a b s t r a c tIncreasing drought poses a big threat to food security over recent decades, highlighting the need for effective tools and adequate information for drought monitoring and mitigation. This study analyzed the performance of five climate-based drought indices and soil moisture measurements for monitoring winter wheat drought threat in China. We employed the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), the Palmer Drought Severity Index (PDSI), the Palmer Z index and the self-calibrated Palmer Drought Severity Index (scPDSI). On average, soil moisture at 50-cm depth correlated better with winter wheat yield during October-December of the previous year of harvest compared to soil moisture at 10-cm and 20-cm depths. Moreover, the 3-layer soil moisture and reference evapotranspiration (ETo) correlated weakly (Pearson's r < 0.3) and even negatively with winter wheat yield. The SPI and SPEI at shorter (1-5 months) timescales during September-December in the previous year of harvest showed higher correlations with winter wheat yield. The SPEI trend in March-June has a significant positive influence on trend in winter wheat yield (r > 0.40, p < 0.05). The climate-based drought indices can facilitate crop drought monitoring in water-limited regions due to the wide-availability of climatic data, particularly in the light of uncertainties arising from the crop model. Among the investigated indices, results revealed that the SPEI is advantageous for drought monitoring over the study area due to its multiscalarity and effective characterization of agricultural droughts.

Soil moisture shortages adversely affecting agriculture are significantly associated with meteorological drought. Because of limited soil moisture observations with which to monitor agricultural drought, characterizing soil moisture using drought indices is of great significance. The relationship between commonly used drought indices and soil moisture is examined here using Chinese surface weather data and calculated station-based drought indices. Outside of northeastern China, surface soil moisture is more affected by drought indices having shorter time scales while deep-layer soil moisture is more related on longer index time scales. Multiscalar drought indices work better than drought indices from two-layer bucket models. The standardized precipitation evapotranspiration index (SPEI) works similarly or better than the standardized precipitation index (SPI) in characterizing soil moisture at different soil layers. In most stations in China, the Z index has a higher correlation with soil moisture at 0–5 cm than the Palmer drought severity index (PDSI), which in turn has a higher correlation with soil moisture at 90–100-cm depth than the Z index. Soil bulk density and soil organic carbon density are the two main soil properties affecting the spatial variations of the soil moisture–drought indices relationship. The study may facilitate agriculture drought monitoring with commonly used drought indices calculated from weather station data.

Climate change has significantly influenced vegetation dynamics on the Tibetan Plateau (TP). Past research mainly focused on vegetation responses to temperature variation and water stress, but the influence of sunshine duration on NDVI and vegetation phenology on the TP is not well understood. In this study, NDVI time series from 1982-2008 were used to retrieve spatiotemporal vegetation dynamics on the TP. Empirical orthogonal function (EOF) analysis was conducted to understand the spatiotemporal variations of NDVI. The Start of Season (SOS) was estimated from NDVI time series with a local threshold method. The first EOF, accounting for 35.1% of NDVI variations on the TP, indicates that NDVI variations are larger in areas with shorter sunshine duration. The needle-leaved forest and shrub in the southeastern TP are more sensitive to sunshine duration anomalies (p < 0.01) than broad-leaved forest, steppe, and meadow due to spatial and altitudinal distribution of sunshine duration and vegetation types. The decrease in sunshine duration for the growing season on the TP has resulted in a decreased NDVI trend in some areas of southeastern TP (p ranging from 0.32-0.05 with threshold ranging from 0.05 to 0.25) in spite of the overall NDVI increase. SOS dynamics in most parts of the TP were mainly related to temperature variability, with precipitation and sunshine duration playing a role in a few regions. This study enhances our understanding of vegetation responses to climatic change on the TP.

The extreme drought in Southwest China during 2009-2010 caused great damages to vegetation in that area. In this study, we analyse the relationship between remotely sensed drought monitoring and meteorological drought monitoring. Vegetation Health Index (VHI) was calculated using multitemporal Normalized Difference Vegetation Index and land surface temperature from 2001 to 2010. VHI was adopted to characterize vegetation responses to southwestern drought characterized by Standard Precipitation Index (SPI). At the beginning of drought, vegetation has little response (VHI > 50). As drought aggravates, VHI shows consistent and significant response (VHI < 30 in most areas). VHI and 3-month SPI have highest correlation for croplands, whereas VHI and 6-month SPI have highest correlation for forest. SPI and VHI have good spatiotemporal consistency during drought period in Southwest China. Our study proves meteorological drought index combined with remote sensing drought index can enhance our understanding of vegetation responses to drought threat.

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This study explores the effects of different environmental variables on the accuracy of species distribution models. Forest inventory and analysis data sets were used to generate absence and pseudo-absence points of chestnut oak (Quercus prinus) in the central and southern Appalachian mountain region of the US. We simulate chestnut oak distribution using different criteria for selecting environmental variables: (1) the selection of sensitive variables using factor analysis and the calculation of a sensitivity index, (2) principal components analysis. Factor analysis to environmental variables at both occurrence and pseudo-absence points was conducted to calculate the sensitivity index for each environmental variable. The identification of sensitive variables may use the factor loadings of first one or two factors of environmental variables. Modelling with sensitive variables (mean Kappa > 0.60; mean true skill statistic (TSS) > 0.60) can enhance model accuracy more than using PCA variables or all available environmental variables (mean Kappa ranges from 0.45 to 0.65; mean TSS ranges from 0.40 to 0.70). Modelling with leading principal components (larger than 90% variations) can achieve similar or higher accuracy than modelling with all variables. The influence of redundant information on species modelling varies with the model used. Our results suggest that selecting environmental variables using a sensitivity index defined by factor analysis may improve model accuracy and reduce redundant information in species modelling. The proposed method for selecting sensitive variables is easy to implement and has strong ecological interpretability. ARTICLE HISTORY