Water Level Measurements Research Articles

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

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.

Development and Calibration of a Low-Cost LIDAR Sensor for Water Level Measurements

Development and Calibration of a Low-Cost LIDAR Sensor for Water Level Measurements

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).

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.

Skills Development in Hydrologic Sciences for Cohorts of Graduate Students from Morocco, Egypt, Türkiye, and Indonesia

AbstractIn developing countries in Africa and Asia, meeting challenges of water scarcity and pollution has often been hampered by shortcomings in higher education, including insufficient research productivity and funding, lack of opportunity for university graduates, and a mismatch between university activities and societal needs. To address these issues, we developed novel programs integrating technical instruction and preparation for professional practice in hydrology for cohorts of graduate students from Morocco and Egypt (2012–2013) and from Türkiye and Indonesia (2013–2014). Students participated in an initial online course and a follow‐up workshop featuring geographic information systems (GIS), remote sensing, and hydrologic modeling with internet‐based data sets. Field activities in the USA (first cohort) and in the students’ home countries (second cohort) included stream gauging, measurement of water levels in wells, water sampling, and measurement of hydrochemical parameters. A subsequent online course focused on research ethics, preparing proposals and publications, and presenting findings to technical audiences and the public, culminating in presentations at conferences in the USA. Participants mentored other students at their home institutions and at K‐12 schools in Türkiye and Indonesia. Participant feedback during and after the programs tended to be strongly positive, and participants have continued to engage with project leaders and mentor students in their home countries and the USA. Our modular, hybrid approach offers a template for students in hydrology and related fields to develop relevant skills and engage internationally.

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.

Testing the Feasibility of an Agent-Based Model for Hydrologic Flow Simulation

Modeling streamflow is essential for understanding flow inundation. Traditionally, this involves hydrologic and numerical models. This research introduces a framework using agent-based modeling (ABM) combined with data-driven modeling (DDM) and Artificial Intelligence (AI). An agent-driven model simulates streamflow and its interactions with river courses and surroundings, considering hydrologic phenomena related to precipitation, water level, and discharge as well as channel and basin characteristics causing increased water levels in the Medio River. A five-year dataset of hourly precipitation, water level, and discharge measurements was used to simulate streamflow. The model’s accuracy was evaluated using statistical metrics like correlation coefficient (r), coefficient of determination (R2), root mean squared error (RMSE), and percentage error in peak discharge (Qpk). The ABM’s simulated peak discharge (Qpk) was compared with the measured peak discharge across four experimental scenarios. The best simulations occurred in scenario 3, using only rainfall and streamflow data. Data management and visualization facilitated input, output, and analysis. This study’s ABM combined with DDM and AI offers a novel approach for simulating streamflow and predicting floods. Future studies could extend this framework to other river basins and incorporate advanced sensor data to enhance the accuracy and responsiveness of flood forecasting.

Enhanced water level monitoring for small and complex inland water bodies using multi-satellite remote sensing

Water level monitoring in lakes and reservoirs is essential for effective water resource management, especially in remote areas where traditional ground sensors are costly and difficult to maintain. Remote sensing offers an alternative, but improving the quality, resolution, and accuracy of satellite data remains crucial. This paper introduces MoRLa (Measurement of Reservoir Level from Altimetry), a data filtering procedure designed to enhance satellite altimetry retrievals. MoRLa increases the acceptance of satellite observations and improves the quality of water level estimates by using physical characteristics of water bodies to exclude non-conforming measurements. Unlike previous studies with static masks, MoRLa employs a dynamic filter adaptable to actual water levels at specific times. Tested on reservoirs in the Korean Peninsula, including the Hwang-Gang dam, MoRLa shows significant improvements in water level measurements using Cryosat-2, ICESat-2, and Sentinel-3A and B satellites.

Water level measurement with a low-cost smartphone using GNSS-IR: an over 2-year study case in Buenos Aires, Argentina

Water level measurement with a low-cost smartphone using GNSS-IR: an over 2-year study case in Buenos Aires, Argentina

Modelling seasonal variation of sediment connectivity and its interplay with river forms

Functional connectivity refers to the system dynamics and sediment transfer in a geomorphological system. It is usually quantified through digital elevation models of difference at two time-steps but measuring and quantifying sediment connectivity variation between the time-steps remains a challenge. In this study we utilised a combined approach of morphodynamic model and field observation for assessing seasonal spatio-temporal variation of longitudinal functional connectivity over a 6-kilometre sub-arctic river reach. Comprehensive field datasets including sediment sampling, monitoring of morphological change and continuous discharge and water level measurements, were collected multiple times during the year to calibrate and validate the model for one year. The results revealed that connectivity inside the reach is episodical and not all the morphological adjustment was tied to discharge. Connectivity varied spatio-temporally inside the reach and sediment source and sink areas could be detected. Outside of the hydrological controlled episodes, meander evolutional stage and grain size had notable role in sediment connectivity in the short-term, and therefore underlaying morphological adjustment in the long-term. This approach can contribute to enhancing already existing connectivity indices towards a functional model considering the seasonality and periodicity of connectivity in more detail and would therefore enhance river management and prediction of geomorphic systems functioning.

A low-cost IoT-based deep learning method of water gauge measurement for flood monitoring

Real-time and accurate measurement of the water level is a critical step in flood monitoring and management of water resources. In recent years, with the advent of the Internet of Things (IoTs) and cloud computing platforms and resources, the surveillance technology for water monitoring has been revolutionized due to the availability of high-resolution and portable cameras, robust image processing techniques, and cloud-enabled data fusion centers. However, despite the potential advantages of online water level monitoring of the rivers and lakes, some technical challenges need to be addressed before they can be fully utilized. Submersible sensor devices are frequently used for measuring water levels but are prone to damage from sediment deposition and many gauge detection techniques are inefficient at nighttime. In response, this paper presents a novel Internet of Things (IoT) based deep learning methodology that uses Mask-RCNN to accurately segment gauges from images even when there are distortions present. An automated and immediate water stage estimate is provided by this simple, low-cost method. The methodology’s applicability to water resource management systems and flood disaster prevention engineering opens up new possibilities for the deployment of intelligent IoT-based flood monitoring systems in the future.