Climate change—linear models

2021 ◽  
pp. 199-216
Keyword(s):  
Climate Change ◽  
Linear Models

scholarly journals Configuration and intercomparison of deep learning neural models for statistical downscaling

2020 ◽  
Vol 13 (4) ◽  
pp. 2109-2124 ◽  
Author(s):  
Jorge Baño-Medina ◽  
Rodrigo Manzanas ◽  
José Manuel Gutiérrez
Keyword(s):  
Climate Change ◽  
Deep Learning ◽  
Statistical Downscaling ◽  
Linear Models ◽  
Regional Climate ◽  
Added Value ◽  
Climate Conditions ◽  
Continental Scale ◽  
Spatial Features ◽  
Learning Techniques

Abstract. Deep learning techniques (in particular convolutional neural networks, CNNs) have recently emerged as a promising approach for statistical downscaling due to their ability to learn spatial features from huge spatiotemporal datasets. However, existing studies are based on complex models, applied to particular case studies and using simple validation frameworks, which makes a proper assessment of the (possible) added value offered by these techniques difficult. As a result, these models are usually seen as black boxes, generating distrust among the climate community, particularly in climate change applications. In this paper we undertake a comprehensive assessment of deep learning techniques for continental-scale statistical downscaling, building on the VALUE validation framework. In particular, different CNN models of increasing complexity are applied to downscale temperature and precipitation over Europe, comparing them with a few standard benchmark methods from VALUE (linear and generalized linear models) which have been traditionally used for this purpose. Besides analyzing the adequacy of different components and topologies, we also focus on their extrapolation capability, a critical point for their potential application in climate change studies. To do this, we use a warm test period as a surrogate for possible future climate conditions. Our results show that, while the added value of CNNs is mostly limited to the reproduction of extremes for temperature, these techniques do outperform the classic ones in the case of precipitation for most aspects considered. This overall good performance, together with the fact that they can be suitably applied to large regions (e.g., continents) without worrying about the spatial features being considered as predictors, can foster the use of statistical approaches in international initiatives such as Coordinated Regional Climate Downscaling Experiment (CORDEX).

Intensification of future low-flow events in relation to projected changes in large-scale climate drivers due to climate change

2021 ◽  
Author(s):  
Pallavi Goswami ◽  
Arpita Mondal ◽  
Christoph Rüdiger ◽  
Tim J. Peterson
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Linear Models ◽  
Southern Oscillation ◽  
Value Theory ◽  
Extreme Value ◽  
Low Flow ◽  
Generalised Linear Models ◽  
Southeast Australia ◽  
The Future

<p>Large-scale climate processes such as the El Nino Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and Southern Annular Mode (SAM) influence the hydro-climatology of Southeast Australia (SEA). In the present study, we show that low-flow events in many catchments in SEA are significantly influenced by variability in these climate drivers. Extreme value distributions and Generalised Linear Models (GLMs) are used here to model low-flow characteristics such as intensity, duration and frequency with respect to these climate drivers. Further, we study how the future projections of ENSO, IOD and SAM are likely to evolve under climate change by examining the projected values of their representative indices and how they will impact low-flow events in the region. It is found that the future dry phases of these climate drivers are likely to be more dry than those in the historic period. This in turn is expected to lead to intensification of low-flow events in the future, resulting in lower availability of fresh water during occurrences of the dry phases of these climate drivers. Thus, climate change in the future is expected to significantly influence future low-flow events in the region thereby making it even more crucial for water managers to adequately manage and ensure water availability.</p><p><br>Keywords: low-flows, ENSO, IOD, SAM, Extreme Value Theory, Generalised Linear Models, Southeast Australia, CMIP5, RCP8.5.</p>

scholarly journals Estimating the impact of climate change on residential water use using panel data analysis: a case study of Lilongwe, Malawi

2017 ◽  
Vol 8 (2) ◽  
pp. 217-226 ◽  
Author(s):  
Chikondi Makwiza ◽  
Musandji Fuamba ◽  
Fadoua Houssa ◽  
Heinz Erasmus Jacobs
Keyword(s):  
Climate Change ◽  
Water Use ◽  
Water Conservation ◽  
Linear Models ◽  
Panel Data Analysis ◽  
Empirical Relationship ◽  
Residential Water Use ◽  
The Impact ◽  
Residential Water ◽  
Emissions Scenario

Abstract In this study, panel linear models were used to develop an empirical relationship between metered household water use and the independent variables plot size and theoretical irrigation requirement. The estimated statistical model provides a means of estimating the climate-sensitive component of residential water use. Ensemble averages of temperature and rainfall projections were used to quantify potential changes in water use due to climate change by 2050. Annual water use per household was estimated to increase by approximately 1.5% under the low emissions scenario or 2.3% under the high emissions scenario. The model results provide information that can enhance water conservation initiatives relating particularly to outdoor water use. The model approach presented utilizes data that are readily available to water supply utilities and can therefore be easily replicated elsewhere.

scholarly journals Linking zooplankton assemblages with oceanographic zones in an Atlantic coastal ecosystem

2018 ◽  
Vol 75 (6) ◽  
pp. 868-882 ◽  
Author(s):  
Allan J. Debertin ◽  
J. Mark Hanson ◽  
Simon C. Courtenay
Keyword(s):  
Climate Change ◽  
Linear Models ◽  
Human Impacts ◽  
Human Populations ◽  
Ecosystem Structure ◽  
Intergovernmental Panel ◽  
Bottom Water Temperature ◽  
Surface Water Salinity ◽  
State Of The Environment ◽  
Atlantic Coastal

Shallow (5–35 m depth) coastal waters, with their proximity to human populations, are likely to experience greater changes to ecosystem structure and functions from climate change and human impacts than offshore waters. Concerns of declining fisheries landings and deteriorating habitat quality in Northumberland Strait led to an assessment by Fisheries and Oceans Canada of the state of the environment and biota including zooplankton during the summer. In this paper we describe spatial structure of zooplankton (three distinct assemblages) and determined that two oceanographic zones can explain the spatial variation. Using distance-based linear models, bottom water temperature and surface water salinity explained 16% to 25% of the variation in zooplankton composition for each year of the survey. We used retrospective analyses to estimate what the zooplankton assemblage may have resembled in the early 1990s from data of oceanographic conditions. Given the prediction of warming oceans by the Intergovernmental Panel on Climate Change, we provide a means of predicting zooplankton composition and their distribution, with implications for the planktivorous fishes that prey upon them.

scholarly journals The role of climate change perceptions and sociodemographics on reported mitigation efforts and performance among households in northeastern Mexico

2022 ◽  
Author(s):  
D. Liliana González-Hernández ◽  
Raúl A. Aguirre-Gamboa ◽  
Erik W. Meijles
Keyword(s):  
Climate Change ◽  
Greenhouse Gas Emissions ◽  
Greenhouse Gas ◽  
Linear Models ◽  
Sociodemographic Characteristics ◽  
Gas Emissions ◽  
Household Level ◽  
Change Perceptions ◽  
Northeastern Mexico ◽  
Mitigate Climate Change

AbstractManaging and reducing the impacts of climate change depends on efficient actions from all societal scales. Yet, the household component is often missing from climate research, debate, and policies. This is problematic because households have been found to significantly contribute to of global greenhouse gas emissions and therefore have the potential to be part of a solution to climate change by mitigating climate change. This study seeks to understand which factors drive household-level mitigation actions. We conducted a household survey in Nuevo Leon, located in northeastern Mexico, to explore the extent to which climate change perceptions and the sociodemographic characteristics of households influence their reported mitigation performances and their perceived mitigation efforts. Results from linear regression analyses and generalized linear models revealed that sociodemographic characteristics are key drivers of the households’ perceived mitigation efforts and reported mitigation performances and. We also found that climate change perceptions drive a household’s efforts to mitigate climate change. These results could partly explain why despite the efforts households take to mitigate climate change, achieving an effective reduction of greenhouse gas emissions is challenging without further access to resources such as education and financial support. If governments intend to realize substantial reductions in future emission pathways, then household-level mitigation should be addressed with proper support.

scholarly journals Configuration and Intercomparison of Deep Learning Neural Models for Statistical Downscaling

2019 ◽  
Author(s):  
Jorge Baño-Medina ◽  
Rodrigo Manzanas ◽  
José Manuel Gutiérrez
Keyword(s):  
Climate Change ◽  
Deep Learning ◽  
Statistical Downscaling ◽  
Linear Models ◽  
Added Value ◽  
Climate Conditions ◽  
Continental Scale ◽  
Temperature And Precipitation ◽  
Spatial Features ◽  
Learning Techniques

Abstract. Deep learning techniques (in particular convolutional neural networks, CNNs) have recently emerged as a promising approach for statistical downscaling due to their ability to learn spatial features from huge spatio-temporal datasets. However, existing studies are based on complex models, applied to particular case studies and using simple validation frameworks, which makes difficult a proper assessment of the (possible) added value offered by these techniques. As a result, these models are usually seen as black-boxes generating distrust among the climate community, particularly in climate change problems. In this paper we undertake a comprehensive assessment of deep learning techniques for continental-scale statistical downscaling, building on the VALUE validation framework. In particular, different CNN models of increasing complexity are applied for downscaling temperature and precipitation over Europe, comparing them with a few standard benchmark methods from VALUE (linear and generalized linear models) which have been traditionally used for this purpose. Besides analyzing the adequacy of different components and topologies, we also focus on their extrapolation capability, a critical point for their possible application in climate change studies. To do this, we use a warm test period as surrogate of possible future climate conditions. Our results show that, whilst the added value of CNNs is mostly limited to the reproduction of extremes for temperature, these techniques do outperform the classic ones for the case of precipitation for most aspects considered. This overall good performance, together with the fact that they can be suitably applied to large regions (e.g. continents) without worrying about the spatial features being considered as predictors, can foster the use of statistical approaches in international initiatives such as CORDEX.

scholarly journals Role of Mixed-Species Stands in Attenuating the Vulnerability of Boreal Forests to Climate Change and Insect Epidemics

2021 ◽  
Vol 12 ◽  
Author(s):  
Raphaël D. Chavardès ◽  
Fabio Gennaretti ◽  
Pierre Grondin ◽  
Xavier Cavard ◽  
Hubert Morin ◽  
...  
Keyword(s):  
Climate Change ◽  
Populus Tremuloides ◽  
Tree Growth ◽  
Host Species ◽  
Boreal Forests ◽  
Linear Models ◽  
Black Spruce ◽  
Trembling Aspen ◽  
Mixed Stands ◽  
Species Mixture

We investigated whether stand species mixture can attenuate the vulnerability of eastern Canada’s boreal forests to climate change and insect epidemics. For this, we focused on two dominant boreal species, black spruce [Picea mariana (Mill.) BSP] and trembling aspen (Populus tremuloides Michx.), in stands dominated by black spruce or trembling aspen (“pure stands”), and mixed stands (M) composed of both species within a 36 km2 study area in the Nord-du-Québec region. For each species in each stand composition type, we tested climate-growth relations and assessed the impacts on growth by recorded insect epidemics of a black spruce defoliator, the spruce budworm (SBW) [Choristoneura fumiferana (Clem.)], and a trembling aspen defoliator, the forest tent caterpillar (FTC; Malacosoma disstria Hübn.). We implemented linear models in a Bayesian framework to explain baseline and long-term trends in tree growth for each species according to stand composition type and to differentiate the influences of climate and insect epidemics on tree growth. Overall, we found climate vulnerability was lower for black spruce in mixed stands than in pure stands, while trembling aspen was less sensitive to climate than spruce, and aspen did not present differences in responses based on stand mixture. We did not find any reduction of vulnerability for mixed stands to insect epidemics in the host species, but the non-host species in mixed stands could respond positively to epidemics affecting the host species, thus contributing to stabilize ecosystem-scale growth over time. Our findings partially support boreal forest management strategies including stand species mixture to foster forests that are resilient to climate change and insect epidemics.

scholarly journals Temperature and Patterns of Occurrence and Abundance of Key Copepod Taxa in the Northeast Pacific

2021 ◽  
Vol 8 ◽  
Author(s):  
Lauren Ashlock ◽  
Marisol García-Reyes ◽  
Chelle Gentemann ◽  
Sonia Batten ◽  
William Sydeman
Keyword(s):  
Climate Change ◽  
Heat Wave ◽  
Linear Models ◽  
Heat Waves ◽  
Food Resource ◽  
Temperature Sensitive ◽  
Thermal Threshold ◽  
Northeast Pacific

The Northeast Pacific is a highly heterogeneous and productive ecosystem, yet it is vulnerable to climate change and extreme events such as marine heat waves. Recent heat wave induced die-offs of fish, marine mammals, and seabirds in the Gulf of Alaska were associated with the loss of large, lipid-rich copepods, which are a vital food resource for forage fishes. The critical and temperature sensitive role of copepods in this ecosystem motivates our investigation into the impacts of temperature on copepod occurrence, abundance, and phenology. Here, we pair long term in situ copepod data from Continuous Plankton Recorder surveys with satellite temperature data to determine the influence of water temperature on three key copepod taxa: Neocalanus plumchrus, Calanus pacificus, and Oithona spp. Through the use of linear models and thermal threshold methods, we demonstrate that N. plumchrus is most vulnerable to warming and future marine heat waves in this region. Linear models demonstrate that N. plumchrus abundance is negatively related to temperature, and thermal threshold methods reveal that N. plumchrus has an upper thermal threshold of 11.5°C for occurrence, and 10.5°C for abundance. Additionally, examining N. plumchrus abundance before and during the 2014–2016 marine heat wave demonstrates reduced species abundance during past warming events. Oithona spp. and C. pacificus appear to be less vulnerable to warm temperatures. However, their presence will not be sufficient to supplement the loss of the larger-bodied and lipid-rich N. plumchrus. Our findings demonstrate the power of using long-term in situ data to determine thermal tolerances, and suggest the need to further examine the potential resilience of N. plumchrus to climate change.

scholarly journals Contemporary range expansion of the Virginia opossum (Didelphis virginiana) impacted by humans and snow cover

2018 ◽  
Vol 96 (2) ◽  
pp. 107-115 ◽  
Author(s):  
L.L. Walsh ◽  
P.K. Tucker
Keyword(s):  
Climate Change ◽  
Lake Michigan ◽  
Agricultural Land ◽  
Linear Models ◽  
Land Development ◽  
Didelphis Virginiana ◽  
Peripheral Populations ◽  
Climate Niche ◽  
Genetic Clusters ◽  
Virginia Opossum

Range expansions are key demographic events driven by factors such as climate change and human intervention that ultimately influence the genetic composition of peripheral populations. The expansion of the Virginia opossum (Didelphis virginiana Kerr, 1792) into Michigan has been documented over the past 200 years, indicating relatively new colonizations in northern Michigan. Although most contemporary expansions are a result of shifts in climate regimes, the opossum has spread beyond its hypothesized climate niche, offering an opportunity to examine the compounding influence that climate change and humans have on a species’ distribution. The genetic consequences of two range expansions were investigated using genotypic data for nine microsatellite markers from opossums collected in Michigan, Ohio, and Wisconsin, USA. Two genetic clusters were identified: one on either side of Lake Michigan. Using general linear models, we found that measurements of genetic diversity across 15 counties are best explained by days of snow on the ground. Next best models incorporate anthropogenic covariates including farm density. These models suggest that opossum expansion may be facilitated by agricultural land development and at the same time be limited by their inability to forage in snow.

scholarly journals Disentangling the Effects of Genotype and Environment on Growth and Wood Features of Balfourodendron riedelianum Trees by Common Garden Experiments in Brazil

Forests ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 905
Author(s):  
Jane Rodrigues da Silva ◽  
Sergio Rossi ◽  
Siddhartha Khare ◽  
Eduardo Luiz Longui ◽  
Carmen Regina Marcati
Keyword(s):  
Climate Change ◽  
Tree Growth ◽  
Linear Models ◽  
Transfer Functions ◽  
Mechanical Damage ◽  
Common Garden ◽  
Growth And Survival ◽  
Functional Perspective ◽  
Common Garden Experiments ◽  
Growth Features

Intraspecific studies with populations replicated in different sites allow the effects of genotype and environment on wood features and plant growth to be distinguished. Based on climate change predictions, this distinction is important for establishing future patterns in the distribution of tree species. We quantified the effects of genotype and environment on wood features and growth of 30-year-old Balfourodendron riedelianum trees. We used three provenances planted in two common garden experiments with difference in precipitation and temperature. We applied linear models to estimate the variability in wood and growth features and transfer functions to evaluate the responses of these features to temperature, precipitation, and the standardized precipitation evapotranspiration index (SPEI). Our results showed that genotype had an effect on vessels and rays, where narrower vessels with thinner walls and larger intervessel pits, and shorter, narrower and more numerous rays were observed in provenances from drier sites. We also observed the effect of the environment on wood features and growth. Trees growing in the wetter site were taller and larger, and they had wider vessels with thicker walls and lower ray density. Transfer functions indicated that an increase in temperature results in larger vessels with thicker walls, taller and denser rays, shorter and narrower fibers with thinner walls, and lower wood density. From a functional perspective, these features make trees growing in warmer environments more prone to drought-induced embolisms and more vulnerable to mechanical damage and pathogen attacks. Tree growth varied with precipitation and SPEI, being negatively affected in the drier site. Overall, we demonstrated that both genotype and environment affect wood features, while tree growth is mainly influenced by the environment. Plastic responses in hydraulic characteristics could represent important functional traits to mitigate the consequences of ongoing climate change on the growth and survival of the species within its natural range.

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