Historically, Hungary has witnessed numerous waves of drought episodes, causing significant agro-economic loss. Over the recent decades, the intensity, severity and frequency of drought occurrence have dramatically shifted, with undisputable upward tendencies across many areas. Thus, the main aim of this study was to characterize drought trends, intensity and duration over Hungary during 1961-2010. To attain the study goals, the present analyses utilized climate datasets obtained from Climate of the Carpathian region project-CARPATCLIM for 1045 gridded points covering entire Hungary. Meanwhile, a well-known drought index, namely; standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI) at 12-month timescales were employed for drought characterization. Furthermore, the sub-set regions of drought in Hungary were identified using S-mode of the principal component analysis. The Mann-Kendall trend test analysis showed a significant negative SPI-12 trend (P \ 0.05) in 11.5% of the total points over the western part of Hungary. In comparison, 43.2% of the total numbers of the SPEI-12 time series gridded points showed a significant negative trend (P \ 0.05) over the similar locale. However, both indices' trends highlighted the fact that the northeastern region is less sensitive to drought despite experiencing the highest of total drought duration. Results also suggested that the SPI-12 indicates that no significant change can be detected from 1961 to 2010 over Hungary. In contrast, the SPEI-12 exhibits that the drought waves that hit Hungary were more pronounced, with a significant positive (P \ 0.05) trend of ? 1.4% per decade being detected for the area affected by very extreme drought. All in all, this study is one of the primary steps toward a better understanding of drought vulnerability assessment in Hungary.

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Soils in the coastal region of Syria (CRoS) are one of the most fragile components of natural ecosystems. However, they are adversely affected by water erosion processes after extreme land cover modifications such as wildfires or intensive agricultural activities. The main goal of this research was to clarify the dynamic interaction between erosion processes and different ecosystem components (inclination, land cover/land use, and rainy storms) along with the vulnerable territory of the CRoS. Experiments were carried out in five different locations using a total of 15 erosion plots. Soil loss and runoff were quantified in each experimental plot, considering different inclinations and land uses (agricultural land (AG), burnt forest (BF), forest/control plot (F)). Observed runoff and soil loss varied greatly according to both inclination and land cover after 750 mm of rainfall (26 events). In the cultivated areas, the average soil water erosion ranged between 0.14 ± 0.07 and 0.74 ± 0.33 kg/m2; in the BF plots, mean soil erosion ranged between 0.03 ± 0.01 and 0.24 ± 0.10 kg/m2. The lowest amount of erosion was recorded in the F plots where the erosion ranged between 0.1 ± 0.001 and 0.07 ± 0.03 kg/m2. Interestingly, the General Linear Model revealed that all factors (i.e., inclination, rainfall and land use) had a significant (p < 0.001) effect on the soil loss. We concluded that human activities greatly influenced soil erosion rates, being higher in the AG lands, followed by BF and F. Therefore, the current study could be very useful to policymakers and planners for proposing immediate conservation or restoration plans in a less studied area which has been shown to be vulnerable to soil erosion processes.

The Mediterranean part of Syria is affected by soil water erosion due to poor land management. Within this context, the main aim of this research was to track soil erosion and runoff after each rainy storm between September 2013 and April 2014 (rainy season), on two slopes with different gradients (4.7%; 10.3%), under three soil cover types (SCTs): bare soil (BS), metal sieve cover (MC), and strip cropping (SC), in Central Syria. Two statistical multivariate models, the general linear model (GLM), and the random forest regression (RFR) were applied to reveal the importance of SCTs. Our results reveal that higher erosion rate, as well as runoff, were recorded in BS followed by MC, and SC. Accordingly, soil cover had a significant effect (p < 0.001) on soil erosion, and no significant difference was detected between MC and SC. Different combinations of slopes and soil cover had no effect on erosion, at least in this experiment. RFR performed better than GLM in predictions. GLM’s median of mean absolute error was 21% worse than RFR. Nonetheless, 25 repetitions of 2-fold cross-validation ensured the highest available prediction accuracy for RFR. In conclusion, we revealed that runoff, rain intensity and soil cover were the most important factors in erosion.

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