Exploring the links between variations in snow cover area and climatic variables in a Himalayan catchment using earth observations and CMIP6 climate change scenarios

Singh, Dharmaveer; Zhu, Yu; Liu, Shiyin; Srivastava, Prashant K.; Dharpure, Jaydeo K.; Chatterjee, Debrupa; Sahu, Rakesh; Gagnon, Alexandre S.

doi:10.1016/j.jhydrol.2022.127648

Cited by 14 publications

(3 citation statements)

References 78 publications

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“…However, perennial rivers maintain their lean flow from subsurface/baseflow. Scientific literature outlined that a long-term decline of the glacier have influences on estuaries (Beaudreau et al, 2022), streamflow pattern (Rashid et al, 2020), and decline in the river flow (Singh et al, 2022), and others.…”

Section: Assessment Of Hydrological Extremities Under Climate Changementioning

confidence: 99%

Modelling of hydrological and environmental flow dynamics over a central Himalayan river basin through satellite altimetry and recent climate projections

Dubey

Goswami

Singh

2022

Intl Journal of Climatology

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The Himalayan region is vulnerable to climate change, which is triggering extreme hydrological events. To understand the impact of climate emission scenarios on different hydrological flow dynamics of the Himalayan region, a thorough hydrological investigation is needed for improved water resource management. Hence, this research exemplifies the use of remote sensing, modelling techniques, and recent climate projections (CMIP5 and CMIP6) for the assessment of hydrological flow dynamics in the central Himalayan catchment. The SWAT model is calibrated/validated using gauge observation and satellite altimetry-derived river discharge and used for long-term hydrological simulations . CMIP6 models predicted that there would be a steady rise in high flows in all the projected scenarios. Flow duration curve for CMIP5 and CMIP6 models depicted that high flow of short duration would be frequent in future. The environmental flow components (EFCs) were calculated using the indicator of hydrologic alteration tool. The SWAT model simulations of hydrological and EFCs under CMIP6 climate projections are found to be significantly more vulnerable to climate warming than CMIP5, which could be due to socioeconomic pathway emission scenarios.

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Section: Assessment Of Hydrological Extremities Under Climate Changementioning

confidence: 99%

Modelling of hydrological and environmental flow dynamics over a central Himalayan river basin through satellite altimetry and recent climate projections

Dubey

Goswami

Singh

2022

Intl Journal of Climatology

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“…The results showed that their pixel-point index based on the grassland's yield, livestock carrying capacity, coefficient of available grassland area, and seasonal grazing utilization scenarios could quantitatively and comprehensively reflect the ability of grasslands to resist snow disasters [19]. Singh et al first investigated changes in the Snow Cover Area (SCA) within the Bhagirathi River Basin using satellite imagery from a Moderate Resolution Imaging Spectroradiometer (MODIS) and the Gravity Recovery and Climate Experiment [20]. Liu et al employed a multi-level synthesis method and a multi-objective linear weighting function method to establish early warning snow disaster models, snow disaster identification models, and risk assessment models.…”

Section: Introductionmentioning

confidence: 99%

Snow Disaster Risk Assessment Based on Long-Term Remote Sensing Data: A Case Study of the Qinghai–Tibet Plateau Region in Xizang

Sun,

Miao,

Feng

et al. 2024

Remote Sensing

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The risk analysis and assessment of snow disasters are essential foundational tasks in natural disaster management and profoundly impact the scientific and precise formulation of disaster prevention, preparedness, and mitigation strategies. Employing the theory and methodology of snow disaster assessment, this research focuses on historical and potential snow disasters in the Qinghai–Tibetan Plateau (QTP) Region. Utilizing a long-time-series snow depth remote sensing dataset, we extracted six assessment indicators for historical snow disaster risk factors and potential snow disaster risk factors. We determined the weights of these six assessment indicators using the entropy weight method. Subsequently, we established a snow disaster assessment model to evaluate the grade distribution of snow disasters in the study area. This method can effectively solve the problem of the sparse data distribution of meteorological stations and reflect degrees of snow disaster risk on a large spatial scale. The findings reveal that areas with a relatively high snow disaster risk are primarily concentrated in the western part of the Ali Region, the central part of Chamdo, and near the border in Southern Xizang. Additionally, regions with a high frequency of snow disasters are predominantly located at the junction of Nagchu, Chamdo, and Nyingchi in the eastern part of Xizang. These results contribute valuable insights into the risk assessment of snow disasters and facilitate the development of effective strategies for disaster management in the region.

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“…Human-induced global warming has altered patterns of the rainfall worldwide (Goswami et al, 2006;Trenberth, 2011), and also increased risks of extreme events such as the droughts and floods (Easterling et al, 2000;Trenberth et al, 2015;Otto et al, 2017). It has impacted hydrology of many river basins globally, including variation in streamflow (Gerten et al, 2008;Lutz et al, 2014;Nepal and Shrestha, 2015;Singh et al 2015a;Ali et al, 2018;Singh et al, 2022). A study of long-term (1948-2004) streamflow (discharge) data of 200 largest rivers of the globe showed considerable change in their annual discharge, however, results were statistically significant only for 64 rivers.…”

Section: Introductionmentioning

confidence: 99%

Machine learning based streamflow prediction in a hilly catchment for future scenarios using CMIP6 data

Singh

Vardhan

Sahu

et al. 2022

Preprint

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Abstract. The alteration in river flow patterns, particularly those that originate in the Himalayas, has been caused by the increased temperature and rainfall variability brought on by climate change. Due to the impending intensification of extreme climate events, as predicted by the Intergovernmental Panel on Climate Change (IPCC) in its sixth assessment report, it is more essential than ever to predict changes in streamflow for future periods. Despite the fact that some research has utilised machine learning-based models to predict streamflow patterns in response to climate change, very few studies have been undertaken for a mountainous catchment, with the number of studies for the western Himalaya being so small as to be considered insignificant. This study investigates the capability of different machine learning (ML) models, including the Gaussian Linear Regression Model (GLM), Gaussian Generalized Additive Model (GAM), Multivariate Adaptive Regression Splines (MARS), Artificial Neural Network (ANN), and Random Forest (RF), in streamflow prediction over the Sutlej River Basin in western Himalaya during the periods 2041–2070 (2050s) and 2071–2100 (2080s) for two greenhouse gas trajectories (SSP245 and SSP585). Coupled Model Intercomparison Project Phase 6 (CMIP6) bias corrected data downscaled at grid resolution of 0.25°× 0.25° for 6 General Circulation Models (GCMs) were used for this purpose. Four different rainfall scenarios (R0, R1, R2, and R3) were applied to the models trained with daily data (1979–2009) at Kasol (the outlet of the basin) in order to better understand how catchment size and the geo-hydro-morphological aspects of the basin affect runoff. RF model with rainfall scenario R3 which outperformed other models during the training and testing period therefore was chosen to simulate streamflow in the Sutlej River in the 2050s and 2080s under the SSP245 and SSP585 scenarios. The mean ensemble of model results show that the mean annual streamflow of the Sutlej River is expected to rise between 2050s and 2080s by 5.51 to 6.04 % for SSP585 and by 6.65 to 6.75 % for SSP245. The seasonal streamflow also is expected to increase in the 2050s and 2080s under both emission scenarios, with the exception of the pre-monsoon, where a decline in streamflow is anticipated for SSP585 in the 2080s. However, under both the emission scenarios, there seemed to be significant variation in the streamflow simulations among the individual models for various time periods. It has been found that the pattern of this variability is highly correlated with the pattern of precipitation and temperature predicted by various GCMs for future emission scenarios. The present study will therefore assist in strategy planning for ensuring the sustainable use of water resources downstream by acquiring a knowledge of the nature and causes of unpredictable streamflow patterns.

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Exploring the links between variations in snow cover area and climatic variables in a Himalayan catchment using earth observations and CMIP6 climate change scenarios

Cited by 14 publications

References 78 publications

Modelling of hydrological and environmental flow dynamics over a central Himalayan river basin through satellite altimetry and recent climate projections

Modelling of hydrological and environmental flow dynamics over a central Himalayan river basin through satellite altimetry and recent climate projections

Snow Disaster Risk Assessment Based on Long-Term Remote Sensing Data: A Case Study of the Qinghai–Tibet Plateau Region in Xizang

Machine learning based streamflow prediction in a hilly catchment for future scenarios using CMIP6 data

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