Soil Morphological Data Research Articles

Derivation of physically based soil hydraulic parameters in New Zealand by combining soil physics and hydropedology

AbstractField‐characterised soil morphological data (to 1 m depth) and modelled soil water release characteristics are recorded in the S‐map database for soils covering approximately 40% of New Zealand's soil area. This paper shows the development of the Smap‐Hydro database that estimates hydraulic parameters by synergising soil morphologic data recorded in S‐map and soil physics. The Smap‐Hydro parameters were derived using the bi‐modal Kosugi hydraulic function. The validity of the Smap‐Hydro parameters was tested by applying them within an uncalibrated physically based hydrological model (HyPix) and comparing results with soil water content, θ, measured with Aquaflex soil moisture probes (0–40 cm deep) at 24 sites across New Zealand. The HyPix model provided an excellent fit with observed soil water content for 25% of the sites, a good fit for 33% of the sites and a poor fit for 42% of the sites. Applying the model to all soils in the S‐map database required adjustments for the occurrence of rock fragments, hydraulic discontinuities caused by soil pans and required the addition of boundary conditions for water tables and the occurrence of impermeable rock. A discussion on how we can further synergise the development of pedotransfer functions with knowledge of soil physics is provided.

Информационный потенциал разрушенных погребений саргатской культуры: курган Новопокровка 16 в Среднем Прииртышье

The article deals with a complex study of the materials obtained during archaeological excavation of the kurgan Novopokrovka 16 in the middle Irtysh river basin (Omsk region). The site, which is attributed to the Sargat culture (5th century BC – second half of the 3rd century AD), was located farmost on the right bank of the river Irtysh, nearby the group of “Princely kurgans”. Despite almost total disturbance by robbers and agricultural activity, common scholars’ efforts demonstrate high informative capacity of the multidisciplinary approach, while modern level of undertaken research is not just being declared but enables to reconstruct seemingly lost information. The kurgan erection stages and mound structure features have been decoded using the results of a geophysical survey and soil morphology data. The paleoanthropological study of the incomplete skeletal remains does not support multiple or inlet interment inside the central grave, and provides sexing and aging of the buried individuals as well as some paleopathological observations. Apart from species examination, archaeozoological data testify that a warm period from spring to early autumn was the season of animal slaughter. Characteristics of mortuary rituals and direction of intercultural contacts of the ancient forest-steppe groups have been completed by new details based on the results of strontium isotope analyses (dental enamel of humans and animals) and evidence non-local origin of the individual buried under the excavated kurgan, what is different from local origin of horses butchered for funereal feasts. The proposed hypothesis supports previously suggested archaeological, paleoanthropological and paleogenetic facts on the origin of the forest-steppe population, indicating an involvement of various population groups with notable external components. Based on the archaeological materials, the kurgan under study might be dated back to mid-4th – 3rd centuries BC.

A minimum data set of soil morphological properties for quantifying soil quality in coastal agroecosystems

Soil quality in coastal agroecosystems changes rapidly owing to coastal dynamics and land-use change. The potential of soil morphological properties as soil quality indicators is mostly unexplored. We present a minimum data set (MDS) of soil morphological properties to quantify the coastal soil quality. We compiled a dataset including 18 soil morphological properties from 468 soil profiles, representing five land-use types (plantation crops, sugarcane, grassland, rice, and cotton + pigeon pea) of the north-western coastal region of India. The categorical variables were transformed into numerical variables using the optimal scaling method, and categorical principal component analysis (CATPCA) was used to identify the MDS. The CATPCA produced five principal components explaining 60% of the variability. The MDS comprises pore abundance, structure size, drainage, pore size, and colour (value) with 32, 22, 21, 14, and 12% contribution to soil quality, respectively. The morphological soil quality index (MSQI) varied from 0.26 to 0.99 for surface soils, and from 0.11 to 0.94 for the subsurface soils. Among the land-use types, the rice-growing soils were low in their morphological quality due to structural degradation. Land-use types significantly influenced the MSQI in both surface and subsurface soils, and hence, we recommend the inclusion of subsurface soils for soil quality evaluation. The strong relationship of MSQI with saturated hydraulic conductivity (R2 = 0.56) validated the suitability of the MDS for assessment of soil quality by the farmers, and non-experts in the coastal regions. Further, the higher variability explained by the soil morphology data indicates that the MDS identified in this study could be effectively used to evaluate soil quality in areas with limited data.

Validating a spatially distributed hydrological model with soil morphology data

Abstract. Spatially distributed models are popular tools in hydrology claimed to be useful to support management decisions. Despite the high spatial resolution of the computed variables, calibration and validation is often carried out only on discharge time series at specific locations due to the lack of spatially distributed reference data. Because of this restriction, the predictive power of these models, with regard to predicted spatial patterns, can usually not be judged. An example of spatial predictions in hydrology is the prediction of saturated areas in agricultural catchments. These areas can be important source areas for inputs of agrochemicals to the stream. We set up a spatially distributed model to predict saturated areas in a 1.2 km2 catchment in Switzerland with moderate topography and artificial drainage. We translated soil morphological data available from soil maps into an estimate of the duration of soil saturation in the soil horizons. This resulted in a data set with high spatial coverage on which the model predictions were validated. In general, these saturation estimates corresponded well to the measured groundwater levels. We worked with a model that would be applicable for management decisions because of its fast calculation speed and rather low data requirements. We simultaneously calibrated the model to observed groundwater levels and discharge. The model was able to reproduce the general hydrological behavior of the catchment in terms of discharge and absolute groundwater levels. However, the the groundwater level predictions were not accurate enough to be used for the prediction of saturated areas. Groundwater level dynamics were not adequately reproduced and the predicted spatial saturation patterns did not correspond to those estimated from the soil map. Our results indicate that an accurate prediction of the groundwater level dynamics of the shallow groundwater in our catchment that is subject to artificial drainage would require a model that better represents processes at the boundary between the unsaturated and the saturated zone. However, data needed for such a more detailed model are not generally available. This severely hampers the practical use of such models despite their usefulness for scientific purposes.

Building and testing conceptual and empirical models for predicting soil bulk density

AbstractThe development of pedotransfer functions offers a potential means of alleviating cost and labour burdens associated with bulk‐density determinations. As a means of incorporating a priori knowledge into the model‐building process, we propose a conceptual model for predicting soil bulk density from other more regularly measured properties. The model considers soil bulk density to be a function of soil mineral packing structures (ρm) and soil structure (Δρ). Bulk‐density maxima were found for soils with approximately 80% sand. Bulk densities were also observed to increase with depth, suggesting the influence of over‐burden pressure. Residuals from the ρm model, hereby known as Δρ, correlated with organic carbon. All models were trained using Australian soil data, with limits set at bulk densities between 0.7 and 1.8 g cm−3 and containing organic carbon levels below 12%. Performance of the conceptual model (r2 = 0.49) was found to be comparable with a multiple linear regression model (r2 = 0.49) and outperformed models developed using an artificial neural network (r2 = 0.47) and a regression tree (r2 = 0.43). Further development of the conceptual model should allow the inclusion of soil morphological data to improve bulk‐density predictions.

Computer processing of soil profile data from surveys in New Zealand

Special field description cards were designed for recording soil morphological data. Data from these cards were punched straight onto paper-tape for transfer to a computer system. This system provided checking and editing facilities and produced longhand profile descriptions equivalent to the data on each card, various indexes to the date, and information on the variability of each soil taxonomic unit described. The system reduced the time taken in data preparation for writing reports by up to one third. Other advantages were that cards of uniform design helped to achieve a consistent standard of profile description and reduced the time required to make a description.