Water Level Measurements Research Articles

Intelligent system for measuring and monitoring water levels

Abstract Measuring and controlling water levels at water management facilities is one of the important tasks. In this research work, we, the authors, have studied the available means of measuring water level. Some of their drawbacks were brought to light throughout the investigation. For instance, if you take a water level control gauge, you have to keep an eye on it all the time, and automation is challenging. These water level measurement devices are not up to date with modern standards. At present, there is a need for systems for continuous monitoring of water resources, collection of necessary information and, if necessary, delivery of processed information to the user or decision-making in complex situations. Taking this into account, it was aimed to develop an intelligent system for measuring and controlling the water level that meets the requirements of the time. As the basis of the intelligent system, the JSN SR-04T ultrasound sensor, which is resistant to various aggressive conditions and has high measurement accuracy, and the Arduino Nano board were taken. This intelligent system was tested at the facility and the developed system performed all tasks reliably and qualitatively.

A Low-Cost Ultrasonic Sensor for Online Monitoring of Water Levels in Rivers and Channels

The measurement of flow parameters in rivers and channels is essential for anticipating floods, hydraulic modeling, and conducting water resource management studies. However, the high cost of existing equipment has resulted in a shortage of monitoring stations compared to actual requirements. This research focused on developing and validating a low-cost, highly accurate ultrasonic device that minimizes malfunctions for online monitoring of water levels in channels and streams. Considering the availability of components, cost, and the reported resolution of the product, the GY-Us42 sensor was selected for constructing the level gauge. The sensor operates within a range of 20 cm to 720 cm with a measurement accuracy of approximately 1 mm. The device integrates several error reduction mechanisms to enhance accuracy, including averaging multiple readings and temperature correction techniques. Laboratory data and field data gathered by both staff and shaft encoder were used for device validation. After constructing the main prototype and conducting various tests, multiple devices were installed in different locations within the Tehran rivers, and water depth data were collected over a specific period. Subsequently, data from other field sensors were also collected and used for device validation and accuracy assessment. The results indicate that the device's average error is below 3%, with a Root Mean Squared Error (RMSE) of 5.00 cm during field tests, demonstrates that the constructed device is an efficient tool for measuring water levels in streams, particularly during flood events.

Ship detection and water level measurement system based on 3D point cloud

ABSTRACT With the continuous development of the water transportation and shipping industries, the number of ships in rivers has steadily multiplied, followed by the increasing complexity of the ship routes. These changes have highlighted the growing importance of ship detection and water level measurement systems. Such systems not only enhance the management efficiency of waterborne traffic, ensure navigation safety, and reduce congestion and collision accidents, but also effectively safeguard the integrity of riverside and bridge structures. Ship detection and river elevation measurement based on 3D point clouds can directly acquire depth information without being affected by lighting conditions. So it has a good research prospect. Therefore, this paper proposes a novel ship detection algorithm based on improved PointRCNN and a novel method for riverbank line extraction and water level measurement based on 3D point clouds, respectively. For ship detection, the improved PointRCNN algorithm can increase the performance of data processing and keypoint extraction techniques, and make the network to keep more foreground point clouds and learn more effective features. This improves the recognition capability of distant ships. Compared to the original PointRCNN algorithm, the improved PointRCNN algorithm has achieved a 3.84% increase in detection precision in practical scenarios. Regarding riverbank extraction and water level measurement, the proposed method based on 3D point clouds can directly extract riverbank lines with depth information, obtaining water level height without direct contact with the river surface. Within a distance range between 15 and 45 m from the LiDAR, the average absolute error using this measurement method is less than 5 cm, demonstrating the good detection accuracy of this method.

Cascade method for water level measurement based on computer vision

Computer vision-based methods of water level measurement that utilize cameras to capture and process images of water bodies and their surroundings are gaining attention due to their advantages over non-visual sensors. This study aims to improve the generalization ability of the water level measurement algorithm based on computer vision to promote the application of the method in a broader range of scenarios. First, we briefly introduce a pipeline consisting of two main steps: calibration and measurement. Second, we propose a novel cascade model that comprises global and local subnetworks to achieve a more precise waterline position coarse-to-fine. In the training phase, apart from basic data augmentation methods, we employ a multiscale training approach to utilize samples more effectively. Finally, compared with other methods, this study increases the accuracy rate and showcases superior accuracy, generalization ability, and application potential.

ENVIRONMENTAL ASPECTS OF IN-SITU GROUNDWATER TREATMENT IN WELLS FOR SMALL SETTLEMENTS

The article describes the characteristics of sandy aquifers, the formation of sanitary zones of wells, the creation of an oxidizing zone of wells, measurements of water levels and their annual precipitation were made, the calculation of the colmatation of the aquifer was performed.

Description and In-Flight Assessment of the POSEIDON-3C Altimeter of the SWOT Mission

The Surface Water and Ocean Topography (SWOT) mission was launched on 16 December 2022 to measure water levels over both open ocean and inland waters. To achieve these objectives, the SWOT Payload contains an innovative Ka-band radar interferometer, called KaRIn, completed with a nadir altimeter called POSEIDON-3C that was switched on a month after launch and a few days before KaRIn. POSEIDON-3C measurements provide a link between large-scale phenomena and high resolution. The POSEIDON-3C design is based on POSEIDON-3B, its predecessor on board JASON-3. It is also a dual-frequency radar altimeter operating in C- and Ku-bands, but with some improvements to enhance its performance. Even though it is a Low Resolution Mode altimeter, its performance over open ocean, inland waters and coastal zones are indeed excellent. This paper first describes the POSEIDON-3C design and its modes with a focus on its new features and the Digital Elevation Model that drives its open-loop tracking mode. Then, we assess the in-flight performances of the altimeter from an instrumental point of view. For that purpose, special and routine calibrations have been realized. They show the good performance and stability of the radar. In-flight assessments thus provide confidence when it comes to ensuring excellent altimeter measurement stability throughout the mission duration.

Deep Learning and object detection for water level measurement using patterned visual markers

Deep Learning and object detection for water level measurement using patterned visual markers

Development and calibration of a low-cost LIDAR sensor for water level measurements

Environmental disasters increase along with disorganized urban growth and irresponsible exploitation of natural resources. The recurrence of hydrological disasters in the municipality of Poços de Caldas (Minas Gerais, Brazil), combined with the geomorphological specificities of the region, result in a demand for an in-depth analysis related to intense precipitation, considering its characteristics and consequences. This study presents a comparative analysis between a linigraph and a laser sensor for fluviometric measurement, pointing out the main differences, similarities, advantages and disadvantages of both systems. The values obtained by the low-cost LIDAR sensor were in agreement with those achieved using the linigraph, which is more expensive, thus demonstrating that the laser sensor can also replace it due to both installation and usage advantages. The laser sensor showed an accuracy of ±0.05 m relative to a metric physical scale, which is considered suitable for applications involving water level measurements in water bodies.

The station system measures the water level and amount of rain that can save the data and online through Web Server

This research paper, the aim is to create a water level and rain gauge station system that can record data and online through Web Server. To be monitor the water level of the river and the amount of water in the rainy season easily by looking through the website. The design of the water level measurement system and the amount of rain is measured with a hydrostatic sensor, Tipping Bucket. A small measuring station, easy to install, uses solar energy and has a Data Logger to record data in parallel with an online database. In the research process, the principle of correlation is used to find the relationship between two variables to find the error of the sensor, namely the water level sensor (Hydrostatic) and the rain sensor (Tipping Bucket). Through research, it was found that the measuring station, the database system can work correctly and with high stability, the water level measurement sensor is highly accurate, the error value is between 0.01 - 0.36 m, the accuracy rate is 97.38%. 98.97% compared to water level measurement stations in nearby areas. For the rain sensor, from the actual count compared to the count with the program 3 times, it is seen that there is 1 error with a deviation value of 0.3 mm. From the recorded data of 2 months (July and August) it was found that the water level is still low to moderate, away from the dangerous water level of 6.84 meters and the amount of rain is still quite high with the amount of rain. The days account for 64.51% and 54.83% of July and August respectively, but the amount of rain will be moderate to moderate. The speed used to send data from the measuring station to the web server via 3G Internet will be between 4 – 6 seconds.

Assessment of daily altimeter-based open ocean water level with hindcast and forecast efficiency

Satellite altimetry water level measurements are valuable in episodic and climate change related hydrodynamic impact studies, despite their sparse temporal distribution over the global ocean. This study presents the spatiotemporal characteristics of the open-ocean satellite derived water level measurements globally for the period 31/12/1992-15/10/2019 and evaluates their efficacy to represent the water level even during intense atmospheric conditions. Water level measurements from 23 different satellite missions are compared with tide gauge records and hydrodynamic simulations. The satellite measurements reproduce the water-level variations with good to excellent skill for ~60% of the areas considered. Additionally, satellite measurements and local atmospheric conditions are utilized in order to examine whether statistical data driven models can contribute to decreasing the temporal sparseness of the water level data over the global ocean. The suitability of this low computational-cost method is demonstrated by deriving a 63-year hindcast of the daily maximum water level for the global ocean, and for a medium-term 15-day ensemble forecast. The publicly available long-term water-level hindcast and the parameters of the data-driven statistical model derived can serve as a tool for designing and facilitating local and global coastal risk-assessment studies.

Geo-Spatial Technology for Identifying Optimal Well Locations in Kolugala Pahalagama Grama Niladhari Division for Effective Groundwater Management

This study utilized Geographic Information System (GIS) technology to pinpoint optimal locations for establishing wells, addressing the issue of declining water yields in the Kolugala Pahalagama Grama Niladhari Division (GND); a geographically diverse region. The research integrated geospatial data, including geolocations of sample wells, garbage pits and quarterly water level measurements collected through field surveys. Several thematic layers, namely, geology, elevation, slope, drainage density, soil type, and land use types were analysed to delineate groundwater potential zones within the GND. Satti's analysis hierarchy was employed to assign appropriate weights to these factors, reflecting their influence on groundwater recharge and well productivity. Spatial analysis revealed that 9.64% of the area displayed very good groundwater potential, 42.17% had good potential, 39.76% showed moderate potential, and 7.23% exhibited poor potential. The distribution of existing wells was also evaluated, showing that 6% were located in very good groundwater potential zones, 56% in good groundwater potential zones and 38% in moderate groundwater potential zones. Despite this, only 34% of the wells were placed in regions suitable for the establishment of wells, while the majority, 66%, were located in unsuitable areas. Notably, 18 wells displayed lower water levels, likely due to their excessive distance from neig-bouring wells. Further spatial analysis identified 29 hectares as unsuitable and 54 hectares as suitable for the future establishment of wells. These findings underscore the significance of geographic factors in the placement of wells, emphasising the need for strategic planning to ensure that wells are sited in areas with high groundwater potential, thus improving long-term water yield sustain-ability in the region.

Experimental investigation of equilibrium and temporal scour around the round head of the rubble mound breakwater in case of breaking and non-breaking waves

In this study, local sediment movements caused by regular wave effects around the head section of rubble mound breakwater were investigated experimentally. The experiments were performed in a wave channel with a length of 33 m, a width of 3.6 m and a depth of 1.2 m. A plunger-type wave generator was located at the offshore side, generating regular waves of varying heights and periods. Uniform sandbed material of three different grain sizes was used, alongside two distinct breakwater slopes and six different wave characteristics. Experiments were performed under both breaking and non-breaking wave conditions. Time-dependent scour depth and water level measurements were determined by using measuring equipment working with an ultrasonic method. In the paper, new empirical relations were derived for temporal relative scour depths and the non-dimensional time scale parameter and the non-dimensional time scale parameter. The determination coefficient and scatter index of these derived relations were calculated and evaluated statistically. It was observed that the relations are in good agreement with those obtained from experiments. Additionally, the findings indicate that wave breaking significantly reduces the equilibrium scour depth.

Use of the SNOWED Dataset for Sentinel-2 Remote Sensing of Water Bodies: The Case of the Po River.

The paper demonstrates the effectiveness of the SNOWED dataset, specifically designed for identifying water bodies in Sentinel-2 images, in developing a remote sensing system based on deep neural networks. For this purpose, a system is implemented for monitoring the Po River, Italy's most important watercourse. By leveraging the SNOWED dataset, a simple U-Net neural model is trained to segment satellite images and distinguish, in general, water and land regions. After verifying its performance in segmenting the SNOWED validation set, the trained neural network is employed to measure the area of water regions along the Po River, a task that involves segmenting a large number of images that are quite different from those in SNOWED. It is clearly shown that SNOWED-based water area measurements describe the river status, in terms of flood or drought periods, with a surprisingly good accordance with water level measurements provided by 23 in situ gauge stations (official measurements managed by the Interregional Agency for the Po). Consequently, the sensing system is used to take measurements at 100 "virtual" gauge stations along the Po River, over the 10-year period (2015-2024) covered by the Sentinel-2 satellites of the Copernicus Programme. In this way, an overall space-time monitoring of the Po River is obtained, with a spatial resolution unattainable, in a cost-effective way, by local physical sensors. Altogether, the obtained results demonstrate not only the usefulness of the SNOWED dataset for deep learning-based satellite sensing, but also the ability of such sensing systems to effectively complement traditional in situ sensing stations, providing precious tools for environmental monitoring, especially of locations difficult to reach, and permitting the reconstruction of historical data related to floods and draughts. Although physical monitoring stations are designed for rapid monitoring and prevention of flood or other disasters, the developed tool for remote sensing of water bodies could help decision makers to define long-term policies to reduce specific risks in areas not covered by physical monitoring or to define medium- to long-term strategies such as dam construction or infrastructure design.

Evaluating D-InSAR performance to detect small water level fluctuations in two small lakes in Sweden

Monitoring lake water level fluctuations is critical for managing water resources, predicting the impacts of climatic change, and preserving ecosystem services lakes provide. However, traditional gauging stations are insufficient to monitor all lakes worldwide due to the large number of existing lakes, the challenges of installation and maintenance, and the remote locations of some. Although satellite altimetry is an alternative for measuring water levels, it cannot monitor small lakes effectively. This study evaluates the potential of Differential Interferometric Synthetic Aperture Radar (D-InSAR) for tracking minor water level changes in small lakes, a method more typically used in wetland studies. We investigate two Swedish lakes using Sentinel-1A and Sentinel-1B data from 2019, generating six-day interferograms and filtering out those with in situ water level changes exceeding one phase cycle. Our results show that D-InSAR can detect small water level changes with Lin’s correlations up to 0.63 and 0.89 and RMSE values of approximately 9 and 4 mm, respectively. These results evidence the potential of future L-band SAR missions with larger wavelengths, such as the NASA-ISRO SAR (NISAR) of the National Aeronautics and Space Administration (NASA) and the Indian Space Research Organisation (ISRO), to track water level changes in lakes and aid water tracking missions such as the SWOT (Surface Water and Ocean Topography).

Research on magnetostrictive liquid level gauge for water level measurement of steam generator

The accurate measurement of the water level on the secondary side of the steam generator (SG) plays a crucial role in the safe and stable operation of the primary and secondary circuits of the nuclear power plant. In order to study the feasibility of applying the magnetostrictive liquid level gauge to the water level measurement of SG, the measurement results of the magnetostrictive liquid level gauge under the steady and transient conditions were obtained and compared with the traditional differential pressure liquid level gauge. The results indicated that the magnetostrictive liquid level gauge had good measurement accuracy under the cold-steady condition, thermal-steady condition and transient pressurization condition. The design parameters of float and operating parameters were necessary to correct the measurement results of magnetostrictive water level gauge. The measurement results under the transient depressurization condition were affected by the fluid movement in the container, resulting in a decrease in measurement accuracy. The results indicated that the magnetostrictive liquid level gauge had the potential to be used for water level measurement of SG in nuclear power plant.

A new model for high-accuracy monitoring of water level changes via enhanced water boundary detection and reliability-based weighting averaging

Accurate measurement of water levels is essential for effectively managing reservoirs to proactively mitigate flooding and drought. Nonetheless, the inaccuracies in measurements derived from gauging station and remote sensing images impose constraints to water resource management. In this study, we developed a novel water level estimation model which utilizes solely the altitude of reliable water boundary pixels to improve the accuracy. The enhanced water boundary detection, incorporating preprocessing steps such as image filtering, resampling, and polarization multiplication, was applied to achieve sub-pixel precision in detecting water boundaries. The water boundary pixels located in layover and shadow regions, which could be misidentified due to distortion error, are eliminated based on backward geolocation. Ambiguous water boundaries, potentially indicating land with low intensity, were defined by computing their absolute derivatives, and removed. Finally, to enhance water level precision, the model computed water levels by averaging the altitudes of boundary pixels with weighting factors of local incidence angle, derivatives of detected water boundaries, and altitude distribution. Compared with the previous studies utilizing water boundaries, the proposed model demonstrated outstanding performance in improving the accuracy, up to 1/40th smaller than the spatial resolution of SAR images in Mean Absolute Error (MAE). The validation was executed over the results from >700 Sentinel-1 against the in-situ measurements obtained from multiple reservoirs and streams with significant water level fluctuations in the Korean peninsula. In this process, we found that the water boundaries located on the layover and shadow regions significantly influence the dispersion of altitude of reliable water boundary pixels. This study demonstrates the proposed model relying on remote sensing data without in-situ measurements, which holds potential applicability under situations where in-situ data are unavailable.

Estimating velocity distribution and flood discharge at river bridges using entropy theory – insights from computational fluid dynamics flow fields

Abstract. Estimating the flow velocity and discharge in rivers is of particular interest for monitoring, modeling, and research purposes. Instruments for measuring water level and surface velocity are generally mounted on bridge decks, and this poses a challenge because the bridge structure, with piers and abutments, can perturb the flow field. The current research aims to investigate the applicability of entropy theory to estimate the velocity distribution and the discharge in the vicinity of river bridges. For this purpose, a computational fluid dynamics (CFD) model is used to obtain three-dimensional flow fields along a stretch of the Paglia River (central Italy), where a historical multi-arch bridge strongly affects flood flows. The input data for the entropy model include the cross-sectional bathymetry and the surface velocity provided by the numerical simulations. A total of 12 samples, including three different flow conditions at four cross-sections, one upstream and three downstream of the bridge, are considered. It is found that the entropy model can be reliably applied upstream of the bridge, also when forced with a single (i.e., the maximum) value of the surface velocity, with errors on total discharge below 13 % in the considered case. By contrast, downstream of the bridge, the wakes generated by the bridge piers strongly affect the velocity distribution, both in the spanwise and in the vertical directions and for very long distances. Here, notwithstanding the complex and multimodal spanwise distribution of flow velocity, the entropy model estimates the discharge with error lower than 8 % if forced with the river-wide distribution of the surface velocity. The present study has important implications for the optimal positioning of sensors and suggests the potential of using CFD modeling and entropy theory jointly to foster greater knowledge of river systems.

Multi-year climate memory in shallow lake water levels

Landscape hydrologic memory of meteorological cycles can have an important impact on catchment hydrological responses by propagating clustering of wet or dry conditions into extreme events. The non-linear and hysteretic hydrologic response functions driven by memory are often only studied at shorter temporal scales (event, season) despite larger interannual hydrologic responses evident in some systems. Within the Canadian Boreal, lakes and lake water levels provide an important indicator that can be used to assess the role of landscape memory on catchment hydrological function. Landscape memory has also been hypothesised to control the hydrological dynamics of shallow lake ecosystems that are also important biogeochemically and ecologically. Here we combine measurements of lake water levels in 26 lakes of varying type at varying temporal frequencies within the glaciated sub-humid Boreal Plain, to examine the impact and variability of interdecadal, decadal, multi-year, and intra-annual memory on lake water levels. We show multi-annual hysteresis of precipitation-lake water levels with varied characteristics in space and time. These spatial variations in landscape memory are driven by differences in storage capacities controlled by heterogeneity in glacial landforms, wetland-forest landcover and lake properties. Thus, the propensity for drought years or wet years to persist or accumulate into extreme landscape drying or wetting varies significantly between different lake-landscape characteristics. We show how landscape memory is crucial to project lake water levels by defining spatial variability of the impact of periods of meteorological drought and deluge vital for understanding system sensitivity, duration of recovery and in turn infer resilience on Boreal Plain hydrology.

Non-contact and non-invasive water level measurement outside metal pipes with electromagnetic acoustic resonance

Accurate measurements of water levels within metal pipes are vital, particularly in environments where thick-walled pipes serve as critical components, such as in nuclear facilities. Measuring water levels in pipes becomes more difficult under high temperatures and pressures. In response to this need, a method involving electromagnetic acoustic measurement is proposed. This method begins with a transducer emitting a high-frequency pulse designed for precise measurements of wall thickness, then calculates the resonance frequency using the time intervals between echoes. Finally, the transducer emits an excitation signal at the fundamental resonance frequency to measure the water level. At low water levels, the measurement is conducted by manually scanning along the pipes, utilizing varying energy losses. At high water levels, the resonance echo method is employed. Numerical simulations have demonstrated that this approach effectively improves signal amplitude, thereby ensuring the robustness of the measurement. Experimental results also demonstrated that the proposed method boosted the echo signal-to-noise ratio to approximately 15 dB. Additionally, it successfully detected water levels in both aluminum and stainless-steel pipes. Therefore, it is considered to be a highly efficient non-contact and non-invasive method to measure liquid levels in metal pipes, and it has proved to hold significant potential for engineering applications.

Global sensitivity analysis of water level response to harmonic aquifer disturbances through a Monte-Carlo based surrogate model with random forest algorithm

Oscillatory water levels are often observed in monitoring wells, when aquifers are disturbed by periodical pressure diffusion (PD) sources, such as oscillatory pumping and ocean tide, and by volumetric strain (VS) sources including the far-field seismic wave and earth tide. The measured water level responses are extensively utilized to interpret aquifer properties and seismic hydrological phenomena. However, owing to wellbore effects, water levels are not always truly representative of pore pressure in aquifers, and can be significantly attenuated or even unobservable in wells. To clarify how water level responds to these disturbances, this study analyzes a global parameter sensitivity of the coupled well-aquifer model based on the assumption of a confined homogeneous aquifer. Parameter uncertainties concerning aquifer properties, source characteristics, and wellbore construction are first analyzed separately for the PD and VS sources. Subsequently, Monte-Carlo based surrogate models linking parameters and response indices are developed using the random forest algorithm. For the PD source, sensitivity results indicate that hydraulic conductivity is the most influential parameter for the yes/no responses. When hydraulic conductivity exceeds 10-4 m/s, water level observations basically represent pore pressure in aquifers. Significant amplitude changes and phase delay occur when the period of disturbances is less than 50 s, majorly due to water column inertia. For VS sources, water level responses are typically observed in aquifers with hydraulic conductivity larger than 10-7 m/s and when disturbed by a source with amplitude greater than 10-3 MPa. However, pronounced amplitude changes and phase shifts occur when the source period varies within 100 s. Consideration of the impact of wellbore effects is recommended, when interpreting water level responses induced by all periodic aquifer disturbances, especially in low-conductivity aquifers with conductivities less than 10-4 m/s.