Abstract:Snow cover and glaciers are the most important long-term forms of water storage and, hence, the main sources of runoff during the ablation period for many alpine headwater basins. We therefore investigated the application of the conceptual, distributed hydrological precipitation runoff evapotranspiration hydrological response unit model (PREVAH) to the alpine glacierized headwater basin of the Upper Salzach (593 km 2 , 5% glacierized) river in Austria. Hourly meteorological data from 17 stations for a 6-year period were available for the calibration and validation of the hourly runoff simulations. Multi-criteria validation included hourly discharge, snow covered area (SCA), and glacier mass balances. SCA maps were generated from optical satellite images for six dates. These maps were compared to simulated maps of SCA to (1) calculate differences in SCA, (2) calculate altitudinal differences, and (3) show the ability to accurately model snow cover on different aspects. The differences between observed and simulated SCA for glacierized areas were between 1 and 9% during June and July, and between 10 and 36% during August and September observations. In general, the model overestimated SCA, which is the result of PREVAH not including redistribution of snow by wind or avalanches. The temporal variability of the simulated mass balance agreed well with observations from surrounding glaciers. Nash-Sutcliffe Efficiency Criteria (R 2 ) of the hourly discharge simulations were between 0Ð83 and 0Ð89 with the exception of the extreme summer of 2003 which had an R 2 of 0Ð74. Contributions of glacier melt (firn/ice melt) to annual total runoff were between 1 and 4%. Again, the exception was 2003, when glaciers contributed 15% of the annual runoff and 60% to the August runoff alone.

Most applications of land cover maps that have been derived from satellite data over the Arctic require higher thematic detail than available in current global maps. A range of application studies has been reviewed, including up-scaling of carbon fluxes and pools, permafrost feature mapping and transition monitoring. Early land cover mapping studies were driven by the demand to characterize wildlife habitats. Later, in the 1990s, up-scaling of in situ measurements became central to the discipline of land cover mapping on local to regional scales at several sites across the Arctic. This includes the Kuparuk basin in Alaska, the Usa basin and the Lena Delta in Russia. All of these multi-purpose land cover maps have been derived from Landsat data. High resolution maps (from optical satellite data) serve frequently as input for the characterization of periglacial features and also flux tower footprints in recent studies. The most used map to address circumpolar issues is the CAVM (Circum Arctic Vegetation Map) based on AVHRR (1 km) and has been manually derived. It provides the required thematic detail for many applications, but is confined to areas north of the treeline, and it is limited in spatial detail. A higher spatial resolution circumpolar land cover map with sufficient thematic content would be beneficial for a range of applications. Such a land cover classification should be compatible with existing global maps and applicable for multiple purposes. The thematic content of existing global maps has been assessed by comparison to the CAVM and regional maps. None of the maps provides the required thematic detail. Spatial resolution has been compared to used classes for local to regional applications. The required thematic detail increases with spatial resolution since coarser datasets are usually applied over larger areas covering more relevant landscape units. This is especially of concern when the entire Arctic is addressed. A spatial resolution around 30 m has been shown to be suitable for a range of applications. This implies that the current Landsat-8, as well as Sentinel-2 missions would be adequate as input data. Recent studies have exemplified the value of Synthetic Aperture Radar (SAR) in tundra regions. SAR missions may be therefore of added value for large-scale high latitude land cover mapping. Remote Sens. 2016, 8, 979 2 of 27with climate change in the upcoming decades [2]. It has been pointed out by a range of studies (e.g., Westermann et al. [1] and Ottle et al. [3]) that there is a need for accurate land cover description in the northern high latitudes. A suitable circumpolar map does not yet exist to date, although a range of studies demonstrated the suitability of satellite data for this purpose on local and regional scales.A first review on traditional Arctic land cover maps as a baseline for the development of a circumpolar map was already published in 1995 by Walker et al. [4]. The Circum-Arctic Vegetation Map (CAVM) has in the following been developed using satellite data (Advanced Very Hi...

Livestock systems play a key role in global sustainability challenges like food security and climate change, yet, many unknowns and large uncertainties prevail. We present a systematic, spatially explicit assessment of uncertainties related to grazing intensity (GI), a key metric for assessing ecological impacts of grazing, by combining existing datasets on a) grazing feed intake, b) the spatial distribution of livestock, c) the extent of grazing land, and d) its net primary productivity (NPP). An analysis of the resulting 96 maps implies that on average 15% of the grazing land NPP is consumed by livestock. GI is low in most of worlds grazing lands but hotspots of very high GI prevail in 1% of the total grazing area. The agreement between GI maps is good on one fifth of the world's grazing area, while on the remainder it is low to very low. Largest uncertainties are found in global drylands and where grazing land bears trees (e.g., the Amazon basin or the Taiga belt). In some regions like India or Western Europe massive uncertainties even result in GI > 100% estimates. Our sensitivity analysis indicates that the input-data for NPP, animal distribution and grazing area contribute about equally to the total variability in GI maps, while grazing feed intake is a less critical variable. We argue that a general improvement in quality of the available global level datasets is a precondition for improving the understanding of the role of livestock systems in the context of global environmental change or food security. Plain Language SummaryLivestock systems play a key role in global sustainability challenges like food security and climate change, yet, many unknowns and large uncertainties prevail. We present a systematic assessment of uncertainties related to the intensity of grazing, a key metric for assessing ecological impacts of grazing. We combine existing datasets on a) grazing feed intake, b) the spatial distribution of livestock, c) the extent of grazing land, and d) the biomass available for grazing. Our results show that most grasslands are used with low intensity but hotspots of high intensity prevail on 1% of the global grazing area, mainly located in drylands and where grazing land bears trees. The agreement between all maps is good on one fifth of the global grazing area, while on the remainder it is low to very low. Our sensitivity analysis indicates that the input-data for available biomass, animal distribution and grazing area contribute about equally to the total variability of our maps, while grazing feed intake is a less critical variable. We argue that a general improvement in quality of the available datasets is a precondition for improving the understanding of livestock systems in the context of global environmental change or food security.

No abstract

Ground subsidence monitoring by Synthetic Aperture Radar interferometry (InSAR) over Arctic permafrost areas is largely limited by long revisit intervals, which can lead to signal decorrelation. Recent satellite missions such as COSMO-Skymed (X-band) and Sentinel-1 (C-band) have comparably short time intervals of a few days. We analyze dense records of COSMO-Skymed from 2013 and 2016 and of Sentinel-1 from 2016, 2017, and 2018 for the unfrozen period over central Yamal (Russia). These years were distinct in environmental conditions and 2016 in particular was unusually warm. We evaluate the InSAR-derived displacement with in situ subsidence records, active-layer thickness measurements, borehole temperature records, meteorological data, C-band scatterometer records, and a land-cover classification based on Sentinel-1 and -2 data. Our results indicate that a comparison of seasonal thaw evolution between years is feasible after accounting for the early thaw data gap in InSAR time series (as a result of snow cover) through an assessment with respect to degree-days of thawing. Average rates of subsidence agree between in situ and Sentinel-1 (corrected for viewing geometry), with 3.9 mm and 4.3 mm per 100 degree-days of thaw at the test site. X-band and C-band records agree well with each other, including seasonal evolution of subsidence. The average displacement is more than twice in magnitude at the active-layer monitoring test site in 2016 compared to the other years. We further demonstrate that InSAR displacement can not only provide information on the magnitude of ground thaw but also on soil properties through analyses of seasonal evolution in extreme years.