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The future of global river health monitoring [1]

['Lauren M. Kuehne', 'Omfishient Consulting', 'Bremerton', 'Washington', 'United States Of America', 'Chris Dickens', 'International Water Management Institute', 'Silverton', 'South Africa', 'David Tickner']

Date: 2023-10

Rivers are the arteries of human civilisation and culture, providing essential goods and services that underpin water and food security, socio-economic development and climate resilience. They also support an extraordinary diversity of biological life. Human appropriation of land and water together with changes in climate have jointly driven rapid declines in river health and biodiversity worldwide, stimulating calls for an Emergency Recovery Plan for freshwater ecosystems. Yet freshwater ecosystems like rivers have been consistently under-represented within global agreements such as the UN Sustainable Development Goals and the UN Convention on Biological Diversity. Even where such agreements acknowledge that river health is important, implementation is hampered by inadequate global-scale indicators and a lack of coherent monitoring efforts. Consequently, there is no reliable basis for tracking global trends in river health, assessing the impacts of international agreements on river ecosystems and guiding global investments in river management to priority issues or regions. We reviewed national and regional approaches for river health monitoring to develop a comprehensive set of scalable indicators that can support “top-down” global surveillance while also facilitating standardised “bottom-up” local monitoring efforts. We evaluate readiness of these indicators for implementation at a global scale, based on their current status and emerging improvements in underlying data sources and methodologies. We chart a road map that identifies data and technical priorities and opportunities to advance global river health monitoring such that an adequate monitoring framework could be in place and implemented by 2030, with the potential for substantial enhancement by 2050. Lastly, we present recommendations for coordinated action and investment by policy makers, research funders and scientists to develop and implement the framework to support conservation and restoration of river health globally.

Funding: This work was supported by the CGIAR Initiative on NEXUS Gains and the World Wildlife Fund (staff support of CD and NE; contracting of LMK), the Natural Sciences and Engineering Research Council of Canada (Vanier Canada Graduate Scholarship to MLM), the Université de Lyon (H2O’Lyon Doctoral Fellowship ANR-17-EUR-0018 to MLM) and the School of Aquatic and Fishery Sciences, University of Washington (Richard C. and Lois M. Worthington Endowment to JDO). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Besides scalability, a second foundational challenge in monitoring river health is the inherent topological structure of fluvial systems. In contrast to terrestrial systems, river health is determined by conditions in upstream drainage areas as well as more proximate influences. Point-based biological monitoring or instream water quality data can provide an integrated perspective (i.e., a healthy local habitat may indicate good conditions across the entire upstream catchment), and novel approaches such as eDNA analyses and improved chemical detectors offer promising avenues to scale up in situ sampling in the future [ 30 ]. But these methods are not yet available at a global scale, and may continue to require modelling support (e.g., for chemical compounds that are difficult to detect in the field) [ 1 ]. Using top-down approaches, upstream influences can be integrated through data processing and modelling that nests information within hierarchical catchments and allows routing of physical or biological properties along river networks (e.g., using accumulation and decay functions) (e.g., [ 24 ]). Of particular importance is the ability to evaluate longitudinal, lateral, vertical, and temporal connectivity, which is vital to freshwater ecosystems as it defines the fluxes, movement or dispersal of species, materials (including water and sediments), nutrients, and energy [ 31 ]. For this reason, a recommended framework should include a versatile geospatial data concept that can incorporate topological information of upstream, downstream and lateral connections as well as nested and hierarchical relationships between hydrographic features.

To blend both bottom-up and top-down approaches in a global framework requires a scalable geospatial method that can discern local hydrographic features at high spatial resolution, while also allowing linkages to coarser global datasets or modelling results. A standardised, common geospatial framework based on pre-defined spatial units (e.g., river reaches and their catchments) can leverage the strengths of both approaches and adaptively incorporate higher-quality data over time. For example, results from top-down analyses can serve as initial placeholders until more precise local information (e.g., from national or regional monitoring programs) becomes available. These types of multi-scale hydrographic frameworks are increasingly available at full global coverage and high spatial resolution (e.g., HydroATLAS [ 28 ] MERIT Hydro-Vector [ 29 ]).

The quality of data available at varying scales is a foundational challenge. Bottom-up approaches to assess river health rely on collecting and compiling local information, often with high precision in situ data; yet this makes the expansion to larger regions time-consuming or even practically impossible. Indeed, given the paucity of river health monitoring in many parts of the world [ 18 ], monitoring global river health based on compilation of local information is a formidable task. However, top-down approaches, which usually rely on remote sensing or modelling, tend to use limited amounts of input data and are often coarse in their spatial or temporal resolution, rendering local interpretations inaccurate and uncertain.

The final critical component in designing a river health framework is the availability and quality of data to capture the chosen indicators. Data sources can be grouped into three, not necessarily mutually exclusive, classes: in situ monitoring data (including conventional biological sampling and in situ sensors as well as novel approaches of uncrewed vehicles, crowd-sourcing, or the analysis of environmental DNA), remote sensing imagery (including prospective satellite missions), and modelling (including predictive hydrological models, statistical models, or artificial intelligence and machine learning approaches). While data sources are generally expanding, fundamental challenges persist. The data required for multi-indicator monitoring frameworks are collected by myriad entities, many of which may not openly share data; datasets may also be scale-dependent, discontinuous, or come in incompatible formats (e.g. raster, vector, tables). A forward-thinking river health framework should, therefore, consider both current and anticipated data developments.

The challenges, however, with such models are that pressures on aquatic ecosystems may or may not reflect ecosystem condition or state [ 23 , 24 ], and therefore can offer only limited insight into their relationship with driving forces and responses that can be adjusted through management, mitigation, or restoration [ 25 ]. Weak or unverifiable linkages between ecosystem state, pressures, and driving forces are unlikely to offer the strong justification needed to support policy changes or investments in river health [ 17 , 25 ]. Our focus on ecological state excludes human valuations of rivers, which may encompass socioeconomic, cultural, or spiritual values [ 26 ]. Although such valuations can and have been included in large-scale assessments of river health (e.g., [ 27 ]), we advance that the first priority is to address the considerable knowledge gaps and technical barriers to evaluating the biophysical state of rivers at a global scale. Our position is that there is an urgent need to develop a framework around ecosystem state, and forward the corresponding scientific and research agenda. Such a framework can then provide the foundation to subsequently incorporate relationships with responses and driving forces, as well as holistic social and human values.

The design of any monitoring program begins with the definition of ecosystem health. In this paper, we define ecosystem health as ’The ability of the aquatic ecosystem to support and maintain key ecological processes and a community of organisms with a species composition, diversity, and functional organisation as comparable as possible to that of undisturbed habitats within the region’ (Schofield & Davies, 1996 after Karr & Dudley, 1981). Assessments of river health can emphasise different types of indicators, which are often classified as driving forces, pressures, state, impact, and response (i.e., the DPSIR framework; [ 19 ]). Our definition of river health sets the stage for selecting indicators that reflect biophysical conditions, thus focusing on ecosystem state. This focus represents a strong departure from previous or ongoing evaluations of river health at global scales. Global assessments to date have instead concentrated on pressures on aquatic ecosystems, largely because of the relative ease of computing changes—actual or projected—in land use, anthropogenic impacts, and climate—based on remote sensing and modelled data. These pressure-based models have yielded estimates of water security and risk of biodiversity loss [ 20 ], human use of ecosystem services [ 21 ], and temporal and geographic changes in biodiversity [ 22 ].

The first step toward a global river health framework is to evaluate current policies, concepts, and data for monitoring and assessment of river health at large-scales (i.e., national to global). The most important development in the last century is undoubtedly the passage of national—or, in the case of the European Union, regional—laws and regulations that mandate restoration and maintenance of freshwater ecosystems to meet water quality or condition standards. Examples of such laws, most of which have been enacted in the last 50 years include the Clean Water Act (1972) in the USA, the Resource Management Act (1991) in New Zealand, the European Water Framework Directive (2000), the CONAMA—Conselho Nacional do Meio Ambiente 357/2005 in Brazil (2005) and the Water Act of South Africa (1998). These laws created legal and financial incentives within nations to develop and refine consistent monitoring and assessment methods over time ( Fig 1 ), including indicators and metrics to evaluate the status of river ecosystems [ 16 , 17 ]. Although mature, large-scale programs are mostly restricted to a handful of economically advantaged nations, they offer a critical foundation for international or intercontinental knowledge transfers to support monitoring of river health globally [ 18 ].

This paper seeks to chart a path toward policy-relevant, global river health monitoring. Our approach synthesises and builds on the substantial work to monitor river health at national and regional scales over recent decades. Leveraging knowledge established through mature large-scale programs, we identify a robust framework of indicators that can be refined and improved over time through coordinated global policy, research, and data infrastructure developments (i.e., top-down efforts). This common framework can also support local, national, and regional efforts to monitor river health with protocols that are flexible but sufficiently standardised for results to be compared between contexts (i.e., bottom-up efforts). Our objective is to align diverse monitoring and research efforts toward strategic actions over the next two decades that will ultimately facilitate robust, comprehensive, and feasible reporting on global trends in river health.

A significant hurdle to addressing the freshwater biodiversity crisis is that the major global initiatives working to thrust ecosystems onto the global development agenda consistently lack robust representation of freshwater health. These initiatives include The Economics of Ecosystems and Biodiversity (TEEB) for water and wetlands [ 12 ], the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), Agenda 2030 on Sustainable Development (the SDGs), the post-2020 Kunming-Montreal global biodiversity framework (GBF) and the UN Decade on Ecosystem Restoration. All of these, together with globally impactful periodic reports such as the Ecological Footprint indicator [ 13 ], the Planetary Boundaries framework [ 14 ], the Water Footprint indicator [ 15 ], and others, lack comprehensive indicators of freshwater ecosystem health. Instead, these efforts generally rely on single or small numbers of proxies. For these reasons, the Emergency Recovery Plan for Freshwater Biodiversity [ 9 ], and subsequently the Sustainable Freshwater Transition set out by the UN Convention on Biological Diversity, called for the development of a more robust and inclusive suite of freshwater biodiversity and ecosystem health indicators. The aim of such indicators would be to provide a foundation for consistent, widespread monitoring as part of international environmental and sustainability agreements, whether focused on fresh waters generally or river health specifically.

Nearly all aspects of human society are impacted by the health of rivers. Flowing waters act as centres of organisation within the landscape, offering countless cultural and ecological services, and supporting a rich diversity of plants and animals. However, rapid changes in water and land use, climate change and a host of other anthropogenic stressors threaten the biodiversity and ecological integrity of these ecosystems [ 1 ]. As long ago as 2005, the global Millennium Ecosystems Assessment [ 2 ] concluded that freshwater ecosystems were among the most degraded and being used unsustainably. Despite the prominence and persistence of challenges including water security and impacts of climate change on hydrology, attention to the conservation of freshwater ecosystems—including rivers—has nonetheless lagged at the global scale [ 3 ]. This may be due to the perception of freshwater systems as a resource for human use rather than a precious habitat [ 4 ], their more limited spatial extent that reduces public awareness [ 5 ], a historical lack of conservation champions [ 6 ], and inadequate transdisciplinary scholarship [ 7 ]. Additional hurdles are associated with understanding and managing rivers as complex networks [ 8 ], and longstanding traditions of large-scale regulation (i.e., dams, diversions) and water extraction. In response, recent years have witnessed mounting calls for global-scale research and policy actions to stem further losses and degradation of freshwater habitats [ 9 – 11 ].

Based on the above review, we develop a framework of indicators for global river health monitoring, which we evaluated for feasibility of implementation at two time periods: by 2030 and by 2050. Rather than including indicators based on current feasibility, we considered all potential indicators, regardless of the state of readiness of the data sources or prognosis for development, even by 2050. For example, careful research and expert knowledge have gone into identifying Essential Biodiversity Variables [ 34 , 35 ], a suite of variables that are broadly agreed as necessary and sufficient to monitor national to global biodiversity. However, much coordinated and systematic development of data sources is still needed to characterise these variables at global scales [ 36 ]. Building on these and other studies that have evaluated and identified “ideal” indicators allows us to diagnose research and data gaps that, if addressed, could substantially advance monitoring of global river health. The 2030 and 2050 time horizons were chosen in light of technical considerations as well as the anticipated updating of global sustainability and ecological health initiatives (e.g., the SDGs which expire, but may be refreshed, in 2030). Given the time frames required for research and operationalizing new data sources and methods, the 2030 framework represents data sources and methods that are effectively available in the near term. The second time period of 2050 allows us to evaluate the technical horizon for promising yet feasible technologies, methods or infrastructure that can advance the accuracy and informativeness of these indicators in the near-to-medium future.

From this initial review, we identified 10 programs or approaches that met the majority of the seven criteria ( Fig 1 ). From each, we summarised the features or attributes that have the greatest potential to inform the design of a global framework: the indicators used in the assessment, the biophysical components that the indicators reflect, data types, methods of data harmonisation and integration, and practical insights applicable to the design of a global monitoring framework.

We compared each approach against the following seven criteria that are considered integral to a successful framework: consistency, representativeness, robustness, flexibility, scalability, feasibility, and informativeness [ 33 ]. To assess consistency, representativeness , and robustness , we evaluated methods of data collection and the number and types of indicators used. Flexibility, scalability , and feasibility were explored qualitatively, primarily based on the indicators and methods of data collection, followed by the complexity of methods used to harmonise and integrate data. Lastly, informativeness was qualitatively assessed in the conceptual design and methods that were used to frame and report results.

We initiated this work by comprehensively reviewing existing programs and approaches to monitor and assess river health around the world [ 32 ]. We restricted our review to those implemented at a national, regional, or global scale, as we considered these most likely to possess the properties necessary to inform a global monitoring framework. We focused on operational programs that were developed for monitoring (i.e., surveillance) of river health, rather than investigative approaches. In addition to these, we also evaluated existing global indices of river health; although differing greatly from monitoring programs, their design and implementation offer important insights into the current state of global river health monitoring.

Results

Review of monitoring approaches Because existing major river health monitoring programs stem from national or regional legislation, they vary in purpose (i.e., matched to specific regulatory context) and are influenced by the approaches and technologies in use at the time of their development (Fig 1; Table 1). PPT PowerPoint slide

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TIFF original image Download: Table 1. Large-scale programs and approaches used to inform a global monitoring framework. Description and attributes of selected large-scale (i.e., national and regional) monitoring programs or global assessment methods determined as meeting a majority of seven criteria for a successful framework [33]. In any monitoring program, a conceptual framework designates relationships between Indicators, which are often grouped by broader biophysical Components (i.e., themes or categories). Further, the conceptual framework of a specific program may treat Components as driving forces (D), pressures (P), state (S), impact (I), or responses (R) (i.e., the DPSIR framework). [Note: Components and Indicators are presented using the terminology of the individual frameworks]. A majority of frameworks include procedures where indicator data are harmonised (i.e., standardised), integrated (i.e., combined) into Component scores, and assigned to condition classes for reporting the condition of the ecosystem. https://doi.org/10.1371/journal.pwat.0000101.t001 Nearly two decades separate early, well-established frameworks [i.e, National Aquatic Resources Survey (NARS) and Water Framework Directive (WFD)] from later ones whose implementation is less well-documented [i.e., Freshwater Biophysical Ecosystem Health Framework (FBEHF) and River Health Index (RHI)]. More recent frameworks tend to include variables based on remote-sensing data, and more explicitly incorporate scale and catchment hierarchy into the conceptual designs and reporting of results [32]. Each of the large-scale river health programs has perceived advantages and disadvantages (Table 1), and collectively they exemplify the key attributes a large-scale framework should possess. These include: a clear definition of ecosystem health; indicators of multiple biophysical components; standardised methods and protocols of data collection and integration; and scale independence (Table 1). The main reasons for differences in the choice and treatment of components originate from the definition of “freshwater health”, which underlies the conceptual design that combines driving forces, pressures, state, impact and responses. For example, New Zealand’s FBEHF defines ecological integrity as the maintenance of structure and function “in the face of external stress”. Therefore, while the FBEHF relies on biophysical indicators to assess condition (i.e., state), it also recommends conceptual modelling of stressor pathways (i.e., pressures and driving forces) to guide management and policy actions for remediating indicators that are below targets [33]. The biophysical categories (hereafter, ‘components’) that are monitored, and the range and type of associated indicators differ greatly among river health monitoring frameworks. The four most commonly included components are 1) biology (aquatic life), 2) water quality (physicochemical conditions), 3) physical habitat, and 4) hydrology (water quantity & dynamics) (Table 1), suggesting broad agreement that these components constitute a robust and comprehensive basis for river health monitoring. A majority of the national and regional programs concur in relying on the state of the biological indicators as the primary reflection of aquatic ecosystem or river health; greater differences occur in the treatment of abiotic indicators. Abiotic features may be considered primarily with respect to influencing the biological responses (e.g., NARS, National River Health Program) or incorporated as part of the definition and measurement of ecological health (e.g., WFD, RHI, FBEHF). Limitations associated with the scale of the monitoring also drive some design decisions, such as availability of data that can reflect indicators of ecosystem health at large scales. This can be seen most starkly in the strong contrast between regional or national-scale monitoring programs and existing global frameworks. Global frameworks tend to emphasise pressures rather than ecosystem state (Table 1) [32], reflecting current data constraints to assess river health at large scales. Regardless of the source and types of data that inform indicators, frameworks share similarities in the methods used to translate indicator data into an assessment of ecological condition. Large-scale frameworks consistently entail harmonisation (i.e., standardisation) of indicator data to a common scale (e.g., 0–100) against benchmark conditions; these may be relative to local, regional, or environmentally similar reference sites, based on the distribution of values, or predetermined targets. It is typical for assessments to integrate harmonised indicator scores to the level of components and/or to an overall score (though exceptions exist, e.g. NARS). Methods of integration range from simple or geometric averaging to expert ratings. Lastly, for reporting assessment results, integrated scores are commonly classified into 3–6 condition categories (e.g., NARS, RHI, WFD) or a condition gradient (e.g., River EcoStatus Monitoring Programme), although reporting may also include fixed thresholds (e.g., NARS) or proximity to goals (e.g., Environmental Performance Index).

Development of the global framework Across the four biophysical components, we identified a comprehensive but parsimonious suite of indicators of river health, and provide a prognosis of their readiness at a global scale in 2030 and 2050 (Table 2; S1 Table). Since these indicators were selected to represent ecosystem state rather than driving forces, pressures, impacts, or responses, all data sources must ultimately be based on either direct measurements or estimation of biological, water quality, physical habitat, or hydrologic parameters (S1 Table). The exception is where an explicit decision is made to use proxies as an interim measure, due to data or modelling limitations. Direct measurement may be done via remote sensing or compilation of in situ data, while estimation involves modelling—including interpolation and/or fate-based modelling—that is calibrated with direct measurements. PPT PowerPoint slide

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TIFF original image Download: Table 2. Proposed framework of indicators to monitor global river health. Each indicator is described with both challenges and opportunities for application to global monitoring, the methods available for implementation by 2030, and recommendations for development toward more accurate and widespread implementation by 2050 [see S1 Table for sources of in situ, remote sensing, and modelled datasets to support development of specific indicators]. Inclusion of each indicator within national or regional and global monitoring programs is shown (● = included, × = not included). https://doi.org/10.1371/journal.pwat.0000101.t002 The framework emphasises modelling approaches, which can produce spatially-explicit estimates of all indicators for all rivers. This stems from the fact that both in situ and remote sensing datasets have inherent (often differing) limitations in their utility to reflect the health of freshwater ecosystems at large scales. Modelling can incorporate multiple data sources for the calibration of indicators (or, potentially, interim use of pressure-based proxies), can yield predictions for smaller rivers which are often more data-limited, and offers a way to harmonise indicators into the same hydrographic structure and scale. Importantly, modelling allows representation of uncertainty, which can be reduced as better or more data are incorporated over time and also indicate priorities for improvement (e.g., by highlighting geographic disparities in in situ data). We organise our presentation of the state-of the-art methods for river health monitoring by biophysical component, first evaluating indicators based on their current feasibility for implementation at a global scale (i.e., 2030) followed by recommendations for development over the next two decades (i.e., 2050).

Biology We recommend five biological indicators of river health, which are fish abundance, fish diversity, invertebrate diversity, aquatic invasive species, and primary productivity. Because biological indicators integrate catchment conditions and anthropogenic influences, they are typically considered to best reflect the ecological state and health of riverine (and other aquatic) ecosystems [18]; it is not surprising that they are well represented in biological monitoring programs, even at large scales. However, the state of current data sources demonstrates consistent bottlenecks and limitations to implementation at global scales, which will require considerable investment and focus to overcome. For example, the IUCN Red List and the Living Planet Index, arguably the most developed and recognised global systems to assess conservation status of species, suffer from well-documented taxonomic and geographic bias [45–47]. The most obvious challenge for global monitoring of biological indicators is that remote sensing data offer little utility; the possible exception to this is primary productivity, for which chlorophyll can be used as a (limited) proxy [48]. Capacity to measure or model biological indicators is therefore dictated by the status and availability of global in situ datasets, which currently suffer from strong geographic limitations and bias (S1 Table). Given that widespread development of new large scale (i.e., regional or national) monitoring programs is not expected [18], developing untapped sources of in situ biological data are essential. The rapid development of eDNA in recent years offers particular promise to help overcome the lack of biological data, but will require investment toward application for specific indicators [49] and implementation at large-scales [50]. For example, although eDNA analyses of aquatic diversity is improving with community approaches such as universal markers or meta-barcoding—and with comparable or greater sensitivity than traditional methods [51]—considerable effort is yet needed to build reference sequences and the genetic databases that these approaches rely on [52]. For these reasons, the potential for other mechanisms to bolster global datasets of aquatic diversity should not be ignored, including processing of museum specimens [53], fostering citizen science observation networks and platforms [54], and collating data from national biomonitoring programs [24]. These options also require little technology, which may be of particular importance for broadening representation in geographically remote areas [55, 56]. Model-based data integration methods also provide ways to leverage this growing quantity and types of biological data being collected [57]. Even anticipated development and expansion of in situ data from the above sources will likely contribute little to the improvement of two indicators, which are fish abundance and primary productivity. A primary drawback of eDNA metabarcoding to evaluate biodiversity is limited ability to infer abundance [58]. Citizen-science projects equally emphasise reporting species presence rather than abundance [56]. As such, the dominant sources of in situ data for these two indicators are most likely to come from ongoing compilation of data from disparate research and monitoring efforts, the most informative of which include multi-year time series for detection of trends [59, 60]. However, it is not possible to recommend or rely on contributions by local or even national researchers and networks without acknowledging systemic and well-documented barriers to data sharing, ranging from lack of financial support for data synthesis to cultural perspectives [61]. Given the importance of in situ data to inform biological indicators for rivers (and other aquatic ecosystems), we recommend that research funders support the creation and expansion of large-scale datasets [62], as well as adopting data sharing criteria to promote behavioural and cultural shifts in scientific practice [63].

Water quality Four water quality indicators are recommended to reflect ecological health of rivers: water temperature, nutrient concentrations, suspended sediments, and ecotoxicants (Table 2). Despite current shortcomings, we find that several of these indicators are in a favourable position for implementation at a global scale in the near future. We attribute this readiness to the fact that the respective water quality constituents (temperature, nutrients, sediments) are closely tied to physical landscape features and catchment processes that can be modelled, and that are also more readily measured using remote sensing or in situ methods. Indicators of water quality are commonly included in large-scale monitoring programs (Table 2), and compilation of in situ data that can support improvements in modelling is—albeit in early stages–underway in many places (S1 Table). Indeed, the priority opportunities for improvement that we foresee and recommend are in broadening the collection and global compilation of in situ data, including through citizen science monitoring [64], for improved calibration of existing physical models [65, 66]. While satellite-based remote sensing of suspended sediments is already well-developed [67, 68] and will continue to progress thanks to the increasing resolution of satellite-based optical imagery, the resolution of thermal infrared imagery is still relatively coarse (≥ 100-m pixel size) and can thus be applied to estimate temperature for only very large rivers at present. Nonetheless, higher resolution data afforded by the imminent launch of new sensors is expected to extend thermal remote sensing to smaller rivers and substantially improve the accuracy of global water temperature models [69]. Of the four recommended water quality indicators, ecotoxicants are currently the least feasible indicator for implementation in global river health monitoring. This is due to the fact that ecotoxicants are optically inactive and highly variable and localised in their release, concentrations and behaviour [70, 71]. The diversity and sources of organic and synthetic chemicals that may be present in fresh waters is daunting. Although agricultural pesticides are often the dominant sources of chemical risk in freshwaters [72, 73], other sources may range from heavy metal effluents from mining operations and urban land uses to endocrine-disrupting pharmaceuticals and engineered nanomaterials [1, 74]. This heterogeneity makes it very difficult to monitor or model ecotoxicants at large scales based on relationships with catchment development or physical processes. We therefore recommend further exploration of pressure-based proxies for the near-term, and that new research advances our understanding and modelling of persistence, fate and ecotoxicology of chemicals in river ecosystems [71].

Physical habitat Four indicators are recommended to reflect physical habitat quality of rivers: connectivity, channel feature diversity, riparian vegetation, and instream vegetation. As with water quality, we find that several of these indicators are relatively well-situated for implementation at a global scale in the near future. However, unlike with water quality, this readiness is not due to the existence of in situ datasets but rather that physical aspects of rivers are more easily characterised from remote sensing data. As a result, anticipated increases in the resolution and capacity of remote sensing products will substantially improve our ability to measure these indicators at a global scale (S1 Table). For example, recent launches of high-resolution global multispectral sensors have greatly advanced our ability to measure the extent and structure of riparian vegetation even for narrow riparian corridors, while hyperspectral sensors will increasingly enable species identification, to the extent of differentiating non-native species invasions or quantifying the prevalence of non-native species [75]. Launches of spaceborne LiDAR sensors will further improve this capacity [76], as well as the identification of channel feature diversity [77]. Metrics for assessing longitudinal connectivity (i.e., river fragmentation) at large scales are already well-developed [78]; however, the global datasets of river barriers that are needed to calculate these metrics are still highly incomplete [79]. Nonetheless, broader characterization of longitudinal barriers (i.e., beyond the current emphasis on relatively large dams, diversions and road crossings) is advancing with novel remote sensing techniques [80, 81] and manual mapping [82], including citizen-science based efforts [83]. Lateral connectivity is an important aspect of physical habitat that has lagged substantially behind measurement of longitudinal connectivity, but is poised for substantial advancement at the global scale [e.g., 84]. Higher-resolution remote sensing products will allow identification of lateral barriers and lateral surface water coverage and dynamics [85], while automated and machine learning approaches (e.g., convolutional neural networks) are increasing the accuracy of surface water classifications, resulting in improved capacity to characterise lateral connectivity at large scales [86, 87] and over time [88]. Although we predict consistent improvements in most physical habitat indicators based on higher resolution and better classification of remote sensing products, the exception to this trend is the characterization of instream vegetation, which is very challenging to assess remotely [89]. This is due to tradeoffs between spatial, temporal, spectral and radiometric resolution, all of which are needed for accurate estimation of aquatic vegetation. Aquatic plants are not routinely or comprehensively measured as part of large-scale monitoring programs, resulting in limited in situ datasets to support modelling and remote sensing classification training efforts. We recommend that developing this indicator will benefit most from modelling aquatic plant diversity and abundance based on hydrographic characteristics (e.g., discharge, floodplain extent, inundation) and nutrients [90, 91], supported by the anticipated expansion of hyperspectral imagery [89]. It is also possible that environmental DNA (eDNA) sampling—if conducted at large scales–might inform measurement of aquatic plant diversity [92]. However, development of eDNA for aquatic plants is behind even other freshwater taxonomic groups [93]; and the relation of eDNA with abundance of plants (and other biological organisms) seems likely to remain elusive for some time.

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