Exploring use of climate information for optimal design of water resources infrastructure

We conducted surveys of fire and fuels managers at local, regional, and national levels to gain insights into decision processes and information flows in wildfire management. Survey results in the form of fire managers’ decision calendars show how climate information needs vary seasonally, over space, and through the organizational network, and help determine optimal points for introducing climate information and forecasts into decision processes. We identified opportunities to use climate information in fire management, including seasonal to interannual climate forecasts at all organizational levels, to improve the targeting of fuels treatments and prescribed burns, the positioning and movement of initial attack resources, and staffing and budgeting decisions. Longer-term (5–10 years) outlooks also could be useful at the national level in setting budget and research priorities. We discuss these opportunities and examine the kinds of organizational changes that could facilitate effective use of existing climate information and climate forecast capabilities.

Solar energy decision support system

Energy plays a prominent role in human society. As a result of technological and industrial development,the demand for energy is rapidly increasing. Existing power sources that are mainly fossil fuel based are leaving an unacceptable legacy of waste and pollution apart from diminishing stock of fuels.Hence, the focus is now shifted to large-scale propagation of renewable energy. Renewable energy technologies are clean sources of energy that have a much lower environmental impact than conventional energy technologies. Solar energy is one such renewable energy. Most renewable energy comes either directly or indirectly from the sun. Estimation of solar energy potential of a region requires detailed solar radiation climatology, and it is necessary to collect extensive radiation data of high accuracy covering all climatic zones of the region. In this regard, a decision support system (DSS)would help in estimating solar energy potential considering the region’s energy requirement.This article explains the design and implementation of DSS for assessment of solar energy. The DSS with executive information systems and reporting tools helps to tap vast data resources and deliver information. The main hypothesis is that this tool can be used to form a core of practical methodology that will result in more resilient in time and can be used by decision-making bodies to assess various scenarios. It also offers means of entering, accessing, and interpreting the information for the purpose of sound decision making.

Geographic Resources Decision Support System – an Open Source GIS

Fully structured and matured open source spatial and temporal analysis technology seems to be the official carrier of the future for planning of the natural resources especially in the developing nations. This technology has gained enormous momentum because of technical superiority, affordability and ability to join expertise from all sections of the society. Sustainable development of a region depends on the integrated planning approaches adopted in decision making which requires timely and accurate spatial data. With the increased developmental programmes, the need for appropriate decision support system has increased in order to analyse and visualise the decisions associated with spatial and temporal aspects of natural resources. In this regard Geographic Information System (GIS) along with remote sensing data support the applications that involve spatial and temporal analysis on digital thematic maps and the remotely sensed images. Open source GIS would help in wide scale applications involving decisions at various hierarchical levels (for example from village panchayat to planning commission) on economic viability, social acceptance apart from technical feasibility. GRASS (Geographic Resources Analysis Support System, http://wgbis.ces.iisc.ernet.in/grass) is an open source GIS that works on Linux platform (freeware), but most of the applications are in command line argument, necessitating a user friendly and cost effective graphical user interface (GUI). Keeping these aspects in mind, Geographic Resources Decision Support System (GRDSS) has been developed with functionality such as raster, topological vector, image processing, statistical analysis, geographical analysis, graphics production, etc. This operates through a GUI developed in Tcltk (Tool command language / Tool kit) under Linux as well as with a shell in X-Windows. GRDSS include options such as Import /Export of different data formats, Display, Digital Image processing, Map editing, Raster Analysis, Vector Analysis, Point Analysis, Spatial Query, which are required for regional planning such as watershed Analysis, Landscape Analysis etc. This is customised to Indian context with an option to extract individual band from the IRS (Indian Remote Sensing Satellites) data, which is in BIL (Band Interleaved by Lines) format. The integration of PostgreSQL (a freeware) in GRDSS aids as an efficient database management system.

Solar energy potential assessment using GIS

Renewable energy resources are those having a cycling time less than 100 years and are renewed by the nature and their supply exceeds the rate of consumption. Renewable energy systems use resources that are constantly replaced in nature and are usually less polluting. In order to tap the potential of renewable energy sources, there is a need to assess the availability of resources spatially as well as temporally. Geographic Information Systems (GIS) along with Remote Sensing (RS) helps in mapping on spatial and temporal scales of the resources and demand. The spatial database of resource availability and the demand would help in the regional energy planning. This paper discusses the application of geographical information system (GIS) to map the solar potential in Karnataka state, India. Regions suitable for tapping solar energy are mapped on the basis of global solar radiation data, and this analysis provides a picture of the potential. The study identifies that Coastal parts of Karnataka with the higher global solar radiation is ideally suited for harvesting solar energy. The potential analysis reveals that, maximum global solar radiation is in districts such as Uttara Kannada and Dakshina Kannada. Global solar radiation in Uttara Kannada during summer, monsoon and winter are 6.31, 4.40 and 5.48 kWh/sq.m, respectively. Similarly, Dakshina Kannada has 6.16, 3.89 and 5.21 kWh/sq.m during summer, monsoon and winter.

Distributed Source Coding for Sensor Data Model and Estimation of Cluster Head Errors Using Bayesian and K-Near Neighborhood Classifiers in Deployment of Dense Wireless Sensor Networks

The lifetime calculation of large dense sensor networks with fixed energy resources and the remaining residual energy have shown that for a constant energy resource in a sensor network the fault rate at the cluster head is network size invariant when using the network layer with no MAC losses.Even after increasing the battery capacities in the nodes the total lifetime does not increase after a max limit of 8 times. As this is a serious limitation lots of research has been done at the MAC layer which allows to adapt to the specific connectivity, traffic and channel polling needs for sensor networks. There have been lots of MAC protocols which allow to control the channel polling of new radios which are available to sensor nodes to communicate. This further reduces the communication overhead by idling and sleep scheduling thus extending the lifetime of the monitoring application. We address the two issues which effects the distributed characteristics and performance of connected MAC nodes. (1) To determine the theoretical minimum rate based on joint coding for a correlated data source at the singlehop, (2a) to estimate cluster head errors using Bayesian rule for routing using persistence clustering when node densities are the same and stored using prior probability at the network layer, (2b) to estimate the upper bound of routing errors when using passive clustering were the node densities at the multi-hop MACS are unknown and not stored at the multi-hop nodes a priori. In this paper we evaluate many MAC based sensor network protocols and study the effects on sensor network lifetime. A renewable energy MAC routing protocol is designed when the probabilities of active nodes are not known a priori. From theoretical derivations we show that for a Bayesian rule with known class densities of omega1, omega2 with expected error P* is bounded by max error rate of P=2P* for single-hop. We study the effects of energy losses using cross-layer simulation of - large sensor network MACS setup, the error rate which effect finding sufficient node densities to have reliable multi-hop communications due to unknown node densities. The simulation results show that even though the lifetime is comparable the expected Bayesian posterior probability error bound is close or higher than Pges2P*.

Rural electrification: Optimising the choice between decentralised renewable energy sources and grid extension

The study presents an analysis aimed at choosing between off-grid solar photovoltaic, biomass gasifier based power generation and conventional grid extension for remote village electrification. The model provides a relation between renewable energy systems and the economical distance limit (EDL) from the existing grid point, based on life cycle cost (LCC) analysis, where the LCC of energy for renewable energy systems and grid extension will match. The LCC of energy feed to the village is arrived at by considering grid availability and operating hours of the renewable energy systems. The EDL for the biomass gasifier system of 25 kW capacities is 10.5 km with 6 h of daily operation and grid availability. However, the EDL for a similar 25 kW capacity photovoltaic system is 35 km for the same number of hours of operation and grid availability. The analysis shows that for villages having low load demand situated far away from the existing grid line, biomass gasification based systems are more cost competitive than photovoltaic systems or even compared to grid extension. (C) 2012 Elsevier Inc. All rights reserved.

Enhanced Raman Spectroscopy of Molecules Adsorbed on Carbon-Doped TiO2 Obtained from Titanium Carbide: A Visible-Light-Assisted Renewable Substrate

Titanium carbide (TiC) is an electrically conducting material with favorable electrochemical properties. In the present studies, carbon-doped TiO2 (C-TiO2) has been synthesized from TiC particles, as well as TiC films coated on stainless steel substrate via thermal annealing under various conditions. Several C-TiO2 substrates are synthesized by varying experimental, conditions and characterized by UV-visible spectroscopy, photoluminescence, X-ray diffraction and X-ray photoelectron spectroscopic techniques. C-TiO2 in the dry state (in powder form as well as in film form) is subsequently used as a substrate for enhancing Raman signals corresponding to 4-mercaptobenzoic acid and 4-nitrothiophenol by utilizing chemical enhancement based on charge-transfer interactions. Carbon, a nonmetal dopant in TiO2, improves the intensities of Raman signals, compared, to undoped TiO2. Significant dependence of Raman intensity on carbon doping is observed. Ameliorated performance obtained using C-TiO2 is attributed to the presence of surface defects that originate due to carbon as a dopant, which, in turn,, triggers charge transfer between TiO2 and analyte. The C-TiO2 substrates are subsequently regenerated for repetitive use by illuminating an analyte-adsorbed substrate with visible light for a period of 5 h.

Algal biofuel production and mitigation potential in India

Energy and energy services are the backbone of growth and development in India and is increasingly dependent upon the use of fossil based fuels that lead to greenhouse gases (GHG) emissions and related concerns. Algal biofuels are being evolved as carbon (C)-neutral alternative biofuels. Algae are photosynthetic microorganisms that convert sunlight, water and carbon dioxide (CO2) to various sugars and lipids Tri-Acyl-Glycols (TAG) and show promise as an alternative, renewable and green fuel source for India. Compared to land based oilseed crops algae have potentially higher yields (5-12 g/m(2)/d) and can use locations and water resources not suited for agriculture. Within India, there is little additional land area for algal cultivation and therefore needs to be carried out in places that are already used for agriculture, e.g. flooded paddy lands (20 Mha) with village level technologies and on saline wastelands (3 Mha). Cultivating algae under such conditions requires novel multi-tier, multi-cyclic approaches of sharing land area without causing threats to food and water security as well as demand for additional fertilizer resources by adopting multi-tier cropping (algae-paddy) in decentralized open pond systems. A large part of the algal biofuel production is possible in flooded paddy crop land before the crop reaches dense canopies, in wastewaters (40 billion litres per day), in salt affected lands and in nutrient/diversity impoverished shallow coastline fishery. Mitigation will be achieved through avoidance of GHG, C-capture options and substitution of fossil fuels. Estimates made in this paper suggest that nearly half of the current transportation petro-fuels could be produced at such locations without disruption of food security, water security or overall sustainability. This shift can also provide significant mitigation avenues. The major adaptation needs are related to socio-technical acceptance for reuse of various wastelands, wastewaters and waste-derived energy and by-products through policy and attitude change efforts.

Application of Artificial Neural Networks and Particle Swarm Optimization for the Management of Groundwater Resources

Ground management problems are typically solved by the simulation-optimization approach where complex numerical models are used to simulate the groundwater flow and/or contamination transport. These numerical models take a lot of time to solve the management problems and hence become computationally expensive. In this study, Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) models were developed and coupled for the management of groundwater of Dore river basin in France. The Analytic Element Method (AEM) based flow model was developed and used to generate the dataset for the training and testing of the ANN model. This developed ANN-PSO model was applied to minimize the pumping cost of the wells, including cost of the pipe line. The discharge and location of the pumping wells were taken as the decision variable and the ANN-PSO model was applied to find out the optimal location of the wells. The results of the ANN-PSO model are found similar to the results obtained by AEM-PSO model. The results show that the ANN model can reduce the computational burden significantly as it is able to analyze different scenarios, and the ANN-PSO model is capable of identifying the optimal location of wells efficiently.

Dynamic mechanism design for markets with strategic resources

The assignment of tasks to multiple resources becomes an interesting game theoretic problem, when both the task owner and the resources are strategic. In the classical, nonstrategic setting, where the states of the tasks and resources are observable by the controller, this problem is that of finding an optimal policy for a Markov decision process (MDP). When the states are held by strategic agents, the problem of an efficient task allocation extends beyond that of solving an MDP and becomes that of designing a mechanism. Motivated by this fact, we propose a general mechanism which decides on an allocation rule for the tasks and resources and a payment rule to incentivize agents' participation and truthful reports. In contrast to related dynamic strategic control problems studied in recent literature, the problem studied here has interdependent values: the benefit of an allocation to the task owner is not simply a function of the characteristics of the task itself and the allocation, but also of the state of the resources. We introduce a dynamic extension of Mezzetti's two phase mechanism for interdependent valuations. In this changed setting, the proposed dynamic mechanism is efficient, within period ex-post incentive compatible, and within period ex-post individually rational.

Remote sensing applications in water resources

With the introduction of the earth observing satellites, remote sensing has become an important tool in analyzing the Earth's surface characteristics, and hence in supplying valuable information necessary for the hydrologic analysis. Due to their capability to capture the spatial variations in the hydro-meteorological variables and frequent temporal resolution sufficient to represent the dynamics of the hydrologic processes, remote sensing techniques have significantly changed the water resources assessment and management methodologies. Remote sensing techniques have been widely used to delineate the surface water bodies, estimate meteorological variables like temperature and precipitation, estimate hydrological state variables like soil moisture and land surface characteristics, and to estimate fluxes such as evapotranspiration. Today, near-real time monitoring of flood, drought events, and irrigation management are possible with the help of high resolution satellite data. This paper gives a brief overview of the potential applications of remote sensing in water resources.