Insufficient availability of weather stations recording air temperature is a common problem in many alpine regions. The low station density combined with the high variability of air temperature means that interpolated fields based on simple or more complex interpolation techniques are unlikely to be representative of the real patterns of air temperature. In this study, a novel method was developed to tackle this problem, following initial investigation of lapse rate variability in the study domain: the alpine Clutha catchment, New Zealand. Owing to a series of complexities in lapse rate variability, a multi‐layer approach was adopted to produce 1 km2 daily fields of maximum (Tmax) and minimum air temperature (Tmin). The first layer of the Tmax and Tmin models was calculated using a trivariate thin‐plate spline, which was constrained to the elevations of the continuous network to avoid unrealistic extrapolation. To compensate for missing continuous high elevation records, two lapse rate models were implemented to scale air temperature above the first layer. The two lapse rate models were based on the dominant processes driving lapse rate variation, which were found to be cold air ponding (Tmin) and spatial differences in relative humidity (Tmax). Independent station records were used to assess accuracy and compare the resultant fields to an existing product (the Virtual Climate Station Network) and a more conventional method based on a bivariate spline and a constant lapse rate. The validation revealed that the new methods developed here have led to a substantial improvement in producing spatial estimates of Tmax, with a mean root mean square error (RMSE) of 2.38 °C, while progress in regard to Tmin was more limited (mean RMSE of 2.93 °C). As such, this work demonstrates that inclusion of the driving processes controlling lapse rates in interpolation routines can lead to improvements in accuracy.

Abstract. As climate change is projected to alter both temperature and precipitation, snow-controlled mid-latitude catchments are expected to experience substantial shifts in their seasonal regime, which will have direct implications for water management. In order to provide authoritative projections of climate change impacts, the uncertainty inherent to all components of the modelling chain needs to be accounted for. This study assesses the uncertainty in potential impacts of climate change on the hydro-climate of a headwater sub-catchment of New Zealand's largest catchment (the Clutha River) using a fully distributed hydrological model (WaSiM) and unique ensemble encompassing different uncertainty sources: general circulation model (GCM), emission scenario, bias correction and snow model. The inclusion of snow models is particularly important, given that (1) they are a rarely considered aspect of uncertainty in hydrological modelling studies, and (2) snow has a considerable influence on seasonal patterns of river flow in alpine catchments such as the Clutha. Projected changes in river flow for the 2050s and 2090s encompass substantial increases in streamflow from May to October, and a decline between December and March. The dominant drivers are changes in the seasonal distribution of precipitation (for the 2090s +29 to +84 % in winter) and substantial decreases in the seasonal snow storage due to temperature increase. A quantitative comparison of uncertainty identified GCM structure as the dominant contributor in the seasonal streamflow signal (44-57 %) followed by emission scenario (16-49 %), bias correction (4-22 %) and snow model (3-10 %). While these findings suggest that the role of the snow model is comparatively small, its contribution to the overall uncertainty was still found to be noticeable for winter and summer.

Abstract.As climate change is projected to alter both temperature and precipitation, snow controlled mid-latitude catchments are expected to experience substantial shifts in their seasonal regime, which will have direct implications for water management. In order to provide authoritative projections of climate change impacts, the uncertainty inherent to all 10 components of the modelling chain needs to be accounted for. This study assesses the uncertainty in potential impacts of climate change on the hydro-climate of New Zealand's largest catchment (the Clutha River) using a fully distributed hydrological model (WaSiM) and unique ensemble encompassing different uncertainty sources: General Circulation Model (GCM), emission scenario, bias correction and snow model. The inclusion of snow models is particularly important, given that (1) they are a rarely considered aspect of uncertainty in hydrological modelling studies, and (2) snow has a considerable 15 influence on seasonal patterns of river flow in alpine catchments such as the Clutha. Projected changes in river flow for the 2050s and 2090s encompass substantial increases in streamflow from May to October, and a decline between December and March. The dominant drivers are changes in the seasonal distribution of precipitation (for the 2090s +25 to +76% in winter) and substantial decreases in the seasonal snow storage due to temperature increase. A quantitative comparison of uncertainty identified GCM structure as the dominant contributor in the seasonal streamflow signal (44-57%) followed by emission 20 scenario (16-49%), bias correction (4-22%) and snow model (3-10%). While these findings suggest that the role of the snow model is comparatively small, its contribution to the overall uncertainty was still found to be noticeable for winter and summer.