基于多源信息的降水空间估计及其水文应用研究
|
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%。
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%.
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%。
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%.
引文
Ahnert P R, Krajewski W F, Johnson E R.1986. Kalman filter estimation of radar-rainfall field bias.Preprints of23rd Radar Meteorology Conference, Snowmass: AMS:33-37.
Ahrens B.2006. Distance in spatial interpolation of daily rain gauge data, Hydrology and EarthSystem Science,10(2):197-208.
Allen R G, Pereira L S, Raes D, et al.1998. Crop evapotranspiration: Guidelines for computingcrop requirements. Irrigation and Drainage Paper No.56, FAO, Rome, Italy.
Barnes S L.1964. A technique for maximizing details in numerical weather map analysis. Journalof Applied Meteorology,3:396-409.
Bhargava M, Danard M.1994. Application of optimum interpolation to the analysis of precipitationin complex terrain. Journal of Applied Meteorology,33:508-518.
Boushaki, Farid I, Hsu K L, et al.2009. Bias adjustment of satellite precipitation estimation usingground-based measurement: a case study evaluation over the southwestern UnitedStates. Journal of Hydrometeorology,10:1231-1242.
Brunsdon C, Fotheringham A S, Charlton M.1996. Geographically weighted regression: a methodfor exploring spatial nonstationarity. Geographical analysis,28(4):281-298.
Brunsdon C.1998. Geographically weighted regression: a natural evolution of the expansionmethod for spatial data analysis. Environment and Planning A,30:1905-1927.
Brunsdon C, McClatchey J, Unwin D J.2001. Spatial variations in the average rainfall–altituderelationship in Great Britain: an approach using geographically weighted regression.International Journal of Climatology21(4):455-466.
Chiang Y M, Hsu K L, Chang F J, et al.2007. Merging multiple precipitation sources for flashflood forecasting. Journal of Hydrology,340(3):183-196.
Chiang S H, Chang K T.2009. Application of radar data to modeling rainfall-induced landslides.Geomorphology,103(3):299-309.
Chiew F H S, Peel M C，Western A W.2002. Application and testing of the simple rainfall-runoffmodel SIMHYD. In: Mathematical Models of Watershed Hydrology, Water ResourcesPublication, Littleton, Colorado.
Cleveland W S.1979. Robust locally weighted regression and smoothing scatter plots. Journal ofThe American statistical Association,74(368):829-836.
Collischonn, B, Collischonn W, Tucci C.E.M.2008. Daily hydrological modeling in the Amazonbasin using TRMM rainfall estimates. Journal of Hydrology,360(1-4),207-216.
Daly C, Neilson R P, Phillips D L.1994. A statistical-topographic model for mapping climatologicalprecipitation over mountainous terrain. Journal of Appllied Meteorology,33:140-158.
Dinku T, Ruiz F, Connor S J, et al.2010. Validation and intercomparison of satellite rainfallestimates over Colombia. Journal of Applied Meteorology and Climatology,49:1004-1014.
Duan Q, Sorooshian S, Gupta V K.1994.Optimal use of the SCE-UA global optimization methodfor calibrating watershed models. Journal of Hydrology,158(3):265-284.
Duan Z, Bastiaanssen W.2013. First results from Version7TRMM3B43precipitation product incombination with a new downscaling-calibration procedure. Remote Sensing of Environment131(15):1-13.
Ebert E E, Janowiak J E, Kidd C.2007. Comparison of near-real-time precipitation estimates fromsatellite observations and numerical models. Bulletin of the American Meteorological Society,88:47-64.
Edijatno, Nascimento N, Yang X, et al.1999. GR3J: a daily watershed model with three freeparameters. Hydrological Sciences Journal,44(2):263-277.
Ehret U.2002. Rainfall and flood nowcasting in small catchments using weather radar. PhD Thesis,University of Stuttgart.
Engeland K, Renard B, Steinsland I, et al.2010. Evaluation of statistical models for forecast errorsfrom the HBV model. Journal of Hydrology,384(1-2):142-155.
Foody G M.2003. Geographical weighting as a further refinement to regression modeling: Anexample focused on the NDVI-rainfall relationship. Remote Sensing of Environment,88,283-293.
Fotheringham A S, Brunsdon C, Charlton M.2002. Geographically weighted regression: theanalysis of spatially varying relationships. John Wiley&Sons, New York.
Fu Q, Liu R. B.2011. Accuracy assessment of global satellite mapping of precipitation (gsmap)product over Poyang Lake Basin, China. Procedia Environmental Sciences10:2265-2271.
Gebregiorgis A, Hossain F.2011. How much can a priori hydrologic model predictability help inoptimal merging of satellite precipitation products?. Journal of Hydrology,12(6):1287-1298.
Goodrich D C, Faures J, Woohiser D A, et al.1995. Measurement and analysis of small scaleconvective storm rainfall variability. Journal of Hydrology,173:283-3081.
Goovaerts P.1997. Geostatistics for natural resources evaluation. New York: Oxford UniversityPress.
Goovaerts P.2000. Geostatistical approaches for incorporating elevation into the spatialinterpolation of rainfall. Journal of Hydrology,228(1):113-129.
Gorenburg, I. P., D. McLaughlin, Entekhabi D.2001. Scale-recursive assimilation of precipitationdata. Advances in Water Resources,24(9):941-953.
Goudenhoofdt E, Delobbe L.2009. Evaluation of radar-gauge merging methods for quantitativeprecipitation estimates. Hydrology and Earth System Sciences,13(2):195.
Grimes D I F, Pardo-Iguzquiza E, Bonifacio R.1999. Optimal areal rainfall estimation usingraingauges and satellite data. Journal of Hydrology,222(1):93-108.
Grist J, Nicholson S E, Mpolokang A.1996. On the use of NDVI for estimating rainfall fields in theKalahari of Botswana. Journal of Arid Environments,35(2):195–214.
Gruber A, Su X J, Kanamitsu M, et al.2000. The comparison of two merged rain gauge-satelliteprecipitation datasets. Bulletin of American Meteorology Society,81:2631-2644.
Gupta R V. Venugopal, Foufoula-Georgiou E.2006. A methodology for merging multisensorprecipitation estimates based on expectation-maximization and scale-recursive estimation.Journal Geophysics Research,111(D2): D02102.
Hengl T, Heuvelink G B M, Stein A.2004. A generic framework for spatial prediction of soilvariables based on regression kriging. Geoderma,120(1):75-93.
Hengl T, Gerard B M. Heuvelink, et al.2007. About regression-kriging: From equations to casestudies. Computers&Geosciences,33(10):1301-1315.
Hewitson B C, Crane R G.2005. Gridded area-averaged daily precipitation via conditionalinterpolation. Journal of Climate.18(1):41-57.
Hirpa F A, Gebremichael M, Hopson T.2009. Evaluation of high resolution satellite precipitation5products over very complex terrain in Ethiopia. Journal of Applied Meteorology andClimatology,49:1044-1051.
Hofstra N, Haylock M, New M, et al.2008. Comparison of six methods for the interpolation ofdaily, European climate data. Journal of Geophysical Research,113(D21): D21110.
Hong Y, Adler R F, Huffman G J.2007. An experimental global prediction system forrainfall-triggered landslides using satellite remote sensing and geospatial datasets. IEEETransactions on Geoscience and Remote Sensing,45(6):1671-1680.
Hurvich C M, Simonoff J S, Tsai C L.2002. Smoothing parameter selection in nonparametricregression using an improved Akaike information criterion. Journal of the Royal StatisticalSociety: Series B (Statistical Methodology),60:271-293.
Huffman G J, Bolvin D T, Nelkin E J, et al.2007. The TRMM Multisatellite Precipitation Analysis(TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J.Hydrometeor,8:38-55.
Huffman G J, Bolvin D T.2011. TRMM and other data precipitation data set documentation[EB/OL].(2013-01-28). ftp://precip.gsfc.nasa.gov/pub/trmmdocs/3B42_3B43_doc.pdf.
Hsu K, Gupta H V, Gao X, et al.1999. Estimation of physical variables from multichannelremotely sensed imagery using a neural network: Application to rainfall estimation. WaterResource Research,35:1605-1618.
Immerzeel W W, Rutten M. M, Droogers P.2009. Spatial downscaling of TRMM precipitationusing vegetation response on the Iberian Peninsula. Remote Sensing of Environment,113(2):362-370.
Isaaks E H, Srivastava R M.1989. Applied Geostatistics. New York: Oxford University Press.
Jiang S, Ren L, Yong B, et al.2010. Evaluation of high-resolution satellite precipitation productswith surface rain gauge observations from Laohahe Basin in northern China. Water Scienceand Engineering,3:405-417.
Jiang S, Ren L, Hong Y, et al.2012. Comprehensive evaluation of multi-satellite precipitationproducts with a dense rain gauge network and optimally merging their simulated hydrologicalflows using the Bayesian model averaging method. Journal of Hydrology,452-453:213-225.
Jiang W G, Hou P, Zhu X H et al.2011. Analysis of vegetation response to rainfall with satelliteimages in Dongting Lake. Journal of Geographical Science,21(1):135–149.
Jia S, Zhu W, Lu A, et al.2011. A statistical spatial downscaling algorithm of TRMM precipitationbased on NDVI and DEM in the Qaidam Basin of China. Remote Sensing of Environment115(12):3069-3079.
Joyce J J, Janowiak J E, Arkin P A, et al.2004. CMORPH: A method that produces globalprecipitation estimates from passive microwave and infrared data at high spatial and temporalresolution. Journal of Hydrometeorology,5:487-503.
Kavetski D, Kuczera G, Franks S W.2006. Bayesian analysis of input uncertainty in hydrologicalmodeling:2. Application. Water Resources Research,42,(W03408)(doi:10.1029/2005WR004376).
Kidd C, Levizzani V, Turk J, Ferraro R.2009. Satellite precipitation measurements for waterresources monitoring. Journal of the American Water Resources Association,45(3):567-579.
Koppel J V, Rietkerk M, Langevelde F V.2002. Spatial heterogeneity and irreversible vegetationchange in semiarid grazing systems. The American Naturalist,159(2):209-218.
Krajewski W.1987. Cokriging radar-rainfall and rain gage data. Journal of Geophysical Research92(D8):9571-9580.
Krzyzstofowicz R.1999. Bayesian theory of probabilistic forecasting via deterministic hydrologicalmodel. Water Resources Research,35(9),2739-2750.
Krzyzstofowicz R.2001. The case for probabilistic forecasting in hydrology. Journal of Hydrology,249(1):2-9.
Kummerow C, William B, Toshiaki K, et al.1998. The Tropical Rainfall Measuring Mission(TRMM) Sensor Package. Journal of Atmosphere and Oceanic Technology,15(3):809-817.
Kubota T, Ushio T, Shige S, et al.2009. Verification of high-resolution satellite-based rainfallestimates around Japan using a gauge-calibrated ground-radar dataset. Journal of theMeteorological Society of Japan,87(A):203-222.
Lall U, Moon Y I, Kwon H H, et al.2006. Locally weighted polynomial regression: Parameterchoice and application to forecasts of the Great Salt Lake. Water Resources Research,42(5):W05422.
Lesage J P.1999. A spatial econometric examination of China's economic growth. GeographicInformation Sciences,5(2):143-15.
Leung Y, Mei C L, Zhang W X.2000. Statistical tests fors patial nonstationarity based on thegeographyically weightedreg ressionmodel. Environment and Planning A,32(1):9-32.
LeungY, Mei C L, Zhang W X.2000.Testing for spatial autocorrelation among the residuals of thegeographically weighted regression. Enviroment and Plaiming A,32(5):871-890.
Lewis R M, Virginia T, A globally convergent augmented lagrangian pattern search algorithm foroptimization with general constraints and simple bounds. SIAM Journal on Optimization,12(4):1075-1089.
Li M, Shao Q.2010. An improved statistical approach to merge satellite rainfall estimates and raingauge data. Journal of Hydrology,385(1):51-64.
Loader C.1999. Local Regression and Likelihood. New York: Springer.
Mair A, Fares A.2010. Comparison of rainfall interpolation methods in a mountainous region of atropical island. Journal of Hydrologic Engineering,16(4),371-383.
Michaelides S, Levizzani V, Anagnostou E, et al.2009. Precipitation: measurement, remote sensing,climatology and modeling. Atmospheric Research,94(4):512-533.
Mitra A. K, Bohra A, Rajeevan M N, et al.2009. Daily Indian precipitation analysis formed from amerge of rain-gauge data with the TRMM TMPA satellite-derived rainfall estimates. Journal ofthe Meteorological Society of Japan,87A:265-279.
Ninomiya K, Akiyama T.1978. Objective analysis of heavy rainfalls based on radar and gaugemeasurement. Journal of Meteorology Society of Japan,50:206-210．
Oke A, Frost A, Beesley C.2009. The use of TRMM satellite data as a predictor in the spatialinterpolation of daily precipitation over Australia, in Proceedings of the18th WorldIMACS/MODSIM Congress, edited by Anderssen R, Braddock R, Newham L C, Australia.
Ouma Y O, Owiti T, Kipkorir E, et al.2012.Multitemporal comparative analysis of TRMM-3B42satellite-estimated rainfall with surface gauge data at basin scales: daily, decadal and monthlyevaluations. International Journal of Remote Sensing,33:7662-7684.
Páez A, Uchida T, Miyamoto K.2002. A general framework for estimation and inference ofgeographically weighted regression models:1. Location-specific kernel bandwidths and a testfor locational heterogeneity. Environment and Planning A,34(4):733-754.
Pagano T C, Hapuarachchi P, Wang Q J.2010. Continuous rainfall-runoff model comparison andshort-term daily streamflow forecast skill evaluation. CSIRO Tech. Rep.
Pardo-Igu′zquiza E.1998. Comparison of geostatistical methods for estimating the areal averageclimatological rainfall mean using information of precipitation and topography. InternationalJournal of Climatology,18:1031-1047.
Pereira-Filho A J, Crawford K C, Hartzell C,1998. Improving WSR-88D Hourly Rainfall Estimates.Weather and Forecasting,13(4):1016-1028.
Perrin C, Michel C, Andréassian V.2003. Improvement of a parsimonious model for streamflowsimulation. Journal of Hydrology,279:275-289.
Rojas O, Rembold F, Delincé J.2011.Using the NDVI as auxiliary data for rapid quality assessmentof rainfall estimates in Africa. International Journal of Remote Sensing,32(12):3249-3265.
Romilly T, Gebremichael M.2011. Evaluation of satellite rainfall estimates over Ethiopian riverbasins, Hydrology and Earth System Sciences,15:1505-1514.
Rozante J R, Demerval S M, Luis G G, et al.2010. Combining TRMM and surface observations ofprecipitation: technique and validation over South America. Weather Forecasting,25:885-894.
Sasaki.1970. Some basic formulas in numerical variation analysis．Monthly Weather Review,98:875-883.
Scheel M, Rohrer M, Huggel C, et al.2010. Evaluation of TRMM Multi-satellite PrecipitationAnalysis (TMPA) performance in the Central Andes region and its dependency on spatial andtemporal resolution. Hydrology and Earth System Sciences Discussions,7:8545-8586.
Seo D J.1998. Real-time estimation of rainfall fields using radar rainfall and rain gage data.Journal of Hydrology,208(1):37-52.
Simonneaux V, Hanich L, Boulet G, et al.2008. Modelling runoff in the Rheraya Catchment (HighAtlas, Morocco) using the simple daily model GR4J. Trends over the last decades[C]//13thIWRA World Water Congress, Montpellier, France.
Smith E G. Ghassem A, Yoji F, et al.2007. International global precipitation measurement (GPM)program and mission: An overview. Measuring Precipitation from Space:611-653. InMeasuring Precipitation from Space: EURAINSAT and the Future, Levizzani V, and Turk F J,Eds. Norwell, MA: Kluwer.
Sinclair S, Pegram G.2005. Combining radar and rain gauge rainfall estimates using conditionalmerging. Atmospheric Science Letters,6,19-22.
Sokol Z.2003. Utilization of regression models for rainfall estimates using radar-derived rainfalldata and rain gauge data. Journal of Hydrology,278(1):144-152.
Sorooshian S, Hsu K, Gao X, et al.2000. Evolution of PERSIANN system satellite-based estimatesof tropical rainfall. Bulletin of American Meteorology Society,81:2035-2046.
Sohn B, Han H J, Seoe K.2010.Validation of satellite-based high-resolution rainfall products overthe Korean peninsula using data from a dense rain gauge network. Journal of AppliedMeteorology and Climatology,49:701-714.
Shrestha M G, Artan S B, Sharma R.2008. Using satellite-based rainfall estimates for streamflowmodelling: Bagmati Basin, Journal of Flood Risk Management,1(2):89-99.
Shrestha M S.2011. Bias-adjustment of satellite-based rainfall estimates over the CentralHimalayas of Nepal for flood prediction. PhD Thesis. Kyoto, Japan: Kyoto University.
Stillman ST, Wilson J P, Daly C, et al.1996. Comparison of ANUSPLIN, MTCLIM-3D, andPRISM precipitation estimates. in The Third International Conference/Workshop onIntegrating GIS and Environmental Modeling. National Center for Geographic Informationand Analysis, Santa Fe, NM.
Stisen S, Sandholt I.2010. Evaluation of remote-sensing-based rainfall products through predictivecapability in hydrological runoff modelling. Hydrological Processes,24(7):879-891.
Su, F, Y. Hong, Lettenmaier D P.2008. Evaluation of TRMM Multisatellite Precipitation Analysis(TMPA) and its utility in hydrologic prediction in the La Plata Basin. Journal ofHydrometeorology,9(4):622-640.
Su F, Gao H, Huffman G J, et al.2011. Potential utility of the real-time TMPA-RT precipitationestimates in streamflow prediction. Journal of Hydrometeorology,12(3):444-455.
Sun R, Chen L, Fu B.2011. Predicting monthly precipitation with multivariate regression methodsusing geographic and topographic information. Physical Geography,32(3):269-285.
Syed T H, Lakshmi V, Paleologos E, et al.2004. Analysis of process controls in land surfacehydrological cycle over the continental United States. Journal of Geophysical Research,109(D22105),(doi:10.1029/2004JD004640).
Takara K, Yamashiki Y, Sassa K, et al.2010. A distributed hydrological-geotechnical model usingsatellite-derived rainfall estimates for shallow landslide prediction system at a catchment scale.Landslides,7(3):237-258.
Teo C K.2006. Application of satellite-based rainfall estimates to crop yield forecasting in Africa.Ph. D. Thesis, University of Reading, UK.
Thornton P E. Running S W, White M A.1997. Generating surfaces of daily meteorologicalvariables over large regions of complex terrain. Journal of Hydrology,190(3):214-251.
Thornton P.1999. DAYMET US Data Center for daily surface weather data and climatologicalsummaries. University of Montana, Numerical Terradynamic Simulation Group.
Tian Y, Peters-Lidard C D, Adler R F, et al.2010. Evaluation of GSMaP precipitation estimatesover the contiguous United States. Journal of Hydrometeorology,11:566-574.
Tian Y, Xu Y P, Zhang X J.2013. Assessment of climate change impacts on river high flowsthrough comparative Use of GR4J, HBV and Xinanjiang models. Water ResourcesManagement,27(191):1-18.
Tobin K J, Marvin E B.2010. Adjusting satellite precipitation data to facilitate hydrologicmodeling. Journal of Hydrometeorology,11(4):966-978.
Tobler W.2004. On the First Law of Geography: a reply. Annals of the Association of AmericanGeographers,94(2):304-310.
Turk F J, Bauer P, Arkin P A.2005. Satellite-derived precipitation verification activities within theInternational Precipitation Working Group (IPWG). AGU Fall Meeting.
Ushio T, Sasashige K, Kubota T, et al.2009. A Kalman filter approach to the Global SatelliteMapping of Precipitation (GSMaP) from combined passive microwave and infraredradiometric data. Journal of the Meteorological Society of Japan,87A:137-151.
Vila D A, Goncalves L G G, Toll D L, et al.2009. Statistical evaluation of combined daily gaugeobservations and rainfall satellite estimates over continental South America. Journal ofHydrometeorology,10(2):533-543.
Wang J, Price K P, Rich P M.2001. Spatial patterns in response of NDVI to precipitation andtemperature in the central Great Plains. International Journal of Remote Sensing,22(18):3827-3844.
Wang Q, Ni J, Tenhunen J.2005. Application of a geographically-weighted regression analysis toestimate net primary production of Chinese forest ecosystems. Global Ecology andBiogeography,14(4):379-393.
Wang H, Ren L L, Liu G H.2009. A regression-kriging model for estimation of rainfall in theLaohahe basin. Proceedings of SPIE.
Wang K, Zhang C, Li W.2012. Comparison of Geographically Weighted Regression andRegression Kriging for estimating the spatial distribution of soil organic matter. GIScience&Remote Sensing,49(6):915-932.
WMO.1987. Guide to hydrological practices Vol1: Data acquisition and processing. Geneva.
Xu C Y, Singh V P.1998. A review on monthly water balance models for water resourcesinvestigations. Water Resources Management,12(1):20-50.
Yilmaz M, Tugrul P H, Roshan S, Valentine G A.2010. Optimally merging precipitation tominimize land surface modeling errors. Journal of Applied Meteorology and Climatology,49:415-423.
Yong B, Ren L, Hong Y, et al.2010. Hydrologic evaluation of TRMM standard precipitationproducts in basins beyond its inclined latitude band: a case study in Laohahe Basin, China.Water Resources Research,46: W07542.
Zhu Q, Chen X W, Fan Q X, et al.2011. A new procedure to estimate the rainfall erosivity factorbased on Tropical Rainfall Measuring Mission (TRMM) data. Science China TechnologicalSciences,54(9):2437-2445.
比斯瓦斯.2007.水文学史.刘国纬,译.北京:科学出版社.
陈晶晶,胡蓓蓓,王军,等.2010.天津降水数据的空间插值分析.安徽师范大学学报(自然科学版),33(4):382-387.
崔林丽,史军,杨引明,等.2009.中国东部植被NDVI对气温和降水的旬响应特征.地理学报,64(7):850-860.
陈利群,刘昌明,郝芳华.2005.站网密度和地形对模拟产流量和产沙量的影响.水土保持学报,19(1):18-21.
陈渭民.2005.卫星气象学.北京：气象出版社.
程正泉,陈联寿,李英.2012.登陆热带气旋与夏季风相互作用对暴雨的影响.应用气象学报,23(6):660-671.
杜国明,贾良文.2009.薄板样条函数在空间数据插值中的应用.计算机工程与应用,45(36)：238-240.
傅抱璞.1981.论陆面蒸发的计算.大气科学,5(1):23-31.
傅抱璞.1983.山地气候.北京:科学出版社.
傅抱璞.1997.我国不同自然条件下的水域气候效应.地理学报,52(3):246-252.
傅抱璞.2011.傅抱璞论文选集.北京：气象出版社.
房彬,班显秀,郭学良,等.2011.雷达-雨量计-粒子激光探测仪联合估算降水量.大气科学34(3):513-519.
封志明,杨艳昭,丁晓强,等.2004.气象要素空间插值方法优化.地理研究,23(3):357-364.
谷黄河,余钟波,杨传国,等.2011.卫星雷达测雨在长江流域的精度分析.水电能源科学.28(8):3-6.
葛朝霞，曹丽青.2009.气象学与气候学教程.北京:中国水利水电出版社.
辜智慧,陈晋,陈晋.2005.锡林郭勒草原1983-1999年NDVI逐旬变化量与气象因子的相关分析.植物生态学报,29(5):753-765.
郝芳华,陈利群,刘昌明,等.2003.降雨的空间不均性对模拟产流量和产沙量不确定的影响.地理科学进展,22(5):447-453.
何惠.中国水文站网.2010.水科学进展,21(4):460-465.
何红艳,郭志华,肖文发.2005.降水空间插值技术的研究进展.生态学杂志,24(10):117-1191.
胡庆芳,王银堂,刘克琳,等.2007.基于改进的两参数月水量平衡模型的月径流模拟.河海大学学报(自然科学版),35(6):638-642.
胡庆芳,尚松浩,温守光,等.2006.潇河冬小麦水肥生产函数偏最小二乘回归建模及分析.节水灌溉,1:1-4.
胡庆芳,杨大文,王银堂,等.2012.利用全局与局部相关函数分析流域降水空间变异性.清华大学学报(自然科学版):52(6):778-784.
郝振纯,童凯,张磊磊,等.2011. TRMM降水资料在青藏高原的适用性分析.水文,31(5):18-23.
江西省水文局.2010.江西省暴雨洪水查算手册.
Kalnay E.2005.大气模式、资料同化和可预报性.薄朝霞,杨福全,邓北胜,等,译.北京:气象出版社.
廖菲,洪延超,郑国光.2007.地形对降水的影响研究概述.气象科技,35(3):310-316.
陆桂华,吴志勇,何海,等.2010.水文循环过程及定量预报.北京:科学出版社.
刘俊峰,陈仁升,卿文武,等.2011.基于TRMM降水数据的山区降水垂直分布特征.水科学进展,22(4):447-454.
卢江涛.2011.利用天气雷达估测和预报降雨分布的研究.[硕士学位论文].北京:清华大学.
李景刚,李纪人,黄诗峰,等.2010.基于TRMM数据和区域综合Z指数的洞庭湖流域近10年旱涝特征分析.资源科学,32(6):1103-1110.
李建通,杨维生,郭林.2000.提高最优插值法测量区域降水量精度的探讨.大气科学,24(2):263-270.
李娜,许有鹏,陈爽.2006.苏州城市化进程对降雨特征影响分析.长江流域资源与环境,15(3):335-339.
李帅,熊立华,万民.2012.月水量平衡模型的比较研究.水文,31(5):35-41.
林文鹏,石婧,王长耀.2005.滨江流域GPS降水预报系统的研究与实现.长江科学院院报,22(3):25-28.
刘元波，傅巧妮，宋平，等．2011.卫星遥感反演降水研究综述．地球科学进展，26(11):1162-1172.
李小文,曹春香,常超一.2007.地理学第一定律与时空邻近度的提出.自然杂志,29(2):69-72.
李相虎,张奇,邵敏.2012.基于TRMM数据的鄱阳湖流域降雨时空分布特征及其精度评价.地理科学进展,31(9):1164-1170.
刘晓阳,毛节泰,李纪人,等.2002.雷达联合雨量计估测降水模拟水库入库流量.水利学报38(4):51-55.
李跃清,张晓春.2011.“雅安天漏”研究进展.暴雨灾害,30(4):289-295.
卢毅敏,岳天祥,陈传法.2010.中国区域年降水空间分布高精度曲面建模.自然资源学报25(7):1194-1205.
刘钰, Pereira L S, Teixeira J L,等.1997.参照腾发量的新定义及计算方法对比.水利学报,28(6):27-33.
李延兴,徐宝祥,胡新康,等.2000.用地基GPS观测站遥测大气含水量和可降雨量的理论基础和试验结果,中国科学(A辑),30(增刊):107-110.
罗阳,赵伟,翟景秋.2009.两类天气预报评分问题研究及一种新评分方法.应用气象学报,20(2):129-136.
林忠辉,莫兴国.2008.一种改进的生成区域日降水场的方法及精度分析.地理研究,(5):732-736.
林之光.1995.地形降水气候学.北京:科学出版社.
李致家,刘金涛,葛文忠，等.2005.雷达估测降雨与水文模型的耦合在洪水预报中的应用.河海大学学报(自然科学版),32(6):601-606.
梅长林.2000.泛函参数回归模型的研究及其应用.[博士学位论文].西安:西安交通大学.
梅长林,王宁.2003.回归模型误差相关性的统一检验方法.高校应用数学学报A辑,18(3):318-326.
毛江玉,吴国雄.2012.基于TRMM卫星资料揭示的亚洲季风区夏季降雨日变化.中国科学:地球科学,42(4):564-576.
钱永兰,杨邦杰,雷延武,等.2004.数据融合及其在农情遥感监测中的应用与展望.农业工程学报,20(4):286-290.
钱永兰，吕厚荃，张艳红.2010.基于ANUSPLIN软件的逐日气象要素插值方法应用与评估.气象与环境学报,26(2):7-15.
覃文忠.2007.地理加权回归基本理论与应用研究[博士学位论文].上海：同济大学.
芮孝芳.2004.水文学原理.南京:河海大学出版社.
邵惠芳.2006.水文时空变异性分析方法及其在降水分析中的应用.[硕士学位论文].北京:清华大学.
孙海燕.2006.薄板样条函数及复杂曲面的数学表示.测绘工程，15(2):7-8.
宋丽琼,田原,邬伦,等.2008.日降水量的空间插值方法与应用对比分析—以深圳市为例.地球信息科学,10(5):566-572.
孙俊,潘玉君,和瑞芳,等.2012.地理学第一定律之争及其对地理学理论建设的启示.地理研究，31(10):1749-1763.
石朋,芮孝芳.2005.降雨空间插值方法的比较与改进.河海大学学报,33(4):361-365.
舒守娟,喻自凤,王元,等.2005.西藏地区复杂地形下的降水空间分布估算模型.地球物理学报,48(3):535-542.
邵薇薇.2009.中国非湿润地区植被与流域水循环相互作用机理研究[博士学位论文].北京：清华大学.
沈艳,游然,冯明农.2010. PEHRPP计划简介及在中国大陆区域的数据质量评估.第七届全国优秀青年气象科技工作者学术研讨会论文集.
沈艳,潘旸,宇婧婧,等.2013.中国区域小时降水量融合产品的质量评估.大气科学学报,36(1):37-46.
孙智辉,刘志超,雷延鹏,等.2010.延安北部丘陵沟壑区植被指数变化及其与气候的关系.生态学报,30(2):533-540.
滕召胜.2000.基于多传感器数据融合的热处理炉温度测量方法.计量学报,21(2):148-152.
王惠文.1999.偏最小二乘回归方法及其应用.北京:国防工业出版社.
王国庆,张建云,张明,等.2009.雨量站网密度对不同气候区月径流模拟的影响.人民长江,40(8):45-49.
王国庆,王军平,荆新爱,等.2006. SIMHYD模型在清涧河流域的应用.人民黄河,28(3):29-30.
文迁,谭国良,罗嗣林.1997.降水分布受地形影响的分析.水文,(S1):63-65.
吴息,王晓云,曾宪宁,等.2000.城市化效应对北京市短历时降水特征的影响.南京气象学院学报,23(1):68-72.
王舒,严登华,秦天玲,等.2011.基于PER-Kriging插值方法的降水空间展布.水科学进展,22(6):756-763.
王跃山.1999.数据同化—它的缘起,含义和主要方法.海洋预报,16(1):11-20.
徐国强,胡欣,苏华.1999.太行山地形对“96.8”暴雨影响的数值试验研究.气象,25(7):3-7.
许继军，杨大文，蔡治国.2007.分布式水文模型结合雷达测雨用于三峡区间的洪水预报[J].长江科学院院报，24(6):42-48.
熊隽,闫娜娜,毛德发,等.2009. TRMM卫星估算降雨量在海河流域的应用评价[EB/OL].(2009-05-27). http://www.cjk3d.net/viewnews-52502.
熊立华,郭生练.2004.分布式流域水文模型.北京:中国水利水电出版社.
徐天献,王玉宽,傅斌.2010.四川省降水空间分布的插值分析.人民长江,41(10):9-12.
徐翔宇.2012.气候变化下典型流域的水文响应研究[博士学位论文].北京:清华大学.
杨传国,余钟波,林朝晖,等.2009.基于TRMM卫星雷达降雨的流域陆面水文过程.水科学进展,20(4):461-466.
杨汉波.2008.流域水热耦合平衡方程推导及其应用[博士学位论文].北京:清华大学.
杨特群,王春青,张勇.2002.利用卫星云图估算黄河中游地区平均面雨量.河南气象,2:1-3.
余锦华,卢莹.2010.热带气旋活动对我国大陆降水影响的统计分析.河海大学学报(自然科学版),38(6):665-670.
殷健,梁珊珊.2010.城市化对上海市区域降水的影响.水文,30(2):66-72.
俞亚勋.2011.东亚夏季风雨带进退与西太副高活动、降水年代际变化及江苏气候若干问题[博士学位论文].兰州：兰州大学.
于淑秋.2007.北京地区降水年际变化及其城市效应的研究.自然科学进展,17(5):632-638.
袁晓清.2009.北京市新一代天气雷达定量估测降雨研究[硕士学位论文].北京:清华大学.
尹忠海,张沛源.2005.利用卡尔曼滤波校准方法估算区域降水量.应用气象学报16(2):213-219.
赵传成,丁永建,叶柏生,等.2011.天山山区降水量的空间分布及其估算方法.水科学进展,22(3):315-322.
赵登忠,张万昌,刘三超.2004.基于DEM的地理要素PRISM空间内插研究.地理科学,24(2):205-211.
詹道江，叶守泽.2000.工程水文学.北京:中国水利水电出版社.
曾红伟,李丽娟.2011.澜沧江及周边流域TRMM3B43数据精度检验.地理学报,66(7):994-1004.
紫金雨量产业联盟.2012.高分辨区域面雨量自动监测系统(PRS-11)技术报告.
周建康,黄红虎,唐运忆,等.2003.城市化对南京区域降水量变化的影响.长江科学院院报,20(4):44-46.
张景雄.2008.空间信息的尺度、不确定性与融合.武汉:武汉大学出版社.
张杰,李栋梁,何金梅,等.2007.地形对青藏高原丰枯水年雨季降水量空间分布的影响.水科学进展,18(3):318-326.
赵坤,葛文忠,李致家.2005.在雷达测雨和洪水预报中自适应卡尔曼滤波法的应用.高原气象,24(6):956-965.
张利平,李璐,叶爱中,夏军.2007.雷达联合雨量计估算区域降水量精度对比.武汉大学学报(工学版),40(1):1-5.
张利平,赵志朋,胡志芳,等.2008.雷达测雨及其在水文水资源中的应用研究进展.暴雨灾害,27(4):373-377.
张蒙蒙.2012.对我国高分辨率融合降水资料的适用性评估[硕士学位论文].南京:南京信息工程大学.
张培昌,杜秉玉,戴铁丕.2001.雷达气象学.北京：气象出版社.
朱强,陈秀万,樊启祥,等.2011.基于TRMM的降侵蚀力计算方法.中国科学:技术科学,41(11):1483-1492.
朱芮芮,李兰,王浩,等.2004.降水量的空间变异性和比较研究.中国农村水利水电,25(4):25-27.
赵蕊,贺建军.2007.多传感器信息融合技术.计算机测量与控制,15(9):1124-1126.
张仁铎.2005.空间变异理论及应用.北京:科学出版社.
赵润华,江静.2009.南京大学学报(自然科学版),45(3):365-376.
张士锋,贾绍凤.2001.降水不均匀性对黄河天然径流量的影响.地理科学进展,20(4):355-363.
张亚萍,程明虎,徐慧，等.2007.雷达定量测量降水在佛子岭流域径流模拟中的应用.应用气象学报,18(3):295-305.
张学勇,谢会兰.1991.岗南,黄壁庄水库降水效应的分析.河北省科学院学报,2(6):40-45.
Ahrens B.2006. Distance in spatial interpolation of daily rain gauge data, Hydrology and EarthSystem Science,10(2):197-208.
Allen R G, Pereira L S, Raes D, et al.1998. Crop evapotranspiration: Guidelines for computingcrop requirements. Irrigation and Drainage Paper No.56, FAO, Rome, Italy.
Barnes S L.1964. A technique for maximizing details in numerical weather map analysis. Journalof Applied Meteorology,3:396-409.
Bhargava M, Danard M.1994. Application of optimum interpolation to the analysis of precipitationin complex terrain. Journal of Applied Meteorology,33:508-518.
Boushaki, Farid I, Hsu K L, et al.2009. Bias adjustment of satellite precipitation estimation usingground-based measurement: a case study evaluation over the southwestern UnitedStates. Journal of Hydrometeorology,10:1231-1242.
Brunsdon C, Fotheringham A S, Charlton M.1996. Geographically weighted regression: a methodfor exploring spatial nonstationarity. Geographical analysis,28(4):281-298.
Brunsdon C.1998. Geographically weighted regression: a natural evolution of the expansionmethod for spatial data analysis. Environment and Planning A,30:1905-1927.
Brunsdon C, McClatchey J, Unwin D J.2001. Spatial variations in the average rainfall–altituderelationship in Great Britain: an approach using geographically weighted regression.International Journal of Climatology21(4):455-466.
Chiang Y M, Hsu K L, Chang F J, et al.2007. Merging multiple precipitation sources for flashflood forecasting. Journal of Hydrology,340(3):183-196.
Chiang S H, Chang K T.2009. Application of radar data to modeling rainfall-induced landslides.Geomorphology,103(3):299-309.
Chiew F H S, Peel M C，Western A W.2002. Application and testing of the simple rainfall-runoffmodel SIMHYD. In: Mathematical Models of Watershed Hydrology, Water ResourcesPublication, Littleton, Colorado.
Cleveland W S.1979. Robust locally weighted regression and smoothing scatter plots. Journal ofThe American statistical Association,74(368):829-836.
Collischonn, B, Collischonn W, Tucci C.E.M.2008. Daily hydrological modeling in the Amazonbasin using TRMM rainfall estimates. Journal of Hydrology,360(1-4),207-216.
Daly C, Neilson R P, Phillips D L.1994. A statistical-topographic model for mapping climatologicalprecipitation over mountainous terrain. Journal of Appllied Meteorology,33:140-158.
Dinku T, Ruiz F, Connor S J, et al.2010. Validation and intercomparison of satellite rainfallestimates over Colombia. Journal of Applied Meteorology and Climatology,49:1004-1014.
Duan Q, Sorooshian S, Gupta V K.1994.Optimal use of the SCE-UA global optimization methodfor calibrating watershed models. Journal of Hydrology,158(3):265-284.
Duan Z, Bastiaanssen W.2013. First results from Version7TRMM3B43precipitation product incombination with a new downscaling-calibration procedure. Remote Sensing of Environment131(15):1-13.
Ebert E E, Janowiak J E, Kidd C.2007. Comparison of near-real-time precipitation estimates fromsatellite observations and numerical models. Bulletin of the American Meteorological Society,88:47-64.
Edijatno, Nascimento N, Yang X, et al.1999. GR3J: a daily watershed model with three freeparameters. Hydrological Sciences Journal,44(2):263-277.
Ehret U.2002. Rainfall and flood nowcasting in small catchments using weather radar. PhD Thesis,University of Stuttgart.
Engeland K, Renard B, Steinsland I, et al.2010. Evaluation of statistical models for forecast errorsfrom the HBV model. Journal of Hydrology,384(1-2):142-155.
Foody G M.2003. Geographical weighting as a further refinement to regression modeling: Anexample focused on the NDVI-rainfall relationship. Remote Sensing of Environment,88,283-293.
Fotheringham A S, Brunsdon C, Charlton M.2002. Geographically weighted regression: theanalysis of spatially varying relationships. John Wiley&Sons, New York.
Fu Q, Liu R. B.2011. Accuracy assessment of global satellite mapping of precipitation (gsmap)product over Poyang Lake Basin, China. Procedia Environmental Sciences10:2265-2271.
Gebregiorgis A, Hossain F.2011. How much can a priori hydrologic model predictability help inoptimal merging of satellite precipitation products?. Journal of Hydrology,12(6):1287-1298.
Goodrich D C, Faures J, Woohiser D A, et al.1995. Measurement and analysis of small scaleconvective storm rainfall variability. Journal of Hydrology,173:283-3081.
Goovaerts P.1997. Geostatistics for natural resources evaluation. New York: Oxford UniversityPress.
Goovaerts P.2000. Geostatistical approaches for incorporating elevation into the spatialinterpolation of rainfall. Journal of Hydrology,228(1):113-129.
Gorenburg, I. P., D. McLaughlin, Entekhabi D.2001. Scale-recursive assimilation of precipitationdata. Advances in Water Resources,24(9):941-953.
Goudenhoofdt E, Delobbe L.2009. Evaluation of radar-gauge merging methods for quantitativeprecipitation estimates. Hydrology and Earth System Sciences,13(2):195.
Grimes D I F, Pardo-Iguzquiza E, Bonifacio R.1999. Optimal areal rainfall estimation usingraingauges and satellite data. Journal of Hydrology,222(1):93-108.
Grist J, Nicholson S E, Mpolokang A.1996. On the use of NDVI for estimating rainfall fields in theKalahari of Botswana. Journal of Arid Environments,35(2):195–214.
Gruber A, Su X J, Kanamitsu M, et al.2000. The comparison of two merged rain gauge-satelliteprecipitation datasets. Bulletin of American Meteorology Society,81:2631-2644.
Gupta R V. Venugopal, Foufoula-Georgiou E.2006. A methodology for merging multisensorprecipitation estimates based on expectation-maximization and scale-recursive estimation.Journal Geophysics Research,111(D2): D02102.
Hengl T, Heuvelink G B M, Stein A.2004. A generic framework for spatial prediction of soilvariables based on regression kriging. Geoderma,120(1):75-93.
Hengl T, Gerard B M. Heuvelink, et al.2007. About regression-kriging: From equations to casestudies. Computers&Geosciences,33(10):1301-1315.
Hewitson B C, Crane R G.2005. Gridded area-averaged daily precipitation via conditionalinterpolation. Journal of Climate.18(1):41-57.
Hirpa F A, Gebremichael M, Hopson T.2009. Evaluation of high resolution satellite precipitation5products over very complex terrain in Ethiopia. Journal of Applied Meteorology andClimatology,49:1044-1051.
Hofstra N, Haylock M, New M, et al.2008. Comparison of six methods for the interpolation ofdaily, European climate data. Journal of Geophysical Research,113(D21): D21110.
Hong Y, Adler R F, Huffman G J.2007. An experimental global prediction system forrainfall-triggered landslides using satellite remote sensing and geospatial datasets. IEEETransactions on Geoscience and Remote Sensing,45(6):1671-1680.
Hurvich C M, Simonoff J S, Tsai C L.2002. Smoothing parameter selection in nonparametricregression using an improved Akaike information criterion. Journal of the Royal StatisticalSociety: Series B (Statistical Methodology),60:271-293.
Huffman G J, Bolvin D T, Nelkin E J, et al.2007. The TRMM Multisatellite Precipitation Analysis(TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J.Hydrometeor,8:38-55.
Huffman G J, Bolvin D T.2011. TRMM and other data precipitation data set documentation[EB/OL].(2013-01-28). ftp://precip.gsfc.nasa.gov/pub/trmmdocs/3B42_3B43_doc.pdf.
Hsu K, Gupta H V, Gao X, et al.1999. Estimation of physical variables from multichannelremotely sensed imagery using a neural network: Application to rainfall estimation. WaterResource Research,35:1605-1618.
Immerzeel W W, Rutten M. M, Droogers P.2009. Spatial downscaling of TRMM precipitationusing vegetation response on the Iberian Peninsula. Remote Sensing of Environment,113(2):362-370.
Isaaks E H, Srivastava R M.1989. Applied Geostatistics. New York: Oxford University Press.
Jiang S, Ren L, Yong B, et al.2010. Evaluation of high-resolution satellite precipitation productswith surface rain gauge observations from Laohahe Basin in northern China. Water Scienceand Engineering,3:405-417.
Jiang S, Ren L, Hong Y, et al.2012. Comprehensive evaluation of multi-satellite precipitationproducts with a dense rain gauge network and optimally merging their simulated hydrologicalflows using the Bayesian model averaging method. Journal of Hydrology,452-453:213-225.
Jiang W G, Hou P, Zhu X H et al.2011. Analysis of vegetation response to rainfall with satelliteimages in Dongting Lake. Journal of Geographical Science,21(1):135–149.
Jia S, Zhu W, Lu A, et al.2011. A statistical spatial downscaling algorithm of TRMM precipitationbased on NDVI and DEM in the Qaidam Basin of China. Remote Sensing of Environment115(12):3069-3079.
Joyce J J, Janowiak J E, Arkin P A, et al.2004. CMORPH: A method that produces globalprecipitation estimates from passive microwave and infrared data at high spatial and temporalresolution. Journal of Hydrometeorology,5:487-503.
Kavetski D, Kuczera G, Franks S W.2006. Bayesian analysis of input uncertainty in hydrologicalmodeling:2. Application. Water Resources Research,42,(W03408)(doi:10.1029/2005WR004376).
Kidd C, Levizzani V, Turk J, Ferraro R.2009. Satellite precipitation measurements for waterresources monitoring. Journal of the American Water Resources Association,45(3):567-579.
Koppel J V, Rietkerk M, Langevelde F V.2002. Spatial heterogeneity and irreversible vegetationchange in semiarid grazing systems. The American Naturalist,159(2):209-218.
Krajewski W.1987. Cokriging radar-rainfall and rain gage data. Journal of Geophysical Research92(D8):9571-9580.
Krzyzstofowicz R.1999. Bayesian theory of probabilistic forecasting via deterministic hydrologicalmodel. Water Resources Research,35(9),2739-2750.
Krzyzstofowicz R.2001. The case for probabilistic forecasting in hydrology. Journal of Hydrology,249(1):2-9.
Kummerow C, William B, Toshiaki K, et al.1998. The Tropical Rainfall Measuring Mission(TRMM) Sensor Package. Journal of Atmosphere and Oceanic Technology,15(3):809-817.
Kubota T, Ushio T, Shige S, et al.2009. Verification of high-resolution satellite-based rainfallestimates around Japan using a gauge-calibrated ground-radar dataset. Journal of theMeteorological Society of Japan,87(A):203-222.
Lall U, Moon Y I, Kwon H H, et al.2006. Locally weighted polynomial regression: Parameterchoice and application to forecasts of the Great Salt Lake. Water Resources Research,42(5):W05422.
Lesage J P.1999. A spatial econometric examination of China's economic growth. GeographicInformation Sciences,5(2):143-15.
Leung Y, Mei C L, Zhang W X.2000. Statistical tests fors patial nonstationarity based on thegeographyically weightedreg ressionmodel. Environment and Planning A,32(1):9-32.
LeungY, Mei C L, Zhang W X.2000.Testing for spatial autocorrelation among the residuals of thegeographically weighted regression. Enviroment and Plaiming A,32(5):871-890.
Lewis R M, Virginia T, A globally convergent augmented lagrangian pattern search algorithm foroptimization with general constraints and simple bounds. SIAM Journal on Optimization,12(4):1075-1089.
Li M, Shao Q.2010. An improved statistical approach to merge satellite rainfall estimates and raingauge data. Journal of Hydrology,385(1):51-64.
Loader C.1999. Local Regression and Likelihood. New York: Springer.
Mair A, Fares A.2010. Comparison of rainfall interpolation methods in a mountainous region of atropical island. Journal of Hydrologic Engineering,16(4),371-383.
Michaelides S, Levizzani V, Anagnostou E, et al.2009. Precipitation: measurement, remote sensing,climatology and modeling. Atmospheric Research,94(4):512-533.
Mitra A. K, Bohra A, Rajeevan M N, et al.2009. Daily Indian precipitation analysis formed from amerge of rain-gauge data with the TRMM TMPA satellite-derived rainfall estimates. Journal ofthe Meteorological Society of Japan,87A:265-279.
Ninomiya K, Akiyama T.1978. Objective analysis of heavy rainfalls based on radar and gaugemeasurement. Journal of Meteorology Society of Japan,50:206-210．
Oke A, Frost A, Beesley C.2009. The use of TRMM satellite data as a predictor in the spatialinterpolation of daily precipitation over Australia, in Proceedings of the18th WorldIMACS/MODSIM Congress, edited by Anderssen R, Braddock R, Newham L C, Australia.
Ouma Y O, Owiti T, Kipkorir E, et al.2012.Multitemporal comparative analysis of TRMM-3B42satellite-estimated rainfall with surface gauge data at basin scales: daily, decadal and monthlyevaluations. International Journal of Remote Sensing,33:7662-7684.
Páez A, Uchida T, Miyamoto K.2002. A general framework for estimation and inference ofgeographically weighted regression models:1. Location-specific kernel bandwidths and a testfor locational heterogeneity. Environment and Planning A,34(4):733-754.
Pagano T C, Hapuarachchi P, Wang Q J.2010. Continuous rainfall-runoff model comparison andshort-term daily streamflow forecast skill evaluation. CSIRO Tech. Rep.
Pardo-Igu′zquiza E.1998. Comparison of geostatistical methods for estimating the areal averageclimatological rainfall mean using information of precipitation and topography. InternationalJournal of Climatology,18:1031-1047.
Pereira-Filho A J, Crawford K C, Hartzell C,1998. Improving WSR-88D Hourly Rainfall Estimates.Weather and Forecasting,13(4):1016-1028.
Perrin C, Michel C, Andréassian V.2003. Improvement of a parsimonious model for streamflowsimulation. Journal of Hydrology,279:275-289.
Rojas O, Rembold F, Delincé J.2011.Using the NDVI as auxiliary data for rapid quality assessmentof rainfall estimates in Africa. International Journal of Remote Sensing,32(12):3249-3265.
Romilly T, Gebremichael M.2011. Evaluation of satellite rainfall estimates over Ethiopian riverbasins, Hydrology and Earth System Sciences,15:1505-1514.
Rozante J R, Demerval S M, Luis G G, et al.2010. Combining TRMM and surface observations ofprecipitation: technique and validation over South America. Weather Forecasting,25:885-894.
Sasaki.1970. Some basic formulas in numerical variation analysis．Monthly Weather Review,98:875-883.
Scheel M, Rohrer M, Huggel C, et al.2010. Evaluation of TRMM Multi-satellite PrecipitationAnalysis (TMPA) performance in the Central Andes region and its dependency on spatial andtemporal resolution. Hydrology and Earth System Sciences Discussions,7:8545-8586.
Seo D J.1998. Real-time estimation of rainfall fields using radar rainfall and rain gage data.Journal of Hydrology,208(1):37-52.
Simonneaux V, Hanich L, Boulet G, et al.2008. Modelling runoff in the Rheraya Catchment (HighAtlas, Morocco) using the simple daily model GR4J. Trends over the last decades[C]//13thIWRA World Water Congress, Montpellier, France.
Smith E G. Ghassem A, Yoji F, et al.2007. International global precipitation measurement (GPM)program and mission: An overview. Measuring Precipitation from Space:611-653. InMeasuring Precipitation from Space: EURAINSAT and the Future, Levizzani V, and Turk F J,Eds. Norwell, MA: Kluwer.
Sinclair S, Pegram G.2005. Combining radar and rain gauge rainfall estimates using conditionalmerging. Atmospheric Science Letters,6,19-22.
Sokol Z.2003. Utilization of regression models for rainfall estimates using radar-derived rainfalldata and rain gauge data. Journal of Hydrology,278(1):144-152.
Sorooshian S, Hsu K, Gao X, et al.2000. Evolution of PERSIANN system satellite-based estimatesof tropical rainfall. Bulletin of American Meteorology Society,81:2035-2046.
Sohn B, Han H J, Seoe K.2010.Validation of satellite-based high-resolution rainfall products overthe Korean peninsula using data from a dense rain gauge network. Journal of AppliedMeteorology and Climatology,49:701-714.
Shrestha M G, Artan S B, Sharma R.2008. Using satellite-based rainfall estimates for streamflowmodelling: Bagmati Basin, Journal of Flood Risk Management,1(2):89-99.
Shrestha M S.2011. Bias-adjustment of satellite-based rainfall estimates over the CentralHimalayas of Nepal for flood prediction. PhD Thesis. Kyoto, Japan: Kyoto University.
Stillman ST, Wilson J P, Daly C, et al.1996. Comparison of ANUSPLIN, MTCLIM-3D, andPRISM precipitation estimates. in The Third International Conference/Workshop onIntegrating GIS and Environmental Modeling. National Center for Geographic Informationand Analysis, Santa Fe, NM.
Stisen S, Sandholt I.2010. Evaluation of remote-sensing-based rainfall products through predictivecapability in hydrological runoff modelling. Hydrological Processes,24(7):879-891.
Su, F, Y. Hong, Lettenmaier D P.2008. Evaluation of TRMM Multisatellite Precipitation Analysis(TMPA) and its utility in hydrologic prediction in the La Plata Basin. Journal ofHydrometeorology,9(4):622-640.
Su F, Gao H, Huffman G J, et al.2011. Potential utility of the real-time TMPA-RT precipitationestimates in streamflow prediction. Journal of Hydrometeorology,12(3):444-455.
Sun R, Chen L, Fu B.2011. Predicting monthly precipitation with multivariate regression methodsusing geographic and topographic information. Physical Geography,32(3):269-285.
Syed T H, Lakshmi V, Paleologos E, et al.2004. Analysis of process controls in land surfacehydrological cycle over the continental United States. Journal of Geophysical Research,109(D22105),(doi:10.1029/2004JD004640).
Takara K, Yamashiki Y, Sassa K, et al.2010. A distributed hydrological-geotechnical model usingsatellite-derived rainfall estimates for shallow landslide prediction system at a catchment scale.Landslides,7(3):237-258.
Teo C K.2006. Application of satellite-based rainfall estimates to crop yield forecasting in Africa.Ph. D. Thesis, University of Reading, UK.
Thornton P E. Running S W, White M A.1997. Generating surfaces of daily meteorologicalvariables over large regions of complex terrain. Journal of Hydrology,190(3):214-251.
Thornton P.1999. DAYMET US Data Center for daily surface weather data and climatologicalsummaries. University of Montana, Numerical Terradynamic Simulation Group.
Tian Y, Peters-Lidard C D, Adler R F, et al.2010. Evaluation of GSMaP precipitation estimatesover the contiguous United States. Journal of Hydrometeorology,11:566-574.
Tian Y, Xu Y P, Zhang X J.2013. Assessment of climate change impacts on river high flowsthrough comparative Use of GR4J, HBV and Xinanjiang models. Water ResourcesManagement,27(191):1-18.
Tobin K J, Marvin E B.2010. Adjusting satellite precipitation data to facilitate hydrologicmodeling. Journal of Hydrometeorology,11(4):966-978.
Tobler W.2004. On the First Law of Geography: a reply. Annals of the Association of AmericanGeographers,94(2):304-310.
Turk F J, Bauer P, Arkin P A.2005. Satellite-derived precipitation verification activities within theInternational Precipitation Working Group (IPWG). AGU Fall Meeting.
Ushio T, Sasashige K, Kubota T, et al.2009. A Kalman filter approach to the Global SatelliteMapping of Precipitation (GSMaP) from combined passive microwave and infraredradiometric data. Journal of the Meteorological Society of Japan,87A:137-151.
Vila D A, Goncalves L G G, Toll D L, et al.2009. Statistical evaluation of combined daily gaugeobservations and rainfall satellite estimates over continental South America. Journal ofHydrometeorology,10(2):533-543.
Wang J, Price K P, Rich P M.2001. Spatial patterns in response of NDVI to precipitation andtemperature in the central Great Plains. International Journal of Remote Sensing,22(18):3827-3844.
Wang Q, Ni J, Tenhunen J.2005. Application of a geographically-weighted regression analysis toestimate net primary production of Chinese forest ecosystems. Global Ecology andBiogeography,14(4):379-393.
Wang H, Ren L L, Liu G H.2009. A regression-kriging model for estimation of rainfall in theLaohahe basin. Proceedings of SPIE.
Wang K, Zhang C, Li W.2012. Comparison of Geographically Weighted Regression andRegression Kriging for estimating the spatial distribution of soil organic matter. GIScience&Remote Sensing,49(6):915-932.
WMO.1987. Guide to hydrological practices Vol1: Data acquisition and processing. Geneva.
Xu C Y, Singh V P.1998. A review on monthly water balance models for water resourcesinvestigations. Water Resources Management,12(1):20-50.
Yilmaz M, Tugrul P H, Roshan S, Valentine G A.2010. Optimally merging precipitation tominimize land surface modeling errors. Journal of Applied Meteorology and Climatology,49:415-423.
Yong B, Ren L, Hong Y, et al.2010. Hydrologic evaluation of TRMM standard precipitationproducts in basins beyond its inclined latitude band: a case study in Laohahe Basin, China.Water Resources Research,46: W07542.
Zhu Q, Chen X W, Fan Q X, et al.2011. A new procedure to estimate the rainfall erosivity factorbased on Tropical Rainfall Measuring Mission (TRMM) data. Science China TechnologicalSciences,54(9):2437-2445.
比斯瓦斯.2007.水文学史.刘国纬,译.北京:科学出版社.
陈晶晶,胡蓓蓓,王军,等.2010.天津降水数据的空间插值分析.安徽师范大学学报(自然科学版),33(4):382-387.
崔林丽,史军,杨引明,等.2009.中国东部植被NDVI对气温和降水的旬响应特征.地理学报,64(7):850-860.
陈利群,刘昌明,郝芳华.2005.站网密度和地形对模拟产流量和产沙量的影响.水土保持学报,19(1):18-21.
陈渭民.2005.卫星气象学.北京：气象出版社.
程正泉,陈联寿,李英.2012.登陆热带气旋与夏季风相互作用对暴雨的影响.应用气象学报,23(6):660-671.
杜国明,贾良文.2009.薄板样条函数在空间数据插值中的应用.计算机工程与应用,45(36)：238-240.
傅抱璞.1981.论陆面蒸发的计算.大气科学,5(1):23-31.
傅抱璞.1983.山地气候.北京:科学出版社.
傅抱璞.1997.我国不同自然条件下的水域气候效应.地理学报,52(3):246-252.
傅抱璞.2011.傅抱璞论文选集.北京：气象出版社.
房彬,班显秀,郭学良,等.2011.雷达-雨量计-粒子激光探测仪联合估算降水量.大气科学34(3):513-519.
封志明,杨艳昭,丁晓强,等.2004.气象要素空间插值方法优化.地理研究,23(3):357-364.
谷黄河,余钟波,杨传国,等.2011.卫星雷达测雨在长江流域的精度分析.水电能源科学.28(8):3-6.
葛朝霞，曹丽青.2009.气象学与气候学教程.北京:中国水利水电出版社.
辜智慧,陈晋,陈晋.2005.锡林郭勒草原1983-1999年NDVI逐旬变化量与气象因子的相关分析.植物生态学报,29(5):753-765.
郝芳华,陈利群,刘昌明,等.2003.降雨的空间不均性对模拟产流量和产沙量不确定的影响.地理科学进展,22(5):447-453.
何惠.中国水文站网.2010.水科学进展,21(4):460-465.
何红艳,郭志华,肖文发.2005.降水空间插值技术的研究进展.生态学杂志,24(10):117-1191.
胡庆芳,王银堂,刘克琳,等.2007.基于改进的两参数月水量平衡模型的月径流模拟.河海大学学报(自然科学版),35(6):638-642.
胡庆芳,尚松浩,温守光,等.2006.潇河冬小麦水肥生产函数偏最小二乘回归建模及分析.节水灌溉,1:1-4.
胡庆芳,杨大文,王银堂,等.2012.利用全局与局部相关函数分析流域降水空间变异性.清华大学学报(自然科学版):52(6):778-784.
郝振纯,童凯,张磊磊,等.2011. TRMM降水资料在青藏高原的适用性分析.水文,31(5):18-23.
江西省水文局.2010.江西省暴雨洪水查算手册.
Kalnay E.2005.大气模式、资料同化和可预报性.薄朝霞,杨福全,邓北胜,等,译.北京:气象出版社.
廖菲,洪延超,郑国光.2007.地形对降水的影响研究概述.气象科技,35(3):310-316.
陆桂华,吴志勇,何海,等.2010.水文循环过程及定量预报.北京:科学出版社.
刘俊峰,陈仁升,卿文武,等.2011.基于TRMM降水数据的山区降水垂直分布特征.水科学进展,22(4):447-454.
卢江涛.2011.利用天气雷达估测和预报降雨分布的研究.[硕士学位论文].北京:清华大学.
李景刚,李纪人,黄诗峰,等.2010.基于TRMM数据和区域综合Z指数的洞庭湖流域近10年旱涝特征分析.资源科学,32(6):1103-1110.
李建通,杨维生,郭林.2000.提高最优插值法测量区域降水量精度的探讨.大气科学,24(2):263-270.
李娜,许有鹏,陈爽.2006.苏州城市化进程对降雨特征影响分析.长江流域资源与环境,15(3):335-339.
李帅,熊立华,万民.2012.月水量平衡模型的比较研究.水文,31(5):35-41.
林文鹏,石婧,王长耀.2005.滨江流域GPS降水预报系统的研究与实现.长江科学院院报,22(3):25-28.
刘元波，傅巧妮，宋平，等．2011.卫星遥感反演降水研究综述．地球科学进展，26(11):1162-1172.
李小文,曹春香,常超一.2007.地理学第一定律与时空邻近度的提出.自然杂志,29(2):69-72.
李相虎,张奇,邵敏.2012.基于TRMM数据的鄱阳湖流域降雨时空分布特征及其精度评价.地理科学进展,31(9):1164-1170.
刘晓阳,毛节泰,李纪人,等.2002.雷达联合雨量计估测降水模拟水库入库流量.水利学报38(4):51-55.
李跃清,张晓春.2011.“雅安天漏”研究进展.暴雨灾害,30(4):289-295.
卢毅敏,岳天祥,陈传法.2010.中国区域年降水空间分布高精度曲面建模.自然资源学报25(7):1194-1205.
刘钰, Pereira L S, Teixeira J L,等.1997.参照腾发量的新定义及计算方法对比.水利学报,28(6):27-33.
李延兴,徐宝祥,胡新康,等.2000.用地基GPS观测站遥测大气含水量和可降雨量的理论基础和试验结果,中国科学(A辑),30(增刊):107-110.
罗阳,赵伟,翟景秋.2009.两类天气预报评分问题研究及一种新评分方法.应用气象学报,20(2):129-136.
林忠辉,莫兴国.2008.一种改进的生成区域日降水场的方法及精度分析.地理研究,(5):732-736.
林之光.1995.地形降水气候学.北京:科学出版社.
李致家,刘金涛,葛文忠，等.2005.雷达估测降雨与水文模型的耦合在洪水预报中的应用.河海大学学报(自然科学版),32(6):601-606.
梅长林.2000.泛函参数回归模型的研究及其应用.[博士学位论文].西安:西安交通大学.
梅长林,王宁.2003.回归模型误差相关性的统一检验方法.高校应用数学学报A辑,18(3):318-326.
毛江玉,吴国雄.2012.基于TRMM卫星资料揭示的亚洲季风区夏季降雨日变化.中国科学:地球科学,42(4):564-576.
钱永兰,杨邦杰,雷延武,等.2004.数据融合及其在农情遥感监测中的应用与展望.农业工程学报,20(4):286-290.
钱永兰，吕厚荃，张艳红.2010.基于ANUSPLIN软件的逐日气象要素插值方法应用与评估.气象与环境学报,26(2):7-15.
覃文忠.2007.地理加权回归基本理论与应用研究[博士学位论文].上海：同济大学.
芮孝芳.2004.水文学原理.南京:河海大学出版社.
邵惠芳.2006.水文时空变异性分析方法及其在降水分析中的应用.[硕士学位论文].北京:清华大学.
孙海燕.2006.薄板样条函数及复杂曲面的数学表示.测绘工程，15(2):7-8.
宋丽琼,田原,邬伦,等.2008.日降水量的空间插值方法与应用对比分析—以深圳市为例.地球信息科学,10(5):566-572.
孙俊,潘玉君,和瑞芳,等.2012.地理学第一定律之争及其对地理学理论建设的启示.地理研究，31(10):1749-1763.
石朋,芮孝芳.2005.降雨空间插值方法的比较与改进.河海大学学报,33(4):361-365.
舒守娟,喻自凤,王元,等.2005.西藏地区复杂地形下的降水空间分布估算模型.地球物理学报,48(3):535-542.
邵薇薇.2009.中国非湿润地区植被与流域水循环相互作用机理研究[博士学位论文].北京：清华大学.
沈艳,游然,冯明农.2010. PEHRPP计划简介及在中国大陆区域的数据质量评估.第七届全国优秀青年气象科技工作者学术研讨会论文集.
沈艳,潘旸,宇婧婧,等.2013.中国区域小时降水量融合产品的质量评估.大气科学学报,36(1):37-46.
孙智辉,刘志超,雷延鹏,等.2010.延安北部丘陵沟壑区植被指数变化及其与气候的关系.生态学报,30(2):533-540.
滕召胜.2000.基于多传感器数据融合的热处理炉温度测量方法.计量学报,21(2):148-152.
王惠文.1999.偏最小二乘回归方法及其应用.北京:国防工业出版社.
王国庆,张建云,张明,等.2009.雨量站网密度对不同气候区月径流模拟的影响.人民长江,40(8):45-49.
王国庆,王军平,荆新爱,等.2006. SIMHYD模型在清涧河流域的应用.人民黄河,28(3):29-30.
文迁,谭国良,罗嗣林.1997.降水分布受地形影响的分析.水文,(S1):63-65.
吴息,王晓云,曾宪宁,等.2000.城市化效应对北京市短历时降水特征的影响.南京气象学院学报,23(1):68-72.
王舒,严登华,秦天玲,等.2011.基于PER-Kriging插值方法的降水空间展布.水科学进展,22(6):756-763.
王跃山.1999.数据同化—它的缘起,含义和主要方法.海洋预报,16(1):11-20.
徐国强,胡欣,苏华.1999.太行山地形对“96.8”暴雨影响的数值试验研究.气象,25(7):3-7.
许继军，杨大文，蔡治国.2007.分布式水文模型结合雷达测雨用于三峡区间的洪水预报[J].长江科学院院报，24(6):42-48.
熊隽,闫娜娜,毛德发,等.2009. TRMM卫星估算降雨量在海河流域的应用评价[EB/OL].(2009-05-27). http://www.cjk3d.net/viewnews-52502.
熊立华,郭生练.2004.分布式流域水文模型.北京:中国水利水电出版社.
徐天献,王玉宽,傅斌.2010.四川省降水空间分布的插值分析.人民长江,41(10):9-12.
徐翔宇.2012.气候变化下典型流域的水文响应研究[博士学位论文].北京:清华大学.
杨传国,余钟波,林朝晖,等.2009.基于TRMM卫星雷达降雨的流域陆面水文过程.水科学进展,20(4):461-466.
杨汉波.2008.流域水热耦合平衡方程推导及其应用[博士学位论文].北京:清华大学.
杨特群,王春青,张勇.2002.利用卫星云图估算黄河中游地区平均面雨量.河南气象,2:1-3.
余锦华,卢莹.2010.热带气旋活动对我国大陆降水影响的统计分析.河海大学学报(自然科学版),38(6):665-670.
殷健,梁珊珊.2010.城市化对上海市区域降水的影响.水文,30(2):66-72.
俞亚勋.2011.东亚夏季风雨带进退与西太副高活动、降水年代际变化及江苏气候若干问题[博士学位论文].兰州：兰州大学.
于淑秋.2007.北京地区降水年际变化及其城市效应的研究.自然科学进展,17(5):632-638.
袁晓清.2009.北京市新一代天气雷达定量估测降雨研究[硕士学位论文].北京:清华大学.
尹忠海,张沛源.2005.利用卡尔曼滤波校准方法估算区域降水量.应用气象学报16(2):213-219.
赵传成,丁永建,叶柏生,等.2011.天山山区降水量的空间分布及其估算方法.水科学进展,22(3):315-322.
赵登忠,张万昌,刘三超.2004.基于DEM的地理要素PRISM空间内插研究.地理科学,24(2):205-211.
詹道江，叶守泽.2000.工程水文学.北京:中国水利水电出版社.
曾红伟,李丽娟.2011.澜沧江及周边流域TRMM3B43数据精度检验.地理学报,66(7):994-1004.
紫金雨量产业联盟.2012.高分辨区域面雨量自动监测系统(PRS-11)技术报告.
周建康,黄红虎,唐运忆,等.2003.城市化对南京区域降水量变化的影响.长江科学院院报,20(4):44-46.
张景雄.2008.空间信息的尺度、不确定性与融合.武汉:武汉大学出版社.
张杰,李栋梁,何金梅,等.2007.地形对青藏高原丰枯水年雨季降水量空间分布的影响.水科学进展,18(3):318-326.
赵坤,葛文忠,李致家.2005.在雷达测雨和洪水预报中自适应卡尔曼滤波法的应用.高原气象,24(6):956-965.
张利平,李璐,叶爱中,夏军.2007.雷达联合雨量计估算区域降水量精度对比.武汉大学学报(工学版),40(1):1-5.
张利平,赵志朋,胡志芳,等.2008.雷达测雨及其在水文水资源中的应用研究进展.暴雨灾害,27(4):373-377.
张蒙蒙.2012.对我国高分辨率融合降水资料的适用性评估[硕士学位论文].南京:南京信息工程大学.
张培昌,杜秉玉,戴铁丕.2001.雷达气象学.北京：气象出版社.
朱强,陈秀万,樊启祥,等.2011.基于TRMM的降侵蚀力计算方法.中国科学:技术科学,41(11):1483-1492.
朱芮芮,李兰,王浩,等.2004.降水量的空间变异性和比较研究.中国农村水利水电,25(4):25-27.
赵蕊,贺建军.2007.多传感器信息融合技术.计算机测量与控制,15(9):1124-1126.
张仁铎.2005.空间变异理论及应用.北京:科学出版社.
赵润华,江静.2009.南京大学学报(自然科学版),45(3):365-376.
张士锋,贾绍凤.2001.降水不均匀性对黄河天然径流量的影响.地理科学进展,20(4):355-363.
张亚萍,程明虎,徐慧，等.2007.雷达定量测量降水在佛子岭流域径流模拟中的应用.应用气象学报,18(3):295-305.
张学勇,谢会兰.1991.岗南,黄壁庄水库降水效应的分析.河北省科学院学报,2(6):40-45.