Abstract: In this paper, a new numerical model has been developed for simulation of Qanat-aquifer system. This model employs quasi-3D mixed-form of Richards’ equation and 1D fully-hydrodynamic form of Saint-Venant equations to simulate subsurface and overland flow, respectively. In order to handle non-orthogonal grids, subsurface flow module benefits from coordinate transformation technique. Using the above-mentioned governing equations, the presented model is able to simulate water flow inside both collection and conveyance sections of the gallery as well as dynamics of groundwater and vadose zone from impermeable bed rock to the soil-air interface. Since measured data corresponding to the hydraulics of Qanats is scarce, the overland and subsurface modules have been validated with analytical, numerical and experimental benchmarks in the literature. Subsequently, the model was employed to simulate ten different hypothetical aquifer-Qanat systems with different properties including the depth of groundwater aquifer, roughness of the gallery and saturated hydraulic conductivity of the gallery-aquifer boundary and the influence of each the parameters was monitored on the outflow rate at the appearance point of each Qanat. Furthermore, the advance of water inside two initially dry galleries were simulated at different time levels up to steady state. Eventually, the streamlines have been shown at the steady state for two Qanat-aquifer systems. Although, the presented study sheds light on some aspects of Qanat-aquifer hydraulics, the validation of the presented model with in-lab or on-field data remains ongoing for the future researches.

Abstract: Drawing on advances in nonstationary frequency analysis and the science of causation and attribution, this study employs a newly developed nonstationary stochastic paired watershed approach to determine the effect of forest harvesting on snowmelt-generated floods. Moreover, this study furthers the application of stochastic physics to evaluate the environmental controls and drivers of flood response. Physically-based climate and time-varying harvesting data are used as covariates to drive the nonstationary flood frequency distribution parameters to detect, attribute, and quantify the effect of harvesting on floods in the snow-dominated Deadman River (878 km2) and nested Joe Ross Creek (99 km2) watersheds. Harvesting only 21% of the watershed caused a 38% and 84% increase in the mean but no increase in variability around the mean of the frequency distribution in the Deadman River and Joe Ross Creek, respectively. Consequently, the 7-year, 20-year, 50-year, and 100-year flood events became approximately two, four, six, and ten times more frequent in both watersheds. An increase in the mean is posited to occur from an increase in moisture availability following harvest from suppressed snow interception and increased net radiation reaching the snowpack. Variability was not increased because snowmelt synchronization was inhibited by the buffering capacity of abundant lakes, evenly distributed aspects, and widespread spatial distribution of cutblocks in the watersheds, preventing any potential for harvesting to increase the efficiency of runoff delivery to the outlet. Consistent with similar recent studies, the effect of logging on floods is controlled not only by the harvest rate but most importantly the physiographic characteristics of the watershed and the spatial distribution of the cutblocks. Imposed by the probabilistic framework to understanding and predicting the relation between extremes and their environmental controls, commonly used in the general sciences but not forest hydrology, it is the inherent nature of snowmelt-driven flood regimes which cause even modest increases in magnitude, especially in the upper tail of the distribution, to translate into surprisingly large changes in frequency. Contrary to conventional wisdom, harvesting influenced small, medium, and very large flood events, and the sensitivity to harvest increased with increasing flood event size and watershed area.