Waves and water level setup during storms can create overwashing flows across barrier islands. Overwashing flows can cause erosion, barrier breaching, and inlet formation, but their sediments can also be deposited and form washover fans. These widely different outcomes remain difficult to predict. Here we suggest that a breach develops when the sediment volume transported by overwashing flows exceeds the barrier subaerial volume. We form a simple analytical theory that estimates overwashing flows from storm characteristics, barrier morphology, and dune vegetation, and which can be used to assess washover deposition and breaching likelihood. Our theory suggests that barrier width and storm surge height are two important controls on barrier breaching. We test our theory with the hydrodynamic and morphodynamic model Delft3D as well as with field observations of 21 washover fans and 6 breaches that formed during hurricane Sandy. There is reasonable correspondence for natural but not for developed barrier coasts, where traditional sediment transport equations do not readily apply. Our analytical formulations for breach formation and overwash deposition can be used to improve long-term barrier island models.
Through machine learning and remote sensing, a high-end model with a finer resolution for groundwater recharge has been developed for the region of South-East Asia. The groundwater recharge coefficient can be found by the application of Random Forest regression followed by the implication of the water budget method to calculate the Groundwater Recharge values. Climatic factors such as precipitation and actual evapotranspiration to map Groundwater Recharge has been framed with a sophisticated machine learning method to be considered as a scale predicting model. A comprehensive visualization of the dataset has been done; the accuracy of the model is noted through random forest regression. Thus, the model can be used for various regions of the dataset specifically for the area where there is a lack of reach for data. It can be successfully used to form a sophisticated end-to-end ML model. Keywords: Machine Learning, Remote Sensing, Groundwater Recharge, Climate science.
Anthropogenic litter is omnipresent in terrestrial and freshwater systems, and can have major economic and ecological impacts. Monitoring and modelling of anthropogenic litter comes with large uncertainties due to the wide variety of litter characteristics, including size, mass, and item type. It is unclear as to what the effect of sample set size is on the reliability and representativeness of litter item statistics. Reliable item statistics are needed to (1) improve monitoring strategies, (2) parameterize litter in transport models, and (3) convert litter counts to mass for stock and flux calculations. In this paper we quantify sample set size requirement for riverbank litter characterization, using a database of more than 14,000 macrolitter items (>0.5 cm), sampled for one year at eight riverbank locations along the Dutch Rhine, IJssel and Meuse rivers. We use this database to perform a Monte Carlo based bootstrap analysis on the item statistics, to determine the relation between sample size and variability in the mean and median values. Based on this, we present sample set size requirements, corresponding to selected uncertainty and confidence levels. Optima between sampling effort and information gain is suggested (depending on the acceptable uncertainty level), which is a function of litter type heterogeneity. We found that the heterogeneity of the characteristics of litter items varies between different litter categories, and demonstrate that the minimum required sample set size depends on the heterogeneity of the litter category. More items of heterogeneous litter categories need to be sampled than of heterogeneous item categories to reach the same uncertainty level in item statistics. For example, to describe the mean mass the heterogeneous category soft fragments (>2.5cm) with 90% confidence, 990 items were needed, while only 39 items were needed for the uniform category metal bottle caps. Finally, we use the heterogeneity within litter categories to assess the sample size requirements for each river system. All data collected for this study are freely available, and may form the basis of an open access global database which can be used by scientists, practitioners, and policymakers to improve future monitoring strategies and modelling efforts.
The grandest geotourism attractions in the southern hemisphere, in the nineteenth century were the siliceous Pink and White Terraces, the lost New Zealand Eighth Wonder of the World. In 1886, the Tarawera eruption buried the terraces. In the absence of a government survey or evidence of their locations; public debate over their survival ensued until the 1940s. Recently, a unique survey was uncovered and led researchers at last to the Terrace locations. Early colonial visitors were told by traditional landowners, that the major White Terrace spring erupted in strong easterly winds. Having researched the Pink and White Terraces for some years, this 1859 report puzzled me, as it did Ferdinand Hochstetter to whom the first report was made in 1859. From previous studies in automotive crankcase ventilation, I could see a potential causal pathway for these east-wind spring eruptions. After examining the topography of the White Terrace spring, embankment and apron: I suggest the puzzling eruptions were a product of three phenomenae: the Venturi and Coandă effects, with Bernoulli’s principle. This paper presents the evidence for the presence of Venturi and Coandă effects at the Lake Rotomahana Basin. More importantly, it discusses how these effects contributed to postulated spring eruptions during the 1886 eruptions; which created so far unexplained water ponding around the Pink, Black and White Terrace locations. These surface waters contribute to the new paradigm for the Rotomahana Basin during the 1886 eruptions; where the topographic changes lead today’s researchers to the lost Terrace locations around the shores of the new Lake Rotomahana.
Rock glaciers manifest the creep of mountain permafrost occurring in the past or at present. Their presence and dynamics are indicators of permafrost distribution and changes in response to climate forcing. Knowledge of rock glaciers is completely lacking in the West Kunlun, one of the driest mountain ranges in Asia, where widespread permafrost is rapidly warming. In this study, we first mapped and quantified the kinematics of active rock glaciers based on satellite Interferometric Synthetic Aperture Radar (InSAR) and Google Earth images. Then we trained DeepLabv3+, a deep learning network for semantic image segmentation, to automate the mapping task. The well-trained model was applied for a region-wide, extensive delineation of rock glaciers from Sentinel-2 images to map the landforms that were previously missed due to the limitations of the InSAR-based identification. Finally, we mapped 413 rock glaciers across the West Kunlun: 290 of them were active rock glaciers mapped manually based on InSAR and 123 of them were newly identified and outlined by deep learning. The rock glaciers are categorized by their spatial connection to the upslope geomorphic units. All the rock glaciers are located at altitudes between 3,389 m and 5,541 m with an average size of 0.26 km2 and a mean slope angle of 17°. The mean and maximum surface downslope velocities of the active ones are 24 cm yr-1 and 127 cm yr-1, respectively. Characteristics of the rock glaciers of different categories hold implications on the interactions between glacial and periglacial processes in the West Kunlun.
The expected increase in rates of sea level rise during the 21st century and beyond may cause tidal inlets to expand and barrier islands to drown. However, many aspects remain unclear, e.g., the timescales involved in the drowning process have received little attention. To gain insight into the morphodynamics of barrier systems subject to sea level rise, we here present results obtained with a novel barrier island model, BRIE-D. This new model allows for changes in the alongshore extent of the barrier lying below sea level. These concern reductions in barrier width, barrier height, as well as lateral expansion of tidal inlets. Model results show that the evolution of barrier islands is susceptible to the wave height and the rate of sea level rise that they experience. It takes hundreds of years for barrier islands to drown in response to high rates of sea level rise (more than 15 mm/yr). Furthermore, increasing rates of sea level rise cause an earlier and more severe barrier drowning in environments with low waves. Barrier systems that face higher waves can undergo more frequent inlet closures (due to a larger amount of sediment imported into the inlets), but also the degree of barrier drowning might increase (due to a deepening of the toe of the shoreface). The latter process dominates over the former when rates of sea level rise are higher than 5 mm/yr.
Natural hazards such as floods, hurricanes, heatwaves, and wildfires cause significant economic losses (e.g., agricultural and property damage) as well as a high number of fatalities. Natural hazards are often driven by univariate or multivariate hydrometeorological drivers. Therefore, it is crucial to understand how and which hydrometeorological variables (i.e., drivers) combine to contribute to the impacts of these hazards. Additionally, when multiple drivers are associated with a hazard, traditional univariate risk assessment approaches are insufficient to cover the full spectrum of impact-relevant conditions originating from different combinations of multiple drivers. Based on historical socioeconomic loss data, we develop an impact-based approach to assess the influence of different hydrometeorological drivers on the impacts caused by different hazard event types. We use the Spatial Hazard Events and Losses Database for the United States (SHELDUS™) to identify the historical hazard events that caused socioeconomic impacts (property and crop damage, injuries, and fatalities) in our case study area, Miami-Dade County, in south Florida. For 9 different hazard types, we obtained data for 13 hydrometeorological drivers from historical in-situ observations and reanalysis products corresponding to the timing and locations of the hazard events found in the SHELDUS database. The relative importance of each hazard driver in generating impacts and the frequency of multiple drivers was then assessed. We found that many high-impact events were caused by multiple hydrometeorological drivers (i.e., compound events). For example, 61% of the recorded flooding events were compound events rather than univariate hazards and these contributed 99% of total property damage and 98.2% of total crop damage in Miami-Dade County. For several hazards, such as hurricanes/tropical storms and wildfires, all the events that caused damage are classified as compound events in our framework. Our findings emphasize the benefit of including socioeconomic impact information when analyzing hazard events, as well as the importance of analyzing all relevant hydrometeorological drivers to identify compound events.
Current approaches to estimate NOx emissions fail to account for new and small sources, biomass burning, and sources which change rapidly in time, generally don’t account for measurement error, and are either based on models, or do not consider wind, chemistry, and dynamical effects. This work introduces a new, model-free analytical environment that assimilates daily TROPOMI NO2 measurements in a mass-conserving manner, to invert daily NOx emissions. This is applied over a rapidly developing and energy-consuming region of Northwest China, specifically chosen due to substantial economic and population changes, new environmental policies, large use of coal, and access to independent emissions measurements for validation, making this region representative of many rapidly developing regions found across the Global South. This technique computes a net NOx emissions gain of 70% distributed in a seesaw manner: a more than doubling of emissions in cleaner regions, chemical plants, and regions thought to be emissions-free, combined with a more than halving of emissions in city centers and at well-regulated steel and powerplants. The results allow attribution of sources, with major contributing factors computed to be increased combustion temperature, atmospheric transport, and in-situ chemical processing. It is hoped that these findings will drive a new look at emissions estimation and how it is related to remotely sensed measurements and associated uncertainties, especially applied to rapidly developing regions. This is especially important for understanding the loadings and impacts of short-lived climate forcers, and provides a bridge between remotely sensed data, measurement error, and models, while allowing for further improvement of identification of new, small, and rapidly changing sources.
Detection and monitoring of tropical forest degradation is crucial to climate change mitigation and biodiversity conservation efforts. Several algorithms have been recently developed to monitor forest degradation and disturbance using remote sensing. However, these algorithms differ in local predictions due to the variation in the biogeophysical parameters used as degradation proxies. It is crucial to assess their relative performance and shortcomings in order to develop a clear understanding of the conditions under which each algorithm will detect a disturbance. In this study, we used GEDI lidar data on forest structure to examine the sensitivity of widely used forest disturbance and degradation products in a frontier tropical forest landscape in the Peruvian Amazon. We compared a leading spectral-based degradation algorithm (Continuous Degradation Detection (CODED)) with a radar-based algorithm (ALOS-2 PalSAR-2 based Radar Forest degradation Index (RFDI)). Given the sensitivity of radar to canopy cover and volume, we hypothesized that a single radar observation may detect degradation better than a long spectral time series. We first identified stable forests for reference structure in two ways: using disturbance stratification data from CODED, and using Peruvian protected areas. Our analysis showed that CODED performed below expectations in detecting forest degradation, often including patches that were regrowing after clear-felling in its “degraded” class. As CODED classified spectral changes over time rather than capturing structural variability, it classified 82% of palm plantations area as “degraded.” CODED also failed to detect degradation in forest areas that were likely partially disturbed (i.e., with low height and high cover). By contrast, the PalSAR-2 RFDI showed a significant relationship with forest height (detecting low height in degraded forests), although its predictive ability was limited due to high variability and signal saturation. Our study supports the conclusion that radar-based observation can detect forest degradation that the time series observation failed to detect. Given the limited correspondence between radar and spectral algorithms, we suggest that integrations of spectral and radar data may be beneficial for mapping forest degradation.
In a model simulating dynamics of a system, parameters can represent system sensitivities and unresolved processes, therefore affecting model accuracy and uncertainty. Taking a light use efficiency (LUE) model as an example, which is a typical approach to estimate gross primary productivity (GPP), we propose a Simultaneous Parameter Inversion and Extrapolation approach (SPIE) to overcome issues stemming from plant-functional-type(PFT)-dependent parameterizations. SPIE refers to predicting model parameters using an artificial neural network based on collected variables, including PFT, climate types, bioclimatic variables, vegetation features, atmospheric nitrogen and phosphorus deposition and soil properties. The neural network was optimized to minimize GPP errors and constrain LUE model sensitivity functions. We compared SPIE with 11 typical parameter extrapolating methods, including PFT- and climate-specific parameterizations, global and PFT-based parameter optimization, site-similarity, and regression approaches. All methods were assessed using Nash-Sutcliffe model efficiency(NSE), determination coefficient and normalized root mean squared error, and contrasted with site-specific calibrations. Ten-fold cross-validated results showed that SPIE had the best performance across sites, various temporal scales and assessing metrics. None of the approaches performed similar to site-level calibrations(NSE=0.95), but SPIE was the only approach showing positive NSE(0.68). The Shapley value, layer-wise relevance and partial dependence showed that vegetation features, bioclimatic variables, soil properties and some PFTs are determining parameters. The proposed parameter extrapolation approach overcomes strong limitations observed in many standard parameterization methods. We argue that expanding SPIE to other models overcomes current limits and serves as an entry point to investigate the robustness and generalization of different models.
Extensive loss of salt marshes in back-barrier tidal embayments is undergoing worldwide as a consequence of land-use changes, wave-driven lateral marsh erosion, and relative sea-level rise compounded by mineral sediment starvation. However, how salt-marsh loss affects the hydrodynamics of back-barrier systems and feeds back into their morphodynamic evolution is still poorly understood. Here we use a depth-averaged numerical hydrodynamic model to investigate the feedback between salt-marsh erosion and hydrodynamic changes in the Venice Lagoon, a large microtidal back-barrier system in northeastern Italy. Numerical simulations are carried out for past morphological configurations of the lagoon dating back up to 1887, as well as for hypothetical scenarios involving additional marsh erosion relative to the present-day conditions. We demonstrate that the progressive loss of salt marshes significantly impacted the Lagoon hydrodynamics, both directly and indirectly, by amplifying high-tide water levels, promoting the formation of higher and more powerful wind waves, and critically affecting tidal asymmetries across the lagoon. We also argue that further losses of salt marshes, partially prevented by restoration projects and manmade protection of salt-marsh margins against wave erosion, which have been put in place over the past few decades, limited the detrimental effects of marsh loss on the lagoon hydrodynamics, while not substantially changing the risk of flooding in urban lagoon settlements. Compared to previous studies, our analyses suggest that the hydrodynamic response of back-barrier systems to salt-marsh erosion is extremely site-specific, depending closely on the morphological characteristics of the embayment as well as on the external climatic forcings.
Salt marshes are ecosystems with significant economic and environmental value. With accelerating rate in sea-level rise, it is not clear whether salt marshes will be able to retain their resilience. Field and numerical investigations have shown that storms play a significant role in marsh accretion and that they might be crucial to salt marsh survival to sea-level rise. Here we present the results from two studies (Pannozzo et al., 2021a,b; Pannozzo et al., 2022) that used numerical and field investigations to quantify the impact of storm surges on the sediment budget of salt marshes within different sea-level scenarios and to investigate how sediment transport pathways determine marsh response to storm sediment input. The Ribble Estuary, North-West England, was used as a test case. The hydrodynamic model Delft3D was used to simulate the estuary morpho-dynamics under selected storm surge and sea-level scenarios. In addition, sediment samples collected with a monthly frequency from different areas of the marsh were analysed with sediments collected from possible sources to integrate field observations with the numerical investigation of sediment transport pathways during stormy and non-stormy conditions. Results showed that, although sea-level rise threatens the estuary and marsh stability by promoting ebb dominance and triggering a net export of sediment, storm surges promote flood dominance and trigger a net import of sediment, increasing the resilience of the estuary and salt marsh to sea-level rise, with the highest surges having the potential to offset sea-level effects on sediment transport and sediment budget of the system. However, although storm sediment input resulted to be significant for the accretion of the marsh platform and particularly for the marsh interior, data showed that storms mainly remobilise sediments already present in the intertidal system and only to a minor extent transport new sediment from external sources.ReferencesPannozzo N. et al., 2021. Salt marsh resilience to sea-level rise and increased storm intensity. Geomorphology, 389 (4): 107825.Pannozzo N. et al., 2021. Dataset of results from numerical simulations of increased storm intensity in an estuarine salt marsh system. Data in Brief, 38 (6): 107336.Pannozzo N. et al., 2022. Sediment transport pathways determine the sensitivity of salt marshes to storm sediment input. In preparation.
Extreme weather conditions are associated with a variety of water quality issues that can pose harm to humans and aquatic ecosystems. Under dry extremes, contaminants become more concentrated in streams with a greater potential for harmful algal blooms, while wet extremes can cause flooding and broadcast pollution. Developing appropriate interventions to improve water quality in a changing climate requires a better understanding of how extremes affect watershed processes, and which places are most vulnerable. We developed a Soil and Water Assessment Tool model of the Cape Fear River Basin (CFRB) in North Carolina, USA, representing contemporary land use, point and non-point sources, and weather conditions from 1979 to 2019. The CFRB is a large and complex river basin undergoing urbanization and agricultural intensification, with a history of extreme droughts and floods, making it an excellent case study. To identify intervention priorities, we developed a Water Quality Risk Index (WQRI) using the load average and load variability across normal conditions, dry extremes, and wet extremes. We found that the landscape generated the majority of contaminants, including 90.1% of sediment, 85.4% of total nitrogen, and 52.6% of total phosphorus at the City of Wilmington’s drinking water intake. Approximately 16% of the watershed contributed most of the pollutants across conditions—these represent high priority locations for interventions. The WQRI approach considering risks to water quality across different weather conditions can help identify locations where interventions are more likely to improve water quality under climate change.
The entire Earth was bombarded c.4100-3800 Ma, establishing initial conditions for all later regional geology. Deep impact-fractures have been regenerated upward from the brittle-ductile boundary by the action of convection, outgassing, circulating fluids, the twice-daily earth-tide, and earthquakes throughout all subsequent time. In consequence, many such fractures have never entirely healed or been eliminated. The two-dimensional map-outlines and circular curvature of numerous three-dimensional “craterform” scars can be readily seen, once the observer has been alerted to the possibility of their existence. Many other Hadean/EoArchean impact-scars are covered over, as is the case at present, at any given time. These impact features have been regenerated “cold” from below and are fundamentally different from astroblemes, as presently defined, whose rocks were directly subjected to the high temperatures and pressures that accompany hypervelocity extra-terrestrial impacts. Melt rock filled the largest impact sites and produced cratons, with overflow producing platforms. In later times, craton rims buckled during collisions, producing orogens. Crater rims originally entered the Earth at near-vertical angles but after sufficient net erosion following Snowball Earth episodes, deeply exposed rim-zones entered the Earth at lower angles, thereby facilitating deep subduction. Renewed activation of earliest Precambrian fractures from below is a recurrent geological phenomenon. The largest scar, approximately 5350 kilometers in diameter, encompasses Asia and has Novaya Zemlya as part of an outer rim. Our vision has greatly improved since 1788 when James Hutton could find “no vestige of a beginning".
[This presentation is published at https://doi.org/10.1111/1440-1703.12317] Dead organic matter (DOM), which consists of leaf litter, fine woody debris (FWD; < 3 cm diameter), downed coarse woody debris (CWDlog), and standing or suspended coarse woody debris (CWDsnag), plays a crucial role in forest carbon cycling. However, the contributions of each DOM type on stand-scale carbon storage (necromass) and stand-scale CO2 efflux (Rstand) estimates are not well understood. In addition, there is little knowledge of the effect of each DOM type on the accuracy of stand-scale estimates of total necromass and Rstand. This study investigated characteristics of necromass and Rstand from DOM in a subtropical forest in Okinawa island, Japan, to quantify the effect of each DOM type on total necromass, total Rstand, and estimate error of total necromass and Rstand. The CWDsnag accounted for the highest proportion (54%) of total necromass (1499.7 g C m–2), followed by CWDlog (24%), FWD (11%), and leaf litter (11%). Leaf litter accounted for the highest proportion (37%) of total Rstand (340.6 g C m–2 yr–1), followed by CWDsnag (25%), CWDlog (20%), and FWD (17%). The CWDsnag was distributed locally with 173% of the coefficient of variation for necromass, which was approximately two times higher than those of leaf litter and FWD (72–73%). Our spatial analysis revealed, for accurate estimates of CWDsnag and CWDlog necromass, sampling areas of ≥ 28750 m2 and ≥ 2058‒42875 m2 were required, respectively, under the condition of 95% confidence level and 0.1 of accepted error. In summary, CWD considerably contributed to stand-scale carbon storage and efflux in this subtropical forest, resulting in a major source of errors in the stand-scale estimates. In forests where frequent tree death is likely to occur, necromass and Rstand of CWD are not negligible in considering the carbon cycling as in this study, and therefore need to be estimated accurately.
Earth Observations (EO) systems aim to monitor nearly all aspects of the global Earth environment. Observations of Essential Water Variables (EWVs) together with advanced data assimilation models, could provide the basis for systems that deliver integrated information for operational and policy level decision making that supports the Water-Energy-Food-Nexus (EO4WEF), and concurrently the UN Sustainable Development Goals (SDGs), and UN Framework Convention on Climate Change (UNFCCC). Implementing integrated EO for GEO-WEF (EO4WEF) systems requires resolving key questions regarding the selection and standardization of priority variables, the specification of technologically feasible observational requirements, and a template for integrated data sets. This paper presents a concise summary of EWVs adapted from the GEO Global Water Sustainability (GEOGLOWS) Initiative and consolidated EO observational requirements derived from the GEO Water Strategy Report (WSR). The UN-SDGs implicitly incorporate several other Frameworks and Conventions such as The Sendai Framework for Disaster Risk Reduction; The Ramsar Convention on Wetlands; and the Aichi Convention on Biological Diversity. Primary and Supplemental EWVs that support WEF Nexus & UN-SDGs, and Climate Change are specified. The EO-based decision-making sectors considered include water resources; water quality; water stress and water use efficiency; urban water management; disaster resilience; food security, sustainable agriculture; clean & renewable energy; climate change adaptation & mitigation; biodiversity & ecosystem sustainability; weather and climate extremes (e.g., floods, droughts, and heat waves); transboundary WEF policy.
Arctic-Boreal lakes emit methane (CH₄), a powerful greenhouse gas. Recent studies suggest ebullition may be a dominant methane emission pathway in lakes but its drivers are poorly understood. Various predictors of lake methane ebullition have been proposed, but are challenging to evaluate owing to different geographical characteristics, field locations, and sample densities. Here we compare large geospatial datasets of lake area, lake perimeter, permafrost, landcover, temperature, soil organic carbon content, depth, and greenness with remotely sensed methane ebullition estimates for 5,143 Alaskan lakes. We find that lake wetland fraction (LWF), a measure of lake wetland and littoral zone area, is a leading predictor of methane ebullition (adj. R² = 0.211), followed by lake surface area (adj. R² = 0.201). LWF is inversely correlated with lake area, thus higher wetland fraction in smaller lakes may explain a commonly cited inverse relationship between lake area and methane ebullition. Lake perimeter (adj. R² = 0.176) and temperature (adj. R² = 0.157) are moderate predictors of lake ebullition, and soil organic carbon content, permafrost, lake depth, and greenness are weak predictors. The low adjusted R² values are typical and informative for methane attribution studies. A multiple regression model combining LWF, area, and temperature performs best (adj. R² = 0.325). Our results suggest landscape-scale geospatial analyses can complement smaller field studies, for attributing Arctic-Boreal lake methane emissions to readily available environmental variables.
Human populations and infrastructure in high mountain regions are exposed to a wide range of natural hazards, the frequency, magnitude, and location of which are extremely sensitive to climate change. In cases where several hazards can occur simultaneously or where the occurrence of one event will change the disposition of another, assessments need to account for complex process chains. While process chains are widely recognized as a major threat, no systematic analysis has been undertaken. We therefore assemble a broad set of process chain events from across the globe to establish new understanding on the factors that directly trigger or alter the disposition for subsequent events in the chain. Based on this new understanding, we derive a novel classification scheme and parameters to aid natural hazard assessment. Most process chains in high mountains are commonly associated with glacier retreat or permafrost degradation. Regional differences exist in the nature and rate of sequencing---some process chains are almost instantaneous, while other linkages are delayed. Process chains involving rapid sequences are difficult to predict or mitigate, and impacts are often devastating. We demonstrate that process chains are initialized most frequently as threshold failures, being the result of gradual landscape weakening and not due to the occurrence of a distinct trigger. The co-occurrence of fluvial processes or activation of sediment deposition areas increases the reach of process chains. Climate change is therefore expected to increase the reach of events in the future, as glacial environments transform into sediment-rich paraglacial and fluvial landscapes.