Large Rivers in the Anthropocene: Insights and tools for understanding climatic, land use, and reservoir influences

Habersack, Helmut; Haspel, Daniel; Kondolf, G. Mathias

doi:10.1002/2013wr014731

Cited by 24 publications

(12 citation statements)

References 41 publications

(59 reference statements)

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“…River systems, erstwhile governed by natural flow regimes on the basis of which aquatic and riparian communities have evolved and adapted specifically (Poff et al, ), have become broadly flow regulated (Petts & Gurnell, ) and morphologically altered (Darby & Simon, ) to satisfy human needs such as water supply or irrigation needs (Habersack, Haspel, & Kondolf, ; Meybeck, ). This situation has led to modified flow regimes with increased hydraulic homogeneity, reduced flooding frequencies, and increased low‐water stage during summer (Costa, Martínez‐Capel, Muñoz‐Mas, Alcaraz‐Hernández, & Garófano‐Gómez, ; Petts & Gurnell, ).…”

Section: Introductionmentioning

confidence: 99%

Exploring the key drivers of riparian woodland successional pathways across three European river reaches

et al. 2017

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International audienceClimate change and river regulation are negatively impacting riparian vegetation. To evaluate these impacts, process‐based models are preferred over data‐driven approaches. However, they require extensive knowledge about ecohydrological processes. To facilitate the implementation of such process‐based models, the key drivers of riparian woodland successional pathways across three river reaches, in Austria, Portugal, and Spain, were explored, employing two complementary approaches. The principal component analyses highlighted the importance of the physical gradients determining the placement of the succession phases within the riparian and floodplain zones. The generalized additive models revealed that the initial and pioneer succession phases, characteristic of the colonization stage, appeared in areas highly morphodynamic, close in height and distance to the water table, and with coarse substrate, whereas elder phases within the transitional and mature stages showed incremental differences, occupying less dynamic areas with finer substrate. The Austrian site fitted well the current successional theory (elder phases appearing sequentially further up and distant), but at the Portuguese site, the tolerance of the riparian species to drought and flash flood events governed their placement. Finally, at the Spanish site, the patchy distribution of the elder phases was the remnants of formative events that reshaped the river channel. These results highlight the complex relationships between flow regime, channel morphology, and riparian vegetation. The use of succession phases, which rely on the sequential evolution of riparian vegetation as a response to different drivers, may be potentially better reproducible, within numerical process‐based models, and transferable to other geographical regions

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Section: Introductionmentioning

confidence: 99%

Exploring the key drivers of riparian woodland successional pathways across three European river reaches

et al. 2017

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“…Since the industrial revolution, worldwide human impacts on landscapes and river systems have intensified significantly (Habersack et al, 2014). Therefore, one area of research that has grown steadily in the last few decades is that of ecohydraulics (Casas-Mulet et al, 2016).…”

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confidence: 99%

Revisiting probabilistic neural networks: a comparative study with support vector machines and the microhabitat suitability for the Eastern Iberian chub (Squalius valentinus)

Muñoz‐Mas

Fukuda

Pórtoles³

et al. 2018

Ecological Informatics

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Probabilistic Neural Networks (PNNs) and Support Vector Machines (SVMs) are flexible classification techniques suited to render trustworthy species distribution and habitat suitability models. Although several alternatives to improve PNNs' reliability and performance and/or to reduce computational costs exist, PNNs are currently not well recognised as SVMs because the SVMs were compared with standard PNNs. To rule out this idea, the microhabitat suitability for the Eastern Iberian chub (Squalius valentinus Doadrio & Carmona, 2006) was modelled with SVMs and four types of PNNs (homoscedastic, heteroscedastic, cluster and enhanced PNNs); all of them optimised with differential evolution. The fitness function and several performance criteria (correctly classified instances, true skill statistic, specificity and sensitivity) and partial dependence plots were used to assess respectively the performance and reliability of each habitat suitability model. Heteroscedastic and enhanced PNNs achieved the highest performance in every index but specificity. However, these two PNNs rendered ecologically unreliable partial dependence plots. Conversely, homoscedastic and cluster PNNs rendered ecologically reliable partial dependence plots. Thus, Eastern Iberian chub proved to be a eurytopic species, presenting the highest suitability in microhabitats with cover present, low flow velocity (approx. 0.3 m/s), intermediate depth (approx. 0.6 m) and fine gravel (64-256 mm). PNNs outperformed SVMs; thus, based on the results of the cluster PNN, which also showed high values of the performance criteria, we would advocate a combination of approaches (e.g., cluster & heteroscedastic or cluster & enhanced PNNs) to balance the trade-off between accuracy and reliability of habitat suitability models. 1 Introduction Humans have facilitated species extinctions, invasions, increased soil erosion, altered fire frequency and hydrology, and incited profound changes in primary productivity and other key biogeochemical and ecosystems processes (Ellis et al., 2010). Therefore, in the face of this global change, forecasting future ecosystem states, such as future species geographic distributions or land-use patterns, is currently a central priority in biogeographical and ecological sciences (Eberenz et al., 2016; Evans et al., 2016). As a consequence of this priority, scientists, conservationists and managers are repeatedly compelled to confront new problems requiring data analysis (LaDeau et al., 2016). Data analysis is largely classified into two broad categories: unsupervised and supervised (Olden et al., 2008). The former focus on revealing patterns and structures in data (e.g., finding groups of co-occurring species), such as the renowned Self-Organising Maps (SOM) (Kohonen, 1982) or the laureate t-Distributed Stochastic Neighbour Embedding (t-SNE) (Van Der Maaten and Hinton, 2008). Conversely, supervised approaches, such as decision trees (e.g., CART; Breiman et al., 1984) or the Generalised Additive Models (GAMs) (Hastie and Tibshirani, 1990),...

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“…Connectivity in fluvial systems embodies magnitude and timing of transport processes ranging from the routing of discharge [ Rinaldo et al ., ], to the travel of aquatic species, pathogens [ Gatto et al ., ], or sediment [ Czuba and Foufoula‐Georgiou , ]. Sediment connectivity is a determinant of river geomorphic processes [ Hooke , ] and concerns fluvial ecosystem integrity, access to water resources [ Trush et al ., ], delivery of nutrients or pollutants [ Walling , ], natural hazard risks [ Bechtol and Laurian , ], and, ultimately, human livelihoods in fluvial systems [ Habersack et al ., ].…”

Section: Introductionmentioning

confidence: 99%

Tracking multiple sediment cascades at the river network scale identifies controls and emerging patterns of sediment connectivity

Schmitt

Bizzi

Castelletti

2016

Water Resources Research

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Sediment connectivity in fluvial networks results from the transfer of sediment between multiple\ud sources and sinks. Connectivity scales differently between all sources and sinks as a function of distance,\ud source grain size and sediment supply, network topology and topography, and hydrologic forcing. In this\ud paper, we address the challenge of quantifying sediment connectivity and its controls at the network scale.\ud We expand the concept of a single, catchment-scale sediment cascade toward representing sediment transport\ud from each source as a suite of individual cascading processes. We implement this approach in the\ud herein presented CAtchment Sediment Connectivity And DElivery (CASCADE) modeling framework. In CASCADE,\ud each sediment cascade establishes connectivity between a specific source and its multiple sinks.\ud From a source perspective, the fate of sediment is controlled by its detachment and downstream transport\ud capacity, resulting in a specific trajectory of transfer and deposition. From a sink perspective, the assemblage\ud of incoming cascades defines provenance, sorting, and magnitude of sediment deliveries. At the network\ud scale, this information reveals emerging patterns of connectivity and the location of bottlenecks,\ud where disconnectivity occurs. In this paper, we apply CASCADE to quantitatively analyze the sediment connectivity\ud of a major river system in SE Asia. The approach provides a screening model that can support analyses\ud of large, poorly monitored river systems. We test the sensitivity of CASCADE to various parameters and\ud identify the distribution of energy between the multiple, simultaneously active sediment cascades as key\ud control behind network sediment connectivity. To conclude, CASCADE enables a quantitative, spatially\ud explicit analysis of network sediment connectivity with potential applications in both river science and\ud management

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Large Rivers in the Anthropocene: Insights and tools for understanding climatic, land use, and reservoir influences

Cited by 24 publications

References 41 publications

Exploring the key drivers of riparian woodland successional pathways across three European river reaches

Exploring the key drivers of riparian woodland successional pathways across three European river reaches

Revisiting probabilistic neural networks: a comparative study with support vector machines and the microhabitat suitability for the Eastern Iberian chub (Squalius valentinus)

Tracking multiple sediment cascades at the river network scale identifies controls and emerging patterns of sediment connectivity

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