Spatial Downscaling Of Precipitation By Using Multi-source Remote Sensing Data

Precipitation data with high spatial resolution is deemed necessary for hydrology,meteorology, ecology and other applications. Currently there are mainly two sourcesof precipitation estimates: meteorological stations and remote sensing technology.However, a large number of studies demonstrated that measured by conventionalmeteorological stations are single points of data，and cannot reflect the spatialvariation of precipitation effectively, especially in the study of more complex areas.While the technology of remote sensing can not only improve the quality of the actualobservations, and be able to produce reasonably high resolution gridded precipitationfields. These products obtained by satellites have been widely used in previous studiesof the world. However, when applied to local basins and regions, the spatial resolutionof these products is too coarse and data accuracy is not high. Therefore, we present astatistical downscaling algorithm for high spatial resolution and further use in otherrelated applied research. This study holds great significance in theSichuan-Chongqing region.In this study, the author validated the applicability of TRMM precipitation datain the Sichuan-Chongqing region with measured data of meteorological stations instudy area and remote sensing and geographic information technologies. Usingdownscaling method which based on the relationships between precipitation and otherenvironmental associated factors in the Sichuan-Chongqing region such astopography and vegetation obtained precipitation data with higher spatial resolution.Finally, using downscaling precipitation data, combined with Normalized DifferenceVegetation Index (NDVI) and Land Surface Temperature (LST) data from Moderateresolution Imaging Spectroradiometer (MODIS)13A3and11A2dataset for droughtmonitoring in the Sichuan-Chongqing region. The conclusions are as follows: (1)TRMM precipitation data is applicable to Sichuan-Chongqing region.Compared with the72rain gauges, TRMM precipitation data shows strong correlation(R generally reached above0.8) and little numerical biases in the whole study area atannal, seasonal and monthly time scale, which indicates a higher precision in Sichuanand Chongqing. But complex terrain factors (e.g. elevation, slope) ofSichuan-Chongqing region have a great impact on rainfall data of the satellite.(2)Downscaling algorithm based on NDVI data and geographic factors isfeasible. We explored the relation among precipitation, Geographical factors andNormalized Difference Vegetation Index (NDVI), which is a proxy for vegetation;therefore, multiple regression models was established under0.25°. By selecting oneof the best downscaling methodologies as the final downscaling algorithm, TRMM3B430.25°×0.25°precipitation fields were downscaled to1×1km pixel precipitationfor each year from2000to2011in this study. Secondly, the calibration ofdownscaling precipitation was based on Geograhical Difference Analysis (GDA) andGeographical Ratio Analysis (GRA). The final downscaling estimation results werevalidated by applying TRMM3B43precipitation data and part of meteorologicalstations measured precipitation data for a duration of12years in Sichuan-Chongqingregion. AS a whole, these results indicated the best1km annual precipitation data areachieved through downscaling followed by GDA calibration for most cases. Themonthly fractions derived from the downscaling results can be used to disaggregate1km annual precipitation to1km monthly precipitation. The disaggregated1kmmonthly precipitation has not only significant improvement in the spatial resolution,but also good agreements with meteorological stations data were achieved forSichuan-Chongqing region. Precipitation of a similar calibration procedure usingremaining rain gauges at monthly time scale improve slightly than the disaggregated1km monthly precipitation.(3) Using downscaling precipitation data, combined with NDVI and LST datafrom MODIS13A3and11A2dataset for drought monitoring in theSichuan-Chongqing region. In this paper, the synthesized drought index (SDI) isdefined as a principal component of vegetation condition index (VCI), temperaturecondition index (TCI) and precipitation condition index (PCI). SDI integratesmulti-source remote sensing data and it synthesizes precipitation deficits, soil thermalstress and vegetation growth status in drought process. Therefore, this method isfavorable to monitor the comprehensive drought. As an example, the monthly synthesized drought index of Sichuan and Chongqing was used to analyze the spatialand temporal variations of drought in2006. The results indicated that the evolution ofthe whole process of drought event was monitored and it in accord with relatedresearch. In our research, a heavy drought process was accurately explored using SDIin the Sichuan-Chongqing region from2000to2011. Finally, a validation wasimplemented and its results show that SDI is not only strongly correlated with3-month scales standardized precipitation index (SPI3), but also with variation of cropyield and drought-affected crop areas. It was proved that this index is acomprehensive drought monitoring indicator and it can contain not only themeteorological drought information but also it can reflect the drought influence onagriculture.