Based on the research results in the field of predictions in ungauged basins (PUB) available from the literatures and our own research experiences in PUB, the methodology of PUB is generalized to include "borrowing", "replacing" and "creating" methods, according to the logic of a Chinese saying: "even a clever housewife can not cook a meal without rice". Borrowing method is defined as the method to get the data by transplanting the data from a similar region or doing interpolation among the data from the neiboughood area. Replacing method is defined as the method to mine the target data from the related information either from the same research area by simulation, assimilation and interdisciplinary studying or from other areas by upscaling and paired-catchment analysis, all with similar geographic characteristics. Creating method is defined as the method to get the data via field or indoor observation. By combining the above research methods available so far, some clues for breaking through the barriers of PUB research to find the right solutions are proposed.

根据国内外研究成果，结合自身的科研体会，借用“巧妇面临无米之炊”时可能用到的“借、替和种米”的应对逻辑，将无测站流域水文预测(Predictions in Ungauged Basins, PUB)的研究方法归纳为移植法、替代法和生成法。将移植法归类为两种，一是当研究地区没有测站资料(简称无资料)而周边某个地区有测站资料(简称有资料)时，如果有资料区的自然地理环境与研究地区相似，直接移用该有资料地区的资料到无资料区，即直接移植法；二是倘若周边不只一个区域有资料，那么就借用所有这些点的信息，采用插值法得到无资料区的资料，即间接移植法。定义替代法为如果本研究区域或者相似区域没有资料，但有可能“求出”资料的辅助信息，则挖掘这些信息的方法。将替代法分为两类，一是根据本研究区域其他信息，通过模型模拟、同化、融合、从本学科和多学科领域挖掘信息等技术，得到本研究区所需资料。二是根据其它研究区域其他信息得到本研究区所需资料，包括就地外延和对比流域法。定义生成法为通过获取第一手资料开展水文预测的方法，包括野外实验和室内实验。通过梳理PUB的这些研究途径，探索了PUB方法发展的可能突破口，旨为推动PUB的研究提供思路。

. A considerable number of river basins around the world lack sufficient ground observations of hydro-meteorological data for effective water resources assessment and management. Several approaches can be developed to increase the quality and availability of data in these poorly gauged or ungauged river basins; among them, the use of Earth observations products has recently become promising. Earth observations of various environmental variables can be used potentially to increase knowledge about the hydrological processes in the basin and to improve streamflow model estimates, via assimilation or calibration. The present study aims to calibrate the large-scale hydrological model PCRaster GLOBal Water Balance (PCR-GLOBWB) using satellite-based products of evapotranspiration and soil moisture for the Moroccan Oum er Rbia River basin. Daily simulations at a spatial resolution of 5  ×  5 arcmin are performed with varying parameters values for the 32-year period 1979–2010. Five different calibration scenarios are inter-compared: (i) reference scenario using the hydrological model with the standard parameterization, (ii) calibration using in situ-observed discharge time series, (iii) calibration using the Global Land Evaporation Amsterdam Model (GLEAM) actual evapotranspiration time series, (iv) calibration using ESA Climate Change Initiative (CCI) surface soil moisture time series and (v) step-wise calibration using GLEAM actual evapotranspiration and ESA CCI surface soil moisture time series. The impact on discharge estimates of precipitation in comparison with model parameters calibration is investigated using three global precipitation products, including ERA-Interim (EI), WATCH Forcing methodology applied to ERA-Interim reanalysis data (WFDEI) and Multi-Source Weighted-Ensemble Precipitation data by merging gauge, satellite and reanalysis data (MSWEP). Results show that GLEAM evapotranspiration and ESA CCI soil moisture may be used for model calibration resulting in reasonable discharge estimates (NSE values from 0.5 to 0.75), although better model performance is achieved when the model is calibrated with in situ streamflow observations. Independent calibration based on only evapotranspiration or soil moisture observations improves model predictions to a lesser extent. Precipitation input affects discharge estimates more than calibrating model parameters. The use of WFDEI precipitation leads to the lowest model performances. Apart from the in situ discharge calibration scenario, the highest discharge improvement is obtained when EI and MSWEP precipitation products are used in combination with a step-wise calibration approach based on evapotranspiration and soil moisture observations. This study opens up the possibility of using globally available Earth observations and reanalysis products of precipitation, evapotranspiration and soil moisture in large-scale hydrological models to estimate discharge at a river basin scale.\n

Evapotranspiration (ET) is one of the important components of the global hydrologic cycle, energy exchange, and carbon cycle. However, basin scale actual ET (hereafter ETa) is difficult to estimate accurately. We present an evaluation of four actual ET products (hereafter ETp) in seven sub-basins in the Tibetan Plateau. The actual ET calculated by the water balance method (hereafter ETref) was used as the reference for correction of the different ETp. The ETref and ETp show obvious seasonal cycles, but the ETp overestimated or underestimated the ET of the sub-basins in the Tibetan Plateau. A simple and effective method was proposed to correct the basin-scale ETp. The method was referred to as ratio bias correction, and it can effectively remove nearly all biases of the ETp. The proposed method is simpler and more effective in correcting the four ETp compared with the gamma distribution bias correction method. The reliability of the ETp is significantly increased after the ratio bias correction. The ratio bias correction method was used to correct the ETp in the seven sub-basins in the Tibetan Plateau, and regional ET was significantly improved. The results may help improve estimation of the ET of the Tibetan Plateau and thereby contribute to a better understanding of the hydrologic cycle of the plateau.

We present Multi-Source Weather (MSWX), a seamless global gridded near-surface meteorological product featuring a high 3-hourly 0.1° resolution, near-real-time updates (∼3-h latency), and bias-corrected medium-range (up to 10 days) and long-range (up to 7 months) forecast ensembles. The product includes 10 meteorological variables: precipitation, air temperature, daily minimum and maximum air temperature, surface pressure, relative and specific humidity, wind speed, and downward shortwave and longwave radiation. The historical part of the record starts 1 January 1979 and is based on ERA5 data bias corrected and downscaled using high-resolution reference climatologies. The data extension to within ∼3 h of real time is based on analysis data from GDAS. The 30-member medium-range forecast ensemble is based on GEFS and updated daily. Finally, the 51-member long-range forecast ensemble is based on SEAS5 and updated monthly. The near-real-time and forecast data are statistically harmonized using running-mean and cumulative distribution function-matching approaches to obtain a seamless record covering 1 January 1979 to 7 months from now. MSWX presents new and unique opportunities for hydrological modeling, climate analysis, impact studies, and monitoring and forecasting of droughts, floods, and heatwaves (within the bounds of the caveats and limitations discussed herein). The product is available at www.gloh2o.org/mswx.

We present Multi-Source Weighted-Ensemble Precipitation, version 2 (MSWEP V2), a gridded precipitation P dataset spanning 1979–2017. MSWEP V2 is unique in several aspects: i) full global coverage (all land and oceans); ii) high spatial (0.1°) and temporal (3 hourly) resolution; iii) optimal merging of P estimates based on gauges [WorldClim, Global Historical Climatology Network-Daily (GHCN-D), Global Summary of the Day (GSOD), Global Precipitation Climatology Centre (GPCC), and others], satellites [Climate Prediction Center morphing technique (CMORPH), Gridded Satellite (GridSat), Global Satellite Mapping of Precipitation (GSMaP), and Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42RT)], and reanalyses [European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) and Japanese 55-year Reanalysis (JRA-55)]; iv) distributional bias corrections, mainly to improve the P frequency; v) correction of systematic terrestrial P biases using river discharge Q observations from 13,762 stations across the globe; vi) incorporation of daily observations from 76,747 gauges worldwide; and vii) correction for regional differences in gauge reporting times. MSWEP V2 compares substantially better with Stage IV gauge–radar P data than other state-of-the-art P datasets for the United States, demonstrating the effectiveness of the MSWEP V2 methodology. Global comparisons suggest that MSWEP V2 exhibits more realistic spatial patterns in mean, magnitude, and frequency. Long-term mean P estimates for the global, land, and ocean domains based on MSWEP V2 are 955, 781, and 1,025 mm yr−1, respectively. Other P datasets consistently underestimate P amounts in mountainous regions. Using MSWEP V2, P was estimated to occur 15.5%, 12.3%, and 16.9% of the time on average for the global, land, and ocean domains, respectively. MSWEP V2 provides unique opportunities to explore spatiotemporal variations in P, improve our understanding of hydrological processes and their parameterization, and enhance hydrological model performance.

. Accurate high-resolution actual evapotranspiration (ET)\nand gross primary production (GPP) information is essential for\nunderstanding the large-scale water and carbon dynamics. However,\nsubstantial uncertainties exist in the current ET and GPP datasets in China\nbecause of insufficient local ground measurements used for model constraint. This study utilizes a water–carbon coupled model, Penman–Monteith–Leuning Version 2 (PML-V2), to estimate 500 m ET and GPP at a daily scale. The parameters of PML-V2(China) were well calibrated against observations of 26 eddy covariance flux towers across nine plant functional types in China,\nindicated by a Nash–Sutcliffe efficiency (NSE) of 0.75 and a root mean square error (RMSE) of 0.69 mm d−1 for daily ET, respectively, and a NSE of 0.82 and a RMSE of 1.71 g C m−2 d−1 for daily GPP. The model estimates get a small Bias of 6.28 % and a high NSE of 0.82 against water-balance annual ET estimates\nacross 10 major river basins in China. Further evaluations suggest that the\nnewly developed product is better than other typical products (MOD16A2,\nSEBAL, GLEAM, MOD17A2H, VPM, and EC-LUE) in estimating both ET and GPP. Moreover, PML-V2(China) accurately monitors the intra-annual variations in ET and GPP in the croplands with a dual-cropping system. The new data showed\nthat, during 2001–2018, the annual GPP and water use efficiency experienced a significant (p<0.001) increase (8.99 g C m−2 yr−2 and\n0.02 g C mm−1 H2O yr−1, respectively), but annual ET showed a\nnon-significant (p>0.05) increase (0.43 mm yr−2). This\nindicates that vegetation in China exhibits a huge potential for carbon\nsequestration with little cost in water resources. The PML-V2(China) product\nprovides a great opportunity for academic communities and various agencies\nfor scientific studies and applications, freely available at\nhttps://doi.org/10.11888/Terre.tpdc.272389 (Zhang and He, 2022).\n

Monthly gravity field estimates made by the twin Gravity Recovery and Climate Experiment (GRACE) satellites have a geoid height accuracy of 2 to 3 millimeters at a spatial resolution as small as 400 kilometers. The annual cycle in the geoid variations, up to 10 millimeters in some regions, peaked predominantly in the spring and fall seasons. Geoid variations observed over South America that can be largely attributed to surface water and groundwater changes show a clear separation between the large Amazon watershed and the smaller watersheds to the north. Such observations will help hydrologists to connect processes at traditional length scales (tens of kilometers or less) to those at regional and global scales.

. Soil moisture is an important parameter required for agricultural drought monitoring and climate change models. Passive microwave remote sensing\ntechnology has become an important means to quickly obtain soil moisture across large areas, but the coarse spatial resolution of microwave data\nimposes great limitations on the application of these data. We provide a unique soil moisture dataset (0.05∘, monthly) for China from\n2002 to 2018 based on reconstruction model-based downscaling techniques using soil moisture data from different passive microwave products – including\nAMSR-E and AMSR2 (Advanced Microwave Scanning Radiometer for Earth Observing System) JAXA (Japan Aerospace Exploration Agency) Level 3 products and SMOS-IC (Soil Moisture and Ocean Salinity designed by the Institut National de la Recherche Agronomique, INRA, and Centre d’Etudes Spatiales de la BIOsphère, CESBIO) products – calibrated with a consistent model in combination with ground observation\ndata. This new fine-resolution soil moisture dataset with a high spatial resolution overcomes the multisource data time matching problem between\noptical and microwave data sources and eliminates the difference between the different sensor observation errors. The validation analysis indicates\nthat the accuracy of the new dataset is satisfactory (bias: −0.057, −0.063 and −0.027 m3 m−3; unbiased root mean square error\n(ubRMSE): 0.056, 0.036 and 0.048; correlation coefficient (R): 0.84, 0.85 and 0.89 on monthly, seasonal and annual scales,\nrespectively). The new dataset was used to analyze the spatiotemporal patterns of soil water content across China from 2002 to 2018. In the past\n17 years, China's soil moisture has shown cyclical fluctuations and a slight downward trend and can be summarized as wet in the south and dry in\nthe north, with increases in the west and decreases in the east. The reconstructed dataset can be widely used to significantly improve hydrologic\nand drought monitoring and can serve as an important input for ecological and other geophysical models. The data are published in Zenodo at\nhttps://doi.org/10.5281/zenodo.4738556 (Meng et al., 2021a).\n

The HydroATLAS database provides a standardized compendium of descriptive hydro-environmental information for all watersheds and rivers of the world at high spatial resolution. Version 1.0 of HydroATLAS offers data for 56 variables, partitioned into 281 individual attributes and organized in six categories: hydrology; physiography; climate; land cover & use; soils & geology; and anthropogenic influences. HydroATLAS derives the hydro-environmental characteristics by aggregating and reformatting original data from well-established global digital maps, and by accumulating them along the drainage network from headwaters to ocean outlets. The attributes are linked to hierarchically nested sub-basins at multiple scales, as well as to individual river reaches, both extracted from the global HydroSHEDS database at 15 arc-second (~500 m) resolution. The sub-basin and river reach information is offered in two companion datasets: BasinATLAS and RiverATLAS. The standardized format of HydroATLAS ensures easy applicability while the inherent topological information supports basic network functionality such as identifying up- and downstream connections. HydroATLAS is fully compatible with other products of the overarching HydroSHEDS project enabling versatile hydro-ecological assessments for a broad user community.

The "Global Aridity Index and Potential Evapotranspiration Database - Version 3" (Global-AI_PET_v3) provides high-resolution (30 arc-seconds) global hydro-climatic data averaged (1970-2000) monthly and yearly, based upon the FAO Penman-Monteith Reference Evapotranspiration (ET) equation. An overview of the methods used to implement the Penman-Monteith equation geospatially and a technical evaluation of the results is provided. Results were compared for technical validation with weather station data from the FAO "CLIMWAT 2.0 for CROPWAT" (ET: r = 0.85; AI: r = 0.90) and the U.K. "Climate Research Unit: Time Series v 4.04" (ET: r = 0.89; AI: r = 0.83), while showing significant differences to an earlier version of the database. The current version of the Global-AI_PET_v3 supersedes previous versions, showing a higher correlation to real world weather station data. Developed using the generally agreed upon standard methodology for estimation of reference ET, this database and notably, the accompanying source code, provide a robust tool for a variety of scientific applications in an era of rapidly changing climatic conditions.© 2022. The Author(s).

Based on monthly streamflow data from 372 stations covering the period 1960-2000 and the monthly streamflow data from 41 stations covering the period 2001-2014 across China, human- and climate-induced impacts on hydrological processes were quantified for 10 river basins in China based on development of Budyko-based coupled water-energy balance model. Penman-Monteith potential evapotranspiration model was used to analyze evapotranspiration processes. Besides, elasticity coefficient was also quantified for the impacts of meteorological variables on streamflow changes. The results indicated that: (1) Compared to southern China, streamflow changes are more sensitive to climate changes and human activities in northern China. Generally, relative humidity changes have positive impacts on streamflow changes. However, the maximum temperature, minimum temperature, solar radiation, wind speed and LUCC changes tend to go against streamflow changes. The elasticity coefficients of streamflow changes for meteorological variables are: precipitation > LUCC > relative humidity > solar radiation > maximum temperature > wind speed > minimum temperature; (2) Climate changes during 1980-2000 generally help to increase annual streamflow, and the increase of streamflow by precipitation changes is most evident, and the increase of streamflow depth reaches 12.1 mm. However, impacts of meteorological variables on streamflow changes are shifting from one river basin to another, e.g. the maximum temperature and relative humidity help to increase streamflow in northern China but decrease streamflow magnitude in southern China; (3) In general, human activities tend to decrease streamflow. Changes of streamflow in the Yangtze, Songhua, Northwest, and Southeast river basins are 78.7%, 76.9%, 65.7%, and 84.2%, respectively, which can be attributed to climate changes. However, human activities play a dominant role in modifications of streamflow changes, such as Pearl River basin, Huaihe River basin, Haihe River basin, Yellow River basin, Liaohe River basin and southwest river basins, with fractional contribution being 59.4%, 77.3%, 66.2%, 69.7%, 75.3%, and 70.4%, respectively. Generally, the fraction of human activities and climate changes to streamflow changes in the river basins across China can reach 71.0% and 29.0% respectively in river basins, where climate changes play a dominant role in streamflow changes. The results of this study can be helpful to human mitigation to climate changes in terms of water resources management.

利用中国372个水文站月径流数据（1960-2000年）及41个水文站年径流数据（2001-2014年）,采用基于Budyko假设的水热耦合平衡方程,构建气候变化和人类活动对径流变化影响定量评估模型,在Penman-Monteith潜在蒸发分析基础上,进一步分析气象因子对径流变化的弹性系数,量化气候变化和人类活动对径流变化的影响。结果表明：① 中国北方地区流域径流变化对各气象因子弹性系数明显大于中国南方地区。就全国而言,径流变化对各因子的弹性系数为：降水>土地利用/土地覆盖变化（LUCC）>相对湿度>太阳辐射>最高气温>风速>最低气温;② 1980-2000年,气候变化总体上有利于增加中国年径流量,而降水对年径流量增加的贡献最为显著;③ 1980-2000年,中国南方流域中,气候变化对年径流变化的影响以增加作用为主,而北方流域,以减少年径流作用为主。对中国大多数流域径流变化而言,人类活动的影响主要以减少年径流量为主。2001-2014年,气候变化以减少径流量为主,人类活动对径流变化的影响程度明显增强,气候变化与人类活动对径流变化的贡献率分别为53.5%、46.5%。该研究对气候变化与人类活动影响下,中国水资源规划管理、防灾减灾及保障水资源安全具有重要理论与现实意义。

For implementing water resources management in the Yellow River Basin, water resources assessment is very necessary and important. The accuracy of water resources assessment relies on predictability of the hydrological cycle. Different land uses, topographical features, geological and soil conditions, and artificial water uses (mainly agricultural irrigation) determine the complexity of hydrological characteristics in this basin. With the limited observation of the river discharge, it is difficult to develop a lumped model for simulating hydrology in different sub-basins based on parameter calibration. The physically-based hydrological model can be helpful in this case. The present research attempts to incorporate all available spatial information into the hydrological modeling by a distributed approach. A physical model is developed using the physical governing equations for description of the hydrological processes. It carries out a 10-year (1980-1989) simulation of the natural hydrological cycle. Based on the hydrological simulation, the paper discusses the spatial-temporal hydrological characteristics and the status of water resources in the Yellow River Basin.