Study region China Study focus We developed machine learning (ML) models for design flood estimation in mountainous catchments (<= 500 km(2)) across China. This process considered different ML algorithms (random forest, extreme gradient boosting, and support vector regression), model scopes (nation and hydrological zones), and feature input sets (1-14 features) to optimize model development strategies. New hydrological insights for the region Based on estimation performance and hyperparameter tuning efficiency, random forest was found to be the optimal algorithm. The optimal model scope resulted in five distinct models: a single lumped model encompassing six eastern zones and four separate zonal models for the western zones. Considering both accuracy and efficiency, the optimal number of input features ranged from 5 to 14 for different models. High estimation accuracy was observed in the Qinba-Dabie North, Southeast, Southwest, and Yunnan-Tibet Zone, with average RMSE, R-2, MQE, and QR ranging from 55.90 to 103.97, 0.83-0.93, 45.62-65.77 %, and 55.90-60.98 %, respectively, for the test set across different return periods. The remaining zones exhibited moderate accuracy, with the Northwest Basin Zone demonstrating particularly low accuracy due to fewer catchments. Notably, catchments with areas > 100 km(2) demonstrated higher estimation accuracy, with an average 60 % reduction in MQE and a 30 % increase in QR compared to catchments of all sizes. This study provides crucial reference and data support for national flash flood prevention efforts.

The existing flood stochastic simulation methods are mostly applied to the stochastic simulation of flood intensity characteristics, with less consideration for the randomness of the flood hydrograph shape and its correlation with intensity characteristics. In view of this, this paper proposes a flood stochastic simulation method that combines intensity and morphological indicators. Using the Foziling and Xianghongdian reservoirs in the Pi River basin in China as examples, this method utilizes a three-dimensional asymmetric Archimedean M6 Copula to construct stochastic simulation models for peak flow, flood volume, and flood duration. Based on K-means clustering, a multivariate Gaussian Copula is employed to construct a dimensionless flood hydrograph stochastic simulation model. Furthermore, separate two-dimensional symmetric Copula stochastic simulation models are established to capture the correlations between flood intensity characteristics and shape variables such as peak shape coefficient, peak occurrence time, rising inflection point angle, and coefficient of variation. By evaluating the fit between the simulated flood characteristics and the dimensionless flood hydrograph, a complete flood hydrograph is synthesized, which can be applied in flood control dispatch simulations and other related fields. The feasibility and practicality of the proposed model are analyzed and demonstrated. The results indicate that the simulated floods closely resemble natural floods, making the simulation outcomes crucial for reservoir scheduling, risk assessment, and decision-making processes.

For the purpose of improving the scientific nature, reliability, and accuracy of flood forecasting, it is an effective and practical way to construct a flood forecasting scheme and carry out real-time forecasting with consideration of different rain patterns. The technique for rain pattern classification is of great significance in the above-mentioned technical roadmap. With the rapid development of artificial intelligence technologies such as machine learning, it is possible and necessary to apply these new methods to assist rain classification applications. In this research, multiple machine learning methods were adopted to study the time-history distribution characteristics and conduct rain pattern classification from observed rainfall time series data. Firstly, the hourly rainfall data between 2003 and 2021 of 37 rain gauge stations in the Pi River Basin were collected to classify rain patterns based on the universally acknowledged dynamic time warping (DTW) algorithm, and the classifications were treated as the benchmark result. After that, four other machine learning methods, including the Decision Tree (DT), Long- and Short-Term Memory (LSTM) neural network, Light Gradient Boosting Machine (LightGBM), and Support Vector Machine (SVM), were specifically selected to establish classification models and the model performances were compared. By adjusting the sampling size, the influence of different sizes on the classification was analyzed. Intercomparison results indicated that LightGBM achieved the highest accuracy and the fastest training speed, the accuracy and F-1 score were 98.95% and 98.58%, respectively, and the loss function and accuracy converged quickly after only 20 iterations. LSTM and SVM have satisfactory accuracy but relatively low training efficiency, and DT has fast classification speed but relatively low accuracy. With the increase in the sampling size, classification results became stable and more accurate. Besides the higher accuracy, the training efficiency of the four methods was also improved.

在新安江模型参数优化领域,洗牌复合形进化(Shuffled Complex Evolution,简称SCE-UA)算法得到了广泛的应用.但在长序列降雨径流模拟和模型参数优化中面临一些问题需要解决.当模型参数适配不当时,土壤含水量易于出现负值,此外,不同径流成分对应的汇流参数需要满足一定的大小关系.上述问题的处理,决定着优化算法能否找到具有正确物理意义的最优模型参数,以及径流模拟结果的合理性和可靠性.针对这些问题,提出了一种约束SCE-UA算法,通过罚函数法对土壤含水量和汇流参数施加约束,引导优化算法向具有正确物理意义的参数可行域展开搜索,在寻优的同时确保了物理意义的正确性.在呈村流域开展了基于约束SCE-UA算法的新安江模型参数优化研究,数值模拟结果表明,约束SCE-UA算法能够确保模型参数物理意义的正确性,解决了土壤含水量出现负值和汇流参数大小关系错误的问题.利用优化获得的最优参数开展水文模拟,能够取得更好的水量平衡效果和更高的模拟精度.

In the field of hydrological model parameter uncertainty analysis, sampling methods such as Differential Evolution based on Monte Carlo Markov Chain (DE-MC) and Shuffled Complex Evolution Metropolis (SCEM-UA) algorithms have been widely applied. However, there are two drawbacks which may introduce bad effects into the uncertainty analysis. The first disadvantage is that few optimization algorithms consider the physical meaning and reasonable range of the model parameters. The traditional sampling algorithms may generate non-physical parameter values and poorly simulated hydrographs when carrying out the uncertainty analysis. The second disadvantage is that the widely used sampling algorithms commonly involve only a single objective. Such sampling procedures implicitly introduce too strong an “exploitation” property into the sampling process, consequently destroying the diversity property of the sampled population, i.e., the “exploration” property is bad. Here, “exploitation” refers to using good already-existing solutions and making refinements to them, so that their fitness will improve further; meanwhile, “exploration” denotes that the algorithm searches for new solutions in new regions. With the aim of improving the performance of uncertainty analysis algorithms, in this research, a constrained multi-objective intelligent optimization algorithm is proposed that preserves the physical meaning of the model parameter using the penalty function method and maintains the population diversity using a Non-dominated Sorted Genetic Algorithm-II (NSGA-II) multi-objective optimization procedure. The representativeness of the parameter population is estimated on the basis of the mean and standard deviation of the Nash–Sutcliffe coefficient, and the diversity is evaluated on the basis of the mean Euclidean distance. The Chengcun watershed is selected as the study area, and uncertainty analysis is carried out. The numerical simulations indicate that the performance of the proposed algorithm is significantly improved, preserving the physical meaning and reasonable range of the model parameters while significantly improving the diversity and reliability of the sampled parameter population.

The small-sized catchment in China often characterized with complex topography and geomorphology. And the mountainous small-sized catchments has distributed widely, which often suffered by serious flash flood disasters. Moreover, most of those catchment are mainly ungauged that brings big challenges in flood modelling and forecast. According the problem of low modelling accuracy, this paper presents a new operational approach which integrated the spatiotemporally-mixed runoff model and machine learning algorithms for the parameter regionalization application and further verified with the data collected from 19 small-sized catchments in Henan province. The results shows that by using more physically-based model in combination with principal component analysis could effectively reduce the noise in the machine learning algorithms caused by over fitting problem. The cross-validation method has been applied in this study to evaluate the effects of different parameter regionalization methods such as machine learning algorithms, shortest distance method and behavior similarity method. The machine learning algorithm (CART model) has been proved as the best approach for the parameter regionalization application. The approach presented in this paper shows higher level promising applicability for solving the problem of low modeling accuracy in small-sized ungauged catchments in China.

雷达资料同化是提高局地数值降雨预报精度的重要手段.以2014年6月17日和2016年7月9日福建梅溪流域发生的2场典型降雨洪水事件为例,设计9种不同的雷达资料同化模式,并引入到以WRF模式和基于CNFF-HM的分布式水文模型为主体构建的陆气耦合模型中,开展降雨-径流预报试验;探究中小流域尺度合理的雷达资料同化模式;分析雷达资料同化对降雨-径流预报结果的影响.结果表明,雷达资料同化能够提高中小流域陆气耦合降雨-径流预报精度;当同化时间间隔为6h时,同化雷达反射率的降雨预报效果最好,但随着同化时间间隔缩短,雷达径向风的同化效果得到明显改善;逐小时同化雷达径向风支持下的陆气耦合模型,其降雨-径流预报结果最佳,2场降雨的累积雨量误差分别仅为7.55％和5.80％,相应洪水的洪峰流量误差为-12.65％和-18.26％,峰现时间均提前1h,NSE为0.866和0.927,在中小流域暴雨洪水预报中有一定的应用前景.

气象水文耦合预报能够延长洪水预报预见期,针对预报结果不确定性大的问题,选取东南沿海地区的梅溪流域为研究区,以2012年8月3日"苏拉"台风和2014年6月17日"海贝思"台风引发的降雨洪水为例,开展气象水文耦合下的集合预报研究.依托WRF(weather research and forecasting)模式建立基于36种物理参数化方案组合的降雨集合预报集,并通过耦合WRF模式和梅溪流域分布式水文模型,实现降雨径流集合预报.结果表明:在不同物理参数化方案下,数值降雨预报结果有一定差异,且对降雨空间分布的预报效果优于降雨时间分布,更容易准确描述时空分布均匀的降雨,很难捕捉短历时强降雨;采用集合预报的方式能够有效降低洪水预报的不确定性,当预见期超过6 h时,对于时空分布均匀的降雨,相应洪水过程的洪峰流量预报误差Rf为11.30％,能够准确反映洪峰量级,峰现时间提前2 h,相比基于"落地雨"开展的洪水预报有一定优势;基于异方差扩展型Logistic算法对预报降雨进行处理后,能够有效提高降雨预报精度,但对于时空分布不均匀的降雨,洪峰流量误差Rf由处理前的-86.89％降低至-48.95％,仍有较大的提升空间.

为实现大规模降雨监测数据的异常识别和快速处理,基于Hampel法、格拉布斯准则、周边测站分析法和雷达辅助校验等方法,建立了递进式异常站点筛查体系,通过K-d tree(K-dimension tree)高级数据结构和并行计算方法提高计算效率,并以福建省5234个具有雨量监测功能的地面站2015-2021年雨量数据进行了验证,结果表明福建省地面站雨量监测数据质量逐年提升;各类测站中,雨量站异常站点占全部异常站点的比例最高,各类异常站点在全省相应类型站点中,雨量站异常站点的占比也最高;雷达辅助校验能够有效解决在雨区与非雨区边界、雨强差异较大的雨区边界的正常站点易被误判为异常值的问题,校验前异常识别准确率为90％左右,校验后准确率提高为95％左右.通过K-d tree和并行计算,全省测站完成一次异常识别需约5～8 min,为大规模降雨监测数据异常识别、充分利用雨量监测站有效信息提供可靠的方法.

The economic loss caused by frequent flood disasters poses a great threat to China’s economic prosperity. This study analyzes the driving factors of flood-related economic losses in China. We used the extended Kaya identity to establish a factor decomposition model and the logarithmic mean Divisia index decomposition method to identify five flood-related driving effects for economic loss: demographic effect, economic effect, flash flood disaster control effect, capital efficiency effect, and loss-rainfall effect. Among these factors, the flash flood disaster control effect most obviously reduced flood-related economic losses. Considering the weak foundation of flash flood disaster prevention and control in China, non-engineering measures for flash flood prevention and control have been implemented since 2010, achieving remarkable results. Influenced by these measures, the loss-rainfall effect also showed reduction output characteristics. The demographic, economic, and capital efficiency effects showed incremental effect characteristics. China’s current economic growth leads to an increase in flood control pressure, thus explaining the incremental effect of the economic effect. This study discusses the relationship between flood-related economic loss and flash flood disaster prevention and control in China, adding value for the adjustment and formulation of future flood disaster prevention policies.

堤基管涌抢险和防治是堤防工程安全的心腹之患,水利部防洪抗旱减灾工程技术研究中心自成立以来,持续开展堤基管涌机理和防治设计准则方面的研究.系统深入研究了典型堤基结构管涌破坏机理和模式、管涌自愈现象,发现了不同堤基结构管涌发展机理和破坏模式的特点和区别;提出了管涌抢险合理范围和盖重合理宽度的确定方法建议;提出并论证了悬挂式短墙布置在背水侧更能有效控制管涌发展的作用机理和设计方法;研发了管涌动态模拟数学模型和软件.提升了对管涌机理的认识,发展了传统渗流控制理论,对管涌防治和抢险有重要指导作用,成果已应用于堤防设计和抢险中.

With the permeability enlargement method, we carried out Finite Element Analysis on the piping development in a double-stratum dike foundation with suspended cut-off wall. Piping initialization as well as its development, erosion domain and seepage distribution under various hydraulic head is studied for double strata. The influences of the wall’s position and penetration on the seepage control are mainly analyzed. Results show that: (1) the erosion is composed of five stages, (a) sudden local piping after the formation of a passageway exit, (b) the channel developing towards the cut-off wall, along the surface of the sand layer, (c) lateral development along the cut-off wall, (d) vertical development leaping over the cut-off wall, and (e) horizontal development after leaping over. The failure mode and mechanism remain consistent with the phenomenon observed in the flume model test; (2) piping erosion transforms from horizontal to vertical, improving the critical hydraulic head effectively and reducing the risk of the dike collapse. The wall positioned at the downstream of the dike works more efficiently; (3) the suspended cut-off wall has little influence on the erosion initialization but exerts an effective control on its development. The seepage control efficiency increases as the wall penetration increases.

Under the influence of multiple factors, such as extreme weather conditions and human activities, flash flood disasters occur frequently in China and are very difficult to predict and anticipate. Based on the national flash flood disaster investigation and assessment project, this article focuses on small (10–50 km2) catchments and extracts 83 alternative indicators from the perspectives of rainfall, underlying surfaces, present social and economic conditions, flood control capacity, wading engineering, and monitoring and early-warning facilities. A dimension reduction processing is conducted using a principal component analysis, and 10 core and independent indicators are obtained. A risk evaluation indicator system is proposed that establishes the risk cube model, which is verified with data from 53,235 historical flash flood events in China from 1949 to 2018. The results show that flash flood risk identification based on small watersheds may effectively reflect the disaster response relationship based on rainfall and the underlying surface. In addition, 91% of historical flash floods occurred in high-, medium-, and low-risk areas, and the occurrence density in high-risk areas was double of that in low-risk areas. These evaluation results provide data support for the accurate defence, forecasting and early warning of flash flood disasters in China.

The middle route project (MRP) of the South to North Water Diversion Project is a significant infrastructure and alleviating water scarcity in Northern China. MRP suffered from untraditional siltation problems. Obvious siltation occurred in the regulating reservoir at the end of the channel and some locations with weak hydrodynamic conditions in the channel when the mineral siltation concentration in the flow is very low. To study the characteristics of the siltation and the siltation time period, an IoT based automatic siltation monitoring system using cloud was installed at the outlet of the inverted siphon project on Xiao River. Three years of online monitoring data since 2018 and the siltation samples at five sites for particle size analysis were collected. The monitoring data shows that siltation mainly occurs during March to October, and almost no siltation occurs in winter. The maximum siltation speed can reach 390 mm per day. The particle size of the siltation gradually increases from upstream to downstream, which mainly occurs in the range above 100 m. The organic matter contained in the siltation shows a significant increase from 40.3 to 86.4% at upstream and downstream sampling position, respectively. Monitoring results shows the main body of the siltation in the MRP is not the traditional siltation but the remnants of the algae that proliferate in large numbers. During March to October, the temperature is suitable for the proliferation of algae which attaches to the sediment particles and gradually grows downstream with the flow.

Flood control risk is one of the main risks affecting the safe operation of large-scale water transfer projects. Systematically identifying the flood control risk in the project and carrying out risk classification and hierarchical management are problems for project managers. Based on the theory of system and risk assessment, this paper starts with the various risk sources and risk events involved in the whole process of the flood disaster chain, the risk of flood disaster factors, the exposure of the disaster-bearing body, and the vulnerability of the disaster-originating environment are combined. Then, we systematically and comprehensively identify the flood control risks of a large-scale water transfer project, which are divided into four types of risk elements: rainfall–runoff; confluence and flow capacity; the geological characteristics of canal section; economic and social layouts. Specific risk factors are identified for each type of risk element, and a flood control risk evaluation index system for a water transfer project is proposed. According to the framework of the analytic hierarchy process (AHP), a quantitative assessment of comprehensive flood control for water transfer projects is carried out. Taking the middle route of the South-to-North Water Transfer Project in China as an example, this paper evaluates the integrated flood control risks of 39 engineering units, identifies six units with higher risk levels, analyzes the causes, and suggests engineering and non-engineering countermeasures to prevent and reduce the occurrence of risk accidents. This method is not only used for comprehensive flood control risk assessment and risk management in the operation and management stage of the large-scale inter-basin water transfer project, but also has a reference value in considering the optimal layout of the project water transmission line from the perspective of flood control in the planning and design stage.

Since the 19th National Congress of the Communist Party of China, the high-quality development of China’s economy has put forward higher requirements for scientific and technological support of water safety guarantee. The flood control conditions of the design unit of water transfer project have changed to a certain extent compared with the initial design stage, especially the underlying surface of rivers and engineering conditions in the northern region have changed greatly, which has seriously affected the conveyance safety of water transfer project and the supply guarantee of water receiving area. Therefore, it is necessary to evaluate the change degree of flood control conditions in the design unit of water transfer project, and point out the weak links of flood control, so as to guide the flood control safety construction of the left bank area. In this paper, from the aspects of the change of underlying surface conditions, the actual flood discharge conditions of upstream and downstream, and the actual discharge capacity of buildings, the change of actual flood discharge conditions of water transfer project design unit compared with the original flood control design conditions is comprehensively considered. According to the principle of relatively independent, quantifiable and easy to obtain between indexes, the evaluation index system of flood control design condition change is established, On the basis of determining the classification standard for the quantitative and qualitative indexes, the entropy weight and fuzzy mathematics comprehensive evaluation model are used to evaluate the change degree of the current flood discharge conditions and the original flood control design conditions of each design unit of the water transfer project. For the units with large changes in flood control design conditions in the evaluation results, corresponding measures to eliminate risk factors and risk control will be taken. Finally, taking the flood control risk evaluation of a management office in a large-scale water transfer project as an example, this paper evaluates the change degree of the current flood discharge conditions and the original flood control design conditions of 16 three-level units in the management office. The results show that there are 6 three-level units with changes in the original flood control design conditions, including 4 great changes and 2 significant changes. The application example shows that the entropy weight and fuzzy mathematics comprehensive evaluation model can accurately reflect the change degree of each three-level design unit in the management office, and play a certain technical support role in the study of the performance change law in the life cycle of water transfer project and the theory of water transfer project and service environment evaluation. PACS: J0101

When carrying out the inter-basin water diversion project, we must face problems such as the long river routing distance and complex hydro-meteorological conditions along the main channel. Under the condition of the unchangeable standard of the designed rainstorm, the changes of the underlying surface could inflect the designed flood. As a result, the drainage channel is not as consistent as the designed condition, therefore the flood discharge condition in the upper and lower sub-reaches changes, and the actual flow capacity of the hydraulic structure is reduced compared with the designed conditions. The above-mentioned situations could cause risk event of the rising of designed flood level in the left bank and will cause local flood risk, and also cause corresponding economic losses and social impacts. In this study, for the first time, the fishbone-diagram method is applied to identify the local flood risk factors of the inter-basin water diversion project. By using this method, the changes of characteristic values of watershed above cross section, villages, factories or other buildings at the outlet location of the drainage structures etc. are identified as the local flood risk factors, and there are mainly eight types of local flood risk factors which can be taken into consideration when selecting engineering and non-engineering measures to reduce or eliminate the risk factors. PACS: J0101

针对山丘区中小流域洪水预报面临的产流机制混合多变和模型参数难以获取的问题,提出了适用于缺资料地区的中小流域时空变源混合产流模型和基于机器学习CART的参数区域化方法.在小流域地貌水文响应单元划分基础上,利用GARTO非饱和下渗计算模型,从超渗/蓄满机制的平面混合、垂向混合和时段混合三个方面构建时空变源混合产流模型,并采用机器学习CART方法进行模型参数区域化研究.选取不同地貌类型区的15个流域和河南省19个小流域实测降雨径流资料分别对模型适用性和参数区域化方法进行了验证.结果表明,通过与国内外8个水文模型的对比验证,时空变源混合产流模型模拟平均纳什系数为0.78,比其他模型提高约20％;利用本模型和CART参数区域化方法在河南省19个流域计算的平均纳什系数为0.70,比参数随机移植结果提高了35％,本模型和参数区域化方法在山丘区中小流域洪水模拟中应用效果较好.

The spatiotemporal evolution of vegetation and its influencing factors can be used to explore the relationships among vegetation, climate change, and human activities, which are of great importance for guiding scientific management of regional ecological environments. In recent years, remote sensing technology has been widely used in dynamic monitoring of vegetation. In this study, the normalized difference vegetation index (NDVI) and standardized precipitation–evapotranspiration index (SPEI) from 1998 to 2017 were used to study the spatiotemporal variation of NDVI in China. The influences of climate change and human activities on NDVI variation were investigated based on the Mann–Kendall test, correlation analysis, and other methods. The results show that the growth rate of NDVI in China was 0.003 year−1. Regions with improved and degraded vegetation accounted for 71.02% and 22.97% of the national territorial area, respectively. The SPEI decreased in 60.08% of the area and exhibited an insignificant drought trend overall. Human activities affected the vegetation cover in the directions of both destruction and restoration. As the elevation and slope increased, the correlation between NDVI and SPEI gradually increased, whereas the impact of human activities on vegetation decreased. Further studies should focus on vegetation changes in the Continental Basin, Southwest Rivers, and Liaohe River Basin.

As an effective technique to improve the rainfall forecast, data assimilation plays an important role in meteorology and hydrology. The aim of this study is to explore the reasonable use of Doppler radar data assimilation to correct the initial and lateral boundary conditions of the numerical weather prediction (NWP) systems. The Weather Research and Forecasting (WRF) model is applied to simulate three typhoon storm events on the southeast coast of China. Radar data from a Doppler radar station in Changle, China, are assimilated with three-dimensional variational data assimilation (3-DVar) model. Nine assimilation modes are designed by three kinds of radar data and at three assimilation time intervals. The rainfall simulations in a medium-scale catchment, Meixi, are evaluated by three indices, including relative error (RE), critical success index (CSI), and root mean square error (RMSE). Assimilating radial velocity at a time interval of 1 h can significantly improve the rainfall simulations, and it outperforms the other modes for all the three storm events. Shortening the assimilation time interval can improve the rainfall simulations in most cases, while assimilating radar reflectivity always leads to worse simulations as the time interval shortens. The rainfall simulations can be improved by data assimilation as a whole, especially for the heavy rainfall with strong convection. The findings provide references for improving the typhoon rainfall forecasts at catchment scale and have great significance on typhoon rainstorm warning.