Articles | Volume 17, issue 1
https://doi.org/10.5194/tc-17-211-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-17-211-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Estimating degree-day factors of snow based on energy flux components
Muhammad Fraz Ismail
CORRESPONDING AUTHOR
TUM School of Engineering and Design, Technical University of Munich, Munich, Germany
Department of Civil Engineering, Koblenz University of Applied
Sciences, Koblenz, Germany
Wolfgang Bogacki
Department of Civil Engineering, Koblenz University of Applied
Sciences, Koblenz, Germany
Markus Disse
TUM School of Engineering and Design, Technical University of Munich, Munich, Germany
Michael Schäfer
Department of Civil Engineering, Koblenz University of Applied
Sciences, Koblenz, Germany
Faculty of Agriculture, Yamagata University, Tsuruoka, Japan
Lothar Kirschbauer
Department of Civil Engineering, Koblenz University of Applied
Sciences, Koblenz, Germany
Related authors
No articles found.
Timo Schaffhauser, Florentin Hofmeister, Gabriele Chiogna, Fabian Merk, Ye Tuo, Julian Machnitzke, Lucas Alcamo, Jingshui Huang, and Markus Disse
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-89, https://doi.org/10.5194/hess-2024-89, 2024
Preprint under review for HESS
Short summary
Short summary
The glacier-expanded SWAT version, SWAT-GL, was tested in four different catchments. The assessment highlighted the capabilities of the glacier routine. It was evaluated based on the representation of glacier mass balance, snow cover and glacier hypsometry. It was shown that glacier changes over a long time scale could be adequately represented, leading to promising potential future applications in glaciated and high mountain environments.
Fabian Merk, Timo Schaffhauser, Faizan Anwar, Ye Tuo, Jean-Martial Cohard, and Markus Disse
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-131, https://doi.org/10.5194/hess-2024-131, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
ET is computed from vegetation (plant transpiration) and soil (soil evaporation). In Western Africa, plant transpiration correlates with vegetation growth. Vegetation is often represented with the leaf-area-index (LAI). In this study, we evaluate the importance of LAI for the ET calculation. We take a close look at the LAI-ET interaction and show the relevance to consider both, LAI and ET. Our work contributes to the understanding of the processes of the terrestrial water cycle.
Lu Tian, Markus Disse, and Jingshui Huang
Hydrol. Earth Syst. Sci., 27, 4115–4133, https://doi.org/10.5194/hess-27-4115-2023, https://doi.org/10.5194/hess-27-4115-2023, 2023
Short summary
Short summary
Anthropogenic global warming accelerates the drought evolution in the water cycle, increasing the unpredictability of drought. The evolution of drought is stealthy and challenging to track. This study proposes a new framework to capture the high-precision spatiotemporal progression of drought events in their evolutionary processes and characterize their feature further. It is crucial for addressing the systemic risks within the hydrological cycle associated with drought mitigation.
Punit K. Bhola, Jorge Leandro, and Markus Disse
Nat. Hazards Earth Syst. Sci., 20, 2647–2663, https://doi.org/10.5194/nhess-20-2647-2020, https://doi.org/10.5194/nhess-20-2647-2020, 2020
Short summary
Short summary
In operational flood risk management, a single best model is used to assess the impact of flooding, which might misrepresent uncertainties in the modelling process. We have used quantified uncertainties in flood forecasting to generate flood hazard maps that were combined based on different exceedance probability scenarios with the purpose to differentiate impacts of flooding and to account for uncertainties in flood hazard maps that can be used by decision makers.
Yang Yu, Markus Disse, Philipp Huttner, Xi Chen, Andreas Brieden, Marie Hinnenthal, Haiyan Zhang, Jiaqiang Lei, Fanjiang Zeng, Lingxiao Sun, Yuting Gao, and Ruide Yu
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2020-80, https://doi.org/10.5194/hess-2020-80, 2020
Manuscript not accepted for further review
Short summary
Short summary
The afforestation actions in China have attracted widely attention in recent years. This paper presents a hydro-ecological modeling approach to assess environmental changes and ecosystem services in the largest inland river basin in China. Our result indicates China's tree-planting in the Tarim River Basin is strictly strained by water stress and 25.9 % of the existing area of natural vegetation will be degraded by 2050. It is a warning for decision-makers and stakeholders.
Punit Kumar Bhola, Jorge Leandro, and Markus Disse
Nat. Hazards Earth Syst. Sci., 19, 1445–1457, https://doi.org/10.5194/nhess-19-1445-2019, https://doi.org/10.5194/nhess-19-1445-2019, 2019
Short summary
Short summary
This study investigates the use of measured water levels to reduce uncertainty bounds of two-dimensional hydrodynamic model output. Uncertainty assessment is generally not reported in practice due to the lack of best practices and too wide uncertainty bounds. Hence, a novel method to reduce the bounds by constraining the model parameter, mainly roughness, is presented. The operational practitioners as well as researchers benefit from the study in the field of flood risk management.
Dagnenet Fenta Mekonnen, Zheng Duan, Tom Rientjes, and Markus Disse
Hydrol. Earth Syst. Sci., 22, 6187–6207, https://doi.org/10.5194/hess-22-6187-2018, https://doi.org/10.5194/hess-22-6187-2018, 2018
Short summary
Short summary
Understanding responses by changes in land use and land cover (LULC) and climate over the past decades on streamflow in the upper Blue Nile River basin is important for water management and water resource planning. Streamflow in the UBNRB has shown an increasing trend over the last 40 years, while rainfall has shown no trend change. LULC change detection findings indicate increases in cultivated land and decreases in forest coverage prior to 1995.
Beatrice Dittes, Maria Kaiser, Olga Špačková, Wolfgang Rieger, Markus Disse, and Daniel Straub
Nat. Hazards Earth Syst. Sci., 18, 1327–1347, https://doi.org/10.5194/nhess-18-1327-2018, https://doi.org/10.5194/nhess-18-1327-2018, 2018
Short summary
Short summary
We study flood protection options in a pre-alpine catchment in southern Germany. Protection systems are evaluated probabilistically, taking into account climatic and other uncertainties as well as the possibility of future adjustments. Despite large uncertainty in damage, cost, and climate, we arrive at a rough recommendation. Hence, one can make good decisions under large uncertainty. The results also show it is preferable to plan risk-based rather than protecting from a specific design flood.
Dagnenet Fenta Mekonnen and Markus Disse
Hydrol. Earth Syst. Sci., 22, 2391–2408, https://doi.org/10.5194/hess-22-2391-2018, https://doi.org/10.5194/hess-22-2391-2018, 2018
Short summary
Short summary
In this study we used multimodel GCMs (because of recognized intervariable biases in host GCMs) and two widely used statistical downscaling techniques (LARS-WG and SDSM) to see comparative performances in the Upper Blue Nile River basin, where there is high climate variability. The result from the two downscaling models suggested that both SDSM and LARS-WG approximate the observed climate data reasonably well and project an increasing trend for precipitation and maximum and minimum temperature.
Muhammad Fraz Ismail and Wolfgang Bogacki
Hydrol. Earth Syst. Sci., 22, 1391–1409, https://doi.org/10.5194/hess-22-1391-2018, https://doi.org/10.5194/hess-22-1391-2018, 2018
Erwin Isaac Polanco, Amr Fleifle, Ralf Ludwig, and Markus Disse
Hydrol. Earth Syst. Sci., 21, 4907–4926, https://doi.org/10.5194/hess-21-4907-2017, https://doi.org/10.5194/hess-21-4907-2017, 2017
Short summary
Short summary
In this research, SWAT was used to model the upper Blue Nile Basin where comparisons between ground and CFSR data were done. Furthermore, this paper introduced the SWAT error index (SEI), an additional tool to measure the level of error of hydrological models. This work proposed an approach or methodology that can effectively be followed to create better and more efficient hydrological models.
Wolfgang Bogacki and M. Fraz Ismail
Proc. IAHS, 374, 137–142, https://doi.org/10.5194/piahs-374-137-2016, https://doi.org/10.5194/piahs-374-137-2016, 2016
Markus Disse
Proc. IAHS, 373, 25–29, https://doi.org/10.5194/piahs-373-25-2016, https://doi.org/10.5194/piahs-373-25-2016, 2016
Short summary
Short summary
The Tarim Basin in Xinjiang province in northwest China is characterized by a hyper arid climate. Climate change and a strong increase in agricultural land use are major challenges for sustainable water management. The largest competition for water resources exists between irrigated fields and natural riparian vegetation. The Sino-German project SuMaRiO provided a decision support system based on ecosystem services and will implement sustainable water management measures in the next 5-year plan.
C. Rumbaur, N. Thevs, M. Disse, M. Ahlheim, A. Brieden, B. Cyffka, D. Duethmann, T. Feike, O. Frör, P. Gärtner, Ü. Halik, J. Hill, M. Hinnenthal, P. Keilholz, B. Kleinschmit, V. Krysanova, M. Kuba, S. Mader, C. Menz, H. Othmanli, S. Pelz, M. Schroeder, T. F. Siew, V. Stender, K. Stahr, F. M. Thomas, M. Welp, M. Wortmann, X. Zhao, X. Chen, T. Jiang, J. Luo, H. Yimit, R. Yu, X. Zhang, and C. Zhao
Earth Syst. Dynam., 6, 83–107, https://doi.org/10.5194/esd-6-83-2015, https://doi.org/10.5194/esd-6-83-2015, 2015
P. Fiener, K. Auerswald, F. Winter, and M. Disse
Hydrol. Earth Syst. Sci., 17, 4121–4132, https://doi.org/10.5194/hess-17-4121-2013, https://doi.org/10.5194/hess-17-4121-2013, 2013
Related subject area
Discipline: Snow | Subject: Energy Balance Obs/Modelling
Modeling snowpack dynamics and surface energy budget in boreal and subarctic peatlands and forests
Understanding wind-driven melt of patchy snow cover
An 11-year record of wintertime snow-surface energy balance and sublimation at 4863 m a.s.l. on the Chhota Shigri Glacier moraine (western Himalaya, India)
Sensitivity of modeled snow grain size retrievals to solar geometry, snow particle asphericity, and snowpack impurities
Metamorphism of snow on Arctic sea ice during the melt season: impact on spectral albedo and radiative fluxes through snow
GABLS4 intercomparison of snow models at Dome C in Antarctica
Divergence of apparent and intrinsic snow albedo over a season at a sub-alpine site with implications for remote sensing
Modelling surface temperature and radiation budget of snow-covered complex terrain
Snow model comparison to simulate snow depth evolution and sublimation at point scale in the semi-arid Andes of Chile
Brief communication: Evaluation of multiple density-dependent empirical snow conductivity relationships in East Antarctica
Effect of small-scale snow surface roughness on snow albedo and reflectance
Impact of forcing on sublimation simulations for a high mountain catchment in the semiarid Andes
Intercomparison and improvement of two-stream shortwave radiative transfer schemes in Earth system models for a unified treatment of cryospheric surfaces
A key factor initiating surface ablation of Arctic sea ice: earlier and increasing liquid precipitation
Forcing the SURFEX/Crocus snow model with combined hourly meteorological forecasts and gridded observations in southern Norway
Observations and simulations of the seasonal evolution of snowpack cold content and its relation to snowmelt and the snowpack energy budget
Jari-Pekka Nousu, Matthieu Lafaysse, Giulia Mazzotti, Pertti Ala-aho, Hannu Marttila, Bertrand Cluzet, Mika Aurela, Annalea Lohila, Pasi Kolari, Aaron Boone, Mathieu Fructus, and Samuli Launiainen
The Cryosphere, 18, 231–263, https://doi.org/10.5194/tc-18-231-2024, https://doi.org/10.5194/tc-18-231-2024, 2024
Short summary
Short summary
The snowpack has a major impact on the land surface energy budget. Accurate simulation of the snowpack energy budget is difficult, and studies that evaluate models against energy budget observations are rare. We compared predictions from well-known models with observations of energy budgets, snow depths and soil temperatures in Finland. Our study identified contrasting strengths and limitations for the models. These results can be used for choosing the right models depending on the use cases.
Luuk D. van der Valk, Adriaan J. Teuling, Luc Girod, Norbert Pirk, Robin Stoffer, and Chiel C. van Heerwaarden
The Cryosphere, 16, 4319–4341, https://doi.org/10.5194/tc-16-4319-2022, https://doi.org/10.5194/tc-16-4319-2022, 2022
Short summary
Short summary
Most large-scale hydrological and climate models struggle to capture the spatially highly variable wind-driven melt of patchy snow cover. In the field, we find that 60 %–80 % of the total melt is wind driven at the upwind edge of a snow patch, while it does not contribute at the downwind edge. Our idealized simulations show that the variation is due to a patch-size-independent air-temperature reduction over snow patches and also allow us to study the role of wind-driven snowmelt on larger scales.
Arindan Mandal, Thupstan Angchuk, Mohd Farooq Azam, Alagappan Ramanathan, Patrick Wagnon, Mohd Soheb, and Chetan Singh
The Cryosphere, 16, 3775–3799, https://doi.org/10.5194/tc-16-3775-2022, https://doi.org/10.5194/tc-16-3775-2022, 2022
Short summary
Short summary
Snow sublimation is an important component of glacier surface mass balance; however, it is seldom studied in detail in the Himalayan region owing to data scarcity. We present an 11-year record of wintertime snow-surface energy balance and sublimation characteristics at the Chhota Shigri Glacier moraine site at 4863 m a.s.l. The estimated winter sublimation is 16 %–42 % of the winter snowfall at the study site, which signifies how sublimation is important in the Himalayan region.
Zachary Fair, Mark Flanner, Adam Schneider, and S. McKenzie Skiles
The Cryosphere, 16, 3801–3814, https://doi.org/10.5194/tc-16-3801-2022, https://doi.org/10.5194/tc-16-3801-2022, 2022
Short summary
Short summary
Snow grain size is important to determine the age and structure of snow, but it is difficult to measure. Snow grain size can be found from airborne and spaceborne observations by measuring near-infrared energy reflected from snow. In this study, we use the SNICAR radiative transfer model and a Monte Carlo model to examine how snow grain size measurements change with snow structure and solar zenith angle. We show that improved understanding of these variables improves snow grain size precision.
Gauthier Vérin, Florent Domine, Marcel Babin, Ghislain Picard, and Laurent Arnaud
The Cryosphere, 16, 3431–3449, https://doi.org/10.5194/tc-16-3431-2022, https://doi.org/10.5194/tc-16-3431-2022, 2022
Short summary
Short summary
Snow physical properties on Arctic sea ice are monitored during the melt season. As snow grains grow, and the snowpack thickness is reduced, the surface albedo decreases. The extra absorbed energy accelerates melting. Radiative transfer modeling shows that more radiation is then transmitted to the snow–sea-ice interface. A sharp increase in transmitted radiation takes place when the snowpack thins significantly, and this coincides with the initiation of the phytoplankton bloom in the seawater.
Patrick Le Moigne, Eric Bazile, Anning Cheng, Emanuel Dutra, John M. Edwards, William Maurel, Irina Sandu, Olivier Traullé, Etienne Vignon, Ayrton Zadra, and Weizhong Zheng
The Cryosphere, 16, 2183–2202, https://doi.org/10.5194/tc-16-2183-2022, https://doi.org/10.5194/tc-16-2183-2022, 2022
Short summary
Short summary
This paper describes an intercomparison of snow models, of varying complexity, used for numerical weather prediction or academic research. The results show that the simplest models are, under certain conditions, able to reproduce the surface temperature just as well as the most complex models. Moreover, the diversity of surface parameters of the models has a strong impact on the temporal variability of the components of the simulated surface energy balance.
Edward H. Bair, Jeff Dozier, Charles Stern, Adam LeWinter, Karl Rittger, Alexandria Savagian, Timbo Stillinger, and Robert E. Davis
The Cryosphere, 16, 1765–1778, https://doi.org/10.5194/tc-16-1765-2022, https://doi.org/10.5194/tc-16-1765-2022, 2022
Short summary
Short summary
Understanding how snow and ice reflect solar radiation (albedo) is important for global climate. Using high-resolution topography, darkening from surface roughness (apparent albedo) is separated from darkening by the composition of the snow (intrinsic albedo). Intrinsic albedo is usually greater than apparent albedo, especially during melt. Such high-resolution topography is often not available; thus the use of a shade component when modeling mixtures is advised.
Alvaro Robledano, Ghislain Picard, Laurent Arnaud, Fanny Larue, and Inès Ollivier
The Cryosphere, 16, 559–579, https://doi.org/10.5194/tc-16-559-2022, https://doi.org/10.5194/tc-16-559-2022, 2022
Short summary
Short summary
Topography controls the surface temperature of snow-covered, mountainous areas. We developed a modelling chain that uses ray-tracing methods to quantify the impact of a few topographic effects on snow surface temperature at high spatial resolution. Its large spatial and temporal variations are correctly simulated over a 50 km2 area in the French Alps, and our results show that excluding a single topographic effect results in cooling (or warming) effects on the order of 1 °C.
Annelies Voordendag, Marion Réveillet, Shelley MacDonell, and Stef Lhermitte
The Cryosphere, 15, 4241–4259, https://doi.org/10.5194/tc-15-4241-2021, https://doi.org/10.5194/tc-15-4241-2021, 2021
Short summary
Short summary
The sensitivity of two snow models (SNOWPACK and SnowModel) to various parameterizations and atmospheric forcing biases is assessed in the semi-arid Andes of Chile in winter 2017. Models show that sublimation is a main driver of ablation and that its relative contribution to total ablation is highly sensitive to the selected albedo parameterization and snow roughness length. The forcing and parameterizations are more important than the model choice, despite differences in physical complexity.
Minghu Ding, Tong Zhang, Diyi Yang, Ian Allison, Tingfeng Dou, and Cunde Xiao
The Cryosphere, 15, 4201–4206, https://doi.org/10.5194/tc-15-4201-2021, https://doi.org/10.5194/tc-15-4201-2021, 2021
Short summary
Short summary
Measurement of snow heat conductivity is essential to establish the energy balance between the atmosphere and firn, but it is still not clear in Antarctica. Here, we used data from three automatic weather stations located in different types of climate and evaluated nine schemes that were used to calculate the effective heat diffusivity of snow. The best solution was proposed. However, no conductivity–density relationship was optimal at all sites, and the performance of each varied with depth.
Terhikki Manninen, Kati Anttila, Emmihenna Jääskeläinen, Aku Riihelä, Jouni Peltoniemi, Petri Räisänen, Panu Lahtinen, Niilo Siljamo, Laura Thölix, Outi Meinander, Anna Kontu, Hanne Suokanerva, Roberta Pirazzini, Juha Suomalainen, Teemu Hakala, Sanna Kaasalainen, Harri Kaartinen, Antero Kukko, Olivier Hautecoeur, and Jean-Louis Roujean
The Cryosphere, 15, 793–820, https://doi.org/10.5194/tc-15-793-2021, https://doi.org/10.5194/tc-15-793-2021, 2021
Short summary
Short summary
The primary goal of this paper is to present a model of snow surface albedo (brightness) accounting for small-scale surface roughness effects. It can be combined with any volume scattering model. The results indicate that surface roughness may decrease the albedo by about 1–3 % in midwinter and even more than 10 % during the late melting season. The effect is largest for low solar zenith angle values and lower bulk snow albedo values.
Marion Réveillet, Shelley MacDonell, Simon Gascoin, Christophe Kinnard, Stef Lhermitte, and Nicole Schaffer
The Cryosphere, 14, 147–163, https://doi.org/10.5194/tc-14-147-2020, https://doi.org/10.5194/tc-14-147-2020, 2020
Cheng Dang, Charles S. Zender, and Mark G. Flanner
The Cryosphere, 13, 2325–2343, https://doi.org/10.5194/tc-13-2325-2019, https://doi.org/10.5194/tc-13-2325-2019, 2019
Tingfeng Dou, Cunde Xiao, Jiping Liu, Wei Han, Zhiheng Du, Andrew R. Mahoney, Joshua Jones, and Hajo Eicken
The Cryosphere, 13, 1233–1246, https://doi.org/10.5194/tc-13-1233-2019, https://doi.org/10.5194/tc-13-1233-2019, 2019
Short summary
Short summary
The variability and potential trends of rain-on-snow events over Arctic sea ice and their role in sea-ice losses are poorly understood. This study demonstrates that rain-on-snow events are a critical factor in initiating the onset of surface melt over Arctic sea ice, and onset of spring rainfall over sea ice has shifted to earlier dates since the 1970s, which may have profound impacts on ice melt through feedbacks involving earlier onset of surface melt.
Hanneke Luijting, Dagrun Vikhamar-Schuler, Trygve Aspelien, Åsmund Bakketun, and Mariken Homleid
The Cryosphere, 12, 2123–2145, https://doi.org/10.5194/tc-12-2123-2018, https://doi.org/10.5194/tc-12-2123-2018, 2018
Short summary
Short summary
Knowledge of the snow reservoir is important for energy production and water resource management. In this study, a detailed snow model is run over southern Norway with two different sets of forcing data. The results show that forcing data consisting of post-processed data from a numerical weather model (observations assimilated into the raw weather predictions) are most promising for snow simulations when larger regions are evaluated.
Keith S. Jennings, Timothy G. F. Kittel, and Noah P. Molotch
The Cryosphere, 12, 1595–1614, https://doi.org/10.5194/tc-12-1595-2018, https://doi.org/10.5194/tc-12-1595-2018, 2018
Short summary
Short summary
We show through observations and simulations that cold content, a key part of the snowpack energy budget, develops primarily through new snowfall. We also note that cold content damps snowmelt rate and timing at sub-seasonal timescales, while seasonal melt onset is controlled by the timing of peak cold content and total spring precipitation. This work has implications for how cold content is represented in snow models and improves our understanding of its effect on snowmelt processes.
Cited articles
Ahmad, M. J. and Tiwari, G. N.: Solar radiation models-A review, Int. J.
Energy Res., 35, 271–290, https://doi.org/10.1002/er.1690, 2011.
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M.: Crop
evapotranspiration – guidelines for computing crop water requirements, FAO Irrigation and Drainage Paper 56, FAO, Rome,
Italy, 300 pp., 1998.
Amaral, T., Wake, C. P., Dibb, J. E., Burakowski, E. A., and Stampone, M.: A
simple model of snow albedo decay using observations from the Community
Collaborative Rain, Hail, and Snow-Albedo (CoCoRaHS-Albedo) Network, J.
Glaciol., 63, 877–887, https://doi.org/10.1017/jog.2017.54, 2017.
Ambach, W.: Characteristics of the Heat Balance of the Greenland Ice sheet
for Modelling, J. Glaciol., 31, 3–12,
https://doi.org/10.3189/S0022143000004925, 1985.
Anderson, E. A.: National Weather Service river forecast system-snow accumulation
and ablation model. National Oceanographic and Atmospheric Administration (NOAA), Tech.
Mem., NWS
HYDRO-17, US Dept. of Commerce, Silver Spring, MD, 217 pp.,
https://repository.library.noaa.gov/view/noaa/13507 (last access: 3 January 2023), 1973.
Anderson, E. A.: Snow Accumulation and Ablation Model – SNOW-17, NOAA's
National Weather Service, Office of Hydrologic Development, Silver Spring, https://www.weather.gov/media/owp/oh/hrl/docs/22snow17.pdf (last access: 3 January 2023),
2006.
Annandale, J., Jovanovic, N., Benadé, N., and Allen, R.: Software for
missing data error analysis of Penman-Monteith reference evapotranspiration,
Irrigation Sci., 21, 57–67, https://doi.org/10.1007/s002710100047, 2002.
Arendt, A. A. and Sharp, M. J.: Energy balance measurements on a Canadian
high Arctic glacier and their implications for mass balance modelling,
IAHS-AISH publication, 256, 165–172, 1999.
Asaoka, Y. and Kominami, Y.: Incorporation of satellite-derived snow-cover
area in spatial snowmelt modeling for a large area: determination of a
gridded degree-day factor, Ann. Glaciol., 54, 205–213,
https://doi.org/10.3189/2013AoG62A218, 2013.
Badescu, V. (Ed.): Modeling solar radiation at the earth's surface: recent
advances, Springer, Berlin, 517 pp., https://doi.org/10.1007/978-3-540-77455-6, 2008.
Badescu, V. and Paulescu, M.: Statistical properties of the sunshine number
illustrated with measurements from Timisoara (Romania), Atmos.
Res., 101, 194–204, https://doi.org/10.1016/j.atmosres.2011.02.009,
2011.
Bagchi, A. K.: Areal value of degree-day factor/Valeur spatiale du facteur
degré-jour, Hydrolog. Sci. J., 28, 499–511,
https://doi.org/10.1080/02626668309491991, 1983.
Bergström, S.: Development and application conceptual runoff model for
scandinavian catchments, SMHI, Research Department, Hydrology, 162 pp., http://urn.kb.se/resolve?urn=urn:nbn:se:smhi:diva-5738 (last access: 3 January 2023),
1976.
Bogacki, W. and Ismail, M. F.: Seasonal forecast of Kharif flows from Upper Jhelum catchment, Proc. IAHS, 374, 137–142, https://doi.org/10.5194/piahs-374-137-2016, 2016.
Bolibar, J., Rabatel, A., Gouttevin, I., Zekollari, H., and Galiez, C.:
Nonlinear sensitivity of glacier mass balance to future climate change
unveiled by deep learning, Nat. Commun., 13, 409,
https://doi.org/10.1038/s41467-022-28033-0, 2022.
Bormann, K. J., Evans, J. P., and McCabe, M. F.: Constraining snowmelt in a
temperature-index model using simulated snow densities, J.
Hydrol., 517, 652–667, https://doi.org/10.1016/j.jhydrol.2014.05.073,
2014.
Braithwaite, R. J.: Positive degree-day factors for ablation on the
Greenland ice sheet studied by energy-balance modelling, J. Glaciol., 41,
153–160, https://doi.org/10.3189/S0022143000017846, 1995.
Braithwaite, R. J.: Temperature and precipitation climate at the
equilibrium-line altitude of glaciers expressed by the degree-day factor for
melting snow, J. Glaciol., 54, 437–444,
https://doi.org/10.3189/002214308785836968, 2008.
Braithwaite, R. J. and Hughes, P. D.: Positive degree-day sums in the Alps:
a direct link between glacier melt and international climate policy, J.
Glaciol., 68, 901–911, https://doi.org/10.1017/jog.2021.140, 2022.
Braithwaite, R. J., Konzelmann, T., Marty, C., and Olesen, O. B.:
Reconnaissance Study of glacier energy balance in North Greenland, 1993–94,
J. Glaciol., 44, 239–247, https://doi.org/10.3189/S0022143000002586, 1998.
Braun, L., Grabs, W., and Rana, B.: Application of a Conceptual
Precipitation Runoff Model in the Langtang Kfaola Basin, Nepal Himalaya,
Snow and Glacier Hydrology, 1993.
Braun, L. N.: Simulation of snowmelt-runoff in lowland and lower alpine
regions of Switzerland, PhD Thesis, ETH Zurich,
https://doi.org/10.3929/ETHZ-A-000334295, 1984.
Bristow, K. L. and Campbell, G. S.: On the relationship between incoming
solar radiation and daily maximum and minimum temperature, Agri.
Forest Meteorol., 31, 159–166,
https://doi.org/10.1016/0168-1923(84)90017-0, 1984.
Brutsaert, W.: On a derivable formula for long-wave radiation from clear
skies, Water Resour. Res., 11, 742–744,
https://doi.org/10.1029/WR011i005p00742, 1975.
Brutsaert, W.: Evaporation into the Atmosphere, Springer Netherlands,
Dordrecht, https://doi.org/10.1007/978-94-017-1497-6, 1982.
Campbell, G. S. and Norman, J. M.: Introduction to environmental biophysics,
2nd Edn., Springer, New York, 286 pp., https://www.umfcv.ro/files/!/x/4/_/4_ Intro_Env_MED_.pdf (last access: 3 January 2023), 1998.
Carenzo, M., Pellicciotti, F., Rimkus, S., and Burlando, P.: Assessing the
transferability and robustness of an enhanced temperature-index glacier-melt
model, J. Glaciol., 55, 258–274,
https://doi.org/10.3189/002214309788608804, 2009.
DeWalle, D. R. and Rango, A.: Principles of Snow Hydrology, Cambridge
University Press, Cambridge, https://doi.org/10.1017/CBO9780511535673, 2008.
Doorenbos, J. and Pruitt, W. O.: Guidelines for predicting crop water
requirements, Rev., Food and Agriculture Organization of the United Nations,
Rome, 144 pp., https://www.posmet.ufv.br/wp-content/uploads/2015/08/LIVRO-385-Doorenbos-e-Pruitt-Guidelines-for-predicting-crop-water-requirements.pdf (last access: 3 January 2023) 1977.
Ekici, C.: Total Global Solar Radiation Estimation Models and Applications:
A review, International Journal of Innovative Technology and Interdisciplinary Sciences, 2, 212–228,
https://doi.org/10.15157/IJITIS.2019.2.2.212-228, 2019.
Evrendilek, F. and Ertekin, C.: Assessing solar radiation models using
multiple variables over Turkey, Clim. Dynam., 31, 131–149,
https://doi.org/10.1007/s00382-007-0338-6, 2008.
Frieler, K., Lange, S., Piontek, F., Reyer, C. P. O., Schewe, J., Warszawski, L., Zhao, F., Chini, L., Denvil, S., Emanuel, K., Geiger, T., Halladay, K., Hurtt, G., Mengel, M., Murakami, D., Ostberg, S., Popp, A., Riva, R., Stevanovic, M., Suzuki, T., Volkholz, J., Burke, E., Ciais, P., Ebi, K., Eddy, T. D., Elliott, J., Galbraith, E., Gosling, S. N., Hattermann, F., Hickler, T., Hinkel, J., Hof, C., Huber, V., Jägermeyr, J., Krysanova, V., Marcé, R., Müller Schmied, H., Mouratiadou, I., Pierson, D., Tittensor, D. P., Vautard, R., van Vliet, M., Biber, M. F., Betts, R. A., Bodirsky, B. L., Deryng, D., Frolking, S., Jones, C. D., Lotze, H. K., Lotze-Campen, H., Sahajpal, R., Thonicke, K., Tian, H., and Yamagata, Y.: Assessing the impacts of 1.5 °C global warming – simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b), Geosci. Model Dev., 10, 4321–4345, https://doi.org/10.5194/gmd-10-4321-2017, 2017.
Gafurov, A.: Water balance modeling using remote sensing information: focus
on Central Asia, PhD Thesis, Inst. für Wasserbau, Stuttgart, 116 pp., 2010.
Hargreaves, G. H. and Samani, Z. A.: Estimating Potential
Evapotranspiration, J. Irrig. Drain. Div., 108, 225–230,
https://doi.org/10.1061/JRCEA4.0001390, 1982.
Harpold, A. A. and Brooks, P. D.: Humidity determines snowpack ablation
under a warming climate, P. Natl. Acad. Sci. USA, 115, 1215–1220,
https://doi.org/10.1073/pnas.1716789115, 2018.
Hasson, S., Saeed, F., Böhner, J., and Schleussner, C.-F.: Water
availability in Pakistan from Hindukush–Karakoram–Himalayan watersheds at
1.5 ∘C and 2 ∘C Paris Agreement targets, Adv.
Water Resour., 131, 103365,
https://doi.org/10.1016/j.advwatres.2019.06.010, 2019.
He, Z. H., Parajka, J., Tian, F. Q., and Blöschl, G.: Estimating degree-day factors from MODIS for snowmelt runoff modeling, Hydrol. Earth Syst. Sci., 18, 4773–4789, https://doi.org/10.5194/hess-18-4773-2014, 2014.
Hempel, S., Frieler, K., Warszawski, L., Schewe, J., and Piontek, F.: A trend-preserving bias correction – the ISI-MIP approach, Earth Syst. Dynam., 4, 219–236, https://doi.org/10.5194/esd-4-219-2013, 2013.
Hinzman, L. D. and Kane, D. L.: Snow hydrology of a headwater Arctic basin:
2. Conceptual analysis and computer modeling, Water Resour. Res., 27,
1111–1121, https://doi.org/10.1029/91WR00261, 1991.
Hock, R.: A distributed temperature-index ice- and snowmelt model including
potential direct solar radiation, J. Glaciol., 45, 101–111,
https://doi.org/10.3189/S0022143000003087, 1999.
Hock, R.: Temperature index melt modelling in mountain areas, J.
Hydrol., 282, 104–115, https://doi.org/10.1016/S0022-1694(03)00257-9,
2003.
Hock, R.: Glacier melt: a review of processes and their modelling, Prog.
Phys. Geog., 29, 362–391,
https://doi.org/10.1191/0309133305pp453ra, 2005.
Hock, R. and Noetzli, C.: Areal melt and discharge modelling of
Storglaciären, Sweden, Ann. Glaciol., 24, 211–216,
https://doi.org/10.3189/S0260305500012192, 1997.
Huss, M. and Hock, R.: Global-scale hydrological response to future glacier
mass loss, Nat. Clim. Change, 8, 135–140,
https://doi.org/10.1038/s41558-017-0049-x, 2018.
Immerzeel, W. W., Droogers, P., de Jong, S. M., and Bierkens, M. F. P.:
Large-scale monitoring of snow cover and runoff simulation in Himalayan
river basins using remote sensing, Remote Sens. Environ., 113,
40–49, https://doi.org/10.1016/j.rse.2008.08.010, 2009.
Immerzeel, W. W., Lutz, A. F., Andrade, M., Bahl, A., Biemans, H., Bolch,
T., Hyde, S., Brumby, S., Davies, B. J., Elmore, A. C., Emmer, A., Feng, M.,
Fernández, A., Haritashya, U., Kargel, J. S., Koppes, M., Kraaijenbrink,
P. D. A., Kulkarni, A. V., Mayewski, P. A., Nepal, S., Pacheco, P., Painter,
T. H., Pellicciotti, F., Rajaram, H., Rupper, S., Sinisalo, A., Shrestha, A.
B., Viviroli, D., Wada, Y., Xiao, C., Yao, T., and Baillie, J. E. M.:
Importance and vulnerability of the world's water towers, Nature, 577,
364–369, https://doi.org/10.1038/s41586-019-1822-y, 2020.
Ismail, M. F. and Bogacki, W.: Scenario approach for the seasonal forecast of Kharif flows from the Upper Indus Basin, Hydrol. Earth Syst. Sci., 22, 1391–1409, https://doi.org/10.5194/hess-22-1391-2018, 2018.
Ismail, M. F., Bogacki, W., and Muhammad, N.: Degree-day factor models for
forecasting the snowmelt runoff for Naran watershed, Sci. Int., 27, 1951–1960,
2015.
Ismail, M. F., Naz, B. S., Wortmann, M., Disse, M., Bowling, L. C., and
Bogacki, W.: Comparison of two model calibration approaches and their
influence on future projections under climate change in the Upper Indus
Basin, Climatic Change, 163, 1227–1246,
https://doi.org/10.1007/s10584-020-02902-3, 2020.
Jansson, P., Hock, R., and Schneider, T.: The concept of glacier storage: a
review, J. Hydrol., 282, 116–129,
https://doi.org/10.1016/S0022-1694(03)00258-0, 2003.
Jennings, K. S., Kittel, T. G. F., and Molotch, N. P.: Observations and simulations of the seasonal evolution of snowpack cold content and its relation to snowmelt and the snowpack energy budget, The Cryosphere, 12, 1595–1614, https://doi.org/10.5194/tc-12-1595-2018, 2018.
Jin, Z., Yezheng, W., and Gang, Y.: General formula for estimation of
monthly average daily global solar radiation in China, Energ. Convers.
Managem., 46, 257–268, https://doi.org/10.1016/j.enconman.2004.02.020,
2005.
Kane, D. L., Gieck, R. E., and Hinzman, L. D.: Snowmelt Modeling at Small
Alaskan Arctic Watershed, J. Hydrol. Eng., 2, 204–210,
https://doi.org/10.1061/(ASCE)1084-0699(1997)2:4(204), 1997.
Kayastha, R. B. and Kayastha, R.: Glacio-Hydrological Degree-Day Model (GDM)
Useful for the Himalayan River Basins, in: Himalayan Weather and Climate and
their Impact on the Environment, edited by: Dimri, A. P., Bookhagen, B.,
Stoffel, M., and Yasunari, T., Springer International Publishing, Cham,
379–398, https://doi.org/10.1007/978-3-030-29684-1_19, 2020.
Kayastha, R. B., Ageta, Y., and Nakawo, M.: Positive degree-day factors for
ablation on glaciers in the Nepalese Himalayas: case study on Glacier AX010
in Shorong Himal, Nepal, Bulletin of Glaciological Research, 17, 1–10, 2000.
Kayastha, R. B., Yutaka, A., Masayoshi, N., Koji, F., Akiko, S., and
Yoshihiro, M.: Positive degree-day factors for ice ablation on four glaciers
in the Nepalese Himalayas and Qinghai-Tibetan Plateau, Bulletin of
Glaciological Research, 20, 7–14, 2003.
Klok, E. J., Jasper, K., Roelofsma, K. P., Gurtz, J., and Badoux, A.:
Distributed hydrological modelling of a heavily glaciated Alpine river
basin, Hydrolog. Sci. J., 46, 553–570,
https://doi.org/10.1080/02626660109492850, 2001.
Kopp, G. and Lean, J. L.: A new, lower value of total solar irradiance:
Evidence and climate significance: FRONTIER, Geophys. Res. Lett., 38, L01706,
https://doi.org/10.1029/2010GL045777, 2011.
Kopp, M., Tuo, Y., and Disse, M.: Fully automated snow depth measurements
from time-lapse images applying a convolutional neural network, Sci.
Total Environ., 697, 134213,
https://doi.org/10.1016/j.scitotenv.2019.134213, 2019.
Kustas, W. P., Rango, A., and Uijlenhoet, R.: A simple energy budget
algorithm for the snowmelt runoff model, Water Resour. Res., 30, 1515–1527,
https://doi.org/10.1029/94WR00152, 1994.
Lang, H.: Forecasting Meltwater Runoff from Snow-Covered Areas and from
Glacier Basins, in: River Flow Modelling and Forecasting, edited by:
Kraijenhoff, D. A. and Moll, J. R., Springer Netherlands, Dordrecht, vol. 3,
99–127, https://doi.org/10.1007/978-94-009-4536-4_5, 1986.
Lang, H. and Braun, L.: On the information content of air temperature in the
context of snow melt estimation, IAHS-AISH P., 190, 347–354, 1990.
Lawrence, M. G.: The Relationship between Relative Humidity and the Dewpoint
Temperature in Moist Air: A Simple Conversion and Applications, B. Am.
Meteorol. Soc., 86, 225–234, https://doi.org/10.1175/BAMS-86-2-225, 2005.
Lehning, M., Bartelt, P., Brown, B., and Fierz, C.: A physical SNOWPACK
model for the Swiss avalanche warning, Cold Reg. Sci. Technol.,
35, 169–184, https://doi.org/10.1016/S0165-232X(02)00072-1, 2002.
Liu, Y., Tan, Q., and Pan, T.: Determining the Parameters of the
Ångström-Prescott Model for Estimating Solar Radiation in Different
Regions of China: Calibration and Modeling, Earth Space Sci., 6,
1976–1986, https://doi.org/10.1029/2019EA000635, 2019.
Luo, Y., Arnold, J., Liu, S., Wang, X., and Chen, X.: Inclusion of glacier
processes for distributed hydrological modeling at basin scale with
application to a watershed in Tianshan Mountains, northwest China, J.
Hydrol., 477, 72–85, https://doi.org/10.1016/j.jhydrol.2012.11.005,
2013.
Lutz, A. F., Immerzeel, W. W., Kraaijenbrink, P. D. A., Shrestha, A. B., and
Bierkens, M. F. P.: Climate Change Impacts on the Upper Indus Hydrology:
Sources, Shifts and Extremes, PLoS ONE, 11, e0165630,
https://doi.org/10.1371/journal.pone.0165630, 2016.
MacDougall, A. H., Wheler, B. A., and Flowers, G. E.: A preliminary assessment of glacier melt-model parameter sensitivity and transferability in a dry subarctic environment, The Cryosphere, 5, 1011–1028, https://doi.org/10.5194/tc-5-1011-2011, 2011.
Marks, D., Domingo, J., Susong, D., Link, T., and Garen, D.: A spatially
distributed energy balance snowmelt model for application in mountain
basins, Hydrol. Process., 13, 1935–1959,
https://doi.org/10.1002/(SICI)1099-1085(199909)13:12/13<1935::AID-HYP868>3.0.CO;2-C, 1999.
Marsh, C. B., Pomeroy, J. W., and Spiteri, R. J.: Implications of mountain
shading on calculating energy for snowmelt using unstructured triangular
meshes: Implication of Mountains shading for snowmelt, Hydrol. Process., 26,
1767–1778, https://doi.org/10.1002/hyp.9329, 2012.
Martinec, J.: The degree-day factor for snowmelt-runoff forecasting, IAHS
Commission of Surface Waters, 51, 468–477, 1960.
Martinec, J.: Snowmelt – runoff model for stream flow forecasts, Nord.
Hydrol., 6, 145–154, 1975.
Martinec, J., Rango, A., and Roberts, R.: Snowmelt Runoff Model (SRM) User's
Manual, 180 pp., https://jornada.nmsu.edu/bibliography/08-023.pdf (last access: 3 January 2023), 2008.
Masters, G. M.: Renewable and efficient electric power systems, John Wiley
& Sons, Hoboken, NJ, 654 pp., http://www.a-ghadimi.com/files/Courses/Renewable Energy/REN_Book.pdf (last access: 3 January 2023), 2004.
Matthews, T. and Hodgkins, R.: Interdecadal variability of degree-day
factors on Vestari Hagafellsjökull (Langjökull, Iceland) and the
importance of threshold air temperatures, J. Glaciol., 62, 310–322,
https://doi.org/10.1017/jog.2016.21, 2016.
McGinn, R. A.: Degree-day snowmelt runoff experiments; Clear Lake Watershed,
Riding Mountain National Park, Geographical Essays, 15, ISSN 1911-5814, https://pcag.uwinnipeg.ca/Prairie-Perspectives/PP-Vol15/McGinn.pdf (last access: 3 January 2023), 2012.
Meeus, J.: Astronomical algorithms, 1st English Edn., Willmann-Bell,
Richmond, Va, 429 pp., https://www.agopax.it/Libri_astronomia/pdf/Astronomical Algorithms.pdf (last access: 3 January 2023), 1991.
Monteith, J. L. and Unsworth, M. H.: Principles of environmental physics:
plants, animals, and the atmosphere, 4th Edn., Elsevier/Academic Press,
Amsterdam, Boston, 401 pp., https://denning.atmos.colostate.edu/readings/Monteith.and.Unsworth.4thEd.pdf (last access: 3 January 2023), 2013.
Muhammad, S., Tian, L., Ali, S., Latif, Y., Wazir, M. A., Goheer, M. A.,
Saifullah, M., Hussain, I., and Shiyin, L.: Thin debris layers do not
enhance melting of the Karakoram glaciers, Sci. Total Environ.,
746, 141119, https://doi.org/10.1016/j.scitotenv.2020.141119, 2020.
Murray, F. W.: On the Computation of Saturation Vapor Pressure, J. Appl.
Meteorol., 6, 203–204, https://doi.org/10.1175/1520-0450(1967)006<0203:OTCOSV>2.0.CO;2, 1967.
Musselman, K. N., Clark, M. P., Liu, C., Ikeda, K., and Rasmussen, R.:
Slower snowmelt in a warmer world, Nat. Clim. Change, 7, 214–219,
https://doi.org/10.1038/nclimate3225, 2017.
Oerlemans, J.: Glaciers and climate change, A.A. Balkema Publishers, Lisse,
Exton (PA), 148 pp., 2001.
Pelkowski, J.: A physical rationale for generalized
Ångström–Prescott regression, Sol. Energy, 83, 955–963,
https://doi.org/10.1016/j.solener.2008.12.011, 2009.
Pellicciotti, F., Brock, B., Strasser, U., Burlando, P., Funk, M., and
Corripio, J.: An enhanced temperature-index glacier melt model including the
shortwave radiation balance: development and testing for Haut Glacier
d'Arolla, Switzerland, J. Glaciol., 51, 573–587,
https://doi.org/10.3189/172756505781829124, 2005.
Prasad, V. H. and Roy, P. S.: Estimation of Snowmelt Runoff in Beas Basin,
India, Geocarto Int., 20, 41–47,
https://doi.org/10.1080/10106040508542344, 2005.
Quick, M. C. and Pipes, A.: U.B.C. Watershed model/Le modèle du bassin
versant U.C.B, Hydrolog. Sci. Bull. 22, 153–161,
https://doi.org/10.1080/02626667709491701, 1977.
Rango, A. and Martinec, J.: Application of a Snowmelt-Runoff Model Using
Landsat Data, Hydrol. Res., 10, 225–238,
https://doi.org/10.2166/nh.1979.0006, 1979.
Rango, A. and Martinec, J.: Revisiting the degree-day method for snowmelt
computations, J. Am. Water Resour. Assoc., 31, 657–669,
https://doi.org/10.1111/j.1752-1688.1995.tb03392.x, 1995.
Reda, I. and Andreas, A.: Solar position algorithm for solar radiation
applications, Sol. Energy, 76, 577–589,
https://doi.org/10.1016/j.solener.2003.12.003, 2004.
Rensheng, C., Shihua, L., Ersi, K., Jianping, Y., and Xibin, J.: Estimating
daily global radiation using two types of revised models in China, Energ.
Convers. Manage., 47, 865–878,
https://doi.org/10.1016/j.enconman.2005.06.015, 2006.
Schaefli, B. and Huss, M.: Integrating point glacier mass balance observations into hydrologic model identification, Hydrol. Earth Syst. Sci., 15, 1227–1241, https://doi.org/10.5194/hess-15-1227-2011, 2011.
Schmid, M.-O., Gubler, S., Fiddes, J., and Gruber, S.: Inferring snowpack ripening and melt-out from distributed measurements of near-surface ground temperatures, The Cryosphere, 6, 1127–1139, https://doi.org/10.5194/tc-6-1127-2012, 2012.
Schreider, S. Yu., Whetton, P. H., Jakeman, A. J., and Pittock, A. B.:
Runoff modelling for snow-affected catchments in the Australian alpine
region, eastern Victoria, J. Hydrol., 200, 1–23,
https://doi.org/10.1016/S0022-1694(97)00006-1, 1997.
Shea, J. M., Dan Moore, R., and Stahl, K.: Derivation of melt factors from
glacier mass-balance records in western Canada, J. Glaciol., 55, 123–130,
https://doi.org/10.3189/002214309788608886, 2009.
Swinbank, W. C.: Long-wave radiation from clear skies, Q. J. Roy. Meteor. Soc.,
89, 339–348, https://doi.org/10.1002/qj.49708938105, 1963.
Tahir, A. A., Chevallier, P., Arnaud, Y., and Ahmad, B.: Snow cover dynamics and hydrological regime of the Hunza River basin, Karakoram Range, Northern Pakistan, Hydrol. Earth Syst. Sci., 15, 2275–2290, https://doi.org/10.5194/hess-15-2275-2011, 2011.
US Army Corps of Engineers (USACE): Snow hydrology: Summary report of the snow investigations, US Army Corps of Engineers, North Pacific Division, Portlandm
https://usace.contentdm.oclc.org/digital/collection/p266001coll1/id/4172/ (last access: 3 January 2023), 1956.
US Army Corps of Engineers (USACE): Runoff from Snowmelt, Engineer Manual reference no. 1110-2-1406, 142 pp., https://www.publications.usace.army.mil/Portals/76/Publications/EngineerManuals/EM_1110-2-1406.pdf (last access: 3 January 2023),
1998.
Vincent, C. and Thibert, E.: Brief communication: Nonlinear sensitivity of glacier-mass balance attested by temperature-index models, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2022-210, in review, 2022.
Walter, M. T., Brooks, E. S., McCool, D. K., King, L. G., Molnau, M., and
Boll, J.: Process-based snowmelt modeling: does it require more input data
than temperature-index modeling, J. Hydrol., 300, 65–75, 2005.
Warren, S. G.: Optical properties of snow, Rev. Geophys., 20, 67–89,
https://doi.org/10.1029/RG020i001p00067, 1982.
Warren, S. G. and Wiscombe, W. J.: A Model for the Spectral Albedo of Snow.
II: Snow Containing Atmospheric Aerosols, J. Atmos. Sci., 37, 2734–2745,
https://doi.org/10.1175/1520-0469(1980)037<2734:AMFTSA>2.0.CO;2, 1980.
Wheler, B. A.: Glacier melt modelling in the Donjek Range, St. Elias
Mountains, Yukon Territory, MS thesis, Dept. of Earth Sciences, Simon
Fraser University, 283 pp., 2009.
Wiscombe, W. J. and Warren, S. G.: A Model for the Spectral Albedo of Snow.
I: Pure Snow, J. Atmos. Sci., 37, 2712–2733,
https://doi.org/10.1175/1520-0469(1980)037<2712:AMFTSA>2.0.CO;2, 1980.
Yang, K. and Koike, T.: A general model to estimate hourly and daily solar
radiation for hydrological studies: GENERAL SOLAR RADIATION, Water Resour.
Res., 41, W10403, https://doi.org/10.1029/2005WR003976, 2005.
Zhang, Y., Liu, S., Xie, C., and Ding, Y.: Application of a degree-day model
for the determination of contributions to glacier meltwater and runoff near
Keqicar Baqi glacier, southwestern Tien Shan, Ann. Glaciol., 43, 280–284,
https://doi.org/10.3189/172756406781812320, 2006.
Zingg, T.: Beziehung zwischen Temperature und Schmelzwasser und ihre
Bedeutung für Niederschlags- und Abflussfragen, Publication of
Association of Hydrological Sciences, 32, 266–269, 1951.
Short summary
Fresh water from mountainous catchments in the form of snowmelt and ice melt is of critical importance especially in the summer season for people living in these regions. In general, limited data availability is the core concern while modelling the snow and ice melt components from these mountainous catchments. This research will be helpful in selecting realistic parameter values (i.e. degree-day factor) while calibrating the temperature-index models for data-scarce regions.
Fresh water from mountainous catchments in the form of snowmelt and ice melt is of critical...