scholarly journals Estimation of return period and its uncertainty for the recent 2013–2015 drought in the Han River watershed in South Korea

2018 ◽  
Vol 49 (5) ◽  
pp. 1313-1329 ◽  
Author(s):  
Jin-Young Kim ◽  
Byung-Jin So ◽  
Hyun-Han Kwon ◽  
Tae-Woong Kim ◽  
Joo-Heon Lee
Keyword(s):  
South Korea ◽  
Return Period ◽  
Drought Severity ◽  
Copula Model ◽  
Return Periods ◽  
Drought Duration ◽  
River Watershed ◽  
Han River ◽  
Joint Return ◽  
Very High

Abstract This study introduces an approach to copula model parameter estimation within a Bayesian framework. The model is applied to estimate the return periods of the 2013–2015 drought in the Han River watershed (South Korea) as observed through a network of 18 rainfall stations in the watershed. For the univariate return periods, some of the 2013–2015 drought durations (i.e., 9–24 months) have return periods of around 30 years while the drought severities (which range from about 400 to 1,400 mm) at certain stations might be regarded as extreme events with return periods in the hundreds of years. The recent drought is extraordinary at many stations in the Han River watershed although the associated uncertainty is quite large (e.g., for the Seoul station, a range of about 180 to 2,250 years). Moreover, it is clearly seen that the unprecedented joint return periods of the 2013–2015 drought at many stations are consistent with the very high drought severity. The joint return period of the drought duration and severity is above 100 years for many of the stations and upwards of 1,000 years for a few stations.

scholarly journals Geographical Distribution and Risk Assessment of Volatile Organic Compounds in Tributaries of the Han River Watershed

Agronomy ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 956
Author(s):  
Jong-Kwon Im ◽  
Yong-Chul Cho ◽  
Hye-Ran Noh ◽  
Soon-Ju Yu
Keyword(s):  
Volatile Organic Compounds ◽  
South Korea ◽  
Organic Compounds ◽  
Control Strategies ◽  
Anthropogenic Activities ◽  
River Watershed ◽  
Han River ◽  
Detection Frequency ◽  
Volatile Organic ◽  
Mean Concentration

Volatile organic compounds (VOCs), with negative impacts on the aquatic ecosystem, are increasingly released into the environment by anthropogenic activities. Water samples were collected from five areas of the Han River Watershed (HRW) tributaries, South Korea, to detect 11 VOCs, which were classified as halogenated aliphatic hydrocarbons (HAHs) and aromatic hydrocarbons (AHs). Among the 11 VOCs, 1,1-dichloroethylene, 1,1,1-trichloroethane, and vinyl chloride were undetected. The highest concentration compounds were chloroform (0.0596 ± 0.1312 µg/L), trichloroethylene (0.0253 ± 0.0781 µg/L), and toluene (0.0054 ± 0.0139 µg/L). The mean concentration (0.0234 µg/L) and detection frequency (37.0%) of HAHs were higher than those of AHs (0.0036 µg/L, 21.0%, respectively). The Imjin Hantan River area exhibited the highest mean concentration (0.2432 µg/L) and detection frequency (22.9%), because it is located near industrial complexes, thus, highlighting their role as important VOC sources. However, the detected VOCs had lower concentrations than those permitted by the EU, WHO, USA, and South Korea drinking water guidelines. Ecological risks associated with the VOCs were estimated by risk quotient (RQ); consequently, the predicted no-effect concentration was 0.0029 mg/L, and the toluene and styrene RQ values were >1 and >0.5, respectively. The findings may facilitate policymakers in designing pollution control strategies.

scholarly journals Joint return periods in hydrology: a critical and practical review focusing on synthetic design hydrograph estimation

2012 ◽  
Vol 9 (5) ◽  
pp. 6781-6828 ◽  
Author(s):  
S. Vandenberghe ◽  
M. J. van den Berg ◽  
B. Gräler ◽  
A. Petroselli ◽  
S. Grimaldi ◽  
...  
Keyword(s):  
Frequency Analysis ◽  
Return Period ◽  
Three Dimensional ◽  
Distribution Functions ◽  
Return Periods ◽  
Design Variables ◽  
Joint Return ◽  
Design Values ◽  
Multivariate Frequency Analysis ◽  
Joint Return Period

Abstract. Most of the hydrological and hydraulic studies refer to the notion of a return period to quantify design variables. When dealing with multiple design variables, the well-known univariate statistical analysis is no longer satisfactory and several issues challenge the practitioner. How should one incorporate the dependence between variables? How should the joint return period be defined and applied? In this study, an overview of the state-of-the-art for defining joint return periods is given. The construction of multivariate distribution functions is done through the use of copulas, given their practicality in multivariate frequency analysis and their ability to model numerous types of dependence structures in a flexible way. A case study focusing on the selection of design hydrograph characteristics is presented and the design values of a three-dimensional phenomenon composed of peak discharge, volume and duration are derived. Joint return period methods based on regression analysis, bivariate conditional distributions, bivariate joint distributions, and Kendal distribution functions are investigated and compared highlighting theoretical and practical issues of multivariate frequency analysis. Also an ensemble-based method is introduced. For a given design return period, the method chosen clearly affects the calculated design event. Eventually, light is shed on the practical implications of a chosen method.

Spatial analysis of return periods of hydrological drought of Nile River Basin

2020 ◽  
Author(s):  
Gizaw Mengistu Tsidu
Keyword(s):  
River Basin ◽  
Water Resource ◽  
Return Period ◽  
Arrival Time ◽  
Population Increase ◽  
Drought Severity ◽  
Nile River ◽  
Return Periods ◽  
The Mean ◽  
Nile River Basin

<p>The Nile River Basin has been vital source of water to Riparian countries in both upper and lower catchments of the Basin. However, the states in the lower catchment namely Sudan and Egypt have exploited this resource without significant competition from countries in the upper catchments in the past. Recently, due to population increase in the basin and climate change, there are some initiatives by Riparian States such as Ethiopia to use this vital water resource (e.g., for energy generation). Therefore, it is important to understand recurrent drought characteristics and its potential impacts on the water resource in the basin. Drought events in the Nile Basin have been extracted using run theory based on the Standardized Precipitation Evapotranspiration Index (SPEI) accumulated on the time scale of 12 months using CRU rainfall and evapotranspiration data, which covers the period 1901–2018. The drought events are characterized by four variables: duration, severity. Intensity and Inter-arrival time. The mean duration and severity of drought during the last 118 years over the Basin are generally short and moderate over upper catchments. Conversely, the mean duration various from 4 to 8 months and up to 14 months over the middle and lower catchments of the Basin respectively while the mean drought severity increases from -2 at upper catchment to -7 at lower catchment. Gamma, Weibull, Gamma and Exponential functions are then selected to describe the marginal distribution of severity, duration, intensity and inter-arrival time, respectively. The Gumbel–Hougaard Copula was used to construct the joint distribution of duration, severity, intensity and/or inter-arrival time. The results indicate that the return period is dependent on the location within the basin, variable type and the combination of variables. For extreme droughts with severity index of -10 and duration of 14 months, return periods are longer than 40 years over south parts of the Basin and it barely exceeds 25 years over northern parts of the Basin. Generally, the short return period is mainly distributed in lower catchments of the Basin. This study on the identification of spatial distributions of drought return periods across the Basin is therefore important for drought mitigation and strategic planning on the water resource.</p>

scholarly journals A maximum entropy copula-based frequency analysis method for assessing bivariate drought risk: a case study of the Kaidu River Basin

2021 ◽  
Author(s):  
X. Yang ◽  
Y. P. Li ◽  
G. H. Huang
Keyword(s):  
Maximum Entropy ◽  
River Basin ◽  
Frequency Analysis ◽  
Return Period ◽  
Drought Risk ◽  
Return Periods ◽  
Drought Frequency ◽  
Joint Return ◽  
Joint Return Period ◽  
Kaidu River

Abstract In this study, a maximum entropy copula-based frequency analysis (MECFA) method is developed through integrating maximum entropy, copulas and frequency analysis into a general framework. The advantages of MECFA are that the marginal modeling requires no assumption and joint distribution preserves the dependence structure of drought variables. MECFA is applied to assessing bivariate drought frequency in the Kaidu River Basin, China. Results indicate that the Kaidu River Basin experienced 28 drought events during 1958–2011, and drought inter-arrival time is 10.8 months. The average duration is 6.2 months (severity 4.6), and the most severe drought event lasts for 35 months (severity 41.2) that occurred from June 1977 to March 1980. Results also disclose that hydrological drought index (HDI) 1 is suitable for drought frequency analysis in target year of return periods of 5 and 10, HDI 3, HDI 6 and HDI 12 are fit for the target year of return periods of 20, 50 and 100. The joint return period can be used as the upper bound of the target return period, and the joint return period that either duration or severity reaches the drought threshold can be used as the lower bound of the target return period.

Pharmaceutical compounds in tributaries of the Han River watershed, South Korea

2020 ◽  
Vol 188 ◽  
pp. 109758 ◽  
Author(s):  
Jong Kwon Im ◽  
Moon Young Hwang ◽  
Eun Hee Lee ◽  
Hye Ran Noh ◽  
Soon Ju Yu
Keyword(s):  
South Korea ◽  
Pharmaceutical Compounds ◽  
River Watershed ◽  
Han River

Characteristics of Fish Communities in Namyang Lake and its Tributaries in the Estuary of Han River Watershed, South Korea

2021 ◽  
Vol 54 (1) ◽  
pp. 1-11
Author(s):  
Yeong-Ho Kwak ◽  
Seung-Young Kim ◽  
Ha-Yun Song ◽  
Hyoung-Joo Jeon ◽  
Mi-Young Song
Keyword(s):  
South Korea ◽  
Fish Communities ◽  
River Watershed ◽  
Han River

scholarly journals Analysis and Application of Drought Characteristics Based on Theory of Runs and Copulas in Yunnan, Southwest China

2020 ◽  
Vol 17 (13) ◽  
pp. 4654
Author(s):  
Liping Wang ◽  
Xingnan Zhang ◽  
Shufang Wang ◽  
Mohamed Khaled Salahou ◽  
Yuanhao Fang
Keyword(s):  
Short Duration ◽  
Return Period ◽  
Joint Distribution ◽  
Yunnan Province ◽  
Cumulative Probability ◽  
Drought Duration ◽  
Drought Characteristics ◽  
Joint Return ◽  
Drought Duration And Severity ◽  
Drought Characteristic

Drought is a complex natural disaster phenomenon. It is of great significance to analyze the occurrence and development of drought events for drought prevention. In this study, two drought characteristic variables (the drought duration and severity) were extracted by using the Theory of Runs based on four drought indexes (i.e., the percentage of precipitation anomaly, the standardized precipitation index, the standardized precipitation evapotranspiration index and the improved comprehensive meteorological drought index). The joint distribution model of drought characteristic variables was built based on four types of Archimedean copulas. The joint cumulative probability and the joint return period of drought events were analyzed and the relationship between the drought characteristics and the actual crop drought reduction area was also studied. The results showed that: (1) The area of the slight drought and the extreme drought were both the zonal increasing distribution from northeast to southwest in Yunnan Province from 1960 to 2015. The area of the high frequency middle drought was mainly distributed in Huize and Zhanyi in Northeast Yunnan, Kunming in Central Yunnan and some areas of Southwest Yunnan, whereas the severe drought was mainly occurred in Deqin, Gongshan and Zhongdian in Northwest Yunnan; (2) The drought duration and severity were fitted the Weibull and Gamma distribution, respectively and the Frank copula function was the optimal joint distribution function. The Drought events were mostly short duration and high severity, long duration and low severity and short duration and low severity. The joint cumulative probability and joint return period were increased with the increase of drought duration and severity; (3) The error range between the theoretical return period and the actual was 0.1–0.4 a. The year of the agricultural disaster can be accurately reflected by the combined return period in Yunnan Province. The research can provide guidelines for the agricultural management in the drought area.

scholarly journals Use of several indices of event severity for floods and droughts

2003 ◽  
Vol 7 (5) ◽  
pp. 642-651 ◽  
Author(s):  
D. A. Jones
Keyword(s):  
Return Period ◽  
Composite Index ◽  
Model Fitting ◽  
Severity Index ◽  
Drought Severity ◽  
Return Periods ◽  
Practical Procedure ◽  
Composite Indices ◽  
Floods And Droughts ◽  
The Relationship

Abstract. An examination is made of the true return periods associated with certain types of composite indices for the rareness of events. In particular, return periods are evaluated separately for several different ways of describing how bad an event was, and the composite index, or apparent return period, is defined as the largest of these component return periods. Such apparent return periods give an incorrect indication of how often a larger value for the composite index will occur. Simulations are used to study the relationship between the true and apparent return periods for some simple cases, and an assessment is made of the extent of the error made if the apparent return period is used directly. A simple practical procedure is described for dealing with real datasets without model-fitting, and this is assessed using further simulations. An example is given relating to a possible flood situation where a composite index is constructed as the largest of the return periods of high rainfall-accumulations over a number of durations. Keywords: drought severity index, composite index, event severity, return period

scholarly journals Risk Assessment of Resources Exposed to Rainfall Induced Landslide with the Development of GIS and RS Based Ensemble Metaheuristic Machine Learning Algorithms

2021 ◽  
Vol 13 (2) ◽  
pp. 457
Author(s):  
Javed Mallick ◽  
Saeed Alqadhi ◽  
Swapan Talukdar ◽  
Majed AlSubih ◽  
Mohd. Ahmed ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Natural Resources ◽  
Return Period ◽  
Learning Algorithms ◽  
Landslide Hazard ◽  
Machine Learning Algorithms ◽  
Return Periods ◽  
Grey Wolf ◽  
Very High

Disastrous natural hazards, such as landslides, floods, and forest fires cause a serious threat to natural resources, assets and human lives. Consequently, landslide risk assessment has become requisite for managing the resources in future. This study was designed to develop four ensemble metaheuristic machine learning algorithms, such as grey wolf optimized based artificial neural network (GW-ANN), grey wolf optimized based random forest (GW-RF), particle swarm optimization optimized based ANN (PSO-ANN), and PSO optimized based RF for modeling rainfall-induced landslide susceptibility (LS) in Aqabat Al-Sulbat, Asir region, Saudi Arabia, which observes landslide frequently. To obtain very high precision and robust prediction from machine learning algorithms, the grey wolf and PSO optimization algorithms were integrated to develop new ensemble machine learning techniques. Subsequently, LS maps produced by training dataset were validated using the receiver operating characteristics (ROC) curve based on the testing dataset. Based on the area under curve (AUC) value of ROC curve, the best method for LS modeling was selected. We developed ROC curve-based sensitivity analysis to investigate the influence of the parameters for LS modeling. The Gumble extreme value distribution was employed to estimate the rainfall at 2, 5, 10, 20, 50, and 100 year return periods. Then, the landslide hazard maps were prepared at different return periods by integrating the best LS model and estimated rainfall at different return periods. The theory of danger pixels was employed to prepare a final risk assessment of the resources, which have been exposed to the landslide. The results showed that 27–42 and 6–15 km2 were predicted as the very high and high LS zones using four ensemble metaheuristic machine learning algorithms. Based on the area under curve (AUC) of ROC, GR-ANN (AUC-0.905) appeared as the best model for LS modeling. The areas under high and very high landslide hazard were gradually increased over the progression of time (26 km2 at the 2 year return period and 40 km2 at the 100 year return period for the high landslide hazard zone, and 6 km2 at the 2 year return period and 20 km2 at the 100 year return period for the very high landslide hazard zone). Similarly, the areas of danger pixel also increased gradually from the 2 to 100 year return periods (37 km2 to 62 km2). Various natural resources, such as scrubland, built up, and sparse vegetation, were identified under risk zone due to landslide hazards. In addition, these resources would be exposed extensively to landslides over the advancement of return periods. Therefore, the outcome of the present study will help planners and scientists to propose high precision management plans for protecting natural resources, which have been exposed to landslides.

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