Optimizing Snowfall Correction Factor for Radar-Amedas Precipitation Using Distributed Snow Model (Web-DHM-S) and Modis Snow Cover Data

Shrestha, Maheswor; Koike, Takao; Wang, Lei; Tsutsui, Hiroyuki; Xue, Yongkang; Hirabayashi, Yukiko

doi:10.2208/jscejhe.70.i_223

Cited by 2 publications

(11 citation statements)

References 21 publications

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“…This is especially true when such methods are used to optimize snowfall correction factors through a similar framework adopted in this study, such as in Shrestha, Koike, et al. (2014) and Shrestha, Wang et al. (2014), as this can lead to erroneous snowfall estimates at high elevations.…”

Section: Discussionmentioning

confidence: 98%

“…To overcome this underestimation in Radar‐AMeDAS snowfall, Shrestha, Koike, et al. (2014) and Shrestha, Wang et al. (2014) applied a snowfall correction factor to the snow precipitation estimates provided by Radar‐AMeDAS.…”

Section: Methodsmentioning

confidence: 99%

“…In this study, we set the air temperature threshold for phase partitioning (

{Th}_{\mathrm{s}\mathrm{n}\mathrm{o}\mathrm{w}})

{0}^{\mathrm{o}}\mathrm{C}

based on the findings of Shrestha, Koike, et al. (2014) and Shrestha, Wang et al. (2014), who used a similar modeling and optimization framework for a different river basin in Japan.…”

Section: Methodsmentioning

confidence: 99%

“…Despite the calibration of the radar product by AMeDAS observations, Radar‐AMeDAS is known to underestimate snowfall in mountainous regions of Japan (Motoya et al., 2001; Shrestha, Koike, et al., 2014; Yamamoto et al., 2011). This is because of the reason that there are insufficient AMeDAS gauges in the mountainous regions, leading to poor Radar‐AMeDAS snowfall estimates even after calibration with AMeDAS observations.…”

Section: Methodsmentioning

confidence: 99%

“…Following Shrestha, Koike, et al. (2014) and Shrestha, Wang et al. (2014),

\alpha

should be defined in such a way that both error components are given equal weights.…”

Section: Methodsmentioning

confidence: 99%

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Improving Snow‐Process Modeling by Evaluating Reanalysis Vertical Temperature Profiles Using a Distributed Hydrological Model

et al. 2022

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The use of the Japanese 55‐year Reanalysis (JRA55) vertical temperature profile (VTP) has been suggested in previous studies for estimating gridded air temperatures at high elevations in data‐scarce regions to conduct hydrological simulations. In this study, we investigate and further enhance the applicability of VTP for hydrological simulations through several experimental sets using a distributed hydrological model with improved snow physics (WEB‐DHM‐S) based on water and energy budget to simulate the hydrology of a snowfed river basin in Japan, Kurobe Basin, while simultaneously correcting the snow precipitation estimates through optimization. We examined the simulated discharge, point snow depth, and snow cover, finding that the snow processes were highly sensitive to the individual station used with VTP for extrapolating air temperature to higher elevations, with corrected snowfall estimates differing by as much as 18%. We further compared this with simulations based on linear regression‐based lapse rates derived from station observations (scarce and often distributed at low elevations). It was found that the use of VTP improved discharge simulations but deteriorated point snow depth simulations at low elevations, compared with linear regression‐based lapse rates. A hybrid of the two methods led to better discharge and point snow depth simulations with reasonable snow cover simulations. The study advances the application of snow‐process models in data‐scarce regions by enhancing our understanding about the usage of VTP.

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Section: Discussionmentioning

confidence: 98%

Section: Methodsmentioning

confidence: 99%

“…In this study, we set the air temperature threshold for phase partitioning (

{Th}_{\mathrm{s}\mathrm{n}\mathrm{o}\mathrm{w}})

{0}^{\mathrm{o}}\mathrm{C}

based on the findings of Shrestha, Koike, et al. (2014) and Shrestha, Wang et al. (2014), who used a similar modeling and optimization framework for a different river basin in Japan.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…Following Shrestha, Koike, et al. (2014) and Shrestha, Wang et al. (2014),

\alpha

should be defined in such a way that both error components are given equal weights.…”

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

Improving Snow‐Process Modeling by Evaluating Reanalysis Vertical Temperature Profiles Using a Distributed Hydrological Model

et al. 2022

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

et al. 2019

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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.

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Optimizing Snowfall Correction Factor for Radar-Amedas Precipitation Using Distributed Snow Model (Web-DHM-S) and Modis Snow Cover Data

Cited by 2 publications

References 21 publications

Improving Snow‐Process Modeling by Evaluating Reanalysis Vertical Temperature Profiles Using a Distributed Hydrological Model

Improving Snow‐Process Modeling by Evaluating Reanalysis Vertical Temperature Profiles Using a Distributed Hydrological Model

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

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