The Role of Auxiliary Variables in Deterministic and Deterministic-Stochastic Spatial Models of Air Temperature in Poland

Szymanowski, Mariusz; Kryza, Maciej

doi:10.1007/s00024-015-1199-2

Cited by 10 publications

(4 citation statements)

References 60 publications

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“…To incorporate altitude corrections, we applied the kriging with external drift method using the digital elevation model as an explanatory variable in the linear regression model. A similar approach was adopted by Ustrnul and Czekierda [29], Czernecki and Miętus [30], Szymanowski and Kryza [31] showing good accuracy in terms of recognizing spatial patterns and seasonal dynamics in Poland.…”

Section: Database Reconstruction 2008-2014 -Kriging With External Driftmentioning

confidence: 99%

Reconstruction of late spring phenophases in Poland and their response to climate change, 1951–2014

Czernecki

Jabłońska

2016

Acta Agrobot

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Phenology is primarily seen as an indicator of the impacts of climate change. The strongest biological signal of climatic change is revealed by phenological data from the period after 1990. Unfortunately, the Polish nationwide network of phenological monitoring was terminated in 1992, and was only reactivated in 2005. Here, we attempt to reconstruct late spring phenophases of flowering of Syringa vulgaris L. and Aesculus hippocastanum L. across several sites in Poland from 1951 to 2014 using the GIS-based approach (if observations from neighboring stations were available) and multiple regression modeling with stepwise screening and bootstrap resampling. It was found that the air temperature and its indices explain over 60% of the variance, giving an accuracy of 3.0-3.4 days (mean absolute error) and correlation coefficients of 0.83 and 0.78 for lilac and horse chestnut, respectively. Altogether, both plant species showed a statistically significant advancement in the onset of flowering with an average rate of 1.7 days per decade. We also found that the final trend is the result of rapid acceleration of the increase in air temperature after the 1990s, while most of the trends for late spring were ambiguous before that period.

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Section: Database Reconstruction 2008-2014 -Kriging With External Driftmentioning

confidence: 99%

Reconstruction of late spring phenophases in Poland and their response to climate change, 1951–2014

Czernecki

Jabłońska

2016

Acta Agrobot

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“…We also assessed the spatial distribution of the regression residuals. If they were found to be significantly spatially autocorrelated (Moran's I statistic) for mapping purposes, we would need to consider extending the spatial interpolation model to a residual kriging formula (Szymanowski & Kryza, 2017).…”

Section: Methodsmentioning

confidence: 99%

“…In these analyses, we no longer considered the momentary effects expressed by the retrieved LST. Since some of the sensors failed over (Szymanowski & Kryza, 2017).…”

Section: Topographical Determinants Of Ground Temperaturementioning

confidence: 99%

Spatial and temporal patterns of near‐surface ground temperature in the Arctic mountain catchment

Kasprzak

Szymanowski

2023

Land Degrad Dev

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We consider the case of a mountain catchment representative of southwest Spitsbergen to investigate the spatial and temporal variation in temperature of the near‐surface ground. We set up 20 thermistor strings distributed in different parts of the catchment, combine the measured data with land surface temperature (LST) retrieved from Landsat imagery, and verify the depth of ground thawing using electrical resistivity tomography. Under current climatic conditions, the thickness of the active layer (ALT) is at least 1.5 m, and in the lower parts of the catchment, between 3.5 and 4 m. The maximum ground surface temperature in the study area can be 27°C, and the temperature at a depth of 1.5 m, 5.4°C. We identified unfrozen ground (taliks) to a depth of 10–30 m in zones of water concentration at foot slopes and under river channels. The persistence of year‐round taliks at lake shores is possible. We verify the hypothesis that topographic parameters significantly determine the spatial variation of near‐surface ground temperature. For each level of temperature aggregation (from 6 h to 12 months data) and each examined the depth of the active layer, it is possible to identify environmental variables—related to energy conditions, heights, terrain relief, and ground surface moisture—significantly correlated with ground temperature, and consequently to specify multiple regression models. Topographic exposure and LST (used only in daily models), altitude, and various approximations of relative height are crucial in their construction. The ground temperature variance, unexplained in the regression analysis, prompts recognition of the critical role of factors not included in the modelling (groundwater, soil structure).

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“…High resolution is of special importance when the interpolated variable is potentially strongly dependant on local factors (including changes in altitude). The choice of spatial resolution applied is supported by earlier studies of Szymanowski et al ( , 2013 and Szymanowski and Kryza (2015). Based on the 250-m DEM, the concavity/convexity index (CCI) expressing the cool air accumulation effects of concave terrain forms and the foehn index (FI) illustrating the thermal impact of the foehn wind were calculated (Szymanowski et al 2007).…”

Section: Environmental Variablesmentioning

confidence: 99%

Spatial Interpolation of Ewert’s Index of Continentality in Poland

Szymanowski

Bednarczyk

Kryza

et al. 2016

Pure Appl. Geophys.

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Abstract-The article presents methodological considerations on the spatial interpolation of Ewert's index of continentality for Poland. The primary objective was to perform spatial interpolation and generate maps of the index combined with selection of an optimal interpolation method and validation of the use of the decision tree proposed by Szymanowski et al. (Meteorol Z 22:577-585, 2013). The analysis involved four selected years and a multi-year average of the period 1981-2010 and was based on data from 111 meteorological stations. Three regression models: multiple linear regression (MLR), geographically weighted regression (GWR), and mixed geographically weighted regression were used in the analysis as well as extensions of two of them to the residual kriging form. The regression models were compared demonstrating a better fit of the local model and, hence, the non-stationarity of the spatial process. However, the decisive role in the selection of the interpolator was assigned to the possibility of extension of the regression model to residual kriging. A key element here is the autocorrelation of the regression residuals, which proved to be significant for MLR and irrelevant for GWR. This resulted in exclusion of geographically weighted regression kriging from further analysis. The multiple linear regression kriging was found as the optimal interpolator. This was confirmed by cross validation combined with an analysis of improvement of the model in accordance with the criterion of the mean absolute error (MAE). The results obtained facilitate modification of the scheme of selection of an optimal interpolator and development of guidelines for automation of interpolation of Ewert's index of continentality for Poland.

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The Role of Auxiliary Variables in Deterministic and Deterministic-Stochastic Spatial Models of Air Temperature in Poland

Cited by 10 publications

References 60 publications

Reconstruction of late spring phenophases in Poland and their response to climate change, 1951–2014

Reconstruction of late spring phenophases in Poland and their response to climate change, 1951–2014

Spatial and temporal patterns of near‐surface ground temperature in the Arctic mountain catchment

Spatial Interpolation of Ewert’s Index of Continentality in Poland

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