Comment on tc-2022-64

Abstract

Melt water from snow and ice dominated mountainous catchments is a valuable source of fresh water in many regions. Seasonal snow cover and glaciers act like a natural reservoir by storing precipitation during winter and releasing it in spring and summer. Snowmelt runoff is usually modelled either by energy balance or by temperature-index approaches. The energy balance approach is process-based and more sophisticated but requires extensive input data, while the temperature-index approach uses the degree-day factor (DDF) as key parameter to estimate melt merely from air temperature. Despite its simplicity, the temperature-index approach has proved to be a powerful tool for simulating the melt process especially in large and data scarce catchments. The present study attempts to quantify the effects of spatial, temporal, and climatic conditions on the DDF, in order to gain a better understanding which influencing factors are decisive under which conditions. The analysis is physically based on the individual energy flux components, however approximate formulas for estimating the DDF are presented to account for situations where observed data is limited. A detailed comparison between observed and estimated DDF values yielded a fair agreement with BIAS= 0.2 mm °C-1 d-1 and RMSE=1.1 mm °C-1 d-1. The analysis of the energy balance processes controlling snowmelt indicates that cloud cover and under clear sky snow albedo are the most decisive factors for estimating the DDF. The results of this study further underline that the DDF changes as the melt season progresses and thus also with altitude, since melting conditions arrive later at higher elevations. A brief analysis of the DDF under the influence of climate change shows that the DDFs are expected to decrease when comparing periods of similar degree-days, as melt will occur earlier in the year and albedo is then likely to be higher. Therefore, the DDF cannot be treated as a constant parameter especially when using temperature-index models for forecasting present or predicting future water availability.