Annual Maximum Precipitation Research Articles

Temporal variability of daily precipitation concentration in Japan for a century: Effects of air temperature rises on extreme rainfall events

Climate change induced by air temperature rises may have a significant impact on change in rainfall patterns. Indeed, flood disasters due to downpours have increased over the last few decades in Japan. This study considers the Lorenz Curve, a concept used in economic theory, to describe spatial-temporal variability in the daily time series of precipitation concentration. The results reveal that annual average air temperature has risen at 10 rural sites and 5 urban sites over the period from 1901 to 2020 (120 years). The obtained trends are positive, and statistically meaningful at the 1% level. Whereas, annual precipitation has not changed significantly in the period (1901–2020) since obtained trends at all 15 sites are non-statistically meaningful (p > 0.01). In the meantime, annual maximum daily precipitation has increased for 1 of 15 sites, i.e. Ishigaki, while annual maximum hourly precipitation has increased for 4 of 15 sites, i.e. Choshi, Sapporo, Tokyo and Fukuoka. Calculated Gini coefficient, i.e. the precipitation concentration index (CI) has increased at all 15 sites in the period (1901–2020). The trends are positive, and statistically significant (p < 0.01). This indicates that rainfall has become more concentrated into a fewer rainy days throughout a year as the air temperature has risen for the 120 year period. The correlation between the precipitation CI and warming trends of individual monthly average air temperatures are weak, i.e. almost no correlation, for April, May, June, and November. Whereas, the obtained Spearman's rank correlation coefficient ρs = 0.78 and 0.72 indicate a strong correlation between the trend in air temperature rises and the trend in precipitation CI for July and August. This suggests that air temperature rises during summer (July and August) play an important role in the change in precipitation characteristics throughout Japan.

Severe Precipitation Phenomena in Crimea in Relation to Atmospheric Circulation

The increase in the frequency and intensity of hazardous hydrometeorological phenomena is one of the most dangerous consequences of climate instability. In this study, we summarize the data on severe weather phenomena using the data from 23 meteorological stations in Crimea from 1976 to 2020. Particular attention was paid to the precipitation phenomena descriptions. For the last 45 years, a significant positive trend of interannual variability of the annual occurrence of severe weather phenomena was estimated to be +2.7 cases per decade. The trend for severe precipitation phenomena was estimated to be +1.3 cases per decade. The probable maximum annual daily precipitation as a quantitative indicator of hazardous events was estimated for each station by using both the stationary and the non-stationary GEV models. For at least half of the meteorological stations, a non-stationary GEV model was more appropriate for the estimation of the precipitation extremes. An analysis of the main synoptic processes that drive severe weather phenomena of precipitation was carried out. The greatest contribution to the formation of severe precipitation was made by Mediterranean–Black Sea cyclones. At the same time, half of all of the cases of extreme precipitation were caused by cyclones generated over the Black Sea only, in all seasons apart from winter. In the mid-troposphere, four types of meridional circulation were identified depending on the location of troughs and ridges, with respect to the Black Sea region. More than 42% of severe precipitation phenomena were accompanied by an isolated high-altitude cyclone in the mid-troposphere over the Black Sea region. The main recommendation that can be drawn from this study is that long-term climatic non-stationarity should be taken into account whenever the risk assessment or hazard analysis is to be carried out. The results can also favor the designing of drainage and sewerage systems in urban areas. The findings of atmospheric patterns can be used for the improvement of extreme precipitation forecasts.

Evaluation of change factor methods in downscaling extreme precipitation over India

Flood is one of the most prevalent natural disasters, and extreme precipitation events are the principal cause of most flooding. Understanding climate change impacts on extreme precipitation is vital for sustainable infrastructure development and water resources management. There are different methods to downscale precipitation to local scale from the Global Climate Models’ simulations available at coarse resolution. The change factor (CF) method is one of those widely used methods for downscaling extreme precipitation. However, there are many variations in the CF method, and no study is available reporting the best CF method for downscaling extreme precipitation until today. Consequently, this study aims to evaluate different variants of four change factor methods, namely Additive Constant Scaling (ACS), Additive Daily Scaling (ADS), Multiplicative Constant Scaling (MCS) and Multiplicative Daily Scaling (MDS), for simulating extreme precipitation over India. Further, the optimum size of the baseline period for all four change factor methods and the optimum number of change factors for the daily scaling method are also evaluated. A total of 80 variants of four change factor methods, namely ACS, ADS, MCS and MDS, are formulated by varying the size of the baseline period and the number of change factors in the daily scaling method. Change factor methods are evaluated in terms of their capability in downscaling the Annual Maximum (AM) precipitation series and four extreme precipitation series extracted with Peaks Over Threshold (POT) method. The results indicate that the daily scaling methods (ADS and MDS) are the best change factor method for more than 75% (3713 out of 4948) of IMD grids for downscaling extreme precipitation. It is also observed that the variants of the daily scaling method having 100 change factors are alone the best change factor method for 35% (1708 out of 4948) of IMD grids for downscaling extreme precipitation. However, change factor methods having a smaller number of change factors (including constant scaling variants) are consistent compared to the variants of the daily scaling method having 100 change factors.

Agrupamento Espacial da Precipitação Anual Extrema no Uruguai

Abstract The main objective of this work is to study the existence of spatial patterns maximum annual rainfall (through daily observations) within the territory of Uruguay and to show the application of two new statistical tools recently proposed. In the first stage, the distributions of maximum annual precipitation at each meteorological station will be studied. In the second stage, spatial clustering methods will be applied. To get the distribution of the maximum of each station, we have used a truncated Cramér-von Mises hypothesis test (the first statistical tool) and showed that it improves on the performance of the classic likelihood ratio test. It was found that in 18 study locations the distribution that best fits the data is of the Gumbel type, and for the other two, it is of the Fréchet type. Regarding the clustering methods, two methodologies were used, one of them was to perform clustering with the estimated parameters and the other was the PAM methodology using the F-madogram as distance, highlighting the homogeneity throughout the Uruguayan territory. Another novelty of this work (the second statistical tool) consists in including, as a complement to the clustering, the recently proposed independence test based on recurrence rates.

Changes in extreme daily precipitation over Africa: Insights from a non-asymptotic statistical approach

Extreme precipitation heavily affects society and economy in Africa because it triggers natural hazards and contributes large amounts of freshwater. Understanding past changes in extreme precipitation could help us improve our projections of extremes, thus reducing the vulnerability of the region to climate change. Here, we combine high-resolution satellite data (1981–2019) with a novel non-asymptotic statistical approach, which explicitly separates intensity and occurrence of the process. We investigate past changes in extreme daily precipitation amounts relevant to engineering and risk management. Significant (α=0.05) positive and negative trends in annual maximum daily precipitation are reported in ∼20 % of Africa both at the local scales (0.05°) and mesoscales (1°). Our statistical model is able to explain ∼90% of their variance, and performs well (72% explained variance) even when annual maxima are explicitly censored from the parameter estimation. This suggests possible applications in situations in which the observed extremes are not quantitatively trusted. We present results at the continental scale, as well as for six areas characterized by different climatic characteristics and forcing mechanisms underlying the ongoing changes. In general, we can attribute most of the observed trends to changes in the tail heaviness of the intensity distribution (25% of explained variance, 38% at the mesoscale), while changes in the average number of wet days only explain 4% (12%) of the variance. Low-probability extremes always exhibit faster trend rates than annual maxima (∼44% faster, in median, for the case of 100-year events), implying that changes in infrastructure design values are likely underestimated by approaches based on trend analyses of annual maxima: flexible change-permitting models are needed. No systematic difference between local and mesoscales is reported, with locally-varying impacts on the areal reduction factors used to transform return levels across scales.

Trends in annual maximum precipitation in selected locations in New Jersey, USA

Trends in annual maximum precipitation in selected locations in New Jersey, USA

Instrumental agreement and retrospective analysis of trends in precipitation extremes in the French Mediterranean Region

In this letter we show the emergence of an agreement between the instruments of a rain-gauge network to point toward a positive trend in daily precipitation extremes since 1960 in the French Mediterranean Region. We identify for each gauge the time varying parameters of the generalized extreme value distribution of annual maximum precipitation over incremental time-windows. These distributions provide for each station of the network a trend assessment over a chosen period that can be interpreted for instance as a trend of the mean or as the trend of a chosen quantile. The incremental window, i.e. a window containing the series of data available at a given date, mimics the annual assessment of the trends that could have been made through time. Each year we thus have one trend per gauge that we can look in distribution through the network in order to assess the level of consensus among instruments. We show how the increasing size of the datasets used over a period of possible climate non-stationarity progressively leads from a dissensus anarchically pointing to no trend (before the 2000s) to a consensus where a majority of gauges points toward a positive trend (after the 2000s). The detected trend in this Mediterranean Region is quite substantial. For instance the 20 year return period precipitation in 1960 turns out to become a 8 year return period precipitation in 2020. Using a simulation basis we try to characterize the effect of decadal variability that is quite readable in the consensus evolution. The proposed metrics is thought to be a good candidate for the assessment of the local time and rate of emergence of climate change that has important implications in regards to adaptation of human and natural systems.

An Effective Trend Surface Fitting Framework for Spatial Analysis of Extreme Events

AbstractThe estimation of exceedance probabilities for extreme climatic events is critical for infrastructure design and risk assessment. Climatic events occur over a greater space than they are measured with point‐scale in situ gauges. In extreme value theory, the block maxima approach for spatial analysis of extremes depends on properly modeling the spatially varying Generalized Extreme Value marginal parameters (i.e., trend surfaces). Fitting these trend surfaces can be challenging since there are numerous spatial and temporal covariates that are potentially relevant for any given event type and region. Traditionally, covariate selection is based on assumptions regarding the topmost relevant drivers of the event. This work demonstrates the benefit of utilizing elastic‐net regression to support automatic selection from a relatively large set of physically relevant covariates during trend surface estimation. The trend surfaces presented are based on 24‐hr annual maximum precipitation for northeastern Colorado and the Texas‐Louisiana Gulf Coast.

Clustering for Regional Time Trend in the Nonstationary Extreme Distribution

Since the estimation of tail properties requires a stationarity of observations, it is necessary to develop a de-trending method not dependent on underlying distributions for nonstationary hydrological processes. Moreover, de-trending has been independently applied to hydrological processes, even though the processes are observed in geometrically adjacent sites. This paper presents a distribution-free de-trending method for nonstationary hydrological processes. Our method also provides clustered regional trends obtained by sparse regularization in a general distribution. It aggregates the parameter estimation and clustering within a unified framework. In the simulation study, our proposed method has superiority over other compared methods with respect to MSE and variance of coefficients. In real data analysis, the clustered trends of the annual maximum precipitation in the South Korean peninsula are reported, and the patterns of the estimated trends are visualized.

Estimation of design precipitation in Beijing–Tianjin–Hebei region under a changing climate

ABSTRACT Under changing environments, conventional methods based on an assumption of stationarity can lead to unreliable estimates of design rainfall quantiles. In this study, the trend of six extreme precipitation indices in the Beijing–Tianjin–Hebei region was analysed using different methods. Nonstationary models were established for annual maximum one-day precipitation (RX1d) and annual total precipitation when daily precipitation is greater than 95% of daily precipitation in a year (R95p) exhibiting significant trend, while stationary models were established for the two indices showing no significant trend. Finally, the nonstationary and stationary design precipitation were estimated using the average design life level method and the stationary method, respectively. The results indicated that for some stations in the east and south, a significant decreasing trend was observed for RX1d and R95p. This resulted in the overestimation of their design precipitation using the stationary methods and altering the spatial distribution of precipitation extremes for future period near these stations.

Identifying Characteristics of Guam’s Extreme Rainfalls Prior to Climate Change Assessment

Extreme rainfall and its consequential flooding account for a devastating amount of damage to the Pacific Islands. Having an improved understanding of extreme rainfall patterns can better inform stormwater managers about current and future flooding scenarios, so they can minimize potential damages and disruptions. In this study, the scaling invariant properties of annual maximum precipitations (AMPs) are used for describing the regional patterns of extreme rainfalls over Guam. AMPs are calculated at seven stations in Guam and exhibit distinct simple scaling behavior for two different time frames: (1) from 15 min to 45 min; and (2) from 45 min to 24 h. With these two different behaviors, the conventional estimation methods for sub-hourly durations overestimate the frequencies at a site in which breakpoints are clearly observed, while the proposed Scaling Generalized Extreme Value (GEV) method, based on the Scaling Three-NCM (S3NCM) method, provides comparable estimates. A new regional extreme rainfall analysis approach based on scaling exponents is introduced in this study. Results show distinct extreme rainfall patterns over Guam. Moreover, the numerical and graphical analyses identify that a tropical cyclone may increase daily AMPs by 3%, on average.

EPPO Datasheet: Naupactus xanthographus

EPPO Datasheet: <i>Naupactus xanthographus</i>

Probabilistic Approach to Determine the Spatial Distribution of the Antecedent Moisture Conditions for Different Return Periods in the Atlántico Region, Colombia

Previous soil moisture conditions play an important role in the design of hydraulic structures because they are directly related to the runoff threshold associated with a return period. These represent one of the main determinants of the runoff response of a drainage basin. One of the main difficulties facing hydrologists in Colombia lies in the time spent gathering and analyzing information related to the selection of antecedent moisture conditions. In this study, complete records from 19 rainfall stations located in the Atlántico region, Colombia, were used to analyze the cumulative precipitation during the 5 days prior to the annual maximum daily precipitation associated with different return periods using the Gev, Gumbel, Pearson Type III and Log Pearson Type III probability distributions. Different interpolation methods (IDW, kriging and spline) were applied to evaluate the spatial distribution of the antecedent moisture conditions. The main contribution of this research is establishing, using a probabilistic approach, the behavior of antecedent moisture conditions in a particular region, which can be used by engineers and designers to plan water infrastructure. This probabilistic approach was applied to a case study of the Atlántico region, Colombia, where the spatial distribution of antecedent moisture conditions was calculated for several return periods. The results indicate that the better results were obtained with the IDW interpolation method, and the Pearson Type III and Gumbel distributions also showed the best fits based on the Akaike criterion.

Discrepancies in changes in precipitation characteristics over the contiguous United States based on six daily gridded precipitation datasets

Variability and spatiotemporal changes in precipitation characteristics can have profound socioenvironmental impacts. Several studies have shown that the frequency and/or magnitude of precipitation events have changed over the contiguous United States (CONUS) in the past decades. Most previous studies used only one precipitation dataset and only investigated mean or extreme precipitation. Here, using 6 gridded daily precipitation datasets, we show that there are substantial discrepancies in the changes in characteristics of both extreme and non-extreme precipitation events from 1983 to 2017. Our results highlight that using a single record to study precipitation changes can potentially lead to biased results. Using different datasets enables examining the overall agreements and discrepancies in precipitation characteristics. For example, we show that almost all datasets agree that some areas show statistically significant changes in the annual precipitation maxima; however, the locations and signs of changes are not consistent across datasets. There is a relative agreement between datasets on changes in the total annual precipitation. When examining other percentiles of the precipitation distribution, including non-extreme values, however, we find widespread discrepancies among different precipitation products (e.g., what part of the precipitation distribution is changing). In fact, depending on the source of data, there exist opposing trends and patterns of change in precipitation characteristics. This highlights the need to further investigate non-extreme precipitation events to unravel potential non-extreme but “unexpected” or “unusual” patterns. Finally, we argue that protocols for data selection are needed to address the issue of inter-data variability and to ensure reliability of statistical analysis.

Mixture frequency analysis for tropical cyclone and non‐tropical cyclone extreme precipitation in the coastal areas: A case of Fujian in China

AbstractThe precipitation is classified into tropical cyclone (TC)‐induced precipitation and others, that is, non‐TC precipitation in the Asian continent and the western North Pacific. Traditional frequency analysis for the precipitation, which is a mixture series of annual maximum precipitation generated by TC and non‐TC, does not consider the difference in the generation mechanism with the assumption of independently and identically distributed. To reveal the effect of different generation mechanisms on the frequency analysis of the extreme precipitation, a multiplicative mixture frequency analysis (MMFA) model is adopted to study the interaction of non‐TC maximum precipitation (NTCMP) and TC maximum precipitation (TCMP) to the total precipitation frequency. Applying the NTCMP and TCMP data in Fujian Province located in southeast China, the results indicated that (a) the inconsistencies between NTCMP and TCMP are common, and different inconsistent patterns existed in space; (b) Different characteristics of inconsistency between the two types of precipitation had distinct effect on the estimation results of MMFA model, especially for the high return levels; and (c) The estimation results of MMFA model were demonstrated to be more secure and reliable. Therefore, the MMFA model is a suitable approach to solve the problem of mixture precipitation frequency caused by two generation mechanisms of non‐TC and TC.

Investigating Temporal and Spatial Precipitation Patterns in the Southern Mid-Atlantic United States

The investigation of regional vulnerability to extreme hydroclimatic events (e.g., floods and hurricanes) is quite challenging due to its dependence on reliable precipitation estimates. Better understanding of past precipitation trends is crucial to examine changing precipitation extremes, optimize future water demands, stormwater infrastructure, extreme event measures, irrigation management, etc., especially if combined with future climate and population projections. The objective of the study is to investigate the spatial-temporal variability of average and extreme precipitation at a sub-regional scale, specifically in the Southern Mid-Atlantic United States, a region characterized by diverse topography and is among the fastest-growing areas in North America. Particularly, this work investigates past precipitation trends and patterns using the North American Land Data Assimilation System, Version 2 (NLDAS-2, 12 km/1 h resolution) reanalysis dataset during 1980–2018. Both parametric (linear regression) and non-parametric (e.g., Theil-Sen) robust statistical tools are employed in the study to analyze trend magnitudes, which are tested for statistical significance using the Mann-Kendall test. Standard precipitation indices from ETCCDI are also used to characterize trends in the relative contribution of extreme events to precipitation in the area. In the region an increasing trend (4.3 mm/year) is identified in annual average precipitation with ~34% of the domain showing a significant increase (at the 0.1 significance level) of +3 to +5 mm/year. Seasonal and sub-regional trends are also investigated, with the most pronounced increasing trends identified during summers along the Virginia and Maryland border. The study also finds a statistically significant positive trend (at a 0.05 significance level) in the annual maximum precipitation. Furthermore, the number of daily extremes (daily total precipitation higher than the 95th and 99th percentiles) also depicts statistically significant increases, indicating the increased frequency of extreme precipitation events. Investigations into the proportion of annual precipitation occurring on wet days and extremely wet days (95th and 99th percentile) also indicate a significant increase in their relative contribution. The findings of this study have the potential to improve local-scale decision-making in terms of river basin management, flood control, irrigation scheme scheduling, and stormwater infrastructure planning to address urban resilience to hydrometeorological hazards.

Impacts of Climate Change and Variability on Precipitation and Maximum Flows in Devil’s Creek, Tacna, Peru

Global projections of climate change indicate negative impacts on hydrological systems, with significant changes in precipitation and temperature in many parts of the world. As a result, floods and droughts are expected. This article discusses the potential effects of climate change and variability on the maximum precipitation, temperature, and hydrological regime in Devil’s Creek, Tacna, Peru. The outputs of precipitation and daily temperature of fifteen regional climate models were used for the RCP4.5 and RCP8.5 emission scenarios. The methodology used includes the bias correction and downscaling of meteorological variables using the quintiles mapping technique, hydrological modeling, the evaluation of two emission scenarios, and its effect on the maximum flows of the stream. The results of the multi-model ensemble show that the maximum annual precipitation will probably increase by more than 30% for the RCP4.5 and RCP8.5 scenarios for the 2021–2050 period relative to the 1981–2005 period. Likewise, as expected, the maximum flows could increase by 220% and 154% for the RCP4.5 scenarios for the 2021–2050 and 2051–2080 terms, respectively, and 234% and 484% for the RCP8.5 scenarios and for the 2021–2050 and 2051–2080 terms, respectively, concerning the recorded historical value, increasing the probability of flood events and damage in populations located downstream.

Annual Maximum Precipitation in Indonesia and Its Association to Climate Teleconnection Patterns: An Extreme Value Analysis

Annual Maximum Precipitation in Indonesia and Its Association to Climate Teleconnection Patterns: An Extreme Value Analysis

PHENOLOGICAL SENSITIVITIES TO CLIMATE ARE SIMILAR IN TWO CLARKIA CONGENERS: INDIRECT EVIDENCE FOR FACILITATION, CONVERGENCE, NICHE CONSERVATISM, OR GENETIC CONSTRAINTS

To date, most herbarium-based studies of phenological sensitivity to climate and of climate-driven phenological shifts fall into two categories: detailed species-specific studies vs. multi-species investigations designed to explain inter-specific variation in sensitivity to climate and/or the magnitude and direction of their long-term phenological shifts. Few herbarium-based studies, however, have compared the phenological responses of closely related taxa to detect: (1) phenological divergence, which may result from selection for the avoidance of heterospecific pollen transfer or competition for pollinators, or (2) phenological similarity, which may result from phylogenetic niche conservatism, parallel or convergent adaptive evolution, or genetic constraints that prevent divergence. Here, we compare two widespread Clarkia species in California with respect to: the climates that they occupy; mean flowering date, controlling for local climate; the degree and direction of climate change to which they have been exposed over the last ∼115 yr; the sensitivity of flowering date to inter-annual and to long-term mean maximum spring temperature and annual precipitation across their ranges; and their phenological change over time. Specimens of C. cylindrica were sampled from sites that were chronically cooler and drier than those of C. unguiculata, although their climate envelopes broadly overlapped. Clarkia cylindrica flowers ∼ 3.5 d earlier than C. unguiculata when controlling for the effects of local climatic conditions and for quantitative variation in the phenological status of specimens. However, the congeners did not differ in their sensitivities to the climatic variables examined here; cumulative annual precipitation delayed flowering and higher spring temperatures advanced flowering. In spite of significant spring warming over the sampling period, neither species exhibited a long-term phenological shift. Precipitation and spring temperature interacted to influence flowering date: the advancing effect on flowering date of high spring temperatures was greater in dry than in mesic regions, and the delaying effect of high precipitation was greater in warm than in cool regions. The similarities between these species in their phenological sensitivity and behavior are consistent with the interpretation that facilitation by pollinators and/or shared environmental conditions generate similar patterns of selection, or that limited genetic variation in flowering time prevents evolutionary divergence between these species.

Evaluation of Generalized Extreme (GEV), Log-Pearson Type 3 (LP3), Pearson Type 3(P3) and Gumbel (EV1) Distributions for Development of IDF Equations for Warri, Nigeria

The application of Gumbel (EVI) to the development of rainfall intensity– duration – frequency (IDF) curves has often been criticized on theoretical and empirical grounds as it may underestimate the largest extreme rainfall amounts. The consequences of underestimation are economic losses, property damages, and loss of life. Therefore, it is important that water resources engineering infrastructure be accurately design to avoid these consequences. This paper evaluates the performances of four probability distributions; GEV, EV1, LP3 and P3 using the annual maxima precipitation series of 26 years for Warri Metropolis obtained from Nigerian Meteorological Agency (NiMet). The strength and weakness of the four probability distributions were examined with the goodness of fit (GOF) module of Easyfit software which implemented Kolmogorov - Smirnov (KS) and Anderson - Darling (AD) tests at 5% significance level. The Easyfit software fitted the precipitation series data to the four probability distributions and ranked the four probability distributions across the fifteen rainfall durations. Results show that for both KS and AD tests, GEV distribution was found to be best-fit distribution and it was applied to the development of IDF curves in Warri Metropolis, Nigeria. Furthermore, the IDF values obtained were applied in the development of three-parameter IDF models for return periods of 10 - , 15 -, 20 -, 25 - , 50 -, and 100-years. The mean absolute error, Nash – Sutcliffe Efficiency (NSE) and Root Mean Square Error (RMSE) indices computed for the IDF models increase with increasing return periods. The IDF curves and models depicted the general attributes of IDF curves and models. This study could be of significant academic value and improvement to professional practice in the design of storm water drainage systems. Therefore, the developed IDF curves and models are recommended to the Warri Urban Authority for inclusion in her stormwater handbooks and manuals.