A key challenge in groundwater management is understanding the shape and structure of aquifers that lie hidden below the surface. By constructing 3D models of hydrostratigraphic units using well logs, drill cores, and geophysical surveys, you can visualize the true geometry of aquifers and aquitards. This enables stakeholders to clearly see thickness, depth, and boundaries, all of which are crucial for identifying storage capacity and vulnerable recharge zones. The result? More accurate water budgeting, improved regulation, and a stronger foundation for sustainable pumping practices that prevent over-extraction.

The relationship between rivers and aquifers is complex. And in 2D, it’s often misunderstood. Integrating surface topography, river bathymetry, and subsurface models into a single 3D data visualization reveals how and where these systems interact. For instance, you can pinpoint where a river is gaining water from an aquifer or losing it to recharge the groundwater below. This visual clarity leads to better environmental protection, equipping resource managers to define minimum environmental flows, mitigate drought impacts, and safeguard ecosystems that depend on balanced water exchange.

When contamination occurs, response time and precision matter. 3D data visualization helps you create volumetric renderings of contaminant plumes based on monitoring well data. By overlaying these plumes with geological layers and satellite imagery, you clearly see the full path and spread of contamination in context. This helps you strategically place recovery wells in the most effective locations while giving regulators and nearby communities a transparent, easy-to-understand visual of the risks and remediation plan.

Flood risk management depends on understanding how water flows across terrain—and 3D data visualization delivers that insight with clarity. Using high-resolution elevation models, hydrologists can create 3D watershed models that visualize flow paths, drainage basins, and inundation depths during flood events. These models highlight high-risk areas for erosion, overflow, or deep flooding, helping planners design safer infrastructure, develop targeted mitigation strategies, and create more intuitive evacuation maps that could save lives.

At first glance, a lagoon might look calm and still, but below the surface, sludge accumulates year after year. That buildup can drastically reduce the lagoon’s retention time, creating a costly liability—but understanding when that liability actually needs to be treated is difficult. Fortunately, that’s where Water Treat Technology comes in.

Using an RC boat equipped with sonar and GPS, Robert’s team collects depth readings across the entire lagoon. They run roughly 30 to 40 sludge surveys each year—and sometimes more—for clients who treat their lagoons as valuable assets. 

“The lagoon is basically an asset, and the sludge is a liability,” Robert explained. “So our customers want regular updates. They want to know what the liability is and when to start planning for removal.”

Once the data is collected, Robert and his team need to communicate it effectively. After all, data is valuable—but only when it’s understandable. To get the job done well, Water Treat Technology relies on Surfer and its data visualization tools. 

“There are plenty of programs out there that give us the numbers,” Robert said. “But what we like about Surfer is that it gives us clean, palatable, and professional visuals.”

Using Surfer, Robert’s team creates clear, compelling, and accurate 3D topographic maps that help stakeholders—from wastewater operators to board members—actually see the sludge that’s building inside their lagoons. These 3D visuals put everything into perspective, giving stakeholders the insight they need but would’ve struggled to get with a 2D format.

“People don’t always understand what a topographic map is,” Robert said. “But show them a 3D image and it clicks.”

That clarity is essential when Robert’s clients must make important decisions about sludge removal or grant applications. Because his team wraps all the data into one compelling, easy-to-understand visual that ties the technical and the practical together, manufacturers and municipalities know when it’s time to treat their lagoons and can pursue grants if necessary. 

Understanding when it’s time for treatment isn’t the only benefit Water Treat Technology provides. Robert and his team also offer strategic value to every project using Surfer’s 3D View.

“When looking at a body of water, it’s hard to know where the sludge is just from the numbers,” Robert explained. “But Surfer’s 3D View gives us the ability to actually see where the buildup is occurring and why it’s happening there.” 

This kind of information helps Robert’s team not only diagnose sludge accumulation but also strategize where to treat or remove it. Whether they’re recommending targeted aeration or  pinpointing ideal treatment zones, the models made in Surfer’s 3D View help Water Treat Technology back up every recommendation with clear, visual evidence. 

“The 3D images offer a very good visual reference, especially whenever we have to present to somebody in a governing agency and need to tell them what we want to do and why we want to do it,” Robert said. “The visual makes it all click. It puts two and two together so they can say, ‘Okay, if we get rid of this big pile of sludge here, that’ll lead to the most effective treatment.” 

These 3D images are also a major advantage if stakeholders in the governing agencies aren’t familiar with wastewater and its related terms. With the clarity and straightforward nature of 3D visualizations, everyone in the room can get on the same page regarding the issue and precise strategy.

While Robert’s team is seeing success with Surfer, it wasn’t always easy to quickly create amazing data visualizations. The right workflow was needed, and two people helped provide it: Water Treat Technology’s Software Analyst Nikki Ellis and Golden Software’s Customer Support Engineer Rachel VanOsdol. Together, they helped Water Treat Technology develop a streamlined workflow tailored specifically to the company’s needs. The same process that used to take days can be done in a matter of minutes.

“We spent a long time developing it,” Robert explained. “But it’s completely transformed the way we do things.” 

His field team can now collect data, process it, and deliver visual results to the client—all in the same day. Sometimes, Field Technician Eric Engele even handles the mapping on-site and emails the results to Robert in real time. That speed and flexibility have set Water Treat Technology apart. 

“We’re literally able to give customers visual reports right there if we need to,” Robert said. “That’s definitely set us apart from everybody else.” 

This efficient and effective Surfer workflow was something Rachel was committed to providing, a desire that’s characteristic of Golden Software’s customer support team. She had no problem partnering with Nikki to accomplish that goal and ensure Robert’s team had a process that produced high-quality final outputs.

“Rachel has done a fantastic job,” Robert said. “She’s so nice to work with. She knows Surfer inside and out. She’s very knowledgeable about it, and she puts it in simple terms that we can understand. When we hit a snag, she helps us get through it.”

That kind of personable, knowledgeable support has proven invaluable—especially when dealing with complex projects. In one recent case, Robert’s team ran into challenges, which led them to reach out to Rachel for assistance. 

“Field Technician Eric was working on a sludge survey project in Canada, and he had to call Rachel because we couldn’t figure out an issue,” Robert recalled. “Well, Canada uses a different set of georeferences, and Rachel gave them to Eric. Then, we were able to continue and give the customer what he wanted. It’s rare that you find somebody who knows their software like a second language, but she’s so fluent in it that it makes it easier for us to do our job.” 

The story of SisBaHiA begins with a moment of both opportunity and bold thinking. In the late 1980s, Professor Rosman had just earned his doctorate in coastal engineering from MIT and was already making strides in the field of environmental hydrodynamics. His early research centered around simulating the complex interactions of natural water bodies—places where ocean tides, river flows, and salinity meet in constantly shifting patterns.

Fast forward to the late 1990s, and a major project in Brazil set the stage for SisBaHiA’s breakthrough. The state of Bahia launched an international competition to select a modeling system for managing Todos los Santos Bay—a massive estuarine system in Bahia. The winning bid came from CH2M Hill, a global environmental consulting powerhouse at the time, which partnered with a local firm in Bahia.

Initially, Professor Rosman was brought in as a translator and technical expert to help adapt CH2M Hill’s proposed American model to Portuguese and teach local agencies how to use it. But during discussions, he made a daring offer: rather than translating their model, he proposed that they use the environmental hydrodynamics model he and his team had developed at the Federal University of Rio.

“I was a little audacious,” Professor Rosman said. “I told the people from CH2M Hill, ‘Listen, the model we developed at the university is better than this one that you are proposing.’ They said, ‘Oh, is that so? We want to see it.'”

Professor Rosman and his team’s model was built to tackle the unique challenges of bays and estuaries and deliver more accurate, meaningful insights. After a demonstration of the model’s capabilities, CH2M Hill saw the potential and agreed to pivot, making Professor Rosman’s role shift dramatically—from translator to principal developer—and SisBaHiA was born.

Before SisBaHiA could take center stage, Professor Rosman knew the system needed to be accessible and effective for users. Ultimately, this meant two things. First, the model needed to perform well, and the person who stepped in to achieve that goal was Patricia Rosman, Professor Rosman’s wife who’s been behind the evolutive maintenance of SisBaHiA since its first version launched in July 2000. Secondly, the model had to offer more than just advanced hydrodynamic calculations. It needed to deliver a user-friendly interface that could display the power of its results—clear visuals, maps, time series data, and dynamic insights that could be easily shared and understood.

This is where Surfer and Grapher came in. As Professor Rosman and his team at the university began to shape SisBaHiA into a tool that scientists and engineers could readily use, they recognized that Surfer and Grapher offered exactly what they needed: robust visualization tools that could be integrated into another program.

With Surfer and Grapher’s capabilities to create maps, plots, and time series graphs—while being able to operate as subroutines within a larger system—SisBaHiA found a perfect match. From then on, the two data visualization tools became an integral part of SisBaHiA’s DNA, bridging the gap between advanced hydrodynamic modeling and easily creating clear, compelling visual outputs.

With an innovative but user-friendly modeling system ready to go, Professor Rosman and his team made SisBaHiA broadly available. Since then, it’s been helping users visualize and analyze their data so they can effectively manage water resources in Brazil and beyond.

For instance, users working on mesh and domain modeling can create detailed bathymetry maps, highlight bottom roughness, or generate colorful isoline maps that show every contour in vivid detail. And when it comes to simulating dynamic environments—like flowing water, shifting currents, or changing water quality—the program helps users dive deep. They can visualize everything from water velocity to water levels to other hydrodynamic patterns in real time. They can also see how water flows from a lagoon to the sea by mapping that journey in a way that’s both intuitive and impressive, equipping them to spot issues and share solutions with ease.

“Whatever the user wants to do, they can do,” Professor Rosman explained. “Since they’re in Surfer when using SisBaHiA, there’s liberty for them to manipulate the map the way they want, so it’s really, really effective.”

SisBaHiA also gives users the power to explore time-series data in rich, interactive ways. For example, if they want to take the readings from multiple monitoring stations and turn them into detailed graphs that show how dissolved oxygen, nutrients, or salinity levels change over time, the Grapher integration in SisBaHia won’t just plot the number—it’ll open up a whole world of statistical analysis, trend line modeling, and post-processing, helping users uncover trends and make smarter decisions.

“Grapher offers a ton of post-processing options,” Professor Rosman said. “It’s very, very powerful, not only because you can put the result you want in a time series, but you can use the whole power of Grapher to calculate statistics and trend lines and do all sorts of stuff. It’s fantastic.”

Because SisBaHiA is such a powerful modeling system, it’s tackling some of the toughest environmental challenges in Brazil. One critical challenge is centered around a lagoon system in the bustling metropolitan area of Rio de Janeiro. The lagoon, like many others, faces serious environmental issues, including pollution, sediment buildup, and declining water quality. And while there’s a big push to restore it, there’s also a need to know what actions will actually make a difference. That’s where SisBaHiA has made an impact. 

Recently, a sewage management company took on the enormous task of managing and cleaning up the lagoon system. They’re required to invest the equivalent of $50 to $60 million in the first year alone. But before making any decisions—like whether to dredge the canals or upgrade the sewage outflows—they turned to SisBaHiA for answers. With this modeling system, they could simulate countless scenarios and pinpoint the most effective investments. They didn’t have to rely on guesswork. Instead, they could see exactly how different interventions would impact water quality, sediment patterns, and overall environmental health—and the stakes were high.

Community members expected that a $60 million investment would magically fix the entire lagoon system, but as Professor Rosman explained to them, that’s just the start. True restoration would take more effort and potentially involve not just private companies but also public investments from the state and local municipalities.

“It’s not in the power of the sewage company to operate it alone,” Professor Rosman said frankly. “How can we demonstrate that? Well, you can demonstrate that by using the SisBaHiA. We could simulate what sort of improvements $60 million would give to the lagoon system and how much more you’d have to invest to really get things done. We simulated a number of scenarios, considering different levels of investment up to nearly $500 million. This modeling system has been used for decision-making involving this sort of investment into the lagoon system.”

First things first: what insights does Igor’s plot reveal? At its core, the visualization is a window into the changing dynamics and climate state of the Labrador Sea, a critical area between Greenland and Canada. This sea is one of the few places in the world’s ocean where deep convection happens, a process of intense vertical ocean mixing driven by cold winter air outbreaks that cool the ocean surface, increasing the density of surface water and causing it to sink and mix with the water below. During some years, this process of vertical mixing is capable of homogenizing to “visible” uniformity layers up to 2,500 meters thick. However, during mild winters, most of the water column remains unmixed, with convection reaching just a few hundred meters. Given the variability, creating accurate, thorough, and detailed visualizations of this process is instrumental for ocean research and mapping year-to-year changes and trends. That’s where Igor’s plot enters the picture.

The visualization Igor created illustrates the temperature and salinity of the water column in the Labrador Sea over multiple years. One of the most striking insights revealed by the plot is the freshening of the Labrador Sea in the 1990s. As more freshwater entered the system—whether through continents, air, or melted sea ice—it mixed with the saltwater, making the upper layer fresher. Once the cool winter air decreased the temperature of the ocean surface, that water began to sink, bringing large amounts of fresh water into the deep layers of the ocean, freshening a significant part of the water column. By showcasing this and tracking long-term patterns, the plot has provided a clear, data-driven story of ocean change, offering valuable insights into climate variability and the broader impact of shifting temperature and salinity levels in the North Atlantic.

Because Igor’s plot has become a defining representation of oceanographic change, it’s received significant recognition over the years. This single visualization and its variations have appeared in scientific papers like Nature and even influenced popular culture. To put its impact into perspective, here’s a look at some of the main places Igor’s plot has appeared and what it’s influenced.

The plot’s first major feature was in the 2001 book titled Ocean Circulation and Climate: Observing and Modelling the Global Ocean. It was published as part of the first compilation of the results of the World Ocean Circulation Experiment (WOCE). At the time, Igor was a postdoctoral researcher in Canada, contributing to a large oceanographic program. His visualization caught the attention of Bob Dickson, a distinguished oceanographer and lead author who was compiling work for the book. Impressed by the clarity of the plot, Dickson invited Igor to contribute it to a chapter—a major opportunity for a young researcher.

Following its book debut, the plot became even more influential when it was featured in the 2002 Nature paper Rapid Freshening of the Deep North Atlantic Ocean Over the Past Four Decades. This study revealed a striking long-term trend: significant freshening of the deep ocean, driven by increased freshwater input from Arctic ice melt, rivers, and precipitation. You can see the plot in Figure 1a. Igor fine-tuned every possible detail of the visualization in Surfer to ensure clarity—and the overall paper gained widespread recognition, accumulating over 800 citations in scientific journals.

This paper also led to unexpected cultural influence. Robert Gagosian, Director of the Woods Hole Oceanographic Institution (WHOI) at the time, was impressed by the work, leading him to publish a commentary article where he misinterpreted the key message of the paper, believing the observed freshening would inhibit deep convection and Meridional Overturning Circulation (MOC), potentially triggering a dramatic man-made ice age. While the interpretation was inaccurate, it inspired the climate disaster film The Day After Tomorrow.

What Igor’s plot and the 2002 Nature paper really revealed was quite the opposite of what the WHOI director had claimed. The freshening of the North Atlantic actually coincided with the strongest and deepest winter convection in the Labrador Sea. Rather than allowing the lighter, fresher water to remain trapped in the upper layer, this deep convection mixed it throughout the entire water column, illuminating the danger of a convective shutdown and a new ice age from a broken MOC. In a way, Igor claimed that the ocean and the Labrador Sea had a hidden ability to sustain stress to a certain limit, a critical reason why deep convection must be observed in real-time, mapped, diagnosed, and understood. 

By 2007, the plot had reached another milestone—it was included in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report, which was awarded half of a Nobel Peace Prize, while the other half went to Al Gore. As a contributing author to Chapter 5 of the report, which examined oceanic climate change and sea level shifts, Igor was a member of the IPCC team that became a Nobel Peace Prize laureate, bringing the plot made with Surfer on board. Section 5.3.2.1 of the report specifically highlighted the North Atlantic Subpolar Gyre, Labrador Sea, and Nordic Seas, featuring a version of Igor’s now-iconic visualization.

As oceanographic research progressed, so did Igor’s plot. Different versions of the full-depth multidecadal observation-based Labrador Sea variability series entered many peer-reviewed publications and reports and were reproduced in several textbooks and publications. One example is the refined version that appeared in the 2016 Journal of Geophysical Research: Oceans paper, Recurrent Replenishment of Labrador Sea Water and Associated Decadal‐Scale Variability. This version—which is Figure 4 in the paper—incorporated more nuanced design elements, including fewer contour labels and a subtle shading technique to indicate data gaps, all of which were done in Surfer. Looking back at earlier versions, Igor realized he had initially over-labeled the contours and adjusted his approach for improved readability.

Most recently, the plot appeared in 2024 in Communications Earth & Environment, a journal under the Nature publishing group. This iteration was Figure 10 in the paper and featured new added layers and improved spacing of axis titles, ensuring an optimal balance between aesthetics and data clarity. The overall layout became more poster-like, equipping readers to dive deeper into the content. Unlike some of the earlier versions of the plot, this final publication also remained free from typesetter modifications, ensuring Igor could achieve his intended design.

Igor’s iconic plot has kept its relevancy for a major reason: it’s continuously evolved alongside various advancements to provide an ever-clearer picture of ocean dynamics. Over the past two decades, ocean observation technology and data visualization software have undergone a series of changes to equip scientists like Igor to analyze and create the best work possible—and that’s evident when you consider what technology could do when Igor first created his plot and what technology can do today. 

Decades ago, measuring ocean properties was time-constrained, limited in spatial and temporal coverage, expensive, and labor-intensive. Researchers had to deploy a weighted wire from a ship, lowering instruments that collected water samples at different depths and taking point measurements of seawater temperature using reversing thermometers. This practice, pioneered by oceanographer Fridtjof Nansen over 120 years ago, remained in use for a while. But it was limited by sporadic sampling, low vertical resolution, and the logistical challenges of ship-based research.

A little before the 1970s, oceanographers started transitioning to seawater profiling and sampling, a significantly more advanced technique involving specially designed devices that could continuously record temperature and salinity as they were lowered through the water column. These tools allowed for a much more consistent look at ocean properties compared to sporadic analysis. However, even with this improvement, ship-based data collection had major gaps in time—research vessels would only collect data once a year at best, meaning long-term trends were reconstructed from relatively sparse data points. 

When Igor began working on his plot, he relied primarily on these ship-based measurements, carefully combining profiles from different years to create a cohesive visualization. Fortunately, things became easier when a revolution in data collection reshaped—or rather expanded—the view of ocean changes like those depicted in Igor’s plot.

The biggest transformation in ocean data collection came with the massive introduction of profiling Argo floats in the early 2000s. These autonomous, free-drifting instruments revolutionized how scientists like Igor observed the ocean. Instead of relying solely on research vessels, Argo floats are deployed from ships, then operate independently—sinking to 1,000 meters, drifting with ocean currents for 10 days, then diving to 2,000 meters before surfacing to transmit data via satellite. Of course, some of these devices profile the ocean on a more frequent basis, but 10 days is the basic requirement to enter the standard Argo program.

Unlike ship-based measurements, which are limited to specific seasons and locations, making them quite infrequent, Argo floats collect continuous, global-scale data in real-time. Over the years, Argo technology has advanced even further, with newer models reaching depths of 5,000 meters and some profiling the ocean daily, dramatically increasing the resolution and frequency of available oceanographic data.

For Igor’s plot, the access to Argo float data has equipped him to expand his research from tracking year-to-year changes to also week-to-week and even day-to-day ocean variations. The recent versions of his plot take into consideration this high-frequency data, delivering a much clearer understanding of short-term fluctuations in ocean salinity, temperature, and circulation. That’s why, from 2008 onward, Igor’s papers and reports include not only long-term yearly-resolve plots but also a set of similar figures covering a shorter time period with a much higher temporal resolution.

The evolution of Igor’s plot hasn’t just happened from better data collection—it’s also been shaped by advancements in data processing and visualization software. Igor uses Surfer as one of his primary visualization tools, and his workflow has evolved to incorporate the new features and improvements in the program. For example, one major feature Igor uses is Scripter, a tool in Surfer that equips him to automate his workflow while still designing a high-quality plot. 

With millions of oceanographic profiles now available, manually improving his plot would be impractical. To maintain efficiency, Igor uses Scripter to jumpstart his visualization process, ensuring that every figure is generated consistently, with the same precision as if it were done by hand. He also prepares boundary and bottom layer masks in MATLAB, which allows him to define custom regions and refine graphical elements before bringing them into Surfer for final visualization.

By integrating scripting and automation, Igor has no problem enhancing his iconic plot efficiently while maintaining high accuracy and applying consistent design elements to maintain a similar feel over time.

The Ricker Method® is a robust statistical tool designed to assess the stability of an entire contamination plume. Developed by Joe Ricker in 2008, this method offers a comprehensive approach to evaluating whether a contamination plume is expanding, contracting, or remaining stable over time. Rather than evaluable contaminant concentration changes well-by-well like many traditional methods, the Ricker Method provides a clearer picture of plume behavior by leveraging detailed statistical analyses of multiple sampling events for all monitoring wells.  This is essential for making consistent informed decisions about site remediation strategies.

Since it’s development, the Ricker method has been used to derive several other valuable analysis methods including the Remediation System Benefit Analysis (RSBA®), Spatial Change Indicator (SCI) analysis, and Well Sufficiency Analysis tools.