Local Basin Research Articles

Coexistence behavior of asymmetric attractors in hyperbolic-type memristive Hopfield neural network and its application in image encryption

The neuron model has been widely employed in neural-morphic computing systems and chaotic circuits. This study aims to develop a novel circuit simulation of a three-neuron Hopfield neural network (HNN) with coupled hyperbolic memristors through the modification of a single coupling connection weight. The bistable mode of the hyperbolic memristive HNN (mHNN), characterized by the coexistence of asymmetric chaos and periodic attractors, is effectively demonstrated through the utilization of conventional nonlinear analysis techniques. These techniques include bifurcation diagrams, two-parameter maximum Lyapunov exponent plots, local attractor basins, and phase trajectory diagrams. Moreover, an encryption technique for color images is devised by leveraging the mHNN model and asymmetric structural attractors. This method demonstrates significant benefits in correlation, information entropy, and resistance to differential attacks, providing strong evidence for its effectiveness in encryption. Additionally, an improved modular circuit design method is employed to create the analog equivalent circuit of the memristive HNN. The correctness of the circuit design is confirmed through Multisim simulations, which align with numerical simulations conducted in Matlab.

PALEOPROTEROZOIC TECTONICS AND EVOLUTIONARY MODEL OF THE ONEGA SYNCLINORIUM

Consideration is being given to the Onega Paleoproterozoic structure (Onega synclinorium, OS) as a tectonotype of intraplate negative structures, which experience intermittent subsidence over a long period of time. The paper presents a model of the OS and discusses its tectonic evolution. The model is based on the geological and structural data, already published and collected so far by the authors, as well as on the data concerning the OS deep structure, particularly on the interpretation of the 1-EV seismic profile and potential fields. The proposed model illustrates an example of conjectured interaction between different geodynamic factors and explains reasons for the development of the OS throughout the Paleoproterozoic, including the periods of intense subsidence and magmatism, inversions of local basins comprising the Onega trough, and deformations of the Paleoproterozoic strata. An important role in the formation of the OS was played by shear dislocations within an imbricate fan of its controlling Central-Karelian shear zone. The shear dislocations were accompanied by rotation of a large block located to the west of the OS, which led to the rotational-indentational interaction between adjacent blocks and to compensated coexistence among transtensional and transpressional regimes along their separating shear zone. Compensatory dynamic mechanism also manifested itself in crustal layers at the base of the OS. Horizontal flow of the mid-crustal masses and their outflow from the depression were compensated by the development of deep-seated thrust duplexes and uplifts around the depression as well as by the upper crustal extension associated with low-angle dilatant normal faulting. Successive propagation of these faults, dynamically related to shear dislocations within an imbricate fan of the Central Karelia zone, controlled the formation features and southward migration of the OS-contained basins as well as magmatic and syllogenesis-related occurrences. Multilayered subhorizontal flow of low-viscosity rocks at the base and inside the OS section against the background of shear dislocations gave rise to the occurrence of crest-like and diapir-like folding. The processes of OS formation occurred amid the development and localization of active mantle plumes and asthenospheric diapirs. One of the factors of their development and localization were the phenomena of relative decompression within the imbrication fan of the Central Karelian shear zone.

A novel approach to precipitation prediction using a coupled CEEMDAN-GRU-Transformer model with permutation entropy algorithm.

The accurate forecasting of precipitation in the upper reaches of the Yellow River is imperative for enhancing water resources in both the local and broader Yellow River basin in the present and future. While many models exist for predicting precipitation by analyzing historical data, few consider the impact of different frequency sequences on model accuracy. In this study, we propose a coupled monthly precipitation prediction model that leverages the adaptive noise complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), gated recurrent unit neural network (GRU), and attention mechanism-based transformer model. The permutation entropy (PE) algorithm is employed to partition the data processed by CEEMDAN into different frequencies, with different models utilized to predict different frequencies. The predicted results are subsequently combined to obtain the monthly precipitation prediction value. The model is applied to precipitation prediction in four regions in the upper reaches of the Yellow River and compared with other models. Evaluation results demonstrate that the CEEMDAN-GRU-Transformer model outperforms other models in predicting precipitation for these regions, with a coefficient of determination R2 greater than 0.8. These findings suggest that the proposed model provides a novel and effective method for improving the accuracy of regional medium and long-term precipitation prediction.

Predicting Models for Local Sedimentary Basin Effect Using a Convolutional Neural Network

Although the numerical models can estimate the significant influence of local site conditions on the seismic propagation characteristics near the surface in many studies, they cannot feasibly predict the seismic ground motion amplification in regular engineering practice or earthquake hazard assessment due to the high computational cost and their complex implementation. In this paper, the scattering problem of trapezoidal sedimentary basins, one of the representatives of local complex sites with a relatively small model size, and simplified by practice in this type of study, was selected as the basin model. A series of standard basin models were built to quantify the relationship between the site condition parameters and the site amplification parameters (the peak ground acceleration and the hazard location). In addition, the factors that influence seismic ground motion amplification, such as the basin shape ratio, the soil depth, the basin edge dip angle, the ratio of shear wave velocity between the bedrock and the soil layer, the damping coefficient, and the fundamental frequency, were selected to investigate the sensitivity. A convolutional neural network (CNN) algorithm based on deep learning replacing traditional recursive algorithms was explored to establish a prediction model of basin amplification characteristics. By the Bayesian optimization method, the structural parameters of the CNN predicting model were selected to improve the accuracy of the prediction model. The results show that the optimized CNN models could predict the amplification characteristics of the basin better than the un-optimized CNN models. Three prediction models were established with the site condition parameters as the input parameters and their output parameters were the maximum amplification value of the peak ground acceleration (PGA), the hazard location, and their combination for each basin. To analyze the CNN’s prediction ability, each CNN model used about 80% of the data from the seismic model repose results for training and the remaining data (20%) for testing. By comparing the CNN prediction results with the FE simulation results, the accuracy and rationality of each prediction model were studied. The results show that, compared to a single numerical model, the CNN prediction results of the site amplification features could be quickly obtained by inputting the relevant parameters. Compared to recursive class models, the established CNN prediction model can directly establish the relationships among multiple input and multiple output parameters. A comparison of the three kinds of CNN models shows that the prediction accuracy of the joint parameter model was slightly lower than that of the two single-output models.

Mud retention in hydrologically closed basins promoted pre-vegetation meandering: evidence from the Neoproterozoic Diabaig Formation, Scotland

Abstract Fluvial point-bar deposits are described from the c. 1 Ga Diabaig Formation, representing some of the most compelling architectural evidence of planform sinuosity recognized in pre-vegetation sedimentary strata to date. The stratigraphical architecture of the deposits indicates different planform transformations in response to meander-bend expansion and downstream migration. Point-bar architectural motifs are locally abundant in the unit and this is attributed to elevated amounts of mud-calibre cohesive sediment. Mud afforded landscape stability that, together with limited stream power, was sufficient to corral sediment into single-thread, sinuous channels. Significant volumes of fine-grained sediment were encouraged to accumulate by the local basin setting of several topographically confined endorheic depressions. These conditions ceased once the hydrologically closed valleys were filled, with the overlying sandstone-dominated Applecross Formation subsequently deposited by low-sinuosity, braided rivers that bypassed fine-grained sediment oceanward. Other endorheic basins conducive to the formation of depositional meanders should be expected to have been relatively widespread on pre-vegetation Earth, though many individually limited in size, confined by their inherited topographies. Depositional architectures representative of sinuous channel planforms remain scarce within more extensive, externally drained basins.

A multiplier-free Rulkov neuron under memristive electromagnetic induction: Dynamics analysis, energy calculation, and circuit implementation

Establishing a realistic and multiplier-free implemented biological neuron model is significant for recognizing and understanding natural firing behaviors, as well as advancing the integration of neuromorphic circuits. Importantly, memristors play a crucial role in constructing memristive neuron and network models by simulating synapses or electromagnetic induction. However, existing models lack the consideration of initial-boosted extreme multistability and its associated energy analysis. To this end, we propose a multiplier-free implementation of the Rulkov neuron model and utilize a periodic memristor to represent the electromagnetic induction effect, thereby achieving the biomimetic modeling of the non-autonomous memristive Rulkov (mRulkov) neuron. First, theoretical analysis demonstrates that the stability distribution of the time-varying line equilibrium point is determined by both the parameters and the memristor’s initial condition. Furthermore, numerical simulations show that the mRulkov neuron can exhibit parameter-dependent local spiking, local hidden spiking, and periodic bursting firing behaviors. In addition, based on the periodic characteristics of the memductance function, the topological invariance of the mRulkov neuron is comprehensively proved. Therefore, local basins of attraction, bifurcation diagrams, and attractors related to extreme multistability can be boosted by switching the memristor’s initial condition. Significantly, the novel boosted extreme multistability is discovered in the Rulkov neuron for the first time. More importantly, the energy transition associated with the boosting dynamics is revealed through computing the Hamilton energy distribution. Finally, we develop a simulation circuit for the non-autonomous mRulkov neuron and confirm the effectiveness of the multiplier-free implementation and the accuracy of the numerical results through PSpice simulations.

What Was the Level of Ground Motion across Europe during the Great A.D. 365 Crete Earthquake?

Abstract Historical and archeological data report that an earthquake was felt over the whole eastern Mediterranean on 21 July A.D. 365. The impact of the tsunami it generated, which may have caused several thousands of fatalities, has been widely studied, whereas the impact of the seismic waves has hardly been explored. Here, we present simulations of the ground motion caused by the A.D. 365 event at 316 sites now instrumented by seismological stations throughout Europe. The simulation approach is based on the modeling of a series of rupture scenarios coupled with empirical Green’s functions (EGFs) obtained at the stations from a recent Mw 6.4 earthquake. The broadband and remarkably also the accelerometric records in urbanized areas can be exploited at distances as far as ∼2000 km. Then, we use three empirical models to estimate the macroseismic intensity across Europe from the simulated peak ground accelerations and peak ground velocities. The presence of stations in thick sedimentary basins (lower Danube valley, Po plain, urban accelerometric network in the alpine valley of Grenoble) shows that local basin amplification effects can dominate acceleration values at frequencies favorable to human earthquake perception (~0.5–1 Hz), even for basins located at more than 1500 km from the earthquake. Thus, our simulations indicate that the A.D. 365 earthquake was likely felt by the populations as far as the Po plain and as the city of Grenoble, about 1800 km away, and presumably in other large European basins such as the Pannonian basin. It is possible that the perception of the earthquake up to such distances contributed to its “universal” character perpetuated in archival sources. At closer distances (300–500 km), the simulated intensity levels indicate that the earthquake probably caused no damage.

Tracing surface water pollution in China’s supply chain

Decades of economic growth in China were enabled by rapid industrialization with insufficient water quality controls. Previous studies have traced water pollution discharges or grey water footprint using the multi-regional input–output (MRIO) model. However, there is a research gap in understanding the relation between surface water pollutant concentrations and final consumption of local and external basins. Here, we present the first national analysis to map surface water quality degradation in watersheds embedded in China’s supply chains. To do this, we developed a basin-specific relationship between surface water pollution concentration and discharge, and combined it with the MRIO model to trace the water pollution of different basins through the trade of products and services. We find that ∼50% chemical oxygen demand (COD) and ∼46% ammonium nitrogen (NH4+-N) discharges from production processes can be traced to consumer demands beyond the basin where the pollution was initially released. 0.3–2.2 mg/L COD and 0.03–0.31 mg/L NH4+-N water quality degradation (the range indicates pollution concentration in different basins) can be attributed to final consumption of commodities from other basins in China. International consumers contributed to increased degradation of water quality (0.43 mg/L COD in Huai River Basin and 0.07 mg/L NH4+-N in Hai River Basin). High pollution concentrations were often concentrated in dry North China, because water scarce basins in this region are more susceptible to human pollution loadings. Basins outsourcing water pollution were mainly developed economies that outsourced production and subsequent water quality impairments to other basins. This study highlights the interactions between water quality and supply chains.

The "Beacon" Structural Model of Protein Folding: Application for Trp-Cage in Water.

Protein folding is a process in which a polypeptide must undergo folding process to obtain its three-dimensional structure. Thermodynamically, it is a process of enthalpy to overcome the loss of conformational entropy in folding. Folding is primarily related to hydrophobic interactions and intramolecular hydrogen bondings. During folding, hydrophobic interactions are regarded to be the driving forces, especially in the initial structural collapse of a protein. Additionally, folding is guided by the strong interactions within proteins, such as intramolecular hydrogen bondings related to the α-helices and β-sheets of proteins. Therefore, a protein is divided into the folding key (FK) regions related to intramolecular hydrogen bondings and the non-folding key (non-FK) regions. Various conformations are expected for FK and non-FK regions. Different from non-FK regions, it is necessary for FK regions to form the specific conformations in folding, which are regarded as the necessary folding pathways (or "beacons"). Additionally, sequential folding is expected for the FK regions, and the intermediate state is found during folding. They are reflected on the local basins in the free energy landscape (FEL) of folding. To demonstrate the structural model, molecular dynamics (MD) simulations are conducted on the folding pathway of the TRP-cage in water.

Sediment provenance signal of the discontinuous retroarc topography in the northern Andes during the Early Cretaceous

AbstractA large dataset of detrital zircon U–Pb ages (N = 5940) of Aptian‐Albian strata of the Colombian‐Ecuadorian retroarc region suggests that these rocks were sourced from Proterozoic cratonic and Permian–Triassic to Cretaceous rocks of the Andean proto‐Cordilleras. The nonconformity between Aptian‐Albian strata and Proterozoic rocks of the Garzón Massif indicates the existence of positive relief in this region. Topographic highs could have caused local basin compartmentalization and the prevalence of a localized provenance of coeval strata. Areas of positive relief were seemingly exposed to intense chemical weathering as suggested by the high Chemical Index of Alteration values, and low Rb/Sr and SiO2/Al2O3 ratios of analysed strata. Our results highlight the value of provenance analysis to study the ancient topography of retroarc systems and open the avenue for further research on the role of extensional tectonics in the topographic and basin evolution in the Andes.

Methodology for a preliminary assessment of water use sustainability in industries at sub-basin level

The sustainability of industrial production, especially for highly water-demanding processes, is strictly related to water resource availability and to the dynamic interactions between natural and anthropogenic requirements over the spatial and temporal scales. The increase in industrial water demand raises the need to assess the related environmental sustainability, facing the occurrence of global and local water stress issues. The identification of reliable methodologies, based on simple indices and able to consider the impact on local water basins, may play a basilar role in water sustainability diagnosis and decision-making processes for water management and land use planning. The present work focalized on the definition of a methodology based on the calculation of indicators and indices in the view of providing a synthetic, simple, and site-specific assessment tool for industrial water cycle sustainability. The methodology was built starting from geo-referenced data on water availability and sectorial uses derived for Italian sub-basins. According to the data monthly time scale, the proposed indices allowed for an industrial water-related impacts assessment, able to take into account the seasonal variability of local resources. Three industrial factories, located in northern (SB1, SB2) and central (SB3) Italian sub-basins, were selected as case studies (CS1, CS2, CS3) to validate the methodology. The companies were directly involved and asked to provide some input data. The methodology is based on the calculation of three synthetic indexes: the Withdrawal and Consumption water Stress Index (WCSI) allowed for deriving a synthetic water stress level assessment at the sub-basin scale, also considering the spatial and temporal variations; the industrial water use sustainability assessment was achieved by calculating the Overall Factory-to-Basin Impact (OFBI) and the Internal Water Reuse (IWR) indices, which allowed a preliminary evaluation of the factories' impacts on the sub-basin water status, considering the related water uses and the overall pressures on the reference territorial context. The WCSI values highlighted significant differences between the northern sub-basins, characterised by limited water stress (WCSISB1 = 0.221; WCSISB2 = 0.047), and the central ones, more subjected to high stress (WCSISB3 = 0.413). The case studies CS1 and CS3 showed to exert a more significant impact on the local water resource (OFBICS1 = 0.18%; OFBICS2 = 0.192%) with respect to CS2 (OFBI = 0.002%), whereas the IWR index revealed the different company's attitude in implementing water reuse practices (IWRCS1 = 40%; IWRCS1 = 27%; IWRCS1 = 99%). The proposed methodology and the indices may also contribute to assessing the effectiveness of river basin management actions to pursue sustainable development goals.

GEB v0.1: a large-scale agent-based socio-hydrological model – simulating 10 million individual farming households in a fully distributed hydrological model

Abstract. Humans play a large role in the hydrological system, e.g. by extracting large amounts of water for irrigation, often resulting in water stress and ecosystem degradation. By implementing large-scale adaptation measures, such as the construction of irrigation reservoirs, water stress and ecosystem degradation can be reduced. Yet we know that many decisions, such as the adoption of more effective irrigation techniques or changing crop types, are made at the farm level by a heterogeneous farmer population. While these decisions are usually advantageous for an individual farmer or their community, aggregate effects of those decisions can have large effects downstream. Similarly, decisions made by other stakeholders, such as governments, often have basin-wide effects and affect each farmer differently. To fully comprehend how the human–natural water system evolves over time and space and to explore which interventions are suitable to reduce water stress, it is important to consider human behaviour and feedbacks to the hydrological system simultaneously at the local household and large basin scales. Therefore, we present the Geographical, Environmental, and Behavioural (GEB) model, a coupled agent-based hydrological model that simulates the behaviour and daily bidirectional interaction of more than 10 million individual farm households with the hydrological system on a personal laptop. Farmers exhibit autonomous heterogeneous behaviour based on their characteristics, assets, environment, management policies, and social network. Examples of behaviour are irrigation, generation of income from selling crops, and investment in adaptation measures. Meanwhile, reservoir operators manage the amount of water available for irrigation and river discharge. All actions can be taken at a daily time step and influence the hydrological system directly or indirectly. GEB is dynamically linked with the spatially distributed grid-based hydrological model CWatM at 30′′ resolution (< 1 km at the Equator). Because many smallholder farm fields are much smaller than 1 × 1 km, CWatM was specifically adapted to implement dynamically sized hydrological response units (HRUs) at the farm level, providing each agent with an independently operated hydrological environment. While the model could be applied anywhere globally at both large and small scales, we explore its implementation in the heavily managed Krishna basin in India, which encompasses ∼ 8 % of India's land area and ∼ 12.1 million farmers.

Optimizing Managed Aquifer Recharge Locations in California's Central Valley Using an Evolutionary Multi‐Objective Genetic Algorithm Coupled With a Hydrological Simulation Model

AbstractManaged aquifer recharge (MAR) can provide long‐term storage of excess surface water for later use. While decades of research have focused on the physical processes of MAR and identifying suitable MAR locations, very little research has been done on how to consider competing factors and tradeoffs in siting MAR facilities. This study proposes the use of a simulation‐optimization (SO) framework to map out a cost‐effectiveness frontier for MAR by combining an evolutionary algorithm with two objective functions that seek to maximize groundwater storage gains while minimizing MAR cost. We present the theoretical framework along with a real‐world application to California's Central Valley. The result of the SO framework is a Pareto front that allows identifying suitable MAR locations for different levels of groundwater storage gain and associated MAR project costs, so stakeholders can evaluate different choices based on cost, benefits, and tradeoffs of MAR sites. Application of the SO framework to the Central Valley shows groundwater can be recharged from high‐magnitude (95th percentile) flows at a marginal cost of $57 to $110 million per km3. If the 10 percent largest flows are recharged the total groundwater storage gain would double and the marginal costs would drop to between $30 and $50 million per km3. If recharge water is sourced from outside local basins (e.g., the Sacramento‐San Joaquin Delta), groundwater storage gain is approximately 25%–80% greater than can be achieved by recharging local flows, but the total cost is about 10%–15% higher because of additional lift cost.

Understanding the spatiotemporal variability in nonpoint source nutrient loads and its effect on water quality in the upper Xin’an river basin, Eastern China

Understanding the spatiotemporal characteristics of major nonpoint sources (NPSs) are crucial for protecting river water quality in drinking water source areas. This study employed a geomorphology-based nonpoint source pollution (GBNP) model to simulate hydrological processes together with NPS pollutant transport and transformation processes from 1980 to 2019 in the upper Xin’an River basin, an important drinking water headwater. The simulations revealed the spatiotemporal characteristics of nitrogen and phosphorus loads and their influences on river water quality. The results showed that the long-term average total nitrogen (TN) and total phosphorus (TP) loads were 3219 kg∙km−2∙yr−1 and 293 kg∙km−2∙yr−1, respectively. The proportional coefficients for TN and TP entering the rivers from the watershed were 0.40 and 0.37. The TN and TP loads during the April to July flood season accounted for 65.0 % and 63.2 % of the annual total, respectively. Annual TN load was significantly correlated with annual runoff depth, soil erosion and TN input in the interannual variation, while the interannual variation of TP load was significantly correlated with annual runoff depth and soil erosion. The long-term average TN and TP loads showed spatial patterns similar to those of land use and runoff depth, respectively. The daily TN concentrations showed a clockwise hysteresis relationship with discharge during flood events, while the daily TP concentrations had a single positive correlation with discharge throughout a whole year. Without atmospheric N deposition, the average TN load would decrease from 3122 kg∙km−2∙yr−1 to 1266 kg∙km−2∙yr−1 in 2000–2009. The TN load from atmospheric deposition accounted for 65 %-71 % of the total load, which implies that river water quality protection must include local basin eco-environmental protection and air pollution control over broader areas. This study showed that the distributed modelling of hydrological and biogeochemical processes is crucial to understand the spatiotemporal variability in nonpoint source nutrient loads over the watershed and is helpful to identifying key factors affecting river water quality.

Tectonic evolution of the Aggeneys-Gamsberg Ore District, South Africa, and implications for the geodynamic setting of the Namaqua Sector

Understanding the deformation history of the structurally complex Aggeneys-Gamsberg Ore District (A-GOD), in the Namaqua Sector of the Mesoproterozoic Namaqua-Natal Metamorphic Province of South Africa, is key to discovering additional metasediment-hosted base metal sulphide mineralization. Although several studies have examined the structure of the main ore deposits and outcrops in the district, and others have investigated the timing of sedimentation, magmatism, and metamorphism, no previous studies have focused on integrating these two datasets. We therefore combine structural observations and the resulting relative timeline of events with new and existing zircon U-Pb geochronology, to yield a temporally constrained tectonic history for the district.The regionally recognized Paleoproterozoic D1 deformation is not recorded in the A-GOD, which preserves only Mesoproterozoic rocks. The largely stratiform base metal deposits occur near the top of a sequence of metamorphosed siliciclastic sediments (Wortel and Hotson Formations), deposited after 1261 ± 5 Ma (youngest reliable detrital zircons). These metasediments are tectonically interleaved with the intrusive ∼ 1190 Ma Little Namaqualand Suite (Aroams Gneiss) and younger ∼ 1175–1145 Ma metagranitoids (Achab Gneiss, Hoogoor Gneiss, aplite sills). Magmatism occurred early during D2a deformation, which created kilometre-scale isoclinal folds and thrusts during S/SW-directed contraction, as recorded at the Swartberg and Broken Hill-Deeps deposits. Associated M2a metamorphism is dated at ∼ 1200 Ma (metamorphic zircon rims), but this was not the peak metamorphic episode as previously thought. The sequence was then exhumed, and the unconformably overlying clastics and bimodal volcanics of the Koeris Formation were deposited in local transtensional pull-apart basins at ∼ 1130 Ma. Thrust stacking in the Aggeneys Mountains may have occurred during subsequent D2c contraction, associated with ∼ 1075 Ma M2c metamorphism, but evidence remains cryptic. The entire sequence is affected by kilometre-scale sheath folds that close downwards to the ∼ ENE (as at Gamsberg and the Quarry Fold Nappe), associated with ∼ ENE-trending open, upright folds. These developed during D3a ∼ ENE-directed extensional to constrictional shearing at peak M3 amphibolite-facies conditions, which re-used the gently dipping D2 regional fabric and overprinted it with new ∼ E-W trending D3 linear features. Abundant metamorphic zircon dates suggest an age of ∼ 1025 Ma for D3a and M3. Ongoing extension at retrograde conditions reactivated earlier structures, such as the Swartberg Thrust, as D3b low-angle, ductile–brittle detachment faults. Finally, dextral transform motion created local D4 monoclines and dextral strike-slip faults, possibly associated with ∼ 1000 Ma M4 metamorphic zircon growth and emplacement of a regional pegmatite swarm.The A-GOD has traditionally been fitted into a regional tectonic model involving accretion of distinct terranes onto the Kaapvaal Craton, during the polyphase ∼ 1.2–1.0 Ga Namaquan Orogeny. In contrast, recent studies propose that the entire Namaqua Sector instead occupied a back-arc setting between ∼ 1.35 and < 1.1 Ga. Our results support a hybrid model, involving accretion and translation of previously extended crustal fragments against the craton, with initially the entire Namaqua Sector - but later only the western portion - located in a hot back-arc setting influenced by subducting slab dynamics.

Modeling Large River Basins and Flood Plains with Scarce Data: Development of the Large Basin Data Portal

Hydrological modeling of large river basins and flood plains continues to be challenged by the low availability and quality of observed data for modeling input and model calibration. Global datasets are often used to bridge this gap, but are often difficult and time consuming to acquire, particularly in low resource regions of the world. Numerous calls have been made to standardize and share data to increase local basin modeling capacities and reduce redundancy in efforts, but barriers still exist. We discuss the challenges of hydrological modeling in data-scarce regions and describe a freely available online tool site developed to enable users to extract input data for any basin of any size. The site will allow users to visualize, map, interpolate, and reformat the data as needed for the intended application. We used our hydrological model of the Upper Zambezi basin and the Chobe-Zambezi floodplains to illustrate the use of this online toolset. Increasing access and dissemination of hydrological modeling data is a critical need, particularly among users where data requirements and access continue to impede locally driven management of hydrological systems.

Land-use changes in Amazon and Atlantic rainforests modify organic matter and black carbon compositions transported from land to the coastal ocean

This study assessed black carbon (BC) dynamics, concentrations, and the organic matter (OM) isotopic carbon composition in northeastern South America drainage basin coastal sediments. Paraíba do Sul (PSR; Atlantic Rainforest, Brazil) coastal sediments displayed more 13C-enriched values (−22.6 ± 1.3 ‰ [n = 13]) than Amazon and Sinnamary (Amazon Rainforest in French Guiana and Brazil) sediments (−25.0 ± 3.1 ‰ [n = 14] and − 26.1 ± 1.0 ‰ [n = 6], respectively), indicating that local land-use basin changes have altered the OM composition, i.e., from natural C3 plant to C4 plants contributions. BC contents normalized to total organic carbon (TOC) content were 0.32 ± 0.24 (n = 8), 0.73 ± 0.67 (n = 6), and 0.95 ± 0.74 (n = 13) mg g−1 TOC for Amazon, Sinnamary and PSR samples, respectively, with BC sources appearing to differ according to different drainage basin vegetation covers. With increasing distance from the river mouths, BC contents exhibited different trends between the coastal zones, with values increasing for the PSR and decreasing values for the Amazon samples. BC distribution in Sinnamary coastal sediments did not display specific patterns. Regarding the Amazon coastal zone, BC contents decreased while the B6CA:B5CA ratios did not show a pattern, which could indicate that BC in the area originates from river transport (aged BC) and that the hydrophobic component of dissolved BC is removed. The BC content mostly increased in the PSR coastal zone, while the B6CA:B5CA ratios were not altered for the entire gradient, indicating the BC stability and possible atmospheric deposition of soot. Our findings indicate that different sources, transformation processes, and hydrological conditions affect BC contents within coastal zones. Continuous land cover changes in both the Amazon and Atlantic Rainforests may result in large-scale marine carbon cycling impacts.

Drainage pattern recognition method considering local basin shape based on graph neural network

ABSTRACT Drainage pattern recognition is crucial for geospatial understanding and hydrologic modelling. Currently, drainage pattern recognition methods employ geometric measures of overall and local features of river networks but lack measures of river basin unit shape features, so that potential correlations between river segments are usually ignored, resulting in poor drainage pattern recognition results. In order to overcome this problem, this paper proposes a supervised graph neural network method that considers the local basin unit shape of river networks. First, based on the overall hierarchy of the river networks, the confluence angle of river segments and the shape of river basin units, multiple drainage pattern classification features are extracted. Then, typical drainage pattern samples from the multi-scale NSDI and USGS databases are used to complete the training, validation and testing steps. Experimental results show that the drainage pattern indexes proposed can describe the characteristics of different drainage patterns. The method can effectively sample the adjacent river segments, flexibly transfer the associated pattern features among river segment neighbours, and aggregate the deeper characteristics of the river networks, thus improving the drainage pattern recognition accuracy relative to other methods and reliably distinguishing different drainage patterns.

Efficacy and toxicity of adjuvant radiotherapy in recurrent melanoma after adjuvant immunotherapy

BackgroundIn patients with stage III melanoma, despite surgical resection and adjuvant systemic therapy, locoregional recurrences still occur. The randomized, phase III Trans-Tasman Radiation Oncology Group (TROG) 02.01 trial demonstrated that...

An exploratory landscape analysis driven artificial bee colony algorithm with maximum entropic epistasis

Continuous real-valued optimization problems are existed in various engineering optimization scenarios. The artificial bee colony algorithm (ABC) which is inspired by the foraging behavior of bee colonies is an efficient optimization algorithm to address complex continuous real-valued optimization problems. In this paper, the maximum entropic epistasis (MEE) in the exploratory landscape analysis (ELA) is adopted in the ABC algorithm (MEEABC) to improve the performance of the ABC algorithm. The dimension interaction of the continuous functions computed by the MEE is introduced to guide the search process of the MEEABC algorithm during the employed bee phase and onlooker bee phase. The adaptive mutation methods and the strategy of dynamic population size reduction are implemented to increase the convergence speed and dynamically ameliorate the local exploitation capability. The solutions in the fitness landscape are automatically divided into different clusters to explore the local basin of the fitness landscape via the collaboration between MEE and adaptive mutation methods. The performance of MEEABC is tested on CEC 2017 benchmark test suite. From the experimental results, the MEEABC is superior to ABC variants and state-of-art algorithms regarded with the performance of effectiveness and robustness.