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. There is a complete lack of knowledge about rock glaciers in the Western Kunlun Mountains, one of the driest mountain ranges in Asia, where extensive 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 Western Kunlun Mountains: 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 3,390 m and 5,540 m with an average size of 0.26 km2 and a mean slope angle of 17°. The median and maximum surface downslope velocities of the active ones are 17±1 cm yr-1 and 127±6 cm yr-1, respectively. Characteristics of the inventoried rock glaciers provided insights into permafrost distribution in the Western Kunlun Mountains.
Geomorphologists have long debated the relative importance of disturbance magnitude, duration and frequency in shaping landscapes. For river-channel adjustment during floods, some argue that cumulative flood ‘power’, rather than magnitude or duration, matters most. However, studies of flood-induced river-channel change often draw upon small datasets. Here, we combine Sentinel-2 imagery with flow data from laterally-active rivers to address this question using a larger dataset. We apply automated algorithms in Google Earth Engine to map rivers and detect their lateral shifting; we generate a large dataset to quantify channel change during 160 floods across New Zealand, Russia, and South America. Widening during these floods is best explained by their duration and cumulative hydrograph. We use a random forest regression model to predict flood-induced channel widening, with potential applications for hazard management. Ultimately, better global data on sediment supply and caliber would help us to understand flood-driven change to river planforms.
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.
Meandering channels display complex planform configurations with upstream- and downstream- skewed bends. Bend orientation is linked to near-field hydrodynamics, bed morphodynamic regime, bank characteristics, riparian vegetation, and geological environment, which are the modulating factors that act specially in high-amplitude and high-sinuosity conditions. Based on the interaction between hydrodynamics and morphodynamics, previous studies have suggested that sub- (β < βR) and super-resonant (β > βR) morphodynamic regimes (where β is the half width-to-depth ratio of the channel, and βR is the resonance condition) may trigger a particular bend orientation (upstream- and downstream-skewed, respectively). However, natural rivers exhibit both US-skewed and DS-skewed bend patterns along the same reach, independently of the morphodynamic regime. Little is known about the hydrogeomorphology (forced and free morphodynamic patterns) under these bend orientations. Herein, using the asymmetric Kinoshita laboratory channel, experiments under sub- and super-resonant conditions (with presence or absence of free bars) for upstream-and downstream-skewed conditions are performed. Additional, detailed field measurements at US-skewed and DS-skewed bends of different skewness along the Tigre River in Peru are presented. Conditions at field scale at high-sinuosity and high-amplitude bends filter out the influence of the morphodynamic regime, where nonlinear processes (e.g. width variation) directly the development of the three-dimensional flow structure, then to the erosional and depositional patterns, and then to the lateral migration patterns.
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.
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.
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.
[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.
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.
Sustainably managing resources in a transboundary freshwater basin is a complex problem, particularly when considering the compounding impacts of climate change, hydropower development, and evolving water governance paradigms. In this study, we used a mixed methods approach to analyze potential impacts of climate change on regional hydrology, the ability of dam operation rules to keep downstream flow within acceptable limits, and the present state of water governance in Laos, Vietnam, and Cambodia. Our results suggest that future river flows in the 3S river system could move closer to natural (i.e., pre-development) conditions during the dry season and experience increased floods during the wet season. This anticipated new flow regime in the 3S region would require a shift in the current dam operations, from maintaining minimum flows to reducing flood hazards. Moreover, our Governance and Stakeholders survey assessment results revealed that existing water governance systems in Laos, Vietnam, and Cambodia are ill-prepared to address such anticipated future water resource management problems. Our results indicate that the solution space for addressing these complex issues in the 3S river basins will be highly constrained unless major deficiencies in transboundary water governance, strategic planning, financial capacity, information sharing, and law enforcement are remedied in the next decade. This work is part of an ongoing research partnership between the National Aeronautical and Space Agency (NASA) and the Conservation International (CI) dedicated to improving natural resources assessment for conservation and sustainable management.
Pool-riffle sediment and flow dynamics have been studied for many years, but there has been relatively little investigation on how field observations might be influenced by the variability of pool-riffle morphologies. In this letter we present a database of quantitative and qualitative measurements from sites where pool-riffle morphologies have been studied and apply two morphologic classifications that compare a) discharge and slope, and b) specific stream power and a representative coarse bed particle size. The classifications show that pool-riffles appear in different positions relative to mobility and morphologic transitions, which indicates that they occur within different flow and sediment regimes. Patterns in the distribution of studied pool-riffles show useful trends as well as an important diversity, the appreciation of which may help to clarify the importance of different flow and sediment transport phenomenon that underlie pool-riffle mechanics.
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.
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.
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.
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.
There is no doubt anymore that Earth Observation (EO) is contributing toward meeting the Sustainable Development Goals and addressing environmental challenges. Digital Earth Africa’s objective is to make freely available an EO data cube for all of Africa that democratizes the capacity to process and analyse satellite data. It allows to track changes across Africa in unprecedented detail and will provide data on a vast number of issues, including soil and coastal erosion, agriculture, forest and desert development, water quality, and changes to human settlements. To realise full benefits of an advanced Platform like Digital Earth Africa, Digital Earth Africa has co-designed and co-developed with five institutions namely the Regional Centre For Mapping Of Resources For Development (RCMRD, Kenya), Centre de Suivi Écologique (Senegal), l’observatoire du Sahara et du Sahel (Tunisia), AFRIGIST (Nigeria) and AGRHYMET (Niger). This was meant to ensure it meets end-users needs, this program has been developed by the future deliverers of the program. From the trainers’ perspective, the program is built to consider the recent changes in teaching approaches and methodologies including pedagogy that emerged from a Covid-19, and post Covid-19, pandemic world. On the end-user side, the curriculum covered a wide spectrum of topics, from understanding satellite images, python scripting in the JupyterLab environment to identifying solutions to SDGs challenges through use cases, available in English and French. Digital Earth Africa’s Gender Equity, Diversity and Social Inclusion principles strategy (GEDSI) is imprinted as a watermark across the whole program. It prioritises gender equality, diversity, and social inclusion so that women, people with disabilities and marginalised individuals and communities have the same opportunities to benefit from EO data. In addition, Digital Earth Africa started live virtual sessions, to stay connected with end users, who have developed impactive stories in their communities. Digital Earth Africa seeks to support the capacity development of individuals, academic and governmental institutions, and private sector organisations to empower present and next generation of decision makers to drive toward a sustainable future, leaving on one and place behind.
The uncontrolled rapid population growth in our regions and strong industrialization are putting pressure on natural resources, accelerating climate change and desertification. This study aims to follow the evolution of land use in the N’ZI watershed. Three images from Landsat 4 & 5 (1986), Landsat 7 (2000), and Landsat 8 (2020) were used to carry out this study. Remote sensing and geographic information systems (GIS) were used to monitor land cover as a whole. Various treatments were performed using Envi 5.1 and ArcGIS 10.4.1 software programs. The results showed that changes took place during the periods of 1986-2000, 2000-2020, and 1986-2020. The results of the analysis showed the regression of water surfaces from -64.95% to -52.47% during the period of2000-2020 and 1986-2020, on the other hand, there is a great increase in bare-ground dwellings (373.63%) and low-cover soils (10.60%). These progressions were due to the destruction of forests -86.93%, savannas -3.97%, and agricultural areas -9.30% between 1986 and 2020
We present the first investigation of subsidence due to sediment compaction and consolidation in two laboratory-scale river delta experiments. Spatial and temporal trends in subsidence rates in the experimental setting may elucidate behavior which cannot be directly observed at sufficiently long timescales, except for in reduced scale models such as the ones studied. We compare subsidence between a control experiment using steady boundary conditions, and an otherwise identical experiment which has been treated with a proxy for highly compressible marsh deposits. Both experiments have non-negligible compactional subsidence rates across the delta-top, comparable in magnitude to our boundary condition relative sea level rise of 250 μm/h. Subsidence in the control experiment (on average 54 μm/h) is concentrated in the lowest elevation (<10mm above sea level) areas near the coast and is likely due to creep induced by a rising water table near the shoreface. The treatment experiment exhibits larger (on average 126 μm/h) and more spatially variable subsidence rates controlled mostly by compaction of recent marsh deposits within one channel depth (_10 mm) of the sediment surface. These rates compare favorably with _eld and modeling based subsidence measurements both in relative magnitude and location. We find that subsidence “hot spots” may be relatively ephemeral on longer timescales, but average subsidence across the entire delta can be variable even at our shortest measurement window. This suggests that subsidence rates in a given decade or century may exceed thresholds for marsh platform drowning, even if the long term trend does not.