A Feasibility Study of Fitting the Normal Distribution and Gamma Distribution to Rainfall Data at Kuantan River Basin

Ghani, Nadiatul Adilah Ahmad Abdul; Senawi, Azlyna; Subramaniam, Roshan

doi:10.1007/978-981-19-5947-9_3

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…maximum likelihood analysis of the daily data sets (Figure 8) revealed that they can be fitted to gamma distributions. Fitting a gamma distribution to daily precipitation values to generate synthetic data has been widely reported in the literature (Coe & Stern, 1982;Ghani et al, 2023;Hasan et al, 2019;Lung, 2016;Martinez-Villalobos & Neelin, 2019). The summary statistics of the daily precipitation values from the simulation and target datasets in the historical period for each month is illustrated in Figure 9.…”

Section: Results Of the Integrated (En-rf) Modelsmentioning

confidence: 99%

“…The maximum likelihood analysis of the daily data sets (Figure 8) revealed that they can be fitted to gamma distributions. Fitting a gamma distribution to daily precipitation values to generate synthetic data has been widely reported in the literature (Coe & Stern, 1982; Ghani et al, 2023; Hasan et al, 2019; Lung, 2016; Martinez‐Villalobos & Neelin, 2019).…”

Section: Resultsmentioning

confidence: 99%

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Triple coupling random forest approach for bias correction of ensemble precipitation data derived from Earth system models for Divandareh‐Bijar Basin (Western Iran)

Zebarjadian,

Dolatabadi,

Zahraie

et al. 2024

Intl Journal of Climatology

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Climate change is expected to change the frequency, duration, intensity, and pattern of precipitation, underscoring the need for accurate predictive tools. Earth system models (ESMs) serve as invaluable instruments in this endeavour, simulating climate variable variations across temporal and spatial dimensions. This study aims to develop a methodology for generating precise daily precipitation maps by rectifying biases inherent in ESM outputs. The proposed methodology includes downscaling ESM outputs to simulate historical daily grid‐based precipitation, thereby enhancing the fidelity of daily precipitation representation. For this purpose, 14 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) were employed. Random forest (RF) machine learning method was used to correct biases in these ESM outputs. This study's novelty lies in integrating results of a grid‐based RF classification model, employed to distinguish between rainy and non‐rainy days, with those obtained by two RF regression models, to estimate precipitation amounts for grid cells receiving extreme and non‐extreme precipitation, to generate an ensemble of ESM outputs. The resulting method, termed the triple coupling method (EN‐RF), was validated using precipitation data from the Divandareh‐Bijar Basin in western Iran to simulate historical climate conditions. Furthermore, the accuracy of the developed triple coupling approach was compared with that of a commonly used single machine learning‐based downscaling model (EN‐Single‐RF). Comparative analysis against a commonly used single machine learning‐based downscaling model (EN‐Single‐RF) revealed the superior performance of the EN‐RF approach in replicating the intensity and distribution of daily precipitation. Furthermore, within the triple coupling framework, support vector machine (SVM) was utilized to simulate daily historical precipitation (EN‐SVM), while the quantile mapping (QM) method served as a benchmark. Comparison of the results showed superiority of the EN‐RF to other methods (EN‐Single‐RF, EN‐SVM, and QM) in terms of various accuracy metrics (Kling‐Gupta Efficiency = 0.95, mean square error = 0.22). The findings indicated the capability of the proposed triple coupling framework using the RF algorithm to simulate the spatio‐temporal distribution of precipitation using the ESM precipitation outputs. The developed framework can be used to produce reliable projections to gain deeper insights into the potential impacts of climate change on regional precipitation patterns.

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Section: Results Of the Integrated (En-rf) Modelsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Triple coupling random forest approach for bias correction of ensemble precipitation data derived from Earth system models for Divandareh‐Bijar Basin (Western Iran)

Zebarjadian,

Dolatabadi,

Zahraie

et al. 2024

Intl Journal of Climatology

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Determinación de los periodos de retorno utilizando el ajuste de distribución de probabilidad para la estación meteorológica de La Playa Norte de Santander

Álvarez-Bayona,

MacGregor-Torrado

2023

Rev. Ingenio

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La estimación de los periodos de retorno se desarrolla para duraciones máximas de precipitación de 1 día, 2 días ypicos continuos sucesivamente mayores, hasta tamaños de duración de 1 año. Son indispensables para determinar las máximas profundidades de precipitación anual de acuerdo con el mejor ajuste de las distribuciones probabilísticas, son de gran utilidad para el desarrollo de los diferentes estudios hidrológicos que requieren una región. La presente investigación tiene como propósito determinar los periodos de retorno de la precipitación máxima anual en un periodo de 24 horas, de la estación meteorológica La Playa en Norte de Santander, para lo cual se realizó el análisis de frecuencia mediante la prueba de bondad y ajuste, Chi cuadrado, teniendo como hipótesis nula una distribución normal, la cual fue aceptada. Posteriormente, se analizó mediante el software Minitab el tipo de distribución que tenían los datos estableciendo que la normal, lognormal, Weibull, logística, Gamma y el valor extremo por máximos se ajustan a ellos

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A Feasibility Study of Fitting the Normal Distribution and Gamma Distribution to Rainfall Data at Kuantan River Basin

Cited by 2 publications

References 8 publications

Triple coupling random forest approach for bias correction of ensemble precipitation data derived from Earth system models for Divandareh‐Bijar Basin (Western Iran)

Triple coupling random forest approach for bias correction of ensemble precipitation data derived from Earth system models for Divandareh‐Bijar Basin (Western Iran)

Determinación de los periodos de retorno utilizando el ajuste de distribución de probabilidad para la estación meteorológica de La Playa Norte de Santander

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