【作者】 胡庆芳；

【导师】 杨大文；

【作者基本信息】 清华大学 ， 水利工程， 2013， 博士

【摘要】 精确推求降水空间分布是水文气象领域一项重要的科学研究目标，也是水文分析预报、自然灾害防治等工作的重要基础。近年，联合地表雨量站网观测数据和卫星遥测信息估计降水空间分布成为重要的热点问题之一。本文以我国多雨区之一的赣江流域为例，在研究传统的降水空间估计方法和代表性卫星降水数据精度的基础上，重点探讨了地表雨量站网和卫星降水信息的融合方法，深入剖析了降水信息融合的效果，并将降水融合数据应用于流域水文模拟中。首先引入地理加权回归方法建立了流域降水空间估计模型，改进了传统的空间插值中降水的局部空间自相关性以及与地理信息互相关性的定量描述方式。以此为基础，在统计平均意义上进一步揭示了赣江流域降水空间估计精度随雨量站网密度的变化特征，证实了大至当站网密度低于1站/1300km~2时，降水空间估计精度随站网密度变化而急剧变化；而当雨量站网密度约高于1站/380km~2时，降水空间估计精度随站网密度的变化不明显。同时，针对赣江流域，在日、月两种时间尺度上和0.25°×0.25°栅格单元、子流域和全流域三种空间尺度上，系统评价了TRMM3B42/3B43V7、TRMM3B42RTV7、CMORPH、PERSIANN四种代表性卫星降水数据的精度及其季节性变化特征、空间差异，发现卫星降水数据的定量误差虽然比较明显，但能动态提供有效的流域降水时空信息，对地面雨量站网观测具有较好的补充作用。采用地理加权回归方法，构建了降水信息融合模型，开展了赣江流域地表雨量站网观测与TRMM3B42/3BV43V7或CMORPH数据的融合试验。发现仅当雨量站网密度约低于1站/2500km~2时，降水信息融合模型的估计精度才逐步高于传统的空间插值模型。在雨量站网密度约为1站/7500km~2时，相对传统插值模型，雨量站网观测信息融合CMORPH日数据，可提高降水估计的空间相关系数约33%、降低平均绝对值误差约16%。在赣江流域19个集水区域，通过大量的水文模拟试验，证实了在雨量站网比较稀疏的条件下，相对于空间插值数据，降水融合数据可显著提高月、日径流模拟精度。当雨量站网密度约为1站/7500km~2时，采用降水融合数据，多数集水区域日径流模拟效率系数可提高0.15以上、最大超过0.40,日径流总量相对误差可削减5%左右、最大超过20%。更多还原

【Abstract】 Accurately mapping precipitation spatial distribution has been an important task inhydrometeorology. It also plays a fundamental role in hydrology analysis, naturaldisasters control, and so on. Recently, combining ground observed and satellite sensedprecipitation to obtain regional estimates has inspired a flurry of research. Thisdissertation therefore aims to estimate precipitation spatial distribution usingmulti-source information including surface rainfall observation and satellite sensing.Particularly, selecting the Gangjiang River Basin as the study area, we carried outresearch from4major aspects. First, we studied traditional precipitation interpolationmodels. Second, we evaluated the accuracy of representative satellite precipitationproducts. Third, rainfall data merging methods were proposed and evaluated. Finally,the merged data was fed to drive hydrology models to understand the merits ofprecipitation merging in improving runoff simulation accuracy.Based on the geographically weighted regression (GWR), we proposed aprecipitation spatial interpolation scheme. This scheme improves the traditionalrepresentation of precipitation locally spatial autocorrelation and its cross-correlationwith geographic factors. Using it, we explored the relationship between the accuracy ofprecipitation spatial estimation and the ground gauges density. When the ground gaugesdensity is approximately lower than1300km~2per gauge, the estimation accuracy variesdramatically with the gauges number; whereas if the density is above380km~2per gaugeapproximately, the accuracy is insensitive to the gauge numbers variation.We then comprehensively evaluated the spatial and temporal accuracy of4representative satellite precipitation products，and analyzed their seasonal andspatial variability. The selected satellite products include TRMM3B42/3B43V7,3B42RTV7, CMORPH and PERSIANN. We focused on2different temporalscales, i.e., daily and monthly scales, and3spatial scales, i.e.,0.25°×0.25°grid,sub-basin, and the whole study area scales. Analyses indicate that thequantitative error of satellite precipitation is significant. However, they coulddynamically provide useful information for rainfall spatio-temporal distributionand thus can complement relatively sparse ground observations. In order to combine the advantages of both ground observed and satelliteprecipitation, we then developed GWR-based data merging methods. With thesemethods, we conducted precipitation merging tests. We combined daily andmonthly ground observations from gauge networks of different densities, withTRMM3B43/3B42V7and CMOPRH satellite data, respectively. When thegauge network density is below2500km~2per gauge approximately, the accuracyof spatial precipitation estimation obtained by data merging is gradually greaterthan that obtained by traditional interpolation methods only using groundobservations. Pertaining to daily precipitation, when the ground gauges densityis about7500km~2per gauge, compared with the traditional spatial interpolationmodel, spatial estimation obtained by merging ground observations andCMORPH increases approximately by33%in spatial correlation coefficient anddecreases by16%in average absolute error.Through extensive hydrologic simulations in19catchments within thestudy area, we further investigated the effect of merged precipitation data onrunoff simulation. When the gauge network is relatively sparse, combinedprecipitation can significantly improve runoff simulation accuracy. Moreprecisely, when the ground gauge number throughout the study area is11, thedetermination coefficient of simulated daily runoff can be improved by morethan0.15with a maximum value over0.4; the relative error of total runoffvolume can be reduced by greater than5%with a maximum values over20%.更多还原