Identification of Influential Climate Indicators, Prediction of Long-Term Streamflow and Great Salt Lake Elevation Using Machine Learning Approach

To meet the surging water demand due to rapid population growth and changing climatic conditions around the world, and to reduce the impact of floods and droughts, comprehensive water management and planning is necessary. Climatic variability, hydrologic uncertainty and variability of hydrologic quantities in time and space are inherent to hydrological modeling. Hydrologic modeling using a physically-based model can be very complex and typically requires detailed knowledge of physical processes. The availability of data is an important issue to justify the use of these models. Data-driven models are an alternative choice. This is a relatively new and efficient approach to modeling. Data-drive models bridge the gap between the classical regression and physically-based models. By using a data-driven model that relies on the machine learning approach, it is possible to produce reasonable predictions from a limited data set and limited knowledge of underlying physical processes of the system by just relating input and output. This dissertation uses the Multivariate Relevance Vector Machine (MVRVM) and Support Vector Machine (SVM) for predicting a variety of hydrological quantities. These models are used in this dissertation for identifying influential climate indicators, and are used for long-term streamflow prediction for multiple lead times at different locations in Utah. They are also used for prediction of Great Salt Lake (GSL) elevation series. They provide reasonable predictions of hydrological quantities from the available data. The predictions from these models are robust and parsimonious. This research presents the first attempt to identify influential climate indicators and predict long lead-time streamflow in Utah, and to predict lake elevation using machine learning models. The approach presented herein has potential value for water resources planning and management especially for irrigation and flood management

Integrated modelling of ecohydrological processes along ephemeral rivers.

Ephemeral rivers are located throughout the world’s arid regions. They are characterised by temporary surface flow that strongly varies between seasons and years. Along the river course often a coupled eco-hydrological vegetation-groundwater system has established, which is referred to as linear oasis, reflecting the ecological and socio-economic importance of ephemeral rivers in otherwise dry areas. The Kuiseb River in Namibia denotes such a linear oasis with eco-hydrological feedbacks between the vegetation and the ground water resource. Temporary floods infiltrate into sediments, which are accumulated in geological pools of impermeable bedrocks. This enables the formation of shallow ground water. The low depth to ground water allows root water uptake by plants and the establishment of a thriving ecosystem. Besides, the river and its environment is diversely used by humans, e.g. by exploiting the ground water for drinking, farming, and mining. Further, it is essential for the survival of the rural Topnaar community and economical important due to its touristic attraction. Therefore, a sustainable resource management is needed, which clearly requires a well developed understanding of the ecohydrologcial processes along ephemeral rivers. The objective of this research was to develop a model framework based on the Kuiseb River that integrates both ecological and hydrological system dynamics. Such a framework helps to increase the mechanistic understanding of driving ecohydrological processes along ephemeral rivers by testing assumptions and generating hypotheses. Further, it can be applied to investigate management strategies in terms of their ability to sustainably exploit the ground water resource while preserving the natural vegetation structure. Uncertainty played a critical role throughout this research due to the scarce information available for both the eco- and the hydrosystem. In particular, the research focused on three types of uncertainty: (1) The parameterisation of the population model, which was challenging as this requires long-term observation of species abundance that is not available. This parameterisation problem was addressed by using pattern-oriented model calibration, in that the species parameters were adjusted such that the resulting parameterisation reproduces the observed three species coexistence pattern along the river course under study. (2) The inherent uncertainty in the occurrence of flood events, which is driven by unpredictable rainfall events. Throughout this study the unpredictability was described with a stochastic process characterised by parameters such as frequency, duration and short/long term memory of flood events. In order to address the parameterisation problem to this type of uncertainty, for each parameter combination the model run 100 times with stochastic identical flood realisations, eventually leading to a quantification of the uncertainty in parameterisation. (3) The uncertainty in parameters describing the (stochastic) flood regimes. This uncertainty arises due to the scarce information about the runoff data along ephemeral rivers because often monitoring systems are rare and the temporary character of the flood events hinders the measurement of large time series. The influence of this particular type of uncertainty on the robustness and significance of integrated management strategies was investigated without neglecting the preceding types of uncertainty

Fractional Calculus and the Future of Science

Newton foresaw the limitations of geometry’s description of planetary behavior and developed fluxions (differentials) as the new language for celestial mechanics and as the way to implement his laws of mechanics. Two hundred years later Mandelbrot introduced the notion of fractals into the scientific lexicon of geometry, dynamics, and statistics and in so doing suggested ways to see beyond the limitations of Newton’s laws. Mandelbrot’s mathematical essays suggest how fractals may lead to the understanding of turbulence, viscoelasticity, and ultimately to end of dominance of the Newton’s macroscopic world view.Fractional Calculus and the Future of Science examines the nexus of these two game-changing contributions to our scientific understanding of the world. It addresses how non-integer differential equations replace Newton’s laws to describe the many guises of complexity, most of which lay beyond Newton’s experience, and many had even eluded Mandelbrot’s powerful intuition. The book’s authors look behind the mathematics and examine what must be true about a phenomenon’s behavior to justify the replacement of an integer-order with a noninteger-order (fractional) derivative. This window into the future of specific science disciplines using the fractional calculus lens suggests how what is seen entails a difference in scientific thinking and understanding

ESSE 2017. Proceedings of the International Conference on Environmental Science and Sustainable Energy

Environmental science is an interdisciplinary academic field that integrates physical-, biological-, and information sciences to study and solve environmental problems. ESSE - The International Conference on Environmental Science and Sustainable Energy provides a platform for experts, professionals, and researchers to share updated information and stimulate the communication with each other. In 2017 it was held in Suzhou, China June 23-25, 2017

Special Functions: Fractional Calculus and the Pathway for Entropy

Historically, the notion of entropy emerged in conceptually very distinct contexts. This book deals with the connection between entropy, probability, and fractional dynamics as they appeared, for example, in solar neutrino astrophysics since the 1970's (Mathai and Rathie 1975, Mathai and Pederzoli 1977, Mathai and Saxena 1978, Mathai, Saxena, and Haubold 2010). The original solar neutrino problem, experimentally and theoretically, was resolved through the discovery of neutrino oscillations and was recently enriched by neutrino entanglement entropy. To reconsider possible new physics of solar neutrinos, diffusion entropy analysis, utilizing Boltzmann entropy, and standard deviation analysis was undertaken with Super-Kamiokande solar neutrino data. This analysis revealed a non-Gaussian signal with harmonic content. The Hurst exponent is different from the scaling exponent of the probability density function and both Hurst exponent and scaling exponent of the Super-Kamiokande data deviate considerably from the value of ½, which indicates that the statistics of the underlying phenomenon is anomalous. Here experiment may provide guidance about the generalization of theory of Boltzmann statistical mechanics. Arguments in the so-called Boltzmann-Planck-Einstein discussion related to Planck's discovery of the black-body radiation law are recapitulated mathematically and statistically and emphasize from this discussion is pursued that a meaningful implementation of the complex ‘entropy-probability-dynamics’ may offer two ways for explaining the results of diffusion entropy analysis and standard deviation analysis. One way is to consider an anomalous diffusion process that needs to use the fractional space-time diffusion equation (Gorenflo and Mainardi) and the other way is to consider a generalized Boltzmann entropy by assuming a power law probability density function. Here new mathematical framework, invented by sheer thought, may provide guidance for the generalization of Boltzmann statistical mechanics. In this book Boltzmann entropy, generalized by Tsallis and Mathai, is considered. The second one contains a varying parameter that is used to construct an entropic pathway covering generalized type-1 beta, type-2 beta, and gamma families of densities. Similarly, pathways for respective distributions and differential equations can be developed. Mathai's entropy is optimized under various conditions reproducing the well-known Boltzmann distribution, Raleigh distribution, and other distributions used in physics. Properties of the entropy measure for the generalized entropy are examined. In this process the role of special functions of mathematical physics, particularly the H-function, is highlighted

Remote Sensing of the Oceans

This book covers different topics in the framework of remote sensing of the oceans. Latest research advancements and brand-new studies are presented that address the exploitation of remote sensing instruments and simulation tools to improve the understanding of ocean processes and enable cutting-edge applications with the aim of preserving the ocean environment and supporting the blue economy. Hence, this book provides a reference framework for state-of-the-art remote sensing methods that deal with the generation of added-value products and the geophysical information retrieval in related fields, including: Oil spill detection and discrimination; Analysis of tropical cyclones and sea echoes; Shoreline and aquaculture area extraction; Monitoring coastal marine litter and moving vessels; Processing of SAR, HF radar and UAV measurements