Active wildfire seasons in the western U.S. warrant the evaluation of post-fire forest management strategies. Ground-based salvage logging is often used to recover economic loss of burned timber. In unburned forests, ground-based logging often follows best management practices by leaving undisturbed areas near streams called stream buffers. However, the effectiveness of these buffers has not been tested in a post-wildfire setting. This experiment tested buffer width effectiveness with a novel field-simulated rill experiment using sediment-laden runoff (25 g/L) released over 40 min at evenly timed flow rates (50, 100 and 150 L/min) to measure surface runoff travel length and sediment concentration under unburned and high and low soil burn severity conditions at 2-, 10- and 22-month post-fire. High severity areas 2-month post-fire had rill lengths of up to 100 m. Rill length significantly decreased over time as vegetation regrowth provided ground cover. Sediment concentration and sediment dropout rate also varied significantly by soil burn severity. Sediment concentrations were 19 g/L for the highest flow 2-month post-fire and reduced to 6.9–14 g/L 10-month post-fire due to abundant vegetation recovery. The amount of sediment dropping out of the flow consistently increased over the study period with the low burn severity rate of 1.15 g L−1 m−1 approaching the unburned rate of 1.29 g L−1 m−1 by 2-year post-fire. These results suggest that an often-used standard, 15 m buffer, was sufficient to contain surface runoff and reduce sediment concentration on unburned sites, however buffers on high burn severity sites need to be eight times greater (120 m) immediately after wildfire and four times greater (60 m) 1-year post-fire. Low burn severity areas 1-year post-fire may need to be only twice the width of an unburned buffer (30 m), and 2-year post-fire these could return to unburned widths. 相似文献
Simulating land use/cover change (LUCC) and determining its transition rules have been a focus of research for several decades. Previous studies used ordinary logistic regression (OLR) to determine transition rules in cellular automata (CA) modeling of LUCC, which often neglected the spatially non-stationary relationships between driving factors and land use/cover categories. We use an integrated geographically weighted logistic regression (GWLR) CA-Markov method to simulate LUCC from 2001–2011 over 29 towns in the Connecticut River Basin. Results are compared with those obtained from the OLR-CA-Markov method, and the sensitivity of LUCC simulated by the GWLR-CA-Markov method to the spatial non-stationarity-based suitability map is investigated. Analysis of residuals indicates better goodness of fit in model calibration for geographically weighted regression (GWR) than OLR. Coefficients of driving factors indicate that GWLR outperforms OLR in depicting the local suitability of land use/cover categories. Kappa statistics of the simulated maps indicate high agreement with observed land use/cover for both OLR-CA-Markov and GWLR-CA-Markov methods. Similarity in simulation accuracy between the methods suggests that the sensitivity of simulated LUCC to suitability inputs is low with respect to spatial non-stationarity. Therefore, this study provides critical insight on the role of spatial non-stationarity throughout the process of LUCC simulation. 相似文献
We analyzed the spatial local accuracy of land cover (LC) datasets for the Qiangtang Plateau, High Asia, incorporating 923 field sampling points and seven LC compilations including the International Geosphere Biosphere Programme Data and Information System (IGBPDIS), Global Land cover mapping at 30 m resolution (GlobeLand30), MODIS Land Cover Type product (MCD12Q1), Climate Change Initiative Land Cover (CCI-LC), Global Land Cover 2000 (GLC2000), University of Maryland (UMD), and GlobCover 2009 (Glob-Cover). We initially compared resultant similarities and differences in both area and spatial patterns and analyzed inherent relationships with data sources. We then applied a geographically weighted regression (GWR) approach to predict local accuracy variation. The results of this study reveal that distinct differences, even inverse time series trends, in LC data between CCI-LC and MCD12Q1 were present between 2001 and 2015, with the exception of category areal discordance between the seven datasets. We also show a series of evident discrepancies amongst the LC datasets sampled here in terms of spatial patterns, that is, high spatial congruence is mainly seen in the homogeneous southeastern region of the study area while a low degree of spatial congruence is widely distributed across heterogeneous northwestern and northeastern regions. The overall combined spatial accuracy of the seven LC datasets considered here is less than 70%, and the GlobeLand30 and CCI-LC datasets exhibit higher local accuracy than their counterparts, yielding maximum overall accuracy (OA) values of 77.39% and 61.43%, respectively. Finally, 5.63% of this area is characterized by both high assessment and accuracy (HH) values, mainly located in central and eastern regions of the Qiangtang Plateau, while most low accuracy regions are found in northern, northeastern, and western regions.
The motion of a submarine in liquid under an ice plate covered with flooded snow is considered. The ice is modelled as an elastic plate and the snow cover is modelled as a viscous layer on the top of the plate. The submarine is modelled as a slender solid of revolution with scale 1:300. The experimental and theoretical study of the influence of the viscous snow layer on deflections of the floating ice plate is conducted. The viscous layer reduces the amplitudes of flexural-gravity waves. The greatest influence of the viscous layer on the plate deflections is achieved for velocities of the submarine, where the waves of maximum amplitude are generated. Theoretical results are in good qualitative and quantitative agreement with the model experiments. 相似文献
For snowmelt-driven flood studies, snow water equivalent (SWE) is frequently estimated using snow depth data. Accurate measurements of snow depth are important in providing data for continuous hydrologic simulations of such watersheds. A new hydrologic fidelity metric is proposed in this study to evaluate the potential contribution of particular snow depth datasets to flow characteristics using observed data and hydrologic modeling using the Variable Infiltration Capacity (VIC) model. Data-based hydrologic fidelity of snow depth measurements is defined as a categorical skill score between the snow depth in the watershed and the hydrograph peak or volume at the watershed outlet. Similarly, model-based hydrologic fidelity is defined as a categorical skill score between the model-simulated snow depth and the model-simulated hydrograph peak or volume. The proposed framework is illustrated using the Pecatonica River watershed in the USA, indicating which sites have a higher hydrologic fidelity, which is preferred in hydrologic studies. 相似文献
A 10‐km gridded snow water equivalent (SWE) dataset is developed over the Saint‐Maurice River basin region in southern Québec from kriging of observed snow survey data for evaluation of SWE products. The gridded SWE dataset covers 1980–2014 and is based on manual gravimetric snow surveys carried out on February 1, March 1, March 15, April 1, and April 15 of each snow season, which captures the annual maximum SWE (SWEM) with a mean interpolation error of ±19%. The dataset is used to evaluate SWEM from a range of sources including satellite retrievals, reanalyses, Canadian regional climate models, and the Canadian Meteorological Centre operational snow depth analysis. We also evaluate a number of solid precipitation datasets to determine their contribution to systematic errors in estimated SWEM. None of the evaluated datasets is able to provide estimates of SWEM that are within operational requirements of ±15% error, and insufficient solid precipitation is determined to be one of the main reasons. The Climate System Forecast Reanalysis is the only dataset where snowfall is sufficiently large to generate SWEM values comparable to observations. Inconsistencies in precipitation are also found to have a strong impact on year‐to‐year variability in SWEM dataset performance and spread. Version 3.6.1 of the Canadian Land Surface Scheme land surface scheme driven with ERA‐Interim output downscaled by Version 5.0.1 of the Canadian Regional Climate Model was the best physically based model at explaining the observed spatial and temporal variability in SWEM (root‐mean‐square error [RMSE] = 33%) and has potential for lower error with adjusted precipitation. Operational snow products relying on the real‐time snow depth observing network performed poorly due to a lack of real‐time data and the strong local scale variability of point snow depth observations. The results underscore the need for more effort to be invested in improving solid precipitation estimates for use in snow hydrology applications. 相似文献