Does Anthropogenic Land Use Change Play a Role in Changes of Precipitation Frequency and Intensity over the Loess Plateau of China?

Climatic Warming-Induced Drought Stress Has Resulted in the Transition of Tree Growth Sensitivity from Temperature to Precipitation in the Loess Plateau of China

Ongoing climate warming poses significant threats to forest ecosystems, particularly in drylands. Here, we assess the intricate responses of tree growth to climate change across two warming phases (1910-1940 and 1970-2000) of the 20th century in the Loess Plateau of China. To achieve this, we analyzed a dataset encompassing 53 ring-width chronologies extracted from 13 diverse tree species, enabling us to discern and characterize the prevailing trends in tree growth over these warming phases. The difference in the primary contributors over two warming phases was compared to investigate the association of tree growth with climatic drivers. We found that the first warming phase exerted a stimulating effect on tree growth, with climate warming correlating to heightened growth rates. However, a contrasting pattern emerged in the second phase as accelerated drought conditions emerged as a predominant limiting factor, dampening tree growth rates. The response of tree growth to climate changed markedly during the two warming phases. Initially, temperature assumed a dominant role in driving the tree growth of growth season during the first warming phase. Instead, precipitation and drought stress became the main factors affecting tree growth in the second phase. This drought stress manifested predominantly during the early and late growing seasons. Our findings confirm the discernible transition of warming-induced tree growth in water-limited regions and highlight the vulnerability of dryland forests to the escalating dual challenges of heightened warming and drying. If the warming trend continues unabated in the Loess Plateau, further deterioration in tree growth and heightened mortality rates are foreseeable outcomes. Some adaptive forest managements should be encouraged to sustain the integrity and resilience of these vital ecosystems in the Loess Plateau and similar regions.

INDMF Based Regularity Calculation Method and Its Application in the Recognition of Typical Loess Landforms

The topographical morphology of the loess landform on the Loess Plateau exhibits remarkable textural features at different spatial scales. However, existing topographic texture analysis studies on the Loess Plateau are usually dominated by statistical characteristics and are missing structural characteristics. At the same time, there is a lack of regularity calculation methods for DEM digital terrain analysis. Taking the Loess Plateau as the study area, a regularity calculation method based on the improved normalized distance matching function (INDMF) is proposed and applied to the classification of a loess landform. The regularity calculation method used in this study (INDMF regularity) mainly includes two key steps. Step 1 calculates the INDMF sequence value and the peak and valley values for the terrain data. Step 2 calculates the significant peak and valley, constructs the significant peak and valley sequences, and then obtains the regularity using the normalised ratio value. The experimental results show that the proposed method has good anti-interference ability and can effectively extract the regularity of the main landform unit. Compared with previous methods, adding structural features (i.e., INDMF regularity) can effectively distinguish loess hill and loess ridge in the hilly and gully region. For the loess hill and loess ridge, the recognition rates of the proposed method are 84.62% and 92.86%, respectively. Combined with the existing topographic characteristics, the proposed INDMF regularity is a topographic structure feature extraction method that can effectively discriminate between loess hill and loess ridge areas on the Loess Plateau.

Climate Dynamics of the Spatiotemporal Changes of Vegetation NDVI in Northern China from 1982 to 2015

As an important part of a terrestrial ecosystem, vegetation plays an important role in the global carbon-water cycle and energy flow. Based on the Global Inventory Monitoring and Modeling System (GIMMS) third generation of Normalized Difference Vegetation Index (NDVI3g), meteorological station data, climate reanalysis data, and land cover data, this study analyzed the climate dynamics of the spatiotemporal variations of vegetation NDVI in northern China from 1982 to 2015. The results showed that growth season NDVI (NDVIgs) increased significantly at 0.006/10a (p < 0.01) in 1982–2015 on the regional scale. The period from 1982 to 2015 was divided into three periods: the NDVIgs increased by 0.026/10a (p < 0.01) in 1982–1990, decreased by −0.002/10a (p > 0.1) in 1990–2006, and then increased by 0.021/10a (p < 0.01) during 2006–2015. On the pixel scale, the increases in NDVIgs during 1982–2015, 1982–1990, 1990–2006, and 2006–2015 accounted for 74.64%, 85.34%, 48.14%, and 68.78% of the total area, respectively. In general, the dominant climate drivers of vegetation growth had gradually switched from solar radiation, temperature, and precipitation (1982–1990) to precipitation and temperature (1990–2015). For woodland, high coverage grassland, medium coverage grassland, low coverage grassland, the dominant climate drivers had changed from temperature and solar radiation, solar radiation and precipitation, precipitation and solar radiation, solar radiation to precipitation and solar radiation, precipitation, precipitation and temperature, temperature and precipitation. The areas controlled by precipitation increased significantly, mainly distributed in arid, sub-arid, and sub-humid areas. The dominant climate drivers for vegetation growth in the plateau climate zone or high-altitude area changed from solar radiation to temperature and precipitation, and then to temperature, while in cold temperate zone, changed from temperature to solar radiation. These results are helpful to understand the climate dynamics of vegetation growth, and have important guiding significance for vegetation protection and restoration in the context of global climate change.

Using GIS, Remote Sensing, and Machine Learning to Highlight the Correlation between the Land-Use/Land-Cover Changes and Flash-Flood Potential

The aim of the present study was to explore the correlation between the land-use/land cover change and the flash-flood potential changes in Zăbala catchment (Romania) between 1989 and 2019. In this regard, the efficiency of GIS, remote sensing and machine learning techniques in detecting spatial patterns of the relationship between the two variables was tested. The paper elaborated upon an answer to the increase in flash flooding frequency across the study area and across the earth due to the occurred land-use/land-cover changes, as well as due to the present climate change, which determined the multiplication of extreme meteorological phenomena. In order to reach the above-mentioned purpose, two land-uses/land-covers (for 1989 and 2019) were obtained using Landsat image processing and were included in a relative evolution indicator (total relative difference-synthetic dynamic land-use index), aggregated at a grid-cell level of 1 km2. The assessment of runoff potential was made with a multilayer perceptron (MLP) neural network, which was trained for 1989 and 2019 with the help of 10 flash-flood predictors, 127 flash-flood locations, and 127 non-flash-flood locations. For the year 1989, the high and very high surface runoff potential covered around 34% of the study area, while for 2019, the same values accounted for approximately 46%. The MLP models performed very well, the area under curve (AUC) values being higher than 0.837. Finally, the land-use/land-cover change indicator, as well as the relative evolution of the flash flood potential index, was included in a geographically weighted regression (GWR). The results of the GWR highlights that high values of the Pearson coefficient (r) occupied around 17.4% of the study area. Therefore, in these areas of the Zăbala river catchment, the land-use/land-cover changes were highly correlated with the changes that occurred in flash-flood potential.

Vegetation change in response to climate factors and human activities on the Mongolian Plateau

Background

Vegetation in the Mongolian Plateau is very sensitive to climate change, which has a significant impact on the regulation of terrestrial carbon cycle.

Methods

We analyzed spatio-temporal changes of both growing season and the seasonal Normalized Difference Vegetation Index (NDVI) using simple linear trend analysis. Besides, correlation analysis was applied to explore the climate factors’ effects on vegetation growth at temporal and spatial scale. Potential effects of human factors on vegetation growth were also explored by residual trend analysis.

Results

The results indicated that vegetation growth showed a greening trend in the Mongolian Plateau over the past 30 years. At the temporal scale, the growing season NDVI showed an insignificant increasing trend (at a rate of 0.0003 yr⁻¹). At the spatial scale, a large region (53.8% of the whole Mongolian Plateau) with an increasing growing season NDVI, was primarily located in the southern and northern parts of the plateau. The correlation analysis suggested that temperature and precipitation were the main limiting factors that affected vegetation growth in spring and the growing season, respectively. The residual trend analysis showed that human activities primarily stimulated the growth of grasslands and shrublands, while croplands displayed a decreasing trend due to human disturbances, implying that anthropogenic factors may lead to croplands abandonment in favor of grasslands restoration. Our results provided detailed spatial and temporal changes of vegetation growth, and explored how climate and human factors affected vegetation growth, which may offer baseline data and scientific suggestions for local land and resources management, and facilitate the sustainable development of the terrestrial ecosystems.