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Initiated in 2018, solinst levellogger data is available until 2023. Data is collected each year and is uploaded soon after the water year is over. Some loggers were moved to different sites or had technical difficulties, therefore dates that have gaps in data represent a time period where data is not available.

The release rate of CO2 gas can be influenced by peatlands' physical properties, such as water level and soil moisture, and rainfall. To anticipate the unstable condition which is when the peatland emit more carbon, we developed the Generalized Space Time Autoregressive (GSTAR) model in predicting these physical properties for the following weeks. As the innovation in modelling, the spatial weight matrix was based on three-dimensional coordinates with a modification on the height factor. The data we used are real-time data of water level on the peatlands in Pulang Pisau Regency, Central Kalimantan Province from 20 February 2021 to 18 March 2023. We then used Ordinary Kriging interpolation on the prediction results to create contour maps on different dates. There were empty data on several dates, especially from 24 March until 3 August 2022. To fill the empty data, we used linear interpolation and then we added white noise to the interpolation results. From the data, the water level has a downward trend pattern from around November to September and an upward trend pattern from October to November. Furthermore, we found that the best model for water level was GSTAR (2;0.1) with a modified matrix a = 0.1 and b = 1.1 . Based on the predicted water level, there is a risk of changes in the properties of the peatlands in several areas in Pulang Pisau Regency. [ABSTRACT FROM AUTHOR]

Understanding groundwater–surface water interaction is essential for water resource management and watershed ecological protection. However, the existing studies often emphasize the tracer role of hydrogeochemical methods (including hydrochemistry and isotopes) while underestimating the importance of analyzing watershed hydraulic characteristics, thus neglecting the indications of the driving mechanisms (hydraulic head difference) for the water exchange. Taking the Songhua River in the Sanjiang Plain as an example, this study combines hydraulic, hydrochemical, and isotopic methods to clarify the groundwater–surface water interactions from both a driving mechanism perspective and a hydrogeochemical characterization perspective within the water cycle. The results indicate that human exploitation has caused river water to infiltrate into groundwater, converting the section into a losing river, where surface water consistently exhibits a hydraulic tendency to recharge the aquifer. The influence zone of the river extends up to 3.5 km from the riverbank, with an average recharge rate from the river reaching 78.04% within this area. This recharge mixes and dilutes the adjacent groundwater, impacting its hydrogeochemical characteristics. This study enhances the understanding of combined methods for groundwater–surface water interaction and provides a scientific basis for water resource management and pollution control strategies in the local agricultural regions. [ABSTRACT FROM AUTHOR]

Long-term variations of ecological and hydrological parameters governing the natural reproduction of sturgeon are analyzed. It was found that long-term variations of hydrological characteristics are taking place against the background of cyclic development of phases with different water abundance and transformation of the annual redistribution of runoff. The contribution of long-term runoff regulation by the Iriklinskoe Reservoir to water regime variations in the Ural River is considered for its middle reaches. A conclusion is made regarding the need to restore the sturgeon herd in the Northern Caspian Sea. Recommendations are made regarding the priority measures for preserving the potential of sturgeon spawning grounds in the middle reaches of the Ural River. [ABSTRACT FROM AUTHOR]

It is vital to provide useful hydrological forecasts for urban and agricultural water management, hydropower generation, flood protection and management, drought mitigation and alleviation, and river basin planning and management, among other things. This paper introduces a simple and flexible hydrological time series forecasting framework. Predicting water levels is crucial, given the need for sustainable environmental management. The prognosis should be reasonable to persuade individuals to take proper precautions. While many methods have been developed to predict water levels, here the effectiveness of two approaches to predicting river water levels was assessed. For this purpose, nine years of data were used, which were divided into input data (2014–2021) and validation data (2022), on water levels in the Morava e Binçës river for the Vitia station, in the form of monthly time series records, to identify the best model among those used and to identify the information necessary for water resource management and hazard control. Models Autoregressive Integrated Moving Average(ARIMA) and Error Trend and Seasonality, or Exponential Smoothing (ETS), were examined using the R package to determine the most accurate. The results indicate the applicability of both models, as evidenced by the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE). The predictive analysis based on historical water levels allowed the identification of distinct periods characterized by high and low water levels between 2022 and 2024, which is important for the area in question due to the numerous flood events occurring here. [ABSTRACT FROM AUTHOR]

Dongting Lake is the second largest freshwater lake in China, located in the middle reaches of the Yangtze River. Since the 21st century, it has faced intensified human activities, particularly the Three Gorges Dam impoundment and sand mining. The water quality of Dongting Lake has significantly changed due to human activities and climate change. Currently, quantitative studies on the spatial–temporal variations of total suspended matter (TSM) during Dongting Lake's dry season and the human impacts on its concentration are lacking. This study utilizes Landsat-5 TM and Landsat-8 OLI data to estimate the changes in TSM concentration during the dry season from 1986 to 2021, analyzing their spatial–temporal variations and driving mechanisms. By evaluating the atmospheric calibration accuracy and model precision metrics, we select a model based on the ratio of red to green band, achieving an R 2 of 0.84, R M S E of 18.94 mg/L, and M R E of 27.32%. Applying this model to the images, we map the distribution of the TSM concentration during the dry season from 1986 to 2021, analyzing its spatial pattern and inter-annual variation, and further investigate the impacts of natural factors and human activities on the TSM concentration. Our results show the following: (1) From 1986 to 2021, the TSM concentration during the dry season ranges from 0 to 200 mg/L of Dongting Lake, with an area-wide average value between 41.61 and 75.44 mg/L. (2) The TSM concentration from 1986 to 2021 is significantly correlated with the water level. Before 2006, it correlates positively, but no significant correlation exists from 2006 onward. (3) From 2006 onward, the mean TSM concentration is notably decreased compared to that before 2006, likely due to the Three Gorges Dam, while our analysis indicates a significant positive correlation between the TSM concentration and sand mining intensity during this period. This study highlights the influence of the Three Gorges Dam and sand mining on the TSM concentration in Dongting Lake during the dry season, providing valuable insights for related research on similar lakes. [ABSTRACT FROM AUTHOR]

Based on the Xiaolangdi North Bank Irrigation Area Project, this study combines numerical simulation and BP neural network methods to investigate the sensitivity of tunnel soil and its parameter inversion under continuous heavy rainfall. The research results indicate that changes in water-level and soil strength parameters have a significant impact on the deformation of tunnel surrounding rock. By comparing the sensitivity factors of different parameters, the main parameter sensitivities affecting the displacement of tunnel surrounding rock were determined to be water level, internal friction angle, and cohesion. The mechanical characteristics of the tunnel construction process were analyzed using finite difference method numerical analysis software FLAC3D, and the results were used as a sample dataset for inversion analysis. Through neural network inverse analysis based on orthogonal design method, the cohesion and internal friction angle of loess layer ④, loess layer ④-1, and loess layer ⑤ were determined, and the data of groundwater level elevation were obtained. Field applications proved the effectiveness and rationality of this method. [ABSTRACT FROM AUTHOR]

Reservoir impoundment and water level fluctuations often trigger landslides and their secondary disasters, such as potential impulse waves, posing a serious threat to the safety of people along the reservoir and dam areas, causing economic losses and even catastrophic consequences. This study delves into a comprehensive field investigation and monitoring of the engineering geological conditions and deformation mechanisms of the Mogu rock landslide. The impoundment is identified as the primary factor inducing slope deformation, with the cumulative displacement of the sliding body showing no signs of convergence, indicating potential instability. By coupling the elasto-visco-plasticity model and the RNG turbulence model in FLOW-3D, an actual impulse wave disaster near the Lianghekou reservoir dam area is replicated to validate the reliability of the numerical method. Building upon this, a three-dimensional model is established to calculate potential impulse waves generated by the Mogu rock landslide, and the risk to the dam is evaluated. Under different water level conditions, the simulated run-ups of impulse waves do not surpass the dam elevation, demonstrating a satisfactory safety margin. Given the inherent danger of landslide-induced wave disasters, continuous attention is warranted, and preventive measures and suggestions are proposed to address these concerns. Additionally, the study explores the contributions of water level fluctuations to the primary tsunami amplitude, the maximum run-up on the opposite bank and the dam, and the attenuation rate of the tsunami amplitude along the river channel. The results provide significant reference values for the early warning and prevention of comparable reservoir landslides and potential landslide-induced waves worldwide. [ABSTRACT FROM AUTHOR]

The middle and lower reaches of the Yangtze River, a primary region for freshwater lakes in China, have undergone significant transformations throughout the Holocene. These changes, driven by factors such as sea-level rise, climate change, and human activities, have led to the progressive elevation of water levels in this area. As a result, a floodplain has emerged, characterized by the formation of numerous shallow lakes along the river course. However, the pattern of water-level changes in the main channel of the Yangtze River during the Holocene remains unclear. This gap in knowledge poses challenges for understanding sediment transport dynamics, the interactions between the river and its adjacent lakes, and the prevention and control of flood disasters in the Yangtze River basin. To shed light on these issues, our study compiled data on the surface elevation and water depth of 81 lakes in the mid-lower reaches of the Yangtze River basin. Additionally, we analyzed historical water-level records from the 1900s to the 1970s at eight gauging stations from Shashi to Jiangyin along the river's main stream. Our findings reveal that, particularly along the Jingjiang section, the basal elevation of most lakes is lower than the Yangtze River's water level during the dry season. Conversely, the water level of the main stream exceeds that of both the floodplain and the lakes enclosed by the Jingjiang embankment. In the tidal reach, especially within the Taihu Lake basin, the basal elevation of lakes typically falls below sea level. Meanwhile, lakes located along the section from Chenglingji to Wuhu exhibit basal elevations that correspond with the Yangtze River's annual average and dry season water levels. Given the widespread presence of lakes along the middle and lower reaches of the Yangtze River, our study introduces a new proxy for reconstructing the mean water level of the mid-lower Yangtze River in the Holocene. By analyzing sediments from Nanyi Lake and Chenyao Lake in the lower Yangtze River, we attempted to reconstruct the water level of the Yangtze River's main channel since 8 ka BP. [ABSTRACT FROM AUTHOR]

Copyright of Acta Geoscientica Sinica is the property of Acta Geoscientica Sinica Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract The release rate of CO2 gas can be influenced by peatlands’ physical properties, such as water level and soil moisture, and rainfall. To anticipate the unstable condition which is when the peatland emit more carbon, we developed the Generalized Space Time Autoregressive (GSTAR) model in predicting these physical properties for the following weeks. As the innovation in modelling, the spatial weight matrix was based on three-dimensional coordinates with a modification on the height factor. The data we used are real-time data of water level on the peatlands in Pulang Pisau Regency, Central Kalimantan Province from 20 February 2021 to 18 March 2023. We then used Ordinary Kriging interpolation on the prediction results to create contour maps on different dates. There were empty data on several dates, especially from 24 March until 3 August 2022. To fill the empty data, we used linear interpolation and then we added white noise to the interpolation results. From the data, the water level has a downward trend pattern from around November to September and an upward trend pattern from October to November. Furthermore, we found that the best model for water level was GSTAR (2;0.1) with a modified matrix $$a=0.1$$ a = 0.1 and $$b=1.1$$ b = 1.1 . Based on the predicted water level, there is a risk of changes in the properties of the peatlands in several areas in Pulang Pisau Regency.

Abstract It is vital to provide useful hydrological forecasts for urban and agricultural water management, hydropower generation, flood protection and management, drought mitigation and alleviation, and river basin planning and management, among other things. This paper introduces a simple and flexible hydrological time series forecasting framework. Predicting water levels is crucial, given the need for sustainable environmental management. The prognosis should be reasonable to persuade individuals to take proper precautions. While many methods have been developed to predict water levels, here the effectiveness of two approaches to predicting river water levels was assessed. For this purpose, nine years of data were used, which were divided into input data (2014–2021) and validation data (2022), on water levels in the Morava e Binçës river for the Vitia station, in the form of monthly time series records, to identify the best model among those used and to identify the information necessary for water resource management and hazard control. Models Autoregressive Integrated Moving Average(ARIMA) and Error Trend and Seasonality, or Exponential Smoothing (ETS), were examined using the R package to determine the most accurate. The results indicate the applicability of both models, as evidenced by the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE). The predictive analysis based on historical water levels allowed the identification of distinct periods characterized by high and low water levels between 2022 and 2024, which is important for the area in question due to the numerous flood events occurring here.

Regularly monitoring pond water quality in fish farming is a crucial practice often neglected, negatively impacting goldfish yields. Addressing this issue, a sophisticated device leveraging fuzzy logic has been engineered to accurately regulate acidity, temperature, and water levels, with real-time data accessible through the Blynk smartphone application. This innovative system employs a trio of sensors—namely an acidity sensor, a DS18B20 temperature sensor, and an HCSR04 ultrasonic sensor—coupled with three output mechanisms: an inlet pump, an outlet pump, and a heater, to ensure precise control. Rigorous testing under various conditions at different times of the day, lasting approximately one hour each, demonstrated the device's capability to adjust water's acidity by about 0.1 units per minute, reflecting the influences of fish activity and water temperature, with a deficient accuracy error of 0.19%. Additionally, the system's effectiveness in maintaining a consistent water level was confirmed, exhibiting a refill rate of 1.2 cm per minute as detected by the sensor. This integrated system is instrumental in safeguarding goldfish health and optimizing their productivity by ensuring water quality remains within the desired acidity, temperature, and volume parameters.

Drainage is the most important single factor affecting mires globally but its effects vary depending on drainage type, climate and wetland ecosystem. Sixteen transects in fifteen drained raised bogs in the hemiboreal zone of Estonia were examined to determine the spatial effects of drainage on vegetation and environmental factors. We found that the effects of drainage along a hydrosequence depended on the drainage type; i.e., whether it took the form of a cutoff ditch intercepting surface and subsurface water flow around the perimeter of the bog massif, or the drainage ditch crossed the central part of the bog. Some drains of the latter type were still functioning whereas old hand-dug examples seemed to be derelict and inactive. For both of the active drainage types, maximum water levels stabilised within 25 m of the ditch but drawdown effects on minimum and average water levels extended to 450 m. The vegetation variables that were most sensitive to drainage were the number and the percentage of bog-specific species, the number of ground vegetation species, the total cover of Sphagnum species, and the height and canopy cover of trees. Trees were rare when the minimum water level was higher than -20 cm, and the number of bog-specific species increased rapidly as the minimum water level rose towards the surface from -90 cm. Total cover of the shrub layer decreased and total cover of Sphagnum species increased almost linearly with a rise of minimum water level up to 40 cm, whereas the total cover of field layer species stabilised when the minimum water level was at -100 cm. Total cover of Sphagnum species in the field layer increased over distances of up to 250–300 m from the drainage ditch and the percentage of bog-specific and fen-specific species stabilised 190–300 m from the ditches.

[Objective] The lakes of the Tibetan Plateau are important indicators of climate change, and their expansion or contraction also have an important impact on the natural environment of the Tibetan Plateau. [Methods] Three typical lakes located in different climatic sub-regions of the Qinghai-Tibet Plateau (Qinghai Lake, Yangzhuo Yongcuo, and Ulan Ula Lake) were selected to study the temporal and spatial variation of the hydrological characteristics of the three typical lakes from 1985 to 2021 by remote sensing monitoring methods, to reveal the role of key climatic factors, and to further explore the influence of glaciers and permafrost on the typical lakes. [Results] The area and water level of Qinghai Lake showed a significant upward trend, with an area increase of 238.68 km2 and a water level increase of 1.32 m. In space, it showed a trend of expansion in the east-west direction. The area and water level of Yangzhuo Yongcuo Lake first fluctuated and then decreased, decreasing by 16.31 km2 and 3.25 m respectively. In space, the overall area showed a shrinking trend from the surrounding to the center. The area and water level of Ulan Ula Lake showed a significant upward trend, increasing by 125.57 km2 and 8.12 m, respectively, and the expansion area was mainly concentrated in the south. [Conclusion] The analysis of key climate factors shows that the lake expansion caused by the increase of precipitation and the accelerated melting of glacial permafrost in the warm and humid environment is the most prominent environmental change characteristics of the Qinghai-Tibet Plateau. The main reason for the rise in the water level of Ulan Ula Lake is the seasonal thaw of permafrost caused by rising temperatures. Exploring the changes in lake area on the Tibetan Plateau is of great guiding significance for in-depth research on global climate change and surface water resource assessment.

Lake Urmia in the northwest of Iran is one of the largest vanishing lakes in the world. Several water-saving strategies have been implemented in the lake basin over the last decade, but they are not producing efficient results. This study employed a modified version of the Soil and Water Assessment Tool (SWAT), an agrohydrological model, to investigate the basin-scale effectiveness of some water-saving plans implemented at the farm scale. Data collected from 301 monitoring sites in the Lake Urmia basin were applied to the modified SWAT model. The modified SWAT model was used to estimate water balance components and was coupled with the Water Accounting Plus framework (WA+). Six discharge stations, crop yields, evapotranspiration, and groundwater level tables were calibrated and validated from 1987 to 2015 within the Zarrineh Rud Basin (ZRB), the most crucial subbasin in the Lake Urmia basin. Next, four individual water-saving plans—changes in irrigation management and developments in irrigation systems, changes in fertilizer type and regime, changes in the type and method of cultivation, and farm size and shape adjustments—as well as seven combinations of the water-saving plans, were applied to the agrohydrological model. The results reveal that assessing restoration plans for Lake Urmia without considering both farm and basin scales provides no reliable results. With changes in management and the development of irrigation systems, considerable differences in water withdrawal were observed. Developing irrigation systems leads to enhanced water consumption and evapotranspiration, which is expected to improve water yield and crop productivity. However, individual plans such as developments in irrigation systems cannot increase the inflow to Urmia Lake, and combined water-saving strategies can help restore the lake only to a limited extent, because the changes in inflow are not substantial. [ABSTRACT FROM AUTHOR]

Water air cooler is a process that uses an evaporative system. In its first generation, there was no temperature sensor, and its fan was turned by an electric motor in an open loop system with a fixed speed. As well, the water's flow and level in the tank are governed by a mechanical system, which is generally a floating ball attached to a shaft. In order to ameliorate this classical system with more advantages and performances, many ideas are included in subsequent generations such the integration of embedded systems, intelligent control and components of manufacturing materials. In this paper, the current study aims to integrate an intelligent system, which is the neural networks by using R language to give a smart decision model to command relays switching dedicated to control the electric motors, where the first one is tied up with a fan and the other to an electro-pump. The HCSR04 ultrasonic and DHT11 sensors supervise the two desired parameters control, water level in the tank and the outside temperature successively. [ABSTRACT FROM AUTHOR]

In the context of escalating climate change-induced impacts on water resources, robust monitoring tools are imperative. Satellite altimetry, benefiting from technical improvement such as the use of SAR and InSAR techniques and tracking modes considering topography, is emerging as a crucial means of estimating lake levels, data that are fundamental to understanding climate dynamics. This study delves into the use of satellite-altimetry-determined water levels to analyze changes in water storage and superficial area in Lake Ranco, in south-central Chile, from 1995 to 2023. The main objective is to provide valuable information for water-resource management and policy formulation. Leveraging AlTiS software (v2.2.9-0-gf5938ab), radar-altimetry data from the missions ERS-2, ENVISAT, SARAL, and Sentinel-3A were processed, generating a complete time series of water levels. The lake-level data were complemented by the bathymetric data for the lake to obtain the variation in the area and volume in the period 1995–2023. These results were analyzed with respect to hydrometeorological data from the study area, such as precipitation, temperature, relative humidity, and potential evapotranspiration. Additionally, the effects of ENSO (ENSO 3.4 index) and the Pacific Decadal Oscillation index (PDO) were considered. Results reveal a strong correlation between altimetry-derived lake levels and observed in situ data, with a mean square error of 0.04 m, a coefficient of determination of 0.99, an index of agreement of 0.99, and a Kling−Gupta efficiency of 0.90. The analysis of climatic variables showed that variations in lake level coincide with changes in precipitation within the study area and also showed the influence of variations in temperature and potential evapotranspiration. Additionally, the effects of the ENSO phenomenon can be seen within the study area for its cold phase (i.e., La Niña) in the 2010–2012 period and for its warm phase (i.e., El Niño) in the 2015–2016 period, with a decrease and increase in precipitation, respectively. These effects were enhanced when the cold and warm phases of the ENSO and PDO phenomena occured. The successful application of satellite altimetry demonstrated in this study underscores its critical role in advancing our understanding and management of water resources amidst changing climate scenarios. [ABSTRACT FROM AUTHOR]

This research paper analyzes satellite altimetry data for the northeastern and middle parts of the Caspian Sea for the period from 1993 to 2023. The verification of altimeter data from the data of marine hydrometeorological stations of nasal observations of the permanent sea at two points was carried out: M Peshnoy and MHP Fort Shevchenko. The results showed that the altimetric data have a high correlation with ground observations (0.89-0.94), with a mean absolute error ranging from 17 cm to 23 cm, and a consistency index above 0.7. The assessment of data applicability according to statistical criteria showed that there are minor deviations. Altimetric data are most accurate for monitoring water levels from April to October and can be used for monitoring sea levels in the open parts of the Caspian Sea. [ABSTRACT FROM AUTHOR]

This study presents a new approach for predicting water levels of the Odra/Oder river using vector autoregressive models (VAR). We use water level time series from 27 gauging stations, on which we interpolate no-data gaps using the LinAR method and detect outliers with two separate methods: the extreme values (EV) approach and the isolation forest (IFO) algorithm. Before removing potential outliers, we propose a hydrological evaluation based on multivariate data analysis. Finally, we consider three separate data scenarios, i.e. LinAR (no outlier rejection), EV, and IFO. VAR models for six prediction gauges were built in a moving window manner on the most recent 720 hourly water levels prior to each prediction. The analysis covered the time range from January 2016 to May 2022 and resulted in ≈ 1,000,000 water level forecasts (3 scenarios x 6 gauges x 55,000 hourly time steps) with lead time of 72 h. The analysis of root mean squared error (RMSE) indicates that the VAR model performs well, especially for 24-hour predictions, with RMSE values ranging from 8 to 28 cm. The model was also found to have skills in predicting a rising limb of a hydrograph. Our numerical experiments showed the susceptibility of the VAR predictions to artefacts. The IFO method was found to detect outliers skilfully, which allowed to produce the most accurate VAR-based predictions. [ABSTRACT FROM AUTHOR]

The northern section of the Longxian-Baoji fault zone has a strong degree of locking, but the locking degree of its southern section is less studied. The observation well of a confined aquifer is considered a sensitive "volumetric strain gauge", and the rise or fall of the well water level often reflects the state of regional stress in tension or compression. Fengxiang Well in Shaanxi is located in the central and southern section of the Longxian-Baoji Fault. Based on the analysis of the variation trend of the water level in Fengxiang Well from 2013 to 2023, the porosity of the aquifer, the volume compression coefficient of the solid skeleton, and the volume compression coefficient of the water were calculated for the aquifer of the well, under un-drained conditions, by using sliding fits such as the barometric pressure coefficient and the M2-wave tidal factor. The temporal characteristics of the tectonic stress field around the well were quantitatively analyzed by using the relationship between the water level variation of the well and the vertical stress variation of the aquifer. Based on the GNSS baseline and surface strain variation characteristics within the region, as well as the seismic activity characteristics since 2013, a comprehensive analysis suggests that the stress field variation characteristics in the region can be divided into two stages: 1 From 2013 to 2017, the water level of Fengxiang well decreased, and the vertical stress weakened, showing a tensile environment with low seismic frequency. 2 From 2017 to 2023, the water level of Fengxiang well tended to stabilize, and the vertical stress fluctuated around zero. The tension weakened and gradually transformed into a stage of stress accumulation and enhancement, with a significant increase in the number of earthquakes. Based on the analysis of the water level, vertical stress, GNSS baseline and surface strain, as well as seismic activity characteristics in the Fengxiang well over the past decade, it can be concluded that the central and southern sections of the Longxian Baoji fault zone is in a relatively stable state with weak stress accumulation and small deformation. [ABSTRACT FROM AUTHOR]

Using regional hydrogeological data, water temperature gradient test results, regional groundwater exploitation data, and precipitation data, the factors affecting the multi-year and interannual changes in water level and temperature in the Tongliao earthquake observation well in Inner Mongolia were analyzed. On this basis, a hydrodynamic geological model was constructed to explore the mechanism of quasi synchronous changes in water level and temperature in the well. The results indicate that regional groundwater extraction and rainfall are the main influencing factors for the quasi synchronous changes of water level and temperature in the well, and the cold water infiltration theory can well explain these quasi synchronous changes. [ABSTRACT FROM AUTHOR]

Previous studies have introduced a resonant caisson designed to enhance wave energy extraction in regions with low wave energy density; however, its operational mechanism remains poorly understood. This paper seeks to elucidate the operational mechanism of the resonant caisson by leveraging Star-CCM+ for Computational Fluid Dynamics (CFD) simulations, focusing on the influence of guides and their dimensions on the water levels, flow velocities, and vortex dynamics. The findings demonstrate the remarkable wave-amplification capabilities of the resonant caisson, with the maximum amplification factor reaching 2.31 at the calculated frequency in the absence of guides. Incorporating guides and expanding their radii substantially elevate the flow rates, accelerate the water currents, and alter the vortex patterns, thereby further enhancing the amplification factor. This study will provide a reference for optimizing the design of resonant caissons and wave energy converters based on resonant caissons, thus promoting the effective use of wave energy resources. [ABSTRACT FROM AUTHOR]

Recently, surveillance technology was proposed as an alternative to flood monitoring systems. This study introduces a novel approach to flood monitoring by integrating surveillance technology and LiDAR data to estimate river water levels. The methodology involves deep learning semantic segmentation for water extent extraction before utilizing the segmented images and virtual markers with elevation information from light detection and ranging (LiDAR) data for water level estimation. The efficiency was assessed using Spearman's rank-order correlation coefficient, yielding a high correlation of 0.92 between the water level framework with readings from the sensors. The performance metrics were also carried out by comparing both measurements. The results imply accurate and precise model predictions, indicating that the model performs well in closely matching observed values. Additionally, the semi-automated procedure allows data recording in an Excel file, offering an alternative measure when traditional water level measurement is not available. The proposed method proves valuable for on-site water-related information retrieval during flood events, empowering authorities to make informed decisions in flood-related planning and management, thereby enhancing the flood monitoring system in Malaysia. [ABSTRACT FROM AUTHOR]

Anguillid eel populations are under threat globally. A particularly vulnerable life‐cycle stage is the migration of mature adult eels downstream from freshwater habitats through estuaries into the sea to spawn. This study investigated the factors associated with downstream migration of the short‐finned eel Anguilla australis (Richardson 1841) from a coastal wetland (Lake Condah) in south‐east Australia, using acoustic telemetry. Migration was associated with time of the year, higher water level and river flows, decreasing water temperature, and darker moon phases. Larger individuals and those in better condition were more likely to migrate from the wetland. Downstream migration peaked in spring, in contrast to the typical autumn migration period for other temperate anguillids. Variable responses, in comparison to other studies, highlight how migration cues may not be universal. In south‐east Australia, short‐finned eels may have evolved to migrate in multiple phases by first migrating to the estuary during typical seasonal spring flow pulses (e.g., to avoid being stranded in upland reaches during dry summer periods) and then migrating into the ocean in autumn. More research is needed to unravel these processes and causes, especially considering that the relationship between migration and hydrology may be complex and confounded (e.g., by human‐induced disruptions to migratory pathways). [ABSTRACT FROM AUTHOR]

The possibility of short-term and long-term forecasting of water levels, including those associated with dangerous hydrological phenomena on the Marmarik River, using various probabilistic approaches, including regression dependencies, an integrated moving average autoregression model, and multilayer perceptron models, is considered. To evaluate the effectiveness of prognostic methods, the statistical parameters of a random process are calculated, while recommendations are given using the classical criteria for the effectiveness of issued forecasts. For long-term forecasting, the expediency of using the integrated moving average autoregression model was assessed, while it is noted that these models in the classical representation are not applicable due to time gaps, and therefore it is recommended to focus on the mathematical expectation of a random process. For short-term forecasting one or two steps ahead, the method of training artificial neural networks was used. The analysis carried out in the work revealed that in the case of short-term forecasting of water levels for one period in advance (12 h), it is most expedient to focus on the value of the water level attributable to the date of issue of the forecast, the standard error of such a forecast is 5 cm. For a 24-h water level forecast forward, it is expedient to develop neural network forecasting models, taking into account the development of the situation on Gomraget-Meghradzor. A further increase in the quality of the outputs is possible when using data for a longer observation period and a whole year. At the same time, as an alternative to neural network forecasting models, physical and mathematical (hydraulic) models of the formation of water levels can be used. [ABSTRACT FROM AUTHOR]

Degraded peat has a high potential for use in agricultural production, especially food crops. The water table, which impacts the production of greenhouse gases (GHGs), is the main problem concerning peat utilization. The study aimed to determine the production of greenhouse gases at various peat water levels. The study was conducted in a laboratory setting utilizing the soil column of undisturbed peat soil. The factorial experiment was arranged in randomized block design, with three replicates, with the first factor was peat cropping use: Maize (Zea mays L.), pineapple (Ananas comosus (L.) Merr.), and scrubs. The second factor was water level (0, 10, 20, 30 cm depth). Variables measured were GHG flux (CO2 and CH4), pH, redox potential, total C content, and ash content. The peat cropping use interacted significantly with the peat water level on the potential for CH4 and CO2 production and the value of global warming potential. Water table depth significantly increased CO2 flux and global warming potential (GWP) in all three peat cropping uses. The lowest GWP at a 0 cm peat water level was 944 (pineapple use), 961 (maize use), and 1097 mg CO2e m-2 d-1 (scrub use). Peat for pineapple cultivation produces the lowest CO2 production and GWP compared to maize cultivation and scrubs. The negative relationship between redox potential and GWP is significant in peat for scrub. The relationship between pH and GWP is significant in peat for pineapple and scrub. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Soil & Water Conservation (1009-2242) is the property of Institute of Soil & Water Conservation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Automatisierungstechnik is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Mangroves face decline and degradation due to human activities and natural forces, making their accurate mapping and dynamic monitoring essential. However, most of the existing mangrove indices that rely on multispectral image spectral characteristics suffer from limitations in terms of recognition accuracy and universality. Therefore, this study aimed to develop a robust and efficient Phenology and Water level Time-series Mangrove Index (PWTMI) for mangrove monitoring. PWTMI is constructed by combining spectral and temporal characteristics from dense time-series multispectral data, wherein phenology and water level time-series characteristics are extracted from NDVI and MNDWI time series. The results show that PWTMI outperforms existing multispectral-based mangrove indices and has an accuracy similar to a hyperspectral-based mangrove index, with overall accuracy ranging from 91.49% to 98.83% and F1 score ranging from 0.91 to 0.98 in four typical areas in China, indicating great potential for long time-series and large-scale mangrove monitoring.

Zizania latifolia has been observed to generate floating mats due to water level fluctuations and wind-waves, leading to significant detrimental impacts on both water quality and biodiversity. The key to controlling and monitoring the spread and movement of Zizania floating is the early detection of outbreaks and the clarification of its formation mechanisms in response to water levels and wind actions. The temporal and spatial resolutions of conventional remote sensing monitoring methods are challenging to satisfy the requirements for continuous and accurate tracking of Zizania floating, which undergoes daily movement. This study develops a timely and accurate monitoring method for Zizania floating by calculating the Normalized Difference Vegetation Index (NDVI) from high spatiotemporal resolution PlanetScope (PS) imagery and building a decision tree on the Google Earth Engine (GEE) platform. Taking the Zizania floating outbreak in Honghu Wetland as the subject of our study, we extracted the continuous movement trajectories of Zizania floating from May to September 2019. Subsequently, we analyzed the interrelationship between the formation of Zizania floating and hydrometeorological factors. We then investigated the effects of Zizania floating on water quality and biodiversity. The results show that: (1) The integration of PS imagery and the GEE platform holds the potential to simplify the workload of remote sensing data processing. Based on the difference in NDVI between water bodies and Zizania floating, the dynamic expansion and reduction areas of the Zizania floating were successfully extracted by setting the threshold at 0.3, enabling timely and accurate tracking. (2) Abrupt fluctuations in water levels during brief time intervals serve as the primary trigger for Zizania floating. Additionally, wind speed during the same timeframe acts as a catalyst for the emergence of Zizania floating, with wind direction influencing its movement. (3) The outbreak of Zizania floating significantly elevates the concentrations of total nitrogen (TN), total phosphorus (TP), chemical oxygen demand (CODmn), and total ammonia nitrogen (TAN), while concurrently reducing the dissolved oxygen (DO) concentration in water bodies. The prolonged presence and movement of Zizania floating has reduced the biodiversity of the wetland ecosystem. Zizania floating mats present unique challenges due to their unpredictability, complicating the monitoring and tracking processes. It is necessary to develop a prompt, accurate, and cost-effective detection program for Zizania floating to reduce the risk it poses to wetland ecosystems.

Purpose: experimental study and assessment of technical capabilities of a device for aerating the water bottom layers in reservoirs using solar radiation in Syrian conditions. Materials and methods. Empirical method was adopted as a research method. In accordance with the patent for a utility model, the experimental unit was a metal cylindrical body of a storage tank, in the lower part of which there was a fitting connected to a plastic air tube. Results. In the process of laboratory studies under conditions of different air temperatures, when assessing the capabilities of the unit, three characteristic periods of its operation were identified: intensive – a period with active air output lasting 110–120 minutes; transitional – with a gradual drop in intensity by more than half, lasting from 10 to 30 minutes; stable – a period with an almost uniform air output with a maximum observable duration of up to 60 minutes. An analysis of research materials showed that, depending on the daytime temperature, the air volume released during more than 180 minutes of observation was up to 7.6 liters. Moreover, in all experiments, more than 90 % of the air volume came out during the first 120 minutes, which should be considered as the period of active operation of the unit. Conclusions. Based on the results of experiments in an open area in the climatic conditions of Syria, the possibility of aerating natural reservoirs using a device for aerating bottom layers of water, the operation of which is based on the use of solar radiation without traditional energy sources, was proven.

Based on the coseismic response data of water temperature and water level of 26 observation wells in Yunnan well network from 2008 to 2023, the coseismic response characteristics of water temperature and water level of each observation well were compared and analyzed with 8 earthquakes that have a significant impact on Yunnan region. The coseismic response mechanism was discussed from the perspectives of seismic wave energy density, lithology, control of fault zone and dynamic coordination. The results indicate that the response of water temperature and water level in the well pattern of Yunnan to the far-field earthquakes is more significant than near earthquakes, with the response primarily concentrated in the middle of Yunnan. The water level exhibits a more pronounced response than the water temperature. The coseismic variation of water level is mainly characterized by oscillation and rise, while the water temperature is mainly characterized by pulses. Dehong Fapa Dian No.22 well and Lijiang Xiangshan well have better seismic reflection performance in Yunnan well pattern. On the other hand, No.06 well in Jianchuan, No.01 well in Huize Gangou Dian, Xiaoshao well in Kunming Guandu District, Dayao geothermal well, and Nanhua Zhen 2 well exhibit weaker coseismic responses compared to the other 21 wells. Further analysis shows that the linear fitting correlation coefficient between seismic wave energy density and epicenter distance is greater than −0.9, which is significantly negative correlation and positive correlation with magnitude, and shows the corresponding relationship between energy density and permeability coefficient. Finally, we analyzed the mechanism of coseismic response of water temperature and water level.

Abstract The spatial response mechanism of hydrology and water quality of large river-connected lakes is very complicated. In this study, we developed a spatial response analysis method that couples wavelet correlation analysis (WTC) with self-organizing maps (SOM), revealing the spatial response and variation of water level and water quality in Poyang Lake, China's largest river-connected lake, over the past decade. The results show that: (1) there was significant spatial heterogeneity in water level and quality during the dry seasons (2010–2018) compared to other hydrological stages. (2) We identified a more pronounced difference in response of water level and quality between northern and southern parts of Poyang Lake. As the distance increases from the northern lake outlet, the impact of rising water levels on water quality deterioration intensified during the dry seasons. (3) The complex spatial heterogeneity of water level and quality response in the dry seasons is primarily influenced by water level fluctuations from the northern region and the cumulative pollutant entering the lake from the south, which particularly leads to the reversal of the response in the central area of Poyang Lake. The results of this study can contribute to scientific decision-making regarding water environment zoning management in large river-connected lakes amidst complex environment conditions.

The amount of annual rainfall in the Bengawan Solo watershed causes high water flow (water discharge) in several rivers. In addition, high flow rates significantly increased the water surface level at some observation sites. The Bengawan Solo River burst its banks in November 2016, causing flooding in several areas in eastern Solo. At that time, the river stage at the Jurug monitoring post passed ten. Therefore, a flood early warning system would be useful for predicting water levels in this context. Every day, one post on the Bengawan Solo River measures the water level. The time series data used in this study is the water level. Autoregressive Moving Average (ARMA) is a predictive method for measuring time set data. The assumption of homoscedasticity or constant error variance is used in this model. However, the study will use the ARMA model if the variance changes randomly. The study used 60 pieces of data from January to February 2018. This study can directly use ARMA because the data results are stationary based on ADF value 0.0036. After the first pause, the ACF and PACF are disconnected based on the correlogram pattern. This shows that the water level of the Bengawan Solo River in that period can appear on the Autoregressive Moving Average with orders p = 1 and q = 1 ARMA(1,1). Thus, the total average residue is 0.0668384, so the short error is 6.68384%.

This study navigates the intricate landscape of the Mureș River's upper course, specifically through the contrasting terrains of the Giurgeu Depression and the Toplița-Deda Gorge. It examines the unique relief features and substrates that define the vertical dynamics of the riverbed. The investigation focuses on data collected from four pivotal hydrometric stations: Suseni, Toplița, Stânceni, and Gălăoaia. Here, we determine the thickness of the rock layer (hp) by calculating the difference between the water level and the maximum depth, in relation to the gauge's zero plane. Our analysis highlights the riverbed's monthly regradation patterns within these locales. We uncover notable variations in the scope of vertical dynamics, delineate characteristic periods, and decipher both linear and polynomial trends.

The use of low-cost sensors, with open-source code, facilitates greater spatial resolution and flexibility of environmental monitoring, thus generating more information and overcoming limitations of traditional commercial sensors. Measurement of water turbidity using submerged sensors can be problematic in that rapid biofouling requires frequent site visits to remove, clean, calibrate and replace the sensor. We therefore designed an automated system using low-cost commercially-available sensors that pumps water from the stream, samples it for turbidity and purges remaining water, leaving the turbidity sensor dry between measurements, thus greatly reducing the biofouling problem and minimizing operation costs. Our station was able to estimate suspended sediment concentrations between 0 and 6 g/L with a root mean square error (RMSE) around 5 % of the total range, which meets typical research and operational study requirements. The results showed that the monitoring station is capable of monitoring water level and turbidity for long periods without the need of cleaning the turbidity sensor, due to its purge function. We demonstrated that spatially intense measurement of turbidity within catchments and drainage networks can be achieved at a relatively low cost, which allows a better understanding of the main sources of suspended sediments and their spatial and temporal variability.

Study region: Yamuna River (Delhi), India. Study focus: The anthropogenic activities within the vicinity of the floodplain reduce the river's margin and subsequently alter the magnitude of the river's flow. The encroachment of riverbeds leads to waterlogging and flooding in urban areas, thereby causing damage to property, human life, etc. It necessitates a comprehensive study of the floodplain and changes in its proximity such as encroachment of floodplains to carry out any further activities with certainty. This study employs a two-dimensional model to simulate the Yamuna River's (YR) hydrodynamic characteristics, focusing on India's Delhi region. New hydrological insights: Simulated flood flows are employed to evaluate floods of once in 10, 20, 25, and 30-year return periods using the flood frequency analysis for 1951–2013. The model validation results indicated that the model could mimic the flood depth in YR. Simulation results revealed that the floodplain's encroachment had increased the severity of the floods. The increase in the extremeness of flooding events, i.e., from once in a 10-year return period to a 30-year return period event, is expected to increase the areas at risk of floods by 12 %. The model also offers a potential platform for evaluating other alternatives, such as further encroachment, for a business-as-usual scenario or for restoring the Yamuna floodplains. With such a comprehensive perspective, floodplains' role enhances river basin resilience to climate and anthropogenic changes and increases flood safety.

Estimating river flow is a key parameter for effective water resource management, flood risk prevention, and hydroelectric facilities planning. Yet, traditional gauging methods are not reliable under very high flows or extreme events. Hydrometric network stations are often sparse, and their spatial distribution is not optimal. Therefore, many river sections cannot be monitored using traditional flow measurements and observations. In the last few decades, satellite sensors have been considered as complementary observation sources to traditional water level and flow measurements. This kind of approach has provided a way to maintain and expand the hydrometric observation network. Remote sensing data can be used to estimate flow from rating curves that relate instantaneous flow (Q) to channel cross-section geometry (effective width or depth of the water surface). Yet, remote sensing has limitations, notably its dependence on rating curves. Due to their empirical nature, rating curves are limited to specific river sections (reaches) and cannot be applied to other watercourses. Recently, deep-learning techniques have been successfully applied to hydrology. The primary goal of this study is to develop a deep-learning approach for estimating river flow in the Boreal Shield ecozone of Eastern Canada using RADARSAT-1 and -2 imagery and convolutional neural networks (CNN). Data from 39 hydrographic sites in this region were used in modeling. A new CNN architecture was developed to provide a straightforward estimation of the instantaneous river flow rate. Our results yielded a coefficient of determination (R2) and a Nash–Sutcliffe value of 0.91 and a root mean square error of 33 m3/s. Notably, the model performs exceptionally well for rivers wider than 40 m, reflecting its capability to adapt to varied hydrological contexts. These results underscore the potential of integrating advanced satellite imagery with deep learning to enhance hydrological monitoring across vast and remote areas. [ABSTRACT FROM AUTHOR]

Recently, heavy rains caused by climate change have resulted in dam failures due to overtopping. This study presents a design method aiming to prevent overtopping failures by applying gabion retaining walls at the dam crest. Simulations, experiments, and measurements were conducted to evaluate the effectiveness of this design. The design framework aims to establish a system in which gabion retaining walls prevent overtopping when water levels exceed the crest of the dam, efficiently draining seepage water into the dam body through vertical filters. Research findings indicate that implementing dam crest core and geomembrane design effectively prevents seepage and saturation of the downstream slope during overtopping events. Notably, the reservoir dam operates in a stable manner, as seepage water passing through the dam body is directed solely to the toe drain. Overall, this design approach suggests its potential as a practical solution by significantly reducing hazards resulting from heavy rainfall. [ABSTRACT FROM AUTHOR]

Synthetic aperture radars (SARs) have enabled allweather sensing of land surfaces, which has significantly benefitted surface hydrology. In this article, we explore various hydrological applications enabled by Indian Space Research Organisation’s (ISRO’s) recently launched Earth observation satellite-04 (EOS-04) C-band SAR satellite. We present the preliminary results of EOS-04 driven flood inundation mapping, monitoring of water bodies and study of river dynamics. Novel application of EOS-04 SAR data in estimation of sedimentation rate is presented for Jayakwadi reservoir, Maharashtra, India. Additionally, this article explores the integrated use of SAR and altimeter data for estimation of lake and reservoir water level and volume. We have used multidate observations from EOS-04 SAR and Sentinel-3A/ 3B altimeters to derive river width and estimate area– water level hypsometric curves for reservoirs. Long-term and near real-time availability of EOS-04 data can provide an indispensable tool for monitoring water bodies and extreme events like floods, at regional scales. [ABSTRACT FROM AUTHOR]

Wetlands of the Laurentian Great Lakes of North America (i.e. lakes Superior, Michigan, Huron, Erie, and Ontario) provide critical habitat for marsh birds. We used 11 years (2011–2021) of data collected by the Great Lakes Coastal Wetland Monitoring Program at 1,962 point-count locations in 792 wetlands to quantify the first-ever annual abundance indices and trends of 18 marsh-breeding bird species in coastal wetlands throughout the entire Great Lakes. Nine species (50%) increased by 8–37% per year across all of the Great Lakes combined, whereas none decreased. Twelve species (67%) increased by 5–50% per year in at least 1 of the 5 Great Lakes, whereas only 3 species (17%) decreased by 2–10% per year in at least 1 of the lakes. There were more positive trends among lakes and species (n  = 34, 48%) than negative trends (n  = 5, 7%). These large increases are welcomed because most of the species are of conservation concern in the Great Lakes. Trends were likely caused by long-term, cyclical fluctuations in Great Lakes water levels. Lake levels increased over most of the study, which inundated vegetation and increased open water-vegetation interspersion and open water extent, all of which are known to positively influence abundance of most of the increasing species and negatively influence abundance of all of the decreasing species. Coastal wetlands may be more important for marsh birds than once thought if they provide high-lake-level-induced population pulses for species of conservation concern. Coastal wetland protection and restoration are of utmost importance to safeguard this process. Future climate projections show increases in lake levels over the coming decades, which will cause "coastal squeeze" of many wetlands if they are unable to migrate landward fast enough to keep pace. If this happens, less habitat will be available to support periodic pulses in marsh bird abundance, which appear to be important for regional population dynamics. Actions that allow landward migration of coastal wetlands during increasing lake levels by removing or preventing barriers to movement, such as shoreline hardening, will be useful for maintaining marsh bird breeding habitat in the Great Lakes. [ABSTRACT FROM AUTHOR]

Floods occur when water levels exceed normal limits, causing rivers to overflow and inundate low-lying areas. Early warning systems for flood disasters are crucial to mitigate the damage caused, such as loss of life and property. A flood classification system can be developed by utilizing water level data from the Department of Water Resources to predict the likelihood of flooding using the K-Nearest Neighbors (KNN) algorithm. This study aims to determine the flood status classification based on water levels using the KNN method in the Ciliwung River. The research data were obtained from the DKI Jakarta open data site, consisting of 564 samples. The study evaluated K values ranging from 1 to 10. The average accuracy across all K scenarios was 99%, with the best K value being 1, which provided 100% accuracy, sensitivity, and specificity. These results indicate that the KNN method is effective in classifying flood status based on water level data, making it a reliable tool for early warning systems. This system is expected to help reduce the negative impacts of floods by providing accurate and timely information to the public and authorities. This research makes a significant contribution to the development of disaster mitigation technology, particularly in flood risk management in urban areas. [ABSTRACT FROM AUTHOR]