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.

The Sahel, a semi-arid climatic zone with highly seasonal and erratic rainfall, experienced severe droughts in the 1970s and 1980s. Based on remote sensing vegetation indices since early 1980, a clear greening trend is found, which can be attributed to the recovery of contemporaneous precipitation. Here, we present an analysis using long-term leaf area index (LAI), precipitation, and sea surface temperature (SST) records to investigate their trends and relationships. LAI and precipitation show a significant positive trend between 1982 and 2016, at 1.72 × 10 −3 yr −1 (p < 0.01) and 4.63 mm yr−1 (p < 0.01), respectively. However, a piecewise linear regression approach indicates that the trends in both LAI and precipitation are not continuous throughout the 35 year period. In fact, both the greening and wetting of the Sahel have been leveled off (pause of rapid growth) since about 1999. The trends of LAI and precipitation between 1982 and 1999 and 1999–2016 are 4.25 × 10 − 3 yr −1 to − 0.27 × 10 −3 yr −1, and 9.72 mm yr −1 to 2.17 mm yr −1, respectively. These declines in trends are further investigated using an SST index, which is composed of the SSTs of the Mediterranean Sea, the subtropical North Atlantic, and the global tropical oceans. Causality analysis based on information flow theory affirms this precipitation stabilization between 2003 and 2014. Our results highlight that both the greening and the wetting of the Sahel have been leveled off, a feature that was previously hidden in the apparent long-lasting greening and wetting records since the extreme low values in the 1980s.

Extensive studies have been performed on water and sediment transport of the Yangtze River in recent decades, but very few studies were focused on seasonal and daily variations. After the construction of Three Gorges Dam (TGD) in 2003, several key cascade dams were built on the Yangtze upstream. Annual, monthly, and daily data were used to study the seasonality change on water level, water discharge, and sediment load in response to multiple reservoir impoundments. TGD impacted the water level inside the reservoir greatly by shifting the peak water level from summer to winter and made the hydrograph more asymmetrical. In contrast, minor changes in water levels (<2 m) occurred at stations downstream of the TGD mainly due to channel incision. Downstream of TGD at Yichang station, annual hydrograph was flattened slightly: water discharge in the dry season increased 49% whereas that of flood season decreased 14% from 1956–2002 to 2013–2017. Mean annual water discharge downstream of the TGD was slightly impacted because the impounded water was <5% of annual inflow of the reservoir. Comparing with the Yangtze, the annual hydrographs of other intensively managed rivers like the Yellow, Missouri, and Colorado have been flattened more effectively by reservoir impoundments. The sediment rating curves at Yichang and Hankou changed from an oval‐like shape before 2013 to two overlapping and flattened lines in 2013–2017. This shift indicates that the rising and falling limbs of the hydrograph experienced similar sediment concentrations in 2013–2017 and suggests a less obvious hysteresis effect. Over 50% of the sediment load observed at the seaward‐most Datong station was contributed by the large tributaries in the upper reach, like Jinsha and Jialing rivers, before 2003. The channel scour downstream of TGD contributed 49%–64% of the Datong sediment load after 2003.

The greening of the Earth over the last decades is predominantly indicated by the enhancements of leaf area index (LAI). Quantifying the relative contribution of multiple determinants, especially changes in climate and in land management changes (LMC), remains an arduous challenge. To solve this problem, we develop a simple yet novel data‐driven method, called the Paired Land Use Experiment (PLUE), for mesoscale analysis. Using PLUE, we analyze vegetation development of the Sanjiang Plain, a transboundary plain between China and Russia, with roughly homogeneous climate but with distinct land management practices across the border‐intensified agricultural development on China side (CNSP) versus largely little‐disturbed natural vegetation on Russia side (RUSP). Both CNSP and RUSP LAI show significant trends (p < 0.05), with the annual variability reaching values of 9.8 × 10−3 yr−1 and 11.3 × 10−3 yr−1, respectively. However, in CNSP, the LAI increase is concentrated in the middle of the year, especially in five 8‐day periods from 26 June to 28 July. During this period, the LAI trend of CNSP is much higher than that of RUSP, at 92.7 × 10−3 yr−1 (p < 0.01) and 43.8 × 10−3 yr−1 (p < 0.01), respectively. Meanwhile, LAI decreased in CNSP at the begging and end of the growing season. The results show that different LMC practices lead to notably different seasonal variability in vegetation changes. The PLUE method offers a new potential tool in driver identification of vegetation greenness change based on observations. We argue for the necessity of parameterizing these different LMC in Earth system models.

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.