Multiple satellite-based earth observations and traditional station data along with the Soil & Water Assessment Tool (SWAT) hydrologic model were employed to enhance the Lower Mekong River Basin region's hydrological decision support system. A nearest neighbor approximation methodology was introduced to fill the Integrated Multi-satellite Retrieval for the Global Precipitation Measurement mission (IMERG) grid points from 2001 to 2014, together with the Tropical Rainfall Measurement Mission (TRMM) data points for continuous precipitation forcing for our hydrological decision support system. A software tool to access and format satellite-based earth observation systems of precipitation and minimum and maximum air temperatures was developed and is presented. Our results suggest that the model-simulated streamflow utilizing TRMM and IMERG forcing data was able to capture the variability of the observed streamflow patterns in the Lower Mekong better than model-simulated streamflow with in-situ precipitation station data. We also present satellite-based and in-situ precipitation adjustment maps that can serve to correct precipitation data for the Lower Mekong region for use in other applications. The inconsistency, scarcity, poor spatial representation, difficult access and incompleteness of the available in-situ precipitation data for the Mekong region make it imperative to adopt satellite-based earth observations to pursue hydrologic modeling.

Hydrological modeling in the Third Pole remains challenging due to the complex topography and scarcity of in-situ precipitation observations. In this study, we assessed five satellite precipitation products (SPPs) including TRMM3B42, PERSIANN-CDR, GPM-IMERG, CMORPH, and GSMaP, and simulated daily streamflow in the Yarlung Zangbo River Basin (YZRB) with VIC model. The performance of SPPs was evaluated by CC, RB, RMSE, POD and FAR, to compare with daily observations. Overall, all SPPs showed decreasing trends of precipitation from east to west compared to 10 km rainfall data. PERSIANN had the highest values of POD (0.65), RB (91.6%) and FAR (0.59) but worst performed in streamflow. CMORPH, GPM and TRMM fit well with the observations annually but overestimate the precipitation in the southeast during wet seasons. Simulation from GPM and CMORPH yield satisfactory results (NSE of 0.86 and 0.82, RE of - 20% and - 13%, respectively), while TRMM outperformed GPM in modeling runoff with smaller relative error. Results indicated the potential of GPM and CMORPH in providing alternative rainfall information in YZRB. Accurate evaluation of multi-source SPPs and their hydrological utility in YZRB would benefit further hydrometeorological studies and water resources management in this area.© 2022. The Author(s).

论文对比分析了1980—2016年基于站点插值降水数据CMA(China Meteorological Administration)和APHRODITE(Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation)、卫星遥感降水数据PERSIANN-CDR(Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Climate Data Record)和GPM(Global Precipitation Measurement)、大气再分析数据GLDAS(Global Land Data Assimilation System)以及区域气候模式输出数据HAR(High Asia Refined analysis)在雅鲁藏布江7个子流域的降水时空描述,利用国家气象站点数据对各套降水数据进行单点验证,并以这6套降水数据驱动VIC(Variable Infiltration Capacity)大尺度陆面水文模型反向评估了各套降水产品在雅鲁藏布江各子流域径流模拟中的应用潜力。结果表明：① PERSIANN-CDR和GLDAS年均降水量最高(770~790 mm),其次是HAR和GPM(650~660 mm),CMA和APHRODITE年均降水量最低(460~500 mm)。除GPM外,其他降水产品在各子流域都能表现季风流域的降水特征,约70%~90%的年降水量集中在6—9月份。② 除PERSIANN-CDR和GLDAS外,其他降水产品皆捕捉到流域降水自东南向西北递减的空间分布特征。其中,HAR数据空间分辨率最高,表现出更详细的流域内部降水空间分布特征。③ 与对应网格内的国家气象站降水数据对比显示,APHRODITE、GPM和HAR降水整体低估(低估10%~30%),且严重低估的站点主要集中在下游(低估40%~120%)。PERSIANN-CDR和GLDAS整体表现为高估上游流域站点降水(高估28%~60%),但低估下游流域站点降水(低估11%~21%)。④ 在流域径流模拟上,当前的6套降水产品在精度或时段上仍无法满足水文模型模拟的需求。⑤ 通过水文模型反向评估,6套降水产品中区域气候模式输出的HAR在流域平均降水量和季节分配上更合理。

The gauge-based precipitation data from the National Climate Center, China Meteorological Administ-ration (CMA), Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Climate Data Record (PERSIANN-CDR), Global Precipitation Measurement (GPM), Global Land Data Assimilation System (GLDAS), High Asia Refined analysis (HAR) are compared with each other and evaluated by the precipitation data from 16 national meteorological stations during 1980-2016 in the Yarlung Zangbo River and its sub-basins. The potential utilities of these multiple precipitation datasets are then systematically evaluated as inputs for the variable infiltration capacity (VIC) macroscale land surface hydrologic model. The results show that: 1) PERSIANN-CDR and GLDAS contain the largest precipitation estimates among the six datasets with mean annual precipitation of 770-790 mm, followed by the HAR and GPM (650-660 mm), while CMA and APHRODITE contain the lowest precipitation estimates with mean annual precipitation of 460-500 mm. All the products can detect the large-scale monsoon-dominated precipitation regime in the Yarlung Zangbo River and its sub-basins with 70%-90% of annual total precipitation occurring in June-September except the GPM. 2) The general spatial pattern of the annual mean precipitation fields is roughly in agreement among the six datasets, with a decreasing trend from the southeast to the northwest in the Yarlung Zangbo River Basin except the PERSIANN-CDR and GLDAS. 3) Relative to the data from the national meteorological stations, APHRODITE, GPM, and HAR generally underestimate precipitation by 10%-30%, while PERSIANN-CDR and GLDAS overestimate precipitation from stations in upstream sub-basins by 28%-60% and underestimate precipitation from stations in downstream sub-basins by 11%-21%. 4) The six precipitation datasets cannot satisfy the needs of hydrological simulation in term of accuracy or period in the basin. 5) HAR precipitation data—output of regional climate model—show more reasonable amount and seasonal pattern among the six datasets in the upper Brahmaputra according to the inverse evaluation by VIC hydrological model.

洪水过程的特征指标不仅包括洪水量级,还包括时间、形态、动力学等指标。现有模型和方法重点关注洪水量级指标的模拟,对其他指标的模拟仍有待深入。如何实现对洪水过程所有特征指标的模拟已成为目前洪水预报的技术瓶颈。论文采用4种机器学习模型(多元线性回归、多层感知器、随机森林和支持向量机)对淮河上游长台关流域59场降雨—洪水场次7个特征指标(洪水总量、洪峰流量、洪水历时、洪峰时间偏度、高流量历时占比、涨洪和落洪速率)进行模拟,评估不同模型对不同洪水类型和特征指标的模拟效果。结果显示：① 长台关流域洪水过程可分为3类,第1类洪量中等、历时长且洪峰出现时间偏前(16场);第2类洪量低、形态矮胖且洪峰出现时间靠后(34场);第3类洪量大、涨落水迅速、形态尖瘦(9场)。② 时间指标模拟效果最优,动力学指标模拟效果最差。多元线性回归和随机森林模拟效果随所有特征指标数值的增加而增强;支持向量机的模拟效果随着洪水历时指标数值的增加而降低,随着其余特征指标数值的增加而增强;多层感知器模拟效果随着洪水总量、洪峰流量、高流量历时占比和涨洪速率等指标值的增加而增强。③ 从各类型洪水特征模拟精度来看,4种模型对第3类洪水特征模拟均为最佳,第2类最差;随机森林在第1类和第3类洪水特征模拟中效果最优,支持向量机对第2类洪水特征模拟效果最优。④ 从综合模拟精度来看,支持向量机效果最优,然后依次为随机森林、多层感知器和多元线性回归。上述4种模型率定和验证期相对误差分别为23%和98%、21%和109%、37%和75%、41%和102%。研究可为流域洪水过程深度挖掘和防洪措施制定提供参考和借鉴。

The characteristic indicators of flood process include not only flood magnitude, but also flood duration, dynamics, and so on. The existing models and methods focus on the simulation of flood magnitude indicators, but the simulation of other indicators remains insufficient. In this study, four machine learning models — multiple linear regression, multi-layer perceptron, random forest, and support vector machine — were used to simulate seven characteristic indicators — total flood volume, peak flow, flood duration, time deviation of flood peak, proportion of high flow duration, flood rise and fall rates — of 59 rainfall-flood events in the Changtaiguan Basin in the upper reaches of the Huaihe River. The simulation performance of the models for different flood types and characteristic indicators were evaluated. The results show that: 1) The flood process in the Changtaiguan Basin can be divided into three categories. The first category is characterized by moderate flood volume, long duration, and earlier peak time (16 fields); the second type has low flood volume, short and fat shape, and the flood peak appears later (34 fields); the third type has large flood volume, sharp and thin shape, and flood rises and falls rapidly (9 fields). 2) Time indicator simulation performed the best and dynamic indicator simulation performed the worst; the performance of multiple linear regression and random forest simulation increased with the increase of all characteristic indicator values. The simulation performance of support vector machine decreased with the increase of flood duration, and increased with the increase of value of the remaining characteristic indicators. The simulation performance of multi-layer perceptron increased with the increase of value of four indicators, namely, total flood volume, peak flow, proportion of high flow duration, and flood rise rate. 3) With regard to the simulation accuracy of characteristic indicators of various types of floods, the four models performed the best for the simulation of the third type of floods, but the worst for the second type; random forest showed the best simulation performance for the first and third types of floods, and support vector machine showed better simulation performance for the second type of floods. 4) According to the comprehensive simulation accuracy, the support vector machine model performed the best, followed by random forest, multi-layer perceptron, and multiple linear regression. Relative errors for the calibration and validation periods were 23% and 98%, 21% and 109%, 37% and 75%, and 41% and 102%, respectively. The study results may provide some references for flood type simulation and countermeasures in the Huaihe River Basin.

作为联结大气圈和地圈的纽带,水文循环同时承受气候变化和土地利用/覆被变化（LUCC）的双重影响,然而大多数的水文响应研究主要关注未来气候变化对径流的影响,忽略了未来LUCC的作用。因此,本文的研究目的是评估未来气候变化和LUCC对径流的共同影响。首先采用2种全球气候模式（BCC-CSM1.1和BNU-ESM）输出,基于DBC降尺度模型得到未来气候变化情景;然后,利用CA-Markov模型预测未来LUCC情景;最后,通过设置不同的气候和LUCC情景组合,采用SWAT模型模拟汉江流域的未来径流过程,定量评估气候变化和LUCC对径流的影响。结果表明：① 未来时期汉江流域的年降水量、日最高、最低气温相较于基准期（1966—2005年）,在RCP 4.5和RCP 8.5浓度路径下,分别增加4.0%、1.8 ℃、1.6 ℃和3.7%、2.5 ℃、2.3 ℃;② 2010—2050年间,流域内林地和建设用地的面积占比将分别增加2.8%和1.2%,而耕地和草地面积占比将分别减少1.5%和2.5%;③ 与单一气候变化或LUCC情景相比,气候变化和LUCC共同影响下的径流变化幅度最大,在RCP 4.5和RCP 8.5浓度路径下未来时期年平均径流分别增加5.10%、2.67%,且气候变化对径流的影响显著大于LUCC。本文的研究结果将有助于维护未来气候变化和LUCC共同影响下汉江流域的水资源规划与管理。

As a link between the atmosphere and the geosphere, the hydrological cycle is affected by both climate change and Land Use/Cover Change (LUCC). However, most existing research on runoff response focused mainly on the impact of the projected climate variation, neglecting the influence of future LUCC variability. Therefore, the objective of this study is to examine the co-impacts of both projected climate change and LUCC on runoff generation. Firstly, the future climate scenarios under BCC-CSM1.1 and BNU-ESM are both downscaled and corrected by the Daily Bias Correction (DBC) model. Secondly, the LUCC scenarios are predicted based on the Cellular Automaton-Markov (CA-Markov) model. Finally, the Soil and Water Assessment Tool (SWAT) model is used to simulate the hydrological process under different combinations of climate and LUCC scenarios, with the attempt to quantitatively evaluate the impacts of climate change and LUCC on runoff generation. In this study, the Hanjiang River basin is used as the case study area. The results show that: (1) compared with the base period (1966-2005), the annual rainfall, daily maximum and minimum air temperatures during 2021-2060 will have an increase of 4.0%, 1.8 ℃, 1.6 ℃ in RCP4.5 scenario, respectively, while 3.7%, 2.5 ℃, 2.3 ℃ in RCP8.5 scenario, respectively. (2) During 2010-2050, the area proportions of forest land and construction land in the study area will increase by 2.8% and 1.2%, respectively, while those of farmland and grassland will decrease by 1.5% and 2.5%, respectively. (3) Compared with the single climate change or LUCC scenario, the variation range of future runoff under both climate and LUCC is the largest, and the influence of climate change on future runoff is significantly greater than that of LUCC. This study is helpful to maintain the future water resources planning and management of the Hanjiang River basin under future climate and LUCC scenarios.

Based on the precipitation data of Poyang lake basin based on TRMM (tropical rainfall measuring mission) satellite 3B42 V6 during 1998-2007, this paper studies the spatio-temporal distribution of precipitation. And the precisions of TRMM data are evaluated for different sub-basins, rainfall intensities and seasons through the observation rainfall data obtained from 40 weather stations, which fills in a gap in the previous studies. The results show that the heavy rain events generally occur in the northern part of Poyang lake basin (Xiushui and Raohe sub-basins) based on TRMM and observation data, which can cause signal attenuation of radar and make a large error for TRMM. The class of 10~50 mm makes the greatest contribution to the total rainfall, accounting for about 60%. The temporal distribution of precipitation in the lake basin shows that there is a dry spell from January to mid-March and a wet spell from late March to September, and another dry spell is observed after September. The spatial distribution presents that more rainfall is observed in the eastern and western parts of the basin than in the central parts. It is also found that the rainfall is 300~400 mm less than observed data in the mountainous areas of southern Jiangxi province, which may be affected by elevation and gradient of landform. So, there is a limitation for TRMM to measure the heavy rain events and the rainfall in mountainous areas.

基于高空间分辨率0.25°的ERA-Interim再分析资料、TRMM 3B43 Version7数据、气象站点实测数据等多源数据，本文采用一种新的流域边界水汽通量概化和提取方法，揭示了夏季怒江流域水汽输送多支特征，并分析了其对降水时空分异的影响。研究表明，在高黎贡山南部、北部，伯舒拉岭北部及念青唐古拉山中部，有4支区域性水汽输送高值区，多年平均输送通量分别达102.6 kg/(m·s)、66.3 kg/(m·s)、39.7 kg/(m·s)和41.3 kg/(m·s)。多支水汽输送不仅深刻影响流域水汽输送格局，而且对降水时空分异也有不同程度影响。年际变化上，中下游横断山区水汽输送对降水的影响较小，上游青藏高原区影响较大，尤其以那曲—比如—索县一带影响最为显著。空间分布上，流域降水与水汽输送通量呈显著正相关，受多支水汽输送影响形成多个区域性多雨带。

Water vapor transport is a key factor in the transformation of atmospheric water resources to terrestrial water resources. In order to reveal the characteristics of water vapor transport and its influence on precipitation over the Nujiang River basin, high spatial resolution (0.25°) ERA-Interim reanalysis data, TRMM 3B43 Version7 data, meteorological station measured data and SRTM Version4.1 DEM data were used, and a new method to generalize and extract water vapor transport data along the watershed boundary was adopted. Through analysis and research, the multiple branches water vapor transports over the Nujiang River Basin in summer were detected, and their effects on the spatiotemporal distribution of precipitation were analyzed. The research shows that there are four regional high-value areas of water vapor transport on the western boundary of the watershed, which are located in the southern and northern parts of Gaoligong Mountain, the northern part of Boshula Mountain, and the middle of Nyenchen Tanglha Mountains, with the annual average water vapor flux being 102.6 kg/(m·s), 66.3 kg/(m·s), 39.7 kg/(m·s), and 41.3 kg/(m·s), respectively. Multiple branches water vapor transport affects not only the water vapor transport in different areas of the basin, but also the spatiotemporal characteristics of precipitation. In terms of interannual variation, the influence of water vapor transport on precipitation is weaker in the middle and downstream area (Hengduan Mountains), while it is greater in the upstream area (Qinghai-Tibet Plateau), especially in the Nagqu-Biru-Suoxian area. In terms of spatial distribution, annual precipitation in the basin is significantly positively correlated with water vapor flux and negatively correlated with water vapor flux divergence. Because of multiple branches of water vapor transport, four regional rainy areas are formed by the west side of the basin.

SMAP(Soil Moisture Active Passive，SMAP)产品空间分辨率低的特征限制了在地表高异质性的干旱区沙漠稀疏植被区的适用性。考虑到干旱区沙漠植被区特殊的环境特征，在地表温度(Land Surface Temperature，LST)、归一化植被指数(Normalized Difference Vegetation Index，NDVI)、数字高程模型(Digital Elevation Model，DEM)等传统降尺度因子的基础上，增加了与荒漠地表土壤水分关联性更强的增强型修改土壤植被指数(Enhanced Modified Soil-Adjusted Vegetation Index，EMSAVI)与比值沙地亮度指数(Ratio Sand Brightness Index，RSBI)分别作为反映研究区植被盖度和裸沙分布状况的降尺度因子，利用随机森林算法(Random Forest，RF)，构建了干旱区土壤水分降尺度模型。结果表明：（1） 由相关性分析可知，EMSAVI（r_干=－0.37，r_湿=－0.34）、RSBI（r_干=－0.42，r_湿=－0.25）对荒漠土壤水分均有较好的指示作用且效果优于NDVI（r_干=－0.21，r_湿=0.08）；（2） EMSAVI和NDVI重要性分别为18.7%、13.2%，EMSAVI在构建降尺度模型时贡献度更高。（3） 构建的干、湿季干旱区土壤水分降尺度模型得到的结果与SMAP产品的R²分别达到了0.916，0.910，RMSE分别达到了0.0075 cm³·cm^-3、0.0063 cm³·cm^-3，较传统模型的RMSE均降低了0.0013 cm³·cm^-3。（4） 通过计算LBP(Local Binary Patterns)的差值（LBP_C）对空间一致性评价，新构建降尺度模型的结果（0.0585）优于传统降尺度（0.0645）。研究结果将短波红外波段引入到植被指数建立的EMSAVI，可较好地应用于干旱区沙漠稀疏植被区土壤水分降尺度研究。

The low spatial resolution of SMAP products limits its applicability to sparsely vegetated arid regions and deserts with high surface heterogeneity. Considering the special environmental characteristics of sparsely vegetated desert areas in arid regions, traditional downscaling methods such as land surface temperature (LST), normalized difference vegetation index (NDVI), and digital elevation model (DEM) have been used, among others. Based on the scale factor, the enhanced modified soil-adjusted vegetation index (Enhanced Modified Soil-Adjusted Vegetation Index, EMSAVI) and the ratio sand brightness index (RSBI), which are more correlated with the desert surface soil moisture, were added to reflect the study area. For the downscaling factors of vegetation coverage and bare sand distribution, the random forest (RF) algorithm was used to build a soil moisture downscaling model in arid areas. The results showed the following: (1) Correlation analysis showed that EMSAVI (rdry = -0.37, rwet = -0.34) and RSBI (rdry = -0.42, rwet = -0.25) were good indicators of desert soil moisture, being superior to NDVI (rdry = -0.21, rwet = 0.08). (2) The importance of EMSAVI and NDVI was 18.7% and 13.2%, respectively, and EMSAVI contributed more to the construction of the downscaling model. (3) The results obtained from the soil moisture downscaling model in dry and wet season arid regions and R2 of the SMAP product reached 0.916 and 0.910, and the RMSE reached 0.0075 cm3·cm-3 and 0.0063 cm3·cm-3, respectively, which are lower than the RMSE of the traditional model of 0.0013 cm3·cm-3. (4) By calculating the difference (LBPC) of LBP (local binary patterns) to evaluate the spatial consistency, the result of the newly constructed downscaling model (0.0585) was better than that of traditional downscaling (0.0645). This research shows that introduction of the short-wave infrared band into the EMSAVI established by the vegetation index enables its better application to the study of soil moisture downscaling in sparsely vegetated desert areas in arid regions.

城市是区域人口、产业等要素集聚的主要空间载体，系统揭示黄河流域城市建设用地结构演化及其驱动机制对于深入推进黄河流域生态保护和高质量发展战略落实具有重要意义。基于黄河流域2007—2019年城市建设用地数据，论文运用信息熵模型分析了黄河流域城市建设用地结构的时空演化特征，并用随机森林模型探讨其驱动机制。结果表明：① 2007—2019年，黄河流域城市建设用地面积快速增长，年均增速达5.34%，但城市建设用地结构信息熵不断降低，经历了“高位无序—相对稳定—低位有序”的变化过程；② 黄河上、中、下游城市建设用地规模增速依次加大，年均增速分别达到3.91%、5.06%、6.22%；中游地区城市建设用地结构信息熵下降最多，上游地区和下游地区降幅相近；③ 黄河流域各类建设用地主要配置在省会及经济发达城市；黄河流域城市用地结构信息熵存在空间异质性，省会城市及经济发达城市建设用地结构信息熵存在降低趋势，而欠发达城市的信息熵逐步增大；④ 城市用地规模和城市人口规模对城市建设用地结构演化的作用最强，产业结构升级和财政支出规模的作用强度次之，经济发展水平和政策支持力度的作用较小，上中下游影响因子的作用强度存在异质性；⑤ 城市用地规模、城市人口规模、财政支出规模与城市建设用地结构信息熵呈正向变动关系，经济发展水平和产业结构升级与城市建设用地结构信息熵呈负向变动关系，政策支持力度则与城市建设用地结构信息熵呈现出“倒U”型关系。

Cities are the main spatial carriers for the agglomeration of regional population, industry and other factors, and systematically revealing the evolution of urban construction land structure and its driving mechanism is of great significance for further promoting the implementation of ecological conservation and high-quality development strategy of the Yellow River Basin. Based on the urban construction land data of the Yellow River Basin from 2007 to 2019, the spatiotemporal change characteristics and differentiation patterns of the construction land structure in the basin were analyzed by using the information entropy model, and the formation mechanism of the driving factors was examined by using the random forest model. The results show that: 1) From 2007 to 2019, the area of urban construction land in the Yellow River Basin continued to grow, with an average annual growth rate of 5.34%, but the information entropy of urban construction land structure decreased, and experienced a change process of high-level disorder, relatively stable, and low-level orderly. 2) The growth rate of urban construction land use in the upper, middle, and lower reaches of the Yellow River increased successively, with the annual growth rate reaching 3.91%, 5.06% and 6.22% respectively. The information entropy of urban construction land use structure in the middle reaches of the Yellow River decreased the most, and the decline rates in the upper reaches and lower reaches were similar. 3) All types of construction land in the basin are mainly distributed in provincial capitals and cities in economically developed areas, and there is a clear spatial heterogeneity in the information entropy of urban construction land use structure in the region. The information entropy of urban construction land use structure in provincial capital cities and economically developed cities showed a decreasing trend, while the information entropy of less developed cities gradually increased. 4) The scale of urban land use and the size of urban population have the strongest effect on the change of urban construction land use structure, followed by the intensity of industrial structure upgrading and scale of fiscal expenditure. The role of economic development level and policy support is small, and the intensity of impact of upstream, middle reaches, and downstream influencing factors is heterogeneous. 5) The scale of urban land use, the size of urban population, and the scale of fiscal expenditure showed a positive change relationship with the information entropy of urban construction land use structure, the level of economic development and industrial structure upgrading showed a negative change relationship with the information entropy of urban construction land use structure, and the policy support showed an inverted U-shaped change relationship with the information entropy of urban construction land use structure.

IMERG和GSMaP是全球降水计划（GPM）时代最主要的高分辨率降水产品。为研究其在中国四川地区的适用性,以中国气象局提供的自动气象站融合降水数据为参考基准,采用6种统计指数分析了IMERG（IMERG_Uncal, IMERG_Cal）和GSMaP（GSMaP_MVK, GSMaP_Gauge）系列产品在四川的误差特征。结果表明：① 在日和小时尺度上,GSMaP系列产品均高估地面降水观测,GSMaP_MVK高估最显著,校正产品GSMaP_Gauge的相关系数（CC）、相对偏差（BIAS）和均方根误差（RMSE）较GSMaP_MVK均有较大提高,尤其对川西高原降水的高估现象改善明显,而IMERG_Uncal存在低估川西高原降水、轻微高估四川盆地降水的问题,校正产品IMERG_Cal一定程度上降低了对川西高原降水的低估现象,但整体精度（CC, RMSE）提高不明显。② IMERG系列产品对降水事件的探测准确性更好,GSMaP_Gauge虽然在四川表现出较高的命中率（POD）,但存在较多的误报降水,在盆地和四川南部各产品均表现出较高的POD和关键成功指数（CSI）以及低误报率（FAR）,而四川西北部表现最差,尤其是在无自动站分布地区。③ 4套降水产品中,IMERG_Cal表现出最好的探测强降水和弱降水的能力,具有一定的监测极端降水的潜力。总体上,IMERG和GSMaP在盆地的反演精度优于高原山区,校正产品精度优于纯卫星产品,不同地形地区精度差异明显,表明对卫星降水产品进行不同地形误差订正仍是未来降水反演工作的重点和难点。

The Integrated Multi-satellitE Retrievals for GPM (IMERG) and Global Satellite Mapping of Precipitation (GSMaP) are two high precisely multi-satellite precipitation estimates in the GPM era. In order to evaluate the applicability of both IMERG and GSMaP series products (IMERG_Uncal and IMERG_Cal, GSMaP_MVK and GSMaP_Gauge) over the Sichuan region in China, six statistical indices are used to systematically analyze the error characteristics of these products, benchmarked by a set of ground-based dataset from China Meterological Administration (CMA). Results show that: (1) All products show the dramatic regional difference over Sichuan at both daily and hourly scales. The GSMaP series products overestimate precipitation and the most overestimations occur over the high altitude areas located in the Western Sichuan. GSMaP_Gauge shows relatively higher correlation coefficient and lower relative bias and root mean square error due to the employment of gauge-based adjustments. On the contrary, IMERG_Uncal shows underestimation over the mountainous areas, while the relatively slight overestimation appears in the basin area with lower elevation at both daily and hourly time scales, suggesting that gauge-calibrated dataset IMERG_Cal has effectively improved the relative bias in the mountainous areas but not in the flat basin area. (2) By synthesizing the three classified statistical indices, IMERG series products exhibit better potentials in detecting precipitation events. Although GSMaP_Gauge shows a higher hit rate of precipitation, it has more false alarm ratios of precipitation. All products show better hit rate and lower false alarm rate over basin area and southern Sichuan. Furthermore, it is found that the ground-based dataset has some errors in those areas without meteorological stations, which leads to the apparent uncertainty in assessing the accuracy of satellite precipitation products over the Northwest Sichuan Plateau. (3) IMERG_Cal performs better in capturing the rainfall amounts and events compared with other products, especially for the lowest and highest rainfall intensity ranges, demonstrating its application potential for monitoring the extreme weather events. Overall, both IMERG and GSMaP estimates have relatively high uncertainties over the mountainous areas than ones over the flat basin areas. Additionally, the gauge-calibrated products obviously outperform the uncalibrated datasets. On the basis of the findings, future efforts focus on reducing and correcting the errors and biases of satellite precipitation estimates by considering both spatio-temporal characteristics and the topographical information.

遥感降水产品相对于气象站观测数据能够更好地反映降水的空间分布特征,对其进行不同气候区上的差异性评价对数据产品选择和遥感降水反演算法改进均有重大意义。本文选择中国典型气候区（干旱区、过渡区、湿润区和青藏高原地区）,以649个经偏差矫正后的气象站降水数据为标准,评估了5种国际常用的遥感降水产品 (CHIRPS v2.0, CMORPH v1.0, MSWEP v2.0, PERSIANN-CDR, TRMM 3B42v7) 在中国典型气候区的适用性。研究发现,各产品的性能存在空间差异性。MSWEP在各气候区的相关系数(CC),Kling-Gupta efficiency（KGE）,均方根误差（RMSE）等基本统计性能指标均优于其他4种产品。相对偏差（BIAS）方面,在干旱区、湿润区、青藏高原、过渡区表现较优越的产品分别为MSWEP、CHIRPS、PERSIANN、TRMM。在评估遥感降水产品对降水事件发生概率的估算能力方面,选择了误报率（FAR,降水事件预报错误的比例）、命中率（POD,降水事件预报正确的比例）、关键成功指数（CSI,降水事件正确预报综合性能）、精度指数（ACC,等级预报综合性能）和降水等级概率分布（PDF）5个指标作为评估依据,结果表明,就POD,CSI和ACC而言,在各气候区MSWEP表现明显优于其他产品;对于FAR,TRMM和CMORPH产品在湿润区表现优越,其余气候区仍以MSWEP表现较为优越;就PDF而言,PERSIANN和MSWEP产品对1~20 mm的日降水量的估算偏高,特别是MSWEP在青藏高原和湿润区对小雨的估算频率明显偏高, MSWEP有待在该区提高频率预报的能力。综合而言,多源数据融合的MSWEP在各气候区的基本统计性能和降水等级性能较好,可作为可靠的降水数据源用于中国水文气象研究,同时也表明多源数据融合产品具有良好的应用前景。