scholarly journals Impacts of a Changing Earth on Microbial Dynamics and Human Health Risks in the Continuum between Beach Water and Sand

2019 ◽  
Author(s):  
Chelsea J. Weiskerger ◽  
João Brandão ◽  
Warish Ahmed ◽  
Asli Aslan ◽  
Lindsay Avolio ◽  
...  
Keyword(s):  
Climate Change ◽  
Infectious Disease ◽  
Human Health ◽  
Human Exposure ◽  
Microbial Population ◽  
Disease Risk ◽  
Climatic Variability ◽  
Extreme Weather Events ◽  
Climate Change Scenarios ◽  
Waterborne Pathogens

Humans may be exposed to microbial pathogens at recreational beaches via environmental sources, such as water, sand, and aerosols. Although infectious disease risk from exposure to waterborne pathogens has been an active area of research for decades, sand is a relatively unexplored reservoir of pathogens and fecal indicator bacteria (FIB). Beach sand and water habitats provide unique advantages and challenges to pathogen introduction, growth, and persistence, as well as continuous exchange between habitats. Models of FIB and pathogen fate and transport in sandy beach habitats can help predict the risk of infectious disease from recreational water use, but filling knowledge gaps such as decay rates and potential for microbial growth in beach habitats is necessary for accurate modeling. Climatic variability, whether natural or anthropogenically-induced, adds complexity to predictive modeling, but may increase human exposure to waterborne pathogens via extreme weather events, warming of water bodies and sea level rise in many regions. The popularity of human recreational beach activities, combined with predicted climate change scenarios, could amplify the risk of human exposure to pathogens and related illnesses. Other global change trends such as increased population growth and urbanization are expected to exacerbate contamination events and the predicted impacts of increasing levels of waterborne pathogens on human health. Such changes will alter microbial population dynamics in beach habitats, and will consequently affect the assumptions and relationships used in population models and quantitative microbial risk assessment (QMRA). Here, we discuss the literature on microbial population and transport dynamics in sand-water continuum habitats at beaches, how these dynamics can be modeled, and how climate change and other anthropogenic influences (e.g., land use, urbanization) should be considered when using and developing more holistic, beachshed-based models.

scholarly journals Climate change impacts on hydrology and water resources of Indian River basin

2018 ◽  
Vol 13 (1) ◽  
pp. 32-43 ◽  
Author(s):  
Umesh Kumar Singh ◽  
Balwant Kumar
Keyword(s):  
Climate Change ◽  
Groundwater Recharge ◽  
River Basin ◽  
Greenhouse Gas Emission ◽  
Hydrological Cycle ◽  
Indian Subcontinent ◽  
Climatic Variability ◽  
River Basins ◽  
Extreme Weather Events ◽  
Runoff Generation

Anthropogenic greenhouse gas emission is altering the global hydrological cycle due to change in rainfall pattern and rising temperature which is responsible for alteration in the physical characteristics of river basin, melting of ice, drought, flood, extreme weather events and alteration in groundwater recharge. In India, water demand for domestic, industrial and agriculture purposes have already increased many folds which are also influencing the water resource system. In addition, climate change has induced the surface temperature of the Indian subcontinent by 0.48 ºC in just last century. However, Ganges–Brahmaputra–Meghna (GBM) river basins have great importance for their exceptional hydro-geological settings and deltaic floodplain wetland ecosystems which support 700 million people in Asia. The climatic variability like alterations in precipitation and temperature over GBM river basins has been observed which signifies the GBM as one of the most vulnerable areas in the world under the potential impact of climate change. Consequently, alteration in river discharge, higher runoff generation, low groundwater recharge and melting of glaciers over GBM river basin could be observed in near future. The consequence of these changes due to climate change over GBM basin may create serious water problem for Indian sub-continents. This paper reviews the literature on the historical climate variations and how climate change affects the hydrological characteristics of different river basins.

scholarly journals Impactos das mudanças climáticas na demanda hídrica e duração do ciclo do sorgo forrageiro e feijão-caupi no estado de Pernambuco

2015 ◽  
Vol 8 ◽  
pp. 542
Author(s):  
José Edson Florentino de Morais ◽  
Thieres George Freire da Silva ◽  
Marcela Lúcia Barbosa ◽  
Wellington Jairo da Silva Diniz ◽  
Carlos André Alves de Souza ◽  
...  
Keyword(s):  
Climate Change ◽  
Relative Humidity ◽  
Air Temperature ◽  
Water Demand ◽  
Stomatal Resistance ◽  
The State ◽  
Extreme Weather Events ◽  
Climate Change Scenarios ◽  
Area Index ◽  
Forage Sorghum

O aumento na ocorrência de eventos climáticos extremos nas últimas décadas é uma forte evidência das mudanças climáticas. Em regiões Semiáridas, onde a pressão de desertificação tem se intensificado, são esperadas diminuição da disponibilidade de água e maior ocorrência de períodos seca, e, consequentemente, efeitos na resposta fisiológica das plantas. Assim, objetivou-se analisar os impactos dos cenários de mudanças climáticas sobre a duração do ciclo fenológico e a demanda de água do sorgo forrageiro e do feijão-caupi cultivados no Estado de Pernambuco. Foram utilizados os valores mensais da normal climatológica brilho solar, temperatura do ar, umidade relativa do ar e velocidade do vento de dez municípios. Considerou-se um aumento de 1,8°C (Cenário B2) e 4,0°C (Cenário A1F1) na temperatura do ar e um decréscimo de 5,0% dos valores absolutos de umidade relativa do ar, além do aumento de 22% na resistência estomática e de 4% no índice de área foliar. Com base nessas informações foram gerados três cenários: situação atual e projeções futuras para B2 e A1F1. Os resultados revelaram uma redução média de 11% (B2) e 20% (A1F1), e de 10% (B2) e 17% (A1F1) na duração do ciclo, e de 4% (B2) e 8% (A1F1), e 2% (B2) e 5% (A1F1) na demanda de água acumulada para o sorgo forrageiro e feijão-caupi, respectivamente. Conclui-se que a magnitude das reduções da duração do ciclo e a demanda de água simulada para as culturas do sorgo forrageiro e do feijão-caupi variaram espaço-temporalmente no Estado de Pernambuco com os cenários de mudanças climáticas.ABSTRACT The increase in the occurrence of extreme weather events in recent decades is a strong evidence of climate change. In semiarid regions, where the pressure of desertification has intensified, are expected to decrease in the availability of water and higher occurrence of drought periods, and, consequently, effects on physiological response of plants. Thus, the objective of analyzing the impacts of climate change scenarios on the duration of phenological cycle and water demand of forage sorghum and cowpea, grown in the State of Pernambuco. Monthly values were used normal climatological solar brightness, air temperature, relative humidity and wind speed of ten municipalities. It was considered an increase of 1.8° C (B2 Scenario) and 4.0° C (A1F1 Scenario) on air temperature and a decrease of 5.0% of the absolute values of relative humidity, in addition to the 22% increase in stomatal resistance and 4% in leaf area index. Based on this information were generated three scenarios: current situation and future projections for B2, A1F1. The results revealed an average reduction of 11% (B2) and 20% (A1F1), and 10% (B2) and 17% (A1F1) for the duration of the cycle, and 4% (B2) and 8% (A1F1), and 2% (B2) and 5% (A1F1) in accumulated water demand for forage sorghum and cowpea, respectively. It is concluded that the magnitude of the reductions in the duration of the cycle and the simulated water demand for crops of forage sorghum and cowpea ranged space-temporarily in the State of Pernambuco with climate change scenarios.

scholarly journals The potential habitat of desert locusts is contracting: predictions under climate change scenarios

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12311
Author(s):  
Jingyun Guan ◽  
Moyan Li ◽  
Xifeng Ju ◽  
Jun Lin ◽  
Jianguo Wu ◽  
...  
Keyword(s):  
Climate Change ◽  
Food Security ◽  
Radiative Forcing ◽  
Social Impact ◽  
Regional Cooperation ◽  
Extreme Weather Events ◽  
Climate Change Scenarios ◽  
Desert Locust ◽  
Maxent Model ◽  
Potential Habitat

Desert locusts are notorious for their widespread distribution and strong destructive power. Their influence extends from the vast arid and semiarid regions of western Africa to northwestern India. Large-scale locust outbreaks can have devastating consequences for food security, and their social impact may be long-lasting. Climate change has increased the uncertainty of desert locust outbreaks, and predicting suitable habitats for this species under climate change scenarios will help humans deal with the potential threat of locust outbreaks. By comprehensively considering climate, soil, and terrain variables, the maximum entropy (MaxEnt) model was used to predict the potential habitats of solitary desert locusts in the 2050s and 2070s under the four shared socioeconomic pathways (SSP126, SSP245, SSP370, and SSP585) in the CMIP6 model. The modeling results show that the average area under the curve (AUC) and true skill statistic (TSS) reached 0.908 ± 0.002 and 0.701, respectively, indicating that the MaxEnt model performed extremely well and provided outstanding prediction results. The prediction results indicate that climate change will have an impact on the distribution of the potential habitat of solitary desert locusts. With the increase in radiative forcing overtime, the suitable areas for desert locusts will continue to contract, especially in the 2070s under the SSP585 scenario, and the moderately and highly suitable areas will decrease by 0.88 × 106 km2 and 1.55 × 106 km2, respectively. Although the potentially suitable area for desert locusts is contracting, the future threat posed by the desert locust to agricultural production and food security cannot be underestimated, given the combination of maintained breeding areas, frequent extreme weather events, pressure from population growth, and volatile sociopolitical environments. In conclusion, methods such as monitoring and early warning, financial support, regional cooperation, and scientific prevention and control of desert locust plagues should be further implemented.

Monitoring EU Emerging Infectious Disease Risk Due to Climate Change

Science ◽  
2012 ◽  
Vol 336 (6080) ◽  
pp. 418-419 ◽  
Author(s):  
E. Lindgren ◽  
Y. Andersson ◽  
J. E. Suk ◽  
B. Sudre ◽  
J. C. Semenza
Keyword(s):  
Climate Change ◽  
Infectious Disease ◽  
Disease Risk ◽  
Emerging Infectious Disease

Implications of climate change on long-lead forecasting and global agriculture

2007 ◽  
Vol 58 (10) ◽  
pp. 939 ◽  
Author(s):  
Raymond P. Motha
Keyword(s):  
Climate Change ◽  
Crop Yields ◽  
Southern Oscillation ◽  
Climatic Variability ◽  
Weather Conditions ◽  
Extreme Weather Events ◽  
Enso Cycle ◽  
Strategic Decisions ◽  
Global Circulation Models ◽  
The North

Variations in crop yields and agricultural productivity are strongly influenced by fluctuations in seasonal weather conditions during the growing season. The El Niño/Southern Oscillation (ENSO) cycle, and other similar ocean/atmosphere teleconnections in the North Pacific and North Atlantic, contribute to extreme weather events and climatic variability. As seasonal forecasting skills improve with greater knowledge of these teleconnections and improved Global Circulation Models (GCMs), farmers and agricultural planners will be able to make better use of long-lead forecasts for strategic decisions in agriculture. Issues related to climate variability and climate change pose significant risks to agriculture as the frequency of natural disasters tends to increase worldwide.

scholarly journals Resilience of vernacular and modernising dwellings in three climatic zones to climate change

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Khadeeja Henna ◽  
Aysha Saifudeen ◽  
Monto Mani
Keyword(s):  
Climate Change ◽  
Economic Growth ◽  
Thermal Stresses ◽  
Climate Change Impacts ◽  
Extreme Weather Events ◽  
Climate Change Scenarios ◽  
Rural Settlements ◽  
Climate Resilience ◽  
Rapid Economic Growth ◽  
The Impact

AbstractClimate change impacts buildings in multiple ways, including extreme weather events and thermal stresses. Rural India comprising 65% of the population is characterised by vernacular dwellings evolved over time to passively regulate and maintain comfortable indoors. Increasing modernization in rural habitations (transitions) evident from the ingress of modern materials and electro-mechanical appliances undermines the ability of building envelopes to passively regulate and maintain comfortable indoors. While such trends are deemed good for the economy, their underlying implications in terms of climate change have not been adequately examined. The current study evaluates the climate-resilience of vernacular dwellings and those undergoing transitions in response to three climate-change scenarios, viz, A1B (rapid economic growth fuelled by balanced use of energy sources), A2 (regionally sensitive economic development) and B1 (structured economic growth and adoption of clean and resource efficient technologies). The study examines dwellings characteristic to three rural settlements representing three major climate zones in India and involves both real-time monitoring and simulation-based investigation. The study is novel in investigating the impact of climate change on indoor thermal comfort in rural dwellings, adopting vernacular and modern materials. The study revealed higher resilience of vernacular dwellings in response to climate change.

scholarly journals Climate change and disease risk in the Himalayas

2011 ◽  
Vol 6 (8) ◽  
pp. 7-8 ◽  
Author(s):  
Sahotra Sarkar
Keyword(s):  
Climate Change ◽  
Infectious Disease ◽  
Water Quality ◽  
Spatial Distribution ◽  
Predictive Models ◽  
Disease Risk ◽  
Quantitative Estimates

A new challenge is unfolding in the Himalayas: a significant increase in the burden of infectious disease, driven by climate change. Vectors are moving beyond their historic ranges to higher elevations; water quality is deteriorating, and the available supply is diminishing. Preventive and ameliorative measures to address these problems require robust quantitative estimates of the size and spatial distribution of disease risk. Once enough data are available, disease risk can be mapped with predictive models so that appropriate policies can be formulated and implemented. Unfortunately, there has been virtually no quantitative epidemiological attention to this region.DOI: http://dx.doi.org/10.3126/hjs.v6i8.4921 Himalayan Journal of Sciences Vol.6 Issue 8 2010 pp.7-8

scholarly journals Subjective Attribution of Infectious Disease Outbreaks to Climate Change is Associated with Mitigation Behavioural Intentions and COVID-19 Recovery Policy Support

2020 ◽  
Author(s):  
Jagadish Thaker
Keyword(s):  
Climate Change ◽  
Infectious Disease ◽  
Human Impact ◽  
Public Engagement ◽  
Attribution Theory ◽  
Disease Outbreaks ◽  
Policy Support ◽  
Extreme Weather Events ◽  
Behavioural Intentions ◽  
Infectious Disease Outbreaks

Scholars argue that personal experience with climate change related impacts has the potential to increase public engagement. Yet, previous studies, which have almost exclusively focussed on experience with extreme weather events, provide mixed results. Based on experiential learning and attribution theory, this article argues that unless individuals’ attribute an event as related to or caused by climate change, their responses may be misdirected. Results based on survey data from a nationally representative sample of the New Zealand public indicates that subjective attribution of infectious disease outbreaks to climate change and to the human impact on the environment is positively associated with mitigation behavioural intentions and policy support. In addition, political affiliation moderates the relationship between subjective attribution and mitigation policy support, indicating a higher potential for right-leaning compared to moderates or left-leaning respondents to learn about climate change through health-related climate change impacts. Helping people understand the role of human impact on the environment and climate change in infectious disease outbreaks is likely to increase public engagement.

scholarly journals ClimateNet: an expert-labeled open dataset and deep learning architecture for enabling high-precision analyses of extreme weather

2021 ◽  
Vol 14 (1) ◽  
pp. 107-124
Author(s):  
◽  
Karthik Kashinath ◽  
Mayur Mudigonda ◽  
Sol Kim ◽  
Lukas Kapp-Schwoerer ◽  
...  
Keyword(s):  
Climate Change ◽  
Deep Learning ◽  
Climate Model ◽  
Extreme Weather ◽  
Training Data ◽  
Extreme Weather Events ◽  
Climate Change Scenarios ◽  
Model Output ◽  
Weather And Climate ◽  
Climate Model Output

Abstract. Identifying, detecting, and localizing extreme weather events is a crucial first step in understanding how they may vary under different climate change scenarios. Pattern recognition tasks such as classification, object detection, and segmentation (i.e., pixel-level classification) have remained challenging problems in the weather and climate sciences. While there exist many empirical heuristics for detecting extreme events, the disparities between the output of these different methods even for a single event are large and often difficult to reconcile. Given the success of deep learning (DL) in tackling similar problems in computer vision, we advocate a DL-based approach. DL, however, works best in the context of supervised learning – when labeled datasets are readily available. Reliable labeled training data for extreme weather and climate events is scarce. We create “ClimateNet” – an open, community-sourced human-expert-labeled curated dataset that captures tropical cyclones (TCs) and atmospheric rivers (ARs) in high-resolution climate model output from a simulation of a recent historical period. We use the curated ClimateNet dataset to train a state-of-the-art DL model for pixel-level identification – i.e., segmentation – of TCs and ARs. We then apply the trained DL model to historical and climate change scenarios simulated by the Community Atmospheric Model (CAM5.1) and show that the DL model accurately segments the data into TCs, ARs, or “the background” at a pixel level. Further, we show how the segmentation results can be used to conduct spatially and temporally precise analytics by quantifying distributions of extreme precipitation conditioned on event types (TC or AR) at regional scales. The key contribution of this work is that it paves the way for DL-based automated, high-fidelity, and highly precise analytics of climate data using a curated expert-labeled dataset – ClimateNet. ClimateNet and the DL-based segmentation method provide several unique capabilities: (i) they can be used to calculate a variety of TC and AR statistics at a fine-grained level; (ii) they can be applied to different climate scenarios and different datasets without tuning as they do not rely on threshold conditions; and (iii) the proposed DL method is suitable for rapidly analyzing large amounts of climate model output. While our study has been conducted for two important extreme weather patterns (TCs and ARs) in simulation datasets, we believe that this methodology can be applied to a much broader class of patterns and applied to observational and reanalysis data products via transfer learning.

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