Geostatistical analysis and conditional simulation for estimating the spatial variability of hydraulic conductivity in the Choushui River alluvial fan, Taiwan

Abstract

Abstract:This work evaluated the spatial variability and distribution of heterogeneous hydraulic conductivity (K) in the Choushui River alluvial fan in Taiwan, using ordinary kriging (OK) and mean and individual sequential Gaussian simulations (SGS). A baseline flow model constructed by upscaling parameters was inversely calibrated to determine the pumping and recharge rates. Simulated heads using different K realizations were then compared with historically measured heads. A global/local simulated error between simulated and measured heads was analysed to assess the different spatial variabilities of various estimated K distributions. The results of a MODFLOW simulation indicate that the OK realization had the smallest sum of absolute mean simulation errors (SAMSE) and the SGS realizations preserved the spatial variability of the measured K fields. Moreover, the SAMSE increases as the spatial variability of the K field increases. The OK realization yields small local simulation errors in the measured K field of moderate magnitude, whereas the SGS realizations have small local simulation errors in the measured K fields, with high and low values. The OK realization of K can be applied to perform a deterministic inverse calibration. The mean SGS method is suggested for constructing a K field when the application focuses on extreme values of estimated parameters and small calibration errors, such as in a simulation of contaminant transport in heterogeneous aquifers. The individual SGS realization is useful in stochastically assessing the spatial uncertainty of highly heterogeneous aquifers.

Heat as a Tracer for Examining Depth‐Decaying Permeability in Gravel Deposits

“…This study assumed R ( s ) as a regional variable with the same variogram throughout the analysis domain, as detailed geologic information for dividing the monotonic sequence was not obtained. Sequential Gaussian simulation (SGS) was used for its stochastic realization as in other sandy gravel aquifer modeling (Eggleston et al ; Jang and Liu ; Lee et al ). The SGS process has six steps (Deutsch and Journel ): (1) input the honor data (solid squares in Figure ), which are equal to the estimates at the nodes, (2) generate a random path though the grid nodes, (3) select the first node in that path and estimate a mean and standard deviation at that node by ordinary kriging based on the surrounding honor data, (4) obtain a random value from the mean and standard deviation and set the value at the node, (5) include the newly simulated value as part of the honor data, and (6) repeat Steps 2 to 5 until all the grid nodes have simulated values.…”

Temporal and spatial variation of infiltration in urban green infrastructure

“…Statistics, such as variance and mean, obtained from equivalent PDFs of two different Ksat populations, can then be compared (Muñoz‐Carpena et al, ). The field Ksat PDF is typically a log‐normal distribution (Ahmed et al, ; Asleson et al, ; Bjerg, Hinsby, Christensen, & Gravesen, ; Jang & Liu, ; Regalado & Muñoz‐Carpena, ; Tsegaye & Hill, ; Vauclin et al, ). However, in some cases, although the conductivity data were skewed towards the lower end, the best distribution was not log normal (e.g., beta distribution in Cooke, Mostaghimi, & Woeste, ; three‐parameter log‐normal distribution in Zhai & Benson, ).…”

Multivariate approaches optimize locations of groundwater pumping facilities for different hydrogeological scales

“…The water level variations of the two main rivers (Laonong Stream and Cishan Stream) adopted were 0 to 6 m and 0 to 4 m with time, respectively. When certain parameters are inversely calibrated in a numerical model, a zonation distribution is generally applied to scale up the calibration parameters (Yeh, 1986;Gau et al, 1998;Jang and Liu, 2004). For numerical calibration, a zonation distribution was adopted to establish the spatial patterns of the pumping and recharge rates.…”

“…Many approaches have employed automated calibration algorithms [e.g., PEST (Doherty ) or UCODE (Poeter and Hill, )] to estimate aquifer properties and manage water resources (Barthel et al ., ; Liu et al ., ). Jang and Liu () adopted the numerical model MODFLOW to simulate the spatial distribution of groundwater flow with time in the heterogeneous field and came up with a better recharge zone for a regional model. In addition, numerous researchers have applied hydraulic tomography using the sequential successive linear estimator (SSLE) algorithm to estimate transmissivity (T) and storage coefficient distributions for a regional flow model or small‐scale flow model (Yeh and Liu, ; Zhu and Yeh, ; Kuhlman et al ., ).…”