Characteristics Of Extreme Precipitation Research Articles

Determination of Mountain Equivalent Rainstorm (MER) in Qinba Maintain Area Based on TRMM

The study of extreme precipitation is a significant aspect for investigating rainstorms, flash floods, and unpredictable disasters. Qinba mountain, Shaanxi province, China, is sensitive to extreme climate and rainstorm events. It is crucial to investigate the feature of precipitation extremes in this region with satellite data. According to this, the paper using the 1Day extreme precipitation datasets of TRMM and rain-gauge to calculate the mountain rainstorm, then the statistical metrics (CC, MBE, RMSE) was used in validation as the performance measure. The 1Day, 3Day, 5Day, and 7Day extreme precipitation was identified by the 95<sup>th</sup> percentile method. Thus to determine the Mountain Equivalent Rainstorm (MER). As the results, (1) Based on the comparison, the TRMM satellite product can capture the extreme precipitation mostly at the station below 433m (R<sup>2 </sup>>0.5) for 5Day datasets, while 7Day datasets reveal contrast patterns. (2) By applying the MER concept, the TRMM-based and gauge-based ratio revealed a similar pattern of mountain rainstorms at higher elevations and slightly different in the middle region. The mountain rainstorm amount was double the extreme rainfall at a higher elevation. Therefore, the defined extreme precipitation characteristics can assist the disaster risk reduction and mitigation strategy in the Qinba mountain of Shaanxi Province, China, and also provide a reference for improving the satellite algorithm in extreme precipitation measurement.

Changes in the spatial variability of extreme precipitation characteristics across Peninsular India

Through a comprehensive analysis, this study portrays the changing spatial variability of extreme precipitation characteristics as a consequence of a gradually warming climate in peninsular India. In particular, it emphasizes the coastal areas that are under increased exposure to frequent extreme events in the recent past. Different extreme precipitation characteristics are considered, and the change points are identified based on their trend, mean and standard deviation. Changes in the spatiotemporal variability of extreme precipitation characteristics are identified through empirical orthogonal functions (EOFs). Our findings illustrate the occurrence of discernible changes almost all over the region with varying time points (1970 to 2011), and the extremes with higher thresholds exhibit more prominent changes. More importantly, a notable disparity in extreme indices expressing intensity is observed between the eastern and western coastal regions: change points for the eastern coastal areas (the Bay of Bengal side) predominantly emerged in the post-1980s, in contrast to the pre-1980s points across the western coastal (the Arabian Sea side) regions. Furthermore, after 2001, the spatial coverage of the western region notably expanded, as indicated by a significant increase in wet extremes, including those at the southernmost tip of India. Concurrently, extreme dry events significantly decreased across most of southern India during this period. On the other hand, the intensification of precipitation has become more prominent towards the Bay of Bengal side than towards the Arabian Sea side. This may be attributed to the increased cyclonic activity in the Bay of Bengal. Overall, the findings of this study will aid in understanding the evolving spatial pattern of extreme precipitation indices and will contribute to better management of extreme events and related hazards across peninsular India.

Intensified response of extreme precipitation to rising temperature over the Tibetan Plateau from CMIP6 multi-model ensembles

This study conducted bias correction of the high-performing general circulation model (GCM) data in combination with observed data based on the latest GCM data released by the Coupled Model Intercomparison Project Phase 6 (CMIP6). The ability of GCMs to simulate the characteristics of extreme precipitation over the Tibetan Plateau (TP) was systematically assessed, and the CMIP6 multi-model ensemble was used to accurately project extreme precipitation. Furthermore, the response of extreme precipitation over the TP to global warming was explored. The results demonstrated that daily translation efficiently addressed the issue of apparent overestimation in the model’s raw output, and CMIP6 multi-model ensemble mean exhibited better simulation performance for all extreme precipitation indices over the TP. Additionally, the simulation performance of each index exhibited a divergence across various elevation bands as altitude increased, with the best and worst performance observed at the altitude bands of 1500–2000 m and above 5000 m, respectively. For future changes in extreme precipitation, the indices representing the amount and intensity of extreme precipitation increase significantly, whereas the indices representing the duration of precipitation decrease. Moreover, the plateau will show a spatial pattern of “wet regions becoming dry and dry regions becoming wet”. The relationship between extreme precipitation and temperature follows a positive and peak pattern over most areas of the plateau, while the response of extreme precipitation to temperature will increase by 0.6 %–52.6 % with rising temperature. The present results have important implications for investigating the impacts of climate change on the water cycle in alpine regions.

Characterisation of extreme precipitation changes in the upper reaches of the Yangtze River, China

ABSTRACT The paper analyses the spatial and temporal characteristics of extreme precipitation in the upper reaches of the Yangtze River through extreme precipitation indicators based on the trend method, the Mann–Kendall trend test, and the rescaled extreme deviation extreme deviation using daily precipitation data from 1961 to 2021. The following conclusions were obtained: The overall precipitation in the upper reaches of the Yangtze River is reduced, and the number of rainy days is reduced. The frequency of extreme precipitation is generally reduced, but the spatial difference in the intensity of extreme precipitation is greater, which makes the occurrence of extreme precipitation more concentrated and more destructive. Extreme precipitation indicators showed relatively large fluctuations after 2000, especially in terms of extreme precipitation intensity. The frequency of extreme precipitation in the upper reaches of the Yangtze River is the highest in the main stream of the Yangtze River Basin and the Wujiang River Basin, the intensity of extreme precipitation is in the Jialing River and the Wujiang River Basin, and the accumulation of extreme precipitation is the highest in the Jialing River and the Wujiang River Basin, whereas the maximum value of the station extreme precipitation intensity and frequency is in the Minjiang River Basin.

Impacts of extreme precipitation on water conservation in Beijiang River Basin, China

Water conservation, which has been regarded as one of the most crucial ecosystem service functions of river basins, is a key factor for sustainable development, that has garnered widespread attention in the context of climate change. However, little attention has been paid to daily water conservation and its response to extreme precipitation events. In this study, we selected the Beijiang River Basin (BJRB) as the study area, which is an important ecological barrier for the Guangdong-Hong Kong-Macau Greater Bay Area (GBA), based on the distributed hydrological model WEP-L (Water and Energy transfer Processes in Large River basin) and water balance equation, the spatiotemporal characteristics of extreme precipitation and four daily water conservation indicators (maximum 1 day, maximum 3 day, maximum 5 day, maximum 7 day water conservation) in the BJRB from 1965 to 2009 were analysed, and the responses of these indicators to extreme precipitation were investigated. The results reveal that (1) the amount (RX1day, RX3day, RX5day, RX7day, R95p, and R99p), intensity (SDII), and frequency (R50mm) of extreme precipitation increased in the BJRB during 1965–2009, with SDII undergoing a significant increase. (2) The four water conservation indicators were positive, indicating that the watershed reduced peak flow during the extreme precipitation period by accumulating precipitation. The rate of increase of the four water conservation indicators were lower than that of extreme precipitation indices, there will be more extreme precipitation converted into surface runoff, thereby increasing the risk of flooding. The four indicators increased in the southern and northern regions of the BJRB, while decreased in the central region during 1965–2009. (3) The four indicators were generally positively correlated with most of the extreme precipitation indices in the northern BJRB, especially RX1day, RX3day, RX5day, RX7day, R99p, and R50mm. The results provide a scientific reference for water conservation adaptation to extreme precipitation variations, protection of the ecological environment, and the prevention and reduction of disasters in the region.

Understanding flash flooding in the Himalayan Region: a case study

The Himalayan region, characterized by its substantial topographical scale and elevation, exhibits vulnerability to flash floods and landslides induced by natural and anthropogenic influences. The study focuses on the Himalayan region, emphasizing the pivotal role of geographical and atmospheric parameters in flash flood occurrences. Specifically, the investigation delves into the intricate interactions between atmospheric and surface parameters to elucidate their collective contribution to flash flooding within the Nainital region of Uttarakhand in the Himalayan terrain. Pre-flood parameters, including total aerosol optical depth, cloud cover thickness, and total precipitable water vapor, were systematically analyzed, revealing a noteworthy correlation with flash flooding event transpiring on October 17th, 18th, and 19th, 2021. Which resulted in a huge loss of life and property in the study area. Contrasting the October 2021 heavy rainfall with the time series data (2000–2021), the historical pattern indicates flash flooding predominantly during June to September. The rare occurrence of October flash flooding suggests a potential shift in the area's precipitation pattern, possibly influenced by climate change. Robust statistical analyses, specifically employing non-parametric tests including the Autocorrelation function (ACF), Mann–Kendall (MK) test, Modified Mann–Kendall, and Sen's slope (q) estimator, were applied to discern extreme precipitation characteristics from 2000 to 201. The findings revealed a general non-significant increasing trend, except for July, which exhibited a non-significant decreasing trend. Moreover, the results elucidate the application of Meteosat-8 data and remote sensing applications to analyze flash flood dynamics. Furthermore, the research extensively explores the substantial roles played by pre and post-atmospheric parameters with geographic parameters in heavy rainfall events that resulted flash flooding, presenting a comprehensive discussion. The findings describe the role of real time remote sensing and satellite and underscore the need for comprehensive approaches to tackle flash flooding, including mitigation. The study also highlights the significance of monitoring weather patterns and rainfall trends to improve disaster preparedness and minimize the impact of flash floods in the Himalayan region.

Evaluation of Multisource Datasets in Characterizing Spatiotemporal Characteristics of Extreme Precipitation from 2001 to 2019 in China

Abstract The spatiotemporal characteristics of extreme precipitation intensity are crucial for hydroclimatic studies. This study delineates the spatiotemporal distribution features of extreme precipitation intensity across China from 2001 to 2019 using the gridded daily precipitation dataset CN05.1, constructed from an observation network of over 2400 stations. Furthermore, we evaluate the reliability of 12 widely used precipitation datasets (including gauge-based, satellite retrieval, reanalysis, and fusion products) in monitoring extreme precipitation events. Our findings indicate the following: 1) CN05.1 reveals a consistent spatial distribution characterized by a decline in extreme precipitation intensity from the southeastern coastal regions toward the northwestern inland areas of China. From 2001 to 2019, more pronounced declining intensity trends are discernible in the northern and southwestern regions of China, whereas marked increasing trends manifest in the northeastern and the Yangtze River plain regions. National mean extreme precipitation indices consistently exhibit significant increasing trends throughout China. 2) Datasets based on station observations generally exhibit superior applicability concerning spatiotemporal distribution. 3) Multisource weighted precipitation fusion products effectively capture the temporal variability of extreme precipitation indices. 4) Satellite retrieval datasets exhibit notable performance disparities in representing various intensity indices. Most products tend to overestimate the increasing trends of national mean intensity indices. 5) Reanalysis datasets tend to overestimate extreme precipitation indices, and inadequately capture the trends. ERA5 and JRA-55 underestimate trends, while CFSR and MERRA-2 significantly overestimate the trends. These findings serve as a basis for selecting reliable precipitation datasets for extreme precipitation and hydrological simulation research in China. Significance Statement Extreme precipitation events have increasingly become more widespread, posing significant threats to human lives and property. Accurately understanding the spatiotemporal patterns of these events is imperative for effective mitigation. Despite the proliferation of precipitation products, their capacity to faithfully represent extreme events remains inadequately validated. In this study, we utilize a gauge-based dataset derived from over 2400 gauge stations across China to investigate the spatiotemporal changes in extreme precipitation events from 2001 to 2019. Subsequently, we conduct a rigorous evaluation of 12 widely used precipitation datasets to assess their efficacy in depicting extreme events. The results of this research offer valuable insights into the strengths and weaknesses of various precipitation products in depicting extreme events.

Attributing Extreme Precipitation Characteristics in South China Pearl River Delta Region to Anthropogenic Influences Based on Pseudo Global Warming

AbstractIn the context of human‐induced warming climate, the atmosphere is expected to hold a greater amount of water vapor, leading to heavier precipitation on a global scale. However, the extent to which changes in extreme rainfall can be attributed to human influences varies at regional scales. Here we conduct attribution analyses on 40 extreme precipitation events in different seasons during 1998–2018 over the Pearl River Delta (PRD), by using the Weather Research and Forecasting (WRF) model and applying the pseudo global warming (PGW) method. The model was integrated with the factual and counterfactual conditions separately, with the latter derived from differences between the Coupled Model Intercomparison Project Phase 5 (CMIP5) historical and historical‐natural runs. By comparing parallel experiments, extreme daily rainfall (>95th percentile) in PRD enhanced by 8%–9.5% for 0.9–1.1 K near‐surface warming (nearly Clausius‐Clapeyron, or CC scaling) in the May‐to‐September (MJJAS) and 12.4% at most for 0.6–0.8 K warming (∼2 × CC rate) in non‐MJJAS seasons; the probability of those extremes increased by 10%–30% during MJJAS (20%–40% in other seasons). While moisture‐related thermodynamic effects play a similar role in modulating rainfall, the wind circulation‐related dynamic effects act differently in different seasons. Changes in MJJAS extremes are related to stronger low‐level southerly winds, while non‐MJJAS rainfall is exacerbated by strengthened low‐level wind convergence and updrafts. Moisture budget analysis suggests that thermodynamic effects associated with the increased moisture amount account for the mean rainfall increase, whereas dynamic effects related to wind circulation changes are responsible for extreme precipitation, regardless of seasons.

Construction of daily precipitation series and the observational characteristics of extreme precipitation in Tianjin, China during 1888–2022

Given the difficulties in rescuing and ensuring the quality of long-term climate data, current studies on century-scale climate change are usually limited to annual and monthly data, resulting in the poor detection of extreme climate events and their changes before 1950. In this study, we reconstructed a daily precipitation series for Tianjin from 15 September 1887 to 31 December 2022 on the basis of the most comprehensive daily precipitation records collected from the Tianjin Meteorological Archive, China, and in reference to the precipitation analysis results based on the datasets developed by the Climatic Research Unit Time-Series version 4.06, Global Precipitation Climatology Centre and University of Delaware along with the application of various homogenisation methods for climate series. Our approach provides a complete and reliable century-long daily precipitation series for the study of regional or local extreme weather and climate events. The reconstructed daily dataset reveals that the annual precipitation amount and R95 intensity in Tianjin during 1888–2022 lack significant trends and have values of 0.74 ± 6.99 and −1.84 ± 3.22 mm per decade, respectively. On the annual and seasonal scales, the precipitation amount and R95 intensity, particularly those in autumn, have increased since the latter half of the 20th century relative to those in 1888–1950. However, the increase in precipitation amount and R95 intensity is relatively limited compared with that in atmospheric water vapour content due to surface warming, indicating the highly sensitive response of extreme precipitation events to warming. In addition, the estimates for the return periods of 5, 10, 20, 50 and 100 years covering 1888–2022, 1888–1950 and 1951–2022 depict that the intensity of heavy rain and above magnitude was highest in 1888–1950 and decreased in 1951–2022.

Characteristics of extreme precipitation and its sensitivity to regional climate change in the upper and middle reaches of the Yellow River Basin

Characteristics of extreme precipitation and its sensitivity to regional climate change in the upper and middle reaches of the Yellow River Basin

The Principal Modes of Morning Extreme Precipitation over Inland Guangdong, China during Pre-Summer Rainy Season

The study explores the characteristics of morning extreme precipitation (MEP) during the pre-summer in inland Guangdong. Based on the principal modes, MEP events can be classified into four groups. The first group of MEP (G1) is a typical southeastward-propagating rainfall system originating from the northwestern mountains. This is caused by the strongest accelerated southwesterly winds at night, which bring abundant moist and warm air from the South China Sea (SCS) along with the shear line and the highest convective available potential energy (CAPE). The second group of MEP (G2) is warm-sector heavy rainfall with large-scale warming and higher CAPE. This local rainfall system originates in the south of Nanling mountains at night and reaches its mature stage in the morning. The rainfall system of the third group (G3) originates in central Guangxi and propagates to the southern inland region. The southeasterly winds in Guangxi intensify at night due to the anomalous cyclonic circulation. However, in the morning, the easterly winds shift to the westerlies, favoring eastward propagation. After SCS monsoon onset, cold air intrudes southward, colliding with moist warm air from the SCS, leading to heavy frontal precipitation in the inland region, classified as the fourth group MEP (G4).

Climatic characteristics and main weather patterns of extreme precipitation in the middle Yangtze River valley

Abstract Based on the daily precipitation data and ERA5 reanalysis data of 40 years from 1981 to 2018 in the middle Yangtze River Valley (MYRV), the climatic characteristics of extreme precipitation are analyzed using statistical methods. The multivariate empirical orthogonal functions and spectral clustering methods are used to classify and synthesize the extreme precipitation weather. The results show that: (1) The spatial distribution of the extreme precipitation threshold is uneven due to the regional topography. The spatial distribution of the average precipitation and frequency of extreme precipitation days is characterized by the north-south antiphase distribution. (2) According to the main influencing systems, the 215 regional extreme precipitation days in the MYRV in the past 40 years can be classified into three types: southwest vortex type, typhoon type, and cold trough shear line type. (3) The southwest vortex type of extreme precipitation occurs in the deep warm and humid airflow in front of the southwest vortex trough, but the typhoon type has better thermal dynamic conditions, and the cold and warm airflow convergence of the cold trough shear line type is more obvious. The rainfall area of three types of extreme precipitation is the result of the synergistic effect of the system.

Evaluation of present-day extreme precipitation over the United States: an inter-comparison of convection and dynamic permitting configurations of E3SMv1

Accurate simulation of the present-day characteristics of mean and extreme precipitation at regional scales remains a challenge for Earth system models, which is due in part to deficiencies in model physics such as convective parameterization (CP), and coarse resolution. High horizontal resolution (HR, ∼25 km) and multiscale modeling framework (MMF, i.e. replacing conventional CP with embedded km-scale cloud-resolving models) are two promising directions that could help improve the interaction between subgrid-scale physical processes and large-scale climate. Here, we evaluate simulated extreme precipitation over the United States (US) across three configurations (i.e. low-resolution [LR], HR, and MMF) of the Energy Exascale Earth System Model (E3SMv1) and intercompare them against two gridded observation datasets (climate prediction center daily US precipitation and integrated multi-satellite retrievals for global precipitation measurement). We assess the model’s ability to simulate very heavy seasonal precipitation (illustrated by the difference between the 99th and 90th percentile values) as well as the spatial distributions of several extreme precipitation indices defined by the expert team on climate change detection and indices. Our results show that both the dry (i.e. consecutive dry days (CDD)) and wet (i.e. consecutive wet days, maximum 5 day precipitation, and very wet days) extremes evaluated herein show some improvement as well as degradation with MMF and HR relative to LR. These results vary across seasons and US subregions. For instance, only the very heavy precipitation of winter is improved with MMF and HR. Both configurations alleviate the well-known drizzling bias evident in LR across both winter and summer in many parts of the US, largely due to the overall improvement in intensity and frequency of precipitation. Additionally, our results suggest that while E3SMv1-MMF has higher intensity rates when it does rain, it has too many CDD during the summer, contributing to a low mean precipitation bias.

Evaluation of precipitation extremes over the Tibetan plateau using the NASA global daily downscaled datasets NEX-GDDP-CMIP6

The utility of statistically downscaled data is in its provision of detailed, high-resolution insights, surpassing global models, which is essential for precise assessments of climate impact. This study delves into the changes in extreme precipitation across the Tibetan Plateau (TP) and its five subregions, thereby deepening our understanding of climate impacts in the region. Employing NASA Earth Exchange Global Daily Downscaled Climate Projections (NEX-GDDP-CMIP6), which are bias-corrected and statistically downscaled, in conjunction with three gauge-based datasets (CN05.1, CMFD, GMFD) spanning from 1979 to 2014, this study conducts an extensive analysis using eight extreme precipitation indices in the TP. The findings reveal significant spatial disparities in extreme precipitation, with the southern subregion experiencing a significant increase while the two northern subregions show statistically insignificant changes. NEX-GDDP-CMIP6 performs best in the eastern subregion but fails to capture extreme precipitation characteristics in the southern and western subregions. Averaging over the whole period of 1979–2014 (climatology), the NEX-GDDP-CMIP6 dataset performs better on the intensity and persistence indices than on the absolute and relative threshold indices. NEX-GDDP-CMIP6's performance on climate trends is significantly below that of climatology. The NEX-GDDP-CMIP6 dataset has limitations over the TP because of its reliance on the underperforming GMFD reference data. This study provides a comprehensive understanding of the variability of extreme precipitation and disparities among different subregions of the TP, laying a robust scientific groundwork for climate impact studies and future projection in this region.

Assessment of the Spatiotemporal Changes in the Extreme Precipitation Climate Indices over the Chungcheong Region of South Korea during 1973–2020

This study analyzed the changes and trends in twelve extreme precipitation-based climate indices obtained using daily data from 10 synoptic stations in the Chungcheong region of South Korea during the 1973–2020 period. The climate indices were used to assess the trends in the extreme precipitation characteristics of duration, frequency, and intensity using the innovative trend analysis (ITA) method. The results of the ITA were further compared with two other non-parametric test methods such as Mann–Kendall (MK) and Spearman’s rho (SR). The results showed that most stations exhibited significant increasing trends in all the investigated climate indices at a 95% confidence level as indicated by the ITA method, with only a few stations indicating significant decreasing trends in R95p, R99p, Rx3day, and Rx5day. The sub-trend analysis further revealed the dominance of neutral behavior around the low-value cluster, especially for the extreme precipitation duration. At the same time, increasing trends dominate the high-value cluster at most stations. Meanwhile, only R10mm, R99p, and R95p exhibited monotonic trends in the Boeun and Seosan stations, respectively. Further, the ITA exhibited superior performance over the MK and SR methods by indicating the presence of more significant trends in the climate indices at most stations. The distribution of the extreme precipitation indices for duration, frequency, and intensity indicate the pronounced risk of flood conditions around the north–central and some parts of southern regions, while the western region indicates a potential drought risk, which could greatly impact the water resources and consequently agricultural activities in the study area. The results of this study provide essential information for addressing the climate-related problems of water resource management and agriculture in the study area and other related climatic regions.

Future Joint Probability Characteristics of Extreme Precipitation in the Yellow River Basin

The relationship between climate change and extreme precipitation is extremely complex. From a probabilistic perspective, a proper understanding of the response of extreme precipitation to climate change is of significant importance. This study was based on daily precipitation provided by CMIP6 climate models and employed copula functions to construct joint distributions of precipitation amount and precipitation intensity indices at different quantile levels. A spatial–temporal assessment of the susceptibility areas for extreme precipitation in the Yellow River Basin was conducted while considering bivariate return periods and design values. The results indicate that there were significant spatial differences in the bivariate return periods. Taking the R90P-SDII (90) index for a 20a return period as an example, the difference between the maximum and minimum joint return periods within the Yellow River Basin was 1.4 times, while the co-occurring return period was 7.0 times, and the Kendall return period was 4 times. Moreover, this difference increased with the increase in the return period. The magnitude order of the four return periods is as follows: TAnd > TKendall > TSingle-variable > TOr. Joint return periods (Or) and co-occurring return periods (And) could be considered as the extreme cases under single-variable return periods, serving as an estimation interval for actual return periods. Under the influence of climate change, the bivariate design values for future periods exhibited a variability increase of 6.76–28.8% compared to historical periods, and this increase grew with higher radiative forcing scenarios, ranking as SSP126 < SSP245 < SSP585. The bivariate design values showed a noticeable difference in variability compared to the single-variable design values, ranging from −0.79% to 18.67%. This difference increased with higher quantile values, with R95P-SDII (95) > R90P-SDII (90) > PRCPTOT-SDII.

Clustering Daily Extreme Precipitation Patterns in China

Different regions exhibit significant differences in the characteristics of extreme precipitation, and the differentiation of such characteristics is not reported for all regions. To distinguish the characteristics of extreme precipitation in different regions in China, we extracted the distribution characteristics of extreme precipitation from daily precipitation data at 649 stations in China, 1961–2017, spatially grouped them by cluster analysis, and, finally, evaluated the effectiveness of zoning by a homogeneity test. Results showed that China can be divided into 33–35 extreme precipitation zones. Our map exhibits better homogeneity than the conventional climate map and other methods reported in the literature and better characterizes the regional distribution characteristics of extreme precipitation. It is noted that long–duration extreme precipitation has a more significant regional distribution consistency than short–duration extreme precipitation. Further, the western and northern regions of China are more prone to sudden, high–intensity extreme precipitation events, whereas the southeastern region is more vulnerable to frequent, high–intensity extreme precipitation events.

Re-inventing Bethlahmy method for estimating probable maximum precipitation

The design and risk analysis of large-scale hydraulic structures (e.g., dams) and sensitive installations (e.g., nuclear facilities) downstream of those structures rely on design flood corresponding to probable maximum precipitation (PMP). In areas where there is a sparsity of information on hydrometeorological variables, practitioners use various statistical methods to arrive at a PMP estimate, assuming it to be the possible upper bound for precipitation. However, the assumption is violated in different parts of the world. Hence, there is a need to improve the existing statistical methods and develop their potential alternatives. Against this backdrop, this paper proposes a new variant of a non-parametric method (Bethlahmy) to facilitate the estimation of PMP at locations with sparse records of extreme precipitation. It involves mapping of datapoints in annual maximum series and their ranks to a non-dimensional space (NDS) and using the information on sample size and observed maximum precipitation in the NDS to arrive at a surrogate variable representing PMP, which is eventually mapped back to PMP in the original space. The effectiveness of the proposed Bethlahmy variant over various existing statistical techniques is illustrated through Monte Carlo Simulation experiments and a case study on 37,872 stations from a global precipitation database. The existing techniques include the original Bethlahmy and Hershfield methods, conventional probabilistic approach, and relevant variant(s). Insight is provided into the relative performance of these methods, as there is a dearth of such attempts in the literature. Results indicate that the proposed Bethlahmy variant exhibits better performance than other methods/variants across samples varying in size and extreme precipitation characteristics, making it a promising statistical alternative for PMP estimation.

Analysis of Extreme Precipitation Variation Characteristics and the Influencing Factors in the Yunnan-Guizhou Plateau Region, China

The increase of extreme precipitation (EP) frequency and the aggravation of disasters have seriously disrupted the normal economic and social development of human beings. The complex topography of the Yunnan-Guizhou Plateau region (YGPR) and the fact that moisture originates from two different directions, the Pacific Ocean and the Indian Ocean, make the mechanism of EP more complicated. Exploring the variation characteristics and influencing factors of EP in YGPR is of great significance for regional disaster prevention and mitigation and water resources management. In this study, 11 extreme precipitation indices (EPIs) defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) were calculated based on daily precipitation data of 1960–2020 from 83 national meteorological stations in the study area. The Mann–Kendall test and Wavelet analysis were used to analyze the variation characteristics of EP and explore the influence mechanisms of geographical factors and atmospheric circulation on EP in the spatial and temporal perspective. The conclusions are as follows: (1) The EP of the study area has an overall increasing trend in the research period, with the increase of persistent dry days, the precipitation concentration, intensity, and extreme heavy precipitation (EHP); (2) It shows the obvious spatial difference in the study area, with the high-value areas of extreme drought (ED) in the northwestern region and the total annual precipitation, EHP, and intensity in the southeastern region. In addition, ED and EHP tends to increase in the western region of the study area as well as in the middle east and southeast; (3) EHP is significantly positively correlated with longitude and highly negatively with latitude. Meanwhile, EHP shows a correlation with altitude (negative at low altitude and positive at high altitude); (4) The degree of drought change is greatly affected by North Atlantic Oscillation/El Niño-Southern Oscillation (ENSO) events. The variation of extreme heavy precipitation is greatly influenced by the summer monsoon of South Asian, East Asian, and South China Sea; (5) All the EPIs show persistence. The study results can contribute to the understanding of EP variation in the study area and provide some scientific references for regional water resource management, meteorological warning, and agricultural production safeguard.

Effects of extreme precipitation intensity and duration on the runoff and nutrient yields

Extreme precipitation and its subsequent natural disasters, including soil erosion and flood, have been confirmed to have major impacts on watershed hydrology and water quality. However, the contributions of the intensity and duration of extreme precipitation to the changes in runoff and nutrient yields have not been fully understood. In this study, we quantified the contributions of extreme precipitation intensity and duration to the changes in watershed runoff and nutrient yields through constructing a quantitative simulation protocol in a distributed hydrological model. The results showed that the historical extreme precipitation, runoff, and nutrient yields have similar inter-annual variation trends. Between 2010 and 2020, extreme precipitation events induced more than 90% of annual runoff and total phosphorus (TP) yields. Runoff and TP yields increased with the increase in extreme precipitation intensity, while total nitrogen (TN) yields did not always decrease linearly with reduced intensity. The impacts of the duration in extreme precipitation on runoff and nutrient yields were less than intensity. Nutrient yields from farmland were stably influenced by extreme precipitation intensity, while that from forestland and grassland have higher inter-annual variability. The contributions of duration to TP yields were higher in farmland and forestland than in grassland and urban and rural areas. Changes in the intensity and duration of extreme precipitation transformed watershed nutrient composition pattern. With the increase of intensity, the contribution ratio of farmland increased while that of urban and rural areas decreased. Moreover, the increase in intensity decreased the spatial autocorrelation degree of TN yields between sub-watersheds, increasing the spatial heterogeneity in TN yields. This study improved the understanding for the responses of runoff and nutrient yields to the variations in extreme precipitation characteristics, which is helpful for more targeted water quality management to cope with increasing climate extremes.