Space-time kriging of precipitation variability in Turkey for the period 1976–2010

Raja, Nussaïbah B.; Aydın, Olgu; Türkoğlu, Necla; Çiçek, İhsan

doi:10.1007/s00704-016-1788-8

Cited by 26 publications

(18 citation statements)

References 41 publications

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“…In the mesoregion of Sertão, smaller values of RMSE were noted between January and May, because it was during this period that the lowest variability of precipitation occurred (Table 1). These results are similar to those found in References [15,27]. The rainy seasons presented high spatial variability due to the actions of different weather systems, such as the Intertropical Convergence Zone (ITCZ) and Upper-Level Cyclonic Vortex (ULCV), as described by Macedo et al [28].…”

Section: Selection and Validation Of The Modelsupporting

confidence: 91%

“…This low R 2 strongly indicates that the trend component was unable to explain the spatiotemporal variability in the precipitation. Moreover, it was challenging to provide a precise estimate for climatic variables, especially precipitation, because the spatial and temporal distributions of such variables exhibited large variations, causing the trend model to present a low R 2 [15]. It is worth noting that the methodology proposed in this paper to estimate the precipitation accounts not only for the trend but also for the dependence of spatiotemporal data.…”

Section: Regression Analysis For the Trend Componentmentioning

confidence: 99%

“…In recent years, studies have appeared analyzing the regional spatiotemporal distribution of precipitation. Raja et al [15] applied the spatiotemporal kriging technique to rainfall data from Turkey. They consequently observed that Turkey is located in a region with a higher risk of possible effects from climate change, similar to the state of Paraíba, Brazil.…”

Section: Introductionmentioning

confidence: 99%

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Modeling Spatiotemporal Rainfall Variability in Paraíba, Brazil

Medeiros

Lima

Olinda

et al. 2019

Water

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The purpose of this study was to provide a detailed framework to use the spatiotemporal kriging to model the space-time variability of precipitation data in Paraíba, which is located in the northeastern region of Brazil (NEB). The NEB is characterized by an irregular, highly variable distribution of rainfall in space and time. In this region, it is common to find high rates of rainfall at locations adjacent to those with no record of rain. Paraíba experiences localized periods of drought within rainy seasons and distinct precipitation patterns among the state’s mesoregions. The mean precipitation values observed at several irregularly spaced rain gauge stations from 1994 to 2014 showed remarkable variations among the mesoregions in Paraíba throughout the year. As a consequence of this behavior, there is a need to model the rainfall distribution jointly with space and time. A spatiotemporal geostatistical methodology was applied to monthly total rainfall data from the state of Paraíba. The rainfall data indicate intense spatial and temporal variabilities that directly affect the water resources of the entire region. The results provide a detailed spatial analysis of sectors experiencing precipitation conditions ranging from a scarcity to an excess of rainfall. The present study should help drive future research into spatiotemporal rainfall patterns across all of NEB.

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Section: Selection and Validation Of The Modelsupporting

confidence: 91%

Section: Regression Analysis For the Trend Componentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling Spatiotemporal Rainfall Variability in Paraíba, Brazil

Medeiros

Lima

Olinda

et al. 2019

Water

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“…In general, annual mean precipitation decreases from the coastal regions to the continental regions parts due to the adiabatic process (i.e., orographic). For example, due to the mountain ranges of the northern and southern coasts—the North Anatolian Mountains and the Taurus Mountains—the reaching of coastal effects to Central Anatolia is hindered (Raja, Aydın, Türkoğlu, & Çiçek, ). Turkey has different climate types with respect to zones as a result of being affected by different meteorological factors and climate control elements.…”

Section: Study Area and Datamentioning

confidence: 99%

Decadal variability analysis of extreme precipitation in Turkey and its relationship with teleconnection patterns

Düzenli

Tabari

Willems

et al. 2018

Hydrological Processes

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Natural disasters such as droughts and floods originate as a consequence of excessive high or low precipitation amount and/or frequency. Due to the temporal persistence of the latter, the disasters tend to cluster in time. Because global ocean–atmosphere teleconnection patterns with (multi‐)decadal oscillations are tightly related with the precipitation variability, it is useful to analyse precipitation variability at the same timescale to understand any possible connection between them. In this study, decadal oscillations of daily extreme precipitation are investigated using quantile perturbation method for 65 stations in Turkey for the period 1955–2014. Arctic Oscillation (AO), North Atlantic Oscillation (NAO), Southern Oscillation Index (SOI), and Western Mediterranean Oscillation teleconnection patterns are examined for their relation with the extreme precipitation variability. The analyses are conducted for four climatic seasons and seven subregions. According to the analysis based on single drivers, NAO is identified as the most effective driver of Turkish extreme precipitation variability in winter, especially over regions influenced by the Mediterranean climate, whereas AO has a similar effect. When the teleconnection patterns are investigated in pairs, the combination of the NAO and SOI results in the strongest influence on the winter extremes. Though obvious differences are not recorded between the results of linear and nonlinear methods for single driver analysis, the nonlinear method is superior for the multiple driver analysis.

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“…The usefulness of geostatistics in fisheries has been well established (Morfin et al, 2012;Petitgas et al, 2014;Rufino, Gaspar, Maynou, & Monteiro, 2008;Rufino, Maynou, Abelló, Gil de Sola, & Yule, 2005;Rufino, Maynou, Abelló, Yule, & Gil de Sola, 2006;Saraux et al, 2014). Space-time geostatistics have been used in other research fields, such as high-resolution global temperature (Kilibarda et al, 2014), precipitation (Raja, Aydin, Türkoğlu, & Çiçek, 2017), economy (Welkenhuysen et al, 2017), disease mapping (Marek, Tuček, & Pászto, 2015), reconstruction of past conditions using tree ring data (Biondi, 2013), and parasite propagation (Hu et al, 2015), but to the authors' best knowledge, these methods have never been applied in the field of fisheries biology. On the other hand, the patterns of temporal series of continuous maps can be summarized using empirical orthogonal functions analysis, a method similar to a principal component analysis (PCA), and often used in meteorology, but in the spatio-temporal domain (Morfin et al, 2012;Petitgas et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Bivalve sanctuaries to enhance stocks along the Algarve coast of southern Portugal: A spatio‐temporal approach

Rufino

Vasconcelos

Pereira

et al. 2018

Aquatic Conservation

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Populations of marine bivalves are subject to large interannual fluctuations as a result of their sensitivity to unfavourable environmental conditions. These populations have a strong dependence on recruitment, which constitutes a risk to stock conservation and the sustainability of the commercially exploited species. Bivalve sanctuaries comprising no‐take areas can promote the restocking of large coastal areas as a result of the high fecundity and the potentially long‐distance larval dispersal of bivalves. The present study explores a novel spatio‐temporal approach to investigate suitable locations for bivalve sanctuaries, aimed at protecting and enhancing stocks along the Algarve coast in southern Portugal. In the context of small‐scale fisheries, bivalve dredging constitutes an important socio‐economic activity. Three bivalve species are targeted: the striped venus (Chamelea gallina), the donax clam (Donax trunculus), and the surf clam (Spisula solida). Data gathered from 36 bivalve fishing surveys conducted over three decades (1986–2015) were analysed using geostatistical methods, accounting for temporal and spatial autocorrelation to produce continuous prediction maps of species distribution for each survey. The approach taken permitted the use of all scattered historical data collected by different methods. The predictions were then used to summarize and illustrate the spatial distribution of species across time, in terms of average biomass, variability, and maximum potential biomass, and to delimit the areas as a function of the species persistence (recurrent, occasional, or rare). The temporal patterns through space were further investigated using empirical orthogonal function (EOF) analysis. The areas accounting for 80% of the species biomass were also estimated. Results of this set of analyses led to the proposal of locations for establishing bivalve sanctuaries, aimed at enhancing the stocks of commercial bivalve species in the long term. Such an innovative spatio‐temporal approach and management strategy is replicable in other fishing resources and geographic areas.

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Space-time kriging of precipitation variability in Turkey for the period 1976–2010

Cited by 26 publications

References 41 publications

Modeling Spatiotemporal Rainfall Variability in Paraíba, Brazil

Modeling Spatiotemporal Rainfall Variability in Paraíba, Brazil

Decadal variability analysis of extreme precipitation in Turkey and its relationship with teleconnection patterns

Bivalve sanctuaries to enhance stocks along the Algarve coast of southern Portugal: A spatio‐temporal approach

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