Monitoring the Seasonal Hydrology of Alpine Wetlands in Response to Snow Cover Dynamics and Summer Climate: A Novel Approach with Sentinel-2

Climate change and the biodiversity of alpine ponds: Challenges and perspectives

Inland waters are among the most threatened biodiversity hotspots. Ponds located in alpine areas are experiencing more rapid and dramatic water temperature increases than any other biome. Despite their prevalence, alpine ponds and their biodiversity responses to climate change have been poorly explored, reflecting their small size and difficult access. To understand the effects of climate change on alpine pond biodiversity, we performed a comprehensive literature review for papers published since 1955. Through analysis of their geographic distribution, environmental features, and biodiversity values, we identified which environmental factors related to climate change would have direct or indirect effects on alpine pond biodiversity. We then synthesized this information to produce a conceptual model of the effects of climate change on alpine pond biodiversity. Increased water temperature, reduced hydroperiod, and loss of connectivity between alpine ponds were the main drivers of biodiversity geographic distribution, leading to predictable changes in spatial patterns of biodiversity. We identified three major research gaps that, if addressed, can guide conservation and restoration strategies for alpine ponds biodiversity in an uncertain future.

Classification of Alpine Grasslands in Cold and High Altitudes Based on Multispectral Landsat-8 Images: A Case Study in Sanjiangyuan National Park, China

Land-use–cover change (LUCC)/vegetation cover plays a critical role in Earth system science and is a reflection of human activities and environmental changes. LUCC will affect the structure and function of ecosystems and a series of other terrestrial surface processes, such as energy exchange, water circulation, biogeochemical circulation, and vegetation productivity. Therefore, accurate LUCC mapping and vegetation cover monitoring are the bases for simulating the global carbon and hydrological cycles, studying the interactions of the land surface and climate, and assessing land degradation. Based on field GPS surveys and UAV data, with cloud-free and snow/glacier algorithms and the SVM classifier to train and model alpine grassland, the alpine grassland and LUCC were extracted by using Landsat-8 OLI satellite images in Sanjiangyuan National Park in this paper. The latest datasets of vegetation types with 30 m × 30 m spatial resolution in the three parks were prepared and formed. The classification results show that the SVM classifier could better distinguish the major land-use types, and the overall classification accuracy was very high. However, in the alpine grassland subcategories, the classification accuracies of the four typical grasslands were relatively low, especially between desert steppes and alpine meadows, and desert steppes and alpine steppes. It manifests the limitations of Landsat-8 multispectral remote sensing imageries in finer-resolution grassland classifications of high-altitude alpine mountains. The method can be utilized for other multispectral satellite imageries with the same band matching, such as Landsat 7, Landsat 9, Sentinel-2, etc. The method described in this paper can rapidly and efficiently process annual alpine grassland maps of the source areas of the Yellow River, the Yangtze River, and the Lancang River. It can provide timely and high-spatial-resolution datasets for supporting scientific decisions for the sustainable management of Sanjiangyuan National Park.

Nonlinear sensitivity of glacier mass balance to future climate change unveiled by deep learning

Glaciers and ice caps are experiencing strong mass losses worldwide, challenging water availability, hydropower generation, and ecosystems. Here, we perform the first-ever glacier evolution projections based on deep learning by modelling the 21st century glacier evolution in the French Alps. By the end of the century, we predict a glacier volume loss between 75 and 88%. Deep learning captures a nonlinear response of glaciers to air temperature and precipitation, improving the representation of extreme mass balance rates compared to linear statistical and temperature-index models. Our results confirm an over-sensitivity of temperature-index models, often used by large-scale studies, to future warming. We argue that such models can be suitable for steep mountain glaciers. However, glacier projections under low-emission scenarios and the behaviour of flatter glaciers and ice caps are likely to be biased by mass balance models with linear sensitivities, introducing long-term biases in sea-level rise and water resources projections.

Deep learning unveils a nonlinear sensitivity of glacier mass changes to future climate warming, with important implications for water resources and sea-level rise coming from glaciers and particularly ice caps.

Reviewing the Potential of Sentinel-2 in Assessing the Drought

This paper systematically reviews the potential of the Sentinel-2 (A and B) in assessing drought. Research findings, including the IPCC reports, highlighted the increasing trend in drought over the decades and the need for a better understanding and assessment of this phenomenon. Continuous monitoring of the Earth’s surface is an efficient method for predicting and identifying the early warnings of drought, which enables us to prepare and plan the mitigation procedures. Considering the spatial, temporal, and spectral characteristics, the freely available Sentinel-2 data products are a promising option in this area of research, compared to Landsat and MODIS. This paper evaluates the recent developments in this field induced by the launch of Sentinel-2, as well as the comparison with other existing data products. The objective of this paper is to evaluate the potential of Sentinel-2 in assessing drought through vegetation characteristics, soil moisture, evapotranspiration, surface water including wetland, and land use and land cover analysis. Furthermore, this review also addresses and compares various data fusion methods and downscaling methods applied to Sentinel-2 for retrieving the major bio-geophysical variables used in the analysis of drought. Additionally, the limitations of Sentinel-2 in its direct applicability to drought studies are also evaluated.

Earlier Snowmelt Advances Breeding Phenology of the Common Frog (Rana temporaria) but Increases the Risk of Frost Exposure and Wetland Drying

The alarming decline of amphibians around the world calls for complementary studies to better understand their responses to climate change. In mountain environments, water resources linked to snowmelt play a major role in allowing amphibians to complete tadpole metamorphosis. As snow cover duration has significantly decreased since the 1970s, amphibian populations could be strongly impacted by climate warming, and even more in high elevation sites where air temperatures are increasing at a higher rate than at low elevation. In this context, we investigated common frog (Rana temporaria) breeding phenology at two different elevations and explored the threats that this species faces in a climate change context. Our objectives were to understand how environmental variables influence the timing of breeding phenology of the common frog, and explore the threats that amphibians face in the context of climate change in mountain areas. To address these questions, we collected 11 years (2009–2019) of data on egg-spawning date, tadpole development stages, snowmelt date, air temperature, rainfall and drying up of wetland pools at ∼1,300 and ∼1,900 m a.s.l. in the French Alps. We found an advancement of the egg-spawning date and snowmelt date at low elevation but a delay at high elevations for both variables. Our results demonstrated a strong positive relationship between egg-spawning date and snowmelt date at both elevations. We also observed that the risk of frost exposure increased faster at high elevation as egg-spawning date advanced than at low elevation, and that drying up of wetland pools led to tadpole mortality at the high elevation site. Within the context of climate change, egg-spawning date is expected to happen earlier in the future and eggs and tadpoles of common frogs may face higher risk of frost exposure, while wetland drying may lead to higher larval mortality. However, population dynamics studies are needed to test these hypotheses and to assess impacts at the population level. Our results highlight climate-related threats to common frog populations in mountain environments, but additional research should be conducted to forecast how climate change may benefit or harm amphibian populations, and inform conservation and land management plans in the future.