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6. River Forcing
It can be challenging to construct appropriate river forcing data that are compatible with the atmospheric data and are of the desired spatial and temporal resolution for the region of study. Conveniently, surface-atmospheric datasets aimed for driving ocean-sea ice models often provide gridded river outflow to the ocean that is by designed compatible with the atmospheric forcing such that the global freshwater budget is closed. Such datasets include the forcing for Coordinated Ocean-ice Reference Experiments (CORE) (Large et al., 2009) and the JRA-55 dataset based on the Japanese 55-year atmospheric reanalysis (Tsujino et al., 2018).
The matlab script generate_riverF.m and function remap_river.m describe the method and can be used for extracting the river forcing for a regional model from a gridded global dataset aimed for forcing ocean models (e.g. the JRA55 rivers)
1. Download JRA55 river dataset and some associated required files (like the area of each cell for the river gridded dataset): https://climate.mri-jma.go.jp/pub/ocean/JRA55-do/
2. In generate_riverF.m modify/provide some information about the year that you want to run and the file for your model domain (i.e., your domain_cfg.nc) and the name and path of your JRA55 downloaded files.
3. The code then will proceed to converted the JRA river flux from mass flux (kg/m2/s) to volume transport (m3/s), then to find the coastline of your provided model domain, and then to re-map the JRA river flux to the closest grid point along your coastline using the remap_river.m
4. The extend of the river mouth (i.e., location of the freshwater output) depends on, and is restricted by, the dataset’s resolution. Therefore, the river outflow from major rivers, with extending Deltas, may have to be spread over several grid cells in a higher resolution regional model. Additionally, very high freshwater outflow concentrated in only one grid point may lead to unrealistic features like freshwater "building up", as it gets trapped along complex coastline features, rather than mixing and advected off-coast in the model. Hence, after re-mapping generate_riverF.m spreads the river outflow along and off the coast as a function of the magnitude of the river discharge. Specifically, first a maximum threshold for river discharge in a grid cell is imposed and then the river outflow is spread, starting from adjacent ocean points until this threshold is satisfied. You can control this spreading by the variable THRESOLD in the script, which corresponds to the maximum river flow value (in m^3/s) allowed in a grid cell. The THRESOLD value is set based on judgement but also based on the model resolution and so the area occupied by the grid cell, for example a very high resolution model should have a smaller value while a coarse resolution model a larger value.
The script checks for any problems such that your total re-mapped flux is not the same as the JRA flux and will display an Error if there is a problem.
5. Finally the script converts the re-mapped river outflow to mass flux and creates a netcdf file compatible to be used by NEMO.
The rivers outflow may be introduced over a defined depth rather than imposed in the top layer to avoid numerical problems in models with a very thin top layer. By default, the rivers are assigned zero temperature and salinity which may lead to unrealistic cooling and freshening in the vicinity of river discharge. Optimally observations from river salinity and temperature should be used to assign these values, but these observations are sparse.In the absence of observations, judgement and short sensitivity experiments can be used to assign these values. For example, for salinity it may be, it may be desirable to assign a non-zero value for rivers that are known to experience strong saltwater intrusions (e.g., Mekong Delta), such that in the vicinity of their mouth the discharge is assumed as being already a mixture of fresh and ocean water.
Check GlobalNews and the SANH wiki: GlobalNEWS is forced by information such as river networks and runoffs, precipitation, water and land use, and nutrient sources (agricultural and natural), and can accommodate future projected anthropogenic and climate changes.