Figure 2. Distributed sensor networks can be developed to support water resource management : (a) REACTIVE ; a river catchment with sensor locations denoted by numbers (1-7) spanning river channels. At t1, a stressor (e.g. organic pollution, sedimentation) appears (upstream of location 7) leading to enhanced ecosystem respiration (ER). Real-time analytics and visualisation allow pollutant tracking through t2-t4, enabling water abstraction (denoted by x) to be deactivated at t3. (b)PROACTIVE ; a river catchment with a large headwater reservoir. Hydrograph shows discharge (Q) scenarios f1-4. Low flow f1 elevates ER in the mainstem. With a regulatory target of ER 1-2.5, water release in f2 modifies only the segment below the reservoir. Excessive water release in f3 leads to overshoot of targets, allowing an optimal solution in f4 to trade-off ecosystem recovery and water supply.
For example, abstractors using river water for drinking water supply can identify contamination issues, such as high dissolved organic carbon (DOC) concentrations upstream, thus avoiding problems whereby disinfection byproducts make water unsuitable for human consumption (Valdivia-Garcia et al., 2019). Specific examples include water utilities and hydropower companies that withdraw, store, and redistribute water around river systems facing management challenges related to altered water quality (Gillespie et al., 2015). Such approaches are already being tested, by diverting episodic events with elevated DOC in raw water sources away from water treatment works (Yorkshire Water, 2023), but sensor networks can be costly to implement and maintain. The integration of forecasting into ML architecture promises to strengthen and advance scientific understanding further by feeding back to field sensors and samplers to collect higher resolution data. For example, enhanced data collection during contamination events could be used to support regulator investigations, and during storms where runoff peaks are often missed, for enhanced understanding of water quality and carbon cycle dynamics.
Regulators and decision makers need access to high-quality data to develop, monitor and enforce catchment management plans and legislation, and identify areas where persistent problems highlight the need for restoration, such as through payment for ecosystem service or nature-based solution initiatives. Additionally, managers of agricultural basins, which are recognized as a leading source of global water contamination (Liu et al., 2022) need evidence to manage and reduce the effects of sediment loads and adsorbed contaminants originating from soil erosion, and the use of agrochemicals (nutrients, herbicides, pesticides), all of which can lead to elevated GHG emissions from rivers (Xiao et al., 2021). By pinpointing river sections or sub-catchments suffering from stressors, prioritized and targeted management practices can meet multiple objectives to reduce emissions as part of the water-energy-food nexus in global resource systems.