The Mann-Kendall (MK) statistical test has been widely applied in the trend detection of the hydrometeorological time series. Previous studies have mainly focused on the null hypothesis of “no trend” or the “Type I Error.” However, few studies address the capability of the MK test to successfully recognize the trends. In some cases, especially when the trend test is jointly applied with hydropower station design, flood risk assessment, and water quality evaluation, the “Type II error” is equally important and should not be neglected. To cope with this problem, we carry out Monte Carlo simulations and the results indicate that in addition to the significance level and the sample length, the MK test power has a close relationship with the sample variance and the magnitude of the trend. For a given time series with fixed length, the power of the MK test increases as the slope increases and declines with increasing sample variance. A deterministic relationship between the slope and the standard deviation of the white noise that can be used for evaluating the power of the MK test has also been detected. Furthermore, we find that a positive autocorrelation contained in the time series will increase both the Type I and the Type II errors due to the enlargement of the variance in the MK statistics. Finally, we recommend that researchers slightly increase the significance level and lengthen the time series sample to improve the power of the MK test in future studies.

A rigorous calculation and analysis of the coupling and coordination relationship between China’s ecological environment and economic development, as well as its spatiotemporal evolution trend, is undoubtedly the first step in implementing the new development concept. This paper constructs China’s ecological environment-economic development evaluation index system based on the index data of China’s provincial areas from 2013 to 2022, measures the comprehensive development level, and calculates the coupling coordination degree of the two systems. We also use a coupling coordination model to calculate the coupling coordination degree of the two systems and evaluate the trend of coordinated development in each province. The results show that: (1) The integrated development of ecological environment-economic development in all provinces of China is unbalanced, and in general, the integrated development of the ecological environment and economy in the southeast region is better than that in the northwest region. (2) From 2013 to 2022, the coupling coordination between the ecological environment and economic development in various provinces of China has shown an upward trend. In 2022, Guangdong Province had the highest coupling coordination (0.99), but Gansu Province had the fastest annual growth rate (13.4%). Meanwhile, the relative gap in coupling and coordination among provinces is constantly narrowing. (3) Overall, the coupling and coordination between China’s ecological environment and economic development present a spatial pattern of “high in the east and low in the west”, with Guangdong and Jiangsu provinces having the highest coordination, while Guizhou and Gansu provinces have the lowest coordination

Across the globe, flood control standards for reservoir engineering appear different due to various deciding factors such as flood features, society, economy, culture, morality, politics, and technology resources, etc. This study introduces an in-depth comparison of flood control standards for reservoir engineering for different countries. After the comparison and analysis, it is concluded that the determination of flood control standards is related to engineering grade, dam type, dam height, and the hazard to downstream after dam-breaking, etc. Each country should adopt practical flood control standards according to the characteristics of local reservoir engineering. The constitutive flood control standards should retain certain flexibility in the basis of constraint force. This review could offer a reference for developing countries in the enactment of flood control standards for reservoir engineering.

Similar to the rainfall depth, duration and intensity, the temporal pattern is also an important characteristic of rainstorm events. Studies have shown that temporal patterns will influence runoff modelling, flash flood warning thresholds as well as urban and infrastructure flood inundation simulations. In this study, a time series clustering method using dynamic time warping (DTW) as similarity measurement criteria is proposed to analyze rainfall temporal patterns. Compared with the existing approaches, it can better reflect the real rainfall processes. Based on this novel method, five representative temporal patterns were extracted from 13,299 rainstorm events during the flood season in China. Through the analysis of their statistical characteristics, the disaster-causing risks of each temporal pattern were compared. Furthermore, we found that for rainstorm events whose durations are less than 24 h, the rainfall is mainly concentrated in 3 to 6 h, which proposes higher requirements for the design of flood control and drainage projects compared with those using average intensities of 12 or 24 h as design standards. Finally, through regional analysis, we found that the rainfall depth, intensity and peak value are affected by the macroclimate. However, the temporal patterns are not strongly related to the macroclimate but are more likely to be affected by the local climate and topography, which needs further studies at smaller scales.

The fast‐flowing watershed is characterized by rapid runoff and confluence, posing challenges for accurate flood prediction. We introduce three flood forecasting model structures, namely GRU‐ED, LSTM‐FED, and LSTM‐DSA to address this issue. Through application research in three representative watersheds, we found that: First, as input information attenuates, the predictive ability of the models may decline with an extended lead time. The incorporation of a feedback mechanism effectively addresses this issue, resulting in an average 5% improvement in Nash efficiency and a significant 26.4% reduction in the interquartile range of relative peak error. Second, the performance of the model is influenced by various factors, including the watershed characteristics, sample size, and temporal resolution. Further investigation is required to determine the extent of their influence. The attention mechanism dynamically assigns weights to input data, significantly improving model performance, especially for larger catchments. This leads to an average increase in Nash efficiency of approximately 7.86% and a reduction in the interquartile range of relative peak error by about 30.7%. Finally, the proposed models demonstrate a high level of accuracy in flood forecasting within a specific lead time, offering an innovative deep learning‐based solution to the problem of fast‐flowing watershed flood forecasting.

Flood forecasting plays a crucial role in supporting decision-making for flood management. In addition to conceptual and physical-based models, the data-driven models have garnered increasing attention in recent years. The proposed model in this study employs LSTM networks, Encoder-Decoder framework, as well as feedback and attention mechanism to effectively utilize diverse observed data and future rainfall as inputs for multiple time steps flood forecasting. The accuracy and reliability of the model have been validated across case studies in multiple watersheds in China. The results demonstrate the high performance of the LSTM-based flood forecasting model. Meanwhile, the efficacy of both the feedback mechanism and attention mechanism has been validated in the domain of flood prediction.