Abstract. Climate change and human activities have significant impacts on terrestrial vegetation. Syria is a typical arid region with a water-limited ecosystem and has experienced severe social unrest over the last decades. In this study, changes in vegetation and potential drivers in Syria are investigated. By using an enhanced vegetation index (EVI), a general browning trend is found in Syria during 2001–2018, with the EVI decreasing at a rate of −0.8 × 10−3 yr−1 (p<0.1). The decrease of the EVI is mainly found in the north region, whereas the west region still maintains an increasing trend. The residual analysis indicates that besides precipitation, human activities also contribute significantly to the EVI decrease, which is confirmed by the decrease in rainfall use efficiency. Moreover, a paired land-use experiment (PLUE) analysis is carried out in the Khabur River basin where croplands are widely distributed in adjacent regions of Syria and Turkey. The time series of the EVIs over these two regions are highly correlated (r=0.8027, p<0.001), indicating that both regions are affected by similar climate forcing. However, vegetation in Syria and Turkey illustrates contrary browning (−3 × 10−3 yr−1, p<0.01) and greening trends (4.5 × 10−3 yr−1, p<0.01), respectively. Relevant reports have noted that social unrest induced insufficient irrigation and lack of seeds, fertilizers, pesticides and field management. Therefore, we concluded that the decline in vegetation in the north Syria is driven by the change of land management.

Gross primary production (GPP) is the total amount of carbon sequestered by plants in an ecosystem through photosynthesis (Beer et al., 2010;Turner et al., 2006). GPP is the largest carbon flux of the terrestrial carbon cycle, which has significant impacts on atmospheric CO 2 concentration and terrestrial ecosystem carbon cycle (

The leaf area index (LAI) shows a significant increasing trend from global to regional scales, which is known as greening. Greening will further enhance photosynthesis, but it is unclear whether the contribution of greening has exceeded the CO 2 fertilization effect and become the dominant factor in the gross primary productivity (GPP) variation. We took the Yangtze River Delta (YRD) of China, where cropland and natural vegetation are significantly greening, as an example. Based on the boreal ecosystem productivity simulator (BEPS) and Revised-EC-LUE models, the GPP in the YRD from 2001 to 2020 was simulated, and attribution analysis of the interannual variation in GPP was performed. In addition, the reliability of the GPP simulated by the dynamic global vegetation model (DGVM) in the area was further investigated. The research results showed that GPP in the YRD had three significant characteristics consistent with LAI: (1) GPP showed a significant increasing trend; (2) the multiyear mean and trend of natural vegetation GPP were higher than those of cropland GPP; and(3) cropland GPP showed double-high peak characteristics. The BEPS and Revised-EC-LUE models agreed that the effect of LAI variation (4.29 Tg C yr -1 for BEPS and 2.73 Tg C yr -1 for the Revised-EC-LUE model) determined the interannual variation in GPP, which was much higher than the CO 2 fertilization effect (2.29 Tg C yr -1 for BEPS and 0.67 Tg C yr -1 for the Revised-EC-LUE model). The GPP simulated by the 7 DGVMs showed a huge inconsistency with the GPP estimated by remote sensing models. The deviation of LAI simulated by DGVM might be a potential cause for this phenomenon. Our study highlights that in significant greening areas, LAI has dominated GPP variation, both spatially and temporally, and DGVM can correctly simulate GPP only if it accurately simulates LAI variation.

Vegetation greening, which refers to the interannual increasing trends of vegetation greenness, has been widely found on the regional to global scale. Meanwhile, climate extremes, especially several drought, significantly damage vegetation. The Southwest China (SWC) region experienced massive drought from 2009 to 2012, which severely damaged vegetation and had a huge impact on agricultural systems and life. However, whether these extremes have significantly influenced long-term (multiple decades) vegetation change is unclear. Using the latest remote sensing-based records, including leaf area index (LAI) and gross primary productivity (GPP) for 1982–2016 and enhanced vegetation index (EVI) for 2001–2019, drought events of 2009–2012 only leveled off the greening (increasing in vegetation indices and GPP) temporally and long-term greening was maintained. Meanwhile, drying trends were found to unexpectedly coexist with greening.

Persistence is an important feature of soil moisture, which affects many important processes such as land–air interaction and ecohydrological processes. Soil moisture datasets from reanalysis, remote-sensing observations and land surface models have been widely used in various ecohydrological studies, however, due to the complexity of hydrological processes, the essential features of soil moisture such as spatial-temporal characteristics and persistence still need to be further quantified. This study focused on the Australia region and used in situ observation from fourteen International Soil Moisture Network sites to evaluate soil moisture from six gridded products, including satellite remote-sensing records (ESA CCI), output of reanalysis (ERA5-Land) and land surface models (GLDAS and GLEAM). High correlation coefficients between observations and the other soil moisture datasets were gotten. Regional averaged inter-annual variations of soil moisture were relatively large with some dry periods (2002–2010, 2013–2016) and wet periods (2011–2012) indicated by these gridded products. General coherent spatial patterns were found in long-term soil moisture with large differences in the lateral inflow area of the Great Artesian Basin. The coefficient of variation of these soil moisture datasets generally decreased from northwest to southeast, but the enhanced vegetation index coefficient of variation was larger in the southwest corner, northeast (non-coastal areas) and the lateral inflow area. Persistence calculated from various soil moisture datasets had quite large differences compared with measurements. Meanwhile, little coherence was gotten among different surface soil moisture datasets, the persistence of deep soil moisture seemed to be significantly overestimated. Therefore, models still need to improve the temporal characteristics with the persistence rather than the correlation coefficient.