Integrating Remotely Sensed Leaf Area Index with Biome-BGC to Quantify the Impact of Land Use/Land Cover Change on Water Retention in Beijing

Spatio-temporal dynamics of land use transitions associated with human activities over Eurasian Steppe: Evidence from improved residual analysis

We presented a framework to evaluate the land use transformations over the Eurasian Steppe (EUS) driven by human activities from 2000 to 2020. Framework involves three main components: (1) evaluate the spatial-temporal dynamics of land use transitions by utilizing the land change modeler (LCM) and remote sensing data; (2) quantifying the individual contributions of climate change and human activities using improved residual trend analysis (IRTA) and pixel-based partial correlation coefficient (PCC); and (3) quantifying the contributions of land use transitions to Leaf Area Index Intensity (LAII) by using the linear regression. Research findings indicate an increase in cropland (+1.17 % = 104,217 km2) over EUS, while a - 0.80 % reduction over Uzbekistan and - 0.16 % over Tajikistan. From 2000 to 2020 a slight increase in grassland was observed over the EUS region by 0.05 %. The detailed findings confirm an increase (0.24 % = 21,248.62 km2) of grassland over the 1st half (2000-2010) and a decrease (-0.19 % = -16,490.50 km2) in the 2nd period (2011-2020), with a notable decline over Kazakhstan (-0.54 % = 13,690 km2), Tajikistan (-0.18 % = 1483 km2), and Volgograd (-0.79 % = 4346 km2). Area of surface water bodies has declined with an alarming rate over Kazakhstan (-0.40 % = 10,261 km2) and Uzbekistan (-2.22 % = 8943 km2). Additionally, dominant contributions of human activities to induced LULC transitions were observed over the Chinese region, Mongolia, Uzbekistan, and Volgograd regions, with approximately 87 %, 83 %, 92 %, and 47 %, respectively, causing effective transitions to 12,997 km2 of cropland, 24,645 km2 of grassland, 16,763 km2 of sparse vegetation in China, and 12,731.2 km2 to grassland and 15,356.1 km2 to sparse vegetation in Mongolia. Kazakhstan had mixed climate-human impact with human-driven transitions of 48,568 km2 of bare land to sparse vegetation, 27,741 km2 to grassland, and 49,789 km2 to cropland on the eastern sides. Southern regions near Uzbekistan had climatic dominancy, and 8472 km2 of water bodies turned into bare soil. LAII shows an increasing trend rate of 0.63 year-1, particularly over human-dominant regions. This study can guide knowledge of oscillations and reduce adverse impacts on ecosystems and their supply services.

The impact of ecological restoration on ecosystem services change modulated by drought and rising CO2

Ecological restoration projects (ERPs) are an indispensable component of natural climate solutions and have proven to be very important for reversing environmental degradation in vulnerable regions and enhancing ecosystem services. However, the level of enhancement would be inevitably influenced by global drought and rising CO2 , which remain less investigated. In this study, we took the Beijing-Tianjin sand source region (which has experienced long-term ERPs), China, as an example and combined the process-based Biome-BGCMuSo model to set multiple scenarios to address this issue. We found ERP-induced carbon sequestration (CS), water retention (WR), soil retention (SR), and sandstorm prevention (SP) increased by 22.21%, 2.87%, 2.35%, and 28.77%, respectively. Moreover, the ecosystem services promotion from afforestation was greater than that from grassland planting. Approximately 91.41%, 98.13%, and 64.51% of the increased CS, SR, and SP were contributed by afforestation. However, afforestation also caused the WR to decline. Although rising CO2 amplified ecosystem services contributed by ERPs, it was almost totally offset by drought. The contribution of ERPs to CS, WR, SR, and SP was reduced by 5.74%, 32.62%, 11.74%, and 14.86%, respectively, under combined drought and rising CO2 . Our results confirmed the importance of ERPs in strengthening ecosystem services provision. Furthermore, we provide a quantitative way to understand the influence rate of drought and rising CO2 on ERP-induced ecosystem service dynamics. In addition, the considerable negative climate change impact implied that restoration strategies should be optimized to improve ecosystem resilience to better combat negative climate change impacts.

Sensitivity Analysis of Biome-BGC for Gross Primary Production of a Rubber Plantation Ecosystem: A Case Study of Hainan Island, China

Accurate monitoring of forest carbon flux and its long-term response to meteorological factors is important. To accomplish this task, the model parameters need to be optimized with respect to in situ observations. In the present study, the extended Fourier amplitude sensitivity test (eFAST) method was used to optimize the sensitive ecophysiological parameters of the Biome BioGeochemical Cycles model. The model simulation was integrated from 2010 to 2020. The results showed that using the eFAST method quantitatively improved the model output. For instance, the R² increased from 0.53 to 0.72. Moreover, the root-mean-square error was reduced from 1.62 to 1.14 gC·m^-2·d^-1. In addition, it was reported that the carbon flux outputs of the model were highly sensitive to various parameters, such as the canopy average specific leaf area and canopy light extinction coefficient. Moreover, long-term meteorological factor analysis showed that rainfall dominated the trend of gross primary production (GPP) of the study area, while extreme temperatures restricted the GPP. In conclusion, the eFAST method can be used in future studies. Furthermore, eFAST could be applied to other biomes in response to different climatic conditions.