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Nicola Montaldo, Matteo Curreli, Roberto Corona, Andrea Saba, and John D. Albertson

to a lesser extent. Peak discharge is the key hydrograph characteristic, commonly used in designs for flood protection and hydrologic forecasting (e.g., Bennett and Mays 1985 ; Ponce 1989 ; Montaldo et al. 2004 ; Mediero et al. 2010 ). Although we were not investigating hydrologic processes at basin scale, our results showed that at plot scale the peak flow was significantly affected by grass growth, and this factor may potentially impact the flood hydrograph, especially for small basins with

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Hanh Nguyen, Jason A. Otkin, Matthew C. Wheeler, Pandora Hope, Blair Trewin, and Christa Pudmenzky

the ET fraction, and r E T ¯ and σ ( r ET ) are its climatological mean and standard deviation, respectively, for that time of the year computed over the period 1975–2018. Note that 2019 was not included in the climatological mean because at the time of computation the year was not complete. Therefore, we fixed the climatology to 1975–2018. Further details on the AWRA-L land surface model and the ESI computational technique can be found in Nguyen et al. (2019) . Areas that lack enough in situ

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Liang Chen, Trent W. Ford, and Priyanka Yadav

fully coupled atmosphere and land components in accordance with the Atmospheric Model Intercomparison Project (AMIP) protocol. Considering the model uncertainties, the second experiment is a Control simulation with atmospheric nudging to effectively drive the model states toward observations ( Wehrli et al. 2019 ). Following the approach in reference ( Wehrli et al. 2019 , 2018 ), we relax the horizontal winds toward the 6-hourly European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis

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Sha Lu, Marie-claire ten Veldhuis, and Nick van de Giesen

drainage systems, QPE performance needs to be satisfactory at subdaily scales. Upper and lower boundary of the lag time for lagged detection could also be adapted, for instance, to the range of [−3, 0] to exclude QPE which would trigger delayed flood forecast. Acceptable FBias, SR or POD score ranges can be determined by the user’s inclination to less false alarms or less misses when choosing among the QPEs. g. Evaluation reliability and uncertainty in the QPEs The outcomes of this study are similar to

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Peter J. Shellito, Sujay V. Kumar, Joseph A. Santanello Jr., Patricia Lawston-Parker, John D. Bolten, Michael H. Cosh, David D. Bosch, Chandra D. Holifield Collins, Stan Livingston, John Prueger, Mark Seyfried, and Patrick J. Starks

radiation is partitioned between sensible and latent heating. These fluxes, in turn, affect transpiration rates, the carbon cycle, weather and climate forecasts, and drought and flood assessments, all of which have humanitarian and environmental impacts. One way to enhance the utility of a hydrologic LSM is to pass it observational information through data assimilation (DA) using a formal framework such as the ensemble Kalman filter (EnKF; Kumar et al. 2014a ; Reichle et al. 2002 ). At the simplest

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Theodore J. Bohn, Mergia Y. Sonessa, and Dennis P. Lettenmaier

application in meteorological and climate modeling dates at least to Krishnamurti et al. (1999) , who found that the simple average of weather forecasts produced by several meteorological models resulted in greater forecast skill than any individual model. A variety of techniques have been developed for combining models, which amounts to selecting weights. Among the methods that have been explored are equal-weight average, multiple linear regression ( Krishnamurti et al. 1999 ; 2000 ), maximum

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Zhongkun Hong, Zhongying Han, Xueying Li, Di Long, Guoqiang Tang, and Jianhua Wang

America ( He et al. 2016 ). In addition, the random forest–based merging procedure (RF-MEP) that combines gridded precipitation products performs well over Chile for 2000–16 ( Baez-Villanueva et al. 2020 ). A recurrent neural network (RNN) model for simulating the hydrological response from various sources of rainfall was used to merge multiple precipitation sources for flash flood forecasting in Taiwan in China, indicating the potential of neural networks in merging multisource information ( Chiang

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Sharon E. Nicholson, Douglas Klotter, and Adam T. Hartman

African gauge data, using “smart” interpolation techniques that take the spatial correlation structure into account. The CHIRPS2 data have low bias and better gauge coverage over Africa compared to other similar products ( Dezfuli et al. 2017 ). PERSIANN-CDR ( Ashouri et al. 2015 ) is also based on geostationary thermal IR brightness temperature, with a neural network approach applied to produce the precipitation estimates. The product is calibrated using NCEP/NCAR precipitation forecasts. It is bias

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Dashan Wang, Xianwei Wang, Lin Liu, Dagang Wang, and Zhenzhong Zeng

heterogeneity of precipitation from 2008 to 2015 ( Fig. 1a ). The CMPA was derived from merging dense rain gauge network observations (~30 000 automatic weather stations) from the China Meteorology Administration (CMA) with the Climate Prediction Center morphing technique (CMORPH) satellite-based quantitative precipitation estimates ( Joyce et al. 2004 ). It is in 0.1°/hourly resolution and starts from January 2008 to present over mainland China ( Shen et al. 2014 ). This product shows good performance over

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Linlin Wang, Zhiqiu Gao, Zaitao Pan, Xiaofeng Guo, and Elie Bou-Zeid

using the transfer-function technique with the cospectral models of Kaimal et al. (1972) (see Moore 1986 for algorithm). The planar fit coordinate is then applied by following the recommendation of Wilczak et al. (2001) . The above corrections are implemented by using the EdiRe software, developed by the University of Edinburgh (see http://www.geos.ed.ac.uk/abs/research/micromet/EdiRe/ ). Following Foken and Wichura (1996) and Foken et al. (2004) , a stationarity test is made for the

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