Statistical analysis of annual maximum daily rainfall for Nelspruit and its environs

Masereka, Eric M.; Ochieng, George M.; Snyman, Jacques

doi:10.4102/jamba.v10i1.499

Cited by 10 publications

(5 citation statements)

References 15 publications

(11 reference statements)

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“…Namun demikian, dalam hidrologi, istilah ini digunakan secara khusus untuk merujuk pada suatu rangkaian waktu di mana hanya sebagian kecil dari waktu memiliki sifat yang tidak dapat diprediksi (Geurink & Ross, n.d.). Menurut Masereka et al, (2018)), hidrologi stokastik memiliki kemampuan untuk mengisi celah antara hidrologi probabilitas dan metode deterministik. Penelitian ini akan menggunakan pemodelan Autoregressive.…”

Section: Hidrologi Statistikunclassified

Pemodelan Stokastik Curah Hujan Harian Stasiun Hujan Di Kabupaten Lombok Tengah

M Arifudin Fahmi,

L Ade Arsuliwininggrat,

Sugiharta

2024

KoNTekS

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Sebagian besar wilayah Kabupaten Lombok Tengah memiliki iklim sabana tropis (Aw) dengan dua musim, yaitu musim penghujan dan musim kemarau, menurut klasifikasi iklim Koppen. Musim penghujan biasanya terjadi dari bulan November hingga bulan April, dengan bulan terbasah Januari yang memiliki curah hujan rata-rata lebih dari 300 milimeter per bulan. Musim kemarau biasanya terjadi dari bulan Mei hingga bulan Oktober, dengan bulan terkering Agustus memiliki curah hujan rata-rata lebih dari 300 milimeter per bulan. Tujuan penelitian ini adalah untuk menyelidiki unsur stokastik dalam data curah hujan harian yang dikumpulkan dari enam stasiun hujan yang terletak di Kabupaten Lombok Tengah. Data curah hujan harian dari tahun 2010–2020 digunakan dalam penelitian ini. Metode analisis yang digunakan termasuk pemodelan periodik dan stokastik menggunakan Fast Fourier Transform (FFT) dan kuadrat terkecil untuk membuat model periodik curah hujan harian. Untuk menghitung model periodik, perbedaan antara curah hujan yang diukur dan model periodik dihitung. Ada perbedaan, menurut hasil penelitian metode stokastik yang dilakukan berdasarkan data curah hujan harian selama sepuluh tahun. Autogresif model orde tiga digunakan untuk menampilkan model stokastik. Untuk validasi seri stokastik, koefisien korelasi antara data dan model dapat dihitung. Hasil penelitian menunjukkan bahwa data curah hujan harian sintetik yang diperoleh melalui seri waktu sangat mirip dengan curah hujan yang diukur. Untuk model periodik, koefisien korelasi (R) rata-rata adalah 0,9770, sedangkan untuk model stokastik adalah 0,9979, dan untuk model periodik stokastik adalah 0,99991.

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Section: Hidrologi Statistikunclassified

Pemodelan Stokastik Curah Hujan Harian Stasiun Hujan Di Kabupaten Lombok Tengah

M Arifudin Fahmi,

L Ade Arsuliwininggrat,

Sugiharta

2024

KoNTekS

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“…The return period of a rainfall event was computed using Weibull's formula (Masereka et al 2018) where the values of annual daily maximum rainfall of a particular catchment area for a number of consecutive years are shown in descending order of magnitude and the probability P of each event being equal or exceeded is given by:…”

Section: Estimation Of Rainfall Return-periodmentioning

confidence: 99%

Assessing the impacts of land use–land cover changes on direct surface runoff: a remote sensing approach in Khulna City

Das

Esraz-Ul-Zannat

2022

Water Science and Technology

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The increased risks of storm flood occurrence in large cities are the resultants of land use changes due to rapid urbanization. This study examines the influence of land-use changes in Khulna City Corporation (KCC) area on surface runoff over a period of 14 years, from 2005 to 2020. Land use-land cover maps for 2005, 2010, 2015, and 2020 were created employing Support Vector Machine (SVM) based supervised image classification using time-series satellite data, and the surface runoff was determined using SCS-CN model. The major land-use change drivers of surface runoff were determined through a correlation analysis. Surface runoff was observed to follow a similar trend in impervious urban areas, which rose by 5.44% from 2005 to 2020 (17.00 mm average runoff depth increment) and the opposite trend was found in vegetation land cover, which declined by 13.34% in areal extent throughout the study period. In comparison with other types of land use, surface runoff changes were most significantly associated with the changes in urban impervious areas and vegetation LULC class. In fast-growing cities across the world, and especially in developing nations, the results of this study may serve as a guide for urban storm flood management and urban planning efforts.

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“…The probability of exceedance of the AMS events are determined by the rank-order method. This method involves ordering the events from the largest events ranked as 1 to the lowest as m, being the sample size of events [32,33].…”

Section: Parametric Idf Modelingmentioning

confidence: 99%

A Review Study on Stationary and Non-Stationary IDF Models Used in Rainfall Data Analysis around the World from 1951-2020

Nwaogazie

Sam

2020

IJECC

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This article focuses on an overview of the processes of generating rainfall intensity-duration-frequency (IDF) models, the different types and applications. IDF model is an important tool applied in the design of either hydrologic or hydraulic design such as prediction of rainfall intensities to estimate peak runoff volumes for mitigation of flooding. IDF models evolved from stationary – parametric (empirical) and non-parametric (stochastic) models, to non-stationary models in which variables vary with time. Each category controls the ways models predict rainfall intensities, and reveals their strength and weaknesses. IDF models must therefore, be chosen in terms of the project objective, data availability, size of the study, location, output needed, and the desired simplicity. For instance, while the parametric model predicts better for shorter durations and return periods only, the non-parametric models predict better for both shorter and longer durations and return periods. For projects requiring change of input data over time and evaluation of uncertainty bounds, risk assessment, including incorporation of changes in extreme precipitation, the non-stationary model approach must be selected. Also, of importance for catchments without rainfall amount and corresponding duration records but has daily (24-hourly) record of rainfall depth, the Indian Meteorological Department (IMD) method of shorter duration disaggregation can be adopted to generate in-put data for the development of IDF curves for such a location. Therefore, each model type has limitations that may make it unsuitable for some projects. Reviewing input data and output requirements, and simplicity are all necessary to decide on which model type should be selected.

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Statistical analysis of annual maximum daily rainfall for Nelspruit and its environs

Cited by 10 publications

References 15 publications

Pemodelan Stokastik Curah Hujan Harian Stasiun Hujan Di Kabupaten Lombok Tengah

Pemodelan Stokastik Curah Hujan Harian Stasiun Hujan Di Kabupaten Lombok Tengah

Assessing the impacts of land use–land cover changes on direct surface runoff: a remote sensing approach in Khulna City

A Review Study on Stationary and Non-Stationary IDF Models Used in Rainfall Data Analysis around the World from 1951-2020

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