Revisiting 2013 Uttarakhand flash floods through hydrological evaluation of precipitation data sources and morphometric prioritization

Patel, Pratiman; Thakur, Praveen K.; Garg, Vaibhav; Dhote, Pankaj R.; Nikam, Bhaskar R.; Swain, Sarat Chandra; Al‐Ansari, Nadhir

doi:10.1080/19475705.2022.2038696

Cited by 40 publications

(12 citation statements)

References 63 publications

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“…This integration enhances precision in water resource management, addressing the intricacies posed by the challenging Himalayan terrain 4 . This aligns with the study of Patel et al 2022 5 , who concentrate on the 2013 Uttarakhand flash floods, underlining the importance of hydrological assessments and the development of disaster preparedness strategies in the region. The catastrophic nature of flash floods caused by cloud bursts and landslides in mountainous regions is highlighted as the most devastating natural disaster 6 .…”

Section: Introductionsupporting

confidence: 86%

Understanding flash flooding in the Himalayan Region: a case study

Nagamani,

Mishra,

Meer

et al. 2024

Sci Rep

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The Himalayan region, characterized by its substantial topographical scale and elevation, exhibits vulnerability to flash floods and landslides induced by natural and anthropogenic influences. The study focuses on the Himalayan region, emphasizing the pivotal role of geographical and atmospheric parameters in flash flood occurrences. Specifically, the investigation delves into the intricate interactions between atmospheric and surface parameters to elucidate their collective contribution to flash flooding within the Nainital region of Uttarakhand in the Himalayan terrain. Pre-flood parameters, including total aerosol optical depth, cloud cover thickness, and total precipitable water vapor, were systematically analyzed, revealing a noteworthy correlation with flash flooding event transpiring on October 17th, 18th, and 19th, 2021. Which resulted in a huge loss of life and property in the study area. Contrasting the October 2021 heavy rainfall with the time series data (2000–2021), the historical pattern indicates flash flooding predominantly during June to September. The rare occurrence of October flash flooding suggests a potential shift in the area's precipitation pattern, possibly influenced by climate change. Robust statistical analyses, specifically employing non-parametric tests including the Autocorrelation function (ACF), Mann–Kendall (MK) test, Modified Mann–Kendall, and Sen's slope (q) estimator, were applied to discern extreme precipitation characteristics from 2000 to 201. The findings revealed a general non-significant increasing trend, except for July, which exhibited a non-significant decreasing trend. Moreover, the results elucidate the application of Meteosat-8 data and remote sensing applications to analyze flash flood dynamics. Furthermore, the research extensively explores the substantial roles played by pre and post-atmospheric parameters with geographic parameters in heavy rainfall events that resulted flash flooding, presenting a comprehensive discussion. The findings describe the role of real time remote sensing and satellite and underscore the need for comprehensive approaches to tackle flash flooding, including mitigation. The study also highlights the significance of monitoring weather patterns and rainfall trends to improve disaster preparedness and minimize the impact of flash floods in the Himalayan region.

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Section: Introductionsupporting

confidence: 86%

Understanding flash flooding in the Himalayan Region: a case study

Nagamani,

Mishra,

Meer

et al. 2024

Sci Rep

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“…The increase in flash floods in recent past decades, especially over the mountainous regions, has advised developing a reliable flood forecasting system (Patel et al, 2022). The climatic changes increasing the precipitation extremes cause further damaging consequences to the public (Mittal et al, 2014;Mishra and Lilhare, 2016;Swain et al, 2021a).…”

Section: Introductionmentioning

confidence: 99%

Analyzing hydro-meteorological data from Yamuna river basin, western Himalaya: Using a Markov Chain and LSTM approach to forecast future disasters

Chauhan

Akıner

Sain

2023

Preprint

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This research aims to evaluate hydro-meteorological data from the discharge site at the Dakpathar barrage in the Yamuna River basin, Uttarakhand, India, utilizing Extreme Value Distribution of Frequency Analysis and the Markov Chain Approach. This method assesses persistence and allows for combinatorial probability estimations such as initial and transitional probabilities. The hydrologic data was generated (in-situ) and received from Uttarakhand Jal Vidut Nigam Limited (UJVNL), and meteorological data was acquired from NASA's archives MERRA-2 product. A total of sixteen years (2005-2020) of data was used to foresee daily precipitation from 2020 to 2022. Precipitation data for 2021 and 2022 were also retrieved from MERRA-2 products and utilized as observed and forecast values for daily precipitation throughout the monsoon season, which runs from July to September. Markov Chain and Long Short-Term Memory (LSTM) findings for 2020, 2021, and 2022 were observed and anticipated values for daily rainfall during the monsoon season between July and September. According to test findings, the artificial intelligence technique cannot anticipate future regional meteorological formations; the correlation coefficient R2 is around 0.12. According to the randomly verified precipitation data findings, the Markov Chain model has a success rate of 79.17 percent. The results suggest that extended return periods should be seen as a warning sign for drought and flood risk in the Himalayan region. This study gives a better knowledge of the water budget and better evaluations of the overall state of the climate change variability, impact for global warning, ultimately leading to improved water resource management and better emergency planning to establishment of the Early Warning System (EWS) for extreme occurrences such as cloudbursts, flash floods, landslides and other hydro-meteorological related hazards in the complex Himalayan region.

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“…Yuan et al [30] and Jiang et al [31] presented the comprehensive studies on the feasibility and hydrologic utility of GPM IMERG for flood simulations over the Chindwin River and Mishui basins in China using XAJ, respectively. Gilewski and Nawalany [32], Saouable et al [33], Benkirane et al [34], Min et al [35], Tang et al [36], Soo et al [37], and Patel et al [38] applied the HEC-HMS model to the river basins in Poland (Upper Skawa River), Morocco (Ghdat River and Tensift River), China (Xin'an River and Shouxi River), Malaysia (Langat River), and India (Upper Ganga River) for flood modeling, respectively. Nonetheless, these IMERG-used flood analyses lack sufficient evaluation of the modeling accuracy compared to traditional methods that rely on gauge observations or precipitation estimates from weather radars, such as those conducted by Li et al [10] and Gilewski and Nawalany [32].…”

Section: Introductionmentioning

confidence: 99%

Comparative Application of Rain Gauge, Ground- and Space-Borne Radar Precipitation Products for Flood Simulations in a Dam Watershed in South Korea

Cho

2023

Water

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This study presents a comparative analysis of flood simulations using rain gauge, ground- and space-borne radar precipitation products. The objectives are to assess the effectiveness of two radar-based data sources, namely the Radar-AWS Rainrates (RAR) and Integrated Multi-satellite Retrievals for GPM (IMERG), in a dam watershed with gauge observations, and explore the modeling feasibility of integrating the half-hourly IMERG satellite precipitation in regions with ungauged or limited observational area. Two types of HEC-HMS models were developed, considering areal-averaged and spatially distributed gridded data simulations utilizing eight selected storm events. The findings indicate that the RAR data, although slightly underestimate precipitation compared to the gauge measurements, accurately reproduce hydrographs without requiring parameter adjustments (Nash–Sutcliffe efficiency, ENS, 0.863; coefficient of determination, R2, 0.873; and percent bias, PBIAS, 7.49%). On the other hand, flood simulations using the IMERG data exhibit lower model efficiency and correlation, suggesting potential limitations in ungauged watersheds. Nevertheless, with available discharge data, the calibrated model using IMERG shows prospects for utilization (ENS 0.776, R2 0.787, and PBIAS 7.15%). Overall, this research offers insights into flood simulations using various precipitation products, emphasizing the significance of reliable discharge data for accurate hydrological modeling and the need for further evaluation of the IMERG product.

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Revisiting 2013 Uttarakhand flash floods through hydrological evaluation of precipitation data sources and morphometric prioritization

Cited by 40 publications

References 63 publications

Understanding flash flooding in the Himalayan Region: a case study

Understanding flash flooding in the Himalayan Region: a case study

Analyzing hydro-meteorological data from Yamuna river basin, western Himalaya: Using a Markov Chain and LSTM approach to forecast future disasters

Comparative Application of Rain Gauge, Ground- and Space-Borne Radar Precipitation Products for Flood Simulations in a Dam Watershed in South Korea

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