The Project

The new challenges for Integrated Catchment Management

Changes in land use, climate change, soil erosion, flow regime modification, morphological changes to habitats, wastewater inputs, and the increase of non-native species represent the major threats to river systems worldwide.

Maximizing societal returns from fluvial landscapes while ensuring resilience and aquatic biodiversity conservation is a formidable challenge for ICM. In many cases, management for services involves trade-offs, such that increasing the supply of one reduces the supply of another. For example, water for irrigation is made possible by massive storage in reservoirs although this may also have serious implications for the provisioning of other services (e.g. fish biomass or water quality). Service trade-offs will also exist between riverine and terrestrial ecosystems. In this case, agriculture may enhance food production but reduce water yield to rivers. Thus, water managers require tools to guide them through complex natural resource decisions that seek meeting multiple objectives (Brown et al. 2010).

Riverine ecosystem services and Fluvial landscapes

Understanding how riverine ecosystem services are affected by human actions remains a long-standing challenge. Analysis of ecosystem services must address the complex and often indirect linkages between interconnected organisms and processes (Fig. 1). Significant advances have been made in understanding the relationship between river biodiversity and functioning in the last decade, however the narrow focus of most studies does not contribute to the understanding of the same relationships at larger spatial scales.


Figure 1. Diagram showing theoretical linkages between different biophysical ecosystem components (EC) and riverine ecosystem services (OM: Organic Matter; SS: Suspended Solids). Adapted from (Barquín et 2015.)

Most current assessments and evaluations of ecosystem services (e.g. LUCI, INVEST, ARIES) have incorporated analytical tools mainly dealing with ecosystem services linked to catchment or terrestrial processes (e.g., Irrigation, Drinking water, Hydroelectric; Fig. 1), but few have incorporated approaches in which models include in-stream elements (i.e., biofilm, macroinvertebrates or fish) to characterise services that are mainly generated within the riverine domain (e.g., Water purification, Food-Fish; Fig. 1). Thus, new approaches are needed to increase our understanding of how river biodiversity and functioning are actually connected to the provision of services.

Assessing riverine ecosystem services using the ARIES approach has a number of advantages over other approaches as it provides with (1) spatial explicit information on modalities of ecosystem services sources, sinks and flows, (2) actual service use versus potential, (3) flexible statement on services values, (4) simultaneous analysis of services trade-offs, and (5) uncertainty estimates (Villa et al. 2014b). In addtition, the potential of ARIES as a large-scale meta-modelling framework will be greatly expanded by coupling it with the Virtual Watershed approach.

Finally, remarkable scientific progress has also been achieved over the last decade increasing our understanding on the organization of river processes across scales, including: (1) the role of river network structure and topology (Benda et al. 2004), (2) the importance of hierarchical patch dynamics (Thorp et al. 2006), (3) the dependency of biodiversity on hydrological dynamics (Poff et al. 1997), and (4) the role of spatial heterogeneity, connectivity, and asynchrony in river dynamics (McCluney et al. 2014). In this regard, Virtual Watersheds offer advantages as a digital analysis because they explicitly account for river network structure and topology incorporating a wide range of terrestrial-riverine interactions at different spatial scales (Benda et al, 2015).

Improving the assessment of hydrological alteration

The influence of the natural flow regime on river processes and functions is well known (Poff et al. 2010). It is now widely accepted that maintaining some degree of similarity to the various pre-impacted combinations of flow magnitude, timing, duration, frequency and rate of change is required to maintain river processes and functions (Schneider et al. 2013). Reservoirs vary in size, level of impoundment, function and operational rules, so generalizations of their potential hydrologic alteration (HA) and ecological impact are difficult (Magilligan and Nislow 2001). The same applies to different landscape configurations due to land use changes. Hence, one of the most robust approach to determine the HA is the site-specific comparison of pre- and post-impact flow series. In this regard, the “Indicators of Hydrologic Alteration” (IHA) method developed by The Nature Conservancy (Richter et al. 1996) has been used worldwide and is currently seen as one of the most effective approaches for assessing the HA. Despite its widespread application and acceptance, the IHA presents several drawbacks that should be tackle to completely understand and determine the degree of HA.

Integrating existing and new databases to account for different levels of biological organization

One of the key advantages of the proposed Virtual Watershed-ARIES platform is that it can incorporate existing and new data from many different sources. This will allow significant progress with current available data (e.g. biomonitoring and hydromorphological data gathered through national or regional monitoring programmes). However, biodiversity indicators currently used to reflect the state of the environment are structural in nature and concern only a few levels of biological organisation, situated mainly at the level of populations and/or communities (Gray et al. 2014). Moreover, dam operation or land use changes have notable effects on river systems, although the causal links and cascade effects on different levels of biological organization (from Dissolved Organic Matter to Ecosystem metabolism) are still poorly understood mainly because of the many interacting effects of different factors (Fig. 1). HYDRA will produce data spanning multiple levels of biological organization and ecosystem functions, and with a spatially explicit design.


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