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The occurrence of drought is related to complicated interactions between many factors, such as precipitation, temperature, evapotranspiration and vegetation. In this study, the relationships between drought and precipitation, temperature, vegetation and evapotranspiration were investigated with a random forest (RF), and a new combined drought monitoring index (CDMI) was constructed. The effectiveness of the CDMI in monitoring drought in Shaanxi Province was verified by the in situ 1 ∼ 12-month standardized precipitation index (SPI); relative soil moisture (RSM) and four other commonly used remote sensing drought monitoring indices. The results show that CDMI is more correlated with the SPI and RSM than the four indices. Moreover, the spatial distributions of drought for the CDMI and RSM are similar. Therefore, the CDMI can be used to monitor droughts in Shaanxi Province, and machine learning can explore the relationships between various factors and establish a drought index without knowledge of the causal mechanisms of these factors. [ABSTRACT FROM AUTHOR]

Soil organic carbon stocks (SOCS) are critical for both maintaining many ecosystem functions and regulating global climate. To address the serious soil erosion and land degradation problems on the Loess Plateau (LP), the Chinese government launched the famous "Grain for Green Program" (GGP) in 1999. However, how the SOCS change under the impacts of both GGP and climate change remains unclear. Here, we investigated the impacts of climate change and the GGP on SOCS of the top 1 m soil on the LP during 1980–2015, using a data-driven model and a multi-period land use/cover dataset. Our SOCS estimates showed that SOCS of grasslands and forestlands on the LP ranged from approximately 1400 ± 75 to 1700 ± 77 Tg C during 1980–2015. The SOCS of grasslands ranged from approximately 1000 ± 56 to 1200 ± 58 Tg C, which were roughly twice those of forestlands. Our analyses suggested that SOCS of grasslands and forestlands on the LP varied in the years before the GGP was launched in 1999, due to the continued warming and frequent drought. Unexpectedly, the SOCS did not significantly enhance after the GGP was implemented. Compared to 1995, the SOCS of grasslands and forestlands on the LP only increased on average by approximately 3.7% during 2000–2015, due mainly the adverse impact of climate change, as well as the limited effect of vegetation restoration on SOC at the early stage of the vegetation restoration programs. The study highlights the noticeably adverse effects of climate change on SOCS in the LP, providing valuable scientific support for the implementation of large-scale ecological restoration initiatives in the region. [Display omitted] • Soil organic carbon stocks (SOCS) of grasslands and forestlands on the LP varied significantly over 1980–2015. • Climate changes had important influences on soil organic carbon stocks on the LP. • Management of grasslands and forests are crucial for enhancing carbon storage on the LP. [ABSTRACT FROM AUTHOR]

Abstract Remote sensing based vegetation index provides a practical method for the monitoring of vegetation dynamics at regional and global scales. Here, using a long-term remotely sensed normalized difference vegetation index (NDVI) dataset, we quantified the vegetation changes in the Loess Plateau (LP) over the last three decades (1982–2015), which includes the period before the Chinese"Grain for Green Program"(GGP) was launched (1982–1999), and the period after the GGP (1999–2015). The correlations between the NDVI and four climate related variables, i.e., precipitation, temperature, root-soil moisture (RSM), and a drought proxy-standardized evapotranspiration deficit index (SEDI), were also examined. The results indicated that, (i) the GGP strongly changed the vegetation in the LP. The growing-season mean NDVI (GSM-NDVI) and the annual mean NDVI (AM-NDVI) decreased slightly before the GGP launched in 1999, with slopes of −3.38 × 10−3 and − 8.00 × 10−4 year−1, respectively. However, they showed slight and significant (p < 0.05) increases after the GGP, with slopes of 4.75 × 10−3 and 2.32 × 10−3 year−1, respectively. (ii) Climate change (i.e., warming and drying) resulted in adverse effects on vegetation in the LP during the period before the GGP. However, the observed changes (i.e., wetting and reduced drought) exerted positive effects on the vegetation during the period after the GGP. (iii) Inter-annual variations of spatially averaged NDVI over the LP were primarily determined by RSM rather than any other climate related variables. In the southeastern LP, the inter-annual variation of GSM-NDVI was mainly determined by precipitation and SEDI, while the inter-annual variation of AM-NDVI was mainly caused by SEDI and RSM. Inter-annual variations of both GSM-NDVI and AM-NDVI were mainly determined by SEDI and RSM in the northwestern LP, and by temperature in the southwestern LP. Graphical abstract Unlabelled Image Highlights • The Grain for Green Program (GGP) greatly changed vegetation in the Loess Plateau. • Climate changes exerted adverse effects on vegetation in the LP before the GGP. • Wetting and reduced drought positively affected vegetation in the LP after the GGP. • The inter-annual variation of NDVI was primarily determined by soil moisture. [ABSTRACT FROM AUTHOR]

Satellite observations of atmospheric carbon dioxide (CO) provide a useful way to improve the understanding of global carbon cycling. In this paper, we present a comparison between simulated CO concentrations from an inversion model of the CarbonTracker Data Assimilation System (CTDAS) and satellite-based CO measurements of column-averaged dry air mole fraction (denoted XCO) derived from version 3.3 Atmospheric CO Observations from Space retrievals of the Greenhouse Gases Observing SATellite (ACOS-GOSAT) L2 data products. We examine the differences of CTDAS and GOSAT to provide important guidance for the further investigation of CTDAS in order to quantify the corresponding flux estimates with satellite-based CO observations. We find that the mean point-by-point difference (CTDAS-GOSAT) between CTDAS and GOSAT XCO is −0.11 ± 1.81 ppm, with a high agreement (correlation r = 0.77, P < 0.05) over the studied period. The latitudinal zonal variations of CTDAS and GOSAT are in general agreement with clear seasonal fluctuations. The major exception occurs in the zonal band of 0°-15°N where the difference is approximately 4 ppm, indicating that large uncertainty may exist in the assimilated CO for the low-latitude region of the Northern Hemisphere (NH). Additionally, we find that the hemispherical/continental differences between CTDAS and GOSAT are typically less than 1 ppm, but obvious discrepancies occur in different hemispheres/continents, with high consistency (point-by-point correlation r = 0.79, P < 0.05) in the NH and a weak correlation (point-by-point correlation r = 0.65, P < 0.05) in the Southern Hemisphere. Overall, the difference of CTDAS and GOSAT is small, and the comparison of CTDAS and GOSAT will further instruct the inverse modeling of CO fluxes using GOSAT. [ABSTRACT FROM AUTHOR]

As the most frequent and longest-lasting climate-related disasters, droughts are complex processes caused by hydrological imbalance, and have significant effects on ecosystem function and socioeconomic development. However, most conventional satellite-based drought indices and combined drought indices are mechanically constrained by the fact that they were developed and evaluated for a specific climate or critical ecological region, especially in China. A comprehensive drought index should be developed for extensive application in different climate-hydrology regions. To address this problem, an optimised principal component analysis method (PCA-RF) was proposed to support drought assessment across the transitional area between the humid and arid regions in central China, including the Loess Plateau (LP) and Qinling Mountains (QL). This method eliminated the causal relationships of non-linear changes for multi-aspect parameters such as land surface temperature, precipitation and soil moisture, and was used in developing a new index named "Meteorological drought index by PCA-RF methods (PMDI)". The results showed that PMDI had higher correlations and lower root mean square error than other drought indices (established using the empirical-weights method) with the 1-month standardized precipitation evapotranspiration index (SPEI-1). That means PMDI performed well in meteorological drought monitoring. Additionally, the normalized difference vegetation index (NDVI) and solar-induced chlorophyll fluorescence (SIF) were utilised to evaluate PMDI effectiveness in detecting vegetation response to drought, which represents agricultural drought. The results indicated that correlations between PMDI and vegetation indices (VIs) were stronger when severe or extreme droughts occurred, and that their relationships were affected by the high spatiotemporal heterogeneity in vegetation change over the LP and QL. Overall, PMDI was shown here to be a very suitable index for monitoring meteorological drought (short-term) and has effective action in capturing the response of vegetation signals to agriculture drought events (long-term). Given the excellent performance of PMDI in diverse climate-hydrology conditions of our study regions, we concluded that the PMDI has considerable potential as a drought monitoring methodology on a larger spatial scale. • A new drought index was developed (PMDI), which integrated multiple sources of information describing different aspects. • PMDI performed well in different climate-hydrology regions for short- and long-term drought monitoring. • PMDI has reasonably good potential applicability in drought monitoring on a larger spatial scale. [ABSTRACT FROM AUTHOR]