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

https://doi.org/10.1016/j.scitotenv.2019.133766Get rights and content

Highlights

  • A data-driven method is presented to assess the UN SDG-6 water stress indicator.

  • The method allows the spatial and temporal disaggregation of the indicator.

  • Remotely sensed irrigation data was used as surrogate to governmental data.

  • The Medjerda basin reached an increasingly sever water scarcity in recent years.

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.

Introduction

In the past 50 years, water has become a limited resource in many nations all over the world. Nowadays, about 4 billion people face severe water stress at least one month per year, in particular in arid and semi-arid regions like in North Africa (Mekonnen and Hoekstra, 2016). According to FAO (2018), North African countries face severe water scarcity, which exerts a significant burden on the overall development of these countries.

Given the global water crisis, a specific goal dedicated to water (SDG-6) has been integrated in the recently adopted UN 2030 Agenda for Sustainable Development. The different targets and indicators of SDG-6 are linked to all water functions and services and considered to be important for sustaining life on Earth. Indicator 6.4.2 of target 6.4 measures the level of water stress (WS). It is a blue water WS indicator. It is defined as the ratio of total yearly national freshwater withdrawn by all sectors to the total of fresh blue water resources minus the Environmental Flow Requirements (EFR) (Vanham et al., 2018). The quality of the assessment of this indicator depends primarily on the quality of available water data. Governments generally use traditional hydrometric data to assess the WS indicator. Yet, traditional hydrometric data archives suffer from data gaps, incomplete time series, poor spatial resolution, often leading to poor quality databases. Alternatively, Remote Sensing (RS) offers hydrometric attributes with a high space-time resolution that may complete the poorly monitored national data such as precipitation, land cover and irrigation consumption. Yang et al. (2018) showed for instance that RS data could be used to estimate water consumption of ecosystems including arable lands using different evapotranspiration (ET) products such as MODIS. RS data can, therefore, be used to reduce the uncertainty in assessing the WS indicator of SDG-6. In addition, with the availability of new RS products and RS processing platforms such as the Google Earth Engine, a capacity exists to downscale SDG-6 indicators at higher spatial and temporal resolutions. This offers an opportunity to address the recommendation of Vanham et al. (2018) stating that more advanced monitoring levels of the WS indicator are needed. It offers also perspectives to support more efficiently regional water management design and planning. Yet the processing streams of RS based methods for assessing SDG-6 indicators have not been tested, and the final quality of the disaggregated indicators is not yet known.

The objective of this study is to present a data-driven method allowing to evaluate and disaggregate the SDG-6 indicator using traditional and RS based data products. We evaluate the method for the case study of the Medjerda catchment in Tunisia. Tunisia is considered as one of the most arid countries in the Mediterranean region, suffering from extreme water scarcity in recent years (FAO, 2018). Tunisia's Medjerda catchment represents the most important river basin in the country, however, it is highly affected by climate change and the pressure on its water resources is high. We assess the WS indicator at the scale of the entire Tunisian catchment and at the scale of four administrative regions (the “governorates”) within the catchment (Jendouba, Beja, El kef and Siliana), both at the yearly and monthly time scales. Annual assessment of the WS indicator might not be sufficient enough to obtain insights into its intra-annual variations. Therefore, the disaggregation of the indicator at a monthly time scale is useful. The monthly assessment provides more knowledge about WS seasonality and its intra-annual variability which are not revealed by assessment at an annual temporal resolution (Degefu et al., 2018; Gain and Wada, 2014). We propose two data-driven approaches: (1) measurement using nationally produced data; and (2) measurement based on ET-derived irrigation water consumption as a substitute for governmental irrigation data. We include First Order Uncertainty Analysis (FUOA) to characterize in each approach the uncertainty in the measurement of WS resulting from uncertainties in the input data.

Section snippets

Study area

Tunisia lies between the hot desert in the south and the Mediterranean in the north. It is dominated by arid and semi-arid climates and affected by limited water supplies. Most of the country's water resources are concentrated in the Medjerda river basin, which represents the most important catchment in the country. The upstream is located in the semi-arid Atlas Mountains of eastern Algeria and it runs over a distance of 312 km in the North of Tunisia. The study area lies in the sub-humid to

WS estimates at basin and administrative region scale using governmental data

Fig. 2 shows the yearly WS at the scale of the Medjerda catchment for the period 2000–2016. It follows a significant increasing trend (Mann-Kendall trend test, Pvalue < 0.05). Lowest values of WS were in the early 2000s with only 30% of WS in 2000 and 36%, 41% and 42% of WS in 2001, 2002 and 2003 respectively. This low level of WS is a result of the low demand for irrigation (only 117.44 Mm3 in 2000), the presence of important hydrological events (especially between 2002 and 2003), and the low

Conclusion

We disaggregated in this study the SDG-6 WS indicator for the Tunisian case study of the Medjerda catchment at different spatial and temporal scales. The disaggregation was conducted by the means of two approaches to assess irrigation water withdrawals. The major conclusions are as follows:

  • (1)

    Yearly WS in the Medjerda basin showed an increasing trend for the period 2000–2016. The level of WS remained acceptable for the first four years with 42% in 2003. However, from 2004 to 2012, WS ranged

Acknowledgment

We acknowledge the support of the Université catholique de Louvain (UCL) and the Wallonie-Bruxelles International (WBI), and the Islamic Development Bank (IDB) Excellence Merit Scholarship. In addition, we acknowledge the remarkable support of the different Tunisian administrations involved in this research (DGRE, SONEDE, CRDA, and the Ministry of Agriculture) for their support to our data collection campaign.

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