added figures

ssmahto · ssmahto · commit b4e46fd135ec · 2023-09-02T14:37:27.000+08:00
diff --git a/paper/paper.md b/paper/paper.md
@@ -36,12 +36,12 @@ bibliography: paper.bib
 
 # Statement of need
 
-Accurate information concerning the amount of water stored by dams is crucial for a variety of engineering applications, such as water resources management ['@Giuliani:2021'], hydrological modelling ['@Dang:2020'], or hydropower systems management ['@Galelli:2022']. Unfortunately, these data are rarely available, particularly in developing countries and transboundary river basins, where dam operations are often a point of contention ['@Warner:2012']. A user necessitating information about dam storage is, in general, left with two alternatives. The first and simplest one is to retrieve such information from a database containing actual observations, such as ResOpsUS ['@Steyaert:2022']. The problem is that the creation of these databases is still at its infancy; at this stage, their spatial coverage is limited to the United States. The second alternative is to bank on the information contained in satellite observations. By combining information on a reservoir’s bathymetry (elevation-area-storage curve) with information
-on water level (measured by an altimeter), or water surface (retrieved from satellite images), one can, in principle, infer the corresponding time series of water storage ['@Gao:2015']. The availability of such data has indeed opened up the possibility of monitoring reservoirs in ungauged areas ['@Bonnema:2017','@Busker:2019'], and also led to the development of online monitoring tools ['@Das:2022'].
+Accurate information concerning the amount of water stored by dams is crucial for a variety of engineering applications, such as water resources management (@Giuliani:2021), hydrological modelling (@Dang:2020), or hydropower systems management (@Galelli:2022). Unfortunately, these data are rarely available, particularly in developing countries and transboundary river basins, where dam operations are often a point of contention (@Warner:2012). A user necessitating information about dam storage is, in general, left with two alternatives. The first and simplest one is to retrieve such information from a database containing actual observations, such as ResOpsUS (@Steyaert:2022). The problem is that the creation of these databases is still at its infancy; at this stage, their spatial coverage is limited to the United States. The second alternative is to bank on the information contained in satellite observations. By combining information on a reservoir’s bathymetry (elevation-area-storage curve) with information
+on water level (measured by an altimeter), or water surface (retrieved from satellite images), one can, in principle, infer the corresponding time series of water storage (@Gao:2015). The availability of such data has indeed opened up the possibility of monitoring reservoirs in ungauged areas (@Bonnema:2017, @Busker:2019), and also led to the development of online monitoring tools (@Das:2022).
 
-Creating storage time series for a given reservoir network is, however, not simple: water level observations provided by altimeters are relatively easy to retrieve and use, but are not available for every dam; moreover, satellite missions that included altimeters were launched only recently ['@Schwatke2015']. Alternatively, satellite images, such as those provided by the Landsat missions, are available for longer periods of time (almost 40 years) and with global coverage. Yet, working with Landsat images requires familiarity with geospatial analysis software (e.g., ArcGIS, QGIS) as well as the statistical or traditional image analysis tools that are needed to process the images affected by cloud cover ['@Zhao:2018']—a rather common problem in many regions, particularly during the monsoon seasons. 
+Creating storage time series for a given reservoir network is, however, not simple: water level observations provided by altimeters are relatively easy to retrieve and use, but are not available for every dam; moreover, satellite missions that included altimeters were launched only recently (@Schwatke2015). Alternatively, satellite images, such as those provided by the Landsat missions, are available for longer periods of time (almost 40 years) and with global coverage. Yet, working with Landsat images requires familiarity with geospatial analysis software (e.g., ArcGIS, QGIS) as well as the statistical or traditional image analysis tools that are needed to process the images affected by cloud cover (@Zhao:2018)—a rather common problem in many regions, particularly during the monsoon seasons. 
 
-Motivated by these modelling challenges, we sought to develop a package that could be potentially used to study any reservoir for a period of time that goes beyond the availability of altimeter observations. This is achieved by developing a package that can extract information from Landsat images, which have a spatial resolution of 30 meters and frequency of 16 days. `InfeRes` was created with this goal: based on the methodology first presented in ['@Vu:2022'], the NASA Landsat Collection 2 top-of-atmosphere (TOA) reflectance, the OSGeo package ['@Coetzee:2020'], and scikit-learn library ['@Hao:2019']. `InfeRes` automates three key modelling steps, namely data download, image processing, and time series reconstruction. With minimal input data required (dam location and design specifications), `InfeRes` is easy to use, even for users unfamiliar with remote sensing and image classification techniques and thus expected to provide an entry point for many users. 
+Motivated by these modelling challenges, we sought to develop a package that could be potentially used to study any reservoir for a period of time that goes beyond the availability of altimeter observations. This is achieved by developing a package that can extract information from Landsat images, which have a spatial resolution of 30 meters and frequency of 16 days. `InfeRes` was created with this goal: based on the methodology first presented in (@Vu:2022), the NASA Landsat Collection 2 top-of-atmosphere (TOA) reflectance, the OSGeo package (@Coetzee:2020), and scikit-learn library (@Hao:2019). `InfeRes` automates three key modelling steps, namely data download, image processing, and time series reconstruction. With minimal input data required (dam location and design specifications), `InfeRes` is easy to use, even for users unfamiliar with remote sensing and image classification techniques and thus expected to provide an entry point for many users. 
 
 # Functionality
 
@@ -61,22 +61,15 @@ In this example, since Xiaowan reservoir area is falling within one tile, we sim
 
 Operatively, we assigned a parent directory and ran the module `data_download.py`, which created a folder (*LandsatData*) and downloaded all the requested Landsat images (NDWI and QA_PIXEL). We then provided the static reservoir information (location, extent, maximum and dead water level) and ran the second module, `data_processing.py`. Please note that we specified both the extents (bigger portion and complete reservoir) for the Nuozhadu reservoir in the user input section (see \autoref{fig:F1}). Then, the package created another folder (*LandsatData_Clip*) and stored the modified NDWI images after applying the cloud mask information from the QA_PIXEL. Subsequently, it also generated the intermediate files and saved them into a different folder (*Outputs*). The intermediate files obtained for the Xiaowan reservoir are shown in \autoref{fig:F2}. The desired final outputs for Xiaowan and Nuozhadu were finally saved in **WSA_Complete_Xiaowan.csv** and **WSA_Complete_Nuozhadu.csv**, respectively.
 
-![(a) DEM-based mask- a spatial mask of the reservoir’s maximum surface water extent created using digital elevation model corresponding to the full reservoir level, (b) Landsat-based mask- same as (a) but created using average of all selected non-cloudy (< 20% cloud coverage over the reservoir) Landsat images, (c) Frequency map- map showing the number of images used to make the Landsat-based mask, and (d) Zone mask- rescaled frequency map ranging from 0 and 50 (user defined range).\label{fig:F2}](Figure 2.png)   
+![(a) DEM-based mask- a spatial mask of the reservoir’s maximum surface water extent created using digital elevation model corresponding to the full reservoir level, (b) Landsat-based mask- same as (a) but created using average of all selected non-cloudy (<20% cloud coverage over the reservoir) Landsat images, (c) Frequency map- map showing the number of images used to make the Landsat-based mask, and (d) Zone mask- rescaled frequency map ranging from 0 and 50 (user defined range).\label{fig:F2}](Figure 2.png)   
 
 # Development Notes
 
 `InfeRes` is developed on GitHub as an open-source package, and the authors welcome
 contributions and feature suggestions. We ensure the code's quality with an extensive suite of tests on multiple reservoirs. Note that the package is only tested on Python 3.8, because of its dependency on the GDAL package. The documentation is created with Sphinx and hosted on Read the Docs. 
 
-# Figures
-
-![Caption for example figure.\label{fig:example}](Figure 1.png)
-![Caption for example figure.\label{fig:example}](Figure 2.png)
-and referenced from text using \autoref{fig:example}.
-
 # Acknowledgments 
 
 This research ‘On the origin of droughts in Mainland Southeast Asia—implications for water and energy security’ is supported by Singapore’s Ministry of Education (MoE) under its Academic Research Fund Tier 2, Project ID: MOE-000379-00 / MOE-000379-01, Award Number: MOE-T2EP50122-0004” XX).
 
-
 # References
