Increased flood risk in Indian sub-continent under the warming climate

Ali, Haider; Modi, Parthkumar; Mishra, Vimal

doi:10.1016/j.wace.2019.100212

Cited by 116 publications

(67 citation statements)

References 66 publications

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“…As an example, we use the bias-corrected projections to estimate the frequency of precipitation and temperature extremes for an administrative region (state of Uttar Pradesh, India) and a river basin (Godavari, India) [Figs. 8,9]. The frequency of extreme precipitation was estimated using 95 th percentiles of rainy days (precipitation more than 1 mm).…”

Section: Usage Notesmentioning

confidence: 99%

Bias-corrected climate projections for South Asia from Coupled Model Intercomparison Project-6

2020

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Climate change is likely to pose enormous challenges for agriculture, water resources, infrastructure, and livelihood of millions of people living in South Asia. Here, we develop daily bias-corrected data of precipitation, maximum and minimum temperatures at 0.25° spatial resolution for South Asia (India, Pakistan, Bangladesh, Nepal, Bhutan, and Sri Lanka) and 18 river basins located in the Indian sub-continent. The bias-corrected dataset is developed using Empirical Quantile Mapping (EQM) for the historic (1951–2014) and projected (2015–2100) climate for the four scenarios (SSP126, SSP245, SSP370, SSP585) using output from 13 General Circulation Models (GCMs) from Coupled Model Intercomparison Project-6 (CMIP6). The bias-corrected dataset was evaluated against the observations for both mean and extremes of precipitation, maximum and minimum temperatures. Bias corrected projections from 13 CMIP6-GCMs project a warmer (3–5°C) and wetter (13–30%) climate in South Asia in the 21st century. The bias-corrected projections from CMIP6-GCMs can be used for climate change impact assessment in South Asia and hydrologic impact assessment in the sub-continental river basins.

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Section: Usage Notesmentioning

confidence: 99%

Bias-corrected climate projections for South Asia from Coupled Model Intercomparison Project-6

2020

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“…Further analysis was performed to detect long term (1981-2018) rainfall trend during JJA. [32] and Sharmila S, [62] where surface hydrological model was utilized to observed and projected changes in extreme precipitation and flood events. Their study demonstrate that the multi-day flood events are projected to increase with a faster rate in the future than the single day events.…”

Section: Plos Onementioning

confidence: 99%

“…Hunt et al, explored the circumstances which majorly caused this extreme flood by using Weather Research and Forecasting (WRF) model in combination with a hydrological model (WRF-Hydro, run at 125 m resolution) [ 31 ]. Haider et al, used downscaled future and historic climate projections from Coupled Model Inter comparison Project (CMIP5) and the Noah-MP land surface hydrological model, to simulate and observe the projected changes in extreme precipitation and flood events [ 32 ]. The extreme precipitation and runoff conditions observations made by Mishra et al using the Variable Infiltration Capacity (VIC) model, have shown that the 2018 extreme flood event was unprecedented in the last seven decades from 1951–2018 [ 33 ].…”

Section: Introductionmentioning

confidence: 99%

Flood inundation mapping- Kerala 2018; Harnessing the power of SAR, automatic threshold detection method and Google Earth Engine

Tiwari¹,

Kumar²,

Matin³

et al. 2020

PLoS ONE

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Flood inundation maps provide valuable information towards flood risk preparedness, management, communication, response, and mitigation at the time of disaster, and can be developed by harnessing the power of satellite imagery. In the present study, Sentinel-1 Synthetic Aperture RADAR (SAR) data and Otsu method were utilized to map flood inundation areas. Google Earth Engine (GEE) was used for implementing Otsu algorithm and processing Sentinel-1 SAR data. The results were assessed by (i) calculating a confusion matrix; (ii) comparing the submerge water areas of flooded (Aug 2018), non-flooded (Jan 2018) and previous year's flooded season (Aug 2016, Aug 2017), and (iii) analyzing historical rainfall patterns to understand the flood event. The overall accuracy for the Sentinel-1 SAR flood inundation maps of 9th and 21st August 2018 was observed as 94.3% and 94.1% respectively. The submerged area (region under water) classified significant flooding as compared to the non-flooded (January 2018) and previous year's same season (August 2015-2017) classified outputs. Summing up, observations from Sentinel-1 SAR data using Otsu algorithm in GEE can act as a powerful tool for mapping flood inundation areas at the time of disaster, and enhance existing efforts towards saving lives and livelihoods of communities, and safeguarding infrastructure and businesses.

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“…The projected changes in the frequency of extreme flooding events of 1-day, 3-day and 5-day duration for the periods 2020-2059 and 2060-2099 estimated based on the 20-year return period streamflow values with respect to the historical base period are provided in Fig. 6.8 (modified from Ali et al 2019). A higher increase in 1-day flood events is projected for the far future than that of the near future under RCP 8.5 scenario (Fig.…”

Section: Floodsmentioning

confidence: 99%

Droughts and Floods

Mujumdar

Preethi

Ramarao

et al. 2020

Assessment of Climate Change Over the Indian Region

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Key Messages • The frequency and spatial extent of droughts over India have increased significantly during 1951-2015. An increase in drought severity is observed mainly over the central parts of India, including parts of Indo-Gangetic Plains (high confidence). These changes are consistent with the observed decline in the mean summer monsoon rainfall. • Increased frequency of localized heavy rainfall on sub-daily and daily timescales has enhanced flood risk over India (high confidence). Increased frequency and impacts of floods are also on the rise in urban areas. • Climate model projections indicate an increase in frequency, spatial extent and severity of droughts over India during the twenty-first century (medium confidence), while flood propensity is projected to increase over the major Himalayan river basins (e.g. Indus, Ganga and Brahmaputra) (high confidence).

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Increased flood risk in Indian sub-continent under the warming climate

Cited by 116 publications

References 66 publications

Bias-corrected climate projections for South Asia from Coupled Model Intercomparison Project-6

Bias-corrected climate projections for South Asia from Coupled Model Intercomparison Project-6

Flood inundation mapping- Kerala 2018; Harnessing the power of SAR, automatic threshold detection method and Google Earth Engine

Droughts and Floods

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