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Water PredictionsforLife Decisions
Water PredictionsforLife Decisions
OHD/HL Distributed Hydrologic Modeling
Pedro RestrepoHydrology Group
HIC ConferenceJan. 24-27, 2006
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Water PredictionsforLife Decisions
Water PredictionsforLife Decisions
Goal
• R&D for improved products and services:– RFC Operations– WFO Flash Flood Prediction– NOAA Water Resources Program
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Water PredictionsforLife Decisions
Water PredictionsforLife Decisions
R&D Topics
• Prototype Water Resources products (e.g. soil moisture) • Parameterization/calibration (with U. Arizona and Penn
State U.)• Flash Flood Modeling: statistical distributed model• Impacts of spatial variability of precipitation• Data assimilation• Snow (Snow-17 and energy budget models in HL-
RDHM)• Spatial and temporal scale issues• Data issues• Links to FLDWAV
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Water PredictionsforLife Decisions
Water PredictionsforLife Decisions
NOAA Water Resources Program:Prototype Products
• Initial efforts focus on soil moisture
Soil moisture (m3/m3)
HL-RDHM soil moisture for April 5m 2002 12z
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Water PredictionsforLife Decisions
Water PredictionsforLife Decisions
UZTWC UZFWC
LZ
TW
C
LZ
FS
C
LZ
FP
C
UZTWC UZFWC
LZ
TW
C
LZ
FS
C
LZ
FP
C
SMC1
SMC3
SMC4
SMC5
SMC2
Sacramento Model Storages
Sacramento Model Storages
Physically-basedSoil Layers andSoil Moisture
Modified Sacramento Soil Moisture Accounting Model
In each grid and in each time step, transform conceptual soil water content to physically-based
water content
Modified Sacramento Soil Moisture Accounting Model
Gridded precipitation, temperature
CONUS scale 4km gridded soil moisture products using SAC and Snow-17
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Water PredictionsforLife Decisions
Water PredictionsforLife Decisions
Distributed ModelParameterization-Calibration
• Explore SSURGO fine scale soils data for initial SAC model parameters (deliverable: parameter data sets in CAP)
• Investigate auto-calibration techniques– HL: Simplified Line Search with Koren’s initial SAC
estimates.– U. Arizona: Multi-objective techniques with HL-RDHM
and Koren’s initial SAC parameters.
• Continue expert-manual calibration• Evaluate gridded values of Snow-17 parameters
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Water PredictionsforLife Decisions
Water PredictionsforLife Decisions
Hydrograph Comparison__ Observed flow
__ SSURGO-based
__ STATSGO-based
Distributed Model ParameterizationUse of SSURGO Data for SAC Parameter Derivation
SSURGO data has showimprovements in certain cases; more work is needed
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Water PredictionsforLife Decisions
Water PredictionsforLife DecisionsForecasted
frequencies
A Statistical-Distributed Model for Flash Flood Forecasting at Ungauged Locations
HistoricalReal-time
simulated historical
peaks (Qsp)
Simulated peaks distribution (Qsp) (unique for each
cell)
Archived
QPE
Initial hydro model states
StatisticalPost-processor
Distributed hydrologic model (HL-
RDHM)
Distributed hydrologic model (HL-
RDHM)
Real-time
QPE/QPF
Max forecasted
peaks
Why a frequency- based approach?
Frequency grids provide a well-understood historical context for characterizing flood severity; values relate to engineering design criteria for culverts, detention ponds, etc.
Computation of frequencies using model-based statistical distributions can inherently correct for model biases
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Water PredictionsforLife Decisions
Water PredictionsforLife Decisions
14 UTC15 UTC
16 UTC 17 UTC
Statistical Distributed Flash Flood Modeling-Example Forecasted Frequency Grids Available at 4 Times on
1/4/1998
In these examples, frequencies are derived from routed flows, demonstrating the capability to forecast floods in locations downstream of where the rainfall occurred.
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Water PredictionsforLife Decisions
Water PredictionsforLife Decisions
Method to Calculate “Adjusted” Peaks
• Probability matching was used to compute adjusted flows at validation points.
• For implementation we can only assume a similar implicit correction if we are considering frequency-based flood thresholds at ungauged locations.
DUTCH
0
0.2
0.4
0.6
0.8
1
1 10 100 1000
Flow (cms)
Pro
b o
f O
ccu
rre
nce
Simulated
Observed157 cms(simulated) 247 cms
(adjusted)
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Water PredictionsforLife Decisions
Water PredictionsforLife Decisions
Eldon (795 km2)
Dutch (105 km2)
Implicitstatistical adjustment
0
200
400
600
800
1000
1/4/98 0:00 1/4/98 12:00 1/5/98 0:00 1/5/98 12:00 1/6/98 0:00 1/6/98 12:00 1/7/98 0:00 1/7/98 12:00
Date
Flo
w (
CM
S)
0
10
20
30
40
50
Simulated flow
Observed flow
QPF - 1/4/1998 3:00:00 PM UTC
Adjusted fcst peak
Fcst Time
0
100
200
300
400
500
600
1/4/98 0:00 1/4/98 12:00 1/5/98 0:00 1/5/98 12:00 1/6/98 0:00 1/6/98 12:00
Date
Flo
w (
CM
S)
0
20
40
60
80
100
Simulated flow
Observed flow
QPF - 1/4/1998 3:00:00 PM UTC
Adjusted fcst peak
Forecast time
~11 hr lead time
~1 hr lead time
Statistical Distributed Flash Flood Modeling -Example Forecast Grid and Corresponding Forecast Hydrographs for 1/4/1998 15z
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Water PredictionsforLife Decisions
Water PredictionsforLife Decisions
Distributed Model Intercomparison Project (DMIP)
Nevada
California
Texas
Oklahoma
Arkansas
MissouriKansas
Elk River
Illinois River
Blue River
AmericanRiver
CarsonRiver
Additional Tests in DMIP 1 Basins1. Routing2. Soil Moisture3. Lumped and Distributed4. Prediction Mode
Tests with Complex Hydrology1. Snow, Rain/snow events2. Soil Moisture3. Lumped and Distributed4. Data Requirements in West
Phase 2 Scope
HMT
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Water PredictionsforLife Decisions
Water PredictionsforLife Decisions
DMIP 2 Science Questions
• Confirm basic DMIP 1 conclusions with a longer validation period and more test basins
• Improve our understanding of distributed model accuracy for small, interior point simulations: flash flood scenarios
• Evaluate new forcing data sets (e.g., HMT)• Evaluate the performance of distributed models in prediction mode • Use available soil moisture data to evaluate the physics of distributed models • Improve our understanding of the way routing schemes contribute to the success of
distributed models • Continue to gain insights into the interplay among spatial variability in rainfall,
physiographic features, and basin response, specifically in mountainous basins • Improve our understanding of scale issues in mountainous area hydrology• Improve our ability to characterize simulation and forecast uncertainty in different
hydrologic regimes• Investigate data density/quality needs in mountainous areas
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Water PredictionsforLife Decisions
Water PredictionsforLife Decisions
Basic DMIP 2 Schedule
• Feb. 1, 2006: all data for OK basins available
• July 1, 2006: all basic data for western basins available
• Feb 1, 2007: OK simulations due from participants
• July 1, 2007: basic simulations for western basins due from participants
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Water PredictionsforLife Decisions
Water PredictionsforLife Decisions
DMIP 2: Potential Participants
• Witold Krajewski• Praveen Kumar• Mario DiLuzio, Jeff Arnold• Sandra Garcia (Spain)• Eldho T. Iype (India)• John McHenry• Konstantine Georgakakos• Ken Mitchell (NCEP)• Hilaire F. De Smedt
(Belgium)• HL
• Thian Gan, (Can.) • Newsha Ajami (Soroosh)• Vazken Andreassian
(Fra)• George Leavesley
(USGS)• Kuniyoshi Takeuchi
(Japan)• Baxter Vieux• John England (USBR)• Dave Garen, Dennis
Lettenmaier• Martyn Clarke
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Water PredictionsforLife Decisions
Water PredictionsforLife Decisions
DMIP 2 Website
• http://www.nws.noaa.gov/oh/hrl/dmip/2/index.html
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Water PredictionsforLife Decisions
Water PredictionsforLife Decisions
Impact of Spatial Variability
• Question: how much spatial variability in precipitation and basin features is needed to warrant use of a distributed model?
• Goal: provide guidance/tools to RFCs to help guide implementation of distributed models, i.e., which basins will show most ‘bang for the buck’?
• HOSIP documents in preparation
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Water PredictionsforLife Decisions
Water PredictionsforLife Decisions
flow
time
output
input
precipitation at time tprecipitation at time t +t
precipitation at time t + 2t
Impact of Precipitation Spatial Variability
‘filter’
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Water PredictionsforLife Decisions
Water PredictionsforLife Decisions
Data Assimilation
• Strategy based on Variational Assimilation developed and tested for lumped SAC model
• HOSIP documents in preparation
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Water PredictionsforLife Decisions
Water PredictionsforLife Decisions
Distributed Snow-17
• Strategy: use distributed Snow-17 as a step in the migration to energy budget modeling: what can we learn?
• Snow-17now in HL-RDHM• Tested in MARFC area and over CONUS• Further testing in DMIP 2• Gridded Snow-17 parameters for CONUS under
review.• Related work: data needs for energy budget
snow models
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Water PredictionsforLife Decisions
Water PredictionsforLife Decisions
Thank You!
North Fork Dam, AmericanRiver, California.
Used with permission