Active‐layer thickness in north central Alaska: Systematic sampling, scale, and spatial autocorrelation

Abstract

Abstract. Active-layer thickness was determined in late August 1995 and 1996 at 100 m intervals over seven 1 km 2 grids in the Arctic Coastal Plain and Arctic Foothills physiographic provinces of northern Alaska. Collectively, the sampled areas integrate the range of regional terrain, soil, and vegetation characteristics in this region. Spatial autocorrelation analysis indicates that patterns of active-layer thickness are governed closely by topographic detail, acting through near-surface hydrology. On the coastal plain, maximum variability occurs at scales involving hundreds of meters, and patterns were similar in the two years. Substantially less spatial structure and interannual correspondence were found within the foothill sites, where high variability occurs over smaller distances. The divergence in patterns of thaw depth between the two provinces reflects the scale of local terrain features, which predetermines the effectiveness of fixed sampling intervals. Exploratory analysis should be performed to ascertain the scale(s) of maximum variability within representative areas prior to selection of sampling intervals and development of long-term monitoring programs. IntroductionThe active layer, a thickness of soil or other earth material above permafrost, experiences freezing and thawing on an annual basis. Given similar surface cover and soil properties, the active layer thins progressively toward the poles in response to shorter, cooler summers. Under a warming climate, such as that predicted by many general circulation models

Evaluating Land Subsidence Rates and Their Implications for Land Loss in the Lower Mississippi River Basin

“…In the New Orleans metropolitan area, interpolated rates from EBK are similar to the observations from Dixon et al [15,25,36]. In the Mississippi River Delta around Plaquemines parish, previous studies [34,35] derived a faster subsidence rate than EBK calculation. In addition to the studies listed in Table 2 [25,[37][38][39], have used the same TR50 benchmark survey data from NGS and obtained similar subsidence estimations at various parts of the study region.…”

“…The overlap factor x representing the number of subsets in which an observation will participate was set to 1.0 (meaning each observation is only being considered once), and the number of simulated semivariograms n was 100. The neighborhood search radius R was defined as 86 kilometers, which was determined by Moran's I autocorrelation analysis as the distance at which Moran's I is the highest [34]. Since the output pixel size is 30ˆ30 meters, which is very time consuming for a study area with nearly 50 million pixels, we used the parallel computing option available in ArcGIS to accelerate the processing.…”

An approach to understanding hydrologic connectivity on the hillslope and the implications for nutrient transport

“…The climate, hydrology, and energy balance of Imnavait Creek have been studied continuously since the mid-1980s [Cooper et al, 1996;Hinzman et al, , 1996Hinzman et al, , 1998Kane et al, 1991;Michaelson et al, 1998;McNamara et al, 1998]. Multiyear records of soil thaw, precipitation, and streamflow exist Nelson et al, 1998], and a weather station with winter snowfall records has operated year-round since 1975. Imnavait Creek has also been the focus of several hydrological and linked hydrological-biological modeling efforts [e.g., McNamara et al, 1997;Stieglitz et al, 2000].…”

Active-Layer Thickness across Alaska: Comparing Observation-Based Estimates with CMIP5 Earth System Model Predictions

“…The results from these CMIP5 models are being used in the current IPCC fifth assessment report. Though these models predict general patterns of permafrost distribution and their responses to climate change, they are difficult to use for land-use planning and management and for ecological monitoring and assessment (Zhang et al, 2012) because they are unable to represent the fine-scale spatial heterogeneity in climate (e.g., temperature, moisture) and environmental parameters (e.g., topography, vegetation, soil, and hydrologic conditions) reported to affect AL thickness (Nelson et al, 1998;Riseborough et al, 2008).…”

“…The AL thickness or depth is measured as the maximum depth of thaw in any particular year. The spatial variability of current AL thickness is a critical component in prediction of the C-climate feedbacks associated with permafrostaffected soils (Nelson et al, 1998;Zhang et al, 2012).…”