Variability Of Extreme Precipitation Research Articles

Changes in extreme precipitation indices across Algeria climate zones

AbstractExtreme precipitation events can have a significant impact on the environment, agriculture, economy and safety, making close monitoring of their short‐ and long‐term trends essential for the development of effective mitigation and adaptation strategies. In this study, we analysed 16 in situ observation datasets from four different climate zones in Algeria, spanning from 1969 to 2021. The trend analysis was conducted using the original Mann–Kendall test and seven modified tests to eliminate the effects of short‐term persistence. Our findings reveal a significant increasing trend of extreme precipitation variability for most stations in the Warm Mediterranean climate zone, except for the Consecutive dry days index, which showed a negative trend for the same zone, while stations in the Cold/Warm semi‐arid climate and Cold desert climate (Bwk) zones showed a decreasing trend. Additionally, all index series with significant long‐term trends were affected by a significant shift in their means, which was confirmed by both the Lombard and Pettitt tests. However, when we used the modified MPT and the test eliminating the effects of long‐term persistence, the significance of the shifts and the trend decreased. Our results suggest that while extreme precipitation events have been increasing in some parts of Algeria; the trend may not be statistically significant in the long‐run, indicating the necessity of revisiting and refreshing the findings of previous studies for a more current perspective.

Extreme precipitation detection ability of four high-resolution precipitation product datasets in hilly area: a case study in Nepal

Given Nepal's vulnerability to extreme precipitation (EP), it is imperative to conduct a comprehensive analysis to comprehend the historical trends of such events. However, acquiring precise precipitation data for EP remains challenging in mountainous countries like Nepal owing to the scarcity of densely gauged networks. This limitation impedes the dissemination of knowledge pertaining to EP variability events in Nepal. The current research on this topic is deficient for two main reasons: 1) there is a lack of studies leveraging recently released high-resolution precipitation products to identify their EP detection capabilities, which further hinders the usability of those products in data-scarce regions like Nepal, and 2) most studies have focused on the characterisation of EP events in Nepal rather than their spatial and temporal variability. To address these issues, this study evaluated the EP detection capabilities of four high-resolution precipitation product datasets (PPDs) across Nepal from 1985 to 2020. These datasets include the ERA5 Land reanalysis data, satellite-based precipitation data (PERSIANN_CCS_CDR and CHIRPS_V2.0) and a merged dataset (TPHiPr). We used various statistical and categorical indices to assess their ability to capture the spatial and temporal variability of EP events. The annual EP events were characterised by 11 indices divided into frequency and intensity categories. The TPHiPr merged dataset offered a robust depiction of monthly precipitation estimates, achieving the highest critical success index, accuracy, probability of detection and a low false alarm ratio for daily precipitation detection of 0.1 mm in Nepal. Conversely, the PERSIANN_CCS_CDR dataset exhibited poor performance. Most PPDs showed increasing trends in EP indices. However, the TPHiPr dataset showcased those trends with fewer errors and stronger correlations for many frequency (R10mm, R20mm and R25mm) and intensity (RX1day, RX5day, PRCPTOT and R99p) indices. The results indicate that TPHiPr outperformed other PPDs in accurately representing the spatial distribution of EP trends in Nepal from 1985 to 2020, particularly noting an exacerbation of EP events mostly in the eastern region of Nepal throughout the study period. While TPHiPr demonstrated superior performance in detecting various EP indices across Nepal, individual products like the ERA5 Land reanalysis dataset showed enhanced performance in the western region of Nepal. Conversely, PERSIANN_CCS_CDR and CHIRPS_V2.0 performed well in the eastern region compared to other PPDs.

Spatiotemporal variability of extreme precipitation in east of northwest China and associated large-scale circulation factors.

Spatial and temporal distributions and influencing factors of extreme precipitation are important bases for coping with future climate change. The spatiotemporal variability and affecting factors of extreme precipitation indices (EPIs) in east of northwest China (ENW) during 1961-2015 were investigated using a series of approaches such as modified Mann-Kendall trend test, Hurst exponent, ensemble empirical mode decomposition (EEMD), and geodetector model. The results showed that CDD and CWD decreased significantly (P < 0.01), with rates of 1.4days/decade and 0.07days/decade, respectively. EPIs in ENW exhibited an obvious heterogeneity. CDD gradually increased from the southeast to the northwest. The remaining EPIs generally showed the opposite trend. Geodetector results demonstrated that large-scale circulation factors had a significant impact on EPIs in ENW. The influence of large-scale climate factors on EPIs was concentrated in nonlinear enhancement, and Nino3.4 and SO were the dominant driving factors that played a major role in the variability of EPIs. The results of this study provided a reference for ENW and other arid and semi-arid regions to cope with extreme climates and develop corresponding strategies.

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.

Increasing multiscale variability in extreme precipitation under global warming in the Tienshan Mountains, Central Asia

Changes in precipitation variability not only have a significant impact on water cycle processes, but also pose an additional challenge to society's climate resilience. Extreme precipitation is more severe, more abrupt, and more sensitive to temperature changes than mean-state precipitation. However, our understanding of the features of extreme precipitation variability over a broad range of temporal scales, as well as the differences between wet and dry seasons, is limited. In this study, we perform filtering on a daily (2–5 days) to an interannual (2–8 years) scale, using daily precipitation data from detrended APHRODITE and bias-corrected CMIP6 based on zero-phase Butterworth filters. We then analyze the changes in climatological extreme precipitation variability at different time scales, looking at the features along geographical gradients and in wet and dry seasons. At various warming levels, extreme precipitation variability is projected. The findings reveal that the longer the time scale, the higher the overall variability of both extreme and mean-state precipitation, with extreme precipitation showing greater variability. From daily to interannual scales, the variability of R95p increases from 142.79 mm to 875.05 mm, representing a 6.13-fold increase in volatility, but the variability in mean precipitation increases just 1.43-fold, from 2.27 mm to 3.24 mm. The variability in the eastern Tienshan Mountains (80°E ∼ 95°E) is greater than that in the western Tienshan Mountains (66°E ∼ 80°E). Furthermore, along longitudinal and latitudinal gradients, extreme precipitation variability exhibits considerable time-scale differences, with the more extreme the event, the greater the variability. R10mm variability increases from 15.28 d to 76.74 d on a daily to interannual scale. The variability increase (61.46 d) is roughly twice that for R5mm. Seasonally, the wet season is more variable than the dry. The total variability of R10mm was 4.57 days in the wet season and 1.78 days in the dry season. Compared with the reference period (1976–2005), there is an overall increase in extreme precipitation variability at different warming levels, along with an increasing sensitivity to temperature. With the exception of consecutive dry days, the degree of response of filtered extreme precipitation variability to temperature (absolute value of response rate) shows obvious increases with time scale and accounts for a greater proportion of the total variability response rate.

Behavior of IMERG precipitation extremes with temperature at different spatial resolution

The Clausius-Clapeyron equation determines how saturation vapor pressure increases with temperature, which is important in determining variations in extreme precipitation. Regionally, the scaling of C–C does not vary significantly, but the relationship with extreme precipitation does. In this study, the precipitation from Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) is tested for its accuracy in scaling extreme precipitation rates with temperature (termed scaling factor). We utilized the IMERG precipitation data across the Indian Sub-continent at 0.1° × 0.1°, 0.25° × 0.25° and 0.5° × 0.5° spatial resolution from 2001 to 2020 datasets. Our findings show that, there is a transition in Global Precipitation Measurement’s precipitation extremes estimations (95th percentile) around 30 °C over spatial resolution of 0.25° and 0.1° from C–C to sub C–C. This study also evaluates the sensitivity of C–C scaling in different regimes of India having homogeneous precipitation climatology. It is found that southeast India is highly sensitive to the spatial resolution, as it shows steep slopes in extreme precipitation rates at high dew point temperatures. This is the first study to evaluate the sensitivity of spatial resolution on C–C analysis as most of the previous studies have considered temporal variations.

Projections of flood regime changes over the upper-middle Huaihe River Basin in China based on CMIP6 models

Introduction: Climate change alters the hydrological cycle to different extents, in particular the intensification of extreme precipitation and floods, which has garnered more attention as a significant scientific issue in the last few decades. The last Coupled Model Inter-comparison Project 6 (CMIP6) was designed with new shared socioeconomic pathways (SSPs) to combine socioeconomic development with greenhouse gas emissions to project future climate.Method: In this study, we used 22 global climate models (GCMs) from CMIP6 to investigate future variations in extreme precipitation and temperature under SSP2-4.5 and SSP5-8.5 scenarios over the upper-middle Huaihe River Basin (UMHRB). Then, the modified Xinanjiang model integrating the flood control module was driven to obtain projections of the daily streamflow and to evaluate the future variations in flood regimes.Results: The results show that 1) the characteristics of future extreme precipitation, such as the average intensity and amount of annual precipitation and extreme precipitation, are projected to increase, and the average, maximum, and minimum temperature values also display substantial increasing trends in the future over the UMHRB; 2) warmer climate will lead to a more severe flood magnitude under the SSP5-8.5 scenario in the far future (2071–2100) over the UMHRB. The results of the multi-model ensemble show that the annual maximum flood peaks (15-day flood volumes) of Wangjiaba and Wujiadu stations are projected to increase by 46.4% (43.1%) and 45.4% (51.1%), respectively, in the far future (2071–2100) under the SSP5-8.5 scenario; and 3) variations in the flood frequency tend to resemble variations in flood magnitude, and the return period of the design flood will obviously decrease under future climate scenarios. For instance, in the far future, under SSP5-8.5 scenarios, the return period of the design flood with a 100-year return period will become 38 years and 31 years for Wangjiaba and Wujiadu stations, respectively.Discussion: The study enhances a more realistic understanding that the occurrence of future extreme precipitation and floods is projected to be more frequent and severe, thereby resulting in an urgent imperative to develop pertinent adaptation strategies to enhance social resilience toward the warming climate.

Evaluation of CMIP6 Models and Multi-Model Ensemble for Extreme Precipitation over Arid Central Asia

Simulated historical extreme precipitation is evaluated for Coupled Model Intercomparison Project Phase 6 (CMIP6) models using precipitation indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). The indices of 33 Global Circulation Models (GCMs) are evaluated against corresponding indices with observations from the Global Climate Center Precipitation Dataset (GPCC V2020) over five sub-regions across Arid Central Asia (ACA), using the Taylor diagram, interannual variability skill score (IVS) and comprehensive rating index (MR). Moreover, we compare four multi-model ensemble approaches: arithmetic average multi-model ensemble (AMME), median multi-model ensemble (MME), pattern performance-based multi-model ensemble (MM-PERF) and independence weighted mean (IWM). The results show that CMIP6 models have a certain ability to simulate the spatial distribution of extreme precipitation in ACA and the best ability to simulate simple daily intensity (SDII), but it is difficult to capture the spatial bias of consecutive wet days (CWD). Almost all models represent different degrees of wet bias in the southern Xinjiang (SX). Most GCMs are generally able to capture extreme precipitation trends, but to reproduce the performance of interannual variability for heavy precipitation days (R10mm), SDII and CWD need to be improved. The four multi-model ensemble methods can reduce the internal system bias and variability within individual models and outperform individual models in capturing the spatial and temporal variability of extreme precipitation. However, significant uncertainties remain in the simulation of extreme precipitation indices in SX and Tianshan Mountain (TM). Comparatively, IWM simulations of extreme precipitation in the ACA and its sub-regions are more reliable. The results of this study can provide a reference for the application of GCMs in ACA and sub-regions and can also reduce the uncertainty and increase the reliability of future climate change projections through the optimal multi-model ensemble method.

Event‐based extreme precipitation variability analysis over a part of the Hindu Kush Himalayan region

AbstractExtreme precipitation is a primary driver of flood hazards causing damage to humans and infrastructure. Analysis of extreme precipitation without considering temporally compounding events underestimates the severity of the hazard. Especially mountainous regions such as the Hindu Kush Himalayan region are highly vulnerable to multiday extreme precipitation events. However, analysis of such events and their time distribution patterns are still unknown, and a comprehensive assessment is needed. To this, the concept of event‐based extreme precipitation, which accounts for the preceding and succeeding precipitation, is employed for the Upper Indus basin using three reference gridded datasets, namely, Indian Meteorological Department (IMD), Multi‐Source Weighted Ensemble Precipitation (MSWEP) and Himalayan Adaption, Water and Resilience (HI‐AWARE). Four different types of time distribution patterns were identified based on the occurrence of extreme precipitation during an event. We identified that the time distribution pattern with the peak on the right side is predominant among the four types. Subsequently, trend analysis on the characteristics, namely amount, frequency, duration and concentration ratio for all four events, display negative trends with IMD and MSWEP datasets, whereas HI‐AWARE displays positive trends in the northwestern part of the catchment. Further, the 100‐year return level of the amount of multiday extreme precipitation is computed along with the traditional method of single‐day extremes considering nonstationarity. The differences between the return values obtained using the traditional method and the event‐based extreme precipitation concept were distinct and substantial (&gt;50 mm for three datasets). The findings clearly show that the analysis of multiday events is much more essential than single‐day extreme events, particularly for events like floods. Moreover, the results of the study were found to be conflicting among reference datasets, demonstrating the importance of identifying the suitability of reference datasets in extreme event analysis.

Interannual variability of extreme precipitation in late summer over west China during 1961–2021

The characteristics and related mechanisms of the interannual variability of late summer (August) extreme precipitation in West China (WC) were investigated from 1961 to 2021. Precipitation and extreme precipitation (defined as the 99th percentile) generally decreased in the southeast-northwest direction, with a maximum in the Sichuan Basin. The non-linear trends in extreme precipitation have increased since the 1980s. Therefore, we further found that the interannual increase in extreme precipitation in the WC was significantly related to the eastward-strengthened South Asian high, western-stretched Western Pacific Subtropical high, enhanced westerly jet, anomalous cyclone in Mongolia, and anomalous anticyclone in the western Pacific. The anti-cyclonic anomaly is a Gill-type response to increase the sea surface temperature in the western Pacific. A mid-high latitude barotropic Rossby-wave train can be induced and has essential effects on the above key circulation patterns, further cooperating with the strong updrafts rather than strengthening and maintaining extreme precipitation in the WC.

Spatial coherence patterns of extreme winter precipitation in the U.S.

Extreme precipitation events have a significant impact on life and property. The U.S. experiences huge economic losses due to severe floods caused by extreme precipitation. With the complex terrain of the region, it becomes increasingly important to understand the spatial variability of extreme precipitation to conduct a proper risk assessment of natural hazards such as floods. In this work, we use a complex network-based approach to identify distinct regions exhibiting spatially coherent precipitation patterns due to various underlying climate mechanisms. To quantify interactions between event series of different locations, we use a nonlinear similarity measure, called the edit-distance method, which considers not only the occurrence of the extreme events but also their intensity, while measuring similarity between two event series. Using network measures, namely, degree and betweenness centrality, we are able to identify the specific regions affected by the landfall of atmospheric rivers in addition to those where the extreme precipitation due to storm track activity is modulated by different mountain ranges such as the Rockies and the Appalachians. Our approach provides a comprehensive picture of the spatial patterns of extreme winter precipitation in the U.S. due to various climate processes despite its vast, complex topography.

Effects of variations in precipitation extremes on sediment load in the Second Songhua River Basin, Northeast China

Effects of variations in precipitation extremes on sediment load in the Second Songhua River Basin, Northeast China

Interannual Variability of Extreme Precipitation during the Boreal Summer over Northwest China

Herein, we investigated the characteristics and mechanisms of interannual variability of extreme summer precipitation in northwest China (NWC). The four high-resolution precipitation predicting products under assessment indicated that both the accumulation of summer daily precipitation ≥95th percentile, and the summer maxima of daily precipitation generally decreased in a southeast—northwest direction, while relatively high values were observed in the Tienshan and Qilian Mountain areas. In turn, the Tropical Rainfall Measuring Mission (TRMM) satellite dataset underestimated extreme precipitation in mountainous areas, while Asian precipitation highly—resolved observational data integration towards evaluation (APHRODITE) and Climate Prediction Center (CPC) captured the characteristics of extreme precipitation in NWC. AMIP-type simulations of the interannual variability of extreme summer precipitation in NWC were quite unsuccessful. However, all of them can capture the increasing trends. Therefore, we further found that the interannual increase in extreme precipitation in NWC is strongly correlated with the weakened South Asian high, strengthened Western Pacific Subtropical high, the enhanced westerly jet, and the mid- to high-latitude Rossby wave trains, whose formation and sustenance can be traced back to sea surface temperature-anomalies in the western Pacific in May, June, and July. An increased sea surface temperature promotes convection and induces diabatic heating, which stimulates anticyclonic anomalies that disturb the enhanced westerly jet, resulting in a barotropic Rossby wave train via the Gill-type response. Additionally, it guides more water vapor convergence to NWC and enhances upward motion via anticyclonic anomalies over western Europe and Eastern Asia, and cyclonic anomalies over Central Asia.

Spatial-Temporal Seasonal Variability of Extreme Precipitation under Warming Climate in Pakistan

Climate science has confirmed the alteration of the hydrological cycle attributed to global warming. This warming tendency affects the monsoon precipitation in Pakistan with an unprecedented intensity, causing severe flooding. Therefore, it is inevitable to observe the recent spring and summer monsoon changes in extreme precipitation throughout Pakistan. The present study examined 8 precipitation indices in the past 50-year period (1971–2020) (stretched to two data periods; 1971–1998 and 1999–2020) using Mann–Kendall and Sen’s method to investigate the direction and magnitude of the observed trends. Spring and summer wet days significantly increased in the central eastern (Kakul, Kotli, Jhelum) and western (Cherat, Chitral, Peshawar) regions in the 1st data period but significantly decreased in areas including the southern region in the 2nd data period. We further observed the high-intensity precipitation days (R10, R20) in the same seasons. The intensity of summer R20 was much stronger throughout Pakistan in the 1st data period which reduced significantly during the 2nd data period in northern and southern regions. We extended the circle of investigation to very heavy and extreme precipitation (R30 and R50). The intensity of R30 and R50 in summer followed the same pattern as observed for R10 and R20. However, R30 and R50 in pre-monsoon significantly increased in the northern, east-western, and south-eastern regions during the 2nd data period. Summer monsoon and westerly humid regions experienced a decreasing tendency of very heavy and severe precipitation in the 1st data period. Our results concluded that the most significant changes in precipitation extremes occurred with higher intensity and recurring frequency for all indices in spring and summer monsoon during the 2nd data period.

Variability of extreme precipitation in West Antarctica and its response to the Amundsen Sea Low changes

Variability of extreme precipitation in West Antarctica and its response to the Amundsen Sea Low changes

Temporal Variability of Extreme Precipitation in Jiangxi during 1961-2018

Temporal Variability of Extreme Precipitation in Jiangxi during 1961-2018

Spatiotemporal variations in precipitation extremes based on CMIP6 models and Shared Socioeconomic Pathway (SSP) scenarios over MENA

Spatiotemporal variations in precipitation extremes based on CMIP6 models and Shared Socioeconomic Pathway (SSP) scenarios over MENA

Spatiotemporal variability of extreme precipitation at different time scales and quantitative analysis of associated driving teleconnection factors: Insights from Taihu Basin, China

The increasing frequency of extreme precipitation (EP) has become an important issue owing to its high degree of destruction. Studies on the spatiotemporal distribution of EP variability and its teleconnection with meteorological factors are extremely important but lacking in the Taihu Lake basin (TLB). Based on the daily precipitation data from 50 hydrological stations in the TLB from 1965 to 2018, we introduced a new circular distribution method to statistically delineate the different flood seasons and assess the spatiotemporal evolution characteristics and future persistence of extreme precipitation indices (EPIs) at different time scales. In addition, geodetector was used to quantify the effects of interactions between meteorological factors on the EPIs changes. The results showed that the EP in the TLB tended to increase significantly (P < 0.05) over the past 54 years, with faster growth in the urban cluster in the northeastern part of the basin. The increase in intensity and maximum precipitation amount was dominant during the plum rain period (PRP), whereas the increase in the number of days of precipitation was dominant during the typhoon period (TP). The scaling exponents reveal that the increasing of EP had long-range correlation at different time scales. Furthermore, the influence of climate factors on EP was nonlinear, and the Western Pacific Subtropical High (WPSH) intensity were the climate factor that played a major role in the variability of the EPIs. The results of this study provide a reference for TLB and other monsoon coastal areas worldwide to cope with extreme climate and develop corresponding strategies.

Potential Effects of Urbanization on Precipitation Extremes in the Pearl River Delta Region, China

Rapid urbanization plays an indelible role in modifying local climate, with more extreme precipitation in urban areas. Understanding the mechanism of urban-induced precipitation changes and quantifying the potential effects of urbanization on the changes in precipitation extremes have become hotspot issues in hydrometeorology. We examine the spatiotemporal changes of precipitation extremes over the Pearl River Delta region in China using the homogenized daily precipitation dataset from the period 1961–2017, and quantify the urbanization effects on these changes. Most of the extreme precipitation indices show increasing trends, but only the mean precipitation intensity has a significant increase. Urbanization could induce the intensification of extreme precipitation, with a higher amount, intensity, and frequency of precipitation extremes and a larger magnitude of their trends in urban areas by comparison with those rural areas. Moreover, high-level urbanization tends to make a greater contribution to the temporal changes in precipitation extremes, indicating that urbanization effects on precipitation extremes may be related to urbanization levels. However, urbanization level shows little effect on the changes in the spatial patterns of precipitation extremes, with similar spatial distribution in different urbanization stages. Our findings highlight the important role of urbanization in precipitation extremes and offer insights into the feedback of anthropogenic changes into variations in precipitation extremes.

Changes in temperature and precipitation extremes in the arid regions of China during 1960–2016

Extreme climate events have a greater impact on natural and human systems than average climate. The spatial and temporal variation of 16 temperature and nine precipitation extremal indices was investigated using the daily maximum and minimum surface air temperature and precipitation records from 113 meteorological stations in China’s arid regions from 1960 to 2016. The warmth indices [warm spell duration (WSDI); numbers of warm nights, warm days, tropical nights (TR), and summer days (SU)] increased significantly. On the contrary, the cold indices [numbers of frost days (FD), ice days (ID), cool days, and cool nights; cold spell duration (CSDI)] decreased significantly. The number of FD decreased fastest (−3.61 days/decade), whereas the growing season length (GSL) increased fastest (3.17 days/decade). The trend was strongest for diurnal temperature range (DTR) (trend rate = −7.29, P &amp;lt; 0.001) and minimum night temperature (trend rate = 7.70, P &amp;lt; 0.001). The cold extreme temperature events increased with increasing latitude, but the warm extreme temperature events decreased. Compared with temperature indices, the precipitation indices exhibited much weaker changes and less spatial continuity. Overall, changes in precipitation extremes present wet trends, although most of the changes are insignificant. The regionally averaged total annual precipitation for wet days increased by 4.78 mm per decade, and extreme precipitation events have become more intense and frequent during the study period. The spatial variability of extreme precipitation in the region was primarily influenced by longitude. Furthermore, the climate experienced a warm-wet abrupt climate change during 1990s.