Extreme Precipitation Data Research Articles

Modeling Extreme Precipitation Data in a Mining Area

In recent decades, extreme precipitation events have increased in frequency and intensity in Greece and across regions of the Mediterranean, with significant environmental and socioeconomic impacts. Therefore, extensive statistical analysis of the extreme rainfall characteristics on a dense temporal scale is crucial for areas with important economic activity. For this reason, this paper uses the daily precipitation measurements of four meteorological stations in a mining area of northeastern Chalkidiki peninsula from 2006 to 2021. Three statistical approaches were carried out to develop the best-fitting probability distribution for annual extreme precipitation conditions, using the maximum likelihood method for parameter estimation: the block maxima of the generalized extreme value (GEV) distribution and the peak over threshold of the generalized Pareto distribution (GPD) based on extreme value theory (EVT), and the gamma distribution. Based upon this fitting distribution procedure, return periods for the extreme precipitation values were calculated. Results indicate that EVT distributions satisfactorily fit extreme precipitation, with GPD being the most appropriate, and lead to similar conclusions regarding extreme events.

Flood detection using Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage and extreme precipitation data

Abstract. A complete global flood event record would aid researchers to analyze the distribution of global floods and, thus, better formulate and manage disaster prevention and reduction policies. This study used Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage and precipitation data combined with high-frequency filtering, anomaly detection and flood potential index methods to successfully extract historical flood days globally between 1 April 2002 and 31 August 2016; these results were then further compared and validated with Dartmouth Flood Observatory (DFO) data, Global Runoff Data Centre (GRDC) discharge data, news reports and social media data. The results showed that GRACE-based flood days could cover 81 % of the flood events in the DFO database, 87 % of flood events extracted by MODIS and supplement many additional flood events not recorded by the DFO. Moreover, the probability of detection greater than or equal to 0.5 reached 62 % among 261 river basins compared to flood events derived from the GRDC discharge data. These detection capabilities and detection results are both good. Finally, we provided flood day products with a 1∘ spatial resolution covering the range between 60∘ S and 60∘ N from 1 April 2002 to 31 August 2016; these products can be obtained from https://doi.org/10.5281/zenodo.6831384 (Zhang et al., 2022b). Thus, this research contributes a data foundation for the mechanistic analysis and attribution of global flood events.

Assessment of the Uncertainty Associated with Statistical Modeling of Precipitation Extremes for Hydrologic Engineering Applications in Amman, Jordan

Estimates of extreme precipitation are commonly associated with different sources of uncertainty. One of the primary sources of uncertainty in the statistical modeling of precipitation extremes comes from extreme data series (i.e., sampling uncertainty). Therefore, this research aimed to quantify the sampling uncertainty in terms of confidence intervals. In addition, this article examined how the data record length affects predicted extreme precipitation estimates and data set statistics. A nonparametric bootstrap resample was utilized to quantify the precipitation quantile sampling distribution at a particular non exceedance probability. This sampling distribution can provide a point estimation of the precipitation quantile and the confidence interval at a particular non exceedance probability. It has been shown that the different types of probability distributions fit the extreme precipitation data series of various weather stations. Therefore, the uncertainty analysis should be conducted using the best-fit probability distribution for extreme precipitation data series rather than a predefined single probability distribution for all stations based on modern extreme value theory. According to the 95% confidence intervals, precipitation quantiles are subject to significant uncertainty and the band of the uncertainty intervals increases with the return period. These uncertainty bounds need to be integrated into any frequency analysis from historical data. The average, standard deviation, skewness and kurtosis are highly affected by the data record length. Thus, a longer record length is desirable to decrease the sampling uncertainty and, therefore, decrease the error in the predicted quantile values. Moreover, the results suggest that a series of at least 40 years of data records is needed to obtain reasonably accurate estimates of the distribution parameters and the precipitation quantiles for 100 years return periods and higher. Using only 20 to 25 years of data to obtain estimates of the higher return period quantile is risky, since it created high sampling variability relative to the full data length.

Performance Evaluation of ERA5 Extreme Precipitation in the Yangtze River Delta, China

Accurate extreme precipitation information is crucial for disaster risk management, social and economic development security, and climate change research. Taking the Yangtze River Delta (YRD), China, a high-impact area of extreme precipitation, as an example, this study evaluates the spatiotemporal performance of extreme precipitation in the latest fifth-generation reanalysis dataset from the European Centre for Medium-Range Weather Forecasts (i.e., ECMWF ERA5) for 1961–2018 based on surface observational precipitation data. The results showed that the 90th-percentile threshold of extreme precipitation extracted from ERA5 data with a daily precipitation amount >1 mm is closer to the actual observations. The ERA5 data can effectively capture the spatiotemporal patterns of the observed extreme precipitation in the YRD. The ERA5 data can successfully represent the seasonal cycle and interannual variability of daily, daytime, and nighttime extreme precipitation. However, the daytime (nighttime) extreme precipitation frequencies and amounts tend to be overestimated (underestimated) for the period 1961–2000, whereas they were significantly underestimated for the period 2000–2018. The trend estimation of seasonal and annual extreme precipitation in ERA5 needs to be improved. The ERA5 data revealed that the extreme precipitation in the YRD was dominated by large-scale precipitation, followed by convective precipitation, but their long-term trends were not clear. This study has conducted a detailed and reliable evaluation of the ERA5 extreme precipitation data. The findings serve as valuable guidance and provide accurate references to extreme climatic variables for data users and algorithm developers.

Will Climate Change Exacerbate the Economic Damage of Flood to Agricultural Production? A Case Study of Rice in Ha Tinh Province, Vietnam

In developing countries in general and in Vietnam in particular, flood induced economic loss of agriculture is a serious concern since the livelihood of large populations depends on agricultural production. The objective of this study was to examine if climate change would exacerbate flood damage to agricultural production with a case study of rice production in Huong Son District of Ha Tinh Province, North-central Vietnam. The study applied a modeling approach for the prediction. Extreme precipitation and its return periods were calculated by the Generalized Extreme Value distribution method using historical daily observations and output of the MRI-CGCM3 climate model. The projected extreme precipitation data was then employed as an input of the Mike Flood model for flood modeling. Finally, an integrated approach employing flood depth and duration and crop calendar was used for the prediction of potential economic loss of rice production. Results of the study show that in comparison with the baseline period, an increase of 49.14% in the intensity of extreme precipitation was expected, while the frequency would increase 5 times by 2050s. As a result, the seriousness of floods would increase under climate change impacts as they would become more intensified, deeper and longer, and consequently the economic loss of rice production would increase significantly. While the level of peak flow was projected to rise nearly 1 m, leading the area of rice inundated to increase by 12.61%, the value of damage would rise by over 21% by 2050s compared to the baseline period. The findings of the present study are useful for long-term agricultural and infrastructural planning in order to tackle potential flooding threats to agricultural production under climate change impacts.

Probability of occurrence of extreme precipitation events and natural disasters in the city of Natal, Brazil

This study aimed at estimating the probability of occurrence and return period of extreme daily precipitation events, associating them with natural disasters that occurred in the city of Natal, Northeast Brazil. For this purpose, generalized extreme value (GEV) distribution models were adjusted to the daily extreme precipitation data observed for a period of 31 years (1984–2014). The results indicated that the intensity of expected extreme precipitation depends on the month of its occurrence. October, November and December have low probabilities of rainfall, while April, May, June and July stand out as the period when the highest intensities of extreme precipitation are expected. On average, the daily accumulated precipitation of the days when natural disaster events occurred in Natal was 69.2 mm. The probability of rainfall greater than 60 mm/day varied between 37.7% and 74.8% between March and July. Furthermore, pluvial flooding was the type of natural disaster occurring most frequently in the city. This information can be useful to support the formulation of public policies in terms of planning, adaptation and mitigation of urban risks caused by extreme precipitation events.

Study of regional regulation on “Kawasan Bandung Utara” impact on flood discharges from the perspective of spatial variations of extreme precipitation in Bandung basin

This research was built from the probability of shifting urbanization patterns as a result of the Regional Regulation about Northern Bandung Region (Perda KBU). Moreover, extreme precipitation in the Bandung Basin not only happens in KBU but also varies spatially. The probability of urbanization shifting and spatial variation of extreme precipitation can increase flood discharge in other places. Three stages to understand how the spatial variations of extreme precipitation and land use affect flood discharge in Bandung Basin, namely: 1) Extreme precipitation data processing, 2) Land-use data processing, and 3) Hydrology Model simulation using HEC-HMS. Urbanization shifting to north non-KBU and east areas occurs as an impact of Perda KBU. It significantly affects flood discharge in the east area but relatively no impact in the north non-KBU area. Additionally, despite extreme precipitation frequency and volume, the south area has the biggest value. It opens the possibility that the South Bandung area has the potential to be the biggest contributor to Bandung Basin discharge. Therefore, flood study in the south area is needed as a priority.

Spatial distribution of the level of return of extreme precipitation events in Northeast Brazil

AbstractThis study aimed to estimate levels of return of extreme daily precipitation events, associating them with natural disasters in Northeast Brazil (NEB), a region characterized by different climatic conditions and low rates of social and economic development. For this, generalized Pareto distribution (GPD) models were adjusted to the daily extreme precipitation data estimated by the Tropical Rainfall Measuring Mission (TRMM) 3B42 product of the multisatellite precipitation analysis for a period of 16 years (2000–2015). In addition, the estimates of the GPD model were compared using two data sources, TRMM and pluviometer. The investigation showed that the results of the GPD model estimated by means of the extreme data from the rain gauge and the TRMM were statistically the same, with 95% confidence. Thus, using the data referring to the 2,082 grid points of the TRMM, it was possible to map the spatial distribution of the estimates of the levels of return of extreme precipitation to the return periods of 2, 5 and 10 years, per seasonal period. In general, the results indicated that the intensity of expected extreme precipitation depends on the seasonal period and the place of occurrence of precipitation. The eastern NEB stood out as the region where the highest intensities of extreme precipitation are expected. Extreme precipitation values of up to 178 mm are expected in 2 years. The areas where natural disasters occurred in the years 2016, 2017 and 2018 are similar to those in which the highest rainfall intensities are expected. The results of this study can allow the evaluation of the spatial distribution of risks related to extreme precipitation events, and therefore, support the planning of regional public policies and environmental management for the prevention of natural disasters in NEB.

Intensity–duration–frequency curves exploiting neighbouring extreme precipitation data

ABSTRACTRecords of precipitation extremes are essential for hydrological design. In urban hydrology, intensity–duration–frequency curves are typically estimated from observation records. However, conventional approaches seldom consider the areal extent of events. If they do, duration-dependent area reduction factors are used, but precipitation is measured at only a few locations. Due to the high spatial variability of precipitation, it is relatively unlikely that a gauged observation network will capture the extremes that occur during a precipitation event. Therefore, the area reduction approach cannot be regarded as the reduction of an observed maximum. To investigate precipitation extremes, spatial aspects need to be considered using different approaches. Here, we both address the conventional practice of area reduction and consider a within-area chance of increased precipitation, defined as the maximum precipitation intensity observed in a cluster within a selected domain. The results show that (1) the risk of urban flooding is routinely underestimated in current design practice, and (2) traditional calculations underestimate extremes by as much as 30–50%. We show how they can be revised sensibly.

Respuesta hidrológica al cambio climático en regiones áridas: caso de estudio en los Comondú, Baja California Sur, México

Background. Currently, climate change is one of the most relevant phenomena within the scientific environment; this change has been recorded as an increase in temperatures on a global scale. For the central zone of Baja California Sur (BCS), Mexico, climate models also indicate an increase in the frequency of extreme precipitation. The study area covers two towns (San Jose and San Miguel de Comondu) whose urban and rural areas are located in a ravine of approximately 16 km in length and up to 800 m in width. Goals. To carry out a historical, statistical analysis of the climatic variability and model the response of the Comondu stream to extreme precipitation events. Methods. Climatic variability was analyzed with databases from the Comondu weather station, operated by the National Meteorological Service (SMN). In addition, the runoff pattern in the Comondu stream was ascertained in the event of extreme precipitation events, similar to or close to events with a return time of 1000 years, using hydrological and hydraulic models of HEC-1 and HEC-RAS software. Results. In the case of Los Comondu, BCS, there is a clear upward trend in the 24-hour extreme precipitation data, which implies that the danger of flooding in populated areas and crops increases considerably. The occurrence of a 1000-year event would affect about 86,864 m2 of the urban area of both towns and 201,570 m2 of the area under cultivation. Conclusions. The results of our modeling indicate an increase in area, depth, and speed of the stream, increasing the risk of flooding in the lower areas of Los Comondu, BCS. Based on our modeling’s results, measures should be promoted to safeguard the population and the economy of Los Comondu.

Bayesian change point analysis for extreme daily precipitation

ABSTRACTChange point (CP) analysis of extreme precipitation plays a key role to incorporate non‐stationarity in flood predictions under climate change. This article provides a Bayesian method to detect theCPfrequently appearing in extreme precipitation data. Unlike most published work based on a normal distribution, we allow for the model to follow a generalized Pareto distribution to fit extreme precipitation over a high threshold with aCP, which can effectively utilize tail behaviour of the distribution. The BayesianCPdetection is investigated on four models: a no change model, a shape change model, a scale change model, and both a shape and scale change model. Model selection is performed using the Bayes factor and model posterior probability; the posterior means of the unknownCPand the model parameters before and after theCPcan be obtained based on the selectedCPmodel. Simulation studies and a real data example are provided to demonstrate the proposed methodologies. Finally, model uncertainty issues in the frequency analysis are extensively discussed. It is found that considering the abrupt and sustainedCPin extreme precipitation is important when performing hydraulic or hydrologic design.

Continental Portuguese Territory Flood Susceptibility Index – contribution to a vulnerability index

Abstract. This work defines a national flood susceptibility index for the Portuguese continental territory, by proposing the aggregation of different variables which represent natural conditions for permeability, runoff and accumulation. This index is part of the national vulnerability index developed in the scope of Flood Maps in Climate Change Scenarios (CIRAC) project, supported by the Portuguese Association of Insurers (APS). This approach expands on previous works by trying to bridge the gap between different flood mechanisms (e.g. progressive and flash floods) occurring at different spatial scales in the Portuguese territory through (a) selecting homogeneously processed data sets and (b) aggregating their values to better translate the spatially continuous and cumulative influence in floods at multiple spatial scales. Results show a good ability to capture, in the higher susceptibility classes, different flood types: fluvial floods and flash floods. Lower values are usually related to mountainous areas, low water accumulation potential and more permeable soils. Validation with independent flood data sets confirmed these index characteristics, although some overestimation can be seen in the southern region of Alentejo where, due to a dense hydrographic network and an overall low slope, floods are not as frequent as a result of lower precipitation mean values. Future work will focus on (i) including extreme precipitation data sets to represent the triggering factor, (ii) improving representation of smaller and stepper basins, (iii) optimizing variable weight definition process and (iii) developing more robust independent flood validation data sets.

Influences of Atlantic multidecadal oscillation phases on spatial and temporal variability of regional precipitation extremes

Summary A major teleconnection, Atlantic multidecadal oscillation (AMO) under two phases (cool and warm) influencing precipitation extremes in Florida, USA, is the main focus of this study. Long-term extreme precipitation data from several rain gages from temporal windows that coincide with the AMO phases are evaluated for changes in spatial and temporal variability across the region. Assessments of precipitation extremes for nine durations in different meteorologically homogenous rainfall areas as well as in the entire region are carried out. Methods of assessment included parametric unpaired t -tests and nonparametric Mann–Whitney U tests, kernel density estimates using Gaussian kernel for distribution-free comparative analysis and bootstrap sampling-based confidence intervals. Depth-duration-frequency (DDF) curves are also developed using generalized extreme value (GEV) distributions characterizing the extremes. Analysis of data indicated increase in precipitation extremes in warm phases of AMO for durations greater than 24 h. The influence of warm or cool phases of AMO on precipitation extremes is not spatially uniform in the region. Temporal shifts in occurrences of extremes from the later part of the year in warm phase to earlier in the year for the cool phase are evident. These shifts will have implications on flooding events in different regions of Florida. Magnitudes of extremes for a 25 year return period based on DDF curves were higher for all nine durations when data from cool or warm phase alone were compared to those obtained from data from two phases. Precipitation extremes for durations longer than a day are associated with increased landfalls of hurricanes occurring in the region in the AMO warm phases.

회귀분석 방법을 이용한 서울지점 극치 수문량 산정

본 연구에서는 서울지점의 목표연도별(2020, 2030, 2040년) 재현기간에 따른 확률강수량을 산정하기 위하여 1973년부터 2009년까지 지속시간 1시간 및 24시간에 대한 연 최대 강수량 자료를 사용하였다. Gumbel 분포를 이용해 빈도해석을 실시하였으며, 확률가중모멘트법을 사용하여 분포형의 위치매개변수와 축척매개변수를 산정하였다. 서울지점의 누적평균 극치강수량에 대하여 선형회귀분석을 실시하는 방법과 한강유역에 속하는 14개 지점들의 누적평균 극치강수량에 대하여 Logistic 회귀분석을 실시하는 방법을 이용해 각 방법에 따른 확률강수량을 정상성임을 가정하여 산정된 재현기간에 따른 확률강수량과 비교하였다. 선형회귀분석에 비해 Logistic 회귀분석의 결정계수가 더욱 높은 것으로 나타났고, 확률강수량의 경우, 선형회귀분석을 통한 확률강수량은 정상성에 비하여 매우 높게 나타남을 보였으나, Logistic 회귀분석에서 최대편차를 이용해 산정된 확률강수량의 경우 선형회귀분석에 비해 비교적 안정적인 증가를 보였다. 확률강수량 산정에 있어 정상성 가정에 기초한 전형적인 빈도해석과 비교하였을 때, Logistic 회귀분석은 극치의 선형 증가 거동을 반영하면서도 선형회귀분석을 사용한 목표연도별 확률강우량 산정시 나타나는 지속적 선형 증가의 문제를 보완할 수 있을 것으로 판단된다. In this study, we estimated probable precipitation amounts at Seoul for the target year(2020, 2030, 2040 year) using the 1 and 24 hour annual maximum precipitation data from 1973 through 2009. The Gumbel distribution was chosen and the probability weighted moment method was used to estimate model parameters. The behavior of the mean of extreme precipitation data, scale parameter, and location parameter amount using the linear regression and the logistic regression methods. The probable precipitation amount for the target years using the linear regression methods is much higher than that using a stationary frequency analysis. The probable precipitation amount using the logistic regression showed stable increase but probable precipitation amounts was almost same for different target years since the behavior of logistic curve converges before 2040 year. In estimating probable precipitation, the logistic regression is able to reflect the increase behavior of hydrologic extreme reasonably while probable precipitation amounts for target years using a linear regression method have continuous increase.

Decadal modelling of rainfall–runoff erosivity in the Euro-Mediterranean region using extreme precipitation indices

This paper presents and assesses the Decadal Rainfall Erosive Multiscale Model (DREMM), in which extreme precipitation data (95th percentile) are used to estimate decadal-scale rainfall–runoff erosivity values compatible with the Universal Soil Loss Equation and its revision — (R)USLE. The test area was a large region including central Europe and Mediterranean countries, in which 111 decades from 88 weather stations (ranging from about 3 to 1680m above sea level) with rain and (R)USLE rainfall–runoff erosivity data were available. The construction of the model is simplified to a location-explicit term and to the understanding that the most erosive rainfalls are those recorded during the summertime and the beginning of autumn (May–October). These precipitation events are suitable for use in spatial and temporal climate variability studies on decadal time-scales. Once parameterized to capture decadal rainfall–runoff erosivity variability over central Europe and the Mediterranean, the DREMM was run to produce spatial patterns of rainfall–runoff erosivity in Germany and Bulgaria, compared to maps from the (R)USLE approach for 1961–1990. Implications for rainfall–runoff erosivity modelling were discussed concluding that a limited number of parameters may be sufficient to represent decadal rainfall–runoff erosivity for the central European and Mediterranean region. A transferable approach is recommended by employing the DREMM to assess the spatial impact of rainfall extremes. Its use to characterize the long-term dynamics of rainfall–runoff erosivity may also be of value in climate investigation.

Bayesian inference from composite likelihoods, with an application to spatial extremes

Composite likelihoods are increasingly used in applications where the full likelihood is analytically unknown or computationally prohibitive. Although some frequentist properties of the maximum composite likelihood estimator are akin to those of the maximum likelihood estimator, Bayesian inference based on composite likelihoods is in its early stages. This paper discusses inference when one uses composite likelihood in Bayes' formula. We establish that using a composite likelihood results in a proper posterior density, though it can differ considerably from that stemming from the full likelihood. Building on previous work on composite likelihood ratio tests, we use asymptotic theory for misspecified models to propose two adjustments to the composite likelihood to obtain appropriate inference. We also investigate use of the Metropolis Hastings algorithm and two implementations of the Gibbs sampler for obtaining draws from the composite posterior. We test the methods on simulated data and apply them to a spatial extreme rainfall dataset. For the simulated data, we find that posterior credible intervals yield appropriate empirical coverage rates. For the extreme precipitation data, we are able to both effectively model marginal behavior throughout the study region and obtain appropriate measures of spatial dependence.

Construction of Intensity–Duration–Frequency (IDF) curves for precipitation at Lubumbashi, Congo, under the hypothesis of inadequate data

Intensity–Duration–Frequency (IDF) curves for precipitation constitute a probabilistic tool and have proven useful in water resources management. In particular, IDF curves for precipitation enable questions on the extreme character of precipitation to be answered. The construction of IDF curves for precipitation is difficult or impossible in tropical areas due to the lack of long-term extreme precipitation data. A technique is proposed to overcome this shortcoming by combining limited high-frequency information on rainfall extremes with long-term daily rainfall information. It may be regarded as an extension of Koutsoyiannis' approach. Using this technique, IDF curves for precipitation are produced for Lubumbashi in Congo. Citation Van de Vyver, H. & Demarée, G. R. (2010) Construction of Intensity–Duration–Frequency (IDF) curves for precipitation at Lubumbashi, Congo, under the hypothesis of inadequate data. Hydrol. Sci. J. 55(4), 555–564.

A Smirnov test–based clustering algorithm with application to extreme precipitation data

A clustering algorithm is developed to form regions with similar extreme rainfall cumulative distribution function (CDF) characteristics. Stations are grouped on the basis of minimum geographic distance and acceptance of the null hypothesis of equal CDF between all station pairs within a cluster. During each iteration previously clustered stations can be regrouped on the basis of the results of a suite of Smirnov tests. This process continues until all possible cluster mergers have been disallowed and thus the final number of clusters is determined solely by the grouping process. The Smirnov test–based algorithm is applied to extreme rainfall data from West Virginia. The results are compared based on the L moments heterogeneity measure. With minor exceptions, the resulting subregions were deemed homogeneous by this measure. Thus it is possible that the Smirnov test‐based clustering procedure can be used as a guide for the otherwise subjective formation of precipitation regions that is a prerequisite of L moments distribution fitting routines.