scholarly journals Observed snow depth trends in the European Alps 1971 to 2019

2021 ◽  
Author(s):  
Michael Matiu ◽  
Alice Crespi ◽  
Giacomo Bertoldi ◽  
Carlo Maria Carmagnola ◽  
Christoph Marty ◽  
...  
Keyword(s):  
Snow Depth ◽  
Principal Component ◽  
European Alps ◽  
Climatic Forcing ◽  
Modes Of Variability ◽  
Regional Trends ◽  
Spatial Coverage ◽  
Trend Assessment ◽  
Snow Cover Duration ◽  
Regional Analyses

<p>The European Alps stretch over a range of climate zones, which affect the spatial distribution of snow. Previous analyses of station observations of snow were confined to regional analyses, which complicates comparisons between regions and makes Alpine wide conclusions questionable. Here, we present an Alpine wide analysis of snow depth from six Alpine countries: Austria, France, Germany, Italy, Slovenia, and Switzerland; including altogether more than 2000 stations, of which more than 800 were used for the trend assessment. Using a principal component analysis and k-means clustering, we identified five main modes of variability and five regions, which match the climatic forcing zones: north & high Alpine, northeast, northwest, southeast, and south & high Alpine. Linear trends of monthly mean snow depth between 1971 and 2019 showed decreases in snow depth for most stations for November to May. The average trend among all stations for seasonal (November to May) mean snow depth was -8.4 % per decade, for seasonal maximum snow depth -5.6 % per decade, and for seasonal snow cover duration -5.6 % per decade. However, regional trends differed substantially after accounting for elevation, which challenges the notion of generalizing results from one region to another or to the whole Alps. This study presents an analysis of station snow depth series with the most comprehensive spatial coverage in the European Alps to date.</p>

scholarly journals Observed snow depth trends in the European Alps: 1971 to 2019

2021 ◽  
Vol 15 (3) ◽  
pp. 1343-1382
Author(s):  
Michael Matiu ◽  
Alice Crespi ◽  
Giacomo Bertoldi ◽  
Carlo Maria Carmagnola ◽  
Christoph Marty ◽  
...  
Keyword(s):  
Snow Depth ◽  
Principal Component ◽  
European Alps ◽  
North West ◽  
Modes Of Variability ◽  
North East ◽  
Regional Trends ◽  
Spatial Coverage ◽  
Snow Cover Duration ◽  
Regional Analyses

Abstract. The European Alps stretch over a range of climate zones which affect the spatial distribution of snow. Previous analyses of station observations of snow were confined to regional analyses. Here, we present an Alpine-wide analysis of snow depth from six Alpine countries – Austria, France, Germany, Italy, Slovenia, and Switzerland – including altogether more than 2000 stations of which more than 800 were used for the trend assessment. Using a principal component analysis and k-means clustering, we identified five main modes of variability and five regions which match the climatic forcing zones: north and high Alpine, north-east, north-west, south-east, and south and high Alpine. Linear trends of monthly mean snow depth between 1971 and 2019 showed decreases in snow depth for most stations from November to May. The average trend among all stations for seasonal (November to May) mean snow depth was −8.4 % per decade, for seasonal maximum snow depth −5.6 % per decade, and for seasonal snow cover duration −5.6 % per decade. Stronger and more significant trends were observed for periods and elevations where the transition from snow to snow-free occurs, which is consistent with an enhanced albedo feedback. Additionally, regional trends differed substantially at the same elevation, which challenges the notion of generalizing results from one region to another or to the whole Alps. This study presents an analysis of station snow depth series with the most comprehensive spatial coverage in the European Alps to date.

scholarly journals Observed snow depth trends in the European Alps 1971 to 2019

2020 ◽  
Author(s):  
Michael Matiu ◽  
Alice Crespi ◽  
Giacomo Bertoldi ◽  
Carlo Maria Carmagnola ◽  
Christoph Marty ◽  
...  
Keyword(s):  
Snow Depth ◽  
Principal Component ◽  
Alpine Region ◽  
European Alps ◽  
Climatic Forcing ◽  
Climate Zones ◽  
Modes Of Variability ◽  
Regional Trends ◽  
Spatial Coverage ◽  
Regional Analyses

Abstract. The European Alps stretch over a range of climate zones, which affect the spatial distribution of snow. Previous analyses of station observations of snow were confined to regional analyses. Here, we present an Alpine wide analysis of snow depth from six Alpine countries: Austria, France, Germany, Italy, Slovenia, and Switzerland; including altogether more than 2000 stations. Using a principal component analysis and k-means clustering, we identified five main modes of variability and five regions, which match the climatic forcing zones: north and high Alpine, northeast, northwest, southeast and southwest. Linear trends of mean monthly snow depth between 1971 to 2019 showed decreases in snow depth for 87 % of the stations. December to February trends were on average −1.1 cm decade−1 (min, max: −10.8, 4.4; elevation range 0–1000 m), −2.5 (−25.1, 4.4; 1000–2000 m) and −0.1 (−23.3, 9.9; 2000–3000 m), with stronger trends in March to May: −0.6 (−10.9, 1.0; 0–1000 m), −4.6 (−28.1, 4.1; 1000–2000 m) and −7.6 (−28.3, 10.5; 2000–3000 m). However, regional trends differed substantially, which challenges the notion of generalizing results from one Alpine region to another or to the whole Alps. This study presents an analysis of station snow depth series with the most comprehensive spatial coverage in the European Alps to date.

Soil sampling design optimization by using conditioned Latin Hypercube sampling    

2021 ◽  
Author(s):  
Fatemeh Hateffard ◽  
Tibor József Novák
Keyword(s):  
Principal Component ◽  
Latin Hypercube Sampling ◽  
Feature Space ◽  
Simple Random Sampling ◽  
Soil Sampling ◽  
Latin Hypercube ◽  
Landsat Images ◽  
Original Distribution ◽  
Spatial Coverage ◽  
Elevation Model

<p>One of the most critical steps in digital soil mapping is finding a sampling approach to cover a good spatial coverage of the area regarding the soil spatial variation. In this matter, environmental variables can aid in taking samples in more innovative and more precise locations while reducing the soil sampling efforts such as time and costs. Conditioned Latin hypercube sampling (cLHS) is a stratified random design strategy that perfectly represents the variability of auxiliary variables in feature space. This study applied this method and compared it to simple random sampling to optimize sampling designs for mapping in the agricultural study site in Hungary. The covariates were indices extracted by the digital elevation model and Landsat images. The principal component analysis (PCA) was applied to reduce the data overlap and select the most important variables as the model's inputs. By computing the statistical criteria (mean, variance, standard deviation, etc.) for covariates and comparing these results between the sampling populations and the entire one, we may conclude that both designs gave almost similar predictions. However, for most covariates, statistical means of cLHS provide the closest approximation compared to the random approach sampling method, but the statistical variances and SDs retrieved similar results. Furthermore, the histogram distribution of most variables in the cLHS was following more closely to the original distribution of the environmental covariates. Overall, considering the type of the study site and the chosen variables, it seems that cLHS is a more applicable method.</p> <p> </p>

Observing Snow Depth at Sub-Kilometer Resolution over the European Alps from Sentinel-1

2021 ◽  
Author(s):  
Hans Lievens ◽  
Isis Brangers ◽  
Hans-Peter Marshall ◽  
Tobias Jonas ◽  
Marc Olefs ◽  
...  
Keyword(s):  
Snow Depth ◽  
European Alps

Geographic Variation in Habitat Structure of the Black-Throated Green Warbler (Dendroica virens)

The Auk ◽  
1983 ◽  
Vol 100 (2) ◽  
pp. 382-389 ◽  
Author(s):  
Scott L. Collins
Keyword(s):  
New York ◽  
Plant Community ◽  
Habitat Structure ◽  
Habitat Management ◽  
Principal Component ◽  
Structural Features ◽  
Wide Range ◽  
White Mountain National Forest ◽  
Study Sites ◽  
Regional Analyses

Abstract Habitat structure of the Black-throated Green Warbler (Dendroica virens) was examined at five study sites: (1) Mount Desert Island, Maine; (2) Mount Blue State Park, Maine; (3) White Mountain National Forest, New Hampshire; (4) southern Adirondacks, New York; and (5) Itasca State Park, Minnesota. Principal component analysis of 13 habitat-structure variables measured at each site produced habitat gradients from tall to shorter canopies, large to smaller trees, and coniferous to deciduous forests. A second ordination indicated that the habitat sampled included five plant-community types: pine forests, spruce-arbor vitae, balsam fir, mixed spruce-fir-deciduous, and beech-maple-birch. Consistent structural features within the total range of habitats sampled were difficult to identify. I suggest that widely occurring species such as the Black-throated Green Warbler have a wide range of habitats with a suitable structure and that regional analyses, even within a single plant-community type, may be of limited value with regard to habitat management when considering the entire range of many species.

Trends and Natural Variability of Spring Onset in the Coterminous United States as Evaluated by a New Gridded Dataset of Spring Indices

2015 ◽  
Vol 28 (21) ◽  
pp. 8363-8378 ◽  
Author(s):  
Toby R. Ault ◽  
Mark D. Schwartz ◽  
Raul Zurita-Milla ◽  
Jake F. Weltzin ◽  
Julio L. Betancourt
Keyword(s):  
United States ◽  
Large Scale ◽  
Meteorological Data ◽  
Ecological Systems ◽  
Agricultural Activity ◽  
Activity Tracking ◽  
Modes Of Variability ◽  
Gridded Dataset ◽  
Regional Trends ◽  
Consistent Manner

Abstract Climate change is expected to modify the timing of seasonal transitions this century, impacting wildlife migrations, ecosystem function, and agricultural activity. Tracking seasonal transitions in a consistent manner across space and through time requires indices that can be used for monitoring and managing biophysical and ecological systems during the coming decades. Here a new gridded dataset of spring indices is described and used to understand interannual, decadal, and secular trends across the coterminous United States. This dataset is derived from daily interpolated meteorological data, and the results are compared with historical station data to ensure the trends and variations are robust. Regional trends in the first leaf index range from −0.8 to −1.6 days decade−1, while first bloom index trends are between −0.4 and −1.2 for most regions. However, these trends are modulated by interannual to multidecadal variations, which are substantial throughout the regions considered here. These findings emphasize the important role large-scale climate modes of variability play in modulating spring onset on interannual to multidecadal time scales. Finally, there is some potential for successful subseasonal forecasts of spring onset, as indices from most regions are significantly correlated with antecedent large-scale modes of variability.

scholarly journals Exploring Atmosphere–Ocean Coupling Using Principal Component and Redundancy Analysis

2010 ◽  
Vol 23 (18) ◽  
pp. 4926-4943 ◽  
Author(s):  
Faez Bakalian ◽  
Harold Ritchie ◽  
Keith Thompson ◽  
William Merryfield
Keyword(s):  
Random Vector ◽  
Redundancy Analysis ◽  
Climate Model ◽  
Climate Modeling ◽  
State Space Model ◽  
Principal Component ◽  
Climate Science ◽  
Coupled Modes ◽  
Modes Of Variability ◽  
Advantages And Disadvantages

Abstract Principal component analysis (PCA), which is designed to look at internal modes of variability, has often been applied beyond its intended design to study coupled modes of variability in combined datasets, also referred to as combined PCA. There are statistical techniques better suited for this purpose such as singular value decomposition (SVD) and canonical correlation analysis (CCA). In this paper, a different technique is examined that has not often been applied in climate science, that is, redundancy analysis (RA). Similar to multivariate regression, RA seeks to maximize the variance accounted for in one random vector that is linearly regressed against another random vector. RA can be used for forecasting and prediction studies of the climate system. This technique has the added advantage that the time-lagged redundancy index offers a robust method of identifying lead–lag relations among climate variables. In this study, combined PCA and RA of global sea surface temperatures (SSTs) and sea level pressures (SLPs) are carried out for the National Centers for Environmental Prediction (NCEP) reanalysis data and a simulation of the Canadian Centre for Climate Modeling and Analysis (CCCma) climate model. A simplified state-space model is also constructed to aid in the diagnosis and interpretation of the results. The relative advantages and disadvantages of combined PCA and RA are discussed. Overall, RA tends to provide a clearer and more consistent picture of the underlying physical processes than combined PCA.

scholarly journals Streamflow droughts in the Iberian Peninsula between 1945 and 2005: spatial and temporal patterns

2012 ◽  
Vol 9 (7) ◽  
pp. 8063-8103 ◽  
Author(s):  
J. Lorenzo-Lacruz ◽  
E. Morán-Tejeda ◽  
S. M. Vicente-Serrano ◽  
J. I. López-Moreno
Keyword(s):  
Iberian Peninsula ◽  
Principal Component ◽  
Threshold Level ◽  
Climatic Conditions ◽  
Drought Severity ◽  
Spatial And Temporal Patterns ◽  
Drought Duration ◽  
Spatial Coverage ◽  
Duration Magnitude ◽  
High Level

Abstract. In this study we analyzed the spatio-temporal variability of streamflow droughts in the Iberian Peninsula from 1945 to 2005. Streamflow series (187) homogeneously distributed across the study area were used to develop a streamflow index (the Standardized Streamflow Index; SSI), which facilitated comparison among regimes and basins, regardless of streamflow magnitudes. A principal component analysis was performed to identify homogeneous hydrological regions having common features, based on the temporal evolution of streamflows. Identification of drought events was carried out using a threshold level approach. We assessed the duration and magnitude of drought episodes and the changes that occurred between two contrasting periods for each hydrological region. The results showed a trend to greater drought severity in the majority of regions. Drought duration, magnitude and spatial coverage was found to depend mainly on the climatic conditions and the water storage strategies in each basin. In some basins these strategies have altered river regimes, and in others created a high level of dependence on storage and water transfer rates.

scholarly journals Principal Metabolic Flux Mode Analysis

2017 ◽  
Author(s):  
Sahely Bhadra ◽  
Peter Blomberg ◽  
Sandra Castillo ◽  
Juho Rousu
Keyword(s):  
Metabolic Flux ◽  
Principal Component ◽  
Supplementary Information ◽  
Mode Analysis ◽  
Flux Analysis ◽  
Large Set ◽  
Elementary Mode Analysis ◽  
Modes Of Variability ◽  
Link Type ◽  
Flux Mode

AbstractMotivationIn the analysis of metabolism using omics data, two distinct and complementary approaches are frequently used: Principal component analysis (PCA) and Stoichiometric flux analysis. PCA is able to capture the main modes of variability in a set of experiments and does not make many prior assumptions about the data, but does not inherently take into account the flux mode structure of metabolism. Stoichiometric flux analysis methods, such as Flux Balance Analysis (FBA) and Elementary Mode Analysis, on the other hand, produce results that are readily interpretable in terms of metabolic flux modes, however, they are not best suited for exploratory analysis on a large set of samples.ResultsWe propose a new methodology for the analysis of metabolism, called Principal Metabolic Flux Mode Analysis (PMFA), which marries the PCA and Stoichiometric flux analysis approaches in an elegant regularized optimization framework. In short, the method incorporates a variance maximization objective form PCA coupled with a Stoichiometric regularizer, which penalizes projections that are far from any flux modes of the network. For interpretability, we also introduce a sparse variant of PMFA that favours flux modes that contain a small number of reactions. Our experiments demonstrate the versatility and capabilities of our methodology.AvailabilityMatlab software for PMFA and SPMFA is available in https://github.com/ aalto-ics-kepaco/[email protected], [email protected], [email protected], [email protected] informationDetailed results are in Supplementary files. Supplementary data are available at https://github.com/aalto-ics-kepaco/PMFA/blob/master/Results.zip.

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