Improving estimation of spatial precipitation in mountain regions

Around 22% of the global population depend on mountain runoff for their water supply. Due to its importance for future water resources, as well as flood and drought planning, an improved understanding of spatial precipitation patterns in mountain regions is needed. Precipitation gauge networks are sparse and traditional methods of interpolation yield inadequate precipitation fields for poorly gauged mountain catchments. This research project builds on a new method, Random Mixing, to generate multiple random spatial daily precipitation fields, conditioned on gauge observations. The Random Mixing algorithm has so far been tested on larger, densely gauged catchments. This project adapts the approach for a sparsely gauged, small 9.1 km2 mountain catchment, Marmot Creek Research Basin in Alberta, Canada, where elevations range between 1600 m and 2825 m above sea level (a.s.l.). Quality-controlled total precipitation (i.e., rainfall and snowfall) gauge observations, for an 11-year period, from three weather stations around the catchment have been used to condition the random spatial fields. Three modifications have been made to the Random Mixing method: improving spatial covariance, introducing elevation dependence and evaluating seasonal effects. Leave-one-out cross-validation is used, comparing spatial fields from the new method with other spatial interpolation techniques, including Inverse Distance Weighting and Kriging with External Drift. Results are promising: even with very few gauges, improving the way that spatial covariance relationships between gauge locations are represented in the model has enhanced the quality of the spatial fields. To optimise selection of the most plausible fields, ensemble hydrological simulations are run, using a modified version of the HBV spatially-distributed conceptual model, and the physically-based Cold Regions Hydrological Model (CRHM), with spatial precipitation fields generated on a 50 m2 regular model grid. Optimisation involves the use of metrics, primarily Nash-Sutcliffe Efficiency (NSE) and bias, to identify the fields that result in the best match between observed and simulated streamflows. Outputs from HBV and CRHM ensemble simulations are compared to evaluate the impact of model structure on catchment response and spatial precipitation field optimisation.