Incorporation of potential natural vegetation into revegetation programmes for sustainable land management

Abstract

Knowledge of potential natural vegetation (PNV) is imperative when implementing revegetation to mitigate land degradation and promote sustainable land management. To date, however, PNV has received relatively little attention. One challenge is determining which type of PNV is the best‐fit ecosystem under investigation. The objective of this study was to develop a revegetation program and evaluate its potential efficacy at correcting existing land use patterns. The PNV pattern was generated using a physically based ecosystem model called Lund–Potsdam–Jena General Ecosystem Simulator. The Loess Plateau of China was selected as the study area because it has undergone substantial degradation and revegetation. The results showed that from 1981 to 2014, forest and steppe accounted for 29.8% and 68.2% of the entire area, respectively. Comparisons between observed and simulated land use patterns indicated that current forestland and grassland distributions must be changed. Only 55.2 ± 1.4% of the existing forestland aligned with the PNV model. A more appropriate vegetation type for the remaining forestland would be grass. It was found that 78.4 ± 1.3% of the existing grassland was consistent with the PNV whereas the remainder should be forested. Comparisons between simulated PNV and existing farmland indicated that the steeper zones could be converted to forestland or grassland. They account for 40.6% and 58.7% of the steeper farmland area, respectively. The framework developed herein and the results derived therefrom may assist other regions facing similar land degradation issues in the development of revegetation programs and the promotion of sustainable land management.

Hydrochemical Characteristics, Controlling Factors, and Solute Sources of Streamflow and Groundwater in the Hei River Catchment, China

“…Thus far, extensive engineering and biological measures have been implemented to reduce soil and water loss. Consequently, the land use structure has been substantially changed by converting steep farmlands to grasslands or forestlands; simultaneously, large areas of farmland have been converted to apple orchards for income improvement [11,12]. With these changes in surface conditions, soil erosion has been effectively reduced; however, river flow has significantly reduced as well, threatening the sustainability of the region [13,14].…”

1 km monthly temperature and precipitation dataset for China from 1901 to 2017

“…High-spatial-resolution and long-term climate data are required for accurate investigations of changes in climate and climate-related phenomena that affect hydrology, vegetation cover, and crop production (Gao et al, 2018;Caillouet et al, 2019;Peng and Li, 2018). Although meteorological observation networks are increasingly incorporating data from a greater number of weather stations and contributions from an increasing number of governments and researchers around the world, observation networks still suffer from low station density and spatial resolution (Caillouet et al, 2019;Peng et al, 2014), especially in mountainous areas (Gao et al, 2018), where the installation and maintenance of weather stations are challenging (Rolland, 2003). Accordingly, several interpolation methods such as inverse distance weighting, kriging methods, and regression analysis are usually used to generate meteorological data for such ungauged areas (Li et al, 2010(Li et al, , 2012Zhao et al, 2004;Attaur-Rahman and Dawood, 2017;Peng et al, 2014).…”

“…Although meteorological observation networks are increasingly incorporating data from a greater number of weather stations and contributions from an increasing number of governments and researchers around the world, observation networks still suffer from low station density and spatial resolution (Caillouet et al, 2019;Peng et al, 2014), especially in mountainous areas (Gao et al, 2018), where the installation and maintenance of weather stations are challenging (Rolland, 2003). Accordingly, several interpolation methods such as inverse distance weighting, kriging methods, and regression analysis are usually used to generate meteorological data for such ungauged areas (Li et al, 2010(Li et al, , 2012Zhao et al, 2004;Attaur-Rahman and Dawood, 2017;Peng et al, 2014). However, as the accuracy of the corresponding results depends on station density (Gao et al, 2018;Peng et al, 2014), one needs to use climatic proxy data to generate long-term and highspatial-resolution climate data.…”

“…Accordingly, several interpolation methods such as inverse distance weighting, kriging methods, and regression analysis are usually used to generate meteorological data for such ungauged areas (Li et al, 2010(Li et al, , 2012Zhao et al, 2004;Attaur-Rahman and Dawood, 2017;Peng et al, 2014). However, as the accuracy of the corresponding results depends on station density (Gao et al, 2018;Peng et al, 2014), one needs to use climatic proxy data to generate long-term and highspatial-resolution climate data.…”

“…However, little work has been done to address this issue. Previous studies indicated that topographic information (e.g., elevation, location, slope, and aspect) may be the key factor for correcting deviations, especially in mountainous areas (Gao et al, 2018Peng et al, 2014). Therefore, a high-resolution reference climatology dataset containing detailed topographic information, as well as the effects of distance to the nearest coast and satellite-derived covariates, was used in this study to downscale the 30 original CRU dataset to a 0.5 dataset comprising monthly TMPs and PRE from January 1901 to December 2017 across China, which has a low density of weather stations in mountainous areas.…”