Evaluation and bias correction of SNODAS snow water equivalent (SWE) for streamflow simulation in eastern Canadian basins (original) (raw)
Related papers
Journal of Geophysical Research, 2003
This is the second part of a study on the cold season process modeling in the North American Land Data Assimilation System (NLDAS). The first part concentrates on the assessment of model simulated snow cover extent. In this second part, the focus is on the evaluation of simulated snow water equivalent (SWE) from the four land surface models (Noah, MOSAIC, SAC and VIC) in the NLDAS. Comparisons are made with observational data from the Natural Resources Conservation Service's SNOTEL network for a 3-year retrospective period at selected sites in the mountainous regions of the western United States. All models show systematic negative bias in the maximum annual simulated SWE that is most notable in the Cascade and Sierra Nevada regions where differences can approach 1000mm. Comparison of NLDAS precipitation forcing with SNOTEL measurements revealed a large bias in the NLDAS annual precipitation which may be lower than the SNOTEL record by up to 2000mm at certain stations. Experiments with the VIC model indicated that most of the bias in SWE is removed by scaling the precipitation by a regional factor based on the regression of the NLDAS and SNOTEL precipitation. Individual station errors may be reduced further still using precipitation scaled to the local station SNOTEL record. Furthermore, the NLDAS air temperature is shown to be generally colder in winter months and biased warmer in spring and summer when compared to the SNOTEL record, although the level of bias is regionally dependent. Detailed analysis at a selected station indicate that errors in the air temperature forcing may cause the partitioning of precipitation into snowfall and rainfall by the models to be incorrect and thus may explain some of the remaining errors in the simulated SWE.
Improving the Accuracy of Snow and Hydrological Models Using Assimilation by Snow Depth
Journal of Hydrologic Engineering, 2021
The main aim of this study is to improve the underestimation of spatial snowfall distributions by using assimilation. Although measuring snowfall depth is crucial to evaluate snow water resources and predict snowmelt runoff in spring season, it is difficult to measure snow depth correctly with a gauge because wind speed strongly influences the capture ratio. Snowfall observation errors have a significant influence on the accuracy of hydrological model output. An evaluation of the distributed hydrological model was carried out in the Yoneshiro River Basin in Japan with a modification of the model using snow depth data. To reduce the measurement error using the snowmelt-runoff model, an assimilation policy based on the observed snow depth is included in the snow water equivalent (SWE) model at regular intervals. As a result, the assimilation improves the accuracy of both the snow depth estimation and the snowmelt-runoff simulation. The Nash-Sutcliffe coefficient is improved from 0.63 to 0.86 throughout the year and from 0.21 to 0.82 from March to May. The assimilation of snow depth can contribute to improvement of the hydrological model with higher accuracy compared with direct use of gauge data. Also, how to assimilate snow depth, such as an interval of the assimilation and its applicable timing, is discussed. The model suggested in this study can be helpful for water management-related activities and decision making.
Journal of Geophysical Research, 2003
This is the second part of a study on the cold season process modeling in the North American Land Data Assimilation System (NLDAS). The first part concentrates on the assessment of model simulated snow cover extent. In this second part, the focus is on the evaluation of simulated snow water equivalent (SWE) from the four land surface models (Noah, MOSAIC, SAC and VIC) in the NLDAS. Comparisons are made with observational data from the Natural Resources Conservation Service's SNOTEL network for a 3-year retrospective period at selected sites in the mountainous regions of the western United States. All models show systematic negative bias in the maximum annual simulated SWE that is most notable in the Cascade and Sierra Nevada regions where differences can approach 1000mm. Comparison of NLDAS precipitation forcing with SNOTEL measurements revealed a large bias in the NLDAS annual precipitation which may be lower than the SNOTEL record by up to 2000mm at certain stations.
A methodology for snow data assimilation in a land surface model
Journal of Geophysical Research, 2004
1] Snow cover has a large influence on heat fluxes between the land and atmosphere because of its high albedo and insulating thermal properties. Hence accurate snow representation in coupled land-ocean-atmosphere global climate models has the potential to greatly increase prediction accuracy. To this end, a one-dimensional extended Kalman filter analysis scheme has been developed to assimilate observed snow water equivalent into the NASA Seasonal-to-Interannual Prediction Project (NSIPP) catchment-based land surface model. This study presents the results from a set of data assimilation ''twin'' experiments using an uncoupled version of the land surface model. First, ''true'' snow states are generated by spinning-up the land surface model for 1987 using an observationconstrained version of the European Centre for Medium-Range Weather Forecasts (ECMWF) 15-year Re-Analysis (ERA-15) data set for atmospheric forcing. A degraded 1987 simulation was then made by initializing the model with no snow on 1 January 1987. A third simulation assimilated the synthetically generated snow water equivalent ''observations'' from the true simulation into the degraded simulation once a day. This study illustrates that by assimilating snow water equivalent observations, which are readily available from remote sensing satellites, other state variables (i.e., snow depth and temperature) can be retrieved and effects of poor initial conditions removed. Runoff and atmospheric flux predictions are also improved.
Assimilation of snow covered area information into hydrologic and land-surface models
Advances in Water Resources, 2006
This paper describes a data assimilation method that uses observations of snow covered area (SCA) to update hydrologic model states in a mountainous catchment in Colorado. The assimilation method uses SCA information as part of an ensemble Kalman filter to alter the sub-basin distribution of snow as well as the basin water balance. This method permits an optimal combination of model simulations and observations, as well as propagation of information across model states. Sensitivity experiments are conducted with a fairly simple snowpack/water-balance model to evaluate effects of the data assimilation scheme on simulations of streamflow. The assimilation of SCA information results in minor improvements in the accuracy of streamflow simulations near the end of the snowmelt season. The small effect from SCA assimilation is initially surprising. It can be explained both because a substantial portion of snowmelts before any bare ground is exposed, and because the transition from 100% to 0% snow coverage occurs fairly quickly. Both of these factors are basin-dependent. Satellite SCA information is expected to be most useful in basins where snow cover is ephemeral. The data assimilation strategy presented in this study improved the accuracy of the streamflow simulation, indicating that SCA is a useful source of independent information that can be used as part of an integrated data assimilation strategy.
Hydrological Processes
A 10-km gridded snow water equivalent (SWE) dataset is developed over the Saint-Maurice River basin region in southern Québec from kriging of observed snow survey data for evaluation of SWE products. The gridded SWE dataset covers 1980-2014 and is based on manual gravimetric snow surveys carried out on February 1, March 1, March 15, April 1, and April 15 of each snow season, which captures the annual maximum SWE (SWEM) with a mean interpolation error of ±19%. The dataset is used to evaluate SWEM from a range of sources including satellite retrievals, reanalyses, Canadian regional climate models, and the Canadian Meteorological Centre operational snow depth analysis. We also evaluate a number of solid precipitation datasets to determine their contribution to systematic errors in estimated SWEM. None of the evaluated datasets is able to provide estimates of SWEM that are within operational requirements of ±15% error, and insufficient solid precipitation is determined to be one of the main reasons. The Climate System Forecast Reanalysis is the only dataset where snowfall is sufficiently large to generate SWEM values comparable to observations. Inconsistencies in precipitation are also found to have a strong impact on yearto-year variability in SWEM dataset performance and spread. Version 3.6.1 of the Canadian Land Surface Scheme land surface scheme driven with ERA-Interim output downscaled by Version 5.0.1 of the Canadian Regional Climate Model was the best physically based model at explaining the observed spatial and temporal variability in SWEM (root-mean-square error [RMSE] = 33%) and has potential for lower error with adjusted precipitation. Operational snow products relying on the real-time snow depth observing network performed poorly due to a lack of real-time data and the strong local scale variability of point snow depth observations. The results underscore the need for more effort to be invested in improving solid precipitation estimates for use in snow hydrology applications.
Generating Observation-Based Snow Depletion Curves for Use in Snow Cover Data Assimilation
Geosciences, 2018
Snow depletion curves (SDC) are functions that are used to show the relationship between snow covered area and snow depth or water equivalent. Previous snow cover data assimilation (DA) studies have used theoretical SDC models as observation operators to map snow depth to snow cover fraction (SCF). In this study, a new approach is introduced that uses snow water equivalent (SWE) observations and satellite-based SCF retrievals to derive SDC relationships for use in an Ensemble Kalman filter (EnKF) to assimilate snow cover estimates. A histogram analysis is used to bin the SWE observations, which the corresponding SCF observations are then averaged within, helping to constrain the amount of data dispersion across different temporal and regional conditions. Logarithmic functions are linearly regressed with the binned average values, for two U.S. mountainous states: Colorado and Washington. The SDC-based logarithmic functions are used as EnKF observation operators, and the satellite-bas...
Hydrology and Earth System Sciences Discussions, 2009
A new technique for constructing spatial fields of snow characteristics for runoff simulation and forecasting is presented. The technique incorporates satellite land surface monitoring data and available ground-based hydrometeorological measurements in a physical based snowpack model. The snowpack model provides simulation of tem-5 poral changes of the snow depth, density and water equivalent (SWE), accounting for snow melt, sublimation, refreezing melt water and snow metamorphism processes with a special focus on forest cover effects. The model was first calibrated against available ground-based snow measurements and then was applied to calculate the spatial distribution of snow characteristics using satellite data and interpolated ground-based 10 25 5506 HESSD 6, 5505-5536Abstract Introduction Conclusions References Tables Figures Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion 25 mapping due to snow interception and ability to mask snow cover on the forest floor. A possible way to improve characterization of the snow cover spatial distribution and temporal variability consists in coupling satellite data with ground-based hydromete-5507 HESSD 6, 5505-5536Abstract 20 5511 HESSD 6, 5505-5536
Modelling snow water equivalent and spring runoff in a boreal watershed, James Bay, Canada
Hydrological Processes, 2013
The hydrology of boreal regions is strongly influenced by seasonal snow accumulation and melt. In this study, we compare simulations of snow water equivalent (SWE) and streamflow by using the hydrological model HYDROTEL with two contrasting approaches for snow modelling: a mixed degree-day/energy balance model (small number of inputs, but several calibration parameters needed) and the thermodynamic model CROCUS (large number of inputs, but no calibration parameter needed). The study site, in Northern Quebec, Canada was equipped with a ground-based gamma ray sensor measuring the SWE continuously for 5 years in a small forest clearing. The first simulation of CROCUS showed a tendency to underestimate SWE, attributable to bias in the meteorological inputs. We found that it was appropriate to use a threshold of 2°C to separate rain and snow. We also applied a correction to account for snowfall undercatch by the precipitation gauge. After these modifications to the input dataset, we noticed that CROCUS clearly overestimated the SWE, likely as a result of not including loss in SWE because of blowing snow sublimation and relocation. To correct this, we included into CROCUS a simple parameterisation effective after a certain wind speed threshold, after which the thermodynamic model performed much better than the traditional mixed degree-day/energy balance model. HYDROTEL was then used to simulate streamflow with both snow models. With CROCUS, the main peak flow could be captured, but the second peak because of delayed snowmelt from forested areas could not be reproduced due to a lack of sub-canopy radiation data to feed CROCUS. Despite the relative homogeneity of the boreal landscape, data inputs from each land cover type are needed to generate satisfying simulation of the spring runoff.