Space–Time Kriging of Precipitation: Modeling the Large-Scale Variation with Model GAMLSS

Medeiros, Elias Silva de; Lima, Renato Ribeiro de; Olinda, Ricardo Alves de; Dantas, Leydson Galvíncio; Santos, Carlos Antonio Costa dos

doi:10.3390/w11112368

Cited by 15 publications

(9 citation statements)

References 32 publications

(59 reference statements)

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“…Moreover, co‐kriging involves regression on covariates that are known beforehand; on the contrary, semantic kriging involves covariates that are not known in advance and thus need to be predicted in turn 34 . In machine learning, kriging often uses rich and nonlinear trend formulations, 35 see, for instance, the polynomial chaos approach 36 . Intrinsic kriging revolves around the process of differences, which is approximately de‐trended if the mean varies slowly with distance 37 .…”

Section: Literature Reviewmentioning

confidence: 99%

Separable spatio‐temporal kriging for fast virtual sensing

Miniato

Bellio

Grassetti

et al. 2022

Appl Stoch Models Bus & Ind

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Environmental monitoring is a task that requires to surrogate system-wide information with limited sensor readings. Under the proximity principle, an environmental monitoring system can be based on the virtual sensing logic and then rely on distance-based prediction methods, foremostly spatio-temporal kriging. The last one is cumbersome with large datasets, but we show that a suitable separability assumption reduces its computational cost to an extent broader than considered typically. Only spatial interpolation needs to be performed in a centralized way, while forecasting can be delegated to each sensor. This simplification is related to the fact that two separate models are involved, one in time and one in the space domain. Any of the two models can be replaced without re-estimating the other under a composite likelihood (CL) approach. Moreover, the use of convenient spatial and temporal models eases up computation, not only in the CL approach, but also in maximum likelihood estimation. We show that this perspective on kriging allows to perform virtual sensing even in the case of tall datasets.

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Section: Literature Reviewmentioning

confidence: 99%

Separable spatio‐temporal kriging for fast virtual sensing

Miniato

Bellio

Grassetti

et al. 2022

Appl Stoch Models Bus & Ind

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“…A widely used approach to model nonstationary hydro-climatic series is the timevarying moments method, indicated by Khaleq et al [42], which incorporates time-varying parameters into probability models with the same form of stationary condition. The GAMLSS is a popular tool to achieve this purpose in hydrology and dynamically detects evolution of probability distributions with time or other covariates [43][44][45][46].…”

Section: The Generalized Additive Models For Location Scale and Shape (Gamlss)mentioning

confidence: 99%

Nonstationary Analyses of the Maximum and Minimum Streamflow in Tamsui River Basin, Taiwan

Shiau

Liu

2021

Water

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This study aims to detect non-stationarity of the maximum and minimum streamflow regime in Tamsui River basin, northern Taiwan. Seven streamflow gauge stations, with at least 27-year daily records, are used to characterize annual maximum 1- and 2-day flows and annual minimum 1-, 7-, and 30-day flows. The generalized additive models for location, scale, and shape (GAMLSS) are used to dynamically detect evolution of probability distributions of the maximum and minimum flow indices with time. Results of time-covariate models indicate that stationarity is only noted in the 4 maximum flow indices out of 35 indices. This phenomenon indicates that the minimum flow indices are vulnerable to changing environments. A 16-category distributional-change scheme is employed to classify distributional changes of flow indices. A probabilistic distribution with complex variations of mean and variance is prevalent in the Tamsui River basin since approximate one third of flow indices (34.3%) belong to this category. To evaluate impacts of dams on streamflow regime, a dimensionless index called the reservoir index (RI) serves as an alternative covariate to model nonstationary probability distribution. Results of RI-covariate models indicate that 7 out of 15 flow indices are independent of RI and 80% of the best-fitted RI-covariate models are generally worse than the time-covariate models. This fact reveals that the dam is not the only factor in altering the streamflow regime in the Tamsui River, which is a significant alteration, especially the minimum flow indices. The obtained distributional changes of flow indices clearly indicate changes in probability distributions with time. Non-stationarity in the Tamsui River is induced by climate change and complex anthropogenic interferences.

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“…At the same time, the remaining 23 are points around the hydrographic region, which served only for spatial interpolation. The filling of gaps in the monthly historical series was carried out with the help of the kriging geostatistical interpolator, which considers the spatial correlation and reproduces good estimates (MEDEIROS et al, 2019;BRUBACHER;GUASSELI, 2020). In addition, the homogeneity of the series was also assessed, using the RHtest package developed by Wang (2008aWang ( , 2008b, a program that contains statistical tests that check the significance of the points of change within the series, identify temporal noise, and harmonise the new data set.…”

Section: Datasetmentioning

confidence: 99%

Trends in pluviometric precipitation and climatic water balance in the hydrographic region of Paraguaçu – BA

Souza

Nascimento

2021

Cad. Geogr.

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Trend analysis of hydroclimatic data is essential in the development of water resources management, as it can envisage changes in the pattern of behaviour, helping develop strategies for adaptation in the face of imminent climate change. This study aimed to investigate possible annual and seasonal trends in rainfall and climatological water balance in the hydrographic region of Paraguaçu - BA. From the historical series of precipitation, deficiency and water surpluses, between 1989 and 2018, two analysis scenarios were conducted: the first to verify the annual and seasonal trends of each station, using the traditional Mann-Kendall (MK) methods and Sen’s estimator; and the second for each sub-region of Paraguaçu, by comparing MK with the Innovative Trend Analysis (ITA). The results of the annual series, regardless of the methodology adopted, point to negative trends in rainfall, positive trends in deficit and negative trends in water surplus. Seasonally, in the autumn and winter seasons, generally considered to be drought, there were more trends of increasing rainfall and decreasing water deficiency. A comparison between the MK and ITA models showed that both have similar results for indicating trends in the sub-regions of Paraguaçu. However, the ITA has shown a higher number of significant trends.

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Space–Time Kriging of Precipitation: Modeling the Large-Scale Variation with Model GAMLSS

Cited by 15 publications

References 32 publications

Separable spatio‐temporal kriging for fast virtual sensing

Separable spatio‐temporal kriging for fast virtual sensing

Nonstationary Analyses of the Maximum and Minimum Streamflow in Tamsui River Basin, Taiwan

Trends in pluviometric precipitation and climatic water balance in the hydrographic region of Paraguaçu – BA

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