A conditional random field-based downscaling method for assessment of climate change impact on multisite daily precipitation in the Mahanadi basin

Downscaling to station-scale hydrologic variables from large-scale atmospheric variables simulated by general circulation models (GCMs) is usually necessary to assess the hydrologic impact of climate change. This work presents CRF-downscaling, a new probabilistic downscaling method that represents the daily precipitation sequence as a conditional random field (CRF). The conditional distribution of the precipitation sequence at a site, given the daily atmospheric (large-scale) variable sequence, is modeled as a linear chain CRF. CRFs do not make assumptions on independence of observations, which gives them flexibility in using high-dimensional feature vectors. Maximum likelihood parameter estimation for the model is performed using limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) optimization. Maximum a posteriori estimation is used to determine the most likely precipitation sequence for a given set of atmospheric input variables using the Viterbi algorithm. Direct classification of dry/wet days as well as precipitation amount is achieved within a single modeling framework. The model is used to project the future cumulative distribution function of precipitation. Uncertainty in precipitation prediction is addressed through a modified Viterbi algorithm that predicts the n most likely sequences. The model is applied for downscaling monsoon (June-September) daily precipitation at eight sites in the Mahanadi basin in Orissa, India, using the MIROC3.2 medium-resolution GCM. The predicted distributions at all sites show an increase in the number of wet days, and also an increase in wet day precipitation amounts. A comparison of current and future predicted probability density functions for daily precipitation shows a change in shape of the density function with decreasing probability of lower precipitation and increasing probability of higher precipitation.

Vertical structure and horizontal gradients of aerosol extinction coefficients over coastal India inferred from airborne lidar measurements during the Integrated Campaign for Aerosol, Gases and Radiation Budget (ICARB) field campaign

Quantitative estimates of the vertical structure and the spatial gradients of aerosol extinction coefficients have been made from airborne lidar measurements across the coastline into offshore oceanic regions along the east and west coasts of India. The vertical structure revealed the presence of strong, elevated aerosol layers in the altitude region of similar to 2-4 km, well above the atmospheric boundary layer (ABL). Horizontal gradients also showed a vertical structure, being sharp with the e(-1) scaling distance (D-0H) as small as similar to 150 km in the well-mixed regions mostly under the influence of local source effects. Above the ABL, where local effects are subdued, the gradients were much shallower (similar to 600-800 km); nevertheless, they were steep compared to the value of similar to 1500-2500 km reported for columnar AOD during winter. The gradients of these elevated layers were steeper over the east coast of India than over the west coast. Near-simultaneous radio sonde (Vaisala, Inc., Finland) ascents made over the northern Bay of Bengal showed the presence of convectively unstable regions, first from surface to similar to 750-1000 m and the other extending from 1750 to 3000 m separated by a stable region in between. These can act as a conduit for the advection of aerosols and favor the transport of continental aerosols in the higher levels (> 2 km) into the oceans without entering the marine boundary layer below. Large spatial gradient in aerosol optical and hence radiative impacts between the coastal landmass and the adjacent oceans within a short distance of < 300 km (even at an altitude of 3 km) during summer and the premonsoon is of significance to the regional climate.

Image reconstruction with laterally truncated projections in helical cone-beam CT: Linear prediction based projection completion techniques

Lateral or transaxial truncation of cone-beam data can occur either due to the field of view limitation of the scanning apparatus or iregion-of-interest tomography. In this paper, we Suggest two new methods to handle lateral truncation in helical scan CT. It is seen that reconstruction with laterally truncated projection data, assuming it to be complete, gives severe artifacts which even penetrates into the field of view. A row-by-row data completion approach using linear prediction is introduced for helical scan truncated data. An extension of this technique known as windowed linear prediction approach is introduced. Efficacy of the two techniques are shown using simulation with standard phantoms. A quantitative image quality measure of the resulting reconstructed images are used to evaluate the performance of the proposed methods against an extension of a standard existing technique.

Tree-ring based hydroclimate reconstruction over northern Vietnam from Fokienia hodginsii: eighteenth century mega-drought and tropical Pacific influence

We present here the first statistically calibrated and verified tree-ring reconstruction of climate from continental Southeast Asia.The reconstructed variable is March-May (MAM) Palmer Drought Severity Index (PDSI) based on ring widths from 22 trees (42 radial cores) of rare and long-lived conifer, Fokienia hodginsii (Po Mu as locally called) from northern Vietnam. This is the first published tree ring chronology from Vietnam as well as the first for this species. Spanning 535 years, this is the longest cross-dated tree-ring series yet produced from continental Southeast Asia. Response analysis revealed that the annual growth of Fokienia at this site was mostly governed by soil moisture in the pre-monsoon season. The reconstruction passed the calibration-verification tests commonly used in dendroclimatology, and revealed two prominent periods of drought in the mid-eighteenth and late-nineteenth enturies. The former lasted nearly 30 years and was concurrent with a similar drought over northwestern Thailand inferred from teak rings, suggesting a ``mega-drought'' extending across Indochina in the eighteenth century. Both of our reconstructed droughts are consistent with the periods of warm sea surface temperature (SST)anomalies in the tropical Pacific. Spatial correlation analyses with global SST indicated that ENSO-like anomalies might play a role in modulating droughts over the region, with El Nio (warm) phases resulting in reduced rainfall. However, significant correlation was also seen with SST over the Indian Ocean and the north Pacific,suggesting that ENSO is not the only factor affecting the climate of the area. Spectral analyses revealed significant peaks in the range of 53.9-78.8 years as well as in the ENSO-variability range of 2.0 to 3.2 years.

Constraining uncertainty in regional hydrologic impacts of climate change: Nonstationarity in downscaling

Regional impacts of climate change remain subject to large uncertainties accumulating from various sources, including those due to choice of general circulation models (GCMs), scenarios, and downscaling methods. Objective constraints to reduce the uncertainty in regional predictions have proven elusive. In most studies to date the nature of the downscaling relationship (DSR) used for such regional predictions has been assumed to remain unchanged in a future climate. However,studies have shown that climate change may manifest in terms of changes in frequencies of occurrence of the leading modes of variability, and hence, stationarity of DSRs is not really a valid assumption in regional climate impact assessment. This work presents an uncertainty modeling framework where, in addition to GCM and scenario uncertainty, uncertainty in the nature of the DSR is explored by linking downscaling with changes in frequencies of such modes of natural variability. Future projections of the regional hydrologic variable obtained by training a conditional random field (CRF) model on each natural cluster are combined using the weighted Dempster-Shafer (D-S) theory of evidence combination. Each projection is weighted with the future projected frequency of occurrence of that cluster (''cluster linking'') and scaled by the GCM performance with respect to the associated cluster for the present period (''frequency scaling''). The D-S theory was chosen for its ability to express beliefs in some hypotheses, describe uncertainty and ignorance in the system, and give a quantitative measurement of belief and plausibility in results. The methodology is tested for predicting monsoon streamflow of the Mahanadi River at Hirakud Reservoir in Orissa, India. The results show an increasing probability of extreme, severe, and moderate droughts due to limate change. Significantly improved agreement between GCM predictions owing to cluster linking and frequency scaling is seen, suggesting that by linking regional impacts to natural regime frequencies, uncertainty in regional predictions can be realistically quantified. Additionally, by using a measure of GCM performance in simulating natural regimes, this uncertainty can be effectively constrained.

Surprising observation of large anthropogenic aerosol fraction over the ``near-pristine'' southern Bay of Bengal: Climate implications

The Bay of Bengal (BoB), a small oceanic region surrounded by landmasses with distinct natural and anthropogenic activities and under the influence of seasonally changing airmass types, is characterized by a rather complex and highly heterogeneous aerosol environment. Concurrent measurements of the physical, optical, and chemical (offline analysis) properties of BoB aerosols, made onboard extensive ship-cruises and aircraft sorties during Integrated Campaign for Aerosols, gases and Radiation Budget of March-April 2006, and satellite-retrieved aerosol optical depths and derived parameters, were synthesized following a synergistic approach to delineate the anthropogenic fraction to the composite aerosol parameters and its spatial variation. Quite interestingly and contrary to the general belief, our studies revealed that, despite of the very high aerosol loading (in the marine atmospheric boundary layer as well as in the vertical column) over the northern BoB and a steep decreasing gradient toward the southern latitudes, the anthropogenic fraction showed a steady increase from North to South (where no obvious anthropogenic source regions exist). Consequently, the direct radiative forcing at the top of the atmosphere due to anthropogenic aerosols remained nearly constant over the entire BoB with values in the range from -3.3 to -3.6 Wm(-2). This interesting finding, beyond doubts calls for a better understanding of the complex aerosol system over the BoB through more focused field campaigns.

Hydrologic drought prediction under climate change: Uncertainty modeling with Dempster-Shafer and Bayesian approaches

Representation and quantification of uncertainty in climate change impact studies are a difficult task. Several sources of uncertainty arise in studies of hydrologic impacts of climate change, such as those due to choice of general circulation models (GCMs), scenarios and downscaling methods. Recently, much work has focused on uncertainty quantification and modeling in regional climate change impacts. In this paper, an uncertainty modeling framework is evaluated, which uses a generalized uncertainty measure to combine GCM, scenario and downscaling uncertainties. The Dempster-Shafer (D-S) evidence theory is used for representing and combining uncertainty from various sources. A significant advantage of the D-S framework over the traditional probabilistic approach is that it allows for the allocation of a probability mass to sets or intervals, and can hence handle both aleatory or stochastic uncertainty, and epistemic or subjective uncertainty. This paper shows how the D-S theory can be used to represent beliefs in some hypotheses such as hydrologic drought or wet conditions, describe uncertainty and ignorance in the system, and give a quantitative measurement of belief and plausibility in results. The D-S approach has been used in this work for information synthesis using various evidence combination rules having different conflict modeling approaches. A case study is presented for hydrologic drought prediction using downscaled streamflow in the Mahanadi River at Hirakud in Orissa, India. Projections of n most likely monsoon streamflow sequences are obtained from a conditional random field (CRF) downscaling model, using an ensemble of three GCMs for three scenarios, which are converted to monsoon standardized streamflow index (SSFI-4) series. This range is used to specify the basic probability assignment (bpa) for a Dempster-Shafer structure, which represents uncertainty associated with each of the SSFI-4 classifications. These uncertainties are then combined across GCMs and scenarios using various evidence combination rules given by the D-S theory. A Bayesian approach is also presented for this case study, which models the uncertainty in projected frequencies of SSFI-4 classifications by deriving a posterior distribution for the frequency of each classification, using an ensemble of GCMs and scenarios. Results from the D-S and Bayesian approaches are compared, and relative merits of each approach are discussed. Both approaches show an increasing probability of extreme, severe and moderate droughts and decreasing probability of normal and wet conditions in Orissa as a result of climate change. (C) 2010 Elsevier Ltd. All rights reserved.

Life-history of Neurospora intermedia in a sugar cane field

The life-history of Neurospora in nature has remained largely unknown. The present study attempts to remedy this. The following conclusions are based on observation of Neurospora on fire-scorched sugar cane in agricultural fields, and reconstruction experiments using a colour mutant to inoculate sugar cane burned in the laboratory. The fungus persists in soil as heat-resistant dormant ascospores. These are activated by a chemical(s) released into soil from the burnt substrate. The chief diffusible activator of ascospores is furfural and the germinating ascospores infect the scorched substrate. An invasive mycelium grows progressively upwards inside the juicy sugar cane and produces copious macroconidia externally through fire-induced openings formed in the plant tissue, or by the mechanical rupturing of the plant epidermal tissue by the mass of mycelium. The loose conidia are dispersed by wind and/or foraged by microfauna. It is suggested that the constant production of macroconidia, and their ready dispersal, serve a physiological role: to drain the substrate of minerals and soluble sugars, thereby creating nutritional conditions which stimulate sexual reproduction by the fungus. Sexual reproduction in the sugar-depleted cellulosic substrate occurs after macroconidiation has ceased totally and is favoured by the humid conditions prevailing during the monsoon rains. Profuse microconidiophores and protoperithecia are produced simultaneously in the pockets below the loosened epidermal tissue. Presumably protoperithecia are fertilized by microconidia which are possibly transmitted by nematodes active in the dead plant tissue. Mature perithecia release ascospores in situ which are passively liberated in the soil by the disintegration of the plant material and are, apparently, distributed by rain or irrigation water.

Multidimensional data reconstruction for two color fluorescence microscopy

We propose an iterative data reconstruction technique specifically designed for multi-dimensional multi-color fluorescence imaging. Markov random field is employed (for modeling the multi-color image field) in conjunction with the classical maximum likelihood method. It is noted that, ill-posed nature of the inverse problem associated with multi-color fluorescence imaging forces iterative data reconstruction. Reconstruction of three-dimensional (3D) two-color images (obtained from nanobeads and cultured cell samples) show significant reduction in the background noise (improved signal-to-noise ratio) with an impressive overall improvement in the spatial resolution (approximate to 250 nm) of the imaging system. Proposed data reconstruction technique may find immediate application in 3D in vivo and in vitro multi-color fluorescence imaging of biological specimens. (C) 2012 American Institute of Physics. http://dx.doi.org/10.1063/1.4769058]

Impacts of climate change on regional water resources

Water is the most important medium through which climate change influences human life. Rising temperatures together with regional changes in precipitation patterns are some of the impacts of climate change that have implications on water availability, frequency and intensity of floods and droughts, soil moisture, water quality, water supply and water demands for irrigation and hydropower generation. In this article we provide an introduction to the emerging field of hydrologic impacts of climate change with a focus on water availability, water quality and irrigation demands. Climate change estimates on regional or local spatial scales are burdened with a considerable amount of uncertainty, stemming from various sources such as climate models, downscaling and hydrological models used in the impact assessments and uncertainty in the downscaling relationships. The present article summarizes the recent advances on uncertainty modeling and regional impacts of climate change for the Mahanadi and Tunga-Bhadra Rivers in India.

Reconstruction of Two-Dimensional Spectra from One-Dimensional Projections-Examples from Solid State NMR

The use of Projection Reconstruction (PR) to obtain two-dimensional (2D) spectra from one-dimensional (1D) data in the solid state is illustrated. The method exploits multiple 1D spectra obtained using magic angle spinning and off-magic angle spinning. The spectra recorded under the influence of scaled heteronuclear scalar and dipolar couplings in the presence of homonuclear dipolar decoupling sequences have been used to reconstruct J/D Resolved 2D-NMR spectra. The use of just two 1D spectra is observed sufficient to reconstruct a J-resolved 2D-spectrum while a Separated Local Field (SLF) 2D-NMR spectrum could be obtained from three 1D spectra. The experimental techniques for recording the 10 spectra and procedure of reconstruction are discussed and the reconstructed results are compared with 20 experiments recorded in traditional methods. The application of the technique has been made to a solid polycrystalline sample and to a uniaxially oriented liquid crystal. Implementation of PR-NMR in solid state provides high-resolution spectra as well as leads to significant reduction in experimental time. The experiments are relatively simple and are devoid of several technical complications involved in performing the 2D experiments.

Multispectral Bayesian reconstruction technique for real-time two color fluorescence microscopy

We have developed a real-time imaging method for two-color wide-field fluorescence microscopy using a combined approach that integrates multi-spectral imaging and Bayesian image reconstruction technique. To enable simultaneous observation of two dyes (primary and secondary), we exploit their spectral properties that allow parallel recording in both the channels. The key advantage of this technique is the use of a single wavelength of light to excite both the primary dye and the secondary dye. The primary and secondary dyes respectively give rise to fluorescence and bleed-through signal, which after normalization were merged to obtain two-color 3D images. To realize real-time imaging, we employed maximum likelihood (ML) and maximum a posteriori (MAP) techniques on a high-performance computing platform (GPU). The results show two-fold improvement in contrast while the signal-to-background ratio (SBR) is improved by a factor of 4. We report a speed boost of 52 and 350 for 2D and 3D images respectively. Using this system, we have studied the real-time protein aggregation in yeast cells and HeLa cells that exhibits dot-like protein distribution. The proposed technique has the ability to temporally resolve rapidly occurring biological events.

Isotopic and geochemical characterization of invader tilapia fishes from water bodies of West Bengal and Karnataka, India

The otoliths (N = 12) of freshwater invasive species tilapia (Tilapia mossambicus) collected from two water bodies located at Kolkata and Bangalore, India, were analyzed for stable isotopes (delta 18O, delta 14C) and major and trace elements in order to assess the suitability of using otoliths as a tracer of aquatic environmental changes. The stable isotope analysis was done using the dual inlet system of a Finnigan-MAT 253 isotope ratio mass spectrometer (Thermo-Fisher, Bremen, Germany). Concentrations of major and trace elements were determined using a Thermo X-Series II quadrupole mass spectrometer. The stable isotope composition in tilapia otolith samples from Bangalore and Kolkata water bodies are quite good agreeing with that of the respective lake/pond and rain water. Elemental composition revealed in a pattern of Ca > Fe > Na > Sr > K > Ba > Cr > Mg > As > Mn > Zn > Co > Cu > Cd > Pb. The otoliths from Kolkata pond water are more enriched in Ba, Zn, Pb, Mn, Se, Cu, Zn, Cd, and Ni whereas Cr and As were found to be higher in otolith samples from Bangalore lake. The enrichment factor (EF) values of Cr were higher for both the sampling location in comparison with other metals, although all the studied metals exhibited EF values >1. The PCA shows clustering of metals in the otolith which are related either with the metabolic and physiological attributes or waterborne source. The study demonstrated the potential of stable isotope techniques to distinguish otolith specimens from varied climatic zone, while elemental composition recorded the quality of water at both the locations. The role of climate driving the quality of water can be understood by detailed and continuous monitoring of otolith specimens in the future. Future method allows reconstruction of climate and water quality from old specimens from field exposures or museum collection.