ALBERT

Description: 〈p〉Publication date: Available online 28 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 Geomorphology〈/p〉 〈p〉Author(s): V.K.E. Duvat, V. Pillet, N. Volto, H. Terorotua, V. Laurent〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉While the geomorphic impacts of extreme climate events, including in particular tropical cyclones and distant-source swells, on atoll islands are widely acknowledged, the influence of moderate climate events, such as tropical lows that do not reach the cyclone stage and low-magnitude distant-source swells, has to date been overlooked. Based on multi-date image analysis and field observations, the geomorphic impacts of a moderate climate event, tropical low 13F (February 2017), were assessed in the northern part of Fakarava Atoll, northwestern Tuamotu Archipelago, in French Polynesia. Results show that this event, which generated strong swells, predominantly caused shoreline retreat (noted along 54.14% of transects, with the minimum Net Shoreline Movement reaching −45.46 m) along the highly-exposed shoreline section, while the rest of the shoreline predominantly exhibited stability. The effects of this swell event were mainly constructional, as shown by the formation of two small storm ramparts and of extensive sediment sheets and tracts on the conglomerate platform. In low-lying areas, the waves crossed over the islands from ocean to lagoon, depositing sediment inland. The erosional impacts of the storm waves and overwash-induced sediment deposition were lower along shoreline exhibiting high vegetation density. One year after the event, intertidal sediment deposits had significantly migrated landward. This study, which reveals an important research gap in atoll geomorphic studies, calls for the more systematic assessment of the geomorphic impacts of moderate climate events on atoll islands, especially in atoll regions that are located outside the cyclone belt (e.g. Tuamotu, Maldives, etc.), where such events can be hypothesised to be more influential than extreme climate events.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: Available online 27 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 Geomorphology〈/p〉 〈p〉Author(s): G. Sofia〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In recent years, the wealth of technological development revolutionised our ability to collect data in geosciences. Due to the unprecedented level of detail of these datasets, geomorphologists are facing new challenges, giving more in-depth answers to a broad(er) range of fundamental questions across the full spectrum of the Earth's (and Planetary) processes. This contribution builds on the existing literature of geomorphometry (the science of quantitative land-surface analysis) and feature extraction (translate land surface parameters into extents of geomorphological elements). It provides evidence of critical themes as well as emerging fields of future research in the digital realm, supporting the likely effectiveness of geomorphometry and feature extractions as they are advancing the theoretical, empirical and applied dimension of geomorphology. The review further discusses the role of geomorphometric legacies, and scientific reproducibility, and how they can be implemented, in the hope that this will facilitate action towards improving the transparency, and efficiency of scientific research, and accelerate discoveries in geomorphology. In the current landscape, substantial changes in landforms, ecosystems, land use, hydrological routing, and direct anthropogenic modifications impact systems across the full spectrum of geomorphological processes. Although uncertainties in the precise nature and likelihood of changes exist, geomorphometry and feature extraction can aid exploring process regimes and landscape responses. Taken together, they can revolutionise geomorphology by opening the doors to improved investigations crossing space and time scales, blurring the boundaries between traditional approaches and computer modelling, and facilitating cross-disciplinary research. Ultimately, the exploitation of the available wealth of digital information can help to translate our understanding of geomorphic processes, which is often based on observations of past or current conditions, into the rapidly changing future.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: 1 April 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 Geomorphology, Volume 354〈/p〉 〈p〉Author(s): Yin-Hsuen Chen, Joann Mossa, Kunwar K. Singh〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The natural flood pulse maintains river-floodplain ecosystems through the exchange of freshwater resources between the main-stem and floodplain habitats. Few prior studies have quantified the relationship between flows and floodplain response including estimating inundation area, floodwater volumes, and slough connectivity. Floodplain modeling typically uses the flow-stage height relationship at river gauge stations. In this study, we compared a relative elevation model (REM) and the Hydrologic Engineering Center River Analysis System (HEC-RAS 1D) model using events from 2015 and 2016 covering a range of flows from the 1st to the 99th percentile for the Apalachicola River, Florida. Because digital elevation models (DEM) from LiDAR (light detection and ranging) data lack details of riverbed topography, we compared a LiDAR-alone and LiDAR-sonar combined DEM to assess their differences. Estimates from the REM and HEC-RAS models were compared to maps based on Landsat imagery-derived water and vegetation indices. In this river, we found a non-linear relationship between the inundated area and flow, increasing markedly through the 90th flow percentile after which increases are minimal. Inundated areas from both REM and HEC-RAS models were similar for all selected flow levels except at the 74th percentile (708 m〈sup〉3〈/sup〉/s) flow at which the REM produced 11% higher inundated area than HEC-RAS. Near the median flows, major sloughs were fully connected with backswamps and low-lying patches being inundated. At the higher flows, only a few anthropogenic features were exposed. Floodplain inundation estimates from Landsat performed poorly, detecting 9% with the modified normalized difference water index (mNDWI) and 41% with the open water likelihood index (OWL). These estimates were much lower than the HEC-RAS model (96% flooded), largely because the satellite is unable to penetrate dense forests and examine the floodplain surface, and the Landsat pixel size is twice the width of floodplain sloughs. The LiDAR-sonar combined DEM produced a higher floodwater volume estimate with the HEC-RAS model than using LiDAR-alone. The difference of 1,368,000 m〈sup〉3〈/sup〉 at the 1st percentile (142 m〈sup〉3〈/sup〉/s) and 2,825,000 m〈sup〉3〈/sup〉 at the 89th percentile (1133 m〈sup〉3〈/sup〉/s) demonstrate the limitation of using a LiDAR-alone DEM, which cannot penetrate the water surface, and the importance of surveying floodplains using sonar. Modeling results under predict historical wetting of the floodplain because Corps dredging made the main channel approximately 13% wider from its historical width in 1941. Further, in the past few decades, droughts and low flows have become more common because of varied upstream water uses, resulting in less inundation than in the past. Frequent high flows are required to maintain river-floodplain connectivity, floodplain forests, and other hydroecological functions in the Apalachicola River floodplain. Our findings present a basis to assess the legacy of past and ongoing disturbances, inform potential policy decisions for water and floodplain management, and provide a baseline for further research.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: 1 April 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 Geomorphology, Volume 354〈/p〉 〈p〉Author(s): Sijin Li, Liyang Xiong, Guoan Tang, Josef Strobl〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Landform classification is one of the most important aspects in geomorphological research, dividing the Earth's surface into diverse geomorphological types. Thus, an accurate classification of landforms is a key procedure in describing the topographic characteristics of a given area and understanding their inner geomorphological formation processes. However, landform types are not always independent of one another due to the complexity and dynamics of interior and external forces. Furthermore, transitional landforms with gradually changing surface morphologies are widely distributed on the Earth's surface. With this situation, classifying these complex and transitional landforms with traditional landform classification methods is hard. In this study, a deep learning (DL) algorithm was introduced, aiming at automatically classifying complex and transitional landforms. This algorithm was trained to learn and extract landform features from integrated data sources. These integrated data sources contain different combinations of imagery, digital elevation models (DEMs), and terrain derivatives. The Loess Plateau in China, which contains complex and transitional loess landforms, was selected as the study area for data training. In addition, two sample areas in the Loess Plateau with complex and transitional loess hill and ridge landforms were used to validate the classified landform types by using the proposed DL method. Meanwhile, a comparative analysis between the proposed DL and random forest (RF) methods was also conducted to investigate their capabilities in landform classification. The proposed DL approach can achieve the highest landform classification accuracy of 87% in the transitional area with data combination of DEMs and images. In addition, the proposed DL method can achieve a higher accuracy of landform classification with better defined landform boundaries compared to the RF method. The classified loess landforms indicate the different landform development stages in this area. Finally, the proposed DL method can be extended to other landform areas for classifying their complex and transitional landforms.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: 1 April 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 Geomorphology, Volume 354〈/p〉 〈p〉Author(s): Sergio R. Dillenburg, Patrick A. Hesp, Robert Keane, Graziela Miot da Silva, André O. Sawakuchi, Ian Moffat, Eduardo G. Barboza, Volney J.B. Bitencourt〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This study examines the southeastern end of the Younghusband Peninsula in South Australia at a location called The Granites in order to gain a better understanding of the processes of formation of the foredune ridge system, and to investigate the drivers that controlled its progradational development during the Holocene. Our findings are based on a morphological analysis, a ground penetrating radar survey, and 〈sup〉14〈/sup〉C and OSL dating. The Younghusband Peninsula at The Granites was formed by an initial aggradational phase resulting in a single complex foredune ridge, and which ended around 4.3 ka, and by a regressive (progradational) barrier phase (750 m wide) that developed in the last 4.3 ka, under very low rates of progradation (0.38 to 0.09 m/yr). The last part of this phase shows significant foredune ridge building in the last 1000 years or so. Barrier progradation via foredune ridge development is likely an effect driven by low wave energy that favored conditions for coastal stability and foredune formation. Paleontological and GPR data indicate a maximum sea-level of +1.23 to +1.5 m, respectively, during initial barrier development. The foredune ridge plain of the barrier experienced at least three phases of significant aeolian activity with ages centered at around 3.9, 3.4 and 3.0 ka suggesting their occurrence at 500 to 400-year events. Computer modelling indicates that sediments for the progradational phase of the barrier were provided by the forced regression produced by a sea-level fall over the past 4.3 ka. The large foredune complex formed during the last phase of progradation could be the result of both the very low progradation rate of 0.09 m/yr, and periods of disturbance possibly related to enhanced storm activity.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: 1 April 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 Geomorphology, Volume 354〈/p〉 〈p〉Author(s): Yu Zhang, Marwan A. Hassan, Leonora King, Xudong Fu, Erkan Istanbulluoglu, Guangqian Wang〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The Loess Plateau in China is the largest and deepest loess deposit in the world. The surficial geology is dominated by Quaternary loess deposits of varying thicknesses, which exhibit some of the fastest erosion rates in the world. Although the plateau has been traditionally thought of as morphologically homogenous, river network morphometrics in this area are spatially variable, suggesting that landscape evolution processes across the plateau are sensitive to variability in local conditions. We divided the plateau into sub-basins and examined six DEM-derived morphometric parameters: Aspect Ratio, Anisotropic Coefficient Variation (ACV), Bifurcation Ratio, Concavity, Steepness, and Mean Junction Angle. The sub-basins exhibited low aspect ratios, high bifurcation ratios and large junction angles relative to landscapes around the world, however they varied intra-regionally relative to each other. Through the use of Principal Component Analysis and Cluster Analysis, we observed specific spatial variation in morphometrics attributable to differences in climate, lithology and tectonics along a NW-SE transect through the plateau. Sub-basins in the elevated plateau regions are more oval with higher aspect ratios and lower ACV values while those located in down-dropped valley regions have smaller junction angles, lower concavity and steepness index. Sub-basins in the transition area between down-dropped and elevated parts of the plateau are distinguished by their high bifurcation ratios and elongated shape. We propose a conceptual model to explain the relationship between these morphometrics and the local environmental conditions.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: Available online 13 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 Geomorphology〈/p〉 〈p〉Author(s): Piotr Cienciala, Andrew D. Nelson, Andrew D. Haas, Zewei Xu〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The rates at which sediment from the landscape is recruited to channel networks reflect lateral geomorphic connectivity and have significant implications for biophysical river systems. By strongly influencing sediment supply regime, such inputs control the dynamics and characteristics of the channel as well as the associated habitat and biota. The main objective of this research was to disentangle the relative importance of geomorphic and biological factors which define spatial patterns in lateral connectivity by promoting or restricting bank erosion, avulsions, and slope failures. Focusing on a forested, formerly glaciated mountain basin in the interior Pacific Northwest, we combined extensive field surveys with remote sensing data (LiDAR, aerial imagery) to obtain metrics representing this complex suite of landscape characteristics. The interpretation of evidence yielded by correlations between these variables, a regime model, and qualitative observations, suggest that, at a multi-decadal timescale between major landscape disturbances, lateral connectivity patterns in Sullivan Creek may be, to a large extent, governed by a combination of biotic factors and glacial legacies. In particular, forest canopy metrics were key predictors of hillslope stability as well as bank and valley floor resistance, suggesting a pivotal influence of root reinforcement. On the other hand, instream large wood abundance was closely related to channel splitting, which reflects its role in promoting channel avulsions. More extensive bank erosion in reaches dominated by drift deposits appeared to indicate glacial legacies. Furthermore, a strong association between bank erosion and colluvial inputs suggested a close coupling within the channel-hillslope system. Lateral confinement – defined as the proportion of the channel length abutting against bedrock, terraces, fans and roads – was negatively associated with channel braiding; the relationships linking confinement with bank erosion and the magnitude of colluvial inputs (negative and positive association, respectively) were relatively weak. Taken together, findings emerging from this research suggest that better understanding of lateral geomorphic connectivity patterns in mountain river basins similar to our study site requires that both channel processes and broader, biophysical landscape system be considered.〈/p〉〈/div〉 〈/div〉 〈div xml:lang="en"〉 〈h5〉Graphical abstract〈/h5〉 〈div〉〈p〉〈figure〉〈img src="https://ars.els-cdn.com/content/image/1-s2.0-S0169555X20300064-ga1.jpg" width="245" alt="Unlabelled Image" title="Unlabelled Image"〉〈/figure〉〈/p〉〈/div〉 〈/div〉