Distributed Hydrological Modeling Framework for Quantitative and Spatial Bias Correction for Rainfall, Snowfall, and Mixed‐Phase Precipitation Using Vertical Profile of Temperature. Issue 9 (7th May 2019)

• Record Type: Journal Article
• Authors: Naseer, Asif; Koike, Toshio; Rasmy, Mohamad; Ushiyama, Tomoki; Shrestha, Maheswor
• Is Part Of: Journal of geophysical research. Volume 124:Issue 9(2019)
• Page Start: 4985
• Page End: 5009
• Publication Date: 2019-05-07
• Subjects: vertical profile of temperature -- distributed hydrologic model lapse rate -- mountainous region; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1029/2018JD029811
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: Mountain snowpack and its distribution both have intimate connections to regional hydrology by preserving winter precipitation to sustain streamflows during the summer months. One of the key knowledge gaps in mountainous region is the interplay of precipitation and temperature with changing altitudes. Three‐dimensional temperature distribution is pivotal for the realistic temporal and spatial distribution of precipitation with pattern (rain/snow). The environmental/linear lapse rates are inadequate to address snow processes, resulting in significant uncertainties. An effort is made in this study to develop a vertical profile of temperature (VPT) and apply it as a dynamic temperature lapse rate to curtail uncertainties. The VPT was used for the spatiotemporal bias correction of precipitation by targeting accessible data sources based on the quantitative and spatial analysis in a distributed hydrologic modeling framework with a logical calibration and validation. The water and energy budget‐based distributed hydrological model with snow was utilized to simulate the streamflows and spatial distribution of snow cover based on VPT and corrected precipitation. During calibration and validation phase, the simulated discharge resulted with Nash‐Sutcliffe Efficiency over 0.76 and 0.71, respectively. Moreover, the output for the spatial distribution of snow cover evaluated against Moderate Resolution Imaging Spectroradiometer‐derived 8‐day maximum snow cover extents by employing pixel‐by‐pixel analysis with average model accuracy over 88.28% and 85.89%. To the authors' knowledge, it is the first study to integrate VPT in hydrologic modeling with robust potential for optimal water resource management in the data scarce region. Key Points: Vertical profile of temperature (VPT) is derived from merged reanalysis and in situ data for classifying rain, snow, and mixed precipitation Mountainous snow is quantified using a snow‐hydrological model with snow‐covered area derived from satellites and observed discharge A logical model calibration and validation framework is developed in an operational way