Ecosystem‐based environmental flow assessment in a Greek regulated river with the use of 2D hydrodynamic habitat modelling

Abstract

Despite the long-term research on the use of hydraulic-hydrodynamic habitat models (HHMs) for predicting the response of aquatic biota to habitat alteration, their practical application in model-based environmental flow assessments (EFAs) has been limited due to reasons mainly associated with cost-effectiveness, time-efficiency, required expertise, and availability of hydroecological information. In this study, we demonstrate a cost-effective and time-efficient application of a benthic-invertebrate, two-dimensional, fuzzy rule-based EFA in a 277-m long reach in the downstream route of a regulated river in western Greece. Apart from developing ecosystem-based environmental flow (eflow) scenarios, we highlight the valuable features of HHMs, comment on their disadvantages, and propose working solutions to overcome them.The results of the study show that the hydrology-based eflow of 0.2 m 3 /s, initially proposed by the managing authorities, is not sufficient to ensure the long-term functionality of the downstream benthic communities, as the ecosystem-based eflow ranged between 0.6 and 2 m 3 /s. As social resilience relies heavily on ecological resilience, ecosystem-based approaches can ensure the sustainability of aquatic ecosystems. This study demonstrates, inter alia, that HHMs-based EFAs can be implemented cost-effectively and time-efficiently to serve as an accurate scientific basis for water managers and stakeholders, in search of the fine balance between anthropogenic water demand and long-term ecosystem integrity and functionality.

Comparing environmental flow scenarios from hydrological methods, legislation guidelines, and hydrodynamic habitat models downstream of the Marathon Dam (Attica, Greece)

“…In the absence of well‐established fish communities in the Oinoi stream (possibly due to the extreme long‐term hydrological alteration caused by the presence of the upstream reservoir), we used benthic macroinvertebrates as our target aquatic community, which have been found to be more widely distributed (Monk et al, ) and are commonly used in such cases elsewhere (Waddle & Holmquist, ). Their habitat preferences were acquired from the benthos‐GR dataset (Theodoropoulos, Skoulikidis, et al, ), which consists of 380 microhabitat observations sampled in Greek streams and rivers of similar environmental and hydraulic properties. The benthos‐GR dataset calculates habitat suitability ( K ) using benthic community metrics (no.…”

“…The dataset was used to train and cross‐validate a fuzzy Bayesian algorithm, described in detail in Theodoropoulos, Skoulikidis, et al () and implemented using the Habfuzz software (Theodoropoulos, Skoulikidis, & Stamou, ). In the fuzzy Bayesian algorithm, the numerical inputs of V and D are converted to overlapping, five‐class, trapezoidal‐shaped membership functions (called fuzzy sets).…”

“…Following the approach of Theodoropoulos, Skoulikidis, et al (), the optimal ecosystem‐based eflow scenario was selected on the basis of the combination of the following indicators–variables: Overall suitability index (OSI): Average OSI (nOSI): where K i (from 0 to 1) denotes the habitat suitability and w denotes the total no. of wetted nodes in the computational grid at each Q scenario.…”

“…Environmental flows (eflows) describe the quantity, timing, and quality of the water flows required to sustain freshwater and estuarine ecosystems and the human livelihoods and well‐being that depend on these ecosystems (Brisbane Declaration, ). During 40 years of research, more than 200 methods for assessing eflows emerged worldwide (Tharme, ) and have been recently categorized in three groups depending on the type of data they use to calculate eflows: (a) hydrological methods, which assess eflows based on long‐term historical hydrological information (Richter, Baumgartner, Powell, & Braun, ; Tennant, ); (b) habitat simulation (also known as hydraulic/hydrodynamic habitat modelling) methods, which develop eflow scenarios based on the interaction between habitat suitability–availability and the distribution of aquatic biota (Koutrakis et al, ; Leitner, Hauer, & Graf, ; Theodoropoulos, Skoulikidis, et al, ; Vezza, Muñoz‐Mas, Martínez‐Capel, & Mouton, ); and (c) holistic methods, which combine hydrological and hydroecological/habitat information to define eflows for multiple biotic elements of the aquatic ecosystem (Water Framework Directive Common Implementation Strategy [WFD CIS] Guidance Document No. 31, ), including fish, benthic macroinvertebrates, and aquatic and riparian vegetation (King, Tharme, & De Villiers, ; Poff et al, ; Solans & Jalón, ).…”

Spatiotemporal Variation in Benthic-Invertebrates-Based Physical Habitat Modelling: Can We Use Generic Instead of Local and Season-Specific Habitat Suitability Criteria?

“…The habitat preference datasets are then used as training data in hydraulic habitat models, that is, the reference data which will be used by the model to predict the habitat suitability (K) in samples/microhabitats with known V, D, and S values and unknown K. Currently, there are various alternatives available for the development of habitat suitability criteria, reflecting the aforementioned challenging effort to balance the sources of error and variation towards cost-effectiveness and time-efficiency. In this effort, hydroecological data from geographically and hydrologically various river types and in different time periods, often collected within different projects, are either treated separately to develop site-and season-specific criteria or are aggregated to increase sample size and/or extend the geographical and typological applicability of a model [21][22][23]. In BMI-based studies, typical aggregation schemes include spatiotemporal pooling-of samples from different sites and/or seasons-without pre-treatment [17,24]; spatial aggregation of samples only from specific, usually low-flow periods [25,26]; and spatiotemporal pooling of samples after proper pre-treatment [4,19,27].…”