Sensitivity Analysis of the Physiographic Parameters of the Teesta River Basin by Hydrological Modelling

Estimating the discharge from a rainfall event is a challenging task because of a number of affecting elements. A multitude of physiographic factors are essential for both channel and surface flow. In a developing nation like Bangladesh, discharge measurement is critical for forecasting floods, managing land, measuring sediment, nutrients and promoting sustainable development. It is possible to measure the discharge and physiographic parameter using a hydrological model. Using a semi-distributed model Soil and water Assessment Tool (SWAT), the discharge of the Teesta River Basin, one of the most significant basins of Bangladesh, is simulated for the years 2003 to 2020. Sequential Uncertainty Fitting version2 (SUFI-2) technique within SWAT-CUP (SWAT Calibration Uncertainty Program) is used to accomplish model calibration and validation for daily time periods utilizing physiographic parameters. The simulation period of this study spans from 2003 to 2020, and the meteorological data utilized includes temperature, wind speed, relative humidity and rainfall. With  and , our calibration results for the time period 2003–2011 demonstrated a strong correlation between observed and simulated discharge. Reasonable values are obtained for the  and , which are 0.65 and 0.70 for the validation period 2012-2020. Sensitivity analysis is an integral part of model development and involves analytical examination of input parameters to aid in model validation and provide guidance for future research and sustainable development. Sensitivities of physiographic parameters have been analyzed using SUFI-2 algorithm in SWAT-CUP. It is done by global and one-at-a-time sensitivity procedures. For the Teesta river basin parameters coefficient curve number CN2.mgt, saturated hydraulic conductivity of the soil lair SOL_K ().sol and soil bulk density SOL_BD ().sol show most sensitivity for both of global and one-at-a-time sensitivity procedures. The findings contribute to predict the discharge in period of no observe data as well as to enhance the understanding and informing decision-making processes for sustainable water resource management.