Regional evapotranspiration from an image-based implementation of the Surface Temperature Initiated Closure (STIC1.2) model and its validation across an aridity gradient in the conterminous US

Recent studies have highlighted the need for improved characterizations of aerodynamic conductance and temperature (gA and T0) in thermal remote sensing-based surface energy balance (SEB) models to reduce uncertainties in regional-scale evapotranspiration (ET) mapping. By integrating radiometric surface temperature (TR) into the Penman-Monteith (PM) equation and finding analytical solutions of gA and T0, this need was recently addressed by the Surface Temperature Initiated Closure (STIC) model. However, previous implementations of STIC were confined to the ecosystem-scale using flux tower observations of infrared temperature. This study demonstrates the first regional-scale implementation of the most recent version of the STIC model (STIC1.2) that physically integrates Moderate Resolution Imaging Spectroradiometer (MODIS)-derived TR and ancillary land surface variables in conjunction with NLDAS (North American Land Data Assimilation System) atmospheric variables into a combined structure of the PM and Shuttleworth-Wallace framework for estimating ET at 1 km × 1 km spatial resolution. Evaluation of STIC1.2 at thirteen core AmeriFlux sites covering a broad spectrum of climates and biomes across an aridity gradient in the conterminous US suggests that STIC1.2 can provide spatially explicit ET maps with reliable accuracies from dry to wet extremes. When observed ET from one wet, one dry, and one normal precipitation year from all sites were combined, STIC1.2 explained 66 % of the variability in observed 8-day cumulative ET with a root mean square error (RMSE) of 7.4 mm/8-day, mean absolute error (MAE) of 5 mm/8-day, and percent bias (PBIAS) of -4 %. These error statistics show higher accuracies than a widely-used SEB-based Surface Energy Balance System (SEBS) and PM-based MOD16 ET, which were found to overestimate (PBIAS = 28 %) and underestimate ET (PBIAS = -26 %), respectively. The performance of STIC1.2 was better in forest and grassland ecosystems as compared to cropland (20 % underestimation) and woody savanna (40 % overestimation). Model inter-comparison suggested that ET differences between the models are robustly correlated with gA and associated roughness length estimation uncertainties which are intrinsically connected to TR uncertainties, vapour pressure deficit (DA), and vegetation cover. A consistent performance of STIC1.2 in a broad range of hydrological and biome categories as well as the capacity to capture spatio-temporal ET signatures across an aridity gradient points to its potential for near real time ET mapping from regional to continental scales.NASA Land-Cover Land-Use Change Grant (NNX17AH97G)NASA new investigator program award (NNX16AI19G)BIOTRANS (grant number, 00001145)CAOS-2 project grant (INTER/DFG/14/02)STEREOIII (INTER/STEREOIII/13/03/HiWET; CONTRACT NR SR/00/301)https://deepblue.lib.umich.edu/bitstream/2027.42/143157/1/hess-2017-535.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/143157/4/hess-22-2311-2018.pdfDescription of hess-2017-535.pdf : SUPERSEDED: for historical purposes onl

Reinvestigating Groundwater Drought Using In Situ and GRACE Data

editorial reviewedGroundwater plays a unique role in the terrestrial water cycle. It is one of the prime sources of water during periods of severe drought. Depletion of groundwater reaching certain thresholds substantially lead to the degradation of water quality. Among all the hydrological variables, it has a characteristics behavior due to its lagged response to precipitation, evapotranspiration, soil water content variations, and surface water variation due to anthropogenic activities. Groundwater drought has been studied in various regions in the world, which revealed significant correlation among hydrological factors, including precipitation, soil water content, and various terrestrial water storage. Terrestrial water storage variables used for monitoring groundwater drought are total water storage change (TWSC) and groundwater storage change (GWSC). While the TWSC can be estimated from the Gravity Recovery and Climate Experiment (GRACE), GWSC can be estimated from in situ groundwater level within the network of well records using relevant hydrogeological information. Previous studies showed the ability and reliability of GRACE data in groundwater monitoring in the regions under extreme drought. Hydrological model outputs, e.g., the Global Land Data Assimilation System (GLDAS), have been used to derive groundwater drought indicators that reached certain reliability. The present study conducts a systematic investigation on the ability of the GRACE data to reflect the groundwater drought conditions, by comparing in situ groundwater data, TWSC estimated from GRACE (TWSCGRACE), GWSC estimated from the conjuncture of GRACE and GLDAS (GWSCGLDAS), Standardized Precipitation Index (SPI), and satellite land surface temperature. Further, by estimating the vadose zone water storage change (VZWC) using TWSC and in situ groundwater data (VZWCin situ), as well as using TWSC and GLDAS (VZWCGLDAS), we investigate the ability of GRACE and in situ data to monitor the vadose zone water content. Our results show that TWSCGRACE correlates better with in situ groundwater data as compared to GWSCGLDAS in all three study areas located in India, Australia, and Belgium, which are some of the hotspots suffering from intensive flash drought in the recent decade. TWSCGRACE shows stronger correlation and better consistency with SPI and land surface temperature as compared to in situ groundwater data. VZWCin situ correlates well with VZWCGLDAS but is limited to data availability from the well network. Results from GWSCGLDAS and VZWCGLDAS show that hydrological model outputs can serve as replacement or supplement to estimate GWSC and VZWC when in situ groundwater data is significantly missing

Assimilation of Soil Moisture and Ocean Salinity (SMOS) brightness temperature into a large-scale distributed conceptual hydrological model to improve soil moisture predictions : the Murray-Darling basin in Australia as a test case

The main objective of this study is to investigate how brightness temperature observations from satellite microwave sensors may help to reduce errors and uncertainties in soil moisture and evapotranspiration simulations with a large-scale conceptual hydro-meteorological model. In addition, this study aims to investigate whether such a conceptual modelling framework, relying on parameter calibration, can reach the performance level of more complex physically based models for soil moisture simulations at a large scale. We use the ERA-Interim publicly available forcing data set and couple the Community Microwave Emission Modelling (CMEM) platform radiative transfer model with a hydro-meteorological model to enable, therefore, soil moisture, evapotranspiration and brightness temperature simulations over the Murray-Darling basin in Australia. The hydrometeorological model is configured using recent developments in the SUPERFLEX framework, which enables tailoring the model structure to the specific needs of the application and to data availability and computational requirements. The hydrological model is first calibrated using only a sample of the Soil Moisture and Ocean Salinity (SMOS) brightness temperature observations (2010-2011). Next, SMOS brightness temperature observations are sequentially assimi-lated into the coupled SUPERFLEX-CMEM model (20102015). For this experiment, a local ensemble transform Kalman filter is used. Our empirical results show that the SUPERFLEX-CMEM modelling chain is capable of predicting soil moisture at a performance level similar to that obtained for the same study area and with a quasi-identical experimental set-up using the Community Land Model (CLM). This shows that a simple model, when calibrated using globally and freely available Earth observation data, can yield performance levels similar to those of a physically based (uncalibrated) model. The correlation between simulated and in situ observed soil moisture ranges from 0.62 to 0.72 for the surface and root zone soil moisture. The assimilation of SMOS brightness temperature observations into the SUPERFLEX-CMEM modelling chain improves the correlation between predicted and in situ observed surface and root zone soil moisture by 0.03 on average, showing improvements similar to those obtained using the CLM land surface model. Moreover, at the same time the assimilation improves the correlation between predicted and in situ observed monthly evapotranspiration by 0.02 on average

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

Bridging Thermal Infrared Sensing and Physically-Based Evapotranspiration Modeling : From Theoretical Implementation to Validation Across an Aridity Gradient in Australian Ecosystems

Thermal infrared sensing of evapotranspiration (E) through surface energy balance (SEB) models is challenging due to uncertainties in determining the aerodynamic conductance (g(A)) and due to inequalities between radiometric (T-R) and aerodynamic temperatures (T-0). We evaluated a novel analytical model, the Surface Temperature Initiated Closure (STIC1.2), that physically integrates T-R observations into a combined Penman-Monteith Shuttleworth-Wallace (PM-SW) framework for directly estimating E, and overcoming the uncertainties associated with T0 and gA determination. An evaluation of STIC1.2 against high temporal frequency SEB flux measurements across an aridity gradient in Australia revealed a systematic error of 10-52% in E from mesic to arid ecosystem, and low systematic error in sensible heat fluxes (H) (12-25%) in all ecosystems. Uncertainty in TR versus moisture availability relationship, stationarity assumption in surface emissivity, and SEB closure corrections in E were predominantly responsible for systematic E errors in arid and semi-arid ecosystems. A discrete correlation (r) of the model errors with observed soil moisture variance (r = 0.33-0.43), evaporative index (r = 0.77-0.90), and climatological dryness (r = 0.60-0.77) explained a strong association between ecohydrological extremes and T-R in determining the error structure of STIC1.2 predicted fluxes. Being independent of any leaf-scale biophysical parameterization, the model might be an important value addition in working group (WG2) of the Australian Energy and Water Exchange (OzEWEX) research initiative which focuses on observations to evaluate and compare biophysical models of energy and water cycle components. Plain Language Summary Evapotranspiration modeling and mapping in arid and semi-arid ecosystems are uncertain due to empirical approximation of surface and atmospheric conductances. Here we demonstrate the performance of a fully analytical model which is independent of any leaf-scale empirical parameterization of the conductances and can be potentially used for continental scale mapping of ecosystem water use as well as water stress using thermal remote sensing satellite data.dPeer reviewe

Mapping Root-Zone Soil Moisture Using a Temperature–Vegetation Triangle Approach with an Unmanned Aerial System: Incorporating Surface Roughness from Structure from Motion

High resolution root-zone soil moisture (SM) maps are important for understanding the spatial variability of water availability in agriculture, ecosystems research and water resources management. Unmanned Aerial Systems (UAS) can flexibly monitor land surfaces with thermal and optical imagery at very high spatial resolution (meter level, VHR) for most weather conditions. We modified the temperature⁻vegetation triangle approach to transfer it from satellite to UAS remote sensing. To consider the effects of the limited coverage of UAS mapping, theoretical dry/wet edges were introduced. The new method was tested on a bioenergy willow short rotation coppice site during growing seasons of 2016 and 2017. We demonstrated that by incorporating surface roughness parameters from the structure-from-motion in the interpretation of the measured land surface-atmosphere temperature gradients, the estimates of SM significantly improved. The correlation coefficient between estimated and measured SM increased from not significant to 0.69 and the root mean square deviation decreased from 0.045 m3∙m−3 to 0.025 m3∙m−3 when considering temporal dynamics of surface roughness in the approach. The estimated SM correlated better with in-situ root-zone SM (15⁻30 cm) than with surface SM (0⁻5 cm) which is an important advantage over alternative remote sensing methods to estimate SM. The optimal spatial resolution of the triangle approach was found to be around 1.5 m, i.e. similar to the length scale of tree-crowns. This study highlights the importance of considering the 3-D fine scale canopy structure, when addressing the links between surface temperature and SM patterns via surface energy balances. Our methodology can be applied to operationally monitor VHR root-zone SM from UAS in agricultural and natural ecosystems

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

A coupled ground heat flux-surface energy balance model of evaporation using thermal remote sensing observations

One of the major undetermined problems in evaporation (ET) retrieval using thermal infrared remote sensing is the lack of a physically based ground heat flux (G) model and its integration within the surface energy balance (SEB) equation. Here, we present a novel approach based on coupling a thermal inertia (TI)-based mechanistic G model with an analytical surface energy balance model, Surface Temperature Initiated Closure (STIC, version STIC1.2). The coupled model is named STIC-TI. The model is driven by noon–night (13:30 and 01:30 local time) land surface temperature, surface albedo, and a vegetation index from MODIS Aqua in conjunction with a clear-sky net radiation sub-model and ancillary meteorological information. SEB flux estimates from STIC-TI were evaluated with respect to the in situ fluxes from eddy covariance measurements in diverse ecosystems of contrasting aridity in both the Northern Hemisphere and Southern Hemisphere. Sensitivity analysis revealed substantial sensitivity of STIC-TI-derived fluxes due to the land surface temperature uncertainty. An evaluation of noontime G (Gi) estimates showed 12 %–21 % error across six flux tower sites, and a comparison between STIC-TI versus empirical G models also revealed the substantially better performance of the former. While the instantaneous noontime net radiation (RNi) and latent heat flux (LEi) were overestimated (15 % and 25 %), sensible heat flux (Hi) was underestimated (22 %). Overestimation (underestimation) of LEi (Hi) was associated with the overestimation of net available energy (RNi−Gi) and use of unclosed surface energy balance flux measurements in LEi (Hi) validation. The mean percent deviations in Gi and Hi estimates were found to be strongly correlated with satellite day–night view angle difference in parabolic and linear pattern, and a relatively weak correlation was found between day–night view angle difference versus LEi deviation. Findings from this parameter-sparse coupled G–ET model can make a valuable contribution to mapping and monitoring the spatiotemporal variability of ecosystem water stress and evaporation using noon–night thermal infrared observations from future Earth observation satellite missions such as TRISHNA, LSTM, and SBG