Hydrology

Six years ago, Wellntel posted a blog titled “In a nonstationary world, continuity of observations is critical” - the title was a statement taken from an article in Science by Milly and others (2008). The message of the blog was that the physical drivers of hydrologic systems have increasingly been altered by climate change and as a result, greater temporal and spatial density of data are required to build the hydrologic understanding to meet local and regional groundwater challenges.

‍

Six years on, we are all experiencing the continued disruption of climate patterns, the increasing variability of precipitation, and the growing reliance on groundwater by industry, agriculture and communities. Nonstationarity, whether inherently natural or anthropogenic, is a necessary and growing consideration in the engineering and design of water systems and infrastructure.

‍

‍

The stationarity/nonstationarity of hydrologic systems is a fundamental principle of water management and important to understand. So, what is the difference between a stationary and nonstationary time-series dataset? The figure above presents the difference, showing:

• (a) a stationary time series with constant mean and variance, and
• (b) three nonstationary time series in the form of a shift in mean (trend and step change) and a shift in variance. Solid and dashed black lines represent the mean and the variance of the time series, respectively. (Figure and explanation are copied directly from Slater and others, 2021)

Water supply and management infrastructure is designed based on best knowledge of the local hydrology. Historically, stationarity has been assumed for these plans and designs, but increasingly elements of nonstationarity, from a variety of sources, can no longer be ignored.

‍

Through much of the last century, available data suggested that the hydroclimate and hydrologic systems were generally stationary around mean values. This stationarity was the foundation of many approaches and tools for the planning and designing of water supply and management infrastructure. But as hydrologic records became longer, the new view suggested that natural variability in the hydroclimate was not truly stationary in the sense it had been applied. According to R. M. Hirsch (2010) it has for awhile been widely recognized and accepted in the hydrologic community that the stationarity assumption may be invalid in many cases. He suggests the following three reasons why hydrologic systems may not exhibit stationarity.

1. "Human modifications to the hydrologic system upstream of the project. These modifications include urbanization (increased impervious surfaces), land-use modification such as conversion of forest land to cropland, or groundwater development leading to decreases in base flow to streams.” “These problems are relatively tractable, but they do present significant scientific challenges, particularly for some of the processes with long lag times, such as deforestation-reforestation or groundwater depletion.”
2. "Natural climate phenomena are quasi-periodic and lead to high degrees of hydrologic persistence. This category centers on phenomena such as El Niño Southern Oscillation, Pacific Decadal Oscillation, and Atlantic Multidecadal Oscillation. All of these phenomena have characteristic temporal scales, and all have documented impacts on temperature, precipitation, and hydrologic conditions on land.” “Regardless of whether this is called “non-stationarity,” it points to the importance of hydrologic analysis making maximum use of a wide range of information sources to help characterize the system. These include historic records, paleo-records, and linkages to distant land or ocean systems for which better records are available.”

3. "Climate change induced by human-driven changes in the global atmosphere, primarily the enrichment of greenhouse gases.” “Many climate researchers suggest that very intense precipitation events are becoming more common, and that prolonged periods of low precipitation are also becoming more common. However, when it comes to the response of rivers and groundwater to these changing climate phenomena, the results are much less clear.”

‍

With these different potential sources of nonstationarity in hydrologic datasets described by Hirsch (2010), he calls it “imperative” that measurement of precipitation and streamflow, soil moisture and groundwater, and snowpack and glaciers continue with a high priority. Further, Hirsch (2010) suggests that the continuity of data and data analysis at established sites have higher priority than beginning the collection of data from more locations.

‍

Milly and others (2015) re-emphasize the importance of data in their response to critiques of their 2008 article ‘Stationarity is Dead: Whither Water Management?’’. Because of the uniqueness and immensity of climate change on the global hydrologic and energy cycles, Milly and others (2015) state again in 2015 as they did in 2008 ‘‘In a nonstationary world, continuity of observations is critical.’’ Long-term hydrologic observations are essential to quantify, on an ongoing basis, how the physical drivers of hydrologic systems are being altered by climate change, and in turn, how approaches and tools must evolve for planning and designing water supply and management infrastructure.

‍

From California comes an example of a response to the need for robust hydrologic observations and for the continual updating of hydrologic frequency analysis. The State Climatologist has improvements underway to the state’s hydrologic forecasting (Anderson, 2022), including adjusting Hydrologic Averages to better reflect most recent hydrologic experiences, refining statistical models using updated data, and taking new approaches to improve the forecasting of extreme weather events (10% and 90% exceedance).

‍

Water supply and management infrastructure is designed based on best knowledge of the local hydrology. Historically, stationarity has been assumed for these plans and designs, but increasingly elements of nonstationarity, from a variety of sources, must be accounted for. The need for continuous, local hydrological data is increasing, while the ability of government agencies to collect and interpret these data is flat or declining. Even when funds for science are more readily available, state and federal agencies can not collect data at a pace and in enough places to address issues most important to local communities for water management. Fortunately, municipalities, communities, and other stakeholders can now join together to create powerful networks of Wellntel systems to provide the “continuity of observations” that focuses directly on their most critical groundwater resource concerns.

‍

‍

Sources

Anderson, M. L., Forecast Improvement to Adapt to Climate Change, California Water Commission Workshop, February 16, 2022

‍

Hirsch, R. M.: A Perspective on Nonstationarity in Water Management, Colorado Water — March / April, 2010, Volume 27, Issue 2, 5-6,  accessed on March 20, 2023 at A Perspective on Nonstationarity in Water Management (wucaonline.org)

‍

Milly, P. C., Betancourt, J., Falkenmark, M., Hirsch, R. M., Kundzewicz, Z. W., Lettenmaier, D. P., and Stouffer, R. J.: Stationarity is dead: Whither water management?, 2008, Science, 319, 573–574, accessed on March 20, 2023 at https://www.law.berkeley.edu/files/CLEE/Milly_2008_Science_StationarityIsDead.pdf

‍

Milly, P. C. D., J. Betancourt, M. Falkenmark, R. M. Hirsch, Z. W. Kundzewicz, D. P. Lettenmaier, R. J. Stouffer, M. D. Dettinger, and V. Krysanova, 2015, On Critiques of ‘‘Stationarity is Dead: Whither Water Management?,’’ Water Resour. Res., 51, 7785–7789, doi:10.1002/ 2015WR017408. accessed on March 20, 2023 at On Critiques of “Stationarity is Dead: Whither Water Management?” - Milly - 2015 - Water Resources Research - Wiley Online Library

‍

Slater, L. J., Anderson, B., Buechel, M., Dadson, S., Han, S., Harrigan, S., Kelder, T., Kowal, K., Lees, T., Matthews, T., Murphy, C., and Wilby, R. L.: Nonstationary weather and water extremes: a review of methods for their detection, attribution, and management, 2021, Hydrol. Earth Syst. Sci., 25, 3897–3935, accessed on March 20, 2023 at https://hess.copernicus.org/articles/25/3897/2021/

‍

The preceding parts of this series explored how the monitoring of water levels in a well is critical to understanding the groundwater resource available to support a home or community, a farm, or a business. In addition, monitoring the fluctuations of water level related to pumping activity provides insight into the local aquifer characteristics. This last part of the series uses the hydrograph from the same well in the Hill Country of Texas. (Figure 1). The highlighted section of the hydrograph is expanded (Figure 2) to show that this particular pumping event is made up of many short pumping events each with a Pump Start and Pump Stop measurement event. In each case, following the Pump Stop event, recovery water levels are measured until the pump starts again causing the measurement of level for Pump Start, followed by Pump Stop and Recovery. After some time, and about 30 short pumping cycles, the condition is ended and the well begins its recovery to a true static water level.

Pump cycling like this could have any number of causes, intentional or not. Regardless, it is important for the well owner/operator to be aware of the pumping activity and be able to monitor the dynamics of the pumping and drawdown.

Part 4 concludes this series of short articles Identifying Three Critical Local Elements of Aquifer Health.

The Wellntel sensor is unique among water-level sensors in that it tags pumping-related measurements so that they can be used in ways important to data analysis. In the preceding Part 2 of this series, all the pumping-tagged measurements were filtered out in the example so that the non-pumping water levels - the static saturated thickness - could be highlighted.

But how does the Wellntel sensor system assign tags to measurements?

Using a current transducer that clips around the power lead to the submersible pump (Figure 1), the Wellntel sensor recognizes when pumping begins and when pumping stops, and takes a water-level measurement at the instant each occurs. And after the sensor records the measurement when pumping stops, the system will take a sequence of measurements to capture the recovery profile.

The different tags (Events) that are applied to Wellntel water-level measurements (Figure 2) are:

• Timed - measurements taken at a constant time interval, set by the network sponsor
• Pump start - a measurement taken when power to the pump is first sensed by the current transducer
• Pump run - a measurement taken if the pump has continued to run for the set Timed interval beyond the Pump start time stamp
• Pump stop - a measurement taken when power to the pump ceases as sensed by the current transducer, reflective of total pumping drawdown
• Pump recovery - a series of measurements beginning with one taken 30 seconds after the Pump stop timestamp and then 120 seconds after that measurement, and then 240 seconds after that last measurement, with the interval between recovery level measurements continuing to double until the the interval reaches that of the set Timed measurement 
• Pump influence - a Timed measurement that senses that the pump is running

Timed water-level measurements are important in illustrating the trends of saturated thickness of the groundwater resource as illustrated in the preceding part of this series. The pumping-related measurements provide important information on the effect of pumping stress on the resource. Each measurement Event type (Figure 2) can be included or filtered out of any hydrograph or other visualization, or data download, as might be appropriate to the analysis.

Focusing on the time interval indicated by the blue box on the hydrograph from a well in the Texas Hill Country (Figure 3) the details of several pumping events are visible (Figure 4). Timed measurements prior to pumping events are noted as are water-level measurements at Pump start, Pump stop, and Pump recovery.

As noted on Figure 4, water-level data show three pumping events in a row that are essentially identical - the same pumping duration (stress) and resulting drawdown, followed by the same recovery profile. Following these three pumping events, data show a pumping event with substantially longer pumping duration resulting in much greater pumping drawdown and a very different recovery profile. The physical characteristics of both the aquifer and the well are reflected in the response to the pumping stress.

A different Texas Hill Country well demonstrates how recurring pumping events can establish the specific capacity of a well. A hydrograph representing about a year includes data of all events (Figure 5), but can be filtered to plot only the Timed and Pump Stop measurements (Figure 6). Specific capacity - the discharge rate (in gpm) divided by the water level drawdown (in feet) - can be calculated at any point along the record. The value of specific capacity integrates characteristics of both the aquifer and the well and is often tracked over time to recognize any changes to conditions in the aquifer, the well or the pump.

By plotting this particular Hill Country well together with a neighboring well, specific insight is gained by observing the influence of pumping of one well on the other (Figure 7). During a period of frequent pumping the overall lowering of the saturated thickness is noted in both wells in response to the stress.

Focusing on the time interval indicated by the blue box on the hydrograph, detail is shown of the direct influence of the stress of pumping on the non-pumping monitoring well (Figure 8). When a pumping event begins, the monitoring well has a measurable downward trend in water level. When the pumping ceases, the monitoring well reverses the trend and levels recover. Because the distance between these two wells is known, as is the pumping rate (stress), data from the recovery curve can be used for estimating local hydraulic parameters of the aquifer.

A last example comes from a Concentrated Animal Feeding Operation in Northeast Wisconsin. The first portion of the hydrograph (Figure 9) is similar to the preceding example in which the influence of a pumping well is observed on the water levels in a monitoring well, and there may be an opportunity to estimate local aquifer characteristics. However, the latter portion of the hydrograph shows a period when both wells are pumping and influencing each other (Figures 9, 10). This is a much more challenging situation in which unknown well losses complicate the use of data from recovery curves for estimating aquifer characteristics. 

Perhaps there are sophisticated numerical approaches that can be applied here, but there is certainly useful insight gained by seeing the pumping influence of one well on the other as the farmer works to optimize operations and protect the water supply infrastructure.

The final part in this series will continue with discussion of real time, tagged water-level data collected by Wellntel sensors providing insight into water supply infrastructure and operations, reducing resource and business risk.

‍

With continuing concern about aridification and deepening drought in many locations, characterizing aquifer health on pace with change is becoming increasingly important for the well-being of businesses, farms and communities. 

Using the Wellntel sensor and Analytics Dashboard, water managers have the data and tools needed to identify the dynamics of three critical local elements: groundwater resource, aquifer and operations. 

Over the next few weeks, we’ll be sharing three short articles by Wellntel’s VP Hydrology, Charles Dunning, PhD, who will define each of these critical elements and provide insight on how to identify and understand them. Stay tuned!

A fundamental reason for monitoring water levels in a well is to understand the groundwater resource available to support a home or community, a farm, or a business. The exploitable groundwater resource is directly related to the measurable saturated thickness of the aquifer.

‍

Presenting an example from the Hill Country of Texas, the hydrograph (Figure 1) shows that, over a roughly four-week period, the water level elevation ranges from a low of about 787 ft to a high of about 854 ft AMSL. The low elevation levels are measured during pumping events and the high levels are measured during non-pumping periods.

The Wellntel sensor uniquely tags measurements that are related to pumping activity so that they can be filtered in ways important to data analysis. To better illuminate the short- and long-term trends in aquifer saturated thickness the pumping-tagged measurements can be identified (Figure 2) and then removed from the hydrograph (Figure 3).

This is accomplished in the Wellntel Analytics Dashboard by simply unselecting pumping-tagged measurements (Figure 4). If the pump is idle or the well is a dedicated monitoring well without a pump, the saturated thickness will be obvious with no pumping-tagged measurements to filter out.

With pumping-tagged measurements removed from the hydrograph, the changes in aquifer saturated thickness over this time period are more clear (Figure 5). Specifically the saturated thickness increased by about 10 ft over 17 days of the record, with a 5-day period responsible for about 6.5 ft of that increase. After the water level has reached its highest elevation, it has exhibited a slow decline.

Being able to identify and separate out water-level elevation measurements that are influenced by pumping activity is critical to evaluating fluctuations in the saturated thickness of an aquifer. As data collection continues over years and decades, local understanding of the dynamics of the groundwater resource increases - in particular the relation of aquifer saturated thickness to normal seasonal influences, longer weather trends, local extraction, and anthropogenic climate change. Integrating data from a network of wells completed in the same aquifer can provide regional insight into the same dynamics.

‍

The next part in this series will present how the pumping-tagged measurements, uniquely collected by the Wellntel acoustic sensor, can be used to increase understanding of local aquifer characteristics.

‍

‍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.

Impacts of hydrological droughts are profoundly local – to understand and manage affected groundwater resources, monitoring networks must be local as well

‍

The National Oceanic and Atmospheric Administration reports that the climatological community, following the work of Wilhite and Glantz¹, recognizes four types of drought:

• meteorological drought
• hydrological drought
• agricultural drought, and
• socioeconomic drought.

The first three are physical phenomenon, whereas the last deals with drought in terms of supply and demand, and its manifestation in socioeconomic systems. Droughts are extensively studied and monitored by industry, academe, and governmental agencies, greatly driven by interest in the socioeconomic impacts.

‍

Hydrological Drought

A hydrological drought is characterized by water supply that is persistently low relative to historic norms – especially in streams, reservoirs, and shallow aquifers. Though droughts originate from abnormally low precipitation, the evolution from meteorological drought to hydrological drought is the result of complicated water cycle processes. Because of this, not all meteorological droughts will lead to hydrological droughts  – a meteorological drought can begin and end rapidly before that precipitation deficit can affect local hydrological resources. A hydrological drought will typically take much longer to develop and then recover than a related meteorological drought.

‍

Groundwater Drought – local monitoring is fundamental

Drought in shallow groundwater can be viewed as a specific subset of hydrological drought – groundwater drought is characterized by groundwater levels that are persistently below the long-term average, resulting in reduced saturated thickness and availability. During a groundwater drought, water withdrawals from the shallow aquifer for irrigation, municipal, industrial, and other purposes will likely continue and may increase – this is a human influence that can amplify the meteorological drivers.

The saturated thickness and availability of a groundwater resource at any point in time is best determined by an appropriately dense (spatially and temporally) network of wells that monitors the depth to the water table. However, there is limited spatial coverage of monitoring wells and networks across the country with which to observe and quantify groundwater droughts. The U.S. Geological Survey operates the Climate Response Network, designed to portray the effect of droughts and other climate variability on groundwater levels in shallow confined or unconfined aquifers. However, this national network includes 684 monitoring points – points whose spatial distribution appear to be skewed to the eastern US.

The value of the Climate Response Network for portraying effects at the state and regional scale is clear, but because the socioeconomic impacts of groundwater droughts are profoundly local, the monitoring networks designed to understand and manage these impacts must be local as well. Managing through the socioeconomic impacts of a groundwater drought that has reduced saturated thickness and availability of a critical groundwater resource, requires a density of data that is appropriately local.

‍

Central Texas – the power of local, dense data

A monitoring network that clearly shows the local impact of a groundwater drought is demonstrated in hydrographs from four residential pumping wells located in the same residential development in Central Texas (Figure 1). The water level in this shared aquifer trends lower in these wells through late August or so and then a partial recovery through the end of the year is visible. This particular groundwater drought coincides well with a documented meteorological drought, but that is not always the case. These data demonstrate a local consistency in the drought effect that is important for characterizing the groundwater drought and for defining potential management responses.

The appropriate spatial density of any network designed to monitor saturated thickness and availability in the context of groundwater droughts will be influenced by local hydrogeological and water-use considerations. Water management that occurs at the county, township, or village scale, will have priorities for monitoring that should be reflected in the spatial scale and character of their network. Figure 2 is a map of a monitoring network in Eastern Wisconsin that is well suited for capturing the local village-scale impact of a drought on the groundwater resource. In addition, because a fair number of the monitored wells are close to local streams and lakes, this network will be particularly useful in relating lower levels in the shallow aquifer to changes in baseflow contribution to the surface-water features. Thus linking a local groundwater drought more broadly to the local hydrological drought.

The impacts of hydrological droughts and associated groundwater droughts are often felt most profoundly by local communities and businesses. Monitoring of groundwater levels is fundamental to recognizing and understanding groundwater droughts, and for local communities to define and plan for management responses.

A Wellntel network is the perfect platform on which to design and implement local groundwater-monitoring programs and to enable local experts to identify and quantify groundwater droughts and to manage the impacts.

‍

Sources

¹Wilhite, D.A.; and M.H. Glantz. 1985. Understanding the Drought Phenomenon: The Role of Definitions. Water International 10(3):111–120.

‍

Groundwater-dependent growers should establish monitoring networks early in the water year to quantify saturated thickness before the growing season.

‍

A direct relation exists between the saturated thickness of an aquifer and the groundwater volume available for extraction constrained by regulation and/or principles of sustainability.

‍

Under natural conditions, the water table of an unconfined aquifer will rise and fall as a function of recharge, discharge, and evapotranspiration. Saturated thickness of the groundwater resource – defined in simplest terms as the distance between the water table and the base of the aquifer (Figure 1) – will increase and decrease in response to the fluctuating water table.

‍

The changes in saturated thickness of an aquifer will usually be observed to repeat seasonally within a historical range driven by typical changes in those same factors – recharge, discharge, and evapotranspiration – but can also reflect longer-term climatic shifts. Imposed on top of these natural fluctuations will be changes caused by any irrigation pumping, managed recharge, or other influences by man.

‍

‍

Because the saturated thickness of an aquifer is directly related to the groundwater volume available for extraction, quantifying the saturated thickness at the beginning of the growing season is critical to irrigation planning and perhaps even to crop selection. Key to quantifying the dynamically changing saturated thickness of an aquifer is, of course, closely monitoring the transient depth to the water table.

‍

Hydrologic observations are usually considered within the framework of the Water Year, which is defined as the 12-month period beginning October 1 of a given year and ending on September 30 of the following year. Establishing a new groundwater-level monitoring network – or expanding an existing network – will increase the understanding of the short- and long-term fluctuations of the water table. By adding these monitoring points early in the water year, saturated thickness can be quantified with greater spatial and temporal confidence heading into the irrigating season.

‍

The following examples illustrate fluctuations in the water table – and by extension aquifer saturated thickness – observed in wells in California and Texas.

‍

The first is a hydrograph of a water-supply well at a California winery, illustrating fluctuations of the water table influenced by pumping of the well and by seasonal hydrologic conditions (Figure 2). This hydrograph makes clear to the vineyard operator that the saturated thickness of this groundwater resource was about 15 feet less at the beginning of Water Year 2020 than it was at the beginning of Water Year 2019, and changes to irrigation operations may be warranted.

A second example is from the Ogallala Aquifer in Gray County within the Llano Estacado Region of Texas – presenting a well with longer records of water-level data collected by the Texas Water Development Board and reported by John E. Stout, of the USDA-ARS (Stout, 2018). The hydrograph of the recorder well provides data from 2005 through 2017, clearly showing the influence of seasonal pumping by irrigation wells neighboring the monitoring well (Figure 3). It is noted that the depth to water at the start of seasonal pumping in Water Years 2014 and 2015 is clearly greater compared to Water Years 2005 and 2006. And even though it appears the difference is less than one meter, such a reduction in saturated thickness and available groundwater may influence local agriculture operations.

These data from the Llano Estacado Region of Texas illustrate the value of long-term data collection and in particular the value of quantifying the saturated thickness of the groundwater resource at the beginning of the water year to enable better and more informed agricultural water management of the Ogallala Aquifer.

‍

Whether the groundwater volume available for extraction is constrained by regulation or by principles of sustainability, is it critical for private well owners to have data and information about the changing saturated thickness of their local aquifer. Wellntel enables groundwater-level monitoring networks composed of private, submersible pump wells, as well as monitoring wells, and provides an Analytics Dashboard with powerful tools for data management, analytics, and visualization.

‍

Sources:

Stout, J.E., 2018, Seasonal water-level perturbations beneath the high plains of the Llano Estacado; Journal of Hydrology: Regional Studies, Volume 18, August 2018, accessed January 18, 2020 at https://www.sciencedirect.com/science/article/pii/S2214581817303373

StudyBlue crowdsourced library, access January 26, 2020 https://www.studyblue.com/?src_url=https://www.studyblue.com/notes/note/n/water-beneath-the-surface-ch-6-3/deck/13436902#flashcard/flip/13436902

‍

In the late summer and early autumn of 2019, Texas experienced severe to extreme drought over portions of Central, Southern, and eastern West Texas Regions. That drought is captured in weekly map images provided by the United States Drought Monitor, through a partnership between the National Drought Mitigation Center at the University of Nebraska-Lincoln, the United States Department of Agriculture, and the National Oceanic and Atmospheric Administration.

‍

Being able to anticipate the impact of dry weather or drought on water levels in local aquifers is critical to the long-term management of shared groundwater resources. Fundamental to these planning efforts must, therefore, be the collection of accurate, continuous, time-series water-level data that will capture periods of drought during the water year. Every drought is inherently unique in character. By collecting continuous data over many years a greater understanding is gained of the background variations in aquifer water levels – both seasonal and event-driven.

‍

No detailed analysis of the relation between drought and groundwater levels has been attempted here – rather what follows is a simple juxtaposition of Drought Intensity against the hydrographs of wells located in areas affected by the 2019 drought. The intent is to document that juxtaposition, to highlight the importance of data collection and continued monitoring, and to encourage further inquiry.

‍

The 2019 Texas Drought

Three selected map images (Figure 1) provide a view of the progression of the drought intensity that was at its greatest in late September 2019 and early October 2019. These maps use a color range from yellow (D0), indicating “Abnormally Dry” conditions, to brown (D4), indicating “Exceptional Drought” conditions (Figure 2). Considering the total area of Texas, < 1% of the state was experiencing Severe or Extreme Drought Intensity (D2-D3) during the week of July 23 (Figures 1 and 3). By the week of October 15, that state-wide percentage had grown to about 26% with much of the drought appearing in the Central, Southern, and eastern West Texas Regions. By the week of December 3, much of the Extreme Drought had abated and the area of Severe Drought had lessened as well – together those categories of Drought Intensity (D2-D3) had dropped to about 8% of the area of Texas (Figures 1 and 3).

Aquifers Under Drought Conditions

Drought Intensity is an interpretation – undertaken by the United States Drought Monitor – of many climatological, agricultural, and hydrologic inputs. A comparison of Drought Intensity to changing water levels in wells over any selected period of time may provide insight to the effect of drought conditions on local aquifers.

‍

Downward trending water levels in an aquifer during a period of drought can have numerous causes, including:

• increased water withdrawals from the aquifer to meet greater dry weather needs,
• reduced or no recharge from precipitation,
• reduced or no recharge from surface water that is typically contributing to groundwater.

‍

The shallow aquifers of the Central, Southern, and eastern West Texas Regions are quite variable in their hydrogeology – including their primary and secondary permeability – and so their response to climatic and hydrologic conditions can be very different. To understand the contributing factors to water-level changes in a specific monitoring or pumping well requires a deliberate investigation of each well or well network. No such Investigations have been conducted here, so the factors contributing to any relation between Drought Intensity and observed changes in water level in the well hydrographs presented below, remain to be explored.

‍

Observed Water-Levels in Pumping Wells

Each of the following hydrographs present water levels measured in wells from Wellntel monitoring networks and were created using the Wellntel Analytics Dashboard. The data presented are for the period of June 1 to December 8, 2019.
Figure 4 presents hydrographs from four pumping wells located in the same residential development in Central Texas – data presented are weekly maximum water elevations. The water level in this shared aquifer trends lower in these wells through late August or so and then a partial recovery is visible.

The Wellntel Analytics Dashboard provides the opportunity to view the data from Figure 4 as water-level change from a common zero reference point using the “Change” feature set. Figure 5 shows this plot – Depth to Water Level Change – highlighting the change between June 1 to August 8 – to have been about 44, 47, 49, and 56ft for the four wells.

Figure 6 presents the hydrograph from a residential pumping well in the South Texas Region – data presented are discrete depth measurements, both timed and pumping-influenced measurements. About 20ft of water-level decline is revealed from June to early October – however, this well continues a downward trend falling another 10 ft through early December. Note that the non-pumping (static) level and depth of pumping drawdown trend lower proportionally. This is a rural area with low well density – a consideration in determining the extent to which the temporal change in water level has been influenced by local pumping versus drought impacts.

Not very distant from the well of Figure 6 is a well with a very different hydrograph over the same period of time – data are presented as weekly minimum depth measurements. As presented in Figure 7, water levels in this well fluctuated only around 0.5ft, but with a rising trend. Hydrogeologic factors likely are a prime influence on the difference in the observed response in these two wells.

Figure 8 presents a Central Texas Region water supply well – data presented are discrete depth measurements, both timed and pumping-influenced measurements. Pumping frequency appears relatively consistent throughout the period so water withdrawals may not be a substantial factor contributing to the water level decline. Note that the non-pumping level and depth of pumping drawdown generally trend lower proportionally. Water level reduction from June to October is about 10 ft with a hint of recovery into December.

The climatic variability of droughts and the hydrogeologic variability of aquifers contribute to the complexity of observed data sets. The collection of statistically appropriate, continuous, time-series water-level data (to support acceptable uncertainty) is foundational to unravelling the combined influence of drought, recharge, and water withdrawals on well hydrographs.

‍

Wellntel enables groundwater-level monitoring networks composed of private, submersible pump wells. Please contact us about your needs for developing a groundwater monitoring network that will help you sustainably manage your groundwater resource through extreme weather and changing climate.

‍

Contact Wellntel’s VP Hydrology Charles Dunning, PhD to learn more about Wellntel networks, groundwater science, and extreme weather.

‍

‍

Citizen science is increasingly crucial to sustainable resource management.

A Community-Based Groundwater-Level Monitoring Network (Community Network) is a group of private wells – often pumping from the same local aquifer – in which groundwater level is measured regularly. The owners of these private wells – residential, industrial, agricultural – have made the decision to share their water-level data and are communicating with each other or with local groundwater professionals. The spatial extent of a Community Network composed by private wells is typically bounded by hydrogeologic, geographic, or political boundaries. An example is presented in Figure 1, a Community Network that is defined by the extent of three counties in West Central Wisconsin – Dunn, Chippewa, and Eau Claire Counties.

‍

Motivation for Joining or Creating a Community Network

A common motivation for private well owners to join or create a Community Network is a perceived or quantified threat to their groundwater resource – this threat can be to the water quality, the water availability, or both. Other motivations include the interest in gaining a better understanding of the hydrogeology of their groundwater resource or the interest in sustainably managing their groundwater resource in a changing climate. And a contributing factor to the need for a Community Network is often the decreasing ability of regulatory or science agencies to meet monitoring needs.

‍

Recognizing the need for a Community Network and the value it brings to groundwater resource management, is based on a firm understanding of the competing demands on a local groundwater resource. In creating a network, it is important to clearly articulate the motivation or threat so that the required spatial and temporal density of network data can be defined. It is also important to identify the stakeholders who depend on the resource, to define points of collaboration and dispute, and to plan the network so that collected data will be relevant to the stakeholders. Education and outreach across the community, not just the participating well owners, is important as well. Identify ways to educate stakeholders and the public on the value of collected data and to collaborate with local organizers to conduct outreach and public meetings to recruit well-owners to the network.

‍

Characteristics of Community Networks

While successful Community Networks might have features unique to their own setting and situation, they typically share these important characteristics:

• the network has spatial and temporal data density that is appropriate for the critical hydrogeologic questions about the local resource,
• data collected by the network are of high quality,
• data collected by the network are shared with all participants,
• the network embraces the scientific method for collecting and interpreting data,
• the network respectfully engages qualified professionals to interpret data, characterize the groundwater resource, and guide sustainable management,
• the network accepts that while some data tell a very intuitive and straightforward story, it is often the case that networks providing spatially-distributed time-series data sets will reveal a complicated and non-intuitive story.

‍

Wellntel makes Community Networks easy, efficient, and valuable

Wellntel is the first real-time groundwater-level information system designed to create community-based monitoring networks that provide the insight needed to sustainably manage groundwater resources. The system uses simple, reliable and accurate sensors and a cloud-based visual and analytics platform to turn private domestic, industrial, and agriculture wells into real-time monitoring networks, to engage stakeholders, to identify risk, and inform management decisions.

‍

With Wellntel, communities can:

• build real-time, continuous groundwater-level monitoring networks quickly and cost-effectively
• identify pumping impact and environmental influences to understand risk and opportunity
• engage local stakeholders
• create dense, rich datasets to inform management decisions and improve groundwater-flow models

‍

Figure 2 provides an example of groundwater-level data collected by a Wellntel community network in Texas. Water levels in these residential wells decline in the summer months due to reduced recharge and increased pumping. Water levels begin to recover in October as precipitation returns to the area. The spatial and temporal density of this network has been designed to provide insight into questions around permitted spacing of wells that will contribute to sustainability of the local groundwater resource.

Planning for the appropriate number and locations of monitoring wells in community networks is important to provide the required spatial density for the hydrogeologic problem to be investigated. Figure 3a presents an appropriately defined Community Network that is composed of 29 Wellntel systems and is defined by the extent of the Village of Richfield in Washington County, Wisconsin. Figure 3b presents a Bubble Map depiction of changes in water level in wells over a 6-month period of time in 2019.

Community networks have been created in many states across the US by Wellntel and our partners. Contact Wellntel’s VP Hydrology Charles Dunning, PhD, to learn more about Wellntel’s Community-Based Groundwater-Level Monitoring Networks and to explore possibilities for your community.

California’s Sustainable Groundwater Management Act of 2014 (SGMA) requires that Groundwater Sustainability Agencies (GSAs) manage groundwater resources to avoid significant and unreasonable adverse impacts on beneficial uses of interconnected surface water^{1, 2}. Surface Water Depletion is one of the six SGMA Sustainability Factors that GSAs and their Groundwater Sustainability Plans (GSPs) must consider and endeavor to avoid or minimize³.
‍

‍

Beneficial uses of surface water include both human consumptive uses and environmental uses – therefore the SGMA obligates GSAs to manage the groundwater resource to also protect groundwater-dependent ecosystems³. Interconnected surface water is defined as “surface water that is hydraulically connected at any point by a continuous saturated zone to the underlying aquifer and the overlying surface water is not completely depleted.” ⁴

‍

As a result of the SGMA mandate to protect beneficial uses of surface water, GSAs will need to develop a deep understanding of the dynamic interactions between groundwater and surface water in their basin – flow can occur in either direction, driven by hydraulic gradients that fluctuate on a range of time scales. In the following figure, A is a gaining stream reach that receives water from the groundwater system, whereas, B is a losing reach that contributes water to the groundwater system⁵. A stream can have both gaining and losing reaches along its length, depending on many local factors.

‍

‍

While there are a number of tools and methods for quantifying flow between groundwater and surface water (see Table 2 (p 9-10) of Cantor and others, 20182), the flow-net analysis method, often called the “Darcy approach,” is probably the most frequently used⁶. In this method, a combination of measurements of levels in near-shore water-table wells and measurements of water stage of adjacent surface-water bodies are used to calculate water-table gradients between the wells and the surface-water body. Quantifying hydraulic gradients in space and time and applying the Darcy equation, enables the GSAs to better understand the magnitude and dynamics of flow between surface water and groundwater.

‍

It is critical for GSPs to recognize the spatial and temporal scale of the groundwater/ surface-water interaction to be managed, so that networks can be designed to provide appropriately scaled data. The following table shows that the appropriate temporal scale of data collection for “Monitoring ground-water and surface-water interaction” can range from days/weeks to decades⁷. For the seasonal- and hydrological event-driven conditions many GSA basins will be experiencing, monitoring frequency at the shortest temporal scale will likely be most valuable.

Creating groundwater and surface-water monitoring networks to achieve the appropriate spatial and temporal density is key to achieving the deep understanding needed by the GSAs. To this end, leveraging existing groundwater monitoring networks will be important, as will be, taking advantage of new technology. The Wellntel Groundwater System (sensor and analytics platform) complements and extends existing networks by adding private pumping wells as water-level monitoring points, and by integrating data from all network sources and third-party data into a powerful Analytics Dashboard to streamline data management, analysis, visualization, and reporting. The Wellntel Groundwater System is the most effective approach to provide the spatial and temporal density that will be required to meet the SGMA mandate to protect beneficial uses of surface water.

‍

Sources

¹ Cal. Water Code § 10721(x)

² Cantor, Alida, Dave Owen, Thomas Harter, Nell Green Nylen, and Michael Kiparsky, 2018, Navigating Groundwater-Surface Water Interactions under the Sustainable Groundwater Management Act, Center for Law, Energy & the Environment, UC Berkeley School of Law, Berkeley, CA. 50 pp, accessed at https://www.law.berkeley.edu/wp-content/uploads/2018/03/Navigating_GW-SW_Interactions_under_SGMA.pdf

³ California Department of Water Resources, 2017, DRAFT Best Management Practices for the Sustainable Management of Groundwater, Sustainable Management Criteria, accessed at https://water.ca.gov/LegacyFiles/groundwater/sgm/pdfs/BMP_Sustainable_Management_Criteria_2017-11-06.pdf

⁴ Cal. Code Regs. tit. 23, § 351(o)

⁵ Barlow, P.M., and Leake, S.A., 2012, Streamflow depletion by wells—Understanding and managing the effects of groundwater pumping on streamflow: U.S. Geological Survey Circular 1376, 84 p., accessed at https://pubs.usgs.gov/circ/1376/pdf/circ1376_barlow_report_508.pdf

⁶ Donald O. Rosenberry, James W. LaBaugh, and Randall J. Hunt, 2008, Use of Monitoring Wells, Portable Piezometers, and Seepage Meters to Quantify Flow Between Surface Water and Ground Water, Chapter 2, in Rosenberry, D.O., and LaBaugh, J.W., 2008, Field techniques for estimating water fluxes between surface water and ground water: U.S. Geological Survey Techniques and Methods 4–D2, 128 p., accessed at https://pubs.usgs.gov/tm/04d02/pdf/TM4-D2-chap2.pdf

⁷ Taylor, C. J., and Alley, W. M., 2001, Ground-water-level monitoring and the importance of long term water-level data: U.S. Geological Survey Circular 1217, accessed at https://pubs.usgs.gov/circ/circ1217/pdf/circ1217_final.pdf

‍

Wellntel systems collect detailed data on pumping activity, tagging these data to be useful in understanding the groundwater aquifer and the operational characteristics of network wells.

‍

Data Tagging

The data that can be collected, tagged, and displayed in real time are:

• the water level in the well during each timed measurement – tagged as Timed
• the water level in the well at the start and stop of each pumping event – tagged as Pump Start or Pump Stop
• the water level in the well during each pumping event – tagged as Pump Run, and
• the recovery of the water level following the end of pumping – tagged as Well Recovery.

Since every measurement taken by Wellntel systems is tagged in this way, data can be filtered and sorted to create data sets most useful for analysis across a network. For instance, analysing specifically the Well Recovery data to quantify aquifer characteristics, or looking at only Timed measurements to understand trends in static water level, or looking only at Pump Run measurements to track changes to the pumping drawdown.

‍

Aquifer characteristics

The dynamic response to a pumping event is a direct reflection of the physical nature of the aquifer and to a varying extent the well. In particular, measurements that record the water-level recovery following a pumping event – tagged by Wellntel as Well Recovery – are an extremely valuable data set for quantifying aquifer characteristics. When combined with an understanding of the rate and cumulative water withdrawal of any pumping event, the aquifer characteristics that can be estimated include specific capacity, hydraulic conductivity, transmissivity, and storage properties.

The six plots of Wellntel data that follow, illustrate a range of examples of Well Recovery curves that provide this critical data for aquifer analysis.

‍

High Capacity Irrigation Well:

With Wellntel sensors, network sponsors may also have the opportunity to measure the effect in one network well of the pumping in another network well – this is an additional type of analysis that can provide important insight into aquifer characteristics.

‍

Frequency and depth of pumping

Very often, Wellntel systems are installed on wells for which there have been no water level measurements collected other than an occasional tape down – in particular, there have typically been no measurements collected during pumping. So, the data collected by Wellntel provides the first view of how pumping is affecting water levels and the operation of the water-supply systems. As a result, assumptions that network sponsors and well owners may have about the frequency and depth of their pumping will often change when these data become visible. And it is not uncommon that these data will prompt a well owner to make adjustments to the settings/conditions that trigger pump runs or even conduct an integrity evaluation to check for system leaks or other problems.

The plots that follow present hydrographs of pumping activity revealed by Wellntel tagged measurements – some activity was quite unexpected by the network sponsor and well owner.

‍

Residential Wells:

Municipal well:

Well operational characteristics

As a history of tagged water-level measurements is collected, the individual characteristic of pumping frequency, drawdown, and recovery for a well become visible. As shown in the preceding and following hydrographs, there is a great variety of well operational characteristics revealed. The individuality is a function of the aquifer, the well, the pump, the well head and surface equipment, and operational practices. These data can inform the wellowner of the aquifer response to pumping operations and suggest whether those operations need to be changed in any way in the present, or in anticipation of seasonal trends or extreme weather.

‍

Municipal wells

Tagging of water-level data to identify the type of measurement enables sophisticated sorting and filtering of network data to streamline analysis and increases the value of local and regional interpretations. Contact Wellntel to receive more information on this and other features of the Wellntel Groundwater Information platform.

The Wellntel Groundwater Information System provides a complete analytics platform – sensors, telemetry, and realtime analytics – that integrate real-time water-supply data with consumptive-use requirements to provide decision support for operational hydrology.

‍

As agricultural operations continue to expand in areas where available groundwater supplies are restricted either by lesser saturated thickness or by allocation limits, often the full consumptive-use demands of some crops cannot be met using typical irrigation approaches. Under these conditions, Limited Irrigation Management is being applied to influence crop rotation decisions and water management during the vegetative growth stages – these farming practices are intended to minimize water stress during critical growth stages and to protect crop yields.

‍

Groundwater managers, irrigation consultants, and research scientists are expanding groundwater-level monitoring networks to provide the greater spatial and temporal density of data required to manage these restricted groundwater supplies. However, monitoring expansion has been traditionally constrained by the costs of siting and installing new monitoring wells and equipment, and the inability to monitor on private water supply wells. With the Wellntel Groundwater Information System, expanding monitoring to private agricultural production is now a cost-effective reality.

‍

Simple non-invasive sensors at the wellhead eliminate the risk of contamination to groundwater or damage to equipment in wells with submersible pumps. Connecting these sensors via telemetry to a cloud-based data visualization and analytics platform provides the means to track and understand real-time water-level measurements that are tagged to quantify pumping influence. The Wellntel system provides real-time online access to the analytics platform providing network scale research- and management-ready data.

‍

An example of data provided through the Wellntel Analytics Dashboard is shown below – the hydrograph for this well reveals that the non-pumping water level has stayed fairly consistent over the past 9 months. However, the trend of the drawdown curves clearly indicate the impact of continued pumping in this aquifer. If supply were limited by the need to maintain a minimum saturated thickness, observing these trends in real time would be invaluable to optimizing operations. The detailed data collected for each individual pumping and recovery event also records pumping duty and helps quantify aquifer properties. Cumulative discharge can also be an output of the analytics platform so as to monitor production relative to allocation.

With the Wellntel Groundwater Information System, operators employing Limited Irrigation Management can now integrate a real-time assessment of the restricted groundwater supply with the field-scale data that define consumptive-use requirements. By integrating water availability and the consumptive-use need, the best irrigation options can be identified to minimize crop stress during critical growth stages and to maximize crop yield under these water-limited conditions.

The Comal Trinity Groundwater Conservation District is employing Wellntel technology to create a cost-effective and powerful groundwater-monitoring network that will greatly increase hydrogeologic understanding and science-based resource management. Their network build-out began in October 2018 with 4 wells, scaling to 10 in early 2019.

‍

“The plan to establish monitoring wells in the Trinity Aquifer has been accelerated since the expense is much less than anticipated with the application of the Wellntel product” said Larry Hull, Board President, Comal Trinity GCD.

‍

In Comal County, Texas are two near-by residential wells completed in the same aquifer within the Trinity Group. During 2019 the changes in groundwater level in these two wells have tracked very closely as seen in the composite hydrograph of Figure 1, rising about 3 1/2 feet in January and then exhibiting a gradual decline of about 7 feet during February through April. The first two weeks of May have seen a rise of about 4 feet.

‍

The hydrograph also shows every occurance of pumping in these two wells, recording the depth to water at Pump Start, Pump Stop, and then through each following defined Well Recovery period. Pumping drawdown has typically been about 12 to 14 feet. All of these data – both timed and pumping measurements – are provided to network sponsors and managers in real time through the Wellntel Analytics Dashboard.

‍

Of particular interest is a period at the end of April during which the well represented by the blue hydrograph was pumped repeatedly over about 25 hours. The pumping events are not individually distinguishable when the entire data set is plotted, but are visible in the closer view when zooming tools are used, as shown in Figure 1.

‍

The Wellntel sensors for both these wells were set to collect measurements during every pumping event and, when not pumping, timed measurements were on a 4-hour interval. In order to see the hydrologic influence of the extended pumping period on both the pumping well and the neighboring well (red hydrograph)the timed measurement interval was shortened to 10 minutes. This change would have the added benefit of collecting Pump Run depth-to-water measurements since pumping duration is typically shorter than the previously set 4-hour interval.

‍

The active telemetry that is standard with the Wellntel platform made it possible to remotely change the measurement interval for both of these wells. The change was made for the pumping well near the beginning of the pumping period and for the neighboring well about midway through the pumping period. The time during which the 10-minute measurement interval was set for each well is noted on Figure 2.

‍

‍

The influence of the pumping over time is clearly seen for the pumping well in both its non-pumping level and the depth of drawdown. And a related lowering of the water level is seen in the neighboring well. The relation between the cumulative discharge over this pumping period and the quantified response of the aquifer seen in both hydrographs will contribute to the understanding of the aquifer characteristics – such as hydraulic conductivity and storage properties – in Comal County.

‍

A closer look at the data is provided in Figure 3, revealing the significant hydraulic connection that exists in the aquifer between the pumping well and the neighboring well. Once depth-to-water measurements were being collected on a 10-minute interval, the data in the neighboring well (red hydrograph) clearly demonstrate falling water levels during periods of pumping and rising water levels when the pump is off.

‍

The collection of these data is made possible by Wellntel sensor technology that turns submersible pump wells into monitoring points, that tags depth-to-water measurements as timed or relating to pumping activity, and that provides data in real time for action by well owners, network sponsors, or resource managers.  

‍

According to Mr. Hull, “Wellntel’s monitoring system and associated analytical tools have provided real time data across the diverse hydrology within the Trinity Aquifer.”

‍

We look forward to a continuing collaboration with the Comal Trinity GCD to provide important insights – such as groundwater flow dynamics, spatial distribution of recharge, and hydraulic gradients between major aquifers – that the dense data collected from a well-designed network can provide.

Dense groundwater-level data – as gathered by Wellntel Community Groundwater Networks – are essential to meet TMDL testing needs

Total Maximum Daily Load (TMDL) guides restoration

The US EPA’s 303(d) Program of the Clean Water Act assists states, territories and authorized tribes in submitting lists of impaired waters and developing the Total Maximum Daily Load (TMDL) for a stream or river. TMDL is a regulatory term describing a plan for restoring impaired waters that identifies the maximum amount of a pollutant – the load – that a body of water can receive on a daily basis while still meeting water-quality standards. A TMDL is composed of load allocations for both point sources and nonpoint sources.

Excessive nutrient levels measured in >40% of US river miles studied

While nitrogen and phosphorus are essential for a healthy aquatic ecosystem, an excess of these nutrients in a waterbody results in overgrowth of aquatic plants, increased harmful algal blooms, decreased light penetration and decreased levels of dissolved oxygen. Any of these conditions reduces the recreational use of the water for people and makes it difficult for fish and other aquatic animals to live. The US EPA National Rivers and Streams Assessment (2008/2009) evaluated 1.2 million miles of rivers and streams in the US and found that more than 40% of river and stream miles have levels of nutrients that are too high.

As reported by the US EPA, “The primary sources of nitrogen and phosphorous pollution are runoff of fertilizers, animal manure, sewage treatment plant discharges, stormwater runoff, car and power plant emissions, and failing septic tanks. In the Mississippi River Basin, which spans 31 states and ultimately drains into the Gulf of Mexico, nutrients from row crops and concentrated animal feeding operations contribute the most nutrient pollution.”.

‍

A source of nutrients that is often overlooked is groundwater baseflow to streams and rivers.

Broadly, baseflow is the flow of a stream or river that exists even when there is no direct runoff of precipitation over the land. In other words, baseflow is the flow of the stream when it has not rained for many days and so there is no precipitation-related runoff. Man-made point discharges of treated wastewater or permitted releases can contribute to baseflow. However, groundwater discharge is usually the primary contributor of sustained baseflow in a stream or river.

‍

Impossible to meet a TMDL if the nutrient contribution of groundwater baseflow is not accounted for

If the nutrient load from groundwater baseflow is not well quantified or is ignored, it will be very difficult or impossible for local managers to meet the nutrient management objectives established by a TMDL. What is known about the nutrient load of groundwater? A comprehensive national analysis of nutrients in streams and groundwater was conducted by the US Geological Survey (USGS) using data collected from 1992 through 2004. The analysis was published in 2010 by the National Water-Quality Assessment Program as USGS Circular 1350.

‍

A major finding of the analysis was that nitrate concentrations greater than the Maximum Contaminant Level (MCL) are more prevalent and widespread in groundwater than in streams. In fact, of studies in shallow groundwater in agricultural areas, 83% percent had one or more samples (of 20 to 30 wells sampled) with a nitrate concentration greater than the MCL. Only 28% of sreams in the agricultural areas had one or more exceedence. In urban settings, 52% of studies documented one or more samples with a nitrate concentration MCL exceedance, compared to only 7% of streams.

The analysis goes on to state that  “Groundwater contributions of nutrients to streams can be significant — particularly for nitrate.”. As part of the study, 148 small streams across the Nation were assessed and it was found that for two-thirds of these streams, at least one-third of the total annual load of nitrate load was derived from baseflow. In certain hydrogeologic settings, groundwater can also contribute a significant phosphorus load to streams.

‍

The USGS analysis in Circular 1350 documents at the national level what has also been observed in numerous local and smaller-scale studies. One example is demonstrated by the following figure from an urban study conducted by the Institute on the Environment at the University of Minnesota. The graph shows that the contribution of groundwater baseflow to the total nutrient load of surface water is significant for both phosphorus and nitrogen, but particularly nitrogen. This urban area was almost completely sewered for stormwater and the year-round, dry-weather flow in the stormwater system was monitored for flow, nutrients, and other chemistry.

For communities involved in developing a TMDL to protect and manage their water resources, USGS Circular 1350 provides a very important caution, quoted here –

“Full accounting and assessment of groundwater contributions of nutrients to surface water is a critical step in developing management strategies to meet water-quality goals for protection of drinking-water supplies and aquatic life. For example, omission of groundwater contributions of nutrients from Total Maximum Daily Loads (TMDL) calculations can result in errors in the allocation of loads to other sources that need to be controlled.”

‍

Hydraulic gradients must be defined to quantify nutrient transport and baseflow loads

To fully account for the contribution of groundwater to an established TMDL, the flow and dynamics of the groundwater system must be well known. In Relevance of Water-Level Data to Ground-Water Quality Issues, USGS Circular 1217 published in 2001, it is stated that predictions of the speed and direction of movement of ground-water contaminants – including nutrients – are “based on determination of the gradient (slope) of the water table or potentiometric surface in the affected aquifer”.

Periodic measurements of groundwater level are often insufficient for defining a potentiometric surface that is dynamic and changing in response to seasonal hydrologic events or pumping stresses. As also stated in Circular 1217 ”longer term water-level measurements are often needed to develop an understanding of how ground-water contaminants migrate from their sources through the ground-water system.”. This is particularly true if groundwater flow models are used in predicting nutrient transport in groundwater. Water-level data of “sufficient duration and frequency of measurement are needed to calibrate and evaluate the reliability of the flow component of these models before realistic simulations of contaminant transport can be made.”.

‍

Wellntel networks provide the critical time-series data necessary to define hydraulic gradients at a scale to quantify local groundwater flow and nutrient transport

Local, state, and federal mandates for assessing and monitoring local water resource continue to expand. The successful management of water quality in watersheds requires understanding the dynamic contributions of both groundwater and surface water. TMDLs will be successful only if the groundwater baseflow contribution of nutrients is understood and accounted for.

‍

Wellntel sensor systems have been designed explicitly to provide the temporal density of data necessary to observe dynamic groundwater systems responding to a range of stresses. And networks of Wellntel systems provide the spatial density necessary to define potentiometric surfaces, quantify hydraulic gradients, and support the assessment of contribution of groundwater baseflow loads to a TMDL.

‍

Wellntel provides the means to quantify the groundwater nutrient contribution with greater accuracy at a much lower cost that has been possible before. In quantifying more accurately the groundwater contribution to nutrient loading, a more precise TMDL can be developed which will then have a greater likelihood of success.

The need for continuous hydrological data is growing, while the ability of government agencies to collect and interpret these data is flat or declining.

The responsibility for groundwater resource management is shifting from governments to citizens

Recent legislation and legal rulings on groundwater use in Wisconsin have continued the trend toward less state-level regulation and oversight. If groundwater use and resource stewardship is to become increasingly a local responsibility, improving the effectiveness of management will require that neighbors and communities pool resources to ensure that “much is known by many”. In the context of groundwater, “much” describes information that is dense, accurate, and objective, and “many” describes all affected stakeholders. Historically, the data available for groundwater assessment and management would best be described as “little is known by few”. In this case, the amount of data is “little” (or modest) because of the cost of institutional data gathering by the “few”, those being the centralized government agencies who have had responsibility for collecting and interpreting groundwater data.

Contributors of water data

Ideally, every stakeholder contributes to the knowledge that supports science-based water resource management. Data collected by community volunteers and local organizations (often called citizen science) are essential because they often fill gaps in institutional data to paint a more complete picture of a dynamic groundwater resource. In addition, citizen science can play a critical role in the management of groundwater resources and become the foundation for education and engagement of local communities most affected by changes to their groundwater resource. The interest of these communities in having a voice in local management of groundwater has grown substantially over recent years.

Leveraging community-level data collection and engagement

Lacking until now has been a way to apply the skills and enthusiasm of citizen scientists to the collection of groundwater-level data for understanding the dynamics of local groundwater resources. Opportunities to access non-pumping monitoring wells are generally limited and measuring the depth to water in pumping wells (residential and farm) is difficult. The best attributes of citizen science will be enhanced by enabling communities to collect groundwater-level data that are accurate, consistent, continuous, and that can be easily shared in real-time with the entire community.

Wellntel provides the solution for communities and other stakeholders to work together with their citizen scientists to collect high-quality data in pumping and non-pumping wells with greater spatial and temporal density, and at much lower cost than traditional methods. Wellntel systems consist of a simple, easy-to-install groundwater-level sensor, a receiver plugged into a router in a nearby building that will push data to the internet, cloud-based computational processing, and real-time presentation of hydrographs. A system deployed on a private well provides that owner with important operational data, such as pumping water levels in relation to pump depth or the effect of pumping by neighboring wells. Systems deployed across a number of private wells can be considered in aggregate, creating a new network or augmenting an existing monitoring network with geo-spatially- and temporally-dense datasets.

Wellntel systems have been proven to provide accurate and objective groundwater-level data on a platform that is as easily used by citizen scientists as by professional scientists.

Wellntel fills a critical gap in meeting the growing need for continuous groundwater data

As stewardship of local groundwater resources falls more on local organizations and community volunteers, collecting data that are dense and accurate, as well as relatively budget friendly, is increasingly essential. With Wellntel, municipalities, communities, and the broader scientific community can for the first time join together to provide the “continuity of observations” that focuses directly on their most critical groundwater resource concerns. A network of Wellntel systems deployed on public and private wells will provide time-series data that are critical for broadly visualizing and managing local groundwater resources, and making local sense of regional trends. This new opportunity to create community groundwater networks moves us all toward the goal that “much is known by many”, a requirement for effective resource management.

‍

Sources

• Bradbury, K.R., Fienen, M.N., Kniffin, M.L., Krause, J.J., Westenbroek, S.M., Leaf, A.T., and Barlow, P.M., 2017, Groundwater flow model for the Little Plover River basin in Wisconsin’s Central Sands: Wisconsin Geological and Natural History Survey Bulletin 111, 82 p.

Groundwater isn’t static anymore…

‍

It is true that groundwater as a natural resource has never been truly static, in the sense that it is constantly moving from points of recharge to points of natural discharge. All components of the hydrological cycle, including groundwater, move continually in response to large-scale physical drivers and the energy dynamics of phase changes.

However, these physical drivers have increasingly been altered as a result of climate change. As presented in the POLICY FORUM of the periodical Science, a column titled Stationarity is Dead: Whither Water Management* states “climate change undermines a basic assumption that historically has facilitated management of water supplies, demands, and risks.”

‍

….in that the foundational assumption of hydrologic stationarity is no longer valid.

Climate change undermines the foundational assumption of stationarity. Simply, stationarity is the premise that measurable processes of natural systems vary in predictable ways, within unchanging bounds of variability. The column further states “Stationarity is dead because substantial anthropogenic change of Earth’s climate is altering the means and extremes of precipitation, evapotranspiration, and rates of discharge of rivers.”*

All hydrological sciences have been built around the assumption of stationarity, as have the practical applications of water management and engineering. The effect of global climate change on the probability of hydrologic events and the traditional practice of water science is a profound complication.

‍

Understanding local hydrology and groundwater resources requires, more than ever before, the collection of local, continuous hydrologic data.

As stated in the Science column “In a nonstationary world, continuity of observations is critical.”* This argues that greater temporal and spatial density of data are required to build the hydrologic understanding to meet local and regional groundwater challenges.

‍

How this affects you.

The need for continuous hydrological data is increasing, while the ability of government agencies to collect and interpret these data is flat or declining. Even when funds for science are more readily available, state and federal agencies can not collect data at a pace and in enough places to address issues most important to local communities. Fortunately, municipalities, communities, and other stakeholders can now join together as SPONSORS and MEMBERS to create powerful networks of Wellntel systems to provide the “continuity of observations” that focuses directly on their most critical groundwater resource concerns.

‍

Sources

*Milly and others, Stationarity Is Dead: Whither Water Management?, Science  01 Feb 2008: Vol. 319, Issue 5863, pp. 573-574.

‍