The role of aerodynamic resistance in thermal remote sensing-based evapotranspiration models

Aerodynamic resistance (hereafter ra) is a preeminent variable in evapotranspiration (ET) modelling. The accurate quantification of ra plays a pivotal role in determining the performance and consistency of thermal remote sensing-based surface energy balance (SEB) models for estimating ET at local to regional scales. Atmospheric stability links ra with land surface temperature (LST) and the representation of their interactions in the SEB models determines the accuracy of ET estimates. The present study investigates the influence of ra and its relation to LST uncertainties on the performance of three structurally different SEB models. It used data from nine Australian OzFlux eddy covariance sites of contrasting aridity in conjunction with MODIS Terra and Aqua LST and leaf area index (LAI) products. Simulations of the sensible heat flux (H) and the latent heat flux (LE, the energy equivalent of ET in W/m2) from the SPARSE (Soil Plant Atmosphere and Remote Sensing Evapotranspiration), SEBS (Surface Energy Balance System) and STIC (Surface Temperature Initiated Closure) models forced with MODIS LST, LAI, and in-situ meteorological datasets were evaluated against flux observations in water-limited (arid and semi-arid) and energy-limited (mesic) ecosystems from 2011 to 2019. Our results revealed an overestimation tendency of instantaneous LE by all three models in the water-limited shrubland, woodland and grassland ecosystems by up to 50% on average, which was caused by an underestimation of H. Overestimation of LE was associated with discrepancies in ra retrievals under conditions of high atmospheric instability, during which uncertainties in LST (expressed as the difference between MODIS LST and in-situ LST) apparently played a minor role. On the other hand, a positive difference in LST coincided with low ra (high wind speeds) and caused a slight underestimation of LE at the water-limited sites. The impact of ra on the LE residual error was found to be of the same magnitude as the influence of LST uncertainties in the semi-arid ecosystems as indicated by variable importance in projection (VIP) coefficients from partial least squares regression above unity. In contrast, our results for the mesic forest ecosystems indicated minor dependency on ra for modelling LE (VIP \u3c 0.4), which was due to a higher roughness length and lower LST resulting in the dominance of mechanically generated turbulence, thereby diminishing the importance of buoyancy production for the determination of ra

Insights Into the Aerodynamic Versus Radiometric Surface Temperature Debate in Thermal-Based Evaporation Modeling

Global evaporation monitoring from Earth observation thermal infrared satellite missions is historically challenged due to the unavailability of any direct measurements of aerodynamic temperature. State-of-the-art one-source evaporation models use remotely sensed radiometric surface temperature as a substitute for the aerodynamic temperature and apply empirical corrections to accommodate for their inequality. This introduces substantial uncertainty in operational drought mapping over complex landscapes. By employing a non-parametric model, we show that evaporation can be directly retrieved from thermal satellite data without the need of any empirical correction. Independent evaluation of evaporation in a broad spectrum of biome and aridity yielded statistically significant results when compared with eddy covariance observations. While our simplified model provides a new perspective to advance spatio-temporal evaporation mapping from any thermal remote sensing mission, the direct retrieval of aerodynamic temperature also generates the highly required insight on the critical role of biophysical interactions in global evaporation research

Insights into the aerodynamic versus radiometric surface temperature debate in thermal-based evaporation modeling

Global evaporation monitoring from Earth observation thermal infrared satellite missions is historically challenged due to the unavailability of any direct measurements of aerodynamic temperature. State-of-the-art one-source evaporation models use remotely sensed radiometric surface temperature as a substitute for the aerodynamic temperature and apply empirical corrections to accommodate for their inequality. This introduces substantial uncertainty in operational drought mapping over complex landscapes. By employing a non-parametric model, we show that evaporation can be directly retrieved from thermal satellite data without the need of any empirical correction. Independent evaluation of evaporation in a broad spectrum of biome and aridity yielded statistically significant results when compared with eddy covariance observations. While our simplified model provides a new perspective to advance spatio-temporal evaporation mapping from any thermal remote sensing mission, the direct retrieval of aerodynamic temperature also generates the highly required insight on the critical role of biophysical interactions in global evaporation research

Behaviour of the foramen ovale flow in fetuses with intrauterine growth restriction

Foramen ovale (FO) flow may be altered in IUGR. .is study was designed to test this hypothesis. Methods. Forty pregnant women (24–38 weeks) were divided into 3 groups: group I (IUGR), group II (adequate growth and maternal hypertension), and group III (normal controls). Impedance across the FO was assessed by the FO pulsatility index (FOPI): (systolic velocity − presystolic velocity)/mean velocity. Statistical analysis utilized ANOVA, Tukey test, and ROC curves. Results. Mean FOPI in IUGR fetuses (n=15) was 3.70 ± 0.99 (3.15–4.26); in the group II (n=12), it was 2.84 ± 0.69 (2.40–3.28), and in the group III (n=13), it was 2.77 ± 0.44 (2.50–3.04) (p=0.004). FOPI and UtA RI were correlated (r= 0.375, p= 0.017), as well as FOPI and UA RI (r= 0.356, p= 0.024) and, inversely, FOPI and MCA RI (r= −0.359, p= 0.023). Conclusions. .e FO flow pulsatility index is increased in fetuses with IUGR, probably as a result of impaired left ventricular diastolic functio

Global groundwater modeling and monitoring: opportunities and challenges

Groundwater is by far the largest unfrozen freshwater resource on the planet. It plays a critical role as the bottom of the hydrologic cycle, redistributing water in the subsurface and supporting plants and surface water bodies. However, groundwater has historically been excluded or greatly simplified in global models. In recent years, there has been an international push to develop global scale groundwater modeling and analysis. This progress has provided some critical first steps. Still, much additional work will be needed to achieve a consistent global groundwater framework that interacts seamlessly with observational datasets and other earth system and global circulation models. Here we outline a vision for a global groundwater platform for groundwater monitoring and prediction and identify the key technological and data challenges that are currently limiting progress. Any global platform of this type must be interdisciplinary and cannot be achieved by the groundwater modeling community in isolation. Therefore, we also provide a high-level overview of the groundwater system, approaches to groundwater modeling and the current state of global groundwater representations, such that readers of all backgrounds can engage in this challenge

Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review

Remote detection and monitoring of the vegetation responses to stress became relevant for sustainable agriculture. Ongoing developments in optical remote sensing technologies have provided tools to increase our understanding of stress-related physiological processes. Therefore, this study aimed to provide an overview of the main spectral technologies and retrieval approaches for detecting crop stress in agriculture. Firstly, we present integrated views on: i) biotic and abiotic stress factors, the phases of stress, and respective plant responses, and ii) the affected traits, appropriate spectral domains and corresponding methods for measuring traits remotely. Secondly, representative results of a systematic literature analysis are highlighted, identifying the current status and possible future trends in stress detection and monitoring. Distinct plant responses occurring under short-term, medium-term or severe chronic stress exposure can be captured with remote sensing due to specific light interaction processes, such as absorption and scattering manifested in the reflected radiance, i.e. visible (VIS), near infrared (NIR), shortwave infrared, and emitted radiance, i.e. solar-induced fluorescence and thermal infrared (TIR). From the analysis of 96 research papers, the following trends can be observed: increasing usage of satellite and unmanned aerial vehicle data in parallel with a shift in methods from simpler parametric approaches towards more advanced physically-based and hybrid models. Most study designs were largely driven by sensor availability and practical economic reasons, leading to the common usage of VIS-NIR-TIR sensor combinations. The majority of reviewed studies compared stress proxies calculated from single-source sensor domains rather than using data in a synergistic way. We identified new ways forward as guidance for improved synergistic usage of spectral domains for stress detection: (1) combined acquisition of data from multiple sensors for analysing multiple stress responses simultaneously (holistic view); (2) simultaneous retrieval of plant traits combining multi-domain radiative transfer models and machine learning methods; (3) assimilation of estimated plant traits from distinct spectral domains into integrated crop growth models. As a future outlook, we recommend combining multiple remote sensing data streams into crop model assimilation schemes to build up Digital Twins of agroecosystems, which may provide the most efficient way to detect the diversity of environmental and biotic stresses and thus enable respective management decisions

Evaluating the Influence of Plant-Specific Physiological Parameterizationson the Partitioning of Land Surface Energy Fluxes

Plant physiological properties have a significant influence on the partitioning of radiative forcing, the spatial and temporal variability of soil water and soil temperature dynamics, and the rate of carbon fixation. Because of the direct impact on latent heat fluxes, these properties may also influence weather-generating processes, such as the evolution of the atmospheric boundary layer (ABL). In this work, crop-specific physiological characteristics, retrieved from detailed field measurements, are included in the biophysical parameterization of the Terrestrial Systems Modeling Platform (TerrSysMP). The physiological parameters for two typical European midlatitudinal crops (sugar beet and winter wheat) are validated using eddy covariance fluxes over multiple years from three measurement sites located in the North Rhine–Westphalia region of Germany. Comparison with observations and a simulation utilizing the generic crop type shows clear improvements when using the crop-specific physiological characteristics of the plant. In particular, the increase of latent heat fluxes in conjunction with decreased sensible heat fluxes as simulated by the two crops leads to an improved quantification of the diurnal energy partitioning. An independent analysis carried out using estimates of gross primary production reveals that the better agreement between observed and simulated latent heat adopting the plant-specific physiological properties largely stems from an improved simulation of the photosynthesis process. Finally, to evaluate the effects of the crop-specific parameterizations on the ABL dynamics, a series of semi-idealized land–atmosphere coupled simulations is performed by hypothesizing three cropland configurations. These numerical experiments reveal different heat and moisture budgets of the ABL using the crop-specific physiological properties, which clearly impacts the evolution of the boundary layer

Advances in catchment science through integrated hydrological modelling and monitoring

none3siopenBertoldi G.; Camporese M.; Sulis M.Bertoldi, G.; Camporese, M.; Sulis, M

Analysis of coupling errors in a physically-based integrated surface water-groundwater model

International audienceAbstract: Several physically-based models that couple 1D or 2D surface and 3D subsurface flow have recently been developed, but few studies have evaluated the errors directly associated with the different coupling schemes. In this paper we analyze the causes of mass balance error for a conventional and a modified sequential coupling scheme in worst-case scenario simulations of Hortonian runoff generation on a sloping plane catchment. The conventional scheme is noniterative, whereas for the modified scheme the surface-subsurface exchange fluxes are determined via a boundary condition switching procedure that is performed iteratively during resolution of the nonlinear subsurface flow equation. It is shown that the modified scheme generates much lower coupling mass balance errors than the conventional sequential scheme. While both coupling schemes are sensitive to time discretization, the iterative control of infiltration in the modified scheme greatly limits its sensitivity to temporal resolution. Little sensitivity to spatial discretization is observed for both schemes. For the modified scheme the different factors contributing to coupling error are isolated, and the error is observed to be highly correlated to the flood recession duration. More testing, under broader hydrologic contexts and including other coupling schemes, is recommended so that the findings from this first analysis of coupling errors can be extended to other surface water-groundwater models