State-Space Approach to Understand Soil-Plant-Atmosphere Relationships

Timm, Luís Carlos; Reichardt, Klaus; Lima, Cláudia Liane Rodrigues de; Aquino, Leandro Sanzi; Penning, Letiane Helwig; Neto, Durval Dourado

doi:10.1007/978-3-319-06013-2_5

Cited by 2 publications

(1 citation statement)

References 36 publications

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“…where X i is the state vector (of a set of p variables) at the ith location, F is a p ´ p matrix of state coefficients consisting of autoregressive coefficients, and w i is the model error vector (Timm et al, 2003). The model error, which is assumed to be uncorrelated, is a zero mean and normally distributed noise with a p ´ p covariance matrix Q, where the latter is the variance per unit space that depends on the interval between observations.…”

Section: Theory Of State-space Modelingmentioning

confidence: 99%

Factors that Influence the Vertical Distribution of Soil Water Content in the Critical Zone on the Loess Plateau, China

Qiao

Zhu

Jia

et al. 2018

Vadose Zone Journal

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Core Ideas Soil water distributions in deep profiles were described and analyzed. Soil water storage distributions were evaluated. Bulk density significantly affected soil water distribution of deep profiles. In arid and semiarid regions, determining the vertical distribution of the soil water content (SWC) in the Earth's Critical Zone is important for understanding hydrological processes and for evaluating soil water storage (SWS) levels. However, the vertical distribution of SWC and its storage in deeper layers are unclear due to the difficulties associated with soil sampling. In this study, we investigated the vertical distribution of the SWC and SWS, and analyzed the relationship between SWC and related soil properties, including bulk density (BD), sand, silt, clay, and soil organic C (SOC), from the top of the soil profile to the bedrock at five sampling sites on the Loess Plateau in China (Yangling, Changwu, Fuxian, An'sai, and Shenmu) by soil core drilling. The results showed that the SWC variations at the five sampling sites tended to become weak as the depth increased. The mean SWC of all sampling sites exhibited a decreasing trend from south to north, with a significant difference (P < 0.01). Stepwise multiple regression analysis and state‐space modeling showed that the BD was an important factor that affected the variations in the SWC in the deep soil layer. The trend of vertical distribution of the SWS was similar to that of the SWC. The results of this study deepen our understanding of the water conditions in deep soil layers, as well as the evaluation of SWS on the Loess Plateau in China.

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Section: Theory Of State-space Modelingmentioning

confidence: 99%