Crop Season#
Author: Dunstan Matekenya
Affiliation: DECAT, The World Bank Group
Date: May 18, 2023
Background#
In this notebook, we generate geovariables in the category of crop season for the National Panel Survey (NPS 2014-2015) in Tanzania. The following geovariables are computed.
Average 12-month total rainfall (mm) for July-June
Average total rainfall in wettest quarter (mm) within 12-month periods from July-June
Average start of wettest quarter in dekads 1-36, where first dekad of July =1
12-month total rainfall (mm) in July-June, starting July 2013
Total rainfall in wettest quarter (mm) within 12- month periods starting July 2013
Start of wettest quarter in dekads 1-36, where first dekad of July 2013 =1
12-month total rainfall (mm) in July-June, starting July 2014
Total rainfall in wettest quarter (mm) within 12- month periods starting July 2014
Start of wettest quarter in dekads 1-36, where first dekad of July 2014 =1
For full variables metadata, see Data-Cover-Page for details about definition of the metrics and other important information.
Input datasets#
The following datasets have been used for all the variables.
Data source: The source for this data is the USGS FEWS NET Data Portal
Input data files The data files used in this notebook were downloaded from the site mentioned above and kept in this OneDrive folder[1]
Python setup#
The following packages are required for the processing in this notebook.
import warnings
warnings.simplefilter(action='ignore')
import sys
import itertools
from pathlib import Path
from datetime import datetime
import geopandas as gpd
import pandas as pd
import numpy as np
spanon = Path.cwd().parents[2].joinpath("Spatial-Anonymization/src/")
sys.path.append(str(spanon))
from spatial_anonymization import utils
from spatial_anonymization import point_displacement
Setup input files#
Define paths to input data files as global variables using Path package.
# =============================
# BASE WORKING DIR
# ============================
DIR_DATA = Path.cwd().parents[1].joinpath('data', 'TZA')
DIR_SPATIAL_DATA = DIR_DATA.joinpath('spatial')
DIR_NPS_W4 = DIR_DATA.joinpath('surveys', 'NPS_w4')
DIR_OUTPUTS = DIR_NPS_W4.joinpath('geovars', 'puf')
# =================
# GEOVARS DATA
# =================
DIR_RFE = Path.cwd().parents[1].joinpath('data', 'RFE', 'dekad')
# =============================
# SURVEY COORDINATES
# ============================
# Public/displaced GPS oordinates files
FILE_HH_PUB_GEOVARS = DIR_NPS_W4.joinpath('geovars', 'npsy4.ea.offset.dta')
# =============================
# ADMIN BOUNDARIES
# =============================
# use geoboundaries API for country bounds
FILE_ADM0 = DIR_SPATIAL_DATA.joinpath('geoBoundaries-TZA-ADM0-all',
'geoBoundaries-TZA-ADM0.shp')
Setup processing global variables#
For variables we will use often such as column name containing latitude and longitude, set them as global variables.
# ====================
# GENERAL
# =====================
DEBUG = False
# in case we need to download OSM data
GEOFABRICK_REGION = 'africa'
COUNTRY_NAME = 'Tanzania'
COUNTRY_ISO = 'TZA'
FILENAME_SUFFIX = 'npsw4'
# When survey was conducted
SURVEY_CALENDAR_YR = 2015
DATA_EXTRACT_YEARS = ['2013-2014', '2014-2015']
# Year starts on July 1 (dekad-19) instead of
# Jan-1 (dekad01)
DEKAD_YEAR_START = 19
DEKADS = ['d{}'.format(str(i).zfill(2)) for i in range(DEKAD_YEAR_START, 37)] + \
['d{}'.format(str(i).zfill(2)) for i in range(1, DEKAD_YEAR_START)]
if DEBUG:
for i in range(28):
q = [DEKADS[i] for i in range(i, i+9)]
assert len(q) == 9
print('Quarter-{}: {}'.format(i+1, q))
# ==============
# BUFFER
# ==============
# whether to buffer points or
# extract values at point
BUFFER = False
BUFF_ZONE_DIST = 1000
# ====================
# INPUT DATA SPECS
# =====================
# Cols for input coordinates file
LAT = 'lat_modified'
LON = 'lon_modified'
HH_ID = 'clusterid'
# =============================
# VARIABLE LABELS
# ============================
VAR_LABELS = {'anntot_avg': 'Average 12-month total rainfall for July-June',
'wetQ_avg': 'Average total rainfall in wettest quarter within 12-month periods from July-June',
'wetQ_avgstart': 'Average start of wettest quarter in dekads 1-36, where first dekad of July =1',
'anntot_2013_2014': '12-month total rainfall in July-June, starting July 2013',
'wetQ_2013_2014': 'Total rainfall in wettest quarter within 12- month periods starting July 2013',
'wetQstart_2013_2014': 'Start of wettest quarter in dekads 1-36, where first dekad of July 2013=1',
'anntot_2014_2015': '12-month total rainfall in July-June, starting July 2014',
'wetQ_2014_2015': 'Total rainfall in wettest quarter within 12- month periods starting July 2014',
'wetQstart_2014_2015': 'Start of wettest quarter in dekads 1-36, where first dekad of July 2014 =1'
}
for name, label in VAR_LABELS.items():
try:
assert len(label) <= 80, 'LABEL TOO LONG'
except AssertionError:
print(name)
Define helper functions for computations#
def get_year_and_tif_list(stop_year, year_start=1):
"""
Retrieve filepath list and years
"""
# ===================================
# RETRIVE FILES BY CALENDAR YEAR
# ===================================
rfe_tifs = [i for i in DIR_RFE.iterdir() if i.suffix == ".tif"]
yrs = {}
for tif in rfe_tifs:
yr = tif.parts[-1][4:8]
if yrs.get(yr, None):
items = yrs[yr]
items.append(tif)
yrs[yr] = items
else:
yrs[yr] = [tif]
# Keep only the years we need based on survey year
# global variable se
sorted_yrs = sorted(list(yrs.keys()))
stop_idx = sorted_yrs.index(str(stop_year))
calend_yrs = dict(sorted(yrs.items())[:stop_idx+1])
# ===================================
# DEAL WITH CROSS CALENDAR YEARS
# ===================================
if year_start != 1:
cross_calend_yrs = {}
files_lst = sorted(list(itertools.chain.from_iterable(list(calend_yrs.values()))))
yr_start_id = int(list(calend_yrs.keys())[0])
while year_start < len(files_lst):
yr_end_id = yr_start_id + 1
yr_id = '{}-{}'.format(yr_start_id, yr_end_id)
yr_start_id += 1
files = files_lst[year_start-1:year_start+35]
cross_calend_yrs[yr_id] = files
year_start += 36
return cross_calend_yrs
else:
return calend_yrs
def get_wettest_quarter(df_yr, dekads=None):
"""
Given one year data, get the quarter with maximum rains
and start dekad of the quarter
"""
dfq = pd.DataFrame(index=df_yr.index)
quarter_start = {}
if dekads:
for i in range(28):
q = [dekads[i] for i in range(i, i+9)]
assert len(q) == 9
colname = 'q{}'.format(i+1)
quarter_start[colname] = int(q[0][1:])
dfq[colname] = df_yr[q].sum(axis=1)
else:
for i in range(1,29):
q = ['d{}'.format(str(i).zfill(2)) for i in range(i, i+9)]
assert len(q) == 9
colname = 'q{}'.format(i)
quarter_start[colname] = int(i)
dfq[colname] = df_yr[q].sum(axis=1)
dfq['qrt_max_val'] = dfq.max(axis=1)
dfq['qrt_max_id'] = dfq.idxmax(axis=1)
dfq['qrt_max_start'] = dfq.qrt_max_id.apply(lambda x: quarter_start[x])
return dfq[['qrt_max_val', 'qrt_max_id', 'qrt_max_start']]
def generate_yr_rfe_stats(tif_list, yr, gdf_coords, uniq_id):
"""
Generates RFE stats for a single year as follows
"""
df_lst = []
for file in tif_list:
colname = 'd{}'.format(file.parts[-1][8:10])
df = utils.point_query_raster(in_raster=file, buffer=BUFFER,
point_shp=gdf_coords, statcol_name=colname
,id_col=uniq_id,xcol=LON, ycol=LAT)
df.set_index(uniq_id, inplace=True)
df_lst.append(df)
df = pd.concat(df_lst, axis=1)
dekad_cols = sorted(df.columns)
df = df[dekad_cols]
# Annual total and max dekadal value
df.loc[:,'total'] = df.sum(axis=1)
df.loc[:,'max'] = df[dekad_cols].max(axis=1)
# Wettest quarter
dfq = get_wettest_quarter(df_yr=df, dekads=DEKADS)
# merge
df2 = pd.concat([dfq, df[['total', 'max']]], axis=1)
df2['yr'] = yr
return df2
def generate_rfe_stats(yr_tif_dict, pts_shp, id_col):
"""
Helper to loop through all years and run generate_yr_rfe_stats()
"""
# ========================
# CALCULATE YEARLY STATS
# ========================
df_yr_list = []
# Loop years and calculate stats for that year
cnt_yrs = 0
for yr, files in yr_tif_dict.items():
if len(files) != 36:
print('SKIPPING {} BECAUSE WE DONT HAVE ALL DEKADS'.format(yr))
continue
df_yr = generate_yr_rfe_stats(files, yr, pts_shp, uniq_id=id_col)
df_yr_list.append( df_yr)
cnt_yrs += 1
if DEBUG:
if cnt_yrs == 5:
break
# concat into a single dataframe
df = pd.concat(df_yr_list, axis=0)
df = df[['yr', 'total', 'max', 'qrt_max_val', 'qrt_max_start']]
assert len(df) == len(pts_shp)*cnt_yrs
# ==================================
# CALCULATE AVERAGES ACROSS YEARS
# ==================================
# Calculate the averages
df_avg = pd.DataFrame(index=df.index.drop_duplicates())
vars = ['total', 'max', 'qrt_max_val', 'qrt_max_start']
print('Long average years: {}'.format(df.yr.unique()))
for idx, row in df_avg.iterrows():
data = df.loc[idx]
for var in vars:
new_colname = 'avg_{}'.format(var)
avg = data[var].mean()
df_avg.loc[idx, new_colname] = int(avg)
# Add the averages to the original dataframe
df2 = df.merge(df_avg, left_index=True, right_index=True)
assert len(df2) == len(df)
return df2
Generate stats#
def generate_stats(target_yrs):
"""
Helper function to put everything together and generate stats
"""
# ===========================
# LOAD COORDINATES
# ===========================
df_pub_coords = pd.read_stata(FILE_HH_PUB_GEOVARS, convert_categoricals=False)
df_coords = df_pub_coords[[HH_ID, LAT, LON]]
gdf_coords = gpd.GeoDataFrame(df_coords,
geometry=gpd.points_from_xy(df_coords[LON],df_coords[LAT]), crs=4326)
# ===========================
# GENERATE ALL YEARS STATS
# ===========================
start = datetime.now()
files_yrs = get_year_and_tif_list(stop_year=SURVEY_CALENDAR_YR, year_start=DEKAD_YEAR_START)
df = generate_rfe_stats(files_yrs, pts_shp=gdf_coords, id_col=HH_ID)
end = datetime.now()
time_taken = (end - start).total_seconds()/60
print('Time taken: {}'.format(int(time_taken)))
# ================================
# EXTRACT DATA FOR TARGET YEARS
# ================================
df_target_yrs = df.query('yr in @target_yrs')
required_vars = {'qrt_max_start': 'wetQstart', 'avg_total': 'anntot_avg',
'avg_qrt_max_start': 'wetQ_avgstart', 'total': 'anntot',
'avg_qrt_max_val': 'wetQ_avg', 'qrt_max_val': 'wetQ'}
df_target_yrs.rename(columns=required_vars, inplace=True)
df_target_yrs = df_target_yrs[['yr', 'anntot', 'wetQ', 'wetQstart', 'anntot_avg','wetQ_avg', 'wetQ_avgstart']]
# ===========================================
# GENERATE YEAR BASED VARIABLES (E.G.,
# ANNTOT_2011-2012,ANNTOT_2012-2013
# ===========================================
df_yr_based = df_target_yrs[['yr', 'anntot', 'wetQ', 'wetQstart']]
df_tmp = pd.DataFrame(index=df_yr_based.index.drop_duplicates())
yr_based_vars = df_yr_based.columns.difference(['yr'])
# There is probably a shorter pivot approach but couldnt
# figure it out, so
for idx, row in df_tmp.iterrows():
df_idx = df_yr_based.loc[idx]
for yr in target_yrs:
for var in yr_based_vars:
val = df_idx.query('yr == @yr').iloc[0][var]
new_col_name = '{}_{}'.format(var, yr.replace("-","_"))
df_tmp.loc[idx, new_col_name] = val
# ===========================================
# PUT EVERYTHING BACK TOGETHER
# ===========================================
# We can drop everything else and remain with unique-locs
df_target_yrs2 = df_target_yrs[~df_target_yrs.index.duplicated(keep='first')]
df = df_target_yrs2.merge(df_tmp, left_index=True, right_index=True, how='inner')
assert len(df) == len(gdf_coords)
# Drop columns we dont need
df.drop(columns=['yr', 'anntot', 'wetQ', 'wetQstart'], inplace=True)
# ===========================================
# SAVE THE OUTPUTS
# ===========================================
out_vars = list(df.columns.difference([HH_ID, LON, LAT]))
out_csv = DIR_OUTPUTS.joinpath('crop-season-{}.csv'.format(FILENAME_SUFFIX))
df.to_csv(out_csv)
# Save variable labels
out_csv_labels = DIR_OUTPUTS.joinpath('crop-season-{}-labels.csv'.format(FILENAME_SUFFIX))
df_var_labels = pd.DataFrame(list(VAR_LABELS.values()), index=list(VAR_LABELS.keys()))
df_var_labels.index.name = 'var_name'
df_var_labels.rename(columns={0:'label'}, inplace=True)
df_var_labels.to_csv(out_csv_labels)
print('-'*30)
print('VARIABLES SAVED TO FILE BELOW')
print('-'*30)
print("/".join(out_csv.parts[6:]))
return out_csv, out_vars
# Generate the variables
out_csv, out_vars = generate_stats(target_yrs=DATA_EXTRACT_YEARS)
---------------------------------------------------------------------------
KeyboardInterrupt Traceback (most recent call last)
Cell In[9], line 2
1 # Generate the variables
----> 2 out_csv, out_vars = generate_stats(target_yrs=DATA_EXTRACT_YEARS)
Cell In[8], line 18, in generate_stats(target_yrs)
16 start = datetime.now()
17 files_yrs = get_year_and_tif_list(stop_year=SURVEY_CALENDAR_YR, year_start=DEKAD_YEAR_START)
---> 18 df = generate_rfe_stats(files_yrs, pts_shp=gdf_coords, id_col=HH_ID)
19 end = datetime.now()
20 time_taken = (end - start).total_seconds()/60
Cell In[7], line 15, in generate_rfe_stats(yr_tif_dict, pts_shp, id_col)
13 print('SKIPPING {} BECAUSE WE DONT HAVE ALL DEKADS'.format(yr))
14 continue
---> 15 df_yr = generate_yr_rfe_stats(files, yr, pts_shp, uniq_id=id_col)
16 df_yr_list.append( df_yr)
17 cnt_yrs += 1
Cell In[6], line 8, in generate_yr_rfe_stats(tif_list, yr, gdf_coords, uniq_id)
6 for file in tif_list:
7 colname = 'd{}'.format(file.parts[-1][8:10])
----> 8 df = utils.point_query_raster(in_raster=file, buffer=BUFFER,
9 point_shp=gdf_coords, statcol_name=colname
10 ,id_col=uniq_id,xcol=LON, ycol=LAT)
11 df.set_index(uniq_id, inplace=True)
12 df_lst.append(df)
File ~/anaconda3/lib/python3.10/site-packages/spatial_anonymization/utils.py:746, in point_query_raster(in_raster, point_shp, statcol_name, class_pct_code, id_col, stat, buffer, buff_dist, xcol, ycol)
743 if rst_obj.crs != point_shp.crs:
744 point_shp = point_shp.to_crs(rst_obj.crs)
--> 746 point_shp[statcol_name] = list(gen_point_query(point_shp, in_raster, interpolate='nearest'))
747 try:
748 assert point_shp[statcol_name].isna().sum() == 0, 'THERE SHOULD BE NO NULLS'
File ~/anaconda3/lib/python3.10/site-packages/rasterstats/point.py:174, in gen_point_query(vectors, raster, band, layer, nodata, affine, interpolate, property_name, geojson_out, boundless)
171 if interpolate not in ["nearest", "bilinear"]:
172 raise ValueError("interpolate must be nearest or bilinear")
--> 174 features_iter = read_features(vectors, layer)
176 with Raster(raster, nodata=nodata, affine=affine, band=band) as rast:
177 for feat in features_iter:
File ~/anaconda3/lib/python3.10/site-packages/rasterstats/io.py:125, in read_features(obj, layer)
122 elif isinstance(obj, bytes):
123 # Single binary object, probably a wkb
124 features_iter = [parse_feature(obj)]
--> 125 elif hasattr(obj, "__geo_interface__"):
126 mapping = obj.__geo_interface__
127 if mapping["type"] == "FeatureCollection":
File ~/anaconda3/lib/python3.10/site-packages/geopandas/geodataframe.py:761, in GeoDataFrame.__geo_interface__(self)
730 @property
731 def __geo_interface__(self):
732 """Returns a ``GeoDataFrame`` as a python feature collection.
733
734 Implements the `geo_interface`. The returned python data structure
(...)
759
760 """
--> 761 return self._to_geo(na="null", show_bbox=True, drop_id=False)
File ~/anaconda3/lib/python3.10/site-packages/geopandas/geodataframe.py:870, in GeoDataFrame._to_geo(self, **kwargs)
862 def _to_geo(self, **kwargs):
863 """
864 Returns a python feature collection (i.e. the geointerface)
865 representation of the GeoDataFrame.
866
867 """
868 geo = {
869 "type": "FeatureCollection",
--> 870 "features": list(self.iterfeatures(**kwargs)),
871 }
873 if kwargs.get("show_bbox", False):
874 geo["bbox"] = tuple(self.total_bounds)
File ~/anaconda3/lib/python3.10/site-packages/geopandas/geodataframe.py:813, in GeoDataFrame.iterfeatures(self, na, show_bbox, drop_id)
810 ids = np.array(self.index, copy=False)
811 geometries = np.array(self[self._geometry_column_name], copy=False)
--> 813 properties_cols = self.columns.difference([self._geometry_column_name])
815 if len(properties_cols) > 0:
816 # convert to object to get python scalars.
817 properties = self[properties_cols].astype(object).values
File ~/anaconda3/lib/python3.10/site-packages/pandas/core/indexes/base.py:3604, in Index.difference(self, other, sort)
3602 self._validate_sort_keyword(sort)
3603 self._assert_can_do_setop(other)
-> 3604 other, result_name = self._convert_can_do_setop(other)
3606 # Note: we do NOT call _deprecate_dti_setop here, as there
3607 # is no requirement that .difference be commutative, so it does
3608 # not cast to object.
3610 if self.equals(other):
3611 # Note: we do not (yet) sort even if sort=None GH#24959
File ~/anaconda3/lib/python3.10/site-packages/pandas/core/indexes/base.py:3745, in Index._convert_can_do_setop(self, other)
3742 other = Index(other, name=self.name, dtype=other.dtype)
3743 else:
3744 # e.g. list
-> 3745 other = Index(other, name=self.name)
3746 result_name = self.name
3747 else:
File ~/anaconda3/lib/python3.10/site-packages/pandas/core/indexes/base.py:568, in Index.__new__(cls, data, dtype, copy, name, tupleize_cols, **kwargs)
565 subarr = com.asarray_tuplesafe(data, dtype=_dtype_obj)
566 if dtype is None:
567 # with e.g. a list [1, 2, 3] casting to numeric is _not_ deprecated
--> 568 subarr = _maybe_cast_data_without_dtype(
569 subarr, cast_numeric_deprecated=False
570 )
571 dtype = subarr.dtype
572 return Index(subarr, dtype=dtype, copy=copy, name=name, **kwargs)
File ~/anaconda3/lib/python3.10/site-packages/pandas/core/indexes/base.py:7414, in _maybe_cast_data_without_dtype(subarr, cast_numeric_deprecated)
7396 def _maybe_cast_data_without_dtype(
7397 subarr: np.ndarray, cast_numeric_deprecated: bool = True
7398 ) -> ArrayLike:
7399 """
7400 If we have an arraylike input but no passed dtype, try to infer
7401 a supported dtype.
(...)
7411 np.ndarray or ExtensionArray
7412 """
-> 7414 result = lib.maybe_convert_objects(
7415 subarr,
7416 convert_datetime=True,
7417 convert_timedelta=True,
7418 convert_period=True,
7419 convert_interval=True,
7420 dtype_if_all_nat=np.dtype("datetime64[ns]"),
7421 )
7422 if result.dtype.kind in ["i", "u", "f"]:
7423 if not cast_numeric_deprecated:
7424 # i.e. we started with a list, not an ndarray[object]
File ~/anaconda3/lib/python3.10/site-packages/pandas/_libs/lib.pyx:2515, in pandas._libs.lib.maybe_convert_objects()
File ~/anaconda3/lib/python3.10/site-packages/numpy/core/numeric.py:344, in full(shape, fill_value, dtype, order, like)
342 dtype = fill_value.dtype
343 a = empty(shape, dtype, order)
--> 344 multiarray.copyto(a, fill_value, casting='unsafe')
345 return a
File <__array_function__ internals>:179, in copyto(*args, **kwargs)
File ~/anaconda3/lib/python3.10/site-packages/numpy/core/multiarray.py:1071, in copyto(dst, src, casting, where)
1023 """
1024 unravel_index(indices, shape, order='C')
1025
(...)
1066
1067 """
1068 return (indices,)
-> 1071 @array_function_from_c_func_and_dispatcher(_multiarray_umath.copyto)
1072 def copyto(dst, src, casting=None, where=None):
1073 """
1074 copyto(dst, src, casting='same_kind', where=True)
1075
(...)
1101 wherever it contains the value True.
1102 """
1103 return (dst, src, where)
KeyboardInterrupt:
Quality checks#
Run summary statistic to generare minimum, maximum values etc and inspect them manually.
# Only use describe for continuos variables
print('-'*50)
print(' SUMMARY STATISTICS FOR CROP SEASON VARS')
print('-'*50)
stats = df[out_vars].describe().T
print(stats[['mean', 'min', '50%', '75%', 'max']])
print('-'*70)
--------------------------------------------------
SUMMARY STATISTICS FOR CROP SEASON VARS
--------------------------------------------------
mean min 50% 75% max
anntot_2013_2014 887.973747 249.0 898.0 1038.5 2111.0
anntot_2014_2015 878.959427 278.0 846.0 1036.0 1967.0
anntot_avg 829.522673 322.0 850.0 967.0 1817.0
wetQ_2013_2014 570.584726 180.0 539.0 718.5 1008.0
wetQ_2014_2015 517.694511 165.0 538.0 628.0 937.0
wetQ_avg 495.923628 211.0 500.0 563.0 821.0
wetQ_avgstart 13.852029 5.0 11.0 19.0 29.0
wetQstart_2013_2014 11.630072 1.0 4.0 30.0 36.0
wetQstart_2014_2015 13.892601 1.0 7.0 30.0 36.0
----------------------------------------------------------------------
Summary#
The following variables below were generated.
ID |
Theme |
Variable Description |
Variable ID |
Units |
Resolution |
Data sources |
Required |
Reference period |
|---|---|---|---|---|---|---|---|---|
1 |
Crop Season |
Average 12-month total rainfall for July-June |
anntot_avg |
mm |
0.008333 dd |
Yes |
2001-2015 |
|
2 |
Crop Season |
Average total rainfall in wettest quarter within 12-month periods from July-June |
wetQ_avg |
mm |
0.008333 dd |
Yes |
2001-2015 |
|
3 |
Crop Season |
Average start of wettest quarter in dekads 1-36, where first dekad of July =1 |
wetQ_avgstart |
dekads |
0.008333 dd |
Yes |
2001-2015 |
|
4 |
Crop Season |
12-month total rainfall in July-June, starting July 2013 |
anntot_2013-2014 |
mm |
0.008333 dd |
Yes |
2013-2014 |
|
5 |
Crop Season |
Total rainfall in wettest quarter within 12- month periods starting July 2013 |
wetQ_2013-2014 |
mm |
0.008333 dd |
Yes |
2013-2014 |
|
6 |
Crop Season |
Start of wettest quarter in dekads 1-36, where first dekad of July 2013 =1 |
wetQstart_2013-2014 |
dekads |
0.008333 dd |
Yes |
2013-2015 |
|
7 |
Crop Season |
12-month total rainfall in July-June, starting July 2014 |
anntot_2014-2015 |
mm |
0.008333 dd |
Yes |
2014-2015 |
|
8 |
Crop Season |
Total rainfall in wettest quarter within 12- month periods starting July 2014 |
wetQ_2014-2015 |
mm |
0.008333 dd |
Yes |
2014-2015 |
|
9 |
Crop Season |
Start of wettest quarter in dekads 1-36, where first dekad of July 2014 =1 |
wetQstart_2014-2015 |
dekads |
0.008333 dd |
Yes |
2014-2015 |