Spatial Variability Research Articles

Colonization & Decomposition Pattern of Water Borne Conidial Fungi in Freshwater Stream of Central Himalaya

In the freshwater streams the leaf litter substrate is colonized by early fungal colonizer and helps in decomposition of leaf litter and makes the substrate more suitable for the recruitment of late successional species. The study was carried out at Central Himalayan stream (Vinayak) to examine the colonization and decomposition pattern of Water Borne Conidial Fungi as all the streams are naturally subjected to great spatial and temporal variability, which is likely to affect the distribution and activity of organism in aquatic ecosystem. In this study nylon-mesh bags technique was used for the period of twelve months, using Angiospermic and Gymnospermic leaves as leaf litter substrate. Decomposition (weight loss) of different substrates in different months of submergence was analyzed statistically and was found that species number significantly affect the weight loss of tested leaf litter. KEY WORDS: Water Borne Conidial Fungi, Fresh Water Streams, Colonization, Decomposition.

Analysis of sensitive clay landslide mechanism and impact pipeline bearing characteristics considering spatial variability

Landslides caused by the progressive failure of sensitive clay slopes may cause substantial economic losses and environmental pollution to pipelines. This study utilizes the Karhunen-Loève (K-L) expansion method, the coupled Eulerian-Lagrangian (CEL) method, and the Monte Carlo method to model the large deformation behaviour of slopes with spatial variability. The proposed RCEL-MC framework for joint analysis of extensive deformation-probability statistics of landslide impact on pipelines confirms that the spatial variability clay slope instability failure mechanism, considering the soil softening effect under earthquake action, aligns more closely with real-world scenarios. Furthermore, based on the statistical failure probability of pipelines impacted by landslides, a sensitive clay slope pipeline safety design method is suggested. The research highlights that the random field parameters significantly influence the load-bearing characteristics of pipelines affected by spatially variable slope progressive failure landslides. The uncertainty surrounding the maximum impact force on pipelines notably increases with the rise of the horizontal correlation length and variability coefficient. When accounting for spatial variability, the maximum impact force on landslide pipelines surpasses the deterministic model analysis results by 28-69 %. It is emphasized that the deterministic model analysis, which neglects the spatial variability of soil and the soil softening effect, may seriously underestimate landslide risk. Based on the criteria for pipeline buckling failure and yield failure, it is advised that the safe design parameters for pipelines in practical projects should fall within the intersection of the secure regions defined by the two failure criteria.

Exploring how the heterogeneous urban landscape influences CO2 concentrations: The case study of the Metropolitan Area of Barcelona

Monitoring CO2 concentrations in urban areas is crucial for determining the efficacy of climate change mitigation policies. However, highly heterogeneous land use, local geography, and local convection patterns, which vary throughout the urban landscape, complicate this task. To establish continuous monitoring programs, it is important to first determine the heterogeneity of urban landscapes on the ground. To understand the role these factors play in the distribution of CO2 over an urban area, we conducted a CO2 measurement campaign over the Metropolitan Area of Barcelona (AMB) over four urban land uses: impervious, green, forest, and agricultural. There is a clear tendency for CO2 mixing ratios to decrease as the degree of urban vegetation increases, even in the midst of a developed boundary layer. For example, CO2 concentrations were 429 and 427 ppm at forest and agricultural sites, respectively, while 485 ppm was reported at urban sites. A decrease in atmospheric CO2 was observed from 458 to 428 ppm in the gradient from urban to suburban areas, in which the biosphere component increased. The biosphere component of the CO2 signal was significant and was observed in the gradient from urban to suburban areas, which averaged a reduction from 458 to 428 ppm. Our findings show that the large spatial variability in CO2 concentrations (ranging from 410 to 495 ppm) is best explained by anthropogenic activity. We propose increasing the spatiotemporal resolution of CO2 monitoring in the AMB to determine these trends more precisely over longer periods of time.

Coral reefs at Qiziwan National Marine Park in 2023: spatial variability and their relationship with environmental factors

Coral reefs at Qiziwan National Marine Park in 2023: spatial variability and their relationship with environmental factors

ECOSENSE - Multi-scale quantification and modelling of spatio-temporal dynamics of ecosystem processes by smart autonomous sensor networks

Global climate change threatens ecosystem functioning worldwide. Forest ecosystems are particularly important for carbon sequestration, thereby buffering climate change and providing socio-economic services. However, recurrent stresses, such as heat waves, droughts and floods can affect forests with potential cascading effects on their carbon sink capacity, drought resilience and sustainability. Knowledge about the stress impact on the multitude of processes driving soil-plant-atmosphere interactions within these complex forest systems is widely lacking and uncertainty about future changes extremely high. Thus, forecasting forest response to climate change will require a dramatically improved process understanding of carbon and water cycling across various temporal (minutes to seasons) and spatial (leaf to ecosystem) scales covering atmosphere, biosphere, pedosphere and hydrosphere components. Many relevant processes controlling carbon and water exchange occur at small scales (e.g. rhizosphere, single leaf) with a high spatial and temporal variability, which is poorly constrained. However, interactions and feedback loops can be key players that amplify or dampen a system’s response to stress. Moreover, spatial and temporal scaling rules for these non-linear processes in structurally and functionally diverse ecosystems are unknown. Legacy effects, for example, altered response after previous stress and retarded recovery of forests after climate extremes, are not captured in state-of-the-art models. Currently, we are lacking the appropriate and interconnected measurement, data assimilation and modelling tools allowing for a comprehensive, real-time quantification of key processes at high spatio-temporal coverage in heterogeneous environments. Moreover, since climate impacts are highly unpredictable with respect to timing and location, future research will require novel mobile, easily deployable and cost-efficient approaches. ECOSENSE, therefore, assembles expertise from environmental and engineering sciences, both being excellently paired at the University of Freiburg. Our interdisciplinary research project will investigate all relevant scales in a next-generation ecosystem research assessment (ECOSENSE). Our vision is to detect and forecast critical changes in ecosystem functioning, based on the understanding of hierarchical process interaction. In the first phase, ECOSENSE will explore these process interactions by investigating pools and fluxes of water and carbon, i.e. CO2 exchange, isotope discrimination and volatile organic compounds (VOC), as well as stress indicators by remotely and in situ sensed chlorophyll fluorescence. To address these research tasks, ECOSENSE will develop, implement and test a distributed, autonomous, intelligent sensor network, based on novel microsensors tailored to the specific needs in remote and harsh forest environments. They will measure the spatio-temporal dynamics of ecosystem pools and fluxes in a naturally complex structured forest system with minimal physiological impact. Measured data will be transferred in real-time into a sophisticated database, which will be explored for process analysis, conducted by Artificial Intelligence and close to real-time process-based ecosystem models for now- and forecasting applications. Thereby, ECOSENSE will: i) break new ground for integrative ecosystem research by identifying hierarchies and interactions of abiotic and physiological processes of forest carbon and water exchange, ii) provide a profound understanding of complex ecosystem responses to environmental stressors and iii) enable the prediction of process-based alterations in ecosystem functioning and sustainability. Our novel ECOSENSE toolkit, tested and validated in controlled climate extreme experiments and our ECOSENSE Forest, will open new horizons for rapid assessment in vast and remote ecosystems. Thereby, ECOSENSE will allow for a unique avenue of data acquisition and, consequently, for unprecedented scale-crossing ecosystem understanding and modelling.

Spatiotemporal variability of pesticides concentration in honeybees (Apis mellifera) and their honey from western Mexico. Risk assessment for honey consumption

The study conducted in the state of Colima, western Mexico, aimed to assess the 1) occurrence, 2) temporal variability, 3) spatial variability, and 4) potential risk for honeybees and human consumption of pesticide-contaminated honey. For that purpose, 48 pesticides were determined in bees and their honey during both dry and wet seasons. The research considered two variables: land use categorization (irrigated agriculture, rainfed agriculture, grassland, and forest area) and location (coastal, valley, and mountain). Bee and honey samples were collected, pre-treated using solid-phase extraction (SPE), and analyzed using LC-MS/MS and GC–MS techniques. Occurrence: of the total number of pesticides, 17 were detected in the bee samples and 12 in the honey samples. The pesticides with the highest concentrations in the bee samples were glufosinate ammonium, picloram, and permethrin, while in the honey samples, picloram, permethrin, and atrazine were the most prevalent. Temporal variability: analyses revealed significant differences between dry and wet seasons for glufosinate ammonium and DEET in bee samples and only for glufosinate ammonium in honey samples. Spatial variability: analyses showed a trend in the number of detected pesticides, with irrigated agriculture areas having the highest detection and grassland areas having the least. The human potential risk assessment of contaminated honey consumption indicated no risk. The bee's potential risk for consumption of pesticides contaminated honey revealed chronic effects due to permethrin in a general scenario, and carbofuran, diazinon and permethrin in the worst scenario, and potential risk of acute effects by permethrin. The findings of this study contribute to understanding the contamination levels of pesticides in bees and their honey, emphasizing the importance of monitoring and mitigating the adverse effects of pesticide exposure on bee populations and environmental health.

Assessment of the piroxicam-incited model of synchronized colitis in T-cell receptor alpha chain-deficient mice.

A multitude of mouse models are utilized to emulate and study intestinal inflammation. T-cell receptor alpha chain (TCRα)-deficient mice are used as a model of spontaneous colitis that has similarities with human ulcerative colitis. However, colitis is triggered late in the life of the mouse (age: 4-5 months), and inflammation does not develop at the same time in different mice. A previously conducted study reported that the administration of the drug piroxicam triggered predictable and early colitis in TCRα-deficient mice at the age of 6-8 weeks. However, a detailed characterization of ensuing inflammation was not provided. We conducted an in-depth examination of piroxicam-triggered colitis in TCRα-deficient mice, with emphasis on spatial histopathologic changes and analysis of expression of inflammatory markers. Furthermore, we tested amelioration of colitis with dexamethasone. We confirmed that piroxicam induced a time-prescribed colitis and did so in the proximal colon as well as the cecum of TCRα-deficient mice. Piroxicam administration was observed to induce epithelial hyperplasia, goblet cell loss, and leukocyte infiltration with occasional ulceration. A Swiss roll technique was used to examine the colon and cecum in its entirety. Importantly, we observed that inflammation was multifocal segmental, with areas of tissue damage in between healthy tissue. In addition, we observed variability in the severity of inflammation among replicate animals and treatments, and that the administration of dexamethasone only partially ameliorated inflammation in the proximal colon. Piroxicam consistently induced multifocal segmental colitis in the proximal colon and cecum, although the degree of inflammation was reduced in the latter. Importantly, spatial variability in inflammation in the large intestine and the inter-replicate variation in the severity of inflammation must be taken into consideration when utilizing this murine model of synchronized colitis.

Morphological patterns and spatial probability maps of the inferior frontal sulcus in the human brain.

The inferior frontal sulcus (ifs) is a prominent sulcus on the lateral frontal cortex, separating the middle frontal gyrus from the inferior frontal gyrus. The morphology of the ifs can be difficult to distinguish from adjacent sulci, which are often misidentified as continuations of the ifs. The morphological variability of the ifs and its relationship to surrounding sulci were examined in 40 healthy human subjects (i.e., 80 hemispheres). The sulci were identified and labeled on the native cortical surface meshes of individual subjects, permitting proper intra-sulcal assessment. Two main morphological patterns of the ifs were identified across hemispheres: in Type I, the ifs was a single continuous sulcus, and in Type II, the ifs was discontinuous and appeared in two segments. The morphology of the ifs could be further subdivided into nine subtypes based on the presence of anterior and posterior sulcal extensions. The ifs was often observed to connect, either superficially or completely, with surrounding sulci, and seldom appeared as an independent sulcus. The spatial variability of the ifs and its various morphological configurations were quantified in the form of surface spatial probability maps which are made publicly available in the standard fsaverage space. These maps demonstrated that the ifs generally occupied a consistent position across hemispheres and across individuals. The normalized mean sulcal depths associated with the main morphological types were also computed. The present study provides the first detailed description of the ifs as a sulcal complex composed of segments and extensions that can be clearly differentiated from adjacent sulci. These descriptions, together with the spatial probability maps, are critical for the accurate identification of the ifs in anatomical and functional neuroimaging studies investigating the structural characteristics and functional organization of this region in the human brain.

Interpretable data-driven prediction methods for atmospheric chloride deposition rate

Precise estimation of atmospheric chloride deposition is crucial in environmental science, providing valuable insights into atmospheric pollution, metal corrosion, and the hydrologic cycle. The distribution of atmospheric chloride deposition involves multiple processes of production, transportation, and deposition, making it a problem with well-defined physical mechanisms. Although numerous parametric models have been proposed for atmospheric chloride distribution prediction, they present challenges in terms of generalizability and prediction accuracy due to simplifications of the holistic process. In contrast, non-parametric machine learning algorithms can analyze complex issues and have good prediction performance, but they are used less often in atmospheric chloride deposition modeling. Therefore, we develop a genetic algorithm optimized quantile regression forest (GA-QRF) model, incorporating environmental variables associated with the holistic deposition process to predict the distribution of atmospheric chloride deposition. QRF is an ensemble learning method known for delivering precise and interpretable predictions, coupled with effective uncertainty estimation. The proposed model results achieved an R2 value of 0.935 for median prediction and 98.2% coverage of the intervals for uncertainty quantification on a dataset collected from six countries. In addition, in order to verify the consistency of the black-box machine learning model with the domain knowledge, several interpretation methods are used to deconstruct the proposed model. Considering the spatial variability of atmospheric chloride deposition, the proposed model was further used to create deposition distribution maps.

Influence of geological uncertainty and soil spatial variability on tunnel deformation and their importance evaluation

In geotechnical engineering, geological uncertainty and the inherent spatial variability of soil properties are two primary sources of uncertainty, significantly impacting embedded geo-structures. While the influence of the soil spatial variability and geological uncertainty has been separately investigated, the coupling effect of these two types of uncertainties on tunnel is still limited. Meanwhile, little effort has been devoted to quantifying the degree of importance of these two types of uncertainties. This study quantitatively evaluates the effects of geological uncertainty and soil spatial variability on tunnels, and first assess the importance of these two uncertainties based on a novel approach. Improved coupled Markov chain model was utilized to simulate the geological uncertainty based on sparse and limited boreholes. The coefficient of variation (COV) was adopted to characterize the different levels of soil spatial variability in this study. The results illuminated the role of geological uncertainty and spatial variability in tunnel probabilistic analysis by comparing different boreholes schemes and COV. In addition, the geological uncertainty index (GUI) was presented to quantitatively estimate the degree of uncertainty in the simulated strata. A novel approach for evaluating the significance of geological uncertainty and soil spatial variability on tunnel was proposed. Based on the Kendall correlation coefficient, the classification was also given, divided into high, strong, moderate, weak and no correlation. The geological uncertainty should be considered in high and strong correlation groups. Both two types of uncertainties should be simultaneously taken into account in moderate and weak correlation divisions.

Spatially-explicit land use change emissions and carbon payback times of biofuels under the Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA)

The Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA) requires airlines to offset their greenhouse gas (GHG) emissions above 2019 levels by either buying carbon offsets or using Sustainable Aviation Fuels (SAFs). These are drop-in jet fuels made from biomass or other renewable resources that reduce GHG emissions by at least 10 % compared to kerosene and meet certain sustainability criteria. This study assesses the direct land use change (DLUC) emissions of SAF, i.e., GHG emissions from on-site land conversion from previous uses (excluding primary forests, peatlands, wetlands, and protected and biodiversity-rich areas) into alternative feedstocks, considering spatial variability in global yields and land carbon stocks. The results provide DLUC values and carbon payback times at 0.5-degree resolution for six SAF pathways, with and without irrigation and a medium-input intensity, according to CORSIA sustainability criteria. When excluding CORSIA non-compliant areas, soybean SAF shows the highest mean DLUC factor (31.9 ± 20.7 gCO2/MJ), followed by reed canary grass and maize. Jatropha SAF shows the lowest mean DLUC factor (3.6 ± 31.4 gCO2/MJ), followed by miscanthus and switchgrass. The latter feedstocks show potential for reducing GHG emissions over large areas but with relatively greater variability. Country-average DLUC values are higher than accepted ILUC ones for all pathways except for maize. To ensure the GHG benefits of CORSIA, feedstocks must be produced in areas where not only carbon stocks are relatively low but also where attainable yields are sufficiently high. The results help identify locations where the combination of these two factors may be favourable for low-DLUC SAF production. Irrigated miscanthus offers the highest SAF production potential (2.75 EJ globally) if grown on CORSIA-compliant cropland and grassland areas, accounting for ∼1/5 of the total kerosene used in 2019. Quantifying other environmental impacts of SAFs is desirable to understand sustainability trade-offs and financial constraints that may further limit production potentials.

Enhancement of Comparative Assessment Approaches for Synthetic Aperture Radar (SAR) Vegetation Indices for Crop Monitoring and Identification—Khabarovsk Territory (Russia) Case Study

Crop identification at the field level using remote sensing data is a very important task. However, the use of multispectral data for the construction of vegetation indices is sometimes impossible or limited. For such situations, solutions based on the use of time series of synthetic aperture radar (SAR) indices are promising, eliminating the problems associated with cloudiness and providing an assessment of crop development characteristics during the growing season. We evaluated the use of time series of synthetic aperture radar (SAR) indices to characterize crop development during the growing season. The use of SAR imagery for crop identification addresses issues related to cloudiness. Therefore, it is important to choose the SAR index that is the most stable and has the lowest spatial variability throughout the growing season while being comparable to the normalized difference vegetation index (NDVI). The presented work is devoted to the study of these issues. In this study, the spatial variabilities of different SAR indices time series were compared for a single region for the first time to identify the most stable index for use in precision agriculture, including the in-field heterogeneity of crop sites, crop rotation control, mapping, and other tasks in various agricultural areas. Seventeen Sentinel-1B images of the southern part of the Khabarovsk Territory in the Russian Far East at a spatial resolution of 20 m and temporal resolution of 12 days for the period between 14 April 2021 and 1 November 2021 were obtained and processed to generate vertical–horizontal/vertical–vertical polarization (VH/VV), radar vegetation index (RVI), and dual polarimetric radar vegetation index (DpRVI) time series. NDVI time series were constructed from multispectral Sentinel-2 images using a cloud cover mask. The characteristics of time series maximums were calculated for different types of crops: soybean, oat, buckwheat, and timothy grass. The DpRVI index exhibited the highest stability, with coefficients of variation of the time series that were significantly lower than those for RVI and VH/VV. The main characteristics of the SAR and NDVI time series—the maximum values, the dates of the maximum values, and the variability of these indices—were compared. The variabilities of the maximum values and dates of maximum values for DpRVI were lower than for RVI and VH/VV, whereas the variabilities of the maximum values and the dates of maximum values were comparable for DpRVI and NDVI. On the basis of the DpRVI index, classifications were carried out using seven machine learning methods (fine tree, quadratic discriminant, Gaussian naïve Bayes, fine k nearest neighbors or KNN, random under-sampling boosting or RUSBoost, random forest, and support vector machine) for experimental sites covering a total area of 1009.8 ha. The quadratic discriminant method yielded the best results, with a pixel classification accuracy of approximately 82% and a kappa value of 0.67. Overall, 90% of soybean, 74.1% of oat, 68.9% of buckwheat, and 57.6% of timothy grass pixels were correctly classified. At the field level, 94% of the fields included in the test dataset were correctly classified. The paper results show that the DpRVI can be used in cases where the NDVI is limited, allowing for the monitoring of phenological development and crop mapping. The research results can be used in the south of Khabarovsk Territory and in neighboring territories.

Modelling and mapping carbon capture potential of farmed blue mussels in the Baltic Sea region

This study applies a regional Dynamic Energy Budget (DEB) model, enhanced to include biocalcification processes, to evaluate the carbon capture potential of farmed blue mussels (Mytilus edulis/trossulus) in the Baltic Sea. The research emphasises the long-term capture of carbon associated with shell formation, crucial for mitigating global warming effects. The model was built using a comprehensive pan-Baltic dataset that includes information on mussel growth, filtration and biodeposition rates, and nutrient content. The study also examined salinity, temperature, and chlorophyll a as key environmental factors influencing carbon capture in farmed mussels. Our findings revealed significant spatial and temporal variability in carbon dynamics under current and future environmental conditions. The tested future predictions are grounded in current scientific understanding and projections of climate change effects on the Baltic Sea. Notably, the outer Baltic Sea subbasins exhibited the highest carbon capture capacity with an average of 55 t (in the present scenario) and 65 t (under future environmental conditions) of carbon sequestrated per farm (0.25 ha) over a cultivation cycle – 17 months. Salinity was the main driver of predicted regional changes in carbon capture, while temperature and chlorophyll a had more pronounced local effects. This research advances our understanding of the role low trophic aquaculture plays in mitigating climate change. It highlights the importance of developing location-specific strategies for mussel farming that consider both local and regional environmental conditions. The results contribute to the wider discourse on sustainable aquaculture development and environmental conservation.

Functional ultrasound imaging and neuronal activity: how accurate is the spatiotemporal match?

Over the last decade, functional ultrasound (fUS) has risen as a critical tool in functional neuroimaging, leveraging hemodynamic changes to infer neural activity indirectly. Recent studies have established a strong correlation between neural spike rates (SR) and functional ultrasound signals. However, understanding their spatial distribution and variability across different brain areas is required to thoroughly interpret fUS signals. In this regard, we conducted simultaneous fUS imaging and Neuropixels recordings during stimulus-evoked activity in awake mice within three regions the visual pathway. Our findings indicate that the temporal dynamics of fUS and SR signals are linearly correlated, though the correlation coefficients vary among visual regions. Conversely, the spatial correlation between the two signals remains consistent across all regions with a spread of approximately 300 micrometers. Finally, we introduce a model that integrates the spatial and temporal components of the fUS signal, allowing for a more accurate interpretation of fUS images.

Compilation of Consolidation Properties Data of Champlain Sea Clay from Ottawa Region

AbstractEstimation of consolidation settlements in fine-grained soils due to various civil infrastructure loads is traditionally based on results derived from consolidation tests performed on undisturbed soil samples, combined with the data of other soil properties. In many geotechnical engineering applications, consolidation settlements are also estimated using empirical consolidation parameters derived from basic soil properties. This approach relies on correlations from the literature to bypass the time-consuming and expensive sampling techniques, laboratory testing, and other associated expenses. However, these correlations may not provide reasonable consolidation settlement estimations as these correlations are typically developed without considering the influence of stress history, geology, salinity of pore water, gradation, soil fabric, and chemical properties of the soils. This is especially true for Champlain Sea clay deposits from Eastern Ontario region of Canada that are typically with heterogeneous site conditions and exhibit spatial variability of soil properties. In this paper, data from the published literature and industrial and government reports on sensitive Champlain Sea clays were gathered for the Ottawa region. The data collection and clean-up methodology towards enhancing the reliability of the gathered data is comprehensively discussed. The summarized data from this study can be used with a greater degree of confidence towards developing reliable correlations in the estimation of consolidation settlements in geotechnical engineering practice.

Spatial variation of the source mechanisms in the Egyptian continental margin

The spatial variability of the focal mechanism solutions in the NW Egypt Cyrenaica and Levant-north Egypt domains of the Egyptian Continental margin is investigated based on the polarity of the P-wave first motions of eleven earthquakes with local magnitudes ML ranging from 4.2 to 6.2. The identified focal mechanism solutions in the Levant-north Egypt domain are evaluated in two sub-provinces with different geological settings. The first sub-province shows strike-slip faulting mechanisms with right-lateral motion along the NE-trending Rosetta-Qattara fault zone, while the NW-trending Temsah fault zone produces left-lateral strike-slip motion. The inland extension of the Rosetta-Qattara fault zone towards the Western Desert reflects the same sense of motion. Strike-slip faulting with thrust components dominates the focal mechanisms in the second sub-province but with a different stress orientation. A single thrust faulting mechanism event which occurred on the periphery of the continental shelf shows the same common orientation of the stress field at the Hellenic Arc. This type of mechanism in this locality can be explained by the mean of compressional stress transfer towards the coast generated by the accelerated southwest retreat of the Hellenic Trench. The complex regional stress along the Cyprian and Hellenic arcs, the current local structure features, and the dominant orientation of the pre-existing fault are the factors that contribute to the changeable stress pattern field in the Levant-north Egypt domain. A single event in the second domain, the NW Egypt Cyrenaica, is a strike-slip with a right-lateral sense of motion on a WNW-oriented plane, consistent with motion along the WNW tectonic fault which separates the continental shelf from the oceanic crust.

ТРАНСФОРМАЦИЯ ПЛОДОРОДИЯ ПОЧВ ЗАЛЕЖЕЙ ЛЕСОСТЕПНОЙ ЗОНЫ ПРИ РАЗЛИЧНОМ НАПРАВЛЕНИИ ИХ ИСПОЛЬЗОВАНИЯ

The purpose of research is to provide a comprehensive comparative assessment of the fertility of gray fallow soils in the forest-steppe zone of the Krasnoyarsk Region, overgrown with forest, used for hayma-king and re-developed into arable land. The studies were conducted in 2007–2020 in the Krasnoyarsk and Achinsk-Bogotol forest-steppe on gray soils of fallow lands no longer used for agricultural purposes. Stu-dies were carried out to assess the indicators of the potential and effective fertility of gray soils of fallow lands of the Krasnoyarsk and Achinsk-Bogotol forest-steppe with their various post-agrogenic use. It has been established that forests regenerated on fallow lands do not lead to degradation of soil fertility, especially in terms of the content of humus and agronomically valuable aggregates. The emerging forest litter and tree litter accelerate the accumulation of organic matter, a source of humus. There is weak spatial va¬riation in most indicators of effective and potential fertility in soils under regenerating forest compared to pure fallow lands. With the re-involvement of fallow land into arable land, nitrification somewhat increases, spatial variation in soil properties decreases, and the content of humus and absorbed bases decreases. The structural composition of the soils of all study objects is characterized as excellent (ACF 88–77 %) with an insignificant value of the coefficient of variation of fractions (4–7 %). When reclaiming fallow lands into arable land, the proportion of the blocky fraction is significantly reduced. The spatial variability of the structural composition of soils in fallow lands and hayfields is higher than on arable land, which is asso¬ciated with the “clumpiness” of the ground cover and root system of plants on uncultivated soils. In terms of fertility, post-agrogenic gray soils used for haymaking occupy an intermediate position between clean fallow lands and those overgrown with forest. The productivity of the phytomass of fallow lands, arable land and hayfields in the Krasnoyarsk and Achinsk-Bogotol forest-steppe is more closely correlated with the indicators of the potential fertility of post-agrogenic gray soils. In most cases, the values of the correlation coefficients are closer between phytomass reserves and indicators of effective fertility in the Achinsk-Bogotol forest-steppe, which is associated with more favorable hydrothermal conditions in this research area and optimal water-physical properties.

Unraveling spatial heterogeneity of soil legacy phosphorus in subtropical grasslands.

Humans have profoundly altered phosphorus (P) cycling across scales. Agriculturally driven changes (e.g., excessive P-fertilization and manure addition), in particular, have resulted in pronounced P accumulations in soils, often known as "soil legacy P." These legacy P reserves serve as persistent and long-term nonpoint sources, inducing downstream eutrophication and ecosystem services degradation. While there is considerable scientific and policy interest in legacy P, its fine-scale spatial heterogeneity, underlying drivers, and scales of variance remain unclear. Here we present an extensive field sampling (150-m interval grid) and analysis of 1438 surface soils (0-15 cm) in 2020 for two typical subtropical grassland types managed for livestock production: Intensively managed (IM) and Semi-natural (SN) pastures. We ask the following questions: (1) What is the spatial variability, and are there hotspots of soil legacy P? (2) Does soil legacy P vary primarily within pastures, among pastures, or between pasture types? (3) How does soil legacy P relate to pasture management intensity, soil and geographic characteristics? and (4) What is the relationship between soil legacy P and aboveground plant tissue P concentration? Our results showed that three measurements of soil legacy P (total P, Mehlich-1, and Mehlich-3 extractable P representing labile P pools) varied substantially across the landscape. Spatial autoregressive models revealed that soil organic matter, pH, available Fe and Al, elevation, and pasture management intensity were crucial predictors for spatial patterns of soil P, although models were more reliable for predicting total P (68.9%) than labile P. Our analysis further demonstrated that total variance in soil legacy P was greater in IM than SN pastures, and intensified pasture management rescaled spatial patterns of soil legacy P. In particular, after controlling for sample size, soil P was extremely variable at small scales, with variance diminished as spatial scale increased. Our results suggest that broad pasture- or farm-level best management practices may be limited and less efficient, especially for more IM pastures. Rather, management to curtail soil legacy P and mitigate P loading and losses should be implemented at fine scales designed to target spatially distinct P hotspots across the landscape.

Sea ice mass balance during the MOSAiC drift experiment: Results from manual ice and snow thickness gauges

Precise measurements of Arctic sea ice mass balance are necessary to understand the rapidly changing sea ice cover and its representation in climate models. During the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, we made repeat point measurements of snow and ice thickness on primarily level first- and second-year ice (FYI, SYI) using ablation stakes and ice thickness gauges. This technique enabled us to distinguish surface and bottom (basal) melt and characterize the importance of oceanic versus atmospheric forcing. We also evaluated the time series of ice growth and melt in the context of other MOSAiC observations and historical mass balance observations from the Surface Heat Budget of the Arctic (SHEBA) campaign and the North Pole Environmental Observatory (NPEO). Despite similar freezing degree days, average ice growth at MOSAiC was greater on FYI (1.67 m) and SYI (1.23 m) than at SHEBA (1.45 m, 0.53 m), due in part to initially thinner ice and snow conditions on MOSAiC. Our estimates of effective snow thermal conductivity, which agree with SHEBA results and other MOSAiC observations, are unlikely to explain the difference. On MOSAiC, FYI grew more and faster than SYI, demonstrating a feedback loop that acts to increase ice production after multi-year ice loss. Surface melt on MOSAiC (mean of 0.50 m) was greater than at NPEO (0.18 m), with considerable spatial variability that correlated with surface albedo variability. Basal melt was relatively small (mean of 0.12 m), and higher than NPEO observations (0.07 m). Finally, we present observations showing that false bottoms reduced basal melt rates in some FYI cases, in agreement with other observations at MOSAiC. These detailed mass balance observations will allow further investigation into connections between the carefully observed surface energy budget, ocean heat fluxes, sea ice, and ecosystem at MOSAiC and during other campaigns.

Effects of soil spatial variability on the behaviour of the embankment supported with a combined retaining structure

In this study, the effects of soil spatial variability on the behaviour of the embankment supported with a combined retaining structure (CRS) were investigated. The numerical model of the CRS embankment was established and validated with the field data. An application programming interface (API) was developed to deal with the data exchanging issue between the numerical model and the spatial variability characterization model. Based on the verified numerical model and the API, the probabilistic analysis with 500 Monte Carlo simulations was automatically computed. Three influencing factors of the retained soil (the mean of the friction angle, the variation of the friction angle and the vertical correlation length of the random field) are analysed by parametric analysis. The results show that the vertical correlation length of the random field is most important in the earth pressure calculation process, while the mean of the friction angle is the factor with least impact. On the whole, the spatial variability of soil properties has minimal impact on the distribution and magnitude of earth pressure behind the retaining structure.