Bibliografie tematyczne: „Theil-Sen slope”

1

Vannest, Kimberly J., Richard I. Parker, John L. Davis, Denise A. Soares i Stacey L. Smith. "The Theil–Sen Slope for High-Stakes Decisions from Progress Monitoring". Behavioral Disorders 37, nr 4 (sierpień 2012): 271–80. http://dx.doi.org/10.1177/019874291203700406.

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Yacoub, Ely, i Gokmen Tayfur. "Trend analysis of temperature and precipitation in Trarza region of Mauritania". Journal of Water and Climate Change 10, nr 3 (28.06.2018): 484–93. http://dx.doi.org/10.2166/wcc.2018.007.

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Abstract Trend analysis of annual temperature and precipitation time series data collected from three stations (Boutilimit (station 1), Nouakchott (station 2) and Rosso (station 3)) has been used to detect the impacts of climate change on water resources in Trarza region, Mauritania. The Mann–Kendall, the Spearman's rho, and the Şen trend test were used for the trend identification. Pettitt's test was used to detect the change point of the series while the Theil–Sen approach was used to estimate the magnitude of the slope in the series. For precipitation, two stations (1 and 3) indicated statistically significant increase in trends. In the case of temperature, almost all the stations show statistically significant increasing trends in the maximum, minimum, and average temperatures. The magnitude of precipitation detected by the Theil–Sen test for stations 1 and 3, respectively, was found to be at the rate of 2.93 and 3.35 mm/year at 5% significance level. The magnitude trend of temperature detected by the Theil–Sen approach was found to be at the rate of 0.2–0.4 °C per decade for almost all the stations. The change points of temperature trends detected by Pettitt test are found to be in the same year (1995) for all the stations.

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Ahmed, Shamseddin Musa. "Assessment of irrigation system sustainability using the Theil–Sen estimator of slope of time series". Sustainability Science 9, nr 3 (28.11.2013): 293–302. http://dx.doi.org/10.1007/s11625-013-0237-1.

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M. M. LUNAGARIA, VYAS PANDEY i H. R. PATEL. "Climatic trends in Gujarat and its likely impact on different crops". Journal of Agrometeorology 14, nr 1 (1.06.2012): 41–44. http://dx.doi.org/10.54386/jam.v14i1.1379.

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Maximum temperature, minimum temperature and rainfall of Anand, Junagadh, Mahuva, Navsari and SK Nagar stations of Gujarat were analyzed on seasonal (winter, summer, monsoon and postmonsoon) and annual time scales using long period data. Linear regression/least squares time series slope (parameteric) and Theil-Sen slope (non-parameteric) were used to investigate the trends of climate va riability. Parametric and non-parametric trend analysis showed fair agreement in result except some cases where the non-parametric approach revealed very high magnitude in slope. During winter season minimum temperature is increasing and maximum temperature is decreasing at Junagadh. At Mahuva minimum temperature is decreasing and maximum temperature is increasing during summer. Only Anand station showed statistically significant increasing annual trend for minimum and maximum temperatures. There was no significant trend for any temperature time series of SK Nagar station. The rainfall of Saurashtra region (Junagadh and Mahuva) showed increasing trend. The impact of increasing temperature on different crops was found negative while decreasing temperature was found positive in most of crop studied.

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5

Amarouche, Khalid, i Adem Akpınar. "Increasing Trend on Storm Wave Intensity in the Western Mediterranean". Climate 9, nr 1 (8.01.2021): 11. http://dx.doi.org/10.3390/cli9010011.

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Annual trends in storm wave intensity over the past 41 years were evaluated during the present study. Storm wave intensity is evaluated in terms of total storm wave energy (TSWE) and storm power index (SPI) of Dolan and Davis (1992). Using an accurate long-term wave hindcast developed using a calibrated SWAN model, all storm wave events occurring over the past 41 years were characterized in terms of significant wave height (Hs) and total storm duration. Thus, both SPI and TSWE was computed for each storm wave event. The Theil–Sen slope estimator was used to estimate the annual slopes of the SPI and TSWE and the Mann–Kendall test was used to test the trend significance with different confidence levels. The present study is spatially performed for the western Mediterranean Sea basin considering 2308 grid points in a regular grid of 0.198° resolution in both directions. Results allow as to define five hotspots covering a large area, experienced a significant increasing slope in both SPI and TSWE (annual maxima and average). The confidence level in this area exceed 95%, with a steep slope between 100 kWh·m−1·year−1 and 240 kWh·m−1·year−1 for annual max TSWE and between 28 m²·h·year−1 and 49 m²·h·year−1 for annual max SPI. Consideration of the present findings is strongly recommended for risk assessment and for sustainable development in coastal and offshore area and to identify areas sensitive to global climate change in the western Mediterranean Sea.

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Amarouche, Khalid, i Adem Akpınar. "Increasing Trend on Storm Wave Intensity in the Western Mediterranean". Climate 9, nr 1 (8.01.2021): 11. http://dx.doi.org/10.3390/cli9010011.

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Annual trends in storm wave intensity over the past 41 years were evaluated during the present study. Storm wave intensity is evaluated in terms of total storm wave energy (TSWE) and storm power index (SPI) of Dolan and Davis (1992). Using an accurate long-term wave hindcast developed using a calibrated SWAN model, all storm wave events occurring over the past 41 years were characterized in terms of significant wave height (Hs) and total storm duration. Thus, both SPI and TSWE was computed for each storm wave event. The Theil–Sen slope estimator was used to estimate the annual slopes of the SPI and TSWE and the Mann–Kendall test was used to test the trend significance with different confidence levels. The present study is spatially performed for the western Mediterranean Sea basin considering 2308 grid points in a regular grid of 0.198° resolution in both directions. Results allow as to define five hotspots covering a large area, experienced a significant increasing slope in both SPI and TSWE (annual maxima and average). The confidence level in this area exceed 95%, with a steep slope between 100 kWh·m−1·year−1 and 240 kWh·m−1·year−1 for annual max TSWE and between 28 m²·h·year−1 and 49 m²·h·year−1 for annual max SPI. Consideration of the present findings is strongly recommended for risk assessment and for sustainable development in coastal and offshore area and to identify areas sensitive to global climate change in the western Mediterranean Sea.

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7

Bai, Bingxin, Yumin Tan, Dong Guo i Bo Xu. "Dynamic Monitoring of Forest Land in Fuling District Based on Multi-Source Time Series Remote Sensing Images". ISPRS International Journal of Geo-Information 8, nr 1 (16.01.2019): 36. http://dx.doi.org/10.3390/ijgi8010036.

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Time series remote sensing images can be used to monitor the dynamic changes of forest lands. Due to consistent cloud cover and fog, a single sensor typically provides limited data for dynamic monitoring. This problem is solved by combining observations from multiple sensors to form a time series (a satellite image time series). In this paper, the pixel-based multi-source remote sensing image fusion (MulTiFuse) method is applied to combine the Landsat time series and Huanjing-1 A/B (HJ-1 A/B) data in the Fuling district of Chongqing, China. The fusion results are further corrected and improved with spatial features. Dynamic monitoring and analysis of the study area are subsequently performed on the improved time series data using the combination of Mann-Kendall trend detection method and Theil Sen Slope analysis. The monitoring results show that a majority of the forest land (60.08%) has experienced strong growth during the 1999–2013 period. Accuracy assessment indicates that the dynamic monitoring using the fused image time series produces results with relatively high accuracies.

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Balkema, Guus, i Paul Embrechts. "Linear Regression for Heavy Tails". Risks 6, nr 3 (10.09.2018): 93. http://dx.doi.org/10.3390/risks6030093.

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There exist several estimators of the regression line in the simple linear regression: Least Squares, Least Absolute Deviation, Right Median, Theil–Sen, Weighted Balance, and Least Trimmed Squares. Their performance for heavy tails is compared below on the basis of a quadratic loss function. The case where the explanatory variable is the inverse of a standard uniform variable and where the error has a Cauchy distribution plays a central role, but heavier and lighter tails are also considered. Tables list the empirical sd and bias for ten batches of one hundred thousand simulations when the explanatory variable has a Pareto distribution and the error has a symmetric Student distribution or a one-sided Pareto distribution for various tail indices. The results in the tables may be used as benchmarks. The sample size is n = 100 but results for n = ∞ are also presented. The error in the estimate of the slope need not be asymptotically normal. For symmetric errors, the symmetric generalized beta prime densities often give a good fit.

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Ghanim, Abdulnoor Ali Jazem, Muhammad Naveed Anjum, Ghulam Rasool, Saifullah, Muhammad Irfan, Saifur Rahman, Salim Nasar Faraj Mursal i Usama Muhammad Niazi. "Assessing spatiotemporal trends of total and extreme precipitation in a subtropical highland region: A climate perspective". PLOS ONE 18, nr 8 (4.08.2023): e0289570. http://dx.doi.org/10.1371/journal.pone.0289570.

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This study used a dataset of 30 years (1990–2020) of daily observations from 24 meteorological stations in the northern highlands of Pakistan to assess trends in extreme precipitation indices. The RClimDex model was used to analyze the indices, and the Modified Mann-Kendal test and the Theil-Sen slope estimator were applied to determine trends and slopes, respectively. The results showed a significant decrease in total annual precipitation amount (PRCPTOT) with varying rates of negative trend from -4.44 mm/year to -19.63 mm/year. The total winter and monsoon precipitation amounts were also decreased during the past three decades. The intensity-based precipitation indices (RX1Day, RX5Day, R95p, R99p, and SDII) showed a significant decrease in extreme intensity events over time, while the count of consecutive dry days (CDD) and consecutive wet days (CWD) indicated a significant decrease in duration at multiple stations. The annual counts of days with precipitation more than or equal to 10 mm (R10), 20 mm (R20), and 25 mm (R25) exhibited a significant decrease in frequency of extreme precipitation events, with the decrease more pronounced in the northern parts of the study domain. The findings of this study indicate a significant decline in the intensity, frequency, and extent of precipitation extremes across the northern highlands of Pakistan over the past 30 years.

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10

Köylü, Ü., i A. Geymen. "MODELLING THE EFFECTS OF LAND-USE CHANGES ON CLIMATE: A CASE STUDY ON YAMULA DAM". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W1 (26.10.2016): 147–49. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w1-147-2016.

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Dams block flow of rivers and cause artificial water reservoirs which affect the climate and the land use characteristics of the river basin. In this research, the effect of the huge water body obtained by Yamula Dam in Kızılırmak Basin is analysed over surrounding spatial’s land use and climate change. Mann Kendal non-parametrical statistical test, Theil&Sen Slope method, Inverse Distance Weighting (IDW), Soil Conservation Service-Curve Number (SCS-CN) methods are integrated for spatial and temporal analysis of the research area. For this research humidity, temperature, wind speed, precipitation observations which are collected in 16 weather stations nearby Kızılırmak Basin are analyzed. After that these statistical information is combined by GIS data over years. An application is developed for GIS analysis in Python Programming Language and integrated with ArcGIS software. Statistical analysis calculated in the R Project for Statistical Computing and integrated with developed application. According to the statistical analysis of extracted time series of meteorological parameters, statistical significant spatiotemporal trends are observed for climate change and land use characteristics. In this study, we indicated the effect of big dams in local climate on semi-arid Yamula Dam.

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