Abstract

Soil organic carbon (SOC) simply cannot be managed if its amounts, changes and locations are not well known. Thus, evaluations of the spatio-temporal dynamics of SOC stock under future climate change are crucial for the adaptive management of regional carbon sequestration. Here, we evaluated the dynamics of SOC stock to a 60 cm depth in the middle Qilian Mountains (1755–5051 m a.s.l.) by combining systematic measurements from 138 sampling sites with a machine learning model. Our results reveal that the combination of systematic measurements with the machine learning model allowed spatially explicit estimates of SOC change to be made. The average SOC stock in the middle Qilian Mountains was expected to decrease under future climate change, while the size and direction of SOC stock changes seemed to be elevation-dependent. Specifically, in comparison with the 2000s, the mean annual precipitation was projected to increase by 18.37, 19.80 and 30.80 mm, and the mean annual temperature was projected to increase by 1.9, 2.4 and 2.9 °C under the Representative Concentration Pathway (RCP) 2.6 (low-emissions pathway), RCP4.5 (low-to-moderate-emissions pathway), and RCP8.5 (high-emissions pathway) scenarios by the 2050s, respectively. Accordingly, the area-weighted SOC stock and total storage for the whole study area were estimated to decrease by 0.43, 0.63 and 1.01 kg m–2 and 4.55, 6.66 and 10.62 Tg under the RCP2.6, RCP4.5 and RCP8.5 scenarios, respectively. In addition, the mid-elevation zones (3100–3900 m), especially the subalpine shrub-meadow Mollic Leptosols, were projected to experience the most intense carbon loss. However, the higher elevation zones (>3900 m), especially the alpine desert zone, were characterized by significant carbon accumulation. As for the low-elevation zones (<2900 m), SOC was projected to be less varied under future climate change scenarios. Thus, the mid-elevation zones, especially the subalpine shrub-meadows and Mollic Leptosols, should be given priority in terms of reducing CO2 emissions in the Qilian Mountains.

Highlights

  • In alpine regions, elevation-dependent warming probably results in a higher temperature increase over higher elevation regions

  • The spatial distribution of normalized difference vegetation index (NDVI) varied significantly from the 2000s to the 2050s (Figure 7), and NDVI was estimated to increase in high-elevation zones while decrease in mid-elevation zones (Table 1)

  • Considering the ideal performance of the random forest algorithm in capturing the non-linear relationships between the soil organic carbon (SOC) and other environmental covariates, we employed this algorithm to construct the data-driven model for future SOC dynamics projection based on the space-for-time substitution method [45,46]

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Summary

Introduction

Elevation-dependent warming probably results in a higher temperature increase over higher elevation regions. The patterns and dynamics of soil organic carbon (SOC) in alpine ecosystems are being markedly reshaped [1,2,3]. A comprehensive evaluation of the spatio-temporal patterns of SOC stocks in alpine regions may enable us to better understand the carbon–climate feedback [9,10,11]. This knowledge will help us to take dedicated adaptive measures to improve the soil carbon stock in alpine regions under future climate change [12,13]

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