Trend analysis of long-term rainfall and temperature data for Ethiopia

Elzopy, Karam A.; Chaturvedi, Ashish; Chandran, K. Madhava; Gopinath, Girish; Naveena, K; Surendran, U.

doi:10.1080/03736245.2020.1835699

Cited by 25 publications

(15 citation statements)

References 23 publications

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“…Higher inter-annual variability of maximum daily and belg rainfall was observed in all ACZs. The results of this study are generally consistent with other research results which were conducted in different parts of the country and reported that rainfall in belg/spring exhibits higher inter-annual variability than Kiremt/summer rainfall [5,32,46,55]. Less than 20 % CV is observed in annual rainfall of higher elevation climatic zones which implies that there is low variability of rainfall in the annual time series.…”

Section: Rainfall Variability In the Basinsupporting

confidence: 91%

“…In this study, the analysis of rainfall variability revealed that low rainfall areas tend to experience high rainfall variability. Similarly, previous studies found that the areas with high rainfall amounts exhibit a low coefficient of variations at different time series [4,17,24,33,46,55]. From MK analysis, daily maximum rainfall detected a statistically significant increasing trend in ACZ1, ACZ5, and ACZ6 with increases of 1.02, 0.72, and 0.52 mm/year, respectively.…”

Section: Rainfall Variability In the Basinsupporting

confidence: 72%

“…wwhere σ is the annual precipitation standard deviation and µ is the mean annual precipitation. The degree of variability of rainfall events classified as low (CV < 20), moderate (20 < CV < 30), and high (CV > 30) [7,17,55].…”

Section: Precipitation Seasonal and Annual Variabilitymentioning

confidence: 99%

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Agroclimatic Zone-Based Analysis of Long-Term Rainfall Trends and Variability in the Wabi Shebele River Basin, Ethiopia.

Toni¹,

Malcherek²,

Kebede³

2022

Preprint

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Any change in the amount and annual distribution of rainfall causes a major socioeconomic and environmental problem where rainfed agriculture is predominant. For that reason, the objective of this study was to determine the long-term variability and trends of precipitation in the Wabi Shebele River Basin (WSRB), Ethiopia. The basin was discretized into 7 local agroclimatic zones (ACZ) based on annual rainfall and elevation. In this study, the coefficient of variation (CV) was used to check the variability of rainfall and modified Mann-Kendall (MK) and Innovative Trend Analysis (ITA) methods were used to detect rainfall trends. For each ACZ, stations with long-term records and less than 10 % missing data were selected for further analysis. The mean annual rainfall in the basin ranges from 227.2 mm to 1047.4 mm. The study revealed most of the ACZs showed a very high variation in Belg/Spring season rainfall (CV % > 30) than Kiremt/Summer and annual rainfall. Trend analysis revealed that no uniform trend was detected among ACZs at each temporal scale. But, most ACZ in the arid and semi-arid areas showed a non-significant decreasing trend. In comparison, a similar result was observed using MK and ITA methods.

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Section: Rainfall Variability In the Basinsupporting

confidence: 91%

Section: Rainfall Variability In the Basinsupporting

confidence: 72%

See 1 more Smart Citation

Agroclimatic Zone-Based Analysis of Long-Term Rainfall Trends and Variability in the Wabi Shebele River Basin, Ethiopia.

Toni¹,

Malcherek²,

Kebede³

2022

Preprint

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“…Rainfall seasonality refers to patterns of increased rainfall at a given location. The rainfall seasonality index (RSI) was estimated using Equation ( 6) for certain months of each year (Elzopy, et al, 2020). When RSI = 0, indicates rainfall spread throughout the year, RSI between 0.6 and 0.79, indicates seasonal and RSI > 1.2 rainfall occurs in 1-2 months, extreme seasonality (Sharma and Singh, 2019).…”

Section: Materials and Methods Methods Used To Evaluate Rainfall Vari...mentioning

confidence: 99%

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

JOHN

2022

International Journal of Environment and Geoinformatics

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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.

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“…Mitigation and adaptation to the impact of climate change have gained unprecedented momentum in recent years (IPCC, 2018;Saint-Lu, et al, 2019). Especially after the 1990s, trend analysis has become a preferred topic among researchers and there are many trend methodology and application studies in the literature recently (Mohorji et al, 2017;Bera, 2017;Preethi et al, 2017;Bisht et al, 2018;Choudhury et al, 2019;Calojero, 2020;Fukushima et al, 2019;Gedefew et al, 2018;Gaponov et al, 2019;Gebrechorkos et al, 2019;Elzopy et al, 2020;Portela et al, 2020;Praveen et al, 2020;Wang et al, 2020;Saini and Sahu, 2021;Skea et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Over-whitening or Pre-whitening in Hydro-climatological Trend Analysis?

Şen

2022

Preprint

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To meet the basic assumption of classical Mann-Kendall (MK) trend analysis, which requires serially independent time series, a pre-whitening (PW) procedure is proposed to alleviate the serial correlation structure of a given hydro-meteorological time series records for application. The procedure is simply to take the lagged differences in a given time series in the hope that the new time series will have an independent serial correlation coefficient. The whole idea was originally based on the first-order autoregressive AR (1) process, but such a procedure has been documented to damage the trend component in the original time series. On the other hand, the over-whitening procedure (OW) proposes a white noise process superposition of the same length with zero mean and some standard deviation on the original time series to convert it into serially independent series without any damage to the trend component. The stationary white noise addition does not have any trend components. For trend identification, annual average temperature records in New Jersey and Istanbul are presented to show the difference between PW and OW procedures. It turned out that the OW procedure was superior to the PW procedure, which did not cause a loss in the original trend component.

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Trend analysis of long-term rainfall and temperature data for Ethiopia

Cited by 25 publications

References 23 publications

Agroclimatic Zone-Based Analysis of Long-Term Rainfall Trends and Variability in the Wabi Shebele River Basin, Ethiopia.

Agroclimatic Zone-Based Analysis of Long-Term Rainfall Trends and Variability in the Wabi Shebele River Basin, Ethiopia.

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

Over-whitening or Pre-whitening in Hydro-climatological Trend Analysis?

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