scholarly journals Estimation of Potential Economic Losses Due to Flooding Considering Variations of Spatial Distribution of Houses and Firms in a City

2021 ◽  
Vol 16 (3) ◽  
pp. 329-342
Author(s):  
Kaito Kotone ◽  
Kenji Taniguchi ◽  
Koichi Nakamura ◽  
Yuki Takayama ◽  
◽  
...  
Keyword(s):  
Global Warming ◽  
Spatial Distribution ◽  
River Basin ◽  
Heavy Rainfall ◽  
Economic Losses ◽  
Urban Structure ◽  
Flood Inundation ◽  
Return Periods ◽  
Expected Values ◽  
Kakehashi River Basin

In Japan, flood disasters caused by record-breaking heavy rainfall frequently cause significant damages. It is also great concern that heavy rainfall may increase and occur more frequently due to global warming. In July 2013, a heavy rainfall event caused record-flooding of the Kakehashi River in Ishikawa Prefecture. In this study, pseudo global warming (PGW) experiments were implemented for the heavy rainfall in 1998 and 2013 around the Kakehashi River basin. Based on the results of PGW simulations, rainfall with different return periods were generated. Runoff analyses and inundation simulations were carried out by forcings with multiple return periods, and the results were used to estimate the economic losses due to flood inundation. Expected values of the economic losses were calculated using two methods for multiple return periods. Differences between the two expected values indicates the importance of the weighting method for the result of each return period. In addition, variations of spatial distribution of houses and firms in a city (i.e., urban structure) were simulated using a computable urban economics (CUE) model for the area of middle-lower reach of the Kakehashi River basin to examine its impact on economic loss due to flooding. In the simulation using the CUE model, a more severe flood inundation risk and an additional insurance burden for general households were added, and possible variations of urban structure were estimated around the lower part of the Kakehashi River basin. Under the more severe risk condition, relocation proceeded from higher risk areas to safer areas, and possible economic losses decreased in the target area. This result indicates that proper recognition of risk can reduce flood damages. On the other hand, there were small variations in economic losses under the condition with the additional flood insurance burden.

scholarly journals Spatial Drought Characterization for Seyhan River Basin in the Mediterranean Region of Turkey

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1331 ◽  
Author(s):  
Yonca Cavus ◽  
Hafzullah Aksoy
Keyword(s):  
Spatial Distribution ◽  
River Basin ◽  
Frequency Analysis ◽  
Time Scale ◽  
Mediterranean Region ◽  
Eastern Mediterranean ◽  
Eastern Mediterranean Region ◽  
Natural Phenomenon ◽  
Total Probability ◽  
Return Periods

Drought is a natural phenomenon that has great impacts on the economy, society and environment. Therefore, the determination, monitoring and characterization of droughts are of great significance in water resources planning and management. The purpose of this study is to investigate the spatial drought characterizations of Seyhan River basin in the Eastern Mediterranean region of Turkey. The standardized precipitation index (SPI) was calculated from monthly precipitation data at 12-month time scale for 19 meteorological stations scattered over the river basin. Drought with the largest severity in each year is defined as the critical drought of the year. Frequency analysis was applied on the critical drought to determine the best-fit probability distribution function by utilizing the total probability theorem. The sole frequency analysis is insufficient in drought studies unless it is numerically related to other factors such as the severity, duration and intensity. Also, SPI is a technical tool and thus difficult to understand at first glance by end-users and decision-makers. Precipitation deficit defined as the difference between precipitation threshold at SPI = 0 and critical precipitation is therefore more preferable due to its usefulness and for being physically more meaningful to the users. Precipitation deficit is calculated and mapped for 1-, 3-, 6- and 12-month drought durations and 2-, 5-, 10-, 25-, 50- and 100-year return periods at 12-month time scale from the frequency analysis of the critical drought severity. The inverse distance weighted (IDW) interpolation technique is used for the spatial distribution of precipitation deficit over the Seyhan River basin. The spatial and temporal characteristics of drought suggest that the Seyhan River Basin in the Eastern Mediterranean region of Turkey experiences quite mild and severe droughts in terms of precipitation deficit. The spatial distribution would alter greatly with increasing return period and drought duration. While the coastal part of the basin is vulnerable to droughts at all return periods and drought durations, the northern part of the basin would be expected to be less affected by the drought. Another result reached in this study is that it could be common for one point in the basin to suffer dry conditions, whilst surrounding points in the same basin experience normal or even humid conditions. This reinforces the importance of spatial analysis over the basin under investigation instead of the point-scale temporal analysis made in each of the meteorological stations. With the use of spatial mapping of drought, it is expected that the destructive and irreversible effects of hydrological droughts can be realized in a more physical sense.

scholarly journals Analysis of extreme rainfall in Oti River Basin (West Africa)

2021 ◽  
Author(s):  
Komi S. Klassou ◽  
Kossi Komi
Keyword(s):  
River Basin ◽  
Heavy Rainfall ◽  
Extreme Rainfall ◽  
Value Theory ◽  
Extreme Value ◽  
Return Periods ◽  
Maximum Rainfall ◽  
Daily Data ◽  
Dry Spell ◽  
Annual Maximum Rainfall

Abstract Understanding how extreme rainfall is changing locally is a useful step in the implementation of efficient adaptation strategies to negative impacts of climate change. This study aims to analyze extreme rainfall over the middle Oti River Basin. Ten moderate extreme precipitation indices as well as heavy rainfall of higher return periods (25, 50, 75, and 100 years) were calculated using observed daily data from 1921 to 2018. In addition, Mann–Kendall and Sen's slope tests were used for trend analysis. The results showed decreasing trends in most of the heavy rainfall indices while the dry spell index exhibited a rising trend in a large portion of the study area. The occurrence of heavy rainfall of higher return periods has slightly decreased in a large part of the study area. Also, analysis of the annual maximum rainfall revealed that the generalized extreme value is the most appropriate three-parameter frequency distribution for predicting extreme rainfall in the Oti River Basin. The novelty of this study lies in the combination of both descriptive indices and extreme value theory in the analysis of extreme rainfall in a data-scarce river basin. The results are useful for water resources management in this area.

scholarly journals Probability Distribution of Precipitation Extremes for Weather Index–Based Insurance in the Zhujiang River Basin, South China

2012 ◽  
Vol 13 (3) ◽  
pp. 1023-1037 ◽  
Author(s):  
Thomas Fischer ◽  
Buda Su ◽  
Yong Luo ◽  
Thomas Scholten
Keyword(s):  
Probability Distribution ◽  
River Basin ◽  
Distribution Functions ◽  
Precipitation Extremes ◽  
Economic Losses ◽  
Return Periods ◽  
Weather Index ◽  
Maximum Precipitation ◽  
Insurance Program ◽  
Zhujiang River

Abstract In a changing climate, understanding the frequency of weather extremes is crucial to improving the management of the associated risks. The concept of weather index–based insurance is introduced as a new approach in weather risk adaptation. It can decrease the vulnerability to precipitation extremes that cause floods and economic losses in the Zhujiang River basin. The probability of precipitation extremes is a key input and the probability distribution of annual precipitation extremes is analyzed with four distribution functions [gamma 3, generalized extreme value (GEV), generalized Pareto, and Wakeby]. Three goodness-of-fit tests (Kolmogorov–Smirnov, Anderson–Darling, and Chi Squared) are applied to the distribution functions for annual time series (1961–2007) of 192 meteorological stations. The results show that maximum precipitation and 5-day-maximum precipitation are best described by the Wakeby distribution. On a basin scale, the GEV is the most reliable and robust distribution for estimating precipitation indexes for an index-based insurance program in the Zhujiang River basin. However, each station has to be analyzed individually as GEV is not always the best-fitting distribution function. Based on the distribution functions, spatiotemporal characteristics of return periods for maximum precipitation and 5-day-maximum precipitation are determined. The return levels of the 25- and 50-yr return periods show similar spatial pattern: they are higher in the southeast and lower in the southwest of the basin. This spatial distribution is in line with the annual averages. The statistical distribution of precipitation indexes delivers important information for a theoretical weather index–based insurance program.

Exploring stage-based flood frequency analysis for flood inundation mapping

2021 ◽  
Author(s):  
Shobhit Singh ◽  
Somil Swarnkar ◽  
Rajiv Sinha
Keyword(s):  
Probability Distribution ◽  
River Basin ◽  
Frequency Analysis ◽  
Probability Distributions ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Flood Inundation ◽  
Return Periods ◽  
Theoretical Probability ◽  
Inundation Maps

<p>Floods are one of the worst natural hazards around the globe and around 40% of all losses worldwide due to natural hazard have been caused by floods since 1980s. In India, more than 40 million hectares of area are affected by floods annually which makes it one of the worst affected country in the world. In particular, the Ganga river basin in northern India which hosts nearly half a billion people, is one of the worst floods affected regions in the country. The Ghaghra river is one of the highest discharge-carrying tributaries of the Ganga river, which originates from High Himalaya. Despite severally affected by floods each year, flood frequencies of the Ghaghra river are poorly understood, making it one of the least studied river basins in the Ganga basin. It is important to note that, like several other rivers in India, the Ghaghra also has several hydrological stations where only stage data is available, and therefore traditional flood frequency analysis using discharge data becomes difficult. In this work, we have performed flood frequency analysis using both stage and discharge dataset at three different gauge stations in the Ghaghra river basin to compare the results using statistical methods. The L-moment analysis is applied to assess the probability distribution for the flood frequency analysis. Further, we have used the TanDEM-x 90m digital elevation model (DEM) to map the flood inundation regions. Our results suggest the Weibull is statistically significant distribution for the discharge dataset. However, stage above danger level (SADL) follows General Pareto (GP3) and Generalized Extreme Value (GEV) distributions. The quantile-quantile plot analysis suggests that the SADL probability distributions (GP3 and GEV) are closely following the theoretical probability distributions. However, the discharge distribution (Weibull) is showing a relatively weak corelation with the theoretical probability distribution. We further used the probability distribution to assess the SADL frequencies at 5-, 10-, 20-, 50- and 100-year return periods. The magnitudes of SADL at different return periods were then used to map the water inundation areas around different gauging stations. These inundation maps were cross-validated with the globally available flooding extent maps provided by Dartmouth flood observatory. Overall, this work exhibits a simple and novel technique to generate inundation maps around the gauging locations without using any sophisticated hydraulics models.</p>

Extreme Rainfall Events in the Hawaiian Islands

2009 ◽  
Vol 48 (3) ◽  
pp. 502-516 ◽  
Author(s):  
Pao-Shin Chu ◽  
Xin Zhao ◽  
Ying Ruan ◽  
Melodie Grubbs
Keyword(s):  
Return Period ◽  
Heavy Rainfall ◽  
Hawaiian Islands ◽  
Daily Rainfall ◽  
Extreme Rainfall ◽  
Generalized Extreme Value Distribution ◽  
Economic Losses ◽  
Return Periods ◽  
Extreme Rainfall Events ◽  
Rainfall Events

Abstract Heavy rainfall and the associated floods occur frequently in the Hawaiian Islands and have caused huge economic losses as well as social problems. Extreme rainfall events in this study are defined by three different methods based on 1) the mean annual number of days on which 24-h accumulation exceeds a given daily rainfall amount, 2) the value associated with a specific daily rainfall percentile, and 3) the annual maximum daily rainfall values associated with a specific return period. For estimating the statistics of return periods, the three-parameter generalized extreme value distribution is fit using the method of L-moments. Spatial patterns of heavy and very heavy rainfall events across the islands are mapped separately based on the aforementioned three methods. Among all islands, the pattern on the island of Hawaii is most distinguishable, with a high frequency of events along the eastern slopes of Mauna Kea and a low frequency of events on the western portion so that a sharp gradient in extreme events from east to west is prominent. On other islands, extreme rainfall events tend to occur locally, mainly on the windward slopes. A case is presented for estimating return periods given different rainfall intensity for a station in Upper Manoa, Oahu. For the Halloween flood in 2004, the estimated return period is approximately 27 yr, and its true value should be no less than 13 yr with 95% confidence as determined from the adjusted bootstrap resampling technique.

Estimating floodwater depth using SAR-derived flood inundation maps and geomorphic model in kosi river basin (India)

2021 ◽  
pp. 1-26
Author(s):  
Bikash Ranjan Parida ◽  
Gaurav Tripathi ◽  
Arvind Chandra Pandey ◽  
Amit Kumar
Keyword(s):  
River Basin ◽  
Flood Inundation ◽  
Inundation Maps

scholarly journals Flood Inundation Assessment in the Low-Lying River Basin Considering Extreme Rainfall Impacts and Topographic Vulnerability

Water ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 896
Author(s):  
Thanh Thu Nguyen ◽  
Makoto Nakatsugawa ◽  
Tomohito J. Yamada ◽  
Tsuyoshi Hoshino
Keyword(s):  
River Basin ◽  
Extreme Rainfall ◽  
Flood Damage ◽  
Water Levels ◽  
Flood Inundation ◽  
Inundation Area ◽  
Inundation Depth ◽  
Study Results ◽  
Inundation Duration ◽  
Short Time

This study aims to evaluate the change in flood inundation in the Chitose River basin (CRB), a tributary of the Ishikari River, considering the extreme rainfall impacts and topographic vulnerability. The changing impacts were assessed using a large-ensemble rainfall dataset with a high resolution of 5 km (d4PDF) as input data for the rainfall–runoff–inundation (RRI) model. Additionally, the prediction of time differences between the peak discharge in the Chitose River and peak water levels at the confluence point intersecting the Ishikari River were improved compared to the previous study. Results indicate that due to climatic changes, extreme river floods are expected to increase by 21–24% in the Ishikari River basin (IRB), while flood inundation is expected to be severe and higher in the CRB, with increases of 24.5, 46.5, and 13.8% for the inundation area, inundation volume, and peak inundation depth, respectively. Flood inundation is likely to occur in the CRB downstream area with a frequency of 90–100%. Additionally, the inundation duration is expected to increase by 5–10 h here. Moreover, the short time difference (0–10 h) is predicted to increase significantly in the CRB. This study provides useful information for policymakers to mitigate flood damage in vulnerable areas.

scholarly journals A Spatial Improved-kNN-Based Flood Inundation Risk Framework for Urban Tourism under Two Rainfall Scenarios

2021 ◽  
Vol 13 (5) ◽  
pp. 2859
Author(s):  
Shuang Liu ◽  
Rui Liu ◽  
Nengzhi Tan
Keyword(s):  
Nearest Neighbor ◽  
Scientific Information ◽  
Climate Extremes ◽  
Extreme Rainfall ◽  
Tourism Industry ◽  
Urban Tourism ◽  
Evaluation Framework ◽  
Economic Losses ◽  
Flood Inundation ◽  
K Nearest Neighbor

Urban tourism has been suffering socio-economic challenges from flood inundation risk (FIR) triggered by extraordinary rainfall under climate extremes. The evaluation of FIR is essential for mitigating economic losses, and even casualties. This study proposes an innovative spatial framework integrating improved k-nearest neighbor (kNN), remote sensing (RS), and geographic information system (GIS) to analyze FIR for tourism sites. Shanghai, China, was selected as a case study. Tempo-spatial factors, including climate, topography, drainage, vegetation, and soil, were selected to generate several flood-related gridded indicators as inputs into the evaluation framework. A likelihood of FIR was mapped to represent possible inundation for tourist sites under a moderate-heavy rainfall scenario and extreme rainfall scenario. The resultant map was verified by the maximum inundation extent merged by RS images and water bodies. The evaluation outcomes deliver the baseline and scientific information for urban planners and policymakers to take cost-effective measures for decreasing and evading the pressure of FIR on the sustainable development of urban tourism. The spatial improved-kNN-based framework provides an innovative, effective, and easy-to-use approach to evaluate the risk for the tourism industry under climate change.

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