Assessing biodiversity in arctic-alpine ecosystems is a costly task. We test in the current study whether we can map the spatial patterns of spider alpha and beta diversity using remotely-sensed surface reflectance and topography in a heterogeneous alpine environment in Central Norway. This proof-of-concept study may provide a tool for an assessment of arthropod communities in remote study areas. Data on arthropod species distribution and richness were collected through pitfall trapping and subjected to a detrended correspondence analysis (DCA) to extract the main species composition gradients. The DCA axis scores as indicators of species composition as well as trap species richness were regressed against a combined data set of surface reflectance as measured by the Sentinel-2 satellite and topographical parameters extracted from a digital elevation model. The models were subsequently applied to the spatial data set to achieve a pixel-wise prediction of both species richness and position in the DCA space. The spatial variation in the modelled DCA scores was used to draw conclusions regarding spider beta-diversity. The species composition was described with two DCA axes that were characterized by post hoc-defined indicator species, which showed a typical annidation in the arctic-alpine environment under study. The fits of the regression models for the DCA axes and species richness ranged from R-2 = 0.25 up to R-2 = 0.62. The resulting maps show strong gradients in alpha and beta diversity across the study area. Our results indicate that the diversity patterns of spiders can at least partially be explained by means of remotely sensed data. Our approach would likely benefit from the additional use of high resolution aerial photography and LiDAR data and may help to improve conservation strategies in arctic-alpine ecosystems.

A process-based, spatially distributed hydrological model was developed to quantitatively simulate the energy and mass transfer processes and their interactions within arctic regions (arctic hydrological and thermal model, ARHYTHM). The model first determines the flow direction in each element, the channel drainage network and the drainage area based upon the digital elevation data. Then it simulates various physical processes: including snow ablation, subsurface flow, overland flow and channel flow routing, soil thawing and evapotranspiration. The kinematic wave method is used for conducting overland flow and channel flow routing. The subsurface flow is simulated using the Darcian approach. The energy balance scheme was the primary approach used in energy-related process simulations (snowmelt and evapotranspiration), although there are options to model snowmelt by the degree-day method and evapotranspiration by the Priestley-Taylor equation. This hydrological model simulates the dynamic interactions of each of these processes and can predict spatially distributed snowmelt, soil moisture and evapotranspiration over a watershed at each time step as well as discharge in any specified channel(s). The model was applied to Imnavait watershed (about 2.2 km(2)) and the Upper Kuparuk River basin (about 146 km(2)) in northern Alaska. Simulated results of spatially distributed soil moisture content, discharge at gauging stations, snowpack ablations curves and other results yield reasonable agreement, both spatially and temporally, with available data sets such as SAR imagery-generated soil moisture data and field measurements of snowpack ablation, and discharge data at selected points. The initial timing of simulated discharge does not compare well with the measured data during snowmelt periods mainly because the effect of snow damming on runoff was not considered in the model. Results from the application of this model demonstrate that spatially distributed models have the potential for improving our understanding of hydrology for certain settings. Finally, a critical component that led to the performance of this modelling is the coupling of the mass and energy processes. Copyright (C) 2000 John Wiley & Sons, Ltd.