Comment on “Rescaling the complementary relationship for land surface evaporation” by R. Crago et al.

2017 ◽  
Vol 53 (7) ◽  
pp. 6340-6342 ◽  
Author(s):  
Ning Ma ◽  
Yinsheng Zhang
Keyword(s):  
Land Surface ◽  
Complementary Relationship ◽  
Surface Evaporation

Reply to comment by Ma and Zhang on “Rescaling the complementary relationship for land surface evaporation”

2017 ◽  
Vol 53 (7) ◽  
pp. 6343-6344 ◽  
Author(s):  
Richard Crago ◽  
Russell Qualls ◽  
Jozsef Szilagyi ◽  
Justin Huntington
Keyword(s):  
Land Surface ◽  
Complementary Relationship ◽  
Surface Evaporation

scholarly journals Rescaling the complementary relationship for land surface evaporation

2016 ◽  
Vol 52 (11) ◽  
pp. 8461-8471 ◽  
Author(s):  
R. Crago ◽  
J. Szilagyi ◽  
R. Qualls ◽  
J. Huntington
Keyword(s):  
Land Surface ◽  
Complementary Relationship ◽  
Surface Evaporation

scholarly journals Evaporation from a large lowland reservoir – (dis)agreement between evaporation models from hourly to decadal timescales

2020 ◽  
Vol 24 (3) ◽  
pp. 1055-1072 ◽  
Author(s):  
Femke A. Jansen ◽  
Adriaan J. Teuling
Keyword(s):  
Land Surface ◽  
Positive Trend ◽  
Hydrological Models ◽  
Water Losses ◽  
Surface Evaporation ◽  
Diurnal Cycles ◽  
Method Selection ◽  
Lowland Reservoir ◽  
Selection For ◽  
Evaporation Models

Abstract. Accurate monitoring and prediction of surface evaporation become more crucial for adequate water management in a changing climate. Given the distinct differences between characteristics of a land surface and a water body, evaporation from water bodies requires a different parameterization in hydrological models. Here we compare six commonly used evaporation methods that are sensitive to different drivers of evaporation, brought about by a different choice of parameterization. We characterize the (dis)agreement between the methods at various temporal scales ranging from hourly to 10-yearly periods, and we evaluate how this reflects in differences in simulated water losses through evaporation of Lake IJssel in the Netherlands. At smaller timescales the methods correlate less (r=0.72) than at larger timescales (r=0.97). The disagreement at the hourly timescale results in distinct diurnal cycles of simulated evaporation for each method. Although the methods agree more at larger timescales (i.e. yearly and 10-yearly), there are still large differences in the projected evaporation trends, showing a positive trend to a more (i.e. Penman, De Bruin–Keijman, Makkink, and Hargreaves) or lesser extent (i.e. Granger–Hedstrom and FLake). The resulting discrepancy between the methods in simulated water losses of the Lake IJssel region due to evaporation ranges from −4 mm (Granger–Hedstrom) to −94 mm (Penman) between the methods. This difference emphasizes the importance and consequence of the evaporation method selection for water managers in their decision making.

scholarly journals Simulation of Land Surface Climate over China with RegCM4.5: Verification and Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Minghao Yang ◽  
Ruiting Zuo ◽  
Liqiong Wang ◽  
Xiong Chen
Keyword(s):  
Heat Flux ◽  
Soil Moisture ◽  
Surface Temperature ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Land Surface Temperature ◽  
South China ◽  
Land Surface ◽  
Reanalysis Data ◽  
Surface Evaporation

The ability of RegCM4.5 using land surface scheme CLM4.5 to simulate the physical variables related to land surface state was investigated. The NCEP-NCAR reanalysis data for the period 1964–2003 were used to drive RegCM4.5 to simulate the land surface temperature, precipitation, soil moisture, latent heat flux, and surface evaporation. Based on observations and reanalysis data, a few land surface variables were analyzed over China. The results showed that some seasonal features of land surface temperature in summer and winter as well as its magnitude could be simulated well. The simulation of precipitation was sensitive to region and season. The model could, to a certain degree, simulate the seasonal migration of rainband in East China. The overall spatial distribution of the simulated soil moisture was better in winter than in summer. The simulation of latent heat flux was also better in winter. In summer, the latent heat flux bias mainly arose from surface evaporation bias in Northwest China, and it primarily arose from vegetation evapotranspiration bias in South China. In addition, the large latent heat flux bias in South China during summer was probably due to less precipitation generated in the model and poor representation of vegetation cover in this region.

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