Around the world human communities and natural ecosystems both rely on headwater regions for vital resources. Resources provided include groundwater sto- rage （Flint et al., 2008; Clilverd et al., 2011; Cao et al., 2...Around the world human communities and natural ecosystems both rely on headwater regions for vital resources. Resources provided include groundwater sto- rage （Flint et al., 2008; Clilverd et al., 2011; Cao et al., 2012） in addition to soil moisture and forest ecosystem dynamics （McNamara et al., 2005; Williams et al., 2009; Jencso and McGlynn, 2011; Smith et al., 2011; Harpold et al., 2015; Webb et al., 2015）. Furthermore these regions are ecologically vital zones （Schlosser, 1995; Lowe and Likens, 2005） that impact downstream water quality （Peterson et al., 2001; Alexander et al., 2007）.展开更多
In high elevation semi-arid rangelands, sage- brush and other shrubs can affect transport and deposition of wind-blown snow, enabling the formation of snowdrifts. Datasets from three field experiments were used to inv...In high elevation semi-arid rangelands, sage- brush and other shrubs can affect transport and deposition of wind-blown snow, enabling the formation of snowdrifts. Datasets from three field experiments were used to investigate the scales of spatial variability of snow depth around big mountain sagebrush （Artemisia tridentata Nutt.） at a high elevation plateau rangeland in North Park, Colorado, during the winters of 2002, 2003, and 2008. Data were collected at multiple resolutions （0.05 to 25 m） and extents （2 to 1000 m）. Finer scale data were collected specifically for this study to examine the correlation between snow depth; sagebrush microtopogra- phy, the ground surface, and the snow surface, as well as the temporal consistency of snow depth patterns. Vario- grams were used to identify the spatial structure and the Moran＇s I statistic was used to determine the spatial correlation. Results show some temporal consistency in snow depth at several scales. Plot scale snow depth variability is partly a function of the nature of individual shrubs, as there is some correlation between the spatial structure of snow depth and sagebrush, as well as between the ground and snow depth. The optimal sampling resolution appears to be 25-cm, but over a large area, this would require a multitude of samples, and thus a random stratified approach is recommended with a fine measurement resolution of 5-cm.展开更多
文摘Around the world human communities and natural ecosystems both rely on headwater regions for vital resources. Resources provided include groundwater sto- rage （Flint et al., 2008; Clilverd et al., 2011; Cao et al., 2012） in addition to soil moisture and forest ecosystem dynamics （McNamara et al., 2005; Williams et al., 2009; Jencso and McGlynn, 2011; Smith et al., 2011; Harpold et al., 2015; Webb et al., 2015）. Furthermore these regions are ecologically vital zones （Schlosser, 1995; Lowe and Likens, 2005） that impact downstream water quality （Peterson et al., 2001; Alexander et al., 2007）.
文摘In high elevation semi-arid rangelands, sage- brush and other shrubs can affect transport and deposition of wind-blown snow, enabling the formation of snowdrifts. Datasets from three field experiments were used to investigate the scales of spatial variability of snow depth around big mountain sagebrush （Artemisia tridentata Nutt.） at a high elevation plateau rangeland in North Park, Colorado, during the winters of 2002, 2003, and 2008. Data were collected at multiple resolutions （0.05 to 25 m） and extents （2 to 1000 m）. Finer scale data were collected specifically for this study to examine the correlation between snow depth; sagebrush microtopogra- phy, the ground surface, and the snow surface, as well as the temporal consistency of snow depth patterns. Vario- grams were used to identify the spatial structure and the Moran＇s I statistic was used to determine the spatial correlation. Results show some temporal consistency in snow depth at several scales. Plot scale snow depth variability is partly a function of the nature of individual shrubs, as there is some correlation between the spatial structure of snow depth and sagebrush, as well as between the ground and snow depth. The optimal sampling resolution appears to be 25-cm, but over a large area, this would require a multitude of samples, and thus a random stratified approach is recommended with a fine measurement resolution of 5-cm.
