Rainstorm Events Research Articles

High-frequency monitoring during rainstorm events reveals nitrogen sources and transport in a rural catchment

Rural areas lacking essential sewage treatment facilities and collection systems often experience eutrophication due to elevated nutrient loads. Understanding nitrogen (N) sources and transport mechanisms in rural catchments is crucial for improving water quality and mitigating downstream export loads, particularly during storm events. To further elucidate the sources, pathways, and transport mechanisms of N from a rural catchment with intensive agricultural activities during storm events, we conducted an analysis of 21 events through continuous sampling over two rainy seasons in a small rural catchment from the lower reaches of the Yangtze River. The results revealed that ammonia-N (NH4+-N) and nitrate-N (NO3−-N) exhibited distinct behaviors during rainstorm events, with NO3−-N accounting for the primary nitrogen loss, its load being approximately forty times greater than that of NH4+-N. Through examinations of the concentration-discharge (c-Q) relationships, the findings revealed that, particularly in prolonged rainstorms, NH4+-N exhibited source limited pattern (b = −0.13, P < 0.01), while NO3−-N displayed transport limited pattern (b = −0.21, P < 0.01). The figure-eight hysteresis pattern was prevalent for both NH4+-N and NO3−-N (38.1% and 52.0%, respectively), arising from intricate interactions among diverse sources and pathways. For NO3−-N, the hysteresis pattern shifted from clockwise under short-duration rainstorms to counter-clockwise under long-duration rainstorms, whereas hysteresis remained consistently clockwise for NH4+-N. The hysteresis analysis further suggests that the duration of rainstorms modifies hydrological connectivity, thereby influencing the transport processes of N. These insights provide valuable information for the development of targeted management strategies to reduce storm nutrient export in rural catchments.

Effect of Nostoc commune cover on shallow soil moisture, runoff and erosion in the subtropics

Nostoc commune (Nostoc commune Vaucher ex Born & Flahault) is widely distributed in grasslands within the subtropical region. Because of its morphological changes are more sensitive to moisture, it may affect the soil’s response to infiltration-runoff processes, thus significantly influencing soil moisture dynamics, runoff, and soil loss. However, the exact effects of this phenomenon are not yet fully understood. This study established a one-year monitoring experiment of soil moisture, runoff and soil erosion in the karst area of Southwest China from September 2021 to November 2022. Using bare soil (CK) as the control, two treatments of Nostoc commune cover at 100 % coverage (C1) and 50 % coverage (C2) were set up in 3 repetitions to investigate the shallow soil’s response to Nostoc commune cover and rainfall and to quantify the effects of land cover and rainfall type on runoff and soil loss. The results showed that Nostoc commune cover significantly increased the shallow-soil water content, and the effect of increasing soil water content was more significant with increasing coverage. The cumulative soil water increment (CSWI) varied greatly under different types of rainfall due to the high water-absorbing capacity of Nostoc commune. The CSWI showed C1 < C2 < CK under light rain and moderate rain events, while the CSWI showed C1 > C2 > CK under heavy rain and rainstorm events. The temporal pattern of soil moisture changed greatly under different cover treatments and rainfall events. In particular, the response time under different rainfall types varied from 10 min to 250 min. The runoff depth and soil loss in CK were 1.19–2.15 times and 1.92–14.43 times that of C1 and C2, respectively. This indicates that Nostoc commune cover has a significant effect on controlling runoff and soil erosion. In addition, the sensitivity of runoff and soil loss to the maximum 10-min rainfall intensity was higher than that of the maximum 30-min rainfall intensity, which was attributed to the rapid swelling of Nostoc commune in the hydration process, which greatly reduced runoff and soil erosion. This study can contribute to further understanding the hydrological process and soil erosion characteristics under different surface covers in humid karst areas.

Regional difference and prevention strategy of farmland erosion induced by extreme rainstorms in the Loess area and Rocky mountain area of Northern China

AbstractElucidating the mechanism of soil erosion caused by extreme rainstorms could improve agroecosystems' resilience to the extreme climate. However, less attention has been paid to the regional difference of soil erosion during extreme rainstorms through multiple field investigations. Using six field surveys of extreme rainstorms, soil erosion and its driving mechanism were investigated on farmland in the Loess area and the Rocky mountain area. Conclusions are as follows: (1) Farmland was severely eroded in comparison with abandoned land and forest–grass land. The erosion modulus of farmland in the Loess area (51 606.51 t/km2) was greater than that in the Rocky mountain area (2582.75 t/km2). (2) In both the Loess area and the Rocky mountain area, the main erosion types on sloped cropland and terraced land were rills/gullies and embankment failure, respectively. Terracing is an appropriate measure that can effectively control farmland erosion in rainstorm events. (3) Concentrated flow is a major driver of soil erosion, with nearly all rills/gullies occurring in the flow paths. Drainage measures can prevent the formation of concentrated flow, weakening rills/gullies erosion in farmland. (4) Building bio‐embankment, planting high‐coverage crops, and implementing local protection measures could reduce the farmland erosion influenced by extremely heavy rainfall. Combining drainage and agronomic measures is the common strategy to combat extreme rainstorm events for reducing farmland erosion in the two regions. These results can be applied to improve the ability of agricultural systems to cope with extreme rainstorms.

Development of an Integrated Urban Flood Model and Its Application in a Concave-Down Overpass Area

Urban floods caused by extreme rainstorm events have increased in recent decades, particularly in concave-down bridge zones. To simulate urban flooding processes accurately, an integrated urban flood model (IUFM) was constructed by coupling a distributed urban surface runoff model based on the cellular automata framework (CA-DUSRM), a widely used pipe convergence module in the storm water management model (SWMM), with an inundation module that describes the overflow expansion process associated with terrain and land-cover. The IUFM was used in a case study of the Anhua Bridge (a typical concave-down overpass) study area in Beijing, China. The spatial-temporal variations in flood depth modeled by the IUFM were verified to be reliable by comparison with actual measurements and other simulations. The validated IUFM was used to obtain temporal variations in flood range, depth, and volume under four rainstorm scenarios (return periods of 3-year, 10-year, 50-year, and 100-year). The results showed that the surface runoff process, overflow from drainage networks, and overflow expansion process could affect the flooding status by changing the composition and spatial configuration of pervious or impervious patches, drainage capacity, and underlying surface characteristics (such as terrain and land-cover). Overall, although the simulation results from the IUFM contain uncertainties from the model structures and inputs, the IUFM is an effective tool that can provide accurate and timely information to prevent and control urban flood disasters and provide decision-making support for long-term storm water management and sponge city construction.

Resilience evaluation research on coping with rainstorm inundation under the perspective of full cycle: A case study of Beijing-Tianjin-Hebei region

With the development of global warming and urbanization, the risk of cities being affected by rainstorms is increasing and the safety of lives and properties of residents as well as the sustainable development of the urban economy and society, are facing crucial threats. As a giant complex system, the interior of a city is highly complex and interconnected. The concept of urban resilience has become an effective way of guiding cities to cope with rainstorm inundation perturbations from the perspective of the wholeness of a system. We aimed to integrate the time dimension into the study of resilience in response to rainstorm inundation to gain a deeper understanding of the process of rainstorm inundation generation and development as well as the resilience mechanism in this process. In this study, based on the basic principles of urban resilience and the occurrence and evolution mechanisms of rainstorm inundation, we propose a novel resilience evaluation method to cope with rainstorm inundation from the perspective of the entire cycle. The full-cycle process from ``rainstorm event'' to ``inundation disaster'' is divided into four phases: rainstorm incubation, rainstorm occurrence, inundation persistence, and inundation recession phases and the resilience objectives of each phase are proposed to form a regional resilience indicator set for coping with rainstorm inundation. The Google Earth Engine (GEE) platform was used to identify the spatial and temporal patterns of regional rainstorm inundation, identify the contributing indicators of specific regional rainstorm inundation, combine hierarchical analysis and expert decision analysis to determine the weight of each indicator, and build a regional resilience evaluation model to cope with rainstorm inundation. On this basis, we conducted regional resilience evaluation and type spectrum research to cope with rainstorm inundation, identify the resilience index of the research unit based on the VIKOR method, identify the regional resilience pattern under the full-cycle perspective, divide the resilience types, generate the resilience zoning map and resilience obstacle indicators, and provide guidance on the timeline for improving resilience in the region.

Analysis of the Occurrent Models of Potential Debris-Flow Sources in the Watershed of Ching-Shuei River

The areas around the Ching-Shuei River saw numerous landslides (2004–2017) after the Jiji earthquake, profoundly harming the watershed’s geological environment. The 33 catchment areas in the Ching-Shuei River watershed and five typhoon and rainstorm events, with a total of 165 occurrences and non-occurrences, were analyzed, and the training and validation were categorized into 70% training and 30% validation. A landslide disaster is deemed, for the purposes of this research, to have taken place if SPOT satellite images taken before and after an incident show a Normalized Difference Vegetation Index difference larger than 0.25, a slope of less than 30 degrees, and a number of connected grids greater than 10. The analysis was carried out using the instability index method analysis with Rogers regression analysis and artificial neural network. The accuracy rates of neural network, logit regression, and instability index analyses were, respectively, 93.3%, 80.6%, and 70.9%. The neural network’s area under the curve was 0.933, indicating excellent discrimination ability; that of the logit regression analysis was 0.794, which is considered good; and that of the instability index analysis was 0.635, or fair. This suggests that any of the three models are suitable for the danger assessment of large post-earthquake debris flows. The results of this study also provide a reference and evidence for specific sites’ potential susceptibility to debris flows.

Assessment of urban waterlogging-induced road traffic safety risk and identification of its driving factors: A case study of Beijing

Due to the increasing number of urban waterlogging events, systematic risk assessment and cause exploration are needed to ensure the safety of people's lives and property on roads. In this paper, the urban waterlogging-induced road traffic safety risk (UWRTSR) in the central part of Beijing was assessed and its driving factors were investigated. We obtained the simulation data of road inundation for five rainstorm return periods (5-year, 10-year, 20-year, 50-year and 100-year) through the InfoWorks ICM hydrodynamic model. Then, the safety risk levels of pedestrians and vehicles are evaluated with safety criteria formulas and standards, and the driving factors are identified by the Geodetector model. The results show that the proportion of the total length of road sections at different risk levels for pedestrians, stationary vehicles and moving vehicles increased with increasing rainstorm return period, while for the same road section, the risk level for pedestrians or vehicles may increase due to a higher intensity rainstorm event. The results of subsequent driving factor analysis show that slope, terrain ruggedness index (TRI) and LS factor were the top three single-factor explanatory powers, with the joint effect of slope and rain pipe capacity (RPC) possessing the largest explanatory power. It is found that a high level UWRTSR is to be generated in road sections with low drain network specifications and high slope gradients. Our research may help smooth emergency resource management, road network construction and reconstruction to mitigate the waterlogging risk.

Time-dependent probabilistic evaluation of rainstorm-induced shallow landslides considering influence of impermeable bedrock

ABSTRACT A time-dependent probabilistic evaluation method is proposed for assessing the stability of soil slopes during rainstorm events. The method considers multiple failure scenarios, including sliding along the wetting front and shallow soil–rock interface. The application of the method is demonstrated by a typical rainstorm-induced shallow landslide in Zhejiang Province, China. The results indicate that the landslides occurred when the failure probability exceeded a certain level, providing references for real-time landslide warnings. Moreover, the factors influencing the probabilistic results have been investigated. A two-dimensional limit state surface is used to explain the change of slope failure scenarios during the rainfall process, confirming the controlling role of the impermeable bedrock surface. The probabilistic evaluation results using the actual rainfall data can better reflect slope performance via comparing the computed failure probability using idealised rainfall patterns and actual rainfall data. Additionally, the parameter sensitivity analysis shows that the matric suction, the effective cohesion and the effective internal friction angle significantly influence the slope performance during the early stage of rainfall. However, during the later stage of rainfall, the saturated permeability coefficient becomes the most sensitive parameter. The applicability of the proposed method was further verified via three other typical rainstorm-induced shallow landslide cases.

Ecological Restoration and Renovation of Xin River Based on Sponge City Concept

China is currently facing various water problems such as water scarcity, water pollution, floods, and loss of aquatic habitats. These water problem syndromes are systemic and comprehensive problems that urgently require a more comprehensive solution. The proposal of the "Sponge City" theory is based on this background. According to Jiaozuo "7.20 extremely heavy rainstorm Event", based on the sponge city theory, citing ecological principles, and following ecological principles, this paper discusses the ecological transformation of the Xin River to achieve the purpose of artificially rebuilding the river ecosystem.

A Framework Based on LIDs and Storage Pumping Stations for Urban Waterlogging

Climate change has resulted in an increase in extreme rainstorm events, posing the challenges of urban waterlogging and runoff pollution. Low Impact Development (LID) is widely used to address the issues above, but its effectiveness is unknown in mountainous areas. Due to a flash flood and high flood peak, storage pumping stations are also needed to drain. Thus, a framework composed of storage pumping stations and Low Impact Developments (LIDs) was proposed based on the topography and the regional upstream and downstream relationships. The water quantity in this framework is applied to YI County in Hebei Province, China. The results showed that individual LIDs effectively reduced runoff volume, with the implementation area being more crucial than the location. Combining storage pumping stations with LIDs significantly reduces peak outflow and delays it by 5 to 51 min. The combined downstream implementation of storage pumping stations and LIDs yielded the most effective results. These findings offer important insights and management strategies for controlling waterlogging in mountainous cities of developing countries.

The Construction of Urban Rainstorm Disaster Event Knowledge Graph Considering Evolutionary Processes

Rainstorm disasters pose a significant threat to the sustainable development of urban areas, and effectively organizing diverse information sources about them is crucial for emergency management. In light of recent advances in knowledge graph theory and application technology, their notable knowledge integration and representation capabilities may offer support for dynamic monitoring and decision-making processes concerning urban rainstorm disaster events. However, conventional models do not adequately capture the spatiotemporal characteristics of these events. To fill this gap, we analyze the essence of urban rainstorm disaster events and divide their evolution into four stages, namely, pregnant, development, continuous, and decline stages. On this basis, a multilevel knowledge representation model is proposed from four layers, which are event, object–state, feature, and relationship layers, by analyzing the components and dynamic characteristics from the mechanism of urban rainstorm disaster events. The proposed model can not only express the comprehensive structure and relationships within urban rainstorm events, but also emphasize the evolution of disaster events through a series of ordered states. Moreover, we test the utility of the constructed knowledge graph through a case study of the Zhengzhou 720 rainstorm. The case study first validates that the selected machine learning models can extract the urban rainstorm disaster event information accurately by comparing them with some mainstream models. Then, it demonstrates that the knowledge graph is practical in the field of disaster knowledge representation, and disaster condition retrieval. Additionally, since the knowledge graph can show the evolution of a disaster event throughout its full life cycle, it can promote the understanding of the mechanisms of urban rainstorm disasters and pave the way for future applications of disaster prevention and reduction.

Climate change and La Niña increase the likelihood of the ‘7·20’ extraordinary typhoon‐rainstorm in Zhengzhou, China

AbstractOn 20 July, 2021, an extraordinary rainstorm occurred in Zhengzhou in Central‐North China, which caused hundreds of deaths and serious property damages, and affected 14 million people. The compound anomalous atmospheric circulations were found to be responsible for the occurrence of the extraordinary rainstorm in Zhengzhou, but the influence of large‐scale sea surface temperature anomaly (SSTA) and the contribution of climate change received less attention. This study conducts an attribution analysis based on atmospheric reanalysis and sea surface temperature (SST) observation data combined with Coupled Model Intercomparison Project Phase 6 (CMIP6) model simulation data. The results show that the return period for the extraordinary rainstorm was 250 years, which was caused by the interaction between the abnormally northerly shift of Western North Pacific Subtropical High (WNPSH) that was associated with the negative SSTA over the tropical central‐eastern Pacific (i.e., a moderate La Niña condition) and Typhoon In‐fa and Typhoon Cempaka. A persistent convergence was formed over Zhengzhou by transporting a large amount of water vapour to hinterland. The lagged response of La Niña affected WNPSH and the rainband in China. The attribution analysis shows that climate change due to anthropogenic forcings such as greenhouse gas emissions increased the risk of such a rainstorm event by 12% compared to the conditions under the natural forcings. Under the combined effects of anthropogenic forcings and La Niña, the risk of occurrence increased 47% compared to the effect of natural forcings only on similar precipitation events. This study suggests a synergistic effect of climate change and large‐scale tropical climate variability on compound extremes.

Efficiency evaluation of low impact development practices on urban flood risk

Urban flood risk assessment delivers invaluable information regarding flood management as well as preventing the associated risks in urban areas. The present study prepares a flood risk map and evaluate the practices of low-impact development (LID) intended to decrease the flood risk in Shiraz Municipal District 4, Fars province, Iran. So, this study investigate flood vulnerability using MCDM models and some indices, including population density, building age, socio-economic conditions, floor area ratio, literacy, the elderly population, and the number of building floors to. Then, the map of thematic layers affecting the urban flood hazard, including annual mean rainfall, land use, elevation, slope percentage, curve number, distance from channel, depth of groundwater, and channel density, was prepared in GIS. After conducting a multicollinearity test, data mining models were used to create the urban flood hazard map, and the urban flood risk map was produced using ArcGIS 10.8. The evaluation of vulnerability models was shown through the use of Boolean logic that TOPSIS and VIKOR models were effective in identifying urban flooding vulnerable areas. Data mining models were also evaluated using ROC and precision-recall curves, indicating the accuracy of the RF model. The importance of input variables was measured using Shapley value, which showed that curve number, land use, and elevation were more important in flood hazard modeling. According to the results, 37.8 percent of the area falls into high and very high categories in terms of flooding risk. The study used a stormwater management model (SWMM) to simulate node flooding and provide management scenarios for rainfall events with a return period ranging from 2 to 50 years and five rainstorm events. The use of LID practices in flood management was found to be effective for rainfall events with a return period of less than 10 years, particularly for two-year events. However, the effectiveness of LID practices decreases with an increase in the return period. By applying a combined approach to a region covering approximately 10 percent of the total area of Shiraz Municipal District 4, a reduction of 2–22.8 percent in node flooding was achieved. The analysis of data mining and MCDM models with a physical model revealed that more than 60% of flooded nodes were classified as "high" and "very high" risk categories in the RF-VIKOR and RF-TOPSIS risk models.

The Spatiotemporal Clustering of Short‐Duration Rainstorms in Shanghai City Using a Sub‐Hourly Gauge Network

AbstractImpacted by both the large‐scale climate change and the local hydrometeorological atmosphere, the spatiotemporal structure and properties of short‐duration extreme rainfall events in urban areas remain a complex issue. This study explores the spatiotemporal pattern variability of short‐duration rainstorm events using an exceptional sub‐hourly gauge network in Shanghai, China. A catalog of 207 “independent” rainstorms is extracted. The hierarchical clustering method is used to examine the spatial and temporal features including rainfall duration, intensity, and coverage for each district with different degrees of urbanization in the 2014–2018 period. The results point to a pronounced spatial heterogeneity with heavier rainfall magnitude in the north. In the urban core area, intensive short‐duration rainstorms hit more frequently than in other districts. Rainstorms can be divided into three types from the spatiotemporal process: Type delayed‐slow, Type advanced‐fast, and Type advanced‐slow. The delayed‐peak with a higher slope occurs more in the west and the advanced‐peak more along the coastal line. The other type of advanced‐peak storm with a reverse trend of intensity and coverage concentrates in June–August of the year and the 16:00–20:00 of the day. While 65.6% of the delayed‐peak storms only occur in one district, more than half of the advanced‐peak appear without other types at the same time. These findings show the importance of observation‐based analysis in understanding the spatiotemporal pattern of short‐duration storms and provide a reference for storm design of sub‐regions in urban areas.

Prior-Guided gated convolutional networks for rainstorm forecasting

Accurate rainstorm forecasting is crucial for the sustainable development of human society. Recently, machine learning-based rainstorm prediction methods have shown promising results. However, these methods often fail to adequately consider the prior knowledge of rainstorms and do not explicitly account for the dynamic spatio-temporal patterns of rainstorm events. This study introduces a novel end-to-end prior-informed rainstorm forecasting model that incorporates both fundamental physical priors and the spatio-temporal development patterns of rainstorms. The model utilizes a gated convolutional encoder-decoder network to effectively represent the spatio-temporal patterns of rainstorm events. A key component of the representation network is the Substantial Derivative-GuIded gated convolutional Unit (SDGiU), which updates latent states under the constraints of physical priors. Additionally, an integrated loss function is designed to minimize reconstruction errors on multiple scales and facilitate the generation of forecasts that reproduce the actual spatio-temporal patterns of rainstorm formation, development and dissipation. Experimental results on two reanalysis datasets show that the proposed forecasting model outperforms competing state-of-the-art baselines by at least 19.7% (15.0%) in overall Critical Success Index (Heidke Skill Score). Qualitative analysis indicates that the proposed model can generate predictions that are both physically consistent and spatially-temporally coherent.

Evolutionary analysis of rainstorm momentum and non-stationary variating patterns in response to climatic changes across diverse terrains

This study aims to analyze time-series measurements encompassing rainstorm events with over a century of datasets to identify rainstorm evolution and dimensional transitions in non-stationarity. Rainstorm events are identified using partial duration series (PDS) to extract changes in rainstorm characteristics, namely maximum intensity (MAXI), duration (D), total rainfall (TR), and average rainfall intensity (ARI), in response to climate change. Ensemble empirical mode decomposition is used for trend filtering and non-stationary identification to explore spatiotemporal insight patterns. Trend models for the first–second-order moments of rainstorm characteristics are used to formulate the identified mean–variance trends using combined multi-dimensional linear-parabolic regression. Best-fitting combinations of various distributions (probability density functions) and trend models for multiple characteristic series are identified based on the Akaike information criterion. We analyze the dimensional transition in rainfall non-stationarity based on sensitivity analysis using PDS to determine its natural geophysical causes. The integrated methodology was applied to the data retrieved from nine weather stations in Taiwan. Our findings reveal rainstorm characteristics of “short D but high rainfall intensity” or “lower MAXI but high TR” across multiple stations. The parabolic trend of the first-order moment (i.e., mean) of ARI, D, and TR appears at the endpoint of the mountain ranges. Areas receiving monsoons and those on the windward plain show a rising parabolic trend in the first- and second-order moments (i.e., mean–variance) characterizing MAXI, implying that the occurrence frequency and magnitude of extreme MAXI increases. Non-stationary transitions in MAXI appear for mountain ranges exposed to the monsoon co-movement effect on both windward and leeward sides. Stations in the plains and rift valleys show upgraded and downgraded transitions in the non-stationary dimensions for D, respectively.

Effects of storm runoff on the spatial-temporal variation and stratified water quality in Biliuhe Reservoir, a drinking water reservoir.

Stormflow runoff is an important non-point source of pollution in drinking water reservoirs. Storm runoff is usually very turbid and contains a high concentration of organic matter, therefore affecting water quality when it enters reservoirs. In order to investigate the impact of storm runoff on spatial-temporal variation and stratification of water quality during this rainstorm event, the inflow process of the storm runoff was studied through a combination of field investigation and simulation using the Delft3D-Flow model. Water samples were collected from Biliuhe Reservoir at four different periods: before storm runoff, storm runoff flood peak period, 1week after storm runoff, and 5weeks after storm runoff. The results showed that the input of storm runoff resulted in a significant increase in the nitrogen (N) and phosphorus (P) in the reservoir water, especially in the reservoir entrance. The concentrations of total nitrogen (TN) and total phosphorus (TP) gradually decreased after the flood peak period; however, the average concentrations of TN and TP in the entire reservoir remained higher than those before the storm runoff levels for an extended duration. The storm runoff will greatly contribute to the contamination of water quality in a reservoir, and the water quality cannot be quickly restored by self-purification in the short term. During the flood peak period, under the influence of density current, the electrical conductivity (EC) and turbidity increased significantly in the water depth of 10-15m, so that the reservoir water had obvious stratification between 10 and 15m. The form of pollutants in storm runoff was mostly in particle phosphorus. Total particulate phosphorus (TPP) concentration was 0.015 ± 0.011mg/L, accounting for 44.12% of total phosphorus (TP) concentration in storm runoff flood peak period. The process of a rainstorm caused runoff, which carried high levels of turbidity, particulate phosphorus, and organic matter. The storm runoff disrupts the stratification of the reservoir water. In terms of vertical distribution, the turbidity in the reservoir area increased to 73.75 NTU. Therefore, the occurrence of significant turbidity density flow in the reservoir is frequently accompanied by intense rainfall events. Gaining insights into the impact of storm runoff on the vertical distribution of reservoir turbidity can help managers in selecting an appropriate inlet height to mitigate high turbidity outflow.

Modeling soil loss under rainfall events using machine learning algorithms

Soil loss is an environmental concern of global importance. Accurate simulation of soil loss in small watersheds is crucial for protecting the environment and implementing soil and water conservation measures. However, predicting soil loss while meeting the criteria of high precision, efficiency, and generalizability remains a challenge. Therefore, this study first used three machine learning (ML) algorithms, namely, random forest (RF), support vector machine (SVM), and artificial neural network (ANN) to develop soil loss models and predict soil loss rates (SLRs). These soil loss models were constructed using field observation data with an average SLR of 1756.48 t/km2 from rainfall events and small watersheds in the hilly-gully region of the Loess Plateau, China. During training, testing and generalizability stages, the average coefficients of determination from the RF, SVM, and ANN models were 0.903, 0.860, and 0.836, respectively. Similarly, the average Nash-Sutcliffe coefficients of efficiency from the RF, SVM and ANN models were 0.893, 0.791 and 0.814, respectively. These results indicated that MLs have superior predictive performance and generalizability, and broad prospects for predicting SLRs. This study also demonstrated that the RF model outperformed better than the SVM and ANN models. Therefore, the RF model was used to simulate the SLR of each small watershed in the Chabagou watershed. Our results showed the four-year (2017–2020) average annual SLR of the small watersheds ranged from 0.73 to 1.63 × 104 t/(km2∙a) in the Chabagou watershed. Additionally, the results also indicated the SLR of small watersheds under the rainstorm event with a 100-year recurrence interval was 4.4–51.3 times that of other rainfall events.Furthermore, this study confirmed that bare land was the predominant source of soil loss in the Chabagou watershed, followed by cropland land and grassland. This study helps to provide the theoretical basis for deploying soil and water conservation measures to realize the sustainable utilization of soil resources in the future.

A fluctuation data grey model and its prediction of rainstorm days

Rainstorm is one of the most severe and frequent meteorological disasters. The rainstorm event brings considerable risks to the natural ecosystem and human life. There is a limited body of research on applying the grey prediction model for predicting rainstorm-related data. Based on the extreme precipitation index defined by the Expert Team on Climate Change Detection and Indices, this paper uses the data of rainstorm days from 493 weather stations in the middle and lower reaches of the Yangtze River to establish a prediction model. Improving the accuracy of rainstorm days prediction can bring valuable results to decision-makers in a specific region. The annual rainstorm days series exhibits randomness, irregular nonlinearity, fluctuations, etc.; however, the traditional grey prediction model can only identify the trend of a series but not its fluctuations, making its prediction extremely challenging. Therefore, this paper introduces a new dynamic grey action quantity based on the traditional grey model, and the parameters are solved by the hybrid technique of the genetic algorithm and the constrained fmincon function. The prediction results of the new model are compared with those of the traditional grey model, the seasonal grey model, the neural network, and the seasonal autoregressive integrated moving average. In terms of prediction accuracy and similarity of fluctuations, the results demonstrate that the new model outperforms these models.

Changes in Population Exposure to Rainstorm Waterlogging for Different Return Periods in the Xiong’an New Area, China

In the context of global climate change and urban expansion, urban residents are encountering greater rainstorm waterlogging risk. Quantifying population exposure to rainstorms is an important component of rainstorm waterlogging risk assessments. This study utilized a two-dimensional hydrodynamic model to simulate the inundation water depth and inundation area resulting from rainstorms, with return periods of 5, 10, 50, and 100 years, in the Xiong’an New Area, and overlaid the gridded population data in 2017 and in 2035 under SSP2 to assess the change in population exposure. The results show that the average inundation depth and area increase were from 0.11 m and 207.9 km2 to 0.18 m and 667.2 km2 as the rainstorm return period increased from once in 5 years to once in 100 years. The greatest water depths in the main urban areas were mainly located in the low-lying areas along the Daqing River. The total population exposed to rainstorm waterlogging for the 5-, 10-, 50-, and 100-year return periods was 0.31, 0.37, 0.50, and 0.53 million, respectively, in 2017. However, this is projected to rise significantly by 2035 under SSP2, increasing 2–4-fold compared with that in 2017 for the four return periods. Specifically, the projected population exposure is expected to be 0.7, 1.0, 1.8, and 2.0 million, respectively. The longer the return period, the greater the increase in population exposure. The proportion of the population exposed at the 0.05–0.2 m water depth to the total population exposure decreases as the return periods increases, whereas the proportion changes in the opposite direction at the 0.2–0.6 m and &gt;0.6 m depth intervals. Spatially, high-exposure areas are concentrated in densely populated main urban regions in the Xiong’an New Area. In the future, more attention should be paid to densely populated low-lying areas and extreme recurrence rainstorm events for urban flood-risk management to ensure population safety and sustainable urban development.