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

Trade-off between spring phenological sensitivities to temperature and precipitation across species and space in alpine grasslands over the Qinghai-Tibetan Plateau

Elucidating climatic drivers of spring phenology in alpine grasslands is critical. However, current statistical estimates of spring phenological sensitivities to temperature and precipitation (βT and βP) might be biased and their variability across sites and species are not fully explained. We benchmarked species-level βT and βP statistically inferred from historical records with observations from a field manipulative experiment. We then analyzed landscape scale βT and βP estimated from the best statistical approach in the benchmark analysis across 57 alpine grassland sites in the Qinghai-Tibetan Plateau. Compared with manipulative experiment results, process-agnostic regression-based approaches underestimate βT by 2.36-3.87 d °C-1 (54-88%) while process-based phenology model fitting predicts comparable βT and βP. Process-based estimates of βT and βP are negatively correlated across species (R = -0.94, P < 0.01) and across sites (R = -0.45, P < 0.01). βT is positively correlated with mean annual temperature, and βP is negatively correlated with elevation at the regional scale. Using process-based model fitting can better estimate spring phenological sensitivities to climate. The trade-off between βT and βP contributes to species-level and site-level variabilities in phenological sensitivities in alpine grasslands, which needs to be incorporated in predicting future phenological changes.

The Relationship Between Phenological Characteristics and Life Forms Within Temperate Semi-Natural Grassland Ecosystems in the Central Himalaya Region of India

The seasonal phenological segregation observed among various species within a plant community can be interpreted as a form of niche differentiation that facilitates the coexistence of these species. In the present study, life forms and phenological attributes of dominant plant species in temperate semi-natural grasslands of Central Himalaya, India, were assessed between January 2022 and December 2022. This study was carried out in three sites in different forest zones, viz. oak, cypress and pine. In each site, plots measuring 0.5 hectares were established and phenological assessments were conducted within each of these plots. A total of 50, 36, and 49 herbaceous species were identified in the grasslands of oak, cypress and pine zones, respectively, with these species categorized into five distinct life form classes. In the grasslands of both oak and pine zones, hemicryptophytes emerged as the predominant life form, whereas in the cypress zone grasslands, it was found that chamaephytes take precedence. The differences observed in the classifications of life forms can be ascribed to the geographical distribution and the biotic interactions present in these sites. The three grasslands exhibit comparable climatic conditions and day lengths, resulting in no significant variations in soil temperature, light intensity or overall climatic factors. The majority of species commenced their flowering phase during the monsoon season, attributed to the favorable conditions characterized by warm, humid weather and adequate soil moisture. Various phenological events, including germination, growth, and senescence, are significantly affected by weather and climate, and their timing subsequently influences ecosystem processes in a reciprocal manner. This study provides valuable foundational data for ecological and environmental research, aiding in the comparison and distinction of plant compositions across the Himalayas and its ecosystems.

Later-melting rather than thickening of snowpack enhance the productivity and alter the community composition of temperate grassland

Snowpack is closely related to vegetation green-up in water-limited ecosystems, and has effects on growing-season ecosystem processes. However, we know little about how changes in snowpack depth and melting timing affect primary productivity and plant community structure during the growing season. Here, we conducted a four-year snow manipulation experiment exploring how snow addition, snowmelt delay and their combination affect aboveground net primary productivity (ANPP), species diversity, community composition and plant reproductive phenology in seasonally snow-covered temperate grassland in northern China. Snow addition alone increased soil moisture and nutrient availability during early spring, while did not change plant community structure and ANPP. Instead, snowmelt delay alone postponed plant reproductive phenology, and increased ANPP, decreased species diversity and altered species composition. Grasses are more sensitive to changes in snowmelt timing than forbs, and early-flowering forbs showed a higher sensitivity compared to late-flowering forbs. The effect of snowmelt delay on ANPP and species diversity was offset by snow addition, probably because the added snow unnecessarily lengthens the snow-covering duration. The disparate effects of changes in snowpack depth and snowmelt timing necessitate their discrimination for more mechanistic understanding on the effects of snowpack changes on ecosystems. Our study suggests that it is essential to incorporate non-growing-season climate change events (in particular, snowfall and snowpack changes) to comprehensively disclose the effects of climate change on community structure and ecosystem functions.

Effects of climate change on vegetation and snow cover area in Gilgit Baltistan using MODIS data

The Hindukush-Karakoram-Himalaya (HKH) mountain ranges are the sources of Asia's most important river systems, which provide fresh water to 1.4 billion inhabitants in the region. Environmental and socioeconomic conditions are affected in many ways by climate change. Globally, climate change has received widespread attention, especially regarding seasonal and annual temperatures. Snow cover is vulnerable to climate warming, particularly temperature variations. By employing Moderate Resolution Imaging Spectroradiometer (MODIS) datasets and observed data, this study investigated the seasonal and interannual variability using snow cover, vegetation and land surface temperature (LST), and their spatial and temporal trend on different elevations from 2001 to 2020 in these variables in Gilgit Baltistan (GB), northern Pakistan. The study region was categorized into five elevation zones extending from < 2000 to > 7000 masl. Non-parametric Mann-Kendall trend tests and Sen's slope estimates indicate snow cover increases throughout the winter, but decreases significantly between June and July. In contrast, GB has an overall increasing annual LST trend. Pearson correlation coefficient (PCC) reveals a significant positive relationship between vegetation and LST (PCC = 0.73) and a significant negative relationship between LST and snow cover (PCC =  - 0.74), and vegetation and snow cover (PCC =  - 0.78). Observed temperature data and MODIS LST have a coefficient of determination greater than 0.59. Snow cover decreases at 3000-2000 masl elevations while increases at higher  5000 masl elevations.The vegetation in low and mid-elevation < 4000 masl zones decreases significantly annually. The temperature shows a sharply increasing trend at lower 2000-3000 masl elevations in the autumn, indicating the shifting of the winter seasons at this elevation zone. These findings better explain the spatiotemporal variations in snow cover, vegetation, and LST at various elevation zones and the interactions between these parameters at various elevations across the HKH region.

Vessels in a Rhododendron ferrugineum (L.) population do not trace temperature anymore at the alpine shrubline

INTRODUCTION

Mean xylem vessel or tracheid area have been demonstrated to represent powerful proxies to better understand the response of woody plants to changing climatic conditions. Yet, to date, this approach has rarely been applied to shrubs.

METHODS

Here, we developed a multidecadal, annually-resolved chronology of vessel sizes for Rhododendron ferrugineum shrubs sampled at the upper shrubline (2,550 m asl) on a north-facing, inactive rock glacier in the Italian Alps.

RESULTS AND DISCUSSION

Over the 1960-1989 period, the vessel size chronology shares 64% of common variability with summer temperatures, thus confirming the potential of wood anatomical analyses on shrubs to track past climate variability in alpine environments above treeline. The strong winter precipitation signal recorded in the chronology also confirms the negative effect of long-lasting snow cover on shrub growth. By contrast, the loss of a climate-growth relation signal since the 1990s for both temperature and precipitation, significantly stronger than the one found in radial growth, contrasts with findings in other QWA studies according to which stable correlations between series of anatomical features and climatic parameters have been reported. In a context of global warming, we hypothesize that this signal loss might be induced by winter droughts, late frost, or complex relations between increasing air temperatures, permafrost degradation, and its impacts on shrub growth. We recommend future studies to validate these hypotheses on monitored rock glaciers.

Growth of alpine grassland will start and stop earlier under climate warming

Alpine plants have evolved a tight seasonal cycle of growth and senescence to cope with a short growing season. The potential growing season length (GSL) is increasing because of climate warming, possibly prolonging plant growth above- and belowground. We tested whether growth dynamics in typical alpine grassland are altered when the natural GSL (2-3 months) is experimentally advanced and thus, prolonged by 2-4 months. Additional summer months did not extend the growing period, as canopy browning started 34-41 days after the start of the season, even when GSL was more than doubled. Less than 10% of roots were produced during the added months, suggesting that root growth was as conservative as leaf growth. Few species showed a weak second greening under prolonged GSL, but not the dominant sedge. A longer growing season under future climate may therefore not extend growth in this widespread alpine community, but will foster species that follow a less strict phenology.

Optimal ranges of social-environmental drivers and their impacts on vegetation dynamics in Kazakhstan

Kazakhstan is part of the Eurasian Steppes, the world's largest contiguous grassland system. Kazakh grassland systems are largely understudied despite being historically important for agropastoral practices. These grasslands are considered vulnerable to anthropogenic activities and climatic variability. Few studies have examined vegetation dynamics in Central Asia owing to the complex impacts of moisture, climatic and anthropogenic forcings. A comprehensive analysis of spatiotemporal changes of vegetation and its driving factors will help elucidate the causes of grassland degradation. Here, we investigated the individual and pairwise interactive influences of various social-environmental system (SES) drivers on greenness dynamics in Kazakhstan. We sought to examine whether there is a relationship between peak season greenness and its drivers - spring drought, preceding winter freeze-thaw cycles, percent snow cover and snow depth - for Kazakhstan during 2000-2016. As hypothesized, snow depth and spring drought accounted for 60 % and 52 % of the variance in the satellite-derived normalized difference vegetation index (NDVI) in Kazakhstan. The freeze-thaw process accounted for 50 % of NDVI variance across the country. In addition, continuous thawing during the winter increased vegetation greenness. We also found that moisture and topographic factors impacted NDVI more significantly than socioeconomic factors. However, the impacts of socioeconomic drivers on vegetation growth were amplified when they interacted with environmental drivers. Terrain slope and soil moisture had the highest q-values or power of determinant, accounting for ~70 % of the variance in NDVI across the country. Socioeconomic drivers, such as crop production (59 %), population density (48 %), and livestock density (26 %), had significant impacts on vegetation dynamics in Kazakhstan. We found that most of the pairwise interactive influences of the drivers exhibited bi-factor enhancement, and the interaction between soil moisture and elevation was the largest (q = 0.92). Our study revealed the optimal ranges and tipping points of SES drivers and quantified the impacts of various driving factors on NDVI. These findings can help us identify the factors causing grassland degradation and provide a scientific basis for ecological protection in semiarid regions.

Intermediate snowpack melt-out dates guarantee the highest seasonal grasslands greening in the Pyrenees

In mountain areas, the phenology and productivity of grassland are closely related to snow dynamics. However, the influence that snow melt timing has on grassland growing still needs further attention for a full understanding, particularly at high spatial resolution. Aiming to reduce this knowledge gap, this work exploits 1 m resolution snow depth and Normalized Difference Vegetation Index observations acquired with an Unmanned Aerial Vehicle at a sub-alpine site in the Pyrenees. During two snow seasons (2019-2020 and 2020-2021), 14 NDVI and 17 snow depth distributions were acquired over 48 ha. Despite the snow dynamics being different in the two seasons, the response of grasslands greening to snow melt-out exhibited a very similar pattern in both. The NDVI temporal evolution in areas with distinct melt-out dates reveals that sectors where the melt-out date occurs in late April or early May (optimum melt-out) reach the maximum vegetation productivity. Zones with an earlier or a later melt-out rarely reach peak NDVI values. The results obtained in this study area, suggest that knowledge about snow depth distribution is not needed to understand NDVI grassland dynamics. The analysis did not reveal a clear link between the spatial variability in snow duration and the diversity and richness of grassland communities within the study area.

Phenological Responses to Snow Seasonality in the Qilian Mountains Is a Function of Both Elevation and Vegetation Types

In high-elevation mountains, seasonal snow cover affects land surface phenology and the functioning of the ecosystem. However, studies regarding the long-term effects of snow cover on phenological changes for high mountains are still limited. Our study is based on MODIS data from 2003 to 2021. First, the NDPI was calculated, time series were reconstructed, and an SG filter was used. Land surface phenology metrics were estimated based on the dynamic thresholding method. Then, snow seasonality metrics were also estimated based on snow seasonality extraction rules. Finally, correlation and significance between snow seasonality and land surface phenology metrics were tested. Changes were analyzed across elevation and vegetation types. Results showed that (1) the asymmetry in the significant correlation between the snow seasonality and land surface phenology metrics suggests that a more snow-prone non-growing season (earlier first snow, later snowmelt, longer snow season and more snow cover days) benefits a more flourishing vegetation growing season in the following year (earlier start and later end of growing season, longer growing season). (2) Vegetation phenology metrics above 3500 m is sensitive to the length of the snow season and the number of snow cover days. The effect of first snow day on vegetation phenology shifts around 3300 m. The later snowmelt favors earlier and longer vegetation growing season regardless of the elevation. (3) The sensitivity of land surface phenology metrics to snow seasonality varied among vegetation types. Grass and shrub are sensitive to last snow day, alpine vegetation to snow season length, desert to number of snow cover days, and forest to first snow day. In this study, we used a more reliable NDPI at high elevations and confirmed the past conclusions about the impact of snow seasonality metrics. We also described in detail the curves of snow seasonal metrics effects with elevation change. This study reveals the relationship between land surface phenology and snow seasonality in the Qilian Mountains and has important implications for quantifying the impact of climate change on ecosystems.

Land Surface Snow Phenology Based on an Improved Downscaling Method in the Southern Gansu Plateau, China

Snow is involved in and influences water–energy processes at multiple scales. Studies on land surface snow phenology are an important part of cryosphere science and are a hot spot in the hydrological community. In this study, we improved a statistical downscaling method by introducing a spatial probability distribution function to obtain regional snow depth data with higher spatial resolution. Based on this, the southern Gansu Plateau (SGP), an important water source region in the upper reaches of the Yellow River, was taken as a study area to quantify regional land surface snow phenology variation, together with a discussion of their responses to land surface terrain and local climate, during the period from 2003 to 2018. The results revealed that the improved downscaling method was satisfactory for snow depth data reprocessing according to comparisons with gauge-based data. The downscaled snow depth data were used to conduct spatial analysis and it was found that snow depth was on average larger and maintained longer in areas with higher altitudes, varying and decreasing with a shortened persistence time. Snow was also found more on steeper terrain, although it was indistinguishable among various aspects. The former is mostly located at high altitudes in the SGP, where lower temperatures and higher precipitation provide favorable conditions for snow accumulation. Climatically, factors such as precipitation, solar radiation, and air temperature had significantly singular effectiveness on land surface snow phenology. Precipitation was positively correlated with snow accumulation and maintenance, while solar radiation and air temperature functioned negatively. Comparatively, the quantity of snow was more sensitive to solar radiation, while its persistence was more sensitive to air temperature, especially extremely low temperatures. This study presents an example of data and methods to analyze regional land surface snow phenology dynamics, and the results may provide references for better understanding water formation, distribution, and evolution in alpine water source areas.

Alpine Grassland Reviving Response to Seasonal Snow Cover on the Tibetan Plateau

Season snow cover plays an important role in vegetation growth in alpine regions. In this study, we analyzed the spatial and temporal variations in seasonal snow cover and the start of the growing season (SOS) of alpine grasslands and preliminarily studied the mechanism by which snow cover affects SOS changes by modifying the soil temperature (ST) and soil moisture (SM) in spring. The results showed that significant interannual trends in the SOS, snow end date (SED), snow cover days (SCD), ST, and SM existed over the Tibetan Plateau (TP) in China from 2000 to 2020. The SOS advanced by 2.0 d/10 a over the TP over this period. Moreover, the SOS showed advancing trends in the eastern and central parts of the TP and a delayed trend in the west. The SED and SCD exhibited an advancing trend and a decreasing trend in high-elevation areas, respectively, and the opposite trends in low-elevation areas. The ST showed a decreasing trend in low-elevation areas and an increasing trend in high-elevation areas. The SM tended to increase in most areas. The effects of the seasonal snow cover on the ST and SM indirectly influenced the SOS of alpine grasslands. The delayed SEDs and more SCD observed herein could provide increasingly wet soil conditions optimal for the advancement of the SOS, while less snow and shorter snow seasons could delay the SOS of alpine grasslands on the TP.

The tempo of greening in the European Alps: Spatial variations on a common theme

The long-term increase in satellite-based proxies of vegetation cover is a well-documented response of seasonally snow-covered ecosystems to climate warming. However, observed greening trends are far from uniform, and substantial uncertainty remains concerning the underlying causes of this spatial variability. Here, we processed surface reflectance of the moderate resolution imaging spectroradiometer (MODIS) to investigate trends and drivers of changes in the annual peak values of the Normalized Difference Vegetation Index (NDVI). Our study focuses on above-treeline ecosystems in the European Alps. NDVI changes in these ecosystems are highly sensitive to land cover and biomass changes and are marginally affected by anthropogenic disturbances. We observed widespread greening for the 2000-2020 period, a pattern that is consistent with the overall increase in summer temperature. At the local scale, the spatial variability of greening was mainly due to the preferential response of north-facing slopes between 1900 and 2400 m. Using high-resolution imagery, we noticed that the presence of screes and outcrops locally magnified this response. At the regional scale, we identified hotspots of greening where vegetation cover is sparser than expected given the elevation and exposure. Most of these hotspots experienced delayed snow melt and green-up dates in recent years. We conclude that the ongoing greening in the Alps primarily reflects the high responsiveness of sparsely vegetated ecosystems that are able to benefit the most from temperature and water-related habitat amelioration above treeline.

Continuous warming shift greening towards browning in the Southeast and Northwest High Mountain Asia

Remote sensing and ground vegetation observation data show that climate warming promotes global vegetation greening, and the increase in air temperature in High Mountain Asia (HMA) is more than twice the global average. Under such a drastic warming in climate, how have the vegetation dynamics in HMA changed? In this study, we use the Normalized Difference Vegetation Index (NDVI) from 1982 to 2015 to evaluate the latest changes in vegetation dynamics in HMA and their climate-driving mechanisms. The results show that over the past 30 years, HMA has generally followed a "warm-wet" trend, with temperatures charting a continuous rise. During 1982-1998 precipitation increased (1.16 mm yr-1), but depicted to reverse since 1998 (- 2.73 mm yr-1). Meanwhile, the NDVI in HMA increased (0.012 per decade) prior to 1998, after which the trend reversed and declined (- 0.005 per decade). The main reason for the browning of HMA vegetation is the dual effects of warming and precipitation changes. As mentioned, the increase in air temperature in HMA exceeds the global average. The increase of water vapor pressure deficit caused by global warming accelerates the loss and consumption of surface water, and also aggravates the soil water deficit. That is to say, the abnormal increase of land evapotranspiration far exceeds the precipitation, and the regional water shortage increases. Climate change is the primary factor driving these vegetation and water dynamics, with the largest proportion reaching 41.9%.

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

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.