Stationary rainfall intensity duration frequency curves have historically influenced urban infrastructure designs. In contrast to the stationary model, which takes constant parameters into account throughout the observation periods, the non-stationary method takes into account changes in the extreme parameters that determine the distribution of precipitation over time. The parameters were estimated using maximum likelihood estimator method. The best model were computed using the R-studio software by comparing information criteria then model parameters, return levels, rainfall intensity are computed. The National Meteorological Agency, situated in Addis Ababa, Ethiopia, provided the essential historical rainfall data of the Debre Tabor rainfall station for this study, Tests and trends were looked for in the rainfall data. Due to its ability to produce the lowest Akaike, corrected Akaike information criteria, and diagnosis test of goodness of fitness Model Type-MV was chosen for Debre Tabor stations. The parameters of the best models were used to forecast the return levels for each of the following return periods: 2, 5, 10, 25, 50, and 100 years. Because the non-stationary technique has varied intensity levels over time, the annual maximum rainfall from the best appropriate model was calculated using its exceedance probability. Using the 95% of exceedance of the return level, the highest rainfall in each fit was determined. In comparison to the stationary model, the non-stationary model produced higher rainfall intensity values. Therefore, when developing IDF curves, the non-stationary approach should be taken into consideration.
Published in | Journal of Civil, Construction and Environmental Engineering (Volume 9, Issue 5) |
DOI | 10.11648/j.jccee.20240905.12 |
Page(s) | 151-174 |
Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Stationary Model, Non-stationary Model, R-Studio, Debre Tabor Town, Diagnostic Test
[1] | WMO, (2023). World Meteorological Manual on intensity-duration-frequency (IDF) curves. Geneva, Switzerland: WMO. |
[2] | ASC (2016). Climate Change and Rainfall Intensity–Duration–Frequency Curves Rerview of Science and Guidelines for Adaptation. 18. |
[3] | Alemayehu et al., (2020). Development of IDF curves for Debre Tabor Town. Journal of water resources, 12. |
[4] | Agency, S. (2007). Central Statistical Agency of Ethiopia. 80. |
[5] | Agency, E. m. (2022). National Meteorological Agency of Ethiopia. 109. |
[6] | Andre Schardong, S. P. (2020). Web-Based Tool for the Development of Intensity Duration Frequency Curves under Changing Climate at Gauged and Ungauged Locations. Water, 31. |
[7] | Masi G. Sam, I. L. (2023). Modeling Rainfall Intensity-Duration-Frequency (IDF) and Establishing Climate Change Existence in Uyo-Nigeria UsingNon-Stationary Approach. Journal of Water Resource and Protecti, 8. |
[8] | Asikoglu, O. L. (2017). Outlier Detection in Extreme Value Series. Journal of Multidisciplinary Engineering Science and Technology, 5. |
[9] | Berger., G. C. (2021). Statistical Inference. Newdelhi. |
[10] | Bougadis, K. A. (2003). Detection of trends in annual extreme rainfall. Hydrological Processes, 14. |
[11] | Chaw, V. T. (1988). Applied Hydrology. California: McGRAW-Hill international Edition. |
[12] | Christian Charron, T. B. (2018). Non-stationary intensity-duration-frequency curves integratinginformation concerning teleconnections and climate change. Inernational Journal of Climatology, 18. |
[13] | Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values. Springer, 17. |
[14] | Conover, W. J. (1981). Rank transformations as a bridge between parametric and nonparametric statistics. The American Statistician, 35(3), 124-129. |
[15] | Daniele Feitoza Silva, l. P. (2021). Introducing Non-Stationarity Into the Development of Intensity-Duration-Frequency Curves under a Changing Climate. Water, 21. |
[16] | Elizabeth, M. (1994). Hydrology in Practice. In M. Elizabeth, Hydrology in Practice (p. 628). Tottenham: Chapman & Hall. |
[17] | Haktanir, T. (2014). Trend, independence, stationarity, and homogeneity tests on maximum rainfall series of standard durations recorded in Turkey. Journal of Hydrologic Engineering, 38. |
[18] | Han Jiqina, F. (2023). Aplication of MK trend and test of Sen’s slope estimator to measure impact of climate change on the adoption of conservation agriculture in Ethiopia. Journal of Water and Climate Change, 12. |
[19] | Hargreaves, G. L. (2014). Assessing the quality of weather data and its relevance to horticultural science. HortScience, 47. |
[20] | Henson, R. &. (2019). Rainfall: Weather Past, Present and Future. Journal of Hydrology, 20. |
[21] | Hershey, J. R. (1955). Frequency analysis of rainfall. Hydrology, 1-10. |
[22] | IPCC. (AR5). provide comprehensive information on observed and projected changes in rainfall patterns due to climate change. Neyork: IPCC. |
[23] | K. Subramanya. (2005). Engineering Hydrology. New Delhi: Tata McGraw-Hill. |
[24] | Temesgen Zelalem, Kasiviswanathan (2023). A Bayesian modelling approach for assessing non A Bayesian modelling approach for assessing non changing climate. changing climate, 2020. |
[25] | Katz, L. C. (2014). Non-stationary extreme value analysis in a changing climate. Climatic Change, 17. |
[26] | Lalani Jayaweera, C. W. (2023). Non-stationarity in extreme rainfalls across Australia. Journal of Hydrology, 15. |
[27] | Linyin Cheng, A. A. (2014). Non-stationary extreme value analysis in a changing climate. Climatic Change, 17. |
[28] | Mohammed S. Shamkhi. (2022). Deriving rainfall intensity–duration–frequency (IDF) testing the best distributionusing EasyFit software 5.5 for Kut city, Iraq. De Gruyter, 1-10. |
[29] | Mohita Anand Sharma, J. B. (2010). Use of Probability Distribution in Rainfall Analysis. New York Science Journal, 10. |
[30] | Petitjean, M. (1999). On the Root Mean Square quantitative chirality and quantitative symmetry measures. Journal of Mathematical Physics, 9. |
[31] | Prerana Chitrakar a, A. S. (2023). Regional distribution of intensity–duration–frequency (IDF) relationships in Sultanate of Oman. Journal of King Saud University – Science, 14. |
[32] | Raghunath. (2006). Hydrology principles analysis design. In Raghunath, Hydrology principles analysis design (p. 477). Newdelhi: New Age International puplisher. |
[33] | Raúl Rodríguez-Solà, a. M.-C. (2017). A study of the scaling properties of rainfall in spain and its appropriateness to generate intensity-duration-frequency urves from daily records. INTERNATIONAL JOURNAL OF CLIMATOLOGY, 11. |
[34] | R Soumya1, U. G. (2023). Incorporation of non-stationarity in precipitation intensity duration-frequency curves for Kerala, India. Earth and Environmental Science, 13. |
[35] | Sattari, A. R.-J. (2017). Assessment of different methods for estimation of missing data in precipitation studies. Hydrology Research, 13. |
[36] | Silva, D. F. (2021). Introducing Non-Stationarity Into the Development of Intensity-Duration-Frequency Curves under a Changing Climate. Water, 22. |
[37] | Simeneh Melesse, G. (2016). Development of Intensity Duration Frequency (IDF) Curves for Bahir Dar City from Daily Rainfall Data by Using Simple Scaling Method, Bahir Dar, Ethiopia. Addis abeba university, 89. |
[38] | Tanchev, L. (2014). Dams and appurtenant Hydraulic structures. London, UK: CPI Group (UK) Ltd. |
[39] | Tegenu, Moges Tariku. (2021). Development of Intensity Duration Frequency Curves for Wolkite Town. International Journal on Data Science and Technology, 9. |
[40] | Thein, Sai Htun. (2019). Modelling of Short Duration Rainfall IDF Equation for Sagaing Region, Myanmar. American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS), 14. |
APA Style
Alemu, T. D., Addis, T. Z., Yihun, Y. M. (2024). Development of Rainfall Intensity Duration Frequency Curves for Debre Tabor Town, Ethiopia Using Non-stationary Method. Journal of Civil, Construction and Environmental Engineering, 9(5), 151-174. https://doi.org/10.11648/j.jccee.20240905.12
ACS Style
Alemu, T. D.; Addis, T. Z.; Yihun, Y. M. Development of Rainfall Intensity Duration Frequency Curves for Debre Tabor Town, Ethiopia Using Non-stationary Method. J. Civ. Constr. Environ. Eng. 2024, 9(5), 151-174. doi: 10.11648/j.jccee.20240905.12
@article{10.11648/j.jccee.20240905.12, author = {Tebikew Dereje Alemu and Temesgen Zelalem Addis and Yenesew Mengiste Yihun}, title = {Development of Rainfall Intensity Duration Frequency Curves for Debre Tabor Town, Ethiopia Using Non-stationary Method }, journal = {Journal of Civil, Construction and Environmental Engineering}, volume = {9}, number = {5}, pages = {151-174}, doi = {10.11648/j.jccee.20240905.12}, url = {https://doi.org/10.11648/j.jccee.20240905.12}, eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jccee.20240905.12}, abstract = {Stationary rainfall intensity duration frequency curves have historically influenced urban infrastructure designs. In contrast to the stationary model, which takes constant parameters into account throughout the observation periods, the non-stationary method takes into account changes in the extreme parameters that determine the distribution of precipitation over time. The parameters were estimated using maximum likelihood estimator method. The best model were computed using the R-studio software by comparing information criteria then model parameters, return levels, rainfall intensity are computed. The National Meteorological Agency, situated in Addis Ababa, Ethiopia, provided the essential historical rainfall data of the Debre Tabor rainfall station for this study, Tests and trends were looked for in the rainfall data. Due to its ability to produce the lowest Akaike, corrected Akaike information criteria, and diagnosis test of goodness of fitness Model Type-MV was chosen for Debre Tabor stations. The parameters of the best models were used to forecast the return levels for each of the following return periods: 2, 5, 10, 25, 50, and 100 years. Because the non-stationary technique has varied intensity levels over time, the annual maximum rainfall from the best appropriate model was calculated using its exceedance probability. Using the 95% of exceedance of the return level, the highest rainfall in each fit was determined. In comparison to the stationary model, the non-stationary model produced higher rainfall intensity values. Therefore, when developing IDF curves, the non-stationary approach should be taken into consideration. }, year = {2024} }
TY - JOUR T1 - Development of Rainfall Intensity Duration Frequency Curves for Debre Tabor Town, Ethiopia Using Non-stationary Method AU - Tebikew Dereje Alemu AU - Temesgen Zelalem Addis AU - Yenesew Mengiste Yihun Y1 - 2024/11/12 PY - 2024 N1 - https://doi.org/10.11648/j.jccee.20240905.12 DO - 10.11648/j.jccee.20240905.12 T2 - Journal of Civil, Construction and Environmental Engineering JF - Journal of Civil, Construction and Environmental Engineering JO - Journal of Civil, Construction and Environmental Engineering SP - 151 EP - 174 PB - Science Publishing Group SN - 2637-3890 UR - https://doi.org/10.11648/j.jccee.20240905.12 AB - Stationary rainfall intensity duration frequency curves have historically influenced urban infrastructure designs. In contrast to the stationary model, which takes constant parameters into account throughout the observation periods, the non-stationary method takes into account changes in the extreme parameters that determine the distribution of precipitation over time. The parameters were estimated using maximum likelihood estimator method. The best model were computed using the R-studio software by comparing information criteria then model parameters, return levels, rainfall intensity are computed. The National Meteorological Agency, situated in Addis Ababa, Ethiopia, provided the essential historical rainfall data of the Debre Tabor rainfall station for this study, Tests and trends were looked for in the rainfall data. Due to its ability to produce the lowest Akaike, corrected Akaike information criteria, and diagnosis test of goodness of fitness Model Type-MV was chosen for Debre Tabor stations. The parameters of the best models were used to forecast the return levels for each of the following return periods: 2, 5, 10, 25, 50, and 100 years. Because the non-stationary technique has varied intensity levels over time, the annual maximum rainfall from the best appropriate model was calculated using its exceedance probability. Using the 95% of exceedance of the return level, the highest rainfall in each fit was determined. In comparison to the stationary model, the non-stationary model produced higher rainfall intensity values. Therefore, when developing IDF curves, the non-stationary approach should be taken into consideration. VL - 9 IS - 5 ER -