scholarly journals Verification of the multi-layer SNOWPACK model with different water transport schemes

2015 ◽  
Vol 9 (2) ◽  
pp. 2655-2707 ◽  
Author(s):  
N. Wever ◽  
L. Schmid ◽  
A. Heilig ◽  
O. Eisen ◽  
C. Fierz ◽  
...  
Keyword(s):  
Water Transport ◽  
Preferential Flow ◽  
Snow Water Equivalent ◽  
Temporal Evolution ◽  
Winter Season ◽  
Model Performance ◽  
Water Dynamics ◽  
Richards Equation ◽  
Snow Density ◽  
Data Set

Abstract. The widely-used detailed SNOWPACK model has undergone constant development over the years. A notable recent extension is the introduction of a Richards Equation (RE) solver as an alternative for the bucket-type approach for describing water transport in the snow and soil layers. In addition, continuous updates of snow settling and new snow density parametrisations have changed model behaviour. This study presents a detailed evaluation of model performance against a comprehensive multi-year data set from Weissfluhjoch near Davos, Switzerland. The data set is collected by automatic meteorological and snowpack measurements and manual snow profiles. During the main winter season, snow height (RMSE: <4.2 cm), snow water equivalent (SWE, RMSE: <40 mm w.e.), snow temperature distributions (typical deviation with measurements: <1.0 °C) and snow density (typical deviation with observations: <50 kg m−3) as well as their temporal evolution are well simulated in the model and the influence of the two water transport schemes is small. The RE approach reproduces internal differences over capillary barriers but fails to predict enough grain growth since the growth routines have been calibrated using the bucket scheme in the original SNOWPACK model. The agreement in both density and grain size is sufficient to parametrise the hydraulic properties. In the melt season, a more pronounced underestimation of typically 200 mm w.e. in SWE is found. The discrepancies between the simulations and the field data are generally larger than the differences between the two water transport schemes. Nevertheless, the detailed comparison of the internal snowpack structure shows that the timing of internal temperature and water dynamics is adequately and better represented with the new RE approach when compared to the conventional bucket scheme. On the contrary, the progress of the meltwater front in the snowpack as detected by radar and the temporal evolution of the vertical distribution of melt forms in manually observed snow profiles do not support this conclusion. This discrepancy suggests that the implementation of RE partly mimics preferential flow effects.

scholarly journals Verification of the multi-layer SNOWPACK model with different water transport schemes

2015 ◽  
Vol 9 (6) ◽  
pp. 2271-2293 ◽  
Author(s):  
N. Wever ◽  
L. Schmid ◽  
A. Heilig ◽  
O. Eisen ◽  
C. Fierz ◽  
...  
Keyword(s):  
Water Transport ◽  
Preferential Flow ◽  
Snow Water Equivalent ◽  
Temporal Evolution ◽  
Winter Season ◽  
Model Performance ◽  
Water Dynamics ◽  
Richards Equation ◽  
Snow Density ◽  
Data Set

Abstract. The widely used detailed SNOWPACK model has undergone constant development over the years. A notable recent extension is the introduction of a Richards equation (RE) solver as an alternative for the bucket-type approach for describing water transport in the snow and soil layers. In addition, continuous updates of snow settling and new snow density parameterizations have changed model behavior. This study presents a detailed evaluation of model performance against a comprehensive multiyear data set from Weissfluhjoch near Davos, Switzerland. The data set is collected by automatic meteorological and snowpack measurements and manual snow profiles. During the main winter season, snow height (RMSE: < 4.2 cm), snow water equivalent (SWE, RMSE: < 40 mm w.e.), snow temperature distributions (typical deviation with measurements: < 1.0 °C) and snow density (typical deviation with observations: < 50 kg m−3) as well as their temporal evolution are well simulated in the model and the influence of the two water transport schemes is small. The RE approach reproduces internal differences over capillary barriers but fails to predict enough grain growth since the growth routines have been calibrated using the bucket scheme in the original SNOWPACK model. However, the agreement in both density and grain size is sufficient to parameterize the hydraulic properties successfully. In the melt season, a pronounced underestimation of typically 200 mm w.e. in SWE is found. The discrepancies between the simulations and the field data are generally larger than the differences between the two water transport schemes. Nevertheless, the detailed comparison of the internal snowpack structure shows that the timing of internal temperature and water dynamics is adequately and better represented with the new RE approach when compared to the conventional bucket scheme. On the contrary, the progress of the meltwater front in the snowpack as detected by radar and the temporal evolution of the vertical distribution of melt forms in manually observed snow profiles do not support this conclusion. This discrepancy suggests that the implementation of RE partly mimics preferential flow effects.

scholarly journals Modelling liquid water transport in snow under rain-on-snow conditions – considering preferential flow

2017 ◽  
Vol 21 (3) ◽  
pp. 1741-1756 ◽  
Author(s):  
Sebastian Würzer ◽  
Nander Wever ◽  
Roman Juras ◽  
Michael Lehning ◽  
Tobias Jonas
Keyword(s):  
Water Transport ◽  
Liquid Water ◽  
Field Experiments ◽  
Preferential Flow ◽  
Richards Equation ◽  
European Alps ◽  
Wet Snow ◽  
Snow Conditions ◽  
Dual Domain ◽  
Grain Properties

Abstract. Rain on snow (ROS) has the potential to generate severe floods. Thus, precisely predicting the effect of an approaching ROS event on runoff formation is very important. Data analyses from past ROS events have shown that a snowpack experiencing ROS can either release runoff immediately or delay it considerably. This delay is a result of refreeze of liquid water and water transport, which in turn is dependent on snow grain properties but also on the presence of structures such as ice layers or capillary barriers. During sprinkling experiments, preferential flow was found to be a process that critically impacted the timing of snowpack runoff. However, current one-dimensional operational snowpack models are not capable of addressing this phenomenon. For this study, the detailed physics-based snowpack model SNOWPACK is extended with a water transport scheme accounting for preferential flow. The implemented Richards equation solver is modified using a dual-domain approach to simulate water transport under preferential flow conditions. To validate the presented approach, we used an extensive dataset of over 100 ROS events from several locations in the European Alps, comprising meteorological and snowpack measurements as well as snow lysimeter runoff data. The model was tested under a variety of initial snowpack conditions, including cold, ripe, stratified and homogeneous snow. Results show that the model accounting for preferential flow demonstrated an improved overall performance, where in particular the onset of snowpack runoff was captured better. While the improvements were ambiguous for experiments on isothermal wet snow, they were pronounced for experiments on cold snowpacks, where field experiments found preferential flow to be especially prevalent.

scholarly journals Spatiotemporal Characteristics of Snowpack Density in the Mountainous Regions of the Western United States

2008 ◽  
Vol 9 (6) ◽  
pp. 1416-1426 ◽  
Author(s):  
Naoki Mizukami ◽  
Sanja Perica
Keyword(s):  
United States ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Winter Season ◽  
Western United States ◽  
Densification Rate ◽  
Snow Density ◽  
Mountainous Regions ◽  
Spatiotemporal Characteristics ◽  
Snow Water

Abstract Snow density is calculated as a ratio of snow water equivalent to snow depth. Until the late 1990s, there were no continuous simultaneous measurements of snow water equivalent and snow depth covering large areas. Because of that, spatiotemporal characteristics of snowpack density could not be well described. Since then, the Natural Resources Conservation Service (NRCS) has been collecting both types of data daily throughout the winter season at snowpack telemetry (SNOTEL) sites located in the mountainous areas of the western United States. This new dataset provided an opportunity to examine the spatiotemporal characteristics of snowpack density. The analysis of approximately seven years of data showed that at a given location and throughout the winter season, year-to-year snowpack density changes are significantly smaller than corresponding snow depth and snow water equivalent changes. As a result, reliable climatological estimates of snow density could be obtained from relatively short records. Snow density magnitudes and densification rates (i.e., rates at which snow densities change in time) were found to be location dependent. During early and midwinter, the densification rate is correlated with density. Starting in early or mid-March, however, snowpack density increases by approximately 2.0 kg m−3 day−1 regardless of location. Cluster analysis was used to obtain qualitative information on spatial patterns of snowpack density and densification rates. Four clusters were identified, each with a distinct density magnitude and densification rate. The most significant physiographic factor that discriminates between clusters was proximity to a large water body. Within individual mountain ranges, snowpack density characteristics were primarily dependent on elevation.

scholarly journals Large-scale snow data assimilation using a spatialized particle filter: recovering the spatial structure of the particles

2021 ◽  
Author(s):  
Jean Odry ◽  
Marie-Amélie Boucher ◽  
Simon Lachance-Cloutier ◽  
Richard Turcotte ◽  
Pierre-Yves St-Louis
Keyword(s):  
Data Assimilation ◽  
Spatial Structure ◽  
Particle Filter ◽  
Large Scale ◽  
Snow Water Equivalent ◽  
Random Noise ◽  
Winter Season ◽  
Loop Model ◽  
Eastern Canada ◽  
Data Set

Abstract. The use of particle filters for data assimilation is increasingly popular because of its minimal assumptions. Nevertheless, implementing a particle filter over domains of large spatial dimensions remains challenging, as the number of required particles rises exponentially as domain size increases. A common solution to overcome this issue is to localize the particle filter and consider a collection of local applications rather than a single regional one. Although this solution can solve the dimensionality limit, it can also create some spatial discontinuity inside the particles. This issue can become even more problematic when additional data is assimilated. The purpose of this study is to test the possibility of remedying the spatial discontinuities of the particles by locally reordering the particles. We implement a spatialized particle filter to estimate the snow water equivalent (SWE) over a large territory in eastern Canada by assimilating local manual snow survey observations. We apply two reordering strategies based on 1) a simple ascending order sorting and 2) the Schaake Shuffle and evaluate their ability to maintain the spatial structure of the particles. To increase the amount of assimilated data, we investigate the inclusion of a second data set, in which SWE is indirectly estimated from snow depth. The two reordering solutions maintain the spatial structure of the individual particles throughout the winter season, which significantly reduces the random noise in the distribution of the particles and decreases the uncertainty associated with the estimation. The Schaake Shuffle proves to be a better tool for maintaining a realistic spatial structure for all particles, although we also found that sorting provides a simpler and satisfactory solution. The assimilation of the secondary data set improved SWE estimates in ungauged sites when compared with the open-loop model, but we noted no significant improvement when both snow courses and the SR50 data were assimilated.

Changes in snow storages and their impact on occurrence and extremity of runoff caused by rain-on-snow events

2020 ◽  
Author(s):  
Ondrej Hotovy ◽  
Michal Jenicek
Keyword(s):  
Air Temperature ◽  
Snow Water Equivalent ◽  
Hydrological Cycle ◽  
Winter Season ◽  
Model Performance ◽  
Catchment Scale ◽  
The Future ◽  
Snow Water ◽  
The Impact ◽  
Catchment Runoff

&lt;p&gt;Seasonal snowpack significantly influences the catchment runoff and thus represents an important input for the hydrological cycle. Changes in the precipitation distribution and intensity, as well as a shift from snowfall to rain is expected in the future due to climate changes. As a result, rain-on-snow events, which are considered to be one of the main causes of floods in winter and spring, may occur more frequently.&lt;/p&gt;&lt;p&gt;The objective of this study is 1) to evaluate the frequency, inter-annual variability and extremity of rain-on-snow events in the past based on existing measurements and 2) to simulate the effect of predicted increase in air temperature on the occurrence of rain-on-snow events in the future. We selected 59 near-natural mountain catchments in Czechia with significant snow influence on runoff and with available long-time series (&gt;35 years) of daily hydrological and meteorological variables. A semi-distributed conceptual model, HBV-light, was used to simulate the individual components of the water cycle at a catchment scale. The model was calibrated for each of study catchments by using 100 calibration trials which resulted in respective number of optimized parameter sets. The model performance was evaluated against observed runoff and snow water equivalent. Rain-on-snow events definition by threshold values for air temperature, snow depth, rain intensity and snow water equivalent decrease allowed us to analyze inter-annual variations and trends in rain-on-snow events during the study period 1980-2014 and to explain the role of different catchment attributes.&lt;/p&gt;&lt;p&gt;The preliminary results show that a significant change of rain-on-snow events related to increasing air temperature is not clearly evident. Since both air temperature and elevation seem to be an important rain-on-snow drivers, there is an increasing rain-on-snow events occurrence during winter season due to a decrease in snowfall fraction. In contrast, a decrease in total number of events was observed due to the shortening of the period with existing snow cover on the ground. Modelling approach also opened further questions related to model structure and parameterization, specifically how individual model procedures and parameters represent the real natural processes. To understand potential model artefacts might be important when using HBV or similar bucket-type models for impact studies, such as modelling the impact of climate change on catchment runoff.&lt;/p&gt;

scholarly journals Simulating more accurate snow maps for Norway with MCMC parameter estimation method

2014 ◽  
Vol 8 (2) ◽  
pp. 1973-2003 ◽  
Author(s):  
T. M. Saloranta
Keyword(s):  
Snow Water Equivalent ◽  
Probability Distributions ◽  
Estimation Method ◽  
Model Performance ◽  
Simulation Method ◽  
Future Research ◽  
Model Parameters ◽  
Data Set ◽  
Mcmc Simulation ◽  
Snow Model

Abstract. The seNorge snow model produces daily updated maps (1 km × 1 km resolution) of snow conditions for Norway which are used by the national flood, avalanche and landslide forecasting services, among others. The snow model uses gridded observations of daily temperature and precipitation as its input forcing. In this paper the revisions made to the new seNorge snow model code (v.1.1.1) are described, and a systematic model analysis is performed by first revealing the most influential key parameters by the Extended FAST sensitivity analysis and then estimating their probability distributions by the MCMC simulation method, using 565 observations of snow water equivalent (SWE) and snow density (ρ). The MCMC simulation resulted in rather narrow posterior distributions for the four estimated model parameters, and enhanced the model performance and snow map quality significantly, mainly by removing the known significant overestimation biases in SWE and ρ. In the new model version (v.1.1.1) the Nash–Sutcliffe (NS) model performance values are now well positive (NS = 0.61 for SWE and NS = 0.30 for ρ), in contrast to the much lower negative NS-values of the previous model (v.1.1). Moreover, the model evaluation against approximately 400 000 point measurements of snow depth shows improvement in the simulated percentage of "good match"-stations (76–84% before April, and still 65% at the end of April). Future research efforts should focus on decreasing the variability in the model fit with observations (i.e. model precision) by further improvements in the seNorge snow model and its important fundament, the gridded meteorological input data set used as its forcing.

scholarly journals Large-scale analysis of changing frequencies of rain-on-snow events with flood-generation potential

2014 ◽  
Vol 18 (7) ◽  
pp. 2695-2709 ◽  
Author(s):  
D. Freudiger ◽  
I. Kohn ◽  
K. Stahl ◽  
M. Weiler
Keyword(s):  
Central Europe ◽  
Large Scale ◽  
Snow Water Equivalent ◽  
Flood Hazard ◽  
Winter Season ◽  
River Basins ◽  
Data Set ◽  
Entire Study ◽  
Snow Model ◽  
Flood Generation

Abstract. In January 2011 a rain-on-snow (RoS) event caused floods in the major river basins in central Europe, i.e. the Rhine, Danube, Weser, Elbe, Oder, and Ems. This event prompted the questions of how to define a RoS event and whether those events have become more frequent. Based on the flood of January 2011 and on other known events of the past, threshold values for potentially flood-generating RoS events were determined. Consequently events with rainfall of at least 3 mm on a snowpack of at least 10 mm snow water equivalent (SWE) and for which the sum of rainfall and snowmelt contains a minimum of 20% snowmelt were analysed. RoS events were estimated for the time period 1950–2011 and for the entire study area based on a temperature index snow model driven with a European-scale gridded data set of daily climate (E-OBS data). Frequencies and magnitudes of the modelled events differ depending on the elevation range. When distinguishing alpine, upland, and lowland basins, we found that upland basins are most influenced by RoS events. Overall, the frequency of rainfall increased during winter, while the frequency of snowfall decreased during spring. A decrease in the frequency of RoS events from April to May has been observed in all upland basins since 1990. In contrast, the results suggest an increasing trend in the magnitude and frequency of RoS days in January and February for most of the lowland and upland basins. These results suggest that the flood hazard from RoS events in the early winter season has increased in the medium-elevation mountain ranges of central Europe, especially in the Rhine, Weser, and Elbe river basins.

scholarly journals Tree Hydrodynamic Modelling of Soil Plant Atmosphere Continuum (SPAC-3Hpy)

2021 ◽  
Author(s):  
Marcela Silva ◽  
Ashley M. Matheny ◽  
Valentijn R. N. Pauwels ◽  
Dimetre Triadis ◽  
Justine E. Missik ◽  
...  
Keyword(s):  
Water Transport ◽  
Model Performance ◽  
Realistic Model ◽  
Flow In Porous Media ◽  
Richards Equation ◽  
Water Fluxes ◽  
Transport Pathways ◽  
Flow Through ◽  
Model Setup

Abstract. Modelling the water transport along the soil-plant-atmosphere continuum is fundamental to estimating and predicting transpiration fluxes. A tree-hydrodynamic model (SPAC-3Hpy) for the water fluxes across the soil-plant-atmosphere continuum is presented here. The model combines the water transport pathways to one vertical dimension, and assumes that the water flow through the soil, roots, and above-ground xylem can be approximated as a flow in porous media. This results in a system of three partial differential equations resembling the Richardson-Richards equation describing the transport of water through the plant system and with additional terms representing sinks and sources for the transfer of water from to the soil to the roots and from the leaves to the atmosphere. The numerical scheme, developed in Python 3, was tested against exact analytical solutions for steady state and transient conditions using simplified but realistic model parametrizations. The model was also used to simulate a previously published case study where observed transpiration rates were available in order to evaluate model performance. With the same model setup as the published case study, SPAC-3Hpy results were in agreement with observations. Through a rigorous coupling of soil, roots, and hydroactive xylem, SPAC-3Hpy can account for variable capacitance while conserving mass and the continuity of the water potential between these three layers. SPAC-3Hpy provides a ready-to-use open access numerical model for the simulation of water fluxes across the soil-plant-atmosphere continuum.

scholarly journals Investigating ANN architectures and training to estimate snow water equivalent from snow depth

2021 ◽  
Vol 25 (6) ◽  
pp. 3017-3040
Author(s):  
Konstantin F. F. Ntokas ◽  
Jean Odry ◽  
Marie-Amélie Boucher ◽  
Camille Garnaud
Keyword(s):  
Snow Depth ◽  
Regression Models ◽  
Snow Water Equivalent ◽  
Structural Characteristics ◽  
Large Data ◽  
Multilayer Perceptrons ◽  
Snow Density ◽  
General Applicability ◽  
Data Set ◽  
Snow Water

Abstract. Canada's water cycle is driven mainly by snowmelt. Snow water equivalent (SWE) is the snow-related variable that is most commonly used in hydrology, as it expresses the total quantity of water (solid and liquid) stored in the snowpack. Measurements of SWE are, however, expensive and not continuously accessible in real time. This motivates a search for alternative ways of estimating SWE from measurements that are more widely available and continuous over time. SWE can be calculated by multiplying snow depth by the bulk density of the snowpack. Regression models proposed in the literature first estimate snow density and then calculate SWE. More recently, a novel approach to this problem has been developed and is based on an ensemble of multilayer perceptrons (MLPs). Although this approach compared favorably with existing regression models, snow density values at the lower and higher ends of the range remained inaccurate. Here, we improve upon this recent method for determining SWE from snow depth. We show the general applicability of the method through the use of a large data set of 234 779 snow depth–density–SWE records from 2878 nonuniformly distributed sites across Canada. These data cover almost 4 decades of snowfall. First, it is shown that the direct estimation of SWE produces better results than the estimation of snow density, followed by the calculation of SWE. Second, testing several artificial neural network (ANN) structural characteristics improves estimates of SWE. Optimizing MLP parameters separately for each snow climate class gives a greater representation of the geophysical diversity of snow. Furthermore, the uncertainty of snow depth measurements is included for a more realistic estimation. A comparison with commonly used regression models reveals that the ensemble of MLPs proposed here leads to noticeably more accurate estimates of SWE. This study thus shows that delving deeper into ANN theory helps improve SWE estimation.

scholarly journals Evaluation of the snow regime in dynamic vegetation land surface models using field measurements

2014 ◽  
Vol 8 (2) ◽  
pp. 487-502
Author(s):  
E. Kantzas ◽  
S. Quegan ◽  
M. Lomas ◽  
E. Zakharova
Keyword(s):  
Land Surface ◽  
Field Data ◽  
Snow Water Equivalent ◽  
Optimization Techniques ◽  
The Arctic ◽  
Surface Model ◽  
Land Surface Models ◽  
Snow Density ◽  
Data Set ◽  
Surface Models

Abstract. An increasing number of studies have demonstrated significant climatic and ecological changes occurring in the northern latitudes over the past decades. As coupled Earth-system models attempt to describe and simulate the dynamics and complex feedbacks of the Arctic environment, it is important to reduce their uncertainties in short-term predictions by improving the description of both system processes and its initial state. This study focuses on snow-related variables and makes extensive use of a historical data set (1966–1996) of field snow measurements acquired across the extent of the former Soviet Union to evaluate a range of simulated snow metrics produced by several land surface models, most of them embedded in IPCC-standard climate models. We reveal model-specific failings in simulating snowpack properties such as magnitude, inter-annual variability, timings of snow water equivalent and evolution of snow density. We develop novel and model-independent methodologies that use the field snow measurements to extract the values of fresh snow density and snowpack sublimation, and exploit them to assess model outputs. By directly forcing the surface heat exchange formulation of a land surface model with field data on snow depth and snow density, we evaluate how inaccuracies in simulating snow metrics affect soil temperature, thaw depth and soil carbon decomposition. We also show how field data can be assimilated into models using optimization techniques in order to identify model defects and improve model performance.

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