Tackling Uncertainty with Data Science


The management of water resources to satisfy competing demands is becoming increasingly complex, driven by commitments to fair cost and access, to resource resiliency, and to ecological protection.

In practical terms, local-scale water management – whether it is a water utility, conservation district, agricultural operation, or manufacturing operation – is defined by the specific data and information needed to make management decisions and constrained by operational budgets.

The uncertainty (or veracity) of that specific data and information is important to the confidence in the management decisions being made – decisions that affect communities, businesses, farmers and the natural environment. Describing or quantifying the uncertainty of data and information supporting these management decisions is important, as is potentially working to reduce uncertainty by the collection and analysis of additional relevant and accurate data.

Fortunately, data that are critical to water management are becoming easier and cheaper to collect – helped by a new generation of sensors and cloud-based platforms, and increasingly sophisticated remote sensing technology. The utilization of increasingly large hydrologic datasets is facilitated by Data Science, a technical field described broadly as the discipline of data collection, maintenance, processing, analysis, and communication. Data Science extracts value from numbers and information that might otherwise go unrealized and has application across the range of hydrological settings.

Two key values of Data Science are that it can provide:
– a clearer assessment of uncertainty in hydrologic datasets, and
– a measure of the achieved reduction in uncertainty provided by increased data density and new data types.  

We can see this in action in Arkansas and Texas

Increasing Rice Yields by Reducing Water Use in Arkansas: 

In this Forbes article, author Steven Savage relates the experience of five Arkansas farmers using the AWD system:

“The Alternate Wetting and Drying system is just what it sounds like. The rice is established in a flooded field, but then allowed to dry through evaporation and through the transpiration of water from the leaves of the rice. In 10-15 days, depending on rainfall the standing water has all disappeared and with more time the water level descends to a few inches below the level of the soil. Research has shown that the farmer can safely let the water level get to negative four inches before pumping in more water. Drying more than that begins to decrease the final rice yield.”

The AWD system minimizes the use of water and reduces methane emissions in rice fields by remotely collecting field-scale data to control irrigation and water levels. The high density of temporal field data and the Data Science to make it useful, enables these farmers to reduce uncertainty in their operations, optimize management, and maximize the value of AWD. One Arkansas farmer reported being able to reduce water use 50% or more, methane emissions 50% (for which a credit was captured), and nitrogen use 25% – along with increased rice yields.

Reducing Community Water Supply Uncertainty with Real-Time Groundwater Level Monitoring in Texas:

A community water-supply system in Texas provides another example of Data Science yielding important insight and reduced uncertainty through a new and dense real-time dataset. The unincorporated community of Gause is located in Milam County, Texas, and water levels in the municipal well had historically been measured by steel tape about once a year by the Post Oak Savannah Groundwater Conservation District (POSGCD).

Because the steel tape measurements were so infrequent and because there was no way to know whether recent pumping was affecting readings, uncertainty was high around understanding of the groundwater resource and its sustainability based on the collected data.

In 2018 the district installed a Wellntel acoustic water-level sensor on the Gause community well and began collecting water-level data in real time.

Supply well operators can now confidently operate the well, and rely on Wellntel to quantify and visually document the pumping and static levels and monitor the magnitude of the drawdown response. In addition, the recovery profile for each pumping event helps quantify local aquifer characteristics and could refine the local Groundwater Availability Model, also an important step in reducing uncertainty around the sustainability of this groundwater resource.

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