Distributed Hydrological Modeling Framework for Quantitative and Spatial Bias Correction for Rainfall, Snowfall, and Mixed‐Phase Precipitation Using Vertical Profile of Temperature

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.

Intra‐day variability of temperature and its near‐surface gradient with elevation over mountainous terrain: Comparing MODIS land surface temperature data with coarse and fine scale near‐surface measurements

“…The information should be verified with secondary data from stations or sensors because coarsescale satellite data can introduce errors in the characterization of the temperature patterns that can then be propagated to hydrological or agricultural models (Hulley et al, 2012;Ghent et al, 2019). Sometimes when temperature data are limited, satellite data are extrapolated between different scales, usually using geostatistical estimations including secondary information as elevation or NSTGE (Monestiez et al, 2001;Naseer et al, 2019). However, as shown in this study, temperature patterns between scales can vary considerably and a proper characterization of the patterns of temperature variability at differing elevations is needed.…”

Fully-integrated hydrological modelling in steep, snow-dominated, geologically complex Alpine terrain

“…This and other studies (e.g. Naseer et al, 2019) suggest that in complex terrain, such constant lapse rates may be unrealistic. Avoiding the use of such constant gradients can therefore be considered a broadly positive feature of the methodology taken here, since it ultimately retains more of the spatial and temporal structure of the local meteorological measurements to be retained.…”

“…A particular novelty of that study was that the correspondence between simulated and observed patterns was expressed at the pixel level. The same model was subsequently applied by Naseer et al (2019), who -instead of applying traditional linear, elevation-dependent lapse rates for the spatial interpolation of meteorological data, which may break down in complex terrain -attempted to integrate 3D temperature profiles derived from climate model reanalysis data. There was no spatial component to the calibration undertaken in this study, however.…”

Improving snowfall representation in climate simulations via statistical models informed by air temperature and total precipitation

“…Krasting et al (2013) study snowfall in CMIP5 models at the Northern emispheric level, highlighting biases but without suggesting any methodology to reduce them, while Lange (2019) proposes a quantile mapping approach that can be used for univariate BC of snowfall. On the other hand, most efforts are focused on correcting observationa (Karbalaee et al, 2017;Wang et al, 2017;Naseer et al, 2019;Panahi and Behrangi, 2019) or reanalysis data (Cucchi et al, 2020;Panahi and Behrangi, 2019).…”