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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.

Fractional vegetation cover (FVC) is an important biophysical parameter of terrestrial ecosystems. Variation of FVC is a major problem in research fields related to remote sensing applications. In this study, the global FVC from 1982 to 2011 was estimated by GIMMS NDVI data, USGS global land cover characteristics data and HWSD soil type data with a modified dimidiate pixel model, which considered vegetation and soil types and mixed pixels decomposition. The evaluation of the robustness and accuracy of the GIMMS FVC with MODIS FVC and Validation of Land European Remote sensing Instruments (VALERI) FVC show high reliability. Trends of the annual FVCmax and FVCmean datasets in the last 30 years were reported by the Mann–Kendall method and Sen’s slope estimator. The results indicated that global FVC change was 0.20 and 0.60 in a year with obvious seasonal variability. All of the continents in the world experience a change in the annual FVCmax and FVCmean, which represents biomass production, except for Oceania, which exhibited a significant increase based on a significance level of p = 0.001 with the Student’s t-test. Global annual maximum and mean FVC growth rates are 0.14%/y and 0.12%/y, respectively. The trends of the annual FVCmax and FVCmean based on pixels also illustrated that the global vegetation had turned green in the last 30 years. A significant trend on the p = 0.05 level was found for 15.36% of the GIMMS FVCmax pixels on a global scale (excluding permanent snow and ice), in which 1.8% exhibited negative trends and 13.56% exhibited positive trends. The GIMMS FVCmean similarly produced a total of 16.64% significant pixels with 2.28% with a negative trend and 14.36% with a positive trend. The North Frigid Zone represented the highest annual FVCmax significant increase (p = 0.05) of 25.17%, which may be caused mainly by global warming, Arctic sea-ice loss and an advance in growing seasons. Better FVC predictions at large regional scales, with high temporal resolution (month) and long time series, would advance our ability to understand the characteristics of the global FVC changes in the last 30 years and predict the response of vegetation to global climate change.

Water is one of the most essential elements for human life and must be provided for life requirements. Historical changes in meteorological data are vital for the planning and operation of water management. A total of 516-time series were used to evaluate the characteristics of drought in Elazig in Turkey. In this study, meteorological drought analysis was carried out in monthly and annual periods by using the Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Precipitation Index (SPI, Innovative Polygon Trend Analysis (IPTA), and China-Z Index (CZI) drought indices. As a result, it was determined that there was an increase in dry periods for all time scales for eight meteorological stations, especially in 2000 and after. A downward trend was detected in precipitation data, while an upward trend was detected in temperature and evaporation data based on a 95% confidence interval. Although normal drought has the highest share among drought categories, very severe drought has the lowest share. it is determined that SPI gives more sensitive results in the very severe drought category than the SPEI index. As a result, the region's trend of rain and temperature will assist water management for resource planning.

The objective of this study is to analyze and visualize the spatial distribution of trends for 74 climate indices on a monthly time-scale in direction, magnitude, and significance level at a resolution of 0.1° during the period of 1950–2021 over the European region. The Mann–Kendall and Sen's slope estimators reveal that growing degree days with mean air temperature >4 °C (gd4) and heating degree days with mean air temperature <17 °C (hd17) show the largest increase (0.93 °C August) and decrease (1.03 °C July), respectively. The universal thermal climate index (utci), relative humidity (rh), wind chill index (wci), global radiation (bio20), and potential evapotranspiration (pet) are of significant importance due to higher correlation and magnitude of change. Country-specific zoning shows the highest warmer days during August experienced by Bosnia and Herzegovina (southeastern Europe) and lower colder days during January by Belarus (eastern Europe). High wind and high utci were experienced by Liechtenstein (southeastern Europe) region during July. The highest wci was experienced by San Marino (southern Europe) in June and Portugal (southern Europe) in March. Bio20 and rh decline were experienced by Russia (eastern Europe) and Moldova (southeastern Europe) in May and September, respectively. Results are useful to mitigate the risk associated with each of the climate indices for specific European regions.

Fluctuations in the precipitation pattern often tend to have an impact on the availability of water, making it necessary to explore spatiotemporal variations in rainfall. This study explores the time series analysis of the rainfall from 1952 to 2019. The trend was analyzed using the modified Mann–Kendall test (MMK), and innovative trend analysis (ITA). The analysis showed that the northern region received the least rainfall while the southern region received the maximum rainfall except that one of the stations had a positive kurtosis. The kurtosis of the rainfall histogram ranges from −0.69 to 24.13. The trend was very well defined by all the methods, though MMK z statistics showed more occurrences of significant changes in the rainfall. The northeast monsoon carried a significantly decreasing trend at Chikkanayakanahalli station where the z value of MMK and ITA_R test showed values of −1.33 and −2.23, respectively, while all of the significantly increasing trends were defined by the MMK test in the annual and southwest monsoon season. The homogeneity test showed the most correlation between Pettitt and Buishand tests in comparison to SNHT. Later, the ARIMA model was run for the precipitation to predict the rainfall value from 2019 to 2029.

The main purpose of this paper was to investigate the monthly, seasonal, and annual rainfall variability in the Mae Klong River Basin in Thailand using the Mann–Kendall (MK) test, Sen's slope method, Spearman's Rho (SR) test, and the innovative trend analysis (ITA) method. The monthly rainfall data of eight stations for the period 1971–2015 were used for trend analysis. Datasets with significant serial correlation were corrected by the trend-free pre-whitening (TFPW) approach for statistical methods. The MK test showed increasing rainfall trends for five out of eight stations in the dry season while 50% of stations indicated increasing trends in the wet season. On an annual scale, 75% of the stations exhibited increasing rainfall trends. The results of the SR test were in line with the MK test for seasonal and annual rainfall. The ITA method showed comparable findings with those of the statistical methods. For the entire basin, trend analysis found increasing rainfall on both seasonal and annual scales by all the tests. The findings of this study could benefit water supply and management, drought monitoring, agricultural production activities, and socioeconomic development in the Mae Klong River Basin in the future.

Groundwater overexploitation along with decreasing precipitation exacerbates groundwater level decline and causes groundwater drought. Efficient assessment of the drought is critical to water management, especially in drought-prone agriculture regions. It remains challenging to characterize groundwater drought quantitatively due to the difficulty in obtaining groundwater observation data and the complexity of groundwater flow systems. To this end, agglomerative hierarchical cluster analysis was performed on long-term groundwater levels to classify wells in the San Joaquin River Basin, California. A Modified Mann–Kendall (MMK) test was undertaken to detect seasonal groundwater level trends from 1980 to 2019, and the magnitude was calculated using Sen's slope estimator. A nonparametric Standardized Groundwater level Index (SGI) was used to quantify the characteristics of groundwater drought. Results show that long-term (40-year) temporal patterns in groundwater levels varied significantly over the San Joaquin River Basin. Significantly decreasing trends were observed among more than 34.6% of wells, with an average decline of 0.69 m/year. Wells suffered frequent and severe groundwater droughts in the last decade, which were mainly driven by heavy groundwater exploitation. Findings provide useful information about the long-term behavior of regional groundwater levels, which in turn help stakeholders monitor droughts and adapt groundwater management strategies.

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.

Investigating the trends of hydro-meteorological variables and checking its variability are of great importance for water resources management and development in the River Nile basin. The present study aimed to analyze the rainfall variability and trends in the Upper Blue Nile Basin (UBNB) over a period from 1953 to 2014. Variability analysis showed that the basin has been suffering from variable rainfall events causing severe droughts and floods over different years. According to precipitation concentration index calculations, the basin had irregular and strong irregular rainfall distribution over the annual and dry seasons, while the basin had a uniform and moderate rainfall distribution during the rainy season and small rainy season. For the total annual rainfall, Mann–Kendall test indicated that, for the eastern central part of the basin, a significant increasing trend of 12.85 mm/year occurred over the studied period, while, for the southwestern part of the basin, a significant decrease of 17.78 mm/year occurred. For the rainy season, a significant increasing trend over the northeastern and eastern central parts of the basin with the magnitude of 3.330–12.625 mm/year occurred. Trend analysis was applied on the monthly averaged rainfall over the whole basin and revealed that July and August are the most contributors of rainfall to the basin with 23.32% and 22.65%. Changing point assessment revealed that at Lake Tana outlet there is a decreasing of the rainfall of 17.7% after 1977 that matched with the trend analysis results. The data and results contained herein provide updated information about the current situation in the UBNB. The results can be used to predict future precipitation and estimate the uncertainty in future precipitation prediction models.

This paper has examined the variability and trends of rainfall in the Borana zone, southern Ethiopia. Monthly rainfall data from 1981 to 2018 were obtained from the National Meteorological Agency (NMA) of Ethiopia. Mean, standard deviation and coefficient of variation were employed to analyze temporal variability. Mann–Kendall (MK) test and Sen's slope estimator (SEE) were used to determine trend and its magnitude, respectively. The inverse weighting distance (IDW) interpolation technique was employed to generate surface data and produce spatial rainfall maps. April and Belg were the wettest month and season, respectively. On an annual basis, Arero (741 mm) followed by Teltele (629 mm) were the wettest stations, whereas the Dillo station (285 mm) was the driest. Rainfall is highly variable on a monthly and seasonal basis than annual timescale. Meher rainfall has shown a significant rainfall increase (P-value <0.05) at most stations. A significant increase in annual rainfall was observed at Arero, Dehas, Dillo and Miyo. Spatially, rainfall decreases from the northeast and northwest parts of the Borana towards the southwest. The findings of this study can serve as a reference basis and provides useful information for policymakers to devise and implement better water management strategies in this water-scarce region.