Classic trend analysis methods’ paradoxical results and innovative trend analysis methodology with percentile ranges

Birpınar, Mehmet Emin; Kizilöz, Burak; Şişman, Eyüp

doi:10.1007/s00704-023-04449-6

Cited by 15 publications

(6 citation statements)

References 45 publications

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“…This study analyzed the trends and variability in the maximum, minimum, and mean temperatures and their associated extreme indices, as they have an impact on important key sectors of the economy such as agriculture, health, water resources, infrastructure, and energy. Another method of investigation recently developed can be used for comparison, such as the Innovative Trend Analysis methodology [125,126]. The results obtained will be useful in future studies to make a quantitative analysis of the effects of these trends and variables on these sectors.…”

Section: Discussionmentioning

confidence: 99%

“…Some future directions of research can be mentioned: (i) investigate changes using other methods, i.e., Innovative Trend Analysis methodology recently developed [125,126] for comparison and (ii) recognize that drought is another effect of climate change in Rwanda. Some studies have recently been conducted on the East African region to determine the variability and trends in factors related to drought [127][128][129].…”

Section: Discussionmentioning

confidence: 99%

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Trends and Variability in Temperature and Related Extreme Indices in Rwanda during the Past Four Decades

Safari,

Sebaziga

2023

Atmosphere

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Analysis of the trends and variability of climate variables and extreme climate events is important for climate change detection in space and time. In this study, the trends and variabilities of minimum, maximum, and mean temperatures, as well as five extreme temperature indices, are analyzed over Rwanda for the period of 1983 to 2022. The Modified Mann–Kendall test and the Theil–Sen estimator are used for the analysis of, respectively, the trend and the slope. The standard deviation is used for the analysis of the temporal variability. It is found, on average, over the country, a statistically significant (α = 0.05) positive trend of 0.17 °C/decade and 0.20 °C/decade in minimum temperature, respectively, for the long dry season and short rain season. Statistically significant (α = 0.05) positive trends are observed for spatially averaged cold days (0.84 days/decade), warm nights (0.62 days/decade), and warm days (1.28 days/decade). In general, maximum temperature represents higher variability compared to the minimum temperature. In all seasons except the long dry season, statistically significant (α = 0.05) high standard deviations (1.4–1.6 °C) are observed over the eastern and north-western highlands for the maximum temperature. Cold nights show more variability, with a standard deviation ranging between 5 and 7 days, than the cold days, warm nights, and warm days, having, respectively, standard deviations ranging between 2 and 3, 4 and 5 days, and 3 and 4, and, especially in the area covering the central, south-western, south-central, and northwestern parts of Rwanda. Temperature increase and its variability have an impact on agriculture, health, water resources, infrastructure, and energy. The results obtained from this study are important since they can serve as the baseline for future projections. These can help policy decision making take objective measures for mitigation and adaptation to climate change impacts.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Trends and Variability in Temperature and Related Extreme Indices in Rwanda during the Past Four Decades

Safari,

Sebaziga

2023

Atmosphere

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“…It also gives a more detailed interpretation by identifying trends in low, medium, and high values [47]. The method has been applied by [48] alongside Sen's Slope and Mann-Kendall (MMK) methodologies to identify trends in precipitation in the Assam region of India. It has been applied in streamflow analysis with Mann Kendall (MK) and Sen's method [49], trends detection for annual, autumn, winter, spring, and summer season rains in England [50], for annual and seasonal precipitation in Ningxia, China [51], and assessment of meteorological drought in Northwest of Algeria [52].…”

Section: Introductionmentioning

confidence: 99%

Impact of Climate Variability on Rainfall Characteristics in the Semi-Arid Shashe Catchment (Botswana) from 1981–2050

Matenge.

Parida

Letshwenyo

et al. 2023

Earth

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Futuristic rainfall projections are used in scale and various climate impact assessments. However, the influence of climate variability on spatial distribution patterns and characteristics of rainfall at the local level, especially in semi-arid catchments that are highly variable and are not well explored. In this study, we explore the influence of climate variability on the spatial distribution and rainfall characteristics at a local scale in the semi-arid Shashe catchment, Northeastern Botswana. The LARS-WG, Long Ashton Research Station Weather Generator downscaling method, three representative scenarios (RCP 2.6, RCP 4.5, and RCP 4.5), three trend detection methods (Mann-Kendall, Sen’s slope, and innovative trend analysis) and L-moment method were used to assess climate change impacts on rainfall. Two data sets were used; one with 40 years of observed data from 1981–2020 and the other with 70 years from 1981–2050 (40 years of observed and 30 years of projected data from 2021–2050). Generally, the study found trend inconsistencies for all the trend detection methods. In most cases, Sen’s Slope has a high estimate of observed and RCP 2.6, while ITA overestimates rainfall totals under RCP 4.5 and RCP 8.5. The trend is increasing for annual total rainfall in most gauging stations while decreasing for annual maximum rainfall. The catchment is homogeneous, and Generalized Logistic distribution is the dataset’s best-fit distribution. Spatial coverage of a 100-year rainfall between 151–180 mm will be 81% based on observed data and 87% based on projected data under RCP 2.6 scenario when it happens. A 200-year rainfall between 196–240 mm under RCP 4.5 and 8.5 has high spatial areal coverage, at least 90% of the total catchment. The outcomes of this study will provide insightful information for water resource management and flood risk assessment under climate change. There is a need, however, to assess the transferability of this approach to other catchments in the country and assess the performance of other advanced modelling systems, such as machine learning, in this region.

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“…It is well-known that the MK and MMK methods rely on several assumptions. Therefore, the validity of utilizing these methods and the results they generate is contingent upon adhering to these assumptions [38].…”

Section: Introductionmentioning

confidence: 99%

Trends in Extreme Precipitation Indices in Northwest Ethiopia: Comparative Analysis Using the Mann–Kendall and Innovative Trend Analysis Methods

Likinaw,

Alemayehu,

Bewket

2023

Climate

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This study analyzed long-term extreme precipitation indices using 4 × 4 km gridded data obtained from the National Meteorological Agency of Ethiopia between 1981 and 2018. The study examined trends in extreme precipitation over three districts (Lay Gayint, Tach Gayint, and Simada) in the northwestern highlands of Ethiopia. Innovative Trend Analysis (ITA) and Mann–Kendall (MK) trend tests were used to study extreme precipitation trends. Based on the ITA result, the calculated values of nine indices (90% of the analyzed indices) showed significant increasing trends (p < 0.01) in Lay Gayint. In Tach Gayint, 70% (seven indices) showed significantly increasing trends at p < 0.01. On the other hand, 60% of the extreme indices showed significant downward trends (p < 0.01) in Simada. The MK test revealed that 30% of the extreme indices had significantly increasing trends (p < 0.01) in Lay Gayint. In Tach Gayint, 30% of the extreme indices showed significant increasing trends at p < 0.05, while 10% of the extreme indices exhibited significant increasing trends at p < 0.01. In Simada, 20% of the extreme indices showed significant increasing trends at p < 0.05. Overall, the results showed that the ITA method can identify a variety of significant trends that the MK test misses.

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Classic trend analysis methods’ paradoxical results and innovative trend analysis methodology with percentile ranges

Cited by 15 publications

References 45 publications

Trends and Variability in Temperature and Related Extreme Indices in Rwanda during the Past Four Decades

Trends and Variability in Temperature and Related Extreme Indices in Rwanda during the Past Four Decades

Impact of Climate Variability on Rainfall Characteristics in the Semi-Arid Shashe Catchment (Botswana) from 1981–2050

Trends in Extreme Precipitation Indices in Northwest Ethiopia: Comparative Analysis Using the Mann–Kendall and Innovative Trend Analysis Methods

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