IntroductionExtreme precipitation exerts serious impacts on human livelihood and ecological systems with global climate change and rapid urbanization. While the quantitative evaluation of regional urbanization influences on extreme precipitation change is still limited.MethodsThis study investigates the long-term variations in extreme precipitation indices (EPIs) over the Yangtze River Delta (YRD) during 1971–2023 and quantitatively assesses the contribution of urbanization using a dynamic urban-rural station classification based on the time-varying impervious area proportion (IAP) dataset.ResultsThe results suggested that: (1) most EPIs exhibited significant increasing trends across the YRD, characterized by significant increase in both intensity indices (6.6–43.2 mm decade−1) and frequency indices (0.08–0.84 days decade−1); (2) Urbanization enhances extreme precipitation more strongly at urban stations, contributing approximately 20.8% and 26.3% to the increase in intensidy and frequency indices, respectively. At the seasonal scale, urbanization exerted pronounced enhancement effects on most EPIs in summer and autumn but weakening effects in the spring and winter; (3) Urbanization effects strengthen with increasing urbanization level, with highly urbanized areas showing markedly larger increases in R90pTOT and R95pTOT (4.39 and 4.15 mm decade−1), confirming the non-uniform, level-dependent nature of urbanization’s influences on extreme precipitation regimes.DicussionThis study is anticipated to provide valuable insights for urban landscape planning and the mitigation of adverse impacts arising from extreme climate events in urban areas.

ABSTRACT Urban drainage systems play an essential role in stormwater management. However, conventional evaluation methods typically focused on individual performance indicators and failed to account for the compound effects of multiple environmental factors. To address this gap, an integrated probabilistic‐hydrodynamic framework was proposed, combining dynamic hydraulic simulation (SWMM) with Bayesian network (BN) inference. A total of 150 scenario simulations were conducted, covering six rainfall return periods, five imperviousness levels, and five downstream water levels. Flooding volumes were classified into three severity levels using the 25th and 75th percentiles. Three complementary metrics were adopted: Risk Ratio (RR), Information Gain (IG), and Relative Contribution (RC). The framework was applied to a typical urban area in Yuanjiang City. The results indicated that rainfall was the most dominant factor (IG = 0.45 bits, 71%), followed by downstream water level (IG = 0.07 bits, 11%) and imperviousness (IG = 0.11 bits, 18%). Extreme rainfall events (50–100 years) exhibited the highest RR (2.12) and contributed 35% of the excess flood risk, whereas high water level contributed 32% and high imperviousness contributed 22%. Low‐severity floods were associated with low rainfall intensities and low water levels, while high‐severity floods required the simultaneous occurrence of extreme rainfall, high imperviousness, and elevated downstream water levels. The proposed framework facilitates a transition from single‐factor to multi‐factor assessment and provides a scientific basis for prioritizing drainage system improvements.

ABSTRACT Global warming increases the potential risks of hydrological extremes, such as extreme precipitation and flood. Limited attention has been given to the integrated effects of climate change, land‐use change, and socioeconomic advancement on flood risk under global warming of 1.5°C and 2.0°C threshold outlined in the Paris Agreement. Here, utilizing the latest coupled model Intercomparison Project 6 (CMIP6), the new shared socioeconomic pathway scenarios (SSPs), hydrological model and future land use simulation (FLUS) model, we perform a comprehensive assessment of the flood risk in the Huai River Basin (HRB) under the global warming of 1.5°C and 2.0°C scenarios. The results reveal that (1) more intense extreme precipitation events will occur in the HRB under two global warming scenarios. The increases in extreme precipitation are approximately twice as high under 2.0°C than under 1.5°C global warming scenario; (2) under global warming of 1.5°C and 2.0°C scenarios, future 100‐year floods will increase by 18.4% and 19.2%, respectively, in the HRB; and (3) high flood‐risk areas are expected to primarily locate in regions with unfavorable flood regimes, with increases of 4.3% and 17.8%, and very high flood‐risk areas are projected to expand by 2% and 4.3%, respectively. Considering the holistic effects of future environmental changes on the flood risk, it is imperative to incorporate flood control management and prevention measures into regional adaptation strategies.

Assessing the probability of dry-wet runoff encounters under changing environmental conditions provides critical scientific support for sustainable water resource management and watershed security. Therefore, this study enhances the Generalized Additive Model for Location, Scale, and Shape (GAMLSS) through variable reconstruction and error correction, thus proposing an innovative methodology for assessing high-low runoff encounter probabilities in the Yangtze and Yellow River Basins under changing environmental conditions. Atmospheric circulation pattern analysis is further integrated to elucidate mechanisms underlying concurrent low-flow events. Key findings reveal that: (1) The Log-Normal distribution exhibits superior goodness-of-fit for runoff frequency in the Yangtze River's headwater (source) and downstream regions, while Gamma and Normal distributions emerge as optimal for the upper and middle reaches, respectively. The Inverse Gaussian and Reverse Gumbel distributions demonstrate enhanced performance in the Yellow River Basin. (2) The optimized GAMLSS achieves remarkable accuracy, with empirical-theoretical value deviations constrained between - 0.1 and 0.1, and Nash-Sutcliffe Efficiency (NSE) values ranging from 0.9756 to 0.9966 across basins. (3) Analysis of 1963-2022 data identifies the highest dry-dry encounter probability (23.48 %) in the upper reaches of both basins, followed by headwater (20.89 %) and middle reaches (18.35 %), with the lowest probability (15.37 %) observed in lower reaches. (4) While the Zhimenda-Tangnaihai, Yichang-Toudaoguai, and Datong-Huayuankou combinations show decreasing dry encounter probabilities, the Dajin-Lanzhou combination exhibits a statistically significant upward trend (p < 0.05) in low-flow synchronicity. (5) Concurrent low-flow events in the Yangtze and Yellow Rivers are predominantly linked to two atmospheric circulation patterns: (a) the Lake Baikal high-pressure ridge, and (b) anomalous strengthening of the western Pacific subtropical high. This study advances hydrological extreme event prediction by integrating statistical modeling innovation with climatic mechanism analysis, providing critical insights for adaptive watershed management under global change scenarios.

Quantifying 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.

Introduction: Climate change alters the hydrological cycle to different extents, in particular the intensification of extreme precipitation and floods, which has garnered more attention as a significant scientific issue in the last few decades. The last Coupled Model Inter-comparison Project 6 (CMIP6) was designed with new shared socioeconomic pathways (SSPs) to combine socioeconomic development with greenhouse gas emissions to project future climate. Method: In this study, we used 22 global climate models (GCMs) from CMIP6 to investigate future variations in extreme precipitation and temperature under SSP2-4.5 and SSP5-8.5 scenarios over the upper-middle Huaihe River Basin (UMHRB). Then, the modified Xinanjiang model integrating the flood control module was driven to obtain projections of the daily streamflow and to evaluate the future variations in flood regimes. Results: The results show that 1) the characteristics of future extreme precipitation, such as the average intensity and amount of annual precipitation and extreme precipitation, are projected to increase, and the average, maximum, and minimum temperature values also display substantial increasing trends in the future over the UMHRB; 2) warmer climate will lead to a more severe flood magnitude under the SSP5-8.5 scenario in the far future (2071–2100) over the UMHRB. The results of the multi-model ensemble show that the annual maximum flood peaks (15-day flood volumes) of Wangjiaba and Wujiadu stations are projected to increase by 46.4% (43.1%) and 45.4% (51.1%), respectively, in the far future (2071–2100) under the SSP5-8.5 scenario; and 3) variations in the flood frequency tend to resemble variations in flood magnitude, and the return period of the design flood will obviously decrease under future climate scenarios. For instance, in the far future, under SSP5-8.5 scenarios, the return period of the design flood with a 100-year return period will become 38 years and 31 years for Wangjiaba and Wujiadu stations, respectively. Discussion: The study enhances a more realistic understanding that the occurrence of future extreme precipitation and floods is projected to be more frequent and severe, thereby resulting in an urgent imperative to develop pertinent adaptation strategies to enhance social resilience toward the warming climate.

Polders are low-lying areas located in deltas, surrounded by embankments to prevent flooding (river or tidal floods). They rely on pumping systems to remove water from the inner rivers (artificial rivers inside the polder area) to the outer rivers, especially during storms. Urbanized polders are especially vulnerable to pluvial flooding if the drainage, storage, and pumping capacity of the polder is inadequate. In this paper, a Monte Carlo (MC) framework is proposed to evaluate the benefits of rainfall threshold-based flood warnings when mitigating pluvial flooding in an urban flood-prone polder area based on 24 h forecasts. The framework computes metrics that give the potential waterlogging duration, maximum inundated area, and pump operation costs by considering the full range of potential storms. The benefits of flood warnings are evaluated by comparing the values of these metrics across different scenarios: the no-warning, perfect, deterministic, and probabilistic forecast scenarios. Probabilistic forecasts are represented using the concept of “predictive uncertainty” (PU). A polder area located in Nanjing was chosen for the case study. The results show a trade-off between the metrics that represent the waterlogging and the pumping costs, and that probabilistic forecasts of rainfall can considerably enhance these metrics. The results can be used to design a rainfall threshold-based flood early warning system (FEWS) for a polder area and/or evaluate its benefits.

Abstract Ensemble hydrologic forecasting which takes advantages of multiple hydrologic models has made much contribution to water resource management. In this study, four hydrological models (the Xin’anjiang model (XAJ), Simhyd, GR4J, and artificial neural network (ANN) models) and three ensemble methods (the simple average, black box-based, and binomial-based methods) were applied and compared to simulate the hydrological process during 1979–1983 in three representative catchments (Daixi, Hengtangcun, and Qiaodongcun). The results indicate that for a single model, the XAJ model and the GR4J model performed relatively well with averaged Nash and Sutcliffe efficiency coefficient (NSE) values of 0.78 and 0.83, respectively. For the ensemble models, the results show that the binomial-based ensemble method (dynamic weight) outperformed with water volume error reduced by 0.8% and NSE value increased by 0.218. The best performance on runoff forecasting occurs in the Hengtang catchment by integrating four hydrologic models based on binomial ensemble method, achieving the water volume error of 2.73% and NSE value of 0.923. Finding would provide scientific support to water engineering design and water resources management in the study areas.

Climate and land-use changes are two major factors that significantly affect the watershed hydrology cycle. It is essential for regional water resource management to quantitatively assess the respective hydrological impact of these two factors. In this study, the Soil and Water Assessment Tool (SWAT) was constructed to quantify the contributions of climate and land-use changes to runoff at the annual and seasonal time scales in the Qinhuai River basin (QRB), where significant urbanization occurred from 1986 to 2015. Moreover, based on the partial least squares regression, the specific impact of individual land-use change on major hydrological components was evaluated at the sub-basin scale. The results showed that: (1) the predominant patterns of land-use change in the QRB included the transformations from paddy fields to urban areas and dry lands, forest to dry lands and dry lands to urban areas; (2) the flood seasonal precipitation series and all air temperature series had significant increasing trends over 1986-2015, and annual and seasonal runoff series had significant increasing trends and had an abrupt change point in 2001; (3) the average annual, flood seasonal, and non-flood seasonal runoff increased 238.5, 130.2 and 108.3 mm, of which land-use change was responsible for 77.6, 55.1, and 104.8% of the increases, respectively, while climate change was responsible for 22.4, 44.9, and -4.8%, respectively and (4) the hydrological response to land-use change showed an obvious decrease in actual evapotranspiration (ET) and significant increases in surface runoff and baseflow. The decrease of ET and increase of baseflow could be attributed to the conversion patterns from paddy fields and forest to dry lands, while the conversions from paddy fields and dry lands to urban areas caused a remarkable increase in surface runoff in the QRB. The study demonstrated that these practicable approaches were beneficial for the more unbiased views of the hydrological responses to climate change and land-use changes in the highly urbanized basin, which were also critical for the sustainable development of regional water resource and future land-use planning.

Quantifying the influences of land use/cover (LULC) change on hydrological processes is important for rational utilization of water resources. The objective of this study was to evaluate the impacts of spatiotemporal LULC change on hydrological components in a typical agricultural area located in the North China Plain at both basin and sub-basin scales. LULC change was quantified, and the Soil and Water Assessment Tool was optimized using parameters associated with LULC conditions. We concluded that the urban and forest areas increased by 25.57 and 10.56%, with the cropland area decreased by 36.76%. About half of the surface runoff (SURQ) in the basin was generated from the urban area, with the SURQ increased significantly in the upstream and downstream of the basin where overlapped with urbanized areas. The proportions of evapotranspiration generated by cropland and forest areas increased slightly (0.89 and 0.55%, respectively), especially in sub-basins where the conversion of cropland to forest was obvious. Urban, forest, and cropland were the main types that generated water yield (WYLD). The proportion of WYLD generated on the urban area increased by 9.55% and decreased in other areas, which may be related to the combined effects of urbanization and forest reduction.

选取南方典型城市化流域(秦淮河流域)为研究区,应用极端降雨指标进行极端降雨时空演变和非一致性频率分布研究.结果表明1979—2015年秦淮河流域年降雨量呈小幅增加趋势,夏冬降雨量增大,春秋降雨量减少.极端降雨强度和频次显著上升,突变点和高值区与城市化格局演变的时间点和地区相吻合.皮尔逊Ⅲ型分布是一致性修正后极端降雨序列的最优分布,最大1 d降雨量R1d和特强降雨量R99p具有较大的非平稳性,若仅采用修正前的R1d计算会导致工程设计值偏低;最大3 d、7 d降雨量R3d、R7d和极端降雨量R95p相对平稳,R3d和R95p不同重现期的设计值高值区均分布在流域下游和上游溧水区,R7d高值区分布在上游句容市,上下游极端降雨易产生叠加效应,为下游带来高洪水风险.

The influence of climate change on the regional hydrological cycle has been an international scientific issue that has attracted more attention in recent decades due to its huge effects on drought and flood. It is essential to investigate the change of regional hydrological characteristics in the context of global warming for developing flood mitigation and water utilization strategies in the future. The purpose of this study is to carry out a comprehensive analysis of changes in future runoff and flood for the upper Huai River basin by combining future climate scenarios, hydrological model, and flood frequency analysis. The daily bias correction (DBC) statistical downscaling method is used to downscale the global climate model (GCM) outputs from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and to generate future daily temperature and precipitation series. The Xinanjiang (XAJ) hydrological model is driven to project changes in future seasonal runoff under SSP245 and SSP585 scenarios for two future periods: 2050s (2031–2060) and 2080s (2071–2100) based on model calibration and validation. Finally, the peaks over threshold (POT) method and generalized Pareto (GP) distribution are combined to evaluate the changes of flood frequency for the upper Huai River basin. The results show that 1) GCMs project that there has been an insignificant increasing trend in future precipitation series, while an obvious increasing trend is detected in future temperature series; 2) average monthly runoffs in low-flow season have seen decreasing trends under SSP245 and SSP585 scenarios during the 2050s, while there has been an obvious increasing trend of average monthly runoff in high-flow season during the 2080s; 3) there is a decreasing trend in design floods below the 50-year return period under two future scenarios during the 2050s, while there has been an significant increasing trend in design flood during the 2080s in most cases and the amplitude of increase becomes larger for a larger return period. The study suggests that future flood will probably occur more frequently and an urgent need to develop appropriate adaptation measures to increase social resilience to warming climate over the upper Huai River basin.

Artificial adjustment and urbanization are key factors of global change and have significant influences on hydrological processes. This study focuses on the effects of urban land-use patterns on flood regimes in a typical urbanized basin in eastern China. Comprehensive assessments of urban land-use patterns were implemented on three levels: total imperviousness area (TIA) magnitude, landscape configuration and relative location in the basin. Hydrologic Engineering Center's Modeling System (HEC-HMS) was calibrated and validated using four groups of parameters associated with land-use conditions. Fourteen flood events were simulated based on 10 land-use scenarios with different land-use patterns. The results indicate that floods are closely associated with three landscape pattern indicators. First, over the past 20 years, the impermeability rate has increased from 3.92 to 17.48%, with the landscape pattern converted from extension growth form to fill-up growth form after 2003. Second, the average flood peak discharge increased by 80% due to impermeable surfaces expansion, with minor floods more sensitive to the expansion than major floods. Third, the contribution of imperviousness expansion to peak discharge in the inner basin is more remarkable than downstream of the river basin, with the landscape pattern metrics of TIA, arable land and forest land displaying strong correlations with flood characteristics.

Both flood magnitude and frequency might change under the changing environment. In this study, a procedure combining statistical methods, flood frequency analysis and attribution analysis was proposed to investigate the response of floods to urbanization and precipitation change in the Qinhuai River Basin, an urbanized basin located in Southeast China, over the period from 1986 to 2013. The Mann–Kendall test was employed to detect the gradual trend of the annual maximum streamflow and the peaks over threshold series. The frequency analysis was applied to estimate the changes in the magnitude and frequency of floods between the baseline period (1986–2001) and urbanization period (2002–2013). An attribution analysis was proposed to separate the effects of precipitation change and urbanization on flood sizes between the two periods. Results showed that: (1) there are significant increasing trends in medium and small flood series according to the Mann–Kendall test; (2) the mean and threshold values of flood series in the urbanization period were larger than those in the baseline period, while the standard deviation, coefficient of variation and coefficient of skewness of flood series were both higher during the baseline period than those during the urbanization period; (3) the flood magnitude was higher during the urbanization period than that during the baseline period at the same return period. The relative changes in magnitude were larger for small floods than for big floods from the baseline period to the urbanization period; (4) the contributions of urbanization on floods appeared to amplify with the decreasing return period, while the effects of precipitation diminish. The procedure presented in this study could be useful to detect the changes of floods in the changing environment and conduct the attribution analysis of flood series. The findings of this study are beneficial to further understanding interactions between flood behavior and the drivers, thereby improving flood management in urbanized basins.

为进一步探索提高模型精度的方法,比较不同集合预报方法的优劣,选择了4种水文模型(新安江模型、Simhyd模型、GR4J模型和人工神经网络模型)分别在浙江省西部山区的4个典型流域做模拟对比,分析这4个模型在流域上的适用性,并将模型的模拟结果作为集合成员,使用黑箱集合预报法和诱导有序二项式系数集合预报法对成员进行集合预报,研究模型的应用效果,并进行4种模型和2种集成方法的优势对比.研究结果表明,新安江模型和GR4J模型在研究区域的适用性方面较好.黑箱集合预报法和诱导有序二项式系数集合预报法分别代表固定权重和变动权重的两种集合预报方法,模拟结果显示,后者对集合成员的改进程度更高,说明其在动态权重方法下更能充分发挥各模型的优势之处,达到择优互补的模拟效果,从而提高预报精度.

It is difficult to extract snow cover from multi-temporal high spatial resolution remotely sensed imagery,where the precision and efficiency is relatively low.This study provides a method which can extract multi-temporal snow cover from GF-1 based on multi-view model.Based on three GF-1 satellite images,single image was treated as a view,and multi views were built for multi-temporal snow recognition through extracting the unchanged part of multi-temporal images.In order to overcome the severe influence of terrain shadows,labeled samples of snow in sunlight and snow in shadow were selected separately.Only by selecting labeled samples once,three classifiers were built on different views based on rotation forest algorithm.Then,three classifiers were applied to the classification of three multi temporal images.The accuracy verification showed that the F-score of three images are 0.941,0.951,and 0.945,respectively.In addition,the efficiency is relatively high.

Urbanization has made natural and semi-natural landscape to be gradually replaced by impervious sur-face, which has caused significant reduction of surface permeability in urban region. Along with these changes, profound transformations have occurred in hydrological processes, water environment, urban thermal environ-ment, and ecological service system. Impervious surface is an important indicator of characterizing the urban ex-pansion, which has extremely important ecological implications. We chose the multi-temporal Landsat images as our data sources, and took Qinhuai River Basin as the research area in this study. Rotation forest algorithm, which belongs to the category of ensemble learning that synthesizes the advantages of different classifiers and ef-fectively addresses the limits of the information provided by a single image, was used to produce the nine-year land cover maps of Qinhuai River Basin. Focusing on the large watershed scale, we explored the changing pro-cess of the urban landscape pattern in the research area during the past 30 years. Impervious surface coverage dy-namic analysis was used to reveal the changes of impervious surface area. Land cover change trajectory analysis was used to explore the resources of impervious surface and transformation process of land cover. Landscape metrics analysis was used to quantify the spatial and temporal changes of impervious surface pattern. We aimed to reveal the spatial-temporal changing characteristics of urban landscape configuration against the background of urbanization. The results showed that the landscape pattern has changed significantly during the past 30 years. Overall, the impervious area has increased by nearly four times. The dominance of the impervious surface has in-creased greatly. The analysis suggested that the turning point of urban expansion is between 2001 and 2003. Ur-ban expansion mainly occurred in Nanjing city and Jiangning district before the turning point, while the impervi-ous surface expansion rate of Nanjing city has greatly decreased after that. At the same time, there is a sharp rise in the expansion rate of Lishui and Jurong districts. The impervious surface within the 2001-2003 period had the highest spatial heterogeneity, which then decreased significantly after the turning point until 2015. The shape of the impervious patches has becoming simpler at the latter stage, and the impervious surface has turned from a dispersed distribution into a distribution pattern with higher connectivity. Besides, the area with high level of con-nectivity is mainly distributed in Nanjing city and Jiangning district.

Multi-temporal Landsat 8 OLI image were utilized to extract thematic information of land use based on the study area-Qinhuai river basin which is located at southern hilly region of China. We first conduct the landuse spectrum sampling. Then the optimal index extraction model was selected for each class, based on the spectrum sampling statistical results. Finally, a decision tree classification model with hierarchical extraction features was formed. The research suggested that:①By means of plant phenology, multi-temporal remote sensing data has a good effect in distinguishing pappy field and forest land;②Pappy field and dry land staggeredly distributed in Qin-huai River basin. The pappy field is mainly located at Qinhuai River depression. The difference between pappy filed and dry land is most obious in summer, so it is the best time in extracting these information. Short wave infra-red band is absorbed by the water body,We can use this band for distinguishing the pappy field and dry land;③The farmland and non farmland can be distinguished by NDVI, but there is some building land with high vegetation cov-erage which is mis-classified with farm land. BSI can be used to remove these building land from farmland;④Su-pervised classification has a good effect in distinguishing building land and bare land; ⑤The overall accuracy of Decision tree classification model is 23. 1% higher than supervised classification result.