Generation of a high-resolution snow cover dataset from Sentinel-2 images for snow model calibration

<p>Modeling the runoff generation of high elevation Alpine catchments requires fundamental knowledge of the snow storage and the spatial distribution of snow cover. Since in-situ snow observations are often very scarce and represent only a point information, spatial snow information from satellite data is used since decades. However, the accuracy of snow cover maps through remote sensing products depends strongly on the cloudiness. In order to generate a spatial and temporal highly resolved dataset of snow cover maps, we applied the pixel identification processor (IdePix available in SNAP v7.0) to retrieve diverse cloud layers from Sentinel-2 Level-1C products. This makes it possible to use also high-clouded images for the snow detection, which increases significantly the data availability for the later performed snow model calibration. Cloudy areas, for which snow detection by the NDSI calculation is not possible, are set to no data. Sentinel-2 images that do not have cloud information require an extra correction based on the assumption that the snow cover has a pronounced elevation gradient. The entire NDSI dataset is subdivided into 200 m elevation zones and statistically analyzed. Thereby, the cloud-influenced images clearly stand out as outliers in the elevation zones >3000 m. If an elevation zone is detected as an outlier, the corresponding elevation zone is set to no data as well. After the comprehensive cloud detection, a pixel wise comparison with in-situ snow depth observation of four different sites allows us a first validation of the snow detection quality. In a second step, the generated snow maps are compared with the snow and cloud detection algorithm developed by Eurac Research. The final snow cover maps are used together with the in-situ snow depth observations to calibrate two different snowmelt approaches of the hydrological model WaSiM - the T-index and the energy balance-based approach (including gravitational snow redistribution) - over a mountainous basin in the Eastern Italian Alps.</p>

Dynamics of Maximum Snow Cover Area and Snow Line Altitude Across Nepal (2003-2018) Using Improved MODIS Data

Snow is one of the main components of the cryosphere and plays a vital role in the hydrology and regulating climate. This study presents the dynamics of maximum snow cover area (SCA) and snow line altitude (SLA) across the Western, Central, and Eastern Nepal using improved Moderate Resolution Imaging Spectroradiometer (MODIS; 500 m) data from 2003 to 2018. The results showed a heterogeneous behavior of the spatial and temporal variations of SCA in different months, seasons, and elevation zones across three regions of Nepal. Further, the maximum and minimum SCA was observed in winter (December-February) and post-monsoon (October-November) seasons, respectively. The inter-annual variation of winter SCA showed an overall negative trend of SCA between 2003 to 2018 at the national and regional scales. The SLA was assessed in the post-monsoon season. At the national scale, the SLA lies in an elevation zone of 4500-5000 m, and the approximate SLA of Nepal was 4750 m in 2018. Regionally, the SLA lies in an elevation zone of 4500-5000 m in the Western and Central regions (approx. SLA at 4750 m) and 5000-5500 m in the Eastern region (approx. SLA at 5250 m) in 2018. The SLA fluctuated with the changes in SCA, and the spatio-temporal variations of SLAs were observed in three regions of Nepal. We observed an upward shift of SLA by 33.3 m yr-1 in the Western and Central Nepal and by 66.7 m yr-1 in Eastern Nepal. This study will help to understand the impacts of climate change on snow cover, and the information will be useful for the hydrologist and water resource managers.

Cyprinid Fishes: An Overview on the Present Status in Meghalaya, India

Cyprinid fishes of Meghalaya were investigated from twin drainage basins Brahmaputra and Barak-Surma-Meghna. 27 cyprinid fishes under 14 genus and 7 sub families were recorded from rivers and reservoirs of four different gradient zones. The diversity of Cyprinid fishes was highest with 49% representation of Cyprinids at lower elevation Zone IV below 500 m above MSL and bio diversity indices estimated as H: 3.05, 1-D: 0.10. In contrary lowest diversity with 7% representation of fishes was observed at elevation 1501 to 2000 m above MSL in Zone I with bio diversity indices H: 0.25, 1-D: 0.57. Distribution of commercially important cyprinids under genus Labeo, Systomus and Cirrhinus were found to be restricted to rivers of Barak-Surma-Meghna drainage basin. Catch percentage of cyprinids indicates that 70% of fishes exhibit occasional occurrence and 30% as common occurrence. High percentage of occasional occurrence, low catch composition percentage and with restricted distribution of commercially important fishes to only certain rivers of Barak-Surma-Meghna drainage is an indication of depletion of cyprinid resources in the state and requires taking multi prong conservation measures to protect cyprinid fishes in Meghalaya.

Survival of Whitebark Pine Seedlings Grown from Direct Seeding: Implications for Regeneration and Restoration under Climate Change

Whitebark pine populations are declining nearly range-wide, primarily from the exotic fungal pathogen that causes white pine blister rust (WPBR). Climate change is expected to exacerbate these declines by decreasing climatically suitable areas. Planting WPBR-resistant seedlings is a key restoration action, but it is costly, time consuming, and labor intensive. Direct seeding—sowing seeds rather than planting seedlings—may reduce costs and open remote areas to restoration; however, its efficacy remains largely unexplored. In this case study, we estimated the annual survival rates (ASR) of seedlings grown from directly sown seeds, and the effect of elevation zone and microsite type on survival. For five years we monitored 184 caches containing one or more seedlings within one study area in the Greater Yellowstone Ecosystem. Seed caches were originally stratified between subalpine forest and treeline and among three microsite types defined by a nurse object: Rocks, trees, and no object. To estimate ASR, we selected the most parsimonious model of a set using AICc. ASR was best described by elevation zone and year and ranged from 0.571 to 0.992. The odds of seedling survival were 2.62 times higher at treeline than in subalpine forest and were 4.6 to 36.2 times higher in 2016–2018 than 2014. We estimated the probability that a whitebark pine seed cache would contain one or more living seedlings six years after sowing to be 0.175 and 0.0584 for treeline and subalpine caches, respectively. We estimated that 1410 and 4229 caches ha−1 would need to be sown at treeline and in subalpine forest, respectively, to attain target restoration densities of 247 established trees ha−1. Our findings, although based on one study area, suggest that climate change may be increasing treeline regeneration, and that direct seeding may be a viable restoration option and climate change mitigation tool for whitebark pine.

The structure of forest stands in the Tatra National Park: The results of 2016–2017 inventory

The composition and structure of forest stands in the Tatra National Park were examined using data gathered in 2016 and 2017 from 617 circular sample plots (0.05 ha each). The diameter at breast height of all living trees, standing dead trees, snags, and wind throws was measured along with diameters and lengths of fallen logs within the plot boundaries. Tree height was measured for all living trees within the core (0.01 ha) of the sample plots. Using the obtained data, height-diameter curves were calculated for all major tree species and in the case of spruce, the height-diameter relationships were also calculated separately for each of the three elevation zones (up to 1200 m, between 1200 and 1400 m, above 1400 m). For each elevation zone and park protection zone, we also determined the volumes of live and dead trees. The volume of living trees in the Tatra National Park amounted to 259 m3/ha, which was higher than the volume of dead trees (176 m3/ha). Snags constituted the largest part of the dead wood whilst over 97% of the standing dead trees were spruce Picea abies. Among living trees, the share of spruce ranged from 81% in the low elevation zone to 98% in the middle zone. Other significant species in the lower zone were Abies alba (11%) and Fagus sylvatica (4.5%), while in the middle and upper elevation zones only Sorbus aucuparia occurred in significant numbers. Furthermore, in the lower elevation zone, Fagus sylvatica was the only species displaying significantly higher volumes in the ‘strict protection’ zone compared to the other park areas. In the ‘landscape protection’ zone, Picea abies was the most dominant species and the share of other species in the lowest elevation zones calculated based on tree density was smaller than calculated based on tree volume, indicating problems with stand conversion from spruce monoculture to mixed forest.

How much can we save? Impact of different emission scenarios on future snow cover in the Alps

This study focuses on an assessment of the future snow depth for two larger Alpine catchments. Automatic weather station data from two diverse regions in the Swiss Alps have been used as input for the Alpine3D surface process model to compute the snow cover at a 200 m horizontal resolution for the reference period (1999–2012). Future temperature and precipitation changes have been computed from 20 downscaled GCM-RCM chains for three different emission scenarios, including one intervention scenario (2 °C target) and for three future time periods (2020–2049, 2045–2074, 2070–2099). By applying simple daily change values to measured time series of temperature and precipitation, small-scale climate scenarios have been calculated for the median estimate and extreme changes. The projections reveal a decrease in snow depth for all elevations, time periods and emission scenarios. The non-intervention scenarios demonstrate a decrease of about 50 % even for elevations above 3000 m. The most affected elevation zone for climate change is located below 1200 m, where the simulations show almost no snow towards the end of the century. Depending on the emission scenario and elevation zone the winter season starts half a month to 1 month later and ends 1 to 3 months earlier in this last scenario period. The resulting snow cover changes may be roughly equivalent to an elevation shift of 500–800 or 700–1000 m for the two non-intervention emission scenarios. At the end of the century the number of snow days may be more than halved at an elevation of around 1500 m and only 0–2 snow days are predicted in the lowlands. The results for the intervention scenario reveal no differences for the first scenario period but clearly demonstrate a stabilization thereafter, comprising much lower snow cover reductions towards the end of the century (ca. 30 % instead of 70 %).

How much can we save? Impact of different emission scenarios on future snow cover in the Alps

This study focuses on an assessment of the future snow depth for two larger Alpine catchments. Automatic weather station data from two diverse regions in the Swiss Alps have been used as input for the Alpine3D surface process model to compute the snow cover at 200 m horizontal resolution for the reference period (1999–2012). Future temperature and precipitation change have been computed from 20 downscaled GCM-RCM chains for three different emission scenarios, including one intervention scenario (2° C target) and for three future time periods (2020–2049, 2045–2074, 2070–2099). By applying simple daily change values to measured time series of temperature and precipitation series small-scale climate scenarios have been calculated for the ensemble mean and extreme changes. The projections reveal a decrease in snow depth for all elevations, time periods and emission scenarios. The non-interventions scenarios demonstrate a decrease of about 50 % even for the elevations above 3000 m. The most affected elevation zone for climate change is located below 1200 m, where the simulations show almost no snow towards the end of the century. Depending on the emission scenario and elevation zone the winter season starts half a month to one month later and ends one to three month earlier in this last scenario period. The resultant snow cover changes may roughly be equivalent to an elevation shift of 500–800 m or 700–1000 m for the two non-intervention emissions scenario. At the end of the century the number of snow days may be more than halved at an elevation of around 1500 m and is predicted to only 0–2 snow days in the lowlands. The results for the intervention scenario reveal no differences for the first scenario period, but clearly demonstrate much lower snow cover reductions towards the end of the century (ca. 30 % instead of 70 %).