Trend Analysis Using Mann-kendall And Sen’s Slope Estimator Test for Annual And Monthly Rainfall for Sinjar District, Iraq

Aswad, Fawaz; Yousif, Ali Hameed; Ibrahim, Sayran A.

doi:10.26682/csjuod.2020.23.2.41

Cited by 19 publications

(9 citation statements)

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“…As precipitation does not follow a normal distribution, the Mann–Kendall nonparametric test (Kendall, 1975; Mann, 1945) and Sen's slope estimator (Sen, 1968) were applied, at a 95% confidence level, to the stations and the cluster series, aiming to detect potential trends within the series and precipitation region. These methods are widely used in analysing hydrological data trends as they are less sensitive to extreme values or outliers (e.g., Aditya et al, 2021; Aswad et al, 2020; Gan & Kwong, 1992; Gocic & Trajkovic, 2013; Hirsch et al, 1982). In particular, Sen's slope analysis offers an estimation of the trend's magnitude (expressed in this study in mm·year −1 ), enabling the interpretation of both the direction and extent of the trend, even in cases where the Mann–Kendall test does not yield statistically significant results (e.g., Aditya et al, 2021; Aswad et al, 2020; Gocic & Trajkovic, 2013; Stefanidis & Stathis, 2018).…”

Section: Methodsmentioning

confidence: 99%

“…These methods are widely used in analysing hydrological data trends as they are less sensitive to extreme values or outliers (e.g., Aditya et al, 2021; Aswad et al, 2020; Gan & Kwong, 1992; Gocic & Trajkovic, 2013; Hirsch et al, 1982). In particular, Sen's slope analysis offers an estimation of the trend's magnitude (expressed in this study in mm·year −1 ), enabling the interpretation of both the direction and extent of the trend, even in cases where the Mann–Kendall test does not yield statistically significant results (e.g., Aditya et al, 2021; Aswad et al, 2020; Gocic & Trajkovic, 2013; Stefanidis & Stathis, 2018).…”

Section: Methodsmentioning

confidence: 99%

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Precipitation homogenization and trends in the Usumacinta River Basin (Mexico‐Guatemala) over the period 1959–2018

Jupin,

Garcia‐López,

Briceño‐Zuluaga

et al. 2023

Intl Journal of Climatology

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The precipitation variability and trends were investigated in the Usumacinta River Basin (URB) for the period 1959–2018, based on imputed and homogenized data records from 60 meteorological stations in Mexico and Guatemala. The homogenization process played a crucial role in enhancing the quality of the original precipitation series, reducing regional inconsistencies and improving temporal and spatial coherence. The dataset reliably captured large‐scale climate variations, revealing three regions with similar precipitation variability and trends in the URB. Notably, maximum precipitation occurred at 636 m a.s.l., while minimum precipitation was at 1531 m a.s.l., indicating an orographic effect in the region. Extreme precipitation events were linked to El Niño–Southern Oscillation. Although the Mann–Kendall test showed statistically significant negative trends in only 18% of the stations, integration of Sen's slope analysis and 30‐year normals and dry year occurrences highlighted a progressive shift towards dryer conditions throughout the study period in the URB. These drier conditions could notably affect regions with higher precipitation, requiring special attention due to possible socioeconomic impacts associated with drought events. By identifying these vulnerable regions, policymakers and stakeholders can proactively plan and execute adaptive measures to mitigate the potential impacts of droughts on communities, ecosystems, and economic activities within the basin.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Precipitation homogenization and trends in the Usumacinta River Basin (Mexico‐Guatemala) over the period 1959–2018

Jupin,

Garcia‐López,

Briceño‐Zuluaga

et al. 2023

Intl Journal of Climatology

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“…Moreover, the linear correlation coefficient (r) was calculated to determine the usefulness of the statistical inference; a value greater than 0.97 and a p-value < 0.05 were obtained in all cases, which indicates a good match. The linear regression model is generally described by the following equation [30]:…”

Section: Methodsmentioning

confidence: 99%

Diagnosis of the Extreme Climate Events of Temperature and Precipitation in Metropolitan Lima during 1965–2013

Giráldez

Silva

Rojas

et al. 2022

Climate

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The most extreme precipitation event in Metropolitan Lima (ML) occurred on 15 January 1970 (16 mm), this event caused serious damage, and the real vulnerability of this city was evidenced; the population is still not prepared to resist events of this nature. This research describes the local climate variability and extreme climate indices of temperature and precipitation. In addition, the most extreme precipitation event in ML is analyzed. Extreme climate indices were identified based on the methodology proposed by the Expert Team on Climate Change Detection and Indices (ETCCDI). Some extreme temperature indices highlight an initial trend toward warm conditions (1965–1998); this trend has changed towards cold conditions since 1999, consistent with the thermal cooling during the last two decades in ML (−0.5 °C/decade) and other coastal areas of Peru. The variations of extreme temperature indices are mainly modulated by sea-surface temperature (SST) alterations in the Niño 1 + 2 region (moderate to strong correlations were found). Extreme precipitation indices show trends toward wet conditions after the 1980s, the influence of the Pacific Ocean SST on the extreme precipitation indices in ML is weak and variable in sign. The most extreme precipitation event in ML is associated with a convergence process between moisture fluxes from the east (Amazon region) at high and mid levels and moisture fluxes from the west (Pacific Ocean) at low levels, and near the surface.

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“…Many studies are specifically dedicated to comparing the performance of existing methods for obtaining and improving LST data [61][62][63][64]66]. The main statistical approaches and methods commonly used in the study of LST and especially LULC interdependence are supervised and unsupervised techniques [9,20,25,[67][68][69][70]; Mann-Kendall statistics [13,71]; principal component analysis end ordinary least squares [72,73]; cellular-automata [21,33,48,74] and most widely used linear and multiple linear regression analyses [9,11,15,28,[33][34][35][36][37][38][39]49,57,76]. Particular attention is merited by studies focused on establishing linear and nonlinear dependencies between LST, UHI effects, and various vegetation indices [31,67,[77][78][79][80][81][82][83].…”

Section: Introductionmentioning

confidence: 99%

“…A comprehensive analysis demonstrate an increase in LST as areas transition from dense vegetation to sparser vegetation or bare land, with moisture content being a pivotal factor in this proces [8,9,37,80,89,91]. The reduction of urban greenery directly correlates with the expansion of bare land and built-up surfaces, further exacerbating urban temperatures [67,71]. Research into the spatial distribution of LST across various urban and rural settings reveals that populated areas exhibit the highest LST across all seasonal phases, with agricultural lands, vegetation, and water bodies following in descending order of LST intensity [19,24,26,78,81].…”

Section: Introductionmentioning

confidence: 99%

Temporal Variations in Land Surface Temperature within an Urban Ecosystem: A Comprehensive Assessment of Land Use and Land Cover Change in Kharkiv, Ukraine

Rees,

Hebryn-Baidy,

Belenok

2024

Preprint

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Remote sensing technologies are critical for analyzing the escalating impacts of global climate change and increasing urbanization, providing vital insights into land surface temperature (LST), land use and cover (LULC) changes, and the identification of urban heat island (UHI) and surface urban heat island (SUHI) phenomena. This research focuses on the nexus between LULC alterations and variations in LST and air temperature (Tair), with a specific emphasis on the intensified SUHI effect in Kharkiv, Ukraine. Employing an integrated approach, the study analyzes time-series data from Landsat and MODIS satellites, alongside Tair climate records, utilizing machine learning techniques and linear regression analysis. Key findings indicate a statistically significant upward trend in Tair and LST during summer months from 1984 to 2023, with a notable positive correlation between Tair and LST across both datasets. MODIS data exhibit a stronger correlation R² = 0.879, compared to Landsat R² = 0.663. The application of supervised classification through Random Forest algorithms and vegetation indices on LULC data reveals significant alterations, manifested as a 70.3% increase in urban land, concurrently with a decrement in vegetative cover, especially 15.5% reduction in dense vegetation and a 62.9% decrease in sparse vegetation. Change detection analysis elucidates a 24.6% conversion of sparse vegetation into urban land, underscoring a pronounced trajectory towards urbanization. Temporal and seasonal LST variations across different LULC classes were analyzed using kernel density estimation (KDE) and boxplot analysis. Urban areas and sparse vegetation had the smallest average LST fluctuations, at 2.09°C and 2.16°C, respectively, but recorded the most extreme LST values. Water and dense vegetation classes exhibited slightly larger fluctuations of 2.30°C and 2.24°C, with the bare land class showing the highest fluctuation 2.46°C, but fewer extremes. Quantitative analysis with the application of Kolmogorov-Smirnov tests across various LULC classes substantiated the normality of LST distributions p &gt; 0.05 for both monthly and annual data sets. Conversely, the Shapiro-Wilk test validated the normal distribution hypothesis exclusively for monthly data, indicating deviations from normality in the annual data. Thresholded LST classifies urban and bare lands as warmest classes with 39.51°C, 38.20°C and water by 35.96°C and dense vegetation 35.52°C, sparse vegetation 37.71°C as coldest, a trend consistent annually and monthly. The analysis of SUHI effects demonstrates an increasing trend in UHI intensity, with statistical trends indicating a growth in average SUHI values over time. This comprehensive study underscores the critical role of remote sensing in understanding and addressing the impacts of climate change and urbanization on local and global climates, emphasizing the need for sustainable urban planning and green infrastructure to mitigate UHI effects.

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Trend Analysis Using Mann-kendall And Sen’s Slope Estimator Test for Annual And Monthly Rainfall for Sinjar District, Iraq

Cited by 19 publications

References 0 publications

Precipitation homogenization and trends in the Usumacinta River Basin (Mexico‐Guatemala) over the period 1959–2018

Precipitation homogenization and trends in the Usumacinta River Basin (Mexico‐Guatemala) over the period 1959–2018

Diagnosis of the Extreme Climate Events of Temperature and Precipitation in Metropolitan Lima during 1965–2013

Temporal Variations in Land Surface Temperature within an Urban Ecosystem: A Comprehensive Assessment of Land Use and Land Cover Change in Kharkiv, Ukraine

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