Land surface phenology and greenness in Alpine grasslands driven by seasonal snow and meteorological factors

Xie, Jing; Jonas, Tobias; Rixen, Christian; Jong, Rogier de; Garonna, Irene; Notarnicola, Claudia; Asam, Sarah; Schaepman, Michael E.; Kneubühler, Mathias

doi:10.1016/j.scitotenv.2020.138380

Cited by 31 publications

(31 citation statements)

References 109 publications

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“…The influence of spring temperature on spring phenology in forests (with average r of −0.18 to −0.56 in 17.9%–23.4% NV areas) is similar to that in grasslands (with average r of −0.16 to −0.57 in 18.3%–23.1% NV areas). Our findings are in agreement with previous reports stating that the influence of ST on SOS differs between topographies such as elevation and region (Alikadic et al., 2019; Asam et al., 2018; Pellerin et al., 2012; Vitasse et al., 2018; Xie et al., 2020). The influence of spring temperature on spring phenology is more pronounced in the NE subregion (Figure 4 and Figure ) and at mid elevations between 1,000 and 2,500 m asl.…”

Section: Discussionsupporting

confidence: 94%

“…factors at landscape level, which is due to a relatively short time span of the commonly used data, as well as their limited spatial coverage considered across the European Alps (Xie et al, 2017(Xie et al, , 2018(Xie et al, , 2020. SOS is particularly sensitive to inter-annual variations of climatic factors (Bennie et al, 2018;Fu et al, 2015;Wipf & Rixen, 2010), especially in winter and spring (Euskirchen et al, 2007).…”

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confidence: 99%

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Spring Temperature and Snow Cover Climatology Drive the Advanced Springtime Phenology (1991–2014) in the European Alps

et al. 2021

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The average global temperature has been reported rising in recent decades, particularly in mountainous regions (IPCC, 2018, 2018). In the European Alps, the average temperature has increased by about 1.1°C over the past 100 years (Böhm et al., 2001). The European Alps were reported to be particularly sensitive to inter-annual variations in climatic drivers such as temperature, precipitation, and solar radiation (Beniston et al., 2003; Gobiet et al., 2014), as well as, depending on elevation, snow cover extent and its depth (Jonas et al., 2008; Xie et al., 2017). Recent climatic warming was reported to have led to considerable variance and shifts in plant phenology across ecosystems (Graven et al., 2013; Parmesan, 2006; Parmesan & Yohe, 2003) and thus may threaten species with synchronized life cycles (Dozier & Painter, 2004; Post et al., 2009). The firm link between phenological changes and seasonal temperature variability suggests that broad-scale and long-term phenology observations could serve as a proxy for detecting and tracing global climate change over space and time (

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Section: Discussionsupporting

confidence: 94%

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confidence: 99%

Spring Temperature and Snow Cover Climatology Drive the Advanced Springtime Phenology (1991–2014) in the European Alps

et al. 2021

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“…Like SCF, SDD has been used in wildlife studies for locations where spring snow disappearance affects the survival of young (Van De Kerk et al, 2018). We anticipate that SDD will have value for mountain regions where SDD has been related to the onset of the wildfire season (Westerling et al, 2006) and where spring vegetation phenology varies with snow cover (Huang et al, 2018; Xie et al, 2020).…”

Section: Descriptions Of the New Snow Metricsmentioning

confidence: 99%

New snow metrics for a warming world

Nolin

Sproles

Rupp

et al. 2021

Hydrological Processes

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Snow is Earth's most climatically sensitive land cover type. Traditional snow metrics may not be able to adequately capture the changing nature of snow cover. For example, April 1 snow water equivalent (SWE) has been an effective index for streamflow forecasting, but it cannot express the effects of midwinter melt events, now expected in warming snow climates, nor can we assume that station‐based measurements will be representative of snow conditions in future decades. Remote sensing and climate model data provide capacity for a suite of multi‐use snow metrics from local to global scales. Such indicators need to be simple enough to “tell the story” of snowpack changes over space and time, but not overly simplistic or overly complicated in their interpretation. We describe a suite of spatially explicit, multi‐temporal snow metrics based on global satellite data from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) and downscaled climate model output for the U.S. We describe and provide examples for Snow Cover Frequency (SCF), Snow Disappearance Date (SDD), At‐Risk Snow (ARS), and Frequency of a Warm Winter (FWW). Using these retrospective and prospective snow metrics, we assess the current and future snow‐related conditions in three hydroclimatically different U.S. watersheds: the Truckee, Colorado Headwaters, and Upper Connecticut. In the two western U.S. watersheds, SCF and SDD show greater sensitivity to annual differences in snow cover compared with data from the ground‐based Snow Telemetry (SNOTEL) network. The eastern U.S. watershed does not have a ground‐based network of data, so these MODIS‐derived metrics provide uniquely valuable snow information. The ARS and FWW metrics show that the Truckee Watershed is highly vulnerable to conversion from snowfall to rainfall (ARS) and midwinter melt events (FWW) throughout the seasonal snow zone. In comparison, the Colorado Headwaters and Upper Connecticut Watersheds are colder and much less vulnerable through mid‐ and late‐century.

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“…In the last few years, many works have been published in the literature presenting approaches, or case studies, about this topic with a significant improvement [2][3][4]. With special concern about mountain rangelands, their phenology at high altitudes is mainly related to snowpack that consequently, because of climate change, is drastically reducing [5]. Snowpack reduction depends on many factors related to the global, regional, and local climate.…”

Section: Introductionmentioning

confidence: 99%

Exploring Short-Term Climate Change Effects on Rangelands and Broad-Leaved Forests by Free Satellite Data in Aosta Valley (Northwest Italy)

Orusa

Mondino

2021

Climate

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Satellite remote sensing is a power tool for the long-term monitoring of vegetation. This work, with reference to a regional case study, investigates remote sensing potentialities for describing the annual phenology of rangelands and broad-leaved forests at the landscape level with the aim of detecting eventual effects of climate change in the Alpine region of the Aosta Valley (Northwest (NW) Italy). A first analysis was aimed at estimating phenological metrics (PMs) from satellite images time series and testing the presence of trends along time. A further investigation concerned evapotranspiration from vegetation (ET) and its variation along the years. Additionally, in both the cases the following meteorological patterns were considered: air temperature anomalies, precipitation trends and the timing of yearly seasonal snow melt. The analysis was based on the time series (TS) of different MODIS collections datasets together with Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) collection obtained through Google Earth Engine. Ground weather stations data from the Centro Funzionale VdA ranging from 2000 to 2019 were used. In particular, the MOD13Q1 v.6, MOD16A2 and MOD10A1 v.6 collections were used to derive PMs, ET and snow cover maps. The SRTM (shuttle radar topography mission) DTM (digital terrain model) was also used to describe local topography while the Coordination of Information on the Environment (CORINE) land cover map was adopted to investigate land use classes. Averagely in the area, rangelands and broad-leaved forests showed that the length of season is getting longer, with a general advance of the SOS (start of the season) and a delay in the EOS (end of the season). With reference to ET, significant increasing trends were generally observed. The water requirement from vegetation appeared to have averagely risen about 0.05 Kg·m−2 (about 0.5%) per year in the period 2000–2019, for a total increase of about 1 Kg·m−2 in 20 years (corresponding to a percentage difference in water requirement from vegetation of about 8%). This aspect can be particularly relevant in the bottom of the central valley, where the precipitations have shown a statistically significant decreasing trend in the period 2000–2019 (conversely, no significant variation was found in the whole territory). Additionally, the snowpack timing persistence showed a general reduction trend. PMs and ET and air temperature anomalies, as well as snow cover melting, proved to have significantly changed their values in the last 20 years, with a continuous progressive trend. The results encourage the adoption of remote sensing to monitor climate change effects on alpine vegetation, with particular focus on the relationship between phenology and other abiotic factors permitting an effective technological transfer.

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Land surface phenology and greenness in Alpine grasslands driven by seasonal snow and meteorological factors

Cited by 31 publications

References 109 publications

Spring Temperature and Snow Cover Climatology Drive the Advanced Springtime Phenology (1991–2014) in the European Alps

Spring Temperature and Snow Cover Climatology Drive the Advanced Springtime Phenology (1991–2014) in the European Alps

New snow metrics for a warming world

Exploring Short-Term Climate Change Effects on Rangelands and Broad-Leaved Forests by Free Satellite Data in Aosta Valley (Northwest Italy)

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