Rainfall Probability Research Articles

Safety Evaluation of Major Nuclear Facilities According to Debris Flow Occurrence Scenarios

This study systematically analyzes the impact of sediment disasters, such as those due to debris flows, on key facilities near the Gori nuclear power plant, aiming to identify limitations in the current safety management system. Using 62 years of rainfall data from the Busan weather station, probable rainfall values for return periods of 30, 50, 100, and 200 years were analyzed. These values were applied to various debris flow scenarios to evaluate the flow characteristics and impact forces on structures. The analysis reveals that the impact forces of debris flows increase sharply for return periods of 100 years or more, indicating that existing safety protocols may not adequately account for the topographical characteristics. This study highlights the necessity of a comprehensive safety assessment framework that integrates site-specific risks such as slope and sediment dynamics. By utilizing actual rainfall data, this study provides a safety evaluation that reflects the unique geographical hazards faced by nuclear facilities. The findings offer essential data to guide future policy decisions regarding site selection, design, and operational management of nuclear facilities, ultimately improving the disaster preparedness for high-risk locations in a way that differs from existing studies.

Analysis of the Impact of Rainfall in Mountainous Areas of Jeju Island on Stream Flood Discharge

Jeju Island frequently suffers significant damage from streams and inland flooding during extreme rainfall events. However, disaster-prevention performance targets, often set using ASOS data from low-lying coastal areas, fail to adequately reflect regional characteristics. To address this issue, this study categorized Jeju Island into coastal, mid-mountain, and mountainous zones based on elevation and conducted a comparative analysis of probable rainfall amounts for streams across these regions. Additionally, the impact of rainfall was examined by comparing flood volumes between adjacent streams originating from different source locations. The analysis revealed that rainfall amounts exceeded disaster prevention performance targets by 24.4% to 64.0%, with rainfall in mountainous areas having the greatest impact on flood volumes in downstream regions. These findings underscore the importance of developing rational solutions through economic and other evaluations when implementing disaster prevention projects and setting disaster response performance targets.

Joint Probability of Cyclonic Wind Speed and Maximum Rainfall for Cyclones Affected Sri Lanka

Joint Probability of Cyclonic Wind Speed and Maximum Rainfall for Cyclones Affected Sri Lanka

Landslide hazard mapping of Wayanad District of Kerala, India, incorporating copula-based estimation of joint probability of rainfall

Abstract. The development and integration of the spatial and temporal probabilities of landslides are required for complete landslide hazard mapping at any location. Under changing climate, the computation of the temporal probability of landslides with rainfall magnitude alone is inaccurate. This research proposes a framework based on copula functions to develop a landslide probability map using multi-site rainfall data by accounting for the rainfall variables of intensity and duration using a joint-probability approach. The proposed technique is used for Wayanad District, Kerala, India, considering extreme rainfall events in 2018. Firstly, the landslide susceptibility map of the district was developed using a robust random forest (RF) model. Based on regional geology, geomorphology, and climate, different regions of Wayanad have varying rainfall thresholds assessed according to the intensity and duration of the rainfall. Then, the temporal probability of landslides was developed, accounting for the intensity and duration of rainfall events using the joint-probability estimation using copula. Through the integration of the landslide spatial probability map with the temporal probability, landslide hazard maps (LHMs) for Wayanad were developed for time periods ranging from 1 to 50 years. The results of the study indicate the need for bi- or multi-variate landslide probability modeling in studies on regional landslide hazard assessments.

Developing hillslope hydrology based probabilistic rainfall threshold for shallow landslides

Developing hillslope hydrology based probabilistic rainfall threshold for shallow landslides

The Impacts of Convectively Coupled Equatorial Waves on Extreme Rainfall in Northern Australia

Abstract Convectively coupled equatorial waves (CCEWs) with an off-equatorial convective center, such as equatorial Rossby (ER) waves, mixed Rossby–gravity (MRG) waves, and tropical depression (TD)-type waves, can be the potential sources of predictability for subseasonal to seasonal prediction over northern Australia. To establish the statistical relationship of the wave–rainfall interaction, we investigate the influences of CCEWs on rainfall means and extremes during the austral summer (December–February) and autumn (March–May) from 1981 to 2018. The results show that ER waves increase the average daily rainfall by up to 7 mm day−1 (4 mm day−1) during the austral summer (autumn) and increase the probability of extreme rainfall (above the 90th percentile) by around 1.5–2.4 times (summer) and 1.1–1.8 times (autumn) relative to climatology. MRG and TD-type waves are shown to have a smaller impact, increasing rainfall by around 1–4 mm day−1 (1–1.5 and 1–3 mm day−1) during the summer (autumn) and extreme probability by 1.4–1.6 times and 1.25–1.9 times (1.3–1.8 times and 1.27–1.7 times), respectively. The increase in rainfall can be attributed to the enhancement of moisture convergence during the time of the rainfall. Furthermore, moisture gain and enhancement of moisture advection were found ahead of the convective center. Additionally, we find that interactions between multiple waves can act to amplify or suppress the mean daily and probability of extreme rainfall. This research highlights the important role of CCEWs on northern Australian precipitation variability. Significance Statement This research is the first to delve into the significant role of convectively coupled equatorial waves (CCEWs) on subseasonal rainfall variability over northern Australia, particularly equatorial Rossby (ER) waves, mixed Rossby–gravity (MRG) waves, and tropical-depression-type (TD-type) waves. A robust statistical relationship is found between these waves and rainfall, suggesting these waves are major contributors to increased daily rainfall and extreme rainfall probabilities during austral summer and autumn. These findings emphasize the significance of CCEWs on northern Australian rainfall variability and highlight the potential of CCEWs for subseasonal to seasonal forecasts in Australia.

Further perceptions of probability: Accurate, stepwise updating is contingent on prior information about the task and the response mode.

To adapt to an uncertain world, humans must learn event probabilities. These probabilities may be stationary, such as that of rolling a 6 on a die, or changing over time, like the probability of rainfall over the year. Research on how people estimate and track changing probabilities has recently reopened an old epistemological issue. A small, mostly recent literature finds that people accurately track the probability and change their estimates only occasionally, resulting in staircase-shaped response patterns. This has been taken as evidence that people entertain beliefs about unknown, distal states of the world, which are tested against observations to produce discrete shifts between hypotheses. That idea stands in contrast to the claim that people learn by continuously updating associations between observed events. The purpose of this article is to investigate the generality and robustness of the accurate, staircase-shaped pattern. In two experiments, we find that the response pattern is contingent on the response mode and prior information about the generative process. Participants exist on continua of accuracy and staircase-ness and we only reproduce previous results when changing estimates is effortful and prior information is provided-the specific conditions of previous experiments. We conclude that explaining this solely through either hypotheses or associations is untenable. A complete theory of probability estimation requires the interaction of three components: (i) online tracking of observed data, (ii) beliefs about the unobserved "generative process," and (iii) a response updating process. Participants' overt estimates depend on how the specific task conditions jointly determine all three.

A Study of the Rainwater Pumping Station Operation Plan to Reduce River Water Level

Urban flood damage is caused by various and complex effects such as inland flood and external water flooding, so research on this is necessary. This study presented a rainwater pumping station operation technique that can suppress the rise in water levels in discharge rivers based on rainfall and pump operation scenarios in order to reduce inland water flooding and simultaneously consider external water levels. The research method used genetic algorithm (GA) optimization techniques to calculate the optimal operating level for each pump, and based on this, an operating scenario was created to minimize pump operation. In addition, after constructing a rainfall scenario based on the probability rainfall in Seoul, an optimal pump operation technique that can reduce the external water level while minimizing the inundation of domestic water was proposed. The results of the study showed that as the rainfall intensity increases, the flooding increases and the peak depth of the runoff increases. In addition, as the pumping scenario changes from 1 to 11, the number of pumping operations decreases, which reduces the drainage volume of the pumps, which increases the flooding.

A tracer‐aided 2D numerical framework to define fluvial and pluvial hazard mapping

AbstractFlood hazard is a dynamic nonstationary phenomenon, which can be categorized based on the origin of the inundation. Inland flood hazard arises primarily from pluvial and fluvial inundations, typically modeled separately with respect to the pertaining spatial domains of the assessment, namely the urban areas and the riverine floodplains. When modeling is based on the catchment‐scale hydrological‐hydrodynamic approach, the inundations such as those resulting from pluvial and fluvial processes are usually not discerned, even though disparities in normative flood risk management exist in different countries. This paper establishes a tracer‐aided criterion to discretize between pluvial and fluvial flooding at a catchment scale, relying on the advection process of a conservative tracer. Applied to a small urban catchment for multiple probabilistic rainfall scenarios, our physically based methodology shows that the incorporation of a transport equation within a shallow water model can be used to define the inundation sources. We highlight the advantages of the proposed approach compared to commonly employed modeling techniques for mapping fluvial inundations, while emphasizing the significance of mapping and regulating pluvial hazards in urban areas. The study shows the potential role of an abstraction of the tracers' transport toward identifying the hazard sources in a catchment‐scale 2D numerical framework.

Assessment of Annual Rainfall Probabilities of Zabol Using Fuzzy Probability Rules

Assessment of Annual Rainfall Probabilities of Zabol Using Fuzzy Probability Rules

A New Post-Processing Method for Improving Track and Rainfall Ensemble Forecasts for Typhoons over Eastern China

This paper proposes a new post-processing method for model data in order to improve typhoon track and rainfall forecasts. The model data used in the article include low-resolution ensemble forecasts and high-resolution forecasts. The entire improvement method contains the following three steps. The first step is to correct the typhoon track forecast: three ensemble member optimization methods are applied to the low-resolution ensemble forecasts, and then the best optimization method is selected with the principle of the smallest average distance error. The results of rainfall forecasts show that the corrected rainfall forecast performs better than the original forecasts. The second step is to derive the high-resolution probability rainfall forecast: the neighborhood method is applied to the deterministic high-resolution rainfall forecast. The last step is to correct the typhoon rainfall forecast: the low- and high-resolution forecasts are blended using the probability-matching method with two different schemes. The results show that the forecasts of the two schemes perform better than the original forecast under all rainfall thresholds and all forecast lead times. In terms of bias score, a rain forecast from one scheme corrects the rainfall deviation from observation better for light and moderate rainfall, whereas a rain forecast from another scheme corrects the rainfall deviation better for heavy and torrential rainfall. The better performance of corrected rain forecasts in the case of Typhoon Lekima and Rumbia over eastern China is demonstrated.

Rainfall and Extreme Drought Detection: An Analysis for a Potential Agricultural Region in the Southern Brazilian Amazon

In recent decades, the main commercial crops of Mato Grosso, such as soybeans, corn, and cotton, have been undergoing transformations regarding the adoption of new technologies to increase production. However, regardless of the technological level, the climate of the region, including the rainfall regime, can influence the success of crops and facilitate, or not, the maximum production efficiency. This study aimed to define the behavior of the variability in monthly and annual rainfall and its probability of monthly occurrence and calculate the drought index for the northwestern region of Mato Grosso, in the southern region of the Brazilian Amazon. To carry out the study, daily rainfall records were collected, calculating the totals for each month of the historical series for each of the four National Water and Sanitation Agency (ANA) rain gauge stations, Aripuanã (1985–2020), Colniza (2001–2020), Cotriguaçu (2004–2020), and Juína (1985–2020), representing the northwestern region. The annual distribution of rainfall during the periods studied ranged from 1376.2 to 3017.3 mm. The monthly distribution indicated a typical water shortage in the months of June, July, and August. The probability of rainfall near the average for each month was more than 50%. The monthly SPI-1 index revealed a total of 56 months affected by very dry events and 34 extreme dry events. The annual SPI-12 index pointed to seven very dry years and five extremely dry years. Therefore, the region presented high rainfall rates in most years; however, a significant process of drought was also observed, including in rainy months, which are the periods with the greatest demand for the main agricultural crops.

The performance of a variable‐resolution 300‐m ensemble for forecasting convection over London

AbstractWhen using sub‐km models to forecast convection, it is important to have a large enough domain to allow convection to fully spin‐up from the lateral boundaries. However, running large domains is computationally expensive and while it may be feasible for research purposes it is not yet feasible for routinely run models, such as the Met Office 300‐m London model. To try and mitigate the spin‐up issues in the London model, a variable‐resolution 300‐m London Model (the ‘LMV’) has been developed, which allows the boundaries of the London model to be further away from areas of interest (e.g., London Heathrow) at lower computational cost. Results from several cases of summertime convection show that the convective storms in the variable‐resolution model are more like those in a large fixed‐resolution 300‐m model than those in the much smaller London model. This implies variable resolution is a viable option for increasing the size of the London model domain without increasing the computational costs too much. Extended evaluation of the LMV was conducted during summer 2022, running as an ensemble nested inside the Met Office's operational UK ensemble (MOGREPS‐UK). Overall, the LMV looks promising for high‐impact convective events as it is better able to represent the organisation of convection into lines or larger storms whereas MOGREPS‐UK tends to simulate isolated, circular storms. This often leads to more reliable probabilities of heavy rainfall in the LMV ensemble compared to MOGREPS‐UK. However, there is an issue with the LMV producing too many small precipitating showers in situations where there should only be shallow clouds. This is thought to be a result of shallow clouds getting too deep in the model and precipitating erroneously.

Analysis of the Rainfall Pattern and Rainfall Utilization Efficiency during the Growth Period of Paddy Rice

Rainfall is one of the most important water sources for rice production in China. However, its temporal and spatial variability is leading to water shortages. The present study collected a long series of historical rainfall data from research sites during the rice growth period to analyze the characteristics of rainfall distribution and the correlation with rainfall utilization efficiency, aiming to investigate its impact on rice irrigation practices. It is found that the rainfall distribution varied greatly between the different locations and growth periods. The average rainfall of the whole growth period ranges from 135.5 mm to 694.5 mm. The rainfall curve exhibits a typical unimodal pattern with variations in the intensity, duration, and timing of peak precipitation across different growth periods. During the rice growth period, the cases in southern China are more prone to waterlogging for a high probability of continuous rainfall, and the cases in northern China are more prone to drought. The rainfall utilization efficiency of all cases exhibits a significant inter-year fluctuation range, negatively influenced by the rainfall amount and rainfall inhomogeneity. The efficiency in utilizing precipitation is diminished with greater and more uneven rainfall experienced during the growth period. These findings can provide a decision-making basis for optimizing rice irrigation strategies and enhancing rainfall utilization efficiency in diverse regions across China.

Mitigating blade erosion damage through nowcast-driven erosion-safe mode control

The erosion-safe mode (ESM) is a novel mitigation strategy that reduces rainfall-induced erosion damage by lowering the tip-speed of the turbine during precipitation events. The ESM requires accurate information about future expected rainfall for its control. In current research, it is debated what method or source should be used to this end. This study explores the effectiveness of driving the ESM using a state-of-the-art weather-radar-based probabilistic rainfall nowcast provided by the Royal Netherlands Meteorological Institute (KNMI). The performance of the nowcast is assessed for various lead times with an impingement-based damage model for three sample sites in the Netherlands and for two distinct ESM strategies. The results show that the quality of the nowcast degrades with increasing lead times, where the 5- and 15-minute lead times exhibit sufficiently good accuracy and response time for adjusting turbine speeds. Overall, the results highlight that the probabilistic information in the nowcast can be employed to improve the efficiency and viability of the ESM.

Evaluating the effects of heatwave events on hydrological processes in the contiguous United States (2003–2022)

Extreme heat and drought conditions are affecting water availability in many regions worldwide, leading to negative impacts on human societies, agriculture, and ecosystems. However, current research lacks comprehensive spatiotemporal analysis examining the interplay between multiple hydrological factors and heatwave events, especially in the context of climate change. This research broadly pertains to understanding the dynamics of hydrological factors and their potential responses to heatwave during warm seasons across the contiguous United States for the period from 2003 to 2022. Utilizing data from the Global Land Data Assimilation System (GLDAS), we analyzed surface runoff, evapotranspiration (ET), precipitation, Groundwater Storage (GWS), Root Zone Soil Moisture (RZSM), and Total Water Storage (TWS) to discern annual patterns and the impacts of heatwave. The spatial patterns of heatwave highlighted a higher occurrence in the western, central, and northeastern U.S., with longer average durations in the western and south-central regions. These events are predominantly dry, characterized by low Relative Humidity (RH), except in the southeastern U.S., where heatwave coincide with high RH levels. Post-heatwave analysis indicated a reduction in GWS, TWS, RZSM, and ET, alongside an increase in surface runoff, RH, and precipitation. An in-depth examination of rainfall and temperature dynamics during heatwave revealed weak correlations between rainfall and temperature, as well as between rainfall and heatwave duration, highlighting the complex nature of these interactions. The study also found an enhanced probability of rainfall following heatwave, particularly in the eastern regions, drawing attention to the potential for increased flood risks post-heatwave. Our findings contribute to the growing body of knowledge on the impacts of heatwave on hydrological factors, providing valuable insights for climate change adaptation and water resource management strategies.

Simulation and Management Impact Evaluation of Debris Flow in Dashiling Gully Based on FLO-2D Modeling

Dashiling Gully, located in Miyun District, Beijing, exhibits a high susceptibility to debris flow due to its unique geological and topographical characteristics. The area is characterized by well-developed rock joints and fissures, intense weathering, a steep gradient, and a constricted gully morphology. These factors contribute to the accumulation of surface water and loose sediment, significantly increasing the risk of debris flow events. Following a comprehensive field geological investigation of Dashiling Gully, key parameters for simulation were obtained, including fluid weight, volume concentration, and rainfall. The formation and development conditions of potential mudslides were analyzed, and numerical simulations were conducted using FLO-2D software (version 2009) to assess scenarios with rainfall probabilities of 1 in 30, 50, and 100 years. The simulations accurately reconstructed the movement velocity, deposition depth, and other critical movement characteristics of mudslides under each rainfall scenario. Using ArcGIS, pre- and post-treatment hazard zoning maps were generated for Dashiling Gully. Furthermore, the efficacy of implementing a retaining wall as a mitigation measure was evaluated through additional numerical simulations. The results indicated that mudslide velocities ranged from 0 to 3 m/s, with deposition depths primarily between 0 and 3 m. The maximum recorded velocity reached 3.5 m/s, corresponding to a peak deposition depth of 4.31 m. Following the implementation of the retaining wall, the maximum deposition depth significantly decreased to 1.9 m, and high-risk zones were eliminated, demonstrating the intervention’s effectiveness. This study provides a rigorous evaluation of mudslide movement characteristics and the impact of mitigation measures within Dashiling Gully. The findings offer valuable insights and serve as a reference for forecasting and mitigating similar mudslide events triggered by heavy rainfall in gully mudslides.

Towards evaluating the conditional probability of waterborne microbial risks associated with critical rainfall events following land application of animal waste — A focus on Irish pastures

ABSTRACT Modelling surface runoff and infiltration in an agricultural watershed is essential for understanding the nutrient cycle and water quality management. This research evaluates the critical rainfall events causing maximum surface runoff and infiltration. Empirical models such as Soil Conservation Service (SCS) curve number and Explicit Green-Ampt (EGA) were analysed for different soil hydraulic conditions and storm events with specific return periods. The present study forecasted the maximum daily rainfall and intensity as 113.6 mm and 19.9 cm h–1, respectively, considering a 100-year return period storm. The deterministic part of the model calculates the daily maximum surface runoff as 5.51 cm (from the SCS model). The EGA model revealed that the rainfall intensity of 0.143, 0.163, 0.175, 0.179, 0.181, 0.182, and 0.182 cm h−1 (duration of 24 h) was found critical for 1, 2, 5, 10, 25, 50, 100 year return period of the storm, respectively in different soil texture classes (sand, loamy sand, sandy loam, loam, silt loam, sandy clay loam, clay loam, silty clay soils). Exceptions imply for three occasions where the intensity of 0.407 cm h−1 and duration of 18 h produced maximum infiltration for silty clay loam, and intensity of 0.352 and 0.354 cm h−1 with 9 h rainfall (each) produced maximum infiltration for clay. The probabilistic model revealed that a daily storm event probability of 2.74 × 10–5 is associated with a simulated mean runoff depth of 0.69 cm and infiltration of 20.7 cm. Critical rainfall probabilities calculated in this study can be multiplied with the overall microbial/pollutant human health risk assessment framework to calculate conditional probability and better estimate microbial risk through the drinking water pathway. This study is very relevant for the wet climatic condition of Ireland, where most land use is pastures.

Rainfall Variability and Crop Planning of Dahod: A Case study from Tribal District in Gujarat, India

India is an agrarian economy mostly depends on monsoon for its crop success. The onset and cessation of monsoon along with unpredictability in weather conditions, reflects the statistics in foodgrain production in this region. Planning of crop requires details on period of dry and wet spell of an area. The present study takes into account Markov chain model for rainfall probability analysis. The mean annual rainfall of Dahod district is 1073 mm with 59 rainy days. Only 41% shows rainfall above normal. 95% of annual rainfall is contributed by South-west monsoon whereas only 4% was contributed by North-east monsoon followed by 0.9% and 0.2% in summer and winter season. Analyzed data (1998-2019) reveals that monsoon start effectively from 25th SMW and cease on 40th SMW. The average monsoon period is of 16 weeks, i.e., 112 days. Initial, conditional and consecutive probability of dry and wet week indicates a shift in weather pattern around 20th SMW, transition of dry to wet weeks. More than 75% of wet probability can be observed from 27th to 35th SMW. Onwards 40th SMW, likelihood of P(D/D) keeps increasing up to 52 SMW. P(W/W) probability more than 75% can be observed from 27th to 32nd SMW. The district’s priority lies in poultry farming and vegetable cultivation along with sustaining soybean and chickpea. The cropping pattern of majority of district falls in maize-maize, maize-chickpea, maize-wheat-green gram, soybean-wheat-green gram and pigeon pea- groundnut, respectively. The area frequently experiences extended dry spells between monsoon. To address these challenges, crop diversification and efficient irrigation practices are prioritized.

Cyclone Jasper’s rains in the context of climate change

Cyclone Jasper struck northern Queensland in mid-December, 2023, causing extensive flooding stemming from torrential rain. Many stations reported rainfall totals exceeding 1 m, and a few surpassed 2 m, possibly making Jasper the wettest tropical cyclone in Australian history. To be better prepared for events like Jasper, it is useful to estimate the probability of rainfall events of Jasper's magnitude and how that probability is likely to evolve as climate warms. To make such estimates, we apply an advanced tropical cyclone downscaling technique to nine global climate models, generating a total of 27,000 synthetic tropical cyclones each for the climate of the recent past and that of the end of this century. We estimate that the annual probability of 1 m of rain from tropical cyclones at Cairns increases from about 0.8% at the end of the 20th century to about 2.3% at the end of the 21st, a factor of almost three. Interpolating frequency to the year 2023 suggests that the current annual probability of Jasper's rainfall is about 1.2%, about a 50% increase over that of the year 2000. Further analysis suggests that the primary causes of increasing rainfall are stronger cyclones and a moister atmosphere.