Frequency Of Extreme Precipitation Research Articles

Monthly Prediction on Summer Extreme Precipitation With a Deep Learning Approach: Experiments Over the Mid‐To‐Lower Reaches of the Yangtze River

AbstractAccurate predictions of monthly extremes assume paramount importance in enabling proactive decision‐making, which however are lacked in skills even for state‐of‐the‐art dynamical models. Taking the extreme precipitation prediction over the mid‐to‐lower reaches of the Yangtze River, China, as an instance, a multi‐predictor U‐Net deep learning approach is designed to enhance the prediction over the European Center for Medium‐Range Weather Forecasts (ECMWF) model, with the single‐predictor U‐Net parallelly examined as the benchmark. Focusing on the precipitation extremes, an extreme associated component is incorporated into the model loss function for optimization. Besides, predictions composed by daily outputs with multiple lead times are imported as a comprehensive set in the training phase to augment the deep learning sample size and to emphasize enhancements in predictions at the monthly timescale as a whole. Results indicate that the multi‐predictor U‐Net effectively improves predictions of extreme summer precipitation frequency, showing distinct superiority to the raw ECMWF and the single‐predictor U‐Net. Multiple evaluation metrics indicate that the model shows a significant positive improvement ratio ranging from 65.1% to 80.0% across all grids compared to the raw ECMWF prediction, which has also been validated through applications in the two extreme summer precipitation cases in 2016 and 2020. Besides, a ranking analysis of feature importance reveals that factors such as humidity and temperature play even more crucial roles than precipitation itself in the multi‐predictor extreme precipitation prediction model at the monthly timescale. That is, in such a deep learning approach, the monthly prediction on extreme precipitation benefits significantly from the inclusion of multiple associated predictors.

A Synoptic Scale Perspective of Solar Forcing on Extreme Precipitation and Floods Over Europe During Summer

AbstractThe relationship between total solar irradiance (TSI) forcing and summer extreme precipitation and flood frequency over western Europe is investigated from a synoptic‐scale perspective, with a focus on the role of Rossby wave packets (RWPs). Utilizing observational, model, and proxy data, we reveal a significant increase in RWP frequency along a zonal band centered around 50°N, extending from North America to western Europe, during periods of low TSI. This anomaly in RWP frequency is consistent with a significant increase in the frequency of extreme precipitation events recorded over western Europe. Sensitivity experiments conducted with a state‐of‐the‐art chemistry‐climate model corroborate our findings based on observational data. Additionally, a flood record from western Europe demonstrates a significant increase in flood frequency during low TSI years, a relationship that persists across timescales. We argue that the frequency patterns associated with TSI forcing presented in this study are robust and, therefore, valuable for estimating the frequency of extreme precipitation events over western Europe under various solar irradiance scenarios. Moreover, our findings indicate that the North Atlantic sector is more responsive to changes in solar forcing during the boreal summer than previously thought, with this effect manifesting primarily on synoptic timescales rather than the long‐term climatological mean.

Evaluating Factors Affecting Flood Susceptibility in the Yangtze River Delta Using Machine Learning Methods

AbstractFloods are widespread and dangerous natural hazards worldwide. It is essential to grasp the causes of floods to mitigate their severe effects on people and society. The key drivers of flood susceptibility in rapidly urbanizing areas can vary depending on the specific context and require further investigation. This research developed an index system comprising 10 indicators associated with factors and environments that lead to disasters, and used machine learning methods to assess flood susceptibility. The core urban area of the Yangtze River Delta served as a case study. Four scenarios depicting separate and combined effects of climate change and human activity were evaluated using data from various periods, to measure the spatial variability in flood susceptibility. The findings demonstrate that the extreme gradient boosting model outperformed the decision tree, support vector machine, and stacked models in evaluating flood susceptibility. Both climate change and human activity were found to act as catalysts for flooding in the region. Areas with increasing susceptibility were mainly distributed to the northwest and southeast of Taihu Lake. Areas with increased flood susceptibility caused by climate change were significantly larger than those caused by human activity, indicating that climate change was the dominant factor influencing flood susceptibility in the region. By comparing the relationship between the indicators and flood susceptibility, the rising intensity and frequency of extreme precipitation as well as an increase in impervious surface areas were identified as important reasons of heightened flood susceptibility in the Yangtze River Delta region. This study emphasized the significance of formulating adaptive strategies to enhance flood control capabilities to cope with the changing environment.

A Methodological Approach to Improving Extreme Precipitation Reanalysis Data Using the Clausius-Clapeyron Relationship: A Case Study in a Mediterranean City

Climate change is a crucial issue of the 21st century, leading to more frequent and severe extreme precipitation events globally. These events result in significant social and economic disruptions, including flooding, loss of life, and damage to infrastructure. Projections suggest that extreme rainfall will intensify in the latter half of the century, underscoring the need for accurate and timely forecasting. Despite advancements in meteorological and climate models that offer high accuracy for various weather parameters, these models still struggle to detect extreme values, particularly for precipitation. This research examines the sensitivity of extreme precipitation events to temperature, based on the Clausius-Clapeyron relationship, focusing on Thessaloniki, Greece. It also evaluates the effectiveness of reanalysis data in identifying extreme precipitation and explores how rainfall-temperature relationships can enhance prediction accuracy. The findings are vital for improving the estimation of extreme rainfall events and informing the design of flood-resilient infrastructure.

Integrating heterogeneous information for modeling non-stationarity of extreme precipitation in the Yangtze River Basin

Extreme precipitation exhibits non-stationary characteristics due to climate change and land use/cover change in recent years. In the literature, probability distributions with varying parameters associated with covariates have been applied to describe the non-stationarity of extreme precipitation. However, there is no consensus on the optimal distributions and the number and type of covariates for constructing non-stationary models. This study comprehensively investigates and determines the optimal distribution models, the type of covariates, and the number of covariates to best characterize the non-stationarity of extreme precipitation, using the Yangtze River Basin as the study area. The time series of four indices representing extreme precipitation of different durations and intensities are fitted by stationary and non-stationary Generalized Additive Models for Location, Scale, and Shape. The models are constructed using seven distributions and four types (time, global climate, local climate, and land cover) of covariates. Each model incorporates one to three covariates simultaneously. The results show that the most preferred distribution is the Generalized Extreme Value distribution for very wet-day precipitation, while the Log-Normal distribution is more suitable for other extreme precipitation indices. For the type of covariates, when using a single covariate, the proportion of stations using a global climate covariate as the best covariate (22.3 %–33.2 % of 193 stations) is close to that using a local climate covariate as the best covariate (22.3 %–27.5 %). The proportion of stations using land cover as the best covariate is much lower (7.3 %–10.9 %), while that using time as the best covariate is the lowest (1.6 %–4.1 %). When using multiple covariates, the models with both global and local climate covariates are mostly selected (8.3 % to 17.6 %) for all extreme precipitation indices. For the number of covariates, one or two covariates are enough to describe the non-stationary behaviors of all extreme precipitation indices. This study emphasizes the necessity to use non-stationary models for extreme precipitation frequency analysis with careful consideration and selection of distributions and covariates.

Season-dependent climate sensitivity of the surface runoff of major rivers in Changbai Mountain

Due to the sensitivity of runoff to changes in seasonal snowpack, the changes in surface runoff within high-latitude and high-elevation areas are more complex than in other areas. However, this sensitivity is often overlooked when correlating changes in runoff with meteorological variables. Here, we analyze changes in surface runoff during snowmelt and snowless periods and their sensitivity to climate change from 1960 to 2019 in the upstream watersheds of the Songhua, Yalu, and Tumen Rivers in Changbai Mountain. Most runoff indices, except for low flow in the Yalu River, exhibited decreasing trends during snowmelt and snowless periods. Precipitation was the primary variable responsible for surface runoff during snowless and snowmelt periods. Notably, rainfall replenished all runoff indices during snowless periods, particularly under high flow conditions. The average and high flows were also primarily supplemented by rainfall during snowmelt periods. However, low flow was positively correlated with snowmelt in the Songhua and Tumen Rivers and with temperature in the Songhua, Yalu and Tumen Rivers during snowmelt periods. Additionally, extreme runoff events were closely associated with extreme precipitation. These findings demonstrate the seasonal and index-dependent climate sensitivity of surface runoff within Changbai Mountain, offering insights into the surface runoff response mechanism to climate change in high-altitude mountainous areas. Under future climate change conditions, frequent and intense extreme precipitation events may increase the risk of flooding, owing to the high sensitivity of high flows to precipitation, posing a serious threat to regional ecological security.

Spatiotemporal variation and teleconnections of extreme precipitation in the Upper Indus Basin: insights for natural hazard assessment

ABSTRACT The study investigates trends and teleconnections of extreme precipitation events in the Upper Indus Basin (UIB) within the Indian region, part of the crucial geo-ecological Hindu Kush Himalayan Mountain system. Utilizing high-resolution (10 km × 10 km) HAR data from 2002 to 2013, we analyzed 11 indices established by the Expert Team on Climate Change Detection and Indices (ETCCDI) to observe variations in extreme precipitation at the monthly, seasonal, and annual time scale. The result shows an increase in dry and wet extreme precipitation frequency and intensity, increasing monsoon precipitation and a shift of winter precipitation, increasing continuous dry days. Using the APHRODITE daily (0.25° × 0.25°) data from 1952 to 2014, continuous wavelet transform and wavelet coherence analysis indicate significant periodicities and correlations with large-scale climate anomalies such as the El Niño-Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO). Wavelet coherence analysis reveals that monthly extreme precipitation events significantly correlate with ENSO at a 3–5 years periodicity, while annual extremes show significant coherence with NAO at 8–10 years and over 16 years periodicities. The study highlights the impact of global climate change on regional precipitation and the need for adaptive water management policies to mitigate flood risks during the rainy season and address water scarcity in the dry season.

Ancient wisdom: A new perspective on the past and future Chinese precipitation patterns based on the twenty-four solar terms

Precipitation, as a crucial component of the hydrological cycle, exhibits intense responsiveness to climatic changes. Accurately and finely describing precipitation patterns on a temporal scale within complex climatic regions is a matter that has consistently garnered attention. China, with its intricate geographical environment spanning five climatic zones, has seen its laboring populace develop the unique Twenty-Four Solar Terms (TFST) calendar from ancient times to macroscopically describe climatic shifts and to guide their agricultural activities. Here, based on the chronological framework of the TFST, we revisited and validated the response of typical solar terms to conventional precipitation indicators over the past 40 years in China. By comparing the accumulated precipitation volume and the number of effective precipitation days 30 days before and after the typical solar terms, we established the statistical credibility of this ancient calendar system’s summarization of precipitation patterns. Furthermore, the study examines the response of various future extreme precipitation events to the TFST and their spatial distribution patterns. We identified that more than 70 % of regions in China experienced a concentration of extreme precipitation events during the XiaoShu and DaShu solar terms. Under four different future scenario models, there is prediction that over 55.2 % of the areas in China will also have extreme precipitation events predominantly during the same two solar terms. Additionally, in the Qinghai-Tibet region, there is a trend indicating an increased risk of extreme precipitation frequency during these two critical solar terms associated with rising carbon emissions, with a significance level averaging over 95 %. This research deeply integrates modern meteorological and hydrological science with traditional culture, allowing for a nuanced understanding of the periodicity of extreme precipitation. It also offers a novel perspective and theoretical basis to guide flood prevention efforts and studies on the regularities of extreme precipitation.

Changes in precipitation phases based on the multi-discrimination method in the Tibetan Plateau

Global warming is rapidly changing the precipitation structure, and the shift in precipitation phases is even more pronounced in the Tibetan Plateau (TP), a region sensitive to global climate change. The accuracy of precipitation phase discrimination is essential for studying precipitation variability. Based on the observed data from 112 meteorological stations in the study area obtained from the China Meteorological Administration (CMA) and the National Climatic Data Center (NCDC) before 1980, we compared and assessed six methods for distinguishing precipitation phases and the optimal method was determined to complete the precipitation phase data after 1980 in the study area. Subsequently, the changes in precipitation amount, intensity, and frequency were analyzed by classifying them into light, moderate, heavy, and extreme precipitation using the percentile threshold discrimination method. It found that the dynamic threshold temperature method (presented by Ding et al., 2014) was the most effective to distinguish precipitation phases in the TP, and its performance was further improved from 80.9% to 86.1% after using frequency intersection and probability guarantee methods. Precipitation was mainly concentrated in July. Meanwhile, the proportion of snowfall to total precipitation (S/P) was approximately 9%. From 1961 to 2020, the amount of total precipitation, rainfall and snowfall in the TP exhibited increasing trends, while the sleet demonstrated a persistent decline. Spatial and temporal heterogeneity existed in the changes of precipitation of different phases. The average single precipitation amount increased. Moreover, the changes in the amount and frequency of extreme precipitation were more pronounced. Increased frequency and amount of both rainfall and snowfall were distributed in the central and eastern parts of the study area with a more significant proportion. In contrast, the peripheral areas witnessed a decrease. The spatial distribution of frequency and amount of different precipitation intensities was comparable to that of the total precipitation. This study offers novel procedure into changes of precipitation phases at high altitudes, which will inform future research on climate change and water resource management.

Spatiotemporal patterns in persistent precipitation extremes of the Chinese mainland (1961–2022) and association with the dynamic factors

Previous studies on extreme precipitation events have primarily focused on the intensity of such events during fixed periods, without considering the entirety of extreme precipitation events and conducting a comprehensive examination of the intensity and frequency of more disastrous persistent extreme precipitation (PEP) events. In this paper, persistent extreme precipitation indices (PEPIs) related to intensity, frequency, duration, and contribution rates were constructed, utilizing a gridded daily precipitation dataset and atmospheric circulation indices data from China for the period 1961–2022 to analyze the spatial distribution, spatiotemporal trends, and seasonal patterns and variations of PEPIs across different regions of China and the potential impact of the dynamic factors on the spatiotemporal variations of PEPIs. The results are crucial for effectively predicting future changes and informing planning for adaptation strategies: From 1961 to 2022, (1) China's PEP pattern displays distinct regional variations. PEP has been prevalent in Western Arid (Semi-Arid) Zone (WAS) and Qinghai-Tibet Plateau (QT) regions, with higher occurrence frequency, duration, and contribution rates to total extreme precipitation (44.91% and 42.01%) and overall precipitation (26.35% and 29.08%). Conversely, the humid eastern regions exhibit greater event intensity. (2) Intensity-related PEPIs show a significant increase, while the frequency and contribution rate-related PEPIs tend to decrease, indicating less frequent but more concentrated PEP events. (3) Seasonal distribution of PEP events shows a primary concentration in QT, North China (N), the Eastern arid zone (EA), and Northeast China (NE) from June to September and they occur more frequently during spring and summer in Central (C) and South China (S) regions. Summer variations predominantly account for annual PEP variability and trends. (4) WPSHII, SCSSHII, WPWPSII, APVII, NHPVII, NINO3 predominantly affect the WAS, QT, NE, C, and S regions, with WPSHII having the broadest impact, notably on the QT, C, S, and NE regions. In the WAS region during autumn, the interaction of NAO and WPSHII explains 72% of the intensity of PEP events.

Extreme weather has variable effects on reproductive success of grassland songbirds at the northern extent of their range

Abstract Grassland songbirds breeding in Canada and the United States have experienced significant population declines likely because of habitat loss and degradation. Many climate change models predict an increase in the frequency, intensity, and duration of extreme precipitation and temperature events that could place further pressures on declining species. We monitored the fate of 1,868 individual nesting attempts of 7 grassland songbird species in response to various precipitation and temperature measures over a 10-yr period (1997 to 2002 and 2004 to 2008) in Saskatchewan, Canada. Daily nest survival rates of 5 species, including 3 at-risk species, were negatively influenced by high levels of precipitation, although the amount of precipitation where declines in daily nest survival occurred varied. Daily nest survival rates of 2 species were negatively correlated with high temperatures. We failed to detect any relationship between precipitation or temperature and the number of fledglings produced from successful nests. Extreme weather events could add additional stressors to declining populations of grassland birds in Canada. Increases in the frequency and intensity of extreme weather, specifically extreme precipitation events and short-term high temperatures, will likely lead to lower reproductive success for several species compared to current levels. This may be especially problematic for management of Anthus spragueii (Sprague’s Pipit) and Centronyx bairdii (Baird’s Sparrow), where a large proportion (> 75%) of the breeding population occurs near the northern edge of the Great Plains. The continuing loss and degradation of northern grasslands may limit the ability of these species to disperse and find favorable climate conditions.

Differing Contributions of Anthropogenic Aerosols and Greenhouse Gases on Precipitation Intensity Percentiles Over the Middle and Lower Reaches of the Yangtze River

AbstractIn June–July 2020, the middle and lower reaches of the Yangtze River (MLYR) were hit by a Meiyu event characterized by a long duration, abundant precipitation, and frequent heavy rainfall, resulting in destructive flooding. We found that the extreme cumulative precipitation in 2020 was mainly contributed by more moderate to heavy daily precipitation rather than extreme daily events. Although some previous studies have been conducted to attribute the 2020 Meiyu event in the MLYR, most of them focused on the cumulative precipitation amount. This attribution case study complements previous attribution analyses and reveals many new features. Our results show that anthropogenic climate change—primarily driven by greenhouse gas (GHG) forcing, anthropogenic aerosol (AA) forcing, and land‐use—has led to a decrease in the number of light to heavy precipitation days, while concurrently increasing the number of extreme precipitation days in the MLYR. Specifically, GHG and AA forcings decreased the frequency of light and moderate precipitation, but only GHG increased the frequency of extreme precipitation. An increasing trend in very heavy precipitation days has been observed. The competitive effects of GHG and AA forcings make it challenging to detect the signal of human activities, which could be intermingled with effects from natural variability. Under the SSP5‐8.5 scenario, the probability of experiencing both light and extreme precipitation events will significantly increase in the MLYR. By 2050–2100, these events are projected to be nearly 4 times more frequent compared to the current climate, which poses significant challenges to water security and economic development decision makers.

Risk of Natural Hazards Caused by Extreme Precipitation in Poland in 1951–2020

Extreme precipitation of a minimum daily value of >30 mm often initiates natural hazards such as floods, which in turn may not only lead to property damage but also present a danger to people’s health and lives. This paper mainly focuses on examining the trends and frequency of extreme daily precipitation (EDPr) in Poland. Also, it determines natural risk zones caused by EDPr of >30 mm, >50 mm, >70 mm, and >100 mm. In Poland, a significant positive trend was found for EDPr > 30 mm, >50 mm, and >70 mm in September, and for EDPr >100 mm in May. The most frequently recorded EDPr in Poland was >30 mm, the frequency of which ranged from 0.04% in February to nearly 3% in July. EDPr of >100 mm was recorded in 4 months, from May to August. An increase in the frequency of monthly EDPr in Poland occurred mainly in the southwestern and western parts. In Poland, three hazard zones of various frequencies of EDPr events were determined. In Zone III, which is in the southwestern and southern parts of the country, EDPr events occurred far more often than in Zone I; on average, four times more in the spring–summer season and slightly more than five times more in the autumn–winter season. The obtained results may help in the building of modern management and monitoring systems for the prevention of natural hazards caused by extreme precipitation.

Past and future joint return period of precipitation extremes over South Asia and Southeast Asia

Climate change is one of the major reasons for the increased intensity and frequency of precipitation extremes in tropical regions. This will have a significant impact on underdeveloped countries in South Asia and Southeast Asia. Therefore, this study analyzes changes in spatiotemporal patterns and the joint behavior of precipitation extremes across South Asian and Southeast Asian countries for the historical period (1975 to 2014) and future periods (2021–2060 (F1) and 2061–2100 (F2)). This study develops bias-corrected precipitation data and ranks General Circulation Models (GCMs) using the Empirical Quantile Mapping method and TOPSIS method, respectively. A multi-model ensemble of precipitation extremes is created using the top five GCMs. The Mann Kendall test is used to analyze trends in precipitation extremes. Joint probabilistic analysis is also conducted for different combinations of ETCCDI precipitation-based indices using Archimedes and Elliptical copulas. The results of the trend analysis indicate significant positive trends for R20mm (19.26%), R95pTOT (18.40%), Rx5Day (11.53%), and CWD (10.46%), while CDD (5.07%) shows a significant negative trend across the study area during historical period (1975–2014). The results of R20mm, R95pTOT, and Rx5Day under SSP585 shows almost 19 to 39% of area comes under significant positive trend during the F1 period. This becomes more evident in F2, with trends under SSP585 rising between 32% and 61%. The high-emissions scenario SSP585 reveals an increase in these positive trends compared to SSP126 in both the future periods (F1 and F2). These results indicating a noticeable rise in extreme precipitation events. The analysis of changes in the joint return period of precipitation extremes indicates that the South Peninsular India, North East India, West Central India and Central Northeast India, as well as most parts of Southeast Asian countries, will experience very heavy intensify precipitations in the future. In addition, persistent co-occurrence of dry and wet conditions will be observed in Pakistan, North West India, and Central Northeast India. This study provides useful information on the distribution of precipitation extremes in different regions of the study area.

Deep‐learning‐based downscaling of precipitation in the middle reaches of the Yellow River using residual‐based CNNs

AbstractThe middle reaches of the Yellow River (MRYR), located in northern China, are the transition zone between semi‐arid and semi‐humid climates. As one of the climate‐sensitive regions in China, MRYR has a fragile ecological environment and serious soil loss, which leads to geological disasters such as landslides, collapses, and mudslides caused by extreme precipitation. However, scarceness of high‐resolution precipitation data over MRYR limits assessment of the environmental impacts caused by climate change, especially for extreme precipitation. In this article, we design a Residual‐in‐Residual Dense Block based Network (RRDBNet) model for the statistical downscaling of precipitation in MRYR, and compare the proposed RRDBNet with a generalized linear regression model (GLM) and two popular deep‐learning‐based models. The multi‐level residuals and dense connectivity strategies introduced in RRDBNet help it to learn more abstract features and complex nonlinear relationships among climate variables to improve downscaling performance. The results show that the proposed RRDBNet has good performance in precipitation simulations, which can reproduce the spatial–temporal characteristics of high‐resolution precipitation well. RRDBNet reduces the root‐mean‐squared error (RMSE) by 19% and improves the Pearson correlation coefficient (CC) by 6% relative to GLM for climatology mean precipitation. Especially, RRDBNet has substantial improvements in extreme precipitation compared with other models. It reduces RMSE by 58% (79%) and improves CC by 38% (145%) relative to GLM for R95P (R99P), where R95P and R99P represent extreme precipitation and very extreme precipitation, respectively. For the probability density function of daily precipitation, it is further demonstrated that RRDBNet performs better as regards extreme precipitation frequency. Our results suggest that statistical downscaling based on RRDBNet may be an effective tool for historical and future climate simulations from global climate models.

Projection of extreme climate change in the Asian arid region and the Tibetan Plateau in the early and middle 21st century based on NEX-GDDP-CMIP6

In the context of global warming, it is anticipated that both the intensity and the frequency of future global extreme high precipitation (EHP) and extreme high temperature (EHT) events will increase. To evaluate the future extreme climate changes in the Asian arid region and Tibetan Plateau, this study applied the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) to assess the changes in EHP (Rx5d and R95pTOT) and EHT (TX90p and TXx) under different emission scenarios in the 21st century. Findings suggest that both the frequency and the intensity of the extreme indices will increase, exhibiting accelerated growth under higher emission scenarios, particularly under the SSP5-8.5 emission scenario. It is suggested that the growth of EHT and EHP in the early subperiod of the 21st century (2026–2045) will be relatively moderate, with small differences between different emission scenarios. However, by the middle subperiod of the 21st century (2041–2060), the differences between different emission scenarios will become larger than the 2035s and the growth will become more intense. In western central Asia, TX90p, TXx, Rx5d, and R95pTOT increase by 9.7%–14.2% (13.3%–24.7%), 1.3°C–1.7°C (1.6°C–2.7°C), 6.5%–8.9% (8.2%–8.8%), and 18.1%–27.0% (25.6%–30.0%) by the early (middle) subperiod; in eastern central Asia, TX90p, TXx, Rx5d, and R95pTOT increase 8.1%–12.0% (11.3%–21.1%), 1.4°C–1.8°C (1.9°C–2.9°C), 7.4%–9.7% (10.4%–13.8%), and 20.2%–29.3% (32.0%–40.8%) by the early (middle) subperiod; and over the Tibetan Plateau, TX90p, TXx, Rx5d, and R95pTOT increase 12.5%–17.4% (17.0%–31.0%), 1.2°C–1.5°C (1.6°C–2.5°C), 7.2%–10.0% (9.9%–15.0%), and 26.6%–33.1% (36.1%–55.3%) by the early (middle) subperiod.摘要近年来, 随着全球变暖, 大部分地区极端气候事件的风险都有所增加. 作为中国一带一路建设核心区域的亚洲干旱区和青藏高原区域面临着严峻的极端气候风险. 本文利用NEX-GDDP-CMIP6数据预估了未来21世纪早期 (2026–2045) 和中期 (2041–2060) 不同排放情景下亚洲干旱区和青藏高原相较气候参考期 (1995–2014) 极端高温指数和极端降水指数的变化. 预估结果显示, 在不同排放情景下极端高温指数和极端降水指数都有所上升, 且在SSP5-8.5情景下增长最多, 其中NEX-GDDP-CMIP6不同模式对极端高温指数的模拟一致性高于极端降水指数. 21世纪中期相比早期极端高温指数和极端降水指数的增长更加严重.

Precipitation and hydrological extremes during the warm season in response to climate change: the example from the Polish Carpathians

This study assesses the long-term variability of extreme precipitation events in the Carpathian region of Poland in relation to climate change and its potential impact on river regime. Focusing on eight catchments with undisturbed runoff regimes, the research utilized daily data from May to October between 1951 and 2022. This data included precipitation totals from 23 gauging stations, discharge values from eight water level gauging stations, and gridded precipitation data from the E-OBS dataset. Additionally, future projections for 2026–2060 under two RCP scenarios (4.5 and 8.5) were generated using five GCM-RCM model combinations from the EURO-CORDEX simulations. The study explored the relationship between precipitation and discharge, using probability techniques to identify extreme events. This study highlights the strong linkages between extreme precipitation and discharge in the region, indicating an elevated flood risk due to increased precipitation variability and volume. Despite no significant trends in the frequency of precipitation extremes being observed in the historical analysis, an increase in seasonal precipitation totals is projected, although with considerable uncertainty.

The impact of precipitation changes on the safety of railway operations in China under the background of climate change

Global climate change has intensified the water cycle, leading to frequent extreme precipitation events, posing a significant threat to railway infrastructure and safety operations. Based on the analysis of past and future precipitation changes in China, this study investigates the impact of climate change on railway safety operations. The study reveals the following findings: (1) Under the influence of the intensified East Asian summer monsoon and the northward shift of the subtropical high during the 2017–2021 compared to the 2012–2016, precipitation has significantly decreased (120 mm) in the regions south of the Yangtze River and South China, while it has increased (60 mm) in the regions from the eastern of Northwest China to the middle and lower reaches of Yangtze River; The operational precipitation risk has decreased for Urumqi, Lanzhou, Qinghai-Tibet Group, Xi’an, and Wuhan railway bureaus (abbreviated as Bureau), while it has increased for Nanchang, Chengdu, Zhengzhou, Shanghai, and Shenyang Bureaus. Particularly noteworthy is that despite a decrease in total annual precipitation for Nanchang bureau (15.3 mm/a), the frequency of intense precipitation events has increased, leading to an increased operational precipitation risk. (2) During the 21st century, under high (SSP5-8.5), medium (SSP2-4.5), and low (SSP1-2.6) forcing scenarios, all projections indicate that most of the China will experience an increasing trend in precipitation, with significant increases in precipitation observed in the regions south of the Yangtze Rive, South and Southwest China. The higher the greenhouse gas emissions, the more pronounced the increasing trend in precipitation. (3) Compared to the 20th century, under high (SSP5-8.5), medium (SSP2-4.5), and low (SSP1-2.6) forcing scenarios, all projections indicate that the total annual precipitation hours, railway inspection, speed limit, and closure risk hours have all increased on a national scale during the 21st century. The operational precipitation risk for railways has also increased. The higher the alert level for railway precipitation (precipitation < inspection < speed limit < closure), the higher the proportion of risk hours compared to the 20th century. By the late 21st century, the railway inspection, speed limit, and closure risk hours have increased by 175%, 463%, and 647%, respectively, compared to the 20th century.

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.