Development of Energy Balance Utilization in Distributed Snowmelt Modeling for Improving Monthly Water Balance Model

Document Type : Research Paper

Authors

1 School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran.‎

2 School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran.

10.22059/jwim.2023.362214.1089

Abstract

This study compared three different snowmelt scenarios using a monthly water balance model in the Taleghan-Alamut Basin in north of Iran. Three scenarios were tested in this study: a temperature-based, a net radiation-based, and an energy balance-based. Remote sensing data were utilized to mitigate the challenges of modeling snowmelt in a basin with limited ground information. The calibration and validation processes were carried out in a two-stage method. First, snow modeling was conducted grid-based throughout the basin, and the model parameters were validated. Using snow cover observed by the MODIS sensor, the model dispcipancy between computed and observed snow accumulation was calculated by comparing the percentage of to the calculated snow storage in each cell of the basin. In the second stage, the other model parameters were calibrated as a lumpt hydrologic model across the basin. Ultimately, the net radiation-based and energy balance-based models showed superior performance compared to the temperature-based model. During the validation period, the Kling-Gupta efficiency metric for the temperature-based snowmelt model was 0.72, while for the net radiation-based and energy balance-based models were 0.78 and 0.86, respectively. Additionally, the correlation coefficient between MODIS snow cover data and snow storage calculated in the three models ranged from 0.62 for the energy balance-based model to 0.72 for the temperature-based model. According to the results, the proposed methodology is suitable for assessing snow budget and the snow hydrology in mountainous areas with limited data availability.

Keywords

Main Subjects


  1. Chen, X., Long, D., Hong, Y., Zeng, C., & Yan, D. (2017). Improved modeling of snow and glacier melting by a progressive twostage calibration strategy with GRACE and multisource data: How snow and glacier meltwater contributes to the runoff of the Upper Brahmaputra River basin. Water Resources Research, 53(3), 2431-2466.
  2. Cox, G. M., Gibbons, J. M., Wood, A. T. A., Craigon, J., Ramsden, S. J., & Crout, N. M. J. (2006). Towards the systematic simplification of mechanistic models. Ecological Modelling, 198(1-2), 240-246.
  3. Daly, C., Neilson, R.P., & Phillips, D.L. (1994). A statisticaltopographic model for mapping climatological precipitation over mountainous terrain. Journal of Applied Meteorology, 33, 140-158.
  4. Ebrahimi, R., Hamzeh, S., & Marofi, S. (2016). Modeling the snow cover and snowmelt runoff using a combination of SRM hydrological model and satellite imagery. Journal of Irrigation and Water Engineering, 6(3), 23, 66-77 (In Persian).
  5. Ezzati, M., Shokoohi Langeroodi, A., Singh, V. P., & Noori, M. (2018). Investigating the Trend of Temperature and Rainfall and its Effects on the Taleghan Dam Water Resources. Iranian Journal of Soil and Water Research, 49(4), 705-716, doi: 10.22059/ijswr.2017.210883.667493, (In Persian).
  6. Fattahi, A., Delavar, M., & Ghasemi, A. (2011). Simulation of snowmelt runoff in mountainous basins using SRM Model-case study of Bazoft Basin. Journal of Applied researches in Geographical Sciences, 20(23), 129-141.
  7. Franz, K. J., & Karsten, L. R. (2013). Calibration of a distributed snow model using MODIS snow covered area data. Journal of hydrology, 494, 160-175.
  8. Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18-27.
  9. Guo, Sh., Chen, H., Zhang, H., Xiong, L., Liu, P., Pang, B., Wang, G., & Wang, Y. (2005). A Semi-Distributed Monthly Water Balance Model and its Application in a Climate Change Impact Study in the Middle and Lower Yellow River Basin. Water International, 30(2), 250-260.
  10. Gupta, H. V., Kling, H., Yilmaz, K. K., & Martinez, G. F. (2009). Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. Journal of hydrology, 377(1-2), 80-91.
  11. Karimi, H., Zeinivand, H., Haghizadeh, A., Tahmasebipour, N., & Miryaghoubzadeh, M. (2017). Snow cover simulation and snow melt runoff in Haro-Dehno Basin in Lorestan Province. Journal of Watershed Management Research, 8(16), 77-89.
  12. Kustas, W. P., Rango, A., & Uijlenhoet, R. (1994). A simple energy budget algorithm for the snowmelt runoff model. Water Resources Research, 30(5), 1515-1527.
  13. Largeron, C., Dumont, M., Morin, S., Boone, A., Lafaysse, M., Metref, S., Cosme, E., Jonas, T., Winstral, A., & Margulis, S.A. (2020). Toward Snow Cover Estimation in Mountainous Areas Using Modern Data Assimilation Methods: A Review. Frontiers in Earth Science, 8, 325. doi: 10.3389/feart.2020.00325 .
  14. Magnusson, J., Wever, N., Essery, R., Helbig, N., Winstral, A., & Jonas, T. (2015). Evaluating snow models with varying process representations for hydrological applications. Water Resources Research, 51(4), 2707-2723.
  15. McCabe, G.J., & Markstrom, S.L. (2007). A monthly water balance model driven by a graphical user interface. U.S. Geological Survey Open-File report 2007-1088, 6 p.
  16. Nash, J. E., & Sutcliffe, J. V. (1970). River flow forecasting through conceptualmodels part I-A discussion of principles. Journal of hydrology, 10(3), 282-290.
  17. Nemri, S., & Kinnard, Ch. (2020). Comparing calibration strategies of a conceptual snow hydrology model and their impact on model performance and parameter identifiability. Journal of Hydrology, 582, 124474.
  18. Nester, T., Kirnbauer, R., Parajka, J., & Blöschl, G. (2012). Evaluating the snow component of a flood forecasting model. Hydrology Research, 43(6), 762-779.
  19. Nouri, A., Vafakhah, M., & Alavipanah, S. K. (2016). Estimation of snowmelt-runoff using SRM model in Taleghan watershed. Iranian Water Researches Journal, 10(3), 163-167 (In Persian).
  20. Parajka, J., & Blöschl, G. (2008). The value of MODIS snow cover data in validating and calibrating conceptual hydrologic models. Journal of hydrology, 358(3-4), 240-258.
  21. Riboust, P., Thirel, G., Le Moine, N., & Ribstein, P. (2019). Revisiting a simple degree-day model for integrating satellite data: implementation of SWE-SCA hystereses. Journal of hydrology and hydromechanics, 67(1), 70-81.
  22. Shahraki Mojahed, R., Alizadeh, A., Sanaei-Nejad, S. H., Faridhosseini, A., Zarrin, A. (2022). Modeling snowmelt runoff by SRM model and estimation of degree-day factor parameter using net radiation satellite images (Case study: Kardeh Basin). Journal of Geography and Environmental Hazards. doi: 10.22067/geoeh.2022.78658.1280 (In Persian).
  23. Taheri M, Shamsi Anboohi, M., Mousavi, R., & Nasseri, M. (2022). Hybrid Global Gridded Snow Products and Conceptual Simulations of Distributed Snow Budget: Evaluation of Different Scenarios in a Mountainous Watershed. Frontiers of Earth Science, 14, 1-16.
  24. Tayefeh Neskili, N., Zahraie, B., & Saghafian, B. (2017). Coupling Snow Accumulation and Melt Rate Modules of Monthly Water Balance Models with Jazim Monthly Water Balance Model. Hydrological Sciences Journal, 62(14), 2348-2368.
  25. USACE (U.S. Army Corps of Engineers). (1998). Engineering and Design-Runoff from Snowmelt. U.S. Army Corps of Engineers, Washington, D.C. http://www.usace. army.mil/publications/eng-manuals/em1110-21406/toc.html.
  26. Zandi, O., Zahraie, B., & Nasseri, M. (2020). Spatial estimation of precipitation based on geographical information and PRISM model in Great Sefidrud Basin. Iran Water Resources Research, 16(4), 268-283.
  27. Zandi, O., Zahraie, B., Nasseri, M., & Behrangi, A. (2022). Stacking machine learning models versus a locally weighted linear model to generate high-resolution monthly precipitation over a topographically complex area. Atmospheric Research, 272, 623-641.