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.

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.

Solid-solid collapse transition in a two dimensional model molecular system

Solid-solid collapse transition in open framework structures is ubiquitous in nature. The real difficulty in understanding detailed microscopic aspects of such transitions in molecular systems arises from the interplay between different energy and length scales involved in molecular systems, often mediated through a solvent. In this work we employ Monte-Carlo simulation to study the collapse transition in a model molecular system interacting via both isotropic as well as anisotropic interactions having different length and energy scales. The model we use is known as Mercedes-Benz (MB), which, for a specific set of parameters, sustains two solid phases: honeycomb and oblique. In order to study the temperature induced collapse transition, we start with a metastable honeycomb solid and induce transition by increasing temperature. High density oblique solid so formed has two characteristic length scales corresponding to isotropic and anisotropic parts of interaction potential. Contrary to the common belief and classical nucleation theory, interestingly, we find linear strip-like nucleating clusters having significantly different order and average coordination number than the bulk stable phase. In the early stage of growth, the cluster grows as a linear strip, followed by branched and ring-like strips. The geometry of growing cluster is a consequence of the delicate balance between two types of interactions, which enables the dominance of stabilizing energy over destabilizing surface energy. The nucleus of stable oblique phase is wetted by intermediate order particles, which minimizes the surface free energy. In the case of pressure induced transition at low temperature the collapsed state is a disordered solid. The disordered solid phase has diverse local quasi-stable structures along with oblique-solid like domains. (C) 2013 AIP Publishing LLC.

Basic Needs and Much More With One Kilowatt Per Capita

Conventional thinkin g holds that increased energy consumption is a prerequisite for economic and social development. This belief, together With the prospect of dwindling global petroleum supplies and the high costs of expanding energy supply generally, lead many to believe that it is not feasible to improve living standards substantially in the developing countries. But by shifting to high-quality energy carriers and by exploiting cost-effective opportunities for more efficient energy use, it would be possible to satisfy basic human needs and to provide considerable further improvements in living standards without significantly increasing per-capita energy use above the present level.

Aspects of tautomerism 9-solvolysis of normal and pseudo phthaloyl chlorides

Solvolysis of normal and pseudo phthaloyl chlorides in aqueous acetone and in aqueous dioxane has revealed that the former solvolyses about hundred-fold faster than the latter. Contrary to the accepted belief, there is no evidence for equilibrium between the normal and the pseudo forms of phthaloyl chloride, under a variety of conditions.

Synthesis and Characterization of a Class of Donor-Acceptor Conjugated Molecules: Experiments and Theoretical Calculations

A class of conjugated molecules containing donor (thiophene) and acceptor (malononitrile) is synthesized by Knoevenagel condensation reaction between 2-(2,6-dimethy1-4H-pyran-4-ylidene) malononitrile and thiophene carbaldehyde containing two and three thiophene units. The resulting molecules are characterized by H-1 and C-13 NMR. We have performed UV-vis absorption, fluorescence, and cyclic voltammetry measurements on these materials. The spectroscopic and electrochemical measurements proved beyond doubt that these materials possess lowexcitation gap and are suitable for being an active material in various electronic devices. We have also performed electronic structure calculations using density functional theory (DFT) and INDO/SCI methods to characterize the ground and excited states of this class of molecules. These donor-acceptor molecules show a strong charge transfercharacter that increases with the increase in the number of thiophene rings coupled to the malononitrile acceptor moiety. We have also calculated the pi-coherence length, Stoke's shift, and effect of solvents on excited states for this class of molecules, Our theoretical values agree well with experimental results.

Belief Propagation Based Decoding of Large Non-Orthogonal STBCs

In this paper,we present a belief propagation (BP) based algorithm for decoding non-orthogonal space-time block codes (STBC) from cyclic division algebras (CDA) having large dimensions. The proposed approachinvolves message passing on Markov random field (MRF) representation of the STBC MIMO system. Adoption of BP approach to decode non-orthogonal STBCs of large dimensions has not been reported so far. Our simulation results show that the proposed BP-based decoding achieves increasingly closer to SISO AWGN performance for increased number of dimensions. In addition, it also achieves near-capacity turbo coded BER performance; for e.g., with BP decoding of 24 x 24 STBC from CDA using BPSK (i.e.,n576 real dimensions) and rate-1/2 turbo code (i.e., 12 bps/Hz spectral efficiency), coded BER performance close to within just about 2.5 dB from the theoretical MIMO capacity is achieved.

Gandhi,Mahatma And The Working Scientist - A Reconciliation

IMAGINE a scientist who is a follower of Mahatma Gandhi. What kind of science can he practice? Would it be different from the kind of science that is being practised? I believe it would be and will illustrate this by constructing Mahatma Gandhi's view on science and scientific research based on his writings on related subjects. To me this implies that science is affected by the scientist's subjective values. I will then trace some of the values behind science as practised today and examine their implications for .he relationship between the scientist and the society. I will also present a case for abandoning the belief that science must be universal and show the relevance of Gandhian concepts to scientists.

Belief revision - an axiomatic approach

Belief revision systems aim at keeping a database consistent. They mostly concentrate on how to record and maintain dependencies. We propose an axiomatic system, called MFOT, as a solution to the problem of belief revision. MFOT has a set of proper axioms which selects a set of most plausible and consistent input beliefs. The proposed nonmonotonic inference rule further maintains consistency while generating the consequences of input beliefs. It also permits multiple property inheritance with exceptions. We have also examined some important properties of the proposed axiomatic system. We also propose a belief revision model that is object-centered. The relevance of such a model in maintaining the beliefs of a physician is examined.

Damped Belief Propagation Based Near-Optimal Equalization of Severely Delay-Spread UWB MIMO-ISI Channels

In this paper, we present a belief propagation (BP) based equalizer for ultrawideband (UWB) multiple-input multiple-output (MIMO) inter-symbol interference (ISI) channels characterized by severe delay spreads. We employ a Markov random field (MRF) graphical model of the system on which we carry out message passing. The proposed BP equalizer is shown to perform increasingly closer to optimal performance for increasing number of multipath components (MPC) at a much lesser complexity than that of the optimum equalizer. The proposed equalizer performs close to within 0.25 dB of SISO AWGN performance at 10-3 bit error rate on a severely delay-spread MIMO-ISI channel with 20 equal-energy MPCs. We point out that, although MIMO/UWB systems are characterized by fully/densely connected graphical models, the following two proposed features are instrumental in achieving near-optimal performance for large number of MPCs at low complexities: i) use of pairwise compatibility functions in densely connected MRFs, and ii) use of damping of messages.

Improved large-MIMO detection based on damped belief propagation

In this paper, we consider the application of belief propagation (BP) to achieve near-optimal signal detection in large multiple-input multiple-output (MIMO) systems at low complexities. Large-MIMO architectures based on spatial multiplexing (V-BLAST) as well as non-orthogonal space-time block codes(STBC) from cyclic division algebra (CDA) are considered. We adopt graphical models based on Markov random fields (MRF) and factor graphs (FG). In the MRF based approach, we use pairwise compatibility functions although the graphical models of MIMO systems are fully/densely connected. In the FG approach, we employ a Gaussian approximation (GA) of the multi-antenna interference, which significantly reduces the complexity while achieving very good performance for large dimensions. We show that i) both MRF and FG based BP approaches exhibit large-system behavior, where increasingly closer to optimal performance is achieved with increasing number of dimensions, and ii) damping of messages/beliefs significantly improves the bit error performance.

Theory and Algorithms for Hop-Count-Based Localization with Random Geometric Graph Models of Dense Sensor Networks

Wireless sensor networks can often be viewed in terms of a uniform deployment of a large number of nodes in a region of Euclidean space. Following deployment, the nodes self-organize into a mesh topology with a key aspect being self-localization. Having obtained a mesh topology in a dense, homogeneous deployment, a frequently used approximation is to take the hop distance between nodes to be proportional to the Euclidean distance between them. In this work, we analyze this approximation through two complementary analyses. We assume that the mesh topology is a random geometric graph on the nodes; and that some nodes are designated as anchors with known locations. First, we obtain high probability bounds on the Euclidean distances of all nodes that are h hops away from a fixed anchor node. In the second analysis, we provide a heuristic argument that leads to a direct approximation for the density function of the Euclidean distance between two nodes that are separated by a hop distance h. This approximation is shown, through simulation, to very closely match the true density function. Localization algorithms that draw upon the preceding analyses are then proposed and shown to perform better than some of the well-known algorithms present in the literature. Belief-propagation-based message-passing is then used to further enhance the performance of the proposed localization algorithms. To our knowledge, this is the first usage of message-passing for hop-count-based self-localization.

Superbubble breakout and galactic winds from disc galaxies

We study the conditions for disc galaxies to produce superbubbles that can break out of the disc and produce a galactic wind. We argue that the threshold surface density of supernovae rate for seeding a wind depends on the ability of superbubble energetics to compensate for radiative cooling. We first adapt Kompaneets formalism for expanding bubbles in a stratified medium to the case of continuous energy injection and include the effects of radiative cooling in the shell. With the help of hydrodynamic simulations, we then study the evolution of superbubbles evolving in stratified discs with typical disc parameters. We identify two crucial energy injection rates that differ in their effects, the corresponding breakout ranging from being gentle to a vigorous one. (a) Superbubbles that break out of the disc with a Mach number of the order of 2-3 correspond to an energy injection rate of the order of 10(-4) erg cm(-2) s(-1), which is relevant for disc galaxies with synchrotron emitting gas in the extra-planar regions. (b) A larger energy injection threshold, of the order of 10(-3) erg cm(-2) s(-1), or equivalently, a star formation surface density of similar to 0.1 M-circle dot yr(-1) kpc(-2), corresponds to superbubbles with a Mach number similar to 5-10. While the milder superbubbles can be produced by large OB associations, the latter kind requires super-starclusters. These derived conditions compare well with observations of disc galaxies with winds and the existence of multiphase halo gas. Furthermore, we find that contrary to the general belief that superbubbles fragment through Rayleigh-Taylor (RT) instability when they reach a vertical height of the order of the scaleheight, the superbubbles are first affected by thermal instability for typical disc parameters and that RT instability takes over when the shells reach a distance of approximately twice the scaleheight.

BELIEF PROPAGATION FOR OPTIMAL EDGE COVER IN THE RANDOM COMPLETE GRAPH

We apply the objective method of Aldous to the problem of finding the minimum-cost edge cover of the complete graph with random independent and identically distributed edge costs. The limit, as the number of vertices goes to infinity, of the expected minimum cost for this problem is known via a combinatorial approach of Hessler and Wastlund. We provide a proof of this result using the machinery of the objective method and local weak convergence, which was used to prove the (2) limit of the random assignment problem. A proof via the objective method is useful because it provides us with more information on the nature of the edge's incident on a typical root in the minimum-cost edge cover. We further show that a belief propagation algorithm converges asymptotically to the optimal solution. This can be applied in a computational linguistics problem of semantic projection. The belief propagation algorithm yields a near optimal solution with lesser complexity than the known best algorithms designed for optimality in worst-case settings.