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Rolf H. Reichle, Qing Liu, Joseph V. Ardizzone, Wade T. Crow, Gabrielle J. M. De Lannoy, Jianzhi Dong, John S. Kimball, and Randal D. Koster

fields, including surface (0–5 cm) and root-zone (0–100 cm) soil moisture, soil temperature, and surface fluxes. The L4_SM product also provides important data assimilation diagnostics, including the assimilated Tb observations and corresponding model forecasts. Here, we use 3-hourly instantaneous surface and root-zone soil moisture and brightness temperature from the L4_SM “analysis-update” files ( Reichle et al. 2018a ). We further use 3-hourly time-average total runoff data (including surface

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Zhe Zhang, Youcun Qi, Donghuan Li, Ziwei Zhu, Meilin Yang, Nan Wang, Yin Yang, and Qiyuan Hu

QPE. Furthermore, hydrological disasters such as flood, debris flow, and urban waterlogging are usually attributed to the heavy precipitation caused by strong convection. Therefore, accurately identifying convective precipitation is practically helpful for hydrological forecasting. Previous studies have proposed different algorithms to discriminate convective and stratiform precipitation. Steiner et al. (1995 , hereafter SHY95) proposed a convection and stratiform separation algorithm by

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Ning Zhang, Steven M. Quiring, and Trent W. Ford

, the longer latency is problematic for applications requiring more rapid updates, including flash flood forecasting and field condition monitoring for agriculture. In addition, soil moisture products based entirely on remote sensing observations do not represent soil moisture conditions in the primary root zone. Although we do not examine root zone soil moisture in this study, the methods are easily applicable for blending root zone soil moisture from in situ and model sources. Last, many blended

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Martin G. De Kauwe, Christopher M. Taylor, Philip P. Harris, Graham P. Weedon, and Richard. J. Ellis

failure or screening for pixel contamination by cloud and/or dust. One solution might be to gap-fill the time series using an interpolation technique; however, this can result in bias because of the suppression of high-frequency components ( Schulz and Mudelsee 2002 ). Alternatively, a model may be used to estimate missing data points, using a sequential filtering algorithm such as a Kalman filter to update model forecasts when observations are available. However, this solution requires the necessary

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Wenyi Xie, Xiankui Zeng, Dongwei Gui, Jichun Wu, and Dong Wang

(MODFLOW-2005). USGS Techniques and Methods 6-D1, 240 pp., . 10.3133/tm6D1 Marsh , P. , 1999 : Snowcover formation and melt: Recent advances and future prospects . Hydrol. Processes , 13 , 2117 – 2134 ,<2117::AID-HYP869>3.0.CO;2-9 . 10.1002/(SICI)1099-1085(199910)13:14/15<2117::AID-HYP869>3.0.CO;2-9 Martinec , J. , 1975 : Snowmelt runoff model for stream flow forecasts . Hydrol. Res. , 6 , 145 – 154

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James Cleverly, Chao Chen, Nicolas Boulain, Randol Villalobos-Vega, Ralph Faux, Nicole Grant, Qiang Yu, and Derek Eamus

partitioning ET into soil and plant components for olive orchards in a semi-arid region . Agric. Water Manage. , 97 , 1769 – 1778 , doi:10.1016/j.agwat.2010.06.009 . Hutley, L. B. , Leuning R. , Beringer J. , and Cleugh H. A. , 2005 : The utility of the eddy covariance techniques as a tool in carbon accounting: Tropical savanna as a case study . Aust. J. Bot. , 53 , 663 – 675 , doi:10.1071/BT04147 . Isaac, P. R. , Leuning R. , Hacker J. M. , Cleugh H. A. , Coppin P. A. , Denmead

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Maxime Turko, Marielle Gosset, Modeste Kacou, Christophe Bouvier, Nanee Chahinian, Aaron Boone, and Matias Alcoba

1. Introduction During the last 10 years, rainfall measurement from commercial microwave link (CML) network has gradually emerged as a useful complement to traditional rainfall measurement based on gauges, weather radar or satellites. Uijlenhoet et al. (2018) and Chwala and Kunstmann (2019) provide a good review of the state of the art and the research developed since the pioneering work of Messer et al. (2006) and Leijnse et al. (2007b) . The CML technique is based on the analysis of

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Patricia Lawston-Parker, Joseph A. Santanello Jr., and Sujay V. Kumar

( Findell and Eltahir 1997 ; Koster et al. 2004 ; Seneviratne et al. 2010 ; Yang et al. 2018 ). At the same time, large-scale coordinated projects such as the Global Land Atmosphere Coupling Experiment (GLACE; Koster et al. 2004 ) have worked to quantify the influence of land initialization on forecast skill, finding significant contributions to temperature forecast skill at the subseasonal time scale ( Koster et al. 2010 ). Although emphasis is often placed on LA coupling at large time or space

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Fidele Karamage, Yuanbo Liu, and Yongwei Liu

monthly runoff simulations. A consistent time series of gridded monthly calibrated runoff data was obtained based on a second-order polynomial regression (PR) ( BSL 2018 ; Billo 2007 ; Morrison 2015 ) between monthly downscaled (gridded) observed and CN-based runoff simulated data. PR includes explicit mathematical formulations and is acceptable for streamflow forecasting ( Rezaie-Balf and Kisi 2018 ; Giustolisi and Savic 2006 ). The statistical method used for runoff calibration was evaluated

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Olivier P. Prat, Brian R. Nelson, Elsa Nickl, and Ronald D. Leeper

and climatic analysis and applications. Here, we use “long-term” in the context of satellite observation platforms that have time series spanning over two to three decades. Those SPPs are among the multitude of gridded SPPs that exist and that rely on different physical retrieval principles, different bias adjustment techniques and reference datasets ( Michaelides et al. 2009 ; Kidd et al. 2010 ; Kidd and Huffman 2011 ; Tapiador et al. 2012 ; Prat and Nelson 2020 ). PERSIANN-CDR, GPCP, and

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