Using precipitation, vertical root distribution, and satellite‐retrieved vegetation information to parameterize water stress in a <scp>P</scp>enman‐<scp>M</scp>onteith approach to evapotranspiration modeling under <scp>M</scp>editerranean climate

Bai, Yun; Zhang, Jiahua; Zhang, Sha; Koju, Upama Ashish; Yao, Fengmei; Igbawua, Tertsea

doi:10.1002/2016ms000702

Cited by 43 publications

(26 citation statements)

References 99 publications

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“…Which is central to our method is using microwave EDVI to quantify the stomatal and canopy resistance under both clear sky and cloudy sky. Most of previous resistance-based LE methods are based on optic VIs [17][18][19] which cannot be effectively implemented under cloudy sky. Their validations are thus limited to under clear or partly cloudy sky.…”

Section: Summary Of the Methodsmentioning

confidence: 99%

“…VIs are typically used for the estimation of canopy resistance (or conductance) in the PM model [8]. Previous studies have shown that optical VI-based resistance and LE estimation show good performances at the 8-day or 16-day time scale and under clear or less cloudy sky conditions [11,[17][18][19]. However, since the optical VIs are retrieved based on the visible, near-infrared and shortwave-infrared bands [19] which are very sensitive to clouds and aerosols, their applicability under cloudy and overcast skies is limited.…”

Section: Introductionmentioning

confidence: 99%

“…They illustrated the superiority of such combinations from multiple sensors for LE estimation. However, such an empirical method between canopy conductance and VIs [17][18][19]23,24] is insufficient to reflect the fast dynamics of conductance and LE affected by local environments during short-term periods (e.g., diurnal and daily scale). Furthermore, most of the validations of estimated LE are conducted at the 8-day (or 16-day) time scale and under clear or less cloudy sky since optical VIs are easily contaminated by cloud cover.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Vegetation Latent Heat Flux over Three Forest Sites in ChinaFLUX using Satellite Microwave Vegetation Water Content Index

et al. 2019

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Latent heat flux (LE) and the corresponding water vapor lost from the Earth's surface to the atmosphere, which is called Evapotranspiration (ET), is one of the key processes in the water cycle and energy balance of the global climate system. Satellite remote sensing is the only feasible technique to estimate LE over a large-scale region. While most of the previous satellite LE methods are based on the optical vegetation index (VI), here we propose a microwave-VI (EDVI) based LE algorithm which can work for both day and night time, and under clear or non-raining conditions. This algorithm is totally driven by multiple-sensor satellite products of vegetation water content index, solar radiation, and cloud properties, with some aid from a reanalysis dataset. The satellite inputs and the performance of this algorithm are validated with in situ measurements at three ChinaFLUX forest sites. Our results show that the selected satellite observations can indeed serve as the inputs for the purpose of estimating ET. The instantaneous estimations of LE (LE cal ) from this algorithm show strong positive temporal correlations with the in situ measured LE (LE obs ) with the correlation coefficients (R) of 0.56-0.88 in the study years. The mean bias is kept within 16.0% (23.0 W/m 2 ) across the three sites. At the monthly scale, the correlations between the retrieval and the in situ measurements are further improved to an R of 0.84-0.95 and the bias is less than 14.3%. The validation results also indicate that EDVI-based LE method can produce stable LE cal under different cloudy skies with good accuracy. Being independent of any in situ measurements as inputs, this algorithm shows great potential for estimating ET under both clear and cloudy skies on a global scale for climate study.

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Section: Summary Of the Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimation of Vegetation Latent Heat Flux over Three Forest Sites in ChinaFLUX using Satellite Microwave Vegetation Water Content Index

et al. 2019

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“…We present an implementation of the model that calculates the rooting zone storage for both the overstory and understory. Then, we compare estimates of S r obtained with this parameterization against S r values obtained by calibrating a local water balance model against observations of latent heat flux and soil water content at 15 eddy covariance sites of the FLUXNET network (re3data.org, 2018; Baldocchi et al, 2001). We assess the effect of using optimality-based S r estimates on the performance of the local water balance model during validation.…”

Section: Introductionmentioning

confidence: 99%

Response to review #2

Speich¹

2018

Preprint

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Rooting zone water storage capacity S r is a crucial parameter for modeling hydrology, ecosystem gas exchange and vegetation dynamics. Despite its importance, this parameter is still poorly constrained and subject to high uncertainty. We tested the analytical, optimality-based model of effective rooting depth proposed by Guswa (2008, 2010) with regard to its applicability for parameterizing S r in temperate forests. The model assumes that plants dimension their rooting systems to maximize net carbon gain. Results from this model were compared against values obtained by calibrating a local water balance model against latent heat flux and soil moisture observations from 15 eddy covariance sites. Then, the effect of optimality-based S r estimates on the performance of local water balance predictions was assessed during model validation. The agreement between calibrated and optimality-based S r varied greatly across climates and forest types. At a majority of cold and temperate sites, the S r estimates were similar for both methods, and the water balance model performed equally well when parameterized with calibrated and with optimality-based S r. At spruce-dominated sites, optimalitybased S r were much larger than calibrated values. However, this did not affect the performance of the water balance model. On the other hand, at the Mediterranean sites considered in this study, optimality-based S r were consistently much smaller than calibrated values. The same was the case at pine-dominated sites on sandy soils. Accordingly, performance of the water balance model was much worse at these sites when optimality-based S r were used. This rooting depth parameterization might be used in dynamic (eco)hydrological models under cold and temperate conditions, either to estimate S r without calibration or as a model component. This could greatly increase the reliability of transient climate-impact assessment studies. On the other hand, the results from this study do not warrant the application of this model to Mediterranean climates or on very coarse soils. While the cause of these mismatches cannot be determined with certainty, it is possible that trees under these conditions follow rooting strategies that differ from the carbon budget optimization assumed by the model.

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“…are representative for the actual site conditions, at least at the temperate and boreal sites. Under Mediterranean conditions, transpiration may be more sensitive to the vertical 20 distribution of soil moisture and roots (Bai et al, 2017). Therefore, the use of a bulk !…”

Section: Parameterizationmentioning

confidence: 99%

Testing an optimality-based model of rooting zone water storage capacity in temperate forests

Speich¹,

Lischke²,

Zappa³

2017

Preprint

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Abstract. Rooting zone water storage capacity ! is a crucial parameter in models of hydrology, ecosystem gas exchange 10 and vegetation dynamics. Despite its importance, this parameter is still poorly constrained and subject to high uncertainty.We tested the analytical, optimality-based model of effective rooting depth proposed by Guswa (2010) with regard to its applicability for parameterizing ! in temperate forests. The model assumes that plants dimension their rooting systems in order to maximize net carbon gain. Results from this model were compared against values obtained by calibrating a local water balance model against latent heat flux and soil moisture observations from 15 eddy covariance sites. To increase the 15 applicability of the rooting depth model, we provide a numerical approximation of its underlying probabilistic soil moisture model.The calibration and validation of the local water balance model showed that the concept of a single rooting zone storage capacity was appropriate at most temperate and cold sites, but not at Mediterranean sites and for very coarse soils. At a majority of sites, the estimates of ! are generally in good agreement. However, mismatches were found in stands dominated 20 by Norway spruce, especially at high elevations. These mismatches were attributed to the fact that the model does not consider rooting depth limitations due to oxygen stress and low soil temperature. Also, it is not clear whether the rooting behavior of pines on coarse soils is captured properly. Nevertheless, the overall good agreement suggests that this model may be useful for generating estimates of rooting zone storage capacity for both hydrological and ecological applications.Another potential use is the dynamic parameterization of the rooting zone in process-based models, which greatly increases 25 the reliability of transient climate-impact assessment studies.

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Using precipitation, vertical root distribution, and satellite‐retrieved vegetation information to parameterize water stress in a Penman‐Monteith approach to evapotranspiration modeling under Mediterranean climate

Cited by 43 publications

References 99 publications

Estimation of Vegetation Latent Heat Flux over Three Forest Sites in ChinaFLUX using Satellite Microwave Vegetation Water Content Index

Estimation of Vegetation Latent Heat Flux over Three Forest Sites in ChinaFLUX using Satellite Microwave Vegetation Water Content Index

Response to review #2

Testing an optimality-based model of rooting zone water storage capacity in temperate forests

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