Add Local runoff inflows after ETM step

[rburghol]

Tasks

• [x] ~Add DSN10 to EXT SOURCES?~ (will not do -- see "Prepare model for DSN" below)
• [x] Add in meta-model hsp2
• [x] Add in meta-model hspf
• [x] Add switch LOCAL_TRIBS in script config

Prepare Model for DSN 10

• DO NOT Add line in iovars, rchres_in, Ex: config/catalog/iovars/vahydro_2022/rchres_in
  • It appears that adding DSN10 to EXT sources is problematic https://github.com/HARPgroup/cbp_wsm/issues/96#issuecomment-1483508863
  • It results in double counting upstream inflows
  • However, we can add DSN10 to the wdm, and then it will get imported into the hdf5 file
  • Then we can use it in the TS block
  • Therefore, LOCAL_TRIBS only has effect when used with hsp2
  • Verify in R
    • `library("rhdf5")
    • `dsn10 = h5read("JL1_6562_6560.h5","/TIMESERIES/TS010/table")
• But DO add DSN 10 into the wdm, and populate for use in the hsp2 ops model
• But if you DID add it, this is what it would look like:

  MODULE ID#   GROUP  MEMBER # #    NAME   BMP_TYPE  DESCRIPTION  ***
  HHYDR   10    INFLOW IVOL          WATR   flo       water acft/ivld

Include in In hsp2->river->prep: https://github.com/HARPgroup/meta_model/tree/main/models/hsp2_cbp6/river/prep

• [x] Locate MASS_-LINK def
  • [x] iovars (IOVARS in config/control/river/[scenario].con) points to $CBP_ROOT/config/catalog/iovars/[IOVARS]/rchres_in
  • Ex: $CBP_ROOT/config/catalog/iovars/p600/rchres_in
  • [x] Route DSN 10 into RCHRES with same settings as DSN 11
  • cbp copy_iovars p600 vahydro_2022
• [x] DSN 10: runoff inflows in river -> prep step
  • [x] Export DSN 11 after step 09 etm
  • [ ] If LOCAL_TRIBS = 1 in config/control/script/[scenario].con blank out DSN 11 (replace with export/import from blank.wdm - don't just overwrite since other things like sed and ORG have been set by ETM)
  • [x] Import 011 CSV as DSN 10
  • [x] Note: hsp2 import_uci step imports all DSNs from all WDM files that are linked into the project, however, to have the settings to use in get_timeseries we add this DSN to separate the flows
• [x] Verify loading in python command line (see comment below)
• [ ] Test loading in hsp2 with op model
• [ ] Test water balance

Adding a blank DSN

DSNs must exist before trying to put data in them. So we use wdmtoolbox (a la: sudo pip install wdmtoolbox, see more at https://pypi.org/project/wdmtoolbox/ and https://timcera.bitbucket.io/wdmtoolbox/docs/index.html)

• Add a new DSN to hold runoff only (instead of runoff + upstream, DSN 11)
  • DSN 10
  • add with wdmtoolbox: wdmtoolbox createnewdsn --tstype WATR --tcode 3 river.wdm 10
  • this works well, except that wdm_insert_one cuts the hours from 1 to 24 which causes maybe a 1 hour lag? Either way, wdm2text will NOT export the final year since it is not deemed complete 12/31/2021 23:00:00

[rburghol]

Check variables after running:

. hspf_config
MODEL_ROOT=$CBP_ROOT
META_MODEL_ROOT=/opt/model/meta_model
export MODEL_ROOT META_MODEL_ROOT

/opt/model/meta_model/run_model hsp2_cbp6 hsp2_2022 PS2_5550_5560 auto river 

echo PS2_5550_5560.wdm PS2_5550_5560_0011.csv 10 1 w message.wdm | wdm_insert_one

# note there is strange behavior when using wdm2text to export, then wdm_insert_one to import that file, 
# then wdm2text to export.  While the original wdm2text has data through hour 23 on 12/31, the wdm_insert_one 
# expects hours from 1 to 2 instead of 0 to 23, and therefore, the final `wdm2text` fails if you request the final year 
# of the simulation, as it is one hour shy.
# So, instead of echo PS2_5550_5560.wdm,1984,2020,11 | wdm2text
# We do:
echo PS2_5550_5560.wdm,1984,2019,10 | wdm2text

[rburghol]

Verify location of data in h5 file

cd tmp/scratch/1587 # this was the scratch dir from the last model run 
python3
# the rest is python
import h5py
import numpy as np
import pandas as pd
from collections import defaultdict
from pandas import DataFrame, read_hdf, HDFStore
from numba.typed import Dict

f = h5py.File(fpath,'a')
fpath = 'PS2_5550_5560.h5'
ts = f['TIMESERIES'].items()
dsn10 = f['TIMESERIES/TS010/table']
dsn10[0]
# (441763200000000000, 20.68031311)