Sediment and Temperature Modeling Update

U.S. Department of the Interior

U.S. Geological Survey

Wright, Nelson, Schmeeckle, Wilcock, Sloff, Grams, Korman

Annual Reporting Meeting

20 January 2010

Phoenix AZ

Project history and objectives

Project began in FY09 and is slated to end this year (though 

modeling has a long history in the program)

Objective statement (from the proposal): Development and 

application of numerical models of sand transport, sandbar 

evolution, and water temperature dynamics, leading to improved 

p p y py y p redictive capability of physical system attributes in response to 

changes in driving variables

Proposal contains 5 science/management questions that the 

project is attempting to address

Progress update illustrated by 4 applications

 Sand budgets: evaluate dam operations, flood trigger frequency

 Temperature hindcasting: ecosystem modeling support

 Flood hydrographs: evaluate peak, duration, rise/fall rates

 “Habitat” modeling: nearshore ecology support

Sand budget modeling

We’ve developed a relatively simple “sand routing model” that can be used 

to forecast sand budgets in the 3 monitoring reaches, based on dam 

releases and tributary in puts

Paria sand inputs, 

by grain size

Mainstem flows 

for the reach

Model concept

sand volume by grain 

size in reach

Export predicted 

based on flow, 

grain sizes in reach

Model applies mass conservation to keep track of the volume of each grain size in reach

Model calibration and testing

The model has been calibrated using the sand flux monitoring data from 

Sep-2002 through Mar-2009 (Topping, next talk)

Model calibration and testing

The model is an advance over “stable” rating curves because the 

calculations are done by grain size, allowing the bed to fine and coarsen in 

response to tributary inputs and mainstem flows

Wright, S.A., Topping, D.J., Rubin, D.R., and Melis, T.S., “A modeling approach for longterm sediment budgets in supply-limited rivers”, USGS review completed, submitted 

to Water Resources Research, currently revising in response to reviews
Example – Evaluation of dam operations

A MLFF

B – MLFF without

dail fl ct ations

4 example 

operations

10 8 MAF/yr 

The model can be used to compare various dam operations with 

respect to sand transport and budgets

A - MLFF daily fluctuations

C - Equal monthly volume

with daily fluctuations D - Steady year-round

10.8 MAF/yr 

(~average)

Example – Evaluation of dam operations

average Paria input 

at beginning of 

simulation (~1.5 

million metric tons)

This is just one example, a more rigorous analysis that includes variability 

in tributary inputs and hydrology, and other operations, is possible

Example – Flood trigger frequency

The model can also be used to evaluate the frequency for which a given 

sediment trigger would me met, on average, based on historical data

21 years of tributary inputs 1 year of dam releases

21 one-year 

i 

2006

+ = scenarios

for sand 

retention*

*Assumes tributary inputs and dam releases are uncorrelated

Example – Flood trigger frequency

Model predicts 10 out of 21 years 

with max retention of ~1 million 

mt or greater

Of these 10 years, 8 had 

retention >1 mmt for a 

significant period of time

This approach could incorporate a range of hydrologic and 

operations scenarios, and evaluate various triggering criteria

Progress update illustrated by 4 applications

 Sand budgets: evaluate dam operations, flood trigger frequency

 Temperature hindcasting: ecosystem modeling support

 Flood hydrographs: evaluate peak, duration, rise/fall rates

 “Habitat” modeling: nearshore ecology support

Temperature hindcasting: ecosystem modeling

Walters et al. are applying Ecopath/Ecosim to improve understanding of 

the controls on historical trends in native fish abundance

This requires basic information on historical trends in water temperature 

(e.g. on a monthly average basis)

To support this effort, we’ve applied 

our simplified water temperature 

model to hindcast historical conditions 

in various reaches
Monthly average temperature model

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0.015

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0.025

nb

ar

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Longitudinal temperature gradient ( oC/km)

Warming

Cooling

Temperatures typically warm downstream at ~constant rate JaFe

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temp at site release 

temp flow 

volume

air temp release 

temp

distance d/s 

from GCD

Wright, S.A., Anderson, C.R., and Voichick, N., 2009. “A simplified water 

temperature model for the Colorado River below Glen Canyon Dam”, River 

Res. Applic. 25, 675–686

Example – Temperature hindcasting

Model was used to hindcast temperatures back to 1988 (when 

measurements began at GCD), for mid-Marble Canyon and below the LCR

Progress update illustrated by 4 applications

 Sand budgets: evaluate dam operations, flood trigger frequency

 Temperature hindcasting: ecosystem modeling support

 Flood hydrographs: evaluate peak, duration, rise/fall rates

 “Habitat” modeling: nearshore ecology support

Flood hydrographs

What hydrograph shape (e.g. peak, duration) is most effective 

at building sandbars, for a given antecedent condition?

Sandbar morphology modeling

This requires fine scale models for simulating the detailed evolution 

of individual sandbars (or short reaches) over the flood hydrograph

5.975

5.976

x 105

~10m grid

Solve 3D mass and momentum 

conservation equations, for water and 

sediment, in a ~ meter scale grid

2.186 2.187 2.188 2.189 2.19 2.191 2.192 2.193 2.194

x 105

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Sandbar morphology modeling

These models include a lot of physics, but some simplifications and assumptions 

are still required and therefore field-based calibration is necessary

HFE Project 1B (discussed in detail this afternoon) – Measurements of flow and 

bar morphology at two sites to support model calibration

597,500

597,550

2

3

3

Eminence

219,000 219,100 219,200 219,300 219,400 219,500 597,150

597,200

597,250

597,300

597,350

597,400

597,450

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-5

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500 218,600 218,700 218,800 218,900 218,950 -6

-5

-4

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Willie Taylor 2
Sandbar modeling – calibration

2 1905 2 191 2 1915 2 192 2 1925 2 193 2 1935 2 194 2 1945

5.9725

5.973

5.9735

5.974

5.9745

x 10

Eminence Using “Delft3D”, a commercial, 

general-use, simulation package

Hydraulic calibrations (i.e. depth, 

velocity) were straightforward and are 

completed

Sand transport/morphology calibrations 

are more difficult and are still ongoing

2.1905 2.191 2.1915 2.192 2.1925 2.193 2.1935 2.194 2.1945

5

2.1855 2.186 2.1865 2.187 2.1875 2.188 2.1885 2.189

x 105

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-4 -3 -2 -1 0 1 2 -4

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2

ADCP velocity (m/s

D3D velocity (m/s)

East - lower eddy

North - lower eddy

perfect agreement

East - upper eddy

North - upper eddy

Willie Taylor

Example (future) application – flood peak duration

Once we are satisfied with the model calibration, a set of “quantification 

sites” will be used to evaluate various flood hydrograph shapes

For example, a range of peak 

durations could be evaluated 

with respect to the degree of 

sandbar-building achieved ze enough sand not enough sand

g

Peak duration

Sandbar sizIt won’t be this simple, 

but this is the idea

Progress update illustrated by 4 applications

 Sand budgets: evaluate dam operations, flood trigger frequency

 Temperature hindcasting: ecosystem modeling support

 Flood hydrographs: evaluate peak, duration, rise/fall rates

 “Habitat” modeling: nearshore ecology support

Habitat modeling – Nearshore ecology

One of the goals of the nearshore ecology research project is to establish 

links between native fish presence and habitat conditions along the 

shorelines. But it’s not possible to measure everything throughout the 

electrofishing reaches; modeling can be used to “fill in gaps”

Thermal

imagery

Habitat modeling – Nearshore ecology

Depth Velocity Temperature

Preliminary model testing at Carbon (RM65)

Habitat suitability indices?

Linked with fish data

Evaluate flow 

regimes

Summary

 The range of questions being asked by the program requires a range 

of modeling approaches. Input from resource managers is necessary 

regarding desired simulations (e.g. specifying dam operations to be 

compared)

 Progress has been made on several fronts over the past 1.5 years, 

but there will still be plenty to do when the project ends. Modeling is 

a component of data analysis and interpretation, so as long as 

research it i d d ti t ti i th h, monitoring, and adaptive management are continuing there

will likely be a need for models

 Modeling tends to be most effective when it is directly integrated with 

other research and monitoring projects. Thus, projects that can 

benefit from modeling (e.g. sand mass balance, sandbar monitoring, 

nearshore ecology) should directly incorporate a modeling 

component, as opposed to having a stand-alone “modeling” project 
Questions?

Scott Wright, sawright@usgs.gov, 916-278-3024