Mixed memory, (non) Hurst effect, and maximum entropy of rainfall in the tropical Andes

Poveda, Germán

doi:10.1016/j.advwatres.2010.11.007

Cited by 28 publications

(42 citation statements)

References 91 publications

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“…This figure is available in colour online at wileyonlinelibrary.com/journal/joc of the robustness of our estimation procedure, as it is based on 'measured' data. Now, TRMM information does not capture all extreme events, and for some highly rainy regions, the previous distribution does not adequately represent the heavy tails of actual precipitation data (Poveda, 2010).…”

Section: Quality Control Of the Estimated Rainfall Fieldsmentioning

confidence: 99%

“…the variance of the local (pixel) probability distribution function (Olea, 1991;Goovaerts, 1997). Then, taking the kriging variance as a measure of uncertainty is not rigorously accurate because (1) in general, the probability distribution function of tropical Andean rainfall is not Gaussian and exhibits heavy tails (Poveda, 2010), and (2) very different values of the kriging variance are estimated using different raingauge sets as primary data, which reflects into discontinuities in the four standard deviation fields (Figure 7). Similar values of the variance associated with the probability distribution function of precipitation at any pixel are to be expected using different raingauge sets for uncertainty estimations.…”

Section: Quality Control Of the Estimated Rainfall Fieldsmentioning

confidence: 99%

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Improved long‐term mean annual rainfall fields for Colombia

Álvarez-Villa¹,

Vélez²,

Poveda³

2010

Intl Journal of Climatology

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ABSTRACT:With the aim of improving the long-term mean annual surface water balance of Colombia, four new annual average precipitation fields are estimated at 4 km spatial resolution. To put in context, a concise literature review of rainfall in Colombia is presented. For estimation purposes, diverse multivariate geostatistical methods are implemented by combining information from 1180 raingauges covering the period 1950-2005, and satellite data from the tropical rainfall measuring mission (TRMM) for the period 1999-2005, used as a drift for the following geostatistical methods: (1) kriging with an external drift (KED), (2) standardized cokriging (SCK), (3) colocated cokriging (CCK), and (4) Markov regionalization CCK (CCKM). To ensure the reliability of the estimated precipitation fields, a detailed cross-validation procedure is performed, including univariate and bivariate analyses of residuals, which allows us to conclude that the best estimated rainfall field is obtained with KED, and the worst with SCK. Visual analyses are also performed in the search for consistency of the resulting precipitation fields. Furthermore, local (at-a-pixel) uncertainty modelling analysis is performed using the indicator approach. Conditional cumulative distribution functions (CCDF) are estimated using indicator CCK with Bayes-Markov hypothesis. Statistical descriptors for the pixel's CCDFs are estimated based on the resulting precipitation fields, including long-term mean, conditional variance and the coefficient of variation. These improved precipitation fields along with their estimated uncertainties are available (http://cancerbero.unalmed.edu.co/∼hidrosig/index.php) for the scientific community and constitute useful basic information for diverse applications in water resources, agriculture, hydropower generation, human health, risks and disaster prevention, and many other applied sectors in Colombia.

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Section: Quality Control Of the Estimated Rainfall Fieldsmentioning

confidence: 99%

Section: Quality Control Of the Estimated Rainfall Fieldsmentioning

confidence: 99%

Improved long‐term mean annual rainfall fields for Colombia

Álvarez-Villa¹,

Vélez²,

Poveda³

2010

Intl Journal of Climatology

Self Cite

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“…Additionally, recent studies [28,30,31] have investigated the relationship between the q-entropy and the Generalized Pareto distribution (which is relevant in hydrological analysis). In particular, the maximization of the q-entropy under a prescribed mean leads to a Pareto probability distribution with power-law tail [28,[46][47][48], which belongs to the family of Lévy stable distributions [49], specifically to Type II Generalized Pareto distributions.…”

Section: Q-entropymentioning

confidence: 99%

“…In particular, the maximization of the q-entropy under a prescribed mean leads to a Pareto probability distribution with power-law tail [28,[46][47][48], which belongs to the family of Lévy stable distributions [49], specifically to Type II Generalized Pareto distributions. For 1 < q < 2, the original distribution takes the form of the Zipf-Mandelbrot type [50][51][52], which decays as a power law for large values of x, and all moments are divergent when 3/2 < q < 2 [53].…”

Section: Q-entropymentioning

confidence: 99%

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Testing the Beta-Lognormal Model in Amazonian Rainfall Fields Using the Generalized Space q-Entropy

Salas¹,

Poveda²,

Sánchez³

2017

Entropy

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Abstract:We study spatial scaling and complexity properties of Amazonian radar rainfall fields using the Beta-Lognormal Model (BL-Model) with the aim to characterize and model the process at a broad range of spatial scales. The Generalized Space q-Entropy Function (GSEF), an entropic measure defined as a continuous set of power laws covering a broad range of spatial scales, S q (λ) ∼ λ Ω(q) , is used as a tool to check the ability of the BL-Model to represent observed 2-D radar rainfall fields. In addition, we evaluate the effect of the amount of zeros, the variability of rainfall intensity, the number of bins used to estimate the probability mass function, and the record length on the GSFE estimation. Our results show that: (i) the BL-Model adequately represents the scaling properties of the q-entropy, S q , for Amazonian rainfall fields across a range of spatial scales λ from 2 km to 64 km; (ii) the q-entropy in rainfall fields can be characterized by a non-additivity value, q sat , at which rainfall reaches a maximum scaling exponent, Ω sat ; (iii) the maximum scaling exponent Ω sat is directly related to the amount of zeros in rainfall fields and is not sensitive to either the number of bins to estimate the probability mass function or the variability of rainfall intensity; and (iv) for small-samples, the GSEF of rainfall fields may incur in considerable bias. Finally, for synthetic 2-D rainfall fields from the BL-Model, we look for a connection between intermittency using a metric based on generalized Hurst exponents, M(q 1 , q 2 ), and the non-extensive order (q-order) of a system, Θ q , which relates to the GSEF. Our results do not exhibit evidence of such relationship.

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Statistical analyses of the effect of a drainage tunnel on landslide hydrogeological characteristics

Wang

et al. 2020

Hydrological Processes

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A high groundwater level is highly relevant to the slope instability. Drainage tunnel is an effective method for groundwater level control, but its effect on landslide hydrogeological characteristics is rarely discussed. This study analysed the changes of the landslide hydrogeological characteristics under the effect of a drainage tunnel by real-time monitoring of rainfall, groundwater level, and surface displacement. The trend and mutation of groundwater level are analysed by the Mann-Kendall test and the Mann-Kendall mutation test. The memory effect of groundwater in the landslide area was analysed using autocorrelation analysis. The response characteristics of groundwater level to rainfall were evaluated using cross-correlation analysis and mutual information theory. Variations of groundwater levels were further investigated based on hydrograph analysis. Results showed that the groundwater level had a downward trend from 2016 to 2017. The significant downward trend of groundwater levels began in August 2016. The memory effect of groundwater levels was longer under the effect of the drainage tunnel. Before the construction of the drainage tunnel, the response time of groundwater to rainfall was less than 3 hr and rainfall can generate dramatic groundwater level variations. After the drainage tunnel was completed, time lags can be observed in the groundwater response, and the variation of groundwater levels was smaller than before. A strong correlation was found between groundwater levels and the landslide movement. This study demonstrated that the drainage tunnel had effectively controlled the groundwater level in the landslide and ensured the stability of the landslide.

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Mixed memory, (non) Hurst effect, and maximum entropy of rainfall in the tropical Andes

Cited by 28 publications

References 91 publications

Improved long‐term mean annual rainfall fields for Colombia

Improved long‐term mean annual rainfall fields for Colombia

Testing the Beta-Lognormal Model in Amazonian Rainfall Fields Using the Generalized Space q-Entropy

Statistical analyses of the effect of a drainage tunnel on landslide hydrogeological characteristics

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