Hydrological Extreme Events and Climate Changes

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".

Deadline for manuscript submissions: closed (14 April 2023) | Viewed by 13622

Special Issue Editor


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Guest Editor
Department of Civil Constructional and Environmental Engineering, University of Rome 'La Sapienza', Rome, Italy
Interests: river and coastal hydrodynamics; hydrological extremes; climate change
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Special Issue Information

Dear Colleagues,

Since 1950, a significant increase in the severity, duration, and frequency of droughts as well as an increase in the intensity and frequency of extreme rainfall modulated by large-scale atmospheric circulation has been observed in different regions of the planet. The intensification of these latter extreme events also affects regions subject to more pronounced drought conditions, highlighting an amplification of the range of variability of the hydrological cycle. Some studies have also shown there is an intensification of tropical cyclones which are more intense and destructive.

There is therefore a strong interest in developing studies to explore the climate related causes of such hydrological cycle changes and methods to forecast hydrological extremes events at the different temporal and spatial scales involved: hourly or daily for river or tropical cyclone floods, monthly or seasonal for droughts, decades for climate projections aimed to assess the frequency, magnitude, consequences of these hydrological extreme events in order to plan adaptation actions. This Special Issue is thus aimed at collecting scientific and technical papers on the abovementioned themes which are crucial in the challenges posed by climate changes.

The Paris agreement on the limitation of greenhouse gas emissions and the United Nations Conference in Sendai on the reduction of the risk of disasters (Sendai Framework for Disaster Risk Reduction 2015–2030) underline how the greatest challenges posed by change climate on a planetary scale concern issues related to water safety that occur at a local or regional scale in terms of scarcity of water resources, drought, and flood risks produced by more intense and frequent extreme hydrological events. Therefore, I ask you to contribute to this Special Issue on hydrological extremes and climate change studies and methodologies to shed light on the complex relationship between climate and extremes of the hydrological cycle.

Prof. Dr. Francesco Cioffi
Guest Editor

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Keywords

  • hydrological extremes
  • floods
  • droughts
  • tropical cyclones
  • climate change
  • water-related disasters
  • hydrological extreme forecast

Published Papers (5 papers)

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Research

22 pages, 20041 KiB  
Article
Spatiotemporal Changes in Extreme Precipitation in China’s Pearl River Basin during 1951–2015
by Shirong Cai, Kunlong Niu, Xiaolin Mu, Xiankun Yang and Francesco Pirotti
Water 2023, 15(14), 2634; https://doi.org/10.3390/w15142634 - 20 Jul 2023
Cited by 1 | Viewed by 1251
Abstract
Precipitation is a key component of the hydrological cycle and one of the important indicators of climate change. Due to climate change, extreme precipitation events have globally and regionally increased in frequency and intensity, leading to a higher probability of natural disasters. This [...] Read more.
Precipitation is a key component of the hydrological cycle and one of the important indicators of climate change. Due to climate change, extreme precipitation events have globally and regionally increased in frequency and intensity, leading to a higher probability of natural disasters. This study, using the long-term APHRODITE dataset, employed six precipitation indices to analyze the spatiotemporal changes in extreme precipitation in the Pearl River Basin during 1951–2015. The Mann–Kendall (M–K) test was used to verify the significance of the observed trends. The results indicate that: (1) the interannual PRCPTOT showed a trend with an average positive increase of 0.019 mm/yr, which was followed by an increase in SDII, R95P, and RX1day, and a decrease in R95D and CWD; seasonal PRCPTOT also displayed an increase in summer and winter and a decrease in spring and autumn, corresponding to increases in R95P and SDII in all seasons. (2) The annual precipitation increases from the west to east of the basin, similar to the gradient distribution of SDII, R95P and RX1day, with the high R95D happening in the middle and lower reaches of the Xijiang River, but the CWD increased from the north to south of the basin. The seasonal spatial distributions of PRCPTOT, SDII, and R95P are relatively similar except in autumn, showing an increase from the west to east of the basin in spring and winter and a gradual increase from the north to south of the basin in summer, indicating that the Beijiang and Dongjiang tributary basins are more vulnerable to floods. (3) The MK test results exhibited that the Yunnan–Guizhou Plateau region in the upper reaches of the Xijiang River Basin became drier, and there was an increase in extreme precipitation in the Beijiang and Dongjiang river basins. The study results facilitate valuable flood mitigation, natural hazard control and water resources management in the Pearl River Basin. Full article
(This article belongs to the Special Issue Hydrological Extreme Events and Climate Changes)
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33 pages, 3428 KiB  
Article
Predicting Flood Frequency with the LH-Moments Method: A Case Study of Prigor River, Romania
by Cristian Gabriel Anghel and Cornel Ilinca
Water 2023, 15(11), 2077; https://doi.org/10.3390/w15112077 - 30 May 2023
Cited by 4 | Viewed by 1489
Abstract
The higher-order linear moments (LH-moments) method is one of the most commonly used methods for estimating the parameters of probability distributions in flood frequency analysis without sample censoring. This article presents the relationships necessary to estimate the parameters for eight probability distributions used [...] Read more.
The higher-order linear moments (LH-moments) method is one of the most commonly used methods for estimating the parameters of probability distributions in flood frequency analysis without sample censoring. This article presents the relationships necessary to estimate the parameters for eight probability distributions used in flood frequency analysis. Eight probability distributions of three parameters using first- and second-order LH-moments are presented, namely the Pearson V distribution, the CHI distribution, the inverse CHI distribution, the Wilson–Hilferty distribution, the Pseudo-Weibull distribution, the Log-normal distribution, the generalized Pareto Type I distribution and the Fréchet distribution. The exact and approximate relations for parameter estimation are presented, as are the exact and approximate relations for estimating the frequency factor specific to each method. In addition, the exact and approximate relationships of variation in the LH-skewness–LH-kurtosis are presented, as is the variation diagram of these statistical indicators. To numerically represent the analyzed distributions, a flood frequency analysis case study using the annual maximum series was carried out for the Prigor River. The analysis is presented compared to the linear moments (L-moments) method, which is the method that is intended to be used in the development of a new norm in Romania for determining the maximum flows. For the Prigor River, the most fit distributions are the Pseudo-Weibull and the generalized Pareto Type I for the linear moments method and the CHI and the Wilson–Hilferty distributions for the first higher-order linear moments method. The performance was evaluated using linear and higher-order linear moment values and diagrams. Full article
(This article belongs to the Special Issue Hydrological Extreme Events and Climate Changes)
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18 pages, 8741 KiB  
Article
Evaluation of Statistical PMP Considering RCP Climate Change Scenarios in Republic of Korea
by Miru Seo, Sunghun Kim, Heechul Kim, Hanbeen Kim, Ju-Young Shin and Jun-Haeng Heo
Water 2023, 15(9), 1756; https://doi.org/10.3390/w15091756 - 2 May 2023
Viewed by 1770
Abstract
Extreme rainfall and floods have increased in frequency and severity in recent years, due to climate change and urbanization. Consequently, interest in estimating the probable maximum precipitation (PMP) has been burgeoning. The World Meteorological Organization (WMO) recommends two types of methods for calculating [...] Read more.
Extreme rainfall and floods have increased in frequency and severity in recent years, due to climate change and urbanization. Consequently, interest in estimating the probable maximum precipitation (PMP) has been burgeoning. The World Meteorological Organization (WMO) recommends two types of methods for calculating the PMP: hydrometeorological and statistical methods. This study proposes a modified Hershfield’s nomograph method and assesses the changes in PMP values based on the two representative concentration pathway (RCP4.5 and RCP8.5) scenarios in South Korea. To achieve the intended objective, five techniques were employed to compute statistical PMPs (SPMPs). Moreover, the most suitable statistical method was selected by comparing the calculated SPMP with the hydrometeorological PMP (HPMP), by applying statistical criteria. Accordingly, SPMPs from the five methods were compared with the HPMPs for the historical period of 2020 and the future period of 2100 for RCP 4.5 and 8.5 scenarios, respectively. The results confirmed that the SPMPs from the modified Hershfield’s nomograph showed the smallest MAE (mean absolute error), MAPE (mean absolute percentage error), and RMSE (root mean square error), which are the best results compared with the HPMP with an average SPMP/HPMP ratio of 0.988 for the 2020 historical period. In addition, Hershfield’s method with varying KM exhibits the worst results for both RCP scenarios, with SPMP/HPMP ratios of 0.377 for RCP4.5 and 0.304 for RCP8.5, respectively. On the contrary, the modified Hershfield’s nomograph was the most appropriate method for estimating the future SPMPs: the average ratios were 0.878 and 0.726 for the 2100 future period under the RCP 4.5 and 8.5 scenarios, respectively, in South Korea. Full article
(This article belongs to the Special Issue Hydrological Extreme Events and Climate Changes)
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14 pages, 5472 KiB  
Article
The 50- and 100-year Exceedance Probabilities as New and Convenient Statistics for a Frequency Analysis of Extreme Events: An Example of Extreme Precipitation in Israel
by Isabella Osetinsky-Tzidaki and Erick Fredj
Water 2023, 15(1), 44; https://doi.org/10.3390/w15010044 - 23 Dec 2022
Cited by 4 | Viewed by 6164
Abstract
The misinterpreted statistics of extreme events’ probability in drainage design codes can lead to an insufficient capacity of drainage systems. This is one of the main factors of frequent floods, especially in coastal cities. Heavy rainfalls, the so-called “1-in-50-year” or “1-in-100-year” events followed [...] Read more.
The misinterpreted statistics of extreme events’ probability in drainage design codes can lead to an insufficient capacity of drainage systems. This is one of the main factors of frequent floods, especially in coastal cities. Heavy rainfalls, the so-called “1-in-50-year” or “1-in-100-year” events followed by floods, may occur there every 2–3 years. We hope to contribute to the correct understanding of extreme events’ statistics. To achieve this goal, we first recall the technique of calculating the exceedance probability of the extreme event over a period of years. Then, to illustrate the theoretical results (among which is the well-known 63.2% minimal exceedance probability over a period of N years for an event with a 1/N annual exceedance probability), we use the rainfall intensity data archived at the Israel Meteorological Service for 1938–2021. We estimate their exceedance probabilities over periods of 50 and 100 years and offer the latter as convenient measures for comparative analysis of the intensity of extreme events. These illustrations highlight the importance of considering return periods that are significantly longer than the 50- or 100-year periods currently used in drainage design, to reduce the risk of flooding and damage caused by heavy rainfalls. Full article
(This article belongs to the Special Issue Hydrological Extreme Events and Climate Changes)
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18 pages, 2473 KiB  
Article
Sub-Hourly to Daily Rainfall Intensity-Duration-Frequency Estimation Using Stochastic Storm Transposition and Discontinuous Radar Data
by Christoffer B. Andersen, Daniel B. Wright and Søren Thorndahl
Water 2022, 14(24), 4013; https://doi.org/10.3390/w14244013 - 8 Dec 2022
Cited by 4 | Viewed by 1650
Abstract
Frequency analysis of rainfall data is essential in the design and modelling of hydrological systems but is often statistically limited by the total observation period. With advances in weather radar technology, frequency analysis of areal rainfall data is possible at a higher spatial [...] Read more.
Frequency analysis of rainfall data is essential in the design and modelling of hydrological systems but is often statistically limited by the total observation period. With advances in weather radar technology, frequency analysis of areal rainfall data is possible at a higher spatial resolution. Still, the observation periods are short relative to established rain gauge networks. A stochastic framework, “stochastic storm transposition” shows great promise in recreating rainfall statistics from radar rainfall products, similar to rain gauge-derived statistics. This study estimates intensity–duration–frequency (IDF) relationships at both point and urban catchment scales. We use the stochastic storm transposition framework and a single high-resolution, 17-year long (however, discontinuous), radar rainfall dataset. The IDF relations are directly compared to rain gauge statistics with more than 40 years of observation, and rainfall extremes derived from the original, and untransposed, radar dataset. An overall agreement is discovered, however, with some discrepancies in short-duration storms due to scaling errors between gauge and radar. Full article
(This article belongs to the Special Issue Hydrological Extreme Events and Climate Changes)
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