Climatic Variables Research Articles

Rainfall’s impact on agricultural production and government poverty reduction efficiency in China

The quest to eradicate poverty, central to the United Nations Sustainable Development Goals (SDGs), poses a significant global challenge. Advancement in sustainable rural development is critical to this effort, requiring the seamless integration of environmental, economic, and governmental elements. Previous research often omits the complex interactions among these factors. Addressing this gap, this study evaluates sustainable rural development in China by examining the interconnection between agricultural production and government-led poverty reduction, with annual rainfall considered an influential factor of climate change impacts on these sectors and overall sustainability. Utilizing a Meta-frontier entropy network dynamic Directional Distance Function (DDF) within an exogenous Data Envelopment Analysis (DEA) model, we categorize China’s 27 provinces into southern and northern regions according to the Qinling-Huaihe line for a comparative study of environmental, economic, and governmental efficiency. This innovative approach overcomes the limitations of previous static analyses. The findings reveal: (1) Rainfall, as an exogenous variable, significantly affects agricultural production efficiency. (2) The overall efficiency in both southern and northern regions increases when accounting for rainfall. (3) Government effectiveness in poverty reduction is comparatively lower in the northern region than in the southern region when rainfall is considered. These insights underscore the importance of including climatic variables in sustainable development policies and emphasize the need for region-specific strategies to bolster resilience against climatic challenges.

Vineyard Microclimatic Zoning as a Tool to Promote Sustainable Viticulture under Climate Change

Understanding microclimate spatial variability is crucial for sustainable and optimised grape production within vineyard plots. By employing a combination of a microclimate model (NicheMapR) and multiple climate data sources, this study aimed to achieve microclimatic analysis in two vineyard plots, Quinta do Bomfim (northern Portugal) and Herdade do Esporão (southern Portugal). This approach provides an innovative 10 m spatial resolution for climate variables. This study incorporated local station hourly data with quantile mapping bias correction on the ERA5-land data. The microclimate model output was employed to perform bias correction on a EURO-CORDEX model ensemble. Climate extreme and bioclimatic indices specifically targeted to viticulture were calculated for each vineyard plot. The 10 m scale was analysed to identify potential shifts in temperature extremes, precipitation patterns, and other crucial climatic variables for grape cultivation within each specific plot. The significance of microclimate analyses was higher in areas with intricate topography, while in areas with smooth slopes, the variation of climatic variables was determined to be negligible. There was a projected increase in the median temperature of approximately 3.5 °C and 3.6 °C and a decrease in precipitation of approximately 98 mm and 105 mm in Quinta do Bomfim and Herdade do Esporão, respectively, when comparing a future scenario for the period 2071–2100 against the historical period (1981–2010). Hence, this study offers a comprehensive and future-oriented method for analysing microclimates in vineyard plots. By incorporating geospatial data, ERA5-land data, and the microclimate NicheMapR model, this research aimed to enhance the understanding of current microclimates and future climate scenarios for viticulturists.

A Review on the Relationship of Climate Variability and Extremes with Crop Production

Risks are always present in agriculture for a variety of reasons. Climate-related risks are the most significant among them since they can occur unexpectedly and cannot be avoided. The main climatic factors influencing crop productivity are rising temperatures, altered precipitation patterns, and rising atmospheric CO2. The average global temperature is expected to rise by 2oC until 2100, which would result in significant global economic losses. The average global temperature is currently rising steadily. The concentration of CO2, which makes up a large amount of greenhouse gases, is rising alarmingly. Climate change, with rising temperatures and increased greenhouse gas emissions, impacts agriculture significantly, leading to varied crop yields and potentially catastrophic economic consequences. While some regions may experience favorable effects, overall, climate variability poses challenges such as reduced crop productivity, increased pest activity, and elevated food costs, particularly affecting underdeveloped nations. Environmental policies must be adaptable and flexible to mitigate these impacts effectively. Farmers' responses to climate change, influenced by perceptions and data accessibility, drive both mitigation and adaptation efforts. Sustainable agriculture, conservation practices, and technological innovations play key roles in mitigating climate change impacts and enhancing resilience in farming communities, though success depends on multiple factors including local perceptions, technical feasibility, and economic viability. This paper reviews the causes of climate change, the climate variables affecting crop production and the mitigation and adaptation strategies against climate change.

Hydrogeological trends in an alluvial valley in the Brazilian semiarid: Impacts of observed climate variables change and exploitation on groundwater availability and salinity

Study regionMimoso Alluvial Valley, Semiarid Brazil. Study focusThis study aims to assess the influence of climate variability and exploitation on groundwater accessibility, quantity and quality, based on a spatiotemporal data analysis from long-term monitoring field campaigns, conducted monthly from 2000 to 2019. This study successfully identified representative stable monitoring points, piezometers and wells, for piezometric and salinity levels employing the technique of relative differences, taking into account aquifer hydraulic properties. Trend analysis was then carried out adopting the Mann-Kendall Method, Sen's Slope test, Pettitt test, and the Seasonal Trend decomposition through the Loess (STL) method. Principal Component Multivariate Analysis (PCA) was also employed to validate long-term analysis of annual groundwater levels, rainfall, evapotranspiration, and pumping rates. New hydrological insights for the regionThe spatial-temporal variability of salinity is closely linked to the salt balance in regions with low hydraulic permeability and groundwater use for irrigation. Groundwater salinity presented a strong link between rainfall and groundwater levels. A temporal rise in evapotranspiration alongside declines in rainfall and groundwater levels was observed, potentially exacerbating groundwater scarcity alongside intensified aquifer exploitation. Furthermore, groundwater salinity presented a decreasing trend for the pumping wells and no trends for piezometers. This could potentially disrupt future irrigation plans and cause long-term water shortages in a region already under severe water scarcity. Hence, groundwater pumping constitutes a positive management alternative for controlling groundwater salinity in the domain.

Trend analysis of reference evapotranspiration and climate variables in the main hydrosystems of Senegal: Senegal, Gambia and Casamance River Basins

Trend analysis of reference evapotranspiration and climate variables in the main hydrosystems of Senegal: Senegal, Gambia and Casamance River Basins

The pollinator conservation approach “Farming with Alternative Pollinators”: Success and drivers

Global food security is heavily reliant on crop pollination. However, evidence on pollinator decline has been reported in all continents. Globally affordable conservation strategies need to be developed, as high-cost measures like European agri-environment schemes are not scalable in all countries. Here, we test, if a low-cost conservation approach named “Farming with Alternative Pollinators” (FAP) can benefit wild pollinator abundance and richness in agro-ecosystems and in crops, through establishment of marketable habitat enhancement plants (MHEP). The study was carried out in four Moroccan agro-climatic regions, during two years, using 6 main crops (pumpkin, zucchini, faba bean, tomato, eggplant and apple) and 201 sites. Additionally, we investigated how crop type, crop-MHEP composition (i.e. phylogenetic distances among crop and MHEP) and local climate can drive the success of the approach in comparison to monocultural fields. Based on 7097 recorded specimens, our results show that the wild pollinators of the entire FAP fields (i.e. 75% main crop and 25% MHEP) were significantly more abundant and species-rich than those of control fields (i.e. 100% main crop). Considering the main crop wild pollinators, FAP did not display any significant effect either on wild pollinator abundance or on pollinator richness. The mean phylogenetic distance between the main crop and MHEP, and climatic variables were not correlated with increase in wild pollinator abundance and richness in FAP fields. The crop type was found to influence the effect of the FAP approach. Our study provides strong evidence that FAP constitutes a relevant method for wild pollinator conservation in agro-ecosystems. Further research on additional environmental factors is necessary to outline the circumstances under which the FAP approach can positively affect wild pollinator communities.

Climate-related naturally occurring epimutation and their roles in plant adaptation in A. thaliana.

DNA methylation has been proposed to be an important mechanism that allows plants to respond to their environments sometimes entirely uncoupled from genetic variation. To understand the genetic basis, biological functions and climatic relationships of DNA methylation at a population scale in Arabidopsis thaliana, we performed a genome-wide association analysis with high-quality single nucleotide polymorphisms (SNPs), and found that ~56% on average, especially in the CHH sequence context (71%), of the differentially methylated regions (DMRs) are not tagged by SNPs. Among them, a total of 3235 DMRs are significantly associated with gene expressions and potentially heritable. 655 of the 3235 DMRs are associated with climatic variables, and we experimentally verified one of them, HEI10 (HUMAN ENHANCER OF CELL INVASION NO.10). Such epigenetic loci could be subjected to natural selection thereby affecting plant adaptation, and would be expected to be an indicator of accessions at risk. We therefore incorporated these climate-related DMRs into a gradient forest model, and found that the natural A. thaliana accessions in Southern Europe that may be most at risk under future climate change. Our findings highlight the importance of integrating DNA methylation that is independent of genetic variations, and climatic data to predict plants' vulnerability to future climate change.

Contribution of climatic variables and their interactions to reference evapotranspiration changes considering freeze-thaw cycles in the Tibetan Plateau during 1960–2022

While the direct impact of climate change on reference evapotranspiration (ET0) has been extensively studied, there is limited research on the indirect impact resulting from the interaction between climatic variables. This gap hinders a comprehensive understanding of climate change effects on ET0. Additionally, there is scarce exploration into the quantitative effect of freeze-thaw cycles on ET0 variation. In this study, we employed path analysis and dependent variable variance decomposition methods to discern the direct and interactive effects of climatic variables on ET0 in the Tibetan Plateau from 1960 to 2022. Annual ET0 exhibited variation across basins, with the coefficient of variability during the thawed period smaller than that during the non-thawed period. On an annual scale, the largest contribution to ET0 variation came from water vapor pressure deficit (VPD) at 47.7%. This contribution was amplified by its coupled interaction with temperature (T) at 47.1%, although the contribution was partially offset by the interactive effects of VPD with downward shortwave radiation and wind speed at −2.4% and − 27.6%, respectively. During different freezing-thawing periods, VPD primarily controlled ET0 variation, with its interaction with other climatic variables enhancing its impact. Furthermore, soil moisture, influenced by freeze-thaw cycles, exhibited a strong correlation with T and VPD, indicating the significant effect of freeze-thaw cycles on ET0 variation. The weak correlation between ET0 and NDVI suggested that vegetation growth had a limited regulatory effect on ET0. These findings provide valuable insights into the impact of interactions between climatic variables on hydrological processes, enhancing our understanding of the interactive roles of hydrometeorological variables.

Latitudinal patterns and drivers of plant lignin and microbial necromass accumulation in forest soils: Disentangling microbial and abiotic controls

Plant- and microbial-derived compounds are the two primary sources of the soil organic carbon (SOC) pool. However, whether and how biotic and abiotic factors regulate the means through which microbes and plants contribute to SOC accumulation at a regional scale remains unclear. This study investigated the distribution patterns of plant lignin components and microbial necromass in soils, as indicated by lignin phenols and amino sugars across five latitudinal forests in eastern China. The contents of plant lignin and microbial necromass in the soil increased with increasing latitude. However, their contributions to SOC followed quadratic patterns with increasing latitude. Given its positive relationship with SOC content in all examined forests, microbial necromass seemed to play a more critical role in SOC accumulation than lignin phenols. Moreover, plant lignin and microbial necromass accumulation and their contributions to SOC were mainly influenced by the joint effects of climatic variables, microbial traits, and soil properties, but their importance was divergent. Specifically, the mean annual temperature (MAT) and soil phosphorus content were central to plant lignin accumulation, whereas MAT and the microbial richness: biomass ratio mainly controlled microbial necromass accumulation. These findings demonstrate that microbial necromass plays a more critical role than plant lignin in forest SOC accumulation and further highlight the importance of microbial traits in microbial necromass accumulation.

Future Climate Change and Anthropogenic Disturbance Promote the Invasions of the World's Worst Invasive Insect Pests.

Invasive insect pests adversely impact human welfare and global ecosystems. However, no studies have used a unified scheme to compare the range dynamics of the world's worst invasive insect pests. We investigated the future range shifts of 15 of the world's worst invasive insect pests. Although future range dynamics varied substantially among the 15 worst invasive insect pests, most exhibited large range expansions. Increases in the total habitat suitability occurred in more than ca. 85% of global terrestrial regions. The relative impacts of anthropogenic disturbance and climate variables on the range dynamics depended on the species and spatial scale. Aedes albopictus, Cinara cupressi, and Trogoderma granarium occurred four times in the top five largest potential ranges under four future climate scenarios. Anoplophora glabripennis, Aedes albopictus, and Co. formosanus were predicted to have the largest range expansions. An. glabripennis, Pl. manokwari, Co. formosanus, and So. invicta showed the largest range centroid shifts. More effective strategies will be required to prevent their range expansions. Although the strategies should be species-specific, mitigating anthropogenic disturbances and climate change will be essential to preventing future invasions. This study provides critical and novel insights for developing global strategies to combat the invasions of invasive insect pests in the future.

Unlocking sustainable agriculture: climate adaptation, opportunity costs, and net revenue for Nigeria cassava farmers

Climate change continues to affect food production and farmers incur additional costs to adopt appropriate adaptation strategies to combat its effects and attain food security. To enhance adaptive and sustainable coping strategies and food security, it is necessary to study the opportunity costs of implementing climate adaptations and how they influence the net revenue of farmers. Therefore, the study empirically investigates adaptation to climate change and the net revenue of cassava farmers in Southwest Nigeria. The primary data used for this study were collected through a well-structured questionnaire for 221 respondents. The analytical methods used were descriptive statistics, paired sample tests, and multiple regression. Analysis revealed that the majority (55%) of the sampled cassava farmers employed planting different varieties and using agrochemicals as their main adaptation strategies. The results revealed that insufficient funding and labour shortage were the main barriers to adaptation in the study area. The results of opportunity cost on net revenue and costs using paired samples test revealed that cassava farmers derived and perceived the utility and the net benefits using adaptation measures than when it is not used. The result of the regression showed that climate variables such as adaptation options, rainfall, and access to climate information are co-joint with socioeconomic and production factors to determine the average net revenue in the area. Therefore, the study suggests that the costs of adaptation resources should be subsidized by the government at affordable prices for the farmers.

Resistance of the fiber-derived geotextile from Typha domingensis submitted to field degradation

Geotextiles made from plant fibers creates a suitable environment for plant growth as part of soil bioengineering techniques. The faster decomposition of plant fiber geotextiles compared to synthetic ones demands the use of composites that enhance their waterproofing and extend their durability in the environment. The objective of this work was to evaluate the resistance of a geotextile made with Thypha domingensis to degradation caused by climatic variables. Tensile strength tests were conducted in the laboratory in order to evaluate the degradation of geotextiles treated with single and double layers of waterproofing resin. Based on Scanning Electron Microscopy (SEM) images, it was verified that applying double layer of waterproofing resin delays the fibers degradation up to 120 days of exposure to the effects of climatic variables other than temperature. The maximum resistance losses due to the geotextile's exposure to degradation were statistically significant for all three treatments: control-without waterproofing resin, with one layer resin, and with two layers resin. Therefore, waterproofing resin, provides a long-term protective solution for geotextiles made from cattail fibers.

Enhancing reliability in climate projections: A novel approach for selecting global climate models

Reliable climate projections are essential for informed decision-making in the context of climate change. However, selecting suitable Global Climate Models (GCMs) for such projections remains challenging, especially when computational resources are limited. This study introduced an innovative approach to GCM selection, emphasizing the identification of models that exhibit consistency in projecting future climate changes and skill in representing current climate conditions, including average climate, seasonal patterns, and climatic variations. GCM performance in simulating these critical properties was evaluated for three major climatic variables: rainfall, maximum temperature, and minimum temperature. This assessment results in a structured 3 × 3 performance matrix for each GCM, encapsulating its ability to capture these essential climate features. The matrix distances, quantifying the disparities between each GCM's performance matrix and the ideal reference matrix, were used to collectively represent the overall model performance. Finally, GCMs were ranked based on these differences using the Jenks natural break classification method, a robust statistical technique, to aid in identifying the top-performing GCMs, making them ideal candidates for ensemble model construction. The newly developed method was tested by applying it to select GCMs for Nigeria from a pool of 19 CMIP6 GCMs. The results indicate that 15 of 19 GCMs consistently projected future climate within a 95% confidence interval. Further evaluation of matrix distances and natural classification reveals a subset of GCMs, ACCESS.ESM1.5, BCC.CSM2.MR, CMCC.ESM2 and MRI.ESM2.0 are the most suitable choice for simulating Nigeria's climate. The multimodel ensemble mean of the selected GCMs projected a notable increase in rainfall by 10–40% over most of the country and the maximum and minimum temperatures by 1.0–3.5 °C and 0.5–4.0 °C across the country. The method introduced in this study can be an effective tool for GCM selection to enhance climate projection reliability.

On the attribution of changes in streamflow conditions to climate variability in North Carolina Piedmont, United States

ABSTRACT Changes in streamflow magnitudes have ecological, hydrological, and geomorphological implications. Improved knowledge and understanding of the drivers of changes in streamflow conditions remain critical in water resource management. Spatiotemporal patterns of changes in streamflow conditions for five flow percentiles between two comparative periods: historical period (1944–1980) and recent period (1984‒2022) were determined in 18 watersheds of North Carolina Piedmont. The observed percentage changes in streamflow magnitudes for the five flow percentiles were attributed to change magnitudes in monthly mean precipitation and monthly mean temperature using a statistical modeling framework (i.e. gamma regression model). The results showed reduced streamflow magnitudes for the 10th, 25th, 50th, and 75th percentile flows, respectively, while an increased streamflow magnitude was found for the 90th percentile flow at many of the sites in recent period. The regression models captured the relationships between change magnitudes in streamflow conditions and changes in climate variables across all the sites.

Carbon Monitor Power-Simulators (CMP-SIM v1.0) across countries: a data-driven approach to simulate daily power generation

Abstract. The impact of climate change on power demand and power generation has become increasingly significant. Changes in temperature, relative humidity, and other climate variables affect cooling and heating demand for households and industries and, therefore, power generation. Accurately predicting power generation is crucial for energy system planning and management. It is also crucial to understand the evolution of power generation to estimate the amount of CO2 emissions released into the atmosphere, allowing stakeholders to make informed plans to reduce emissions and to adapt to the impacts of climate change. Artificial intelligence techniques have been used to investigate energy-demand-side responses to external factors at various scales in recent years. However, few have explored the impact of climate and weather variability on power demand. This study proposes a data-driven approach to model daily power demand provided by the Carbon Monitor Power project by combining climate variables and human activity indices as predictive features. Our investigation spans the years 2020 to 2022 and focuses on eight countries or groups of countries selected to represent different climates and economies, accounting for over 70 % of global power consumption. These countries include Australia, Brazil, China, the European Union (EU), India, Russia, South Africa, and the United States. We assessed various machine-learning regressors to simulate daily power demand at the national scale. For countries within the EU, we extended the analysis to one group of countries. We evaluated the models based on key evaluating metrics: coefficient of determination (R2), mean absolute error (MAE), root mean squared error (RMSE), and median absolute error (MedAE). We also used the models to identify the most influential variables that impact power demand and determine their relationship with it. Our findings provide insight into variations in important predictive features among countries, along with the role played by distinct climate variables and indicators of the level of economic activity, such as weekends and working days, vacations and holidays, and the influence of COVID-19.

Evaluating the performance of key ERA‐Interim, ERA5 and ERA5‐Land climate variables across Siberia

AbstractReanalysis datasets provide a continuous picture of the past climate for every point on Earth. They are especially useful in areas with few direct observations, such as Siberia. However, to ensure these datasets are sufficiently accurate they need to be validated against readings from meteorological stations. Here, we analyse how values of six climate variables—the minimum, mean and maximum 2‐metre air temperature, snow depth (SD), total precipitation and wind speed (WSP)—from three reanalysis datasets—ERA‐Interim, ERA5 and ERA5‐Land—compare against observations from 29 meteorological stations across Siberia and the Russian Far East on a daily timescale from 1979 to 2019. All three reanalyses produce values of the mean and maximum daily 2‐metre air temperature that are close to those observed, with the average absolute bias not exceeding 1.54°C. However, care should be taken for the minimum 2‐metre air temperature during the summer months—there are nine stations where correlation values are <0.60 due to inadequate night‐time cooling. The reanalysis values of SD are generally close to those observed after 1992, especially ERA5, when data from some of the meteorological stations began to be assimilated, but the reanalysis SD should be used with caution (if at all) before 1992 as the lack of assimilation leads to large overestimations. For low daily precipitation values the reanalyses provide good approximations, however they struggle to attain the extreme high values. Similarly, for the 10‐metre WSP; the reanalyses perform well with speeds up to 2.5 ms−1 but struggle with those above 5.0 ms−1. For these variables, we recommend using ERA5 over ERA‐Interim and ERA5‐Land in future research. ERA5 shows minor improvements over ERA‐Interim, and, despite an increased spatial resolution, there is no advantage to using ERA5‐Land.

Drought Offsets the Controls on Colored Dissolved Organic Matter in Lakes

The concentration of colored dissolved organic matter (CDOM) in lakes is strongly influenced by climate, land cover, and topographic settings, but it is not known how drought may affect the relative importance of these controls. Here, we evaluate the controls of CDOM during two summers with strongly contrasting values of the Palmer drought index (PDI), indicating wet vs. dry conditions. We hypothesized that lake CDOM during a wet summer season is regulated mainly by the surrounding land cover to which the lakes are hydrologically connected, while, during drought, the lakes are disconnected from the catchment and CDOM is regulated by climatic and morphometric factors that govern the internal turnover of CDOM in the lakes. A suite of climate, land cover, and morphometric variables was assembled and used to explain remotely sensed CDOM values for 255 boreal lakes distributed across broad environmental and geographic gradients in Sweden and Norway. We found that PDI explained the variability in CDOM among lakes in a dry year, but not in a wet year, and that severe drought strongly decreased CDOM during the dry year. Large lakes, especially, with a presumed high degree of catchment uncoupling, showed low CDOM during the dry year. However, in disagreement with our hypothesis, climate, land cover, and morphometry all showed a stronger impact on lake CDOM in wet vs. dry years. Thus, drought systematically weakened the predictability of CDOM variations at the same time as CDOM was offset toward lower values. Our results show that drought not only has a direct effect on CDOM, but also acts indirectly by changing the spatial regulation of CDOM in boreal lakes.

Analysis of Climatic Variables on the Water Resources of the Ouémé River Using the Seasonal ARIMA Approach

Analysis of Climatic Variables on the Water Resources of the Ouémé River Using the Seasonal ARIMA Approach

Influence of climate, physical and chemical variables on the taxonomic and functional responses of macroinvertebrate communities in tropical island rivers

Influence of climate, physical and chemical variables on the taxonomic and functional responses of macroinvertebrate communities in tropical island rivers

Site Quality Models and Fuel Load Dynamic Equation Systems Disaggregated by Size Fractions and Vegetative States in Gorse and High Heath Shrublands in Galicia (NW Spain)

Compatible model systems were developed for estimating fuel load dynamics in Ulex europaeus (gorse) and in Erica australis (Spanish heath) dominated shrub communities at stand level. The models were based on intensive, detailed destructive field sampling and were fitted simultaneously to fulfill the additivity principle. The models enable, for the first time, estimation of the biomass dynamics of the total shrub layer, size fractions and vegetative stage, with reasonably good accuracy. The approach used addresses the high variability in shrub biomass estimates by using a site index (SI) based on biomass levels at a reference age of 10 years. Analysis of the effect of climatic variables on site index confirmed the preference of gorse for mild temperatures and the ability of high heath communities to tolerate a wider range of temperatures. In the gorse communities, SI tended to increase as summer rainfall and the mean temperature of the coldest month increased. However, in the heath communities, no relationships were observed between SI and any of the climatic variables analyzed. The study findings may be useful for assessing and monitoring fuel hazards, updating fuel mapping, planning and implementing fuel reduction treatments and predicting fire behavior, among other important ecological and biomass use-related applications.