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CIG SpecialistIntroductory Meeting

Wednesday, December 20th, 2006Computer Lab Room 105

Farrall Agriculture Engineering HallMichigan State University

East Lansing, MI

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Agenda

10:00 – 10:15 Opening Remarks by IWR and MDA Staff

10:15 – 10:25 CIG Specialist Introductions

10:25 – 11:00 Digital Watershed – IWR’s On-line Watershed Mapping Application

Overview Hands-on ExerciseQuestion and Answer

11:00 – 11:10 Break

11:10 – 12:00 High Impact Targeting (HIT) – IWR’s On-line Sediment Mapping Application

OverviewHands-on ExerciseQuestion and Answer

12:00 – 1:00 Lunch (pizza and subs provided)DiscussionNext Steps

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MDA

IWR

Determine reduction targets

Cost/benefit analysis

of BMPs

Conservation Districts

Develop and deliver outreach plan

Farmers

CIG Specialists

Interface with

Model sediment yield in select watersheds

Build and refine on-line HIT system

Provide user feedback CREP

Technicians

MDEQ

Monitoring

CIG Technical Flow

Feedback provided by a Technical Advisory Committee

Year 1

Year 1

Year 1

Year 1-Year 2

On-going

On-going

On-going

On-going

NRCS

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IWR

Composition of IWR Technical Committee

MSU Ag Engineering

MACD Farm Bureau

Ease of system use

Modelingimprovements

Outreacheffectiveness

Technical Committee

Inputs

HIT utility

Refine and enhance HIT technical capacity

Representatives of

FSA NRCSWatershed Orgs CREP Technicians

CIG Specialists MDA

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High Impact Targeting (H.I.T.)

A web-accessible system that allows users to identify and prioritize, at multiple-scales, areas at high-risk for sediment loading. The data delivered through H.I.T. are the product of results from the Spatially Explicit Delivery Model (SEDMOD)¹ and the Revised Universal Soil Loss Equation (RUSLE)².

1. Fraser. May 1999

2. Renard, Foster, Weesies, McCool, Yoder. 1996.

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Rainfall

SupportPractice

Land Cover

Landuse/Tillage

Soil ClayContent

Soil Erodibility

DEM

Delivery Ratio

Soil Erosion

Sediment Yield

Sediment Yield

SEDMOD/RUSLE Methodology

SurfaceRoughness

SoilTexture

Distance toStream

Weighting

C Factor

K Factor

R Factor

P Factor

LS Factor

RUSLE

SEDMOD

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Prioritization of 8-digit HUCs Using 90m Resolution Data(Great Lakes Basin)

Estimated Potential Sediment LoadingContributed from Cropland (tons/yr.)

Source: Ouyang, Bartholic, Selegean (2005)

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Prioritization of 12-digit HUCs Using 10m Resolution Data (Lower Maumee River Watershed – NW Ohio)

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Watershed Acres TillageTotal Sediment

(tons)Reduction

(tons)Percent Change

Garret 18,065 current practice 1,591 0 0%

Garret no till on worst 5% 1,322 269 17%

Garret no till on worst 10% 1,223 368 23%

Wolf 17,440 current practice 286 0

Wolf no till on worst 5% 216 69

Wolf no till on worst 10% 202 84

Applying BMP (no-till) on highest risk acres in contrasting watersheds

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Slide “A” shows a 30 square mile area of watershed that can be examined to rapidly locate and magnify high risk contributing areas.

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“B” shows an enlarged map area

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“C” shows further enlargement with a photographic image of the area.

C

0 2,000 feet

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“D” shows this resolution with the photo overlaid with the sediment risk layer.

D

0 2,000 feet

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Specific problem areas can be interpreted from slide “E” by overlaying the sediment risk layer over the photograph.

0 1,000 feet

E

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Slide “F” shows contour lines and three example areas of high sediment delivery.

1) High sediment deliveries 2) Potential concentrated flow 3) High sediment delivery no riparian buffer

F

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3

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Point of View in Blue, Model-predicted Flow in Yellow

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Point of View in Blue, Traces of Gullies in Yellow

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Closer Look at Trace of a Gully

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Upstream Site Installed Grass Buffer

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• Benefits– Quick prioritization of areas with a high risk for

sediment loading– Field-level resolution

• Limitations– RUSLE does not account for ephemeral gully

erosion– 10 meter resolution DEMs not available for all

areas

Benefits and Limitations of the Methodology

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Making the Data Web-Accessible:In order to realize the benefits of the H.I.T. modeling process,

the data needs to be readily available to decision makers.

H.I.T. front page.

User selects a watershed.

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Making the Data Web-Accessible:

User has the option to view data for the entire 8-digit watershed in three formats:

Tabular

Bar Graph

Spatial

Those options are also available for viewing sub-watersheds of the 8-digit watershed.

In this example, the user chooses to compare all sub-watersheds.

Users can choose from multiple scales and formats to view data.

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Making the Data Web-Accessible:

User has the option to view data for the entire 8-digit watershed in three formats:

Several watersheds will be compared.

Cost benefit analyses will be run for each of two BMPs:

No Till on the worst 5% of areas

Mulchtill on the worst 5% of areasTotals and rates will be calculated for

each sub-watershed.

Sediment statistics will be calculated.

Build a results table

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Making the Data Web-Accessible:Table Results

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Making the Data Web-Accessible:Closer look at the tabular results

Results sorted by BMP cost per ton reduction (by clicking on column title).

BMP cost/acre provided by NRCS.

This can help an organization determine where (and which) conservation efforts would yield the maximum return in sediment reduction within its budget.

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Making the Data Web-Accessible:Same data displayed in the table is also available in bar graph format.

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Making the Data Web-Accessible:

If Wade Creek is identified as the targeted watershed, the user can use H.I.T. to connect to Digital Watershed in order to explore Wade Creek’s high risk areas spatially.

Viewing the data spatially

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HIT site:

35.9.116.206/hit/hit.asp

CIG Project site:

www.iwr.msu.edu/CIG-MDA/

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• SEDMOD/RUSLE methodology facilitates prioritization of areas in terms of sediment loading at small and large scales.

• H.I.T. system makes SEDMOD/RUSLE results readily accessible over the web.

• H.I.T. allows results to be explored in either tabular, bar graph, or spatial formats.

• Empowers decision makers in targeting areas at high-risk for sediment loading.

Summary

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Thanks for Caring and Acting to Sustain

Water Resources

http://www.iwr.msu.edu

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