scholarly journals Evaluating methods for reconstructing large gaps in historic snow depth time series

2021 ◽  
Author(s):  
Johannes Aschauer ◽  
Christoph Marty
Keyword(s):  
Time Series ◽  
Missing Data ◽  
Snow Depth ◽  
Coefficient Of Determination ◽  
Gap Filling ◽  
Suitable Alternative ◽  
Temperature Index ◽  
Distance Weighted ◽  
Snow Model ◽  
Evaluating Methods

Abstract. Historic measurements are often temporally incomplete and may contain longer periods of missing data whereas climatological analyses require continuous measurement records. This is also valid for historic manual snow depth (HS) measurement time series, where even whole winters can be missing in a station record and suitable methods have to be found to reconstruct the missing data. Daily in-situ HS data from 126 nivo-meteorological stations in Switzerland in an altitudinal range of 230 to 2536 m above sea level is used to compare six different methods for reconstructing long gaps in manual HS time series by performing a "leave-one-winter-out" cross-validation in 21 winters at 33 evaluation stations. Synthetic gaps of one winter length are filled with bias corrected data from the best correlated neighboring station (BSC), inverse distance weighted (IDW) spatial interpolation, a weighted normal ratio (WNR) method, Elastic Net (ENET) regression, Random Forest (RF) regression and a temperature index snow model (SM). Methods that use neighboring station data are tested in two station networks with different density. The ENET, RF, SM and WNR methods are able to reconstruct missing data with a coefficient of determination (r2) above 0.8 regardless of the two station networks used. Median RMSE in the filled winters is below 5 cm for all methods. The two annual climate indicators, average snow depth in a winter (HSavg) and maximum snow depth in a winter (HSmax), can be well reproduced by ENET, RF, SM and WNR with r2 above 0.85 in both station networks. For the inter-station approaches, scores for the number of snow days with HS ≥ 1 cm (dHS1) are clearly weaker and except for BCS positively biased with RMSE of 18–33 days. SM reveals the best performance with r2 of 0.93 and RMSE of 15 days for dHS1. Snow depth seems to be a relatively good-natured parameter when it comes to gap filling of HS data with neighboring stations in a climatological use case. However, when station networks get sparse and if the focus is set on dHS1, temperature index snow models can serve as a suitable alternative to classic inter-station gap filling approaches.

scholarly journals Evaluating methods for reconstructing large gaps in historic snow depth time series

2021 ◽  
Vol 10 (2) ◽  
pp. 297-312
Author(s):  
Johannes Aschauer ◽  
Christoph Marty
Keyword(s):  
Time Series ◽  
Missing Data ◽  
Snow Depth ◽  
Coefficient Of Determination ◽  
Gap Filling ◽  
Suitable Alternative ◽  
Temperature Index ◽  
Distance Weighted ◽  
Snow Model ◽  
Evaluating Methods

Abstract. Historic measurements are often temporally incomplete and may contain longer periods of missing data, whereas climatological analyses require continuous measurement records. This is also valid for historic manual snow depth (HS) measurement time series, for which even whole winters can be missing in a station record, and suitable methods have to be found to reconstruct the missing data. Daily in situ HS data from 126 nivo-meteorological stations in Switzerland in an altitudinal range of 230 to 2536 m above sea level are used to compare six different methods for reconstructing long gaps in manual HS time series by performing a “leave-one-winter-out” cross-validation in 21 winters at 33 evaluation stations. Synthetic gaps of one winter length are filled with bias-corrected data from the best-correlated neighboring station (BSC), inverse distance-weighted (IDW) spatial interpolation, a weighted normal ratio (WNR) method, elastic net (ENET) regression, random forest (RF) regression and a temperature index snow model (SM). Methods that use neighboring station data are tested in two station networks with different density. The ENET, RF, SM and WNR methods are able to reconstruct missing data with a coefficient of determination (r2) above 0.8 regardless of the two station networks used. The median root mean square error (RMSE) in the filled winters is below 5 cm for all methods. The two annual climate indicators, average snow depth in a winter (HSavg) and maximum snow depth in a winter (HSmax), can be reproduced by ENET, RF, SM and WNR well, with r2 above 0.85 in both station networks. For the inter-station approaches, scores for the number of snow days with HS>1 cm (dHS1) are clearly weaker and, except for BCS, positively biased with RMSE of 18–33 d. SM reveals the best performance with r2 of 0.93 and RMSE of 15 d for dHS1. Snow depth seems to be a relatively good-natured parameter when it comes to gap filling of HS data with neighboring stations in a climatological use case. However, when station networks get sparse and if the focus is set on dHS1, temperature index snow models can serve as a suitable alternative to classic inter-station gap filling approaches.

scholarly journals A comparison of methods for smoothing and gap filling time series of remote sensing observations – application to MODIS LAI products

2013 ◽  
Vol 10 (6) ◽  
pp. 4055-4071 ◽  
Author(s):  
S. Kandasamy ◽  
F. Baret ◽  
A. Verger ◽  
P. Neveux ◽  
M. Weiss
Keyword(s):  
Time Series ◽  
Missing Data ◽  
Time Course ◽  
Singular Spectrum Analysis ◽  
Gaussian Function ◽  
Gap Filling ◽  
Area Index ◽  
Filling Time ◽  
Moderate Resolution ◽  
Temporal Profiles

Abstract. Moderate resolution satellite sensors including MODIS (Moderate Resolution Imaging Spectroradiometer) already provide more than 10 yr of observations well suited to describe and understand the dynamics of earth's surface. However, these time series are associated with significant uncertainties and incomplete because of cloud cover. This study compares eight methods designed to improve the continuity by filling gaps and consistency by smoothing the time course. It includes methods exploiting the time series as a whole (iterative caterpillar singular spectrum analysis (ICSSA), empirical mode decomposition (EMD), low pass filtering (LPF) and Whittaker smoother (Whit)) as well as methods working on limited temporal windows of a few weeks to few months (adaptive Savitzky–Golay filter (SGF), temporal smoothing and gap filling (TSGF), and asymmetric Gaussian function (AGF)), in addition to the simple climatological LAI yearly profile (Clim). Methods were applied to the MODIS leaf area index product for the period 2000–2008 and over 25 sites showed a large range of seasonal patterns. Performances were discussed with emphasis on the balance achieved by each method between accuracy and roughness depending on the fraction of missing observations and the length of the gaps. Results demonstrate that the EMD, LPF and AGF methods were failing because of a significant fraction of gaps (more than 20%), while ICSSA, Whit and SGF were always providing estimates for dates with missing data. TSGF (Clim) was able to fill more than 50% of the gaps for sites with more than 60% (80%) fraction of gaps. However, investigation of the accuracy of the reconstructed values shows that it degrades rapidly for sites with more than 20% missing data, particularly for ICSSA, Whit and SGF. In these conditions, TSGF provides the best performances that are significantly better than the simple Clim for gaps shorter than about 100 days. The roughness of the reconstructed temporal profiles shows large differences between the various methods, with a decrease of the roughness with the fraction of missing data, except for ICSSA. TSGF provides the smoothest temporal profiles for sites with a % gap > 30%. Conversely, ICSSA, LPF, Whit, AGF and Clim provide smoother profiles than TSGF for sites with a % gap < 30%. Impact of the accuracy and smoothness of the reconstructed time series were evaluated on the timing of phenological stages. The dates of start, maximum and end of the season are estimated with an accuracy of about 10 days for the sites with a % gap < 10% and increases rapidly with the % gap. TSGF provides more accurate estimates of phenological timing up to a % gap < 60%.

scholarly journals Assessing the benefit of snow data assimilation for runoff modeling in Alpine catchments

2016 ◽  
Vol 20 (9) ◽  
pp. 3895-3905 ◽  
Author(s):  
Nena Griessinger ◽  
Jan Seibert ◽  
Jan Magnusson ◽  
Tobias Jonas
Keyword(s):  
Time Series ◽  
Data Assimilation ◽  
Hydrological Model ◽  
Temperature Index ◽  
Degree Day ◽  
Index Approach ◽  
Snow Model ◽  
Alpine Catchments ◽  
Time Invariant ◽  
Snowmelt Time

Abstract. In Alpine catchments, snowmelt is often a major contribution to runoff. Therefore, modeling snow processes is important when concerned with flood or drought forecasting, reservoir operation and inland waterway management. In this study, we address the question of how sensitive hydrological models are to the representation of snow cover dynamics and whether the performance of a hydrological model can be enhanced by integrating data from a dedicated external snow monitoring system. As a framework for our tests we have used the hydrological model HBV (Hydrologiska Byråns Vattenbalansavdelning) in the version HBV-light, which has been applied in many hydrological studies and is also in use for operational purposes. While HBV originally follows a temperature-index approach with time-invariant calibrated degree-day factors to represent snowmelt, in this study the HBV model was modified to use snowmelt time series from an external and spatially distributed snow model as model input. The external snow model integrates three-dimensional sequential assimilation of snow monitoring data with a snowmelt model, which is also based on the temperature-index approach but uses a time-variant degree-day factor. The following three variations of this external snow model were applied: (a) the full model with assimilation of observational snow data from a dense monitoring network, (b) the same snow model but with data assimilation switched off and (c) a downgraded version of the same snow model representing snowmelt with a time-invariant degree-day factor. Model runs were conducted for 20 catchments at different elevations within Switzerland for 15 years. Our results show that at low and mid-elevations the performance of the runoff simulations did not vary considerably with the snow model version chosen. At higher elevations, however, best performance in terms of simulated runoff was obtained when using the snowmelt time series from the snow model, which utilized data assimilation. This was especially true for snow-rich years. These findings suggest that with increasing elevation and the correspondingly increased contribution of snowmelt to runoff, the accurate estimation of snow water equivalent (SWE) and snowmelt rates has gained importance.

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

&lt;p&gt;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 &amp;#916;snow model, which estimates SWE solely based on snow depth observations. The open source &amp;#916;snow 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/m&amp;#178; and 2 kg/m&amp;#178;, respectively. The &amp;#916;snow model shows the best performance with 90% of the modelled SWE time series having an RMSE lower than 30 kg/m&amp;#178; 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 &amp;#916;snow 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.&lt;/p&gt;

Comparing methods for gap filling in historical snow depth time series

2020 ◽  
Author(s):  
Johannes Aschauer ◽  
Mathias Bavay ◽  
Michael Begert ◽  
Christoph Marty
Keyword(s):  
Time Series ◽  
Snow Depth ◽  
Spatial Interpolation ◽  
Synthetic Data ◽  
Learning Approaches ◽  
Temperature Index ◽  
Target Station ◽  
Data Gaps ◽  
Unique Dataset

&lt;p&gt;Switzerland has a unique dataset of long-term manual daily snow depth time series ranging back more than 100 years for some stations. This makes the dataset predestined to be analyzed in a climatological sense. However, there are sometimes shorter (weeks, months) or longer (years) gaps in these manual snow depth series, which hinder a sound climatological analysis and reasonable conclusions. Therefore, we examine different methods for filling data gaps in daily snow depth series. We focus on longer gaps and use different methods of spatial interpolation, temperature index models and machine learning approaches to fill the data gaps. We assess the performance of the different methods by creating synthetic data gaps and set the applicability of the methods in relation to the density of the available neighboring stations, elevation and climatic setting of the target station.&lt;/p&gt;

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 Assessing the benefit of snow data assimilation for runoff modelling in alpine catchments

2016 ◽  
Author(s):  
Nena Griessinger ◽  
Jan Seibert ◽  
Jan Magnusson ◽  
Tobias Jonas
Keyword(s):  
Time Series ◽  
Data Assimilation ◽  
Hydrological Model ◽  
Temperature Index ◽  
Degree Day ◽  
Index Approach ◽  
Snow Model ◽  
Alpine Catchments ◽  
Time Invariant ◽  
Snowmelt Time

Abstract. Snow models have been developed with a wide range of complexity depending on the purpose of application. In alpine catchments, snowmelt often is a major contribution to runoff; therefore, modeling snow processes is important when concerned with flood or drought forecasting, reservoir operation and inland waterway management. In this study, we address the question, whether the performance of a hydrological model can be enhanced by integrating data from a dedicated external snow monitoring system. As a framework for our tests we used the hydrological model HBV (in the version HBV light), which has been applied in many hydrological studies and is also in use for operational purposes. While HBV originally follows a temperature index approach with time-invariant calibrated degree-day factors to represent snowmelt, in this study the HBV model was modified to use snowmelt time series from an external and spatially distributed snow model as model input. The external snow model integrates three-dimensional sequential assimilation of snow monitoring data with a snowmelt model, which is also based on the temperature-index approach but uses a time-variant degree-day factor. The following three variations of this external snow model were applied: a) the full model with assimilation of observational snow data from a dense monitoring network, b) the same snow model but with data assimilation switched off, c) a downgraded version of the same snow model representing snowmelt with a time-invariant degree-day factor. Model runs were conducted for 20 catchments at different elevations within Switzerland for 15 years. Our results show that at low and mid elevations the performance of the runoff simulations did not vary considerably with the snow model version chosen. At higher elevations, however, best performance in terms of simulated runoff was obtained when using the snowmelt time series from the snow model, which utilized data assimilation. This was especially true for snow-rich years. These findings suggest that with increasing elevation and correspondingly increased contribution of snowmelt to runoff, the accurate estimation of snowmelt rates gains importance.

scholarly journals A Methodology for Fitting the Time Series of Snow Depth on the Arctic Sea Ice

2019 ◽  
Vol 36 (8) ◽  
pp. 1449-1462
Author(s):  
Yuzhe Wang ◽  
Haidong Pan ◽  
Daosheng Wang ◽  
Xianqing Lv
Keyword(s):  
Time Series ◽  
Missing Data ◽  
Sea Ice ◽  
Snow Depth ◽  
Spline Interpolation ◽  
Optimal Number ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Earth Observing System ◽  
Arctic Sea

AbstractSnow depth is an important geophysical variable for investigating sea ice and climate change, which can be obtained from satellite data. However, there is a large number of missing data in satellite observations of snow depth. In this study, a methodology, the periodic functions fitting with varying parameter (PFF-VP), is presented to fit the time series of snow depth on Arctic sea ice obtained from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). The time-varying parameters are obtained by the independent point (IP) scheme and cubic spline interpolation. The PPF-VP is validated by experiments in which part of the observations are artificially removed and used to compare with the fitting results. Results indicate that the PPF-VP performs better than three traditional fitting methods, with its fitting results closer to observations and with smaller errors. In the practical experiments, the optimal number of IPs can be determined by only considering the fraction of missing data, particularly the length of the longest gaps in the snow-depth time series. All the experimental results indicate that the PPF-VP is a feasible and effective method to fit the time series of snow depth and can provide continuous data of snow depth for further study.

scholarly journals A comparison of methods for smoothing and gap filling time series of remote sensing observations: application to MODIS LAI products

2012 ◽  
Vol 9 (12) ◽  
pp. 17053-17097 ◽  
Author(s):  
S. Kandasamy ◽  
F. Baret ◽  
A. Verger ◽  
P. Neveux ◽  
M. Weiss
Keyword(s):  
Time Series ◽  
Missing Data ◽  
Time Course ◽  
Singular Spectrum Analysis ◽  
Gaussian Function ◽  
Gap Filling ◽  
Area Index ◽  
Temporal Smoothing ◽  
Filling Time ◽  
Temporal Profiles

Abstract. Moderate resolution satellite sensors including MODIS already provide more than 10 yr of observations well suited to describe and understand the dynamics of the Earth surface. However, these time series are incomplete because of cloud cover and associated with significant uncertainties. This study compares eight methods designed to improve the continuity by filling gaps and the consistency by smoothing the time course. It includes methods exploiting the time series as a whole (Iterative caterpillar singular spectrum analysis (ICSSA), empirical mode decomposition (EMD), low pass filtering (LPF) and Whittaker smoother (Whit)) as well as methods working on limited temporal windows of few weeks to few months (Adaptive Savitzky-Golay filter (SGF), temporal smoothing and gap filling (TSGF) and asymmetric Gaussian function (AGF)) in addition to the simple climatological LAI yearly profile (Clim). Methods were applied to MODIS leaf area index product for the period 2000–2008 over 25 sites showing a large range of seasonal patterns. Performances were discussed with emphasis on the balance achieved by each method between accuracy and roughness depending on the fraction of missing observations and the length of the gaps. Results demonstrate that EMD, LPF and AGF methods were failing in case of significant fraction of gaps (%Gap > 20%), while ICSSA, Whit and SGF were always providing estimates for dates with missing data. TSGF (respectively Clim) was able to fill more than 50% of the gaps for sites with more than 60% (resp. 80%) fraction of gaps. However, investigation of the accuracy of the reconstructed values shows that it degrades rapidly for sites with more than 20% missing data, particularly for ICSSA, Whit and SGF. In these conditions, TSGF provides the best performances significantly better than the simple Clim for gaps shorter than about 100 days. The roughness of the reconstructed temporal profiles shows large differences between the several methods, with a decrease of the roughness with the fraction of missing data, except for ICSSA. TSGF provides the smoothest temporal profiles for sites with %Gap > 30%. Conversely, ICSSA, LPF, Whit, AGF and Clim provide smoother profiles than TSGF for sites with %Gap < 30%. Impact of the accuracy and smoothness of the reconstructed time series were evaluated on the timing of phenological stages. The dates of start, maximum and end of the season are estimated with an accuracy of about 10 days for the sites with %Gap < 10% and increases rapidly with %Gap. TSGF provides the more accurate estimates of phenological timing up to %Gap < 60%.

Sign in / Sign up

Export Citation Format

Share Document