摘要
高分辨率降水数据有助于刻画降水的时空分异特性,对流域水文、气象和生态等过程的精准模拟具有重要作用,因此对低分辨率降水产品开展空间降尺度,提高其分辨率十分必要。鉴于此,该研究在充分考虑热带降雨测量卫星(Tropical Rainfall Measuring Mission,TRMM)降水产品在渭河流域适用性的基础上,引入归一化差分植被指数(Normalized Difference Vegetation Index,NDVI)、数字高程模型(Digital Elevation Model,DEM)、坡度、坡向和经纬度等地理环境因子,构建了多尺度地理加权回归(Multi-scale Geographically Weighted Regression,MGWR)模型用以分析不同因子对渭河流域降水空间格局影响的尺度差异;并提出了一种针对TRMM降水产品的空间降尺度方法,通过精度评价验证降尺度结果的可靠性。结果表明:1)TRMM降水产品数据相较于站点实测数据存在一定精度误差,年尺度上R2=0.807,BIAS=2.909%,RMSE=83.477 mm,表现较好;季尺度上秋季R2最高,为0.847,夏季RMSE最大,为62.393 mm,四季的BIAS均较低;月尺度R2为0.456~0.815,BIAS的绝对值介于0~14%之间,多数月份为正值,RMSE值域范围为3.507~39.342 mm,精度较好;总体而言,TRMM降水产品数据在年、季和月尺度上均表现出良好的整体适用性。2)不同因子在干湿年份对降水空间分异格局的影响呈现出不同的尺度特征,其中湿润年的DEM、NDVI、坡向和经纬度对降水呈现局部影响,坡度影响具有全局性,而干旱年各因子均表现为局部影响。3)流域和站点尺度上,降尺度TRMM数据相较于降尺度前产品数据精度得到一定改善,流域尺度上,R2和RMSE得到有限提高;站点尺度上,各站点统计指标变化各异,但降尺度后统计指标整体优于降尺度前,并且由于时间尺度上的误差累积,站点年尺度数据精度相比月尺度数据稍差。4)降尺度TRMM数据相比于降尺度前产品数据,空间分布更细腻,细节特征表现更好,且在年、月时间尺度上均具有较高的精度,可为渭河流域资料短缺地区的水文设计提供数据支撑。
High-resolution precipitation data can be directly used to characterize the spatial-temporal differentiation features of precipitation after the accurate simulation of hydrological,meteorological,and biological systems.Therefore,it is crucial to implement the spatial downscaling for the precipitation products with the low spatial resolution.However,it is still lacking on the precision and detail features in the downscaling precipitation data,due to fail to consider the scale variations in the spatial distribution of precipitation.In this study,a spatial downscaling approach was proposed to improve the TRMM precipitation data in the Wei River basin(WRB).A Multi-scale Geographically Weighted Regression(MGWR)model was also integrated to enable the conditional relationships between the response and predictor variables that changed at the spatial scales.Therein,the goodness of fit(R2),relative deviation(BIAS),and Root Mean Square Error(RMSE)were employed to verify the TRMM satellite precipitation product,compared with the actual precipitation data from meteorological stations.Normalized Difference Vegetation Index(NDVI),digital elevation model(DEM),slope,aspect,latitude and longitude were induced as the Geographic Environmental Factors(GEFs).The MGWR models with the monthly TRMM precipitation data were constructed to further investigate the scale effects of factors on precipitation distribution.The spatial downscaling of TRMM production data was then implemented using a scale conversion process.Finally,the reliable spatial downscaling was achieved in the TRMM precipitation products.The results illustrated that:1)The TRMM precipitation data was better suited for use at different scales in the WRB.An acceptable fitness was found in the statistics of R2(0.807),BIAS(2.909%),and RMSE(83.477 mm)at the annual scale.Specifically,the maximum R2 was 0.847 at the seasonal scale,the largest RMSE was 62.393 mm in the summer,and the BIAS values were lower in all four seasons.More importantly,the R2 varied between 0.456 and 0.815 on the monthly scale,with the smallest value in June and the largest value in September.The BIAS was positive in the most months,indicating that the TRMM product data generally overestimated the precipitation.The RMSE index was fallen in the range of 3.507-39.342 mm,which was lower than those on the annual and seasonal scales.2)Different scale characteristics were found in the influence of various GEFs on the spatial pattern of precipitation divergence in wet and dry years.Slope was set as the global scale,whereas the DEM,NDVI,aspect,latitude,and longitude were the local effects on the precipitation in wet years,and all GEFs were used the local impacts in dry years.3)The more precise data was obtained in the downscaled TRMM on the watershed and station scales,compared with the product data,indicating an increase in the R2 of the entire watershed of 3%,while a decrease in the RMSE of 1 mm.However,the accuracy of station downscaling precipitation data at the annual scale was worse than that at the monthly scale,due to the accumulation of errors on the time scale,as shown by the R2 range decreasing,while the RMSE range increasing.4)The downscaled TRMM data presented the better detailed characteristics,the greater precision at annual and monthly scales,and a much more delicate geographical distribution than the product data.The finding can provide a strong data support for the hydrological design in the areas with less precipitation data.
作者
黎扬兵
张洪波
杨天增
吕丰光
王雨巍
姚聪聪
Li Yangbing;Zhang Hongbo;Yang Tianzeng;Lyu Fengguang;Wang Yuwei;Yao Congcong(School of Water and Environment,Chang’an University,Xi’an 710054,China;Key Laboratory of Eco-hydrology and Water Security in Arid and Semi-arid Regions of the Ministry of Water Resources,Xi’an 710054,China)
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2022年第23期141-151,共11页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家自然科学基金项目(51979005)
陕西省自然科学基础研究计划项目(2020JM-250)。
