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The 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). However, UoP has been analyzed to only a limited extent across India. To help bridge this gap, watershed-scale UoP was analyzed 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. The gross UoP was identified by analyzing the extent of the imbalance in the annual water budget of watersheds corresponding to 242 river gauging stations for which quality-controlled data on catchment boundaries and streamflow are available. The water year (WY)-based volume of observed annual P was compared against the observed annual streamflow (R) and the satellite-based actual evapotranspiration (ET). Across many watersheds of both Northern and Peninsular India, spurious water imbalance scenarios (P≤R or P≪R+ET) were realized. It is shown that the management of water, such as groundwater extraction, reservoir storage and water diversion, is generally minimal compared to the annual P in such watersheds. It is also shown that annual changes in terrestrial water storage are minimal compared to the annual P in such watersheds. Assuming that data on R (and, to a lesser extent, ET) are reliable, it is concluded that UoP is very likely the cause of this imbalance. Inter-watershed groundwater flow (IGF) is assumed to be negligible. While the effect of IGF on R is unknown, examples are provided which show that IGF is unlikely to be the cause of the observed imbalance in certain watersheds. All 12 of the P datasets analyzed here suffer from UoP, but the extent of the UoP varies by dataset and region. The reanalysis-based datasets ERA5-Land and IMDAA are less affected by UoP than the IMD dataset. Based on the 30-year period of WY 1985–2014, P for the whole of India could be as much as 19 % (ERA5-Land) to 37 % (IMDAA) higher than that from the IMD, with substantial variability within years and river basins. The actual magnitude of UoP is speculated to be even greater. Moreover, trends seen in the IMD's P are not always present in ERA5-Land and IMDAA. Studies using IMD should exercise caution since UoP could lead to the misrepresentation of water budgets and long-term trends. Limitations of this study are discussed. [ABSTRACT FROM AUTHOR]

Geospatial information on river gauging stations, such as station locations, catchment boundaries, river flow paths and other hydrographic data are basic building blocks of hydrological analyses. Such information for India's river basins is available from Central Water Commission (CWC) and water resources information system (WRIS), but is limited in extent and unreliable. Using widely used global hydrography products, catchment boundaries were delineated for 100 gauging stations in the Ganga river basin (GRB), and their catchment areas were estimated. The delineated boundary for the GRB, and also station-wise catchment areas from the present study were much more consistent with global hydrography products than those available from CWC and WRIS. Lack of reliable, analysis-ready hydrography data is a hindrance to hydro-climatological research. The long-term objective of this study is to help build such a needed dataset using high-resolution remote sensing products. [ABSTRACT FROM AUTHOR]

In this paper, we present a macroscale hydrologic modeling system with an explicit representation of storage and movement of water in river channels and floodplains. The overall modeling system, called the Catchment-Based Hydrologic and Routing Modeling System (CHARMS), is composed of a land surface model and a river routing model that operate on a network of hydrologic catchments (or watersheds). The land surface model in CHARMS is based on the National Center for Atmospheric Research Community Land Model. The river routing model in CHARMS generates river discharge by transporting runoff generated by the catchment-based CLM through the river network. The routing model uses information on channel cross-section geometry, derived from the 90 m Shuttle Radar Topography Mission digital elevation model, to simulate river discharge and the associated flow depth and inundation width. CHARMS was implemented over the Wabash River basin in the central United States (drainage area 72282 km2), and simulated streamflow was validated using daily observations. Simulated flow depth and inundation extent generally followed seasonal variations in observed flooding and droughts. Limitations of some of the assumptions and scaling factors used in this study and the issues that need to be addressed for a continental- or global-scale implementation of CHARMS are discussed. This paper serves as the foundation for a catchment-based, global land surface modeling framework that could incorporate spatiotemporal variations in surface water bodies, as well as satellite measurements of these variations.

Streamflow gauging stations not only track the pulse of rivers but also act as common reference points for hydrologic and other environmental analyses. As such, streamflow data and metadata on gauging stations – Geographic Information System (GIS) data on station locations, their upstream catchment boundaries and river flow networks – are critical for analyses. However, for India's river basins, the availability of such data is limited; when available, data are not in an analysis-ready format and can have substantial errors. Studies often use available information from India's water agencies as is, without checking its validity. This study addresses the above limitations by building a new dataset using existing metadata (from the Central Water Commission, CWC, and the Water Resources Information System, WRIS) and checking it against publicly available information from global data sources (e.g., World Wildlife Fund, Multi-Error-Removed Improved-Terrain Hydro and Copernicus) and online maps (e.g., Google Maps). The quality control process categorizes existing metadata based on their consistency with these sources; also, existing metadata are supplemented with additional information where needed. The new dataset developed here is called the "Geospatial dataset for Hydrologic analyses in India" (GHI) and uses Hydrological data and maps based on Shuttle Elevation Derivatives at multiple Scales (HydroSHEDS) data as the underlying template. GHI has both geospatial and time series information. In this initial version of GHI, the spatial domain includes only the river basins of Peninsular India where daily streamflow data are publicly available. Following the quality control process, the CWC's 645 stations in Peninsular India were categorized into three groups: Group 1 (reliable metadata and adequate daily streamflow data; 213 stations), Group 2 (reliable metadata and inadequate or no daily streamflow data; 259 stations) and Group 3 (missing or unreliable metadata; 173 stations). For each of the 472 stations falling into groups 1 and 2, catchment-specific annual and monthly time series spanning 71 water years (1950–2020) of the following were compiled: observed precipitation from the Indian Meteorological Department (IMD); observed streamflow from WRIS; estimated precipitation, evapotranspiration (ET) and streamflow from ERA5-Land; and ET from the Global Land Evaporation Amsterdam Model (GLEAM). A preliminary analysis of catchment-scale time series of data indicates that, while the compiled data appear reasonable over most of the study domain, spurious runoff–precipitation ratios were observed in the hilly coastal regions of Western India. This adds to yet another data-related obstacle faced by the hydrologic community. In order to quantify historical changes and reconcile them with anticipated future changes, the community needs robust and reliable hydrographic and hydrometeorological datasets as well as unrestricted access to such datasets. The goal of this study is to highlight the limitations of existing datasets and pave the way for a community-led effort towards building the needed datasets. GHI serves as a placeholder until such datasets become available. Potential improvements to GHI are discussed. GHI is publicly available at 10.5281/zenodo.7563599. [ABSTRACT FROM AUTHOR]