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Performance evaluation of satellite precipitation estimation with ground monitoring stations over southern Himalayas in Bhutan

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
High-resolution remote sensing-based hydro-meteorological data products are being increasingly used for various scientifc studies throughout the world. As such, it is important to evaluate the quality of the data retrieved by the means of remotesensing especially for the regions characterized by the drastic variation of topography such as the Himalayas. This work focuses on the comparison and evaluation of the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA–3B43v7) with point-based ground observations recorded by Department of Hydro-Met Services (DHMS) in Bhutan. For this study, 32 rain gauge stations over Southern Himalayas in Bhutan (SHB) were selected and precipitation data for a period of 19 years (from 1998 to 2017) were compared with the TRMM precipitation product. The comparison revealed that the coefcient of correlation between satellite data and ground observation is statistically signifcant at a 95% confdence level. Furthermore, the coefcient of correlation is near unity in some stations and an average of 0.814 over the entire SHB region for 19 years. This fnding imparts that the TRMM can capture the rainfall trend over the SHB. As per the fndings, the average root-mean-square error was 219.1 mm per month considering the entire duration and 344.7 mm per month considering only the rainy season. Similarly, the average Bias was computed at 0.27 for all seasons and 0.32 for the rainy season, indicating TRMM underestimates the precipitation over SHB. The satellite estimate provides a piece of good information on the distribution of rainfall over the SHB. Nevertheless, it is still advisable to correct the bias of the satellite product, as the relative root-mean-square error is larger than 50% for 96% of the evaluated stations.
Czasopismo
Rocznik
Strony
933--943
Opis fizyczny
Bibliogr. 28 poz.
Twórcy
  • College of Science and Technology, Royal University of Bhutan, Thimphu, Bhutan
autor
  • College of Science and Technology, Royal University of Bhutan, Thimphu, Bhutan
  • College of Science and Technology, Royal University of Bhutan, Thimphu, Bhutan
autor
  • College of Science and Technology, Royal University of Bhutan, Thimphu, Bhutan
  • College of Science and Technology, Royal University of Bhutan, Thimphu, Bhutan
autor
  • Department of Hydrology, Indian Institute of Technology Roorkee, Roorkee, India
Bibliografia
  • 1. Bharti V, Singh C (2015) J Geophys Res Atmos https://doi.org/10.1002/2014JD022121
  • 2. Castro LM, Miranda M, Fernández B (2015) Evaluation of TRMM multi-satellite precipitation analysis (TMPA) in a mountainous region of the central Andes range with a Mediterranean climate. Hydrol Res 46(1):89. https://doi.org/10.2166/nh.2013.096
  • 3. Chai T, Draxler RR (2014) Root mean square error (RMSE) or mean absolute error (MAE)? Arguments against avoiding RMSE in the literature. Geosci Model Dev 7(3):1247–1250. https://doi.org/10.5194/gmd-7-1247-2014
  • 4. Condom T, Rau P, Espinoza JC (2011) Correction of TRMM 3B43 monthly precipitation data over the mountainous areas of Peru during the period 1998–2007. Hydrol Process 25(12):1924–1933. https://doi.org/10.1002/hyp.7949
  • 5. Duan Z, Bastiaanssen WG, Liu J (2012) Monthly and annual validation of TRMM mulitisatellite precipitation analysis (TMPA) products in the caspian sea region for the period 1999–2003. IEEE, pp 3696–3699.
  • 6. Franchito SH, Rao VB, Vasques AC, Santo CME, Conforte JC (2009) Validation of TRMM precipitation radar monthly rainfall estimates over Brazil. J Geophys Res Atmos. https://doi.org/10.1029/2007JD009580
  • 7. Gao Y, Leung LR, Zhang Y, Cuo L (2015) Changes in moisture flux over the Tibetan Plateau during 1979–2011: insights from a high-resolution simulation. J Clim 28(10):4185–4197. https://doi.org/10.1175/jcli-d-14-00581.1
  • 8. Gao Y, Vano JA, Zhu C, Lettenmaier DP (2011) Evaluating climate change over the Colorado River basin using regional climate models. J Geophys Res Atmos 116(13):1–20. https://doi.org/10.1029/2010JD015278
  • 9. Gupta V, Jain MK (2018) Investigation of multi-model spatiotemporal mesoscale drought projections over India under climate change scenario. J Hydrol 567:489–509
  • 10. Gupta V, Jain MK, Singh PK, Singh V (2019) An assessment of global satellite-based precipitation datasets in capturing precipitation extremes: a comparison with observed precipitation dataset in India. Int J Climatol. https://doi.org/10.1002/joc.6419
  • 11. Gupta V, Kumar Jain M, Singh VP (2020) Multivariate modeling of projected drought frequency and hazard over India. J Hydrol Eng 25(4):04020003
  • 12. Huffman GJ, Bolvin DT, Nelkin EJ, Wolff DB, Adler RF, Gu G, Hong Y, Bowman KP, Stocker EF (2007) The TRMM multisatellite precipitation analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J Hydrometeorol 8(1):38–55. https://doi.org/10.1175/JHM560.1
  • 13. Islam MN, Uyeda H (2007) Use of TRMM in determining the climatic characteristics of rainfall over Bangladesh. Remote Sens Environ 108(3):264–276. https://doi.org/10.1016/j.rse.2006.11.011
  • 14. Khandu AJL, Forootan E (2016) An evaluation of high-resolution gridded precipitation products over Bhutan (1998–2012). Int J Climatol 36(3):1067–1087. https://doi.org/10.1002/joc.4402
  • 15. Kneis D, Chatterjee C, Singh R (2014) Evaluation of TRMM rainfall estimates over a large Indian river basin (Mahanadi). Hydrol Earth Syst Sci 18(7):2493–2502. https://doi.org/10.5194/hess-18-2493-2014
  • 16. Krakauer NY, Pradhanang SM, Lakhankar T, Jha AK (2013) Evaluating satellite products for precipitation estimation in mountain regions: a case study for Nepal. Remote Sensing 5(8):4107–4123. https://doi.org/10.3390/rs5084107
  • 17. Li XH, Zhang Q, Xu CY (2012) Suitability of the TRMM satellite rainfalls in driving a distributed hydrological model for water balance computations in Xinjiang catchment, Poyang lake basin. J Hydrol 426–427:28–38. https://doi.org/10.1016/j.jhydrol.2012.01.013
  • 18. Liu C, Ikeda K, Thompson G, Rasmussen R, Dudhia J (2011) High-resolution simulations of wintertime precipitation in the Colorado headwaters region: sensitivity to physics parameterizations. Mon Weather Rev 139(11):3533–3553. https://doi.org/10.1175/MWR-D-11-00009.1
  • 19. Mitra AK, Bohra AK, Rajan D (2002) Daily rainfall analysis for Indian summer monsoon region. Int J Climatol 17(10):1083–1092. https://doi.org/10.1002/(sici)1097-0088(199708)17:10%3c1083:aid-joc185%3e3.3.co;2-j
  • 20. Nanda A, Sen S, McNamara JP (2019) How spatiotemporal variation of soil moisture can explain hydrological connectivity of infiltration-excess dominated hillslope: observations from Lesser Himalayan Landscape. J Hydrol 579:124146
  • 21. Nanda A, Sen S, Sharma AN, Sudheer KP (2020) Soil temperature dynamics at hillslope scale—field observation and machine learning-based approach. Water 12(3):713
  • 22. Norris J, Carvalho LMV, Jones C, Cannon F (2020) Warming and drying over the central Himalaya caused by an amplification of local mountain circulation. Clim Atmos Sci 3(1):1–11. https://doi.org/10.1038/s41612-019-0105-5
  • 23. Prakash S, Mitra AK, AghaKouchak A, Liu Z, Norouzi H, Pai DS (2016) A preliminary assessment of GPM-based multi-satellite precipitation estimates over a monsoon dominated region. J Hydrol 2014:1–12. https://doi.org/10.1016/j.jhydrol.2016.01.029
  • 24. Sanjay J, Krishnan R, Shrestha AB, Rajbhandari R, Ren GY (2017) Downscaled climate change projections for the Hindu Kush Himalayan region using CORDEX South Asia regional climate models. Adv Clim Change Res 8(3):185–198. https://doi.org/10.1016/j.accre.2017.08.003
  • 25. Shrestha AB, Bajracharya SR (eds) (2013) Case studies on flash flood risk management in the Himalayas In support of specific flash flood policies. www.icimod.org/publications
  • 26. Tang G, Zeng Z, Long D, Guo X, Yong B, Zhang W, Hong Y (2016) Statistical and Hydrological Comparisons between TRMM and GPM Level-3 Products over a Midlatitude Basin: Is Day-1 IMERG a Good Successor for TMPA 3B42V7? J Hydrometeorol 17(1):121–137. https://doi.org/10.1175/JHM-D-15-0059.1
  • 27. Xu W, Rutledge SA (2014) Morphology, intensity, and rainfall production of MJO convection: observations from DYNAMO Shipborne Radar and TRMM. J Atmos Sci. https://doi.org/10.1175/JAS-D-14-0130.1
  • 28. Xue X, Hong Y, Limaye AS, Gourley JJ, Huffman GJ, Khan SI, Dorji C, Chen S (2013) Statistical and hydrological evaluation of TRMM-based Multi-satellite Precipitation Analysis over the Wangchu Basin of Bhutan: Are the latest satellite precipitation products 3B42V7 ready for use in ungauged basins? J Hydrol 499:91–99. https://doi.org/10.1016/j.jhydrol.2013.06.042
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-99426c38-cded-45e1-8f24-7057d8aed4cd
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