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When did the two highest mega floods in the Amazon occur?

We found two simulated mega-floods, that largely deviated from what can be expected from the observed record. Here we will perform a first analysis to figure out when they happened.

##Load pacakages
import xarray as xr
import matplotlib.pyplot as plt
import numpy as np
import cartopy
import cartopy.crs as ccrs

##This is so variables get printed within jupyter
from IPython.core.interactiveshell import InteractiveShell 
InteractiveShell.ast_node_interactivity = "all"

We are loading the data from (der Wiel et al., 2019), openly available through https://zenodo.org/record/2536396#.XnnBgohKiUk.

Note the error message in regard to the time. I suspect this is because it is just a number from 1:2000 -> there are 2000 present-climate and future-climate years.

dirname = r'/home/tike/Discharge/'

Global_discharge = xr.open_dataset(dirname + 'Zenodo/presentYearMax.nc')
Global_discharge

/home/tike/miniconda3/envs/exp/lib/python3.8/site-packages/xarray/coding/times.py:426: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range
  dtype = _decode_cf_datetime_dtype(data, units, calendar, self.use_cftime)
/home/tike/miniconda3/envs/exp/lib/python3.8/site-packages/numpy/core/_asarray.py:85: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range
  return array(a, dtype, copy=False, order=order)

xarray.Dataset

• Dimensions:
  • lat: 360
  • lon: 720
  • time: 2000
• Coordinates: (3)
  • time
    (time)
    object
    0001-01-01 00:00:00 ... 2000-01-01 00:00:00

    standard_name :

    time

    long_name :

    Days since 1901-01-01

    array([cftime.DatetimeGregorian(0001-01-01 00:00:00),
           cftime.DatetimeGregorian(0002-01-01 00:00:00),
           cftime.DatetimeGregorian(0003-01-01 00:00:00), ...,
           cftime.DatetimeGregorian(1998-01-01 00:00:00),
           cftime.DatetimeGregorian(1999-01-01 00:00:00),
           cftime.DatetimeGregorian(2000-01-01 00:00:00)], dtype=object)

  • lat
    (lat)
    float32
    89.75 89.25 88.75 ... -89.25 -89.75

    long_name :

    latitude

    units :

    degrees_north

    standard_name :

    latitude

    array([ 89.75,  89.25,  88.75, ..., -88.75, -89.25, -89.75], dtype=float32)

  • lon
    (lon)
    float32
    -179.75 -179.25 ... 179.25 179.75

    standard_name :

    longitude

    long_name :

    longitude

    units :

    degrees_east

    array([-179.75, -179.25, -178.75, ...,  178.75,  179.25,  179.75],
          dtype=float32)

• Data variables: (1)
  • discharge
    (time, lat, lon)
    float32
    ...

    standard_name :

    discharge

    long_name :

    discharge

    units :

    m3s-1

    [518400000 values with dtype=float32]

• Attributes: (0)

Selecting the streamflow timeseries for the mouth of the Amazon

The files contain 2000 years of annual monthly maximum streamflow. We use the mouth of the river and select which year resulted in the largest flood.

##Spatial domain of the Amazon basin
lats = [5, -17]
lons = [-80, -45]

## We cut out the Amazon region
Amazon_discharge = Global_discharge['discharge'].sel(lon=slice(lons[0], lons[1]),
                                           lat=slice(lats[0], lats[1]))
Amazon_discharge
Amazon_timeseries = Global_discharge['discharge'].sel(lon=-51.75, lat=-1.25)

xarray.DataArray

'discharge'

• time: 2000
• lat: 44
• lon: 70

• ...

  [6160000 values with dtype=float32]

• Coordinates: (3)
  • time
    (time)
    object
    0001-01-01 00:00:00 ... 2000-01-01 00:00:00

    standard_name :

    time

    long_name :

    Days since 1901-01-01

    array([cftime.DatetimeGregorian(0001-01-01 00:00:00),
           cftime.DatetimeGregorian(0002-01-01 00:00:00),
           cftime.DatetimeGregorian(0003-01-01 00:00:00), ...,
           cftime.DatetimeGregorian(1998-01-01 00:00:00),
           cftime.DatetimeGregorian(1999-01-01 00:00:00),
           cftime.DatetimeGregorian(2000-01-01 00:00:00)], dtype=object)

  • lat
    (lat)
    float32
    4.75 4.25 3.75 ... -16.25 -16.75

    long_name :

    latitude

    units :

    degrees_north

    standard_name :

    latitude

    array([  4.75,   4.25,   3.75,   3.25,   2.75,   2.25,   1.75,   1.25,   0.75,
             0.25,  -0.25,  -0.75,  -1.25,  -1.75,  -2.25,  -2.75,  -3.25,  -3.75,
            -4.25,  -4.75,  -5.25,  -5.75,  -6.25,  -6.75,  -7.25,  -7.75,  -8.25,
            -8.75,  -9.25,  -9.75, -10.25, -10.75, -11.25, -11.75, -12.25, -12.75,
           -13.25, -13.75, -14.25, -14.75, -15.25, -15.75, -16.25, -16.75],
          dtype=float32)

  • lon
    (lon)
    float32
    -79.75 -79.25 ... -45.75 -45.25

    standard_name :

    longitude

    long_name :

    longitude

    units :

    degrees_east

    array([-79.75, -79.25, -78.75, -78.25, -77.75, -77.25, -76.75, -76.25, -75.75,
           -75.25, -74.75, -74.25, -73.75, -73.25, -72.75, -72.25, -71.75, -71.25,
           -70.75, -70.25, -69.75, -69.25, -68.75, -68.25, -67.75, -67.25, -66.75,
           -66.25, -65.75, -65.25, -64.75, -64.25, -63.75, -63.25, -62.75, -62.25,
           -61.75, -61.25, -60.75, -60.25, -59.75, -59.25, -58.75, -58.25, -57.75,
           -57.25, -56.75, -56.25, -55.75, -55.25, -54.75, -54.25, -53.75, -53.25,
           -52.75, -52.25, -51.75, -51.25, -50.75, -50.25, -49.75, -49.25, -48.75,
           -48.25, -47.75, -47.25, -46.75, -46.25, -45.75, -45.25], dtype=float32)

• Attributes: (3)

  standard_name :

  discharge

  long_name :

  discharge

  units :

  m3s-1

Mega-flood indices

We have now selected the timeseries for the mouth of the Amazon. Next step is to extract the two highest events, that are deviating a lot from the rest (see Extreme value analysis.

The highest flood occurs on 1568 (index 1567 in python). The second highest on 1731 (index 1730 in python).

For megaflood1 this corresponds to: Year: 2037 Start: 13 Ensemble: 13

And for megaflood2 this corresponds to: Year: 2035 Start: 14 Ensemble: 21

# List from Niko: 
# S01-E01-2035 S01-E01-2036 S01-E01-2037 S01-E01-2038 S01-E01-2039 S01 E02 -2035…. S01-E16-2039 S02-E01-2035…

Starts=np.arange(1,17)
Ensembles=np.arange(0,25)
years=np.arange(2035,2040)
Starts
Ensembles
years

array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16])

array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
       17, 18, 19, 20, 21, 22, 23, 24])

array([2035, 2036, 2037, 2038, 2039])

years_list=np.tile(years,len(Starts)*len(Ensembles))
Starts_list=np.array([])
for start in Starts:
    Starts_list = np.append(Starts_list, np.repeat(start,len(years)*len(Ensembles)))
Ensembles_list_signlestart=np.array([])
for ensemble in Ensembles:
    Ensembles_list_signlestart = np.append(Ensembles_list_signlestart, np.repeat(ensemble,len(years)))
Ensembles_list=np.tile(Ensembles_list_signlestart,len(Starts))

years_list[0:200]
Ensembles_list[0:200]
Starts_list[0:200]

len(years_list)
len(Starts_list)
len(Ensembles_list)

array([2035, 2036, 2037, 2038, 2039, 2035, 2036, 2037, 2038, 2039, 2035,
       2036, 2037, 2038, 2039, 2035, 2036, 2037, 2038, 2039, 2035, 2036,
       2037, 2038, 2039, 2035, 2036, 2037, 2038, 2039, 2035, 2036, 2037,
       2038, 2039, 2035, 2036, 2037, 2038, 2039, 2035, 2036, 2037, 2038,
       2039, 2035, 2036, 2037, 2038, 2039, 2035, 2036, 2037, 2038, 2039,
       2035, 2036, 2037, 2038, 2039, 2035, 2036, 2037, 2038, 2039, 2035,
       2036, 2037, 2038, 2039, 2035, 2036, 2037, 2038, 2039, 2035, 2036,
       2037, 2038, 2039, 2035, 2036, 2037, 2038, 2039, 2035, 2036, 2037,
       2038, 2039, 2035, 2036, 2037, 2038, 2039, 2035, 2036, 2037, 2038,
       2039, 2035, 2036, 2037, 2038, 2039, 2035, 2036, 2037, 2038, 2039,
       2035, 2036, 2037, 2038, 2039, 2035, 2036, 2037, 2038, 2039, 2035,
       2036, 2037, 2038, 2039, 2035, 2036, 2037, 2038, 2039, 2035, 2036,
       2037, 2038, 2039, 2035, 2036, 2037, 2038, 2039, 2035, 2036, 2037,
       2038, 2039, 2035, 2036, 2037, 2038, 2039, 2035, 2036, 2037, 2038,
       2039, 2035, 2036, 2037, 2038, 2039, 2035, 2036, 2037, 2038, 2039,
       2035, 2036, 2037, 2038, 2039, 2035, 2036, 2037, 2038, 2039, 2035,
       2036, 2037, 2038, 2039, 2035, 2036, 2037, 2038, 2039, 2035, 2036,
       2037, 2038, 2039, 2035, 2036, 2037, 2038, 2039, 2035, 2036, 2037,
       2038, 2039])

array([ 0.,  0.,  0.,  0.,  0.,  1.,  1.,  1.,  1.,  1.,  2.,  2.,  2.,
        2.,  2.,  3.,  3.,  3.,  3.,  3.,  4.,  4.,  4.,  4.,  4.,  5.,
        5.,  5.,  5.,  5.,  6.,  6.,  6.,  6.,  6.,  7.,  7.,  7.,  7.,
        7.,  8.,  8.,  8.,  8.,  8.,  9.,  9.,  9.,  9.,  9., 10., 10.,
       10., 10., 10., 11., 11., 11., 11., 11., 12., 12., 12., 12., 12.,
       13., 13., 13., 13., 13., 14., 14., 14., 14., 14., 15., 15., 15.,
       15., 15., 16., 16., 16., 16., 16., 17., 17., 17., 17., 17., 18.,
       18., 18., 18., 18., 19., 19., 19., 19., 19., 20., 20., 20., 20.,
       20., 21., 21., 21., 21., 21., 22., 22., 22., 22., 22., 23., 23.,
       23., 23., 23., 24., 24., 24., 24., 24.,  0.,  0.,  0.,  0.,  0.,
        1.,  1.,  1.,  1.,  1.,  2.,  2.,  2.,  2.,  2.,  3.,  3.,  3.,
        3.,  3.,  4.,  4.,  4.,  4.,  4.,  5.,  5.,  5.,  5.,  5.,  6.,
        6.,  6.,  6.,  6.,  7.,  7.,  7.,  7.,  7.,  8.,  8.,  8.,  8.,
        8.,  9.,  9.,  9.,  9.,  9., 10., 10., 10., 10., 10., 11., 11.,
       11., 11., 11., 12., 12., 12., 12., 12., 13., 13., 13., 13., 13.,
       14., 14., 14., 14., 14.])

array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
       1., 1., 1., 1., 1., 1., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2.,
       2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2.,
       2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2.,
       2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2.,
       2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2.])

2000

2000

2000

##Select the highest flood
megaflood1 = Amazon_timeseries.isel(
    time=np.argmax(Amazon_timeseries))
megaflood1
## time = 1568, so index is 1567:
Amazon_timeseries[1567]

xarray.DataArray

'discharge'

• 1157535.8

  array(1157535.8, dtype=float32)

• Coordinates: (3)
  • time
    ()
    object
    1568-01-01 00:00:00

    standard_name :

    time

    long_name :

    Days since 1901-01-01

    array(cftime.DatetimeGregorian(1568-01-01 00:00:00), dtype=object)

  • lat
    ()
    float32
    -1.25

    long_name :

    latitude

    units :

    degrees_north

    standard_name :

    latitude

    array(-1.25, dtype=float32)

  • lon
    ()
    float32
    -51.75

    standard_name :

    longitude

    long_name :

    longitude

    units :

    degrees_east

    array(-51.75, dtype=float32)

• Attributes: (3)

  standard_name :

  discharge

  long_name :

  discharge

  units :

  m3s-1

xarray.DataArray

'discharge'

• 1157535.8

  array(1157535.8, dtype=float32)

• Coordinates: (3)
  • time
    ()
    object
    1568-01-01 00:00:00

    standard_name :

    time

    long_name :

    Days since 1901-01-01

    array(cftime.DatetimeGregorian(1568-01-01 00:00:00), dtype=object)

  • lat
    ()
    float32
    -1.25

    long_name :

    latitude

    units :

    degrees_north

    standard_name :

    latitude

    array(-1.25, dtype=float32)

  • lon
    ()
    float32
    -51.75

    standard_name :

    longitude

    long_name :

    longitude

    units :

    degrees_east

    array(-51.75, dtype=float32)

• Attributes: (3)

  standard_name :

  discharge

  long_name :

  discharge

  units :

  m3s-1

arg2=np.argmax(Amazon_timeseries.where(Amazon_timeseries<megaflood1))
megaflood2 = Amazon_timeseries.isel(time=arg2)
megaflood2

xarray.DataArray

'discharge'

• 940480.25

  array(940480.25, dtype=float32)

• Coordinates: (3)
  • time
    ()
    object
    1731-01-01 00:00:00

    standard_name :

    time

    long_name :

    Days since 1901-01-01

    array(cftime.DatetimeGregorian(1731-01-01 00:00:00), dtype=object)

  • lat
    ()
    float32
    -1.25

    long_name :

    latitude

    units :

    degrees_north

    standard_name :

    latitude

    array(-1.25, dtype=float32)

  • lon
    ()
    float32
    -51.75

    standard_name :

    longitude

    long_name :

    longitude

    units :

    degrees_east

    array(-51.75, dtype=float32)

• Attributes: (3)

  standard_name :

  discharge

  long_name :

  discharge

  units :

  m3s-1

year_megaflood1=years_list[1567]
Start_megaflood1=Starts_list[1567]
Ensemble_megaflood1=Ensembles_list[1567]

year_megaflood1
Start_megaflood1
Ensemble_megaflood1

2037

13.0

13.0

year_megaflood2=years_list[1730]
Start_megaflood2=Starts_list[1730]
Ensemble_megaflood2=Ensembles_list[1730]

year_megaflood2
Start_megaflood2
Ensemble_megaflood2

2035

14.0

21.0

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Flood frequency curves for the Amazon

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Checking the spatio-temporal build up of the largest simulated events