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Keywords = historical extraordinary flood

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28 pages, 3494 KiB  
Article
Rain Belt and Flood Peak: A Study of the Extreme Precipitation Event in the Yangtze River Basin in 1849
by Yuda Yang, Zhengrong Xu, Weiwei Zheng, Shuihan Wang and Yibo Kang
Water 2021, 13(19), 2677; https://doi.org/10.3390/w13192677 - 28 Sep 2021
Cited by 7 | Viewed by 3741
Abstract
Floods caused by extreme precipitation events, in the context of climate warming, are one of the most serious natural disasters in monsoon region societies. The great flood in the Yangtze River Basin in 1849, in Eastern China, was a typical extreme flood event. [...] Read more.
Floods caused by extreme precipitation events, in the context of climate warming, are one of the most serious natural disasters in monsoon region societies. The great flood in the Yangtze River Basin in 1849, in Eastern China, was a typical extreme flood event. According to historical archives, local chronicles, diaries, and historical hydrological survey data, this study reconstructed the temporal and spatial patterns of extreme precipitation in 1849, and the flood process of the Yangtze River. We found four major precipitation events at the middle and lower reaches of the Yangtze River, from 18 May to 18 July 1849. The torrential rainfall area showed a dumbbell-like structure along the Yangtze River, with two centers distributed separately in the east and west. For the specific flood process of the Yangtze River, many tributaries of the Yangtze River system entered the flood season consecutively since April, and the mainstream of the Yangtze River experienced tremendous pressure on flood prevention with the arrival of multiple rounds of heavy rainfall. In mid-to-late July, the water level and flow rate of many stations along the mainstream and tributaries had reached their record high. The record-breaking peak flow rate at many stations along the mainstream and tributaries in the middle reaches of the Yangtze River indicated intense precipitation in the area. The heavy rainfall disaster in the Yangtze River Basin could be driven by these reasons. First, the cold air in North China was extraordinary active in 1849, which made it difficult for the subtropical high pressure to move northward. Second, the rain belt stagnated in the Yangtze River Basin for a long time, and the Meiyu period reached 42 days, 62% longer than normal years. Third, the onset of a southwest monsoon was earlier and more active, which provided abundant moisture to the Yangtze River Basin. The great flood disaster was caused by heavy precipitation at the middle reaches, which made it quite different from the other three great floods in the Yangtze River in the 20th century. At present, the large water conservancy projects in the Yangtze River are mainly designed for flood problems caused by rainstorms in the upper reaches of the Yangtze River. The middle reaches of the Yangtze River, however, are facing the weakening of flood diversion capacity, caused by social and economic development. Therefore, future flood prevention measures in the Yangtze River should pay great attention to the threat of this flood pattern. Full article
(This article belongs to the Special Issue Hydrometeorological Observation and Modeling)
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Figure 1

Figure 1
<p>Map of the study area. Figure note: The Yellow River in 1820 is shown. Underlined cities, e.g., “Hangzhou”, are the cities recorded in the diary. The cities with frame, e.g., “<span class="html-fig-inline" id="water-13-02677-i001"> <img alt="Water 13 02677 i001" src="/water/water-13-02677/article_deploy/html/images/water-13-02677-i001.png"/></span>”, are the demarcation points of each section of the Yangtze River.</p>
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<p>Distribution maps of four precipitation events in summer of 1849. (<b>I</b>): spatial distribution of the first precipitation event, (<b>II</b>): spatial distribution of the second precipitation event, (<b>III</b>): spatial distribution of the third precipitation event, (<b>IV</b>): spatial distribution of the fourth precipitation event. The date, such as “5/25–5/28”, is the duration of heavy precipitation in the figure.</p>
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<p>Flood peak flow rates and water levels of the Yangtze River in 1849. (<b>a</b>) shows the peak flow rates and water levels of the stations along the mainstream of the Yangtze River in 1849. Numbers such as “65,000” are the peak discharges of the year (m<sup>3</sup>/s), and numbers marked with “m” are the highest water levels of the year (relative to the base level at Wusong). The peak flow rate in black font is obtained by combining the peak water level/discharge curve of stations in 1954 [<a href="#B31-water-13-02677" class="html-bibr">31</a>]. The water level in black font is drawn based on the water level records extracted from “<span class="html-italic">The Compilation of Survey Data of Historical Great Floods in China</span>” [<a href="#B55-water-13-02677" class="html-bibr">55</a>]. The peak flow rates and water levels in purple font are drawn based on the comprehensive judgment of the peak water level/discharge curve of each station in 1954 and documented historical records. (<b>b</b>) shows the flood peak flow rates and water levels of the hydrological stations where the Yangtze River system reached the highest water level or flood flow rate in history in 1849, which is drawn according to “<span class="html-italic">The Compilation of Survey Data of Historical Great Floods in China</span>” [<a href="#B55-water-13-02677" class="html-bibr">55</a>].</p>
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<p>Records of four sites of diaries. The daily weather classification method during Meiyu period in 1849 adopts the method of Zhimin Man et al. [<a href="#B50-water-13-02677" class="html-bibr">50</a>] with some amendments. 0: sunny, 1: light rain, 2: moderate rain, 3: rainstorm. The gray area is Meiyu period.</p>
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<p>Maps of droughts and floods in summer of the Yangtze River Basin in several great flood years. 1: severe drought, 2: drought, 3: normal, 4: flood, 5: severe flood.</p>
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19 pages, 2850 KiB  
Article
Improved Mixed Distribution Model Considering Historical Extraordinary Floods under Changing Environment
by Jianzhu Li, Yanchen Zheng, Yimin Wang, Ting Zhang, Ping Feng and Bernard A. Engel
Water 2018, 10(8), 1016; https://doi.org/10.3390/w10081016 - 31 Jul 2018
Cited by 9 | Viewed by 2934
Abstract
Historical extraordinary floods are an important factor in non-stationary flood frequency analysis and they may occur at any time, regardless of whether the environment is changing or not. Based on mixed distribution (MD) modeling, this paper proposed an improved mixed distribution (IMD) model [...] Read more.
Historical extraordinary floods are an important factor in non-stationary flood frequency analysis and they may occur at any time, regardless of whether the environment is changing or not. Based on mixed distribution (MD) modeling, this paper proposed an improved mixed distribution (IMD) model to consider the discontinuity and non-stationarity of flood samples simultaneously, which adds historical extraordinary floods in both sub-series divided by a change point. As a case study, the annual maximum peak discharge and volume series of Ankang hydrological station, located in the upper Hanjiang River Basin of China, were selected to identify non-stationarity by using the variation diagnosis system. MD and IMD were used to fit the flood characteristic series and a genetic algorithm was employed to estimate the optimal parameters. Compared with the design flood values fitted by the stationary Pearson type-III distribution, the results computed by IMD decreased at low return periods and increased at high return periods, with the difference varying from −6.67% to 7.19%. The results highlighted that although the design flood values of IMD are slightly larger than those of MD with different return periods, IMD provided a better result than MD. IMD provides a new perspective for non-stationary flood frequency analysis. Full article
(This article belongs to the Special Issue Hydrological Processes under Environmental Change)
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Figure 1
<p>Map of the upper Hanjiang Basin above the Ankang hydrological station.</p>
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<p>The hydrograph of the 1974 typical flood and the 1983 largest flood. (<b>a</b>) The hydrograph of the typical flood in 1974; and (<b>b</b>) The hydrograph of the largest flood in 1983.</p>
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<p>Correlation relationship between the flood peak and volume at the Ankang hydrological station.</p>
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<p>Variation between the design flood value of annual maximum peak discharge series (AMPDS) simulated by Monte Carlo and estimated by genetic algorithm (GA). (<b>a</b>) Design flood values of AMPDS simulated by Monte Carlo and estimated by GA at different return periods; and (<b>b</b>) Quantile-Quantile plots between design flood values of AMPDS simulated by Monte Carlo and estimated by GA.</p>
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<p>95% confidence intervals computed using the bootstrap method for flood characteristic series. (<b>a</b>) 95% confidence intervals of the annual maximum peak discharge; (<b>b</b>) 95% confidence intervals of the annual maximum 24-h flood volume; and (<b>c</b>) 95% confidence intervals of the annual maximum 72-h flood volume.</p>
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<p>Fitting results for each flood characteristic series under four conditions. (<b>a</b>) Fitting results of the annual maximum peak discharge; (<b>b</b>) Fitting results of the annual maximum 24-h flood volume; and (<b>c</b>) Fitting results of the annual maximum 72-h flood volume.</p>
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<p>Fitting results of the MD and IMD methods. (<b>a1</b>) MD fitting results of AMPDS; (<b>a2</b>) IMD fitting results of AMPDS; (<b>b1</b>) MD fitting results of annual maximum 24-h flood volume series (24-h AMFVS); (<b>b2</b>) IMD fitting results of 24-h AMFVS; (<b>c1</b>) MD fitting results of 72-h AMFVS; and (<b>c2</b>) IMD fitting results of 72-h AMFVS.</p>
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4355 KiB  
Article
Climate-Induced Extreme Hydrologic Events in the Arctic
by Toru Sakai, Tsuneo Matsunaga, Shamil Maksyutov, Semen Gotovtsev, Leonid Gagarin, Tetsuya Hiyama and Yasushi Yamaguchi
Remote Sens. 2016, 8(11), 971; https://doi.org/10.3390/rs8110971 - 23 Nov 2016
Cited by 9 | Viewed by 6352
Abstract
The objectives were (i) to evaluate the relationship between recent climate change and extreme hydrological events and (ii) to characterize the behavior of hydrological events along the Alazeya River. The warming rate of air temperature observed at the meteorological station in Chersky was [...] Read more.
The objectives were (i) to evaluate the relationship between recent climate change and extreme hydrological events and (ii) to characterize the behavior of hydrological events along the Alazeya River. The warming rate of air temperature observed at the meteorological station in Chersky was 0.0472 °C·year−1, and an extraordinary increase in air temperatures was observed in 2007. However, data from meteorological stations are somewhat limited in sparsely populated regions. Therefore, this study employed historical remote sensing data for supplementary information. The time-series analysis of the area-averaged Global Precipitation Climatology Project (GPCP) precipitation showed a positive trend because warming leads to an increase in the water vapor content in the atmosphere. In particular, heavy precipitation of 459 ± 113 mm was observed in 2006. On the other hand, the second-highest summer National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution radiometer (AVHRR) brightness temperature (BT) was observed in 2007 when the highest air temperature was observed in Chersky, and the anomaly from normal revealed that the summer AVHRR BTs showed mostly positive values. Conversely, riverbank, lakeshore and seashore areas were much cooler due to the formation, expansion and drainage of lakes and/or the increase in water level by heavy precipitation and melting of frozen ground. The large lake drainage resulted in a flood. Although the flooding was triggered by the thermal erosion along the riverbanks and lakeshores—itself induced by the heat wave in 2007—the increase in soil water content due to the heavy precipitation in 2006 appeared to contribute the magnitude of flood. The flood was characterized by the low streamflow velocity because the Kolyma Lowlands had a very gentle gradient. Therefore, the flood continued for a long time over large areas. Information based on remote sensing data gave basic insights for understanding the mechanism and behavior of climate-induced extreme hydrologic events. Full article
(This article belongs to the Special Issue Remote Sensing of Land Degradation and Drivers of Change)
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Graphical abstract

Graphical abstract
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<p>Location of study area in the Kolyma lowlands.</p>
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<p>Time series of mean (<b>a</b>) annual and (<b>b</b>) monthly air temperatures observed at the meteorological station in Chersky for the period 1960–2015. The red line indicates the air temperatures in 2007, and the gray lines show the air temperatures for the other years.</p>
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<p>Time series of area-averaged (<b>a</b>) brightness temperature (BT) calculated using Advanced Very High Resolution Radiometer (AVHRR) data during the summer period (June–September) and (<b>b</b>) annual precipitation calculated using the Global Precipitation Climatology Project (GPCP) data, which combines meteorological observations and satellite estimates. The average (solid line) and standard deviation (shaded area) of AVHRR summer BT and GPCP annual precipitation data are shown for the region shown in <a href="#remotesensing-08-00971-f001" class="html-fig">Figure 1</a> (67°N–72°N, 147°E–162°E).</p>
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<p>AVHRR summer BT anomaly in 2007 with respect to the 1982–2015 baseline. Red/blue areas indicate higher/lower than normal summer BT values. Black rectangles show the area of Phased Array type L-band Synthetic Aperture Radar (PALSAR) images shown in <a href="#remotesensing-08-00971-f005" class="html-fig">Figure 5</a>.</p>
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<p>Inundated areas around three villages on the Alazeya River visualized as a RGB composite from different three-year PALSAR images (R:G:B=2006:2007:2008). (<b>a</b>) Svatai (upper reaches); (<b>b</b>) Argakhtakh (middle reaches); and (<b>c</b>) Andryushkino (lower reaches). Black areas show continuous water bodies (e.g., rivers and lakes) for the three years. Dark-blue, purple and red areas show the inundated areas in 2006, 2007, and 2007 and 2008, respectively. White circles show the location of the villages.</p>
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<p>Water area detected by PALSAR data during the summer season from 2006 to 2009 in the 20 km × 20 km area shown in <a href="#remotesensing-08-00971-f005" class="html-fig">Figure 5</a>. Blue, green and red dots show the villages of Svatai, Argahtah and Andryushkino, respectively. Closed and open circles show fine and ScanSAR modes of PALSAR, respectively.</p>
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<p>Lake after drainage.</p>
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<p>Argakhtakh village surrounded by the flood in 2007.</p>
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4113 KiB  
Article
Optimization of Upstream Detention Reservoir Facilities for Downstream Flood Mitigation in Urban Areas
by Thi Thuy Ngo, Do Guen Yoo, Yong Sik Lee and Joong Hoon Kim
Water 2016, 8(7), 290; https://doi.org/10.3390/w8070290 - 14 Jul 2016
Cited by 25 | Viewed by 8630
Abstract
A detention reservoir is one of the most effective engineered solutions for flood damage mitigation in urban areas. Detention facilities are constructed to temporarily store storm water and then slowly drain when the peak period has passed. This delayed drainage may coincide with [...] Read more.
A detention reservoir is one of the most effective engineered solutions for flood damage mitigation in urban areas. Detention facilities are constructed to temporarily store storm water and then slowly drain when the peak period has passed. This delayed drainage may coincide with upstream floods and aggravate the flood risk downstream. Optimal operation and design are needed to improve the performance of detention reservoirs for flood reduction. This study couples hydrologic simulation software (EPA-SWMM) with an evolutional optimizer (extraordinary particle swarm optimization, EPSO) to minimize flood damage downstream while considering the inundation risk at the detention reservoir. The optimum design and operation are applied to an urban case study in Seoul, Korea, for historical severe flooding events and designed rainfall scenarios. The optimal facilities outperform the present facilities in terms of flood damage reduction both downstream and in the detention reservoir area. Specifically, the peak water level at the detention pond under optimal conditions is significantly smaller than that of the current conditions. The comparison of the total flooded volume in the whole watershed shows a dramatic reduction of 79% in a severe flooding event in 2010 and around 20% in 2011 and in 180 min designed rainfall scenarios. Full article
(This article belongs to the Special Issue Hydroinformatics and Urban Water Systems)
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<p>Schematic representation of detention reservoir facilities.</p>
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<p>Flowchart of coupled model of EPSO and SWMM5.1.</p>
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<p>Layout of Daerim3 catchment in Seoul.</p>
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<p>Daerim3 conduit system and layout of Daerim3 detention reservoir.</p>
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<p>Flooding area (blue hatch pattern) in Daerim3 catchment in September 2010 [<a href="#B26-water-08-00290" class="html-bibr">26</a>].</p>
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<p>Present and optimal rule curves of pump station D in the detention basin.</p>
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<p>Comparison of current and optimal detention reservoir facilities in terms of water depth at: (<b>a</b>) detention storage unit and (<b>b</b>) control node in September 2010.</p>
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<p>Comparison of current and optimal detention reservoir facilities in terms of water depth at (<b>a</b>) detention storage unit and (<b>b</b>) control node in July 2011.</p>
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