Department Surface Waters - Research and Management

Many components of the Earth system feature self-reinforcing feedback processes that can potentially scale up a small initial change to a fundamental state change of the underlying system in a sometimes abrupt or irreversible manner beyond a critical threshold. Such tipping points can be found across a wide range of spatial and temporal scales and are expressed in very different observable variables. For example, early-warning signals of approaching critical transitions may manifest in localised spatial pattern formation of vegetation within years as observed for the Amazon rainforest. In contrast, the susceptibility of ice sheets to tipping dynamics can unfold at basin to sub-continental scales, over centuries to even millennia. Accordingly, to improve the understanding of the underlying processes, to capture present-day system states and to monitor early-warning signals, tipping point science relies on diverse data products. To that end, Earth observation has proven indispensable as it provides a broad range of data products with varying spatio-temporal scales and resolutions. Here we review the observable characteristics of selected potential climate tipping systems associated with the multiple stages of a tipping process: This includes i) gaining system and process understanding, ii) detecting early-warning signals for resilience loss when approaching potential tipping points and iii) monitoring progressing tipping dynamics across scales in space and time. By assessing how well the observational requirements are met by the Essential Climate Variables (ECVs) defined by the Global Climate Observing System (GCOS), we identify gaps in the portfolio and what is needed to better characterise potential candidate tipping elements. Gaps have been identified for the Amazon forest system (vegetation water content), permafrost (ground subsidence), Atlantic Meridional Overturning Circulation, AMOC (section mass, heat and fresh water transports and freshwater input from ice sheet edges) and ice sheets (e.g. surface melt). For many of the ECVs, issues in specifications have been identified. Of main concern are spatial resolution and missing variables, calling for an update of the ECVS or a separate, dedicated catalogue of tipping variables.

Waterborne pathogens pose a serious threat to public health, emphasizing the need for reliable and efficient water treatment technologies. Wastewater treatment plants employ a range of processes to reduce microbial contamination, with membrane filtration emerging as a promising solution due to its ability to physically remove pathogens without the production of harmful chemical by-products. This study investigates the effectiveness of a gravity-driven membrane (GDM) filtration system for pathogen removal from wastewater and evaluates the associated public health risks with and without treatment. A quantitative microbial risk assessment model was employed to estimate infection probabilities for various waterborne pathogens. The results demonstrated a significant decrease in pathogen concentrations following treatment, with up to a 10⁴-fold reduction in norovirus infection risk. Three critical factors influencing membrane performance were identified: membrane integrity, pore size characteristics, and membrane fouling. Maintaining membrane integrity was found to be essential for ensuring consistent pathogen removal. While nominal pore size is commonly used to predict rejection efficiency, the overall pore size distribution was found to have a greater influence on virus retention. Additionally, although membrane fouling is often considered detrimental, it was shown to enhance virus removal by up to two orders of magnitude. These findings underscore the potential of GDM systems for effective virus removal and highlight the importance of proper membrane design, maintenance, and monitoring in ensuring long-term operational efficiency and maximizing public health protection in wastewater treatment applications.

Amid unprecedented biodiversity loss and water scarcity, calls for corporate responsibility are becoming louder and have led to emerging non-financial disclosure frameworks with demanding data needs. While the role of satellite remote sensing (RS) is highly anticipated to address data needs and boost transparency, critical thought on what is feasible and how to strategically integrate its insights for ambitious corporate biodiversity and water disclosure is lagging behind. To address this, we propose applying a systems perspective to represent the complex, multi-scale interactions between biodiversity, water systems, and corporate operations, and to guide how to integrate RS contributions to analyze the full spectrum of impacts and risks—ranging from direct and concurrent to cascading, cumulative, and emergent. We highlight seven guiding (non-exhaustive) principles for leveraging satellite RS data to assess corporate biodiversity and water impacts and risks. This process requires an effective system boundary (1) set spatially, temporally, and process-wise. Within which, biodiversity and water's multi-dimensionality (2) needs to be addressed to monitor the spatio-temporal dynamics (3) that characterize ecosystem responses. To attribute risk and impact of detected changes, interactions need to be defined by causality (4) and directionality (5), and ultimately consider compound impacts (6) across commodities, supply chains and portfolios, as well as cross-system interactions (7), for example, between climate change, water and biodiversity. We review each of these principles and related challenges individually, providing a system theory definition, relevant RS capabilities, and research directions. Addressing these seven principles will be crucial to harness satellite RS's potential for comprehensive biodiversity and water disclosure for strong corporate accountability.