Climate change and groundwater overdraft impacts on agricultural drought in India: Vulnerability assessment, food security measures and policy recommendation

Integration of SPEI and machine learning for assessing the characteristics of drought in the middle ganga plain, an agro-climatic region of India

Drought, as a natural and intricate climatic phenomenon, poses challenges with implications for both natural ecosystems and socioeconomic conditions. Evaluating the characteristics of drought is a significant endeavor aimed at mitigating its impact on society and individuals. This research paper explores the integration of the Standardized Precipitation Evapotranspiration Index (SPEI) and machine learning techniques for an assessment of drought characteristics in the Middle Ganga Plain, a crucial agro-climatic region in India. The study focuses on evaluating the frequency, intensity, magnitude, and recurrence interval of drought events. Various drought models, including Random Forest (RF), Artificial Neural Networks (ANN), and an ensemble model combining ANN and RF, were employed to analyze and predict drought patterns at different temporal scales (3-month, 6-month, and 12-month). The performance of these models was rigorously validated using key metrics such as precision, accuracy, proportion incorrectly classified, over-all area under the curve (AUC), mean absolute error (MAE), and root mean square error (RMSE). Furthermore, the research extends its application to delineating drought vulnerability zones by establishing demarcations for high and very high drought vulnerability areas for each model and temporal scale. Results indicate that the south-western part of the middle Ganga plain falls under the highly drought-vulnerable zone, which averagely covers 40% of the study region. The core and buffer regions of drought vulnerability have also been identified. The south-western part of the study area is identified as the core region of drought. Ground verification of the drought-vulnerable area has been done by using soil moisture meter. Validation metrics show that the ensemble model of ANN and RF exhibits the highest accuracy across all temporal scales. This research's findings can be applied to improve drought preparedness and water resource management in the Middle Ganga Plain. By identifying high-risk drought zones and utilizing accurate prediction models, policymakers and farmers can implement targeted mitigation strategies. This approach could enhance agricultural resilience, protect livelihoods, and optimize water allocation in this vital agro-climatic region.

Global terrestrial drought and its projected socioeconomic implications under different warming targets

Droughts are increasingly frequent as the Earth warms, presenting adaptation challenges for ecosystems and human communities worldwide. A strategic environmental assessment (SEA) and the integration of adaptation strategies into policies, plans, and programs (PPP) are two important approaches for enhancing climate resilience and fostering sustainable development. This study developed an innovative approach to strengthen the SEA of droughts by quantifying the impacts of future temperature increases. A novel method for projecting drought events was integrated into the SEA process by leveraging multiple data sources, including atmospheric reanalysis, reconstructions, satellite-based observations, and model simulations. We identified drought conditions using terrestrial water storage (TWS) anomalies and applied a random forest (RF) model for disentangling the drivers behind drought events. We then set two global warming targets (2.0 °C and 2.5 °C) and analyzed drought changes under three shared socioeconomic pathways (SSP126, SSP370, SSP585). In a 2.0 °C warming world, over 50 % of the global surface will face increased drought risk. With an additional 0.5 °C increase, >60 % of the land will be prone to further drought escalation. We utilized copulas to build the joint distribution for drought duration and severity, estimating the joint return periods (JRP) for bivariate drought hazard. In tropical and subtropical regions, JRP reductions exceeding half are projected for >33 % of the regional land surface under 2.0 °C warming and for >50 % under 2.5 °C warming. Finally, we projected the impacts of drought events on population and gross domestic product (GDP). Among the three SSPs, under SSP370, population exposure is highest and GDP exposure is minimal under 2.0 °C warming. Global GDP and population risks from drought are projected to increase by 37 % and 24 %, respectively, as warming continues. This study enhances the accuracy of SEA in addressing drought risks and vulnerabilities, supporting climate-resilient planning and adaptive strategies.

Recent Advances in Superabsorbent Hydrogels Derived from Agro Waste Materials for Sustainable Agriculture: A Review

Superabsorbent hydrogels made from agro waste materials have the potential to promote sustainable agriculture and environmental sustainability. These hydrogels not only help reduce water consumption and increase crop yields but also contribute to minimizing waste and lowering greenhouse gas emissions. Recent research on superabsorbent hydrogels derived from agro wastes has focused on the preparation of hydrogels based on natural polymers isolated from agro wastes, such as cellulose, hemicellulose, and lignin. This review provides an in-depth examination of hydrogels developed from raw agro waste materials and natural polymers extracted from agro wastes, highlighting that these studies start with raw wastes as the main materials. The utilization strategies for specific types of agro wastes are comprehensively described. This review outlines different methods utilized in the production of these hydrogels, including physical cross-linking techniques such as dissolution-regeneration and freeze-thawing, as well as chemical cross-linking methods involving various cross-linking agents and graft polymerization techniques such as free radical polymerization, microwave-assisted polymerization, and γ radiation graft polymerization. Specifically, this review explores the applications of agro waste-based superabsorbent hydrogels in enhancing soil properties such as water retention and slow-release of fertilizers for sustainable agriculture.

Regional differences, distributional dynamics and convergence of multidimensional food security levels in China

Food security is one of the important issues in the current world development process. The article takes 31 provinces (districts and cities) in China as the research object and constructs a multidimensional food security level evaluation index system from four dimensions: quantitative security, nutritional security, ecological security, and capacity security. Using the entropy method, China's food security index was calculated for the ten-year period from 2013 to 2022. Overall, China's food security level showed an upward trend during the decade, with the provinces of Shandong, Heilongjiang, and Henan having the highest level of security. The distribution dynamics of food security and its spatiotemporal evolution in the seven regions were examined using the Dagum Gini coefficient and its decomposition, and the absolute and conditional convergence of food security in the different areas was verified. The results of the study show that the provinces within East China have the largest gap in food security levels between them, and there is absolute β-convergence. Looking at China as a whole, the development of its food security level is characterized by significant convergence, which means that provinces with a low level of food security will have a faster rate of growth than those with a high level of food security, resulting in a gradual narrowing of the gap in food security levels between provinces.

Enhancing drought resilience: machine learning-based vulnerability assessment in Uttar Pradesh, India

Drought is a natural and complex climatic hazard. It has both natural and social connotations. The purpose of this study is to use machine learning methods (MLAs) for drought vulnerability (DVM) in Uttar Pradesh, India. There were 18 factors used to determine drought vulnerability, separated into two groups: physical drought and meteorological drought. The study found that the eastern part of Uttar Pradesh is high to very highly prone to drought, which is approximately 31.38% of the area of Uttar Pradesh. The receiver operating characteristic curve (ROC) was then used to evaluate the machine learning models (artificial neural networks). According to the findings, the ANN functioned with AUC values of 0.843. For policy actions to lessen drought sensitivity, DVMs may be valuable. Future exploration may involve refining machine learning algorithms, integrating real-time data sources, and assessing the socio-economic impacts to continually enhance the efficacy of drought resilience strategies in Uttar Pradesh.

Declining groundwater and its impacts along Ganga riverfronts using combined Sentinel-1, GRACE, water levels, and rainfall data

The Indo-Gangetic Plains (IGP) in northern India are vast alluvial tracts with huge shallow aquifers, densely populated and agriculturally productive regions. In the last few decades, IGP has been facing water scarcity driven by erratic monsoon dynamics, anthropogenic activity, and hydroclimatic variability. In urban centers, continuous groundwater withdrawal leads to high stress, affecting surface deformation and a threat to buildings and infrastructures. An attempt has been made to explore the possible linkage and coupling between groundwater level, hydroclimatic variables, and subsidence in the Central Ganga Plains (CGP), in Varanasi metropolis using the combined multisensory multitemporal data, Sentinel-1 (2017-2023), GRACE (2003-2023), groundwater levels (1998-2023), and precipitation (2002-2023). Long-term hydrological response in the CGP shows continuous depletion (14.6 ± 5.6 mm/yr) in response to precipitation variability. Results show spatiotemporal variations between GWS, and precipitation estimate with nonlinear trend response due to associated inter-annual/inter-seasonal climate variability and anthropogenic water withdrawal, specifically during the observed drought years. The significant storage response in the urban center compared to a regional extent suggests the potential impact of exponentially increasing urbanization and building hydrological stress in the cities. The implications of reducing storage capacity show measured land subsidence (∼2-8 mm/yr) patterns developed along the meandering stretch of the Ganga riverfronts in Varanasi. The groundwater level data from the piezometric supports the hydroclimatic variables and subsidence coupling. Considering the vital link between water storage, food security, and socioeconomic growth, the results of this study require systematic inclusion in water management strategies as climate change seriously impacts water resources in the future.

Sustainable management of water resources and assessment of the vulnerability of Moroccan oases to climate change

In the oases of Morocco, climate trends show an increase in average temperatures of 2.2 °C and exacerbated precipitation by + 20% between 2020 and 2050 according to climate change scenarios. The consequences of these changes have a clear decrease in water availability and an increase in water needs. Therefore, analyzing water resource capacity and searching for adequate solutions to water scarcity in oases are essential for developing drylands. In this study, we assess the possible effects of climate change on water scarcity and the oasis ecosystem and its components. The calculated water stress index (WSI) remains very low due to a decrease in the resource impacted by the combined increase in precipitation and temperature. The obtained results indicate that for scenario 1 the WSI varies from 904 to 699 m3/inhab/year in 2030 and for scenario 2 the WSI varies between 583 in 2030 and 451 m3/inhab/year in 2050. The water availability indicator takes a value in scenario 1 of 75% for Zagora and 50% for Ouarzazate at the horizons 2030 and 2050, then increase in scenario 2 to 89% for Zagora and 78% for Ouarzazate at the horizons2030 and 2050. These results were used to develop the adaptation process, which aims to identify needs, activities, and projects in the short, medium, and long term at the horizons 2030 and 2050. In addition, it could shed light on sustainable development in this region. In addition, this study could be a reference for researchers and a decision-support document for decision-makers to place economic development within an environmental management framework.

Assessment of aquifer specific vulnerability to total nitrate contamination using ensemble learning and geochemical evidence

Henan Province's plain area is the granary of China, yet its regional aquifer is being polluted by industrial wastewater, agricultural pesticide, fertilizer and domestic wastewater. In order to safeguard the security of food and drinking water, and in response to the problem of low prediction accuracy caused by the lack of samples and unevenly distributed groundwater monitoring data, we propose a new way to predict the aquifer vulnerability in large areas by rich small-scale data, so as to identify the pollution risks and to address the issue of sample shortage. In small regions with abundant nitrate data, we employed a Random Forest model to screen key impact indicators, using them as features and nitrate-N concentration as the target variable. Consequently, we established six machine learning prediction models, and then selected the best bagging model (R2 = 0.86) to predict the vulnerability of aquifers in larger regions lacking nitrate data. The predicted results showed that highly vulnerable areas accounted for 20 %, which were mainly affected by aquifer thickness (65.91 %). High nitrate-N concentration implies serious aquifer contamination. Therefore, a long series of groundwater nitrate-N concentration monitoring data in a large scale, the trend and slope of nitrate-N concentration showed a significant correlation with the model prediction results (Spearman's correlation coefficients are 0.75 and 0.58). This study can help identify the risk of aquifer contamination, solve the problem of sample shortage in large areas, thus contributing to the security of food and drinking water.

Investigating the impacts of airborne dust on herbicide performance on Amaranthus retroflexus

Dust pollution poses environmental hazards, affecting agriculture through reduced sunlight exposure, photosynthesis, crop yields, and food security. This study explores the interference of dust pollution on herbicide efficacy to control weeds in a semi-arid region. In a factorial experiment conducted in 2019 and replicated in 2020, the interaction of dust and various herbicide applications, including bentazon, sulfosulfuron, tribenuron-methyl, aminopyralid + florasulam, foramsulfuron + iodosulfuron + thiencarbazone, 2,4-D + MCPA, and acetochlor, in controlling Amaranthus retroflexus L. were assessed. Dust induced a 9.2% reduction in the total chlorophyll content of A. retroflexus, while herbicide application independently led to a 67.5% decrease. Contrary to expectations, herbicides performed better in dust, except bentazon, which caused a 28% drop in plant height and a 29% decrease in total biomass compared to non-dust conditions. Both herbicides and dust exerted suppressive effects on A. retroflexus's leaf and stem weights and overall biomass. Despite dust presence, tribenuron-methyl (95.8%), aminopyralid + florasulam (95.7%), sulfosulfuron (96.5%), and foramsulfuron + iodosulfuron + thiencarbazone (97.8%) effectively controlled A. retroflexus. These findings indicate that dust's effect on herbicide efficacy is herbicide-dependent but except bentazon, dust generally increased herbicide efficacy and amplified the control of A. retroflexus.

Differential responses of crop yields to multi-timescale drought in mainland China: Spatiotemporal patterns and climate drivers

Increasingly frequent and severe droughts pose a growing threat to food security in China. However, our understanding of how different crops respond to multi-timescale drought under varying climatic conditions remains limited, hindering effective drought risk management. To address this knowledge gap, we applied spatial principal component analysis (SPCA) to unveil spatiotemporal patterns in annual yields of major grain crops (rice, wheat, maize) and cotton in response to multi-timescale drought, as indicated by the standardized precipitation evapotranspiration index (SPEI) across China. Subsequently, predictive discriminant analysis (PDA) was employed to identify the primary climatic factors driving these response patterns. The findings indicated that drought-induced interannual variability of crop yields were spatially and temporally heterogeneous, closely tied to the timescale used for drought assessment. Crop types displayed distinct responses to drought, evident in the variations of months and corresponding timescales for their strongest reactions. The initial three principal components, capturing over 65 % of drought-related yield variance, unveiled short- to medium-term patterns for rice, maize, and cotton, and long-term patterns for wheat. Specifically, rice was highly susceptible to drought on a 4-month timescale in September, wheat on a 6-month timescale in May, maize on a 3-month timescale in August, and cotton on a 3-month timescale in September. Moreover, the first three discriminant functions explaining over 90 % of the total variance, effectively distinguish spatiotemporal crop yield response patterns to drought. These patterns primarily stem from seasonal climatic averages, with water balance (precipitation minus potential evapotranspiration) and temperature being the most influential variables (p < 0.05). Interestingly, we observed a weak correlation between drought severity and crop yield in humid conditions, with responses tending to manifest over longer timescales. These findings enhance our comprehension of how drought timescales impact crop yields in China, providing valuable insights for the implementation of rational irrigation management strategies.

Insights into long-term changes of groundwater levels in the typical region of Zhangjiakou City, China

Little information is available on the long-term changes of groundwater levels and their associated influencing factors. Zhangjiakou City was chosen as a case to reveal the temporal and spatial dynamics of groundwater level and its driving factors in the long term. Herein, the observation data of groundwater level from 56 wells was investigated from 1981 to 2015, including the collected meteorological data, socio-economic data, and groundwater resource exploitation situation. Results showed that the groundwater level in Zhangjiakou City tended to be decreased, and the decrease rate was gradually accelerated. In the past 35 years, the groundwater level of Bashang Plateau has decreased by 3.59 m < 3.6 m in Yuyang Basin < 7.17 m in Zhuohuai Basin < 20.41 m in Chaixuan Basin. The dynamic changes of groundwater level in four geomorphic units in Zhangjiakou City were significant correlation between the total population and other socio-economic factors, including primary industry production value; common cultivated land area; effective irrigation area; total grain yield; total vegetable yield; total production of pork, beef, and mutton; secondary industry production value; tertiary industry production value; and year-end total population. Furthermore, the principal component analysis of groundwater level variation in Zhangjiakou city showed that the variance contribution rates of the first principal component in the characteristic indicators of the Bashang Plateau, Chaixuan Basin, Zhuohuai Basin, and Yuyang Basin were 75.7%, 83.9%, 66.1%, and 77.8%, respectively, which mainly reflect the information of socio-economic factors. This indicated that socio-economic factors were the main driving factor influencing the continuous decline of groundwater levels in Zhangjiakou City. The obtained findings can provide new insights into the sustainable development of social economy and the rational utilization and allocation of regional water resources.

Using neural networks and remote sensing for spatio-temporal prediction of air pollution during the COVID-19 pandemic

The study aims to monitor air pollution in Iranian metropolises using remote sensing, specifically focusing on pollutants such as O3, CH4, NO2, CO2, SO2, CO, and suspended particles (aerosols) in 2001 and 2019. Sentinel 5 satellite images are utilized to prepare maps of each pollutant. The relationship between these pollutants and land surface temperature (LST) is determined using linear regression analysis. Additionally, the study estimates air pollution levels in 2040 using Markov and Cellular Automata (CA)-Markov chains. Furthermore, three neural network models, namely multilayer perceptron (MLP), radial basis function (RBF), and long short-term memory (LSTM), are employed for predicting contamination levels. The results of the research indicate an increase in pollution levels from 2010 to 2019. It is observed that temperature has a strong correlation with contamination levels (R2 = 0.87). The neural network models, particularly RBF and LSTM, demonstrate higher accuracy in predicting pollution with an R2 value of 0.90. The findings highlight the significance of managing industrial towns to minimize pollution, as these areas exhibit both high pollution levels and temperatures. So, the study emphasizes the importance of monitoring air pollution and its correlation with temperature. Remote sensing techniques and advanced prediction models can provide valuable insights for effective pollution management and decision-making processes.

Edible Insects: Perceptions of Marketing, Economic, and Social Aspects among Citizens of Different Countries

Because edible insects (EI) have been, in recent years, recommended as a nutritious animal protein food with enormous environmental advantages over other sources of animal protein for human consumption, studies aimed at investigating the consumer perspective have become more prominent. Hence, this study intended to examine the perceptions of participants from different countries about the commercialization and economic and social impacts of edible insects. The study was made using a questionnaire survey, and data were collected in Brazil, Croatia, Greece, Latvia, Lebanon, Lithuania, Mexico, Poland, Portugal, Romania, Serbia, Slovenia, Spain, and Turkey. The final number of received answers was 7222 participants. For the treatment of the results, different statistical techniques were used: factor analysis, internal reliability by Cronbach's alpha, cluster analysis, ANOVA to test differences between groups, and Chi-square tests. The results obtained confirmed the validity of the scale, constituted by 12 out of the 14 items initially considered, distributed by 4 factors: the first related to the economic impact of EIs, the second related to the motivation for consumption of EIs, the third related to the places of purchase of EIs, and the fourth corresponding to a question presented to the participants as a false statement. A cluster analysis allowed identifying three clusters, with significant differences between them according to all the sociodemographic variables tested. Also, it was found that the participants expressed an exceptionally high level of agreement with aspects such as the difficulty in finding EIs on sale, knowledge acting as a strong motivator for EI consumption, and the role of personalities and influencers in increasing the will to consume EIs. Finally, practically all sociodemographic variables were found to be significantly associated with perceptions (country, sex, education, living environment, and income), but not age. In conclusion, the perceptions about EI commercialization were investigated and revealed differences among samples originating from different countries. Moreover, the sociodemographic characteristics of the participants were found to be strongly associated with their perceptions.

Groundwater levels and resiliency mapping under land cover and climate change scenarios: a case study of Chitravathi basin in Southern India

Chitravathi basin in India is facing significant challenges as its groundwater resources are facing the impact of land cover and climate change. This study explores the impact of land cover and climate change on groundwater levels and groundwater recharge in the basin using CMIP6 GCMs climate projections data. Taylor Skill Score (TSS) and Rating Metric (RM) were used to rank the GCMs. The top four ranked GCMs, i.e., MPI-ESM1-2-LR, EC-Earth3, MPI-ESM1-2-HR, and INM-CM5-0 were found to produce the most accurate projections under scenarios SSP2-4.5 and SSP5-8.5. Cellular Automata-Artificial Neural Network (CA-ANN) was used to develop future LULC maps. SWAT model was applied for estimating the future groundwater recharge and was calibrated and validated for discharge data, giving the values of R2 = 0.84 and 0.82 and NSE = 0.81 and 0.80 during calibration and validation, respectively. A steady-state groundwater flow model, MODFLOW, was employed to estimate future groundwater levels. Based on the projected groundwater recharge and levels, a resiliency map of the basin was developed. The results indicated that by 2060, under SSP2-4.5 scenario, groundwater levels in the basin would decrease by 54 m, while under the SSP5-8.5 scenario, the decrease would be 62 m. The groundwater resiliency for both SSPs would be poor in 2060. This research will help design and implement adaptation measures to mitigate the impacts of land cover and climate change on Chitravathi basin's groundwater resources. These findings will help to protect and preserve the basin's groundwater supplies.

The COP27 screened through the lens of global water security

Water security is an expression of resilience. In the recent past, scientists and public organizations have built considerable work around this concept launched in 2013 by the United Nations as "the capacity of a population to safeguard sustainable access to adequate quantities of acceptable quality water for sustaining livelihoods, human well-being, and socio-economic development, for ensuring protection against water-borne pollution and water-related disasters, and for preserving ecosystems in a climate of peace and political stability". In the 27th Conference of the Parties (COP27), held in Sharm El-Sheikh (Egypt) in last November, water security was considered a priority in the climate agenda, especially in the adaption and loss and damage axes. This discussion paper represents the authors' opinion about how the conference coped with water security and what challenges remain to attend. As discussion paper, it had the purpose to stimulate further discussion in a broader scientific forum.

Machine learning: An effective technical method for future use in assessing the effectiveness of phosphorus-dissolving microbial agroremediation

The issue of agricultural pollution has become one of the most important environmental concerns worldwide because of its relevance to human survival and health. Microbial remediation is an effective method for treating heavy metal pollution in agriculture, but the evaluation of its effectiveness has been a difficult issue. Machine learning (ML), a widely used data processing technique, can improve the accuracy of assessments and predictions by analyzing and processing large amounts of data. In microbial remediation, ML can help identify the types of microbes, mechanisms of action and adapted environments, predict the effectiveness of microbial remediation and potential problems, and assess the ecological benefits and crop growth after remediation. In addition, ML can help optimize monitoring programs, improve the accuracy and effectiveness of heavy metal pollution monitoring, and provide a scientific basis for the development of treatment measures. Therefore, ML has important application prospects in assessing the effectiveness of microbial remediation of heavy metal pollution in agriculture and is expected to be an effective pollution management technology.