Vertical difference of climate change impacts on vegetation at temporal-spatial scales in the upper stream of the Mekong River Basin

Integrated nexus approach to assessing climate change impacts on grassland ecosystem dynamics: A case study of the grasslands in Tanzania

This study addresses the intricate interplay between climate, vegetation, and livestock dynamics in Tanzania within the Climate-Vegetation-Livestock (CVL) nexus through a quantitative assessment. By examining the temporal and spatial relationships between vegetation indices (NDVI, EVI, NPP) and key climatic variables (Precipitation, Temperature, Evapotranspiration) from 2009 to 2019, and projecting to 2050, this research aims to elucidate vegetation responses to climate change and its subsequent impacts on livestock. To this end, the relationship between the vegetation dynamics indicators (NDVI, NPP) and climate parameters is evaluated to quantify the vegetation response to climate change using statistical models. Next, an examination of multicollinearity is conducted to investigate potential interactions (nexus) between variables, incorporating the correlation among independent variables. Notably, the evaluation of performance and accuracy for the mentioned models is conducted through the cross-validation method and validation indices. Ultimately, the variation between projected NPP and NDVI (average for 2040-2060) and the present NPP and NDVI (average for 2009-2020) identifies the regions that are most likely susceptible, showcasing the vegetation cover's reaction to climate change in different emission scenarios. The results unveil significant spatio-temporal variations in vegetation dynamics influenced by climatic factors, where higher precipitation and temperatures correlate with increased vegetation health and productivity. The projected fluctuations in NDVI and NPP values indicate varying trends across different regions, with a general decrease in vegetation density and productivity from the northeast to the west under both RCP2.6 and RCP8.5 scenarios by 2050. This decline is attributed to anticipated changes in precipitation and temperature patterns driven by climate change. Furthermore, significant declines in vegetation density and productivity under emission scenarios, particularly in the southern regions compared to the present, suggest greater vulnerability to climate change impacts. This highlights the need for targeted mitigation strategies in these vulnerable areas. Meanwhile, northeast areas under both NDVI and NPP will remain unchanged across both climate scenarios. Moreover, analysis of livestock distribution maps indicates areas of vulnerability under climate change scenarios, with implications for future livestock management and agricultural practices. These findings underscore the importance of proactive planning and targeted interventions to enhance resilience and sustainable development in vulnerable regions, emphasizing the need for integrated approaches that consider the complex interactions between climate, vegetation, and livestock dynamics.

Enhanced release, export, and transport of diffuse nutrients from litter in forested watersheds with climate warming

Variations in litter decomposition and nutrient migration are constraints to accurately estimate watershed diffuse forest pollution under the combined effects of topographic heterogeneity and climate change. In this study, remote sensing data, decomposition and leaching experiments, and the Soil and Water Assessment Tool (SWAT) were used to quantify the release, export, and transport characteristics of diffuse nutrients from forest litter under two climate scenarios (the current climate condition [S1] and the future warming and drying climate condition [S2]), and the impacts on aquatic environment were identified. The annual litter decomposition was 27.80 × 10⁶ t in S2, which was 1.39 times that of S1. Additionally, the annual litter nutrient release in S2 (C, N, and P was 8.65 × 10⁶, 3.31 × 10⁵, and 1.57 × 10⁴ t, respectively) also increased by 31.16%-45.62% compared with that of S1. The spatial patterns of nutrient export showed that the annual exports of C, N, and P in S1 were 109.77, 46.85, and 0.43 kg/ha, respectively. The annual nutrient export in S2 increased by 1.44 times, and S2 also had higher values of nutrient transport. In addition, variation trends of temperature and precipitation increased significantly with increasing altitude, which promoted differences in nutrient transport between S1 and S2 in the high-altitude areas. The response analysis of the diffuse nutrient in surface water also indicated that forest nutrient discharge load were critical factors affecting the aquatic environmental quality. This study indicated that climate warming accelerated litter decomposition and made litter a potential source of diffuse forest pollution, and watershed discharge load varied intensively with the terrestrial conditions. The combination of experiments and modeling can improve the accuracy of diffuse forest pollution simulation and provide valuable information for formulating watershed climate change adaptation strategies.

Temporal Spatial Mutations of Soil Erosion in the Middle and Lower Reaches of the Lancang River Basin and Its Influencing Mechanisms

As a major threat to ecosystem functions and national food security, soil erosion also exerts an influence on the water quality in basins and the operation and maintenance of hydropower plants. Existing discussions about trends of soil erosion focus mainly on its variation and mutation over time. Few studies have addressed the spatial mutation of soil erosion and its influence mechanism. In this research, Sen’s slope estimation was coupled with a Mann–Kendall model to explore the spatiotemporal distribution, spatial mutation characteristics and influence mechanisms of soil erosion, and conduct a case study on the Middle and Lower reaches of the Lancang River Basin (ML-LRB) in China. There are three main conclusions from this study: (1) During 2000–2019, the annual soil erosion in the ML-LRB variation ranged from 0 to 7.00 × 103 t/(km2·a) with a multi-year mean of 1.53 × 103 t/(km2·a), decreasing year by year from north to south, while an increasing trend began to appear in the central above region after 2015. (2) The areas with decreased soil erosion were much larger than those with increased soil erosion during 2000–2019, and there was a concentrated increase in soil erosion in Dali and in Xishuangbanna. (3) The mutation of the soil erosion intensity was spatially consistent with that of the Normalized Difference Vegetation Index (NDVI). Overall, this paper provides a new perspective for the study of factors affecting the trends and spatial mutation of soil erosion.

Spatiotemporal dynamics of vegetation in China from 1981 to 2100 from the perspective of hydrothermal factor analysis

The increased growth of vegetation has the potential to slow global climate warming. Therefore, analyzing and predicting the response assessment of Chinese vegetation to climate change is of great significance to studies of global warming. In this paper, we examine the spatiotemporal dynamics of vegetation leaf area index (LAI) values in China from 1981 to 2017 and their correlations with meteorological (hydrothermal) factors based on trend analysis and correlation analysis. We further construct an LAI prediction model based on hydrothermal conditions. The climate data obtained under different scenarios in the CMIP5 and CMIP6 climate models were used to predict the dynamic change trend of vegetation LAI from 2021 to 2100. The results show that most areas of China (72.82%) showed an improving trend in vegetation LAI from 1981 to 2017, during which the annual average LAI value increased at a rate of 0.0029 year-1. Vegetation LAI in China was significantly correlated with climatic factors (temperature, precipitation, and evapotranspiration), and the LAI prediction model constructed based on hydrothermal conditions had a high accuracy (Pearson's Cor value is 0.9729). From 2021 to 2100, approximately 2/3 of China's vegetation LAI area showed an improvement trend, and the impact of climate change on vegetation LAI predictions under the high emission scenario was greater than that under the low emission scenario. This research can provide a basis for studies on the climatic drivers of vegetation change and the global vegetation dynamic model.

Quantitative Contributions of Climate Change and Human Activities to Vegetation Changes in the Upper White Nile River

Vegetation changes in the Upper White Nile River (UWNR) are of great significance to the maintenance of local livelihoods, the survival of wildlife, and the protection of species habitats. Based on the GIMMS NDVI3g and MODIS normalized difference vegetation index (NDVI) data, the temporal and spatial characteristics of vegetation changes in the UWNR from 1982 to 2020 were analyzed by a Theil-Sen median trend analysis and Mann-Kendall test. The future trend of vegetation was analyzed by the Hurst exponential method. A partial correlation analysis was used to analyze the relationship of the vegetation and climate factors, and a residual trend analysis was used to quantify the influence of climate change and human activities on vegetation change. The results indicated that the average NDVI value (0.75) of the UWNR from 1982 to 2020 was relatively high. The average coefficient of variation for the NDVI was 0.059, and the vegetation change was relatively stable. The vegetation in the UWNR increased 0.013/10 year on average, but the vegetation degradation in some areas was serious and mainly classified as agricultural land. The results of a future trend analysis showed that the vegetation in the UWNR is mainly negatively sustainable, and 62.54% of the vegetation will degrade in the future. The NDVI of the UWNR was more affected by temperature than by precipitation, especially on agricultural land and forestland, which were more negatively affected by warming. Climate change and human activities have an impact on vegetation changes, but the spatial distributions of the effects differ. The relative impact of human activities on vegetation change accounted for 64.5%, which was higher than that of climate change (35.5%). Human activities, such as the large proportion of agriculture, rapid population growth and the rapid development of urbanization were the main driving forces. Establishing a cross-border drought joint early warning mechanism, strengthening basic agricultural research, and changing traditional agricultural farming patterns may be effective measures to address food security and climate change and improve vegetation in the UWNR.

Projecting Future Vegetation Change for Northeast China Using CMIP6 Model

Northeast China lies in the transition zone from the humid monsoonal to the arid continental climate, with diverse ecosystems and agricultural land highly susceptible to climate change. This region has experienced significant greening in the past three decades, but future trends remain uncertain. In this study, we provide a quantitative assessment of how vegetation, indicated by the leaf area index (LAI), will change in this region in response to future climate change. Based on the output of eleven CMIP6 global climates, Northeast China is likely to get warmer and wetter in the future, corresponding to an increase in regional LAI. Under the medium emissions scenario (SSP245), the average LAI is expected to increase by 0.27 for the mid-century (2041–2070) and 0.39 for the late century (2071–2100). Under the high emissions scenario (SSP585), the increase is 0.40 for the mid-century and 0.70 for the late century, respectively. Despite the increase in the regional mean, the LAI trend shows significant spatial heterogeneity, with likely decreases for the arid northwest and some sandy fields in this region. Therefore, climate change could pose additional challenges for long-term ecological and economic sustainability. Our findings could provide useful information to local decision makers for developing effective sustainable land management strategies in Northeast China.

Spatiotemporal nexus between vegetation change and extreme climatic indices and their possible causes of change

Climate extremes have a significant impact on vegetation. However, little is known about vegetation response to climatic extremes in Bangladesh. The association of Normalized Difference Vegetation Index (NDVI) with nine extreme precipitation and temperature indices was evaluated to identify the nexus between vegetation and climatic extremes and their associations in Bangladesh for the period 1986-2017. Moreover, detrended fluctuation analysis (DFA) and Morlet wavelet analysis (MWA) were employed to evaluate the possible future trends and decipher the existing periodic cycles, respectively in the time series of NDVI and climate extremes. Besides, atmospheric variables of ECMWF ERA5 were used to examine the casual circulation mechanism responsible for climatic extremes of Bangladesh. The results revealed that the monthly NDVI is positively associated with extreme rainfall with spatiotemporal heterogeneity. Warm temperature indices showed a significant negative association with NDVI on the seasonal scale, while precipitation and cold temperature extremes showed a positive association with yearly NDVI. The DEA revealed a continuous increase in temperature extreme in the future, while no change in precipitation extremes. NDVI also revealed a significant association with extreme temperature indices with a time lag of one month and with precipitation extreme without time lag. Spatial analysis indicated insensitivity of marshy vegetation type to climate extremes in winter. The study revealed that elevated summer geopotential height, no visible anticyclonic center, reduced high cloud cover, and low solar radiation with higher humidity contributed to climatic extremes in Bangladesh. The nexus between NDVI and climatic extremes established in this study indicated that increasing warm temperature extremes due to global warming might have severe implications on Bangladesh's ecology and the environment in the future.

Driving Factor Analysis of Ecosystem Service Balance for Watershed Management in the Lancang River Valley, Southwest China

Revealing the spatio-temporal change of the supply, demand and balance of ecosystem services (ESs) associated with human activities and land-use changes is of great significance for watershed ecosystem management. Taking the Lancang river valley as a case, we explicitly studied the ES spatial characteristics, using the land use/land cover (LULC) matrix model, Optimized Hot Spot Analysis and landscape pattern analysis. Furthermore, we screened out the dominant explanatory variables that had significant influence on the ES supply, demand and balance by means of the Geographical Weighted Regression (GWR) method at pixel scale. The results showed that the ES demand intensity varied little throughout the watershed, while the downstream ES supply capacity and balance values were greater than upstream ones. Meanwhile, the hotspots of ES supply and demand were mainly distributed in the south part with coldspots in the north part. Human activity factors integrating landscape pattern variables were verified to have a negative impact on the ES balance in general. Among them, the Largest Patch Index (LPI) had a negative influence on the majority of pixels, while the Gross Domestic Product (GDP), cultivated land ratio and Area Weighted Average Patch Fractal Dimension (AWAPFD) had positive effects on a few pixels. This study will provide scientific support for regional ecosystem service trade-off and regulation at multiple scales.

Integrating hydrological, landscape ecological, and economic assessment during hydropower exploitation in the upper Yangtze River

Comprehensive investigation of hydrological processes associated with landscape ecology and economic development plays a key role in watershed management, and is less developed in watersheds with large-scale cascade dams. With the abundant hydropower resources and its unprecedented advantages, hydropower exploitation in the upper Yangtze River (Jinsha River) is critical to energy structure adjustment in China. Therefore, we integrated hydrological modeling, landscape ecology analysis, and economic analysis in the dammed Jinsha River. With climate variations in the Jinsha River Basin, the average flow near the uppermost dams in the mainstream grew from 796 m³ s^-1 (1990s), to 918 m³ s^-1 (2000s), and further to 1025 m³ s^-1 (2010s). During 1991 to 2017, the source power in the headwater region grew slightly, but varied little in the downstream area. In the lower dammed Jinsha River, analysis of landscape indicators showed that the landscape was enriched, while the landscape type distribution was more uniform. Moreover, hydropower exploitation brought benefits to regional economic development. Principal component analysis further highlighted the landscape ecological and economic variations with high loadings in the first principal component. With the non-significant temporal variations and normal spatial fluctuations in flow discharge, the landscape pattern was basically stable, and the utilization of hydropower can be sustainable in the Jinsha River. In addition, hydropower development drove local economic development. Based on the integrated analysis of hydrological, landscape ecological, and economic assessment at the watershed scale, our results stressed the significance of hydropower exploitation in the Jinsha River. However, more attention should be paid to the warming climate during hydropower exploitation. These findings are valuable for the scientific planning of hydropower bases in watersheds with large-scale cascade dams, and have substantial implications for sustainable hydropower development.

Determining the Distribution and Interaction of Soil Organic Carbon, Nitrogen, pH and Texture in Soil Profiles: A Case Study in the Lancangjiang River Basin, Southwest China

The profile distributions of soil organic carbon (SOC), soil organic nitrogen (SON), soil pH and soil texture were rarely investigated in the Lancangjiang River Basin. This study aims to present the vertical distributions of these soil properties and provide some insights about how they interact with each other in the two typical soil profiles. A total of 56 soil samples were collected from two soil profiles (LCJ S-1, LCJ S-2) in the Lancangjiang River Basin to analyze the profile distributions of SOC and SON and to determine the effects of soil pH and soil texture. Generally, the contents of SOC and SON decreased with increasing soil depth and SOC contents were higher than SON contents (average SOC vs. SON content: 3.87 g kg−1 vs. 1.92 g kg−1 in LCJ S-1 and 5.19 g kg−1 vs. 0.96 g kg−1 in LCJ S-2). Soil pH ranged from 4.50 to 5.74 in the two soil profiles and generally increased with increasing soil depth. According to the percentages of clay, silt, and sand, most soil samples can be categorized as silty loam. Soil pH values were negatively correlated with C/N ratios (r = −0.66, p < 0.01) and SOC contents (r = −0.52, p < 0.01). Clay contents were positively correlated with C/N ratios (r = 0.43, p < 0.05) and SOC contents (r = 0.42, p < 0.01). The results indicate that soil pH and clay are essential factors influencing the SOC spatial distributions in the two soil profiles.