scholarly journals Assimilation of MODIS snow cover area data in a distributed hydrological model

2011 ◽  
Vol 8 (1) ◽  
pp. 1329-1364 ◽  
Author(s):  
G. Thirel ◽  
P. Salamon ◽  
P. Burek ◽  
M. Kalas
Keyword(s):  
Particle Filter ◽  
Snow Cover ◽  
Hydrological Model ◽  
Snow Water Equivalent ◽  
Water Cycle ◽  
Research Centre ◽  
Distributed Hydrological Model ◽  
Joint Research ◽  
Snow Cover Area ◽  
Depletion Curve

Abstract. Snow is an important component of the water cycle and its estimation in hydrological models is of great importance concerning snow melting flood events simulations and forecasting. The LISFLOOD model is a spatially distributed hydrological model designed at the Joint Research Centre for large European river basins. It is used for a variety of applications including flood forecasting and assessing the effects of land use change and climate change. In order to improve the streamflow simulations of this model, especially with respect to snowmelt induced floods, the assimilation of Snow Cover Area (SCA) has been evaluated in this study. For this purpose daily 420 m-resolution MODIS satellital SCA data have been used, which were then converted in Snow Water Equivalent (SWE) using a Snow Depletion Curve. Tests were performed over the Morava basin, a tributary of the Danube, for a period of almost three years. Two data assimilation techniques, the Ensemble Kalman Filter (EnKF) and the particle filter, were compared, for assimilating the MODIS composites of SCA every seven days. Two approaches were tested, in which the SWE of the model was adjusted either using three altitudinal-based zones or seven sub-basins-based zones. These experiments showed the improvement of the SWE of the model when compared with MODIS-derived snow for both the EnKF and the particle filter. However, on average only the particle filter improved the discharge simulation, because the EnKF imposed too important water balance modifications, which deteriorated the simulation of the discharges during the snow melt periods.

scholarly journals Assimilation of MODIS Snow Cover Area Data in a Distributed Hydrological Model Using the Particle Filter

2013 ◽  
Vol 5 (11) ◽  
pp. 5825-5850 ◽  
Author(s):  
Guillaume Thirel ◽  
Peter Salamon ◽  
Peter Burek ◽  
Milan Kalas
Keyword(s):  
Particle Filter ◽  
Snow Cover ◽  
Hydrological Model ◽  
Distributed Hydrological Model ◽  
Snow Cover Area ◽  
Area Data

scholarly journals Modeling the Spatial Distribution of Snow Cover in the Dudhkoshi Region of the Nepal Himalayas

2012 ◽  
Vol 13 (1) ◽  
pp. 204-222 ◽  
Author(s):  
Maheswor Shrestha ◽  
Lei Wang ◽  
Toshio Koike ◽  
Yongkang Xue ◽  
Yukiko Hirabayashi
Keyword(s):  
Spatial Distribution ◽  
Snow Cover ◽  
Land Surface ◽  
Snow Depth ◽  
Hydrological Model ◽  
Lapse Rate ◽  
Distributed Hydrological Model ◽  
Snow Cover Area ◽  
Temperature Lapse Rate ◽  
Snow Cover Extent

Abstract In this study, a distributed biosphere hydrological model with three-layer energy-balance snow physics [an improved version of the Water and Energy Budget–based Distributed Hydrological Model (WEB-DHM-S)] is applied to the Dudhkoshi region of the eastern Nepal Himalayas to estimate the spatial distribution of snow cover. Simulations are performed at hourly time steps and 1-km spatial resolution for the 2002/03 snow season during the Coordinated Enhanced Observing Period (CEOP) third Enhanced Observing Period (EOP-3). Point evaluations (snow depth and upward short- and longwave radiation) at Pyramid (a station of the CEOP Himalayan reference site) confirm the vertical-process representations of WEB-DHM-S in this region. The simulated spatial distribution of snow cover is evaluated with the Moderate Resolution Imaging Spectroradiometer (MODIS) 8-day maximum snow-cover extent (MOD10A2), demonstrating the model’s capability to accurately capture the spatiotemporal variations in snow cover across the study area. The qualitative pixel-to-pixel comparisons for the snow-free and snow-covered grids reveal that the simulations agree well with the MODIS data to an accuracy of 90%. Simulated nighttime land surface temperatures (LST) are comparable to the MODIS LST (MOD11A2) with mean absolute error of 2.42°C and mean relative error of 0.77°C during the study period. The effects of uncertainty in air temperature lapse rate, initial snow depth, and snow albedo on the snow-cover area (SCA) and LST simulations are determined through sensitivity runs. In addition, it is found that ignoring the spatial variability of remotely sensed cloud coverage greatly increases bias in the LST and SCA simulations. To the authors’ knowledge, this work is the first to adopt a distributed hydrological model with a physically based multilayer snow module to estimate the spatial distribution of snow cover in the Himalayan region.

scholarly journals Variation of snow cover in the Nyang River basin of southeastern Tibetan Plateau, China

2021 ◽  
Author(s):  
Rui Zhang ◽  
Zongxue Xu ◽  
Depeng Zuo ◽  
Chunguang Ban
Keyword(s):  
Snow Cover ◽  
River Basin ◽  
Snow Water Equivalent ◽  
Global Climate ◽  
Remote Sensing Data ◽  
Significant Negative Correlation ◽  
Spatial And Temporal Variation ◽  
Snow Cover Area ◽  
Northern Edge ◽  
Slight Upward Trend

Abstract Snow cover is highly sensitive to global climate change and strongly influences the climate at global and regional scales. Because of limited in situ observations, snow cover dynamics in the Nyang River basin (NRB) have been examined in few studies. Five snow cover indices derived from observation and remote sensing data from 2000 to 2018 were used to investigate the spatial and temporal variation of snow cover in the NRB. There was clear seasonality in the snow cover throughout the entire basin. The maximum snow-covered area was 8,751.35 km2, about 50% of the total basin area, and occurred in March. The maximum snow depth (SD) was 5.35 cm and was found at the northern edge of the middle reaches of the basin. Snow cover frequency, SD, and fraction of snow cover area increased with elevation. The decrease in SD was the most marked in the elevation range of 5,000–6,000 m. Above 6,000 m, the snow water equivalent showed a slight upward trend. There was a significant negative correlation between snow cover and temperature. The results of this study could improve our understanding of changes in snow cover in the NRB from multivariate perspectives. It is better for water resources management.

scholarly journals Sediment transport modelling in a distributed physically based hydrological catchment model

2010 ◽  
Vol 7 (5) ◽  
pp. 7591-7631 ◽  
Author(s):  
M. Konz ◽  
M. Chiari ◽  
S. Rimkus ◽  
J. M. Turowski ◽  
P. Molnar ◽  
...  
Keyword(s):  
Sediment Transport ◽  
Hydrological Model ◽  
Transport Model ◽  
Water Cycle ◽  
Transport Processes ◽  
Distributed Hydrological Model ◽  
Water Fluxes ◽  
Transport Modelling ◽  
Erosion Processes ◽  
Grid Level

Abstract. Sediment transport and erosion processes in channels are important components of water induced natural hazards in alpine environments. A distributed hydrological model, TOPKAPI, has been developed to support continuous simulations of river bed erosion and deposition processes. The hydrological model simulates all relevant components of the water cycle and non-linear reservoir methods are applied for water fluxes in the soil, on the surface and in the channel. The sediment transport simulations are performed on a sub-grid level, which allows for a better discretization of the channel geometry, whereas water fluxes are calculated on the grid level in order to be CPU efficient. Flow resistance due to macro roughness is considered in the simulation of sediment transport processes. Several transport equations as well as the effects of armour layers on the transport threshold discharge are considered. The advantage of this approach is the integrated simulation of the entire water balance combined with hillslope-channel coupled erosion and transport simulation. The comparison with the modelling tool SETRAC and with LiDAR based reconstructed sediment transport rates demonstrates the reliability of the modelling concept. The modelling method is very fast and of comparable accuracy to the more specialised sediment transport model SETRAC.

scholarly journals Using Air Temperature to Quantitatively Predict the MODIS Fractional Snow Cover Retrieval Errors over the Continental United States

2014 ◽  
Vol 15 (2) ◽  
pp. 551-562 ◽  
Author(s):  
Jiarui Dong ◽  
Mike Ek ◽  
Dorothy Hall ◽  
Christa Peters-Lidard ◽  
Brian Cosgrove ◽  
...  
Keyword(s):  
United States ◽  
Snow Cover ◽  
Air Temperature ◽  
Snow Water Equivalent ◽  
Probability Of Detection ◽  
Joint Analysis ◽  
Snow Cover Area ◽  
Fractional Snow Cover ◽  
Retrieval Errors

Abstract Understanding and quantifying satellite-based, remotely sensed snow cover uncertainty are critical for its successful utilization. The Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover errors have been previously recognized to be associated with factors such as cloud contamination, snowpack grain sizes, vegetation cover, and topography; however, the quantitative relationship between the retrieval errors and these factors remains elusive. Joint analysis of the MODIS fractional snow cover (FSC) from Collection 6 (C6) and in situ air temperature and snow water equivalent measurements provides a unique look at the error structure of the MODIS C6 FSC products. Analysis of the MODIS FSC dataset over the period from 2000 to 2005 was undertaken over the continental United States (CONUS) with an extensive observational network. When compared to MODIS Collection 5 (C5) snow cover area, the MODIS C6 FSC product demonstrates a substantial improvement in detecting the presence of snow cover in Nevada [30% increase in probability of detection (POD)], especially in the early and late snow seasons; some improvement over California (10% POD increase); and a relatively small improvement over Colorado (2% POD increase). However, significant spatial and temporal variations in accuracy still exist, and a proxy is required to adequately predict the expected errors in MODIS C6 FSC retrievals. A relationship is demonstrated between the MODIS FSC retrieval errors and temperature over the CONUS domain, captured by a cumulative double exponential distribution function. This relationship is shown to hold for both in situ and modeled daily mean air temperature. Both of them are useful indices in filtering out the misclassification of MODIS snow cover pixels and in quantifying the errors in the MODIS C6 product for various hydrological applications.

scholarly journals Sediment transport modelling in a distributed physically based hydrological catchment model

2011 ◽  
Vol 15 (9) ◽  
pp. 2821-2837 ◽  
Author(s):  
M. Konz ◽  
M. Chiari ◽  
S. Rimkus ◽  
J. M. Turowski ◽  
P. Molnar ◽  
...  
Keyword(s):  
Sediment Transport ◽  
Hydrological Model ◽  
Transport Model ◽  
Water Cycle ◽  
Ground Surface ◽  
Distributed Hydrological Model ◽  
Water Fluxes ◽  
Transport Modelling ◽  
Erosion Processes ◽  
Grid Level

Abstract. Bedload sediment transport and erosion processes in channels are important components of water induced natural hazards in alpine environments. A raster based distributed hydrological model, TOPKAPI, has been further developed to support continuous simulations of river bed erosion and deposition processes. The hydrological model simulates all relevant components of the water cycle and non-linear reservoir methods are applied for water fluxes in the soil, on the ground surface and in the channel. The sediment transport simulations are performed on a sub-grid level, which allows for a better discretization of the channel geometry, whereas water fluxes are calculated on the grid level in order to be CPU efficient. Several transport equations as well as the effects of an armour layer on the transport threshold discharge are considered. Flow resistance due to macro roughness is also considered. The advantage of this approach is the integrated simulation of the entire basin runoff response combined with hillslope-channel coupled erosion and transport simulation. The comparison with the modelling tool SETRAC demonstrates the reliability of the modelling concept. The devised technique is very fast and of comparable accuracy to the more specialised sediment transport model SETRAC.

scholarly journals Impact of model structure on flow simulation and hydrological realism: from a lumped to a semi-distributed approach

2017 ◽  
Vol 21 (8) ◽  
pp. 3937-3952 ◽  
Author(s):  
Federico Garavaglia ◽  
Matthieu Le Lay ◽  
Fréderic Gottardi ◽  
Rémy Garçon ◽  
Joël Gailhard ◽  
...  
Keyword(s):  
Hydrological Model ◽  
Snow Water Equivalent ◽  
Flow Simulation ◽  
Evaluation Framework ◽  
Model Structure ◽  
Runoff Simulation ◽  
Snow Cover Area ◽  
Mountainous Regions ◽  
Distributed Approach ◽  
The Impact

Abstract. Model intercomparison experiments are widely used to investigate and improve hydrological model performance. However, a study based only on runoff simulation is not sufficient to discriminate between different model structures. Hence, there is a need to improve hydrological models for specific streamflow signatures (e.g., low and high flow) and multi-variable predictions (e.g., soil moisture, snow and groundwater). This study assesses the impact of model structure on flow simulation and hydrological realism using three versions of a hydrological model called MORDOR: the historical lumped structure and a revisited formulation available in both lumped and semi-distributed structures. In particular, the main goal of this paper is to investigate the relative impact of model equations and spatial discretization on flow simulation, snowpack representation and evapotranspiration estimation. Comparison of the models is based on an extensive dataset composed of 50 catchments located in French mountainous regions. The evaluation framework is founded on a multi-criterion split-sample strategy. All models were calibrated using an automatic optimization method based on an efficient genetic algorithm. The evaluation framework is enriched by the assessment of snow and evapotranspiration modeling against in situ and satellite data. The results showed that the new model formulations perform significantly better than the initial one in terms of the various streamflow signatures, snow and evapotranspiration predictions. The semi-distributed approach provides better calibration–validation performance for the snow cover area, snow water equivalent and runoff simulation, especially for nival catchments.

scholarly journals Incorporation of satellite-derived snow-cover area in spatial snowmelt modeling for a large area: determination of a gridded degree-day factor

2013 ◽  
Vol 54 (62) ◽  
pp. 205-213 ◽  
Author(s):  
Yoshihiro Asaoka ◽  
Yuji Kominami
Keyword(s):  
Snow Cover ◽  
Snow Water Equivalent ◽  
Sensible Heat Flux ◽  
Spatial Uncertainty ◽  
Large Area ◽  
Snow Cover Area ◽  
Degree Day ◽  
Snow Water ◽  
Snowmelt Modeling

AbstractSpatial degree-day factors (DDFs) are required for spatial snowmelt modeling over large areas by the degree-day method. We propose a method to obtain DDFs by incorporating snow disappearance dates (SDDs), derived from 10 day composites of Satellite Pour l’Observation de la Terre (SPOT)/VEGETATION data, into the degree-day method. This approach allowed determination of DDFs for each gridpoint so as to better reflect regional characteristics than use of spatially constant DDFs obtained from point measurements. Simulations at six observation sites successfully accounted for variations in snow water equivalent (SWE), even at elevations different from the closest measurement site. These results suggest that incorporating satellite-derived SDDs into the degree-day method decreases spatial uncertainty compared with the use of spatially constant DDFs. Application of our method to Japanese cold regions revealed that gridded DDFs were negatively correlated with accumulated positive degree-days (APDDs) and were high only when APDDs were low. These results imply that high DDFs resulted from the dominant contribution of solar radiation to snowmelt at low temperatures and that low DDFs resulted from a relatively high contribution of sensible heat flux at high temperatures. The proposed method seems to adequately account for the main energetic components of snowmelt during the snow-cover season over large areas.

scholarly journals Improving the snow physics of WEB-DHM and its point evaluation at the SnowMIP sites

2010 ◽  
Vol 14 (12) ◽  
pp. 2577-2594 ◽  
Author(s):  
M. Shrestha ◽  
L. Wang ◽  
T. Koike ◽  
Y. Xue ◽  
Y. Hirabayashi
Keyword(s):  
Snow Depth ◽  
Hydrological Model ◽  
Snow Water Equivalent ◽  
Phase 1 ◽  
Forest Site ◽  
Distributed Hydrological Model ◽  
Snowmelt Runoff ◽  
Runoff Generation ◽  
Basin Scale ◽  
Snow Physics

Abstract. In this study, the snow physics of a distributed biosphere hydrological model, referred to as the Water and Energy Budget based Distributed Hydrological Model (WEB-DHM) is significantly improved by incorporating the three-layer physically based energy balance snowmelt model of Simplified Simple Biosphere 3 (SSiB3) and the Biosphere-Atmosphere Transfer Scheme (BATS) albedo scheme. WEB-DHM with improved snow physics is hereafter termed WEB-DHM-S. Since the in-situ observations of spatially-distributed snow variables with high resolution are currently not available over large regions, the new distributed system (WEB-DHM-S) is at first rigorously tested with comprehensive point measurements. The stations used for evaluation comprise the four open sites of the Snow Model Intercomparison Project (SnowMIP) phase 1 with different climate characteristics (Col de Porte in France, Weissfluhjoch in Switzerland, Goose Bay in Canada and Sleepers River in USA) and one open/forest site of the SnowMIP phase 2 (Hitsujigaoka in Japan). The comparisons of the snow depth, snow water equivalent, surface temperature, snow albedo and snowmelt runoff at the SnowMIP1 sites reveal that WEB-DHM-S, in general, is capable of simulating the internal snow process better than the original WEB-DHM. Sensitivity tests (through incremental addition of model processes) are performed to illustrate the necessity of improvements over WEB-DHM and indicate that both the 3-layer snow module and the new albedo scheme are essential. The canopy effects on snow processes are studied at the Hitsujigaoka site of the SnowMIP2 showing that the snow holding capacity of the canopy plays a vital role in simulating the snow depth on ground. Through these point evaluations and sensitivity studies, WEB-DHM-S has demonstrated the potential to address basin-scale snow processes (e.g., the snowmelt runoff), since it inherits the distributed hydrological framework from the WEB-DHM (e.g., the slope-driven runoff generation with a grid-hillslope scheme, and the flow routing in the river network).

scholarly journals The role of liquid water percolation representation to estimate snow water equivalent in a Mediterranean mountain region (Mount Lebanon)

2019 ◽  
Author(s):  
Abbas Fayad ◽  
Simon Gascoin
Keyword(s):  
Snow Cover ◽  
Liquid Water ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Mountain Regions ◽  
Snow Cover Area ◽  
Water Percolation ◽  
Snow Water ◽  
Snow Model ◽  
Snow Mass

Abstract. In many Mediterranean mountain regions, the seasonal snowpack is an essential yet poorly known water resource. Here, we examine, for the first time, the spatial distribution and evolution of the snow water equivalent (SWE) during three snow seasons (2013–2016) in the coastal mountains of Lebanon. We run SnowModel (Liston and Elder, 2006a), a spatially-distributed, process-based snow model, at 100 m resolution forced by new automatic weather station (AWS) data in three snow-dominated basins of Mount Lebanon. We evaluate a recent upgrade of the liquid water percolation scheme in SnowModel, which was introduced to improve the simulation of the snow water equivalent (SWE) and runoff in warm maritime regions. The model is evaluated against continuous snow depth and snow albedo observations at the AWS, manual SWE measurements, and MODIS snow cover area between 1200 m and 3000 m a.s.l.. The results show that the new percolation scheme yields better performance especially in terms of SWE but also in snow depth and snow cover area. Over the simulation period between 2013 and 2016, the maximum snow mass was reached between December and March. Peak mean SWE (above 1200 m a.s.l.) changed significantly from year to year in the three study catchments with values ranging between 73 mm and 286 mm we (RMSE between 160 and 260 mm w.e.). We suggest that the major sources of uncertainty in simulating the SWE, in this warm Mediterranean climate, can be attributed to forcing error but also to our limited understanding of the separation between rain and snow at lower-elevations, the transient snow melt events during the accumulation season, and the high-variability of snow depth patterns at the sub-pixel scale due to the wind-driven blown-snow redistribution into karstic features and sinkholes. Yet, the use of a process-based snow model with minimal requirements for parameter estimation provides a basis to simulate snow mass SWE in non-monitored catchments and characterize the contribution of snowmelt to the karstic groundwater recharge in Lebanon. While this research focused on three basins in the Mount Lebanon, it serves as a case study to highlight the importance of wet snow processes to estimate SWE in Mediterranean mountain regions.

Sign in / Sign up

Export Citation Format

Share Document