Integrated parallelization of computations and visualization for large-scale applications

Critical applications like cyclone tracking and earthquake modeling require simultaneous high-performance simulations and online visualization for timely analysis. Faster simulations and simultaneous visualization enable scientists provide real-time guidance to decision makers. In this work, we have developed an integrated user-driven and automated steering framework that simultaneously performs numerical simulations and efficient online remote visualization of critical weather applications in resource-constrained environments. It considers application dynamics like the criticality of the application and resource dynamics like the storage space, network bandwidth and available number of processors to adapt various application and resource parameters like simulation resolution, simulation rate and the frequency of visualization. We formulate the problem of finding an optimal set of simulation parameters as a linear programming problem. This leads to 30% higher simulation rate and 25-50% lesser storage consumption than a naive greedy approach. The framework also provides the user control over various application parameters like region of interest and simulation resolution. We have also devised an adaptive algorithm to reduce the lag between the simulation and visualization times. Using experiments with different network bandwidths, we find that our adaptive algorithm is able to reduce lag as well as visualize the most representative frames.

Farmers' vulnerability to climate variability in Dimapur district of Nagaland, India

Certain parts of the State of Nagaland situated in the northeastern region of India have been experiencing rainfall deficit over the past few years leading to severe drought-like conditions, which is likely to be aggravated under a climate change scenario. The state has already incurred considerable losses in the agricultural sector. Regional vulnerability assessments need to be carried out in order to help policy makers and planners formulate and implement effective drought management strategies. The present study uses an 'index-based approach' to quantify the climate variability-induced vulnerability of farmers in five villages of Dimapur district, Nagaland. Indicators, which are reflective of the exposure, sensitivity and adaptive capacity of the farmers to drought, were quantified on the basis of primary data generated through household surveys and participatory rural appraisal supplemented by secondary data in order to calculate a composite vulnerability index. The composite vulnerability index of village New Showba was found to be the least, while Zutovi, the highest. The overall results reveal that biophysical characteristics contribute the most to overall vulnerability. Some potential adaptation strategies were also identified based on observations and discussions with the villagers.

Characterizing drought using the reliability-resilience-vulnerability concept

This study borrows the measures developed for the operation of water resources systems as a means of characterizing droughts in a given region. It is argued that the common approach of assessing drought using a univariate measure (severity or reliability) is inadequate as decision makers need assessment of the other facets considered here. It is proposed that the joint distribution of reliability, resilience, and vulnerability (referred to as RRV in a reservoir operation context), assessed using soil moisture data over the study region, be used to characterize droughts. Use is made of copulas to quantify the joint distribution between these variables. As reliability and resilience vary in a nonlinear but almost deterministic way, the joint probability distribution of only resilience and vulnerability is modeled. Recognizing the negative association between the two variables, a Plackett copula is used to formulate the joint distribution. The developed drought index, referred to as the drought management index (DMI), is able to differentiate the drought proneness of a given area when compared to other areas. An assessment of the sensitivity of the DMI to the length of the data segments used in evaluation indicates relative stability is achieved if the data segments are 5years or longer. The proposed approach is illustrated with reference to the Malaprabha River basin in India, using four adjoining Climate Prediction Center grid cells of soil moisture data that cover an area of approximately 12,000 km(2). (C) 2013 American Society of Civil Engineers.

A Comparison of Machine Learning Techniques for Modeling River Flow Time Series: The Case of Upper Cauvery River Basin

Models of river flow time series are essential in efficient management of a river basin. It helps policy makers in developing efficient water utilization strategies to maximize the utility of scarce water resource. Time series analysis has been used extensively for modeling river flow data. The use of machine learning techniques such as support-vector regression and neural network models is gaining increasing popularity. In this paper we compare the performance of these techniques by applying it to a long-term time-series data of the inflows into the Krishnaraja Sagar reservoir (KRS) from three tributaries of the river Cauvery. In this study flow data over a period of 30 years from three different observation points established in upper Cauvery river sub-basin is analyzed to estimate their contribution to KRS. Specifically, ANN model uses a multi-layer feed forward network trained with a back-propagation algorithm and support vector regression with epsilon intensive-loss function is used. Auto-regressive moving average models are also applied to the same data. The performance of different techniques is compared using performance metrics such as root mean squared error (RMSE), correlation, normalized root mean squared error (NRMSE) and Nash-Sutcliffe Efficiency (NSE).

Why is the influence of soil macrofauna on soil structure only considered by soil ecologists?

These last twenty years have seen the development of an abundant literature on the influence of soil macrofauna on soil structure. Amongst these organisms, earthworms, termites and ants are considered to play a key role in regulating the physical, chemical and microbiological properties of soils. Due to these influential impacts, soil ecologists consider these soil macro-invertebrates as `soil engineers' and their diversity and abundance are nowadays considered as relevant bioindicators of soil quality by many scientists and policy makers. Despite this abundant literature, the soil engineering concept remains a `preach to the choir' and bioturbation only perceived as important for soil ecologists. We discussed in this article the main mechanisms by which soil engineers impact soil structure and proposed to classify soil engineers with respect to their capacity to produce biostructures and modify them. We underlined the lack of studies considering biostructure dynamics and presented recent techniques in this purpose. We discussed why soil engineering concept is mainly considered by soil ecologists and call for a better collaboration between soil ecologists and soil physicists. Finally, we summarized main challenges and questions that need to be answered to integrate soil engineers activities in soil structure studies. (C) 2014 Elsevier B.V. All rights reserved.

Perceptions of priority issues in the conservation of biodiversity and ecosystems in India

We report on the results of a country-wide survey of people's perceptions of issues relating to the conservation of biodiversity and ecosystems in India. Our survey, mainly conducted online, yielded 572 respondents, mostly among educated, urban and sub-urban citizens interested in ecological and environmental issues. 3160 ``raw'' questions generated by the survey were iteratively processed by a group of ecologists, environmental and conservation scientists to produce the primary result of this study: a summarized list of 152 priority questions for the conservation of India's biodiversity and ecosystems, which range across 17 broad thematic classes. Of these, three thematic classes-''Policy and Governance'', ``Biodiversity and Endangered Species'' and ``Protection and Conservation''-accounted for the largest number of questions. A comparative analysis of the results of this study with those from similar studies in other regions brought out interesting regional differences in the thematic classes of questions that were emphasized and suggest that local context plays a large role in determining emergent themes. We believe that the ready list of priority issues generated by this study can be a useful guiding framework for conservation practitioners, researchers, citizens, policy makers and funders to focus their resources and efforts in India's conservation research, action and funding landscape. (C) 2015 Elsevier Ltd. All rights reserved.

Investigation of orographic features influencing spatial distribution of rainfall over the Western Ghats of India using satellite data

Using remotely sensed Tropical Rainfall Measuring Mission (TRMM) 3B42 rainfall and topographic data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Digital Elevation Model (DEM), the impact of oroghraphical aspects such as topography, spatial variability of elevation and altitude of apexes are examined to investigate capacious summer monsoon rainfall over the Western Ghats (WG) of India. TRMM 3B42 v7 rainfall data is validated with Indian Meteorological Department (IMD) gridded rainfall data at 0.5 degrees resolution over the WG. The analysis of spatial pattern of monsoon rainfall with orography of the WG ascertains that the grade of orographic precipitation depends mainly on topography of the mountain barrier followed by steepness of windward side slope and altitude of the mountain. Longer and broader, i.e. cascaded topography, elevated summits and gradually increasing slopes impel the enhancement in precipitation. Comparing topography of various states of the WG, it has been observed that windward side of Karnataka receives intense rainfall in the WG during summer monsoon. It has been observed that the rainfall is enhanced before the peak of the mountain and confined up to the height about 800m over the WG. In addition to this, the spatial distribution of heavy and very heavy rainfall events in the last 14 years has also been explored. Heavy and very heavy rain events on this hilly terrain are categorized with a threshold of precipitation (R) in the range 150>R>120mmday(-1) and exceeding 150mmday(-1) using probability distribution of TRMM 3B42 v7 rainfall. The areas which are prone to heavy precipitation are identified. The study would help policy makers to manage the hazard scenario and, to improve weather predictions on mountainous terrain of the WG.