Inter-annual river patterns change detection using machine learning

Copyright © Author(s) 2022. River patterns in the tropics usually exhibit seasonal changes because of strongly seasonal rainfall and its impact on geomorphological processes. However, studies on tropical rivers are much less than those on temperate rivers, and so these seasonal patterns have not been quantified. To fill some of this research gap, this paper employs machine learning methods using Sentinel-2 multispectral remote sensing images to classify geomorphological units in Bislak River, Laoag River and Abra River in west Luzon, the Philippines. In this study, we firstly designed a workflow for river pattern classification, which was validated for the three rivers at different spatial and temporal scales. Then, 5.5 years of river patterns, defined using three morphological units, in the three rivers were generated for further geomorphological analysis. The classification results were analysed in terms of both spatial and temporal aspects. The results show a variety of relationships between channel width and each landform unit (wetted channel; exposed sediment bar; vegetated bar). The analysis shows that channel width has an impact on the area occupied by vegetation (the bigger the river, the stronger the correlation between channel width and vegetation). We present a way to analyse interactions between geomorphic units at seasonal scales using time series of correlations. The rivers were divided into sub-reaches based on observed patterns of water frequency and confinement, and then temporal analysis was undertaken for each sub-reach. This analysis used Ensemble Empirical Mode Decomposition (EEMD) which decomposed the time series and precipitation. The EEMD results indicate that areas occupied by water and vegetation commonly show synchronised fluctuations with precipitation, while sediment bars have an anti-phase oscillation with precipitation. The results suggests that deviations from periodic consistency in patterns may reflect the influence of extreme events and/or human disturbance. Correlation results show that the total area of unvegetated bars is usually the most stable landform unit in all three rivers, and that the vegetated area changes less in narrower channels. Confinement, due to hillslope and terrace topography, and the impact of fault are also considered. The methods for generating time series of landform unit data and time series analysis used here provide a framework for analysis of tropical rivers that are subject to regular, frequent and dynamic changes of planform

Enhancing performance of multi-temporal tropical river landform classification through downscaling approaches

Data availability: Data are available from: https://doi.org/10.5525/gla.researchdata.1355.Supplementary material: Supplemental data for this article can be accessed online at https://doi.org/10.1080/01431161.2022.2139164 .Copyright 2022 The Author(s). Multi-temporal remote sensing imagery has the potential to classify river landforms to reconstruct the evolutionary trajectory of river morphologies. Whilst open-access archives of high spatial resolution imagery are increasingly available from satellite sensors, such as Sentinel-2, there remains a fundamental challenge of maximising the utility of information in each band whilst maintaining a sufficiently fine resolution to identify landforms. Although image fusion and downscaling methods on Sentinel-2 imagery have been investigated for many years, there is a need to assess their performance for multi-temporal object-based river landform classification. This investigation first compared three downscaling methods: area to point regression kriging (ATPRK), super-resolution based on Sen2Res, and nearest neighbour resampling. We assessed performance of the three downscaling methods by accuracy, precision, recall and F1-score. ATPRK was the optimal downscaling approach, achieving an overall accuracy of 0.861. We successively engaged a set of experiments to determine an optimal training model, exploring single and multi-date scenarios. We find that not only does remote sensing imagery with better quality improve river landform classification performance, but multi-date datasets for establishing machine learning models should be considered for contributing higher classification accuracy. This paper presents a workflow for automated river landform recognition that could be applied to other tropical rivers with similar hydro-geomorphological characteristics. Key policy highlights . Choice of downscaling approach influences the performance of river landform classification from satellite imagery and should be considered in river and flood management. . An efficient and straightforward operating workflow was developed for automated river landform classification with high accuracy that supports an improved understanding of the use of machine learning approaches in river landform recognition. . Freely available and easy-to-access remote sensing datasets can help extend the operating workflow to difficult-to-access or remote regions and allow for complete regional and/or national coverage.China Scholarship Council [201908060049] and University of Glasgow [201908060049]

Using delta channel width to estimate paleodischarge in the rock record: geometric scaling and practical sampling criteria

Quantifying paleodischarge from geological field observations remains a key research challenge. Several scaling relationships between paleodischarge and channel morphology (width; depth) have been developed for rivers and river deltas. Previous paleodischarge scaling relationships were based on discharge-catchment area scaling and an empirical flow velocity estimate (e.g. Chézy, Manning formulae) multiplied by channel cross-sectional area to derive discharge. In deltas, where marine (wave, tide) energy causes bidirectional flow within distributary channels, the available paleodischarge scaling relationships are not applicable due to their unidirectional flow assumption. Here, the spatial variability of distributary channel widths from a database of 114 global modern river deltas is assessed to understand the limit of marine influence on distributary channel widths. Using 6213 distributary channel width measurements, the median channel widths of distributary channels for each delta were correlated with bankfull discharge for river-, tide- and wave-dominated deltas, the latter two including the effect of bidirectional flow. Statistically significant width-discharge scaling relationships are derived for river- and wave-dominated deltas, with no significant relationships identified for tide-dominated deltas. By reverse bootstrapping the channel widths measured from modern deltas, the minimum number of width measurements needed to apply width-discharge scaling relationships to ancient deltaic deposits is estimated as 3 and 4 for the upstream parts of river- and wave-dominated deltas, respectively, increasing to 30 in the downstream parts of river-dominated deltas. These estimates will guide sedimentological studies that often have limited numbers of distributary channel widths exposed in the rock record. To test the reliability of these alternative width-discharge scaling relationships in the rock record, paleodischarges were estimated for the well-studied Cretaceous lower Mesa Rica Sandstone Formation, USA . Comparison of these results with the more complex Chézy-derived method suggests that these new scaling relationships are accurate. Hence, it is proposed that the scaling relationships obtained from modern deltas can be applied to the rock record, requiring fewer, and easier to measure, data inputs than previously published methods

Large-scale flood risk assessment under different development strategies: the Luanhe River Basin in China

© The Author(s) 2021. Increasing resilience to natural hazards and climate change is critical for achieving many Sustainable Development Goals (SDGs). In recent decades, China has experienced rapid economic development and became the second-largest economy in the world. This rapid economic expansion has led to large-scale changes in terrestrial (e.g., land use and land cover changes), aquatic (e.g., construction of reservoirs and artificial wetlands) and marine (e.g., land reclamation) environments across the country. Together with climate change, these changes may significantly influence flood risk and, in turn, compromise SDG achievements. The Luanhe River Basin (LRB) is one of the most afforested basins in North China and has undergone significant urbanisation and land use change since the 1950s. However, basin-wide flood risk assessment under different development scenarios has not been considered, although this is critically important to inform policy-making to manage the synergies and trade-offs between the SDGs and support long-term sustainable development. Using mainly open data, this paper introduces a new framework for systematically assessing flood risk under different social and economic development scenarios. A series of model simulations are performed to investigate the flood risk under different land use change scenarios projected to 2030 to reflect different development strategies. The results are systematically analysed and compared with the baseline simulation based on the current land use and climate conditions. Further investigations are also provided to consider the impact of climate change and the construction of dams and reservoirs. The results potentially provide important guidance to inform future development strategies to maximise the synergies and minimise the trade-offs between various SDGs in LRB.Natural Environment Research Council (NERC) of the UK Research and Innovation (UKRI) through the Towards a Sustainable Earth (TaSE) programme (NE/S012427/1)