Impact of aridization on soil cover transformation of the Aral Sea and the modern Syr-Darya Delta

The effects of human activities on the soil cover transformation in the eastern part of Kazakhstan were investigated during the period of 1956-2008.The results of the research for different soil types in Priaralye indicated that there was 643.3×103 hm2 solonchaks,accounting for 38.5 % of the total area(1670.5×103 hm2) in 2008.Vast areas are occupied with dried lakeshore soil(311.1× 103 hm2),sandy soils(147.6×103 hm2) and grey-brown desert soils and solonetzes(146.7×103 hm2).In 2001 the area of solonchak was 755×103 hm2 and decreased to 643.3×103 hm2 in 2008,which due to the shrinkage of the Aral Sea,the areas of marsh and lakeshore solonchaks decreased with the increase of dried bottom of the Aral Sea.The level of soil cover transformation in the modern delta of the Syr-Darya River can be seen from the comparison of the results obtained from the different years in the study area.The area of solonchaks increased by 10×103 hm2 and the area of alluvial-meadow salinizied soils increased by 17.9×103 hm2 during the period of 1956-1969.It means that many non-salinizied soils were transformed into salinizied ones.Striking changes occurred in the structure of soil cover as a result of aridization.So,the researches in1969 significantly determined the areas of hydromorphic soils subjected to desertification(it was not fixed on the map before 1956).Later,these soils were transformed into takyr-like soils.The area of takyr-like soils increased almost by 3 times for 34 years(from 1956 to 1990).The long-term soil researches on soil cover transformation in Priaralye have shown that the tendencies of negative processes(salinization and deflation) are being kept and lead to further soil and eco-environment degradation in the region.     

Spatiotemporal variation in snow cover and its effects on grassland phenology on the Mongolian Plateau

Snow cover is an important water source for vegetation growth in arid and semi-arid areas,and grassland phenology provides valuable information on the response of terrestrial ecosystems to climate change.The Mongolian Plateau features both abundant snow cover resources and typical grassland ecosystems.In recent years,with the intensification of global climate change,the snow cover on the Mongolian Plateau has changed correspondingly,with resulting effects on vegetation growth.In this study,using MOD10A1 snow cover data and MOD13A1 Normalized Difference Vegetation Index(NDVI)data combined with remote sensing(RS)and geographic information system(GIS)techniques,we analyzed the spatiotemporal changes in snow cover and grassland phenology on the Mongolian Plateau from 2001 to 2018.The correlation analysis and grey relation analysis were used to determine the influence of snow cover parameters(snow cover fraction(SCF),snow cover duration(SCD),snow cover onset date(SCOD),and snow cover end date(SCED))on different types of grassland vegetation.The results showed wide snow cover areas,an early start time,a late end time,and a long duration of snow cover over the northern Mongolian Plateau.Additionally,a late start,an early end,and a short duration were observed for grassland phenology,but the southern area showed the opposite trend.The SCF decreased at an annual rate of 0.33%.The SCD was shortened at an annual rate of 0.57 d.The SCOD and SCED in more than half of the study area advanced at annual rates of 5.33 and 5.74 DOY(day of year),respectively.For grassland phenology,the start of the growing season(SOS)advanced at an annual rate of 0.03 DOY,the end of the growing season(EOS)was delayed at an annual rate of 0.14 DOY,and the length of the growing season(LOS)was prolonged at an annual rate of 0.17 d.The SCF,SCD,and SCED in the snow season were significantly positively correlated with the SOS and negatively correlated with the EOS and LOS.The SCOD was significantly negatively correlated with the SOS and positively correlated with the EOS and LOS.The SCD and SCF can directly affect the SOS of grassland vegetation,while the EOS and LOS were obviously influenced by the SCOD and SCED.This study provides a scientific basis for exploring the response trends of alpine vegetation to global climate change.     

Scenario simulation of water retention services under land use/cover and climate changes: a case study of the Loess Plateau,China

Comprehensive assessments of ecosystem services in environments under the influences of human activities and climate change are critical for sustainable regional ecosystem management. Therefore, integrated interdisciplinary modelling has become a major focus of ecosystem service assessment. In this study, we established a model that integrates land use/cover change (LUCC), climate change, and water retention services to evaluate the spatial and temporal variations of water retention services in the Loess Plateau of China in the historical period (2000-2015) and in the future (2020-2050). An improved Markov-Cellular Automata (Markov-CA) model was used to simulate land use/land cover patterns, and ArcGIS 10.2 software was used to simulate and assess water retention services from 2000 to 2050 under six combined scenarios, including three land use/land cover scenarios (historical scenario (HS), ecological protection scenario (EPS), and urban expansion scenario (UES)) and two climate change scenarios (RCP4.5 and RCP8.5, where RCP is the representative concentration pathway). LUCCs in the historical period (2000-2015) and in the future (2020-2050) are dominated by transformations among agricultural land, urban land and grassland. Urban land under UES increased significantly by 0.63×10³ km²/a, which was higher than the increase of urban land under HS and EPS. In the Loess Plateau, water yield decreased by 17.20×10⁶ mm and water retention increased by 0.09×10⁶ mm in the historical period (2000-2015), especially in the Interior drainage zone and its surrounding areas. In the future (2020-2050), the pixel means of water yield is higher under RCP4.5 scenario (96.63 mm) than under RCP8.5 scenario (95.46 mm), and the pixel means of water retention is higher under RCP4.5 scenario (1.95 mm) than under RCP8.5 scenario (1.38 mm). RCP4.5-EPS shows the highest total water retention capacity on the plateau scale among the six combined scenarios, with the value of 1.27×10⁶ mm. Ecological restoration projects in the Loess Plateau have enhanced soil and water retention. However, more attention needs to be paid not only to the simultaneous increase in water retention services and evapotranspiration but also to the type and layout of restored vegetation. Furthermore, urbanization needs to be controlled to prevent uncontrollable LUCCs and climate change. Our findings provide reference data for the regional water and land resources management and the sustainable development of socio-ecological systems in the Loess Plateau under LUCC and climate change scenarios.     

Spatial and temporal variations of vegetation cover and the relationships with climate factors in Inner Mongolia based on GIMMS NDVI3g data

Variation in vegetation cover in Inner Mongolia has been previously studied by the remote sensing data spanning only one decade. However, spatial and temporal variations in vegetation cover based on the newly released GIMMS NDVI3g data spanning nearly thirty years have yet to be analyzed. In this study, we applied the methods of the maximum value composite(MVC) and Pearson's correlation coefficient to analyze the variations of vegetation cover in Inner Mongolia based on GIMMS NDVI3g data spanning from 1982 to 2013. Our results indicate that the normalized difference vegetation index(NDVI) increased at a rate of 0.0003/a during the growing seasons despite of the drier and hotter climate in Inner Mongolia during the past three decades. We also found that vegetation cover in the southern agro-pastoral zone significantly increased, while it significantly decreased in the central Alxa. The variations in vegetation cover were not significant in the eastern and central regions. NDVI is positively correlated with precipitation(r=0.617, P=0.000) and also with air temperature(r=0.425, P=0.015), but the precipitation had a greater effect than the air temperature on the vegetation variations in Inner Mongolia.     

Impacts of snow on seed germination are independent of seed traits and plant ecological characteristics in a temperate desert of Central Asia

Seed germination profoundly impacts plant community composition within the plant life cycle. Snow is an important source of water for seed germination in the temperate deserts of Central Asia. Understanding how seed germination responds to variations in snow cover in relation to seed traits and plant ecological characteristics can help predict plant community sustainability and stability in Central Asia under a scenario climate change. This study investigated the seed germination of 35 plant species common to the Gurbantunggut Desert in Central Asia under the three snow treatments: (1) snow addition; (2) ambient snow; and (3) snow removal. Two-way analysis of variance (ANOVA) tests were performed to assess interactions among the impacts of snow treatments, seed traits and plant ecological characteristics on seed germination. Phylogenetic generalized least-squares (PGLS) model was used to test the relationships between seed traits and seed germination. The results demonstrated that snow variations had no significant impacts on seed germination overall. Seed germination under the snow addition treatment was similar with that under the ambient snow treatment, irrespective of seed traits and plant ecological characteristics. Snow removal only had negative impacts on seed germination for certain groups of seed traits and plant ecological characteristics. Seed mass positively affected seed germination, showing a linear increase of arcsin square root-transformed seed germination with log-transformed seed mass. Seed shape also profoundly impacted seed germination, with a higher germination percentage for elongated and flat seeds. Seed germination differed under different plant life forms, with semi-shrub species showing a significantly higher germination percentage. Most importantly, although snow treatments, seed traits and plant ecological characteristics had no interactive effects on seed germination overall, some negative impacts from the snow removal treatment were detected when seeds were categorized on the basis of seed mass and shape. This result suggests that variations of snow cover may change plant community composition in this temperate desert due to their impacts on seed germination.     

Effects of climate change and land use/cover change on the volume of the Qinghai Lake in China

Qinghai Lake is the largest saline lake in China.The change in the lake volume is an indicator of the variation in water resources and their response to climate change on the Qinghai-Tibetan Plateau(QTP)in China.The present study quantitatively evaluated the effects of climate change and land use/cover change(LUCC)on the lake volume of the Qinghai Lake in China from 1958 to 2018,which is crucial for water resources management in the Qinghai Lake Basin.To explore the effects of climate change and LUCC on the Qinghai Lake volume,we analyzed the lake level observation data and multi-period land use/land cover(LULC)data by using an improved lake volume estimation method and Integrated Valuation of Ecosystem Services and Trade-offs(InVEST)model.Our results showed that the lake level decreased at the rate of 0.08 m/a from 1958 to 2004 and increased at the rate of 0.16 m/a from 2004 to 2018.The lake volume decreased by 105.40×10⁸ m³ from 1958 to 2004,with the rate of 2.24×10⁸ m³/a,whereas it increased by 74.02×10⁸ m³ from 2004 to 2018,with the rate of 4.66×10⁸ m³/a.Further,the climate of the Qinghai Lake Basin changed from warm-dry to warm-humid.From 1958 to 2018,the increase in precipitation and the decrease in evaporation controlled the change of the lake volume,which were the main climatic factors affecting the lake volume change.From 1977 to 2018,the measured water yield showed anincrease-decrease-increasefluctuation in the Qinghai Lake Basin.The effects of climate change and LUCC on the measured water yield were obviously different.From 1977 to 2018,the contribution rate of LUCC was -0.76% and that of climate change was 100.76%;the corresponding rates were 8.57% and 91.43% from 1977 to 2004,respectively,and -4.25% and 104.25% from 2004 to 2018,respectively.Quantitative analysis of the effects and contribution rates of climate change and LUCC on the Qinghai Lake volume revealed the scientific significance of climate change and LUCC,as well as their individual and combined effects in the Qinghai Lake Basin and on the QTP.This study can contribute to the water resources management and regional sustainable development of the Qinghai Lake Basin.     

Impact of land use/land cover types on surface humidity in northern China in the early 21st century

In the context of global change, it is essential to promote the rational development and utilization of land resources, improve the quality of regional ecological environment, and promote the harmonious development of human and nature for the regional sustainability. We identified land use/land cover types in northern China from 2001 to 2018 with ENVI images and ArcGIS software. Meteorological data were selected from 292 stations in northern China, the potential evapotranspiration was calculated with the Penman-Monteith formula, and reanalysis humidity and observed humidity data were obtained. The reanalysis minus observation (RMO, i.e., the difference between reanalysis humidity and observed humidity) can effectively characterize the impact of different land use/land cover types (forestland, grassland, cultivated land, construction land, water body and unused land) on surface humidity in northern China in the early 21^st century. The results showed that from 2001 to 2018, the area of forestland expanded (increasing by approximately 1.80×10⁴ km²), while that of unused land reduced (decreasing by approximately 5.15×10⁴ km²), and the regional ecological environment was improved. Consequently, land surface in most areas of northern China tended to be wetter. The contributions of land use/land cover types to surface humidity changes were related to the quality of the regional ecological environment. The contributions of the six land use/land cover types to surface humidity were the highest in northeastern region of northern China, with a better ecological environment, and the lowest in northwestern region, with a fragile ecological environment. Surface humidity was closely related to the variation in regional vegetation coverage; when the regional vegetation coverage with positive (negative) contributions expanded (reduced), the land surface became wetter. The positive contributions of forestland and water body to surface humidity were the greatest. Unused land and construction land were associated with the most serious negative contributions to surface humidity. Affected by the regional distribution pattern of vegetation, surface humidity in different seasons decreased from east to west in northern China. The seasonal variation in surface humidity was closely related to the growth of vegetation: surface humidity was the highest in summer, followed by autumn and spring, and the lowest in winter. According to the results, surface humidity is expected to increase in northeastern region of northern China, decrease in northern region, and likely increase in northwestern region.     

Effects of agriculture,climate, and policy on NDVI change in a semi-arid river basin of the Chinese Loess Plateau

In 1999, the Grain for Green Project was implemented by the Chinese government. Since then, the vegetation of Zuli River Basin, a semi-arid river basin of the Chinese Loess Plateau, has been greatly changed. Clearly understanding the impact of natural and artificial factors on vegetation change is important for policy making and ecosystem management. In this study, spatio-temporal variations in vegetation cover in Chinese Zuli River Basin during 1999–2016 were investigated using Landsat normalized difference vegetation index (NDVI) data. Analyses of several indicators, including changes in NDVI in different slopes and land use changes and the relationships between climatic factors and NDVI change, were presented to quantitatively evaluate the effects of agriculture, climate, and policy on NDVI change. The NDVI in the Zuli River Basin increased during the study period, and the main contributors to this change were forest in 1999–2011, cropland, abandoned farmland, and grassland in 2009–2016, and land with slopes ≤ 15°. Land with slope > 15°, where the “Project” was implemented, slightly contributed to the increase in regional NDVI. In 1999–2011, the project (?98.16%) combined with climate change (?68.18%) showed negative effects on the increase in NDVI in the Zuli River Basin, but agriculture (22.28%) played a positive role in increasing this index. In 2009–2016 and 1999–2016, the project (38.45% and 35.25%, respectively), the project combined with climate change (49.83% and 46.30%, respectively), agriculture (18.61% and 23.30%, respectively), promoted increases in NDVI in the basin.     

Modelling the impact of climate change on rangeland forage production using a generalized regression neural network: a case study in Isfahan Province,Central Iran

Monitoring of rangeland forage production at specified spatial and temporal scales is necessary for grazing management and also for implementation of rehabilitation projects in rangelands. This study focused on the capability of a generalized regression neural network(GRNN) model combined with GIS techniques to explore the impact of climate change on rangeland forage production. Specifically, a dataset of 115 monitored records of forage production were collected from 16 rangeland sites during the period 1998–2007 in Isfahan Province, Central Iran. Neural network models were designed using the monitored forage production values and available environmental data(including climate and topography data), and the performance of each network model was assessed using the mean estimation error(MEE), model efficiency factor(MEF), and correlation coefficient(r). The best neural network model was then selected and further applied to predict the forage production of rangelands in the future(in 2030 and 2080) under A1 B climate change scenario using Hadley Centre coupled model. The present and future forage production maps were also produced. Rangeland forage production exhibited strong correlations with environmental factors, such as slope, elevation, aspect and annual temperature. The present forage production in the study area varied from 25.6 to 574.1 kg/hm~2. Under climate change scenario, the annual temperature was predicted to increase and the annual precipitation was predicted to decrease. The prediction maps of forage production in the future indicated that the area with low level of forage production(0–100 kg/hm~2) will increase while the areas with moderate, moderately high and high levels of forage production(≥100 kg/hm~2) will decrease both in 2030 and in 2080, which may be attributable to the increasing annual temperature and decreasing annual precipitation. It was predicted that forage production of rangelands will decrease in the next couple of decades, especially in the western and southern parts of Isfahan Province. These changes are more pronounced in elevations between 2200 and 2900 m. Therefore, rangeland managers have to cope with these changes by holistic management approaches through mitigation and human adaptations.