inital commit

Author: ssmahto

InfeRes_package/paper.md

@@ -45,11 +45,11 @@ Motivated by these modelling challenges, we sought to develop a package that cou
 
 # Functionality
 
-`InfeRes` is available on GitHub (https://github.com/ssmahto/InfeRes_test). Its documentation (https://inferes-test.readthedocs.io/en/latest/index.html) provides a detailed explanation of the installation steps and guidelines for running the code. This includes the preparation of the required modules, which can be easily imported in the Python environment via the pip, conda, or conda-forge package manager.
+`InfeRes` is available on GitHub (https://github.com/Critical-Infrastructure-Systems-Lab/InfeRes). Its documentation (https://inferes-test.readthedocs.io/en/latest/index.html) provides a detailed explanation of the installation steps and guidelines for running the code. This includes the preparation of the required modules, which can be easily imported in the Python environment via the pip, conda, or conda-forge package manager.
 
 The package's core functionality is divided into two main modules, which are run in sequence. The first module (`data_download.py`) downloads the Landsat imageries using the Earth Engine Python API. To that purpose, the user needs to install the *earthengine-api* package and authenticate with the Google Earth Engine account. In principle, the user can download any set of data from the Google Earth Engine using `data_download.py`; however, for the current objective, we simply use the Normalised Difference Water Index (NDWI) and Quality Assessment Bands (QA_PIXEL) from the Landsat data collection. Alternatively, the user can also download the GREEN and NIR bands to calculate NDWI, instead of downloading NDWI directly from the Earth Engine. Note that more storage and time would be required in such case. The data download module also helps users change the data specifications, such as satellite sensor, area of interest, spatial resolution, and selection of bands. The time needed for data download depends on the size of the Landsat images. For instance, when tested on one of the biggest reservoir area (3010 x 5413 pixels of 30 m resolution), it took around 6-8 hours to download 1330 Landsat images (Landsat 5, 7, and 8), which required nearly 45 GB of storage. 
 
-The second module (`data_processing.py`) includes four sub-modules that are designed to execute one after another to get the complete set of results. The four sub modules are: **CURVE.py**, **MASK.py**, **WSA.py**, and **CURVE_Tile.py**. Apart from the Landsat images, the user needs the DEM of the selected reservoir (with the same dimension of the Landsat images), which is an input to the sub-modules. We recommend users to get the DEM separately before running the code. Please note that the DEM should have been created before the reservoir is filled. We tested our code with SRTM DEM (30m) which was acquired in the year 2000; therefore, reservoirs built before 2000 were not taken into account. As the user runs the data processing module, the **CURVE.py** is executed first, generating the reservoir's bathymetric curve (using the information contained in the DEM). Next, **MASK.py** takes the Landsat images and DEM as input (together with the design specifications i.e. dam location, extent of the reservoir, maximum and dead storage level, and year of commission) and generates the supporting intermediate files required to run **WSA.py**. This sub-module works with supporting files and processed Landsat images to calculate the reservoir surface water area. Finally, **CURVE_Tile.py** translates the reservoir's surface water area into its corresponding storage volume and level using the previously constructed bathymetric curve.
+The second module (`data_processing.py`) includes four sub-modules that are designed to execute one after another to get the complete set of results. The four sub modules are: **CURVE.py**, **MASK.py**, **WSA.py**, and **CURVE_Tile.py**. Apart from the Landsat images, the user needs the DEM of the selected reservoir (with the same dimension of the Landsat images), which is an input to the sub-modules. We recommend users to get the DEM separately before running the code. Please note that the DEM should have been created before the reservoir is filled. We tested our code with SRTM DEM (30m) which was acquired in the year 2000. If the reservoir is constructed before the DEM was created, the software provides the bathymetric curves for water level above the DEM observation point (so this may require the user to complete the bathymetric curve via extrapolation). As the user runs the data processing module, the **CURVE.py** is executed first, generating the reservoir's bathymetric curve (using the information contained in the DEM). Next, **MASK.py** takes the Landsat images and DEM as input (together with the design specifications i.e. dam location, extent of the reservoir, maximum and dead storage level, and year of commission) and generates the supporting intermediate files required to run **WSA.py**. This sub-module works with supporting files and processed Landsat images to calculate the reservoir surface water area. Finally, **CURVE_Tile.py** translates the reservoir's surface water area into its corresponding storage volume and level using the previously constructed bathymetric curve.
              
 # Example Use Case