Evaluation of Remote Sensing Rainfall Products, Bias Correction and Temporal Disaggregation Approaches, for Improved Accuracy in Hydrologic Simulations

P. Yaswanth (), B. Arul Malar Kannan (), V. M. Bindhu (), C. Balaji () and Balaji Narasimhan ()
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P. Yaswanth: Indian Institute of Technology-Madras
B. Arul Malar Kannan: India Meteorological Department
V. M. Bindhu: Indian Institute of Technology-Madras
C. Balaji: IIT Madras
Balaji Narasimhan: Indian Institute of Technology-Madras

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2023, vol. 37, issue 8, No 10, 3069-3092

Abstract: Abstract Flood risk management studies require reliable estimates of extreme precipitation at high spatial–temporal distribution to force hydrologic models. Recently, Remote Sensing Rainfall Products (RRPs) have gained significant importance in the field of hydrometeorology, but their applicability in urban hydrologic predictions remains uncertain. The current study evaluates the accuracy of RRPs in comparison with observed rainfall and the significance of space–time representation of rain in simulating single and bimodal flood hydrographs. The study is conducted for the Adyar river basin, a rapidly developing urban area in Chennai experiencing frequent floods. Sub-daily rainfall retrievals from Integrated Multi-satellite Retrievals for Global Precipitation Measurement version 6 final run product (IMERG GPM), the near-real-time Global Satellite Mapping of Precipitation (GSMaP_NRT) version 6, GSMaP gauge adjusted (GSMaP_Gauge), Precipitation Dynamic Infrared Rain Rate near real-time (PDIR-Now) and Doppler Weather Radar (DWR) are the Remote sensing Rainfall products (RRPs) selected in the present study. Continuous and categorical statistical indices are selected to evaluate the performance of satellite rainfall estimates for the period from 2001 to 2015. Then the hydrologic utility of RRPs is conducted using the Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) model for five extreme precipitation events. The RRPs simulated the rising and recession portion of flood hydrographs accurately with a bias in peak discharge. Then, two approaches are selected to further improve the flood hydrograph simulations; 1) Hydrologic model simulations after disaggregating the daily station data to sub-daily scale using time characteristics of RRPs, 2) Hydrologic simulations after bias adjusting the RRPs with station data. The study finds substantial improvements in model results in the two approaches. The disaggregation approach using satellite rainfall estimates has overcome the insufficiency of sub-daily rainfall observations. The bias adjusted radar rainfall data is found as best performing for the flood hydrograph simulations.

Keywords: Weather radar and satellite precipitation estimates; Hydrologic simulation; Extreme floods; HEC-HMS; Rainfall spatial variability (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11269-023-03486-0

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