New dilemmas, old problems: advances in data analysis and its geoethical implications in groundwater management

Author(s)

de Oliveira Ferreira Silva, Cesar ^[1] ; Matulovic, Mariana ^[2] ; Lilla Manzione, Rodrigo ^[2]

Affiliation

^[1] Agroicone, Sao Paulo, SP - Brazil

^[2] Sao Paulo State Univ UNESP, Sch Sci & Engn, Biosyst Engn Dept, Tupa, SP - Brazil

• Document type: Journal article
• Source: SN APPLIED SCIENCES; v. 3, n. 6 JUN 2021.
• Web of Science Citations: 0

Abstract

Groundwater governance uses modeling to support decision making. Therefore, data science techniques are essential. Specific difficulties arise because variables must be used that cannot be directly measured, such as aquifer recharge and groundwater flow. However, such techniques involve dealing with (often not very explicitly stated) ethical questions. To support groundwater governance, these ethical questions cannot be solved straightforward. In this study, we propose an approach called ``open-minded roadmap{''} to guide data analytics and modeling for groundwater governance decision making. To frame the ethical questions, we use the concept of geoethical thinking, a method to combine geoscience-expertise and societal responsibility of the geoscientist. We present a case study in groundwater monitoring modeling experiment using data analytics methods in southeast Brazil. A model based on fuzzy logic (with high expert intervention) and three data-driven models (with low expert intervention) are tested and evaluated for aquifer recharge in watersheds. The roadmap approach consists of three issues: (a) data acquisition, (b) modeling and (c) the open-minded (geo)ethical attitude. The level of expert intervention in the modeling stage and model validation are discussed. A search for gaps in the model use is made, anticipating issues through the development of application scenarios, to reach a final decision. When the model is validated in one watershed and then extrapolated to neighboring watersheds, we found large asymmetries in the recharge estimatives. Hence, we can show that more information (data, expertise etc.) is needed to improve the models' predictability-skill. In the resulting iterative approach, new questions will arise (as new information comes available), and therefore, steady recourse to the open-minded roadmap is recommended. (AU)

• FAPESP's process: 14/04524-7 - Monitoring water table depths at Bauru Aquifer System in a conservation reserv in Águas de Santa Bárbara, SP - Brazil
• Grantee: Rodrigo Lilla Manzione
• Support Opportunities: Regular Research Grants
• FAPESP's process: 16/09737-4 - MODELLING GROUNDWATER SPATIO-TEMPORAL VARIABILITY FROM WATER TABLE MONITORING DATA USING COVARIANCE FUNCTIONS
• Grantee: Rodrigo Lilla Manzione
• Support Opportunities: Regular Research Grants