PARTNERSHIPS FOR ENHANCED ENGAGEMENT IN RESEARCH (PEER) Cycle 7 (2018 Deadline) Improving sustainable groundwater management: A major challenge in the over-exploited Medjerda basin (North Tunisia) PI: Fatma Trabelsi (trabelsifatma@gmail.com), Higher School of Engineers of Medjez El Bab IRESA/University of Jendouba U.S. Partner: Clifford I. Voss, U.S. Geological Survey (retired) and Amir AghaKouchak, University of California, Irvine Dates: January 2019 - April 2023 Project Overview Water scarcity and pollution are severe problems in Tunisia, seriously affecting socioeconomic development. In the northwestern regions of Tunisia crossed by the Medjerda River, groundwater resources are being increasingly exploited. Unfortunately, not only over-exploitation of groundwater but also agricultural and industrial practices in the region and saltwater intrusion into the coastal aquifers have led to significant water quality degradation. Moreover, the population is not sufficiently aware of how critical the water resources situation is to become positively involved in water resources management. The lack of accurate data on Medjerda basin groundwater resources undermines the capacity of decision-makers and water users to understand and sustainably manage water resources. In particular, when this project began, there was no existing GIS-based tool for Integrated Water Resources Management (IWRM) for the Medjerda River basin, which is Tunisia’s main water resource. Surface and groundwater resource spatial data were scattered among various organizations and in formats that are difficult for non-experts to understand or use. The consequences of these factors are poor planning and unclear understanding of groundwater resources management by decision makers. Thus, the key objective of this project was to implement an innovative approach to improve sustainable groundwater management for the Medjerda basin. The expected outcomes of the project are based on three main pillars: (1) overall initial assessment of groundwater resources availability and quality, (2) data management and numerical simulation of water resources, and (3) capacity development. The PI and her team aimed to implement a “smart” water monitoring system and develop a GIS-integrated modelling platform for simulation of groundwater quantity and quality that can be used by managers in water resource decision making. Final Summary of Project Activities The PI Dr. Trabelsi and her team developed a surface and subsurface geodatabase for the Lower valley of the Medjerda basin (LVM) and used it to establish a geospatial platform for strengthening evidence-based decision-making in the water resource management sector. This platform boasts several geospatial layers and a dashboard of statistical series with metadata records, bringing together geographic information and statistical data. It serves as a digital public good to create interactive data maps, analyze trends, and identify real-time gaps and opportunities. The researchers also created a Decision Support System Tool (DSS) by setting up a smart groundwater monitoring system to gather accurate time-continuous data needed for groundwater stakeholders. As of the project’s completion date in 2023, this smart system is the first real-time groundwater system connected with the IoT platform set up in Tunisia, and it is is hosted on the website of the Ministry of Agriculture, Water Resources and Fisheries of Tunisia. The PEER team has also created and honed models simulating groundwater flow and contamination and the impact of climate change on water resources. This PEER project had a strong impact on capacity building in data science and modeling for several Tunisian researchers, professionals, and students through the large number of workshops and trainings organized by the project team. In total, the project organized three international conferences, two national conferences, six workshops, two summer schools, and four webinars. The project also produced a new course entitled Application of Remote Sensing and IoT in Water Management, which has been added to the curriculum of the research Master’s program at the Higher School of Engineers of Medjez El Bab (ESIM). PEER funds also supported the visits of the U.S. partners, Clifford Voss and Amir AghaKouchak. Furthermore, the project has strengthened the role of women scientists in water management by improving their participation in scientific events, training, and meetings with decision-makers. The project funded research activities that enabled several participants to obtain advanced degrees: one Habilitation Universitaire (a Tunisian degree beyond the PhD that allows its holder to lead research projects), two PhD, eight Master’s degrees, and four engineering degrees. PEER funds supported a six-month fellowship in Spain by a female PhD student, Salsebil Bel Hadj Ali, as well as collaborative research and conference travel to France and the United States by the PI and other team members. Additionally, the project has improved the scientific collaboration and synergy between young researchers and government, non-government, and private organizations. Stakeholder engagement and participatory approaches for water diplomacy were used during the implementation of project activities for developing trust, consensus, and communication and stimulating the water reform process. Dr. Trabelsi and her team convened numerous awareness and technical meetings and signed cooperative agreements to implement project activities. Thanks to the new groundwater sensor network funded by PEER and the database and decision support system Dr. Trabelsi and her colleagues have created, they are well positioned to contribute to the aim of better water resource management in Tunisia and beyond. Publications Fatma Trabelsi, Salsebil Bel Hadj Ali, and Saro Lee. 2022. Comparison of novel hybrid and benchmark machine learning algorithms to predict groundwater potentiality: case of a drought-prone region of Medjerda basin, Northern Tunisia. Remote Sensing, 15(1), 152. https://doi.org/10.3390/rs15010152 Fatma Trabelsi and Salsebil Bel Hadj Ali. 2022. Exploring machine learning models in predicting irrigation groundwater quality indices for effective decision making in Medjerda River Basin, Tunisia. Sustainability 2022, 14, 2341. https://doi.org/10.3390/su14042341 Back to PEER Cycle 7 Grant Recipients