Evaluation of future wetland changes under optimal scenarios and land degradation neutrality analysis in the Guangdong-Hong Kong-Macao Greater Bay Area

Abstract

Wetlands are one of the most productive ecosystems on Earth and are also focused on by the Sustainable Development Goals (SDGs). However, global wetlands have suffered from considerable degradation due to rapid urbanization and climate change. To support wetland protection and SDG reporting, we predicted future wetland changes and assessed land degradation neutrality (LDN) from 2020 to 2035 under four scenarios in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). A simulation model combining random forest (RF), CLUE-S and multi-objective programming (MOP) methods was developed to predict wetland patterns under the natural increase scenario (NIS), economic development scenario (EDS), ecological protection and restoration scenario (ERPS) and harmonious development scenario (HDS). The simulation results indicated that the integration of RF and CLUE-S achieved good simulation accuracy, with OA over 0.86 and kappa indices over 0.79. From 2020 to 2035, the mangrove, tidal flat and agricultural pond increased while the coastal shallow water decreased under all scenarios. The river decreased under NIS and EDS, while increased under ERPS and HDS. The Reservoir decreased under NIS, while increased under the remaining scenarios. Among scenarios, the EDS had the largest built-up land and agricultural pond, and the ERPS had the largest forest and grassland. The HDS was a coordinated scenario that balanced economic development and ecological protection. Its natural wetlands were almost equal to these of ERPS, and its built-up land and cropland were almost equal to these of EDS. Then, the land degradation and SDG 15.3.1 indicators were calculated to support the LDN target. From 2020 to 2035, the ERPS had a smallest gap of 705.51 km2 from the LDN target, following the HDS, EDS and NIS. The SDG 15.3.1 indicator was lowest under the ERPS, with a value of 0.85 %. Our study could offer strong support for urban sustainable development and SDGs reporting.