Observations and stochastic modeling of soil moisture control on evapotranspiration in a Californian oak savanna

Rubin, Yoram; Ma, Siyan; Baldocchi, Dennis

doi:10.1029/2007wr006646

Cited by 49 publications

(43 citation statements)

References 82 publications

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“…This mechanism is tightly coupled to the available soil moisture [18] and the interannual variability in rainfall [19,20]. Variability in precipitation and soil moisture leads to alterations of the carbon flux via the water use efficiency [14,21], which has led to the examination of the role of optimal water use, particularly during drought conditions [22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Multiscale Interactions betweenWater and Carbon Fluxes and Environmental Variables in A Central U.S. Grassland

Brunsell

Wilson

2013

Entropy

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Abstract:The temporal interactions between water and carbon cycling and the controlling environmental variables are investigated using wavelets and information theory. We used 3.5 years of eddy covariance station observations from an abandoned agricultural field in the central U.S. Time-series of the entropy of water and carbon fluxes exhibit pronounced annual cycles, primarily explained by the modulation of the diurnal flux amplitude by other variables, such as the net radiation. Entropies of soil moisture and precipitation show almost no annual cycle, but the data were collected during above average precipitation years, which limits the role of moisture stress on the resultant fluxes. We also investigated the information contribution to resultant fluxes from selected environmental variables as a function of time-scale using relative entropy. The relative entropy of latent heat flux and ecosystem respiration show that the radiation terms contribute the most information to these fluxes at scales up to the diurnal scale. Vapor pressure deficit and air temperature contribute to the most information for the gross primary productivity and net ecosystem exchange at the daily time-scale. The relative entropy between the fluxes and soil moisture illustrates that soil moisture contributes information at approximately weekly time-scales, while the relative entropy with precipitation contributes information predominantly at the monthly time-scale. The use of information theory metrics is a relatively new technique for assessing biosphere-atmosphere interactions, and this study illustrates the utility of the approach for assessing the dominant time-scales of these interactions.

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Section: Introductionmentioning

confidence: 99%

Multiscale Interactions betweenWater and Carbon Fluxes and Environmental Variables in A Central U.S. Grassland

Brunsell

Wilson

2013

Entropy

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“…Soil saturation pdfs are less sensitive to the many sources of uncertainty and common gaps in soil moisture observations and do not require high quality co-located and concurrent hydrologic measurements that are often lacking. A number of studies have combined inverse modeling 10 approaches with ground and remotely sensed soil moisture data to successfully extract meaningful hydrologic information (Xu et al, 2006;Miller et al, 2007;Chen et al, 2008;Volo et al, 2014;Wang et al, 2016;Baldwin et al, 2017). In particular, Bayesian inference methods are effective in relating prior pdfs of observations to posterior estimates of model parameters (Xu et al, 2006;Chen et al, 2008;Baldwin et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Miller et al, (2007) calibrated soil moisture pdfs to project vegetation stress in a changing climate. Chen et al, (2008) related evapotranspiration observations at the stand scale to soil moisture values using a Bayesian inversion approach, and Volo et al, (2014) calibrated the soil moisture loss curve to investigate effects of irrigation scheduling and precipitation on soil moisture dynamics and plant stress. The functional form of the soil moisture losses was 25 approximated using conditionally averaged precipitation (Salvucci, 2001;Saleem and Salvucci, 2002) and remotely sensed data (Tuttle and Salvucci, 2014).…”

Section: Introductionmentioning

confidence: 99%

Probabilistic inference of ecohydrological parameters using observations from point to satellite scales

Bassiouni¹,

Higgins²,

Still³

et al. 2017

Preprint

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Abstract. Ecohydrological parameters that describe vegetation controls on soil moisture dynamics are not easy to measure at hydrologically meaningful scales and site-specific values are rarely available. We hypothesize that sufficient information required to determine these ecohydrological parameters is encoded in empirical probability density functions (pdfs) of soil 10 saturation, and that this information can be extracted through inverse modeling of the commonly used stochastic soil water balance. We developed a generalizable Bayesian inference approach to estimate soil saturation thresholds at which plants control soil water losses, based only on soil texture, rainfall and soil moisture data at point, footprint, and satellite scales. The optimal analytical soil saturation pdfs were statistically consistent with empirical pdfs and parameter uncertainties were on average under 10 %. Parameter estimates were most constrained for scales and locations at which soil water dynamics are 15 more sensitive to the fitted ecohydrological parameters of interest. The algorithm convergence was most successful and the best goodness-of-fit statistics were obtained at the satellite scale. Robust and accurate results were obtained with as little as 75 daily observations randomly sampled from the full records, demonstrating the advantage of analyzing soil saturation pdfs instead of time series. A sensitivity analysis showed that estimates of soil saturation thresholds at which plants control soil water losses were not sensitive to soil depth and near-surface observations are valuable to characterize ecohydrological 20 factors driving soil water dynamics at ecosystem scales. This work combined modeling and empirical approaches in ecohydrology and provided a simple framework to obtain analytical descriptions of soil moisture dynamics at a range of spatial scales that are consistent with soil moisture observations.

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“…The map below (b) shows the locations of the 12 sites within 8 NEON Ecoclimatic domains. Data sources: COSMOS, ORNL-DAAC (2015), Goulden et al (2012), Anderson and Goulden (2012), , Chen et al (2008). et al, 2012), COSMOS-UK (Evans et al, 2016), the Australian National Cosmic-Ray Soil Moisture Monitoring Facility CosmOZ (Hawdon et al, 2004), and TERrestrial ENvironmental Observatoria, TERENO (Baatz et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Land surface model performance using cosmic-ray and point-scale soil moisture measurements for calibration

Iwema

Rosolem

Rahman

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. At very high resolution scale (i.e. grid cells of 1 km 2 ), land surface model parameters can be calibrated with eddy-covariance flux data and point-scale soil moisture data. However, measurement scales of eddy-covariance and point-scale data differ substantially. In our study, we investigated the impact of reducing the scale mismatch between surface energy flux and soil moisture observations by replacing point-scale soil moisture data with observations derived from Cosmic-Ray Neutron Sensors (CRNSs) made at larger spatial scales. Five soil and evapotranspiration parameters of the Joint UK Land Environment Simulator (JULES) were calibrated against point-scale and Cosmic-Ray Neutron Sensor soil moisture data separately. We calibrated the model for 12 sites in the USA representing a range of climatic, soil, and vegetation conditions. The improvement in latent heat flux estimation for the two calibration solutions was assessed by comparison to eddy-covariance flux data and to JULES simulations with default parameter values. Calibrations against the two soil moisture products alone did show an advantage for the cosmic-ray technique. However, further analyses of two-objective calibrations with soil moisture and latent heat flux showed no substantial differences between both calibration strategies. This was mainly caused by the limited effect of calibrating soil parameters on soil moisture dynamics and surface energy fluxes. Other factors that played a role were limited spatial variability in surface fluxes implied by soil moisture spatio-temporal stability, and data quality issues.

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Observations and stochastic modeling of soil moisture control on evapotranspiration in a Californian oak savanna

Cited by 49 publications

References 82 publications

Multiscale Interactions betweenWater and Carbon Fluxes and Environmental Variables in A Central U.S. Grassland

Multiscale Interactions betweenWater and Carbon Fluxes and Environmental Variables in A Central U.S. Grassland

Probabilistic inference of ecohydrological parameters using observations from point to satellite scales

Land surface model performance using cosmic-ray and point-scale soil moisture measurements for calibration

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