Spatial Heterogeneity Of Soil Properties Research Articles

Physical property responses of soils subjected to different degrees of erosion and seasonal freeze-thaw cycles in Northeast China

Changes in soil pores and aggregate stability due to freeze-thaw cycles (FTCs) are important causes of increased soil erosion during snowmelt. Soil erosion causes spatial redistribution of soils, enhancing soil heterogeneity and potentially impacting soil responses to FTCs. Nonetheless, there is minimal knowledge of the responses of soils subjected to different degrees of erosion to seasonal FTCs. To reveal the impact of seasonal FTCs, the dynamic variations of pore characteristics and aggregates of soils with four different degrees of erosion (original, degraded, deposited and parent soil) were measured, and the connections between influencing factors and soil properties were analyzed. The results showed that FTCs altered the structure of the soils and weakened their resistance to erosion and that soils with different degrees of erosion responded differently to FTCs. After seasonal FTCs, soil porosity increased (0.4 %-11.9 %) to some extent in all soils, with greater changes observed in the more eroded soils. Notably, capillary porosity exhibited a complex changing trend compared to total porosity. Degraded and parent soils showed a stable bulk density, while the original soil showed a decrease (2.1 %) in bulk density and the deposited soil showed an increase (18.4 %) in bulk density. With the increase of FTCs, the field capacity of original, degraded, and deposited soils exhibited a gradual decrease (15.1 %-18.5 %), while that of the parent soil slightly increased (0.9 %). After seasonal FTCs, the saturated hydraulic conductivity decreased for original and deposited soils (19.5 %-41.5 %), while it increased for degraded and parent soils (29.2 %-41.6 %). Throughout the FTCs, the proportion of the large aggregates decreased and the small aggregates increased, and the transformation was greater on the less eroded soils. The mean weight diameter and geometric mean diameter of the soils gradually decreased with increasing FTCs, while the change was smaller for the more eroded soils. After seasonal FTCs, the less eroded soils were at greater risk of erosion. Our results demonstrated that the number of FTCs had a more significant impact on soil physical properties compared to the temperature difference and soil water content. Overall, freeze-thaw action reinforced the spatial heterogeneity of soil properties, potentially intensifying soil erosion. These findings help reveal the effects of FTCs on the physical properties of soils with different degrees of erosion and deepen the understanding of the mechanism of FTCs on soil erosion processes.

Assessing and mitigating health risks of workers exposed to volatile organic compounds in contaminated soils during active pit excavation: Accounting for exposure variability and uncertainty

Health risks to workers involved in the active pit excavation of volatile organic compound (VOC)-contaminated soils have been overlooked, despite the prevalence of ex-situ remediation practices in China. This study developed a new VOC exposure in pit excavation (VEPE) model that comprehensively accounts for the release of gas-phase VOCs in soil pores, surface volatilization of newly exposed agglomerates, and volatilization of newly exposed excavation pit bottom surfaces. Additionally, the model incorporates the concentration variation of soil contaminants, spatial heterogeneity of soil properties, and the variability and uncertainty of worker exposure using two-dimensional Monte Carlo simulation (2D MCS), highlighting the importance of controlling area of new exposure surfaces, soil saturation, and air exchange during the pit excavation to mitigate risks. The study evaluated worker-health-based risks during the excavation of a VOC-contaminated site in Beijing and revealed that the inhalation cancer risk for trichloroethylene (2.35 × 10−6) and its hazard quotient (143.92) exceed the acceptable levels (10−6 and 1.0, respectively). The comparison of models demonstrated the theoretical advancements of the VEPE model over existing pit excavation models, while the field test comparison showed a significant improvement in the predication accuracy. This study addresses the under-researched area of worker safety in contaminated excavation sites. The findings have the potential to promote cleaner and more sustainable practices in ex-situ contaminated site remediation by providing valuable insights for implementing effective control measures to mitigate the emission of toxic VOCs, thereby reducing adverse social and environmental impacts.

Soil organic carbon as an indicator of land use impacts in life cycle assessment

PurposeAnthropogenic activities are a major driver of soil and land degradation. Due to the spatial heterogeneity of soil properties and the global nature of most value chains, the modelling of the impacts of land use on soil quality for application in life cycle assessment (LCA) requires a regionalised assessment with global coverage. This paper proposes an approach to quantify the impacts of land use on soil quality, using changes in soil organic carbon (SOC) stocks as a proxy, following the latest recommendation of the Life Cycle Initiative.MethodsAn operational set of SOC-based characterisation factors for land occupation and land transformation were derived using spatial datasets (1 km resolution) and aggregated at the national and global levels. The developed characterisation factors were tested by means of a case study analysis, investigating the impact on soil quality caused by land use activities necessary to provide three alternative energy supply systems for passenger car transport (biomethane, ethanol, and solar electricity). Results obtained by applying characterisation factors at local, regional, and national levels were compared, to investigate the role of the level of regionalisation on the resulting impacts.Results and discussionGlobal maps of characterisation factors are presented for the 56 land use types commonly used in LCA databases, together with national and global values. Urban and industrial land uses present the highest impacts on SOC stocks, followed by severely degraded pastures and intensively managed arable lands. Instead, values obtained for extensive pastures, flooded crops, and urban green areas often report an increase in SOC stocks. Results show that the ranking of impacts of the three energy systems considered in the case study analysis is not affected by the level of regionalisation of the analysis. In the case of biomethane energy supply, impacts assessed using national characterisation factors are more than double those obtained with local characterisation factors, with less significant differences in the other two cases.ConclusionsThe integration of soil quality aspects in life cycle impact assessment methods is a crucial challenge due to the key role of soil conservation in ensuring food security and environmental protection. This approach allows the quantification of land use impacts on SOC stocks, taken as a proxy of soil quality. Further research needs to improve the assessment of land use impacts in LCA are identified, such as the ability to reflect the effects of agricultural and forestry management practices.

Erosion and deposition regulate soil carbon by mediating Fe-Carbon fixation mode in a typical catchment in the black soil region of Northeastern China

Soil carbon is critical to soil quality and the global carbon cycle, while Fe is highly sensitive to redox processes and can affect soil carbon stocks by sequestrating and releasing carbon. Soil erosion and deposition can cause the spatial heterogeneity of soil properties along landscapes and alter the redox environment, thus further influence carbon fixation by Fe in situ. Carbon, especially Fe-oxide-bound carbon (OC-Fe) were poorly studied in different erosion intensities. Here, we investigated OC-Fe, soil organic carbon (SOC), and inorganic carbon (SIC) contents at erosion and deposition sites in a typical catchment of Northeast China, and evaluated the responses of oxygen diffusion state (DCo), Fe oxides (Fep, Fed) and valence state of Fe (Fe(II), Fe(III)) to erosion and deposition. We have also quantified factors affecting Fe and carbon to elucidate the drivers of Fe-mediated carbon stock variations induced by erosion. Results showed that with a higher erosion intensity, a greater redistribution was emerged on OC-Fe and SIC in the 0–10 cm soil layer of study catchment. Compared to deposition sites, OC-Fe and SIC increase by 63.32 % and 11.41 times at erosion sites, respectively. However, SOC increased 18.56 % at deposition sites. In the 10–20 cm soil layer, where erosion forces were more pronounced, the binding mode of Fe oxides to carbon changed from co-precipitation to adsorption. The presence of Fe(II) promoted OC-Fe formation. Structural equation modelling（SEM）constructed by the varied particle composition with erosion and deposition explained 79 % of the variation in carbon stock in 0–20 cm soil layer. This study concluded that subsoil carbon stocks (>10 cm) are mainly affected by the application rate of exogenous carbon and variations of its mineral content. Erosion-induced variations of carbon sequestration patterns have continuous effects on farmland carbon turnover capacity.

Analysis of spatial heterogeneity of soil physicochemical properties in northern Malawi

Topography and fertility management practices are critical when examining distribution of spatial heterogeneity of soil properties. The study was carried out on a 52-ha land with undulating topography of a village in Northern Malawi to analyze spatial soil heterogeneity with attention to topography and soil fertility management practices in 2021. The spatial heterogeneity of 12 soil chemical properties and soil texture were examined using geostatistical analysis. At 0–20 cm depth, 115 geocoded surface soil samples were collected. Remotely sensed images were captured using an unmanned aerial vehicle, processed, and analyzed to generate and visualize topographic data. Soil fertility management practices were described using field observations and questionnaire survey. The geostatistical analysis revealed spatial heterogeneity of most soil properties at a scale of 80–650 m. The spatial structure of soil pH, available phosphorus (P), total carbon (TC), total nitrogen (TN), and clay showed strong spatial autocorrelation (Q value <0.25). Calcium (Ca2+) and potassium (K+) had moderate spatial autocorrelations (Q values >0.25–< 0.75), whereas magnesium (Mg2+), sodium (Na+), sand, and silt had weak spatial autocorrelations (Q value >0.75). The spatial heterogeneity in TC, K+, Mg2+, CEC, clay, silt, and sand was influenced by the area's topography, whereas pH, P, TN, and Ca2+ were influenced by soil fertility management practices. While change can be negative or positive, differences in topography and fertility management practices significantly changed the physicochemical soil properties, and consequently affected their distribution. TN and P increased with the application of fertilizer, and manure, respectively. Increase in elevation and slope induced positive changes in CEC, K+, Mg2+ and clay. These results could be helpful in the development and application of precision agriculture in the village.

Implementing Cloud Computing for the Digital Mapping of Agricultural Soil Properties from High Resolution UAV Multispectral Imagery

The spatial heterogeneity of soil properties has a significant impact on crop growth, making it difficult to adopt site-specific crop management practices. Traditional laboratory-based analyses are costly, and data extrapolation for mapping soil properties using high-resolution imagery becomes a computationally expensive procedure, taking days or weeks to obtain accurate results using a desktop workstation. To overcome these challenges, cloud-based solutions such as Google Earth Engine (GEE) have been used to analyze complex data with machine learning algorithms. In this study, we explored the feasibility of designing and implementing a digital soil mapping approach in the GEE platform using high-resolution reflectance imagery derived from a thermal infrared and multispectral camera Altum (MicaSense, Seattle, WA, USA). We compared a suite of multispectral-derived soil and vegetation indices with in situ measurements of physical-chemical soil properties in agricultural lands in the Peruvian Mantaro Valley. The prediction ability of several machine learning algorithms (CART, XGBoost, and Random Forest) was evaluated using R2, to select the best predicted maps (R2 &gt; 0.80), for ten soil properties, including Lime, Clay, Sand, N, P, K, OM, Al, EC, and pH, using multispectral imagery and derived products such as spectral indices and a digital surface model (DSM). Our results indicate that the predictions based on spectral indices, most notably, SRI, GNDWI, NDWI, and ExG, in combination with CART and RF algorithms are superior to those based on individual spectral bands. Additionally, the DSM improves the model prediction accuracy, especially for K and Al. We demonstrate that high-resolution multispectral imagery processed in the GEE platform has the potential to develop soil properties prediction models essential in establishing adaptive soil monitoring programs for agricultural regions.

Soil Organic Carbon Stock Prediction: Fate under 2050 Climate Scenarios, the Case of Eastern Ethiopia

Soil Organic carbon (SOC) is vital to the soil’s ecosystem functioning as well as improving soil fertility. Slight variation in C in the soil has significant potential to be either a source of CO2 in the atmosphere or a sink to be stored in the form of soil organic matter. However, modeling SOC spatiotemporal changes was challenging due to lack of data to represent the high spatial heterogeneity in soil properties. Less expensive techniques, digital soil mapping (DSM) combined with space-for-time substitution (SFTS), were applied to predict the present and projected SOC stock for temperature and rainfall projections under different climate scenarios represented by the four Representative Concentration Pathways (RCPs): RCP2.6, RCP4.5, RCP6, and RCP8.5). The relationship between environmental covariates (n = 16) and measured SOC stock (148 samples) was developed using a random forest model. Then, the temporal changes in SOC stock over the baseline were developed for the top 30 cm soil depth of the selected districts (Chiro Zuria, Kuni, Gemechis and Mieso) of West Hararghe Zone at 30 m resolution. The model validation using the random sample of 20% of the data showed that the model explained 44% of the variance (R2) with a root mean square error (RMSE) of 8.96, a mean error (ME) of 0.16, and a Lin’s concordance correlation coefficient (CCC) of 0.88. Temperature was the most important predictor factor influencing the spatial distribution of SOC stock. An overall net gain of SOC stock over the present C stock was expected in the study area by 2050. The gain in areas with the lower baseline SOC stock counterbalanced the loss in areas with the higher baseline stock. The changes in the SOC stock depended on land use land cover (LULC), soil type, and agro-ecological zones. By 2050, cropland is supposed to lose its SOC stock under all RCPs; therefore, appropriate decisions are crucial to compensate for the loss of C.

Spatial heterogeneity of soil properties of recreational areas of Volgograd

The article presents the developed soil maps and maps of spatial variability of soil properties (pH, total salt and carbonate content, organic carbon, density) in some recreational areas of Volgograd. The anthropogenic soils in the studied recreational areas include various urbostratozems (Urbic Technosols (Transportic)), Ekranic Technosols (Transportic), as well as quasizems (Phaeozems (Tehnic)), among the agrogenic ones – accumulation-carbonate agrozems (Cambisols (Aric, Protocalcic). Natural soils are represented by light and dark humus soils, chestnut soils, solonetz, as well as stratozems and psammozems. According to the data of isolinear mapping, it was found that acid-alkaline properties of soils of green zones vary from slightly alkaline to alkaline depending on the soil horizon and anthropogenic load. The mapping did not reveal salinization of the upper 0–30 cm layer. Values of organic carbon vary greatly (1–10%) and are conditioned by the presence or absence of meliorative artificial layers, as well as by the character of territory use. The content of calcium carbonates in the upper 30 cm is small and varies from 0.7 to 5%. It is established that the physical properties (density) directly depend on the level of anthropogenic load, increasing in footpaths and lawns. Spatial assessment of the properties of urban soils is important for identifying areas with an excess or on the contrary – low content of certain chemical and physical indicators for the planning of landscaping and gardening of urban areas.

Comprehensive assessment of soil heterogeneity by crop canopy status

One of the most important applications of soil knowledge is in agricultural processes and crop production. Crop and forage yields strongly depend on soil conditions. Therefore, accurate soil information is an important component of informational support for agricultural production. A soil map is used as the basis for obtaining the soil data of a land plot. But the information on the soil map in many cases does not reflect the spatial variation of all agronomically important properties of soils, which leads to oversimplified and inaccurate analysis of soil information when making management decisions. A new methodology of applied soil mapping for agricultural purposes, based on a detailed analysis of the state of crops in a crop rotation with the use of multi-year satellite data archives, is proposed. The maps of soil inhomogeneity of individual fields, obtained on the basis of the proposed approaches, differ significantly from the traditionally developed soil maps and more accurately reflect the spatial heterogeneity of soil properties, predetermining their actual fertility.

습지 생태계에서 사면유형에 따른 토양특성의 공간적 이질성 : 고창 운곡 습지를 사례로

본 연구는 전라북도 고창군 운곡습지를 대상으로 지형경관의 시공간적 변화를 살펴보고, 습지 생태계에서 지형에 따른 토양의 공간적 이질성을 파악하고자 한다. 이를 위해 지형 · 수문학적 조건을 결정하는 위치적 · 형태적 특징에 따라 summit, shoulder, backslope, footslope, toeslope 등 5가지 사면유형으로 구분하였다. 더불어 운곡습지의 전반적인 토양특성을 확인하기 위해 사면유형별 토양 조사를 실시하였다. 사면유형에 따라 각 토양특성의 공간적 분포를 살펴보면 모래 함량은 사면하부로 갈수록 낮아지다 높아지는 U자형 패턴이 나타나고 미사 함량의 분포는 반대의 경향을 보였다. 유기물 함량의 경우 상부 사면에서 하부 사면으로 갈수록 낮아졌다. pH는 하부 사면으로 갈수록 대체로 높아지는 경향을 보이지만 backslope에서 가장 큰 값을 보였고, 유효인산은 toeslope에서 가장 크게 나타났다. 또한 주변 전라북도 논토양과 산림토양 그리고 운곡습지의 저습지에 해당하는 toeslope의 유기물 함량을 비교한 결과, 저습지가 각각 4.3배, 1.6배 더 큰 값을 보였다. 현재 운곡습지는 교란 후 생태회복이 진행되는 과정에 있다고 본다면 향후 저습지에 더 많은 유기물이 축적되면서 현재보다 높은 토양 유기물 함량을 보일 수 있다. 따라서 운곡습지의 탄소 격리 기능은 더욱 강화될 것으로 판단된다. 본 연구를 통해 운곡습지와 같은 비슷한 지형 및 환경 조건을 갖춘 묵논이나 논을 대상으로 생태복원을 시도한다면 효과가 커질 수 있을 것으로 기대한다.

Quantifying the Spatial Distribution of Soil Nitrogen under Long-Term Drip Fertigation

Quantifying the spatial distribution of nitrogen (N) in the soil under long-term drip fertigation events is essential for the optimal regulation of drip fertigation systems. In this study, a greenhouse soil that had been under drip irrigation for 20 years was selected as the research object, and soil samples were collected from the 0–50 cm soil depth. The concentrations of N in the soil samples were measured and their spatial distribution characteristics were quantified by classical statistical analysis and multifractal analysis. The results showed that long-term drip fertigation and the influence of natural factors resulted in the nitrate N mainly accumulating in the shallow layer of the soil and within a distance from the drip irrigation belt, and the spatial heterogeneity gradually decreased with increasing depth. The content of ammonium N was low, and its distribution was observed in the whole section. Multifractal analysis indicated that the Δα value of nitrate N and inorganic N gradually increased with the increase in the research scale, i.e., the spatial heterogeneity gradually increased, and it did not appreciably change for ammonium N. Meanwhile, the local high value region was the main factor leading to the spatial heterogeneity of N, and this dominant effect gradually increased with increasing depth. Multifractal analysis can effectively reflect the local information of the N spatial distribution in the soil and provide a more detailed description of the spatial heterogeneity of soil properties.

Spatial variability of soil available potassium in rubber plantation based on coKriging

The coKriging method of selecting effective variables is helpful to improve the spatial prediction accuracy of soil available potassium in county-scale rubber plantation, which is of significance in precision fertilization management of rubber plantation. In this study, we analyzed the spatial variability characteristics of soil available potassium in 0-20 cm layer in the rubber plantation of Baisha County, Hainan Province, by geostatistics. The significant characteristic variables were screened by correlation analysis, and the spatial interpolation precisions of coKriging (COK) of different variables were compared. The results showed that the average soil available potassium content in the study area was 44.65 g·kg-1, generally at a state of shortage. The variable coefficient was 52.6%, which was a moderate intensity of variation. The nugget effect was 12.5%, with a strong spatial autocorrelation. The organic matter and elevation were closely related to soil available potassium content. The COK spatial interpolation prediction precisions of the three covariates of organic matter (COK1), elevation (COK2), and organic matter+elevation (COK3) were all higher than ordinary Kriging (OK), and the fitting precision of the cross-validation model was COK1&gt;COK3&gt;COK2&gt;OK. The fitting precision was not proportional to the number of covariates selected. Selecting more correlated covariates was more conducive to reflecting the spatial heterogeneity of soil properties. The soil available potassium content was higher in the northwest and lower in the central and eastern regions, which provided a theoretical basis for the further development of soil potassium management in rubber plantations.

Probabilistic modelling of the inherent field-level pesticide pollution risk in a small drinking water catchment using spatial Bayesian belief networks

Abstract. Pesticides are contaminants of priority concern that continue to present a significant risk to drinking water quality. While pollution mitigation in catchment systems is considered a cost-effective alternative to costly drinking water treatment, the effectiveness of pollution mitigation measures is uncertain and needs to be able to consider local biophysical, agronomic, and social aspects. We developed a probabilistic decision support tool (DST) based on spatial Bayesian belief networks (BBNs) that simulates inherent pesticide leaching risk to ground- and surface water quality to inform field-level pesticide mitigation strategies in a small (3.1 km2) drinking water catchment with limited observational data. The DST accounts for the spatial heterogeneity in soil properties, topographic connectivity, and agronomic practices; the temporal variability of climatic and hydrological processes; and uncertainties related to pesticide properties and the effectiveness of management interventions. The rate of pesticide loss via overland flow and leaching to groundwater and the resulting risk of exceeding a regulatory threshold for drinking water was simulated for five active ingredients. Risk factors included climate and hydrology (e.g. temperature, rainfall, evapotranspiration, and overland and subsurface flow), soil properties (e.g. texture, organic matter content, and hydrological properties), topography (e.g. slope and distance to surface water/depth to groundwater), land cover and agronomic practices, and pesticide properties and usage. The effectiveness of mitigation measures such as the delayed timing of pesticide application; a 10 %, 25 %, or 50 % reduction in the application rate; field buffers; and the presence/absence of soil pan on risk reduction were evaluated. Sensitivity analysis identified the month of application, the land use, the presence of buffers, the field slope, and the distance as the most important risk factors, alongside several additional influential variables. The pesticide pollution risk from surface water runoff showed clear spatial variability across the study catchment, whereas the groundwater leaching risk was uniformly low, with the exception of prosulfocarb. Combined interventions of a 50 % reduced pesticide application rate, management of the plough pan, delayed application timing, and field buffer installation notably reduced the probability of a high risk of overland runoff and groundwater leaching, with individual measures having a smaller impact. The graphical nature of BBNs facilitated interactive model development and evaluation with stakeholders to build model credibility, while the ability to integrate diverse data sources allowed a dynamic field-scale assessment of “critical source areas” of pesticide pollution in time and space in a data-scarce catchment, with explicit representation of uncertainties.

Effects of spatial variations in rock fragments related to tillage on hydrological processes and sediment transport

Cultivation activities play an important role in shaping the spatial distribution of rock fragments in hilly agricultural landscapes. The spatial variation in rock fragment contents (SVRFC) can exert strong effects on the spatial heterogeneity of soil properties with great implications for hydrological fluxes and soil erosion. However, previous research always assumed that rock fragments were homogenously distributed, but little has been known concerning the SVRFC over the hillslope and its influence on hydrological and erosional processes. In this study, simulated rock fragment contents (RFCs) in soil layers at the upper (0.6 kg kg−1) and lower slope positions (0, 0.2, 0.4 and 0.6 kg kg−1) were established in combination with artificial rainfall experiments, to investigate the influence of RFC difference between the upper and lower slope positions on hydrological and erosional processes. Results show that the decreasing RFC difference reduced the yield of surface flow (5–100%), but increased yields of subsurface flow (58–792%) and deep percolation (19–130%). Additionally, the decreasing RFC difference reduced the runoff velocity of surface flow and surface erosion rate, but aggravated subsurface erosion. Especially under the 0 kg kg−1 RFC difference, the intense soil loss occurred at the subsurface which may be due to the highly developed preferential flow. With increasing rainfall intensity, great increments in yields of subsurface flow, deep percolation and subsurface sediments were found under the small RFC difference which had small increments in yields of surface flow and surface sediments. These results suggest that the changing SVRFC related to tillage erosion can exert strong influence on behaviors of hydrological and erosional processes. Our study provides a better understanding of the interaction between agricultural cultivation and soil–water loss in hilly agricultural landscapes.

До питання про роль рельєфу у формуванні просторової неоднорідності ґрунтових параметрів

The results of the study of the role of relief in the formation of the spatial heterogeneity of humus content, avalable compounds of phosphorus and potassium, the total content of nitrogen and pHH2O within the arable layer are highlighted. The study was carried out on two fields in the Kharkiv region in the zone of the Left-Bank Forest-Steppe of Ukraine. The soil cover of one of the fields is represented by gray forest (Haplic Luvisol) and dark gray podzolized (Luvic Greyzemic Phaeozem) soils, the second – by podzolized chernozem (Chernic Phaeozem) (including reclaimed) and dark gray podzolized (Luvic Gremizem) with eroded counterparts. According to the granulometric composition, all soils are heavy loamy. Relationships were found between the main indicators of soil fertility and the characteristics of the relief of the surveyed land plots, cartographic materials were created for the spatial distribution of nutrients using statistical methods of data processing. The existence of spatial heterogeneity of soil properties (Cv &gt; 25 %) was confirmed and factors influencing the features of their spatial distribution were determined. It was found that the existence of heterogeneous landforms and, in particular, the presence of microdepressions in the surveyed areas, causes an uneven distribution of precipitation, which significantly affects the formation of heterogeneity of soil quality within the field – the content of nutrients (nitrogen, phosphorus and potassium) and soil pH. The results show that the soil at high relief points and in micro-depressions is better provided with humus and available phosphorus compounds. The zones of available potassium and nitrogen enrichment coincide with depressions in the relief. The reason for the distribution of nutrients is the migration of flows of substances, the intensity of which depends on the topographic features of the territory. It was found that the soil at higher parts of the field is characterized by pHH2O values close to neutral. Relative acidification of the soil was found in areas with a sharp slope of the surface. Based on the results, recommended to take into account the peculiarities of the spatial distribution of soil properties in the practice of applying fertilizers and chemical ameliorants.

Anthropogenic erosion-induced small-scale soil heterogeneity in South African rangelands

Land-use-induced soil erosion in semi-arid drylands induced by land-use changes creates patchworks of soils and vegetation different from those of natural conditions. Knowledge of the highly dynamic spatial heterogeneity in soil properties, not depicted in conventional soil maps, is important for managing land uses sustainably, and for understanding soil-climate interactions. This analysis of soil redistribution in a degraded rangeland in South Africa assessed the significance of anthropogenic soil heterogeneity within a small catchment with a silted reservoir. This study carries out analysis of soil redistribution in a degraded rangeland in South Africa, to assess the significance of anthropogenic soil heterogeneity within a small catchment with a silted-up reservoir. Surface soil (N = 51) and soil profile (N = 29) samples were collected from areas of various degrees of degradation and analysed for texture, pH, total nitrogen (TN), total organic carbon (TOC), available phosphorus (P), and potassium (K). Diverse vegetation cover is reflected in a high soil heterogeneity, showing differences in soil texture. Average surface soil nutrient content was significantly higher in vegetated areas (grassland: TOC 1.08 %, TN 0.10 %, P 20.12 mg kg −1 , mixed vegetation: TOC 0.93 %, TN 0.08 %, P 13.54 mg kg −1 , depositional: TOC 1.68 %, TN 0.18 %, P 34.67 mg kg −1 ) than at degraded sites (TOC 0.47 %, TN 0.06 %, P 7.52 mg kg −1 ). K content was low to moderate but did not show any significant difference between landscape units. Potential exists for the formation of distinct young anthropogenic soils on the silted-up reservoir where deposited sediments differed in TOC, TN, P, and profile depth from the shallow natural soils. Consequently, azonal soils on dam-deposits are different from those occurring naturally, and not depicted on conventional soil maps. Their different water and nutrient cycling may affect vegetation and biogeochemical fluxes. Land-use management should consider such soils, as they play an important role in rangeland ecology.

High fire frequency reduces soil fertility underneath woody plant canopies of Mediterranean ecosystems

Spatial heterogeneity of soil properties plays a major role in regulating ecosystem structure and functioning. In general, soil resources accumulate beneath woody plant-covered patches more than in the open interspace, making them function as fertility islands. Whilst wildfire is a common disturbance, little information is available on the role of particular plant species in maintaining soil fertility underneath in areas that are subjected to recurrent fires. This is an important issue given that land abandonment, together with a warmer and drier climate, is increasing fire danger in regions such as the Mediterranean. We determined whether increasing fire frequency, producing changes from a Quercus ilex L., woodland to a shrubland, modifies the effect of woody plant canopy on soil fertility. Additionally, the effect of fire history on species-specific leaf and litter nutrient concentration was assessed. Areas affected by none, one, two or three fires were selected. Within each area, soil fertility was measured underneath Cistus ladanifer L., Retama sphaerocarpa L., Phillyrea angustifolia L. and Quercus ilex canopies and in open interspace. Unburned soils located underneath P. angustifolia and Q. ilex canopies were significantly more fertile than in open interspaces. The microsite effect on soil fertility was fire frequency dependent. As fire frequency increased, the plant canopy microsite effect decreased for soil organic matter (SOM), cation exchange capacity (CEC), total C, P, Ca, K and Mg, labile phosphate, arylsulfatase and acid phosphatase activities. Total N, ammonium, nitrate and β-glucosidase activity decreased with increasing fire frequency, but their spatial variability was maintained along all fire frequency scenarios. Fire frequency decreased foliar N concentration but increased P concentration in some species, leading to a decrease in their N:P ratio. Our findings suggest that soil fertility heterogeneity will be reduced with increasing fire frequency. This could compromise the recovery of soil and ecosystem functioning.

The role of spatiotemporal plant trait variability in model predictions of ecohydrological responses to climate change in a desert shrubland

Although spatial heterogeneity of soil properties, topography, and climate is commonly incorporated into ecohydrological models, the spatial and temporal variability in plant functional traits is typically overlooked. The objective of our study is to evaluate the impact of trait parameter variability on modeled ecohydrological processes. We implemented a model-data fusion approach to constrain spatiotemporally dynamic parameters in plant functional traits at two desert shrubland sites located along a topographic and climate gradient in the Mojave Desert. Our results showed that the estimates for specific leaf area and rooting depth for the broadleaf-evergreen-shrub plant-functional-type showed spatial variability, with lower specific leaf area and deeper rooting depth found at the low elevation site. We also found that the specific leaf area estimates changed over time at both sites in response to water stress, but with different sensitivities, possibly depending on species and/or climate. The spatial variability in trait parameter estimates was greater than temporal variability and played a more important role in accurately simulating ecohydrological processes, but including the temporal variability in specific leaf area further improved seasonal predictions. In simulations forced by future climate projections under the Representative Concentration Pathway 4.5 (RCP 4.5) and 8.5 (RCP 8.5) greenhouse gas emissions scenarios, spatial variability in trait parameters impacted predictions of both carbon and water fluxes, while temporal variability in trait parameters resulted in predictions of higher ecological function and water use efficiency. The higher water use efficiency led to improved ecohydrological function in simulations under RCP 4.5, but it showed little capacity for buffering intensive water stresses under the more pessimistic RCP 8.5 scenario, indicating that with spatiotemporally variable trait parameters, the impact on predicted ecohydrological processes depends on the climate projections. Overall, our modeling results prompt further field-based examination of temporal and belowground trait variability in desert shrublands, and they raise the question of how combined spatiotemporal variabilities of multiple traits may support ecohydrological function under water stress.

Multifractal and multiscale entropy scaling of in-situ soil moisture time series: Study of SMOSMANIA network data, southwestern France

Soil moisture processes exhibit a strong variability in space and time due to the variability of the meteorological forcing and the spatial heterogeneity of soil properties. This study aims at providing a statistical description of soil moisture variability by analyzing data from nine in situ stations located over an West-East gradient in southwestern France (SMOSMANIA stations, distributed over an area of about 300 × 200 km). For each station, four time series of soil moisture observed at four different depths ranging from 5 cm to 30 cm are analyzed. First, possible scaling properties are investigated within the Fourier domain with the help of spectral analysis tools. Red noise-like (1/f2) scaling properties could be noticed over a fairly wide scale range (1000 h–1 h) with relatively homogeneous scaling parameters for 5 cm depth soil moistures regardless of the station. These properties are confirmed at other depths with slightly steeper spectra and more heterogeneity across stations. In a second step, multifractal analysis has been carried out on the same data. Multifractal scaling is observed over a narrower scaling range (128 h–1 h). Moment scaling functions can be parameterized within the framework of Universal Multifractals: typical parameters are C1≈0.25 and α ≈ 1.6–1.8 for surface data while both parameters are subject to strong changes (C1 increases and α decreases) as the depth increases. In a third step, Multiscale Entropy (MSE) analysis has been applied in order to analyze the dataset from an information theory point of view and to infer whether multifractal properties could have a signature on MSE estimates. The MSE function has been found to follow a power law of the aggregation time with a scaling exponent close to 0.3–0.4 for surface data. These exponents were generally close to the Hurst exponent H estimated by first-order structure functions. While it is already known that MSE should follow scaling properties in the case of monofractal signals, the results suggest that the latter property holds for natural multifractal processes. Finally, complementary numerical tests based on synthetic multifractal time series are done in order to assess the relationship between the MSE scaling exponent H′ and multifractal parameters C1, α and H. A dependency on the three multifractal parameters has been observed, yet in practice the approximation H′ ≈ H seems somewhat acceptable for processes with relatively large Hurst exponents and/or low multifractal intermittency (C1). On a more broad perspective, the existence of relationships between multifractal scaling and MSE properties could help to refine the physical interpretation of observed scaling properties in many geophysical processes.

Estimating soil organic carbon and pH in Jilin Province using Landsat and ancillary data

AbstractRemote sensing provides possibilities to support digital soil mapping at large scales, but this technology over complex landscapes is still challenging due to the high spatial heterogeneity of soil properties. The objective of this work was to check the predictive capability of SOC and soil pH over different ecoregions. First, we collected a series of satellite images from 2017 and ancillary variables (e.g., climate and terrain) as spectral and environmental indicators, respectively. A total of 201 topsoil samples (0–20 cm) from the Northeastern Black Soil Region of China were also used. Then, the relationships between the two soil properties and indicators were analyzed. The indicators that showed significant correlation with SOC or soil pH were involved in the subsequent models. Finally, the SOC and soil pH prediction models were constructed by using a stepwise regression model. The results showed that the visible and shortwave infrared bands, along with the thermal infrared band, had strong correlations with the two soil properties. Environmental factors such as precipitation, temperature, and elevation all had an impact on the distribution of SOC and soil pH. The SOC and soil pH prediction models had R2 values of 0.66 and 0.73, root mean square errors of 0.21 and 0.58, and residual prediction deviations of 1.46 and 1.53, respectively, indicating reliable precision and average prediction abilities. This approach to estimating SOC and soil pH can contribute to improving our understanding and the modeling of soil processes at a large scale and in areas with relatively complex landscapes.