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Python implementation of the Forecasting Inundation Extents using REOF method

Based off of the methods from Chang et al., 2020

Installation

$ conda create -n fier -c conda-forge python=3.8 numpy scipy xarray pandas sckit-learn eofs geoglows

$ pip install git:https://github.com/servir/fierpy.git

Requirements

  • numpy
  • xarray
  • pandas
  • eofs
  • geoglows
  • scikit-learn
  • rasterio

Example use

import xarray as xr
import fierpy

# read sentinel1 time series imagery
ds = xr.open_dataset("sentine1.nc")

# apply rotated eof process
reof_ds = fierpy.reof(ds.VV,n_modes=4)

# get streamflow data from GeoGLOWS
# select the days we have observations
lat,lon = 11.7122,104.9653
q = fierpy.get_streamflow(lat,lon)
q_sel = fierpy.match_dates(q,ds.time)

# apply polynomial to different modes to find best stats
fit_test = fierpy.find_fits(reof_ds,q_sel,ds)