Benchmarking inference methods for water quality monitoring and status classification

Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends

This paper reviews the current GeoAI and machine learning applications in hydrological and hydraulic modeling, hydrological optimization problems, water quality modeling, and fluvial geomorphic and morphodynamic mapping. GeoAI effectively harnesses the vast amount of spatial and non-spatial data collected with the new automatic technologies. The fast development of GeoAI provides multiple methods and techniques, although it also makes comparisons between different methods challenging. Overall, selecting a particular GeoAI method depends on the application’s objective, data availability, and user expertise. GeoAI has shown advantages in non-linear modeling, computational efficiency, integration of multiple data sources, high accurate prediction capability, and the unraveling of new hydrological patterns and processes. A major drawback in most GeoAI models is the adequate model setting and low physical interpretability, explainability, and model generalization. The most recent research on hydrological GeoAI has focused on integrating the physical-based models’ principles with the GeoAI methods and on the progress towards autonomous prediction and forecasting systems.

Transferring and extrapolating estimates of cost-effectiveness for water quality outcomes: Challenges and lessons from the Great Barrier Reef

In recent decades the declining health of the Great Barrier Reef has led to a number of government policies being implemented to reduce pollutant loads from the adjacent agricultural-based catchments. There is increasing use of cost-effectiveness measures to help prioritise between different programs and actions to reduce pollutants, given limited resources and the scale of the issues. However there are a small number of primary studies available, and the consistency of cost-effectiveness measures and their application is limited, particularly given the various uncertainties that underlie the measures. Unlike Europe and the United States of America water policy or benefit transfer approaches, there are no procedural guidance studies that must be followed in the context of the Great Barrier Reef catchments. In this study we review the use of cost effectiveness estimates for pollutant reduction into the Great Barrier Reef in the context of a benefit transfer framework, where estimates of costs from a particular case study are transferred to various scenarios within different catchments. The conclusions suggest a framework be developed for the Great Barrier Reef, which is consistent, transparent, and rigorous.

Modeling Water Quality in Watersheds: From Here to the Next Generation

In this synthesis, we assess present research and anticipate future development needs in modeling water quality in watersheds. We first discuss areas of potential improvement in the representation of freshwater systems pertaining to water quality, including representation of environmental interfaces, in-stream water quality and process interactions, soil health and land management, and (peri-)urban areas. In addition, we provide insights into the contemporary challenges in the practices of watershed water quality modeling, including quality control of monitoring data, model parameterization and calibration, uncertainty management, scale mismatches, and provisioning of modeling tools. Finally, we make three recommendations to provide a path forward for improving watershed water quality modeling science, infrastructure, and practices. These include building stronger collaborations between experimentalists and modelers, bridging gaps between modelers and stakeholders, and cultivating and applying procedural knowledge to better govern and support water quality modeling processes within organizations.