ohd/hl distributed hydrologic modeling

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Water PredictionsforLife Decisions


OHD/HL Distributed Hydrologic Modeling

Pedro RestrepoHydrology Group

HIC ConferenceJan. 24-27, 2006

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Goal

• R&D for improved products and services:– RFC Operations– WFO Flash Flood Prediction– NOAA Water Resources Program

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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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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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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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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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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 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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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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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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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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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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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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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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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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DMIP 2 Website

• http://www.nws.noaa.gov/oh/hrl/dmip/2/index.html

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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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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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Data Assimilation

• Strategy based on Variational Assimilation developed and tested for lumped SAC model

• HOSIP documents in preparation

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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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Thank You!

North Fork Dam, AmericanRiver, California.

Used with permission

ohd/hl distributed hydrologic modeling

Documents

gridded soil moisture

flash flood forecasting

zdistributed model intercomp

conceptual soil water

frequency based approach

korens initial sac parameters

korens initial sac estimates

parameter data sets