Blue and green water re-distribution dependency on precipitation datasets for a tropical Indian River basin

Study region: The Damodar River basin, India.Study focus: Water resource assessment at the river basin scale is crucial for human well-being and ecosystem health, and it can be performed quantitatively through green water (GW) and blue water (BW) flow evaluation. However, the quantification of BW and GW through land surface models (LSMs) is significantly influenced by the accuracy of precipitation datasets. In this present study, the spatiotemporal variation of blue and green water resources under the influence of six secondary precipitation products (SPPs) [APHRODITE; IMDAA; WFDEI; PRINCETON; CHIRPS, PERSIANN-CDR] are investigated using JULES LSM, considering India Meteorological Depart-ment (IMD) precipitation product as an observed precipitation dataset. This will help in exam-ining the dependencies of precipitation datasets on partitioning between BW and GW distribution in a tropical River basin. New hydrological insights for the study region: The results suggest significant differences in BW and GW simulation with the use of six aforementioned SPPs. In comparison to the reference IMD-based JULES model simulation, the annual average BW and GW estimates varied significantly: from 9.05% to -40% and 15.24% to -11.79%, respectively due to changes in the SPPs. Furthermore, the higher correlation between BW and precipitation datasets (R2 = 0.84-0.96) suggested that BW is more sensitive to precipitation changes in comparison to GW. Overall, our study emphasizes the importance of carefully considering the specification of precipitation da-tabases for estimating BW and GW in a tropical River basin.

Automated summary

This study investigates the influence of six secondary precipitation products (SPPs) on the simulation of blue water (BW) and green water (GW) using land surface models (LSMs). The results show significant differences in BW and GW simulation with the use of different SPPs. It is also found that BW is more sensitive to precipitation changes compared to GW.