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Mike Smith, Victor Koren, Ziya Zhang,Brian Cosgrove, Zhengtao Cui, Naoki Mizukami

OHD/HLHydrologic Science and Modeling Branch

Introduction

Lecture 1 DHM/HL-RDHM Training

MARFCJuly 21-24, 2009

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Overview

• Expectations

• Strategy for use

• Results of DMIP 2

• Overview of HL-RDHM capabilities

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Goals and Expectations

• Potential– History

• Lumped modeling took years to enter operations and is a good example of implementation time

• We’re second to do operational forecasting with dist. models– Expectations

• ‘As good or better than lumped’• Limited, but growing experience with calibration• May not yet show (statistical) improvement in all cases due to errors

and insufficient spatial variability of precipitation and basin features… but is proper future direction!

– New capabilities• Gridded water balance values and variables e.g., soil moisture• Flash flood predictions, e.g., DHM-TF• Frozen ground• New evapotranspiration component to SAC-SMA

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Strategy: Use

• Use with, not instead of, lumped model at same time step

• Part of natural progression to finer scalesLumped 6-hr Lumped 1-hour Distributed 1-hour• Calibration is good training process for

forecasting• Current:

– DHM: AWIPS operation for headwaters, locals– HL-RDHM: Large area, soil moisture, SNOW-17,

GFFG, DHM-TF, etc• Feedback to OHD

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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 vs. Distributed4. Prediction mode

Tests with Complex Hydrology1. Snow, Rain/snow events2. Soil Moisture3. Lumped vs. Distributed

Phase 2 Scope

6Overall Results: Rmod, calibrated models, all periods

ARS

AZ1

AZ2

CEM

DH1

DH2

EMC

ILL

LMP

NEB

OHD

UAE

UOK

WHU

ICL

UAE

UCI

Median uncalb

Median Calb

VUB

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Basin (smallest to largest)

Mo

difi

ed

Co

rre

latio

n C

oe

ff R

mo

d

Parent Basins

Basin Area Km2 37 49 90 105 285 337 365 420 433 619 795 1233 1489 2258 2484

Results of DMIP 2: Oklahoma Basins

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North Fork American River

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ILL

UOB

Observed

OHD

Lumped

HRC

North Fork American RiverSimulated and Observed Hydrographs

ILL

UOB

Observed

OHD

Lumped

HRC

North Fork American RiverSimulated and Observed Hydrographs

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NEB

HRC

UOB

ILL

OHD

Lumped

Observed

East Fork Carson RiverDiurnal Runoff Patterns

NEB

HRC

UOB

ILL

OHD

Lumped

Observed

East Fork Carson RiverDiurnal Runoff Patterns

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Calibrated and Uncalibrated Dist. Model Simulations of SWE

Ebbets Pass

Blake

Sprat Creek

Poison Flats

ObservedCalibratedUncalibrated

East Fork Carson RiverOHD Calibrated and Uncalibrated Simulations of SWE

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-30

-25

-20

-15

-10

-5

0

5

10

15

20

0.2 0.4 0.6 0.8 1Rmod

% B

ias

LMP

OHD

ILL

NEB

UOB

HRC

North fork, calibrated, 1987-2002

North Fork American RiverModified R vs %Bias

Results of Sierra Nevada Experiments

Calb. and Ver. PeriodsCalibrated Models

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DHM/HL-RDHM Workshop

A. DHM and HL-RDHM Overview

B. HL-RDHM and CHPS/FEWS

C. Capabilities1. SAC-SMA, SAC-HT, SAC-HT with new ET (in

progress)

2. Snow-17

3. DHM-TF

4. Hillslope and channel routing

5. Manual and auto calibration

Overview of Capabilities

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HL-RDHM

SAC-SMA, SAC-HT

Channel routing

SNOW -17

P, T, & ET

surface runoff

rain + melt

Flows and state variables

base flowHillslope routing

SAC-SMA

Channel routing

P & ET

surface runoff

rain

Flows and state variables

base flowHillslope routing

AWIPS DHM

Mods

Auto Calb& ICP

DHM-TF

ForecastingCalibration/Forecasting

(Available on AWIPS LAD)

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Distributed Modeling and CHPS/FEWS

• CHPS BOC 1 will not have distributed modeling

• OHD will migrate HL-RDHM components to CHPS/FEWS

• Official OHD distributed model in CHPS/FEWS to come later

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INTERFLOWSURFACERUNOFF

INFILTRATIONTENSION

TENSION TENSION

PERCOLATION

LOWERZONE

UPPERZONE

PRIMARYFREE

SUPPLE-MENTAL

FREE

RESERVED RESERVED

FREE

EVAPOTRANSPIRATION

BASEFLOW

SUBSURFACEOUTFLOW

DIRECTRUNOFF

Precipitation 1. Sacramento Soil MoistureAccounting Model

Source: U. Arizona

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UZTWC UZFWCL

ZT

WC

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

1. Modified Sacramento Soil Moisture Accounting Model (Victor Koren)

In each grid and in each time step, transform conceptual soil water content to physically-based water content

SAC-HT

Soil moisture productsSoil temperature products

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1. SAC-HT Background

• Originally developed for the Noah Land Surface Model

• Designed as replacement of the existing conceptual SAC-SMA frozen ground option.– Does not need calibration– Generates soil moisture and temperature versus depth– Can be used with local soil properties to adjust soil moisture to

local conditions.

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Observed (white) and simulated (red) soil temperature and moisture at 20cm, 40cm, and 80cm depths. Valdai, Russia, 1971-1978.

Soil Moisture

Soil Temperature

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Validation of SAC-HT

Comparison of observed, non-frozen ground, and frozen ground simulations: Root River, MN

Observed

Frozen ground

Non frozen ground

2. SAC-HT

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NOAA Water Resources Program:Prototype Products

Soil moisture (m3/m3)

HL-RDHM soil moisture for April 5th 2002 at 12Z

2. SAC-HT

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Soil moisture for top 10 cm layer April 06, 2002 00z

Local Soil Texture

4km Gridded Soil Moisture

Distributed Modeling for New Products and Services

Time

LocalSoil

Moisture

“Long-term soil moisture forecasts, when used to manage livestock and forage production, can increase ranch profits by as much as 40% ($1.05/acre)”

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SAC-SMA Parameters

1. Based on STATSGO + constant CN– Assumed “pasture or range land use” under “fair”

hydrologic conditions – National coverage – Available now via CAP

2. Based on STATSGO + variable CN– National coverage– Updated for dry area effects (Victor)– New ZPERC values

3. Based on SSURGO + variable CN– Parameters for CONUS – Updated for dry area effects (Victor)

Objective estimation procedure: produce spatially consistent and physically realistic parameter values

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Demonstration of scale difference between polygons in STATSGO and SSURGO

SSURGO

STATSGO

Soils Data for SAC ParametersSoils Data for SAC Parameters

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2. Distributed SNOW-17 Model

• SNOW-17 model is run at each pixel (hourly ok)• Gridded precipitation from multiple sensor products

are provided at each pixel• Gridded temperature inputs are adjusted by using

DEM and regional temperature lapse rate • The areal depletion curve is modified depending on

the topography of the basins. • Other parameters are either replaced by physical

properties or related to physical properties• Melt factors can be related to topographic properties:

slope & aspect

… HL-RDHM and Distributed Snow-17

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2. Distributed modeling and snow

Parameterization of Distributed Snow-17

Min Melt Factor

Max Melt Factor

Derived from:1. Aspect2. Forest Type3. Forest Cover, %4. Anderson’s rec’s.

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Overland flow routed independently for each

hillslope

(adapted from Chow et al., 1988)

HRAP Cell (~ 4 km x 4 km) Uniform, conceptual hillslopes within a modeling unit are assumed

• Drainage density illustrated is ~1.1 km/km2• Number of hillslopes depends on drainage density

Conceptual channel provides cell-

to-cell link

Overland flow routed independently for each

hillslope

(adapted from Chow et al., 1988)

HRAP Cell (~ 4 km x 4 km) Uniform, conceptual hillslopes within a modeling unit are assumed

• Drainage density illustrated is ~1.1 km/km2• Number of hillslopes depends on drainage density

Conceptual channel provides cell-

to-cell link

Real HRAP Cell

Hillslope model

Cell-to-cell channel routing

3. Routing Model

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ABRFC ~33,000 cells

MARFC ~14,000 cells

• OHD delivers baseline HRAP resolution connectivity, channel slope, and hillslope slope grids for each CONUS RFC

• HRAP cell-to-cell connectivity and slope grids are derived from higher resolution DEM data.

HRAP Cell-to-cell Connectivity Examples

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3. Channel Routing Model

• Uses implicit finite difference solution technique• Solution requires a unique, single-valued

relationship between cross-sectional area (A) and flow (Q) in each grid cell (Q=q0Aqm)

• Distributed values for the parameters q0 and qm in this relationship are derived by using – USGS flow measurement data at selected points– Connectivity/slope data– Geomorphologic relationships

• USGS recently removed its measurements from the web pages; must specifically request them.