lisflood-utilities 0.11.2

A set of utilities for lisfloodutilities users. pcr2nc: Convert PCRaster files to netCDF; cutmaps: cut netCDF files;compare: to compare two set of netcdf files

Lisflood Utilities

This repository hosts source code of LISFLOOD utilities. Go to Lisflood OS page for more information.

Other useful resources

Project	Documentation	Source code
Lisflood	Model docs	https://github.com/ec-jrc/lisflood-code
	User guide
Lisvap	Docs	https://github.com/ec-jrc/lisflood-lisvap
Calibration tool	Docs	https://github.com/ec-jrc/lisflood-calibration
Lisflood Utilities		https://github.com/ec-jrc/lisflood-utilities (this repository)
Lisflood Usecases		https://github.com/ec-jrc/lisflood-usecases

User guide of each tool is placed under the relative directory in a README.md file.

pcr2nc

pcr2nc is a tool to convert PCRaster maps to netCDF4 files. It's developed at JRC E1 directorate, as a Floods group initiative.

• convert a single map into a NetCDF4 file
• convert a time series of maps into a NetCDF4 mapstack
• support for WGS84 and ETRS89 (LAEA) reference systems
• fine tuning of output files (compression, significant digits, etc.)

Installation

There are two ways to install the software: one via pip tool and one by cloning the repository.

Requisites

Ensure you have properly installed the following software:

• Python 3.5+
• GDAL C library and software
• netCDF4 C library

Create a python3 virtualenv for using the software and activate it.

If you have virtualenvwrapper:

$ workon pcr2nc

Otherwise just execute the activate script

$ source /path/to/virtualenvs/pcr2nc/bin/activate

Install via pip tool

Activate the virtualenv and then:

$ pip install pcr2nc

After the install was complete, you still have to install the proper GDAL package, according to the version of gdal library that is installed on your machine. You will also need C GDAL headers to properly install python GDAL wrapper.

E.g.

sudo apt-get install libgdal-dev libgdal
export CPLUS_INCLUDE_PATH=/usr/include/gdal
export C_INCLUDE_PATH=/usr/include/gdal
gdal-config --version  # 2.2.3
pip install GDAL==2.2.3

Install by cloning the repository

Assuming your Python 3 virtualenv is called pcr2nc and you have virtualenvwrapper installed:

git clone https://github.com/ec-jrc/lisflood-utilities
cd lisflood-utilities/pcr2nc
workon pcr2nc

Install requirements

$ pip install -r requirements.txt

If GDAL library fails to install, ensure to install the same package version of the library you have on your system. To check which version of GDAL libraries you have installed on your computer, use gdal-config

gdal-config --version
2.1

Example: you have installed gdal 2.1, then:

$ pip install GDAL==2.1
$ pip install -r requirements.txt

Usage

Note: This guide assumes you have installed the program with pip tool. If you cloned the source code instead, just substitute the executable pcr2nc with python pcr2nc_script.py that is in the root folder of the cloned project.

The tool takes three command line input arguments:

• -i, --input: It can be a path to a single file, a folder or a unix-like widlcard expression like /path/to/files/dis00*
• -o, --output_file: Path to the output nc file
• -m, --metadata: Path to a yaml or json file containing configuration for the NetCDF4 output file.

Unless the input is a single file, the resulting NetCDF4 file will be a mapstack according to a time dimension.

Example of usages:

Input as a folder containing PCRaster maps. In this case, the folder must contain ONLY PCraster files and the output will be a mapstack.

pcr2nc -i /path/to/input/ -o /path/to/output/out.nc -m ./nc_metadata.yaml

Input as a path to a single map. In this case, the output won't be a mapstack.

pcr2nc -i /path/to/input/pcr.map -o /path/to/output/out.nc -m ./nc_metadata.yaml

Input as a Unix style pathname pattern expansion. The output will be a mapstack. Note that in this case the input argument must be contained in double quotes!

pcr2nc -i "/path/to/input/pcr00*" -o /path/to/output/out.nc -m ./nc_metadata.json

Writing metadata configuration file

Format of resulting NetCDF4 file is configured into a metadata configuration file. This file can be written in YAML or JSON format.

An example of a metadata configuration file is the following

variable:
  shortname: dis
  description: Discharge
  longname: discharge
  units: m3/s
  compression: 9
  least_significant_digit: 2
source: JRC Space, Security, Migration
reference: JRC Space, Security, Migration
geographical:
  datum: WGS84
time:
  calendar: proleptic_gregorian
  units: days since 1996-01-01

Variable section

In variable section you can configure metadata for the main variable:

• shortname: A short name for the variable
• longname: The long name version
• description: A description for humans
• units: The units of the variable
• compression: Optional, integer number between 1 and 9, default 0 (no compression). If present the output nc file will be compressed at this level.
• least_significant_digit: Optional, integer number, default 2. From NetCDF4 documentation:

If your data only has a certain number of digits of precision (say for example, it is temperature data that was measured with a precision of 0.1 degrees), you can dramatically improve zlib compression by quantizing (or truncating) the data using the least_significant_digit keyword argument to createVariable. The least significant digit is the power of ten of the smallest decimal place in the data that is a reliable value. For example if the data has a precision of 0.1, then setting least_significant_digit=1 will cause data the data to be quantized using numpy.around(scale*data)/scale, where scale = 2**bits, and bits is determined so that a precision of 0.1 is retained (in this case bits=4). Effectively, this makes the compression 'lossy' instead of 'lossless', that is some precision in the data is sacrificed for the sake of disk space.

Source and reference

source and reference add information for the institution that is providing the NetCDF4 file.

Geographical section

In geographical section the only setting to configure is datum. Currently, pcr2nc supports the following list:

• WGS84
• ETRS89
• GISCO

Time section

This section is optional and is only required if the output file is a mapstack (a timeseries of georeferenced 2D arrays) In this section you have to configure units and calendar.

• units: Can be one of the following strings (replacing placeholders with the actual date):
  • hours since YYYY-MM-DD HH:MM:SS
  • days since YYYY-MM-DD
• calendar: A recognized calendar identifier, like proleptic_gregorian, gregorian etc.

Cutmaps: a NetCDF files cookie-cutter

This tool accepts a template/cookie-cutter as input, along with a list of files to cut, and produces cut netcdf files.

Usage:

The tool accepts as input:

• a mask map (either PCRaster or netCDF format) or alternatively, passing directly indices in the form min_x_ind-max_x_ind:min_y_ind-max_y_ind
• a textfile with a list of filepaths for files to cut or alternatively a path to a folder containing netCDF files to cut
• a path to a folder where to write cut files.

Examples of usage:

The following command will cut all netcdf files inside /workarea/Madeira/lai/ folder and produced files will be writte in current folder. The cookie-cutter that will be used is /workarea/Madeira/maps/MaskMap/Bacia_madeira.nc. This file is a mask (boolean map with 1 only in the area of interest) where cutmaps takes the bounding box from. The mask can also be in PCRaster format.

cutmaps -m /workarea/Madeira/maps/MaskMap/Bacia_madeira.nc -f /workarea/Madeira/lai/ -o ./

Indices can also be passed as an argument (using -c argument instead of -m). Knowing your area of interest from your netCDF files, you can determine indices of the array and you can pass in the form i_min-i_max:j_min-j_max.

cutmaps -c 150-350:80:180 -f /workarea/Madeira/lai/ -o ./

gfit

Introduction

The tool Gfit2 was created to extract flood warning thresholds from a map stack of daily discharge for several years, given in NetCDF format. It is designed to work with any other variable as well as with different sampling (sub- or super- daily)

The tool is made of three files:

1. Gfit2.sh: the main file, used to run the tool (e.g., ./Gfit2.sh ./settingFile_test.sh)
2. settingFile_test.sh: a setting file, where paths and values of the input variables are defined. It can be renamed, as long as the correct name is called in the script (e.g.: set1.sh --> ./Gfit2.sh ./set1.sh)
3. gfit2.r: the r script that performs the extreme value analysis

Requirements

You need to have installed the following:

• CDO
• R

How to run

The tool operates in a Linux environment (also as a job with qsub). To run the tool you'll need:

• R (the script was tested with R version 3.5.0)
• the following R packages: ncdf4, lmomco, ismev
• CDO (the script was tested with CDO version 1.6.5.1)

What does the tool do

The Gfit2 tool performs the following steps:

• It takes the input file in NetCDF and extract the series of annual maxima. an optional number of warm-up years ($warmup_yrs) are excluded at the beginning of the data, to remove potential spin-up effect. Also the map of average value is computed
• An extreme value fitting is performed on each pixel of the map, using L-moments and a 2-parameter Gumbel distribution. As option, the user can limit the analysis to a specific number of years. Also, one can use the option to remove from the fitting all values smaller than the long-term average. The fitting is performed only where at least 5 annual maxima are available, otherwise a NA is returned on the output map
• Output return level maps (corresponding to user-selected years of recurrence interval) are saved in a netcdf file (return_levels.nc) and as ascii files. If a clone map in PCRaster format is provided, maps are also saved in PCRaster (return level maps and parameters of the Gumbel distribution)
• After the EV fitting, the tool estimates some further statistics from the long-term input file, including minimum, maximum, and different percentile maps. This part can be commented out if not of interest

Reference

L-Moments: Analysis and Estimation of Distributions Using Linear Combinations of Order Statistics

Hosking, J.R.M., 1990. L-Moments: Analysis and Estimation of Distributions Using Linear Combinations of Order Statistics. J. R. Stat. Soc. Ser. B Methodol. 52, 105124.