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This study analyzes and forecasts the long-term Spatio-temporal changes in rainfall using the data from 1901 to 2015 across India at meteorological divisional level. The Pettitt test was employed to detect the abrupt change point in time frame, while the Mann-Kendall (MK) test and Sen’s Innovative trend analysis were performed to analyze the rainfall trend. The Artificial Neural Network-Multilayer Perceptron (ANN-MLP) was employed to forecast the upcoming 15 years rainfall across India. We mapped the rainfall trend pattern for whole country by using the geo-statistical technique like Kriging in ArcGIS environment. Results show that the most of the meteorological divisions exhibited significant negative trend of rainfall in annual and seasonal scales, except seven divisions during. Out of 17 divisions, 11 divisions recorded noteworthy rainfall declining trend for the monsoon season at 0.05% significance level, while the insignificant negative trend of rainfall was detected for the winter and pre-monsoon seasons. Furthermore, the significant negative trend (−8.5) was recorded for overall annual rainfall. Based on the findings of change detection, the most probable year of change detection was occurred primarily after 1960 for most of the meteorological stations. The increasing rainfall trend had observed during the period 1901–1950, while a significant decline rainfall was detected after 1951. The rainfall forecast for upcoming 15 years for all the meteorological divisions’ also exhibit a significant decline in the rainfall. The results derived from ECMWF ERA5 reanalysis data exhibit that increasing/decreasing precipitation convective rate, elevated low cloud cover and inadequate vertically integrated moisture divergence might have influenced on change of rainfall in India. Findings of the study have some implications in water resources management considering the limited availability of water resources and increase in the future water demand.

Depletion of groundwater resources has been identified in numerous global aquifers, suggesting that extractions have exceeded natural recharge rates in critically important global freshwater supplies. Groundwater depletion has been ascribed to groundwater pumping, often ignoring influences of direct and indirect consequences of climate variability. Here, we explore relations between natural and human drivers and spatiotemporal changes in groundwater storage derived from the Gravity Recovery and Climate Experiment (GRACE) satellites using regression procedures and dominance analysis. Changes in groundwater storage are found to be influenced by direct climate variability, whereby groundwater recharge and precipitation exhibited greater influence as compared to groundwater pumping. Weak influence of groundwater pumping may be explained, in part, by quasi-equilibrium aquifer conditions that occur after “long-time” pumping, while precipitation and groundwater recharge records capture groundwater responses linked to climate-induced groundwater depletion. Evaluating groundwater response to climate variability is critical given the reliance of groundwater resources to satisfy water demands and impending changes in climate variability that may threaten future water availability.

In summer (pre-monsoon) of recent years, low water level among the last few decades, has been observed in several lower Indian reaches of the Ganges (or Ganga) river (with estimated river water level depletion rates at the range of −0.5 to −38.1 cm/year between summers of 1999 and 2013 in the studied reaches). Here, we show this Ganges river depletion is related to groundwater baseflow reduction caused by ongoing observed groundwater storage depletion in the adjoining Gangetic aquifers (Ganges basin, −0.30 ± 0.07 cm/year or −2.39 ± 0.56 km 3 /year). Our estimates show, 2016-baseflow amount (~1.0 × 10 6 m 3 /d) has reduced by ~59%, from the beginning of the irrigation-pumping age of 1970s (2.4 × 10 6 m 3 /d) in some of the lower reaches. The net Ganges river water reduction could jeopardize domestic water supply, irrigation water requirements, river transport, ecology etc. of densely populated northern Indian plains. River water reduction has direct impact on food production indicating vulnerability to more than 100 million of the population residing in the region. The results of this study could be used to decipher the groundwater-linked river water depletion as well as the regional water security in other densely populated parts of the globe.

Since 2015 the greater Cape Town area (∼3.7 million people) has been experiencing the worst drought of the last century. The combined effect of this prolonged dry period with an ever-growing demand for water culminated in the widely publicized 'Day Zero' water crisis. Here we show how: (i) consecutive significant decreases in rainfall during the last three winters led to the current water crisis; (ii) the 2015-2017 record breaking drought was driven by a poleward shift of the Southern Hemisphere moisture corridor; (iii) a displacement of the jet-stream and South Atlantic storm-track has imposed significantly drier conditions to this region. Decreasing local rainfall trends are consistent with an expansion of the semi-permanent South Atlantic high pressure, and reflected in the prevalence of the positive phase of the Southern Annular Mode. Large-scale forcing mechanisms reveal the intensification and migration of subtropical anticyclones towards the mid-latitudes, highlighting the link between these circulation responses and the record warm years during 2015-2017 at the global scale.

Accumulation of stress ethylene in plants due to osmotic stress is a major challenge for the achievement of optimum sweet corn crop yield with limited availability of irrigation water. A significant increase in earth’s temperature is also making the conditions more crucial regarding the availability of ample quantity of irrigation water for crops production. Plant growth promoting rhizobacteria (PGPR) can play an imperative role in this regard. Inoculation of rhizobacteria can provide resistance and adaptability to crops against osmotic stress. In addition, these rhizobacteria also have potential to solve future food security issues. That's why the current study was planned to examine the efficacious functioning of Pseudomonas fluorescens strains on yields and physiological characteristics of sweet corn ( Zea mays L. var saccharata) under different levels of irrigation. Three irrigation levels i.e., 100% (I 100 no stress), 80% (I 80 ), and 60% (I 60 ) were used during sweet corn cultivation. However, there were four rhizobacteria strains i.e., P. fluorescens P 1 , P. fluorescens P 3 , P. fluorescens P 8 , P. fluorescens P 14 which were used in the experiment. The results showed that severe water stress (60% of plant water requirement) decreased chlorophyll a , chlorophyll b , and total chlorophyll contents, Fv/Fm ratio and nutrients uptake. A significant increase in F 0 , F m , proline, total soluble sugars, catalase (CAT) and peroxidase (POX) activity led to less ear yield and canned seed yield. Combination of four strains significantly increased the yield traits of sweet corn i.e., ear and (44%) and canned seed yield (27%) over control. The highest promoting effect was observed in the combination of four strains treatment and followed by P 1 strain in reducing the harmful effects of drought stress and improving sweet corn productivity. However, P 14 gave minimum improvement in growth and yield indices under limited availability of water. In conclusion, combination of four strains inoculation is an efficacious approach for the achievement of better yield of sweet corn under osmotic stress.

This study aims to evaluate the quality of groundwater in the most arid province of Iran, Yazd. It is highly dependent on groundwater resources to meet the domestic, industrial, and agricultural water demand. Position of water samples on the modified Gibbs diagram demonstrates that the interaction with silicates and the increase in direct cation exchange are responsible for the increased salinity of groundwater. Based on entropy theory, the decreasing order of importance of variables in controlling groundwater chemistry is Fe > As > Ba > Hg > NO 2  > Pb > K > Cl > Na > Mg > SO 4  > NO 3  > HCO 3  > Ca. The results of entropy weighted water quality index (EWWQI) calculation show that about 34 and 32% of 206 samples in the wet and dry seasons, respectively, are classified as extremely poor quality (ranks 4 and 5). Approximately 60 and 55% of 206 samples in wet and dry seasons, respectively, have excellent, good, and medium quality (ranks 1, 2, and 3). The non-carcinogenic human health risk (NHHR) from intake and dermal contact pathways using deterministic approach show that 36 and 17 samples in both seasons are not suitable for drinking by children. Furthermore, 9 and 2 samples are not suitable for drinking by adults. The results show that children are more vulnerable than adults to these health risks. The non-carcinogenic risks through dermal contact were negligible.

Nearly one-sixth of U.S. river basins are unable to consistently meet societal water demands while also providing sufficient water for the environment. Water scarcity is expected to intensify and spread as populations increase, new water demands emerge, and climate changes. Improving water productivity by meeting realistic benchmarks for all water users could allow U.S. communities to expand economic activity and improve environmental flows. Here we utilize a spatially detailed database of water productivity to set realistic benchmarks for over 400 industries and products. We assess unrealized water savings achievable by each industry in each river basin within the conterminous U.S. by bringing all water users up to industry- and region-specific water productivity benchmarks. Some of the most water stressed areas throughout the U.S. West and South have the greatest potential for water savings, with around half of these water savings obtained by improving water productivity in the production of corn, cotton, and alfalfa. By incorporating benchmark-meeting water savings within a national hydrological model (WaSSI), we demonstrate that depletion of river flows across Western U.S. regions can be reduced on average by 6.2-23.2%, without reducing economic production. Lastly, we employ an environmentally extended input-output model to identify the U.S. industries and locations that can make the biggest impact by working with their suppliers to reduce water use 'upstream' in their supply chain. The agriculture and manufacturing sectors have the largest indirect water footprint due to their reliance on water-intensive inputs but these sectors also show the greatest capacity to reduce water consumption throughout their supply chains.

Increasing meteorological drought frequency and rising water demand drive groundwater exploitation beyond sustainable limits. In heavily-stressed aquifers mitigation strategies, such as Managed Aquifer Recharge (MAR), are needed to restore depleted groundwater storage. MAR is also designed to overcome short dry periods. However, wider impacts of MAR as a drought mitigation strategy remain to be quantified. The objective of this study is to assess impacts of MAR in heavily-stressed aquifers using a case study of the Central Valley in California (USA). The novelty of this study lies in its analytical approach based on long-term observational data of precipitation, groundwater levels, and MAR operations. The impact of MAR operations is assessed regionally and for different temporal scales. Results show spatially-coherent clusters of groundwater level time series in the Central Valley representing three main patterns that manifest themselves in different groundwater drought characteristics and long-term trends. The first regional pattern shows lengthened groundwater droughts and declining groundwater levels over time, indicating effects of over abstraction in aquifer sections without MAR. The second regional pattern shows reduced groundwater drought duration and magnitude related to periodically rising groundwater levels, showing short-term MAR impacts. The third regional pattern shows alleviated groundwater droughts and groundwater levels show a long-term rise, representing long-term MAR impacts. Mitigated groundwater droughts and long-term rise in groundwater levels reveal the value of long-term MAR operations and their contribution toward sustainable groundwater management. Increased institutional support is recommended to ensure longevity of MAR and thereby amplify its success as regional drought mitigation strategy in heavily-stressed aquifers.

With the advancement of technologies, things became intelligent with the capabilities of self-communication between them. Internet of Things (IoT) connected daily household things to the Internet and make them able to make decisions like the human mind. Sensors collect the real atmospheric data and with the help of Artificial intelligence (AI) algorithms analysis of data takes place so that devices behave more smartly. The present article discusses how IoT revolutionized the agricultural community. According to the study, it is analyzed that 70% population is dependent on agriculture for their livelihood in India, but the status of agriculture is no more concealed from society. With the involvement of technology, it becomes easy to predict temperature, rainfall, humidity, the need for fertilizers, water requirements, etc. The introduction of modern agriculture techniques using IoT & AI is revolutionizing the traditional agriculture methodologies and are making farming a profitable venture also.

Ski tourism is a major sector of mountain regions economy, which is under the threat of long-term climate change. Snow management, and in particular grooming and artificial snowmaking, has become a routine component of ski resort operations, holding potential for counteracting the detrimental effect of natural snow decline. However, conventional snowmaking can only operate under specific meteorological conditions. Whether snowmaking is a relevant adaptation measure under future climate change is a widely debated issue in mountainous regions, with major implications on the supply side of this tourism industry. This often lacks comprehensive scientific studies for informing public and private decisions in this sector. Here we show how climate change influences the operating conditions of one of the main ski tourism markets worldwide, the French Alps. Our study addresses snow reliability in 129 ski resorts in the French Alps in the 21st century, using a dedicated snowpack model explicitly accounting for grooming and snowmaking driven by a large ensemble of adjusted and downscaled regional climate projections, and using a geospatial model of ski resorts organization. A 45% snowmaking fractional coverage, representative of the infrastructures in the early 2020s, is projected to improve snow reliability over grooming-only snow conditions, both during the reference period 1986–2005 and below 2 °C global warming since pre-industrial. Beyond 3 °C of global warming, with 45% snowmaking coverage, snow conditions would become frequently unreliable and induce higher water requirements.