flash flood case study

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• Published: 25 March 2024

Understanding flash flooding in the Himalayan Region: a case study

• Katukotta Nagamani 1 ,
• Anoop Kumar Mishra 1 , 2 ,
• Mohammad Suhail Meer 1 &
• Jayanta Das 3  

Scientific Reports volume  14 , Article number:  7060 ( 2024 ) Cite this article

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• Climate sciences
• Cryospheric science
• Natural hazards

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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Introduction.

The most significant challenges affecting a country's long-term social, economic, and environmental well-being stem from natural disasters. This includes extreme hydro-meteorological events like cloudbursts and excessive rainfall, which, due to their severe complications and intensity, have become a focal point of research, particularly in mountainous areas. The exploration of these events is crucial for developing strategies to mitigate their impact for mountainous region 1 . In the Himalayan context, the discernment of topographical intricacies assumes paramount importance due to their potential rapid escalation into calamitous events 2 . Consequently, a comprehensive understanding of hydrological challenges and water resource resilience becomes imperative, as these phenomena manifest in diverse catastrophic forms 3 . To delineate and analyze these hydrological challenges and resilience, hydrological modeling emerges as a crucial tool. The efficacy of such modeling is contingent upon the utilization of high-resolution geospatial data, particularly within the Soil and Water Assessment Tool (SWAT) framework. 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 . Instances of such disasters precipitate multifaceted consequences, encompassing loss of life, infrastructural degradation, and disruption of financial operations. Mitigating these adversities necessitates the systematic monitoring and analysis of flood events. A historical examination underscores the pivotal role of floods, emerging as the foremost impactful natural calamity, with an annual average impact on over 80 million individuals globally over the past few decades. The substantial global impact, as evidenced by floods contributing to annual economic losses exceeding US$11 million worldwide 7 , is further underscored by the difficulty in collecting information on land use, topography, and hydro-meteorological conditions. Anticipating an increased frequency of precipitation extremes and associated flooding in Asia, Africa, and Southeast Asia in the coming decades, this challenge has prompted a debate on the necessary adaptations in flood management policies to address this evolving reality 8 . India, facing the highest flood-related fatalities among Asian countries 9 , 10 , encounters heightened vulnerability to disaster threats. This susceptibility is further exacerbated by the country's extensive geographic variability, making the development and implementation of a climate response strategy considerably more challenging 11 .

The Indian Himalayan Region, being crucial to the national water, energy, and food linkage due to its variety of political, economic, social, and environmental systems, is uniquely vulnerable to hydro-meteorological catastrophes, including floods, cloudbursts, glacier lake eruptions, and landslides 12 , 13 , 14 , 15 . During monsoon season the cloud burst is increasing in the Himalayan region.This phenomenon is closely tied to the unique climatic conditions prevalent in the Himalayas during this period. Monsoons in this region bring intense and sustained rainfall, characterized by the convergence of moisture-laden air masses, especially from the Bay of Bengal, attributing to landslides, debris flows, and flash flooding 16 . These result in significant loss of life, property, infrastructure, agriculture, forest cover, and communication systems 17 . In 2013, the Himalayan state of Uttarakhand experienced devastating floods and landslides due to multiple heavy rainfall spells 17 , 18 . On February 7th, 2021, a portion of the Nanda Devi glacier in Uttarakhand's Chamoli district broke off, causing an unanticipated flood 19 , 20 , 21 . During this sudden flood, 15 people were killed, and 150 went missing. These disasters have disrupted the Himalayan ecology in several states, including Uttarakhand, and the cause and magnitude of these disasters have been made worse by human activities, including building highways, dams, and deforestation 22 . When we check the flood record of Uttarakhand, Himalaya, the area has experienced catastrophes during 1970, 1986, 1991, 1998, 2001, 2002, 2004, 2005, 2008, 2009, 2010, 2012, 2013, 2016, 2017, 2019, 2020, and 2021, making them among the most significant natural disasters to have struck Uttarakhand 16 , 21 .

The rising trend of the synoptic scale of Western Disturbance (WD) activity and precipitation extremes over the Western Himalayan (WH) region during the last few decades is the result of human-induced climate change, and these changes cannot be fully explained by natural forcing alone. This phenomenon is observed over the large expanse of the high-elevation eastern Tibetan Plateau, where a higher surface warming in response to climate change is noted compared to the western side 22 , 23 . Since the Industrial Revolution, the Himalaya and the Tibetan plateau have warmed at an increased rate of 0.2 degrees each decade (1951–2014) 24 . In the Himalayan region, the mean surface temperature has increased by almost 0.5˚C during 2000–2014. This alteration in climate (temperature) has resulted in a decrease in the amount of apples produced in low-altitude portions of the Himalaya. The warming of the planet is directly responsible for these effects. The Himalayan region has experienced a decline in pre-monsoon precipitation towards the end of the century, leading to new societal challenges for local farmers due to the socioeconomic shifts that have taken place 25 . Simultaneously, there has been an increase in the highest recorded temperature observed throughout the monsoon season. In tandem with heightened levels of precipitation, an elevation in the maximum attainable temperature has the potential to amplify the occurrence of torrential rainfall events during the monsoon season 26 . This long-term change in atmospheric parameters, known as climate change, may affect river hydrology and biodiversity. The associated shifts in climate pose a significant risk to hydropower plants if certain climate change scenarios materialize. As part of this broader context, the dilemma of spring disappearance should be thoroughly analyzed to provide scientific, long-term remedies and mitigation strategies for potential hydrogeological disasters. This is crucial due to the observed increase in the frequency of landslides, avalanches, and flash floods in recent years 24 .

El Niño–Southern Oscillation (ENSO) and Equatorial Indian Ocean Oscillation (EQUINOO) play a crucial role in the teleconnection of India's Monsoon, as well as in determining rainfall patterns and the occurrence of flash floods across different regions of India. At a regional level, a study was conducted to examine the impact of various types of climatic fluctuations on the onset dates of the monsoon. Northern India, specifically northern northwest India, referred to as SR15, consistently experiences a delayed start to its seasons, regardless of the climatic phase 27 . The occurrence of significant anomalies in sea surface temperatures (SST) in the tropical Pacific region, associated with ENSO and EQUINOO, is accompanied by large-scale tropical Sea Level Pressure (SLP) anomalies related to the Southern Oscillation (SO) 28 , 29 . The Equatorial Indian Ocean Oscillation (EIO) represents the oscillation between these two states, manifested in pressure gradients and wind patterns along the equator (EQUINOO).

The negative anomaly of the zonal component of surface wind in the equatorial Indian Ocean region (60°–90°, 2.5° S—2.5° N) is the foundation for the EQUINOO index 30 . Additionally, they demonstrated that between 1979 and 2002, any season with excessive rainfall or drought could be "explained" in terms of the favorable or unfavorable phase of either the EQUINOO, the ENSO, or both. For instance, in 1994, EQUINOO was favorable, but ENSO was negative, resulting in above-average rainfall in India. Conversely, ENSO was favorable, EQUINOO was unfavorable between 1979 and 1985, and India saw below-average rainfall. They, therefore, proposed that by combining those two climate indices, it would be possible to increase the predictability of rainfall during the Indian monsoon. The quantity of rainfall throughout a storm event that might cause a significant discharge in a particular river segment is known as a "rainfall threshold" 31 , 32 . Different techniques, indicators, and predictor variables can be used to derive rainfall thresholds. There are four categories of methodology: empirical, hydrological/hydrodynamic, probabilistic, and compound approaches. Empirical rainfall thresholds are among the most popular methods for constructing EWS in local, regional, and national areas 33 , 34 , 35 . Empirical methods use historical flood reports and rainfall amounts to perform a correlation analysis linking the frequency of event to the amount and length of essential precipitation 36 , 37 , 38 . Several empirical rainfall threshold curves may be found in literature from various countries 32 , 39 , 40 , 41 . Although this research concentrated on various shallow landslides and mudflows, flash flood risk systems can be set using actual rainfall thresholds 42 . Similarly, the principles of the Flood Risk Guideline (FFG) method serve as the foundation for hydrogeological precipitation limits 30 , 41 , 43 , 44 . The fundamental concept of FFG is to use reverse hydrologic modelling to identify the precipitation that produces the slightest flood flow at the basin outlet. Alerts are sent out whenever the threshold is exceeded for a specific time for the real-time actual daily rainfall or the precipitation forecast. This method needs data on precipitation collected using radar or real-time rainfall sensors 45 , 46 . Other threshold approaches for rainfall, however, require the same data. The modelling of various synthetic photographs, regionally dispersed models, and the prior soil moisture status have all been incorporated into the FFG, which is widely used worldwide 46 . Hydraulic models have been developed recently, allowing the threshold to be determined by the canal design, features, and the link between the achieved water table and the inundated area 47 , 48 .

Recent flood events underscore the inadequacy of relying solely on structural safeguards for comprehensive protection against such catastrophes. The imperative for an effective flood management approach becomes paramount to preemptively mitigate these calamities and ensure sustainable safety measures. The present study generates rainfall product that uses real-time satellite data from Meteosat-8 to summarize the significant short-lived localised multiple rainfall events that result flash flooding in the Nainital, Uttrakhand, during October 2021 48 . This method was utilized to investigate the flood events over J&K 2014 49 . Rajasthan in 2019 50 and Bihar and Assam in 2019 51 . This study introduces a pioneering approach by precisely measuring the peak rainfall hours and correlating them with daily rainfall, elucidating their direct correlation with flash flooding in the study area. A distinctive feature of this research is its integration of time series rainfall data with socioeconomic metrics to underscore the significant damage caused by a major flash flood incident. The exploration of the role of sheer slope in flooding provides a unique angle to flood dynamics. Additionally, the study delves into pre-atmospheric parameters specific to the study area that played a pivotal role in initiating flash flooding. By shedding light on these intricate details, this study establishes itself as a trailblazer in disaster mitigation strategies, emphasizing its pivotal role in advancing our understanding of flash flood dynamics and fortifying disaster response frameworks.

The economic and climatic conditions of India are intricately linked to the region of Himalaya, renowned for its delicate ecosystems and geological intricacies 52 . Spanning a vast area, the Indian, Himalaya is among the recent mountain ranges on the surface of earth, marked by the study delves into the vulnerability of the region of Himalaya, examining the intricate interplay of geographical and atmospheric parameters in flash flood occurrences. The area has susceptibility to geological hazards, topographical nuances, biodiversity, and water resource dynamics 53 . Geographically positioned between latitudes 28.44° to 31.28°N and longitudes 77.35° to 81.01°E, with elevations ranging from 7409 to 174 m, Uttarakhand, depicted in Fig.  1 , covers 53,483 square kilometers. Approximately 64% of the land is forested, and 93% is mountainous terrain, bordered by Himachal Pradesh, Uttar Pradesh, China, and Nepal. Serving as the source of major rivers, the state encompasses six significant basins: Yamuna, Alaknanda, Ganga, Kali, Bhagirathi, and Ramganga. Data analysis utilized Shuttle Radar Topographic Mission information obtained from Earth Explorer ( https://earthexplorer.usgs.gov ) via Arc GIS Version 10.5, as shown in Fig.  1 .

( a ) Showing Uttarakhand North western Himalayan state of India ( b ) Nainital district of Uttarakhand with Digital elevation model.

Climate characteristics

The climate of study area exhibits notable variations, ranging from humid subtropical conditions in the Terai region to tundra-like environments in the Greater Himalaya. Substantial transformations occur across the landscape, with high altitudes housing glaciers and lower elevations supporting subtropical forests. Annual precipitation contributes nourishing snowfall to the Himalaya, particularly above 3000 meters 54 . Temperature variations are influenced by elevation, geographical position, slope, and topographical factors. In March and April, southern areas experience average maximum temperature between 34 °C and 38 °C, with average minimum temperatures ranging from 20 °C to 24 °C. Temperatures peak in May and June, reaching up to 42 °C in the lowlands and around 30 °C at elevations exceeding two kilometers. A decline in temperatures begins in late September, reaching their lowest points in January and early February, with January being the coldest month. Southern regions and river valleys witness an average maximum temperature of approximately 20 °C and an average minimum temperature of about 6 °C, while elevation of 2 km above sea level range from 10 °C to 12 °C 55 .

Materials and methodology

Radar is used to collect the rainfall observation remotely. A rain gauge is a conventional method located on the ground for recording rainfall depth in millimeters. Radar systems and rain gauges are standard equipment for tracking significant rainfall events. If there is a widespread, uniform network of rain gauges, it is possible to monitor rainfall accurately unfortunately, there is no such system in Nainital, Uttarakhand, or other parts of India. With the diverse topography of Nainital, Uttrakhand, it is challenging to observe accuracy for extreme rainfall events using radar and rain gauge stations. Satellite observation is the only tool available for monitoring these events. The extreme rainfall event over Nainital, Uttarakhand, was tracked in this study using hourly measurements of rainfall from Meteosat-8 geostationary satellite data. Hourly rainfall measurement was estimated at five kilometres by integrating observation from the Meteosat-8 satellite with space-borne precipitation Radar (PR) from the tropical rainfall measuring mission (TRMM). To estimate rainfall using Meteosat-8 IR and water vapour (WV) channels at 5 km resolution, we have employed the rain index-based technique created by Mishra, 2012 48 . The techniques use TRMM (Tropical rainfall Measuring Mission), space-borne precipitation radar (PR), and Meteosat-8 multispectral satellite data to create the rain analysis. The technique uses Infrared and water vapour observation from Meteosat-8 on 17, 18 and 19 October 2021 to estimate the amount of rainfall over the Nainital, Uttarakhand. By using the infrared (IR) and water vapour (WV) channel observations from Meteosat-8, a new rain index (RI) was computed. The procedure for calculating the rain index is as follows. Non-rainy clouds are filtered out using spatial and temporal gradient approach and brightness temperature from thermal Infrared (TIR) and WV are collocated against rainfall from precipitation radar (PR) to derive non-rainy thresholds of brightness temperature from TIR and WV channels. Now TIR and WV rain coefficient is computed by dividing the brightness temperature from TIR and WV channels with non-rainy thresholds. The TIR ad WV, rain coefficient product, is defined as the rain index (RI). RI is collocated against rainfall from PR to develop a relationship between rainfall and RI using large data sets of heavy rainfall events during the monsoon season of multiple years. The following equation is developed between rain rate (RR) and RI:

Finally, the rainfall rate (RR) is calculated using Eq. ( 1 ). For the Indian subcontinent, a, b, and c are calculated as a = 8.4969, b = 2.7362, and c = 4.27. Using RI generated from Meteosat-8 measurements, this model may be used to estimate hourly rainfall.

The current equation (I) was verified using observations from a strong network of ground-based rain gauges. Hourly rain gauge readings over India during the south-west monsoon season were observed to have a correlation coefficient of 0.70, a bias of 1.37 mm/h, a root mean square error of 3.98 mm/h, a chance of detection of 0.87, a false alarm ratio of 0.13, and a skill score of 0.22 48 . The method used by Mishra 48 outperformed other methods for examining the diurnal aspects of heavy rain over India compared to currently available worldwide rainfall statistics. If both satellite spectral responses to the channels used to produce the rain signatures are similar, the equation developed to estimate rainfall using the rain signature from one satellite can also be used to estimate rainfall using the rain signature from another satellite.

Within the framework of this investigation, Meteosat-8 Second Generation (MSG) measurements were harnessed to scrutinize rainfall characteristics with a heightened focus on fine geographical and temporal scales. Employing the mentioned technique facilitated the calculation of spatial rainfall distribution, as well as the meticulous quantification of hourly and daily rainfall. Subsequently, a comprehensive analysis of cumulative rainfall was conducted, unraveling nuanced patterns and trends within the meteorological data. Following an in-depth examination of intense rainfall episodes, the atmospheric datasets, incorporating cloud optical thickness, total precipitable water vapor, and aerosol optical depth, were procured from Modern-Era Retrospective Analysis for Research and Applications, the National Centers for Environmental Prediction (NCEP), and the National Centre for Atmospheric Research (NCAR). These datasets underwent meticulous scrutiny to unravel the intricate interconnections between atmospheric parameters and heavy rainfall, specifically flash flooding, across the study area. The central objective was to decipher the meteorological conditions catalyzing the genesis of a low-pressure system, subsequently triggering heightened convective activities. To comprehend the dynamics of aerosols within the study domain, trajectory analysis through HYSPLIT was implemented, elucidating trajectories and dispersion patterns of aerosols for comprehensive insights. To comprehensively comprehend episodes of heavy rainfall in the Nainital region of Uttarakhand, particularly during the flash flooding events of October 2021, this study systematically delves into pre-flood parameters. The investigation focuses on Nainital and systematically analyzes time series rainfall data (Modern-Era Retrospective Analysis for Research and Applications) spanning from 2000 to 2021. Monthly rainfall for each year and the long-term mean (accumulated rainfall) were meticulously calculated. Robust statistical tests applied to the time series data unveiled trends, indicating a non-significant increase overall, except for a notable decrease in July. The study further integrates Shuttle Radar Topography Mission (SRTM) topographic data and the total number of cloud burst events ( https://dehradun.nic.in/ ) to elucidate the role of elevation in cloud burst occurrences. Exploring the relationship between elevation, annual rainfall, and maximum temperature, the research establishes critical links between heavy rainfall episodes, flash flooding, and associated loss of lives from 2010 to 2022. The study strategically correlates these aspects with time-series data, presenting instances of heavy rainfall and rapid-onset flooding. Utilizing Meteosat-8 data and remote sensing, our research pioneers dynamic flash flood analysis, shedding light on the pivotal roles played by atmospheric and geographic parameters. The time series precipitation data, spanning from 2001 to 2021, underwent rigorous trend analysis employing statistical methodologies, including Autocorrelation function (ACF), Mann–Kendall (MK) test, Modified Mann–Kendall test, and Sen's slope (q) estimator. These analyses were conducted to elucidate and characterize the prevailing trends within the rainfall dataset over the specified temporal interval.

Autocorrelation function (ACF)

Autocorrelation or serial dependency is one of the severe drawbacks for analyzing and detecting trends of time series data. The existence of autocorrelation in the time series data may affect MK test statistic variance (S) 56 , 57 . Hence, the ACF at lag-1 was calculated using the following equation.

where, \({r}_{k}\) denotes the ACF (autocorrelation function) at lag k, \({x}_{t}\) and \({x}_{p}\) is the utilized rainfall data, \(\overline{x}\) denotes the mean of utilized data \(\left({x}_{p}\right)\) , \(N\) signify the total length of the time-series ( \({x}_{p})\) , k refers to the maximum lag.

Mann–Kendall (MK) test

In hydroclimatic investigations, the MK test is extensively employed for evaluating trends 58 , 59 , 60 . The-MK test 61 , 62 was conferred by the World-Meteorological-Organization (WMO), which has a number of benefits 63 . The following equations can be used to construct MK test-statistic

In Eq. ( 5 ), n denotes the size of the sample, whereas \({x}_{p}\) and- \({x}_{q}\) denote consecutive data within a series.

The variance of \(S\) is assessed in the following way

whereas \({t}_{p}\) and \(q\) denotes the number of ties for the \({p}^{th}\) value. Equation ( 9 ) shows how to calculate Z statistic, the standardized-test for the MK test-(Z)

The trend's direction is indicated by the letter Z. A negative Z value specifies a diminishing trend and vice versa. The null hypothesis of no trend will be rejected when the absolute value of Z would be greater than 2.576 and 1.960 at 1% and 5% significant level.

Modified Mann–Kendall test

Hamed and Rao (1998) 64 introduced the modified MK test for auto-correlated data. In the case of auto-correlated data, variance (s) is underestimated 65 ; hence, the following correction factor \(\left(\frac{n}{{n}_{e}^{*}}\right)\) is proposed to deal with serially dependency data.

where \(n\) is the total number of observations and \({\rho }_{e}\left(f\right)\) denotes the autocorrelation function of the time series, and it is estimated using the following equation

Sen's slope (q) estimator

Sen 66 proposed the non-parametric technique to obtain the quantity of trends in the data series. The Sen’s slope estimator can calculate in the time series from N pairs of data using this formula

where \({Q}_{i}\) refers to the Sen’s slope estimator, \({x}_{n}\) and \({x}_{m}\) are scores of times \(n\) and \(m\) , respectively.

Results and discussion

The Himalaya, renowned for their massive size and elevated altitude, possess distinctive geological characteristics that render them vulnerable to sudden and intense floods 67 . These rapid floods are the outcome of a combination of natural and human factors, including geological movements, glacial lakes, steep topography, deforestation, alterations in land usage, and the monsoon season 68 . In the Himalayan region, the primary trigger for these abrupt floods is often linked to instances of cloud bursts accompanied by heavy rainfall episodes 69 . This study aims to provide insight into historical and recent instances of significant rainfall that have resulted in flash floods, while also examining the relationship between these events with atmospheric and other relevant factors. The study also elaborates on the discussion on flash flooding on the 17th, 18 and 19 October 2021. In Fig.  2 we have illustrated the elevation and cloud burst events that occurred between 2020 and 2021 across different districts in Uttarakhand, Himalaya. The elevation map (Fig.  2 ), was generated by Arc GIS 10.5. Using cloud burst data from ( https://dehradun.nic.in/ ). After statistical analyses, the same data was imported to Arc GIS 10.5 and was shown in the form of Fig.  2 . The figure underscores that the northern areas, located within the central portion of Uttarakhand, witnessed a higher frequency of cloud bursts compared to the southern areas. The observed divergence, attributed to steeper slopes in the northern region as opposed to the southern region, is further complemented by an intriguing revelation in our study 70 . Specifically, we noted significantly fewer cloud burst events in the areas of both lower and sharply higher elevations during the period of 2020–2021, particularly when compared with the occurrences at medium elevations from (1000 to 2500)m illustrated in Fig.  2 . Thus, emphasizing a noteworthy and substantiated relationship between cloud bursts and elevation 70 .

Location map of cloudbursts hit area from 2020 to 2021 over Uttrakhand.

Within the specified timeframe, a total of 30 significant cloudburst incidents were documented during 2020–2021, with 17 of these incidents transpiring in 2021. Among the districts, Uttarkashi recorded the highest number of cloudburst occurrences (07), trailed by Chamoli with 05 incidents, while Dehradun and Pithoragarh each registered 04 instances. Rudraprayag accounted for 03 incidents, whereas Tehri, Almora, and Bageshwar each reported 01 cloudburst occurrence, according to reports from the Dehradun District Administration and the India Meteorological Department in 2021.

Due to high topography, the area has faced many flash flood events in history. Figure  3 presents a graphical representation of the total monthly rainfall data for the Nainital district in Uttarakhand from 2000 to 2021. The graph reveals the amount of rainfall received each month throughout this period. A noteworthy observation from the graph is that most of the years between 2000 and 2021 experienced substantial rainfall, with the majority surpassing 300 mm. However, 2010 is an exceptional case of rainfall in the Nainital area. The region received an astounding 500 mm monthly rainfall during this particular year. This extraordinary amount of rainfall was unprecedented and broke the records of the last few decades. Such a significant monthly rainfall level had not been observed in the region for quite some time. The spike in rainfall during 2010 might have considerably impacted the local environment, water bodies, and overall hydrological conditions in the area. Given the intensity of the rainfall, It could have caused flooding, landslides, and other related hazards. The data presented in Fig.  3 is crucial for understanding the long-term trends and patterns of rainfall in Nainital over the past two decades. In Fig.  3 , another intriguing aspect emerges, shedding light on the fact that the South-west monsoon exhibits its peak rainfall during the months of June, July, August, and September across the study area.( https://mausam.imd.gov.in/Forecast/mcmarq/mcmarq_data/SW_MONS OON_2022_UK.pdf).The region could be subject to recurring heavy rainfall episodes, potentially resulting in flash flooding over specific temporal intervals.

Time series monthly rainfall of study area. J(January),F(February),M(March),A(April),M(May),Ju(June)Jl(July),Ag(August), S(September),Oc(October), N(November), D(December).

Figure  4 offers a visual representation of the long-term average of monthly recorded rainfall data in the study area from 2000 to 2021 to gain insight into the average rainfall during the same timeframe. The graph illustrates a significant rise in the average long-term rainfall within the study area. This increase is particularly notable during the months spanning from June to September. Notably, the figure underscores that during the years 2000 to 2021, the months of July and August in the area witnessed multiple heavy rainfall episodes due to monsoon. For these two months, the long-term average surpasses the 300 mm mark. In our results and discussion, we unravel the ramifications of persistent and substantial rainfall throughout these crucial months. The enduring deluge sets in motion a series of impactful consequences, ranging from escalated surface runoff and heightened river discharge to the looming specter of rapid flooding and landslides. This intricate web of effects intricately influences the stability of the soil, the vitality of vegetation, and the delicate balance within local ecosystems 71 . The findings highlighted in Fig. (3 and 4) underscore the critical significance of examining monthly rainfall data to comprehend the relationship with average monthly rainfall trends from (2000–2021) in the Himalayan region. The figure specifically draws attention to the months characterized by substantial rainfall, which may have result in disasters such as flash flooding and landslides. So we have concluded the study area may have received flash flooding by heavy rainfall during June to September (2000–2021).The daily rainfall data from 2001 to 2021 was allowed for non parametric trend analyses using Mann–Kendall test, Sen’s slope analysis. Modified Mann–Kendall and autocorrelation function for trend analysis.

Accumulated rainfall (Long-term mean) over the Study area.

Our analysis delved into daily rainfall data, downloaded from (ww.nasa.giovanni.com). We aimed to discern trends in key parameters, including monthly rainfall during the monsoon season (June to September), monsoon season data, annual rainfall, heavy rainfall events (> 50 mm/Day), and the number of wet days (> 2.5 mm/Day). Table 1 provides a comprehensive analysis of rainfall trends and extreme rainfall events from 2000 to 2021. In June, a negative autocorrelation was observed, and the findings are statistically significant at a 95% confidence level, so we considered modified MK test instead of original MK test. Employing the non-parametric Mann–Kendall test (MK/mMK) for trend analysis, our findings revealed a general non-significant increasing trend, with the exception of July, which exhibited a non-significant decreasing trend. Noteworthy was the significant increase in the number of wet days at a 0.05% significance level. Sen’s slope analysis further emphasized an annual increase in rainfall at a rate of 4.558 mm. These results provide valuable insights into the evolving rainfall patterns in the studied region, with implications for understanding climate variations.

Topographic influence on rainfall and temperature over the study area

Exploring the realm of abundant rainfall at lofty Himalayan elevations delves into the captivating interplay between topography and the dynamic shifts in atmospheric parameters. Our investigation ventures beyond the surface, intricately analyzing the elevations across diverse districts within our study area. Figure  5 serves as a visual gateway, unraveling the fascinating discourse on how these elevational nuances weave a compelling narrative of change, orchestrating the dance between rainfall patterns and temperature shifts across our meticulously examined landscape. Using Fig.  5 , we can correlate the significant relationship between the amount of rainfall and the topography over the Himalayan region of Uttarakhand. The figure distinctly delineates various districts of Uttarakhand, such as Bageshwar, Chamoli, Nainital, Pithoragarh, Rudraprayag, and Tehri Garhwal, positioned at elevations surpassing 7000 m. The presented data establishes a conspicuous correlation between the received rainfall and the elevated nature of these districts, showcasing those areas above 7000 m experience substantial annual rainfall exceeding 1500 mm. This correlation underscores the notable influence of elevation on the precipitation patterns in the Himalayan region. Higher elevations tend to attract more moisture from the atmosphere, leading to increased rainfall 72 .

Topographic influence on the atmospheric parameter (Temperature and rainfall).

Figure  5 , in conjunction with the citation of Rafiq et al. 2016 73 , emphasizes the significant connection between mean maximum temperature and elevation within the Himalayan region. The figure illustrates that as elevation increases, there is a corresponding decline in mean maximum temperature. This well-known phenomenon is called the "lapse rate," which describes the temperature decrease with rising altitude. Areas above 7000 m experience notably lower temperatures than those at lower elevations. The lapse rate is a fundamental climatic characteristic particularly relevant in mountainous terrains like the Himalaya. As air ascends along the slopes, it cools down due to decreasing atmospheric pressure, forming clouds through condensation. These clouds subsequently contribute to rainfall, as discussed in the study by Wang Keyi et al. 72 . Higher elevations experience a more pronounced temperature decrease, resulting in elevated rainfall levels.

The steep slopes in the Himalayan region significantly correlate with the number of casualties resulting from cloud bursts, landslides, and flash floods caused by heavy rainfall events. The presence of steep gradients exacerbates the impact of sudden and intense rainfall, leading to flash floods and landslides. Topography is crucial in disasters, particularly flash flooding and landslides, commonly observed in the Himalayan region 2 . These natural disasters have resulted in substantial loss of life and livelihood, as depicted in Fig.  6 .  Over 300 casualties were reported due to landslides, flash flooding, and cloud bursts in Uttarakhand during 2021. From 2010 and 2013, the loss was restricted to nearly 230 causalities each year. The Himalayan steep gradients are especially vulnerable to the effects of rainfall and climate change 74 .

Number of human lives lost during heavy rainfall episodes in Uttrakhand.

Moreover, these mountainous regions' ecological and socioeconomic systems are becoming increasingly vulnerable due to the rising human population 2 . These disasters cause severe damage to infrastructure, properties, human lives, and the environment. Furthermore, they can exacerbate other hardships, including the spread of diseases, financial instability, environmental degradation, and social conflicts 74 .

In summary, the steep slopes in the Himalayan region play a critical role in the occurrence and severity of disasters such as flash floods and landslides. The susceptibility of these areas to heavy rainfall and climate impacts poses significant challenges for ecological and socioeconomic systems, particularly with the increasing human population. The aftermath of these disasters is far-reaching and extends beyond the immediate loss of life and property, affecting various aspects of human life and the environment in the region.

Flash flood event during October 2021

As delineated in Fig. 7 , our investigation reveals a distinctive pattern in precipitation dynamics. Traditionally, the region encounters heightened rainfall exclusively from June to September, aligning with the monsoon season. Flash flooding, consequently, primarily manifests during this period. However, the anomalous occurrence in October 2021 is unprecedented in our dataset. For the first time, our analysis, depicted in Fig.  7 , captures the manifestation of intense rainfall episodes leading to flash flooding in the Nainital region, Uttarakhand. As this was the rare case the study area has received heavy raifnall during month of october 2021. This may be due to western distribuance that area very rarely is receiving. The infrequency of such events in the area may be attributed to the rarity of western disturbances impacting the region. Utilizing the technique developed by Mishra 48 , we conducted the study to map daily monthly and spatial distribution of rainfall amount using Meteosat-8 data. The study employs real-time monitoring to track and analyze flash flooding, shedding light on the atmospheric parameters that contributed to the occurrence of this unique episode.

During October 2021, the region of Nainital, Uttarakhand experienced a series of rainfall events. From 12 to 15 September 2021, the area witnessed the development of low-pressure systems from the Bay of Bengal, as documented in the IMD Report 2021. This convergence of low-pressure systems led to several episodes of heavy rainfall over the Himalayan region 74 . Unfortunately, the consequences of these multiple rainfall episodes were severe, causing flash flooding and triggering landslides in various parts of the Indian Himalaya. Over the past few decades, there has been a noticeable upward trend in flash flooding incidents, particularly in the Himalayan region, which can be attributed to the effects of climate change 75 . As global temperatures rise and weather patterns become more erratic, the delicate balance of the Himalayan ecosystem is being disrupted, leading to intensified rainfall events and a higher risk of natural disasters like flash floods and landslides. These alarming changes underscore the urgent need for climate action and measures to address the impacts of climate change on vulnerable regions like the Himalaya. In October 2021, Nainital, Uttarakhand experienced an unusual and devastating flood event, an occurrence that is typically rare during this particular month. The torrential floodwaters swept away numerous homes and disrupted transportation networks, leaving the region in turmoil. In response to this calamity, various defence groups, such as the army and national defense forces, were promptly deployed to the Himalayan state to conduct rescue operations for residents and tourists. The impact of the flood was further exacerbated by landslides, which severed many districts from the rest of the region, as roads were blocked by mud and debris. The region's vulnerability to such natural disasters can be traced back to historical records, as it has been experiencing substantial rainfall since as early as 1857 76 . During 17th, 18th, and 19th of October 2021 a series of heavy rainfall episodes in Nainital, Uttarakhand, leading to flash flooding and landslides. The dire consequences resulted in widespread destruction of both lives and livelihoods 2 . Figure  7 highlights the visual representation of rainfall distribution over three days. The illustration provides valuable insights into the amount and pattern of rainfall that occurred during this critical period. Notably, the data reveals a remarkable occurrence on the 18th and 19th of October, where the study area experienced an abrupt 270 mm of rainfall. This substantial rainfall in just two days is an alarming and unprecedented event, signifying the intensity and severity of the weather system that hit the region. Moreover, it is essential to note that the 270 mm rainfall figure is not solely confined to those two days but is the cumulative result of heavy rainfall from multiple rainy spells that persisted during the specified period. The confluence of these rain events led to an overwhelming deluge, which became a primary driver of the extreme flooding that engulfed Nainital, Uttarakhand.

Time series heavy rainfall episodes over the Study area.

The analysis of near real-time monitoring of flash flooding in the area involved examining pre-flood atmospheric data related to aerosol optical depth, cloud optical thickness and total perceptible water vapour over the study area, as depicted in Fig.  8 b,c,d. The study revealed a significant correlation between the pre-flood atmospheric data and the occurrence of extreme and multiple rainfall episodes in the region. This indicates that cloud formation and the presence of moisture are closely linked to the presence of aerosol particles 77 . The analysis of aerosol data in the study area revealed a significant presence of aerosol content in the atmosphere before the flood. This observation was particularly evident from the data recorded between the 5th and 8th of October 2021, as depicted in Fig.  8 . The aerosol optical depth during this period was measured to be around 0.8, a noteworthy value for its potential impact in inducing heavy rainfall and flash flooding 78 , 79 . Aerosols are tiny particles suspended in the air, which can have important implications for weather and climate patterns 80 .  High aerosol optical depth, as indicated by the measurement of 0.8, suggests a relatively dense concentration of aerosol particles in the atmosphere during the specified timeframe. Such high aerosol levels can act as cloud condensation nuclei, providing necessary sites for water vapour to condense and form cloud droplets. This phenomenon is crucial for cloud formation and rainfall processes 81 .  The significance of aerosols in cloud formation lies in their ability to serve as nuclei for the aggregation of water vapour, leading to the development of clouds. This thick cloud cover resulted in considerable precipitable water vapour from the 17th to 19th of October, as shown in Fig.  8 82 , 83 . These atmospheric parameters resulted in favorable conditions for extreme with multiple rainfall episodes over the study area from 17 to 19th October 2021,finally, the extreme rainfall episodes attributed to flash flooding over the Nainital, Uttarakhand.

( a ) Cumulative rainfall over the Nanital Utrankhand, ( b ) Aerosol optical depth over the Nanital Utrankhand, ( c ) Cloud optical thickness over the Nanital Utrankhand, ( d ) Total Perceptible water Vapor over the Nanital Utrankhand.

When moisture condenses around aerosol particles, it contributes to the formation of larger cloud droplets. These larger droplets can result in more intense rainfall events, potentially leading to flash flooding under certain conditions 82 , 83 . Furthermore, the HYSPLIT trajectory analysis revealed a profound influence of air masses originating or passing through western regions on the Himalayan radiation budget. This suggests that atmospheric dynamics from these areas significantly impact the weather patterns and climate in the Himalayan region. To gain deeper insights into the role of aerosols in the Himalayan radiation budget, the study also examined the Atmospheric Radiative Forcing (ARF) 14 . In the investigation of aerosol data, a backward trajectory analysis was conducted depicted in Fig.  9 , focusing on the 17th and 18th of October 2021. The analysis aimed to trace the movement and direction of aerosols in the atmosphere 48 h before reaching the target area encircled in Fig.  9 . The findings of figure demonstrated journey of aerosol during these days, shedding light on their movement and behavior in the study area. Specifically, on the 17th of October, the source of aerosols was observed at an altitude of 3500 m above Mean Sea Level (MSL). The tracked trajectory of aerosols reveals a gradual descent from an initial altitude of 3500 m above Mean Sea Level (MSL), ultimately reaching the research target at 1096 m MSL. This horizontal movement of aerosols suggests a potential influential role in the occurrence of heavy rainfall that result flash flooding over the study area by providing the favorable atmospheric conditions.

Backward trajectory of Aerosol during 17th, 18th and 19th October 2021 over the study area source encircled.

The comprehensive analysis conducted in this study has significantly advanced our understanding of the intricate interactions between various atmospheric parameters, aerosols, and rainfall patterns, all of which collectively contribute to heavy with multiple rainfall episodes that resulted flash flooding event in the Nainital region of Uttarakhand. The severity of such flash floods is starkly evident from the tragic loss of fifty lives and the extensive damage to property and infrastructure.

A key highlight of this study is the application of remote sensing data, including total aerosol optical depth, cloud cover thickness, total precipitable water vapour, and rainfall product (Meteosat-8), for real-time monitoring of flash floods. The use of cutting-edge satellite technology and geospatial data has proven to be pivotal in closely monitoring and tracking flash floods, enabling timely and efficient responses to mitigate the impact of these disasters. The findings of this research underscore the vital importance of leveraging advanced technology and scientific research to address the challenges posed by flash flooding in the Himalayan region. To effectively combat these challenges, a comprehensive and multi-faceted approach is imperative. This may encompass implementing measures to counteract the impact of climate change on weather patterns, advocating for sustainable land use practices to reduce vulnerability, and bolstering the resilience of critical infrastructure to withstand the impacts of extreme weather events like flash floods.

Furthermore, the study presents a unique occurrence in the Nainital region of Uttarakhand, Himalaya, wherein heavy rainfall, marked by multiple episodes, led to flash flooding during October 2021, an unusual event when compared to the time series precipitation analyzed in the study. The investigation emphasizes the significant role of elevation in influencing rainfall and temperature variations in the region. The study emphasizes the significance of continuous scientific research and monitoring efforts to gain invaluable insights into the underlying patterns and drivers of flash flooding in the Himalaya. Armed with this knowledge, authorities can formulate robust strategies and policies to minimize the impact of future flash floods and safeguard the lives and livelihoods of the communities residing in the region. This study reaffirms the crucial role that satellite data and geospatial technology play in effective disaster management. It underscores the urgency of adopting proactive measures to address the mounting risks of flash floods in vulnerable regions like Nainital, Uttarakhand. By synergizing scientific research, advanced monitoring techniques, and community engagement, authorities can work towards building a more resilient future, better equipped to respond to and mitigate the repercussions of flash flooding events.

With their immense size and unique geological features, the Himalaya are prone to flash flooding incidents that pose significant risks to human life and infrastructure. Natural factors, such as tectonic activities and glacial lakes, and human-induced changes, including deforestation and land use alterations, influence these flash floods. In the Nainital region of Uttarakhand, the primary cause of flash floods is often attributed to cloud bursts accompanied by heavy rainfall episodes. The study highlights the crucial role of rainfall product and remote sensing data including total aerosol optical depth, cloud cover thickness and total precipitable water vapour, in real-time short-lived flash flood monitoring. The study emphasizes the significant role of elevation in influencing rainfall and temperature variations in the region. The application of satellite technology and geospatial data has proven to be instrumental in promptly tracking and responding to flash flood events. A comprehensive approach is necessary to address the challenges of flash flooding in the Himalaya. This may involve implementing measures to mitigate the impact of climate change, promoting sustainable land use practices, and enhancing infrastructure resilience. The study highlights a significant shift in precipitation patterns of Nainital, with usual heightened rainfall and flash floods. The rarity of such events in the region may be linked to infrequent western disturbances.

The research contributes valuable historical data and insights into the patterns of heavy rainfall and flash floods in the region. It underscores the alteration in precipitation patterns attributed to variations in atmospheric parameters over the study area. The findings demonstrate continuous monitoring and scientific research are critical for developing effective strategies to mitigate the impact of flash floods and safeguard communities in vulnerable regions like Nainital Uttarakhand. Overall, this study emphasizes the urgent need for climate action and proactive measures to address the rising risks of flash floods. By integrating advanced technology, scientific research, and community engagement, authorities can work towards building a more resilient future and better preparedness to tackle extreme weather events ( Supplementary Information ).

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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The work was funded by HRDG CSIR through Grant Number 23(0034)/19/EMR-II. CSIR.

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Conceptualization, validation, writing review, editing and supervision was carried out by K.N. and A.K.M. Methodology, software, formal analysis, writing original draft preparation was carried out by M.S.M. and J.D. All authors have read and agreed to the published version of the manuscript.

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Nagamani, K., Mishra, A.K., Meer, M.S. et al. Understanding flash flooding in the Himalayan Region: a case study. Sci Rep 14 , 7060 (2024). https://doi.org/10.1038/s41598-024-53535-w

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Disaster management in flash floods in leh (ladakh): a case study

Affiliation.

• 1 Regimental Medical Officer, Leh, Ladakh, India.
• PMID: 23112446
• PMCID: PMC3483513
• DOI: 10.4103/0970-0218.99928

Background: On August 6, 2010, in the dark of the midnight, there were flash floods due to cloud burst in Leh in Ladakh region of North India. It rained 14 inches in 2 hours, causing loss of human life and destruction. The civil hospital of Leh was badly damaged and rendered dysfunctional. Search and rescue operations were launched by the Indian Army immediately after the disaster. The injured and the dead were shifted to Army Hospital, Leh, and mass casualty management was started by the army doctors while relief work was mounted by the army and civil administration.

Objective: The present study was done to document disaster management strategies and approaches and to assesses the impact of flash floods on human lives, health hazards, and future implications of a natural disaster.

Materials and methods: The approach used was both quantitative as well as qualitative. It included data collection from the primary sources of the district collectorate, interviews with the district civil administration, health officials, and army officials who organized rescue operations, restoration of communication and transport, mass casualty management, and informal discussions with local residents.

Results: 234 persons died and over 800 were reported missing. Almost half of the people who died were local residents (49.6%) and foreigners (10.2%). Age-wise analysis of the deaths shows that the majority of deaths were reported in the age group of 25-50 years, accounting for 44.4% of deaths, followed by the 11-25-year age group with 22.2% deaths. The gender analysis showed that 61.5% were males and 38.5% were females. A further analysis showed that more females died in the age groups <10 years and ≥50 years.

Conclusions: Disaster preparedness is critical, particularly in natural disasters. The Army's immediate search, rescue, and relief operations and mass casualty management effectively and efficiently mitigated the impact of flash floods, and restored normal life.

Keywords: Disaster management; humanitarian assistance; mass casualty management.

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Impacts of a flash flood on drinking water quality: case study of areas most affected by the 2012 Beijing flood

Hongjuan qi.

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Corresponding author. [email protected]

The first three authors contributed equally to this work.

Received 2015 Nov 6; Revised 2015 Dec 29; Accepted 2016 Jan 28; Collection date 2016 Feb.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

In this study, we present a method for identifying sources of water pollution and their relative contributions in pollution disasters. The method uses a combination of principal component analysis and factor analysis. We carried out a case study in three rural villages close to Beijing after torrential rain on July 21, 2012. Nine water samples were analyzed for eight parameters, namely turbidity, total hardness, total dissolved solids, sulfates, chlorides, nitrates, total bacterial count, and total coliform groups. All of the samples showed different degrees of pollution, and most were unsuitable for drinking water as concentrations of various parameters exceeded recommended thresholds. Principal component analysis and factor analysis showed that two factors, the degree of mineralization and agricultural runoff, and flood entrainment, explained 82.50% of the total variance. The case study demonstrates that this method is useful for evaluating and interpreting large, complex water-quality data sets.

Keywords: Risk assessment processes, Pollution, Mathematics, Health sciences, Applied sciences

1. Introduction

On July 21, 2012, torrential rain hit the city of Beijing, China. The average rainfall over the whole city for the same period was 170 mm, the highest recorded rainfall since 1951. The rainfall event was caused by long-term regional rainfall and affected a significant part of Beijing. Within a day, there were many obvious effects of the flood, including damage to property and infrastructure. The floodwater killed 79 people ( Gui-Feng, 2012 ), and 56,933 people were evacuated, causing damages of 11.64 billion Yuan and destroying at least 8,200 homes ( Sha-Sha, 2012 ). Overall, more than 1.9 million people were affected by the flood ( Liu, 2012 ). Fangshan District, in the southwest of Beijing, received a record-breaking 460 mm of rain and was the most heavily affected area. The torrential rain triggered at least three types of natural disasters in this district, including flash floods, ponding, and mudslides.

Inevitably, after a flash flood, there is an immediate response by government agencies, as relief operations get underway to try and restore basic infrastructure and provide the fundamental items that are necessary for survival and subsequent recovery. Floodwater will often produce many health problems because of, among other things, damage to water supply systems, insufficient drinking-water supplies, and disruption of transport systems ( Michelozzi and de' Donato, 2014 ; Bich et al., 2011 ; Carroll et al., 2010 ; Fundter et al., 2008 ). However, the most serious consequence of flooding is large-scale contamination of drinking water (surface water, groundwater, and distribution systems). Drinking water can be contaminated with microorganisms such as bacteria, sewage, heating oil, agricultural or industrial waste, chemicals, and other substances that can cause serious illnesses ( Murshed et al., 2014 ; Yard et al., 2014 ; Chaturongkasumrit et al., 2013 ). In such situations, water-borne illnesses that are usually associated with poor hygiene and sanitation can affect a large part of the population ( Baig et al., 2012 ); therefore access to clean drinking water and adequate sanitation is a priority.

To improve our understanding of pollution patterns and to support decision making concerning effective control and prevention of disease, it is very important to be able to identify hidden sources of drinking water pollution. To date, principal component analysis (PCA) and factor analysis (FA) are the most commonly used, multivariate statistical tools in water environmental science ( Shyu et al., 2011 ; Liu et al., 2011 ). These methods can be used to interpret complex databases to obtain an improved understanding of water quality. These techniques also permit identification of the possible factors or sources that are responsible for variations in water quality and that influence the water system; they can therefore support the development of appropriate strategies for effective management of water resources and provide rapid solutions for pollution issues ( Singh et al., 2004 ; Li et al., 2007 ; Kazi et al., 2009 ). However, to date, no studies have been carried out to determine either the safety of water for human consumption or the sources of water pollution after severe floods.

As stated above, the identification of hidden sources is critical to our understanding of water pollution patterns and to support decision making about site remediation. Therefore, multivariate statistical methods should be applied in disaster impact analysis. The objectives of this study were, i) to describe the changes in water quality because of the 2012 flash flood using laboratory analysis methods; ii) to use the PCA and FA method to identify hidden pollution sources and their contributions after the flash flood, and iii) to demonstrate the merits of the suggested method using a case study.

2. Materials and methods

2.1. description of sampling sites.

The villages of Dahanji, Louzishui, and Huangshandian are located in the rural zone of Beijing. These three villages are in the Fengtai District of Beijing, the area worst hit by the flash flood. The main drinking water source for the population of the region is groundwater. We carried out a detailed investigation of drinking water in the study area on July 27, 2012. Nine water samples were collected at key sites to be analyzed for a wide range of determinants that were considered to represent the water quality of the groundwater system. The first two sites (1 and 2) were in the area around Dahanji; four sites (3–6) were in the environs of Louzishui, and three sites (7–9) were in the area around Hangshandian ( Fig. 1 ).

Map of the study area and sampling sites.

2.2. Sample collection

Samples were collected, preserved, and transported as outlined in the Chinese National Quality Standards for Drinking Water (GB/T 5750.2-2006). In brief, water samples were collected from the sampling sites using a sterilized sampler. After adding a preserving agent, the choice of which was dependent on the test variable, the samples were packed in sealed plastic bags, and then transported to the laboratory.

2.3. Analytical methods

The samples were analyzed in the laboratory of the Institute of Disease Control and Prevention, Academy of Military Medical Sciences, Beijing, following the methods outlined in the Chinese National Quality Standards for Drinking Water (GB/T 5749-2006 and GB/T 5750-2006). Samples were analyzed for turbidity by the scattering method; for total hardness by the titrimetric method; for total dissolved solids by the gravimetric method; for sulfates, chlorides, and nitrates by spectrophotometry; for total bacterial counts (TBC) by the plate count method, and for total coliform groups by the multiple tube method. Data quality was ensured through careful standardization, procedural blank measurements, and spiked and duplicate samples. The laboratory also participates in regular national programs for analytical quality control. The analytical precision for replicate samples was within ±10% and the measurement errors between determined and certified values were less than 5%.

2.4. Statistical analysis

Principal component analysis provides information on the most meaningful parameters, which describe the whole data set through data reduction with minimum loss of the original information ( Alberto et al., 2001 ). It is a powerful technique for pattern recognition that attempts to explain the variance between a large set of inter-correlated variables and transforms it into a smaller set of independent (uncorrelated) variables (principal components). The principal component (PC) is expressed as:

where a is the component loading; z is the component score; x is the measured value of a variable; i is the component number; j is the sample number, and m is the total number of variables.

Factor analysis attempts to extract a lower dimensional linear structure from the data set. It further reduces the contribution of the less significant variables obtained from PCA and extracts a new group of variables, known as varifactors (VFs), by rotating the axis defined by PCA. The basic concept of FA is expressed in Eq. (2) :

where z is the measured value of a variable; a is the factor loading; f is the factor score; e is the residual term accounting for errors or other sources of variation; i is the sample number; j is the variable number, and m is the total number of factors.

PCA and FA of water quality data were carried out using SPSS version 18.0 (SPSS Inc., Chicago, IL, USA). PCA of the normalized variables (water quality data set) was used to extract significant PCs and to further reduce the contribution of variables with minor significance; these PCs were subjected to varimax rotation (raw) to generate VFs. VFs can be hypothetical underlying, yet convenient, variables for the purposes of water quality assessment ( Vega et al., 1998 ; Helena et al., 2000 ). Each original water quality variable is the linear combination of common factors and one unusual factor that explains the errors or other sources of variation.

3. Results and discussion

3.1. water quality with parameter variations.

Understanding drinking water quality is important, given that it is the main factor that determines its suitability for drinking ( Wang, 2013 ; Kumar et al., 2007 ). Summary data for eight parameters, including the mean and standard deviation, are reported in Table 1 . The maximum permissible limit for turbidity in drinking water is 1.0 nephelometric turbidity units (NTU). The values of turbidity varied widely and ranged from 0.48 to 9.99 NTU, with a mean of 3.26 NTU. Turbidity exceeded the permissible limit at six sites (sites 3–7, and site 9). Water hardness is primarily caused by the presence of cations, such as calcium and magnesium, and anions, such as carbonate, bicarbonate, chloride, and sulfate ( Ravikumar et al., 2011 ). Drinking water with a hardness value that exceeds the limit of 450 mg/L is considered to be very hard. Total hardness (TH) ranged from 218 to 481 mg/L, with a mean value of 369.2 mg/L as CaCO 3 ( Table 1 ). Samples from two sites (site 1 and site 2) fell into the very hard category, indicating that some of the water was unsuitable for drinking purposes. TDS in water are determined by evaporating a water sample to dryness, and weighing the residue that remains ( Bahar and Reza, 2010 ). They comprise compounds of inorganic salts (principally calcium, magnesium, potassium, sodium, bicarbonates, chlorides, and sulfates) and small amounts of organic matter that are dissolved in water. TDS ranged from 243 to 587 mg/L and had an average value of 368 mg/L.

Description of water-quality parameters.

Values indicate exceedances of standard values.

The abundance of the major anions in this study decreased in the following order: SO 4 2− >Cl − >NO 3 − . The concentrations of sulfate, the first dominant anion, ranged from 60.7 to 211.6 mg/L, and the average was 131.3 mg/L. Chloride was the second dominant anion. Its concentrations ranged from 13.0 to 144.5 mg/L and the average value was 54.3 mg/L. Nitrates are the end product of aerobic stabilization of organic nitrogen, and a product of the conversion of nitrogenous material, a phenomenon that occurs in polluted water. The nitrate concentrations of groundwater samples ranged from 1.84 to 15.9 mg/L, with an average value of 8.88 mg/L. Nitrate concentrations of four samples exceeded the maximum permissible limit of 10 mg/L.

Information about bacterial colonies in the water samples is also provided in Table 1 . TBC ranged from 28 to 2000 CFU/cm 3 , with an average value of 1081 CFU/cm 3 in the sampled drinking waters. Water samples from only two sites (site 1 and site 5) were within the maximum permissible limit of TBC, while all the others exceeded the limit. Coliform bacteria, which are not an actual cause of disease, are commonly used as a bacterial indicator of water pollution. In the study area, coliform groups (TCG) were detected in seven groundwater samples (from sites 3–9). When compared with the maximum limits for microbial parameters in drinking water, the data indicate that most of the samples were unsuitable for drinking water purposes. The above results show that it is imperative to have sufficient information to be able to make reliable statements about water quality. It is, however, often difficult to interpret and draw meaningful conclusions from a huge complex data set comprising a large number of parameters.

3.2. Source identification

Further, for effective pollution control and water resource management, pollution sources and their relative contributions need to be identified. PCA was used to support the identification and analysis of sources of water pollution. All of the data were standardized with a mean of 0 and variance of 1. The results of Kaiser–Meyer–Olkin (KMO = 0.548) and Bartlett's sphericity tests (P = 0) indicated that parameters of these samples were suitable for PCA ( Table 2 ). The greater the calculated eigenvalues, the more significant the corresponding factors. Following Pekey et al. (2004) , only eigenvalues ≥1 were selected. The results of PCA after applying varimax rotation for the water-quality parameters are presented in Table 3 , while Fig. 2 shows the variation diagram in rotated space. The results indicate that PCA reduced the number of variables to two principal components (PCs), which explained 82.503% of the data variance. PC1 and PC2 accounted for 59.225% and 23.278% of the total variance, respectively.

Results of KMO and Bartlett's tests.

Total variance explained.

Principal component analysis loading plot for the eight parameters.

The rotated component matrix was then obtained by orthogonal rotation. VFs were obtained by applying FA to the PCs. The VFs and the corresponding variable loadings are presented in Table 4 . According to Liu et al. (2003) , factor loadings >0.75, between 0.5 and 0.75, and between 0.3 and 0.5 are considered to be strong, moderate, and weak, respectively. Out of the two VFs, VF1 had strong positive loadings for sulfates, TDS, chlorides, and nitrates ( Table 4 ). Concentrations of TDS in water vary considerably in different geological regions owing to differences in the solubility of minerals ( WHO, 2004 ). Further, agriculture is very developed in the study area, and agricultural fertilizers are extensively used. Therefore, VF1 could reflect both the mineral components of the drinking water and the influence of agricultural runoff from the soil. VF2 has strong positive loadings for TCG and TBC, a moderate loading for turbidity, and a strong negative loading for TH ( Table 4 ). The communalities of TCG, TBC, and turbidity were relatively high, suggesting complex influences of multiple sources on these variables. However, the high microbial loads and turbidity arising from the floods resulted in water quality contamination. Therefore, the results show that several microorganisms were transferred via floodwater to different parts of this region and water for human consumption was cross-contaminated by floodwater.

Rotated component matrix.

3.3. Source spatial distribution

The factor score of each sampling point VF can easily be calculated when SPSS is used for FA. The factor score of each VF multiplied by its variance contribution rate accounts for the extraction of a common factor, which is then weighted to obtain composite scores for each sampling site. The higher the factor score of a sampling point, the more serious the pollution at that point. Fig. 3 shows clearly that the different sampling points had different sources of pollution. The common factor score of VF1, which represented the degree of mineralization and agricultural runoff of the sampling points, was highest at site 7, followed by site 1, site 2, site 9, site 4, site 6, site 8, site 5, and site 3 ( Fig. 3 ), which shows that variations in this region were mainly influenced by geological conditions and agricultural production. Our survey also demonstrated that these spatial distributions represented the degree of variation in agricultural runoff from the soil. The common factor score of VF2 was highest at site 4, followed by site 7, site 9, site 8, site 6, site 5, site 3, site 2, and site 1 ( Fig. 3 ). Previous analysis showed that VF2 mainly represented the effects of the flood. Results showed that the floodwater introduced large amounts of impurities and microbial contaminants into drinking water. These findings mirror those of the actual survey, and confirm that the different sampling points suffered flood damage to varying degrees. Thus, the method that we have presented appears to be an effective tool for water pollution source apportionment and identification, and may provide valuable reference information for pollution control and emergency management.

Spatial distribution of the factor scores for each VF. The size of the circle represents the size of the factor score of each VF.

4. Conclusions

The aim of this study was to identify the sources and the geographical distribution of water pollution in the areas worst hit by a flash flood by interpreting analysis results of the major water-quality parameters. The main conclusions are as follows:

1. The eight parameters for which the samples were analyzed highlight the variations in water quality. The results indicate that the nine samples were unsuitable for drinking purposes; the results also indicate that it is difficult to interpret and draw meaningful conclusions from a complex data set.

2. PCA and FA can provide useful information for assessing water quality. The combination of these two methods showed that the pollution levels in the study area were mainly influenced by two factors, the degree of mineralization and agricultural runoff, and flood entrainment. Moreover, maps can present information about spatial variations in drinking water quality in an easily understood format.

3. This study demonstrates that the combination of PCA and FA provides a useful and efficient method for summarizing data and reporting information to decision makers to ensure an improved understanding of the quality status of drinking water. This method should be very useful in the future.

Declarations

Author contribution statement.

Rubao Sun: Performed the experiments; Analyzed and interpreted the data.

Daizhi An, Wei Lu: Performed the experiments.

Yun Shi, Lili Wang, Can Zhang, Ping Zhang, Hongjuan Qi: Contributed reagents, materials, analysis tools or data.

Qiang Wang: Conceived and designed the experiments; Wrote the paper.

Rubao Sun, Daizhi An and Wei Lu contributed equally to this study.

Competing interest statement

The authors declare no conflict of interest.

Funding statement

This project was supported by grants from the National Natural Science Foundation of China (81472478 and 81200298).

Additional information

No additional information is available for this paper.

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Environmental and economic impact of cloudburst-triggered debris flows and flash floods in Uttarakhand Himalaya: a case study

• Vishwambhar Prasad Sati   ORCID: orcid.org/0000-0001-6423-3119 1 &
• Saurav Kumar 1  

Geoenvironmental Disasters volume  9 , Article number:  5 ( 2022 ) Cite this article

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This paper examines the environmental and economic impact of cloudburst-triggered debris flow and flash flood in four villages of Uttarkashi district, Uttarakhand Himalaya. On 18th July 2021 at 8:30 p.m., a cloudburst took place on the top of the Hari Maharaj Parvat, which triggered a huge debris flows and flash floods, affecting 143 households of four villages of downstream areas. Immediately after the cloudburst occurred, the authors visited four affected villages—Nirakot, Mando, Kankrari, and Siror. A structured questionnaire was constructed and questions were framed and asked from 143 heads of affected households on the impact of debris flows and flash floods on people’s life, settlements, cowsheds, bridges, trees, forests, and arable land in and around the villages. The volume of debris, boulders, pebbles, gravels, and mud was assessed. It was noticed that all four villages got lots of destructions in terms of loss of life—people and animals, and property damage—land, crops, and infrastructural facilities. This study shows that the location of the settlements along with the proximity of the streams, which are very violent during the monsoon season, has led to the high impact of debris flow on the affected villages. We suggest that the old inhabited areas, which are located in the risk zones, can be relocated and the new settlements can be constructed in safe places using suitability analyses.

Introduction

Cloudburst, a geo-hydrological hazard, refers to a sudden and heavy rainfall that takes place within a short span of time and a particular space (Sati 2013 ). The intensity of rainfall is often more than 100 mm/h (Das et al. 2006 ). The disruptive events, cloudbursts occur during the monsoon season in the Himalaya and trigger debris flows, flash floods, landslides, and mass movements (Fig.  1 ). Fragile landscape, rough and rugged terrain, and precipitous slope accentuate the magnitude of geo-hydrological hazards. Cloudburst-triggered debris flows, flash floods, landslides, and mass movements have become more intensive and frequent worldwide, mainly in the mountainous regions, causing large-scale destruction of people, land, and property (Houghton et al. 1996 ; Wang et al. 2014 ; Mayowa et al. 2015 ; Malla et al. 2020 ; Sim et al. 2022 ). Similarly, the Himalayan region is prone to the occurrences of cloudburst-triggered hazards, causing huge loss of life and property and degradation of forest and arable lands (Bohra et al. 2006 ; Allen et al. 2013 ; Balakrishnan 2015 ; Ruiz-Villanueva et al. 2017 ).

Cloudburst-triggered hazards in the Uttarakhand Himalaya

The Uttarakhand Himalaya, one of the integrated parts of the Himalaya, is the most fragile landscape and prone to geo-hydrological hazards—cloudbursts, avalanches, and glacier bursts (Sati 2019 ). It receives many hazards mainly cloudburst-triggered debris flows, flash floods, landslides, and mass movements during the monsoon season every year. The intensity, frequency, and severity of these hazards have been observed to increase during the recent past. Devi ( 2015 ) stated that the changing monsoon patterns and increasing precipitation in the Himalaya are associated with catastrophic natural hazards. However, these hazards are the least understood because of the remoteness of the areas and lacking meteorological stations (Thayyen et al. 2013 ).

The Uttarakhand Himalaya has many eco-sensitive zones, vulnerable to natural hazards mainly for geo-hydrological hazards. Every year, many cloudburst events occur here, cause to roadblocks, land degradation, forest and cropland loss, and losses of life and infrastructural facilities. One of the most devastating cloudburst-triggered debris flow events of this century occurred on the night of 16th and 17th June 2013 in the famous Hindu pilgrimage ‘Kedarnath’, which killed more than 10,000 people and devastated the entire Mandakini and Alaknanda river valleys (Upadhyay 2014 ; Sati 2013 ). The entire region had received 16 major geo-hydrological and terrestrial hazards within the last 50 years (Bhambri et al. 2016 ). Some of the devastating cloudburst-triggered debris flows and flash floods that occurred in the Uttarakhand Himalaya are Rudraprayag on 14th September 2012, Munsiyari on 18th August 2010, Kapkot on 19th August 2010, Nachni on 7th August 2009, Malpa and Ukhimath on 17th August 1998, Badrinath on 24th July 2004, and the Alaknanda River valley on 1970. About 20,000 people died and a huge loss of property took place due to these calamities (Das 2015 ). It has been noticed that these catastrophic events occurred mainly during the three months of the monsoon season—July, August, and September.

Debris flows and flash floods caused by glacier-bursts incidences were although not much frequent and intensive yet, during the recent past, their number has increased owing to changes in the climatic conditions. The increasing number of infrastructural facilities on the valley bottom has accelerated damages owing to exposed elements in risk-prone areas (Sati 2014 ; ICIMOD 2007a , b ; Chalise and Khanal 2001 ; Bhandari 1994 ; Uttarakhand 2017 ). Many drivers exist, which affect the severity of cloudburst-triggered hazards in the Uttarakhand Himalaya. Growing population and the construction of settlements and infrastructural facilities on the fragile slopes and along the river valleys have also caused severe hazards. The Uttarakhand region is home to world-famous pilgrimages and natural tourism. Mass tourism during the rainy season enhances the intensity of disasters.

Several studies have been carried out on glacier-bursts and cloudburst-triggered debris flows and flash floods in the Himalaya (Shugar et al. 2021 ; Byers et al. 2018 ; Cook et al. 2018 ; Asthana and Sah 2007 ; Bhatt 1998 ; Joshi and Maikhuri 1997 ; NIDM 2015 ; IMD 2013 ; Khanduri et al. 2018 ; Sati 2006 , 2007 , 2009 , 2011 , 2018a , b , 2020 ; Naithani et al. 2011 ). These studies were conducted from broader perspectives, mostly covering the entire Himalaya. However, the present paper looks into the case study of four villages of the Uttarakhand Himalaya, which were severely affected and damaged by cloudburst-triggered debris flows and flash floods, which occurred on July 18th, 2021. It analyses the environmental impact of cloudbursts in terms of forest and fruit trees dislocation, land degradation, and soil erosion—arable, forests, and barren land of the four affected villages. It also evaluates the human and economic losses like the killing of people, loss of existing crops, and damage of houses and cowsheds, respectively. The study suggests policy measures to risk reduction and rehabilitation of settlements from danger zones to safer areas after suitability analysis.

The Uttarakhand Himalaya is located in the north of India and south of the Himalaya. It is also called the Indian Central Himalayan Region. Out of the total 93% mountainous area, 16% is snow-capped, called the Greater Himalaya. The terrain is undulating and precipitous and the landscape is fragile, vulnerable to natural hazards. This catastrophic event occurred in the four villages of Uttarkashi district. The Uttarkashi town lies about 10 km downstream of the affected villages. A National Highway number 108, connecting Haridwar and Gangotri, is passing through Uttarkashi town. The four affected villages—Nirakot, Mando, Kankrari, and Siror are located in the upper Bhagirathi catchment, which is prone to geo-hydrological hazards. The slope gradient of these villages varies from 15° to 70°. Indravati is a perennial stream, a tributary of the Bhagirathi River that meets Bhagirathi from its left bank. All three Gadheras (streams)—Mando, Diya, and Siror are seasonal but violent during the monsoon season. Nirakot (1530 m) village is located in the middle altitude of the Hari Maharaj Parvat (2350 m) in a steep slope, Mando village (1180 m) is located on the left bank of the Bhagirathi River along the Mando Gadhera with gentle to a steep slope, Kankrari (1620 m) village is located on the moderate to the gentle slope on the bank of the Diya Gadhera, and Siror village (1280 m) is situated on the left bank of both Bhagirathi and Siror Gadhera with gentle to the steep slope (Fig.  2 ). One of the prominent eco-sensitive zones of the Uttarakhand Himalaya, the ‘Bhagirathi Eco-Sensitive Zone’ is 120 km long, spanning from Uttarkashi to Gaumukh, along the Bhagirathi River valley (Sati 2018a , b ). The rural people depend on the output of the traditional farming systems, often face intensive natural hazards. The settlements are located either on the fragile and steep slopes or on the banks of streams, which are very violent during the monsoon season when a heavy downpour occurs. Therefore, heavy losses of life and property in these areas are common, taking place every year.

Location map of cloudburst source and hit areas and their surroundings

Methodology

This study was empirically tested and a qualitative approach was employed to describe data. A structured questionnaire was constructed. The main questions framed and asked from the heads of households were—human and animal death, damage to self property—houses and cowsheds, and existing crops—cereals, fruits, and vegetables. Loss to public properties such as bridges, public institutions, and forest land was assessed. Based on the questions framed, we surveyed 143 heads of households of four villages, which were partially or fully affected due to cloudburst-triggered debris flow. These villages are Nirakot, Mando, Kankrari, and Siror. To assess the debris and the damaging areas, the authors travelled from the source areas to the depositional zones and measured the volume of debris—boulders, pebbles, sands, and soils using a formula; circumference = 2πR and area = π * R 2 . The slope gradient, accessibility, economic conditions, and climate of the villages were assessed and based on which, the susceptibility analysis of the villages was carried out. The villages were divided into very high susceptibility, high susceptibility, and moderate susceptibility levels. Both environmental degradation and economic losses in four villages were assessed. We used Geographical Positioning System (GPS) to obtain the data of altitude, longitude, and latitude. Two maps—case study villages and the major cloudburst incidences—2020 and 2021 were prepared and data were also presented using graphs. Photographs of four villages were used to present the destruction of villages due to the cloudburst event.

Results and analysis

Major cloudburst incidences in the uttarakhand himalaya.

Past incidences depict that the Uttarakhand Himalaya suffered tremendously due to cloudburst-triggered calamities. We gathered data on the major cloudburst incidences in Uttarakhand in the monsoon seasons of 2020 and 2021 from the state disaster relief force (SDRF), Dehradun. From May to September 2020, 13 major cloudburst incidences were noticed in Uttarakhand (Table 1 ). These incidences resulted in the death of 22 people and 77 animals, and 19 houses were fully damaged. Similarly, from May to September 2021, 17 major cloudburst incidences were occurred in the Uttarakhand Himalaya, resulting in the death of 34 people and 144 animals, and 106 houses were buried. Besides, it caused a huge loss to public property and landscape degradation.

The economic losses in 2021 were much higher than the losses in 2020 (Fig.  3 ). In 2021, the frequency and intensity of cloudburst-triggered calamities were also higher. The loss of animals was quite high both the years. Houses that collapsed due to calamity were six times higher in 2021 than in 2020. The loss of human life was substantial in both years. Several bridges were washed away.

Loss of human lives, livestock, houses and bridges due to cloudburst in Uttarakhand during the 2020 and 2021

District-wise major cloudburst events of 2020–2021 are shown in the map of the Uttarakhand Himalaya (Fig.  4 ). A total of 30 major cloudburst incidences were recorded, out of which, 17 occurred in 2021. The Uttarkashi district received the highest incidences (07), followed by the Chamoli district (05). Dehradun and Pithoragarh districts have recorded 04 incidences each. Rudraprayag 03 and Tehri, Almora, Bageshwar have recorded 01 each. It has been observed that cloudburst-triggered incidences mainly occurred in remote places along the fragile river valleys and middle slopes.

Location map of cloudbursts hit areas in 2020 and 2021

Case study of affected villages

On July 18, 2021, a cloudburst hits the Hari Maharaj Parvat (hilltop) at an altitude of 2350 m at 8:30 p.m., which triggered huge debris flows and flash floods. The four villages—Nirakot, Mando, Kankrari, and Siror of Uttarkashi district, located down slopes of the hilltop and close to the Uttarkashi town, were severely affected due to debris flow (Table 2 ). At the cloudburst hit area, it formed three gullies, which later on merged into three streams, along which these villages are located. Debris, from the source i.e. hilltop of Hari Maharaj Parvat, equally flew in three directions. Since the cloudburst event occurred at 8:30 p.m., the people did not have time to move with their movable property and therefore, the magnitude of damage was enormous.

The villages are located from the altitudes of 1180 m (lowest) to 1620 m (highest). Mando village is located at 1180 m, Kankrari village at 1620 m, Nirakot at 1530 m, and Siror has 1280 m altitude. The two villages—Nirakot and Mando have west-facing slopes, Kankrari has a south-facing slope, and Siror has a north-facing slope. These villages are located along the tributaries of the Bhagirathi River, with 2 to 5 km distance from the road. The intensity and volume of debris were different in different villages, therefore, the casualties and losses were also varied. The villages are surrounded by agricultural and forestlands. The farmers mainly grow subsistence cereal crops—paddy, wheat, pulses, oilseeds, fruits, and vegetables. Forest types comprise pine (sub-tropical) and oak and coniferous forests (temperate), used for fodder, firewood, and wild fruits.

Located at the high-risk zones, these villages face several disaster incidences every year. Out of the total 143 heads of households surveyed, more than 80% of heads were in favour of rehabilitating them in the safer areas. They wanted to relocate their houses and cowshed within the village territory with financial assistance from the state government. The streams, along which the settlements are constructed, are fragile and highly vulnerable to landslide hazards. Further, the cloudburst incidences are increasing due to climate change, the heads of households perceived.

Figure  5 shows four villages—Nirakot, Mando, Kankrari, and Siror, which were severely affected by cloudburst-triggered debris flow and flash flood. The volume of debris and boulders can be seen in all the villages. These villages are surrounded by dense sub-tropical and temperate forests that vary from pine to mixed-oak and deodar. Kharif crops were growing in the arable land whereas a large cropland has been washed away.

Cloudburst affected villages a Nirakot, b Mando, c Kankrari, d Siror; Photo: by authors

Impact of cloudburst-triggered debris flow and flash flood

Environmental impact.

The environmental impact of cloudburst-triggered debris flow and flash flood in four villages of Uttarkashi district was analyzed (Table 3 ). The major variables were the number of forest trees dislocated, total land degradation, land degradation under existing crops, number of fruit trees dislocated, land degradation under arable land, number of buildings were damaged, number of bridges damaged, and boulders’ volume. Forest trees, which dislocated were pine in the middle altitude and mixed-oak and deodar in the higher altitude. A total of 770 forest trees were dislocated from all four villages, out of which, 500 were from the Kankrari village (highest). The lowest trees dislocated were from Siror village (70). The total land degradation from the cloudburst hit areas to the affected areas was huge, however, we have measured the land which was within and surrounding each village. The total land degradation was 52.5 acres with the highest in Kankrari (45 acres) and the lowest in Siror (0.5 acres). The land degradation under existing crops was 22.6 acres in all four villages, varying from 0.1 acres in Siror to 20.6 acres in Kankrari. The total number of fruit trees dislocated was 486. Land degradation under arable land was 22.6 acres. It includes the area under existing crops both agriculture and horticulture. A total of 19 buildings were damaged whereas a total of 14 bridges, connecting the affected villages were washed away.

Economic impact

The economic impact due to cloudburst calamity was tremendous in the forms of a household affected, loss of human and animal life, building loss, forest loss, loss of existing crops including fruits, loss of arable land, and loss of bridges (Table 4 ). The value of all these assets was calculated in Indian Rupees (INR) at the current price. The total number of households affected was 143, of which, 100 households belonged to the Kankrari village (highest) and three households (lowest) were from Siror village. Four people died due to the calamity—three women from Mando village and 1 man from Kankrari village. Two cows from Mando village died. The total loss from the collapse of the building was 1.7 million INR, with the highest (1.1 million INR) from Kankrari village. A total of 0.77 million INR was lost due to forest loss, and the loss from existing crops was 3.35 million INR. Loss from dislocation of fruit trees was noted high, which was about 0.5 million INR. A large portion of arable land was flown which value was 11.3 million INR. About 14 million INR was lost due to the collapse of bridges. As a whole, about 31.62 million INR was lost due to cloudburst calamity. Per household loss by the cloudburst calamity was noted 0.22 million INR.

Average circumference, area, and volume of boulders

We calculated the average circumference, area, and volume of boulders in the case study villages using a formula: circumference = 2πR; Area = π * R 2 ; volume = length × width × depth (Table 5 ). We noticed that the highest average area of boulders was in Mando village, which is 28.3 m 2 followed by Kankrari 19.6 m 2 , Nirakot 12.57 m 2 , and Siror 7.1 m 2 . In terms of the total volume of debris, it was the highest in Kankrari village, followed by Mando, Nirakot, and Siror villages.

Figure  6 shows the average diameter of boulders in the cloudburst-affected villages. We drew the figure with a scale of 1 cm is equal to 1 m. The average biggest diameter of boulders was found in Mando village (6 m), followed by Kankrari (5 m) and Nirakot (4 m) villages. The average smallest diameter of boulders was found in Siror village (3 m).

Village-wise average diameter of boulders

Susceptibility analysis

Based on the above description, susceptibility analysis of the case study villages was carried out (Table 6 ). The main variables of susceptibility were slope gradient, accessibility of villages, economic conditions of households, and climatic conditions. We noticed that Nirakot village has very high susceptibility, Kankrari has high, and Siror and Mando have moderate susceptibility.

The Uttarakhand Himalaya is highly vulnerable to geo-hydrological disasters because of its geological formation (Vaidya 2019 ). It is an ecologically fragile, geologically sensitive, and tectonically and seismically very active mountain range (Sati 2019 ). The geo-hydrological events—cloudbursts and glacier bursts-triggered catastrophes are very common and devastating. The monsoon season poses severe threats to natural hazards because of heavy downpours. About 93% of the Uttarakhand Himalaya is mountainous mainland, of which 16% is snow-capped. The undulating and precipitous terrain and remoteness are the most vulnerable for disaster risks.

This study reveals that most of the cloudbursts incidences in 2020–21 occurred mainly in the remote mountainous districts of the Uttarakhand Himalaya. The villages in the Uttarakhand Himalaya are located on the sloppy land and along the river valleys, which are fragile and very vulnerable to disasters. The rivers flow above danger marks during the monsoon season cause threats to rural settlements. The roads of Uttarakhand are constructed along the river banks and on fragile lands. These roads lead to the highland and river valley pilgrimages where the number of tourists and pilgrims visit every year mainly during the monsoon season. There are many locations along the river valleys where the houses are constructed on the debris, deposited by rivers during debris flow events. Therefore, the environmental and economic losses due to debris flows and flash floods are high. The construction of hydropower projects along the river valleys without using sufficient technology further accentuates the vulnerability of debris flows and flash floods. One of the recent examples is the Rishi Ganga tragedy in Chamoli district where more than 200 people died with a huge loss to property (Sati 2021 ). We observed that the cloudburst triggered calamity in 2021 was higher than in 2020. The trend of occurring natural hazards has been increasing. Similarly, the intensity and frequency of natural hazards were observed high.

The present study shows that the environmental and economic loss in the four villages of the Bhagirathi River valley was huge due to cloudburst-triggered debris flows and flash floods. Almost every household of the villages were affected by cloudburst calamity. There were large forest and arable land degradation, forest and fruit trees were dislocated, loss of life—human and animal, and the houses and bridges were collapsed. The calamity also poses threat to the future, in terms of, the large deposition of debris including boulders, pebbles, and gravels in the villages along the streams and gullies. The rural people are poor and their livelihood is dependent on practicing subsistence agriculture. Many of them are living below the poverty line in these villages. Because the existing crops have been lost, they are facing food insecurity. Further, the psychological problems are immense. The fear of another calamity is always there in the mind of people as all villages are situated in very high to moderate susceptible areas. The national highway is passing through the right bank of the Bhagirathi River and the affected villages are situated on the left bank. The connectivity problem is immense all the time in these villages. The entire rural areas of the Uttarakhand Himalaya are facing similar problems.

Cloudburst-triggered debris flows and flash floods are natural calamities in the Himalayan regions. They occur naturally and cannot be stopped. The losses—environmental and economic are also huge. However, the severity of these natural calamities can be minimized. For example, the high impact of cloudburst-triggered debris flow on the four study villages was mainly due to their location along the streams and on the fragile slopes. This can be avoided by constructing the settlements in safer places generally away from the violent streams. In the disaster risk zones, scenario analysis can be carried out under which, identifying driving forces of disaster risks is the first step. Then, the critical uncertainties are to be identified, and finally, a possible scenario can be developed. Nature-based eco-disaster risk reduction can be adopted to prevent further disaster risks. A large-scale plantation drive in the degraded land will restore the fragile landscape. Both pre and post-disaster risk reduction measures can be adopted to reduce the economic and environmental impact of debris flows. There must be policies implementation programmes for providing immediate relief packages for the affected people in terms of food and shelters. In a long run, susceptibility analyses should be carried out to understand the risk to the settlements so that the settlements can be replaced on the safer side if needed. A special budget can be allocated to hazard-prone villages during adverse situations.

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Sati, V.P., Kumar, S. Environmental and economic impact of cloudburst-triggered debris flows and flash floods in Uttarakhand Himalaya: a case study. Geoenviron Disasters 9 , 5 (2022). https://doi.org/10.1186/s40677-022-00208-3

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• Debris flow
• Flash flood