Abstract
A geostatistical downscaling scheme is presented and can generate fine scale precipitation information from coarse scale Tropical Rainfall Measuring Mission (TRMM) data by incorporating auxiliary fine scale environmental variables. Within the geostatistical framework, the TRMM precipitation data are first decomposed into trend and residual components. Quantitative relationships between coarse scale TRMM data and environmental variables are then estimated via regression analysis and used to derive trend components at a fine scale. Next, the residual components, which are the differences between the trend components and the original TRMM data, are then downscaled at a target fine scale via area-to-point kriging. The trend and residual components are finally added to generate fine scale precipitation estimates. Stochastic simulation is also applied to the residual components in order to generate multiple alternative realizations and to compute uncertainty measures. From an experiment using a digital elevation model (DEM) and normalized difference vegetation index (NDVI), the geostatistical downscaling scheme generated the downscaling results that reflected detailed characteristics with better predictive performance, when compared with downscaling without the environmental variables. Multiple realizations and uncertainty measures from simulation also provided useful information for interpretations and further environmental modeling.
Highlights
Precipitation information has been regarded as one of the important information sources for understanding hydrological, ecological, and environmental systems [1,2,3]
In addition to the two variables, the interaction of digital elevation model (DEM) with normalized difference vegetation index (NDVI) was included in the multiple regression analysis, since forest areas showing high NDVI values tend to be located in high altitude zones
DEM and the interaction of DEM with NDVI were chosen as statistically significant variables and the regression relation between precipitation and those two variables was modeled as z∗ (Vk) = 17.508 + 0.513 DEM (Vk) (5)
Summary
Precipitation information has been regarded as one of the important information sources for understanding hydrological, ecological, and environmental systems [1,2,3]. Jia et al [3] proposed a statistical scheme for the downscaling of TRMM data with fine scale elevation and normalized difference vegetation index (NDVI) They first conducted multiple regression analysis at various spatial scales interpolated the residuals to the fine scale grid, and generated downscaling results by adding the trend component by regression analysis at an optimal spatial scale to the residuals at a fine scale. Regression analysis is first conducted to derive statistical relationships between TRMM data and auxiliary environmental variables at an original coarse scale. The final downscaling results are obtained by adding the downscaled residuals to the trend components estimated by regression analysis This approach can reproduce the original TRMM precipitation values when the downscaling results at a fine scale are upscaled or aggregated to the coarse scale. South Korea is carried out to examine the potential and demonstrate the applicability of the presented geostatistical scheme for downscaling
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have