The Impact of Precipitation Uncertainty on Hydrological Modeling: An Analysis over Indian basins

Gridded precipitation products are naturally uncertain due to different factors like measurement errors, precipitation undercatch, and errors introduced by the different interpolation algorithms. Hydrological modelling is significantly influenced by the uncertainty in meteorological data. The effectiveness of advanced data assimilation systems and other tools in land surface and hydrological modeling is limited due to the lack of quantitative estimates of uncertainty for hydro-meteorological data. We have created a high-resolution ensemble precipitation dataset, Indian Precipitation Ensemble Dataset (IPED), at 0.1° resolution from 1991 to 2023 over the Indian region. This dataset is derived from observation-based precipitation data using a locally weighted linear regression algorithm. However, its potential in evaluating the uncertainties in hydrological modeling is yet to be explored. This study investigates the impact of uncertainties in precipitation data on hydrological modeling across 18 basins in India by utilizing IPED dataset into Indian Land Data Assimilation System (ILDAS), a hydrologic hydrodynamic model. The ensemble simulation employs IPED's probabilistic estimates to perform uncertainty analysis. The results highlight the extent, spatial distribution, and magnitude of uncertainties in precipitation and streamflow variables from 1991 to 2023. A key finding is that uncertainties in streamflow are significantly affected by uncertainties in precipitation. Both spatial and temporal averaging have distinctive effects on the uncertainty of different variables across the study area. In conclusion, this investigation provides a thorough understanding of how IPED dataset improves and quantifies the uncertainties in streamflow over Indian river basins that arise from precipitation data uncertainties.