Comparison of the Methods to Estimate Missing Values in Monthly Precipitation Data

Sanusi, Wahidah; Zin, Wan Zawiah Wan; Mulbar, Usman; Danial, Muhammad; Side, Syafruddin

doi:10.18517/ijaseit.7.6.2637

Cited by 12 publications

(4 citation statements)

References 12 publications

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“…From the five stations, two (Hosana and Shone) are located in Hadiya Zone and three stations (Areka, Bele, and Boditi) are located in Wolaita Zone. e five meteorological stations considered in this study are sufficient and comparable with the four stations [46,47] and six stations [48] of similar studies. e stations are located from 6.92 to 7.57°(latitude) and from 37.5 to 37.95°(longitude) and in the altitudinal ranges of 1240-2397 meters above sea level (see Table 1).…”

Section: Study Area and Data Descriptionsupporting

confidence: 61%

Evaluation of Seven Gap-Filling Techniques for Daily Station-Based Rainfall Datasets in South Ethiopia

Chinasho

Bedadi

Lemma

et al. 2021

Advances in Meteorology

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Meteorological stations, mainly located in developing countries, have gigantic missing values in the climate dataset (rainfall and temperature). Ignoring the missing values from analyses has been used as a technique to manage it. However, it leads to partial and biased results in data analyses. Instead, filling the data gaps using the reference datasets is a better and widely used approach. Thus, this study was initiated to evaluate the seven gap-filling techniques in daily rainfall datasets in five meteorological stations of Wolaita Zone and the surroundings in South Ethiopia. The considered gap-filling techniques in this study were simple arithmetic means (SAM), normal ratio method (NRM), correlation coefficient weighing (CCW), inverse distance weighting (IDW), multiple linear regression (MLR), empirical quantile mapping (EQM), and empirical quantile mapping plus (EQM+). The techniques were preferred because of their computational simplicity and appreciable accuracies. Their performance was evaluated against mean absolute error (MAE), root mean square error (RMSE), skill scores (SS), and Pearson’s correlation coefficients (R). The results indicated that MLR outperformed other techniques in all of the five meteorological stations. It showed the lowest RMSE and the highest SS and R in all stations. Four techniques (SAM, NRM, CCW, and IDW) showed similar performance and were second-ranked in all of the stations with little exceptions in time series. EQM+ improved (not substantial) the performance levels of gap-filling techniques in some stations. In general, MLR is suggested to fill in the missing values of the daily rainfall time series. However, the second-ranked techniques could also be used depending on the required time series (period) of each station. The techniques have better performance in stations located in higher altitudes. The authors expect a substantial contribution of this paper to the achievement of sustainable development goal thirteen (climate action) through the provision of gap-filling techniques with better accuracy.

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Section: Study Area and Data Descriptionsupporting

confidence: 61%

Evaluation of Seven Gap-Filling Techniques for Daily Station-Based Rainfall Datasets in South Ethiopia

Chinasho

Bedadi

Lemma

et al. 2021

Advances in Meteorology

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“…The primary reliance of this method is on the average ratio of rainfall data between adjacent stations and the designated station, which is used to assess the influence of each nearby gauge. The computation of missing rainfall data can be achieved through the utilization of the following equation (Sanusi et al, 2017):…”

Section: Analysis Methods 241 Missing Rainfall Data Analysismentioning

confidence: 99%

Rainfall Analysis for Creating Intensity-Duration-Frequency (Idf) Curve of Pontianak City

Hidayati,

Soeryamassoeka,

Herawati

2023

JTS

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Pontianak is a lowland area prone to flooding if it rains with high rainfall intensity. Analysis of the planned flood discharge is needed to overcome the flood problem. The design flood discharge analysis can be calculated using the rational method, which requires rain intensity data in terms of duration and frequency, which can be described as an IDF curve.This research was conducted by calculating the average daily maximum rainfall after conducting consistency and homogeneity tests on the rainfall data used and determining the selected distribution based on the results of statistical parameters. The rain design with various return periods is calculated with the desired distribution that matches Pontianak City. The rainfall intensity calculation uses the Mononobe method because the rainfall data owned is daily rainfall data.The analysis results show that the Log Pearson Type III distribution corresponds to the data distribution in Pontianak. From the IDF curve, it can be seen that high rainfall intensity lasts for a short duration; this can prove the general nature of rain that the faster the rain stays, the higher the rainfall intensity and vice versa. The IDF curve can assist in calculating the design flood discharge.

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“…Data tersebut diperoleh dari Dinas Sumber Daya Air, Cipta Karya, dan Tata Ruang Provinsi Sulawesi Selatan, dan Balai Besar BMKG Wilayah IV Provinsi Sulawesi Selatan. Data yang tidak lengkap diestimasi menggunakan metode Rasio Normal termodifikasi [12].…”

Section: Dataunclassified

“…Vektor-vektor ini menunjukkan bahwa semua stasiun mempunyai nilai periode ulang yang tinggi untuk kategori lembab dan mempunyai nilai periode ulang yang rendah untuk kategori basah, kecuali di stasiun BBMKG. Sementara itu, Persamaan(12) memperlihatkan bahwa semua stasiun mempunyai durasi yang tinggi dalam keadaan basah dibanding keadaan lainnya, kecuali di stasiun BBMKG. Sementara keadaan lembab mempunyai durasi yang singkat dibanding keadaan lainnya.…”

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Estimasi Bayes Empirik pada Model Rantai Markov untuk Menggambarkan Karakteristik Curah Hujan di Kota Makassar

Sanusi

2024

Jomta

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Peristiwa banjir dan kekeringan merupakan dua peristiwa yang masih menjadi topik menarik bagi para peneliti karena kedua peristiwa tersebut merugikan kelangsungan hidup manusia, baik secara langsung maupun tidak langsung. Kedua fenomena tersebut juga tidak dapat dipastikan kapan mulai atau berakhirnya, sehingga diperlukan suatu model probabilistik seperti model rantai Markov. Model ini dapat menggambarkan karakteristik curah hujan, seperti nilai peluang, durasi dan periode ulang kejadian basah dan kering. Penentuan peluang transisi suatu model rantai Markov dapat dilakukan dengan menggunakan pendekatan klasik maupun pendekatan Bayes. Penelitian ini bertujuan untuk menentukan peluang transisi model rantai Markov menggunakan pendekatan Bayes empirik, dan untuk memperoleh gambaran karakteristik curah hujan Kota Makassar. Penelitian ini menggunakan data curah hujan bulanan dari empat stasiun curah hujan di Kota Makassar untuk periode tahun 1988 sampai 2017. Data diperoleh dari Dinas Pengelolaan Sumber Daya Air dan Balai Besar Badan Meteorologi, Klimatologi, dan Geofisika Wilayah IV Provinsi Sulawesi Selatan. Berdasarkan estimasi Bayes empirik peluang transisi model Rantai Markov, hasil penelitian ini menunjukkan bahwa kota Makassar akan mengalami keadaan basah dua bulan berturut-turut, begitupun dengan keadaan kering. Selain itu, hasil penelitian ini juga menunjukkan bahwa pada umumnya kota Makassar akan lebih sering mengalami keadaan basah dibanding keadaan lainnya dengan rata-rata durasi keadaan basah sekitar 7 bulan.

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Comparison of the Methods to Estimate Missing Values in Monthly Precipitation Data

Cited by 12 publications

References 12 publications

Evaluation of Seven Gap-Filling Techniques for Daily Station-Based Rainfall Datasets in South Ethiopia

Evaluation of Seven Gap-Filling Techniques for Daily Station-Based Rainfall Datasets in South Ethiopia

Rainfall Analysis for Creating Intensity-Duration-Frequency (Idf) Curve of Pontianak City

Estimasi Bayes Empirik pada Model Rantai Markov untuk Menggambarkan Karakteristik Curah Hujan di Kota Makassar

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