Residual Kriging Research Articles

Responsive high-resolution air quality index mapping using model, regulatory monitor, and sensor data in real-time

Interpolated regulatory monitor measurements that are used to calculate the Air Quality Index (AQI) have low spatial resolution and are less accurate where regulatory monitors are sparsely distributed, especially during episodic events such as wildfires and dust storms. In this paper, an AQI map that is more responsive and accurate than current formulations with 5 km spatial and hourly temporal resolution is constructed for Southern California using interpolated concentration fields. The fine particle mass (PM2.5) fields are calculated by combining regulatory-grade monitored concentrations, low-cost sensor measurements, and Community Multiscale Air Quality Model forecasts, using Residual Kriging (RK) interpolation to account for the uncertainty of each input component. The Ozone fields are calculated by combining regulatory monitor and model forecasted concentrations using RK. The interpolated concentration fields have root mean square interpolation errors (RMSE) of 5.59 µg m−3 (PM2.5) and 6.66 ppb (Ozone), smaller than corresponding RMSE of current inverse distance weighting methods used by the U.S. Environmental Protection Agency AirNOW system, and a previous interpolation method used by the South Coast Air Quality Management District. The inclusion of low-cost sensor measurements makes the AQI more responsive to wildfire events, with a PM2.5 RMSE of 7.73 µg m–3, a significant improvement from the other interpolation methods. The mass of particles with an aerodynamic diameter smaller than 10 micrometers (PM10) calculated using natural neighbor interpolation of the difference, PM10—PM2.5, and carbon monoxide and nitrogen dioxide fields calculated using natural neighbor interpolation, have small but usually not statistically significant reductions of RMSE relative to the other interpolation methods. Quality control and correction procedures applied to the low-cost sensor measurements are described. Display of the blended AQI data on an interactive map in real time provides an important tool for the public to minimize their exposure to poor air quality in one of the most polluted regions in the United States.

Integrating Ground-based Observations and Radar Data Into Gridding Sub-daily Precipitation

A new and general approach is proposed for interpolating 6-h precipitation series over large spatial areas. The outputs are useful for distributed hydrological modelling and studies of flooding. We apply our approach to large-scale data, measured between 2014 and 2016 at 159 weather stations network of Meteo Romania, using weather radar information and local topography as ancillary data. Novelty of our approach is in systematic development of a statistical model underlying the interpolation. Seven methods have been tested for the interpolation of the 6-h precipitation measurements: four regression methods (linear regression via ordinary least squares (OLS), with and without logarithmic transformation, and two models of generalized additive model (GAM) class, with logarithmic and identity links), and three regression-kriging models (one uses semivariogram fitted separately every 6-h, based on the residuals of the GAM with identity links models, and other two with pooled semivariograms, based on the OLS and GAM with identity links models). The prediction accuracy of the spatial interpolation methods was evaluated on a part of the dataset not used in the model-fitting stage. Due to the good results in interpolating sub-daily precipitation, normal general additive model with identity link followed with kriging of residuals with kriging parameters estimated from pooled semivariograms was applied to construct the final 6-h precipitation maps (PRK-NGAM). The final results of this work are the 6-h precipitation gridded datasets available in high spatial resolution (1000 m × 1000 m), together with their estimated accuracy.

Source apportionment of soil nitrogen and phosphorus based on robust residual kriging and auxiliary soil-type map in Jintan County, China

Previous studies on the source apportionment of soil total nitrogen (TN) and total phosphorus (TP) mainly focused on specific spatial point sites and the closely related information such as soil-type map was seldom used. Moreover, traditional geostatistical models are usually sensitive to outliers. To address these problems, this study first proposed robust residual kriging (RRK) with soil-type data as auxiliary information to increase the spatial prediction accuracies for TN and TP in topsoil (0–20 cm) in Jintan County, China. Then, the spatial distribution patterns of the background concentrations of soil TN and TP were quantitatively assessed based on profile samples (80–100 cm) and the soil-type map. Last, the spatial distribution patterns of exogenous net inputs of TN and TP in topsoil and the corresponding contribution ratios were further calculated. Model comparisons showed that the relative improvement (RI) accuracies of RRK and residual kriging (RK) over the traditionally-used ordinary kriging (OK) were 23.91% and 15.22% for soil TN and 24.14% and 20.69% for soil TP, respectively. The results of the source apportionment indicated that (i) the exogenous net inputs of TN and TP had similar spatial distribution patterns, with the low inputs mainly in the west and high inputs mainly in the northeast and east of the county; (ii) high contribution ratio of exogenous net input of TN (≥67%) in topsoil was mainly located in the north, southeast, and middle of the study area, and the area with the contribution ratio over 70% is 294.16 km2, covering 30.16% of the area of the county; (iii) high contribution ratio of exogenous net input of TP (≥53%) in topsoil was mainly located in the north, northeast, and southeast of the county, and the area with contribution ratio over 70% is 24.35 km2, covering 2.50% of the area of the county. These results provided more accurate spatial decision support for effectively regulating TN and TP in topsoil.

APLICATION OF KRIGING WITH TREND TO SOIL CHARACTERIZATION IN TOP HOLE DESIGN

In top hole designs, geotechnical characterization is fundamental in the study of the axial load capacity of the soil and its implications on the design of the conductor casing. The present work estimates soil parameters from CPTu (piezocone penetration test) data using geostatistical methods, with evaluation of trends, variograms, and kriging. Results suggest that universal and residual kriging are good tools for soil characterization in top hole section design, considering their capacity to support spatiality of the phenomenon and the mapping of the uncertainties involved.

Using climatic and biophysical attributes to model the presence and severity of root disease across the U.S. Northern Rocky Mountains

Root disease fungi are native, soil-borne pathogens that impact forest stand dynamics and productivity through direct tree mortality or reduced yield. Despite being a major contributor of mortality in forests of western North America, particularly in the northern Rocky Mountains in the United States, little is known about the underlying factors that influence root disease occurrence and severity at landscape and broader scales. We used a database with >15,000 geographically-distributed, spatially-referenced root disease assessments from United States Department of Agriculture (USDA) Forest Service Forest Inventory and Analysis (FIA) plots from across the US Northern Rocky mountains and high resolution (30 m) climate and soil-water balance grids to examine climatic and biophysical factors associated with presence and severity of root disease. We used a two-stage modeling approach that combined boosted regression-tree models and residual kriging to predict the probability of occurrence and severity of root disease across the area of analysis. A best-fit model explained root disease occurrence moderately well (AUC = 0.77) and included average annual dewpoint temperature, evapotranspiration and climatic water deficit. Root disease occurrence was associated with moist, humid sites subject to low or moderate climatic water deficits. Root disease severity was not well explained by climatic and biophysical factors alone (R2 = 0.19). Residual kriging only marginally improved model fit (R2 = 0.20). We used the fitted models to produce 30 m resolution gridded root disease occurrence and severity maps across the US Forest Service Northern Region for use in broad-scale planning and analysis. Weak spatial autocorrelation at distances beyond 5 km and relatively low accuracy of the severity model suggests that the FIA plot distribution is not suited to predict spatial patterns of root disease severity. Additional factors related to root disease severity, such as vegetation type and site-specific disturbance histories, are likely needed for fine-scale severity predictions.

Anomaly Kriging Helps to Remove Bias in Spatial Model Runoff Estimates

AbstractThe low spatial density of streamflow gauging stations limits the accuracy of spatial streamflow estimates in many parts of the world. Strategies to improve runoff estimates in the absence of dense measurements have tended to focus on estimating parameters of runoff models in ungauged regions, through so‐called parameter regionalization methods. However, parameter regionalization can be affected by overdependence on calibration at gauged sites, model parameter equifinality, and ensuing estimation errors. As a result, spatial model runoff estimates typically exhibit spatially correlated biases. This analysis attempts to enhance the use of observations in spatial runoff estimation. Specifically, we assessed the potential to reduce systematic errors by spatially interpolating residuals (i.e., errors) between prior grid‐based streamflow estimates for Australia at 0.05° × 0.05° grid from the Australian Bureau of Meteorology's calibrated, operational Australian Water Resources Assessment Landscape model (AWRA‐L) and streamflow gauging records from 780 unimpeded, relatively small catchments. We analyzed spatial autocorrelation in residuals and tested an efficient two‐step correction approach involving a uniform correction and subsequent kriging of residuals. The approach removed an average of 41% of systematic bias in the model estimates and also improved other model performance measures. Further reduction in errors at shorter timescales may be achievable through a temporally hierarchical correction scheme.

Use of Topographic Models for Mapping Soil Properties and Processes

Landscape topography is an important driver of landscape distributions of soil properties and processes due to its impacts on gravity-driven overland and intrasoil lateral transport of water and nutrients. Rapid advancements in aerial, space, and geographic technologies have led to large scale availability of digital elevation models (DEMs), which have proven beneficial in a wide range of applications by providing detailed topographic information. In this report, we presented a summary of recent topography-based soil studies and reviewed five main groups of topographic models in geospatial analyses widely used for soil sciences. We then compared performances of two types of topography-based models—topographic principal component regression (TPCR) and TPCR-kriging (TPCR-Kr)—to ordinary kriging (OKr) models in mapping spatial patterns of soil organic carbon (SOC) density and redistribution (SR) rate. The TPCR and OKr models were calibrated at an agricultural field site that has been intensively sampled, and the TPCR and TPCR-Kr models were evaluated at another field of interest with two sampling transects. High-resolution topographic variables generated from light detection and ranging (LiDAR)-derived DEMs were used as inputs for the TPCR model building. Both TPCR and OKr models provided satisfactory results on SOC density and SR rate estimations during model calibration. The TPCR models successfully extrapolated soil parameters outside of the area in which the model was developed but tended to underestimate the range of observations. The TPCR-Kr models increased the accuracies of estimations due to the inclusion of residual kriging calculated from observations of transects for local correction. The results suggest that even with low sample intensives, the TPCR-Kr models can reduce estimation variances and provide higher accuracy than the TPCR models. The case study demonstrated the feasibility of using a combination of linear regression and spatial correlation analysis to localize a topographic model and to improve the accuracy of soil property predictions in different regions.

Conditional simulation of surface rainfall fields using modified phase annealing

Abstract. The accuracy of quantitative precipitation estimation (QPE) over a given region and period is of vital importance across multiple domains and disciplines. However, due to the intricate temporospatial variability and the intermittent nature of precipitation, it is challenging to obtain QPE with adequate accuracy. This paper aims to simulate rainfall fields while honoring both the local constraints imposed by the point-wise rain gauge observations and the global constraints imposed by the field measurements obtained from weather radar. The conditional simulation method employed in this study is modified phase annealing (PA), which is practically an evolution from the traditional simulated annealing (SA). Yet unlike SA, which implements perturbations in the spatial field, PA implements perturbations in Fourier space, making it superior to SA in many respects. PA is developed in two ways. First, taking advantage of the global characteristic of PA, the method is only used to deal with global constraints, and the local ones are handed over to residual kriging. Second, except for the system temperature, the number of perturbed phases is also annealed during the simulation process, making the influence of the perturbation more global at initial phases and decreasing the global impact of the perturbation gradually as the number of perturbed phases decreases. The proposed method is used to simulate the rainfall field for a 30 min event using different scenarios: with and without integrating the uncertainty of the radar-indicated rainfall pattern and with different objective functions.

Three-dimensional digital soil mapping of multiple soil properties at a field-scale using regression kriging

Three-dimensional digital soil mapping (3D-DSM) quantifies both the horizontal and the vertical variability of soil properties. Most current studies in 3D-DSM were based on either one-dimensional profile depth functions or two-dimensional horizontal interpolation techniques, which did not allow true 3D visualization of spatial soil heterogeneity. Only a few studies have utilized the 3D variograms for mapping. Recent advances in proximal soil sensing technologies allow measurement and prediction of soil properties rapidly at multiple depths which could serve as input data for DSM. Various soil physical and chemical properties have already shown either direct or indirect relationships with the proximal soil sensing data. This study aims to test the methodology of 3D-DSM by incorporating a 3D regression kriging (RK) with multiple proximal soil sensing techniques. In this study, vis-NIR spectra were collected in-situ at 148 locations to about 1-m depth using the Veris® P4000 soil profiler at Field 26 of Macdonald Farm, McGill University. Additionally, 32 soil cores were collected out of the 148 locations to 1-m maximum depth and sectioned at 10-cm depth intervals for laboratory analysis of volumetric water content (VWC), soil organic matter (SOM), and clay content. Cubist spectral models were developed for each soil property at the 32 locations and then predicted to the 148 locations, which were then randomly split into calibration (70%, 103 locations) and validation (30%, 45 locations) datasets for mapping. The 3D-RK method included a trend prediction between calibration dataset and environmental covariates (including apparent soil electrical conductivity, gamma-ray radiation, and elevation) and a residual kriging. The generalized linear model (GLM), regression tree (RT), and random forest (RF) models were compared for trend prediction. The covariates were also simulated 100 times using sequential Gaussian simulations to fit into 3D-RK and calculate model uncertainty. As a result, complete 3D digital soil maps with uncertainty were developed. We found that the RF model outperformed GLM and RT in regard to interpreting non-linear soil-landscape relationships and resulting in marginally higher validation accuracy and smaller prediction uncertainty for VWC and clay. The GLM model resulted in slightly better validation results and smaller model uncertainty for SOM only. SOM and clay showed large horizontal and vertical variability and affected the spatial distribution of VWC. The validation accuracy was higher in the soil surface for most soil properties due to the uniform environment in the plow layer and sufficient environmental covariates collected at the soil surface. The mapping uncertainty increased with depth for VWC and clay content but decreased with depth for SOM because SOM content decreases with depth.

Comparison of interpolation methods for soil moisture prediction on China's Loess Plateau

AbstractDue to limited in situ observations, prediction of large‐scale soil moisture content (SMC) for deep soil layers via interpolation is usually very challenging. This is especially true for regions with high spatial variations of terrain features. For precise prediction at a regional scale, SMC data for the 0‐ to 500‐cm soil profile across China's Loess Plateau (CLP) region were collected and interpolated using four different methods. The methods included inverse distance weighting (IDW), ordinary kriging (OK), multiple linear regression with residual kriging (MLR‐RK), and radial basis function neural network with residual kriging (RBFNN‐RK). The objective of the study was to determine the optimal interpolation method for predicting regional SMC at various soil layers. The study showed that the performances of IDW, OK, and RBFNN‐RK in predicting SMC were generally much better than that of MLR‐RK. Specifically, IDW performed best for soil depths of 200‒300 and 400‒500 cm. This was attributed to the more uniform distribution (smoother change of spatial clusters) of SMC in these two layers. The OK method performed best for the 10‐ to 40‐ and 40‐ to 100‐cm soil layers, which was due to the strong spatial dependence of the two layers. The RBFNN‐RK performed best for the 0‐ to 10‐, 100‐ to 200‐, and 300‐ to 400‐cm soil layers, because RBFNN‐RK captures nonlinear relations of SMC with environmental factors. Ordinary kriging, IDW, and RBFNN‐RK interpolation can therefore be used to predict regional SMC for different soil layers in CLP region. The RBFNN‐RK method was recommended for predicting regional SMC in complex topographic hilly‐gully regions where there is nonlinear relation between SMC and environmental variables.

Predicting regional spatial distribution of soil texture in floodplains using remote sensing data: A case of southeastern Iran

Soil texture is a physical property of soil and knowledge on its spatial distribution is essential for many agricultural and environmental activities especially in alluvial plains. This study aimed to predict the spatial distribution of soil fractions (i.e. percentages of sand, silt, and clay) in Sistan floodplain on a regional scale (area: 1500 km2). Random forest (RF), regression kriging-neural network residual kriging (RKNNRK), neural network residual kriging (NNRK), regression kriging (RK), and cokriging (COK) methods were used to map soil texture components over the region. Soil fractions were measured for 160 soil samples taken from the surface layer (0–30 cm) of various soil series in agriculture land of Sistan floodplain. The additive log-ratio (alr) transformation was applied to transform texture components prior to prediction. Remote sensing data including Landsat 8′ Bands (1–8), Band 4 to Band 8 ratio, Band 4 to Band 3 ratio, NDVI, GSI, Brightness Index, and Clay Index were used as auxiliary variables for interpolation of soil fractions. 80% of actual data was used for prediction and 20% of data was used for validation. The performance of methods used was evaluated using RMSE, ME and MAE criteria. The results showed that the RKNNRK model has the highest accuracy for prediction of sand (RMSE = 15.04%) and clay (RMSE = 8.77%) contents while the most accurate model for predicting silt content is NNRK (RMSE = 12.68%). RK and COK performed worse than NNRK and RKNNRK. A relatively high value of RMSE obtained for sand (17.89%), silt (14.15%), and clay (8.89%) contents by the RF model. This could be due to the low relief of study area, regional scale effects, low density of sampling points and high heterogeneity of soil texture components in floodplains. Our findings revealed that RKNNRK and NNRK models, when combined with remote sensing data, produce more accurate results and therefore can be used for appropriate mapping of soil fractions in floodplains and on a regional scale.

The SAFRAN daily gridded precipitation product in Tunisia (1979–2015)

AbstractA new high‐resolution (5 km) gridded daily precipitation dataset for Tunisia between 1979 and 2015 is introduced. This product combines 960 rain gauges with the SAFRAN analysis to produce the precipitation gridded data. A validation approach on two different datasets reveals that the SAFRAN analysis outperforms other standard interpolation methods such as Inverse Distance, Nearest Neighbors, Ordinary Kriging or Residual Kriging with altitude. When compared to EOBS, a widely used gridded dataset over Europe, a strong negative bias in EOBS precipitation is found. However due to the aridity and the low density of rain gauges in south Tunisia, results in this region must be analyzed with care. The SAFRAN product could be useful for various purposes such as climate model evaluation, climate studies, hydrological modelling to support the planning and management of surface water resources in Tunisia.

Prediction of the chrome distribution in subarctic Noyabrsk using co-kriging, generalized regression neural network, multilayer perceptron, and hybrid technics

Combination of geostatistical interpolation (kriging) and machine learning (artificial neural networks, ANN) methods leads to an increase in the accuracy of forecasting. The paper considers the application of residual kriging of an artificial neural network to predicting the spatial contamination of the surface soil layer with chromium (Cr). We reviewed and compared two neural networks: the generalized regression neural network (GRNN) and multilayer perceptron (MLP), as well as the combined method: multilayer perceptron residual kriging (MLPRK). The study is based on the results of the screening of the surface soil layer in the subarctic Noyabrsk, Russia. The models are developed based on computer modeling with minimization of the RMSE. The MLPRK model showed the best prognostic accuracy.

Spatiotemporal geostatistical modeling of groundwater levels under a Bayesian framework using means of physical background

The joint spatiotemporal modeling of aquifer level fluctuations provides a significant tool in the prediction of groundwater levels at unvisited locations. Two types of variogram functions, appropriately modified to incorporate physical information, are presented and compared under Space-time Residual kriging methodology using the Bayesian Bootstrap idea to quantify uncertainty. The spatiotemporal trend is approximated using a component of physical meaning. A recently developed Spartan type non-separable variogram function employs tools of physical meaning to enhance the efficiency and reliability of spatiotemporal geostatistical modeling in groundwater applications. In addition, the product-sum space-time variogram is applied and involves in the separable variogram functions a scale factor of hydraulic conductivity directional ratio. The proposed variogram functions involve a non-Euclidean distance metric and are mathematically valid (i.e., constitutes permissible models). Herein, the efficiency of the proposed tools is tested using groundwater level data from an alluvial unconfined aquifer. Both functions provide improved results compared to those of the Space-time Ordinary kriging. Between the two, the non-separable one has the most efficient performance.

Pre-stratified modelling plus residuals kriging reduces the uncertainty of aboveground biomass estimation and spatial distribution in heterogeneous savannas and forest environments

Mapping aboveground biomass (AGB) is a challenge in heterogeneous environments, such as the Brazilian savannas and tropical forests located in Minas Gerais state (MG), Brazil. The factors linked to AGB stocks vary in climate, soil characteristics, and stand-level structural attributes over short distances, making generalization of AGB difficult over regional-scales. We offer the hypothesis that stratification into vegetation types at the plot level plus a regression kriging technique, can reduce the variability of factors controlling AGB, helping to select the appropriate predictor variables and result in an ability to produce reliable models and maps. To do so, we incorporate remotely sensed data (Landsat and MODerate resolution Imaging Spectroradiometer-MODIS), spatio-environmental variables, and forest inventory data to develop spatial-explicit maps of AGB across three important Brazilian biomes (savanna, Atlantic forest, and semi-arid woodland). We modelled and predicted the spatial distribution of AGB of six individual vegetation types of savanna-forest biomes (shrub savanna, woodland savanna, densely wooded savanna, deciduous forest, semi-deciduous forest and rain forest), utilizing a random forests (RF) algorithm plus residual kriging, selecting the lowest number of variables that offer the best predictive performance. The stratified models notably improved the AGB prediction by reducing the mean absolute error – MAE (%) and the root-mean-square error – RMSE (Mg/ha) for all vegetation types, mainly for shrub savanna (MAE reduced from 82.69 to 54.73%). The AGB spatial distribution is governed mainly by precipitation and seasonality. The south and east of MG presented high values of AGB due to the predominance of semi-deciduous trees and rain forest conditions within Atlantic forest biome (total of 491,456,607 Mg), with a higher amount rain over the year, lower temperatures, and lower precipitation seasonality. Rain forests have the largest mean AGB per area (157.71 Mg/ha) while semi-deciduous forests hold the largest AGB stocks in the state (583,176,472 Mg). Shrub savannas, located in the central, northwest and north regions of MG (lower amount of rain, higher temperatures and strong seasonality), accounted the lowest amount of AGB in both total AGB (27,906,281 Mg) and AGB per area (18.80 Mg/ha). Our study demonstrates that stratification can reduce variability and improve estimates by developing individual models and selecting optimal predictor variables dependent on the characteristics of specific vegetation types. The methods demonstrated and the resultant maps and estimates improve the quality of regional biomass estimates needed to understand and mitigate climate change, enabling researchers to refine estimates of greenhouse gas emissions.

Predictive modeling of elevated groundwater nitrate in a karstic spring-contributing area using random forests and regression-kriging

Many of Florida’s large springs have seen an order of magnitude increase in nitrate concentration since the mid-twentieth century, which has contributed to the proliferation of nuisance algae and alteration of spring ecosystems. Cost-effective strategies to limit nitrate inputs require identification of contributing land areas within springs (springsheds) where surficial nitrogen sources are most likely to be transported to the underlying aquifer. To address spatial variability in vulnerability to nitrogen loading, spatial models specific to nitrate were developed for the Silver Springs springshed (Florida, USA). Random forest classification models were trained using an extensive (1554 wells) groundwater nitrate dataset assembled from public water system and agency monitoring data. Spatial layers representing soil hydrology, subsurface geology, recharge potential, and nitrogen sources were used as predictor variables. Random forest models produced out-of-bag error estimates of 21% or less, and variable importance plots indicated that a subset of subsurface geological predictors was the most important contributors to overall model accuracy. Although predictors representing land use and nitrogen sources contributed less to overall model accuracy, they were still important in the final spatial discrimination of the most vulnerable areas. Random forest model accuracy was further improved by kriging of model residuals, and kriged residuals were added to model estimates to produce final prediction maps. The models developed are well suited for a management decision framework for environmental restoration, as it informs the manager with maps of probabilistic information. Recognizing the potential for legacy nitrate impacts, we recommend the current models be adopted as a part of a tiered approach to restoration projects that first prioritizes critical areas using the models presented herein and subsequently uses site-specific information to verify local impacts.

Digital mapping of topsoil pH by random forest with residual kriging (RFRK) in a hilly region

Soil pH is a vital attribute of soil fertility. The accurate and efficient prediction of soil pH can provide the necessary basic information for agricultural development. In the present study, random forest with residual kriging (RFRK) was used to predict soil pH based on stratum, climate, vegetation and topography in a hilly region. The performance of RFRK was compared with those of the classification and regression tree (CART) and the random forest (RF). Comparative results showed that RFRK provided the best performance. The corresponding values of Lin’s concordance correlation coefficient, coefficient of determination, mean absolute error and root mean square error were as follows: 0.70, 0.51, 0.44 and 0.61 for CART; 0.80, 0.70, 0.34 and 0.48 for RF; and 0.88, 0.80, 0.25 and 0.39 for RFRK. Stratum and average annual temperature were the most important factors affecting the soil pH in the study area. Results indicate that RFRK is a feasible and reliable tool for predicting soil pH in hilly regions.

On the Performance of Neural Network Residual Kriging in Radio Environment Mapping

This paper addresses the research question: can feedforward neural network (FFNN)-based path loss modeling improve the accuracy of Kriging? Radio propagation factors, which consist of path loss and shadowing, can accurately be obtained via crowdsourcing with Kriging. In most works on Kriging-aided radio environment mapping, measurement datasets are first regressed via linear path loss modeling to ensure spatial stationarity of the shadowing. However, in practical situations, the path loss often contains an anisotropy owing to terrain and obstacle effects. Thus, Kriging may not perform an optimal interpolation because of the errors in path loss modeling. In this paper, an FFNN is used for path loss modeling. Then, ordinary Kriging is applied to interpolate the shadowing. We first evaluate the performance of this method in a case where the transmitter is fixed. It is shown that this method does not improve Kriging in a large-scale and fixed transmitter system; although the FFNN outperforms OLS in path loss modeling. Then, this method is extended to distributed wireless networks where transmitters are arbitrarily located, such as in mobile ad hoc networks (MANETs) and vehicular ad hoc networks (VANETs). The results of a measurement-based experiment show that the FFNN is capable of improving Kriging in such a distributed network case.

Modeling Spatial Patterns of Humus Forms in Montane and Subalpine Forests: Implications of Local Variability for Upscaling

Humus forms are a distinctive morphological indicator of soil organic matter decomposition. The spatial distribution of humus forms depends on environmental factors such as topography, climate and vegetation. In montane and subalpine forests, environmental influences show a high spatial heterogeneity, which is reflected by a high spatial variability of humus forms. This study aims at examining spatial patterns of humus forms and their dependence on the spatial scale in a high mountain forest environment (Val di Sole/Val di Rabbi, Trentino, Italian Alps). On the basis of the distributions of environmental covariates across the study area, we described humus forms at the local scale (six sampling sites), slope scale (60 sampling sites) and landscape scale (30 additional sampling sites). The local variability of humus forms was analyzed with regard to the ground cover type. At the slope and landscape scale, spatial patterns of humus forms were modeled applying random forests and ordinary kriging of the model residuals. The results indicate that the occurrence of the humus form classes Mull, Mullmoder, Moder, Amphi and Eroded Moder generally depends on the topographical position. Local-scale patterns are mostly related to micro-topography (local accumulation and erosion sites) and ground cover, whereas slope-scale patterns are mainly connected with slope exposure and elevation. Patterns at the landscape scale show a rather irregular distribution, as spatial models at this scale do not account for local to slope-scale variations of humus forms. Moreover, models at the slope scale perform distinctly better than at the landscape scale. In conclusion, the results of this study highlight that landscape-scale predictions of humus forms should be accompanied by local- and slope-scale studies in order to enhance the general understanding of humus form patterns.

Strong Ground Motion Characteristics from 2016 Central Italy Earthquake Sequence

The Central Italy earthquake sequence has, to date, generated three mainshocks: M6.1 24 August, M5.9 26 October, and M6.5 30 October 2016. These events, along with aftershocks, were well recorded by Italian networks, and are among the normal fault earthquakes with the highest number of recordings globally. We process records for six events using procedures developed during the latest Next Generation Attenuation (NGA-West2) project, coordinated by the Pacific Earthquake Engineering Research Center (PEER). Many recording sites lacked VS30assignments, which we provide using measured shear wave velocity profiles where available and a local geology proxy otherwise. Stations at close distance, including near the hanging wall, exhibit fling step in some cases but no obvious rupture directivity. The data exhibit fast anelastic attenuation at large distances (>100 km), as predicted by recent Italy-adjusted global models, but not by Italy-specific models. We partition residuals from Italy-adjusted global models, finding negative event terms at short periods (weaker than average shaking). We apply Kriging of within-event peak acceleration and velocity residuals using a global semi-variogram model to estimate the spatial distribution of peak accelerations and velocities, which are generally most intense southwest of Mt. Vettore.