Data assimilation with machine learning for constructing gridded rainfall time series data to assess long-term rainfall changes in the northeastern regions in India

Author:

Singh Vishal1,Bansal Joshal Kumar2,Rani Deepti1,Singh Pushpendra Kumar3,Nema Manish Kumar3,Singh Sudhir Kumar4ORCID,Jain Sanjay Kumar2

Affiliation:

1. a Centre For Cryosphere and Climate Change Studies, National Institute of Hydrology, Roorkee 247667, Uttarakhand, India

2. b Centre of Excellence in Disaster Mitigation and Management, Indian Institute of Technology Roorkee, Roorkee 247667, Uttarakhand, India

3. c Water Resources System Division, National Institute of Hydrology, Roorkee 247667, Uttarakhand, India

4. d K. Banerjee Centre of Atmospheric and Ocean Studies, University of Allahabad, Prayagraj 211002, Uttar Pradesh, India

Abstract

ABSTRACT Data scarcity and unavailability of observed rainfalls in the northeastern states of India limit prediction of extreme hydro-climatological changes. To fill this gap, a data assimilation approach has been applied to re-construct accurate high-resolution gridded (5 km2) daily rainfall data (2001–2020), which include seasonality assessment, statistical evaluation, and bias correction. Random forest (RF) and support vector regression were used to predict rainfall time series, and a comparison between machine learning and data assimilation-based gridded rainfall data was performed. Five gridded rainfall datasets, namely, Indian Monsoon Data Assimilation and Analysis (IMDAA) (12 km2), APHRODITE (25 km2), India Meteorological Department (25 km2), PRINCETON (25 km2), and CHIRPS (25 and 5 km2), have been utilized. For re-constructed rainfall datasets (5 km2), the comparative seasonality and change assessment have been performed with respect to other rainfall datasets. CHIRPS and APHRODITE datasets have shown better similarities with IMDAA. The RF and assimilated rainfall (AR) have superiority based on bias and extremity, and AR data were recognized as the best accurate data (>0.8). Precipitation change analysis (2021–2100) performed utilizing the bias-corrected and downscaled CMIP6 datasets showed that the dry spells will be enhanced. Considering the CMIP6 moderate emission scenario, i.e., SSP245, the wet spell will be enhanced in future; however, when considering SSP585 (representing the extreme worst case), the wet spells will be decreased.

Publisher

IWA Publishing

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