Multimodel response assessment for monthly rainfall distribution in some selected Indian cities using best-fit probability as a tool

Abstract Spatial interpolation methods usually differ in their underlying mathematical concepts, each with inherent advantages and drawbacks depending on the properties of data. This paper, therefore, aims to compare and evaluate the performances of well-established interpolation techniques for estimating monthly rainfall data in Thailand. The selected methods include the inverse distance-based method, multiple linear regression (MLR), artificial neural networks (ANN), and ordinary kriging (OK). The technique of searching nearest stations is additionally imposed for some aforementioned schemes. The k -fold cross-validation method is exploited to assess the efficiency of each method, then the metric scores, RMSE, and MAE are used for comparisons. The results suggest the ANN might be the least favorite as it underperforms in many folds. While the OK method provides the most accurate prediction, the inverse distance weighting (IDW), particularly inverse exponential weighting (IEW), and MLR are considerably comparative. Overall, IEW is plausible for monthly rainfall estimation of Thailand because it is less computationally expensive than the OK and its flexible computation.

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Rainfall Distribution in the Upper East Region of Ghana, 1976 – 2016

Ghana Journal of Science, Technology and Development ◽

10.47881/168.967x ◽

2020 ◽

Vol 6 (2) ◽

pp. 45-59

Author(s):

Boateng AMPADU ◽

Isaac SACKEY ◽

Eugene CUDJOE

Keyword(s):

Agricultural Sector ◽

Moving Average ◽

Monthly Rainfall ◽

Rainfall Distribution ◽

East Region ◽

Dry Spells ◽

Early Maturing ◽

Upper East Region ◽

The Mean ◽

High Precipitation

The knowledge and understanding of rainfall distribution of a region are very essential and useful in determining the overall impacts of climate change, especially to the agricultural sector. Monthly rainfall data from 1976-2016 for five selected stations were acquired and subjected to various statistical techniques namely coefficient of variation, 5-year moving average and departure from the mean to obtain the variability and trends in the data. The results showed that the selected stations have uni-modal rainfall distribution and that the rain mostly starts in May and ends in September. High precipitation occurs in July, August and September, with August recording the highest amount with a low variability, indicating the reliable occurrence of precipitation within this period of the year. This is of high importance to farmers and the recharging of aquifers. The wettest station was Zuarungu, with a mean total monthly rainfall of 89.55 mm followed by Navrongo, Bolgatanga, Garu and Manga-Bawku with their respective mean total monthly rainfall as 81.08 mm, 80.59 mm, 79.64 mm and 78.86 mm. High annual variability was found in all the stations and long dry spells were observed from November to March. The rainfall season wet period is between July and September at all the stations and it is recommended that farmers should cultivate early-maturing crops and adopt irrigation farming practices as well as practices which utilize water efficiently.

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Best-Fit Probability Distributions and Return Periods for Maximum Monthly Rainfall in Bangladesh

Climate ◽

10.3390/cli6010009 ◽

2018 ◽

Vol 6 (1) ◽

pp. 9 ◽

Cited By ~ 31

Author(s):

Md Alam ◽

Kazuo Emura ◽

Craig Farnham ◽

Jihui Yuan

Keyword(s):

Probability Distributions ◽

Monthly Rainfall ◽

Return Periods ◽

Best Fit

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Monthly Rainfall Analysis in West Part of Chongqing

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.580-583.2019 ◽

2014 ◽

Vol 580-583 ◽

pp. 2019-2022

Author(s):

Lin Qin ◽

Jun Ying Jin ◽

Qian Zhang ◽

Da Ke Wang

Keyword(s):

Large Scale ◽

Global Scale ◽

Monthly Rainfall ◽

Statistical Characteristics ◽

The West ◽

Rainfall Distribution ◽

Rainfall Analysis ◽

Complex Interactions ◽

West Part ◽

Average Monthly Rainfall

Rainfall information is critical in understanding the hydrologic balance on a global scale and the complex interactions among the small-and large-scale components within the hydrologic cycle [1]. In this study, the monthly rainfall data from 1976 to 2006 at the stations of Rongchang, Dazu, Tongliang and Yongchuan were used to analyze the statistical characteristics and trends of rainfall changes in the west of Chongqing. The average monthly rainfall at Rongchang, Dazu, Tongliang and Yongchuan were 89.4mm, 83.5mm, 88.3mm and 84.8mm in thirty years. The probability distribution of rainfall is a normal distribution. Moreover, the histogram of frequencies showed a clear tendency toward a obvious seasonality of the rainfall distribution [2].

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Influence of topography on monthly rainfall distribution over East Africa

Climate Research ◽

10.3354/cr028199 ◽

2005 ◽

Vol 28 ◽

pp. 199-212 ◽

Cited By ~ 47

Author(s):

P Oettli ◽

P Camberlin

Keyword(s):

East Africa ◽

Monthly Rainfall ◽

Rainfall Distribution

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PEWILAYAHAN AGROKLIMAT TANAMAN NILAM (Pogostemon spp.) BERBASIS CURAH HUJAN DI PROVINSI LAMPUNG (AGROCLIMATE ZONING OF PATCHOULY (Pogostemon ssp.) BASED ON RAINFALL IN LAMPUNG PROVINCE)

Agromet ◽

10.29244/j.agromet.20.2.14-24 ◽

2006 ◽

Vol 20 (2) ◽

pp. 14

Author(s):

I. G. Darmaputra ◽

Y. Koesmaryono ◽

I. Santoso

Keyword(s):

Cluster Analysis ◽

Principle Component Analysis ◽

Monthly Rainfall ◽

Seasonal Rainfall ◽

Type I ◽

Type Ii ◽

Rainfall Distribution ◽

Principle Component ◽

Rainfall Map ◽

Type V

The research purposed to determine the patchouly cropping in Lampung Province based on the agroclimate feasibility of area rainfall and to determine the monthly rainfall probability which less than the patchouly requirement. There are four steps on this research such as to determine the seasonal rainfall distribution by Principle Component Analysis, seasonal rainfall zoning by Cluster Analysis, and agroclimate zoning of patchouly by superimpossed annual region rainfall map, regional map of wet month, topography map to patchouly agroclimate requirement, and to determine the monthly rainfall probability which less than the patchouly crop requirement. The result of research shows 2,069,005 ha of Lampung Province area most feasible and feasible to patchouly cropping, which are spread in Lampung Barat Regency (15.7%), Lampung Tengah Regency (15.5%), Way Kanan Regency (14.3%), Tanggamus Regency (14%), Lampung Utara Regency (12.5%), Lampung Timur Regency (10.8%), Tulang Bawang Regency (8.5%), Lampung Selatan Regency (8.2%), Bandar Lampung City (0.4%) and Metro City (0.1%). The results also reveal that the seasonal rainfall in Lampung Province can be grouped in seven types (I-VII), and the most feasible area lay on type I-IV, but for feasible area lay on type I-VI. Related to rainfall probability, at type I there is not occured monthly rainfall ≤ 200 mm with probability ≥ 60%, where as at type II the condition can be occurred 5 months, at type III and IV occurred 4 months, and at type V and VI occurred 7 months.

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Average Monthly and Annual Rainfall Distribution in Puerto Rico

The Journal of Agriculture of the University of Puerto Rico ◽

10.46429/jaupr.v70i4.7103 ◽

1969 ◽

Vol 70 (4) ◽

pp. 267-275

Author(s):

Eliodoro J. Ravalo ◽

Megh R. Goyal ◽

Carlos R. Almodóvar

Keyword(s):

Puerto Rico ◽

Dry Season ◽

Monthly Rainfall ◽

Annual Rainfall ◽

North Coast ◽

Southern Coast ◽

Rainfall Distribution

Average annual and monthly rainfall distribution in Puerto Rico are presented in isohyetal charts which indicate that January, February, March, July and December belong to the dry season. The southern coast of Puerto Rico receives the least rainfall compared to that of the southern and northern slopes, north coast, eastern and western interiors, respectively.

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