Local Effects Of Climate Change Research Articles

Advancing neighbourhood climate action: opportunities, challenges and way ahead

Cities are emerging as key sites for action on climate change. Within cities, urban neighbourhoods are increasingly taking leadership in addressing local effects of climate change through mitigation and adaptation programs. Bottom-up action on climate change through neighbourhood scale programs presents opportunities in terms of getting the community to partner and participate in climate action. However, neighbourhood scale programs often run into challenges in terms of limited participation, impact and resources to keep the programs running. In this paper, we advance the literature on the opportunities and challenges of neighbourhood scale climate action. We do so by analysing three neighbourhood scale programs that address climate action in Canada and in Australia. We adopt online workshops as a research methodology where volunteers from the three programs share their experiences of opportunities and ways of overcoming challenges of neighbourhood climate action. Our findings illustrate that collaborative governance between the city and the neighbourhoods, incremental community building and consolidating local resources are important for advancing neighbourhood climate action. This paper adds to the thin body of knowledge on neighbourhood scale climate action and presents ways of overcoming the challenges of bottom-up climate action.

Variability and drivers of winter near-surface temperatures over boreal and tundra landscapes

Abstract. Winter near-surface air temperatures have important implications for ecosystem functioning such as vegetation dynamics and carbon cycling. In cold environments, the persistence of seasonal snow cover can exert a strong control on the near-surface temperatures. However, the lack of in situ measurements of both snow cover duration and surface temperatures over high latitudes has made it difficult to estimate the spatio-temporal variability in this relationship. Here, we quantified the fine-scale variability in winter near-surface air temperatures (+2 cm) and snow cover duration (calculated from temperature time series) using a total of 441 microclimate loggers in seven study areas across boreal and tundra landscapes in Finland during 2019–2021. We further examined the drivers behind this variation using a structural equation model and the extent to which near-surface air temperatures are buffered from free-air temperatures during winter. Our results show that while average winter near-surface temperatures stay close to 0 ∘C across the study domain, there are large differences in their fine-scale variability among the study areas. Areas with large topographical variation, as well as areas with shallow snowpacks, showed the greatest variation in near-surface temperatures and in snow cover duration. In the tundra, for example, differences in minimum near-surface temperatures between study sites were close to 30 ∘C and topography was shown to be an important driver of this variability. In contrast, flat topography and long snow cover duration led to little spatial variation, as well as long periods of decoupling between near-surface and air temperatures. Quantifying and understanding the landscape-wide variation in winter microclimates improves our ability to predict the local effects of climate change in the rapidly warming boreal and tundra regions.

Digital mapping of the soil available water capacity: tool for the resilience of agricultural systems to climate change

Soil available water capacity (AWC) is a key function for human survival and well-being. However, its direct measurement is laborious and spatial interpretation is complex. Digital soil mapping (DSM) techniques emerge as an alternative to spatial modeling of soil properties. DSM techniques commonly apply machine learning (ML) models, with a high level of complexity. In this context, we aimed to perform a digital mapping of soil AWC and interpret the results of the Random Forest (RF) algorithm and, in a case study, to show that digital AWC maps can support agricultural planning in response to the local effects of climate change. To do so, we divided this research into two approaches: In the first approach, we showed a DSM using 1857 sample points in a southeastern region of Brazil with laboratory-determined soil attributes, together with a pedotransfer function (PTF), remote sensing and DSM techniques. In the second approach, the constructed AWC digital soil map and weather station data were used to calculate climatological soil water balances for the periods between 1917–1946 and 1991–2020. The result showed the selection of covariates using Shapley values as a criterion contributed to the parsimony of the model, obtaining goodness-of-fit metrics of R2 0.72, RMSE 16.72 mm m−1, CCC 0.83, and Bias of 0.53 over the validation set. The highest contributing covariates for soil AWC prediction were the Landsat multitemporal images with bare soil pixels, mean diurnal, and annual temperature range. Under the current climate conditions, soil available water content (AW) increased during the dry period (April to August). May had the highest increase in AW (∼17 mm m−1) and decrease in September (∼14 mm m−1). The used methodology provides support for AWC modeling at 30 m resolution, as well as insight into the adaptation of crop growth periods to the effects of climate change.

A brief overview of the studies on the climatic impact of heavy precipitation events in Hungary

Climate change is one of the major problems of our day. In different areas of the earth, climate change causes different problems. On the European continent, according to observations, the dry and rainy periods have changed significantly. During rainy periods, the amount of water from the rain falling in a short period increased significantly. The authors' research focused on a smaller East-European area, Hungary. A significant part of damage events in Hungarian settlements is related to surface and underground waters and rainfall activity. The road network, the cellars and the gutters of municipalities are often subject to damage due to the inappropriate construction of the drainage system. The article analyzes the local effects of climate change based on the analysis of the weather data of the past years. The change in the number of rainy days and the amount of rain is evidence of a change in the weather. This work is an overview based on previous studies and statistical data showing the climatic impact in Hungary.

The role of citizen science in demonstrating the local effects of climate change

Climate change is the greatest global challenge for present and future generations, destabilizing life-support systems with its diverse interrelationships and interactions. The real solutions to global problems at the local levels can only be expected through the widest possible social cohesion and the effective transmission of the results of science to all ages and social strata. One of the best ways to do this is by involving present or historical data from reliable sources of qualified and reliable amateur (citizen) scientists, which is an outstanding methodological opportunity to expand and increase the efficiency of scientific research. Therefore, citizen science has become more and more widespread within and between different disciplines in recent decades. One of the significant practical examples of this, with the persistent and accurate work of a late enthusiastic amateur data collector, Mr. László Cseh), who measured, collected and preserved main local climate data (that is, the average and maximum temperature and precipitation) over several decades in Csem?-Ereklyés region near Cegléd. The comparison of these data sets with the dynamics of the national middle-term data and the possibilities of their use in explaining plant phenology shifts are presented in this paper. According to our results, these local data sets fit well with the national trend and can increase the effectiveness of nature conservation by demonstrating the effects of climate change on plant phenology. Community science provides an opportunity for greater social recognition and acknowledgement of scientific results.

Indigenous and improved adaptation technologies in response to climate change adaptation and barriers among smallholder farmers in the East Wollega Zone of Oromia, Ethiopia

Agriculture is the foundation of economy for Ethiopia. The majority of people in the study area rely on rain-fed subsistence agriculture for their livelihood. As a result, smallholder farmers were more susceptible to climate change due to their reliance on a single rain season and lack of climate change adaptation technologies. The sustainability of food crop production, which employs the majority of the labor force in the nation, is threatened by climate change. The study's objectives were to look at indigenous and improved adaptation technologies in reaction to local climate change effects and barriers to adaptation among smallholder farmers. A closed-ended, semi-structured questionnaire was used to gather the data from 400 smallholder farmer household heads. An analysis of the variables influencing farmers' decisions regarding climate change adaptation was conducted using binary logistic regression model. The fitness and accuracy of the model were examined using an omnibus test of model coefficients. The study's findings showed that absence of crop insurance, a lack of training and demonstration, a lack of non-farm employment, a lack of weather forecasting and early warning, shortage of finance, the high price of farm inputs, and reliance on a single rain season were the main barriers preventing farmers from adapting to climate change. The most popular indigenous adaptation techniques used by smallholder farmers included mixed cropping of local variety, reducing social and religious rituals, mixed farming (local crop and livestock), and reducing the amount of food consumed. The primary improved adaptation strategies used by the farmers included the use of chemical fertilizers, various agrochemicals, and soil and water conservation methods. The binary logistic regression model's findings showed that access to extension services by household heads significantly and favorably influenced farmers' decisions to scale back social and religious rituals, reduce food consumption, sell livestock, and adopt improved mixed farming as a means of adapting to climate change. Farm experience significantly influenced the farmers’ decision to practice improved mixed farming; access to the market significantly affected farmers’ decision on soil and water conservation practices; and the age of the respondents significantly influenced the farmers’ decision to practice improved crop variety as a response to climate change adaptation. The study's conclusions showed that a number of factors limited farmers' attempts to adapt to climate change. The study recommends that decisions regarding adaptation plans should concentrate on reducing these barriers.

Climate Change Adaptation Policies of Metropolitan Municipalities in Turkey

Climate change, which is one of the most prominent global security problems of today, adversely affects the natural and socio-economic environment from the decline in biodiversity to food security, from rising sea-water levels to water scarcity. The global, national, and local effects of climate change make the preparation of policies related to this issue more crucial and necessitate global, regional, and local approaches to these policies. The study focuses on the local level management process of the adaptation process to climate change. Establishing and putting into action local cohesion policies is an extensive field of study. In the study, strategic plans and organizational charts of metropolitan municipalities are examined to determine their awareness of climate change. The frequency and context of the words “global warming, greenhouse gas, and climate change” are evaluated in the scanning conducted in the 2020-2024 strategic plans. It is ascertained at the institutional level that local policies are an emerging field on climate change. However, it would be possible to see the contribution of the work of these units in the fight against climate change in the coming years.

Understanding the Knowledge, Attitudes, and Practices of Healthcare Professionals toward Climate Change and Health in Minnesota

Climate change is an urgent public health issue that is impacting health locally and across the world. Healthcare professionals are on the front lines for public health, caring for people affected by climate change; yet few studies have assessed their knowledge and experiences of local climate change effects. The purpose of this study was to improve our understanding of the health impacts of climate change in Minnesota from the perspective of healthcare professionals. An electronic survey was administered by the Minnesota Department of Health (MDH) to a convenience sample of Board-certified nurses and physicians in Minnesota. Seventy-five percent of respondents agreed that climate change is happening, and 60% agreed that it is currently impacting the health of their patients. However, only 21% felt well prepared to discuss climate change, and only 4% discussed climate change with all or most of their patients. Similarly, results from open-ended questions highlighted the importance of climate change and acknowledged the challenges of discussing this topic. While most respondents recognized the health impacts of climate change, they also reported feeling uncomfortable discussing climate change with patients. Thus, there is an opportunity to develop targeted resources to support healthcare professionals in addressing climate change.

Aligning citizen science and remote sensing phenology observations to characterize climate change impact on vegetation

Phenology observations are essential indicators to characterize the local effects of climate change. Citizen participation in the collection of phenological observations is a potential approach to provide data at both high temporal scale and fine grain resolution. Traditional observation practices of citizen science (CS), although precise at the species scale, are limited to few observations often closely located to an observer’s residence. These limitations hinder coverage of the great variability of vegetation phenology across biomes and improvement of the knowledge of vegetation changes due to climate change impacts. This study presents a new approach to overcome these limitations by improving CS guidance and feedback as well as expanding phenology report sites and observations across different habitats and periods to contribute to monitoring climate change. This approach includes: (a) a new methodology focused on harmonizing remote sensing phenology products with traditional CS phenology observations to direct volunteers to active phenology regions and, (b) a new protocol for citizen scientists providing tools to guide them to specific regions to identify, collect and share species phenological observations and their phenophases. This approach was successfully tested, implemented and evaluated in Catalonia with more than 5000 new phenologically interesting regions identified and more than 200 observations collected and Sentinel-2 derived phenometrics were demonstrated as of good quality.

Seeing climate change: psychological distance and connection to nature

To develop targeted climate change curricula, it is imperative to understand how students perceive and observe the localized effects of climate change. Our work used a quantitatively driven, parallel mixed methods research design to study potential factors that contribute to undergraduate biology students seeing the local effects of climate change. Our research questions asked students to provide examples of climate change occurring in their state and examined the predictive power of connection to nature, psychological distance, and other factors on whether students see climate change or not. Our quantitative data show a student’s connection to nature, their spatial climate awareness, and their geographic location were the three most important factors in predicting a student seeing and discussing climate change occurring in their state. Further, our qualitative data support our quantitative findings indicating that personal experiences in nature are integral for students connecting with nature and seeing climate change at scales smaller than their state. Supplemental data for this article is available online at https://doi.org/10.1080/13504622.2022.2042205.

Building Climate Change Adaptation Scenarios with Stakeholders for Water Management: A Hybrid Approach Adapted to the South Indian Water Crisis

Climate change threatens the sustainability of agriculture and natural resources. Adaptive solutions must be designed locally with stakeholders. We developed the Approach for Building Adaptation Scenarios with Stakeholders (ABASS), which aims to identify adaptation policies and corresponding scenarios of natural resource management in the context of climate change. Its originality is the combination of different existing participatory methods, organized in three phases. In step 1, experts identify local environmental problems on a map and build the assumption tree of local climate change effects. In step 2, experts identify stakeholders. Step 3 leads to the construction of adaptation scenarios with stakeholders in two phases. First, in a participatory workshop gathering numerous stakeholders, the assumption tree is presented to help stakeholders identify potential policies that address the effects of climate change. Then, using the map produced in step 1, each group of stakeholders separately translates each potential policy into a detailed scenario. We applied ABASS to the context of groundwater overexploitation in South India. Two policies at the farm level emerged as consensual: (i) ponds to harvest runoff water and (ii) drip irrigation to conserve water; but their implementation highlights the differences of opinion among stakeholders.

Local effects of climate change on row crop production and irrigation adoption

Global climate change will affect crop productivity, technology adoption, and commodity food and fiber prices. This research investigates the ex-ante effects of climate change on cropping mix and irrigation decisions at a watershed scale by integrating a downscaled General Circulation Model (GCM) projections, a crop growth model, and an economic model of the row crop sector typical of corn, soybean, wheat, and sorghum operations in Tennessee, United States. The downscaled GCM is used to generate weather patterns for Tennessee’s watersheds to 2049, under moderate and high greenhouse gas (GHG) emission assumptions. Crop yields and commodity prices were also estimated under these prevailing climate scenarios. Compared with the moderate emission level scenario, greater GHG emissions decrease dryland crop productivity and cause commodity prices to trend upward. A row crop optimization model was developed under the assumption that producers are profit maximizers who base their cropping decisions on the previous yield performance of a cropping system, commodity prices, and production functions, subject to resource constrains. Results suggest that producers in some watersheds adopt irrigation to minimize variability in net returns if water is available. Dryland and irrigated soybeans, dryland corn, and dryland soybean-wheat double cropping could become the dominant cropping systems in watersheds located in western Tennessee while irrigated and dryland soybeans could become the dominant row crop in watersheds located in middle Tennessee.

Geometry morphometrics of plant structures as a phenotypic tool to differentiate Polylepis incana Kunth. and Polylepis racemosa Ruiz & Pav. reforested jointly in Ecuador

ABSTRACT In Ecuador, there is a progressive loss of the native forest. To mitigate these problems, several reforestation plans have been carried out in the country. To recover the Andean forest, Polylepis racemosa was introduced from Peru, due to its similarity to native species like Polylepis incana. This genus fulfills important ecological functions that help mitigate the effects of local climate change. However, reforestation of introduced species close to natural populations threatens the genetic diversity of Polylepis native forests. In the long term, it can trigger hybridization processes and create problems for ecosystem conservation. This study used geometry morphometric (GM), to differentiate species quickly and economically, using the form of leaves, stomata, flowers, and pollen of P. incana and P. racemosa in three populations: Illinizas Ecological Reserve, Mojanda Lagoons and Cayambe-Coca National Park. To obtain the data, the composite leaves were scanned, the stomata and pollen were photographed with an increase of 400× and on flowers with 100×. 15, 10, 12, and 9 landmarks (LM) were digitized for leaves, stomata, flowers, and pollen, respectively, using ImageJ software. Then, the shape variables and statistical analysis were performed in MorphoJ software. As a result, it was obtained that the discriminant function analysis (DFA) shows that leaves and flowers can be used as indicators to differentiate species from this genus, discarding stomata and pollen. After that, Canonical Variate Analysis (CVA) of leaves and flowers showed that reforested species jointly are separated into two different species; except for the flowers of the population of Mojanda, where there is an intersection of few individuals, which share similar phenotypic characteristics so they can be considered as potentially hybrid individuals. This study generates information on the location of species introduced inside and outside the National System of Protected Areas that threaten the Andean highlands. Finally, GM is an accessible tool for monitoring biodiversity through morphological characteristics and discriminating against species with complex taxonomic problems.

Planning for Climate Change

Problem, research strategy, and findings Climate scientists have developed high-resolution climate models to project local effects of climate change. However, such high-resolution climate information is not widely incorporated into climate change adaptation plans. In this study we analyze high-resolution (1.24 × 1.24-mile grid) temperature data generated by a climate model for Los Angeles County (CA). The data include projected surface air temperature and the number of extreme heat days (daily maximum temperature exceeding 95 °F) between a baseline period (1981–2000) and a mid-21st-century period (2041–2060). The data assume that human-induced greenhouse gas emissions will continuously increase at the current level and no change in existing built environments. We find significant geographic heterogeneity in terms of climate change effects in Los Angeles County. Also, given the assumptions, we expect inland suburban areas where urban expansion has been occurring to have more severe extreme heat effects than central and coastal areas by mid-century. Takeaway for practice Planners should engage with climate scientists to access and use high-resolution climate data. The use of high-resolution climate data needs to be mandated in plan-making in the era of climate change. Many regions may have different spatial patterns of extreme heat events and require tailored climate adaptation plans. In Los Angeles County, for example, continuing suburbanization in inland valley areas may need to be discouraged, whereas coastal areas with the least effects may consider denser, inclusive redevelopment strategies.

Impact of Barrier Breaching on Wetland Ecosystems under the Influence of Storm Surge, Sea-Level Rise and Freshwater Discharge

Coastal wetland ecosystems and biodiversity are susceptible to changes in salinity brought about by the local effects of climate change, meteorological extremes, coastal evolution and human intervention. This study investigates changes in the salinity of surface water and the associated impacts on back-barrier wetlands as a result of breaching of a barrier beach and under the compound action of different surge heights, accelerated sea-level rise (SLR), river discharge and rainfall. We show that barrier breaching can have significant effects in terms of vegetation die-back even without the occurrence of large storm surges or in the absence of SLR, and that rainfall alone is unlikely to be sufficient to mitigate increased salinity due to direct tidal flushing. Results demonstrate that an increase in sea level corresponding to the RCP8.5 scenario for year 2100 causes a greater impact in terms of reedbed loss than storm surges up to 2 m with no SLR. In mitigation of the consequent changes in wetland ecology, regulation of relatively small and continuous river discharge can be regarded as a strategy for the management of coastal back-barrier wetland habitats and for the maintenance of brackish ecosystems. As such, this study provides a tool for scoping the potential impacts of storms, climate change and alternative management strategies on existing wetland habitats and species.

Soil Phosphorus Modeling for Modern Agriculture Requires Balance of Science and Practicality: A Perspective.

The use of phosphorus (P) fertilizers in arable crop and pastoral systems is expected to change as modern agriculture is challenged to produce more food with fewer inputs. Agricultural systems models offer a dual purpose to support and integrate recent scientific advances and to identify strategies for farmers to improve nutrient efficiency. However, compared with nitrogen and carbon, advances in P modeling have been less successful. We assessed the potential opportunity of P modeling to increase P efficiency for modern agriculture and identified the current challenges associated with modeling P dynamics at the field scale. Three major constraints were (i) a paucity of detailed field datasets to model strategies aimed at increasing P use efficiency, (ii) a limited ability to predict P cycling and availability under the local effects of climate change, and (iii) a restricted ability to match measured soil P fractions to conceptual and modelable pools in soils with different mineral properties. To improve P modeling success, modelers will need to walk a tightrope to balance the roles of assisting detailed empirical research and providing practical land management solutions. We conclude that a framework for interdisciplinary collaboration is needed to acquire suitable datasets, continually assess the need for model adjustment, and provide flexibility for progression of scientific theory. Such an approach is likely to advance P management for increased P use efficiency.

The role of landscape installations in climate change communication

Engaging the public in the issue of climate change is critical in fostering the support required for climate change adaptation. Designers and artists can contribute to public engagement using the landscape as a setting and medium to visualize climate change futures. This research note presents the case example of High Tide, a temporary landscape installation in Boston, MA, designed to bring attention to projected flooding in the area due to sea level rise. Our study sought to pilot the use of social science methods to gain initial insight on whether a landscape installation, through its accessible and site-specific qualities, could engage local audiences in the subject of climate change. Our findings provide an initial proof-of-concept for the role of public art in contributing to public engagement by bringing attention to and visualizing local effects of climate change using the landscape as a publicly accessible setting. Future research using robust social science methods would further illuminate these issues.

Are values related to culture, identity, community cohesion and sense of place the values most vulnerable to climate change?

Values related to culture, identity, community cohesion and sense of place have sometimes been downplayed in the climate change discourse. However, they have been suggested to be not only important to citizens but the values most vulnerable to climate change. Here we test four empirical consequences of the suggestion: (i) at least 50% of the locations citizens' consider to be the most important locations in their municipality are chosen because they represent these values, (ii) locations representing these values have a high probability of being damaged by climate change induced sea level rise, (iii) citizens for which these values are particularly strongly held less strongly believe in the local effects of climate change, and (iv) citizens for which these values are particularly strongly held less strongly believe that they have experienced the effects of climate change. The tests were made using survey data collected in 2014 from 326 citizens owning property in Höganäs municipality, Sweden, and included values elicited using a new methodology separating instrumental values from end values, and using the former (which strictly speaking should be seen as estimates of usefulness rather than as aims in themselves) as stepping stones to pinpoint the latter, that represent the true interests of the respondents. The results provide the first evidence that, albeit frequent, values related to culture, identity, community cohesion and sense of place are not the values most vulnerable to climate change. This in turn indicates a need to further investigate the vulnerability of these values to climate change, using a methodology that clearly distinguishes between instrumental and end values.

Intercomparison of machine learning methods for statistical downscaling: the case of daily and extreme precipitation

Statistical downscaling of Global Climate Models (GCMs) allows researchers to study local climate change effects decades into the future. A wide range of statistical models have been applied to downscaling GCMs but recent advances in machine learning have not been explored compared to traditional approaches. In this paper, we compare five Perfect Prognosis (PP) approaches, Ordinary Least Squares, Elastic-Net, and Support Vector Machine along with two machine learning methods Multi-task Sparse Structure Learning (MSSL) and Autoencoder Neural Networks. In addition, we introduce a hybrid Model Output Statistics and PP approach by modeling the residuals of Bias Correction Spatial Disaggregation (BCSD) with MSSL. Metrics to evaluate each method’s ability to capture daily anomalies, large-scale climate shifts, and extremes are analyzed. Generally, we find inconsistent performance between PP methods in their ability to predict daily anomalies and extremes as well as monthly and annual precipitation. However, results suggest that L1 sparsity constraints aid in reducing error through internal feature selection. The MSSL+BCSD coupling, when compared with BCSD, improved daily, monthly, and annual predictability but decreased performance at the extremes. Hence, these results suggest that the direct application of state-of-the-art machine learning methods to statistical downscaling does not provide direct improvements over simpler, longstanding approaches.

Projected climate and agronomic implications for corn production in the Northeastern United States.

Corn has been a pillar of American agriculture for decades and continues to receive much attention from the scientific community for its potential to meet the food, feed and fuel needs of a growing human population in a changing climate. By midcentury, global temperature increase is expected to exceed 2°C where local effects on heat, cold and precipitation extremes will vary. The Northeast United States is a major dairy producer, corn consumer, and is cited as the fastest warming region in the contiguous U.S. It is important to understand how key agronomic climate variables affect corn growth and development so that adaptation strategies can be tailored to local climate changes. We analyzed potential local effects of climate change on corn growth and development at three major dairy locations in the Northeast (Syracuse, New York; State College, Pennsylvania and Landisville, Pennsylvania) using downscaled projected climate data (2000–2100) from nine Global Climate Models under two emission pathways (Representative Concentration Pathways (RCP) 4.5 and 8.5). Our analysis indicates that corn near the end of the 21st century will experience fewer spring and fall freezes, faster rate of growing degree day accumulation with a reduction in time required to reach maturity, greater frequencies of daily high temperature ≥35°C during key growth stages such as silking-anthesis and greater water deficit during reproductive (R1-R6) stages. These agronomic anomalies differ between the three locations, illustrating varying impacts of climate change in the more northern regions vs. the southern regions of the Northeast. Management strategies such as shifting the planting dates based on last spring freeze and irrigation during the greatest water deficit stages (R1-R6) will partially offset the projected increase in heat and drought stress. Future research should focus on understanding the effects of global warming at local levels and determining adaptation strategies that meet local needs.