Hybrid global gridded snow products and conceptual simulations of distributed snow budget: evaluation of different scenarios in a mountainous watershed

Abstract

Considering snowmelt in mountainous areas as the important source of streamflow, the snow accumulation/melting processes are vital for accurate simulation of the hydrological regimes. The lack of snow-related data and its uncertainties/conceptual ambiguity in snowpack modeling are the different challenges of developing hydro-climatological models. To tackle these challenges, Global Gridded Snow Products (GGSPs) are introduced, which effectively simplify the identification of the spatial characteristics of snow hydrological variables. This research aims to investigate the performance of multi-source GGSPs using multi-stage calibration strategies in hydrological modeling. The used GGSPs were Snow-Covered Area (SCA) and Snow Water Equivalent (SWE), implemented individually or jointly to calibrate an appropriate water balance model. The study area was a mountainous watershed located in Western Iran with a considerable contribution of snowmelt to the generated streamflow. The results showed that using GGSPs as complementary information in the calibration process, besides streamflow time series, could improve the modeling accuracy compared to the conventional calibration, which is only based on streamflow data. The SCA with NSE, KGE, and RMSE values varying within the ranges of 0.47–0.57, 0.54–0.65, and 4–6.88, respectively, outperformed the SWE with the corresponding metrics of 0.36–0.59, 0.47–0.60, and 5.22–7.46, respectively, in simulating the total streamflow of the watershed. In addition to the superiority of the SCA over SWE, the two-stage calibration strategy reduced the number of optimized parameters in each stage and the dependency of internal processes on the streamflow and improved the accuracy of the results compared with the conventional calibration strategy. On the other hand, the consistent contribution of snowmelt to the total generated streamflow (ranging from 0.9 to 1.47) and the ratio of snow melting to snowfall (ranging from 0.925 to 1.041) in different calibration strategies and models resulted in a reliable simulation of the model.

Graphical abstract

Keywords

global gridded snow products / snow hydrology / multi-stage calibration strategy / hydro-climatological modeling / mountainous watershed

References

[1] Ajami N K, Gupta H, Wagener T, Sorooshian S (2004). Calibration of a semi-distributed hydrologic model for streamflow estimation along with a river system. J Hydrol (Amst), 298(1−4): 112−135

[2] Amini Y, Nasseri M (2021). Improving spatial estimation of hydrologic attributes via optimized moving search strategies.Arab J Geosci, 14(8): 723

[3] Andreadis K M, Lettenmaier D P (2006). Assimilating remotely sensed snow observations into a macroscale hydrology model.Adv Water Resour, 29(6): 872–886

[4] Beven K J (2011). Rainfall-Runoff Modeling: the Primer. New York: John Wiley & Sons

[5] Bian Q, Xu Z, Zhao L, Zhang Y F, Zheng H, Shi C, Zhang S, Xie C, Yang Z L (2019). Evaluation and intercomparison of multiple snow water equivalent products over the Tibetan Plateau.J Hydrometeorol, 20(10): 2043–2055

[6] Bigdeli M, Taheri M, Mohammadian A (2021). Spatial sensitivity analysis of COVID-19 infections concerning the satellite-based four air pollutants levels.Int J Environ Sci Technol, 18(3): 751–760

[7] Chen X, Long D, Hong Y, Zeng C, Yan D (2017). Improved modeling of snow and glacier melting by a progressive two-stage calibration strategy with GRACE and multisource data: How snow and glacier meltwater contributes to the runoff of the Upper Brahmaputra River basin? Water Resour Res, 53(3): 2431–2466

[8] Clark M P, Slater A G, Barrett A P, Hay L E, McCabe G J, Rajagopalan B, Leavesley G H (2006). Assimilation of snow-covered area information into hydrological and land-surface models.Adv Water Resour, 29(8): 1209–1221

[9] Cline D, Elder K, Bales R (1998). Scale effects in a distributed snow water equivalence and snowmelt model for mountain basins. Hydrol Processes, 12(10−11): 1527−1536

[10] Corbari C, Ravazzani G, Martinelli J, Mancini M (2009). Elevation-based correction of snow coverage retrieved from satellite images to improve model calibration.Hydrol Earth Syst Sci, 13(5): 639–649

[11] Dai L, Che T, Ding Y (2015). Inter-calibrating SMMR, SSM/I and SSMI/S data to improve the consistency of snow-depth products in China.Remote Sens (Basel), 7(6): 7212–7230

[12] Debele B, Srinivasan R, Gosain A K (2010). Comparison of process-based and temperature-index snowmelt modeling in SWAT.Water Resour Manage, 24(6): 1065–1088

[13] Derksen C, Walker A, Goodison B (2005). Evaluation of passive microwave snow water equivalent retrievals across the boreal forest/tundra transition of western Canada. Remote Sens Environ, 96(3−4): 315−327

[14] Dietz A J, Kuenzer C, Gessner U, Dech S (2012). Remote sensing of snow–a review of available methods.Int J Remote Sens, 33(13): 4094–4134

[15] Dressler K A, Leavesley G H, Bales R C, Fassnacht S R (2006). Evaluation of gridded snow water equivalent and satellite snow cover products for mountain basins in a hydrological model.Hydrol Process, 20(4): 673–688

[16] Duan Q, Sorooshian S, Gupta V (1992). Effective and efficient global optimization for conceptual rainfall-runoff models.Water Resour Res, 28(4): 1015–1031

[17] Duethmann D, Peters J, Blume T, Vorogushyn S, Güntner A (2014). The value of satellite-derived snow cover images for calibrating a hydrological model in snow-dominated catchments in Central Asia.Water Resour Res, 50(3): 2002–2021

[18] Engeset R V, Udnæs H C, Guneriussen T, Koren H, Malnes E, Solberg R, Alfnes E (2003). Improving runoff simulations using satellite-observed time-series of snow-covered areas.Nord Hydrol, 34(4): 281–294

[19] Finger D, Pellicciotti F, Konz M, Rimkus S, Burlando P (2011). The value of glacier mass balance, satellite snow cover images, and hourly discharge for improving the performance of a physically based distributed hydrological model. Water Resour Res, 47(7)

[20] Franz K J, Karsten L R (2013). Calibration of a distributed snow model using MODIS snow covered area data.J Hydrol (Amst), 494: 160–175

[21] Gao H, Dong J, Chen X, Cai H, Liu Zh, Jin Zh, Mao D, Yang Z, Duan Z (2020). Stepwise modeling and the importance of internal variables validation to test model realism in a data scarce glacier basin.J Hydro, 591: 125457

[22] Güntner A, Uhlenbrook S, Seibert J, Leibundgut C (1999). Multi-criterial validation of TOPMODEL in a mountainous catchment.Hydrol Processes, 13(11): 1603–1620

[23] Guo S, 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 Int, 30(2): 250–260

[24] Hall D K, Riggs G A (2007). Accuracy assessment of the MODIS snow products.Hydrol Processes, 21(12): 1534–1547

[25] Hall D K, Riggs G A (2015). MODIS/Terra Snow Cover Monthly L3 Global 0.05Deg CMG, Version 6. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center

[26] Hall D K, Riggs G A (2021). MODIS/Terra Snow Cover Monthly L3 Global 0.05Deg CMG, Version 61. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center

[27] Han P, Long D, Han Z, Du M, Dai L, Hao X (2019). Improved understanding of snowmelt runoff from the headwaters of China’s Yangtze River using remotely sensed snow products and hydrological modeling.Remote Sens Environ, 224: 44–59

[28] He Z H, Parajka J, Tian F Q, Blöschl G (2014). Estimating degree day factors from MODIS for snowmelt runoff modeling.Hydrol Earth Syst Sci, 18(12): 4773–4789

[29] Immerzeel W W, Droogers P, De Jong S M, Bierkens M F P (2009). Large-scale monitoring of snow cover and runoff simulation in Himalayan river basins using remote sensing.Remote Sens Environ, 113(1): 40–49

[30] Jazim A A (2006). A monthly six-parameter water balance model and its application at arid and semiarid low yielding catchments.J King Saud U Eng Sci, 19(1): 65–81

[31] Karpouzos D K, Baltas E A, Kavalieratou S, Babajimopoulos C (2011). A hydrological investigation using a lumped water balance model: the Aison River Basin case (Greece).Water Environ J, 25(3): 297–307

[32] Khakbaz B, Imam B, Hsu K, Sorooshian S (2012). From lumped to distributed via semi-distributed: calibration strategies for semi-distributed hydrologic models.J Hydrol (Amst), 418−419: 61–77

[33] Kim R S, Kumar S, Vuyovich C, Houser P, Lundquist J, Mudryk L, Durand M, Barros A, Kim E J, Forman B A, Gutmann E D, Wrzesien M L, Garnaud C, Sandells M, Marshall H P, Cristea N, Pflug J M, Johnston J, Cao Y, Mocko D, Wang S (2021). Snow Ensemble Uncertainty Project (SEUP): quantification of snow water equivalent uncertainty across North America via ensemble land surface modeling.Cryosphere, 15(2): 771–791

[34] Knoben W J, Freer J E, Woods R A (2019). Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores..Hydrol Earth Syst Sci, 23(10): 4323–4331

[35] Lévesque E, Anctil F, Van Griensven A N N, Beauchamp N (2008). Evaluation of streamflow simulation by SWAT model for two small watersheds under snowmelt and rainfall.Hydrol Sci J, 53(5): 961–976

[36] Li X, Williams M W (2008). Snowmelt runoff modelling in an arid mountain watershed, Tarim Basin, China.Hydrol Processes, 22(19): 3931–3940

[37] Li S, Liu M, Adam J C, Pi H, Su F, Li D, Liu Z, Yao Z (2021). Contribution of snow-melt water to the streamflow over the Three-River Headwater Region, China.Remote Sens (Basel), 13(8): 1585

[38] Martinec J, Rango A, Roberts R (1994). The snowmelt runoff model user’s manual. In: Baumgartner M F, ed. Geographica Bernensia. University of Berne

[39] McCabe G J, Markstrom S L (2007). A monthly water-balance model driven by a graphical user interface (Vol. 1088). Reston, VA: US Geological Survey

[40] Muñoz-Sabater J (2019). ERA5-Land monthly averaged data from 1981 to present. In: Copernicus Climate Change Service (C3S) Climate Data Store (CDS)

[41] Nemri S, Kinnard Ch (2020). Comparing calibration strategies of a conceptual snow hydrology model and their impact on model performance and parameter identifiability.J Hydrol (Amst), 582: 124474

[42] Orsolini Y, Wegmann M, Dutra E, Liu B, Balsamo G, Yang K, de Rosnay P, Zhu C, Wang W, Senan R, Arduini G (2019). Evaluation of snow depth and snow cover over the Tibetan Plateau in global reanalyses using in situ and satellite remote sensing observations.Cryosphere, 13(8): 2221–2239

[43] Parajka J, Blöschl G (2008). The value of MODIS snow cover data in validating and calibrating conceptual hydrologic models. J Hydrol (Amst), 358(3−4): 240−258

[44] Parajka J, Merz R, Blöschl G (2007). Uncertainty and multiple objective calibration in regional water balance modelling: case study in 320 Austrian catchments.Hydrol Processes, 21(4): 435–446

[45] Pulliainen J (2006). Mapping of snow water equivalent and snow depth in boreal and sub-arctic zones by assimilating space-borne microwave radiometer data and ground-based observations.Remote Sens Environ, 101(2): 257–269

[46] Rabuffetti D, Ravazzani G, Corbari C, Mancini M (2008). Verification of operational Quantitative Discharge Forecast (QDF) for a regional warning system? the AMPHORE case studies in the upper Po River..Nat Hazards Earth Syst Sci, 8(1): 161–173

[47] Rao A R, Al-Wagdany A (1995). Effects of climatic change in Wabash river basin.J Irrig Drain Eng, 121(2): 207–215

[48] Reed S, Koren V, Smith M, Zhang Z, Moreda F, Seo D J, Participants D M I P (2004). Overall distributed model intercomparison project results. J Hydrol (Amst), 298(1−4): 27−60

[49] Refsgaard J C (1997). Parameterisation, calibration and validation of distributed hydrological models. J Hydrol (Amst), 198(1−4): 69−97

[50] 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.J Hydrol Hydromech, 67(1): 70–81

[51] Roebber P J (2009). Visualizing multiple measures of forecast quality.Weather Forecast, 24(2): 601–608

[52] Rodell M, Houser P R (2004). Updating a land surface model with MODIS-derived snow cover.J Hydrometeorol, 5(6): 1064–1075

[53] Roy A, Royer A, Turcotte R (2010). Improvement of springtime streamflow simulations in a boreal environment by incorporating snow-covered area derived from remote sensing data. J Hydrol (Amst), 390(1−2): 35−44

[54] Seibert J (2000). Multi-criteria calibration of a conceptual runoff model using a genetic algorithm.Hydrol Earth Syst Sci, 4(2): 215–224

[55] Shrestha M, Koike T, Hirabayashi Y, Xue Y, Wang L, Rasul G, Ahmad B (2015). Integrated simulation of snow and glacier melt in water and energy balance-based, distributed hydrological modeling framework at Hunza River Basin of Pakistan Karakoram region.J Geophys Res Atmos, 120(10): 4889–4919

[56] Shrestha M, Wang L, Koike T, Tsutsui H, Xue Y, Hirabayashi Y (2014). Correcting basin-scale snowfall in a mountainous basin using a distributed snowmelt model and remote-sensing data.Hydrol Earth Syst Sci, 18(2): 747–761

[57] Slater A G, Clark M P (2006). Snow data assimilation via an ensemble Kalman filter.J Hydrometeorol, 7(3): 478–493

[58] Smith M B, Koren V, Zhang Z, Zhang Y, Reed S M, Cui Z, Moreda F, Cosgrove B A, Mizukami N, Anderson E A, Andreassian V, Lerat J, Loumagne C, Perrin C, Ribstein P, Gupta H V, Yilmaz K K, Pokhrel P, Wagener T, Butts M, Yamagata K, Sorooshian S, Khakbaz B, Behrangi A, Hsu K, Imam B, De Smedt F, Safari A, Tavakoli M, Li L, Wang X, Wu J, Yang C, Yang M, Yu Z, Gan T, Islam Z, Vieux B, Looper J, Xia Y, Mitchell K, Ek M, McIntyre N, Orellana B, Sivapalan M, Li H, Tian F, Ryu J, Arnold J, Whittaker G, Confesor R, Di Luzio M (2012). Results of the DMIP 2 Oklahoma experiments.J Hydrol (Amst), 418-419: 17–48

[59] Şorman A A, Şensoy A, Tekeli A E, Şorman A Ü, Akyürek Z (2009). Modelling and forecasting snowmelt runoff process using the HBV model in the eastern part of Turkey.Hydrol Processes, 23(7): 1031–1040

[60] Taheri M, Dolatabadi N, Nasseri M, Zahraie B, Amini Y, Schoups G (2020). Localized linear regression methods for estimating monthly precipitation grids using elevation, rain gauge, and TRMM data.Theor Appl Climatol, 142(1–2): 623–641

[61] Taheri M, Mohammadian A, Ganji F, Bigdeli M, Nasseri M (2022). Energy-based approaches in estimating actual evapotranspiration focusing on land surface temperature: a review of methods, concepts, and challenges.Energies, 15(4): 1264

[62] Tekeli A E, Akyürek Z, Şorman A A, Şensoy A, Şorman A Ü (2005). Using MODIS snow cover maps in modeling snowmelt runoff process in the eastern part of Turkey.Remote Sens Environ, 97(2): 216–230

[63] Thirel G, Salamon P, Burek P, Kalas M (2013). Assimilation of MODIS snow cover area data in a distributed hydrological model using the particle filter.Remote Sens (Basel), 5(11): 5825–5850

[64] Udnæs H C, Alfnes E, Andreassen L M (2007). Improving runoff modelling using satellite-derived snow covered area?.Nord Hydrol, 38(1): 21–32

[65] Vogel R M, Sankarasubramanian A (2003). Validation of a watershed model without calibration. Water Resour Res, 39(10)

[66] Wang G Q, Zhang J Y, Xuan Y Q, Liu J F, Jin J L, Bao Z X, He R M, Liu C S, Liu Y L, Yan X L (2013). Simulating the impact of climate change on runoff in a typical river catchment of the Loess Plateau, China.J Hydrometeorol, 14(5): 1553–1561

[67] Widén-Nilsson E, Halldin S, Xu C Y (2007). Global water-balance modelling with WASMOD-M: parameter estimation and regionalisation. J Hydrol (Amst), 340(1−2): 105−118.

[68] Xu C Y (1999). Estimation of parameters of a conceptual water balance model for ungauged catchments.Water Resour Manage, 13(5): 353–368

[69] Yang J W, Jiang L M, Lemmetyinen J, Luojus K, Takala M, Wu S L, Pan J M (2020). Validation of remotely sensed estimates of snow water equivalent using multiple reference datasets from the middle and high latitudes of China.J Hydrol (Amst), 590: 125499

[70] Yatheendradas S, Lidard C D P, Koren V, Cosgrove B A, De Goncalves L G, Smith M, Geiger J, Cui Z, Borak J, Kumar S V, Toll D L, Riggs G, Mizukami N (2012). Distributed assimilation of satellite‐based snow extent for improving simulated streamflow in mountainous, dense forests: an example over the DMIP2 western basins.Water Resour Res, 48(9): 2011WR011347

[71] Zaitchik B F, Rodell M (2009). Forward-looking assimilation of MODIS-derived snow-covered area into a land surface model.J Hydrometeorol, 10(1): 130–148

[72] Zappa M, Pos F, Strasser U, Warmerdam P, Gurtz J (2003). Seasonal water balance of an Alpine catchment as evaluated by different methods for spatially distributed snowmelt modelling.Nord Hydrol, 34(3): 179–202

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