On the Use of Gridded Data Products for Trend Assessment and Aridity Classification in a Mediterranean Context: The Case of the Apulia Region

Abstract

Large-scale gridded climatic data can be useful for the assessment of climate variability and change as a basis for understanding and monitoring natural hazards, as well as for determining appropriate coping strategies. However, an evaluation of the accuracy of these data products against local observational measurements over the different regions of the globe is always required, as these large-scale data may be affected by systematic errors, which can affect the results of downstream applications. Therefore, this study was carried out to evaluate the performances of two long-term gridded datasets in reproducing station-based precipitation and temperature data over the Apulia region (southern Italy) for the period 1956–2019, with a particular focus on the effect of using the different data sources on the results of trend analyses and aridity classification. The results revealed that the considered gridded data products allow only general indications on the spatial and temporal behavior of climatic variables over the Apulia region, especially in regard to precipitation data.

Assessment of Water Stress Conditions in Central Italy by the Use of Ground and Remotely Sensed Weather Datasets

“…That method predicts daily RSWC by weighting the AW factors of vegetation and soil for the respective cover fractions: RSWC = FVC (0.5 + 0.5 AW) + (1 − FVC) AW (2) Within this formulation, the fractional vegetation cover (FVC) is considered to be responsive to the soil water condition integrated over a sufficient time period (one or two months) and was consequently used to modulate the intensity of SWC variations. Equation ( 2) was therefore applied to estimate the RSWC for the Cascine meadow and olive grove deriving the respective FVC from the NDVI MSI images, as fully described in [24]. The AW meteorological factors were computed using both the ground and combined datasets cumulated over 1 or 2 months, respectively.…”

“…This makes the inter/extrapolation of precipitation quite difficult to perform without the use of extremely dense networks of rain gauges, which are not always available. This issue actually affects the gridded precipitation datasets produced by such techniques, as the Pan-European E-OBS product, which has been evaluated in Italy both at national and regional scales [23,24]. A valid alternative could be represented by precipitation observations taken by ground-based weather radars, which are now available in a pre-processed format.…”

Interpolating climate variables by using INLA and the SPDE approach

“…A selected collection of these products at the European (e.g., E‐OBS) and global scale (e.g., CRU, GISTEMP, Berkeley Earth) is available through the Copernicus Climate Change Service (https://doi.org/10.24381/cds.11dedf0c). A review of the E‐OBS dataset and its applications is available, among others, in Cornes et al (2018), Ledesma and Futter (2017) and Raymond et al (2017), while an example of evaluation of the accuracy of the E‐OBS dataset against local observational measurements in Italy (Apulia region) can be found in My et al (2022).…”

Evaluation of Reanalysis Data in Meteorological and Climatological Applications: Spatial and Temporal Considerations

“…In this context, this Special Issue (SI) entitled "Evaluation of Reanalysis Data in Meteorological and Climatological Applications: Spatial and Temporal Considerations", includes articles dedicated not only to the evaluation of reanalysis products against observations [4][5][6][7] but also to exploring the effects of uncertainties using reanalysis data in model outputs [8,9].…”

“…My et al [9] evaluated the performance of two long-term gridded datasets (E-OBS and CRU) for reproducing station-based precipitation and temperature data (with a particular focus on trends and aridity classification results) over the Apulia region in southern Italy for the period from 1956-2019. The main conclusion of this study was that gridded datasets, especially for complex topographic and/or climatic regions, should be used with caution or only after a preliminary evaluation against observational data before any climatological application to ensure their proper reliability.…”