Degree-day model, based on a linear relationship between glacier/snow ablation and positive accumulated temperature, is widely used for estimating glacier and snow melt for its simplicity and outstanding performance. However, degree-day factor (DDF), as the most important parameter in this model, is difficult to obtain in an extensive region, especially in Yarlung Zangbo River Basin, which is the highest basin in the world and characterized by rugged and complex terrain. With the development of distributed hydrological model, increasing demand for spatial dataset of DDFs has become a challenging issue in the relevant fields. The objective of this study is to develop an algorithm to spatially estimate the positive DDFs based on available observed DDFs. Topographical factors are fully taken into account to correlate with DDFs, and spatial DDFs in moderate resolution are generated by stepwise and neighboring mean filtering improved in this study. Finally, the produced DDFs are indirectly validated by snow lines in different orientations, and the correlation coefficient of up to 0.82 proves that the DDFs generated in the present study are reasonable in applications. The spatial distribution of DDFs in Yarlung Zangbo River Basin shows that DDFs in downstream were lower than those in upstream and midstream, which is probably because there is less precipitation and more solar radiation in upstream and midstream than in downstream, due to monsoon from India Ocean. To better estimate glacial meltwater runoff and manage water resources in Yarlung Zangbo River Basin, further studies on spatial variability in DDFs are yet needed.
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