Flow rate and water quality are often analyzed independently, but doing so can obscure important relationships. For example, a sudden increase in streamflow may dilute contaminant concentrations, making pollutant levels appear lower than they would under normal conditions. 

Conversely, prolonged low-flow conditions can concentrate those same contaminants, resulting in higher measured concentrations even when pollutant inputs remain relatively unchanged. If flow rate and water quality are viewed in separate graphs, it’s harder to see an accurate picture.

Where a sample is collected is often just as important as the measurement itself. A table of monitoring results may show elevated nitrate concentrations at one site and lower concentrations downstream, but it doesn’t explain why those differences exist. Without geographic context explicitly built into the visualization, it becomes much harder to perform upstream-versus-downstream analysis. You lose the ability to easily see how point sources of pollution (like an industrial discharge pipe) or non-point sources (like agricultural runoff from a specific farming valley) physically intersect the river network.  

Environmental conditions in a watershed rarely remain static over time. The relationship between streamflow, temperature, and water quality changes dramatically depending on the season, storm events, or even the time of day.

If temporal data is isolated on separate charts, crucial cyclical relationships are easily missed. For instance, a spike in sediment runoff during spring snowmelt looks completely different on a graph than a spike caused by a sudden summer flash flood, even if the peak flow rates are identical.

Similarly, agricultural fertilizer runoff often peaks during specific planting windows. If you view nutrient concentrations on one calendar plot and river discharge on another, you lose the ability to see how timing and flow volume collide to create high-risk “loading” events.

Even before analyzing the individual values, several patterns begin to emerge.

• The largest bubbles are concentrated near the headwaters, indicating that streamflow is greatest in the upper reaches of the river.
• Bubble size generally decreases downstream, suggesting lower discharge at many of the remaining monitoring stations.
• Bubble color gradually shifts from darker shades to brighter yellow tones, showing that specific conductance generally increases farther downstream.

Even before analyzing the individual numbers, a crucial environmental relationship stands out immediately: as river discharge decreases downstream, specific conductance sharply increases.

Near the headwaters (the top-left of the plot), the large, dark-blue bubbles represent a high volume of cold, dilute snowmelt. With so much fresh water and very little dissolved mineral content to carry a current, specific conductance stays low.

As you trace the river’s path downstream to the right, the bubbles shrink and shift toward bright yellow. This shift is a classic dilution response: as agricultural and municipal diversions pull fresh water off the river, less flow remains to dilute the river’s dissolved load. At the same time, irrigation return flows wash natural salts out of the valley’s agricultural soils and underlying marine shales, while evaporation further concentrates what’s left. Ultimately, less water carrying more salt is what drives specific conductance sharply upward through the lower, heavily managed reaches of the watershed.

The graph not only compares flow and water quality but also shows where those relationships occur across the landscape. Because every measurement is tied to its exact geographic location (Longitude and Latitude), you can quickly identify stretches of the river where lower flows coincide with higher specific conductance.

For example, the saltiest, lowest-flow stations cluster along the eastern stretch of the map—in the exact same reaches as the valley’s heavily irrigated farmland. This is precisely where you’d expect to see irrigation return flows and evapoconcentration driving up dissolved mineral levels. That spatial context lets you isolate the primary drivers behind water quality shifts rather than guessing, making it easier to evaluate factors such as:

• Diversions, where water is drawn off for farms and towns, leaving less flow in that section of river
• Tributary inputs and saltier groundwater baseflow
• Local geology, such as salt-bearing marine shales
• Irrigation return flows loaded with leached salts and urban land use

Instead of interpreting isolated measurements, you can see how conditions evolve across the watershed and communicate those findings faster and with much greater confidence.

When a graph communicates multiple variables simultaneously, the legend becomes key to interpreting the visualization correctly. An effective legend for a multivariate plot should clearly explain what each visual property represents. 

In the Arkansas River example, the bubble-size legend immediately shows how marker size corresponds to discharge, while the color scale explains how specific conductance changes across monitoring stations. Together, these two legend elements equip decision-makers to interpret both variables without referring to additional documentation.

Multivariate graphs communicate several variables at once, which means every visual element should earn its place. While it may be tempting to add extra labels, symbols, gridlines, or decorative formatting, too many competing elements can make the graph harder to understand.

Instead, keep the design as clean as possible so the relationships between your variables remain the focus. Use labels selectively, minimize overlapping symbols whenever possible, and avoid unnecessary colors or formatting that draw attention away from the data. A clean, well-organized graph empowers stakeholders to quickly recognize patterns across multiple variables without getting distracted by visual noise.

Even a well-designed multivariate graph may contain important findings that deserve additional emphasis. That’s when strategic labels and callouts are essential, as they can help guide decision-makers directly to those insights.

For example, you might annotate:

• Monitoring stations that exceed regulatory water quality thresholds
• Locations where discharge changes dramatically between sampling sites
• Areas where water quality shifts unexpectedly along the river
• Levels of concern for drinking water or irrigation

Instead of expecting stakeholders to discover these patterns on their own, concise annotations help the graph communicate key findings immediately. This is especially valuable when presenting results to people who may not have a technical background or extensive hydrological expertise.