Examining the spatiotemporal dynamics of meteorological variables in the context of changing climate, particularly in countries where rainfed agriculture is predominant, is vital to assess ...climate-induced changes and suggest feasible adaptation strategies. To that end, trend analysis has been employed to inspect the change of rainfall and temperature in northcentral Ethiopia using gridded monthly precipitation data obtained from Global Precipitation and Climate Centre (GPCC V7) and temperature data from Climate Research Unit (CRU TS 3.23) with 0.5° by 0.5° resolution from 1901 to 2014. Data have been analyzed using coefficient of variation, anomaly index, precipitation concentration index and Palmer drought severity index. Furthermore, Mann-Kendall test was used to detect the time series trend. The result revealed intra- and inter-annual variability of rainfall while Palmer drought severity index value proved the increasing trend of the number of drought years. Annual, belg and kiremt rainfall have decreased with a rate of 15.03, 1.93 and 13.12 mm per decade respectively. The declining trend for annual and kiremt rainfall was found to be statistically significant while that of belg was not significant. The rate of change of temperature was found to be 0.046, 0.067 and 0.026 °C per decade for mean, minimum and maximum respectively. The Mann-Kendall trend analysis test result revealed increasing trend for mean and minimum average temperatures through time significantly while the trend for maximum temperature exhibited a non-significant increasing trend. We recommend strategies designed in the agricultural sector have to take the declining and erratic nature of rainfall and increasing trend of temperature into consideration.
•Pre-whitening cannot really improve trend identification when using the MK test.•Series’ trend magnitudes greatly influence trend identification of series.•EMD method can be an effective alternative ...for trend identification of series.•EMD method can identify the specific shape of the analyzed series’ trend.
Trend identification is an important issue in hydrological time series analysis, but it is also a difficult task due to the diverse performances of methods. This paper mainly investigated the performances between the Mann–Kendall (MK) test and the empirical mode decomposition (EMD) method for trend identification of series. Analyses of both synthetic and observed series indicate the better performance of EMD compared with the other. The results show that pre-whitening cannot really improve trend identification when using the MK test, but cause wrong results sometimes. It can be due to the good correlation of trend, so pre-whitening would weaken trend’s magnitude. If the trend of the analyzed series has small magnitude, it cannot be accurately identified by the MK test, because the trend would be submerged too severely by other components of series to accurately identify trend. When the analyzed series has short length, its trend cannot be accurately identified by the MK test. However, the EMD method can eliminate the influences of trends’ magnitude and series’ length, so it has more effective power for trend identification. As a result, it is suggested that series’ trend can be directly identified by the MK test but need not do pre-whitening; moreover, the influences of trends’ magnitude should be carefully considered for trend identification. Comparatively, the EMD method can adaptively determine the specific shape of the nonlinear and non-stationary trend of series by considering statistical significance, so it can be an effective alternative for trend identification of hydrological time series.
Analyzing hourly ozone data from 214 European background sites over the time period 2000–2010, we demonstrated for the first time that the ozone control measures are effective at rural sites, while ...ozone concentrations are still increasing in the cities. The Western European Mediterranean basin is expected to be more strongly affected by climate change, including ozone pollution, than most of the other regions of the world. At 58% of the rural sites significant decreases were found resulting in an average – 0.43% per year while an increase was recorded in urban and suburban stations (+0.64% year−1 and +0.46% year−1, respectively). At cities ozone average levels increased, but the peak ozone concentrations decreased. In all station types, a significant reduction in the amplitude of peak ozone concentrations was found at more than 75% of stations (98th percentile, −0.77% year−1; hourly peak, −1.14% year−1 and daily average peak, −0.76% year−1). The peak reduction may largely be attributed to the reduction in NOx and VOC emissions within the European Union which started in the early 1990s. The results suggested a convergence of ozone pollution at remote and urban sites all around the Western European Mediterranean basin.
•We calculate annual trends for ozone and associated statistics.•We discuss of spatial distribution of levels and changes in ozone concentrations.•We use an innovative method by co-kriging to map results.•We discuss of possible explanations of observed trends.•We discuss of the convergence of ozone pollution at remote and urban sites all around the Mediterranean Europe.
Long‐term precipitation monitoring plays a vital role in water resource management and disaster prevention and mitigation. This study assesses spatial and temporal trends in seasonal and annual ...precipitation in Pakistan between 1960 and 2016 at an interannual scale. The Mann–Kendall (MK) test, Sen's slope (SS) estimator, and Sequential Mann–Kendall (SQMK) test were employed to assess trends. Cluster analysis and L‐moment approach were used to identify the homogenous precipitation regions. In general, increasing precipitation trends between 1960 and 2016 were evident. Results indicated increasing precipitation in winter, autumn, summer and annual scale at the rates of 0.20, 2.18, 5.16, and 10.89 mm·decade−1, respectively. In spring, the precipitation trend shows a decreasing trend at −0.67 mm·decade−1. Moreover, a significant decreasing trend occurred in winter in southern Pakistan. The overall increasing trends were more noticeable between 1960 and 1988, compared to the declining precipitation during 1989–2016. SQMK analysis indicates a clear downward trend in most regions during 1989–2016, except in autumn. Annual precipitation has increased topographically except at 500 m and 1,500 m during 1960–2016 with a significant increase of 1.37 mm·decade−1 at elevation <250 m. Results indicate a negative correlation in SS test value with seasonal and annual precipitation with elevation and a positive correlation in winter. The seasonal and annual precipitation trends exhibit increasing and decreasing trends before and after 1990, respectively, in most subregions. The notable finding based on the outcomes of this study is that the whole country observed an increasing trend during 1960–1988, followed by a decreasing trend in during 1989–2016. This decreasing tendency is particularly pronounced between 1985 and 1995, except in autumn. Agriculture production is largely reliant on precipitation in many regions. So, a detailed study of the influence of monsoon trends and large‐scale climatic variability controls over Pakistan is vital for improved water resource management in the context of global warming and rising human activity. The results will help policy makers while establishing and updating water‐related initiatives and regulations.
During 1960–2016, results indicated increasing precipitation in winter, autumn, summer, and annual scale at the rates of 0.20, 2.18, 5.16, and 10.89 mm·decade−1, respectively. Moreover, a significant decreasing trend occurred in winter in southern Pakistan. The overall increasing trends were more noticeable between 1960 and 1988, compared to the declining precipitation during 1989–2016.
The principle of maximum entropy can provide consistent basis to analyze water resources and geophysical processes in general. In this paper, we propose to assess the space-time variability of ...rainfall and streamflow in northeastern region of Brazil using the Shannon entropy. Mean values of marginal and relative entropies were computed for a 10-year period from 189 stations in the study area and entropy maps were then constructed for delineating annual and seasonal characteristics of rainfall and streamflow. The Mann–Kendall test was used to evaluate the long-term trend in marginal entropy as well as relative entropy for two sample stations. High degree of similarity was found between rainfall and streamflow, particularly during dry season. Both rainfall and streamflow variability can satisfactorily be obtained in terms of marginal entropy as a comprehensive measure of the regional uncertainty of these hydrological events. The Shannon entropy produced spatial patterns which led to a better understanding of rainfall and streamflow characteristics throughout the northeastern region of Brazil. The total relative entropy indicated that rainfall and streamflow carried the same information content at annual and rainy season time scales.
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•Entropy can be used for assessing rainfall and streamflow variability•The uncertainty level in streamflow data is higher than in rainfall data•Rainfall and streamflow variability can be obtained in terms of marginal entropy•Rainfall and streamflow carry the same information content
As abundant distribution of glaciers and snow, the Tianshan Mountains are highly vulnerable to changes in climate. Based on meteorological station records during 1960–2016, we detected the variations ...of air temperature and precipitation by using non-parametric method in the different sub-regions and different elevations of the Tianshan Mountains. The mutations of climate were investigated by Mann-Kendall abrupt change test in the sub-regions. The periodicity is examined by wavelet analysis employing a chi-square test and detecting significant time sections. The results show that the Tianshan Mountains experienced an overall rapid warming and wetting during study period, with average warming rate of 0.32°C/10a and wet rate of 5.82mm/10a, respectively. The annual and seasonal spatial variation of temperature showed different scales in different regions. The annual precipitation showed non-significant upward trend in 20 stations, and 6 stations showed a significant upward trend. The temperatures in the East Tianshan increased most rapidly at rates of 0.41°C/10a. The increasing magnitudes of annual precipitation were highest in the Boertala Vally (8.07mm/10a) and lowest in the East Tianshan (2.64mm/10a). The greatest and weakest warming was below 500m (0.42°C/10a) and elevation of 1000–1500m (0.23°C/10a), respectively. The increasing magnitudes of annual precipitation were highest in the elevation of 1500m–2000m (9.22mm/10a) and lowest in the elevation of below 500m (3.45mm/10a). The mutations of annual air temperature and precipitation occurred in 1995 and 1990, respectively. The large atmospheric circulation influenced on the mutations of climate. The significant periods of air temperature were 2.4–4.1years, and annual precipitation was 2.5–7.4years. Elevation dependency of temperature trend magnitude was not evidently in the Tianshan Mountains. The annual precipitation wetting trend was amplified with elevation in summer and autumn. The strong elevation dependence of precipitation increasing trend appeared in summer.
•Temporal and spatial trends of air temperature and precipitation in different sub-regions and elevations of Tianshan Mountains were analysed.•Mutations and significant periodicity of air temperature and precipitation were examined.•Large large scale atmospheric circulation and elevation dependence of trends in air temperature and precipitation were analysed.
Extreme cold events (“cold waves”) have disastrous impacts on ecosystem and human health. Evidence shows that these events will still occur under current increasing mean temperatures. Little research ...has been done on extreme cold events, especially in developing countries such as South Africa. These events pose a significant threat due to the low adaptive capacity, urgent development needs and relatively inadequate infrastructure in South Africa. This study presents annual and seasonal, spatial and temporal trend analyses of extreme cold temperature events for the period 1960–2016. We apply the World Meteorological Organisation Commission for Climatology and Indices Expert Team on Sector‐Specific Climate Indices (ET‐SCI) to South Africa for the first time, with comparison to the World Meteorological Organisation Expert Team on Climate Change Detection (ETCCDI) indices previously used in South Africa. The extreme cold indices are calculated using the RClimDex and ClimPACT, respectively. Trends were calculated using the non‐parametric Mann‐Kendall test, Spearman Rank Correlation Coefficient and Sen's slope estimates. A decreasing trend is found for annual cold spell duration and cold wave frequency, at rates of 0.10 days.day−1 and 0.02 events.day−1, respectively. Seasonally, coldest day temperatures increased in autumn, with increases of 0.02°C.day−1 for the period 1960–2016. Regionally, increasing trends in annual cold spell duration days were evident in stations located in the Western Cape, Eastern Cape, North‐West Province, at a rate of 0.03 days.day−1. Increasing trends in cold waves were observed for stations in Northern Cape, Gauteng, KwaZulu‐Natal and the Eastern Cape Province, at a rate of 0.01 events.day−1. These results contribute to the awareness and recognition of the incidence and duration of cold extreme events in South Africa, seeing that studies suggest that anomalously cold events may persist in a warming world.
A decreasing trend is found for annual cold spell duration and cold wave frequency, at rates of 0.10 days.day−1 and 0.02 events.day−1, respectively. Regionally, increasing trends in annual cold spell duration days were evident in stations located in the Western Cape, Eastern Cape, North‐West Province, at a rate of 0.03 days.day−1. Increasing trends in cold waves were observed for stations in Northern Cape, Gauteng, Kwazulu‐Natal and the Eastern Cape Province, at a rate of 0.01 events.day−1.
Gridded rainfall data of 0.5×0.5° resolution (CRU TS 3.21) was analysed to study long term spatial and temporal trends on annual and seasonal scales in Wainganga river basin located in Central India ...during 1901–2012. After testing the presence of autocorrelation, Mann–Kendall (Modified Mann–Kendall) test was applied to non-auto correlated (auto correlated) series to detect the trends in rainfall data. Theil and Sen׳s slope estimator test was used for finding the magnitude of change over a time period. For detecting the most probable change year, Pettitt–Mann–Whitney test was applied. The Rainfall series was then divided into two partial duration series for finding changes in trends before and after the change year. Arc GIS was used to explore spatial patterns of the trends over the entire basin. Though most of the grid points shows a decreasing trend in annual rainfall, only seven grids has a significant decreasing trend during 1901–2012. On the basis of seasonal trend analysis, non-significant increasing trend is observed only in post monsoon season while seven grid points show significant decreasing trend in monsoon rainfall and non-significant in pre-monsoon and winter rainfall over the last 112 years. During the study period, overall a 8.45% decrease in annual rainfall is estimated. The most probable year of change was found to be 1948 in annual and monsoonal rainfall. There is an increasing rainfall trend in the basin during the period 1901–1948, which is reversed during the period 1949–2012 resulting in decreasing rainfall trend in the basin. Homogeneous trends in annual and seasonal rainfall over a grid points is exhibited in the basin by van Belle and Hughes׳ homogeneity trend test.
In the last few decades, climate changes have become the most important topic in the field of climatology. Reference evapotranspiration (ET0) is often used to identify regions prone to drought or ...aridity. In this paper, we used monthly data recorded in 57 weather stations in Romania over the period 1961–2007. The FAO Penman–Monteith method, based on air temperature, sunshine duration, relative humidity and wind speed, was employed in order to calculate ET0. Seasonal, annual, winter wheat and maize growing seasons data sets of ET0 were generated. The trends were detected using the Mann–Kendall test and Sen's slope, while an ArcGIS software was employed for mapping the results. The main findings of the study are: positive slopes were found in 71% of the data series considered and almost 30% of the total number of series were found significant at α=0.05; the highest frequency of the increasing trends as well as their absolute maximum magnitude were detected during summer and maize growing season; in winter, significant increasing changes are specific mainly to the extra-Carpathians regions; in autumn decreasing ET0 is specific to more than 80% of the locations, but the significant decrease characterizes mainly the southern half of the country; during the growing seasons of maize and winter wheat, the increase of the ET0 is dominant for the entire country. The relative change decreases with the increase of the length of the period considered: the most intense changes were detected for climatic seasons, followed by crop growing seasons and annual values. Among the climatic seasons, the highest relative increase is specific to winter followed by summer, spring and autumn, while for the crop growing seasons the values detected are similar.
•Annual ET0 is higher than precipitation, mainly in the extra-Carpathian regions.•The annual ET0 is increasing in Romania in more than 70% of the locations.•ET0 increases during summer in 91% of the 57 weather stations considered.•In autumn, ET0 is decreasing in 84% of the locations.•Relative change of ET0 is more intense in winter than in summer.
Detecting change-points and trends are common tasks in the analysis of remote sensing data. Over the years, many different methods have been proposed for those purposes, including (modified) ...Mann–Kendall and Cox–Stuart tests for detecting trends; and Pettitt, Buishand range, Buishand U, standard normal homogeneity (Snh), Meanvar, structure change (Strucchange), breaks for additive season and trend (BFAST), and hierarchical divisive (E.divisive) for detecting change-points. In this paper, we describe a simulation study based on including different artificial, abrupt changes at different time-periods of image time series to assess the performances of such methods. The power of the test, type I error probability, and mean absolute error (MAE) were used as performance criteria, although MAE was only calculated for change-point detection methods. The study reveals that if the magnitude of change (or trend slope) is high, and/or the change does not occur in the first or last time-periods, the methods generally have a high power and a low MAE. However, in the presence of temporal autocorrelation, MAE raises, and the probability of introducing false positives increases noticeably. The modified versions of the Mann–Kendall method for autocorrelated data reduce/moderate its type I error probability, but this reduction comes with an important power diminution. In conclusion, taking a trade-off between the power of the test and type I error probability, we conclude that the original Mann–Kendall test is generally the preferable choice. Although Mann–Kendall is not able to identify the time-period of abrupt changes, it is more reliable than other methods when detecting the existence of such changes. Finally, we look for trend/change-points in land surface temperature (LST), day and night, via monthly MODIS images in Navarre, Spain, from January 2001 to December 2018.
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