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Advances in hydrologic prediction in the western U.S.

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Title: Advances in hydrologic prediction in the western U.S.


1
Advances in hydrologic prediction in the western
U.S.
  • Dennis P. Lettenmaier
  • Department of Civil and Environmental Engineering
  • University of Washington
  • Catchment-scale hydrological modeling and data
    assimilation international workshop (CAHMDA) III
  • Melbourne, Australia
  • January 10, 2008

2
Talk Outline
  • Background
  • Seasonal hydrologic prediction approaches
  • The University of Washington west-wide seasonal
    hydrologic forecast system
  • Current and recent research
  • Assimilation of satellite data
  • Is calibration necessary?
  • Processing of real-time forcings
  • Is there hydrologically relevant skill in climate
    forecasts?
  • Conclusions and challenges

3
1. Background The importance of Seasonal
Hydrologic Forecasting
water management hydropower irrigation flood
control water supply fisheries recreation
navigation water quality
4
2. Seasonal hydrologic prediction approaches
  • Statistical and stochastic methods
  • Simple to apply
  • Accuracy depends on sample size
  • Difficulties with extremes and nonstationarity
  • Dynamic hydrological modeling (via Ensemble
    Streamflow Prediction) most of this talk
  • Requires model calibration (Achilles Heel of
    hydrologic prediction
  • More or less immune from deficiencies of
    statistical and stochastic methods
  • Consistency with coupled modeling approaches used
    in numerical weather and climate prediction
  • Hybrid approaches
  • Not widely used, but deserving of more attention

5
Application of statistical methods to seasonal
hydrologic prediction in the western U.S.
PNW
Snow water content on April 1
SNOTEL Network
McLean, D.A., 1948 Western Snow Conf.
April to August runoff
6
Typical SNOTEL Site
7
Overview ESP Hydrologic prediction strategy
ESP data flow
The ESP spider web
8
ESP Implementation by NWS Lake Tahoe inflow
forecasts
9
3. The University of Washington west-wide
seasonal hydrologic forecast system
6-month ESP streamflow forecasts for western U.S.
and Mexico effective 1/1/08
10
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11
UW Seasonal HydrologicForecast System Website
12
Forecast System Initial State information
Snowpack Simulated Initial Condition
Observed SWE
Soil Moisture Simulated Initial Condition
13
Forecast System Initial State Snow Adjustment
  • 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

14
Streamflow Forecast Details
15
Streamflow Forecast Results Westwide at a Glance
16
Winter 2006-07 seasonal volume forecast for
APR-SEP
OBS
Forecasts made on 1st of Month
17
User Interactions associated with these
research applications
U. Arizona / USBR forecast study, Lower Colorado
basin
NWS Hydrologic Ensemble Prediction Experiment
Princeton University Hydrologic Forecast System
3TIER Environmental Forecast Group
Miscellaneous Seattle City Light, energy
traders, hydropower utilities, NOAA regional
climate offices
NRCS National Water and Climate Center
UW Climate Impacts Group (CIG) Annual Water
Outlook meetings
UW Hydrologic Forecast and Nowcast Systems
NOAA National Centers for Environmental
Prediction (NCEP) testbed activities
UW Rick Palmer Group Puget Sound region flow
forecasts
Klamath R. Basin Bureau of Reclamation
new
US Drought Monitor
NOAA Climate Prediction Centers US Drought
Outlook
Columbia River Inter-tribal Fish Commission
UCI / California Dept of Water Resources
WA State Dept of Ecology Yakima R. Basin Bureau
of Reclamation
18
4a Current and recent research Snow data
assimilation
19
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20
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21
MODIS updating of snow covered area
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
25th Day of Month 0
1-2 years back
Change in Snowcover as a Result of MODIS Update
for April 1, 2004 Forecast
Snowcover before MODIS update
Snowcover after MODIS update
22
Unadjusted vs adjusted forecast errors,
2001-2003, for reservoir inflow volumes (left
plot) and reservoir storage (right)
23
Passive microwave remote sensing for snow water
equivalent
  • In principle, attractive since it measures the
    right variable (water equivalent rather than
    extent)
  • AMSR-E product probably is best current
    generation, but numerous problems (mostly
    generic)
  • Coarse resolution ( 15-25 km)
  • Saturation at 100-200 mm SWE
  • Requires dry snowpacks (algorithms fail if there
    is liquid water in the pack)
  • Algorithms unreliable for mixed pixels
    (especially forest)
  • Signature is highly sensitive to grain size (and
    other snow microphysical properties)

24
4b Current and recent research Is calibration
necessary?
  • Approach Use percentile mapping bias correction
    on uncalibrated forecasts, compare Cp (1
    forecast MSE/unconditional variance) for
    calibrated and uncalibrated (using N-LDAS
    parameters) for a range of forecast dates and
    lead times at 8 forecast sites throughout the
    western U.S.
  • Result Bias corrected uncalibrated forecasts
    did nearly as well at most sites, and better at
    some
  • Conclusion Perhaps calibration (the Achilles
    Heel of dynamic hydrologic forecasting methods)
    isnt really necessary

25
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26
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27
4c Processing of real-time forcings
Problem Generally there are many fewer
real-time stations (station numbers shown in
figure) than retrospective (all stations
28
Question How to interpolate data from real-time
stations so as to respect orographic variations
in precipitation (most stations are at low
elevation, most precipitation at high elevation)?
  • Options tested
  • a) Grid real-time stations directly, with PRISM
    orographic adjustment
  • b) interpolate percentiles to locations of
    non-reporting stations, then grid data as if all
    had reported

29
Generally, percentile interpolation methods
(Index) do better than direct interpolation
methods (Maurer)
30
4d Is there hydrologically relevant skill in
climate forcings
31
Results of previous seasonal hydrologic
predictability studies for continental U.S.
  1. Wood et al, A retrospective assessment of NCEP
    climate model-based ensemble hydrologic
    forecasting in the western United States, JGR,
    2005
  2. Wood, An ensemble-based framework for
    characterizing sources of uncertainty in
    hydrologic prediction
  3. Work in progress, DEMETER forecasts over
    continental U.S.

32
Wood et al 2005 Retrospective Assessment
Results using GSM
General finding is that NCEP GSM climate
forecasts do not add to skill of ESP forecasts,
except
April GSM forecast with respect to climatology
(left) and to ESP (right)
33
Wood et al 2005 Retrospective results for ENSO
years
Summary During strong ENSO events, for some
river basins (California, Pacific Northwest)
runoff forecasts improved with strong-ENSO
composite but Colorado River, upper Rio Grande
River basin RO forecasts worsened.
October GSM forecast w.r.t ESP unconditional
(left) and strong-ENSO (right)
34
Wood (2002) Reverse ESP
35
Reverse ESP vs ESP typical results for the
western U.S.
Columbia R. Basin
fcst more impt
ICs more impt
Rio Grande R. Basin
36
DEMETER forecast evaluation
  • VIC model long-term (1960-99) simulations at ½
    degree spatial resolution assumed to be truth
  • DEMETER reforecasts with ECMWF seasonal forecast
    model for 6 month lead, forecasts made on Feb 1,
    May 1, Aug 1, Nov 1 1960-99
  • 9 forecast ensembles on each date
  • Forecast forcings (precipitation and temperature)
    downscaled and bias corrected using Wood et al
    approach (also incorporated in UW West-wide
    system)
  • On each forecast date, 9 ensemble members also
    resampled at random from 1960-99 to form ESP
    ensemble
  • Forecast skill evaluated using Cp for unrouted
    runoff

37
Test sites
38
Missouri River at Fort Benton
39
Snake River at Milne
40
Owyhee River
41
Riley
42
Rosco
43
USGS streamflow gauges that are used in the
evaluation of streamflow predictions
Luo, L. and E. F. Wood (2007) Seasonal
Hydrologic Prediction with the VIC Hydrologic
Model for the Eastern U.S. Journal of
Hydrometeorology. In review.
44
The evaluation of streamflow predictions over
selected gauges. The ranked probability score
(RPS) for monthly streamflow for the first three
months are examined against the offline
simulation. The bars are for CFS, CFSDEMETER
and ESP from the left to the right,
respectively. RPS 01 with 0 being the perfect
forecast 3 tercels, below normal, normal and
above normal with probability of 1/3 each.
Luo, L. and E. F. Wood (2007) Seasonal
Hydrologic Prediction with the VIC Hydrologic
Model for the Eastern U.S. Journal of
Hydrometeorology. In review.
45
Concluding thoughts
  • Hydrologic prediction skill at S/I lead times
    comes mostly from initial conditions.
  • Hence more focus on data assimilation, and its
    implications for hydrologic forecast skill, needs
    more attention.
  • The role of model error in hydrologic predictions
    needs more focus how do we best weight land
    models in multimodel ensemble?
  • Do hydrologists (and the land data assimilation
    community) need to expend more effort on
    hydrologic forecasting?
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