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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 l.jpg
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 l.jpg
Outline of this talk western U.S.

  • 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 l.jpg

Reservoir Storage development

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 l.jpg
Introduction: developmentHydrologic 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 l.jpg

recently observed development

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 l.jpg
Introduction: developmentCurrent 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 l.jpg
Introduction: developmentResearch 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 l.jpg

ESP forecast development

“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 l.jpg
Initial Conditions: developmentBalancing 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 l.jpg
Initial Conditions: developmentHydrologic 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 l.jpg
Initial Conditions: developmentestimating 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 l.jpg
Initial Conditions: developmentsnow 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 l.jpg

spatial weighting function development

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 l.jpg
Initial Conditions: developmentSnow state assimilation

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


Initial conditions final product l.jpg
Initial Conditions: developmentfinal product

Snow Water Equivalent (SWE) and Soil Moisture


Slide21 l.jpg

MODIS Update development

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 l.jpg

Result of MODIS Update on Streamflow and Storage development

April 1, 2004 Forecast

Unadjusted

MODIS Unadjusted

MODIS Adjusted


Slide23 l.jpg

Unadjusted vs adjusted forecast errors, 2001-2003, for reservoir inflow volumes (left plot) and reservoir storage (right)


Forecast approaches based on ensemble climate prediction l.jpg
Forecast approaches based on ensemble climate prediction reservoir inflow volumes (left plot) and reservoir storage (right)


Climate forecasts seasonal prediction l.jpg
Climate Forecasts: reservoir inflow volumes (left plot) and reservoir storage (right)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


Climate forecasts scale issues l.jpg
Climate Forecasts: reservoir inflow volumes (left plot) and reservoir storage (right)Scale Issues


Overview hydrologic forecast approach l.jpg

reservoir inflow volumes (left plot) and reservoir storage (right)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 l.jpg
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 l.jpg

T from NCEP/EMCOBS

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 forecast use challenges l.jpg
Climate Forecasts: from NCEP/EMCforecast use challenges


Climate forecasts bias correction l.jpg

specific to calendar month from NCEP/EMC

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 operational products l.jpg
Climate Forecasts: from NCEP/EMCOperational Products


Climate forecasts skill deficit problem l.jpg

streamflow forecast skill score from NCEP/EMCbold: 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 l.jpg

masked for local significance from NCEP/EMC

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 l.jpg
Forecasting Project: winters 2002-3 and 2003-4Background

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 l.jpg
Current season forecasts winters 2002-3 and 2003-4

September/October 2003: Soil Moisture


Current season forecasts38 l.jpg
Current season forecasts winters 2002-3 and 2003-4

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


Current season forecasts39 l.jpg
Current season forecasts winters 2002-3 and 2003-4

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


Current season forecasts40 l.jpg
Current season forecasts winters 2002-3 and 2003-4

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


Current season forecasts41 l.jpg
Current season forecasts winters 2002-3 and 2003-4

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


2003 04 spatial forecasts l.jpg
2003-04 winters 2002-3 and 2003-4Spatial Forecasts


Current season forecasts locations l.jpg
Current season forecasts, locations winters 2002-3 and 2003-4


Overview streamflow forecasts l.jpg
Overview: winters 2002-3 and 2003-4Streamflow Forecasts

hydrographs

targeted statistics

raw ensemble data


Streamflow products l.jpg

raw ensemble data winters 2002-3 and 2003-4

Streamflow products

targeted statistics

hydrographs



Comparison with rfc regression forecast for columbia river at the dalles l.jpg
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 l.jpg
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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