Development and application of a spatially distributed snowmelt runoff model for limited data condition

  • S. Rajkumari
  • N. Chiphang
  • Liza G. Kiba
  • A. BandyopadhyayEmail author
  • A. Bhadra
Original Paper


Two main approaches are used to calculate snowmelt, namely, energy balance (physically based) and temperature index (conceptual). Energy balance requires comprehensive data to solve physical equations. In this study, a distributed conceptual model, “spatially distributed snowmelt runoff model”, based on modified temperature index approach was developed to estimate snow-related parameters like snow density and degree day factor in a distributed manner over space and time to meet the hydrological demands of data-scarce snow-dominated mountainous catchment. The model needs only daily temperature, precipitation, snow cover, and albedo as basic inputs. It gives pixel-wise output of snow density, snow depth, snow water equivalent, degree day, net radiation, snowmelt depth, rain-induced runoff, snowmelt runoff, actual evaporation, and infiltration, while routing the flow up to the catchment outlet to estimate discharge as time series along with contribution breakup between snowmelt and rain-induced runoff. The developed model was tested for a small-sized watershed Nuranang (58 km2) as well as for a medium-sized watershed Mago basin (844 km2) both located in the Tawang district of Arunachal Pradesh, India, and was successfully calibrated for 2004–2005 and validated for 2008–2009 for Nuranang watershed. In the case of Mago basin, a single year (2007) was used for calibration and validation was also performed for a single year (2009) as well due to the unavailability of data. Performance of the model was found satisfactory for both the basins with modelling efficiency (ME) greater than 0.6 and coefficient of residual mass (CRM) in between − 0.2 and 0.2 for both calibration and validation years in these data-scarce eastern Himalayan watersheds. Considering the satisfactory performance of the model in both the small- and medium-sized watersheds, the model can be very useful for any un-instrumented mountainous basins for pixel-wise snowmelt runoff computation and gridded output generation using degree day approach, where observed snow density data is not available.


Distributed snowmelt runoff model Modified temperature index approach Snow cover Snow albedo Modified Clark routing Limited data condition 



The authors gratefully acknowledge the help, encouragement, and financial support provided by the Space Application Centre, Ahmedabad, under PRACRITI-II Hydrology Project, and express their sincere thanks to Central Water Commission (Itanagar) for providing the data for use in this study.

Software availability

The developed software is freely available for use in non-commercial educational and research purposes. The compiled executable version of the developed software can be requested by email to the corresponding author of this paper.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. Adnan M, Nabi G, Poommee MS, Ashraf A (2016) Snowmelt runoff prediction under changing climate in the Himalayan cryosphere: a case of Gilgit river basin. Geosci Front 8(5):941–949CrossRefGoogle Scholar
  2. Arora M, Singh P, Goel NK (2008) Climate variability influences on hydrological responses of a large Himalayan basin. Water Resour Manag 22:1461–1475CrossRefGoogle Scholar
  3. Bavera D, Michele CD (2009) Snow water equivalent estimation in the Mallero basin using snow gauge data and MODIS images and fieldwork validation. Hydrol Process 23:1961–1972CrossRefGoogle Scholar
  4. Bhadra A, Bandyopadhyay A, Raghuwanshi NS, Singh R (2009) Integrated reservoir-based canal irrigation model (IRCIM) - II: application. J Irrig Drain Eng 135(2):158–168. CrossRefGoogle Scholar
  5. Bloschl G, Reszler C, Komma J (2007) A spatially distributed flash flood forecasting model. Environ Model Softw 23:464–478CrossRefGoogle Scholar
  6. Debele B, Srinivasan R, Gosain AK (2010) Comparison of process-based and temperature-index snowmelt modelling in SWAT. Water Resour Manag 24(6):1065–1088. CrossRefGoogle Scholar
  7. Hassan QK, Sekhon NS, Magai R, McEachern P (2012) Reconstruction of snow water equivalent and snow depth using remote sensing data. J Environ Inf 20:67–74CrossRefGoogle Scholar
  8. He ZH, Parajka J, Tian FQ, Bloschl G (2014) Estimating degree day factors from MODIS for snowmelt runoff modelling. Hydrol Earth Syst Sci 18:4773–4789CrossRefGoogle Scholar
  9. Hinzman LD, Kane DL (1991) Snow hydrology of a headwater Arctic basin. 2. Conceptual analysis and computer modelling. Water Resour Res 27(6):1111–1121CrossRefGoogle Scholar
  10. Hock R (2003) Temperature index melt modelling in mountain areas. J Hydrol 282:104–115CrossRefGoogle Scholar
  11. Hughes DA, Hannart P, Watkins D (2003) Continuous baseflow separation from time series of daily and monthly streamflow data. Water SA 29(1):43–48Google Scholar
  12. Jansson P, Hock R, Schneider T (2003) The concept of glacier storage: a review. J Hydrol 282:116–129CrossRefGoogle Scholar
  13. Jonas T, Marty C, Magnusson J (2009) Estimating the snow water equivalent from snow depth measurements in the Swiss Alps. J Hydrol 378:161–167CrossRefGoogle Scholar
  14. Koivusalo H, Heikinheimo M, Karvonen T (2001) Test of a simple two-layer parameterisation to simulate energy balance and temperature of a snowpack. Theor Appl Climatol 70:65–79CrossRefGoogle Scholar
  15. Kokkonen T, Jolma A, Koivusalo H (2003) Interfacing environmental simulation models and databases using XML. Environ Model Softw 18:463–471CrossRefGoogle Scholar
  16. Kuusisto E (1980) On the values and variability of degree-day melting factor in Finland. Nord Hydrol 11:235–242CrossRefGoogle Scholar
  17. Linacre ET (1994) Estimating U.S. Class-A pan evaporation from few climate data. Water Int 19:5–14CrossRefGoogle Scholar
  18. Linsley RK, Kohler MA, Paulhus JLH (1982) Hydrology for engineers (3rd Edition). McGraw-Hill, New YorkGoogle Scholar
  19. Lu XF (2009) Simulation of the upper Waimakariri River catchment by observed rain and radar reflectivity. M.Sc. Thesis, Lincoln University, New ZealandGoogle Scholar
  20. Martinec J (1960) The degree-day factor for snowmelt-runoff forecasting. IUGG General Assembly of Helsinki, IAHS-AISH P No. 51:468–477Google Scholar
  21. Martinec JM, Rango A, Roberts R (2008) Snowmelt runoff model (SRM) user’s manual. New Mexico State University, Las CrucesGoogle Scholar
  22. Pietroniro A, Prowse T, Hamlin L, Kouwen N, Soulis E (1996) Application of a grouped response unit hydrological model to a northern wetland region. Hydrol Process 10:1245–1261CrossRefGoogle Scholar
  23. Pohl S, Davidson B, Marsh P, Pietroniro A (2005) Modelling spatially distributed snowmelt and meltwater runoff in a small Arctic catchment with a hydrology land-surface scheme (WATCLASS). Atmosphere-Ocean 43(3):193–211CrossRefGoogle Scholar
  24. Qi J, Li S, Jamieson R, Xing Z, Meng F (2017) Modifying SWAT with an energy balance module to simulate snowmelt for maritime regions. Environ Model Softw 93:146–160CrossRefGoogle Scholar
  25. Quick MC, Pipes A (1988) High mountain snowmelt and application of runoff forecasting, Proceedings of Workshop on Snow Hydrology, 23–26 Nov, Manila, IndiaGoogle Scholar
  26. Rango A (1992) Worldwide testing of the snowmelt runoff model with applications for predicting the effects of climate change. Nord Hydrol 23:155–172CrossRefGoogle Scholar
  27. Senzeba KT, Bhadra A, Bandyopadhyay A (2015) Snowmelt runoff modelling in data scarce Nuranang catchment of eastern Himalayan region. Remote Sens Appl: Soc Environ 1:20–35Google Scholar
  28. Sexstone GA, Fassnacht SR (2014) What drives basin scale spatial variability of snowpack properties in northern Colorado? Cryosphere 8:329–344CrossRefGoogle Scholar
  29. Singh P, Haritashya UK, Kumar N (2010) Modelling and estimation of different components of streamflow for Gangotri glacier basin, Himalayas. Hydrol Sci J 53(3):309–322Google Scholar
  30. Smith JL, Halverson HG (1979) Estimating snowpack density from Albedo measurement. Res. Pap. PSW-RP-136. Berkeley, CA: U.S. Department of Agriculture, Forest Service, Pacific Southwest Forest and Range Experiment Station. 13 pGoogle Scholar
  31. Smith TJ, Marshall LA (2010) Exploring uncertainty and model predictive performance concepts via a modular snowmelt-runoff modeling framework. Environ Model Softw 25:691–701CrossRefGoogle Scholar
  32. Uhlenbrook S, Sieber A (2005) On the value of experimental data to reduce the prediction uncertainty of a process-oriented catchment model. Environ Model Softw 20:19–32CrossRefGoogle Scholar
  33. Viessmann W, Lewis GL (1996) Introduction to hydrology, 4th edn. HarperCollins, New YorkGoogle Scholar
  34. Welderufael WA, Woyessa YE (2010) Stream flow analysis and comparison of base flow separation methods – case study of the Modder river basin in central south Africa. Eur Water 31:3–12Google Scholar
  35. WMO (1986) Intercomparison of models of snowmelt runoff. Operational Hydrology Report No. 23, WORLD METEO No. 646, GenevaGoogle Scholar

Copyright information

© Saudi Society for Geosciences 2019

Authors and Affiliations

  1. 1.National Institute of HydrologyRoorkeeIndia
  2. 2.Department of Agricultural EngineeringNorth Eastern Regional Institute of Science and TechnologyItanagarIndia

Personalised recommendations