Seasonally, snow‐covered forests are a critical source of water in the Western United States and are subject to major disturbances, including fire, harvest, disease and insect‐caused mortality, that have relatively unknown effects on water availability. In this study, we investigated changes in winter season snow accumulation and ablation in a forest following the Las Conchas fire in the Jemez Mountains of New Mexico. We investigated two competing sets of processes that should determine the peak annual snowpack prior to snowmelt: (1) reduced interception by forest canopy results in greater new snow accumulation and (2) increased winter season ablation of the snowpack results in reduced peak snowpack volumes. These processes were evaluated with approximately 800 spatially distributed manual observations of new snow, 1500 manual observations of peak snowpack, and light detection and ranging‐derived snow depth, vegetation and terrain datasets collected prior to the fire. A single snowfall event yielded significantly larger snow depths in the post‐burn area versus the unburned area (p < 0.001), with 25% to 45% interception in the unburned area and near zero in the post‐burn area. Conversely, the peak snowpack depths were significantly larger in the unburned area compared with the post‐burn area (mean of 55 and 47 cm, respectively), despite nearly identical peak snowpacks prior to the fire (72 and 72 cm, respectively). The lack of strong vegetation controls led to less variability at peak snowpack in the post‐burn area and a shift towards topographically controlled variability, caused by differences in elevation and aspect, occurring at larger spatial scales. The unburned area had roughly 10% more water available for melt than the post‐burn area, with winter season ablation reducing snowpacks by nearly 50% prior to melt in the post‐burn area. The relative importance of shortwave radiation to the snowpack energy balance and sublimation suggests that the 10% reductions in peak snow water storage found in these north‐facing areas could be a conservative estimate for winter season ablation following fire. Further work is necessary to assess the role that topography plays in altering water partitioning following forest disturbance and the potential implications for ecological health and downstream water resources. Copyright © 2013 John Wiley & Sons, Ltd.

Abstract. We analyze scale-dependent statistics of correlated random hydrogeological variables and their extremes using neutron porosity data from six deep boreholes, in three diverse depositional environments, as example. We show that key statistics of porosity increments behave and scale in manners typical of many earth and environmental (as well as other) variables. These scaling behaviors include a tendency of increments to have symmetric, non-Gaussian frequency distributions characterized by heavy tails that decay with separation distance or lag; power-law scaling of sample structure functions (statistical moments of absolute increments) in midranges of lags; linear relationships between log structure functions of successive orders at all lags, known as extended self-similarity or ESS; and nonlinear scaling of structure function power-law exponents with function order, a phenomenon commonly attributed in the literature to multifractals. Elsewhere we proposed, explored and demonstrated a new method of geostatistical inference that captures all of these phenomena within a unified theoretical framework. The framework views data as samples from random fields constituting scale mixtures of truncated (monofractal) fractional Brownian motion (tfBm) or fractional Gaussian noise (tfGn). Important questions not addressed in previous studies concern the distribution and statistical scaling of extreme incremental values. Of special interest in hydrology (and many other areas) are statistics of absolute increments exceeding given thresholds, known as peaks over threshold or POTs. In this paper we explore the statistical scaling of data and, for the first time, corresponding POTs associated with samples from scale mixtures of tfBm or tfGn. We demonstrate that porosity data we analyze possess properties of such samples and thus follow the theory we proposed. The porosity data are of additional value in revealing a remarkable cross-over from one scaling regime to another at certain lags. The phenomena we uncover are of key importance for the analysis of fluid flow and solute as well as particulate transport in complex hydrogeologic environments.

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