scholarly journals Characterizing and Modeling Temporal and Spatial Trends in Rainfall Extremes

2009 ◽  
Vol 10 (1) ◽  
pp. 241-253 ◽  
Author(s):  
Santosh K. Aryal ◽  
Bryson C. Bates ◽  
Edward P. Campbell ◽  
Yun Li ◽  
Mark J. Palmer ◽  
...  
Keyword(s):  
Southern Oscillation ◽  
Probability Distributions ◽  
Temporal Trends ◽  
Spatial Coherence ◽  
Spatial Models ◽  
Daily Rainfall ◽  
Summer Rainfall ◽  
Return Levels ◽  
Rainfall Extremes ◽  
Hierarchical Spatial Model

Abstract A hierarchical spatial model for daily rainfall extremes that characterizes their temporal variation due to interannual climatic forcing as well as their spatial pattern is proposed. The model treats the parameters of at-site probability distributions for rainfall extremes as “data” that are likely to be spatially correlated and driven by atmospheric forcing. The method is applied to daily rainfall extremes for summer and winter half years over the Swan–Avon River basin in Western Australia. Two techniques for the characterization of at-site extremes—peaks-over-threshold (POT) analysis and the generalized extreme value (GEV) distribution—and three climatic drivers—the El Niño–Southern Oscillation as measured by the Southern Oscillation index (SOI), the Southern Hemisphere annular mode as measured by an Antarctic Oscillation index (AOI), and solar irradiance (SI)—were considered. The POT analysis of at-site extremes revealed that at-site thresholds lacked spatial coherence, making it difficult to determine a smooth spatial surface for the threshold parameter. In contrast, the GEV-based analysis indicated smooth spatial patterns in daily rainfall extremes that are consistent with the predominant orientation of storm tracks over the study area and the presence of a coastal escarpment near the western edge of the basin. It also indicated a linkage between temporal trends in daily rainfall extremes and those of the SOI and AOI. By applying the spatial models to winter and summer extreme rainfalls separately, an apparent increasing trend in return levels of summer rainfall to the northwest and decreasing trends in return levels of winter rainfall to the southwest of the region are found.

scholarly journals Probability distributions of daily rainfall extremes in Lazio and Sicily, Italy, and design rainfall inferences

2021 ◽  
Vol 33 ◽  
pp. 100771
Author(s):  
Benedetta Moccia ◽  
Claudio Mineo ◽  
Elena Ridolfi ◽  
Fabio Russo ◽  
Francesco Napolitano
Keyword(s):  
Probability Distributions ◽  
Daily Rainfall ◽  
Design Rainfall ◽  
Rainfall Extremes

scholarly journals Characteristics of the Northern Australian Rainy Season

2008 ◽  
Vol 21 (17) ◽  
pp. 4298-4311 ◽  
Author(s):  
I. N. Smith ◽  
L. Wilson ◽  
R. Suppiah
Keyword(s):  
Rainy Season ◽  
Climate Model ◽  
Southern Oscillation ◽  
Monsoon Season ◽  
Daily Rainfall ◽  
Summer Rainfall ◽  
Average Intensity ◽  
Australian Monsoon ◽  
Enso Events ◽  
Independent Features

Abstract A trend of increasing rainfall over much of north and northwest Australia over recent decades has contrasted with decreases over much of the rest of the continent. The increases have occurred during the summer months when the rainy season is dominated by the Australian monsoon but is also affected by other events such as tropical cyclones, Madden–Julian oscillations, and sporadic thunderstorms. The problem of diagnosing these trends is considered in terms of changes in the timing of the rainy season. While numerous definitions for rainy/monsoon season onset exist, most are designed to be useful in a predictive sense and can be limited in their application to diagnostic studies, particularly when they involve predetermined threshold amounts. Here the authors define indices, based on daily rainfall observations, that provide relatively simple, robust descriptions of each rainy season at any location. These are calculated using gridded daily rainfall data throughout the northern Australian tropics and also for selected stations. The results indicate that the trends in summer rainfall totals over the period from 1950 to 2005 appear to be mainly the result of similar trends in average intensity. Furthermore, the links between the September–October average Southern Oscillation index indicate that ENSO events affect season duration rather than average intensity. Because duration and average intensity are derived as independent features of each season, it is argued that the trends in rainfall totals are largely unrelated to trends in ENSO and most likely reflect the influence of other factors. Finally, diagnosing these features of the rainy season provides a basis for assessing the confidence one can attach to different climate model projections of changes to rainfall.

scholarly journals How extreme is extreme? An assessment of daily rainfall distribution tails

2012 ◽  
Vol 9 (5) ◽  
pp. 5757-5778 ◽  
Author(s):  
S. M. Papalexiou ◽  
D. Koutsoyiannis ◽  
C. Makropoulos
Keyword(s):  
Extreme Event ◽  
Probability Distributions ◽  
Daily Rainfall ◽  
Rainfall Distribution ◽  
Empirical Distributions ◽  
Gamma Distributions ◽  
Tail Behaviour ◽  
The World ◽  
Rainfall Extremes ◽  
Heavy Tailed

Abstract. The upper part of a probability distribution, usually known as the tail, governs both the magnitude and the frequency of extreme events. The tail behaviour of all probability distributions may be, loosely speaking, categorized in two families: heavy-tailed and light-tailed distributions, with the latter generating more "mild" and infrequent extremes compared to the former. This emphasizes how important for hydrological design is to assess correctly the tail behaviour. Traditionally, the wet-day daily rainfall has been described by light-tailed distributions like the Gamma, although heavier-tailed distributions have also been proposed and used, e.g. the Lognormal, the Pareto, the Kappa, and others. Here, we investigate the issue of tails for daily rainfall by comparing the upper part of empirical distributions of thousands of records with four common theoretical tails: those of the Pareto, Lognormal, Weibull and Gamma distributions. Specifically, we use 15 029 daily rainfall records from around the world with record lengths from 50 to 163 yr. The analysis shows that heavier-tailed distributions are in better agreement with the observed rainfall extremes than the more often used lighter tailed distributios, with clear implications on extreme event modelling and engineering design.

scholarly journals Projected intensification of sub-daily and daily rainfall extremes in convection-permitting climate model simulations over North America: implications for future intensity–duration–frequency curves

2019 ◽  
Vol 19 (2) ◽  
pp. 421-440 ◽  
Author(s):  
Alex J. Cannon ◽  
Silvia Innocenti
Keyword(s):  
Global Warming ◽  
Climate Model ◽  
Daily Rainfall ◽  
Scaling Exponent ◽  
Model Simulations ◽  
Return Levels ◽  
Rainfall Extremes ◽  
Idf Curves ◽  
Climate Model Simulations ◽  
Intensity Duration Frequency

Abstract. Convection-permitting climate models have been recommended for use in projecting future changes in local-scale, short-duration rainfall extremes that are of the greatest relevance to engineering and infrastructure design, e.g., as commonly summarized in intensity–duration–frequency (IDF) curves. Based on thermodynamic arguments, it is expected that rainfall extremes will become more intense in the future. Recent evidence also suggests that shorter-duration extremes may intensify more than longer durations and that changes may depend on event rarity. Based on these general trends, will IDF curves shift upward and steepen under global warming? Will long-return-period extremes experience greater intensification than more common events? Projected changes in IDF curve characteristics are assessed based on sub-daily and daily outputs from historical and late 21st century pseudo-global-warming convection-permitting climate model simulations over North America. To make more efficient use of the short model integrations, a parsimonious generalized extreme value simple scaling (GEVSS) model is used to estimate historical and future IDF curves (1 to 24 h durations). Simulated historical sub-daily rainfall extremes are first evaluated against in situ observations and compared with two high-resolution observationally constrained gridded products. The climate model performs well, matching or exceeding performance of the gridded datasets. Next, inferences about future changes in GEVSS parameters are made using a Bayesian false discovery rate approach. Large portions of the domain experience significant increases in GEVSS location (>99 % of grid points), scale (>88 %), and scaling exponent (>39 %) parameters, whereas almost no significant decreases are projected to occur (<1 %, <5 %, and <5 % respectively). The result is that IDF curves tend to shift upward (increases in location and scale), and, with the exception of the eastern US, steepen (increases in scaling exponent), which leads to the largest increases in return levels for short-duration extremes. The projected increase in the GEVSS scaling exponent calls into question stationarity assumptions that form the basis for existing IDF curve projections that rely exclusively on simulations at the daily timescale. When changes in return levels are scaled according to local temperature change, median scaling rates, e.g., for the 10-year return level, are consistent with the Clausius–Clapeyron (CC) relation at 1 to 6 h durations, with sub-CC scaling at longer durations and modest super-CC scaling at sub-hourly durations. Further, spatially coherent but small increases in dispersion – the ratio of scale and location parameters – of the GEVSS distribution are found over more than half of the domain, providing some evidence for return period dependence of future changes in extreme rainfall.

scholarly journals How extreme is extreme? An assessment of daily rainfall distribution tails

2013 ◽  
Vol 17 (2) ◽  
pp. 851-862 ◽  
Author(s):  
S. M. Papalexiou ◽  
D. Koutsoyiannis ◽  
C. Makropoulos
Keyword(s):  
Extreme Event ◽  
Probability Distributions ◽  
Daily Rainfall ◽  
Rainfall Distribution ◽  
Empirical Distributions ◽  
Gamma Distributions ◽  
Tail Behaviour ◽  
The World ◽  
Rainfall Extremes ◽  
Heavy Tailed

Abstract. The upper part of a probability distribution, usually known as the tail, governs both the magnitude and the frequency of extreme events. The tail behaviour of all probability distributions may be, loosely speaking, categorized into two families: heavy-tailed and light-tailed distributions, with the latter generating "milder" and less frequent extremes compared to the former. This emphasizes how important for hydrological design it is to assess the tail behaviour correctly. Traditionally, the wet-day daily rainfall has been described by light-tailed distributions like the Gamma distribution, although heavier-tailed distributions have also been proposed and used, e.g., the Lognormal, the Pareto, the Kappa, and other distributions. Here we investigate the distribution tails for daily rainfall by comparing the upper part of empirical distributions of thousands of records with four common theoretical tails: those of the Pareto, Lognormal, Weibull and Gamma distributions. Specifically, we use 15 029 daily rainfall records from around the world with record lengths from 50 to 172 yr. The analysis shows that heavier-tailed distributions are in better agreement with the observed rainfall extremes than the more often used lighter tailed distributions. This result has clear implications on extreme event modelling and engineering design.

scholarly journals Extreme avalanche cycles: Return levels and probability distributions depending on snow and meteorological conditions

2021 ◽  
pp. 100344
Author(s):  
Guillaume Evin ◽  
Pascal Dkengne Sielenou ◽  
Nicolas Eckert ◽  
Philippe Naveau ◽  
Pascal Hagenmuller ◽  
...  
Keyword(s):  
Probability Distributions ◽  
Meteorological Conditions ◽  
Return Levels

Differential Credibility of Climate Modes in CMIP6

2021 ◽  
pp. 1
Author(s):  
Jacob Coburn ◽  
S.C. Pryor
Keyword(s):  
Southern Oscillation ◽  
Probability Distributions ◽  
Critical Role ◽  
Regional Scale ◽  
Southern Annular Mode ◽  
Climate Projections ◽  
Annular Mode ◽  
Climate Modes ◽  
First Order ◽  
Skill Scores

AbstractThis work quantitatively evaluates the fidelity with which the Northern Annular Mode (NAM), Southern Annular Mode (SAM), Pacific-North American pattern (PNA), El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO) and Atlantic Multidecadal Oscillation (AMO) and the first-order mode interactions are represented in Earth System Model (ESM) output from the CMIP6 archive. Several skill metrics are used as part of a differential credibility assessment (DCA) of both spatial and temporal characteristics of the modes across ESMs, ESM families and specific ESM realizations relative to ERA5. The spatial patterns and probability distributions are generally well represented but skill scores that measure the degree to which the frequencies of maximum variance are captured are consistently lower for most ESMs and climate modes. Substantial variability in skill scores manifests across realizations from individual ESMs for the PNA and oceanic modes. Further, the ESMs consistently overestimate the strength of the NAM-PNA first-order interaction and underestimate the NAM-AMO connection. These results suggest that the choice of ESM and ESM realizations will continue to play a critical role in determining climate projections at the global and regional scale at least in the near-term.

scholarly journals Using high-resolution regional climate models to estimate return levels of daily extreme precipitation over Bavaria

2021 ◽  
Vol 21 (11) ◽  
pp. 3573-3598
Author(s):  
Benjamin Poschlod
Keyword(s):  
High Resolution ◽  
Climate Models ◽  
Regional Climate ◽  
Daily Rainfall ◽  
Regional Climate Models ◽  
Reanalysis Data ◽  
Extreme Value ◽  
Return Level ◽  
Gev Distribution ◽  
Return Levels

Abstract. Extreme daily rainfall is an important trigger for floods in Bavaria. The dimensioning of water management structures as well as building codes is based on observational rainfall return levels. In this study, three high-resolution regional climate models (RCMs) are employed to produce 10- and 100-year daily rainfall return levels and their performance is evaluated by comparison to observational return levels. The study area is governed by different types of precipitation (stratiform, orographic, convectional) and a complex terrain, with convective precipitation also contributing to daily rainfall levels. The Canadian Regional Climate Model version 5 (CRCM5) at a 12 km spatial resolution and the Weather and Forecasting Research (WRF) model at a 5 km resolution both driven by ERA-Interim reanalysis data use parametrization schemes to simulate convection. WRF at a 1.5 km resolution driven by ERA5 reanalysis data explicitly resolves convectional processes. Applying the generalized extreme value (GEV) distribution, the CRCM5 setup can reproduce the observational 10-year return levels with an areal average bias of +6.6 % and a spatial Spearman rank correlation of ρ=0.72. The higher-resolution 5 km WRF setup is found to improve the performance in terms of bias (+4.7 %) and spatial correlation (ρ=0.82). However, the finer topographic details of the WRF-ERA5 return levels cannot be evaluated with the observation data because their spatial resolution is too low. Hence, this comparison shows no further improvement in the spatial correlation (ρ=0.82) but a small improvement in the bias (2.7 %) compared to the 5 km resolution setup. Uncertainties due to extreme value theory are explored by employing three further approaches. Applied to the WRF-ERA5 data, the GEV distributions with a fixed shape parameter (bias is +2.5 %; ρ=0.79) and the generalized Pareto (GP) distributions (bias is +2.9 %; ρ=0.81) show almost equivalent results for the 10-year return period, whereas the metastatistical extreme value (MEV) distribution leads to a slight underestimation (bias is −7.8 %; ρ=0.84). For the 100-year return level, however, the MEV distribution (bias is +2.7 %; ρ=0.73) outperforms the GEV distribution (bias is +13.3 %; ρ=0.66), the GEV distribution with fixed shape parameter (bias is +12.9 %; ρ=0.70), and the GP distribution (bias is +11.9 %; ρ=0.63). Hence, for applications where the return period is extrapolated, the MEV framework is recommended. From these results, it follows that high-resolution regional climate models are suitable for generating spatially homogeneous rainfall return level products. In regions with a sparse rain gauge density or low spatial representativeness of the stations due to complex topography, RCMs can support the observational data. Further, RCMs driven by global climate models with emission scenarios can project climate-change-induced alterations in rainfall return levels at regional to local scales. This can allow adjustment of structural design and, therefore, adaption to future precipitation conditions.

Reliability of global gridded precipitation products in assessing extremes

2021 ◽  
Author(s):  
Chandra Rupa Rajulapati ◽  
Simon Michael Papalexiou ◽  
Martyn P Clark ◽  
Saman Razavi ◽  
Guoqiang Tang ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Extreme Precipitation ◽  
Probability Distributions ◽  
Extreme Precipitation Events ◽  
Return Levels ◽  
Time Period ◽  
Precipitation Gauge ◽  
Global Precipitation ◽  
Surface Observations ◽  
Precipitation Events

&lt;p&gt;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. &amp;#160;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 K&amp;#246;ppen-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.&lt;/p&gt;

Elemental proxy evidence for late Quaternary palaeoenvironmental change in southern African sedimentary records: interpretation and applications

2021 ◽  
Author(s):  
M.S. Humphries
Keyword(s):  
Environmental Change ◽  
Southern Africa ◽  
Southern Oscillation ◽  
Late Quaternary ◽  
Rainfall Variability ◽  
Analytical Techniques ◽  
Summer Rainfall ◽  
Geochemical Data ◽  
Complex Nature ◽  
Cape Coast

Abstract Sediments are the most important source of Late Quaternary palaeoclimate information in southern Africa, but have been little studied from a geochemical perspective. However, recent advances in analytical techniques that allow rapid and near-continuous elemental records to be obtained from sedimentary sequences has resulted in the increasing use of elemental indicators for reconstructing climate. This paper explores the diverse information that can be acquired from the inorganic component of sediments and reviews some of the progress that has been made over the last two decades in interpreting the climatic history of southern Africa using elemental records. Despite the general scarcity of elemental records, excellent examples from the region exist, which provide some of the longest and most highly resolved sequences of environmental change currently available. Records from Tswaing crater and marine deposits on the southern KwaZulu-Natal coastline have provided rare glimpses into hydroclimate variability over the last 200 000 years, suggesting that summer rainfall in the region responded predominantly to insolation forcing on glacial-interglacial timescales. Over shorter timescales, lakes and wetlands found in the Wilderness embayment on the southern Cape coast and along the Maputaland coast in north-eastern South Africa have yielded highly-resolved elemental records of Holocene environmental change, providing insight into the changing interactions between tropical (e.g., El Niño-Southern Oscillation) and temperate (e.g., mid-latitude westerlies) climate systems affecting rainfall variability in the region. The examples discussed demonstrate the multiple environmental processes that can be inferred from elemental proxies and the unique insight this can provide in advancing our understanding of past climate change on different timescales. The interpretation of geochemical data can be complicated by the complex nature of sedimentary environments, various proxy assumptions and analytical challenges, and the reliability of sediment-based climate reconstructions is substantially enhanced through multi-proxy approaches.

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