Vegetation Index‐Based Partitioning of Evapotranspiration Is Deficient in Grazed Systems

Raghav, Pushpendra; Wagle, Pradeep; Kumar, Mukesh; Banerjee, Tirtha; Neel, J. P. S.

doi:10.1029/2022wr032067

Cited by 8 publications

(3 citation statements)

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“…Another source of uncertainty is the absence of data related to partitioning of ET into E and T. In our analysis, for simplicity, we also implicitly assumed that either ET retrieved from the remotely sensed data is largely contributed by transpiration (Subsection 3.3), which is the water extracted from bls by the plants (see Equations and ), or the transpired water is closely correlated with total ET (see Equation ). In reality, this may not be true, depending on the contributions from soil evaporation and interception (Raghav et al., 2022). Again, an advanced partitioning of ET into E and T than the one adopted here, which could generate a more accurate estimate of the two components and their seasonal variability, is expected to further improve seasonal recharge estimates.…”

Section: Methodological Limitations and Discussionmentioning

confidence: 99%

Influence of Shallow Groundwater Evapotranspiration on Recharge Estimation Using the Water Table Fluctuation Method

Boumis

Kumar

Nimmo

et al. 2022

Water Resources Research

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Groundwater is a vital resource for both natural ecosystems and humans (Wada et al., 2014). Over 2 billion people rely on groundwater as their main freshwater resource (Famiglietti, 2014). Groundwater is also extensively used in irrigated agriculture (Siebert et al., 2010). The increased water extraction to satisfy human needs has led to groundwater depletion in many parts of the world (Aeschbach-Hertig & Gleeson, 2012;Rodell et al., 2009;Scanlon et al., 2007). Given these evolving challenges, which are expected to get worse with increasing population, assessment of groundwater recharge, that is, the amount of water that replenishes aquifers after escaping the vadose zone , is crucial for sustainable management and development of groundwater resources (Moon et al., 2004). Groundwater recharge is, however, an inherently complex process controlled by multiple factors including climate, geomorphology, vegetation characteristics and antecedent soil moisture conditions, among others (De Vries & Simmers, 2002). Several methods to obtain estimates of groundwater recharge exist, including those based on direct measurements (Flint et al., 2002) and indirect methods that often use empirical models (Reitz & Sanford, 2020), physically based land surface models (

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Section: Methodological Limitations and Discussionmentioning

confidence: 99%

Influence of Shallow Groundwater Evapotranspiration on Recharge Estimation Using the Water Table Fluctuation Method

Boumis

Kumar

Nimmo

et al. 2022

Water Resources Research

Self Cite

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“…In [128], the authors explore similar relationships between leaf area index (LAI) and ETa and determined that LAI explains 43% of variability in global T/ET. However, more robust methodologies that consider factors such as water use efficiency and vapor pressure deficit may lead to improved estimates of E and T [108,129]. Those variables were not measured at our site at the time of this study.…”

Section: Restoration Landscapementioning

confidence: 99%

An Ensemble Mean Method for Remote Sensing of Actual Evapotranspiration to Estimate Water Budget Response across a Restoration Landscape

Petrakis,

Norman,

Villarreal

et al. 2024

Remote Sensing

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Estimates of actual evapotranspiration (ETa) are valuable for effective monitoring and management of water resources. In areas that lack ground-based monitoring networks, remote sensing allows for accurate and consistent estimates of ETa across a broad scale—though each algorithm has limitations (i.e., ground-based validation, temporal consistency, spatial resolution). We developed an ensemble mean ETa (EMET) product to incorporate advancements and reduce uncertainty among algorithms (e.g., energy-balance, optical-only), which we use to estimate vegetative water use in response to restoration practices being implemented on the ground using management interventions (i.e., fencing pastures, erosion control structures) on a private ranch in Baja California Sur, Mexico. This paper describes the development of a monthly EMET product, the assessment of changes using EMET over time and across multiple land use/land cover types, and the evaluation of differences in vegetation and water distribution between watersheds treated by restoration and their controls. We found that in the absence of a ground-based monitoring network, the EMET product is more robust than using a single ETa data product and can augment the efficacy of ETa-based studies. We then found increased ETa within the restored watershed when compared to the control sites, which we attribute to increased plant water availability.

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“…Notably, a few recent studies have indicated improved capabilities of plant hydraulic models to capture ET, though primarily during specific hydroclimatic periods such as periods that are dry 32 or have high vapor pressure deficit (VPD) 33 . Given that model performance and ET partition changes with hydroclimatic regimes [34][35][36] different model representations likely result in distinct ET partitions. Here we evaluate the limitation of model structures on accurately estimating ET and ET partitioning by comparing the performances of empirical and plant hydraulic models with a machine-learning (ML) based model, which is not restricted by assumptions of model structure or pre-defined relations between hydroclimatic conditions and stomatal response.…”

Section: Mainmentioning

confidence: 99%

Structural constraints in current stomatal conductance models preclude accurate estimation of evapotranspiration and its partitions

Raghav

Kumar

Liu

2023

Preprint

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Evaporation (E) and Transpiration (T) play a critical role in water and energy budgets at regional to global scales. T is regulated via stomatal conductance (gsc), which depends on a multitude of plant physiological processes and hydrometeorological forcings. In recent years, significant advances have been made toward estimating gsc using a variety of models, ranging from relatively simple empirical models to more complex and data-intensive plant hydraulics-based models. However, a detailed assessment of the ability of these models for predicting evapotranspiration components (E and T) remains lacking. Using machine learning and eddy covariance flux tower data of 642 years, distributed across 84-sites and ten land covers globally, here we show that structural constraints in both empirical and plant hydraulics-based models of gsc limit their effectiveness for predicting evapotranspiration (ET) and its components, i.e., E and T. Notably, even when the current generation gsc models are calibrated locally, their limiting structures don’t allow them to use the information contained in the data optimally. Performance of empirical models, which are still widely used for ET estimation, is observed to be especially underwhelming for partitioning T from ET. While the plant hydraulics-based model structure is relatively effective because of its ability to capture the inextricably-linked stomatal response to soil moisture (SM) and vapor pressure deficit (VPD), we show that there still is a significant room for improvement in the structure of these models. These results underscore the need to prioritize improvements in models of gsc to constraint estimates of E and T, and thus to reduce uncertainties in assessments of plants’ role in regulating the earth’s climate.

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Vegetation Index‐Based Partitioning of Evapotranspiration Is Deficient in Grazed Systems

Abstract: Around 60% of the precipitated water is returned to the atmosphere by evapotranspiration (ET) (Oki & Kanae, 2006). Unsurprisingly, ET plays a major role in influencing the water and climate cycles components at both local and global scales (

Cited by 8 publications

References 115 publications

Influence of Shallow Groundwater Evapotranspiration on Recharge Estimation Using the Water Table Fluctuation Method

Influence of Shallow Groundwater Evapotranspiration on Recharge Estimation Using the Water Table Fluctuation Method

An Ensemble Mean Method for Remote Sensing of Actual Evapotranspiration to Estimate Water Budget Response across a Restoration Landscape

Structural constraints in current stomatal conductance models preclude accurate estimation of evapotranspiration and its partitions

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