scholarly journals Relationship between Spatiotemporal Variations of Climate, Snow Cover and Plant Phenology over the Alps—An Earth Observation-Based Analysis

2018 ◽  
Vol 10 (11) ◽  
pp. 1757 ◽  
Author(s):  
Sarah Asam ◽  
Mattia Callegari ◽  
Michael Matiu ◽  
Giuseppe Fiore ◽  
Ludovica De Gregorio ◽  
...  

Alpine ecosystems are particularly sensitive to climate change, and therefore it is of significant interest to understand the relationships between phenology and its seasonal drivers in mountain areas. However, no alpine-wide assessment on the relationship between land surface phenology (LSP) patterns and its climatic drivers including snow exists. Here, an assessment of the influence of snow cover variations on vegetation phenology is presented, which is based on a 17-year time-series of MODIS data. From this data snow cover duration (SCD) and phenology metrics based on the Normalized Difference Vegetation Index (NDVI) have been extracted at 250 m resolution for the entire European Alps. The combined influence of additional climate drivers on phenology are shown on a regional scale for the Italian province of South Tyrol using reanalyzed climate data. The relationship between vegetation and snow metrics strongly depended on altitude. Temporal trends towards an earlier onset of vegetation growth, increasing monthly mean NDVI in spring and late summer, as well as shorter SCD were observed, but they were mostly non-significant and the magnitude of these tendencies differed by altitude. Significant negative correlations between monthly mean NDVI and SCD were observed for 15–55% of all vegetated pixels, especially from December to April and in altitudes from 1000–2000 m. On the regional scale of South Tyrol, the seasonality of NDVI and SCD achieved the highest share of correlating pixels above 1500 m, while at lower elevations mean temperature correlated best. Examining the combined effect of climate variables, for average altitude and exposition, SCD had the highest effect on NDVI, followed by mean temperature and radiation. The presented analysis allows to assess the spatiotemporal patterns of earth-observation based snow and vegetation metrics over the Alps, as well as to understand the relative importance of snow as phenological driver with respect to other climate variables.

2021 ◽  
Author(s):  
Martin Ménégoz ◽  
Julien Beaumet ◽  
Hubert Gallée ◽  
Xavier Fettweis ◽  
Samuel Morin ◽  
...  

<p>The evolution of temperature, precipitation and snow cover in the European Alps have been simulated with the regional climate model MAR applied with a 7 kilometre horizontal resolution and driven by the ERA-20C (1902-2010) and the ERA5 reanalyses (1981-2018). A comparison with observational datasets, including French and Swiss local meteorological stations, in-situ glacier mass balance measurements and reanalysis product demonstrates high model skill for snow cover duration and snow water equivalent (SWE) as well as for the climatology and the inter-annual variability of both temperature and precipitation. The relatively high resolution allows to estimate the meteorological variables up to 3000m.a.s.l. The vertical gradient of precipitation simulated by MAR over the European Alps reaches 33% km-1 (1.21 mmd-1.km-1) in summer and 38%km-1 (1.15mmd mmd-1.km-1) in winter, on average over 1971–2008 and shows a large spatial variability. This study evidences seasonal and altitudinal contrasts of climate trends over the Alps. A significant (pvalue< 0.05) increase in mean winter precipitation is simulated in the northwestern Alps over 1903–2010, with changes typically reaching 20% to 40% per century, a signal strongly modulated by multi-decadal variability during the second part of the century. A general drying is found in summer over the same period, exceeding 20% to 30% per century in the western plains and 40% to 50% per century in the southern plains surrounding the Alps but remaining smaller (<10%) and not significant above 1500ma.s.l. Over 1903–2010, the maximum of daily precipitation (Rx1day) shows a general and significant increase at the annual timescale and also during the four seasons, reaching local values between 20% and 40% per century over large parts of the Alps and the Apennines. Trends of Rx1day are significant (pvalue<0.05) only when considering long time series, typically 50 to 80 years depending on the area considered. Some of these trends are nonetheless significant when computed over 1970–2010, suggesting a recent acceleration of the increase in extreme precipitation. Rx1day increase occurs where the annual correlation between temperature and intense precipitation is high. The highest warming rates in MAR are found at low elevations (< 1000 m a.s.l) in winter, whereas they are found at high elevations (> 2000 m a.s.l) in summer. In spring, warming trends show a maximum at intermediate elevations (1500 m to 1800 m). Our results suggest that higher warming at these elevations is mostly linked with the snow-albedo feedback in spring and summer.</p>


2013 ◽  
Vol 7 (3) ◽  
pp. 3001-3042 ◽  
Author(s):  
F. Hüsler ◽  
T. Jonas ◽  
M. Riffler ◽  
J. P. Musial ◽  
S. Wunderle

Abstract. Seasonal snow cover is of great environmental and socio-economic importance for the European Alps. Therefore a high priority has been assigned to quantifying its temporal and spatial variability. Complementary to land-based monitoring networks, optical satellite observations can be used to derive spatially comprehensive information on snow cover extent. For understanding long-term changes in alpine snow cover extent, the data acquired by the Advanced Very High Resolution Radiometer (AVHRR) sensors mounted onboard the National Oceanic and Atmospheric Association (NOAA) and Meteorological Operational satellite (MetOp) platforms offer a~unique source of information. In this paper, we present the first space-borne 1 km snow extent climatology for the Alpine region derived from AVHRR data over the period 1985–2011. The objective of this study is twofold: first, to generate a new set of cloud-free satellite snow products using a specific cloud gap-filling technique and second, to examine the spatiotemporal distribution of snow cover in the European Alps over the last 27 yr from the satellite perspective. For this purpose, snow parameters such as snow onset day, snow cover duration (SCD), melt-out date and the snow cover area percentage (SCA) were employed to analyze spatio-temporal variability of snow cover over the course of 3 decades. On the regional scale, significant trends were found toward a shorter SCD at lower elevations in the south-east and south-west. However, our results do not show any significant trends in the monthly mean SCA over the last 27 yr. This is in agreement with other research findings and may indicate a~deceleration of the decreasing snow trend in the Alpine region. Given the importance of mountain regions for climate change assessment, this study recommends the complementary use of remote sensing data for long-term snow applications. It bears the potential to provide spatially and temporally comprehensive snow information for use in related research fields or to serve as a reference for climate models.


2004 ◽  
Vol 38 ◽  
pp. 245-252 ◽  
Author(s):  
Nando Foppa ◽  
Stefan Wunderle ◽  
David Oesch ◽  
Florian Kuchen

AbstractThis study is part of research activities concentrating on the real-time application of the U.S. National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) sensor for snow-cover analysis of the European Alps. For mapping snow cover in heterogeneous terrain, we implement the widely used linear spectral mixture algorithm to estimate snow cover at sub-pixel scale. Principal component analysis, including the reflective part of AVHRR channel 3, is used to estimate fractions of “snow” and “not snow” within a pixel, using linear mixture modeling. The combination of these features leads to a fast, simple solution for operational and near-real-time processing. The presented algorithm is applied on the European Alps on 17 January 2003 and successfully maps snow at sub-pixel scale. The detailed snow-cover information makes it easy to recognize the complex topography of the Alps, more so than with either a classic binary map or a Moderate Resolution Imaging Spectroradiometer (MODIS) snow product. The sub-pixel algorithm reasonably identifies snow-cover fractions in regions and at altitudes where neither the classic binary map nor the MODIS algorithm detects any snow. Differences concerning the snow distribution are found in forested areas as well as in the lowest-elevation zones. The algorithm substantially improves snow mapping over complex topography for operational and near-realtime applications.


2021 ◽  
Vol 13 (21) ◽  
pp. 4316
Author(s):  
Mariapina Castelli

In the Alps, understanding how climate change is affecting evapotranspiration (ET) is relevant due to possible implications on water availability for large lowland areas of Europe. Here, changes in ET were studied based on 20 years of MODIS data. MOD16 and operational Simplified Surface Energy Balance (SSEBop) products were compared with eddy-covariance data and analyzed for trend detection. The two products showed a similar relationship with ground observations, with RMSE between 0.69 and 2 mm day−1, and a correlation coefficient between 0.6 and 0.83. A regression with the potential drivers of ET showed that, for climate variables, ground data were coherent with MOD16 at grassland sites, where r2 was 0.12 for potential ET, 0.17 for precipitation, and 0.57 for air temperature, whereas ground data agreed with SSEBop at forest sites, with an r2 of 0.46 for precipitation, no correlation with temperature, and negative correlation with potential ET. Interestingly, ground-based correlation corresponded to SSEBop for leaf area index (LAI), while it matched with MOD16 for land surface temperature (LST). Through the trend analysis, both MOD16 and SSEBop revealed positive trends in the south-west, and negative trends in the south and north-east. Moreover, in summer, positive trends prevailed at high elevations for grasslands and forests, while negative trends dominated at low elevations for croplands and grasslands. However, the Alpine area share with positive ET trends was 16.6% for MOD16 and 3.9% for SSEBop, while the share with negative trends was 1.2% for MOD16 and 15.3% for SSEBop. A regression between trends in ET and in climate variables, LST, and LAI indicated consistency, especially between ET, temperature, and LAI increase, but low correlation. Overall, the discrepancies in the trends, and the fact that none of the two products outperformed the other when compared to ground data, suggest that, in the Alps, SSEBop and MOD16 might not be accurate enough to be a robust basis to study ET changes.


2014 ◽  
Vol 8 (1) ◽  
pp. 73-90 ◽  
Author(s):  
F. Hüsler ◽  
T. Jonas ◽  
M. Riffler ◽  
J. P. Musial ◽  
S. Wunderle

Abstract. Seasonal snow cover is of great environmental and socio-economic importance for the European Alps. Therefore a high priority has been assigned to quantifying its temporal and spatial variability. Complementary to land-based monitoring networks, optical satellite observations can be used to derive spatially comprehensive information on snow cover extent. For understanding long-term changes in alpine snow cover extent, the data acquired by the Advanced Very High Resolution Radiometer (AVHRR) sensors mounted onboard the National Oceanic and Atmospheric Association (NOAA) and Meteorological Operational satellite (MetOp) platforms offer a unique source of information. In this paper, we present the first space-borne 1 km snow extent climatology for the Alpine region derived from AVHRR data over the period 1985–2011. The objective of this study is twofold: first, to generate a new set of cloud-free satellite snow products using a specific cloud gap-filling technique and second, to examine the spatiotemporal distribution of snow cover in the European Alps over the last 27 yr from the satellite perspective. For this purpose, snow parameters such as snow onset day, snow cover duration (SCD), melt-out date and the snow cover area percentage (SCA) were employed to analyze spatiotemporal variability of snow cover over the course of three decades. On the regional scale, significant trends were found toward a shorter SCD at lower elevations in the south-east and south-west. However, our results do not show any significant trends in the monthly mean SCA over the last 27 yr. This is in agreement with other research findings and may indicate a deceleration of the decreasing snow trend in the Alpine region. Furthermore, such data may provide spatially and temporally homogeneous snow information for comprehensive use in related research fields (i.e., hydrologic and economic applications) or can serve as a reference for climate models.


Author(s):  
Wolfgang Schöner

Glaciers are probably the most obvious features of Earth’s changing climate. They enable one to see the effects of a warming or a cooling of the atmosphere by landscape changes on time scales short enough to be perceived or recognized by humans. However, the relationship between a retreating and advancing glacier and the climate is not linear, as glacier flow can filter the direct signal of the climate. Thus, glaciers can advance during periods of warming or, vice versa, retreat during periods of cooling. In fact, it is the mass change of the glacier (i.e., the mass balance) that directly links the glacier reaction to an atmospheric signal. The mechanism-based understanding of the relationship between the changing climate and glacier reaction received important and significant momentum from the science of the Alpine region. This strong momentum from the Alps has to do with the well-established science tradition in Europe in the 19th and beginning of the 20th century, which resulted in a series of important inventions to measure climate and glacier properties. Even at that time, knowledge was gained that is still valid in the early 21st century (e.g., the climate is changing and fluctuating; glacier changes are caused by changing climate; and the ice age was the result of shifting climate). Above all others, Albrecht Penck and Eduard Brückner were the key scientists in this blossoming era of glacier climatology. Interest in a better understanding of the relationship of climate to glaciers was not only driven by curiosity, but also by several impacts of glaciers on human life in the Alps. Investigations of climate–glacier relationships in the Alps began with the expiration of the Little Ice Age (LIA) period when glaciers were particularly large but began to retreat significantly. Observations of post-LIA glacier front positions showed a sharp decline after their maximum extent in about 1850 until the turn of the 19th to 20th centuries, when they began to grow and advance again. They were also forming a prominent moraine around 1920, which was, however, far behind the 1850 extent. Interestingly, climate time series of the post LIA period show a general long-term cooling of summer temperatures and several decades of precipitation deficit in the second half of the 19th century. Thus, the retreat forced by climate changes cannot be simply explained by increasing air temperatures, though calibrated glacier mass balance models are able to simulate this period quite well. Additional effects related to the albedo could be a source for a better understanding. From 1920 onward, the climate moved into a period of warm and high-sunshine summers, which peaked in the 1940s until 1950. Glaciers started again to melt strongly and related discharges of pro-glacial rivers were exceptionally high during this period as glaciers were still quite large and the available energy for melt from radiation was enhanced. With the shift of the Atlantic meridional overturning (AMO), which is an important driver of European climate, into a negative mode in the 1960s, the mass balances of Alpine glaciers experienced more and more positive mass balance years. This finally resulted in a period of advancing glaciers and the development of frontal moraines around 1980 for a large number of glaciers. Thereafter, from 1980 onward, Alpine glaciers moved into an era of continuous negative mass balances and particularly strong retreat. The anthropogenic forcing from greenhouse gases together with global brightening and the increase of anticyclonic weather types in summer moved the climate and thus the mass balances of glaciers into a state far away from equilibrium. Given available scenarios of future climate, this retreat will continue and, even under the optimistic RCP2.6 scenario, glaciers (as derived from model simulations for the future) will not return to an equilibrium mass balance before the end of the 21st century. According to a glacier inventory for the European Alps from Landsat Thematic Mapper scenes of 2003, published by Paul and coworkers in 2011, the total surface of all glaciers and ice patches in the European Alps in 2003 was 2,056 km² (50% in Switzerland, 19% in Italy, 18% in Austria, 13% in France, and <1% in Germany). Generally, the reaction of Alpine glaciers to climate perturbations is rather well understood. For the glaciers of the Alps, important processes of glacier changes are related to the surface energy balance during the ablation season when radiation is the primary source of energy for snow and ice melt. Other ablation processes, such as sublimation and internal and basal ablation, are small compared to surface melt. This specificity enables the use of simple temperature-based models to simulate the mass balance of glaciers sufficiently well. Besides atmospheric forcing of glacier mass balance, glacier flow (which is related to englacial temperature distribution) plays a role, in particular, for observed front position changes of glaciers. Glaciers are continuously adapting their size to the climate, which could work much faster for smaller glaciers compared to large valley glaciers of the Alps having a response time of about 100 years.


Author(s):  
Mark Vellend

This chapter highlights the scale dependence of biodiversity change over time and its consequences for arguments about the instrumental value of biodiversity. While biodiversity is in decline on a global scale, the temporal trends on regional and local scales include cases of biodiversity increase, no change, and decline. Environmental change, anthropogenic or otherwise, causes both local extirpation and colonization of species, and thus turnover in species composition, but not necessarily declines in biodiversity. In some situations, such as plants at the regional scale, human-mediated colonizations have greatly outnumbered extinctions, thus causing a marked increase in species richness. Since the potential influence of biodiversity on ecosystem function and services is mediated to a large degree by local or neighborhood species interactions, these results challenge the generality of the argument that biodiversity loss is putting at risk the ecosystem service benefits people receive from nature.


Water ◽  
2021 ◽  
Vol 13 (16) ◽  
pp. 2161
Author(s):  
Fuxiang Zhang ◽  
Bo Meng ◽  
Shang Gao ◽  
Rupert Hough ◽  
Peng Hu ◽  
...  

Snow cover is a unique environmental medium in cold regions that can cause potential risks to the ecological environment, due to the release of pollutants that are stored in it. In this study, the Qixing River wetland, located in the Sanjiang Plain of China, was taken as the target research area. Heavy metals in snow cover, including Cu, Ni, Cr, Cd, Pb, and Zn were measured at 19 sampling sites. The results showed that the average concentrations of heavy metals were: Zn (103.46 ± 39.16) > Pb (13.08 ± 4.99) > Cr (11.97 ± 2.82) > Ni (9.55 ± 4.96) > Cu (6.19 ± 1.79) > Cd (0.55 ± 0.25) μg·L−1. Cr and Zn were between Class I and Class II in the “Environmental Quality Standards for Surface Water” of China (GB3838-2002). Pb in snow exceeded the upper limit of Class II, and was significantly higher than concentrations measured in water samples from the Qixing River wetland (p < 0.05), indicating that atmospheric deposition during winter was the major source of Pb. The water pollution index (WPI) indicated that 61.0% of samples could be considered of “clean” status, while the contribution of Zn, Pb, and Cr to WPI were 33.3%, 21.0%, and 19.3%, respectively. A preliminary evaluation of heavy metal inventory in snow cover showed that the residue level of Zn was the highest (2313.57 ± 1194.67 μg·m−2), while Cd was the lowest (13.91 ± 10.45 μg·m−2). The areas with high residues of heavy metals were all located near the buffer zone of the wetland (except for Zn), where snow depth tended to be greatest. Exposure analysis indicated that the risks to winter resident birds from snow ingestion was minimal, but it should be noted that the exposure risk was higher in birds with lower bodyweights. This study provides important information and scientific knowledge on the pollution characteristics and residue inventory of heavy metals in wetland ecosystems, while the results can also provide a monitoring method, reflecting atmospheric environmental quality at a local or regional scale.


2021 ◽  
Vol 13 (9) ◽  
pp. 1835
Author(s):  
Yared Bayissa ◽  
Semu Moges ◽  
Assefa Melesse ◽  
Tsegaye Tadesse ◽  
Anteneh Z. Abiy ◽  
...  

Drought is one of the least understood and complex natural hazards often characterized by a significant decrease in water availability for a prolonged period. It can be manifested in one or more forms as meteorological, agricultural, hydrological, and/or socio-economic drought. The overarching objective of this study is to demonstrate and characterize the different forms of droughts and to assess the multidimensional nature of drought in the Abbay/ Upper Blue Nile River (UBN) basin and its national and regional scale implications. In this study, multiple drought indices derived from in situ and earth observation-based hydro-climatic variables were used. The meteorological drought was characterized using the Standardized Precipitation Index (SPI) computed from the earth observation-based gridded CHIRPS (Climate Hazards Group InfraRed Precipitation with Station) rainfall data. Agricultural and hydrological droughts were characterized by using the Soil Moisture Deficit Index (SMDI) and Standardized Runoff-discharge Index (SRI), respectively. The monthly time series of SMDI was derived from model-based gridded soil moisture and SRI from observed streamflow data from 1982 to 2019. The preliminary result illustrates the good performance of the drought indices in capturing the historic severe drought events (e.g., 1984 and 2002) and the spatial extents across the basin. The results further indicated that all forms of droughts (i.e., meteorological, agricultural, and hydrological) occurred concurrently in Abbay/Upper Blue Nile basin with a Pearson correlation coefficient ranges from 0.5 to 0.85 both Kiremt and annual aggregate periods. The concurrent nature of drought is leading to a multi-dimensional socio-economic crisis as indicated by rainfall, and soil moisture deficits, and drying of small streams. Multi-dimensional drought mitigation necessitates regional cooperation and watershed management to protect both the common water sources of the Abbay/Upper Blue Nile basin and the socio-economic activities of the society in the basin. This study also underlines the need for multi-scale drought monitoring and management practices in the basin.


2021 ◽  
Vol 13 (9) ◽  
pp. 1843
Author(s):  
Xiaona Chen ◽  
Yaping Yang ◽  
Yingzhao Ma ◽  
Huan Li

Snow cover phenology has exhibited dramatic changes in the past decades. However, the distribution and attribution of the hemispheric scale snow cover phenology anomalies remain unclear. Using satellite-retrieved snow cover products, ground observations, and reanalysis climate variables, this study explored the distribution and attribution of snow onset date, snow end date, and snow duration days over the Northern Hemisphere from 2001 to 2020. The latitudinal and altitudinal distributions of the 20-year averaged snow onset date, snow end date, and snow duration days are well represented by satellite-retrieved snow cover phenology matrixes. The validation results by using 850 ground snow stations demonstrated that satellite-retrieved snow cover phenology matrixes capture the spatial variability of the snow onset date, snow end date, and snow duration days at the 95% significance level during the overlapping period of 2001–2017. Moreover, a delayed snow onset date and an earlier snow end date (1.12 days decade−1, p < 0.05) are detected over the Northern Hemisphere during 2001–2020 based on the satellite-retrieved snow cover phenology matrixes. In addition, the attribution analysis indicated that snow end date dominates snow cover phenology changes and that an increased melting season temperature is the key driving factor of snow end date anomalies over the NH during 2001–2020. These results are helpful in understanding recent snow cover change and can contribute to climate projection studies.


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