Participatory approaches to monitor water-related Sustainable Development Goal (SDG 6) in Tunisia

The Sustainable Development Goals (SDGs) are a global initiative lead by the United Nations to achieve "a better and more sustainable future for all". Given the increasing impact of climate change on the available water resources over the past two decades, a particular attention is paid to water through SDG 6. The SDG-6 aims to "ensure availability and sustainable management of water and sanitation for all". This goal comes with a set of highly interconnected targets and indicators that are essential for its global and local implementation. Tunisia, as a member of the United Nations, adopted the framework of the SDGs with a particular interest in SDG 6 since Tunisia is considered as one of the highly water stressed countries in North Africa. Despite the growing need for a rapid, yet consistent, implementation of the goal in the country, very few scientific initiatives are working on monitoring the different indicators at the national scale using nationally-produced data. However, lower scale implementation and monitoring of SDG 6 indicators are essential to provide a consistent overview of the current state of water resources in the country. The current data infrastructure in Tunisia represents a major constraint, since the water-related information is poorly monitored and not accessible at the regional scale. This is due to the complex water institutional system, which is mostly dominated by the lack of coordination and communication between the different data providers. Nevertheless, over the past decade, participatory approaches for water monitoring have been getting more attention in many parts of the world, especially with the emergence of novel cost-effective smart technologies. Such participatory approaches are often referred to as Citizen Science (CS). With CS, citizens are involved in scientific activities, such as environmental monitoring, and they also can be a valuable part of decision making and policy formulation. Together with the generic available data, in particular from remote sensing technology, CS could be tested and implemented in Tunisia in order to improve the access to available and reliable data. The main objective of this thesis was to strengthen the scientific basis for participatory approaches in conjunction with public available environmental data to support the monitoring of water resources in Tunisia. The strengthening of water observation is expected to improve the monitoring of the SDG 6 indicators in Tunisia. The study is focused on the Medjerda catchment, which is the most important river basin in Tunisia and considered as the largest national water resource. The focus was first put on the calculation of the water stress SDG 6 indicator (6.4.2). This indicator was selected as a specific case in this study since water stress is very relevant for the considered catchment. The indicator was evaluated at the scale of the Medjerda catchment and 4 regions within for the period 2000-2016. The spatial and temporal disaggregation was based on analysis of both governmental and remote sensing data, and provided a valuable understanding of the seasonal and regional behavior of water stress. The lack of access to reliable official data was the major constraint in measuring the water stress indicator. The second research section of the thesis was then dedicated to the improvement of the water-related database of the Medjerda catchment through data collection and the creation of a publicly available and standardized online platform "Together4Water", which provides access and dynamic visualizations of the collected data as well as interactive real-time communication tool with users. Consequently, we introduced in the third section of this work the concept of CS as a complementary source of data in Tunisia. The approach was based on the engagement of everyday citizens of a test region within the Medjerda catchment in monitoring rainfall, discharge, and water quality using cost-effective tools. Focus was mainly put on rainfall and discharge monitoring. The Together4Water website was the base for reaching a wide range of citizens from different generations in the test region allowing the initiative to engage a group of volunteers in data collection. For rainfall, traditional low-cost rain gauges were used at multiple locations, while the data transmission was ensured through the Together4Water website. Results yield a good agreement between citizens' observations and the official rainfall data. For discharge, a publicly available smartphone app "Discharge app" was used to estimate river flow of the Medjerda at two locations. A Data fusion technique was then used to validate and combine citizens' estimations to generate unique yet higher quality discharge datasets for both locations. Finally, an assessment of the potential contribution of the CS initiative to the SDG 6 monitoring was performed. We conclude that around 54% of the goal's indicators could be supported by our CS initiative, which emphasizes the value of public engagement in SDG 6 monitoring.(AGRO - Sciences agronomiques et ingénierie biologique) -- UCL, 202

Scientific and participatory approaches to monitor water related SDG (SDG-6) in Tunisia

The 2030 Agenda was accepted in September 2015 in New York by the global heads of state and have launched 17 goals and 169 targets including a dedicated goal on water and sanitation (SDG-6) that calls on states to “Ensure availability and sustainable management of water and sanitation for all”. The Agenda includes 6 objectives and 7 goals directly and indirectly related to water. This new Agenda have been defined through a bottom-up approach intended to be applicable in all nations either they are developed or developing. All the goals are inter-connected and all must be achieved which can push the humanity to change, to think differently and can involve making very big fundamental changes in how we live on earth. We introduce a concept for monitoring the implementation of water-related sustainable development goals (SDG-6) in Tunisia and present an approach for testing the concept at the scale of the Medjerda Catchment. The Medjerda catchment is the most important river basin of the country. The monitoring concept is coherent with the indicator framework that is negotiated at the UN level (UN Statistical Commission) but consider the specificity of current and future water data infrastructure of the Medjerda catchment. In order to boost water data availability in the near future, we propose to integrate approaches from the Citizen Science domain in the monitoring concept. Also, we propose to explore the concept of the internet of things as a new way to process and share water-related data. The robustness of the indicators that should be integrated in the monitoring concept must be SMART. This implies that they should be statistically evaluated to assess the quality of outcomes (data). We also propose Bayesian Data Fusion (BDF) techniques as a way to combine data from different quality in robust indicators

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

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

Scientific and participatory approaches to monitor water related SDG (SDG-6) in Tunisia

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Testing a citizen science water monitoring approach in Tunisia

Citizen Science (CS) has been emerging in the last decade as a new field of environmental monitoring involving a direct collaboration between everyday citizens and scientists. In Tunisia, several recent governmental efforts aimed at reinforcing and renovating the existing official water monitoring systems. However, the lack of reliable hydrological data is still an issue, which could be better addressed through integrating a CS approach. The latter approach is tested for rainfall monitoring in the Medjerda catchment in Tunisia using cost-effective and publicly available manual rain gauges. We used a step-by-step approach to target, engage and train citizens on using the monitoring tools and transmitting the data to a user-friendly online platform. The ongoing approach involved 7 citizens from different generations and different educational backgrounds. The collected daily CS data are compared with data from reference stations. Results yield a significant correlation between CS data collected at 3 different sites and the reference stations with r (Pearson Correlation Coefficient (PCC)) ranges between 0.91 and 0.98 for all citizens. Student’s t-test was applied to evaluate the significance of the agreement between the CS and reference data. In addition, the variability of the CS data is compared with the variability associated with the official governmental data. The CS approach delivered consistent outcomes to complement existing Tunisian monitoring systems, and also to enhance innovation, adaptation, and local capacity building in the Tunisian water sector

Scientific and participatory approaches to monitor water related SDG (SDG-6) in Tunisia

The 2030 Agenda was accepted in September 2015 in New York by the global heads of state and have launched 17 goals and 169 targets including a dedicated goal on water and sanitation (SDG-6) that calls on states to “Ensure availability and sustainable management of water and sanitation for all”. The Agenda includes 6 objectives and 7 goals directly and indirectly related to water. This new Agenda have been defined through a bottom-up approach intended to be applicable in all nations either they are developed or developing. All the goals are inter-connected and all must be achieved which can push the humanity to change, to think differently and can involve making very big fundamental changes in how we live on earth. We introduce a concept for monitoring the implementation of water-related sustainable development goals (SDG-6) in Tunisia and present an approach for testing the concept at the scale of the Medjerda Catchment. The Medjerda catchment is the most important river basin of the country. The monitoring concept is coherent with the indicator framework that is negotiated at the UN level (UN Statistical Commission) but consider the specificity of current and future water data infrastructure of the Medjerda catchment. In order to boost water data availability in the near future, we propose to integrate approaches from the Citizen Science domain in the monitoring concept. Also, we propose to explore the concept of the internet of things as a new way to process and share water-related data. The robustness of the indicators that should be integrated in the monitoring concept must be SMART. This implies that they should be statistically evaluated to assess the quality of outcomes (data). We also propose Bayesian Data Fusion (BDF) techniques as a way to combine data from different quality in robust indicators

Exploring causes of streamflow alteration in the Medjerda river, Algeria

Study region: The Medjerda is a transboundary catchment located in North-Eastern Algeria and shared with Tunisia. Study focus: In this study, we explore the causes of hydrological alteration of streamflow in a subcatchment of the Medjerda in Algeria. The hydrological alteration was explored through the application of Mann-Kendall test based on possible explanatory factors, namely, precipitation, evapotranspiration, temperature, irrigation, and Normalized Difference of Vegetation Index (NDVI). Furthermore, the causal factors of streamflow variation were addressed using Convergent Cross Mapping (CCM) method. New hydrological insights for the region: Results of the trend analysis yield that the streamflow is altered during the period 1981 2012. This is consistent with the trends of the possible explanatory factors of this alteration. The Convergent Cross Mapping (CCM) method showed that streamflow alteration is unidirectionally caused by changes in patterns of precipitation, temperature, evapotranspiration, irrigation, and NDVI, and that there is little feedback from streamflow alteration to these causing factors. Overall, our assessment showed that the method used to identify the causal relationships in dynamical systems based on the CCM algorithm is suitable for exploring the drivers of the hydrologic alteration in multivariate time series, in particular when nonlinear dynamics determine the system

Data fusion of citizen-generated smartphone discharge measurements in Tunisia

Water resources management techniques have been evolving over the years, introducing new ways of monitoring and collecting data that improve both the quality and quantity of water-related information. Among them, Citizen Science (CS) has been introduced in the field of environmental monitoring as a novel approach that involves a direct collaboration between citizens, scientists and local authorities. In Tunisia, the lack of reliable hydrological data about rivers’ discharge remains a major issue, despite the recent governmental efforts to reinforce the existing official monitoring systems. In this study, we show how this problem can be efficiently addressed using a CS methodology. Citizens at two locations in Tunisia (Slouguia and Medjez-N5) have monitored discharge of the Medjerda river (the most important water resource in the country) using a mobile phone application for a series of hydrological events. Using a Best Linear Unbiased Predictor (BLUP) as a data fusion procedure for combining the various CS measurements, the results show that predicted discharge at both locations is in very good agreement with the reference data collected for the same hydrological events. Based on a variance decomposition, this approach allows us as well to properly assess the respective part of the random errors that are related to the citizens and measuring devices. It is concluded that CS-based discharge data collection is a promising cost-effective way for obtaining reliable and numerous measurements. The monitoring approach based on the use of a mobile phone application is thus quite valuable for complementing the existing Tunisian monitoring system, as well as for empowering local communities. Furthermore, this approach can be applied at larger scales in the country by involving more citizens and adding other sites, which should support the national efforts for better and smarter water resources management

Assessment of Citizen's Measurements Using Test Strips for Water Quality in Medjerda Watershed (Northern Tunisia)

Water resources in Africa are subjected to many pressures related to urban growth and agricultural expansions which will be exarcebated by climate change. These pressures jeopardise achieving the UN-Sustainable Development Goal 6 (SDG6). Water resources in Africa are subjected to many pressures related to urban growth, agricultural expansion, and climate change (Bahri et al., 2016). These pressures jeopardise reaching the UN-Sustainable Development Goal 6 (SDG6). Efficient monitoring of water systems is pivotal for designing efficient water management strategies that alleviates aforementioned pressures (Mutambara et al., 2016). Yet, the water monitoring capacity in Africa is often very poor, in particular for Water Quality Monitoring (WQM). Citizen Science (CS) based WQM is nowadays proposed as an innovative approach to strengthen the WQM capacity (Fehri et al., 2020; Njue et al., 2019; Jollymore et al., 2017). The concept of CS is based on the potential social benefits of engaging, collaborating and actively involving citizens in data collection and knowledge generation. Yet, the quality of CS-based WQM is different as compared to reference WQM. CS-based WQM programmes need therefore to be thoroughly validated. The main objective of this study is to assess the quality of a CSbased WQM program for the Medjerda river in Tunisia

Can remotely sensed data reduce uncertainty in SDG-6 water stress indicator?

The different targets and indicators of the water-related Sustainable Development Goal (SDG-6) are related to the different water related functions and services, considered to be important for sustaining life on Earth. Target 6.4 deals with water scarcity and availability. This target is evaluated by means of two indicators. Indicator 6.4.1 is related to water use efficiency, while indicator 6.4.2 measures the level of water stress. In this work, we focus on the water stress indicator for the Medjerda catchment in Tunisia. The Medjerda catchment is expected to be heavily impacted by climate change and it is considered as the most important river basin in the country from an economical point of view. We assess the value and the quality of the water stress indicator for this catchment. The quality of the indicator is evaluated by quantifying the uncertainty associated with the indicator. First Order Uncertainty Analysis (FOUA) is used to assess indicator uncertainty. We process the indicator using two data flows. First, we process the indicator using data provided by official governmental institutions. Subsequently, we add generic remote-sensed data derived from the cloud-based analysis platform “Google Earth Engine”. Differences inquantified uncertainty on the water stress indicator for both procedures demonstrates the added value of remote-sensed data on the calculation of the water stress indicator SDG 6.4.2 for this case study