Water Level Monitoring Research Articles

Developing simple indicators of nitrogen and phosphorus removal in constructed stormwater wetlands

Quantifying pollutant removal by stormwater wetlands requires intensive sampling which is cost-prohibitive for authorities responsible for a large number of wetlands. Wetland managers require simple indicators that provide a practical means of estimating performance and prioritising maintenance works across their asset base. We therefore aimed to develop vegetation cover and metrics derived from monitoring water level, as simple indicators of likely nutrient pollutant removal from stormwater wetlands. Over a two-year period, we measured vegetation cover and water levels at 17 wetlands and used both to predict nitrogen (N) and phosphorus (P) removal. Vegetation cover explained 48 % of variation in total nitrogen (TN) removal; with a linear relationship suggesting an approximate 9 % loss in TN removal per 10 % decrease in vegetation cover. Vegetation cover is therefore a useful indicator of TN removal. Further development of remotely-sensed data on vegetation configuration, species and condition will likely improve the accuracy of TN removal estimates. Total phosphorus (TP) removal was not predicted by vegetation cover, but was weakly related to the median water level which explained 25 % of variation TP removal. Despite weak prediction of TP removal, metrics derived from water level sensors identified faults such as excessive inflow and inefficient outflow, which in combination explained 50 % of the variation in the median water level. Monitoring water levels therefore has the potential to detect faults prior to loss of vegetation cover and therefore TN removal, as well as inform the corrective action required.

THE FEASIBILITY OF FUSING SATELLITE IMAGERIES FOR HIGH-FREQUENCY SEA LEVEL MONITORING

Enhancing the capacity to monitor swift environmental shifts at finer scales requires satellite image that offers high spatial and temporal resolution. However, no individual satellite can offer images meeting both criteria simultaneously. To tackle this challenge, spatial temporal fusion algorithms have been developed to derive fine-scale and time-series images. Conversely, effective monitoring of water levels is crucial for preventing natural disaster, such as flood and tsunami mitigation. Yet, monitoring these natural changes regularly poses challenges for remote sensing satellites, given their limitations in either spatial or temporal resolution. For instance, the spatial resolution of 30 meters of Landsat 8 provides imagery with a but lacks the temporal resolution needed to capture dynamic events. On the contrary, the Himawari 8 has the capability to monitor the entire hemisphere every 10 minutes. However, its inadequate resolution affects the precision of sea water change mapping. This research seeks to utilize Landsat OLI and Himawari-8 images jointly for tracking sea level variation patterns. Our approach involves calculating a water index from both Landsat and Himawari images, then using an image fusion algorithm to merge these indices. Next, we identify water coverage by applying a specific threshold on the water index. The comparison of water percentages with reference water height observations has delivered encouraging outcomes.

SWOT Level 2 Lake Single-Pass Product: The L2_HR_LakeSP Data Preliminary Analysis for Water Level Monitoring

The Surface Water and Ocean Topography (SWOT) mission, launched in December 2022, aims to address the crucial environmental goal of water monitoring to support preparedness for extreme events and facilitate adaptation to climate change on global and local scales. This mission will provide a comprehensive inventory of worldwide water resources, lakes, reservoir storage, and river dynamics. In this work, we carried out a preliminary assessment of SWOT’s Lake product Level 2 version 1.1, also known as “L2_HR_LakeSP”. The analysis was performed across six diverse lakes on three continents, revealing an average median bias of 0.08 m with respect to the considered reference, after suitable outlier removal. An overall precision of 0.22 m was found, combined with an average correlation of 68% between SWOT and reference time series. Moreover, the accuracy varied in the considered six lakes, since biases up to some decimeters were found for some of them; they could be due to residual inconsistencies between the vertical reference frame of SWOT and that of the considered reference. In summary, the first analysis of the “L2_HR_LakeSP” product, Version 1.1, demonstrated the promising potential of SWOT for monitoring seasonal variations in water levels. Nevertheless, notable anomalies were found in the water masks, particularly in higher latitudes, suggesting potential difficulties in accurately delineating water bodies in those regions. Additionally, a discernible reduction in accuracy was observed towards the end of the monitoring period. These preliminary findings indicate some issues that should be addressed in future investigations about the quality and potential of SWOT’s lake products for advancing our understanding of global water dynamics.

Assessing the impacts of ice penetration on monitoring water levels of high-latitude and -altitude lakes from CryoSat-2 altimetry

The CryoSat-2 satellite provides high-precision and long-term monitoring of global lake levels, offering advantages for water resource management, ecological protection, and disaster warning. However, the lake ice penetration by radar electromagnetic waves of CryoSat-2 altimetry tends to result in underestimation of water levels for lakes in high latitudes and altitudes. By contrast, the laser altimetry of ICESat-2 enables us to obtain the height measurements from lake ice surface and the ice penetration impacts on lake levels can be nearly ignored in the ICESat-2 elevation product. After converting the CryoSat-2 and ICESat-2 altimetry data to the same datum and height reference system, the water level difference of (near-) synchronous observations between the two satellites could be mainly attributed to the influences by the lake ice penetration from CryoSat-2 data. In this study, totally 257 lakes in high-latitude (north of 50°N) and high-altitude (the Tibetan Plateau, TP) regions that are covered by both CryoSat-2 and ICESat-2 satellite tracks are examined to explore the characteristics of ice penetration impacts on water level measurements. The altimetry data during unfreezing seasons (June to October) are used to correct the systematic height biases of these two satellite measurements, thus their water level differences during freezing seasons (November to May of the following year) are deemed as the influences of ice penetration on lake level measurements by CryoSat-2 radar altimetry. The results show that the maximum underestimation depth of water level for each lake varies within the range of 0.01–3.47 m, with an average of 0.90 m. Particularly in North America with a dense distribution of lakes, the underestimation of lake levels by CryoSat-2 gradually ascends with increasing latitude. CryoSat-2 underestimates lake levels in Europe and the TP less than in North America. Among different months, the lake ice leads to the maximum underestimation on the water level by CryoSat-2 altimetry in March. The spatial and temporal characteristics of lake ice impacts on the water level measurements by CryoSat-2 altimetry would be an essential reference for quantifying and reducing the uncertainties of satellite radar altimetry for monitoring inland water dynamics.

Monitoring model group of seepage behavior of earth-rock dam based on the mutual information and support vector machine algorithms

The establishment of a high-precision piezometric water level monitoring model ensures the safe operation of earth-rock dams. The hysteresis effect of the upstream water level and rainfall should be considered during modeling. In the traditional method, the average factors are used to express this effect, and linear regression modeling is adopted. These factors reduce the accuracy of the model. In this paper, the mutual information (MI) and support vector machine (SVM) algorithms are proposed. MI has a powerful correlation analysis capability, and it is innovatively used to address hysteresis effects. SVM has a strong nonlinear modeling ability, and it is used as a modeling algorithm. During this study, it was found that the lag time of rainfall varied. In view of this characteristic, the concept of an innovative model group, which is an important extension of the traditional single model, is proposed. In the example, the mean square error (MSE) is used as the precision index. Compared with the traditional single model established by linear regression, the MSE of the MI–SVM model group can be reduced by approximately 60.98%–68.75%. Compared with the model group established by linear regression, the MSE of the MI–SVM model group can be reduced by approximately 41.28%–45.45%. The new method effectively improves the accuracy of the model and can precisely monitor the seepage state of the dam. Moreover, it is beneficial for improving the level of dam safety management and can be extended to other fields involving hysteresis effects and nonlinear modeling.

Characterizing soil hydrology in the Indo-Gangetic plain of Bihar, India: Methods and preliminary results

In the Eastern Gangetic Plain (EGP) soil hydrology is a major determinant of land use and also governs the ecosystem services derived from cropping systems, particularly greenhouse gas (GHG) emissions from rice fields. To characterize patterns of soil hydrology in these, daily field monitoring of water levels was conducted during the monsoon (kharif) season in a comparatively wet (2021) and dry (2022) year with flooding depth and drainage tracked with field water tubes across 47 (2021) and 183 (2022) locations. Fields were clustered into hydrologic response types (HRT) which can then be used for land surface modelling, land use recommendations, and to target agronomic interventions that contribute to sustainable development outcomes. Clusters based on two methods of summarizing a single information source were compared. The information source was a time-series of field water-level observations, and the two methods were (1) the original time-series and their first differences and (2) a set of derived hydrologic descriptors that are conceptually related to greenhouse gas (GHG) emissions. Clustering was (1) by k-means with an optimization of cluster numbers and (2) by hierarchical clustering with the same number of clusters as identified by k-means. Hydrologic behaviour shifted dramatically between growing seasons, and it was not possible to identify consistent HRT's across years. The clusters had only a weak relation with soil properties, almost no relation with farmer perception of relative landscape position, and no relation with rice establishment method. Clusters based on time-series were moderately well predicted in the dry year 2022 by optimized random forest models, with the most important predictors being the number of irrigations, seasonal precipitation, pre-monsoon groundwater levels, seasonal groundwater level change, and pH, this latter as a surrogate for landscape position and other soil properties. In the wet year 2021 clusters were (poorly) predicted by just seasonal precipitation and pre-monsoon groundwater levels. This shows the complex relation of soil hydrology with landscape position and land management, as well as synoptic climate. By contrast, clusters based on the descriptors were not well-matched with those from the time-series, and could not be well predicted by random forest models. This shows that different clustering criteria may result in different interpretations of the landscape hydrology and thus different heuristics for anticipating the hydrology of a given field under different management choices.

Design and Development of a Flood Detection Device for Drainage Systems Utilizing Float Switch Water Level Sensors

Indonesia ranks second in Asia Pacific in the list of the highest number of deaths due to natural disasters. In a year, more than 300 flood events occur, resulting in large losses, environmental damage and even loss of life. To overcome this problem, it is necessary to design a tool for monitoring water levels and rainfall. This research aims to design a flood detection system using the fuzzy method and using a float switch water level sensor. The results of the research show that this system can monitor water levels and rainfall using a web server. In the first test, it produces a fuzzy warning level in a safe condition. In the second test, the water level reached 51 cm with rainfall of 0.47 mm, and the fuzzy warning level was in a safe condition. Finally, in the third test, the average water level reached 99 cm with rainfall of 0.69 mm, resulting in a fuzzy warning level in an alert state. It can be concluded that this system needs to be improved in quality by adding items to the system.

A Comparative Study of Narrow/Ultra-Wideband Microwave Sensors for the Continuous Monitoring of Vital Signs and Lung Water Level.

This article presents an in-depth investigation of wearable microwave antenna sensors (MASs) used for vital sign detection (VSD) and lung water level (LWL) monitoring. The study looked at two different types of MASs, narrowband (NB) and ultra-wideband (UWB), to decide which one was better. Unlike recent wearable respiratory sensors, these antennas are simple in design, low-profile, and affordable. The narrowband sensor employs an offset-feed microstrip transmission line, which has a bandwidth of 240 MHz at -10 dB reflection coefficient for the textile substrate. The UWB microwave sensor uses a CPW-fed line to excite an unbalanced U-shaped radiator, offering an extended simulated operating bandwidth from 1.5 to 10 GHz with impedance matching ≤-10 dB. Both types of microwave sensors are designed on a flexible RO 3003 substrate and textile conductive fabric attached to a cotton substrate. The specific absorption rate (SAR) of the sensors is measured at different resonant frequencies on 1 g and 10 g of tissue, according to the IEEE C95.3 standard, and both sensors meet the standard limit of 1.6 W/kg and 2 W/kg, respectively. A simple peak-detection algorithm is used to demonstrate high accuracy in the detection of respiration, heartbeat, and lung water content. Based on the experimental results on a child and an adult volunteer, it can be concluded that UWB MASs offer superior performance when compared to NB sensors.

Wetlands Water Level Measurements From the New Generation of Satellite Laser Altimeters: Systematic Spatial‐Temporal Evaluation of ICESat‐2 and GEDI Missions Over the South Florida Everglades

AbstractThe ICESat‐2 and GEDI missions were launched in 2018, becoming the new generation of space‐borne laser altimeters. These missions provide unprecedented global geodetic elevations, opening great opportunities for water level monitoring. The potential of these altimeters has been demonstrated in open‐water environments such as lakes, rivers, and reservoirs. However, detailed evaluations in vegetated environments, such as wetlands, floodplains, and other areas not constrained by water canal networks, are essential for continued improvement and further hydrological application. We developed a systematic accuracy assessment of ICESat‐2 ATL08, and GEDI L2A products to monitor spatial‐temporal water level and depth dynamics over the South Florida Everglades wetlands. The evaluation was performed on data acquired between 2020 and 2021, using gauge‐based water level and depth estimates as references. The results showed an RMSE of 0.17 m (water level) and 0.15 m (water depth) for ICESat‐2 and 0.75 m (water level) and 0.37 m (water depth) for GEDI. The analysis suggested that nighttime acquisitions were more accurate for both missions than daytime ones. The low‐power beams achieved slightly higher accuracies than those of the high‐power beams over the evaluated wetlands. Water level retrieval was more problematic in densely vegetated areas; however, we derived a correction model based on the leaf area index that improved the accuracy by up to 75% for water depth retrievals from GEDI. Furthermore, the analysis provides new insights to understand the potential of the altimeters in monitoring the spatial‐temporal dynamics of water levels in the evaluated wetlands.

Real‐Time Water Levels Using GNSS‐IR: A Potential Tool for Flood Monitoring

AbstractGlobal Navigation Satellite System Interferometric Reflectometry (GNSS‐IR) using low‐cost antennas is a practical solution for monitoring water levels from rivers, lakes and seas that does not require submerging any instruments in water. Here we present a novel method for obtaining real‐time water levels using multiple low‐cost antennas that we validate by comparing with measurements from a co‐located pressure gauge at two sites with variable tides. Additionally, we use survey measurements to show that there is a site‐dependant mean bias in GNSS‐IR measurements up to a few centimeters, but this mean bias can be effectively removed by using a correction for the effect of tropospheric delay. We conclude that GNSS‐IR water level sensors could be a powerful tool for real‐time applications such as flood or storm surge monitoring and water resource management, as well as for improving the spatial coverage of sensors in remote regions.

IOT-BASED WATER LEVEL MONITORING SYSTEM OF SITU RAWA BESAR

Pancoran Mas, Depok city has a water catchment area, namely Situ Rawa Besar. Situ Rawa Besar has the potential to overflow during the rainy season which caused flooding in 2020. The water level of Situ Rawa Besar is not monitored in real time so that information on the increase in water level is late to reach the community. Thus, an IoT-based water level monitoring system using ultrasonic sensors was made so that the community was able to monitor the state of Situ Rawa Besar in real time through a prepared website. In order to use the ultrasonic sensor module, there was a failure to read the water level in previous studies. So in addition to monitoring the water level in Situ Rawa Besar, the purpose of making this system is to prove that ultrasonic sensors can be used in water environments such as situ. System testing was carried out at Situ Rawa Besar and at the Prototype. This system is able to monitor the water level for 24 hours with electrical power provided from batteries integrated with solar panels. Testing was carried out on 6 days with an average water level of 70-72cm (alert level). All height data for 6 days is uploaded to the Thingspeak website and can be seen by anyone. Based on the research on the prototype, the system is able to measure the height from 0-182 cm and turn on the alarm when the height is more than equal to 100 cm.

Estimating and Assessing Monthly Water Level Changes of Reservoirs and Lakes in Jiangsu Province Using Sentinel-3 Radar Altimetry Data

Generating accurate monthly estimations of water level fluctuations in reservoirs and lakes is crucial for supporting effective water resource management and protection. The dual-satellite configuration of Sentinel-3 makes it possible to monitor water level changes with great coverage and short time intervals. However, the potential of Sentinel-3’s Synthetic Aperture Radar Altimetry (SRAL) data to enable operational monitoring of water levels across Jiangsu Province on a monthly basis has not yet been fully explored. This study demonstrated and validated the use of Sentinel-3’s SRAL to generate accurate monthly water level estimations needed to inform water management strategies. The monthly water levels of lakes and reservoirs from 2017 to 2021 were produced using Sentinel-3 level-2 land products. Results showed that, compared with in situ data across eight studied lakes, all lakes presented R (Pearson correlation coefficient) values greater than 0.5 and Root Mean Square Error (RMSE) values less than 1 m. Notably, water level estimates for Tai Lake, Gaoyou Lake, and Luoma Lake were particularly accurate, with R above 0.9 and RMSE below 0.5 m. Furthermore, the monthly water level estimates derived from the Sentinel-3 data showed consistent seasonal trends over the multi-year study period. The annual water level of all lakes did not change significantly, except for Shijiu Lake, of which the difference between the highest and lowest water level was up to about 5 m. Our findings confirmed the water level observation ability of Sentinel-3. The accuracy of water level monitoring could be influenced by internal water level differences, terrain features, as well as the area and shape of the lake. Larger lakes with more altimetry sampling points tended to yield higher accuracy estimates of water level fluctuations. These results demonstrate that the frequent, wide-area coverage offered by this satellite platform provides valuable hydrological information, especially across remote regions lacking in situ data. Sentinel-3 has immense potential to support improved water security in data-scarce regions.

RANCANG BANGUN SISTEM PEMANTAU KETINGGIAN AIR OTOMATIS MENGGUNAKAN SENSOR ULTRASONIK (HC-SR04) BERBASIS ARDUINO UNO DENGAN ANTARMUKA KOMPUTER BERBASIS MICROSOFT VISUAL BASIC 6.0

Efforts can be made to overcome this natural disaster, including providing a drainage system, maintaining forests and monitoring river water levels to determine the possibility of flooding if the level exceeds normal. In this research the author designed a tool to provide warnings before a flood disaster occurs. The design of an automatic water level monitoring system using an ultrasonic sensor (HC-SR04) based on Arduino UNO with a computer interface based on Microsoft Visual Basic 6.0 was created using HC-SR04 to measure distance, Arduino UNO as the main controller for the entire tool, LED and buzzer. When the HC-SR04 measures the distance by emitting waves from the trigger pin as the trigger and the echo pin as the receiver, the distance will then be processed by the Arduino UNO to calculate how many "cm" distances have been measured. If the distance calculated by the Arduino UNO is < 30 cm then the green LED will light up, if the distance is < 20 cm then the yellow LED will light up and the buzzer will sound slowly. However, if the distance is <10 cm, the red LED and yellow LED will light up alternately and the buzzer will sound loudly. Next, the data will be displayed on the PC interface with Microsoft Visual Basic 6.0 according to the distance received.

Design and development of arduino-based automation home system using the internet of things

<p><span>The home automation system described in this paper is low-cost, dependable, and versatile. It uses an Arduino microcontroller and Bluetooth internet protocol (IP) connectivity to allow authorized users to remotely access and control devices. The suggested system employs the internet of things (IoT), which is server-independent, to manage human-desired appliances ranging from industrial machinery to consumer products. In this project, we have taken a Bluetooth module that is programmed through an Arduino Nano to control various devices auto-switching of mechanical devices and monitoring of water level within a range of 130 m using an Android application. This is done to show the effectiveness and viability of this system. Each bulb was switched on/off remotely using a mobile phone successfully. The operation of the water pump attached to the source bucket were controlled from the phone while in manual mode and controlled by an ultrasonic sensor while in automatic mode. It enables remote control of a number of devices, including lights and pumps, and decision-making based on sensor feedback.</span></p>

ArduHydro: a low-cost device for water level measurement and monitoring

In this paper, we present ArduHydro, a low-cost device for water level measurement and monitoring designed for employment in controlled and outdoor environments. It measures the water level through an ultrasonic sensor and elaborates the signals through an Arduino microcontroller. The small size of this device, its robustness and accuracy make ArduHydro properly versatile for different applications in the field of water control and management. This article describes the design, the components, the costs, and the performance of ArduHydro. The performance was assessed with a laboratory test inside an open-channel flume and comparing ArduHydro measurements with those obtained with a traditional ultrasonic sensor. Furthermore, an example of ArduHydro application for detecting the wavefront evolution during surface irrigation of a maize crop is presented. The results revealed that ArduHydro measurements were, on average, very consistent with those obtained by the traditional ultrasonic sensor in all different flow conditions. The application of ArduHydro during a surface watering of an agricultural field allowed us to obtain important spatiotemporal information about the water depth along the longitudinal direction of the field, paying the way for a real comprehension of the dynamics of wavefront evolution in a real-world case study.

Peringatan Dini Banjir Berbasis Internet Of Things (IOT) dan Telegram

Flooding is a natural event that can have serious impacts on ecosystems, the economy and human life. In efforts to mitigate and control flood risks, early warning plays an important role in providing quick information to the public and the authorities. In this context, the application of the Internet of Things (IoT) provides an innovative solution by utilizing the latest technology to create an efficient and accurate flood early warning system. This research is intended to create a prototype river water level monitoring system using NodeMCU and the Telegram application programming interface as a first step in anticipating flooding. The main objective is to provide assistance to supervisory officers in monitoring river water levels in real time. This research method includes the development and integration of sensors connected to an IoT network to accurately detect changes in water level. The system also implements efficient data analysis algorithms to determine whether conditions require early warning. Telegram was chosen as communication platform to convey warning messages to the public. The IoT device is connected to a buzzer which will provide a sound warning when the water level reaches a threshold. The research results obtained show that water level conditions in the range of 2cm-50cm are categorized as Alert 1 status, so the buzzer (Alarm) is On. In the 51cm-99cm range, the status is Alert 2, and in the 100cm-400cm range the status is Alert 3 with the buzzer Off. Apart from that, via the Telegram bot the status of Alert 3, Alert 2, and Alert 1 will be informed

A Multi-Parameter Flexible Smart Water Gauge for the Accurate Monitoring of Urban Water Levels and Flow Rates

Urban drainage and waterlogging prevention are critical components of urban water management systems, as they help to mitigate the risks of flooding and water damage in cities. The accurate collection of liquid level and flow rate data at the end of these systems is crucial for their effective monitoring and management. However, existing water equipment for this purpose has several shortcomings, including limited accuracy, inflexibility, and difficulty in operation under specific working conditions. A new type of multi-parameter flexible smart water gauge was developed to address these issues. This technology uses underwater simulation robot technology and is designed to overcome the deficiencies of existing water equipment. The flexibility of the gauge allows it to be adapted to different working conditions, ensuring accurate data collection even in challenging environments. The accuracy of the new water gauge was tested through a series of experiments, and the results showed that it was highly accurate in measuring both liquid level and flow rate. This new technology has the potential to be a key tool in smart water conservancy, enabling the more efficient and accurate monitoring of water levels and flow rates. By providing a new solution to the problem of collecting terminal equipment for urban drainage and waterlogging prevention, this technology can help to improve the resilience and sustainability of urban water management systems.

Visualization and monitoring dynamic water levels of steam generators based on deep learning

In a nuclear power system, the steam generator (SG) is the most important part of the second loop, and accurate monitoring of the internal water level of the SG has always been a challenge. In this study, a two-loop steam generator is mounted on a six-degrees-of-freedom (6-DoF) platform to fully imitate the marine condition of the SG, and the internal water level is visualized by opening a window on the SG. The heating power, period and angle of the 6-DoF platform are considered. By calibrating the relationship between vertical pixels and actual height of the water level, the recognition of water level position in the image is mapped to the actual water level height. 700 water level images were obtained for constructing the dataset. An enhanced deep learning recognition network (YOLO-v7-SE) is proposed to focus on the water level of SG, which is added a channel attention mechanism to the backbone network for enhancing the extraction of target features. Compared with the original YOLO-v7 network, the enhanced YOLO-v7-SE network enhanced the detection accuracy of water level recognition by 12.5% and the mean average accuracy (mAP) by 13.3%. In addition, as the heating power increases, the water level without feeding water drops faster, and that with feeding water keeps constant. The swing periods and angles have complex effects on the water level. In contrast, the traditional magnetic flap level gauge could not obtain the variation of the water level. This shows that the enhanced YOLO-v7-SE network can accuracy recognize the real-time water level change, and achieve fast dynamic response to the water level change, which is a reliable and powerful guarantee for the safety of the SG.

Spring Design of Triboelectric Nanogenerator with MXene-Modified Interface for Fluid Energy Harvesting and Water Level Monitoring.

The introduction of two-dimensional materials with high capacitance that are dielectric into the triboelectric interface is critical for the development of a highly efficient triboelectric nanogenerator (TENG) due to its excellent electrical conductivity and versatile surface chemistry. This paper reports a spring-structured multilayer TENG (S-TENG), where a Nb2CTx MXene-PVDF composite was chosen as the triboelectric electrode for increasing the dielectric and surface charge density. The intense electrostatic interaction of the strong hydrogen bonds between anions on the MXene surface and hydrogen atoms of PVDF chains not only creates a dipole in responding to the applied electric field but also promotes the formation of a piezoelectric phase and induces a strong interface coupling effect. Consequently, an output power enhancement of 300% was shown in comparison with pure PVDF, and a spring-like design with a multilayer structure further increases the space utilization and contact area and presents an output voltage of 420 V, a current density of 1.47 mA/m2, and a maximal output power density of 619 mW/m2. In addition, the as-prepared S-TENG can serve as both a fluid energy harvester on an urban river and a real-time monitor to realize the automatic alarm of water level warning.

GNSS-IR water level monitoring toward particular narrow scenarios: Application to South-to-North Water Diversion Project in China

River water level monitoring plays a crucial role in hydrological practice. Global Navigation Satellite Systems Interferometry Reflectometry (GNSS-IR) has been demonstrated to help retrieve absolute water levels in expansive and unobstructed scenarios. However, how to measure in rivers remains a significant challenge for practical use due to the restricted reflection area. To address these issues, we propose the Dynamic Masking Elevation Threshold Selection Method (DMETSM), which effectively broadens the selection of available reflective signals. Through analyzing actual field data collected along the South-to-North Water Diversion Project, we have found that DMETSM almost doubles the effective monitoring area compared to the classical method. Furthermore, the multi-GNSS water level retrievals at two sites can achieve high accuracy, with root-mean-square errors of 1.36 cm and 2.57 cm, respectively. Results show that it improves accuracy by 30.6 % and 42.6 %. This highlights the considerable potential and value of applications in narrow and irregular areas.