Model and parameter uncertainty in IDF relationships under climate change

Quantifying distributional behavior of extreme events is crucial in hydrologic designs. Intensity Duration Frequency (IDF) relationships are used extensively in engineering especially in urban hydrology, to obtain return level of extreme rainfall event for a specified return period and duration. Major sources of uncertainty in the IDF relationships are due to insufficient quantity and quality of data leading to parameter uncertainty due to the distribution fitted to the data and uncertainty as a result of using multiple GCMs. It is important to study these uncertainties and propagate them to future for accurate assessment of return levels for future. The objective of this study is to quantify the uncertainties arising from parameters of the distribution fitted to data and the multiple GCM models using Bayesian approach. Posterior distribution of parameters is obtained from Bayes rule and the parameters are transformed to obtain return levels for a specified return period. Markov Chain Monte Carlo (MCMC) method using Metropolis Hastings algorithm is used to obtain the posterior distribution of parameters. Twenty six CMIP5 GCMs along with four RCP scenarios are considered for studying the effects of climate change and to obtain projected IDF relationships for the case study of Bangalore city in India. GCM uncertainty due to the use of multiple GCMs is treated using Reliability Ensemble Averaging (REA) technique along with the parameter uncertainty. Scale invariance theory is employed for obtaining short duration return levels from daily data. It is observed that the uncertainty in short duration rainfall return levels is high when compared to the longer durations. Further it is observed that parameter uncertainty is large compared to the model uncertainty. (C) 2015 Elsevier Ltd. All rights reserved.

Influence of fuel hydrogen fraction on syngas fueled SI engine: Fuel thermo-physical property analysis and in-cylinder experimental investigations

Hydrogen, either in pure form or as a gaseous fuel mixture specie enhances the fuel conversion efficiency and reduce emissions in an internal combustion engine. This is due to the reduction in combustion duration attributed to higher laminar flame speeds. Hydrogen is also expected to increase the engine convective heat flux, attributed (directly or indirectly) to parameters like higher adiabatic flame temperature, laminar flame speed, thermal conductivity and diffusivity and lower flame quenching distance. These factors (adversely) affect the thermo-kinematic response and offset some of the benefits. The current work addresses the influence of mixture hydrogen fraction in syngas on the engine energy balance and the thermo-kinematic response for close to stoichiometric operating conditions. Four different bio-derived syngas compositions with fuel calorific value varying from 3.14 MJ/kg to 7.55 MJ/kg and air fuel mixture hydrogen fraction varying from 7.1% to 14.2% by volume are used. The analysis comprises of (a) use of chemical kinetics simulation package CHEMKIN for quantifying the thermo-physical properties (b) 0-D model for engine in-cylinder analysis and (c) in-cylinder investigations on a two-cylinder engine in open loop cooling mode for quantifying the thermo-kinematic response and engine energy balance. With lower adiabatic flame temperature for Syngas, the in-cylinder heat transfer analysis suggests that temperature has little effect in terms of increasing the heat flux. For typical engine like conditions (700 K and 25 bar at CR of 10), the laminar flame speed for syngas exceeds that of methane (55.5 cm/s) beyond mixture hydrogen fraction of 11% and is attributed to the increase in H based radicals. This leads to a reduction in the effective Lewis number and laminar flame thickness, potentially inducing flame instability and cellularity. Use of a thermodynamic model to assess the isolated influence of thermal conductivity and diffusivity on heat flux suggests an increase in the peak heat flux between 2% and 15% for the lowest (0.420 MW/m(2)) and highest (0.480 MW/m(2)) hydrogen containing syngas over methane (0.415 MW/m(2)) fueled operation. Experimental investigations indicate the engine cooling load for syngas fueled engine is higher by about 7% and 12% as compared to methane fueled operation; the losses are seen to increase with increasing mixture hydrogen fraction. Increase in the gas to electricity efficiency is observed from 18% to 24% as the mixture hydrogen fraction increases from 7.1% to 9.5%. Further increase in mixture hydrogen fraction to 14.2% results in the reduction of efficiency to 23%; argued due to the changes in the initial and terminal stages of combustion. On doubling of mixture hydrogen fraction, the flame kernel development and fast burn phase duration decrease by about 7% and 10% respectively and the terminal combustion duration, corresponding to 90%-98% mass burn, increases by about 23%. This increase in combustion duration arises from the cooling of the near wall mixture in the boundary layer attributed to the presence of hydrogen. The enhancement in engine cooling load and subsequent reduction in the brake thermal efficiency with increasing hydrogen fraction is evident from the engine energy balance along with the cumulative heat release profiles. Copyright (C) 2015, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.

Isolating the impacts of land use and climate change on streamflow

Quantifying the isolated and integrated impacts of land use (LU) and climate change on streamflow is challenging as well as crucial to optimally manage water resources in river basins. This paper presents a simple hydrologic modeling-based approach to segregate the impacts of land use and climate change on the streamflow of a river basin. The upper Ganga basin (UGB) in India is selected as the case study to carry out the analysis. Streamflow in the river basin is modeled using a calibrated variable infiltration capacity (VIC) hydrologic model. The approach involves development of three scenarios to understand the influence of land use and climate on streamflow. The first scenario assesses the sensitivity of streamflow to land use changes under invariant climate. The second scenario determines the change in streamflow due to change in climate assuming constant land use. The third scenario estimates the combined effect of changing land use and climate over the streamflow of the basin. Based on the results obtained from the three scenarios, quantification of isolated impacts of land use and climate change on streamflow is addressed. Future projections of climate are obtained from dynamically downscaled simulations of six general circulation models (GCMs) available from the Coordinated Regional Downscaling Experiment (CORDEX) project. Uncertainties associated with the GCMs and emission scenarios are quantified in the analysis. Results for the case study indicate that streamflow is highly sensitive to change in urban areas and moderately sensitive to change in cropland areas. However, variations in streamflow generally reproduce the variations in precipitation. The combined effect of land use and climate on streamflow is observed to be more pronounced compared to their individual impacts in the basin. It is observed from the isolated effects of land use and climate change that climate has a more dominant impact on streamflow in the region. The approach proposed in this paper is applicable to any river basin to isolate the impacts of land use change and climate change on the streamflow.

Evaluation of Precipitation Retrievals From Orbital Data Products of TRMM Over a Subtropical Basin in India

The spatial error structure of daily precipitation derived from the latest version 7 (v7) tropical rainfall measuring mission (TRMM) level 2 data products are studied through comparison with the Asian precipitation highly resolved observational data integration toward evaluation of the water resources (APHRODITE) data over a subtropical region of the Indian subcontinent for the seasonal rainfall over 6 years from June 2002 to September 2007. The data products examined include v7 data from the TRMM radiometer Microwave Imager (TMI) and radar precipitation radar (PR), namely, 2A12, 2A25, and 2B31 (combined data from PR and TMI). The spatial distribution of uncertainty from these data products were quantified based on performance metrics derived from the contingency table. For the seasonal daily precipitation over a subtropical basin in India, the data product of 2A12 showed greater skill in detecting and quantifying the volume of rainfall when compared with the 2A25 and 2B31 data products. Error characterization using various error models revealed that random errors from multiplicative error models were homoscedastic and that they better represented rainfall estimates from 2A12 algorithm. Error decomposition techniques performed to disentangle systematic and random errors verify that the multiplicative error model representing rainfall from 2A12 algorithm successfully estimated a greater percentage of systematic error than 2A25 or 2B31 algorithms. Results verify that although the radiometer derived 2A12 rainfall data is known to suffer from many sources of uncertainties, spatial analysis over the case study region of India testifies that the 2A12 rainfall estimates are in a very good agreement with the reference estimates for the data period considered.

A Cause and Effect Analysis of Motion Trajectories for Modeling Actions

An action is typically composed of different parts of the object moving in particular sequences. The presence of different motions (represented as a 1D histogram) has been used in the traditional bag-of-words (BoW) approach for recognizing actions. However the interactions among the motions also form a crucial part of an action. Different object-parts have varying degrees of interactions with the other parts during an action cycle. It is these interactions we want to quantify in order to bring in additional information about the actions. In this paper we propose a causality based approach for quantifying the interactions to aid action classification. Granger causality is used to compute the cause and effect relationships for pairs of motion trajectories of a video. A 2D histogram descriptor for the video is constructed using these pairwise measures. Our proposed method of obtaining pairwise measures for videos is also applicable for large datasets. We have conducted experiments on challenging action recognition databases such as HMDB51 and UCF50 and shown that our causality descriptor helps in encoding additional information regarding the actions and performs on par with the state-of-the art approaches. Due to the complementary nature, a further increase in performance can be observed by combining our approach with state-of-the-art approaches.

How much Dillenia indica seed predation occurs from Asian elephant dung?

Elephants are thought to be effective seed dispersers, but research on whether elephant dung effectively protects seeds from seed predation is lacking. Quantifying rates of seed predation from elephant dung will facilitate comparisons between elephants and alternative dispersers, helping us understand the functional role of megaherbivores in ecosystems. We conducted an experiment to quantify the predation of Dillenia indica seeds from elephant dung in Buxa Reserve, India from December 2012 to April 2013. Using dung boluses from the same dung pile, we compared the number of seeds in boluses that are a) opened immediately upon detection (control boluses), b) made available only to small seed predators (<= 3 mm wide) for 1-4 months, and c) made available to all seed predators and secondary dispersers for 1-4 months. Using a model built on this experiment, we estimated that seed predation by small seed predators (most likely ants and termites) destroys between 82.9% and 96.4% of seeds in elephant dung between the time of defecation and the median germination date for D. indica. Exposure to larger seed predators and secondary dispersers did not lead to a significant additional reduction in the number of seeds per dung bolus. Our findings suggest that post-dispersal seed predation by small insects (<3 mm) substantially reduces but does not eliminate the success of elephants as dispersers of D. indica in a tropical moist forest habitat. (C) 2015 Elsevier Masson SAS. All rights reserved.

Detection of quantum well induced single degenerate-transition-dipoles in ZnO nanorods

Quantifying and characterising atomic defects in nanocrystals is difficult and low-throughput using the existing methods such as high resolution transmission electron microscopy (HRTEM). In this article, using a defocused wide-field optical imaging technique, we demonstrate that a single ultrahigh-piezoelectric ZnO nanorod contains a single defect site. We model the observed dipole-emission patterns from optical imaging with a multi-dimensional dipole and find that the experimentally observed dipole pattern and model-calculated patterns are in excellent agreement. This agreement suggests the presence of vertically oriented degenerate-transition-dipoles in vertically aligned ZnO nanorods. The HRTEM of the ZnO nanorod shows the presence of a stacking fault, which generates a localised quantum well induced degenerate-transition-dipole. Finally, we elucidate that defocused wide-field imaging can be widely used to characterise defects in nanomaterials to answer many difficult questions concerning the performance of low-dimensional devices, such as in energy harvesting, advanced metal-oxide-semiconductor storage, and nanoelectromechanical and nanophotonic devices.