Estimation of Snow Water Equivalent in Semiarid Zone from Data of Global Numerical Models ICON and GFS/NCEP (Case Study of the Selenga River Basin)

Мұқаба

Дәйексөз келтіру

Толық мәтін

Аннотация

The possibility to use the global numerical (NWP) models ICON and GFS/NCEP for We consider the applicability of ICON and GFS/NCEP global numerical atmospheric model data for calculating the snow water equivalent (SWE) in the Selenga River basin located the semiarid zone. SWE was calculated for the cold periods of 2020–2022 based on the empirical methodology previously developed for the Kama River basin and adapted to the semiarid conditions. The main components of the SWE balance that are taken into account in the calculation are atmospheric precipitation (liquid or solid phase), snowmelt, sublimation from the snow surface and precipitation interception by vegetation with subsequent sublimation. The validation of the results was performed for the Russian part of the basin using the data of snow surveys carried out in the second half of the winter of 2021/22. In general, reasonable estimates of the SWE spatial distribution were obtained. While in 2021, both overestimation and underestimation by 1–15 mm (20–50%) of the calculated SWE was observed at different sites compared to the measurements, in 2022, its systematic underestimation was observed, especially significant in calculations using the ICON model data. In the steppe zone, SWE is significantly underestimated, which may be due to overestimation of the intensity of sublimation from the snow surface. The comparison of these results with the ERA5-Land reanalysis data and MODIS satellite images showed that the ERA5-Land reanalysis significantly overestimates the SWE and the snow cover area. The simulation results based on the GFS/NCEP and ICON models underestimated the snow cover area in 2022 and reproduced well in 2021, which correlates with the results of the SWE calculation.

Авторлар туралы

A. Shikhov

Perm State University; Kazan Federal University

Хат алмасуға жауапты Автор.
Email: and3131@inbox.ru
Russia, Perm; Russia, Kazan

V. Chernykh

Baikal Institute of Nature Management Siberian branch of the RAS

Email: and3131@inbox.ru
Russia, Ulan-Ude

A. Aurzhanaev

Kazan Federal University

Email: and3131@inbox.ru
Russia, Kazan

S. Pyankov

Perm State University

Email: and3131@inbox.ru
Russia, Perm

R. Abdullin

Perm State University

Email: and3131@inbox.ru
Russia, Perm

Әдебиет тізімі

  1. Vinogradov Yu.B., Vinogradova T.A. Matematicheskoye mo-delirovaniye v gidrologii. Mathematical modeling in hydrology. Moscow: Academy Center, 2010: 304 p. [In Russian].
  2. Gavrilova S.Yu. Ustranenie neodnorodnosti vremennykh ryadov atmosfernykh osadkov i ikh ispol’zovanie dlya analiza izmenenii rezhima uvlazhneniya na territorii Rossii. Elimination of the non-stationarity of precipitations time series and their use for the analysis of changes in the moisture regime in Russia: Abstract of the PhD-thesis. Saint Petersburg: Voeikov Main Geophysical Observatory 2010: 111 p. [In Russian].
  3. Garmaev E.Zh., Khristoforov A.V. Vodnyye resursy rek basseyna ozera Baykal: osnovy ikh ispol’zovaniya i okhrany. Water resources of the rivers of the Baikal basin: the fundamentals of their use and protection. Novosibirsk: GEO, 2010: 227 p. [In Russian].
  4. Gordeev I.N. The method of snowmelt intensity calculation for the forecasts of spring runoff of Siberian rivers. Scientific-practical school-seminar for young scientists and specialists in the field of hydrometeorology. Novosibirsk, 2012. Retrieved from: http://sibnigmi.ru/documents/school/Gordeev.pdf. (Last access: 18.10.2022) [In Russian].
  5. Gusev E.M., Nasonova O.N. Modelirovaniye teplo- i vlagoobmena poverkhnosti sushi s atmosferoy. Modeling of heat and moisture exchange of the land surface with the atmosphere. Moscow: Nauka, 2010: 327 p. [In Russian].
  6. Kazakova E.V. Yezhednevnaya otsenka lokal’nykh znacheniy i ob"yektivnyy analiz kharakteristik snezhnogo pokrova v ramkakh sistemy chislennogo prognoza pogody COSMO-Ru. Daily assessment of local values and objective analysis of snow cover characteristics in the framework of the COSMO-Ru numerical weather forecast system. PhD. Moscow: Hydrometeorological center of Russia, 2015: 181 p. [In Russian].
  7. Karpechko Yu.V., Bondarik N.L. Gidrologicheskaya rol’ lesokhozyaistvennykh i lesopromyshlennykh rabot v taezhnoi zone Evropeiskogo Severa Rossii. Hydrological role of forestry and logging in the taiga zone of the Russian European North. Petrozavodsk: Karelian Research Centre of RAS, 2010: 225 p. [in Russian].
  8. Koren’ V.I. Matematicheskiye modeli v prognozakh rechnogo stoka Mathematical models in river runoff forecasts. Leningrad: Hydrometeoizdat, 1991: 199 p. [In Russian].
  9. Kuzmin P.P. Process tayaniya snezhnogo pokrova. The process of melting snow cover. Leningrad: Hydrometeoizdat, 1961: 346 p. [In Russian].
  10. Millionshchikova T.D. Modelirovaniye i predvychisleniye mnogoletnikh izmeneniy stoka r. Selengi. Modeling and prediction of long-term changes in the runoff of the Selenga River. PhD. Moscow: Water problem Institute of RAS, 2019: 133 p. [In Russian].
  11. Motovilov Yu.G., Gelfan A.N. Modeli formirovaniya stoka v zadachakh gidrologii rechnykh basseynov. Models of runoff formation for the challenges of river basins hydrology. Moscow: Water problem Institute of RAS, 2018: 296 p. [In Russian].
  12. Pyankov S.V., Shikhov A.N., Mikhaylyukova P.G. Simulation of snow accumulation and melting in the Kama river basin using data from global prognostic models. Led i Sneg. Ice and Snow. 2016, 59 (4): 494‒508 [In Russian]. https://doi.org/10.15356/2076-6734-2019-4-423
  13. Sandakova S.L., Dangasuren B. Influence of natural disasters on the state of sheep populations in Eastern Mongolia. Vestnik Buryatskogo gosudarstvennogo universiteta. Bulletin of the Buryat State University. 2014, 11 (4): 80‒82 [In Russian].
  14. Turkov D.V., Sokratov V.S. Calculation of snow cover characteristics on lowland areas with the use of the SPONSOR model of local heat and moisture exchange and reanalysis data on the example of the Moscow region. Led i Sneg. Ice and Snow. 2016, 56 (3): 369–380 [In Russian]. https://doi.org/10.15356/ 2076-6734-2016-3-369-380
  15. Churyulin E.V. Ispol’zovaniye sputnikovoy i model’noy informatsii o snezhnom pokrove pri raschetakh kharakteristik vesennego polovod’ya. Using satellite-based and simulated snow cover information for calculating spring flood characteristics. PhD. Moscow: Lomonosov Moscow State University, 2019: 175 p. [In Russian].
  16. Churyulin E.V., Kopeikin V.N., Rozinkina I.A., Frolova N.L., Churyulina A.G. Analysis of snow cover characteristics using satellite and model data for various basins on the European territory of Russian Federation. Gidrometeorologicheskie issledovaniya i prognozy. Hydro-meteorological studies and forecasts. 2018, 2 (368): 120–143 [In Russian].
  17. Arino O., Bicheron P., Achard F., Latham J., Witt R., Weber J.-L. GLOBCOVER: the most detailed portrait of EarthIn. European Space Agency Bulletin. 2008, 136: 24–31.
  18. Bellaire S., Jamieson J.B., Fierz C. Forcing the snow-cover model SNOWPACK with forecasted weather data. The Cryosphere. 2011, 5: 1115–1125. https://doi.org/10.5194/tc-5-1115-2011
  19. Bigio E.R., Swetnam T.W., Baisan C.H., Guiterman C.Y., Kisilyakhov V.K., Andreev S.G., Batotsyrenov E.A., Ayurzhanaev A.A. The influence of land-use activities and regional drought on historical fire regimes of Buryatia, Siberia. Environmental Research Letters. 2022, 17 (5): 054043. https://doi.org/10.1088/1748-9326/ac6964
  20. Frolova N.L., Belyakova P.A., Grigoriev V.Y., Sazonov A.A. Runoff fluctuations in the Selenga River Basin. Regional Environmental Change. 2017, 17 (7): 1–12. https://doi.org/0.1007/s10113-017-1199-0
  21. Hall D.K., Riggs G.A., Salomonson V.V. Development of methods for mapping global snow cover using moderate resolution imaging spectroradiometer data. Remote Sensing of Environment. 1995, 54: 127–140.
  22. Kazakova E.V., Chumakov M.M., Rozinkina I.A. The system for computing snow cover parameters for forming initial fields for numerical weather prediction based on the COSMO-Ru model, Russian Meteorology and Hydrology. 2015, 40 (5): 296–304. https://doi.org/10.3103/S1068373915050027
  23. Kuchment L.S., Gelfan A.N., Demidov V.N. A distributed model of runoff generation in the permafrost regions. Journ. of Hydrology. 2000, 240: 1–22.
  24. Kuchment L.S., Romanov Р.Yu., Gelfan А.N., Demidov V.N. 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 Science. 2010, 14 (2): 339–350. https://doi.org/10.5194/hess-14-339-2010
  25. Kukavskaya E.A., Buryak L.V., Shvetsov E.G., Conard S.G., and Kalenskaya O.P. The impact of increasing fire frequency on forest transformations in southern Siberia. Forest Ecology and Management. 2016, 82: 225–235. https://doi.org/10.1016/j.foreco.2016.10.015
  26. Motovilov Yu., Gottschalk L. Engeland K., Belokurov A. ECOMAG – regional model of hydrological cycle. Application to the NOPEX region. Department of Geophysics, University of Oslo, 1999: 88.
  27. Muñoz-Sabater J., Dutra E., Agustí-Panareda A., Albergel C., Arduini G., Balsamo G., Boussetta S., Choulga M., Harrigan S., Hersbach H., Martens B., Miralles D.G., Piles M., Rodríguez-Fernández N.J., Zsoter E., Buontempo C., Thépaut J.-N. ERA5-land: A state-of-the-art global reanalysis dataset for land applications. Earth System Science Data, 2021, 13 (9): 4349–4383. https://doi.org/10.5194/essd-13-4349-2021
  28. Myneni R.B., Hoffman S., Knyazikhin Y., Privette J.L., Glassy J., Tian Y., Wang Y., Song X., Zhang Y., Smith G.R., Lotsch A., Friedl M. Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sensing of Environment. 2002, 83: 214–231. https://doi.org/10.1016/S0034-4257(02)00074-3
  29. Quéno L., Vionnet V., Dombrowski-Etchevers I., Lafaysse M., Dumont M., Karbou F. Snowpack modelling in the Pyrenees driven by kilometric-resolution meteorological forecasts. The Cryosphere. 2016, 10: 1571–1589. https://doi.org/10.5194/tc-10-1571-2016
  30. Pyankov S.V., Shikhov A.N., Kalinin N.A., Sviyazov E.M. A GIS-based modeling of snow accumulation and melt processes in the Votkinsk reservoir basin. Journ. of Geographical Sciences, 2018. 28 (2): 221–237. https://doi.org/10.1007/s11442-018-1469-x
  31. Romasko V.Y., Burakov D.A. Space Monitoring of Snow Cover of River Watersheds. Journ. of Siberian Federal University. Engineering & Technologies. 2017, 10 (6): 704–713. https://doi.org/10.17516/1999-494X-2017-10-6-704-713
  32. Shikhov A.N., Churiulin E.V., Abdullin R.K. Assessment of the accuracy of snow water equivalent calculation with the use of global numerical weather prediction models and SnoWE snowpack model (by the example of the Kama River basin). Vestnik of Saint Petersburg University. Earth Sciences. 2021, 66 (1). https://doi.org/10.21638/SPBU07.2021.110
  33. Wilson J.P., Gallant J.C. Terrain Analysis – Principles and Applications. New York, John Wiley & Sons. 2000: 520.
  34. WGNE Overview of Plans at NWP Centres with Global Forecasting Systems – 2022. Retrieved from: http://wgne.meteoinfo.ru/nwp-systems-wgne-table/wgne-table/ (Last access: 21.01.2023).

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