Yue Qin

Alpine vegetation phenology dynamic over 16 years and its covariation with climate in a semi-arid region of China

Vegetation phenology is a sensitive indicator of ecosystem response to climate change, and plays an important role in the terrestrial biosphere. Improving our understanding of alpine vegetation phenology dynamics and the correlation with climate and grazing is crucial for high mountains in arid areas subject to climatic warming. Using a time series of SPOT Normalized Difference Vegetation Index (NDVI) data from 1998 to 2013, the start of the growing season (SOS), end of the growing season (EOS), growing season length (GSL), and maximum NDVI (MNDVI) were extracted using a threshold-based method for six vegetation groups in the Heihe River headwaters. Spatial and temporal patterns of SOS, EOS, GSL, MNDVI, and correlations with climatic factors and livestock production were analyzed. The MNDVI increased significantly in 58% of the study region, whereas SOS, EOS, and GSL changed significantly in <5% of the region. The MNDVI in five vegetation groups increased significantly by a range from 0.045 to 0.075. No significant correlation between SOS and EOS was observed in any vegetation group. The SOS and GSL were highly correlated with temperature in May and April-May, whereas MNDVI was correlated with temperature in August and July-August. The EOS of different vegetation groups was correlated with different climatic variables. Maximum and minimum temperature, accumulated temperature, and effective accumulated temperature showed stronger correlations with phenological metrics compared with those of mean temperature, and should receive greater attention in phenology modeling in the future. Meat and milk production were significantly correlated with the MNDVI of scrub, steppe, and meadow. Although the MNDVI increased in recent years, ongoing monitoring for rangeland degradation is recommended.

Impacts of climate warming on the frozen ground and eco-hydrology in the Yellow River source region, China

The Yellow River source region is located in the transition region between permafrost and seasonally frozen ground on the northeastern Qinghai-Tibet Plateau. The region has experienced severe climate change, especially air temperature increases, in past decades. In this study, we employed a geomorphology-based eco-hydrological model (GBEHM) to assess the impacts of climate change on the frozen ground and eco-hydrological processes in the region. Based on a long-term simulation from 1981 to 2015, we found that the areal mean maximum thickness of seasonally frozen ground ranged from 1.1-1.8m and decreased by 1.2cm per year. Additionally, the ratio of the permafrost area to the total area decreased by 1.1% per year. These decreasing trends are faster than the average in China because the study area is on the sensitive margin of the Qinghai-Tibet Plateau. The annual runoff exhibited variations similar to those of the annual precipitation (R2=0.85), although the annual evapotranspiration (ET) exhibited an increasing trend (14.3mm/10a) similar to that of the annual mean air temperature (0.66°C/10a). The runoff coefficient (annual runoff divided by annual precipitation) displayed a decreasing trend because of the increasing ET, and the vegetation responses to climate warming and permafrost degradation were manifested as increases in the leaf area index (LAI) and ET at the start of the growing season. Furthermore, the results showed that changes to the frozen ground depth affected vegetation growth. Notably, a rapid decrease in the frozen ground depth (< -3.0cm/a) decreased the topsoil moisture and then decreased the LAI. This study showed that the eco-hydrological processes in the headwater area of the Yellow River have changed because of permafrost degradation, and these changes could further influence the water resources availability in the middle and lower reaches of the basin.

Spatial Interpolation of Daily Precipitation in a High Mountainous Watershed Based on Gauge Observations and a Regional Climate Model Simulation

AbstractPrecipitation is a primary climate forcing factor in catchment hydrology, and its spatial distribution is essential for understanding the spatial variability of ecohydrological processes in a catchment. In mountainous areas, meteorological stations are generally too sparse to represent the spatial distribution of precipitation. This study develops a spatial interpolation method that combines meteorological observations and regional climate model (RCM) outputs. The method considers the precipitation–elevation relationship in the mountain region and the topographic effects, especially the mountain blocking effect. Furthermore, using this method, this study produced a 3-km-resolution precipitation dataset from 1960 to 2014 in the middle and upper reaches of the Heihe River basin located on the northern slope of the Qilian Mountains in the northeastern Tibetan Plateau. Cross validation based on the station observations showed that this method is reasonable. The rationality of the interpolated precipit...

Data-driven mapping of the spatial distribution and potential changes of frozen ground over the Tibetan Plateau

Frozen ground degradation profoundly impacts the hydrology, ecology and human society on the Tibetan Plateau (TP) and the downstream regions. The spatial distribution and potential changes of permafrost and maximum thickness of seasonally frozen ground (MTSFG) on the TP is of great importance and needs more in-depth studies. This study maps the permafrost and MTSFG distribution in the baseline period (2003-2010) and in the future (2040s and 2090s) with 1-km resolution. Logistic regression (LR), support vector machine (SVM) and random forest (RF) are validated using 106 borehole observations and proved to be applicable in estimating permafrost distribution. According to the majority voting results of the three algorithms, 45.9% area of the TP is underlain by permafrost in the baseline period, and respectively 25.9% and 43.9% of the current permafrost will disappear by the 2040s and the 2090s projected by mean of the projections from the five General Circulation Models under the Representative Concentration Pathway 4.5 scenario. SVM performs better in spatial generalization than RF based on the results of nested cross validation. According to the MTSFG results derived from SVM, the most dramatic decrease in MTSFG will occur in the southwestern TP, which is projected to exceed 50 cm in the 2090s compared with the baseline period. This study introduces the statistics and machine learning algorithms to frozen ground estimation on the TP, and the high resolution permafrost and MTSFG maps produced by this study can provide useful information for future studies on the third pole region.