Disaggregating SDG-6 water stress indicator at different spatial and temporal scales in Tunisia

Raed Fehri; Slaheddine Khlifi; Marnik Vanclooster

doi:10.1016/j.scitotenv.2019.133766

Disaggregating SDG-6 water stress indicator at different spatial and temporal scales in Tunisia

Sci Total Environ. 2019 Dec 1:694:133766. doi: 10.1016/j.scitotenv.2019.133766. Epub 2019 Aug 5.

Authors

Raed Fehri¹, Slaheddine Khlifi², Marnik Vanclooster³

Affiliations

¹ Université Catholique de Louvain, Earth and Life Institute (ELI), GERU, Croix du Sud 2, 1348 Louvain La Neuve, Belgium. Electronic address: raed.fehri@uclouvain.be.
² Ecole Supérieure des Ingénieurs de Medjez el Bab (ESIM), UR-Gestion Durable des Ressources en Eau et en Sol, P5, 9070, Tunisia.
³ Université Catholique de Louvain, Earth and Life Institute (ELI), GERU, Croix du Sud 2, 1348 Louvain La Neuve, Belgium.

PMID: 31756819
DOI: 10.1016/j.scitotenv.2019.133766

Abstract

The recently adopted UN Sustainable Development Goals (SDGs) encompasses a specific goal for water (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 the WS indicator from the 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 of WS. Results reveal that the WS at the scale of the catchment increases considerably in recent years, exceeding 50% from 2005 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 higher WS values compared to the first approach (144% versus 108% in 2016). The monthly-disaggregated WS at catchment scale exhibits a similar increasing trend. The highest WS values are at the end of the fall and during the summer season, which is mainly due to the increasing demand for irrigation and drinking water. Siliana region is the most affected by WS, while Beja is the least affected. The FOUA shows that the integration of remote sensing-based irrigation data reduces the WS uncertainty.

Keywords: Disaggregation; SDG-6 indicators; Sustainable development goals; Water resources management; Water stress.