Assessing severe Drought and wet events over India in a future climate using a nested bias-correction approach

General circulation models (GCMs) are routinely used to simulate future climatic conditions. However, rainfall outputs from GCMs are highly uncertain in preserving temporal correlations, frequencies, and intensity distributions, which limits their direct application for downscaling and hydrological modeling studies. To address these limitations, raw outputs of GCMs or regional climate models are often bias corrected using past observations. In this paper, a methodology is presented for using a nested bias-correction approach to predict the frequencies and occurrences of severe droughts and wet conditions across India for a 48-year period (2050-2099) centered at 2075. Specifically, monthly time series of rainfall from 17 GCMs are used to draw conclusions for extreme events. An increasing trend in the frequencies of droughts and wet events is observed. The northern part of India and coastal regions show maximum increase in the frequency of wet events. Drought events are expected to increase in the west central, peninsular, and central northeast regions of India. (C) 2013 American Society of Civil Engineers.

Assessing resilience and state-transition models with historical records of cheatgrass Bromus tectorum invasion in North American sagebrush-steppe

1. Resilience-based approaches are increasingly being called upon to inform ecosystem management, particularly in arid and semi-arid regions. This requires management frameworks that can assess ecosystem dynamics, both within and between alternative states, at relevant time scales. 2. We analysed long-term vegetation records from two representative sites in the North American sagebrush-steppe ecosystem, spanning nine decades, to determine if empirical patterns were consistent with resilience theory, and to determine if cheatgrass Bromus tectorum invasion led to thresholds as currently envisioned by expert-based state-and-transition models (STM). These data span the entire history of cheatgrass invasion at these sites and provide a unique opportunity to assess the impacts of biotic invasion on ecosystem resilience. 3. We used univariate and multivariate statistical tools to identify unique plant communities and document the magnitude, frequency and directionality of community transitions through time. Community transitions were characterized by 37-47% dissimilarity in species composition, they were not evenly distributed through time, their frequency was not correlated with precipitation, and they could not be readily attributed to fire or grazing. Instead, at both sites, the majority of community transitions occurred within an 8-10year period of increasing cheatgrass density, became infrequent after cheatgrass density peaked, and thereafter transition frequency declined. 4. Greater cheatgrass density, replacement of native species and indication of asymmetry in community transitions suggest that thresholds may have been exceeded in response to cheatgrass invasion at one site (more arid), but not at the other site (less arid). Asymmetry in the direction of community transitions also identified communities that were at-risk' of cheatgrass invasion, as well as potential restoration pathways for recovery of pre-invasion states. 5. Synthesis and applications. These results illustrate the complexities associated with threshold identification, and indicate that criteria describing the frequency, magnitude, directionality and temporal scale of community transitions may provide greater insight into resilience theory and its application for ecosystem management. These criteria are likely to vary across biogeographic regions that are susceptible to cheatgrass invasion, and necessitate more in-depth assessments of thresholds and alternative states, than currently available.

Estimation of Series Capacitance for a Three-Phase Transformer Winding From Its Measured Frequency Response

Recently, authors published a method to indirectly measure series capacitance (C-s) of a single, isolated, uniformly wound transformer winding, from its measured frequency response. The next step was to implement it on an actual three-phase transformer. This task is not as straightforward as it might appear at first glance, since the measured frequency response on a three-phase transformer is influenced by nontested windings and their terminal connections, core, tank, etc. To extract the correct value of C-s from this composite frequency response, the formulation has to be reworked to first identify all significant influences and then include their effects. Initially, the modified method and experimental results on a three-phase transformer (4 MVA, 33 kV/433 V) are presented along with results on the winding considered in isolation (for cross validation). Later, the method is directly implemented on another three-phase unit (3.5 MVA, 13.8 kV/765 V) to show repeatability.

On Computing Amplitude, Phase, and Frequency Modulations Using a Vector Interpretation of the Analytic Signal

The amplitude-modulation (AM) and phase-modulation (PM) of an amplitude-modulated frequency-modulated (AM-FM) signal are defined as the modulus and phase angle, respectively, of the analytic signal (AS). The FM is defined as the derivative of the PM. However, this standard definition results in a PM with jump discontinuities in cases when the AM index exceeds unity, resulting in an FM that contains impulses. We propose a new approach to define smooth AM, PM, and FM for the AS, where the PM is computed as the solution to an optimization problem based on a vector interpretation of the AS. Our approach is directly linked to the fractional Hilbert transform (FrHT) and leads to an eigenvalue problem. The resulting PM and AM are shown to be smooth, and in particular, the AM turns out to be bipolar. We show an equivalence of the eigenvalue formulation to the square of the AS, and arrive at a simple method to compute the smooth PM. Some examples on synthesized and real signals are provided to validate the theoretical calculations.

Temporal Envelope Fit of Transient Audio Signals

We address the problem of temporal envelope modeling for transient audio signals. We propose the Gamma distribution function (GDF) as a suitable candidate for modeling the envelope keeping in view some of its interesting properties such as asymmetry, causality, near-optimal time-bandwidth product, controllability of rise and decay, etc. The problem of finding the parameters of the GDF becomes a nonlinear regression problem. We overcome the hurdle by using a logarithmic envelope fit, which reduces the problem to one of linear regression. The logarithmic transformation also has the feature of dynamic range compression. Since temporal envelopes of audio signals are not uniformly distributed, in order to compute the amplitude, we investigate the importance of various loss functions for regression. Based on synthesized data experiments, wherein we have a ground truth, and real-world signals, we observe that the least-squares technique gives reasonably accurate amplitude estimates compared with other loss functions.

Maximum likelihood algorithms for joint estimation of synchronisation impairments and channel in multiple input multiple output-orthogonal frequency division multiplexing system

Maximum likelihood (ML) algorithms, for the joint estimation of synchronisation impairments and channel in multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) system, are investigated in this work. A system model that takes into account the effects of carrier frequency offset, sampling frequency offset, symbol timing error and channel impulse response is formulated. Cramer-Rao lower bounds for the estimation of continuous parameters are derived, which show the coupling effect among different impairments and the significance of the joint estimation. The authors propose an ML algorithm for the estimation of synchronisation impairments and channel together, using the grid search method. To reduce the complexity of the joint grid search in the ML algorithm, a modified ML (MML) algorithm with multiple one-dimensional searches is also proposed. Further, a stage-wise ML (SML) algorithm using existing algorithms, which estimate less number of parameters, is also proposed. Performance of the estimation algorithms is studied through numerical simulations and it is found that the proposed ML and MML algorithms exhibit better performance than SML algorithm.

Eigen-profiles of spatio-temporal fragments for adaptive region-based tracking

We propose a novel space-time descriptor for region-based tracking which is very concise and efficient. The regions represented by covariance matrices within a temporal fragment, are used to estimate this space-time descriptor which we call the Eigenprofiles(EP). EP so obtained is used in estimating the Covariance Matrix of features over spatio-temporal fragments. The Second Order Statistics of spatio-temporal fragments form our target model which can be adapted for variations across the video. The model being concise also allows the use of multiple spatially overlapping fragments to represent the target. We demonstrate good tracking results on very challenging datasets, shot under insufficient illumination conditions.

A nearly constant switching frequency current hysteresis controller with generalized parabolic boundaries using 12-sided polygonal voltage space vectors for IM drives

In this paper, a current hysteresis controller with parabolic boundaries for a 12-sided polygonal voltage space vector inverter fed induction motor (IM) drive is proposed. Parabolic boundaries with generalized vector selection logic, valid for all sectors and rotational direction, is used in the proposed controller. The current error space phasor boundary is obtained by first studying the drive scheme with space vector based PWM (SVPWM) controller. Four parabolas are used to approximate this current error space phasor boundary. The system is then run with space phasor based hysteresis PWM controller by limiting the current error space vector (CESV) within the parabolic boundary. The proposed controller has simple controller implementation, nearly constant switching frequency, extended modulation range and fast dynamic response with smooth transition to the over modulation region.

A Medium-Voltage Inverter-Fed IM Drive Using Multilevel 12-Sided Polygonal Vectors, With Nearly Constant Switching Frequency Current Hysteresis Controller

In this paper, a current error space vector (CESV)-based hysteresis current controller for a multilevel 12-sided voltage space vector-based inverter-fed induction motor (IM) drive is proposed. The proposed controller gives a nearly constant switching frequency operation throughout different speeds in the linear modulation region. It achieves the elimination of 6n +/- 1, n = odd harmonics from the phase voltages and currents in the entire modulation range, with an increase in the linear modulation range. It also exhibits fast dynamic behavior under different transient conditions and has a simple controller implementation. Nearly constant switching frequency is obtained by matching the steady-state CESV boundaries of the proposed controller with that of a constant switching frequency SVPWM-based drive. In the proposed controller, the CESV reference boundaries are computed online, using the switching dwell time and voltage error vector of each applied vector. These quantities are calculated from estimated sampled reference phase voltages. Vector change is decided by projecting the actual current error along the computed hysteresis space vector boundary of the presently applied vector. The estimated reference phase voltages are found from the stator current error ripple and the parameters of the IM.

Call intensity is a repeatable and dominant acoustic feature determining male call attractiveness in a field cricket

Acoustic signal variation and female preference for different signal components constitute the prerequisite framework to study the mechanisms of sexual selection that shape acoustic communication. Despite several studies of acoustic communication in crickets, information on both male calling song variation in the field and female preference in the same system is lacking for most species. Previous studies on acoustic signal variation either were carried out on populations maintained in the laboratory or did not investigate signal repeatability. We therefore used repeatability analysis to quantify variation in the spectral, temporal and amplitudinal characteristics of the male calling song of the field cricket Plebeiogryllus guttiventris in a wild population, at two temporal scales, within and across nights. Carrier frequency (CF) was the most repeatable character across nights, whereas chirp period (CP) had low repeatability across nights. We investigated whether female preferences were more likely to be based on features with high (CF) or low (CP) repeatability. Females showed no consistent preferences for CF but were significantly more attracted towards signals with short CPs. The attractiveness of lower CP calls disappeared, however, when traded off with sound pressure level (SPL). SPL was the only acoustic feature that was significantly positively correlated with male body size. Since relative SPL affects female phonotaxis strongly and can vary unpredictably based on male spacing, our results suggest that even strong female preferences for acoustic features may not necessarily translate into greater advantage for males possessing these features in the field. (C) 2013 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved.

Formulation of a mathematical approach to regional frequency analysis

Estimation of design quantiles of hydrometeorological variables at critical locations in river basins is necessary for hydrological applications. To arrive at reliable estimates for locations (sites) where no or limited records are available, various regional frequency analysis (RFA) procedures have been developed over the past five decades. The most widely used procedure is based on index-flood approach and L-moments. It assumes that values of scale and shape parameters of frequency distribution are identical across all the sites in a homogeneous region. In real-world scenario, this assumption may not be valid even if a region is statistically homogeneous. To address this issue, a novel mathematical approach is proposed. It involves (i) identification of an appropriate frequency distribution to fit the random variable being analyzed for homogeneous region, (ii) use of a proposed transformation mechanism to map observations of the variable from original space to a dimensionless space where the form of distribution does not change, and variation in values of its parameters is minimal across sites, (iii) construction of a growth curve in the dimensionless space, and (iv) mapping the curve to the original space for the target site by applying inverse transformation to arrive at required quantile(s) for the site. Effectiveness of the proposed approach (PA) in predicting quantiles for ungauged sites is demonstrated through Monte Carlo simulation experiments considering five frequency distributions that are widely used in RFA, and by case study on watersheds in conterminous United States. Results indicate that the PA outperforms methods based on index-flood approach.

A rain forest dusk chorus: cacophony or sounds of silence?

A rain forest dusk chorus consists of a large number of individuals of acoustically communicating species signaling at the same time. How different species achieve effective intra-specific communication in this complex and noisy acoustic environment is not well understood. In this study we examined acoustic masking interference in an assemblage of rain forest crickets and katydids. We used signal structures and spacing of signalers to estimate temporal, spectral and active space overlap between species. We then examined these overlaps for evidence of strategies of masking avoidance in the assemblage: we asked whether species whose signals have high temporal or spectral overlap avoid calling together. Whereas we found evidence that species with high temporal overlap may avoid calling together, there was no relation between spectral overlap and calling activity. There was also no correlation between the spectral and temporal overlaps of the signals of different species. In addition, we found little evidence that species calling in the understorey actively use spacing to minimize acoustic overlap. Increasing call intensity and tuning receivers however emerged as powerful strategies to minimize acoustic overlap. Effective acoustic overlaps were on average close to zero for most individuals in natural, multispecies choruses, even in the absence of behavioral avoidance mechanisms such as inhibition of calling or active spacing. Thus, call temporal structure, intensity and frequency together provide sufficient parameter space for several species to call together yet communicate effectively with little interference in the apparent cacophony of a rain forest dusk chorus.

Bivariate frequency analysis of floods using a diffusion based kernel density estimator

Recent focus of flood frequency analysis (FFA) studies has been on development of methods to model joint distributions of variables such as peak flow, volume, and duration that characterize a flood event, as comprehensive knowledge of flood event is often necessary in hydrological applications. Diffusion process based adaptive kernel (D-kernel) is suggested in this paper for this purpose. It is data driven, flexible and unlike most kernel density estimators, always yields a bona fide probability density function. It overcomes shortcomings associated with the use of conventional kernel density estimators in FFA, such as boundary leakage problem and normal reference rule. The potential of the D-kernel is demonstrated by application to synthetic samples of various sizes drawn from known unimodal and bimodal populations, and five typical peak flow records from different parts of the world. It is shown to be effective when compared to conventional Gaussian kernel and the best of seven commonly used copulas (Gumbel-Hougaard, Frank, Clayton, Joe, Normal, Plackett, and Student's T) in estimating joint distribution of peak flow characteristics and extrapolating beyond historical maxima. Selection of optimum number of bins is found to be critical in modeling with D-kernel.