Temporal trends and spatial patterns of chironomid communities in alpine lakes recovering from acidification under accelerating climate change

2021 ◽  
Author(s):  
Marek Svitok ◽  
Vladimír Kubovčík ◽  
Jiří Kopáček ◽  
Peter Bitušík
Keyword(s):  
Climate Change ◽  
Spatial Patterns ◽  
Temporal Trends ◽  
Alpine Lakes

Bayesian uncertainty quantification of spatio-temporal trends in soil organic carbon using INLA and SPDE

2020 ◽  
Author(s):  
Nicolas P.A. Saby ◽  
Thomas Opitz ◽  
Bifeng Hu ◽  
Blandine Lemercier ◽  
Hocine Bourennane
Keyword(s):  
Climate Change ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Spatial Patterns ◽  
Temporal Trends ◽  
Model Parameters ◽  
Spatial And Temporal Variability ◽  
Statistical Inferences ◽  
Spatio Temporal ◽  
General Response

<p>The assumption of spatial and temporal stationarity does not hold for many ecological and environmental processes. This is particularly the case for many soil processes like carbon sequestration, often driven by factors such as biological dynamics, climate change and anthropogenic influences. For better understanding and predicting such phenomena, we develop a Bayesian inference framework that combines the integrated nested Laplace approximation (INLA) with the stochastic partial differential equation approach (SPDE). We put focus on modeling complex temporal trends varying through space with an accurate assessment of uncertainties, and on spatio-temporal mapping of processes that are only partially observed.</p><p>We model observed data through a latent (i.e., unobserved) smooth process whose additive components are endowed with Gaussian process priors. We use the SPDE approach to implement flexible sparse-matrix approximations of the Matérn covariance for spatial fields. The separate specification of the spatially varying linear trend allows us to conduct component-specific statistical inferences (range and variance estimates, standard errors, confidence bounds), and to provide maps to stakeholders for time-invariant spatial patterns, spatial patterns in slopes of time trends, and the associated uncertainties. For observed data following a Gaussian distribution, we add independent measurement errors, but more general response distributions of the data can be implemented. We also include in our model covariate information on parent material, climate and seasonality.</p><p>The INLA method and its implementation in the R-INLA library provide a rich toolbox for statistical space-time modelling while sidestepping typical convergence problems arising with simulation-based techniques using Markov Chain Monte–Carlo codes for large and complex hierarchical models such as ours. Uncertainties arising in model parameters and in pointwise spatio-temporal predictions are naturally captured in the posterior distributions computed through INLA using appropriate approximation techniques, and we can communicate on them through maps of various properties. Moreover, INLA also allows for direct simulation from the estimated posterior model, such that we can conduct statistical inferences on more complex functionals of the multivariate predictive distributions by analogy with MCMC frameworks.</p><p>Soil organic carbon is a major compartment of the global carbon cycle and small variations of its level can largely impact atmospheric CO<sub>2</sub> concentrations. In the context of global climate change, it is important to be able to quantify and explain spatial and temporal variability of SOC in order to forecast future changes. In this work, we used this approach to study possible trends in space and time of soil carbon stock of three agricultural fields in France. Fitted models reveal significant temporal trends with strong spatial heterogeneity. The Matérn model and SPDE approach provide a flexible framework with respect to field design.</p>

Alien vascular plants in Iceland: Diversity, spatial patterns, temporal trends, and the impact of climate change

Flora ◽  
2013 ◽  
Vol 208 (10-12) ◽  
pp. 648-673 ◽  
Author(s):  
Pawel Wasowicz ◽  
Ewa Maria Przedpelska-Wasowicz ◽  
Hörður Kristinsson
Keyword(s):  
Climate Change ◽  
Spatial Patterns ◽  
Vascular Plants ◽  
Temporal Trends ◽  
Impact Of Climate Change ◽  
The Impact

scholarly journals Methodology for classifying the ecosystem integrity of forests in Germany using quantified indicators

2021 ◽  
Vol 33 (1) ◽  
Author(s):  
Martin Jenssen ◽  
Stefan Nickel ◽  
Winfried Schröder
Keyword(s):  
Climate Change ◽  
National Park ◽  
Temporal Trends ◽  
Water Flux ◽  
Ecosystem Functions ◽  
Specific Reference ◽  
Atmospheric Nitrogen ◽  
Complete Coverage ◽  
Ecosystem Integrity ◽  
Quantitative Indicators

Abstract Background Atmospheric deposition of nitrogen and climate change can have impacts on ecological structures and functions, and thus on the integrity of ecosystems and their services. Operationalization of ecosystem integrity is still an important desideratum. Results A methodology for classifying the ecosystem integrity of forests in Germany under the influence of climate change and atmospheric nitrogen deposition is presented. The methodology was based on 14 indicators for six ecosystem functions: habitat function, net primary function, carbon sequestration, nutrient and water flux, resilience. It allows assessments of ecosystem integrity changes by comparing current or prospective ecosystem states with ecosystem-type-specific reference states as described by quantitative indicators for 61 forest ecosystem types based on data before 1990. Conclusion The method developed enables site-specific classifications of ecosystem integrity as well as classifications with complete coverage and determinations of temporal trends as shown using examples from the Thuringian Forest and the “Kellerwald-Edersee” National Park (Germany).

Spatial patterns and temporal trends of daily precipitation indices in Iran

2014 ◽  
Vol 124 (1-2) ◽  
pp. 239-253 ◽  
Author(s):  
Tayeb Raziei ◽  
Jamal Daryabari ◽  
Isabella Bordi ◽  
Reza Modarres ◽  
Luis S. Pereira
Keyword(s):  
Spatial Patterns ◽  
Daily Precipitation ◽  
Temporal Trends ◽  
Precipitation Indices

Assessing the contribution of multiple forcings to changes in temperature extremes 1981–2020 using CMIP6 climate models

2021 ◽  
Author(s):  
Mastawesha Misganaw Engdaw ◽  
Andrew Ballinger ◽  
Gabriele Hegerl ◽  
Andrea Steiner
Keyword(s):  
Climate Change ◽  
Greenhouse Gases ◽  
Climate Models ◽  
Temporal Trends ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Temperature Extremes ◽  
Cold Temperatures

<p>In this study, we aim at quantifying the contribution of different forcings to changes in temperature extremes over 1981–2020 using CMIP6 climate model simulations. We first assess the changes in extreme hot and cold temperatures defined as days below 10% and above 90% of daily minimum temperature (TN10 and TN90) and daily maximum temperature (TX10 and TX90). We compute the change in percentage of extreme days per season for October-March (ONDJFM) and April-September (AMJJAS). Spatial and temporal trends are quantified using multi-model mean of all-forcings simulations. The same indices will be computed from aerosols-, greenhouse gases- and natural-only forcing simulations. The trends estimated from all-forcings simulations are then attributed to different forcings (aerosols-, greenhouse gases-, and natural-only) by considering uncertainties not only in amplitude but also in response patterns of climate models. The new statistical approach to climate change detection and attribution method by Ribes et al. (2017) is used to quantify the contribution of human-induced climate change. Preliminary results of the attribution analysis show that anthropogenic climate change has the largest contribution to the changes in temperature extremes in different regions of the world.</p><p><strong>Keywords:</strong> climate change, temperature, extreme events, attribution, CMIP6</p><p> </p><p><strong>Acknowledgement:</strong> This work was funded by the Austrian Science Fund (FWF) under Research Grant W1256 (Doctoral Programme Climate Change: Uncertainties, Thresholds and Coping Strategies)</p>

Investigating the species-specific association between anuran calling phenology and air temperature

2021 ◽  
Author(s):  
Samantha Wong
Keyword(s):  
Climate Change ◽  
Air Temperature ◽  
Management Strategies ◽  
Temporal Trends ◽  
Interspecific Variation ◽  
Northern Ontario ◽  
Anuran Species ◽  
Calling Behaviour ◽  
Survival And Development ◽  
Species Specific

Climate change has been associated in phenological shifts for a variety of taxa. Amphibians, specifically the order Anura (frogs and toads), are considered particularly vulnerable due to their sensitivity to anthropogenic and environmental change. Previous research has documented shifts in the timing of anuran breeding that can be attributed, in part, to climate change, with potential implications for reproduction, survival, and development. This study aims to investigate how air temperature is associated with anuran calling phenology. I will examine the temporal trends in spring and summer air temperature in a lake in northern Ontario, Canada. and quantify seasonal patterns of calling anuran species using acoustic monitoring over a four-month period. I predict that there will be interspecific variation in peak calling associated with air temperature. Additionally, I expect to find asymmetrical association between air temperature and anuran species’ calling behaviour – wherein prolonged breeding species will have a larger optimal temperature range for calling compared to explosive breeding species. The findings of this research will aid in future conservation and provide insight for management strategies of anurans in Canada in response to anticipated climate warming.

scholarly journals The Fingerprint of Climate Change and Urbanization in South Korea

Atmosphere ◽  
2018 ◽  
Vol 9 (7) ◽  
pp. 273 ◽  
Author(s):  
Won-Ho Nam ◽  
Guillermo Baigorria ◽  
Eun-Mi Hong ◽  
Taegon Kim ◽  
Yong-Sang Choi ◽  
...  
Keyword(s):  
Climate Change ◽  
South Korea ◽  
Urban Areas ◽  
Temporal Trends ◽  
Weather Data ◽  
Maximum Temperature ◽  
Island Effect ◽  
Temperature Trends ◽  
Agricultural Areas ◽  
Precipitation And Temperature

Understanding long-term changes in precipitation and temperature patterns is important in the detection and characterization of climate change, as is understanding the implications of climate change when performing impact assessments. This study uses a statistically robust methodology to quantify long-, medium- and short-term changes for evaluating the degree to which climate change and urbanization have caused temporal changes in precipitation and temperature in South Korea. We sought to identify a fingerprint of changes in precipitation and temperature based on statistically significant differences at multiple-timescales. This study evaluates historical weather data during a 40-year period (1973–2012) and from 54 weather stations. Our results demonstrate that between 1993–2012, minimum and maximum temperature trends in the vicinity of urban and agricultural areas are significantly different from the two previous decades (1973–1992). The results for precipitation amounts show significant differences in urban areas. These results indicate that the climate in urbanized areas has been affected by both the heat island effect and global warming-caused climate change. The increase in the number of rainfall events in agricultural areas is highly significant, although the temporal trends for precipitation amounts showed no significant differences. Overall, the impacts of climate change and urbanization in South Korea have not been continuous over time and have been expressed locally and regionally in terms of precipitation and temperature changes.

NDVI dynamics as reflected in climatic variables: spatial and temporal trends – a case study of Hungary

2019 ◽  
Vol 56 (4) ◽  
pp. 624-644 ◽  
Author(s):  
Szabó ◽  
Elemér ◽  
Kovács ◽  
Püspöki ◽  
Kertész ◽  
...  
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Local Level ◽  
Temporal Trends ◽  
Maximum Temperature ◽  
Time Range ◽  
Climatic Data ◽  
Spatial Pattern Analysis ◽  
Short Period ◽  
Temporal Variance

Understanding climate change and revealing its future paths on a local level is a great challenge for the future. Beside the expanding sets of available climatic data, satellite images provide a valuable source of information. In our study we aimed to reveal whether satellite data are an appropriate way to identify global trends, given their shorter available time range. We used the CARPATCLIM (CC) database (1961–2010) and the MODIS NDVI images (2000–2016) and evaluated the time period covered by both (2000–2010). We performed a regression analysis between the NDVI and CC variables, and a time series analysis for the 1961–2008 and 2000–2008 periods at all data points. The results justified the belief that maximum temperature (TMAX), potential evapotranspiration and aridity all have a strong correlation with the NDVI; furthermore, the short period trend of TMAX can be described with a functional connection with its long period trend. Consequently, TMAX is an appropriate tool as an explanatory variable for NDVI spatial and temporal variance. Spatial pattern analysis revealed that with regression coefficients, macro-regions reflected topography (plains, hills and mountains), while in the case of time series regression slopes, it justified a decreasing trend from western areas (Transdanubia) to eastern ones (The Great Hungarian Plain). This is an important consideration for future agricultural and land use planning; i.e. that western areas have to allow for greater effects of climate change.

Mosses as biomonitors of atmospheric heavy metal deposition: Spatial patterns and temporal trends in Europe

2010 ◽  
Vol 158 (10) ◽  
pp. 3144-3156 ◽  
Author(s):  
H. Harmens ◽  
D.A. Norris ◽  
E. Steinnes ◽  
E. Kubin ◽  
J. Piispanen ◽  
...  
Keyword(s):  
Heavy Metal ◽  
Spatial Patterns ◽  
Temporal Trends ◽  
Metal Deposition ◽  
Heavy Metal Deposition
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