Human, agricultural, and industrial processes rely on river water as a vital source ([18]). However, due to recent environmental and climatic changes, river water management has become a formidable obstacle, particularly in regions where climate change has led to a reduced water supply. Measuring river discharge frequently and precisely is essential for water resource management, flood forecasting, and ecosystem conservation. River discharge, the volume of water that flows through a river channel per unit of time, is typically expressed in cubic meters per second, the sum of the river channel’s cross-section, flow rate, and water level ([6]).

In recent years the pressure on this precious commodity has been rising. Groundwater is being abstracted at far higher rates than it can be naturally replenished, and environmental and climatic changes have negatively impacted water availability ([2]). To balance the changing demand in water supply, there has been a rapid shift towards increased river abstraction, both legal and illegal within basins globally ([15]). Consequently, hydrological modelling [1] studies using earth observation data ([18]) have revealed alarming global groundwater depletion rates ([20]).

Due to environmental and climatic changes, Kenya is experiencing unprecedented desertification ([16]). This poses significant water management challenges in previously non-ASAL regions, resulting in severe water supply shortages. This situation has resulted in the loss of livelihoods, decreased crop yields, and increased food insecurity. Reservoirs, which are the primary source of reticulated water supply for agricultural and domestic use for many rural households, have dried up due to climate change. Consequently, there has been a dramatic increase in abstraction of rivers and streams to compensate for the water shortage ([7]). This trend is particularly evident in the Muringato catchment, where abstraction has risen, creating significant river water management challenges resulting in reduced water supply.

To manage this abstraction and shortage crisis it is paramount to ensure equitable and controlled distribution through sustainable abstraction, flood forecasting, and ecosystem conservation. Sustainable abstraction aligns with Sustainable Development Goals (SDG), particularly goal 6 on clean water and sanitation. However, studies show an excessive reliance on groundwater has caused continuous decline in water table ([18]). Therefore, sustainable water management requires continuous and cost-effective river discharge monitoring systems.

Recent research has explored the application of the internet of things (IoT) in river water monitoring. [14] presented a study on IoT applications for monitoring water levels using cellular communication. They focused on the hardware development of the RiverCore and defined its applicability in a specific hydrological region of Colima, Mexico. The system utilizes MQTT architecture for transmission of the telemetry and 3G cellular networks for communication. The authors found the potential of the data generated by the monitoring network in reducing impacts of floods and providing valuable information for city planning. They further emphasized on the importance of building a reliable and low-cost water level monitoring network ([14]). They, however, acknowledged data loss, which is accustomed to low cellular connectivity, and recommended further improvements on the RiverCore IoT device.

Similarly, [4] developed a system for river monitoring using various sensors for quality assessment. The system collected data such as temperature, pH, turbidity, and ORP values with unique features sin frequency, mobility and power consumption for sustainability. The research demonstrated that integrating IoT with big data analytics enables real-time monitoring of water quality, enhancing public awareness and pollution mitigation. They recommended further systematic experimentation using technologies such as spark streaming analysis, deep learning neural network models, and belief rule-based systems for analyzing river water in Bangladesh ([4]).

Although several studies have explored IoT-based water monitoring systems, most have focused on water level or quality monitoring rather than direct river discharge measurement. Moreover, many of these systems, such as those developed by [4] and [14] depend heavily on stable cellular networks and are designed for regions with advanced infrastructure. These limitations make their direct application challenging in rural and data-scarce environments such as the Muringato Catchment in Kenya.

Consequently, there remains a critical gap in the development of low-cost, reliable, and context-appropriate IoT systems capable of measuring river discharge in real time ([19]), particularly in areas with limited connectivity and financial resources. The lack of such systems has led to inadequate discharge data, hindering effective water abstraction monitoring, flood forecasting, and sustainable resource management. Addressing this gap is therefore essential to support equitable water distribution, safeguard ecosystems, and advance the Sustainable Development Goals (SDG 6) on clean water and sanitation ([21]).

The RDMS (Figure 3) device encompasses the three sensors: flow sensor, ultrasonic, and temperature. Additionally, the RDMS features a solar panel for recharge and a 3.7-volt, 6600 mAh lipo battery that is recharged by the 12-volt solar panels. With a protruding antenna, the RDMS uses it for transmission of data to the Things Network and consequently to personal databases using a webhook linked in the TTN dashboard.

Figure 3. RDMS prototype system for data collection housed in a 100*100 mm enclosure. This features the three sensor module terminals, the microcontroller, the communication module, and the antennae housed in an enclosure.

The RDMS prototype was deployed (Figure 4) along one of the rivers at latitude −0.41, longitude 36.97 that’s covered with deep canopy cover; the system enclosure holding the PCB and the ultrasonic sensor and the solar panel was mounted on a wooden log at 3 feet from the floor of the stream. The flow sensor was immersed in water and mounted vertically with the sensor position parallel to the stream flow at 2 cm above the floor of the stream. The antennae of the communication module (LoRa) was positioned to face vertically for optimal signal transmission and command reception.

Figure 4. This is the deployed RDMS at the centerline, 140.2 cm from the river banks, of the river Muringato (0˚24'28.89"S, 36˚58'11.42"E) with real time telemetry being transmitted to the cloud (7th August 2025).

Prior to deployment, the system underwent calibration to ensure accurate and reliable measurements ([10]). The ultrasonic sensor was tested at distances of 100 cm, 200 cm, and 300 cm from a vertical wall, with continuous measurements taken at each distance to evaluate accuracy (Figure 5). Flow and temperature sensors were calibrated using a water rig to simulate open-channel flow and a hydraulic workbench (F110) to assess performance under controlled flow and thermal conditions (Figures 6(a)-(c)). During calibration, the flow sensor readings were compared with a standard water meter ([13]), and the temperature sensor was validated against a mercury thermometer. These calibration steps confirmed the precision and consistency of all sensors prior to field deployment.

Figure 5. Ultrasonic sensor distance range testing against a wall obstacle measurement for sensor performance over distances of 100 cm, 200 cm and 300 cm.

(a) (b)

(c)

Figure 6. (a) System simulation in a water rig for sensor operation testing; (b) hydraulic workbench flow sensor calibration process; and (c) a standardized water meter.

Field measurements collected during deployment included water level, flow rate, temperature, and battery voltage (Figure 7). Battery consumption was monitored throughout the deployment period, as the dense canopy cover reduced solar charging efficiency (Figure 8). The voltage measurements highlighted intermittent charging due to limited solar exposure, emphasizing the need for monitoring in shaded locations.

Figure 7. The velocity measurements between the flow rate sensor and the F1-10 were carried out at two different velocity measurements, showing a high level of correlation.

Figure 8. Battery usage discharge rate over time showing the voltage drop as well as the recharge duration over time using the solar panel.

Water level measurements ranged from 15 cm to 61 cm during the deployment, with variations likely due to upstream abstraction activities, such as water use by a coffee processing facility (Figure 9). Temperature readings from the onboard sensor ranged between 19.55˚C and 20.15˚C, with minor fluctuations attributed to solar penetration in the sensor location (Figure 10).

River discharge was calculated using the measured flow rate, water level, and stream width, according to:

flow rate * level * width = Discharge (Q) (1)

Figure 9. Water level data plotted against time.

Figure 10. A graph of temperature against time over time for the deployment of the system.

The computed discharge data reflected low river flow during the observation period, consistent with stream width, season, and upstream abstraction impacts (Figure 11).

Figure 11. Discharge comparison over time.

The deployment confirmed that the RDMS could reliably capture continuous, high-frequency river discharge data with minimal human intervention. The system successfully transmitted telemetry via LoRa to TTN and through a webhook to a personal database, demonstrating the feasibility of a low-cost, IoT-based river monitoring system suitable for field applications. Further, after the stepwise validation (Table 1) of the system was carried out R² of the system was carried out (Figure 12).

Figure 12. Validation results for temperature, discharge, water level and flow rate measurements.

Table 1. Tabular representation of the validation results of the 4 parameters of the system.

The validation results in Table 1 show that the regression model performs very well for discharge and water level, with high R² values of 0.995 and 0.866, respectively, indicating strong predictive capability. Temperature and flow rate have moderate R² values around 0.527 and 0.634, suggesting acceptable but less accurate predictions. The low root mean square errors (RMSE) and mean absolute errors (MAE) across all parameters demonstrate that the model’s predictions closely match observed values with minimal average deviation. Additionally, the near-zero mean bias error (MBE) values indicate the model is unbiased, neither consistently overestimating nor underestimating. Overall, the system shows reliable performance, particularly for discharge and water level, while there is room for improving temperature and flow rate predictions.

The validation results from this study demonstrate that the developed River Discharge Monitoring System (RDMS) performs reliably for measuring discharge and water level, with R² values of 0.995 and 0.866 respectively, indicating strong agreement with reference measurements. These methodologies yielded high accuracy in river discharge monitoring ([12]) The moderate performance on temperature (R²= 0.53) and a flow rate (R²= 0.63) aligns with challenges often reported in capturing highly dynamic environmental parameters, where sensor sensitivity and natural variability can affect precision.

Compared to established hydrological methods, such as current meter and float techniques commonly used in stream discharge assessments, the RDMS’ integration of low-cost sensors and IoT-based data transmission provides a comparable level of discharge measurement accuracy while offering advantages in real-time monitoring and remote deployment ([8]). Traditional methods, though reliable, involve labor-intensive fieldwork and infrastructure, which may be impractical for continuous measurements in resource-limited rural settings like the Muringato Catchment.

The system’s low root mean square error (RMSE) and mean absolute error (MAE) across all parameters indicate good calibration and minimal prediction bias, supporting its suitability for real-time river management applications. The near-zero-mean bias error (MBE) values confirm that the system does not systematically over or underestimate key variables, a critical aspect for sustainable water resource management and flood forecasting.

Despite these results, the moderate R² values for temperature and flow rate suggest that further sensor enhancements or multi-sensor data fusion techniques could improve the system’s overall performance. This is in line with recommendations from previous IoT-based aquatic monitoring studies emphasizing continuous refinement to overcome environmental noise and sensor limitations ([4]; [14]).

While the RDMS demonstrates strong potential for low-cost river discharge monitoring, several limitations were observed during design, development, and field deployment. First, sensor operation efficiency can be affected by environmental conditions such as stream debris pileup in the sensor, soil and rocks deposition, which may lead to erroneous readings in high turbid rivers streams. Regular maintenance or the integration of self-cleaning mechanisms and developing AI solutions to detect anomalous data collection may be required to ensure consistent data quality.

Second, operational durability remains a challenge during high-flow or flood events. Strong currents, floating debris, and bank erosion can physically damage or displace the sensor housing, posing risks to long-term deployment. Designing more robust mounting structures or using protective casings could improve resilience.

Third, the reliance on battery power and solar charging introduces additional constraints. In densely vegetated riparian zones, canopy cover can limit solar panel exposure, reducing charging efficiency and potentially causing power interruptions. This highlights the need for optimized power management strategies or alternative energy solutions for heavily forested environments.

Finally, the reliability of LoRaWAN transmission may change based on the terrain, the plants around it, and how far away the nearest TTN gateway is. In areas with a lot of trees or complicated terrain, signal loss can cause data loss or transmission intervals that aren’t regular. This means that gateway densification or hybrid communication solutions may be needed.

In summary, the RDMS prototype offers a promising low-cost, scalable solution for regular river discharge monitoring, particularly suited to data-scarce, rural environments. It bridges the gap between laborious traditional methods and the need for cost-effective, reliable hydrological data to support sustainable abstraction, ecosystem conservation, and achievement of SDG 6 targets ([21]) in Kenya and similar context globally.

Based on the system development, deployment, initial calibration and validation results, it has been proved that it generates reliable data that can be utilized in the monitoring of the stream flow parameters. This calls for the need to have a higher density of sensors in the entire catchment for more discharge monitoring capabilities, stream health determination, and information management. With decreasing river gauges poses a threat to stage measurement and with the densification of the network poses a better solution to the data archiving and collection across the catchment.

Beyond data collection, real-time information generated by a series of networks of RDMS units has direct added operational value for water resources management authorities. Continuous discharge and water level measurements can support the Water Resources Authority (WRA) and local WRUAs in setting scientifically informed abstraction limits based on current flow conditions, especially during dry seasons, while near real-time stage monitoring can strengthen early warning systems by enabling the rapid detection of rising water levels, thereby supporting the timely issuance of flood alerts to at-risk communities. Additionally, long-term datasets from multiple stations bridge the data gap and can guide catchment restoration priorities, drought planning, and licensing decisions by providing accurate, spatially distributed hydrological evidence.

The RDMS portrays the need to continuously undertake research and development within the hydrology field for ecosystem monitoring; continuous improvement of the system performance is paramount to archiving reliable data for modeling of the catchment and hence the need to continuously undertake system calibration and validation.

With the community engagement, there is need to continuously engage the community for tool development, testing and validation, as well as training on the use of these tools for the sustenance of their catchment health through the locals and Water Resources Users Associations (WRUAs).

The design files for the IoT are available upon request and will be made available on MIT license through a GitHub repository.