基于降水Z指数的朝鲜降水及旱涝时空特征

利用TRMM（Tropical Rainfall Measuring Mission,TRMM）3B43月降水数据,并结合降水Z指数,研究朝鲜1998—2018年的降水和旱涝时空格局。结果表明：TRMM与站点观测降水数据有显著的相关性。朝鲜降水季节性特征明显,57.29%的降水集中在夏季,空间上自东北部沿海岸线向东南递增。朝鲜的区域综合旱涝等级基本处于正常状态,偏旱和偏涝发生的频率最高,其次是大旱和大涝,极旱和极涝发生的频率最低,夏季和冬季干旱发生最为频繁,而洪涝在秋季频发。朝鲜北部发生旱涝事件的频率明显高于南部,咸镜山脉和盖马高原是旱涝的多发地区,温泉平原则最不易受到旱涝影响。春季、夏季和秋季均呈现洪涝强度增强的趋势。夏季洪涝强度加剧的趋势明显,中北部地区通过95%的显著性检验。     

TRMM 3B42卫星降水数据在赣江流域径流模拟中的应用

以赣江流域为研究区,基于观测降水和TRMM准实时数据(3B42RTV6、3B42RTV7)和分析数据(3B42V6、3B42V7),驱动VIC水文模型,开展卫星降水产品在赣江流域的水文模拟,评估TRMM降水产品在水文模拟中的应用能力。结果表明:(1)在赣江流域,3B42V7估算的降水与实测降水的对比结果最好,3B42RTV6的估算精度最低,3B42RTV7较3B42RTV6在赣江流域的降水估算精度提升非常明显;(2)在径流模拟方面,3B42V6和3B42V7在日尺度上尽管对洪峰的模拟有所偏差,但模拟结果仍能反映径流变化特征,在月尺度上模拟结果精度较高,纳什系数均在0.9以上,并且二者在4、5月的径流模拟结果较好,7、8月的模拟结果较差,而3B42RTV6对径流的模拟能力较差,日径流量和月径流量均呈现明显低估,3B42RTV7对径流的模拟结果比3B42RTV6有明显改善,可以满足实时水文预报的需求。     

GPM卫星降水数据在天山山区的适用性分析

卫星降水产品在山区的适用性具有较高的不确定性。论文基于天山山区2014-2015年167个气象站点数据,应用较为广泛的两套卫星降水产品--TRMM与CMORPH,选用均方根误差（RMSE）、相关系数（R）、相对误差（PB）,以及分类统计分析指标（错报率FAR、探测率POD、公正先兆评分ETS、频率偏差BIAS）等,评估了新一代卫星降水产品--Global Precipitation Measurement（GPM）在天山山区的适用性。结果表明：相对于以上两种产品,GPM在山区的精度最高。具体表现在：1）3套产品在降水较多的夏秋季均表现较好,相对于TRMM与CMORPH,GPM与观测数据的相关系数最高（R≥0.6）,相对误差最小（PB≈10%）;2）在整个天山山区,GPM相对于其他两套产品表现出较低的误差范围（-55%~55%）;3）GPM在不同的高程带内,均表现出同观测站点较低的误差与较高的相关系数;4）3套降水产品均表现出对探测弱降水事件较高的准确性（POD≈0.58）和较低的错误率（FAR≈0.63）,但综合分析4种指数,GPM表现最佳,能够以较准确的精度和较低的误差估测降水系统。     

基于TRMM订正数据的横断山区降水时空分布特征

鉴于高时空变异地区降水观测的需求,论文提出了基于ISODATA动态聚类法和最大似然法分区逐月回归的TRMM数据订正方法,并以横断山区为例,利用地基台站降水数据对1998-2012 年的TRMM 3B43 V7 数据进行了实验研究,并探讨了过去15 a 横断山区的降水时空分布特征及变化趋势。结果表明,TRMM 3B43 V7 数据在横断山区的总体精度较高,单站精度较低。订正后的TRMM 3B43 数据,与实测值偏差大为减少,降水相对偏差大于10%的站点由原始数据的16 个(42.1%)减少为7 个(18.4%),有81.6%的站点年降水量相对偏差小于10%,且相对偏差大于等于20%的站点仅3 个,能够基本满足横断山区降水时空分布特征研究的精度需要,有效地弥补了有限站点观测的不足。研究区内年降水量从东南向西北递减,与东亚季风在该区域的走向一致;1998-2012 年横断山区的降水量主要呈减少趋势,减少地区主要分布在南部以及中西部地区。夏季降水减少趋势最为突出,秋季次之,冬春变化趋势不明显。横断山区大部分区域的降水量与东亚夏季风指数正相关,与东亚冬季风指数负相关;最近15 a,研究区东亚季风指数持续变小,恰好与研究结果中的降水减少趋势一致。     

TRMM降水数据在东北地区的精度验证与应用

利用东北地区2000-2007 年的APHRODITE降水数据,基于GWR方法对TRMM降水数据进行修正,分析新的TRMM降水数据精度,并基于修正的TRMM降水数据对东北地区降水进行时空分布特征分析.结果表明:①APHRODITE降水数据与观测数据之间的线性相关性更高、均方根误差RMSE更小,数据具有较高的精度;②修正后的TRMM降水数据相关系数R有所提高,且RMSE值均有降低.整体来看,TRMM降水数据的降水量数值偏大于观测值;③修正TRMM降水数据在5-10 月的误差相对较小,整体来看,在大部分区域的误差在0~30%之间;④东北地区降水分布极不均匀,整体呈从东南向西北减少趋势.11 月到翌年3 月的降水稀少,降水主要集中在夏季,其中7月降水量最大.     

TRMM卫星降水数据在雅鲁藏布江流域的适用性分析

以雅鲁藏布江流域为研究区,利用16个气象站点的实测降水量在月尺度和日尺度上验证了TRMM(Tropical Rainfall Measurement Mission)卫星降水数据的精度,并在此基础上基于TRMM月降水数据分析了雅鲁藏布江流域的降水时空分布特征。结果表明:在整体上,TRMM月降水数据与站点实测降水量相关系数R=0.902,斜率K=0.849,数据精度较高,数值上比站点实测降水量略微偏低;就单个站点而言,大部分站点相关系数较高,偏差较小,但波密站相关系数相对较低,江孜站和南木林站数据偏差相对较大。TRMM日降水数据与站点实测降水量相关系数R=0.466,斜率K=0.451,数据精度较低,与站点实测降水量一致性较差。在降水空间分布上,雅鲁藏布江流域整体呈现由西向东逐渐递增的趋势,不同区域间差异极其明显;在降水时间分布上,大部分降水集中在6至9月,12月至第二年2月很少有降水发生。     

Using SWAT to Model Streamflow in Two River Basins With Ground and Satellite Precipitation Data1

Abstract: Both ground rain gauge and remotely sensed precipitation (Next Generation Weather Radar – NEXRAD Stage III) data have been used to support spatially distributed hydrological modeling. This study is unique in that it utilizes and compares the performance of National Weather Service (NWS) rain gauge, NEXRAD Stage III, and Tropical Rainfall Measurement Mission (TRMM) 3B42 (Version 6) data for the hydrological modeling of the Middle Nueces River Watershed in South Texas and Middle Rio Grande Watershed in South Texas and northern Mexico. The hydrologic model chosen for this study is the Soil and Water Assessment Tool (SWAT), which is a comprehensive, physical‐based tool that models watershed hydrology and water quality within stream reaches. Minor adjustments to selected model parameters were applied to make parameter values more realistic based on results from previous studies. In both watersheds, NEXRAD Stage III data yields results with low mass balance error between simulated and actual streamflow (±13%) and high monthly Nash‐Sutcliffe efficiency coefficients (NS > 0.60) for both calibration (July 1, 2003 to December 31, 2006) and validation (2007) periods. In the Middle Rio Grande Watershed NEXRAD Stage III data also yield robust daily results (time averaged over a three‐day period) with NS values of (0.60‐0.88). TRMM 3B42 data generate simulations for the Middle Rio Grande Watershed of variable qualtiy (MBE = +13 to ?16%; NS = 0.38‐0.94; RMSE = 0.07‐0.65), but greatly overestimates streamflow during the calibration period in the Middle Nueces Watershed. During the calibration period use of NWS rain gauge data does not generate acceptable simulations in both watersheds. Significantly, our study is the first to successfully demonstrate the utility of satellite‐estimated precipitation (TRMM 3B42) in supporting hydrologic modeling with SWAT; thereby, potentially extending the realm (between 50°N and 50°S) where remotely sensed precipitation data can support hydrologic modeling outside of regions that have modern, ground‐based radar networks (i.e., much of the third world).     

天山山区TRMM降水数据的空间降尺度研究

山区降水是干旱区水资源的重要补给源,但由于山区地形复杂、监测困难造成资料缺乏,水文预报的误差较大。近年来,TRMM3B43降水数据得到了大量应用,但受其较低空间分辨率的影响,使得应用精度受到限制。论文以2001—2010年TRMM3B43数据为基础,结合提取的7个数字地形因子（经度、纬度、坡度、坡向、海拔、地形开阔度、地形起伏度）,构建了天山山区年、季的降水主成分-逐步回归降尺度模型。分析结果表明：主成分-逐步回归降尺度模型有效地将TRMM3B43数据的空间分辨率由0.25°×0.25°提高到1 km×1 km。通过站点实测降水数据对比验证,决定系数均在0.85以上,降尺度后的数据精度显著优于原始TRMM3B43数据。该方法对研究干旱区空间降水精细化具有一定参考意义。     

基于TRMM降水订正数据的祁连山地区最大降水高度带研究

采用TRMM卫星反演月降水数据和气象台站实测降水数据,通过误差评估等数理统计方法验证数据,并结合Kriging法对TRMM降水数据进行订正,以此研究了祁连山地区最大降水高度带的时空变化。结果表明：（1）TRMM降水数据在祁连山地区的整体适用性较好。其中TRMM降水数据与台站实测值的年均降水量相关系数达0.94;季节平均降水量的相关系数分别为春季（0.87）、夏季（0.89）、秋季（0.88）、冬季（0.70）。（2）祁连山地区27个气象台站实测值与TRMM降水数据的相关性较好,但在个别台站误差较大且存在低值高估、高值低估的现象。（3）祁连山地区年均降水量自东向西呈减少趋势。东、中、西三段最大降水高度带分别为4100 m、4500 m、4700 m,年均降水量的垂直变化率分别为16.6 mm/100 m、10.8 mm/100 m、9.8 mm/100 m。（4）1998-2016年祁连山地区东、中、西三段降水量均呈波动增加,最大降水高度带也呈波动上升趋势,祁连山地区年内各季节最大降水高度带按夏、春、秋、冬的次序降低。     

Evaluation of Satellite‐Derived Rainfall Data for Multiple Physio‐Climatic Regions in the Santiago River Basin,Mexico

Assessment of water resources requires reliable rainfall data, and rain gauge networks may not provide adequate spatial representation due to limited point measurements. The Tropical Rainfall Measuring Mission (TRMM) provides rainfall data at global scale, and has been used with good results. However, TRMM data are an indirect measurement of rainfall, and therefore must be validated for its proper use. In this work, a validation scheme was designed and implemented to compare the TRMM Version 7 (V7) monthly rainfall product at different time frames with data measured in two hydrologic subregions of the Santiago River Basin (SRB) in Mexico: Río Alto Santiago and Río Bajo Santiago (RBS). Additionally, three physio‐climatic regions provide an assessment of the interplay of topography, distance from coastal regions, and seasonal weather patterns on the correspondence between both datasets. The TRMM V7 rainfall product exhibited good agreement with the rain gauge data particularly for the RBS and for the whole SRB during wettest summer and autumn seasons. However, strong regional dependence was observed due to differences in climate and topography. Overall, in spite of some noted underestimations, the monthly TRMM V7 rainfall product was found to provide useful information that can be used to complement limited monitoring as is the case of RBS. An improved combined rainfall product could be generated and thus gaining the most benefits from both data sources.