Land resources are closely intertwined with human survival, making it crucial to explore the spatiotemporal changes and driving forces of land use. In this study, the Yan River Basin in the Loess Plateau was selected as the study area. The Mann–Kendall trend test, Pettitt’s test, landscape pattern indices, and other methods were employed to explore characteristics and driving factors of land use change from 1990 to 2020. The results indicate that: (1) The areas of forest and impervious showed a significant increasing trend and suddenly changed in 2004. Change-point years for the area of cropland and grassland were 2005 and 1999, respectively. The main transition of land use types was from cropland to grassland and from grassland to forest. (2) Patches showed a trend towards regularization and simplification, indicating the strengthening of human activities’ impact on spatial patterns. (3) Both social and natural factors jointly influenced land use change in the Yan River Basin. The Grain for Green (GfG) project was the main driving factor in accelerating land use transformation. This study aims to provide a basis for the scientific management of land resources and serve as an example for related research on land use change in the Loess Plateau.
Abrupt runoff reduction in the Wei River Basin (WRB) has attracted extensive attention owing to climate change and human activities. Nevertheless, previous studies have inadequately assessed the respective contributions of climate variability and human activities to runoff change on different spatial scales. Using Mann–Kendall and Pettitt’s methods, this study identified long-term (1970–2018) changes in hydro-meteorological variables. Furthermore, the Budyko-based method was used to quantify the influence of climate change and human activities on runoff change at different spatial scales of the WRB, including the whole WRB, three sub-basins, and sixteen catchments. The results show that a significant decrease trend was identified in runoff at different spatial scales within the WRB. Runoff in almost all catchments showed a significant downward trend. Temperature, potential evapotranspiration, and the parameter n showed significant increases, whereas no significant trend in precipitation was observed. The change in runoff was mainly concentrated in the mid-1990s and early 2000s. Anthropogenic activities produced a larger impact on runoff decrease in the WRB (62.8%), three sub-basins (53.9% to 65.8%), and most catchments (–47.0% to 147.3%) than climate change. Dramatic catchment characteristic changes caused by large-scale human activities were the predominant reason of runoff reduction in the WRB. Our findings provide a comprehensive understanding of the dominate factors causing runoff change and contribute to water resource management and ecosystem health conservation in the WRB.
Vegetation greenness plays a crucial role in assessing vegetation dynamics and evaluating the effectiveness of ecological governance on the Loess Plateau. However, the limited spatial resolution of remote sensing products poses challenges in capturing local vegetation characteristics and the spatiotemporal variation of vegetation greenness lines. In this study, we utilized the Google Earth Engine (GEE) platform and Landsat images to investigate the spatiotemporal evolution of vegetation greenness and the shift of vegetation greenness lines (VGL) during the growing season on the Loess Plateau from 1987 to 2020. The results show that the average annual growth rates of NDVI and EVI in the Loess Plateau growing season from 1987 to 2020 were 0.0042/year (P<0.01) and 0.0023/year (P<0.01), respectively, with the growth rate after 2000 being three to four times higher. Spatially, significant and extremely significant increases in NDVI and EVI were observed, covering 78.8% and 69.2% of the total area of the Loess Plateau, respectively. Furthermore, the VGLNDVI and VGLEVI on the Loess Plateau in 1987-2020 experienced a northward shift at rates of 5.5 km/year and 4.6 km/year, respectively, resulting in an average movement of 173.9 km and 131.6 km. The most significant shifts occurred between 2005 and 2010. The prolongation of the growing season due to climate warming and humidification, coupled with the “Grain for Green” Project, played significant roles in the increase in vegetation greenness and the northward shift of the VGL on the Loess Plateau. Our study provides valuable insights into the spatiotemporal dynamics of vegetation greenness and the shift of vegetation greenness lines, contributing to improved ecological management and land restoration efforts on the Loess Plateau.
Understanding the spatial patterns and driving mechanisms of net primary productivity (NPP) and precipitation utilization efficiency (PUE) is crucial for assessing ecosystem services. This study analyzed the variations in NPP and PUE in Heilongjiang Province from 2001 to 2020, using MOD17A3 NPP products and meteorological, topographic, and land use data. The distribution of the NPP and PUE of seven land use categories was determined in the study, namely, cropland, forest, grassland, water, barren, impervious and wetland. The multi-year spatial averages for NPP and PUE were 428.96 gC·m−2·a−1 and 0.74 gC·m−2·mm−1, respectively, with forests showing the highest values and barren lands the lowest. During the study period, 91.4% of the NPP increased at an average rate of 3.36 gC·m−2·a−1, while PUE exhibited a polarized trend. Changes in land use, especially conversions involving cropland and forest, along with climatic factors such as rising precipitation and temperature, significantly influenced NPP and PUE dynamics. These findings provide a scientific basis for ecological restoration and the assessment of ecosystem function under changing climatic conditions.
Understanding the impact of climate change and human activities on runoff is crucial for water resources management. However, an evaluation of available methods for analysing this impact is lacking. In this study, we comprehensively reviewed four commonly used quantitative methods: the Soil and Water Assessment Tool (SWAT) model, Budyko-based approach, and two empirical methods, i.e., Double mass curve (DMC) and Modified DMC (MDMC). Using the Wei River basin as a case study, we assessed the runoff reduction influenced by climate change and human activities from 1970 to 2017. The results show that human activities are the primary driver for runoff reduction. The highest contribution of human activities was estimated by the DMC (93.2%–99.9%), followed by MDMC and SWAT (65.6%–87.1%), while the Budyko-based had the smallest estimates (55.3%–61.2%). Each method has advantages and limitations, so the appropriate method should be selected based on research objectives and data availability/quality.
Abstract Accurate vegetation cover data are important for realistic simulation of regional climate. The default vegetation parameters from Global Land Cover 2000, currently incorporated into global climate models and used in regional climate model RegCM, are not realistic for China, which may have contributed to serious bias in surface climate simulation. In this study, a new set of vegetation parameters considering the Plant Functional Type (PFT) fractions and the corresponding monthly leaf area index (PFT_LAI), were developed based on the land cover and MODIS LAI data sets. The regional climate model RegCM4.5 coupled with the land surface model CLM4.5 were utilized to test the performance of the new vegetation parameters by comparing simulations with observations using different surface parameters. The surface energy balance was analysed to examine the effects of changed vegetation parameters on regional climate. The results showed that the new parameters were more accurate than the GLC2000 parameters when describing the distribution of crops, grassland, and forests over China. The improved vegetation parameters reduced model biases for winter air temperature and precipitation over southern China by 0.9°C and 8%, respectively, and reduced the winter temperature and summer precipitation biases over northeastern China by approximately 0.7°C and 8%, respectively. More accurate surface albedo are the main reasons for reductions in model bias. However, certain biases, such as the cold and dry bias over the Tibetan Plateau, still remained in the simulation results using our new vegetation data.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)