Impacts of Light-Absorbing Impurities in Snow and Ice on Hydrology
About one sixth of the world’s population is dependent on fresh water supply from snow or glacier melt. A detailed understanding of what drives the timing and quantity of snow melt and streamflow is therefore paramount, particularly in a changing climate. This PhD thesis focused on a mechanism that...
| Published in: | Hydrology and Earth System Sciences |
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| Main Author: | |
| Format: | Doctoral or Postdoctoral Thesis |
| Language: | English |
| Published: |
2019
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10852/71261 http://urn.nb.no/URN:NBN:no-74396 |
| Summary: | About one sixth of the world’s population is dependent on fresh water supply from snow or glacier melt. A detailed understanding of what drives the timing and quantity of snow melt and streamflow is therefore paramount, particularly in a changing climate. This PhD thesis focused on a mechanism that affects both the timing and amount of snow melt: small particles mixed within the snow that darken the snow surface and accelerate snow melt through increased absorption of solar radiation. These particles originate from both human and natural sources, such as the combustion of fossil fuels, forest fires, and dust from soils and deserts. The main contribution of this thesis was the development of a snow model that allows the consideration of this darkening effect and the application within a hydrological model. The model simulations reveal significant consequences of the effect for snow melt and streamflow in study regions located in Norway and the Indian Himalayas. Another major outcome was the development of an approach that allowed to improve model predictions by using satellite data giving an estimate on the darkening effect. Combining model predictions with satellite data further allowed to improve the estimate of the amount of particles transported to the study region in the Himalayas. Finally, this thesis also raised attention to the limitations of the model and quantified the uncertainties of the model predictions. |
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