Water engineering can be defined as an amalgam of engineering aspects of hydraulics, hydrology, ecosystems, and environmental and water resources as well as non-engineering aspects of social, economic and political sciences. Each of these looks at problems using different techniques that are based on different concepts and assumptions and that vary in complexity. The second law of thermodynamics (entropy theory) permits us to develop a theory that helps address these issues in a unified manner. This paper discusses rudimentary aspects of the entropy theory and illustrates its potential by addressing some of the above-mentioned issues.
Owing to the rise in water demand and looming climate change, recent years have witnessed much focus on global drought scenarios. As a natural hazard, drought is best characterized by multiple climatological and hydrological parameters. An understanding of the relationships between these two sets of parameters is necessary to develop measures for mitigating the impacts of droughts. Beginning with a discussion of drought definitions, this paper attempts to provide a review of fundamental concepts of drought, classification of droughts, drought indices, historical droughts using paleoclimatic studies, and the relation between droughts and large scale climate indices. Conclusions are drawn where gaps exist and more research needs to be focussed. (c) 2010 Elsevier B.V. All rights reserved.
An entropy theory, comprising the Shannon entropy, the principle of maximum entropy, and the concentration theorem, has been applied in recent years to a wide range of problems in hydrology. From a hydrologic point of view, the applications can be organized into three classes: (1) physical, (2) statistical, and (3) mixed. This study focuses on the physical applications of the entropy theory, wherein the theory is coupled with the laws of mathematical physics and solutions are derived either in a time or space domain rather than the frequency domain. It is shown that a general framework can be developed to derive solutions to a wide range of seemingly disparate problems. The theory seems to have much potential that remains yet to be fully exploited.
In recent years droughts have been occurring frequently, and their impacts are being aggravated by the rise in water demand and the variability in hydro-meteorological variables due to climate change. As a result, drought hydrology has been receiving much attention. A variety of concepts have been applied to modeling droughts, ranging from simplistic approaches to more complex models. It is important to understand different modeling approaches as well as their advantages and limitations. This paper, supplementing the previous paper (Mishra and Singh, 2010) where different concepts of droughts were highlighted, reviews different methodologies used for drought modeling, which include drought forecasting, probability based modeling, spatio-temporal analysis, use of Global Climate Models (GCMs) for drought scenarios, land data assimilation systems for drought modeling, and drought planning. It is found that there have been significant improvements in modeling droughts over the past three decades. Hybrid models, incorporating large scale climate indices, seem to be promising for long lead-time drought forecasting. Further research is needed to understand the spatio-temporal complexity of droughts under climate change due to changes in spatio-temporal variability of precipitation. Applications of copula based models for multivariate drought characterization seem to be promising for better drought characterization. Research on decision support systems should be advanced for issuing warnings, assessing risk, and taking precautionary measures, and the effective ways for the flow of information from decision makers to users need to be developed. Finally, some remarks are made regarding the future outlook for drought research. (C) 2011 Elsevier B.V. All rights reserved.
The interest in using Jatropha curcas L. (JCL) as a feedstock for the production of bio-diesel is rapidly growing. The properties of the crop and its oil have persuaded investors, policy makers and clean development mechanism (CDM) project developers to consider JCL as a substitute for fossil fuels to reduce greenhouse gas emissions. However, JCL is still a wild plant of which basic agronomic properties are not thoroughly understood and the environmental effects have not been investigated yet. Gray literature reports are very optimistic on simultaneous wasteland reclamation capability and oil yields, further fueling the Jatropha bio-diesel hype. In this paper, we give an overview of the currently available information on the different process steps of the production process of bio-diesel from JCL, being cultivation and production of seeds, extraction of the oil, conversion to and the use of the bio-diesel and the by-products. Based on this collection of data and information the best available practice, the shortcomings and the potential environmental risks and benefits are discussed for each production step. The review concludes with a call for general precaution and for science to be applied. (C) 2008 Elsevier Ltd. All rights reserved.
The absence of a generic modeling framework in hydrology has long been recognized. With our current practice of developing more and more complex models for specific individual situations, there is an increasing emphasis and urgency on this issue. There have been some attempts to provide guidelines for a catchment classification framework, but research in this area is still in a state of infancy. To move forward on this classification framework, identification of an appropriate basis and development of a suitable methodology for its representation are vital. The present study argues that hydrologic system complexity is an appropriate basis for this classification framework and nonlinear dynamic concepts constitute a suitable methodology. The study employs a popular nonlinear dynamic method for identification of the level of complexity of streamflow and for its classification. The correlation dimension method, which has its base on data reconstruction and nearest neighbor concepts, is applied to monthly streamflow time series from a large network of 117 gaging stations across 11 states in the western United States (US). The dimensionality of the time series forms the basis for identification of system complexity and, accordingly, streamflows are classified into four major categories: low-dimensional, medium-dimensional, high-dimensional, and unidentifiable. The dimension estimates show some 'homogeneity' in flow complexity within certain regions of the western US, but there are also strong exceptions.
Cobalt(II), nickel(II), copper(II) and zinc(II) complexes with 2-acetylthiophene benzoylhydrazone have been synthesized and characterized by elemental analyses, magnetic susceptibility measurements, electronic, IR, NMR and ESR spectral techniques. The molecular structures of ligand and its copper(II) complex have been determined by single crystal X-ray diffraction technique. The Cu(II) complex possesses a CuN2O2 chromophore with a considerable delocalization of charge. The structure of the complex is stabilized by intermolecular pi-pi stacking and C-H center dot center dot center dot pi interactions. Hatbh acts as a monobasic bidentate ligand in all the complexes bonding through a deprotonated C-O and >C=N groups. Electronic spectral studies indicate an octahedral geometry for the Ni(II) complex while square planar geometry for the Co(II) and Cu(II) complexes. ESR spectrum of the Cu(II) complex exhibits a square planar geometry in solid and in DMSO solution. The trend g(parallel to) > g(perpendicular to) > 2.0023 indicates the presence of an unpaired electron in the d(x2) (y2) orbital of Cu(II). The electro-chemical study of Cu(II) complex reveals a metal based reversible redox behavior. The Ni(II) complex shows exothermic multi-step decomposition pattern of the bonded ligand. The ligand and its most of the metal complexes show appreciable corrosion inhibition properties for mild steel in 1 M HCl medium. [Co(atbh)(2)] complex exhibited the greatest impact on corrosion inhibition among the other compounds. (C) 2011 Elsevier B.V. All rights reserved.
This study develops a Two-source Trapezoid Model for Evapotranspiration (TTME) from satellite imagery by interpreting the remotely sensed fractional vegetation cover (f(c))-radiative surface temperature (T-rad) space and the concept of soil surface moisture availability isopleths superimposed on the space. The theoretical upper boundary condition of TTME is determined by solving for temperatures of the driest bare surface (T-s,T-max) and the driest fully vegetated surface (T-c,T-max) both implicit in radiation budget and energy balance equations. Air temperature (T-a) constitutes the lower boundary of TTME. T-rad of a pixel within the f(c)-T-rad space is decomposed into temperature components (T-c and T-s) by interpolating the slope of the theoretical boundaries and interpreting variation in T-rad with f(c) for each isopiestic line going across the pixel. Vegetation transpiration and soil surface evaporation are then separately parameterized. TTME was applied to the Soil Moisture-Atmosphere Coupling Experiment (SMACEX) site in central Iowa, U.S., on three days in 2002 during the period of rapid growth in corn and soybean when three scenes of Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+) images and one scene of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image were acquired. Results indicate that TTME is capable of reproducing latent heat flux (LE) with a mean absolute percentage difference (MAPD) of similar to 10%, and a root mean square difference (RMSD) of 45.6 W m(-2) and 63.1 W m(-2) for Landsat TM/ETM+ and ASTER images, respectively. Comparison of TTME with other one-source and two-source models using the same data set suggests that TTME shows comparable accuracy as the Two-Source Energy Balance (TSEB), but requires relatively fewer inputs and obviates the computation of resistance networks in the modeling domain and the overestimation of vegetation transpiration incurred by using the Priestley-Taylor equation. Sensitivity analysis suggests that TTME is most sensitive to T-rad and T-a, but not sensitive to a range of meteorological observations and variables and parameters derived/specified. (c) 2012 Elsevier Inc. All rights reserved.
An entropy theory is formulated for one-dimensional movement of moisture in unsaturated soils in the vertically downward direction. The theory is composed of five parts: (1) Tsallis entropy, (2) principle of maximum entropy, (3) specification of information on soil moisture in terms of constraints, (4) maximization of the Tsallis entropy, and (5) derivation of the probability distributions of soil moisture. The theory is applied to determine the soil moisture profile under three conditions: (1) the moisture is maximum at the soil surface and decreases downward to a minimum value at the bottom of the soil column (it may be near the water table); (2) the moisture is minimum at the soil surface and increases downward to a maximum value at the end of the soil column (this case is the opposite of case 1); and (3) the moisture at the soil surface is low and increases downward up to a distance and then decreases up to the bottom (this case combines case 2 and case 1). The entropy-based soil moisture profiles are tested using experimental observations reported in the literature, and properties of these profiles are enumerated.