Spatial downscaling of TRMM‐based precipitation data using vegetative response in Xinjiang, China

Abstract

Station-based observed precipitation data are available in a limited way and only at low spatial resolution. High-resolution satellite rainfall products can help to monitor precipitation changes over large areas. The Tropical Rainfall Measuring Mission 3B43 (TRMM 3B43) precipitation data with coarse spatial resolution and low data accuracy are capable of depicting the spatial variability of precipitation, but fail to estimate the accurate magnitude. These data available in Xinjiang, China, need to be evaluated, especially because Xinjiang has a complex terrain and application of such data becomes a challenging task. Based on the relation between precipitation and normalized difference vegetation index (NDVI), a proxy for vegetation, a new statistical downscaling algorithm is proposed in this study. The calibration was based on geographical difference analysis (GDA) and the monthly fractions derived from the un-calibrated TRMM data can be used to disaggregate high-resolution annual precipitation to high-resolution monthly precipitation. The accuracy of downscaled TRMM precipitation was evaluated based on station-based observed precipitation for a period of 1998-2010. Results indicated that: (1) optimal relations shown by R 2 between TRMM precipitation and NDVI at different temporal and spatial scales were different: the largest R 2 value was 0.69 for the period of 1998-2010 at a spatial resolution of 1.00 ∘ × 1.00 ∘ , was 0.70 for the dry year (2001 in this study) at a spatial resolution of 0.75 ∘ × 0.75 ∘ , and was 0.67 for the wet year (2010 in this study) at a spatial resolution of 1.25 ∘ × 1.25 ∘ ; (2) the downscaled TRMM precipitation data obtained using NDVI described spatial patterns of precipitation reasonably well at a spatial resolution of 8 km × 8 km with more detailed information when compared to the raw TRMM precipitation at a spatial resolution of 0.25 ∘ × 0.25 ∘ ; (3) the downscaled TRMM precipitation with GDA calibration, P DSGDA , can exclude the regions with irrigation-and/or ground water-induced vegetation coverage. Therefore, P DSGDA obtained in this study can be regarded as estimated alternative precipitation data across Xinjiang for management of water resources and agricultural irrigation activities.

Fine-Resolution Precipitation Mapping in a Mountainous Watershed: Geostatistical Downscaling of TRMM Products Based on Environmental Variables

“…The GDA and GRA methods have proven to be simple and effective methods to correct the errors in the downscaled data [41,74,75]. In this study, calibration using GDA and GRA methods gave better annual precipitation in terms of statistical indicators for the three reference years and the entire time period.…”

Spatial Downscaling of GPM Annual and Monthly Precipitation Using Regression-Based Algorithms in a Mountainous Area

“…Jia et al [14] introduced both NDVI and Digital Elevation Model (DEM) as the explanatory variables in a Multiple Linear Regression (MLR) model. Independent station validation in the two studies and later studies [15,22,24,30] showed that the downscaled precipitation based on ER or MLR after residual correction was comparable with the original TRMM product but at much improved resolution.…”

“…Therefore, it is a feasible approach to downscale precipitation through establishing statistical models of precipitation and these factors, and this is termed as statistical downscaling algorithms. Their fundamental statistical theories could be classified into regression analysis [14,15,[22][23][24] and machine learning methods such as Artificial Neural Network [25,26] and random forests [27][28][29].…”

Evaluation and hydrologic validation of TMPA satellite precipitation product downstream of the Pearl River Basin, China

“…In order to provide an appropriate application guidance of the satellite‐based precipitation products in different regions (Li et al, ), both evaluation and validation of the accuracy and applicability of satellite‐based precipitation products over different regions around the world have become a necessary requirement. During past decades, numerous relevant studies have been carried out at national or basin scale in different regions (Behrangi et al, ; Diem et al, ; Kumar et al, ; Li, Zhang, & Xu, ; Liu, Chen, Lian, & Lou, ; Prakash et al, ; Ricardo et al, ; Su et al, ; Tekeli et al, ; Worqlul et al, ; Xue et al, ; Zhao & Yatagai, ), including in China such as the Tibetan Plateau (Meng et al, ; Tong et al, ; Zeng, Li, & Li, ), the north‐west China (Yang & Luo, ; Zhang et al, ), south‐west China (Liu et al, ), north‐east China (Yong et al, , ), and south China (Jiang et al, ; Hu, Yang, Wang, & Yang, ; Hu, Yang, Wang, Yang, & Liu, ; Li et al, , ; Tang et al, ; Yong et al, ). In these studies, the in situ observation‐based comparisons were widely used as the method of evaluation.…”