Frequency Of Heavy Rainfall Research Articles

Comprehensive Impacts of Climate Change on Rice Production and Adaptive Strategies in China.

The rice production system is one of the most climate change sensitive agro-ecosystems. This paper reviews the effects of current and future climate change on rice production in China. In recent decades, thermal resources have increased during the rice growing season, while solar radiation resources have decreased, and precipitation heterogeneity has increased. The increasing frequency of high-temperature stress, heavy rainfall, drought, and flood disasters may reduce the utilization efficiency of hydrothermal resources. Climate change, thus far, has resulted in a significant northward shift in the potential planting boundaries of single- and double-cropping rice production systems, which negatively affects the growth duration of single-, early-, and late-cropping rice. Studies based on statistical and process-based crop models show that climate change has affected rice production in China. The effects of climate change on the yield of single rice (SR), early rice (ER), and late rice (LR) were significant; however, the results of different methods and different rice growing areas were different to some extent. The trend of a longer growth period and higher yield of rice reflects the ability of China’s rice production system to adapt to climate change by adjusting planting regionalization and improving varieties and cultivation techniques. The results of the impact assessment under different climate scenarios indicated that the rice growth period would shorten and yield would decrease in the future. This means that climate change will seriously affect China’s rice production and food security. Further research requires a deeper understanding of abiotic stress physiology and its integration into ecophysiological models to reduce the uncertainty of impact assessment and expand the systematicness of impact assessment.

Effect of Rainfall Pattern on the Tea Production in Bangladesh: An Analysis of Socio-economic Perspectives

In Bangladesh, tea is a cash crop that significantly benefits both our economy and food security. Hence, the study aimed to assess the present pattern of tea production and the impacts of rainfall patterns on tea yield and made tea quality. The empirical data was collected from ten tea gardens covering three tea-producing administrative divisions and six districts of Bangladesh. The cost of production and profit-loss were examined to link with rainfall distribution. Likewise, the daily income of permanent workers, the job market of the contractual workers, and the workers' associated welfare were critically analyzed to understand the social impact. The results showed that the Rangpur division faces incremental dryness; in contrast, the Sylhet division exhibited a steady pattern with a high frequency of continuous heavy rainfall. The rainfall pattern of the Chottogram division was highly unpredictable and oscillated. Long spell drought and heavy showers followed by flash floods affect tea yield. According to respondents' perception, the rainfall pattern showed changing behaviours that are not favourable for tea production. The emergence of new pests and deterioration of soil characteristics were reported. Bangladesh enjoyed the most pleasant weather for tea production in 2019. The cost of production increases with the adverse weather, which increases the loss for the growers. Profit loss of growers is a determinant of the welfare of the workers impacting their livelihoods. The organic tea garden is not affected by climatic events. Consequently, the socio-economic conditions of the organic garden are safeguarded. Hence, the study recommends following organic farming to cope with climate change. Differently, it was reported that the intermediaries' hegemony in the market chain is more problematic than climate change. Policy initiatives are warranted to correct the market and to establish gardeners' and consumers' rights.

Backwater Effect of Clogging of Aquatic Plants at Fine-Particle Screens on Inland Flooding in Okayama

In low-lying Okayama city, Japan, the heavy rainfall frequency and intensity have recently increased, subjecting the city to inland flooding. Another factor increasing the inland flooding risk is fine-particle screen clogging by aquatic plants at drainage pump stations, which obstructs drainage and causes backwater. In this study, water level simulations were conducted in drainage pump station channels to clarify the inland flooding risks with and without aquatic plant clogging-induced backwater at fine-particle screens. In the Urayasu West Drainage Pump Station channels, without backwater, no inland flooding occurred under an initial water level of 70% of the channel depth and a 40 m3/s discharge. However, when backwater deeper than 0.2 m occurred under the same conditions, inland flooding occurred, indicating an increased inland flooding risk associated with backwater. Additionally, we conducted an aquatic plant distribution survey in the main Okayama city channels and proposed six priority control sections based on sections with thriving aquatic plants. Although no previous inland flooding studies have considered aquatic plant clogging-induced backwater at fine-particle screens, aquatic plants cause clogging problems and drainage obstructions at water control facilities worldwide. Therefore, this study reveals the importance of conducting water level simulations and distribution surveys in areas other than Okayama city.

New Insights into Ice Avalanche-Induced Debris Flows in Southeastern Tibet Using SAR Technology

Drastic climate change has led to glacier retreat in southeastern Tibet, and the increased frequency and magnitude of heavy rainfall and intense snow melting have intensified the risk of ice avalanche-induced debris flows in this region. To prevent and mitigate such hazards, it is important to derive the pre-disaster evolutionary characteristics of glacial debris flows and understand their triggering mechanisms. However, ice avalanche-induced debris flows mostly occur in remote alpine mountainous areas that are hard for humans to reach, which makes it extremely difficult to conduct continuous ground surveys and optical remote sensing monitoring. To this end, synthetic aperture radar (SAR) images were used in this study to detect and analyze the pre-disaster deformation characteristics and spatial evolution in the Sedongpu Basin and to detect changes in the snowmelt in the basin in order to improve our understanding of the triggering mechanism of the ice avalanche-induced debris flows in this region. The results revealed that the maximum average deformation rate in the basin reached 57.3 mm/year during the monitoring period from January 2016 to October 2018. The deformation displacement in the gully where the ice avalanche source area was located was intimately correlated with the summer snowmelt and rainfall and was characterized by seasonal accumulation. Clear acceleration of the deformation was detected after both the most recent earthquake and the strong rainfall and snowmelt processes in the summer of 2018. This suggests that earthquakes, snowmelt, and rainfall were significant triggers of the Sedongpu ice avalanche-induced debris flows. The results of this study provide new insights into the genesis of the Sedongpu ice avalanche-induced debris flows, which could assist in disaster warning and prevention in alpine mountain regions.

Differences in Tropical Rainfall in Aquaplanet Simulations With Resolved or Parameterized Deep Convection

AbstractThis study investigates the effects of resolved deep convection on tropical rainfall and its multi‐scale variability. A series of aquaplanet simulations are analyzed using the Model for Prediction Across Scales‐Atmosphere with horizontal cell spacings from 120 to 3 km. The 3‐km experiment uses a novel configuration with 3‐km cell spacing between 20°S and 20°N and 15‐km cell spacing poleward of 30°N/S. A comparison of those experiments shows that resolved deep convection yields a narrower, stronger, and more equatorward intertropical convergence zone, which is supported by stronger nonlinear horizontal momentum advection in the boundary layer. There is also twice as much tropical rainfall variance in the experiment with resolved deep convection than in the experiments with parameterized convection. All experiments show comparable precipitation variance associated with Kelvin waves; however, the experiment with resolved deep convection shows higher precipitation variance associated with westward propagating systems. Resolved deep convection also yields at least two orders of magnitude more frequent heavy rainfall rates (>2 mm hr−1) than the experiments with parameterized convection. A comparison of organized precipitation systems demonstrates that tropical convection organizes into linear systems that are associated with stronger and deeper cold pools and upgradient convective momentum fluxes when convection is resolved. In contrast, parameterized convection results in more circular systems, weaker cold pools, and downgradient convective momentum fluxes. These results suggest that simulations with parameterized convection are missing an important feedback loop between the mean state, convective organization, and meridional gradients of moisture and momentum.

A framework for quantifying climate-informed heavy rainfall change: Implications for adaptation strategies

To understand the influence of climate change on heavy rainfalls and reduce the consequential multidimensional risks, we develop a climate-informed and adaptation strategies-related framework by using the information on heavy rainfalls and various socioeconomic factors. For this purpose, we firstly quantify the spatiotemporal characteristics of heavy rainfalls with various durations (1 h to multiple days) and return periods (2-year to 50-year) for the flood-prone country Cambodia, as a case study, during the historical period (1980–2005), mid-century (2040–2065), and late-century (2070–2095), using the latest three hourly climate model datasets under RCP 8.5 and 1 hourly ERA5 reanalysis datasets. A novel conditional artificial neural network (CANN) model is employed for temporal disaggregation to obtain the monthly maximum of 1 hourly rainfall in the future periods and subsequently, a zero-inflated generalized extreme value function (ZIGEV) is applied for extreme value analysis (EVA) to obtain rainfall intensity with different return periods. Secondly, the province-level flood risk change maps are developed based on a novel flood risk change index. The combination of CANN and ZIGEV performs better in EVA than traditional approaches by reducing the uncertainty from the stationarity assumption of temporal disaggregation and bias in the disaggregated rainfall. Rainfall intensity is projected to increase more in higher return periods and shorter durations towards the late-century, predominantly over Southern and Central Cambodia. Projected rainfall intensity-duration-frequency (IDF) curves in the capital city, Phnom Penh, reveal that the occurrence frequency of heavy rainfall in a given duration (e.g., 48 h) is likely to become ~10-fold in the mid-century. Results of province-level flood risk change maps indicate that Southeastern and Northwestern regions should be prioritized for employing adaption strategies. Our results will assist the policymakers in further mapping the flood susceptibility and vulnerability in different spatiotemporal scales across various communities and localities in the country and beyond.

Impact analysis of hydraulic loading rate on constructed wetland: Insight into the response of bulk substrate and root-associated microbiota

Constructed wetland (CW) is an environment-friendly and low-cost technology for nutrients removal from domestic wastewater. For a well-tuned CW, hydraulic loading rate (HLR) is one of the critical factors, particularly under the challenging circumstance of more frequent heavy rainfall events brought by global warming. In this study, a comprehensive investigation was conducted to explore the influence of different HLRs on the CW's bulk substrate and root-associated microbiota aiming to yield new insight for CW management from a hybrid perspective of environmental microbiology and engineering science. The response of the microbial community and associated nutrients removal performance under different HLR settings were analyzed after a one-year operation. Results showed that the bulk substrate and rhizosphere genera involved in desulfurization and denitrification, such as Ferritrophicum, Sulfurimonas, and Sulfurisoma, were enriched in the higher HLR condition and associated with the higher total nitrogen (TN) and nitrate nitrogen (NO3−-N) removal compared to the lower HLR condition. Co-occurrence network analysis demonstrated a more complex network under the higher HLR condition. Besides, it was observed that more stochastic in microbial assembly under the higher HLR condition. Surprisingly, zoonotic pathogens were observed and showed a greater prevalence under the higher HLR condition, indicating the potential correlation between HLR and pathogen intrusion. Collectively, this study revealed that the microbiota could be significantly altered under different HLR conditions, thereby resulting in differences in nutrients removal performance.

Spatiotemporal Variation and Ecological Risk Assessment of Heavy Metals in Industrialized Urban River Sediments: Fengshan River in Southern Taiwan as a Case Study

The sediment pollution index acts as a useful indicator for assessing anthropogenic pollution within river drainage basins. An industrialized urban river, Fengshan River in Kaohsiung City, southern Taiwan has been suffering heavy metal pollution from surrounding factories. In this study, spatial and seasonal variations in heavy metals in sediments from seven sampling sites of Fengshan River were determined to assess sediment pollution status and potential ecological risk using multiple sediment pollution indices. Results showed that the heavy metal concentrations displayed large spatial variations. Severe contamination of heavy metals, especially for Cr, Hg, and Zn in the lower reaches of Fengshan River, may attribute to wastewater discharges from leather processing and metal finishing factories along the river drainage basin. An increase in metal concentrations from upstream to downstream indicated that heavy metals tend to accumulate in tidal reaches, probably as a result of the flocculation effect. Frequent heavy rainfall in the wet season can enhance surface runoff to discharge metal pollutants from non-point sources (scattered factories) into the river. Assessment of multiple pollution indices showed moderately polluted (mCd = 3.9, PLI = 2.6) and considerable ecological risk (RI = 540, mERMQ = 0.55), indicating Fengshan River sediments, particularly in the lower reaches, are considered toxic and can cause adverse effects to benthic organisms. Organic matters showed a good correlation with heavy metals, which play an important role in the spatiotemporal variations in heavy metal pollutants in the Fengshan River sediments. This study can provide valuable information for river pollution remediation, and urban planning and management.

Development of effective pre-release method for dams during super typhoons using long-term ensemble rainfall forecasts

Against the background of increasing frequency of heavy rainfall due to climate change, an analytical approach to maximize the effective use of the reservoir from both flood control and water utilization was conducted. Simulations by applying statistical down-scaling method to the long-term ensemble rainfall forecast by ECMWF on a dam basin where a large inflow occurred during the Typhoon Hagibis in 2019 were shown. The results show that it is possible to use long-term ensemble rainfall forecasts to gain a temporal, stochastic understanding of total rainfall, required flood storage volumes, and recoverable reserves. Based on this understanding, a calculation method for setting release rates that correspond to effective pre-release start times and changes in rainfall forecasts in order to minimize both flood control and water service risks was proposed.

Heavy rainfall frequency analysis in the Benin section of the Niger and Volta Rivers basins: is the Gumbel's distribution a one-size-fits-all model?

Abstract. West African populations are increasingly exposed to heavy rainfall events which cause devastating floods. For the design of rainwater drainage facilities (to protect populations), practitioners systematically use the Gumbel distribution regardless of rainfall statistical behaviour. The objective of this study is twofold. The first is to update existing knowledge on heavy rainfall frequency analysis in West Africa to check whether the systematic preference for Gumbel's distribution is not misleading, and subsequently to quantify biases induced by the use of the Gumbel distribution on stations fitting other distributions. Annual maximum daily rainfall of 12 stations located in the Benin sections of the Niger and Volta Rivers' basins covering a period of 96 years (1921–2016) were used. Five statistical distributions (Gumbel, GEV, Lognormal, Pearson type III, and Log-Pearson type III) were used for the frequency analysis and the most appropriate distribution was selected based on the Akaike (AIC) and Bayesian (BIC) criteria. The study shows that the Gumbel's distribution best represents the data of 2/3 of the stations studied, while the remaining 1/3 of the stations fit better GEV, Lognormal, and Pearson type III distributions. The systematic application of Gumbel's distribution for the frequency analysis of extreme rainfall is therefore misleading. For stations whose data best fit the other distributions, annual daily rainfall maxima were estimated both using these distributions and the Gumbel's distribution for different return periods. Depending on the return period, results demonstrate that the use of the Gumbel distribution instead of these distributions leads to an overestimation (of up to +6.1 %) and an underestimation (of up to −45.9 %) of the annual daily rainfall maxima and therefore to an uncertain design of flood protection facilities. For better validity, the findings presented here should be tested on larger datasets.

The Economic Loss Prediction of Flooding Based on Machine Learning and the Input-Output Model

As climate change becomes increasingly widespread, rapid, and intense, the frequency of heavy rainfall and floods continues to increase. This article establishes a prediction system using feature sets with multiple data dimensions, including meteorological data and socio-economic data. Based on data of historical floods in 31 provinces and municipalities in China from 2006 to 2018, five machine learning methods are compared to predict the direct economic losses. Among them, GBR performs the best with a goodness-of-fit of 90%. Combined with the input-output (IO) model, the indirect economic losses of agriculture to other sectors are calculated, and the total economic losses caused by floods can be predicted effectively by using the GBR-IO model. The model has a strong generalization ability with a minimum requirement of 80 pieces of data. The results of the data show that in China, provinces heavily reliant on agriculture suffered the most with the proportion of direct economic losses to provincial GDP exceeding 1‰. Therefore, some policy implications are provided to assist the government to take timely pre-disaster preventive measures and conduct post-disaster risk management, thereby reducing the economic losses caused by floods.

Spatiotemporal variation of flash floods in the Hengduan Mountains region affected by rainfall properties and land use

Understanding the spatiotemporal characteristics of flash floods is important for reasonable and accurate identification of high-risk regions and for prediction of future hydrological regimes. Therefore, using time series datasets (1979–2015) of historical flash flood events, rainfall, and land use in the Hengduan Mountains region (HMR) of China, this study analyzed the effects of changes in precipitation and land use on spatiotemporal variation of flash floods. Analysis of the linear trend revealed that flash floods increased by 12 events/10 years and that 82% of events occurred in the flood season (June–August). The increase in flash flood events was found closely correlated with the increased frequency (rainstorm days increased by 3.5 d/10 years) and magnitude (rainstorm amount increased by 10.2 mm/10 years) of heavy rainfall as well as the expansion of the area of artificial surfaces (increase of 999 km2). Morlet wavelet analysis revealed significant periodic variation in the occurrence of flash floods on scales of 3–7, 8–15, and 21–31 years. On the basis of the standard elliptical difference, we identified displacement of the center of gravity of flash flooding from the northwest toward the southeast. Generally, more disasters were recorded in southern parts of the HMR owing to the frequent occurrence of rainstorms and the increase in area of both cultivated land and artificial surfaces with higher runoff potential. These findings improve understanding regarding the spatiotemporal dynamics of flash floods in the HMR and could support policymakers in identifying high-risk areas in mountainous watershed.

Consistent intensity-duration-frequency curves by post-processing of estimated Bayesian posterior quantiles

As a warming climate leads to more frequent heavy rainfall, the importance of accurate rainfall statistics is increasing. Rainfall statistics are often presented as intensity-duration-frequency (IDF) curves showing the rainfall intensity (return level) that can be expected at a location for a duration, and the frequency of this intensity (return period). IDF curves are commonly constructed by fitting generalized extreme value (GEV) distributions to observed annual maximum rainfall for several target durations. As the estimation is performed independently across durations, the resulting IDF curves may be inconsistent across durations and return periods. This paper proposes to ensure consistency by post-processing the estimated IDF curves. Two post-processing approaches are considered, a quantile selection algorithm that searches for consistent return levels within the posterior quantiles of a Bayesian inference approach, and adjustments based on isotonic regression. The methods are evaluated for simulated data and for Norwegian rainfall data from 83 locations, for hourly and sub-hourly durations. The post-processing yields consistent estimates that are at least as accurate as the unadjusted, inconsistent estimates. We also demonstrate how our approach differs from d-GEV, a method that performs simultaneous estimation across durations. An R implementation for the post-processing methods is available athttps://github.com/ClimDesign/fixIDF.

Probing the Crisis of Regional Connectivity Instigated by the Natural Disasters, Mizoram, India

Mizoram, in eastern Himalayas of India, is educated but thinly populous young state of area 21081sq km with hilly forest cover of (≈90.68%) The major connectivity is by roads of length 8912km (2019) that connects India’s mainland, Myanmar and Bangladesh and about no rail tracks. The state is housed in various folds and faults over Indo-Burmese tectonic zone (Indo Eurasian plate tectonics), and housed adjacent to 950 Ridges. The booming craggy state is combatting with its road network progress due to frequent low magnitude tremors, landslides, floods, and heavy rainfalls.
 The data of earthquakes, landslides, rainfall, and road progress were collected, and analyzed. The connectivity growth is reported tough and challenging within forests, broken relief, frequent ghats roads, and deep gorges, in topography that hinders the progress in pavement growth. Present study focuses on the ameliorative measures of hill road construction in Mizoram by adhering to the IRC: 52-2019, changing modus operandi of national contract bids, amateurish engineering design, procurement policies, bidder’s expertise and commitment for execution.
 The non-availability menial labours and technocrats have stemmed many legal issues & disputes between the government and the contractors. Faulty planning, engineering design, executant’s approach, procurement policies and the contractor’s skill, contract administration and construction management, road safety challenges, contract documentation, and inability of deployed bidders needs to be improved in Mizoram. To cater the needs of up-gradation/ new constructions, the hurdles warrant cutting edge impulses through EIA, EMP, DPR, amateurish design, and updating procurement policies that should supersede the orthodox practices.

Evaluating the Hydrologic Risk of n-Year Floods According to RCP Scenarios

Recent climate change has brought about irregular rainfall patterns along with an increased frequency of heavy rainfall, and flood damage in Korea is increasing accordingly. The increased rainfall amount and intensity during the rainy season lead to flood damage on a massive scale every year in Korea. In order to reduce such flood damage and secure the stability of hydraulic structures, evaluation of hydrologic risk corresponding to design floods is necessary. As Korea’s current climate change scenarios are generally applied to mid-sized watersheds, there is no practical application method to calculate the hydrologic risk of local floods corresponding to various future climate change scenarios. Using the design flood prediction model, this study evaluated the hydrologic risks of n-year floods according to 13 climate change scenarios. The representative concentration pathway (RCP) 8.5 scenario resulted in the 100-year floods increasing 134.56% on average, and 132.30% in the Han River, 132.81% in the Nakdong River, 142.42% in the Gum River, and 135.47% in the Seomjin-Youngsan River basin, compared with the RCP 4.5. The 100-year floods at the end of the 21st century increased by +3% and +13% according to the RCP 4.5 and 8.5, respectively. The corresponding hydrologic flood risk increased by 0.53% and 8.68% on average according to the RCP 4.5 and RCP 8.5, respectively, compared with the current level of hydrologic risk of a 100-year flood.

Flash flood modeling in the data-poor basin: A case study in Matina River Basin

Forecasting flooding hazards is a very effective non-engineering measure for flood control. Presently, the commonly used forecasting method in simulating flash flood events is through a watershed hydrological model. Many Asia-Pacific countries like the Philippines are prone to frequent hydrometeorological hazards such as tropical cyclones, resulting in frequent heavy rainfall events. However, most rivers in the many basins lack water flow observation data, which makes it challenging to use lumped and data-driven models for flash flood forecasting. With the continuous progress of remote sensing (RS) and geographic information system (GIS) techniques, the physically-based distributed hydrological model (PBDHMs) has rapidly advanced. PBDHMs can directly determine the model parameters according to the underlying surface characteristics from remotely-sensed data, which makes it possible for flood forecasting in areas with little to virtually no data. In this study, the Matina River basin in Davao City was selected as a case study in simulating a small data-poor basin in the region. The Liuxihe model was used to formulate a forecasting scheme and simulated the past flash flood events. The results show that there is a good correspondence between the past heavy rainfall events and their corresponding simulated river discharges. The results conform to the hydrological regularities, which can be used for flood forecasting and can serve as a baseline for the development of a flood forecasting system in the rivers of Davao City and elsewhere.

Evolutionary Transition of Stormwater Pump System in Tokyo

One of the roles of sewerage is to effectively discharge stormwater and protect urban areas from inland flooding, but there are several challenges to overcome. Urban development is progressing in Tokyo, and the amount of stormwater flowing into the sewers is increasing. In order to respond to the increase in the frequency of heavy rainfall in recent years, it is necessary to improve the capacity of stormwater pumps. The Bureau of Sewerage Tokyo Metropolitan Government has been working on the technological development of higher-performance stormwater pumps since the 1950s, when initial-type stormwater pumps were installed. The technological development history of stormwater pumps in Tokyo can be broadly divided into four periods. The first period spanned approximately 30 years when initial-type pumps, which start to operate after stormwater flows into the pump station, were active. In the second period, which spans approximately 20 years from 1980, two types of pumps were developed; a non-water supply pump that could operate reliably even in an earthquake, and a standby operation pump that could respond to sudden stormwater inflow during torrential rains. The third period spans approximately 15 years from 1999, when the waterless standby pump, which integrated the features of the two pumps developed in the second period, was developed and introduced. The fourth period is the era of pumps with higher performances than third period pumps developed after 2015. Currently, these stormwater pumps are operated together with a rainfall radar system that accurately grasps the rainfall situation and protects the citizens of Tokyo from inland flooding.

Impact of Climate Change on Flood Hazard at Airports on Pacific Islands: A Case Study of Faleolo International Airport, Samoa

Climate change is believed to have increased the intensity and frequency of heavy rainfall, and also to have caused sea level rises over this century and beyond. There is widespread concern that small-island nations are particularly vulnerable to increasing risk of inland flood due to such climate change. Understanding the impact of climate change on flood hazard is of great importance for these countries for the development of better protection and adaptation strategies. This study conducted a case study focusing on the impact of climate change on flood hazard at Faleolo International Airport (FIA), Samoa. FIA is a typical small islands airport, located on the lowland along the coast fronted by a fringing reef. Annual maximum daily rainfalls for different return periods were first estimated for the present and future climate around FIA. The estimated rainfalls were input as the forcing of a two-dimensional flood inundation model to investigate the flooding behavior and effectiveness of probable drainage systems. Results showed that a part of the runway can be inundated under heavy rainfall. Construction of drainage pipes significantly contributes to reducing the flood hazard level. Sensitivity analysis showed that the astronomical tide level has relatively little influence on the performance of the drainage system, while the combination of sea level rise and the sea level anomaly induced by stormy waves on the fringing reef could have non-negligible impacts on the drainage system. Location of the drainage pipe is also important to effectively mitigate flooding. The time-concentration of torrential rainfall may also significantly impact the overall performance of the drainage system.

The Crucial Role of Synoptic Pattern in Determining the Spatial Distribution and Diurnal Cycle of Heavy Rainfall over the South China Coast

AbstractThe South China coast suffers frequent heavy rainfall every warm season. Based on the objective classification method of principal components analysis, the key role of the synoptic pattern in determining the heavy rainfall processes that occurred over the South China coast in the warm season during 2008–18 is examined in this study. We found that heavy rainfall occurs most frequently under three typical synoptic patterns (P1–P3 hereafter) characterized by strong low-level onshore winds. P1 and P3 feature a prevailing southwesterly monsoonal flow in the lower troposphere, with heavy rainfall frequently occurring over the inland windward region in the afternoon associated with the orographic lifting and solar heating. The onshore wind of P3 is stronger than P1 as the western Pacific subtropical high extends more westward to 122°E, which induces stronger low-level convergence along the coastline than P1 when the ageostrophic wind veers from the offshore to onshore direction in the early morning. Hence, a secondary early morning rainfall peak can be found along the coastline. P2 is characterized by a low-level vortex located over the southwest portion of south China. Heavy rainfall under P2 usually initiates over the western part of the coastal region in the morning and then propagates inland in the afternoon. Overall, the synoptic patterns strongly determine the spatial distribution and diurnal cycle of heavy rainfall over the South China coast. This heavy rainfall is closely related to the diurnally varying low-level onshore winds rather than the low-level jets, as well as the different interactions between the low-level onshore winds and the local orography, coastline, and land–sea breeze circulations under different synoptic patterns.

Statistical characteristics and risk zoning of different duration heavy rainfall in Shanxi

Based on different duration heavy rainfall data and the associated disaster situation, geographic information, socio-economic characteristics, river network density, and geological hazard-prone zoning of 109 stations in Shanxi Province in the past 38 years, a statistical analysis of different duration heavy rainfall events in Shanxi Province was carried out. In addition, a comprehensive risk zoning was performed using hierarchy analysis and weighted comprehensive evaluation methods. The results show that: (1) the spatial distribution of the extreme value indicates the following: the mountain area is larger than that of the basin, and the south is greater than the north and has a shorter time-effect and a stronger local distribution of the extreme value. Yuanqu County (YuanQ) has the highest concentration area of all different duration heavy rainfall events within 12 h. (2) Within 12 h., the occurrence frequency of different duration heavy rainfall has the spatial distribution characteristics of being higher in the south and lower in the north, higher in the mountain area than in the basin area, higher in the eastern mountain area than in the western mountain area, and obviously concentrated in the southeast. YuanQ is a high incidence area of short-duration heavy rainfall over 1 h. and 3 h., and Jincheng City (JinC) is a high incidence area of heavy rainfall over 6 h and 12 h.. (3) Severe precipitation with different durations occurs in July–August every year, and the frequency of hourly heavy rainfall is the highest. (4) The intra-day distributions of heavy rainfall over a period of 1 h., 3 h., and 12 h. are single-peak curves, with peaks at 18:00, 19:00 and 09:00, respectively; the intra-day distribution of heavy rainfall over 6 h. is a double peak curve, with peaks at 06:00 and 20:00. (5) The variation trend of the annual occurrence times of short-duration heavy precipitation is similar for 1 h, 3 h, and 6 h, and the growth rate is the highest in southeastern Shanxi Province. The variation trend of the occurrence times of heavy rainfall over 12-h. is different from that of 1-h., 3-h., and 6-h. short-duration heavy rainfall. The rate of growth is the largest in the eastern and western mountain areas of Shanxi Province. (6) The whole area of JinC, the east of Yuncheng City (YunC), and the southeast of Changzhi City (CZ) are the high-risk areas for all different duration heavy precipitation disaster-causing factors within 12 h. As the duration of heavy rainfall increases, the high-risk areas with respect to disaster factors in Linfen City (LF) and CZ increase. (7) JinC, YunC, and Liulin County (LL) are high-risk areas for the comprehensive risk of different duration heavy rainfall within 12 h, and LF and CZ districts increase the extent of high-risk areas with the increase in the duration of heavy precipitation.