Coastal wetlands can be saved from sea level rise by recreating past tidal regimes

Climate change driven Sea Level Rise (SLR) is creating a major global environmental crisis in coastal ecosystems, however, limited practical solutions are provided to prevent or mitigate the impacts. Here, we propose a novel eco-engineering solution to protect highly valued vegetated intertidal ecosystems. The new ‘Tidal Replicate Method’ involves the creation of a synthetic tidal regime that mimics the desired hydroperiod for intertidal wetlands. This synthetic tidal regime can then be applied via automated tidal control systems, “SmartGates”, at suitable locations. As a proof of concept study, this method was applied at an intertidal wetland with the aim of restabilising saltmarsh vegetation at a location representative of SLR. Results from aerial drone surveys and on-ground vegetation sampling indicated that the Tidal Replicate Method effectively established saltmarsh onsite over a 3-year period of post-restoration, showing the method is able to protect endangered intertidal ecosystems from submersion. If applied globally, this method can protect high value coastal wetlands with similar environmental settings, including over 1,184,000 ha of Ramsar coastal wetlands. This equates to a saving of US$230 billion in ecosystem services per year. This solution can play an important role in the global effort to conserve coastal wetlands under accelerating SLR

Profiling resilience and adaptation in mega deltas: a comparative assessment of the Mekong, Yellow, Yangtze, and Rhine deltas

River deltas and estuaries are disproportionally-significant coastal landforms that are inhabited by nearly 600 M people globally. In recent history, rapid socio-economic development has dramatically changed many of the World's mega deltas, which have typically undergone agricultural intensification and expansion, land-use change, urbanization, water resources engineering and exploitation of natural resources. As a result, mega deltas have evolved into complex and potentially vulnerable socio-ecological systems with unique threats and coping capabilities. The goal of this research was to establish a holistic understanding of threats, resilience, and adaptation for four mega deltas of variable geography and levels of socio-economic development, namely the Mekong, Yellow River, Yangtze, and Rhine deltas. Compiling this kind of information is critical for managing and developing these complex coastal areas sustainably but is typically hindered by a lack of consistent quantitative data across the ecological, social and economic sectors. To overcome this limitation, we adopted a qualitative approach, where delta characteristics across all sectors were assessed through systematic expert surveys. This approach enabled us to generate a comparative assessment of threats, resilience, and resilience-strengthening adaptation across the four deltas. Our assessment provides novel insights into the various components that dominate the overall risk situation in each delta and, for the first time, illustrates how each of these components differ across the four mega deltas. As such, our findings can guide a more detailed, sector specific, risk assessment or assist in better targeting the implementation of risk mitigation and adaptation strategies

InletTracker - A python toolkit for monitoring coastal inlets via Landsat and Sentinel-2

This repository contains the entire open-source code of InletTracker as well as the key input data necessary to reproduce the results of the corresponding journal publication. InletTracker is a Google Earth Engine enabled open-source python software package that first uses a novel least-cost path finding approach to trace inlet channels along and across the berm/barrier/bar, and then analyses the resulting transects to infer whether an entrance is open or closed. Our study highlighted that InletTracker is able to consistently and accurately infer open vs. closed inlet states and can even provide an indication of the degree of inlet opening for larger inlets. The data that InletTracker can generate will help to answer a range of remaining questions around processes, dynamics, and drivers (i.e., waves vs. rainfall vs. tide) of inlets in different coastal and hydroclimatic settings around the globe

InletTracker - A python toolkit for monitoring coastal inlets via Landsat and Sentinel-2

This repository contains the entire open-source code of InletTracker as well as the key input data necessary to reproduce the results of the corresponding journal publication. InletTracker is a Google Earth Engine enabled open-source python software package that first uses a novel least-cost path finding approach to trace inlet channels along and across the berm/barrier/bar, and then analyses the resulting transects to infer whether an entrance is open or closed. Our study highlighted that InletTracker is able to consistently and accurately infer open vs. closed inlet states and can even provide an indication of the degree of inlet opening for larger inlets. The data that InletTracker can generate will help to answer a range of remaining questions around processes, dynamics, and drivers (i.e., waves vs. rainfall vs. tide) of inlets in different coastal and hydroclimatic settings around the globe

Addressing spatio-temporal resolution constraints in Landsat and MODIS-based mapping of large-scale floodplain inundation dynamics

Recent studies have developed novel long-term records of surface water (SW) maps on continental and global scales but due to the spatial and temporal resolution constraints of available satellite sensors, they are either of high spatial and low temporal resolution or vice versa. In this study, we address this limitation by exploring two approaches for generating an 8-day series of Landsat resolution (30 m) SW maps for three floodplain sites in south-eastern Australia during the 2010 La Nina Floods. Firstly, we applied a generalized additive regression model (GAM) that empirically relates Landsat-based SW extent to in-situ river flow to then predict additional time steps. Secondly, we used the STARFM and ESTARFM blending algorithms for predicting the Open Water Likelihood at 8-daily intervals and 30 m resolution from Landsat and MODIS imagery. ESTARFM outperformed STARFM and best blending accuracies were achieved in the floodplain site with the slowest changes in inundation extent through time. There was good agreement between the blended and statistically-modeled 8-day SW series and both series provided new and temporally consistent information about changes in inundation extent throughout the flooding cycles. After careful consideration of accuracy limitations and model assumptions, these SW records hold great potential for assimilation into hydrodynamic and hydrological models as well as improved management of terrestrial freshwater ecosystems

Modeling 25 years of spatio-temporal surface water and inundation dynamics on large river basin scale using time series of Earth observation data

The usage of time series of Earth observation (EO) data for analyzing and modeling surface water extent (SWE) dynamics across broad geographic regions provides important information for sustainable management and restoration of terrestrial surface water resources, which suffered alarming declines and deterioration globally. The main objective of this research was to model SWE dynamics from a unique, statistically validated Landsat-based time series (1986-2011) continuously through cycles of flooding and drying across a large and heterogeneous river basin, the Murray-Darling Basin (MDB) in Australia. We used dynamic linear regression to model remotely sensed SWE as a function of river flow and spatially explicit time series of soil moisture (SM), evapotranspiration (ET), and rainfall (P). To enable a consistent modeling approach across space, we modeled SWE dynamics separately for hydrologically distinct floodplain, floodplain-lake, and non-floodplain areas within eco-hydrological zones and 10km × 10km grid cells. We applied this spatial modeling framework to three sub-regions of the MDB, for which we quantified independently validated lag times between river gauges and each individual grid cell and identified the local combinations of variables that drive SWE dynamics. Based on these automatically quantified flow lag times and variable combinations, SWE dynamics on 233 (64%) out of 363 floodplain grid cells were modeled with a coefficient of determination (r2) greater than 0.6. The contribution of P, ET, and SM to the predictive performance of models differed among the three sub-regions, with the highest contributions in the least regulated and most arid sub-region. The spatial modeling framework presented here is suitable for modeling SWE dynamics on finer spatial entities compared to most existing studies and applicable to other large and heterogeneous river basins across the world