The authors developed an open-source package to ease the access, reformat, and visualization of remote sensing earth observation data from NASA, to facilitate data dissemination, assist hydrological modeling, and support decision making. The package currently supports multiple NASA climate datasets, and the reformatted data could be seamlessly ingested into several mainstream hydrological models. The manuscript has clearly described the functionalities and methodology of the package, and given detailed instructions on installation requirements and steps, as well as how to use it with a case study. All the technical instructions are easy to follow. Also, the package has already been demonstrated in two published articles.  Below are comments that suggest the authors to address.

1.    What are the benefits of NASAaccess comparing with existing tools listed in Table 1? In Table 1, it would help if you could list more information of existing tools to illustrate the necessity and benefits of NASAaccess, such as open source or not, supported datasets, programming language, operation system, and other pros and cons related to the purpose of this work.

2.    For some abbreviations, please only give full name when one item is first time mentioned in the manuscript, for examples, NASA on Page 3 and 16, GeoGloWS on Page 17, GLDAS on Page 15, GES on Page 28, 29, and 30, DISC on Page 28, 29, and 30, CMIP on Page 18, 24, and 26 (twice), OSSI on Page 16, SWAT on Page 6, etc.

3.    In Section 3.1, these three functions in NASAaccess are mentioned here for the first time. Please explain them here for readers to better understand or give a note to inform readers to find “further explanation in XXXX of the Appendix”?

4.    In Section 3.1, please give full name and explain the “data of IMERG”.  It would be better to give the website of IMERG data https://gpm.nasa.gov/data/imerg

5.    Where can readers find the shapefile and DEM for the case study?

6.    Is there any limitation on how long the data could be retrieved? For years of data, how long it would take with different functions?

This paper presents novel open-source software  packages and web-based environmental modeling applications for Earth observation data accessing, reformation, and presenting quantitative data products. This software is very  useful for the scientists and stakeholders to further provide support for environmental modeling . This software can help to  lower the technical barriers and leverage  the distributed computing resources for environmental modeling. The user manuals are described very well and easily to follow. And the paper is well written. 

However, I have some suggestions/questions:

1. There are some similar functionality between the NASAaccess and  other open-sources mentioned in Table 1. What is the gap between NASAaccess and other software in the Table? What benefit can we obtain by using NASAaccess compared to other software?
2. Line 130 - 135, once the data were generated and downloaded, what kind of data format it would be? And could you show some examples?This could be important information for the end-user to further use the processed data.
3. Line 175-176. The authors mentioned the .netrc file for storing the credentials information. What is the”.urs_cookies” file for? 
4. Line 175-177 is the same contents  as 185-190
5. I am not sure if I understand correctly. The raw data is saved in NASA’ server with PostgreSQL database? The users can download the data which is not saved in the PostgresQL as mentioned in Line 114 . Then why the database is required to be downloaded by the database server as stated in Line 200 - 205. Indeed, it wouldl be great if the authors can add a data flow and data information , i.e. where is the raw data, what are formats of the data, how the data is processed, where is the processed data stored,what is the processed data format etc.
6. The tool seems to be supported by R and Python. But the examples in the manuscript are only given to R.  I would like to suggest authors to add examples that use Python. Or you can point to the document link where there is any example for Python