Ahead of the curve : channel pattern formation of low-energy rivers

Many rivers have been channelized in large parts of the world in the past centuries. However, in the last decades, realization has grown that channelization has negative consequences: it results in loss of ecological niches and high discharge peaks that may lead to flooding. Therefore, rivers worldwide are currently being restored. One of the most used restoration measures in small, low-energy rivers is to re-meander the river channel pattern, often by mimicking the sinuous pattern from before channelization. However, it remains largely unknown how sinuous patterns of low-energy rivers naturally form and develop with time, because they do not have sufficient energy to erode their banks, and generally do not show lateral migration. In this doctoral thesis, the aim is to understand and predict the channel pattern formation of low-energy rivers. Distinctive channel patterns form in valleys with a peaty, heterogeneous and sandy floodplain. For each river type, a palaeogeographic reconstruction was performed using coring, ground-penetrating radar and geochronological data from different valley cross-sectional research sites. Based on these reconstructions, conceptual models were developed on how these channel patterns develop. The bank strength was identified as a key forming factor of the channel pattern of low-energy rivers, and incorporated in a newly developed channel pattern prediction tool, which has a high prediction success. River restoration can benefit from the insights of this research and focus on restoring natural processes of low-energy rivers in a scientifically sound way

Storylines for practice: a visual storytelling approach to strengthen the science‑practice interface

A growing number of scientifc publications is available to promote sustainable river management. However, these publications target researchers rather than water management professionals who are responsible for the implementation of management practices. To bridge this science-to-practice gap, we conceptualize and propose a series of steps to prepare efective storylines targeted at a practitioner audience. We developed this approach within a research program that supports integrated and collaborative river management. We prepared three storylines, each based on one scientifc publication. The storylines combined text and interactive visuals using the ESRI StoryMaps tool to make them available online. Via focus groups with 44 participants from research and practice, we evaluated the perceived usefulness of and engagement with the content and design. We collected feedback from participants using a survey as well as via audio and screen recordings. Our fndings show that we should narrow down the audience of the storylines by tailoring them to the needs of project managers rather than specialized advisors. Therefore, the content should ofer more than a visual summary of the research by showing examples of the management application. A more engaging sequence with a clear protagonist is further required to better relate to the problem and the potential application. Although visuals and interactive elements were considered attractive, a multi-disciplinary editorial team is necessary to better complement the visuals’ design to the text. The level of detail of participants’ feedback shows that involving project managers to co-create storylines can be an important step for improvement.Peer reviewe

Comparing geomorphological maps made manually and by deep learning

Geomorphological maps provide information on the relief, genesis and shape of the earth's surface and are widely used in sustainable spatial developments. The quality of geomorphological maps is however rarely assessed or reported, which limits their applicability. Moreover, older geomorphological maps often do not meet current quality requirements and require updating. This updating is time-consuming and because of its qualitative nature difficult to reproduce, but can be supported by novel computational methods. In this paper, we address these issues by (1) quantifying the uncertainty associated with manual geomorphological mapping, (2) exploring the use of convolutional neural networks (CNNs) for semi-automated geomorphological mapping and (3) testing the sensitivity of CNNs to uncertainties in manually created evaluation data. We selected a test area in the Dutch push-moraine district with a pronounced relief and a high variety of landforms. For this test area we developed five manually created geomorphological maps and 27 automatically created landform maps using CNNs. The resulting manual maps are similar on a regional level. We could identify the causes of disagreement between the maps on a local level, which often related to differences in mapping experience, choices in delineation and different interpretations of the legend. Coordination of mapping efforts and field validation are necessary to create accurate and precise maps. CNNs perform well in identifying landforms and geomorphological units, but fail at correct delineation. The human geomorphologist remains necessary to correct the delineation and classification of the computed maps. The uncertainty in the manually created data that are used to train and evaluate CNNs have a large effect on the model performance and evaluation. This also advocates for coordinated mapping efforts to ensure the quality of manually created training and test data. Further model development and data processing are required before CNNs can act as standalone mapping techniques

Anthropogenic drivers for exceptionally large meander formation during the Late Holocene

Large-amplitude meanders may form in low-energy rivers despite generally limited mobility in theses systems. Exceptionally large meanders which even extend beyond the valley sides have developed in the Overijsselse Vecht river (the Netherlands) between ca. 1400 CE (Common Era) and the early 1900s, when channelization occurred. Previous studies have attributed the enhanced lateral dynamics of this river to changes in river regime due to increased discharges, reflecting climate and/or land-use alterations in the catchment. This paper focuses on local aspects that may explain why exceptionally large meanders developed at specific sites. Through an integrated analysis based on archaeological, historical, and geomorphological data along with optically stimulated luminescence dating, we investigated the relative impact of three direct and indirect anthropogenic causes for the local morphological change and enhanced lateral migration rates: (1) lack of strategies to manage fluvial erosion; (2) a strong increase in the number of farmsteads and related intensified local land use from the High Middle Ages onwards; and (3) (human-induced) drift-sand activity directly adjacent to the river bends, causing a change in bank stability. Combined, these factors led locally to meander amplitudes well beyond the valley sides. Lessons learned at this site are relevant for management and restoration of meandering rivers in similar settings elsewhere, particularly in meeting the need to estimate spatial demands of (restored) low-energy fluvial systems and manage bank erosion.</p

Channel pattern prediction tool

This tool can be used to predict river channel patterns, based on the publication by Candel, J.H.J., Kleinhans, M.G., Makaske, B., Wallinga, J. Predicting river channel pattern based on stream power, bed material and bank strength. Progress in Physical Geograph

Channel pattern prediction tool

This tool can be used to predict river channel patterns, based on the publication by Candel, J.H.J., Kleinhans, M.G., Makaske, B., Wallinga, J. (in review). Predicting river channel pattern based on stream power, bed material and bank strength. Progress in Physical Geograph

Replication Data for Oblique aggradation: a novel explanation for sinuosity of low-energy streams in peat-filled valley systems

Low-energy streams in peatlands often have a high sinuosity. However, it is unknown how this sinuous planform formed, since lateral migration of the channel is hindered by relatively erosion-resistant banks. We present a conceptual model of Holocene morphodynamic evolution of a stream in a peat-filled valley, based on a palaeohydrological reconstruction. Coring, ground-penetrating radar (GPR) data, and 14C and OSL dating were used for the reconstruction. We found that the stream planform is partly inherited from the Late-Glacial topography, reflecting stream morphology prior to peat growth in the valley. Most importantly, we show that aggrading streams in a peat-filled valley combine vertical aggradation with lateral displacement caused by attraction to the sandy valley sides, which are more erodible than the co-evally aggrading valley-fill. Owing to this oblique aggradation in combination with floodplain widening, the stream becomes stretched out as channel reaches may alternately aggrade along opposed valley sides, resulting in increased sinuosity over time. Hence, highly sinuous planforms can form in peat-filled valleys without the traditional morphodynamics of alluvial bed lateral migration. Improved understanding of the evolution of streams provides inspiration for stream restoration

Replication Data for: Late Holocene channel pattern change from laterally stable to meandering – a palaeohydrological reconstruction

This excel file can be used to make palaeohydrological reconstructions from scroll bars. Monte Carlo runs are 10,000 times. This spreadsheet is based on the publication of Candel et al. 2018 in Earth Surface Dynamics

Channel pattern prediction tool

This tool can be used to predict river channel patterns, based on the publication by Candel, J.H.J., Kleinhans, M.G., Makaske, B., Wallinga, J. (in review). Predicting river channel pattern based on stream power, bed material and bank strength. Progress in Physical Geograph