Estimation Of Hydraulic Conductivity Research Articles

Comparison of aquifer parameters inferred from water level changes induced by slug test, earth tide and earthquake – A case study in the three Gorges area

Hydraulic conductivity plays an important role in groundwater systems, but the estimation of hydraulic conductivity has many difficulties (the costs, feasibility and the scale-dependent results). In this study, we compared the hydraulic conductivity inferred from the conventional method (slug test) and the natural perturbation methods (tidal response and post-seismic water level recovery methods) at two confined wells, DJP well and MP well. The results show that the hydraulic conductivities estimated by the conventional method and the natural perturbation methods deviate somewhat from each other. The reason for the difference in hydraulic conductivities inferred from three methods is the variations of wellbore skin. By analyzing the uncertainty of estimation, we conclude that the different assumptions of the models cause inherent uncertainty and that the parameter uncertainty is secondary. The calculation results of these methods represent the different scales of the aquifer properties: 5–10 m for the slug test and 10–25 m for the tidal response and the post-seismic water level recovery methods, averaged by the hydraulic properties of the fault zone. The calculation errors of the three methods are 25% for the slug test, less than 10% for the tidal analysis and larger than 10% for the third method. Based on these comparisons, we conclude that the tidal response method is the most favorable way to infer aquifer parameters in this study. Both the tidal response and post-seismic water level recovery method provide a way to estimate aquifer properties as well as the mechanism of the co-seismic water level responses.

In-situ estimation of unsaturated hydraulic conductivity in freezing soil using improved field data and inverse numerical modeling

Hydraulic property determination in freezing soils continues to be a substantial challenge. Our overall objective was to present a novel method for in-situ estimation of unsaturated hydraulic conductivity in freezing soil using a combination of improved field data and a simplified inverse modeling approach. Dielectric sensor readings in the field were corrected using knowledge of ice permittivity, temperature and water redistribution to achieve higher accuracy in determination of liquid soil water content and soil ice content. The equation describing unsaturated hydraulic conductivity in freezing soil with an added impedance parameter was inversely estimated by firstly fitting soil freezing and thawing characteristic curves using the measured liquid soil water content and soil temperature, and then minimizing differences of the measured and simulated total soil water content with a coupled heat- and water-transfer model in frozen soil. Field measurements were made over two winters between 2011 and 2013 (year-1: winter of 2011–2012; year-2: winter of 2012–2013) in Beijing, China. Results suggested that (i) the relative error of liquid soil water content measurement in freezing soil was up to 53% if the knowledge of ice permittivity, temperature and water redistribution was neglected; (ii) the estimated impedance parameter in year-1 (1.152) was an order of magnitude higher than in year-2 (0.117), possibly because a faster freezing rate generated more fine ice in year-2, resulting in reduced tortuosity; (iii) the estimated impedance parameter uncertainty likely comes from the model assumptions, the measurement accuracies of model inputs and the inverse modeling parameter estimates. Based on these results and analysis, we conclude that the impedance parameter in frozen soil is strongly related to both the soil ice content and the size of formed ice, which is affected by the freezing rate and by the size of soil pores related to the soil texture.

Estimation of Unsaturated Hydraulic Conductivity of Granular Soils from Particle Size Parameters

Estimation of unsaturated hydraulic conductivity could benefit many engineering or research problems such as water flow in the vadose zone, unsaturated seepage and capillary barriers for underground waste isolation. The unsaturated hydraulic conductivity of a soil is related to its saturated hydraulic conductivity value as well as its water retention behaviour. By following the first author’s previous work, the saturated hydraulic conductivity and water retention curve (WRC) of sandy soils can be estimated from their basic gradation parameters. In this paper, we further suggest the applicable range of the estimation method is for soils with d10 > 0.02mm and Cu < 20, in which d10 is the grain diameter corresponding to 10% passing and Cu is the coefficient of uniformity (Cu=d60d10). The estimation method is also modified to consider the porosity variation effect. Then the proposed method is applied to predict unsaturated hydraulic conductivity properties of different sandy soils and also compared with laboratory and field test results. The comparison shows that the newly developed estimation method, which predicts the relative permeability of unsaturated sands from basic grain size parameters and porosity, generally has a fair agreement with measured data. It also indicates that the air-entry value is mainly relative to the mean grain size and porosity value change from the intrinsic value. The rate of permeability decline with suction is mainly associated with grain size polydispersity.

Estimation of hydraulic conductivity of saturated frozen soil from the soil freezing characteristic curve

Knowledge of hydraulic conductivity is crucial for determining water movement in frozen soil, and the objective of this study is to introduce the soil freezing characteristic curve to estimate the hydraulic conductivity of saturated frozen soil. Based on the non-uniform tortuous capillary bundle model and the assumption that the ice was first formed in the pores with largest size, a physical infiltration model of saturated frozen soil was developed. On the basis of this physical infiltration model, a new approach for estimating the hydraulic conductivity of saturated frozen soil was developed by using the Hagen-Poiseuille equation and Darcy's law. To verify the validity of this approach, five soil data sets, including 29 data points with hydraulic conductivity between 10−6 cm/s and 10−11 cm/s, were used to compare the predicted results and experimental data. The results show that the new approach fits the experimental data well. This approach is more convenient than the soil water characteristic curve in numerical modeling, and it can be used to describe the relationship between hydraulic conductivity and minus temperature. Moreover, the new approach and the results in this study maybe also can provide a reference for the research on water flow and the related numerical modeling in cold regions environmental engineering.

Hydraulic conductivity estimation by using groundwater modelling system program for upper zone of Iraqi aquifers

Estimating of soil hydraulic conductivity was so important and essential parameter for designing systems. Hydraulic conductivity measurements need many activities beginning from extract samples. Therefore it is having been found costly and time-consuming. At the present time, geo-statistical ways were used to estimate the hydraulic conductivity on the basis of restricted data. The aim of this research is to characterize hydraulic conductivity for the upper zone of Iraqi aquifers using groundwater modelling system (GMS) software and to build two dimensions geo-statistical analysis model depending on the application of 868 wells.These wells were divided into 778 wells for building the model and 90 wells for the verification. The hydraulic conductivity parameter which was obtained from the geo-statistical analysis model results indicated that the best fitted theoretical model was the inverse distance weighted (IDW) model with a coefficient of determination (R2 ) equal to 87.43% and root mean square error (RMSE) equal to 1.853. Also, by conducting many trails, it was found that the optimal distance range for estimating the hydraulic conductivity was 3800 to 5000 meters. A comparison between the kriging and IDW interpolation methods indicated that both methods could estimate the hydraulic conductivity with good accuracy and less error.

Analytical models for effective hydraulic sorptivity, diffusivity and conductivity of concrete with interfacial transition zone

This paper presents analytical models for evaluating effective hydraulic conductivity, diffusivity and sorptivity of concrete considering the properties of the Interfacial Transition Zone (ITZ) and the aggregate size distribution. Results of the proposed models compare well with the experimental results and those obtained from rigorous numerical Finite Element (FE) analysis. It is also found that a change of Interfacial Transition Zone (ITZ) properties has more influence on the effective hydraulic conductivity than it has on the effective hydraulic sorptivity. The significance of the proposed models is that they can estimate the hydraulic sorptivity and diffusivity of concrete by only knowing the hydraulic properties of mortar, effectively extricating the need for sophisticated and time-consuming FE analysis required for estimation of the effective hydraulic conductivity of concrete at the meso-scale. Furthermore, these models offer scientific insight into the effect of different components of concrete on its effective hydraulic properties.

Random forest, M5P and regression analysis to estimate the field unsaturated hydraulic conductivity

Hydraulic conductivity of soil reveals its influencing role in the studies related to management of surface and subsurface flow, e.g. irrigation and drainage projects, and solute mass transport models. Direct measurements of hydraulic conductivity have many difficulties due to spatial variation of the property in the field. Pertaining to this problem, in this study, estimation models have been developed using machine learning methods (M5 tree model and random forest model) in an attempt to estimate the accurate values of unsaturated hydraulic conductivity related to basic soil properties (clay, silt and sand content, bulk density and moisture content). Data set was collected from the experimental measurements of cumulative infiltration using mini disc infiltrometer at the study area (Kurukshetra, India). A multivariate nonlinear regression (MNLR) relationship was derived, and the performance of this model was compared with the machine learning-based models. The evaluation of the results, based on statistical criteria (R2, RMSE, MAE), suggested that random forest regression model is superior in accurate estimations of the unsaturated hydraulic conductivity of field data relative to M5 model tree and MNLR.

Revising tortuosity and multi-fractal assumptions of unsaturated hydraulic conductivity from critical path analysis of percolation theory

The relation between soil pore structure and water retention is complex and is often not well determined. We present a novel approach based on critical path analysis from percolation theory to refine hydraulic conductivity estimation from soil water retention curve by introducing a new tortuosity parameter as a function of scaling factor. We generalize this model to account for large shifts in the relation between soil pore structure and water retention, which are indicative of soils with multi-fractal properties, by employing a t-test on scaled saturation and suction data. The proposed model relaxes the constraints that were set on model parameters for multi-fractal soils in the literature by tuning “all” parameters against observed data using a multiple-start gradient-based optimization algorithm, and is applicable to a wider variety of soil textures. The optimization results are further evaluated against those of a Markov Chain Monte Carlo algorithm to ensure global optimum is found. Goodness-of-fit (GOF) measures, including geometric mean and standard deviation error ratios, and Nash-Sutcliffe efficiency, show that the proposed model presents less bias across the entire range of matric potential compared to its predecessor that under-estimate hydraulic conductivity in all studied cases.

Estimation of saturated hydraulic conductivity with pedotransfer functions: A review

Saturated hydraulic conductivity (Ks) is a singular parameter in earth system science. Ks not only governs the rate of flow of water under a hydraulic gradient as specified by the Darcy equation for saturated conditions, but also acts as a scaling factor in many unsaturated flow and transport applications that involve pore-size distribution models. Without knowledge of saturated hydraulic conductivity, it would be difficult to accurately describe the transport of water and dissolved or suspended constituents in soils and sediments, or calculate groundwater transport and recharge, and quantify the exchange between soils and the atmosphere. While the determination of Ks is not especially difficult, it is expensive and (in many cases) infeasible to carry out field or lab experiments for large-scale applications. Pedotransfer functions (PTFs) are a class of largely data-driven empirical models that aim to estimate Ks (and often other hydraulic quantities such as water retention characteristics) from easily available data. In this review, we first briefly discuss the history of the development of the concept of saturated hydraulic conductivity and its relation to the Kozeny-Carman (KC) equation. The KC equation serves as a central point in this review because it determines which soil variables affect saturated flow at the pore-scale, a domain which now can also be visited by computational fluid dynamics models. The KC equation also provides us with a structure in which we can classify the large number of PTFs that have been developed for estimating Ks. Datasets and statistical techniques available for PTF development are discussed, and we also describe common metrics used to assess the accuracy and reliability of PTF estimates. The mutual agreement of two main classes (i.e., an effective porosity KC-based and soil texture-based) of PTFs is analyzed using a number of global maps of predicted Ks. Finally, we discuss challenges and perspectives that might lead to PTFs with improved estimates of Ks. In particular, we suggest establishing and utilizing large and completely independent databases to assess the accuracy and reliability of PTFs for global use, while also drawing in information from pedological and remote sensing sources.

Estimation of saturated hydraulic conductivity of coarse-grained soils using particle shape and electrical resistivity

Based on the similarity in the underlying mechanisms between electrical and hydraulic conduction in porous media, Archie's equation can be combined with the Kozeny-Carman (KC) equation to estimate the hydraulic conductivity (K) of coarse-grained soils. However, the assumption of the exponent m in Archie's equation, which is equivalent to the assumed porosity at specific soil electrical properties, reduces the accuracy of the value of K predicted using the combination of Archie's equation and the KC equation. Therefore, this study introduces a depolarization factor, which allows the exponent m in Archie's equation to be estimated from the shape of the particles. Consequently, this study proposes a formula for estimating K for coarse-grained soils based on the combination of Archie's equation, the KC equation, and the depolarization factor. Data from laboratory experiments performed in this work and available data from the literature were used to validate the proposed model. In addition, the optimal value for the exponent m was recommended for predicting hydraulic conductivity using the proposed K estimating formula in the absence of particle shape data. Data obtained in the experiments and literature revealed that the proposed model is comparatively reliable in predicting K.

Fracture controlled permeability of ultramafic basement aquifers. Inferences from the Koniambo massif, New Caledonia

We study the relationship between fracture distribution and permeability in weathered peridotites exposed to tropical climate using the data from the Koniambo massif (New Caledonia, South Pacific). The vertical distribution of hydraulic conductivity is obtained from packer tests performed on four 200-m-deep boreholes. A positive correlation is observed between the cumulated width of weathered fractures and the hydraulic conductivity in a given depth-interval. Therefore, a two-scale model of weathered fractures is developed with small fractures (5 m radius) deduced from outcrop data and large fractures (50 m radius) from a remote sensing analysis. Hydraulic conductivity is 10−5 m/s in the fractures and 10−8 m/s in the matrix. Permeability fields are computed for the synthetic fractures network models, either from flow along the three axes of a periodic cube or from injection through a slit. Then, results are compared to packer tests and show that when both sets of large and small fractures are included, the proposed model reproduces reasonably in situ measured data. Analytical formulae are proposed for computing an approximate permeability from field data. The decrease of hydraulic conductivity with depth is also discussed. We show that the proposed model for peridotites aquifers is similar to the one currently admitted for granitic rock aquifers. It relies on weathering of a pre-existing serpentine-bearing fractures network which controls permeability in the weathering profile itself, but also deeper in the fresh and non-weathered rock. The proposed conceptual model allows deriving quantitative estimate of hydraulic conductivity from field data which are of primary interest for managing the groundwater resource of peridotite aquifers, particularly where mining operations impact groundwater flow.

Capillary pressure–saturation curves of thin hydrophilic fibrous layers: effects of overburden pressure, number of layers, and multiple imbibition–drainage cycles

Unsaturated fluid flow in thin porous media depends on hydraulic properties, such as the capillary pressure, P c, as a function of saturation, S. We measured this relationship for two different types of compressible thin hydrophilic fibrous layers under varying conditions. Among other factors, we changed the number of layers and the overburden pressure (i.e. the confined solid pressure applied on top of the sample) imposed on one layer or a stack of layers. Applying an overburden pressure drastically affected the [Formula: see text] curves. However, increasing the number of fibrous layers had little impact on the capillary pressure–saturation curves. We also investigated the effect of multiple imbibition–drainage cycles on the [Formula: see text] data. Measured data points were used to find general expressions for the [Formula: see text] relationships of compressible thin porous media. Existing quasi-empirical correlations used in vadose zone hydrology, notably expressions by van Genuchten (Van Genuchten MTh. A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci Soc Am J 1980; 44: 892-898) and Durner (Durner W. Hydraulic conductivity estimation for soils with heterogeneous pore structure. Water Resour Res 1994; 32: 211–223) for single- and dual-porosity media, respectively, were employed to fit the measured data points.

Estimation of soil subsurface hydraulic conductivity based on inverse modelling and soil morphology

Soil hydraulic properties play a primary role in water infiltration and movement, and transport of nutrients and contaminants through the soil profile. However, the determination of soil hydraulic properties, especially the hydraulic conductivity at lower depths within the soil profile is very challenging. We investigated the use of inverse modelling based on APSIM (Agricultural Production Simulation Model) to determine likely ranges of the hydraulic conductivity at saturation (Ksat) and field capacity (KDUL) for a slowly permeable subsurface horizon. Simulations were run for the poorly drained Otokia silt loam, in the Otago region of New Zealand. APSIM simulations were set up either according to the site specific soil profile description or based on the national soils database (S-map) pedotransfer functions. Simulated temporal soil moisture data were compared to measurements from a field experiment. Within a sensitivity analysis Ksat values of the subsurface soil were varied from 2.4 to 192 mm/d, and values for KDUL from 0.05 to 1 mm/d. Finally simulations were run over 45 years using three climatic conditions, with annual rainfall amounts of 582, 628 and 730 mm. The effect of varying the hydraulic conductivity of the subsurface fragipan horizon (Cx) on the wetness status of the overlying B2g horizon was determined by calculating the average annual duration of soil moisture above field capacity. These were then used to estimate ranges of the hydraulic conductivity that contributes to a soil being imperfectly or poorly drained. The results highlight the significant influence that KDUL has on the reliability of modelling the hydraulic behaviour of soils with slowly permeable subsoils.

Estimation of the soil hydraulic properties from the transient infiltration curve measured on soils affected by water repellency

Estimation of soil sorptivity (S) and hydraulic conductivity (K) is fundamental to model the water infiltration into the soil. This process can be affected by soil water repellency, which is defined as a reduction in soil wettability due to coating of soil particles by hydrophobic substances. Unlike to wettable soils, this phenomenon can generate infiltration curves with double-slope shape: a transient infiltration curve followed by a steady-state section. Because the topsoil final volumetric water content (θ1) of the transient phase of the double-slope curve is not a measurable data, in principle, the standard model based on the Haverkamp et al. (1994) model cannot be used to estimate S and K. This work presents two different approaches based on the Haverkamp et al. (1994) equation, which allow estimating S and K from the first phase of a double-slope infiltration curve, when θ1 data are not available. The methods, which are based on the analysis of both short-medium time transient infiltration curve (Tr) and the combination of both short-medium transient and steady-state infiltration steps (Mx), were applied on 20 soils affected by different degrees of water repellency. The Haverkamp et al. (1994) model was also valid for infiltration curves measured on hydrophobic soils, and the final volumetric water content was not an essential data to estimate K and S. Although the steady-state infiltration rate (q1) calculated with Mx was about 26% larger than that estimated with Tr, comparable K and S values were obtained with both methods. Overall, a large dispersion on the estimate of θ1 was observed with both methods. The gravimetric time, tgrav, estimated in the studied soils was low, <500 s. While the Mx method required simpler numerical calculus, Tr looked like to be more robust and less subjective.

Microbial effects on hydraulic conductivity estimation by single-well injection tests in a petroleum-contaminated aquifer

Single-well injection tests in a petroleum-contaminated region stimulate microbial growth and cause bioclogging because of the intrusion of elevated dissolved oxygen (DO), which may affect the hydraulic conductivity (K) estimation. In this study, we developed a mathematical model considering multispecies and multiphase reactive transport and bioclogging for a single-well injection test in a confined aquifer to investigate the microbial effects on parameter estimation. The finite-difference method was used to solve the model. The temporal heads in the injection well in both cases of clean and petroleum-contaminated aquifers were compared. The results indicated that the bioclogging led to an abnormal bulge of the drawdown-time curve at an intermediately late time of injection. Based on sensitivity analysis and Cooper-Jacob straight-line fitting associated with the least square method, the underestimation degree of K was 17.7% with default parameters, 11.9% with double injection rate Q, 31.1% with double maximum biomass growth rate μmax,j and 39.5% with double initial contaminant concentration C2ini. Moreover, introducing an approximate Damköhler number indicated that increasing Q reduced the underestimation by flushing and diluting the essential materials for microbial growth to the downstream and that larger C2ini and μmax,j resulted in an increase in underestimation due to the increase in favorable conditions and strong growth ability of microbes. Finally, we suggest that when petroleum contamination is inevitable in a single-well injection test, moderate increases in Q and the test duration can reduce the underestimation degree of K to a considerable extent.

On the estimation of hydraulic conductivity of layered vadose zones with limited data availability

Vadose zone acts as a controlling agent for recharge and transport of contaminants into aquifers. Hence, for modelling and quantifying flow and transport processes in subsurface environments, hydraulic conductivity (K) of the vadose zone plays an important role. However, given the heterogeneity and anisotropy of subsurface systems, the in-situ measurement of K is a daunting task at a larger scale. The present study was conceived to evaluate the efficacy of salient pedotransfer functions (PTFs) to indirectly estimate the saturated hydraulic conductivity \((K_{\mathrm{s}})\) of a lateritic vadose zone of eastern India. Also, in-situ hydraulic conductivity along with basic soil physical properties was determined in different vadose-zone layers at three locations (bare plot, cultivated field and orchard). Four PTFs [Campbell, Rawls–Brakensiek/Cronican–Gribb (R–B/C–G), and Models 2 and 3 of Rosetta] were selected to estimate \(K_{\mathrm{s}}\) and their performances were evaluated. Based on the statistical indicators, it is concluded that Model 3 of Rosetta is capable of predicting relatively close values of \(K_{\mathrm{s}}\) for the lateritic vadose zones to some extent. To generalise the findings of this study, it is recommended that such field-based studies should be carried out at a larger scale in lateritic terrains with varying land use/land cover.

Reliability-based optimum design of hydraulic water retaining structure constructed on heterogeneous porous media: utilizing stochastic ensemble surrogate model-based linked simulation optimization model

Seepage characteristics under hydraulic water retaining structures (HWRSs) significantly affect the hydraulic serviceability and stability of such structures. The expected hydraulic conductivity value and its spatial and directional variation substantially influence seepage characteristics. Furthermore, the uniform and homogenous hydraulic conductive is rarely seen in the real field. To study the effects of uncertainty and variation in hydraulic conductivity, a random field concept was used to generate different realizations of heterogeneous hydraulic conductivity with constant mean and varied standard deviation. Consequently, the seepage characteristics stochastically varied, creating uncertainty in seepage characteristics which influenced the HWRS design. Therefore, the objective of this paper was to integrate the reliability concept in the linked simulation optimization (S-O) model to address the uncertainty of hydraulic conductivity. Hence, the safest and most cost-effective HWRS design could be attained considering the effects of the uncertainty of hydraulic conductivity. The reliability-based optimum design (RBOD) concept was implemented utilizing the multiple realization optimization technique based on stochastic ensemble surrogate models. The reliability degree of each candidate design was measured based on the number of stochastic constraints satisfying the design requirements to the total number of constraints. The S-O model-based RBOD was formulated to find the most cost-effective HWRS design satisfying a beforehand desired degree of reliability. Each surrogate model was trained utilizing several (input–output) data sets simulated using numerical seepage modeling code (SEEPW). The Gaussian process regression machine learning technique was used to train several surrogate models to imitate the responses of the numerical seepage modeling. Each single input data set was solved (4 × 5) times to simulate four different realizations of spatial variation resulting from one of the five different standard deviation values (0.85, 1.55, 2.25, 2.95, 3.65 m/day) with constant mean (2 m/day). Each group of data for single standard deviation was utilized to train a single surrogate model. The genetic algorithm-based S-O model was utilized as an efficient optimization solver for such complex optimization task. The results of this study demonstrated that the reliability significantly influenced the design of HWRS. Further, the deterministic optimum design of HWRS was insufficient to be considered a reliable design, especially when a high degree of uncertainty due to the hydraulic conductivity estimation is included. Hence, RBOD for such problem is essential and helps the designer make a decision.

On the estimation of spatially representative plot scale saturated hydraulic conductivity in an agricultural setting

Spatially representative estimates of saturated hydraulic conductivity, Ks, are needed for simulating catchment scale surface runoff and infiltration. Classical methods for measuring Ks are time-consuming so sampling campaigns need to be designed economically. Important insights can be obtained by experiments directed to understand the controls of Ks in an agricultural setting and identify the minimum number of samples required for estimating representative plot scale Ks. In this study, a total of 131 double-ring infiltrometer measurements were made on 12 plots in a small Austrian catchment. A statistical analysis of Ks across the catchment suggests Ks to be only slightly influenced by physical and topographical soil characteristics while land use is the main control. The highest values of Ks were observed in arable fields, with a median of about 3 times and a coefficient of variation (CV) of about 75% of those in grassland areas. An uncertainty analysis aimed at determining the minimum number of Ks measurements necessary for estimating the geometric mean of Ks over a given area with a specified accuracy suggests that, beyond a specific and plot-size dependent number of measurements, the benefit of any extra measurement is small. The confidence interval of the geometric mean of Ks decreases with the number of measurements and increases with the size of the plot sampled. Applications of these findings for designing field campaigns are discussed.

Infiltration characteristics of lateritic vadose zones: Field experiments and modeling

Lateritic vadose zone, mostly found in tropical humid regions, is very complex and hence behaves in a different way than other vadose zones. Until now, little is known about the impact of agricultural practices on the infiltration dynamics of such vadose zones. To this end, the present study was conceived to explore infiltration characteristics of lateritic soil-water zones by conducting 72 double-ring infiltration experiments at multiple sites considering both plot and field scales having paddy-wheat cultivation practices. Furthermore, five infiltration models, two empirical (Kostiakov and Kostiakov-Lewis) and three process-based (Philip Two-Term, Swartzendruber and Brutsaert) were fitted to the site-specific infiltration data and their performance was critically evaluated for modeling infiltration behavior of the lateritic vadose zone. Results indicated that the mean ‘quasi-steady infiltration rate’ of the Experimental Plot measured before paddy cultivation was about 0.23 ± 0.2 mm/min, which decreased by around 80% after paddy cultivation and then increases by more than 200% after wheat cultivation. Similarly, at the field scale, the ‘quasi-steady infiltration rate’ decreases by about 60% after paddy cultivation. The Swartzendruber model followed by the Brutsaert model provided the most reliable estimates of saturated hydraulic conductivity. The Swartzendruber, Brutsaert and Kostiakov-Lewis models showed a greater fitting ability in predicting ‘cumulative infiltration’ under all cultivation practices at both plot and field scales. In contrast, all the five infiltration models predicted infiltration rate with a lower accuracy than the cumulative infiltration. It is concluded that the infiltration characteristics of lateritic vadose zones are greatly influenced by cultivation practices as well as by macro-pores and low-permeability layers present in the soil profile. Given the significant field heterogeneity, future studies should explore the efficacy of other process-based infiltration models [e.g., the Smith and Parlange (1978) model, the Kutílek and Krejča model (1987), and the modified Green-Ampt models] in predicting the infiltration characteristics of lateritic terrains.

Application of an improved global-scale groundwater model for water table estimation across New Zealand

Abstract. Many studies underline the importance of groundwater assessment at the larger, i.e. global, scale. The groundwater models used for these assessments are dedicated to the global scale and therefore not often applied for studies in smaller areas, e.g. catchments, because of their simplifying assumptions. In New Zealand, advanced numerical groundwater flow models have been applied in several catchments. However, that application is piecemeal: only for a limited amount of aquifers and through a variety of groundwater model suites, formats, and developers. Additionally, there are large areas where groundwater models and data are sparse. Hence, an inter-catchment, inter-regional, or nationwide overview of important groundwater information, such as the water table, does not exist. The investment needed to adequately cover New Zealand with high-resolution groundwater models in a consistent approach would be significant and is therefore not considered possible at this stage. This study proposes a solution that obtains a nationwide overview of groundwater that bridges the gap between the (too-)expensive advanced local models and the (too-)simple global-scale models. We apply an existing, global-scale, groundwater flow model and improve it by feeding in national input data of New Zealand terrain, geology, and recharge, and by slight adjustment of model parametrisation and model testing. The resulting nationwide maps of hydraulic head and water table depths show that the model points out the main alluvial aquifers with fine spatial detail (200 m grid resolution). The national input data and finer spatial detail result in better and more realistic variations of water table depth than the original, global-scale, model outputs. In two regional case studies in New Zealand, the hydraulic head shows excellent correlation with the available groundwater level data. Sensitivity and other analyses of our nationwide water tables show that the model is mostly driven by recharge, model resolution, and elevation (gravity), and impeded by the geology (permeability). The use of this first dedicated New Zealand-wide model can aid in provision of water table estimates in data-sparse regions. The national model can also be used to solve inconsistency of models in areas of trans-boundary aquifers, i.e. aquifers that cover more than one region in New Zealand. Comparison of the models, i.e. the national application (National Water Table model: NWT) with the global model (Equilibrium Water Table model: EWT), shows that most improvement is achieved by feeding in better and higher-resolution input data. The NWT model still has a bias towards shallow water tables (but less than the EWT model because of the finer model resolution), which could only be solved by feeding in a very high resolution terrain model that incorporates drainage features. Although this is a model shortcoming, it can also be viewed as a valuable indicator of the pre-human water table, i.e. before 90 % of wetlands were drained for agriculture since European settlement in New Zealand. Calibration to ground-observed water level improves model results but can of course only work where there are such data available. Future research should therefore focus on both model improvements and more data-driven, improved estimation of hydraulic conductivity, recharge, and the digital elevation model. We further surmise that the findings of this study, i.e. successful application of a global-scale model at smaller scales, will lead to subsequent improvement of the global-scale model equations.