scholarly journals Exploration of the Snow Ablation Process in the Semiarid Region in China by Combining Site-Based Measurements and the Utah Energy Balance Model—A Case Study of the Manas River Basin

Water ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 1058 ◽  
Author(s):  
Yan Liu ◽  
Pu Zhang ◽  
Lei Nie ◽  
Jianhui Xu ◽  
Xinyu Lu ◽  
...  
Keyword(s):  
Time Series ◽  
Energy Balance ◽  
River Basin ◽  
Time Scales ◽  
Snow Depth ◽  
Snow Accumulation ◽  
Semiarid Region ◽  
Snow Density ◽  
Ablation Process ◽  
Manas River Basin

Understanding the snow accumulation and melting process is of great significance for the assessment and regulation of water resources and the prevention of meltwater flooding, especially for the semiarid region in the Manas River Basin. However, the lack of long snow measurement time series in this semiarid region prevents a full understanding of the detailed local-scale snow ablation process. Additionally, the modeling of snow accumulation and melting is challenging due to parameter uncertainty. In this study, the snow ablation process in the Manas River Basin was quantitatively explored with long time-series of 3-h measurements of snow depth, snow density and snow water equivalent (SWE) at the Wulanwusu (WLWS), Hanqiazi (HQZ), and Baiyanggou (BYG) sites. This study explored the ability of the Utah energy balance (UEB) snow accumulation and melt model to simulate SWE, energy flux and water loss in the study area. Furthermore, the uncertainty in the ground surface aerodynamic roughness index zos in the UEB model was also analyzed. The results showed that: (1) noticeable variations in snow depth, SWE and snow density occurred on seasonal and interannual time scales, and variations in melting time and melting ratios occurred on short time scales; (2) a rapid decrease in snow depth did not influence the variations in SWE, and snow melting occurred during all time periods, even winter, which is a typical characteristic of snow accumulation in arid environments; (3) the UEB model accurately simulated the snow ablation processes, including SWE, snow surface temperature, and energy flux, at WLWS, HQZ, and BYG sites; (4) the lowest contribution of net radiation to melting occurred in the piedmont clinoplain, followed by the mountain desert grassland belt and mountain forest belt, whereas the contributions of net turbulence exhibited the opposite pattern; (5) the optimal zos in the UEB model was experimentally determined to be 0.01 m, and the UEB model-simulated SWE based on this value was the most consistent with the measured SWE; and (6) the results may provide theoretical and data foundations for research on the snow accumulation process at the watershed scale.

scholarly journals Modeling Snow Depth and Snow Water Equivalent Distribution and Variation Characteristics in the Irtysh River Basin, China

2021 ◽  
Vol 11 (18) ◽  
pp. 8365
Author(s):  
Liming Gao ◽  
Lele Zhang ◽  
Yongping Shen ◽  
Yaonan Zhang ◽  
Minghao Ai ◽  
...  
Keyword(s):  
Snow Cover ◽  
River Basin ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Snow Accumulation ◽  
Implicit Scheme ◽  
Snow Water ◽  
Irtysh River ◽  
Simulation Results ◽  
The Irtysh River

Accurate simulation of snow cover process is of great significance to the study of climate change and the water cycle. In our study, the China Meteorological Forcing Dataset (CMFD) and ERA-Interim were used as driving data to simulate the dynamic changes in snow depth and snow water equivalent (SWE) in the Irtysh River Basin from 2000 to 2018 using the Noah-MP land surface model, and the simulation results were compared with the gridded dataset of snow depth at Chinese meteorological stations (GDSD), the long-term series of daily snow depth dataset in China (LSD), and China’s daily snow depth and snow water equivalent products (CSS). Before the simulation, we compared the combinations of four parameterizations schemes of Noah-MP model at the Kuwei site. The results show that the rainfall and snowfall (SNF) scheme mainly affects the snow accumulation process, while the surface layer drag coefficient (SFC), snow/soil temperature time (STC), and snow surface albedo (ALB) schemes mainly affect the melting process. The effect of STC on the simulation results was much higher than the other three schemes; when STC uses a fully implicit scheme, the error of simulated snow depth and snow water equivalent is much greater than that of a semi-implicit scheme. At the basin scale, the accuracy of snow depth modeled by using CMFD and ERA-Interim is higher than LSD and CSS snow depth based on microwave remote sensing. In years with high snow cover, LSD and CSS snow depth data are seriously underestimated. According to the results of model simulation, it is concluded that the snow depth and snow water equivalent in the north of the basin are higher than those in the south. The average snow depth, snow water equivalent, snow days, and the start time of snow accumulation (STSA) in the basin did not change significantly during the study period, but the end time of snow melting was significantly advanced.

Time-scale dependent mechanism of atmospheric CO2 concentration drivers of watershed water-energy balance

2021 ◽  
Author(s):  
Jing Zhao
Keyword(s):  
Energy Balance ◽  
River Basin ◽  
Time Scales ◽  
Time Scale ◽  
Hydrological Processes ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
The Wei River Basin ◽  
Balance Dynamics ◽  
The Impact

<p>The elevated atmospheric carbon dioxide concentration (CO<sub>2</sub>), as a key variable linking human activities and climate change, seriously affects the watershed hydrological processes. However, whether and how atmospheric CO<sub>2</sub> influences the watershed water-energy balance dynamics at multiple time scales have not been revealed. Based on long-term hydrometeorological data, the variation of non-stationary parameter n series in the Choudhury's equation in the mainstream of the Wei River Basin (WRB), the Jing River Basin (JRB) and Beiluo River Basin (BLRB), three typical Loess Plateau regions in China, was examined. Subsequently, the Empirical Mode Decomposition method was applied to explore the impact of CO<sub>2</sub> on watershed water-energy balance dynamics at multiple time scales. Results indicate that (1) in the context of warming and drying condition, annual n series in the WRB displays a significantly increasing trend, while that in the JRB and BLRB presents non-significantly decreasing trends; (2) the non-stationary n series was divided into 3-, 7-, 18-, exceeding 18-year time scale oscillations and a trend residual. In the WRB and BLRB, the overall variation of n was dominated by the residual, whereas in the JRB it was dominated by the 7-year time scale oscillation; (3) the relationship between CO<sub>2 </sub>concentration and n series was significant in the WRB except for 3-year time scale. In the JRB, CO<sub>2 </sub>concentration and n series were significantly correlated on the 7- and exceeding 7-year time scales, while in the BLRB, such a significant relationship existed only on the 18- and exceeding 18-year time scales. (4) CO<sub>2</sub>-driven temperature rise and vegetation greening elevated the aridity index and evaporation ratio, thus impacting watershed water-energy balance dynamics. This study provided a deeper explanation for the possible impact of CO<sub>2</sub> concentration on the watershed hydrological processes.</p>

Watershed water-energy balance dynamics and their association with diverse influencing factors at multiple time scales

2020 ◽  
Author(s):  
Shengzhi Huang ◽  
Jing Zhao ◽  
Kang Ren
Keyword(s):  
Energy Balance ◽  
Influencing Factors ◽  
River Basin ◽  
Time Scales ◽  
Time Scale ◽  
Dynamic Change ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Plateau Region ◽  
Balance Dynamics

<p>The Budyko curve is an effective tool for estimating how precipitation (P) partition into evapotranspiration (E) and streamflow (Q). Controlling the shape of the Budyko curve, the Budyko parameter represents the superimposed impact of various periodic factors (including climatic factors, catchment characteristics, teleconnection factors and anthropogenic activities) on the watershed water-energy balance dynamics, and such superimposed impact is not conducive to identifying the driving factors of the dynamic change of Budyko parameter at different time scales, and thus affect the parameter estimation. Here we obtain the dynamic change of Budyko parameter for the Wei River Basin (WRB)-a typical Loess Plateau region in China based on a 11-years moving window, and then adopt the Empirical Mode Decomposition (EMD) method to reveal the relationships between influencing factors and Budyko parameter series at multiple time scales by considering the interplay among different influencing factors. Results indicate that (1) Budyko parameter series are decomposed into 4-, 12-, 20-, exceeding 20-year time scale oscillations and a residual component with an significantly increasing trend in the upstream of the WRB (UWR) and the middle and lower reaches of the WRB (MDWR), a non-significantly decreasing trend in the Jing River Basin (JRB) and Beiluo River Basin (BLRB); (2) by analyzing the residual trend component, evaporation ratio (E/P), soil moisture (SM) and effective irrigated area (EIA) are found to induce the significant increase of parameter in the UWR, whereas that in the MDWR is dominated by baseflow (BF) and Niño 3.4; (3) parameter dynamics at the 4-year time scale is dominated by E/P, aridity index (E<sub>P</sub>/P), BF and SM; BF, PDO and sunspots attribute to the dynamics at 12-year time scale; all the factors except BF and SM contribute to the dynamics at 20- or exceeding 20-year time scales. The results of this study will help identify the connection between watershed water-energy balance dynamics and changing environment at multiple time scales, and also be beneficial for guiding water resources management and ecological development planning on the Loess Plateau region.</p>

Dynamic Snow Distribution Modeling using the Fokker-Planck Equation Approach

2021 ◽  
Author(s):  
Noriaki Ohara
Keyword(s):  
Time Series ◽  
Snow Depth ◽  
Planck Equation ◽  
Snow Accumulation ◽  
Airborne Lidar ◽  
Equation Approach ◽  
Diffusion Type ◽  
Fokker Planck Equation ◽  
Time Space ◽  
Fokker Planck

<p>The Fokker-Planck equation (FPE) describes the time evolution of the distribution function of fluctuating macroscopic variables.  Although the FPE was originally derived for the Brownian motion, this framework can be applied to various physical processes.  In this presentation, applications in the snow accumulation and thaw process, which attributes to considerable spatial and temporal variations, are discussed. It is well known that snow process is a major source of heterogeneity in hydrological systems in high altitude or latitude regions; therefore, better treatment of the snow sub-grid variability is desirable. The main advantage of the FPE approach is that it can dynamically compute the probability density function (PDF) governed by an advection-diffusion type FPE without a prescribed PDF.</p><p>First, a bivariate FPE was derived from point scale process-based governing equations (Ohara et al., 2008). This FPE can express the evolution of the PDF of snow depth and temperature within a finite space, possibly a computational cell or small basin, whose shape is irrelevant. This conceptual model was proven to be effective through comparing to the corresponding Monte-Carlo simulation.  Then, the more realistic single variated FPE model for snow depth was implemented with the snow redistribution and snowmelt rate as the main sources of stochasticity. In this study, several realistic approximations were proposed to compute the time-space covariances describing effects induced by uneven snowmelt and snow redistribution.</p><p>Meanwhile, observed high-resolution snow depth data was analyzed using statistical methods to characterize the sub-grid variability of snow depth, which is essential to validate the FPE model for representing such sub-grid variability.  Airborne light detection and ranging (Lidar) provided the snow depth measurements at 0.5 m resolution over two mountainous areas in southwestern Wyoming, Snowy Range and Laramie Range (He et al., 2019). It was found that PDFs of snow depth tend to be Gaussian distributions in the forest areas. However, due to the no-snow areas effect, mainly caused by snow redistribution and uneven snowmelt, the PDFs are eventually skewed as non-Gaussian distribution.</p><p>The simulated results of the FPE model were validated using the measured time series of snow depth at one site and the spatial distributions of snow depth measured by ground penetrating radar (GPR) and airborne Lidar. The modeled and observed time series of the mean snow depth agreed very well while the simulated PDFs of snow depth within the study area were comparable to the observed PDFs of snow depth by GPR and Lidar (He and Ohara, 2019). Accordingly, the FPE model is capable to capture the main characteristics of the snow sub-grid variability in the nature.</p><p><strong>References</strong></p><p>Ohara, N., Kavvas, M. L., & Chen, Z. Q. (2008). Stochastic upscaling for snow accumulation and melt processes with PDF approach. Journal of Hydrologic Engineering, 13(12), 1103-1118.</p><p>He, S., Ohara, N., & Miller, S. N. (2019). Understanding subgrid variability of snow depth at 1‐km scale using Lidar measurements. Hydrological Processes, 33(11), 1525-1537.</p><p>He, S., & Ohara, N. (2019). Modeling subgrid variability of snow depth using the Fokker‐Planck equation approach. Water Resources Research, 55(4), 3137-3155.</p>

SWE modelling: comparison between different approaches applied to Germany

2021 ◽  
Author(s):  
Fabiana Castino ◽  
Bodo Wichura ◽  
Harald Schellander ◽  
Michael Winkler
Keyword(s):  
Time Series ◽  
Snow Depth ◽  
Cross Validation ◽  
Snow Water Equivalent ◽  
Snow Density ◽  
Design Standards ◽  
Model Based ◽  
Meteorological Observations ◽  
Snow Model ◽  
Leave One Out

<p>The characterization of the snow cover by snow water equivalent (SWE) is fundamental in several environmental applications, e.g., monitoring mountain water resources or defining structural design standards. However, SWE observations are usually rare compared to other snow measurements as snow depth (HS). Therefore, model-based methods have been proposed in past studies for estimating SWE, in particular for short timescales (e.g., daily). In this study, we compare two different approaches for SWE-data modelling. The first approach, based on empirical regression models (ERMs), provides the regional parametrization of the bulk snow density, which can be used to estimate SWE values from HS. In particular, we investigate the performances of four different schemes based on previously developed ERMs of bulk snow density depending on HS, date, elevation, and location. Secondly, we apply the semi-empirical multi-layer Δsnow model, which estimates SWE solely based on snow depth observations. The open source Δsnow model has been recently used for deriving a snow load map for Austria, resulting in an improved Austrian standard. A large dataset of HS and SWE observations collected by the National Weather Service in Germany (DWD) is used for calibrating and validating the models. This dataset consists of daily HS and three-times-a-week SWE observations from in total ~1000 stations operated by DWD over the period from 1950 to 2020. A leave-one-out cross validation is applied to evaluate the performance of the different model approaches. It is based on 185 time series of HS and SWE observations that are representative of the diversity of the regional snow climatology of Germany. Cross validation reveals for all ERMs: 90% of the modelled SWE time series have a root mean square error (RMSE) and a bias lower than 45 kg/m² and 2 kg/m², respectively. The Δsnow model shows the best performance with 90% of the modelled SWE time series having an RMSE lower than 30 kg/m² and bias similar to the ERMs. This comparative study provides new insights on the reliability of model-based methods for estimating SWE values. The results show that the Δsnow model and, to a lower degree, the developed ERMs can provide satisfactory performances even on short timescales. This suggest that these models can be used as reliable alternative to more complex thermodynamic snow models, even more if long-term meteorological observations aside HS are scarce.</p>

scholarly journals Direct Insertion of NASA Airborne Snow Observatory‐Derived Snow Depth Time Series Into the iSnobal Energy Balance Snow Model

2018 ◽  
Vol 54 (10) ◽  
pp. 8045-8063 ◽  
Author(s):  
Andrew R. Hedrick ◽  
Danny Marks ◽  
Scott Havens ◽  
Mark Robertson ◽  
Micah Johnson ◽  
...  
Keyword(s):  
Time Series ◽  
Energy Balance ◽  
Snow Depth ◽  
Snow Model

scholarly journals Technical note: Snow Water Equivalence Estimation (SWEE) Algorithm from Snow Depth Time Series Using a Snow Density Model

2018 ◽  
Author(s):  
Noriaki Ohara ◽  
Siwei He ◽  
Andrew D. Parsekian ◽  
Thijs Kelleners
Keyword(s):  
Time Series ◽  
Snow Depth ◽  
Temperature Sensors ◽  
Technical Note ◽  
Density Model ◽  
Snow Density ◽  
Maintenance Costs ◽  
The Us ◽  
Snow Water ◽  
Weight Sensor

Abstract. Snow water equivalence (SWE) is typically computed from snow weight by the SNOTEL system in the US. However, a snow pillow, the main snow weight sensor used by SNOTEL, requires a large, open, flat area (at least 9 square meters) and substantial maintenance costs. This article presents the snow water equivalence estimation (SWEE) algorithm that estimates the SWE evolution merely from continuous snow depth and temperature measurements using common sensors. The key component is a depth-averaged snow density model that is available in the literature, but is underutilized. Here, we demonstrate that the snow density model can estimate mass exchanges (SWE changes due to snowfall, erosion, deposition, and snowmelt) as well as the SWE. The SWEE algorithm can potentially increase the number of snow monitoring locations because snow depth and temperature sensors are considerably more accessible and economical than snow weighing sensor.

scholarly journals The Measurement Factors in Estimating Snowfall Derived from Snow Cover Surfaces Using Acoustic Snow Depth Sensors

2011 ◽  
Vol 50 (3) ◽  
pp. 681-699 ◽  
Author(s):  
Alexandre P. Fischer
Keyword(s):  
Time Series ◽  
Snow Cover ◽  
Time Scales ◽  
Snow Depth ◽  
Strong Dependence ◽  
Time Interval ◽  
Reasonable Accuracy ◽  
Mathematical Methods ◽  
Depth Sensors ◽  
Ultrasonic Ranging

Abstract At three Canadian test locations during the cold seasons of 2006 and 2007/08, snowfall measurements are derived from changes in the total depth of snow on the ground using multiple Campbell Scientific, Inc., SR50 ultrasonic ranging sensors over very short (minute–hour) time scales. Data analysis reveals that, because of the interplay of numerous essential factors that influence snow cover levels, the measurements exhibit a strong dependence on the time interval between consecutive measurements used to generate the snowfall value. This finding brings into question the reasonable accuracy of snowfall measurements that are derived from the snow cover surface using automated methods over very short time scales. In this study, two mathematical methods are developed to assist in quantifying the magnitude of the snowfall measurement error. From time-series analysis, the suggested characteristics of the snowdrift signal in the snow depth time series is shown by using measurements taken by FlowCapt Snowdrift acoustic sensors. Furthermore, the use of three collocated SR50s shows that repeated snow depth measurements represent three pairwise essentially different time series. These results question the reasonable accuracy of snowfall measurements derived using only a single ultrasonic ranging sensor, especially in cases in which the snow cover is redistributed by the wind and in which snow depth spatial variability is prominent.

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