A novel fuzzy <Emphasis Type="Italic">K</Emphasis>-nearest neighbor inference model with differential evolution for spatial prediction of rainfall-induced shallow landslides in a tropical hilly area using GIS

This research represents a novel soft computing approach that combines the fuzzy k-nearest neighbor algorithm (fuzzy k-NN) and the differential evolution (DE) optimization for spatial prediction of rainfall-induced shallow landslides at a tropical hilly area of Quy Hop, Vietnam. According to current literature, the fuzzy k-NN and the DE optimization are current state-of-the-art techniques in data mining that have not been used for prediction of landslide. First, a spatial database was constructed, including 129 landslide locations and 12 influencing factors, i.e., slope, slope length, aspect, curvature, valley depth, stream power index (SPI), sediment transport index (STI), topographic ruggedness index (TRI), topographic wetness index (TWI), Normalized Difference Vegetation Index (NDVI), lithology, and soil type. Second, 70 % landslide locations were randomly generated for building the landslide model whereas the remaining 30 % landslide locations was for validating the model. Third, to construct the landslide model, the DE optimization was used to search the optimal values for fuzzy strength (fs) and number of nearest neighbors (k) that are the two required parameters for the fuzzy k-NN. Then, the training process was performed to obtain the fuzzy k-NN model. Value of membership degree of the landslide class for each pixel was extracted to be used as landslide susceptibility index. Finally, the performance and prediction capability of the landslide model were assessed using classification accuracy, the area under the ROC curve (AUC), kappa statistics, and other evaluation metrics. The result shows that the fuzzy k-NN model has high performance in the training dataset (AUC?=?0.944) and validation dataset (AUC?=?0.841). The result was compared with those obtained from benchmark methods, support vector machines and J48 decision trees. Overall, the fuzzy k-NN model performs better than the support vector machines and the J48 decision trees models. Therefore, we conclude that the fuzzy k-NN model is a promising prediction tool that should be used for susceptibility mapping in landslide-prone areas.     

Coseismic landslides triggered by the 8th August 2017 <Emphasis Type="Italic">M</Emphasis><Subscript>s</Subscript> 7.0 Jiuzhaigou earthquake (Sichuan,China): factors controlling their spatial distribution and implications for the seismogenic blind fault identification

On 8th August 2017, a magnitude M_s 7.0 earthquake struck the County of Jiuzhaigou, in Sichuan Province, China. It was the third M_s ≥?7.0 earthquake in the Longmenshan area in the last decade, after the 2008 M_s 8.0 Wenchuan earthquake and the 2013 M_s 7.0 Lushan earthquake. The event did not produce any evident surface rupture but triggered significant mass wasting. Based on a large set of pre- and post-earthquake high-resolution satellite images (SPOT-5, Gaofen-1 and Gaofen-2) as well as on 0.2-m-resolution UAV photographs, a polygon-based interpretation of the coseismic landslides was carried out. In total, 1883 landslides were identified, covering an area of 8.11 km², with an estimated total volume in the order of 25–30?×?10⁶ m³. The total landslide area was lower than that produced by other earthquakes of similar magnitude with strike-slip motion, possibly because of the limited surface rupture. The spatial distribution of the landslides was correlated statistically to a number of seismic, terrain and geological factors, to evaluate the landslide susceptibility at regional scale and to identify the most typical characteristics of the coseismic failures. The landslides, mainly small-scale rockfalls and rock/debris slides, occurred mostly along two NE-SW-oriented valleys near the epicentre. Comparatively, high landslide density was found at locations where the landform evolves from upper, broad valleys to lower, deep-cut gorges. The spatial distribution of the coseismic landslides did not seem correlated to the location of any known active faults. On the contrary, it revealed that a previously-unknown blind fault segment—which is possibly the north-western extension of the Huya fault—is the plausible seismogenic fault. This finding is consistent with what hypothesised on the basis of field observations and ground displacements.     

Evaluating the susceptibility of landslide landforms in Japan using slope stability analysis: a case study of the 2016 Kumamoto earthquake

This study analyzed 267 landslide landforms (LLs) in the Kumamoto area of Japan from the database of about 0.4 million LLs for the whole of Japan identified from aerial photos by the National Research Institute for Earth Science and Disaster Resilience of Japan (NIED). Each LL in the inventory is composed of a scarp and a moving mass. Since landslides are prone to reactivation, it is important to evaluate the sliding-recurrence susceptibility of LLs. One possible approach to evaluate the susceptibility of LLs is slope stability analysis. A previous study found a good correlation (R ² = 0.99) between the safety factor (F _s ) and slope angle (α) of F _s  = 17.3α ^?0.843. We applied the equation to the analysis of F _s for 267 LLs in the area affected by the 2016 Kumamoto earthquake (M _j  = 7.3). The F _s was calculated for the following three cases of failure: scarps only, moving mass only, and scarps and moving mass together. Verification with the 2016 Kumamoto earthquake event shows that the most appropriate method for the evaluation of LLs is to consider the failure of scarps and moving mass together. In addition, by analyzing the relationship between the factors of slope of entire landslide and slope of scarp for LLs and comparing the results with the Aso-ohashi landslide, the largest landslide caused by the 2016 Kumamoto earthquake, we also found that morphometric analysis of LLs is useful for forecasting the travel distance of future landslides.     

A new region-based preparatory factor for landslide susceptibility models: the total flux

This paper presents a new region-based preparatory factor, total flux (TF), for landslide susceptibility models (LSMs). TF takes into account the topography and hydrology conditions upstream of each gridded data cell and represents the total flux of water in the stream. The results show that TF is strongly associated with the occurrence of landslides and is a good preparatory factor for LSM. Using TF instead of a drainage distance factor in I-Lan region in Taiwan shows an improvement in the accuracy of the cumulative percentage of landslide occurrence of 44 and 14 % for the top 1 and 10 % susceptible areas, respectively. This significant improvement in accuracy in these high-risk areas is critical for preventing and mitigating the economic and human losses due to landslides.     

Geometrical characteristics of earthquake-induced landslides and correlations with control factors: a case study of the 2013 Minxian,Gansu, China, <Emphasis Type="Italic">Mw</Emphasis> 5.9 event

Geometric parameters are useful for characterizing earthquake-triggered landslides. This paper presents a detailed statistical analysis on this issue using the landslide inventory of the 2013, Minxian, China Mw 5.9 earthquake. Based on GIS software and a 5-m resolution DEM, geometric parameters of 635 coseismic landslides (with areas larger than 500 m²) were obtained, including height, length, width, reach angle (arc tangent of the height-length ratio), and aspect ratio (length-width ratio). The fitting relationship of height and length from these data is H = 0.6164L + 0.4589, with an average reach angle of 31.65°. The landslide aspect ratios concentrate in the range of 1.4～2.6, with an average of 2.11. According to the plane geometric shapes and aspect ratios, the landslides are classified into four categories: transverse landslide (LA1, L/W ≤ 0.8), isometric landslide (LA2, 0.8 < L/W ≤ 1.2), longitudinal landslide (LA3, 1.2 < L/W ≤ 3), and elongated landslide (LA4, L/W > 3). Statistics of these four types of landslides versus ten classified control factors (elevation, slope angle, slope aspect, curvature, slope position, distance to drainages, lithology, seismic intensity, peak ground acceleration, and distance to seismogenic fault) are used to examine their possible correlations and the landslide-prone areas, which would be helpful to the landslide disaster mitigation in the affected area.     

Shear wave velocity imaging of landslide debris deposited on an erodible bed and possible movement mechanism for a loess landslide in Jingyang,Xi’an,China

The South Jingyang Plateau, with a total area of 70 km², is located in Shaanxi Province, China. Since 1976, more than 50 landslides of different types have occurred repeatedly on the edge slopes of the plateau due to the start of diversion irrigation on the plateau, resulting in great loss of lives and property. To better understand the initiation and movement mechanisms of these loess landslides, we surveyed them and carried out a detailed investigation of a large landslide in the Xihetan area. Our field survey results revealed that although most of these landslides had a long runout with high mobility, most of the landslide materials originating from the edge slopes may have been in an unsaturated state when the landslide occurred. This suggests that the materials at the toe of the edge slope as well as on the travel path along the river terrace might have played a key role in landslide movement. To examine how the materials on the travel path were involved in the landsliding, we used a multichannel surface wave technique and surveyed shear wave velocity (V _s ) profiles of the landslide deposits. We also examined the internal geometry of the deposits that outcropped on the right-side slope of the landslide foot. The longitudinal profile of V _s along the direction of movement showed that terrace deposits near the toe of the edge slope may have been sheared upward, indicating that at the toe, the surface of rupture might be located inside the terrace deposits. The V _s contours showed an A-shaped fold within the landslide deposits in the middle part of the travel path and became greater in the most distal toe part. The V _s profile across the deposits showed a U-shaped belt, in which the soil layers have smaller V _s . This belt may be the boundary between the sliding landslide debris and terrace deposits. The observed internal geometry of the landslide deposits indicates that a sliding surface developed within the sandy layer underlying the gravel layer. Therefore, we inferred that after failure, the displaced landslide materials overrode and sheared the terrace deposits along its main sliding direction, resulting in the formation of thrust folds within the terrace deposits, and greater V _s on the distal toe part of the landslide.     

Does parameterization influence the performance of slope stability model results? A case study in Rio de Janeiro,Brazil

We produce factor of safety (FOS) and slope failure susceptibility index (SFSI) maps for a 4.4-km² study area in Rio de Janeiro, Brazil, in order to explore the sensitivity of the geotechnical and geohydraulic parameterization on the model outcomes. Thereby, we consider parameter spaces instead of combinations of discrete values. SFSI is defined as the fraction of tested parameter combinations within a given space yielding FOS <1. We repeat our physically based calculations for various parameter spaces, employing the infinite slope stability model and the sliding surface model of the software r.slope.stability for testing the geotechnical parameters and the Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Model (TRIGRS) for testing the geohydraulic parameters. Whilst the results vary considerably in terms of their conservativeness, the ability to reproduce the spatial patterns of the observed landslide release areas is relatively insensitive to the variation of the parameterization as long as there is sufficient pattern in the results. We conclude that landslide susceptibility maps yielded by catchment-scale physically based models should not be interpreted in absolute terms and suggest that efforts to develop better strategies for dealing with the uncertainties in the spatial variation of the key parameters should be given priority in future slope stability modelling efforts.     

The formation and development of debris flows in large watersheds after the 2008 Wenchuan Earthquake

The Wenchuan earthquake has caused abundance of loose materials supplies for debris flows. Many debris flows have occurred in watersheds in area beyond 20 km², presenting characteristics differing from those in small watersheds. The debris flows yearly frequency decreases exponentially, and the average debris flow magnitude increases linearly with watershed size. The rainfall thresholds for debris flows in large watersheds were expressed as I?=?14.7 D ^?0.79 (2 h?<?D?<?56 h), which is considerably higher than those in small watersheds as I?=?4.4 D ^?0.70 (2 h?<?D?<?37 h). A case study is conducted in Ergou, 39.4 km² in area, to illustrate the formation and development processes of debris flows in large watersheds. A debris flow develops in a large watershed only when the rainfall was high enough to trigger the wide-spread failures and erosions on slope and realize the confluence in the watershed. The debris flow was supplied by the widely distributed failures dominated by rill erosions (14 in 22 sources in this case). The intermittent supplying increased the size and duration of debris flow. While the landslide dam failures provided most amounts for debris flows (57 % of the total amount), and amplified the discharge suddenly. During these processes, the debris flow velocity and density increased as well. The similar processes were observed in other large watersheds, indicating this case is representative.     

Predicting saturated hydraulic conductivity using particle swarm optimization and genetic algorithm

Soil saturated hydraulic conductivity (K_s) is considered as soil basic hydraulic property, and its precision estimation is a key element in modeling water flow and solute transport processes both in the saturated and vadose zones. Although some predictive methods (e.g., pedotransfer functions, PTFs) have been proposed to indirectly predict K_s, the accuracy of these methods still needs to be improved. In this study, some easily available soil properties (e.g., particle size distribution, organic carbon, calcium carbonate content, electrical conductivity, and soil bulk density) are employed as input variables to predict K_s using a fuzzy inference system (FIS) trained by two different optimization techniques: particle swarm optimization (PSO) and genetic algorithm (GA). To verify the derived FIS, 113 soil samples were taken, and their required physical properties were measured (113 sample points?×?7 factors?=?791 input data). The initial FIS is compared with two methods: FIS trained by PSO (PSO-FIS) and FIS trained by GA (GA-FIS). Based on experimental results, all three methods are compared according to some evaluation criteria including correlation coefficient (r), modeling efficiency (EF), coefficient of determination (CD), root mean square error (RMSE), and maximum error (ME) statistics. The results showed that the PSO-FIS model achieved a higher level of modeling efficiency and coefficient of determination (R²) in comparison with the initial FIS and the GA-FIS model. EF and R² values obtained by the developed PSO-FIS model were 0.69 and 0.72, whereas they were 0.63 and 0.54 for the GA-FIS model. Moreover, the results of ME and RMSE indices showed that the PSO-FIS model can estimate soil saturated hydraulic conductivity more accurate than the GA-FIS model with ME?=?10.4 versus 11.5 and RMSE?=?5.2 versus 5.5 for PSO-FIS and GA-FIS, respectively.     

Seasonal Factors Influencing Copepod Abundance in the Maryland Coastal Bays

Maryland Coastal Bays differ in hydrography from river-dominated estuaries because of limited freshwater inflow from tributary creeks and more marine influence. Consequently, the copepod community structure may be different from that of the coastal ocean and river-dominated estuaries in the mid-Atlantic region. A 2-year study was conducted to describe copepod species composition and seasonal patterns in abundance and factors influencing the community structure. Seven copepod genera, Acartia, Centropages, Pseudodiaptomus, Parvocalanus, Eurytemora, Oithona, and Temora, in addition to harpacticoids were found. The copepod community was dominated by Acartia spp. (64%), followed by Centropages spp. (30%), unlike in river-dominated estuaries in the region where the copepod community is usually dominated by Acartia spp. followed by Eurytemora affinis. Acartia tonsa was the most abundant in summer and fall whereas Centropages spp., Temora sp., Oithona similis, E. affinis, and harpacticoids were most abundant in winter and early spring. Parvocalanus crassirostris and Pseudodiaptomus pelagicus were present in fall and winter but at relatively low densities. The highest mean density of copepods occurred in winter 2012 (36,437 m^?3) and the lowest in spring 2013 (347 m^?3). Low densities occurred through early summer (614 m^?3) coinciding with peak spawning by bay anchovy (Anchoa mitchilli). Bottom-up control via low phytoplankton biomass coupled with top-down control by ctenophores (Mnemiopsis sp.), mysids (Neomysis americana), and bay anchovy was probably responsible for the low copepod densities in spring and early summer. Temperature and salinity were also important factors that influenced the seasonal patterns of copepod species occurrence. The observed seasonal differences in the abundance of copepods have important implications for planktivorous fishes as they may experience lower growth rates and survival due to food limitation in spring/early summer when copepod densities are relatively low than in late summer/fall when copepod abundance is higher.     

Salinity Tolerance and Competition Drive Distributions of Native and Invasive Submerged Aquatic Vegetation in the Upper San Francisco Estuary

Both abiotic and biotic factors govern distributions of estuarine vegetation, and experiments can reveal effects of these drivers under current and future conditions. In upper San Francisco Estuary (SFE), increased salinity could result from sea level rise, levee failure, or water management. We used mesocosms to test salinity effects on, as well as competition between, the native Stuckenia pectinata (sago pondweed) and invasive Egeria densa (Brazilian waterweed), species with overlapping distributions at the freshwater transition in SFE. Grown alone at a salinity of 5, E. densa decreased fivefold in biomass relative to the freshwater treatment and decomposed within 3 weeks at higher salinities. In contrast, S. pectinata biomass accumulated greatly (~4× initial) at salinities of 0 and 5, doubled at 10, and was unchanged at 15. When grown together in freshwater, S. pectinata produced 75 % less biomass than in monoculture and significantly more nodal roots (suggesting increased nutrient foraging). At a salinity of 5, a decline in E. densa performance coincided with a doubling of S. pectinata shoot density. Additional experiments on E. densa showed elevated temperature (26 and 30 °C) suppressed growth especially at higher salinities (≥5). We conclude that salinity strongly influences distributions of both species and that competition from E. densa may impose limits on S. pectinata abundance in the fresher reaches of SFE. With a salinity increase of 5, S. pectinata is likely to maintain its current distribution while spreading up-estuary at the expense of E. densa, especially if increased temperature also reduces E. densa biomass.     

Setting up evaluate indicators for slope control engineering based on spatial clustering analysis

Rainfall-induced landslides (RILs) have been a source of social and economic disruption in the mountainous Baguio area in northern Philippines. Prolonged heavy rainfall usually happens during tropical cyclone and southwest monsoon activity. A pragmatic approach to RIL mitigation is to develop rainfall-based early warning. We implemented a modified regression method to derive the empirical minimum intensity (I)–duration (D) threshold I = 6.46 D ^?0.28 and a normalized ID threshold NI = 0.002 D ^?0.28 for rainfall duration ranging between 24 and 264 h. Using a separate data set to evaluate the applicability of the threshold, 93% of the landslide-triggering rainfall events fell above the derived threshold. RILs also occurred when 24-h rainfall was 0.02–28% of the mean annual precipitation or after accumulating at least 500 mm of rainfall from the onset of the rainy season. The thresholds may be further refined as more landslide data become available in the future.     

Reliability back analysis of shear strength parameters of landslide with three-dimensional upper bound limit analysis theory

It is essential to determine the shear strength parameters c and φ on the sliding surface for stability evaluation and engineering design of a landslide. In this study, a new parameter back analysis method is proposed by combining the 2D/3D upper bound method of limit analysis and reliability theory to accurately determine the shear strength parameters for a 3D slope with a single failure surface. The proposed reliability back analysis method overcomes the shortcomings of the traditional deterministic analysis method of slope stability that cannot take into account the randomness and uncertainty of geotechnical parameters. Based on the reliability theory, two methods were studied: first-order reliability method (implemented by spreadsheet and Matlab, called spreadsheet method and constrained optimization method, respectively, in this paper) and Monte Carlo simulation. The optimized values of c and φ were obtained by establishing only one balance equation with the consideration of the pore water pressure or other complex conditions, which can solve the problem of the back analysis of strength parameters for a single 3D sliding surface condition. The correlation research showed that the negative correlation between c and φ greatly affected the back analysis results, and the reliability index values were conservative without considering such a negative correlation. A case study for the back analysis of shear strength parameters is conducted based on a practical landslide model with a broken line slip surface slope in Zhuquedong village, Luxi town, Xiangxi County, Hunan Province, China, and a suggestion for the selection of landslide cross section is presented. The results show that the back analysis results determined by the reliability theory coincide well with the survey and experimental results. The proposed method is found to be more accurate and effective in determining the values of shear parameters than that of the traditional deterministic inversion method.     

Modelling malaria susceptibility using geographic information system

Identifying and/or predicting the geography of malaria will help decision makers locate the particular area with the health problem, and to design area-specific interventions. Using GIS (ArcMap 10.1), a spatial analysis of environmental factors that contribute to the spread of malaria vector was conducted to develop a malaria susceptibility model that could be used in effective malaria control planning. The study first determined malaria susceptibility index and combined it with geospatial modelling to predict malaria susceptibility. Clinical malaria cases were then geocoded and tested to determine the accuracy of the prediction. The results show that 72.3, 24.5, 3.1 and 0.1 % of the clinical malaria incidence were found in areas that were predicted to have very high, high, low and very low susceptibility levels. Hence, the model, to a large extent, predicted malaria occurrences. The conclusion is that modelling such as this can help determine spatio-temporal prediction and mapping of malaria incidence to aid in the design and administration of appropriate interventions.     

Innovative landslide susceptibility mapping supported by geomorphon and geographical detector methods

Landslides are among the most common and dangerous natural hazards in mountainous regions that can cause damage to properties and loss of lives. Landslide susceptibility mapping (LSM) is a critical tool for preventing or mitigating the negative impacts of landslides. Although many previous studies have employed various statistical methods to produce quantitative maps of the landslide susceptibility index (LSI) based on inventories of past landslides and contributing factors, they are mostly ad hoc to a specific area and their success has been hindered by the lack of a methodology that could produce the right mapping units at proper scale and by the lack of a general framework for objectively accounting for the differing contribution of various preparatory factors. This paper addresses these issues by integrating the geomorphon and geographical detector methods into LSM to improve its performance. The geomorphon method, an innovative pattern recognition approach for identifying landform elements based on the line of sight concept, is adapted to delineate ridge lines and valley lines to form slope units at self-adjusted spatial scale suitable for LSM. The geographical detector method, a spatial variance analysis method, is integrated to objectively assign the weights of contributing factors for LSM. Applying the new integrated approach to I-Lan, Taiwan produced very significant improvement in LSI mapping performance than a previous model, especially in highly susceptible areas. The new method offers a general framework for better mapping landslide susceptibility and mitigating its negative impacts.     

Absorption in the H(93–95)<Emphasis Type="Italic">α</Emphasis> and H(78–79)<Emphasis Type="Italic">α</Emphasis> recombination lines in the HII region of the quasar 3C 345

Absorption of the synchrotron emission of the quasar 3C 345 in the continuum and H(93–95)α and H(78–79)α radio recombination lines is studied. The upper limit for absorption in the H(93–95)α lines is T_al/T_ac < 0.7%; absorption in the H(78–79)α lines with antenna temperature T_al = 25 mK, linewidth Δf = 5.3 ± 0.08 MHz, and T_al/T_ac ≥ 0.3% has been detected. A correction to the redshift Δz = 0.00135 ± 0.00008 (z = 0.59365) has been determined.     

Developing an algorithm for automated geometric analysis and classification of landslides incorporating LiDAR-derived DEM

Amending landslides inventories is immensely important to policy and decision makers alike. Sliding creates geometric shapes on the Earth’s surface. This study presents the utilization of LiDAR high-resolution digital elevation model (DEM) in the Alborz Mountains, Iran to refurbish the existing landslide inventory dataset by implementing the proposed algorithm. The method consists of the automated derivation of landslide geometry (length, width, and area) followed by classification of landslide types considering length, width and flow direction. This study has used the trapezoidal rule for numerical integration to develop the proposed algorithm. The landslides were then classified into four types (very long, long, very wide, and wide) based on slope, length, and width. This geometric classification of landslides is based on the geographical coordinates, slope angle (θ), length (L), and width (W), and further failure flow direction. A total of 95 landslides were updated from the existing inventory database. The proposed method was verified and evaluated by field observations; and 14 samples were tested to determine the relative error. The results demonstrated that the mean percentage relative error is 0.496% in length and width and 0.008% in area, related to the GIS analysis. The accuracy performance of determining the landslide’s type is 92%. The purposefulness of this algorithm is to increase the accuracy performance of landslides geometry analysis and automated measurements associated with the usual GIS platforms such as ArcGIS.     

Stable carbon and oxygen isotope study on benthic foraminifera: Implication for microhabitat preferences and interspecies correlation

Stable isotopes of benthic foraminifera have widely been applied in micropalaeontological research to understand vital effects in foraminifera. Isotopic fractionations are mainly controlled by ontogeny, bottom/pore water chemistry, habitat preference, kinetic effect and respiration. Discontinuous abundance of a species for isotopic analysis has forced us to select multiple species from down-core samples. Thus standardisation factors are required to convert isotopic values of one species with respect to other species. The present study is pursued on isotopic values of different pairs of benthic foraminifera from the Krishna–Godavari basin and Peru offshore to understand habitat-wise isotopic variation and estimation of isotopic correction factors for the paired species (Cibicides wuellerstorfi–Bulimina marginata, Ammonia spp.–Loxostomum amygdalaeformis and Bolivina seminuda–Nonionella auris). Infaunal species (B. marginata, Ammonia spp. and N. auris) show a lighter carbon isotopic excursion with respect to the epifaunal to shallow infaunal forms (C. wuellerstorfi, L. amygdalaeformis and B. seminuda). These lighter \(\updelta ^{13}\) \(\hbox {C}\) values are related to utilisation of \(\hbox {CO}_{2}\) produced by anaerobic remineralisation of organic matter. However, enrichment of \(\updelta ^{18}\) \(\hbox {O}\) for the deeper microhabitat (bearing lower pH and decreased \({\hbox {CO}_{3}}^{2-})\) is only recorded in case of B. marginata. It is reverse in case of N. auris and related to utilisation of respiratory \(\hbox {CO}_{2}\) and internal dissolve inorganic carbon pool. Estimation of interspecies isotopic correction factors for the species pairs (\(\updelta ^{13}\) \(\hbox {C}\) of C. wuellerstorfi–B. marginata, L. amygdalaeformis–Ammonia spp., N. auris–B. seminuda) and \(\updelta ^{18}\) \(\hbox {O}\) of C. wuellerstorfi–B. marginata are statistically reliable and may be used in palaeoecological studies.     

Comparison of the magnetic properties of leading and following spots and the overlying ultraviolet emission

SDO/HMI and SDO/AIA data for the 24th solar-activity cycle are analyzed using a quicker and more accurate method for resolving π ambiguities in the transverse component of the photospheric magnetic field, yielding new results and confirming some earlier results on the magnetic properties of leading and following magnetically connected spots and single spots. The minimum inclination of the field lines to the positive normal to the solar surface α _min within umbrae is smaller in leading than in following spots in 78% of the spot pairs considered; the same trend is found for the mean angle 〈α〉 in 83% of the spot pairs. Positive correlations between the α _min values and the 〈α〉 values in leading and following spots are also found. On average, in umbrae, the mean values of 〈B〉, the umbra area S, and the angles α _min and 〈α〉 decrease with growth in the maximum magnetic field B _max in both leading and following spots. The presence of a positive correlation between B _max and S is confirmed, and a positive correlation between 〈B〉 and S in leading and following spots has been found. Themagnetic properties of the umbrae of magnetically connected pairs of spots are compared with the contrast of the He II 304 emission above the umbrae, C ₃₀₄. Spots satisfying certain conditions display a positive correlation between C _304?L and 〈α _L 〉 for the leading (L) spots, and between C _304?L /C _304?F and l _L /l _F , where l _L (l _F ) are the lengths of the field lines connecting leading (L) or following (F) spots from the corresponding spot umbrae to the apex of the field line.