Accurate and robust river flow measurements are essential under complex environmental conditions. In this study, an improvement of Deep Learning-based STIV combined with scene classification is proposed. We build datasets from real rivers and labels the Main Orientation of Texture (MOT) using scene classification and semiautomatic labeling. The STI classification model, built with EfficientNetV2 as the backbone, divides STIs into three classes, achieving an accuracy of over 97.6 % on the validation set and 91 % in generalization experiments. For detecting valid STIs, the MOT regression model employs Group Convolution and Convolutional Block Attention Module (CBAM), with a MAE of 0.49 degrees on the validation set. The velocities corresponding to uncertain, invalid and blind areas are corrected utilizing the distribution law of section velocity. The proposed method achieves a MRE of 3.90 % in general environments and 9.48 % in extreme environments, outperforming both Gradient Tensor and Fast Fourier Transform methods.

The image-based water flow measurement technology based on the Direct Linear Transformation (DLT) method relies on ground control points, and has problems such as low efficiency, high risk, and difficult operation in natural rivers with wide sections. A camera attitude angle calibration method based on Cross-Section Waterline Orientation (CSWO) is proposed. In this method, the calibration process is divided into two steps: laboratory calibration and field calibration, and the latter is divided into camera positioning and orientation. In the orientation step, the optical axis is required to be aligned with the cross-section when the camera is installed, so that the azimuth angle is set to zero. The roll angle is calculated by the slope of the straight waterline marked manually in the undistorted image. Then the sub-pixel image coordinate of the intersection point between the waterline and the vertical axis of the image is calculated. Finally, the pitch angle is calculated according to the perspective projection imaging model combined with the object coordinates obtained by the water level and the interpolated elevation of cross-section. This method has been applied to Space-Time Image Velocimetry (STIV) to measure the image-free surface velocity of a river with a width of 200 m. The results show that the maximum absolute error of starting distance is 0.59 m, the maximum relative error is 0.45%, and the maximum relative error of surface velocity is less than 6.3%. © 2024 Science Press. All rights reserved.

River discharge is an essential hydrological index of the global water cycle and is important for flood and drought forecast. Ultrahigh frequency (UHF) radar has gained much attention since it can remotely retrieve river discharge from the surface flow velocity measurement in real-time and all-weather condition. However, it becomes challenging for the mountain river case where the water level changes more significant than that of the plain river due to the deep-narrow channel, which breaks the previous empirical relationship between surface flow velocity and discharge. In this letter, an improved index-velocity method is proposed to address this issue. Through modeling the nonuniform variation of the cross-sectional area with water level, a more accurate surface flow velocity-discharge relationship is established. Experimental results show that the proposed method produces higher calculation accuracy than that of the previous methods, which enlarges the applicable scenario of UHF radar in discharge measurement.

水口水电站坝下治理工程实施后,电站坝下尾水位—流量关系发生变化,直接影响电站平稳运行。为分析这种变化,基于一维恒定非均匀流水力学方法,对水口水电站坝下工程实施后水电站坝下尾水位—流量关系进行数值模拟,并结合河道来沙量、采砂量及河道大断面变化资料,分析工程建设前、后水电站尾水位—流量变化规律。结果表明,河道地形变化是尾水位—流量关系变化的主要原因,坝下治理工程建成后,水电站尾水位—流量关系与现状相比整体升高1.20～3.01m,发电净水头减小。

The dynamic risk assessment of drought is crucial in the transition from the crisis management model to the risk management model, which can reveal the evolution mechanism of drought disasters. Due to a lack of data and research perspectives, most current studies are still based on static risk assessment. This study proposes a conceptual model for the dynamic risk assessment of droughts based on the probability of their occurrence and potential impacts. The developed dynamic risk index considers the hazard, exposure, vulnerability, and capacity for drought mitigation. The analytic hierarchy process (AHP) method was used to determine the weight coefficient of each indicator in the model. The novelty of the proposed model lies in the integration of four elements of drought disasters with spatiotemporal characteristics. Jiangxi Province, which is frequently affected by drought, was selected as the study area to validate the proposed model. Experimental results demonstrate that the proposed model rapidly reflects the degree of drought disaster risk caused by drought events and the influencing factors at monthly and annual scales. Moreover, the datasets based on the influencing factors of drought disasters in different regions have a good commonality in the proposed model. © 2021 The Authors.

Human activities have substantially altered present-day flow regimes. The Headwater Area of the Yellow River (HAYR, above Huanghe'yan Hydrological Station, with a catchment area of 21,000 km(2) and an areal extent of alpine permafrost at similar to 86%) on the northeastern Qinghai-Tibet Plateau, Southwest China has been undergoing extensive changes in streamflow regimes and groundwater dynamics, permafrost degradation, and ecological deterioration under a warming climate. In general, hydrological gauges provide reliable flow records over many decades and these data are extremely valuable for assessment of changing rates and trends of streamflow. In 1998-2003, the damming of the Yellow River by the First Hydropower Station of the HAYR complicated the examination of the relations between hydroclimatic variables and streamflow dynamics. In this study, the monthly streamflow rate of the Yellow River at Huanghe'yan is reconstructed for the period of 1955-2019 using the double mass curve method, and then the streamflow at Huagnhe'yan is forecasted for the next 20 years (2020-2040) using the Elman neural network time-series method. The dam construction (1998-2000) has caused a reduction of annual streamflow by 53.5-68.4%, and a more substantial reduction of 71.8-94.4% in the drier years (2003-2005), in the HAYR. The recent removal of the First Hydropower Station of the HAYR dam (September 2018) has boosted annual streamflow by 123-210% (2018-2019). Post-correction trends of annual maximum (Q(Max)) and minimum (Q(Min)) streamflow rates and the ratio of the Q(Max)/Q(Min) of the Yellow River in the HAYR (0.18 and 0.03 m(3).(-)s(-1).yr(-1) and -0.04 yr(-1), respectively), in comparison with those of precorrection values (-0.11 and -0.004 m(3).s(-1).yr(-1) and 0.001 yr(-1), respectively), have more truthfully revealed a relatively large hydrological impact of degrading permafrost. Based on the Elman neural network model predictions, over the next 20 years, the increasing trend of flow in the HAYR would generally accelerate at a rate of 0.42 m(3).s(-1).yr(-1). Rising rates of spring (0.57 m(3).s(-1).yr(-1)) and autumn (0.18 m(3).s(-1).yr(-1)) discharge would see the benefits from an earlier snow-melt season and delayed arrival of winter conditions. This suggests a longer growing season, which indicates ameliorating phonology, soil nutrient availability, and hydrothermal environments for vegetation in the HAYR. These trends for hydrological and ecological changes in the HAYR may potentially improve ecological safety and water supplies security in the HAYR and downstream Yellow River basins.

利用雨滴谱仪对2019年5—10月和2020年6—7月的雨滴特性进行分析,并与实验场内0.1 mm和0.5 mm分辨率的雨量计观测数据对比,得到雨滴到达地面时的尾速、直径及其两者之间的关系特征,并探究不同雨强范围雨滴直径的分布情况。结果表明:2019

The calibration of tipping bucket rain gauges (TBRs) before installation is an important but very time-consuming task, especially at low rainfall intensities. To address the time consumption, this paper developed a novel method and calibration system to improve the TBR calibration efficiency. Two peristaltic pumps were adopted in this method: one pump provided some simulated rainfall intensities, and another pump provided high rainfall intensities (termed accelerated rainfall intensities) to quickly inject a given amount of water into the TBR bucket before tipping. To verify the feasibility of the novel method, this investigation performed comparison experiments with three different types of TBRs. In addition, various accelerated rainfall intensities and ratios of the injected water volume to the total volume of the bucket were combined to conduct experiments at three simulated rainfall intensities of less than 1 mm min(-1) to obtain the optimal schemes and maximize the timesaving ratio (beta). The results of the comparison experiments demonstrated that the performances of the new and conventional methods were quite close; notably, the average errors differed by less than 0.32%. According to the ANOVA results, all P-values were greater than 0.05, and most were greater than 0.1. Therefore, there was no significant difference between the results of the two methods, verifying the feasibility of the new method. The scheme-based experimental results show that beta can exceed 45% and reach 89%; thus, the novel method can greatly reduce the required calibration time. In summary, the fast calibration method proposed in this work is feasible for solving the time consumption problem and can increase the availability of TBRs in hydrology, meteorology and other sciences.