Impact Of Climate Variability Research Articles

The impact of internal climate variability on OH trends between 2005 and 2014

The impact of internal climate variability on OH trends between 2005 and 2014

Quantitative analysis of the impact of climate variability and human activities on grassland productivity of the Qilian Mountain National Park, China.

To quantitatively analyze the impact of climate variability and human activities on grassland productivity of China's Qilian Mountain National Park, this study used Carnegic-Ames-Stanford Approach model (CASA) and Integrated Vegetation model improved by the Comprehensive and Sequential Classification System (CSCS) to assess the trends of grassland NPP from 2000 to 2015, the residual trend analysis method was used to quantify the impact of human activities and climate change on the grassland based on the NPP changes. The actual grassland NPP accumulation mainly occurred in June, July and August (autumn); the actual NPP showed a fluctuating upward trend with an average increase of 2.2 g C·m-2 a-1, while the potential NPP increase of 1.6 g C·m-2 a-1 and human-induced NPP decreased of 0.5 g C·m-2 a-1. The annual temperature showed a fluctuating upward trend with an average increase of 0.1°C 10a-1, but annual precipitation showed a fluctuating upward trend with an average annual increase of 1.3 mm a-1 from 2000 to 2015. The area and NPP of grassland degradation caused by climate variability was significantly greater than that caused by human activities and mainly distributed in the northwest and central regions, but area and NPP of grassland restored caused by human activities was significantly greater than that caused by climate variability and mainly distributed in the southeast regions. In conclusion, grassland in Qilian Mountain National Park showed a trend of degradation based on distribution area, but showed a trend of restoration based on actual NPP. Climate variability was the main cause of grassland degradation in the northwestern region of study area, and restoration of grassland in the eastern region was the result of the combined effects of human activities and climate variability. Under global climate change, the establishment of Qilian Mountain National Park was of great significance to the grassland's protection and the grasslands ecological restoration that have been affected by humans.

A long-term study on the impact of climatic variables on two common nest-dwelling ectoparasites of the Eurasian blue tit (Cyanistes caeruleus).

We explored the potential influence of temperature and precipitation on the abundance of two nest-dwelling ectoparasites (blowflies and mites) of Eurasian blue tits (Cyanistes caeruleus) during a period of 21 years and compared the results with those of a shorter period. The abundance of blowflies was negatively related to precipitation, which could prevent flies from locating their host, and laying date. In addition, blowflies were positively related to brood size (more food implies more parasites) and the interaction between precipitation and temperature. The highest abundances of blowfly pupae were attained in conditions of increasing precipitation and decreasing temperature, which should be more common at the beginning of the bird breeding season. Mites were significantly and positively related to laying date and the interaction between average precipitation and temperature but only for the larger dataset. Higher abundances of mites were related to intermediate values of temperature and precipitations, conditions that are found at the end of the breeding season. These results imply that optimal conditions for both parasites differ, with blowflies preferring earlier breeders and colder and more humid conditions than mites. Thus, the effects of the climatic conditions studied on parasite abundances are non-monotonic and can vary with years and parasite species. Finally, the fact that average temperature and precipitation decreases across the years of study is probably due to the advancement in Eurasian blue tit laying date because we calculated those variables for the period of birds' reproduction. This earlier nesting does not affect parasite abundance.

Spatially Weighted Multinomial Modelling of Comorbidity of Anaemia and Malaria among Under Five Children in Nigeria

Children in sub-Saharan African countries especially Nigeria continue to suffer increased mortality owing to comorbidity of infections such as anaemia and malaria, which are known to epidemiologically overlap. In order to examine the risk factors and spatial patterns of comorbidity of anaemia and malaria using 2021 Nigeria malaria indicator survey (NMIS), multinomial logit model was extended by incorporating spatially weighted random effect. The impact of climatic variation on the childhood disease comorbidity was explored by weighting the spatial structured component based on the 2021 NMIS average cluster temperature of each state in Nigeria. A number of spatial weighted geo-additive models were fitted and compared using defiance information criterion. Inference was fully Bayesian and Intrinsic Conditional Autoregressive prior was used for structured random effects. Based on the map generated from the best- fitted model which unveiled states that are more susceptible to the risk of disease comorbidity, the average temperature used as a weighting factor however has strong relationship with the spatial pattern of disease comorbidity. States with low temperature have higher risk of comorbidity of anaemia and malaria compared to states with higher average temperature.Area of residence, level of education of the mother, economic status of the mother and owning mosquito-treated nets were identified as the significant risk factors associated with the disease comorbidity.Findings from this study will be helpful to policymakers and health authorities in their effort to combat the comorbidity of childhood anaemia and malaria thereby reducing child mortality in Nigeria.

Evaluation of the Impact of Climate Variability on the Soil-Water Characteristics Curve

The hydro-mechanical behavior of unsaturated soil, particularly expansive soil, is influenced significantly by cyclic wetting and drying. Understanding the soil parameters is crucial when evaluating the performance of infrastructures constructed on expansive clay. As a result of extreme rainfall events, highway slopes containing highly expansive Yazoo clay in Mississippi, U.S., become vulnerable to volume change. The phenomenon creates perched water zones within the slopes and poses a risk of slope failure. The soil-water characteristic curve (SWCC) defines the relationship between water content and soil suction, which can be obtained from different laboratory procedures. However, conventional laboratory methods have some limitations. To address this, various analytical and predictive models have been developed, but they can only offer estimates based on soil characteristics and lack seasonal variations occurring in field conditions. Studying seasonal SWCC through field measurements can help understand soil responses to changing moisture conditions. The current study utilized field data from six highway slopes in Mississippi and classified the data into different seasons: spring, summer, and fall. After obtaining van Genuchten parameters from the fitted curve for each season, the finite element method was applied to evaluate the parameters for accurate numerical analysis of infrastructures containing expansive clay. The study observed the variations in flow parameters with seasonal change that cannot be achieved when data from only one season is considered. The findings underscore the importance of field instrumentation data for developing SWCC and the significance of seasonal flow parameters in infrastructure design.

Impacts of climate variations and human influences on groundwater recharge in the semi-arid Trarza aquifer (SW Mauritania)

Impacts of climate variations and human influences on groundwater recharge in the semi-arid Trarza aquifer (SW Mauritania)

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.

Determinants of farmers’ choice of adaptation strategies against climate variability and change: Lessons from central Tanzania in Manyoni district

Farmers’ adaptation strategies against climate variability and change impact are fundamental in abating the effects in this epoch. Previous studies have confirmed the presence of context-specific farmers’ adaptation strategies in Tanzania. However, it is not clear what factors influence farmers to select specific adaptation strategies. This study contributes to filling this knowledge gap. We used the cross-sectional research design in a survey of a random sample of 330 small-scale farmers. The multivariate probit was used to model the factors determining the choice of adaptation strategies. Farmers adapted a number of strategies, nonetheless, there were four key adaptation strategies: drought-resistant varieties, use of early maturing varieties, resistant livestock breeds, and conservation agriculture. Farmers used more than one strategy, and the choice was determined by multiple factors that showed a statistically significant impact at 5 %. We categorize the factors into four groups: (i) individual farmer and or household characteristics covering age, farming experience of the household head; household size; and household wealth, especially livestock ownership (ii) farm characteristics like location of the farm and farm size (iii) institutional factors that include access to extension services, technology, and provision of title deeds for land ownership to the farmers; and (iv) knowledge of manifestation of CV & C like shifting of rain seasons, and awareness of using meteorological information in decision making. These factors should be considered in policy development to heighten the effectiveness of the adaptation strategies in cushioning climate variability and change impact in the study area and central Tanzania at large.

Determinants of COVID-19 Prevalence Rate in Asia: A study using Spatial Analysis

This study aims at finding out the important determinants of prevalence rate of COVID-19 in the Asian continent using spatial analysis. The impact of climatic, socioeconomic, demographic, and health status variables on the prevalence rate of COVID-19 is seen through various spatial models such as Spatial lag, Spatial error, Geographically Weighted regression model, and Multiscale Geographically Weighted regression model. The performance of the models is compared under different comparison criteria. It is found that among all, Multiscale Geographically Weighted regression model outperformed other competitive models. Findings also indicate that cardiovascular health, prevalence of smoking habit, human development index, and net migration rate played significant role in defining the prevalence rate of COVID-19 in Asia.

Spatially Weighted Multinomial Modelling of Comorbidity of Anaemia and Malaria among Under-Five Children in Nigeria

Spatially Weighted Multinomial Modelling of Comorbidity of Anaemia and Malaria among Under-Five Children in Nigeria

Deterioration of steel structures due to corrosion considering the global effects of climate change

The randomness of atmospheric conditions is among the key contributing factors that affect the ability to accurately predict the corrosion growth in steel structures. Climate change has the potential to alter the long-term characteristics of these factors over the lifespan of steel structures, both those already existing and those newly built. The impact of climate variability on the stochastic nature of atmospheric variables, which greatly influence corrosion conditions, can add complexity to corrosion predictions in these structures. This paper introduces an integrated framework to quantify the impact of climate change on corrosion rates of steel structures worldwide. It considers the changes in environmental conditions, specifically temperature, relative humidity and wind speed and their effects on atmospheric corrosion. Global Climate Models are employed to evaluate the long-term impacts of climate change on these environmental conditions. An analytical model for predicting corrosion rate is integrated with climate change models to predict alterations in the corrosion rates of steel components relative to historical conditions. This paper also discusses the impact of climate change on the variations of these climatic parameters and offers a comparison between historical data and projected conditions across the globe. The results indicate that the effect of climate change on the corrosion rate depends on the considered region. While regions such as Australia, North America and Europe see an increase, others such as Asia and Africa observe a decline. Notably, all oceans, especially the Antarctic and Arctic, experience a significant increase in corrosion by the end of the century.

Impact of climatic variations in horticulture sector, Kinnaur, Himachal Pradesh, India

Climate change has an impact on the horticulture crops of Himachal Pradesh in relation to the varying climatic conditions in the Himalayan Region. The purpose of the study was to determine how frequently these differences in horticultural crops occur in the Kinnaur district of Himachal Pradesh since the horticulture industry in Himachal Pradesh greatly benefits from this district.  Long-term temperature (maximum, minimum, and diurnal) and rainfall data from 1990 to 2020 were analyzed for different phenological stages (pre-flowering, flowering, and fruit setting). The findings revealed that the average maximum and diurnal temperatures were found to have increased significantly, at the rate by 0.027 °C and 0.042 °C, respectively, during the flowering stages. However, there were no appreciable changes in the variations for other phenological stages. Under trend analysis of crop yield, the productivity of Pear and Almond notably fell over the past 20 years, at -0.029 t/ha/year and -0.016 t/ha/year, respectively, whereas Walnut productivity climbed at 0.008 t/ha/year. The relationship or impact between the climate and the crop was also examined in addition to the trend analysis for crop and climatic variables. The results showed that among the three phenological stages, Apple and Apricot were highly impacted (54.4%) during pre-flowering stage, followed by Apricot, i.e., 72.6% during flowering, and Grapes (53.2%) during the fruit setting stage. In the current situation, climate fluctuations had a favorable effect on Apples, Walnuts and Grapes while having a negative effect on Apricots, Plums, Pears and Almonds.

Simple hydrological models for assessing the impact of forest cover change on streamflow for large catchments with a dry tropical climate

ABSTRACT Hydrological models are widely used to assess the impact of climate variability and land use change on streamflow. However, it is uncertain whether these models are capable of simulating the impact given parameter interactions and inter-annual climate variability for large dry catchments. Two such catchments with extensive land clearing were selected to test three models – AWBM, Sacramento and SimHyd – in terms of their capacity to simulate streamflow during periods of vastly different levels of forest cover. AWBM outperformed others, and thus was further simplified as AWBM_C a with minimum performance deterioration to have only one transferable parameter: the average storage capacity, C a. A reduction of 21–28% in the C a value for the post-clearing period indicates a decrease in the amount of water stored for transpiration and consequently an increase in runoff-generation capacity. AWBM_C a can be used to predict the impact of forest cover change on streamflow with different C a values.

Anatomically modern human dispersals into Europe during MIS 3: Climate stability, paleogeography and habitat suitability

The initial large-scale dispersal of Anatomically Modern Humans (AMHs) into Europe, associated with the Aurignacian technocomplex, occurred during Marine Isotope Stage 3 (MIS 3), a critically unstable climatic period. The impact of climate change (millennial-scale Dansgaard-Oeschger events) and climate variability (annual and seasonal variation) on the mobility and initial dispersal of AMHs on the continent is not fully demonstrated. Here we show that both climate change and variability affected the spatial behavior of Aurignacian groups and structured their arrival on the continent. Using Random Forest, a machine learning algorithm, we produced habitat suitability (HS) models for AMHs under stadial (GS) and interstadial (GI) climate conditions. These models demonstrate that climate variability was a key factor governing the spatial behavior of human groups across MIS 3. They also illustrate that the structure and distribution of suitable habitat in Europe were affected by climatic conditions, with implications in terms of our species’ adaptability and behavioral plasticity. Finally, our results support the suggestion that initial dispersals followed a Mediterranean coastal route, likely under interstadial conditions.

Enhancing solids deposit prediction in gully pots with explainable hybrid models: A review.

Urban flooding has made it necessary to gain a better understanding of how well gully pots perform when overwhelmed by solids deposition due to various climatic and anthropogenic variables. This study investigates solids deposition in gully pots through the review of eight models, comprising four deterministic models, two hybrid models, a statistical model, and a conceptual model, representing a wide spectrum of solid depositional processes. Traditional models understand and manage the impact of climatic and anthropogenic variables on solid deposition but they are prone to uncertainties due to inadequate handling of complex and non-linear variables, restricted applicability, inflexibility and data bias. Hybrid models which integrate traditional models with data-driven approaches have proved to improve predictions and guarantee the development of uncertainty-proof models. Despite their effectiveness, hybrid models lack explainability. Hence, this study presents the significance of eXplainable Artificial Intelligence (XAI) tools in addressing the challenges associated with hybrid models. Finally, crossovers between various models and a representative workflow for the approach to solids deposition modelling in gully pots is suggested. The paper concludes that the application of explainable hybrid modeling can serve as a valuable tool for gully pot management as it can address key limitations present in existing models.

Land use changes and climate parameters assessments in a tropical highland region of Cameron Highlands, Malaysia

Abstract Tropical highland regions are susceptible to climate change and natural disasters due to their geographical location and hilly terrain. The objectives of this study are to determine land use land cover (LULC) changes in the Cameron Highlands and analyse the climatic parameters of the Cameron Highlands. This study integrates LULC analysis using remote sensing techniques and 10-year climatic parameters data to evaluate the impact of climate variability on the sustainability of the Cameron Highlands. Based on the validation results, the overall accuracy of LULC was 95.42% in 2016, 96.60% in 2018, and 97.40% in 2020. The results show an 18% rise in agriculture, a 16% increase in urban growth, and an 8.14% decline in forest coverage in Tanah Rata and Ringlet, Cameron Highlands, from 2016 to 2020. The Mann -Kendall and Sen slope indicated a statistically significant increasing trend in rainfall (Kendall's Tau, z = 0.102, p < 0.0001 and Sen value= 0.131, p < 0.001, respectively) and temperature (Kendall's Tau, z = 0.151, p < 0.001 and Sen value = 0.294, p < 0.001, respectively) from 2012 to 2021, increasing the area's susceptibility towards climate change impact and natural disasters. This study highlights the vulnerability of the Cameron Highlands to natural disasters, emphasizing the crucial need for efficient land management in slope areas.

Changes in climate, vegetation cover and vegetation composition affect runoff generation in the Gulf of Guinea Basin

AbstractAlthough considerable effort has been deployed to understand the impact of climate variability and vegetation change on runoff in major basins across Africa, such studies are scarce in the Gulf of Guinea Basin (GGB). This study combines the Budyko framework and elasticity concept along with geospatial data to fill this research gap in 44 nested sub‐basins in the GGB. Annual rainfall from 1982 to 2021 show significant decreasing and increasing trends in the northern and southern parts of the GGB, respectively. Annual potential evapotranspiration (PET) also shows significant increasing trends with higher magnitudes observed in the northern parts of the GGB. Changing trends in climate variables corroborates with shift to arid and wetter conditions in the north and south, respectively. From 2000 to 2020 vegetation cover estimated using enhanced vegetation index (EVI) shows significant increasing trends in all sub‐basins including those experiencing a decline in annual rainfall. Vegetation composition measured using vegetation continuous fields (VCFs) from 2000 to 2020 show an increase in tree canopy cover (TC), a decline in short vegetation cover and marginal changes in bare ground cover (BG). Elasticity coefficients show that a 10% increase in annual rainfall and PET may lead to a 33% increase and 24% decline in runoff, respectively. On the other hand, a 10% increase in EVI may lead to a 4% decline in runoff while a 10% increase in TC, SV and BG may reduce runoff by 4% and increase runoff by 3% and 2%, respectively. Even though changes are marginal, decomposing vegetation into different parameters using EVI and VCFs may lead to different hydrological effects on runoff which is one of the novelties of this study that may be used for implementing nature‐based solutions. The study also demonstrates that freely available geospatial data together with analytical methods are a promising approach for understanding the impact of climate variability and vegetation change on hydrology in data‐scarce regions.

Influence of climatic variation on microbial communities during organic Pinot noir wine production.

To assess the possible impact of climatic variation on microbial community composition in organic winemaking, we employed a metabarcoding approach to scrutinize the microbiome in a commercial, organic, Pinot noir wine production system that utilizes autochthonous fermentation. We assessed microbial composition across two vintages (2018 and 2021) using biological replicates co-located at the same winery. Microbial dynamics were monitored over four important fermentation time points and correlated with contemporaneous climate data. Bacterial (RANOSIM = 0.4743, p = 0.0001) and fungal (RANOSIM = 0.4738, p = 0.0001) compositions were different in both vintages. For bacteria, Lactococcus dominated the diversity associated with the 2018 vintage, while Tatumella dominated the 2021 vintage. For fungal populations, while Saccharomyces were abundant in both vintages, key differences included Starmerella, copious in the 2018 vintage; and Metschnikowia, substantive in the 2021 vintage. Ordination plots correlated the climatic variables with microbial population differences, indicating temperature as a particularly important influence; humidity values also differed significantly between these vintages. Our data illustrates how climatic conditions may influence microbial diversity during winemaking, and further highlights the effect climate change could have on wine production.

Geographic range of plants drives long-term climate change

Long computation times in vegetation and climate models hamper our ability to evaluate the potentially powerful role of plants on weathering and carbon sequestration over the Phanerozoic Eon. Simulated vegetation over deep time is often homogenous, and disregards the spatial distribution of plants and the impact of local climatic variables on plant function. Here we couple a fast vegetation model (FLORA) to a spatially-resolved long-term climate-biogeochemical model (SCION), to assess links between plant geographical range, the long-term carbon cycle and climate. Model results show lower rates of carbon fixation and up to double the previously predicted atmospheric CO2 concentration due to a limited plant geographical range over the arid Pangea supercontinent. The Mesozoic dispersion of the continents increases modelled plant geographical range from 65% to > 90%, amplifying global CO2 removal, consistent with geological data. We demonstrate that plant geographical range likely exerted a major, under-explored control on long-term climate change.

Temporal and Spatial Assessment of Carbon Flux Dynamics: Evaluating Emissions and Sequestration in the Three Northern Protection Forest Project Areas Supported by Google Earth Engine

Contemporary research on terrestrial carbon exchange processes is paramount for a nuanced comprehension of global and local climatic fluctuations and their interaction with anthropogenic activities. This study delves into the spatiotemporal dynamics of vegetation carbon exchanges within the Three Northern Protection Forest Project Area, leveraging two decades of MODIS NPP data and an innovative NEP estimation model. Our analysis highlights a generally increasing trend in Net Ecosystem Productivity (NEP) from 2000 to 2020, with significant growth in approximately 32.97% of the study area and slight increases in 24.18%. Notably, lower NEP values were found in desert and arid zones, whereas higher values were observed in more vegetated regions like Ningxia, Hebei, Inner Mongolia, and the northeast. The study also assesses the impact of climate variables and land-use changes on NEP, identifying both negative and positive correlations in specific regions. Despite the overall positive trend towards ecological restoration and enhancement, significant uncertainties remain, emphasizing the urgent need for further research to support ecosystem resilience and sustainable management practices.