Monthly runoff

• convert monthly runoff to annual by summing :

if file_name == "mrunoff.nc":
        #convert number of months to years
        nb_year = int(len(years)/12) #years in months here
        tmp = np.zeros([nb_year,len(latitudes),len(longitudes)])
        #calculation of the sum over each year
        for k in range(0,nb_year,1):
            tmp[k] = np.sum(data[k*12:k*12+12],axis=0)
        data = tmp  #[months,latitudes,longitudes]

Harvest

• Convert harvest to yield :

#gC/m2 -> tDM/ha (dry matter)
harvests_tdm = np.multiply(harvests,0.02222222)
#tDM/ha -> tFM/ha (fresh matter)
harvests_tfm = np.multiply(harvests_tdm,1.14) #tonne / ha
#production = harvest * surface  [years 101; latitude 30; longitude 33]
yields = np.multiply(harvests_tfm,cft_surface) #tonne

Convert latitude and longitude into lpjml 30 minutes system

pts = [ 
(-43.42368753718026, -12.682038634410734), (-42.71628164452376, -10.559820956441273), (-41.70570179787164, -9.549241109789147), (-42.81733962918898, -15.71377817436711)]
for pt in pts :
    new_pt = []
    for i in [1,0]:
        p = pt[i]
        if round(p) > p :
            p = round(p)-0.25
        else : 
            p = round(p)+0.25
        new_pt.append(p)
    print(new_pt)  

Cell fraction to area

30 minutes resolution

gridhectares = np.zeros((len(latitudes),len(longitudes)))
for lat,latitude in enumerate(latitudes) :
     gridhectares[lat,:] = 110 * 110 * math.cos(math.pi*latitude/180) * 1/2 * 1/2 * 100

5 minutes resolution

surfaces = np.zeros_like(cftfracs)
for lat,latitude in enumerate(latitudes) :
    surfaces[:,:,lat,:] = 110 * 110 * math.cos(math.pi*latitude/180) * 0.083 * 0.083 * 100

Moving average

ma_harvest = []
window = 10
for y,year in enumerate(years[window:]):
    ma_harvest.append(np.average(harvests_regions[id_region][y-window:y]))
ma_harvest = np.asarray(ma_harvest)

Regional selection (biome in brazil)

def region_selection (latitudes,longitudes,region):
    nc = Dataset("/home/marie/ownCloud/Documents/spitfire grassland paper/visualization/python_toolbox/brazil_biomes/brazilian_biomes_newversion.nc", mode='r')
    lat = nc.variables['latitude'][:]
    lon = nc.variables['longitude'][:]
    biomes = nc.variables['biome'][:]
    nc.close()

    new_latitudes,new_longitudes = np.where(region==biomes)

    return (new_latitudes,new_longitudes)
biomes =  ["amazon","caatinga","cerrado","mataatlantica","pampas","pantanal"]