Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Spatiotemporal sampling strategy for characterization of hydraulic properties in heterogeneous soils
AU - Yu, D.
AU - Zha, Y.
AU - Shi, L.
AU - Bolotov, A.
AU - Tso, C.-H.M.
PY - 2021/3/1
Y1 - 2021/3/1
N2 - Accurate characterization and prediction of soil moisture distribution and solute transport in vadose zone require detailed knowledge of the spatial distribution of soil hydraulic properties. Since the direct measurements of these unknown properties are challenging, many studies invert the soil hydraulic parameters by incorporating observation data (e.g., soil moisture and pressure head) at selected point sampling locations into soil moisture flow models. However, a cost-effective sampling strategy for where and when to collect the data, which is vital for saving the costs for monitoring and data interpretation, is relatively rare compared to the direct parameter retrieving efforts. Here, an optimal spatial–temporal sampling strategy was proposed based on cross-correlation analysis between observed state variables and soil hydraulic parameters. Besides, the effects of meteorological condition, observation type, bottom boundary condition, and correlation scale of soil hydraulic parameters are also demonstrated. The proposed sampling strategy was assessed by both synthetic numerical experiments and a real-world case study. Results suggest the retrieval accuracy of heterogeneous soil is acceptable if the spatial/temporal sampling interval is set to be one spatial/temporal correlation length of soil moisture. Besides, surface observation contains the most plentiful information which could be used to derive root-zone soil moisture/parameters, but this ability depends on the correlation scale of soil hydraulic parameters. Besides, the temporal value of soil moisture depends on meteorological condition. It is not necessary to sample repeatedly during dry periods, but more attention should be paid to the observations after rainfall events. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.
AB - Accurate characterization and prediction of soil moisture distribution and solute transport in vadose zone require detailed knowledge of the spatial distribution of soil hydraulic properties. Since the direct measurements of these unknown properties are challenging, many studies invert the soil hydraulic parameters by incorporating observation data (e.g., soil moisture and pressure head) at selected point sampling locations into soil moisture flow models. However, a cost-effective sampling strategy for where and when to collect the data, which is vital for saving the costs for monitoring and data interpretation, is relatively rare compared to the direct parameter retrieving efforts. Here, an optimal spatial–temporal sampling strategy was proposed based on cross-correlation analysis between observed state variables and soil hydraulic parameters. Besides, the effects of meteorological condition, observation type, bottom boundary condition, and correlation scale of soil hydraulic parameters are also demonstrated. The proposed sampling strategy was assessed by both synthetic numerical experiments and a real-world case study. Results suggest the retrieval accuracy of heterogeneous soil is acceptable if the spatial/temporal sampling interval is set to be one spatial/temporal correlation length of soil moisture. Besides, surface observation contains the most plentiful information which could be used to derive root-zone soil moisture/parameters, but this ability depends on the correlation scale of soil hydraulic parameters. Besides, the temporal value of soil moisture depends on meteorological condition. It is not necessary to sample repeatedly during dry periods, but more attention should be paid to the observations after rainfall events. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.
KW - Cross-correlation analysis
KW - Data assimilation
KW - Sampling strategy
KW - Soil heterogeneity
KW - Variably saturated flow
KW - Cost effectiveness
KW - Sampling
KW - Soil moisture
KW - Solute transport
KW - Bottom boundary conditions
KW - Meteorological condition
KW - Root zone soil moistures
KW - Soil hydraulic parameters
KW - Soil hydraulic properties
KW - Soil moisture distribution
KW - Spatio-temporal samplings
KW - Soil surveys
U2 - 10.1007/s00477-020-01882-1
DO - 10.1007/s00477-020-01882-1
M3 - Journal article
VL - 35
SP - 737
EP - 757
JO - Stochastic Environmental Research and Risk Assessment
JF - Stochastic Environmental Research and Risk Assessment
SN - 1436-3240
ER -