Preprints
https://doi.org/10.5194/hess-2024-18
https://doi.org/10.5194/hess-2024-18
25 Jan 2024
 | 25 Jan 2024
Status: this preprint is currently under review for the journal HESS.

Extent of gross underestimation of precipitation in India

Gopi Goteti and James Famiglietti

Abstract. Underestimation of precipitation (UoP) in the hilly and mountainous parts of South Asia is estimated by some studies to be as large as the observed precipitation (P). For instance, correction factors (CFs) developed by the recent PBCOR dataset have values exceeding 2.0 across the wettest regions of India, some of which have experienced catastrophic flooding in the recent past. However, UoP has been analyzed only to a limited extent across India. Towards bridging this gap, this study analyzes watershed-scale UoP using various P datasets within a water imbalance analysis. Among these P datasets, the often-used Indian Meteorological Department (IMD) dataset is of primary interest.

Gross UoP was identified by analyzing the extent of imbalance in the annual water budget of watersheds corresponding to 242 river gauging stations where quality controlled data on catchment boundaries and streamflow is available. Water year (WY) based volume of observed annual P was compared against observed annual streamflow (R) and satellite-based actual evapotranspiration (ET). Across many watersheds of both Northern and Peninsular India, the spurious water imbalance scenarios of PR, or P << R + ET, were realized. It is shown that management of water, such as groundwater extraction, reservoir storage and water diversion (imports or exports), is generally minimal compared to annual P in such watersheds. It is also shown that annual changes in terrestrial water storage are also minimal compared to annual P in such watersheds. Assuming data on R (and ET to a lesser extent) to be reliable, it is concluded that UoP is very likely the cause of such imbalance.

All 12 of the P datasets analyzed here suffer from UoP, but the extent of UoP varies by dataset and region. The reanalysis-based datasets ERA5-Land and IMDAA are less affected by UoP than IMD, and the spatial patterns of estimated CFs based on these two datasets are also consistent with those made independently by the PBCOR dataset. Based on the 30-year period of WY 1985–2014, P for the whole of India could be up to 19 % (ERA5-Land) to 37 % (IMDAA) higher than IMD, with substantial variability within years and river basins. For instance, P for the Indian portion of the Ganga River Basin, for the same 30-year period, could be up to 36 % (ERA5-Land) to 54 % (IMDAA) higher than IMD. The actual magnitude of UoP is speculated to be even greater. Moreover, trends in IMD's P are not always present in ERA5-Land and IMDAA. Studies using IMD should exercise caution since UoP could lead to misrepresentation of water budgets and long-term trends.

The empirical approach of identifying watersheds affected by UoP using a water imbalance approach is contingent on data availability. It is speculated that if additional data on R becomes available, particularly in Northern India, many other watersheds affected by UoP would be identified. While the scientific community is striving to continually improve P products, India's water agencies can help the community better quantify UoP by making observed hydrometeorological data more widely available. Limitations of this study are discussed.

Gopi Goteti and James Famiglietti

Status: open (until 29 Mar 2024)

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Gopi Goteti and James Famiglietti
Gopi Goteti and James Famiglietti

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Short summary
Underestimation of precipitation (UoP) in India is a substantial issue not just within gauge-based precipitation datasets but also within state-of-the-art satellite and reanalysis-based datasets. UoP is prevalent across most river basins of India, including those that have experienced catastrophic flooding in the recent past. This paper not only highlights a major limitation of existing precipitation products over India but also other data-related obstacles faced by the research community.