Probabilistic estimation of irrigation requirement under climate uncertainty using dichotomous and marked renewal processes |
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| Institution: | 1. Department of Water Resources Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran;2. Department of Civil Engineering, Near East University, P.O. Box: 99138, Nicosia, North Cyprus, Mersin 10, Turkey;3. Department of Mechanical Engineering of Technical Faculty, University of Bihac, Bihac, Bosnia and Herzegovina;1. Department of Civil Engineering, Isfahan University of Technology (IUT), Isfahan, Iran;2. Department of Land, Air and Water Resource, University of California, Davis, CA, USA;1. College of Environmental Science and Engineering, Ocean University of China, Qingdao 266100, China;2. Key Laboratory of Marine Environment and Ecological Education, Ministry of Education, Ocean University of China, Qingdao 266100, China;3. Technische Universität Dresden, Faculty of Environmental Sciences, Department of Forest Sciences, Chair of Forest Biometrics and Forest Systems Analysis, 01062 Dresden, Germany;1. Atmospheric Research Laboratory, Department of Physics, Institute of Science, Banaras Hindu University, Varanasi 221005, India;2. Department of Earth and Planetary Sciences, V.B.S. Purvanchal University, Jaunpur 222003, India;3. DST - Mahamana Centre of Excellence in Climate Change Research, Banaras Hindu University, Varanasi, India;4. Kashi Naresh Government Post Graduate College Gyanpur, Bhadohi 221304, India;1. Watershed Science & Modeling Laboratory, Department of Earth and Atmospheric Sciences, University of Alberta, Edmonton, AB T6G 2R3, Canada;2. Global Institute for Water Security, School of Envionment and Sustainability, and Department of Civil, Geological, and Environmental Enigeering, University of Saskatchewan, Canada |
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| Abstract: | This study addresses estimation of net irrigation requirement over a growing season under climate uncertainty. An ecohydrological model, building upon the stochastic differential equation of soil moisture dynamics, is employed as a basis to derive new analytical expressions for estimating seasonal net irrigation requirement probabilistically. Two distinct irrigation technologies are considered. For micro irrigation technology, probability density function of seasonal net irrigation depth (SNID) is derived assessing transient behavior of a stochastic process which is time integral of dichotomous Markov process. Probability mass function of SNID which is a discrete random variable for traditional irrigation technology is also presented using a marked renewal process with quasi-exponentially-distributed time intervals. Comparing the results obtained from the presented models with those resulted from a Monte Carlo approach verified the significance of the probabilistic expressions derived and assumptions made. |
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