<p>The river discharge data is one of the most important pieces of information to regulate various water resources, including flood frequency analysis, drought and flood prediction, etc. The missing observer discharge data, even a short gap, influences the whole analysis and gives a totally different result. Filling data gaps in streamflow data is thus a critical step in any hydrological study. Interpolation, regression-based analysis, artificial neural networks, and modeling are all methods for generating missing data. While using the hydrological model to generate the data, we first need to calibrate the hydrological model. The single-site calibration of the hydrological model has its own limitations, due to which it does not correctly predict the streamflow at intermediate gauge locations. This is because, while calibrating the model for the final outlet, we tune the parameters that affect the results for the final outlet only and neglect the intermediate sites' output. In this study, we demonstrate the importance of multi-site calibration and use the calibrated hydrological model to generate the missing data at intermediate sites.</p> <p>For this study, we selected the Godavari River basin and calibrated it at the final outlet (single-site calibration) and at 18 + 1 outlets (multi-site calibration). The whole basin is divided into 103 subbasins, and the Soil and Water Assessment Tool (SWAT) hydrological model is used for this study. After the successful multi-site calibration, we generated the missing data at 25 different gauging locations. The initial results from single-site calibration (NSE (0.57) and R2 (0.61)) show good agreement between observed and simulated discharge for the final outlet. The multi-site calibration analysis is in progress, and full results will be presented at the conference.</p>
<p>The hydraulic structures, such as dams and reservoirs, are built for flood mitigation, drinking & irrigation water supply, and hydropower generation. Despite their positive roles, large dams and reservoirs are well known to trap a significant portion of the incoming sediment fluxes. In turn, sedimentation reduces the reservoir's water storage capacity. The Indra Sagar dam, located in the Narmada River Basin, is the largest reservoir in India (total capacity ~ 12.2 Bm<sup>3</sup>). Therefore, in this study, our objective is to set up a data-driven, i.e., Generalized Additive Model Location Scale and Shape (GAMLSS) to simulate the impact of the Indira Sagar dam on the downstream sediment transport. The daily sediment and water discharge data are used from 1987 to 2019, from June to November, at upstream and downstream gauge stations. Preliminary analysis reveals a significant alteration in downstream sediment discharge after constructing the Indira Sagar dam. However, the pre-dam period doesn't significantly alter sediment transport behavior. In addition, pre-and post-dam water discharge behaviors do not exhibit considerable alteration. The difference between 5-yearly sediment duration curves reveals around 60% to 95% reduction in high and moderate magnitudes sediment load. Further observation suggests an increase in low sediment magnitude flows downstream after the dam construction from the base period 1989-1993. The significance of the study is that it will help water managers in understanding the dam's water storage capacity, which may be affected due to sediment deposition. It is also crucial to understand the geomorphological changes and implications of less sediment supply in the downstream region. The results obtained from this study will further provide additional insights into evolving flood and drought processes and their forecasting around the dam-affected region. This work is in progress, and further results will be presented at the conference.</p>
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