Ambitious investments in expanding forest cover globally need more careful analysis to avoid severe unintended consequences.

Accurate rainfall prediction remains a challenging problem because of the high volatility and complicated essence of atmospheric data. This study proposed a hybrid model (DSP) that combines the advantages of discrete wavelet transform (DWT), support vector regression (SVR), and Prophet to forecast rainfall data. First, the rainfall time series is decomposed into high-frequency and low-frequency subseries using discrete wavelet transform (DWT). The SVR and Prophet models are then used to predict high-frequency and low-frequency subsequences, respectively. Finally, the predicted rainfall is determined by summing the predicted values of each subsequence. A case study in China is conducted from 1 January 2014 to 30 June 2016. The results show that the DSP model provides excellent prediction, with RMSE, MAE, and R² values of 6.17, 3.3, and 0.75, respectively. The DSP model yields higher prediction accuracy than the three baseline models considered, with the prediction accuracy ranking as follows: DSP > SSP > Prophet > SVR. In addition, the DSP model is quite stable and can achieve good results when applied to rainfall data from various climate types, with RMSEs ranging from 1.24 to 7.31, MAEs ranging from 0.52 to 6.14, and R² values ranging from 0.62 to 0.75. The proposed model may provide a novel approach for rainfall forecasting and is readily adaptable to other time series predictions.

Specific forest restoration aims to maximum ecosystem services (ESs); however, the complex trade-offs among ecosystem services pose considerable challenges for fulfilling such goals. Based on forest restoration on Hainan Island, China, we integrated spatially explicit models of ecosystem services and spatial prioritization techniques based on the efficiency frontier between habitat quality and plantation revenue to analyze the impacts of decision-makers’ preferences on optimal configurations of forest restoration. We then investigated the effects of different optimal restoration schemes on water purification, soil retention, carbon sequestration, and coastal hazard mitigation. Based on our results, plantation revenue and habitat quality exhibited an obvious trade-off during the process of restoration. Forest restoration patterns also varied with the degree of preference for plantation yield or habitat quality, indicating that understanding ecosystem service tradeoffs can support the optimal selection of forest restoration schemes under different preferences. However, when the values of multiple ecosystem services associated with forest restoration were considered (e.g., water purification, soil retention, carbon sequestration, and coastal hazard mitigation), the optimal solution choice varied. Our results suggest the application of the efficiency frontier can deepen quantitative understanding of ecosystem service trade-offs, and the addition of multi-benefit evaluation based on optimal solutions can provide a more detailed and broader picture of forest restoration plans. Integrated efficiency frontier assessment with the valuation of ecosystem services associated with forest restoration provides a quantitative approach for optimal forest restoration, which can be applied in broad forest restoration programs.

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.