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Z. Long, W. Perrie, J. Gyakum, D. Caya, and R. Laprise

simulations of local temperature, evaporation, and precipitation compared to simulations that neglect the lake effects. For example, the presence of the Great Lakes results in a phase shift in the annual cycles of latent and sensible heat fluxes, increases of the local evaporation and precipitation during the autumn and winter, and alters the meridional air temperature gradient ( Lofgren 1997 ; Bates et al. 1993 ; Hostetler et al. 1993 ; Bonan 1995 ). While most atmosphere–lake studies have focused on

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Lingjing Zhu, Jiming Jin, and Yimin Liu

at a rate of 46.5 mm decade −1 ( Zhu et al. 2019 ). Therefore, the effects of TP lakes and their changes on local and regional climate are worth characterizing and quantifying. Due to their wide distribution and ability to modulate energy and water transfer, TP lakes are likely to affect TP precipitation at diurnal and seasonal scales. TP precipitation is generated mainly by small (<100 km 2 ) and medium (100–10 000 km 2 ) convective systems ( Hirose and Nakamura 2005 ) and characterized by a

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G. Q. Wang, J. Y. Zhang, Y. Q. Xuan, J. F. Liu, J. L. Jin, Z. X. Bao, R. M. He, C. S. Liu, Y. L. Liu, and X. L. Yan

38.2° and 39.8°N, 109.5° and 111.1°E in the East Asian monsoon climate zone, it is generally arid to semiarid, with average areal precipitation of 414 mm yr −1 . There is no permanent snow in this catchment, but seasonal snowfall and snow accumulation exist in this catchment owing to lower temperature in winter and high temperature in summer. The drainage area of the Kuye River catchment is 8645 km 2 , with a mainstream length of 242 km. The catchment contains two major tributaries (the

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Lifeng Luo and Eric F. Wood

these stream gauges are indicated in Fig. 3 . As discussed earlier, the simulated streamflow agrees well with observations, but there are periods with significant differences that may arise from water management, especially reservoir operations. One way to avoid these complicating effects is to verify the seasonal hydrologic predictions against offline streamflow simulated with our best-known, in situ forcing data. An analysis of Fig. 8 leads to similar results, as does the precipitation and soil

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Ipshita Majhi and Daqing Yang

constructions, interbasin water diversions, and water withdrawal for industrial and agricultural uses also affect river discharge regimes and changes over space and time ( Miah 2002 ; Vörösmarty et al. 1997 ; Revenga et al. 1998 ; Dynesius and Nilsson 1994 ). Slow economic growth and low population in the high-latitude regions have resulted in low impact by humans ( Shiklomanov et al. 2000 ; Lammers et al. 2001 ). Among the human impacts, reservoir regulation has the most direct effects on hydrologic

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Xiaogang Shi, Andrew W. Wood, and Dennis P. Lettenmaier

upstream diversion and storage effects had been removed. All forecast points have lengthy streamflow records (spanning at least the period 1971–2001) of high quality. The basins represent different hydroclimatic conditions and are distributed across the western United States. The seasonal hydrologic cycle of all basins is dominated by spring snowmelt runoff. Six of the gages, the Animas River at Durango (ANIMA), Bruneau River near Hot Spring (BRUNE), Yellowstone River (YELLO), Salmon River at Whitebird

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H. Biemans, R. W. A. Hutjes, P. Kabat, B. J. Strengers, D. Gerten, and S. Rost

expect to be the largest source of uncertainty from input data. For water resources assessments, the intra-annual dynamics of discharge are important, because both water demand and supply vary throughout the year. Therefore, the impact of uncertainty should also be investigated on a seasonal time scale. The objective of this paper is to quantify the global distribution of the uncertainty in annual as well as seasonal estimates of precipitation on a basin scale and the resulting uncertainty in

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Jiamin Li and Chenghai Wang

the effects of heat (radiation) and dynamics (wind speed) on evaporation, as shown in Eq. (1) : (1) PE = Δ Δ + γ ⁡ ( R n ) λ + γ Δ + γ 6.43 ⁡ ( f u ) D λ , where PE is the potential open-water evaporation (mm day −1 ); R n is the net radiation at the surface (MJ m day −1 ); Δ is the slope of the saturation vapor pressure curve (kPa °C −1 ); γ is the psychrometric coefficient (kPa °C −1 ); λ is the latent heat of vaporization (MJ kg −1 ); f u is the wind function; and D is the vapor

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James V. Rudolph and Katja Friedrich

relate seasonal and annual reflectivity patterns to atmospheric stability and cloud microphysics observed during individual cases. Furthermore, we examine the effects of surface temperature on the vertical structure of reflectivity and implications for the impact of climate change on the hydrologic cycle. To our knowledge, multiple-year observations of vertical precipitation structure in the European Alps have not been investigated. Most precipitation climatologies are based on surface observations

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Xiang Gao, Paul A. Dirmeyer, Zhichang Guo, and Mei Zhao

interannual variability of vegetation properties and the other using their multiyear mean climatology (the approach typically employed by most climate models). Our experience with uncoupled land surface modeling (without atmosphere feedback) suggests that there exist sensitivities in simulations of surface fluxes and state variables to the choice of mean seasonal cycle versus time-varying vegetation properties ( Guo et al. 2006 ). Discovery of these sensitivities helps to motivate the coupled studies and

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