High Water Levels Research Articles

Plant growth and physiological responses of the invasive plant Acmella radicans to contrasting light and soil water conditions

Acmella radicans (Jacquin) R.K. Jansen is a new invasive species record for Yunnan Province, China, as of 2017 and little is known about its invasion mechanisms. To better understand its invasive strategies, we investigated the growth, physiological and soil nutrient use parameters of the invader under combined conditions of light (25%, 50%, 75%, and 100% of light availability) and soil water content (full, high, medium, and low soil water content) in the glasshouse. The results showed that light level, soil water content and their interaction had a significant effect on all plant morphological, physiological and soil nutrient parameters for A. radicans (P < 0.05). For the most part, plant height, total branch length, leafstalk length, leaf area, inflorescence number, seed number, leaf biomass, stem biomass, aboveground biomass, and total biomass of A. radicans were significantly increased with increased shading rate and soil water content and were generally greater at intermediate light levels and intermediate to high soil water levels. Under high-irradiance conditions, the chlorophyll content was greatly reduced compared to other treatments. We also observed that leafstalk length, leaf area, Pn, chlorophyll a, and chlorophyll b of A. radicans were markedly increased with increased shading rate and soil water content, and were generally highest under intermediate irradiance and soil water conditions, but many plant physiological parameters also exhibited relatively high values under waterlogging conditions. The concentrations of organic matter, available N and available K of A. radicans soils at high-irradiance and full-high soil water content treatments and medium-irradiance and high-medium soil water content treatments were often less than those of other treatments indicated that hypothetically soil absorption was increased by moderate shading and high soil water. This was the first study demonstrating that A. radicans thrives best under moderate light conditions in combination with high soil water. Furthermore, we surmised that its higher phenotypic and physiological plasticity contributes to its invasion success.

Potential Use of Portulaca Plant Species in Removing Estradiol Hormone Pollutants in the Surface Water of Bengawan Solo River

Bengawan Solo River water is a source of drinking water and raw materials for the government of Surakarta city, but the water has been mixed with domestic, industrial, and agricultural wastes. The waste contains estradiol-17 derived from urine and feces, both from livestock and humans as well as industries around the sub-watershed Bengawan Solo River. The content of estradiol-17 in the Bengawan Solo sub-watershed is quite high. This study is the first conducted in Bengawan Solo River to look at natural estrogens that are very rarely studied in the environment, which are likely could cause several health effects in humans and wildlife due to their relatively strong estrogenic potential and high levels in wastewater and river water. Therefore, research on the elimination of these compounds using effective, energy-efficient, and low-maintenance technologies for water treatment such as phytoremediation is highly expected. The purposes of this study were to identify estradiol, to measure the estradiol levels through HPLC tests as well as to test the effectiveness of phytoremediation with Portulaca plant as biological agents. The results show that the water of Bengawan Solo River contained estradiol substances ranging from 3.88 ppm to 5.76 ppm. The Portulaca plant species was effective at eliminating estrogenic waste up to 99.89%.

The Timing, Magnitude, and Relative Composition of Extreme Total Water Levels Vary Seasonally Along the U.S. Atlantic Coast

AbstractThe annual maximum (AM) method, which subsamples time series to retain the maximum event per year, and the peak‐over‐threshold (POT) method, which extracts values exceeding a threshold to define extremes, have long histories in determining flood event frequency. In practice, extreme value distributions applied to AM and POT events often assume that the data comes from the same statistical population. Locations across the world, like the United States (U.S.) Atlantic coastline, however, experience high coastal water levels driven by various individual processes and storms with different driving mechanisms during different seasons. This research investigates when extreme total water levels (TWLs) occur during the year along the U.S. Atlantic coast and whether individual components, like waves, tides, and storm surge, contributing to TWLs vary across regions and during the year. From 1980 to 2020, extreme TWLs occurred during the extratropical and tropical seasons, with the relative proportion of extreme TWLs occurring during the extratropical season increasing northward. Still water levels drive spatial variability in extreme TWL magnitude, while wave climate drives differences in extreme TWL magnitude between extratropical and tropical seasons. Month‐to‐month variability in the composition of extreme TWLs varies more than spatial variability, highlighting the importance of understanding the components driving extremes at different times of the year. Variations across storm seasons in the processes contributing to extreme TWLs may have implications for how large‐scale changes to the climate impact hazards along open sandy coastlines and influence the robustness of extrapolating rare events from models fit to a single population.

GSM Based Canal Gate and Flood Monitoring and Control System

Technology based modern Irrigation systems are the recent requirement in every part of world today. The irrigation water is being used for agricultural, industrial and domestic purposes. Owing to mismanagement and inequitable distribution of water, it is necessary to have a fool-proof system where water is supplied to the end-users judiciously. Due to the variable atmospheric circumstances these conditions sometimes may vary from place to place, which makes very difficult to operate the canal gate manually and instantly. Therefore, we proposed. The “GSM based Canal gate and Flood monitoring and control system’’ canal gates are monitored and controlled after sensing the water level and flow speed. In our project the water level and flow will be calculated automatically and send towards the operator through SMS and the Canal Gate is operated according to the collected data, this system also Announce flood in case of high level of water and speed of water in River or canals by the help of Alarming as well as lightning tower with green, yellow and red lights indication of normal, intermediate and flood conditions respectively.

Application of Methodology for Defining the Adjoining Lands and Flooding River Strips in Bulgaria in a GIS Environment Upon Bridge Facility Designing

The "Methodology for defining the adjoining lands and flooding rivers stripes in Bulgaria” (MALFARS) has been developed in 2012 based on the requirements of the EU Water Framework Directive 2000/60. In this article we investigated another aspect of MALFARS application. We have reviewed the possibilities for methodology application upon designing of bridge facilities within the researched river section. The study presents the application of the Methodology for the designing of a bridge facility over the Dzherman River upon the maximum dimensional water quantities calculated for the studied section of the river. We have used GIS instruments to analyze and evaluate the results obtained for the parameters of the maximum runoff and the conductivity of the river section. The results present the defined flooding zones of the adjoining river areas/stripes, good conductivity of the road facility, hydraulic and geometric characteristics of the runoff in the investigated river section in cases of high water levels

Seismic Vulnerability Assessment of Non-Overflow Concrete Gravity Dam Section

Abstract As the economy advances, there is a growing need for energy. Harnessing hydroelectric energy has emerged as a crucial strategy for addressing the energy crisis. To promote hydropower development, numerous tall concrete dams are either under construction or in the planning stages. These dams are frequently situated in areas with elevated water levels and face the challenge of being located in regions with high seismic intensity. An accident resulting from the failure of concrete dams can pose significant threats to both economic development and public safety. Therefore, it is of crucial importance to investigate the stability of such dams, particularly under conditions of high water levels and seismic activity. The finite element program Geostudio is being undertaken to evaluate a complete numerical analysis of the dam. In this paper, Longtan Dam has been selected as the case study for the research. This study is applied in two cases: seismic and static conditions. A two-dimensional seismic numerical analysis was carried out using the Koyna earthquake acceleration time records in 1976. The results of the FEM model concluded that the dam is unsafe under sesmic conditions. The concrete-rock interface, particularly susceptible to sliding, was identified as the most critical part of the dam, presenting the most likely failure mode. The study highlights the significance of its methodology in investigating earthquake effects on dams, prioritizing it over the achieved results. The aim is for the conclusions and recommendations to provide valuable guidance for future studies and initiatives focused on enhancing safety at hydropower dams.

Adjoint-based sensitivity analysis and assimilation of multi-source data for the inference of spatio-temporal parameters in a 2D urban flood hydraulic model

This contribution presents a novel approach for the calibration of distributed parameters in a 2D urban flood hydraulic model. It focuses on the challenging issue of inferring distributed friction parameters from multi-source heterogeneous spatio-temporal observations of their hydraulic signatures in the context of an urban flash flood in a complex street network. A variational data assimilation algorithm is used to infer high-dimensional multi-variate parameters (spatialized friction and inflow discharge time series) using multi-source observations. This method relies on a differentiable 2D shallow water hydraulic model which enables to generate high-resolution sensitivity maps of local gradients and Derivative-based Global Sensitivity Measures (DGSM), enabling to guide adequate definition of parameter spatialization for the data assimilation process. Assimilated data include real local limnigraphic measurements and high-water marks collected after a major flood event, as well as modeled flow velocity used in twin experiments setups. This study is the first to leverage high-water marks with a variational method for the calibration of distributed parameters in an urban flood model. The multi-source data is used to infer inflow hydrographs and distributed friction parameters in setups of varying complexity. In the main setup, the complex structure of the street network, along with the sensitivity maps and hydraulics expertise led to define a model configuration with 45 friction patches. A high-dimensional parameter vector composed of these friction values and an upstream inflow is inferred simultaneously by assimilating real limnigraphic data and high-water marks. This leads to an increase in model fit to observations and satisfying parameter estimates.

Flood risk assessment of a small river with limited available data

Flood risk modeling of small watercourses is challenging when only limited input data are available. Therefore, this study assessed the flood characteristics of a small river (Tarna River: entire watershed-C, upper-VS, middle-TMS, and lower section-TOS) from 1990 to 2019. The assessment focused on modeling, model calibration, and validation using feature event-based time-series data in data-scarce environments. We showed that since the 2000s, the number of high-water levels above 250 cm, and the frequency of three flood types had increased. Flood simulation results showed the largest flooded area in the TMS section, followed by the VS, and then the TOS. The outcomes from the VS, TMS, and TOS sections did not exhibit superior performance compared to the C area. Models performed well for larger flood events, with Kling Gupta Efficiency corresponding well to NRMSE and Nash-Sutcliffe efficiency metrics. Accordingly, flood events characterized by the longest duration and high-water levels yielded outstanding results across all areas, followed by moderate flood events with good accuracy. Normal water level events exhibited significant deviations from the reference across all sections. In summary, despite the event-based modeling challenges in data-limited environments, such models can still mitigate potential flood events and improve decision-making processes.

Emigration of Juvenile Tarpon Megalops atlanticus from Ephemerally Connected Coastal Ponds

Worldwide, coastal wetlands are threatened by disrupted hydrology, urbanization, and sea-level rise. In southwest Florida, coastal wetlands include tidal creeks and coastal ponds, which are the primary habitats used by juvenile Tarpon, Megalops atlanticus, an important sport fish. Coastal ponds can occur near uplands and are ephemerally connected to the open estuary, creating conditions of variable dissolved oxygen and salinity. Juveniles can tolerate wide-ranging abiotic conditions, but little is known about how they egress from their remote nursery habitats, which often requires them to cross > 1 km of mangrove forest to reach the open estuary. The objective of this study was to (1) compare Tarpon body condition among ponds close to the open estuary versus those ponds farther away on the Cape Haze peninsula of Charlotte Harbor, Florida, and (2) using acoustic telemetry determine what factors contribute to Tarpon emigration from the ponds to open estuarine waters. We tested the hypothesis that distinct groups of Tarpon occur in isolated ponds, leading to variation in fish length and body condition, and that opportunities for emigration from these ponds hinge on high water events. No pond stood out as having Tarpon of low body condition. Factors contributing to increased probabilities of Tarpon emigration were low barometric pressure, high-water level, and Tarpon body length. Tarpon emigrated from ponds near tidal creeks during summer king tides, while tropical cyclone conditions were needed to allow for movement from ponds farther in the landscape. The juvenile Tarpon were later detected at the mouths of large rivers 30 km up-estuary. The characterizations of water levels and event criteria needed for successful Tarpon nurseries should aid in habitat conservation and the creation of Tarpon nursery habitat in restoration designs.

Growth conditions of tree species relative to climate change and sea level rise in low-lying Mid Atlantic coastal forests

IntroductionCoastal forests occupy low-lying elevations, typically adjacent to tidal salt marshes. Exposed to increased flooding with sea level rise, coastal forests have retreated as salt marshes advance upslope. Coastal forests likely currently experience periodic tidal flooding, but whether they temporarily accommodate or quickly succumb to rising sea level under changing climatic conditions remains a complex question. Disentangling how tidal flooding and climate affect tree growth is important for gauging which coastal forests are most at risk of loss with increasing sea levels.MethodsHere, dendrochronology was used to study tree growth relative to climate variables and tidal flooding. Specifically, gradients in environmental conditions were compared to species-specific (Pinus taeda, Pinus rigida, Ilex opaca) growth in coastal forests of two estuaries (Delaware and Barnegat Bays). Gradient boosted linear regression, a machine learning approach, was used to investigate tree growth responses across gradients in temperature, precipitation, and tidal water levels. Whether tree ring widths increased or decreased with changes in each parameter was compared to predictions for seasonal climate and mean high water level to identify potential vulnerabilities.ResultsThese comparisons suggested that climate change as well as increased flood frequency will have mixed, and often non-linear, effects on coastal forests. Variation in responses was observed across sites and within species, supporting that site-specific conditions have a strong influence on coastal forest response to environmental change.DiscussionSite- and species-specific factors will be important considerations for managing coastal forests given increasing tidal flood frequencies and climatic changes.

Time‐varying copula‐based compound flood risk assessment of extreme rainfall and high water level under a non‐stationary environment

AbstractQuantifying flood risk depends on accurate probability estimation, which is challenging due to non‐stationarity and the combined effects of multiple factors in a changing environment. The threat of compound flood risks may spread from coastal areas to inland basins, which have received less attention. In this study, a framework based on time‐varying copulas was introduced for the treatment of compound flood risk and bivariate design in non‐stationary environments. Archimedean copulas were developed to diagnose the non‐stationary trends of flood risk. Return periods, average annual reliabilities, and bivariate designs were estimated. Model uncertainty was analyzed by comparing the results for stationary and non‐stationary conditions. The case study investigated the extreme rainfall and water level series from the Qinhuai River Basin and the Yangtze River in China. The results showed that marginal distributions and correlations are non‐stationary in all bivariate combinations. Ignoring composite effects may lead to inappropriate quantification of flood risk. Excluding non‐stationarity may lead to risk over or underestimation. It showed the limitations of the 1‐day scale and quantified the uncertainty of non‐stationary models. This study provided a flood risk assessment framework in a changing environment and a risk‐based design technique, which is essential for climate change adaptation and water management.

Fixed-time fractional-order sliding mode controller with disturbance observer for U-tube steam generator

Water level control of a U-tube steam generator plays a critical and challenging role in ensuring safe and reliable operation for a pressurized-water reactor-type nuclear power plant, as both excessively high and low water level in the U-tube steam generator would affect the normal operation of the nuclear power plant and even lead to unplanned shutdown events. This work presents a fixed-time fractional-order sliding mode water level controller coupled with a fixed-time disturbance observer, aiming to further improve the water level control performance under full operating conditions, considering uncertainties and actuator saturation. The proposed scheme is formulatedbased on the popular Irving water level model with uncertainties and the pre-existing actual water level control framework, while at the same time incorporating the benefits of the state-of-the-art anti-windup techniques, disturbance observer-based feedforward compensation techniques, fractional-order calculus, sliding mode control techniques, and fixed-time stability theorem, such that the transient water level response and steady-state control accuracy can be further enhanced even in the presence of uncertainties and actuator saturation phenomenon. It is theoretically proved that under the proposed scheme, both the estimation error of the uncertainties and the water level control error can be driven to zero within a fixed time regardless of their initial values by Lyapunov’s theorem. Meanwhile, simulation studies, involving the comparisons with a real-world adopted water level controller and a pre-existing integer-order sliding mode water level controller coupled with an uncertainty estimator, reveal the feasibility and superiority of the proposed approach.

Impact of dye treatment as management strategy on available light may favour a highly invasive alien aquatic plant

Attempts to control massive proliferations of invasive alien aquatic plants (IAAP) are often ineffective. A renewed interest for dye treatment is emerging aiming to control the submerged macrophyte Myriophyllum heterophyllum in France. We aimed to understand the effects of dye on this plant knowing about its adaptation to low light. In a 2 × 2 factorial design experiment, we assessed the effect of a dye mixture based on Brilliant Blue (E133) and Allura Red (E129) at high and low light intensities on light quantity and quality and how this might affect the plant’s performance by measuring morphological and physiological traits. A multivariate analysis identified three groups – high light (HL) plants, plants in high light with dye (HLD) or low light (LL), and low light with dye (LLD) plants. HL plants performed well but showed stress signs, with a reduced main shoot length, a higher leaf dry matter content (LDMC) and a lower nitrogen content (N), not observed in plants grown in HLD or LL. HLD and LL plants exhibited a comparable total length growth rate to those in HL, but had lower LDMC and higher N contents. LLD plants performed poorly with the lowest growth and signs of physiological stress. Dye-induced changes in light quality only marginally affected the absorbance range of chlorophyll b, which apparently did not affect photosynthesis. Commercially available dyes currently used to control nuisance aquatic plants thus seem to have little or no effect on submerged macrophytes. The presence of dye may exacerbate negative effects of very low light intensities on the plant’s growth. However, these very low intensities would only be reached during high-water levels or in winter, periods where the dye would rapidly be diluted. During summer, however, the application of dye may protect the plant from damaging light intensities and thus not be a good management strategy to control low light adapted invasive submerged macrophytes.

An Enhanced SL-YOLOv8-Based Lightweight Remote Sensing Detection Algorithm for Identifying Broken Strands in Transmission Lines

Power transmission lines frequently face threats from lightning strikes, severe storms, and chemical corrosion, which can lead to damage in steel–aluminum-stranded wires, thereby seriously affecting the stability of the power system. Currently, manual inspections are relatively inefficient and high risk, while drone inspections are often limited by complex environments and obstacles. Existing detection algorithms still face difficulties in identifying broken strands. To address these issues, this paper proposes a new method called SL-YOLOv8. This method incorporates an improved You Only Look Once version 8 (YOLOv8) algorithm, specifically designed for online intelligent inspection robots to detect broken strands in transmission lines. Transmission lines are susceptible to lightning strikes, storms, and chemical corrosion, which is leading to the potential failure of steel- and aluminum-stranded lines, and significantly impacting the stability of the power system. Currently, manual inspections come with relatively low efficiency and high risk, and Unmanned Aerial Vehicle (UAV) inspections are hindered by complex situations and obstacles, with current algorithms making it difficult to detect the broken strand lines. This paper proposes SL-YOLOv8, which is a broken transmission line strand detection method for an online intelligent inspection robot combined with an improved You Only Look Once version 8 (YOLOv8). By incorporating the Squeeze-and-Excitation Network version 2 (SENet_v2) into the feature fusion network, the method effectively enhances adaptive feature representation by focusing on and amplifying key information, thereby improving the network’s capability to detect small objects. Additionally, the introduction of the LSKblockAttention module, which combines Large Selective Kernels (LSKs) and the attention mechanism, allows the model to dynamically select and enhance critical features, significantly enhancing detection accuracy and robustness while maintaining model precision. Compared with the original YOLOv8 algorithm, SL-YOLOv8 demonstrates improved precision recognition accuracy in Break-ID-1632 and cable damage datasets. The precision is increased by 3.9% and 2.7%, and the recall is increased by 12.2% and 2.3%, respectively, for the two datasets. The mean average precision (mAP) at the Intersection over Union (IoU) threshold of 0.5 is also increased by 4.9% and 1.2%, showing the SL-YOLOv8’s effectiveness in accurately identifying small objects in complex situations.

The effect of redox fluctuation on carbon mineralization in riparian soil: An analysis of the hotspot zone of reactive oxygen species production

Riparian zones are important depositional environments at the catchment scale and provide environmental services such as carbon sequestration. This zone is a highly dynamic interface for oxygen and electron exchange, which confers the basis for reactive oxygen species (ROS) production. However, the differences in soil ROS production and their impact on carbon turnover across various redox locations within the riparian zone remain to be fully elucidated. In this study, we investigated the distribution characteristics and generation mechanism of ROS in riparian soil based on soil samples collected in a three-month field monitoring experiment, with additional incubation experiments conducted to examine the effect of hydroxyl radical (•OH) on soil organic carbon (SOC) mineralization. The obtained results demonstrated that the riverine wetland was the hotspot zone for •OH production, with the production flux of 13.05 μmol kg−1 d−1, which was significantly higher than that in floodplain (7.29 μmol kg−1 d−1) and riverbank soils (8.61 μmol kg−1 d−1). Moreover, •OH levels displayed distinct rhythmic fluctuations, with significantly higher concentrations at low water levels compared to those at high water levels, and remained essentially flat over three cycles. The statistic analysis revealed that the ROS production was highly dependent on reduced species and microbial community structure, which function as biogeochemical batteries and electron shuttles under redox fluctuations. Furthermore, the generated •OH involved in the abiotic mineralization of SOC, contributing to 13.1‒21.8 % of total CO2 efflux. Compared to particulate organic carbon (POC), mineral-associated organic carbon (MAOC) fractions of SOC were more susceptible to •OH attacks. The findings provide a novel insight to comprehensively assess the redox process on riparian carbon turnover.

Quantifying risk contagion of fluvial flood disaster chain

ABSTRACT The fluvial flood disaster (FFD) poses a great threat to human civilisation. The high water level can cause overtopping, piping, and even breach of the levees, which, in turn, further results in urban flood. These secondary disasters form a disaster chain with multipaths that amplify the risk of the fluvial flood. This work develops a stochastic multi-path network method to quantify the risk contagion for the FFD chain. First, the multi-path network structure for the chain was identified, characterised by reflecting the uncertainties of levee failure modes. Second, a model for the evolutionary probabilities of the chain was proposed by combining the directed network technique with the network structure. Finally, the systematic risk of the chain was quantified by considering the consequence of the disasters. A risk contagion factor was proposed to quantify the attenuation or amplification effect of the risk during the evolution of the chain. A case study in Huizhou, China, shows that the FFD chain is characterised by “small probability” but “extremely high risk”. The most likely path is overtopping-urban flooding, indicating the priority of the area is to heighten the levees. This work paves the way for a more entrepreneurial future on disaster prevention and mitigation.

Is the ordinary high water mark ordinarily at bankfull? Applying a weight‐of‐evidence approach to stream delineation

AbstractThe ordinary high water mark (OHWM) is a regulatory boundary essential to identifying the lateral jurisdictional limits of rivers and streams in the United States (U.S.). Bankfull is a scientific concept that has been defined and identified in a multitude of ways by scientists. Geomorphologist and hydrologist have long recognized that there can be variability in the identification of bankfull depending on how bankfull is defined. Furthermore, this variability is only increased by the inherent variability in stream characteristics that occurs along a reach of channel. Because of the overlap in the regulatory definition of OHWM and the scientific definitions of bankfull, one of the primary purposes of the study is to apply the definition of OHWM and compare it to bankfull in a variety of channel types in different climatic, hydrologic, and geologic settings. Our results show that there is a clear overlap between the identification of the OHWM and bankfull elevations. Regulatory practitioners are generally not specialized in fluvial geomorphology and yet are tasked with consistently and accurately identifying the OHWM in a variety of stream types throughout the U.S. Therefore, we also present how to apply a weight‐of‐evidence approach through a clear step‐by‐step process to potentially improve consistency and accuracy in identification of OHWM and bankfull by both scientists and non‐scientists.

Factors influencing fish migration in one of the world's largest inland fisheries

Fish from Cambodia's Tonle Sap Lake are economically, culturally, and nutritionally significant for people in the Lower Mekong Basin, providing income, livelihoods, and protein. Fish in this system generally migrate toward upstream Mekong River in dry season and return in early wet season. However, drivers of fish migration from Tonle Sap Lake to the Mekong River are not well-understood. In this paper, we utilized Mixed Effects Random Forest to predict the catch weight of six fish species migrating from the Tonle Sap Lake to the Mekong River using precipitation, lunar cycle, and hydrologic conditions like river stage, streamflow, flow magnitude, and timing as predictors. As a surrogate for fish migration, we used daily fish catch weight from 2002 through 2008 at the bagnet, or Dai, fisheries along Tonle Sap River, a migration corridor connecting Tonle Sap Lake to the Mekong River. We found that migration of large fish was mainly cued by streamflow and flow magnitude, while smaller fish migrate depending on the combination of streamflow and flow timing. Streamflow less than average cumulative flow was the most important driver for migration of Pangasianodon hypophthalmus, and Cirrhinus microlepis. Migration of Cyclocheilichthys enoplos and Osteochilus melanopleurus was highly dependent on the number of low- and minimum-flow days. Cumulative flows, period of high flow and water level were the main predictors of the small mud-carp Henicorhynchus entmema's migration, while individuals of Labiobarbus leptocheilus migrated out of the Tonle Sap Lake depending on the number of days after 7-, 30-, and 90-day minimum flows. These results suggest that flow characteristics can be used to aid conservation and adaptive management of Cambodia's Dai fisheries.

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

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

Predicting flood stages in watersheds with different scales using hourly rainfall dataset: A high-volume rainfall features empowered machine learning approach

Accurate prediction of instantaneous high lake water levels and flood flows (flood stages) from micro-catchments to big river basins are critical for flood forecasting. Lake Carl Blackwell, a small-watershed reservoir in the south-central USA, served as a primary case study due to its rich historical dataset. Bearing knowledge that both current and previous rainfall contributes to the reservoirs' water body, a series of hourly rainfall features were created to maximize predicting power, which include total rainfall amounts in the current hour, the past 2 h, 3 h, …, 600 h in addition to previous-day lake levels. Notedly, the rainfall features are the accumulated rainfall amounts from present to previous hours rather than the rainfall amount in any specific hour. Random Forest Regression (RFR) was used to score the features' importance and predict the flood stages along with Neural Network – Multi-layer Perceptron Regression (NN-MLP), Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), and the ordinary multi-variant linear regression (MLR) together with dimension reduced linear models of Principal Component Regression (PCR) and Partial Least Square Regression (PLSR). The prediction accuracy for the lake flood stages can be as high as 0.95 in R2, 0.11 ft. in mean absolute error (MAE), and 0.21 ft. in root mean square error (RMSE) for the testing dataset by the RFR (NN-MLP performed equally well), with small accuracy decreases by the other two non-linear algorithms of XGBoost and SVR. The linear regressions with dimension reductions had the lowest accuracy. Furthermore, our approach demonstrated high accuracy and broad applicability for surface runoff and streamflow predictions across three different-sized watersheds from micro-catchment to big river basins in the region, with increases of predicting power from earlier rainfall for larger watersheds and vice versa.