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Connectivity describes the efficiency of material transfer between geomorphic system components such as hillslopes and rivers or longitudinal segments within a river network. Representations of geomorphic systems as networks should recognize that the compartments, links, and nodes exhibit connectivity at differing scales. The historical underpinnings of connectivity in geomorphology involve management of geomorphic systems and observations linking surface processes to landform dynamics. Current work in geomorphic connectivity emphasizes hydrological, sediment, or landscape connectivity. Signatures of connectivity can be detected using diverse indicators that vary from contemporary processes to stratigraphic records or a spatial metric such as sediment yield that encompasses geomorphic processes operating over diverse time and space scales. One approach to measuring connectivity is to determine the fundamental temporal and spatial scales for the phenomenon of interest and to make measurements at a sufficiently large multiple of the fundamental scales to capture reliably a representative sample. Another approach seeks to characterize how connectivity varies with scale, by applying the same metric over a wide range of scales or using statistical measures that characterize the frequency distributions of connectivity across scales. Identifying and measuring connectivity is useful in basic and applied geomorphic research and we explore the implications of connectivity for river management. Common themes and ideas that merit further research include; increased understanding of the importance of capturing landscape heterogeneity and connectivity patterns; the potential to use graph and network theory metrics in analyzing connectivity; the need to understand which metrics best represent the physical system and its connectivity pathways, and to apply these metrics to the validation of numerical models; and the need to recognize the importance of low levels of connectivity in some situations. We emphasize the value in evaluating boundaries between components of geomorphic systems as transition zones and examining the fluxes across them to understand landscape functioning. © 2018 John Wiley & Sons, Ltd. Geomorphic connectivity emphasizes hydrological, sediment, or landscape connectivity. Common themes and ideas that merit further research include; understanding the importance of capturing landscape heterogeneity and connectivity patterns; using graph and network theory metrics in analyzing connectivity; understanding which metrics best represent the physical system and its connectivity pathways, and applying these metrics to the validation of numerical models; and recognizing the importance of low levels of connectivity in some situations. [ABSTRACT FROM AUTHOR]

Abstract: The exposure time is a water transport time scale defined as the cumulative amount of time a water parcel spends in the domain of interest regardless of the number of excursions from the domain. Transport time scales are often used to characterize the nutrient removal potential of aquatic systems, but exposure time distribution estimates are scarce for deltaic systems. Here we analyze the controls on exposure time distributions using a hydrodynamic model in two domains: the Wax Lake delta in Louisiana, USA, and an idealized channel‐island complex. In particular, we study the effects of river discharge, vegetation, network geometry, and tides and use a simple model for the fractional removal of nitrate. In both domains, we find that channel‐island hydrological connectivity significantly affects exposure time distributions and nitrate removal. The relative contributions of the island and channel portions of the delta to the overall exposure time distribution are controlled by island vegetation roughness and network geometry. Tides have a limited effect on the system's exposure time distribution but can introduce significant spatial variability in local exposure times. The median exposure time for the WLD model is 10 h under the conditions tested and water transport within the islands contributes to 37–50% of the network‐scale exposure time distribution and 52–73% of the modeled nitrate removal, indicating that islands may account for the majority of nitrate removal in river deltas. [ABSTRACT FROM AUTHOR]

River deltas are dynamic systems whose channels can widen, narrow, migrate, avulse, and bifurcate to form new channel networks through time. With hundreds of millions of people living on these globally ubiquitous systems, it is critically important to understand and predict how delta channel networks will evolve over time. Although much work has been done to understand drivers of channel migration on the individual channel scale, a globalscale analysis of the current state of delta morphological change has not been attempted. In this study, we present a methodology for the automatic extraction of channel migration vectors from remotely sensed imagery by combining deep learning and principles from particle image velocimetry (PIV). This methodology is implemented on 48 river delta systems to create a global dataset of decadal-scale delta channel migration. By comparing delta channel migration distributions with a variety of known external forcings, we find that global patterns of channel migration can largely be reconciled with the level of fluvial forcing acting on the delta, sediment fluxmagnitude, and frequency of flood events. An understanding of modern rates and patterns of channel migration in river deltas is critical for successfully predicting future changes to delta systems and for informing decision makers striving for deltaic resilience. [ABSTRACT FROM AUTHOR]

Advances in computing technology, new and ongoing restoration initiatives, concerns about climate change's effects, and the increasing interdisciplinarity of research have encouraged the development of landscape-scale mechanistic models of coupled ecological-geophysical systems. However, communication barriers and uneven infiltration of new strategies for data-driven induction persist in the context of simulation model development across disciplines. One challenge is that ecology and the geosciences have embraced different modeling epistemologies, with ecologists historically favoring inductive inference from generalized, phenomenological models and geoscientists favoring deductive inference from detailed first-principles models. Today, many models used for environmental management, particularly for aquatic ecosystems, tend to be highly detailed, with ecological and geophysical components represented in different modules that are linked but often not closely integrated. These observations highlight a need for cross-disciplinary dialogue about landscape-scale modeling objectives and approaches. The philosophies of pattern-oriented modeling in ecology and exploratory modeling in geophysics have yielded advances in theoretical and applied knowledge in both of those disciplines, but they are not comprehensive across all aspects of landscape-scale modeling. Here we define and synthesize the “Appropriate-Complexity Method” (ACME), which builds upon these two philosophies to guide the development of process-oriented models across a spectrum of scientific and management objectives. ACME helps modelers efficiently converge upon an optimal modeling structure through: i) systematic evaluation of the attributes that comprise computational and representational detail, for which we have developed an operational decision tree; ii) iterative adjustment of models based on pattern-oriented model evaluation strategies; and iii) the use of appropriate datasets (where applicable) to build conceptual models and formulate predictions. Decisions about aspects of computational and representational detail are based on the landscape's emergent properties. They are also based on a hierarchy of classes of questions governing model objectives that represent a multi-attribute tradeoff among validation potential, interpretability, tractability, and generality as functions of computational and representational detail. Tradeoff curves, together with model objectives, provide further guidance for determining the “appropriate” level of complexity for representation of processes in models. Once deemed adequate for addressing the original research question of interest, models may be used for projection and scenario testing. They may next undergo expansion that moves them down the hierarchy, where they can then be used to address research questions of higher specificity, detail, and validation potential, though at a cost of lower tractability and interpretability on the tradeoff curves. This practical, systematic procedure provides clear guidance for the design and improvement of landscape models that may be used to address a wide variety of questions relevant to restoration, over a spectrum of scales. [ABSTRACT FROM AUTHOR]

The Ganges Brahmaputra Meghna Delta (GBMD) is a large and complex coastal system whose channel network is vulnerable to morphological changes caused by sea level rise, subsidence, anthropogenic modifications, and changes to water and sediment loads. Locating and characterizing change is particularly challenging because of the wide range of forcings acting on the GBMD and because of the large range of scales over which these forcings act. In this study, we examine the spatial variability of change in the GBMD channel network. We quantify the relative magnitudes and directions of change across multiple scales and relate the spatial distribution of change to the spatial distribution of a variety of known system forcings. We quantify how the channelization varies by computing the Channelized Response Variance (CRV) on 30 years of remotely sensed imagery of the entire delta extent. The CRV analysis reveals hotspots of morphological change across the delta. We find that the magnitude of these hotspots are related to the spatial distribution of the dominant physiographic forcings in the system (tidal and fluvial influence levels, channel connectivity, and anthropogenic interference levels). We find that the anthropogenically modified embanked regions have much higher levels of geomorphic change than the adjacent natural Sundarban forest and that this change is primarily due to channel infilling and increased rates of channel migration. Having a better understanding of how anthropogenic changes affect delta channel networks over human timescales will help to inform policy decisions affecting the human and ecological presences on deltas around the world. [ABSTRACT FROM AUTHOR]

Drainage network types give indications of the landscape evolution processes and the lithologic and structural conditions that produced them. Moreover, drainage network attributes affect the hydrologic response of basins. Several network types have been identified through visual inspection and, more recently, through quantitative methods developed and applied primarily on relatively low-relief regions. These methods have not been tested in high-relief environments where other factors can control network evolution and other network types may exist. In this work, we study the occurrence of network types in high-relief environments and their potential identification through quantitative classification methods. We analyze 29 high-relief basins in the Chilean Andean region. After exploring the contextual properties (geological, climatic and landcover) of these networks, we calculate several metrics describing their horizontal and vertical attributes and benchmark them against those for network types in low-relief regions. Results show that the high-relief networks have three different types (herringbone, barbed, and high-relief pinnate), which differ in terms of their contextual properties as well as their horizontal and vertical attributes. Classification trees built using only vertical attributes have accuracies of 93% and 84% when differentiating among the high- and low-relief networks, respectively. Furthermore, an overall accuracy of 91% is reached when classifying the entire set of high- and low-relief networks with a combination of vertical and horizontal attributes. Overall, we conclude that classification of drainage patterns through vertical and horizontal attributes is feasible and more accurate than with horizontal attributes alone. • Herringbone and barbed drainage networks can develop in high-relief regions. • High-relief pinnate networks have no dominant stream and more irregular courses. • Joint use of vertical and planform attributes enhances network classification. • Log-log slope-area plot differentiates barbed from high-relief pinnate networks. • A strong linkage between planform and vertical properties of river basins was found. [ABSTRACT FROM AUTHOR]

Summary Mark Melton in 1957 found that climate, basin morphometry, and surficial variables control drainage density ( D d ), but differences observed between field surveyed channels and those mapped on topographic contours or blue lines left doubts on these results. Later, several landscape evolution model and observational studies analyzed the behavior of D d . However, only a few studies have been performed over a large number of landscapes of different characteristics and have relied on high resolution topography data. We revisit Melton’s hypothesis by using meter-resolution digital terrain models (DTMs) in 101 subbasins in the USA. We first propose a dimensionless drainage density ( D dd ) metric based on the ratio of likely channelized pixels to total number of basin pixels, which has the advantage of eliminating the computation of the channel network. Our analysis shows that D dd is a weak scaling function of the input DTM resolution compared to the classic dimensional D d metric (ratio of total channel length to total basin area). We analyze the correlation of D dd and mean annual precipitation ( MAP ) with a Gaussian mixture model which identifies two sub-groups displaying different correlation; negative in arid and semi-arid environments, and positive in humid environments. The transition in correlation is around 1100 ± 100 mm/yr of MAP and is accompanied by the occurrence of thick soil layers and high available water capacity that promote dense vegetation cover ( V cov ) and low D dd . While small variation in D dd is observed across vegetation types, increasing V cov corresponds to decreasing D dd . We also explore the relationship between D dd and relief R , and D dd and lithology. D dd and R are weakly correlated in arid and semi-arid environments, while they have strong positive correlation in humid environments. No significant correlation is found between D dd and lithology although the results are likely affected by our sample choice. [ABSTRACT FROM AUTHOR]