Time series of spring phytoplankton bloom and model forcing at station S3 from Feb 15th - June 15th 2015¶

In [1]:

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib as mpl
import netCDF4 as nc
import datetime as dt
from salishsea_tools import evaltools as et, places, viz_tools, visualisations
import xarray as xr
import pandas as pd
import pickle
import os
import bloomdrivers

%matplotlib inline

In [2]:

start=dt.datetime(2015,2,15)
end=dt.datetime(2015,6,15)
year=str(start.year)
modver='201812'

Location of station S3¶

In [3]:

loc='S3'

# lat and lon informatin for place:
lon,lat=places.PLACES['S3']['lon lat']
# get place information on SalishSeaCast grid:
ij,ii=places.PLACES['S3']['NEMO grid ji']
# GEM2.5 grid ji is atm forcing grid for ops files
jw,iw=places.PLACES['S3']['GEM2.5 grid ji']

fig, ax = plt.subplots(1,1,figsize = (6,6))
with nc.Dataset('/data/vdo/MEOPAR/NEMO-forcing/grid/bathymetry_201702.nc') as grid:
    viz_tools.plot_coastline(ax, grid, coords ='map', isobath=.1)
ax.plot(lon, lat, '.', markersize=14, color='red')
ax.set_ylim(48,50)
ax.set_xlim(-125,-122)
ax.set_title('Location of Station S3')
ax.set_xlabel('Longitude')
ax.set_ylabel('Latitude')

Out[3]:

Text(0, 0.5, 'Latitude')

load data¶

In [4]:

savedir='/ocean/aisabell/MEOPAR/extracted_files'
#savedir='/data/eolson/results/MEOPAR'
fname=f'springTimeSeries_{year}_{loc}_{modver}.pkl'
savepath=os.path.join(savedir,fname)
recalc=False

In [5]:

if recalc==True or not os.path.isfile(savepath):
    basedir='/results/SalishSea/nowcast-green.201812/'
    nam_fmt='nowcast'
    flen=1 # files contain 1 day of data each
    ftype= 'ptrc_T' # load bio files
    tres=24 # 1: hourly resolution; 24: daily resolution  
    flist=et.index_model_files(start,end,basedir,nam_fmt,flen,ftype,tres)
    # flist contains paths: file pathes; t_0 timestemp of start of each file; t_n: timestamp of start of next file
    # a list of the files we want between start and end date
    print(flist)
    fliste3t = et.index_model_files(start,end,basedir,nam_fmt,flen,"carp_T",tres)

    ik=0
    with xr.open_mfdataset(flist['paths']) as bio:
        bio_time=np.array(bio.time_centered[:])
        sno3=np.array(bio.nitrate.isel(deptht=ik,y=ij,x=ii))
        sdiat=np.array(bio.diatoms.isel(deptht=ik,y=ij,x=ii))
        sflag=np.array(bio.flagellates.isel(deptht=ik,y=ij,x=ii))
        scili=np.array(bio.ciliates.isel(deptht=ik,y=ij,x=ii))
        no3_alld=np.array(bio.nitrate.isel(y=ij,x=ii)) 
        diat_alld=np.array(bio.diatoms.isel(y=ij,x=ii))
        flag_alld=np.array(bio.flagellates.isel(y=ij,x=ii))
        cili_alld=np.array(bio.ciliates.isel(y=ij,x=ii))
        with xr.open_mfdataset(fliste3t['paths']) as carp:
            intdiat=np.array(np.sum(bio.diatoms.isel(y=ij,x=ii)*carp.e3t.isel(y=ij,x=ii),1)) # depth integrated diatom
            intphyto=np.array(np.sum((bio.diatoms.isel(y=ij,x=ii)+bio.flagellates.isel(y=ij,x=ii)\
                            +bio.ciliates.isel(y=ij,x=ii))*carp.e3t.isel(y=ij,x=ii),1))
            spar=np.array(carp.PAR.isel(deptht=ik,y=ij,x=ii))
    fracdiat=intdiat/intphyto # depth integrated fraction of diatoms

    sphyto=sdiat+sflag+scili
    phyto_alld=diat_alld+flag_alld+cili_alld
    percdiat=sdiat/sphyto # percent diatoms

    opsdir='/results/forcing/atmospheric/GEM2.5/operational'

    flist2=et.index_model_files(start,end,opsdir,nam_fmt='ops',flen=1,ftype='None',tres=24)
    with xr.open_mfdataset(flist2['paths']) as winds:
        u_wind=np.array(winds.u_wind.isel(y=jw,x=iw))
        v_wind=np.array(winds.v_wind.isel(y=jw,x=iw))
        twind=np.array(winds.time_counter)
        solar=np.array(winds.solar.isel(y=jw,x=iw))
    # wind speed:
    wspeed=np.sqrt(u_wind**2 + v_wind**2)
    # wind direction in degrees from east
    d = np.arctan2(v_wind, u_wind)
    winddirec=np.rad2deg(d + (d < 0)*2*np.pi)

    # reading Fraser river flow files
    dfFra=pd.read_csv('/ocean/eolson/MEOPAR/obs/ECRivers/Flow/FraserHopeDaily__Dec-2-2020_10_31_05PM.csv',
                      skiprows=1)
    # the original file contains both flow and water level information in the same field (Value)
    # keep only the flow data, where PARAM=1 (drop PARAM=2 values, water level data)
    # flow units are m3/s
    # DD is YD, year day (ie. 1 is jan 1)
    dfFra.drop(dfFra.loc[dfFra.PARAM==2].index,inplace=True)  

    # rename 'Value' column to 'Flow' now that we have removed all the water level rows
    dfFra.rename(columns={'Value':'Flow'}, inplace=True) 
        # inplace=True does this function on the orginal dataframe

    # no time information so use dt.date
    dfFra['Date']=[dt.date(iyr,1,1)+dt.timedelta(days=idd-1) for iyr, idd in zip(dfFra['YEAR'],dfFra['DD'])]
    # taking the value from the yr column, jan1st date, and making jan1 column to be 1 not 0
    dfFra.head(2)

    # select portion of dataframe in desired date range
    dfFra2=dfFra.loc[(dfFra.Date>=start.date())&(dfFra.Date<=end.date())]
    riv_time=dfFra2['Date'].values
    rivFlow=dfFra2['Flow'].values
    # could also write dfFra['Date'], sometimes this is required
    # newstart is a datetime object, so we convert it to just a date with .date
    pickle.dump((bio_time,sno3,sdiat,sflag,scili,diat_alld,no3_alld,flag_alld,cili_alld,phyto_alld,intdiat,intphyto,spar,fracdiat,sphyto,percdiat,
            u_wind,v_wind,twind,solar,wspeed,winddirec,riv_time,rivFlow),open(savepath,'wb'))
else:
    bio_time,sno3,sdiat,sflag,scili,diat_alld,no3_alld,flag_alld,cili_alld,phyto_alld,intdiat,intphyto,spar,fracdiat,sphyto,percdiat,\
            u_wind,v_wind,twind,solar,wspeed,winddirec,riv_time,rivFlow=pickle.load(open(savepath,'rb'))

Spring Bloom Timing Metrics (3 ways)¶

Metric 1: Allen and Wolfe definition: “peak phytoplankton concentration (averaged from the surface to 3 m depth) within four days of the average 0-3 m nitrate concentration going below 0.5 uM (the half-saturation concentration) for two consecutive days”¶

a) Average phytoplankton concentration over upper 3 m

b) Average nitrate over upper 3 m

c) Find first location where nitrate crosses below 0.5 micromolar and stays there for 2 days

d) Find date with maximum phytoplankton concentration within four days (say 9 day window) of date in c).

In [6]:

bloomtime1=bloomdrivers.metric1_bloomtime(phyto_alld,no3_alld,bio_time)
print(f'The spring phytoplankton bloom according to metric 1 occurs at {bloomtime1}')

The spring phytoplankton bloom according to metric 1 occurs at 2015-03-06 12:00:00

Metric 2: the first peak in which chlorophyll concentrations are above 5 ug/L for more than two days (Olson et. al 2020)¶

Take first peak, check if it has two days around have concentrations>5, if no, move to next peak

In [7]:

bloomtime2=bloomdrivers.metric2_bloomtime(sphyto,sno3,bio_time)
print(f'The spring phytoplankton bloom according to metric 2 occurs at {bloomtime2}')

The spring phytoplankton bloom according to metric 2 occurs at 2015-03-04 12:00:00

Metric 3: For a given year, bloom initiation is determined to be the week that first reaches the threshold value (by looking at weekly averages) as long as one of the two following weeks was >70% of the threshold value. (Karyn’s method)¶

The median + 5% of the annual Chl concentration is deemed “threshold value” for each year.

In [8]:

bloomtime3=bloomdrivers.metric3_bloomtime(sphyto,sno3,bio_time)
print(f'The spring phytoplankton bloom according to metric 3 occurs at {bloomtime3}')

The spring phytoplankton bloom according to metric 3 occurs at 2015-03-01 00:00:00

Total surface phytoplankton and nitrate:¶

In [13]:

fig,ax=plt.subplots(1,1,figsize=(12,3))
p1=ax.plot(bio_time,sphyto,
           '-',color='forestgreen',label='Phytoplankton')
p2=ax.plot(bio_time,sno3,
           '-',color='orange',label='Nitrate')
ax.legend(handles=[p1[0],p2[0]],loc=1)
ax.set_ylabel('Concentration ($\mu$M N)')
ax.set_title('Surface Phytoplankton and Nitrate at Station S3')

ax.axvline(x=bloomtime1, label='Metric 1 Bloom Date:{}'.format(bloomtime1), color='r')
ax.axvline(x=bloomtime2, label='Metric 2 Bloom Date:{}'.format(bloomtime2), color='k')
ax.axvline(x=bloomtime3, label='Metric 3 Bloom Date:{}'.format(bloomtime3), color='b')
ax.legend()

Out[13]:

<matplotlib.legend.Legend at 0x7f0a629d1820>

Fraction of surface phytoplankton that is diatoms¶

In [10]:

fig,ax=plt.subplots(1,1,figsize=(12,3))
ax.plot(bio_time,percdiat, '-',color='orchid')
ax.set_ylabel('Diatoms / Total Phytoplankton')
ax.set_title('Fraction of Diatoms in Total Surface Phytoplankton')
ax.set_ylim(0,1)

Out[10]:

(0.0, 1.0)

Depth integrated phytoplankton:¶

In [11]:

%%time
fig,ax=plt.subplots(1,1,figsize=(12,3))
ax.plot(bio_time,intphyto,'-',color='forestgreen',label='Phytoplankton')
ax.legend(loc=2);
ax.set_ylabel('Concentration (mmol N/m2)')
ax.set_xlim(bio_time[0],bio_time[-1])
ax.set_title('Depth Integrated Phytoplankton Concentration')

CPU times: user 20 ms, sys: 0 ns, total: 20 ms
Wall time: 17.1 ms

Out[11]:

Text(0.5, 1.0, 'Depth Integrated Phytoplankton Concentration')

Fraction of depth integrated phytoplankton that is diatoms¶

In [12]:

%%time
fig,ax=plt.subplots(1,1,figsize=(12,3))
ax.plot(bio_time,fracdiat,'-',color='orchid')
ax.set_ylabel('Diatoms / Total Phytoplankton')
ax.set_xlim(bio_time[0],bio_time[-1])
ax.set_title('Fraction of Diatoms in Total Depth Integrated Phytoplankton')
ax.set_ylim(0,1)

CPU times: user 16 ms, sys: 0 ns, total: 16 ms
Wall time: 16.2 ms

Out[12]:

(0.0, 1.0)

Fraser River Flow¶

In [13]:

# plot phytoplankton on top:
fig,ax1=plt.subplots(1,1,figsize=(12,3))
p1=ax1.plot(bio_time,sphyto,
           '-',color='forestgreen',label='Phytoplankton')
p2=ax1.plot(bio_time,sno3,
           '-',color='orange',label='Nitrate')
ax1.set_ylabel('Concentration ($\mu$M N)')
ax1.set_ylim(0,18)

# Now plot Fraser Flow
ax2=ax1.twinx()
p3=ax2.plot(riv_time,rivFlow,'c-', label='Fraser Flow')
ax2.set_ylabel('Flow (m$^3$s$^{-1}$)')
ax2.set_title('Fraser Flow at Hope and Surface Phytoplankton at Station S3')
ax1.legend(handles=[p1[0],p2[0],p3[0]],loc='upper center')

Out[13]:

<matplotlib.legend.Legend at 0x7f340221b220>

Forcing (ops): Wind Speed¶

In [14]:

fig,ax=plt.subplots(1,1,figsize=(18,2))
ax.plot(twind,u_wind,'c-')
ax.plot(twind,v_wind,'b-')
ax.set_xlim(start,end)
ax.set_title('Wind speed')
ax.set_ylabel('m/s')

Out[14]:

Text(0, 0.5, 'm/s')

In [15]:

fig,ax=plt.subplots(1,1,figsize=(20,6))
q=ax.quiver(twind, np.zeros(len(twind)), u_wind, v_wind,scale=200, width=0.001); # change the scale
ax.set_yticklabels([]);
fig.autofmt_xdate(bottom=0.3, rotation=30, ha='right')
yearsFmt = mdates.DateFormatter('%b %d')
ax.xaxis.set_major_formatter(yearsFmt)
ax.set_xlim(start,end)
ax.set_title('Wind Vectors in Geographic Coordinates')
# this can probably be done better?

Out[15]:

Text(0.5, 1.0, 'Wind Vectors in Geographic Coordinates')

Daily average wind speed¶

In [16]:

# calculate daily average wind speed:
ttday=twind[12::24] # start at 12th value and take every 24th
wsdaily=list()
for ii in range(0,int(len(wspeed)/24)):
    wsdaily.append(np.mean(wspeed[(ii*24):((ii+1)*24)]))
wsdaily=np.array(wsdaily) # convert to numpy array from list to be able to plot

In [17]:

fig,ax=plt.subplots(1,1,figsize=(18,2))
ax.plot(ttday,wsdaily,'b-')
ax.set_xlim(start,end)
ax.set_title('Daily average wind speed')
ax.set_ylabel('m/s')

Out[17]:

Text(0, 0.5, 'm/s')

Daily average wind speed cubed¶

In [18]:

wscubed=wsdaily**3

# plot phytoplankton on top:
fig,ax1=plt.subplots(1,1,figsize=(12,3))
p1=ax1.plot(bio_time,sphyto,
           '-',color='forestgreen',label='Phytoplankton')
p2=ax1.plot(bio_time,sno3,
           '-',color='orange',label='Nitrate')
ax1.set_ylabel('Concentration ($\mu$M N)')
ax1.set_ylim(0,18)

ax2=ax1.twinx()
p3=ax2.plot(ttday,wscubed,'b-',label='Wind Speed Cubed')
ax2.set_xlim(start,end)
ax2.set_title('Daily Average Wind Speed cubed and Surface Phytoplankton at Station S3')
ax2.set_ylabel('$\mathregular{m^3}$/$\mathregular{s^3}$')
ax1.legend(handles=[p1[0],p2[0],p3[0]],loc='upper center')

Out[18]:

<matplotlib.legend.Legend at 0x7f340209aa30>

Photosynthetically Available Radiation (PAR) at Surface¶

In [19]:

# plot phytoplankton on top:
fig,ax1=plt.subplots(1,1,figsize=(12,3))
p1=ax1.plot(bio_time,sphyto,
           '-',color='forestgreen',label='Phytoplankton')
p2=ax1.plot(bio_time,sno3,
           '-',color='orange',label='Nitrate')
ax1.set_ylabel('Concentration ($\mu$M N)')
ax1.set_ylim(0,18)

ax2=ax1.twinx()
p3=ax2.plot(bio_time,spar,
           '-',color='red',label='Model PAR')
ax2.set_ylabel('PAR (W/$\mathregular{m^2}$)') # say its model PAR
ax2.set_title('Modeled PAR and Surface Phytoplankton at Station S3')
ax1.legend(handles=[p1[0],p2[0],p3[0]],loc='center left')

Out[19]:

<matplotlib.legend.Legend at 0x7f340208ee50>

Forcing: Solar radiation¶

In [20]:

fig,ax=plt.subplots(1,1,figsize=(18,2))
ax.plot(twind,solar,'r-')
ax.set_xlim(start,end)
ax.set_title('Solar radiation')
ax.set_ylabel('W/$\mathregular{m^2}$')

Out[20]:

Text(0, 0.5, 'W/$\\mathregular{m^2}$')

In [21]:

# calculate daily average solar radiation:
ttday=twind[12::24] # start at 12th value and take every 24th
solardaily=list()
for ii in range(0,int(len(solar)/24)):
    solardaily.append(np.mean(solar[(ii*24):((ii+1)*24)]))
solardaily=np.array(solardaily) # convert to numpy array from list to be able to plot

In [22]:

# plot phytoplankton on top:
fig,ax1=plt.subplots(1,1,figsize=(12,3))
p1=ax1.plot(bio_time,sphyto,
           '-',color='forestgreen',label='Phytoplankton')
p2=ax1.plot(bio_time,sno3,
           '-',color='orange',label='Nitrate')
ax1.set_ylabel('Concentration ($\mu$M N)')
ax1.set_ylim(0,18)

ax2=ax1.twinx()
p3=ax2.plot(ttday,solardaily,'m-',label='Solar Radiation')
ax2.set_xlim(start,end)
ax2.set_title('Daily Average Solar Radiation and Surface Phytoplankton at Station S3')
ax2.set_ylabel('W/$\mathregular{m^2}$')
ax1.legend(handles=[p1[0],p2[0],p3[0]],loc='upper center')

Out[22]:

<matplotlib.legend.Legend at 0x7f3401d653a0>

In [ ]: