Disaggregating SDG-6 water stress indicator at different spatial and temporal scales in Tunisia
(2019) Science of the Total Environment — Vol. 694, p. 133766 (2019)
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- Authors
Fehri, Raed 
- Abstract
- The recently adopted UN Sustainable Development Goals (SDGs) encompasses a specific goal forwater (SDG-6). The target 6.4 deals with water scarcity and refers to two main indicators: water use efficiency and water stress (WS), monitored by the UN statistical services yearly at the country level. Yet, for more efficient development planning, indicators should also be provided with higher spatial and temporal resolutions. This study presents a data-driven method allowing to disaggregate the WS indicator at higher spatial and temporal resolution. We applied the method for the Medjerda catchment in Tunisia, known as being severely water-stressed. We disaggregated theWS indicator fromthe overall catchment to the administrative regional level at yearly and monthly scales. In order to overcome poorly documented irrigation water withdrawals, two approaches were adopted: 1)we used yearly governmental data at both catchment and regions scales; 2)we replaced governmental irrigation data by remote sensing-based irrigation estimation. First Order Uncertainty Analysis (FOUA)was performed to characterize the uncertainty associated with the assessment ofWS. Results reveal that theWS at the scale of the catchment increases considerably in recent years, exceeding 50% from2005 and surpassing the 100% threshold in 2015 and 2016 (102%, 108% respectively). The two adopted approaches result in similar WS trends. However, the second approach yields higherWS values compared to the first approach (144% versus 108% in 2016). Themonthly-disaggregatedWSat catchment scale exhibits a similar increasing trend. The highestWSvalues are at the end of the fall and during the summer season,which ismainly due to the increasing demand for irrigation and drinking water. Siliana region is the most affected byWS, while Beja is the least affected. The FOUA shows that the integration of remote sensing-based irrigation data reduces theWS uncertainty.
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Citations
Fehri, R., Khlifi, S., & Vanclooster, M. (2019). Disaggregating SDG-6 water stress indicator at different spatial and temporal scales in Tunisia. Science of the Total Environment, 694, 133766. https://doi.org/10.1016/j.scitotenv.2019.133766 (Original work published 2019)