The evaluation of climate change impact on hydrologic processes of a mountain river basin

Adhikari, Tika B.; Talchabhadel, Rocky; Shrestha, Suraj; Sharma, Sanjib; Aryal, Dibit; Pradhanang, Soni M.

doi:10.1007/s00704-022-04204-3

Cited by 9 publications

(4 citation statements)

References 79 publications

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“…These datasets are collected from the Department of Hydrology and Meteorology (DHM). The climatic datasets processing in this study using normal ratio method to fill in the rainfall data (Shrestha et al, 2019a;Shrestha et al, 2019b), evaporation data was calculated using Thornwaite method based on the mean monthly temperature and the lapse rate method is used to fill in the temperature data (Adhikari et al, 2022). The dynamic datasets are discharge and climatic stations used in this research in Table 1.…”

Section: Input Datamentioning

confidence: 99%

“…Utilizing temperature and precipitation data as inputs, the Precipitation-Runoff Model assesses stream flow and simulates streamflow (Vormoor et al, 2018). By combining evaluations of precipitation, climate, and land use, basin responses to water balance, flow regimes, climate change, flood peaks and volumes, the interaction between soil and water, and sediment yields are assessed (Adhikari et al, 2022). According to Siebert et al ( 2012), the HBV light version features a routine for precipitation in snow, soil moisture, reaction, and routing that includes lakes and glaciers (Siebert., 2005).…”

Section: Hbv Modelmentioning

confidence: 99%

“…Snow and glacier areas that significantly melt contribute to streamflow in snow and glacier-fed river basin systems (Khadka et al, 2014). The changing climate influences the hydrological processes that impact river flow regimes and freshwater availability in catchment-scale hydrology in the Himalayan River basin (Adhikari et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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Application of hydrological model to simulate streamflow contribution on water balance in Himalaya river basin, Nepal

et al. 2023

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Hydrological models are widely used and often regarded as reliable tools for accurately estimating various components of the water balance. In a remote Himalayan catchment, such as Tamakoshi basin, where limited hydrometric dataset is available, such models often provide essential insights that are crucial to water researchers and planners. In this regard, we employed the semi-distributed HBV-light (version 4.0.0.25) hydrological model for glacierized Tamakoshi river basin and attempted to quantify various water balance components. For our model tests, using the daily streamflow records, we selected two distinct periods, i.e., 2004–2008 as a calibration period whilst 2011–2012 for model validation. Based on our findings, the model was able to reasonably predict the streamflow (validation efficiency: Nash-Sutcliffe Efficiency of 0.82 and percent bias −21%). At our site, HBV-light model predicted that the change in streamflow was mostly governed by monsoonal rain (62%) followed by baseflow (20%), glacier melt (13%) and snowmelt (5%). As expected, the streamflow peaked during the month of August where monsoon-induced rain and melting of glaciers significantly contributed to river flow. As a result, monsoon period showcased largest fluctuation in water storage while negligible change was observed during post-monsoon season. Nonetheless, our findings revealed that the baseflow contribution to streamflow was maximum during the month of October and lowest during February. Our findings indicated that the water balance of the Tamakoshi basin is largely influenced by monsoonal rain during June–September window as well as baseflow and glacier melt during the dry season. Runoff components contribution to streamflow was increasing but water storage changes was decreasing in recent decade (2011–2020). We believe our findings are crucial for future initiatives involving water resources, water-induced disaster management, and studies of climate change may benefit from the findings of this study, especially in a region with limited hydrometric data availability.

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Section: Input Datamentioning

confidence: 99%

Section: Hbv Modelmentioning

confidence: 99%

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Application of hydrological model to simulate streamflow contribution on water balance in Himalaya river basin, Nepal

et al. 2023

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“…Naturally, snowmelt runoff is affected by a variety of meteorological factors and involves many complex physical processes [5]; as such, modeling snowmelt runoff is more challenging than that for conventional rainfall-runoff processes [6,7]. These challenges become even more exacerbated when it comes to representing the climate change impact on hydrological processes [8,9].…”

Section: Introductionmentioning

confidence: 99%

Improvement of the SWAT Model for Snowmelt Runoff Simulation in Seasonal Snowmelt Area Using Remote Sensing Data

Zhao

Xuan

et al. 2022

Remote Sensing

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The SWAT model has been widely used to simulate snowmelt runoff in cold regions thanks to its ability of representing the effects of snowmelt and permafrost on runoff generation and confluence. However, a core method used in the SWAT model, the temperature index method, assumes both the dates for maximum and minimum snowmelt factors and the snowmelt temperature threshold, which leads to inaccuracies in simulating snowmelt runoff in seasonal snowmelt regions. In this paper, we present the development and application of an improved temperature index method for SWAT (SWAT+) in simulating the daily snowmelt runoff in a seasonal snowmelt area of Northeast China. The improvements include the introduction of total radiation to the temperature index method, modification of the snowmelt factor seasonal variation formula, and changing the snowmelt temperature threshold according to the snow depth derived from passive microwave remote sensing data and temperature in the seasonal snowmelt area. Further, the SWAT+ model is applied to study climate change impact on future snowmelt runoff (2025–2054) under the climate change scenarios including SSP2.6, SSP4.5, and SSP8.5. Much improved snowmelt runoff simulation is obtained as a result, supported by several metrics, such as MAE, RE, RMSE, R2, and NSE for both the calibration and validation. Compared with the baseline period (1980–2019), the March–April ensemble average snowmelt runoff is shown to decrease under the SSP2.6, SSP4.5, and SSP8.5 scenario during 2025–2054. This study provides a valuable insight into the efficient development and utilization of spring water resources in seasonal snowmelt areas.

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Spatial and temporal variability of climate extremes and their impact on maize yield in North-Central Nigeria

Adeyemi,

Ahmad,

Adelodun

et al. 2023

Theor Appl Climatol

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The evaluation of climate change impact on hydrologic processes of a mountain river basin

Cited by 9 publications

References 79 publications

Application of hydrological model to simulate streamflow contribution on water balance in Himalaya river basin, Nepal

Application of hydrological model to simulate streamflow contribution on water balance in Himalaya river basin, Nepal

Improvement of the SWAT Model for Snowmelt Runoff Simulation in Seasonal Snowmelt Area Using Remote Sensing Data

Spatial and temporal variability of climate extremes and their impact on maize yield in North-Central Nigeria

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