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Investigating the critical influencing factors of snowmelt runoff and development of a mid-long term snowmelt runoff forecasting

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Abstract

Snowmelt runoff is a vital source of fresh water in cold regions. Accurate snowmelt runoff forecasting is crucial in supporting the integrated management of water resources in these regions. However, the performances of such forecasts are often very low as they involve many meteorological factors and complex physical processes. Aiming to improve the understanding of these influencing factors on snowmelt runoff forecast, this study investigated the time lag of various meteorological factors before identifying the key factor in snowmelt processes. The results show that solar radiation, followed by temperature, are the two critical influencing factors with time lags being 0 and 2 days, respectively. This study further quantifies the effect of the two factors in terms of their contribution rate using a set of empirical equations developed. Their contribution rates as to yearly snowmelt runoff are found to be 56% and 44%, respectively. A mid-long term snowmelt forecasting model is developed using machine learning techniques and the identified most critical influencing factor with the biggest contribution rate. It is shown that forecasting based on Supporting Vector Regression (SVR) method can meet the requirements of forecast standards.

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References

  • Adnan M, Nabi G, Saleem Poomee M et al., 2017. Snowmelt runoff prediction under changing climate in the Himalayan cryosphere: A case of Gilgit River Basin. Geoscience Frontiers, 8(5): 941–949.

    Article  Google Scholar 

  • Barnett T P, Adam J C, Lettenmaier D P, 2005. Potential impacts of a warming climate on water availability in snow-dominated regions. Nature, 438(7066): 303–309.

    Article  Google Scholar 

  • Bergström S, Lindström G, 2015. Interpretation of runoff processes in hydrological modelling: Experience from the HBV approach. Hydrological Processes, 29(16): 3535–3545.

    Article  Google Scholar 

  • Bookhagen B, Burbank D W, 2010. Toward a complete Himalayan hydrological budget: Spatiotemporal distribution of snowmelt and rainfall and their impact on river discharge. Journal of Geophysical Research: Earth Surface, 115(F3).

  • Callegari M, Mazzoli P, De Gregorio L et al., 2015. Seasonal river discharge forecasting using support vector regression: A case study in the Italian Alps. Water, 7(5): 2494–2515.

    Article  Google Scholar 

  • Che T, Li X, Jin R et al., 2008. Snow depth derived from passive microwave remote-sensing data in China. Annals of Glaciology, 49: 145–154.

    Article  Google Scholar 

  • Chen G, Wang X, Zhuang Z et al., 1996. Genetic Algorithm and Its Application. Beijing: People Post and Communication Publisher of China.

    Google Scholar 

  • Cidan Y, Li H, Yang W et al., 2021. Method to identify composition and production phases of spring runoff in high-latitude mid-temperate regions: A case study in the Second Songhua River Basin, China. Journal of Water and Climate Change, 12(8): 3786–3800.

    Article  Google Scholar 

  • Conway H, Benedict R, 1994. Infiltration of water into snow. Water Resources Research, 30(3): 641–649.

    Article  Google Scholar 

  • Corzo G, Solomatine D, 2007. Knowledge-based modularization and global optimization of artificial neural network models in hydrological forecasting. Neural Networks, 20(4): 528–536.

    Article  Google Scholar 

  • Costa D, Shook K, Spence C et al., 2020. Predicting variable contributing areas, hydrological connectivity, and solute transport pathways for a Canadian Prairie Basin. Water Resources Research, 56(12): e2020W–e27984W.

    Article  Google Scholar 

  • 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 Sensing, 7(6): 7212–7230.

    Article  Google Scholar 

  • Dewalle D R, Rango A, 2008. Principles of Snow Hydrology. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Dibike Y B, Velickov S, Solomatine D et al., 2001. Model induction with support vector machines: Introduction and applications. Journal of Computing in Civil Engineering, 15(3): 208–216.

    Article  Google Scholar 

  • Duffie J A, Beckman W A, 1980. Solar Engineering of Thermal Processes. New York: Wiley.

    Google Scholar 

  • Feng Z, Niu W, Tang Z et al., 2020. Monthly runoff time series prediction by variational mode decomposition and support vector machine based on quantum-behaved particle swarm optimization. Journal of Hydrology, 583: 124627.

    Article  Google Scholar 

  • Gaur S, Johannet A, Graillot D et al., 2021. Modeling of groundwater level using artificial neural network algorithm and WA-SVR model. In: Groundwater Resources Development and Planning in the Semi-arid Region. Springer: 129–150.

  • Guo J, Zhou J, Qin H et al., 2011. Monthly streamflow forecasting based on improved support vector machine model. Expert Systems with Applications, 38(10): 13073–13081.

    Article  Google Scholar 

  • Hagg W, Hoelzle M, Wagner S et al., 2013. Glacier and runoff changes in the Rukhk catchment, upper Amu-Darya Basin until 2050. Global and Planetary Change, 110: 62–73.

    Article  Google Scholar 

  • Hasson S U, Saeed F, Böhner J et al., 2019. Water availability in Pakistan from Hindukush-Karakoram-Himalayan watersheds at 1.5°C and 2°C Paris Agreement targets. Advances in Water Resources, 131: 103365.

    Article  Google Scholar 

  • He Y, Pu T, Li Z et al., 2010. Climate change and its effect on annual runoff in Lijiang Basin-Mt. Yulong Region, China. Journal of Earth Science, 21(2): 137–147.

    Article  Google Scholar 

  • He Z, Vorogushyn S, Unger-Shayesteh K et al., 2018. The value of hydrograph partitioning curves for calibrating hydrological models in glacierized basins. Water Resources Research, 54(3): 2336–2361.

    Article  Google Scholar 

  • Hock R, Rees G, Williams M W et al., 2010. Contribution from glaciers and snow cover to runoff from mountains in different climates. Hydrological Processes, 20(10): 2089–2090.

    Article  Google Scholar 

  • Jassim M S, Coskuner G, Zontul M, 2022. Comparative performance analysis of support vector regression and artificial neural network for prediction of municipal solid waste generation. Waste Management & Research, 40(2): 195–204.

    Article  Google Scholar 

  • Jenicek M, Ledvinka O, 2020. Importance of snowmelt contribution to seasonal runoff and summer low flows in Czechia. Hydrology and Earth System Sciences, 24(7): 3475–3491.

    Article  Google Scholar 

  • Jin H, Ju Q, Yu Z et al., 2019. Simulation of snowmelt runoff and sensitivity analysis in the Nyang River Basin, southeastern Qinghai-Tibetan Plateau, China. Natural Hazards, 99(2): 931–950.

    Article  Google Scholar 

  • Krzysztofowicz R, 1999. Bayesian theory of probabilistic forecasting via deterministic hydrologic model. Water Resources Research, 35(9): 2739–2750.

    Article  Google Scholar 

  • Krzysztofowicz R, 2002. Bayesian system for probabilistic river stage forecasting. Journal of Hydrology, 268(1–4): 16–40.

    Article  Google Scholar 

  • Legates D R, Mccabe Jr G J, 1999. Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation. Water Resources Research, 35(1): 233–241.

    Article  Google Scholar 

  • Li B, Chen Y, Chen Z et al., 2013. Variations of temperature and precipitation of snowmelt period and its effect on runoff in the mountainous areas of Northwest China. Journal of Geographical Sciences, 23(1): 17–30.

    Article  Google Scholar 

  • Li H, Xie M, Jiang S, 2012. Recognition method for mid- to long-term runoff forecasting factors based on global sensitivity analysis in the Nenjiang River Basin. Hydrological Processes, 26(18): 2827–2837.

    Article  Google Scholar 

  • Li W, Cidan Y, Wang A et al., 2019. Identification of influencing factors and machanism of spring runoff in Baishan watershed, China. Water Resources and Hydropower Engineering, 50(5): 63–72.

    Google Scholar 

  • Li W, Li H, Guo X, 2019. Analysis and trend prediction of sunspot activity cycle. Water Resources and Hydropower Engineering, 50(5): 53–62.

    Google Scholar 

  • Li Z, Lyu S, Chen H et al., 2021. Changes in climate and snow cover and their synergistic influence on spring runoff in the source region of the Yellow River. Science of The Total Environment, 799: 149503.

    Article  Google Scholar 

  • Li Z, Shi X, Tang Q et al., 2020. Partitioning the contributions of glacier melt and precipitation to the 1971–2010 runoff increases in a headwater basin of the Tarim River. Journal of Hydrology, 583: 124579.

    Article  Google Scholar 

  • Luo K, Tao F, Deng X et al., 2017. Changes in potential evapotranspiration and surface runoff in 1981–2010 and the driving factors in upper Heihe River Basin in Northwest China. Hydrological Processes, 31(1): 90–103.

    Article  Google Scholar 

  • Martinec J, Rango A, Roberts R, 2008. Snowmelt runoff model (SRM) user’s manual. Geographica Bernensia P, 35.

  • Meriö L, 2015. The measurement and modeling of snowmelt in sub-Arctic site using low cost temperature loggers. Environmental Engineering: 60–63.

  • Montero R A, Schwanenberg D, Krahe P et al., 2016. Moving horizon estimation for assimilating H-SAF remote sensing data into the HBV hydrological model. Advances in Water Resources, 92: 248–257.

    Article  Google Scholar 

  • Nabi G, Latif M, Azhar A H, 2011. The role of environmental parameter (degree day) of snowmelt runoff simulation. Soil & Environment, 30(1).

  • Nafees Ahmad H M, Sinclair A, Jamieson R et al., 2011. Modeling sediment and nitrogen export from a rural watershed in eastern Canada using the soil and water assessment tool. Journal of Environmental Quality, 40(4): 1182–1194.

    Article  Google Scholar 

  • Nash J E, Sutcliffe J V, 1970. River flow forecasting through conceptual models (Part I): A discussion of principles. Journal of Hydrology, 10(3): 282–290.

    Article  Google Scholar 

  • Neitsch S L, Arnold J G, Kiniry J R et al., 2011. Soil and water assessment tool theoretical documentation version 2009. Texas Water Resources Institute.

  • Niu D, Wang Y, Wu D D, 2010. Power load forecasting using support vector machine and ant colony optimization. Expert Systems with Applications, 37(3): 2531–2539.

    Article  Google Scholar 

  • Ocampo Melgar D, Meza F J, 2020. Exploring the fingerprints of past rain-on-snow events in a central Andean mountain range basin using satellite imagery. Remote Sensing, 12(24): 4173.

    Article  Google Scholar 

  • Ouyang R, Ren L, Cheng W et al., 2010. Similarity search and pattern discovery in hydrological time series data mining. Hydrological Processes, 24(9): 1198–1210.

    Article  Google Scholar 

  • Pomeroy J W, Gray D M, Brown T et al., 2007. The cold regions hydrological model: A platform for basing process representation and model structure on physical evidence. Hydrological Processes, 21(19): 2650–2667.

    Article  Google Scholar 

  • Prescott J A, Collins J A, 1951. The lag of temperature behind solar radiation. Quarterly Journal of the Royal Meteorological Society, 77(331): 121–126.

    Article  Google Scholar 

  • Prestrud P, 2007. Global outlook for ice & snow. UNEP/Earthprint.

  • Pyankov S V, Shikhov A N, Kalinin N A et al., 2018. A GIS-based modeling of snow accumulation and melt processes in the Votkinsk Reservoir Basin. Journal of Geographical Sciences, 28(2): 221–237.

    Article  Google Scholar 

  • Qu Y, Li H, Liu H, 2003. Method for optimizing initial weights of ANNS by GAS. Journal of Jilin University Engineering and Technology Edition, 33(2): 11–14.

    Google Scholar 

  • Ren G, Cao Y, Wen S et al., 2018. A modified Elman neural network with a new learning rate scheme. Neurocomputing, 286: 11–18.

    Article  Google Scholar 

  • Ren W W, Yang T, Huang C S et al., 2018. Improving monthly streamflow prediction in alpine regions: Integrating HBV model with Bayesian neural network. Stochastic Environmental Research and Risk Assessment, 32(12): 3381–3396.

    Article  Google Scholar 

  • Sciarretta A, Trematerra P, Baumgärtner J, 2001. Geostatistical analysis of Cydia Funebrana (lepidoptera: tortricidae) pheromone trap catches at two spatial scales. American Entomologist, (3): 174–184.

  • Sedgwick P, 2012. Pearson’s correlation coefficient. Bmj, 345.

  • Siderius C, Biemans H, Wiltshire A et al., 2013. Snowmelt contributions to discharge of the Ganges. Science of the Total Environment, 468: S93–S101.

    Article  Google Scholar 

  • Singh P, Ramasastri K S, Kumar N et al., 2000. Correlations between discharge and meteorological parameters and runoff forecasting from a highly glacierized Himalayan Basin. Hydrological Sciences Journal, 45(5): 637–652.

    Article  Google Scholar 

  • Stigter E E, Wanders N, Saloranta T M et al., 2017. Assimilation of snow cover and snow depth into a snow model to estimate snow water equivalent and snowmelt runoff in a Himalayan catchment. The Cryosphere, 11(4): 1647–1664.

    Article  Google Scholar 

  • Tian L, 2019. Research on spring snowmelt runoff in the middle temperate zone: A case study of Baishan Reservoir Basin in the Second Songhua River [D]. Changchun: Jilin University.

    Google Scholar 

  • Tian L, Li H, Li F et al., 2018. Identification of key influence factors and an empirical formula for spring snowmelt-runoff: A case study in mid-temperate zone of Northeast China. Scientific Reports, 8(1): 1–12.

    Article  Google Scholar 

  • Vapnik V N, 1999. An overview of statistical learning theory. IEEE Transactions on Neural Networks, 10(5): 988–999.

    Article  Google Scholar 

  • Vasil Ev D Y, Gavra N K, Kochetkova E S et al., 2013. Correlation between the total precipitation and the mean and maximum runoff during the snowmelt flood in the Belaya River Basin. Russian Meteorology and Hydrology, 38(5): 351–358.

    Article  Google Scholar 

  • Wang H, Li Y P, Liu Y R et al., 2021. Analyzing streamflow variation in the data-sparse mountainous regions: an integrated CCA-RF-FA framework. Journal of Hydrology, 596: 126056.

    Article  Google Scholar 

  • Wang S, Huang G H, Lin Q G et al., 2014. Comparison of interpolation methods for estimating spatial distribution of precipitation in Ontario, Canada. International Journal of Climatology, 34(14): 3745–3751.

    Article  Google Scholar 

  • Wang S, Huang G H, Veawab A, 2013. A sequential factorial analysis approach to characterize the effects of uncertainties for supporting air quality management. Atmospheric Environment, 67: 304–312.

    Article  Google Scholar 

  • Wang X W, 2010. Study of soil freezing and thawing law and simulation of hydrologic properties in the northern seasonlly frozen soil area [D]. Harbin: Northeast Agricultural University.

    Google Scholar 

  • Wheeler D, Shaw G, Barr S, 2013. Statistical Techniques in Geographical Analysis. Routledge.

  • Xie S, Du J, Zhou X et al., 2018. A progressive segmented optimization algorithm for calibrating time-variant parameters of the snowmelt runoff model (SRM). Journal of Hydrology, 566: 470–483.

    Article  Google Scholar 

  • Xiong L, Wan M, Wei X et al., 2009. Indices for assessing the prediction bounds of hydrological models and application by generalised likelihood uncertainty estimation. Hydrological Sciences Journal, 54(5): 852–871.

    Article  Google Scholar 

  • Xu Z, Lan Y, Cheng G, 2000. A study on runoff forecast by artificial neural network model. Journal of Glaciology and Geocryology, 22(4): 372–375.

    Google Scholar 

  • Yan Z, Liu W, Wen S et al., 2019. Multi-label image classification by feature attention network. IEEE Access, 7: 98005–98013.

    Article  Google Scholar 

  • Yang Q, 2015. Study on spatio-temporal distribution of snow cover in Northeast China and its simulation on snowmelt runoff [D]. Changchun: Jilin University.

    Google Scholar 

  • Zhang F, Ahmad S, Zhang H et al., 2016. Simulating low and high streamflow driven by snowmelt in an insufficiently gauged alpine basin. Stochastic Environmental Research and Risk Assessment, 30(1): 59–75.

    Article  Google Scholar 

  • Zhang F, Li L, Ahmad S et al., 2014. Using path analysis to identify the influence of climatic factors on spring peak flow dominated by snowmelt in an alpine watershed. Journal of Mountain Science, 11(4): 990–1000.

    Article  Google Scholar 

  • Zhang Y, Gulimire H, Sulitan D et al., 2022. Monitoring and analysis of snow cover change in an alpine mountainous area in the Tianshan Mountains, China. Journal of Arid Land, 14(9): 962–977.

    Article  Google Scholar 

  • Zhang Z, Kane D L, Hinzman L D, 2000. Development and application of a spatially-distributed arctic hydrological and thermal process model (ARHYTHM). Hydrological Processes, 14(6): 1017–1044.

    Article  Google Scholar 

  • Zhu J, Qi F, Mu X et al., 2015. Snowmelt runoff characteristics and its influencing factors of Songhua River. Bulletin of Soil and Water Conservation, 35(2): 125–130.

    Google Scholar 

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Authors and Affiliations

  1. College of New Energy and Environment, Jilin University, Changchun, 130021, China

    Hongling Zhao, Hongyan Li, Yangzong Cidan, Yingying Liu, Changhai Li & Meichu Yao

  2. Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun, 130021, China

    Hongling Zhao, Hongyan Li, Yangzong Cidan, Yingying Liu, Changhai Li & Meichu Yao

  3. Jilin Provincial Key Laboratory of Water Resources and Environment, Jilin University, Changchun, 130021, China

    Hongling Zhao, Hongyan Li, Yangzong Cidan, Yingying Liu, Changhai Li & Meichu Yao

  4. Department of Civil Engineering, Swansea University Bay Campus, Fabian Way, Swansea, SA1 8EN, UK

    Yunqing Xuan

  5. Yellow River Engineering Consulting Co. Ltd., Zhengzhou, 450003, China

    Shanshan Bao

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  1. Hongling Zhao
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  2. Hongyan Li
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  3. Yunqing Xuan
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Correspondence to Hongyan Li.

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Foundation: The Key Program of National Natural Science Foundation of China, No.42230204

Author: Zhao Hongling (1994−), PhD Candidate, specialized in hydrology and water resources.

Corresponding author: Li Hongyan, Professor, specialized in hydrology and water resources.

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Zhao, H., Li, H., Xuan, Y. et al. Investigating the critical influencing factors of snowmelt runoff and development of a mid-long term snowmelt runoff forecasting. J. Geogr. Sci. 33, 1313–1333 (2023). https://doi.org/10.1007/s11442-023-2131-9

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