Water Level Monitoring Research Articles

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.

A dataset of lake level changes in China between 2002 and 2023 using multi-altimeter data

ABSTRACT Lake water levels are an important indicator of water balance and water cycles, and are essential for climate and environmental change studies and water resource evaluation. Currently, lake level measurements are scarce or inconsistent throughout the country, and traditional gauge measurements of many lakes are not feasible, so satellite altimetry is a vital alternative to gauge lake levels. However, the accuracy and sampling frequency of lake level time series are usually low because of time and space coverage limitations; therefore, it is necessary to utilize multi-altimeter data to monitor lake levels and obtain lake level changes over long time series. In this study, we extracted the water level changes in 988 lakes (>10 km2) in China between 2002 and 2023 based on ICESat/-2, Cryosat-2, Jason-1/2/3, and Sentinel-3A/3B altimetry data using waveform retracking, lake level extraction, lake level time series construction, the fusion of multi-altimeter lake level time series, and outlier removal. A total of 55% of the lakes in this dataset have been monitored for more than 10 years, and 34% have more than 12 times the annual average water level monitoring. At the same time, in situ data from 21 lakes were used for validation, and the average root mean square error (RMSE) for each of the datasets of ICESat/-2, Cryosat-2, Jason-1/2/3, and Sentinel-3A/3B versus the in situ lake levels are 0.223 m, 0.163 m, 0.207 m, 0.596 m, 0.295 m, 0.275 m, 0.243 m, and 0.317 m, respectively, and the mean RMSE of the fused lake levels reaches 0.332 m. During the monitoring period, the water levels in Chinese lakes generally increased. The overall annual average rate of change at the 20 and 10-year scales was 0.123 m/a and 0.151 m/a, respectively, among which the overall water levels in large lakes increased significantly. The lakes with a faster rate of decline in the water level were primarily small. The water storage in each lake region in China shows an upward trend, with the most significant increase in the Tibetan Plateau region, where the average annual water level change rate has remained above 0.15 m/a over the past two decades. This dataset has high spatiotemporal coverage and accuracy and can support the estimation of changes in lake water storage, analysis of lake level trends, plateau flooding, and the relationship between lake ecosystems and water resources.

Identifying Issues in the Katingan I Swamp Irrigation Area Through Field Survey and Monitoring Approaches

The Katingan irrigation area is a swamp area that has undergone agricultural development. The optimization of yields necessitates the adaptation of management strategies in response to the challenges encountered within the swamp irrigation area. The objective of this study is to discern the issues in the Katingan I swamp irrigation area by conducting field surveys and monitoring. The field survey was conducted from December 2022 to March 2023. The primary data collected are water level, quality, agricultural products, and field documentation. Secondary data such as rainfall, irrigation network schemes, and institutions were also acquired. The survey result found water acidity issues due to water coming from peat forests and acid sulphate soils. The acidity of the water rises during low tide and when rain carries acidic water. There is a problem with high salinity, particularly during high tide in dry seasons. Optimal water management is achievable through continuous monitoring of water level and quality. Another issue is a lack of human resources to cultivate the land, especially because the irrigation service area has recently been expanded. The results of this study hopefully provide valuable insights for developing future management strategies in Katingan.

Smart Water-Irrigated Management System (SWIMS): Automated Water Level Monitoring, Notification, and Irrigation Control for Enhanced Crop Cultivation

Provinces in the Philippines, such as Ilocos Norte, have areas that struggle to manage water efficiently and mitigate disasters brought on by numerous annual storms despite existing irrigation designs. A more pragmatic approach to irrigation designs is needed to resolve this issue. This study aimed to design and develop a miniature smart water management prototype that combines irrigation and floodwater systems featuring disaster preparedness that the country could implement. The study employed an operational research design to construct a scaled-down smart and autonomous water management system with Internet of Things (IoT) capabilities. The prototype featured contingency measures, irrigation control, and short message services (SMS) through ultrasonic sensors, a soil moisture sensor, and actuators. The results showed that the precision tests of ultrasonic sensors and the soil moisture sensor were precise between readings. All components were functional and in good condition. Acceptability and adaptability tests yielded a weighted mean of 4.07 and 4.13, respectively, translating high levels in each. Ultrasonic and soil moisture sensors have minimal variations in each reading, inferring reliability and consistency. Every constituent component is functional, and the prototype is highly acceptable in design. The prototype is capable of being adapted for modifications and upgrades to accommodate larger scales, suggesting implementation near areas experiencing agricultural challenges.

Development and Deployment of a Virtual Water Gauge System Utilizing the ResNet-50 Convolutional Neural Network for Real-Time River Water Level Monitoring: A Case Study of the Keelung River in Taiwan

Climate change has exacerbated severe rainfall events, leading to rapid and unpredictable fluctuations in river water levels. This environment necessitates the development of real-time, automated systems for water level detection. Due to degradation, traditional methods relying on physical river gauges are becoming progressively unreliable. This paper presents an innovative methodology that leverages ResNet-50, a Convolutional Neural Network (CNN) model, to identify distinct water level features in Closed-Circuit Television (CCTV) river imagery of the Chengmei Bridge on the Keelung River in Neihu District, Taiwan, under various weather conditions. This methodology creates a virtual water gauge system for the precise and timely detection of water levels, thereby eliminating the need for dependable physical gauges. Our study utilized image data from 1 March 2022 to 28 February 2023. This river, crucial to the ecosystems and economies of numerous cities, could instigate a range of consequences due to rapid increases in water levels. The proposed system integrates grid-based methods with infrastructure like CCTV cameras and Raspberry Pi devices for data processing. This integration facilitates real-time water level monitoring, even without physical gauges, thus reducing deployment costs. Preliminary results indicate an accuracy range of 83.6% to 96%, with clear days providing the highest accuracy and heavy rainfall the lowest. Future work will refine the model to boost accuracy during rainy conditions. This research introduces a promising real-time river water level monitoring solution, significantly contributing to flood control and disaster management strategies.

Earthquake prediction based on the analysis of water level fluctuations in the control well

This study examines the methodology and results of earthquake forecasting based on the assumption that the earthquake preparation process can be linked to the development of rupture mechanisms. The analysis focuses on water fluctuations in a control monitoring well, which provides valuable information about the earthquake’s preparation period well in advance. Studies on the territory of the Kryvyi Rih iron ore basin (Kryvbas) confirmed that the underground waters in a deep well (815 m deep) depend on the processes of the current lithosphere deformations evidenced by the changing elastic-deformation state of the crust in tectonic areas. The Kryvyi Rih-Kremenchuk crust-mantle fault produces a system of different-scale faults in the crystalline basement which determine the hydrodynamics of the Kryvbas underground filtration. The earthquake preparation process exhibits clear indicators of the rupture process, characterized by a periodic component of water fluctuations superimposed on the main level-changing process during the preparatory phase. The frequency of the periodic component increases as the date of the earthquake approaches. With this approach, an earthquake with a magnitude of 4.1 inKryvbas was successfully predicted one and a half year before the event, based on monitoring water level fluctuations for 120 days between February and June 2016. The efficacy of forecasting local earthquakes was evaluated using the retro-forecast of earthquakes in Kryvbas on July 29, 2017. The methodology was validated through testing, confirming the initial assumptions. This technique was tested to predict the exacerbation of the seismic situation in the iron ore mining area caused by powerful blasting activities.

Enhancing agricultural productivity through a semi-autonomous IOT robot in smart farming systems

In addressing the challenge of enhancing agricultural productivity in developing countries, this research introduces a semi-autonomous IoT robot designed to modernize traditional farming practices in regions like Bangladesh. The study explores whether such a robot can effectively integrate with existing farming practices and assesses its impact on agricultural productivity, resource optimization, and most importantly cost-efficiency. The literature reveals a push towards smart farming technologies, but their adoption in less affluent regions is hindered by cost and resource constraints. Employing a mixed-methods case study approach, the research developed and tested a robot equipped with an NPK sensor for detecting levels of nitrogen, phosphorus, and potassium in the soil, a water level indicator to measure flood water levels in millimeters, and a soil moisture sensor. These data were transmitted to the user's phone over the internet, allowing for remote monitoring of fertilizers and water levels. Additionally, the system included a remote-controllable water dispenser for irrigation and a fruit-picking mechanism for harvesting. The results indicated that all intended data collection was executed accurately, enabling users to remotely monitor soil conditions and effectively control the robot's actions. However, the initial cost of the robot may be slightly expensive for individual farmers, though mass production is anticipated to reduce the price to a level that is reasonably affordable for widespread adoption. However, limitations in sensor calibration for different soil types are acknowledged. Future research suggested exploring sensor calibration precision, extending system capabilities, and integrating predictive AI for a comprehensive agricultural solution. Bangladesh J. Agri. 2023, 48(2): 95-105

Dual Axis Solar Tracking of Solar Radiation for Agriculture usage

Energy is one of the important parts of our life. As there is decline in fossil fuels and increasing demand for energy an alternate energy source is required which is renewable energy source like solar, wind etc. So, we use solar panels which trap the energy from the sun and produce electricity, and this energy is used for agriculture purpose like to run water pumps and to meet other energy requirements in agriculture. Due to rotation of earth the stationery solar panel will receive energy only for smaller duration so to overcome this we use dual axis tracking system which rotates solar panel according to direction of sun and helps in producing more solar energy. Agriculture is one of the major contributing sectors to the economy of a country and it requires automation and advanced technology so that it helps farmers in producing more yield and better crops. So, in agriculture continuous monitoring of soil and water level is required so we can automate this which helps the farmers where the device continuously monitors and depending upon the moisture level of the soil the water pumps get on automatically and we can use this for different crops and set threshold depending upon the crop type. And we can also integrate this idea with IOT technology for improvements. By this we create sustainable energy indirectly producing sustainable environment.

DISASTER PREPAREDNESS OF THE HISTORIC CHURCHES (HOUSE OF WORSHIP) IN THE PROVINCE OF CAVITE

Cavite is a historical province in the Philippines the province is also known for historical Churches. This study assessed the disaster preparedness of the historical churches. The method is qualitative in nature and utilized interviews as the instrument of the study. The respondents are representatives of each historical church. The results show that NHCP is aware of the ancient churches' vulnerability to disasters based on their findings. Disaster response is a problem since historical churches aren't given priority and because of the geographical characteristics that rendered them vulnerable. Due to their significance to religion, culture, history, and identity, historic churches have social significance. Due to its age, worth to the community, and importance, historic museums must be prepared for disasters. While local governments have developed measures to reduce flooding, floods can be avoided provided that government policies and regular water level monitoring are put into practice. Earthquakes can cause the historical churches to shake. For disaster readiness in the event of ground shaking, the NHCP and the local government coordinate. Finally, since ground shaking cannot be entirely avoided, precautions like routine structural inspections are made.

DEVELOPMENT OF WATER LEVEL MONITORING APPLICATIONS IN SMART HOME SYSTEMS USING FLUTTER

Monitoring the water level at smart home system is an effort to increase the comfort and safety of its residents. Therefore, this study aims to design and develop a water level monitoring application that can be implemented to smart home system using the ESP8266 NodeMCU, HC-SR04 ultrasonic sensor, MQTT protocol, and the Flutter framework. By implementing Flutter as an open-source framework for creating applications, it can be monitor water levels in real-time accessed from various platforms: such as Android, iOS, Web, Windows, macOS, and Linux. The test expected to function correctly and provide accurate and real-time information regarding water levels in the surrounding area and can be accessed using various platforms. This research likely to be successful because previous studies conducted the same components in this study.

Design of mechanical digitalization automatic water level recorder for river water level monitoring

The Welang River Pasuruan has an Automatic Water Level Recorder (AWLR) tool that measures water levels at that location. This tool operates using mechanical movement to rotate the roller on the AWLR, causing the float to move up and down. This movement results in ink marks on the water level validator paper. The recorded water level data is then processed into an Excel chart. This study aims to digitize the AWLR tool so that the monitoring and calculation of the water level is more precise and can be monitored in online. The water level detector will work in real time using a rotary encoder sensor and data will be sent to database and monitor water level monitoring through a series of data processing which later the data will be sent via MiFi. The method of testing the 2-component connecting system is the accuracy of reading the water level and the accuracy of sending data. The results will be displayed every 1 second on the monitoring dashboard and a standard deviation of 0.0002792 and an error of 0.43% is obtained which indicates the accuracy of the reading is quite accurate and real time.

Analysis of Water Level Monitoring using GNSS Interferometric Reflectometry in River Waters

GNSS Interferometric Reflectometry (GNSS-IR) is one of the newest explorations of Global Navigation Satellite System (GNSS) signals which utilizes multipath signals to calculate the vertical distance from the reflecting surface to the geodetic antenna/receiver. However, scientific-grade or geodetic GNSS instruments are expensive, which is a limiting factor for their prompt and more widespread deployment as a dedicated environmental sensor. Hence, in this study, low-cost GNSS receiver devices were used to monitor the surface level of PT Garam River in Pamekasan Regency using the GNSS-IR method. The study location of PT Garam River which is quite close to the sea causes the water level to vary following the tides at the sea. This is because there are needs for advanced methodologies to limit GNSS observations in a much narrower area. The river in this case is a more challenging study location in terms of monitoring the water level. Calculation of river water level height with GNSS-IR observations is based on determining the peak frequency on the periodogram resulting from signal-to-noise ratio (SNR) data extraction. The two-weeks GNSS-IR observation in this study were applied by installing sideway orientation of GNSS antenna towards the river. This will be done to see how effective the strength of the reflected signal received by the low-cost GNSS devices are in narrow footprint area. Conventional tide gauge near the GNSS-IR site was used to evaluate the accuracy of GNSS-IR in river water level monitoring. Tide analysis is carried out to obtain tidal constituents and tide predictions using least square harmonics estimation (LSE). The resulting accuracy of the validation data is at an RMSE of 15.35 cm, with the correlation value of 0.94. The type of river tides in the study location based on the calculation results is mixed tide prevailing semidiurnal. Tide prediction for 20 days gives promising results with an RMSE of 16 cm. These results indicate that low-cost GNSS device has a promising capability for water level monitoring using GNSS-IR method in a narrow reflector area.

Estimation of the Water Level in the Ili River from Sentinel-2 Optical Data Using Ensemble Machine Learning

Monitoring of the water level and river discharge is an important task, necessary both for assessment of water supply in the current season and for forecasting water consumption and possible prevention of catastrophic events. A network of ground hydrometric stations is used to measure the water level and consumption in rivers. Rivers located in sparsely populated areas in developing countries of Central Asia have a very limited hydrometric network. In addition to the sparse network of stations, in some cases remote probing data (virtual hydrometric stations) are used, which can improve the reliability of water level and discharge estimates, especially for large mountain rivers with large volumes of suspended sediment load and significant channel instability. The aim of this study is to develop a machine learning model for remote monitoring of water levels in the large transboundary (Kazakhstan-People’s Republic of China) Ili River. The optical data from the Sentinel-2 satellite are used as input data. The in situ (ground-based) data collected at the Ili-Dobyn gauging station are used as target values. Application of feature engineering and ensemble machine learning techniques has achieved good accuracy of water level estimation (Nash–Sutcliffe model efficiency coefficient (NSE) >0.8). The coefficient of determination of the model results obtained using cross-validation of random permutations is NSE = 0.89. The method demonstrates good stability under different variations of input data and ranges of water levels (NSE > 0.8). The average absolute error of the method ranges from 0.12 to 0.18 meters against the background of the maximum river water level spread of more than 4 meters. The obtained result is the best current result of water level prediction in the Ili River using the remote probing data and can be recommended for practical use for increasing the reliability of water level estimation and reverse engineering of data in the process of river discharge monitoring.

Monitoring Ketinggian Air Sebagai Pendeteksi Banjir Menggunakan Notifikasi Telegram Berbasis Internet Of Things

Floods are stagnant water caused by blocked rivers or rainfall. Currently residents estimate the potential for flooding is still done manually, there is no monitoring system for detecting water levels and flood warnings. There is no use of ultrasonic-based technology and telegram information notifications for monitoring water level levels as flood detectors. In this study the authors used data analysis methods consisting of observations, interviews and literature studies. The stages of designing a water level monitoring tool system as a flood detector using the Rapid Application Development (RAD) method consist of Requirements Analysis, Design Workshop (Modeling) and Implementation (Implementation). The purpose of this research is to make a water level monitoring tool as a flood detector using an ultrasonic sensor. Assisting online monitoring of water levels via smartphone telegrams as early information on flooding and increasing citizen awareness to detect water levels. The results of this study are that the water level monitoring tool for flood detection can detect the water level properly and can help minimize losses from the impact of flooding on residents by using this tool. The ultrasonic sensor provides accurate information about the water level distance. The buzzer sounds according to the expected distance and sound intensity. Telegram warning notifications provide monitoring information and water level warnings as flood detectors according to the specified water level settings. The mini water pump pumps standing water and drains the water to a safe place when the ultrasonic sensor detects that the distance has reached the maximum limit.

Reconstructing flood level timeseries at seasonal wetlands in Ireland using Sentinel-1

Wetlands are vital habitats which play a critical role in global biodiversity. Their functioning is intrinsically linked with their hydrological regime and so the monitoring of water level data is critical to their management and conservation. However, monitoring the hydrology of wetlands can be challenging. Their large number and wide distribution can make them impractical to monitor using traditional field instrumentation. In this context, this paper presents a novel approach for monitoring water levels in gauged and ungauged seasonally flooded wetlands using Sentinel-1 Synthetic Aperture Radar (SAR) data. The methodology, which was fully automated, used multitemporal sequences of SAR imagery to reconstruct hydrometric data at 44 seasonally flooded wetland sites across the Republic of Ireland. The procedure downloaded, processed and checked the suitability of SAR images for water classification. Suitable images were classified and filtered to remove common sources of misclassification, and the flood area in each image was cross-referenced against a predefined stage-area relationship (derived from a digital terrain model) to determine water level. The methodology was calibrated using observed field data and performance evaluated in two ways: 1) generic calibration whereby the process was optimised collectively to achieve the best accuracy for ungauged sites, and 2) site-specific calibration whereby the process was optimised for each site individually to achieve the best possible accuracy for each gauged site. The generic calibration yielded an average Nash-Sutcliffe Efficiency of 0.80 for both calibration and validation datasets while the site-specific optimisation produced results of 0.87 for both calibration and validation datasets. Results show the methodology was capable of accurately reproducing water levels in easonal wetlands for floods as small as 3 Ha. This demonstrates its potential as a viable tool for the remote monitoring of ecologically significant wetlands, and a means of providing the observational data necessary for their long-term sustainable management.

Water level identification with laser sensors, inertial units, and machine learning

Flood risk management usually hinges on accurate water level identification in urban streams such as rivers or creeks. Although research has emphasised the applicability of ultrasonic sensors as a contactless technology for sensor-based water level monitoring, Light Detection and Ranging (LiDAR) sensors are less sensitive to weather conditions that typically happen during flood events, such as dust, fog and rainfall. However, there has been little research on the applicability of LiDAR sensors in this field. No previous literature has analysed the impact of complicating variables on the quality of predictions or evaluated the possible benefits of using a combined approach with Inertial Measurement Units (IMU) and machine learning to produce superior predictions. In this work, we collected a dataset in a laboratory condition synchronising data from a LiDAR, an ultrasonic sensor and an IMU in an experimental device. We controlled the incidence angle, the distance, and the water turbidity to analyse their effect on the predictions. Traditional machine-learning techniques were evaluated as models to combine data from distance and inertial sensors, reducing the error rates compared to individual sensors’ predictions. Results indicated a sharp drop in the mean absolute error, root mean squared error and coefficient of determination for all water turbidity and incidence angles considered, especially when tree-based ensembles were used. The ultrasonic sensor led to improved results for low water turbidity and increased incidence angle, but statistically significant differences were not found in the other cases.

BROACHING UNCOUTH WATER LEVEL SNAG IN UNDERGROUND AGRICULTURE FIELD THROUGH WIRELESS SENSORS

The major problem faced by the agriculture industry is the growth of seeds at optimal fertility. Such uncouth fertility occurs when either no water reaches under soil seed or some obstacles prevent it. This issue degrades agriculture growth at an aggravation level and consequently barren the land. A wireless sensor-based communication model has been developed to notice the accurate status of the water level reaching seeds of various crops such as wheat, maize, barley, and cotton with soil types Loamy, Silty clay, and Black soil. In the proposed model, "Broadening Uncouth Water Level Snag in Underground Agriculture Field with Wireless Sensors" (BUWLN), the water level would be measured in real time and the data would be transmitted to a base station accordingly. The 8 sensor nodes and a base station would be deployed accompanied by an Arduino package in an area of 90 x 30 square feet to monitor water levels efficiently. The study will keep track of water levels, and the farmer will be informed of the water level so they may manage water resources more effectively. In addition, farmers will also be aware of the field if water is distributed uniformly so that the crops yield increases. The performance of the proposed BUWLN model has been compared with Remote water-level monitoring system (RWMS), Low cost wireless sensor network (LCSN), and Ensemble groundwater level prediction (E-GWLP) by conducting simulation using PROTEUS. result showed that the proposed BUWLN model has enough potential for future growth.

Water Area Extraction and Water Level Prediction of Dongting Lake Based on Sentinel-1 Dual-Polarization Data Decomposition

The Sentinel-1 imaging radar mission provides a short revisit-time, continuous all-weather, and day-and-night imagery at the C-band, which brings opportunities for the dynamic extraction of lake water areas. For wetland-type lakes, it is difficult to distinguish between the water, submerged plants, and mudflats at the edge of a lake, which leads to difficulty in recognizing the water edge of a lake and affects the accuracy of lake water area extraction. In this paper, a water area extraction and water level prediction algorithm based on Sentinel-1 dual-polarization data decomposition is proposed to solve the problem. We can accurately extract lake water through generalized Stokes polarization decomposition. At the same time, we can verify the accuracy of water area extraction by establishing the water area and in situ water level elevation (A–E) relationship, and predicting the water level according to the calculated water area. In this study, dual-polarization Sentinel-1 time series SAR data covering the Dongting Lake wetland from 2018 to 2022 are used to verify the proposed water area extraction algorithm and establish the A–E relationship of the East Dongting Lake basin. The results show that the generalized Stokes decomposition parameters are very sensitive to the water boundary, and the R2 of the water area and the water level can reach 0.98 by using the piecewise linear function. It confirms the accuracy of the water area inversion, which is of extremely important significance for the high-precision monitoring of the water area of Dongting Lake with long-term Sentinel-1 data. Meanwhile, the predicted lake water level acquired using the A–E relationship established in this paper is compared with the field water level, with an RMSE of 0.4857 m and R2 of 0.9930. This means that the water level derived using the method in this study is quite compatible with the field observations, which provides a good idea for the water level monitoring of lakes lacking hydrological monitoring stations.

Monitoring Lake Level Changes in Bosten Lake from 2003-2021 based on Multi-source Satellite Altimetry Data

The investigation of freshwater lake water resource changes in arid regions is crucial for understanding the evolution of regional lake dynamics under climate change, given their value and limited availability. This study aims to overcome the limitations of traditional water level monitoring methods and address the practical need for monitoring water level changes in Lake Bosten, located in the arid and semi-arid regions of China. To this end, we employed ICESat, ICESat-2, and CryoSat-2 altimetry data, as well as Landsat 8-OLI optical remote sensing data, to obtain the water level information for Bosten Lake from 2003 to 2022. We analyzed the temporal variation of the lake level and discussed the response of the lake level to climate and human activities in the context of temperature, precipitation data, and human water use. The water level of Lake Bosten exhibited a decreasing and then increasing trend, with three rising and three falling intervals, respectively. The intra-year variation of the lake water level exhibited bimodal characteristics, and the fluctuation range of water level during abundant water periods was greater than that during dry periods. Finally, we correlated the extracted lake water level with the measured water level at the hydrological station and found a significant positive linear correlation between the observed water level of Bosten Lake based on satellite altimetry and the measured water level at the hydrological station, with an overall correlation coefficient of 0.896 and 0.934 (P < 0.01), indicating high accuracy of the water level results obtained in this study. The dynamics of Lake Bosten are influenced by a combination of climate change, hydrological elements, and human activities, with a relatively weak relationship between climate change and lake volume and a strong influence of human activities. Multi-source altimetry satellites provide a powerful tool for monitoring lake water levels over a long time scale, which is critical for studying lake water level changes and their response to climate and the environment.