Inflow/Outflow Boundaries

Inflow/outflow boundaries locations specified in hgrid.gr3, usually with the preprocessor identifying the start and end of the string of nodes. The fact that a boundary is a flow or tracer boundaries is specified in bctides.in. The actual data are provided in flux.th for flow and SAL_1.th, TEM_1.th for other tracers. For info on the .th file format, you can refer to the SCHISM manual on the topic.

Where are the data?

In BayDeltaSCHISM/data we provide flux.th, salinity.th (maps to SAL_1.th) and temperature.th (maps to TEM_1.th) that go back to roughly 2008. Before that, operations were different and some of our better data sources were unavailable.

Clipping to run dates and conversion to elapsed time

The .th format uses elapsed seconds from run start for time and has no headers. Our data are provided in what we call dated format. We mix it down before the run.

What if I have to add or move a boundary?

Moving the grid relative to the boundary or a boundary relative to the grid has consequences in some far flung places. You’ll want to cover our checklist of consequences and consider which apply to your case.

Please also note that our boundary condition file is constructed, in order, from our most common boundaries. If you add a boundary you may have to inject a column into the flux.th file.

Adding Data From Other Sources (e.g. Forecasts, other models)

The port_boundary.py script in BayDeltaSCHISM/bdschism/bdschism/ contains utilities to convert or graft outputs from various data formats (other models or forecasts) onto files in SCHISM time history (.th) formats.

port_boundary.read_csv(file, var, name, p=2.0)

Reads in a csv file of monthly boundary conditions and interpolates Outputs an interpolated DataFrame of that variable

File Descriptions

port_boundary.py: main script

port_boundary_map.csv: configuration file

Configuring the mapping file

The mapping file port_boundary_map.csv is the main configuration file in which the SCHISM variables are mapped to their respective substitution variables and sources.

1. schism_boundary: the SCHISM boundary variable

2. boundary_kind: type of boundary (flow, ec, temp)

3. source_kind: the type of file to convert from (SCHISM, CSV, DSS, CONSTANT)

Note

If SCHISM is used as the source, values from the designated SCHISM .th file are copied over and used directly.

Note

for CSV source_kind: pd.read_csv() is used to parse the csv and, as coded, parses monthly data. The format of a csv source_kind is shown in the table below.

date	RSAC155	RSAN112	var_n
1/1/2014	5609	1233	…
1/2/2014	4771	1162	…
…	…	…	…

Note

for DSS source_kind, CalSim or DSM2 standard DSS files may be used.

Note

for CONSTANT source kind, input a value in the ‘var’ column. This may be used when an input boundary is generlly constant (.e.g, Sacramento River boundary EC).

4. derived: whether to use the variable directly, or calculate based on other variables. For example SCHISM variable ‘east’ is sum of DSM2 variables RCSM075 and RMKL070.

5. source_file: path of the source_file (relative path)

6. var: variable name within the source file.

7. sign: multiplied by timeseries to change direction of flow.

8. convert: apply unit conversions (CFS_CMS, EC_PSU):

   if convert == 'CFS_CMS':
       dfi = dfi * CFS2CMS * sign
   elif convert == 'EC_PSU':
       dfi = ec_psu_25c(dfi) * sign
   

9. rhistinterp_p: this script uses rhistinterp to interpolate and smooth timeseries of coarser timesteps. This field sets the parameter p

10. formula: the formula for the derived timeseries Used if derived = TRUE.

Note

Python’s eval() function is used to parse the code entered in the cell. Examples:

(csv.RCSM075+csv.RMKL070)

The above entry would sum up RCSM075 and RMKL070 from the CSV source

(flux.american/(flux.sac+flux.american)).mul(csv.RSAC155)

The above entry would multiply the RSAC155 from the CSV source by the ratio of the (Amer River/Amer+Sac River). This is used to disaggregate the total Sacramento R. flow above the confluence (i.e., from DSM2) into its American River component, based on an existing SCHISM flux.th input.

The dataframes are named:

flux: DataFrame containing the historical SCHISM flux.th file

salt: DataFrame containing the historical SCHISM salt.th file

temp: DataFrame containing the historical SCHISM temp.th file

csv: DataFrame containing the values from the CSV source

dss: DataFrame containing the values from the DSS source

Note

the SCHISM time history files used are the ones in this repository.

schism_flux_file = '../data/time_history/flux.th'
schism_salt_file = '../data/time_history/salt.th'
schism_temp_file = '../data/time_history/temp.th'