The aim of this study was to evaluate the frequency and severity of drought over the arable lands of Romania using the Normalized Difference Drought Index (NDDI). This index was obtained from the Moderate Resolution Imaging Spectro-Radiometer (MODIS) sensor of the Terra satellite. The interval between March and September was investigated to study the drought occurrence from the early stage of crop growth to its harvest time. The study covered a long period (2001–2020), hence it is able to provide a sound climatological image of crop vegetation conditions. Corine Land Cover 2018 (CLC) was used to extract the arable land surfaces. According to this index, the driest year was 2003 with 25.6% of arable land affected by drought. On the contrary, the wettest year was 2016, with only 10.8% of arable land affected by drought. Regarding the multiannual average of the period 2001–2020, it can be seen that drought is not a phenomenon that occurs consistently each year, therefore only 11.7% of arable land was affected constantly by severe and extreme drought. The correlation between NDDI and precipitation amount was also investigated. Although the correlations at weekly or monthly levels are more complicated, the annual regional mean NDDI is overall negatively correlated with annual rainfall. Thus, from a climatic perspective, we consider that NDDI is a reliable and valuable tool for the assessment of droughts over the arable lands in Romania.

Land degradation (LD) and desertification (DS) are a sensitive global issue including southern and south-eastern Europe, which is severely affected by climate change. In this study, a state-of-the-art approach for assessing the intensity of LD and DS processes using remote-sensing-derived indicators within a GIS environment was proposed. The analysis was carried out using the Principal Component Analysis based on integrating the significant trends of relevant biophysical parameters in Romania. The methodology was tested and validated at the national level in Romania. In total, 7.76% of the area was identified as LD and 60.8% of the total area tended to improve, and 31.44% was stable. Most of the regions with LD overlapped with the dryland areas, while improvement areas were identified outside of the drylands. In forested areas from high altitudes, a tendency to improve the condition of vegetation was observed, and most of the surfaces being protected were natural areas that have benefited from proper management. All these results can be used to adapt management practices to avoid, reduce, or restore the LD. The proposed model was based on globally available remote sensing datasets, with a high frequency of data acquisition and collection history that allows for the statistical analyses of changes on a global scale.

The management of groundwater resources must take into account their variation trends. In this sense, 3 statistical methods were used to identify seasonal and annual groundwater level trends: Mann-Kendall test (MK), Innovative Analysis Method (ITA) and Spearman’s Rho test (SR). Each method was applied for 5 time series (one annual and four seasonal) from 148 hydrological wells from Eastern Romania. The wells were classified in 8 cluster groups based on water depth, using the cluster analysis, covering the full range of depths from under 1.4 to over 15.5 m. Coupling statistical methods (MK and SR test) with one based on graphical analysis (ITA method) offers the possibility of obtaining statistically significant results (between 53% and 69% for spring season, 68% and 96% for autumn season and 68% and 81% from annual values). The decreasing trend of water depth is more obvious for summer and autumn season, for 72%–74% from analyzed wells (based on SR and ITA method) and 68% for annual series (based on MK test). The spatial distribution of seasonal and annual trends highlights that in the northern and central parts of the region, the groundwater depth suffers depletion induced by the effects of prolonged meteorological and hydrological drought manifested in this area in the last decades.

The Landsat 8 satellites have retrieved land surface temperature (LST) resampled at a 30-m spatial resolution since 2013, but the urban climate studies frequently use a limited number of images due to the problems related to missing data over the city of interest. This paper endorses a procedure for building a long-term gap-free LST data set in an urban area using the high-resolution Landsat 8 imagery. The study is applied on 94 images available through 2013–2018 over Bucharest (Romania). The raw images containing between 1.1% and 58.4% missing LST data were filled in using the Data INterpolating Empirical Orthogonal Functions (DINEOF) algorithm implemented in the sinkr R packages. The resulting high-spatial-resolution gap-filled land surface temperature data set was used to explore the LST climatology over Bucharest (Romania) an urban area, at a monthly, seasonal, and annual scale. The performance of the gap-filling method was checked using a cross-validation procedure, and the results pledge for the development of an LST-based urban climatology.