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Many stochastic process models for environmental data sets assume a process of relatively simple structure which is in some sense partially observed. That is, there is an underlying process (Xn, n 0) or (Xt, t 0) for which the parameters are of interest and physically meaningful, and an observable process (Yn, n 0) or (Yt, t 0) which depends on the X process but not otherwise on those parameters. Examples are wide ranging: the Y process may be the X process with missing observations; the Y process may be the X process observed with a noise component; the X process might constitute a random environment for the Y process, as with hidden Markov models; the Y process might be a lower dimensional function or reduction of the X process. In principle, maximum likelihood estimation for the X process parameters can be carried out by some form of the EM algorithm applied to the Y process data. In the paper we review some current methods for exact and approximate maximum likelihood estimation. We illustrate some of the issues by considering how to estimate the parameters of a stochastic Nash cascade model for runoff. In the case of k reservoirs, the outputs of these reservoirs form a k dimensional vector Markov process, of which only the kth coordinate process is observed, usually at a discrete sample of time points. 相似文献
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A Method for Direct Assessment of the “Non Rainfall” Atmospheric Water Cycle: Input and Evaporation From the Soil 总被引：1，自引：0，他引：1
Kudzai Farai Kaseke Anthony J. Mills Roger Brown Karen J. Esler Johannes. R. Henschel Mary K. Seely 《Pure and Applied Geophysics》2012,169(5-6):847-857
“Non rainfall” atmospheric water (dew, fog, vapour adsorption) supplies a small amount of water to the soil surface that may be important for arid soil micro-hydrology and ecology. Research into the direct effects of this water on soil is, however, lacking due to instrument and technical constraints. We report on the design, development, construction and findings of an automated microlysimeter instrument to directly measure this soil water cycle in Stellenbosch, South Africa during winter. Performance of the microlysimeter was satisfactory and results obtained were compared to literature and fell within the expected range. “Non rainfall” atmospheric water input into bare soil (river sand) was between 0.88 and 1.10?mm per night while evaporation was between 1.39 and 2.71?mm per day. The study also attempted to differentiate the composition of “non rainfall” atmospheric water and results showed that vapour adsorption contributed the bulk of this input. 相似文献
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M. E. Thompson T. N. Kaseke 《Stochastic Environmental Research and Risk Assessment (SERRA)》1995,9(1):33-47
Many stochastic process models for environmental data sets assume a process of relatively simple structure which is in some sense partially observed. That is, there is an underlying process (Xn, n 0) or (Xt, t 0) for which the parameters are of interest and physically meaningful, and an observable process (Yn, n 0) or (Yt, t 0) which depends on the X process but not otherwise on those parameters. Examples are wide ranging: the Y process may be the X process with missing observations; the Y process may be the X process observed with a noise component; the X process might constitute a random environment for the Y process, as with hidden Markov models; the Y process might be a lower dimensional function or reduction of the X process. In principle, maximum likelihood estimation for the X process parameters can be carried out by some form of the EM algorithm applied to the Y process data. In the paper we review some current methods for exact and approximate maximum likelihood estimation. We illustrate some of the issues by considering how to estimate the parameters of a stochastic Nash cascade model for runoff. In the case of k reservoirs, the outputs of these reservoirs form a k dimensional vector Markov process, of which only the kth coordinate process is observed, usually at a discrete sample of time points. 相似文献
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K. F. Kaseke A. J. Mills K. Esler J. Henschel M. K. Seely R. Brown 《Pure and Applied Geophysics》2012,169(12):2217-2229
“Non-rainfall” water is important to the soil water balance and ecology of arid ecosystems. A component of this “non-rainfall” water in the Namib Desert, fog, exhibits spatial variation implying variability in composition and significance of each vector (dew, fog, vapour adsorption) to ecology at different locations. The composition of “non -rainfall” water input directly into soil was investigated at two sites in the Central Namib Desert, Kleinberg and Gobabeb. Results showed spatial variation in composition between the sites, although vapour adsorption dominated input at both sites. Fog contributed more to total “non rainfall” atmospheric water closer to the coast (Kleinberg) compared to further inland (Gobabeb) but was lower than expected at both sites. Absolute values of fog input at both sites showed the opposite trend, Kleinberg 0.38?mm per night compared to Gobabeb 8.7?mm per night. This difference was attributed to the development of a mechanical crust on the soil surface at Kleinberg, which resulted in a significant reduction of vapour adsorption compared to Gobabeb. The crust also led to a significant reduction in evaporation from the sample at Kleinberg compared to the one at Gobabeb. Ecological differences between the two sites can be attributed to the development of the soil crust on the sample at Kleinberg and not on the sample at Gobabeb. 相似文献
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K. F. Kaseke A. J. Mills J. Henschel M. K. Seely K. Esler R. Brown 《Pure and Applied Geophysics》2012,169(5-6):873-880
The effect of desert pavements (gravel mulch) on near surface soil micro-hydrology has been inadequately studied. Micro-hydrology in arid ecosystems occurs due to a daily non rainfall atmospheric water cycle, consisting of an input phase (dew, fog, vapour adsorption) and an evaporation phase. A winter comparative study between a bare soil (control) and gravel mulch using the automated microlysimeter approach was conducted in Stellenbosch, South Africa in 2008. Results showed that dew deposition and direct water vapour adsorption were significantly higher into bare soil compared to gravel mulch. In contrast, however, soil moisture from rain persists for a longer time under gravel mulch compared to bare soil. This result suggests that the greatest impact of gravel mulch on soil micro-hydrology is towards conserving moisture and could explain why the treatment is used in dry-land agriculture in Mediterranean regions. 相似文献
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T. N. Kaseke M. E. Thompson 《Stochastic Environmental Research and Risk Assessment (SERRA)》1997,11(1):1-16
Abstract: Linear continuous time stochastic Nash cascade conceptual models for runoff are developed. The runoff is modeled
as a simple system of linear stochastic differential equations driven by white Gaussian and marked point process noises. In
the case of d reservoirs, the outputs of these reservoirs form a d dimensional vector Markov process, of which only the dth
coordinate process is observed, usually at a discrete sample of time points. The dth coordinate process is not Markovian.
Thus runoff is a partially observed Markov process if it is modeled using the stochastic Nash cascade model. We consider how
to estimate the parameters in such models. In principle, maximum likelihood estimation for the complete process parameters
can be carried out directly or through some form of the EM (estimation and maximization) algorithm or variation thereof, applied
to the observed process data. In this research we consider a direct approximate likelihood approach and a filtering approach
to an algorithm of EM type, as developed in Thompson and Kaseke (1994). These two methods are applied to some real life runoff
data from a catchment in Wales, England. We also consider a special case of the martingale estimating function approach on
the runoff model in the presence of rainfall. Finally, some simulations of the runoff process are given based on the estimated
parameters. 相似文献
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Rain-fed agriculture is threatened by an increased frequency of droughts worldwide thereby putting millions of livelihoods at risk especially in sub-Saharan Africa. This makes drought preparedness critical. In this study, we sought to establish whether maize yield can be predicted using the number of dry dekads that occur at specific maize growth stages for purposes of yield early warning. The dry dekads were derived from remotely sensed Vegetation Condition Index calculated from the SPOT NDVI time series ranging from 1998 to 2013. Regression between dry dekads and maize yield show a negative linear relationship for four growing seasons (2010–2013) and indicates that dry dekads at both the vegetative and reproductive stages are important for predicting maize yield. Results suggest that early warning alert could be given using dry dekads that occur at the vegetative stage, while those at the reproductive stage can be used to give better yield estimate later on. 相似文献
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