel sites
  • (and scan in curve method figure)

NRCS

SNOTEL Network

SNOTEL network

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

April to August runoff

introduction hydrologic prediction and nws
recently observed

meteorological data

ensemble of met. data

to generate forecast

ESP forecast

Spin-up

ICs

Forecast

obs

hydrologic

state

RMSE

Introduction:Hydrologic prediction and NWS
  • 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
  • The RFC final seasonal
  • forecasts also incorporate
  • NRCS results.

Neither NWS or NRCS objectively use contemporary climate forecasts

introduction current operational methods
Introduction:Current Operational Methods

A primary seasonal forecasting product is the runoff volume forecast

UPPER COLUMBIA BASINS

Mar-04MIDMN W A T E R S U P P L Y F O R E C A S T S

FORECAST RUNOFF AVERAGE RO PREV

STREAM AND STATION PERIOD PROBABLE % MAXIMUM % MINIMUM % RUNOFF PERIOD

COLUMBIA RIVER

MICA RESERVOIR INFLOW, BC JAN-JUL 9100.0 95 10400.0 108 7770.0 81 9619. 1049

FEB-SEP 12200.0 94 13600.0 105 10900.0 84 12960. 13000

APR-SEP 11800.0 94 13200.0 106 10500.0 84 12500. 12590

REVELSTOKE, BC JAN-JUL 13200.0 95 14400.0 104 12100.0 87 13880. 15070

ARROW LAKES INFLOW JAN-JUL 19300.0 92 21300.0 102 17200.0 82 20960. 21970

FEB-SEP 24400.0 92 26500.0 100 22300.0 84 26460. 26070

APR-SEP 23100.0 92 25100.0 100 21000.0 84 25110. 24950

BIRCHBANK, BC (1) JAN-JUL 34900.0 90 40100.0 103 29600.0 76 38930. 42950

APR-SEP 39000.0 90 44200.0 102 33700.0 77 43500. 46150

GRAND COULEE, WA (1) JAN-JUL 55500.0 88 64000.0 102 46900.0 75 62900. 68020

APR-SEP 56400.0 88 65000.0 102 47900.0 75 63990. 68220

ROCK ISLAND DAM BLO, WA (1) JAN-JUL 61000.0 89 70400.0 102 51600.0 75 68910. 74830

APR-SEP 61500.0 88 71000.0 102 52100.0 75 69540. 74300

THE DALLES NR, OR (1) JAN-JUL 92000.0 86 106000.0 99 78400.0 73 107300. 103800

APR-AUG 79800.0 86 93400.0 100 66200.0 71 93090. 93800

APR-SEP 84600.0 86 98200.0 100 71000.0 72 98650. 98080

introduction research rationale
Introduction: Research Rationale

Are current seasonal hydrologic forecasts all that they can be?

How can ongoing research on land-atmosphere interactions

help to improve seasonal streamflow forecasts in the western U.S.?

  • Potential sources of improvement since inception of regression/ESP methods:
  • operational seasonal climate forecasts (model-based and otherwise)
  • greater availability of station data
  • computing
  • new satellite-based products (primarily snow cover)
  • distributed, physical hydrologic modeling for macroscale regions
slide13
ESP forecast

“Reverse-ESP” forecast

ensemble of met data to

generate ensemble of ICs

perfect retrospective

met forecast

recently observed

meteorological data

ensemble of met. data

to generate forecast

Spin-up

ICs

Forecast

Spin-up

ICs

Forecast

obs

“obs” = perfect spinup + perfect fcst

simulation

hydrologic

state

obs

RMSE

hydrologic

state

Analysis performed over 21-year period (1979-99), from which spinup and fcst traces were taken.

RMSE

Estimating relative impact of initial

conditions and forecast accuracy

Retrospective ESP-type simulations can shed light on the relative value of initial conditions to a given forecast application.

initial conditions balancing ic and forecast accuracy
Initial Conditions:Balancing IC and forecast accuracy

Columbia R. Basin

fcst more impt

ICs more impt

Rio Grande R. Basin

RMSE (perfect IC, uncertain fcst)

RMSE (perfect fcst, uncertain IC)

RE =

initial conditions hydrologic simulations
Initial Conditions: Hydrologic Simulations

start of month 0

end of mon 6-12

1-2 years back

forecast ensemble(s)

model spin-up

initial conditions

climatology ensemble

NCDC met. station obs. up to 2-4 months from current

2000-3000 stations in west

LDAS/other real-time met. forcings for remaining spin-up

~300-400 stations in west

climate forecast information

data sources

Forecast Products

streamflow

soil moisture

runoff

snowpack

derived products

e.g., reservoir system forecasts

obs snow state information

(eg, SNOTEL)

initial conditions estimating run up conditions
Initial Conditions:estimating run-up conditions

Problem: met. data availability in 3 months prior to forecast has only a tenth of long term stations used to calibrate model

Solution: use interpolated monthly index station precip percentiles and temperature anomalies to extract values from higher quality retrospective forcing data -- then disaggregate using daily index station signal.

sparse station network in real-time

dense station network for model calibration

initial conditions snow state assimilation
Initial Conditions: snow state assimilation

Problem

sparse station spin-up period incurs some systematic errors, but snow state estimation is critical

Solution

use SWE anomaly observations (from the 600+ station USDA/NRCS SNOTEL network and a dozen ASP stations in BC, Canada) to adjust snow state at the forecast start date

initial conditions initial snow state assimilation
spatial weighting function

elevation

weighting

function

SNOTEL/ASP

VIC cell

Initial Conditions: Initial snow state 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
  • 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
initial conditions snow state assimilation19
Initial Conditions: Snow state assimilation

SWE state differences due to assimilation of SNOTEL/ASP observations, Feb. 25, 2004

initial conditions final product
Initial Conditions:final product

Snow Water Equivalent (SWE) and Soil Moisture

slide21
MODIS Update

Description and Products of MODIS Updated Forecasts

local scale weather inputs

Initial Conditions:

soil moisture,

snowpack

Hydrologic model spin up

Hydrologic simulation

Ensemble Forecast:

streamflow, soil moisture, snowpack, runoff

NCDC met. station obs. up to 2-4 months from current

LDAS/other real-time met. forcings for remaining spin-up

End of Month 6 - 12

1-2 years back

25th Day of Month 0

Change in Snowcover as a Result of MODIS Update for April 1, 2004 Forecast

Snowcover before MODIS update

Snowcover after MODIS update

slide22
Result of MODIS Update on Streamflow and Storage

April 1, 2004 Forecast

Unadjusted

MODIS Unadjusted

MODIS Adjusted

slide23
Unadjusted vs adjusted forecast errors, 2001-2003, for reservoir inflow volumes (left plot) and reservoir storage (right)
climate forecasts seasonal prediction
Climate Forecasts:Seasonal prediction
  • Climate prediction has markedly advanced in the last several decades
    • better monitoring of oceans and atmosphere
    • deeper understanding of ocean-atmosphere teleconnections
    • Monthly / seasonal climate forecasting has become operational at a number of research centers

Circulation Features

Sea Surface Temps

Typical climate model spatial resolution

e.g

El Nino / La Nina

overview hydrologic forecast approach
hydrologic model inputs

streamflow, soil moisture,

snowpack,

runoff, derived products

  • climate forecast
  • ~2-3 degree resolution (T42-T62)
  • monthly total P, avg T
  • Use 2 step approach:1) statistical bias correction
  • 2) downscaling

1.

climate model ensemble outputs

  • 1/8-1/4 degree resolution
  • daily P, Tmin, Tmax

2.

Extended (“Ensemble) Streamflow Prediction (ESP)

  • 1/8-1/4 degree resolution
  • daily P, Tmin, Tmax
  • No bias-correction or downscaling needed
  • Met. traces can be composited before/after ensemble is run to represent ENSO, PDO conditions, etc.
Overview: Hydrologic Forecast Approach
overview global spectral model gsm ensemble forecasts from ncep emc
Overview: 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
overview climate model forecast processing sequence
TOBS

a. b. c.

TGSM

Overview: climate model forecast processing sequence

a) bias correction:

climate model climatology  observed climatology

b) spatial interpolation:

e.g., GSM (1.8-1.9 deg.)  VIC (1/8 deg)

c) temporal disaggregation (via resampling of observed patterns):

monthly  daily

climate forecasts bias correction
specific to calendar month

and climate model grid cell

Climate Forecasts: Bias Correction
  • numerous methods of downscaling and/or bias correction exist
  • the relatively simple one we’ve settled on requires a sufficient retrospective climate model climatology, e.g.,
    • NCEP: hindcast ensemble climatology, 21 years X 10 member
    • NSIPP-1: AMIP run climatology, > 50 years, 9 member
climate forecasts skill deficit problem
streamflow forecast skill score bold: GSM signif. better

underline: GSM signif. worse

Climate Forecasts:“skill deficit” problem
  • Retrospective skill analysis for NCEP-GSM found:
  • bias-correction sufficient to put T & P forecasts into plausible range w.r.t. observations
  • where/when model shows good rank correlation with observations, this can produce a better (than ESP) streamflow forecast
  • “sweet spots” are few and far between:
  • -- strong ENSO-anomaly conditions
  • -- ENSO-sensitive regions
skill assessment retrospective analysis
masked for local significance

local significance at a 0.05 level (based on a binomial model of success/failure, and assuming zero spatial and zero autocorellation, so that N=21 and p(success)=.33)

Skill Assessment:Retrospective analysis

tercile prediction skill of GSM ensemble forecast averages, JAN FCST

forecasting project background
Forecasting Project: Background

1998-9

Ohio R. basin w/ COE: First tried climate model-based seasonal forecasts on experimental (retrospective) basis

Eastern US: First attempted real-time seasonal forecasts during drought condition in southeastern states -- results published in: Wood et al. (2001), JGR

2000

Columbia R. basin: Implemented approach during the PNW drought, again using climate model based approach

2001

Western US: Retrospective analysis of forecasts over larger domain (for one climate model and for ESP)

2002

Columbia R. basin: New funding for “pseudo-operational” implementation for western US; began with pilot project in CRB

2003

  • (Funding from: NASA NSIPP; IRI/ARCS; NOAA GCIP/GAPP)

Western US: expanded to western U.S domain for real-time forecasts; working to improve and evaluate methods each forecast cycle

2004

current season forecasts
Current season forecasts

September/October 2003: Soil Moisture

current season forecasts38
Current season forecasts

November 25: Snow Water Equivalent (SWE) and Soil Moisture

current season forecasts39
Current season forecasts

December 25: Snow Water Equivalent (SWE) and Soil Moisture

current season forecasts40
Current season forecasts

January 25: Snow Water Equivalent (SWE) and Soil Moisture

current season forecasts41
Current season forecasts

February 25: Snow Water Equivalent (SWE) and Soil Moisture

overview streamflow forecasts
Overview: Streamflow Forecasts

hydrographs

targeted statistics

raw ensemble data

streamflow products
raw ensemble dataStreamflow products

targeted statistics

hydrographs

comparison with rfc regression forecast for columbia river at the dalles
Comparison with RFC regression forecast for Columbia River at the Dalles

UW forecasts made on

25th of each month

RFC forecasts made

several times monthly:

1st, mid-month, late

(UW ESP unconditional forecasts shown)

UW

RFC

some obstacles and opportunities in hydrological application of climate information
Some obstacles and opportunities in hydrological application of climate information
  • The “one model” problem
  • Calibration and basin scale (post-processing as an alternative to calibration)
  • The value of visualization
  • Opportunities to utilize non-traditional data (e.g. remote sensing)

For more information: www.hydro.washington.edu/Lettenmaier/Projects/fcst/

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