以全球变暖为主的气候变化已成为当前世界最重要的环境问题之一。气候变化对水循环与水资源影响的研究越来越引起国内外学者的的高度关注和重视。简要回顾了国内外气候变化对水文水资源影响研究的发展历程,着重论述了目前气候变化对水文水资源影响的重点研究领域：水循环要素变化的检测与归因分析、气候变化与人类活动对水循环与水资源影响的定量评估、未来气候变化情景下水循环与水资源的演变趋势预估、气候变化对极端水文事件的影响研究和应对气候变化的水资源适应性管理策略;并介绍了气候变化对水文水资源影响研究中的气候变化情景、水文模拟及陆-气模型耦合等重要技术手段。最后,针对目前研究中存在的问题及薄弱环节,提出未来研究的发展趋势和亟需解决的关键问题。

Climate change characterized by global warming has become one of the world ′s most important environmental issues. Its impact on hydrology and water resources has attracted more and more concern from international scholars. This article briefly reviews the development of studies for impact of climate change on hydrology and water resources, and then focuses on the key fields in the studies: analysis of variation trends in runoff and related driving forces, quantitative assessment for climate change and human activities impact on water resources, watershed modeling to assess impact of potential climate change on hydrology and water resources, study of extreme hydrologic events under climate change scenarios, and adaptation strategy for climate change impact on water resources. It also introduces the important techniques in the related studies, including hydrologic simulation, future climate change scenarios, and connection of hydrological model with climatic model. Finally, it raises problems and weakness in study and puts forward some suggestions, including development trends of future research and key problems to be solved.

分析了1880年以来华北地区(太原、北京、济南、烟台和郑州)降水的季节、年际和年代际变化。研究表明:华北降水存在着显著的年际和年代际变化,1883~1898年和1949~1964年是华北降水较丰沛时段,1899~1920年和1965~1997年华北降水处于偏少阶段,其中1980~1993年降水持续偏少,干旱现象严重。研究指出:华北夏季降水的年际和年代际变化与夏季东亚——西太平洋地区上空500 hPa位势高度场以及西太平洋副热带高压脊线位置的年际和年代际变化密切相关;90年代后期,夏季西太平洋副热带高压脊线的位置偏北,估计未来5~10年华北地区持续干旱现象将结束,降水可能会恢复到20世纪50~60年代中期水平,有利于华北水资源增加。

A study on the precipitation in North China is presented since 1880.There were two plentiful rainfall periods,one was 1883~1898,the other was 1949~1964.There were two less rainfall periods,one was 1899~1920,the other was 1965~1997.The decade precipitation and evaporation in North China and their associated 500 hPa patters and west Pacific subtropical high during 1951~1997 are studied.The analysis of the summer dry and wet periods in North China reveals a reversal in the sign of anomalies over 500 hPa between middle latitude and low latitude.The precipitation in North China is positive (negative) anomaly when the west Pacific subtropical high lies north (south) than normal.

Precipitation affects many aspects of our everyday life. It is the primary source of freshwater and has significant socioeconomic impacts resulting from natural hazards such as hurricanes, floods, droughts, and landslides. Fundamentally, precipitation is a critical component of the global water and energy cycle that governs the weather, climate, and ecological systems. Accurate and timely knowledge of when, where, and how much it rains or snows is essential for understanding how the Earth system functions and for improving the prediction of weather, climate, freshwater resources, and natural hazard events. The Global Precipitation Measurement (GPM) mission is an international satellite mission specifically designed to set a new standard for the measurement of precipitation from space and to provide a new generation of global rainfall and snowfall observations in all parts of the world every 3 h. The National Aeronautics and Space Administration (NASA) and the Japan Aerospace and Exploration Agency (JAXA) successfully launched the Core Observatory satellite on 28 February 2014 carrying advanced radar and radiometer systems to serve as a precipitation physics observatory. This will serve as a transfer standard for improving the accuracy and consistency of precipitation measurements from a constellation of research and operational satellites provided by a consortium of international partners. GPM will provide key measurements for understanding the global water and energy cycle in a changing climate as well as timely information useful for a range of regional and global societal applications such as numerical weather prediction, natural hazard monitoring, freshwater resource management, and crop forecasting.

Ensembles used for probabilistic weather forecasting often exhibit a spread-error correlation, but they tend to be underdispersive. This paper proposes a statistical method for postprocessing ensembles based on Bayesian model averaging (BMA), which is a standard method for combining predictive distributions from different sources. The BMA predictive probability density function (PDF) of any quantity of interest is a weighted average of PDFs centered on the individual bias-corrected forecasts, where the weights are equal to posterior probabilities of the models generating the forecasts and reflect the models' relative contributions to predictive skill over the training period. The BMA weights can be used to assess the usefulness of ensemble members, and this can be used as a basis for selecting ensemble members; this can be useful given the cost of running large ensembles. The BMA PDF can be represented as an unweighted ensemble of any desired size, by simulating from the BMA predictive distribution.

Accurate rainfall estimations based on ground-based rainfall observations and satellite-based rainfall measurements are essential for hydrological and environmental modeling in the Qaidam Basin of China. We evaluated the accuracy of daily and monthly scale Tropical Rainfall Measuring Mission (TRMM) rainfall products in the Qaidam Basin. A Geographically Weighted Regression (GWR) was used to estimate the spatial distribution of the TRMM product error using altitude and geographical latitude and longitude as independent variables. Finally, a rainfall model was developed by combining ground-based and satellite-based rainfall measurements, and the model precision was validated with a cross-validation method based on rainfall gauge measurements. The TRMM precipitation observations may contain errors compared with the ground-measured precipitation, and the error for daily data was higher than that for monthly data. A time series of TRMM rainfall measurements at the same location showed errors at certain time intervals. The ground-based and satellite-based rainfall GWR model improved the error in the TRMM rainfall products. This rainfall estimation model with a 1-km spatial resolution is applicable in the Qaidam Basin in which there is a sparse network of rainfall gauges, and is significant for spatial investigations of hydrology and climate change.

高质量的水文气象观测数据是开展气象和水文灾害监测、预报预警及长期气候变化趋势分析的基本支撑。针对流域尺度内现有融合降水数据空间分辨率低的问题，通过集合卡尔曼滤波（EnKF）融合算法对清江流域33个地面站点和TRMM及CMORPH两种卫星产品的降水数据在日尺度上进行融合，得到了0.05°×0.05°的清江流域融合降水数据MSAP。利用留一交叉验证的方式对卫星降水、地面插值和MSAP融合降水数据进行定量分析，证明EnKF融合算法能从相关性系数R、平均绝对误差MAE和均方根误差RMSE 3个方面改善清江流域降水的精度，并且融合算法改善了卫星数据和地面数据在流域边界部分区域精度低的缺点，展现了EnKF融合算法在降水数据融合中的应用潜力。进一步地，将MSAP与CMFD、ERA5和MSWEP等3种主流再分析数据进行对比，并对1998年汛期清江流域与长江干流四次洪峰遭遇中最大的两次遭遇所对应的场次强降水进行了空间分析。结果表明，在时间尺度上，MSAP数据具有最高的R和最小的MAE及RMSE；在误差的空间分布上，MSAP数据在各站点精度评价指标的空间差异最小，四种再分析数据精度由高到低排序为MSAP > CMFD > MSWEP > ERA5；在历时5日和历时2日的场次强降水过程中，CMFD、MSWEP和MSAP均能在一定程度上反映出暴雨中心，在空间分布和降水量级上，MSAP和CMFD基本保持一致。

High-quality hydrometeorological observation data is the basic support for meteorological and hydrological disaster monitoring， forecasting and warning， and long-term climate change trend analysis. To solve the problem of low spatial resolution of existing integrated precipitation data at the watershed scale， this paper uses ensemble Kalman Filter （EnKF） fusion algorithm to merge precipitation data from 33 ground stations and TRMM and CMORPH satellite products at the daily scale in Qingjiang River Basin， yielding the 0.05°×0.05° fusion precipitation production of Qingjiang River Basin i.e.， MSAP. The Leave-One-Out Cross-Validation method is used to quantitatively analyze the satellite precipitation data， ground interpolation data and MSAP fused precipitation data. It is proved that the EnKF fusion algorithm can improve the precision of precipitation in Qingjiang River Basin from three aspects： correlation coefficient R， mean absolute error MAE and root mean square error RMSE. In addition， the fusion algorithm overcame the shortcomings of low accuracy of satellite data and ground data in part of the watershed boundary area， which shows that the EnKF fusion algorithm has application potential in precipitation data fusion. Furthermore， MSAP is compared with CMFD， ERA5 and MSWEP reanalysis data， and the spatial distribution of heavy precipitation events corresponding to the two largest flood peaks in Qingjiang River Basin and Yangtze River during the flood season in 1998 is analyzed. The results also show that the MSAP data has the highest R and the smallest MAE， RMSE in terms of the time scale. In terms of the spatial distribution of errors， the spatial difference of accuracy evaluation index of MSAP data in each site is the smallest， and the order of accuracy of the four kinds of reanalysis data from high to low is MSAP > CMFD > MSWEP > ERA5. CMFD， MSWEP and MSAP can reflect the center of rainstorm to some extent in the process of 5-day and 2-day heavy precipitation events. In terms of spatial distribution and precipitation amount， MSAP and CMFD are basically consistent.

气象站点观测降水难以精确反映降水时空分布与变化，而雷达降水存在复杂地形区域精度不高等问题。为了最大限度发挥两者的优势，文章以广东省北部山区为研究区域，选择2018-08-26—30一次暴雨过程为研究对象，结合地形、与海岸线距离、植被指数、经纬度等地表辅助参量，分析地面站点降水与地表辅助参量、雷达降水的相关关系，利用XGBoost算法与克里金插值方法，构建地面-雷达日降水数据融合模型，得到了空间分辨率为1 km的日降水融合数据集。此外，采用多元线性回归（LM）与克里金插值方法，实现了地面-雷达日降水数据的融合，并利用地面降水数据分别对XGBoost与LM日降水融合性能进行精度验证。结果表明：1）地面降水与雷达降水存在显著的正相关，地面降水与地表辅助参量之间的相关性随时间变化；2）XGBoost预测精度整体上高于LM预测结果；经模型残差校正后，XGBoost融合模型的精度整体上优于LM融合模型，这是因为XGBoost方法在捕捉地面降水与地表辅助参量、雷达降水之间关系性能上优于LM方法。

降水作为连接着大气过程与地表过程的一个水分通量，在水文、气象、生态等方面具有重要意义。由于降水较强的时空变异性，使其成为目前最不易准确测量的水文变量之一。准确、高效地获取高时空分辨率、高精度的降水数据对于水文以及气象分析研究是十分有意义的。以汉江流域为研究区，提出了两步降尺度融合方案，利用雨量站观测降水和卫星反演降水数据在可用性和准确性方面具有互补的特点，通过融合雨量站观测值和全球降水观测任务（Global Precipitation Measurement，GPM）卫星降水产品，生成0.01°的空间分辨率高精度的日降水产品。将获得的融合降水产品驱动分布式水文模型WASMOD-D来模拟降雨—径流过程，验证其径流模拟效果。结果表明：①基于随机森林模型的降尺度算法不仅显著提高了GPM降水的空间分辨率，而且也保持了较好的精度。②基于协同克里金法的降水数据线性融合模型，融合方法大大提高了GPM降水的估算精度，平均绝对误差和均方根误差分别降低了32.38%和21.38%，偏差下降到了1%以下；③综合两种不同情景下的日径流模拟效果来看，由于结合了卫星降水数据和站点降水数据的优势，融合降水数据的整体模拟效果最好，整体改善效果较为显著。研究为基于卫星—地面雨量站（Satellite-Gauge，S-G）降水数据融合的方法提供了新思路，研究结果可作为获取高分辨率、高精度的降水数据方法的参考。

Precipitation is the basic component of the earth water cycle. As a water flux， it connects the atmospheric process with the surface process， and has important significance in meteorology， climatology and hydrology. Due to the strong temporal and spatial variability of precipitation， it is one of the most difficult hydrological variables to measure accurately at present. Accurate precipitation data with high temporal and spatial resolution is very important for many applications such as hydrological and meteorological analysis. This paper takes Hanjiang River Basin as the research area and puts forward a two-step downscaling-merging method. By using the complementary characteristics of data availability and accuracy of precipitation observed by rainfall gauges and retrieved by satellites， a high-quality daily precipitation product with a spatial resolution of 0.01 can be generated by fusing gauge observations and GPM satellite precipitation products. The obtained fused precipitation product is driven by semi-distributed hydrological model WASMOD-D to simulate the rainfall-runoff process， and its runoff simulation effect is verified. The results show that： ① The downscaling algorithm based on random forest model not only significantly improves the spatial resolution of GPM precipitation， but also maintains good accuracy. ②The linear fusion model of precipitation data based on the co-Kriging method， the fusion scheme greatly improves the estimation accuracy of GPM precipitation， with the average absolute error and root mean square error reduced by 32.38% and 21.38% respectively， and the bias dropped below 1%； ③ Considering the simulation results of daily runoff under two different scenarios， the overall simulation effect of integrating precipitation data is the best because of combining the advantages of satellite precipitation data and gauge observations， and the overall improvement effect is obvious. This paper provides a new idea for the data fusion method based on Satellite-Gauge（S-G），and the research results can be used as a reference for obtaining high-resolution and high-precision precipitation data.

. Framed within the Copernicus Climate Change Service (C3S) of the European Commission,\nthe European Centre for Medium-Range Weather Forecasts (ECMWF) is producing an enhanced global dataset for the land component of the fifth generation of European ReAnalysis (ERA5), hereafter referred to as ERA5-Land. Once completed, the period covered will span from 1950 to the present, with continuous updates to support land monitoring applications. ERA5-Land describes the evolution of the water and energy cycles over land in a consistent manner over the production period, which, among others, could be used to analyse trends and anomalies.\nThis is achieved through global high-resolution numerical integrations of the ECMWF land surface model driven by the downscaled meteorological forcing from the ERA5 climate reanalysis, including an elevation correction for the thermodynamic near-surface state. ERA5-Land shares with ERA5\nmost of the parameterizations that guarantees the use of the state-of-the-art land surface modelling applied to numerical weather prediction (NWP) models.\nA main advantage of ERA5-Land compared to ERA5 and the older ERA-Interim is the horizontal resolution, which is enhanced globally to 9 km compared to 31 km (ERA5) or 80 km (ERA-Interim), whereas the temporal resolution\nis hourly as in ERA5. Evaluation against independent in situ observations\nand global model or satellite-based reference datasets shows the added value\nof ERA5-Land in the description of the hydrological cycle, in particular\nwith enhanced soil moisture and lake description, and an overall better agreement of\nriver discharge estimations with available observations. However, ERA5-Land snow depth fields present a mixed performance when compared to those of ERA5, depending on geographical location and altitude.\nThe description of the\nenergy cycle shows comparable results with ERA5. Nevertheless, ERA5-Land reduces the global averaged root mean square error of the skin temperature, taking as\nreference MODIS data, mainly due to the contribution of\ncoastal points where spatial resolution is important.\nSince January 2020, the ERA5-Land period available has extended from January 1981 to the near present, with a\n2- to 3-month delay with respect to real time. The segment prior to 1981 is in production, aiming for a release of the whole dataset in summer/autumn 2021.\nThe high spatial and temporal resolution of ERA5-Land, its extended period, and the consistency of the fields produced makes it a valuable dataset to support hydrological studies,\nto initialize NWP and climate models,\nand to support diverse applications dealing with water resource, land, and environmental management. The full ERA5-Land hourly (Muñoz-Sabater, 2019a) and monthly (Muñoz-Sabater, 2019b) averaged datasets presented in this paper are available through the C3S Climate Data Store at https://doi.org/10.24381/cds.e2161bac and https://doi.org/10.24381/cds.68d2bb30, respectively.\n

As the U.S. Science Team's globally gridded precipitation product from the NASA-JAXA Global Precipitation Measurement (GPM) mission, the Integrated Multi-Satellite Retrievals for GPM (IMERG) estimates the surface precipitation rates at 0.1 degrees every half hour using spaceborne sensors for various scientific and societal applications. One key component of IMERG is the morphing algorithm, which uses motion vectors to perform quasi-Lagrangian interpolation to fill in gaps in the passive microwave precipitation field using motion vectors. Up to IMERG V05, the motion vectors were derived from the large-scale motions of infrared observations of cloud tops. This study details the changes introduced in IMERG V06 to derive motion vectors from large-scale motions of selected atmospheric variables in numerical models, which allow IMERG estimates to be extended from the 60 degrees N-60 degrees S latitude band to the entire globe. Evaluation against both instantaneous passive microwave retrievals and ground measurements demonstrates the general improvement in the precipitation field of the new approach. Most of the model variables tested exhibited similar performance, but total precipitable water vapor was chosen as the source of the motion vectors for IMERG V06 due to its competitive performance and global completeness. Continuing assessments will provide further insights into possible refinements of this revised morphing scheme in future versions of IMERG.

Reanalysis products produced at the various centers around the globe are utilized for many different scientific endeavors, including forcing land surface models and creating surface flux estimates. Here, flux tower observations of temperature, wind speed, precipitation, downward shortwave radiation, net surface radiation, and latent and sensible heat fluxes are used to evaluate the performance of various reanalysis products [NCEP–NCAR reanalysis and Climate Forecast System Reanalysis (CFSR) from NCEP; 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) and ECMWF Interim Re-Analysis (ERA-Interim) from ECMWF; and Modern-Era Retrospective Analysis for Research and Applications (MERRA) and Global Land Data Assimilation System (GLDAS) from the Goddard Space Flight Center (GSFC)]. To combine the biases and standard deviation of errors from the separate stations, a ranking system is utilized. It is found that ERA-Interim has the lowest overall bias in 6-hourly air temperature, followed closely by MERRA and GLDAS. The variability in 6-hourly air temperature is again most accurate in ERA-Interim. ERA-40 is found to have the lowest overall bias in latent heat flux, followed closely by CFSR, while ERA-40 also has the lowest 6-hourly sensible heat bias. MERRA has the second lowest and is close to ERA-40. The variability in 6-hourly precipitation is best captured by GLDAS and ERA-Interim, and ERA-40 has the lowest precipitation bias. It is also found that at monthly time scales, the bias term in the reanalysis products are the dominant cause of the mean square errors, while at 6-hourly and daily time scales the dominant contributor to the mean square errors is the correlation term. Also, it is found that the hourly CFSR data have discontinuities present due to the assimilation cycle, while the hourly MERRA data do not contain these jumps.