A GIS-based modeling of snow accumulation and melt processes in the Votkinsk reservoir basin

doi:10.1007/s11442-018-1469-x

Abstract

Abstract:

Coupled hydrological and atmospheric modeling is an efficient method for snowmelt runoff forecast in large basins. We use short-range precipitation forecasts of mesoscale atmospheric Weather Research and Forecasting (WRF) model combining them with ground-based and satellite observations for modeling snow accumulation and snowmelt processes in the Votkinsk reservoir basin (184,319 km²). The method is tested during three winter seasons (2012-2015). The MODIS-based vegetation map and leaf area index data are used to calculate the snowmelt intensity and snow evaporation in the studied basin. The GIS-based snow accumulation and snowmelt modeling provides a reliable and highly detailed spatial distribution for snow water equivalent (SWE) and snow-covered areas (SCA). The modelling results are validated by comparing actual and estimated SWE and SCA data. The actual SCA results are derived from MODIS satellite data. The algorithm for assessing the SCA by MODIS data (ATBD-MOD 10) has been adapted to a forest zone. In general, the proposed method provides satisfactory results for maximum SWE calculations. The calculation accuracy is slightly degraded during snowmelt periods. The SCA data is simulated with a higher reliability than the SWE data. The differences between the simulated and actual SWE may be explained by the overestimation of the WRF-simulated total precipitation and the unrepresentativeness of the SWE measurements (snow survey).

Key words: snow accumulation and snowmelt processes, snow water equivalent, GIS-based modeling, WRF-ARW model

V. PYANKOV Sergey, N. SHIKHOV Andrey, A. KALININ Nikolay, M. SVIYAZOV Eugene. A GIS-based modeling of snow accumulation and melt processes in the Votkinsk reservoir basin[J].Journal of Geographical Sciences, 2018, 28(2): 221-237.

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References 36

[1]	Addor N, Jaun S, Fundel Fet al., 2011. An operational hydrological ensemble prediction system for the city of Zurich (Switzerland): Skill, case studies and scenarios.Hydrology and Earth System Sciences, 15: 2327-2347. doi: 10.5194/hess-15-2327-2011
[2]	Bl¨oschl G, 1999. Scaling issues in snow hydrology.Hydrological Processes, 13: 2149-2175. doi: 10.1002/(ISSN)1099-1085
[3]	Carroll S S, Cressie N, 1996. A comparison of geostatistical methodologies used to estimate snow water equivalent.Water Resources Bulletin, 32: 267-278. doi: 10.1111/j.1752-1688.1996.tb03450.x
[4]	Chang A T C, Rango A, 2000. Algorithm Theoretical Basis Document for the AMSR-E Snow Water Equivalent Algorithm, Version 3.1. Greenbelt, MD, USA, NASA Goddard Space Flight Center, 49 pp. doi: 10.1506/car.25.2.3
[5]	Elder K, Rosenthal W, Davis R, 1998. Estimating the spatial distribution of snow water equivalence in a montane watershed.Hydrological Processes, 12: 1793-1808. doi: 10.1002/(SICI)1099-1085(199808/09)12:10/11<1793::AID-HYP695>3.0.CO;2-K
[6]	Erxleben J, Elder K, Davis R, 2002. Comparison of spatial interpolation methods for estimating snow distribution in the Colorado Rocky Mountains.Hydrological Processes, 16: 3627-3649. doi: 10.1002/hyp.1239
[7]	Garen D C, Marks D, 2005. Spatially distributed energy balance snowmelt modelling in a mountainous river basin: Estimation of meteorological inputs and verification of model results.Journal of Hydrology, 315: 126-153. doi: 10.1016/j.jhydrol.2005.03.026
[8]	Gelfan A N, Pomeroy J W, Kuchment L S, 2004. Modeling forest cover influences on snow accumulation, sublimation, and melt.Journal of Hydrometeorology, 5: 785-803. doi: 10.1175/1525-7541(2004)0052.0.CO;2
[9]	Georgakakos K P, Graham N E, Modrick T Met al., 2014. Evaluation of real-time hydrometeorological ensemble prediction on hydrologic scales in northern California.Journal of Hydrology, 519: 2978-3000. doi: 10.1016/j.jhydrol.2014.05.032
[10]	Gordeev I N, 2013. Simulation of the dynamics of snow albedo during the snowmelt in the basin of the Yenisei River.Earth’s Cryosphere, 17: 47-50. (in Russian)
[11]	Hall D K, Riggs G A, 2007. Accuracy assessment of the MODIS snow products.Hydrological Processes, 21: 1534-1547. doi: 10.1002/hyp.6715
[12]	Hall D K, Riggs G A, Salomonson V Vet al., 2002. MODIS snow-cover products.Remote Sensing of Environment, 83: 181-194. doi: 10.1016/S0034-4257(02)00095-0
[13]	Karpechko Yu V, Bondarik N L, 2010. Hydrological role of agricultural & wood activities in the taiga zone of European Russian north. Karelian Scientific Center of RAS: Petrozavodsk. (in Russian)
[14]	Klok E J, Jasper K, Roelofsma K Pet al., 2002. Distributed hydrological modelling of a heavily glaciated Alpine river basin.Hydrological Sciences ~ Journal ~ des Sciences Hydrologiques, 46(4): 553-570.
[15]	Koren’ V I, 1991. Mathematical Models in River Runoff Forecasting. Leningrad: Gidrometeoizdat Publishers. (in Russian)
[16]	Kuchment L S, Gelfan A N, Demidov V N, 2000. A distributed model of runoff generation in the permafrost regions.Journal of Hydrology, 240: 1-22. doi: 10.1016/S0022-1694(00)00318-8
[17]	Kuchment L S, Romanov P Yu, Gelfan A Net al., 2010. Use of satellite-derived data for characterization of snow cover and simulation of snowmelt runoff through a distributed physically based model of runoff generation. Hydrology and Earth System Sciences, 14: 339-350. doi: 10.5194/hess-14-339-2010
[18]	Kumar S V, Peters-Lidard C D, Eastman J Let al., 2008. An integrated high-resolution hydrometeorological modeling testbed using LIS and WRF.Environmental Modelling and Software, 23: 169-181. doi: 10.1016/j.envsoft.2007.05.012
[19]	Kunstmann H, Stadler C, 2005. High resolution distributed atmospheric-hydrological modelling for Alpine catchments.Journal of Hydrology, 314: 105-124. doi: 10.1016/j.jhydrol.2005.03.033
[20]	Kuzmin P P, 1961. The Process of Snow Cover Melting. Leningrad: Gidrometeoizdat Publishers. (in Russian)
[21]	Lehning M, Völksch Ingo I, Gustafsson Det al., 2006. ALPINE3D: A detailed model of mountain surface processes and its application to snow hydrology.Hydrological Processes, 20: 2111-2128. doi: 10.1002/hyp.6204
[22]	Lopez-Moreno J I, Nogues-Bravo D, 2006. Interpolating local snow depth data: An evaluation of methods.Hydrological Processes, 20: 2217-2232. doi: 10.1002/hyp.6199
[23]	Marks D, Domingo J, Susong Det al., 1999. A spatially distributed energy balance snowmelt model for application in mountain basins.Hydrological Processes, 13: 1935-1959. doi: 10.1002/(SICI)1099-1085(199909)13:12/133.0.CO;2-C
[24]	Melloh R A, Hardy J P, Bailey R Net al., 2001. An efficient snow albedo model for the open and sub-canopy.Hydrological Processes, 16: 3571-3584. doi: 10.1002/hyp.1229
[25]	Motoya K, Yamazaki T, Yasuda N, 2001. Evaluating the spatial and temporal distribution of snow accumulation, snowmelts and discharge in a multi basin scale: An application to the Tohoku Region, Japan.Hydrological Processes, 15: 2101-2129. doi: 10.1002/hyp.279 pmid: 2048967
[26]	Myneni R B, Hoffman S, Knyazikhin Yet al., 2002. Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data.Remote Sensing of Environment, 83: 214-231. doi: 10.1016/S0034-4257(02)00074-3
[27]	Nosenko O A, Dolgih N A, Nosenko G A, 2006. Snow cover in the Central European Russia under the data derived from AMSR-E and SSM/I. In: Recent Problems of the Land Surface Remote Sensing from the Space: Physical Basis, Technologies of Monitoring of the Environment and Dangerous Phenomena, “Azbuka-2000”, Moscow, Russia, 296-301. (in Russian)
[28]	Pomeroy J W, Gray D M, Shook K Ret al., 1998. An evaluation of snow accumulation and ablation processes for land surface modelling.Hydrological Processes, 12: 2339-2367. doi: 10.1002/(ISSN)1099-1085
[29]	Quéno L, Vionnet V, Dombrowski-Etchevers Iet al., 2016. Snowpack modelling in the Pyrenees driven by kilometric-resolution meteorological forecasts.Cryosphere, 10: 1571-1589. doi: 10.5194/tc-2016-20
[30]	Shutov V A, 1998. Investigations, analysis and modeling of different scaled spatial variability of snow storage.Izvestiya - Akademiya Nauk, Seriya Geograficheskaya, 1: 122-132. (in Russian)
[31]	Skamarock W, Klemp J, Dudhia Jet al., 2008. A description of the Advanced Research WRF version 3. NCAR Tech., Note NCAR/TN-475+STR. doi: 10.5065/D68S4MVH
[32]	Tarboton D G, Luce C H, 1996. Utah energy balance snow accumulation and melt model (UEB): Computer model technical description and users guide. Utah Water Research Laboratory and USDA Forest Service Intermountain Research Station, Logan, Utah.
[33]	Verbunt M, Zappa M, Gurtz Jet al., 2006. Verification of a coupled hydrometeorological modelling approach for alpine tributaries in the Rhine basin.Journal of Hydrology, 324: 224-238. doi: 10.1016/j.jhydrol.2005.09.036
[34]	Wigmosta M S, Vail L W, Lettenmaier D P, 1994. A distributed hydrology-vegetation model for complex terrain.Water Resources Research, 30(6): 1665-1679. doi: 10.1029/94WR00436
[35]	Wilson, J P, Gallant J C, 2000. Terrain Analysis: Principles and Applications. New York: John Wiley & Sons.
[36]	Zhao Q, Liu Z, Ye Bet al., 2009. A snowmelt runoff forecasting model coupling WRF and DHSVM.Hydrology and Earth Systems Sciences, 13: 925-940. doi: 10.5194/hess-13-1897-2009

Parameters	Year	Month
Parameters	Year	November	December	January	February	March
X_m	2012/13	61.8	29.5	33.8	15.1	50.5
	2013/14	65.0	59.5	39.6	34.7	44.9
	2014/15	24.1	45.1	42.3	28.6	17.0
X_s	2012/13	65.0	32.6	33.9	21.9	66.6
	2013/14	64.7	62.5	44.0	32.2	67.2
	2014/15	28.0	54.0	49.2	43.0	27.8
$\Delta \overline{X}$	2012/13	10.0	4.9	5.2	7.7	17.8
	2013/14	12.0	9.3	7.2	6.2	24.1
	2014/15	5.8	9.3	9.5	15.0	11.1
RMSE	2012/13	12.1	6.9	6.2	8,6	21.1
	2013/14	15.4	11.6	9.1	8.7	27.0
	2014/15	6.6	11.1	12.0	16.1	12.5
RMSE/X_m,%	2012/13	20.0	23.0	18.0	57	42.0
	2013/14	23.0	20.0	23.0	25	60.0
	2014/15	27.0	25.0	28.0	56	73.0
	2014/15	6.1	10.7	10.8	16.1	11.2