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Theoretical and Applied Climatology

, Volume 138, Issue 3–4, pp 1991–2006 | Cite as

Bayesian evaluation of meteorological datasets for modeling snowmelt runoff in Tizinafu watershed in Western China

  • Wenyi Xie
  • Xiankui ZengEmail author
  • Shuang Zhang
  • Jichun Wu
  • Dong Wang
  • Xiaobin Zhu
Original Paper

Abstract

The Tizinafu watershed is characterized by extremely scarce precipitation and low temperature, and snow and glacier melting which dominate the main water resource of this area. Thus, assessing the snowmelt water resource has great significance to local residents and ecological systems. Based on snowmelt runoff model (SRM) and Markov chain Monte Carlo (MCMC) simulation, four combinations of meteorological data consisting of two daily temperature data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the variable infiltration capacity (VIC) model and two daily precipitation data from Tropical Rainfall Measuring Mission (TRMM) and VIC, respectively, are evaluated for this ungauged basin in Bayesian uncertainty analysis. By comparing the performances of the four meteorological data combinations based on SRM (SRMMODIS + TRMM, SRMMODIS + VIC, SRMVIC + TRMM and SRMVIC + VIC, the former and latter subscripts denote temperature and precipitation data, respectively), results show that the four SRMs are both capable of reproducing the runoff process of the Tizinafu watershed on daily runoff simulation, and SRMVIC + VIC has the best performance. In addition, the impact of climate change on the water resources of the Tizinafu watershed is also evaluated in Bayesian uncertainty analysis, and four climate change scenarios under the condition of Representative Concentration Pathway 8.5 of CMIP5 projection are considered. The results demonstrate that the mean annual runoff prediction of the 2090s increased by 17.2%, 20.2%, 24.1%, and 16.2% compared to that of base year for the four scenarios, respectively. In addition, with the increase of the cryosphere area, snow cover area will have an increasing impact on the mean annual runoff of the study area in the 2090s. The runoff components of snowpack melt and new snow are both sensitive to the cryosphere area, while the runoff component of rainfall is not sensitive to the cryosphere area.

Notes

Funding information

This study was supported by the National Key Research and Development Program of China (2016YFC0402802), NSFC projects (41672233 and 41571017), and the Fundamental Research Funds for the Central Universities (0206-14380040).

References

  1. Adam JC, Hamlet AF, Lettenmaier DP (2009) Implications of global climate change for snowmelt hydrology in the twenty-first century. Hydrol Process 23:962–972CrossRefGoogle Scholar
  2. Adnan M, Nabi G, Poomee MS, Ashraf A (2016) Snowmelt runoff prediction under changing climate in the Himalayan cryosphere: a case of Gilgit River Basin. Geosci Front 8:941–949CrossRefGoogle Scholar
  3. Adnan M, Nabi G, Poomee MS, Ashraf A (2017) Snowmelt runoff prediction under changing climate in the Himalayan cryosphere: a case of Gilgit River Basin. Geosci Front 8:941–949CrossRefGoogle Scholar
  4. Barnett TP, Adam JC, Lettenmaier DP (2005) Potential impacts of a warming climate on water availability in snow-dominated regions. Nature 438:303–310CrossRefGoogle Scholar
  5. Bookhagen B, Burbank DW (2010) Toward a complete Himalayan hydrological budget: spatiotemporal distribution of snowmelt and rainfall and their impact on river discharge. J Geophys Res-Earth 115Google Scholar
  6. Box GEP, Tiao GC (1992) Bayesian inference in statistical analysis. Wiley:478–479Google Scholar
  7. Chen H, Xiang T, Zhou X, Xu C-Y (2012) Impacts of climate change on the Qingjiang Watershed’s runoff change trend in China. Stoch Env Res Risk A 26:847–858CrossRefGoogle Scholar
  8. de la Paix MJ, Lanhai L, Jiwen G, de Dieu HJ, Theoneste N (2012) Analysis of snowmelt model for flood forecast for water in arid zone: case of Tarim River in Northwest China. Environ Earth Sci 66:1423–1429CrossRefGoogle Scholar
  9. Dou Y, Chen X, Bao A, Li L (2011) The simulation of snowmelt runoff in the ungauged Kaidu River Basin of TianShan Mountains, China. Environ Earth Sci 62:1039–1045CrossRefGoogle Scholar
  10. Elias EH, Rango A, Steele CM, Mejia JF, Smith R (2015) Assessing climate change impacts on water availability of snowmelt-dominated basins of the Upper Rio Grande basin. Journal of Hydrology Regional Studies 3:525–546CrossRefGoogle Scholar
  11. Fan YR, Huang GH, Baetz BW, Li YP, Huang K (2017) Development of a copula-based particle filter (CopPF) approach for hydrologic data assimilation under consideration of parameter interdependence. Water Resour Res 53:4850–4875CrossRefGoogle Scholar
  12. Ferguson R (1999) Snowmelt runoff models. Prog Phys Geogr 23:205–227CrossRefGoogle Scholar
  13. Gassman PW, Reyes MR, Green CH, Arnold JG (2007) The soil and water assessment tool: historical development, applications, and future research directions. Trans ASABE 50:1211–1250CrossRefGoogle Scholar
  14. Gelman A, Rubin DB (1992) Inference from iterative simulation using multiple sequences. Stat Sci 7(4):457–472Google Scholar
  15. Georgievsky MV (2009) Application of the snowmelt runoff model in the Kuban River basin using MODIS satellite images. Environmental Research Letters 4:045017CrossRefGoogle Scholar
  16. Hock R (2003) Temperature index melt modelling in mountain areas. J Hydrol 282:104–115CrossRefGoogle Scholar
  17. IPCC (2014) Climate change 2014—impacts, adaptation and vulnerability: regional aspects: Cambridge University PressGoogle Scholar
  18. Jordan R (1991) A one-dimensional temperature model for a snow cover: technical documentation for SNTHERM, p 89Google Scholar
  19. Kult J, Choi W, Choi J (2014) Sensitivity of the snowmelt runoff model to snow covered area and temperature inputs. Appl Geogr 55:30–38Google Scholar
  20. Kumar M, Marks D, Dozier J, Reba M, Winstral A (2013) Evaluation of distributed hydrologic impacts of temperature-index and energy-based snow models. Adv Water Resour 56:77–89CrossRefGoogle Scholar
  21. Laloy E, Vrugt JA (2012) High-dimensional posterior exploration of hydrologic models using multiple-try DREAM(ZS) and high-performance computing. Water Resour Res 50:182–205Google Scholar
  22. Leavesley GH, Lichty R, Troutman BM, Saindon L (1983) Precipitation-runoff modeling system: user’s manual. USGS, Washington, DCGoogle Scholar
  23. Leng G, Tang Q, Rayburg S (2015) Climate change impacts on meteorological, agricultural and hydrological droughts in China. Global and Planetary Change 126:23–34CrossRefGoogle Scholar
  24. Li XG, Williams MW (2008) Snowmelt runoff modelling in an arid mountain watershed, Tarim Basin, China. Hydrol Process 22:3931–3940CrossRefGoogle Scholar
  25. Li X, Jia X, Dong G (2006) Influence of desertification on vegetation pattern variations in the cold semi-arid grasslands of Qinghai-Tibet Plateau, North-west China. J Arid Environ 64:505–522CrossRefGoogle Scholar
  26. Liang X, Lettenmaier DP, Wood EF, Burges SJ (1994) A simple hydrologically based model of land surface water and energy fluxes for general circulation models. J Geophys Res: Atmos 99:14415–14428CrossRefGoogle Scholar
  27. Liston GE, Elder K (2006) A distributed snow-evolution modeling system (SnowModel). J Hydrometeorol 7:1259–1276CrossRefGoogle Scholar
  28. Lu D, Ye M, Hill MC, Poeter EP, Curtis GP (2014) A computer program for uncertainty analysis integrating regression and Bayesian methods. Environ Model Softw 60:45–56Google Scholar
  29. Ma YG, Huang Y, Chen X, Li YP, Bao AM (2013, 2013) Modelling snowmelt runoff under climate change scenarios in an ungauged mountainous watershed, Northwest China. Math Probl Eng:9 pagesGoogle Scholar
  30. Marsh P (1999) Snowcover formation and melt: recent advances and future prospects. Hydrol Process 13:2117–2134CrossRefGoogle Scholar
  31. Martinec J (1975) Snowmelt - runoff model for stream flow forecast. Hydrol Res 6(3):145–154Google Scholar
  32. Martinec J, Rango A, Roberts R (2008) WinSRM 1.11—snowmelt runoff model user’s manual. Editörler: Gomez-Landesa E. ve Bleiweiss, PM, New Mexico State University, Las Cruces, New Mexico, ABDGoogle Scholar
  33. Nathan RJ, McMahon TA (1990) Evaluation of automated techniques for base flow and recession analyses. Water Resour Res 26(7):1465–1473Google Scholar
  34. Panday PK, Williams C, Frey K, Brown M (2010) Snowmelt runoff modeling in the Tamor River Basin in the eastern Nepalese Himalaya. Department of Geography, Clark University 950Google Scholar
  35. Panday PK, Williams CA, Frey KE, Brown ME (2014) Application and evaluation of a snowmelt runoff model in the Tamor River basin, Eastern Himalaya using a Markov Chain Monte Carlo (MCMC) data assimilation approach. Hydrol Process 28:5337–5353CrossRefGoogle Scholar
  36. Qiu L, You J, Qiao F, Peng D (2014) Simulation of snowmelt runoff in ungauged basins based on MODIS: a case study in the Lhasa River basin. Stoch Env Res Risk A 28:1577–1585CrossRefGoogle Scholar
  37. Rango A, Katwijk V (1990) Development and testing of a snowmelt-runoff forecasting technique. JAWRA J Am Water Resour Assoc 26:135–144CrossRefGoogle Scholar
  38. Regine H (1999) A distributed temperature-index ice- and snowmelt model including potential direct solar radiation. J Glaciol 45:101–111CrossRefGoogle Scholar
  39. Shang H et al (2018) Long-term variation of streamflow in Tizinafu River based on tree-ring hydrology. J China HydrolGoogle Scholar
  40. Tahir AA, Chevallier P, Arnaud Y, Neppel L, Ahmad B (2011) Modeling snowmelt-runoff under climate scenarios in the Hunza River basin, Karakoram Range, Northern Pakistan. J Hydrol 409:104–117CrossRefGoogle Scholar
  41. Tahir AA, Hakeem SA, Hu T, Hayat H, Yasir M (2017) Simulation of snowmelt-runoff under climate change scenarios in a data-scarce mountain environment. Int J Digit Earth:1–21Google Scholar
  42. Tekeli AE, Akyurek Z, Sorman AA, Sensoy A, Sorman AU (2005) Using MODIS snow cover maps in modeling snowmelt runoff process in the eastern part of Turkey. Remote Sens Environ 97:216–230CrossRefGoogle Scholar
  43. Vrugt JA (2011) DREAM(D): an adaptive Markov chain Monte Carlo simulation algorithm to solve discrete, noncontinuous, posterior parameter estimation problems. Hydrol Earth Syst Sci Discuss 8:3701–3713CrossRefGoogle Scholar
  44. Wang J, Li S (2006) Effect of climatic change on snowmelt runoffs in mountainous regions of inland rivers in Northwestern China. Science in China Series D: Earth Sciences 49:881–888CrossRefGoogle Scholar
  45. Wang GQ, Zhang JY, Jin JL, Pagano TC (2012) Assessing water resources in China using PRECIS projections and VIC model. Hydrol Earth Syst Sci Discuss 16:231–240CrossRefGoogle Scholar
  46. Wöhling T, Vrugt JA (2011) Multiresponse multilayer vadose zone model calibration using Markov chain Monte Carlo simulation and field water retention data. Water Resour Res 47:W04510CrossRefGoogle Scholar
  47. Xu C-y (1999) Climate change and hydrologic models: a review of existing gaps and recent research developments. Water Resour Manag 13:369–382CrossRefGoogle Scholar
  48. Xu H, Hou Z, Ai L, Tan L (2007) Precipitation at Lake Qinghai, NE Qinghai–Tibet Plateau, and its relation to Asian summer monsoons on decadal/interdecadal scales during the past 500 years. Palaeogeogr Palaeoclimatol Palaeoecol 254:541–549CrossRefGoogle Scholar
  49. Zeng X, Wu J, Wang D, Zhu X (2015) Assessing the pollution risk of a groundwater source field at western Laizhou Bay under seawater intrusion. Environ Res 148:586–594CrossRefGoogle Scholar
  50. Zhang YC, Ll BL, Bao AM, Zhou CH, Chen X, Zhang XR (2007) Study on snowmelt runoff simulation in the Kaidu River basin. Sci China Ser D 50:26–35CrossRefGoogle Scholar
  51. Zhang XJ, Tang QH, Pan M, Tang Y (2014) A long-term land surface hydrologic fluxes and states dataset for China. J Hydrometeorol 15:2067–2084CrossRefGoogle Scholar
  52. Zhang JL, Li YP, Huang GH, Wang CX, Cheng GH (2016) Evaluation of uncertainties in input data and parameters of a hydrological model using a Bayesian framework: a case study of a snowmelt-precipitation-driven watershed. J Hydrometeorol 17:2333–2350CrossRefGoogle Scholar
  53. Zobitz JM, Desai AR, Moore DJP, Chadwick MA (2011) A primer for data assimilation with ecological models using Markov chain Monte Carlo (MCMC). Oecologia 167:599–611CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Key Laboratory of Surficial Geochemistry, Ministry of Education, School of Earth Sciences and EngineeringNanjing UniversityNanjingChina

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