Sea level rise (SLR) poses a hazard to ecosystems and economies in low-lying coastal and estuarine areas. To better understand the potential impacts of SLR in estuaries, a comprehensive review of existing theory, literature, and assessment tools is undertaken. In addition, several conceptual models are introduced to assist in understanding interlinked estuarine processes and their complex responses to SLR. This review indicates that SLR impacts in estuaries should not be assessed via static (bathtub) approaches as they fail to consider important hydrodynamic effects such as tidal wave amplification, dampening, and reflection. Where hydrodynamic models are used, the existing literature provides a relatively detailed understanding of how SLR will affect estuarine hydrodynamics (e.g., tides and inundation regimes). With regards to the current understanding of, and ability to model, the connections between altered hydrodynamics (under SLR) and dependent geomorphic, ecological, and bio-geochemical processes, significant knowledge gaps remain. This is of particular concern as there is currently a paradigm shift towards more integrated and holistic management of estuaries. Estuarine management under accelerating SLR is likely to become increasingly complex, as decision-making will be undertaken with uncertainty. As such, this review highlights that there is a fundamental requirement for more sophisticated and interdisciplinary studies that integrate physical, ecological, bio-geochemical, and geomorphic responses of estuaries to SLR.

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 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 10 km × 10 km 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.

River deltas and estuaries belong to the most significant coastal landforms on our planet and are usually very densely populated. Nearly 600 million people live in river deltas, benefiting from the large variety of locational advantages and rich resources. Deltas are highly dynamic and vulnerable environments that are exposed to a wide range of natural and man-made threats. Sustainable management of river deltas therefore requires a holistic assessment of historic and recent ongoing changes and the dynamics in settlement sprawl, land cover and land use change, ecosystem development, as well as river and coastline geomorphology, all of which is difficult to achieve solely with traditional land-based surveying techniques. This review paper presents the potential of Earth Observation for analyses and quantification of land surface dynamics in the large river deltas globally, emphasizing the different geo-information products that can be derived from medium resolution, high resolution and highest resolution optical, multispectral, thermal and SAR data. Over 200 journal papers on remote sensing related studies for large river deltas and estuaries have been analyzed and categorized into thematic fields such as river course morphology, coastline changes, erosion and accretion processes, flood and inundation dynamics, regional land cover and land use dynamics, as well as the monitoring of compliance with respect to anthropogenic activity such as industry expansion-related habitat destruction. Additionally, our own exemplary analyses are interwoven into the review to visualize related delta work.