A terrain openness index for the extraction of karst Fenglin and Fengcong landform units from DEMs

The Fenglin and Fengcong landform units are considered to be an important representation for defining the degree of development of Karst landforms. However, these terrain features have been proven difficult to delineate and extract automatically because of their complex morphology. In this paper, a new method for identifying the Fenglin and Fengcong landform units is proposed. This method consists of two steps: (1) terrain openness calculation and (2) toe line extraction. The proposed method is applied and validated in the Karst case area of Guilin by using ASTER GDEM with one arc-second resolution. The openness of both the positive and negative terrain and a threshold were used to extract toe lines for segmenting depressions and pinnacles in Fenglin and Fengcong landforms. A comparison between the extracted Fenglin and Fengcong landform units and their real units from high resolution images was carried out to evaluate the capability of the proposed method. Results show the proposed method can effectively extract the Fenglin and Fengcong landform units, and has an overall accuracy of 93.28%. The proposed method is simple and easy to implement and is expected to play an important role in the automatic extraction of similar landform units in the Karst area.     

Approximate optimal tracking control for near-surface AUVs with wave disturbances

This paper considers the optimal trajectory tracking control problem for near-surface autonomous underwater vehicles (AUVs) in the presence of wave disturbances. An approximate optimal tracking control (AOTC) approach is proposed. Firstly, a six-degrees-of-freedom (six-DOF) AUV model with its body-fixed coordinate system is decoupled and simplified and then a nonlinear control model of AUVs in the vertical plane is given. Also, an exosystem model of wave disturbances is constructed based on Hirom approximation formula. Secondly, the time-parameterized desired trajectory which is tracked by the AUV’s system is represented by the exosystem. Then, the coupled two-point boundary value (TPBV) problem of optimal tracking control for AUVs is derived from the theory of quadratic optimal control. By using a recently developed successive approximation approach to construct sequences, the coupled TPBV problem is transformed into a problem of solving two decoupled linear differential sequences of state vectors and adjoint vectors. By iteratively solving the two equation sequences, the AOTC law is obtained, which consists of a nonlinear optimal feedback item, an expected output tracking item, a feedforward disturbances rejection item, and a nonlinear compensatory term. Furthermore, a wave disturbances observer model is designed in order to solve the physically realizable problem. Simulation is carried out by using the Remote Environmental Unit (REMUS) AUV model to demonstrate the effectiveness of the proposed algorithm.     

Automated Prediction of CMEs Using Machine Learning of CME – Flare Associations

Machine-learning algorithms are applied to explore the relation between significant flares and their associated CMEs. The NGDC flares catalogue and the SOHO/LASCO CME catalogue are processed to associate X and M-class flares with CMEs based on timing information. Automated systems are created to process and associate years of flare and CME data, which are later arranged in numerical-training vectors and fed to machine-learning algorithms to extract the embedded knowledge and provide learning rules that can be used for the automated prediction of CMEs. Properties representing the intensity, flare duration, and duration of decline and duration of growth are extracted from all the associated (A) and not-associated (NA) flares and converted to a numerical format that is suitable for machine-learning use. The machine-learning algorithms Cascade Correlation Neural Networks (CCNN) and Support Vector Machines (SVM) are used and compared in our work. The machine-learning systems predict, from the input of a flare’s properties, if the flare is likely to initiate a CME. Intensive experiments using Jack-knife techniques are carried out and the relationships between flare properties and CMEs are investigated using the results. The predictive performance of SVM and CCNN is analysed and recommendations for enhancing the performance are provided.     

Application of different clustering approaches to hydroclimatological catchment regionalization in mountainous regions,a case study in Utah State

With respect to the different hydrological responses of catchments, even the adjacent ones, in mountainous regions, there are a great number of motivations for classifying them into homogeneous clusters. These motivations include prediction in ungauged basins (PUB), model parameterization, understanding the potential impact of environmental changes, transferring information from gauged catchments to the ungauged ones. The present study investigated the similarity of catchments through the hydro-climatological pure time-series of a 14-year period from 2001 to 2015. Data sets encompass more than 13,000 month-station streamflow, rainfall, and temperature data obtained from 27 catchments in Utah State as one of the eight mountainous states of the USA. The identification, analysis, and interpretation of homogeneous catchments were investigated by applying the four approaches of clustering, K-means, Ward, and SOM (Self-Organized Map) and a newly proposed Wavelet-Entropy-based (WE-SOM) clustering method. By using two clustering evaluation criteria, 3, 5, and 6 clusters were determined as the best numbers of clusters, depending on the method employed, where each cluster represents different hydro-climatological behaviors. Despite the absence of geographic characteristics in input data matrix, the results indicated a regionalization in agreement with topographic characteristics. Considering the dependency of the hydrological behavior of catchments on the physiographic field aspects and characteristics, WE-SOM method demonstrated a more acceptable performance, compared to the other three conventional clustering methods, by providing more clusters. WE-SOM appears to be a promising approach in catchment clustering. It preserves the topological structure of data which can, as a result, be proofed in a greater number of clusters by dividing data into higher numbers of distinct clusters with similar altitudes of catchments in each cluster. The results showed the aptitude of wavelets to quantify the time-based variability of temperature, rainfall and streamflow, in the way contributing to the regionalization of diverse catchments.     

Global Warming and Tropical Land-Use Change: Greenhouse Gas Emissions from Biomass Burning, Decomposition and Soils in Forest Conversion, Shifting Cultivation and Secondary Vegetation

Tropical forest conversion, shiftingcultivation and clearing of secondary vegetation makesignificant contributions to global emissions ofgreenhouse gases today, and have the potential forlarge additional emissions in future decades. Globally, an estimated 3.1×10⁹ t of biomasscarbon of these types is exposed to burning annually,of which 1.1×10⁹ t is emitted to the atmospherethrough combustion and 49×10⁶ t is converted tocharcoal (including 26–31×10⁶ t C of blackcarbon). The amount of biomass exposed to burningincludes aboveground remains that failed to burn ordecompose from clearing in previous years, andtherefore exceeds the 1.9×10⁹ t of abovegroundbiomass carbon cleared on average each year. Above-and belowground carbon emitted annually throughdecomposition processes totals 2.1×10⁹ t C. Atotal gross emission (including decomposition ofunburned aboveground biomass and of belowgroundbiomass) of 3.41×10⁹ t C year^-1 resultsfrom clearing primary (nonfallow) and secondary(fallow) vegetation in the tropics. Adjustment fortrace gas emissions using IPCC Second AssessmentReport 100-year integration global warming potentialsmakes this equivalent to 3.39×10⁹ t ofCO₂-equivalent carbon under a low trace gasscenario and 3.83×10⁹ t under a high trace gasscenario. Of these totals, 1.06×10⁹ t (31%)is the result of biomass burning under the low tracegas scenario and 1.50×10⁹ t (39%) under thehigh trace gas scenario. The net emissions from allclearing of natural vegetation and of secondaryforests (including both biomass and soil fluxes) is2.0×10⁹ t C, equivalent to 2.0–2.4×10⁹ t of CO₂-equivalent carbon. Adding emissions of0.4×10⁹ t C from land-use category changesother than deforestation brings the total for land-usechange (not considering uptake of intact forest,recurrent burning of savannas or fires in intactforests) to 2.4×10⁹ t C, equivalent to 2.4–2.9×10⁹ t of CO₂-equivalent carbon. The totalnet emission of carbon from the tropical land usesconsidered here (2.4×10⁹ t C year^-1)calculated for the 1981–1990 period is 50% higherthan the 1.6×10⁹ t C year^-1 value used by the Intergovernmental Panel on Climate Change. The inferred (= `missing') sink in the global carbonbudget is larger than previously thought. However,about half of the additional source suggested here maybe offset by a possible sink in uptake by Amazonianforests. Both alterations indicate that continueddeforestation would produce greater impact on globalcarbon emissions. The total net emission of carboncalculated here indicates a major global warmingimpact from tropical land uses, equivalent toapproximately 29% of the total anthropogenic emissionfrom fossil fuels and land-use change.     

Singularly perturbed solution of coupled model in atmosphere-ocean for global climate

A box model of the interhemispheric thermohaline circulation （THC） in atmosphere-ocean for global climate is considered. By using the multi-scales method, the asymptotic solution of a simplified weakly nonlinear model is discussed. Firstly, by introducing first scale, the zeroth order approximate solution of the model is obtained. Secondly, by using the multi-scales, the first order approximate equation of the model is found. Finally, second order approximate equation is formed to eliminate the secular terms, and a uniformly valid asymptotic expansion of solution is decided. The multi-scales solving method is an analytic method which can be used to analyze operation sequentially. And then we can also study the diversified qualitative and quantitative behaviors for corresponding physical quantities. This paper aims at providing a valid method for solving a box model of the nonlinear equation.     

Global-Mode Analysis of Full-Disk Data from the <Emphasis Type="Italic">Michelson Doppler Imager</Emphasis> and the <Emphasis Type="Italic">Helioseismic and Magnetic Imager</Emphasis>

Building upon our previous work, in which we analyzed smoothed and subsampled velocity data from the Michelson Doppler Imager (MDI), we extend our analysis to unsmoothed, full-resolution MDI data. We also present results from the Helioseismic and Magnetic Imager (HMI), in both full resolution and processed to be a proxy for the low-resolution MDI data. We find that the systematic errors that we saw previously, namely peaks in both the high-latitude rotation rate and the normalized residuals of odd \(a\)-coefficients, are almost entirely absent in the two full-resolution analyses. Furthermore, we find that both systematic errors seem to depend almost entirely on how the input images are apodized, rather than on resolution or smoothing. Using the full-resolution HMI data, we confirm our previous findings regarding the effect of using asymmetric profiles on mode parameters, and also find that they occasionally result in more stable fits. We also confirm our previous findings regarding discrepancies between 360-day and 72-day analyses. We further investigate a six-month period previously seen in \(f\)-mode frequency shifts using the low-resolution datasets, this time accounting for solar-cycle dependence using magnetic-field data. Both HMI and MDI saw prominent six-month signals in the frequency shifts, but we were surprised to discover that the strongest signal at that frequency occurred in the mode coverage for the low-resolution proxy. Finally, a comparison of mode parameters from HMI and MDI shows that the frequencies and \(a\)-coefficients agree closely, encouraging the concatenation of the two datasets.