Search Results (1,090)

Changes in global temperature and precipitation over the past few decades have caused significant alterations in global climate patterns. However, the impact of these changes on global vegetation productivity remains unclear. This article evaluates the effect of converging climate patterns on global vegetation productivity, focusing on the land outside Antarctica as the study area, and theoretically substantiates the validity of the findings. The study reveals the climate status of the historical period of 1980–2022 and the SSP126 scenario, where convergence in precipitation patterns leads to a significant increase in global NPP, while the convergence of temperature patterns has a much smaller impact on NPP than precipitation. Under the high-emission scenarios SSP245 and SSP585, the laws are reversed: converging temperature patterns lead to a decrease in NPP, while converging precipitation patterns have an insignificant impact on NPP. Climate change under these three scenarios indicates the detrimental effects of climate patterns under high emissions on vegetation productivity. This study fills a gap in the literature on the impact of climate patterns on vegetation productivity. Full article

Carya dabieshanensis is a species of significant economic value due to its unique flavor and nutritional properties as a snack food, as well as its durable wood, which is highly suitable for furniture production. Known for its remarkable adaptability to environmental stress, this species serves as a valuable genetic resource for enhancing hickory cultivars. However, its restricted distribution and limited availability of high-quality germplasm have impeded large-scale cultivation and hindered industry development. While the genetic diversity and genomic basis of its environmental adaptation hold great promise for future breeding programs, no studies to date have utilized SNP markers to explore its genetic diversity or the genomic mechanisms underlying environmental adaptability. In this study, we analyzed 60 samples from 12 natural populations of C. dabieshanensis, representing its global distribution. Using the Carya illinoinensis (Wangenh. and K. Koch) genome as a reference, we employed Specific Locus Amplified Fragment Sequencing (SLAF-seq) to generate high-quality SNP data. By integrating population and landscape genomics approaches, we investigated the genetic structure and diversity of wild populations and identified key environmental factors driving genetic differentiation. Our population genomics analysis revealed 9,120,926 SNP markers, indicating substantial genetic diversity (π = 1.335 × 10⁻³ to 1.750 × 10⁻³) and significant genetic differentiation among populations (F_ST = 0.117–0.354). Landscape genomics analysis identified BIO3 (Isothermality), BIO6 (Min Temperature of Coldest Month), and BIO14 (Precipitation of Driest Month) as critical environmental factors shaping genetic diversity. This study provides essential insights into the genetic resources of C. dabieshanensis, facilitating the development of climate-resilient cultivars and offering a scientific foundation for the conservation and sustainable management of its wild populations. Full article

Evapotranspiration (ET) and water availability (WA) are critical components of the global water cycle. Although the effects of ecological restoration on ET and WA have been widely investigated, quantifying the impacts of multiple environmental factors on plant water consumption and regional water balance in dryland areas remains challenging. In this study, we investigated the spatial and temporal trends of ET and WA and isolated the contributions of vegetation restoration and climate change to variations in ET and WA in the Beijing–Tianjin Sand Source Region (BTSSR) in Northern China from 2001 to 2021, using the remote sensing-based Priestley–Taylor-Jet Propulsion Laboratory (PT-JPL) model and scenario simulation experiments. The results indicate that the estimated ET was consistent with field observations and state-of-the-art ET products. The annual ET in the BTSSR increased significantly by 1.28 mm yr⁻¹ from 2001 to 2021, primarily driven by vegetation restoration (0.78 mm yr⁻¹) and increased radiation (0.73 mm yr⁻¹). In contrast, the drier climate led to a decrease of 0.56 mm yr⁻¹ in ET. In semiarid areas, vegetation and radiation were the dominant factors driving the variability of ET, while in arid areas, relative humidity played a more critical role. Furthermore, reduced precipitation and increased plant water consumption resulted in a decline in WA by −0.91 mm yr⁻¹ during 2001–2021. Climate factors, rather than vegetation greening, determined the WA variations in the BTSSR, accounting for 77.6% of the total area. These findings can provide valuable insights for achieving sustainable ecological restoration and ensuring the sustainability of regional water resources in dryland China under climate change. This study also highlights the importance of simultaneously considering climate change and vegetation restoration in assessing their negative impacts on regional water availability. Full article

It is imperative to assess and comprehend the hydrological processes of the river basin in light of the potential effects of land use/land cover and climate changes. The study’s main objective was to evaluate hydrologic response of water balance components to the projected land use/land cover (LULC) and climate changes in the Omo–Gibe River Basin, Ethiopia. The study employed historical precipitation, maximum and minimum temperature data from meteorological stations, projected LULC change from module for land use simulation and evaluation (MOLUSCE) output, and climate change scenarios from coupled model intercomparison project phase 6 (CMIP6) global climate models (GCMs). Landsat thematic mapper (TM) (2007) enhanced thematic mapper plus (ETM+) (2016), and operational land imager (OLI) (2023) image data were utilized for LULC change analysis and used as input in MOLUSCE simulation to predict future LULC changes for 2047, 2073, and 2100. The predictive capacity of the model was evaluated using performance evaluation metrics such as Nash–Sutcliffe Efficiency (NSE), the coefficient of determination (R2), and percent bias (PBIAS). The bias correction and downscaling of CMIP6 GCMs was performed via CMhyd. According to the present study’s findings, rainfall will drop by up to 24% in the 2020s, 2050s, and 2080s while evapotranspiration will increase by 21%. The findings of this study indicate that in the 2020s, 2050s, and 2080s time periods, the average annual Tmax will increase by 5.1, 7.3, and 8.7%, respectively under the SSP126 scenario, by 5.2, 10.5, and 14.9%, respectively under the SSP245 scenario, by 4.7, 11.3, and 20.7%, respectively, under the SSP585 scenario while Tmin will increase by 8.7, 13.1, and 14.6%, respectively, under the SSP126 scenario, by 1.5, 18.2, and 27%, respectively, under the SSP245 scenario, and by 4.7, 30.7, and 48.2%, respectively, under the SSP585 scenario. Future changes in the annual average Tmax, Tmin, and precipitation could have a significant effect on surface and subsurface hydrology, reservoir sedimentation, hydroelectric power generation, and agricultural production in the OGRB. Considering the significant and long-term effects of climate and LULC changes on surface runoff, evapotranspiration, and groundwater recharge in the Omo–Gibe River Basin, the following recommendations are essential for efficient water resource management and ecological preservation. National, regional, and local governments, as well as non-governmental organizations, should develop and implement a robust water resources management plan, promote afforestation and reforestation programs, install high-quality hydrological and meteorological data collection mechanisms, and strengthen monitoring and early warning systems in the Omo–Gibe River Basin. Full article

In the Himalayan regions of complex terrains, such as Himachal Pradesh, the occurrence of extreme rainfall events (EREs) has been increasing, triggering landslides and flash floods. Investigating the dynamics and precipitation characteristics and improving the prediction of such events are crucial and could play a vital role in contributing to sustainable development in the region. This study employs a high-resolution numerical weather prediction framework, the weather research and forecasting (WRF) model, to deeply investigate an ERE which occurred between 8 July and 13 July 2023. This ERE caused catastrophic floods in the Mandi and Kullu districts of Himachal Pradesh. The WRF model was configured with nested domains of 12 km and 4 km horizontal grid resolutions, and the results were compared with global high-resolution precipitation products and the fifth-generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis dataset. The selected case study was amplified by the synoptic scale features associated with the position and intensity of the monsoon trough, including mesoscale processes like orographic lifting. The presence of a western disturbance and the heavy moisture transported from the Arabian Sea and the Bay of Bengal both intensified this event. The model has effectively captured the spatial distribution and large-scale dynamics of the phenomenon, demonstrating the importance of high-resolution numerical modeling in accurately simulating localized EREs. Statistical evaluation revealed that the WRF model overestimated extreme rainfall intensity, with the root mean square error reaching 17.33 mm, particularly during the convective peak phase. The findings shed light on the value of high-resolution modeling in capturing localized EREs and offer suggestions for enhancing disaster management and flood forecasting. Full article

Agricultural productivity is significantly influenced by climate-related factors. Understanding the impacts of climate change on agroclimatic conditions is critical for ensuring sustainable agricultural practices. This study investigates how key agroclimatic variables—temperature, moisture conditions, and length of the growing season (LGS)—influence wheat suitability in the Upper Blue Nile Basin (UBNB), Ethiopia. The Global Agroecological Zones (GAEZ) methodology was employed to assess agroclimatic suitability, integrating climate projections from Climate Models Intercomparison Project v6 (CMIP6) under shared socioeconomic pathway (ssp370 and ssp585) scenarios. The CMIP6 data provided downscaled projections for temperature and precipitation, while the GAEZ framework translated these climatic inputs into agroclimatic indicators, enabling spatially explicit analyses of land suitability. Projections indicate significant warming, with mean annual temperatures expected to rise between 1.13 °C and 4.85 °C by the end of the century. Precipitation levels are anticipated to increase overall, although spatial variability may challenge moisture availability in some regions. The LGS is projected to extend, particularly in the southern and southeastern UBNB, enhancing agricultural potential in these areas. However, wheat suitability faces considerable declines; under ssp585, the highly suitable area is expected to drop from 24.21% to 13.31% by the 2080s due to thermal and moisture stress. This study highlights the intricate relationship between agroclimatic variables and agricultural productivity. Integrating GAEZ and CMIP6 projections provides quantified insights into the impacts of climate change on wheat suitability. These findings offer a foundation for developing adaptive strategies to safeguard food security and optimize land use in vulnerable regions. Full article

Ecosystem services (ESs) are essential for balancing environmental sustainability and socio-economic development. However, the sustainability of ESs and their relationships are increasingly threatened by global climate change and intensifying human activities, particularly in ecologically sensitive and agriculturally-intensive regions. The Songnen Plain, a crucial agricultural region in Northeast China, faces considerable challenges in sustaining its ESs due to the overexploitation of agricultural land, environmental degradation, and climate variability. This study assessed five key ESs in the Songnen Plain from 2000 to 2020 across multiple scales: habitat quality (HQ), soil conservation (SC), water yield (WY), food production (FP), and windbreaking and sand fixing (WS). We evaluated the trade-offs and synergies between these ESs, as well as the driving factors of the main ES trade-offs. Our findings indicate that provisioning services (WY and FP) and regulating services (SC and WS) improved over time, with FP exhibiting the most significant increase at 203.90%, while supporting services (HQ) declined by 32.61%. The primary ecosystem service multifunctionality areas were those that provided FP, SC, and WY, accounting for 58% of the total. ES trade-offs and synergies varied across spatial scales, with stronger synergies being observed at the pixel scale and more pronounced trade-offs at the county scale. Climate factors, particularly precipitation and temperature, played a more significant role in shaping ES trade-offs than anthropogenic factors. Our study provides valuable insights into the restoration and sustainable management of ESs in temperate agriculturally-intensive regions, with significant implications for the protection of the northeastern black soil region and safeguarding national food security. Full article

AgERA5 (ECMWF) is a relatively new climate dataset specifically designed for agricultural applications. MERRA-2 (NASA) is also used in agricultural applications; however, it was not specifically designed for this purpose. Despite the proven value of these datasets in assessing global climate patterns, their effectiveness in small-scale agricultural contexts remains unclear. This research aims to fill this gap by assessing the suitability and performance of AgERA5 and MERRA-2 in precision irrigation management, which is crucial for regions with limited ground data availability. The wine-making region of Nemea, Greece, with its complex and challenging terrain is used as a characteristic case study. The datasets are assessed for key weather variables and for irrigation planning, using detailed local meteorological station data as a reference. The results reveal that both products have serious limitations in small scale irrigation scheduling applications in contrast to what was reported in previous studies for other regions. The uneven performance of global datasets in different regions due to lack of sufficient observation data for reanalysis data calibration was also indicated. Comparing the two datasets, AgERA5 outperforms MERRA-2, especially in precipitation and reference evapotranspiration. MERRA-2 shows comparable potential in irrigation planning, as it occasionally matches or exceeds AgERA5’s performance. The study findings underscore the importance of evaluating metanalysis datasets in the application area before their use for precision agriculture, particularly in regions with complex topography. Full article

High-altitude mountainous regions are highly vulnerable to climate and environmental shifts, with the current global climate change exerting a profound influence on the ecological landscape of the Tianshan Mountains in China. This study assesses the ecological security quality in the Tianshan Mountains of China from 2001 to 2020 by employing various remote sensing techniques such as the Remote Sensing Ecological Index (RSEI) for evaluation, Normalized Difference Vegetation Index (NDVI) for fractional vegetation cover (FVC) analysis, the CASA model for estimating vegetation primary productivity (NPP), and a carbon source/sink model for calculating the net ecosystem productivity (NEP) of vegetation. The research also delves into the evolutionary trends and impact mechanisms on the ecological environment using land use and meteorological data. The findings reveal that the RSEI’s principal component (PC1) exhibits significant explanatory power, showing a notable increase of 5.90% from 2001 to 2020. Despite relatively stable changes in the RSEI over the past two decades covering 61.37% of the study area, there is a prevalent anti-persistence pattern at 72.39%. Notably, NDVI, FVC, and NPP display upward trends in vegetation characteristics. While most areas in the Tianshan Mountains continue to emit carbon, there is a marked increase in NEP, signifying an enhanced carbon absorption capacity. The partial correlation coefficients between the RSEI and temperature, as well as precipitation, demonstrate statistically significant relationships (p < 0.05), encompassing 6.36% and 1.55% of the study area, respectively. Temperature displays a predominantly negative correlation in 98.71% of the significantly correlated zones, while precipitation exhibits a prevalent positive correlation. An in-depth analysis of how climate change affects the quality of the ecological environment provides crucial insights for strategic interventions to enhance regional environmental protection and promote ecological sustainability. Full article

A comprehensive evaluation of the variations in carbon use efficiency (CUE) and water use efficiency (WUE) in the Nanling Mountains Region (NMR) is crucial for gaining insights into the intricate relationships between climate change and ecosystem processes. This study evaluates the spatiotemporal rates of dynamics in CUE, WUE, gross primary productivity (GPP), net primary productivity (NPP), and evapotranspiration (ET) over the period from 2001 to 2023, using remote sensing data and linear regression analysis. Trend analysis, Hurst exponent analysis, and stability analysis were applied to examine the long-term patterns of CUE and WUE, while partial correlation analysis was employed to explore the spatial relationships between these efficiencies and climatic factors. The main findings of the study are as follows: (1) The CUE and WUE of the NMR decreased geographically from 2001 to 2023, and both the CUE and WUE of NMR showed a significant declining trend (p < 0.05) with the CUE decreasing at a rate of 0.0014/a (a: year) and the WUE falling at a rate of 0.0022/a. (2) The average values of the CUE and WUE of the NMR from 2001 to 2023 were 0.47 and 0.82 g C·m⁻²·mm⁻¹, respectively, with a clear geographical difference. (3) The CUE and WUE in the NMR showed widespread degradation trends with some localized improvements, yet sustainability analysis indicates a likely continued decline across most areas, particularly for forests, while grasslands exhibit the greatest resilience. (4) Precipitation had a significantly stronger impact on WUE, while temperature appeared to exert a more substantial effect on CUE, with vegetation types responding differently; notably, shrubland displayed a direct association between CUE and temperature. In summary, multi-source data were employed to comprehensively analyze the spatiotemporal dynamics of CUE and WUE in the NMR over the past 23 years. We also examined the features of their responses to global warming, offering valuable theoretical insights into the carbon and water dynamics within the terrestrial ecosystems of the NMR. Full article

Rainfall–runoff models are crucial tools for managing water resources. The absence of reliable rainfall data in many regions of the world is a major limitation for these models, notably in many African countries, although some recent global rainfall products can effectively monitor rainfall from space. In Algeria, to identify a relevant modeling approach using this new source of rainfall information, the present research aims to (i) compare a conceptual model (GR4J) and seven machine learning algorithms (FFNN, ELM, LSTM, LSTM2, GRU, SVM, and GPR) and (ii) compare different types of precipitation inputs, including four satellite products (CHIRPS, SM2RAIN, GPM, and PERSIANN), one reanalysis product (ERA5), and observed precipitation, to assess which combination of models and precipitation data provides the optimal performance for river discharge simulation. The results show that the ELM, FFNN, and LSTM algorithms give the best performance (NSE > 0.6) for river runoff simulation and provide reliable alternatives compared to a conceptual hydrological model. The SM2RAIN-ASCAT and ERA5 rainfall products are as efficient as observed precipitation in this data-scarce context. Consequently, this work is the first step towards the implementation of these tools for the operational monitoring of surface water resources in Algeria. Full article

Climate change is increasingly recognized as a significant issue facing humanity. The World Health Organization (WHO) designates climate change as the greatest threat to global health in the 21st century. Bulgaria is under imminent threat from climate change. The country is projected to experience a temperature increase of up to 4 °C by 2100. This will lead to changes in precipitation patterns, resulting in numerous consequences. These include reduced water storage, impacts on public health, disruptions in agricultural production, stress on the country’s biodiversity and forests, damage to infrastructure and private property, changes in tourism patterns, and many other potential issues. Climate change has recently become a significant concern in Bulgaria due to its impact on ecosystems, the economy, society, and infrastructure. This study provides a comprehensive analysis of the barriers to climate adaptation in Bulgaria, integrating sources from the literature with empirical data gathered from a survey. By employing cluster analysis, this research identifies five primary groups of barriers, offering a fresh perspective on the complexities involved in this process. The findings contribute to the existing body of knowledge on climate adaptation and hold the potential to guide policy development aimed at addressing these challenges. Full article

Reference precipitation (RP) serves as a benchmark for evaluating the accuracy of precipitation products; thus, the selection of RP considerably affects the evaluation. In order to quantify this impact and provide guidance for RP selection, three interpolation methods, namely inverse distance weighting (IDW), ordinary kriging (OK), and geographical weighted regression (GWR), along with six groups of station densities, were adopted to generate different RPs, based on the super-high-density rainfall observations as true values, and we analyzed the errors of different RPs and the impacts of RP selection on the assessment of GPM precipitation products. Results indicate that the RPs from IDW and GWR both approached the true values as the station density increased (CC > 0.90); while the RP from OK showed some differences (CC < 0.80), it was similar to GWR when the station density was low, but the accuracy improved at first and then worsened as the station density continued to increase; the evaluation results based on different RPs showed remarkable differences even under the same conditions; when the average distance between rainfall gauges that were utilized to generate RPs was below the medium value (i.e., d < 20 km), the evaluation based on RP derived from IDW and GWR was close enough to that based on the true precipitation, which indicates its feasibility in evaluating satellite precipitation products. Full article

Over the last century, significant climate changes, including more intense droughts and floods, have impacted agriculture and socio-economic development, particularly in rain-dependent regions like Marrakech–Safi (MS) in Morocco. Limited data availability complicates the accurate monitoring and assessment of these natural hazards. This study evaluates the role of satellite data in drought monitoring in the MS region using rain gauge observations from 18 stations, satellite-based precipitation estimates from Climate Hazards Group InfraRed Precipitation with Station (CHIRPS), and temperatures from the fifth generation of the atmospheric global climate reanalyzed Era5-Land data. The Standardized Precipitation Evapotranspiration Index (SPEI) was calculated at various timescales to characterize droughts. Statistical analysis was then performed to assess the correlation between the SPEI and the cereal yields. The results show that CHIRPS effectively monitors droughts, demonstrating strong statistically significant correlations (r ~ 0.9) with the observed data in the plains, the plateaus, Essaouira–Chichaoua Basin, and the coastal zones, along with a good BIAS score and lower root mean square error (RMSE). However, discrepancies were observed in the High Atlas foothills and the mountainous regions. Correlation analysis indicates the significant impact of droughts on agricultural productivity, with strong correlations between the Standardized Yield Residual Series (SYRS) and SPEI-6 in April and SPEI-12 in June (r ~ 0.80). These findings underscore the importance of annual and late-season precipitation for cereal yields. Analysis provides valuable insights for decision-makers in designing adaptation strategies to enhance small-scale farmers’ resilience to current and projected droughts. Full article

Geothermal energy exploitation has emerged as a critical solution to combat global climate crises, such as reducing CO₂ emissions and climate warming. Scaling is the process of mineral precipitation in fluid pathways and geothermal equipment. It is known to significantly hamper geothermal energy production by decreasing the rates of heat extraction. Numerous research efforts are dedicated to characterising dissolution and precipitation processes, not only to provide know-how for further and safer developments in geothermal energy, but also to adapt such findings to the ever emerging field of geothermal energy recovery. This paper presents an overview of experiments—performed under variable pressure and temperature conditions—with a focus on scaling. We assess the different factors that influence disequilibrium reactions in carbonate rocks, the different experimental setups, and their application to the field. The influence of experimental variables (such as temperature and pressure) on mineral dissolution and precipitation is discussed, and the main learning points from experiments are compared and contrasted. We address techniques for preventing and controlling scaling in geothermal wells based on a comprehensive analysis of experimental studies in carbonate rocks. We propose that the intelligent combination of fieldwork, numerical approaches, and laboratory experience provides a foundation for the success of future work in this field. Full article