Variability Of Extreme Precipitation Research Articles

Variability of extreme precipitation and rainfall erosivity and their attenuated effects on sediment delivery from 1957 to 2018 on the Chinese Loess Plateau

Variability of extreme precipitation and rainfall erosivity and their attenuated effects on sediment delivery from 1957 to 2018 on the Chinese Loess Plateau

Understanding the sensitivity of hourly precipitation extremes to the warming climate over Eastern China

Theoretically, under the warming climate, due to the increase of the atmospheric water vapor, the precipitation extremes would increase. The Clausius-Clapeyron (CC) scaling indicates an increase of precipitation extremes at a rate of about 7% globally. However, the response of precipitation extremes to global warming is complex globally. In this study, we investigated the hourly precipitation extremes-dew-point temperature scaling relation in eastern China and the theoretical explanation. It is shown here that the hourly precipitation extreme intensity increases with the dew-point temperature (DPT), following the CC scaling rate. Conversely, as DPT exceeds ∼22 °C, the negative scaling exists, particularly for the southern part of China. The change in the scaling direction as the DPT exceeds 22 °C may be attributed to the negative scaling of precipitation efficiency/vertical velocity with DPT. The increase in the convective inhibition and decrease in the temperature advection above ∼22 °C, partially explain the negative scaling of precipitation efficiency and vertical velocity with DPT. Our results may help understanding the variations in precipitation extremes under future warming.

Statistical downscaling of daily precipitation over southeastern South America: Assessing the performance in extreme events

AbstractThe performance of multiple empirical statistical downscaling (ESD) methods was assessed for simulating daily precipitation during 1979–2017 over southeastern South America (SESA), a region where extremes are remarkable. Meteorological stations were used as reference and three gridded precipitation products were included to account for observational uncertainty. The set of ESD models involved different configurations of the analogue method (ANs), deterministic and stochastic versions of neural networks (NNs) and generalized linear models (GLMs) and circulation‐conditioned GLMs (GLM_WTs). The years with the largest number of extreme events (wet years) were calibrated separately. The spatio‐temporal variability of extremes was assessed by analysing their intensity, spatial extent, frequency and interannual variability. An overall good performance of the ESD models was found for several aspects of daily precipitation. ESD performance dispersion was usually contained in the observational spread. No particular model configuration was found to perform best in all aspects, indicating the advantage of considering a multi‐model ensemble. The ANs tended to follow the stations, satisfactorily simulating daily precipitation and its extremes. The deterministic GLMs strongly underestimated precipitation estimates and were not able to represent extreme frequencies and intensities, but this was alleviated by employing a stochastic version of the method. Furthermore, the use of weather types to condition the GLMs (GLM_WTs) considerably improved model performance, particularly for the annual cycle and the spatial structure of extreme precipitation. The NNs adequately reproduced the spatial behaviour and intra‐annual variability of extreme precipitation, although they underestimated its intensity in their deterministic version. An analysis of the regional time series of extremes showed consistency among datasets and evidenced the influence of the ENSO teleconnection on the wet years, which were commonly well‐simulated by the statistical models. ESD models presented good skills in simulating a wetter climate over SESA, which is of particular importance in a climate change scenario.

Simulation of Extreme Precipitation in Four Climate Regions in China by General Circulation Models (GCMs): Performance and Projections

In the context of global climate change, it is important to monitor abnormal changes in extreme precipitation events that lead to frequent floods. This research used precipitation indices to describe variations in extreme precipitation and analyzed the characteristics of extreme precipitation in four climatic (arid, semi-arid, semi-humid and humid) regions across China. The equidistant cumulative distribution function (EDCDF) method was used to downscale and bias-correct daily precipitation in eight Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCMs). From 1961 to 2005, the humid region had stronger and longer extreme precipitation compared with the other regions. In the future, the projected extreme precipitation is mainly concentrated in summer, and there will be large areas with substantial changes in maximum consecutive 5-day precipitation (Rx5) and precipitation intensity (SDII). The greatest differences between two scenarios (RCP4.5 and RCP8.5) are in semi-arid and semi-humid areas for summer precipitation anomalies. However, the area of the four regions with an increasing trend of extreme precipitation is larger under the RCP8.5 scenario than that under the RCP4.5 scenario. The increasing trend of extreme precipitation in the future is relatively pronounced, especially in humid areas, implying a potential heightened flood risk in these areas.

Spatiotemporal variations in extreme precipitation on the middle and lower reaches of the Yangtze River Basin (1970–2018)

Extreme precipitation events are likely occur more frequently and lead to casualties and socio-economic losses as global warming intensifies. Therefore, exploring the characteristics of extreme precipitation events is essential. Based on meteorological station data from the middle and lower reaches of the Yangtze River Basin (MLYRB) from 1970 to 2018, we analyzed the spatiotemporal distributions of 11 extreme precipitation indices and considered the correlation between altitude and large-scale atmospheric circulation with extreme precipitation. To this end, we employed Sen's trend analysis, rescaled range analysis, the Mann-Kendall test, and continuous wavelet transform. The results indicate that on this spatial scale, extreme precipitation indices in the northwestern region of the MLYRB have mainly decreased, but they have increased along the eastern coastal region. From the perspective of the entire region, Hurst values of extreme precipitation indices are >0.5. Areas with consistent increases are mainly concentrated in the central region, while those with consistent decreases are mainly scattered in the northern and southern regions. Abrupt change in extreme precipitation indices mostly occurred in the 1980s and 1990s. The periodic oscillations of most indices tendtoward high time-frequency scales (2.3 years or less). Consecutive wet days increased with increasing altitude; other indices changed similarly with increasing altitude, first increasing then decreasing before increasing again. Additionally, we found significant resonance periods between the extreme precipitation indices and the atmospheric circulation index, however, there were obvious differences in time domains.

Contributions of natural climate variability on the trends of seasonal precipitation extremes over China

AbstractThe increased trends of extreme precipitation events over China have been partially attributed to global warming, while it is unclear how the natural climate variability affect these variations in extreme rainfall trends. Here we examine the contributions of large‐scale modes climate variability from the Pacific Ocean, which are represented by El Niño‐Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO), on the trends of seasonal extreme events across China. Results show that the effects of climate variability modes on precipitation extremes exhibit evidently spatiotemporal characteristics at the seasonal scale. A significant positive trend is found over southeastern and northwestern China in four seasons, and substantial increases in two types of extreme events are also seen over northwestern China in spring and winter. Most of the seasonal trends of extreme precipitation events can be explained by the trends of the principal components. El Niño (La Niña) and PDO–like sea surface temperature (SST) anomalies over the Pacific have a close linkage with the variations in precipitation extremes. By modulating the large‐scale atmospheric circulations, these Pacific SST anomalies associated with climatic oscillations, such as ENSO and PDO, provide the conducive conditions responsible for the occurrences of seasonal precipitation extremes across China. The seasonal trends of extreme events can be largely explained by the variability of the ENSO and PDO episodes, with a relatively larger modulation of PDO as compared to ENSO. These researches have important implications for improving seasonal flood disaster predictability and climate model performances.

Relationship of extreme precipitation, surface air temperature, and dew point temperature across the Tibetan Plateau

Global warming is expected to have profound socio-economic and environmental consequences, and one of the key concerns is extreme precipitation. The classic theory indicates that the variation of extreme precipitation will follow the thermodynamically based Clausius-Clapeyron relation (increasing at a rate of approximately 7%/°C). However, the interaction and the seasonal variation of the relationship between extreme precipitation and temperature has not been thoroughly integrated. Here, we use quantile regression and the binning method to process meteorological station data and the reanalysis data from 78 stations on the Tibetan Plateau (TP), and estimate the sensitivity and dependency of extreme precipitation to both surface air temperature and dew point temperature. The results indicate that the majority of the meteorological stations experience a positive scaling between extreme precipitation and surface air temperature, with the median of surface air temperature scaling close to 2.5%/°C. With regard to scaling during individual seasons, 80% of stations possess negative scaling of the 99th percentile of extreme precipitation with temperature in summer, while 63 stations (81% of the total) display positive trends in winter, and the other 19% have negative scaling. A stronger scaling (3.5%/°C) occurs between dewpoint temperature and extreme precipitation. For surface air temperature 8 °C, relative humidity decreases with increasing temperature and dewpoint depression increases, so dewpoint temperature increases more slowly than temperature, resulting in the stronger scaling relationship with dewpoint temperature. The depression of dewpoint temperature presents a slight decrease in −3–8 °C interval, but dewpoint temperature increases faster than other intervals, and the scaling relationship of dewpoint temperature is consistently larger than with surface air temperature due to the small increasing rate with dewpoint temperature and increasing extreme precipitation intensity. Our results emphasize that the increasing temperature has clear scaling and seasonal relationship with extreme precipitation on the TP, and the atmospheric humidity variation has an effect on the overall difference in the scaling relationships of extreme precipitation with surface air temperature and dewpoint temperature. The variations of seasonality and the effects of atmospheric humidity for extreme precipitation should also be carefully considered in future research.

Impacts of Urbanization on Variations of Extreme Precipitation over the Yangtze River Delta

The urbanization process is the hallmark of the population’s economic activities and land-use types, including population-, economic-, and landscape-urbanization. The question of how to classify the stations into urbanized and suburbanized stations is important for detecting the contribution rates of urbanization to precipitation extremes. This study used the fuzzy c-means clustering method to classify different urbanized level stations by population, economy, and impervious surface in the Suzhou-Wuxi-Changzhou urban agglomeration. Based on the change trends of six extreme precipitation indices, the contribution rates of urbanization to the precipitation extremes were estimated. The results show that the increasing indices were the intensity indices, while the decreasing indices were the duration indices during 1980–2015. Moreover, high urbanization tended to have a higher contribution to the most extreme precipitation indices, especially the intensity indices, than urbanization in the medium-size cities, indicating the urbanization leads to the phenomenon of extreme precipitation enhancement. The results of the three kinds of classification methods were different, especially the classification by the impervious area. This paper investigated the spatiotemporal changes in precipitation extremes and the contribution of urbanization to extreme precipitation, which will provide support for the development of urban agglomeration in the future.

Variations in extreme precipitation and relation to the Asia summer monsoon over the Qinling-Dabashan Mountains, China

Variations in extreme precipitation and relation to the Asia summer monsoon over the Qinling-Dabashan Mountains, China

鄱阳湖流域极端降水时空变化特征及ENSO事件影响分析

鄱阳湖流域极端降水时空变化特征及ENSO事件影响分析

Variations in precipitation extremes in the arid and semi‐arid regions of China

AbstractPrecipitation regimes have been predicted to shift to more extreme patterns that are characterized by heavy rainfall events and longer dry intervals. Spatiotemporal variation of precipitation extremes may significantly influence the local ecosystem, especially in the arid and semi‐arid regions. This study investigated the variations in extreme precipitation indices across the arid and semi‐arid land of China from 1960 to 2018. Spatial autocorrelation analysis was also conducted to evaluate spatial autocorrelations within the study area. There was generally an increasing trend of precipitation intensity, however, most stations in the low‐altitude semi‐arid regions showed non‐significant trend. The frequency indices generally showed a decreasing trend in consecutive dry days, and an increasing trend in precipitation days in the arid land and high‐altitude semi‐arid regions, suggesting that the arid and high‐altitude semi‐arid regions are becoming wetter. Signals of decreasing precipitation intensity and frequency were still found, such as increasing trends in consecutive dry days in the stations from the low‐altitude semi‐arid regions, implying the possibility of increased drought risk at small spatial scale. The global Moran's I values indicated that the spatial autocorrelation of the trend in the low‐altitude semi‐arid region is high. The local Moran's I showed that the number of stations with wetting trend in the arid region were much higher than that in the semi‐arid regions. These observations imply that the precipitation extremes in the arid and semi‐arid regions of China are becoming more homogeneous. Hence, this study can serve as a useful reference to the policymakers and stakeholders for framing water resource and ecosystem management strategies in arid regions.

Daily precipitation characteristics of RegCM4 and WRF in China and their interannual variations

To evaluate and clarify the daily precipitation characteristics (i.e. amount, frequency and intensity) of the regional climate models (RCMs) in China, long-term simulations were carried out using RegCM4.5 and Weather Research and Forecasting model (WRF), which were nested within the European Centre for Medium-Range Weather Forecasts (ECMWF)’s 20th century reanalysis (ERA-20C) between 1901 and 2010. The 2 RCMs were initially run at a resolution of 50 km. Analyses mainly compared the model-simulated climatic means and interannual variations of precipitation characteristics with those of dense and high-quality station observations (STN) from 1961-2010. Both models satisfactorily reproduced the seasonal mean precipitation amount, but they overestimated its frequency and underestimated its intensity. Extreme rainfall frequency was also underestimated by both RCMs. In winter (DJF), the interannual variabilities in dry days, light precipitation and moderate precipitation were well represented by both models. However, they poorly reproduced the counterparts of extreme precipitation in winter. In summer (JJA), the 2 RCMs performed well in simulating the interannual variability of extreme precipitation. Comparably, RegCM outperformed WRF in reproducing the spatial patterns of precipitation amount, interannual variations in extreme precipitation and rain events. By contrast, WRF better represented precipitation frequency in different sub-regions overall. Moreover, when the horizontal resolution of RegCM was increased from 50 to 25 km, there was a slight improvement in the representation of precipitation amount and intensity. Our results show that RCMs perform well in reproducing actual climatic means and interannual variations of daily precipitation characteristics in China, and that high-resolution RCM simulations can produce improved results for precipitation amount and intensity.

The contribution of anthropogenic influence to more anomalous extreme precipitation in Europe

Anthropogenic influences can modulate the low‐frequency variability of extreme precipitation and increase the likelihood of flooding events. It is not, however, clear how much and in what manner the low-frequency variability has changed in recent decades as global warming has intensified. Here, we investigate the contribution of anthropogenic influences to the time evolution of extreme precipitation anomalies in different seasons using Coupled Model Intercomparison Project Phase 6 (CMIP6) and CMIP5 model simulations and observations over Europe. Our results show a latitudinal dependence of changes in extreme precipitation anomalies for all seasons due to anthropogenic impacts. While the contribution of anthropogenic influences to extreme precipitation anomalies at low latitudes (<50°) is less than 8% in all seasons, it goes up to 26% and 41% at mid (50°–60°) and high (>60°) latitudes. Without the offsetting effect of anthropogenic aerosols, anthropogenic emissions of greenhouse gases alone should have produced larger anomalies than observed. For all seasons, the more extreme the precipitation, the larger the anthropogenic influences.

Evaluation of MEVD-based precipitation frequency analyses from quasi-global precipitation datasets against dense rain gauge networks

Precipitation extremes and associated hydrological hazards pose a significant global risk to society and economy. To be effective, mitigation strategies require the best possible estimation of the intensity and frequency of precipitation extremes. Traditional approaches to precipitation frequency analysis rely on long-term records from in-situ observations, which are limited in terms of global coverage. Satellite-based precipitation products provide global coverage, but errors in these estimates may lead to large biases in the quantification of extremes. Previous studies have demonstrated the ability of the novel Metastatistical Extreme Value Distribution (MEVD) framework to provide robust estimates of high quantiles in the presence of short-term data records and the uncertainties typical of remote sensing precipitation products. Here, we evaluate MEVD-based precipitation frequency analyses for four widely used quasi-global precipitation products (IMERG-v6, GSMaP-v6, CMORPH-v1.0, and MSWEP-v2) over high-density gauge networks in five hydroclimatic regions (Austria, Italy, Florida, Texas, and Arizona). We show dependence of MEVD-based estimation error on the characteristics of each dataset and the hydroclimatic region. Additionally, we evaluate the sub-grid variability of extreme precipitation and demonstrate the impact of spatial scale mismatch (that is, single in-situ gauge versus satellite pixel) on the frequency analysis of extremes. This work provides an assessment of the use of MEVD for estimating precipitation extremes from globally available datasets and an understanding of the variability of sub-daily precipitation extremes in different hydroclimatic regions of the world.

Urban signatures of sub-daily extreme precipitation events over a metropolitan region

Previous research on the relationship between urbanization and extreme precipitation has shown that regional and global anthropogenic changes have made extreme precipitation events more frequent. The purpose of this research was to explore changes in the variability of extreme precipitation in urban areas that underwent different degrees of urbanization intensity, and to explore the possible reasons using data from 7 meteorological stations in Shenzhen, China with hourly precipitation records dating back to the 60's. Shenzhen is one of the fastest growing metropolitan areas on earth with heavy urbanization occurring since 1979. We used k-means clustering to categorize areas of the city into steadily urbanized regions (SUR) or rapidly urbanized regions (RUR). Next, the variations in the linear trends of annual maximum precipitation (AMP) were analyzed, followed by water vapor analysis and the random forest algorithm to analyze the causes of the variation. The results showed that the severity of the urbanization process had a large and significant impact on extreme precipitation. The AMP analysis showed that at the higher temporal resolutions (< 6 h) extreme precipitation was only significant at RUR stations, while at low temporal resolutions precipitation did not significantly change at any station. An analysis of the diurnal patterns of precipitation and the water vapor analysis over Shenzhen showed that the increase in short-duration extreme precipitation events may be related to atmospheric instability caused by heat emitted as a byproduct of the urbanization process. Furthermore, the differences between the variations in the precipitation trends of SUR and RUR may be related to differences in thermal contrast between high and low altitudes. The results of the random forest analysis supported the correlations between the severity of urban development and changes in precipitation variability. Our findings have highlighted the need for a better understanding of the local and regional anthropogenic effects on the climate and the intensification of extreme climate events due to urbanization.

Changes in Precipitation Extremes across Vietnam and Its Relationships with Teleconnection Patterns of the Northern Hemisphere

Understanding changes in precipitation extremes is critical for designing mitigation measures for the potential implications of a warming climate. This study assessed changes in the magnitude and frequency of precipitation extremes over Vietnam using high-quality gridded daily precipitation observations from 1980 to 2010. The annual maxima precipitation was analyzed to detect historical changes in the magnitude of precipitation extremes, while the number of heavy precipitation events, defined using the peak-over-threshold approach, was used to assess changes in the frequency of precipitation extremes. We found a strong signal of changes in the frequency of heavy precipitation, with 28.3% of Vietnam’s landmass exhibiting significant increasing trends. The magnitude of annual maxima precipitation shows a mixed pattern of changes, with less than 10% of Vietnam’s landmass exhibiting significant (both increasing and decreasing) trends. To identify possible mechanisms driving changes in precipitation, we assessed the relationship between inter-annual variations in precipitation extremes and climate variability represented by the teleconnection patterns of the Northern Hemisphere. Using five climate indices, we found that teleconnections across the Indian and Pacific Oceans have implied large control over the characteristics of precipitation extremes across Vietnam, with up to 30% of Vietnam’s landmass exhibiting a significant relationship.

Potential linkages of precipitation extremes in Beijing-Tianjin-Hebei region, China, with large-scale climate patterns using wavelet-based approaches

In this study, various wavelet analysis methods are used to investigate possible influences of large-scale climate patterns, such as El Nino-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), and Indian Ocean Dipole (IOD), on precipitation extremes over Beijing-Tianjin-Hebei region in China at different time scales. Firstly, the temporal patterns of precipitation extremes are detected by wavelet transform. Significant annual or inter-annual oscillations for the precipitation extremes during 1958–2017, with periodicities of around 0.5–1 year, 1–2 years, and 2–5 years were being found for monthly, seasonal, and annual time series, respectively. Subsequently, wavelet coherence method is used to identify the dominant driving factors of precipitation extremes, with ENSO, IOD, and NAO showing stronger correlations with monthly, seasonal, and annual precipitation extremes, respectively. Meanwhile, partial wavelet coherence analyses indicate that the standalone influences of climate factors may be weak, and the influences seem to be stronger because of their interdependences on other climate indices. Finally, multiple wavelet coherences reveal that variations of precipitation extremes could be better explained by combinations of two or more factors, although the additional explanatory variable may have not a significant increase in percent number of significant coherence.

Greater probability of extreme precipitation under 1.5 °C and 2 °C warming limits over East-Central Asia

East-Central Asia is one of the most vulnerable and sensitive regions to climate change, and the variability of extreme precipitation attracts great attention due to the large population and the importance of its economy. Here, three special runs with the Community Earth System Model (CESM) are used to project the changes in representative extreme precipitation indices (Rx1day, Rx5day, R95p, SDII) over East-Central Asia under the 1.5 °C and 2 °C Paris Agreement limits. The results indicate that Rx1day and Rx5day will increase by 28% and 15%, respectively, under the 1.5 °C warming level relative to the historical period (1971–2000). Most areas over East-Central Asia are projected to experience an accelerated increase in response to a further 0.5 °C warming. Specifically, humid areas (HAs) are projected to experience a greater increase in R95p annual days and area fraction, whereas arid and semiarid areas (ASAs) may have threefold higher risks. The proportion of extreme precipitation in total will increase ~10% in most HAs in response to the 0.5 °C additional warming. Holding global warming at 1.5 °C instead of 2 °C reduces the occurrence of R95p annual days by ~3 days/year in humid areas and ~1 day/year in ASAs. For SDII, most HAs will experience 0.2–0.6 mm/day and 0.2–0.4 mm/day increases in 1.5 °C or 2 °C warming limits, especially in Southeast China and the Himalayas. Therefore, limiting global warming to under 1.5 °C is beneficial to reducing the occurrence and associated impact of precipitation extremes in East-Central Asia.

Trends and variability of precipitation extremes in the Peruvian Altiplano (1971–2013)

AbstractPrecipitation extremes have been investigated throughout the last decades in different regions of the Andes. However, little attention has been paid to the Altiplano region (Central Andes), especially to the Peruvian Altiplano (PA) that represents a complex area and is highly vulnerable to extreme events, such as floods and droughts, driven by the strong variability of precipitation. This study focuses on the analysis of 11 extreme precipitation indices (EPIs) in the period 1971–2013. In this context, commonly used statistical trend and break analyses were applied and a false discovery procedure was used in order to reduce the number of artificial significant tests. Additionally, the relative dominance of precipitation frequency and intensity in interannual precipitation datasets was determined. Finally, the correlation between EPIs and six oceanic‐atmospheric indices were analysed. The results indicate that there is no significant global trend towards wet or dry conditions in the PA, although a signal of a more slightly decrease of precipitation is presented in the Southern PA. Additionally, interannual variability of total precipitation is mainly dominated by precipitation frequency. The Central Tropical Pacific sea surface temperature plays a major role for the maximum and average length of wet periods as well as for total precipitation. This finding is particularly relevant for the southwestern PA. Our results have important implications for risk management and adaptation planning related to extreme hydrological events in the PA.

Rainfall regionalization and variability of extreme precipitation using artificial neural networks: a case study from western central Morocco

Abstract Here, we investigate the precipitation regionalization and the spatial variability of rainfall extremes, using a 47-year long station-based dataset from western central Morocco, a region with marked topographic and climatic variations. The principal component analysis revealed three homogeneous rainfall regimes, consistent with topographic features: the coastal area receives heavy rainfall during autumns and winters, whereas the inner lowlands, in the middle of the study area, are characterized by an overall rainfall deficit regardless of their high water demand for irrigation, and the highest rainfall amounts take place in the mid-mountain area, including the summer seasons. Furthermore, the frequency analysis of daily rainfall extremes revealed high ten-year precipitation amounts in the coastal region (about 88 mm) and exceptional daily precipitation for longer return periods (182 mm for a 100-year period). Using artificial neural networks, the spatialization of these extreme precipitation events shows that they increase from the plain to the Atlas mountains and especially from the plain to the Atlantic Ocean. The spatial distribution of extreme precipitation highlights the areas where stormwater management needs to be improved, such as efficient stormwater drainage, and where floods are more likely to take place in the future.