• Nenhum resultado encontrado

Estimating spatial mean root-zone soil moisture from point-scale observations

N/A
N/A
Protected

Academic year: 2017

Share "Estimating spatial mean root-zone soil moisture from point-scale observations"

Copied!
39
0
0

Texto

Loading

Imagem

Fig. 1. Location of the study areas and observation sites. (a) Louvain-la-Neuve (0.5 m interval contour lines), (b) Tarrawarra (2 m interval contour lines), (c) R-5 ( ∼ 3 m interval contour lines).
Fig. 2. Time series of spatial mean and variability. Error bars indicate ± one standard deviation.
Fig. 3. Normal probability plot of the normalized spatial soil moisture fields. Dots indicate the median value, error bars indicate 25% and 75% percentiles of time variability
Fig. 4. Rank stability plots for the three datasets. The sites have been ranked according to their mean di ff erence with the spatial mean
+7

Referências

Documentos relacionados

Mean annual values of soil temperature at 5 cm depth, soil moisture of the top 5 cm soil layer, soil respiration rate, fine root biomass and soil microbial biomass under

Moisture data for Station 84 (red clay): (a) Time series for each sensor; (b) Horizontal moisture isochrones A comparison between the precipitation and the gravimetric water content

Semivariogram models, adjusted for soil moisture, had strong spatial dependence, but the relationship between soil moisture and soil ECa was obtained only in one of the

For the Red- Yellow Ultisol and Litholic Neosol, the largest amplitudes followed the sequence of the greatest water content retained by the soil (field capacity > wilting point

Decomposition of the Catchment near-surface soil moisture time series at Little River, using the harmonic (HA; black) and moving average (MA; cyan) methods, for (a) the original

The main conclusions of this study are (1) soil moisture in near-surface and root zone layers in Oklahoma and Nebraska are strongly coupled, (2) the exponential filter method

A linear interpolation of Ŵ soil between the six sampling dates in combination with hourly soil temperature measurements were used to generate a time series of soil compensation point

(2011) developed a dual Kalman filter for estimating the root zone soil moisture using a model based on the Richards equation, by combining the EKF to update the state variables,