Evaporation into the Atmosphere
Chapters (11)
Since time immemorial human beings have observed evaporation of water in their surroundings, and they undoubtedly have speculated on the nature of this phenomenon. For a better understanding of the discovery of our present knowledge, it is appropriate to review briefly some of the concepts of the past and their evolution.
For many practical purposes, the air of the lower atmosphere can be considered as a mixture of perfect gases; in the present context these may conveniently be assumed to be dry air of constant composition and water vapor.
In this chapter the so-called flux-profile relationships are presented for specific humidity, and other relevant quantities such as wind speed and temperature in the different sublayers of the ABL. Because of the above-mentioned difficulties of closure, these relationships are not derived by the solution of the transport equations; rather they are arrived at by invoking similarity, through the application of dimensional analysis. Thus, after the relevant physical quantities are identified from the governing equations or simply by inspection, they are organized into a reduced number of dimensionless quantities. Dimensional analysis only establishes the possible existence of a functional relationship between these dimensionless quantities; however, the function itself must usually be determined by experiment. Still, in some cases the functional form of the relationship may be inferred theoretically by means of a conceptual transport model or by applying a plausible closure assumption to the transport equations, and only some unknown constants need be determined experimentally. Especially in recent years numerous similarity models for the ABL have been proposed in the literature. This chapter does not present an exhaustive review but only the more important schemes that appear applicable in the determination of water vapor transport.
The momentum roughness, z
0m
, is an important parameter, not only for the wind profile, but it is also essential in the calculation of z
0υ
, for water vapor, z0h
for heat and the roughness parameters for other scalars.
Evaporation and sensible heat flux into the atmosphere require the availability of some form of energy at the earth-atmosphere interface. This question can be treated quantitatively by considering the equation for the energy budget for a layer of surface material. Depending on the nature of the surface, this layer may consist of water, or of some other substrate like soil, canopy or snow; although this layer can be taken to be infinitesimally thin, it may sometimes even comprise a lake or a vegetational canopy over its entire depth.
The concepts reviewed up to this point are applicable to study local evaporation from surfaces, which are sufficiently uniform and large, so that edge effects involving horizontal advection by the mean wind are relatively unimportant. The assumption of a horizontally homogeneous and steady boundary layer allows a one-dimensional treatment of the transport phenomena near the surface. However, under natural conditions, this assumption is often invalid. In the case of evaporation from surfaces of limited extent, such as finite-size lakes or irrigated areas surrounded by arid land, the horizontal inhomogeneity can be very important.
Equations for the means, such as (3.44), (3.62) and (3.67) constitute the basis for the eddy-correlation method. This method consists of determining the turbulent fluxes of water vapor, momentum, sensible heat, or any other admixture from covariances. Hence, over a uniform surface under steady conditions, the surface fluxes E, H and u
* can be obtained from (3.74), (3.75) and (3.76), respectively. In practice, the flux E is determined by measuring the fluctuations w′ and q′ and then computing the cross-correlation over a suitable averaging period, and similarly for u
* and H. Equations (3.74) and (3.75) for scalars were first applied by Dyer (1961) and Swinbank (1951), respectively.
Over a uniform surface with an adequate fetch, these methods are based directly on the similarity theories for the atmospheric boundary layer treated in Chapter 4. In the present chapter, first, a brief account is given of the application of profile expressions; second, a summary is given of different forms and applications of some related bulk transfer coefficients.
These methods involve either the direct application, or some approximation of the equation for the energy budget. One form of this equation is given in (6.1) but in many practical situations it can be simplified considerably. A common characteristic of most energy budget methods is that they require the determination of the net radiation, R
n
. In general, the energy budget method allows the determination of one of the terms of (6.1), or a simplified form of it, when all the remaining terms can be determined by some independent method.
Mass budget methods are based on the principle of conservation of mass applied to some part of the hydrological cycle. Conservation of mass, formulated as a mass budget equation, requires that, in general, for any given control volume, the inflow rate minus the outflow rate equal the rate of change of the water stored. Accordingly, evaporation can be determined as the only unknown rest term in the budget equation if all the other terms can be determined independently. Although, from the conceptual point of view, mass budget methods are by far the simplest, their application is often difficult and impractical. Therefore, they are less commonly used than aerodynamic or energy budget methods. Nevertheless, their conceptual simplicity is an appealing feature and, in certain situations, a mass budget approach can be quite appropriate. In this chapter a brief description is given of several ways in which the mass budget can be applied in practice.
... The net longwave radiation is determined using the following equation (Brutsaert 1982): ...
... R lu is calculated using Eq. (6), under the assumption that the surface temperature T s (K) equals the air temperature T a (Brutsaert 1982). ...
... On the other hand, the value of ε s is commonly considered to be a constant with a value of 0.97 to 0.98 (Brutsaert 1982). ...
Net longwave radiation, which is an essential factor in evapotranspiration, is generally estimated by multiplying the net emissivity under clear-sky conditions by the effect of cloudiness. In this study, we proposed a phenomenon-specific daily net longwave radiation function, in the form of a difference between the upward longwave radiation and the downward longwave radiation, for the Penman–Monteith evapotranspiration equation and the Penman evaporation equation. In addition, we verified the net longwave radiation equation by comparing the observed downward longwave radiation with that estimated by the downward longwave radiation equation derived from the well-known net longwave radiation equations in the Penman–Monteith evapotranspiration equation and the Penman evaporation equation. The downward longwave radiation equation constituting the proposed net longwave radiation equation w1ith four calibrated parameters had an RMSE of 8.60 W m⁻² and MBE of − 4.37 W m⁻² and is more accurate than the downward longwave radiation equations derived from the general net longwave radiation equations at Tateno, Japan.
... The history relating EW to meteorological variables can be traced early nineteenth century (Dalton 1802).From then, traditional EW empirical models, such as Stelling (Brutsaert 2013), Thornthwaite-Holzman (Thornthwaite and Holzman 1939) and Ryan-Harlenman (Ryan et al. 1974), have been developed. Based on the evaporation physical process and experiment in the specific area, the empirical models provide combined effects of wind speed, vapor pressure and temperature on the EW. ...
... The above three empirical equations are widely applied for EW. The Stelling model was proposed in 1882, which replaced the wind speed coefficient in Dalton's formula with a primary linear function of wind speed (Brutsaert 2013). Thornthwaite-Holzman model was proposed by Thornthwaite and Holzman (1939).The T-H model is based on boundary layer similarity theory. ...
The current study aims to investigate the applicability of ensemble modeling in improving the simulation of water surface evaporation (EW) in the Three Gorges Reservoir. To achieve this objective, a sensitivity analysis is performed to determine the most influential model inputs. Various models are employed for the simulation of EW, including empirical models such as Stelling (STE), Thornthwaite Holzman (T-H), and Ryan Harleman (R-H); statistical models including multiple-linear regression (MLR), Ridge regression (Ridge), and Lasso regression (Lasso); and different Artificial Intelligence (AI) techniques such as Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and General Regression Neural Network (GRNN). The performance of these models is evaluated. Additionally, three ensemble methods, namely simple averaging, weighted averaging, and neural ensemble, are utilized in strategy 1 for empirical models, strategy 2 for statistical models, strategy 3 for AI models, and strategy 4 for multi-class mixed models, with the aim of improving the simulation performance. The results indicate that the dominant parameters are water surface temperature, water surface area, relative humidity, temperature difference of vapor, and wind speed. For the single model, empirical and statistical models can yield valuable results, while most AI models suffer from overfitting issues. Among the ensemble models, the neural ensemble method outperforms the simple averaging and weighted averaging methods. The multi-class mixed ensemble model exhibits the highest simulation accuracy, with NSE values of 0.95 and 0.86 in the training and validation phases, respectively. Compared to the best single model, the ensemble approaches proposed in this study improve the performance of single models in the validation phase by up to 11.63%, 8.21%, 6.88%, and 6.96% for strategies 1 ~ 4, respectively. Furthermore, the results demonstrate that the multi-class mixed ensemble modeling approach is preferable over empirical, statistical, and AI ensemble modeling.
... The air temperature served as the background temperature in these corrections. The emissivities of the soil and vegetation targets were taken as 0.96 and 0.98, respectively, while the atmosphere's apparent emissivity (emissivity of the background) was estimated using method in [37], [38]; i.e., ε = ε = ε ; where ε = 1.24(e T ⁄ ) / is the clear sky apparent emissivity. ...
... e and T are the air vapour pressure and temperature, respectively. is a parameterized factor that scales the clear-sky emissivity to cloudy conditions [38], [39]. Further correction for the sensing wave-band was done using expressions from [40] and [41]. ...
Land surface temperature is an essential climate variable that can serve as a proxy for detecting water deficiencies in croplands and wooded areas. Its measurement can however be influenced by anisotropic properties of surface targets leading to occurrence of directional effects on the signal. This may lead to an incorrect interpretation of thermal measurements. In this study, we perform model assessments and check the influence of thermal radiation directionality using data over a vineyard. To derive the overall directional surface temperatures, elemental values measured by individual cameras were aggregated according to the respective cover fractions/weights in viewing direction. Aggregated temperatures from the turbid model were compared to corresponding temperatures simulated by the 3D DART radiative transfer model. The reconstructed temperatures were then used in surface-energy-balance (SEB) simulations to assess the impact of the Sun-target-sensor geometry on retrievals. Here, the pseudo-isotropic Soil-Plant-Atmosphere-Remote-Sensing-of-Evapotranspiration (SPARSE) dual-source model together with the non-isotropic version (SPARSE4), were used. Both schemes were able to retrieve overall fluxes satisfactorily, confirming a previous study. However, the sensitivity (of flux and component temperature estimates) of the schemes to viewing direction was tested for the first time using reconstructed sets of directional thermal data to force the models. Degradation (relative to nadir) in flux retrieval cross-row was observed, with better consistency along rows. Overall, it was nevertheless shown that SPARSE4 is less influenced by the viewing direction of the temperature than SPARSE, particularly for strongly off-nadir viewing. Some directional/asymmetrical artefacts are however not well reproduced by the simple Radiative Transfer Methods (RTM), which can then manifest in and influence the subsequent thermal-infrared-driven SEB modelling.
... where Δ [= 4098e*(T a + 237.3) -2 ] denotes the slope of the saturation vapor pressure curve (hPa • C − 1 ) at the measured air temperature, T a . The empirical wind function, f u (mm d -1 hPa − 1 ), is traditionally formulated (Brutsaert, 1982) as f u = 0.26(1 + 0.54u 2 ). Here u 2 (m s − 1 ) is the horizontal wind speed at 2-m above the ground/canopy surface and can be estimated by a power function (Brutsaert, 1982) from measurements (u h ) at h meters above the surface as u 2 = u h (2 / h) 1/7 , and γ = c p p (0.622L) -1 is the psychrometric constant, where c p the specific heat of air under constant pressure, L the latent heat of vaporization and p atmospheric pressure. ...
... The empirical wind function, f u (mm d -1 hPa − 1 ), is traditionally formulated (Brutsaert, 1982) as f u = 0.26(1 + 0.54u 2 ). Here u 2 (m s − 1 ) is the horizontal wind speed at 2-m above the ground/canopy surface and can be estimated by a power function (Brutsaert, 1982) from measurements (u h ) at h meters above the surface as u 2 = u h (2 / h) 1/7 , and γ = c p p (0.622L) -1 is the psychrometric constant, where c p the specific heat of air under constant pressure, L the latent heat of vaporization and p atmospheric pressure. E p dry is the theoretical maximum value of E p when the air/land becomes completely devoid of moisture (i.e., e a is zero). ...
... where Z is the observation height from the ground of wind speed and air temperature; k ¼ 0.41 is the von-Karman constant; u z is the observed wind speed at Z height above the ground; d o,v , Z om,v , and Z oh,v are the zero-plane displacement, momentum roughness length, and heat roughness length of vegetation, respectively, which can be estimated according to Brutsaert (1982) and Kustas et al. (1989); d o,s , Z om,s , and Z oh,s are the zero-plane displacement, momentum roughness length, and heat roughness length of soil, respectively, which can be estimated according to Liu et al. (2007) and Brutsaert (1982); and L is the Monin-Obukhov length. ...
... where Z is the observation height from the ground of wind speed and air temperature; k ¼ 0.41 is the von-Karman constant; u z is the observed wind speed at Z height above the ground; d o,v , Z om,v , and Z oh,v are the zero-plane displacement, momentum roughness length, and heat roughness length of vegetation, respectively, which can be estimated according to Brutsaert (1982) and Kustas et al. (1989); d o,s , Z om,s , and Z oh,s are the zero-plane displacement, momentum roughness length, and heat roughness length of soil, respectively, which can be estimated according to Liu et al. (2007) and Brutsaert (1982); and L is the Monin-Obukhov length. ...
Evapotranspiration (ET) is an important process of the regional hydrothermal cycle. However, it is unclear how the mitigation of urban ET in urban thermal environments occurs in a spatial context. Landsat 8 satellite images from 2014 to 2018 of Xuzhou and corresponding meteorological observations were selected, and the improved mono-window algorithm (IMW) and urban RS-PM model were applied to invert land surface temperature (LST) and ET, respectively. In addition, spatial analysis methods (a profile analysis, standard deviation ellipse (SDE) analysis, and bivariate Moran's I) were employed to quantify and simulate the spatial characteristics of the ET effect on LST. The results indicated the following: (1) There was a significant linear negative correlation between ET and LST, which confirms that ET has a negative effect on LST; (2) the SDE overlap ratios between patches with higher ET and lower LST imply that higher ET patches have a significant impact on the spatial distribution of LST; and (3) bivariate Moran's I between ET and LST and their linear mixture spectral analysis (LISA) maps reveal a significant negative spatial correlation between ET and LST. In addition, the landscape pattern of higher ET parches is also an important factor affecting the environmental temperature.
HIGHLIGHTS
A significant negative spatial correlation between ET and LST was found.;
Areas with various ET intensities have different regulatory effects on LST.;
Higher ET intensity is not an absolute factor in mitigating the urban thermal environment.;
Landscape pattern of the patches with higher ET also has a significant impact on LST regulation.;
... where S is solar radiation and α is surface albedo taken for the water body as 0.05, L ↓ and L ↑ are incoming and outgoing longwave radiation. Atmospheric radiation is calculated using an expression presented by Brutsaert (1982), L ↓ = ε a. σT a 4 (1 + ω cc ς ) (7). where ε a is clear sky atmospheric emissivity, σ is the Steffan -Boltzmann constant (5.667 × 10 -8 W m − 2 K − 4 ), cc is cloud cover fraction, ω and ς are correction terms for cloud contribution (see Oroud and Nasrallah, 1998). ...
... where ε a is clear sky atmospheric emissivity, σ is the Steffan -Boltzmann constant (5.667 × 10 -8 W m − 2 K − 4 ), cc is cloud cover fraction, ω and ς are correction terms for cloud contribution (see Oroud and Nasrallah, 1998). Atmospheric emissivity is calculated using the procedure presented by Brutsaert (1982), ...
An energy balance model run on a monthly time step for 800 years was developed to predict the future level, areal extent and temperature of the Dead Sea under different scenarios of freshwater input and atmospheric boundary conditions. The model integrates energy, water and salt balances. The bathymetry of the Dead Sea was derived from high-resolution digital elevation data. Model results were verified against measured lake level for the period 1928 through 2022. Predicted levels are very close to observed values as demonstrated by three statistical measures. The monthly temperatures of the mixed layer as predicted by the model were also commensurate with observational results and satellite derived data. Future simulation predictions were verified against a novel diagnostic analytic method developed in this investigation. The newly developed method has the potential to approximate the equilibrium activity and temperature of hypersaline lakes under specified freshwater inflow and atmospheric forcings. Results show that the future level, areal extent and temperature of the Dead Sea will be contingent on freshwater inflow and the prevailing atmospheric forcings. Projections indicate that the Dead Sea will end up as a dwarfed hypersaline hot lake. The time span needed for the Dead Sea to reach a quasi-steady state equilibrium is several hundred years. Simulations results presented in this investigation are expected to be approximate given the complexity of the system, the long integration time involved, and uncertainties brought about by climate vagaries and model parameterizations.
... and ℎ are the surface exchange coefficients for heat and moisture, respectively. They are assumed to be equal in this study and calculated based on 165 the Monin-Obukhov (M-O) similarity theory (Brutsaert, 1982) : ...
The rapid warming of the Arctic, accompanied by glacier and sea ice melt, has significant consequences for the Earth's climate, ecosystems, and economy. Recent evidence suggests that the snow-darkening effect (SDE) induced by light-absorbing particles, such as black carbon (BC) deposition, could greatly influence rapid warming in the Arctic. However, there is still a lack of ensemble simulations using high-resolution models for investigating the impacts of the SDE resulting from BC deposition on the Arctic surface energy balance. By integrating the physically based Snow, Ice, Aerosol, and Radiation (SNICAR) model with a polar-optimized version of the Weather Research and Forecasting model (Polar-WRF), this study aimed to quantify the impacts of the SDE due to BC deposition and analyze the relationship between BC aerosol mass in snow (represented by snow depth) and snow albedo reduction. The simulation results indicate that BC deposition can directly affect the surface energy balance by decreasing snow albedo and its corresponding radiative forcing (RF). On average, BC deposition at 50 ng g-1 causes a radiative forcing (RF) of 1.6 W m-2 in off-line simulations (without surface feedbacks) and 1.4 W m-2 in on-line simulations (with surface feedbacks). The high RF caused by BC deposition reached 1–4 W m-2 and mainly occurred in Greenland, Baffin Island and East Siberia, where areas with deep snow depths and large snow densities are prevalent. The changes in snow albedo are indeed strongly linked to the mass of BC aerosols. Notably, a clear linear relationship was established between snow depth and the reduction in snow albedo, with a correlation coefficient exceeding 0.9 and an R-squared value greater than 0.85 when the snow depth is shallow. However, as snow depth increases, the impact of BC on snow albedo gradually diminishes until it reaches its maximum value when the snowpack becomes sufficiently optically thick. Regions with deep snowpack, such as Greenland, tend to exhibit greater sensitivity to BC deposition due to the higher absolute mass of BC and the longer duration of the SDE. For a given column-mean BC concentration in snow, the impacts of the SDE are approximately 25–41 % greater in deep snow-covered areas than in shallow snow-covered areas, leading to a 19–40 % increase in snowmelt. A comparison between off-line and on-line coupled simulations using Polar-WRF/Noah-MP and SNICAR has provided valuable insights into the critical mechanisms and key factors influencing changes in surface heat transfer due to the impacts of the SDE induced by BC deposition in the Arctic. It has been observed that various processes, such as snow melting and land‒atmosphere interactions, play significant roles in assessing changes in the surface energy balance caused by BC deposition. Notably, off-line simulations tend to overestimate the impacts of the SDE, sometimes by more than 50 %, due to the lack of relevant processes. This study emphasized the importance of the impacts of snow conditions and land‒atmosphere interactions on evaluating the impacts of the SDE by BC deposition. It is therefore necessary to prioritize high-resolution modeling studies that incorporate detailed physical processes to enhance our understanding of the impacts of the SDE on Arctic climate change.
... λ is the latent heat of vaporization (MJ kg −1 ) and was calculated as a function of T air . ρ a is the density of air at constant pressure (kg m −3 ) from the ideal gas law, see Brutsaert (1982). We present values of maximum ∆S H (∆S H max ) and ∆S LE (∆S LE max ) as seasonal values of storage in general balance to zero. ...
... The evaporation term in Eq (7) is based on Dalton's law, which is a fundamental aerodynamic principle that explains evaporation [34]. This theory states that the movement of water molecules on a free surface is proportional to the vertical vapor pressure gradient. ...
Black ice, a phenomenon that occurs abruptly owing to freezing rain, is difficult for drivers to identify because it mirrors the color of the road. Effectively managing the occurrence of unforeseen accidents caused by black ice requires predicting their probability using spatial, weather, and traffic factors and formulating appropriate countermeasures. Among these factors, weather and traffic exhibit the highest levels of uncertainty. To address these uncertainties, a study was conducted using a Monte Carlo simulation based on random values to predict the probability of black ice accidents at individual road points and analyze their trigger factors. We numerically modeled black ice accidents and visualized the simulation results in a geographical information system (GIS) by employing a sensitivity analysis, another feature of Monte Carlo simulations, to analyze the factors that trigger black ice accidents. The Monte Carlo simulation allowed us to map black ice accident occurrences at each road point on the GIS. The average black ice accident probability was found to be 0.0058, with a standard deviation of 0.001. Sensitivity analysis using Monte Carlo simulations identified wind speed, air temperature, and angle as significant triggers of black ice accidents, with sensitivities of 0.354, 0.270, and 0.203, respectively. We predicted the probability of black ice accidents per road section and analyzed the primary triggers of black ice accidents. The scientific contribution of this study lies in the development of a method beyond simple road temperature predictions for evaluating the risk of black ice occurrences and subsequent accidents. By employing Monte Carlo simulations, the probability of black ice accidents can be predicted more accurately through decoupling meteorological and traffic factors over time. The results can serve as a reference for government agencies, including road traffic authorities, to identify accident-prone spots and devise strategies focused on the primary triggers of black ice accidents.
... In terms of model physics parameterization, we used approaches that have been previously recommended for nonatmospherically coupled Noah-MP simulations (He et al., 2021). These options include using table-specified vegetation fractions and interpolated monthly LAI (i.e., no dynamic vegetation model or crop model), the Ball-Berry formulation for canopy stomatal resistances, the CLM formulation for soil transpiration reduction factor (Niu et al., 2011), the original Unified Noah surface and subsurface runoff (He et al., 2023;Schaake et al., 1996), the Monin Obukov similarity theory solver for surface layer coefficients (Brutsaert, 1982), linear effects of frozen soil on permeability, direct solving of supercooled liquid water within the soil (Niu & Yang, 2006), the CLASS formulation for dynamic ground snow surface albedo (Verseghy, 2007), the Jordan approach for partitioning precipitation into rainfall or snowfall (Jordan, 1991), a semi-implicit flux top boundary condition for top layer soil temperature, and the Sakaguchi and Zeng (2009) approach for surface resistance to evaporation and sublimation. All simulations used default parameters in the Noah-MP unless otherwise specified (He et al., 2023;Niu et al., 2011). ...
Although urbanization fundamentally alters water and energy cycles, contemporary land surface models (LSMs) often do not include key urban vegetation processes that serve to transfer water and energy laterally across heterogeneous urban land types. Urban water/energy transfers occur when rainfall landing on rooftops, sidewalks, and driveways is redirected to lawns or pervious pavement and when transpiration occurs from branches overhanging impervious surfaces with the corresponding root water uptake takes place in nearby portions of yards. We introduce Noah‐MP for Heterogenous Urban Environments (Noah‐MP HUE), which adds sub‐grid water transfers to the widely used Noah‐MP LSM. We examine how sub‐grid water transfers change surface water and energy balances by systematically increasing the amount of simulated water transfer for four scenarios: tree canopy expanding over pavement (Urban Tree Expansion), tree canopy shifting over pavement (Urban Tree Shift), and directing impermeable runoff onto surrounding vegetation (Downspout Disconnection) or into an engineered pavement (Permeable Pavement). Even small percentages of sub‐grid water transfer can reduce runoff and enhance evapotranspiration and deep drainage. Event‐scale runoff reduction depends on storm depth, rainfall intensity, and antecedent soil moisture. Sub‐grid water transfers also tend to enhance (reduce) latent (sensible) heat. Results highlight the importance not only of fine‐scale heterogeneity on larger scale surface processes, but also the importance of urban management practices that enhance lateral water transfers and water storage–so‐called green infrastructure–as they change land surface fluxes and, potentially, atmospheric processes. This work opens a pathway to directly integrate those practices in regional climate simulations.
... This value for z 0 is found by performing LES following the configuration of DNS by Chandrakar et al. (2022) and matching the Nusselt and Sherwood numbers of our LES to their DNS (see Appendix B for details). The relationships among the roughness lengths of momentum, temperature, and moisture are found by performing a set of additional DNS, which are similar to those found in a channel flow with rough walls (Brutsaert, 1982;Garratt, 1994). The top and bottom walls are saturated with respect to liquid water in all presented simulations. ...
Collisional growth of cloud droplets is an essential yet uncertain process for drizzle and precipitation formation. To improve the quantitative understanding of this key component of cloud‐aerosol‐turbulence interactions, observational studies of collision‐coalescence in a controlled laboratory environment are needed. In an existing convection‐cloud chamber (the Pi Chamber), collisional growth is limited by low liquid water content and short droplet residence times. In this work, we use numerical simulations to explore various configurations of a convection‐cloud chamber that may intensify collision‐coalescence. We employ a large‐eddy simulation (LES) model with a size‐resolved (bin) cloud microphysics scheme to explore how cloud properties and the intensity of collision‐coalescence are affected by the chamber size and aspect ratio, surface roughness, side‐wall wetness, side‐wall temperature arrangement, and aerosol injection rate. Simulations without condensation and evaporation within the domain are first performed to explore the turbulence dynamics and wall fluxes. The LES wall fluxes are used to modify the Scalar Flux‐budget Model, which is then applied to demonstrate the need for non‐uniform side‐wall temperature (two side walls as warm as the bottom and the two others as cold as the top) to maintain high supersaturation in a tall chamber. The results of LES with full cloud microphysics reveal that collision‐coalescence is greatly enhanced by employing a taller chamber with saturated side walls, non‐uniform side‐wall temperature, and rough surfaces. For the conditions explored, although lowering the aerosol injection rate broadens the droplet size distribution, favoring collision‐coalescence, the reduced droplet number concentration decreases the frequency of collisions.
... This equation is based on the assumption that the surface boundary layer is a fully turbulent region where the vertical distribution of vapor fluxes is relatively constant. This is usually valid except when there is condensation or, under stable atmospheric conditions, when stratification in the vertical distribution of vapor fluxes predominates (Brutsaert, 1982). ...
... In both cases, Equations 7-9 are solved iteratively, starting from neutral conditions (L = ∞) to calculate initial fluxes, and continuing until the difference between successive solutions for L is below some minimal threshold (0.001) or a maximum number of iterations has been reached (n = 15). In all cases, we use d 0 = 0.65 hr (Brutsaert, 1982), z 0m = 0.125 hr (Norman et al., 1995), and z 0h = 0.1z 0m (Bonan, 2016). ...
Accurate and timely observations of individual‐scale transpiration are critical for predicting ecosystem responses to climate change. Existing remote sensing methods for measuring transpiration lack the spatial resolution needed to resolve individual plants, and their sources of uncertainty are not well‐constrained. We present two novel approaches for independently quantifying fine‐scale transpiration using thermal imagery and a suite of environmental sensors mounted on an unmanned aerial vehicle (UAV) platform. The first is a surface energy balance (SEB) approach designed for fine‐scale thermal imagery; the second uses profiles of air temperature (Ta) and humidity (hr) to calculate transpiration from the Bowen Ratio. Both approaches derive the energy equivalent of transpiration, latent heat flux (λE), solely using data acquired from the UAV. We compare the two approaches and their sources of uncertainty using data from several flights at a grassland eddy covariance site in 2021 and 2022 and using typical diurnal conditions to evaluate the uncertainty of λE estimates for each approach. The SEB approach generated independent, UAV‐based estimates of λE within ∼20% of eddy covariance measurements and was most sensitive to surface temperature and resistance to heat transfer. λE calculated from the Bowen Ratio approach was ∼30% higher than tower values due to inaccuracies in Ta and hr, the main sources of uncertainty in this approach. The Bowen Ratio approach has a lower overall potential uncertainty, indicating its potential for improvement over the SEB approach. Our results are the first physically‐based observations of transpiration derived solely from a UAV platform, with no ancillary data inputs.
... In this study, PAR, T a , VPD, and SWC were considered in the r c calculation using the following respective stress functions: where B is the ratio of the asymptotic value of transpiration at infinite radiation without other constraints [W m − 2 ]; T opt is the optimal air temperature for transpiration [℃]; VPD opt is the optimal VPD for transpiration [℃]; k 1 and k 2 are empirical coefficients; s is the threshold value of soil water deficit at which point transpiration begins to decrease; f 0 is the fraction of transpiration when soil water deficit = 1; SMD is the soil moisture deficit calculated by (SWC max -SWC)/(SWC max -SWC min ), where SWC max and SWC min are the maximum and minimum soil water contents in the root zone, respectively (Poyatos et al., 2007). The aerodynamic resistances (r a ) in orange and jujube orchards were calculated using the classical formula that accounts for the stability correction for wind and temperature described in Brutsaert (1982) and Lhomme and Monteny (2000). In the grape greenhouse, r a was computed by the convective heat transfer equation with heat transfer coefficient calculated under different convection conditions classified by the Grashof number and the Reynolds number (Bailey et al., 1993). ...
... where the Businger-Dyer non-dimensional stability functions for heat and water vapour are expressed as a function of the bulk 860 Richardson number Ri b (Eq. A3) (Brutsaert, 1982;Oke, 1987) ...
We estimate the surface energy balance (SEB) of the Murtèl rock glacier, a seasonally snow-covered permafrost landform with a ventilated coarse-blocky active layer (AL) located in the eastern Swiss Alps. We focus on the parametrisation of the turbulent heat fluxes. Seasonally contrasting atmospheric conditions occur in the Murtèl cirque, with down-slope katabatic jets in winter and a strongly unstable atmosphere over the heated blocky surface in summer. We use a novel comprehensive sensor array both above ground surface and in the coarse-blocky AL to track the rapid coupling by convective heat and moisture fluxes between the atmosphere, the snow cover and the AL for the time period September 2020–September 2022. The in situ sensor array includes a sonic anemometer for eddy-covariance flux above ground and sub-surface long-wave radiation measurements in a natural cavity between the AL blocks. During the thaw seasons, the measurements suggest an efficient (ca. 90 %) export of the available net radiation by sensible and latent turbulent fluxes, thereby strongly limiting the heat available for melting ground ice. Turbulent export of heat and moisture drawn from the porous/permeable AL contributes to the well-known insulating effect of the coarse-blocky AL and partly explains the climate resiliency of rock glaciers. This self-cooling capacity is counteracted by an early snow melt-out date, exposing the low-albedo blocky surface to the intense June–July insolation, and reduced evaporative cooling due to exacerbated moisture scarcity in the near-surface AL during dry spells. With climate change, earlier snow melting and increased frequency, duration and intensity of heat waves and droughts are projected. Regarding the parametrisation of the turbulent fluxes, we successfully estimated the year-round turbulent fluxes using a modified Louis 1979 scheme despite seasonally contrasting atmospheric conditions and closed the monthly SEB within 20 W m−2, except during the snow melt-out months. Detected sensible turbulent fluxes from nocturnal ventilation processes, although a potentially important ground cooling mechanism, are within our 20 W m−2 uncertainty, because nighttime wind speeds are low. Wintertime katabatic wind speeds had to be scaled to close the SEB, which hints at the limits of parametrisations based on the Monin–Obukhov theory in complex mountain terrain and katabatic drainage flows. The present work contributes to the process understanding of the SEB and climate sensitivity of coarse-blocky landforms.
... λ is the latent heat of vaporization (MJ kg −1 ) and was calculated as a function of T air . ρ a is the density of air at constant pressure (kg m −3 ) from the ideal gas law, see Brutsaert (1982). We present values of maximum ∆S H (∆S H max ) and ∆S LE (∆S LE max ) as seasonal values of storage in general balance to zero. ...
Understanding the effects of intensification of Amazon basin hydrological cycling—manifest as increasingly frequent floods and droughts—on water and energy cycles of tropical forests is essential to meeting the challenge of predicting ecosystem responses to climate change, including forest “tipping points”. Here, we investigated the impacts of hydrological extremes on forest function using 12+ years of observations (between 2001–2020) of water and energy fluxes from eddy covariance, along with associated ecological dynamics from biometry, at the Tapajós National Forest. Measurements encompass the strong 2015–2016 El Niño drought and La Niña 2008–2009 wet events. We found that the forest responded strongly to El Niño‐Southern Oscillation (ENSO): Drought reduced water availability for evapotranspiration ( ET ) leading to large increases in sensible heat fluxes ( H ). Partitioning ET by an approach that assumes transpiration ( T ) is proportional to photosynthesis, we found that water stress‐induced reductions in canopy conductance ( G s ) drove T declines partly compensated by higher evaporation ( E ). By contrast, the abnormally wet La Niña period gave higher T and lower E , with little change in seasonal ET . Both El Niño‐Southern Oscillation (ENSO) events resulted in changes in forest structure, manifested as lower wet‐season leaf area index. However, only during El Niño 2015–2016, we observed a breakdown in the strong meteorological control of transpiration fluxes (via energy availability and atmospheric demand) because of slowing vegetation functions (via shutdown of G s and significant leaf shedding). Drought‐reduced T and G s , higher H and E , amplified by feedbacks with higher temperatures and vapor pressure deficits, signaled that forest function had crossed a threshold, from which it recovered slowly, with delay, post‐drought. Identifying such tipping point onsets (beyond which future irreversible processes may occur) at local scale is crucial for predicting basin‐scale threshold‐crossing changes in forest energy and water cycling, leading to slow‐down in forest function, potentially resulting in Amazon forests shifting into alternate degraded states.
... where R n , H, and G, are net radiation, sensible heat, and soil heat flux, respectively; R s,in is the incoming shortwave radiation flux; α is surface albedo; σ is the Stefan-Boltzmann constant; ε s and ε a are surface and atmospheric emissivity, respectively (Brutsaert, 1982;Herrero & Polo, 2012); δ is the thickness of air viscous sublayer (i.e., a function of wind speed) (Haghighi & Or, 2013); T s is surface temperature; T a is air temperature; and k a is the air thermal conductivity. A comprehensive representation of subsurface thermal regimes requires solution of energy balance equation in soil profile with certain assumptions about soil thermal characteristics and moisture content in depth. ...
Extremely high land surface temperatures affect soil ecological processes, alter land‐atmosphere interactions, and may limit some forms of life. Extreme surface temperature hotspots are presently identified using satellite observations or deduced from complex Earth system models. We introduce a simple, yet physically based analytical approach that incorporates salient land characteristics and atmospheric conditions to globally identify locations of extreme surface temperatures and their upper bounds. We then provide a predictive tool for delineating the spatial extent of land hotspots at the limits to biological adaptability. The model is in good agreement with satellite observations showing that temperature hotspots are associated with high radiation and low wind speed and occur primarily in Middle East and North Africa, with maximum temperatures exceeding 85°C during the study period from 2005 to 2020. We observed an increasing trend in maximum surface temperatures at a rate of 0.17°C/decade. The model allows quantifying how upper bounds of extreme temperatures can increase in a warming climate in the future for which we do not have satellite observations and offers new insights on potential impacts of future warming on limits to plant growth and biological adaptability.
... It means, that basic part of energy reaching such impermeable surface is not used as latent heat of evaporation, but will be heating impermeable surface and adjoining layer of atmosphere. Impermeable surfaces (roofs, asphalt) are usually dark with low albedo, which means high ratio of solar energy absorption (Brutsaert, 1982;Novák, 2012). ...
In this study, we present an extension to the Monin–Obukov similarity theory (MOST) for the roughness sublayer (RSL) over short vegetation. We test our theory using temperature measurements from fiber optic cables in an array-shaped set-up. This provides a high vertical measurement resolution that enables us to measure the sharp temperature gradients near the surface. It is well-known that MOST is invalid in the RSL as the flow is distorted by roughness elements. However, to derive the surface temperature, it is common practice to extrapolate the logarithmic profiles down to the surface through the RSL. Instead of logarithmic behaviour defined by MOST near the surface, our observations show near-linear temperature profiles. This log-to-linear transition is described over an aerodynamically smooth surface by the Van Driest equation in classical turbulence literature. Here we propose that the Van Driest equation can also be used to describe this transition over a rough surface, by replacing the viscous length scale with a surface length scale Ls that represents the size of the smallest eddies near the grass structures. We show that Ls scales with the geometry of the vegetation and that the model shows the potential to be scaled up to tall canopies. The adapted Van Driest model outperforms the roughness length concept in describing the temperature profiles near the surface and predicting the surface temperature.
The quantification of uncertainties in runoff over the Tibetan Plateau (TP), simulated by land surface models (LSMs), is of paramount importance for effective water resources management within this region. However, the interactions of land surface processes on simulated runoff, where the effectiveness of one process depends on the chosen scheme for another, have rarely been studied. To address this gap, we conducted ensemble simulations with the Noah‐MP (Noah with multiparameterization) LSM by varying the optional parameterization schemes of six land surface processes and quantified the sensitivities of the simulated runoff to these processes. Results showed that the simulated runoff over the TP was most sensitive to the RUN (runoff‐groundwater) process. The interplay of RUN and FRO (frozen soil permeability) accounted for up to 30% of the variation in the annual mean surface runoff in the TP's permafrost regions. The interactions of RUN and VEG (dynamic vegetation) on summer and autumn subsurface runoff exceeded 10% in the southeast TP. In regions where these interactions among land surface processes significantly affected simulated runoff, we observed elevated model errors and reduced model controllability. Therefore, this study underscores the imperative need to categorize land regions based on the interactions of land surface processes as a foundational step toward enhancing the performance of LSMs. Prioritizing improvements in model physics should be particularly directed toward regions marked by high interactions.
Turbulent fluxes of heat, momentum, and humidity in the atmospheric boundary layer are pivotal to the evolution of geology, weather and climate, and the possibility of life. Here we extend recent advances in calculating these near‐surface turbulent fluxes in the Earth's atmospheric boundary layer to any terrestrial planetary body with an atmosphere. These improvements include: (a) incorporating Monin‐Obukhov similarity functions that encompass the entire range of atmospheric stability expected on terrestrial planetary bodies, (b) accounting for the additional shear associated with buoyant plumes under unstable conditions, (c) using surface renewal theory to calculate transfer rates within the interfacial layer adjacent to the surface, and (d) explicitly accounting for key humidity effects that become especially important when a volatile is more buoyant than the ambient gas (e.g., on Mars where H2O is lighter than CO2). We tested and validated our model using in situ data collected on Earth, Mars, and Titan under a wide range of atmospheric stability, pressure, and surface roughness conditions. The model shows up to 71% better agreement with measurements compared to methods commonly used on Mars for evaporation/sublimation. Compared to previous estimates for H2O ice on Mars, our model predicts up to 1.5–190x lower latent heat fluxes under stable atmospheric conditions (depending on the wind speed) and 1.78x higher latent heat fluxes under unstable conditions. Our results provide improved constraints on the stability of ice on Mars and will help determine whether ice can melt under present‐day conditions.
Thermal infrared (TIR) remote sensing of the land-surface temperature (LST) provides an invaluable diagnostic of surface fluxes and vegetation state, from plant and sub-field scales up to regional and global coverage. However, without proper consideration of the nuances of the remotely sensed LST signal, TIR imaging can give poor results for estimating sensible and latent heating. For example, sensor view angle, atmospheric impacts, and differential coupling of soil and canopy sub-pixel elements with the overlying atmosphere can affect the use of satellite-based LST retrievals in land-surface modeling systems. A concerted effort to address the value and perceived shortcomings of TIR-based modeling culminated in the Workshop on Thermal Remote Sensing of the Energy and Water Balance, held in La Londe les Maures, France in September of 1993. One of the outcomes of this workshop was the Two-Source Energy Balance (TSEB) model, which has fueled research and applications over a range of spatial scales.
In this paper we provide some historical context for the development of TSEB and TSEB-based multi-scale modeling systems (ALEXI/DisALEXI) aimed at providing physically based, diagnostic estimates of latent heating (evapotranspiration, or ET, in mass units) and other surface energy fluxes. Applications for TSEB-based ET retrievals are discussed: in drought monitoring and yield estimation, water and forest management, and data assimilation into – and assessment of – prognostic modeling systems. New research focuses on augmenting temporal sampling afforded in the thermal bands by integrating cloud-tolerant, microwave-based LST information, as well as evaluating the capabilities of TSEB for separating ET estimates into evaporation and transpiration components. While the TSEB has demonstrated promise in supplying water use and water stress information down to sub-field scales, improved operational capabilities may be best realized in conjunction with ensemble modeling systems such as OpenET, which can effectively combine strengths of multiple ET retrieval approaches.
Carbonyl sulfide (COS) measurements provide an important mechanism for quantifying the terrestrial carbon and water cycles. Terrestrial photosynthesis can be inferred from vegetative uptake of COS due to the shared diffusion pathway of COS and CO2 into the chloroplasts. However, the efficacy of different leaf‐to‐canopy upscaling approaches for estimating plant COS uptake and photosynthesis at the canopy scale remains uncertain. In this study, the daytime canopy COS uptake rate (Fcosc) and gross primary productivity (GPP) were simulated using big leaf (BL), two big leaf (TBL) and two leaf (TL) upscaling approaches in the Boreal Ecosystem Productivity Simulator terrestrial biosphere model for two forest sites. We found that TL and TBL performed better than BL in estimating GPP as evaluated by the eddy covariance derived GPP at the studied sites. BL overestimated GPP and canopy stomatal conductance to COS (Gs_cos), resulting from the overestimation of canopy intercepted solar radiation. The positive biases of GPP by TBL could be attributed to the positive biases in GPP of shaded leaves by TBL. The results in this study demonstrate the improved accuracy in using TL for estimating GPP at the canopy scale. Improved leaf‐to‐canopy upscaling approach in terrestrial biosphere models will improve the accuracy of canopy COS uptake and GPP estimation on regional to global scales.
The reliability of evapotranspiration (ET) models is crucial to comprehending land–atmosphere interactions and water balance dynamics in various available resources of the model. This study compared two different models based on energy and water balance models, a surface energy balance system (SEBS) and SPHY, and evaluated against ground observation data from flux towers for different land cover characteristics (forest and savanna) in Southeast Africa. We found that both models have a good correlation with flux tower data for both sites (ZM-Mon and ZM-Kru). The SEBS model showed a lower root-mean-square error (RMSE; 2.17 mm day−1) at the savanna site (ZM-Kru) than the SPHY model (2.27 mm day−1). However, at the forest site (ZM-Mon), the SEBS model showed a higher RMSE value (1.90 mm day−1) than the SPHY model (0.88 mm day−1). Then, we analyzed the ET model's sensitivity to the precipitation variable. We found that SPHY overestimated ET during the winter season and underestimated it during the summer season, which might be influenced by the dependency of the SPHY model to water excess and water shortage stress parameters in ET calculations. Overall, SPHY, with fewer input data, showed a reasonably good result compared to the SEBS. The results revealed that each model possesses its unique strengths and limitations in relation to specific land covers and vegetation composition, offering opportunities for improvement and optimization.
Understanding and projecting drought, especially in the face of climate change, is crucial for assessing its impending risks. However, the causes of drought are multifaceted. As the environmental research paradigm pivots towards machine learning (ML) for predictions, our investigation contrasted multiple ML techniques to simulate the Standardized Precipitation Evapotranspiration Index (SPEI) from 2009 to 2022, utilizing various potential evapotranspiration (PET) methods. Our primary focus was Australia, the world’s driest inhabited continent. Given the challenges with ML model interpretation, SHAP (SHapley Additive exPlanations) values were employed to decipher SPEI variations and to gauge the relative importance of precipitation (Prec) and PET in six key Australian cities. Our findings revealed that while different PET methods resulted in distinct mean values, their trends remained consistent. Post the Millennium Drought, Australia experienced several drought events. SPEI discrepancies based on PET methods were minimal in humid regions like Brisbane and Darwin. However, for arid cities, the Priestley–Taylor equation-driven SPEI differed notably from other methods. Ridge regression was the most adept at mirroring SPEI changes among the assessed ML models. Furthermore, the SHAP explainer discerned that PET-related climate variables had a greater impact on SPEI in drier cities, whereas in humid cities, Prec was more influential. Notably, the research emphasised CO2′s role in influencing drought dynamics in humid cities. These insights are invaluable for enhancing drought mitigation strategies and refining predictive models. Such revelations are crucial for stakeholders aiming to improve drought prediction and management, especially in drought-prone regions like Australia.
Evaporation is known as one of the main factors in reducing surface water resources such as lakes and dam reservoirs. Due to the multitude of components affecting evaporation, its correct estimation has always been of interest to researchers in the field of hydrology. In this research, the daily evaporation from the surface of the Ekbatan dam reservoir has been estimated using the Bowen ratio energy budget method as the basic method and compared with the results of 20 different equations. Investigations indicate that the Brutsaert-Stricker equation with MSE, MAE, and RMSE values equal to 0.36, 0.41, and 0.60 mm/day, respectively, and the NSE and R2 equal to 0.97 and 0.98, respectively, had the best performance in estimating daily evaporation rate from the surface of Ekbatan dam reservoir. On the other hand, Harbeck and Ryan-Harleman equations, in the group of mass transfer equations, have performed the weakest. Also, the relationship presented in this research to calculate evaporation from Ekbatan dam reservoir using weather parameters and without the need to calculate thermal storage of water with MSE, MAE, and RMSE values equal to 0.81, 0.67 and 0.90 mm/day, respectively, and the NSE and R2 equal to 0.93 and 0.94, respectively, for the test data have a good performance in estimating the evaporation rate.
The generalized complementary relationship (CR) rooted on land–atmosphere coupling has been widely used to study evaporation over various ecosystems. Evaporation estimation models based on CR usually contain two parameters. The first one controls the shape of CR, and the second one is related to potential evaporation (Epo). Previous studies have found substantial variations in these parameters, but how they are affected by land surface and/or the atmosphere under conditions with varying land–atmosphere coupling remains unclear. In this study, the land and atmospheric controls for the generalized CR were comparatively investigated focusing on the two parameters of the sigmoid type function by using the data of 24 grassland and 19 forest flux sites and by considering the contrasting land–atmosphere coupling strength. The strong coupling to the outer atmosphere at the forest sites resulted in a large variation in the Epo‐related parameter and its significant correlation with the mean relative humidity. The shape parameter had a large variation at the grassland sites due to the weak coupling with the outer atmosphere and was significantly correlated with the mean soil water content. The simple parameterizations for the two parameters based on the correlations improved the predictions of monthly actual evaporation compared with the parameterizations for the fixed parameters corresponding to ecosystem types. The effects of land surface wetness and local advections on the generalized CR over grasslands and forests were also discussed respectively.
An airborne gamma-ray remote-sensing technique provides a strong potential to estimate a reliable snow water equivalent (SWE) in forested environments where typical remote-sensing techniques have large uncertainties. This study explores the utility of assimilating the temporally (up to four measurements during a winter period) and spatially sparse airborne gamma SWE observations into a land surface model (LSM) to improve SWE estimates in forested areas in the northeastern US. Here, we demonstrate that the airborne gamma SWE observations add value to the SWE estimates from the Noah LSM with multiple parameterization options (Noah-MP) via assimilation despite the limited number of measurements. Improvements are witnessed during the snow accumulation period, while reduced skills are seen during the snowmelt period. The efficacy of the gamma data is greater for areas with lower vegetation cover fraction and topographic heterogeneity ranges, and it is still effective at reducing the SWE estimation errors for areas with higher topographic heterogeneity. The gamma SWE data assimilation (DA) also shows a potential to extend the impact of flight-line-based measurements to adjacent areas without observations by employing a localization approach. The localized DA reduces the modeled SWE estimation errors for adjacent grid cells up to 32 km distance from the flight lines. The enhanced performance of the gamma SWE DA is evident when the results are compared to those from assimilating the existing satellite-based SWE retrievals from the Advanced Microwave Scanning Radiometer 2 (AMSR2) for the same locations and time periods. Although there is still room for improvement, particularly for the melting period, this study shows that the gamma SWE DA is a promising method to improve the SWE estimates in forested areas.
Cloud microphysical processes are an important facet of atmospheric modeling, as they can control the initiation and rates of snowfall. Thus, parameterizations of these processes have important implications for modeling seasonal snow accumulation. We conduct experiments with the Weather Research and Forecasting (WRF V4.3.3) model using three different microphysics parameterizations, including a sophisticated new scheme (ISHMAEL). Simulations are conducted for two cold-seasons (2018 and 2019) centered on the Colorado Rockies’ ∼750 km ² East River Watershed. Precipitation efficiencies are quantified using a drying-ratio mass budget approach and point evaluations are performed against three NRCS SNOTEL stations. Precipitation and meteorological outputs from each are used to force a land-surface model (Noah-MP) so that peak snow accumulation can be compared against airborne snow lidar products. We find that microphysical parameterization choice alone has a modest impact on total precipitation on the order of ± 3% watershed-wide, and as high as 15% for certain regions, similar to other studies comparing the same parameterizations. Precipitation biases evaluated against SNOTEL are 15 ± 13%. WRF Noah-MP configurations produced snow water equivalents with good correlations with airborne lidar products at a 1-km spatial resolution: Pearson’s r values of 0.9, RMSEs between 8-17 cm and percent-biases of 3-15%. Noah-MP with precipitation from the PRISM geostatistical precipitation product leads to a peak SWE underestimation of 32% in both years examined, and a weaker spatial correlation than the WRF configurations. We fall short of identifying a clearly superior microphysical parameterization, but conclude that snow lidar is a valuable non-traditional indicator of model performance.
High Mountain Asia (HMA) is among the most vulnerable water towers globally and yet future projections of water availability in and from its high‐mountain catchments remain uncertain, as their hydrologic response to ongoing environmental changes is complex. Mechanistic modeling approaches incorporating cryospheric, hydrological, and vegetation processes in high spatial, temporal, and physical detail have never been applied for high‐elevation catchments of HMA. We use a land surface model at high spatial and temporal resolution (100 m and hourly) to simulate the coupled dynamics of energy, water, and vegetation for the 350 km ² Langtang catchment (Nepal). We compare our model outputs for one hydrological year against a large set of observations to gain insight into the partitioning of the water balance at the subseasonal scale and across elevation bands. During the simulated hydrological year, we find that evapotranspiration is a key component of the total water balance, as it causes about the equivalent of 20% of all the available precipitation or 154% of the water production from glacier melt in the basin to return directly to the atmosphere. The depletion of the cryospheric water budget is dominated by snow melt, but at high elevations is primarily dictated by snow and ice sublimation. Snow sublimation is the dominant vapor flux (49%) at the catchment scale, accounting for the equivalent of 11% of snowfall, 17% of snowmelt, and 75% of ice melt, respectively. We conclude that simulations should consider sublimation and other evaporative fluxes explicitly, as otherwise water balance estimates can be ill‐quantified.
Land cover controls the land‐atmosphere exchange of water and energy through the partitioning of solar energy into latent and sensible heat. Observations over all land cover types at the regional scale are required to study these turbulent flux dynamics over a landscape. Here, we aim to study how the control of daily and midday latent and sensible heat fluxes over different land cover types is distributed along three axes: energy availability, water availability and exchange efficiency. To this end, observations from 19 eddy covariance flux tower sites in the Netherlands, covering six different land cover types located within the same climatic zone, were used in a regression analysis to explain the observed dynamics and find the principle drivers. The resulting relative position of these sites along the three axes suggests that land cover partly explains the variance of daily and midday turbulent fluxes. We found that evaporation dynamics from grassland, peatland swamp and cropland sites could mostly be explained by energy availability. Forest evaporation can mainly be explained by water availability, urban evaporation by water availability and exchange efficiency, and open water evaporation can almost entirely be explained by exchange efficiency. We found that the sensible heat flux is less sensitive to land cover type. This demonstrates that the land‐atmosphere interface plays an active role in the shedding of sensible heat. Our results contribute to a better understanding of the dynamics of evaporation over different land cover types and may help to optimise, and potentially simplify, models to predict evaporation.
Periodic observations of vegetation index, such as the normalized difference vegetation index (NDVI), can be used for data assimilation in heterogenous ecosystems. Indeed, the new Sentinel 2 Multispectral instrument and Landsat 8 Operational Land Imager sensor data are available at such high temporal and spatial resolutions that can be used to detect the patches of the main vegetation components (grass and trees) of heterogenous ecosystems, and capture their dynamics. We demonstrate the possibility to merge grass and tree NDVI observations and models, to optimally provide robust predictions of grass and tree leaf area index. The proposed assimilation approach assimilates NDVI data through the Ensemble Kalman filter (EnKF) and dynamically calibrates a key vegetation dynamic model parameter, the maintenance respiration coefficient (ma). In the presence of large bias of the grass and tree ma base values, only the use of the proposed assimilation approach removes the large bias in the biomass balance, dynamically calibrating maintenance respiration coefficients, and corrects the model. The performance of a land surface – vegetation model was improved by assimilating observations of NDVI. The effective impact of the proposed assimilation approach on the evapotranspiration and CO2 uptake predictions in the heterogenous ecosystem is also demonstrated.
The mechanisms underlying the soil CO2 flux (Fs) in dry desert soils are not fully understood. To better understand these processes, we must accurately estimate these small fluxes. The most commonly used method, static chambers, inherently alters the conditions that affect the flux and may introduce errors of the same order of magnitude as the flux itself. Regional and global assessments of annual soil respiration rates are based on extrapolating point measurements conducted with flux chambers. Yet, studies conducted in desert ecosystems rarely discuss potential errors associated with using static chambers in dry and bare soils. We hypothesized that a main source of error is the collar protrusion above the soil surface. During the 2021 dry season, we deployed four automated chambers on collars with different configurations in the Negev, Israel. Fs exhibited a repetitive diel cycle of nocturnal uptake and daytime efflux. CO2 uptake measured over the conventionally protruding collars was significantly lower than over the collars flushed with the soil surface. Using thermal imaging, we proved that the protruding collar walls distorted the ambient heating and cooling regime of the topsoil layer, increasing the mean surface temperatures. Higher soil temperatures during the night suppressed the flux driving forces, i.e., soil–atmosphere CO2 and temperature gradients, ultimately leading to an underestimation of up to 50 % of the actual Fs. Accordingly, the total daily CO2 uptake by the soil in the conventionally deployed collars was underestimated by 35 %. This suggests that desert soils are a larger carbon sink than previously reported and that drylands, which cover approximately 40 % of Earth's terrestrial surface, may play a significant role in the global carbon balance.
Non‐water‐limited canopy resistance (r cs , also known as the bulk stomatal resistance or surface resistance) is a critical variable in estimating potential evapotranspiration (PET), which is widely used in ecohydrology related fields. However, quantifying r cs is a challenging work. Here we develop an approach for estimating r cs over the globe. Comparing results over the globe and across ten ET datasets (used as inputs), which are based on diverse mechanisms and algorithms, we find that the approach can capture canopy resistance well (mean correlation of 0.84 ± 0.04, mean relative Root Mean Squared Error of 4.4 ± 1.0%, and mean relative Mean Absolute Error of 5.8 ± 1.4%), and the estimated r cs are very close to those estimated using another method ( R ² = 0.92), which is based on a quite different hypothesis that is only suitable for saturated regions. Based on these, we find that the r cs shows an overall increasing trend (0.43 ± 0.13 s m ‐1 year ‐1 ) over the globe (at 77.6 ± 3.9% of the land grid cells) during 1982‐2014, and the air temperature dominates the variabilities of r cs in regions with decreasing r cs (mean relative contribution of 57.9 ± 11.4%), while air CO 2 concentration controls the changes in r cs in regions with increasing r cs (mean relative contribution of 47.3 ± 8.0%). Moreover, we also find that the traditional PET estimator explicitly overestimates the increasing trends in PET, and tends to overestimate (underestimate) the increasing (decreasing) trends in regions with increasing (decreasing) PET. These findings can improve our knowledge on the complex water‐vegetation‐environment interactions and would be helpful for developing more accurate models for quantifying the impacts of global change on water resources.
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Terrestrial water budget (TWB) data over large domains are of high interest for various hydrological applications. Spatiotemporally continuous and physically consistent estimations of TWB rely on land surface models (LSMs). As an augmentation of the operational North American Land Data Assimilation System Phase 2 (NLDAS-2) four-LSM ensemble, this paper describes a dataset simulated from an ensemble of 48 physics configurations of the Noah LSM with multi-physics options (Noah-MP). The 48 Noah-MP physics configurations are selected to give a representative cross-section of commonly used LSMs for parameterizing runoff, atmospheric surface layer turbulence, soil moisture limitation on photosynthesis, and stomatal conductance. The dataset spans from 1980 to 2015 over the conterminous United States (CONUS) at a monthly temporal resolution and a 1/8 • spatial resolution. The dataset variables include total evapotranspiration and its constituents (canopy evaporation, soil evaporation, and transpiration), runoff (the surface and subsurface components), as well as terrestrial water storage (snow water equivalent, four-layer soil water content from the surface down to 2 m, and the groundwater storage anomaly). The dataset is available at https://doi.org/10.5281/zenodo.7109816 (Zheng et al., 2022). Evaluations carried out in this study and previous investigations show that the ensemble performs well in reproducing the observed terrestrial water storage, snow water equivalent, soil moisture, and runoff. Noah-MP complements the NLDAS models well, and adding Noah-MP consistently improves the NLDAS estimations of the above variables in most areas of CONUS. Besides, the perturbed-physics ensemble facilitates the identification of model deficiencies. The parameterizations of shallow snow, spatially varying groundwater dynamics, and near-surface atmospheric turbulence should be improved in future model versions.
Soil heat flux at the ground surface (G) is an essential component of energy for the recirculation of water and other masses on the land surface. However, direct measurement is difficult and inaccurate, and available data are inadequate owing to the inconvenience of installing the heat sensor plate immediately beneath the true surface. Additionally, thermal properties such as the diffusivity, conductivity, and volumetric heat capacity are intricately influenced by water, vegetation, labile weather, and environmental conditions. Errors of estimation for them from indirect measurement of water content can be substantial and almost impossible without adequately and accurately determination of the soil parameters in situ. In this study, a more general parabolic heat equation is analytically solved for thermal diffusion of gradually varying state properties, and an inverse method is used to determine the parameters so that the properties and G can be simultaneously obtained at a site of measurement. Comparison and validation against data from a numerical model and experimental‐site measurements indicate well agreement and consistency. The retrieved G reveals relatively larger in scale that indicates a non‐negligible disparity from the under‐surface plate measured and the estimations by previous heat‐correction methods, suggesting that G should be regarded more significant in roles for the studies of near‐surface processes, particularly in rather dry conditions and in sub‐diurnal time scale. The present inversion provides some insights into the soil thermal physics of the land surface. Finally, a formula is suggested for the completion of the plate‐based heat flux measurements.
Flooded rice paddies are important for modifying land surface energy and water budgets, especially in Asian countries. This study incorporated shallow paddy water into the Noah with Multi‐Parameterization (Noah‐MP) model to enhance its performance in capturing the distinct features of small Bowen ratios over flooded rice fields. The paddy surface water was parameterized as one integrated layer along with the top soil layer, and meteorological measurements from two crop sites in Japan, that is, SAITO (early rice) and SAGA (late rice), were employed for model evaluation at the field scale. The simulation results show that the model performance was significantly improved by combining the incorporation of paddy water and the calibration of rice crop parameters, particularly at SAGA. Compared with the reference run using the original version of Noah‐MP for SAGA, the underestimation in latent heat and the overestimation in sensible heat during daytime were decreased by ∼74 W m⁻² (∼67%) and ∼92 W m⁻² (∼55%), respectively. Approximately 60%–70% of this improvement was contributed by using calibrated rice crop parameters, while the rest of 30%–40% was from further incorporating paddy water. The decreased ground surface resistance owing to the presence of paddy water was crucial for capturing the features of small Bowen ratios. The observed water depth might help mitigate the underestimation of latent heat nonlinearly. This work may benefit the study of land‐atmosphere interactions and local and regional weather and climate in Asia with the widely used coupled Weather Research and Forecasting/Noah‐MP model.
Accurate simulation of evapotranspiration (ET) is essential to enhance efficient irrigation management in the maize field. Here, we evaluated the performance of four mathematical models for estimating the ET of maize. The four models based on surface resistance calculate ET from different vapor sources, which are Penman‐Monteith (PM) through the “big leaf” model, the Shuttleworth‐Wallace (SW) model for distinguishing between soil and canopy, the clumping (C) model for distinguishing between canopy, soils under the canopy and bare soil, and the seasonal clumping (Cj) model for dividing ET into transpiration of sunlit leaves and shaded leaves, evaporation of bare soil surface, sunlit soil surface of canopy gap fraction, and canopy shaded soil surfaces. The models were calibrated by ET measured from a weighing lysimeter, transpiration by the sap flux method, and soil evaporation by micro‐lysimeters in 2014, 2015, and 2017. Results showed that the measured daily transpiration was 3.32 mm/day during the full‐grown stage of maize, and the mean measured daily soil evaporation was 1.46 mm/day. The performance of the sap flow for transpiration plus micro‐lysimeter for soil evaporation method was consistent with the large‐weighted lysimeter method in measuring daily ET. For simulating versus measuring hourly transpiration, the Cj model performed better than the C model with a slope of 0.94, determination coefficient ( R ² ) of 0.85, mean absolute error (MAE) of 0.08 mm/h, and modified agreement index ( d ) of 0.81. In simulating daily soil evaporation, the Cj model also had a higher slope and less MAE than the C and SW models. Nevertheless, the Cj model yielded increased slope and d and decreased MAE between simulated and measured daily ET. The most sensitive environmental factor in the Cj model is temperature. With a 50% increase in temperature, ET, transpiration, and evaporation increase by 45%, 36%, and 69%, respectively. In summary, the Cj model improved the accuracy for hourly and daily ET of maize and helped separate plant transpiration and soil evaporation, thus giving an available approach for precision irrigation in water management of maize planting systems.
This study mainly aims to estimate the actual evapotranspiration rate and calculate the water requirement of pistachio crops in the central plateau of Iran applying satellite remote sensing products. In order to achieve this main goal, 12 images from the Operational Earth Imager (OLI) of the Landsat 8 satellite for the water year 2018-2019 were downloaded from NASA's Earth Surface Processes Distribution Active Archive Centre. The daily data of the five variables namely, maximum temperature, minimum temperature, wind speed, relative humidity and sunshine hours of Taft station were also obtained from Iran Meteorological Organization as the closest meteorological station to the study area after preparing images and collecting data, first, the actual evapotranspiration rate of the pistachio product was estimated on a monthly scale for every 12 months of the study, using two surface energy balance (SEBAL) and Surface Energy Balance Index (SEBS). Evapotranspiration potential was also acquired in a station scale applying 12 experimental methods. In a comparative study, the results revealed that the evapotranspiration values achieved from the four experimental models H-S, B-C, Trabert and Rn-Based have the highest correlation and the lowest error value with the values estimated from the two SEBAL and SEBS models. Finally, the water requirement of the pistachio crop during its growth period was estimated separately using two models namely, SEBAL, SEBS and confirmed experimental models.
The effect of microstructure on the hydromechanical behavior of unsaturated soils is an issue that is frequently addressed in literature. A simple procedure is proposed in this study to characterize the soil microstructure through evaporation tests. Time-series gravimetric water content data together with the drying rate curve of an evaporated soil specimen are used to reveal soil’s microstructural properties. The experimental output is used for calibrating the parameter for shear strength model proposed by Alonso et al. (2010). A test program consisting of constant suction and constant water content triaxial tests on various types of soils from non-plastic to highly plastic has been carried out. The shear strength of soils determined from the experiments is predicted with the shear strength model that is calibrated through evaporation tests on the same soils. The model predictions have matched the experimental results satisfactorily. The shear strength of the unsaturated soils is predicted from a quite simple experimental procedure.
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