Variogram Analysis Research Articles

Estimasi Sumberdaya Bauksit Menggunakan Metode Ordinary Kriging Pada PT. Wahana Petra Sejahtera Kabupaten Bengkayang Provinsi Kalimantan Barat

PT. Wahana Petra Sejahtera, which operates in the bauxite mining sector, requires resource calculations as a basis for carrying out its mining activities. This research aims to determine the levels and distribution of Al2O3, SiO2, and Fe2O3 and estimate bauxite resources using the ordinary kriging method. This research uses secondary data in coordinates, elevation, test pit depth, and slope data. Data processing uses software in database creation, statistical analysis, block and composite modeling, variogram analysis, resource estimation, and model validation. The calculation results for the Jirak block show that the Al2O3 waste content is 1,488,000 tons, low-grade 4,532,000 tons, medium-grade 1,824,000 tons, and high-grade 32,000 tons. For SiO2 the average content is 13.54% and the average Fe2O3 content is 13.50%. Meanwhile, in the Sindu block the Al2O3 waste content is 64,000 tons, medium-grade 1,188,000 tons, and high-grade 3,056,000 tons. Meanwhile, the average SiO2 content is 9.13% and the average Fe2O3 content is 12.06%. The total resources in the Jirak block are 7,696,000 tons with the amount meeting the COG of 800 tons. Meanwhile, in the Sindu block, the total resources are 5,008,000 tons with an amount that meets the COG of 1,280,000 tons.

FACIES PARTITIONING AT REGIONAL AND FIELD SCALES IN THE BARREMIAN KHARAIB‐2 CARBONATES, UAE

Carbonates in the Lower Cretaceous (Barremian to early Aptian) Kharaib Formation are reservoir rocks at giant oil fields in the UAE and Qatar. The Barremian Kharaib‐2 member (K60), the focus of this study, is in general composed of a regionally continuous succession of high‐energy, shallow‐water limestones bounded above and below by “dense” low‐energy mud‐rich strata. Despite several decades of research, conventional carbonate facies classification schemes and resulting facies groupings for the Kharaib‐2 member have failed to show a statistically acceptable correlation with core‐ and log‐derived petrophysical data. Moreover, sedimentary bodies potentially responsible for dynamic reservoir heterogeneities have not clearly been identified. This paper proposes a standardized facies classification scheme for the Kharaib‐2 carbonates based on vertical facies proportion curves (VPCs) and variogram analyses of core data to construct stratigraphic correlations at both field and regional scales. Data came from 295 cored wells penetrating the Kharaib‐2 member at ten fields in the on‐ and offshore UAE. Thin, dense intervals separating reservoir units were adopted as fourth‐order transgressive units and were used for stratigraphic correlation. Field‐scale probability maps were used to identify sedimentary bodies such as shallow‐water rudistid shoals.Regional stratigraphic correlations of the Kharaib‐2 member carbonates based on the VPCs identified variations in depositional environments, especially for the lower part of the reservoir unit; depositional facies at fields in the SE of the UAE were interpreted to be more distal compared to those at offshore fields to the NW. At a field scale, the VPCs failed to identify significant lateral variations in the carbonates. However, variogram analyses of cored wells showed spatial concentrations of specific facies in the inner ramp domain which could be correlated with high‐energy depositional bodies such as shoals dominated by rudist debris. The bodies were sinusoidal in plan view with lengths of up to 8 km and widths of ca. 1 km. Although similar‐shaped bodies with these dimensions have been reported from other carbonate depositional systems, they have not previously been reported in the Kharaib Formation. At a regional (inter‐field) scale, the stratigraphic correlation of standardized sedimentary facies remains problematic; however, mapping of facies associations and their relative proportions relative to their environments of deposition demonstrated new patterns for the stratigraphic architecture of the Kharaib‐2 member in the UAE.

The interaction between vegetation patchiness and tidal flows in a shortleaf seagrass meadow

AbstractSeagrasses are critical coastal habitats that provide numerous ecosystem services. The inherent patchiness within meadows exerts a significant influence on the flow‐vegetation interaction, which, in turn, affects the array of services provided by these environments. We present field observations of vegetation distribution and hydrodynamic measurements within and surrounding an approximately 2 m diameter gap (bare sediment) in a fragmented seagrass meadow. We show that variability in mean flows and turbulence is correlated with meadow structure at small (O (100 m)) and large (O (102 m)) spatial scales. Our observations reveal that bare gaps within seagrass meadows lead to faster flows and lower bed elevations. Despite slower flow speeds above the dense seagrass adjacent to the gap, the rates of dissipation of turbulent energy above the vegetation are typically around an order of magnitude larger than above the bare bed. Associated with this enhanced dissipation of turbulent energy, we observed a dominance of down‐deceleration events promoting fluid exchange and mixing and driving mass flux into the canopy. Analysis of directional variograms demonstrates that the major continuity axis within NDVI (normalized difference vegetation index) imagery (used as a proxy for vegetation biomass) coincides with the major axis of flow on both gap and meadow scales. Conversely, along the axis of minor flow variance, vegetation remains dense and exhibits greater uniformity. These findings indicate a feedback mechanism between seagrass meadow patchiness and spatial structure through flow modification, which may be beneficial for plant development.

Spatial variability of geotechnical parameters of lateritic gravels in an Olama–Ngomedzap road section in a humid tropical area (Cameroon)

Searching for borrow pits often used during civil engineering constructions is an important step that requires a lot of time and money. The nature of the parent rock, climate, topography, vegetation and time influence the formation of lateritic gravels with different geotechnical properties within the same region. The present study aims at finding the spatial variability of geotechnical parameters of lateritic gravels from two different deposits using a variogram and statistical analysis. Out of 122 lateritic gravel samples collected, 69 were from underdeveloped lithic ferrallitic soils (LFS) and 53 from under developed non-lithic ferrallitic soils (NLFS). The mean values of the coefficient of variation (CV) shows that the geotechnical parameters of LFS-derived lateritic gravels are more dispersed than those developed on NLFS. The fines content, plasticity index (PI) and Californian bearing ratio (CBR) variograms observed mainly in the North and NNE parts of the study area, suggest that, the lateritic gravels in this area have average to good geotechnical characteristics. The principal component analysis (PCA) indicates that the CBR and the maximum dry density (MDD) are inversely related to the optimum moisture content (OMC). The discriminant factor analysis (DFA) coupled to the ascending hierarchical classification (AHC) shows three families of lateritic gravels: (1) class 1/3, lateritic gravels characterized by MDD, CBR, consistency index (CI), gravels content, and plastic limit (PL); (2) class 2/3, lateritic gravels characterized by fines content, and maximum grain diameter (Dmax) and, (3) class 3/3, lateritic gravels characterized by OMC, liquid limit (LL), PI, PL, and fines content, that fit for road construction.

Predicting the thermal regime of the Po Plain subsurface (Italy) using geostatistical modeling constrained by legacy wells

Italy boasts a long history of hydrocarbon exploration. The Po Basin, in particular, has been extensively explored due to its peculiar geological setting, arising from the convergence of the Southern Alps and Northern Apennines. Because of this, substantial subsurface data have been collected and made publicly available. Today, data from past oil and gas exploration campaigns can find new life in decarbonization programs, especially for geothermal exploration. Geothermal data on the Po Basin, including bottom hole temperature (BHT) and drill stem test (DST) records, play a crucial role in understanding the thermal state of the subsurface, and can therefore be employed in the exploration of geothermal resources. This study presents a 3D model of the thermal regime in the Po Basin based on the analysis of legacy hydrocarbon wells. Geostatistical models of subsurface temperatures are produced via direct sequential simulations that consider: (i) the overall geothermal gradient across the region, (ii) anisotropy in the temperature field revealed by variogram analysis, and (iii) temperature data in the surrounding geological volumes. A base-case model of expected temperature values and associated conditional variance are thus generated from which depth-to-the-isotherm maps were extracted for temperatures in the low-enthalpy range (<100 °C). The study confirms that promising thermal anomalies are mapped in the Ferrara Arc and the buried Southern Alps. These areas offer opportunities for new developments and the repurposing of depleted oil wells for district heating. Additionally, the findings highlight other areas in the Po Plain (e.g., Emilian Arc) where there exists more subtle geothermal potential for further exploration and development.

Uncertainty and sensitivity analysis of a travel‐time distribution‐based stream water electrical conductivity model

AbstractIn this paper, we analyse the uncertainty and parameter sensitivity of a conceptual water quality model, based on a travel time distribution (TTD) approach, simulating electrical conductivity (EC) in the Duck River, Northwest Tasmania, Australia for a 2‐year period. Dynamic TTDs of stream water were estimated using the StorAge Selection (SAS) approach, which was coupled with two alternate methods to model stream water EC: (1) a solute‐balance approach and (2) a water age‐based approach. Uncertainty analysis using the Differential Evaluation Adoptive Metropolis (DREAM) algorithm showed that: 1. parameter uncertainty was a small contribution to the overall uncertainty; 2. most uncertainty was related to input data uncertainty and model structure; 3. slightly lower total error was obtained in the water age‐based model than the solute‐balance model; 4. using time‐variant SAS functions reduced the model uncertainty markedly, which likely reflects the effect of dynamic hydrological conditions over the year affecting the relative importance of different flow pathways over time. Model parameter sensitivity analysis using the Variogram Analysis of Response Surfaces (VARS‐TOOL) framework found that parameters directly related to the EC concentration were most sensitive. In the solute‐balance model, the rainfall concentration Crain and in the age‐based model, the parameter controlling the rate of change of EC with age (λ) were the most sensitive parameter. Model parameters controlling the age mixes of both evapotranspiration and streamflow water fluxes (i.e., the SAS function parameters) were influential for the solute‐balance model. Little change in parameter sensitivity over time was found for the age‐based concentration relationship; however, the parameter sensitivity was quite dynamic over time for the solute‐balance approach. The overarching outcomes provide water quality modellers, engineers and managers greater insight into catchment functioning and its dependence on hydrological conditions.

Geostatistics and GIS as a Technique for Chemical Soil Properties Mapping

Soil properties play a fundamental role in agriculture, and understanding their spatial distribution is essential for effective land management and increasing agricultural productivity. This study focuses on the application of geostatistics and Geographic Information Systems (GIS) as techniques for mapping soil properties, in the case of the Mozambique Sugar cane company (ADM) in Mafambisse, in the province of Sofala, Mozambique. The central objective is to demonstrate how geostatistics and GIS can be used to characterize and map soil properties. The research was materialized using field surveys that estimated the pH in the field of several sampling points at different depths in the irrigated perimeter of the ADM. Geostatistics is applied, using methods such as kriging and variogram analysis, allowing the creation of continuous maps that represent soil properties in unsampled locations. GIS is an essential tool for integrating geostatistical data and creating accessible and informative maps. The results of this study indicate that the combination of geostatistics and GIS offers an effective approach for mapping soil properties. This knowledge is crucial for Mozambique sugar cane company, an important company in the sugar industry, to optimize its agricultural practices, increase productivity and minimize environmental impacts. Thus, offering solid bases for making informed and sustainable agricultural decisions, contributing to efficient agricultural development in Mozambique and other regions with similar challenges.

Surrogate-Based Uncertainty Analysis for Groundwater Contaminant Transport in a Chromium Residue Site Located in Southern China

Numerical modeling is widely acknowledged as a highly precise method for understanding the dynamics of contaminant transport in groundwater. However, due to the intricate characteristics of environmental systems and the lack of accurate information, the results are susceptible to a significant degree of uncertainty. Numerical models must explicitly consider related uncertainties in parameters to facilitate robust decision-making. In a Chromium Residue Site located in southern China (the study area), this study employed Monte Carlo simulation to assess the impact of variability in key parameters uncertainty on the simulation outcomes. Variogram analysis of response surface (VARS), global sensitivity analysis, and an XGBoost (version 2.0.0)-based surrogate model was employed to overcome the substantial computational cost of Monte Carlo simulation. The results of numerical simulation indicate that the contaminant is spreading downstream towards the northern boundary of contaminated site near Lianshui River, threatening water quality. Furthermore, migration patterns are complex due to both downstream convection and upstream diffusion. Sensitivity analysis identified hydraulic conductivity, recharge rate, and porosity as the most influential model parameters, selected as key parameters. Moreover, uncertainty analysis indicated that the variability in key parameters has a minimal impact on the simulation outcomes at monitoring wells near the contaminant source. In contrast, at wells positioned a considerable distance from the contaminant source, the variability in key parameters significantly influences the simulation outcomes. The surrogate model markedly mitigated computational workload and calculation time, while demonstrating superior precision and effectively capture the non-linear correlations between input and output of the simulation model.

Rock typing and uncertainty assessment in geological and petrophysical properties by integrating electrofacies, hydraulic flow units, and geostatistical techniques in the Kangan gas field, Zagros basin

The structural complexities of hydrocarbon reservoirs make it difficult to correlate geological and petrophysical properties. A successful field development depends on accurately mapping the spatial distributions of reservoir key parameters. In this study, we present results on rock type analysis, estimation, and uncertainty evaluation of geological and petrophysical data of 33 wells in one of the south Iranian gas fields. This paper is divided into two parts. In the first part, we used a reservoir rock classification theme based on identifying electrofacies (EFs) and hydraulic flow units by analyzing both log and core data. In the second part of the paper, we performed estimation, uncertainty evaluation, and assessment of the porosity–thickness relationship of the high-quality EFs using geostatistical techniques. We used sequential simulation schemes to map the spatial distributions of porosity and thickness of the high-quality EFs across the field. Using probabilistic approaches, the generated multiple realizations were used to quantify the local and spatial uncertainties. Variogram analysis showed that property distributions had a higher continuity and minimum variance in the NW–SE direction. Based on spatial uncertainty analysis, we found that the indicator-based sequential simulated maps exhibited less spatial uncertainty. Furthermore, the obtained probability maps indicated that the SE part of the study area is more suitable for drilling and production scenarios.

Spatial structure of in situ reflectance in coastal and inland waters: implications for satellite validation

Validation of satellite-derived aquatic reflectance involves relating meter-scale in situ observations to satellite pixels with typical spatial resolution ∼ 10–100 m within a temporal “match-up window” of an overpass. Due to sub-pixel variation these discrepancies in measurement scale are a source of uncertainty in the validation result. Additionally, validation protocols and statistics do not normally account for spatial autocorrelation when pairing in situ data from moving platforms with satellite pixels. Here, using high-frequency autonomous mobile radiometers deployed on ships, we characterize the spatial structure of in situRrs in inland and coastal waters (Lake Balaton, Western English Channel, Tagus Estuary). Using variogram analysis, we partition Rrs variability into spatial and intrinsic (non-spatial) components. We then demonstrate the capacity of mobile radiometers to spatially sample in situRrs within a temporal window broadly representative of satellite validation and provide spatial statistics to aid satellite validation practice. At a length scale typical of a medium resolution sensor (300 m) between 5% and 35% (median values across spectral bands and deployments) of the variation in in situRrs was due to spatial separation. This result illustrates the extent to which mobile radiometers can reduce validation uncertainty due to spatial discrepancy via sub-pixel sampling. The length scale at which in situRrs became spatially decorrelated ranged from ∼ 100–1,000 m. This information serves as a guideline for selection of spatially independent in situRrs when matching with a satellite image, emphasizing the need for either downsampling or using modified statistics when selecting data to validate high resolution sensors (sub 100 m pixel size).

Influence of nanoscale spatial architecture of seed and root mucilage on its wettability

Mucilage secreted by root-tips may alter hydraulic conditions at the root-soil interface and seed coat mucilage increases water availability for seeds. Both significantly affect the water content dynamics of the rhizosphere. Therefore, we examined the nanostructure of different types of dried mucilage (root or seed mucilage and mucilage from different species) and how it affects their wettability. We hypothesized that with increasing roughness wettability of dried mucilage decreases based on the Cassie−Baxter relation due to entrapped air in holes formed in the mucilage layer during drying. In order to visualize the nanostructure of different mucilage species, we dried root mucilage from aeroponically grown seedlings (maize, cress, flax, wheat, barley) and seed mucilage (cress and flax) as thin layers on flat mica surfaces. Spatial structures of the samples were visualized by atomic force microscopy. Variogram analysis was used to analyze the spatially resolved topography data, and the coverage of mucilage was quantified using GeoDict® software. Moreover, by the pore size distribution, the effect of hole-size dispersity on mucilage's wettability was investigated. Our results demonstrate that mucilage of different species have different nanostructures. In contrast to expectations, the wettability of dried mucilage does not follow the Cassie−Baxter relation and does not decrease with increasing roughness. We conclude that wettability of surfaces decreases with increasing mucilage coverage. The lower the coverage the more hydrophilic area of the underlying mineral surface contributes to the overall wettability.

Lateral and vertical characteristics of floodplain aggradation cycles in the lower Eocene Willwood Formation, Bighorn Basin, Wyoming, USA

Sedimentation on river floodplains is a complex process that involves overbank flooding, crevasse splaying, and river avulsion. The resulting floodplain stratigraphy often exhibits floodplain aggradation cycles with alternating fine-grained overbank flooding deposits that underwent significant petrogenesis, and coarser-grained, avulsion-belt deposits largely devoid of pedogenic impact. These cycles are linked to lateral migration and avulsion of channels driven by internal dynamics, external factors, or a combination of both. To better understand the spatial and vertical variability of such floodplain aggradation cycles, we map these in three dimensions using a photogrammetric model of the lower Eocene Willwood Formation in the northern Bighorn Basin, Wyoming, USA. This allows identifying 44 floodplain aggradation cycles in ∼300 m of strata with an average thickness of 6.8 m and a standard deviation of 2.0 m. All the cycles are traceable over the entire model, pointing to their spatial consistency over the 10 km2 study area. At the same time, rapid lateral thickness changes of the floodplain aggradation cycles occur with changes up to 4 m over a lateral distance of 400 m. Variogram analyses of both field and numerical-model results reveal stronger consistency of floodplain aggradation cycle thicknesses along the paleoflow direction compared to perpendicular to paleoflow. Strong compensational stacking occurs at the vertical scale of 2−3 floodplain aggradation cycles (14−20 m), while full compensational stacking occurs at larger scales of more than six floodplain aggradation cycles (&amp;gt;41 m). The lateral and vertical thickness variability of the floodplain aggradation cycles, as well as their compensational stacking behavior, are interpreted to be dominantly driven by autogenic processes such as crevasse splaying and avulsing that preferentially fill topographic lows. External climate forcing may have interacted with these autogenic processes, producing the laterally persistent and vertically repetitive floodplain aggradation cycles. The spatial variability of floodplain aggradation cycles demonstrated in this study highlights again the need for three-dimensional data collection in alluvial floodplain settings rather than depending on one-dimensional records.

THE ROLE OF REMOTE SENSING OF THE EARTH IN PRECISION AGRICULTURE

The paper provides a historical assessment of the increasing role of remote sensing data, the methods, services and tools used in their acquisition and applied in the information support of precision agriculture technologies. The main problems constraining the scalability of aerospace imagery application in precision crop production have been considered. The necessity to create a new research methodology, the formation of an appropriate physical, technical and experimental infrastructure to overcome them has been emphasized. The application of methodology for planning and conducting specialized experiments in conjunction with remote and ground-based monitoring of field experiments with test plots for detection within-field heterogeneity and the degree of its intensity has been depicted. For this purpose, has been created the functional that implements the capabilities of two new methods for within-field variability delineation and border marking based on aerospace images. The first one based on the main geostatistical tool – variogram analysis, and the other one is based on the systematic quantitative assessment of the dynamics of changes in the integral optical characteristics of the crop.

Snowpack relative permittivity and density derived from near‐coincident lidar and ground‐penetrating radar

AbstractDepth‐based and radar‐based remote sensing methods (e.g., lidar, synthetic aperture radar) are promising approaches for remotely measuring snow water equivalent (SWE) at high spatial resolution. These approaches require snow density estimates, obtained from in‐situ measurements or density models, to calculate SWE. However, in‐situ measurements are operationally limited, and few density models have seen extensive evaluation. Here, we combine near‐coincident, lidar‐measured snow depths with ground‐penetrating radar (GPR) two‐way travel times (twt) of snowpack thickness to derive &gt;20 km of relative permittivity estimates from nine dry and two wet snow surveys at Grand Mesa, Cameron Pass, and Ranch Creek, Colorado. We tested three equations for converting dry snow relative permittivity to snow density and found the Kovacs et al. (1995) equation to yield the best comparison with in‐situ measurements (RMSE = 54 kg m−3). Variogram analyses revealed a 19 m median correlation length for relative permittivity and snow density in dry snow, which increased to &gt;30 m in wet conditions. We compared derived densities with estimated densities from several empirical models, the Snow Data Assimilation System (SNODAS), and the physically based iSnobal model. Estimated and derived densities were combined with snow depths and twt to evaluate density model performance within SWE remote sensing methods. The Jonas et al. (2009) empirical model yielded the most accurate SWE from lidar snow depths (RMSE = 51 mm), whereas SNODAS yielded the most accurate SWE from GPR twt (RMSE = 41 mm). Densities from both models generated SWE estimates within ±10% of derived SWE when SWE averaged &gt;400 mm, however, model uncertainty increased to &gt;20% when SWE averaged &lt;300 mm. The development and refinement of density models, particularly in lower SWE conditions, is a high priority to fully realize the potential of SWE remote sensing methods.

Пространствено вариране на физикохимичните свойства на почвите от Ботевградската котловина

The aim of the present study is a spatial analysis and assessment of the main physicochemical properties of the soils in Botevgrad Valley. The soil samples were taken along a pre-selected grid to cover the areas selected for research as uniformly as possible. The data were interpolated using the geostatistical methods of kriging and variogram analysis. The soils have relatively favourable physicochemical properties, such as the cation sorption capacity with values from 10.80 to 24.50 mequ/100 g of soil. The basic cations, such as calcium and magnesium, dominate anywhere, and those with acidic functions, such as exchangeable aluminium and hydrogen, have minimal values. The base saturation is almost above 50%, and the soils are defined as Eutric. Soil reaction also varies from strongly acidic to slightly alkaline.

Spatial extrapolation of downscaled geochemical data using conditional GAN

Multi-scale geochemical data play an important guiding role in mineral exploration and environmental assessment. However, due to the influence of certain factors such as terrain, sampling cost and time, and instrument detection limit, geochemical data obtained from a study area are often incomplete or sparse. In this study, a conditional generative adversarial network (GAN) model was developed as a stochastic simulation technique for spatial extrapolation of downscaled geochemical data by learning nonlinear relationships between two soil geochemical datasets with different sampling densities, one set called fine data and the other coarse data. Taking Qianjiang City, Hubei Province, China as an example, first the fine data and coarse data were divided separately into training area and verification area for simulation in the GAN model. Then, exploratory data analysis, error analysis and variogram analysis were used to evaluate the realizations. Finally, Ni anomalies related to soil heavy metal pollution were extracted by combined concentration–area (C–A) fractal model and uncertainty analysis, in the verification area. Our studies indicate that the proposed conditional GAN model is efficient in extrapolating downscaled geochemical data, since both the realizations and fine data maintain similar spatial autocorrelation and statistical distribution.

Short-Scale Soil Spatial Variability of a Salt-Affected Land Allotment in &lt;em&gt;Maha-Illuppallama&lt;/em&gt;, Sri Lanka

The development of salt-affected soils is a serious limitation for sustainable agricultural crop production in Sri Lanka. This study was conducted to explore the short-scale spatial variability of a salt-affected land allotment at Maha-Illupallama in dry zone, Sri Lanka. Seventy soil samples from a depth of 0 - 30 cm were randomly collected within the study site. Soil Electrical Conductivity (EC), pH, Cation Exchange Capacity (CEC), Exchangeable (Ex.) cations; Ex.K, Ex.Na, Ex.Mg and Ex.Ca were measured using established techniques. Sodium Adsorption Ratio (SAR), Exchangeable Na percentage (ESP), K (EPP), Mg (EMP), and Ca (ECP) were estimated based on the relevant measured soil properties. Exploratory data analysis found considerable spatial variability in EC, ESP, SAR, and EPP while CEC, EMP, and ECP showed moderate spatial variability. The soil pH and BS exhibited lesser spatial variability within the study field. Variogram analyses revealed the presence of strong structured spatial variability for each analyzed soil parameter within the study site. Four Potential Management Zones (PMZs) i.e., PMZ1, PMZ2, PMZ3, and PMZ4 were demarcated using Fuzzy k means classification. Highest EC (1.19 ± 0.70 dS m-1) and SAR (19.05 ± 13.09) mean values were observed in PMZ2. Higher EMP mean values were observed in PMZ 1 and 3 in comparison to PMZ 2 and 4. Other evaluated soil properties varied among the designated PMZs. Soils in several places revealed pH &gt; 8.5, EC &lt; 4.5 dS m-1, ESP &gt; 15%, and SAR &gt; 13 values indicating an occurrence of sodic soil. The results underline the considerable potential for the use of Site-Specific Soil Management (SSSM) practices based on the PMZs in the examined salt-affected soil to boost productivity.

Spatio-temporal sensitivity analysis of the wetland modules of a semi-distributed hydrological model

Hydrological models offer the opportunity to evaluate wetland conservation networks as nature-based solutions to mitigate hydrological extremes. To optimize the replication of a hydrological model response (i.e., stream flows), the models rely on input data (land cover, hydrographic network, soil types, topography, meteorological data) and parameters. However, the effect of parametric uncertainty on the simulated variables and the spatio-temporal variability of this sensitivity are insufficiently documented. This study presents an application of the novel global sensitivity analysis method, the Variogram Analysis of Response Surface, to the isolated and riparian wetland modules of HYDROTEL, a semi-distributed, process-based, deterministic model, on two watersheds located in the province of Quebec, Canada. The analysis accounted for: (i) the spatial variability by assessing the sensitivity at various locations in the watersheds and by compiling sensitivity indices for wetland networks discretized following the Strahler order classification which quantifies the structure of hydrographic networks and (ii) the temporal variability of the sensitivity using the Generalized Global Sensitivity Matrix. The results indicate that the parameters defining the geometry of wetlands (area, depth) are the most sensitive for both isolated and riparian wetlands, and for all Strahler orders. The temporal assessment of sensitivity highlights the seasonal controlling processes, including a peaking sensitivity of the maximum water depth in wetlands during spring snowmelt, whereas the sensitivity of the evapotranspiration parameter increases during summer, but is null during winter. Results also indicate that the parameters of the isolated modules are more sensitive for wetlands located on lower stream order (upstream/headwaters) while those of the riparian modules display a greater sensitivity when wetlands are located on higher Strahler orders (downstream/main channel). These findings deepen our understanding of the impact of wetland features on stream flows and provide guidelines to plan future data acquisition campaigns in wetlandscapes.

Mapping spatial and temporal trends of atmospheric deposition of nitrogen at the landscape level in Germany 2005, 2015 and 2020 and their comparison with emission data

Mosses are particularly suitable for recording the accumulation of atmospheric substance inputs in large areas at relatively many locations. In Europe, this has been done every five years since 1990 as part of the European Moss Survey. In this framework, mosses were collected at up to 7312 sites in up to 34 countries and chemically analyzed for metals (since 1990), nitrogen (since 2005), persistent organic pollutants (since 2010) and microplastic (since 2015). The present investigation aimed at determining the nitrogen accumulated in three-year-old shoots from mosses collected in Germany in 2020 by quality-controlled sampling and chemical analysis according to the European Moss Survey Protocol (ICP Vegetation 2020). The spatial structure of the measurement values was analyzed by means of Variogram Analysis, and the respective function was used for Kriging-Interpolation. In addition to mapping the nitrogen values according to the international classification, maps based on 10 percentile classes were calculated. Maps for the Moss Survey 2020 data were compared with respective maps produced from the 2005 and 2015 Moss Survey data. Trends in Germany-wide nitrogen medians over the past three campaigns (2005, 2015 and 2020) show that nitrogen medians decreased by −2 % between 2005 and 2015 and increased by +8 % between 2015 and 2020. These differences are not significant and do not match the emission trends. Therefore, emission register data needs to be controlled by monitoring nitrogen deposition with technical and biological samplers and deposition modelling.

Modeling and mapping the spatial variability of soil micronutrients in the Tigris basin

BackgroundCrop production is negatively impacted by excess and lack of soil micronutrients. Due to anthropogenic and natural factors, soil micronutrients vary greatly in space, necessitating time- and money-consuming large-scale sampling. Therefore, modeling their spatial distributions and forecasting in non-sampled areas are essential for high crop production. MethodsIn this study, regional variations in soil micronutrient content of the Upper Tigris Basin were modeled to produce local change maps for the development of site-specific nutrient management systems. The concentrations of extractable zinc (Zn), copper (Cu), manganese (Mn), and iron (Fe) in soil samples taken at 388 different sites between 0 and 20 cm deep were determined. Using variogram and kriging analyses, the spatial distribution of the micro element concentrations was modeled and mapped in a GIS environment. ResultsThe micronutrients demonstrated significant variability with a high coefficient of variation (CV > 35%). It was found that the spatial dependence of the samples ranged from low for Fe and Cu to high for Zn and Mn. The spatial distribution of soil micronutrients was influenced by soil texture in addition to distance. Overall, the results demonstrated that the management of site-specific micronutrients may be aided by the integration of geostatistics and GIS, which is particularly beneficial in terms of effective management of the lands and the optimal use of inputs. ConclusionOverall, the findings showed that the integration of geostatistics and GIS may be helpful in the management of site-specific micronutrients, which is especially advantageous in terms of efficient management of the lands and the best use of inputs.