Thursday, June 15, 2023

Good Science Writing Tips from HBR

 Source -


“Keep it simple.” This classic piece of writing advice stands on the most basic neuroscience research. Simplicity increases what scientists call the brain’s “processing fluency.” Short sentences, familiar words, and clean syntax ensure that the reader doesn’t have to exert too much brainpower to understand your meaning.


Specifics awaken a swath of brain circuits. Think of “pelican” versus “bird.” Or “wipe” versus “clean.” In one study, the more-specific words in those pairs activated more neurons in the visual and motor-strip parts of the brain than did the general ones, which means they caused the brain to process meaning more robustly.


Our brains are wired to make nonstop predictions, including guessing the next word in every line of text. If your writing confirms the readers’ guess, that’s OK, though possibly a yawner. Surprise can make your message stick, helping readers learn and retain information.

Stirring Language

You may think you’re more likely to persuade with logic, but no. Our brains process the emotional connotations of a word within 200 milliseconds of reading it—much faster than we understand its meaning. So when we read emotionally charged material, we reflexively react with feelings—fear, joy, awe, disgust, and so forth—because our brains have been trained since hunter-gatherer times to respond that way. Reason follows. We then combine the immediate feeling and subsequent thought to create meaning.


As humans, we’re wired to savor an­tic­ipation. One famous study showed that people are often happier planning a vacation than they are after taking one. Scientists call the reward “anticipatory utility.” You can build up the same sort of excitement when you structure your writing. In experiments using poetry, researchers found that readers’ reward circuitry reached peak firing several seconds before the high points of emphatic lines and stanzas. Brain images show preemptive spikes of pleasure even in readers with no previous interest in poetry.

Smart Thinking

Making people feel smart—giving them an “aha” moment—is another way to please readers. To show how these sudden “pops” of insight activate the brain, researchers have asked people to read three words (for example, “house,” “bark,” and “apple”) and then identify a fourth word that relates to all three, while MRI machines and EEGs record their brain activity. When the study participants arrive at a solution (“tree”), brain regions near the right temple light up, and so do parts of the reward circuit in the prefrontal cortex and midbrain. The readers’ delight is visible. Psychological research also reveals how people feel after such moments: at ease, certain, and—most of all—happy.

Social Content

Our brains are wired to crave human connection—even in what we read. Consider a study of readers’ responses to different kinds of literary excerpts: some with vivid descriptions of people or their thoughts, and others without such a focus. The passages that included people activated the areas of participants’ brains that interpret social signals, which in turn triggered their reward circuits.


Few things beat a good anecdote. Stories, even fragments of them, captivate extensive portions of readers’ brains in part because they combine many of the elements I’ve described already

Sunday, May 28, 2023

Blog strategy to explain complex modeling concepts to non-modelers, while highlighting the beauty and majesty of domain knowledge:

Blog strategy to explain complex modeling concepts to non-modelers, while highlighting the beauty and majesty of domain knowledge:

  1. Title: "Simplifying Complexity: Unveiling the Beauty and Mastery of Hydrological Modeling"

  2. Introduction: Start by providing a brief overview of what hydrological modeling is and why it's important. Use relatable language and analogies to draw in your audience.

  3. Identify Your Audience's Needs: You're writing for non-modelers, so identity what they need to understand about modeling. What concepts are crucial for them to grasp? What misconceptions might they have?

  4. Break Down Complexity: Break down the complex modeling concepts into simpler, understandable parts. Use analogies or real-world examples to explain these concepts. For instance, you could compare the movement of water in a watershed to traffic flow in a city to explain runoff.

  5. Visualize It: Use visuals to make complex concepts easier to understand. Diagrams, charts, or animations can all be great tools for this. Even a well-placed infographic can do wonders to simplify complex ideas.

  6. Highlight the Beauty: Talk about the elegance of the mathematical models, the way they mimic real-world processes, and the surprising insights they can provide. This is where you can really showcase the "beauty and majesty" of your domain knowledge.

  7. Interactive Elements: Incorporate interactive elements if possible, such as simulations that readers can manipulate. This can help your audience intuitively understand how different factors influence the model.

  8. Case Studies: Use real-world examples or case studies to show the practical applications of these models. Stories can be a powerful tool for making abstract concepts concrete.

  9. Glossary: Include a glossary of key terms that you've simplified for your readers. This can serve as a quick reference guide for readers who may be unfamiliar with certain terms.

  10. Conclusion: Summarize the main points and emphasize the importance of understanding these models, even for non-modelers. End on a note that invites discussion or further inquiry.

  11. Invitation to Engage: Encourage your readers to ask questions or share their thoughts in the comments. This can be a great way to engage your readers and help them learn more.

Remember, the goal is not to turn your readers into modelers but to help them appreciate the beauty and complexity of the models and understand their significance.

Saturday, May 27, 2023

ICM SWMM and Detention Ponds

ICM SWMM and Detention Ponds

InfoWorks Integrated Catchment Model (ICM) with Storm Water Management Model (SWMM) is a sophisticated software tool used for simulating and managing water quantity and quality in urban catchments. It can model a variety of hydraulic structures, including detention ponds.

Here's a simplified explanation of how you can use ICM SWMM to model a detention pond with a box outfall with weirs and an orifice:

  1. Detention Pond: In ICM SWMM, a detention pond can be modeled as a "Storage Unit." The "Storage Curve" feature allows you to define the relationship between the water depth and the storage volume in the pond.

  2. Weirs and Orifices: Weirs and orifices are two types of outlet structures often used in detention ponds to control outflow. In ICM SWMM, you can model these using "Outlet" objects. You can define the type of outlet (weir or orifice), the geometry, and the discharge coefficients. If the pond has both a weir and an orifice, you can model these as separate outlets.

  3. Box Outfall: An outfall in ICM SWMM represents a point where water exits the system. It can be linked to the detention pond via a conduit. The box shape of the outfall doesn't directly impact the hydraulic simulation, but the dimensions can be used to configure the outfall's capacity.

  4. Evaporation: ICM SWMM can model evaporation from the surface of the detention pond. The evaporation rate can be specified in the "Climate" settings of the model. This rate can change over time based on the weather data you input.

  5. Side and Bottom Infiltration: Infiltration from the pond to the surrounding ground can be modeled using the "Exfiltration" feature in ICM SWMM. This allows you to set a rate at which water seeps out from the storage unit into the surrounding soil. You can set different rates for the side and bottom surfaces.

While creating your model, you'd need to calibrate and validate it using observed data to ensure its accuracy. This is a high-level overview and actual modeling might require more specific steps based on the unique characteristics of your detention pond and outfall structure.

How InfoWorks ICM can model landfills

How InfoWorks ICM Can model landfills

InfoWorks Integrated Catchment Model (ICM) is a comprehensive software package that can be used for a wide range of applications, including modeling landfills. The software is designed to model the hydrological processes involved in the urban and rural catchment areas.

While the software isn't specifically designed for landfill modeling, its broad range of capabilities allows it to be adapted for this purpose. Here are some ways that InfoWorks ICM might be used to model landfills:

  1. Leachate Production: Landfills generate leachate, a liquid that contains a variety of contaminants. InfoWorks ICM can model the generation and flow of this leachate, helping to predict where it will go and how it might impact the local environment.

  2. Surface Runoff: Rainfall on a landfill site can cause surface runoff, which can carry contaminants. InfoWorks ICM can model this process, predicting how much runoff will be produced and where it will go.

  3. Groundwater Contamination: Leachate can contaminate groundwater if it is not properly managed. InfoWorks ICM can model the movement of groundwater and the spread of contaminants, helping to predict and prevent this type of contamination.

  4. Stormwater Management: Landfills need effective stormwater management to prevent surface runoff and leachate production. InfoWorks ICM can model stormwater systems, helping to design and optimize these systems for landfill sites.

  5. Flood Risk: Landfills can alter the landscape, potentially increasing the risk of flooding. InfoWorks ICM can model flood risks, helping to predict and mitigate these risks.

These are some of the ways InfoWorks ICM can be used to model landfills. However, it's worth noting that these applications often require specialized knowledge and expertise in both the software and the environmental processes involved. It's recommended to consult with a professional or expert when using InfoWorks ICM for such purposes.

Thursday, May 25, 2023

Knowledge-Centered Service (KCS)

 Knowledge-Centered Service (KCS) is a methodology that focuses on knowledge as a key asset in an organization. It's widely adopted in the IT Service Management (ITSM) industry but also applicable to any business that relies heavily on knowledge to provide their services or products.

Fundamental Concepts:

Knowledge as a Key Asset: The crux of KCS is the recognition of knowledge as a valuable asset. This refers to information, data, and expertise accumulated within an organization. It can include anything from solutions to common customer issues, to internal processes, to expert insights on the industry or products.

Creating Knowledge: KCS encourages organizations to create knowledge content during their problem-solving process. This means that as employees resolve issues, they should document their solutions in a standardized and structured manner.

Sharing Knowledge: After knowledge is created, it should be shared across the organization. This can involve using a central knowledge base where information is stored and can be easily accessed by anyone in the organization.

Updating Knowledge: Knowledge isn't static; it's constantly evolving. KCS recognizes this and emphasizes the need for knowledge to be regularly updated to reflect the latest information.


Benefits of KCS:

Improved Efficiency: By documenting and sharing solutions, employees can solve problems faster and more efficiently. They won't need to "reinvent the wheel" each time a similar issue arises.

Enhanced Customer Support: With a robust knowledge base, customer support agents can provide quicker, more accurate responses. It can also power self-service portals where customers can find solutions themselves.

Empowered Employees: KCS fosters a culture of knowledge sharing and continuous learning, empowering employees to learn from each other and grow their skills.

Organizational Learning: Over time, KCS can enhance an organization's collective knowledge and capabilities, making the business more adaptable and resilient.

To implement KCS, organizations often use a combination of strategies, processes, and tools—including knowledge management systems, training programs, and KCS-aligned roles and responsibilities. It's an iterative methodology, with cycles of knowledge creation, sharing, use, and improvement—guided by the mantra "knowledge as you work".

Sunday, May 21, 2023

Comments on SWMM Version 1 from 1971

 Here is the requested information summarized in a table format from this history post about SWMM from 2001

VolumeTitleReference NumberPagesYear
Volume IFinal Report11024DOC07/71 (NTIS PB?203289)352 pp.1971
Volume IIVerification and Testing11024DOC08/71 (NTIS PB?203290)172 pp.1971
Volume IIIUser’s Manual11024DOC09/71 (NTIS PB?203291)359 pp.1971
Volume IVProgram Listing11024DOC10/71 (NTIS PB?203292)249 pp.1971

The original SWMM proposal was submitted by three groups:

GroupKey People
University of FloridaEd Pyatt (deceased) and John Schaake (now NOAA-NWS)
Water Resources Engineers (now Camp, Dresser and McKee)Bob Shubinski (deceased) and Carl Chen (not sure where now!)
Metcalf and Eddy (Palo Alto)John Lager (retired)

The primary EPA project officer was Darwin Wright.

Responsibility for SWMM blocks was as follows:

SWMM BlockResponsibility
Runoff, quantityWRE
Runoff, qualityM&E
Receiving WaterWRE

Significant contributors to SWMM over time:

Larry RoesnerDeveloper of the WRE Transport Model (EXTRAN)
Richard FieldLong-time EPA SWMM master, benefactor, and visionary
Bob DickinsonThe code effectively became his after about 1980
Bill JamesFirst interactive version
Alan PeltzWorked on the code before Bob Dickinson
Several UF grad students (notably Steve Nix, Miguel Medina, and Don Polmann)Various contributions

Note: This list is not exhaustive and may not include many other valuable contributors.

EPA SWMM versions in table format:

Version 11969 to 1971
Version 21975
Interim Release1977
Version 31981
Version 41988
Version 4.041989
Version 4.051990
Version 4.201992
Version 4.301994
Version 4.401997
Version 4.4g1998

AI Rivers of Wisdom about ICM SWMM

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