Weather Underground is a site that provides excellent local weather information in the form of graphs, tables and csv files. You can use the data very easily in SWMM 5 by copying from Excel to a time series in SWMM 5.

InfoSWMM and H2oMAP SWMM Release Notes

The solution of node depths and link flows in SWMM 5 is an iterative process but the number of iterations for the flows and links depends on how fast the node depth converges. The node continuity equation is ALWAYS solved at each iteration for each node but depending on whether both the link upstream and downstream node depths have converged the the number of iterations for a link may be between 2 and 4. The minimum number of iterations for a link is 2.

Figure 1. Iterations in SWMM 5

Heavier Rainstorms Ahead Due To Global Climate Change, Study Predicts
ScienceDaily (Sep. 27, 2009) — Heavier rainstorms lie in our future. That’s the clear conclusion of a new MIT and Caltech study on the impact that global climate change will have on precipitation patterns.

But the increase in extreme downpours is not uniformly spread around the world, the analysis shows. While the pattern is clear and consistent outside of the tropics, climate models give conflicting results within the tropics and more research will be needed to determine the likely outcomes in tropical regions.
Overall, previous studies have shown that average annual precipitation will increase in both the deep tropics and in temperate zones, but will decrease in the subtropics. However, it’s important to know how the frequency and magnitude of extreme precipitation events will be affected, as these heavy downpours can lead to increased flooding and soil erosion.
It is the frequency of these extreme events that was the subject of this new research, which will appear online in theProceedings of the National Academy of Sciences during the week of Aug. 17. The report was written by Paul O’Gorman, assistant professor in the Department of Earth, Atmospheric and Planetary Sciences at MIT, and Tapio Schneider, professor of environmental science and engineering at Caltech.
Model simulations used in the study suggest that precipitation in extreme events will go up by about 6 percent for every one degree Celsius increase in temperature. Separate projections published earlier this year by MIT’s Joint Program on the Science and Policy of Global Change indicate that without rapid and massive policy changes, there is a median probability of global surface warming of 5.2 degrees Celsius by 2100, with a 90 percent probability range of 3.5 to 7.4 degrees.
Specialists in the field called the new report by O’Gorman and Schneider a significant advance. Richard Allan, a senior research fellow at the Environmental Systems Science Centre at Reading University in Britain, says, “O’Gorman’s analysis is an important step in understanding the physical basis for future increases in the most intense rainfall projected by climate models.” He adds, however, that “more work is required in reconciling these simulations with observed changes in extreme rainfall events.” The basic underlying reason for the projected increase in precipitation is that warmer air can hold more water vapor. So as the climate heats up, “there will be more vapor in the atmosphere, which will lead to an increase in precipitation extremes,” O’Gorman says.
However, contrary to what might be expected, extremes events do not increase at the same rate as the moisture capacity of the atmosphere. The extremes do go up, but not by as much as the total water vapor, he says. That is because water condenses out as rising air cools, but the rate of cooling for the rising air is less in a warmer climate, and this moderates the increase in precipitation, he says.
The reason the climate models are less consistent about what will happen to precipitation extremes in the tropics, O’Gorman explains, is that typical weather systems there fall below the size limitations of the models. While high and low pressure areas in temperate zones may span 1,000 kilometers, typical storm circulations in the tropics are too small for models to account for directly. To address that problem, O’Gorman and others are trying to run much smaller-scale, higher-resolution models for tropical areas.
Massachusetts Institute of Technology (2009, September 27). Heavier Rainstorms Ahead Due To Global Climate Change, Study Predicts.ScienceDaily. Retrieved September 27, 2009, from http://www.sciencedaily.com/releases/2009/08/090817190638.htm

MWH Soft Releases InfoSWMM and H2OMAP SWMM Version 8.5,
Leveraging the Latest EPA SWMM5 Functionality

Newest Iteration of Industry-Leading Geospatial Urban Drainage Modeling and Design Software
Delivers Expanded Engineering Simulation Value

Broomfield, Colorado USA, November 11, 2009 — MWH Soft, the leading global provider of environmental and water resources applications software, today announced the immediate release of Generation V8.5 of H2OMAP SWMM and InfoSWMM for ArcGIS (ESRI, Redlands, CA). The new version adds powerful features and leverages engine enhancements included in the latest release of EPA SWMM5 (5.0.017). It also improves the breadth and performance by extending MWH Soft tradition of including new enhancements specifically requested by customers. Version 8.5 marks a significant evolution of the company’s SWMM-based urban drainage modeling and design products, which continue to be top choices for the effective evaluation, design, management, rehabilitation and operation of wastewater and stormwater collection systems.
Underlining MWH Soft’s leadership in the wastewater industry, InfoSWMM and H2OMAP SWMM reflect the company’s ongoing commitment to delivering pioneering technology that raises the bar for urban drainage network modeling and simulation, helping to shape the future of this critical sector. The full-featured InfoSWMM urban drainage network analysis and design program is the only urban drainage modeling solution certified by the National Association of GIS-Centric Software (www.nagcs.com). It addresses all operations of a typical sewer system — from analysis and design to management functions such as water quality assessment, pollution prediction, sediment transport, urban flooding, real-time control and record keeping — in a single, fully integrated geoengineering environment whose powerful hydraulic computational engine is endorsed by the USEPA and certified by FEMA.

————————
Build 5.0.018 (11/18/09)
————————
Engine Updates

1. Reporting of the total infiltration + evaporation loss for each
Storage Unit (as a percent of total inflow to the unit) was added
to the Storage Volume Summary table in the Status Report. See
objects.h, node.c, stats.c, and statsrpt.c.

2. Double counting the final stored volume when finding the nodes with
the highest mass balance errors has been eliminated. See stats.c.

3. A warning message was added for when a Rain Gage’s recording
interval is less than the smallest time interval appearing in its
associated rainfall time series. (An error message is issued if
the recording interval is greater than the smallest time series
interval.) See gage.c and text.h.

4. Hot Start interface files now contain the final state of each
subcatchment’s groundwater zone in addition to the node and
link information they have always had. See routing.c.

5. To avoid confusion, the actual conduit slope is now listed in the
Link Summary table of the Status Report rather than the adjusted
slope that results from any conduit lengthening. See link.c and
dynwave.c.

6. The Status Report now displays only those summary tables for
which results have been obtained (e.g., if the Flow Routing
option is turned off, then no node or link tables are displayed).
See massbal.c and statsrpt.c.

7. Some code re-factoring was done to place rain gage validation
and initialization in separate functions. See project.c, gage.c,
and funcs.h.

8. The engine version number was updated to 50018 (this update had
been overlooked since release 5.0.010). See consts.h.

GUI Updates

1. A bug that prevented Status Report files from being deleted from
a users TEMP folder when they were no longer in use was corrected.
Users should check their TEMP folders (usually in
c:\Documents and Settings\\Local Settings\Temp)
for old files that begin with “swm”. These can safely be deleted.

2. The project input file created for use by SWMM’s Add-On Tools now
contains all project data, including map coordinates and element
tags.

You can use the geometry of the connecting pipes to divide the flow instead of flow divider in the dynamic wave solution of SWMM 5. You can try to do the same using an Outlet link but the method of using two outlets is sometimes very unstable and requires a small time step just to lower the continuity error. I used two flat rectangular links with the same maximum depth and but different width values (Figure 1). The flow was split based on the value of Q full for the link (which you can see in the text output file (Figure 2).

Figure 1. Link Geometry

Figure 2. Flow Division from a inflow of 10 mgd

Figure 3. SWMM 5.0.016 Cross section geometry for the two rectangular links.

I (and a few others) think a welcome change to the DWF dialog in SWMM 5 would be the addition of another scale factor to modify the average flow field. The purpose of the scale factor would be to allow the users to enter the DWF contributing population * the various DWF patterns * the scale factor (in units of cfs/person or l/s/person) in the Inflows dialog. Some users of SWMM 5 prefer to use population directly in the GUI rather than doing this calculation externally and entering either the flow in cfs or l/s. An example of why this would be useful is a future conditions model in which the population either increases or decreases in the catchment.

These warning and error messages were added in SWMM 5.0.0.14 to 5.0.016 to trap questionable raingage, link and node data. Correcting these errors does make a better model . The list shown below has the major new warnings and errors and help on interpreting the messages.

WARNING 01: wet weather time step reduced to recording interval for Rain Gage

Explanation: The user selected hydrology time step is automatically reduced by the engine to match the rain gage interval. The smallest rainfall interval among all of the gages will be used during the simulation.

WARNING 02: maximum depth increased for Node

Explanation: The rim elevation of a node has to be at least equal to the crown elevation of the highest connecting link to the node. The maximum depth of the node is increased to match the highest crown elevation.

WARNING 03: negative offset ignored for Link

Explanation: Negative Offsets are set to offsets of 0.0

WARNING 04: minimum elevation drop used for Conduit

Explanation: If the elevation across the link length is less than 0.001 feet then the elevation drop is set to 0.001 feet (internal units).

WARNING 05: minimum slope used for Conduit

Explanation: If the link slope is less than the user defined minimum slope then the engine will set the slope of the link to the minimum slope.

WARNING 06: dry weather time step increased to the wet weather time step

Explanation: The Dry hydrology time step cannot be less than the wet weather hydrology time step.

WARNING 07: routing time step reduced to the wet weather time step

Explanation: Routing step set to the wet hydrology time step.

ERROR 112: elevation drop exceeds length for Conduit

Explanation: The drop across the conduit cannot exceed the conduit length. It indicates short conduits that likely have improper offset elevations or incorrectly estimated lengths based on the large offsets of the conduit.

ERROR 134: Node has illegal DUMMY link connections.

Explanation: This means either the model has more than one Ideal Pump connecting the same upstream and downstream nodes or a dummy link with more than one link exiting its upstream node.

ERROR 151: a Unit Hydrograph in set has invalid time base.

Explanation: The value of T*K or the Time Base for a RDII Unit Hydrograph has a value less than the rain gage interval. The fundamental time unit for Unit Hydrographs is the rain gage time interval.

ERROR 157: inconsistent rainfall format for Rain Gage

Explanation: Two or more gages using the same time series have different rainfall types in the rain gage definition. For example, intensity and volume.

ERROR 159: inconsistent time interval for Rain Gage

Explanation: The rain gage user defined rainfall interval does not match the actual rainfall time series.

ERROR 173: Time Series has its data out of sequence

Explanation: A time series has either the same time value for consecutive intervals or the time values are out of numerical order.

Source: http://www.nytimes.com/2009/07/17/world/asia/17daylight.html?_r=1&partner=rss&emc=rss

Image via Wikipedia

Peeling Back Pavement to Expose Watery Havens
By ANDREW C. REVKIN
SEOUL, South Korea — For half a century, a dark tunnel of crumbling concrete encased more than three miles of a placid stream bisecting this bustling city.

The waterway had been a centerpiece of Seoul since a king of the Choson Dynasty selected the new capital 600 years ago, enticed by the graceful meandering of the stream and its 23 tributaries. But in the industrial era after the Korean War, the stream, by then a rank open sewer, was entombed by pavement and forgotten beneath a lacework of elevated expressways as the city’s population swelled toward 10 million.

Today, after a $384 million recovery project, the stream, called Cheonggyecheon, is liberated from its dank sheath and burbles between reedy banks. Picnickers cool their bare feet in its filtered water, and carp swim in its tranquil pools.

The restoration of the Cheonggyecheon is part of an expanding environmental effort in cities around the world to “daylight” rivers and streams by peeling back pavement that was built to bolster commerce and serve automobile traffic decades ago.

In New York State, a long-stalled revival effort for Yonkers’s ailing downtown core that could break ground this fall includes a plan to re-expose 1,900 feet of the Saw Mill River, which currently runs through a giant flume that was laid beneath city streets in the 1920s.

Cities from Singapore to San Antonio have been resuscitating rivers and turning storm drains into streams. In Los Angeles, residents’ groups and some elected officials are looking anew at buried or concrete-lined creeks as assets instead of inconveniences, inspired partly by Seoul’s example.

By building green corridors around the exposed waters, cities hope to attract affluent and educated workers and residents who appreciate the feel of a natural environment in an urban setting.

Environmentalists point out other benefits. Open watercourses handle flooding rains better than buried sewers do, a big consideration as global warming leads to heavier downpours. The streams also tend to cool areas overheated by sun-baked asphalt and to nourish greenery that lures wildlife as well as pedestrians.

Some political opponents have derided Seoul’s remade stream as a costly folly, given that nearly all of the water flowing between its banks on a typical day is pumped there artificially from the Han River through seven miles of pipe.

But four years after the stream was uncovered, city officials say, the environmental benefits can now be quantified. Data show that the ecosystem along the Cheonggyecheon (pronounced chung-gye-chun) has been greatly enriched, with the number of fish species increasing to 25 from 4. Bird species have multiplied to 36 from 6, and insect species to 192 from 15.

The recovery project, which removed three miles of elevated highway as well, also substantially cut air pollution from cars along the corridor and reduced air temperatures. Small-particle air pollution along the corridor dropped to 48 micrograms per cubic meter from 74, and summer temperatures are now often five degrees cooler than those of nearby areas, according to data cited by city officials.

And even with the loss of some vehicle lanes, traffic speeds have picked up because of related transportation changes like expanded bus service, restrictions on cars and higher parking fees.

“We’ve basically gone from a car-oriented city to a human-oriented city,” said Lee In-keun, Seoul’s assistant mayor for infrastructure, who has been invited to places as distant as Los Angeles to describe the project to other urban planners.

Some 90,000 pedestrians visit the stream banks on an average day.

What is more, a new analysis by researchers at the University of California, Berkeley, found that replacing a highway in Seoul with a walkable greenway caused nearby homes to sell at a premium after years of going for bargain prices by comparison with outlying properties.

Efforts to recover urban waterways are nonetheless fraught with challenges, like convincing local business owners wedded to existing streetscapes that economic benefits can come from a green makeover.

Yet today the visitors to the Cheonggyecheon’s banks include merchants from some of the thousands of nearby shops who were among the project’s biggest opponents early on.

On a recent evening, picnickers along the waterway included Yeon Yeong-san, 63, who runs a sporting apparel shop with his wife, Lee Geum-hwa, 56, in the adjacent Pyeonghwa Market.

Mr. Yeon said his family moved to downtown Seoul in the late 1940s, and he has been running the business for four decades. He said parking was now harder for his customers. But “because of less traffic, we have better air and nature,” he said.

He and his wife walk along the stream every day, he added. “We did not think about exercising here when the stream was buried underground,” Mr. Yeon said.

The project has yielded political dividends for Lee Myung-bak, a former leader of construction companies at the giant Hyundai Corporation. He was elected Seoul’s mayor in 2002 largely around his push to remove old roads — some of which he had helped build — and to revive the stream. Today he is South Korea’s president.

Even strong critics of the president tend to laud his approach to the Cheonggyecheon revival, which involved hundreds of meetings with businesses and residents over two years.

A recent newspaper column that criticized the president over a police raid on squatters ended with the words “Please come back, Cheonggyecheon Lee Myung-bak!” — a reference to the nickname he earned during the campaign to revive the stream.

The role of Seoul’s environmental renewal in Mr. Lee’s political ascent is not lost on Mayor Philip A. Amicone of Yonkers, a city of 200,000 where entrenched poverty had slowed a revival project. Once the river restoration was added to the plan, he said, he found new support for redevelopment.

Yonkers has gained $34 million from New York State and enthusiastic support from environmental groups for the river restoration, which is part of a proposed $1.5 billion development that includes a minor-league ballpark. The river portion is expected to cost $42 million over all.

A longtime supporter was George E. Pataki, who helped line up state money in his last year as governor, Mayor Amicone said. “Every time he’d visit, he’d say, ‘You’ve got to open up that river,’ ” he added.

Part of the plan would expose an arc of the river and line it with paths and restaurant patios that would wrap around a shopping complex and the ballpark. Another open stretch would become a “wetland park” on what is now a parking lot.

Mr. Amicone, who has a background as a civil engineer, said the example of Seoul’s success had helped build support in Yonkers. In an interview, he recalled the enthusiasm with which Mr. Lee, then Seoul’s mayor, toured Yonkers in 2006 and discussed the cities’ parallel river projects with him.

“Whether it’s a city of millions or 200,000, the concept is identical,” Mr. Amicone said. “These are no longer sewers, but aesthetically pleasing assets that enhance development.”

Jean Chung contributed reporting.

Source: http://www.nytimes.com/2009/07/17/world/asia/17daylight.html?_r=1&partner=rss&emc=rss

Image via Wikipedia

Peeling Back Pavement to Expose Watery Havens
By ANDREW C. REVKIN
SEOUL, South Korea — For half a century, a dark tunnel of crumbling concrete encased more than three miles of a placid stream bisecting this bustling city.

The waterway had been a centerpiece of Seoul since a king of the Choson Dynasty selected the new capital 600 years ago, enticed by the graceful meandering of the stream and its 23 tributaries. But in the industrial era after the Korean War, the stream, by then a rank open sewer, was entombed by pavement and forgotten beneath a lacework of elevated expressways as the city’s population swelled toward 10 million.

Today, after a $384 million recovery project, the stream, called Cheonggyecheon, is liberated from its dank sheath and burbles between reedy banks. Picnickers cool their bare feet in its filtered water, and carp swim in its tranquil pools.

The restoration of the Cheonggyecheon is part of an expanding environmental effort in cities around the world to “daylight” rivers and streams by peeling back pavement that was built to bolster commerce and serve automobile traffic decades ago.

In New York State, a long-stalled revival effort for Yonkers’s ailing downtown core that could break ground this fall includes a plan to re-expose 1,900 feet of the Saw Mill River, which currently runs through a giant flume that was laid beneath city streets in the 1920s.

Cities from Singapore to San Antonio have been resuscitating rivers and turning storm drains into streams. In Los Angeles, residents’ groups and some elected officials are looking anew at buried or concrete-lined creeks as assets instead of inconveniences, inspired partly by Seoul’s example.

By building green corridors around the exposed waters, cities hope to attract affluent and educated workers and residents who appreciate the feel of a natural environment in an urban setting.

Environmentalists point out other benefits. Open watercourses handle flooding rains better than buried sewers do, a big consideration as global warming leads to heavier downpours. The streams also tend to cool areas overheated by sun-baked asphalt and to nourish greenery that lures wildlife as well as pedestrians.

Some political opponents have derided Seoul’s remade stream as a costly folly, given that nearly all of the water flowing between its banks on a typical day is pumped there artificially from the Han River through seven miles of pipe.

But four years after the stream was uncovered, city officials say, the environmental benefits can now be quantified. Data show that the ecosystem along the Cheonggyecheon (pronounced chung-gye-chun) has been greatly enriched, with the number of fish species increasing to 25 from 4. Bird species have multiplied to 36 from 6, and insect species to 192 from 15.

The recovery project, which removed three miles of elevated highway as well, also substantially cut air pollution from cars along the corridor and reduced air temperatures. Small-particle air pollution along the corridor dropped to 48 micrograms per cubic meter from 74, and summer temperatures are now often five degrees cooler than those of nearby areas, according to data cited by city officials.

And even with the loss of some vehicle lanes, traffic speeds have picked up because of related transportation changes like expanded bus service, restrictions on cars and higher parking fees.

“We’ve basically gone from a car-oriented city to a human-oriented city,” said Lee In-keun, Seoul’s assistant mayor for infrastructure, who has been invited to places as distant as Los Angeles to describe the project to other urban planners.

Some 90,000 pedestrians visit the stream banks on an average day.

What is more, a new analysis by researchers at the University of California, Berkeley, found that replacing a highway in Seoul with a walkable greenway caused nearby homes to sell at a premium after years of going for bargain prices by comparison with outlying properties.

Efforts to recover urban waterways are nonetheless fraught with challenges, like convincing local business owners wedded to existing streetscapes that economic benefits can come from a green makeover.

Yet today the visitors to the Cheonggyecheon’s banks include merchants from some of the thousands of nearby shops who were among the project’s biggest opponents early on.

On a recent evening, picnickers along the waterway included Yeon Yeong-san, 63, who runs a sporting apparel shop with his wife, Lee Geum-hwa, 56, in the adjacent Pyeonghwa Market.

Mr. Yeon said his family moved to downtown Seoul in the late 1940s, and he has been running the business for four decades. He said parking was now harder for his customers. But “because of less traffic, we have better air and nature,” he said.

He and his wife walk along the stream every day, he added. “We did not think about exercising here when the stream was buried underground,” Mr. Yeon said.

The project has yielded political dividends for Lee Myung-bak, a former leader of construction companies at the giant Hyundai Corporation. He was elected Seoul’s mayor in 2002 largely around his push to remove old roads — some of which he had helped build — and to revive the stream. Today he is South Korea’s president.

Even strong critics of the president tend to laud his approach to the Cheonggyecheon revival, which involved hundreds of meetings with businesses and residents over two years.

A recent newspaper column that criticized the president over a police raid on squatters ended with the words “Please come back, Cheonggyecheon Lee Myung-bak!” — a reference to the nickname he earned during the campaign to revive the stream.

The role of Seoul’s environmental renewal in Mr. Lee’s political ascent is not lost on Mayor Philip A. Amicone of Yonkers, a city of 200,000 where entrenched poverty had slowed a revival project. Once the river restoration was added to the plan, he said, he found new support for redevelopment.

Yonkers has gained $34 million from New York State and enthusiastic support from environmental groups for the river restoration, which is part of a proposed $1.5 billion development that includes a minor-league ballpark. The river portion is expected to cost $42 million over all.

A longtime supporter was George E. Pataki, who helped line up state money in his last year as governor, Mayor Amicone said. “Every time he’d visit, he’d say, ‘You’ve got to open up that river,’ ” he added.

Part of the plan would expose an arc of the river and line it with paths and restaurant patios that would wrap around a shopping complex and the ballpark. Another open stretch would become a “wetland park” on what is now a parking lot.

Mr. Amicone, who has a background as a civil engineer, said the example of Seoul’s success had helped build support in Yonkers. In an interview, he recalled the enthusiasm with which Mr. Lee, then Seoul’s mayor, toured Yonkers in 2006 and discussed the cities’ parallel river projects with him.

“Whether it’s a city of millions or 200,000, the concept is identical,” Mr. Amicone said. “These are no longer sewers, but aesthetically pleasing assets that enhance development.”

Jean Chung contributed reporting.

How does the surcharge depth work in SWMM 5?

The surcharge depth from the node attribute table is added to the maximum full depth in the routine dynwave.c as an upper bound check for the new iteration depth of yNew.

    // — determine max. non-flooded depth

    yMax = Node[i].fullDepth;

    if ( canPond == FALSE ) yMax += Node[i].surDepth;

If the new depth yNew is greater then yMax then the program will calculate either the amount of flooding from the node or the ponded depth and volume.  If the node cannot pond (canPond is False) then the amount of overflow is the excess flow in the node and the new depth yNew is set to yMax.

    if ( canPond == FALSE )

    {

        Node[i].overflow = (Node[i].oldVolume + dV – Node[i].fullVolume) / dt;

        Node[i].newVolume = Node[i].fullVolume;

        yNew = yMax;

    }

    else

    {

        Node[i].newVolume = Node[i].fullVolume + (yNew-yMax)*Node[i].pondedArea;

        Node[i].overflow = (Node[i].newVolume – Node[i].fullVolume) / dt;

    }

        if ( Node[i].overflow < FUDGE ) Node[i].overflow = 0.0;

    return yNew;

As an example, if the node floods then the depth will go above the manhole rim elevation as the following image shows.

If the ponded area of the node is zero then any excess flow is lost as overflow and the depth only stays at the rim elevation.

How does the surcharge depth work in SWMM 5?

The surcharge depth from the node attribute table is added to the maximum full depth in the routine dynwave.c as an upper bound check for the new iteration depth of yNew.

    // — determine max. non-flooded depth

    yMax = Node[i].fullDepth;

    if ( canPond == FALSE ) yMax += Node[i].surDepth;

If the new depth yNew is greater then yMax then the program will calculate either the amount of flooding from the node or the ponded depth and volume.  If the node cannot pond (canPond is False) then the amount of overflow is the excess flow in the node and the new depth yNew is set to yMax.

    if ( canPond == FALSE )

    {

        Node[i].overflow = (Node[i].oldVolume + dV – Node[i].fullVolume) / dt;

        Node[i].newVolume = Node[i].fullVolume;

        yNew = yMax;

    }

    else

    {

        Node[i].newVolume = Node[i].fullVolume + (yNew-yMax)*Node[i].pondedArea;

        Node[i].overflow = (Node[i].newVolume – Node[i].fullVolume) / dt;

    }

        if ( Node[i].overflow < FUDGE ) Node[i].overflow = 0.0;

    return yNew;

As an example, if the node floods then the depth will go above the manhole rim elevation as the following image shows.

If the ponded area of the node is zero then any excess flow is lost as overflow and the depth only stays at the rim elevation.

SWMM 5 Calibration Files

Steps for making calibration files from the SWMM 5.

Step 1.  Run the model and graph at least one Object class such as Nodes, Links or Subcatchments

Step 2:  Use the Command Copy To to bring up the Saving Selection Dialog

Step 3.  Select the type of Calibration File you want based on the Selection List

Step 4:  Go to the Calibration Data Selection Menu

Step 5:  Select the correct type of Calibration Data

Step 6:  Click on the Edit Button and paste the data you saved to the clipboard into the Text Box

Step 7: Graph with the Calibration Data

Step 8: Graph with the Calibration Data in Date/Time Format

Step 9: Graph with the Calibration Data in Date/Time Format

SWMM 5 Calibration Files

Steps for making calibration files from the SWMM 5.

Step 1.  Run the model and graph at least one Object class such as Nodes, Links or Subcatchments

Step 2:  Use the Command Copy To to bring up the Saving Selection Dialog

Step 3.  Select the type of Calibration File you want based on the Selection List

Step 4:  Go to the Calibration Data Selection Menu

Step 5:  Select the correct type of Calibration Data

Step 6:  Click on the Edit Button and paste the data you saved to the clipboard into the Text Box

Step 7: Graph with the Calibration Data

Step 8: Graph with the Calibration Data in Date/Time Format

Step 9: Graph with the Calibration Data in Date/Time Format

SWMM 5 Calibration Files

Steps for making calibration files from the SWMM 5.

Step 1.  Run the model and graph at least one Object class such as Nodes, Links or Subcatchments

Step 2:  Use the Command Copy To to bring up the Saving Selection Dialog

Step 3.  Select the type of Calibration File you want based on the Selection List

Step 4:  Go to the Calibration Data Selection Menu

Step 5:  Select the correct type of Calibration Data

Step 6:  Click on the Edit Button and paste the data you saved to the clipboard into the Text Box

Step 7: Graph with the Calibration Data

Step 8: Graph with the Calibration Data in Date/Time Format

Step 9: Graph with the Calibration Data in Date/Time Format

International Conference on Stormwater Urban Water Systems Modeling

Topic:  The Variable time step and the Adjustment Factor.  The adjustment factor lowers the internal time step used in the model.  In the example model the lowering of the Adjustment factor from 75 percent to 25 percent lowers the minimum simulation time step from 20 second to 3 seconds.  The inflow and outflow of the model stays the same, what changes is the computed internal time step based on the Courant CFL condition.

At each time step the minimum CFL time step is calculated based on the length of the link (delta x), the ending velocity at the last time step in the link (V) and the ending link depth at the last time step (D).

Topic:  The Variable time step and the Adjustment Factor.  The adjustment factor lowers the internal time step used in the model.  In the example model the lowering of the Adjustment factor from 75 percent to 25 percent lowers the minimum simulation time step from 20 second to 3 seconds.  The inflow and outflow of the model stays the same, what changes is the computed internal time step based on the Courant CFL condition.

At each time step the minimum CFL time step is calculated based on the length of the link (delta x), the ending velocity at the last time step in the link (V) and the ending link depth at the last time step (D).

Visit SWMM5 – Stormwater Management Model

SWMM 5 Complexity Index

The complexity index for SWMM 5 compares a model to the first Extran example in Extran 3, which would be network #1 in this expanded SWMM 5 network. The baseline network has 22 objects and an 8 hour simulation duration. It took 5 minutes to run this network on a IBM AT in 1988.  The purpose of the complexity index is to supply a means of comparison for a present day model.   The complexity equation compares the number of objects in the new model to the number of objects in the baseline model and also factors in any increase in simulation duration.

The complexity index adds up the of raingages, subcatchments, junctions, outfalls, dividers, storages, conduits, pumps, orifices, weirs, outlets, control curves, diversion curves, pump curves, rating curves, shape curves, storage curves, tidal curves, time series, patterns, transects, hydrographs, aquifers, controls, climate objects and snowpacks objects. The complexity index is then multiplied by the number of pollutants for all subcatchments, junctions, outfalls, dividers, storages, conduits, pumps, orifices, weirs and outlets and it also includes the number of landuses times the number of subcatchment objects.

The complexity index is compared to the network #1 by dividing the calculated complexity index by the base line 22 objects and dividing the duration of the new network by the 8 hour duration of the baseline network. The network shown above has a complexity index of 5.2 and runs in less than 1 second on a Intel Dual Core Processor.

The complexity index was 88.5 for sake of comparison for the model USER4.INP in the zip file DATA.ZIP in epaswmm5_qa.zip on the EPA SWMM 5 QA/QC suite of files.   The complexity index was 7.4 for the model USER1.INP in the zip file DATA.ZIP.  The complexity index was 55 for the model USER2.INP in the zip file DATA.ZIP. The complexity index was20.1 for the model USER3.INP in the zip file DATA.ZIP.  The complexity index was 18.5 for the model USER5.INP in the zip file DATA.ZIP.

SWMM 5 Complexity Index

The complexity index for SWMM 5 compares a model to the first Extran example in Extran 3, which would be network #1 in this expanded SWMM 5 network. The baseline network has 22 objects and an 8 hour simulation duration. It took 5 minutes to run this network on a IBM AT in 1988.  The purpose of the complexity index is to supply a means of comparison for a present day model.   The complexity equation compares the number of objects in the new model to the number of objects in the baseline model and also factors in any increase in simulation duration.

The complexity index adds up the of raingages, subcatchments, junctions, outfalls, dividers, storages, conduits, pumps, orifices, weirs, outlets, control curves, diversion curves, pump curves, rating curves, shape curves, storage curves, tidal curves, time series, patterns, transects, hydrographs, aquifers, controls, climate objects and snowpacks objects. The complexity index is then multiplied by the number of pollutants for all subcatchments, junctions, outfalls, dividers, storages, conduits, pumps, orifices, weirs and outlets and it also includes the number of landuses times the number of subcatchment objects.

The complexity index is compared to the network #1 by dividing the calculated complexity index by the base line 22 objects and dividing the duration of the new network by the 8 hour duration of the baseline network. The network shown above has a complexity index of 5.2 and runs in less than 1 second on a Intel Dual Core Processor.

The complexity index was 88.5 for sake of comparison for the model USER4.INP in the zip file DATA.ZIP in epaswmm5_qa.zip on the EPA SWMM 5 QA/QC suite of files.   The complexity index was 7.4 for the model USER1.INP in the zip file DATA.ZIP.  The complexity index was 55 for the model USER2.INP in the zip file DATA.ZIP. The complexity index was20.1 for the model USER3.INP in the zip file DATA.ZIP.  The complexity index was 18.5 for the model USER5.INP in the zip file DATA.ZIP.

SWMM 5 Variable Time Step

In the SWMM 5 Simulation Options/Dynamic Wave Options is the Variable Time Step Frame which contains the Adjustment Factor Percentage. The Adjustment Factor is a multiplication factor on the CFL condition.

The effiect of changing the Adjustment factor can be seen in the following graph. As the value of the adjustment factor changes from 75 to 50 to 25 the time step used in the program decreases because the time step gets further away from the CFL time step condition.

SWMM 5 Variable Time Step

In the SWMM 5 Simulation Options/Dynamic Wave Options is the Variable Time Step Frame which contains the Adjustment Factor Percentage. The Adjustment Factor is a multiplication factor on the CFL condition.

The effiect of changing the Adjustment factor can be seen in the following graph. As the value of the adjustment factor changes from 75 to 50 to 25 the time step used in the program decreases because the time step gets further away from the CFL time step condition.

SWMM 5 Variable Time Step

In the SWMM 5 Simulation Options/Dynamic Wave Options is the Variable Time Step Frame which contains the Adjustment Factor Percentage. The Adjustment Factor is a multiplication factor on the CFL condition.

The effiect of changing the Adjustment factor can be seen in the following graph. As the value of the adjustment factor changes from 75 to 50 to 25 the time step used in the program decreases because the time step gets further away from the CFL time step condition.

Option “Define Supercritical Flow By” does inside the SWMM 5 engine. The options are called Slope, Froude and Bothin the GUI and in the engine of SWMM 5. A few other variable definitions you need to know to understand this explanation are: (1) Y1 for the upstream link depth, (2) Y2 for the downstream link depth, (3) Q for the flow in the link, (4)Qfull for the full Manning’s equation flow or normal flow for the link based on the bed slope, (5) Froude1 and Froude2 for the Froude Number respectively of the upstream and downstream ends of the link, (6) n for Manning’s roughness, (7) Yfull for the maximum depth of the link and (8) Qnormal for the Normal Flow equation flow based on the upstream area of the link (A1) and the upstream hydraulic radius (R1).

In the SWMM 5 engine these options are used after the dynamic wave equation flow is estimated using the St. Venant equation. The option that you choose is only active for those links that have a flow greater than 0, links with negative flow use the dynamic wave equation flow exclusively. It the flow is positive and the link is an open channel and full then the minimum of the dynamic wave flow or Qfull is used as the new flow in the link. If the flow is positive and the depth at the upstream end of the link or Y1 is less than Yfull then the engine will compare Qnormal to Q using the routines in Check Normal Flow.

If the link gets to the Check Normal Flow routines then it uses the following logic:

• If the Slope or Both option is used or either the upstream node or the downstream node of the link is an outfall AND Y1 is less than Y2 then the minimum of Q from the dynamic wave equation or Q from the Normal Flow equation is used as the current iteration flow in link, or
• If the Froude or Both option AND either the upstream Froude Number or the downstream Froude number is greater than 1 then the minimum of Q from the dynamic wave equation or Q from the Normal Flow equation is used as the current iteration flow in link. This condition is never used if either of the connecting nodes of the link are outfalls.

How does this work in the actual flow that SWMM 5 estimates for a link? Consider this example in which the link flow in blue is plotted with the Qnormal flow in red and the Q dynamic wave equation flow in purple:

Qnormal is

Qnormal is only calculated when the link is not full so in the plot a Qnormal of 0 means that the pipe was full. At other times the flow in the link was equal to Qnormal as the minimum of the dynamic equation flow or the Qnormal flow is used at each iteration in the solution process. The flow is normally bounded by the Qnormal flow in SWMM 5.0.013. Your choice of the options Slope, Froude andBoth really only impact the conditions under which this comparison is true. If you use Froude or Both then Supercritical flow at either end of the link will trigger this comparison will be the dynamic wave equation flow and the Froude number at each end of the link.

Option “Define Supercritical Flow By” does inside the SWMM 5 engine. The options are called Slope, Froude and Bothin the GUI and in the engine of SWMM 5. A few other variable definitions you need to know to understand this explanation are: (1) Y1 for the upstream link depth, (2) Y2 for the downstream link depth, (3) Q for the flow in the link, (4)Qfull for the full Manning’s equation flow or normal flow for the link based on the bed slope, (5) Froude1 and Froude2 for the Froude Number respectively of the upstream and downstream ends of the link, (6) n for Manning’s roughness, (7) Yfull for the maximum depth of the link and (8) Qnormal for the Normal Flow equation flow based on the upstream area of the link (A1) and the upstream hydraulic radius (R1).

In the SWMM 5 engine these options are used after the dynamic wave equation flow is estimated using the St. Venant equation. The option that you choose is only active for those links that have a flow greater than 0, links with negative flow use the dynamic wave equation flow exclusively. It the flow is positive and the link is an open channel and full then the minimum of the dynamic wave flow or Qfull is used as the new flow in the link. If the flow is positive and the depth at the upstream end of the link or Y1 is less than Yfull then the engine will compare Qnormal to Q using the routines in Check Normal Flow.

If the link gets to the Check Normal Flow routines then it uses the following logic:

• If the Slope or Both option is used or either the upstream node or the downstream node of the link is an outfall AND Y1 is less than Y2 then the minimum of Q from the dynamic wave equation or Q from the Normal Flow equation is used as the current iteration flow in link, or
• If the Froude or Both option AND either the upstream Froude Number or the downstream Froude number is greater than 1 then the minimum of Q from the dynamic wave equation or Q from the Normal Flow equation is used as the current iteration flow in link. This condition is never used if either of the connecting nodes of the link are outfalls.

How does this work in the actual flow that SWMM 5 estimates for a link? Consider this example in which the link flow in blue is plotted with the Qnormal flow in red and the Q dynamic wave equation flow in purple:

Qnormal is

Qnormal is only calculated when the link is not full so in the plot a Qnormal of 0 means that the pipe was full. At other times the flow in the link was equal to Qnormal as the minimum of the dynamic equation flow or the Qnormal flow is used at each iteration in the solution process. The flow is normally bounded by the Qnormal flow in SWMM 5.0.013. Your choice of the options Slope, Froude andBoth really only impact the conditions under which this comparison is true. If you use Froude or Both then Supercritical flow at either end of the link will trigger this comparison will be the dynamic wave equation flow and the Froude number at each end of the link.

Option “Define Supercritical Flow By” does inside the SWMM 5 engine. The options are called Slope, Froude and Bothin the GUI and in the engine of SWMM 5. A few other variable definitions you need to know to understand this explanation are: (1) Y1 for the upstream link depth, (2) Y2 for the downstream link depth, (3) Q for the flow in the link, (4)Qfull for the full Manning’s equation flow or normal flow for the link based on the bed slope, (5) Froude1 and Froude2 for the Froude Number respectively of the upstream and downstream ends of the link, (6) n for Manning’s roughness, (7) Yfull for the maximum depth of the link and (8) Qnormal for the Normal Flow equation flow based on the upstream area of the link (A1) and the upstream hydraulic radius (R1).

In the SWMM 5 engine these options are used after the dynamic wave equation flow is estimated using the St. Venant equation. The option that you choose is only active for those links that have a flow greater than 0, links with negative flow use the dynamic wave equation flow exclusively. It the flow is positive and the link is an open channel and full then the minimum of the dynamic wave flow or Qfull is used as the new flow in the link. If the flow is positive and the depth at the upstream end of the link or Y1 is less than Yfull then the engine will compare Qnormal to Q using the routines in Check Normal Flow.

If the link gets to the Check Normal Flow routines then it uses the following logic:

• If the Slope or Both option is used or either the upstream node or the downstream node of the link is an outfall AND Y1 is less than Y2 then the minimum of Q from the dynamic wave equation or Q from the Normal Flow equation is used as the current iteration flow in link, or
• If the Froude or Both option AND either the upstream Froude Number or the downstream Froude number is greater than 1 then the minimum of Q from the dynamic wave equation or Q from the Normal Flow equation is used as the current iteration flow in link. This condition is never used if either of the connecting nodes of the link are outfalls.

How does this work in the actual flow that SWMM 5 estimates for a link? Consider this example in which the link flow in blue is plotted with the Qnormal flow in red and the Q dynamic wave equation flow in purple:

Qnormal is

Qnormal is only calculated when the link is not full so in the plot a Qnormal of 0 means that the pipe was full. At other times the flow in the link was equal to Qnormal as the minimum of the dynamic equation flow or the Qnormal flow is used at each iteration in the solution process. The flow is normally bounded by the Qnormal flow in SWMM 5.0.013. Your choice of the options Slope, Froude andBoth really only impact the conditions under which this comparison is true. If you use Froude or Both then Supercritical flow at either end of the link will trigger this comparison will be the dynamic wave equation flow and the Froude number at each end of the link.

Smaller Storms Drop Larger Overall Rainfall In Hurricane Season

ScienceDaily (Dec. 11, 2007) — Researchers have found that when residents of the U.S. southeastern states look skyward for rain to alleviate a long-term drought, they should be hoping for a tropical storm over a hurricane for more reasons than one. According to a new study using NASA satellite data, smaller tropical storms do more to alleviate droughts than hurricanes do over the course of a season by bringing greater cumulative rainfall.

A new study that provides insight into what kind of storms are best at tackling drought in the southeastern United States. The study focuses on a decade of first-ever daily rainfall measurements by a NASA satellite carrying a weather radar in space. The study’s authors believe the same insights can be applied by meteorologists and public officials to other regions where daily satellite rainfall data and storm tracking data are available.

In the wake of Hurricane Katrina, meteorologist Marshall Shepherd, an associate professor of geography and atmospheric sciences at the University of Georgia, Athens, and colleagues delved into the ongoing debate about whether global warming is leading to an increase in rainfall intensity. The researchers wanted to determine how much rainfall each type of cyclone, from tropical depressions to category five hurricanes, contributes to overall rainfall. They focused the study on the Southeast in the hope that results could be harnessed to improve drought relief information for the region. Their findings were published today in the American Geophysical Union’s Geophysical Research Letters.

“As much of the Southeast experiences record drought, our findings indicate that weak tropical systems could significantly contribute to rainfall totals that can bring relief to the region,” said Shepherd, lead author of the NASA-funded study. “These types of storms are significant rain producers. The larger hurricanes aren’t frequent enough to produce most of the actual rain during the season and therefore are not the primary storm type that relieves drought in the region.”

Shepherd created a new measurement method as an efficient way to get a real sense for how much rainfall each type of storm contributes in a given year around the coastal regions of the southeastern U.S. To do so, he had to distinguish an average rainfall day from an extreme rainfall day. Though data from NASA’s Tropical Rainfall Measuring Mission (TRMM) satellite could offer daily rainfall amounts, the data could not be used to set apart whether rainfall was average or extreme for any given day.

Shepherd and his team modeled their metric on the “cooling degree day” that energy companies use to relate daily temperature to energy needs for air conditioning. A cooling degree day is found by subtracting 65 degrees from the average daily temperature. Values larger than zero give some indication whether a day was abnormally warm. Shepherd used daily rainfall data from TRMM to determine 28.9 as the base value of average daily rainfall at one of the world’s wettest locations, Maui’s Mount Wailea in Hawaii.

In the same way as the cooling degree day, the “millimeter day” metric is calculated by subtracting 28.9 millimeters from the average daily rainfall in each of four ocean basins along coastal areas scattered across the south near Houston and New Orleans, east of Miami and south of North Carolina. Values greater than zero indicate a so-called “wet millimeter day” of extreme rainfall.

Using daily rainfall data from the TRMM satellite from 1998-2006, Shepherd’s team compared the amount of rain that fell in the basins on extreme rainfall days with the location of tropical storms from the National Hurricane Center’s storm tracking database to determine how many extreme rainfall days were associated with a particular type of tropical storm.

The team found that the most extreme rainfall days occurred in September and October, two of the busiest months of the Atlantic hurricane season. They also found that though major hurricanes produced the heaviest rainfall on any given day, the smaller tropical storms and depressions collectively produced the most rainfall over the entire season. Over half of the rainfall during the hurricane season attributed to cyclones of any type came from weaker tropical depressions and storms, compared to 27 percent from category 3-5 hurricanes.

TRMM has transformed the way researchers like Shepherd measure rainfall by providing day-to-day information that did not exist before the satellite’s 1997 launch. “Though we’ve had monthly rainfall data available since 1979 from other sources, it’s the daily rainfall data that allows us to see that tropical storm days contributed most significantly to cumulative rainfall for the season due to how frequently that kind of storm occurs,” said Shepherd.

“It’s important in the future to build a longer record of daily rainfall to establish, with better confidence, whether trends are occurring,” said Shepherd. “This study sets the stage for us to understand how much rainfall weak and strong tropical cyclones contribute annually and whether this contribution is trending upward in response to global warming-fueled growth in tropical cyclones.”

Shepherd believes advances that will improve study of cyclones and rainfall are “just around the corner” with NASA’s Global Precipitation Measurement satellite, scheduled for launch in 2013. An extension of TRMM’s capabilities, it will measure precipitation at higher latitudes, the actual size of snow and rain particles, and distinguish between rain and snow.
Adapted from materials provided by NASA/Goddard Space Flight Center, via EurekAlert!, a service of AAAS.
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NASA/Goddard Space Flight Center (2007, December 11). Smaller Storms Drop Larger Overall Rainfall In Hurricane Season. ScienceDaily. Retrieved November 27, 2008, from http://www.sciencedaily.com­ /releases/2007/12/071210104022.htm

Smaller Storms Drop Larger Overall Rainfall In Hurricane Season

ScienceDaily (Dec. 11, 2007) — Researchers have found that when residents of the U.S. southeastern states look skyward for rain to alleviate a long-term drought, they should be hoping for a tropical storm over a hurricane for more reasons than one. According to a new study using NASA satellite data, smaller tropical storms do more to alleviate droughts than hurricanes do over the course of a season by bringing greater cumulative rainfall.

A new study that provides insight into what kind of storms are best at tackling drought in the southeastern United States. The study focuses on a decade of first-ever daily rainfall measurements by a NASA satellite carrying a weather radar in space. The study’s authors believe the same insights can be applied by meteorologists and public officials to other regions where daily satellite rainfall data and storm tracking data are available.

In the wake of Hurricane Katrina, meteorologist Marshall Shepherd, an associate professor of geography and atmospheric sciences at the University of Georgia, Athens, and colleagues delved into the ongoing debate about whether global warming is leading to an increase in rainfall intensity. The researchers wanted to determine how much rainfall each type of cyclone, from tropical depressions to category five hurricanes, contributes to overall rainfall. They focused the study on the Southeast in the hope that results could be harnessed to improve drought relief information for the region. Their findings were published today in the American Geophysical Union’s Geophysical Research Letters.

“As much of the Southeast experiences record drought, our findings indicate that weak tropical systems could significantly contribute to rainfall totals that can bring relief to the region,” said Shepherd, lead author of the NASA-funded study. “These types of storms are significant rain producers. The larger hurricanes aren’t frequent enough to produce most of the actual rain during the season and therefore are not the primary storm type that relieves drought in the region.”

Shepherd created a new measurement method as an efficient way to get a real sense for how much rainfall each type of storm contributes in a given year around the coastal regions of the southeastern U.S. To do so, he had to distinguish an average rainfall day from an extreme rainfall day. Though data from NASA’s Tropical Rainfall Measuring Mission (TRMM) satellite could offer daily rainfall amounts, the data could not be used to set apart whether rainfall was average or extreme for any given day.

Shepherd and his team modeled their metric on the “cooling degree day” that energy companies use to relate daily temperature to energy needs for air conditioning. A cooling degree day is found by subtracting 65 degrees from the average daily temperature. Values larger than zero give some indication whether a day was abnormally warm. Shepherd used daily rainfall data from TRMM to determine 28.9 as the base value of average daily rainfall at one of the world’s wettest locations, Maui’s Mount Wailea in Hawaii.

In the same way as the cooling degree day, the “millimeter day” metric is calculated by subtracting 28.9 millimeters from the average daily rainfall in each of four ocean basins along coastal areas scattered across the south near Houston and New Orleans, east of Miami and south of North Carolina. Values greater than zero indicate a so-called “wet millimeter day” of extreme rainfall.

Using daily rainfall data from the TRMM satellite from 1998-2006, Shepherd’s team compared the amount of rain that fell in the basins on extreme rainfall days with the location of tropical storms from the National Hurricane Center’s storm tracking database to determine how many extreme rainfall days were associated with a particular type of tropical storm.

The team found that the most extreme rainfall days occurred in September and October, two of the busiest months of the Atlantic hurricane season. They also found that though major hurricanes produced the heaviest rainfall on any given day, the smaller tropical storms and depressions collectively produced the most rainfall over the entire season. Over half of the rainfall during the hurricane season attributed to cyclones of any type came from weaker tropical depressions and storms, compared to 27 percent from category 3-5 hurricanes.

TRMM has transformed the way researchers like Shepherd measure rainfall by providing day-to-day information that did not exist before the satellite’s 1997 launch. “Though we’ve had monthly rainfall data available since 1979 from other sources, it’s the daily rainfall data that allows us to see that tropical storm days contributed most significantly to cumulative rainfall for the season due to how frequently that kind of storm occurs,” said Shepherd.

“It’s important in the future to build a longer record of daily rainfall to establish, with better confidence, whether trends are occurring,” said Shepherd. “This study sets the stage for us to understand how much rainfall weak and strong tropical cyclones contribute annually and whether this contribution is trending upward in response to global warming-fueled growth in tropical cyclones.”

Shepherd believes advances that will improve study of cyclones and rainfall are “just around the corner” with NASA’s Global Precipitation Measurement satellite, scheduled for launch in 2013. An extension of TRMM’s capabilities, it will measure precipitation at higher latitudes, the actual size of snow and rain particles, and distinguish between rain and snow.
Adapted from materials provided by NASA/Goddard Space Flight Center, via EurekAlert!, a service of AAAS.
Email or share this story:
Need to cite this story in your essay, paper, or report? Use one of the following formats:
APA

MLA
NASA/Goddard Space Flight Center (2007, December 11). Smaller Storms Drop Larger Overall Rainfall In Hurricane Season. ScienceDaily. Retrieved November 27, 2008, from http://www.sciencedaily.com­ /releases/2007/12/071210104022.htm

Smaller Storms Drop Larger Overall Rainfall In Hurricane Season

ScienceDaily (Dec. 11, 2007) — Researchers have found that when residents of the U.S. southeastern states look skyward for rain to alleviate a long-term drought, they should be hoping for a tropical storm over a hurricane for more reasons than one. According to a new study using NASA satellite data, smaller tropical storms do more to alleviate droughts than hurricanes do over the course of a season by bringing greater cumulative rainfall.

A new study that provides insight into what kind of storms are best at tackling drought in the southeastern United States. The study focuses on a decade of first-ever daily rainfall measurements by a NASA satellite carrying a weather radar in space. The study’s authors believe the same insights can be applied by meteorologists and public officials to other regions where daily satellite rainfall data and storm tracking data are available.

In the wake of Hurricane Katrina, meteorologist Marshall Shepherd, an associate professor of geography and atmospheric sciences at the University of Georgia, Athens, and colleagues delved into the ongoing debate about whether global warming is leading to an increase in rainfall intensity. The researchers wanted to determine how much rainfall each type of cyclone, from tropical depressions to category five hurricanes, contributes to overall rainfall. They focused the study on the Southeast in the hope that results could be harnessed to improve drought relief information for the region. Their findings were published today in the American Geophysical Union’s Geophysical Research Letters.

“As much of the Southeast experiences record drought, our findings indicate that weak tropical systems could significantly contribute to rainfall totals that can bring relief to the region,” said Shepherd, lead author of the NASA-funded study. “These types of storms are significant rain producers. The larger hurricanes aren’t frequent enough to produce most of the actual rain during the season and therefore are not the primary storm type that relieves drought in the region.”

Shepherd created a new measurement method as an efficient way to get a real sense for how much rainfall each type of storm contributes in a given year around the coastal regions of the southeastern U.S. To do so, he had to distinguish an average rainfall day from an extreme rainfall day. Though data from NASA’s Tropical Rainfall Measuring Mission (TRMM) satellite could offer daily rainfall amounts, the data could not be used to set apart whether rainfall was average or extreme for any given day.

Shepherd and his team modeled their metric on the “cooling degree day” that energy companies use to relate daily temperature to energy needs for air conditioning. A cooling degree day is found by subtracting 65 degrees from the average daily temperature. Values larger than zero give some indication whether a day was abnormally warm. Shepherd used daily rainfall data from TRMM to determine 28.9 as the base value of average daily rainfall at one of the world’s wettest locations, Maui’s Mount Wailea in Hawaii.

In the same way as the cooling degree day, the “millimeter day” metric is calculated by subtracting 28.9 millimeters from the average daily rainfall in each of four ocean basins along coastal areas scattered across the south near Houston and New Orleans, east of Miami and south of North Carolina. Values greater than zero indicate a so-called “wet millimeter day” of extreme rainfall.

Using daily rainfall data from the TRMM satellite from 1998-2006, Shepherd’s team compared the amount of rain that fell in the basins on extreme rainfall days with the location of tropical storms from the National Hurricane Center’s storm tracking database to determine how many extreme rainfall days were associated with a particular type of tropical storm.

The team found that the most extreme rainfall days occurred in September and October, two of the busiest months of the Atlantic hurricane season. They also found that though major hurricanes produced the heaviest rainfall on any given day, the smaller tropical storms and depressions collectively produced the most rainfall over the entire season. Over half of the rainfall during the hurricane season attributed to cyclones of any type came from weaker tropical depressions and storms, compared to 27 percent from category 3-5 hurricanes.

TRMM has transformed the way researchers like Shepherd measure rainfall by providing day-to-day information that did not exist before the satellite’s 1997 launch. “Though we’ve had monthly rainfall data available since 1979 from other sources, it’s the daily rainfall data that allows us to see that tropical storm days contributed most significantly to cumulative rainfall for the season due to how frequently that kind of storm occurs,” said Shepherd.

“It’s important in the future to build a longer record of daily rainfall to establish, with better confidence, whether trends are occurring,” said Shepherd. “This study sets the stage for us to understand how much rainfall weak and strong tropical cyclones contribute annually and whether this contribution is trending upward in response to global warming-fueled growth in tropical cyclones.”

Shepherd believes advances that will improve study of cyclones and rainfall are “just around the corner” with NASA’s Global Precipitation Measurement satellite, scheduled for launch in 2013. An extension of TRMM’s capabilities, it will measure precipitation at higher latitudes, the actual size of snow and rain particles, and distinguish between rain and snow.
Adapted from materials provided by NASA/Goddard Space Flight Center, via EurekAlert!, a service of AAAS.
Email or share this story:
Need to cite this story in your essay, paper, or report? Use one of the following formats:
APA

MLA
NASA/Goddard Space Flight Center (2007, December 11). Smaller Storms Drop Larger Overall Rainfall In Hurricane Season. ScienceDaily. Retrieved November 27, 2008, from http://www.sciencedaily.com­ /releases/2007/12/071210104022.htm

MWH Soft Enters Pipeline Design Market with Best-in-Class Geocentric Technology

Revolutionary New PipePlan Delivers Considerable Productivity Gains and Cost Savings for Detailed Pipeline Design Production and Rapid Return on Investment

Broomfield, Colorado USA, December 3, 2008 — Once again taking waterworks industry innovation to a new level, MWH Soft, a leading global provider of environmental and water resources applications software, today announced the worldwide release of its highly anticipated PipePlan for InfoWater. The state of the art for detailed pipeline design production, PipePlan gives civil engineers the unprecedented ability to quickly and accurately design, plot and complete final drafting of water distribution and transmission pipes in both plan and section views.

The engineering design of pressurized pipelines is a complex, time-consuming task. It requires compliance with a daunting number of design criteria, including pipeline minimum slope, maximum spacing between air release valves, intersection with existing utilities, and pipe cover. Meeting these criteria, while also achieving quality assurance standards, requires repetitive iterations based on sound engineering experience.

The unrivaled productivity and user friendliness of PipePlan will quickly change the way pipelines are engineered and designed. It provides all the capabilities needed to streamline engineering design processes, optimize detailed pipeline design (both horizontal and vertical alignments), increase productivity and accuracy, and significantly shorten project schedules. PipePlan also features a comprehensive set of automated drawing tools that allow users to interactively design pipeline plans and profiles in minutes and generate complete, high quality shop drawings in seconds. (Using disparate hydraulic modeling, CAD and GIS packages with spreadsheets and macros, finished detailed pipeline CAD drawings in plan and profile usually take several days.) These advances in detailed pipeline design production deliver substantial cost savings and productivity gains.

PipePlan has already played a crucial role in automating detailed pipeline designs at Khatib & Alami (K&A), a leading engineering consulting firm in the Middle East and North Africa. “In view of the dramatic success and widespread industry adoption of MWH Soft InfoWater, the most advanced water distribution modeling solution in the market today, the next logical step is streamlining and automating pipeline engineering design,” reports Adel AbouJaoude, Ph.D, K&A Associate and Environmental Engineering Department Manager. “The intuitive PipePlan interface and advanced, fully-automated engineering design tools are enabling us to complete and validate detailed pipeline design projects faster and more accurately than ever before. That allows us to work smarter and faster — delivering better design projects within budget, time and resource limits and ensuring predictable production at the lowest sustainable cost.”

PipePlan greatly extends the functionality of MWH Soft’s InfoWater, a comprehensive geospatial environment for water network analysis and design and the industry-leading GIS-centric water modeling software for ArcGIS (ESRI, Redlands, CA). Users can now leverage their work in building detailed hydraulic network models to produce highly professional distribution and transmission line designs in record time and at minimal cost. They can also preserve their “best practice” design information for reuse on future projects, increasing engineering productivity, ensuring quality designs, and preserving corporate knowledge.

Among many benefits, PipePlan will help users quickly design pipelines within a flexible, easy-to-use graphical interface; directly access and display node (station) information, including slope, angle, pressure, etc.; automatically generate comprehensive pipeline plans and profiles in CAD format along with high quality plan and profile deliverable sheets; and configure graphical presentation of CAD drawings. The software automatically creates all intermediate stations (stakes) and calculates their natural terrain elevations and pipe slopes; defines the location of pipe ancillary fittings such as air valves, blowoffs (washouts), bends and tees; reports interference with existing and proposed utility networks; instantly updates profiles for horizontal alignment changes; and visually checks compliance with design criteria. With these advanced capabilities, PipePlan meets the broader design and documentation challenges faced by EPCs, consulting engineering firms and water utilities, optimizing pipeline design and construction costs and significantly increasing their productivities.

“We are convinced that the water industry simply deserves better solutions than the outdated, inefficient tools still being used today,” said Paul F. Boulos, Ph.D, President and COO of MWH Soft. “PipePlan is a giant step forward for the productivity of the industry. By giving mainstream design engineers easy access to a host of new and unique tools and capabilities for detailed pipeline design, we can help them consistently generate higher-quality designs much faster and at a lower cost. PipePlan sustains MWH Soft’s record for being first — a distinction that is important only because it extends our customers’ competitive advantage. It will influence design engineering for years to come.”

Pricing and Availability
PipePlan for InfoWater is now available by subscription worldwide. Prices start at $12,000. For the latest information on the MWH Soft Subscription Program, including availability and purchase requirements, visit www.mwhsoft.com or contact your local MWH Soft Certified Representative.

About MWH Soft
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Force Main Transition between Partial and Full Flow

1. If the force main is full then the program will use either Hazen-Willams or Darcy-Weisbach to calculate the friction loss (term dq1),

2. If the force main is NOT full then the program will use Manning’s Equation.

// — compute terms of momentum eqn.:
// — 1. friction slope term
if ( xsect->type == FORCE_MAIN && isFull )
dq1 = dt * forcemain_getFricSlope(j, fabs(v), rMid);
else dq1 = dt * Conduit[k].roughFactor / pow(rWtd, 1.33333) * fabs(v);

double forcemain_getFricSlope(int j, double v, double hrad)
//
// Input: j = link index
// v = flow velocity (ft/sec)
// hrad = hydraulic radius (ft)
// Output: returns a force main pipe’s friction slope
// Purpose: computes the headloss per unit length used in dynamic wave
// flow routing for a pressurized force main using either the
// Hazen-Williams or Darcy-Weisbach flow equations.
// Note: the pipe’s roughness factor was saved in xsect.sBot in
// conduit_validate() in LINK.C.
//
{
double re, f;
TXsect xsect = Link[j].xsect;
switch ( ForceMainEqn )
{
case H_W:
return xsect.sBot * pow(v, 0.852) / pow(hrad, 1.1667); //(5.0.012 – LR)
case D_W:
re = forcemain_getReynolds(v, hrad);
f = forcemain_getFricFactor(xsect.rBot, hrad, re);
return f * xsect.sBot * v / hrad;
}
return 0.0;
}

Manual de SWMM 5 en espanol
http://www.gmmf.upv.es/descargas/manualSWMM.pdf

SWMM 5 View Variables

There are four types of graphical variables in SWMM 5: (1) Subcatchements, (2) System, (3) Nodes and (4) Links.  The SWMM 5 Hydrology binary graphics file consists of 21 view variables for each subcatcment simulation in SWMM 5.  The variables are:

    

Subcatchment Variables	Description
SUBCATCH_RAINFALL	rainfall intensity
SUBCATCH_SNOWFALL	snowfall intensity
SUBCATCH_RUNOFF	total runoff flow rate
SUBCATCH_RUNOFF_IMPZero	runoff flow rate from zero imp area feb 2007
SUBCATCH_RUNOFF_IMP	runoff flow rate from imp area feb 2007
SUBCATCH_RUNOFF_Pervious	runoff flow rate from pervious area feb 2007
SUBCATCH_LOSSES	total losses (infil)
SUBCATCH_EVAP	watershed evaporation loss
SUBCATCH_DEPTH	watershed depth
SUBCATCH_GW_FLOW	groundwater flow rate to node
SUBCATCH_GW_FLOW_A1	groundwater flow rate to node
SUBCATCH_GW_FLOW_A2	groundwater flow rate to node
SUBCATCH_GW_FLOW_A3	groundwater flow rate to node
SUBCATCH_GW_ELEV	elevation of saturated gw table
SUBCATCH_GW_THETA	soil moisture
SUBCATCH_GW_PERCOLATION	aquifer deep percolation
SUBCATCH_SNOWMELT	watershed snow melt
SUBCATCH_SNOWDEPTH	watershed snow depth
SUBCATCH_FREEWATER	watershed snow depth
SUBCATCH_COLD	watershed cold content
SUBCATCH_SNOWAREA	watershed snow coverage
SUBCATCH_UL	soil thickness
SUBCATCH_FTOT	infiltration during an event
SUBCATCH_FU	current value of F
SUBCATCH_FUMAX	maximum value of F
SUBCATCH_MOISTURE	current soil mositure (less than porosity)
SUBCATCH_IMD	current IMD (Porisity – Moisture)
SUBCATCH_IMDbyEvent	IMD at the beginning of an event
SUBCATCH_SAT	Flag for saturation (1 is saturated)
SUBCATCH_INFIL_TIME	GA infiltration time
SUBCATCH_WLMAX	current infiltration RATE
SUBCATCH_NETPRECIP	rainfall intensity
SUBCATCH_BUILDUP	pollutant buildup concentration
SUBCATCH_WASHOFF	pollutant washoff concentration

The SWMM 5 system binary graphics file consists of 25 variables on one line for each system variable simulated in SWMM 5.  The variables are: 

The SWMM 5 Node graphics binary file consists of 20 variables on one line for each junction/storage/outfall/divider  simulated in SWMM 5.  The variables are: 

Link Variables

SWMM 5 View Variables

There are four types of graphical variables in SWMM 5: (1) Subcatchements, (2) System, (3) Nodes and (4) Links.  The SWMM 5 Hydrology binary graphics file consists of 21 view variables for each subcatcment simulation in SWMM 5.  The variables are:

    

Subcatchment Variables	Description
SUBCATCH_RAINFALL	rainfall intensity
SUBCATCH_SNOWFALL	snowfall intensity
SUBCATCH_RUNOFF	total runoff flow rate
SUBCATCH_RUNOFF_IMPZero	runoff flow rate from zero imp area feb 2007
SUBCATCH_RUNOFF_IMP	runoff flow rate from imp area feb 2007
SUBCATCH_RUNOFF_Pervious	runoff flow rate from pervious area feb 2007
SUBCATCH_LOSSES	total losses (infil)
SUBCATCH_EVAP	watershed evaporation loss
SUBCATCH_DEPTH	watershed depth
SUBCATCH_GW_FLOW	groundwater flow rate to node
SUBCATCH_GW_FLOW_A1	groundwater flow rate to node
SUBCATCH_GW_FLOW_A2	groundwater flow rate to node
SUBCATCH_GW_FLOW_A3	groundwater flow rate to node
SUBCATCH_GW_ELEV	elevation of saturated gw table
SUBCATCH_GW_THETA	soil moisture
SUBCATCH_GW_PERCOLATION	aquifer deep percolation
SUBCATCH_SNOWMELT	watershed snow melt
SUBCATCH_SNOWDEPTH	watershed snow depth
SUBCATCH_FREEWATER	watershed snow depth
SUBCATCH_COLD	watershed cold content
SUBCATCH_SNOWAREA	watershed snow coverage
SUBCATCH_UL	soil thickness
SUBCATCH_FTOT	infiltration during an event
SUBCATCH_FU	current value of F
SUBCATCH_FUMAX	maximum value of F
SUBCATCH_MOISTURE	current soil mositure (less than porosity)
SUBCATCH_IMD	current IMD (Porisity – Moisture)
SUBCATCH_IMDbyEvent	IMD at the beginning of an event
SUBCATCH_SAT	Flag for saturation (1 is saturated)
SUBCATCH_INFIL_TIME	GA infiltration time
SUBCATCH_WLMAX	current infiltration RATE
SUBCATCH_NETPRECIP	rainfall intensity
SUBCATCH_BUILDUP	pollutant buildup concentration
SUBCATCH_WASHOFF	pollutant washoff concentration

The SWMM 5 system binary graphics file consists of 25 variables on one line for each system variable simulated in SWMM 5.  The variables are: 

The SWMM 5 Node graphics binary file consists of 20 variables on one line for each junction/storage/outfall/divider  simulated in SWMM 5.  The variables are: 

Link Variables

How much does your DNA determine your future? Our reporter has her DNA analyzed by three different labs, and shares every detail of the results… as well as how she copes with them.

read more | digg story

How much does your DNA determine your future? Our reporter has her DNA analyzed by three different labs, and shares every detail of the results… as well as how she copes with them.

read more | digg story

There are four steps to using EMC concentrations in your network:

1. Define your pollutant by adding a pollutant using the Data=>Quality=>Pollutant command:

2. Define the Land Use by using the Data=>Land Uses command or the Land Use Editor:

3. Define Buildup to be None by clicking on the None Tab:

4. Define the EMC Washoff concentration by clicking on the Washoff Tab:

More: http://www.swmm2000.com/

There are four steps to using EMC concentrations in your network:

1. Define your pollutant by adding a pollutant using the Data=>Quality=>Pollutant command:

2. Define the Land Use by using the Data=>Land Uses command or the Land Use Editor:

3. Define Buildup to be None by clicking on the None Tab:

4. Define the EMC Washoff concentration by clicking on the Washoff Tab:

More: http://www.swmm2000.com/

Papaya Bugs and Gators in the Backyard

A bug on our backyard Papaya trees. The papaya trees survived but the frost of January 2008 killed many of the trees. They are growing back, however.

Papaya Bugs and Gators in the Backyard

A bug on our backyard Papaya trees. The papaya trees survived but the frost of January 2008 killed many of the trees. They are growing back, however.

Papaya Bugs and Gators in the Backyard

A bug on our backyard Papaya trees. The papaya trees survived but the frost of January 2008 killed many of the trees. They are growing back, however.

PuddleBlog is the image history of one small to large puddle on an American Street:

What is Puddleblog, you ask? Puddleblog chronicles the epic journey of one puddle, bracing for an uncertain future.

It’s a blog. You know, for a puddle. Specifically, the puddle that graces the corner of Jay and Plymouth, a couple blocks east of the Manhattan Bridge. Maybe if this thing catches on we can think about including other qualified puddles.

Note www.epaswmm.info now forwards to www.swmm2000.com which forwards to swmm2000.ning.com one of the wonderful Ning social network sites.

Note www.epaswmm.info now forwards to www.swmm2008.com which forwards to swmm2008.ning.com one of the wonderful Ning social network sites.

Note www.swmm5.info now forwards to www.swmm2000.com which forwards to swmm2000.ning.com one of the wonderful Ning social network sites.

Note www.swmm5.info now forwards to www.swmm2008.com which forwards to swmm2008.ning.com one of the wonderful Ning social network sites.

Note www.swmm.info now forwards to www.swmm2000.com which forwards to swmm2000.ning.com one of the wonderful Ning social network sites.

Note www.swmm.info now forwards to www.swmm2008.com which forwards to swmm2008.ning.com one of the wonderful Ning social network sites.

SWMM 3,4 to 5 Converter Interface
The SWMM 3 and SWMM 4 converter can convert up to two files at one time to SWMM 5. Typically you would convert a Runoff and Transport file to SWMM 5 or a Runoff and Extran File to SWMM 5. If you have a combination of a SWMM 4 Runoff, Transport and Extran network then you will have to convert it in pieces and copy and past the two data sets together to make one SWMM 5 data set.

The x,y coordinate file is only necessary if you do not have existing x, y coordinates on the D1 line of the SWMM 4 Extran input data set.

You can use the command File=>Define Ini File to define the location of the ini file. The ini file will save your conversion project input data files and directories.

You can use the command File=>Define Your Text Editor to define the location of the text editor program. The ini file will save your conversion project editor name.

You can get a copy of the latest SWMM 3,4 to 5 Converter Here..

SWMM 3,4 to 5 Converter Interface
The SWMM 3 and SWMM 4 converter can convert up to two files at one time to SWMM 5. Typically you would convert a Runoff and Transport file to SWMM 5 or a Runoff and Extran File to SWMM 5. If you have a combination of a SWMM 4 Runoff, Transport and Extran network then you will have to convert it in pieces and copy and past the two data sets together to make one SWMM 5 data set.

The x,y coordinate file is only necessary if you do not have existing x, y coordinates on the D1 line of the SWMM 4 Extran input data set.

You can use the command File=>Define Ini File to define the location of the ini file. The ini file will save your conversion project input data files and directories.

You can use the command File=>Define Your Text Editor to define the location of the text editor program. The ini file will save your conversion project editor name.

You can get a copy of the latest SWMM 3,4 to 5 Converter Here..

Google Sites is a recent addition to the Google family that allows you to set up a collaborative web site focused on one or more topics. In the words of Google:

“Meet Google Sites, the newest addition to the Google Apps product suite. It was designed to allow you to easily create a network of sites and share them with whomever you choose. Google Sites lets you pull together information from across Google Apps by embedding documents, spreadsheets, presentations, videos, and calendars in your sites. Of course, we also harness the power of Google search technology so your search results are always fast and relevant.”

The Google Site SWMM2000+ has so far duplicate information copied from the SWMM Ning Sites and the SWMM5 Blogspot.

Google Sites is a recent addition to the Google family that allows you to set up a collaborative web site focused on one or more topics. In the words of Google:

“Meet Google Sites, the newest addition to the Google Apps product suite. It was designed to allow you to easily create a network of sites and share them with whomever you choose. Google Sites lets you pull together information from across Google Apps by embedding documents, spreadsheets, presentations, videos, and calendars in your sites. Of course, we also harness the power of Google search technology so your search results are always fast and relevant.”

The Google Site SWMM2000+ has so far duplicate information copied from the SWMM Ning Sites and the SWMM5 Blogspot.

Google Sites is a recent addition to the Google family that allows you to set up a collaborative web site focused on one or more topics. In the words of Google:

“Meet Google Sites, the newest addition to the Google Apps product suite. It was designed to allow you to easily create a network of sites and share them with whomever you choose. Google Sites lets you pull together information from across Google Apps by embedding documents, spreadsheets, presentations, videos, and calendars in your sites. Of course, we also harness the power of Google search technology so your search results are always fast and relevant.”

The Google Site SWMM2000+ has so far duplicate information copied from the SWMM Ning Sites and the SWMM5 Blogspot.

A four foot gator living in a Stormwater Outfall in Florida.

A four foot gator living in a Stormwater Outfall in Florida.

A four foot gator living in a Stormwater Outfall in Florida.

If you are new to this site or revisiting please visit Notes Home to see a roadmap to the Notes. SWMM 5 Input Files and Notes can be found in SWMM5. SWMM 4 Input Files and Notes can be found in SWMM4.

If you are new to this site or revisiting please visit Notes Home to see a roadmap to the Notes. SWMM 5 Input Files and Notes can be found in SWMM5. SWMM 4 Input Files and Notes can be found in SWMM4.

SWMM 3,4,5 uses a spatial step equal to the length of the link. Or, in terms of the 1D St. Venant Equation for the calculation of flow used in SWMM 5:

In which is the length of the conduit.

The program will calculate the cross sectional area, hydraulic radius top width and depth at the upstream, midpoint and downstream sections of the link. The link solution is pivoted on the midpoint cross sectional area in the dominant dynamic wave terms and

and the non-linear term in the dynamic wave equation uses the upstream and downstream link cross sectional areas. In the finite difference equation in SWMM 5 the pipe shown below would have one length but use the cross sectional information from the upstream, midpoint and downstream points of the link.

The bend in the pipe would be modeled using the “other” category of losses

SWMM 3,4,5 uses a spatial step equal to the length of the link. Or, in terms of the 1D St. Venant Equation for the calculation of flow used in SWMM 5:

In which is the length of the conduit.

The program will calculate the cross sectional area, hydraulic radius top width and depth at the upstream, midpoint and downstream sections of the link. The link solution is pivoted on the midpoint cross sectional area in the dominant dynamic wave terms and

and the non-linear term in the dynamic wave equation uses the upstream and downstream link cross sectional areas. In the finite difference equation in SWMM 5 the pipe shown below would have one length but use the cross sectional information from the upstream, midpoint and downstream points of the link.

The bend in the pipe would be modeled using the “other” category of losses

In the newest version of EPA SWMM (5.0.1.11), there is a new feature of allowing for Add-ins and third-party tools. One such Add-in, the Microsoft Excel, can be very helpful for input data editing and model calibration.

1. To activate the Add-in
This process is detailed in pp. 141 of the EPA SWMM manual (http://www.epa.gov/ednnrmrl/models/swmm … manual.pdf). Basically the user needs to go to “Tools->Program Preferences->Configure Tools” on SWMM main menu. Then in the pop-up “Tool Options” menu choose “Add.” A “Tool Properties” window will pop-up, and the user can assign a name to the Excel Add-in for the “Name” field. For the “Program” field, the user needs to navigate to the location of the Excel executable file at “C:\Program Files\Microsoft Office\Office10\Excel.exe” (the file path may vary). Leave the “Working Directory” field as blank, and choose “INPFILE” macro for the “Parameters” field. Check both “Disable SWMM while executing” and “Update SWMM after closing.”

After the above is set up, click OK and the Excel Add-in is registered in SWMM5. The Add-in tool is under the “Tools” menu. One important thing now is to go to “Tools->Program Preferences,” and in the pop-up window check “Tab Delimited Project File.”

2. Use the Excel Add-in
The SWMM5 input file by default is a tab-delimited .txt file. The user can view the file using Wordpad, but the editing is not very convenient, especially when it comes to calibration for a watershed with large number of subbasins. The Excel Add-in provides great relief for such operations.

Create a simple watershed model in SWMM, and then go to “Tools->Excel Editor (or whatever the user names the Add-in).” The input file for the watershed model is then displayed in tab-delimited format in Excel. In this environment, the user can edit the input data much easier (as compared to double-click each model component and key in the values in the Graphic User Interface). This becomes more apparent when the number of subbasins increases. When the editing is finished, close the Excel program, and then click “YES” or “OK” to all the pop-up windows. After that, the SWMM model interface pops back and the input parameters are updated.

So with this knowledge the model setup process can be much easier. In the initial model setup, the user may not bother to input any parameter values (i.e. subbasin area, width, slope, etc.). Instead, the model can be delineated and all components represented. Then the user can open the “Excel Editor” and copy/paste the model parameter values from another table of pre-created input parameter values (which is always the case). This process will totally by-pass the manually key-in of parameter values.

The second case of this feature applies is the model calibration. In a traditional way, suppose the user needs to change the value of depression storage for the impervious area. That means for a 30-subbasin watershed, the user needs to roam around the watershed and double-click 30 times to finish that single parameter change. Imagine if it takes five times to find the best value for that single parameter. With this feature, the user can open up the input file, set a depression storage value for the first subbasin, and then drag down for all the other 29 subbasins. Close Excel and go back to SWMM, and the updated model can be ran immediately.

Source: http://ceeforums.com/forum/viewtopic.php?f=26&t=256&p=582#p582

In the newest version of EPA SWMM (5.0.1.11), there is a new feature of allowing for Add-ins and third-party tools. One such Add-in, the Microsoft Excel, can be very helpful for input data editing and model calibration.

1. To activate the Add-in
This process is detailed in pp. 141 of the EPA SWMM manual (http://www.epa.gov/ednnrmrl/models/swmm … manual.pdf). Basically the user needs to go to “Tools->Program Preferences->Configure Tools” on SWMM main menu. Then in the pop-up “Tool Options” menu choose “Add.” A “Tool Properties” window will pop-up, and the user can assign a name to the Excel Add-in for the “Name” field. For the “Program” field, the user needs to navigate to the location of the Excel executable file at “C:\Program Files\Microsoft Office\Office10\Excel.exe” (the file path may vary). Leave the “Working Directory” field as blank, and choose “INPFILE” macro for the “Parameters” field. Check both “Disable SWMM while executing” and “Update SWMM after closing.”

After the above is set up, click OK and the Excel Add-in is registered in SWMM5. The Add-in tool is under the “Tools” menu. One important thing now is to go to “Tools->Program Preferences,” and in the pop-up window check “Tab Delimited Project File.”

2. Use the Excel Add-in
The SWMM5 input file by default is a tab-delimited .txt file. The user can view the file using Wordpad, but the editing is not very convenient, especially when it comes to calibration for a watershed with large number of subbasins. The Excel Add-in provides great relief for such operations.

Create a simple watershed model in SWMM, and then go to “Tools->Excel Editor (or whatever the user names the Add-in).” The input file for the watershed model is then displayed in tab-delimited format in Excel. In this environment, the user can edit the input data much easier (as compared to double-click each model component and key in the values in the Graphic User Interface). This becomes more apparent when the number of subbasins increases. When the editing is finished, close the Excel program, and then click “YES” or “OK” to all the pop-up windows. After that, the SWMM model interface pops back and the input parameters are updated.

So with this knowledge the model setup process can be much easier. In the initial model setup, the user may not bother to input any parameter values (i.e. subbasin area, width, slope, etc.). Instead, the model can be delineated and all components represented. Then the user can open the “Excel Editor” and copy/paste the model parameter values from another table of pre-created input parameter values (which is always the case). This process will totally by-pass the manually key-in of parameter values.

The second case of this feature applies is the model calibration. In a traditional way, suppose the user needs to change the value of depression storage for the impervious area. That means for a 30-subbasin watershed, the user needs to roam around the watershed and double-click 30 times to finish that single parameter change. Imagine if it takes five times to find the best value for that single parameter. With this feature, the user can open up the input file, set a depression storage value for the first subbasin, and then drag down for all the other 29 subbasins. Close Excel and go back to SWMM, and the updated model can be ran immediately.

Source: http://ceeforums.com/forum/viewtopic.php?f=26&t=256&p=582#p582

This is my explanation of the comments on on the blog http://hhwq.blogspot.com about the CDM version of SWMM 5. It was purely a QA/QC testing program used in the code and data set migration of SWMM 4 to SWMM 5 during the years 2004 to 2007.

CDM version of SWMM5

Posted by tsblue at Sunday, August 19, 2007

4 comments:

Robert said…

This version is a QA/QC version of SWMM 5 that was used to more closely compare the SWMM 4 to SWMM 5 results using extra data variables.

April 19, 2008 8:35 PM

Robert said…

It should not be used for modeling. You should use the EPA SWMM Web site to download the latest EPA SWMM version:

http://www.epa.gov/ednnrmrl/models/swmm/index.htm

April 19, 2008 8:36 PM

This is my explanation of the comments on on the blog http://hhwq.blogspot.com about the CDM version of SWMM 5. It was purely a QA/QC testing program used in the code and data set migration of SWMM 4 to SWMM 5 during the years 2004 to 2007.

CDM version of SWMM5

Posted by tsblue at Sunday, August 19, 2007

4 comments:

Robert said…

This version is a QA/QC version of SWMM 5 that was used to more closely compare the SWMM 4 to SWMM 5 results using extra data variables.

April 19, 2008 8:35 PM

Robert said…

It should not be used for modeling. You should use the EPA SWMM Web site to download the latest EPA SWMM version:

http://www.epa.gov/ednnrmrl/models/swmm/index.htm

April 19, 2008 8:36 PM

1. Subcatchment Runoff
2. Subcatchment Washoff
3. Node Water Depth
4. Link Flow Rate
5. Node Water Quality
6. Node Lateral Inflow
7. Node Flooding
8. Groundwater Flow
9. Groundwater Elevation
10. Snow Pack Depth
11. Link Flow Depth
12. Link Flow Velocity