# Notebook from mintproject/topoflow36
Path: TopoFlow_Calibration_Baro_at_Masha.ipynb

# TopoFlow Calibration for Baro River at Masha

#### Table of Contents

1.  Introduction

2.  How to Install the TopoFlow Python Package

3.  Baro River Basin, Gambella, Ethiopia

5.  Calibration vs. Best-Guess Parameters

6.  Defining the Cost Function

7.  River Discharge Data for Baro River at Masha

8.  TopoFlow Model Run for Baro River at Masha

9.  The TopoFlow Calibrate Module

10. TopoFlow Calibration for Baro River at Masha

11. Conclusions

12. Other Jupyter Notebooks for TopoFlow 3.6

13. Key References for TopoFlow

14. Appendix 1: Installing TopoFlow in a conda Environment

_____no_output_____## Introduction

This notebook shows a simple model calibration exercise for the TopoFlow 3.6 hydrologic model that uses river discharge measurements from a gauging station on the Baro River, near Masha in Ethiopia.  Model calibration is computationally expensive and is best performed on supercomputers with specialized toolkits such as [DAKOTA](https://dakota.sandia.gov/) from DOE's Sandia Labs.  It basically involves performing a very large number of model runs with different combinations of the adjustable parameters to find the set of parameters that results in the closest match between observations of some variable and model predictions for the same variable.  The goal of this notebook is to show how calibration works using a grealy simplified example where only one model parameter is considered to be adjustable.
_____no_output_____## How to Install the TopoFlow Python Package

Before you can run the code in this notebook, you will need to install the TopoFlow 3.6 Python package.

Detailed instructions and background information for how to install TopoFlow in a conda environment are given in
Appendix 1:  Installing TopoFlow in a conda Environment._____no_output_____## Baro River Basin, Masha, Ethiopia

The
[Baro River](https://en.wikipedia.org/wiki/Baro_River)
is a large tributary of the
[Sobat River](https://en.wikipedia.org/wiki/Sobat_River)
that flows westward from the
[Ethiopian Highlands](https://en.wikipedia.org/wiki/Ethiopian_Highlands)
and lies partly within both the
[Oromia](https://en.wikipedia.org/wiki/Oromia_Region)
and
[Gambela](https://en.wikipedia.org/wiki/Gambela_Region)
regions.  It is about 150 meters wide as it passes through the
[town of Gambela](https://en.wikipedia.org/wiki/Gambela,_Ethiopia),
and drains a contributing area of over 41,000 square kilometers.

Figure. A color, shaded-relief image of the Baro River basin, in Ethiopia, where red is high elevation and blue is lower elevation.  Black lines show the river network and the white polygon is the watershed boundary._____no_output_____## Calibration vs. Best-Guess Parameters

Model calibration is one commonly-used method for
[parameter estimation](https://en.wikipedia.org/wiki/Estimation_theory)
(PE).  This method requires the use of a
[cost function](https://en.wikipedia.org/wiki/Loss_function)
that compares the observed or measured values of some variable to the values that are predicted by a model for that same variable.  It is important to understand that this method of parameter estimation requires observational data;  it cannot be used when observational data is unavailable.  For most of the river basins in the world, and especially on the African continent, observational data is only available for a relatively small number of river basins;  typically just the largest basins.

For hydrologic models, it is common to use a time series of
[river discharge](https://en.wikipedia.org/wiki/Discharge_(hydrology))
(which has units of volume per time), as the variable that is both observed and predicted, at some river basin outlet where there is a gauging station.  The modeler decides which model parameters will be held fixed and which will be allowed to be set by this method, and the model is then run many, many times, each time using a different set of possible parameter values (over allowed ranges) and each time computing the value of the cost function.  This is an
[optimization problem](https://en.wikipedia.org/wiki/Optimization_problem),
where one seeks the global minimum of the cost function over the model's
[parameter space](https://en.wikipedia.org/wiki/Parameter_space).
The cost function is often simply an
[L2 norm](https://en.wikipedia.org/wiki/Norm_(mathematics)#Euclidean_norm)
(i.e. least squares difference) that measures how close the model predictions are to the observations.  When finished, the model's adjustable parameters are set to those of the model run that minimized the cost function.  Unfortunately, it can happen that many different sets of parameters will minimize the cost function equally well, and this problem is called
[equifinality](https://en.wikipedia.org/wiki/Equifinality).

Since model calibration utilizes observational data, measurement errors of any kind can be a problem.  Also, a model must be recalibrated when it is applied to a different location (e.g. another river basin).  And due to the likely
[nonstationarity](https://en.wikipedia.org/wiki/Stationary_process)
of weather variables, it may also need to be recalibrated when it is applied to different time period or to any circumstances that were not reflected in the observational data (such as a large flood event).

In the common case where observational data is not available, a domain expert will gather as much information about the region (and scenario) of interest as possible, from any available source (e.g. papers, books, websites, global databases, personal communication, etc.), and combine this with his or her understanding of the underlying physics (expert knowledge) and a few assumptions to make best guesses for the values of adjustable parameters.  Neither calibration nor this method is foolproof, so model predictions always have uncertainty.  Note that even a perfect model (if that were possible) will give incorrect and even nonsensical results if its parameters are set incorrectly.

A closely related topic is
[model sensitivity analysis](https://en.wikipedia.org/wiki/Sensitivity_analysis).
As with model calibration, a model sensitivity study requires a model to be run many, many times with different parameter settings.  However, the goal in this case is to determine the degree to which the model predictions (i.e. the numerical values of the output variables) are sensitive to changes in any given parameter.  Physically-based models, where each parameter represents some important aspect of the physics, can be sensitive to many of their parameters.  In some cases a model may even become chaotic or exhibit a discontinuous jump in its predicted values even after a parameter is changed by only a very tiny amount.
_____no_output_____## Defining the Cost Function

Model calibration is an optimization problem, and this type of problem starts with the definition of a cost function, which may also be called an objective function or a
[loss function](https://en.wikipedia.org/wiki/Loss_function).

The cost function must be a
[metric](https://en.wikipedia.org/wiki/Metric_(mathematics))
that measures model's performance, defined as an abstract "distance" between the observed and model-predicted values of some output variable. There are many different metrics that can be used, such as those based on the one-parameter family of Lp norms, given by:

$$ \large{
d \left( \boldsymbol{Y}_{obs}, \boldsymbol{Y}_{sim} \right) =
\left( \sum_{k=1}^n \left| y_k^{(obs)} - y_k^{(sim)} \right|^{\,p} \right)^{1/p}
} $$

where
$Y_{obs}$ is a vector of observed values with components $y_k^{\, (obs)}$,
$Y_{sim}$ is a vector of simulated values with components $y_k^{\, (sim)}$,
and $p > 0$ is a scalar. The case where $p = 2$, the or the $L^2$ norm, is the basis of the popular
[least squares](https://en.wikipedia.org/wiki/Least_squares) metric.  While this metric gives disproportionate weight to outliers, it ensures that the derivative of the cost function is continuous where the error is zero.

When calibrating a hydrologic model, it is common to use an observed and simulated time series of river discharge values (at the outlet of a river basin) for the vectors $Y_{obs}$ and $Y_{sim}$.  In this case, $k$ may be seen as a time index.  Notice that the cost, $d$, can only be zero if all of the simulated and observed values are the same.

While not indicated explicitly in the notation above, it is understood that the vector of simulated values, $Y_{sim}$, is a function of the values of all of the model's adjustable parameters, sometimes called control parameters or design parameters._____no_output_____## River Discharge Data for Baro River, Near Masha

For this calibration exercise, we will use observed river discharge measurements at the mouth of a relatively small subbasin within the Baro River basin, with its outlet near the town of Masha.
_____no_output_____## TopoFlow Model Run for Baro River at Masha

Before starting the calibration exercise, which requires multiple model runs, each taking about 15 minutes on my new MacBook Pro, we perform a run with some "best-guess" parameters to get an idea of what to expect.  First, we import some Python packages and set some directories and file prefixes._____no_output_____

from topoflow import main
from topoflow.utils import visualize as tfvis
import os, os.path

cfg_prefix  = 'Test1'
case_prefix = 'Test1'
home_dir    = os.path.expanduser("~")
basin_dir   = home_dir + '/TF_Tests3/Baro-Gam_60sec/'
cfg_dir     = basin_dir + 'Test1_2015-10_to_2018-10_GLDAS_Bilinear_cfg/'
output_dir  = basin_dir + 'Test1_2015-10_to_2018-10_GLDAS_Bilinear_OUT/'

print('Your Home directory =', home_dir)
print('Basin directory     =', basin_dir)
print('CFG directory =', cfg_dir)
print('Output directory =', output_dir)

time_interp_method = 'None'
# time_interp_method = 'Linear'Importing TopoFlow 3.6 package modules:
   topoflow.utils
   topoflow.utils.tests
   topoflow.components
   topoflow.components.tests
   topoflow.framework
   topoflow.framework.tests

Your Home directory = /Users/peckhams
Basin directory     = /Users/peckhams/TF_Tests3/Baro-Gam_60sec/
CFG directory = /Users/peckhams/TF_Tests3/Baro-Gam_60sec/Test1_2015-10_to_2018-10_GLDAS_Bilinear_cfg/
Output directory = /Users/peckhams/TF_Tests3/Baro-Gam_60sec/Test1_2015-10_to_2018-10_GLDAS_Bilinear_OUT/

### Run the Model with Best-Guess Parameters_____no_output_____

main.run_model(cfg_prefix=cfg_prefix, cfg_directory=cfg_dir,
               time_interp_method=time_interp_method)EMELI: Reading info from provider_file:
    /Users/peckhams/TF_Tests3/Baro-Gam_60sec/Test1_2015-10_to_2018-10_GLDAS_Bilinear_cfg/Test1_providers.txt
EMELI: Reading info from comp_repo_file:
    /Users/peckhams/Dropbox/TopoFlow_3.6/topoflow/framework/component_repository.xml

EMELI: Instantiated component: tf_meteorology
EMELI: Instantiated component: tf_diversions_fraction_method
EMELI: Instantiated component: tf_channels_kin_wave
EMELI: Instantiated component: tf_snow_degree_day
EMELI: Instantiated component: tf_satzone_darcy_layers
EMELI: Instantiated component: tf_evap_priestley_taylor
EMELI: Instantiated component: tf_infil_richards_1d
EMELI: Instantiated component: tf_ice_gc2d
EMELI: Instantiated component: topoflow_driver
Driver component name = topoflow_driver

Meteorology component: Initializing...
Reading config file into component state.
In met_base read_input_files():
   time = 0.0
   min(P) = 0.0 [mmph]
   max(P) = 0.12960001072315208 [mmph]

Imported netCDF4 version: 1.5.3
EMELI: Initialized component: tf_meteorology.

Diversions component: Initializing...
Reading config file into component state.
Diversions component: Disabled in CFG file.
EMELI: Initialized component: tf_diversions_fraction_method.

Channels component: Initializing...
Reading config file into component state.
CHANNELS calling initialize_d8_vars()...
D8 component: Initializing...
Reading config file into component state.
Reading grid values...
Finished reading grid from:
  /Users/peckhams/TF_Tests3/Baro-Gam_60sec/__topo/Baro-Gam_60sec_DEM.rtg
   min(DEM), max(DEM) = 422.70148 2854.459

Filling pits in initial DEM...
Number of nodata and NaN values = 0
Finished initializing "closed" array.

Putting boundary pixels on heap...
Number of pixels on heap = 522
Finished with heap insertion.

n_closed = 0 of 17286
n_closed = 5000 of 17286
n_closed = 10000 of 17286
n_closed = 15000 of 17286
Total pixels   = 17286
Raised  pixels = 0
Drained pixels = 17288
Run time for fill_pits() =     0.3079 [seconds]
Finished with fill_pits().

Imported netCDF4 version: 1.5.3
D8 component: Updating...
Updating D8 flow grid...
   update_d8_codes(): Initializing grid...
   --------------------------------------------
   Data type of flow grid at start = int16
   Number of flats         = 784
   Number of 1-pixel pits  = 29
   Number of nodata/NaN    = 0
   min(codes), max(codes)  = -255, 136
   --------------------------------------------
   update_d8_codes(): Breaking ties...
   update_d8_codes(): Linking flats...
   Number of iterations = 17 (in link_flats())
Updating flow width grid...
    min(dw), max(dw) = 1830.1156, 2604.4915 [m]
Updating flow length grid...
    min(ds), max(ds) = 1830.1156, 2604.4915 [m]
Updating upstream area grid...
    min(A), max(A) = 0.0, 24704.818 [km^2]
    Number of iterations = 170
Saving D8 grids specified in CFG file...
CHANNELS calling read_input_files()...
CHANNELS calling set_computed_input_vars()...
CHANNELS calling initialize_computed_vars()...
    min(nval)       = 0.019999999552965164
    max(nval)       = 1.0
    min(width)      = 1.0
    max(width)      = 400.0
    min(angle)      = 29.999999999999996 [deg]
    max(angle)      = 29.999999999999996 [deg]
    min(sinuosity)  = 1.0
    max(sinuosity)  = 1.0
    min(init_depth) = 0.0
    max(init_depth) = 0.0
Initializing u, f, d grids...
Total number of slope values = 17286
Number of zero values        = 1305 (maybe edge)
Number of negative values    = 0
Number of NaN values         = 0
Number of infinite values    = 0
-------------------------------------------------
WARNING: Zero, negative or NaN slopes found.
         Replacing them with smallest slope.
         Consider using "new_slopes.py" instead.
         min(S) = 4.7353055379062425e-06
         max(S) = 0.31516149640083313
-------------------------------------------------

Imported netCDF4 version: 1.5.3
EMELI: Initialized component: tf_channels_kin_wave.

Snow component: Initializing...
Reading config file into component state.
Snow component: Disabled in CFG file.
EMELI: Initialized component: tf_snow_degree_day.

Groundwater component: Initializing...
Reading config file into component state.
Groundwater component: Disabled in CFG file.
EMELI: Initialized component: tf_satzone_darcy_layers.

Evaporation component: Initializing...
Reading config file into component state.
Imported netCDF4 version: 1.5.3
EMELI: Initialized component: tf_evap_priestley_taylor.

Infiltration component: Initializing...
Reading config file into component state.
Infiltration component: Disabled in CFG file.
EMELI: Initialized component: tf_infil_richards_1d.

Ice component: Initializing...
Reading config file into component state.
Ice component: Disabled in CFG file.
EMELI: Initialized component: tf_ice_gc2d.

TopoFlow component: Initializing...
Reading config file into component state.
Setting stop method to: Until_model_time
Opening log file:
    log_file = /Users/peckhams/TF_Tests3/Baro-Gam_60sec/Test1_2015-10_to_2018-10_GLDAS_Bilinear_OUT/Test1.log
Starting TopoFlow model run...
EMELI: Initialized component: topoflow_driver.

EMELI: Component time step sizes =
    tf_meteorology = 10800.0 [seconds]
    tf_diversions_fraction_method is Disabled.
    tf_channels_kin_wave = 200.0 [seconds]
    tf_snow_degree_day is Disabled.
    tf_satzone_darcy_layers is Disabled.
    tf_evap_priestley_taylor = 3600.0 [seconds]
    tf_infil_richards_1d is Disabled.
    tf_ice_gc2d is Disabled.
    topoflow_driver = 100.0 [seconds]
Converting all time step units to seconds...
Component with smallest time step is: topoflow_driver

Time interpolation method = None

Will print values every 0.5 seconds.
Time =     600.00 [min],  Q_out =    0.00000 [m^3/s]
Time =    1260.00 [min],  Q_out =    0.00000 [m^3/s]
Time =    1823.33 [min],  Q_out =    0.00060 [m^3/s]
Time =    2350.00 [min],  Q_out =    0.00000 [m^3/s]
Time =    2940.00 [min],  Q_out =    0.00000 [m^3/s]
Time =    3516.67 [min],  Q_out =    0.00000 [m^3/s]
Time =    4140.00 [min],  Q_out =    0.02473 [m^3/s]
Time =    4740.00 [min],  Q_out =    0.00055 [m^3/s]
Time =    5333.33 [min],  Q_out =    0.00000 [m^3/s]
Time =    5963.33 [min],  Q_out =    0.00000 [m^3/s]
Time =    6600.00 [min],  Q_out =    0.03036 [m^3/s]
Time =    7200.00 [min],  Q_out =    0.00067 [m^3/s]
Time =    7876.67 [min],  Q_out =    0.00000 [m^3/s]
Time =    8576.67 [min],  Q_out =    0.00000 [m^3/s]
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Time =    9900.00 [min],  Q_out =    0.13621 [m^3/s]
Time =   10486.67 [min],  Q_out =    0.32051 [m^3/s]
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Time =   12410.00 [min],  Q_out =    0.07876 [m^3/s]
Time =   13040.00 [min],  Q_out =    2.03415 [m^3/s]
Time =   13680.00 [min],  Q_out =    3.44248 [m^3/s]
Time =   14400.00 [min],  Q_out =    3.92583 [m^3/s]
Time =   15060.00 [min],  Q_out =    3.77925 [m^3/s]
Time =   15726.67 [min],  Q_out =    4.62159 [m^3/s]
Time =   16380.00 [min],  Q_out =    4.83992 [m^3/s]
Time =   17083.33 [min],  Q_out =   94.78960 [m^3/s]
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Time =   20296.67 [min],  Q_out =  101.31189 [m^3/s]
Time =   20890.00 [min],  Q_out =  133.10446 [m^3/s]
Time =   21565.00 [min],  Q_out =  220.93057 [m^3/s]
Time =   22200.00 [min],  Q_out =  398.53763 [m^3/s]
Time =   22870.00 [min],  Q_out =  340.38052 [m^3/s]
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Time =   24803.33 [min],  Q_out =  588.22440 [m^3/s]
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Time =   26100.00 [min],  Q_out =  651.44562 [m^3/s]
Time =   26740.00 [min],  Q_out =  659.55997 [m^3/s]
Time =   27406.67 [min],  Q_out =  643.60140 [m^3/s]
Time =   28093.33 [min],  Q_out =  580.62814 [m^3/s]
Time =   28760.00 [min],  Q_out =  539.23194 [m^3/s]
Time =   29390.00 [min],  Q_out =  575.34750 [m^3/s]
Time =   30050.00 [min],  Q_out =  667.48226 [m^3/s]
Time =   30670.00 [min],  Q_out =  775.31337 [m^3/s]
Time =   31320.00 [min],  Q_out =  687.88547 [m^3/s]
Time =   32003.33 [min],  Q_out =  613.33276 [m^3/s]
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Time =   33296.67 [min],  Q_out =  682.36723 [m^3/s]
Time =   33946.67 [min],  Q_out =  711.80114 [m^3/s]
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Time =   35916.67 [min],  Q_out =  534.48562 [m^3/s]
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Time =   37200.00 [min],  Q_out =  427.73009 [m^3/s]
Time =   37810.00 [min],  Q_out =  411.85231 [m^3/s]
Time =   38510.00 [min],  Q_out =  389.64282 [m^3/s]
Time =   39133.33 [min],  Q_out =  774.75413 [m^3/s]
Time =   39770.00 [min],  Q_out =  508.46420 [m^3/s]
Time =   40410.00 [min],  Q_out =  526.56181 [m^3/s]
Time =   41040.00 [min],  Q_out =  509.04431 [m^3/s]
Time =   41710.00 [min],  Q_out =  613.30878 [m^3/s]
Time =   42336.67 [min],  Q_out =  793.20902 [m^3/s]
Time =   43003.33 [min],  Q_out =  770.50020 [m^3/s]
Time =   43620.00 [min],  Q_out =  767.83370 [m^3/s]
Time =   44273.33 [min],  Q_out =  691.74097 [m^3/s]
Time =   44880.00 [min],  Q_out =  622.99599 [m^3/s]
Time =   45516.67 [min],  Q_out =  635.09063 [m^3/s]
Time =   46140.00 [min],  Q_out =  567.85653 [m^3/s]
Time =   46765.00 [min],  Q_out =  653.00994 [m^3/s]
Time =   47400.00 [min],  Q_out =  675.36237 [m^3/s]
Time =   48053.33 [min],  Q_out =  630.93792 [m^3/s]
Time =   48720.00 [min],  Q_out =  560.88588 [m^3/s]
Time =   49320.00 [min],  Q_out =  499.94661 [m^3/s]
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Time =   61020.00 [min],  Q_out =  190.07264 [m^3/s]
Time =   61573.33 [min],  Q_out =  208.47590 [m^3/s]
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Time =   62723.33 [min],  Q_out =  312.39442 [m^3/s]
Time =   63330.00 [min],  Q_out =  298.49037 [m^3/s]
Time =   63900.00 [min],  Q_out =  364.64426 [m^3/s]
Time =   64576.67 [min],  Q_out =  393.02709 [m^3/s]
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Time =   65940.00 [min],  Q_out =  476.71847 [m^3/s]
Time =   66600.00 [min],  Q_out =  456.87607 [m^3/s]
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Time =   71100.00 [min],  Q_out =  256.35702 [m^3/s]
Time =   71763.33 [min],  Q_out =  242.54827 [m^3/s]
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Time =   72996.67 [min],  Q_out =  232.80827 [m^3/s]
Time =   73590.00 [min],  Q_out =  252.56969 [m^3/s]
Time =   74220.00 [min],  Q_out =  278.86437 [m^3/s]
Time =   74893.33 [min],  Q_out =  270.63927 [m^3/s]
Time =   75540.00 [min],  Q_out =  258.89088 [m^3/s]
Time =   76200.00 [min],  Q_out =  301.12788 [m^3/s]
Time =   76860.00 [min],  Q_out =  303.93937 [m^3/s]
Time =   77563.33 [min],  Q_out =  272.14078 [m^3/s]
Time =   78148.33 [min],  Q_out =  288.30603 [m^3/s]
Time =   78798.33 [min],  Q_out =  323.53000 [m^3/s]
Time =   79403.33 [min],  Q_out =  426.25620 [m^3/s]
Time =   80040.00 [min],  Q_out =  381.50681 [m^3/s]
Time =   80666.67 [min],  Q_out =  419.90015 [m^3/s]
Time =   81300.00 [min],  Q_out =  533.09473 [m^3/s]
Time =   81970.00 [min],  Q_out =  509.89590 [m^3/s]
Time =   82593.33 [min],  Q_out =  498.58489 [m^3/s]
Time =   83250.00 [min],  Q_out =  555.94063 [m^3/s]
Time =   83880.00 [min],  Q_out =  630.72692 [m^3/s]
Time =   84523.33 [min],  Q_out =  572.86305 [m^3/s]
Time =   85140.00 [min],  Q_out =  526.11836 [m^3/s]
Time =   85796.67 [min],  Q_out =  503.72412 [m^3/s]
Time =   86400.00 [min],  Q_out =  497.97575 [m^3/s]
Time =   87000.00 [min],  Q_out =  455.42166 [m^3/s]
Time =   87663.33 [min],  Q_out =  422.29091 [m^3/s]
Time =   88320.00 [min],  Q_out =  401.25606 [m^3/s]
Time =   88966.67 [min],  Q_out =  374.49210 [m^3/s]
Time =   89616.67 [min],  Q_out =  343.50610 [m^3/s]
Time =   90240.00 [min],  Q_out =  306.06407 [m^3/s]
Time =   90900.00 [min],  Q_out =  281.08297 [m^3/s]
Time =   91553.33 [min],  Q_out =  257.59186 [m^3/s]
Time =   92193.33 [min],  Q_out =  236.54390 [m^3/s]
Time =   92826.67 [min],  Q_out =  220.40930 [m^3/s]
Time =   93476.67 [min],  Q_out =  209.30439 [m^3/s]
Time =   94096.67 [min],  Q_out =  226.71658 [m^3/s]
Time =   94710.00 [min],  Q_out =  289.00521 [m^3/s]
Time =   95340.00 [min],  Q_out =  294.71359 [m^3/s]
Time =   95986.67 [min],  Q_out =  289.28096 [m^3/s]
Time =   96616.67 [min],  Q_out =  287.91755 [m^3/s]
Time =   97246.67 [min],  Q_out =  272.24599 [m^3/s]
Time =   97913.33 [min],  Q_out =  281.73567 [m^3/s]
Time =   98520.00 [min],  Q_out =  343.12940 [m^3/s]
Time =   99196.67 [min],  Q_out =  392.42772 [m^3/s]
Time =   99840.00 [min],  Q_out =  455.34329 [m^3/s]
Time =  100506.67 [min],  Q_out =  456.81562 [m^3/s]
Time =  101140.00 [min],  Q_out =  473.64803 [m^3/s]
Time =  101753.33 [min],  Q_out =  415.75417 [m^3/s]
Time =  102383.33 [min],  Q_out =  390.75314 [m^3/s]
Time =  102996.67 [min],  Q_out =  331.46601 [m^3/s]
Time =  103648.33 [min],  Q_out =  281.19376 [m^3/s]
Time =  104290.00 [min],  Q_out =  243.15519 [m^3/s]
Time =  105000.00 [min],  Q_out =  224.30291 [m^3/s]
Time =  105640.00 [min],  Q_out =  258.35289 [m^3/s]
Time =  106303.33 [min],  Q_out =  376.53813 [m^3/s]
Time =  106920.00 [min],  Q_out =  366.19046 [m^3/s]
Time =  107636.67 [min],  Q_out =  340.51003 [m^3/s]
Time =  108240.00 [min],  Q_out =  353.57035 [m^3/s]
Time =  108893.33 [min],  Q_out =  324.24446 [m^3/s]
Time =  109513.33 [min],  Q_out =  292.98841 [m^3/s]
Time =  110160.00 [min],  Q_out =  260.75749 [m^3/s]
Time =  110860.00 [min],  Q_out =  238.45679 [m^3/s]
Time =  111480.00 [min],  Q_out =  258.76348 [m^3/s]
Time =  112140.00 [min],  Q_out =  307.02073 [m^3/s]
Time =  112770.00 [min],  Q_out =  340.61676 [m^3/s]
Time =  113400.00 [min],  Q_out =  485.65426 [m^3/s]
Time =  114036.67 [min],  Q_out =  716.28673 [m^3/s]
Time =  114660.00 [min],  Q_out = 1378.25608 [m^3/s]
Time =  115306.67 [min],  Q_out = 1731.27778 [m^3/s]
Time =  115920.00 [min],  Q_out = 1538.05893 [m^3/s]
Time =  116570.00 [min],  Q_out = 1268.92876 [m^3/s]
Time =  117180.00 [min],  Q_out = 1136.24047 [m^3/s]
Time =  117840.00 [min],  Q_out = 1103.96573 [m^3/s]
Time =  118440.00 [min],  Q_out =  947.92603 [m^3/s]
Time =  119086.67 [min],  Q_out =  833.55821 [m^3/s]
Time =  119700.00 [min],  Q_out =  732.58430 [m^3/s]
Time =  120353.33 [min],  Q_out =  628.25702 [m^3/s]
Time =  120960.00 [min],  Q_out =  620.08788 [m^3/s]
Time =  121603.33 [min],  Q_out =  611.60227 [m^3/s]
Time =  122233.33 [min],  Q_out =  600.32911 [m^3/s]
Time =  122850.00 [min],  Q_out =  576.10514 [m^3/s]
Time =  123510.00 [min],  Q_out =  525.73911 [m^3/s]
Time =  124183.33 [min],  Q_out =  541.67665 [m^3/s]
Time =  124820.00 [min],  Q_out =  517.48480 [m^3/s]
Time =  125440.00 [min],  Q_out =  510.26693 [m^3/s]
Time =  126058.33 [min],  Q_out =  593.70243 [m^3/s]
Time =  126666.67 [min],  Q_out =  512.69307 [m^3/s]
Time =  127283.33 [min],  Q_out =  472.63213 [m^3/s]
Time =  127980.00 [min],  Q_out =  402.68960 [m^3/s]
Time =  128613.33 [min],  Q_out =  352.09958 [m^3/s]
Time =  129283.33 [min],  Q_out =  295.86311 [m^3/s]
Time =  129923.33 [min],  Q_out =  279.83221 [m^3/s]
Time =  130560.00 [min],  Q_out =  252.71735 [m^3/s]
Time =  131156.67 [min],  Q_out =  237.33431 [m^3/s]
Time =  131746.67 [min],  Q_out =  214.96111 [m^3/s]
Time =  132396.67 [min],  Q_out =  197.39618 [m^3/s]
Time =  133020.00 [min],  Q_out =  212.72391 [m^3/s]
Time =  133680.00 [min],  Q_out =  198.86800 [m^3/s]
Time =  134280.00 [min],  Q_out =  199.56021 [m^3/s]
Time =  134940.00 [min],  Q_out =  290.44277 [m^3/s]
Time =  135560.00 [min],  Q_out =  392.21596 [m^3/s]
Time =  136200.00 [min],  Q_out =  487.94364 [m^3/s]
Time =  136853.33 [min],  Q_out =  438.04311 [m^3/s]
Time =  137486.67 [min],  Q_out =  406.04638 [m^3/s]
Time =  138120.00 [min],  Q_out =  522.20066 [m^3/s]
Time =  138770.00 [min],  Q_out =  902.76805 [m^3/s]
Time =  139413.33 [min],  Q_out = 1013.95867 [m^3/s]
Time =  140076.67 [min],  Q_out =  952.04227 [m^3/s]
Time =  140773.33 [min],  Q_out =  762.33596 [m^3/s]
Time =  141420.00 [min],  Q_out =  639.13559 [m^3/s]
Time =  141993.33 [min],  Q_out =  555.92762 [m^3/s]
Time =  142616.67 [min],  Q_out =  507.63011 [m^3/s]
Time =  143236.67 [min],  Q_out =  572.17072 [m^3/s]
Time =  143903.33 [min],  Q_out =  437.78739 [m^3/s]
Time =  144540.00 [min],  Q_out =  375.42718 [m^3/s]
Time =  145260.00 [min],  Q_out =  307.55901 [m^3/s]
Time =  145916.67 [min],  Q_out =  273.82071 [m^3/s]
Time =  146603.33 [min],  Q_out =  241.09200 [m^3/s]
Time =  147240.00 [min],  Q_out =  220.20414 [m^3/s]
Time =  147913.33 [min],  Q_out =  186.24764 [m^3/s]
Time =  148500.00 [min],  Q_out =  171.49201 [m^3/s]
Time =  149143.33 [min],  Q_out =  156.30791 [m^3/s]
Time =  149760.00 [min],  Q_out =  167.30371 [m^3/s]
Time =  150410.00 [min],  Q_out =  252.81954 [m^3/s]
Time =  151020.00 [min],  Q_out =  370.63543 [m^3/s]
Time =  151680.00 [min],  Q_out =  271.71246 [m^3/s]
Time =  152340.00 [min],  Q_out =  301.20240 [m^3/s]
Time =  152993.33 [min],  Q_out =  305.79139 [m^3/s]
Time =  153600.00 [min],  Q_out =  330.87313 [m^3/s]
Time =  154161.67 [min],  Q_out =  292.99899 [m^3/s]
Time =  154730.00 [min],  Q_out =  293.60121 [m^3/s]
Time =  155400.00 [min],  Q_out =  282.91272 [m^3/s]
Time =  156026.67 [min],  Q_out =  449.74570 [m^3/s]
Time =  156670.00 [min],  Q_out =  314.27121 [m^3/s]
Time =  157280.00 [min],  Q_out =  295.61829 [m^3/s]
Time =  157936.67 [min],  Q_out =  345.33095 [m^3/s]
Time =  158586.67 [min],  Q_out =  338.61643 [m^3/s]
Time =  159240.00 [min],  Q_out =  285.28166 [m^3/s]
Time =  159863.33 [min],  Q_out =  263.37483 [m^3/s]
Time =  160483.33 [min],  Q_out =  261.07528 [m^3/s]
Time =  161050.00 [min],  Q_out =  244.95108 [m^3/s]
Time =  161640.00 [min],  Q_out =  222.26730 [m^3/s]
Time =  162343.33 [min],  Q_out =  197.52136 [m^3/s]
Time =  162940.00 [min],  Q_out =  192.17656 [m^3/s]
Time =  163560.00 [min],  Q_out =  248.06165 [m^3/s]
Time =  164230.00 [min],  Q_out =  279.34192 [m^3/s]
Time =  164858.33 [min],  Q_out =  373.85767 [m^3/s]
Time =  165540.00 [min],  Q_out =  351.50155 [m^3/s]
Time =  166183.33 [min],  Q_out =  348.10699 [m^3/s]
Time =  166860.00 [min],  Q_out =  354.93620 [m^3/s]
Time =  167443.33 [min],  Q_out =  355.63072 [m^3/s]
Time =  168060.00 [min],  Q_out =  328.15068 [m^3/s]
Time =  168660.00 [min],  Q_out =  307.72176 [m^3/s]
Time =  169323.33 [min],  Q_out =  289.15624 [m^3/s]
Time =  169936.67 [min],  Q_out =  279.52205 [m^3/s]
Time =  170580.00 [min],  Q_out =  261.37920 [m^3/s]
Time =  171260.00 [min],  Q_out =  245.28126 [m^3/s]
Time =  171895.00 [min],  Q_out =  293.71756 [m^3/s]
Time =  172526.67 [min],  Q_out =  291.40734 [m^3/s]
Time =  173156.67 [min],  Q_out =  294.42083 [m^3/s]
Time =  173800.00 [min],  Q_out =  337.69242 [m^3/s]
Time =  174410.00 [min],  Q_out =  396.58050 [m^3/s]
Time =  174960.00 [min],  Q_out =  461.82382 [m^3/s]
Time =  175633.33 [min],  Q_out =  408.61529 [m^3/s]
Time =  176270.00 [min],  Q_out =  408.76770 [m^3/s]
Time =  176880.00 [min],  Q_out =  415.83182 [m^3/s]
Time =  177480.00 [min],  Q_out =  369.84779 [m^3/s]
Time =  178140.00 [min],  Q_out =  306.32893 [m^3/s]
Time =  178776.67 [min],  Q_out =  295.54563 [m^3/s]
Time =  179415.00 [min],  Q_out =  289.45859 [m^3/s]
Time =  180036.67 [min],  Q_out =  311.48101 [m^3/s]
Time =  180600.00 [min],  Q_out =  370.93113 [m^3/s]
Time =  181236.67 [min],  Q_out =  416.70396 [m^3/s]
Time =  181860.00 [min],  Q_out =  479.45945 [m^3/s]
Time =  182520.00 [min],  Q_out =  424.43456 [m^3/s]
Time =  183173.33 [min],  Q_out =  408.19936 [m^3/s]
Time =  183823.33 [min],  Q_out =  403.76817 [m^3/s]
Time =  184460.00 [min],  Q_out =  450.21355 [m^3/s]
Time =  185100.00 [min],  Q_out =  522.16105 [m^3/s]
Time =  185776.67 [min],  Q_out =  512.40378 [m^3/s]
Time =  186426.67 [min],  Q_out =  608.56648 [m^3/s]
Time =  187046.67 [min],  Q_out =  516.22463 [m^3/s]
Time =  187613.33 [min],  Q_out =  483.80263 [m^3/s]
Time =  188253.33 [min],  Q_out =  446.32807 [m^3/s]
Time =  188883.33 [min],  Q_out =  453.34930 [m^3/s]
Time =  189520.00 [min],  Q_out =  406.61175 [m^3/s]
Time =  190140.00 [min],  Q_out =  382.23826 [m^3/s]
Time =  190780.00 [min],  Q_out =  373.09623 [m^3/s]
Time =  191440.00 [min],  Q_out =  341.25854 [m^3/s]
Time =  192060.00 [min],  Q_out =  312.48273 [m^3/s]
Time =  192773.33 [min],  Q_out =  266.19288 [m^3/s]
Time =  193386.67 [min],  Q_out =  265.62770 [m^3/s]
Time =  194013.33 [min],  Q_out =  301.73757 [m^3/s]
Time =  194580.00 [min],  Q_out =  295.65942 [m^3/s]
Time =  195168.33 [min],  Q_out =  381.19252 [m^3/s]
Time =  195820.00 [min],  Q_out =  351.72794 [m^3/s]
Time =  196423.33 [min],  Q_out =  388.77832 [m^3/s]
Time =  197080.00 [min],  Q_out =  380.82977 [m^3/s]
Time =  197696.67 [min],  Q_out =  423.85033 [m^3/s]
Time =  198383.33 [min],  Q_out =  458.17707 [m^3/s]
Time =  199080.00 [min],  Q_out =  459.99458 [m^3/s]
Time =  199800.00 [min],  Q_out =  388.78064 [m^3/s]
Time =  200363.33 [min],  Q_out =  353.29105 [m^3/s]
Time =  200913.33 [min],  Q_out =  322.61567 [m^3/s]
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Time =  202856.67 [min],  Q_out =  301.23409 [m^3/s]
Time =  203520.00 [min],  Q_out =  275.34828 [m^3/s]
Time =  204180.00 [min],  Q_out =  265.61667 [m^3/s]
Time =  204783.33 [min],  Q_out =  297.23622 [m^3/s]
Time =  205473.33 [min],  Q_out =  218.35280 [m^3/s]
Time =  206100.00 [min],  Q_out =  227.34189 [m^3/s]
Time =  206756.67 [min],  Q_out =  249.73609 [m^3/s]
Time =  207373.33 [min],  Q_out =  182.82513 [m^3/s]
Time =  208036.67 [min],  Q_out =  189.59818 [m^3/s]
Time =  208720.00 [min],  Q_out =  206.69154 [m^3/s]
Time =  209280.00 [min],  Q_out =  228.72956 [m^3/s]
Time =  209876.67 [min],  Q_out =  268.69397 [m^3/s]
Time =  210460.00 [min],  Q_out =  337.17322 [m^3/s]
Time =  211110.00 [min],  Q_out =  353.94745 [m^3/s]
Time =  211750.00 [min],  Q_out =  378.01649 [m^3/s]
Time =  212413.33 [min],  Q_out =  443.10105 [m^3/s]
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Time =  215590.00 [min],  Q_out =  410.20890 [m^3/s]
Time =  216240.00 [min],  Q_out =  411.78556 [m^3/s]
Time =  216895.00 [min],  Q_out =  413.16344 [m^3/s]
Time =  217440.00 [min],  Q_out =  404.43822 [m^3/s]
Time =  218053.33 [min],  Q_out =  378.61144 [m^3/s]
Time =  218700.00 [min],  Q_out =  380.76488 [m^3/s]
Time =  219330.00 [min],  Q_out =  378.20975 [m^3/s]
Time =  219940.00 [min],  Q_out =  358.97018 [m^3/s]
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Time =  221173.33 [min],  Q_out =  633.96688 [m^3/s]
Time =  221843.33 [min],  Q_out =  779.67213 [m^3/s]
Time =  222505.00 [min],  Q_out = 1457.92468 [m^3/s]
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Time =  223680.00 [min],  Q_out = 1526.04529 [m^3/s]
Time =  224283.33 [min],  Q_out = 1379.44359 [m^3/s]
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Time =  225580.00 [min],  Q_out =  998.83587 [m^3/s]
Time =  226260.00 [min],  Q_out =  892.46748 [m^3/s]
Time =  226980.00 [min],  Q_out =  786.21777 [m^3/s]
Time =  227643.33 [min],  Q_out =  778.94859 [m^3/s]
Time =  228240.00 [min],  Q_out =  679.12615 [m^3/s]
Time =  228950.00 [min],  Q_out =  542.40360 [m^3/s]
Time =  229576.67 [min],  Q_out =  463.11535 [m^3/s]
Time =  230166.67 [min],  Q_out =  410.88012 [m^3/s]
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Time =  232800.00 [min],  Q_out =  439.61866 [m^3/s]
Time =  233460.00 [min],  Q_out =  386.82891 [m^3/s]
Time =  234100.00 [min],  Q_out =  338.46383 [m^3/s]
Time =  234720.00 [min],  Q_out =  298.56164 [m^3/s]
Time =  235340.00 [min],  Q_out =  300.16614 [m^3/s]
Time =  235980.00 [min],  Q_out =  342.73361 [m^3/s]
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Time =  237340.00 [min],  Q_out =  369.96332 [m^3/s]
Time =  237916.67 [min],  Q_out =  416.14706 [m^3/s]
Time =  238440.00 [min],  Q_out =  381.63679 [m^3/s]
Time =  239063.33 [min],  Q_out =  342.45025 [m^3/s]
Time =  239683.33 [min],  Q_out =  348.53258 [m^3/s]
Time =  240300.00 [min],  Q_out =  360.35060 [m^3/s]
Time =  240900.00 [min],  Q_out =  383.40408 [m^3/s]
Time =  241591.67 [min],  Q_out =  386.91775 [m^3/s]
Time =  242233.33 [min],  Q_out =  371.40017 [m^3/s]
Time =  242853.33 [min],  Q_out =  351.77777 [m^3/s]
Time =  243480.00 [min],  Q_out =  332.77312 [m^3/s]
Time =  244080.00 [min],  Q_out =  300.38814 [m^3/s]
Time =  244673.33 [min],  Q_out =  273.65613 [m^3/s]
Time =  245280.00 [min],  Q_out =  258.51639 [m^3/s]
Time =  245920.00 [min],  Q_out =  263.75812 [m^3/s]
Time =  246540.00 [min],  Q_out =  278.09127 [m^3/s]
Time =  247156.67 [min],  Q_out =  312.40773 [m^3/s]
Time =  247800.00 [min],  Q_out =  334.10252 [m^3/s]
Time =  248436.67 [min],  Q_out =  634.79545 [m^3/s]
Time =  249120.00 [min],  Q_out =  773.16927 [m^3/s]
Time =  249716.67 [min],  Q_out =  952.64905 [m^3/s]
Time =  250343.33 [min],  Q_out =  947.83542 [m^3/s]
Time =  250966.67 [min],  Q_out =  877.70505 [m^3/s]
Time =  251603.33 [min],  Q_out =  756.35427 [m^3/s]
Time =  252240.00 [min],  Q_out =  640.62155 [m^3/s]
Time =  252886.67 [min],  Q_out =  533.81235 [m^3/s]
Time =  253513.33 [min],  Q_out =  471.44691 [m^3/s]
Time =  254100.00 [min],  Q_out =  489.61701 [m^3/s]
Time =  254760.00 [min],  Q_out =  576.57366 [m^3/s]
Time =  255380.00 [min],  Q_out =  513.25412 [m^3/s]
Time =  256030.00 [min],  Q_out =  459.91819 [m^3/s]
Time =  256653.33 [min],  Q_out =  479.37562 [m^3/s]
Time =  257220.00 [min],  Q_out =  495.58564 [m^3/s]
Time =  257850.00 [min],  Q_out =  475.29266 [m^3/s]
Time =  258476.67 [min],  Q_out =  556.63471 [m^3/s]
Time =  259140.00 [min],  Q_out =  805.71392 [m^3/s]
Time =  259740.00 [min],  Q_out =  716.36956 [m^3/s]
Time =  260423.33 [min],  Q_out =  582.84663 [m^3/s]
Time =  261033.33 [min],  Q_out =  518.80874 [m^3/s]
Time =  261710.00 [min],  Q_out =  423.43706 [m^3/s]
Time =  262320.00 [min],  Q_out =  367.04700 [m^3/s]
Time =  262996.67 [min],  Q_out =  303.06065 [m^3/s]
Time =  263663.33 [min],  Q_out =  289.41178 [m^3/s]
Time =  264250.00 [min],  Q_out =  343.43166 [m^3/s]
Time =  264846.67 [min],  Q_out =  275.20052 [m^3/s]
Time =  265413.33 [min],  Q_out =  295.18258 [m^3/s]
Time =  266050.00 [min],  Q_out =  279.69093 [m^3/s]
Time =  266700.00 [min],  Q_out =  280.21364 [m^3/s]
Time =  267326.67 [min],  Q_out =  289.18449 [m^3/s]
Time =  267960.00 [min],  Q_out =  276.29773 [m^3/s]
Time =  268560.00 [min],  Q_out =  296.58613 [m^3/s]
Time =  269226.67 [min],  Q_out =  378.51648 [m^3/s]
Time =  269820.00 [min],  Q_out =  384.23465 [m^3/s]
Time =  270486.67 [min],  Q_out =  346.02542 [m^3/s]
Time =  271046.67 [min],  Q_out =  356.44562 [m^3/s]
Time =  271610.00 [min],  Q_out =  375.26876 [m^3/s]
Time =  272233.33 [min],  Q_out =  316.62600 [m^3/s]
Time =  272880.00 [min],  Q_out =  290.24102 [m^3/s]
Time =  273553.33 [min],  Q_out =  276.84623 [m^3/s]
Time =  274173.33 [min],  Q_out =  314.64669 [m^3/s]
Time =  274828.33 [min],  Q_out =  268.87264 [m^3/s]
Time =  275440.00 [min],  Q_out =  265.39148 [m^3/s]
Time =  276086.67 [min],  Q_out =  258.19468 [m^3/s]
Time =  276716.67 [min],  Q_out =  249.78616 [m^3/s]
Time =  277260.00 [min],  Q_out =  240.58722 [m^3/s]
Time =  277875.00 [min],  Q_out =  216.36943 [m^3/s]
Time =  278483.33 [min],  Q_out =  196.95316 [m^3/s]
Time =  279180.00 [min],  Q_out =  190.61290 [m^3/s]
Time =  279806.67 [min],  Q_out =  375.42824 [m^3/s]
Time =  280440.00 [min],  Q_out =  391.82687 [m^3/s]
Time =  281090.00 [min],  Q_out =  554.76735 [m^3/s]
Time =  281706.67 [min],  Q_out =  606.19425 [m^3/s]
Time =  282343.33 [min],  Q_out =  517.42541 [m^3/s]
Time =  282900.00 [min],  Q_out =  435.29410 [m^3/s]
Time =  283493.33 [min],  Q_out =  455.65881 [m^3/s]
Time =  284086.67 [min],  Q_out =  503.73372 [m^3/s]
Time =  284760.00 [min],  Q_out =  547.19681 [m^3/s]
Time =  285460.00 [min],  Q_out =  667.80091 [m^3/s]
Time =  286070.00 [min],  Q_out =  713.73248 [m^3/s]
Time =  286686.67 [min],  Q_out =  715.46249 [m^3/s]
Time =  287303.33 [min],  Q_out =  611.76527 [m^3/s]
Time =  287960.00 [min],  Q_out =  542.74659 [m^3/s]
Time =  288540.00 [min],  Q_out =  545.45047 [m^3/s]
Time =  289173.33 [min],  Q_out =  444.83625 [m^3/s]
Time =  289753.33 [min],  Q_out =  407.67946 [m^3/s]
Time =  290386.67 [min],  Q_out =  365.26710 [m^3/s]
Time =  291020.00 [min],  Q_out =  344.88559 [m^3/s]
Time =  291616.67 [min],  Q_out =  351.76073 [m^3/s]
Time =  292270.00 [min],  Q_out =  299.02330 [m^3/s]
Time =  292910.00 [min],  Q_out =  282.05628 [m^3/s]
Time =  293580.00 [min],  Q_out =  263.74202 [m^3/s]
Time =  294223.33 [min],  Q_out =  248.01192 [m^3/s]
Time =  294840.00 [min],  Q_out =  224.55956 [m^3/s]
Time =  295403.33 [min],  Q_out =  217.29153 [m^3/s]
Time =  296040.00 [min],  Q_out =  207.07488 [m^3/s]
Time =  296686.67 [min],  Q_out =  202.44340 [m^3/s]
Time =  297300.00 [min],  Q_out =  203.27369 [m^3/s]
Time =  297946.67 [min],  Q_out =  182.16322 [m^3/s]
Time =  298573.33 [min],  Q_out =  177.31846 [m^3/s]
Time =  299280.00 [min],  Q_out =  165.52168 [m^3/s]
Time =  299926.67 [min],  Q_out =  160.90197 [m^3/s]
Time =  300568.33 [min],  Q_out =  155.62599 [m^3/s]
Time =  301133.33 [min],  Q_out =  215.65424 [m^3/s]
Time =  301736.67 [min],  Q_out =  317.00217 [m^3/s]
Time =  302400.00 [min],  Q_out =  315.50708 [m^3/s]
Time =  303060.00 [min],  Q_out =  378.89385 [m^3/s]
Time =  303730.00 [min],  Q_out =  446.88252 [m^3/s]
Time =  304380.00 [min],  Q_out =  446.86695 [m^3/s]
Time =  305073.33 [min],  Q_out =  404.04125 [m^3/s]
Time =  305700.00 [min],  Q_out =  394.98079 [m^3/s]
Time =  306360.00 [min],  Q_out =  383.67558 [m^3/s]
Time =  307003.33 [min],  Q_out =  347.41332 [m^3/s]
Time =  307620.00 [min],  Q_out =  311.24826 [m^3/s]
Time =  308320.00 [min],  Q_out =  266.64772 [m^3/s]
Time =  308948.33 [min],  Q_out =  233.16283 [m^3/s]
Time =  309600.00 [min],  Q_out =  255.79450 [m^3/s]
Time =  310226.67 [min],  Q_out =  442.07330 [m^3/s]
Time =  310920.00 [min],  Q_out =  326.76106 [m^3/s]
Time =  311580.00 [min],  Q_out =  286.43011 [m^3/s]
Time =  312266.67 [min],  Q_out =  258.45187 [m^3/s]
Time =  312840.00 [min],  Q_out =  243.27400 [m^3/s]
Time =  313440.00 [min],  Q_out =  208.05877 [m^3/s]
Time =  314056.67 [min],  Q_out =  200.88791 [m^3/s]
Time =  314720.00 [min],  Q_out =  196.15009 [m^3/s]
Time =  315360.00 [min],  Q_out =  186.42980 [m^3/s]
Time =  315983.33 [min],  Q_out =  200.80284 [m^3/s]
Time =  316636.67 [min],  Q_out =  187.54890 [m^3/s]
Time =  317270.00 [min],  Q_out =  152.95250 [m^3/s]
Time =  317930.00 [min],  Q_out =  142.34411 [m^3/s]
Time =  318553.33 [min],  Q_out =  138.00822 [m^3/s]
Time =  319140.00 [min],  Q_out =  128.82370 [m^3/s]
Time =  319720.00 [min],  Q_out =  131.49273 [m^3/s]
Time =  320340.00 [min],  Q_out =  134.67242 [m^3/s]
Time =  321006.67 [min],  Q_out =  132.79434 [m^3/s]
Time =  321650.00 [min],  Q_out =  147.71906 [m^3/s]
Time =  322290.00 [min],  Q_out =  156.05383 [m^3/s]
Time =  322920.00 [min],  Q_out =  162.68909 [m^3/s]
Time =  323580.00 [min],  Q_out =  225.65422 [m^3/s]
Time =  324173.33 [min],  Q_out =  302.69183 [m^3/s]
Time =  324720.00 [min],  Q_out =  358.19669 [m^3/s]
Time =  325353.33 [min],  Q_out =  437.60760 [m^3/s]
Time =  325980.00 [min],  Q_out =  473.36028 [m^3/s]
Time =  326643.33 [min],  Q_out =  583.88978 [m^3/s]
Time =  327276.67 [min],  Q_out =  807.02582 [m^3/s]
Time =  327943.33 [min],  Q_out =  750.14495 [m^3/s]
Time =  328613.33 [min],  Q_out =  637.64751 [m^3/s]
Time =  329256.67 [min],  Q_out =  537.41254 [m^3/s]
Time =  329880.00 [min],  Q_out =  488.42507 [m^3/s]
Time =  330453.33 [min],  Q_out =  429.18749 [m^3/s]
Time =  331063.33 [min],  Q_out =  355.84996 [m^3/s]
Time =  331663.33 [min],  Q_out =  317.83187 [m^3/s]
Time =  332313.33 [min],  Q_out =  272.22061 [m^3/s]
Time =  332940.00 [min],  Q_out =  242.39607 [m^3/s]
Time =  333600.00 [min],  Q_out =  207.73896 [m^3/s]
Time =  334260.00 [min],  Q_out =  186.95055 [m^3/s]
Time =  334920.00 [min],  Q_out =  168.96274 [m^3/s]
Time =  335586.67 [min],  Q_out =  150.74422 [m^3/s]
Time =  336240.00 [min],  Q_out =  129.14004 [m^3/s]
Time =  336955.00 [min],  Q_out =  120.30841 [m^3/s]
Time =  337500.00 [min],  Q_out =  128.20012 [m^3/s]
Time =  338100.00 [min],  Q_out =  139.94380 [m^3/s]
Time =  338756.67 [min],  Q_out =  108.14142 [m^3/s]
Time =  339413.33 [min],  Q_out =   95.61781 [m^3/s]
Time =  340035.00 [min],  Q_out =   95.71263 [m^3/s]
Time =  340673.33 [min],  Q_out =   89.81795 [m^3/s]
Time =  341288.33 [min],  Q_out =   89.37732 [m^3/s]
Time =  341936.67 [min],  Q_out =   85.07149 [m^3/s]
Time =  342590.00 [min],  Q_out =   81.28505 [m^3/s]
Time =  343145.00 [min],  Q_out =   79.51687 [m^3/s]
Time =  343800.00 [min],  Q_out =   78.59199 [m^3/s]
Time =  344520.00 [min],  Q_out =   86.64672 [m^3/s]
Time =  345243.33 [min],  Q_out =   81.47837 [m^3/s]
Time =  345923.33 [min],  Q_out =   79.97991 [m^3/s]
Time =  346610.00 [min],  Q_out =   71.64756 [m^3/s]
Time =  347233.33 [min],  Q_out =   72.82389 [m^3/s]
Time =  347880.00 [min],  Q_out =   67.55517 [m^3/s]
Time =  348480.00 [min],  Q_out =   69.40083 [m^3/s]
Time =  349080.00 [min],  Q_out =   66.39932 [m^3/s]
Time =  349770.00 [min],  Q_out =   64.08105 [m^3/s]
Time =  350423.33 [min],  Q_out =   61.18647 [m^3/s]
Time =  351110.00 [min],  Q_out =   52.38671 [m^3/s]
Time =  351740.00 [min],  Q_out =   48.32071 [m^3/s]
Time =  352450.00 [min],  Q_out =   41.46543 [m^3/s]
Time =  353060.00 [min],  Q_out =   40.98496 [m^3/s]
Time =  353700.00 [min],  Q_out =   36.59981 [m^3/s]
Time =  354266.67 [min],  Q_out =   40.86273 [m^3/s]
Time =  354800.00 [min],  Q_out =   37.40073 [m^3/s]
Time =  355480.00 [min],  Q_out =   29.64046 [m^3/s]
Time =  356080.00 [min],  Q_out =   35.75841 [m^3/s]
Time =  356726.67 [min],  Q_out =   37.66113 [m^3/s]
Time =  357353.33 [min],  Q_out =   61.87881 [m^3/s]
Time =  358016.67 [min],  Q_out =   91.53568 [m^3/s]
Time =  358680.00 [min],  Q_out =  103.51151 [m^3/s]
Time =  359280.00 [min],  Q_out =  117.48442 [m^3/s]
Time =  359880.00 [min],  Q_out =  181.69771 [m^3/s]
Time =  360480.00 [min],  Q_out =  351.06392 [m^3/s]
Time =  361146.67 [min],  Q_out =  344.51078 [m^3/s]
Time =  361780.00 [min],  Q_out =  353.63094 [m^3/s]
Time =  362456.67 [min],  Q_out =  372.21089 [m^3/s]
Time =  363060.00 [min],  Q_out =  384.54824 [m^3/s]
Time =  363720.00 [min],  Q_out =  342.69550 [m^3/s]
Time =  364373.33 [min],  Q_out =  323.76929 [m^3/s]
Time =  364960.00 [min],  Q_out =  314.41951 [m^3/s]
Time =  365521.67 [min],  Q_out =  322.34489 [m^3/s]
Time =  366120.00 [min],  Q_out =  309.94541 [m^3/s]
Time =  366780.00 [min],  Q_out =  287.89266 [m^3/s]
Time =  367440.00 [min],  Q_out =  321.20008 [m^3/s]
Time =  368090.00 [min],  Q_out =  381.90096 [m^3/s]
Time =  368708.33 [min],  Q_out =  380.75269 [m^3/s]
Time =  369360.00 [min],  Q_out =  365.75049 [m^3/s]
Time =  370043.33 [min],  Q_out =  341.39045 [m^3/s]
Time =  370620.00 [min],  Q_out =  320.28886 [m^3/s]
Time =  371216.67 [min],  Q_out =  298.45394 [m^3/s]
Time =  371833.33 [min],  Q_out =  269.37055 [m^3/s]
Time =  372540.00 [min],  Q_out =  270.61351 [m^3/s]
Time =  373163.33 [min],  Q_out =  270.12988 [m^3/s]
Time =  373860.00 [min],  Q_out =  259.97220 [m^3/s]
Time =  374540.00 [min],  Q_out =  385.18854 [m^3/s]
Time =  375203.33 [min],  Q_out =  371.06165 [m^3/s]
Time =  375866.67 [min],  Q_out =  333.29148 [m^3/s]
Time =  376440.00 [min],  Q_out =  308.67669 [m^3/s]
Time =  377066.67 [min],  Q_out =  278.99266 [m^3/s]
Time =  377660.00 [min],  Q_out =  255.86428 [m^3/s]
Time =  378360.00 [min],  Q_out =  216.91878 [m^3/s]
Time =  378976.67 [min],  Q_out =  214.38503 [m^3/s]
Time =  379620.00 [min],  Q_out =  208.87891 [m^3/s]
Time =  380325.00 [min],  Q_out =  168.17423 [m^3/s]
Time =  380943.33 [min],  Q_out =  153.63488 [m^3/s]
Time =  381600.00 [min],  Q_out =  147.52592 [m^3/s]
Time =  382223.33 [min],  Q_out =  184.65987 [m^3/s]
Time =  382856.67 [min],  Q_out =  158.60055 [m^3/s]
Time =  383400.00 [min],  Q_out =  137.27865 [m^3/s]
Time =  384106.67 [min],  Q_out =  139.09191 [m^3/s]
Time =  384776.67 [min],  Q_out =  175.37777 [m^3/s]
Time =  385440.00 [min],  Q_out =  184.79699 [m^3/s]
Time =  386100.00 [min],  Q_out =  139.67878 [m^3/s]
Time =  386736.67 [min],  Q_out =  142.67067 [m^3/s]
Time =  387360.00 [min],  Q_out =  163.87362 [m^3/s]
Time =  387990.00 [min],  Q_out =  217.46983 [m^3/s]
Time =  388620.00 [min],  Q_out =  208.36283 [m^3/s]
Time =  389160.00 [min],  Q_out =  186.83917 [m^3/s]
Time =  389820.00 [min],  Q_out =  164.20335 [m^3/s]
Time =  390450.00 [min],  Q_out =  171.96103 [m^3/s]
Time =  391080.00 [min],  Q_out =  180.21942 [m^3/s]
Time =  391716.67 [min],  Q_out =  182.97848 [m^3/s]
Time =  392340.00 [min],  Q_out =  177.38661 [m^3/s]
Time =  393018.33 [min],  Q_out =  184.86429 [m^3/s]
Time =  393623.33 [min],  Q_out =  206.41592 [m^3/s]
Time =  394256.67 [min],  Q_out =  223.99657 [m^3/s]
Time =  394860.00 [min],  Q_out =  164.33880 [m^3/s]
Time =  395400.00 [min],  Q_out =  152.05837 [m^3/s]
Time =  396000.00 [min],  Q_out =  168.23016 [m^3/s]
Time =  396623.33 [min],  Q_out =  460.66598 [m^3/s]
Time =  397286.67 [min],  Q_out =  605.00520 [m^3/s]
Time =  397920.00 [min],  Q_out =  870.39827 [m^3/s]
Time =  398580.00 [min],  Q_out =  719.51333 [m^3/s]
Time =  399236.67 [min],  Q_out =  821.19256 [m^3/s]
Time =  399843.33 [min],  Q_out =  740.14270 [m^3/s]
Time =  400410.00 [min],  Q_out =  793.18433 [m^3/s]
Time =  400970.00 [min],  Q_out =  896.78499 [m^3/s]
Time =  401550.00 [min],  Q_out =  966.15073 [m^3/s]
Time =  402146.67 [min],  Q_out =  926.83790 [m^3/s]
Time =  402840.00 [min],  Q_out =  814.03153 [m^3/s]
Time =  403473.33 [min],  Q_out =  774.67577 [m^3/s]
Time =  404100.00 [min],  Q_out =  702.29479 [m^3/s]
Time =  404756.67 [min],  Q_out =  612.67561 [m^3/s]
Time =  405390.00 [min],  Q_out =  579.44832 [m^3/s]
Time =  406010.00 [min],  Q_out =  625.55481 [m^3/s]
Time =  406596.67 [min],  Q_out =  631.27806 [m^3/s]
Time =  407220.00 [min],  Q_out =  549.01162 [m^3/s]
Time =  407863.33 [min],  Q_out =  470.91484 [m^3/s]
Time =  408486.67 [min],  Q_out =  417.77082 [m^3/s]
Time =  409140.00 [min],  Q_out =  372.66600 [m^3/s]
Time =  409740.00 [min],  Q_out =  347.24076 [m^3/s]
Time =  410400.00 [min],  Q_out =  329.73573 [m^3/s]
Time =  411033.33 [min],  Q_out =  336.45501 [m^3/s]
Time =  411600.00 [min],  Q_out =  364.56550 [m^3/s]
Time =  412156.67 [min],  Q_out =  450.76737 [m^3/s]
Time =  412793.33 [min],  Q_out =  470.73088 [m^3/s]
Time =  413423.33 [min],  Q_out =  486.05115 [m^3/s]
Time =  414043.33 [min],  Q_out =  456.48355 [m^3/s]
Time =  414670.00 [min],  Q_out =  574.37456 [m^3/s]
Time =  415280.00 [min],  Q_out =  598.61779 [m^3/s]
Time =  415886.67 [min],  Q_out =  569.34278 [m^3/s]
Time =  416453.33 [min],  Q_out =  595.05843 [m^3/s]
Time =  417060.00 [min],  Q_out =  540.40650 [m^3/s]
Time =  417685.00 [min],  Q_out =  504.19649 [m^3/s]
Time =  418236.67 [min],  Q_out =  559.83159 [m^3/s]
Time =  418910.00 [min],  Q_out =  438.52552 [m^3/s]
Time =  419520.00 [min],  Q_out =  390.25358 [m^3/s]
Time =  420146.67 [min],  Q_out =  347.54909 [m^3/s]
Time =  420740.00 [min],  Q_out =  317.70665 [m^3/s]
Time =  421380.00 [min],  Q_out =  289.94813 [m^3/s]
Time =  422013.33 [min],  Q_out =  290.34048 [m^3/s]
Time =  422666.67 [min],  Q_out =  280.50162 [m^3/s]
Time =  423320.00 [min],  Q_out =  257.72066 [m^3/s]
Time =  423900.00 [min],  Q_out =  239.38426 [m^3/s]
Time =  424500.00 [min],  Q_out =  211.87403 [m^3/s]
Time =  425160.00 [min],  Q_out =  186.40264 [m^3/s]
Time =  425823.33 [min],  Q_out =  162.54516 [m^3/s]
Time =  426420.00 [min],  Q_out =  169.91996 [m^3/s]
Time =  427016.67 [min],  Q_out =  187.21978 [m^3/s]
Time =  427640.00 [min],  Q_out =  151.03935 [m^3/s]
Time =  428256.67 [min],  Q_out =  138.02920 [m^3/s]
Time =  428906.67 [min],  Q_out =  116.72910 [m^3/s]
Time =  429480.00 [min],  Q_out =  165.36968 [m^3/s]
Time =  430080.00 [min],  Q_out =  291.29185 [m^3/s]
Time =  430680.00 [min],  Q_out =  256.69520 [m^3/s]
Time =  431330.00 [min],  Q_out =  259.04249 [m^3/s]
Time =  431980.00 [min],  Q_out =  247.59296 [m^3/s]
Time =  432583.33 [min],  Q_out =  209.77549 [m^3/s]
Time =  433260.00 [min],  Q_out =  166.17710 [m^3/s]
Time =  433880.00 [min],  Q_out =  163.45209 [m^3/s]
Time =  434520.00 [min],  Q_out =  143.66148 [m^3/s]
Time =  435140.00 [min],  Q_out =  120.73653 [m^3/s]
Time =  435766.67 [min],  Q_out =  118.35203 [m^3/s]
Time =  436320.00 [min],  Q_out =  119.42042 [m^3/s]
Time =  436906.67 [min],  Q_out =  171.56115 [m^3/s]
Time =  437550.00 [min],  Q_out =  168.93918 [m^3/s]
Time =  438153.33 [min],  Q_out =  184.05613 [m^3/s]
Time =  438840.00 [min],  Q_out =  219.12620 [m^3/s]
Time =  439440.00 [min],  Q_out =  239.10274 [m^3/s]
Time =  440073.33 [min],  Q_out =  279.72875 [m^3/s]
Time =  440653.33 [min],  Q_out =  308.25513 [m^3/s]
Time =  441310.00 [min],  Q_out =  297.95004 [m^3/s]
Time =  441910.00 [min],  Q_out =  283.12724 [m^3/s]
Time =  442440.00 [min],  Q_out =  287.05102 [m^3/s]
Time =  443068.33 [min],  Q_out =  263.61520 [m^3/s]
Time =  443686.67 [min],  Q_out =  275.35723 [m^3/s]
Time =  444210.00 [min],  Q_out =  279.25715 [m^3/s]
Time =  444780.00 [min],  Q_out =  272.71678 [m^3/s]
Time =  445386.67 [min],  Q_out =  313.59499 [m^3/s]
Time =  446040.00 [min],  Q_out =  364.96556 [m^3/s]
Time =  446700.00 [min],  Q_out =  350.26058 [m^3/s]
Time =  447316.67 [min],  Q_out =  341.88812 [m^3/s]
Time =  447900.00 [min],  Q_out =  340.24331 [m^3/s]
Time =  448440.00 [min],  Q_out =  332.70430 [m^3/s]
Time =  449083.33 [min],  Q_out =  312.28187 [m^3/s]
Time =  449693.33 [min],  Q_out =  294.31621 [m^3/s]
Time =  450360.00 [min],  Q_out =  278.91649 [m^3/s]
Time =  451020.00 [min],  Q_out =  283.54994 [m^3/s]
Time =  451680.00 [min],  Q_out =  291.15725 [m^3/s]
Time =  452288.33 [min],  Q_out =  293.83493 [m^3/s]
Time =  452880.00 [min],  Q_out =  291.62320 [m^3/s]
Time =  453558.33 [min],  Q_out =  288.48623 [m^3/s]
Time =  454163.33 [min],  Q_out =  310.64555 [m^3/s]
Time =  454800.00 [min],  Q_out =  301.35426 [m^3/s]
Time =  455423.33 [min],  Q_out =  285.56154 [m^3/s]
Time =  456060.00 [min],  Q_out =  255.76537 [m^3/s]
Time =  456633.33 [min],  Q_out =  248.92737 [m^3/s]
Time =  457213.33 [min],  Q_out =  277.09485 [m^3/s]
Time =  457883.33 [min],  Q_out =  285.61785 [m^3/s]
Time =  458483.33 [min],  Q_out =  330.59320 [m^3/s]
Time =  459126.67 [min],  Q_out =  282.26260 [m^3/s]
Time =  459723.33 [min],  Q_out =  256.65581 [m^3/s]
Time =  460440.00 [min],  Q_out =  249.20527 [m^3/s]
Time =  461063.33 [min],  Q_out =  240.61539 [m^3/s]
Time =  461700.00 [min],  Q_out =  227.76705 [m^3/s]
Time =  462333.33 [min],  Q_out =  262.34533 [m^3/s]
Time =  462863.33 [min],  Q_out =  288.15159 [m^3/s]
Time =  463500.00 [min],  Q_out =  316.56934 [m^3/s]
Time =  464123.33 [min],  Q_out =  328.15006 [m^3/s]
Time =  464760.00 [min],  Q_out =  297.29885 [m^3/s]
Time =  465383.33 [min],  Q_out =  362.42857 [m^3/s]
Time =  466006.67 [min],  Q_out =  604.55343 [m^3/s]
Time =  466673.33 [min],  Q_out =  486.86870 [m^3/s]
Time =  467326.67 [min],  Q_out =  369.41236 [m^3/s]
Time =  468000.00 [min],  Q_out =  329.68966 [m^3/s]
Time =  468660.00 [min],  Q_out =  337.86077 [m^3/s]
Time =  469286.67 [min],  Q_out =  364.77256 [m^3/s]
Time =  469860.00 [min],  Q_out =  395.91004 [m^3/s]
Time =  470503.33 [min],  Q_out =  470.71286 [m^3/s]
Time =  471113.33 [min],  Q_out =  453.89952 [m^3/s]
Time =  471733.33 [min],  Q_out =  407.98732 [m^3/s]
Time =  472433.33 [min],  Q_out =  388.63814 [m^3/s]
Time =  473086.67 [min],  Q_out =  345.26648 [m^3/s]
Time =  473730.00 [min],  Q_out =  315.07904 [m^3/s]
Time =  474340.00 [min],  Q_out =  292.05305 [m^3/s]
Time =  474946.67 [min],  Q_out =  263.70803 [m^3/s]
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Time =  476126.67 [min],  Q_out =  229.88471 [m^3/s]
Time =  476803.33 [min],  Q_out =  211.05819 [m^3/s]
Time =  477423.33 [min],  Q_out =  193.58712 [m^3/s]
Time =  478070.00 [min],  Q_out =  201.34609 [m^3/s]
Time =  478650.00 [min],  Q_out =  248.43889 [m^3/s]
Time =  479303.33 [min],  Q_out =  211.30833 [m^3/s]
Time =  479895.00 [min],  Q_out =  263.07673 [m^3/s]
Time =  480506.67 [min],  Q_out =  377.06529 [m^3/s]
Time =  481083.33 [min],  Q_out =  380.75853 [m^3/s]
Time =  481650.00 [min],  Q_out =  370.04747 [m^3/s]
Time =  482220.00 [min],  Q_out =  365.44375 [m^3/s]
Time =  482830.00 [min],  Q_out =  508.98028 [m^3/s]
Time =  483480.00 [min],  Q_out =  432.19928 [m^3/s]
Time =  484120.00 [min],  Q_out =  452.50317 [m^3/s]
Time =  484710.00 [min],  Q_out =  429.19412 [m^3/s]
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Time =  486510.00 [min],  Q_out =  404.30566 [m^3/s]
Time =  487120.00 [min],  Q_out =  453.50637 [m^3/s]
Time =  487763.33 [min],  Q_out =  443.99950 [m^3/s]
Time =  488406.67 [min],  Q_out =  431.68873 [m^3/s]
Time =  489083.33 [min],  Q_out =  498.21006 [m^3/s]
Time =  489690.00 [min],  Q_out =  809.22537 [m^3/s]
Time =  490303.33 [min],  Q_out =  443.10630 [m^3/s]
Time =  490920.00 [min],  Q_out =  387.60805 [m^3/s]
Time =  491563.33 [min],  Q_out =  416.66510 [m^3/s]
Time =  492220.00 [min],  Q_out =  381.73309 [m^3/s]
Time =  492840.00 [min],  Q_out =  377.81764 [m^3/s]
Time =  493463.33 [min],  Q_out =  462.03493 [m^3/s]
Time =  494020.00 [min],  Q_out =  402.45226 [m^3/s]
Time =  494580.00 [min],  Q_out =  379.37266 [m^3/s]
Time =  495243.33 [min],  Q_out =  497.42853 [m^3/s]
Time =  495900.00 [min],  Q_out =  556.85932 [m^3/s]
Time =  496576.67 [min],  Q_out =  508.26007 [m^3/s]
Time =  497193.33 [min],  Q_out =  531.57797 [m^3/s]
Time =  497853.33 [min],  Q_out =  450.71821 [m^3/s]
Time =  498470.00 [min],  Q_out =  445.80914 [m^3/s]
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Time =  502823.33 [min],  Q_out =  336.56720 [m^3/s]
Time =  503400.00 [min],  Q_out =  362.86884 [m^3/s]
Time =  503963.33 [min],  Q_out =  318.33448 [m^3/s]
Time =  504523.33 [min],  Q_out =  326.30075 [m^3/s]
Time =  505160.00 [min],  Q_out =  310.49005 [m^3/s]
Time =  505795.00 [min],  Q_out =  301.69490 [m^3/s]
Time =  506400.00 [min],  Q_out =  280.37295 [m^3/s]
Time =  507033.33 [min],  Q_out =  310.00411 [m^3/s]
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Time =  508260.00 [min],  Q_out =  556.42521 [m^3/s]
Time =  508820.00 [min],  Q_out =  532.01994 [m^3/s]
Time =  509426.67 [min],  Q_out =  517.10093 [m^3/s]
Time =  510060.00 [min],  Q_out =  478.08406 [m^3/s]
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Time =  512100.00 [min],  Q_out =  350.13689 [m^3/s]
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Time =  513396.67 [min],  Q_out =  310.10011 [m^3/s]
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Time =  514566.67 [min],  Q_out =  284.04855 [m^3/s]
Time =  515213.33 [min],  Q_out =  260.39524 [m^3/s]
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Time =  517198.33 [min],  Q_out =  183.74557 [m^3/s]
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Time =  518470.00 [min],  Q_out =  147.40413 [m^3/s]
Time =  519016.67 [min],  Q_out =  170.48046 [m^3/s]
Time =  519600.00 [min],  Q_out =  183.56825 [m^3/s]
Time =  520200.00 [min],  Q_out =  187.08570 [m^3/s]
Time =  520860.00 [min],  Q_out =  261.94398 [m^3/s]
Time =  521463.33 [min],  Q_out =  256.39060 [m^3/s]
Time =  522080.00 [min],  Q_out =  267.45640 [m^3/s]
Time =  522730.00 [min],  Q_out =  316.28682 [m^3/s]
Time =  523320.00 [min],  Q_out =  371.38145 [m^3/s]
Time =  523950.00 [min],  Q_out =  361.45427 [m^3/s]
Time =  524526.67 [min],  Q_out =  410.55081 [m^3/s]
Time =  525180.00 [min],  Q_out =  394.15863 [m^3/s]
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Time =  526486.67 [min],  Q_out =  329.39418 [m^3/s]
Time =  527100.00 [min],  Q_out =  315.19895 [m^3/s]
Time =  527700.00 [min],  Q_out =  327.82198 [m^3/s]
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Time =  528883.33 [min],  Q_out =  393.30258 [m^3/s]
Time =  529516.67 [min],  Q_out =  373.26820 [m^3/s]
Time =  530143.33 [min],  Q_out =  353.29336 [m^3/s]
Time =  530790.00 [min],  Q_out =  323.60891 [m^3/s]
Time =  531420.00 [min],  Q_out =  312.75854 [m^3/s]
Time =  532073.33 [min],  Q_out =  283.43652 [m^3/s]
Time =  532740.00 [min],  Q_out =  251.36699 [m^3/s]
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Time =  536383.33 [min],  Q_out =  290.78219 [m^3/s]
Time =  537000.00 [min],  Q_out =  267.07384 [m^3/s]
Time =  537658.33 [min],  Q_out =  280.39729 [m^3/s]
Time =  538320.00 [min],  Q_out =  377.60932 [m^3/s]
Time =  538920.00 [min],  Q_out =  756.98877 [m^3/s]
Time =  539490.00 [min],  Q_out =  846.46136 [m^3/s]
Time =  540076.67 [min],  Q_out =  800.37186 [m^3/s]
Time =  540696.67 [min],  Q_out =  680.23489 [m^3/s]
Time =  541356.67 [min],  Q_out =  589.82104 [m^3/s]
Time =  541980.00 [min],  Q_out =  549.37416 [m^3/s]
Time =  542700.00 [min],  Q_out =  469.15636 [m^3/s]
Time =  543368.33 [min],  Q_out =  425.58802 [m^3/s]
Time =  544043.33 [min],  Q_out =  461.59610 [m^3/s]
Time =  544613.33 [min],  Q_out =  474.31822 [m^3/s]
Time =  545166.67 [min],  Q_out =  447.35597 [m^3/s]
Time =  545800.00 [min],  Q_out =  448.77972 [m^3/s]
Time =  546420.00 [min],  Q_out =  500.31514 [m^3/s]
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Time =  547680.00 [min],  Q_out =  430.12302 [m^3/s]
Time =  548343.33 [min],  Q_out =  424.46483 [m^3/s]
Time =  548986.67 [min],  Q_out =  418.81543 [m^3/s]
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Time =  553273.33 [min],  Q_out =  309.33857 [m^3/s]
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Time =  589866.67 [min],  Q_out =  188.11954 [m^3/s]
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Time =  591780.00 [min],  Q_out =  188.51977 [m^3/s]
Time =  592400.00 [min],  Q_out =  211.97330 [m^3/s]
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Time =  596073.33 [min],  Q_out =  228.03168 [m^3/s]
Time =  596676.67 [min],  Q_out =  287.64733 [m^3/s]
Time =  597360.00 [min],  Q_out =  216.11455 [m^3/s]
Time =  598020.00 [min],  Q_out =  218.42504 [m^3/s]
Time =  598680.00 [min],  Q_out =  200.73678 [m^3/s]
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Time =  602343.33 [min],  Q_out =  337.77148 [m^3/s]
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Time =  608030.00 [min],  Q_out =  242.33692 [m^3/s]
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Time =  617823.33 [min],  Q_out =  293.54273 [m^3/s]
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Time =  621000.00 [min],  Q_out =  445.92512 [m^3/s]
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Time =  646893.33 [min],  Q_out =  728.90339 [m^3/s]
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Time =  755743.33 [min],  Q_out =  343.57918 [m^3/s]
Time =  756300.00 [min],  Q_out =  360.69979 [m^3/s]
Time =  756916.67 [min],  Q_out =  434.74747 [m^3/s]
Time =  757560.00 [min],  Q_out =  623.35449 [m^3/s]
Time =  758160.00 [min],  Q_out =  646.37490 [m^3/s]
Time =  758803.33 [min],  Q_out =  824.88338 [m^3/s]
Time =  759373.33 [min],  Q_out =  832.54096 [m^3/s]
Time =  759960.00 [min],  Q_out =  776.95281 [m^3/s]
Time =  760510.00 [min],  Q_out =  751.48186 [m^3/s]
Time =  761110.00 [min],  Q_out =  757.75662 [m^3/s]
Time =  761760.00 [min],  Q_out =  742.48049 [m^3/s]
Time =  762360.00 [min],  Q_out =  679.82859 [m^3/s]
Time =  763000.00 [min],  Q_out =  619.83093 [m^3/s]
Time =  763580.00 [min],  Q_out =  602.69281 [m^3/s]
Time =  764220.00 [min],  Q_out =  565.88331 [m^3/s]
Time =  764850.00 [min],  Q_out =  535.58574 [m^3/s]
Time =  765493.33 [min],  Q_out =  495.86890 [m^3/s]
Time =  766106.67 [min],  Q_out =  444.70537 [m^3/s]
Time =  766646.67 [min],  Q_out =  410.22392 [m^3/s]
Time =  767276.67 [min],  Q_out =  376.83914 [m^3/s]
Time =  767880.00 [min],  Q_out =  395.83345 [m^3/s]
Time =  768505.00 [min],  Q_out =  450.19794 [m^3/s]
Time =  769133.33 [min],  Q_out =  412.76874 [m^3/s]
Time =  769743.33 [min],  Q_out =  449.00325 [m^3/s]
Time =  770366.67 [min],  Q_out =  420.65470 [m^3/s]
Time =  770940.00 [min],  Q_out =  488.71525 [m^3/s]
Time =  771540.00 [min],  Q_out =  466.78740 [m^3/s]
Time =  772090.00 [min],  Q_out =  484.80306 [m^3/s]
Time =  772740.00 [min],  Q_out =  482.88120 [m^3/s]
Time =  773350.00 [min],  Q_out =  538.70070 [m^3/s]
Time =  773980.00 [min],  Q_out =  583.03534 [m^3/s]
Time =  774583.33 [min],  Q_out =  637.72243 [m^3/s]
Time =  775200.00 [min],  Q_out =  629.61276 [m^3/s]
Time =  775873.33 [min],  Q_out =  597.01583 [m^3/s]
Time =  776480.00 [min],  Q_out =  575.56972 [m^3/s]
Time =  777120.00 [min],  Q_out =  532.50263 [m^3/s]
Time =  777760.00 [min],  Q_out =  494.08294 [m^3/s]
Time =  778380.00 [min],  Q_out =  438.32942 [m^3/s]
Time =  779040.00 [min],  Q_out =  401.73359 [m^3/s]
Time =  779640.00 [min],  Q_out =  362.95405 [m^3/s]
Time =  780266.67 [min],  Q_out =  327.07169 [m^3/s]
Time =  780883.33 [min],  Q_out =  304.30073 [m^3/s]
Time =  781530.00 [min],  Q_out =  320.48677 [m^3/s]
Time =  782100.00 [min],  Q_out =  340.61611 [m^3/s]
Time =  782733.33 [min],  Q_out =  306.67996 [m^3/s]
Time =  783360.00 [min],  Q_out =  328.32958 [m^3/s]
Time =  783990.00 [min],  Q_out =  321.28748 [m^3/s]
Time =  784646.67 [min],  Q_out =  313.96024 [m^3/s]
Time =  785250.00 [min],  Q_out =  346.41075 [m^3/s]
Time =  785880.00 [min],  Q_out =  357.54989 [m^3/s]
Time =  786513.33 [min],  Q_out =  377.97087 [m^3/s]
Time =  787180.00 [min],  Q_out =  388.88925 [m^3/s]
Time =  787770.00 [min],  Q_out =  380.92137 [m^3/s]
Time =  788333.33 [min],  Q_out =  380.12459 [m^3/s]
Time =  788940.00 [min],  Q_out =  392.50172 [m^3/s]
Time =  789500.00 [min],  Q_out =  446.66302 [m^3/s]
Time =  790113.33 [min],  Q_out =  377.06457 [m^3/s]
Time =  790670.00 [min],  Q_out =  365.62471 [m^3/s]
Time =  791276.67 [min],  Q_out =  418.85004 [m^3/s]
Time =  791880.00 [min],  Q_out =  435.37623 [m^3/s]
Time =  792480.00 [min],  Q_out =  457.82549 [m^3/s]
Time =  793140.00 [min],  Q_out =  541.21697 [m^3/s]
Time =  793756.67 [min],  Q_out =  594.42995 [m^3/s]
Time =  794360.00 [min],  Q_out =  552.07201 [m^3/s]
Time =  795000.00 [min],  Q_out =  560.90636 [m^3/s]
Time =  795603.33 [min],  Q_out =  534.81654 [m^3/s]
Time =  796276.67 [min],  Q_out =  509.95076 [m^3/s]
Time =  796900.00 [min],  Q_out =  509.57505 [m^3/s]
Time =  797540.00 [min],  Q_out =  482.68803 [m^3/s]
Time =  798126.67 [min],  Q_out =  415.50069 [m^3/s]
Time =  798780.00 [min],  Q_out =  338.41711 [m^3/s]
Time =  799380.00 [min],  Q_out =  307.51627 [m^3/s]
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Time =  801200.00 [min],  Q_out =  239.73194 [m^3/s]
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Time =  803640.00 [min],  Q_out =  242.95293 [m^3/s]
Time =  804220.00 [min],  Q_out =  313.67467 [m^3/s]
Time =  804796.67 [min],  Q_out =  339.15304 [m^3/s]
Time =  805346.67 [min],  Q_out =  323.23728 [m^3/s]
Time =  805923.33 [min],  Q_out =  300.37939 [m^3/s]
Time =  806520.00 [min],  Q_out =  293.54955 [m^3/s]
Time =  807093.33 [min],  Q_out =  281.58913 [m^3/s]
Time =  807660.00 [min],  Q_out =  257.89537 [m^3/s]
Time =  808246.67 [min],  Q_out =  239.86099 [m^3/s]
Time =  808873.33 [min],  Q_out =  225.85661 [m^3/s]
Time =  809460.00 [min],  Q_out =  419.97061 [m^3/s]
Time =  810070.00 [min],  Q_out =  327.31191 [m^3/s]
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Time =  811303.33 [min],  Q_out =  436.27194 [m^3/s]
Time =  811976.67 [min],  Q_out =  494.11111 [m^3/s]
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Time =  813210.00 [min],  Q_out =  572.20142 [m^3/s]
Time =  813816.67 [min],  Q_out =  587.01009 [m^3/s]
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Time =  815660.00 [min],  Q_out =  530.91526 [m^3/s]
Time =  816300.00 [min],  Q_out =  472.45151 [m^3/s]
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Time =  817980.00 [min],  Q_out =  469.69993 [m^3/s]
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Time =  831160.00 [min],  Q_out =  308.74074 [m^3/s]
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Time =  833580.00 [min],  Q_out =  918.69705 [m^3/s]
Time =  834120.00 [min],  Q_out = 1006.84124 [m^3/s]
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Time =  835320.00 [min],  Q_out =  771.62368 [m^3/s]
Time =  835920.00 [min],  Q_out =  743.06023 [m^3/s]
Time =  836580.00 [min],  Q_out =  706.78516 [m^3/s]
Time =  837180.00 [min],  Q_out =  664.11017 [m^3/s]
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Time =  840803.33 [min],  Q_out =  626.70177 [m^3/s]
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Time =  844380.00 [min],  Q_out =  302.38500 [m^3/s]
Time =  845040.00 [min],  Q_out =  265.53438 [m^3/s]
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Time = 1250466.67 [min],  Q_out =  398.16465 [m^3/s]
Time = 1251036.67 [min],  Q_out =  339.36419 [m^3/s]
Time = 1251600.00 [min],  Q_out =  305.79269 [m^3/s]
Time = 1252165.00 [min],  Q_out =  302.66134 [m^3/s]
Time = 1252678.33 [min],  Q_out =  307.82618 [m^3/s]
Time = 1253236.67 [min],  Q_out =  293.66190 [m^3/s]
Time = 1253863.33 [min],  Q_out =  339.13327 [m^3/s]
Time = 1254436.67 [min],  Q_out =  449.88696 [m^3/s]
Time = 1255020.00 [min],  Q_out =  505.89095 [m^3/s]
Time = 1255620.00 [min],  Q_out =  414.18748 [m^3/s]
Time = 1256213.33 [min],  Q_out =  356.72391 [m^3/s]
Time = 1256826.67 [min],  Q_out =  297.94618 [m^3/s]
Time = 1257400.00 [min],  Q_out =  264.43597 [m^3/s]
Time = 1258020.00 [min],  Q_out =  221.08079 [m^3/s]
Time = 1258643.33 [min],  Q_out =  199.75798 [m^3/s]
Time = 1259220.00 [min],  Q_out =  192.84620 [m^3/s]
Time = 1259820.00 [min],  Q_out =  212.28979 [m^3/s]
Time = 1260396.67 [min],  Q_out =  262.68432 [m^3/s]
Time = 1260998.33 [min],  Q_out =  197.31349 [m^3/s]
Time = 1261565.00 [min],  Q_out =  177.55193 [m^3/s]
Time = 1262130.00 [min],  Q_out =  171.67729 [m^3/s]
Time = 1262713.33 [min],  Q_out =  170.21677 [m^3/s]
Time = 1263253.33 [min],  Q_out =  177.83214 [m^3/s]
Time = 1263893.33 [min],  Q_out =  190.15506 [m^3/s]
Time = 1264500.00 [min],  Q_out =  223.12149 [m^3/s]
Time = 1265120.00 [min],  Q_out =  204.78311 [m^3/s]
Time = 1265720.00 [min],  Q_out =  196.40041 [m^3/s]
Time = 1266323.33 [min],  Q_out =  235.03403 [m^3/s]
Time = 1266960.00 [min],  Q_out =  243.47581 [m^3/s]
Time = 1267560.00 [min],  Q_out =  243.03822 [m^3/s]
Time = 1268200.00 [min],  Q_out =  231.99845 [m^3/s]
Time = 1268803.33 [min],  Q_out =  232.48079 [m^3/s]
Time = 1269360.00 [min],  Q_out =  238.08380 [m^3/s]
Time = 1269960.00 [min],  Q_out =  315.45345 [m^3/s]
Time = 1270460.00 [min],  Q_out =  338.86155 [m^3/s]
Time = 1271036.67 [min],  Q_out =  349.25272 [m^3/s]
Time = 1271520.00 [min],  Q_out =  334.58107 [m^3/s]
Time = 1272100.00 [min],  Q_out =  312.85206 [m^3/s]
Time = 1272650.00 [min],  Q_out =  291.22504 [m^3/s]
Time = 1273160.00 [min],  Q_out =  304.40222 [m^3/s]
Time = 1273713.33 [min],  Q_out =  318.27019 [m^3/s]
Time = 1274340.00 [min],  Q_out =  309.30418 [m^3/s]
Time = 1274910.00 [min],  Q_out =  297.53406 [m^3/s]
Time = 1275516.67 [min],  Q_out =  272.79106 [m^3/s]
Time = 1276100.00 [min],  Q_out =  260.10307 [m^3/s]
Time = 1276603.33 [min],  Q_out =  255.28440 [m^3/s]
Time = 1277113.33 [min],  Q_out =  234.05164 [m^3/s]
Time = 1277595.00 [min],  Q_out =  248.66046 [m^3/s]
Time = 1278120.00 [min],  Q_out =  259.99946 [m^3/s]
Time = 1278720.00 [min],  Q_out =  248.69318 [m^3/s]
Time = 1279300.00 [min],  Q_out =  232.27119 [m^3/s]
Time = 1279920.00 [min],  Q_out =  214.35406 [m^3/s]
Time = 1280466.67 [min],  Q_out =  212.71864 [m^3/s]
Time = 1281060.00 [min],  Q_out =  211.99825 [m^3/s]
Time = 1281660.00 [min],  Q_out =  211.90674 [m^3/s]
Time = 1282246.67 [min],  Q_out =  199.83844 [m^3/s]
Time = 1282860.00 [min],  Q_out =  205.05059 [m^3/s]
Time = 1283503.33 [min],  Q_out =  228.47479 [m^3/s]
Time = 1284123.33 [min],  Q_out =  224.70421 [m^3/s]
Time = 1284720.00 [min],  Q_out =  220.19567 [m^3/s]
Time = 1285303.33 [min],  Q_out =  216.87029 [m^3/s]
Time = 1285860.00 [min],  Q_out =  237.57339 [m^3/s]
Time = 1286410.00 [min],  Q_out =  261.60444 [m^3/s]
Time = 1286995.00 [min],  Q_out =  242.07549 [m^3/s]
Time = 1287540.00 [min],  Q_out =  251.60992 [m^3/s]
Time = 1288083.33 [min],  Q_out =  358.32692 [m^3/s]
Time = 1288663.33 [min],  Q_out =  441.47662 [m^3/s]
Time = 1289220.00 [min],  Q_out =  402.22562 [m^3/s]
Time = 1289760.00 [min],  Q_out =  498.38716 [m^3/s]
Time = 1290240.00 [min],  Q_out =  578.96111 [m^3/s]
Time = 1290730.00 [min],  Q_out =  739.72841 [m^3/s]
Time = 1291260.00 [min],  Q_out =  736.77316 [m^3/s]
Time = 1291740.00 [min],  Q_out =  639.04515 [m^3/s]
Time = 1292250.00 [min],  Q_out =  608.50861 [m^3/s]
Time = 1292773.33 [min],  Q_out =  582.58848 [m^3/s]
Time = 1293343.33 [min],  Q_out =  585.72379 [m^3/s]
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Time = 1294473.33 [min],  Q_out =  547.40209 [m^3/s]
Time = 1295040.00 [min],  Q_out =  536.89192 [m^3/s]
Time = 1295640.00 [min],  Q_out =  501.99029 [m^3/s]
Time = 1296193.33 [min],  Q_out =  582.55129 [m^3/s]
Time = 1296723.33 [min],  Q_out =  624.36285 [m^3/s]
Time = 1297276.67 [min],  Q_out =  585.74344 [m^3/s]
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Time = 1301590.00 [min],  Q_out =  513.00875 [m^3/s]
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Time = 1302666.67 [min],  Q_out =  568.42743 [m^3/s]
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Time = 1304200.00 [min],  Q_out =  750.34617 [m^3/s]
Time = 1304740.00 [min],  Q_out =  705.44454 [m^3/s]
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Time = 1340640.00 [min],  Q_out =  683.07716 [m^3/s]
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Time = 1346280.00 [min],  Q_out =  369.84937 [m^3/s]
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Time = 1347480.00 [min],  Q_out =  315.09001 [m^3/s]
Time = 1348080.00 [min],  Q_out =  684.16849 [m^3/s]
Time = 1348680.00 [min],  Q_out =  875.09440 [m^3/s]
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Time = 1354193.33 [min],  Q_out =  395.77097 [m^3/s]
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Time = 1355400.00 [min],  Q_out =  316.37430 [m^3/s]
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Time = 1357730.00 [min],  Q_out =  191.52644 [m^3/s]
Time = 1358243.33 [min],  Q_out =  175.81453 [m^3/s]
Time = 1358806.67 [min],  Q_out =  150.84043 [m^3/s]
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Time = 1362660.00 [min],  Q_out =   91.67490 [m^3/s]
Time = 1363226.67 [min],  Q_out =  106.54435 [m^3/s]
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Time = 1368296.67 [min],  Q_out =  454.50434 [m^3/s]
Time = 1368880.00 [min],  Q_out =  569.81628 [m^3/s]
Time = 1369436.67 [min],  Q_out =  660.54851 [m^3/s]
Time = 1369920.00 [min],  Q_out =  670.68677 [m^3/s]
Time = 1370500.00 [min],  Q_out =  620.84013 [m^3/s]
Time = 1371020.00 [min],  Q_out =  558.98934 [m^3/s]
Time = 1371530.00 [min],  Q_out =  520.37399 [m^3/s]
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Time = 1372610.00 [min],  Q_out =  517.98964 [m^3/s]
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Time = 1373700.00 [min],  Q_out =  456.39661 [m^3/s]
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Time = 1389490.00 [min],  Q_out =  852.90733 [m^3/s]
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Time = 1392120.00 [min],  Q_out =  824.44823 [m^3/s]
Time = 1392650.00 [min],  Q_out =  689.35247 [m^3/s]
Time = 1393140.00 [min],  Q_out =  782.73986 [m^3/s]
Time = 1393680.00 [min],  Q_out =  726.28814 [m^3/s]
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Time = 1399180.00 [min],  Q_out =  734.58282 [m^3/s]
Time = 1399740.00 [min],  Q_out =  677.10791 [m^3/s]
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Time = 1403100.00 [min],  Q_out =  314.07378 [m^3/s]
Time = 1403760.00 [min],  Q_out =  282.94404 [m^3/s]
Time = 1404360.00 [min],  Q_out =  255.55080 [m^3/s]
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Time = 1405440.00 [min],  Q_out =  195.96782 [m^3/s]
Time = 1405990.00 [min],  Q_out =  180.54150 [m^3/s]
Time = 1406600.00 [min],  Q_out =  169.78342 [m^3/s]
Time = 1407120.00 [min],  Q_out =  181.39825 [m^3/s]
Time = 1407660.00 [min],  Q_out =  203.31348 [m^3/s]
Time = 1408170.00 [min],  Q_out =  247.40324 [m^3/s]
Time = 1408686.67 [min],  Q_out =  254.44852 [m^3/s]
Time = 1409220.00 [min],  Q_out =  244.69478 [m^3/s]
Time = 1409760.00 [min],  Q_out =  264.13369 [m^3/s]
Time = 1410333.33 [min],  Q_out =  313.36623 [m^3/s]
Time = 1410901.67 [min],  Q_out =  357.03409 [m^3/s]
Time = 1411446.67 [min],  Q_out =  366.81963 [m^3/s]
Time = 1411980.00 [min],  Q_out =  340.09635 [m^3/s]
Time = 1412485.00 [min],  Q_out =  316.03912 [m^3/s]
Time = 1412986.67 [min],  Q_out =  296.05299 [m^3/s]
Time = 1413486.67 [min],  Q_out =  272.69210 [m^3/s]
Time = 1414023.33 [min],  Q_out =  265.24468 [m^3/s]
Time = 1414560.00 [min],  Q_out =  284.40062 [m^3/s]
Time = 1415160.00 [min],  Q_out =  398.73202 [m^3/s]
Time = 1415700.00 [min],  Q_out =  582.92267 [m^3/s]
Time = 1416240.00 [min],  Q_out =  526.29924 [m^3/s]
Time = 1416830.00 [min],  Q_out =  470.92930 [m^3/s]
Time = 1417336.67 [min],  Q_out =  428.21164 [m^3/s]
Time = 1417863.33 [min],  Q_out =  376.81917 [m^3/s]
Time = 1418400.00 [min],  Q_out =  344.62991 [m^3/s]
Time = 1418940.00 [min],  Q_out =  317.90972 [m^3/s]
Time = 1419536.67 [min],  Q_out =  292.87085 [m^3/s]
Time = 1420100.00 [min],  Q_out =  271.97383 [m^3/s]
Time = 1420703.33 [min],  Q_out =  240.56280 [m^3/s]
Time = 1421280.00 [min],  Q_out =  241.68647 [m^3/s]
Time = 1421870.00 [min],  Q_out =  272.53233 [m^3/s]
Time = 1422498.33 [min],  Q_out =  277.09367 [m^3/s]
Time = 1423033.33 [min],  Q_out =  261.99358 [m^3/s]
Time = 1423603.33 [min],  Q_out =  252.41405 [m^3/s]
Time = 1424160.00 [min],  Q_out =  238.42822 [m^3/s]
Time = 1424716.67 [min],  Q_out =  227.88167 [m^3/s]
Time = 1425330.00 [min],  Q_out =  197.03015 [m^3/s]
Time = 1425910.00 [min],  Q_out =  186.17051 [m^3/s]
Time = 1426500.00 [min],  Q_out =  184.28544 [m^3/s]
Time = 1427100.00 [min],  Q_out =  224.60377 [m^3/s]
Time = 1427620.00 [min],  Q_out =  197.28819 [m^3/s]
Time = 1428223.33 [min],  Q_out =  163.64866 [m^3/s]
Time = 1428710.00 [min],  Q_out =  157.34699 [m^3/s]
Time = 1429216.67 [min],  Q_out =  216.87988 [m^3/s]
Time = 1429763.33 [min],  Q_out =  253.55520 [m^3/s]
Time = 1430316.67 [min],  Q_out =  206.08598 [m^3/s]
Time = 1430843.33 [min],  Q_out =  212.34293 [m^3/s]
Time = 1431360.00 [min],  Q_out =  212.74886 [m^3/s]
Time = 1431876.67 [min],  Q_out =  245.22792 [m^3/s]
Time = 1432410.00 [min],  Q_out =  227.60402 [m^3/s]
Time = 1432920.00 [min],  Q_out =  227.05862 [m^3/s]
Time = 1433423.33 [min],  Q_out =  249.90540 [m^3/s]
Time = 1433973.33 [min],  Q_out =  306.91102 [m^3/s]
Time = 1434483.33 [min],  Q_out =  402.38438 [m^3/s]
Time = 1434986.67 [min],  Q_out =  534.81075 [m^3/s]
Time = 1435546.67 [min],  Q_out =  756.40219 [m^3/s]
Time = 1436080.00 [min],  Q_out =  927.17438 [m^3/s]
Time = 1436623.33 [min],  Q_out =  870.27307 [m^3/s]
Time = 1437173.33 [min],  Q_out =  814.44921 [m^3/s]
Time = 1437703.33 [min],  Q_out =  736.42429 [m^3/s]
Time = 1438260.00 [min],  Q_out =  647.80477 [m^3/s]
Time = 1438773.33 [min],  Q_out =  599.19975 [m^3/s]
Time = 1439340.00 [min],  Q_out =  577.17935 [m^3/s]
Time = 1439916.67 [min],  Q_out =  541.36833 [m^3/s]
Time = 1440450.00 [min],  Q_out =  529.83771 [m^3/s]
Time = 1441080.00 [min],  Q_out =  447.32408 [m^3/s]
Time = 1441633.33 [min],  Q_out =  578.35837 [m^3/s]
Time = 1442170.00 [min],  Q_out =  661.04218 [m^3/s]
Time = 1442740.00 [min],  Q_out =  563.77508 [m^3/s]
Time = 1443300.00 [min],  Q_out =  507.78454 [m^3/s]
Time = 1443890.00 [min],  Q_out =  472.54776 [m^3/s]
Time = 1444480.00 [min],  Q_out =  451.35847 [m^3/s]
Time = 1445030.00 [min],  Q_out =  432.06140 [m^3/s]
Time = 1445543.33 [min],  Q_out =  420.82383 [m^3/s]
Time = 1446100.00 [min],  Q_out =  397.74679 [m^3/s]
Time = 1446626.67 [min],  Q_out =  358.27238 [m^3/s]
Time = 1447140.00 [min],  Q_out =  345.37591 [m^3/s]
Time = 1447640.00 [min],  Q_out =  335.69212 [m^3/s]
Time = 1448190.00 [min],  Q_out =  305.14494 [m^3/s]
Time = 1448703.33 [min],  Q_out =  280.00119 [m^3/s]
Time = 1449206.67 [min],  Q_out =  259.57262 [m^3/s]
Time = 1449736.67 [min],  Q_out =  233.84499 [m^3/s]
Time = 1450260.00 [min],  Q_out =  216.32187 [m^3/s]
Time = 1450776.67 [min],  Q_out =  199.63424 [m^3/s]
Time = 1451313.33 [min],  Q_out =  194.15463 [m^3/s]
Time = 1451843.33 [min],  Q_out =  249.51301 [m^3/s]
Time = 1452386.67 [min],  Q_out =  295.39075 [m^3/s]
Time = 1452960.00 [min],  Q_out =  281.31781 [m^3/s]
Time = 1453500.00 [min],  Q_out =  284.99513 [m^3/s]
Time = 1454100.00 [min],  Q_out =  278.56185 [m^3/s]
Time = 1454676.67 [min],  Q_out =  252.24278 [m^3/s]
Time = 1455300.00 [min],  Q_out =  236.70063 [m^3/s]
Time = 1455900.00 [min],  Q_out =  216.85030 [m^3/s]
Time = 1456478.33 [min],  Q_out =  198.19839 [m^3/s]
Time = 1457090.00 [min],  Q_out =  178.31493 [m^3/s]
Time = 1457640.00 [min],  Q_out =  166.52663 [m^3/s]
Time = 1458300.00 [min],  Q_out =  157.07661 [m^3/s]
Time = 1458900.00 [min],  Q_out =  156.88460 [m^3/s]
Time = 1459486.67 [min],  Q_out =  154.72063 [m^3/s]
Time = 1460086.67 [min],  Q_out =  141.94060 [m^3/s]
Time = 1460653.33 [min],  Q_out =  132.29464 [m^3/s]
Time = 1461266.67 [min],  Q_out =  123.62331 [m^3/s]
Time = 1461863.33 [min],  Q_out =  126.18167 [m^3/s]
Time = 1462450.00 [min],  Q_out =  138.54474 [m^3/s]
Time = 1463040.00 [min],  Q_out =  145.94085 [m^3/s]
Time = 1463640.00 [min],  Q_out =  160.32262 [m^3/s]
Time = 1464243.33 [min],  Q_out =  147.12962 [m^3/s]
Time = 1464816.67 [min],  Q_out =  148.12473 [m^3/s]
Time = 1465386.67 [min],  Q_out =  181.69420 [m^3/s]
Time = 1465956.67 [min],  Q_out =  197.55576 [m^3/s]
Time = 1466520.00 [min],  Q_out =  303.65767 [m^3/s]
Time = 1467133.33 [min],  Q_out =  356.39206 [m^3/s]
Time = 1467720.00 [min],  Q_out =  403.72590 [m^3/s]
Time = 1468300.00 [min],  Q_out =  453.93053 [m^3/s]
Time = 1468820.00 [min],  Q_out =  446.32903 [m^3/s]
Time = 1469340.00 [min],  Q_out =  410.47175 [m^3/s]
Time = 1469896.67 [min],  Q_out =  377.18407 [m^3/s]
Time = 1470420.00 [min],  Q_out =  398.04003 [m^3/s]
Time = 1470946.67 [min],  Q_out =  451.37372 [m^3/s]
Time = 1471488.33 [min],  Q_out =  390.39772 [m^3/s]
Time = 1472003.33 [min],  Q_out =  358.96380 [m^3/s]
Time = 1472515.00 [min],  Q_out =  382.65898 [m^3/s]
Time = 1473000.00 [min],  Q_out =  411.12435 [m^3/s]
Time = 1473496.67 [min],  Q_out =  400.56602 [m^3/s]
Time = 1474030.00 [min],  Q_out =  364.29875 [m^3/s]
Time = 1474560.00 [min],  Q_out =  350.27422 [m^3/s]
Time = 1475093.33 [min],  Q_out =  336.27384 [m^3/s]
Time = 1475640.00 [min],  Q_out =  311.26856 [m^3/s]
Time = 1476180.00 [min],  Q_out =  342.95392 [m^3/s]
Time = 1476720.00 [min],  Q_out =  415.94327 [m^3/s]
Time = 1477260.00 [min],  Q_out =  423.01029 [m^3/s]
Time = 1477800.00 [min],  Q_out =  514.34367 [m^3/s]
Time = 1478373.33 [min],  Q_out =  565.59697 [m^3/s]
Time = 1478960.00 [min],  Q_out =  651.85056 [m^3/s]
Time = 1479540.00 [min],  Q_out =  795.51787 [m^3/s]
Time = 1480173.33 [min],  Q_out =  835.08676 [m^3/s]
Time = 1480806.67 [min],  Q_out =  796.66653 [m^3/s]
Time = 1481430.00 [min],  Q_out =  708.25999 [m^3/s]
Time = 1482003.33 [min],  Q_out =  627.65851 [m^3/s]
Time = 1482600.00 [min],  Q_out =  585.34561 [m^3/s]
Time = 1483200.00 [min],  Q_out =  535.36673 [m^3/s]
Time = 1483783.33 [min],  Q_out =  482.23278 [m^3/s]
Time = 1484413.33 [min],  Q_out =  409.79852 [m^3/s]
Time = 1485000.00 [min],  Q_out =  365.15881 [m^3/s]
Time = 1485616.67 [min],  Q_out =  317.51096 [m^3/s]
Time = 1486200.00 [min],  Q_out =  286.04105 [m^3/s]
Time = 1486740.00 [min],  Q_out =  248.31448 [m^3/s]
Time = 1487316.67 [min],  Q_out =  216.24781 [m^3/s]
Time = 1487820.00 [min],  Q_out =  202.49435 [m^3/s]
Time = 1488360.00 [min],  Q_out =  179.81144 [m^3/s]
Time = 1488893.33 [min],  Q_out =  170.25787 [m^3/s]
Time = 1489380.00 [min],  Q_out =  162.76146 [m^3/s]
Time = 1489906.67 [min],  Q_out =  159.53119 [m^3/s]
Time = 1490460.00 [min],  Q_out =  151.77888 [m^3/s]
Time = 1490976.67 [min],  Q_out =  143.49862 [m^3/s]
Time = 1491553.33 [min],  Q_out =  128.76939 [m^3/s]
Time = 1492073.33 [min],  Q_out =  149.80752 [m^3/s]
Time = 1492563.33 [min],  Q_out =  243.34512 [m^3/s]
Time = 1493100.00 [min],  Q_out =  177.35399 [m^3/s]
Time = 1493640.00 [min],  Q_out =  167.44551 [m^3/s]
Time = 1494180.00 [min],  Q_out =  180.67000 [m^3/s]
Time = 1494720.00 [min],  Q_out =  188.42305 [m^3/s]
Time = 1495260.00 [min],  Q_out =  225.51359 [m^3/s]
Time = 1495863.33 [min],  Q_out =  183.52600 [m^3/s]
Time = 1496460.00 [min],  Q_out =  184.50239 [m^3/s]
Time = 1497060.00 [min],  Q_out =  235.61524 [m^3/s]
Time = 1497603.33 [min],  Q_out =  299.62325 [m^3/s]
Time = 1498140.00 [min],  Q_out =  447.74914 [m^3/s]
Time = 1498740.00 [min],  Q_out =  503.43909 [m^3/s]
Time = 1499306.67 [min],  Q_out =  487.69701 [m^3/s]
Time = 1499858.33 [min],  Q_out =  487.55443 [m^3/s]
Time = 1500420.00 [min],  Q_out =  465.36038 [m^3/s]
Time = 1500960.00 [min],  Q_out =  426.32138 [m^3/s]
Time = 1501560.00 [min],  Q_out =  383.89164 [m^3/s]
Time = 1502123.33 [min],  Q_out =  354.65946 [m^3/s]
Time = 1502683.33 [min],  Q_out =  317.53399 [m^3/s]
Time = 1503240.00 [min],  Q_out =  284.88977 [m^3/s]
Time = 1503736.67 [min],  Q_out =  278.49357 [m^3/s]
Time = 1504260.00 [min],  Q_out =  268.91024 [m^3/s]
Time = 1504800.00 [min],  Q_out =  249.34668 [m^3/s]
Time = 1505340.00 [min],  Q_out =  262.55869 [m^3/s]
Time = 1505905.00 [min],  Q_out =  256.82876 [m^3/s]
Time = 1506420.00 [min],  Q_out =  246.02796 [m^3/s]
Time = 1506960.00 [min],  Q_out =  221.56752 [m^3/s]
Time = 1507500.00 [min],  Q_out =  209.85617 [m^3/s]
Time = 1508036.67 [min],  Q_out =  218.20258 [m^3/s]
Time = 1508580.00 [min],  Q_out =  202.72238 [m^3/s]
Time = 1509120.00 [min],  Q_out =  193.23407 [m^3/s]
Time = 1509610.00 [min],  Q_out =  204.75586 [m^3/s]
Time = 1510173.33 [min],  Q_out =  202.52723 [m^3/s]
Time = 1510680.00 [min],  Q_out =  212.69952 [m^3/s]
Time = 1511220.00 [min],  Q_out =  220.33417 [m^3/s]
Time = 1511820.00 [min],  Q_out =  222.07541 [m^3/s]
Time = 1512336.67 [min],  Q_out =  282.25778 [m^3/s]
Time = 1512840.00 [min],  Q_out =  361.47921 [m^3/s]
Time = 1513386.67 [min],  Q_out =  355.60542 [m^3/s]
Time = 1513920.00 [min],  Q_out =  343.79970 [m^3/s]
Time = 1514486.67 [min],  Q_out =  333.67297 [m^3/s]
Time = 1515060.00 [min],  Q_out =  275.93790 [m^3/s]
Time = 1515600.00 [min],  Q_out =  261.02635 [m^3/s]
Time = 1516200.00 [min],  Q_out =  246.07841 [m^3/s]
Time = 1516776.67 [min],  Q_out =  241.72520 [m^3/s]
Time = 1517350.00 [min],  Q_out =  243.57216 [m^3/s]
Time = 1517910.00 [min],  Q_out =  207.77671 [m^3/s]
Time = 1518450.00 [min],  Q_out =  184.46939 [m^3/s]
Time = 1518966.67 [min],  Q_out =  178.69868 [m^3/s]
Time = 1519543.33 [min],  Q_out =  181.70361 [m^3/s]
Time = 1520146.67 [min],  Q_out =  206.71933 [m^3/s]
Time = 1520743.33 [min],  Q_out =  318.07445 [m^3/s]
Time = 1521300.00 [min],  Q_out =  306.19869 [m^3/s]
Time = 1521900.00 [min],  Q_out =  260.81677 [m^3/s]
Time = 1522500.00 [min],  Q_out =  233.14424 [m^3/s]
Time = 1523040.00 [min],  Q_out =  213.92247 [m^3/s]
Time = 1523570.00 [min],  Q_out =  212.17462 [m^3/s]
Time = 1524060.00 [min],  Q_out =  228.28625 [m^3/s]
Time = 1524600.00 [min],  Q_out =  203.48113 [m^3/s]
Time = 1525130.00 [min],  Q_out =  246.86514 [m^3/s]
Time = 1525626.67 [min],  Q_out =  535.37619 [m^3/s]
Time = 1526180.00 [min],  Q_out =  990.64253 [m^3/s]
Time = 1526736.67 [min],  Q_out =  845.66763 [m^3/s]
Time = 1527300.00 [min],  Q_out =  674.64783 [m^3/s]
Time = 1527840.00 [min],  Q_out =  597.94970 [m^3/s]
Time = 1528366.67 [min],  Q_out =  605.69752 [m^3/s]
Time = 1528903.33 [min],  Q_out =  559.85770 [m^3/s]
Time = 1529400.00 [min],  Q_out =  498.46825 [m^3/s]
Time = 1529940.00 [min],  Q_out =  441.89457 [m^3/s]
Time = 1530510.00 [min],  Q_out =  405.38420 [m^3/s]
Time = 1531010.00 [min],  Q_out =  389.41933 [m^3/s]
Time = 1531500.00 [min],  Q_out =  372.31472 [m^3/s]
Time = 1532003.33 [min],  Q_out =  365.65887 [m^3/s]
Time = 1532506.67 [min],  Q_out =  405.44648 [m^3/s]
Time = 1533016.67 [min],  Q_out =  382.98810 [m^3/s]
Time = 1533540.00 [min],  Q_out =  359.42730 [m^3/s]
Time = 1534046.67 [min],  Q_out =  368.88049 [m^3/s]
Time = 1534566.67 [min],  Q_out =  421.48214 [m^3/s]
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Time = 1536120.00 [min],  Q_out =  544.24320 [m^3/s]
Time = 1536660.00 [min],  Q_out =  513.31121 [m^3/s]
Time = 1537203.33 [min],  Q_out =  438.62109 [m^3/s]
Time = 1537796.67 [min],  Q_out =  393.27796 [m^3/s]
Time = 1538306.67 [min],  Q_out =  372.72492 [m^3/s]
Time = 1538871.67 [min],  Q_out =  354.23885 [m^3/s]
Time = 1539430.00 [min],  Q_out =  448.15456 [m^3/s]
Time = 1539980.00 [min],  Q_out =  454.61497 [m^3/s]
Time = 1540556.67 [min],  Q_out =  394.05751 [m^3/s]
Time = 1541040.00 [min],  Q_out =  356.23987 [m^3/s]
Time = 1541603.33 [min],  Q_out =  312.14660 [m^3/s]
Time = 1542136.67 [min],  Q_out =  281.77506 [m^3/s]
Time = 1542660.00 [min],  Q_out =  255.56552 [m^3/s]
Time = 1543203.33 [min],  Q_out =  229.31155 [m^3/s]
Time = 1543763.33 [min],  Q_out =  216.77663 [m^3/s]
Time = 1544250.00 [min],  Q_out =  211.72247 [m^3/s]
Time = 1544806.67 [min],  Q_out =  305.12969 [m^3/s]
Time = 1545303.33 [min],  Q_out =  482.31825 [m^3/s]
Time = 1545806.67 [min],  Q_out =  331.47357 [m^3/s]
Time = 1546340.00 [min],  Q_out =  241.74637 [m^3/s]
Time = 1546840.00 [min],  Q_out =  213.72210 [m^3/s]
Time = 1547396.67 [min],  Q_out =  180.24001 [m^3/s]
Time = 1547936.67 [min],  Q_out =  166.81369 [m^3/s]
Time = 1548433.33 [min],  Q_out =  166.27752 [m^3/s]
Time = 1548996.67 [min],  Q_out =  181.73780 [m^3/s]
Time = 1549523.33 [min],  Q_out =  181.83852 [m^3/s]
Time = 1550080.00 [min],  Q_out =  203.61397 [m^3/s]
Time = 1550646.67 [min],  Q_out =  236.01479 [m^3/s]
Time = 1551180.00 [min],  Q_out =  256.25295 [m^3/s]
Time = 1551720.00 [min],  Q_out =  231.72388 [m^3/s]
Time = 1552286.67 [min],  Q_out =  209.47655 [m^3/s]
Time = 1552816.67 [min],  Q_out =  199.62578 [m^3/s]
Time = 1553400.00 [min],  Q_out =  187.06527 [m^3/s]
Time = 1553953.33 [min],  Q_out =  181.37383 [m^3/s]
Time = 1554480.00 [min],  Q_out =  187.69893 [m^3/s]
Time = 1555020.00 [min],  Q_out =  186.16757 [m^3/s]
Time = 1555560.00 [min],  Q_out =  159.47091 [m^3/s]
Time = 1556100.00 [min],  Q_out =  186.43578 [m^3/s]
Time = 1556653.33 [min],  Q_out =  237.05236 [m^3/s]
Time = 1557233.33 [min],  Q_out =  313.52797 [m^3/s]
Time = 1557810.00 [min],  Q_out =  311.20778 [m^3/s]
Time = 1558353.33 [min],  Q_out =  435.05882 [m^3/s]
Time = 1558928.33 [min],  Q_out =  636.23125 [m^3/s]
Time = 1559533.33 [min],  Q_out =  750.94236 [m^3/s]
Time = 1560120.00 [min],  Q_out =  857.51433 [m^3/s]
Time = 1560720.00 [min],  Q_out =  868.69697 [m^3/s]
Time = 1561300.00 [min],  Q_out =  945.40354 [m^3/s]
Time = 1561863.33 [min],  Q_out =  720.19908 [m^3/s]
Time = 1562463.33 [min],  Q_out =  601.15065 [m^3/s]
Time = 1562983.33 [min],  Q_out =  551.43018 [m^3/s]
Time = 1563503.33 [min],  Q_out =  511.28972 [m^3/s]
Time = 1564020.00 [min],  Q_out =  477.80105 [m^3/s]
Time = 1564533.33 [min],  Q_out =  466.85685 [m^3/s]
Time = 1565083.33 [min],  Q_out =  421.18044 [m^3/s]
Time = 1565580.00 [min],  Q_out =  428.50918 [m^3/s]
Time = 1566063.33 [min],  Q_out =  450.07611 [m^3/s]
Time = 1566570.00 [min],  Q_out =  460.31602 [m^3/s]
Time = 1567090.00 [min],  Q_out =  489.19469 [m^3/s]
Time = 1567680.00 [min],  Q_out =  467.55302 [m^3/s]
Time = 1568236.67 [min],  Q_out =  459.98523 [m^3/s]
Time = 1568753.33 [min],  Q_out =  438.24644 [m^3/s]
Time = 1569310.00 [min],  Q_out =  396.22826 [m^3/s]
Time = 1569840.00 [min],  Q_out =  374.57618 [m^3/s]
Time = 1570380.00 [min],  Q_out =  349.20789 [m^3/s]
Time = 1570893.33 [min],  Q_out =  332.97777 [m^3/s]
Time = 1571400.00 [min],  Q_out =  317.58717 [m^3/s]
Time = 1571940.00 [min],  Q_out =  292.40578 [m^3/s]
Time = 1572500.00 [min],  Q_out =  280.78997 [m^3/s]
Time = 1573080.00 [min],  Q_out =  254.47131 [m^3/s]
Time = 1573670.00 [min],  Q_out =  222.48061 [m^3/s]
Time = 1574173.33 [min],  Q_out =  214.77085 [m^3/s]
Time = 1574700.00 [min],  Q_out =  224.03158 [m^3/s]
Time = 1575213.33 [min],  Q_out =  213.07344 [m^3/s]
Time = 1575720.00 [min],  Q_out =  194.49699 [m^3/s]
Time = 1576260.00 [min],  Q_out =  198.04338 [m^3/s]
Stopping: Reached stop time = 1576800.0.

Meteorology component: Finished.
Diversions component: Finished.
Channels component: Finished.
Snow component: Finished.
Groundwater component: Finished.
Evaporation component: Finished.
Infiltration component: Finished.
Ice component: Finished.
=======================
Simulation complete.
=======================

------------------------------------------------------------
TopoFlow Version 3.62 (2020-06-02)
Tue Jul  7 10:54:08 2020

Input directory:      /Users/peckhams/TF_Tests3/Baro-Gam_60sec/
Output directory:     /Users/peckhams/TF_Tests3/Baro-Gam_60sec/Test1_2015-10_to_2018-10_GLDAS_Bilinear_OUT/
Site prefix:          Baro-Gam_60sec
Case prefix:          Test1

Simulated time:      1576800.0 [min]
Program run time:    22.80398739973704 [min]

Number of timesteps: 946080
Number of columns:   134
Number of rows:      129

T_stop:            1576800.0 [min]

Main outlet ID:    (79, 4) (row, col)
Basin_area:        24647.3 [km^2]

Min Manning's n:   0.019999999552965164
Max Manning's n:   1.0

Q_final:           203.32360770798405 [m^3/s]
Q_peak:            1882.9428408499414 [m^3/s]
Q_peak_time:       722370.0 [min]
u_peak:            2.021411651311498 [m/s]
u_peak_time:       722366.6666666666 [min]
d_peak:            2.322236737395515 [m]
d_peak_time:       722366.6666666666 [min]

Max(precip rate):  13.199700788391056 [mm/hr]

Total accumulated volumes over entire DEM: (fluxes)
vol_P    (precip):        258531.9079237831 x 10^6 [m^3]   (incl. leq snowfall)
vol_Q    (discharge):     35304.81060147085 x 10^6 [m^3]   (main basin outlet)
vol_SM   (snowmelt):      0.0 [m^3]
vol_MR   (icemelt):       0.0 [m^3]
vol_ET   (evaporation):   427715.32701535244 x 10^6 [m^3]
vol_IN   (infiltration):  0.0 [m^3]
vol_Rg   (recharge):      0.0 [m^3]   (bottom loss)
vol_GW   (baseflow):      0.0 [m^3]
vol_R    (runoff):        -169183.23772112493 x 10^6 [m^3]  R = (P+SM+MR) - (ET+IN)

Total accumulated volumes over entire DEM: (storage)
vol_soil  (infiltration):  0.0 [m^3]  (change in storage)
vol_chan0 (channels):      0.0 [m^3]  (initial)
vol_chan  (channels):      300.27062622473085 x 10^6 [m^3]
vol_land  (surface):       0.0 [m^3]
vol_edge  (boundary):      83409.00569861056 x 10^6 [m^3]
vol_swe   (snowpack):      0.0 [m^3]

Mass balance check:
volume in         = 258531.9079237831 x 10^6 [m^3]
volume out        = 511124.332713963 x 10^6 [m^3]
change in storage = 300.27062622473085 x 10^6 [m^3]
volume gain error = 252892.69541640466 x 10^6 [m^3]
vol_error/ vol_in = 0.9781875569918399

Final grid mins and maxes:
Min(Q), Max(Q):   0.0000, 347.7946 [m^3/s]
Min(u), Max(u):   0.0000, 3.6964 [m/s]
Min(d), Max(d):   0.0000, 4.9279 [m]

Finished.  (Test1)

### Display the Simulated Hydrograph (River Discharge)

Explanation.
_____no_output_____

os.chdir(output_dir)
nc_file = case_prefix + '_0D-Q.nc'
tfvis.plot_time_series(nc_file, output_dir=output_dir, var_index=2,
                       marker=',', REPORT=True, xsize=10, ysize=4)var_names in netCDF file =
['time', 'Q_79_4', 'Q_98_75', 'Q_76_14', 'Q_79_29', 'Q_59_48', 'Q_60_48', 'Q_76_45', 'Q_74_44', 'Q_81_56']
var longitudes =
[34.28791666666567, 35.471249999999, 34.45458333333234, 34.70458333333234, 35.021249999999, 35.021249999999, 34.971249999999, 34.95458333333234, 35.15458333333233]
var latitudes =
[8.670416666665666, 8.987083333332333, 8.620416666665665, 8.670416666665666, 8.337083333332332, 8.353749999999, 8.620416666665665, 8.587083333332334, 8.703749999999]

### Display the Simulated River Depth

The bankfull depth of the Baro River near Masha is estimated to be about ##### meters based on an online literature search (but we were unable to obtain actual measurements).  The next figure shows a time series of simulated river depth for the Baro River at Masha._____no_output_____

os.chdir(output_dir)
nc_file = case_prefix + '_0D-d.nc'
tfvis.plot_time_series(nc_file, output_dir=output_dir, var_index=2,
                       marker=',', REPORT=False, xsize=10, ysize=4)_____no_output_____

### Display the Simulated River Velocity

The next figure shows a time series of simulated river velocity for the Baro River near Masha, with an average value of about ######### meters per second._____no_output_____

os.chdir(output_dir)
nc_file = case_prefix + '_0D-u.nc'
tfvis.plot_time_series(nc_file, output_dir=output_dir, var_index=2,
                       marker=',', REPORT=False, xsize=10, ysize=4)_____no_output_____

## The TopoFlow Calibrate Module

Having completed one model run, we now import TopoFlow's calibrate module from topoflow/utils and explain some of its features.
This module reads required information about the observed and predicted time series from a configuration (CFG) file called calibrate.cfg.  Note that simulated values from TopoFlow will be stored with a fixed time interval between values (i.e. regular spacing in time), but the observed values are often stored with an irregular time interval.  Because of this observed values often need to be regularized, or interpolated to the same times associated with the simulated values before computing the cost function.  Also, observed times (and sometimes dates), may use different formats such as "hhmm", "datetime" (e.g. 2015-10-01 12:34:56), or "timesince".  The calibrate module automatically deals with all of these issues.

The calibrate module contains a class called calibrator.  We first create an instance of this class, call some of its method functions, and show how to print out some of the data it contains.

### Import and Instantiate the Module
_____no_output_____

from topoflow.utils import calibrate

#---------------------------------------------
# Create an instance of the calibrator class
#---------------------------------------------
cal_cfg_file  = basin_dir + 'Test1_2015-10_to_2018-10_GLDAS_Bilinear_cfg/calibrate.cfg'
cal = calibrate.calibrator(cfg_file=cal_cfg_file)_____no_output_____

### Call Some of Its Method Functions_____no_output_____

cal.print_cfg_info()In print_cfg_info():
site_prefix  = Baro-Gam_60sec
cfg_prefix   = Test1
home_dir     = /Users/peckhams
basin_dir    = /Users/peckhams/TF_Tests3/Baro-Gam_60sec/
topo_dir     = /Users/peckhams/TF_Tests3/Baro-Gam_60sec/__topo/
cfg_dir      = /Users/peckhams/TF_Tests3/Baro-Gam_60sec/Test1_2015-10_to_2018-10_GLDAS_Bilinear_cfg/
output_dir   = /Users/peckhams/TF_Tests3/Baro-Gam_60sec/Test1_2015-10_to_2018-10_GLDAS_Bilinear_OUT/
------------------------------------------------------
obs_dir            = /Users/peckhams/TF_Tests3/Baro-Gam_60sec/__observations/
obs_data_file      = /Users/peckhams/TF_Tests3/Baro-Gam_60sec/__observations/Baro-Masha_Discharge_2015-10_to_2018-10.csv
obs_data_delim     = ,
obs_time_format    = date
obs_time_interval  = 1440.0
obs_time_units     = minutes
obs_start_datetime = 2015-10-01 00:00:00
obs_header_lines   = 4
obs_time_column    = 0
obs_value_column   = 1
obs_interp_method  = linear
------------------------------------------------------
sim_dir            = /Users/peckhams/TF_Tests3/Baro-Gam_60sec/Test1_2015-10_to_2018-10_GLDAS_Bilinear_OUT/
sim_data_file      = /Users/peckhams/TF_Tests3/Baro-Gam_60sec/Test1_2015-10_to_2018-10_GLDAS_Bilinear_OUT/Test1_0D-Q.txt
sim_data_delim     = None
sim_time_format    = timesince
sim_time_interval  = 60.0
sim_time_units     = minutes
sim_start_datetime = 2015-10-01T00:00:00
sim_header_lines   = 2
sim_time_column    = 0
sim_value_column   = 1
------------------------------------------------------
cost_fcn_name      = Lp_norm
p_for_Lp_norm      = 2.0

#-------------------------------------------
# Read simulated values from the last run,
# and then read observed values
#-------------------------------------------
cal.get_simulated_values( SILENT=False )
cal.get_observed_values( SILENT=False )In get_simulated_values():
Sim time format    = timesince
Sim time interval  = 60.0
Min(sim_times)     = 0.0
Max(sim_times)     = 1576740.0
Min(sim_values)    = 0.0
Max(sim_values)    = 1882.5703

Reading time series data...
Interpolating obs. time series data...

In get_observed_values():
Obs time format    = date
Obs time interval  = 1440.0
obs_values.size    = 26280
Min(obs_values)    = 1.0809999704360962
Max(obs_values)    = 163.85499572753906

obs_values_orig.size = 1096
Min(obs_values_orig) = 1.081
Max(obs_values_orig) = 163.855

### Print Values of Some of its Variables_____no_output_____

print(cal.sim_start_datetime)
print(cal.sim_time_units)
print(cal.sim_time_format)
print(cal.sim_times[0:5])
print(cal.sim_times.max())
print(cal.sim_times.size)
print()
print(cal.obs_start_datetime)
print(cal.obs_time_units)
print(cal.obs_time_format)
print(cal.obs_times[0:10])
print(cal.obs_times[-10:])
print(cal.obs_times.size)2015-10-01T00:00:00
minutes
timesince
[  0.  60. 120. 180. 240.]
1576740.0
26280

2015-10-01 00:00:00
minutes
date
[  0.  60. 120. 180. 240. 300. 360. 420. 480. 540.]
[1576200. 1576260. 1576320. 1576380. 1576440. 1576500. 1576560. 1576620.
 1576680. 1576740.]
26280

### Compute the Cost Function for One Model Run_____no_output_____

Y_obs = cal.obs_values
Y_sim = cal.sim_values
n_obs = Y_obs.size   #  ndarrays
n_sim = Y_sim.size
if (n_obs < n_sim):
    Y_sim = Y_sim[0:n_obs]
else:
    Y_obs = Y_obs[0:n_sim]
cost = calibrate.Lp_norm(Y_obs, Y_sim)
print( cost )60829.736868013824

### Plot the Observed and Simulated Values with Options_____no_output_____

cal.plot_time_series(obs_only=True, marker=',', title='Observed Values')_____no_output_____cal.plot_time_series(sim_only=True, marker=',', title='Simulated Values')_____no_output_____cal.plot_time_series(marker=',', title='Observed & Simulated Values')_____no_output_____cal.plot_time_series(normalize=True, marker=',', title='Normalized Values')_____no_output_____

## TopoFlow Calibration for Baro River at Masha

This is just a simple calibration where we only allow one parameter to vary, for purposes of demonstration.  The procedure is to perform many model runs where we vary that parameter over its allowed range (i.e. we sample the parameter space) and for each run we compute a cost function that compares predictions to observations.  Finally, we save the value of the parameter that minimizes the cost function.

Here we use a TopoFlow utility in topoflow/utils called calibrate.py.

For a full-fledged calibration exercise, where more parameters would be allowed to vary, the best approach is to use a sophisticated toolkit such as
[DAKOTA](https://dakota.sandia.gov/), developed over decades by DOE's Sandia National Lab.
DAKOTA can more rapidly perform a large ensemble of model runs by automatically taking advantage of parallel processing on a supercomputer or cluster.  Details for how to calibrate TopoFlow with DAKOTA will be provided in a future notebook.  In addition to its computational speed, DAKOTA offers an extensive collection of tools and algorithms that go far beyond optimization problems (like model calibration), to encompass all types of uncertainty quantification and sensitivity analysis._____no_output_____

#---------------------------------------------
# Create an instance of the calibrator class
#---------------------------------------------
cal_cfg_file  = basin_dir + 'Test1_2015-10_to_2018-10_GLDAS_Bilinear_cfg/calibrate.cfg'
cal = calibrate.calibrator(cfg_file=cal_cfg_file)
cal.print_cfg_info()_____no_output_____

First, we choose a calibration variable, cal_var, and perform the calibration exercise. The four calibration variables shown here are used as inputs to a function in TopoFlow's parameterize module that generates an input grid of channel widths or Manning's n roughness values.  These grids have the same dimensions as the DEM, and are estimates computed as power-law functions of the total contributing area (TCA) grid.  The chosen calibration variable is varied over a set range of possible values, and for each of those values a new input grid is created and a new model run is performed.  After each model run, the cost function is evaluated.  Various 1D arrays are saved in the state of the calibrator object, namely

 cal.obs_times     (in minutes)

 cal.obs_values    (observed values)

 cal.sim_times     (in minutes)

 cal.sim_values    (simulated values)

 cal.params        (the set of parameter values used)

 cal.costs         (the cost associated with each parameter value used)

 cal.best_param    (the parameter value with the lowest cost)

Setting the PLOT keyword to True generates a plot after each model run to compare the observed discharge values to the values that are predicted for the given parameter settings.  The orange curve shows the observed values and the blue curve has the predicted values for a given model run.  These plots are created using TopoFlow's visualize module, in topoflow/utils/visualize.py.

Keep in mind that we could get a better fit and lower cost by allowing multiple parameters to vary over their allowed ranges.  That exercise will be added to this notebook soon.

### Case 1:  Calibration Variable = "manning_n_min"_____no_output_____

cal.calibrate( cal_var='manning_n_min', var_range=[0.01, 0.16],
               n_values=16, PLOT=True, NORMALIZE_PLOT=True, SILENT=True)

# cal.calibrate( cal_var='manning_n_max', var_range=[0.1, 1.0],
#  _____no_output_____

### Print out the parameter values and their associated costs

For the case where the adjustable parameter is manning_n_min, a value of about 0.02 minimizes the cost function.

Note that the
[Manning's n roughness parameter](https://en.wikipedia.org/wiki/Manning_formula)
decreases downstream (as total contributing area, or TCA increases), and a typical value for a river channel is around 0.02 or 0.03.  Manning's formula is often used for overland flow as well, where the roughness and momentum loss is much greater.  Some typical overland flow values are:

(0.04: grass meadow, 0.08: harvested corn, 0.16: corn field, 0.32: forest, 0.64: dense forest).
See: http://cidbimena.desastres.hn/pdf/eng/doc5646/doc5646-8b.pdf (Table 8.5).

_____no_output_____

print('best param =', cal.best_param)
print('params =', cal.params)
print('costs  =', cal.costs)_____no_output_____

### Case 2:  Calibration Variable = "channel_width_max"

For this next set of simulations, we vary the maximum channel width, i.e. the maximum river width of any channel in the Baro River, which should occur near the basin outlet, just west of the town of Gambela. _____no_output_____

cal.calibrate( cal_var='channel_width_max', var_range=[100.0, 400.0],
               n_values=10, PLOT=True, NORMALIZE_PLOT=True, SILENT=True)

# cal.calibrate( cal_var='channel_width_power', var_range=[0.1, 1.0],
#                n_values=10, PLOT=True, NORMALIZE_PLOT=True, SILENT=True )
_____no_output_____

## Conclusions

The purpose of this notebook was to explain the concepts and explore the potential for calibrating the TopoFlow hydrologic model using the wetted area of a grid cell from remote sensing as the observed variable and river discharge from TopoFlow as the simulated variable.  For simplicity, only one model parameter at a time was varied.  Observed and predicted values were compared in normalized plots, with a focus on the timing of high and low flow events.  However, there was not a clear correspondence between the two time series, and the computed costs (computed from the Spearman metric) were not seen to change much for the parameters that were varied.  This may have been largely due to the dramatic difference in temporal resolution between the observed and simulated values (12 days vs. 1 hour, respectively).  The
[GLDAS space-time rainfall product](https://disc.gsfc.nasa.gov/datasets/GLDAS_NOAH025_3H_2.1/summary?keywords=GLDAS)
from NASA, with a temporal resolution of 3 hours, was used to drive the model.  It is possible that there are problems with this data, as discussed in another Jupyter notebook in this series:
[TopoFlow Rainfall Inputs](https://github.com/peckhams/topoflow36/blob/master/TopoFlow_Rainfall_Inputs.ipynb).
Later, we will try using the
[GPM space-time rainfall product](https://disc.gsfc.nasa.gov/datasets/GPM_3IMERGHH_06/summary?keywords=GPM) to see if it yields better results (also from NASA).

The TopoFlow utilities calibrate.py, parameterize.py, time_utils.py visualize.py in topoflow/utils were used "under the hood" to do most of the work for this analysis.  In particular, the modules calibrate.py and time_utils.py were extended signficantly to suppor this analysis.

This notebook will be expanded to vary multiple parameters soon, and to discuss the results in more detail._____no_output_____## Other Jupyter Notebooks for TopoFlow 3.6

There is a growing collection of Jupyter notebooks to demonstrate the functionality of the TopoFlow 3.6 hydrologic model and its numerous utilities.  These are all available at:
https://github.com/peckhams/topoflow36.

Peckham, S.D. (2019) A Jupyter notebook for getting started with TopoFlow 3.6. https://github.com/peckhams/topoflow36/blob/master/TopoFlow_Getting_Started.ipynb.

Peckham, S.D. (2019) A Jupyter notebook to illustrate new data preparation utilities in TopoFlow 3.6. https://github.com/peckhams/topoflow36/blob/master/TopoFlow_Utils_Demo1.ipynb.

Peckham, S.D. (2019) A Jupyter notebook to create visualizations of model output from TopoFlow 3.6. (version 2) https://github.com/peckhams/topoflow36/blob/master/TopoFlow_Visualization_v2.ipynb.

Peckham, S.D. (2020) A Jupyter notebook that discusses the use of global rainfall products such as GPM and GLDAS as inputs to TopoFlow.
https://github.com/peckhams/topoflow36/blob/master/TopoFlow_Rainfall_Inputs.ipynb.

Peckham, S.D. (2020) A Jupyter notebook that discusses the flood modeling (overbank flow) capabilities of TopoFlow 3.6.
https://github.com/peckhams/topoflow36/blob/master/TopoFlow_Flood_Modeling.ipynb.

Peckham, S.D. (2020) A Jupyter notebook to check and explore the TopoFlow solar radiation utilities, which includes shortwave and longwave radiation calculators. (Only shows a few of the capabilities so far.)
https://github.com/peckhams/topoflow36/blob/master/TopoFlow_Radiation.ipynb._____no_output_____## Key References for TopoFlow

Peckham, S.D., M. Stoica, E.E. Jafarov, A. Endalamaw and W.R. Bolton (2017) Reproducible, component-based modeling with TopoFlow, a spatial hydrologic modeling toolkit, Earth and Space Science,4(6), 377-394, special issue: Geoscience Papers of the Future, American Geophysical Union, http://dx.doi.org/10.1002/2016EA000237.

Peckham, S.D. (2009) Geomorphometry and spatial hydrologic modeling, In: Hengl, T. and Reuter, H.I. (Eds), Geomorphometry: Concepts, Software and Applications, Chapter 25, Developments in Soil Science, vol. 33, Elsevier, 579-602, http://dx.doi.org/10.1016/S0166-2481(08)00025-1.  (TopoFlow)

Jiang, P., M. Elag, P. Kumar, S.D. Peckham, L. Marini, R. Liu (2017) A service-oriented architecture for coupling web service models using the Basic Model Interface (BMI), Environmental Modelling & Software, 92, 107-118, http://dx.doi.org/10.1016/j.envsoft.2017.01.021.

Zhang, Z., D.L. Kane and L.D. Hinzman (2000) Development and application of a spatially-distributed Arctic hydrological and thermal process model (ARHYTHM),
Hydrological Processes, 14(6), 1017-1044,
<a href="https://doi.org/10.1002/(SICI)1099-1085(20000430)14:6<1017::AID-HYP982>3.0.CO;2-G">
    https://doi.org/10.1002/(SICI)1099-1085(20000430)14:6<1017::AID-HYP982>3.0.CO;2-G.

Peckham, S.D. (2014a) EMELI 1.0:  An experimental smart modeling framework for automatic coupling of self-describing models, Proceedings of HIC 2014, 11th International Conf. on Hydroinformatics, New York, NY. CUNY Academic Works, http://academicworks.cuny.edu/cc_conf_hic/464/.

Peckham, S.D. (2014b) The CSDMS Standard Names:  Cross-domain naming conventions for describing process models, data sets and their associated variables, Proceedings of the 7th Intl. Congress on Env. Modelling and Software, International Environmental Modelling and Software Society (iEMSs), San Diego, CA. (Eds.  D.P. Ames, N.W.T. Quinn, A.E. Rizzoli),  Paper 12. http://scholarsarchive.byu.edu/iemssconference/2014/Stream-A/12/.

Peckham, S.D., E.W.H. Hutton and B. Norris (2013) A component-based approach to integrated modeling in the geosciences: The Design of CSDMS, Computers & Geosciences, special issue: Modeling for Environmental Change, 53, 3-12, http://dx.doi.org/10.1016/j.cageo.2012.04.002.

Peckham, S.D. (2018) Using the open-source TopoFlow Python package for extracting D8 grids from DEMs and for fluvial landscape evolution modeling, Proceedings of Geomorphometry 2018, Boulder, CO, pp. O-11-1 to O-11-4, http://2018.geomorphometry.org/Peckham_2018_geomorphometry.pdf
_____no_output_____## Appendix 1: Installing TopoFlow in a conda Environment

To run this Jupyter notebook, it is recommended to use Python 3.7 from an Anaconda distribution and to install the required Python packages in a conda environment called tf36.  This prevents conflicts with other Python packages you may have installed.
The Anaconda distribution includes many packages from the
[Python Standard Library](https://docs.python.org/3/library/).

First, download the TopoFlow 3.6 package from GitHub repo "topoflow36" at:
https://github.com/peckhams/topoflow36.
Copy or unzip the package into some directory on your computer.  Let's refer to this full path as TF36_DIR.  e.g. TF36_DIR = /Users/peckhams/Dropbox/TopoFlow_3.6

Installing TopoFlow 3.6 with pip causes most of its dependencies to be installed automatically.  However, if you want to run the growing collection of Jupyter notebooks (such as this one) that highlight TopoFlow functionality, you will also need to install the
[nb_conda](https://docs.anaconda.com/anaconda/user-guide/tasks/use-jupyter-notebook-extensions/) package, and optionally the jupyterlab package (see below).
In addition, some of the new TopoFlow utilities (e.g. regrid.py and visualize.py) require the
[gdal](https://pypi.org/project/GDAL/) and
[matplotlib](https://matplotlib.org/) packages.
Simply type the following commands at an OS prompt after installing Anaconda and downloading TopoFlow.

``` bash
% conda update -n base conda
% conda create --name tf36
% conda activate tf36
% conda list
% conda install nb_conda
% conda install gdal
% conda install matplotlib
% conda install imageio
% pip install imageio-ffmpeg  (an imageio extension)
% cd TF36_DIR
% pip install -e .   (-e is the editable/developer option)
```

Note:  The pip package manager is used to install TopoFlow 3.6, since it is not available as a conda package.  (It gets installed when you install nb_conda.) However, like conda, pip will install a package and its dependencies into the currently active conda environment, as explained
[in these docs](https://docs.conda.io/projects/conda/en/4.6.1/user-guide/tasks/manage-pkgs.html#installing-non-conda-packages).  If you switch to another environment with conda activate envname, you can confirm that topoflow (or cfunits, gdal, netcdf4, etc.) is not there with conda list.

Note:  Some of the new TopoFlow utilities use a Python package version of [GDAL](https://pypi.org/project/GDAL/).  At the time of this writing, installing gdal from the conda-forge  with conda install -c conda-forge gdal did not work.

Note: The netCDF4 package will be installed as a TopoFlow dependency.

#### Conda Environments

Note that conda is the name of the package manager for the popular Anaconda Python distribution.  One feature of conda is support for multiple environments, which are isolated from one another.  When you install Anaconda, an environment called base is created for you and a base set of commonly-used Python packages are installed there.  However, you can (and should!) create additional, named environments and install different sets of Python packages into them without worrying about potential conflicts with packages in other environments.  Type conda env list to list your available environments.  You can switch to one of your other environments using the command conda activate envname.  (Replace "envname" with the name of an environment.) You can switch back to the base environment with the command conda deactivate.  It is better not to install new packages into the base environment.  See the online conda documentation on [Managing Environments](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) for more information.

It is always a good idea to update conda itself before creating new environments and installing packages in them. The "-n" flag is followed by the name of the environment to update, and the "-c" flag is followed by the name of the channel from which to get packages.  A channel is a collection of Python packages that are provided and maintained by some group.  The word "defaults" refers to
[Anaconda's own collection](https://docs.anaconda.com/anaconda/packages/pkg-docs/), while
[conda-forge](https://conda-forge.org/feedstocks/)
refers to another popular collection and the GitHub organization that maintains it.  Many Python packages are available from both of these channels.  (However, the ipyleaflet and pydap  packages are currently not available in the Anaconda collection.) When you are installing several packages into an environment, the potential for installation problems seems to be less if you get them all from the same channel.  Keep in mind that packages you install will likely depend on many other Python packages, so there is a potential for conflicts, usually related to different package versions.  Using conda environments helps to mitigate against this and helps with reproducibility.

Once you've switched to an environment with conda activate envname, you can type conda list to see a list of packages.  If you do this right after you create a new environment you will see that it contains no packages.  If you do this right after installing each package above you will see that:

Installing nb_conda triggers installation of nb_conda_kernels (2.2.3),
        ipykernel (5.3.0), notebook (6.0.3), pip (20.0.2),
        setuptools (46.4.0) and traitlets (4.3.3), among many others.

Installing gdal triggers installation of #######.

Installing matplotlib triggers installation of ##########.

#### Jupyter Notebook Extensions

Note that nb_conda is installed first above, and triggers installation of nb_conda_kernels along with notebook.  This is important as it makes your Jupyter notebook app aware of your conda environments and available in the app as "kernels".  Anaconda provides a helpful page on the
[Jupyter Notebook Extensions](https://docs.continuum.io/anaconda/user-guide/tasks/use-jupyter-notebook-extensions/).
That page also explains how you can enable or disable these extensions individually. The command jupyter nbextension list shows you the extensions that are installed and whether they are enabled.  If you run the jupyter notebook or jupyter lab command in an environment that has nb_conda_kernels installed (see below), you will have the ability to associate one of your available conda environments with any new notebook you create.  Different environments give rise to different kernels in Jupyter, and the kernel name includes the environment name, e.g. Python \[conda env:tf36\].  The kernel name is displayed in the upper right corner.  Notebooks typically open with the "environment kernel" they were created with. However, there is a Change Kernel option in the Kernel menu in the Jupyter app menu bar. (After changing the kernel, you may need to choose Restart from the Kernel menu.

#### Cloning a conda Environment

If your notebook is working but then you want to import additional packages (possibly with many dependencies, and potential for problems), you can keep the first environment but clone it with
conda create --name clonename --copy --clone envname,
and then install the additional packages in the clone.  This way, you can switch to the new environment's kernel and try to run your notebook, but if you run into any problems you can easily revert back to the original environment and functionality.

Note: Setting the "--copy" flag installs all packages using copies instead of hard or soft links.  This is necessary to avoid problems when using pip together with conda as described [on this page](https://stackoverflow.com/questions/43879119/installing-tensorflow-in-cloned-conda-environment-breaks-conda-environment-it-wa).

#### Running Notebooks in the Jupyter Notebook App

When you want to run the notebook, type conda activate tf36 (at an OS command prompt) to activate this environment.  Then change to the directory that contains this notebook and type jupyter notebook.  By default, this folder is called Jupyter and is in your home directory.  In the app, choose this notebook by name, "TopoFlow_Getting_Started.ipynb", and make sure to choose the kernel called:  Python \[conda env:tf36\].  See the References section at the end for more info.

#### Running Notebooks in the JupyterLab App

The
[JupyterLab](https://jupyterlab.readthedocs.io/en/stable/index.html)
app is a cool, new successor to the Notebook app and offers many additional features.  If you want to use this notebook in JupyterLab, you need to install one more Python package, as follows.

``` bash
% conda activate tf36
% conda install jupyterlab
```

You launch the JupyterLab app by typing jupyter lab instead of jupyter notebook.  To quit, choose Logout or Shutdown from the app's File menu.

#### JupyterLab Extensions

The Jupyter project provides documentation on
[JupyterLab Extensions](https://jupyterlab.readthedocs.io/en/stable/user/extensions.html)
which add capabilities to JupyterLab.  For example, after installing jupyterlab (see just above), if you want to use the ipywidgets and ipyleaflet Python packages, you need to install two extensions, as follows:
```
% conda activate tf36
% jupyter labextension install jupyter-leaflet
% jupyter labextension install @jupyter-widgets/jupyterlab-manager
```
To list the jupyter labextensions you have, and to see whether or not they are enabled, type jupyter labextension list.  Note: If you start jupyterlab from a conda environment in which a given extension is not installed, and then open or switch to a notebook which uses a different "environment kernel", one that requires that extension, the notebook may not work.

You should only install trusted extensions, due to security concerns, as explained in the documentation.  Third-party extensions pose a potential security risk.  An extension that allows you to play MP4 movie files in JupyterLab is
[available on GitHub](https://github.com/jupyterlab/jupyterlab-mp4)
(by Ian Rose of the JupyterLab organization)
and can be installed with the command:
```
% jupyter labextension install @jupyterlab/mp4-extension
```
Note: This command is from a pull-request on the extension's github repo.
Using the command: "jupyter labextension install jupyterlab-mp4" results in the
following error message:

An error occured.
ValueError: "jupyterlab-mp4" is not a valid npm package

As of May 27, 2020, JupyterLab has added an experimental Extension Manager which can be enabled by choosing Settings > Enable Extension Manager in the app.
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