Model evaluation¶

This example shows how to load model and observation data and evalute model performance.

In [ ]:

# skip this if package has already been installed
!pip install modvis

# skip this if package has already been installed !pip install modvis

In [1]:

from modvis import ATSutils
from modvis import utils
from modvis import general_plots as gp

import matplotlib.pyplot as plt

model_dir = "../data/coalcreek"

from modvis import ATSutils from modvis import utils from modvis import general_plots as gp import matplotlib.pyplot as plt model_dir = "../data/coalcreek"

Download the sample data when running on Google Colab

In [2]:

# import os
# if not os.path.exists(model_dir):
#   !git clone https://github.com/pinshuai/modvis.git
#   %cd ./modvis/examples/notebooks

# import os # if not os.path.exists(model_dir): # !git clone https://github.com/pinshuai/modvis.git # %cd ./modvis/examples/notebooks

Load model data¶

This will load the water_balance-daily.dat file generated from ATS model. The data file includes watershed variables including outlet discharge, ET, and etc.

In [3]:

simu_df = ATSutils.load_waterBalance(model_dir, WB_filename="water_balance-daily.dat",
                                plot = True)

simu_df = ATSutils.load_waterBalance(model_dir, WB_filename="water_balance-daily.dat", plot = True)

Load observation data¶

In [4]:

obs_df = utils.load_nwis(sites= "09111250", start = '2014-10-01')

obs_df = utils.load_nwis(sites= "09111250", start = '2014-10-01')

In [5]:

obs_df.plot()

obs_df.plot()

Out[5]:

<AxesSubplot:>

Streamflow comparison¶

Compare simulated streamflow with observed USGS streamflow.

In [6]:

fig,ax = plt.subplots(1,1, figsize=(8,4))
simu_df['watershed boundary discharge [m^3/d]'].plot(color = 'c',ax=ax, label= "simu_Q")
obs_df['Discharge [m^3/d]'].plot(color = 'k', ax=ax, label = "obs_Q")
ax.set_ylabel("Discharge [m^3/d]")
ax.legend()

fig,ax = plt.subplots(1,1, figsize=(8,4)) simu_df['watershed boundary discharge [m^3/d]'].plot(color = 'c',ax=ax, label= "simu_Q") obs_df['Discharge [m^3/d]'].plot(color = 'k', ax=ax, label = "obs_Q") ax.set_ylabel("Discharge [m^3/d]") ax.legend()

Out[6]:

<matplotlib.legend.Legend at 0x7fd7c97556f0>

FDC comparison¶

Compare the simulated vs. observed flow duration curve.

In [7]:

fig, ax = gp.plot_FDC(dfs=[obs_df['Discharge [m^3/d]'], simu_df['watershed boundary discharge [m^3/d]']],
           labels=['obs_Q','simu_Q'], 
           colors=['k', 'c'],
           start_date="2016-10-01" 
           )

fig, ax = gp.plot_FDC(dfs=[obs_df['Discharge [m^3/d]'], simu_df['watershed boundary discharge [m^3/d]']], labels=['obs_Q','simu_Q'], colors=['k', 'c'], start_date="2016-10-01" )

One-to-one plot¶

One to one scatter plot with metrics.

In [8]:

gp.one2one_plot(obs_df['Discharge [m^3/d]'], simu_df['watershed boundary discharge [m^3/d]'],
               metrics=['R^2', 'NSE', 'mKGE'],
                # metrics='all',
               show_density=False,
                start_date="2016-10-01"
               )

gp.one2one_plot(obs_df['Discharge [m^3/d]'], simu_df['watershed boundary discharge [m^3/d]'], metrics=['R^2', 'NSE', 'mKGE'], # metrics='all', show_density=False, start_date="2016-10-01" )

Out[8]:

{'R^2': 0.8721059035985849,
 'NSE': 0.8386136492066862,
 'mKGE': 0.7947371500678737}

In [ ]: