Lessons learnt from the Deluge of Kedarnath, Uttarakhand, India

Rautela, Piyoosh

doi:10.3850/s1793924013002824

Cited by 34 publications

(18 citation statements)

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“…During the second storm, the peak discharge was observed at Sitapur (Gaurikund) on 16 June, which was consistent with the maximum intensity of rainfall (105 mm/day) on 16 June. The discharge rate on 16 June was significantly high (1378.5 m 3 /s), which was attributed to various reasons, but primarily due to the outburst of the moraine-dammed Chorabari Lake [3] associated with this extreme rainfall. These results showed that the corresponding higher discharge rate during June 2013 has a good correspondence with the maximum rainfall intensity.…”

Section: Sbre Rainfall Products Validation Against Imd Using Statistimentioning

confidence: 99%

“…Due to such extreme rainfall events, many studies have reported that the lake situated in Chorabari glacier above Kedarnath in Rudraprayag district burst, causing flash floods and debris flow [3,52]. This hydro-meteorological disaster caused heavy destruction and inundation of many villages in the state.…”

Section: Gis-based Mapping Of Affected Villages and Populationmentioning

confidence: 99%

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Evaluation of Satellite-Derived Rainfall Estimates for an Extreme Rainfall Event over Uttarakhand, Western Himalayas

et al. 2017

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Abstract:The extreme rainfall event during June 2013 in the Western Himalayas caused widespread flash floods, which triggered landslides, a lake-outburst, and debris flow. For the hydrological study of such an unexpected extreme event, it is essential to have reliable and accurate rainfall predictions based on satellite observations. The mountainous state of Uttarakhand is covered by complex topography, and this state has few, unevenly distributed, rain gauge networks. This unique study was conducted to evaluate three satellite based rainfall products (i.e., TMPA-3B42, Global Satellite Mapping of Precipitation (GSMaP), and NOAA CPC Morphing Technique (CMORPH)) against the observed rain gauge-based India Meteorological Department (IMD) gridded dataset for this rainfall episode. The results from this comprehensive study confirmed that the magnitude of precipitation and peak rainfall intensity were underestimated in TMPA-3B42 and CMORPH against gauge-based IMD data, while GSMaP showed dual trends with under-and over-predictions. From the results of the statistical approach on the determination of error statistic metrics (MAE (mean absolute error), NRMSE (normalized root mean square error), PBIAS (percent bias), and NSE (Nash-Sutcliffe efficiency)) of respective satellite products, it was revealed that TMPA-3B42 predictions were more relevant and accurate compared to predictions from the other two satellite products for this major event. The TMPA-3B42-based rainfall was negatively biased by 18%. Despite these caveats, this study concludes that TMPA-3B42 rainfall was useful for monitoring extreme rainfall event in the region, where rain-gauges are sparse.

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Section: Sbre Rainfall Products Validation Against Imd Using Statistimentioning

confidence: 99%

Section: Gis-based Mapping Of Affected Villages and Populationmentioning

confidence: 99%

Evaluation of Satellite-Derived Rainfall Estimates for an Extreme Rainfall Event over Uttarakhand, Western Himalayas

et al. 2017

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“…The effect of these factors has increased many folds due to rapid population growth (more than 19 % in the Himalayan state of Uttrakhand) as observed during last decade (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011). The latest Kedarnath disaster of 2013 that killed over 5,000 people was a stark reminder of the fact that even the remotest places of Himalaya are extremely prone to natural hazards and their effect can be extremely disastrous (NIDM 2013;Routela 2013;Champati ray et al 2013). The magnitude of the damage was enormous due to both lack of information about impending hazard as well as unscientific development and human settlement at vulnerable locations Champati ray 2013).…”

Section: Introductionmentioning

confidence: 97%

Spatial prediction of landslide susceptibility in parts of Garhwal Himalaya, India, using the weight of evidence modelling

Guri

Ray

Patel

2015

Environ Monit Assess

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Garhwal Himalaya in northern India has emerged as one of the most prominent hot spots of landslide occurrences in the Himalaya mainly due to geological causes related to mountain building processes, steep topography and frequent occurrences of extreme precipitation events. As this region has many pilgrimage and tourist centres, it is visited by hundreds of thousands of people every year, and in the recent past, there has been rapid development to provide adequate roads and building infrastructure. Additionally, attempts are also made to harness hydropower by constructing tunnels, dams and reservoirs and thus altering vulnerable slopes at many places. As a result, the overall risk due to landslide hazards has increased many folds and, therefore, an attempt was made to assess landslide susceptibility using 'Weights of Evidence (WofE)', a well-known bivariate statistical modelling technique implemented in a much improved way using remote sensing and Geographic Information System. This methodology has dual advantage as it demonstrates how to derive critical parameters related to geology, geomorphology, slope, land use and most importantly temporal landslide distribution in one of the data scarce region of the world. Secondly, it allows to experiment with various combination of parameters to assess their cumulative effect on landslides. In total, 15 parameters related to geology, geomorphology, terrain, hydrology and anthropogenic factors and 2 different landslide inventories (prior to 2007 and 2008-2011) were prepared from high-resolution Indian remote sensing satellite data (Cartosat-1 and Resourcesat-1) and were validated by field investigation. Several combinations of parameters were carried out using WofE modelling, and finally using best combination of eight parameters, 76.5 % of overall landslides were predicted in 24 % of the total area susceptible to landslide occurrences. The study has highlighted that using such methodology landslide susceptibility assessment can be carried out in vast stretches of Himalaya in short time in order to assess the impact of development as well as climate change/variability. The resultant map can play a critical role in selecting areas for remedial measures for slope stabilisation as well planning for future development of the region.

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“…One such extreme rainfall event occurred in mid-June 2013 (an extreme 4 d rainfall event), during which 370 mm d −1 of rain was recorded at Dehradun, which corresponds to 27% of the annual rainfall (Gosain et al 2015). Heavy rainfall caused a breach in a moraine dammed lake, leading to severe flooding that resulted in the loss of thousands of lives, and massive infrastructure damage that affected 4200 villages, 1636 roads, 144 bridges, and 19 hydropower plants (Das 2013, Rautela 2013, Dube et al 2014, Kala 2014, Gosain et al 2016. Reconstruction cost after the disaster is estimated to be almost 480 million pounds (Jogesh et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Return period of extreme rainfall substantially decreases under 1.5 °C and 2.0 °C warming: a case study for Uttarakhand, India

Kumari

Haustein

Javid

et al. 2019

Environ. Res. Lett.

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In June 2013, Uttarakhand experienced a hydro-meteorological disaster due to a 4 d extreme precipitation event of return period more than 100 years, claiming thousands of lives and causing enormous damage to infrastructure. Using the weather@home climate modelling system and its Half a degree Additional warming, Prognosis and Projected Impacts simulations, this study investigates the change in the return period of similar events in a 1.5°C and 2°C warmer world, compared to current and pre-industrial levels. We find that the likelihood of such extreme precipitation events will significantly increase under both future scenarios. We also estimate the change in extreme river flow at the Ganges; finding a considerable increase in the risk of flood events. Our results also suggest that until now, anthropogenic aerosols may have effectively counterbalanced the otherwise increased meteorological flood risk due to greenhouse gas (GHG) induced warming. Disentangling the response due to GHGs and aerosols is required to analyses the changes in future rainfall in the South Asia monsoon region. More research with other climate models is also necessary to make sure these results are robust.

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Lessons learnt from the Deluge of Kedarnath, Uttarakhand, India

Cited by 34 publications

References 0 publications

Evaluation of Satellite-Derived Rainfall Estimates for an Extreme Rainfall Event over Uttarakhand, Western Himalayas

Evaluation of Satellite-Derived Rainfall Estimates for an Extreme Rainfall Event over Uttarakhand, Western Himalayas

Spatial prediction of landslide susceptibility in parts of Garhwal Himalaya, India, using the weight of evidence modelling

Return period of extreme rainfall substantially decreases under 1.5 °C and 2.0 °C warming: a case study for Uttarakhand, India

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