Search

This study aimed to analyze the trend in land surface temperature (LST) over time using the entire archive of the available cloud-free Landsat images from 1986 to 2022 for Jordan and its nine local climate zones (LCZs). Two primary datasets were used (i) Landsat-5; -8 imagery, and (ii) map of LCZs of Jordan. All LST images were clipped, preprocessed, and checked for cloud contamination and bad pixels using the quality control bands. Then, time-series of monthly LST images were generated through compositing and mosaicking processes using cloud computing functions and Java scripts in Google Earth Engine (GEE). The Mann–Kendall (MK) test and Sen’s slope estimator (SSE) were used to detect and quantify the magnitude of LST trends. Results showed a warming trend in the maximum LST values for all LCZs while there was annual fluctuation in the trend line of the minimum LST values in the nine zones. The monthly average LST values showed a consistent upward trajectory, indicating a warming condition, but with variations in the magnitude. The annual rate of change in LST for the LCZs showed that the three Saharan zones are experiencing the highest rate of increase at 0.0184 K/year for Saharan Mediterranean Warm (SMW), 0.0185 K/year for Saharan Mediterranean Cool (SMC), and 0.0169 K/year for Saharan Mediterranean very Warm (SMvW), indicating rapid warming in these regions. The three arid zones came in the middle, with values of 0.0156 K/year for Arid Mediterranean Warm (AMW), 0.0151 for Arid Mediterranean very Warm (AMvW), and 0.0139 for Arid Mediterranean Cool (AMC), suggesting a slower warming trend. The two semi-arid zones and the sub-humid zone showed lower values at 0.0138, 0.0127, and 0.0117 K/year for the Semi-arid Mediterranean Cool (SaMC), Semi-arid Mediterranean Warm (SaMW) zones, and Semi-humid Mediterranean (ShM) zones, respectively, suggesting the lowest rate of change compared to other zones. These findings would provide an overall understanding of LST change and its impact in Jordan’s LCZs for sustainable development and water resources demand and management.

In recent decades, an increase in the earth’s atmospheric temperature has been noticed due to the augmentation of the volume of gases with the greenhouse effect (GHG) released into the atmosphere. To reduce this effect, the European Union’s directives indicate the action directions for reducing these emissions, among which carbon dioxide (CO2) recorded the highest amount. In this context, the article analyzes the CO2 series reported in 1990–2021 by 30 European countries. The Kruskal-Wallis test rejected the hypothesis that the series comes from the same underlying distribution. The Anderson-Darling test rejected the normality hypothesis for seven series out of thirty, and Sen’s procedure found a decreasing trend slope only for 17 series. ARIMA models have been built for all individual series. Grouping the series (by the k-means and hierarchical clustering) provided the base for building the Regional series (RegS), which describes the CO2 pollution evolution over Europe. The advantage of this approach is to provide the synthetic image of the regional evolution of the CO2 emission volume (mt), incorporating information from 30 series (one for each country) in only one—RegS. It is also shown that selecting the number of clusters involved in building RegS and assessing their stability is essential for the model’s goodness of fit.

In order to continuously promote water conservation efforts to alleviate the pressure of water diversion, water footprint (WF) is used as an effective tool to measure water utilization in the water-receiving areas of the Middle Route and Eastern Route of the South-to-North Water Diversion Project (SNWDP). The tempo-spatial variations of WF and spatial equilibrium of water footprint intensity (WFI) in the study area are quantified using the Mann–Kandle trend test, Sen’s slope, and Dagum Gini coefficient decomposition method for the years of 2005–2020. The results show that WF has a significant decreasing trend (Sen’s slop < 0, significant level < 0.05) in 17 cities in the study area, whereas WF shows a significant increasing trend (Sen’s slop > 0, significant level < 0.05) in 10 cities. Cities in the water-receiving areas are categorized into three types based on the contribution of the water utilization sector to changes in WF as follows: agriculture water-dominated city (AD), domestic and ecological water-dominated city (DED), and virtual water trade-dominated city (VWTD). Accordingly, targeted water conservation recommendations are made for these three kinds of cities, and it is suggested that AD, DED, and VWTD cities need to focus on advanced irrigation technologies, water reuse, and trade restructuring, respectively. The overall Gini coefficient of WFI fluctuates between 0.219 and 0.267 in the water-receiving areas of the Middle Route, which is dominated by the differences in city level. However, it fluctuates between 0.412 and 0.278 in the water-receiving areas of the Eastern Route, which is dominated by the differences in provincial level. Accordingly, water conservation hotspots are determined at the city level in the Middle Route and at the provincial level in the Eastern Route with different water management policies. These results provide a scientific support for water conservation management in the water-receiving areas of the SNWDP, as well as a methodological reference for the tempo-spatial characteristics of WF and their implications for water conservation.

The radiometric calibration network (RadCalNet) comprises four pseudo-invariant calibration sites (PICS): Gobabeb, Baotou, Railroad Valley Playa, and La Crau. Due to its site stability characteristics, it is widely used for sensor performance monitoring and radiometric calibration, which require high spatiotemporal stability. However, some studies have found that PICS are not invariable. Previous studies used top-of-atmosphere (TOA) data without verifying site data, which could affect the accuracy of their results. In this study, we analyzed the short- and long-term radiometric trends of RadCalNet sites using bottom-of-atmosphere (BOA) data, and verified the trends revealed by the TOA data from Landsat 7, 8, and Sentinel-2. Besides the commonly used methods (e.g., nonparametric Mann–Kendall and sequential Mann–Kendall tests), a more robust Sen’s slope method was used to estimate the magnitude of the change. We found that (1) the trends based on TOA reflectance contrasted with those based on BOA reflectance in certain cases, e.g., the reflectance trends in the red band of BOA data for La Crau in summer and autumn and Baotou were not significant, while the TOA data showed a significant downward trend; (2) the temporal trends showed statistically significant and abrupt changes in all PICS, e.g., the SWIR2 band of La Crau in winter and spring changed by 1.803% per year, and the SWIR1 band of Railroad Valley Playa changed by >0.282% per year, indicating that the real changes in sensor performance are hard to detect using these sites; (3) spatial homogeneity was verified using the coefficient of variation (CV) and Getis statistic (Gi*) for each PICS (CV < 3% and Gi* > 0). Overall, the RadCalNet remains a highly reliable tool for vicarious calibration; however, the temporal stability should be noted for radiometric performance monitoring of sensors.

Daily and monthly rainfall data provided by surface rain gauges in the Amazon Basin are sparse and defective, making it difficult to monitor rainfall patterns for certain portions of its territory, in this sense, estimations of precipitation from remote sensing calibrated with rain gauge data are key to overcome this problem. This paper presents a spatiotemporal analysis of the precipitation distribution for Rondônia State, in southwestern Amazonia. Data from Climate Hazards Group InfraRed Precipitation and Station (CHIRPS) were analyzed, using a pooled time analysis of a forty-year period (1981–2020). Data obtained from remote sensing were validated by rain gauges distributed over the study region. Pixel-by-pixel trend analyzes were developed by applying the Mann-Kendall test and Sen’s slope test to study the magnitude of the trend. The analysis revealed that CHIRPS presents a tendency to underestimate precipitation values in most cases. Among the metrics, mean values between very good (2 > 0.70) for about 64.7% of the stations. Sen’ slope spatialization results show a reduction of approximately −15 mm year−1, with decrease mainly in the Northern Region of Rondônia, which has extensive areas where the native forest has been replaced by pasture.

The western Himalayan region is susceptible to minor climate changes because of its fragile ecology, which might threaten the valley’s prestigious ecosystems and socio-economic components. The Himalayas’s local climate and weather are vulnerable to and interlinked with world-scale climatic changes since the region’s hydrology is predominantly dominated by snow and glaciers. The Himalayas, notably the Jammu and Kashmir region in the western Himalayas, has clearly shown distinct and robust evidence of climate change. This study used observed data to examine the climatic variability and trends of change in precipitation and temperature for the Kashmir valley between 1980 and 2020. Gulmarg, Pahalgam, Kokernag, Qazigund, Kupwara, and Srinagar (Shalimar) meteorological stations in the Kashmir valley were studied in detail for long- and short-term as well as localized fluctuations in temperature and precipitation. The annual temperature and precipitation fluctuations were calculated using Sen’s slope approach, and the sloping trend was determined using linear regression. The research showed statistically insignificant growing trends in maximum and minimum temperatures throughout the Kashmir valley. The average annual temperature in the Kashmir valley increased by 1.55 °C during the last 41 years (from 1980 to 2020), with a higher rise in maximum and minimum temperature by 2.00 and 1.10 °C, respectively. However, precipitation showed a non-significant decreasing trend concerning time series analysis over 1980 to 2020 in Kashmir valley. Results of annual average maximum temperature at all the stations revealed that Pahalgam (2.2 °C), Kokernag (1.8 °C), and Kupwara (1.8 °C) displayed a steep upsurge and statistically significant trends; however, annual average minimum temperature followed an increasing trend from 1980 to 2020 at all the stations except Shalimar. However, non-significant declining trends in precipitation were recorded at all the locations in Kashmir valley. This changing pattern of temperature and precipitation could have significant environmental consequences, affecting the western Himalayan region’s food security and ecological sustainability.

In this study, we analyze sea surface height referenced against the WGS84 ellipsoid at the Hon Dau tidal gauge station (Hai Phong, Vietnam), in front of the Red River Delta, between 1961 and 2020. The annual sea level varied from 165.23 cm to 206.06 cm in this period (+20.28 cm over 60 years). The average water level was 190.87 cm for 60 years, with higher annual values in recent years, especially from 2016 to the present (above 201.5 cm). The Mann–Kendall (MK) test with Sen’s slope estimator and Şen’s innovative trend analysis (ITA) were applied and compared to estimate the sea level rise. These methods showed complete agreement among tests with significant rising trends of about 3.38 mm/year with the MK test and 3.08 mm/year with the ITA method for 1961–2020. During the last 20 years (2001–2020), the mean sea level increased about 7.16 mm/year (MK test and Sen’s slope), 7.38 mm/year (ITA method), and around twice higher than the rate of the region and globally. The MK test and ITA method provided similar results for periods: 1961–2020, 1961–1980, and 2001–2020, with relatively stable monotonic related trend conditions. For the period 1981–2000, with a more nonmonotonic trend, the MK test and ITA method provided different trends and allowed to illustrate the specificity of each method.

As a major threat to ecosystem functions and national food security, soil erosion also exerts an influence on the water quality in basins and the operation and maintenance of hydropower plants. Existing discussions about trends of soil erosion focus mainly on its variation and mutation over time. Few studies have addressed the spatial mutation of soil erosion and its influence mechanism. In this research, Sen’s slope estimation was coupled with a Mann–Kendall model to explore the spatiotemporal distribution, spatial mutation characteristics and influence mechanisms of soil erosion, and conduct a case study on the Middle and Lower reaches of the Lancang River Basin (ML-LRB) in China. There are three main conclusions from this study: (1) During 2000–2019, the annual soil erosion in the ML-LRB variation ranged from 0 to 7.00 × 103 t/(km2·a) with a multi-year mean of 1.53 × 103 t/(km2·a), decreasing year by year from north to south, while an increasing trend began to appear in the central above region after 2015. (2) The areas with decreased soil erosion were much larger than those with increased soil erosion during 2000–2019, and there was a concentrated increase in soil erosion in Dali and in Xishuangbanna. (3) The mutation of the soil erosion intensity was spatially consistent with that of the Normalized Difference Vegetation Index (NDVI). Overall, this paper provides a new perspective for the study of factors affecting the trends and spatial mutation of soil erosion.

Investigating the research trends within a scientific domain by analyzing semantic information extracted from scientific journals has been a topic of interest in the natural language processing (NLP) field. A research trend evaluation is generally based on the time evolution of the term occurrence or the term topic, but it neglects an important aspect—research publication latency. The average time lag between the research and its publication may vary from one month to more than one year, and it is a characteristic that may have significant impact when assessing research trends, mainly for rapidly evolving scientific areas. To cope with this problem, the present paper is the first work that explicitly considers research publication latency as a parameter in the trend evaluation process. Consequently, we provide a new trend detection methodology that mixes auto-ARIMA prediction with Mann–Kendall trend evaluations. The experimental results in an electronic design automation case study prove the viability of our approach.

Dhaka city, having a population of about 18 million, depends heavily on groundwater as a source of quality water. However, the city is encountering a rapid depletion of groundwater, and its groundwater-based water supply is at risk of failure. This study was carried out to analyze the groundwater depletion scenarios occurring from 1970 to 2019 in the city and to find suitable options to sustain its water supply. The trends in groundwater levels (GWLs) were quantified by the non-parametric Sen’s slope and their significances were assessed by the modified Mann-Kendall test. Contour maps of GWL were generated to develop the contemporary GWL scenario in the city. Key informant interviews (KIIs) with the Dhaka Water Supply and Sewerage Authority (DWASA) officials, groundwater experts and researchers, in addition to semi-structured interviews with the DWASA consumers were conducted to assess current adaptation practices and to develop potential adaptation options. The effectiveness of the options was assessed by strengths, weaknesses, opportunities and threats (SWOT) analysis and the options were ranked through a normalization process of the weights given by the KIIs for future adaptabilities. The GWLs were found to be decreasing all over the city, varying from 0.6–2.4 m/year. The contour maps demonstrated that the groundwater of the central area had depleted more than the peripheral areas. The locations vulnerable to severe groundwater depletion were identified to be the Khilgaon, Sobujbagh, Motijheel, Dhanmondi, and Sutrapur areas and some parts of the Cantonment and Mirpur areas. Potential options identified were adopting more surface water treatment plants, rainwater harvesting, implementing a block tariff system, reducing non-revenue water, metering water consumption, and promoting public awareness. Spatially-varying adaptation strategies were also suggested for different zones. Some measures adopted by DWASA were not supported by the respondents.

Climate variability and change constitute major challenges for Africa, especially West Africa (WA), where an important increase in extreme climate events has been noticed. Therefore, it appears essential to analyze characteristics and trends of some key climatological parameters. Thus, this study addressed spatiotemporal variabilities and trends in regard to temperature and precipitation extremes by using 21 models of the Coupled Model Intercomparison Project version 6 (CMIP6) and 24 extreme indices from the Expert Team on Climate Change Detection and Indices (ETCCDI). First, the CMIP6 variables were evaluated with observations (CHIRPS, CHIRTS, and CRU) of the period 1983–2014; then, the extreme indices from 1950 to 2014 were computed. The innovative trend analysis (ITA), Sen’s slope, and Mann–Kendall tests were utilized to track down trends in the computed extreme climate indices. Increasing trends were observed for the maxima of daily maximum temperature (TXX) and daily minimum temperature (TXN) as well as the maximum and minimum of the minimum temperature (TNX and TNN). This upward trend of daily maximum temperature (Tmax) and daily minimum temperature (Tmin) was enhanced with a significant increase in warm days/nights (TX90p/TN90p) and a significantly decreasing trend in cool days/nights (TX10p/TN10p). The precipitation was widely variable over WA, with more than 85% of the total annual water in the study domain collected during the monsoon period. An upward trend in consecutive dry days (CDD) and a downward trend in consecutive wet days (CWD) influenced the annual total precipitation on wet days (PRCPTOT). The results also depicted an upward trend in SDII and R30mm, which, additionally to the trends of CDD and CWD, could be responsible for localized flood-like situations along the coastal areas. The study identified the 1970s dryness as well as the slight recovery of the 1990s, which it indicated occurred in 1992 over West Africa.

This research assessed the changes in spatial patterns and the seasonal trends in temperature, precipitation, and relative humidity over 36 years (1979–2014) using Climate Forecast System Reanalysis (CFSR) datasets. The evaluation of climate deviations was the prime objective of this research. The augmented Dickey–Fuller Test (ADF) was used to scrutinize whether the data was either stationary or non-stationary. The results of the ADF test showed that all the datasets were found to be stationary at lag order 3. To observe undulations in the time series data, trend analyses were done using Sen’s slope (SS), Mann–Kendall (MK), and Cox and Stuart (CS) tests. For all the statistical analyses, we considered the 5% significance level (α = 0.05) and p < 0.05 to be statistically significant. We observed significant (p < 0.05) trends in spring (MAM) and autumn (SON) for minimum temperature (Tmin) in Punjab. We also noted a significant (p < 0.05) trend in precipitation during autumn (SON). Annually, all the variables showed a non-significant (p > 0.05) trend for Punjab, Pakistan, during the period 1979–2014. Climate variability, such as a decrease in precipitation, higher temperature, and relative humidity fluctuations, were the reasons for the imbalance in the sustainability of Punjab, Pakistan.

Rising temperature and shifting precipitation patterns due to climate change are likely to intensify droughts throughout the world. Understanding the drought characteristics of possible future scenarios under climate change requires verification of past drought events using appropriate drought indices. Consequently, this study investigates the application of two widely used drought indices, the standardized precipitation index (SPI) and standardized streamflow index (SSI), to characterize historical droughts, drought trends, and their impact on water quality and stream integrity for a selected study basin in New Jersey. Results indicated that both SPI and SSI were able to identify historical drought events, including three drought emergency periods and the most recent drought-watch periods. A significant positive meteorological drought was observed at the western side of the basin, whilst a significant positive hydrological drought was found in the eastern side. The average pollutant concentration of drought periods were lesser than non-drought periods due to reduction of different processes, such as erosion and transport of sediment and nutrients into rivers and streams, during drought periods as opposed to non-drought periods. The findings from this study will serve to bolster the ongoing efforts to formulate better drought management strategies for future climate change in the Raritan Basin.

Aggregated mining development has direct and indirect impacts on vegetation changes. This impact shows spatial differences due to the complex influence of multiple mines, which is a common issue in resource regions. To estimate the spatial heterogeneity of vegetation response to mining activities, we coupled vegetation changes and mining development through a geographically weighted regression (GWR) model for three cumulative periods between 1999 and 2018 in integrated resource regions of northwestern China. Vegetation changes were monitored by Sen’s slope and the Mann–Kendall test according to a total of 72 Landsat images. Spatial distribution of mining development was quantified, due to four land-use maps in 2000, 2005, 2010, and 2017. The results showed that 80% of vegetation in the study area experienced different degrees of degradation, more serious in the overlapping areas of multiple mines and mining areas. The scope of influence for single mines on vegetation shrunk by about 48%, and the mean coefficients increased by 20%, closer to mining areas. The scope of influence for multiple mines on vegetation gradually expanded to 86% from the outer edge to the inner overlapping areas of mining areas, where the mean coefficients increased by 92%. The correlation between elevation and vegetation changes varied according to the average elevation of the total mining areas. Ultimately, the available ecological remediation should be systematically considered for local conditions and mining consequences.

Global climate change is significant, and the spatiotemporal variations of precipitation associated with it are pronounced. Based on the daily precipitation data from 10 weather stations located from southeast to northwest across China from 1961–2017, the Mann–Kendall trend test was generally applied to analyze the spatiotemporal variations of precipitation. The factors influencing the precipitation changes were investigated. The results revealed that (1) the annual, summer, and winter rainfall amount (RA) exhibited increasing rates of 16.36, 12.31, and 2.49 mm/10 year, respectively. The change rates of annual rainfall days (RD) were 2.68 day/10 year in the northwest region and −1.88 day/10 year in the southeast. The annual and seasonal daily precipitation on rainy days (RP) exhibited an increasing trend. (2) All of the RA, RD, and RP values initially increased, then decreased, and then slightly increased from Southeast to Northwest China. These results proved that the RA increased with the increase of light rain in Northwest China and heavy rain in Southeast China. In addition, changes in the monsoon have altered the rate at which RA, RD, and RP vary with distance from the sea. These findings may help to provide suggestions for the rational spatial utilization of water resources in China.

Many developing nations are facing severe food insecurity partly because of their dependence on rainfed agriculture. Climate variability threatens agriculture-based community livelihoods. With booming population growth, agricultural land expands, and natural resource extraction increases, leading to changes in land use and land cover characterized by persistent vegetation greening and browning. This can modify local climate variability due to changing land–atmosphere interactions. Yet, for landscapes with significant interannual variability, such as the Mount Elgon ecosystem in Kenya and Uganda, characterizing these changes is a difficult task and more robust methods have been recommended. The current study combined trend (Mann–Kendall and Sen’s slope) and breakpoint (bfast) analysis methods to comprehensively examine recent vegetation greening and browning in Mount Elgon at multiple time scales. The study used both Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) and Climate Hazards group Infrared Precipitation with Stations (CHIRPS) data and attempted to disentangle nature- versus human-driven vegetation greening and browning. Inferences from a 2019 field study were valuable in explaining some of the observed patterns. The results indicate that Mount Elgon vegetation is highly variable with both greening and browning observable at all time scales. Mann–Kendall and Sen’s slope revealed major changes (including deforestation and reforestation), while bfast detected most of the subtle vegetation changes (such as vegetation degradation), especially in the savanna and grasslands in the northeastern parts of Mount Elgon. Precipitation in the area had significantly changed (increased) in the post-2000 era than before, particularly in 2006–2010, thus influencing greening and browning during this period. The greenness–precipitation relationship was weak in other periods. The integration of Mann–Kendall and bfast proved useful in comprehensively characterizing vegetation greenness. Such a comprehensive description of Mount Elgon vegetation dynamics is an important first step to instigate policy changes for simultaneously conserving the environment and improving livelihoods that are dependent on it.

The effects of temperature and rainfall changes on hydropower generation in Ghana from 1960–2011 were examined to understand country-wide trends of climate variability. Moreover, the discharge and the water level trends for the Akosombo reservoir from 1965–2014 were examined using the Mann-Kendall test statistic to assess localised changes. The annual temperature trend was positive while rainfall showed both negative and positive trends in different parts of the country. However, these trends were not statistically significant in the study regions in 1960 to 2011. Rainfall was not evenly distributed throughout the years, with the highest rainfall recorded between 1960 and 1970 and the lowest rainfalls between 2000 and 2011. The Mann-Kendall test shows an upward trend for the discharge of the Akosombo reservoir and a downward trend for the water level. However, the discharge irregularities of the reservoir do not necessarily affect the energy generated from the Akosombo plant, but rather the regular low flow of water into the reservoir affected power generation. This is the major concern for the operations of the Akosombo hydropower plant for energy generation in Ghana.

Global climate change has local implications. Focusing on datasets from the topographically-challenging Karnali river basin in Western Nepal, this research provides an overview of hydro-climatic parameters that have been observed during 1981–2012. The spatial and temporal variability of temperature and precipitation were analyzed in the basin considering the seven available climate stations and 20 precipitation stations distributed in the basin. The non-parametric Mann–Kendall test and Sen’s method were used to study the trends in climate data. Results show that the average precipitation in the basin is heterogeneous, and more of the stations trend are decreasing. The precipitation shows decreasing trend by 4.91 mm/year, i.e., around 10% on average. Though the increasing trends were observed in both minimum and maximum temperature, maximum temperature trend is higher than the minimum temperature and the maximum temperature trend during the pre-monsoon season is significantly higher (0.08 °C/year). River discharge and precipitation observations were analyzed to understand the rainfall-runoff relationship. The peak discharge (August) is found to be a month late than the peak precipitation (July) over the basin. Although the annual precipitation in most of the stations shows a decreasing trend, there is constant river discharge during the period 1981–2010.