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Evaluation of Rainfall Extreme Characteristics in Dodoma Urban, A Central Part of Tanzania

Yıl 2022, Cilt: 9 Sayı: 3, 165 - 177, 08.09.2022
https://doi.org/10.30897/ijegeo.1000458

Öz

The occurrence of low rainfall in semi-arid areas including Dodoma urban leads to a shortage of water. This paper addresses the evaluation of rainfall extreme characteristics by analyzing the variability indices, probability distribution, and return periods. The daily rainfall index shown the magnitude of rainfall varied unpredictably within annual wetted days. The study area experienced a period of one to two months per year with extreme seasonality as evaluated using the rainfall seasonality index. The standardized anomaly index indicated the equivalent of 60% of 20 years experienced the dried years with unpredicted occurrence. The Weibull distribution was selected out of Fifteen probability functions when ranked using Kolmogorov–Smirnov and Anderson-Darling tests. The return periods of rainfall had an average rainfall of 576 mm and were predicted using seventeen plotting position methods and Weibull distribution. Therefore, the magnitude of rainfall in the semi-arid areas would not easily be estimated but using more than one technique would improve the evaluation of rainfall characteristics.

Destekleyen Kurum

The University of Dodoma

Proje Numarası

Year 2020

Teşekkür

My thanks go to the supporting institution and all reviewers

Kaynakça

  • USGS, United States Geological Survey. (2021). Droughts: Things to Know. Available Online. https://www.usgs.gov/special-topic/water-science-school/science/droughts-things-know?qt-science_center_objects=0#qt-science_center_objects [accessed on 18/07/2021]
  • S Markovic, N Cerekovic, N Kljajic, and N Rudan, 2012. Rainfall analyses and water deficit during growing season in Banja Luka region," International Congress of Ecologists, Ecological Spectrum, pp. 1167-1176.
  • S Sharma and P K Singh, 2019. Spatial trends in rainfall seasonality: a case study in Jharkhand, India, Weather, vol. 74, no. 1, pp. 31-39.
  • F M.C Vieira, J M.C Machado, E de Souza Vismara, and J C Possenti, 2018. Probability distributions of frequency analysis of rainfall at the southwest region of Paraná State, Brazil, Revista de Ciencias Agroveterinarias, vol. 17, no. 2, pp. 260-266.
  • W Critchley, K Siegert, and C Chapman. (1991). Rainfall-runoff analysis. Available Online. http://www.fao.org/3/u3160e/u3160e05.htm [accessed on 30/April/2021] F De Paola, M Giugni, M E Topa, and E Bucchignani, 2014. Intensity-Duration-Frequency (IDF) rainfall curves, for data series and climate projection in African cities, SpringerPlus, vol. 3, no. 1, pp. 1-18.
  • S Joshi, K Kumar, V Joshi, and B Pande, 2014. Rainfall variability and indices of extreme rainfall-analysis and perception study for two stations over Central Himalaya, India," Natural hazards, vol. 72, no. 2, pp. 361-374.
  • WMO, World Meteorological Organization, 2012. Standardized Precipitation Index User Guide. D Dunkerley, 2019. Sub-Daily Rainfall Intensity Extremes: Evaluating Suitable Indices at Australian Arid and Wet Tropical Observing Sites," Water, vol. 11, no. 2616, pp. 1-18.
  • L V Alexander et al., 2019. On the use of indices to study extreme precipitation on sub-daily and daily timescales," Environmental Research Letters, vol. 14, no. 12, p. 125008.
  • K B Mafuru and T Guirong, 2018. Assessing prone areas to heavy rainfall and the impaction of the upper warm temperature anomaly during March–May rainfall season in Tanzania," Advances in Meteorology, vol. 2018.
  • M A Sharma and J B Singh, 2010. Use of probability distribution in rainfall analysis," New York Science Journal, vol. 3, no. 9, pp. 40-49.
  • C Zhan, W Cao, J Fan, and C K Tse, 2018. Impulse Weibull distribution for daily precipitation and climate change in China during 1961–2011," Physica A, no. https://doi.org/10.1016/j.physa.2018.07.033.
  • R Kumar and A Bhardwaj, 2015. Probability analysis of return period of daily maximum rainfall in annual data set of Ludhiana, Punja," Indian Journal of Agricultural Research, vol. 49, no. 2, pp. 160-164.
  • A Alonge and T Afullo, 2012. Rainfall drop-size estimators for Weibull probability distribution using method of moments technique," South African Institute of Electrical Engineers, vol. 103, no. 2, pp. 83-93.
  • Z Li, Z Li, W Zhao, and Y Wang, 2015. Probability modeling of precipitation extremes over two river basins in northwest of China," Advances in Meteorology, vol. 2015, p. 13.
  • J Suhaila, K Ching-Yee, Y Fadhilah, and F Hui-Mea, 2011. Introducing the Mixed Distribution in Fitting Rainfall Data," Open Journal of Modern Hydrology, vol. 1, pp. 11-22.
  • K Sasireka, C R Suribabu, and T R Neelakantan, 2019. Extreme rainfall return periods using Gumble and Gamma distribution," International Journal of Recent Technology and Engineering, vol. 8, no. 4S2.
  • A H Syafrina, A Norzaida, A Kartini, and K Badron, 2018. Comparison of gamma and weibull distributions in simulating hourly rainfall in peninsular Malaysia," Journal of Fundamental and Applied Sciences, vol. 10, no. 3S, pp. 331-337.
  • G J Husak, J Michaelsen, and C Funk, 2006. Use of the gamma distribution to represent monthly rainfall in Africa for drought monitoring applications," International Journal of Climatology, no. 10.1002/joc.1441.
  • N Sunusi, 2017. A comparison between weibull and power law model in estimating conditional intensity function for rainfall data," International Journal of Management and Applied Science, vol. 3, no. 2, pp. 16-18.
  • A Kist and J S.D Filho, 2015. Probabilistic analysis of the distribution of daily rainfall in the State of Paraná," Ambiente & Água - An Interdisciplinary Journal of Applied Science, vol. 10, no. 1, pp. 172-182.
  • R F.P Marques, C R de Mello, A M da Silva, C S Franco, and A S de Oliveira, 2014. Performance of the probability distribution models applied to heavy rainfall daily events," Ciênc. Agrotec., Lavras, vol. 38, no. 4, pp. 335-342.
  • R H Al-Suhili and R Khanbilvardi, 2014. Frequency Analysis of the Monthly Rainfall Data at Sulaimania Region, Iraq," American Journal of Engineering Research, vol. 3, no. 5, pp. 212-222.
  • I L Nwaogazie and M G Sam, 2019. Probability and non-probability rainfall intensity-duration-frequency modeling for Port-Harcourt metropolis, Nigeria," International Journal of Hydrology, vol. 3, no. 1, pp. 66-75.
  • G Arvind, P A Kumar, S G Karthi, and C R Suribabu, 2017. Statistical analysis of 30 years rainfall data: a case study," In IOP Conference Series: Earth and Environmental Science. IOP Publishing, vol. 80, no. 1, p. p. 012067.
  • P Erto and A Lepore, 2014. Plotting positions close to the exact unbiased solution: application to the Pozzuoli’s bradyseism earthquake data," arXiv preprint arXiv:1412.5663., Naples, Italy.
  • A S Yahaya et al., 2012. Determination of the probability plotting position for type I extreme value distribution," Journal of Applied Sciences, vol. 12, no. 14, pp. 1501-1506.
  • M M Portela and J M Delgado, 2009. A new plotting position concept to evaluate peak flood discharges based on short samples," Water Resources Management. WIT Transactions on Ecology and the Environment, vol. 125, pp. 415-427.
  • R Dirk, 2013. Frequency analysis of rainfall data," Katholieke Universiteit Leuven, College on Soil Physics – 30th Anniversary (1983-2013), Department of Earth and Environmental Sciences, Belgium.
  • A J Saul, 1996. Hydraulic Assessment. Sewers: Repair and Renovation.”: Elsevier, p. 149.
  • S Olivera and C Heard, 2018. Increases in the extreme rainfall events: Using the Weibull distribution," Environmetrics, pp. 1-9, https://doi.org/10.1002/env.2532.
  • K.S P Kumar and S Gaddada, 2015. Statistical scrutiny of Weibull parameters for wind energy potential appraisal in the area of Northern Ethiopia," Renewables: Wind, Water and Solar, vol. 2, no. 14, pp. 1-15,
  • J Hamisi, 2013. Study of rainfall trends and variability over Tanzania," Postgraduate Diploma in Meteorology, University of Nairobi, Nairobi, Kenya.
  • M O Kisaka et al., 2015. Rainfall variability, drought characterization, and efficacy of rainfall data reconstruction: case of Eastern Kenya," Advances in Meteorology, vol. 2015, p. 16.
  • Z Nouaceur and O Mursrescu, 2016. Rainfall variability and trend analysis of annual rainfall in North Africa," International Journal of Atmospheric Sciences, vol. 2016, p. 16.
  • R Zengeni, V Kakembo, and N Nkongolo, 2016. Historical rainfall variability in selected rainfall stations in Eastern Cape, South Africa," South African Geographical Journal, vol. 98, no. 1, pp. 118-137.
  • K A Elzopy, A K Chaturvedi, K M Chandran, G Gopinath, and U Surendran, 2020. Elzopy, K. A., Chaturvedi, A. K., Chandran, K. M., Gopinath, G., & Surendran, U. (2020). Trend analysis of long-term rainfall and temperature data for Ethiopia," South African Geographical Journal, vol. 1-14.
  • B Moccia, C Mineo, E Ridolfi, F Russo, and F Napolitano, 2021. Probability distributions of daily rainfall extremes in Lazio and Sicily, Italy, and design rainfall inferences," Journal of Hydrology: Regional Studies, vol. 33, no. 100771
  • EasyFit 5.0. (2021) MathWave Technologies. Available Online. https://easyfit. soft112.com/ [accessed on 18/06/2021]
  • A K Azad, M G Rasul, and T Yusuf, 2014. Statistical Diagnosis of the Best Weibull Methods for Wind power Assessment for Agricultural Applications," Energies, vol. 7, pp. 3056-3085.
  • S Liu, G LI, H Xie, and X Wang, 2017. Correlation Characteristic Analysis for Wind Speed in Different Geographical Hierarchies," Energies, vol. 10, no. 237, pp. 1-20, Shiyu Liu, Gengfeng Li *, Haipeng Xie and Xifan Wang.
  • A H Shaban, A K Resen, and N Bassil, 2020. Weibull Parameters Evaluation by Different Methods for Windmills Farms," Tmrees, EURACA, 04 to 06 September 2019, vol. 6, pp. 188-199.
  • D Kang, K Ko, and J Huh, 2018. Comparative study of different methods for estimating Weibull parameters: A case study on Jeju Island, South Korea, energies, vol. 11, no. 356, pp. 1-19.
  • D K Kidmo, R Danwe, S Y Doka, and N Djongyang, 2015. Statistical analysis of wind speed distribution based on six Weibull methods for wind power evaluation in Garoua, Cameroon, Revue des Energies Renouvelables, vol. 8, no. 1, pp. 105-125.
  • S S Kutty, M.G M Khan, and M R Ahmed, 2019. Estimation of different wind characteristics parameters and accurate wind resource assessment for Kadavu, Fiji, AIMS Energy, vol. 7, no. 6, pp. 760–791.
Yıl 2022, Cilt: 9 Sayı: 3, 165 - 177, 08.09.2022
https://doi.org/10.30897/ijegeo.1000458

Öz

Proje Numarası

Year 2020

Kaynakça

  • USGS, United States Geological Survey. (2021). Droughts: Things to Know. Available Online. https://www.usgs.gov/special-topic/water-science-school/science/droughts-things-know?qt-science_center_objects=0#qt-science_center_objects [accessed on 18/07/2021]
  • S Markovic, N Cerekovic, N Kljajic, and N Rudan, 2012. Rainfall analyses and water deficit during growing season in Banja Luka region," International Congress of Ecologists, Ecological Spectrum, pp. 1167-1176.
  • S Sharma and P K Singh, 2019. Spatial trends in rainfall seasonality: a case study in Jharkhand, India, Weather, vol. 74, no. 1, pp. 31-39.
  • F M.C Vieira, J M.C Machado, E de Souza Vismara, and J C Possenti, 2018. Probability distributions of frequency analysis of rainfall at the southwest region of Paraná State, Brazil, Revista de Ciencias Agroveterinarias, vol. 17, no. 2, pp. 260-266.
  • W Critchley, K Siegert, and C Chapman. (1991). Rainfall-runoff analysis. Available Online. http://www.fao.org/3/u3160e/u3160e05.htm [accessed on 30/April/2021] F De Paola, M Giugni, M E Topa, and E Bucchignani, 2014. Intensity-Duration-Frequency (IDF) rainfall curves, for data series and climate projection in African cities, SpringerPlus, vol. 3, no. 1, pp. 1-18.
  • S Joshi, K Kumar, V Joshi, and B Pande, 2014. Rainfall variability and indices of extreme rainfall-analysis and perception study for two stations over Central Himalaya, India," Natural hazards, vol. 72, no. 2, pp. 361-374.
  • WMO, World Meteorological Organization, 2012. Standardized Precipitation Index User Guide. D Dunkerley, 2019. Sub-Daily Rainfall Intensity Extremes: Evaluating Suitable Indices at Australian Arid and Wet Tropical Observing Sites," Water, vol. 11, no. 2616, pp. 1-18.
  • L V Alexander et al., 2019. On the use of indices to study extreme precipitation on sub-daily and daily timescales," Environmental Research Letters, vol. 14, no. 12, p. 125008.
  • K B Mafuru and T Guirong, 2018. Assessing prone areas to heavy rainfall and the impaction of the upper warm temperature anomaly during March–May rainfall season in Tanzania," Advances in Meteorology, vol. 2018.
  • M A Sharma and J B Singh, 2010. Use of probability distribution in rainfall analysis," New York Science Journal, vol. 3, no. 9, pp. 40-49.
  • C Zhan, W Cao, J Fan, and C K Tse, 2018. Impulse Weibull distribution for daily precipitation and climate change in China during 1961–2011," Physica A, no. https://doi.org/10.1016/j.physa.2018.07.033.
  • R Kumar and A Bhardwaj, 2015. Probability analysis of return period of daily maximum rainfall in annual data set of Ludhiana, Punja," Indian Journal of Agricultural Research, vol. 49, no. 2, pp. 160-164.
  • A Alonge and T Afullo, 2012. Rainfall drop-size estimators for Weibull probability distribution using method of moments technique," South African Institute of Electrical Engineers, vol. 103, no. 2, pp. 83-93.
  • Z Li, Z Li, W Zhao, and Y Wang, 2015. Probability modeling of precipitation extremes over two river basins in northwest of China," Advances in Meteorology, vol. 2015, p. 13.
  • J Suhaila, K Ching-Yee, Y Fadhilah, and F Hui-Mea, 2011. Introducing the Mixed Distribution in Fitting Rainfall Data," Open Journal of Modern Hydrology, vol. 1, pp. 11-22.
  • K Sasireka, C R Suribabu, and T R Neelakantan, 2019. Extreme rainfall return periods using Gumble and Gamma distribution," International Journal of Recent Technology and Engineering, vol. 8, no. 4S2.
  • A H Syafrina, A Norzaida, A Kartini, and K Badron, 2018. Comparison of gamma and weibull distributions in simulating hourly rainfall in peninsular Malaysia," Journal of Fundamental and Applied Sciences, vol. 10, no. 3S, pp. 331-337.
  • G J Husak, J Michaelsen, and C Funk, 2006. Use of the gamma distribution to represent monthly rainfall in Africa for drought monitoring applications," International Journal of Climatology, no. 10.1002/joc.1441.
  • N Sunusi, 2017. A comparison between weibull and power law model in estimating conditional intensity function for rainfall data," International Journal of Management and Applied Science, vol. 3, no. 2, pp. 16-18.
  • A Kist and J S.D Filho, 2015. Probabilistic analysis of the distribution of daily rainfall in the State of Paraná," Ambiente & Água - An Interdisciplinary Journal of Applied Science, vol. 10, no. 1, pp. 172-182.
  • R F.P Marques, C R de Mello, A M da Silva, C S Franco, and A S de Oliveira, 2014. Performance of the probability distribution models applied to heavy rainfall daily events," Ciênc. Agrotec., Lavras, vol. 38, no. 4, pp. 335-342.
  • R H Al-Suhili and R Khanbilvardi, 2014. Frequency Analysis of the Monthly Rainfall Data at Sulaimania Region, Iraq," American Journal of Engineering Research, vol. 3, no. 5, pp. 212-222.
  • I L Nwaogazie and M G Sam, 2019. Probability and non-probability rainfall intensity-duration-frequency modeling for Port-Harcourt metropolis, Nigeria," International Journal of Hydrology, vol. 3, no. 1, pp. 66-75.
  • G Arvind, P A Kumar, S G Karthi, and C R Suribabu, 2017. Statistical analysis of 30 years rainfall data: a case study," In IOP Conference Series: Earth and Environmental Science. IOP Publishing, vol. 80, no. 1, p. p. 012067.
  • P Erto and A Lepore, 2014. Plotting positions close to the exact unbiased solution: application to the Pozzuoli’s bradyseism earthquake data," arXiv preprint arXiv:1412.5663., Naples, Italy.
  • A S Yahaya et al., 2012. Determination of the probability plotting position for type I extreme value distribution," Journal of Applied Sciences, vol. 12, no. 14, pp. 1501-1506.
  • M M Portela and J M Delgado, 2009. A new plotting position concept to evaluate peak flood discharges based on short samples," Water Resources Management. WIT Transactions on Ecology and the Environment, vol. 125, pp. 415-427.
  • R Dirk, 2013. Frequency analysis of rainfall data," Katholieke Universiteit Leuven, College on Soil Physics – 30th Anniversary (1983-2013), Department of Earth and Environmental Sciences, Belgium.
  • A J Saul, 1996. Hydraulic Assessment. Sewers: Repair and Renovation.”: Elsevier, p. 149.
  • S Olivera and C Heard, 2018. Increases in the extreme rainfall events: Using the Weibull distribution," Environmetrics, pp. 1-9, https://doi.org/10.1002/env.2532.
  • K.S P Kumar and S Gaddada, 2015. Statistical scrutiny of Weibull parameters for wind energy potential appraisal in the area of Northern Ethiopia," Renewables: Wind, Water and Solar, vol. 2, no. 14, pp. 1-15,
  • J Hamisi, 2013. Study of rainfall trends and variability over Tanzania," Postgraduate Diploma in Meteorology, University of Nairobi, Nairobi, Kenya.
  • M O Kisaka et al., 2015. Rainfall variability, drought characterization, and efficacy of rainfall data reconstruction: case of Eastern Kenya," Advances in Meteorology, vol. 2015, p. 16.
  • Z Nouaceur and O Mursrescu, 2016. Rainfall variability and trend analysis of annual rainfall in North Africa," International Journal of Atmospheric Sciences, vol. 2016, p. 16.
  • R Zengeni, V Kakembo, and N Nkongolo, 2016. Historical rainfall variability in selected rainfall stations in Eastern Cape, South Africa," South African Geographical Journal, vol. 98, no. 1, pp. 118-137.
  • K A Elzopy, A K Chaturvedi, K M Chandran, G Gopinath, and U Surendran, 2020. Elzopy, K. A., Chaturvedi, A. K., Chandran, K. M., Gopinath, G., & Surendran, U. (2020). Trend analysis of long-term rainfall and temperature data for Ethiopia," South African Geographical Journal, vol. 1-14.
  • B Moccia, C Mineo, E Ridolfi, F Russo, and F Napolitano, 2021. Probability distributions of daily rainfall extremes in Lazio and Sicily, Italy, and design rainfall inferences," Journal of Hydrology: Regional Studies, vol. 33, no. 100771
  • EasyFit 5.0. (2021) MathWave Technologies. Available Online. https://easyfit. soft112.com/ [accessed on 18/06/2021]
  • A K Azad, M G Rasul, and T Yusuf, 2014. Statistical Diagnosis of the Best Weibull Methods for Wind power Assessment for Agricultural Applications," Energies, vol. 7, pp. 3056-3085.
  • S Liu, G LI, H Xie, and X Wang, 2017. Correlation Characteristic Analysis for Wind Speed in Different Geographical Hierarchies," Energies, vol. 10, no. 237, pp. 1-20, Shiyu Liu, Gengfeng Li *, Haipeng Xie and Xifan Wang.
  • A H Shaban, A K Resen, and N Bassil, 2020. Weibull Parameters Evaluation by Different Methods for Windmills Farms," Tmrees, EURACA, 04 to 06 September 2019, vol. 6, pp. 188-199.
  • D Kang, K Ko, and J Huh, 2018. Comparative study of different methods for estimating Weibull parameters: A case study on Jeju Island, South Korea, energies, vol. 11, no. 356, pp. 1-19.
  • D K Kidmo, R Danwe, S Y Doka, and N Djongyang, 2015. Statistical analysis of wind speed distribution based on six Weibull methods for wind power evaluation in Garoua, Cameroon, Revue des Energies Renouvelables, vol. 8, no. 1, pp. 105-125.
  • S S Kutty, M.G M Khan, and M R Ahmed, 2019. Estimation of different wind characteristics parameters and accurate wind resource assessment for Kadavu, Fiji, AIMS Energy, vol. 7, no. 6, pp. 760–791.
Toplam 44 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Çevre Mühendisliği
Bölüm Research Articles
Yazarlar

Ombeni John 0000-0003-1923-0319

Proje Numarası Year 2020
Yayımlanma Tarihi 8 Eylül 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 9 Sayı: 3

Kaynak Göster

APA John, O. (2022). Evaluation of Rainfall Extreme Characteristics in Dodoma Urban, A Central Part of Tanzania. International Journal of Environment and Geoinformatics, 9(3), 165-177. https://doi.org/10.30897/ijegeo.1000458