loone 0.1.2

LOONE: A comprehensive water balance-nutrient-optimization model

LOONE

The Lake Operation Optimization of Nutrient Exports (LOONE) is a comprehensive water balance-nutrient-optimization model that comprises three coupled modules: A water balance module that simulates the water balance and operations of a reservoir, a nutrient module that simulates nutrient (phosphorus) dynamics in the water column, and an optimization engine that optimizes a reservoir’s releases into its distributaries with the objective of nutrient export minimization and/or water deficit minimization. Python 3 was chosen to develop the code because of its many high-quality libraries and a powerful community support.

Installation

pip install loone

Development Installation

git clone <this repository>
cd ./LOONE
pip install -e .

How to Run LOONE?

"""
Data prep
    1. Add all required data to the workspace directory.
    2. Add a config.yaml file following the example below with the correct variables and file names for required data.
"""
from loone.loone_q import LOONE_Q
from loone.loone_nut import LOONE_NUT
from loone.loone_wq import LOONE_Constituent_SimQ


LOONE_Q(
    workspace="/path/to/workspace",
    p1=0,
    p2=0,
    s77_dv=0,
    s308_dv=0,
    tp_lake_s=0,
)

LOONE_NUT(
    workspace="/path/to/workspace",
    out_file_name="loone_nut_outputs.csv",
    loads_external_filename="lo_external_loads.csv",
    flow_df_filename="flow_df.csv",
    forecast_mode=True,
)

LOONE_Constituent_SimQ(workspace="/path/to/workspace")

Example configuration file

# LOONE Configuration

# predefined variables
schedule: "LORS20082023"
sim_type: 0  # 0:Scenario_Simulation 1:Optimization_Validation 2:Optimization
start_year: 2008
end_year: 2023
start_date_entry: [2008, 1, 1]
beg_date_cs_entry: [2008, 1, 1]
end_date_entry: [2023, 3, 31]
end_date_tc: [2023, 4, 1]
month_n: 183
opt_new_tree: 1  # if New Tree Decision is used enter 1 else enter 0.
code: 6
multiplier: 100
tci: 1
opt_net_inflow: 2
net_inf_const: 0
start_stage: 10.268
beg_stage_cs: 10.268
opt_los_admd: 1
mult_losa: 100
opt_losa_ws: 1  # the option for LOSA Daily Supply where 1: Calculated Function of WSM, 2: Set values to zeros.
opt_dec_tree: 1  # if Tree Decision is used enter 1 else enter 0.
zone_c_met_fcast_indicator: 1  # 0 to use the same tree classifications as SLONINO or 1 to use the same tree classifications as LORS2008 and SFWMM.
wca3a_reg_zone: "ERTP:TopE"
wca3a_offset: 0
wca328_min: 7.5
wca3_nw_min: 11
wca217_min: 11.1
opt_wca_limit_wsa: 2
cs_flag: 1
pls_day_switch: 0  # 0: pulse day counter continues to 10 even if release level increases, 1: pulse day counter is set to zero if release level increases during the 10-day pulse.
max_qstg_trigger: 20  # the maximum stage trigger for maximum discharge if Trib_cond. = XWet.
opt_qreg_mult: 0  # option for using multipliers 0: don't use, 1: apply only during dry season (Nov-May), 2: apply year-round.
alternate_high_qyrs: 0
option_s80_baseflow: 0
s308_bk_const: 1
s308_bk_thr: 14.5
opt_s308: 1
s308_rg_const: 1
option_reg_s77_s308: 0
s80_const: 1
opt_outlet1_dsrg: 0
thc_threshold: 2
low_chance: 50
opt_l_chance_line: 1
opt_date_targ_stg: 1
opt_sal_fcast: 3
ce_sal_threshold: 5
late_dry_season_option: 0
opt_no_ap_above_bf_sb: 1
opt_adap_prot: 1
opt_ceews_lowsm: 0
opt_thc_byp_late_ds: 1
apcb1: 100
apcb2: 100
apcb3: 100
apcb4: 100
cal_est_ews: 300
outlet1_usews_switch: 1
outlet1_usbk_switch: 1  # option for S77BK simulation 0: Use input data or 1: Simulate with LOONE.
outlet1_usbk_threshold: 11.1
option_s77_baseflow: 0  # 0: baseflow supplements daily C43RO, 1: baseflow supplements monthly C43RO.
outlet1_usreg_switch: 1
outlet1_ds_switch: 1
max_cap_reg_wca: 4000
multiplier_reg_wca: 1
option_reg_wca: 2
constant_reg_wca: 400
max_cap_reg_l8_c51: 500
multiplier_reg_l8_c51: 1
option_reg_l8_c51: 2
constant_reg_l8_c51: 200
et_switch: 0
opt_wsa: 0  # Options for Lake O water supply augmentation (WSA) operation (0 = no WSA,1 = use flat trigger stages to activate WSA operation,2 = trigger stages defined using offsets from LOWSM WST line to activate WSA operation)
wsa_thc: 2
wsa_trig1: 12.5
wsa_trig2: 11.5
wsa_off1: 0.5
wsa_off2: 0
mia_cap1: 860
mia_cap2: 1720
nnr_cap1: 900
nnr_cap2: 1800
option_stage: 0
# Water demand cutback for each WSM Zone
z1_cutback: 0.15
z2_cutback: 0.3
z3_cutback: 0.45
z4_cutback: 0.6

dstar_b: 99
dstar_c: 99
dstar_d3: 99
dstar_d2: 99
dstar_d1: 99
astar_b: 1
astar_c: 1
astar_d3: 1
astar_d2: 1
astar_d1: 1
bstar_s77_b: 1
bstar_s77_c: 1
bstar_s77_d3: 0.5
bstar_s77_d2: 0.5
bstar_s77_d1: 0.5
bstar_s80_b: 1
bstar_s80_c: 1
bstar_s80_d3: 0.5
bstar_s80_d2: 0.5
bstar_s80_d1: 0.5

# data
sfwmm_daily_outputs: "SFWMM_Daily_Outputs.csv"
wsms_rsbps: "WSMs_RSBPs.csv"
losa_wkly_dmd: "LOSA_wkly_dmd.csv"
trib_cond_wkly_data: "Trib_cond_wkly_data.csv"
seasonal_lonino: "Seasonal_LONINO.csv"
multi_seasonal_lonino: "Multi_Seasonal_LONINO.csv"
netflows_acft: "Netflows_acft.csv"
water_dmd: "Water_dmd.csv"
rf_vol: "RFVol.csv"
et_vol: "ETVol.csv"
c44ro: "C44RO.csv"
c43ro: "C43RO.csv"
basin_ro_inputs: "Basin_RO_inputs.csv"
c43ro_monthly: "C43RO_Monthly.csv"
c44ro_nonthly: "C44RO_Monthly.csv"
sltrib_monthly: "SLTRIB_Monthly.csv"
s77_regulatory_release_rates: "S77_RegRelRates.csv"
s80_regulatory_release_rates: "S80_RegRelRates.csv"
ce_sle_turns_inputs: "CE_SLE_turns_inputs.csv"
pulses_inputs: "Pulses_Inputs.csv"
june_1st_lake_stage_below_11ft: "Chance of June 1st Lake stage falling below 11.0ft.csv"
may_1st_lake_stage_below_11ft: "Chance of May 1st Lake stage falling below 11.0ft.csv"
estuary_needs_water_input: "Estuary_needs_water_Input.csv"
eaa_mia_ro_inputs: "EAA_MIA_RUNOFF_Inputs.csv"
storage_deviation: "Storage_Dev.csv"
calibration_parameters: "Cal_Par.csv"

# tp variables regions
z_sed: 0.05  # m
per_h2o_m: 0  # 85 #%
per_h2o_s: 0  # 20 #%
per_h2o_r: 0  # 20  #%
per_h2o_p: 0  # 85 #%
n_per: 0.43
s_per: 0.57

bulk_density_m: 0.15  # g/cm3
bulk_density_s: 1.213  # g/cm3
bulk_density_r: 1.213  # g/cm3
bulk_density_p: 0.14  # g/cm3

particle_density_m: 1.2  # g/cm3
particle_density_s: 2.56  # g/cm3
particle_density_r: 2.56  # g/cm3
particle_density_p: 1.2  # g/cm3

a_mud_n: 377415128  # m2 in 1988!
a_mud_s: 394290227  # m2 in 1988!
a_sand_n: 237504380  # m2 in 1988!
a_sand_s: 117504905  # m2 in 1988!
a_rock_n: 17760274  # m2 in 1988!
a_rock_s: 141327951  # m2 in 1988!
a_peat_n: 97497728  # m2 in 1988!
a_peat_s: 301740272  # m2 in 1988!

v_burial_m: 0.0000003  # 1.0e-05 #(m/day)#0.00017333#(m/month)# 0.00208 (m/yr)
v_burial_s: 0.0000003  # 1.0e-05 #(m/day)#0.00017333#(m/month)# 0.00208 (m/yr)
v_burial_r: 0.0000003  # 1.0e-05 #(m/day)#0.00017333#(m/month)# 0.00208 (m/yr)
v_burial_p: 0.0000003  # 1.0e-05 #(m/day)#0.00017333#(m/month)# 0.00208 (m/yr)

nondominated_sol_var: "nondominated_Sol_var.csv"

Case Study:

LOONE was used to simulate operations and phosphorus mass balance of Lake Okeechobee, the largest reservoir by surface area in the US. In addition to its dimensions, we chose Lake Okeechobee as a case study for multiple reasons. First, it is a multi-inlet and multi-outlet reservoir with a complex system of pumps and locks operated with a regulation schedule, which allowed us to evaluate performance of the model in a complex hydrologic system. Second, it is a nutrient impaired lake known to export large amount of nutrients to its surrounding water bodies (Tarabih and Arias, 2021; Walker, 2000), thus LOONE could aid evaluating impacts of lake regulations on the nutrient status of the regional system.

Data Description:

See : Data Description

Data Requirements:

LOONE was used to simulate Lake Okeechobee regulatory releases into St. Lucie Canal, and Caloosahatchee River, meanwhile prescribed flows were used for West Palm Beach Canal, North New River Canal/Hillsboro Canal, Miami Canal, and L-8 Canal as well as water supply flows utilizing the continuity equation and Lake Okeechobee rule curves for the study period. We simulated three different Lake Okeechobee schedules during the study period (1991-2018): RUN25 (1991-1999), WSE (2000-2007), and 2008 LORS (2008-2018). LOONE was used to design optimal releases of Lake Okeechobee into the Caloosahatchee River and St. Lucie Canal, with the goal of demonstrating an operational schedule that can minimize pollutant exports into the estuaries while minimizing LOSA water deficits.

Data type	Explanation	Time step	File name	Data source
Tributary Condition	Net Rainfall, Tributary Flow, Palmer Index, Net inflows	Weekly	Trib_cond_wkly_data_xxx	Rainfall, Tributary flow, and net inflows (DBHYDRO)
Palmer Index	NOAA	Monthly	Palmer_Index_xxx	USACE Monthly Reports
Seasonal LONINO	Seasonal Lake Okeechobee Net Inflow Outlooks	Monthly	Seasonal_LONINO_xxx	USACE Monthly Reports
Multi Seasonal LONINO	Multi Seasonal Lake Okeechobee Net Inflow Outlooks	Monthly	Multi_Seasonal_LONINO_xxx	USACE Monthly Reports
Net Inflows	Net Inflows = All tributary inflows – non simulated outflows	Daily	NetFlows_acft_xxx	DBHYDRO
Water demand	LOSA water demand	Daily	Water_dmd_xxx	SFWMD Reports
Rainfall	Rainfall Volume	Daily	RF_Volume_xxx	DBHYDRO
Evapotranspiration	ET Volume	Daily	ETVol_xxx	DBHYDRO
C44 Runoff	St Lucie Watershed Runoff	Daily	C44RO_xxx	DBHYDRO
C43 Runoff	Caloosahatchee Watershed Runoff	Daily	C43RO_xxx	DBHYDRO
EAA_MIA_Runoff	Daily flow data for Miami Canal at S3, NNR at S2_NNR, WPB at S352, S2 pump, and S3 pump.	Daily	EAA_MIA_RUNOFF_Inputs_xxx	DBHYDRO
Storage Deviation	Storage deviation between simulated storage using observed outflows and observed storage to account for unreported outflows.	Daily	Storage_Dev_xxx	DBHYDRO
External Loads	Phosphorus loads into Lake Okeechobee from the tributaries	Daily	LO_External_Loadings_3MLag_xxx	DBHYDRO
Lake Inflows	Lake Okeechobee inflows from all the tributaries as well as back flows	Daily	LO_Inflows_BK_xxx	DBHYDRO
Wind Shear Stress	Wind shear stress function of wind speed	Daily	WindShearStress_xxx	Calculated
Wind Speed	Mean wind speed	Daily	Mean_WindSpeed_xxx	DBHYDRO
Kinematic viscosity	Kinematic viscosity of Lake Okeechobee water column function of Water Temperature	Daily	nu_xxx	DBHYDRO
Water Temperature	Water column Temperature	Daily	LZ40_T_xxx	DBHYDRO