scholarly journals Event-Based Time Distribution Patterns, Return Levels, and Their Trends of Extreme Precipitation across Indus Basin

Water ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3373
Author(s):  
Muhammad Zaman ◽  
Ijaz Ahmad ◽  
Muhammad Usman ◽  
Muhammad Saifullah ◽  
Muhammad Naveed Anjum ◽  
...  

This study presented the spatio-temporal characteristics of extreme precipitation events in the Northern Highlands of Pakistan (NHPK). Daily precipitation observations of 30 in situ meteorological stations from 1961 to 2014 were used to estimate the 11 extreme precipitation indices. Additionally, trends in time distribution patterns (TDPs) and return periods were also investigated for event based extreme precipitations (EEP). Results found that the precipitation events with an amount of 160–320 mm and with a concentration ratio of 0.8–1.0 and a duration of 4–7 consecutive days were dominant. The frequency of heavy, very heavy and extremely heavy precipitation days decreased, whereas the frequency of wet, very wet and extremely wet days increased. Most of the indices, generally, showed an increasing trend from the northeast to middle parts. The extreme precipitation events of the 20 and 50-year return period were more common in the western and central areas of NHPK. Moreover, the 20 and 50-year return levels depicted higher values (up to 420 mm) for an event duration with all daily precipitation extremes dispersed in the first half (TDP1) in the Chitral, Panjkora and Jhelum Rivers basins, whilst the maximum values (up to 700 mm) for an event duration with all daily precipitation extremes dispersed in the second half (TDP2) were observed in the eastern part of the NHPK for 20-year and eastern and south-west for 50-year, respectively.

2020 ◽  
Author(s):  
Gaby Gründemann ◽  
Ruud van der Ent ◽  
Hylke Beck ◽  
Marc Schleiss ◽  
Enrico Zorzetto ◽  
...  

<p>Understanding the magnitude and frequency of extreme precipitation events is a core component of translating climate observations to planning and engineering design. This research aims to capture extreme precipitation return levels at the global scale. A benchmark of the current climate is created using the global Multi-Source Weighted-Ensemble Precipitation (MSWEP-V2, coverage 1979-2017 at 0.1 arc degree resolution) data, by using both classical and novel extreme value distributions. Traditional extreme value distributions, such as the Generalized Extreme Value (GEV) distribution use annual maxima to estimate precipitation extremes, whereas the novel Metastatistical Extreme Value (MEV) distribution also includes the ordinary precipitation events. Due to this inclusion the MEV is less sensitive to local extremes and thus provides a more reliable and smoothened spatial pattern. The global scale application of methods allows analysis of the complete spatial patterns of the extremes. The generated database of precipitation extremes for high return periods is particularly relevant in otherwise data-sparse regions to provide a benchmark for local engineers and planners.</p>


2018 ◽  
Vol 562 ◽  
pp. 305-317 ◽  
Author(s):  
Xushu Wu ◽  
Shenglian Guo ◽  
Jiabo Yin ◽  
Guang Yang ◽  
Yixuan Zhong ◽  
...  

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 254
Author(s):  
Bikash Nepal ◽  
Dibas Shrestha ◽  
Shankar Sharma ◽  
Mandira Singh Shrestha ◽  
Deepak Aryal ◽  
...  

The reliability of satellite precipitation products is important in climatic and hydro-meteorological studies, which is especially true in mountainous regions because of the lack of observations in these areas. Two recent satellite rainfall estimates (SREs) from Global Precipitation Measurement (GPM)-era—Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG-V06) and gauge calibrated Global Satellite Mapping of Precipitation (GSMaP-V07) are evaluated for their spatiotemporal accuracy and ability to capture extreme precipitation events using 279 gauge stations from southern slope of central Himalaya, Nepal, between 2014 and 2019. The overall result suggests that both SREs can capture the spatiotemporal precipitation variability, although they both underestimated the observed precipitation amount. Between the two, the IMERG product shows a more consistent performance with a higher correlation coefficient (0.52) and smaller bias (−2.49 mm/day) than the GSMaP product. It is worth mentioning that the monthly gauge-calibrated IMERG product yields better detection capability (higher probability of detection (POD) values) of daily precipitation events than the daily gauge calibrated GSMaP product; however, they both show similar performance in terms of false alarm ratio (FAR) and critical success index (CSI). Assessment based on extreme precipitation indices revealed that the IMERG product outperforms GSMaP in capturing daily precipitation extremes (RX1Day and RX5Day). In contrast, the GSMaP product tends to be more consistent in capturing the duration and threshold-based precipitation extremes (consecutive dry days (CDD), consecutive wet days (CWD), number of heavy precipitation days (R10mm), and number of extreme precipitation days (R25mm)). Therefore, it is suggested that the IMERG product can be a good alternative for monitoring daily extremes; meanwhile, GSMaP could be a better option for duration-based extremes in the mountainous region.


2008 ◽  
Vol 21 (1) ◽  
pp. 22-39 ◽  
Author(s):  
Siegfried D. Schubert ◽  
Yehui Chang ◽  
Max J. Suarez ◽  
Philip J. Pegion

Abstract In this study the authors examine the impact of El Niño–Southern Oscillation (ENSO) on precipitation events over the continental United States using 49 winters (1949/50–1997/98) of daily precipitation observations and NCEP–NCAR reanalyses. The results are compared with those from an ensemble of nine atmospheric general circulation model (AGCM) simulations forced with observed SST for the same time period. Empirical orthogonal functions (EOFs) of the daily precipitation fields together with compositing techniques are used to identify and characterize the weather systems that dominate the winter precipitation variability. The time series of the principal components (PCs) associated with the leading EOFs are analyzed using generalized extreme value (GEV) distributions to quantify the impact of ENSO on the intensity of extreme precipitation events. The six leading EOFs of the observations are associated with major winter storm systems and account for more than 50% of the daily precipitation variability along the West Coast and over much of the eastern part of the country. Two of the leading EOFs (designated GC for Gulf Coast and EC for East Coast) together represent cyclones that develop in the Gulf of Mexico and occasionally move and/or redevelop along the East Coast producing large amounts of precipitation over much of the southern and eastern United States. Three of the leading EOFs represent storms that hit different sections of the West Coast (designated SW for Southwest coast, WC for the central West Coast, and NW for northwest coast), while another represents storms that affect the Midwest (designated by MW). The winter maxima of several of the leading PCs are significantly impacted by ENSO such that extreme GC, EC, and SW storms that occur on average only once every 20 years (20-yr storms) would occur on average in half that time under sustained El Niño conditions. In contrast, under La Niña conditions, 20-yr GC and EC storms would occur on average about once in 30 years, while there is little impact of La Niña on the intensity of the SW storms. The leading EOFs from the model simulations and their connections to ENSO are for the most part quite realistic. The model, in particular, does very well in simulating the impact of ENSO on the intensity of EC and GC storms. The main model discrepancies are the lack of SW storms and an overall underestimate of the daily precipitation variance.


2021 ◽  
Author(s):  
Chandra Rupa Rajulapati ◽  
Simon Michael Papalexiou ◽  
Martyn P Clark ◽  
Saman Razavi ◽  
Guoqiang Tang ◽  
...  

<p>Assessing extreme precipitation events is of high importance to hydrological risk assessment, decision making, and adaptation strategies. Global gridded precipitation products, constructed by combining various data sources such as precipitation gauge observations, atmospheric reanalyses and satellite estimates, can be used to estimate extreme precipitation events. Although these global precipitation products are widely used, there has been limited work to examine how well these products represent the magnitude and frequency of extreme precipitation. In this work, the five most widely used global precipitation datasets (MSWEP, CFSR, CPC, PERSIANN-CDR and WFDEI) are compared to each other and to GHCN-daily surface observations. The spatial variability of extreme precipitation events and the discrepancy amongst datasets in predicting precipitation return levels (such as 100- and 1000-year) were evaluated for the time period 1979-2017.  The behaviour of extremes, that is the frequency and magnitude of extreme precipitation, was quantified using indices of the heaviness of the upper tail of the probability distribution. Two parameterizations of the upper tail, the power and stretched-exponential, were used to reveal the probabilistic behaviour of extremes. The analysis shows strong spatial variability in the frequency and magnitude of precipitation extremes as estimated from the upper tails of the probability distributions. This spatial variability is similar to the Köppen-Geiger climate classification. The predicted 100- and 1000-year return levels differ substantially amongst the gridded products, and the level of discrepancy varies regionally, with large differences in Africa and South America and small differences in North America and Europe. The results from this work reveal the shortcomings of global precipitation products in representing extremes. The work shows that there is no single global product that performs best for all regions and climates.</p>


2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Xianghu Li ◽  
Qi Hu

Spatiotemporal changes in extreme precipitation at local scales in the context of climate warming are overwhelmingly important for prevention and mitigation of water-related disasters and also provide critical information for effective water resources management. In this study, the variability and trends of extreme precipitation in both time and space in the Poyang Lake basin over the period of 1960–2012 are analyzed. Also, changes in precipitation extremes with topography are investigated, and possible causes are briefly discussed. The results show that extreme precipitation over the Poyang Lake basin is intensified during the last 50 years, especially the increasing trends are more significant before the end of the 1990s. Moreover, high contribution rates of extreme precipitation to the total rainfall (40–60%) indicated that extreme precipitation plays an important role to the total water resources in this area. The precipitation extremes also exhibited a significant spatial dependence in the basin. The northeastern and eastern areas are exposed to high risk of flood disaster with the higher frequency of extreme precipitation events. In addition, the distribution of precipitation extremes had a clear dependence on elevation, and the topography is an important factor affecting the variability of extreme precipitation over the Poyang Lake basin.


Atmosphere ◽  
2018 ◽  
Vol 9 (8) ◽  
pp. 325 ◽  
Author(s):  
Alexandre M. Ramos ◽  
Ricardo M. Trigo ◽  
Ricardo Tomé ◽  
Margarida L. R. Liberato

The European Macaronesia Archipelagos (Azores, Madeira and Canary Islands) are struck frequently by extreme precipitation events. Here we present a comprehensive assessment on the relationship between atmospheric rivers and extreme precipitation events in these three Atlantic Archipelagos. The relationship between the daily precipitation from the various weather stations located in the different Macaronesia islands and the occurrence of atmospheric rivers (obtained from four different reanalyses datasets) are analysed. It is shown that the atmospheric rivers’ influence over extreme precipitation (above the 90th percentile) is higher in the Azores islands when compared to Madeira or Canary Islands. In Azores, for the most extreme precipitation days, the presence of atmospheric rivers is particularly significant (up to 50%), while for Madeira, the importance of the atmospheric rivers is reduced (between 30% and 40%). For the Canary Islands, the occurrence of atmospheric rivers on extreme precipitation is even lower.


2016 ◽  
Vol 17 (2) ◽  
pp. 693-711 ◽  
Author(s):  
Hamed Ashouri ◽  
Soroosh Sorooshian ◽  
Kuo-Lin Hsu ◽  
Michael G. Bosilovich ◽  
Jaechoul Lee ◽  
...  

Abstract This study evaluates the performance of NASA’s Modern-Era Retrospective Analysis for Research and Applications (MERRA) precipitation product in reproducing the trend and distribution of extreme precipitation events. Utilizing the extreme value theory, time-invariant and time-variant extreme value distributions are developed to model the trends and changes in the patterns of extreme precipitation events over the contiguous United States during 1979–2010. The Climate Prediction Center (CPC) U.S. Unified gridded observation data are used as the observational dataset. The CPC analysis shows that the eastern and western parts of the United States are experiencing positive and negative trends in annual maxima, respectively. The continental-scale patterns of change found in MERRA seem to reasonably mirror the observed patterns of change found in CPC. This is not previously expected, given the difficulty in constraining precipitation in reanalysis products. MERRA tends to overestimate the frequency at which the 99th percentile of precipitation is exceeded because this threshold tends to be lower in MERRA, making it easier to be exceeded. This feature is dominant during the summer months. MERRA tends to reproduce spatial patterns of the scale and location parameters of the generalized extreme value and generalized Pareto distributions. However, MERRA underestimates these parameters, particularly over the Gulf Coast states, leading to lower magnitudes in extreme precipitation events. Two issues in MERRA are identified: 1) MERRA shows a spurious negative trend in Nebraska and Kansas, which is most likely related to the changes in the satellite observing system over time that has apparently affected the water cycle in the central United States, and 2) the patterns of positive trend over the Gulf Coast states and along the East Coast seem to be correlated with the tropical cyclones in these regions. The analysis of the trends in the seasonal precipitation extremes indicates that the hurricane and winter seasons are contributing the most to these trend patterns in the southeastern United States. In addition, the increasing annual trend simulated by MERRA in the Gulf Coast region is due to an incorrect trend in winter precipitation extremes.


2015 ◽  
Vol 16 (6) ◽  
pp. 2537-2557 ◽  
Author(s):  
Laurie Agel ◽  
Mathew Barlow ◽  
Jian-Hua Qian ◽  
Frank Colby ◽  
Ellen Douglas ◽  
...  

Abstract This study examines U.S. Northeast daily precipitation and extreme precipitation characteristics for the 1979–2008 period, focusing on daily station data. Seasonal and spatial distribution, time scale, and relation to large-scale factors are examined. Both parametric and nonparametric extreme definitions are considered, and the top 1% of wet days is chosen as a balance between sample size and emphasis on tail distribution. The seasonal cycle of daily precipitation exhibits two distinct subregions: inland stations characterized by frequent precipitation that peaks in summer and coastal stations characterized by less frequent but more intense precipitation that peaks in late spring as well as early fall. For both subregions, the frequency of extreme precipitation is greatest in the warm season, while the intensity of extreme precipitation shows no distinct seasonal cycle. The majority of Northeast precipitation occurs as isolated 1-day events, while most extreme precipitation occurs on a single day embedded in 2–5-day precipitation events. On these extreme days, examination of hourly data shows that 3 h or less account for approximately 50% of daily accumulation. Northeast station precipitation extremes are not particularly spatially cohesive: over 50% of extreme events occur at single stations only, and 90% occur at only 1–3 stations concurrently. The majority of extreme days (75%–100%) are related to extratropical storms, except during September, when more than 50% of extremes are related to tropical storms. Storm tracks on extreme days are farther southwest and more clustered than for all storm-related precipitation days.


Water ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 1688 ◽  
Author(s):  
Riccardo Hénin ◽  
Margarida Liberato ◽  
Alexandre Ramos ◽  
Célia Gouveia

An assessment of daily accumulated precipitation during extreme precipitation events (EPEs) occurring over the period 2000–2008 in the Iberian Peninsula (IP) is presented. Different sources for precipitation data, namely ERA-Interim and ERA5 reanalysis by the European Centre for Medium-Range Weather Forecast (ECMWF) and Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), both in near-real-time and post-real-time releases, are compared with the best ground-based high-resolution (0.2° × 0.2°) gridded precipitation dataset available for the IP (IB02). In this study, accuracy metrics are analysed for different quartiles of daily precipitation amounts, and additional insights are provided for a subset of EPEs extracted from an objective ranking of extreme precipitation during the extended winter period (October to March) over the IP. Results show that both reanalysis and multi-satellite datasets overestimate (underestimate) daily precipitation sums for the least (most) extreme events over the IP. In addition, it is shown that the TRMM TMPA precipitation estimates from the near-real-time product may be considered for EPEs assessment over these latitudes. Finally, it is found that the new ERA5 reanalysis accounts for large improvements over ERA-Interim and it also outperforms the satellite-based datasets.


Sign in / Sign up

Export Citation Format

Share Document