Three Gorges Reservoir (TGR) has provoked a series of unprecedented environmental problems, many of which are related to the transitional area between the base and capacity water levels of the ...reservoir, commonly referred to as the water-level fluctuation zone. We proposed here that this zone serves as a unique geomorphological unit playing an important role of influencing the life of the TGR and named it as the TGR disturbance zone because it has been intensively interrupted by various human activities. Based on its geomorphological characteristics, we divided this zone into three types that have hillside profiles with distinct shapes and gradients, grain components and sizes, and land uses. Although the three types of the zone have experienced the same set of geomorphological processes under the new annually cyclic hydrological regime, their responses and the associated morphologies are distinguishable. Thus we depicted them separately. Accompanied with these processes are geochemical and biological processes that bring about exacerbated pollution and destroyed ecosystem in this zone. We elaborated these problems and indicated the potential research directions toward fully understanding the complex geomorphological, geochemical, and biological processes prevalent in the zone.
Due to global warming, extreme climate events have become an important issue, and different geographical regions have different sensitivities to climate change. Therefore, temporal and spatial ...variations in extreme temperature and precipitation events in Inner Mongolia were analyzed based on the daily maximum temperature, minimum temperature, and precipitation data during the period of 1960–2017. The results showed that warm extreme indices, such as SU25, TX90p, TN90p, and WSDI, significantly increased, whereas the cold extreme indices, such as FD0, TX10p, TN10p, and CSDI, significantly decreased; all indices have obvious abrupt changes based on the Mann-Kendall test; nighttime warming was higher than daytime warming. Extreme precipitation indices slightly decreased overall. All of the extreme temperature and precipitation indices had long-range correlations based on detrended fluctuation analysis (a > 0.5), thereby indicating that the extreme climate indices will maintain their current trend directions in the future. ENSO, AO, and IOD had a strong positive influence on warm extremes and a strong negative influence on cold extremes in Inner Mongolia. NCEP/NCAR and ERA-20CM reanalysis showed that strengthening anticyclone circulation, increasing geopotential height, decreasing daytime cloudiness and increasing nightime cloudiness contributed to changes in climate extremes in Inner Mongolia.
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•Warm extreme indices significantly increased, while cold extreme indices decreased.•Nighttime warming was higher than daytime warming in Inner Mongolia.•Extreme temperature and precipitation events had long-range correlation based on DFA.•ENSO, AO, and IOD had positive (or negative) impacts on warm (or cold) extremes.
Surface evapotranspiration is a water exchange process between the atmosphere, biosphere, and hydrosphere. Accurate evapotranspiration estimations in arid and semi-arid regions are important for ...monitoring droughts and protecting the ecological environment. The main objective of this study is to build an evapotranspiration estimation model suitable for an effective scientific and objective evaluation of water consumption in the arid and semi-arid regions of the Xilin River Basin based on comprehensive parameters, including meteorological parameters, vegetation coverage, and soil water content. In this study, the community evapotranspiration model was initially constructed using field data, which was then expanded for applicability to the Xilin River Basin based on Geographic Information System technology and spatial heterogeneity characteristics of remote sensing data; both models were significant at the 0.05 level. The monthly evapotranspiration values in July during 2000-2017 and those from April to September (growing season) during the dry, normal, and wet years were calculated using the model at the basin scale. The evapotranspiration showed a generally increasing trend, which was consistent with the fluctuation trend in precipitation in July during 2000-2017. The trend curve for evapotranspiration was gentle during the growing season in dry years, but steep during wet years. The evapotranspiration was the lowest in April, with negligible spatial variations throughout the Xilin River Basin. During May-July, the evapotranspiration was higher than that in other months, in the following order: upper reaches > middle reaches > lower reaches; this was consistent with the vegetation coverage. The evapotranspiration declined and spatial variations were not evident during August-September. The results of this study provide a reference for evapotranspiration model construction and a scientific basis for evaluating regional water resources and protecting the ecological environment.
Inner Mongolia in China is a typically arid and semi-arid region with vegetation prominently affected by global warming and human activities. Therefore, investigating the past and future vegetation ...change and its impact mechanism is important for assessing the stability of the ecosystem and the ecological policy formulation. Vegetation changes, sustainability characteristics, and the mechanism of natural and anthropogenic effects in Inner Mongolia during 2000–2019 were examined using moderate resolution imaging spectroradiometer normalized difference vegetation index (NDVI) data. Theil–Sen trend analysis, Mann–Kendall method, and the coefficient of variation method were used to analyze the spatiotemporal variability characteristics and sustained stability of the NDVI. Furthermore, a trend estimation method based on a Seasonal Trend Model (STM), and the Hurst index was used to analyze breakpoints and change trends, and predict the likely future direction of vegetation, respectively. Additionally, the mechanisms of the compound influence of natural and anthropogenic activities on the vegetation dynamics in Inner Mongolia were explored using a Geodetector Model. The results show that the NDVI of Inner Mongolia shows an upward trend with a rate of 0.0028/year (p < 0.05) from 2000 to 2019. Spatially, the NDVI values showed a decreasing trend from the northeast to the southwest, and the interannual variation fluctuated widely, with coefficients of variation greater than 0.15, for which the high-value areas were in the territory of the Alxa League. The areas with increased, decreased, and stable vegetation patterns were approximately equal in size, in which the improved areas were mainly distributed in the northeastern part of Inner Mongolia, the stable and unchanged areas were mostly in the desert, and the degraded areas were mainly in the central-eastern part of Inner Mongolia, it shows a trend of progressive degradation from east to west. Breakpoints in the vegetation dynamics occurred mainly in the northwestern part of Inner Mongolia and the northeastern part of Hulunbuir, most of which occurred during 2011–2014. The future NDVI trend in Inner Mongolia shows an increasing trend in most areas, with only approximately 10% of the areas showing a decreasing trend. Considering the drivers of the NDVI, we observed annual precipitation, soil type, mean annual temperature, and land use type to be the main driving factors in Inner Mongolia. Annual precipitation was the first dominant factor, and when these four dominant factors interacted to influence vegetation change, they all showed interactive enhancement relationships. The results of this study will assist in understanding the influence of natural elements and human activities on vegetation changes and their driving mechanisms, while providing a scientific basis for the rational and effective protection of the ecological environment in Inner Mongolia.
In recent years, global warming and intense human activity have been responsible for significantly altering vegetation dynamics on the Mongolian Plateau. Understanding the long-term vegetation ...dynamics in this region is important to assess the impact of these changes on the local ecosystem. Long-term (1982–2015), satellite-derived normalized difference vegetation index (NDVI) datasets were used to analyse the spatio-temporal patterns of vegetation activities using linear regression and the breaks for additive season and trend methods. The links between these patterns and changes in temperature, precipitation (PRE), soil moisture (SM), and anthropogenic activity were determined using partial correlation analysis, the residual trends method, and a stepwise multiple regression model. The most significant results indicated that air temperature and potential evapotranspiration increased significantly, while the SM and PRE had markedly decreased over the past 34 years. The NDVI dataset included 71.16% of pixels showing an increase in temperature and evaporation during the growing season, particularly in eastern Mongolia and the southern border of the Inner Mongolia Autonomous region, China. The proportion indicating the breakpoint of vegetation dynamics was 71.34% of pixels, and the trend breakpoints mainly occurred in 1993, 2003, and 2010. The cumulative effects of PRE and SM in the middle period, coupled with the short-term effects of temperature and potential evapotranspiration, have had positive effects on vegetation greening. Anthropogenic factors appear to have positively impacted vegetation dynamics, as shown in 81.21% of pixels. We consider rapid economic growth, PRE, and SM to be the main driving factors in Inner Mongolia. PRE was the main climatic factor, and combined human and livestock populations were the primary anthropogenic factors influencing vegetation dynamics in Mongolia. This study is important in promoting the continued use of green projects to address environmental change in the Mongolian Plateau.
The response of vegetation to regional climate change was quantified between 1982 and 2010 in the Mongolian plateau by integrating the Advanced Very High Resolution Radiometer (AVHRR) Global ...Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI) (1982–2006) and the Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI (2000–2010). Average NDVI values for the growing season (April–October) were extracted from the AVHRR and MODIS NDVI datasets after cross-calibrating and consistency checking the dataset, based on the overlapping period of 2000–2006. Correlations between NDVI and climatic variables (temperature and precipitation) were analyzed to understand the impact of climate change on vegetation dynamics in the plateau. The results indicate that the growing-season NDVI generally exhibited an upward trend with both temperature and precipitation before the mid- or late 1990s. However, a downward trend in the NDVI with significantly decreased precipitation has been observed since the mid- or late 1990s. This is an apparent reversal in the NDVI trend from 1982 to 2010. Pixel-based analysis further indicated that the timing of the NDVI trend reversal varied across different regions and for different vegetation types. We found that approximately 66% of the plateau showed an increasing trend before the reversal year, whereas 60% showed a decreasing trend afterwards. The vegetation decline in the last decade is mostly attributable to the recent tendency towards a hotter and drier climate and the associated widespread drought stress. Monitoring precipitation stress and associated vegetation dynamics will be important for raising the alarm and performing risk assessments for drought disasters and other related natural disasters like sandstorms.
•The linkage of SSC to turbidity was examined against various rainfall conditions.•A significant positive liner correlation between SSC and turbidity was observed.•The linear regression slope ...displays a decreasing trend with larger rainfall intensity.•Rainfall pattern and sediment particle size may determine varied fitting performance.
Suspended sediment concentration (SSC) has been used as a key indicator in various environmental studies concerning, for example, ecological integrity, river morphology, aquatic and riverine biota, and the management of river and reservoir systems. River turbidity has been widely used as a proxy indicator of SSC. However, the relationship between the two parameters varies considerably under different conditions. The present study investigated the relationships between SSC and turbidity under different rainfall conditions in the Baisha River, a mountainous river located in southwestern China. Data were continuously collected on site from 2015 to 2021. The results showed a significant positive correlation between SSC and turbidity, with the median slope model exhibiting the best linear fit. In addition, the linear regression slopes varied significantly between different seasons and rainfall intensities in the following descending orders: dry season (1.05) > rainy season (0.97), no rainfall (1.14) > non-erosive rainfall (0.99) > erosive rainfall (0.95), and mild rainfall (1.05) > moderate rainfall (0.97) > heavy rainfall (0.93) > rainstorm (0.89). Overall, during the rainy season and under higher rainfall intensities, the regression slope was gentler. This study discussed the mechanisms through which rainfall conditions affected the SSC-turbidity relationship. It was shown that such a relationship was generally influenced by sediment transport in the runoff and river water flow flux, and the number of suspended particles as well as their size were considered as main factors. The results of this study revealed the impact of rainfall conditions on the SSC-turbidity relationship and provided a reference for the rapid assessment of the suspended sediment load in mountain river basins at high altitudes. In addition, based on the results obtained, it is expected that a new method for predicting event-based soil erosion can be applied in similar high-altitude mountain valleys.
A simple multi-linear regression model was developed for sea surface salinity, and tested against in situ measurements. The algorithm can be applied to in situ measured reflectance data with a root ...mean square error of 0.833psu (R2=0.64). The model was recalibrated based on remote sensing reflectance data derived from MERIS and from in situ salinity data, yielding a RMSE in modeled salinity (relative to in situ data) of 1.311psu. This recalibrated model can be applied to MODIS data following a linear correction. The spatio-temporal changes in sea surface salinity and in the influence of freshwater flow were analyzed using MERIS, MODIS and river discharge data. Sea surface salinity in Laizhou Bay, Bohai Bay and Liaodong Bay was fresher than that in Qinhuangdao, the central Bohai Sea and Bohai Strait. During 2004–2009, the temporal trends in sea surface salinity varied between sites. Salinity in most parts of the Bohai Sea was increasing. The Yellow River discharge was found to have the greatest influence on sea surface salinity near the river estuary, whereas it had only a weak impact on the salinity far from river mouth. More independent datasets are needed to improve the model and to gain a better understanding of processes controlling changes in sea surface salinity.
•A multi-linear regression model for sea surface salinity was developed and tested.•The model was recalibrated and then applied to MERIS and MODIS data.•The spatio-temporal changes of salinity and freshwater influence were analyzed.•The spatio-temporal patterns of sea surface salinity were complex in the Bohai Sea.•The changing trends of sea surface salinity at different sites were different.
•Soil erosion intensity in the LJRB decreasing during 1990–2020.•Substantial sediment deposited in the LJRB's cascade reservoirs.•Increased non-agricultural patches in LCTs reduce soil ...erosion.•Landscape spatial composition differentially influences soil erosion between LCTs.•Landscape composition and spatial configuration should be considered together.
Landscape heterogeneity, including compositional and spatial configuration, changes soil erosion and sediment yield (SESY) in watersheds by regulating hydrological processes. However, existing research has paid little attention to landscape composition. The Lower Jinsha River Basin (LJRB) has significant landscape heterogeneity and the highest sediment yields in the Yangtze River Basin, China. The spatially distributed soil erosion model WaTEM/SEDEM was used to evaluate the spatio-temporal changes in SESY in the LJRB from 1990 to 2020. Area information conservation method and semi-variogram were utilized to determine the optimal grain and extent size of landscape analysis. The landscape heterogeneity characteristics of the LJRB were analyzed for landscape composition and spatial configuration. Finally, statistical analysis was performed to determine the differences in SESY among different landscape composition types (LCTs) and to explore the relationship between landscape configuration and SESY. The optimal grain and extent size for analysis of the landscape pattern in the LJRB were 150 and 2100 m, respectively. Based on this, three types and ten subtypes of landscape composition were identified. Landscapes composed of forest, shrubs, grassland, and farmland were predominant in the study area, with a relatively even distribution of patches and minimum dominance of specific patch types. Additionally, the landscapes composed of forest, shrubs, grassland, farmland, and impervious surfaces were diverse, with the highest fragmentation. Landscapes comprising farmland and impervious surfaces occupied a minor proportion of the study area, but specific patches exhibited higher landscape connectivity. The magnitude of SESY in the LJRB gradually decreased during 1990–2020. Farmland and grassland were the land types with the highest magntidue of SESY in this region. Substantial sediment has been deposited in Xiangjiaba and Xiluodu reservoirs since they commenced operations. Increased proportions of farmland patches in some LCTs have significantly enhanced SESY. For LCTs dominated by farmland and impervious surfaces, reducing the dominance of the largest patch, decreasing patch aggregation, and increasing landscape-type diversity and evenness have helped mitigate SESY. The opposite is true for LCTs dominated by forests, shrubs, and grasslands. Collectively, the results of this study provide valuable progress in the capacity to support landscape restoration and comprehensive management of soil and water degradation in mountainous watersheds with similar characteristics to the LJRB.
•Soil aggregate stability was determined by wetting and wet-shaking pre-treatment.•Wet-shaking had the highest impacts on aggregate stability decrease than wetting.•ASI reduction caused by ...wet-shaking was negatively correlated with elevation.•CEC was the key internal factor in surface soil aggregate stability.
Soil aggregate stability is a key indicator of soil quality and susceptibility to water erosion. The water level fluctuation zone (WLFZ) of Three Gorges Reservoir (TGR) experiences hydraulic disturbances induced by rainfall, reservoir wave, and water-level fluctuation. Soil aggregate in this region has a unique mechanism of disintegration different from other terrestrial soils. The traditional methods of soil aggregate stability measurement cannot reveal the complex external factors of the soil in the WLFZ. In the present study, an attempt has been made to establish an approach mimicking the real situation in the WLFZ to deeply understand the effects of water movement and periodical wetting on soil aggregate stability in the WLFZ. The soil samples from different elevations were allowed to stay under wetting and wet-shaking conditions for 3 and 81 min, followed by a quantitative separation of disintegrated aggregates by wet-sieving. The mean differences between wetting and wet-shaking for the mean weight diameter (ΔMWD) were highly significant at all elevations for 81 min. Contrary, both treatments applied within a short time period disintegrated aggregates at the same magnitude. Additional to slaking, the kinetic energy applied to soil has induced a mechanical breakdown as a result of water movement. The difference of aggregate stability index (ASI) was highly significant among the elevations p < 0.001 and strongly significant between lower and upper elevations. The Cation Exchange Capacity (CEC) was the most predominant factor determining the stability of soil aggregates with r2 = 0.61, 0.65, 0.69 (p < 0.05) and r2 = 0.71, 0.7, 0.77 (p < 0.05) for ASI, GMD and MWD recorded after wetting and wet-shaking, respectively. Crucially, understanding different effects between arising impacts of water level fluctuations and periodical inundations on soil aggregate stability is a promise for future studies in areas experiencing similar conditions.
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