Developing precipitation datasets for mountain regions in a changing climate

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.

To optimise selection of the most plausible fields, ensemble hydrological simulations are run, initially using a Python-coded version of the HBV spatially-distributed conceptual model, 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. Sensitivity of these fields to seasonality, elevation and precipitation intensity is tested.

Results so far 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. The biggest improvement to date is from explicitly modelling the precipitation / elevation relationship, introducing gradients, and applying daily dry day and wet day parameters to each grid cell across the model domain.

Intended future work will aim to further refine the process using a physically-based spatially distributed model, the Cold Regions Hydrological Model (CRHM). Spatial fields generated using other random methods will be used to evaluate the performance of the new technique. Long time-period flood frequency curves generated using each approach will be compared. Different methods of phase partitioning will be evaluated to identify impacts on extreme flooding which is often controlled by snowpack melt. Climate change perturbations will be applied to generate potential future flood estimates.