Water quality and spatial parameters from the main channel of a 6th order stream collected with an uncrewed surface vehicle
Data files
May 30, 2025 version files 125.29 KB
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Mainstem_AquaBOT_Data_Format.xlsx
123.93 KB
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README.md
1.36 KB
Abstract
Nitrate concentrations in streams and rivers in the midwestern United States are often elevated, reflecting the predominance of agriculture in the surrounding landscape. Recent advances in technology, including surface-water drones and more precise sensors, provide an opportunity to investigate nitrate dynamics with high spatial and temporal resolution. We deployed an aquatic drone, the AquaBOT, in a 6th-order, agriculturally influenced river to examine longitudinal patterns in water quality. Our goal was to measure the spatial and temporal heterogeneity in nitrate and nitrate-removal processes and determine the influence of tributary inputs on mainstem chemistry. We navigated the drone along a 12-km reach of the Des Moines River (Iowa, USA) nine times between June 2021 and August 2022. Across the deployments, mean nitrate concentration was positively related to discharge and was nearly two orders of magnitude higher in spring than summer. We observed contrasting patterns in mainstem nitrate, which decreased downstream during some runs (e.g., 3.1 to 2.7 mg N/L in June 2021), demonstrating nitrate uptake along the reach, and remained constant on other dates. Similarly, tributaries to the Des Moines had varied influence on riverine nitrate, either increasing or decreasing nitrate concentrations depending on the tributary and date. Nitrate removal rates were spatially and temporally variable but showed some consistency at the sub-reach (2 km) scale, with two sub-reaches in particular often showing elevated rates of nitrate removal across dates. Our study reveals nuanced heterogeneity in nitrate dynamics of the Des Moines River despite the homogeneity of agricultural land cover in the watershed.
https://doi.org/10.5061/dryad.np5hqc02x
Description of the data and file structure
This dataset contains water quality parameters (Water temperature, specific conductivity, dissolved oxygen, and nitrate-N) from sensors attached to an aquatic uncrewed surface vehicle (drone) within the main channel of our study reach as well as distance down stream calculated from the drones GPS coordinates. Each tab contains these main channel parameters for one of the 9 sampling dates. These data are essential for understanding the longitudinal patterns in nitrate and conductivity detailed in our research paper.
Files and variables
File: Mainstem_AquaBOT_Data_Format.xlsx
Description: Water quality parameters for each sampling date. Main channel values only.
Variables
- TIMESTAMP: the date and time of each record. Each tab within the file is a separate date.
- Water_Temp_deg C: Water temperature in degrees Celsius
- Sp_Cond_uS/cm: Specific conductivity in microsiemens per centimeter
- ODO_%sat: Dissolved oxygen in percent saturation
- ODO_mg/L: Dissolved oxygen in milligrams per liter
- NO3-N_mg/L: Nitrate-N in milligrams per liter
- distFromStart_m: Distance downstream within the main channel of the river from the starting point of sampling
This dataset was collected along a 12.4-km reach of the Des Moines River, a 6th-order river in Central Iowa, USA. The study reach, located ~50 km northwest of the city of Des Moines, Iowa, begins at the upstream site (UP) near Boone, Iowa at 42°02'13.6"N 93°55'39.8"W and ends at the downstream site (DOWN) at 41°57'59.0"N 93°53'47.0"W. A surface-water drone equipped with water-quality sensors (the “AquaBOT”) measured longitudinal patterns in water chemistry along the 12.4-km reach of the Des Moines River. Nine AquaBOT runs were carried out in the spring through fall across two years (2021, 2022)