http://nm.water.usgs.gov/drought/photos.htm
Drought, hydrologic forecasting, and
water resources in the Pacific
Northwest
Washington Hydrologic Society
April 20, 2005
Dennis P. Lettenmaier
Department of Civil and Environmental Engineering
University of Washington
Outline – Part I (historic droughts)
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Background – Rationale and approach using longterm hydrologic reconstructions
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Motivation
VIC Model
Drought Definitions
Technique of drought classification
Preliminary results for continental U.S.
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1930s, 1950s, current drought
Comparison of most severe agricultural and hydrologic
droughts
Implications for water managers
Future research
Motivation
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Recent availability of precipitation and
temperature data in electronic form from the
beginning of the instrumental record at the
National Climatic Data Center make model
simulation of hydrologic conditions for long
periods possible.
Such simulations provide a spatially and
temporally continuous data set.
They also allow us to investigate historical
droughts in new ways.
VIC Model
Soil Parameterization
• 3 soil layers
• Variable infiltration curve in upper layer
partitions subsurface and quick storm
response
• Gravity-driven vertical
soil drainage
• Non-linear baseflow
drainage from lowest layer
•Energy balance snow
model
Maurer et al., 2002
6 Sample Hydrographs
Good agreement of
•Seasonal cycle
•Low Flows
Model •Peak Flows
Obs.
Maurer et al., 2002
Comparison with Illinois Soil Moisture
19 observing stations are compared to the 17 1/8º modeled grid
cells that contain the observation points.
Moisture Level
Moisture Flux
Variability
Obs.
Model
Persistence
Drought Definitions
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Meteorological Drought
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Agricultural Drought
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Soil Moisture
Hydrologic Drought
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Precipitation and
Temperature
Streamflow/Runoff
Socioeconomic Drought
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Disparity between supply
and demand
http://nm.water.usgs.gov/drought/photos.htm
Palmer Drought Severity Index
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PDSI : measures meteorological drought using a
method that accounts for precipitation,
evaporation, and soil moisture conditions.
Dai & Trenberth (2004) find correlation between
annual PDSI and streamflow and correlation
between PDSI and soil moisture during warm
season. Snow interferes with soil moisture
calculations.
Despite standardization, dependence on
termination criteria results in questionable
distribution of severe droughts.
PDSI-based studies
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Cook et al. (1999) used tree-ring
chronologies to reconstruct US droughts
from 1700-1978
EOFs used for regionalization purposes
Examined PDSI signal over those regions
“Dust Bowl” dominated the entire period
Other notable droughts: 1950, 1965, 1977.
PDSI-based studies
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Dai et al. (1998) used a monthly PDSI
dataset from 1860-1995
2.5o x 2.5o grid over the globe
Major droughts identified: 1930s, 1950s and
1988
Correlation between PDSI and ENSO signals
Increase in percentage areas of severe
drought during the last 2-3 decades, over
many ENSO-sensitive regions
Drought spatial analysis from
other studies
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Most other studies have used station data
Pre-defined climate regions
Statistical methods such as
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Correlation analysis, (Oladipo 1986)
Empirical Orthogonal Functions (EOF), (Cook
et al. 1999, Hisdal and Tallaksen 2003)
Simulation provides continuous spatial and
temporal mapping of hydrologic variables
Hydrologic Simulations
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Based on physical processes
Not dependent on scattered or temporally
disjoint station data
Allow for direct analysis of parameters of
interest, i.e. runoff and soil moisture
Use of percentile values standardizes over
heterogeneous regions and is independent
of initialization and termination criteria
How can we use information from
long term hydrologic model
simulations to synthesize the
following drought characteristics:
severity, intensity, extent, and
duration?
Severity-Area-Duration Analysis
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Based on the Depth-Area-Duration technique
from probable maximum precipitation analysis
Replace depth with measure of drought
severity
S=(1-ΣP/t)
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S=severity, ΣP = total percentile (soil moisture or
runoff), t = duration
How do we define drought extent?
Severity
Droughts change over time!
SAD Construction
Modified from WMO (1960) computational
method of DAD analysis
1. Rank cells by severity & identify potential
drought centers
2)
1)
2. Search 3x3 neighborhood of drought
center
3. Average severities & add areas
4. Output severity and area at specified
area intervals
5. Compare the severity at ~25,000 km2 for
each potential drought center and select
center with maximum severity
3)
Methodology
VIC model
output
Threshold
Weibull
Total column soil
percentiles
moisture
Runoff
Temporal
contiguity
20th percentile and
lower soil moisture
30th percentile and
lower runoff
Spatial
contiguity
Final drought
and
subdrought
classification
Severity-AreaDuration
Highest
severities
Envelope
curve for
each duration
Initial
drought
classification
SAD curves
for each
event
1930s Drought Soil Moisture
Soil moisture-defined drought
80
Percent severity
100
1930s Drought Runoff
Runoff-defined drought
80
Percent severity
100
1950s Drought Soil Moisture
Soil moisture-defined drought
80
Percent severity
100
1950s Drought Runoff
Runoff-defined drought
80
Percent severity
100
2000s western U.S. drought soil
moisture
Soil moisture-defined drought
80
Percent severity
100
2000s western U.S. drought runoff
Runoff-defined drought
80
Percent severity
100
Soil Moisture – entire record
July 2002July 2002
Apr 1934June 1934
3 month
6 month
1 year
2 years
Feb 1955Feb 1956
4 years
8 years
Severity area envelope for soil
moisture, 3 month duration
Severity area envelope for soil
moisture, 12 month duration
Severity area envelope for soil
moisture, 12 month duration
Feb 1977Feb 1977
Jun 2002Jun 2003
Runoff
3 month
6 month
1 year
2 years
Nov 1953Nov 1956
4 years
8 years
Dec 1932Dec 1939
Severity area envelope for soil
moisture, 3 month duration
Severity area envelope for
runoff, 12 month duration
Severity area envelope for
runoff, 48 month duration
Rising Temperatures and
Declining Streamflow in West US
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Higher temperatures are resulting in earlier
snow melts, up to 20-30 days earlier (Pagano
et al., 2004).
Upper Colorado River basin reported to be
experiencing worst streamflow deficit in 80
years & 7th worse in past 500 years (Piechota
et al., 2004).
Mountain snowpack has declined over much
of the western U.S. over the last century,
mostly due to a general warming (Mote et al,
2005)
Implications for Water Management
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Similar to Depth-Area-Duration analysis,
Severity-Area-Duration analysis provides a
basis for a sort of “design drought”
estimation.
This estimates an upper bound for
anticipated drought severities.
Future Research
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Real-time applications!
Figure from Andy Wood.
Conclusions
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The most severe historical US droughts
occurred during the 1930s and 1950s.
The current drought ranks among the
most severe droughts, especially when
averaged over smaller areas.
Future research promises to provide water
managers with new tools for real-time
drought forecasting.
Experimental surface water monitor for the
continental U.S.
Snow water equivalent percentiles 4/19/05
Soil moisture percentiles 4/19/05
Change in soil moisture from previous week
Change in snow water equivalent from previous week
II Advances in hydrologic forecasting
over large areas (with an example from
the PNW this year)
University of Washington experimental west-wide
seasonal hydrologic forecast system
Forecast System Schematic
local scale (1/8 degree)
weather inputs
Hydrologic
model spin up
NCDC met.
station obs.
up to 2-4
months from
current
1-2 years back
LDAS/other
real-time
met. forcings
for spin-up
gap
soil moisture
snowpack
INITIAL
STATE
streamflow, soil moisture,
snow water equivalent, runoff
Hydrologic forecast
simulation
ensemble forecasts
SNOTEL
SNOTEL
/ MODIS*
Update
Update
25th Day, Month 0
ESP traces (40)
CPC-based outlook (13)
NCEP GSM ensemble (20)
NSIPP-1 ensemble (9)
Month 6 - 12
* experimental, not yet in real-time product
Modeling Framework
Snowpack
Initial
Condition
Soil Moisture
Initial
Condition
Forecast points and sample streamflow
forecasts
monthly
hydrographs
targeted statistics e.g., runoff volumes
Background: W. US Forecast System
CCA
NOAA
CAS
OCN
SMLR
Seasonal Climate
Forecast Data Sources
CPC Official
Outlooks
CA
Seasonal
Forecast
Model (SFM)
NASA
VIC
Hydrolog
y Model
NSIPP-1
dynamical
model
ESP
ENSO
UW
ENSO/PDO
Approach: Bias Correction
Scheme
bias-corrected forecast scenario
month m
raw GSM forecast scenario
from COOP observations
month m
from GSM climatological runs
Approach: Bias Example
Regional Bias: spatial example
Sample GSM cell located over Ohio River basin
JULY
obs prcp GSM prcp
obs temp GSM temp
obs
GSM
VIC initial condition estimation:
SNOTEL 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
VIC model spinup methods: 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
• 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
spatial weighting function
elevation
weighting
function
SNOTEL/ASP
VIC cell
Results for Winter 2003-04: volume runoff
forecasts
Comparison with RFC forecast for Columbia River at
the Dalles, OR
UW forecasts made
on 25th of each
month
RFC forecasts made
several times
monthly:
1st, mid-month, late
(UW’s
ESP unconditional
and
CPC forecasts
shown)
UW
RFC
Results for Winter 2003-04: volume runoff
forecasts
Comparison with RFC forecast for Sacramento River
near Redding, CA
UW forecasts made
on 25th of each
month
RFC forecasts made
on 1st of month
(UW’s
ESP unconditional
forecasts shown)
RFC
UW
Results for Winter 2003-04: volume
forecasts
for a sample of PNW locations
OCT 1, 2003 Summer Runoff Volume Forecasts
compared to OBS
100
90
DALLE
DWORS
LGRAN
JLAKE
50
HHORS
60
LIBBY
70
OBS %avg
RFC
UW ESP
DUNCA
80
MICAA
percent of average
110
Results for Winter 2003-04: volume
forecasts
for a sample of PNW locations
NOV 1, 2003 Summer Runoff Volume Forecasts
compared to OBS
100
90
80
70
60
OBS %avg
RFC
UW ESP
DALLE
DWORS
LGRAN
JLAKE
HHORS
LIBBY
DUNCA
50
MICAA
percent of average
110
Seasonal Hydrologic Forecast
Uncertainty
Importance of uncertainty in ICs vs. climate vary with lead time
…
ICs low
climate f’cast high
ICs high
climate f’cast low
Forecast
Uncertainty
high
low
streamflow volume
forecast period
actual
perfect
data, model
model + data
uncertainty
Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep
… hence importance of model & data errors also vary with lead
time.
Relative important of initial
condition and climate forecast
error in streamflow forecasts
Columbia R. Basin
fcst more impt
ICs more impt
Rio Grande R. Basin
RMSE (perfect IC, uncertain fcst)
RE =
RMSE (perfect fcst, uncertain IC)
Expansion to multiple-model framework
It should be possible to balance effort given to
climate vs IC part of forecasts
climate forecasts
more important
N ensembles
high
climate
ensembles
ICs more
important
streamflow volume
forecast period
IC
ensembles
low
Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep
Expansion to multiple-model framework
CCA
NOAA
CAS
OCN
SMLR
CPC Official
Outlooks
NWS
HL-RMS
CA
Seasonal
Forecast
Model (SFM)
NASA
Multiple Hydrologic
Models
VIC
Hydrolog
y Model
NSIPP-1
dynamical
model
others
ESP
ENSO
UW
ENSO/PDO
weightings calibrated via
retrospective analysis
Winter 2004-5 – evolution of a
drought and its prediction
January 1 SWE
forecasts (ensemble
averages) using ESP for
JAN-FEB-MAR
January 1 SWE
forecasts (ensemble
averages) using ESP
for APR-MAY-JUN
January 1 SWE
forecasts (ensemble
averages) using CPC
outlook for JANFEB-MAR
January 1 SWE
forecasts (ensemble
averages) using CPC
outlook for APRMAY-JUN
April 15 forecast – Columbia River at the Dalles
April 1 forecast – Columbia River at the Dalles
April 15 forecast – Columbia River at the Dalles
Summary
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Approach offers a basis for incorporating modern
prediction methods (e.g., data assimilation)
across a large domain 2-week forecasts
Planned improvements include more
sophisticated data assimilation methods, based
on e.g. satellite-derived snow cover extent
Method is applicable (using different modeling
methods) to smaller domains (e.g., Puget Sound
basin) at high resolution
Potential exists to do reservoir contents
forecasting