Articles | Volume 10, issue 5
https://doi.org/10.5194/tc-10-1947-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/tc-10-1947-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
06 Sep 2016
Research article |
| 06 Sep 2016
A model for the spatial distribution of snow water equivalent parameterized from the spatial variability of precipitation
Norwegian Water Resources and energy Directorate, P.O. Box 5091, Maj.
0301 Oslo, Norway
Ingunn H. Weltzien
Norwegian Water Resources and energy Directorate, P.O. Box 5091, Maj.
0301 Oslo, Norway
Department of Geosciences, University of Oslo, Oslo, Norway
now at: Norconsult AS, P.O. Box 626, 1303, Sandvika, Norway
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21 citations as recorded by crossref.
- Modeling the Snow Depth Variability With a High‐Resolution Lidar Data Set and Nonlinear Terrain Dependency T. Skaugen & K. Melvold 10.1029/2019WR025030
- Simulating the hydrological regime of the snow fed and glaciarised Gilgit Basin in the Upper Indus using global precipitation products and a data parsimonious precipitation-runoff model A. Nazeer et al. 10.1016/j.scitotenv.2021.149872
- Hydrological impacts of climate change on small ungauged catchments – results from a global climate model–regional climate model–hydrologic model chain A. Tsegaw et al. 10.5194/nhess-20-2133-2020
- A dynamic river network method for the prediction of floods using a parsimonious rainfall-runoff model A. Tsegaw et al. 10.2166/nh.2019.003
- Impact of climate change on spatiotemporal patterns of snow hydrology: Conceptual frameworks, machine learning versus nested model M. Besharatifar & M. Nasseri 10.1016/j.pce.2024.103691
- Snow redistribution for the hydrological modeling of alpine catchments D. Freudiger et al. 10.1002/wat2.1232
- A parameter parsimonious approach for catchment scale urban hydrology – Which processes are important? T. Skaugen et al. 10.1016/j.hydroa.2020.100060
- Constraining the HBV model for robust water balance assessments in a cold climate H. Erlandsen et al. 10.2166/nh.2021.132
- Shyft v4.8: a framework for uncertainty assessment and distributed hydrologic modeling for operational hydrology J. Burkhart et al. 10.5194/gmd-14-821-2021
- Rainfall-runoff modeling based on HEC-HMS model: a case study in an area with increased groundwater discharge potential M. Herbei et al. 10.3389/frwa.2024.1474990
- Hydrology influences breeding time in the white-throated dipper A. Nilsson et al. 10.1186/s12898-020-00338-y
- Parameter uncertainty analysis for an operational hydrological model using residual-based and limits of acceptability approaches A. Teweldebrhan et al. 10.5194/hess-22-5021-2018
- Spatial Distribution and Scaling Properties of Lidar‐Derived Snow Depth in the Extratropical Andes P. Mendoza et al. 10.1029/2020WR028480
- Estimating catchment-scale groundwater dynamics from recession analysis – enhanced constraining of hydrological models T. Skaugen & Z. Mengistu 10.5194/hess-20-4963-2016
- Evaluation of global forcing datasets for hydropower inflow simulation in Nepal B. Bhattarai et al. 10.2166/nh.2020.079
- Analysing the elevation-distributed hydro-climatic regime of the snow covered and glacierised Hunza Basin in the upper Indus A. Nazeer et al. 10.3389/feart.2023.1215878
- A New Method to Characterize Changes in the Seasonal Cycle of Snowpack A. Evan 10.1175/JAMC-D-18-0150.1
- Single‐Column Validation of a Snow Subgrid Parameterization in the Rapid Update Cycle Land‐Surface Model (RUC LSM) S. He et al. 10.1029/2021WR029955
- In search of operational snow model structures for the future – comparing four snow models for 17 catchments in Norway T. Skaugen et al. 10.2166/nh.2018.198
- Changes in the hydro-climatic regime of the Hunza Basin in the Upper Indus under CMIP6 climate change projections A. Nazeer et al. 10.1038/s41598-022-25673-6
- Predicting hourly flows at ungauged small rural catchments using a parsimonious hydrological model A. Tsegaw et al. 10.1016/j.jhydrol.2019.03.090
21 citations as recorded by crossref.
- Modeling the Snow Depth Variability With a High‐Resolution Lidar Data Set and Nonlinear Terrain Dependency T. Skaugen & K. Melvold 10.1029/2019WR025030
- Simulating the hydrological regime of the snow fed and glaciarised Gilgit Basin in the Upper Indus using global precipitation products and a data parsimonious precipitation-runoff model A. Nazeer et al. 10.1016/j.scitotenv.2021.149872
- Hydrological impacts of climate change on small ungauged catchments – results from a global climate model–regional climate model–hydrologic model chain A. Tsegaw et al. 10.5194/nhess-20-2133-2020
- A dynamic river network method for the prediction of floods using a parsimonious rainfall-runoff model A. Tsegaw et al. 10.2166/nh.2019.003
- Impact of climate change on spatiotemporal patterns of snow hydrology: Conceptual frameworks, machine learning versus nested model M. Besharatifar & M. Nasseri 10.1016/j.pce.2024.103691
- Snow redistribution for the hydrological modeling of alpine catchments D. Freudiger et al. 10.1002/wat2.1232
- A parameter parsimonious approach for catchment scale urban hydrology – Which processes are important? T. Skaugen et al. 10.1016/j.hydroa.2020.100060
- Constraining the HBV model for robust water balance assessments in a cold climate H. Erlandsen et al. 10.2166/nh.2021.132
- Shyft v4.8: a framework for uncertainty assessment and distributed hydrologic modeling for operational hydrology J. Burkhart et al. 10.5194/gmd-14-821-2021
- Rainfall-runoff modeling based on HEC-HMS model: a case study in an area with increased groundwater discharge potential M. Herbei et al. 10.3389/frwa.2024.1474990
- Hydrology influences breeding time in the white-throated dipper A. Nilsson et al. 10.1186/s12898-020-00338-y
- Parameter uncertainty analysis for an operational hydrological model using residual-based and limits of acceptability approaches A. Teweldebrhan et al. 10.5194/hess-22-5021-2018
- Spatial Distribution and Scaling Properties of Lidar‐Derived Snow Depth in the Extratropical Andes P. Mendoza et al. 10.1029/2020WR028480
- Estimating catchment-scale groundwater dynamics from recession analysis – enhanced constraining of hydrological models T. Skaugen & Z. Mengistu 10.5194/hess-20-4963-2016
- Evaluation of global forcing datasets for hydropower inflow simulation in Nepal B. Bhattarai et al. 10.2166/nh.2020.079
- Analysing the elevation-distributed hydro-climatic regime of the snow covered and glacierised Hunza Basin in the upper Indus A. Nazeer et al. 10.3389/feart.2023.1215878
- A New Method to Characterize Changes in the Seasonal Cycle of Snowpack A. Evan 10.1175/JAMC-D-18-0150.1
- Single‐Column Validation of a Snow Subgrid Parameterization in the Rapid Update Cycle Land‐Surface Model (RUC LSM) S. He et al. 10.1029/2021WR029955
- In search of operational snow model structures for the future – comparing four snow models for 17 catchments in Norway T. Skaugen et al. 10.2166/nh.2018.198
- Changes in the hydro-climatic regime of the Hunza Basin in the Upper Indus under CMIP6 climate change projections A. Nazeer et al. 10.1038/s41598-022-25673-6
- Predicting hourly flows at ungauged small rural catchments using a parsimonious hydrological model A. Tsegaw et al. 10.1016/j.jhydrol.2019.03.090
Latest update: 03 Jul 2025
Short summary
In hydrological models it is important to properly simulate the spatial distribution of snow water equivalent (SWE) for the timing of spring melt floods and the accounting of energy fluxes. This paper describes a method for the spatial distribution of SWE which is parameterised from observed spatial variability of precipitation and has hence no calibration parameters. Results show improved simulation of SWE and the evolution of snow-free areas when compared with the standard method.
In hydrological models it is important to properly simulate the spatial distribution of snow...
