Integrated mechanistic and data-driven modeling for risk assessment of greenhouse gas production in an urbanized river system.

Surrounded by intense anthropogenic activities, urban polluted rivers have increasingly been reported as a significant source of greenhouse gases (GHGs). However, unlike pollution and climate change, no integrated urban water models have investigated the GHG production in urban rivers due to system complexity. In this study, we proposed a novel integrated framework of mechanistic and data-driven models to qualitatively assess the risks of GHG accumulation in an urban river system in different water management interventions. Particularly, the mechanistic model delivered elaborated insights into river states in four intervention scenarios in which the installation of a new wastewater treatment plant using two different technologies, together with new sewage systems and additional retention tanks, were assessed during dry and rainy seasons. From the insights, we applied fuzzy rule-based models as a decision support tool to predict the GHG accumulation risks and identify their driving factors in the scenarios. The obtained results indicated the important role of new discharge connection and additional storage capacity in decreasing pollutant concentrations, consequently, reducing the risks. Moreover, among the major variables explaining the GHG accumulation in the rivers, DO level was considerably affected by the reaeration capacity of the rivers that was strongly dependent on river slope and flow. Furthermore, river water quality emerged as the most critical variable explaining the pCO2 and N2O accumulation that implied that the more polluted and anaerobic the sites were, the higher were their GHG accumulation. Given its simplicity and transparency, the proposed modeling framework can be applied to other river basins as a decision support tool in setting up integrated urban water management plans.

Unimodal head-width distribution of the European eel (Anguilla anguilla L.) from the Zeeschelde does not support disruptive selection.

Since the early 20th century, European eels (Anguilla anguilla L.) have been dichotomously classified into 'narrow' and 'broad' heads. These morphs are mainly considered the result of a differential food choice, with narrow heads feeding primarily on small/soft prey and broad heads on large/hard prey. Yet, such a classification implies that head-width variation follows a bimodal distribution, leading to the assumption of disruptive selection. We investigated the head morphology of 272 eels, caught over three consecutive years (2015-2017) at a single location in the Zeeschelde (Belgium). Based on our results, BIC favored a unimodal distribution, while AIC provided equal support for a unimodal and a bimodal distribution. Notably, visualization of the distributions revealed a strong overlap between the two normal distributions under the bimodal model, likely explaining the ambiguity under AIC. Consequently, it is more likely that head-width variation followed a unimodal distribution, indicating there are no disruptive selection pressures for bimodality in the Zeeschelde. As such, eels could not be divided in two distinct head-width groups. Instead, their head widths showed a continuum of narrow to broad with a normal distribution. This pattern was consistent across all maturation stages studied here.

Integrating data-driven ecological models in an expert-based decision support system for water management in the Du river basin (Vietnam).

In this study, classification trees were combined with the Water Framework Directive (WFD)-Explorer, a modular toolbox that supports integrated water management in a river basin to evaluate the impact of different restoration measures on river ecology. First, the WFD-Explorer toolbox analysed the effect of different restoration options on the abiotic river characteristics based on the water and substance balance embedded in the simulation environment. Based on these abiotic characteristics, the biological index Biological Monitoring Working Party for Vietnam was then predicted by classification trees that were trained on biological and abiotic data collected in the Du river basin in northern Vietnam. The ecological status of streams in the basin ranged from nearly pristine headwaters to severely impacted river stretches. Elimination of point sources from ore extraction and decentralised domestic wastewater treatment proved to be the most effective measures to improve the ecological condition of the Du river basin. The combination of the WFD-Explorer results with data-driven models enabled model application in a situation where expert knowledge was lacking. Consequently, this approach appeared promising for decision support in the context of river restoration and conservation management.

Assessment of brown trout habitat suitability in the Jucar River Basin (SPAIN): comparison of data-driven approaches with fuzzy-logic models and univariate suitability curves.

The implementation of the Water Framework Directive implies the determination of an environmental flow (E-flow) in each running water body. In Spain, many of the minimum flow assessments were determined with the physical habitat simulation system based on univariate habitat suitability curves. Multivariate habitat suitability models, widely applied in habitat assessment, are potentially more accurate than univariate suitability models. This article analyses the microhabitat selection by medium-sized (10-20 cm) brown trout (Salmo trutta fario) in three streams of the Jucar River Basin District (eastern Iberian Peninsula). The data were collected with an equal effort sampling approach. Univariate habitat suitability curves were built with a data-driven process for depth, mean velocity and substrate classes; three types of data-driven fuzzy models were generated with the FISH software: two models of presence-absence and a model of abundance. FISH applies a hill-climbing algorithm to optimize the fuzzy rules. A hydraulic model was calibrated with the tool River-2D in a segment of the Cabriel River (Jucar River Basin). The fuzzy-logic models and three methods to produce a suitability index from the three univariate curves were applied to evaluate the river habitat in the tool CASiMiR©. The comparison of results was based on the spatial arrangement of habitat suitability and the curves of weighted usable area versus discharge. The differences were relevant in different aspects, e.g. in the estimated minimum environmental flow according to the Spanish legal norm for hydrological planning. This work demonstrates the impact of the model's selection on the habitat suitability modelling and the assessment of environmental flows, based on an objective data-driven procedure; the conclusions are important for the water management in the Jucar River Basin and other river systems in Europe, where the environmental flows are a keystone for the achievement of the goals established in the European Water Framework Directive.

Abundance versus presence/absence data for modelling fish habitat preference with a genetic Takagi-Sugeno fuzzy system.

This study compared the accuracy of fuzzy habitat preference models (FHPMs) and habitat preference curves (HPCs) obtained from the FHPMs in order to assess the effect of two types of data [log-transformed fish population density (LOG) and presence-absence (P/A) data] on the habitat preference evaluation of Japanese medaka (Oryzias latipes). Three independent data sets were prepared for each type of data. The results differed according to the data sets and the types of data used. The HPCs showed a similar trend, whilst the degrees of preference were different. The model accuracy also differed according to the data sets used. Although almost no statistical difference was observed, on average, the P/A-based models showed a better performance according to the threshold-independent performance measures, whilst the LOG-based models showed better performance in predicting absence of the fish. These results can be explained partly from the different shapes of HPCs. This case study of Japanese medaka demonstrated the effect of different types of data on habitat preference evaluation. Further studies should build on the present finding and evaluate the effects of data characteristics such as the size of data sets and the prevalence for better understanding and reliable assessment of the habitat for target species.