Water Characteristic Data Research Articles

Innovative interpretable AI-guided water quality evaluation with risk adversarial analysis in river streams considering spatial-temporal effects

Water security remains a critical issue given the looming threats of industrial pollution, necessitating comprehensive assessments of water quality to address seasonal fluctuations and influential factors while formulating effective strategies for decision makers. This study introduces a novel approach for evaluating water quality within a complex riverine zone in South Korea: Han River that encompasses five river streams situated at each junction of North and South streams (including Gyeongan Stream) that ultimately leading towards Paldang Lake. By utilizing the monthly water characteristic data from the year 2013–2022 across 14 different locations, the significant seasonal trends and potential influences on water quality are identified. The water quality here is calculated with the proposed method of sub-index water quality index (s-WQI). A combinatorial prediction approach of s-WQI for each location is conducted through a collective of data preprocessing approaches including Hampel filtering and feature selection in prior to the machine learning predictions. In return, light gradient boosting (LGB) is the most accurate predictor by outperforming other prediction algorithms, especially through LGB-Pearson and LGB-Spearman combinations for North and South stream intersections, and LGB-Pearson for Paldang Lake. To further evaluate the robustness of this evaluation and extending the results to a foreseeable scenario, a seasonal based Monte-Carlo Simulation with 10,000 attempts targeting the water characteristic distributions obtained from each location considered are carried out to identify the risk bounds within. The results are further interpreted with SHAP analysis on identifying the contributions of each water characteristics towards the water quality through local and global spectrum. This research yields practical implications, offering tailored strategies for water quality enhancement and early warning systems. The integration of AI-based prediction and feature selection underscores the transformative potential of computational techniques in advancing data-driven water quality assessments, shaping the future of environmental science research.

Dry-air technology for stabilising weak deposits

Many available ground improvement techniques are effective, but involve large amounts of carbon dioxide emissions. Any green ground improvement technique would thus be beneficial. In this work, dry air, supplied at low pressure and relative humidity, was used to remove water from a soft soil deposit. The investigation was carried out at model scale, with a soft soil layer formed in a box of size 1.0 × 1.0 × 0.75 m. The soil bed was fitted with slender granular columns for the injection of dry air. The technique is the reverse process of vacuum consolidation, in which the magnitude of negative pore water pressure that can be applied to the soil is limited and thus requires careful construction procedures. The dry-air approach is simple and does not require any complex construction procedures. The investigations carried out over a limited period showed a significant improvement in the strength of the soil bed, indicating possible full-scale implementation. Full-scale implementation of the technique may not require any new construction methods as the procedure is very similar to that adopted in vacuum consolidation. However, variabilities in ground conditions, including the groundwater table, may pose additional challenges and supplementary information (soil–water characteristic data and numerical modelling) may be necessary to implement this technique at full scale.

A two-point method for determining the soil water characteristic of typical northern temperate boreal soils

Data from Scottish soils were used to test a method to estimate the soil water characteristic from just two measured soil water retention points plus a measure of the water content at saturation. The method produced a good approximation of the measured soil water characteristic for the majority of the samples analysed (R2=0.947; mean absolute error 0.019m2m−2). However it was found that the method was not readily transferable to a group of soils that are very porous due to cryoturbation and have soil water characteristics not well approximated by a power function. It is concluded that the method has value in improving the cost effectiveness of obtaining soil water characteristic data. The assessments of the method, which now span a wide range of different soil materials, give sufficient indication of expected accuracy and precision to enable users to assess if the method is suitable for their particular applications.

Predicting Saturated Hydraulic Conductivity of Golf Course Sands from Particle‐Size Distribution

This research developed a model of saturated hydraulic conductivity for golf course and athletic field media. The model was developed from saturated flow data in packed sand cores, for which a pore‐size distribution was derived from particle‐size distribution, bulk density, and measured soil water characteristic data. The pores were first assumed to form an idealized structure, consisting of non‐tortuous capillary tubes of uniform shape and size, and the Hagen–Poiseuille flow equation was applied to compute idealized saturated flow. The idealized saturated flows were compared with saturated flows derived from the measured saturated hydraulic conductivity data. Subsequently, an empirical relationship was established between the two in the form: Qt(m) = c + dQt(h–p), where Qt(m) is the saturated flow through the natural‐structure sand cores and Qt(–p) is the saturated flow through the idealized pore structure for the same core. In our study, parameters c and d had values of −1.675 and 0.308, respectively, and the r2 of the regression had a value of 0.871. The model was applied to 14 golf course sands and produced excellent results with minor anomalies.

Physical Properties of Natural Organic Soils in Carolina Bays of the Southeastern United States

Hydrologic models are useful tools for designing wetland restoration projects, but they are difficult to use for sites with natural organic soils because few soil property data are available for these soils. The objective of this study was to measure the physical properties of organic soils needed to calibrate hydrologic models of three natural Carolina Bay wetlands in the southeastern USA. Undisturbed soil cores were collected at each site for laboratory measurement of saturated hydraulic conductivity, soil water characteristic curves, bulk density, particle density, and total porosity. Field measurements of saturated hydraulic conductivity were also made. The Oi, Oe, and Oa horizons in the natural organic soils had similar bulk densities (0.16 g cm -3 ), total porosities (0.90 cm 3 cm -3 ), and particle densities (1.50 g cm -3 ). However, field-measured saturated hydraulic conductivities decreased as the level of decomposition increased, going from 45 to 7.1 to 1.5 cm h -1 for the Oi, Oe, and Oa horizons, respectively. Soil water characteristic data revealed an abundance of large pores (>0.3 mm) in the Oi horizons, which likely explains their high saturated hydraulic conductivity relative to the Oe and Oa horizons, which had fewer large pores. Using the data collected in this study, mathematical relationships were developed to predict the total porosity and the saturated hydraulic conductivity of these organic soils based on more easily obtainable data, such as bulk density and the organic horizon type.

Discussion of “Impact of Soil Type and Compaction Conditions on Soil Water Characteristic” by C. J. Miller, N. Yesiller, K. Yaldo, and S. Merayyan

The authors conducted a comparison of four soil water characteristic models, i.e., the Brooks‐Corey model, the van Genuchten model, the Fredlund and Xing four-parameter model, and the Fredlund and Xing five-parameter model, using experimental data of three different soils ~Fredlund and Xing 1994!. The authors found that each of the four models provided an acceptable fit to the experimental data, and the resulting curves of the four models were very similar though each model yielded a different set of best-fit parameters. The authors did not show which model gave a best fit among the four models. Leoung and Rahardjo ~1997! evaluated the van Genuchten and the Fredlund and Xing models using the published soil water characteristic data for soil materials ranging from sand to clay. It is found that the Fredlund and Xing four-parameter model performed marginally better than the van Genuchten and the Fredlund and Xing five-parameter models. Leoung and Rahardjo ~1997! found another interesting result was that for sandy soils the models with u r ~van Genuchten and Fredlund and Xing five-parameter models! performed much better than the Fredlund and Xing four-parameter model. Barbour ~1998! pointed out that the Brooks‐Corey model gave a best fit for sand and silt. Leoung and Rahardjo ~1997! pointed out that the data used should include points after u r in obtaining the parameters of the soil water characteristic models. It is difficult to make a comparison among the soil water characteristic models owing to the lack of the measured data of soil water characteristics after

Scaling Parameter to Predict the Soil Water Characteristic from Particle-Size Distribution Data

The Arya-Paris model is an indirect method to estimate the soil water characteristic from particle-size data. The scaling parameter, α, in the original model was assumed constant for all soil textures. In this study, α is defined as α i = (log N i /log n i ), ), where n i is the number of spherical particles in the ith particle-size fraction (determined by the fraction solid mass, w i , and mean particle radius, R i ) and N i is the number of spherical particles of radius R i required to trace the pore length generated by the same solid mass in a natural structure soil matrix. An estimate for log N i was obtained by either relating log N i to log n i using a logistic growth equation or by relating log N i linearly to log (w i /R 3 i ) based on the similarity principle. For any given texture, both approaches showed that α was not constant but decreased with increasing particle size, especially for the coarse fractions. In addition, α was also calculated as a single-value average for a given textural class. The three formulations of α were evaluated on 23 soils that represented a range in particle-size distribution, bulk density, and organic matter content. The average α consistently predicted higher pressure heads in the wet range and lower pressure heads in the dry range. The formulation based on the similarity principle resulted in bias similar to that of the constant a approach, whereas no bias was observed for the logistic growth equation. The logistic growth equation implicitly accounted for bias in experimental procedures, because it was fitted to log N i values computed from experimental soil water characteristic data. The formulation based on the similarity principle is independent of bias that might be inherent in experimental data.

Temporal variation in ped shape in an old pasture soil

Ped shape can influence soil physical properties by determining the morphology of interpedal pores. Monthly variation in soil ped shape was investigated in conjunction with soil physical properties for a pasture soil uncultivated for some years with the intension of identifying whether simple relationships existed between ped shape parameters and physical properties. A brief description of the ped shape quantification method used (Holden, 1993) and results obtained are presented, in conjunction with methods and data for hydraulic conductivity, soil water release, ped stability and microbial biomass determinations. Results indicate significant variation in ped surface roughness throughout one calendar year, but no significant changes in gross shape. Correlations between surface roughness and other soil properties are presented, the most important being the relationship identified between ped surface roughness and soil water characteristic data for a tension of 10 kPa. A significant correlation between ped shape and hydraulic conductivity could not be established. It is concluded that seasonally driven change in ped shape occurs, but that this is poorly related to bulk physical properties of the soil by simple relationships.

A comparison of various methods to scale soil hydraulic properties

Soil water characteristic curves and hydraulic conductivity functions at 57 locations were scaled by various methods. Optimum scaling results were obtained when pressure head and conductivity data were scaled by different methods. Criteria that supported this conclusion were: (1) the percent reduction in sum of squares; (2) the correlation between unscaled and estimated pressure head values as obtained from the scaled mean hydraulic functions; and (3) correlation between scaling factors determined from soil water characteristic data and those determined from hydraulic conductivity data. Both sets of scaling factors were found to be lognormally distributed. A correlation of R 2 = 0.761 was obtained between scaling factors determined from soil water characteristic data and those determined from hydraulic conductivity data. For the optimum scaling methods, the sum of squares, about the average curve, was reduced by 73 and 42%, for the water characteristic and hydraulic conductivity data, respectively. The results suggest that scaling can successfully be used for describing the variability of soil hydraulic properties of different soil map units and horizons.

Estimating Generalized Soil‐water Characteristics from Texture

AbstractSoil‐water potential and hydraulic conductivity relationships with soil‐water content are needed for many plant and soil‐water studies. Measurement of these relationships is costly, difficult, and often impractical. For many purposes, general estimates based on more readily available information such as soil texture are sufficient. Recent studies have developed statistical correlations between soil texture and selected soil potentials using a large data base, and also between selected soil textures and hydraulic conductivity. The objective of this study was to extend these results by providing mathematical equations for continuous estimates over broad ranges of soil texture, water potentials, and hydraulic conductivities. Results from the recent statistical analyses were used to calculate water potentials for a wide range of soil textures, then these were fit by multivariate analyses to provide continuous potential estimates for all inclusive textures. Similarly, equations were developed for unsaturated hydraulic conductivities for all inclusive textures. While the developed equations only represent a statistical estimate and only the textural influence, they provide quite useful estimates for many usual soil‐water cases. The equations provide excellent computational efficiency for model applications and the textures can be used as calibration parameters where field or laboratory soil water characteristic data are available. Predicted values were successfully compared with several independent measurements of soil‐water potential.

Scaling Soil Hydraulic Properties of a Heterogeneous Field

AbstractField data for soil water characteristics and hydraulic conductivity have been scaled by two methods. The set of scaling factors (αj) determined by one method depends on soil water content. A correlation of r2 = 0.86 is obtained between scaling factors determined from soil characteristic data and those determined from hydraulic conductivity data. In the second method, the set of αj is normally distributed, and is highly correlated with αj obtained by the first method from water characteristic data at water contents near saturation. In one method the sum of squares, about an average curve, was reduced by 44 and 66%, for the water characteristics and hydraulic conductivities data, respectively, while 92 and 82.5% reduction was obtained by the second method, for the same respective data. The results suggest that in practice, a single stochastic variable may represent the hydraulic properties of a heterogeneous field.

Scaling field‐measured soil hydraulic properties using a similar media concept

With a view toward developing techniques for studying water flow in spatially varying soils, field data for soil water characteristic relationships and unsaturated hydraulic conductivity are scaled by using the concept of similar media. Data observed by different investigators at three geographic areas are used. The soil water characteristic data consisted of 840, 900, and 512 observations, while those for the unsaturated hydraulic conductivity (available from only one of the three sources) consisted of 2640 observations. In the process of scaling the data a best fit for the scaled data is defined in terms of a sum of squares about an ‘average’ curve using one value for the scaling parameter for each sampling location. Comparisons made between curves fitting the data and those fitting the scaled data show that scaling reduces the sums of squares by amounts varying from 34 to over 90%. For similar media the scaling parameter determined at a given sampling location for the soil water characteristic relationship (α from h(S)) should be identical to that for the unsaturated hydraulic conductivity (αr from K(S)). Although these parameters were highly correlated (r = 0.91 ) for the only set of data available, α r from h(S) values are shown to be more effective in scaling the unsaturated hydraulic conductivity data than are αr from K(S) values in scaling soil water characteristic data.

Evaluation of a Capillary Bundle Model for Describing Solute Dispersion in Aggregated Soils

AbstractA simple capillary bundle model was evaluated for describing solute dispersion in two well‐aggregated soils of Hawaii. The model enables the use of pore‐water velocity distribution rather than an average pore‐water velocity. The pore‐size distribution was calculated from the soil water characteristic data. The position and relative shape of the break‐through curves calculated by the capillary bundle model was dependent more on the pore‐water velocity distribution than on dispersion owing to mixing within a pore. The predicted breakthrough curves were extremely skewed and did not describe the measured curves. The mixing of solute between adjacent flow paths, a process not accounted for in the model, apparently resulted in failure of the model. A measure of pore accessibility and interconnectedness of pore sequences is essential for quantitative description of the influence of soil pore geometry on solute dispersion.

SPATIAL VARIABILITY OF IN SITU UNSATURATED HYDRAULIC CONDUCTIVITY OF MADDOCK SANDY LOAM

Unsaturated soil hydraulic conductivity (K) versus depth was measured in situ in five infiltration plots within a 0.01 hectare area on a soil developed from lacustrine materials in a glacial lake bed. Sufficient water was allowed to infiltrate each plot to wet the soil profile to 152 cm. The soil surface was covered to prevent evaporation and during the ensuing drainage period, soil water pressure was monitored with triplicate tensiometers at each depth of 15, 30, 45, 61, 91, 122, and 152 cm. Soil water characteristic data determined on triplicate cores taken from the same depth as the tensiometer cups, were used in conjunction with the soil water pressure head data to compute the hydraulic conductivity. Significant spatial variability of hydraulic conductivity at the 1 percent level was found. In addition, K significantly varied with depth, at the 1 percent level, generally increasing, due to the heterogeneous nature of soil in the vertical direction. Hydraulic conductivity as a function of porosity was computed for each site by the modified Green and Corey method. Agreement of these theoretical K values with those measured in situ depended on the soil water content at which the matching factor was selected. Agreement between methods was best when the matching factor was selected at the lowest water content at which K was measured in the field for that particular soil depth.