Hydrological Science

 As a hydrologist, I recently conducted a study to evaluate the performance of three lumped conceptual rainfall-runoff models, GR4J, GR5J, and GR6J, in estimating runoff in a sub-basin of the Bharathapuzha river basin in Kerala. Our findings showed that the GR4J model performed better than the GR5J and GR6J models in estimating streamflow. During the validation period, the NSE and R values were better than those during the calibration period for all three GR models. While the PBIAS values during the calibration period were better than those during the validation period for all three GR models, all three models showed a negative value of PBIAS in both calibration and validation periods, indicating an overestimation of streamflow by the models. Furthermore, we found that the GR4J model overestimated streamflow the least in both the calibration and validation periods. In addition, the GR4J and GR6J models outperformed the GR5J model in terms of NSE, PBIAS, and R values. Overall, our e...

Summary of my EGU 2023 General Assembly Presentation: In our study, we investigated change point detection, trend analysis, and streamflow forecasting for Upper Narmada Basin We presented our findings at the EGU 2023 General Assembly, covering the following key points: Overview of Results: 1. Change Point Detection: Consistent change points detected by Pettitt's test and SNHT Significant alterations in streamflow levels were identified for each river station 2. Trend Analysis: Annual, seasonal, and monthly trends were analyzed using MK, MMK tests, and Sen's Slope method No significant trends were found using MK and MMK tests for annual data Varying patterns observed depending on the location and specific streamflow metric (average, maximum, or minimum) for seasonal and monthly data 3. Seasonal and Monthly Streamflow: Barmanghat: significant increasing trend in average and maximum streamflows in January before the change point Belkedi: the significant decreasing trend for maximu...