HyGBR-Baseflow: A hybrid machine learning approach to enhance the baseflow estimation across India

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Authors: Rajesh Singh, Vimal Mishra, Hiren Solanki
Cited by: 0

Abstract

This dataset contains daily baseflow estimates and associated hydrological analyses for 200+ stations in major river basins across India covering the period 1961–2021. Using a new developed HyGBR framework, baseflow was separated from observed streamflow and evaluated against multiple existing graphical and digital filter methods. The dataset includes daily baseflow time series, basin attributes, climate classifications, baseflow index (BFI), aridity index (AI), and long-term baseflow change (%). These data were developed to investigate the spatial variability, temporal trends, and climatic controls of baseflow across India's diverse hydro-climatic regions. The dataset supports the assessment of groundwater contributions to streamflow, evaluation of baseflow separation methods, and analysis of long-term changes in hydrological regimes under varying climatic conditions.

Fields of Study & Subjects

Hydrology and Watershed Management Studies Hydrological Forecasting Using AI Hydrology and Drought Analysis

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
2 February 2026
Document type
Dataset