"""Prepare data for water use for cooling & energy technologies."""
import pandas as pd
from message_ix import make_df
from message_ix_models.util import broadcast, package_data_path
# water & electricity for irrigation
[docs]def add_irr_structure(context):
"""Add irrigation withdrawal infrastructure
The irrigation demands are added in
Parameters
----------
context : .Context
Returns
-------
data : dict of (str -> pandas.DataFrame)
Keys are MESSAGE parameter names such as 'input', 'fix_cost'.
Values are data frames ready for :meth:`~.Scenario.add_par`.
Years in the data include the model horizon indicated by
``context["water build info"]``, plus the additional year 2010.
"""
# define an empty dictionary
results = {}
# reading basin_delineation
FILE2 = f"basins_by_region_simpl_{context.regions}.csv"
PATH = package_data_path("water", "delineation", FILE2)
df_node = pd.read_csv(PATH)
# Assigning proper nomenclature
df_node["node"] = "B" + df_node["BCU_name"].astype(str)
df_node["mode"] = "M" + df_node["BCU_name"].astype(str)
if context.type_reg == "country":
df_node["region"] = context.map_ISO_c[context.regions]
else:
df_node["region"] = f"{context.regions}_" + df_node["REGION"].astype(str)
# Reference to the water configuration
info = context["water build info"]
# probably can be removed
year_wat = [2010, 2015]
year_wat.extend(info.Y)
inp = make_df(
"input",
technology="irrigation_cereal",
value=1,
unit="-",
level="water_supply",
commodity="freshwater",
mode="M1",
time="year",
time_origin="year",
node_origin=df_node["region"],
node_loc=df_node["region"],
).pipe(broadcast, year_vtg=info.Y)
inp = inp.append(
make_df(
"input",
technology="irrigation_oilcrops",
value=1,
unit="-",
level="water_supply",
commodity="freshwater",
mode="M1",
time="year",
time_origin="year",
node_origin=df_node["region"],
node_loc=df_node["region"],
).pipe(broadcast, year_vtg=info.Y)
)
inp = inp.append(
make_df(
"input",
technology="irrigation_sugarcrops",
value=1,
unit="-",
level="water_supply",
commodity="freshwater",
mode="M1",
time="year",
time_origin="year",
node_origin=df_node["region"],
node_loc=df_node["region"],
).pipe(broadcast, year_vtg=info.Y)
)
# year_act = year_vts for tecs with 1 time-step lifetime
inp["year_act"] = inp["year_vtg"]
# Electricity values per unit of irrigation water supply
# Reference: Evaluation of Water and Energy Use in
# Pressurized Irrigation Networks in Southern Spain
# Diaz et al. 2011 https://ascelibrary.org/
# doi/10.1061/%28ASCE%29IR.1943-4774.0000338
# Low Value :0.04690743
# Average Value :0.101598174
# High Value : 0.017123288
# inp = inp.append(
# make_df(
# "input",
# technology="irrigation_sugarcrops",
# value=0.04690743,
# unit="-",
# level="final",
# commodity="electr",
# mode="M1",
# time="year",
# time_origin="year",
# node_origin=df_node["region"],
# node_loc=df_node["region"],
# ).pipe(broadcast, year_vtg=year_wat, year_act=year_wat)
# )
#
# inp = inp.append(
# make_df(
# "input",
# technology="irrigation_oilcrops",
# value=0.04690743,
# unit="-",
# level="final",
# commodity="electr",
# mode="M1",
# time="year",
# time_origin="year",
# node_origin=df_node["region"],
# node_loc=df_node["region"],
# ).pipe(broadcast, year_vtg=year_wat, year_act=year_wat)
# )
#
# inp = inp.append(
# make_df(
# "input",
# technology="irrigation_cereal",
# value=0.04690743,
# unit="-",
# level="final",
# commodity="electr",
# mode="M1",
# time="year",
# time_origin="year",
# node_origin=df_node["region"],
# node_loc=df_node["region"],
# ).pipe(broadcast, year_vtg=year_wat, year_act=year_wat)
# )
# inp.loc[(inp['node_loc'] == 'R11_SAS') &
# (inp['commodity'] == 'electr'),
# "value",
# ] *= 0.00004690743
results["input"] = inp
irr_out = make_df(
"output",
technology="irrigation_cereal",
value=1,
unit="km3/year",
level="irr_cereal",
commodity="freshwater",
mode="M1",
time="year",
time_dest="year",
node_loc=df_node["region"],
node_dest=df_node["region"],
).pipe(broadcast, year_vtg=info.Y)
irr_out = irr_out.append(
make_df(
"output",
technology="irrigation_sugarcrops",
value=1,
unit="km3/year",
level="irr_sugarcrops",
commodity="freshwater",
mode="M1",
time="year",
time_dest="year",
node_loc=df_node["region"],
node_dest=df_node["region"],
).pipe(
broadcast,
year_vtg=info.Y,
)
)
irr_out = irr_out.append(
make_df(
"output",
technology="irrigation_oilcrops",
value=1,
unit="km3/year",
level="irr_oilcrops",
commodity="freshwater",
mode="M1",
time="year",
time_dest="year",
node_loc=df_node["region"],
node_dest=df_node["region"],
).pipe(
broadcast,
year_vtg=info.Y,
)
)
irr_out["year_act"] = irr_out["year_vtg"]
results["output"] = irr_out
return results