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Paul A. Dirmeyer, Yan Jin, Bohar Singh, and Xiaoqin Yan

climate variability (e.g., Findell et al. 2011 ; Santanello et al. 2011 ), predictability (e.g., Koster et al. 2006 ; Guo et al. 2012 ), and prediction (e.g., Koster et al. 2010 , 2011 ). There are also significant climate effects on longer time scales, from decadal to century periods, caused by land use change (e.g., Snyder 2010 ; Mahmood et al. 2013, manuscript submitted to Int. J. Climatol. ). Because of the lack of long-term, complete, in situ measurements, estimates of the impact of land

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Xuejin Wang, Baoqing Zhang, Feng Li, Xiang Li, Xuliang Li, Yibo Wang, Rui Shao, Jie Tian, and Chansheng He

nutrient loss, surface runoff, and annual discharges ( Woodward et al. 2014 ). Extensive urbanization creates an urban heat island effect ( McCarthy et al. 2010 ; Lin et al. 2016 ) and affects precipitation P considerably ( Wang et al. 2018 ). However, the effects of anthropogenic land use on local climate are complicated and differ between regions. LUCC can directly affect terrestrial evapotranspiration (ET): recent studies have shown that large-scale vegetation restoration has occurred in many

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Xiang Gao, Alexander Avramov, Eri Saikawa, and C. Adam Schlosser

processes and more comprehensive and explicit representation of anthropogenic land management ( Lawrence et al. 2019 ). Specifically relevant to representation of soil and plant hydrology, CLM5 implements: 1) a revised solution to the Richard’s equation which improves the accuracy and stability of the numerical soil water solution ( Zeng and Decker 2009 ); 2) a dry surface layer-based soil evaporation resistance parameterization ( Swenson and Lawrence 2014 ); 3) a spatially variable soil depth (0

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

cumulative function of flux density is expressed as where is the upwind distance (m) from the measurement site, the von Kármán constant ( Andreas et al. 2006 ), is the zero-plane displacement height (m), u * is the friction velocity (m s −1 ), and is the average wind speed (m s −1 ) between the displacement height and the observation height (m) above the surface. Assuming a logarithmic profile (without effects of stratification) for horizontal wind speed , the average wind speed can be

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Bo Dong, John D. Lenters, Qi Hu, Christopher J. Kucharik, Tiejun Wang, Mehmet E. Soylu, and Phillip M. Mykleby

consequences of climate variations in this region in order to better manage water resources and sustain food production. Evidence of long-term hydroclimatic changes in the United States has been reported in numerous studies (e.g., Karl and Knight 1998 ; Lins and Slack 1999 ; Milly et al. 2005 ; Barnett et al. 2008 ; IPCC 2013 , among others). These changes have been found to be highly nonlinear and characterized by decadal to multidecadal variations. Apart from responses to various anthropogenic

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Shida Gao, Pan Liu, and Upmanu Lall

. Lavers , and F. Ralph , 2018 : Global analysis of climate change projection effects on atmospheric rivers . Geophys. Res. Lett. , 45 , 4299 – 4308 , https://doi.org/10.1029/2017Gl076968 . 10.1029/2017GL076968 Fischer , E. M. , and R. Knutti , 2015 : Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes . Nat. Climate Change , 5 , 560 – 564 , https://doi.org/10.1038/nclimate2617 . 10.1038/nclimate2617 Fisman , D. , A. Tuite , and K

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Lingcheng Li, Dunxian She, Hui Zheng, Peirong Lin, and Zong-Liang Yang

– 58 , https://doi.org/10.1038/nclimate1633 . 10.1038/nclimate1633 Dai , A. , K. E. Trenberth , and T. Qian , 2004 : A global dataset of Palmer drought severity index for 1870–2002: Relationship with soil moisture and effects of surface warming . J. Hydrometeor. , 5 , 1117 – 1130 , https://doi.org/10.1175/JHM-386.1 . 10.1175/JHM-386.1 Diffenbaugh , N. S. , D. L. Swain , and D. Touma , 2015 : Anthropogenic warming has increased drought risk in California . Proc. Natl. Acad

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Akihiko Murata, Shun-ichi I. Watanabe, Hidetaka Sasaki, Hiroaki Kawase, and Masaya Nosaka

function (PDF) and cumulative distribution function (CDF), between precipitation with and without the effects of tropical cyclones. Specifically, CDF of precipitation using sample data was compared based on parametric models. The degree of difference between the two CDFs was examined to verify the similarities between them after the contribution of tropical cyclones to total precipitation was excluded. The degree of the similarity after the exclusion was expected to be higher than that obtained from

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Marouane Temimi, Ricardo Fonseca, Narendra Nelli, Michael Weston, Mohan Thota, Vineeth Valappil, Oliver Branch, Hans-Dieter Wizemann, Niranjan Kumar Kondapalli, Youssef Wehbe, Taha Al Hosary, Abdeltawab Shalaby, Noor Al Shamsi, and Hajer Al Naqbi

(station 33). For this station, a large cold bias in excess of 3 K is mostly corrected when the LULC is updated from barren/sparsely vegetated to urban/built-up, due to the effects of the anthropogenic heat. As seen in Fig. 5 , this bias is almost exclusively present at night, with a peak magnitude of about 6 K in the autumn season. When the surface properties are updated, the nighttime bias is significantly reduced to less than 2 K for all seasons and times. Even though the daytime maximum

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Prabhakar Shrestha

exhibits a strong local control on ET and infiltration after interception. For example, transpiration can account for almost 35%–90% of total ET depending on vegetation cover ( Schlesinger and Jasechko 2014 ; Good et al. 2015 ; Maxwell and Condon 2016 ; Fatichi and Pappas 2017 ; Shrestha et al. 2018b ). Hence, daytime clouds and vegetation combined can exert a significant control on the dynamics of shallow GWT depths along with other nonlocal effects. Where observations are sparse, creating spatial

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