Tracing Snowmelt Paths in an Integrated Hydrological Model for Understanding Seasonal Snowmelt Contribution at Basin Scale

Li, Hongyi; Li, Xin; Yang, Dawen; Wang, Jian; Gao, Bing; Pan, Xiaoduo; Zhang, Yanlin; Hao, Xiaohua

doi:10.1029/2019jd030760

Cited by 51 publications

(45 citation statements)

References 58 publications

(91 reference statements)

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“…A new model based on snow hydrological processes and snow radiative transfer processes in cold regions is integrated to simulate the snow spectral albedo with high spectral resolution targeting satellite remote sensing observations. In the new integrated model, we couple the snow radiative transfer process based on the SNICAR model [32] with the snow hydrological process based on the GBEHM [29] and carefully track the transfer paths of solar radiation energy in multilayer snow cover; as a consequence, the simulation of the snow mass-energy balance process is more reasonable, and the limitations of single-model simulations are avoided.…”

Section: Methodsmentioning

confidence: 99%

“…where R snow,direct is the direct solar radiation received from the snow surface, R snow,diffuse is the diffuse solar radiation received from the snow surface, A snow,albedo is the snow albedo, and E snow,effective is the effective radiation of snow. In the original GBEHM [29], the calculations of R snow,direct , R snow,diffuse and A snow,albedo on the right side of the equation all adopt the parameterized scheme of the BATS model without carefully considering the complex radiative transfer process inside the snow layer. In the new integrated model, on the basis of retaining the GBEHM to simulate the snow energy balance process, we introduce the SNICAR model with a more detailed description of the snow radiative transfer process to calculate R snow,direct , R snow,diffuse and A snow,albedo .…”

Section: Integration Of Snow Energy Balance and Snow Radiative Transfmentioning

confidence: 99%

“…Almost all land surface process models can simulate snow albedo only within a simplified band and thus cannot directly simulate continuous snow surface spectral reflectance. For example, the Common Land Model (CoLM) [25], Community Land Model (CLM) [26], Noah-Multiparameterization (MP) model [27], Biosphere Atmosphere Transfer Scheme (BATS) model [28] and Geomorphology-Based Ecohydrological Model (GBEHM) [29] can simulate only visible (VIS) and near-infrared (NIR) snow albedo but cannot directly obtain continuous snow surface reflectance data (Appendix C, Table A1). In contrast, in a forward simulation model, the input parameters are easy to adjust and optimize, the physical process is considered more carefully than in land surface process models, and it is easy to control the simulation direction when simulating the snow radiative transfer process.…”

Section: Introductionmentioning

confidence: 99%

“…The GBEHM is an ecohydrological model specifically developed for the environment of alpine mountainous regions. This model proposes a new method for simulating the snow energy balance process [29]. Compared with other snow hydrological models, the GBEHM can better simulate snow hydrological processes in cold regions and achieves better accuracy when simulating the input parameters (such as the snow cover area, snow depth and snow water equivalent) of the snow radiative transfer process [29].…”

Section: Introductionmentioning

confidence: 99%

“…This model proposes a new method for simulating the snow energy balance process [29]. Compared with other snow hydrological models, the GBEHM can better simulate snow hydrological processes in cold regions and achieves better accuracy when simulating the input parameters (such as the snow cover area, snow depth and snow water equivalent) of the snow radiative transfer process [29]. In addition, when simulating the snow radiative transfer process, the SNICAR model not only considers the detailed dynamic evolution of the snow grain size but also simulates the multiple scattering of multilayer snow cover, thereby yielding a better snow albedo simulation accuracy [32].…”

Section: Introductionmentioning

confidence: 99%

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Modeling Snow Surface Spectral Reflectance in a Land Surface Model Targeting Satellite Remote Sensing Observations

Shao

et al. 2020

Remote Sensing

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Snow surface spectral reflectance is very important in the Earth’s climate system. Traditional land surface models with parameterized schemes can simulate broadband snow surface albedo but cannot accurately simulate snow surface spectral reflectance with continuous and fine spectral wavebands, which constitute the major observations of current satellite sensors; consequently, there is an obvious gap between land surface model simulations and remote sensing observations. Here, we suggest a new integrated scheme that couples a radiative transfer model with a land surface model to simulate high spectral resolution snow surface reflectance information specifically targeting multisource satellite remote sensing observations. Our results indicate that the new integrated model can accurately simulate snow surface reflectance information over a large spatial scale and continuous time series. The integrated model extends the range of snow spectral reflectance simulation to the whole shortwave band and can predict snow spectral reflectance changes in the solar spectrum region based on meteorological element data. The kappa coefficients (K) of both the narrowband snow albedo targeting Moderate Resolution Imaging Spectroradiometer (MODIS) data simulated by the new integrated model and the retrieved snow albedo based on MODIS reflectance data are 0.5, and both exhibit good spatial consistency. Our proposed narrowband snow albedo simulation scheme targeting satellite remote sensing observations is consistent with remote sensing satellite observations in time series and can predict narrowband snow albedo even during periods of missing remote sensing observations. This new integrated model is a significant improvement over traditional land surface models for the direct spectral observations of satellite remote sensing. The proposed model could contribute to the effective combination of snow surface reflectance information from multisource remote sensing observations with land surface models.

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Section: Methodsmentioning

confidence: 99%

Section: Integration Of Snow Energy Balance and Snow Radiative Transfmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling Snow Surface Spectral Reflectance in a Land Surface Model Targeting Satellite Remote Sensing Observations

Shao

et al. 2020

Remote Sensing

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Analysis of seasonal snowmelt contribution using a distributed energy balance model for a river basin in the Altai Mountains of northwestern China

Zhang

et al. 2021

Hydrological Processes

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Snowmelt water is a vital freshwater resource in the Altai Mountains of northwestern China. Yet its seasonal hydrological cycle characteristics could change under a warming climate and more rapid spring snowmelt. Here, we simulated snowmelt runoff dynamics in the Kayiertesi River catchment, from 2000 to 2016, by using an improved hydrological distribution model that relied on high-resolution meteorological data acquired from the National Centers for Environmental Prediction (Fnl-NCEP) that were downscaled using the Weather Research Forecasting model. Its predictions were compared to observed runoff data, which confirmed the simulations' reliability. Our results show the model performed well, in general, given its daily validation Nash-Sutcliffe efficiency (NSE) of 0.62 (from 2013 to 2015) and a monthly NSE score of 0.68 (from 2000 to 2010) for the studied river basin of the Altai Mountains. In this river basin catchment, snowfall accounted for 64.1% of its precipitation and snow evaporation for 49.8% of its total evaporation, while snowmelt runoff constituted 29.3% of the annual runoff volume. Snowmelt's contribution to runoff in the Altai Mountains can extend into non-snow days because of the snowmelt water retained in soils. From 2000 to 2016, the snow-to-rain ratio decreased rapidly, however, the snowmelt contribution remained relatively stable in the study region. Our findings provide a sound basis for making snowmelt runoff predictions, which could be used prevent snowmelt-induced flooding, as well as a generalizable approach applicable to other remote, high-elevation locations where high-density, long-term observational data are currently lacking. How snowmelt contributes to water dynamics and resources in cold regions is garnering greater attention. Our proposed model is thus timely perhaps, enabling more comprehensive assessments of snowmelt contributions to hydrological processes in those alpine regions characterized by seasonal snow cover.

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Identifying river water sources using end‐member mixing analysis in a subtropical monsoon basin China

Xiao

Shi

et al. 2023

Hydrological Processes

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Pressure on river water resources may increase continuously under global climate change and the increasing intensity of human activity, and the identification of river water sources is needed to maintain water resource sustainability. In this study, river water sources were studied using hydrochemical tracers (i.e. the major ions) and end‐member mixing analysis (EMMA) during two hydrological years, in the subtropical monsoon basin of the Xiangjiang River in the south‐central China. Various water types including precipitation, river water, and mountain creek water, were collected in the basin at a high frequency. The results of EMMA showed that the average contributions from the direct input of precipitation, near‐surface runoff (represented by mountain creek water), and groundwater (represented by river water sampled during the base flow period) were 28 ± 24%, 49 ± 24% and 23 ± 16%, respectively. The contributions from these water sources to the river water varied greatly within the year, which may indicate a shift in the major flow pathways from direct precipitation input and near‐surface flow pathways in the spring rainy months (e.g. from March to May), to a deeper flow pathway in the dry or rainless months (e.g. January, February, July and December). The shift of the major flow pathways may be due to the factors such as the seasonality of precipitation, evapotranspiration and soil wetness conditions. Groundwater and near‐subsurface runoff dominated the river water and the direct input of precipitation was negligible during the dry or rainless months, and consequently, the river water chemistry was less diluted and more influenced by water‐soil/rock interactions and anthropogenic pollution during these months. The creek water contribution to the river water was relatively high in summer, especially in June and July 2021, which may indicate the substantial inter‐seasonal carryover of precipitation that contributed to the river water. With the projected increased frequency of extreme weather events such as seasonal drought, streamflow in the subtropical monsoon regions may experience greater recharge by deeper flow pathways, with the enhanced role of base flow in regulating streamflow, and there may be an increased risk to the water resource security under the condition of the decreased river dilution capacity and increased anthropogenic pollution.

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Tracing Snowmelt Paths in an Integrated Hydrological Model for Understanding Seasonal Snowmelt Contribution at Basin Scale

Cited by 51 publications

References 58 publications

Modeling Snow Surface Spectral Reflectance in a Land Surface Model Targeting Satellite Remote Sensing Observations

Modeling Snow Surface Spectral Reflectance in a Land Surface Model Targeting Satellite Remote Sensing Observations

Analysis of seasonal snowmelt contribution using a distributed energy balance model for a river basin in the Altai Mountains of northwestern China

Identifying river water sources using end‐member mixing analysis in a subtropical monsoon basin China

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