Estimating the optimal vegetation coverage for the dominant tree and shrub species over China's northwest drylands

Anthropogenic revegetation is an effective way to control soil erosion and restore degraded ecosystems in China's northwest drylands(NWD).However,excessive vegetation cover expansion has long been known to increase evapotranspiration,leading to reduced local water availability,which can in turn threaten the health and services of restored ecosystems.Determining the optimal vegetation coverage(OVC)is critical for balancing the trade-off between plant growth and water consumption in water-stressed areas,yet quantitative assessments over the entire NWD are still lacking.In this study,a modified Biome BioGeochemical Cycles(Biome-BGC)model was used to simulate the long-term(1961–2020)dynamics of actual evapotranspiration(ET_(a)),net primary productivity(NPP),and leaf area index(LAI)for the dominant non-native tree(R.pseudoacacia and P.sylvestris)and shrub(C.korshinkii and H.rhamnoides)species at 246 meteorological sites over NWD.The modified model incorporated the Richards equation to simulate transient unsaturated water flow in a multilayer soil module,and both soil and eco-physiological parameters required by the model were validated using field-observed ETadata for each species.Spatial distributions of OVC(given by the mean maximum LAI,LAI_(max))for the dominant species were determined within three hydrogeomorphic sub-areas(i.e.,the loess hilly-gully sub-area,the windy and sandy sub-area,and the desert sub-area).The modified Biome-BGC model performed well in terms of simulating ET_(a) dynamics for the four plant species.Spatial distributions of mean ET_a,NPP,and LAI_(max)generally exhibited patterns similar to mean annual precipitation(MAP).In the loess hilly-gully sub-area(MAP:210 to 710 mm),the OVC respectively ranged from 1.7 to 2.9 and 0.8 to 2.9 for R.pseudoacacia and H.rhamnoides.In the windy and sandy sub-area(MAP:135 to 500 mm),the OVC ranged from 0.3 to 3.3,0.5 to 2.6 and 0.6 to 2.1for P.sylvestris,C.korshinkii and H.rhamnoides,respectively.In the desert sub-area(MAP:90 to 500 mm),the OVC ranged from 0.4 to 1.7 for H.rhamnoides.Positive differences between observed and simulated plant coverage were found over 51%of the forest-and shrub-covered area,especially in the loess hilly-gully sub-area,suggesting possible widespread overplanting in those areas.This study provides critical revegetation thresholds for dominant tree and shrub species to guide future revegetation activities.Further revegetation in areas with overplanting should be undertaken with caution,and restored ecosystems that exceed the OVC should be managed(e.g.,thinning)to maintain a sustainable ecohydrological environment in the drylands.

An empirical approach to predict regional organic carbon in deep soils

Deep soil organic carbon(SOC)plays an important role in carbon cycling.Precisely predicting deep SOC at the regional scale is crucial for the accurate assessment of carbon sequestration potential in soils but has been challenging for a century.Herein,we developed a depth distribution function-based empirical approach to predict SOC in deep soils at the regional scale.We validated this approach with a dataset from four regions of the world and examined the application of this approach in China’s Loess Plateau.We found that among the reported depth distribution functions describing vertical patterns of SOC,the negative exponential function performed best in fitting SOC along the soil profile in various regions.Moreover,the parameters(i.e.,Ceand k)of the negative exponential function were linearly correlated to surface SOC(0–20 cm)and the changing rates of SOC within the topsoil(0–40 cm).Based on the above relationships,the empirical equations for predicting the negative exponential parameters are established.The validation results from site-specific and regional dataset showed that combining the negative exponential function and such empirical equations can precisely predict SOC concentration in soils down to 500 cm depth.Our study provides a simple,rapid and accurate method for predicting deep soil SOC at the regional scale,which could simplify the assessment of deep soil SOC in various regions.

Predictions of soil and nutrient losses using a modified SWAT model in a large hilly-gully watershed of the Chinese Loess Plateau

Hydrological models are effective tools for assessing the effects of soil and nutrient losses on land degradation.SWAT(Soil and Water Assessment Tool)model is widely used to simulate soil and nutrient losses caused by various management regimes.However,its performance of predicting nutrient loss has not been assessed adequately on the Loess Plateau.This study proposed a modified SWAT model by incorporating the modified Soil Conservation Service curve number method,the storm-based Chinese soil loss equation and the nutrient loss model.The observed daily data of runoff and sediment over 16 years and the monthly soluble phosphorus(P)and nitrate losses over 9 years and 4 years,respectively at the outlet of the upper Beiluo river(UBR)basin were used to assess the model performances.Global sensitivity and uncertainty analyses of parameters to runoff,sediment,soluble P and nitrate in the modified SWAT were conducted.The findings during calibration and validation showed that the modified SWAT was highly accurate in terms of model efficiency(calibration:0.83,0.83,0.48,and 0.49;validation:0.58,0.57,0.53,and 0.65)for runoff,sediment,soluble P loss and nitrate loss,respectively.High model efficiency indicated that the modified SWAT could accurately predict soil and nutrient losses at the river basin scale for the Loess Plateau.Moreover,the temporal variations from month to year and the spatial variations at the sub-basin scale for soil loss and the total N and P losses were analysed using the data simulated by the modified SWAT.The results indicated that the critical loss period occurred in July and August,and the Grain for Green project significantly affected the hydrological behaviour and reduced the soil and nutrient losses in the UBR basin.