The existence of persistence contaminates the results of the Mann-Kendall (MK) trend test. This paper proposes an improved MK test for hydrological data with both positive and negative persistence. To this end, the variance and distribution patterns of S were modified under the scaling hypothesis. Different estimators of the scaling coefficient were compared. According to the results, the generalized normal distribution is the best approximation to fit the exact distribution of S with both positive and negative persistence. The centred detrending moving average was demonstrated to be the best estimator of the scaling coefficient for series with a trend. The improved MK test was proven to be more advantageous than other trend detection methods for the synthetic time series and observed precipitation data obtained from nine hydrometric stations located in China. With this modification, the application of the MK test can be extended to long-term persistent data.

The prediction of monthly rainfall is greatly beneficial for water resources management and flood control projects. Machine learning (ML) techniques, as an increasingly popular approach, have been applied in diverse climatic regions, showing their respective superiority. On top of that, the ensemble learning model that synthesizes the advantages of different ML models deserves more attention. In this study, an ensemble learning model based on stacking approach was proposed. Four prevalent ML models, namely k-nearest neighbors (KNN), extreme gradient boosting (XGB), support vector regression (SVR), and artificial neural networks (ANN) are taken as base models. To combine the outputs from the base models, the weighting algorithm is used as second-layer learner to generate predictions. Large-scale climate indices, large-scale atmospheric variables, and local meteorological variables were used as predictors. R², RMSE and MAE, were used as evaluation metrics. The results show that the performance of base models varied among the nine stations in the Taihu Basin, while the stacking approach generally performed better than the four base models. The stacking model showed better performance in spring and winter than in summer and autumn. During wet months, the accuracy of model prediction varied more significantly. On the whole, based on performance evaluation measures, it is concluded that the proposed stacking ensemble multi-ML model can provide a flexible and reasonable prediction framework applicable to other regions.

We examine urban flood response through data‐driven analyses for a diverse sample of “small” watersheds (basin scale ranging from 7.0 to 111.1 km²) in the Charlotte Metropolitan region. These watersheds have experienced extensive urbanization and suburban development since the 1960s. The objective of this study is to develop a broad characterization of land surface and hydrometeorological controls of urban flood hydrology. Our analyses are based on peaks‐over‐threshold flood data developed from USGS streamflow observations and are motivated by problems of flood hazard characterization for urban regions. We examine flood‐producing rainfall using high‐resolution (1 km²spatial resolution and 15 min time resolution), bias‐corrected radar rainfall fields that are developed through the Hydro‐NEXRAD system. The analyses focus on the 2001–2015 period. The results highlight the complexities of urban flood response. There are striking spatial heterogeneities in flood peak magnitudes, response times, and runoff ratios across the study region. These spatial heterogeneities are mainly linked to watershed scale, the distribution of impervious cover, and storm water management. Contrasting land surface properties also determine the mixture of flood‐generating mechanisms for a particular watershed. Warm‐season thunderstorm systems and tropical cyclones are main flood agents in Charlotte, with winter/spring storms playing a role in less‐urbanized watersheds. The mixture of flood agents exerts a strong impact on the upper tail of flood frequency distributions. Antecedent watershed wetness plays a minor role in urban flood response, compared with less‐urbanized watersheds. Implications for flood hazard characterization in urban watersheds and for advances in flood science are discussed.

The role of rainfall space–time structure, as well as its complex interactions with land surface properties, in flood response remains an open research issue. This study contributes to this understanding, specifically for small (<15 km²) urban watersheds. Using a flood frequency analysis framework that combines stochastic storm transposition (SST)-based rainfall scenarios with the physically based distributed Gridded Surface Subsurface Hydrologic Analysis (GSSHA) model, we examine the role of rainfall spatial and temporal variability in flood frequency across drainage basin scales in the highly urbanized Dead Run watershed (14.3 km²), Maryland, USA. The results show the complexities of flood response within several subwatersheds for both short (<50 years) and long (>100 years) rainfall return periods. The impact of impervious area on flood response decreases with increasing rainfall return period. For extreme storms, the maximum discharge is closely linked to the spatial structure of rainfall, especially storm core spatial coverage. The spatial heterogeneity of rainfall increases flood peak magnitudes by 50 % on average at the watershed outlet and its subwatersheds for both small and large return periods. The framework of SST–GSSHA-coupled frequency analysis also highlights the fact that spatially distributed rainfall scenarios are needed in quick-response flood frequency, even for relatively small basin scales.

Shanghai is the economic center of China, but is vulnerable to threats from natural hazards. Along with climate change and fast urbanization, flood is the inevitable deterrent to the city’s development. Flood hazard mitigation is considered as a critical work for the city government to ensure safety, maintain sustainable development, and reduce damages and losses. A comprehensive overview of flood management systems and considerations are of great value. So in this paper, the major causes of flooding in Shanghai are investigated and their characteristics are analyzed. Based on the new strategy of flood management aiming at reducing future impacts of flood disaster, the authors’ considerations about the current flood control measurements in Shanghai are demonstrated, especially at the aspects of flood design standard and the potential threats existing in the engineering system and nonengineering system. Though it is clear that Shanghai has made remarkable achievements in flood management, flood management should be conducted from a strategic perspective of disaster prevention.

Urban development, topographic relief, and coastal boundaries can all exert influences on storm hydroclimatology, making rainfall and flood frequency analysis a major challenge. This study explores heterogeneity in extreme rainfall in the Baltimore Metropolitan region at small spatial scales using hydrometeorological analyses of major storm events in combination with hydroclimatological analyses based on storm catalogs developed using a 16‐year record of high‐resolution bias‐corrected radar rainfall fields. Our analyses demonstrate the potential for rainfall frequency methods using storm catalogs combined with stochastic storm transposition (SST); procedures are implemented for Dead Run, a small (14.3 km²) urban watershed located within the Baltimore Metropolitan area. The results point to the pronounced impact of complex terrain (including the Chesapeake Bay to the east, mountainous terrain to the west and urbanization in the region) on the regional rainfall climatology. Warm‐season thunderstorm systems are shown to be the dominant mechanism for generating extreme, short‐duration rainfall that leads to flash flooding. The SST approach is extended through the implementation of a multiplier field that accounts for spatial heterogeneities in extreme rainfall magnitude. SST‐based analyses demonstrate the need to consider rainfall heterogeneity at multiple scales when estimating the rainfall intensity‐duration‐frequency relationships.