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Appraisal of hydro-meteorological factors during extreme precipitation event: case study of Kedarnath cloudburst, Uttarakhand, India

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Abstract

Flash flood is an uncertain and most catastrophic disaster worldwide that causes socio-economic problems, devastation and loss of infrastructure. One of the major triggering factors of flash floods is the extreme events like cloudburst that causes flooding of area within a short span of time. Therefore, this study aims to understand the variations in hydro-meteorological variables during the devastating Kedarnath cloudburst in the Uttarakhand, India. The hydro-meteorological variables were collected from the global satellites such as Moderate Resolution Imaging Spectroradiometer, Tropical Rainfall Measuring Mission, modelled datasets from Decision Support System for Agrotechnology Transfer and National Center for Environmental Prediction (NCEP). For the validation of satellite meteorological data, the NCEP Global analysis data were downscaled using Weather Research and Forecasting model over the study area to achieve the meteorological variables’ information. The meteorological factors such as atmospheric pressure, atmospheric temperature, rainfall, cloud water content, cloud fraction, cloud particle radius, cloud mixing ratio, total cloud cover, wind speed, wind direction and relative humidity were studied during the cloudburst, before as well as after the event. The outcomes of this study indicate that the variability in hydro-meteorological variables over the Kedarnath had played a significant role in triggering the cloudburst in the area. The results showed that during the cloudburst, the relative humidity was at the maximum level, the temperature was very low, the wind speed was slow and the total cloud cover was found at the maximum level. It is expected that because of this situation a high amount of clouds may get condensed at a very rapid rate and resulted in a cloudburst over the Kedarnath region.

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References

  • Ackerman SA, Strabala KI, Menzel WP, Frey RA, Moeller CC, Gumley LE (1998) Discriminating clear sky from clouds with MODIS. J Geophys Res 103:32141–132157

    Google Scholar 

  • Bapalu GV, Sinha R (2005) GIS in flood hazard mapping: a case study of Kosi River Basin, India. GIS Dev Wkly 1:1–3

    Google Scholar 

  • Buchta C, Hornik K, Hahsler M (2008) Getting things in order: an introduction to the R package seriation. J Stat Softw 25:1–34

    Google Scholar 

  • Burton I (1993) The environment as hazard. Guilford Press, London

    Google Scholar 

  • Caraux G, Pinloche S (2005) PermutMatrix: a graphical environment to arrange gene expression profiles in optimal linear order. Bioinformatics 21:1280–1281

    Google Scholar 

  • Chen F, Dudhia J (2001) Coupling an advanced land surface–hydrology model with the Penn State–NCAR MM5 modeling system. Part I: model implementation and sensitivity. Mon Weather Rev 129:569–585

    Google Scholar 

  • Chen Y, Huang C, Ticehurst C, Merrin L, Thew P (2013) An evaluation of MODIS daily and 8-day composite products for floodplain and wetland inundation mapping. Wetlands 33:823–835

    Google Scholar 

  • Dimri A et al (2017) Cloudbursts in Indian Himalayas: a review. Earth Sci Rev 168:1–23

    Google Scholar 

  • Dudhia J (2014) WRF modeling system overview. www.2.mmmucaredu/wrf/users/tutorial/201201/WRF_Overview_Dudhia ppt.pdf

  • Guleria RP et al (2012) Validation of MODIS retrieval aerosol optical depth and an investigation of aerosol transport over Mohal in North Western Indian Himalaya. Int J Remote Sens 33:5379–5401

    Google Scholar 

  • Gupta M, Srivastava PK, Islam T, Ishak AMB (2013) Evaluation of TRMM rainfall for soil moisture prediction in a subtropical climate. Environ Earth Sci. https://doi.org/10.1007/s12665-013-2837-6

    Article  Google Scholar 

  • Gupta M, Srivastava PK, Islam T, Ishak AMB (2014) Evaluation of TRMM rainfall for soil moisture prediction in a subtropical climate. Environ Earth Sci 71:4421–4431

    Google Scholar 

  • Hoaglin DC, Mosteller F, Tukey JW (2011) Exploring data tables, trends, and shapes, vol 101. Wiley, Hoboken

    Google Scholar 

  • Hong S-Y, Lee J-W (2009) Assessment of the WRF model in reproducing a flash-flood heavy rainfall event over Korea. Atmos Res 93:818–831

    Google Scholar 

  • Hong S-Y, Lim J-OJ (2006) The WRF single-moment 6-class microphysics scheme (WSM6). J Korean Meteorol Soc 42:129–151

    Google Scholar 

  • Horváth Á, Davies R (2001) Feasibility and error analysis of cloud motion wind extraction from near-simultaneous multiangle MISR measurements. J Atmos Ocean Technol 18:591–608

    Google Scholar 

  • Huffman GJ, Bolvin DT, Braithwaite D, Hsu K, Joyce R, Xie P, Yoo S-H (2013) NASA Global Precipitation Measurement (GPM) Integrated Multi-satellite retrievals for GPM (IMERG). In: Algorithm theoretical basis document, version 4.1. NASA, IMD Terminologies and Glossary

  • Islam T, Rico-Ramirez MA, Han D, Srivastava PK, Ishak AM (2012) Performance evaluation of the TRMM precipitation estimation using ground-based radars from the GPM validation network. J Atmos Solar Terr Phys 77:194–208

    Google Scholar 

  • Islam T, Srivastava PK, Dai Q, Gupta M, Jaafar WZW (2015) Stratiform/convective rain delineation for TRMM microwave imager. J Atmos Solar Terr Phys 133:25–35

    Google Scholar 

  • Kala CP (2014) Deluge, disaster and development in Uttarakhand Himalayan region of India: challenges and lessons for disaster management. Int J Disaster Risk Reduct 8:143–152

    Google Scholar 

  • Kim H-J, Wang B (2011) Sensitivity of the WRF model simulation of the East Asian summer monsoon in 1993 to shortwave radiation schemes and ozone absorption. Asia Pac J Atmos Sci 47:167–180

    Google Scholar 

  • Manabe S, Wetherald RT (1967) Thermal equilibrium of the atmosphere with a given distribution of relative humidity. J Atmos Sci 24(3):241–259

    Google Scholar 

  • Maurya S, Srivastava PK, Gupta M, Islam T, Han D (2016) Integrating soil hydraulic parameter and microwave precipitation with morphometric analysis for watershed prioritization. Water Resour Manag 30:5385–5405. https://doi.org/10.1007/s11269-016-1494-4

    Article  Google Scholar 

  • Mlawer EJ, Taubman SJ, Brown PD, Iacono MJ, Clough SA (1997) Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J Geophys Res Atmos 102:16663–16682

    Google Scholar 

  • O’Gorman PA, Schneider T (2009) The physical basis for increases in precipitation extremes in simulations of 21st-century climate change. Proc Natl Acad Sci 106:14773–14777

    Google Scholar 

  • Ooyama KV (1990) A thermodynamic foundation for modeling the moist atmosphere. J Atmos Sci 47:2580–2593

    Google Scholar 

  • Pai DS, Bhan SC (2013) Monsoon Report 2013 IMD Met Monograph: ESSO/IMD/SYNOPTIC MET/01-2014/15

  • Pande RK (2010) Flash flood disasters in Uttarakhand. Disaster Prev Manag Int J 19:565–570. https://doi.org/10.1108/09653561011091896

    Article  Google Scholar 

  • Pruppacher H, Klett J (1997) Microphysics of clouds and precipitation: with an introduction to cloud chemistry and cloud electricity. Kluwer, Norwell

    Google Scholar 

  • Radke LF, Coakley JA, King MD (1989) Direct and remote sensing observations of the effects of ships on clouds. Science 246:1146–1149

    Google Scholar 

  • Santo RdE (2012) Principal component analysis applied to digital image compression. Einstein (São Paulo) 10:135–139

    Google Scholar 

  • Sassen K, Wang Z, Liu D (2008) Global distribution of cirrus clouds from CloudSat/Cloud-Aerosol lidar and infrared pathfinder satellite observations (CALIPSO) measurements. J Geophys Res Atmos. https://doi.org/10.1029/2008JD009972

    Article  Google Scholar 

  • Schwartz CS et al (2009) Next-day convection-allowing WRF model guidance: a second look at 2-km versus 4-km grid spacing. Mon Weather Rev 137:3351–3372

    Google Scholar 

  • Shekhar M, Pattanayak S, Mohanty U, Paul S, Kumar MS (2015) A study on the heavy rainfall event around Kedarnath area (Uttarakhand) on 16 June 2013. J Earth Syst Sci 124:1531–1544

    Google Scholar 

  • Singh S, Srivastava PK, Gupta M, Mukherjee S (2012) Modeling mineral phase change chemistry of groundwater in a rural–urban fringe. Water Sci Technol 66:1502

    Google Scholar 

  • Singh SK, Srivastava PK, Pandey AC, Gautam SK (2013) Integrated assessment of groundwater influenced by a confluence river system: concurrence with remote sensing and geochemical modelling. Water Resour Manag 27:4291–4313

    Google Scholar 

  • Skamarock WC, Klemp JB, Dudhia J, Gill DO, Barker DM, Wang W, Powers JG (2005) A description of the advanced research WRF version 2. National Center for Atmospheric Research, Boulder, CO, Mesoscale and Microscale Meteorology Div

  • Srivastava PK, Han D, Gupta M, Mukherjee S (2012) Integrated framework for monitoring groundwater pollution using a geographical information system and multivariate analysis. Hydrol Sci J 57:1453–1472

    Google Scholar 

  • Srivastava PK, Han D, Rico-Ramirez MA, Islam T (2014) Sensitivity and uncertainty analysis of mesoscale model downscaled hydro-meteorological variables for discharge prediction. Hydrol Process 28:4419–4432

    Google Scholar 

  • Srivastava PK, Han D, Rico-Ramirez MA, O’Neill P, Islam T, Gupta M, Dai Q (2015a) Performance evaluation of WRF-Noah land surface model estimated soil moisture for hydrological application: synergistic evaluation using SMOS retrieved soil moisture. J Hydrol 529:200–212

    Google Scholar 

  • Srivastava PK, Islam T, Gupta M, Petropoulos G, Dai Q (2015b) WRF dynamical downscaling and bias correction schemes for NCEP estimated hydro-meteorological variables. Water Resour Manag 29:2267–2284

    Google Scholar 

  • Srivastava PK, Islam T, Singh SK, Petropoulos GP, Gupta M, Dai Q (2016) Forecasting Arabian Sea level rise using exponential smoothing state space models and ARIMA from TOPEX and Jason satellite radar altimeter data. Meteorol Appl 23:633–639

    Google Scholar 

  • Stackhouse Jr PW et al (2018) POWER Release 8.0. 1 (with GIS Applications) Methodology (Data Parameters, Sources, & Validation) Documentation Date December 12, 2018 (All previous versions are obsolete) (Data Version 8.0. 1; Web Site Version 1.1. 0)

  • Su F, Hong Y, Lettenmaier DP (2008) Evaluation of TRMM Multisatellite Precipitation Analysis (TMPA) and its utility in hydrologic prediction in the La Plata Basin. J Hydrometeorol 9:622–640

    Google Scholar 

  • Trenberth KE (1998) Atmospheric moisture residence times and cycling: implications for rainfall rates and climate change. Clim Change 39:667–694

    Google Scholar 

  • Umoh AA, Akpan AO, Jacob BB (2013) Rainfall and relative humidity occurrence patterns in Uyo Metropolis, Akwa Ibom State, South-South Nigeria. IOSRJEN 3:27–31

    Google Scholar 

  • White JW, Hoogenboom G, Wilkens PW, Stackhouse PW, Hoel JM (2011) Evaluation of satellite-based, modeled-derived daily solar radiation data for the continental United States. Agron J 103:1242–1251

    Google Scholar 

  • Whiteman D, Melfi S (1999a) Cloud liquid water and particle size detection using a Raman lidar. Ratio 2:27

    Google Scholar 

  • Whiteman DN, Melfi SH (1999b) Cloud liquid water, mean droplet radius, and number density measurements using a Raman lidar. J Geophys Res Atmos 104:31411–31419

    Google Scholar 

  • Wold S, Esbensen K, Geladi P (1987) Principal component analysis. Chemom Intell Lab Syst 2:37–52

    Google Scholar 

Download references

Acknowledgements

The authors would like to thank the University Grants Commission, Government of India, for providing necessary support and funding for this research. The authors also would like to acknowledge Banaras Hindu University for providing the seed grant. Author is also thankful to University Corporation for Atmospheric Research (UCAR) for providing NCEP-FNL data to perform this study.

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Authors and Affiliations

  1. DST-Mahamana Centre of Excellence in Climate Change Research, Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, Uttar Pradesh, India

    Shailendra Pratap, Prashant K. Srivastava & Rajesh Kumar Mall

  2. National Centre for Medium Range Weather Forecasting (NCMRWF), Ministry of Earth Sciences, Noida, Uttar Pradesh, 201309, India

    Ashish Routray

  3. NASA Jet Propulsion Laboratory, Pasadena, USA

    Tanvir Islam

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  1. Shailendra Pratap
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  2. Prashant K. Srivastava
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  3. Ashish Routray
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  4. Tanvir Islam
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  5. Rajesh Kumar Mall
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Correspondence to Prashant K. Srivastava.

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Pratap, S., Srivastava, P.K., Routray, A. et al. Appraisal of hydro-meteorological factors during extreme precipitation event: case study of Kedarnath cloudburst, Uttarakhand, India. Nat Hazards 100, 635–654 (2020). https://doi.org/10.1007/s11069-019-03829-4

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