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Review

A Review of Data Quality and Cost Considerations for Water Quality Monitoring at the Field Scale and in Small Watersheds

1
Center for Agricultural Resources Research, United State Department of Agriculture–Agricultural Research Service, Fort Collins, CO 80526, USA
2
Department of Agricultural and Biological Engineering, Penn State University, State College, PA 16801, USA
3
Soil Drainage Research Unit, United State Department of Agriculture–Agricultural Research Service, Columbus, OH 43210, USA
4
Pioneer Farm, University of Wisconsin—Platteville, Platteville, WI 53818, USA
5
Department of Biological and Agricultural Engineering, North Carolina State University, Raleigh, NC 27695, USA
6
Department of Agricultural Sciences, Clemson University, Clemson, SC 29634, USA
*
Author to whom correspondence should be addressed.
Water 2023, 15(17), 3110; https://doi.org/10.3390/w15173110
Submission received: 24 July 2023 / Revised: 23 August 2023 / Accepted: 23 August 2023 / Published: 30 August 2023
(This article belongs to the Section Water Quality and Contamination)

Abstract

:
Technological advances and resource constraints present scientists and engineers with renewed challenges in the design of methods to conduct water quality monitoring, and these decisions ultimately determine the degree of project success. Many professionals are exploring alternative lower-cost options because of cost constraints, and research and development is largely focused on in situ sensors that produce high temporal resolution data. While some guidance is available, contemporary information is needed to balance water quality monitoring decisions with financial and personnel constraints, while meeting data quality needs. This manuscript focuses on monitoring constituents, such as sediment, nutrients, and pathogens, at the field scale and in small watersheds. Specifically, the impacts on the costs and data quality of alternatives related to site selection, discharge measurement, and constituent concentration measurement, are explored. The present analysis showed that avoiding sites requiring extensive berm construction and the installation of electric power to reach distant sites greatly reduces the initial costs with little impact on data quality; however, other decisions directly impact data quality. For example, proper discharge measurement, high-frequency sampling, frequent site and equipment maintenance, and the purchase of backup power and monitoring equipment can be costly, but are important for high quality data collection. In contrast, other decisions such as the equipment type (mechanical samplers, electronic samplers, or in situ sensors) and whether to analyze discrete or composite samples greatly affect the costs, but have minimal impact on data quality. These decisions, therefore, can be based on other considerations (e.g., project goals, intended data uses, funding agency specifications, and agency protocols). We hope this guidance helps practitioners better design and implement water quality monitoring to satisfy resource constraints and data quality needs.

1. Introduction

As water quality monitoring is increasingly implemented at the field and farm level to evaluate land management impacts, scientists and engineers are faced with technical and resource-related decisions that ultimately determine the degree of project success. Beginning in the 1980s, most field-scale and small watershed projects shifted away from manual sampling and, instead, used automated equipment to collect runoff water quality samples (Figure 1 [1]). The shift likely occurred due to the financial reality that personnel costs for long-term manual sampling projects are much greater than that for automated equipment purchase and maintenance. In addition, it is very difficult to ensure personnel are available for deployment to conduct manual sampling at numerous, often remote, sampling locations in short duration “wet weather” or “storm water” events that drive non-point source pollution at the field scale and in small watersheds and that regularly occur outside of traditional working hours. Then, in the 1990s, major technological advances ushered in the common use of electronic automated samplers, and general guidance followed on equipment types, monitoring plans, and statistical design [2,3]. In the 2000s, research produced guidance related to critical technical components (e.g., storm thresholds, sample type, sampling frequency) [4,5,6,7,8,9].
Despite the availability of technical guidance, automated water quality sampling projects remain difficult, time consuming, and expensive, with numerous technical challenges (e.g., unpredictable weather, extreme events, instrument malfunction). In fact, previous research has reached a common conclusion: project success requires achieving the proper balance between the monitoring resources and the quality of the collected data [7,10,11,12,13,14]. The issue of data quality (measurement uncertainty) has been the subject of considerable research in recent years and is now included in monitoring guidance in the US and Australia [15,16,17]. Additionally, uncertainty estimation methods have been developed for runoff, sediment, nutrient, and pathogen data [13,18,19,20]. Another source of uncertainty, namely spatial variability, due to the number and location of monitoring sites within larger watersheds does affect data quality in terms of assessing the spatial distribution of sources. This is outside the scope of the present study, but certainly warrants future examination.
While some cursory information is available on the relationship between monitoring costs and data quality (e.g., a pre-calibrated flume is more expensive but produces lesser uncertainty than measuring discharge in a natural channel), additional guidance is needed to better manage costs and produce acceptable data quality. Whether or not this balance between monitoring costs and data quality is acutely considered, it is certainly an implicit consideration because collecting finer resolution/higher quality data tends to cost more. Faced with the monitoring resource constraints and the relatively high cost of off-the-shelf automated sampling systems, scientists and engineers are increasingly exploring alternative systems. This is especially true in developing countries and community science-based monitoring projects in which monitoring resources are severely limited; however, most if not all projects face cost or other resource constraints [14,21]. At the same time, a great deal of research and development is currently focused on in situ sensors because of their ability to collect fine temporal resolution data and avoid the need for sample collection and laboratory analysis; however, in situ sensors can suffer from calibration challenges, drifting, submergence concerns in low flow, and debris and sediment deposition.
Thus, the objective of this manuscript is to provide contemporary guidance on the relationships between monitoring costs and data quality to improve water quality monitoring project design and implementation that reflects advances in modern technology. Specifically, the impacts of key decisions (i.e., establishing monitoring sites, and measuring discharge and constituent concentrations) on monitoring costs and data quality are discussed. In addition, cost comparisons are presented between major project types. These results and recommendations apply to the following conditions: (1) field-scale (edge-of-field) to small watershed (<~10,000 ha); (2) discharge conditions, namely storm events; and (3) constituent types, namely nitrogen (N), phosphorus (P), sediment, and pathogens. Throughout this discussion, we acknowledge that water quality monitoring data have different intended uses, which warrant differing expectations related to data quality.

2. Materials and Methods

To compile state-of-the-art knowledge, we assembled a team with extensive expertise and examined the available literature on the impacts of key water quality monitoring decisions to provide cost and uncertainty estimates. Based on this information, we present herein the impacts and relevant recommendations for alternatives in site selection and establishment, discharge measurement, and constituent concentration measurement. To constrain the comparisons, we analyzed the range of annual, initial (one time), and total costs (using current 2022 costs), for five major project types (Table 1). For each of the five project types, we assumed a set of “typical” characteristics (i.e., 8 sampling sites, 10 events per year, USD 25 per sample for analysis, 2 constituents, 3 project years) to aid in the comparisons.

3. Results and Discussion

3.1. Monitoring Site Selection and Establishment

Information related to site selection and establishment was summarized from previous research, especially [7] (Table 2). The determination of the quantity and location of water quality monitoring sites affects project costs, but does not directly impact data quality. Although spatial variability, in terms of source contribution from various land uses and point sources, does contribute another source of uncertainty within larger watersheds, this is outside the scope of the present study. Each additional site increases the equipment and sample analysis costs, as well as the travel costs because personnel must make frequent site trips to maintain the equipment, collect data, and retrieve water samples (at times outside of normal working hours). It also indirectly affects salary costs and staffing levels.
Decisions related to peripheral equipment at monitoring sites impact both the project costs and data quality. Equipment shelters secure and protect the equipment and prolong its life, and backup (replacement) equipment limits data loss when equipment fails and requires replacement. The value of redundancy in terms of the backup equipment, especially for flow measurement, which seems to be the least reliable, is commonly underappreciated and often decreases data quality. Communication devices using radio, cell, or satellite technology allow personnel to remotely check equipment status, reset operation, retrieve data, and avoid unnecessary travel to check for samples, which saves travel costs; however, frequent travel to sites may still be required for equipment maintenance and sample retrieval.

3.2. Discharge Data Collection

Measurement of field-scale surface runoff or stream flow (discharge) is a critical component that affects project costs and data quality (Table 3). Most discharge measurement techniques are well established [22,23,24,25,26,27]. The most common method utilizes the stage (water surface level or flow depth) and its relationship to discharge (flow). With this method, a stage–discharge relationship (rating curve) is established by repeated measurements that represent the full range of the flow conditions at the site or accompanies pre-calibrated weirs or flumes. Then, continuous stage data are recorded and translated into discharge using the rating curve. Stage–discharge measurement assumes a univocal relationship between the stage and the discharge (i.e., no hysteresis), the stability of the relationship, no downstream flow control, and only applies within the range of measured stages and flows used to develop the relationship. Where these assumptions are not met, large uncertainties can occur [28]. Additionally, rating curve measurements are difficult and often unsafe to make under high flow conditions, thus the validation of stage–discharge relationships at high flows are often lacking.
More recent alternatives utilize in-stream velocity meters, with stage sensors or non-contact sensors (e.g., radar) to estimate or measure depth and velocity to provide continuous discharge measurements. These instruments use velocity data and the corresponding stage data with cross-sectional survey data to calculate the discharge; however, recorded velocities may not adequately represent the mean velocity, especially as the flow cross-section increases. To reduce inaccuracies, measurements should therefore be taken in stable cross sections of known geometry, and velocity methods (e.g., index velocity) should be applied in larger channels to correct the velocity measurement to represent the entire cross-section [28,29,30,31].
Table 2. Key decisions on monitoring site selection and establishment that affect the relationship between the project costs 1 and data quality, based largely on [7]. These decisions affect all the project types listed in Table 1.
Table 2. Key decisions on monitoring site selection and establishment that affect the relationship between the project costs 1 and data quality, based largely on [7]. These decisions affect all the project types listed in Table 1.
DecisionAnnual CostInitial
(One-Time) Cost
Data
Uncertainty
Related Comments
Number of sites$$$:
technical staff
salary
$$$: vehicle-Carefully determine the number of
sampling sites. One full-time technician can typically operate 6–10 sites (including laboratory analysis, data management), but the distance to and proximity of the sites can affect this. For graduate students, the salary costs are less but so is their capacity in terms of time and expertise. Exceeding a reasonable number of sampling sites and utilizing inexperienced technicians will decrease data quality.
Location of sites$-$$:
travel costs =
f (location, proximity); site maintenance; equipment maintenance
$/site:
basic installation
-or- $$/site:
extensive berm
construction required
-Locate sites to minimize travel costs. If possible, install field-scale sites within a natural drainage way to avoid extensive berm construction (USDA, 1996), and avoid unstable sites. Commit to frequent maintenance [2,3,7], as less frequent maintenance will decrease data quality
due to missing or inaccurate data.
Equipment shelter-$/site:
homemade or
repurposed shelter
-or- $-$$/site:
commercial shelter
-Install equipment shelter at each site.
Ensure accessibility during wet weather, above the flood level [3,25], yet as close as possible to the sample point [32].
Duplicate equipment-$$/per every 6–10 sites, backup and replacement equipment-Purchase backup and replacement
equipment to reduce missing data
(increases the data quality).
Power needs$/site:
electrical costs
(if AC power; maintain and
recharge batteries)
$-$$/site:
install AC power at remote site (if
feasible) -and-
$-$$/site: solar panel and battery
See [20,33,34] for
uncertainty related
to preservation and
storage.
Consider power needs based on the
QA/QC requirements for sample
preservation. Refrigerated samplers
often require AC power; however,
battery backup is recommended
for all automated samplers to avoid power loss during storm events.
Communication devices (cell, radio, satellite)$/site: fees$-$$/site:
communication
device
-Consider the costs and benefits of communication devices (i.e., purchase costs and fees relative to salary and travel costs). The communication device can increase data quality through the immediate notification of equipment failure. As the distance to the sampling sites increases, the benefits of communication devices
increases.
Notes: 1 $—less than USD 1000; $$—USD 1000–USD 10,000; $$$—greater than USD 10,000.
Table 3. Key decisions on flow measurement that affect the relationship between the project costs 1 and data quality.
Table 3. Key decisions on flow measurement that affect the relationship between the project costs 1 and data quality.
DecisionAnnual CostInitial
(One-Time) Cost
Data UncertaintyRelated Comments
Are discharge (flow) data needed to meet the project objectives?Cost estimates below apply only
to projects that measure loads
(project types 3–5 in Table 1).
-If load data are not needed, discharge measurement is unnecessary; however, flow and load data are often critical, so carefully
consider the potential future
data uses and project objectives.
If flow data are needed, consider the following options.
Discharge (flow) measurement options:
Measure the stage in the pre-calibrated flow
control structure with the established stage–discharge
relationship [7,35]
$/site:
maintenance
$$/site:
control structure
and stage
measurement equipment
±5–10% depending on
frequency of calibration with a current meter [36]
Follow installation and maintenance recommendations [32,37]. Data quality decreases substantially if the stage exceeds the design capability, which limits use as the contributing area increases.
Measure the stage in
a channel, culvert,
or other stable flow path
$-$$/site:
maintenance; flow
measurements
and cross-section
surveys to confirm
or adjust the stage–discharge relationship
$-$$/site:
survey cross-
section; stage measurement equipment
Stable channel:
±6% [38],
±10% [36]

Shifting channel:
±20% [36]
Selecting sites with an established stage–discharge relationship and in a stable channel avoids the cost and difficulty of developing and adjusting the relationship. Calibrate the stage–discharge
relationship with a current meter and cross-section surveys,
8–12 per year especially
in shifting channels.
Measure discharge
with area–velocity sensor
$-$$/site:
maintenance; flow measurements
and channel surveys to confirm discharge data
$$/site:
survey cross-
section;
velocity
measurement equipment

>10% (King, unpublished data)

±10% in trapezoidal
channel [39]

±0.003 m at 0.01–3 m (depth); ±0.03 m/s at 0–1.5 m/s and ±2% at 1.5–6 m/s
(velocity) [40]
Confirm velocity measurement
accuracy and stage discharge relationship with current meter readings, 8–12 per year, especially in shifting channels. Data quality decreases substantially if the flow cross-section is non-uniform, exceeds the design capability, if the sediment concentration is low, and if the cross-section is unstable.
Estimate discharge with Manning’s equation$$/site:
maintenance;
flow checks
$/site:
survey cross-
section; flow
measurement equipment
~10%-100% [41]

±10–20% for ideal
conditions, but ±25–30% more likely, and
≥ ±50% possible [42]
Not recommended
because of low data
quality without extensive
adjustments.
Measure the stage in the homemade flow
control structure
(i.e., flume, weir)
$/site:
maintenance flow measurements to develop/adjust stage–discharge
relationship
$/site:
structure construction
and flow
measurement equipment
±11% in initial tests,
but higher uncertainty
at high discharge rates
with turbulent flow
(Busch, unpublished data)
Design specifications for flumes and weirs are quite specific; therefore, deviations affect the theoretical discharge relationship. Conduct extensive current meter checks to establish an accurate stage–discharge relationship.
Notes: 1 $—less than USD 1000; $$—USD 1000–USD 10,000.
To reduce costs, some projects have utilized Manning’s equation, which eliminates the need to measure the discharge velocity. Manning’s equation estimates discharge velocity based on the channel roughness, slope, and cross-sectional geometry [25,43]. Then, the cross-sectional flow area and velocity estimates are used to calculate the discharge. Because Manning’s equation was designed for a uniform flow with a constant channel slope, shape, material, cross-sectional area, and velocity, its use in natural channels introduces substantial uncertainty, due to the non-uniform flow and channel roughness coefficient determination [43].
Other projects have utilized homemade weirs/flumes to reduce the costs associated with discharge measurement. For example, Busch et al. (unpublished data) developed a prototype flume, reducing the height of the flume’s converging sidewalls to overcome the challenge of the large footprint of large flumes. This prototype flume was designed to accurately measure a range of flows by allowing high flows to remain within the approach and flume channel, but to overtop the converging sidewalls. Initial test results from the University of Minnesota, Saint Anthony Falls Laboratory, showed average discharge measurements within 11% of the true values across a range of flows, but in-field tests revealed turbulent flow in the flume, at high discharge rates, which made accurate stage measurements difficult. The latest iteration of the prototype uses a combination of a 0.75 ft (0.225 m) HL flume and a 2.25 ft (0.675 m) sharp crested weir on each side of the flume; this is similar to a compound weir but with the flume replacing a v-notch weir for measuring low discharge rates. The initial field tests have shown quiescent flow and accurate stage measurements during large runoff events, and additional lab tests are planned.
Others have used sheet metal or wood to create stable channel sections and/or to control volumes and, thus, create stable stage–discharge relationships [39]. These artificial channels lead to the precise estimation of the cross-section area (typically rectangular or trapezoidal) and encourage laminar flow (if the length is adequate, >~2.5 m), both of which increase the quality for discharge measurements.

3.3. Constituent Concentration Measurement

Prior to the 1990s, the within-event variability of constituent concentrations in small watersheds was only able to be characterized with intense manual sampling programs or with mechanical samplers [44,45,46,47,48]. Then, in the 1990s, the advent of electronic samplers and sensors brought increased scientific capacity, enabling monitoring efforts to focus on the determination of runoff concentration and load dynamics [1,49].
Decisions related to several aspects of water quality concentration measurement (i.e., constituent type, automated approach and sampling frequency, in situ sensors, low-cost components) affect both the monitoring costs and data quality (Table 4). Regardless of the sampling method, the uniformity of the water quality across the cross-section and within the water profile is an important consideration. The assumption of well-mixed conditions is generally true for dissolved constituents at edge-of-field and small watershed sites [50,51]. This can be evaluated with a four-parameter probe (pH, temperature, conductivity, dissolved oxygen), such that if the values vary by more than 5% throughout the channel, then the assumption of well-mixed conditions may not be appropriate and a single sampling location may not represent the cross-section [52]. Even particulate constituents can often be assumed to be uniformly distributed in the flow profile and across the channel in edge-of-field scale sites and small streams because of well-mixed conditions and shallow flows. However, this assumption can be evaluated with integrated manual sampling, as described by [53], with multiple grab samples [6], or with turbidity probes. If the particulate concentrations do, in fact, vary considerably with the flow, then a relationship can be established between the concentrations at the sampling location intake and the total concentration across a range of discharges [51].

3.3.1. Automated Sampling

Small watershed monitoring projects typically utilize automated sampling equipment to collect water quality samples because runoff events often occur with little warning over short durations, outside conventional working hours, and in adverse weather conditions [3]. Additionally, automated samplers can sample within the rapid hydrologic response time of small watersheds, use a consistent sampling procedure at multiple sites, and take multiple samples throughout entire runoff durations. In contrast, manual sampling requires multiple technicians to travel to multiple sites during runoff events and manually collect samples in hazardous conditions, often outside of traditional working hours. Since few projects have sufficient resources to maintain adequate on-call technical staff, small watershed projects rarely, if ever, employ manual storm sampling. Therefore, the following discussion focuses on a comparison of automated sampling (for information on manual storm sampling, see [53,54]).
The two major categories of automated samplers are mechanical and electronic. Mechanical automated samplers have been used for decades for edge-of-field and small watershed monitoring. One class of mechanical automated samplers collects flow-weighted samples with rotating slots (e.g., Coshocton samplers [46,47], modified Coshocton samplers [45,55,56,57], or multi-slot divisors [44,58,59,60]). Rotating slot and multi-slot divisor samplers have near unlimited sampling capacity and can estimate flow volume and the event mean concentration (EMC) through analysis of the single resulting composite sample, but they have difficulty in taking small volume samples relative to the flow volume. In contrast, other mechanical automated samplers use pumps to collect time-weighted samples at pre-specified intervals with which to examine within event concertation variability (e.g., Chickasha samplers [48]). This type of sampler, however, has a limited capacity in terms of the size and number of sample bottles and requires fairly complex fabrication and wiring, which limits its use. Siphon or single-stage samplers can also be used to collect a sample of near-surface water during the hydrograph rising limb [61], but they have several limitations [62,63] and produce greater uncertainty.
Electronic automated samplers have been used since the 1990s. Regardless of the manufacturer, electronic samplers typically have a stage recorder/flow meter (or can be linked to one), a sample collection pump and bottle(s), a data logger/memory, and programmable operation related to the sampling threshold, sample volume, time- or flow-weighted sampling, and discrete or composite sampling.
Sampling theory and several studies have shown that the smaller the sampling interval, the better the characterization of the water quality [4,5,12,14,64,65,66]; however, sampling interval selection must also consider the importance of sampling throughout long duration/large volume events. Frequent volumetric depth intervals (1–2.54 mm) allow smaller storm events to be sampled and moderate-to-large storm events to be sampled more intensively with little to no increase in uncertainty, especially with composite sampling.
Table 4. Key decisions on constituent concentration measurement that affect the relationship between the project costs 1 and data quality.
Table 4. Key decisions on constituent concentration measurement that affect the relationship between the project costs 1 and data quality.
DecisionAnnual CostInitial
(One-Time) Cost
Data UncertaintyRelated Comments
How will the constituent concentrations be determined?Cost estimates below are a
function of the project types
in Table 1.
-Carefully consider the
following options.
Collect samples to measure the constituent concentrations in the labCost estimates in the
following section apply to
project types 1, 3, 4.
Automated sampling with rotating slot or multi-slot divisor mechanical samplers (produces single flow-weighted composite
sample and estimates flow volume, thus the discharge measurement options in Table 2 are not needed for load determination).
$/site:
maintenance
$-$$/site:
mechanical rotating slot or multi-slot sampler (will likely require fabrication)
See [5,12,14] for
uncertainty estimates
related to automated
sampling.
More frequent sampling increases data quality. The unlimited sampling capacity of rotating slot and multi-slot divisor samplers can increase data quality in large events, but they only capture the EMC. Limitation in the size and number of sample bottles in mechanical time-weighted and electronic samplers can decrease data quality in large events, though strategies
exist to overcome this [9].
Automated sampling
with electronicsamplers
$/site:
maintenance
$$/site:
electronic
sampler
Lab analysis$-$$/site:
analysis =
f (number of samples)
-See [67,68,69,70,71,72] for
Uncertainty estimates
related to lab analysis.
Follow sample preservation,
storage, and analysis protocols
to reduce uncertainty. Estimating the annual number of samples
will assist in estimating lab
analysis costs [8].
Utilize in situ sensors to measure the constituent concentrationsCost estimates in the
following section apply to
project types 2, 5.
See [73,74,75] for
additional uncertainty
estimates related to
in situ sensors.
Avoids lab analysis costs. Provides concentration data with the same time resolution as the flow data. Conduct weekly to biweekly maintenance of the optics. Independently obtain discrete samples for instrument calibration.
$/site:
maintenance and calibration
$$$/site:
voltametric and
amperometric
See [76,77]
for nitrate ISE

±0.5 mg N/L
for NH4+ [78]
Much of the uncertainty in ISE
measurements results from the
fouling and drift over time.
In situ ISE is currently used
only for ammonium
> 1 mg N/L.
$$/site:
maintenance and calibration
$$$/site:
optical UV–VIS
spectroscopy and
fluorescence sensors
0.1–12 mg/L NO3-N
(±5% +0.2 mg/L) [79]

0–14 mg/L N (±10%)
and 0–42 mg/L N
(±25%) [80]

EXO NitraLED™
±0.4 mg N/L
or 5% (in pure water)
Proxy techniques require
a conversion algorithm, but
can be highly accurate; subject
to interference from water
color and turbidity.

$$/site:
maintenance and
calibration

$$$/site:
colorimetric sensors with a “lab” onsite

Generally, more accurate
than ion specific electrodes,
but has smaller range and
can require post-correction.
Notes: 1 $—less than USD 1000; $$—USD 1000–USD 10,000; $$$—greater than USD 10,000.
Composite automated sampling in the field increases the sampler capacity by placing two or more sub-samples in each bottle, making it a valuable, cost-saving alternative that introduces less uncertainty than increasing the minimum flow thresholds or increasing the sampling intervals [5,7,12,14]; although, [81] concluded that compositing generally increased the precision of the annual load estimates for tile-drained landscapes, but often decreased the accuracy. Composite samples can also be produced in the lab from discrete field samples [1,18]. This alternative can be attractive if sufficient lab staff are available and if flexibility is needed (i.e., a discrete sample can be analyzed for selected constituents and the composite samples analyzed for others). Irrespective of the compositing method, composite sampling can decrease analysis costs and increase sampling capacity, but it does limit within-event concentration data, which can be critical to understanding temporal and spatial patterns of constituent flux.
To reduce costs, several projects have utilized homemade samplers, pumps, and other automated sampling components. In one example, ref. [82] developed a prototype edge-of-field runoff monitoring station, to reduce costs and address technical issues, for monitoring in northern climates. The initial results showed promise with low-cost ultrasonic stage sensors accurately measuring flow depth in the flume, except at high discharge in which turbulent flow reduced the accuracy. The station used flume heaters and equipment enclosures, which increased the costs but decreased the effort to prepare stations for winter monitoring and improved the working conditions. The low-cost sampler also produced similar estimates on suspended sediment and NO3-N concentrations as a conventional automated sampler.
Another project compared prototype edge-of-field monitoring gauges based on Internet-of-Things (IoT) technology and conventional methods. In this project (Busch and King, unpublished data), water quality and quantity were compared for five edge-of-field runoff and five tile drainage sites during non-frozen field conditions in the Blanchard River Watershed in northwest Ohio. The results of the in-field testing indicated that the IoT prototype, with low-cost ultrasonic and pressure sensors, produced comparable water quality and chemistry results to the conventional system, except for estimates on tile discharge when a tile was impacted by submergence. It should be noted that the IoT prototype was designed to estimate discharge and collect flow-paced samples only, and it lacks the capability to collect discrete time-based or flow-paced water samples.
Additionally, replacement parts produced in-house with 3D printers are being used to reduce costs. These components are showing promise as part of low-cost samplers for edge-of-field load determination, as reported in a recent USDA project [83]; however, additional field testing is needed.

3.3.2. In Situ Sensors

In situ sensors have been conceptualized for decades to overcome difficulties and costs associated with sample collection, transportation, storage, processing, and analysis. To clarify, the term in situ sensors herein refers to waterproof electronic equipment able to collect frequent water quality constituent concentration data in the field without the need to bring numerous water samples to a laboratory for analysis; however, these systems do require the collection of independent field samples to calibrate and validate sensor values. Although not yet widely adopted for edge-of-field research and monitoring, in situ sensors most likely represent the future [14,74,84,85,86]. The capacity to measure instantaneous concentration data in both baseflow and stormflow conditions, as is now conducted for discharge data, is a revolutionary prospect, especially for constituents known to exhibit high concentration variability; however, limitations remain related to the cost, accuracy, robustness, reliability over time, and the parameters measured. In addition, Ref. [87] highlighted the importance of the placement of sensors in protective housing with sufficiently large openings to facilitate water exchange.
The three main in situ sensor types are: voltametric and amperometric sensors, optical sensors, and miniaturized analytical sensors. Starting in the 1990s, voltametric and amperometric sensors, usually with ‘multiparameter probes’, have measured selected parameters (e.g., temperature, pH, dissolved oxygen, conductivity, redox potentials) at a high frequency to quantify the physico-chemical aspects of stream or runoff water quality. Ion-selective sensors (ISEs) using similar technology have been mounted on these multiparameter probes to measure specific constituent concentrations; however, low concentrations for constituents of interest (e.g., ammonium or nitrate, ~0–10 mg N/L), fouling and temporal drift requiring frequent on-site calibration [76,77,88,89], and the limited lifespan of ISE membranes, lead to unreliability and limited use.
Optical sensors measure the tendency of water to absorb or reflect light upon excitation. These instruments are proven and relatively robust for nitrate, C, and sediment concentrations, although fouling of the optics can increase error [90] and the costs remain relatively high. In the late 1990s, turbidity sensors became widely available for measuring either the absorption of direct light for a given optical path length or the scatter at a 90° angle of an emitted visible light due to suspended particles (the nephelometric technique). These sensors, which are typically mounted on multiparameter probes, report values in turbidity units, which can be correlated, on a site-by-site basis, to suspended solid concentrations. With this calibration, high-frequency suspended solid concentrations can be calculated [91,92,93,94,95]. Most optical sensors are now equipped with wiping mechanisms to reduce fouling; however, the wiping mechanism can smear the optics necessitating manual cleaning, especially as the sensor dries during ephemeral conditions [96].
In the 21st century, miniaturized in situ spectrophotometers extended absorbance measurement to the UV range, where nitrate in particular is known to strongly absorb light. These UV–visible spectrophotometric sensors have been successfully used to measure N, P, and sediment concentrations in water [97,98,99,100,101,102,103]. These sensors are standalone instruments (i.e., not mounted on multiparameter probes) and tend to be expensive (USD 20,000–USD 30,000). More recently, fluorescence sensors have become more popular to measure a range of dissolved and suspended constituents [104]. These sensors are the equivalent of nephelometric turbidity sensors for the UV range. An excitation light at a narrow wavelength is sent in water, some of which is absorbed and fluoresces back at lower wavelengths due to the nature and concentration of organic matter in water (e.g., DOC, algal pigments), and the emitted fluorescence is measured at 90° from the excitation light. Fluorescence has been shown to be correlated with algal cell and DOC concentrations [105,106,107,108] and, recently, has been used to monitor pathogen concentrations [109]. Fluorescent sensors tend to be mounted on multiparameter probes, enabling physical, chemical, and biological parameters to be measured simultaneously.
Lastly, miniaturized analytical sensors utilize colorimetric absorbance spectrophotometry methods to measure constituent concentrations of dissolved nutrients, including ammonium, nitrate, and orthophosphate [85]. They require considerable maintenance because of their high potential for fouling and use of reagents that generate waste that must be managed, and they require power that may limit their utility in remote locations. Ref. [110] recommend weekly cleaning and cartridge (solution) replacement every 3–4 months to maintain proper functioning. Additionally, the concentration ranges for these systems may limit their utility in highly impaired water bodies. For example, nitrate analyzers typically have ranges up to the primary drinking water standard of 10 mg N/L. However, nitrate concentrations are known to exceed these levels in small watersheds or field-scale systems that are agriculturally impaired, thereby limiting their utility. These miniaturized colorimetric sensors are currently the only reliable options for measuring phosphorus concentrations at high frequency [110,111]. Refs. [100,111] reported the ability to measure P using UV–vis spectrometers, although phosphate does not absorb light because parameter concentrations may covary with the ‘color of water’, with the correlation established on a site-by-site basis. For detailed additional discussion of in situ sensors, including the current technology and limitations, see [74,84,85,112].

3.4. Decisions with Substantial Impacts on Project Costs and Data Quality

As shown in Table 5, several key decisions common to edge-of-field and small watershed monitoring projects can have substantial impacts on initial (one-time) and annual project costs. The costs presented, subsequently, represent a “typical” project (assuming eight sampling sites, ten events/yr, USD 25/sample analysis, two constituents) based on the market costs at the time of the manuscript preparation in 2022. The following decisions affect the project costs but have little to no effect on data quality. Avoiding sites that require extensive berm construction (− USD 15,200) and selecting sites with nearby power lines for projects requiring AC power (− USD 20,000) can dramatically reduce the initial costs. Using repurposed or homemade equipment shelters instead of commercial options also decreases the initial costs (− USD 5600), although additional staff time will be required. Communication devices linked to electronic samplers and in situ sensors (+ USD 1200) increase the initial costs but can save annual travel costs even when associated fees are considered. Additionally, locating monitoring sites near to the lab/office and near each other, when possible, can reduce annual costs (− USD 2600).
The following decisions are highly recommended, despite the additional costs, because of the high likelihood of reducing the data quality if not implemented. Committing to frequent maintenance (once per week is recommended) and installing a battery/solar panel power system at each site, even where AC power is available, does increase costs but also increases the likelihood of successful data collection in storm events. Similarly, purchasing backup equipment (+ USD 6000 to + USD 20,000) decreases downtime to repair malfunctioning equipment.
The discharge measurement necessary for load determination can increase the initial costs, but collecting only constituent concentration data limits the value of monitoring projects and the utility of the resulting data sets for uses such as the validation and calibration of hydrology and water quality models. The ability of mechanical rotating slot or multi-slot samplers to estimate the total flow volume at no additional cost is a major benefit. In contrast, accessory discharge measurement equipment is needed when using electronic automated samplers or in situ sensors. For electronic samplers or in situ sensors, the initial costs for flow measurement increases: + USD 9000 if an established stage–discharge relationship exists at all sites (which is rare at the edge-of-field and small watershed scale), + USD 17,000 if area–velocity sensors are used, and + USD 57,000 if pre-calibrated weirs/flumes are used. Whereas the cost is substantially higher for pre-calibrated weirs/flumes, they tend to produce lower uncertainty than area–velocity sensors in the author’s experience; however, the uncertainty greatly increases for pre-calibrated weirs if the flow exceeds the design capability and for area–velocity sensors if the flow cross-section exceeds the design capability, the sediment concentration is low, and the cross-section is unstable.
For measuring the constituent concentrations, mechanical rotating slot or multi-slot samplers utilize the most basic and least expensive equipment (USD 6000) to estimate the flow volume and collect a composite sample for analysis. The estimated annual lab analysis costs for mechanical slots samplers is USD 4000 because of the single composite sample generated. In contrast, the initial cost of automated electronic samplers (USD 48,000) and in situ sensors (USD 135,000 to USD 180,000) is much greater. With automated electronic samplers, the major determinant of the annual costs is the decision to take discrete or composite samples, which directly affects the number of samples to be analyzed. The annual laboratory analysis costs range from USD 4000 for single composite samples to USD 40,000 for discrete samples, which illustrates the value of high-frequency composite sampling to control costs. With in situ sensors, there are no laboratory analysis costs.
For each of the five project types under the assumptions made (Table 5), the salary costs (USD 100,000/yr) and the vehicle purchase price (USD 50,000) represent a large portion of the total project costs. Projects that measure concentrations and loads with mechanical rotating slot or multi-slot samplers are by far the least expensive (USD 383,590 to USD 412,190) because of low initial equipment costs and low annual laboratory analysis costs. These mechanical samplers are quite cheap relative to electronic samplers; thus, it is quite surprising that they are not utilized more. In comparison, projects that measure concentrations and loads with electronic samplers cost from USD 447,360 to USD 667,080, whereas projects that measure concentrations and loads with in situ sensors are more expensive (USD 528,480 to USD 695,200). Although laboratory costs are avoided with in situ sensors, the higher initial costs for sensor purchase (USD 15,000 to USD 20,000/site) and the equipment calibration costs (USD 5000 to USD 15,000/yr) are not enough to offset this saving.
Another cost consideration is the re-use of the equipment in subsequent projects. Equipment, such as discharge measurement devices, mechanical and electronic samplers, in situ sensors, and communication devices, can be re-used if carefully removed and properly stored in the interim. This can substantially reduce the costs in future projects because the initial purchase costs for electronic samplers and in situ sensors present a high proportion of the total project costs.
Because of the value of high temporal resolution data, we expect that the costs and capacity related to in situ sensors will improve in the coming decade increasing both the affordability and data quality. In addition, mini-sensors utilizing the Internet of Things (IoT) could revolutionize the collection of water quality data in terms of spatial and temporal variability; however, the stewardship of data produced by IoT sensors may require significant resources.

4. Conclusions

As water resource professionals are faced with the difficult and expensive task of quantifying water quality at edge-of-field and small watershed scales, they continue to struggle in balancing the monitoring costs with data quality (uncertainty). In general terms, collecting more and/or higher quality data tends to cost more. Decisions related to sampling site numbers and locations, discharge measurement, and sampling equipment, all affect a project’s ability to satisfy the monitoring goals within cost constraints. The information herein provides relative cost estimates for various monitoring alternatives to inform the allocation of monitoring resources to produce the highest quality (lowest uncertainty) data.
The present analysis shows that certain decisions produce considerable initial (one-time) and annual cost differences, but not all of these decisions impact data quality. For example, avoiding the selection of sites that require extensive berm construction and the lengthy extension of AC power lines greatly reduces the initial costs but has little impact on data quality.
Other decisions have more than one viable option for producing high quality data. For example, the decision to purchase mechanical samplers, electronic samplers, or in situ sensors greatly affects the initial costs, but each device can produce accurate constituent concentration data. Thus, this decision can be based on relative affordability and simplicity of use, common practice, project goals, temporal data resolution needs, or laboratory capabilities. Similarly, the decision to analyze discrete or composite samples from automated samplers greatly affects annual costs and determines whether within-event concentration changes are quantified, but the impact on data quality is minimal.
Some decisions have a clear option to minimize data uncertainty. For example, discharge (flow) measurement with prefabricated flow control structures or area–velocity meters under proper conditions, although costly relative to lower-cost alternatives (e.g., Manning’s equation), is highly recommended to produce high quality data. Similarly, high-frequency sampling, frequent site and equipment maintenance, battery/solar panel backup power systems, and backup monitoring equipment all increase data quality.
The trend for increasing the use of in situ sensors to monitor water quality in small watersheds is expected to continue because of their relative simplicity and ability to provide continuous concentration and load data; however, more research on the related measurement uncertainty is needed. Automatic sampling will also likely continue because of its affordability and reliability. The Internet-of-Things (IoT) ecosystem of sensors, loggers, controllers, telemetry, and data stewardship protocols may provide a low-cost option with increased capabilities and increased spatial representation in the near future. With any process or equipment, it is critical to utilize proper quality assurance/quality control methods to collect data with the highest quality possible and to report the estimated uncertainty of the resulting data.
Throughout this discussion, we acknowledge that water quality monitoring data have different intended uses, similar to intended modeling uses and ranging from “exploratory” (e.g., pilot projects, stakeholder demonstrations) to “planning” (e.g., practice evaluation, policy formulation) and to “regulatory/legal” (e.g., compliance assessment, standard violations, lawsuits, human health implications), as described in [113]. Thus, differing uses of water quality monitoring data, along with additional requirements (e.g., funding agency specifications, agency protocols), all warrant differing expectations related to data quality. For additional information on uncertainty, decision-making, and documenting water quality benefits at the edge-of-field and small watershed scale, see [114].
We also encourage data quality estimates to be presented with all water quality concentration and load data to facilitate applicability assessment in future, and unexpected uses, as called for in [13,19,20,115]. Simply judging data as “good” or “bad” is rarely appropriate (recognizing that there are some invalid/erroneous data resulting from mistakes in collection, analysis, and transcription).

Author Contributions

Conceptualization, R.D.H., H.E.P. and D.S.; methodology, R.D.H., H.E.P. and D.S.; formal analysis, R.D.H., K.W.K., D.B. and F.B.; investigation, R.D.H., H.E.P., K.W.K., F.B. and D.B.; writing—original draft preparation and review and editing, R.D.H., H.E.P. and D.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received the USDA National Institute of Food and Agriculture Federal Appropriations under Project PEN04574 and Accession number 1004448.

Data Availability Statement

No new data, in addition to those presented in the tables, were created in this research.

Acknowledgments

USDA and the Pennsylvania State University are equal opportunity providers and employers. The mention of trade names, laboratory names, or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendations or endorsements by USDA or the Pennsylvania State University. H.E. Preisendanz is supported, in part, by the USDA National Institute of Food and Agriculture Federal Appropriations under project PEN04574 and accession number 1004448.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Harmel, R.; King, K.; Busch, D.; Smith, D.; Birgand, F.; Haggard, B. Measuring edge-of-field water quality: Where we have been and the path forward. J. Soil Water Conserv. 2018, 73, 86–96. [Google Scholar] [CrossRef]
  2. USDA. Part 600: Introduction. In National Water Quality Handbook; USDA-NRCS: Washington, DC, USA, 1996. [Google Scholar]
  3. USEPA. Monitoring Guidance for Determining the Effectiveness of Nonpoint-Source Controls; EPA 841-B-96-004; USEPA: Washington, DC, USA, 1997.
  4. King, K.W.; Harmel, R.D. Comparison of time-based sampling strategies to determine nitrogen loading in plot-scale runoff. Trans. ASAE 2004, 47, 1457–1463. [Google Scholar] [CrossRef]
  5. King, K.W.; Harmel, R.D. Considerations in selecting a water quality sampling strategy. Trans. ASAE 2003, 46, 63–73. [Google Scholar] [CrossRef]
  6. Harmel, R.D.; Slade, R.M.; Haney, R.L. Impact of sampling techniques on measured storm water quality data for small streams. J. Environ. Qual. 2010, 39, 1734–1742. [Google Scholar] [CrossRef] [PubMed]
  7. Harmel, R.D.; King, K.W.; Haggard, B.E.; Wren, D.G.; Sheridan, J.M. Practical guidance for discharge and water quality data collection on small watersheds. Trans. ASABE 2006, 49, 937–948. [Google Scholar] [CrossRef]
  8. Harmel, R.D.; King, K.W.; Slade, R.M. Automated storm water sampling on small watersheds. Appl. Eng. Agric. 2003, 19, 667–674. [Google Scholar] [CrossRef]
  9. Gall, H.E.; Jafvert, C.T.; Jenkinson, B. Integrating hydrograph modeling with real-time flow monitoring to generate hydrograph-specific sampling schemes. J. Hydrol. 2010, 393, 331–340. [Google Scholar] [CrossRef]
  10. Agouridis, C.T.; Edwards, D.R. The development of relationships between constituent concentrations and generic hydrological variables. Trans. ASAE 2003, 46, 245–256. [Google Scholar] [CrossRef]
  11. King, K.W.; Harmel, R.D.; Fausey, N.R. Development and sensitivity of a method to select time- and flow-paced storm event sampling intervals. J. Soil Water Conserv. 2005, 60, 323–331. [Google Scholar]
  12. Harmel, R.D.; King, K.W. Uncertainty in measured sediment and nutrient flux in runoff from small agricultural watersheds. Trans. ASAE 2005, 48, 1713–1721. [Google Scholar] [CrossRef]
  13. Harmel, R.D.; Cooper, R.J.; Slade, R.M.; Haney, R.L.; Arnold, J.G. Cumulative uncertainty in measured streamflow and water quality data for small watersheds. Trans. ASABE 2006, 49, 689–701. [Google Scholar] [CrossRef]
  14. Miller, P.S.; Mohtar, R.H.; Engel, B.A. Water quality monitoring strategies and their effects on mass load calculation. Trans. ASABE 2007, 50, 817–829. [Google Scholar] [CrossRef]
  15. USDA-NRCS. Natural Resources Conservation Service, Edge-of-Field Water Quality Monitoring Data Collection and Evaluation—Conservation Activity (Code 201); USDA-NRCS: Washington, DC, USA, 2012.
  16. USDA-NRCS. Natural Resources Conservation Service, Edge-of-Field Water Quality Monitoring System Installation—Conservation Activity (Code 202); USDA-NRCS: Washington, DC, USA, 2012.
  17. Jakeman, A.J.; Green, T.R.; Harmel, R.D.; Pembleton, K.; Iwanaga, T. Independent Review of the Paddock to Reef Modelling Program; Queensland Department of Natural Resources, Mines and Energy: Brisbane, Australia, 2019. [Google Scholar]
  18. Harmel, R.; Hathaway, J.; Wagner, K.; Wolfe, J.; Karthikeyan, R.; Francesconi, W.; McCarthy, D. Uncertainty in monitoring E. coli concentrations in streams and stormwater runoff. J. Hydrol. 2016, 534, 524–533. [Google Scholar] [CrossRef]
  19. Harmel, R.; Smith, D.; King, K.; Slade, R. Estimating storm discharge and water quality data uncertainty: A software tool for monitoring and modeling applications. Environ. Model. Softw. 2009, 24, 832–842. [Google Scholar] [CrossRef]
  20. McCarthy, D.; Deletic, A.; Mitchell, V.; Fletcher, T.; Diaper, C. Uncertainties in stormwater E. coli levels. Water Res. 2008, 42, 1812–1824. [Google Scholar] [CrossRef]
  21. Abtew, W.; Powell, B. Water quality sampling schemes for variable flow canals at remote sites. JAWRA J. Am. Water Resour. Assoc. 2004, 40, 1197–1204. [Google Scholar] [CrossRef]
  22. Brakensiek, D.L.; Osborn, H.B.; Rawls, W.J. Field Manual for Research in Agricultural Hydrology; Agriculture Handbook, No. 224; USDA: Washington, DC, USA, 1979.
  23. Buchanan, T.J.; Somers, W.P. Chapter A7: Stage Measurement at Gaging Stations. In Techniques of Water-Resources Investigations of the U.S. Geological Survey, Book 3; USGS: Washington, DC, USA, 1982. [Google Scholar]
  24. Buchanan, T.J.; Somers, W.P. Chapter A8: Discharge Measurements at Gaging Stations. In Techniques of Water-Resources Investigations of the U.S. Geological Survey, Book 3; USGS: Washington, DC, USA, 1976. [Google Scholar]
  25. Haan, C.T.; Barfield, B.J.; Hayes, J.C. Design Hydrology and Sedimentology for Small Catchments; Academic Press: New York, NY, USA, 1994. [Google Scholar]
  26. Kennedy, E.J. Chapter A10: Discharge ratings at gaging stations. In Techniques of Water-Resources Investigations of the U.S. Geological Survey, Book 3; USGS: Washington, DC, USA, 1984. [Google Scholar]
  27. Carter, R.W.; Davidian, J. Chapter A6: General procedure for gaging streams. In Techniques of Water-Resources Investigations of the U.S. Geological Survey, Book 3; USGS: Washington, DC, USA, 1989. [Google Scholar]
  28. Birgand, F.; Lellouche, G.; Appelboom, T. Measuring flow in non-ideal conditions for short-term projects: Uncertainties associated with the use of stage-discharge rating curves. J. Hydrol. 2013, 503, 186–195. [Google Scholar] [CrossRef]
  29. Morlock, S.E.; Nguyen, H.T.; Ross, J.H. Feasibility of Acoustic Doppler Velocity Meters for the Production of Discharge Records from U.S. Geological Survey Streamflow-Gaging Stations; Water-Resources Investigations Report 01-4157; US Geological Survey: Reston, VA, USA, 2002. [CrossRef]
  30. Birgand, F.; Benoist, J.-C.; Novince, E.; Gilliet, N.; Saint-Cast, P.; Le Saos, E. Flow measurements using ultrasonic Doppler meters in small streams. Ingénieries-EAT 2005, 41, 23–38. (In French) [Google Scholar]
  31. ISO15769; Hydrometry—Guidelines for the Application of Acoustic Velocity Meters Using the Doppler and Echo Correlation Methods. ISO: Geneva, Switzerland, 2010.
  32. Stuntebeck, T.D.; Komiskey, M.J.; Owens, D.W.; Hall, D.W. Methods of Data Collection, Sample Processing, and Data Analysis for Edge-of-Field, Streamgaging, Subsurface-Tile, and Meteorological Stations at Discovery Farms and Pioneer Farm in Wisconsin, 2001–2007. US Geol. Surv. Open-File Rep. 2008, 1015, 51. [Google Scholar] [CrossRef]
  33. Kotlash, A.R.; Chessman, B.C. Effects of water sample preservation and storage on nitrogen and phosphorus determinations: Implications for the use of automated sampling equipment. Water Res. 1998, 32, 3731–3737. [Google Scholar] [CrossRef]
  34. Harmel, D.; Wagner, K.; Martin, E.; Smith, D.; Wanjugi, P.; Gentry, T.; Gregory, L.; Hendon, T. Effects of field storage method on E. coli concentrations measured in storm water runoff. Environ. Monit. Assess. 2016, 188, 170. [Google Scholar] [CrossRef]
  35. Holtan, H.N.; Minshall, N.E.; Harrold, L.L. Field Manual for Research in Agricultural Hydrology; Agriculture Handbook, No. 224; Department of Agriculture, Science and Education Administration: Washington, DC, USA, 1962; 215p. [Google Scholar]
  36. Slade, R.M. General Methods, Information, and Sources for Collecting and Analyzing Water-Resources Data. CD-ROM. Copyright 2004 Raymond M. Slade, Jr. 2004.
  37. Komiskey, M.J.; Stuntebeck, T.D.; Cox, A.L.; Frame, D.R. Implications of Flume Slope on Discharge Estimates from 0.762-Meter H Flumes Used in Edge-of-Field Monitoring; USGS Open-File Report 2013-1082; US Department of the Interior, US Geological Survey: Reston, VA, USA, 2013. [Google Scholar] [CrossRef]
  38. Boning, C.W. Policy Statement on Stage Accuracy; Technical Memorandum, No. 93-07; USGS, Office of Water: Washington, DC, USA, 1992.
  39. Birgand, F.; Benoist, J.; Novince, É.; Gilliet, N.; Saint-Cast, P.; Le Saos, É. Guide for application of continuous Doppler flow meters in wooden trapezoidal flume sections. Ingénieries 2005, 41, 77–82. (In French) [Google Scholar]
  40. Teledyne ISCO. 2150 Area Velocity Flow Module Datasheet; Teledyne ISCO: Lincoln, Nebraska, 2022. [Google Scholar]
  41. Tuozzolo, S.; Langhorst, T.; de Moraes Frasson, R.P.; Pavelsky, T.; Durand, M.; Schobelock, J.J. The impact of reach averaging Manning’s equation for an in-situ dataset of water surface elevation, width, and slope. J. Hydrol. 2019, 578, 123866. [Google Scholar] [CrossRef]
  42. Open Channel Flow Blog. 2023. Available online: www.openchannelflow.com/blog/manning-formula-for-determining-open-channel-flows (accessed on 14 April 2023).
  43. Maidment, D.R. (Ed.) Handbook of Hydrology; McGraw-Hill: New York, NY, USA, 1993. [Google Scholar]
  44. Geib, H.V. A new type of installation for measuring soil and water losses from control plots. J. Am. Soc. Agron. 1933, 25, 429–440. [Google Scholar] [CrossRef]
  45. Edwards, W.M.; Frank, H.E.; King, T.E.; Gallwitz, D.R. Runoff Sampling: Coshocton Vane Proportional Sampler; Pub. No. ARS-NC-50; USDA-ARS: Washington, DC, USA, 1976.
  46. Parsons, D.A. Coshocton-Type Runoff Samplers; ARS-41-2; USDA-ARS: Washington, DC, USA, 1955.
  47. Parsons, D.A. Coshocton-Type Runoff Samplers: Laboratory Investigations; SCS-TP-124; USDA-ARS: Washington, DC, USA, 1954.
  48. Allen, P.B.; Welch, N.H.; Rhoades, E.D.; Edens, C.D.; Miller, G.E. The Modified Chickasha Sediment Sampler; Pub. No. ARS-S-107; USDA-ARS: Washington, DC, USA, 1976.
  49. Kirchner, J.W.; Feng, X.; Neal, C.; Robson, A.J. The fine structure of water-quality dynamics: The (high-frequency) wave of the future. Hydrol. Process. 2004, 18, 1353–1359. [Google Scholar] [CrossRef]
  50. Martin, G.R.; Smoot, J.L.; White, K.D. A comparison of surface-grab and cross sectionally integrated stream-water-quality sampling methods. Water Environ. Res. 1992, 64, 866–876. [Google Scholar] [CrossRef]
  51. Ging, P.B. Water-Quality Assessment of South-Central Texas—Comparison of Water Quality in Surface-Water Samples Collected Manually and by Automated Samplers; USGS Fact Sheet FS-172-99; US Department of the Interior, US Geological Survey: Reston, VA, USA, 1999. [CrossRef]
  52. Wilde, F.D.; Radtke, D.B. Chapter A6: Field measurements: General information and guidelines. In Techniques of Water-Resources Investigations of the U.S. Geological Survey, Book 9; USGS: Washington, DC, USA, 2005. [Google Scholar]
  53. Wells, F.; Gibbons, W.; Dorsey, M. Guidelines for Collection and Field Analysis of Water-Quality Samples from Streams in Texas; USGS Open-File Report 90-127; USGS: Austin, TX, USA, 1990.
  54. USGS. Handbooks for Water-Resources Investigations, Section A. National Field Manual for Collection of Water-Quality Data. In Techniques of Water-Resources Investigations of the United States Geological Survey, Book 9; US Department of the Interior, US Geological Survey: Reston, VA, USA, 1999. [Google Scholar]
  55. Bonta, J.V. Modification and performance of the Coshocton wheel with the modified drop-box weir. J. Soil Water Conserv. 2002, 57, 364–373. [Google Scholar]
  56. Bonta, J.V. Water sampler and flow measurement for runoff containing large sediment particles. Trans. ASAE 1999, 42, 107–114. [Google Scholar] [CrossRef]
  57. Malone, R.W.; Bonta, J.V.; Lightell, D. A low-cost composite water sampler for drip and stream flow. Appl. Eng. Agric. 2003, 19, 59–61. [Google Scholar] [CrossRef]
  58. Sheridan, J.M.; Lowrance, R.R.; Henry, H.H. Surface flow sampler for riparian studies. Appl. Eng. Agric. 1996, 12, 183–188. [Google Scholar] [CrossRef]
  59. Pinson, W.T.; Yoder, D.C.; Buchanan, J.R.; Wright, W.C.; Wilkerson, J.B. Design and evaluation of an improved flow divider for sampling runoff plots. Appl. Eng. Agric. 2004, 20, 433–437. [Google Scholar] [CrossRef]
  60. Franklin, D.H.; Cabrera, M.L.; Steiner, J.L.; Endale, D.M.; Miller, W.P. Evaluation of percent flow captured by a small in-field runoff collector. Trans. ASAE 2001, 44, 551–554. [Google Scholar] [CrossRef]
  61. Inter-Agency Committee on Water Resources, Subcommittee on Sedimentation, ICRW-SS. The Single-Stage Sampler for Suspended Sediment; Report 13; St. Anthony Falls Hydraulics Laboratory: Minneapolis, MN, USA, 1961; 105p.
  62. Edwards, T.K.; Glysson, G.D. Field Methods for Measurement of Fluvial Sediment: U.S. Geological Survey Open-File Report 86-531; US Department of the Interior, US Geological Survey: Reston, VA, USA, 1988; 118p.
  63. Graczyk, D.J.; Robertson, D.M.; Rose, W.J.; Steur, J.J. Comparison of Water-Quality Samples Collected by Siphon Samplers and Automatic Samplers in Wisconsin; USGS Fact Sheet FS-067-00; US Geological Survey: Reston, VA, USA, 2000. [CrossRef]
  64. Richards, R.P.; Holloway, J. Monte Carlo studies of sampling strategies for estimating tributary loads. Water Resour. Res. 1987, 23, 1939–1948. [Google Scholar] [CrossRef]
  65. Leecaster, M.K.; Schiff, K.; Tiefenthaler, L.L. Assessment of efficient sampling designs for urban stormwater monitoring. Water Res. 2002, 36, 1556–1564. [Google Scholar] [CrossRef]
  66. Veith, T.; Preisendanz, H.; Elkin, K. Characterizing transport of natural and anthropogenic constituents in a long-term agricultural watershed in the northeastern United States. J. Soil Water Conserv. 2020, 75, 319–329. [Google Scholar] [CrossRef]
  67. Gordon, J.D.; Newland, C.A.; Gagliardi, S.T. Laboratory Performance in the Sediment Laboratory Quality-Assurance Project, 1996–1998; USGS Water Resources Investigations Report 99-4184; US Department of the Interior, US Geological Survey: Reston, VA, USA, 2000. [CrossRef]
  68. Miller, R.O.; Kotuby-Amacher, J. North American Proficiency Testing (NAPT) Program; Colorado State University: Fort Collins, CO, USA; Utah State University: Logan, UT, USA, 2005; unpublished data. [Google Scholar]
  69. Mercurio, G.; Perot, J.; Roth, N.; Southerland, M. Maryland Biological Stream Survey 2000: Quality Assessment Report; Versar, Inc.: Springfield, VA, USA; Maryland Department of Natural Resources: Baltimore, MD, USA, 2002. [Google Scholar]
  70. Ludtke, A.S.; Woodworth, M.T.; Marsh, P.S. Quality Assurance Results for Routine Water Analysis in U.S. Geological Survey Laboratories, Water Year 1998; USGS Water Resources Investigations Report 00-4176; USGS: Washington, DC, USA, 2000.
  71. USEPA. Method 1603: Escherichia coli (E. coli) in Water by Membrane Filtration Using Modified Membrane-Thermotolerant Escherichia coli Agar (Modified mTEC); EPA-821-R-06-011; Environmental Protection Agency, Office of Water: Washington, DC, USA, 2006.
  72. USEPA. Results of the Interlaboratory Testing Study for the Comparison of Methods for Detection and Enumeration of Enterococci and Escherichia coli in Combined Sewer Overflows (CSOs); EPA-821-R-08-006; Environmental Protection Agency, Office of Water: Washington, DC, USA, 2008.
  73. Johengen, T.; Purcell, H.; Tamburri, M.; Loewensteiner, D.; Smith, G.J.; Schar, D.; McManus, M.; Walker, G. Performance Verification Statement for Sea-Bird Scientific HydroCycle Phosphate Analyzer; Alliance for Coastal Technologies (ACT): Solomons, MD, USA, 2017. [Google Scholar] [CrossRef]
  74. Blaen, P.J.; Khamis, K.; Lloyd, C.E.; Bradley, C.; Hannah, D.; Krause, S. Real-time monitoring of nutrients and dissolved organic matter in rivers: Capturing event dynamics, technological opportunities and future directions. Sci. Total Environ. 2016, 569–570, 647–660. [Google Scholar] [CrossRef]
  75. Snazelle, T. Laboratory Evaluation of the Sea-Bird Scientific HydroCycle-PO4 Phosphate Sensor. 2018. Available online: https://pubs.usgs.gov/of/2018/1120/ofr20181120.pdf (accessed on 14 April 2023).
  76. Le Goff, T.; Braven, J.; Ebdon, L.; Scholefield, D. Automatic continuous river monitoring of nitrate using a novel ion-selective electrode. J. Environ. Monit. 2003, 5, 353–358. [Google Scholar] [CrossRef]
  77. Le Goff, T.; Braven, J.; Ebdon, L.; Chilcott, N.P.; Scholefield, D.; Wood, J.W. An accurate and stable nitrate-selective electrode for the in situ determination of nitrate in agricultural drainage waters. Analyst 2002, 127, 507–511. [Google Scholar] [CrossRef]
  78. S::CAN. Jianshan Waste Water Treatment Plant Controls the Nitrogen Removal Process and Optimizes the Carbon Source Dosage. 2017. Available online: www.s-can.at/wp_contents/uploads/2021/09/reference_jianshan_wwtp_cn_en_2017_11_web.pdf (accessed on 14 April 2023).
  79. OTT. Technical Data OTT ecoN; V-06/02/2019; OTT Hydromet GmbH: Kempten, Germany, 2019. [Google Scholar]
  80. OTT. Technical Data Sea-Bird Scientific SUNA Optical Nitrate Sensor; V-06/02/2019; OTT Hydromet GmbH: Kempten, Germany, 2019. [Google Scholar]
  81. Williams, M.R.; King, K.W.; Macrae, M.L.; Ford, W.; Van Esbroeck, C.; Brunke, R.I.; English, M.C.; Schiff, S.L. Uncertainty in nutrient loads from tile-drained landscapes: Effect of sampling frequency, calculation algorithm, and compositing strategy. J. Hydrol. 2015, 530, 306–316. [Google Scholar] [CrossRef]
  82. Mentz, R.S.; Busch, D.L.; Ribikawskis, M.; VanRyswyk, W.S.; Tomer, M.D. Monitoring Edge-of-Field Surface-Water Runoff: A Three-State Pilot Project to Promote and Evaluate a Simple, Inexpensive, and Reliable Gauge; USDA Natural Resources Conservation Service Final Project Report; USDA: Washington, DC, USA, 2016.
  83. Ham, J.; Wardle, E. Next Generation Technology for Monitoring Edge-of-Field Water Quality in Organic Agriculture; USDA-NRCS CIG Final Report; Colorado State University, Department of Soil and Crop Sciences: Washington, DC, USA, 2022. [Google Scholar]
  84. Rode, M.; Wade, A.J.; Cohen, M.J.; Hensley, R.T.; Bowes, M.J.; Kirchner, J.W.; Arhonditsis, G.B.; Jordan, P.; Kronvang, B.; Halliday, S.J.; et al. Sensors in the stream: The high-frequency wave of the present. Environ. Sci. Technol. 2016, 50, 10297–10307. [Google Scholar] [CrossRef]
  85. Pellerin, B.A.; Stauffer, B.A.; Young, D.A.; Sullivan, D.J.; Bricker, S.B.; Walbridge, M.R.; Clyde, G.A., Jr.; Shaw, D.M. Emerging tools for continuous nutrient monitoring networks: Sensors advancing science and water resources protection. J. Am. Water Resour. Assoc. 2016, 52, 993–1008. [Google Scholar] [CrossRef]
  86. Burns, D.A.; Pellerin, B.A.; Miller, M.P.; Capel, P.D.; Tesoriero, A.J.; Duncan, J.M. Monitoring the riverine pulse: Applying high-frequency nitrate data to advance integrative understanding of biogeochemical and hydrological processes. WIREs Water 2019, 6, e1348. [Google Scholar] [CrossRef]
  87. Erwin, E.G.; McLaughlin, D.L.; Stewart, R.D. Installation matters: Implications for in situ water quality monitoring. Water Resour. Res. 2021, 57, e2020WR028294. [Google Scholar] [CrossRef]
  88. Hanrahan, G.; Patil, D.G.; Wang, J. Electrochemical sensors for environmental monitoring: Design, development and applications. J. Environ. Monit. 2004, 6, 657–664. [Google Scholar] [CrossRef] [PubMed]
  89. Scholefield, D.; Stone, A.C.; Braven, J.; Chilcott, N.P.; Ebdon, L.; Sutton, P.G.; Wood, J.W. Field evaluation of a novel nitrate sensitive electrode in drainage waters from agricultural grassland. Analyst 1999, 124, 1467–1470. [Google Scholar] [CrossRef]
  90. Etheridge, J.R.; Birgand, F.; Burchell, M.R.; Smith, B.T. Addressing the fouling of in situ ultraviolet-visual spectrometers used to continuously monitor water quality in brackish tidal marsh waters. J. Environ. Qual. 2013, 42, 1896–1901. [Google Scholar] [CrossRef]
  91. Lenhart, C.F.; Brooks, K.N.; Heneley, D.; Magner, J.A. Spatial and temporal variation in suspended sediment, organic matter, and turbidity in a Minnesota prairie river: Implications for TMDLs. Environ. Monit. Assess. 2009, 165, 435–447. [Google Scholar] [CrossRef]
  92. Jones, A.S.; Horsburgh, J.S.; Mesner, N.O.; Ryel, R.J.; Stevens, D.K. Influence of sampling frequency on estimation of annual total phosphorus and total suspended solids loads. JAWRA J. Am. Water Resour. Assoc. 2012, 48, 1258–1275. [Google Scholar] [CrossRef]
  93. Grayson, R.; Finlayson, B.; Gippel, C.; Hart, B. The potential of field turbidity measurements for the computation of total phosphorus and suspended solids loads. J. Environ. Manag. 1996, 47, 257–267. [Google Scholar] [CrossRef]
  94. Gippel, C.J. Potential of turbidity monitoring for measuring the transport of suspended solids in streams. Hydrol. Process. 1995, 9, 83–97. [Google Scholar] [CrossRef]
  95. Saraceno, J.F.; Pellerin, B.A.; Downing, B.D.; Boss, E.; Bachand, P.A.M.; Bergamaschi, B.A. High-frequency in situ optical measurements during a storm event: Assessing relationships between dissolved organic matter, sediment concentrations, and hydrologic processes. J. Geophys. Res. Atmos. 2009, 114, G00F09. [Google Scholar] [CrossRef]
  96. Moin, S. Evaluating the Benefits of Near-continuous Monitoring, Real-Time Control, and SCM Visibility in Performance of Stormwater Control Measures. Ph.D. Dissertation, North Carolina State University, Raleigh, NC, USA, 2021. [Google Scholar]
  97. Birgand, F.; Lefrançois, J.; Grimaldi, C.; Novince, E.; Gilliet, N.; Gascuel-Odoux, C. Flux measurement and sampling of total suspended solids in small agricultural streams. Ingénieries–EAT 2004, 40, 21–35. (In French) [Google Scholar]
  98. Birgand, F.; Aveni-Deforge, K.; Smith, B.; Maxwell, B.; Horstman, M.; Gerling, A.B.; Carey, C.C. First report of a novel multiplexer pumping system coupled to a water quality probe to collect high temporal frequency in situ water chemistry measurements at multiple sites. Limnol. Oceanogr. Methods 2016, 14, 767–783. [Google Scholar] [CrossRef]
  99. Minella, J.P.G.; Merten, G.H.; Reichert, J.M.; Clarke, R.T. Estimating suspended sediment concentrations from turbidity measurements and the calibration problem. Hydrol. Process. 2007, 22, 1819–1830. [Google Scholar] [CrossRef]
  100. Jones, A.S.; Stevens, D.K.; Horsburgh, J.S.; Mesner, N.O. Surrogate measures for providing high frequency estimates of total suspended solids and total phosphorus Concentrations1. JAWRA J. Am. Water Resour. Assoc. 2010, 47, 239–253. [Google Scholar] [CrossRef]
  101. Navratil, O.; Esteves, M.; Legout, C.; Gratiot, N.; Nemery, J.; Willmore, S.; Grangeon, T. Global uncertainty analysis of suspended sediment monitoring using turbidimeter in a small mountainous river catchment. J. Hydrol. 2011, 398, 246–259. [Google Scholar] [CrossRef]
  102. Thompson, J.; Cassidy, R.; Doody, D.G.; Flynn, R. Assessing suspended sediment dynamics in relation to ecological thresholds and sampling strategies in two Irish headwater catchments. Sci. Total Environ. 2014, 468–469, 345–357. [Google Scholar] [CrossRef] [PubMed]
  103. Etheridge, J.R.; Birgand, F.; Osborne, J.A.; Osburn, C.L.; Burchell, M.R.; Irving, J. Using in situ ultraviolet-visual spectroscopy to measure nitrogen, carbon, phosphorus, and suspended solids concentrations at a high frequency in a brackish tidal marsh. Limnol. Oceanogr. Methods 2014, 12, 10–22. [Google Scholar] [CrossRef]
  104. Khamis, K.; Sorensen, J.P.R.; Bradley, C.; Hannah, D.M.; Lapworth, D.J.; Stevens, R. In situ tryptophan-like fluorometers: Assessing turbidity and temperature effects for freshwater applications. Environ. Sci. Process. Impacts 2015, 17, 740–752. [Google Scholar] [CrossRef] [PubMed]
  105. Tzortziou, M.; Neale, P.J.; Osburn, C.L.; Megonigal, J.P.; Maie, N.; Jaffé, R. Tidal marshes as a source of optically and chemically distinctive colored dissolved organic matter in the Chesapeake Bay. Limnol. Oceanogr. 2008, 53, 148–159. [Google Scholar] [CrossRef]
  106. Osburn, C.L.; Wigdahl, C.R.; Fritz, S.C.; Saros, J.E. Dissolved organic matter composition and photoreactivity in prairie lakes of the U.S. Great Plains. Limnol. Oceanogr. 2011, 56, 2371–2390. [Google Scholar] [CrossRef]
  107. Osburn, C.L.; Mikan, M.P.; Etheridge, J.R.; Burchell, M.R.; Birgand, F. Seasonal variation in the quality of dissolved and particulate organic matter exchanged between a salt marsh and its adjacent estuary. J. Geophys. Res. Biogeosci. 2015, 120, 1430–1449. [Google Scholar] [CrossRef]
  108. Osburn, C.L.; Handsel, L.T.; Peierls, B.L.; Paerl, H.W. Predicting sources of dissolved organic nitrogen to an estuary from an agro-urban coastal watershed. Environ. Sci. Technol. 2016, 50, 8473–8484. [Google Scholar] [CrossRef] [PubMed]
  109. Bedell, E.; Harmon, O.; Frankhauser, K.; Shivers, Z.; Thomas, E. A continuous, in-situ, near-time fluorescence sensor coupled with a machine learning model for detection of fecal contamination in drinking water: Design, characterization, and field validation. Water Res. 2022, 220, 118644. [Google Scholar] [CrossRef]
  110. Dialameh, B.; Ghane, E. Effect of water sampling strategies on the uncertainty of phosphorus load estimation in subsurface drainage discharge. J. Environ. Qual. 2022, 51, 377–388. [Google Scholar] [CrossRef]
  111. Cassidy, R.; Jordan, P. Limitations of instantaneous water quality sampling in surface-water catchments: Comparison with near-continuous phosphorus time-series data. J. Hydrol. 2011, 405, 182–193. [Google Scholar] [CrossRef]
  112. Zhang, J.; Zhang, C. Quality assurance/quality control in surface water sampling. In Quality Assurance and Quality Control of Environmental Field Sampling; Zhang, C., Mueller, J.F., Mortimer, M.R., Eds.; Future Science: London, UK, 2014. [Google Scholar]
  113. Harmel, R.; Smith, P.; Migliaccio, K.; Chaubey, I.; Douglas-Mankin, K.; Benham, B.; Shukla, S.; Muñoz-Carpena, R.; Robson, B. Evaluating, interpreting, and communicating performance of hydrologic/water quality models considering intended use: A review and recommendations. Environ. Model. Softw. 2014, 57, 40–51. [Google Scholar] [CrossRef]
  114. Daniels, M.; Sharpley, A.; Harmel, R.; Anderson, K. The utilization of edge-of-field monitoring of agricultural runoff in addressing nonpoint source pollution. J. Soil Water Conserv. 2018, 73, 1–8. [Google Scholar] [CrossRef]
  115. Montgomery, R.H.; Sanders, T.G. Uncertainty in water quality d57ata. Dev. Water Sci. 1986, 27, 17–29. [Google Scholar] [CrossRef]
Figure 1. Methods for collecting water quality samples for published field-scale and small watershed studies, from [1].
Figure 1. Methods for collecting water quality samples for published field-scale and small watershed studies, from [1].
Water 15 03110 g001
Table 1. Major project types as categorized by the data measured (constituent concentrations and/or loads) and the measurement technique/equipment used.
Table 1. Major project types as categorized by the data measured (constituent concentrations and/or loads) and the measurement technique/equipment used.
Project TypeData
Measured
Discharge MeasurementSample CollectionLab Analysis
1. Measure concentrations with electronic samplers and subsequent lab analysisConcentrationsNAWater 15 03110 i001Water 15 03110 i001
2. Measure concentrations with in situ sensorsConcentrationsNANANA
3. Measure concentrations and loads with mechanical rotating slot or multi-slot samplers and subsequent lab analysisConcentrations and loadsWater 15 03110 i001Water 15 03110 i001Water 15 03110 i001
4. Measure concentrations and loads with electronic samplers and subsequent lab analysisConcentrations and loadsWater 15 03110 i001Water 15 03110 i001Water 15 03110 i001
5. Measure concentrations and loads with in situ sensorsConcentrations and loadsWater 15 03110 i001NANA
Table 5. Costs for major project types, assuming the project is “typical” (i.e., 8 sites, 10 events/yr, USD 25/sample analysis, 2 constituents). See Table 2, Table 3 and Table 4 for the uncertainty considerations.
Table 5. Costs for major project types, assuming the project is “typical” (i.e., 8 sites, 10 events/yr, USD 25/sample analysis, 2 constituents). See Table 2, Table 3 and Table 4 for the uncertainty considerations.
Item(s) for Initial PurchaseInitial CostItem(s) with Annual CostAnnual Cost
All projects
Number of sitesVehicleUSD 50,000Technical staff salaryUSD 100,000
Location of sitesInstallation =
f (berm construction)
USD 800-16,000Site maintenance; travel costs =
f (location, proximity)
USD 3400–6000
Equipment shelterShelter = f (commercial
or homemade/repurposed)
USD 2400–8000--
Duplicate equipment (included in cost below based on equipment type)Backup/replacement
equipment set
(per every 6–10 sites)
---
Power (for electronic samplers and in situ sensors)Electrical = f (battery;
solar or electrical power)
USD 3200–23,200Electricity use (maintain
and recharge batteries)
USD 960
Communication device
(for electronic samplers and in situ sensors)
Communication =
f (device needed)
USD 0–1200Fees = f (data plan requirements)USD 0–1440
1. Projects that measure concentrations with electronic samplers and subsequent lab analysis
Collect samplesSamplerUSD 54,000Equipment maintenance =
f (sampler type)
USD 960
Conduct
lab analysis
--Sample analysis =
f (discrete or
composite samples)
USD 4000– 40,000
Initial cost = USD 110,400–152,400    Annual cost = USD 109,320–149,360   Total (3 yr) project cost = USD 438,360–600,480
2. Projects that measure concentrations with in situ sensors
Determine
concentrations
Sensor = f (sensor type)USD 135,000–USD 180,000Equipment maintenance
and calibration
USD 5000–USD 15,000
Initial cost = USD 191,400–258,400   Annual cost = USD 109,360–123,400   Total (3 yr) project cost = USD 519,480–628,600
3. Projects that measure concentrations and loads with rotating slot or multi-slot samplers and subsequent lab analysis
Collect
composite sample
SamplerUSD 6750Equipment maintenanceUSD 480
Conduct
lab analysis
--Sample analysisUSD 4000
Initial cost = USD 59,950–80,750    Annual cost = USD 107,880–110,480   Total (3 yr) project cost = USD 383,590–412,190
4. Projects that measure concentrations and loads with electronic samplers and subsequent lab analysis
Collect samplesSamplerUSD 54,000Equipment maintenanceUSD 960
Conduct
lab analysis
--Sample analysis = f (discrete
or composite samples)
USD 4000–40,000
Measure
discharge volume
Equipment = f (control
structure, stage/flow
measurement)
USD 9000–57,000Equipment maintenance;
measurement = f (stage–
discharge relationship)
USD 0–3200
Initial cost = USD 119,400–209,200   Annual cost = USD 109,320–152,560   Total (3 yr) project cost = USD 447,360–667,080
5. Projects that measure concentrations and loads with in situ sensors
Determine
concentrations
Sensor =
f (sensor type)
USD 135,000– 180,000Equipment maintenance
and calibration
USD 5000–15,000
Measure
discharge volume
Equipment = f (control
structure, stage/flow
measurement)
USD 9000– 57,000Equipment maintenance;
measurement = f (stage–
discharge relationship)
USD 0–3200
Initial cost = USD 200,400–315,400   Annual cost = USD 109,360–126,600   Total (3 yr) project cost = USD 528,480–695,200
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Harmel, R.D.; Preisendanz, H.E.; King, K.W.; Busch, D.; Birgand, F.; Sahoo, D. A Review of Data Quality and Cost Considerations for Water Quality Monitoring at the Field Scale and in Small Watersheds. Water 2023, 15, 3110. https://doi.org/10.3390/w15173110

AMA Style

Harmel RD, Preisendanz HE, King KW, Busch D, Birgand F, Sahoo D. A Review of Data Quality and Cost Considerations for Water Quality Monitoring at the Field Scale and in Small Watersheds. Water. 2023; 15(17):3110. https://doi.org/10.3390/w15173110

Chicago/Turabian Style

Harmel, Robert Daren, Heather Elise Preisendanz, Kevin Wayne King, Dennis Busch, Francois Birgand, and Debabrata Sahoo. 2023. "A Review of Data Quality and Cost Considerations for Water Quality Monitoring at the Field Scale and in Small Watersheds" Water 15, no. 17: 3110. https://doi.org/10.3390/w15173110

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