Dr Keith Musselman, Postdoctoral Research Associate at the Centre for Hydrology, will present a seminar entitled
Inter-annual snow accumulation and melt patterns in forested and alpine terrain;
a case study from the Sierra Nevada, California
On Wednesday October 3rd, 2012, at 11:30am, in AGRI 1E85
Results are presented from a study of snowpack dynamics in the southern Sierra Nevada, California. The study area covers 1800 km2 and a 3600 m elevation gradient. The accuracy of a distributed snow model is evaluated against a multi-scale suite of field measurements including a network of snow depth sensors, basin-scale manual surveys, and airborne LiDAR. In general, the model accurately simulated the seasonal maximum snow depth and SWE at lower and middle elevations. The model overestimated SWE at upper elevations where wind effects are pronounced and no precipitation measurements were available. The SWE errors were partially explained (R2 > 0.80, p<0.01) by the distance of the SWE measurement from the nearest precipitation gauge. The results suggest that precipitation uncertainty and wind redistribution are both critical limitations on snow model accuracy, particularly at upper elevations. Analyses of snowmelt patterns highlight distinct differences in melt dynamics at lower, middle, and upper elevations. Specifically, forested middle elevations experienced the most sustained snowmelt at relatively low seasonal average melt rates (~ 5 mm day-1). This unique melt timing and rate may be critical to the local forest ecosystem. Furthermore, the three years evaluated in this study indicate a marked sensitivity of this elevation range to seasonal meteorology, suggesting that it could be highly sensitive to future changes in climate.

Dr Taufique Mahmood, of the Global Institute of Water Security and Centre for Hydrology, will present a seminar on
Hydrologic Spatial Patterns in a semiarid Ponderosa Pine hillslopes
on Monday 16 July, 2012, at 2:00pm, in Room 146 Kirk Hall
Ponderosa pine forests are a dominant land cover type in semiarid montane areas. Water supplies in major rivers of the southwestern United States depend on ponderosa pine forests as these ecosystems:
(1) receive a significant amount of rainfall and snowfall,
(2) intercept precipitation and transpire water, and
(3) indirectly influence runoff by impacting the infiltration rate.
However, the hydrologic patterns in these ecosystems with strong seasonality are poorly understood. In this study, we use a distributed hydrologic model to understand hydrologic patterns in a patchy ponderosa pine landscape. Our modeling effort is focused on the hydrologic responses during North American Monsoon (NAM) and winter to summer transitional period.
Our findings indicate that vegetation patterns primarily influence the hillslope hydrologic response during dry summer periods leading to patchiness related to the ponderosa pine stands. The spatial response patterns switch to fine-scale terrain curvature controls during persistently wet NAM periods. Thus, a climatic threshold involving rainfall and weather conditions during the NAM is identified in the hillslope response when sufficient lateral soil moisture fluxes are activated by high rainfall amounts and the lower evapotranspiration induced by cloud cover.
Our findings on the winter to summer transitional period indicate the importance of the relative wetness of each season. For a sequence with a wet winter and a dry summer, a robust snowpack results in abundant soil moisture in the hillslope that persists until the summer season when evapotranspiration consumes it. Under these conditions, the hillslope lateral transport becomes disconnected during the spring transition. We observe an opposite sequence of events when a dry winter is followed by a wet summer period. For each case, the spatial controls on hillslope hydrologic patterns are assessed relative to the terrain and vegetation distributions.
Results from this work have implications on the design of hillslope experiments, the resolution of hillslope scale models, and the prediction of hydrologic conditions in ponderosa pine landscapes. Further, the proposed methodology can be useful for predicting responses to climate and land cover changes that are anticipated for the southwestern United States.

MSc candidate Chris Marsh will present a seminar on
Implications of mountain shading on calculating energy for snowmelt using unstructured triangular meshes

On Thursday June 28, 2012, at 10am, in 146 Kirk Hall
In many parts of the world, snowmelt energy is dominated by solar irradiance. This is the case in the Canadian Rocky Mountains, where clear skies dominate the winter and spring. In mountainous regions, solar irradiance at the snow surface is not only affected by solar angles, atmospheric transmittance, and the slope and aspect of immediate topography, but also by shadows from surrounding terrain. Accumulation of errors in estimating solar irradiation can lead to significant errors in calculating the timing and rate of snowmelt due to the seasonal storage of internal energy in the snowpack. Gridded methods are often used to estimate solar irradiance in complex terrain. These methods work best with high-resolution gridded digital elevation models (DEMs), such as those produced using LiDAR. However, such methods also introduce errors due to the rigid nature of the mesh, creating artefacts and other artificial problems. Unstructured triangular meshes, such as triangulated irregular networks, are more efficient in their use of DEM data than fixed grids when producing solar irradiance information for spatially distributed snowmelt calculations and they do not suffer from the artefact problems of a gridded DEM. This project demonstrates the use of a horizon-shading algorithm model with an unstructured mesh versus standard self-shading algorithms. A systematic over-prediction in irradiance is observed when only self-shadows are considered. This over-prediction can be equivalent to 20% of total pre-melt snow accumulation. The modelled results are diagnosed by comparison to measurements of mountain shadows by time-lapse digital cameras and solar irradiance by a network of radiometers in Marmot Creek Research Basin, Alberta, Canada.