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    Review of 'Assessing SDG indicator 6.4.2 “level of water stress” at major basins level'

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    Assessing SDG indicator 6.4.2 “level of water stress” at major basins levelCrossref
    A useful piece of work that that will contribute towards global monitoring of SDG 6
    Average rating:
        Rated 3 of 5.
    Level of importance:
        Rated 3 of 5.
    Level of validity:
        Rated 3 of 5.
    Level of completeness:
        Rated 3 of 5.
    Level of comprehensibility:
        Rated 3 of 5.
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    Assessing SDG indicator 6.4.2 “level of water stress” at major basins level

     Riccardo Biancalani (corresponding) ,  MIchela Marinelli (2021)
    This paper describes a method to disaggregate the indicator 6.4.2 (level of water stress) by major river basin. The analysis was performed using the GlobWat soil water balance model and global geospatial data consistent with national statistics published in AQUASTAT, the FAO’s global information system on water and agriculture. When a river basin spans across more than one country, the water stress calculated by country can be very different from that calculated by river basin since the counting of the renewable freshwater resources from one country to another is highly dependent on the official agreement and treaties that regulate the flow of those resources between countries. This problem is hydrologically solved once the accounting of the water resources is done on the major river basin as a whole. The disaggregation by river basin allows the identification of hotspots where actions should be prioritized and reveals that the area affected by a high or critical water stress spans across all continents with the exception of Oceania. It offers also the possibility of an analysis of freshwater withdrawals by sector, which may become crucial for the definition of water management policies in the context of the economic development of a country.
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      Review information

      10.14293/S2199-1006.1.SOR-GEO.A4FMT8.v1.RZONCF

      This work has been published open access under Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com.

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      Review text

      This paper presents an interesting technical method to disaggregate the indicator 6.4.2. I found the paper to be overly technical and didn’t bring forth the importance of accurately monitoring and measuring the indicator. The paper could benefit from highlight real-life examples where levels of water stress at major basins have caused human and environmental suffering. I also think the paper missed an opportunity to highlight issues of data collection at country level on the indicator. While the methodology is important in monitoring the indicator, ensuring that accurate and reliable data is collected at national level is critical. I didn’t get a sense of whether the authors found this to be a challenge that would ultimately hinder the ability to report on the indicator. Furthermore, I believe the paper could have benefited from zooming in onto a specific context to demonstrate how robust the indicator is in assessing the indicator at regional and subregional levels.

       

      Other important comments: there are grammatic errors that need to be addressed all through the paper which make it difficult to read.

      For none technical readers the paper might be a little confusing if the methods and formulars are not explicitly explained.

      The results section is clear and reads well. In fact, it allows practitioners to understand issues related to indicator 6.4.2 and the implications of measuring or not measuring this indicator at national level. However, I do feel that you can expand a little bit on population densities as playing a significant role in increasing water stress. Although mentioned on the last paragraph of the results section, it might be beneficial to expand on this a little further and perhaps engage with how the method would, in future, need to take into consideration population densities as a key variable.  

       

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