Analysis of Orographic Precipitation on Jeju-Island Using Regional Frequency Analysis and Regression

Um, Myoung-Jin; Yun, Hyeseon; Cho, Woncheol; Heo, Jun Haeng

doi:10.1007/s11269-009-9509-z

Cited by 14 publications

(8 citation statements)

References 27 publications

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“…Multilinear-regression models were used to estimate the spatial distribution of precipitation over the catchment. The MLR models have previously been used for mountainous regions (e.g., Marquínez et al, 2003;Naoum and Tsanis, 2004;Um et al, 2010). Cumulative precipitation P and geographical position altitude Z, latitude X, and longitude Y of the stations were used as response and predictor variables, respectively.…”

Section: Resultsmentioning

confidence: 99%

A new method to estimate annual and event-based recharge coefficient in karst aquifers; case study: Sheshpeer karst aquifer, south-central Iran

Kavousi¹,

Raeisi²

2016

JCKS

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Quantification of recharge is a fundamental issue in evaluation and management of karst water resources. Recharge coefficient (RC), which is the ratio of aquifer recharged water (VR) to the total precipitation, has been widely used for recharge estimation in karst catchments. Though the RC definition is straightforward, in practice, its application on a fixed short time scale (e.g., monthly or annual) is challenging, because of the problems in determination of change in groundwater storage (DVS) that is included in VR. Some researchers have neglected DVS, estimated VR as equal to the net aquifer outflows; hence they have over-or under-estimated the RC, ignoring the aquifer's antecedent storage condition. In this study, a new method is proposed to estimate the actual event VR, and consequently, the event-based RC, utilizing the MRC displacement method of Kavousi and Raeisi (2015). Applying the method, VR due to a specific event or a combination of events during a hydrological year can be directly estimated by decomposition of the observed hydrograph to the individual hydrographs induced by different events. The method is applicable for karst aquifers with no-flow boundaries, draining by a spring. The calculated event VR takes DVS into account, enabling an accurate event-based RC estimation. The method was applied to the Sheshpeer karst aquifer in Iran. The estimated RCs reasonably matched up to the catchment properties and former field studies. Results showed that the annual RCs would be underestimated if the DVS was neglected, because DVS was positive during the study period.

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Section: Resultsmentioning

confidence: 99%

A new method to estimate annual and event-based recharge coefficient in karst aquifers; case study: Sheshpeer karst aquifer, south-central Iran

Kavousi¹,

Raeisi²

2016

JCKS

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“…The above relationships were analysed using statistical methods mainly used for quantitative data. It has been confirmed many times that qualitative data about land cover, physiographic units or watersheds are useful for indicating or distinguishing precipitation regions (Prudhomme and Reed 1999;Bac-Bronowicz 2005Gouvas et al 2009;Myoung-Jin et al 2010).…”

Section: Topographic Conditions To Separate Regionsmentioning

confidence: 97%

Regionalization of geographical space according to selected topographic factors in reference to spatial distribution of precipitation: application of artificial neural networks in GIS

Bac-Bronowicz

Grzempowski

2018

Environ Earth Sci

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In this paper, the regionalization of geographical space according to selected topographic factors and the spatial distribution of precipitation is discussed. The model takes into account qualitative and quantitative data describing the conditions associated with the studied precipitation. In the modelling, data mining methods including data clustering methods for agglomeration and artificial neural networks for classification have been used. The reason for their use was the classification of the area due to conditions related to precipitation, the distinguishing of similar areas and the delimitation of the propagation of the phenomenon or transition zones. To realize the research aims, professional software for data management, spatial data analysis, mathematical calculations and data mining have been used. The result of the research was a model of the classes representing areas with specific conditions affecting the phenomenon, transition zones between classes and areas with conditions other than those in the surroundings of the measuring stations, which are not classified in any of the classes. Classification results indicate the boundaries of the areas in which we can model the values measured at stations, the transition zones of possible discontinuous change and areas in which the phenomenon should not be modelled due to significantly different conditions from those in the neighbourhoods of measuring stations. Unclassified areas are also potential locations for new measuring stations.

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“…Bradley 1998;Parida et al 1998;Fowler and Kilsby 2003;Kumar and Chatterjee 2005;Wallis et al 2007;Noto and Loggia 2009;Saf 2009;Shahzadi et al 2013;Devi and Choudhury 2013;Liu et al 2015). Um et al (2010) studied five distribution models to examine extreme rainfall events in Jeju Island using elevation and geographic coordinates as modeling inputs. The study discussed multiple non-linear form, linear regressions, an intensity-duration-frequency (IDF) relationship curve and obtained model accuracies in the range of 18-86%.…”

Section: Introductionmentioning

confidence: 99%

Regional frequency analysis for consecutive hour rainfall using L-moments approach in Jeju Island, Korea

Kar

Yang

Lee

et al. 2017

Geoenviron Disasters

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Background: Extreme rainfall events are enormously frequent and abrupt in tropical areas like the Jeju Island of South Korea, impacting the hydrological functions as well as the social and economic situation. Rainfall magnitude and frequency distribution related information are essential for water system design, water resources management and hydro-meteorological emergencies. This study therefore has investigated the use of L-moments approach for hourly regional rainfall frequency estimation so as to ensure better accuracy and efficiency of the estimation process from the usually limited data sets. Results: The Hancheon catchment was considered as the primary study domain and several best fitted statistical tools were used to analyze consecutive hour rainfall data from five hydro-meteorological stations (Jeju, Ara, Eorimok, Witsaeorum and Jindallaebat) adjacent to the area. The cluster analysis and discordancy measure categorized the Hancheon catchment in three regions (1, 2 and 3). Based on the L-moments heterogeneity and goodness-of-fit measure, Gumbel and generalized extreme value (GEV) distribution were identified as robust distributions for the study area. The RMSE ratios for the catchment were found as 0.014 to 0.237 for Gumbel and 0.115 to 0.301 for GEV distribution. The linear regression analysis of the different rainfall quantiles inferred r-square values from 0.842 to 0.974. Conclusions: The L-moments and other statistical information derived from the study can be useful for important hydrological design considerations in connection with flood risk management, mitigation and safety; whereas the methodological framework of the study may be suitable for other small scaled catchment areas with high slope.

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Analysis of Orographic Precipitation on Jeju-Island Using Regional Frequency Analysis and Regression

Cited by 14 publications

References 27 publications

A new method to estimate annual and event-based recharge coefficient in karst aquifers; case study: Sheshpeer karst aquifer, south-central Iran

A new method to estimate annual and event-based recharge coefficient in karst aquifers; case study: Sheshpeer karst aquifer, south-central Iran

Regionalization of geographical space according to selected topographic factors in reference to spatial distribution of precipitation: application of artificial neural networks in GIS

Regional frequency analysis for consecutive hour rainfall using L-moments approach in Jeju Island, Korea

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