Journal articles on the topic 'Theil-Sen slope'
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1
Vannest, Kimberly J., Richard I. Parker, John L. Davis, Denise A. Soares, and Stacey L. Smith. "The Theil–Sen Slope for High-Stakes Decisions from Progress Monitoring." Behavioral Disorders 37, no. 4 (August 2012): 271–80. http://dx.doi.org/10.1177/019874291203700406.
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Yacoub, Ely, and Gokmen Tayfur. "Trend analysis of temperature and precipitation in Trarza region of Mauritania." Journal of Water and Climate Change 10, no. 3 (June 28, 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, no. 3 (November 28, 2013): 293–302. http://dx.doi.org/10.1007/s11625-013-0237-1.
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M. M. LUNAGARIA, VYAS PANDEY, and H. R. PATEL. "Climatic trends in Gujarat and its likely impact on different crops." Journal of Agrometeorology 14, no. 1 (June 1, 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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Amarouche, Khalid, and Adem Akpınar. "Increasing Trend on Storm Wave Intensity in the Western Mediterranean." Climate 9, no. 1 (January 8, 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, and Adem Akpınar. "Increasing Trend on Storm Wave Intensity in the Western Mediterranean." Climate 9, no. 1 (January 8, 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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Bai, Bingxin, Yumin Tan, Dong Guo, and 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, no. 1 (January 16, 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, and Paul Embrechts. "Linear Regression for Heavy Tails." Risks 6, no. 3 (September 10, 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, and Usama Muhammad Niazi. "Assessing spatiotemporal trends of total and extreme precipitation in a subtropical highland region: A climate perspective." PLOS ONE 18, no. 8 (August 4, 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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Köylü, Ü., and 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 (October 26, 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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Felix, Micah Lourdes, Young-kyu Kim, Mikyoung Choi, Joo-Cheol Kim, Xuan Khanh Do, Thu Hien Nguyen, and Kwansue Jung. "Detailed Trend Analysis of Extreme Climate Indices in the Upper Geum River Basin." Water 13, no. 22 (November 10, 2021): 3171. http://dx.doi.org/10.3390/w13223171.
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To investigate the recent effects of climate change in the upper Geum River basin in South Korea, a detailed trend analysis of 17 extreme climate indices based on 33 years (1988–2020) of daily precipitation, and daily (minimum and maximum) temperature data has been analyzed in this study. Out of the 17 extreme climate indices, nine (eight) indices were based on temperature (precipitation) data. Trend analysis based on detailed temporal scales (annual, seasonal, monthly) were performed through the Mann–Kendall trend test and the Theil–Sen slope method. Furthermore, the Mann–Whitney–Pettit test was also applied in this study, to detect abrupt changes in the extreme climate indices. Based on the results of this study, the climate conditions at the upper Geum River basin for the past three decades can be summarized as follows: general increase in temperature intensity, decrease in cold duration, increased heat duration, increased precipitation intensity, and increased consecutive wet and dry durations. Furthermore, a prolonged summer season (shorter spring, and autumn periods) and precipitation shifts, were detected based on trend analysis results of seasonal, and monthly time scales. The results presented in this study can provide supplementary data for improving watershed management strategies in the upper Geum River basin.
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Jain, Piyush, Xianli Wang, and Mike D. Flannigan. "Trend analysis of fire season length and extreme fire weather in North America between 1979 and 2015." International Journal of Wildland Fire 26, no. 12 (2017): 1009. http://dx.doi.org/10.1071/wf17008.
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We have constructed a fire weather climatology over North America from 1979 to 2015 using the North American Regional Reanalysis dataset and the Canadian Fire Weather Index (FWI) System. We tested for the presence of trends in potential fire season length, based on a meteorological definition, and extreme fire weather using the non-parametric Theil–Sen slope estimator and Mann–Kendall test. Applying field significance testing (i.e. joint significance of multiple tests) allowed the identification of the locations of significant trends, taking into account spatial correlations. Fire season length was found to be increasing over large areas of North America, especially in eastern Canada and the south-western US, which is consistent with a later fire season end and an earlier fire season start. Both positive and negative trends in potential fire spread days and the 99th percentile of FWI occurred in Canada and the contiguous United States, although the trends of largest magnitude and statistical significance were mostly positive. In contrast, the proportion of trends with significant decreases in these variables were much lower, indicating an overall increase in extreme fire weather. The smaller proportion of significant positive trends found over Canada reflects the truncation of the time series, necessary because assimilation of precipitation observations over Canada ceased in the reanalysis post-2002.
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Dagnachew, Melku, Asfaw Kebede, Awdenegest Moges, and Adane Abebe. "Effects of Climate Variability on Normalized Difference Vegetation Index (NDVI) in the Gojeb River Catchment, Omo-Gibe Basin, Ethiopia." Advances in Meteorology 2020 (June 10, 2020): 1–16. http://dx.doi.org/10.1155/2020/8263246.
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Vegetation dynamics have been visibly influenced by climate variability. The Normalized Difference Vegetation Index (NDVI) has been the most commonly used index in vegetation dynamics. The study was conducted to examine the effects of climatic variability (rainfall) on NDVI for the periods 1982–2015 in the Gojeb River Catchment (GRC), Omo-Gibe Basin, Ethiopia. The spatiotemporal trend in NDVI and rainfall time series was assessed using a Theil–Sen (Sen) slope and Mann–Kendall (MK) statistical significance test at a 95% confidence interval. Moreover, the residual trend analysis (RESTREND) method was used to investigate the effect of rainfall and human induction on vegetation degradation. The Sen’s slope trend analysis and MK significant test indicated that the magnitude of annual NDVI and rainfall showed significant decrement and/or increment in various portions of the GRC. The concurrent decrement and/or increment of annual NDVI and rainfall distributions both spatially and temporarily could be attributed to the significant positive correlation of the monthly (RNDVI-RF = 0.189, P≤0.001) and annual (RNDVI-RF = 0.637, P≤0.001) NDVI with rainfall in almost all portions of the catchment. In the GRC, a strongly negative decrement and strong positive increment of NDVI could be derived by human-induced and rainfall variability, respectively. Accordingly, the significant NDVI decrement in the downstream portion and significant increment in the northern portion of the catchment could be attributed to human-induced vegetation degradation and the variability of rainfall, respectively. The dominance of a decreasing trend in the residuals at the pixel level for the NDVI from 1982, 1984, 2000, 2008 to 2012 indicates vegetation degradation. The strong upward trend in the residuals evident from 1983, 1991, 1998 to 2007 was indicative of vegetation improvements. In the GRC, the residuals may be derived from climatic variations (mainly rainfall) and human activities. The time lag between NDVI and climate factors (rainfall) varied mainly from two to three months. In the study catchment, since vegetation degradations are mainly caused by human induction and rainfall variability, integrated and sustainable landscape management and climate-smart agricultural practices could have paramount importance in reversing the degradation processes.
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Nguyen, Tuan Hoang, and Truong Thanh Canh. "Researching trends of rainfall change in Ninh Thuan in the context of climate change by the non-parametric method." Science and Technology Development Journal - Natural Sciences 5, no. 1 (December 27, 2020): first. http://dx.doi.org/10.32508/stdjns.v5i1.939.
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The purpose of this study is to analyze the trend of precipitation change in Ninh Thuan province in the context of Climate Change. In this study, the authors used precipitation data at rain gauges with a minimum duration of 24 years and a maximum of 36 years. The main method was used in this study is the non-parametric method, namely Mann-Kendall analysis and the Theil-Sen slope. The research was conducted under the support of ProULC 5.1 and MAKESEN 1.0 software. The results showed that annual rainfall in Ninh Thuan province tended to increase in the time series of observation. Specifically, the downward trend of rainfall was mainly in March and increased from July to November. Along with that, through the index regarding the trend of increase and decrease of rainfall, the study also showed a prediction of the trend of increasing rainfall for the area. Forecast results of 2035 rainfall in Ninh Thuan the highest the increase is expected 7.7% and in 2050 is 13.8%. The study results have reflected the actual situation of rainfall change in the context of climate change with the stations having statistical significance (p <0.05). The research results are the basis for Ninh Thuan province to develop solutions to adapt and mitigate climate change in the fields of Socio-Economic life.
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Feng, Zihao, Jianjun Chen, Renjie Huang, Yanping Yang, Haotian You, and Xiaowen Han. "Spatial and Temporal Variation in Alpine Vegetation Phenology and Its Response to Climatic and Topographic Factors on the Qinghai–Tibet Plateau." Sustainability 14, no. 19 (October 7, 2022): 12802. http://dx.doi.org/10.3390/su141912802.
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Vegetation phenology changes are able to reveal climate-change-associated ecosystem feedback mechanisms. In this study, Qinghai–Tibet Plateau (QTP) alpine vegetation phenological information was extracted from the normalised difference vegetation index of the MOD13Q1 product collected from 2001 to 2020 using TIMESAT3.3 and S-G filtering and threshold dynamics methods. An analysis of data from the start of growth (SOG) and end of growth (EOG) seasons using a Theil–Sen median slope trend and partial correlation analyses revealed spatial and temporal variations in vegetation phenology related to climate change and topography, including: (1) significant spatial variation, gradually increasing southeast-to-northwest SOG delays and northeast-to-southwest EOG delays, with significant variations across vegetation types; (2) significant altitude-associated variations in the meadow, steppe, and shrub alpine vegetation types with high-altitude boundaries of 2400 m, 2800 m, and 2600 m, respectively, with delayed and earlier SOG and EOG below and above each boundary, respectively; and (3) spatial variations in relationships between vegetation phenology changes and climatic factors, where SOG negatively and EOG positively correlated with temperature and precipitation. The mean temperature in the 30 days before SOG and mean total precipitation in the 30 days before EOG were significantly correlated with SOG and EOG timing both negatively and positively, respectively. These results provide guidance for the monitoring of the alpine vegetation phenology on the QTP.
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He, Bing, Xi Wu, Kang Liu, Yuanzhi Yao, Wenjiang Chen, and Wei Zhao. "Trends in Forest Greening and Its Spatial Correlation with Bioclimatic and Environmental Factors in the Greater Mekong Subregion from 2001 to 2020." Remote Sensing 14, no. 23 (November 25, 2022): 5982. http://dx.doi.org/10.3390/rs14235982.
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Understanding trends of vegetation evolution and its spatial characteristics is critical for sustainable social development in the Greater Mekong Subregion (GMS), which is densely populated and still has uneven economic development. Through Theil–Sen/Mann–Kendall tests, polynomial regression and bivariate local autocorrelation analyses, we investigated vegetation greening trends and their spatial correlation with bioclimatic and environmental variables. The study yielded the following results: (1) Land cover in the GMS has changed significantly over the last 20 years. Conversion between forest and grassland was the main type of change. (2) The upward trend in the forest enhanced vegetation index (EVI) significantly exceeded the downward trend in countries over 20 years. In GMS, the spatial variation in forest trend slope values ranged from −0.0297 a−1 to 0.0152 a−1. (3) Anthropogenic activities have played an important role in forest greening; planted, plantation and oil palm forests exhibit the largest contributions to greening. (4) Changes in forest EVI were most spatially correlated with radiation (12.19% for surface net solar radiation and 12.14% for surface solar radiation downwards) and least spatially correlated with seasonality precipitation (8.33%) and mean annual temperature (8.19%). The results of the analysis of EVI trends in vegetation and their spatial correlation with bioclimatic and environmental variables can provide a reference for strategies aimed for protecting the vegetation ecology.
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Zhao, Fengmei, Chaoli Tang, Xiaomin Tian, Xin Wu, Congming Dai, and Heli Wei. "The Global Spatial and Temporal Distribution of Ice Cloud Optical Thickness Based on MODIS Satellite Data during 2000–2021." Atmosphere 14, no. 6 (June 3, 2023): 977. http://dx.doi.org/10.3390/atmos14060977.
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Ice cloud optical thickness (IOT) is an important parameter to characterize ice cloud properties and in the determination of cloud–radiation parameterization schemes, and the variation trend of ice clouds is more concerned with the study of weather and climate. In this paper, we analyzed the spatial and temporal distributions of IOT over the region between ±60° latitude. Cloud product data from March 2000 to February 2021 acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA’s Aqua satellite were used in this study. Theil–Sen median trend analysis and EOF analysis methods were used to study the variation trend of IOT. The research results indicate that the monthly average IOT shows a “W” distribution from January to December, with a maximum reached in July (12.15) and a double bottom reached in March (10.7) and October (10.99), respectively. The average global IOT reaches the maximum in June–August, it tends to decrease with time, and its slope is −0.01 year−1. The statistical analysis results show that the area with an increase accounted for 49.4% of the total ice cloud coverage area; the area with a trend of significant increased and decreased is both 2.2%. The probability distribution of IOT reaches the maximum, around 3.25%, when the IOT is larger than 1.5 and less than or equal to 2.
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Zhang, Wenqi, Huaan Jin, Huaiyong Shao, Ainong Li, Shangzhi Li, and Wenjie Fan. "Temporal and Spatial Variations in the Leaf Area Index and Its Response to Topography in the Three-River Source Region, China from 2000 to 2017." ISPRS International Journal of Geo-Information 10, no. 1 (January 13, 2021): 33. http://dx.doi.org/10.3390/ijgi10010033.
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The Three-River Source Region (TRSR) is an important area for the ecological security of China. Vegetation growth has been affected by the climate change, topography, and human activities in this area. However, few studies have focused on analyzing time series tendencies of vegetation change in various terrain conditions. To address this issue in the TRSR, this study explored vegetation stability, tendency, and sustainability with multiple methods (e.g., coefficient of variation, Theil-Sen median trend analysis, Mann-Kendall test, and Hurst index) based on the 2000–2017 Global LAnd Surface Satellite Leaf Area Index (GLASS LAI) product. The differentiation patterns of LAI variations and multiyear mean LAI value under different topographic factors were also investigated in combination with digital elevation model (DEM). The results showed that (1) the mean LAI value in the study area increased, with a linear tendency of 0.013·10 a−1; (2) LAI values decreased from southeast to northwest in terms of spatial distribution and the CV indicated LAI variations were relatively stable; (3) the trend analysis revealed that the improved area of LAI accounted for 62.72% which was larger than the degraded area (37.28%), and hurst index revealed a weak anti-sustaining effect of the current tendencies; and (4) the increasing trend was found in multiyear mean LAI value as relief amplitude and slope increased, while LAI stability improved with increasing slope. They exhibited a clear regular pattern. Moreover, significant improvement in LAI generally occurred in low-altitude and flat areas. Finally, the overall improvement and sustainability of LAI improved when moving from sunny aspects to shady aspects, but the LAI stability decreased. Note that vegetation degradation was observed in some high slope areas and was further aggravated. This study is beneficial for revealing the spatial and temporal changes of LAI and their changing rules as a function of different topographic factors in the TRSR. Meanwhile, the results of this study provide theoretical support for sustainable development of this area.
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Yang, Chen, Meichen Fu, Dingrao Feng, Yiyu Sun, and Guohui Zhai. "Spatiotemporal Changes in Vegetation Cover and Its Influencing Factors in the Loess Plateau of China Based on the Geographically Weighted Regression Model." Forests 12, no. 6 (May 25, 2021): 673. http://dx.doi.org/10.3390/f12060673.
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Vegetation plays a key role in ecosystem regulation and influences our capacity for sustainable development. Global vegetation cover has changed dramatically over the past decades in response to both natural and anthropogenic factors; therefore, it is necessary to analyze the spatiotemporal changes in vegetation cover and its influencing factors. Moreover, ecological engineering projects, such as the “Grain for Green” project implemented in 1999, have been introduced to improve the ecological environment by enhancing forest coverage. In our study, we analyzed the changes in vegetation cover across the Loess Plateau of China and the impacts of influencing factors. First, we analyzed the latitudinal and longitudinal changes in vegetation coverage. Second, we displayed the spatiotemporal changes in vegetation cover based on Theil-Sen slope analysis and the Mann-Kendall test. Third, the Hurst exponent was used to predict future changes in vegetation coverage. Fourth, we assessed the relationship between vegetation cover and the influence of individual factors. Finally, ordinary least squares regression and the geographically weighted regression model were used to investigate the influence of various factors on vegetation cover. We found that the Loess Plateau showed large-scale greening from 2000 to 2015, though some regions showed decreasing vegetation cover. Latitudinal and longitudinal changes in vegetation coverage presented a net increase. Moreover, some areas of the Loess Plateau are at risk of degradation in the future, but most areas showed a sustainable increase in vegetation cover. Temperature, precipitation, gross domestic product (GDP), slope, cropland percentage, forest percentage, and built-up land percentage displayed different relationships with vegetation cover. Geographically weighted regression model revealed that GDP, temperature, precipitation, forest percentage, cropland percentage, built-up land percentage, and slope significantly influenced (p < 0.05) vegetation cover in 2000. In comparison, precipitation, forest percentage, cropland percentage, and built-up land percentage significantly affected (p < 0.05) vegetation cover in 2015. Our results enhance our understanding of the ecological and environmental changes in the Loess Plateau.
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Wang, Danmeng, Ruolan Li, Guoxi Gao, Nueryia Jiakula, Shynggys Toktarbek, Shilin Li, Ping Ma, and Yongzhong Feng. "Impact of Climate Change on Food Security in Kazakhstan." Agriculture 12, no. 8 (July 23, 2022): 1087. http://dx.doi.org/10.3390/agriculture12081087.
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Global food production faces immense pressure, much of which can be attributed to climate change. A detailed evaluation of the impact of climate change on the yield of staple crops in Kazakhstan, a major food exporter, is required for more scientific planting management. In this study, the Mann–Kendall test and Theil–Sen Median slope were used to determine climate trends and staple food yields over the past 30 years; random forest was used to analyze the importance of monthly climatic factors; states were classified according to climatic factors through systematic clustering method; and lastly, the influence of climate on yield was analyzed using panel regression models. The upward trend in wind speed and potato yield throughout Kazakhstan was apparent. Furthermore, barley and wheat yields had increased in the southeast. We determined that for wheat, frostbite should be prevented after the warmer winters in the high-latitude areas. Except for July–August in the low-latitude areas, irrigation water should be provided in the other growth periods and regions. As similar effects were reported for barley, the same preventive measures would apply. For potatoes, tuber rot, caused by frost or excessive precipitation in May, should be prevented in high-latitude areas; soil dryness should be alleviated during the germination and seedling stages in low-latitude areas; and irrigation and cooling should be maintained during tuber formation and maturation. Furthermore, hot dry air in March and April could damage the crops.
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Mupangwa, Walter, Lovemore Chipindu, Bongani Ncube, Siyabusa Mkuhlani, Nascimento Nhantumbo, Esther Masvaya, Amos Ngwira, Mokhele Moeletsi, Isaiah Nyagumbo, and Feyera Liben. "Temporal Changes in Minimum and Maximum Temperatures at Selected Locations of Southern Africa." Climate 11, no. 4 (April 6, 2023): 84. http://dx.doi.org/10.3390/cli11040084.
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Agriculture is threatened by ever increasing temperatures and this trend is predicted to continue for the near and distant future. The negative impact of rising temperatures on agri-food systems is also compounded by the erratic and highly variable rainfall in most parts of southern Africa. Minimum and maximum temperatures’ variability and trend analysis were undertaken using daily time series data derived from 23 meteorological stations spread across Malawi, Mozambique, South Africa and Zimbabwe. The modified Mann–Kendall and Theil–Sen slope models were used to assess temperature trends and their magnitudes. Temperature varied with location and minimum temperature was more variable than maximum temperature. Semi-arid regions had higher variation in minimum temperature compared to humid and coastal environments. The results showed an upward trend in minimum (0.01–0.83 °C over a 33–38 year period) and maximum (0.01–0.09 °C over a 38–57 year period) temperatures at 9 and15 locations, respectively. A downward trend in minimum temperature (0.03–0.20 °C over 38–41 years) occurred in South Africa at two locations and Dedza (Malawi), while a non-significant decline in maximum temperature (0.01 °C over 54 years) occurred at one location in coastal dry sub-humid Mozambique. The results confirm the increase in temperature over 33–79 years, and highlight the importance of including temperature when designing climate change adaption and mitigation strategies in southern Africa and similar environments.
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Li, Shengkun, Xiaobing Li, Jirui Gong, Dongliang Dang, Huashun Dou, and Xin Lyu. "Quantitative Analysis of Natural and Anthropogenic Factors Influencing Vegetation NDVI Changes in Temperate Drylands from a Spatial Stratified Heterogeneity Perspective: A Case Study of Inner Mongolia Grasslands, China." Remote Sensing 14, no. 14 (July 10, 2022): 3320. http://dx.doi.org/10.3390/rs14143320.
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The detection and attribution of vegetation dynamics in drylands is an important step for the development of effective adaptation and mitigation strategies to combat the challenges posed by human activities and climate change. However, due to the spatial heterogeneity and interactive influences of various factors, quantifying the contributions of driving forces on vegetation change remains challenging. In this study, using the normalized difference vegetation index (NDVI) as a proxy of vegetation growth status and coverage, we analyzed the temporal and spatial characteristics of the NDVI in China’s Inner Mongolian grasslands using Theil–Sen slope statistics and Mann–Kendall trend test methods. In addition, using the GeoDetector method, a spatially-based statistical technique, we assessed the individual and interactive influences of natural factors and human activities on vegetation-NDVI change. The results show that the growing season average NDVI exhibited a fluctuating upward trend of 0.003 per year from 2000 to 2018. The areas with significant increases in NDVI (p < 0.05) accounted for 45.63% of the entire region, and they were mainly distributed in the eastern part of the Mu Us sandy land and the eastern areas of the Greater Khingan Range. The regions with a decline in the NDVI were mainly distributed in the central and western regions of the study area. The GeoDetector results revealed that both natural and human factors had significant impacts on changes in the NDVI (p < 0.001). Precipitation, livestock density, wind speed, and population density were the dominant factors affecting NDVI changes in the Inner Mongolian grasslands, explaining more than 15% of the variability, while the contributions of the two topography factors (terrain slope and slope aspect) were relatively low (less than 2%). Furthermore, NDVI changes responded to the changes in the level of specific influencing factors in a nonlinear way, and the interaction of two factors enhanced the effect of each singular factor. The interaction between precipitation and temperature was the highest among all factors, accounting for 39.3% of NDVI variations. Findings from our study may aid policymakers in better understanding the relative importance of various factors and the impacts of the interactions between factors on vegetation change, which has important implications for preventing and mitigating land degradation and achieving sustainable pasture use in dryland ecosystems.
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Zhao, Haiwei, Chaoyang Wu, and Xiaoyue Wang. "Large-scale forest conservation and restoration programs significantly contributed to land surface greening in China." Environmental Research Letters 17, no. 2 (February 1, 2022): 024023. http://dx.doi.org/10.1088/1748-9326/ac44c5.
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Abstract China has implemented a portfolio of large-scale forest conservation and restoration programs (FCRPs) to advance the sustainable management of forests. However, the contributions of these programs to forest recovery and land surface greening were generally evaluated on a local scale, which hindered the systematic planning of FCRPs. In this study, we analyzed the spatiotemporal patterns of tree cover (TC) change before and after the intensification of FCRPs using the Mann-Kendall test and the Theil–Sen slope estimator. With the improved phenology-based residual trend analysis (P-RESTREND) method, we derived the spatiotemporal patterns of human-induced tree cover (TCH) change on the national scale. Then, we calculated the effectiveness index of FCRPs at the provincial level, based on which the effectiveness levels for the 31 provinces of mainland China were classified. Our study showed that the area of forested lands with a significant greening trend was almost five times larger in the post-intensification phase (1999–2015) than in the pre-intensification phase of FCRPs (1982–1998). More than 29.9% of the forested lands were significantly improved in TC by human activities in the post-intensification phase. Provinces with high effectiveness levels were generally distributed in humid areas, whereas the majority of provinces with low and moderately low effectiveness levels were spread in arid and semi-arid regions. We concluded that the implementation of FCRPs had contributed greatly to the land surface greening in China. Moreover, the effectiveness of FCRPs in forest recovery was heterogeneous at the provincial level and was driven by multiple natural and socioeconomic factors.
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Lim Kam Sian, Kenny Thiam Choy, Jianhong Wang, Brian Odhiambo Ayugi, Isaac Kwesi Nooni, and Victor Ongoma. "Multi-Decadal Variability and Future Changes in Precipitation over Southern Africa." Atmosphere 12, no. 6 (June 9, 2021): 742. http://dx.doi.org/10.3390/atmos12060742.
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The future planning and management of water resources ought to be based on climate change projections at relevant temporal and spatial scales. This work uses the new regional demarcation for Southern Africa (SA) to investigate the spatio-temporal precipitation variability and trends of centennial-scale observation and modeled data, based on datasets from the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The study employs several statistical methods to rank the models according to their precipitation simulation ability. The Theil–Sen slope estimator is used to assess precipitation trends, with a Student’s t-test for the significance test. The comparison of observation and model historical data enables identification of the best-performing global climate models (GCMs), which are then employed in the projection analysis under two Shared Socioeconomic Pathways (SSPs): SSP2-4.5 and SSP5-8.5. The GCMs adequately capture the annual precipitation variation but with a general overestimation, especially over high-elevation areas. Most of the models fail to capture precipitation over the Lesotho-Eswatini area. The three best-performing GCMs over SA are FGOALS-g3, MPI-ESM1-2-HR and NorESM2-LM. The sub-regions demonstrate that precipitation trends cannot be generalized and that localized studies can provide more accurate findings. Overall, precipitation in the wet and dry seasons shows an initial increase during the near future over western and eastern SA, followed by a reduction in precipitation during the mid- and far future under both projection scenarios. Madagascar is expected to experience a decrease in precipitation amount throughout the twenty-first century.
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Martínez, Beatriz, Sergio Sánchez-Ruiz, Manuel Campos-Taberner, F. Javier García-Haro, and M. Amparo Gilabert. "Exploring Ecosystem Functioning in Spain with Gross and Net Primary Production Time Series." Remote Sensing 14, no. 6 (March 8, 2022): 1310. http://dx.doi.org/10.3390/rs14061310.
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The main objective of this study is to analyze the spatial and temporal variability of gross and net primary production (GPP and NPP) in Peninsular Spain across 15 years (2004–2018) and determine the relationship of those carbon fluxes with precipitation and air temperature. A time series study of daily GPP, NPP, mean air temperature, and monthly standardized precipitation index (SPI) at 1 km spatial resolution is conducted to analyze the ecosystem status and adaptation to changing environmental conditions. Spatial variability is analyzed for vegetation and specific forest types. Temporal dynamics are examined from a multiresolution analysis based on the wavelet transform (MRA-WT). The Mann–Kendall nonparametric test and the Theil–Sen slope are applied to quantify the magnitude and direction of trends (increasing or decreasing) within the time series. The use of MRA-WT to extract the annual component from daily series increased the number of statistically significant pixels. At pixel level, larger significant GPP and NPP negative changes (p-value < 0.1) are observed, especially in southeastern Spain, eastern Mediterranean coastland, and central Spain. At annual temporal scale, forests and irrigated crops are estimated to have twice the GPP of rainfed crops, shrublands, grasslands, and sparse vegetation. Within forest types, deciduous broadleaved trees exhibited the greatest annual NPP, followed by evergreen broadleaved and evergreen needle-leaved tree species. Carbon fluxes trends were correlated with precipitation. The temporal analysis based on daily TS demonstrated an increase of accuracy in the trend estimates since more significant pixels were obtained as compared to annual resolution studies (72% as to only 17%).
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Makama, Ezekiel Kaura, and Hwee San Lim. "Variability and Trend in Integrated Water Vapour from ERA-Interim and IGRA2 Observations over Peninsular Malaysia." Atmosphere 11, no. 9 (September 22, 2020): 1012. http://dx.doi.org/10.3390/atmos11091012.
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Integrated water vapour (IWV) is the total amount of precipitable water in an atmospheric column between the Earth’s surface and space. The implication of its variability and trend on the Earth’s radiation budget and precipitation makes its monitoring on a regular basis important. ERA-Interim reanalysis (ERA) and radiosonde (RS) data from 1988 to 2018 were used to investigate variability and trend in IWV over Peninsular Malaysia. ERA performed excellently when gauged with RS. Trend analysis was performed using the non-parametric Mann–Kendall and Theil–Sen slope estimator tests. ERA and RS IWV revealed double fluctuations at the seasonal time scale, with maxima in May and November, which are the respective beginnings of the southwest monsoon (SWM) and northeast monsoon (NEM) seasons, as well as coincidental peaks of precipitation in the region. IWV decreased in a southeast–northwest orientation, with regional maximum domiciled over the southeastern tip of the region. Steep orography tended to shape intense horizontal gradients along the edges of the peninsular, with richer gradients manifesting along the western boundary during SWM, which harbours more water vapour in the peninsular. IWV trends, both at the annual and seasonal time series, were positive and statistically significant at the 95% level across the stations, except at Kota Bharu, where a nonsignificant downward trend manifested. Trends were mostly higher in the NEM, with the greatest rate being 0.20 ± 0.42 kgm−2 found at Penang. Overall, the IWV trend in Peninsular Malaysia was positive and consistent with the upward global changes in IWV reported elsewhere.
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Roessler, Sebastian, and Andreas Jürgen Dietz. "Development of Global Snow Cover—Trends from 23 Years of Global SnowPack." Earth 4, no. 1 (December 20, 2022): 1–26. http://dx.doi.org/10.3390/earth4010001.
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Globally, the seasonal snow cover is the areal largest, the most short-lived and the most variable part of the cryosphere. Remote sensing proved to be a reliable tool to investigate their short-term variations worldwide. The medium-resolution sensor MODIS sensor has been delivering daily snow products since the year 2000. Remaining data gaps due to cloud coverage or polar night are interpolated using the DLR’s Global SnowPack (GSP) processor which produces daily global cloud-free snow cover. With the conclusion of the hydrological year 2022 in the northern hemisphere, the snow cover dynamics of the last 23 hydrological years can now be examined. Trends in snow cover development over different time periods (months, seasons, snow seasons) were examined using the Mann–Kendall test and the Theil–Sen slope. This took place as both pixel based and being averaged over selected hydrological catchment areas. The 23-year time series proved to be sufficient to identify significant developments for large areas. Globally, an average decrease in snow cover duration of −0.44 days/year was recorded for the full hydrological year, even if slight increases in individual months such as November were also found. Likewise, a large proportion of significant trends could also be determined globally at the catchment area level for individual periods. Most drastic developments occurred in March, with an average decrease in snow cover duration by −0.16 days/year. In the catchment area of the river Neman, which drains into the Baltic Sea, there is even a decrease of −0.82 days/year.
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Schmale, Julia, Sangeeta Sharma, Stefano Decesari, Jakob Pernov, Andreas Massling, Hans-Christen Hansson, Knut von Salzen, et al. "Pan-Arctic seasonal cycles and long-term trends of aerosol properties from 10 observatories." Atmospheric Chemistry and Physics 22, no. 5 (March 8, 2022): 3067–96. http://dx.doi.org/10.5194/acp-22-3067-2022.
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Abstract. Even though the Arctic is remote, aerosol properties observed there are strongly influenced by anthropogenic emissions from outside the Arctic. This is particularly true for the so-called Arctic haze season (January through April). In summer (June through September), when atmospheric transport patterns change, and precipitation is more frequent, local Arctic sources, i.e., natural sources of aerosols and precursors, play an important role. Over the last few decades, significant reductions in anthropogenic emissions have taken place. At the same time a large body of literature shows evidence that the Arctic is undergoing fundamental environmental changes due to climate forcing, leading to enhanced emissions by natural processes that may impact aerosol properties. In this study, we analyze 9 aerosol chemical species and 4 particle optical properties from 10 Arctic observatories (Alert, Kevo, Pallas, Summit, Thule, Tiksi, Barrow/Utqiaġvik, Villum, and Gruvebadet and Zeppelin Observatory – both at Ny-Ålesund Research Station) to understand changes in anthropogenic and natural aerosol contributions. Variables include equivalent black carbon, particulate sulfate, nitrate, ammonium, methanesulfonic acid, sodium, iron, calcium and potassium, as well as scattering and absorption coefficients, single scattering albedo and scattering Ångström exponent. First, annual cycles are investigated, which despite anthropogenic emission reductions still show the Arctic haze phenomenon. Second, long-term trends are studied using the Mann–Kendall Theil–Sen slope method. We find in total 41 significant trends over full station records, i.e., spanning more than a decade, compared to 26 significant decadal trends. The majority of significantly declining trends is from anthropogenic tracers and occurred during the haze period, driven by emission changes between 1990 and 2000. For the summer period, no uniform picture of trends has emerged. Twenty-six percent of trends, i.e., 19 out of 73, are significant, and of those 5 are positive and 14 are negative. Negative trends include not only anthropogenic tracers such as equivalent black carbon at Kevo, but also natural indicators such as methanesulfonic acid and non-sea-salt calcium at Alert. Positive trends are observed for sulfate at Gruvebadet. No clear evidence of a significant change in the natural aerosol contribution can be observed yet. However, testing the sensitivity of the Mann–Kendall Theil–Sen method, we find that monotonic changes of around 5 % yr−1 in an aerosol property are needed to detect a significant trend within one decade. This highlights that long-term efforts well beyond a decade are needed to capture smaller changes. It is particularly important to understand the ongoing natural changes in the Arctic, where interannual variability can be high, such as with forest fire emissions and their influence on the aerosol population. To investigate the climate-change-induced influence on the aerosol population and the resulting climate feedback, long-term observations of tracers more specific to natural sources are needed, as well as of particle microphysical properties such as size distributions, which can be used to identify changes in particle populations which are not well captured by mass-oriented methods such as bulk chemical composition.
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Shiferaw, Ashenafie Bereded, Abera Kumie, and Worku Tefera. "The spatial and temporal variation of fine particulate matter pollution in Ethiopia: Data from the Atmospheric Composition Analysis Group (1998–2019)." PLOS ONE 18, no. 3 (March 24, 2023): e0283457. http://dx.doi.org/10.1371/journal.pone.0283457.
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Background Evidence suggests ambient fine particulate matter (PM2.5) is a risk factor for cardiovascular diseases, lung cancer morbidity and mortality, and all-cause mortality. Countries that implement strong policies are able to reduce ambient PM2.5 concentration. In Ethiopia, however, PM2.5 monitoring stations, laboratory technicians, and equipment are staggeringly limited. In this study, the spatial and temporal variation of PM2.5 in Ethiopia was assessed. Methods Satellite-based PM2.5 estimates, from the year 1998 to 2019, by Atmospheric Composition Analysis Group (ACAG) at a spatial resolution of 0.01° X 0.01° was used. The annual mean PM2.5 concentration for all administrative regions and zones in Ethiopia was extracted. The average mean from the twenty-two years was also calculated. The trend of PM2.5 concentration was graphed and quantitatively calculated using the Mann-Kendall test. The slope of the change over time was estimated using the Theil-Sen slope. At the zonal administration level, for the average annual mean, spatial dependency using univariate Global Moran’s I and clustering and outlier tests using Anselin Local Moran’s were performed. Results The country’s average annual mean PM2.5 concentration was 17 μgm-3. The Afar region had the highest concentration, 27.9 μgm-3. The Mann-Kendall S was positive and significant at p<0.001. The spatial distribution of satellite-based ambient PM2.5 concentration was non-random. Significant highest value clustering of ambient total PM2.5 concentration exists in the Afar, Eastern Tigray, and Eastern and Southeastern Amhara while the significant lowest value dispersing was observed in the Southern Oromia and Somali region. Conclusion At the national and regional levels, the annual mean ambient PM2.5 concentration is beyond the World Health Organization (WHO)-recommended level. The ambient PM2.5 concentration distribution is spatially dependent and significantly clustered in space. Installation of additional ground-based PM2.5 monitoring devices, particularly in regions where PM2.5 concentration is higher, is recommended. Validating satellite-based PM2.5 data with ground-based measurements in the country is also advised.
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Pesce, Matteo, Jost von Hardenberg, Pierluigi Claps, and Alberto Viglione. "Correlation between climate and flood indices in Northwestern Italy at different temporal scales." Journal of Hydrology and Hydromechanics 70, no. 2 (May 19, 2022): 178–94. http://dx.doi.org/10.2478/johh-2022-0009.
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Abstract The occurrence of river floods is strongly related to specific climatic conditions that favor extreme precipitation events leading to catchment saturation. Although the impact of precipitation and temperature patterns on river flows is a well discussed topic in hydrology, few studies have focused on the relationship between peak discharges and standard Climate Change Indices (ETCCDI) of precipitation and temperature, widely used in climate research. It is of interest to evaluate whether these indices are relevant for characterizing and predicting floods in the Alpine area. In this study, a correlation analysis of the ETCCDI indices annual time series and annual maximum flows is presented for the Piedmont Region, in North-Western Italy. Spearman’s rank correlation is used to determine which ETCCDI indices are temporally correlated with maximum discharges, allowing to hypothesize which climate drivers better explain the interannual variability of floods. Moreover, the influence of climate (decadal) variability on the tendency of annual maximum discharges is examined by spatially correlating temporal trends of climate indices with temporal trends of the discharge series in the last twenty years, calculated using the Theil-Sen slope estimator. Results highlight that, while extreme precipitation indices are highly correlated with extreme discharges at the annual timescale, with different indices that are consistent with catchment size, the decadal tendencies of extreme discharges may be better explained by the decadal tendencies of the total annual precipitation over the study area. This suggests that future projections of the annual precipitation available from climate models simulations, whose reliability is higher compared to precipitation extremes, may be used as covariates for non-stationary flood frequency analysis.
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Masseroni, Daniele, Stefania Camici, Alessio Cislaghi, Giorgio Vacchiano, Christian Massari, and Luca Brocca. "The 63-year changes in annual streamflow volumes across Europe with a focus on the Mediterranean basin." Hydrology and Earth System Sciences 25, no. 10 (October 25, 2021): 5589–601. http://dx.doi.org/10.5194/hess-25-5589-2021.
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Abstract. Determining the spatiotemporal variability in the annual streamflow volume plays a relevant role in hydrology with regard to improving and implementing sustainable and resilient policies and practices of water resource management. This study investigates annual streamflow volume trends in a newly assembled, consolidated, and validated data set of daily mean river flow records from more than 3000 stations which cover near-natural basins in more than 40 countries across Europe. Although the data set contains streamflow time series from 1900 to 2013 in some stations, the statistical analyses were carried out by including observations from 1950 to 2013 in order to have a consistent and reliable data set over the continent. Trends were detected by calculating the slope of the Theil–Sen line over the annual anomalies of streamflow volume. The results show that annual streamflow volume trends have emerged at European scale, with a marked negative tendency in Mediterranean regions, with about -1×103 m3/(km2 yr−2), and a generally positive trend in northern ones, with about 0.5×103 m3/(km−2 yr−2). The annual streamflow volume trend patterns appear to be in agreement with the continental-scale meteorological observations in response to climate change drivers. In the Mediterranean area, the decline of annual streamflow volumes started in 1965, and since the early 1980s, volumes have consistently been lower than the 1950–2013 average. The spatiotemporal annual streamflow volume patterns observed in this work can help to contextualize short-term trends and regional studies already available in the scientific literature, as well as to provide a valid benchmark for further accurate quantitative analysis of annual streamflow volumes.
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Choi, Yongjoo, Yugo Kanaya, Hisahiro Takashima, Hitoshi Irie, Kihong Park, and Jihyo Chong. "Long-Term Variation in the Tropospheric Nitrogen Dioxide Vertical Column Density over Korea and Japan from the MAX-DOAS Network, 2007–2017." Remote Sensing 13, no. 10 (May 16, 2021): 1937. http://dx.doi.org/10.3390/rs13101937.
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We investigated long-term observations of the tropospheric nitrogen dioxide vertical column density (NO2 TropVCD) from the Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) network in Russia and ASia (MADRAS) from 2007 to 2017 at urban (Yokosuka and Gwangju) and remote (Fukue and Cape Hedo) sites in East Asia. The monthly mean in the NO2 TropVCD from MAX-DOAS measured at ~13:30 local time, which is the Ozone Monitoring Instrument (OMI) overpass time, shows good agreement with OMI data during summer, but differences between the two datasets increase in winter. The Theil-Sen slope of the long-term trend indicate a relatively rapid and gradual reduction in NO2 at Yokosuka and two remote sites (Fukue and Cape Hedo), respectively, regardless of the season except for fall at Fukue, but significant changes in NO2 are not observed at Gwangju, Korea. In contrast, OMI satellite data reveal an increase in the NO2 TropVCD at all sites except for Yokosuka, where a decreasing trend common to MAX-DOAS is found, suggesting that the results from satellites need to be cautiously used for investigating long-term trends in less polluted or remote areas. Using backward trajectories, potential source regions are identified for the two urban sites. The spatial distribution from OMI data shows good agreement with the potential source regions at Yokosuka. The potential source regions in Gwangju are identified as the National Industrial Complex in Yeosu and Gwangyang, while the transport route is not clearly visible with OMI data because of their low sensitivity in less polluted areas. The proposed approach is suitable for identifying potential source areas that might not be recognized by satellite observations.
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Wang, Junyi, Yifei Fan, Yu Yang, Luoqi Zhang, Yan Zhang, Shixiang Li, and Yali Wei. "Spatial-Temporal Evolution Characteristics and Driving Force Analysis of NDVI in the Minjiang River Basin, China, from 2001 to 2020." Water 14, no. 18 (September 18, 2022): 2923. http://dx.doi.org/10.3390/w14182923.
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Monitoring vegetation growth and exploring the driving force behind it is very important for the study of global climate change and ecological environmental protection. Based on Normalized Difference Vegetation Index (NDVI) data from Moderate-Resolution Imaging Spectroradiometer (MODIS), meteorological and nighttime lights data from 2001 to 2020, this study uses the Theil–Sen slope test, Mann–Kendall significance test, Rescaled Range Analysis and partial correlation analysis to investigate the evolution of NDVI in the Minjiang River Basin, China, from three aspects: the spatial-temporal variation characteristics and future trend prediction of NDVI, the variation of climate and human activities in the basin, and the influences of different driving forces on NDVI. The results show that the average NDVI in the growing season was 0.60 in the Minjiang River Basin in the past twenty years, with a growth rate of 0.002/a. The area with high NDVI growth accounts for 66.02%, mainly distributed in the southeast, the central and the northern low-altitude areas of the basin. Combined with the Hurst index, the NDVI in the Minjiang River Basin exhibits an anti-sustainable tendency, with 63.22% of the area changing from improvement to degradation in the future. Meanwhile, the spatial differentiation of NDVI in the Minjiang River Basin is mainly affected by topography and climate factors, followed by human activities. This study not only provides scientific guidelines for the vegetation restoration, soil and water conservation and sustainable development of the Minjiang River Basin, but also provides a scientific basis for making informed decisions on ecological protection under the impacts of climate change and human activities.
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Rajput, Jitendra, NL Kushwaha, DR Sena, DK Singh, and Indra Mani. "Trend assessment of rainfall, temperature and relative humidity using non-parametric tests in the national capital region, Delhi." MAUSAM 74, no. 3 (July 3, 2023): 593–606. http://dx.doi.org/10.54302/mausam.v74i3.4936.
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Understanding rainfall and temperature’s spatio-temporal variations at the local, regional, and global scale is vital for planning soil and water conservation structures and making irrigation decisions. The present investigation attempts to observe the rainfall and temperature variability and trend over 31 years (1990-2020) in the National Capital Region (NCR), Delhi, India, obtained from IARI meteorological station, Pusa, New Delhi. The statistical trend analyses Mann-Kendall (MK) test followed by Theil Sen slope estimator test was used for annual and monthly analysis to assess the trend direction and magnitude of the change over time. Pettitt's test detected the inflection point in the variable time series. The annual Tmax, Tmin, and rainfall showed no trend in the time series data. However, Tmax indicated a statistically significant decreasing trend in January and December. This implies a dip in the temperature during the winter months of January and December. Similarly, Tmin revealed a statistically significant decreasing trend in January and December. But a statistically increasing trend for Tmin was observed in April, which may cause a harsh environment for cultivating the Zaid season crops due to increased warming. The Pettitt test showed no change point in the time series trend in the annual Tmax and Tmin data series. For January Tmax data, the trend change point occurred in 1998. However, it was observed that Tmin in April showed a change point in the time series trend in 1999. The change point in the annual average rainfall data was marked in 2012. A didactic implication of these changes on hydrologic design and crop irrigation decisions was discussed in this paper.
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Rao, GRama, Aleti Sowjanya, D. Shekhar, BNSandeep Naik, and BVS Kiran. "Rainfall analysis over 31 years of Chintapalle, Visakhapatnam, High Altitude and Tribal zone, Andhra Pradesh, India." MAUSAM 74, no. 3 (July 3, 2023): 685–98. http://dx.doi.org/10.54302/mausam.v74i3.818.
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Climate change and variability, particularly which of the annual rainfall, has received a great deal of interest to researchers worldwide. The extent of the variability of rainfall varies according to locations. Consequently, investigating the dynamics of rainfall variable in the perspective of changing climate is important to evaluate the impact of climate change and adapt potential mitigation strategies. To gain insight, trend analysis has been employed to inspect and quantify the rainfall distribution in the Chintapalli, Visakhapatnam district of Andhra Pradesh, India. Thirty-one years for a period of 1990–2020 long historical rainfall data series for different temporal scales (Monthly, Seasonal and Annual) of the study region was used for the analysis. Statistical trend analysis techniques namely Mann–Kendall (MK) test was used to detect the trend. To compute trend magnitude, Theil–Sen approach (TSA) was used for calculation of Sen’s slope. The detailed analysis of the data for 31 years indicates positive increasing trend with 2.13mm per year derived from the linear regression. MK test detected that there were rising and falling trends for various time scales in the study area. Departure analysis of rainfall indicated that a possible chance of normal rainfall, more frequently in the area. Rainfall Anomaly Index (RAI) analysis revealed that normal for most of the years, however, 2002 is the very dry year. While last ten years, the frequency of drought occurrence is thrice, but the magnitude is low. The study results will help in persuading the rainfall risks with effective use of water resources which can increase crop productivity and likely to manage natural resources for sustainability at HAT zone of Andhra Pradesh.
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Chen, Jin, Chongmin Xu, Sen Lin, Zhilong Wu, Rongzu Qiu, and Xisheng Hu. "Is There Spatial Dependence or Spatial Heterogeneity in the Distribution of Vegetation Greening and Browning in Southeastern China?" Forests 13, no. 6 (May 28, 2022): 840. http://dx.doi.org/10.3390/f13060840.
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Vegetation is an indispensable component of terrestrial ecosystems and plays an irreplaceable role in mitigation of climate change. Global vegetation changes (i.e., greening and browning) still occur frequently, however, little is known about the spatial relationships between these two processes. Based on the normalized difference vegetation index (NDVI) dataset from 1998 to 2018 in Fujian Province, China. The Theil-Sen and Mann-Kendall tests were used to explore temporal changes in vegetation growing, then the spatial relationships of greening and browning was distinguished with bivariate spatial autocorrelation analysis, and the spatial variation in the relationship between vegetation changes and driving factors was explored by the geographical detector. The results showed that from 1998 to 2018, the average NDVI value increased from 0.75 to 0.83; 89.61% of the study area experienced vegetation greening, while 5.7% experienced significant browning, with active vegetation changes occurred along roads and nearby cities. The spatial autocorrelation results showed that the spatial relationships between vegetation greening and browning were dominated by spatial heterogeneity (i.e., high greening and low browning, H-L clusters accounting for 60% and low greening and high browning, L-H clusters accounting for 14%), but we also revealed that there were still quite a few places (4%) with spatial dependence (i.e., high greening and browning, H-H clusters), occurring around urban areas and along roads. The factor detector indicated that the nighttime light intensity was among the most dominant factor of vegetation changes, followed by elevation and slope. Although the individual effect of the distance to roads was relatively weak on the vegetation changes, its indirect effect was found to be the strongest by the interaction detector, which was obtained from the interactions much larger than its independent impact. Simultaneously, the risk detector revealed that the greening preferred occurring in places with lower nighttime light intensity (<1.1 nW cm−2sr−1), higher elevation (>43.4 m) and slope (>6.3°). Moreover, we found that the vegetation changes primarily occurred within a distance of 1685.4 m from roads. Our findings could deepen the understanding of vegetation change patterns and provide advice for mitigating the impact on the vegetation changes.
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Jenicek, M., J. Seibert, M. Zappa, M. Staudinger, and T. Jonas. "Importance of maximum snow accumulation for summer low flows in humid catchments." Hydrology and Earth System Sciences Discussions 12, no. 7 (July 24, 2015): 7023–56. http://dx.doi.org/10.5194/hessd-12-7023-2015.
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Abstract. The expected increase of air temperature will increase the ratio of liquid to solid precipitation during winter and, thus decrease the amount of snow, especially in mid-elevation mountain ranges across Europe. The decrease of snow will affect groundwater recharge during spring and might cause low streamflow values in the subsequent summer period. To evaluate these potential climate change impacts, we investigated the effects of inter-annual variations in snow accumulation on summer low flow and addressed the following research questions: (1) how important is snow for summer low flows and how long is the "memory effect" in catchments with different elevations? (2) How sensitive are summer low flows to any change of winter snowpack? To find suitable predictors of summer low flow we used long time series from 14 alpine and pre-alpine catchments in Switzerland and computed different variables quantifying winter and spring snow conditions. We assessed the sensitivity of individual catchments to the change of maximum snow water equivalent (SWEmax) using the non-parametric Theil–Sen approach as well as an elasticity index. In general, the results indicated that maximum winter snow accumulation influenced summer low flow, but could only partly explain the observed inter-annual variations. One other important factor was the precipitation between maximum snow accumulation and summer low flow. When only the years with below average precipitation amounts during this period were considered, the importance of snow accumulation as a predictor of low flows increased. The slope of the regression between SWEmax and summer low flow and the elasticity index both increased with increasing mean catchment elevation. This indicated a higher sensitivity of summer low flow to snow accumulation in alpine catchments compared to lower elevation catchments.
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Tran, Tran, Myint, Latorre-Carmona, Ho, Tran, and Dao. "Assessing Spatiotemporal Drought Dynamics and Its Related Environmental Issues in the Mekong River Delta." Remote Sensing 11, no. 23 (November 21, 2019): 2742. http://dx.doi.org/10.3390/rs11232742.
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Drought is a major natural disaster that creates a negative impact on socio-economic development and environment. Drought indices are typically applied to characterize drought events in a meaningful way. This study aims at examining variations in agricultural drought severity based on the relationship between standardized ratio of actual and potential evapotranspiration (ET and PET), enhanced vegetation index (EVI), and land surface temperature (LST) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) platform. A new drought index, called the enhanced drought severity index (EDSI), was developed by applying spatiotemporal regression methods and time-series biophysical data derived from remote sensing. In addition, time-series trend analysis in the 2001–2018 period, along with the Mann–Kendal (MK) significance test and the Theil Sen (TS) slope, were used to examine the spatiotemporal dynamics of environmental parameters (i.e., LST, EVI, ET, and PET), and geographically weighted regression (GWR) was subsequently applied in order to analyze the local correlations among them. Results showed that a significant correlation was discovered among LST, EVI, ET, and PET, as well as their standardized ratios (|r| > 0.8, p < 0.01). Additionally, a high performance of the new developed drought index, showing a strong correlation between EDSI and meteorological drought indices (i.e., standardized precipitation index (SPI) or the reconnaissance drought index (RDI)), measured at meteorological stations, giving r > 0.7 and a statistical significance p < 0.01. Besides, it was found that the temporal tendency of this phenomenon was the increase in intensity of drought, and that coastal areas in the study area were more vulnerable to this phenomenon. This study demonstrates the effectiveness of EDSI and the potential application of integrating spatial regression and time-series data for assessing regional drought conditions.
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Catarino, Silvia, Maria Manuel Romeiras, Rui Figueira, Valentine Aubard, João M. N. Silva, and José M. C. Pereira. "Spatial and Temporal Trends of Burnt Area in Angola: Implications for Natural Vegetation and Protected Area Management." Diversity 12, no. 8 (August 9, 2020): 307. http://dx.doi.org/10.3390/d12080307.
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Fire is a key driver of natural ecosystems in Africa. However, human activity and climate change have altered fire frequency and severity, with negative consequences for biodiversity conservation. Angola ranks among the countries with the highest fire activity in sub-Saharan Africa. In this study, we investigated the spatial and temporal trends of the annual burnt area in Angola, from 2001 to 2019, and their association with terrestrial ecoregions, land cover, and protected areas. Based on satellite imagery, we analyzed the presence of significant trends in burnt area, applying the contextual Mann–Kendall test and the Theil–Sen slope estimator. Data on burnt areas were obtained from the moderate-resolution imaging spectroradiometer (MODIS) burnt area product and the analyses were processed in TerrSet. Our results showed that ca. 30% of the country’s area burned every year. The highest percentage of annual burnt area was found in northeast and southeast Angola, which showed large clusters of decreasing trends of burnt area. The clusters of increasing trends were found mainly in central Angola, associated with savannas and grasslands of Angolan Miombo woodlands. The protected areas of Cameia, Luengue-Luiana, and Mavinga exhibited large areas of decreasing trends of burnt area. Conversely, 23% of the Bicuar National Park was included in clusters of increasing trends. Distinct patterns of land cover were found in areas of significant trends, where the clusters of increasing trends showed a higher fraction of forest cover (80%) than the clusters of decreasing trends (55%). The documentation of burnt area trends was very important in tropical regions, since it helped define conservation priorities and management strategies, allowing more effective management of forests and fires in countries with few human and financial resources.
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Mainuddin, Mohammed, Jorge L. Peña-Arancibia, Fazlul Karim, Md Masud Hasan, Mohammad A. Mojid, and John M. Kirby. "Long-term spatio-temporal variability and trends in rainfall and temperature extremes and their potential risk to rice production in Bangladesh." PLOS Climate 1, no. 3 (March 8, 2022): e0000009. http://dx.doi.org/10.1371/journal.pclm.0000009.
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Understanding the historical and future spatio-temporal changes in climate extremes and their potential risk to rice production is crucial for achieving food security in Bangladesh. This paper presents results from a study on trend analysis for 13 climate metrics that significantly influence rice production. The analysis was conducted using the non-parametric Mann-Kendall test and the Theil-Sen slope estimator methods. The study included data from all available weather stations in Bangladesh and the assessment was done for both the wet (May to October) and dry (November to April) seasons, which cover the growing seasons of the country’s three types of rice: Aus, Aman and Boro. Results show significant decreasing trends for wet season rainfall (>12 mm/season/year in some stations) in the central and north regions. In addition, dry season rainfall is decreasing significantly in many areas, whilst dry season dry spells are increasing throughout Bangladesh. Decrease in rainfall in some of these areas are of concern because of its impacts on rainfed Aus rice and in the sowing/planting of rainfed Aman rice and irrigated dry season Boro rice. The maximum temperatures in the wet season are increasing throughout the country at 0.5°C every ten years, significantly at most of the climate stations. The analysis shows that the number of days with temperature >36°C has significantly increased in 18 stations over the last three decades, which implies a serious risk to Aman rice yield. The current maximum temperatures (both in the wet and dry seasons) are higher than the optimum temperature ranges for rice production, and this will have likely adverse effects on yield in the face of climate change with increasing temperatures. The results herein have practical implications for planning appropriate adaptation policies to ensure food security in the country.
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Liu, Zhenzhen, Hang Wang, Ning Li, Jun Zhu, Ziwu Pan, and Fen Qin. "Spatial and Temporal Characteristics and Driving Forces of Vegetation Changes in the Huaihe River Basin from 2003 to 2018." Sustainability 12, no. 6 (March 12, 2020): 2198. http://dx.doi.org/10.3390/su12062198.
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In this study, MODIS normalized difference vegetation index (NDVI), TRMM3B43 precipitation, and MOD11A2 land-surface temperature (LST) data were used as data sources in an analysis of temporal and spatial characteristics of vegetation changes and ecological environmental quality in the Huaihe River basin, China, from 2003 to 2018. The Mann–Kendall (MK) non-parametric test and the Theil–Sen slope test were combined for this analysis; then, when combined with the results of the MK mutation test and two introduced indexes, the kurtosis coefficient (KU) and skewness (SK) and correlations between NDVI, precipitation (TRMM), and land-surface temperature (LST) in different time scales were revealed. The results illustrate that the mean NDVI in the Huaihe River basin was 0.54. The annual NDVImax curve fluctuations for different land cover types were almost the same. The main reasons for the decrease in or disappearance of vegetation cover in the Huaihe River basin were the expansion of towns and impact of human activities. Furthermore, vegetation cover around water areas was obviously degraded and wetland protections need to be strengthened urgently. On the same time scale, change trends of NDVI, TRMM, and LST after abrupt changes became consistent within a short time period. Vegetation growth was favored when the KU and SK of TRMM had a close to normal distribution within one year. Monthly TRMM and LST can better reflect NDVI fluctuations compared with seasonal and annual scales. When the precipitation (TRMM) is less than 767 mm, the average annual NDVI of different land cover types is not ideal. Compared with other land cover types, dry land has stronger adaptability to changes in the LST when the LST is between 19 and 22.6 °C. These trends can serve as scientific reference for protecting and managing the ecological environment in the Huaihe River basin.
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Morresi, Donato, Alessandro Vitali, Carlo Urbinati, and Matteo Garbarino. "Forest Spectral Recovery and Regeneration Dynamics in Stand-Replacing Wildfires of Central Apennines Derived from Landsat Time Series." Remote Sensing 11, no. 3 (February 4, 2019): 308. http://dx.doi.org/10.3390/rs11030308.
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Understanding post-fire regeneration dynamics is an important task for assessing the resilience of forests and to adequately guide post-disturbance management. The main goal of this research was to compare the ability of different Landsat-derived spectral vegetation indices (SVIs) to track post-fire recovery occurring in burned forests of the central Apennines (Italy) at different development stages. Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Normalized Burn Ratio (NBR), Normalized Burn Ratio 2 (NBR2) and a novel index called Forest Recovery Index 2 (FRI2) were used to compute post-fire recovery metrics throughout 11 years (2008–2018). FRI2 achieved the highest significant correlation (Pearson’s r = 0.72) with tree canopy cover estimated by field sampling (year 2017). The Theil–Sen slope estimator of linear regression was employed to assess the rate of change and the direction of SVIs recovery metrics over time (2010–2018) and the Mann–Kendall test was used to evaluate the significance of the spectral trends. NDVI displayed the highest amount of recovered pixels (38%) after 11 years since fire occurrence, whereas the mean value of NDMI, NBR, NBR2, and FRI2 was about 27%. NDVI was more suitable for tracking early stages of the secondary succession, suggesting greater sensitivity toward non-arboreal vegetation development. Predicted spectral recovery timespans based on pixels with a statistically significant monotonic trend did not highlight noticeable differences among normalized SVIs, suggesting similar suitability for monitoring early to mid-stages of post-fire forest succession. FRI2 achieved reliable results in mid- to long-term forest recovery as it produced up to 50% longer periods of spectral recovery compared to normalized SVIs. Further research is needed to understand this modeling approach at advanced stages of post-fire forest recovery.
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Cheng, Wei, Beibei Shen, Xiaoping Xin, Qian Gu, and Tao Guo. "Spatiotemporal Variations of Grassland Ecosystem Service Value and Its Influencing Factors in Inner Mongolia, China." Agronomy 12, no. 9 (September 1, 2022): 2090. http://dx.doi.org/10.3390/agronomy12092090.
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The services provided by grassland ecosystems are important and irreplaceable in maintaining the balance and stability of ecosystems. The spatiotemporal variations of grassland ecosystem service value (ESV) and its influencing factors in Inner Mongolia from 2000 to 2019 were studied in this paper. Based on the socio-economic data, remote sensing data, geographic data, and meteorological data, a dynamic ESV assessment method based on the equivalent factors was used to calculate the grassland ESV for each year. The spatiotemporal dynamic variation and future trend of grassland ESV were studied by coefficient of variation index (CV), Theil–Sen median trend analysis, Mann–Kendall test, and Hurst index, and the Geodetector was used to determine the main factors affecting the distribution of ESV. The results indicated that (1) the annual average grassland ESV of Inner Mongolia was higher in the northeast than in southwest, the average ESV was 2.0794 million CNY/km2, and the pixels were concentrated from 1 to 3 million CNY/km2, accounting for 75.46% of the study area; (2) during the study period, the average grassland ESV increased slowly with time at an annual growth rate of 0.2, and the total ESV decreased first and then increased with the change in grassland area; (3) the average volatility was 0.16, and pixels with CV values between 0.1 and 0.2 accounted for 69.2% of the study area, indicating the fluctuation of ESV was relatively stable during the study period; (4) 37.16% of the grassland ESV in Inner Mongolia decreased slightly and 41.77% increased slightly during these years, and the two parts showed opposite trends in the future; and (5) the single factor influencing the spatial distribution of grassland ESV was mainly normalized vegetation index (NDVI) and precipitation, and the multi-factor interactions were NDVI∩slope and NDVI∩precipitation. All influencing factors exhibited a stronger impact through the two-factor interaction. This study can provide reference values for the policymaking of natural resource conservation or restoration.
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Teferi, E., S. Uhlenbrook, and W. Bewket. "Inter-annual and seasonal trends of vegetation condition in the Upper Blue Nile (Abbay) basin: dual scale time series analysis." Earth System Dynamics Discussions 6, no. 1 (February 6, 2015): 169–216. http://dx.doi.org/10.5194/esdd-6-169-2015.
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Abstract. A long-term decline in ecosystem functioning and productivity, often called land degradation, is a serious environmental and development challenge to Ethiopia that needs to be understood so as to develop sustainable land use strategies. This study examines inter-annual and seasonal trends of vegetation cover in the Upper Blue Nile (UBN) or Abbay basin. Advanced Very High Resolution Radiometer (AVHRR) based Global Inventory, Monitoring, and Modelling Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) was used for course scale long-term vegetation trend analysis. Moderate-resolution Imaging Spectroradiometer (MODIS) NDVI data (MOD13Q1) was used for finer scale vegetation trend analysis. Harmonic analyses and non-parametric trend tests were applied to both GIMMS NDVI (1981–2006) and MODIS NDVI (2001–2011) data sets. Based on a robust trend estimator (Theil–Sen slope) most part of the UBN (~77%) showed a positive trend in monthly GIMMS NDVI with a mean rate of 0.0015 NDVI units (3.77% yr−1), out of which 41.15% of the basin depicted significant increases (P < 0.05) with a mean rate of 0.0023 NDVI units (5.59% yr−1) during the period. However, the finer scale (250 m) MODIS-based vegetation trend analysis revealed that about 36% of the UBN shows a significantly decreasing trend (P < 0.05) over the period 2001–2011 at an average rate of 0.0768 NDVI yr−1. This indicates that the greening trend of vegetation condition was followed by browning trend since the mid-2000s in the basin, which requires the attention of land users and decision makers. Seasonal trend analysis was found to be very useful in identifying changes in vegetation condition that could be masked if only inter-annual vegetation trend analysis was performed. The finer scale intra-annual trend analysis revealed trends that were more linked to human activities. This study concludes that integrated analysis of course and fine scale, inter-annual and intra-annual trends enables a more robust identification of changes in vegetation condition.
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Aubard, Valentine, Joana Amaral Paulo, and João M. N. Silva. "Long-Term Monitoring of Cork and Holm Oak Stands Productivity in Portugal with Landsat Imagery." Remote Sensing 11, no. 5 (March 4, 2019): 525. http://dx.doi.org/10.3390/rs11050525.
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Oak stands are declining in many regions of southern Europe. The goal of this paper is to assess this process and develop an effective monitoring tool for research and management. Long-term trends of the Normalized Difference Vegetation Index (NDVI) were derived and mapped at 30-m spatial resolution for all areas with a stable land cover of cork oak (Quercus suber L.) and holm oak (Quercus ilex L.) forests and agroforestry systems in mainland Portugal. NDVI, a good proxy for forest health and productivity monitoring, was obtained for the 1984–2017 period using Landsat-5 TM and Landsat-7 ETM+ imagery. TM values were adjusted to those of ETM+, after a comparison of site-specific and literature linear equations. The spatiotemporal trend analysis was performed using only July and August NDVI values, in order to minimize the spectral contribution of understory vegetation and its phenological variability, and thus, focus on the tree layer. Signs and significance of trends were obtained for six representative oak stands and the whole country with the Mann Kendall and Contextual Mann-Kendall test, respectively, and their slope was assessed with the Theil-Sen estimator. Long-term forest inventories of six study sites and NDVI time series derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) allowed validating the methodology and results with independent data. NDVI has a good relationship with cork production at the forest stand level. Pettitt tests reveal significant change-points within the trends in the period 1996–2005, when changes in drought patterns occurred. Twelve percent of the area of oak stands in Portugal presents significant decreasing trends, most of them located in mountainous regions with shallow soils. Cork oak agroforestry is the most declining oak forest type, compared to cork oak and holm oak forests. The Google Earth Engine platform proved to be a powerful tool to deal with long-term time series and for the monitoring of forests health and productivity.
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Ghanim, Abdulnoor A. J., Muhammad Naveed Anjum, Ghulam Rasool, Saifullah, Muhammad Irfan, Mana Alyami, Saifur Rahman, and Usama Muhammad Niazi. "Analyzing Extreme Temperature Patterns in Subtropical Highlands Climates: Implications for Disaster Risk Reduction Strategies." Sustainability 15, no. 17 (August 23, 2023): 12753. http://dx.doi.org/10.3390/su151712753.
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This study utilized hot and cold indices to evaluate the changes in extreme temperature events that occurred in subtropical highland climates from 1991 to 2020. The modified Mann–Kendall (MMK) test and the Theil–Sen (TS) slope estimator were used to analyze the linear trends in the time series of the extreme temperature indices. The northern highlands of Pakistan (NHP) were considered as a case study region. The results showed that the annual maximum temperature had a slightly increasing tendency (at the rate of 0.14 °C/decade), while the annual minimum temperature had a slightly decreasing tendency (at the rate of −0.02 °C/decade). However, these trends were not significant at the 5% significance level. The decadal averages of the hot indices were the highest in the second decade (2000s), while they were the lowest in the subsequent decade (2010s). In comparison, all the cold indices except the annual minimum value of the maximum temperature (TXn) showed a persistent decline in their decadal averages throughout the 2000s and 2010s. Overall, the frequency of hot days significantly increased in the NHP during the study period. This study found that the hot days and coldest days increased over the past three decades in the NHP. However, there was a decreasing trend in the cold spell duration, cold nights, and the coldest nights over the past three decades, as demonstrated by the trends of the cold spell duration index (CSDI), the temperature of cold nights (TN10p), and the annual minimum value of the minimum temperature (TNn) indices. These changes may impact the environment, human health, and agricultural operations. The findings provide useful insights into the shifting patterns of extreme temperature events in northern Pakistan and have crucial implications for the climate-change-adaptation and resilience-building initiatives being undertaken in the region. It is suggested that the continuous monitoring of extreme temperature events is necessary to comprehend their effects on the region and devise strategies for sustainable development.
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Hu, Kehong, Zhen Zhang, Hongliang Fang, Yijie Lu, Zhengnan Gu, and Min Gao. "Spatio-Temporal Characteristics and Driving Factors of the Foliage Clumping Index in the Sanjiang Plain from 2001 to 2015." Remote Sensing 13, no. 14 (July 16, 2021): 2797. http://dx.doi.org/10.3390/rs13142797.
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The Sanjiang Plain is the largest agricultural reclamation area and the biggest marsh area in China. The regional vegetation coverage in this area is vital to local ecological systems, and vegetation growth is affected by natural and anthropogenic factors. The clumping index (CI) is of great significance for land surface models and obtaining information on other vegetation structures. However, most existing ecological models and the retrieval of other vegetation structures do not consider the spatial and temporal variations of CI, and few studies have focused on detecting factors that influence the spatial differentiation of CI. To address these issues, this study investigated the spatial and temporal characteristics of foliage CI in the Sanjiang Plain, analysing the correlation between CI and leaf area index (LAI) through multiple methods (such as Theil−Sen trend analysis, the Mann−Kendall test, and the correlation coefficient) based on the 2001−2015 Chinese Academy of Sciences Clumping Index (CAS CI) and Global LAnd Surface Satellite Leaf Area Index (GLASS LAI). The driving factors of the spatial differentiation of CI were also investigated based on the geographical detector model (GDM) with natural data (including the average annual temperature, annual precipitation, elevation, slope, aspect, vegetation type, soil type, and geomorphic type) and anthropogenic data (the land use type). The results showed that (1) the interannual variation of foliage CI was not obvious, but the seasonal variation was obvious in the Sanjiang Plain from 2001 to 2015; (2) the spatial distribution of the multiyear mean CI of each season in the Sanjiang Plain was similar to the spatial distribution of the land use type, and the CI decreased slightly with increases in elevation; (3) the correlation between the growing season mean CI (CIGS) and the growing season mean LAI (LAIGS) time series was not significant, but their spatial distributions were negatively correlated; (4) topographic factors (elevation and slope) and geomorphic type dominated the spatial differentiation of foliage CI in the Sanjiang Plain, and the interactions between driving factors enhanced their explanatory power in terms of the spatial distribution of foliage CI. This study can help improve the accuracy of the retrieval of other vegetation structures and the simulation of land surface models in the Sanjiang Plain, providing invaluable insight for the analysis of the spatial and temporal variations of vegetation based on CI. Moreover, the results of this study support a theoretical basis for understanding the explanatory power of natural and anthropogenic factors in the spatial distribution of CI, along with its driving mechanism.
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Guo, Yuchen, Liusheng Han, Dafu Zhang, Guangwei Sun, Junfu Fan, and Xiaoyu Ren. "The Factors Affecting the Quality of the Temperature Vegetation Dryness Index (TVDI) and the Spatial–Temporal Variations in Drought from 2011 to 2020 in Regions Affected by Climate Change." Sustainability 15, no. 14 (July 21, 2023): 11350. http://dx.doi.org/10.3390/su151411350.
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The temperature vegetation dryness index (TVDI) is widely used for the monitoring of global or regional drought because of its strong drought-monitoring capabilities and ease of implementation. However, the temporal errors in the land surface temperature (LST) and normalized difference vegetation index (NDVI) can affect warm and cold edges, thus determining the quality of the TVDI, especially in regions affected by climate change, such as Shandong Province. This paper explores this issue in the region in 2011, using daily MODIS MOD09GA and MOD11A1 data products. For each image acquisition time, the warm and cold edges of the NDVI–LST were extracted based on the NDVI, derived from red and near-infrared reflectance data, and the LST, derived from the MOD11A1 dataset. Then, the variations in the warm and cold edges with the LST and NDVI were analyzed. Subsequently, the influence of warm and cold edges, based on the daily values of the temperature, NDVI and precipitation during the observed period, was assessed using a linear regression. The soil moisture (SM) data obtained from the Global Land Data Assimilation System (GLDAS) datasets and the crop water stress index (CWSI) obtained from the MOD16A2 products were used for the assessment. The spatial and temporal variations in drought in Shandong Province from 2011 to 2020 were measured based on Theil–Sen median trend analysis and the Mann–Kendall test. The results show that apparently random variations were evident in the temporal evolution of the slope of the warm edge, indicating that daily data were appropriate to determine the boundary of the warm edge. Daily data were also appropriate to determine the boundary of the cold edge in a similar way. Additionally, the temperature, NDVI and precipitation in this region affected by climate change had a negative correlation with the slope and a positive correlation with the intercept. The validation results show that there was a significant negative correlation between the observed TVDI and GLDAS soil moisture values (R2 > 0.62) in 12 scatter plots. Therefore, we deduced that the monthly or yearly TVDI product produced by the daily MODIS data has a higher precision than that produced by 8-day or monthly data in regions affected by climate change. The spatial and temporal variations show that the trend of slight and moderate droughts first increased and then decreased, and, in particular, some areas presented severe drought from 2011 to 2015. The results obtained in this study are important for the scheduling of irrigation and drought warnings.
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Zuo, Yiting, Jie Cheng, Hongjie Zhang, Feng Tang, and Meichen Fu. "How Did the Mild and Humid Areas of China Turn Green? A Case Study on Chongqing." Forests 13, no. 8 (August 9, 2022): 1257. http://dx.doi.org/10.3390/f13081257.
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Since the implementation of the Natural Forest Resources Protection Project (NFRP) and the Grain for Green Program (GFGP), two key ecological projects related to forestry, the vegetation cover in Chongqing, has improved significantly. Existing studies have revealed the effects of climate change and human activity on vegetation cover in arid regions. However, more studies are needed to reveal the influence of drivers on vegetation cover in mild and humid areas, to quantify the relative contribution of drivers and to analyze the overall land use characteristics in different regions. In this study, we used Theil–Sen slope analysis and the Mann–Kendall test to investigate the spatial and temporal changes in vegetation cover in Chongqing. Further, we used Pearson correlation analysis to analyze the correlations between vegetation cover and drivers, quantitatively analyzing the relative contributions of these drivers. Complex network model analysis was used for different regions to obtain their land-use system characteristics, and the Hurst index was adopted to predict future vegetation-cover changes. The results of this study showed that the average vegetation cover in Chongqing increased significantly from 2000 to 2020, and the overall greening trend was most obvious in winter. Precipitation and temperature influenced the vegetation cover of Chongqing city to a certain extent, and the positive correlation between vegetation cover and precipitation was more significant than that with temperature. In terms of the precipitation factor, the areas with significant positive correlations were mainly concentrated in the central and southern parts of Chongqing, which could be related to the higher precipitation in the southern part of the city. Under the combined influence of climate change and human activity, vegetation cover increased in 71.95% of the total area. Human activity had a relative contribution of 70.39% and 69.14% in the areas where vegetation cover decreased and increased, respectively. The analysis results of the complex network model showed that woodlands and grasslands contributed more to areas where the vegetation cover exhibited an increasing trend. In the future, it is estimated that 72.92% of the vegetation cover in Chongqing will exhibit a degradation trend. This study helps us further understand vegetation-cover changes in mild and humid areas, providing new research directions for informing forestry-related policies.
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Teferi, E., S. Uhlenbrook, and W. Bewket. "Inter-annual and seasonal trends of vegetation condition in the Upper Blue Nile (Abay) Basin: dual-scale time series analysis." Earth System Dynamics 6, no. 2 (September 25, 2015): 617–36. http://dx.doi.org/10.5194/esd-6-617-2015.
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Abstract. A long-term decline in ecosystem functioning and productivity, often called land degradation, is a serious environmental challenge to Ethiopia that needs to be understood so as to develop sustainable land use strategies. This study examines inter-annual and seasonal trends of vegetation cover in the Upper Blue Nile (UBN) or Abbay Basin. The Advanced Very High Resolution Radiometer (AVHRR)-based Global Inventory, Monitoring, and Modeling Studies (GIMMS) normalized difference vegetation index (NDVI) was used for long-term vegetation trend analysis at low spatial resolution. Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data (MOD13Q1) were used for medium-scale vegetation trend analysis. Harmonic analyses and non-parametric trend tests were applied to both GIMMS NDVI (1981–2006) and MODIS NDVI (2001–2011) data sets. Based on a robust trend estimator (Theil–Sen slope), most parts of the UBN (~ 77 %) showed a positive trend in monthly GIMMS NDVI, with a mean rate of 0.0015 NDVI units (3.77 % yr−1), out of which 41.15 % of the basin depicted significant increases (p < 0.05), with a mean rate of 0.0023 NDVI units (5.59 % yr−1) during the period. However, the MODIS-based vegetation trend analysis revealed that about 36 % of the UBN showed a significant decreasing trend (p < 0.05) over the period 2001–2011 at an average rate of 0.0768 NDVI yr−1. This indicates that the greening trend of the vegetation condition was followed by decreasing trend since the mid-2000s in the basin, which requires the attention of land users and decision makers. Seasonal trend analysis was found to be very useful to identify changes in vegetation condition that could be masked if only inter-annual vegetation trend analysis was performed. Over half (60 %) of the Abay Basin was found to exhibit significant trends in seasonality over the 25-year period (1982–2006). About 17 and 16 % of the significant trends consisted of areas experiencing a uniform increase in NDVI throughout the year and extended growing season, respectively. These areas were found primarily in shrubland and woodland regions. The study demonstrated that integrated analysis of inter-annual and intra-annual trends based on GIMMS and MODIS enables a more robust identification of changes in vegetation condition.