Skip to content

Latest commit

 

History

History
232 lines (174 loc) · 6.06 KB

README.md

File metadata and controls

232 lines (174 loc) · 6.06 KB
Raw

TJ_HYD_NAM

TJ_HYD_NAM is a Python implementation of the NedborAfstromnings Model (NAM), a lumped rainfall–runoff model. This implementation is based on the original code from NAM_Model by hckaraman.

Installation

You can install the package via pip:

pip install tj_hyd_nam

Getting Started

1. Prepare the Dataset

The dataset should contain the following columns: Date, Temperature, Precipitation, Evapotranspiration, and Discharge. Column names can be customized.

Date Temp Q P E
10/9/2016 15.4 0.25694 0 2.79
10/10/2016 14.4 0.25812 0 3.46
10/11/2016 14.9 0.30983 0 3.65
10/12/2016 16.1 0.31422 0 3.46
10/13/2016 20.1 0.30866 0 5.64
10/14/2016 13.9 0.30868 0 3.24
10/15/2016 11.1 0.31299 0 3.41
... ... ... ... ...

Ensure that the time intervals between dates are consistent (e.g., 24 hours) for accurate model performance.

2. Initialize the NAM Model

import pandas as pd
from tj_hyd_nam import TJHydNAM, NAMColNames, NAMConfig

# Load the dataset
df = pd.read_csv('data_example.csv')

# Specify the column names
nam_col_names = NAMColNames(
    date='Date',
    temperature='Temp',
    precipitation='P',
    evapotranspiration='E',
    discharge='Q'
)

# Configure the model parameters
nam_config = NAMConfig(
    area=58.8,
    start_date=None,
    end_date=None,
    interval=24.0,
    spin_off=0.0,
    umax=0.97,
    lmax=721.56,
    cqof=0.18,
    ckif=495.91,
    ck12=25.16,
    tof=0.97,
    tif=0.11,
    tg=0.19,
    ckbf=1121.74,
    csnow=2.31,
    snowtemp=3.51,
)

# Initialize the NAM model
NAM = TJHydNAM(
    dataset=df,
    nam_col_names=nam_col_names,
    nam_config=nam_config
)

print(NAM)

The output will display details of the NAM model based on the loaded dataset.

3. Display and Save Graphs

# Plot and save the discharge comparison graph
NAM.show_discharge(save=True, filename='discharge.png')

# Plot and save all calculated model information
NAM.show(save=True, filename='result.png')

4. Optimize the Model

NAM.optimize()
print(NAM)

The optimization process will refine the model parameters and display the updated configuration.

5. Reconfigure the Model Based on Properties

You can reconfigure the model parameters and recalculate the results based on new date ranges or other properties.

NAM.re_config_by_props(
    start_date=pd.to_datetime('09/10/2016', dayfirst=True, utc=True),
    end_date=pd.to_datetime('20/10/2016', dayfirst=True, utc=True)
)

6. Reconfigure All Parameters

To fully reconfigure the model, use:

NAM.re_config(
    NAMConfig(
        area=60,
        start_date=None,
        end_date=None,
        interval=24.0,
        spin_off=0.0,
        umax=0.8,
        lmax=719.56,
        cqof=0.14,
        ckif=493.86,
        ck12=45.16,
        tof=0.97,
        tif=0.45,
        tg=0.19,
        ckbf=1121.74,
        csnow=2.31,
        snowtemp=3.51,
    )
)

7. Save Calculated Model Data

NAM.save_to_csv('result.csv')

8. Convert Calculated Data to DataFrame

nam_df = NAM.to_dataframe()

9. Save and Load the Model

Save the current model configuration:

NAM.save('nam_model')

Load a saved model:

SAVED_NAM = NAM.load('nam_model.tjnam')

10. Use the Previous Model's Configuration for Prediction

PRED_NAM = TJHydNAM(
    pd.read_csv('future_data.csv'),
    NAMColNames(
        date='Date',
        temperature='Temp',
        precipitation='P',
        evapotranspiration='E',
        discharge='Q'
    ),
    SAVED_NAM.config
)

11. Accessing Calculated Variables (>=1.1.0)

You can access various calculated variables directly:

NAM.size       # Access the value of _size
NAM.date       # Access the value of _date
NAM.T          # Access the value of _T (temperature series)
NAM.P          # Access the value of _P (precipitation series)
NAM.E          # Access the value of _E (evaporation series)
NAM.Q_obs      # Access the value of _Q_obs (observed discharge)
NAM.U_soil     # Access the value of _U_soil (upper soil layer moisture)
NAM.S_snow     # Access the value of _S_snow (snow storage)
NAM.Q_snow     # Access the value of _Q_snow (snowmelt discharge)
NAM.Q_inter    # Access the value of _Q_inter (interflow discharge)
NAM.E_eal      # Access the value of _E_eal (actual evapotranspiration)
NAM.Q_of       # Access the value of _Q_of (overland flow)
NAM.Q_g        # Access the value of _Q_g (groundwater discharge)
NAM.Q_bf       # Access the value of _Q_bf (baseflow)
NAM.Q_sim      # Access the value of _Q_sim (simulated discharge)
NAM.L_soil     # Access the value of _L_soil (soil moisture)

Exception Classes

MissingColumnsException

Raised when one or more required columns are missing from the dataset. The exception message specifies which column is missing, helping users to correct their data.

ColumnContainsEmptyDataException

Raised when a specified column contains empty data. This ensures that critical data is complete before proceeding with the model.

InvalidDatetimeException

Raised when an invalid datetime value is encountered. This could be due to incorrect formatting or values that do not conform to the expected standards.

InvalidDatetimeIntervalException

Raised when an invalid datetime interval is provided. This ensures that the interval between dates or times is logically consistent.

InvalidStartDateException

Raised when the provided start date is invalid. This helps catch errors where the beginning of a time-based event or range is not properly defined.

InvalidEndDateException

Raised when the provided end date is invalid. This ensures the end of a time-based event or range is correctly defined.

InvalidDateRangeException

Raised when the date range provided is invalid, such as when the start date is after the end date.