Drought Analysis in East Nusa Tenggara (Indonesia) Using Regional Frequency Analysis

Drought is a condition of a shortage of water that has an impact on economic activity. This research studies the severe drought area in Indonesia using Regional Frequency Analysis (RFA), based on daily precipitation data recorded at nine stations. The analysis reveals five homogeneous regions, based on discordancy and heterogeneity tests. Furthermore, the L-moment approach is applied to investigate the regional distribution and suggests that the Pearson type III distribution is the distribution that best fits the five regions. This distribution is also used to calculate the regional growth curve that is employed in the drought analysis. The drought return period analysis, for conditions of 40% of normal rainfall, concludes that the region containing the Fransiskus Xaverius, Gewayantana, and Mali stations has the highest drought risk, indicated by the fastest return period estimate of 2 years and 4 months. Moreover, the extreme drought analysis shows that two of the regions have the potential to experience the return of extreme drought, with less than 20% of normal rainfall, in less than four years.

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Regional Frequency Analysis of Precipitation Extremes and Its Spatio-Temporal Patterns in the Hanjiang River Basin, China

Atmosphere ◽

10.3390/atmos10030130 ◽

2019 ◽

Vol 10 (3) ◽

pp. 130 ◽

Cited By ~ 3

Author(s):

Wenlong Hao ◽

Zhenchun Hao ◽

Feifei Yuan ◽

Qin Ju ◽

Jie Hao

Keyword(s):

River Basin ◽

Frequency Analysis ◽

Return Period ◽

Extreme Events ◽

Extreme Precipitation ◽

Precipitation Extremes ◽

Regional Frequency Analysis ◽

Hanjiang River ◽

Spatio Temporal ◽

Regional Frequency

Extreme events such as rainstorms and floods are likely to increase in frequency due to the influence of global warming, which is expected to put considerable pressure on water resources. This paper presents a regional frequency analysis of precipitation extremes and its spatio-temporal pattern characteristics based on well-known index-flood L-moments methods and the application of advanced statistical tests and spatial analysis techniques. The results indicate the following conclusions. First, during the period between 1969 and 2015, the annual precipitation extremes at Fengjie station show a decreasing trend, but the Wuhan station shows an increasing trend, and the other 24 stations have no significant trend at a 5% confidence level. Secondly, the Hanjiang River Basin can be categorized into three homogenous regions by hierarchical clustering analysis with the consideration of topography and mean precipitation in these areas. The GEV, GNO, GPA and P III distributions fit better for most of the basin and MARE values range from 3.19% to 6.41% demonstrating that the best-fit distributions for each homogenous region is adequate in predicting the quantiles estimates. Thirdly, quantile estimates are reliable enough when the return period is less than 100 years, however estimates for a higher return period (e.g., 1000 years) become unreliable. Further, the uncertainty of quantiles estimations is growing with the growing return periods and the estimates based on R95P series have a smaller uncertainty to describe the extreme precipitation in the Hanjiang river basin (HJRB). Furthermore, In the HJRB, most of the extreme precipitation events (more than 90%) occur during the rainy season between May and October, and more than 30% of these extreme events concentrate in July, which is mainly impacted by the sub-tropical monsoon climate. Finally, precipitation extremes are mainly concentrated in the areas of Du River, South River and Daba Mountain in region I and Tianmen, Wuhan and Zhongxiang stations in region III, located in the upstream of Danjiangkou Reservoir and Jianghan Plain respectively. There areas provide sufficient climate conditions (e.g., humidity and precipitation) responsible for the occurring floods and will increase the risk of natural hazards to these areas.

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Comparative Study of Regional Frequency Analysis and Traditional At-Site Hydrological Frequency Analysis

Water ◽

10.3390/w11030486 ◽

2019 ◽

Vol 11 (3) ◽

pp. 486

Author(s):

Mengrui Li ◽

Xiaodong Li ◽

Tianqi Ao

Keyword(s):

Frequency Analysis ◽

Return Period ◽

Flood Control ◽

Regional Frequency Analysis ◽

Optimal Frequency ◽

Frequency Curve ◽

Design Values ◽

Homogeneous Regions ◽

Regional Frequency ◽

Hydrological Frequency Analysis

Hydrological frequency analysis plays an indispensable role in the construction of national flood control projects. This study selects the stations with the smallest and largest discordances in the nine homogeneous regions of Sichuan Province as the representative stations, and results obtained by regional frequency analysis are compared with those obtained by traditional at-site hydrological frequency analysis. The results showed that the optimal frequency distribution of each representative station obtained by traditional at-site hydrological frequency analysis and the ones of corresponding homogeneous regions obtained by regional frequency analysis were not necessarily consistent, which was related to the site and homogeneous regions. At the same time, there were also differences between the fitting of the theoretical rainstorm frequency curve obtained by the two methods and the observation. In general, in each homogeneous region, the results obtained by regional frequency analysis and traditional at-site hydrological frequency analysis at the stations with the largest frequency analysis were quite different. The design values obtained by the two methods were also increasingly different with the increase of the return period. The study has specific reflections on the differences between regional frequency analysis and traditional at-site hydrological frequency analysis.

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Bayesian Parameter Estimation to Bivariate Drought Regional Frequency Analysis Model: Application to Han-River Watershed

10.5194/egusphere-egu2020-20713 ◽

2020 ◽

Author(s):

Ho Jun Kim ◽

Jin-Guk Kim ◽

He Mie Cho ◽

Hyun-Han Kwon

Keyword(s):

Frequency Analysis ◽

Development Program ◽

Copula Function ◽

Regional Frequency Analysis ◽

Drought Risk ◽

Analysis Model ◽

Modeling Framework ◽

Proposed Model ◽

Drought Frequency Analysis ◽

Regional Frequency

Copula-based bivariate drought frequency analysis has been widely employed to evaluate drought risk in the context of point frequency analysis. However, the relatively significant uncertainties in the parameters are problematic when available data are limited. This study developed a bivariate regional frequency analysis model using Copula function within the Bayesian modeling framework. An experimental study is first performed to assure ourselves whether the proposed model can accurately reproduce drought characteristics. The proposed model is capable of effectively representing the recent drought events and can provide drought risk information along with its uncertainty. The results confirm that the proposed model is not only effectively representing correlation with regional dependencies of drought, but also providing the uncertainty of parameters.&#160;KEYWORDS: Copula, Bayesian, Bivariate drought regional frequency analysis, Uncertainty&#160;AcknowledgementThis work was funded by the Korea Meteorological Administration Research and Development Program under Grant KMI 2018-07010.

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Regional frequency analysis of daily maximum rainfall in Haryana

MAUSAM ◽

10.54302/mausam.v72i4.531 ◽

2021 ◽

Vol 72 (4) ◽

pp. 835-846

Author(s):

MOHIT NAIN ◽

B. K. HOODA

Keyword(s):

Frequency Analysis ◽

Return Period ◽

Regional Distribution ◽

Rain Gauge ◽

Quantile Estimation ◽

Regional Frequency Analysis ◽

Daily Maximum ◽

Maximum Rainfall ◽

L Moments ◽

Regional Frequency

This paper is sets-out for the regional frequency analysis of daily maximum rainfall from the 27 rain gauge stations in Haryana using L-moments. As the distribution of rainfall varies spatially in Haryana, the 27 rain gauge stations are grouped into three clusters namely, cluster C1, C2 and C3 using Ward’s clustering method and homogeneity of clusters was confirmed using L-moments-based Heterogeneity measure (H). Using goodness-of-fit measure ( DIST Z ) and L-moment ratios diagram, suitable regional frequency distributions were selected among five candidate distributions;Generalized Logistic (GLO), Generalized Extreme Value (GEV),Generalized Normal (GNO), Generalized Pareto (GPA), and Pearson Type-3 (PE3) for each cluster. Results showed that PE3 and GNO were good fitted regional distribution for the cluster C1 and GEV, PE3 and GNO fitted for cluster C2 while for cluster C3; GLO and GEV were good fitted regional distribution. To select a robust distribution among good fitted distributions accuracy measures calculated using Monte Carlo simulations for each cluster. The simulation result showed that PE3 was the best choice for quantile estimation for cluster C1. For cluster C2, PE3 was the best choicefor a large return period and GEV was best for a small return period. For cluster C3, GEV was the most suitable distribution for quantile estimation. Using these robust distributions rainfall quantiles were estimated at each rain gauge station from 2 to 100 year return periods. These estimated rainfall quantiles may be rough guideline for planning and designing hydraulic structures by policy makers and structural engineers.

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Regional frequency analysis of daily maximum rainfall in Haryana

MAUSAM ◽

10.54302/mausam.v72i4.3551 ◽

2021 ◽

Vol 72 (4) ◽

pp. 835-846

Author(s):

MOHIT NAIN ◽

B. K. HOODA

Keyword(s):

Frequency Analysis ◽

Return Period ◽

Regional Distribution ◽

Rain Gauge ◽

Quantile Estimation ◽

Regional Frequency Analysis ◽

Daily Maximum ◽

Maximum Rainfall ◽

L Moments ◽

Regional Frequency

This paper is sets-out for the regional frequency analysis of daily maximum rainfall from the 27 rain gauge stations in Haryana using L-moments. As the distribution of rainfall varies spatially in Haryana, the 27 rain gauge stations are grouped into three clusters namely, cluster C1, C2 and C3 using Ward’s clustering method and homogeneity of clusters was confirmed using L-moments-based Heterogeneity measure (H). Using goodness-of-fit measure ( ) and L-moment ratios diagram, suitable regional frequency distributions were selected among five candidate distributions; Generalized Logistic (GLO), Generalized Extreme Value (GEV),Generalized Normal (GNO), Generalized Pareto (GPA), and Pearson Type-3 (PE3) for each cluster. Results showed that PE3 and GNO were good fitted regional distribution for the cluster C1 and GEV, PE3 and GNO fitted for cluster C2 while for cluster C3; GLO and GEV were good fitted regional distribution. To select a robust distribution among good fitted distributions accuracy measures calculated using Monte Carlo simulations for each cluster. The simulation result showed that PE3 was the best choice for quantile estimation for cluster C1. For cluster C2, PE3 was the best choicefor a large return period and GEV was best for a small return period. For cluster C3, GEV was the most suitable distribution for quantile estimation. Using these robust distributions rainfall quantiles were estimated at each rain gauge station from 2 to 100 year return periods. These estimated rainfall quantiles may be rough guideline for planning and designing hydraulic structures by policy makers and structural engineers.

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Regional Frequency Analysis of Maximum Monthly Rainfall in Haryana State of India Using L-Moments

Journal of Reliability and Statistical Studies ◽

10.13052/jrss0974-8024.1413 ◽

2021 ◽

Author(s):

Mohit Nain ◽

B. K. Hooda

Keyword(s):

Return Period ◽

Goodness Of Fit ◽

Regional Distribution ◽

Rain Gauge ◽

Monthly Rainfall ◽

Regional Frequency Analysis ◽

Goodness Of Fit Test ◽

Pearson Type ◽

L Moments ◽

Regional Frequency

The paper aims to select the appropriate regional frequency distribution for the maximum monthly rainfall and estimation of quantiles using L-moments for the 27 rain gauge stations in Haryana. These 27 rain gauge stations were grouped into three homogeneous regions (Region-1, Region-2, and Region-3) using Ward’s method of cluster analysis. To confirm the homogeneity of each region, L-moments based measure of heterogeneity was used. For each homogeneous region, a regional distribution was selected with the help of the L-moments ratio diagram and goodness-of-fit test. Results of the goodness-of-fit test and L-moments ratio diagram indicated that Generalized Logistic and Generalized Extreme Value distributions were best- fitted regional frequency distributions for the Region-1 and Region-2 respectively while for Region-3, Pearson Type-3) was best-fitted distribution. The quantiles for each region were calculated and the regional growth curves were developed. The accuracy measurements were determined using Monte Carlo simulations for the regional quantiles. Results of simulations showed that uncertainty in regional quantiles measured by Root Mean Square Error value and 90 percent error limits were small when the return period was low but uncertainty in quantiles increases as the return period increases.

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