[1] The science of forests and floods is embroiled in conflict and is in urgent need of reevaluation in light of changing climates, insect epidemics, logging, and deforestation worldwide. Here we show how an inappropriate pairing of floods by meteorological input in analysis of covariance (ANCOVA) and analysis of variance (ANOVA), statistical tests used extensively for evaluating the effects of forest harvesting on floods smaller and larger than an average event, leads to incorrect estimates of changes in flood magnitude because neither the tests nor the pairing account for changes in flood frequency. We also illustrate how ANCOVA and ANOVA, originally designed for detecting changes in means, do not account for any forest harvesting induced change in variance and its critical effects on the frequency and magnitude of larger floods. The outcomes of numerous studies, which applied ANCOVA and ANOVA inappropriately, are based on logical fallacies and have contributed to an ever widening disparity between science, public perception, and often land-management policies for decades. We demonstrate how only an approach that pairs floods by similar frequency, well established in other disciplines, can evaluate the effects of forest harvesting on the inextricably linked magnitude and frequency of floods. We call for a reevaluation of past studies and the century-old, preconceived, and indefensible paradigm that shaped our scientific perception of the relation between forests, floods, and the biophysical environment.

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Abstract. The L moments are used in the three stages of regional frequency analysis: the delineation of homogeneous regions, the identification of a regional parent distribution, and the estimation of distribution parameters. Numerical analysis is conducted on 5 min to 24 hours annual rainfall extremes from 375 precipitation gaging stations in Canada. The numerical analysis concluded that Canada could be considered as a single homogeneous region in which the L skewness and L kurtosis display no significant spatial variability. Also, on the basis of mean annual precipitation (MAP), Canada can be divided into climatologically homogeneous subregions, in which the L coefficient of variation is virtually constant. The parent distribution was identified as the general extreme value (GEV), the parameters of which depend on the MAP and storm duration. A hierarchical regional approach is proposed for fitting the identified GEV distribution, where the L skewness, L coefficient of variation, and mean are estimated on a regional, subregional, and single-site basis, respectively. Monte Carlo simulations indicate that design storms estimated by the proposed hierarchical approach are substantially more accurate than those estimated by the single-site method. The simulations also demonstrate that the proposed hierarchical approach makes the estimation of design storms at ungaged sites less dependent on the availability of precipitation data. While current procedures for estimating design storms in Canada are exclusively based on single-site frequency analysis, it has long been recognized that regional analysis techniques have the ability to significantly reduce uncertainties in quantile estimates relative to that inherent in the single-site approach [Lettenmaier et al., 1987;Pilon and Adamowski, 1992]. Regionalization procedures can further be considered equivalent to extending of the gaging network and provide planners and designers with a better alternative for the estimation of design storms at ungaged sites than currently used rainfall frequency maps. The advantages of regional frequency models and the large uncertainties involved in single-site approaches justify the need for a new regional methodology leading to more reliable design storm estimates at both short-term record sites and ungaged locations.The purpose of this paper is to develop a regional rainfall frequency approach for estimating design storms in Canada and to evaluate the accuracy of such approach relative to that of the current single-site frequency method. Linear Moment StatisticsThis study draws heavily on the linear moments or L moments [Hosking, 1990]. The L moments suffer less from the effects of sampling variability than the conventional moments, they are more robust to outliers in the data, and hence they enable more secure inferences to be made from samples about the underlying probability distribution [Royston, 1991]. The rth 31,645

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[1] A well-established precept in forest hydrology is that any reduction of forest cover will always have a progressively smaller effect on floods with increasing return period. The underlying logic in snow environments is that during the largest snowmelt events the soils and vegetation canopy have little additional storage capacity and under these conditions much of the snowmelt will be converted to runoff regardless of the amount or type of vegetation cover. Here we show how this preconceived physical understanding, reinforced by the outcomes of numerous paired watershed studies, is indefensible because it is rationalized outside the flood frequency distribution framework. We conduct a meta-analysis of postharvest data at four catchments (3-37 km 2 ) with moderate level of harvesting (33%-40%) to demonstrate how harvesting increases the magnitude and frequency of all floods on record (19-99 years) and how such effects can increase unchecked with increasing return period as a consequence of changes to both the mean (þ11% to þ35%) and standard deviation (À12% to þ19%) of the flood frequency distribution. We illustrate how forest harvesting has substantially increased the frequency of the largest floods in all study sites regardless of record length and this also runs counter to the prevailing wisdom in hydrological science. The dominant process responsible for these newly emerging insights is the increase in net radiation associated with the conversion from longwave-dominated snowmelt beneath the canopy to shortwave-dominated snowmelt in harvested areas, further amplified or mitigated by basin characteristics such as aspect distribution, elevation range, slope gradient, amount of alpine area, canopy closure, and drainage density. Investigating first order environmental controls on flood frequency distributions, a standard research method in stochastic hydrology, represents a paradigm shift in the way harvesting effects are physically explained and quantified in forest hydrology literature.Citation: Green, K. C., and Y. Alila (2012), A paradigm shift in understanding and quantifying the effects of forest harvesting on floods in snow environments, Water Resour. Res., 48, W10503,

[1] This study evaluates the performance and internal structure of the distributed hydrology soil vegetation model (DHSVM) using 1998-2001 data collected at Upper Penticton Creek, British Columbia, Canada. It is shown that clear-cut snowmelt rates calculated using data-derived snow albedo curves are in agreement with observed lysimeter outflow. Measurements in a forest stand with 50% air crown closure suggest that the fraction of shortwave radiation transmitted through the canopy is 0.18-0.28 while the hemispherical canopy view factor controlling longwave radiation fluxes to the forest snowpack is estimated at 0.81 ± 0.07. DHSVM overestimates shortwave transmittance (0.50) and underestimates the view factor (0.50). An alternative forest radiation balance is formulated that is consistent with the measurements. This new formulation improves model efficiency in simulating streamflow from 0.84 to 0.91 due to greater early season melt that results from the enhanced importance of longwave radiation below the canopy. The model captures differences in canopy rainfall interception between small and large storms, tree transpiration measured over a 6-day summer period, and differences in soil moisture between a dry and a wet summer. While the model was calibrated to 1999 snow water equivalent (SWE) and hydrograph data for the untreated control basin, it successfully simulates forest and clear-cut SWE and streamflow for the 3 other years and 4 years of preharvesting and postharvesting streamflow for the second basin. Comparison of model states with the large array of observations suggests that the modified model provides a reliable tool for assessing forest management impacts in the region.

Abstract:The Distributed Hydrology Soil Vegetation Model is applied to the Redfish Creek catchment to investigate the suitability of this model for simulation of forested mountainous watersheds in interior British Columbia and other high-latitude and high-altitude areas. On-site meteorological data and GIS information on terrain parameters, forest cover, and soil cover are used to specify model input. A stepwise approach is taken in calibrating the model, in which snow accumulation and melt parameters for clear-cut and forested areas were optimized independent of runoff production parameters. The calibrated model performs well in reproducing year-to-year variability in the outflow hydrograph, including peak flows. In the subsequent model performance evaluation for simulation of catchment processes, emphasis is put on elevation and temporal differences in snow accumulation and melt, spatial patterns of snowline retreat, water table depth, and internal runoff generation, using internal catchment data as much as possible. Although the overall model performance based on these criteria is found to be good, some issues regarding the simulation of internal catchment processes remain. These issues are related to the distribution of meteorological variables over the catchment and a lack of information on spatial variability in soil properties and soil saturation patterns. Present data limitations for testing internal model accuracy serve to guide future data collection at Redfish Creek. This study also illustrates the challenges that need to be overcome before distributed physically based hydrologic models can be used for simulating catchments with fewer data resources.

Abstract. Preferential flow paths have been found to be important for runoff generation, solute transport, and slope stability in many areas around the world. Although many studies have identified the particular characteristics of individual features and measured the runoff generation and solute transport within hillslopes, very few studies have determined how individual features are hydraulically connected at a hillslope scale. In this study, we used dye staining and excavation to determine the morphology and spatial pattern of a preferential flow network over a large scale (30 m). We explore the feasibility of extending small-scale dye staining techniques to the hillslope scale. We determine the lateral preferential flow paths that are active during the steady-state flow conditions and their interaction with the surrounding soil matrix. We also calculate the velocities of the flow through each cross-section of the hillslope and compare them to hillslope scale applied tracer measurements. Finally, we investigate the relationship between the contributing area and the characteristics of the preferential flow paths. The experiment revealed that larger contributing areas coincided with highly developed and hydraulically connected preferential flow paths that had flow with little interaction with the surrounding soil matrix. We found evidence of subsurface erosion and deposition of soil and organic material laterally and vertically within the soil. These results are important because they add to the understanding of the runoff generation, solute transport, and slope stability of preferential flow-dominated hillslopes.