Search

The prediction of the location of ground water discharge areas is a key aspect for the protection and (re)development of ground water-dependent wetlands. Ground water discharge areas can be simulated with MODFLOW using the DRAIN package by setting the drain level equal to the topography, while the conductance is mostly set to an arbitrary high value. However, conceptual and practical problems arise in the calculation of the ground water discharge by the DRAIN package as calculated water tables above the land surface, difficult parameterization of the conductance, and large water balance errors. To overcome these problems, a new SEEPAGE package for MODFLOW is proposed. The basic idea of this package is an adaptable constant head cell. It has a variable head, unless the ground water rises above the seepage level, in which case it has a constant head cell. The estimation of the ground water discharge location along a homogeneous, isotropic, linear sloping profile is used to verify the model and to compare it to the DRAIN solution. In an application to three basins in Belgium, it is shown that the SEEPAGE package can be used in combination with the DRAIN package in situations where an upper boundary for a free water table and additional resistance for drainage is required. It is clearly demonstrated that the identification and delineation of regional ground water discharge areas is more accurate using the SEEPAGE package., Introduction Our current understanding of wetlands is insufficient to assess the effects of past and future wetland loss. While knowledge of wetland hydrology is crucial, ground water flows are often [...]

This paper presents a Bayesian Monte Carlo method for evaluating the uncertainty in the delineation of well capture zones and its application to a wellfield in a heterogeneous, multiaquifer system. In the method presented, Bayes' rule is used to update prior distributions for the unknown parameters of the stochastic model for the hydraulic conductivity, and to calculate probability-based weights for parameter realizations using head residuals. These weights are then assigned to the corresponding capture zones obtained using forward particle tracking. Statistical analysis of the set of weighted protection zones results in a probability distribution for the capture zones. The suitability of the Bayesian stochastic method for a multilayered system is investigated, using the wellfield Het Rot at Nieuwrode, Belgium, located in a three-layered aquifer system, as an example. The hydraulic conductivity of the production aquifer is modeled as a spatially correlated random function with uncertain parameters. The aquitard and overlying unconfined aquifer are assigned random, homogeneous conductivities. The stochastic results are compared with deterministic capture zones obtained with a calibrated model for the area. The predictions of the stochastic approach are more conservative and indicate that parameter uncertainty should be taken into account in the delineation of well capture zones., Introduction The awareness of the importance to maintain the quality of ground water in the vicinity of supply wells has resulted in regulations aiming to protect ground water supplies from [...]

Agricultural expansion in the Kharga Oasis, in the western desert of Egypt, depends strongly on irrigation. Soil hydraulic conductivity is therefore a key property for reclaiming desert land and planning irrigation schemes. Soil samples collected at 10‐m intervals in a 120 m by 120 m plot were analysed for hydraulic conductivity together with 12 other basic physical and chemical soil properties. The resulting data were analysed statistically using Pearson correlation, principal component analysis and linear regression. The hydraulic conductivity values varied over four orders of magnitude and, because of the saline–sodic nature of the soil, were about two orders of magnitude smaller than what is generally reported in the literature for similar soil textures. Results showed that the hydraulic conductivity was correlated significantly with soil variables that relate to soil structure, such as wilting point, field capacity and SAR, and less to variables that relate to soil texture, such as silt and clay fractions. Pedotransfer functions for hydraulic conductivity were derived by stepwise multiple linear regression and fitted by residual maximum likelihood. The first model was based directly on soil properties, whereas the second model was based on principal components. Both models showed that part of the variation in hydraulic conductivity encountered in the field could be explained, but with large uncertainty probably resulting from sampling and measurement errors, randomness and soil heterogeneity, or other soil properties that were not observed. The most significant predictors for the first model were wilting point, SAR and silt fraction. The second model used the first two principal components of the soil variables as predictors, the first one related to soil structure and the second to soil texture. Highlights: We analysed relations between saturated hydraulic conductivity and other basic soil properties. Principal component analysis showed that the variation in hydraulic conductivity is related to soil structure. A pedotransfer function predicted hydraulic conductivity from wilting point, sodium adsorption ratio and silt fraction. A pedotransfer function was derived from principal components one and two related to structure and texture, respectively. [ABSTRACT FROM AUTHOR]