The Contributions of Climate Changes and Human Activities to Long-Term Variations in Lake Sediments Based on Results from Generalized Additive Models

2019 ◽  
Vol 33 (3) ◽  
pp. 1069-1085 ◽  
Author(s):  
Zhuoshi He ◽  
Shouliang Huo ◽  
Chunzi Ma ◽  
Hanxiao Zhang ◽  
Da An ◽  
...  
Keyword(s):  
Lake Sediments ◽  
Human Activities ◽  
Climate Changes ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Long Term Variations

Environmental and geographical effects on fog occurrence

2021 ◽  
Author(s):  
Iva Hunova ◽  
Marek Brabec ◽  
Marek Malý ◽  
Alexandru Dumitrescu ◽  
Jan Geletič
Keyword(s):  
Air Pollution ◽  
Environmental Factors ◽  
Tensor Product ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Low Rank ◽  
Seasonal Component ◽  
Explanatory Variables ◽  
The Individual

<p>Fog is a very complex phenomenon (Gultepe et al., 2007). In some areas it can contribute substantially to hydrological and chemical inputs and is therefore of high environmental relevance (Blas et al., 2010). Fog formation is affected by numerous factors, such as meteorology, air pollution, terrain (geomorphology), and land-use.</p><p>In our earlier studies we addressed the role of meteorology and air pollution on fog occurrence (Hůnová et al., 2018) and long-term trends in fog occurrence in Central Europe (Hůnová et al., 2020). This study builds on earlier model identification of year-to-year and seasonal components in fog occurrence and brings an analysis of the deformation of the above components due to the individual explanatory variables. The aim of this study was to indicate the geographical and environmental factors affecting the fog occurrence.</p><p>       We have examined the data on fog occurrence from 56 meteorological stations of various types from Romania reflecting different environments and geographical areas. We used long-term records from the 1981–2017 period. </p><p>       We considered both the individual explanatory variables and their interactions. With respect to geographical factors, we accounted for the altitude and landform. With respect to environmental factors,   we accounted for proximity of large water bodies, and proximity of forests. Geographical data from Copernicus pan-European (e.g. CORINE land cover, high resolution layers) and local (e.g. Urban Atlas) projects were used. Elevation data from EU-DEM v1.1 were source for morphometric analysis (Copernicus, 2020).</p><p>        We applied a generalized additive model, GAM (Wood, 2017; Hastie & Tibshirani, 1990) to address nonlinear trend shapes in a formalized and unified way. In particular, we employed penalized spline approach with cross-validated penalty coefficient estimation. To explore possible deformations of annual and seasonal components with various covariates of interest, we used (penalized) tensor product splines to model (two-way) interactions parsimoniously, Wood (2006).</p><p>       The fog occurrence showed significant decrease over the period under review. In general the selected explanatory variables significantly affected the fog occurrence and their effect was non-linear. Our results indicated that, the geographical and environmental variables affected primarily the seasonal component of the model. Of the factors which were accounted for, it was mainly the altitude showing the clear effect on seasonal component deformation (Hůnová et al., in press).</p><p>      </p><p> </p><p>References:</p><p>Blas, M, Polkowska, Z., Sobik, M., et al. (2010). Atmos. Res. 95, 455–469.</p><p>Copernicus Land Monitoring Service (2020). Accessed online at: https://land.copernicus.eu/.</p><p>Gultepe, I., Tardif, R., Michaelidis, S.C., Cermak, J., Bott, A. et al. (2007). Pure Appl Geophys, 164, 1121-1159.</p><p>Hastie, T.J., Tibshirani, R.J. (1990). Generalized Additive Models. Boca Raton, Chapman & Hall/CRC.</p><p>Hůnová, I., Brabec, M., Malý, M., Dumitrescu, A., Geletič, J. (in press) Sci. Total Environ. 144359.</p><p>Hůnová, I., Brabec, M., Malý, M., Valeriánová, A. (2018) Sci. Total Environ. 636, 1490–1499.</p><p>Hůnová, I., Brabec, M., Malý, M., Valeriánová, A. (2020) Sci. Total Environ. 711, 135018.</p><p>Wood, S.N. (2006) Low rank scale invariant tensor product smooths for generalized additive mixed models. Biometrics 62(4):1025-1036</p><p>Wood, S.N. (2017). Generalized Additive Models: An Introduction with R (2nd ed). Boca Raton, Chapman & Hall/CRC.</p><p> </p>

Evidence for temperature-dependent shifts in spawning times of anadromous alewife (Alosa pseudoharengus) and blueback herring (Alosa aestivalis)

2020 ◽  
Vol 77 (4) ◽  
pp. 741-751 ◽  
Author(s):  
Steven M. Lombardo ◽  
Jeffrey A. Buckel ◽  
Ernie F. Hain ◽  
Emily H. Griffith ◽  
Holly White
Keyword(s):  
Generalized Additive Models ◽  
Additive Models ◽  
Alosa Pseudoharengus ◽  
Spawning Habitat ◽  
Temperature Dependent ◽  
Blueback Herring ◽  
River Herring ◽  
Alosa Aestivalis ◽  
Timing Of Spawning

We analyzed four decades of presence–absence data from a fishery-independent survey to characterize the long-term phenology of river herring (alewife, Alosa pseudoharengus; and blueback herring, Alosa aestivalis) spawning migrations in their southern distribution. We used logistic generalized additive models to characterize the average ingress, peak, and egress timing of spawning. In the 2010s, alewife arrived to spawning habitat 16 days earlier and egressed 27 days earlier (peak 12 days earlier) relative to the 1970s. Blueback herring arrived 5 days earlier and egressed 23 days earlier (peak 13 days earlier) in the 2010s relative to the 1980s. The changes in ingress and egress timing have shortened the occurrence in spawning systems by 11 days for alewife over four decades and 18 days for blueback herring over three decades. We found that the rate of vernal warming was faster during 2001–2016 relative to 1973–1988 and is the most parsimonious explanation for changes in spawning phenology. The influence of a shortened spawning season on river herring population dynamics warrants further investigation.

scholarly journals Urbanization and Human Population Favor Species Richness of Alien Birds

Diversity ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 72
Author(s):  
Maria Lazarina ◽  
Mariana A. Tsianou ◽  
Georgios Boutsis ◽  
Aristi Andrikou-Charitidou ◽  
Elpida Karadimou ◽  
...  
Keyword(s):  
Species Richness ◽  
Alien Species ◽  
Human Activities ◽  
Native Species ◽  
Human Population ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Land Uses ◽  
Time Periods ◽  
Native Species Richness

Human activities like urbanization and agriculture affect spatial biodiversity patterns. The presence and activities of humans richly benefit alien species, but native species usually decline in human-impacted areas. Considering that the richness of alien and native species are inter-related, we explored the effect of human population density, human-related land uses (agricultural and urban), and natural land area on avian (alien and native) species richness of Massachusetts for two time periods using Generalized Additive Models. Avian alien species richness increased with native species richness in both time periods. Despite the predominant role of native species richness as a major driver of alien species richness, human activities play an important additional role in shaping species richness patterns of established aliens. Human-related land uses (urban and agricultural) and human population favored alien species richness in both time periods. Counter to expectations, human activities were also positively associated to native avian species richness. Possible explanations of these patterns may include habitat heterogeneity, increased availability of resources, and reduced predation risk.

scholarly journals Risk of Extreme Precipitation under Nonstationarity Conditions during the Second Flood Season in the Southeastern Coastal Region of China

2017 ◽  
Vol 18 (3) ◽  
pp. 669-681 ◽  
Author(s):  
Lu Gao ◽  
Jie Huang ◽  
Xingwei Chen ◽  
Ying Chen ◽  
Meibing Liu
Keyword(s):  
Return Period ◽  
Human Activities ◽  
Extreme Precipitation ◽  
Climate Changes ◽  
Precipitation Amount ◽  
Coastal Region ◽  
Additive Models ◽  
Flood Season ◽  
Extreme Precipitation Events ◽  
Mk Test

Abstract This study analyzes the variation and risk changes of extreme precipitation under nonstationarity conditions using the Generalized Additive Models for Location, Scale, and Shape (GAMLSS) and the Mann–Kendall (MK) test. The extreme precipitation series is extracted from the observations during the second flood season (July–September) from 1960 to 2012 derived from 86 meteorological stations in the southeastern coastal region of China. The trend of mean (Mn) and variance (Var) of extreme precipitation is detected by MK. Ten large-scale circulation variables and four greenhouse gases are selected to construct a climate change index and a human activity index, which are based on principal component analysis. The recurrence risk of extreme precipitation is calculated by GAMLSS while considering climate changes and human activities. The results demonstrate that the nonstationarity characteristic of extreme precipitation is widespread in this region. A significant increasing trend of Mn is found in Shanghai, eastern Zhejiang, and northern and southern Fujian. An enhanced Var is found in eastern Guangdong. A significant positive correlation exists between climate changes/human activities and Mn/Var, especially in Zhejiang and Fujian. Generally, the contribution of climate changes and human activities to Mn is greater than it is to Var. In this region, the precipitation amount of high-frequency (2-yr return period) and low-frequency (100-yr return period) events increases from inland to coastal and from north to south. The government should pay careful attention to these trends because the intensity of extreme precipitation events and their secondary disasters could result in serious losses.

Stock-recruitment models for Pacific sardine (Sardinops sagax)

1995 ◽  
Vol 52 (3) ◽  
pp. 566-577 ◽  
Author(s):  
Larry D. Jacobson ◽  
Alec D. MacCall
Keyword(s):  
Reproductive Success ◽  
Environmental Conditions ◽  
Statistical Power ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Cold Temperatures ◽  
Pacific Sardine ◽  
Sardinops Sagax ◽  
Spawning Biomass

We used generalized additive models to study the recruitment of Pacific sardine (Sardinops sagax) along western North America and to compare the effects of fishing and environmental factors on the stock. We found significant relationships between the logarithm of sardine reproductive success (recruits per unit of spawning biomass) and average sea surface temperature (SST), as well as between sardine recruitment, spawning biomass, and average SST. Simulation and time series analyses were used to evaluate bias, variance, and statistical power for one of our models. Correlation with log reproductive success was highest when average SST data included temperatures after the period of larval development, indicating that recruitment estimates were affected by environmentally driven changes in availability of older sardine to the fishery. Predicted equilibrium biomass and MSY for sardine are lower under environmental conditions that prevail when temperatures are colder. Long-term fluctuations in the environment may cause long-term fluctuations in sardine productivity, and little or no sardine harvest may be sustainable during periods of adverse environmental conditions and cold temperatures.

Modelling the occurrence of convective hazards using ERA5 reanalysis data

2021 ◽  
Author(s):  
Francesco Battaglioli ◽  
Pieter Groenemeijer ◽  
Tomas Pucik ◽  
Uwe Ulbrich ◽  
Henning Rust ◽  
...  
Keyword(s):  
Weather Forecasting ◽  
Generalized Additive Models ◽  
Reanalysis Data ◽  
Severe Weather ◽  
Additive Models ◽  
Ensemble Forecasts ◽  
Probabilistic Forecasts ◽  
Medium Range ◽  
Additive Regression

<p>Convective hazards such as large hail, severe wind gusts, tornadoes, and heavy rainfall cause high economic damages, fatalities, and injuries across Europe. There are insufficient observations to determine whether trends in such local phenomena exist, however recent studies suggest that the conditions supporting such hazards have become more frequent across large parts of Europe in recent decades.</p><p>We model the occurrence of these hazards using Generalized Additive Models (GAM) to investigate the existence of such long-term trends, and to enable objective probabilistic forecasts of these hazards. The models are trained with storm reports from the European Severe Weather Database (ESWD), lightning observations from the EUCLID network, and predictor parameters derived from the ERA5 reanalysis. Our work is based on the framework AR-CHaMo (Additive Regression Convective Hazard Models), previously developed at ESSL.</p><p>Preliminary results include a spatial depiction of the environmental conditions giving rise to convective hazards at a higher resolution than was possible before. The skill of hail models developed using AR-CHaMo has been shown to be superior to composite parameters used by weather forecasters for the occurrence of large hail, such as the Supercell Composite Parameter (SCP) and the Significant Hail Parameter (SHP). Likewise, for tornadoes, more skillful models can be constructed using the AR-CHaMo framework than predictors such as the Significant Tornado Parameter (STP).</p><p>The developed models have use both in climate studies and in medium-range severe weather forecasting. We will report on initial efforts to detect long term (1979-2019) trends of convective hazards and present how these models can be used to support severe weather forecasting using medium-range ensemble forecasts.</p>

Silver and lead in high-altitude lake sediments: Proxies for climate changes and human activities

2012 ◽  
Vol 27 (3) ◽  
pp. 760-773 ◽  
Author(s):  
Marion Garçon ◽  
Catherine Chauvel ◽  
Emmanuel Chapron ◽  
Xavier Faïn ◽  
Mingfang Lin ◽  
...  
Keyword(s):  
High Altitude ◽  
Lake Sediments ◽  
Human Activities ◽  
Climate Changes ◽  
High Altitude Lake

Warming in the upper San Francisco Estuary: Patterns of water temperature change from 5 decades of data

2021 ◽  
Author(s):  
Samuel M. Bashevkin ◽  
Brian Mahardja ◽  
Larry R. Brown
Keyword(s):  
Water Temperature ◽  
Temperature Change ◽  
San Francisco ◽  
Generalized Additive Models ◽  
San Francisco Estuary ◽  
Additive Models ◽  
Spatiotemporal Variability ◽  
Northern Regions ◽  
Migration Corridor

Temperature is a key controlling variable from subcellular to ecosystem scales. Thus, climatic warming is expected to have broad impacts, especially in economically and ecologically valuable systems such as estuaries. The heavily managed upper San Francisco Estuary (SFE) supplies water to millions of people and is home to fish species of high conservation, commercial, and recreational interest. Despite a long monitoring record (> 50 years), we do not yet know how water temperatures have already changed or how trends vary spatially or seasonally. We fit generalized additive models on an integrated database of discrete water temperature observations to estimate long-term trends with spatio-seasonal variability. We found that water temperatures have increased 0.017 °C/year on average over the past 50 years. Rates of temperature change have varied over time, but warming was predominant. Temperature increases were most widespread in the late-fall to winter (November to February) and mid-spring (April to June), coinciding with the winter development of juvenile Chinook Salmon and spring spawning window of the endangered Delta Smelt. Warming was fastest in the northern regions, a key fish migration corridor with important tidal wetland habitat. However, no long-term temperature trends were detected in October and were only observed in some regions in May, July, and August. These results can help identify optimal areas for restoration or refugia to buffer the effects of a warming climate, and the methods can be leveraged to understand the spatiotemporal variability in climate warming patterns in other aquatic systems.

scholarly journals Geocoding and spatio-temporal modeling of long-term PM2.5 and NO2 exposure: the Mexican Teachers' Cohort

2021 ◽  
Author(s):  
Cervantes - Martínez Karla ◽  
Riojas - Rodríguez Horacio ◽  
Díaz - Ávalos Carlos ◽  
Moreno - Macías Hortensia ◽  
López - Ridaura Ruy ◽  
...  
Keyword(s):  
Predictive Accuracy ◽  
Generalized Additive Models ◽  
Epidemiological Studies ◽  
Additive Models ◽  
Temporal Modeling ◽  
Secondary Sources ◽  
Temporal Models ◽  
Out Of Sample ◽  
Spatio Temporal

Abstract Epidemiological studies on the effects of air pollution in Mexico often use the environmental concentrations of monitors closest to the home as exposure proxies, yet this approach disregards the space gradients of pollutants and assumes that individuals have no intra-city mobility. Our aim was to develop high-resolution spatial and temporal models for predicting long-term exposure to PM2.5 and NO2 in a population of ~ 16 500 participants from the Mexican Teachers’ Cohort study. We geocoded the home and work addresses of participants. Using information from secondary sources on geographic and meteorological variables as well as other pollutants, we fitted two generalized additive models to predict monthly PM2.5 and NO2 concentrations in the 2004–2019 period. The models were evaluated through 10-fold cross validation. Both showed high predictive accuracy with out-of-sample data and no overfitting (CV RMSE = 0.102 for PM2.5 and CV RMSE = 4.497 for NO2). Participants were exposed to a monthly average of 24.38 (6.78) µg/m3 of PM2.5 and 28.21 (8.00) ppb of NO2 during the study period. These models offer a solid alternative for estimating PM2.5 and NO2 exposure with high spatio-temporal resolution for epidemiological studies in the Valle de México region.

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