The influence of vegetation cover on summer precipitation in China: A statistical analysis of NDVI and climate data

Zhang, Jingyong; Dong, Wenjie; Fu, Congbin; Wu, Lingyun

doi:10.1007/bf02915523

Cited by 68 publications

(18 citation statements)

References 15 publications

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“…The CART and RF algorithm were used to establish the downscaling model at a grid pixel resolution of 25 km by 25 km. Multiple studies have acknowledged that precipitation data are strongly correlated to NDVI, LST, and DEM [31][32][33][34][35]. As precipitation is a spatially heterogeneous variable, geolocations (longitude and latitude) are also considered variables that reflect spatial variations in precipitation.…”

Section: Downscaling Algorithmmentioning

confidence: 99%

Mapping Fine Spatial Resolution Precipitation from TRMM Precipitation Datasets Using an Ensemble Learning Method and MODIS Optical Products in China

2017

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Precipitation data are important for the fields of hydrology and meteorology, and are fundamental for ecosystem monitoring and climate change research. Satellite-based precipitation products are already able to provide high temporal resolution precipitation information at a global level. However, the coarse spatial resolution has restricted their use in regional level studies. In this study, monthly fine spatial resolution land precipitation data in China was obtained by downscaling the TRMM 3B43 V7 monthly precipitation products. The downscaling model was constructed based on the ensemble learning method called random forest (RF). In addition to the RF model, the classification and regression tree (CART) model was also used to downscale the precipitation data for the purpose of comparison. The results were validated with in situ measurements. Results showed that the RF model outperformed the CART model. The downscaled precipitation data were strongly correlated with the in situ measurements. The downscaling method was applied to mapping fine spatial resolution precipitation over all of China, and is valuable for developing high spatial resolution precipitation products for studies on hydrology, meteorology, and climate science.

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Section: Downscaling Algorithmmentioning

confidence: 99%

Mapping Fine Spatial Resolution Precipitation from TRMM Precipitation Datasets Using an Ensemble Learning Method and MODIS Optical Products in China

2017

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“…Most parts of North China are typical of arid and semi-arid areas; the dry/wet state of the land surface is affected by precipitation hydrological processes [34]. The distribution of vegetation and the vegetation condition are highly correlated to precipitation [35,36]. Thus, the land surface temperature (LST) and NDVI are effective indicators of precipitation [37].…”

Section: Study Areamentioning

confidence: 99%

A Comparison of Different Regression Algorithms for Downscaling Monthly Satellite-Based Precipitation over North China

et al. 2016

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Environmental monitoring of Earth from space has provided invaluable information for understanding land-atmosphere water and energy exchanges. However, the use of satellite-based precipitation observations in hydrologic and environmental applications is often limited by their coarse spatial resolutions. In this study, we propose a downscaling approach based on precipitation-land surface characteristics. Daytime land surface temperature, nighttime land surface temperature, and day-night land surface temperature differences were introduced as variables in addition to the Normalized Difference Vegetation Index (NDVI), the Digital Elevation Model (DEM), and geolocation (longitude, latitude). Four machine learning regression algorithms, the classification and regression tree (CART), the k-nearest neighbors (k-NN), the support vector machine (SVM), and random forests (RF), were implemented to downscale monthly TRMM 3B43 V7 precipitation data from 25 km to 1 km over North China for the purpose of comparison of algorithm performance. The downscaled results were validated based on observations from meteorological stations and were also compared to a previous downscaling algorithm. According to the validation results, the RF-based model produced the results with the highest accuracy. It was followed by SVM, CART, and k-NN, but the accuracy of the downscaled results using SVM relied greatly on residual correction. The downscaled results were well correlated with the observations during the year, but the accuracies were relatively lower in July to September. Downscaling errors increase as monthly total precipitation increases, but the RF model was less affected by this proportional effect between errors and observation compared with the other algorithms. The variable importances of the land surface temperature (LST) feature variables were higher than those of NDVI, which indicates the significance of considering the precipitation-land surface temperature relationship when downscaling TRMM 3B43 V7 precipitation data.

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“…[3] In addition to the ocean, land surface can also provide a critical memory function in the climate system at the monthly and longer time scales [Shukla and Mintz, 1982;Yeh et al, 1984;Koster and Suarez, 1995;Zhang et al, 2003aZhang et al, , 2008Wu and Dickinson, 2004;Liu et al, 2006]. It has been suggested to be an important factor in the modulation of the monsoon circulation, and therefore offers the potential for improving the EASM prediction [e.g., Webster, 1987;Shukla, 1998].…”

Section: Introductionmentioning

confidence: 99%

“…Numerical experiments also showed that human-induced land use and land cover changes in East Asia have brought significant influence on the EASM [e.g., Xue, 1996;Fu, 2003;Gao et al, 2003;Cui et al, 2006;Takata et al, 2009]. In the early 2000s, Zhang et al [2003aZhang et al [ , 2003b and Kaufmann et al [2003] inferred vegetation effects on precipitation and/or temperature from observational records over China and the United States, respectively. Since then many researchers have made efforts to explore the role of vegetation in influencing surface climate using longterm satellite-sensed vegetation index and observational climate data [e.g., Liu et al, 2006;Notaro et al, 2006;Los et al, 2006;Wang et al, 2006;Hua et al, 2008].…”

Section: Introductionmentioning

confidence: 99%

The role of May vegetation greenness on the southeastern Tibetan Plateau for East Asian summer monsoon prediction

Zhang

Huang

et al. 2011

J. Geophys. Res.

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[1] It is well known that the slowly varying oceanic processes provide the primary source for East Asian summer monsoon (EASM) predictability. However, the memory inherent in the land surface state is less well understood or applied toward the EASM prediction. Here we investigate the role of antecedent vegetation conditions over East Asia for the EASM variation and prediction using March, April, May, and spring mean satellite-sensed Normalized Difference Vegetation Index (NDVI) for the period of . Results show that May vegetation greenness on the southeastern Tibetan Plateau (TP) is most closely linked to the EASM, accounting for about half of the total EASM variance. May vegetation greenness on the southeastern TP has significant and positive correlations with summer rainfall over the southeastern TP, East Asian summer subtropical frontal region, and many areas of northern China. We further discuss the possible physical mechanism explaining our findings. It is proposed that increased TP vegetation greenness enhances surface thermal effects, which subsequently warm atmospheric temperature, as well as strengthen ascending motion, convergence at the lower layers and divergence at the higher layers, and summer monsoon circulation. Finally, a linear regression model is developed to predict the EASM strength by combination of El Niño-Southern Oscillation (ENSO) and the vegetation greenness. Hindcast for the period shows that the use of the southeastern TP vegetation information can highly improve the EASM prediction skill compared to that using ENSO alone.

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The influence of vegetation cover on summer precipitation in China: A statistical analysis of NDVI and climate data

Cited by 68 publications

References 15 publications

Mapping Fine Spatial Resolution Precipitation from TRMM Precipitation Datasets Using an Ensemble Learning Method and MODIS Optical Products in China

Mapping Fine Spatial Resolution Precipitation from TRMM Precipitation Datasets Using an Ensemble Learning Method and MODIS Optical Products in China

A Comparison of Different Regression Algorithms for Downscaling Monthly Satellite-Based Precipitation over North China

The role of May vegetation greenness on the southeastern Tibetan Plateau for East Asian summer monsoon prediction

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