Satellite-based precipitation products have been widely used to estimate precipitation, especially over regions with sparse rain gauge networks. However, the low spatial resolution of these products has limited their ...Satellite-based precipitation products have been widely used to estimate precipitation, especially over regions with sparse rain gauge networks. However, the low spatial resolution of these products has limited their application in localized regions and watersheds.This study investigated a spatial downscaling approach, Geographically Weighted Regression Kriging(GWRK), to downscale the Tropical Rainfall Measuring Mission(TRMM) 3 B43 Version 7 over the Lancang River Basin(LRB) for 2001–2015. Downscaling was performed based on the relationships between the TRMM precipitation and the Normalized Difference Vegetation Index(NDVI), the Land Surface Temperature(LST), and the Digital Elevation Model(DEM). Geographical ratio analysis(GRA) was used to calibrate the annual downscaled precipitation data, and the monthly fractions derived from the original TRMM data were used to disaggregate annual downscaled and calibrated precipitation to monthly precipitation at 1 km resolution. The final downscaled precipitation datasets were validated against station-based observed precipitation in 2001–2015. Results showed that: 1) The TRMM 3 B43 precipitation was highly accurate with slight overestimation at the basin scale(i.e., CC(correlation coefficient) = 0.91, Bias = 13.3%). Spatially, the accuracies of the upstream and downstream regions were higher than that of the midstream region. 2) The annual downscaled TRMM precipitation data at 1 km spatial resolution obtained by GWRK effectively captured the high spatial variability of precipitation over the LRB. 3) The annual downscaled TRMM precipitation with GRA calibration gave better accuracy compared with the original TRMM dataset. 4) The final downscaled and calibrated precipitation had significantly improved spatial resolution, and agreed well with data from the validated rain gauge stations, i.e., CC = 0.75, RMSE(root mean square error) = 182 mm, MAE(mean absolute error) = 142 mm, and Bias = 0.78%for annual precipitation and CC = 0.95, RMSE = 25 mm, MAE = 16 mm, and Bias = 0.67% for monthly precipitation.展开更多
The three-dimensional structures of summer precipitation over the South China Sea （SCS） and the East China Sea （ECS） are investigated based on tropical rainfall measurement mission （TRMM）. The primary results ar...The three-dimensional structures of summer precipitation over the South China Sea （SCS） and the East China Sea （ECS） are investigated based on tropical rainfall measurement mission （TRMM）. The primary results are as follows. First, both the convective and stratiform precipitation rates in the SCS are much higher than those of the ECS. The contribution of the convective cloud precipitation to the surface precipitation is primarily over the SCS and the ECS with a proportion of about 70%, but the contribution of convective cloud precipitation is slightly larger in the SCS than the ECS. The contribution of stratus precipitation is slightly larger in the ECS than that in the SCS. Second, the content of cloud particles and precipitation particles in the ECS in June was greater than that in the SCS, while in July and August, the content of cloud and precipitation particles in the ECS was less than that in the SCS. Third, the latent heat profile of the ECS is quite different from that of the SCS. In June, the peak values of evaporation and condensation latent heating rates in the ECS are greater than those in the SCS. In July and August, however, the peak values of evaporation and condensation latent heating rates in the ECS are about 0.05°/h less than those in the SCS.展开更多
High-quality rainfall information is critical for accurate simulation of runoff and water cycle processes on the land surface. In situ monitoring of rainfall has a very limited utility at the regional and global scale...High-quality rainfall information is critical for accurate simulation of runoff and water cycle processes on the land surface. In situ monitoring of rainfall has a very limited utility at the regional and global scale because of the high temporal and spatial variability of rainfall. As a step toward overcoming this problem, microwave remote sensing observations can be used to retrieve the temporal and spatial rainfall coverage because of their global availability and frequency of measurement. This paper addresses the question of whether remote sensing rainfall estimates over a catchment can be used for water balance computations in the distributed hydrological model. The TRMM 3B42V6 rainfall product was introduced into the hydrological cycle simulation of the Yangtze River Basin in South China. A tool was developed to interpolate the rain gauge observations at the same temporal and spatial resolution as the TRMM data and then evaluate the precision of TRMM 3B42V6 data from 1998 to 2006. It shows that the TRMM 3B42V6 rainfall product was reliable and had good precision in application to the Yangtze River Basin. The TRMM 3B42V6 data slightly overestimated rainfall during the wet season and underestimated rainfall during the dry season in the Yangtze River Basin. Results suggest that the TRMM 3B42V6 rainfall product can be used as an alternative data source for large-scale distributed hydrological models.展开更多
Soil erosion by water is the most important land degradation problem worldwide. In this paper a new procedure was developed to estimate the rainfall-runoff erosivity factor （R） based on Tropical Rainfall Measuring M...Soil erosion by water is the most important land degradation problem worldwide. In this paper a new procedure was developed to estimate the rainfall-runoff erosivity factor （R） based on Tropical Rainfall Measuring Mission （TRMM） satellite-estimated precipitation data, which consists of 3-h rainfall intensity data. In this method, R was calculated as the product of the maximum 180-min rainfall intensity and the rainfall energy. This procedure was applied to the Daling River basin in Liaoning Province, China, R in terms of yearly, monthly and event-based rainfall in 2005 was computed separately using TRMM 3B42 data. The TRMM data showed a significant correlation with the interpolated rain-gauge data. Furthermore, because the TRMM data are based on rainfall intensity, they can represent the impact on erosion more accurately. It reflects both the spatial distribution and the intensity of rainfall. The procedure is a new approach to estimate the rainfall erosivity for soil water erosion modeling, especially in areas lacking rain-gange stations.展开更多
Accurate estimates of precipitation are fundamental for hydrometeorological and ecohydrological studies,but are more difficult in high mountainous areas because of the high elevation and complex terrain.This study com...Accurate estimates of precipitation are fundamental for hydrometeorological and ecohydrological studies,but are more difficult in high mountainous areas because of the high elevation and complex terrain.This study compares and evaluates two kinds of precipitation datasets,the reanalysis product downscaled by the Weather Research and Forecasting(WRF)output,and the satellite product,the Tropical Rainfall Measuring Mission(TRMM)Multisatellite Precipitation Analysis(TMPA)product,as well as their bias-corrected datasets in the Middle Qilian Mountain in Northwest China.Results show that the WRF output with finer resolution perfonns well in both estimating precipitation and hydrological simulation,while the TMPA product is unreliable in high mountainous areas.Moreover,bias-corrected WRF output also performs better than bias-corrected TMPA product.Combined with the previous studies,atmospheric reanalysis datasets are more suitable than the satellite products in high mountainous areas.Climate is more important than altitude for the\falseAlarms'events of the TRMM product.Designed to focus on the tropical areas,the TMPA product mistakes certain meteorological situations for precipitation in subhumid and semiarid areas,thus causing significant"falseAlarms"events and leading to significant overestimations and unreliable performance.Simple linear bias correction method,only removing systematical errors,can significantly improves the accuracy of both the WRF output and the TMPA product in arid high mountainous areas with data scarcity.Evaluated by hydrological simulations,the bias-corrected WRF output is more reliable than the gauge dataset.Thus,data merging of the WRF output and gauge observations would provide more reliable precipitation estimations in arid high mountainous areas.展开更多
基金Under the auspices of the National Natural Science Foundation of China(No.41661099)the National Key Research and Development Program of China(No.Grant 2016YFA0601601)
文摘Satellite-based precipitation products have been widely used to estimate precipitation, especially over regions with sparse rain gauge networks. However, the low spatial resolution of these products has limited their application in localized regions and watersheds.This study investigated a spatial downscaling approach, Geographically Weighted Regression Kriging(GWRK), to downscale the Tropical Rainfall Measuring Mission(TRMM) 3 B43 Version 7 over the Lancang River Basin(LRB) for 2001–2015. Downscaling was performed based on the relationships between the TRMM precipitation and the Normalized Difference Vegetation Index(NDVI), the Land Surface Temperature(LST), and the Digital Elevation Model(DEM). Geographical ratio analysis(GRA) was used to calibrate the annual downscaled precipitation data, and the monthly fractions derived from the original TRMM data were used to disaggregate annual downscaled and calibrated precipitation to monthly precipitation at 1 km resolution. The final downscaled precipitation datasets were validated against station-based observed precipitation in 2001–2015. Results showed that: 1) The TRMM 3 B43 precipitation was highly accurate with slight overestimation at the basin scale(i.e., CC(correlation coefficient) = 0.91, Bias = 13.3%). Spatially, the accuracies of the upstream and downstream regions were higher than that of the midstream region. 2) The annual downscaled TRMM precipitation data at 1 km spatial resolution obtained by GWRK effectively captured the high spatial variability of precipitation over the LRB. 3) The annual downscaled TRMM precipitation with GRA calibration gave better accuracy compared with the original TRMM dataset. 4) The final downscaled and calibrated precipitation had significantly improved spatial resolution, and agreed well with data from the validated rain gauge stations, i.e., CC = 0.75, RMSE(root mean square error) = 182 mm, MAE(mean absolute error) = 142 mm, and Bias = 0.78%for annual precipitation and CC = 0.95, RMSE = 25 mm, MAE = 16 mm, and Bias = 0.67% for monthly precipitation.
基金The National Key Basic Research Program of China under contract No.2014CB953903the National Basic Research Programof China under contract No.2011CB403500+1 种基金the National Natural Science Foundation of China under contract Nos 40775066 and 41275145the Fundamental Research Funds for the Central Universities under contract No.13lgjc03
文摘The three-dimensional structures of summer precipitation over the South China Sea （SCS） and the East China Sea （ECS） are investigated based on tropical rainfall measurement mission （TRMM）. The primary results are as follows. First, both the convective and stratiform precipitation rates in the SCS are much higher than those of the ECS. The contribution of the convective cloud precipitation to the surface precipitation is primarily over the SCS and the ECS with a proportion of about 70%, but the contribution of convective cloud precipitation is slightly larger in the SCS than the ECS. The contribution of stratus precipitation is slightly larger in the ECS than that in the SCS. Second, the content of cloud particles and precipitation particles in the ECS in June was greater than that in the SCS, while in July and August, the content of cloud and precipitation particles in the ECS was less than that in the SCS. Third, the latent heat profile of the ECS is quite different from that of the SCS. In June, the peak values of evaporation and condensation latent heating rates in the ECS are greater than those in the SCS. In July and August, however, the peak values of evaporation and condensation latent heating rates in the ECS are about 0.05°/h less than those in the SCS.
基金supported by the National Basic Research Program of China (the 973 Program,Grant No.2010CB951101)the National Natural Science Foundation of China (Grants No. 50979022 and 50679018)+2 种基金the Program for Changjiang Scholars and Innovative Research Teams in Universities (Grant No. IRT0717)the Special Fund of the State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering of Hohai University (Grant No. 1069-50986312)the Open Fund Approval of the State Key Laboratory of Hydraulics and Mountain River Engineering of Sichuan University (Grant No. SKLH-OF-0807)
文摘High-quality rainfall information is critical for accurate simulation of runoff and water cycle processes on the land surface. In situ monitoring of rainfall has a very limited utility at the regional and global scale because of the high temporal and spatial variability of rainfall. As a step toward overcoming this problem, microwave remote sensing observations can be used to retrieve the temporal and spatial rainfall coverage because of their global availability and frequency of measurement. This paper addresses the question of whether remote sensing rainfall estimates over a catchment can be used for water balance computations in the distributed hydrological model. The TRMM 3B42V6 rainfall product was introduced into the hydrological cycle simulation of the Yangtze River Basin in South China. A tool was developed to interpolate the rain gauge observations at the same temporal and spatial resolution as the TRMM data and then evaluate the precision of TRMM 3B42V6 data from 1998 to 2006. It shows that the TRMM 3B42V6 rainfall product was reliable and had good precision in application to the Yangtze River Basin. The TRMM 3B42V6 data slightly overestimated rainfall during the wet season and underestimated rainfall during the dry season in the Yangtze River Basin. Results suggest that the TRMM 3B42V6 rainfall product can be used as an alternative data source for large-scale distributed hydrological models.
基金supported by the National High Technology Research and Development Program of China("863"Program)(Grant No. 2008AA12Z112)
文摘Soil erosion by water is the most important land degradation problem worldwide. In this paper a new procedure was developed to estimate the rainfall-runoff erosivity factor （R） based on Tropical Rainfall Measuring Mission （TRMM） satellite-estimated precipitation data, which consists of 3-h rainfall intensity data. In this method, R was calculated as the product of the maximum 180-min rainfall intensity and the rainfall energy. This procedure was applied to the Daling River basin in Liaoning Province, China, R in terms of yearly, monthly and event-based rainfall in 2005 was computed separately using TRMM 3B42 data. The TRMM data showed a significant correlation with the interpolated rain-gauge data. Furthermore, because the TRMM data are based on rainfall intensity, they can represent the impact on erosion more accurately. It reflects both the spatial distribution and the intensity of rainfall. The procedure is a new approach to estimate the rainfall erosivity for soil water erosion modeling, especially in areas lacking rain-gange stations.
基金Under the auspices of National Natural Science Foundation of China(No.42030501,41877148,41501016,41530752)Scherer Endowment Fund of Department of Geography,Western Michigan University and the Fundamental Research Funds for the Central Universities(No.lzujbky-2019-98)。
文摘Accurate estimates of precipitation are fundamental for hydrometeorological and ecohydrological studies,but are more difficult in high mountainous areas because of the high elevation and complex terrain.This study compares and evaluates two kinds of precipitation datasets,the reanalysis product downscaled by the Weather Research and Forecasting(WRF)output,and the satellite product,the Tropical Rainfall Measuring Mission(TRMM)Multisatellite Precipitation Analysis(TMPA)product,as well as their bias-corrected datasets in the Middle Qilian Mountain in Northwest China.Results show that the WRF output with finer resolution perfonns well in both estimating precipitation and hydrological simulation,while the TMPA product is unreliable in high mountainous areas.Moreover,bias-corrected WRF output also performs better than bias-corrected TMPA product.Combined with the previous studies,atmospheric reanalysis datasets are more suitable than the satellite products in high mountainous areas.Climate is more important than altitude for the\falseAlarms'events of the TRMM product.Designed to focus on the tropical areas,the TMPA product mistakes certain meteorological situations for precipitation in subhumid and semiarid areas,thus causing significant"falseAlarms"events and leading to significant overestimations and unreliable performance.Simple linear bias correction method,only removing systematical errors,can significantly improves the accuracy of both the WRF output and the TMPA product in arid high mountainous areas with data scarcity.Evaluated by hydrological simulations,the bias-corrected WRF output is more reliable than the gauge dataset.Thus,data merging of the WRF output and gauge observations would provide more reliable precipitation estimations in arid high mountainous areas.
文摘利用1998-2008年56个气象台站降水资料,结合TRMM月降水产品,通过对TRMM3B43降水数据在不同气候区、不同时空尺度的精度对比分析,探讨了卫星遥感反演降水产品在中国西北内陆河流域的适应性.结果表明:TRMM探测的月降水数据与实测月降水数据在整体上具有较好的一致性和线性相关性,相关系数为0.76,效率系数为0.58,其探测的降水量比观测值略大;TRMM在高原气候区月降水量的探测效果要优于在西风带区的;TRMM数据所反映的降水量的年内变化过程和实测降水量结果基本一致,但在具体的量上有一定的差异,表现为对降水相对集中的5-9月低估实测降水量,而在降水较少的10月-次年4月高估实测降水量,反映了TRMM对较大强度降水量的探测能力不足.流域多年平均降水量呈现南、北部大,中部小的格局,降水量的高值中心主要出现在高山地区,高达300 mm;而受西风环流影响的塔里木盆地东南面的且末-若羌一带、吐鲁番盆地和受高原区影响的柴达木盆地为极端干旱少雨区,降水量均不足100 mm.
