Quantification and Prediction of Nighttime Evapotranspiration for Two Distinct Grassland Ecosystems

Abstract

Evapotranspiration (ET) is, after precipitation, the second largest flux at the land surface in the water cycle and occurs mainly during daytime. Less attention has been given to water fluxes from the land surface into the atmosphere during nighttime (i.e., between sunset and sunrise). The nighttime ET (ETN) may be estimated based on models that use meteorological data; however, due to missing experimental long‐term data, the verification of ETN estimates is limited. In this paper, the amount of ETN for two grassland ecosystems was determined from highly temporally resolved and precise weighing lysimeter data. We found that annual ETN ranged between 3.5% and 9.5% of daytime annual ET (ETD) and occurred mainly during wet soil and canopy surface conditions, which suggests that ETN is largely related to evaporation. ETN was positively correlated with wind speed. Dew formation, ranging from 4.8% to 6.4% of annual precipitation, was in absolute terms larger than ETN. The prediction of ETN with the Penman‐Monteith model improved if the aerodynamic and surface resistance parameters were based on vegetation height observations and the nighttime stomatal resistance parameter was assumed to be zero. The occurrence of hot days during the observation period showed to increase average ETN rates. Our results suggest that ETN can be observed with precision weighing lysimeters, was a not negligible component in the water balance of the grassland ecosystems, and thus needs more attention when simulating land surface hydrological processes.

Physics‐Constrained Machine Learning of Evapotranspiration

“…Specifically, LE predictions by the hybrid model are most sensitive to h_canopy and SM. The strong dependence of ET and surface resistance on SM is well known (e.g., Akbar et al, ; Dirmeyer, ; Douville et al, ; Entekhabi et al, ; Gentine et al, ; Koster et al, ; Koster et al, ; Seneviratne et al, ; Seneviratne et al, ; Vogel et al, ), but the impact of vegetation height has been only recently recognized (Groh et al, ; Klein et al, ; Ringgaard et al, ; Xu et al, ), and not thoroughly quantified. Here we find that canopy height is even more important than SM.…”

“…We then test the sensitivity of the hybrid model to different environmental variables by adding a perturbation to each input variable (10-90% standard deviation increase, with 10% increment) to understand which variables are the most important regulators of r s and LE (Figures 4a and 4b), as well as to assess their nonlinearity. The most important variables (ranked by their mean R 2 with and without perturbation in Dirmeyer, 2011; Douville et al, 2016;Entekhabi et al, 1996;Gentine et al, 2012;Koster et al, 2004;Koster et al, 2006;Seneviratne et al, 2006;Seneviratne et al, 2010;Vogel et al, 2017), but the impact of vegetation height has been only recently recognized (Groh et al, 2019;Klein et al, 2015;Ringgaard et al, 2014;Xu et al, 2018), and not thoroughly quantified. Here we find that canopy height is even more important than SM.…”

Responses of soil water storage and crop water use efficiency to changing climatic conditions: A lysimeter-based space-for-time approach

“…For the other crops the spread of fungal pathogens under a more humid climate (Talley et al, 2002;Agam and Berliner, 2006) and frequent occurrence of dew formation (Xiao et al, 2009;Groh et al, 2018a;Brunke et al, 2019;Groh et al, 2019) could explain the generally lower yield of grain crops for soils under a wet climate in Selhausen. However, an appropriate crop management with one to three applications of fungicides during the growing season (see appendix impact on crop yield such that other reasons have to be considered.…”

Terrestrial water loss at night: global relevance from observations and climate models

“…Seneviratne et al, 2012 for site details). The sensors are thoroughly described by Hirschi et al (2017). Given that the focus is on sensor comparison in this case, day and night are distinguished using a simple threshold of 10 W m −2 for measured incoming solar radiation; below this threshold, it is assumed that no photosynthesis occurs (Hirschi et al, 2017).…”

“…The EC data are processed using EddyPro (Fratini and Mauder, 2014;LI-COR, 2018) to obtain a latent heat flux time series with a temporal resolution of 30 min. Values are discarded for intervals when rain occurs, when the tower is in the upwind direction affecting the air flow (see Hirschi et al, 2017), and for cases with overly low turbulence (median threshold for friction velocity) based on Wutzler et al (2018). The resulting gaps are filled according to Reichstein et al (2005).…”