Midwest partnership summer meeting july 6 8 2011
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Midwest Partnership Summer Meeting (July 6-8 2011). Bernie Engel, Youn Shik Park and Larry Theller engelb@purdue.edu - theller@purdue.edu Purdue University. Discussion Outline. Case study: Fort McCoy and Low Impact Development L-THIA. Chris Urban

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Midwest Partnership Summer Meeting (July 6-8 2011)

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Midwest Partnership Summer Meeting (July 6-8 2011)

Bernie Engel, Youn Shik Park

and Larry Theller

engelb@purdue.edu - theller@purdue.edu

Purdue University


Discussion Outline

  • Case study: Fort McCoy and Low Impact Development L-THIA. Chris Urban

  • Case study: Desktop Arcview LTHIA in Korea, leads to desktop model for WI DNR. Larry Theller

  • L-THIA IDEM case study leads into Corps project with MSU – major upgrades to LTHIA OWLs. Larry Theller

  • LDC upgrades, LOADEST, etc . Youn Shik Park

  • WQX New Project ( LDC plugs in to WQX and is TMDL friendly, 6 FIFRA data sets uploaded. Larry Theller

  • Driftwatch update. Bernie Engel


Long Term Hydrologic Impact Model

  • As a quick and easy-to-use approach, L-THIA's results can be used to generate community awareness of potential long-term problems and to support planning aimed at minimizing disturbance of critical areas.

  • L-THIA is an ideal tool to assist in the evaluation of potential effects of planned or zoned land use change .

  • It can identify the best location of a particular land use so as to have minimum impact on a community's natural environment.


Fort McCoy Case Study


  • This study was particularly interested in the impact of N and P on algae growth.

  • Algae has been a problem occurrence on an annual average of 45 days in recent years (2005 – 2009.) The accumulation of N and P occurs through both dry and rainy seasons continuously.

  • To support management of the lake (created by a dam in 1980,) discharge and water quality data were collected from five sites including two streams and three sites close to the lake.

  • To estimate load under various conditions the research included simulation using the models Long-Term Hydrologic Impact Assessment (L-THIA) and Soil and Water Assessment Tool (SWAT).


Results

  • For the analysis, Curve Number (CN) values were calculated based on growing season and dormant season and were used for the L-THIA simulation.

  • The L-THIA model was calibrated against observed data and produced a better fit than the SWAT model in the calibration.

  • The L-THIA results are used in the decision support process. Model results were produced to aid in the management of the watershed to control runoff.

  • Eutrophication is the major problem in this lake, and management measures are designed to control the addition of N and P in to the lake.


Desktop L-THIA Calculator


Model may be populated from the online version.


Since 2002, there have been at least 17 watershed management plans (funded by IDEM's 319 or 205(j) program, or submitted to IDEM for review to be eligible for implementation funding) that have used L-THIA. Most commonly, L-THIA was used to estimate existing pollutant loads in a watershed. Sometimes L-THIA was used when there was no monitoring data available, and sometimes it was used to provide a comparison with loads calculated from monitoring data.

Watershed Management Case Study


Feedback from Watershed Managers has guided improvements to the Online version of L-THIA


Floating, semi-transparent toolbars, collapsible menus, open architecture for partners, improved editing perfrormance.

New Area of Interest tool : Polygon

“Select by Polygon” to use a single HUC 12 outline will work for off-site users, such as Michigan State.


Tool will now allow use of a polygon as an area of analysis.

This will improve ability to model zoning and LID BMP areas.


-Display of HIT target layers

-EPA Waters layers

-GIS layers


Results remain in current window, no more spawning of multiple pages.


Load Duration Tool

with

LOADEST

https://engineering.purdue.edu/~ldc/new


To develop only FDC

To develop FDCand LDC,

To run “LOADEST”


FDC only

FDC and LDC with LOADEST execution

: by user’s flow and WQ data

: by USGS flow data and user’s WQ data

: by USGS flow data and user’s WQ data with drainage ratio

: by retrieving both data through the web


Flowdata

- user’s data

- USGS flow data

Develop FDC and LDC

Execute LOADESTon background mode

WQdata

- user’s data

- USGS WQ data

Develop TMDLusing LOADEST result

FuturePlan

Estimate required reduction to meet goal


a

b

Select State (a). (Indiana and Wisconsin are available, so far.)

Three ways to find USGS gauging station :Type address or ZIP (b),Through Google Maps interface (c),Type Station Number (d).

c

d


The tool gives..

.. Information of pre-selected 24 WQ data (a),

.. Information on Flow data (b),

.. Raw data file of flow and WQ data (c).

a

Select period to develop LDC(d),

and click this button.

(period : Jan/1/2008 – Nov/8/2010, WQ : 00530)

b

c

d


a

b

A simple module works to combine flowand WQdata.

Set Water Quality Standard (a).

Also combined data file is downloadable to use in other models (b).


Following slides are common results for those options.


a

FDC

LDC

Time Series Plot

LOADEST can be run with background mode on this page (a).

It provides Mean Daily Load estimated by measured data (b).

Concentration Plot

b


Changed by

LOADEST result

During processing, the figures on the page will be replaced by LOADEST results.

LOADEST Inputs and Outputs are downloadable (a).

a


Changed by

LOADEST result

During processing, the figures will be replaced by LOADEST result.

Estimated Annual Load File is downloadable (a).

Mean Daily Load by LOADEST result will be displayed (b).

a

b


LOADEST results,

Estimated Mean Daily Load compared to

Mean Daily Load from observed data.


Spatial Optimization for Managing Surface Runoff from Urbanization- Parameterization and Application of a Spatial Runoff Minimization Model

Mi HoonJeong

June 20, 2011


3rd Objective – Results

Multiple-Objective Spatial Optimization for New Urban Development

The ROMIN model was adjusted to identify optimal regions in terms of hydrologic management and socioeconomic consideration

The LTM (Land Transformation Model) was chosen as a way to adjust the ROMIN model by providing socioeconomic preference for urban development


Overall Summary

Summary and Implications

Hydrologic management can be supported by better decisions regarding urban development scale and location

The proposed approach identifies optimal locations based on multiple objectives and the solutions are considered more practical than those based on only consideration of hydrologic impact management

The ROMIN model has potential for solving other multiple objective problems


Appendix

(a)

(b)

(c)

Muskegon Watershed

Southfork Wildcat Creek-Cary

Watershed

(c) Little Eagle Creek Watershed


2nd Objective –Results

  • The relationship between urban development scale and hydrologic impact is a characteristic specific to a watershed

Southfork Wildcat

Creek-Cary

Southfork Wildcat

Creek-Cary

Little Eagle Creek


2nd Objective –Results

Optimal locations depend on development scale

40 ha development

(23% urbanized)

120 ha development

(29% urbanized)

200 ha development

(34% urbanized)

360 ha development

(46% urbanized)

280 ha development

(40% urbanized)

320 ha development

(43% urbanized)


2nd Objective –Results

Optimal Location Change

As the required area is increased, the current optimal location isn’t able to satisfy the required shape or area, or minimize runoff increase at the same location; therefore, it is necessary to re-compute the optimal location of development

Critical Points & Slope Changes

- a change in optimal development location or

- Running out of least cost neighbor areas around the current location and inclusion of higher cost areas for development.


3rd Objective - Results

The LTM-socioeconomic preference

Likelihood of urbanization

for each cell

-> a substitute for

socioeconomic preference

Likelihood

  • 0 : no likelihood

  • Higher: higher likelihood of urbanization


1st Objective - Results

ROMIN solutions are better than suitability solutions in terms of contiguousness and area requirement.

The ROMIN Model Solution

(c) Runoff Increase≤0.99cm (d) Runoff Increase ≤ 1.09cm

Suitability Map Solutions

(a) Runoff Increase≤0.1cm (b) Runoff Increase ≤ 0.76cm

Suitability Map Solutions


Overall Summary

Future Possibilities

Potential for a variety of applications by defining the objective function cost such as:

  • Identification of management zones for precision agriculture applications.

  • an optimal site selection whose objective is to minimize NPS pollution or erosion instead of runoff increase.

  • determination of land prioritization for water supply protection.


DriftWatch Overview

Pesticide Sensitive Crops and Habitats Registry

Bernie Engel and Larry Theller

engelb@purdue.edu and theller@purdue.edu

Purdue University

Leighanne Hahn

hahnl@purdue.edu

Office of Indiana State Chemist

www.driftwatch.org


Illinois sensitive crops and habitats registry

theller@purdue.edu


Producer uses mouse to sketch field boundary


theller@purdue.edu


Additional “sensitive areas or habitats” layers are streamed over the Google Maps™ display.

This example contains nearby commercial wind turbines.

theller@purdue.edu


Driftwatch National Expansion.

Join in 2012

Active in 2011


Questions


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