Impact of climate change on snow precipitation and streamflow in the Upper Indus Basin ending twenty-first century

Romshoo, Shakil Ahmad; Marazi, Asif

doi:10.1007/s10584-021-03297-5

Cited by 11 publications

(5 citation statements)

References 51 publications

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“…Intense or prolonged rainfall events decrease the shear strength and internal friction between the soil particles and cause the soil to slide downward, causing often fatal landslides [87,88]. There are also increases in the extreme precipitation events over the study area (Jammu and Kashmir), which have the potential to increase the frequency of natural hazards such as floods, landslides, snow avalanches, floods, GLOF, and LLOF [89,90]. Further, the soil profile on the slopes of this area is loose naturally; hence, a low intensive rainfall event is enough to trigger a landslide [68].…”

Section: Landslide Early Warning System (Lews) and Land System Proces...mentioning

confidence: 99%

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Management of Landslides in a Rural–Urban Transition Zone Using Machine Learning Algorithms—A Case Study of a National Highway (NH-44), India, in the Rugged Himalayan Terrains

Fayaz

Meraj²,

Khader

et al. 2022

Land

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Landslides are critical natural disasters characterized by a downward movement of land masses. As one of the deadliest types of disasters worldwide, they have a high death toll every year and cause a large amount of economic damage. The transition between urban and rural areas is characterized by highways, which, in rugged Himalayan terrain, have to be constructed by cutting into the mountains, thereby destabilizing them and making them prone to landslides. This study was conducted in one most landslide-prone regions of the entire Himalayan belt, i.e., National Highway NH-44 (the Jammu–Srinagar stretch). The main objectives of this study are to understand the causes behind the regular recurrence of the landslides in this region and propose a landslide early warning system (LEWS) based on the most suitable machine learning algorithms among the four selected, i.e., multiple linear regression, adaptive neuro-fuzzy inference system (ANFIS), random forest, and decision tree. It was found that ANFIS and random forest outperformed the other proposed methods with a substantial increase in overall accuracy. The LEWS model was developed using the land system parameters that govern landslide occurrence, such as rainfall, soil moisture, distance to the road and river, slope, land surface temperature (LST), and the built-up area (BUA) near the landslide site. The developed LEWS was validated using various statistical error assessment tools such as the root mean square error (RMSE), mean square error (MSE), confusion matrix, out-of-bag (OOB) error estimation, and area under the receiver operating characteristic (ROC) curve (AUC). The outcomes of this study can help to manage landslide hazards in the Himalayan urban–rural transition zones and serve as a sample study for similar mountainous regions of the world.

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Section: Landslide Early Warning System (Lews) and Land System Proces...mentioning

confidence: 99%

“…In contrast, others are covered with sedimentary rocks and sandstone granules, making these sites highly prone to rainfallinduced land failure [68]. All these factors make the region highly susceptible and prone to landslides [68,89,[100][101][102].…”

Section: Landslide Early Warning System (Lews) and Land System Proces...mentioning

confidence: 99%

Management of Landslides in a Rural–Urban Transition Zone Using Machine Learning Algorithms—A Case Study of a National Highway (NH-44), India, in the Rugged Himalayan Terrains

Fayaz

Meraj²,

Khader

et al. 2022

Land

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“…Climate change can significantly alter global precipitation patterns and water resource availability. 9 , 10 , 11 Streamflow variability across the major global river basins is projected to change because of a warming climate. 12 , 13 Hydropower is susceptible to a warming climate 14 , 15 because of changes in reservoir storage and streamflow.…”

Section: Introductionmentioning

confidence: 99%

“… 17 , 18 , 19 Indian sub-continental river basins (Ganga and Indus) are sensitive to a warming climate. 9 , 20 However, the impacts of climate change on India’s hydropower potential remain unrecognized. Ali et al.…”

Section: Introductionmentioning

confidence: 99%

Increased hydropower but with an elevated risk of reservoir operations in India under the warming climate

Chuphal¹,

Mishra²

2023

iScience

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“…Glaciers in the Upper Indus Basin (UIB) are important because glacier-melt accounts for a major component of the streamflow and supports agriculture, water supply, energy generation and tourism, all of which are important sectors of the economy in the basin 16 , 17 . Glaciers in the UIB, like those elsewhere in the Himalaya, have experienced significant recession and mass loss in recent decades 4 , 18 .…”

Section: Introductionmentioning

confidence: 99%

Towards understanding various influences on mass balance of the Hoksar Glacier in the Upper Indus Basin using observations

Romshoo

Murtaza

Abdullah

2022

Sci Rep

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Mass balance is a good indicator of glacier health and sensitivity to climate change. The debris-covered Hoksar Glacier (HG) in the Upper Indus Basin (UIB) was studied using direct and geodetic mass balances. During the 5-year period from 2013 to 2018, the glacier’s mean in situ mass balance (MB) was − 0.95 ± 0.39 m w.e. a−1. Similarly, the glacier’s mean geodetic MB from 2000 to 2012 was − 1.20 ± 0.35 m w.e. a−1. The continuously negative MB observations indicated that the HG is losing mass at a higher rate than several other Himalayan glaciers. The glacier showed increased mass loss with increasing altitude, in contrast to the typical decreasing MB with increasing elevation, due to the existence of thick debris cover in the ablation zone, which thins out regularly towards the accumulation zone. Rising temperatures, depleting snowfall and increasing black carbon concentration in the region, indicators of climatic change, have all contributed to the increased mass loss of the HG. During the lean period, when glacier melt contributes significantly to streamflow, the mass loss of glaciers has had a considerable impact on streamflow. Water availability for food, energy, and other essential economic sectors would be adversely affected, if, glaciers in the region continued to lose mass due to climatic change. However, long-term MB and hydro-meteorological observations are required to gain a better understanding of glacier recession in the region as climate changes in the UIB.

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Impact of climate change on snow precipitation and streamflow in the Upper Indus Basin ending twenty-first century

Cited by 11 publications

References 51 publications

Management of Landslides in a Rural–Urban Transition Zone Using Machine Learning Algorithms—A Case Study of a National Highway (NH-44), India, in the Rugged Himalayan Terrains

Management of Landslides in a Rural–Urban Transition Zone Using Machine Learning Algorithms—A Case Study of a National Highway (NH-44), India, in the Rugged Himalayan Terrains

Increased hydropower but with an elevated risk of reservoir operations in India under the warming climate

Towards understanding various influences on mass balance of the Hoksar Glacier in the Upper Indus Basin using observations

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