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

Abstract

Maintaining or increasing water retention in ecosystems (WRE) can reduce floods and increase water resource provision. However, few studies have taken the effect of the spatial information of vegetation structure into consideration when assessing the effects of land use/land cover (LULC) change on WRE. In this study, we integrated the remotely sensed leaf area index (LAI) into the ecosystem process-based Biome-BGC model to analyse the impact of LULC change on the WRE of Beijing between 2000 and 2015. Our results show that the volume of WRE increased by approximately 8.58 million m3 in 2015 as compared with 2000. The volume of WRE in forests increased by approximately 26.74 million m3, while urbanization, cropland expansion and deforestation caused the volume of WRE to decline by 11.96 million m3, 5.86 million m3 and 3.20 million m3, respectively. The increased WRE contributed by unchanged forests (14.46 million m3) was much greater than that of new-planted forests (12.28 million m3), but the increase in WRE capacity per unit area in new-planted forests (124.69 ± 14.30 m3/ha) was almost tenfold greater than that of unchanged forests (15.60 ± 7.85 m3/ha). The greater increase in WRE capacity in increased forests than that of unchanged forests was mostly due to the fact that the higher LAI in unchanged forests induced more evapotranspiration to exhaust more water. Meanwhile, the inverted U-shape relationship that existed between the forest LAI and WRE implied that continued increased LAI in forests probably caused the WRE decline. This study demonstrates that integrating remotely sensed LAI with the Biome-BGC model is feasible for capturing the impact of LULC change with the spatial information of vegetation structure on WRE and reduces uncertainty.

Climate Change and Rising CO2 Amplify the Impact of Land Use/Cover Change on Carbon Budget Differentially Across China

“…Source codes of BBMS were open‐access and can be easily deployed for local running (https://nimbus.elte.hu/bbgc/). Meanwhile, BBMS was developed from Biome‐BGC, which has been widely used in China before (B. Huang et al., 2022; Li et al., 2022; Q. Liu et al., 2022; Yan et al., 2016; Zheng et al., 2022). Biophysical parameters of BBMS have already been localized in previous studies.…”

Yield estimation of summer maize based on multi‐source remote‐sensing data

“…Typical models include TEM (Cramer et al, 1999), BIOME-BGC (B. Huang, Yang, et al, 2022;H. Ren et al, 2022), and CENTURY (Nkrumah et al, 2022); however, the process models involve more parameters and are difficult to obtain.…”

“…The process model considers the physiological and ecological processes of plant growth and development, which are estimated by simulating the plant photosynthesis, auto respiration, and transpiration processes. Typical models include TEM (Cramer et al., 1999), BIOME‐BGC (B. Huang, Yang, et al., 2022; H. Ren et al., 2022), and CENTURY (Nkrumah et al., 2022); however, the process models involve more parameters and are difficult to obtain. The theoretical basis of light‐use efficiency models is resource balance.…”

Using GEDI as training data for an ongoing mapping of landscape-scale dynamics of the plant area index

“…(Tharammal et al 2019), the importance of LAI is, for example, evident in runoff models as it affects not only evapotranspiration rates but also immediate water retention (Tesemma et al 2015, Seo and Kim 2021. Since LAI varies greatly due to seasonality, the use of a temporally as well as spatially (Huang et al 2022) detailed data set could help improve dynamic modeling of such variables.…”