Peeling Back Pavement to Expose Watery Havens

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.

Surcharge Level in SWMM 5

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.

Q full vs Q dynamic vs Q normal in SWMM5

Introduction – the reason for these series of blogs are as an expanded view of the input, engine and output of #SWMM5 It is a companion to the EPA Documentation which I describe here:

I have noticed based on email questions and postings to the SWMM List Sever (a great resource hosted by CHI, Inc.) that many SWMM 5 users do not know about the really outstanding documentation on SWMM 5 posted on the EPA Websitehttps://www.epa.gov/water-research/storm-water-management-model-swmm It consists of two now and in the near future three volumes on Hydrology, Water Quality, LID’s and SuDS and Hydraulics. The documentation is fantastically complete with detailed background on the theory, process parameters and completely worked out examples for all of the processes in SWMM5. It is truly an outstanding aid to modelers and modellers worldwide. It would benefit you to read them (if you have not already downloaded the PDF files)
1. It gets more flow than qFull because the water in the pipe has more than just the bed slope to push it - it also has the water surface slope.
There is about a 5 meter head pushing the water out if you the bed slope to the water surface slope - see the HGL Plot.

2. The Q dynamic or St. Venant flow uses ALL of the information you have about the condition in the link (see the next image) so the flow is greater than Qfull and Q normal flow. The information includes the hydraulic radius and cross sectional areas for upstream, midpoint and the downstream ends of the links.

Normal Flow, Q and St Venant Flow.   Fraction Normal Flow Limited is the fraction of time SWMM5 uses Normal Flow for the Conduit.

Future Rainfall

Published: April 2009

Outlook: Extreme

As the planet warms, look for more floods where it’s already wet and deeper drought where water is scarce.

By Elizabth Kolbert

The world's first empire, known as Akkad, was founded some 4,300 years ago, between the Tigris and the Euphrates Rivers. The empire was ruled from a city—also known as Akkad—that is believed to have lain just south of modern-day Baghdad, and its influence extended north into what is now Syria, west into Anatolia, and east into Iran. The Akkadians were well organized and well armed and, as a result, also wealthy: Texts from the time testify to the riches, from rare woods to precious metals, that poured into the capital from faraway lands.

Then, about a century after it was founded, the Akkad empire suddenly collapsed. During one three-year period four men in succession briefly claimed to be emperor. "Who was king? Who was not king?" a register known as the Sumerian King List asks.

For many years, scholars blamed the empire's fall on politics. But about a decade ago, climate scientists examining records from lake bottoms and the ocean floor discovered that right around the time that the empire disintegrated, rainfall in the region dropped dramatically. It is now believed that Akkad's collapse was caused by a devastating drought. Other civilizations whose demise has recently been linked to shifts in rainfall include the Old Kingdom of Egypt, which fell right around the same time as Akkad; the Tiwanacu civilization, which thrived near Lake Titicaca, in the Andes, for more than a millennium before its fields were abandoned around A.D. 1100; and the Classic Maya civilization, which collapsed at the height of its development, around A.D. 800.

The rainfall changes that devastated these early civilizations long predate industrialization; they were triggered by naturally occurring climate shifts whose causes remain uncertain. By contrast, climate change brought about by increasing greenhouse gas concentrations is our own doing. It, too, will influence precipitation patterns, in ways that, though not always easy to predict, could prove equally damaging.

Warm air holds more water vapor—itself a greenhouse gas—so a hotter world is a world where the atmosphere contains more moisture. (For every degree Celsius that air temperatures increase, a given amount of air near the surface holds roughly 7 percent more water vapor.) This will not necessarily translate into more rain—in fact, most scientists believe that total precipitation will increase only modestly—but it is likely to translate into changes in where the rain falls. It will amplify the basic dynamics that govern rainfall: In certain parts of the world, moist air tends to rise, and in others, the moisture tends to drop out as rain and snow.

"The basic argument would be that the transfers of water are going to get bigger," explains Isaac Held, a scientist at the National Oceanic and Atmospheric Administration's Geophysical Fluid Dynamics Laboratory at Princeton University. Climate models generally agree that over the coming century, the polar and subpolar regions will receive more precipitation, and the subtropics—the area between the tropical and temperate zones—will receive less. On a regional scale, the models disagree about some trends. But there is a consensus that the Mediterranean Basin will become more arid. So, too, will Mexico, the southwestern United States, South Africa, and southern Australia. Canada and northern Europe, for their part, will grow damper.

A good general rule of thumb, Held says, is that "wet areas are going to get wetter, and dry areas drier." Since higher temperatures lead to increased evaporation, even areas that continue to receive the same amount of overall precipitation will become more prone to drought. This poses a particular risk for regions that already subsist on minimal rainfall or that depend on rain-fed agriculture.

"If you look at Africa, only about 6 percent of its cropland is irrigated," notes Sandra Postel, an expert on freshwater resources and director of the Global Water Policy Project. "So it's a very vulnerable region."

Meanwhile, when rain does come, it will likely arrive in more intense bursts, increasing the risk of flooding—even in areas that are drying out. A recent report by the United Nations' Intergovernmental Panel on Climate Change (IPCC) notes that "heavy precipitation events are projected to become more frequent" and that an increase in such events is probably already contributing to disaster. In the single dec­ade between 1996 and 2005 there were twice as many inland flood catastrophes as in the three decades between 1950 and 1980.

"It happens not just spatially, but also in time," says Brian Soden, a professor of marine and atmospheric science at the University of Miami. "And so the dry periods become drier, and the wet periods become wetter."

Quantifying the effects of global warming on rainfall patterns is challenging. Rain is what scientists call a "noisy" phenomenon, meaning that there is a great deal of natural variability from year to year. Experts say that it may not be until the middle of this century that some long-term changes in precipitation emerge from the background clatter of year-to-year fluctuations. But others are already discernible. Between 1925 and 1999, the area between 40 and 70 degrees north latitude grew rainier, while the area between zero and 30 degrees north grew drier. In keeping with this broad trend, northern Europe seems to be growing wetter, while the southern part of the continent grows more arid. The Spanish Environment Ministry has estimated that, owing to the combined effects of climate change and poor land-use practices, fully a third of the country is at risk of desertification. Meanwhile, the island of Cyprus has become so parched that in the summer of 2008, with its reservoir levels at just 7 percent, it was forced to start shipping in water from Greece.

"I worry," says Cyprus's environment commissioner, Charalambos Theopemptou. "The IPCC is talking about a 20 or 30 percent reduction of rainfall in this area, which means that the problem is here to stay. And this combined with higher temperatures—I think it is going to make life very hard in the whole of the Mediterranean."

Other problems could follow from changes not so much in the amount of precipitation as in the type. It is estimated that more than a billion people—about a sixth of the world's population—live in regions whose water supply depends, at least in part, on runoff from glaciers or seasonal snowmelt. As the world warms, more precipitation will fall as rain and less as snow, so this storage system may break down. The Peruvian city of Cusco, for instance, relies in part on runoff from the glaciers of the Quelccaya ice cap to provide water in summer. In recent years, as the glaciers have receded owing to rising temperatures, Cusco has periodically had to resort to water rationing.

Several recent reports, including a National Intelligence Assessment prepared for American policymakers in 2008, predict that over the next few decades, climate change will emerge as a significant source of political instability. (It was no coincidence, perhaps, that the drought-parched Akkad empire was governed in the end by a flurry of teetering monarchies.) Water shortages in particular are likely to create or exacerbate international tensions. "In some areas of the Middle East, tensions over water already exist," notes a study prepared by a panel of retired U.S. military officials. Rising temperatures may already be swelling the ranks of international refugees—"Climate change is today one of the main drivers of forced displacement," the United Nations High Commissioner for Refugees, António Guterres, has said—and contributing to armed clashes. Some experts see a connection between the fighting in Darfur, which has claimed an estimated 300,000 lives, and changes in rainfall in the region, bringing nomadic herders into conflict with farmers.

Will the rainfall changes of the future affect societies as severely as some of the changes of the past? The American Southwest, to look at one example, has historically been prone to droughts severe enough to wipe out—or at least disperse—local populations. (It is believed that one such megadrought at the end of the 13th century contributed to the demise of the Anasazi civilization, centered in what currently is the Four Corners.) Nowadays, of course, water-management techniques are a good deal more sophisticated than they once were, and the Southwest is supported by what Richard Seager, an expert on the climatic history of the region, calls "plumbing on a continental scale." Just how vulnerable is it to the aridity likely to result from global warming?

"We do not know, because we have not been at this point before," Seager observes. "But as man changes the climate, we may be about to find out." 

© 2009 National Geographic Society. All rights reserved.

Additional SWMM 3,4 Converter Information

Step 1: Open up or run the converter

Step 2: Define your text editor if you want to use the Edit Button

Step 3: Define the programs ini file if you want to use it multiple times

Step 4: Click on Select to convert either a Runoff, Runoff and Transport or Runoff and Extran

Step 5: Click on Convert to convert the two selected files

Step 6: File Converted Message will tell you that the file9s) were converted correctly.

Step 7: Please make sure to check the log file to confirm that everything was converted successfully.

International Conference on Stormwater and Urban Water Systems Modeling

International Conference on Stormwater Urban Water Systems Modeling

Thursday and Friday February 19-20, 2009

http://www.computationalhydraulics.com/Training/Conferences/program.html

📊 SWMM 5 Complexity Index 📊

📊 SWMM 5 Complexity Index 📊

The SWMM 5 Complexity Index offers a way to measure a model's intricacy against a benchmark: the first Extran example in Extran 3, now referred to as network #1 in this broader SWMM 5 context. The foundational network showcases 22 objects and runs simulations over 8 hours. 🕗 Notably, it took 5 minutes to process this network on an IBM AT back in 1988. 🖥️⏳

The core aim of this complexity index? To provide a comparative tool for contemporary models. 📈🔄 The complexity formula evaluates the object count in the new model versus the baseline, while also accounting for any extensions in simulation time. 🔄🔍📏

📊 Complexity Index Breakdown 📊

The complexity index consolidates the count of various elements: raingages, subcatchments, junctions, outfalls, dividers, storages, conduits, pumps, orifices, weirs, outlets, control curves, and many more, right up to snowpack objects. 🌧️🌍🚰🔀🌊

For a more nuanced understanding, this index is then amplified by tallying pollutants across various elements like subcatchments, junctions, or weirs. Additionally, the multiplication of the number of land uses by the count of subcatchment objects is considered. 🧪🔄🌳🏘️

To gauge its relative complexity, this index is juxtaposed against network #1. This involves dividing the freshly computed complexity index by the foundational 22 objects and contrasting the new network's duration against the 8-hour benchmark of the base network. 🕗📏 The exemplified network flaunts a complexity rating of 5.2 and, impressively, executes in under a second on an Intel Dual Core Processor. 🖥️⚡

🔍 Understanding the Complexity Index

The complexity index is a comprehensive metric that sums up various components of a given hydrologic model. Specifically, it aggregates:

• Rain gauges, subcatchments, junctions, outfalls, dividers, storages, conduits, pumps, orifices, weirs, outlets, and several curve types (control, diversion, pump, rating, shape, storage, tidal), as well as time series, patterns, transects, hydrographs, aquifers, controls, climate objects, and snowpacks. 🌦️🌍🚰

• The index is then adjusted by taking into account the number of pollutants for multiple components like subcatchments, junctions, outfalls, and so forth. 🧪

• Additionally, it factors in the number of land uses multiplied by the count of subcatchment objects. 🌲🏙️

📏 Comparing the Complexity Index:

To gauge the relative complexity of a network:

1. The computed complexity index is divided by a baseline value of 22 objects. 📊
2. The duration of the new network is normalized against an 8-hour duration of a reference network. 🕗

For example, a showcased network had a complexity index of 5.2 and executed in under a second on an Intel Dual Core Processor. 💨🖥️

📂 Complexity Indices from Sample Models:

Using the EPA SWMM 5 QA/QC suite of files, the complexity indices for different models in the DATA.ZIP file are:

• USER4.INP: 88.5 📈
• USER1.INP: 7.4 📉
• USER2.INP: 55 📊
• USER3.INP: 20.1 📉
• USER5.INP: 18.5 📉

---

In essence, the complexity index provides a quantitative measure of a hydrologic model's intricacy, enabling modelers to benchmark and optimize performance efficiently. 👩‍💼🔧📊

SWMM 5 Pond Infiltration

You can model the pond infiltration indirectly by using either:

1. a Pump Type 4 (the classic SWMM 4 solution to this matter), in which the Pump simulates the pond depth - infiltration rate function,

2. alter the SWMM 5 Evap Factor for a pond so that you have seasonal or monthly variation in your infiltration loss simulated as an increase in Pan Evaporation or

3. You can use the newer SWMM 5 Outlet structure and use either a functional or tabular relationship to simulate the infiltration loss as a function of pond depth.

If you search the CHI Knowledge database you can also find some suggestions from Mike Gregory (and others) about modeling infiltration loss from a pond. I would recommend items 2 and 3 because "An outlet curve in SWMM 5 has the same functionality as a SWMM 4 Depth related pump ( Flow versus Depth) but it has the great advantage of being explicitly designed to have multiple functions; does not have the appearance of being an ad hoc solution (as a pump simulating infiltration would be to the casual viewer) and has many wonderful other features (added by Lewis Rossman) that you would not get with a strict pump link."

Here's the expanded version with emojis:

### SWMM 5 Pond Infiltration 🌊💧

You can model the pond infiltration indirectly by using either: 🤔

1\. a Pump Type 4 (the classic SWMM 4 solution to this matter), in which the Pump simulates the pond depth - infiltration rate function, 🆚

2\. alter the SWMM 5 Evap Factor for a pond so that you have seasonal or monthly variation in your infiltration loss simulated as an increase in Pan Evaporation 🌞 or

3\. You can use the newer SWMM 5 Outlet structure and use either a functional or tabular relationship to simulate the infiltration loss as a function of pond depth. 🔬

If you search the CHI Knowledge database 🔍 you can also find some suggestions from Mike Gregory (and others) about modeling infiltration loss from a pond. I would recommend items 2 and 3 because "An outlet curve in SWMM 5 has the same functionality as a SWMM 4 Depth related pump ( Flow versus Depth) but it has the great advantage of being explicitly designed to have multiple functions; does not have the appearance of being an ad hoc solution (as a pump simulating infiltration would be to the casual viewer) and has many wonderful other features (added by Lewis Rossman) that you would not get with a strict pump link." 👌

SWMM 5 Variable Time Step

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.

SWMM5 Normal Flow

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

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

How to Make an InfoSWMM model from the DBF Files

Follow the following steps to create MY .MXD file.

1) Open an empty InfoSWMM project. Do not initialize it.

2) Save the project as MY .MXD within the folder where you have MY. ISDB

3) Initialize the project.

4) Click the reset Display button

That should create the project for you.  If you want an H2OMAP SWMM project, you can save the InfoSWMM model, and then Import it from H2OMAP SWMM.

Force Main Transition in SWMM 5

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;
}

World Rainfall

SCS Rainfall Distributions and Design Storms

SCS Rainfall Distributions for H20MAP and InfoSWMM

The base file has a 24 hyetograph for SCS Type 1A and SCS Type 2 distrubutions. The total of the rainfall is 1 inch and to make a 25 year or 50 year storm you follow these steps:

1. Clone the rainfall time series and

2. Use the Field Calculator in DB Edit to change the total rainfall by using the operand in the Field Calculator dialog. For example, the picture shown below will make a 10 inch 24 hour rainfall in the new time series.

You will end up with two series: (1) the base 1 inch hyetograph and (2) the new design storm of 10 inches.

H2OMAP and InfoSWMM Sediment Transport Modeling

H2OMAP SWMM and InfoSWMM Sediment Transport Modeling

Sanitary and combined sewer systems can carry substantial loads of suspended solids (waste solids) which can accumulate and cause blockages thereby impairing the hydraulic capacity of the sewer pipes (by restricting their flow area and increasing the bed friction resistance). H2OMAP SWMM and InfoSWMM can simulate the transport and gravitational settling of (total suspended solids including grit) over time throughout the sewer collection system under varying hydraulic conditions. As long as flow velocity exceeds the critical/terminal velocity, H2OMAP SWMM and InfoSWMM assumes that the sewage flow has the capacity to transport all incoming . Deposited  particles are also assumed to be scoured and transported downstream when velocity of the sewage flow exceeds the terminal velocity. Settling starts when flow velocity falls below the critical velocity. In the model, transport of thet particles is governed by advection implying that the particles are transported at local flow velocity. 

The sediment transport modeling using H2OMAP SWMM and InfoSWMM  requires only few inputs, namely limiting flow velocity, particle settling velocity, and source node(s) and initial concentrations (in mg/l) at the source nodes. 

In order to specify the first two inputs (i.e., limiting flow velocity and particle settling velocity), the user should first select from the quality tab which in turn activates the editing tabs for particle settling velocity and limiting flow velocity. Specification of source node(s) and its/their initial concentration is similar to the method described above in relation to pollutant transport. The default values used by the model for limiting flow velocity and particle settling velocity are 2 ft/s and 0.1 ft/s, respectively.  User specified values over rid these default figures .

H2OMAP SWMM and InfoSWMM  deposition (in kg)  in pipes and  concentration (in mg/l) at manholes,  wet wells, and outlets are the outputs reported following successful simulation of  transport for a collection system.

Modified Basket Handle Cross Section Warnings

There is a rule in SWMM 5 that the depth cannot be less than half the bottom width for a modified basket handle(see below).  You always have to have a maximum depth less than 50 percent or 1/1 of the bottom width,  If you do not meet this criterion then the program will generate an invalid number warning.  This is the code from xsect.c that checks the validity of the cross section data:

    case MOD_BASKET:

        if ( p[1] <= 0.0 || p[0] <>

        xsect->yFull = p[0]/ucf;

        xsect->wMax  = p[1]/ucf;