Flow in formations having both low-porosity and low-permeability prevalently occurs in fracture networks. Fracture connectivity and permeability are the main features governing flow in these media, and affect the effectiveness of any subsurface operation (e.g. enhanced oil recovery, enhanced geothermal systems). Moreover, the rheology of the fluid is often non-Newtonian due to their complex microstructure, resulting in a non-linear constitutive law at the continuum scale. Modeling flow of complex fluids in a fracture is challenging, when both medium heterogeneity and a realistic fluid rheology, typically shear-thinning, are considered. Full 3-D simulations are computationally expensive and time-consuming; alternatively, a lubrication-based approach can be adopted to implement an efficient 2-D flow solver able to produce vast statistics in reasonable time. We propose a numerical code that solves the 2-D generalized non-linear Reynolds equation for Ellis fluid flow in a single variable aperture fracture, adopting the finite volume method. The inexact Newton-Krylov method solves the associated non-linear system of equations, while a preconditioned conjugate gradient algorithm is used to solve the linear system at each Newton iteration. A parameter continuation strategy is also introduced to handle strongly non-linear cases. A synthetic fracture aperture field w(x) is generated and the flow problem is solved. The numerical scheme is particularly efficient and robust for most input parameters of practical interest. Numerical parameters of the Inexact Newton-Krylov method have been optimized to simulate flow on large mesh (e.g. 1010x1010), considering a range of fracture closure (σw/‹w›) from 0 to 1, a shear-thinning index (n) of the Ellis rheologic model varying from 0.1 (strongly shear-thinning behavior) to 1 (Newtonian case). Pressure gradients (∇P) typical of subsurface flow, either natural or forced, have been considered to study natural phenomena and industrial applications.

Decision makers and water managers throughout New England desire to understand how development and land-use change, especially under a changing climate, will affect high flows, flooding, and stormwater. We use the New England Landscape Futures (NELF) project to demonstrate the potential for translating participatory scenarios to simulations of land use and land cover and the resulting effects on streamflow. In addition to projecting recent trends, four other landscape scenarios were co-designed through a structured process that engaged over 150 stakeholders and scientists from throughout New England. Daily streamflows were simulated with the Soil and Water Assessment Tool (SWAT) to investigate how high flows vary among the scenarios for the less developed Cocheco River watershed in southeast New Hampshire and the more urbanized Charles River watershed in eastern Massachusetts. The hydrologic response of each land-use scenario was simulated for both historic weather (1999-2017) and downscaled weather for 2049-2067 from the CCSM 4.0-RCP 8.5 model-pathway from the Community Earth System Model. Differences among the land-use scenarios led to no differences in average annual water yield and ET. Loss of forest and increase in urban area reduces the baseflow contribution to streamflow while increasing storm runoff. This shift in partitioning did not affect the frequency of high flows (5% exceedence). The increase in runoff did lead to a concomitant increase in the average annual maximum flow, and the effect is larger in the Charles River watershed than in the Cocheco. Under the future climate, a combination of increased precipitation and decreased potential evaporation results in increased streamflow relative to the scenarios modeled with the historic weather. As a fraction of precipitation, surface runoff remains the same, and baseflow increases. The frequency of high flows increases, with the 5%-exceedence flow (under historical weather) being met or exceeded 8-9% of the days. Annual maximum flows also increased for the future climate, and the effects of land-use change and climate on annual maximum flows are comparable. These water-related results fit into a larger framework for evaluating ecosystem services associated with socially relevant landscape scenarios.

Accurately measuring CO2 system variables in the ocean is necessary not only to estimate carbon fluxes, but also to understand changes in seawater chemistry and its effect on marine organisms. Total alkalinity (AT) is one of the most commonly used parameters to characterize the marine CO2 system, but compared to the other measureable CO2 system variables, the measurement of AT necessarily titrates all bases present in seawater, and not just those of interest (carbonate and bicarbonate). While the majority of non-CO2 bases in seawater are identified and easily accounted for, there is extensive evidence for unidentified bases (excess alkalinity, AX) at levels exceeding the uncertainty of the AT measurement. Failure to account for these unidentified bases in a quantitatively meaningful way will lead to errors in interpreting AT as a CO2 system variable. Few methods estimating AX are presented with uncertainty estimates, and none have so far dealt with the uncertainty in the total boron content (BT), the second largest acid-base system in the ocean following the CO2 system. I will describe an open-cell titration approach that can, together with equilibrium modelling, quantify AX as a combination of one or several acid-base systems with apparent total concentration(s) (XT) and acid dissociation constant(s) (KX). The method includes titration of a seawater sample with a strong acid to estimate AT, followed by quantitative removal of CO2. The sample is then titrated with a strong base, and data from this “back-titration” can be used to quantify AX. An uncertainty assessment is provided that includes systematic errors and effects of CO2 contamination, supplemented by colorimetric measurements of BT. This work provides realistic uncertainties of AT measurements made in areas suspected of having a high presence of unidentified acid-base systems, thus contributing to a more accurate assessment of changes in marine CO2 chemistry and speciation in many environments.

Coronal holes (CH) are dark areas in EUV images that are generally associated with open magnetic field regions on the Sun. They can be used to estimate the open magnetic flux in the heliosphere by overlaying them on magnetic field measurements. Accurately measuring the CH boundaries over the whole Sun remains challenging due to many factors, including limited instrument coverage, obstruction by nearby bright structures, and the assumptions of a given detection algorithm. Here we explore the effects of CH obscuration using a global thermodynamic MHD model of the corona. We generate synthetic EUV images for several sets of observer locations, and combine them into maps using current and new obscuration-mitigating strategies. CH maps are generated from each resulting EUV map (using an established CH detection algorithm) and used to estimate the open flux. The importance of synchronizing the effective EUV image height to the height of the magnetic field values is demonstrated. Comparisons of the CH contours and open flux results with the known open field in the simulation gives insight into how much CH obscuration might influence observationally detected CH maps and open flux estimates. Application of obscuration-mitigating mapping techniques to observations is also discussed.

Dam construction is on the rise in developing nations. Monitoring of these dams is necessary to understand downstream hydrologic impacts and for better planning and management of water resources. Satellite observations and advancements in information technology now present a unique opportunity to overcome the traditional limitations of reservoir monitoring. In this study, a global reservoir monitoring framework is developed as an online tool for near-realtime monitoring and impact analysis of existing and planned reservoirs based on publicly available and global satellite observations. The framework uses a mass balance approach to monitor 1598 reservoirs in South America, Africa, and South-East Asia region. The framework simulated streamflow was validated in 18 river basins and the storage change was validated against in-situ data of 77 reservoirs. The framework was able to capture reservoir state realistically for more than 75% of the reservoirs. The tool can now be used to study existing and planned reservoirs for hydrologic impact and operating pattern for short and long term decision making and policy analysis.

Observations of rocks from exhumed shear zones clearly reveal that secondary phases strongly influence the mechanical and microstructural evolution of materials undergoing large-strain shear deformation. Through Zener pinning, secondary phases promote grain size sensitive creep, allowing deformation to localize in fine-grained regions. For the longevity of such shear zones over geological times, fine grain sizes must be maintained between episodes of deformation such that localization will continue in these regions in subsequent deformation events. Experimental studies of static grain growth on single phase materials demonstrate relatively fast rates of grain growth that would serve to undo grain size refinement under natural conditions. Although static grain growth experiments on samples composed of two or more phases indicate slower growth rates of each phase, such studies have typically been carried out on undeformed material. To investigate the effectiveness of secondary phases at inhibiting grain growth after large-strain deformation, analog samples were synthesized from olivine (Ol) and ferropericlase (Fp) powders with Ol:Fp ratios of 1:5 to 5:1. Samples were deformed in torsion in a triaxial gas-medium apparatus to shear strains of γ = 3 - 7 at P = 300 MPa and T = 1523 K to induce mixing between the two phases; specifically, the distribution of phase boundaries followed a random binomial distribution. Subsequently, sections of each sample were statically annealed at P = 0.1 MPa and T = 1523 K for 10 h or 100 h. Grain size measurements obtained via electron backscatter diffraction indicate that after 10 h of post-deformation annealing Ol grains are smaller by a factor of 1.5 and Fp grains are smaller by a factor of 3 than predicted from single-phase grain growth laws. Comparing the ratio of grain sizes of the two phases to the secondary phase fraction yields a power law fit with an exponent of ~ 0.4 in Ol-rich samples and ~ 1 in Fp-rich samples. These results, along with microstructural observations, indicate that secondary phase particles are primarily distributed along grain boundaries in the Ol-rich samples but are randomly dispersed in Fp-rich samples. Our results demonstrate that secondary phases are highly effective at pinning grain size during static annealing following significant deformation.

Ambient air quality (AQ) is an environmental and socio-economic issue increasingly decisive in the sustainable development of a territory. At low density territories of Continental Portugal, a good AQ can contribute to the development of various sectors of activity like health and wellness tourism and organic farming, affecting positively the socio-economic situation. The main goal of this study was to build information on ozone concentration in ambient air for the present and on a climate change scenario. The territory case study – the “cerne do Entre-Norte-e-Centro”–, consists of seven municipalities: Aguiar da Beira, Castro Daire, Moimenta da Beira, Sátão, Sernancelhe, Tarouca and Vila Nova de Paiva. Geographically it belongs to the North and Centre of Mainland Portugal (NUTS II) and the Douro and Dão-Lafões subregions (NUTS III). In this area (151.195 hectares) there is not any station of AQ monitoring. Data provided by the Portuguese Air Quality Network was analysed to determine the spatio-temporal evolution of different pollutants covered by actual Portuguese legislation, with special focus on ozone and nitrogen dioxide. This information was compared with the data provided by Copernicus Atmosphere Monitoring Service. It was build hazard and vulnerability charts as well as the chart of risk to the territory under study. This knowledge will improve the decision-making process in terms of public politics. In addition it contributes to an increased visibility and attractiveness of this area, as a tourism destination through environmental differentiation in this endogenous resource.

Turbulence and icing represent a danger for the aircrafts. In fact, turbulence is the leading cause of aircraft accidents related to meteorology. Both turbulence and icing weather hazards can occur in mountain waves simultaneously. For this reason, it is important to study these weather phenomena. Here, the HARMONIE-AROME model is used in order to analyze mountain wave events in the Guadarrama and the Cantabrian Mountain Ranges (Spain). This short-range mesoscale numerical weather prediction model is used for operational weather forecasts in many European meteorological organizations with a horizontal resolution of 2.5 km. In this study, a spatial resolution of 1 x 1 km is selected. In the analyzed areas, mountain waves are formed on the leeward side when strong winds, perpendicular to the mountains (usually north or northwest winds), are forced to ascend. Simulated total cloud cover and z-wind component have been analyzed and compared with satellite images. The validation of these mountain wave episodes between the HARMONIE-AROME simulations and satellite observations were satisfactory. The most notable differences were observed in the hours of the mountain wave formation/dissipation.

The Neoproterozoic Era represents extreme environmental conditions with three major glaciation events. Associated with these glaciation and deglaciation periods are high amplitude positive and negative δ13C excursions which have been observed in the rock records, suggesting perturbations in oceanic biogeochemical cycles. However, whether these isotopic records reflect primary depositional signature and open ocean condition are debated. The Marwar Basin of the Indian Shield preserves one such record of the Ediacaran time period. The magnitude of negative δ13C excursion from the Marwar Basin is comparable with the Shuram excursion.1 We report elemental compositions of sixty-eight carbonate samples collected from five spatially distributed sections from the Bilara carbonates of the Marwar Basin. Selected samples from two of these sections were analyzed for their radiogenic Sr (87Sr/86Sr) and stable Ca (δ44/40Ca) isotopic compositions. Elemental compositions were measured using an Inductively Coupled Plasma Mass Spectrometer (ICP-MS, X series II) while 87Sr/86Sr and δ44/40Ca (reported relative to NIST SRM 915a) values were measured using a Thermal Ionization Mass Spectrometer (TIMS, Triton Plus), both at the CEaS, IISc Bangalore, India. The Bilara Group carbonates are sub-divided into two populations based on non-redox REY anomalies and the Y/Ho ratio. Super-Chondritic Y/Ho (40-52) and positive La anomaly (1.01-2.65) of some samples suggest deposition under open ocean conditions and connectivity to the global ocean. While heterogeneity in δ13C values is evident in samples with low Y/Ho (<40), samples with high Y/Ho (> 40) preserve low δ13C values. No significant correlation has been observed between δ13C and Ce anomaly (Ce/Ce*) suggesting absence of any paleo-redox gradient. The lowest 87Sr/86Sr (~0.7079) observed in the carbonates is comparable with Ediacaran seawater confirming retention of primary depositional signatures in these samples. These carbonates show heavy δ44/40CaSRM915a compositions (1.44‰-2.21‰) typical of Neoproterozoic post-glacial successions2. Our study confirms primary origin and open ocean nature of the late Neoproterozoic δ13C excursions in the Marwar Basin. [1] Ansari et al., (2018) Precambrian Research, [2] Silva-Tamayo et al., (2010) Precambrian Research

The Plankton, Aerosol, Cloud, and Ocean Ecosystem (PACE) mission is NASA’s next great investment in Earth Science, continuing NASA’s legacy of over forty years of satellite ocean color measurements. PACE, expected to launch in 2023, will advance our Earth-observing and monitoring capabilities through hyperspectral imaging and multi-angle polarimetric observations of ocean, atmosphere, and land ecosystems. PACE will give us an unprecedented view of our home planet and will support user-driven environmental applications through research and applied science to address societal challenges and inform decision-making. An integral component of actionable applied science is Design Thinking - an iterative, problem-solving framework that integrates human perspectives, needs, and experiences at every step of process. In this session, we will present the design process, collaborative activities, and outcomes of the 2021 PACE Applications Water Quality community focus session. A Design Thinking methodology was used in event planning as well as during day-of ideation breakout sessions. To foster empathy and better illuminate the goals, concerns, and needs of the diverse PACE user community, eight draft user personas were created to represent a range of water industry users from research to government to the private sector. Attendees worked together to complete the various personas by identifying different user challenges and pain points, ideal data experiences, and realistic, tailored Earth Observation and PACE Mission specific solutions and opportunities to satisfy users’ needs and goals. As a result, the eight archetype personas and co-production of knowledge will help ensure that PACE data are usable and accessible for a variety of possible users, thereby expanding the eventual reach and societal benefit of PACE. Lastly, we will highlight how Design Thinking will inform future stakeholder engagement efforts and actionable science via the PACE Mission.

The well-investigated aquifer in southern Tunisia have been selected from the literature to test the potential use of uranium isotopic compositions as a groundwater dating method. This is the Senonian carbonate aquifer of the Nefzaoua basin (https://doi.org/10.1016/j.quaint.2020.01.024). For the this aquifer, an increase in U concentrations along the generalized flow path is observed in proportion to an increase in total dissolved solids, which may indicate the predominance of dissolution processes over alpha-recoil processes under oxidizing conditions for uranium. There is also an increase in U concentrations with a decrease in 14C values. For this aquifer, positive results were obtained on uranium-isotope dating. It was found that the groundwater residence time in the aquifer increases from 440 to 11,300 years from the recharge area along the generalized flow path (Figure). Uranium age correction model under oxidizing conditions in the aquifer is given in “Malov, 2018. Evolution of the groundwater chemistry in the coastal aquifers of the south-eastern White Sea area (NW Russia) using 14C and 234U-238U dating. Science of the Total Environment. https://doi.org/10.1016/j.scitotenv.2017.10.197” It is shown that for uranium-isotope dating of groundwater, three main conditions must be met: i) oxidizing conditions for uranium in the aquifer, ii) an increase in uranium concentrations with a decrease in 14C activities, and iii) the homogeneity of the aquifer in terms of hydraulic conductivity and lithological composition. The author understands the reality of the fact that groundwater dating methods are under development and improvement. Nevertheless, analysis of the evolution of the chemical and isotopic composition of groundwater, analysis of the geological and hydrogeological history of the region, hydraulic estimates of groundwater velocities in aquifers, hydrodynamic and balance justifications for the formation of groundwater, and analysis of the composition of stable isotopes can increase the reliability of dating. In the future, it is necessary to continue studies to assess the retardation factor and recoil loss factor in order to improve the uranium-isotope method for dating groundwater under oxidizing conditions for uranium. This work was supported by the RFBR (project no. 20-05-00045_A)

Long-term soil moisture datasets at high spatial resolution are important in agricultural, hydrological, and climatic applications. The soil moisture estimates can be achieved using satellite remote sensing observations. However, the satellite soil moisture data are typically available at coarse spatial resolutions (~ several tens of km), therefore require further downscaling. Different satellite soil moisture products have to be conjointly employed in developing a consistent time-series of high resolution soil moisture, while the discrepancies amongst different satellite retrievals need to be resolved. This study aims to downscale three different satellite soil moisture products, the Soil Moisture and Ocean Salinity (SMOS, 25 km), the Soil Moisture Active Passive (SMAP, 36 km) and the SMAP-Enhanced (9 km), and to conduct an inter-comparison of the downscaled results. The downscaling approach is developed based on the relationship between the diurnal temperature difference and the daily mean soil moisture content. The approach is applied to two sub-catchments (Krui and Merriwa River) of the Goulburn River catchment in the Upper Hunter region (NSW, Australia) to estimate soil moisture at 1 km resolution for 2015. The three coarse spatial resolution soil moisture products and their downscaled results will be validated with the in-situ observations obtained from the Scaling and Assimilation of Soil Moisture and Streamflow (SASMAS) network. The spatial and temporal patterns of the downscaled results will also be analysed. This study will provide the necessary insights for data selection and bias corrections to maintain the consistency of a long-term high resolution soil moisture dataset. The results will assist in developing a time-series of high resolution soil moisture data over the south-eastern Australia.

Any catalogue describing objects will contain metadata: such systems are built to improve findability (F) and accessibility (A). However, a content rich metadata framework, carefully developed by communities with broad interests, can also ensure an interoperability (I) and re-usability (R) of described objects. The Australian Metadata Working Group (MDWG), supported by the Australian and New Zealand Land Information Council and Intergovernmental Committee for Surveying and Mapping, is building one of these FAIR frameworks. The group comprises of federal and jurisdictional governments, research organisations and academia which provides a wide spectrum of use cases of multi-disciplinary community needs. The MDWG recognises multiple aspects of reusability to ensure consistent adoption of the Australian Metadata Profile based on the ISO 19115-1 standard including: Reusability of content: defining a list of elements to ensure content-rich self-describing metadata that can be interpreted by both humans and machines to: capture data dictionaries to enable dataset reconstruction record technical details for services to ensure their correct usage and associated code reuse understand resource quality and provenance to ensure its correct usage specify licence and security conditions to understand preventing the reuse factors consistent reuse of existing community vocabularies (or develop and openly publish new for reuse by others) record resource formats to support access to resources Reusability of the Australian Metadata Profile through publishing its model and XML to ensure consistent adoption of metadata patterns: developing XML examples building user guides and other communication materials Reusability of tools and their deployment: investigation and testing metadata creation, publishing and validation tools, sharing tricks and lessons learnt The MDWG has successfully delivered reusable tools, a consistent profile and user guide plus defined metadata elements with clear purposes. The wealth of combined expertise, sharing of resources and technical support also provided the first example of the ISO 19115-1 standard adoption and re-use of metadata tools, thus, providing great savings to organisations in developing and implementation time and budget.

For decades, agricultural expansion in Southern Amazonia has relied on deforestation to increase the national and global supplies of cattle and soybean products. Research on the use of land and water for increasing the region’s pasture and cropland outputs has provided important insight into the role of producers in managing these resources. However, the roles of other actors on cattle and soybean production systems (e.g. traders) have been less apparent. For instance, some private initiatives, such as the Cattle Agreement or the Soybean Moratorium, have been proposed to curb deforestation through the cattle and soybean supply chains as a means to influence the production process. Here, we highlight the role of producers and consumers in the use of land and water resources for agricultural production in the state of Mato Grosso, Brazil, by combining field measurements and modelling with trade mapping. We use the Transparency for sustainable economies platform (Trase, https://trase.earth) to highlight the role of trade actors by combining high resolution trade information (e.g. custom declarations) with up-to-date deforestation and water scarcity maps. In 2015, up to five traders with zero deforestation commitments exported over 40% of soybean produced in the state of Mato Grosso. Within this context, producers can increase agricultural output by irrigating cropland, and/or concentrating cattle production on current pastureland, but this combined system has the potential to increase water scarcity in the dry season. Our analysis provides additional information on the drivers shaping the Brazilian agricultural frontier and attempts to bring producers and consumers closer together in agricultural supply chains.

A halo Coronal Mass Ejection can have a devastating impact on Earth by causing damage to satellites and electrical transmission line facilities and disrupting radio transmissions. To predict the orientation of the magnetic field (and therefore the occurrence of a geomagnetic storm) associated with an occurring CME, filaments’ sign of magnetic helicity can be used. This would allow us to predict a geomagnetic storm. With the deluge of image data produced by ground-based and space-borne observatories and the unprecedented success of computer vision algorithms in detecting and classifying objects (events) on images, identification of filaments’ chirality appears to be a well-fitted problem in this domain. To be more specific, Deep Learning algorithms with a Convolutional Neural Network (CNN) backbone are made to attack this very type of problem. The only challenge is that these supervised algorithms are data-hungry; their large number of model parameters demand millions of labeled instances to learn. Datasets of filaments with manually identified chirality, however, are costly to be built. This scarcity exists primarily because of the tedious task of data annotation, especially that identification of filaments’ chirality requires domain expertise. In response, we created a pipeline for the augmentation of filaments based on the existing and labeled instances. This Python toolkit provides a resource of unlimited augmented (new) filaments with labeled magnetic helicity signs. Using an existing dataset of H-alpha based manually-labeled filaments as input seeds, collected from August 2000 to 2016 from the big bear solar observatory (BBSO) full-disk solar images, we augment new filament instances by passing labeled filaments through a pipeline of chirality-preserving transformation functions. This augmentation engine is fully compatible with PyTorch, a popular library for deep learning and generates the data based on users requirement.

Open water evaporation from reservoirs and lakes is becoming increasingly important for water management under a changing climate and increasing demands from growing populations. Remotely sensed evapotranspiration (ET) data have significantly advanced from MODIS to Landsat to ECOSTRESS. Here, we evaluate remotely sensed open water evaporation from NASA JPL’s ET data production team (e.g., ECOSTRESS) against in situ measurements of evaporation from multiple sites around the world.

The onset of plant flowering is one of many factors affected by climate change. This has important consequences for ecosystems, where plants and pollinators are at risk of a temporal mismatch. This also has implications for crops. The timing of their highest-sensitivity window is affected, despite some control exerted by agricultural management. Current efforts to monitor the onset of flowering rest on numerous but sparse ground observations, or on proxies like temperature models or vegetation indices. In crop studies, a time-invariant harvesting calendar is most widely used to infer the flowering period for crops. We use spectral data from satellite imagery, combined with convolutional neural networks, to create a large-scale, high-resolution proxy for the timing of flowering. We exploit a transfer learning method to overcome the sparsity of ground truth data on plant phenology. We first train a temporal convolutional neural network to predict a temperature-based proxy for flowering (the First Bloom Index from the National Phenology Network), using as input the 8-day composite from MODIS at a 500m resolution. This model learns relevant temporal patterns on both short and long time scales within a year, thanks to a dilated convolution structure. We then fine-tune the parameters from the last fully connected layer using ground observations from the National Phenology Network as training data. The root mean squared error of our model is about 8.6 days, though this average hides much spatial heterogeneity: in many places, the model performs well-enough to capture year-to-year variations, with a prediction within a couple days of the target, while it performs extremely poorly in a few places. Our model outperforms the temperature-based proxy since the latter has a root mean squared error of about 9.3 days on the same testing data. More importantly, our model extrapolates much better over space: it has a root mean squared error of about 12 days when predicting outside of our study region, compared to a 35-day error for the temperature-based model. This is a key feature, since our goal is to fill in the spatial gaps left by the sparse ground truth observations. These large-scale predictions of annual flowering times could offer insights into plant-pollinator desynchronization and crop calendar variation under climate change, and could be used for further applications like studying the health impacts of shifts in spring flowering times.

Coastal wetlands provide a livelihood for local communities and simultaneously provide services that include coastal protection, enhanced carbon storage and habitats provision for both terrestrial- and marine life. Yet, the development of climate resilient management strategies that need to account for changes that might occur in the coming decades is challenging due to the variable response of coastal ecosystems. This is because coastal ecosystems, including mangroves, wetlands and tidal flats, are directly linked to coastline dynamics, as coastlines can move seaward, when waves are low and sediment availability is high, or retreat when the opposite is true. Particularly the long-term fate of the mud dominated coastline of Suriname, part of the Guianas coastal system stretching from the Amazon to the Orinoco delta, is determined by migrating subtidal mudbanks that cause a cyclic instability of alternating erosion and progradation phases. We present a semi-automatic remote sensing approach to quantify the influence of mudbank migration on coastline dynamics along the entire coast of Suriname. We validated our approach with high resolution drone imagery collected at contrasting locations, indicating average accuracy of changes in coastline position to be within 50 meters. This suggest we can apply our methodology on all available Landsat images between 1985 and 2020, acquired over the heterogonous and complex coastal landscapes. The results show that regional forcing mechanisms, related to migration of six to eight subtidal mudbanks in front of the Suriname coast, have a strong imprint on local coastline dynamics with an average 25 m/yr-1 expansion during mudbank presence, and 8 m/yr-1 retreat of the coastline during mudbank absence between 1986 and 2020. More importantly, we found that not all spatial and temporal variations in the magnitude and timing of local changes can be explained by migrating mudbanks. This demonstrates the importance of incorporating changes that cannot be explained by regional forcing mechanisms in management frameworks that aim at explaining current variability and predicting future coastline changes.