You know most children by the age of six or seven have learned that lying usually only makes things worse. Statements like the dog did it or the baby did it rarely fly with parents. Unfortunately as a society we have learned to condone dishonesty in many forms. 

Yesterday's July 23, 2026 report by GHD sent to the Min. of Environment (MECP) and titled "Municipal Aquifer Steady State Conditions Trial" is an example of what my Blog title is all about. I understand that exceptions to the fibbing rule exist which include two dishonest parties sending knowingly false information back and forth in order to reinforce their agreed upon self-serving "truths". No one is offended because both parties have long ago agreed to the deception. Examples abound such as President Bush lying about Iraq's Weapons of Mass Destruction (WMD). England and the U.S. needed some justification for attacking Iraq so they jointly made sh*#  up. Countries do it, individuals do it and polluting corporations do it. 

There are a host of made up falsehoods regarding the Elmira Water Crisis.  Only a very few to date have been debunked or admitted to such as Allan Deal (GHD) admitting to TRAC that Varnicolor Chemical contributed chlorinated solvents to the Elmira drinking water aquifers (MA) as per the Minutes of their September 12, 2024 meeting in Council Chambers. This was on page 7 and on page 6 of these Minutes is also finally the admission of DNAPL existing near pumping well W4 by the Howard St. Water Tower and beside monitoring well OW57-32R. 

The page 6 admission regarding off-site DNAPL presence is important in the context of yesterday's  report as it clarifies why Figures 5 & 7 clearly show both the remaining Municipal Upper and Municipal Lower Aquifer locations of chlorobenzene in Elmira's subsurface. They are located  to the immediate east of former pumping well W4 as well as of monitoring wells OW57-32R and OW57-38 . The remaining plumes in both aquifers (MU, ML) are directly beneath the former Varnicolor Chemical site with Howard Ave. on their south side and First Ave on their north side. Both plumes cross under Union St. as they are slowly pulled towards pumping wells W5A/B in each aquifer. Also of interest in yesterday's report is the statement on page 15 in the Conclusions that  "GHD notes that extraction wells were drilled in the core of the existing plumes at the time of installation.". This explains the location of pumping well W4 beside the free phase DNAPL clearly discovered prior to the startup of the off-site pumping in August 1998 but denied originally at a CPAC meeting around 2004-5 when I presented a writeup about it found in a monthly Progress Report. It was then denied repeatedly for years until the admission of its' presence at the September 12, 2024 TRAC meeting. 

DNAPLS (chlorobenzene & more) left the Uniroyal Chemical site decades ago in at least two locations ending up by the Howard St. Water Tower and by monitoring well OW58-24 on the property line with Nutrite/Yara via a "window" through the Municipal Aquitard.  Both Uniroyal and the Ontario M.O.E. colluded to hide and deny this fact from the public in order to preserve their sweetheart deal which covered up the Ministry's negligence and incompetence and which reduced Uniroyal's cleanup costs by millions of dollars. 

Sorry for the upset and disruption to thin skinned individuals at Woolwich Township who insist on playing nice with those who don't play nice, much less legally. 

July 21, 2026, was Black Women’s Equal Pay Day here in the U.S. It’s a symbolic date that marks how far a Black woman must work into the current year to make the same amount that a white, non-Hispanic man earned last year.

Another way to look at it? Imagine finding out you’ve been earning just 65 cents for every dollar paid to a coworker. That’s the current wage gap for Black women working full-time, year-round in the U.S.

And there are more equal pay day milestones coming up, each revealing deep disparities:

• Moms’ Equal Pay Day — August 6
• Native Hawaiian and Pacific Islander Women’s Equal Pay Day — September 15
• Latina Equal Pay Day — October 8
• Disabled Women’s Equal Pay Day — October 20
• Native Women’s Equal Pay Day — November 19

While I hope your organization conducts regular pay equity reviews and addresses any inequities, there’s also something we can do on a personal level: Discuss our compensation.

It’s uncomfortable territory for many of us, but when we tell others what we make, we provide them with valuable data points. And data is a helpful tool in any negotiation process.

Share this action on Instagram, LinkedIn, or YouTube.

Should employees and job candidates have to use their own credit card for business travel and other expenses?

Paolo Gaudiano and Cynthia Overton argue in their new book Meritocracy that this common workplace practice is one of many that quietly undermine merit.

They explain that structural enablers and organizational biases prevent meritocracy all around us. For example:

• The enabler: The organization requires individuals to pay business travel costs upfront and request a reimbursement later.
• The bias: This creates bias among employees at different socioeconomic positions. Not everyone can float hundreds — or thousands — of dollars in company expenses. Some people don’t have a credit card. Others have low credit limits or can’t afford to carry the balance while waiting weeks for reimbursement.
• The solution: Hire a company travel agent, provide corporate credit cards paid by accounting, give cash advances.

If someone needs to travel or make purchases for work, don’t assume they can “just put it on their credit card.” Instead, provide another option.

For example, use virtual credit cards (VCCs) or temporary prepaid corporate cards, available from many financial institutions. They allow your business to instantly issue temporary 16-digit card numbers for specific trips, set firm spending limits, and restrict charges to travel categories such as flights and hotels.

“It’s taking longer because the decision maker is older and more cautious.”

I heard a leader make this comment during a recent virtual meeting.

My first instinct was to let it go. I had another meeting starting soon. This leader was someone I respected. He was thoughtful and empathetic. Maybe it wasn’t worth bringing up.

But I try to practice what I preach.

So, as the call was wrapping up, I asked the leader if he could stay on the line for a few more minutes because I had something I wanted to share with him.

After everyone else left, I said, “I hope you’re open to some feedback” and then summarized what I had heard him say. I pointed out that he had reinforced a negative stereotype about older people, and that while I didn’t think he meant any harm, he sent a subtle message to others on that call that it’s okay to assume older folks are going to be slower and more cautious with decision-making or other aspects of their job.

I added, “You could simply have said the decision was taking longer than expected, without attributing it to their age.”

He nodded in agreement and sincerely thanked me for the feedback.

It reminded me that these conversations don’t have to be confrontational. A respectful conversation after the meeting can be enough to help someone notice a stereotype they hadn’t realized they were reinforcing.

I hope you’ll do the same the next time you witness one.

Last week, the US Defense Secretary Pete Hegseth mandated testing testosterone levels for service members over 30 and recommended hormone therapy if needed. Afterward, Fox News host Jesse Watters made an awful joke on air about it. He chuckled, saying,

“Women on base: you better be careful. Port calls, women in Asia: you better be careful. Because these guys are going to be wild animals, and you better watch out.”

Sexual violence is not something to joke about.

And as I shared in a previous newsletter, research shows that when someone tells a denigrating joke, it signals that behavior is acceptable. People who might normally conceal their feelings feel freer to express them.

As better allies, let’s speak up. For example, “We don’t joke about rape here.”

A subscriber from Berlin wrote to tell me about something she’s been noticing on public transportation: there’s a general lack of awareness of people with disabilities and access needs.

Here are some examples she regularly sees:

• A blind person navigating roads, using the markings for blind people, which are fully occupied by people walking with rolling suitcases.
• A parent juggling a stroller, a child, and shopping bags, with no one offering their seat or making space for the stroller.
• A wheelchair user trying to navigate a crowded train, weaving around bags and feet that never move because no one looks up.

She urges all of us to, “Look up and be aware of your surroundings so you’re able to offer help in situations where it’s needed.”

Most people are perfectly capable of managing on their own. But knowing someone noticed and cared enough to make space or ask how they might help can make all the difference.

🙏

If you’ve taken a step towards being a better ally, please reply to this email and tell me about it. And mention if I can quote you by name or credit you anonymously in an upcoming newsletter.

That’s all for this week. I’m glad you’re on this journey with me,

Karen Catlin (she/her), Author of the Better Allies® book series

Copyright © 2026 Karen Catlin. All rights reserved.

Together, we can make a difference with the Better Allies® approach.

• Say thanks to Karen and buy her a coffee ☕ (Need a receipt for educational reimbursement? Reply to this email, and we’ll take care of it.)
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Break the Silence Around Compensation, and Other Actions for Allies was originally published in Code Like A Girl on Medium, where people are continuing the conversation by highlighting and responding to this story.

KITCHENER - On K-W Oktoberfest night, the Berlin Kegtappers and Toronto Maple Leafs played through more twists and turns than a pretzel.

But in the end, the Leafs emerged victorious 9-3 at Jack Couch Park Thursday night.

It didn't start pretty for Berlin starter Jorge Minyety, who gave up a solo shot to Brando Leroux on the fourth pitch of the game.

It was the first of two home runs on the night for Leroux.

He broke open what was a 5-3 Leaf lead in the eighth with a two-run shot in what was an otherwise nail biter of a game.

Minyety struck out seven and gave up four runs (two earned) on seven hits in six innings of work.

The story of the game became the errors that led to runs being scored.

A wild pick-off attempt off the glove of Yosuke Fujie at third allowed Toronto's second run to score in the first inning. 

Fujie redeemed himself later with a pair of spectacular defensive plays at third, and hit a solo home run to make it 4-3 in the sixth inning.

In the eighth, Crix Taveras was caught in no man's land between first and second, but a throwing error and attention away from Yordan Manduley allowed the former Panther to come in to score.

Marcus Knecht hit a two-run homer in the ninth inning to round out the scoring.

Kitchener drops to 10-17 on the season, while Toronto improves to 9-18.

Kitchener is on the road in Hamilton Friday, Barrie Saturday and back home Sunday to take on Welland at 5:05 p.m.

It begins a tough stretch for the Panthers, who play their next seven games against opponents currently in playoff positions.

GET YOUR TICKETS NOW and #PackTheJack!

This is but one of the retreaded wellfields that Waterloo Region are reopening in their desperation to keep builders and developers happy. Don't worry they do the risk calculations and theoretically at least while your risk of getting cancer will certainly rise with all these contaminated aquifers being put back in service; the numbers may be tiny such as an increase from 2 or 3 "excess" cancers in a million to a marginally greater 4 "excess" cancers in a million. No worries there especially as hardly any of us have friends, colleagues or relatives suffering cancers, past cancers or fatal cancers. Right?

 The Greenbrook Wellfield wells, K1, K2, K4B, K5A and K8 located near Homer Watson Blvd. and Stirling Ave. in Kitchener were all on standby status from at least 2003 until 2007. Newspaper accounts also advise us that that situation has continued during the 2020s whether from a chlorine/ammonia screwup and explosion or from the 1,4 dioxane contamination that came from the Ottawa St. Landfill. Now there have also been well redrills and replacements in this wellfield as the current wells are now known as K1A, K2A, K4C, K5A and K8.  These machinations if you will are common practice for the Region when they have "problematic" wells and examples in Cambridge for example include drilling past and through a contaminated zone of a well in order to pump water from a deeper less contaminated or uncontaminated zone.   

It is my opinion that this Grand River Source Protection Area Assessment Report Chapter 8, July 29, 2025 is unnecessarily thick and inflated with data that is at best filler or meant to make reading difficult and tedious. That said with enough patience and tenacity and overall knowledge a serious person can find much to be concerned with regarding this wellfield. Firstly the chances of 1,4 dioxane being the only toxic contaminant to reach the Greenbrook Wellfield from the Ottawa St. Landfill is somewhere between zero and nil. Look for benzene, chlorinated solvents and all the usual array of industrial solvents that were dumped there including by Elmira industries such as Varnicolor Chemical and if I might hazard a guess probably Uniroyal Chemical as well back in the day. Paper restrictions were strong whereas physical restrictions were not.

Other issues include high chlorides and high sodiums maybe from road salt but also common from industrially contaminated aquifers. Also I should applaud at least a couple of the Figures in this report as they do identify the Total Number of Significant Threat Activities as well as the Total Number of Properties with Significant Threats. Those numbers are huge (630, 379). They also indicate both High Unadjusted Intrinsic Vulnerability levels for this wellfield as well as High Adjusted Intrinsic Vulnerability threats to water quality. 

Waterloo Region are desperate to keep the water flowing and in my opinion they are risking your and my health for their own benefit. What else is new when it comes to water protection. 

The lazier way to manage everything docker

Go 52.2k Updated Apr 19

Mars needs allies, intelligence and time.
Only blood and fire can earn them all three.

Read Alien Olympus in ebook, audiobook or paperback now!

♦

Despite sacrifices and even victories, the Protectorate of the Mage-Queen of Mars is losing the war against the Reezh Kazh, the fascist theocracy that rules the only aliens they’ve ever met.

Ambassador Connor O’Hannagain has been sent to find allies among the reezh home worlds, the old and mighty systems that defied the Kazh once—but a centuries-long cold war has left the enemies of his enemy paranoid and violent.

Mike Kelzin, husband, pilot and spy, is the Protectorate’s eyes and ears, seeking every scrap of intelligence that can be found on the Kazh. But access to the enemy worlds is nigh impossible, leaving his best chances in the occupied Chimera System—where even the Resistance is entirely cut off from Mars.

Mage-Admiral Jane Alexander, Crown Princess and fleet commander, knows that they need time more than anything else. Time their enemy is unlikely to give them.

To turn the tide, Mars must buy allies, intelligence, and time—and only the oldest currency can pay for all three.

Blood.

The post Alien Olympus is out now! appeared first on Glynn Stewart.

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The post 2026 Youth Impact Survey Prize Winners appeared first on Children and Youth Planning Table.

Python library for generating ambient music from text descriptions. No GPU required. Turn text into sound with a single line of code.

Python 41 Updated May 29

What does your life look like when you are living at your courageous best?

That question came from a leadership coach during a women’s leadership program I was part of at work. The room went quiet, then people started naming their fears. Talking to a large audience. Doing something new outside their comfort zone. Networking with unknown people.

I sat there realizing I did not share any of those fears. Mine was self-advocacy.

For years my mantra was to let the work speak for itself. It felt like the safer, more dignified choice. But in a world of shrinking attention spans, is it fair to expect someone else to notice what I built? That question sent me into a why-why analysis on what I was missing.

Two problems surfaced immediately. Firstly, I do not always know what I should be advocating for. I do the work, but I rarely stop to name what it proves about me. Moreover, even when I know what to say, I do not know how to say it without sounding like I am bragging.

And I am living this right now. I led a product build that went from zero to one with a successful beta run in three months. We are now moving towards general availability. Scaling it from here on needs more people on my team. But when I raise this with my leaders, I can see they do not instinctively get why.

Because I never told them what it actually took to get here. I never walked them through the build, the partner negotiations, the scaling decisions, the ongoing support, all of it stacked on top of each other for months. I assumed the beta success would speak for the effort behind it. It did not. Success is visible. The work underneath it is not, unless you name it.

This article is my attempt to work through both, using what I learned in the session and what I have tested since.

Self-advocacy begins with self-trust, and self-trust is shakier than it looks.

Every evening a voice shows up and tells me the day did not amount to much. It does not pause to check; it just delivers the verdict. I used to treat that voice as the final word.

But there is a second voice, if I actually let it speak. It does not argue with the critic directly. It just asks one question first. What did I actually get done today? Most evenings, when I stop and answer that honestly, the list is longer than the critic gave me credit for. The day was not wasted. I simply never stopped to notice it before moving on to the next thing.

Call it the critic and the mentor. The critic is fast and loud. It shows up uninvited every single day. The mentor only speaks if you deliberately ask it a question, which is exactly why it loses so often.

Here is the fix I am using. Every time the critic makes a claim, I call in the mentor to check it against evidence, not feeling.

And evidence comes in two forms. Functional evidence talks about what I actually produced. That is a fact, not a feeling, and it directly counters the critic's claim. Emotional evidence involves what I got through. Moments I stayed steady, or recovered from something hard.

The reason self-trust feels shaky is not that the evidence does not exist. It is that we never log it. The critic keeps a precise record of every shortfall. The mentor gets no record at all, unless you build one.

So the practical answer to problem one is simple. Keep a running log, even three lines a week, split into two columns. What did I produce. What did I get through. That log becomes the material your mentor uses to talk back to the critic. It is also, directly, the raw material for what you advocate for later.

Knowing your evidence is only half the problem. The other half is saying it out loud without it sounding like self-promotion.

Here is the distinction that unlocked this for me. Self-advocacy is stating a fact. Self-promotion is trying to win approval. The two get confused because they can use the same words. The difference is in what you attach to the sentence.

If I say, I turned around this initiative despite the constraints, and stop there, that is advocacy. It is a fact, stated plainly, with no ask attached.

If I add, so I hope people notice, or, so I deserve more credit, that same sentence turns into a request for approval, and it starts to feel uncomfortable to say, and to hear.

The technique is to separate the update from the ask. State what happened. Do not hedge it with a joke, do not shrink it with a qualifier, do not attach a request unless you actually have one. If you do have an ask, make it separately, and make it direct.

In practice, this sounds like: This shipped, and it took solving three specific constraints. Not: I don’t know if this matters, but I guess I managed to get this out despite everything. The first is advocacy. The second is an apology wearing the clothes of an update.

Self-advocacy is not one skill. It is knowing what to say, built from evidence you deliberately collect, and knowing how to say it, built from separating fact from request.

1. Log your evidence weekly, split into what you produced and what you got through.
2. When the critic speaks, ask what the mentor would say, using the log as proof, not opinion.
3. When you communicate an update, state the fact plainly, with no self-deprecating qualifier and no unstated ask.
4. If you have an actual request, make it as its own sentence, separate from the update.

I turned this log into a skill called daily self-advocacy log, open source on GitHub: github.com/kaantibshetty/skills-library/tree/main/career/daily-self-advocacy-log. You can schedule a task using this skill on Claude Cowork or Perplexity, and it will auto-trigger a conversation at the end of your day. Each evening it asks two questions: what did you produce and what did you get through, then saves the entry to a running log so the mentor always has evidence on hand.

The courageous version of me is not the one who talks the loudest about her work. It is the one who keeps the receipts and hands them over without apologizing for it.

The Courage Nobody Talks About was originally published in Code Like A Girl on Medium, where people are continuing the conversation by highlighting and responding to this story.

This wellfield is located on Kitchener's west side near Trussler Rd. and the boundary with Wilmot Township. It consists of multiple Supply Wells (i.e. pumping wells K21A, K25, K29) along with Recovery Wells (RCW1-2) originally and now RCW 2-4.  Aquifer Storage & Recovery Wells (ASR1-5) are to accept treated Grand River water for storage to be pumped later into the distribution system by the Recovery Wells RCW 2-4 .   There are also a set of wells known as Peaking wells and they are designated K91-94). 

I would very much like to see audited up to date pumping rates for all these wells because the data I do have does not jive with the reality of a system constantly needing to expand along with our population. I have long suspected that the entire Mannheim  Aquifer Storage and Recovery System has not lived up to expectations whether through misadventure, bad luck or poor planning. Regardless again the data for all these wells that I have shows pumping rates well below the permitted volumes. This alone without ever greater population growth would cause pressure on other nearby well systems to take up the slack. This would include the Mannheim West Wellfield in Wilmot Township as well as the just down the road further west Wilmot Centre Wellfield which has been causing well interference complaints from local residents.

Under all the circumstances well interference is hardly surprising but what is, is the Region's hardas# attitude towards these residents and their water shortages possibly caused by again misadventure, bad luck or poor planning. 

The OTT industry continues to grow rapidly as streaming consumption increases across global markets. In 2026, consumers are looking for seamless playback, a continuous streaming experience, and broadcast-quality service on every device. Streaming reliability has become a key factor influencing user retention, which is why businesses are prioritizing high-quality OTT platform development services to drive long-term growth.

According to the latest market estimates by Ampere Analysis, global OTT revenues are already in the hundreds of billions of dollars, despite ever-growing consumer expectations.

On the OTT platform side, a few moments of delay may cause loss of subscribers, loss of viewing duration, lower ad completions, and reputational damage. With increasing competition and growing numbers of users, operational reliability has become a major competitive differentiator for streaming platforms.

Quality of streams is no longer a technical characteristic that concerns backend systems alone. Rather, stream quality is now considered a key business KPI tied directly to client retention and monetization.

According to industry studies by Conviva in its State of Streaming report, buffering and playback problems have been found to affect viewer engagement and session completion for OTT platforms.

The multi-CDN approach remains one of the most efficient ways of lowering buffering and reducing downtime.

Relying on a single CDN provider creates operational risks. Regional CDN outages, ISP routing issues, and sudden traffic spikes can negatively affect streaming performance if traffic cannot be dynamically redirected.

By implementing multi-CDN infrastructure, an OTT provider may route customers to a high-performance CDN depending on various factors such as:

• Customer’s location
• Network traffic
• Network latency
• Performance of CDN in real time
• Network demand

A 2025-study on CDN-based live-streaming architectures stated that a drop in buffering rate can be achieved due to optimization of edge delivery and CDN routing practices.

Keeping video playback smooth when millions of people log in at once is tough. Live sports, big movie releases, or breaking news can bring in huge crowds in just a few minutes. If a platform isn’t ready, these sudden traffic spikes cause buffering, crashes, login delays, and network overloads.

To handle these crowded moments, advanced OTT app development services focus on a few key operational steps:

• Smart Scaling: Using past data to prepare servers before the crowd arrives.
• Traffic Spreading: Sharing the user load across different regions and servers.
• Auto-Backup Systems: Instantly switching to a backup network if the main one fails.
• Edge Storage: Storing content closer to the users so videos load faster.
• Fast Processing: Speeding up how live video is broken down and sent to devices.
• Stress Tests: Testing the system with fake traffic before the real event starts.

Running these tests helps companies find and fix weak spots in their streaming setup ahead of time. This proactive approach prevents annoying disruptions when viewer numbers — and ad revenues — are at their highest.

Standard infrastructure monitoring is not adequate for today’s streaming services.

Earlier, the main concerns of OTT operations managers were uptime, bandwidth, and server availability. But these indicators don’t capture the full picture of the viewer’s experience.

The recent trend in the streaming world is Quality of Experience (QoE) monitoring that analyzes the performance of content delivery from the viewer’s perspective.

Here are the key factors that successful OTT organizations currently monitor:

1. Rebuffering ratio
2. Video load time
3. Failure rate
4. Adaptive bitrate switch
5. Session abandonment
6. Performance based on specific devices
7. Streaming degradation related to ISPs

This allows OTT teams to detect and resolve playback issues proactively before they impact viewer satisfaction. Moreover, modern monitoring tools utilize AI technologies to analyze potential problems and take immediate action automatically.

Adaptive bitrate (ABR) streaming dynamically adjusts video quality based on network conditions to maintain stable playback. However, improper configuration of bitrate ladders results in fluctuating quality issues, unnecessary buffering, and unstable playback experience for users.

Current best practices for improving OTT streaming are as follows:

• Well-tuned bitrate ladders
• Device-dependent encoding profiles
• Optimized segments
• Increased efficiency of manifest delivery
• Low-latency packaging
• Fast transcoding process

Codec optimization is now gaining popularity among OTT service providers to minimize data usage while preserving high visual quality.

Codec efficiency becomes especially important for global OTT apps serving millions of concurrent users.

Ultra-low-latency streaming demand is increasing rapidly, especially in:

• Live sports coverage
• Streaming interactivity
• Gambling integration
• Gaming content
• Real-time user interaction

Yet, ultra-low latency creates operational challenges.

Lowering latencies aggressively without building resilient systems may lead to higher levels of playback disruptions, synchronization problems, and segment delivery failures.

Successful OTT services focus on balancing latency and reliability rather than chasing the minimum latency possible.

Artificial intelligence is becoming an increasingly important part of OTT infrastructural management.

Advanced AI technologies have the ability to predict:

1. Spikes in traffic
2. CDN congestion
3. Degradation of playback quality
4. Potential risks of viewer churn
5. Regional network instability
6. Infrastructural bottlenecks

AI can also enhance adaptive streaming by taking advantage of user behavior and network conditions. As a result, the quality of bitrate distribution, traffic routing, and fault recovery becomes much better.

OTT infrastructure is gradually evolving toward self-healing systems capable of detecting and resolving issues automatically.

For OTT providers in 2026, minimizing buffering and downtime is no longer just a technical objective — it is a business necessity.

As competition intensifies, investing in reliable OTT app development services is the best way for brands to secure interruption-free streaming, stronger viewer retention, and long-term growth.

Reducing Buffering and Downtime: Operational Best Practices for OTT Platforms was originally published in Code Like A Girl on Medium, where people are continuing the conversation by highlighting and responding to this story.

June brought a modest increase in real estate sales across Waterloo Region, but buyers remained highly selective and home prices continued to soften.

A total of 650 homes sold during the month, representing a 2.7% increase from the 633 sales recorded in May. However, sales remained 4.4% below June 2025 levels. Both the average and median sale prices also declined year-over-year.

The Waterloo Region real estate market remains active, but it is not forgiving. Buyers are responding when they see clear value, while homes that are overpriced, poorly prepared or incorrectly positioned are having a much more difficult time generating meaningful interest.

Compared with May 2026 and June 2025:

• 650 homes sold: Up 2.7% from May, but down 4.4% year-over-year
• $730,668 average sale price: Down approximately 1.9% from May and 6.5% year-over-year
• $690,000 median sale price: Down 5.0% year-over-year
• 1,401 new listings: Down 3.8% from May and 7.5% year-over-year
• 2,053 homes available for sale: Up 1.5% from May, but down 6.7% year-over-year
• 27 average days on market: Unchanged from June 2025
• 4.1 months of inventory: Down slightly from 4.2 months one year ago
• 100.9% of the most recent list price received: Compared with 101.2% in June 2025

Cornerstone Association of REALTORS®

The month-over-month picture was mixed. More homes sold in June, but prices softened and selling timelines generally lengthened compared with May.

Year-over-year, sales, prices, new listings and overall inventory all remained lower.

Beyond the statistics, buyers continue to respond most strongly to homes that are properly prepared, accurately priced and effectively positioned from the beginning.

Today’s buyers have access to more market information than ever. They are comparing each property against competing listings, recent sales, neighbourhood trends, condition, location and overall value.

Strategic pricing is no longer simply about attracting online views or showing activity. It is about creating enough interest and confidence to motivate qualified buyers to act.

The sellers achieving the strongest results are generally those who fully commit to the selling process. They follow the recommended preparation and staging plan, address the details that matter and price according to current market conditions rather than past expectations.

We saw this firsthand with 2 recent listings priced above $1 million. Both homes were priced at current market value rather than intentionally underpriced, and both sold within days, above asking and in multiple offers.

In each case, the sellers fully committed to the recommended preparation, staging, pricing, marketing and launch strategy.

Not every home requires extensive renovations or a major financial investment before selling. The key is understanding which improvements will influence buyers, where money should and should not be spent, and how to position the property so it stands out from competing homes for sale in Waterloo Region.

Single-family homes continued to outperform the townhouse and condominium segment in June.

• 430 sales: Up 2.9% from May, but down 2.5% year-over-year
• $838,386 average sale price: Down 1.7% from May and 7.0% year-over-year
• $775,000 median sale price: Down 0.8% from May and 6.1% year-over-year
• 23 average days on market: Up from 20 days in May and unchanged from June 2025
• 3.4 months of inventory
• 101.4% of the most recent list price received

Cornerstone Association of REALTORS®

Year-to-date single-family home sales were essentially unchanged from 2025, increasing by just 0.1%.

With fewer months of inventory and shorter selling timelines than other property categories, the single-family segment continues to experience stronger relative demand.

Detached homes that are well maintained, properly presented and priced in line with recent neighbourhood sales can still attract significant buyer interest. However, buyers remain highly sensitive to condition and perceived value, particularly when comparing similar homes within the same price range.

The townhouse, semi-detached and condominium segment continues to offer buyers more choice and greater negotiating power.

• 220 sales: Up 2.3% from May, but down 7.9% year-over-year
• $520,128 average sale price: Down 2.8% from May and 7.0% year-over-year
• $525,000 median sale price: Down 4.5% from May and 5.4% year-over-year
• 35 average days on market: Up from 30 days in May and unchanged from June 2025
• 5.4 months of inventory
• 100.0% of the most recent list price received

Cornerstone Association of REALTORS®

With more inventory and longer selling timelines than the single-family segment, this portion of the Waterloo Region housing market remains especially price sensitive.

Accurate pricing and strong presentation become even more important when buyers have several comparable properties to choose from. Small differences in monthly fees, updates, layout, parking, location and overall condition can have a meaningful impact on buyer interest.

Sellers in this category should also be prepared for buyers to take more time, compare more options and negotiate more carefully than they may have during previous markets.

Nationally, Canada’s housing market continued to improve gradually in June.

Home sales increased by 0.5% from May, marking a third consecutive monthly increase and placing activity approximately 7% above March levels. The National Composite MLS® Home Price Index was unchanged from May, while national housing inventory remained close to its long-term average.

The Canadian Real Estate Association also revised its housing-market forecast in July. CREA now expects national home sales to decline modestly by 1.4% in 2026 following a weaker first half of the year, but anticipates a more active second half.

Importantly for the Waterloo Region real estate market, Ontario is now the only province forecast to record an annual increase in home sales in 2026.

National sales are then forecast to rise by 3.7% in 2027 as stable interest rates, modest economic growth and pent-up demand gradually bring more buyers back into the market.

On July 15, the Bank of Canada held its overnight rate at 2.25% for a sixth consecutive meeting. Canada’s annual inflation rate also eased to 2.8% in June from 3.2% in May.

Taken together, these developments support a cautiously optimistic outlook. A dramatic rebound is not expected, but stable interest rates, moderating inflation and improving activity in Ontario may help strengthen buyer and seller confidence as we move toward the fall market.

June demonstrated that strong selling results are still possible, but they are not automatic.

The homes performing best are generally those that are:

• Thoughtfully prepared before reaching the market
• Priced according to current conditions
• Professionally staged and photographed
• Marketed directly to the most likely buyers
• Positioned clearly against competing properties
• Launched with a coordinated strategy from the outset

A home does not need to be perfect, but it does need a clear plan.

That plan should consider the property itself, the surrounding neighbourhood, competing listings, recent sales, the target buyer and the realities of the current market.

Overpricing a home to “test the market” can be especially risky. The first days of a listing are often when buyer interest is strongest. If the price does not align with the home’s condition and competition, buyers may move on before a price adjustment is made.

For buyers, the current Waterloo Region housing market continues to offer more choice, more time to make informed decisions and greater negotiating opportunities than the highly competitive markets of recent years.

That does not mean every home will sell below asking or without competition. Well-priced homes in desirable neighbourhoods can still sell quickly and receive multiple offers.

However, buyers generally have more opportunity to complete proper due diligence, compare available properties and negotiate based on condition, recent sales and market value.

Working with a knowledgeable local real estate team remains important, particularly when evaluating whether a property is priced fairly or determining how aggressively to approach an offer.

June brought slightly stronger sales activity than May, but Waterloo Region home prices remained under pressure and buyers continued to be selective.

The opportunity for a strong result still exists. However, preparation, pricing, presentation and marketing matter more in this market than they did when limited inventory was driving nearly every property toward a quick sale.

Whether you are considering buying, selling, downsizing or relocating, your strategy should be based on current neighbourhood-level information rather than broad headlines or sale prices from several months ago.

If you are wondering what your home may be worth in today’s Waterloo Region real estate market, our team would be happy to provide a personalized market assessment and help you understand the best strategy for your property.

The post Waterloo Region Real Estate Market Report | July 2026 appeared first on Kitchener Waterloo Real Estate Agent - The Deutschmann Team.

If you’re a leader, or in any kind of leadership position, of course, you need vision, integrity, communication & emotional intelligence. But honestly, we have been saying the same things about leadership since the invention of LinkedIn. And while all of those qualities matter, there is genuinely so much more to being a good leader.

As a coach, when I read some of the conversations around leadership, I sometimes wonder whether we are developing leaders or simply finding creative ways to describe the ability to just be a decent human being. So, I want to talk about some of the more basic qualities that don’t always make it onto a leadership competency framework. The things that may sound simple, but actually mean a lot to your team.

Not: “I don’t know, but let me find out.” Just: “I don’t know.”
You are a leader. You are not Google with a job title.
You do not need to have every answer, know every detail or be the smartest person in every room you walk into. Your job is not to know everything.
Your job is to create an environment where people feel comfortable enough to ask questions, share ideas & work out the answers together.

There is something deeply reassuring about a leader who can say: “I’m not sure. Let’s figure it out.” Because the moment you stop pretending to know everything, you give everyone else permission to stop pretending too & honestly, that is usually when the best thinking begins.

This sounds obvious. It isn’t. Especially when:

• You have already explained something three times.
• The person in front of you is making a mistake you made five years ago.
• You are under pressure & everything feels urgent.

A good leader understands that people do not all learn at the same speed, process information in the same way, or gain confidence on the same timeline. Patience does not mean lowering your standards. It means giving people enough space to meet them. Also, if you have to give feedback, you should be able to do it without making the person feel like a personal inconvenience.

You can be honest without being condescending. Or you can be direct without being difficult & you can hold people accountable without making them afraid of you. Because feedback should help people become better at what they do. It should not be an opportunity for you to demonstrate how much you know, how frustrated you are, or how quickly you would have done it yourself.

Everything cannot be urgent. Sometimes the deadline is genuinely critical & sometimes you’re just stressed. A real leader knows the difference. Because your anxiety can quickly become everyone else’s workload.

If your team can only succeed when you are the smartest person in the room, you have not built a team. You have built a very expensive fan club.

A good leader hires people who know things they don’t. People who challenge them & occasionally have better ideas. And then they don’t take it personally.

Your team succeeding does not make you less important. In fact, that is the point.

“I’m sorry I spoke to you that way.” Not: “I’m sorry, but you were also being difficult.” That is not an apology. That is a performance review with emotional lighting.

Remember being new or being nervous before a meeting? Remember making a mistake & waiting for your manager’s reaction. Remember not knowing who to ask or wanting to speak but being afraid you would sound stupid?

If you forget what it felt like to be at the beginning, you may eventually become the reason other people are afraid to begin.

Sometimes your team wins. Not you. Your team.
You do not need to remind everyone that you were in the room when the idea happened. You do not need to explain how you approved it. You can just say: “That was their work. They did an excellent job.” And mean it. Your LinkedIn profile will survive.

Not every quiet employee is disengaged. Not every person who doesn’t speak in meetings lacks confidence. Not every person who doesn’t want to join Friday drinks has a culture problem.

Sometimes people are tired. Sometimes they are introverts, they have a life outside work. A good leader supports people without constantly trying to redesign their personalities.

Because leadership is humbling. You will send an email with the wrong attachment. You will confidently say something that makes absolutely no sense. You will walk into the wrong meeting.

You will have a deeply profound leadership moment & then immediately trip over a chair. It happens. You should be able to laugh. Because the best leaders I have encountered are not the ones who have perfected leadership. They are the ones who are self-aware enough to notice when they are getting it wrong.

They know when to speak.
When to listen.
When to push.
When to step back.
When to give feedback.
When to give people time.
When to say, “I was wrong.”
And when to stop calling every problem a “learning opportunity” & admit that everyone is simply very tired.

And perhaps this is where emotional intelligence matters most. In my opinion, it is probably the most important quality a leader can have. Not because it sounds good on a leadership competency framework, but because it affects everything.

How you speak to people.
How you give feedback.
How you handle pressure.
How you respond when you are wrong.
How you make people feel when they are still learning.
Because leadership is not about becoming the most impressive person in the room.
It is about making the people around you feel more capable, more confident & more able to do their best work.

People may not remember every strategy you created or every meeting you led. But they will remember how they felt around you. I’ve worked with some truly exceptional leaders, & I know I’ve done some of my best work around the leaders who made me feel trusted enough to do it. Whether they felt safe enough to ask questions. People will remember;

Whether they could make mistakes & recover.
Whether your feedback helped them grow or simply made them feel small.
Whether you gave them time.
Whether you gave them credit.
Whether you were patient when they were still learning.
And whether, somewhere along the way, you made them believe they could do more than they thought they could.
Because ultimately, the most underrated leadership quality of all is simply this: Being a human being before being a leader.

That, to me, is leadership.

The Leadership Qualities Nobody Puts on the Job Description was originally published in Code Like A Girl on Medium, where people are continuing the conversation by highlighting and responding to this story.

I used to think Kafka was one of those tools you just name-drop in interviews and hope nobody asks a follow-up question. “We’d use Kafka for that” is a sentence that has gotten a lot of engineers through a lot of rounds without anyone actually explaining what happens after you say it.

So here’s my attempt at the version I wish I’d had going in. Top down, starting from a dumb example and ending at the stuff that actually separates a senior answer from a staff answer.

Imagine you’re running a website that shows real-time World Cup stats. A goal happens, you push an update. A player gets booked, you push an update. Simple enough with one server: a producer puts events on a queue, a consumer reads them off and updates the site.

Now blow the tournament up to a hypothetical 1,000 teams, all playing at once. Your single queue is drowning. Your consumer has its mouth under a firehose. You need more servers.

Here’s where it gets interesting. If you just randomly spray events across new servers, you get chaos. Goals get scored before kickoff. Players get booked for fouls that haven’t happened yet, because ordering just broke.

The fix is almost embarrassingly simple once you see it: distribute events by game ID. Every event for one game always lands on the same partition, so within that game, order is preserved. This is the entire idea Kafka is built around. Everything else is commentary.

• Broker — one server in the cluster. More brokers, more storage, more clients served.
• Partition — an ordered, append-only log. This is the actual unit of scale.
• Topic — a logical grouping of partitions. This is what you publish to and subscribe from.
• Producer / Consumer — write to topics, read from topics. Kafka doesn’t care what’s inside the message.

One thing that trips people up: a topic and a partition are not the same axis. Topic is logical, partition is physical. A topic can span partitions across many different brokers.

Kafka can be either, and the difference comes down to consumption pattern, not the underlying mechanics. Both modes track progress with offset commits.

As a queue: one consumer in a group handles a message, and it’s effectively done (Kafka still holds onto it based on retention, but logically it’s consumed).

As a stream: the log sticks around, it can be replayed, and multiple independent consumer groups can read the same data at their own pace.

Reach for the queue pattern when you have async work (transcode this video whenever), or when order matters (a virtual waiting room), or when you want producer and consumer to scale independently.

Reach for the stream pattern when you need continuous real-time processing (aggregating ad clicks as they happen), or when several totally different consumers need to see the same events (pushing live comments to multiple downstream services).

The Message Body

• Four fields, all technically optional: value (the actual payload), key, timestamp, and headers.
• Order within a partition is decided by offset (position in the log), not by timestamp.

How a message finds its partition (two steps)

• Step 1: Kafka hashes the key and hash(key) % num_partitions to pick a partition. No key? It falls back to a default partitioner that spreads messages round the partitions.
• Step 2: the cluster controller’s metadata tells the producer which broker holds that partition, and the message is sent straight there.

Once it lands: the immutable log

• Messages are appended only, never edited in place.
• Old data leaves only through retention (time/size limits) or compaction (keeping just the latest value per key).
• That immutability is what makes replication simple, recovery fast, and avoids a whole class of bugs that come from mutable state.

Durability: leader-follower replication

• One broker is the leader for a partition, handling all writes (and by default, reads).
• Followers replicate passively in the background.
• If the leader dies, an in-sync follower gets promoted.
• acks=all is the strongest guarantee: a message is only acknowledged once every in-sync replica has it.

Consumers pull, they’re never pushed to

• A deliberate design choice: consumers set their own pace, failure handling stays simple, and one slow consumer can’t take down the whole pipeline.

Key path: the message’s key gets hashed to pick a partition, the leader broker for that partition takes the write, followers replicate it, and it lands in the log at the next offset — ordered by position, not by timestamp.

This is the section that’s easy to skip when you’re first learning Kafka, and the section that gets asked about the moment you’re past mid-level.

One broker on decent hardware holds around 1TB and can push something like a million messages a second. That number is doing a lot of hand waving, since it depends heavily on message size and hardware, but it’s a reasonable anchor to reason from.

A couple of hard rules that come up fast in practice:

• Keep individual messages under 1MB.
• Never put a video file or a big blob directly into Kafka. Drop it in S3, and put the S3 pointer in the message instead.

Kafka is a log, not a filesystem. Treat it like one.

Adding brokers only helps if your topics already have enough partitions to spread across them. This is where an interview answer should live: not “we’ll add more brokers,” but “here’s how I’m choosing the partition key.”

The classic setup: you’re aggregating ad clicks, partitioned by ad ID, and a celebrity ad drops. One partition melts while the rest sit idle.

There are four moves here, and a good answer picks the right one for the situation instead of reciting all four:

1. Salt the key — append a random suffix (or a hash of something high-cardinality) to spread one hot key across multiple partitions.
2. Repartition with a better key — if one dimension is naturally skewed, partition on something more evenly distributed instead.
3. Over-partition upfront — provision more partitions than you think you need, so a spike has somewhere to spread to.
4. Isolate the hot key — give known hot entities (a celebrity account, a major ad campaign) their own dedicated partition or topic, so they don’t drag down everyone else’s throughput.

Worth internalizing: Kafka is always available, sometimes consistent.

If an interviewer asks “what happens when Kafka goes down,” that’s honestly not a very realistic question, and it’s fine to push back on it gently. What’s actually realistic is a consumer dying.

When that happens:

• Its partitions get rebalanced across the surviving consumers.
• On restart, it picks up from its last committed offset.

The catch: don’t commit that offset until the real side effect (writing to blob storage, whatever it is) is actually durable, or you’ll lose work on a crash.

• Producers get automatic retries. Pair that with idempotent producer mode so retries don’t create duplicates.
• Consumers get nothing out of the box. SQS gives you this for free, Kafka doesn’t. You build it yourself: a retry topic, a dedicated consumer for it, and a dead letter queue for anything that fails too many times.

This is actually why some designs pick SQS over Kafka outright, just to skip building this by hand.

Batching messages and compressing them (GZIP, Snappy, LZ4) both help, but neither moves the needle as a good key does. If you only have time to talk about one lever in an interview, make it this one.

Default is 7 days, controlled by retention.ms or retention.bytes. Need longer history for a design? Turn the dial up, and be ready to talk about the storage cost that comes with it.

If you remember nothing else: producers write to topics, topics are made of ordered append-only partitions spread across brokers, consumers pull from them, and almost every interesting design decision in the system comes down to how you pick your partition key. Everything above is really just detail on that one idea.

I used to think the interview-ready version of Kafka was knowing the vocabulary. It’s not. It’s being able to say “here’s how I’d pick the key” without blinking, and being honest that Kafka going down is a less interesting question than a consumer dying mid-batch.

Kafka is always available, sometimes consistent. Design accordingly!

I Said “We’d Use Kafka” in a Dozen Interviews Before I Actually Understood It was originally published in Code Like A Girl on Medium, where people are continuing the conversation by highlighting and responding to this story.

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We’re delighted to share some exciting news and updates at Child Witness Centre.

The post Summer Newsletter: Bright & Uplifting Highlights 🌞😎 first appeared on Child Witness Centre.

In this blog we present Aquanty's recent research into the use of next-generation hybrid "delta-models". Delta-models combine the advantages of data-driven/machine learning techniques and traditional physics-based hydrologic models. A delta-model retains the conceptual structure of a hydrologic model, including state variables such as snow water equivalent and soil moisture, while using differentiable, data-driven training to learn model parameters and improve streamflow simulation performance.

Canadian Lake River Hydrofabric (CLRH)
Tolson, B. A., J. R. Craig, S. Lin, P. Aberi, M. Han, J. Wiebe, S. Babra, S. Kolev, M. Stapleton, H. Shen (2023). Canadian Lake and River Hydrofabric (CLRH) version 1. 2023 Canadian Water Resources Association Annual Conference, June 19-21, 2023, Halifax, Canada.

The Bighorn dam is located in the foothills of the Canadian Rockies in Alberta and is one of TransAlta's major hydroelectric facilities, with a capacity of 120 MW and an average annual generation of approximately 408,000 MWh (Bighorn - TransAlta). Reservoir planning is important for hydropower operations because operators must balance water availability, storage constraints, generation demand, flood risk, and downstream flow requirements. In snowmelt dominated basins (like this one), reservoir inflow relies on both current streamflow and upstream watershed conditions which determine future water volumes over coming days, weeks, and months. These conditions include the amount of water stored as snowpack, the timing/rate of snowmelt, antecedent soil wetness, incoming precipitation, and changes in temperature. Therefore, hydrologic modelling can provide support by mapping meteorological inputs and watershed states into expected inflow timing and volume. A streamflow simulation provides an estimate of incoming water volume, while diagnostic state variables such as snow water equivalent (SWE) and soil wetness help explain whether the basin is primed for sustained runoff. This context can help distinguish between short-lived flow responses and broader watershed conditions that may support persistent flow. In this case study, we examine the North Saskatchewan River at Whirlpool Point (05DA009, 1920 km²), a primary upstream tributary contributing to Lake Abraham. The objective is to demonstrate how hydrologic streamflow simulations, paired with physically meaningful watershed state diagnostics can support reservoir inflow interpretation and planning to help answer questions such as:

• Is the basin storing above normal snow going into the freshet?

• Has the basin transitioned from stored snow into active melt?

• Is inflow likely to persist after the initial rise?

• Are inflows likely snowmelt-driven or rainfall driven?

This case study also highlights an emerging capability within HydroSphereAI. While HydroSphereAI currently provides AI-based streamflow forecasting, this work demonstrates how next-generation hybrid "delta-models" can extend the platform by combining the predictive performance of machine learning with the interpretability of process-based hydrologic models. The result is a forecasting approach that not only predicts reservoir inflows but also provides insight into the watershed conditions driving those forecasts, making them more transparent and actionable for operational decision-making.

Simulated and Observed Streamflow and SWE at Station 05DA009 (2014-2024)

Hydrologic inflow forecasting for reservoir operations is commonly executed through process-based models or data-driven machine learning models. Classical process-based models (e.g., HBV) represent hydrologic processes such as snow accumulation, snowmelt, soil moisture storage, runoff generation, and routing through explicit conceptual or physically based equations. These models are valuable because their internal states can be interpreted, but they often require careful calibration, basin-specific assumptions, and manual effort across many watersheds. In contrast, machine learning models (e.g., LSTM) can learn complex relationships between meteorological inputs, basin specific attributes, and streamflow across numerous watersheds simultaneously from data. These models can achieve strong predictive performance, often better than process-based models, especially when sufficient historical observations are available. However, purely data-driven models are often more difficult to interpret (i.e., a black-box model) because their internal representations do not directly correspond to recognizable hydrologic states such as snowpack or soil moisture. For reservoir operators, this can make it harder to determine why a forecast is changing.

On the HydroClimateSight platform, we are improving our HydroSphereAI machine learning based forecasting capabilities through hybrid delta-models, which combine the advantages of both of these modelling approaches (Feng et al, 2023).

A delta-model retains the conceptual structure of a hydrologic model, including state variables such as snow water equivalent and soil moisture, while using differentiable, data-driven training to learn model parameters and improve streamflow simulation performance. As a result, the model can provide strong predictive performance, similar to LSTM models, while preserving hydrologically interpretable states and the mass-conserving structure. This makes the model useful not only for streamflow prediction, but also as a watershed state diagnostic tool for reservoir planning.

In this case study, we use δHBV, a differentiable version of the widely-used HBV model that has demonstrated strong regional performance, including in ungauged basins (Song et al, 2025). By combining the interpretability of the HBV hydrological model with the trainability of modern data-driven approaches, δHBV provides a useful framework for examining both reservoir inflow simulations and the upstream watershed states that help explain those flows.

Using Watershed State Diagnostics for Reservoir Operations at Station 05DA009 (March 14 to September 30, 2021)

Model Setup and Simulation

In this application, the model was trained across more than 500 basins in Canada, using only streamflow observations as the calibration target (Water Survey of Canada - Canada.ca). The model demonstrated strong performance for station 05DA009 (North Saskatchewan River at Whirlpool Point in Alberta) when evaluated on unseen test period data, indicating that it was able to reproduce the streamflow dynamics at this upstream reservoir tributary. In addition to streamflow,  the extracted δHBV SWE storage showed strong agreement with ERA5-Land SWE, an independent land-surface reanalysis product. Although ERA5-Land is not a direct observation of snowpack, this comparison provides supporting evidence that the δHBV's internal snow state is physically plausible and useful as a diagnostic tool.

Watershed Aggregated Diagnostics

To illustrate how these diagnostic states can support reservoir inflow interpretation, we focus on the 2021 freshet period as a case-study window. The 2021 freshet produced the highest daily peak flow at 05DA009 during the 2014–2024 test period, making it a useful example for examining how the model represents a reservoir-relevant high-inflow year. The objective is not only to evaluate the simulated hydrograph, but to demonstrate how modelled SWE storage, soil wetness, precipitation, and temperature can be interpreted together to explain the upstream basin conditions that contributed to the sustained inflow response.

For 05DA009, the 2021 freshet is better interpreted as a stored-water-volume event rather than a short-duration peak-flow event. During the accumulation and pre-melt period, observed inflow remained low, but δHBV diagnosed roughly 400-490 mm of upstream SWE storage, with SWE in the upper quartile of the day-of-year percentile range. This provided an early indication that the basin contained substantial water available for future inflow before the reservoir observed a major hydrograph response.

During active melt, the model diagnosed rapid snow depletion and a sharp increase in soil wetness. Between active melt and the peak inflow periods, δHBV active SWE storage decreased approximately 382 mm, equivalent to roughly 733 million m³ of water over the basin. Observed inflow over the same period was about 723 million m³. This comparison is a key interpretability result: the model states connect the observed inflow volume to a physically meaningful depletion of upstream snow storage, rather than treating the hydrograph as an isolated discharge signal. Nevertheless, the close agreement in SWE depletion volume and observed inflow volume should only be interpreted as a diagnostic consistency check, not a closed basin water balance proof.

The peak flow on July 2 was important, but it does not fully represent key reservoir relevant decision context for this event. Approximately 63% of the total event inflow volume occurred on and after the peak date, meaning the operational concern was not only the maximum daily discharge, but also the persistence of elevated flows after the peak. The δHBV cumulative-volume curve captured the timing of the incoming volume well, although discrepancies between the simulated and observed cumulative inflow became more apparent during the peak inflow stage.

Animation showing simulated SWE shows how active snowpack storage evolves across the basin through 2020-2021.

After early July, SWE storage had largely depleted and streamflow entered a gradual recession period. At this stage, the dominant flow appears to shift from snowmelt supply toward delayed watershed drainage and baseflow contributions. Later rainfall events are reflected more clearly in the soil wetness index than in the simulated streamflow response (i.e., precipitation is recharging soil moisture rather than producing substantial inflow). For reservoir operations, this distinction is important because the main drivers of inflow (e.g., snowmelt, baseflow, rainfall) imply different planning horizons and operation considerations.

Spatial Watershed Diagnostics

In addition to outlet streamflow and basin averaged state variables, δHBV can also provide spatially distributed diagnostics across the upstream watershed. This is important because reservoir inflow at the outlet integrates many upstream subbasins that can differ in snow accumulation, melt timing, runoff generation, and travel time.

The SWE animation shows how modelled active snowpack storage evolves across the basin through 2020-2021 water year. Here, "active" means the snow storage represented within δHBV's hydrologic state: the portion of snowpack that participates in modelled accumulation, melt, and runoff-generation processes. Rather than representing the basin as a single lumped snow reservoir, the semi-distributed structure allows users to inspect where the model stores snow, how spatial accumulation patterns evolve through winter, and which parts of the basin begin depleting during spring freshet. In this case, the animation indicates larger snow storage in the western, higher elevation subbasins, followed by steady depletion as melt conditions develop. This provides context for why inflow can remain elevated after the initial streamflow rise: upstream active snow storage may still be available even after lower elevation areas begin melting.

A streamflow animation showing how simulated runoff is transferred through the river network toward the 05DA009 outlet.

The routed-flow animation shows how simulated runoff is transferred through the river network toward the 05DA009 outlet. This adds a spatial interpretation to the hydrograph by showing which reaches carry larger flows at different points in the event and how upstream runoff signals propagate downstream. For reservoir operations, this helps distinguish between localized runoff responses from broader basin-wide inflow events. A localized response may create a short-lived rise, whereas widespread high flows across multiple tributaries will generate more sustained reservoir inflow.

Together, these spatial diagnostics demonstrate that δHBV is not limited to producing a single outlet forecast and single basin averaged state. This model can provide an interpretable spatial view of the upstream watershed storage, runoff generation, and downstream routing. This helps users evaluate not only how much inflow may occur, but where that water is coming from and how it moves through the basin. For reservoir planning, this spatial context can make inflow forecasts more transparent and operationally useful, especially during snowmelt events where differences in elevation, aspect, and timing can strongly influence peak flow and inflow persistence.

Conclusion

The streamflow hydrograph tells operators what happens at the gauge; the δHBV state diagnostics help explain why it happens and whether similar inflow conditions are likely to persist. In this case, the model suggests that the basin entered the freshet period with above-normal, but not extreme, SWE conditions. A subsequent warming period triggered the transition to active melt, contributing directly to reservoir inflow as well as basin wetness, which helped to sustain inflows throughout the summer.

This case study demonstrates how HydroSphereAI can evolve beyond streamflow prediction by incorporating process-informed, data-driven delta-models that provide both accurate forecasts and interpretable watershed diagnostics for reservoir operations. Instead of relying only on forecasted discharge, reservoir managers can inspect the watershed state diagnostics to evaluate where the water is coming from, whether the basin is primed for sustained inflow, and whether the future risk is snowmelt-driven, rainfall-driven, or mixed. Physically interpretable model states can improve hydrologic awareness and decision-making by making forecasts more explainable, auditable, and actionable than a "black-box" streamflow prediction.

References

Feng, D., Liu, J., Lawson, K., & Shen, C. (2022). Differentiable, learnable, regionalized process-based models with physical outputs can approach state-of-the-art hydrologic prediction accuracy. arXiv. doi.org/10.48550/ARXIV.2203.14827

Song, Y., Bindas, T., Shen, C., Ji, H., Knoben, W. J. M., Lonzarich, L., Clark, M. P., Liu, J., van Werkhoven, K., Lamont, S., Denno, M., Pan, M., Yang, Y., Rapp, J., Kumar, M., Rahmani, F., Thébault, C., Adkins, R., Halgren, J., … Lawson, K. (2025). High‐Resolution National‐Scale Water Modeling Is Enhanced by Multiscale Differentiable Physics‐Informed Machine Learning. Water Resources Research, 61(4). doi.org/10.1029/2024wr038928

Tolson, B. A., J. R. Craig, S. Lin, P. Aberi, M. Han, J. Wiebe, S. Babra, S. Kolev, M. Stapleton, H. Shen (2023). Canadian Lake and River Hydrofabric (CLRH) version 1. 2023 Canadian Water Resources Association Annual Conference, June 19-21, 2023, Halifax, Canada.

Wow! Today's K-W Record advises us that our vaunted LRT (Light Rail Transit) is having more than teething pains. The lawsuit by  GrandLinq is for $110 million. That's a chunk of change . Regardless of who's right, who's wrong and whether or not we taxpayers yet again end up on the hook; it tends to make us at the very least ask the question: "Are we getting value for money in our political governance?" Or do we have a lot of dead wood on regional council who are simply literally bums in chairs pretending to be administrators and managers of the public interest? Is it possible that half or more of them simply show up in order to receive their pay cheques with absolutely no  concern for the issues or the public interest? It has been said that the greatest and most important skill of a politician is their acting abilities. Pretending to care and pretending to want to resolve problems and disputes would seem beneficial in giving hope and confidence to citizens and residents.

If and when the entire truth is exposed around the destruction of our fabulous water resources; I predict there will be a wholesale abandoning of the ship of state. Politicians may quickly decide that early retirement sure beats being shamed and harassed into resigning mid term. I predict that as early as tomorrow or as late as the August 21 deadline for registering to run in this fall's municipal election; many rats will give varying excuses for stepping down. 

The Region have most recently mistreated Wilmot residents both with the still surreptitious huge land grab as well as the refusal to acknowledge well interference even after breaking the 1980 agreement with Wilmot over the Wilmot Centre Wellfield pumping. They have also broken faith with Woolwich (Elmira) residents for the last 36 years regarding our contaminated local aquifers caused by both private, industrial greed and negligence as well as by provincial oversight gross failures.  The Region's day of reckoning is overdue.

♦

Whatever organizations decree these kinds of things have seen fit to announce that there are not one but two “National Crème Brûlée” days, July 21 and July 27. Perhaps it is an indication of how popular the classic dessert is, so the laurels are likely well-deserved.

It’s true that at one time or another every chef makes a crème brûlée and just about every restaurant has it among its desserts; spend a few moments scanning menus, and I’m sure you will find several.

But popular hardly does it justice: there are crème brûlée recipes, attributed to Francois Massialot, dating to the French court of 1692. That’s over 300 years of the treat.

A custard of scalded milk, sugar and egg yolks, crème brûlée means “burned cream” which entails creating and baking in a water bath a soft, silken custard and chilling it before adding sugar to the top and caramelizing it, either under a hot broiler (in a restaurant kitchen, a salamander) or the fun way with a propane torch.

The key is to keep a cool, silky interior while getting just the right crispness – and not a rock-solid layer of impenetrable sugar – on the top for a well-balanced crunch.

Another reason for the dual national days, aside from its deliciousness, is the versatility that a crème brûlée has: that custard is a blank slate that can scrumptiously admit a vast array of sweet and savoury ingredients, from simple vanilla and chocolate to lavender and maple-bacon.

[Banner image/Wikimedia Commons]

Check out my latest post National Crème Brûlée Day from AndrewCoppolino.com.

Musy, S. L., Dresmann, H., Tomonaga, Y., Sano, Y., & Schilling, O. S. (2025). Modeling a geologically complex volcanic watershed for integrated water resources management in Mt. Fuji, Japan. Scientific Data, 13 (1). doi.org/10.1038/s41597-025-06380-z

CLICK HERE TO READ THE ARTICLE.

“Although HGS supports both finite element and finite difference methods, the present model was developed using the finite element approach. Accordingly, mesh generation was carried out with the finite element mesh generation software AlgoMesh using line and polygon vector files as input. The catchment contours, resampled at a 400 m resolution, define the outer extent of the mesh.”

Fig. 4. Set up for the integrated hydrogeological model of Mt. Fuji catchment (Japan); (a) Elemental zones in the porous medium domain, corresponding to the different hydrofacies in Table 1, as well as the lake and riverbeds (RB) and fault elements, corresponding to the lake and river beds, resp. to the fault zone; (b) Elemental zones for the overland flow domain, corresponding to the categories in Table 3; (c) Model boundary conditions: blue diamonds represent the critical depth boundary conditions for the surface domain in the Suruga Bay area, on the river outflows and on the catchment contours. The Fuji River Termination (SW) and Kise River effluent (SE) are set to no-flow boundary conditions (red spheres). The green boxes represent the fixed head for the subsurface domain in the Suruga bay area, and to the river elevation in the North and East outlets (not visible from this point of view). For the lateral subsurface boundaries for which no other boundary conditions are indicated, as well as for the base of the model, no flow conditions were imposed (not shown).

This publication presents a three-dimensional geological and integrated hydrological modelling dataset developed for the Mt. Fuji volcanic watershed in Japan. This study leverages HydroGeoSphere (HGS) to simulate coupled surface–subsurface flow and transport processes in a geologically complex volcanic catchment, addressing long-standing challenges in representing groundwater flow pathways and hydrologic interactions in structurally heterogeneous mountain environments. The resulting dataset provides a physically consistent modelling framework to support interdisciplinary water resources research and scenario-based hydrologic simulations.

Hydrologic analyses in volcanic terrains are often limited by sparse subsurface observations and simplified conceptual representations of hydrofacies structure. While geological datasets and monitoring networks provide valuable constraints, they rarely capture the full complexity of fractured volcanic aquifers and fault-controlled flow systems. By integrating stratigraphic interpretation, hydrofacies mapping, land-cover parameterization, and numerical mesh development within a HydroGeoSphere modelling workflow, this research establishes a catchment-scale framework capable of representing interactions between surface water, groundwater, and vegetation processes under realistic boundary conditions.

The study applied the HGS model to the Mt. Fuji watershed using a structured workflow that combined geological cross-sections, borehole data, hydrofacies surfaces, and land-use datasets to construct a three-dimensional representation of subsurface structure and hydrologic properties. Simulations successfully reproduced major surface-water features while capturing groundwater flow behaviour across contrasting aquifer thicknesses separated by regional fault systems. Results demonstrated that even a computationally efficient model configuration could reproduce realistic hydrologic system behaviour while supporting sensitivity analyses and scenario-based simulations for future research applications.

Key findings showed that integrated geological–hydrological modelling provides a robust foundation for evaluating groundwater dynamics in volcanic catchments, where structural uncertainty and limited calibration data often restrict predictive modelling efforts. The dataset enables further investigation of fault-controlled flow discontinuities, hydrofacies uncertainty, and climate-driven hydrologic variability within a unified modelling framework designed for extension through automated calibration and scenario testing.

HydroGeoSphere proved essential in enabling this work due to its ability to simulate fully integrated surface flow, variably saturated subsurface flow, discrete fracture flow, vegetation–land–water interactions, density-dependent transport, and winter hydrological processes within a single modelling platform. These capabilities allowed the researchers to construct a catchment-scale representation of the Mt. Fuji hydrologic system that captures the multi-process complexity characteristic of volcanic watersheds.

This research provides critical insights for integrated watershed modelling and groundwater resource assessment in geologically complex regions, demonstrating that advanced modelling approaches like HydroGeoSphere can support the development of reproducible, extensible hydrologic datasets for interdisciplinary research and long-term water management planning.

Abstract:

This dataset provides high-resolution 3D geological and integrated hydrological models of Mt. Fuji watershed in Japan. The watershed’s complex volcanic and tectonic setting, large spatial extent, and limited subsurface data present significant challenges for integrated hydrological modeling. Diverse geological datasets – borehole logs, geological maps, and hydrofacies surfaces – were collected, processed, and used to construct and validate a 3D geological model suitable for integrated hydrological simulations. Building on this, a 3D numerical model for integrated hydrological simulations was constructed. The repository includes 3D hydrofacies surfaces in raster format, numerical mesh files, and input configurations necessary to run simulations with the integrated surface-subsurface hydrological simulator HydroGeoSphere. The preparation of heterogeneous geological data, construction of hydrofacies surfaces, generation of the numerical mesh, and setup of the integrated hydrological model are described in a streamlined, reproducible workflow suited for volcanic contexts and transferable to other geologically complex or data-limited regions. These resources are intended to reduce trial-and-error iterations and support further research in groundwater assessment, model calibration, climate impact studies, and hazard mitigation.

CLICK HERE TO READ THE ARTICLE.

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