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

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

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

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 multidecadal surface water inundation dynamics and key drivers on large river basin scale using multiple time series of Earth-observation and river flow data

Periodically inundated floodplain areas are hot spots of biodiversity and provide a broad range of ecosystem services but have suffered alarming declines in recent history. Despite their importance, their long-term surface water (SW) dynamics and hydroclimatic drivers remain poorly quantified on continental scales. In this study, we used a 26 year time series of Landsat-derived SW maps in combination with river flow data from 68 gauges and spatial time series of rainfall, evapotranspiration and soil moisture to statistically model SW dynamics as a function of key drivers across Australia's Murray-Darling Basin (∼1 million km2). We fitted generalized additive models for 18,521 individual modeling units made up of 10 × 10 km grid cells, each split into floodplain, floodplain-lake, and nonfloodplain area. Average goodness of fit of models was high across floodplains and floodplain-lakes (r2 > 0.65), which were primarily driven by river flow, and was lower for nonfloodplain areas (r2 > 0.24), which were primarily driven by rainfall. Local climate conditions were more relevant for SW dynamics in the northern compared to the southern basin and had the highest influence in the least regulated and most extended floodplains. We further applied the models of two contrasting floodplain areas to predict SW extents of cloud-affected time steps in the Landsat series during the large 2010 floods with high validated accuracy (r2 > 0.97). Our framework is applicable to other complex river basins across the world and enables a more detailed quantification of large floods and drivers of SW dynamics compared to existing methods

The role of GRACE total water storage anomalies, streamflow and rainfall in stream salinity trends across Australia's Murray-Darling Basin during and post the Millennium Drought

By influencing water tables of saline aquifers, multiyear dry or wet periods can significantly delay or accelerate dryland salinity, but this effect remains poorly quantified at the large river basin scale. The Gravity and Climate Recovery Experiment (GRACE) satellite measures changes in the total water storage of river systems, providing a unique opportunity for better understanding connections between stream salinity and changes in catchment water storages at the large river basin scale. Here, we quantified the role of GRACE total water storage anomalies (TWSA) in stream salinity variability across Australia's Murray-Darling Basin ( similar to 1 million km(2)), while also accounting for streamflow and rainfall. We used the MERRA-2 global land surface model to i) place our findings in the context of the longer-term hydroclimatology (1980-present) and ii) to decompose TWSA into groundwater storage as an alternative driver variable. Multivariate time series regression models (generalized additive mixed models or GAMM) showed that the driver variables could explain 20-50% of the variability in stream salinity across 8 sub-catchments in the Murray Darling Basin. TWSA commonly explained as much variability as stream flow, while groundwater storage and TWSA had very similar explanatory power and rainfall only negligible contributions. The 2000-2009 Millennium Drought and the subsequent La Nina Floods had a predominantly decelerating and accelerating effect on stream salinity respectively and these trends were partially explained by trends in TWSA. Our study illustrates that GRACE can be a useful addition for monitoring and modeling dryland salinity over large river basins