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【Objective】 The weir-flume apparatus, consisting of a measuring flume and a measuring weir, is an innovative device for monitoring and controlling open channel flow. Installed across channels, it can measure water flow, elevates upstream water level, and enhances ecological connectivity in rivers in diverse geographical conditions. While measurement formulas and hydraulic characteristics have been experimentally investigated for small flow with shallow water depth, there is a lack of study for large flow. This paper aims to bridge this gap by studying the vertical and longitudinal velocity profiles as well as spatial distribution of flow field under high flow conditions. 【Method】 The analysis was based on numerical modelling. The FLOW-3D software was used to simulate hydraulic performance of a weir-flume device under 17 flow rates ranging from 10.13 to 200 L/s. From the simulated results we analyzed the impact of flow rate on water depth, Froude number (Fr), and velocities within both the in-groove flow and the weir flow. 【Result】 As the flow rate increased, the Fr and velocity in the central longitudinal section of the device initially increased and then tapered off. The flow in the upstream of the device remained stable, with Fr ranging from 0.1 to 0.3 and velocities varying between 0.08 and 0.5 m/s. Both Fr and velocity gradually increased with the increase in flow rate. After entering the device, Fr and flow velocity exhibited a significant surge. A wide range of thin water layers emerged in the downstream of the device, with the maximum Fr and velocities being 3 to 4.6 and 1.05 to 2.06 m/s, respectively. A subdued water jump was observed downstream of the in-groove flow, while the downstream of the weir showed a jet stream with Fr>1. Additionally, diffused water generated a rhomboid wave at the side bank. We derived a formula from the numerical simulations; its maximum relative error was 6.11% for flow rate below 100 L/s, beyond which the relative error gradually increased. 【Conclusion】 The numerical results obtained from FLOW-3D agreed well with the test data, validating the accuracy and consistency of the derived formula for analyzing measurement data from the weir-flume device. This work bridges a critical knowledge gap, enabling more precise assessment and control of open channel flow under various conditions.

【Background and Objective】 Automation of water measurement is a crucial to developing water-saving agriculture and enhancing irrigation water utilization efficiency. Although water measurement technologies for large canals have reached a mature stage, there is a significant technology gap in the context of small channels. The objective of this paper is to address this gap by developing an automatic flow measurement system specifically designed for small rectangular channels. 【Method】 The proposed automatic flow measurement system for small rectangular channels utilizes an innovative design featuring an airfoil-shaped sluice gate. The system comprises an airfoil-shaped measuring flume and a flat gate, with the core functionality implemented using the STC89C51RC microcontroller. The system incorporates essential components such as flow measurement facilities, data processing and communication facilities, as well as power facilities. To assess its performance, extensive hydraulic performance tests were conducted, analyzing the system's behavior under various flow rates and gate openings. 【Result】 The threshold for determining the outflow of the airfoil gate pier was between 0.77 and 0.82. The flow formulae for gate outflow and weir flow were established based on the submerge of the gate at different gate openings. Based on water level and gate opening measured using the ultrasonic sensing technology, an automatic control and communication system was developed using the STC89C51RC microcontroller. The real-time outlet flow was collected by a built-in flow calculation module, and the system was powered by a photovoltaic facility. 【Conclusion】 The proposed automatic flow measurement system using airfoil gate pier is accurate. It has potential for automating flow measurement in small rectangular channels in irrigation districts.

【Objective】 Flow measurement through sluice gates in U-Shaped channels depends on a range of factors. We experimentally measured the characteristics of flow through the gates under different designs and working conditions, and then derived a formula using these data to estimate flow rates based on hydraulic characteristics of the gate and channel, as well as the working conditions. 【Method】 The experiments were conducted by controlling the flow rates from 10 to 50 L/s. The critical ratio of orifice flow to weir flow, as well as the flow capacity were analyzed utilizing hydraulic parameters, including water surface line, Froude number, and water head loss across the gate. These parameters were used in the orifice flow formula to calculate the flow rate. 【Result】 The critical ratio of orifice flow to weir flow was close to 1, the Froude number was uniformly distributed along the flow path, and the average relative head loss across the gate was 7%. Compared to the measured data, the error of the derived formula was less than 3.5%. 【Conclusion】 The sluice gate for the U-Shaped channel works well, and water head loss across it is minor. The derived formula for calculating flow rate is accurate. These results are helpful for designing U-shaped channels in irrigation areas and calculating flow rates through them.

【Background】Most irrigation districts in northern China are flatten and water in their channels is muddy. Existing facilities for measuring water flow in channels could cause backward water flow in their upstream and are prone to sediment deposition. Horizontal plate is a new and simply structured facility to measure water flow and can alleviate sediment deposition. While the impact of the discharge and other factors such as plate deflection angle, water depth in both upstream and downstream, and plate profile on performance of the plate method has been well documented, how bed slope of a channel affects hydraulic characteristics of the plate remains poorly understood. 【Objective】The purpose of this paper is to plug this knowledge gap, systematically investigating the effects of bed slope on reliability and accuracy of the horizontal plate for measuring water flow. 【Method】U-shaped channel commonly used in irrigation areas in northern China was taken to test the method, and the volume of the horizontal plate with optimal contraction ratio of cross-section (0.439) was selected as the test object. With a total of 18 combinations, which included three bed slope ranging from 0.000 2 to 0.001 with each associated with 4~7 flow rates ranging from 10 to 44 L/s, variation of the water-surface line and the plate deflection angles in each combination were measured, from which we analyzed the impact of the bed slope on water-surface line, relative water head loss, energy conversion coefficient, plate deflection angle and comprehensive flow coefficient. Using the formula of sluice flooding discharge, a semi-empirical flow formula with the bed slope as independent variable was used to fit the data. 【Result】Under the same water flow rate, the hydraulic jump length behind the plate, the energy conversion coefficient and the comprehensive flow coefficient all increased as the bed slope became steeper; in contrast, the water-surface line, the relative water head loss and the plate deflection angle all decreased as the bed slope increased. For all tested flow rates, the average relative error of the fitting formula for water flow rate was 2.6%, with a maximum of 6.5%, meeting the requirement for water flow in irrigated areas. 【Conclusion】The bed slope of channel has a significant influence on accuracy of the horizontal plate for measuring water flow in U-shaped channels, and a formula considering the bed slope was proposed to calculate water flow in U-shaped channels.

The aboveground biomass (AGB) of summer maize is an important indicator for assessing crop growth status and predicting yield, playing a significant role in agricultural management and decision-making. Traditional on-site measurements of AGB are limited, due to low efficiency and a lack of spatial information. The development of unmanned aerial vehicle (UAV) technology in agriculture offers a rapid and cost-effective method for obtaining crop growth information, but currently, the prediction accuracy of summer maize AGB based on UAVs is limited. This study focuses on the entire growth period of summer maize. Multispectral images of six key growth stages of maize were captured using a DJI Phantom 4 Pro, and color indices and elevation data (DEM) were extracted from these growth stage images. Combining measured data such as summer maize AGB and plant height, which were collected on the ground, and based on the three machine learning algorithms of partial least squares regression (PLSR), random forest (RF), and long short-term memory (LSTM), an input feature analysis of PH was carried out, and a prediction model of summer maize AGB was constructed. The results show that: (1) using unmanned aerial vehicle spectral data (CIS) alone to predict the biomass of summer maize has relatively poor prediction accuracy. Among the three models, the LSTM (CIS) model has the best simulation effect, with a coefficient of determination (R2) ranging from 0.516 to 0.649. The R2 of the RF (CIS) model is 0.446–0.537. The R2 of the PLSR (CIS) model is 0.323–0.401. (2) After adding plant height (PH) data, the accuracy and stability of model prediction significantly improved. R2 increased by about 25%, and both RMSE and NRSME decreased by about 20%. Among the three prediction models, the LSTM (PH + CIS) model had the best performance, with R2 = 0.744, root mean square error (RSME) = 4.833 g, and normalized root mean square error (NRSME) = 0.107. Compared to using only color indices (CIS) as the model input, adding plant height (PH) significantly enhances the prediction effect of AGB (aboveground biomass) prediction in key growth periods of summer maize. This method can serve as a reference for the precise monitoring of crop biomass status through remote sensing with unmanned aerial vehicles. [ABSTRACT FROM AUTHOR]

N-polar GaN film was obtained by using a high-temperature AlN buffer layer. It was found that the polarity could be inverted by a thin low-temperature AlN interlayer with the same V/III ratio as that of the high-temperature AlN layer. Continuing to increase the V/III ratio of the low-temperature AlN interlayer, the Ga-polarity of GaN film was inverted to N-polarity again but the crystal quality and surface roughness of GaN film greatly deteriorated. Finally, we analyzed the chemical environment of the AlN layer by x-ray photoelectron spectroscopy (XPS), which provides a new direction for the control of GaN polarity. [ABSTRACT FROM AUTHOR]

To address the complexity and high computational burden in the design of drip irrigation networks, the Jaya algorithm is utilized to study factors affecting project costs, including equipment and pipeline depreciation and the operation and management costs of the irrigation area. A mathematical model of synchronization optimal design of pipe layout and pipe diameter selection in a drip irrigation network system with constraints on pipe diameter, flow velocity, and pipe pressure is established. Using an irrigation district in Xinjiang, China, as an example, the Jaya algorithm optimization design program was run independently 50 times, and the relative deviation of each optimization result from the optimal solution was calculated. The results show that the annual cost per unit area o is reduced to 635.99 RMB/hm2, a 25.34% reduction compared to the original engineering program, and the investment-saving effect is obvious. The relative deviation is controlled within 3%, which shows that the algorithm has stable convergence performance and can meet the requirements of actual engineering design. The Jaya algorithm eliminates the need for parameter tuning, and it excels in cost savings, algorithm stability, and computational accuracy, making it an effective method for the single-objective optimization design of drip irrigation networks. [ABSTRACT FROM AUTHOR]

Accurate estimation of soil moisture content (SMC) is vital for effective farmland water management and informed irrigation decision-making. The utilization of unmanned aerial vehicle (UAV)-based remote sensing technology to monitor SMC offers advantages such as mobility, high timeliness, and high spatial resolution, thereby compensating for the limitations of in-situ observations and satellite remote sensing. However, previous research has primarily focused on SMC diagnostics for the entire crop growth period, often neglecting the development of targeted soil moisture modeling paradigms that account for the specific characteristics of the canopy and root zone at different growth stages. Furthermore, the variations in soil moisture status between fields, resulting from the hysteresis of water flow in irrigation channels at different levels, may influence the development of soil moisture modeling schemes, an area that has been seldom explored. In this study, SMC models based on UAV spectral information were constructed using Random Forest (RF) and Particle Swarm Optimization-Support Vector Machine (PSO-SVM) algorithms. The soil moisture modeling paradigms (i.e., input–output mapping) under different growth stages and soil moisture conditions of summer maize were systematically compared and discussed, along with the corresponding physical interpretability. Our results showed that (1) the SMC modeling schemes differ significantly across the various growth stages, with distinct input–output mappings recommended for the early (i.e., jointing, tasselling, and silking stages), middle (i.e., blister and milk stages), and late (i.e., maturing stage) periods. (2) these machine learning-based models performed best at the jointing stage, while subsequently, their accuracy generally exhibited a downward trend as the maize grew. (3) the RF model demonstrates superior robustness in estimating soil moisture status across different fields (moisture conditions), achieving optimal estimation accuracy in fields with overall higher SMC in line with the PSO-SVM model. (4) unlike the RF model's robustness in spatial SMC diagnostics, the PSO-SVM model more reliably captured the temporal dynamics of SMC across different growth stages of summer maize. This study offers technical references for future modelers in UAV-based SMC modeling across various spatial and temporal conditions, addressing both the types of models as well as their input features. [ABSTRACT FROM AUTHOR]