Fluvial sediment supply to a mega-delta reduced by shifting tropical-cyclone activity.

The world's rivers deliver 19 billion tonnes of sediment to the coastal zone annually, with a considerable fraction being sequestered in large deltas, home to over 500 million people. Most (more than 70 per cent) large deltas are under threat from a combination of rising sea levels, ground surface subsidence and anthropogenic sediment trapping, and a sustainable supply of fluvial sediment is therefore critical to prevent deltas being 'drowned' by rising relative sea levels. Here we combine suspended sediment load data from the Mekong River with hydrological model simulations to isolate the role of tropical cyclones in transmitting suspended sediment to one of the world's great deltas. We demonstrate that spatial variations in the Mekong's suspended sediment load are correlated (r = 0.765, P < 0.1) with observed variations in tropical-cyclone climatology, and that a substantial portion (32 per cent) of the suspended sediment load reaching the delta is delivered by runoff generated by rainfall associated with tropical cyclones. Furthermore, we estimate that the suspended load to the delta has declined by 52.6 ± 10.2 megatonnes over recent years (1981-2005), of which 33.0 ± 7.1 megatonnes is due to a shift in tropical-cyclone climatology. Consequently, tropical cyclones have a key role in controlling the magnitude of, and variability in, transmission of suspended sediment to the coast. It is likely that anthropogenic sediment trapping in upstream reservoirs is a dominant factor in explaining past, and anticipating future, declines in suspended sediment loads reaching the world's major deltas. However, our study shows that changes in tropical-cyclone climatology affect trends in fluvial suspended sediment loads and thus are also key to fully assessing the risk posed to vulnerable coastal systems.

Streamflow Prediction in Highly Regulated, Transboundary Watersheds Using Multi-Basin Modeling and Remote Sensing Imagery.

Despite the potential of remote sensing for monitoring reservoir operation, few studies have investigated the extent to which reservoir releases can be inferred across different spatial and temporal scales. Through evaluating 21 reservoirs in the highly regulated Greater Mekong region, remote sensing imagery was found to be useful in estimating daily storage volumes for within-year and over-year reservoirs (correlation coefficients [CC] ≥ 0.9, normalized root mean squared error [NRMSE] ≤ 31%), but not for run-of-river reservoirs (CC < 0.4, 40% ≤ NRMSE ≤ 270%). Given a large gap in the number of reservoirs between global and local databases, the proposed framework can improve representation of existing reservoirs in the global reservoir database and thus human impacts in hydrological models. Adopting an Integrated Reservoir Operation Scheme within a multi-basin model was found to overcome the limitations of remote sensing and improve streamflow prediction at ungauged cascade reservoir systems where previous modeling approaches were unsuccessful. As a result, daily regulated streamflow was predicted competently across all types of reservoirs (median values of CC = 0.65, NRMSE = 8%, and Kling-Gupta efficiency [KGE] = 0.55) and downstream hydrological stations (median values of CC = 0.94, NRMSE = 8%, and KGE = 0.81). The findings are valuable for helping to understand the impacts of reservoirs and dams on streamflow and for developing more useful adaptation measures to extreme events in data sparse river basins.

Development and Testing of a UAV Laser Scanner and Multispectral Camera System for Eco-Geomorphic Applications.

While Uncrewed Aerial Vehicle (UAV) systems and camera sensors are routinely deployed in conjunction with Structure from Motion (SfM) techniques to derive 3D models of fluvial systems, in the presence of vegetation these techniques are subject to large errors. This is because of the high structural complexity of vegetation and inability of processing techniques to identify bare earth points in vegetated areas. Furthermore, for eco-geomorphic applications where characterization of the vegetation is an important aim when collecting fluvial survey data, the issues are compounded, and an alternative survey method is required. Laser Scanning techniques have been shown to be a suitable technique for discretizing both bare earth and vegetation, owing to the high spatial density of collected data and the ability of some systems to deliver dual (e.g., first and last) returns. Herein we detail the development and testing of a UAV mounted LiDAR and Multispectral camera system and processing workflow, with application to a specific river field location and reference to eco-hydraulic research generally. We show that the system and data processing workflow has the ability to detect bare earth, vegetation structure and NDVI type outputs which are superior to SfM outputs alone, and which are shown to be more accurate and repeatable, with a level of detection of under 0.1 m. These characteristics of the developed sensor package and workflows offer great potential for future eco-geomorphic research.

Decoding the drivers of bank erosion on the Mekong river: The roles of the Asian monsoon, tropical storms, and snowmelt.

We evaluate links between climate and simulated river bank erosion for one of the world's largest rivers, the Mekong. We employ a process-based model to reconstruct multidecadal time series of bank erosion at study sites within the Mekong's two main hydrological response zones, defining a new parameter, accumulated excess runoff (AER), pertinent to bank erosion. We employ a hydrological model to isolate how snowmelt, tropical storms and monsoon precipitation each contribute to AER and thus modeled bank erosion. Our results show that melt (23.9% at the upstream study site, declining to 11.1% downstream) and tropical cyclones (17.5% and 26.4% at the upstream and downstream sites, respectively) both force significant fractions of bank erosion on the Mekong. We also show (i) small, but significant, declines in AER and hence assumed bank erosion during the 20th century, and; (ii) that significant correlations exist between AER and the Indian Ocean Dipole (IOD) and El Niño Southern Oscillation (ENSO). Of these modes of climate variability, we find that IOD events exert a greater control on simulated bank erosion than ENSO events; but the influences of both ENSO and IOD when averaged over several decades are found to be relatively weak. However, importantly, relationships between ENSO, IOD, and AER and hence inferred river bank erosion are not time invariant. Specifically, we show that there is an intense and prolonged epoch of strong coherence between ENSO and AER from the early 1980s to present, such that in recent decades derived Mekong River bank erosion has been more strongly affected by ENSO.

Monitoring riverine traffic from space: The untapped potential of remote sensing for measuring human footprint on inland waterways.

Mass urbanisation and intensive agricultural development across river deltas have driven ecosystem degradation, impacting deltaic socio-ecological systems and reducing their resilience to climate change. Assessments of the drivers of these changes have so far been focused on human activity on the subaerial delta plains. However, the fragile nature of deltaic ecosystems and the need for biodiversity conservation on a global scale require more accurate quantification of the footprint of anthropogenic activity across delta waterways. To address this need, we investigated the potential of deep learning and high spatiotemporal resolution satellite imagery to identify river vessels, using the Vietnamese Mekong Delta (VMD) as a focus area. We trained the Faster R-CNN Resnet101 model to detect two classes of objects: (i) vessels and (ii) clusters of vessels, and achieved high detection accuracies for both classes (f-score = 0.84-0.85). The model was subsequently applied to available PlanetScope imagery across 2018-2021; the resultant detections were used to generate monthly, seasonal and annual products mapping the riverine activity, termed here the Human Waterway Footprint (HWF), with which we showed how waterborne activity has increased in the VMD (from approx. 1650 active vessels in 2018 to 2070 in 2021 - a 25 % increase). Whilst HWF values correlated well with population density estimates (R2 = 0.59-0.61, p < 0.001), many riverine activity hotspots were located away from population centres and varied spatially across the investigated period, highlighting that more detailed information is needed to fully evaluate the extent, and type, of human footprint on waterways. High spatiotemporal resolution satellite imagery in combination with deep learning methods offers great promise for such monitoring, which can subsequently enable local and regional assessment of environmental impacts of anthropogenic activities on delta ecosystems around the globe.

A high-resolution daily global dataset of statistically downscaled CMIP6 models for climate impact analyses.

A large number of historical simulations and future climate projections are available from Global Climate Models, but these are typically of coarse resolution, which limits their effectiveness for assessing local scale changes in climate and attendant impacts. Here, we use a novel statistical downscaling model capable of replicating extreme events, the Bias Correction Constructed Analogues with Quantile mapping reordering (BCCAQ), to downscale daily precipitation, air-temperature, maximum and minimum temperature, wind speed, air pressure, and relative humidity from 18 GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6). BCCAQ is calibrated using high-resolution reference datasets and showed a good performance in removing bias from GCMs and reproducing extreme events. The globally downscaled data are available at the Centre for Environmental Data Analysis ( https://doi.org/10.5285/c107618f1db34801bb88a1e927b82317 ) for the historical (1981-2014) and future (2015-2100) periods at 0.25° resolution and at daily time step across three Shared Socioeconomic Pathways (SSP2-4.5, SSP5-3.4-OS and SSP5-8.5). This new climate dataset will be useful for assessing future changes and variability in climate and for driving high-resolution impact assessment models.

Biological-physical interactions are fundamental to understanding and managing coastal dynamics.

There is an urgent need to address coastal dynamics as a fundamental interaction between physical and biological processes, particularly when trying to predict future biological-physical linkages under anticipated changes in environmental forcing. More integrated modelling, support for observational networks and the use of management interventions as controlled experimental exercises should now be vigorously pursued.

Benthic-based contributions to climate change mitigation and adaptation.

Innovative solutions to improve the condition and resilience of ecosystems are needed to address societal challenges and pave the way towards a climate-resilient future. Nature-based solutions offer the potential to protect, sustainably manage and restore natural or modified ecosystems while providing multiple other benefits for health, the economy, society and the environment. However, the implementation of nature-based solutions stems from a discourse that is almost exclusively derived from a terrestrial and urban context and assumes that risk reduction is resolved locally. We argue that this position ignores the importance of complex ecological interactions across a range of temporal and spatial scales and misses the substantive contribution from marine ecosystems, which are notably absent from most climate mitigation and adaptation strategies that extend beyond coastal disaster management. Here, we consider the potential of sediment-dwelling fauna and flora to inform and support nature-based solutions, and how the ecology of benthic environments can enhance adaptation plans. We illustrate our thesis with examples of practice that are generating, or have the potential to deliver, transformative change and discuss where further innovation might be applied. Finally, we take a reflective look at the realized and potential capacity of benthic-based solutions to contribute to adaptation plans and offer our perspectives on the suitability and shortcomings of past achievements and the prospective rewards from sensible prioritization of future research. This article is part of the theme issue 'Climate change and ecosystems: threats, opportunities and solutions'.

Mean flow and turbulence structure over exposed roots on a forested floodplain: Insights from a controlled laboratory experiment.

The time-averaged and instantaneous flow velocity structures of flood waters are not well understood for irregular surfaces such as are created by the presence of roots and fallen branches on forested floodplains. Natural flow structures commonly depart systematically from those described for idealised roughness elements, and an important knowledge gap exists surrounding the effects of natural flow structures on vertical exchanges of fluid and momentum. An improved understanding of the flow structure is required to model flows over forested floodplains more accurately, and to distinguish their dynamics from non-vegetated floodplains or indeed floodplains with other vegetation types, such as reed or grass. Here we present a quantification of the three-dimensional structure of mean flow velocity and turbulence as measured under controlled conditions in an experimental flume using a physical reproduction of a patch of forested floodplain. The results conform in part to existing models of local flow structure over simple roughness elements in aspects such as flow separation downstream of protruding roots, flow reattachment, and the lowering of the velocity maximum further downstream. However, the irregular shape of the surface of the floodplain with exposed roots causes the three-dimensional flow structure to deviate from that anticipated based on previous studies of flows over idealised two-dimensional roughness elements. The results emphasise varied effects of inheritance of flow structures that are generated upstream-the local response of the flow to similar obstacles depends on their spatial organisation and larger-scale context. Key differences from idealised models include the absence of a fully-developed flow at any location in the test section, and various interactions of flow structures such as a reduction of flow separation due to cross-stream circulation and the diversion of the flow over and around the irregular shapes of the roots.

The Southampton-York Natural Scenes (SYNS) dataset: Statistics of surface attitude.

Recovering 3D scenes from 2D images is an under-constrained task; optimal estimation depends upon knowledge of the underlying scene statistics. Here we introduce the Southampton-York Natural Scenes dataset (SYNS: https://syns.soton.ac.uk), which provides comprehensive scene statistics useful for understanding biological vision and for improving machine vision systems. In order to capture the diversity of environments that humans encounter, scenes were surveyed at random locations within 25 indoor and outdoor categories. Each survey includes (i) spherical LiDAR range data (ii) high-dynamic range spherical imagery and (iii) a panorama of stereo image pairs. We envisage many uses for the dataset and present one example: an analysis of surface attitude statistics, conditioned on scene category and viewing elevation. Surface normals were estimated using a novel adaptive scale selection algorithm. Across categories, surface attitude below the horizon is dominated by the ground plane (0° tilt). Near the horizon, probability density is elevated at 90°/270° tilt due to vertical surfaces (trees, walls). Above the horizon, probability density is elevated near 0° slant due to overhead structure such as ceilings and leaf canopies. These structural regularities represent potentially useful prior assumptions for human and machine observers, and may predict human biases in perceived surface attitude.