2023
DOI: 10.3390/w15193338
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Spatial and Temporal Analysis of Hydrological Modelling in the Beas Basin Using SWAT+ Model

Suraj Kumar Singh,
Shruti Kanga,
Bhavneet Gulati
et al.

Abstract: In this research, the SWAT+ model was employed to elucidate hydrological dynamics within the Beas Basin. The primary objectives encompassed the calibration of the SWAT model for accurate water balance quantification, annual simulation of salient hydrological components, and a decadal analysis of trends in fluvial discharge and sediment transport. The methodology encompasses simulating hydrological data with the SWAT+ model, followed by calibration and validation using flow data from Larji and Mahadev hydroelec… Show more

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Cited by 4 publications
(2 citation statements)
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“…The following four performance indicators were extensively employed to qualitatively assess the performance of the generated models in order to further assess the established model and neural network model: The following expressions were used to express the root mean squared error (RMSE), correlation coefficient (CC), Nash-Sutcliffe efficiency coefficient (NSE), and percent bias (PBIAS) [36][37][38][39]:…”
Section: Model Simulation Effect Evaluation Indexmentioning
confidence: 99%
“…The following four performance indicators were extensively employed to qualitatively assess the performance of the generated models in order to further assess the established model and neural network model: The following expressions were used to express the root mean squared error (RMSE), correlation coefficient (CC), Nash-Sutcliffe efficiency coefficient (NSE), and percent bias (PBIAS) [36][37][38][39]:…”
Section: Model Simulation Effect Evaluation Indexmentioning
confidence: 99%
“…Hydrologists have conducted extensive research on the selection [1], calibration [2][3][4][5], and uncertainty assessment [6][7][8][9][10][11] of hydrological models to obtain optimal information from these models. In the calibration or uncertainty assessment of hydrological models, observational data play a pivotal role, with the critical elements being the length of the data and the climatic conditions (e.g., dry years, normal years, and wet years) to which the data belong [12].…”
Section: Introductionmentioning
confidence: 99%