Proactive management responses to sensor data
Proactive uses of sensor networks and analytics portals will benefit
from long-term management planning through research and adaptive
management. For example, experimental campaigns can be initiated to
optimize management by modifying environmental flows from reservoirs
(Figure 2b). At present, reservoir operators release water to support
downstream ecosystems, aiming to maintain the quantity and quality of
water, based on the taxonomic or behavioural response of targeted
biological groups, such as fish and invertebrates (Gillespie et al.,
2015). However, these flows additionally modify downstream water
quality, such as temperature, which is a strong control on carbon cycle
processes (Yvon-Durocher et al., 2011). The water release also alters
emissions from previously dry sediments (Perez-Calpe et al., 2022), and
transfers dissolved GHG from in-reservoir processing (Shi et al., 2023)
to modify downstream emissions (Guerin et al., 2006). With the ability
of sensor networks to provide rapid insights into downstream river
ecosystem responses to changes in outflow volume, reservoir managers
could more effectively balance water supply requirements with minimising
downstream ecosystem damage and emissions.
GHGs are known to be emitted from all freshwaters, but the lack of
direct accounting for many of these systems, despite studies showing
their important role in GHG budgets both naturally and when modified,
can now be remedied with enhanced environmental data collection. Without
such investments, commitments to develop net-zero adaptation policies by
governments and businesses, such as water and hydropower companies, are
likely to be hampered if freshwater systems remain understudied.
Following recent IPCC inventory refinements (IPCC, 2019), emissions from
managed inland waters (e.g. farm ponds, reservoirs, and their outflows)
now need to be quantified, which is adding some impetus to data
collection, but for river systems it is also necessary to consider
business operations effects ‘offsite’ or ‘downstream’, such as lengths
of watercourses influenced by upstream contaminant inputs (Hu et al.,
2018) or flow modifications (Shi et al., 2023). The integration of
sensor networks with ML models will be a key step toward meeting this
need for whole catchment understanding and improved management.