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Remote sensing freely provides many processed image products such as moderate resolution imaging spectroradiometer (MODIS), and long-term data record (LTDR), for the investigation of drought evolution. Our objectives are to investigate drought evolution and spatiotemporal variations from 1982 to 2017 based on two remote-sensing indices, namely, the normalized difference vegetation index (NDVI) and the vegetation condition index (VCI), and a popular meteorological index—standardized precipitation index (SPI)—under four different land cover types, cropland, forestland, grassland, and desertland in China. The modified Mann–Kendall test was used to detect the significance of a trend. The Pearson correlation method was used to find the relationship between NDVI anomaly, VCI, precipitation, and SPI. The results revealed that (a) both mean monthly and yearly precipitation had a general land cover type rank of forestland > grassland ≈ cropland > desertland. (b) A positive correlation was found between drought indices (NDVI anomaly, VCI, SPI) and precipitation for different land cover types. The NDVI anomaly and VCI were well correlated with 3-month SPI for cropland and were well correlated with 6-month SPI for forestland. VCI performed better than NDVI anomaly when correlating with SPI. (c) The coefficient of determination ( R 2 ) was obtained for precipitation and VCI in the driest (2011) and wettest (2016) years. The R 2 values for desert and grassland ranged from 0.70 to 0.90 and for cropland and forestland were lower (0.54–0.69). (d) Only precipitation, SPI, and VCI of cropland had significant increasing trends. The spatial distribution patterns of precipitation, NDVI, and VCI increased with the decreased elevation. The study revealed that desert and grassland had been regularly exposed to moderate or extreme droughts conditions and confirmed that desert and grassland are more sensitive to short-term drought.

Melatonin is an important plant growth regulator which plays a key role in plant growth and development. The objective of the current research was to evaluate the effect of foliar application of melatonin (MF) on photosynthetic efficiency, antioxidant defense mechanism, and its relation with leaf senescence in maize crop grown in a semi-arid region. A field experiment was conducted during 2017 and 2018 growth season, where melatonin was applied to the foliage at concentrations of 0 (MF0), 25 (MF1), 50 (MF2), and 75 (MF3) μM at the ninth leaf stage. Foliar application of melatonin significantly improved chlorophyll content, net photosynthetic rate, soluble sugar content, and soluble protein content during the process of leaf senescence. The application of melatonin also enhanced antioxidant enzyme activities including superoxide dismutase, catalase, and peroxidase, while reduced malondialdehyde and reactive oxygen species accumulation. Melatonin foliar application also increased total leaf area per plant, grains per ear, thousand grain weight and grain yield of maize crop in a semi-arid region. The application of melatonin significantly improved photosynthetic activity, antioxidant defense mechanism, and yield of maize crop in a semi-arid region, where the most effective treatment was MF2.

Melatonin is a ubiquitous signaling plant hormone that plays a crucial role in regulating the growth and development of plants under stress conditions. Since a few species have been investigated to unveil the effect of exogenous melatonin on salt stress, in the current research study, we investigated the effects of melatonin by measuring the photosynthetic characteristics and antioxidant defense system of maize seedling under salt stress (200 mM NaCl), along with different concentrations of melatonin (MT1—30, MT2–60, and MT3–90 µM) treatments. Salt stress reduced the plant growth characteristics and photosynthetic efficiency by increasing the ROS accumulation and reducing the antioxidant enzyme activities of maize seedling. However, pretreatment with melatonin on roots alleviated NaCl-induced decrease in photosynthetic rate and oxidative stress in a dose-dependent manner. Our results revealed that exogenous application of melatonin at an optimum concentration (60 µM) under salt stress conditions significantly increased the growth of plants, chlorophyll content, photosynthetic efficiency, antioxidant enzymes activities, i.e., superoxide dismutase (SOD), peroxide (POD), catalase (CAT) and ascorbate peroxidase (APX), and reduced the reactive oxygen species (ROS). This study unraveled the crucial role of melatonin in salt stress mitigation and thus can be implicated in the management of salinity in maize seedling.

BackgroundMelatonin played an essential role in numerous vital life processes of animals and captured the interests of plant biologists because of its potent role in plants as well. As far as its possible contribution to photoperiodic processes, melatonin is believed to act as a growth regulator and a direct free radical scavenger/indirect antioxidant. The objective of this study to identify a precise melatonin concentration for a particular application method to improve plant growth requires identification and clarification.MethodsThis work establishes unique findings by optimizing melatonin concentration in alleviating the detrimental effects of drought stress in maize. Maize plants were subjected to drought stress (40–45% FC) after treatments of melatonin soil drenching at different concentrations (50, 100, and 150 µM) to consider the changes of growth attribute, chlorophyll contents, photosynthetic rate, relative water content (RWC), chloroplast ultrastructure, endogenous hormonal mechanism, and grain yield.ResultsOur results showed that the application of melatonin treatments remarkably improved the plant growth attributes, chlorophyll contents, photosynthetic rate, RWC, hormonal mechanism, and grain yield plant−1 under drought conditions at a variable rate.ConclusionOur current findings hereby confirmed the mitigating potential of melatonin application 100 µM for drought stress by maintaining plant growth, hormone content, and grain yield of maize. We conclude that the application of melatonin to maize is effective in reducing drought stress tolerance.

In the context of climate change, hydrological processes between surface water and groundwater become increasingly complex, which may aggravate the risk of hydrological drought. As the complexity of factors influencing drought increases, traditional univariate drought indices are likely to be insufficient for accurate drought analysis. Therefore, it is important to develop an appropriate index combining surface water and groundwater for comprehensive monitoring of hydrological drought, and to understand the process of drought propagation to reduce the impact of droughts. This study develops the standardized runoff groundwater drought index (SRGI), which is a multi-variable model based on a combination of the standardized groundwater level drought index (SGI) and the standardized runoff drought index (SRI). The cross-wavelet transforms and correlation coefficient methods are used to reveal the linkages and propagation characteristics between meteorological and hydrological drought. The results indicate that (1) the generalized extreme value (GEV) distribution is generally suitable for the SGI calculation in the midstream of the Heihe River Basin (HHRB); (2) the SRGI, combining the advantages of the SRI and SGI, can detect droughts more accurately and timely than univariate drought indices, and it demonstrates a drought warning capability to some extent; (3) the lag time of the SRI/SRGI responding to the SPI notably varied with seasons, the longer in spring and winter and the shorter in summer and autumn. Additionally, the propagation time from the SRI to the SPI is shorter than that from the SRGI to the SPI in spring and autumn, the opposite is true in summer. (4) Positive correlations exist between the SRI/SRGI and the SPI during the period 1986–2010, which are relatively stable over longer periods, but ambiguous over shorter periods.

Heavy metal contamination of drinking water, primarily driven by industrial activities, represents a critical challenge, with implications for human health and environmental safety. Gujranwala is an industrial and thickly populated city. The current study aimed to assess and compare heavy metal contamination levels in drinking water from five industrial areas and evaluate their potential impacts on human health. Total 100 water samples were collected and analysed for physicochemical parameters and heavy metals. Zonal variations in heavy metal concentrations revealed that Zone 2 had the highest mean levels of cadmium (0.331 mg/L), lead (0.573 mg/L), chromium (0.164 mg/L), arsenic (0.042 mg/L), and aluminium (0.484 mg/L), while Zone 4 showed elevated mean levels of iron (1.88 mg/L) and mercury (0.259 mg/L). Spearman correlation analysis among heavy metals revealed positive relationships among several heavy metals with As notably showing a strong correlation with Hg (0.701**). Kruskal–Wallis test revealed significant spatial variation (p < 0.05) in parameters like pH, EC, TDS, and heavy metals (Cd, Pb, Fe, Cu, Mn, Al), indicating spatial heterogeneity across zones. Spatial distribution maps also depicted heavy metal elevated concentrations of Cd, Pb, Fe, Cr, As, and Hg exceeding in Zone 2 (Engineering industries zone) and 4 (Iron and steel industries zone). The findings revealed a strong link between elevated heavy metal levels and health risks, including dermatological, respiratory, gastrointestinal, and neurological disorders. This study highlights the need for stricter wastewater management, regular monitoring of drinking water, and policies to address water toxicity in industrial premises and to protect public health.

Drought is a major natural disaster that significantly impacts the susceptibility and flexibility of the ecosystem by changing vegetation phenology and productivity. This study aimed to investigate the impact of extreme climatic variation on vegetation phenology and productivity over the four sub-regions of China from 2000 to 2017. Daily rain gauge precipitation and air temperature datasets were used to estimate the trends, and to compute the standardized precipitation-evapotranspiration index (SPEI). Remote sensing–based Enhanced Vegetation Index (EVI) data from a moderate resolution imaging spectroradiometer (MODIS) was used to characterize vegetation phenology. The results revealed that (1) air temperature had significant increasing trends ( P < 0.05) in all sub-regions. Precipitation showed a non-significant increasing trend in Northwest China (NWC) and insignificant decreasing trends in North China (NC), Qinghai Tibet area (QTA), and South China (SC). (2) Integrated enhanced vegetation index (iEVI) and SPEI variations depicted that 2011 and 2016 were the extremely driest and wettest years during 2000–2017. (3) Rapid changes were observed in the vegetation phenology and productivity between 2011 and 2016. In 2011, changes in the vegetation phenology with the length of the growing season (ΔLGS) = was − 14 ± 36 days. In 2016, the overall net effect changed at the onset and end of the growing season with ΔLGS of 34 ± 71 days. The change in iEVI per SPEI increased rapidly with a changing rate of 0.16 from arid (NWC, and QTA) to semi-arid (NWC, QTA and NC) and declined with a rate of − 0.04 from semi-humid (QTA, NC, and SC) to humid (SC) region. A higher association was observed between iEVI and SPEI as compared to iEVI and precipitation. Our finding exposed that north China is more sensitive to climatic variation.

Studying the significant impacts of drought on vegetation is crucial to understand its dynamics and interrelationships with precipitation, soil moisture, and temperature. In North and West Africa regions, the effects of drought on vegetation have not been clearly stated. Therefore, the present study aims to bring out the drought fluctuations within various types of Land Cover (LC) (Grasslands, Croplands, Savannas, and Forest) in North and West Africa regions. The drought characteristics were evaluated by analyzing the monthly Self-Calibrating Palmer Drought Severity Index (scPDSI) in different timescale from 2002 to 2018. Then, the frequency of droughts was examined over the same period. The results have revealed two groups of years (dry years and normal years), based on drought intensity. The selected years were used to compare the shifting between vegetation and desert. The Vegetation Condition Index (VCI), the Temperature Condition Index (TCI), the Precipitation Condition Index (PCI), and the Soil Moisture Condition Index (SMCI) were also used to investigate the spatiotemporal variation of drought and to determine which LC class was more vulnerable to drought risk. Our results revealed that Grasslands and Croplands in the West region, and Grasslands, Croplands, and Savannas in the North region are more sensitive to drought. A higher correlation was observed among the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Tropical Rainfall Measuring Mission (TRMM), and Soil Moisture (SM). Our findings suggested that NDVI, TRMM, and SM are more suitable for monitoring drought over the study area and have a reliable accuracy (R2 > 0.70) concerning drought prediction. The outcomes of the current research could, explicitly, contribute progressively towards improving specific drought mitigation strategies and disaster risk reduction at regional and national levels.

Globally, agricultural productivity is adversely impacted due to climatic changes as the temperatures rises and precipitation decreases, and especially in Pakistan, which ultimately enhanced groundwater salinity and harmed water quality in the country. However, the impacts of groundwater salinity and climate change on farmers’ revenue have not been fully understood in Pakistan. Therefore, the focus of current research is the assessment of shadow price of water, farmers’ revenue, and socioeconomic and environmental indicators affected by variations in groundwater salinity, precipitation, and temperature. The estimation of crop yield sensitivity to groundwater salinity, precipitation, and temperature and their prediction for 2030, 2040, and 2050 time periods was accomplished through the technique of General Maximum Entropy and Response-Yield function. Moreover, the assessment of groundwater quality and climate variable impacts on socioeconomic and environmental indicators was obtained through Target Motad-PMP model. In the end, the most suitable climate change scenario in the study area was established by applying a multi-criteria decision-making method. The results revealed that groundwater salinity and temperature expressed a significantly increasing trend with the Z values of 5.82 and 2.15, respectively. While the precipitation depicted a significantly decreasing trend ( Z value = −3.37). The negative impact of climatic changes and groundwater salinity was revealed for revenue risk and shadow prices of water. The most negative impact on income risk and shadow prices is during 2050 horizon with a decrease by 11.4 and 19.4% respectively. The environmental index is the most important with a priority of 43.4% compared to the socio-economic indicators. The sub-index water use is also significant in the study area with a priority of 28.1%. A2 is the most appropriate climate scenario conferring to the TOPSIS ranking method. Therefore, the A2 scenario should be taken into account for the policy of adaptation to the climate change wonder in district Kohat.

Droughts are one of the world’s most destructive natural disasters. In large regions of Africa, droughts can have strong environmental and socioeconomic impacts. Understanding the mechanism that drives drought and predicting its variability is important for enhancing early warning and disaster risk management. Taking North and West Africa as the study area, this study adopted multi-source data and various statistical analysis methods, such as the joint probability density function (JPDF), to study the meteorological drought and return years across a long term (1982–2018). The standardized precipitation index (SPI) was used to evaluate the large-scale spatiotemporal drought characteristics at 1–12-month timescales. The intensity, severity, and duration of drought in the study area were evaluated using SPI–12. At the same time, the JPDF was used to determine the return year and identify the intensity, duration, and severity of drought. The Mann-Kendall method was used to test the trend of SPI and annual precipitation at 1–12-month timescales. The pattern of drought occurrence and its correlation with climate factors were analyzed. The results showed that the drought magnitude (DM) of the study area was the highest in 2008–2010, 2000–2003, and 1984–1987, with the values of 5.361, 2.792, and 2.187, respectively, and the drought lasting for three years in each of the three periods. At the same time, the lowest DM was found in 1997–1998, 1993–1994, and 1991–1992, with DM values of 0.113, 0.658, and 0.727, respectively, with a duration of one year each time. It was confirmed that the probability of return to drought was higher when the duration of drought was shorter, with short droughts occurring more regularly, but not all severe droughts hit after longer time intervals. Beyond this, we discovered a direct connection between drought and the North Atlantic Oscillation Index (NAOI) over Morocco, Algeria, and the sub-Saharan countries, and some slight indications that drought is linked with the Southern Oscillation Index (SOI) over Guinea, Ghana, Sierra Leone, Mali, Cote d’Ivoire, Burkina Faso, Niger, and Nigeria.