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Huaihe River is one of the seven largest rivers in China, in which floods occurred frequently. Disasters cause huge casualties and property losses to the basin, and also make it famous for high social vulnerability to floods. Based on the latest social-economic data, the index system of social vulnerability to floods was constructed, and Catastrophe theory method was used in the assessment process. The conclusion shows that social vulnerability as a basic attribute attached to urban environment, with significant changes from city to city across the Huaihe River basin. Different distribution characteristics are present in population, economy, flood prevention vulnerability. It is important to make further development of social vulnerability, which will play a positive role in disaster prevention, improvement of comprehensive ability to respond to disasters. [ABSTRACT FROM AUTHOR]

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Runoff prediction has a pivotal role in the flood warning system. For mountainous small-sized watersheds, establishing a reliable and efficient model to forecast flood is multifarious and disorderly work. The ensemble framework for flash flood forecasting (EF5) provides a new opportunity to model simply and practically. However, the EF5 has not successfully verified its feasibility in mountainous small-sized basins and without satellite rainfall products. This paper used the framework to structure a flood forecast model without any satellite rainfall support for a small watershed in China where flash floods occur frequently. The evaluation indicated that the EF5 model performs well in flood prediction cases, with over 0.9 Pearson's linear correlation coefficient (PCC) values and over 0.85 Nash–Sutcliffe coefficient of efficiency (NSE) values during the validation. In addition, statistical results revealed that the EF5 model can maintain a PCC of more than 0.9, NSE of more than 0.7, and flood peak bias (FPB) of more than -0.2 when the forecast lead time exceeds 3 h. Numerous indicators and plots proved the excellent effect of the model forecasting. Considering the convenience and validity of this framework, the research and verification of the EF5 model in the mountainous small-sized basin are of significance to flood prediction. [ABSTRACT FROM AUTHOR]

Reliable urban flood modeling is highly demanded in emergency response, risk management, and urban planning related to urban flooding. In this paper, the Storm Water Management Model (SWMM) is adapted to simulate urban rainfall‐runoff and pipe drainage processes within the Dominant river tracing‐Routing Integrated with VIC Environment (DRIVE) model which accounts for natural river basin runoff generation and routing processes. The integrated DRIVE‐SWMM model (referred to as DRIVE‐Urban) allows to explicitly delineate the mass‐energy interactions between urban drainage system (e.g., pipes and dikes) and river networks. This presents a further step model development for accurate urban flooding prediction which is lacking in existing urban flood models and traditional hydrological models. The validity of the DRIVE‐Urban model is evaluated for three case studies in Haikou City, China, with camera observations of street inundation during typhoon landfalls and heavy rainfall events. The results show that the DRIVE‐Urban model successfully captures 62%, 69%, and 77% of the total observed inundated road‐sections for the three cases respectively. The third case study with severe flooding situation shows that the DRIVE‐Urban performance is further improved when given reliable river and tidal level information, indicating the importance of integrating river‐basin with urban hydrological and hydraulic modeling. Plain Language Summary: This study mainly focuses on the development of an urban flood model (Dominant river tracing‐Routing Integrated with VIC Environment, DRIVE‐Urban), which couples the river‐basin and urban hydrological modeling, with a great potential for large‐scale real‐time urban flood monitoring and forecasting through the current Global Flood Monitoring System. Two inundation algorithms are developed within the DRIVE‐Urban model. One is for the river inundation caused urban flooding, and the other one is for the urban inundation caused by manhole overflow or excessive runoff drainage over the city land surface. Multi‐source observations including the closed‐circuit television video, citizen photos and municipal reports are collected for model validation. The results show that the DRIVE‐Urban model simulates well the different type of urban inundations at the 5‐m high spatial resolution with a promising hit success (i.e., POD >60%) according to three case studies. In particular, the results for the typhoon Kalmaegi case show that the model performance can be improved significantly by coupling the upstream river flow simulation with the urban runoff‐routing process, which validates our strategy of including the upstream river‐basin hydrological process in the urban flood modeling. Key Points: The Dominant river tracing‐Routing Integrated with VIC Environment (DRIVE)‐Urban flood model is developed by coupling river basin and urban hydrological and hydraulic processTwo 2D inundation algorithms are implemented within the model to simulate the manhole inundating and river overflow flooding respectivelyThe validity of the DRIVE‐Urban model is well established through three case studies with observations during typhoon and rainfall events [ABSTRACT FROM AUTHOR]

Due to its wide scope, rapid development and high hazard, remote sensing images with a single time phase and local area coverage cannot meet the needs of large-scale flood detection. The problem of large-scale flooding emergency detection can be well solved by using Google Earth Engine (GEE) platform, which has the characteristics of fast data computing speed, easy image retrieval and rich remote sensing data. In this paper, we propose a method for flood detection using Sentienl-1 imagery based on the GEE platform, which can quickly accomplish a large scale flood detection. The method was validated in a flooding event that occurred around the Pearl River basin, China in June 2022. It shown that using the proposed method can not only meet the needs of basin-wide flood detection, but also extract information on the temporal dimension of the flood development status. [ABSTRACT FROM AUTHOR]

It is well known that flooding brings great losses to people's production and life, just because it is unsuspected, is extremely extensive, and has high frequency. More than half of the people in China live in flood-prone areas, and their lives and properties are threatened. Flood risk assessment is one of the measures of flood management, and it is economically and socially important to assess flood risk. The scope of the traditional monitoring and forecasting early warning system is mainly limited to the area affected by flash floods, while the objective reality of real-time monitoring, forecasting, and early warning of flood disasters in the area affected by river floods and by the scheduling of riverine terrace power stations is not possible. The multiphysics remote sensing big data fusion analysis can divide the data into grids according to the grid method in the sliding time window, filter the normal data by information entropy in each grid, judge the remaining data that may be abnormal by using local abnormality factor, and eliminate the abnormal data according to the judgment result. This paper introduces the application of remote sensing in flood control field, proposes the framework of basin flood prediction based on multiphysics field remote sensing big data fusion analysis, and designs each functional module of the system according to the object-oriented idea to realize the functions of data management, image processing, spatial analysis, and simulation output. The method can change the problems of cumbersome data processing and basic parameter rate determination in traditional hydrological methods and can find certain regularity through the connection between all related factors. Meanwhile, the use of artificial intelligence and other technical means makes the calculation speed faster and the obtained results are closer to the actual measured values, which is beneficial to guide the practical work. [ABSTRACT FROM AUTHOR]

In general, there are no long-term meteorological or hydrological data available for karst river basins. The lack of rainfall data is a great challenge that hinders the development of hydrological models. Quantitative precipitation estimates (QPEs) based on weather satellites offer a potential method by which rainfall data in karst areas could be obtained. Furthermore, coupling QPEs with a distributed hydrological model has the potential to improve the precision of flood predictions in large karst watersheds. Estimating precipitation from remotely sensed information using an artificial neural network-cloud classification system (PERSIANN-CCS) is a type of QPE technology based on satellites that has achieved broad research results worldwide. However, only a few studies on PERSIANN-CCS QPEs have occurred in large karst basins, and the accuracy is generally poor in terms of practical applications. This paper studied the feasibility of coupling a fully physically based distributed hydrological model, i.e., the Liuxihe model, with PERSIANN-CCS QPEs for predicting floods in a large river basin, i.e., the Liujiang karst river basin, which has a watershed area of 58 270 km 2 , in southern China. The model structure and function require further refinement to suit the karst basins. For instance, the sub-basins in this paper are divided into many karst hydrology response units (KHRUs) to ensure that the model structure is adequately refined for karst areas. In addition, the convergence of the underground runoff calculation method within the original Liuxihe model is changed to suit the karst water-bearing media, and the Muskingum routing method is used in the model to calculate the underground runoff in this study. Additionally, the epikarst zone, as a distinctive structure of the KHRU, is carefully considered in the model. The result of the QPEs shows that compared with the observed precipitation measured by a rain gauge, the distribution of precipitation predicted by the PERSIANN-CCS QPEs was very similar. However, the quantity of precipitation predicted by the PERSIANN-CCS QPEs was smaller. A post-processing method is proposed to revise the products of the PERSIANN-CCS QPEs. The karst flood simulation results show that coupling the post-processed PERSIANN-CCS QPEs with the Liuxihe model has a better performance relative to the result based on the initial PERSIANN-CCS QPEs. Moreover, the performance of the coupled model largely improves with parameter re-optimization via the post-processed PERSIANN-CCS QPEs. The average values of the six evaluation indices change as follows: the Nash–Sutcliffe coefficient increases by 14 %, the correlation coefficient increases by 15 %, the process relative error decreases by 8 %, the peak flow relative error decreases by 18 %, the water balance coefficient increases by 8 %, and the peak flow time error displays a 5 h decrease. Among these parameters, the peak flow relative error shows the greatest improvement; thus, these parameters are of the greatest concern for flood prediction. The rational flood simulation results from the coupled model provide a great practical application prospect for flood prediction in large karst river basins. [ABSTRACT FROM AUTHOR]

The peak flow of a flash flood is an important factor concerning flood risk management. Empirical approaches based on measured hydrological data at gauging sites can be applied to estimate flood peaks; however, they are not suitable for catchments with limited available rainfall and streamflow data, especially ungauged small mountainous catchments. This paper presents a novel method for estimating flash flood peaks in small mountainous catchments based on derived distributed geomorphologic unit hydrographs (DGUHs) using fuzzy c -means (FCM) clustering. Data from 25,130 small mountainous catchments in Gansu Province, located in northwest China, are selected for this study. The results show that (1) the derived DGUHs can reflect the strong nonlinearity of the hydrologic process over small mountainous catchments; (2) the FCM clustering method performs well in the clustering of the DGUHs, and the optimal number of clusters in this case is insensitive to the excess rainfall intensity; and (3) empirical formulas based on catchment characteristics (including drainage area, length, and the longitudinal gradient of main channel networks) can estimate flash flood peaks accurately in small mountainous catchments. [ABSTRACT FROM AUTHOR]

The construction and the operation of multi-reservoir has severely altered the downstream design flood. This paper quantified the flood frequency modifications caused by multi-reservoir regulation with recorded data and a numerical model composed of peaks-over-threshold (POT) samples and time-trend models. The whole research was carried out in Dongjiang River Basin, Southern China. Results showed that (1) Sampling with the POT method could eliminate fake changes in change detection and prevent the underestimation of the design floods with an exceeding probability over 90% and the overestimation of the design floods with an exceeding probability less than 80%; (2) Multi-reservoir regulation severely reduced design flood when the exceeding probability was greater than 1% and smaller than 95%. The 10-year floods were mitigated the most. However, when the exceeding probability was over 95%, the impact was insignificant; (3) Flood mitigation was positively correlated to the number of reservoirs, but negatively correlated to the distance between the affecting reservoir and the affected station. However, when multiple reservoirs took effect on the design flood in one station, the closest reservoir did not necessarily take the most effect. The results of this research will amplify the theoretical basis for flood protection and the planning of regulation. [ABSTRACT FROM AUTHOR]

Water-related cultural heritage architecture (WRCHA) represents a globally significant and potentially hybrid heritage found across river basins worldwide. Its spatial and temporal evolution characteristics offer insight into the development trends of river basin environments, yet their value within water-related cultural and environmental systems remain incompletely assessed. This study undertakes qualitative and quantitative analyses of the historical spatial and temporal distributions, influencing factors, and environmental changes affecting the water-related culture, climate, population, and urban areas of 295 WRCHA sites in the Li River Basin of China, employing drought–flood indices, GIS analyses, random forest algorithms, and other methodologies. The results reveal that (1) the Lishui Basin contains a significant distribution pattern of agglomeration for WRCHA within the river basin, concentrated along the river, at low altitudes, with minimal terrain variation, and radiating around ancient governance centers, with varying increases observed across different periods and aggregation zones and with significant spatial and temporal heterogeneities; (2) the distribution pattern is influenced by joint natural and human factors, closely tied to variables such as the river network density, DEM, population changes, and distance to ancient government sites; (3) the combination of the architecture's location and elevation, along with drought–flood curves, reflects the position of the ancient riverbed of the Lishui River and its historical maximum water level. The quantity of new constructions, in conjunction with the distance from ancient government centers and the trends in population change, thus indicates the urban scale and the frequency and severity of disasters. This study provides a research paradigm and historical reference model for investigating environmental changes in watershed systems, aiding in clarifying the historical human–water symbiosis pattern in the middle reaches of the Yangtze River. Such insights will furnish a scientific basis for future regional ecological planning and watershed environmental management. [ABSTRACT FROM AUTHOR]

• A framework is proposed to analyze compound floods with multiple driving factors. • Local rainfall has a larger impact on the flooding levels of the GBA, China. • Flooding area is about 50 km2 and 1.3 m flooded depth for the worst scenario. • Findings have important implications to GBA's flooding control and management. Low-lying coastal cities are vulnerable to compound floods caused by many factors including river flows, tides and local rainfall. Many previous studies focus on the impacts of rainfall and tidal levels (two driving factors) on estuaries or regions near the main single river, while research about the three influencing factors on the floods for complex urban river networks remain relatively scarce. This paper proposes a framework to simulate the coastal compound flooding process, with a series of scenarios designed to explore the main flooding drivers and their worst possible combination. The approach is used to quantify the induced flooding consequences, in which a two-dimensional model is employed to conduct a case study for a city with complex river networks. The Qianshan River basin in Zhuhai City, located in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) of China is used as the case study. Application results show that: (i) the occurrence and properties of flooding in this coastal area are jointly affected by upstream river flows, downstream tidal levels and local rainfall intensity with different contribution levels, (ii) compared to tidal levels, local rainfall intensity has a larger impact on the flooding levels of the study region, and (iii) the worst scenario where a 50-year return period upstream river flow co-occurring with high tide and a 100-year return period rainfall can produce a flooding area up to 50 km2 for the study region, with an average of 1.3 m flooded depth. The proposed framework with its findings offers an in-depth understanding and quantification of coastal urban flood characteristics within the GBA of China. [ABSTRACT FROM AUTHOR]

Accurate flood event simulation and prediction, enabled by effective models and reliable data, are critical for mitigating the potential risk of flood disaster. This study aims to investigate the impacts of spatiotemporal resolutions of precipitation on flood event simulation in a large-scale catchment of China. We use high-spatiotemporal-resolution Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) products and a gauge-based product as precipitation forcing for hydrologic simulation. Three hydrological models (HBV, SWAT and DHSVM) and a data-driven model (long short-term memory (LSTM) network) are utilized for flood event simulation. Two calibration strategies are carried out, one of which targets matching of the flood events, with peak discharge exceeding 8600 m 3 s -1 between January 2015 and December 2017, and the other one is the conventional strategy for matching the entire streamflow time series. The results indicate that the event-based calibration strategy improves the performance of flood event simulation compared with a conventional calibration strategy, except for DHSVM. Both hydrological models and LSTM yield better flood event simulation at a finer temporal resolution, especially in flood peak simulation. Furthermore, SWAT and DHSVM are less sensitive to the spatial resolutions of IMERG, while the performance of LSTM obtains improvement when degrading the spatial resolution of IMERG-L. Generally, LSTM outperforms the hydrological models in most flood events, which implies the usefulness of the deep learning algorithms for flood event simulation. [ABSTRACT FROM AUTHOR]

The seasonal design flood (SDF) plays an important role in guiding water resources management and floodwater utilization. To estimate the SDF, studies have established the joint distribution of flood peak (i.e., annual maximum daily discharge) and timing (i.e., occurrence date) and assumed a monotonic dependence structure (MDS) between these two flood elements within the bivariate framework. However, the MDS assumption cannot fully represent the flood seasonality dynamics, which might affect the reliable assessments of the bivariate flood risks. To solve this issue, this study proposes a mixed-copula function to model a nonmonotonic dependence structure (NMDS) between flood peak and timing, and then derives the SDFs under nonstationary conditions by using the equivalent reliability method. As the NMDS can characterize the turnaround relationship between peak discharge and timing, the NMDS performs better at modeling observed (1951–2019) flood peaks than the traditional MDS, as reported by a case study in the Wujiang River Basin, China. The results suggest that, within the flood season from May to October, the NMDS-based estimation framework produces higher SDFs in June and July but smaller SDFs in the remaining months than the annual design flood. This allows for reducing the flood control storage capacity to improve floodwater utilization downstream without increasing flood risk. After further investigating the potential effects of a changing environment on peak discharges in the future (2020–2099), it is expected that reservoir regulation might play a more important role in flattening the seasonal fluctuations in floods. These results provide rich information as references for flood risk management and floodwater resources utilization under a changing environment. [ABSTRACT FROM AUTHOR]

Concerns regarding urbanization impacts on floods gradually moved from end-of-pipe solutions, based on open channel hydraulics improvement, to imperviousness ratio limiting and then to land use control and to integrated planning at local and large scale levels. The Niushou River basin is one of the fastest urbanizing areas in Nanjing City, East China, however, the high urban land percentage has leaded to series of flooding events. The paper aims to reveal the impact of imperviousness ratio, patterns and drainage system on flooding areas based on the unit of catchment and Storm Water Management Model (SWMM). The following conclusions were reached. 1) The ratio or spatial characteristics of the impervious surface affected the runoff volumes and associated floods areas. Despite the well-established drainage system, the high imperviousness ratio, particularly clustered pattern in locations such as hydrological sensitive zones aggravated the flooding tension across the basin. 2) The poor drainage hydraulic efficiency in local areas, and the lack of integral processes of infiltration, yield, storage and discharge in local catchment and larger basin are also significant factors. 3) The Niushou River basin development should improve the drainage transformations from a single local, short-term drainage process into integral, elastic processes of infiltration, yield, storage, and discharge. [ABSTRACT FROM AUTHOR]

The Huaihe River Basin is a transitional river located in the transitional climate zone in China, and it has been frequently hit by big floods and suffered from flood disasters. Flood control and management of the areas are of vital importance of the Huaihe River Basin in its social and economic development. In this paper, pioneer works of summarizing the flood management has been done for the Huaihe River in China. It firstly introduces flood and flood disasters of the River basin, Moreover, the paper summarizes achievements in flood control and management. Furthermore, it discusses experiences and enlightenment in flood control and management and draws conclusions for the research. [ABSTRACT FROM AUTHOR]

[Display omitted] • We develop a framework to estimate future Flood Regulation Service (FRS) dynamics. • The framework considers the joint impacts of both climate change and urbanization. • Future FRS supply–demand imbalance will continue to intensify. • Future impacts of climate change in Baiyangdian Lake Basin cannot be ignored. • Cities should actively adapt to climate change to reduce flood risks in the future. Estimating the supply–demand relationship's future dynamics and trend of Flood Regulation Service (FRS) is important for preventing flood risks and choose an appropriate future development path. Less attention has been given to assessing the future trends in this relationship under the joint influence of climate change and urbanization. This paper aims to quantify future trends in the FRS supply–demand relationship under the joint impacts of climate change and urbanization. To this end, we took the Baiyangdian Lake Basin as an example and developed a "Climate Change-Urbanization-FRS supply and demand" framework by combining future climate scenarios from the ScenarioMIP (Scenario Model Intercomparison Project) dataset and socioeconomic data from the SSPs (Shared Socioeconomic Pathways) to estimate the FRS supply and demand dynamics during the period from 2020 to 2050. The results show that the framework has a reasonable ability to estimate the FRS supply–demand relationship. The correlation coefficients between estimated supply–demand ratios and the historical values exceeded 0.9. The average FRS supply–demand ratio among the four scenarios shows a significantly downward trend between 2020 and 2050. The FRS is expected to decrease by 0.159 (36.6 %), resulting in the imbalanced area increasing from 38.3 km2 (4.1 % of the whole basin) to 11049.7 km2 (36.2 %). We believe that although urbanization is the dominant factor contributing to the intensified imbalance trend (with a contribution of 71.2 %), influences from climate change also cannot be ignored (with a contribution of 28.8 %). Therefore, adaptation actions to climate change should be actively taken to reduce urban flood risks in the future. [ABSTRACT FROM AUTHOR]

The accurate design flood of hydraulic engineering is an important precondition to ensure the safety of residents, and the high precision estimation of flood frequency is a vital perquisite. The Xiangjiang River basin, which is the largest river in Hunan Province of China, is highly inclined to floods. This paper aims to investigate the annual maximum flood peak (AMFP) risk of Xiangjiang River basin under the climate context employing the Bayesian nonstationary time-varying moment models. Two climate covariates, i.e., the average June-July-August Artic Oscillation and sea level pressure in the Northwest Pacific Ocean, are selected and found to exhibit significant positive correlation with AMFP through a rigorous statistical analysis. The proposed models are tested with three cases, namely, stationary, linear-temporal and climate-based conditions. The results both indicate that the climate-informed model demonstrates the best performance as well as sufficiently explain the variability of extreme flood risk. The nonstationary return periods estimated by the expected number of exceedances method are larger than traditional ones built on the stationary assumption. In addition, the design flood could vary with the climate drivers which has great implication when applied in the context of climate change. This study suggests that nonstationary Bayesian modelling with climatic covariates could provide useful information for flood risk management. [ABSTRACT FROM AUTHOR]

Farmland in southern China is prone to flooding and waterlogging alternation after short-term heavy rainfall. Single drainage form cannot meet the requirements of the farmland flooding and waterlogging removal. Drainage measures and layout forms should be explored to alleviate flooding and waterlogging threat and improve crop yield. So, based on an indoor sand tank experiment, this paper presents a combined drainage form: conventional subsurface drainage as an auxiliary drainage measure and is alternatively combined with open ditch (OD), filter drainage (FD), conventional (CD) and improved subsurface drainage (ID), respectively, under equal and unequal drain depth. The performance of different combined drainage forms and the effect of auxiliary drainage measures are discussed for stable ponding and receding water. During the experiment, two factors—drainage measure and drain depth—are considered. The results indicate that compared with the conventional subsurface drainage alone, the flow rates of the open-ditch, thin-improved, and thick-improved subsurface drainage combined with conventional subsurface drainage can be increased by 22.4–32.3%, 10.6–16.2%, and 29.8–32%, respectively, under equal drain depth in stable ponding water. Among the four combined drainage forms, the flow rate of shallow–deep combination is 8.1–17.1% higher than that of the shallow–shallow combination. Compared with a single drainage form, the flow rates of the combined drainage have the same change characteristics over time. Additionally, the use of auxiliary, conventional, subsurface drainage can improve the flooding and waterlogging removal efficiency in farmland. For the combined drainage forms, the contribution degree of the open-ditch and thin-improved subsurface drainage is 51.3–56.7%, while the thick-improved subsurface drainage is approximately 61.0%, under equal drain depth conditions in the flooding removal process. Moreover, open-ditch and thick-improved subsurface drainage combined with conventional subsurface drainage have significant advantages in flooding and waterlogging removal, which were 11.5–38.1% and 37.1–48.6% faster than conventional subsurface drainage in flooding removal time, 14.3–157.1% and 14.3–44.4% faster than conventional subsurface drainage in the waterlogging removal time. The synergistic application of shallow–deep and medium–medium combinations can be carried out by exploiting the advantages of each drainage measure. The experimental flow rate observation is in good agreement with the theoretically calculated value, with a relative error of less than 5%. These research findings could provide technical support for the increased application of combined drainage forms in areas prone to flooding and waterlogging. [ABSTRACT FROM AUTHOR]

Flood Detection and Service (FD&S) is of great significance to flood management and decision-making. But most of the state-of-art FD&S methods are weak in timeliness and extensibility, and could not reflect flood processes. To resolve the abovementioned problems, this paper proposed the process-based FD&S (PFD&S) method based on Sensor Web. PFD&S is a four-layer architecture the core components of which are the access adapter and process-based detection rules and it is capable of heterogeneous sensor access, flood detection, and flood phase adaptive services. A prototype was developed based on the PFD&S method. Two floods occurring in the Huanghan basin (Hubei, China, with the area of 5.04 × 104 km2) during July 2016, was selected as the case studies to validate the PFD&S method and prototype. The results demonstrated that the proposed PFD&S method and prototype could achieve the instant flood process detection and services in 2.7 min. • The PFD&S method was proposed to achieve the rapid flood process detection and flood phase adaptive services. • The PFD&S prototype was designed and implemented based on the PFD&S method. • The method and prototype were sensor web - based, with universality and extensibility. [ABSTRACT FROM AUTHOR]

This paper investigates the annual and seasonal precipitation variability at 20 stations over upper Indus River basin (UIRB) by using an innovative trend analysis (ITA) method, Mann-Kendall (MK) and Sen's slope estimator tests. Moreover, the annual precipitation time series is investigated for five precipitation intensities i.e. light, low, moderate, high and heavy; and on a seasonal scale for extreme values i.e. light and heavy precipitation intensities. Annual precipitation showed an increasing trend at four stations in the northeast region and a decreasing trend at two stations in the southeast region of UIRB. On a seasonal scale, the results showed that Garidopatta, Peshawar, Astore and Gupis stations are more sensitive to seasonal precipitation variabilities. Changes in heavy precipitation (> 90th percentile) are found more severe in winter and summer seasons in the northern regions suggesting a probable flooding aggravation. However, a reduction in flooding event observed during the spring season in the southern regions of the UIRB indicating a shift in the precipitation regime from south to north. The results of light precipitation (< 10th percentile) intensity showed that winter and spring seasons are more vulnerable to the occurrence of prolonged drought events. Moreover, ITA method has the advantage of detecting the sub-trends in the precipitation series because of its ability to present the results in graphical format. The results of this study could help to understand seasonal and annual precipitation variability over UIRB and will have imperative insinuations for further studies. [ABSTRACT FROM AUTHOR]

The hydrologic data series are nonstationary due to climate change and local anthropogenic activities. The existing nonstationary design flood estimation methods usually focus on the statistical nonstationarity of the flow data series in the catchment, which neglect the hydraulic approach, such as reservoir flood regulation. In this paper, a novel approach to comprehensively consider the driving factors of non-stationarities in design flood estimation is proposed, which involves three main steps: (1) implementation of the candidate predictors with trend tests and change point detection for preliminary analysis; (2) application of the nonstationary flood frequency analysis with the principle of Equivalent Reliability (ER) for design flood volumes; (3) development of a nonstationary most likely regional composition (NS-MLRC) method, and the estimation of a design flood hydrograph at downstream cascade reservoirs. The proposed framework is applied to the cascade reservoirs in the Han River, China. The results imply that: (1) the NS-MLRC method provides a much better explanation for the nonstationary spatial correlation of the flood events in Han River basin, and the multiple nonstationary driving forces can be precisely quantified by the proposed design flood estimation framework; (2) the impacts of climate change and population growth are long-lasting processes with significant risk of flood events compared with stationary distribution conditions; and (3) the swift effects of cascade reservoirs are reflected in design flood hydrographs with lower peaks and lesser volumes. This study can provide a more integrated template for downstream flood risk management under the impact of climate change and human activities. [ABSTRACT FROM AUTHOR]

The stable oxygen isotope composition of precipitation (δ18Op) in southern China is considered as a valuable proxy of climatic conditions. However, their interpretations have been controversial. In this study, based on the observed and simulated data (isoGSM2) on oxygen isotope composition of precipitation, the linkage between summer precipitation (P) and δ18Op in the Dongting Lake basin and their possible influencing factors were investigated. The results indicate that the interannual variation of summer δ18Op is consistent with that of annual δ18Op. They both show a significantly negative correlation with the summer P, suggesting that the stable isotope composition in precipitation may be considered as a proxy of summer precipitation in the Dongting Lake basin. Statistically, the amount effect and circulation effect are significant in the isotope composition of precipitation in the basin. Based on either the observed data in Changsha or the simulated data for the basin, the local amount effect appears more important than large‐scale circulation for δ18Op during extreme summers. These results can potentially improve the reconstruction of paleoclimate in the East Asian monsoon region. Further study is needed to determine the contribution of local and large‐scale factors to the oxygen isotope composition of precipitation and to quantify the integral rainout along the moisture transporting paths. [ABSTRACT FROM AUTHOR]

The check dam is the most important engineering measure in place for controlling water and preventing soil loss in the gully on the Loess Plateau. This study assesses Jiuyuangou, a typical governance watershed of the Loess Plateau, and studies the influence that the check dam system has on the dynamics of sub-storm floods over different return periods by coupling the one-dimensional hydrodynamic MIKE 11 model with the distributed hydrological MIKE SHE model. This research demonstrates the following: The check dam system significantly reduces a flood's peak flow and total volume in the basin and also increases the flood duration. The former effects are greater than the latter effect. The flood peak's rate of reduction, the flood volume's rate of reduction and the flood duration's rate of increase all increase linearly as the number of check dams increase. Of all dam systems, the check dam has the best linear response to a flood peak's rate of reduction. The check dam system reduces the flow rate of flood runoff in the basin. After the dam is built, the average flow rate in the basin decreases by 54%. The runoff velocity of the flood is reduced by different degrees over different return periods and the average peak value decreases compared with a scenario lacking any kind of dam. The check dams have a greater impact on the runoff velocity over a shorter return period, with the velocity being 58.56% slower. Finally, the check dam system significantly reduces the intensity of erosion in the middle and lower reaches of the watershed, and it changes the distribution of the intensity compared to the original channel runoff. These findings are expected to provide a scientific basis for guiding the construction and high-quality development of check dams on the Loess Plateau. In addition, check dam construction in basins can trap water, sediment and nutrients, and it can also contribute to ecosystem diversity and maintaining ecosystem imbalances around the world. [ABSTRACT FROM AUTHOR]

Adopting new technologies to calibrate hydrological models to produce good simulations for water resources or flood risk management is one of the important research topics in hydrology field. Incorporating hydrological signatures and concepts in hydrological model calibration becomes prevalent in recent years. A good realistic simulation of hydrological processes within the catchment is more significant than only achieving good performance at watershed outlet with single-statistical-objective function. This research adopts the concept of flood scaling property, which describes the statistical relationship between flood peak, its contributing areas and additional catchment attributes, to behave as one of the constraints under multi-objective model calibration framework. Several designed calibration scenarios are tested by employing the soil and water assessment tool hydrological model. In comparison with single-objective calibration scenarios, multi-objective calibration method is recommended to obtain both good simulation at catchment outlet and sub-basins reaches. The multi-objective calibration method assists in improving the model performance in terms of flow duration curve and also in reducing the bias of long-term runoff ratio. The proposed model calibration approach reflects the hydrological interaction of flood peaks across sub-basins and takes into account the catchment antecedent wetness and climatic condition for each flood event. [ABSTRACT FROM AUTHOR]

Understanding the spatiotemporal pattern of precipitation concentration is important for the assessment of flood and drought risk and utilization of water resources. In this study, the daily precipitation concentration index in the Hai River basin in China was calculated based on the Gini coefficient obtained from the observed data of 51 meteorological stations from 1951 to 2018 and spatiotemporal pattern variations were investigated. The trends and abrupt changes of DPCI were tested by the Mann–Kendall and the Pettitt methods, respectively. The relationships among DPCI, percentage of precipitation contributed by the rainiest days, and disaster losses were discussed by the linear correlation analysis. The results showed that the DPCI value ranges between 0.6471 and 0.7938, decreases westward and northward, which is negatively related to latitude and elevation, and is positively related to longitude. Negative trends of the DPCI were found at most stations, and the PCI trends of more than 80% stations were statistically significant. Abrupt changes of the DPCI have a postponement trend from west to east with time. Daily heterogeneity of the rainfall in a year is highly correlated with the heavy rainfall amount of the 25% rainiest days. For a year, higher DPCI coupled with more precipitation is easy to cause a flood disaster; conversely, higher DPCI along with less precipitation is easy to cause a drought disaster. In the future, the risk of flood disasters would be reduced, but the drought disasters would be increased in the Hai River basin. [ABSTRACT FROM AUTHOR]

Understanding the hydrological surface condition changes, climate change and their combined impacts on flood runoff are critical for comprehending the hydrology under environmental changes and for solving future flood management challenges. This study was designed to examine the relative contributions of the hydrological surface condition changes and climate change in the flood runoff of a 45,421-km2 watershed in the Loess Plateau region. Statistical analytical methods, including Kendall's trend test, the Theisen median trend analysis, and cumulative anomaly method, were used to detect trends in the relationship between the climatic variables, the normalized difference vegetation index (NDVI), land use/cover change (LUCC) data, and observed flood runoff. A grid-cell distributed rainfall–runoff model was used to detect the quantitative hydrologic responses to the climatic variability and land-use change. We found that climatic variables were not statistically significantly different (p > 0.05) over the study period. From 1985 to 2013, the cropland area continued to decrease, while the forest land, pastures, and residential areas increased in the Jinghe River Basin. Affected by LUCC and climate change, the peak discharges and flood volumes decreased by 8–22% and 5–67%, respectively. This study can provide a reference for future land-use planning and flood runoff control policy formulation and for revision in the study area. [ABSTRACT FROM AUTHOR]

China has suffered from increasingly severe flood events in recent years, most of which are caused by heavy rains. The substantial casualties and damage caused by flooding necessitates a better understanding of precipitation extremes, especially in heavily populated urban areas. Based on L-moments from a regional perspective, this paper analyzes precipitation extremes in the Taihu Basin, utilizing annual maximum daily precipitation and partial duration series at 96 rain gages. The comparison of regional and at-site analysis results shows that the former provides more robust estimates, especially in the upper tail of a distribution (higher quantiles). Also, the use of partial duration series, which captures more information about extreme events, was found to be preferable to describe the extreme precipitation events in the Taihu Basin. Given the recently observed more frequent occurrence and greater magnitude of precipitation extremes, it is suggested that the food design standard used in the basin should be updated, especially for the urbanizing zones. [ABSTRACT FROM AUTHOR]

Precipitation data from 30 stations in the Huaihe River basin (HRB), a climatic transitional zone in east China, were used to investigate the spatiotemporal variability and trends of extreme precipitation on multitimescales for the period 1961–2010. Results indicated that (1) the spatial pattern of the annual precipitation, rainy days, extreme precipitation, and maximum daily precipitations shows a clear transitional change from the south (high) to the north (low) in the HR; it confirmed the conclusion that the HRB is located in the transitional zone of the 800 mm precipitation contour in China, where the 800 mm precipitation contour is considered as the geographical boundary of the south and the north. (2) Higher value of the extreme precipitation intensity mainly occurs in the middle of the east and the central part of the basin; it reveals a relatively distinct west-east spatial disparity, and this is not in line with the spatial pattern of the extreme precipitation total, the sum of the precipitation in 95th precipitation days. (3) Annual precipitation of 22 stations exhibits increasing trend, and these 22 stations are located from the central to the northern part. There is no significant trend detected for the seasonal precipitation. The summer precipitation exhibits a larger change range; this might cause the variation of the flood and drought in the HBR. However, the increasing trend in winter precipitation may be beneficial to the relief of winter agricultural drought. Rainy days in 12 stations, mostly located in and around the central northeastern part, experienced significant decreasing trend. Extreme precipitation days and precipitation intensity have increasing trends, but no station with significant change trend is detected for the maximum precipitation of the basin. (4) The spatiotemporal variability in the HRB is mainly caused by the geographic differences and is largely influenced by the interdecadal variations of East Asian Summer Monsoon in eastern China. The output of this paper could provide references for the practical decision making and integrated basin management of Huaihe River basin. [ABSTRACT FROM AUTHOR]

Gravity Recovery and Climate Experiment (GRACE) and its successor GRACE Follow-on (GRACE-FO) satellite provide terrestrial water storage anomaly (TWSA) estimates globally that can be used to monitor flood in various regions at monthly intervals. However, the coarse temporal resolution of GRACE and GRACE-FO satellite data has been limiting their applications at finer temporal scales. In this study, TWSA estimates have been reconstructed and then temporally downscaled into daily values based on three different learning-based models, namely a multi-layer perceptron (MLP) model, a long-short term memory (LSTM) model and a multiple linear regression (MLR) model. Furthermore, a new index incorporating temporally downscaled TWSA estimates combined with daily average precipitation anomalies is proposed to monitor the severe flood events at sub-monthly timescales for the Yangtze River basin (YRB), China. The results indicated that (1) the MLP model shows the best performance in reconstructing the monthly TWSA with root mean square error (RMSE) = 10.9 mm per month and Nash–Sutcliffe efficiency (NSE) = 0.89 during the validation period; (2) the MLP model can be useful in temporally downscaling monthly TWSA estimates into daily values; (3) the proposed normalized daily flood potential index (NDFPI) facilitates robust and reliable characterization of severe flood events at sub-monthly timescales; (4) the flood events can be monitored by the proposed NDFPI earlier than traditional streamflow observations with respect to the YRB and its individual subbasins. All these findings can provide new opportunities for applying GRACE and GRACE-FO satellite data to investigations of sub-monthly signals and have important implications for flood hazard prevention and mitigation in the study region. [ABSTRACT FROM AUTHOR]

Mapping flash flood susceptibility is effective for mitigating the negative impacts of flash floods. However, a variety of conditioning factors have been used to generate susceptibility maps in various studies. In this study, we proposed combining logistic regression (LR) and random forest (RF) models with embedded feature selection (EFS) to filter specific feature sets for the two models and map flash flood susceptibility in the mainstream basin of the Songhua River. According to the EFS results, the optimized feature sets included 32 and 28 features for the LR and RF models, respectively, and the composition of the two optimal feature sets was similar and distinct. Overall, the relevant vegetation cover and river features exhibit relatively high effects overall for flash floods in the study area. The LR and RF models provided accurate and reliable flash flood susceptibility maps (FFSMs). The RF model (accuracy = 0.8834, area under the curve (AUC) = 0.9486) provided a better prediction capacity than the LR model (accuracy = 0.8634, AUC = 0.9277). Flash flood-prone areas are mainly distributed in the south and southwest and areas close to rivers. The results obtained in this study is useful for flash flood prevention and control projects. [ABSTRACT FROM AUTHOR]

China's economic development has shifted from a high-speed growth stage to a high-quality development stage, which promotes the sustainable economic development of the Yangtze River (YR) and also improves the response capacity of the YR basin's resilience system to floods. Flood resilience (FR) is not only just the achievement of high-quality economic development (HQED) but also the key to mitigating flood damage (FD). Therefore, based on panel data from 2000 to 2019 from provincial administrative regions (PARs) in the Yangtze River basin (YRb), the entropy weight method and the quantitative regression analysis method were used to empirically investigate the impact of HQED and FR on FD in the YRb. The empirical analysis results show the following. (i) HQED has an inverted U-shaped effect on FD in the YRb, meaning that HQED will reach a certain point and reduce flood losses. (ii) The HQED model, which has been pushed forward, will significantly improve the level of FR. Various mediating effect test methods revealed that FR has a significant negative mediating effect on the process of HQED to mitigate FD. Taken together, this reveals the importance of insisting on implementing an HQED model to mitigate FD in the YRb, gradually deriving an FR that is adapted to floods and has a high self-organizing and self-restoring capacity through high-quality development. [ABSTRACT FROM AUTHOR]

Under climate change, flash floods have become more frequent and severe, and are posing a danger to society, especially in the ungauged catchments. The objective of this paper, is to construct a simple and early warning system, serving for flash floods risk management in the ungauged catchments of the Loess Plateau in China, and offer a reference for flash flood warning in other areas in the world. Considering the absence of hydrological data in the ungauged catchments, the early warning system for flash floods is established by combining the regional or watershed isograms of hydrological parameters and local empirical formulas. Therein, rainfall and water stage/flow are used as warning indices for real-time risk estimation of flash flood. For early warning, the disaster water stage was first determined according to the protected objects (e.g., residents and buildings), namely the critical water stage. The critical flow (flow threshold), was calculated based on the water stage, and the established relationship between water stage and flow using the cross-sectional measured data. Then, according to the flow frequency curve of the design flood, the frequency of critical flow was ascertained. Assuming that the rainfall and the flood have the same frequency, the critical rainfall threshold was calculated through the design rainstorm with the same frequency of the design flood. Due to the critical rainfall threshold being sensitive with different soil conditions, the design flood and frequency curve of flood flow were calculated under different soil conditions, and thus the rainfall threshold was given under different soil condition for early warning of the flash flood disaster. Taking two sections in Zichang County (within the Loess Plateau) as an example, we set the rainfall and water stage/flow thresholds to trigger immediate or preparation signals for the migration of the population along the river. The application of this method to the 7.26 flood events in 2017 in China, shows that the early warning system is feasible. It is expected that this simple early warning system can provide early warnings of flash floods in ungauged catchments in the Loess Plateau and other similar areas. [ABSTRACT FROM AUTHOR]

Flood disasters have occurred frequently in recent years, but there is no consensus on the mechanism and influencing factors. Taking the upper reaches of Weihe River Basin as a case in Western China, a soil and water assessment tool (SWAT) model was established to quantitatively simulate the impact of land use and climate change on runoff changes, while 4 extreme land-use scenarios and 24 temperature and precipitation scenarios assumptions were proposed to simulate the response of runoff to land use and climate changes. The SWAT simulation results showed that the sensitivity parameters affecting the model simulation were the CANMX, CN2, SOL_K, CH_N2, and SOL_AWC. The correlation index R2 and the efficiency coefficient ENS of the upper Weihe River were both in the range of 0.75–0.78, the relative error PS between the simulated results and the measured runoff was below 10%, suggesting the good applicability of the SWAT model in this study area. Using the improved SWAT model to simulate the peak runoff (flood) simulation value is generally smaller than the measured value, and the absolute value of the error is less than 6%. The expansion of wasteland increased the runoff by over 90% on average, the expansion of cultivated land increased the runoff by 8% on average, and the expansion of woodland and grassland increased the surface runoff by 6% on average. When the precipitation decreased by 25% and the temperature increased by 22%, the smallest runoff was obtained in the simulation. Accordingly, when the precipitation increased by 25% and the temperature decreased by 22%, the maximum annual runoff was obtained. By decomposing the contribution rate of human activities and climate change to runoff, it showed that the contribution rate of human activities to the reduction of runoff was greater than that of climate change. This study can provide scientific reference for the simulation and prediction of future floods. [ABSTRACT FROM AUTHOR]

Hubei province is located in the center of China with 56% total area characterized with mountainous area. Thus, flash flood caused by extreme rainfall has become one of the significant obstacles that highly affect the social and economic development of the province. In order to scientifically understand the mechanism of flash flood disasters and provide technological support to the local flood prevention and control work, the IWHR designed and developed a new distributed hydrological model named China-FFMS that can simulate the evolution of natural disasters and make an assessment by setting the flood water sources in line with the flow discharge. The FFMS was further applied to simulate the 8.12 flash flood disaster that occurred in the Liulin county of Hubei province on 12 August ("8.12") and fed by the data collected from the national flash flood disaster investigation and assessment. The calculated peak flow was 666.22 m3/s with an error of +13% compared with postdisaster investigation data (589 m3/s). The results showed that using a multisourced modelling approach, e.g., mixing spatiotemporal variables and sources, to simulate the flash flood process was able to accurately reproduce the flood process and the consistence of the flow discharge, thereby explaining the underlying reason of the disaster formation and evolution. Regarding the case of the Liulin county, the main factor leading to the disaster was the overlapped peak flow where the Dunne flood peak of three different tributaries from the upper reach met together at the same time. Moreover, the peak flow of the Lianhua river at the downstream of Liulin County also arrived at the same time as the upstream peak, which obstructed the flood progress and increased the damage of the disaster. According to the analysis, several suggestions and recommendations are proposed such as the improvement of the forecast and early warning system of the upstream areas, the optimization of the current flood defense plan, and the enhancement of the residents' awareness of flash flood disasters. [ABSTRACT FROM AUTHOR]

Five severe floods occurred in the Yangtze River Basin, China, between July and August 2020, and the Three Gorges Reservoir (TGR) located in the middle Yangtze River experienced the highest inflow since construction. The world's largest cascade-reservoir group, which counts for 22 cascade reservoirs in the upper Yangtze River, cooperated in real time to control floods. The cooperation prevented evacuation of 600,000 people and extensive inundations of farmlands and aquacultural areas. In addition, no water spillage occurred during the flood control period, resulting in a world-record annual output of the TGR hydropower station. This work describes decision making challenges in the cooperation of super large reservoir groups based on a case-study, controlling the 4th and 5th floods (from Aug-14 to Aug-22), the efforts of technicians, multi-departments, and the state, and reflects on these. To realize the full potential of reservoir operation for the Yangtze River Basin and other basins with large reservoir groups globally, we suggest: (i) improve flood forecast accuracy with a long leading time; (ii) strengthen and further develop ongoing research on reservoir group cooperation; and (iii) improve and implement institutional mechanisms for coordinated operation of large reservoir groups. [ABSTRACT FROM AUTHOR]

River runoff and suspended sediment play a critical role in the chemical, physical, biological processes of Earth's ecosystems. We assessed the temporal changes in runoff and sediment load at both the annual and event scales in the Gushanchuan watershed in the middle reaches of the Yellow River. A total of 330 events, from 1955 to 2018, were selected to evaluate the relationships between rainfall, runoff, and suspended sediment load at the Gaoshiya gauging station. Significant reductions were examined in the maximum runoff peaks, suspended sediment concentration, total runoff, suspended sediment yield, and ratio of event runoff to annual total, whereas the ratio of event sediment yield to annual total remained relatively stable. Hysteresis analysis suggested that the complex loops, anti‐clockwise, and figure‐eight loops accounted for 93.7% of the total events in the watershed. High positive correlations were observed between the flood‐event sediment yield, runoff volume, maximum runoff peaks, and total rainfall amount. The flow‐sediment relationship can be well fitted by the proportional model, and was weaker in 1997–2018 than those in the periods of 1955–1979 and 1980–1996. Runoff during the rising limb of the event significantly contributed to the total runoff and sediment yield. Land use changes and various soil conservation practices were responsible for the decrease in the flood frequency and magnitude. The results of this study provide useful insights into the relationships between rainfall, runoff, and suspended sediment dynamics, and their responses to soil conservation practices. [ABSTRACT FROM AUTHOR]

Observation schedules depend upon the accurate understanding of a single sensor’s observation capability and the interrelated observation capability information on multiple sensors. The general ontologies for sensors and observations are abundant. However, few observation capability ontologies for satellite sensors are available, and no study has described the dynamic associations among the observation capabilities of multiple sensors used for integrated observational planning. This limitation results in a failure to realize effective sensor selection. This paper develops a sensor observation capability association (SOCA) ontology model that is resolved around the task-sensor-observation capability (TSOC) ontology pattern. The pattern is developed considering the stimulus-sensor-observation (SSO) ontology design pattern, which focuses on facilitating sensor selection for one observation task. The core aim of the SOCA ontology model is to achieve an observation capability semantic association. A prototype system called SemOCAssociation was developed, and an experiment was conducted for flood observations in the Jinsha River basin in China. The results of this experiment verified that the SOCA ontology based association method can help sensor planners intuitively and accurately make evidence-based sensor selection decisions for a given flood observation task, which facilitates efficient and effective observational planning for flood satellite sensors. [ABSTRACT FROM AUTHOR]

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Hydropower dams are proliferating in the riparian countries of the Lancang‐Mekong River Basin (LMRB) driven by the pursuit of renewable electricity and societal resilience to flooding. However, the tradeoffs between hydropower production and flood control are unclear in a changing environment. Here, we use a hydrological variable infiltration capacity model combined with a reservoir module to quantify the relative effects of climate change and reservoir operation on flood and hydropower generation in LMRB. Results show that while climate change would increase flood magnitude and frequency, adaptive reservoir operation can reduce flood magnitude by 5.6%–6.4% and frequency by 17.1%–18.9% at the cost of 9.8%–14.4% of basin‐wide hydropower generation. Particularly, upstream reservoirs suffer more hydropower loss (5.4 times) than downstream ones when flood control is prioritized in reservoir regulation. Our findings have implications for integrated water and energy management at the transboundary river basin under climate change. Plain Language Summary: Dams and reservoirs provide two important services, i.e., flood control and hydropower generation. In this study, we seek to understand the future tradeoff between these two services provided by reservoir regulation in the transboundary Lancang‐Mekong River Basin. Using a modeling based approach, we find that climate change will likely lead to more frequent and larger flood events, but reservoir operation, by regulating water discharges and levels in streams, can effectively reduce flood frequency and magnitude at the expense of hydropower generation. Our results highlight the importance of coordinating water and energy management across countries in this transboundary river basin. Key Points: Reservoir can mitigate future flood risk from climate change at the cost of reducing hydropower generation in the Lancang‐Mekong River BasinReservoir regulation can delay the timing when flood risk exceeds the historical baseline by at least 20 years in Laos and ThailandFlood control measures would reduce hydropower at a magnitude of 5.4 times in China than downstream countries [ABSTRACT FROM AUTHOR]

The contributions of heavy rainfall and/or rapid snow and glacier melting can be quantified to help understand the evolution and recession of flood formation. In this study, the hydrologic components of river discharge in the Manas River Basin were simulated using an energy balance snowmelt model called ?UEBGrid.? The model can identify surface water inputs from rain, evaporation and snow and glacial melting. The 3-h downscaled China meteorological forcing dataset and available geographic information related to basin topography, and vegetation characteristics were used to drive the model. The surface water inputs from rain, snowmelt and glacier ice melt to the river discharge during the rain-on-snow flood on 20 July 2004, and the snowmelt flood on 29 March 2011, were explored. Detailed analyses of the temporal distributions of the three surface water inputs and their contributions to river discharge during two typical floods at a 3-h scale are presented herein. The results show that (1) changes in the hydrologic components were connected to variations in either air temperature or precipitation or a combination of the two. In early spring, sharply rising temperatures likely accelerated the snow melting rate and caused the area to be prone to flooding. In late July, torrential rainfall was the most common cause of flooding. The increased rainfall in the preceding period increased the runoff rate, thereby reducing the runoff delay time by approximately one day. (2) In the flooding example, the contributions of rain, snowmelt and glacier ice melt to the river discharge varied strongly by flood type and showed obvious seasonal characteristics. The surface water inputs to the mountain front discharge were 90% sourced from snowmelt, less than 10% sourced from the glacial melt and less than 1% sourced from effective rainfall during spring flooding. In contrast, the surface water inputs were 90% sourced from effective rainfall and 10% sourced from glacial melt during the torrential rainfall flooding process. These results can help us understand the water sources and magnitude of river discharge when floods occur. Hourly surface water inputs from rain, snowmelt and glacier ice melt to river discharge that lead to hydrologic extremes (flood events) may be of value for flood predictions, climate change risk assessments and other related applications. This study can aid in preventing flood hazards and mitigating flood disasters. [ABSTRACT FROM AUTHOR]

Flash floods present significant heterogeneity over both space and time due to diverse topographic, geomorphologic, and hydro-meteorological conditions of catchments. Accurate identification and simulation of typical flash flood types are of great significance for the mitigation of flash flood disasters at the national scale. Three flood peak indices and dynamic indices were adopted to characterize the behavioral variability of flash floods. The typical flash flood types and corresponding behavior indices were identified and simulated using statistical analysis (i.e., principal component analysis, dynamic K-means clustering, and analysis of similarity) and hydrological modelling (i.e., HEC and XAJ models). There were 177 flash flood events at the hourly scale being selected for case study from eight catchments with various climatic and geographic characteristics. Results showed that all the flash flood events were clustered into three types (named Types 1, 2, and 3). Type 1 was characterized by low peak flow intensity, early flood peak occurrence time, and thin flood process with short duration. Type 2 was characterized by low peak flow intensity, late flood peak occurrence time, and flat flood process with long duration. Type 3 was characterized by high peak flow intensity and late flood peak occurrence time. Flash flood types showed high consistency with their influencing factors (e.g., catchment forest ratio and drainage area, occurrence time and magnitude of maximum storm intensity, and concentration of a storm event). The simulation performances were basically the same for HEC and XAJ models. As for flash flood event simulations, the average relative error varied from 23.25% to 27.98%, from 11.95% to 18.19%, and from 8.30% to 18.25% for Types 1, 2 and 3, respectively. The average Nash-Sutcliffe efficiency coefficient varied from 0.39 to 0.54, from 0.76 to 0.85, and from 0.86 to 0.91, respectively. As for the six flash flood behavior indices simulations, the average relative root-mean-square error (RMSEr) varied from 0.37 to 0.69, from 0.37 to 0.41, and from 0.18 to 0.25 for Types 1, 2, and 3, respectively. The average correlation coefficient (r) varied from 0.52 to 0.68, from 0.78 to 0.85, and from 0.88 to 0.94, respectively. The flood peak indices were the best simulated for Types 2 and 3 with RMSEr varying from 0.18 to 0.28 and r varying from 0.86 to 0.91. The flood dynamic indices were the best simulated for Type 3 with RMSEr varying from 0.19 to 0.21 and r varying from 0.91 to 0.97. The study provided detailed flood information supports for flood management at catchment scale, and also provided new insights into flash flood simulations in small and medium-sized catchments from perspective of flood behavioral processes. [ABSTRACT FROM AUTHOR]

The precipitation pattern in the Taihu Lake basin in East China has significantly changed over the last decades, perhaps due to intensive urbanization. However, there is limited understanding of the effect of large-scale expansion of city groups on the precipitation variability, which causes a challenge for flood control in the basin. Considering the process of urbanization, we investigated in this study the variability of precipitation in different urban zones and characteristic scales and explored the influence of urban development on the rain island effect in the basin. The basin was divided into three zones (old urban area, new urban area, and suburbs) considering different degrees of urbanization. Results indicated that precipitation change and its response to urbanization had spatial differences at various characteristic scales. Urbanization exhibited a significant "precipitation enhancement effect" at the characteristic scales including annual, flood season, summer, autumn, and winter but indicated a "precipitation reduction effect" in spring. Annual (flood season) precipitation in 1990–2013 increased by 30.04 mm (16.94 mm), due to urbanization, compared with that in the preceding period. The rate of contribution of urbanization to precipitation increase was 15.6%, 41.1%, and 14.4% in summer, autumn, and winter. The enhancement effect of short duration rainfall extremes in old urban area was also discernible, and that in new urban areas would enhance along with urban expansion, complicating urban flood and waterlogging control. More effective adaptation strategies should be implemented to handle the unfavorable situation. [ABSTRACT FROM AUTHOR]

Quantifying the effects of human activities on floods is challenging because of limited knowledge and observations. Many previous methods fail to isolate different effects and reduce the uncertainty caused by small samples. We use panel regressions to derive the sensitivity of annual maximum discharges (Q) to the changing values of three human factors: urban areas, cropland areas, and reservoir indexes for large and medium dams. We also test whether the effects increase or decrease with increasing initial values of human factors. This method is applied in 757 non-nested catchments in China. Results show that a 1 % point increase in urban areas causes around a 3.9 % increase in Q with a confidence interval CI = [1.9 %, 5.7 %]. Cropland areas have no significant effect on Q. Reservoir index has a decreasing effect: a 1 unit increase in reservoir index causes a decrease in Q from 21.4 % (with CI = [11.4 %, 29.9 %]) to 6.2 % (with CI = [3.2 %, 9.1 %]) for catchments with initial reservoir indexes from 0 to 3. Among 61 catchments with significant increases in observed Q in 1992–2017, increasing urban areas cause more than 10 % increases in Q in only five (8.2 % of 61) catchments. Among 234 catchments with at least one dam and significant decreases in observed Q in 1960–2017, increasing reservoir indexes cause more than 10 % decreases in Q in 138 (59.8 % of 234) catchments. Among 1249 catchments with limited impacts from urban areas and reservoir indexes, 403 (32.3 %) catchments have significant decreases in Q during 1960–2017, and 46.7 % of the 403 catchments are located in the middle and downstream of the Yellow River Basin and the upper streams of the Hai He River Basin. This study extends the panel regression method in hydrology and sheds light on the attribution of flood changes on a national scale. [ABSTRACT FROM AUTHOR]

Drought and flood are investigated in the Pearl River basin (PRB) using long-term terrestrial water storage anomaly (TWSA) data from the mascon (mass concentration) solutions based on Gravity Recovery and Climate Experiment (GRACE) satellite measurements (2002–19) and reanalysis data (1980–2019). The GRACE mascon solutions capture two major drought periods (2003–06 and 2009–12) with similar onsets and endings over the last two decades, but show considerable differences in quantifying total drought severity. The reanalysis data significantly overestimate drought duration and severity during 1980–2000 owing to overestimated negative TWSA forced by underestimated precipitation. The GRACE mascon solutions identify four major flood events in August 2002, June 2008, and July in 2006 and 2019. The flood potential is influenced by the precipitation in both the current and antecedent months. The flood potential index of the most recent flood in 2008 showed a similar spatial pattern compared to precipitation at monthly and subbasin scales. The precipitation and TWSA in the PRB are mainly influenced by El Niño–Southern Oscillation (ENSO). TWSA exhibits a lag of 1–3 months responding to ENSO during 1980–2019. This study emphasizes the significance of removing water storage changes in new large reservoirs before long-term drought and flood characterization. The inclusion of reservoir water storage would expand (shrink) the drought duration and overestimate (underestimate) drought severity for the period before (after) reservoir impoundment and overestimate flood potential for the period after reservoir impoundment. This study highlights the intensifying drought conditions in the PRB over the last four decades under the circumstances of more frequent human activities (reservoir construction and regulation) and the complex changing climate system. [ABSTRACT FROM AUTHOR]

To assess the impact of four-dimensional variational (4D-Var) data assimilation on the performance of a land--atmosphere coupled model, the satellite precipitation of the Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) was assimilated into the Weather Research Forecast (WRF) Model, and the WRF was coupled to the hydrological model TOPX. Precipitation and evaporation were both investigated as connecting elements in the coupled model WRF--TOPX. Differing in whether the 4D-Var data assimilation and evaporation were applied, one control experiment and four experiments were performed to simulate a historical flood event that happened in the Wangjiaba watershed in eastern China. The key hydrological variables of precipitation, potential evaporation, soil moisture, and discharge in the studied flood process were evaluated. The results showed that 1) the 4D-Var data assimilation with the IMERG could reduce both the overestimations of the WRF-predicted precipitation and potential evaporation; 2) the applied 4D-Var data assimilation could improve considerably the accuracy of the soil moisture and discharges from the coupled model WRF--TOPX; and 3) evaporation was also an important factor to influence the net precipitation to affect the performance of the coupled land--atmosphere model. With the two connecting elements of precipitation and evaporation, the 4D-Var assimilation based on IMERG could improve the Nash--Sutcliffe coefficient of the coupled model WRF--TOPX from 0.483 to 0.521 at the hourly scale. These investigations can provide important implications for the land--atmosphere coupling with both the precipitation and evaporation and using the 4D-Var data assimilation with IMERG for flood simulation at a large scale. [ABSTRACT FROM AUTHOR]

Floods out of all other water problems cause very large damages in China. Previous flood management plans mainly focused on a single river by controlling its water level and conveyance capacity, while research on mitigation solutions to flooding issues in river-lake systems is scarce. This study considers the Huai River - Lake Hongze system, which is one of the largest river-lake systems in China. Very large damages associated with small floods are frequently observed in this river-lake system, although a series of flood management measures have been implemented in the Huai River Basin since the middle of the last century. An unstructured-grid finite-volume numerical model was applied to simulate hydrodynamic processes in this system, which is characterized by discrepant spatial scales between these two types of water bodies. It is found that the lake affects the upstream river flooding, but lowering the lake level would have limited effects that would be rapidly impaired by the sharp meander bend connecting the river and the lake. The artificial cutoff of this intensively-embanked bend has great potential in reducing the river stage and flood damages, as the construction of a diversion channel would shorten the flow path and increase the hydraulic gradient. This study extends the current knowledge about the hydrodynamics of river-lake systems and is beneficial to flood mitigation strategies for similar systems. [ABSTRACT FROM AUTHOR]

• A Bayesian information-theoretic approach for disclosing design flood uncertainty. • How the parameter uncertainty is propagated to bivariate design flood is uncovered. • Uncertainty in design flood is enormous and increases along with return period. • Parameters of marginal distributions contribute most to design flood uncertainty. Copula-based statistical model has been extensively used to characterize the joint probability behaviors of extreme hydro-climatic design events, such as bivariate design floods. However, parameter uncertainty, not desirably but inevitably, accompanies the copula-based design flood estimation (CDFE) model which makes the inference results of design flood ambiguous. In this paper, we develop a Bayesian information-theoretic (BIT) approach to disclose design flood uncertainty arising from parameter uncertainty in CDFE model and furthermore examine how the parameter uncertainty is propagated to design flood estimation. Two catchments located in the Yellow River Basin, China, are selected as study regions. The research we have done indicates that design flood uncertainty is considerably large under parameter uncertainty. Worse still, the uncertainty in design flood increases along with the return period, thus making the design flood inference more ambiguous. Specifically, shape and scale parameters of marginal distributions have the largest contribution to the design flood uncertainty, followed by copula parameter, finally the location parameter of marginal distribution. Furthermore, the interaction between some parameters has dominant effect on design flood uncertainty compared with the individual effect of these parameters. These findings will enable better understanding the design flood uncertainty under parameter uncertainty. Moreover, the proposed BIT approach also offers a promising technical reference for design estimation of other types of extreme hydrological events, not limited to the design flood. [ABSTRACT FROM AUTHOR]

Synchronously and accurately estimating the flood discharges and dynamic changes in the fluid density is essential for hydraulic analysis and forecasting of flash floods, as well as for risk assessment. However, such information is rare for steep mountain catchments, especially in regions that are hotspots for earthquakes. Therefore, six hydrological monitoring sites were established in the main stream and tributaries of the 78.3‐km2 Longxi River catchment, an affected region of the Wenchuan earthquake region in China. Direct real‐time monitoring equipment was installed to measure the flow depths, velocities, and fluid total pressures of the flood hydrographs. On the basis of field measurements, real‐time mean cross‐sectional velocities during the flood hydrographs could be derived from easily obtainable parameters: cross‐sectional maximum velocities and the calibrated dimensionless parameter Kh. Real‐time discharges were determined on the basis of a noncontact method to establish the effective rating curves of this mountainous stream, ranging from 1.46 to 386.34 m3/s with the root mean square errors of ≤10.22 m3/s. Compared with the traditional point‐velocity method and empirical Manning's formula, the proposed noncontact method was reliable and safe for monitoring whole flood hydrographs. Additionally, the real‐time fluid density during the flood hydrographs was calculated on the basis of the direct monitoring parameters for fluid total pressures and water depths. During the flood hydrograph, transient flow behaviour with higher fluid density generally occurred downstream during the flood peak periods when the flow was in the supercritical flow regime. The observed behaviour greatly increased the threat of damage to infrastructure and human life near the river. Thus, it is important to accurately estimate flood discharge and identify for fluid densities so that people at risk from an impending flash flood are given reliable, advanced warning. [ABSTRACT FROM AUTHOR]

Here we for the first time present a nationwide characterization of flood hazards across China. Our analysis is based on an exceptional dataset of 1120 stream gauging stations with continuous records of annual flood peaks for at least 50 years across the entire country. Our results are organized by centering on various aspects of flood peak distributions, including temporal changes in flood series and their spatial variations, the statistical distribution of extreme values, and the properties of storms that lead to annual flood peaks. These aspects altogether contribute to an improved understanding of flood hydrology under a changing environment over China and promote advances in flood science at the global scale. Historical changes in annual flood peaks demonstrate frequent abrupt changes rather than slowly varying trends. The dominance of decreasing annual flood peak magnitudes indicates a weakening tendency of flood hazards over China in recent decades. We model the upper tails of flood peaks based on the generalized extreme value (GEV) distributions. The GEV shape parameter is weakly dependent on drainage area, but it shows spatial splits tied to rainfall climatology between northern and southern China. Landfalling tropical cyclones play an important role in characterizing the upper-tail properties of flood peak distributions especially in northern China and southeastern coast, while the upper tails of flood peaks are dominated by extreme monsoon rainfall in southern China. Severe flood hazards associated with landfalling tropical cyclones are characterized by complex interactions of storm circulations with synoptic environments (i.e., mid-latitude baroclinic disturbances) and regional topography. [ABSTRACT FROM AUTHOR]