Articles published recently
1. Zhang, L. X., Cong, Z. T., Zhang, D. W. and Li, Q. S (2017), Response of vegetation dynamics to climatic variables across a precipitation gradient in the Northeast China Transect, Hydrological Sciences Journal. doi: 10.1080/02626667.2017.1337274.
This study investigated the influence of climatic variables on the spatio-temporal variation of vegetation growth using normalized difference vegetation index (NDVI) data and climate data from 2000 to 2013 in the Northeast China Transect. Partial correlation and linear regression methods were applied to quantify the response of the growing season NDVI to climatic variables. Gradient analysis was used to investigate how the response changes across the precipitation gradient over the transect. The results show that, at the spatial scale, NDVI increases with precipitation in grassland, and the spatial sensitivity is 0.001/mm. At the temporal scale, grassland NDVI is less correlated with precipitation in wet areas where precipitation exceeds a threshold of 250 mm. The temporal sensitivity of grassland NDVI to precipitation is 0.0003–0.0006/mm. Positive correlations between NDVI and temperature dominate in forest areas, and forest NDVI is sensitive to temperature by 0.06–0.12/°C.
2. Cong, Z. T., Li, Q. S., Mo, K. L., Zhang, L. X. and Shen, H. (2017), Ecohydrological optimality in the Northeast China Transect. Hydrology and Earth System Sciences, 21(5), 2449-2462. doi: 10.5194/hess-21-2449-2017.
The Northeast China Transect (NECT) is one of the International Geosphere-Biosphere Program (IGBP) terrestrial transects, where there is a significant precipitation gradient from east to west, as well as a vegetation transition of forest-grassland-desert. It is remarkable to understand vegetation distribution and dynamics under climate change in this transect. We take canopy cover (M), derived from Normalized Difference Vegetation Index (NDVI), as an index to describe the properties of vegetation distribution and dynamics in the NECT. In Eagleson’s ecohydrological optimality theory, the optimal canopy cover (M*) is determined by the trade-off between water supply depending on water balance and water demand depending on canopy transpiration. We apply Eagleson’s ecohydrological optimality method in the NECT based on data from 2000 to 2013 to get M*, which is compared with M from NDVI to further discuss the sensitivity of M* to vegetation properties and climate factors. The result indicates that the average M* fits the actual M well (for forest, M* = 0.822 while M = 0.826; for grassland, M* = 0.353 while M = 0.352; the correlation coefficient between M and M* is 0.81). Results of water balance also match the field-measured data in the references. The sensitivity analyses show that M* decreases with the increase of leaf area index (LAI), stem fraction and temperature, while it increases with the increase of leaf angle and precipitation amount. Eagleson’s ecohydrological optimality method offers a quantitative way to understand the impacts of climate change on canopy cover and provides guidelines for ecorestoration projects.
3. Cong, Z. T., Shen, Q. N., Zhou, L., Sun, T. and Liu, J. H (2017), Evapotranspiration estimation considering anthropogenic heat based on remote sensing in urban area, Science China Earth Sciences, 60(4), 659-671. doi: 10.1007/s11430-016-0216-3.
Urbanization influences hydrologic cycle significantly on local, regional even global scale. With urbanization the water resources demand for dense population sharpened, thus it is a great challenge to ensure water supply for some metropolises such as Beijing. Urban area is traditionally considered as the area with lower evapotranspiration (ET) on account of the impervious surface and the lower wind speed. For most remote sensing models, the ET, defined as latent heat in energy budget, is estimated as the difference between net radiation and sensible heat. The sensible heat is generally higher in urban area due to the high surface temperature caused by heat island, therefore the latent heat (i.e. the ET) in urban area is lower than that in other region. We estimated water consumption from 2003 to 2012 in Beijing based on water balance method and found that the annual mean ET in urban area was about 654 mm. However, using Surface Energy Balance System (SEBS) model, the annual mean ET in urban area was only 348 mm. We attributed this inconsistence to the impact of anthropogenic heat and quantified this impact on the basis of the night-light maps. Therefore, a new model SEBS-Urban, coupling SEBS model and anthropogenic heat was developed to estimate the ET in urban area. The ET in urban area of Beijing estimated by SEBS-Urban showed a good agreement with the ET from water balance method. The findings from this study highlighted that anthropogenic heat should be included in the surface energy budget for a highly urbanized area.
4. Cong, Z. T., Shahid, M., Zhang, D. W., Lei, H. M., & Yang, D. W (2017), Attribution of runoff change in the alpine basin: a case study of the Heihe Upstream Basin, China. Hydrological Sciences Journal, 62(6), 1013-1028. doi: 10.1080/02626667.2017.1283043.
Quantifying the relative contributions of different factors to runoff change is helpful for basin management, especially in the context of climate change and anthropogenic activities. The effect of snow change on runoff is seldom evaluated. We attribute the runoff change in the Heihe Upstream Basin (HUB), an alpine basin in China, using two approaches: a snowmelt-based water balance model and the Budyko framework. Results from these approaches show good consistency. Precipitation accounts for 58% of the increasing runoff. The contribution of land-cover change seems unremarkable for the HUB as a whole, where land-cover change has a major effect on runoff in each sub-basin, but its positive effect on increasing runoff in sub-basins 1 and 3 is offset by the negative effect in sub-basin 2. Snow change plays an essential role in each sub-basin, with a contribution rate of around 30%. The impact of potential evapotranspiration is almost negligible.
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