A changing climate has been posing significant impacts on vegetation growth, especially in the Yellow River Basin (YRB) where agriculture and ecosystems are extremely vulnerable. In this study, the data for normalized difference vegetation index (NDVI) obtained from moderate-resolution imaging spectroradiometer (MODIS) sensors and climate data (precipitation and temperature) derived from the national meteorological stations were employed to examine the spatiotemporal differences in vegetation growth and its reaction to climate changes in the YRB from 2000–2019, using several sophisticated statistical methods. The results showed that both NDVI and climatic variables exhibited overall increasing trends during this period, and positive correlations at different significant levels were found between temperature/precipitation and NDVI. Furthermore, NDVI in spring had the strongest response to temperature/precipitation, and the correlation coefficient of NDVI with temperature and precipitation was 0.485 and 0.726, respectively. However, an opposite situation was detected in autumn (September to November) since NDVIs exhibited the weakest responses to temperatures/precipitation, and the NDVI’s correlation with both temperature and precipitation was 0.13. This indicated that, compared to other seasons, increasing the temperature and precipitation has the most significant effect on NDVI in spring (March to May). Except for a few places in the northern, southern, and southwestern regions of the YRB, NDVI was positively correlated with precipitation in most areas. There was an inverse relationship between NDVI and temperature in most parts of the central YRB, especially in summer (June to August) and growing season (May to September); however, there was a positive correlation in most areas of the YRB in spring. Finally, continuous attention must be given to the influence of other factors in the YRB.

Global warming and rapid socioeconomic development increased the risk of regional and global disasters. Particularly in China, annual heatwaves (HWs) caused many fatalities and substantial property damage, with an increasing trend. Therefore, it is of great scientific value and practical importance to analyze the spatiotemporal changes of HW in China for the sustainable development of regional socioeconomic and disaster risk management. In this study, based on gridded maximum temperature product and specific humidity dataset, an HW evaluation algorithm, considering the impact of humidity on the human body and the characteristics of HW in China, was employed to generate daily HW state at light, moderate, and severe levels for the period 1979–2018. Consequently, the regional differences at three HW levels were revealed, and the changing trend of HW onset, termination, and duration in each subregion was analyzed. The results show that in the three levels, the frequency and duration of HW in China had a significant increasing trend, generally characterized by the advancement of HW onset and the postponement of HW termination. The HW influence at light, moderate and severe levels decreased gradually, with the light level occurring the earliest and terminating the latest. Among the seven subregions, the largest HW frequency happened to be mainly in XJ (Xinjiang), SC (Southern China), and NC (Northern China), while the variations of HW onset and termination had noticeable regional differences at the three levels. The findings presented in this study can provide the essential scientific and technological support for national and regional disaster prevention mitigation and adaptation to extreme climate events.

Given the increasing frequency and severity of floods caused by climate change, there is a pressing requirement for creative ways to improve public comprehension and control of hydrological phenomena. Contemporary technology provides unparalleled possibilities to transform this domain. This project investigates the possibilities for merging gaming engines and digital twins to enhance flood education, data visualization, and interactive monitoring. This study proposes the utilization of immersive digital twins to enhance the comprehension of hydrological and hydraulic systems. The suggested method utilizes game engines to generate dynamic and interactive models that connect raw data to practical insights, enabling a more profound understanding of flood dynamics. This study underscores the wide-ranging usefulness of digital twins in various watersheds by focusing on the development of advanced monitoring systems, the benefits of improved data visualization, and educational outreach. The incorporation of real-time data via IoT technology considerably improves the significance and precision of these virtual models. This novel approach seeks to refashion flood management approaches by cultivating well-informed stakeholders and advocating for effective environmental education, ultimately leading to more resilient and prepared communities. An immersive digital twin of the real world can assist decision-makers technically, psychologically, and mentally by making complex phenomena easier to understand and visualize, thanks to real-time data and simulations that keep the information up-to-date, consequently leading to a more precise and intuitive decision-making process.

Focusing on the Lower Bari Doab Canal (LBDC) command area, characterized by its heavy reliance on agriculture, this study addresses the critical issue of groundwater table fluctuations in response to diverse pumping scenarios. Herein, we comprehensively evaluated the dynamic interplay between crop water requirements and groundwater pumping within the expansive canvas of the LBDC, which is facing water shortages. Using the Penman–Monteith equation, we calculated annual average evapotranspiration for major crops—wheat, maize, cotton, rice, and sugarcane. Three-dimensional MODFLOW-based numerical modeling was used to analyze the dynamics of groundwater regimes. MODFLOW was calibrated from 2010 to 2020. Thereafter, we simulated water table changes under a 20% increase and decrease in groundwater extraction up to 2040s. Results revealed significant variations in water demands among these crops, with sugarcane requiring the highest average annual evapotranspiration at 1281 mm. Spatiotemporal analysis revealed substantial declines in the water table in the tail-end command areas, particularly Sahiwal and Khanewal where the decline was 0.55 m/year between 2010 and 2020. The upper reaches, such as Balloki and Okara, experienced milder declines. In considering management scenarios, a 20% increase in groundwater extraction up to September 2040 was projected to raise pumping to 4650 MCM/year. and decrease the net water balance to −235 MCM/year. Alternatively, a 20% decrease in groundwater extraction up to September 2040 could reduce pumping to 4125 MCM/year and increase the net water balance to 291 MCM/year. This study sheds light on major crop water requirements, spatiotemporal groundwater dynamics, and the implications of groundwater extraction in the LBDC command area. Scenarios presented here, encompassing increased and decreased groundwater extraction, offer invaluable guidance for policymakers and stakeholders seeking a balance between agricultural productivity and long-term groundwater sustainability.

This study assessed the drought susceptibility in Golestan Province, Northeastern Iran, using land use change modeling and climate projections from the CMIP6 framework, under three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5) for 2030–2050. The development of current (2022) and future drought susceptibility maps was based on agrometeorological sample points and 14 environmental factors—such as land use, precipitation, mean temperature, soil moisture, and remote sensing-driven vegetation indices—used as inputs into a machine learning model, maximum entropy. The model showed a very robust predictive capacity, with AUCs for the training and test data of 0.929 and 0.910, thus certifying the model’s reliability. The current analysis identified major hotspots in Gomishan and Aqqala, where 66.12% and 36.12% of their areas, respectively, exhibited “very high” susceptibility. Projections under the SSP scenarios, particularly SSP5-8.5, indicate that the risk of drought will be the most severe in Maraveh Tappeh, where 72.09% of the area exhibits a “very high” risk. The results revealed that Golestan Province is at a crossroads. Rising temperatures, exceeding 35 °C in summer, combined with declining rainfall, intensify agricultural and hydrological droughts. These aggravated risks are compounded with land use transitions from rangelands to bare land, mostly in Aqqala and Gomishan, besides urban expansion in Bandar-e Torkman and Bandar Gaz, all of which face less groundwater recharge and increased surface runoff. Golestan’s drought vulnerability has both local and regional impacts, with its increased susceptibility affecting neighboring communities and ecosystems. Trade, migration, and ecological stresses linked to declining water resources may emerge as critical challenges, requiring regional collaboration for mitigation. Targeted interventions prioritizing sustainable land use practices, regional cooperation, and collaborative strategies are essential to address and mitigate these cascading risks and safeguard vulnerable communities.