Impact Of Spatial Variability Research Articles

Hypoplastic modeling post-failure behavior of sandy slope with porosity spatial variability

Recent probabilistic studies have thoroughly examined how uncertainties in soil shear strength affect slope stability, while the impact of spatial variability in soil porosity on post-failure behaviors of slopes has received less attention. Overlooking the influence of porosity may lead to inaccurate assessments of slope post-failure behaviors. This study presents a random smoothed particle hydrodynamics (RSPH) framework for probabilistic analysis of slope failure with large deformation using a critical state-based hypoplastic constitutive model. The results indicate that the proposed RSPH method provides reasonable estimates for slope post-failure behaviors. Increases in both the coefficient of variation (COV) and the scale of fluctuation (SOF) of porosity give rise to underestimations of the run-out distance and sliding volume. Additionally, slope failure modes are affected by increasing uncertainty of porosity, which may contribute to the occurrence of deep-seated failures.

On the conceptual complexity of non-point source management: impact of spatial variability

Abstract. Non-point source (NPS) pollution has degraded groundwater quality of unconsolidated sedimentary basins over many decades. Properly conceptualizing NPS pollution from the well scale to the regional scale leads to complex and expensive numerical models: key controlling factors of NPS pollution – recharge rate, leakage of pollutants, and soil and aquifer hydraulic properties – are spatially and, for recharge and pollutant leakage, temporally variable. This leads to high uncertainty in predicting well pollution. On the other hand, concentration levels of some key NPS contaminants (salinity, nitrate) vary within a limited range (< 2 orders of magnitude), and significant mixing occurs across the aquifer profile along the most critical compliance surface: drinking water wells with their extended vertical screen length. Given these two unique NPS contamination conditions, we here investigate the degree to which NPS travel time to wells and the NPS source area associated with an individual well can be appropriately captured, for practical applications, when spatiotemporally variable recharge, contaminant leakage rates, or hydraulic conductivity are represented through a sub-regionally homogenized parametrization. We employ a Monte Carlo-based stochastic framework to assess the impact of model homogenization on key management metrics for NPS contamination. Results indicate that travel time distributions are relatively insensitive to the spatial variability of recharge and contaminant loading, while capture zone and contaminant time series exhibit some sensitivity to source variability. In contrast, homogenization of aquifer heterogeneity significantly affects the uncertainty assessment of travel times and capture zone delineation. Surprisingly, the statistics of relevant NPS well concentrations (fast and intermediate travel times) are fairly well reproduced by a series of equivalent homogeneous aquifers, highlighting the dominant role of NPS solute mixing along well screens.

Impact of spatial variability of geotechnical properties on uncertain settlement of frozen soil foundation around an oil pipeline

The spatial variability of geotechnical properties can lead to the uncertainty of settlement for frozen soil foundation around the oil pipeline, and it can affect the stability of permafrost foundation. In this paper, the elastic modulus, cohesion, angle of internal friction and poisson ratio are taken as four independent random fields. A stochastic analysis model for the uncertain settlement characteristic of frozen soil foundation around an oil pipeline is presented. The accuracy of the stochastic analysis model is verified by measured data. Considering the different combinations for the coefficient of variation and scale of fluctuation, the influences of spatial variability of geotechnical properties on uncertain settlement are estimated. The results show that the stochastic effects between elastic modulus, cohesion, angle of internal friction and poisson ratio are obviously different. The deformation parameters have a greater influence on stochastic settlement than the strength parameters. The overall variability of settlement reduces with the increase of horizontal scale of fluctuation and vertical scale of fluctuation. These results can improve our understanding of the influences of spatial variability of geotechnical properties on uncertain settlement and provide a theoretical basis for the reliability analysis of pipeline engineering in permafrost regions.

Impact of spatial variability in hydraulic parameters on plume migration within unsaturated surficial formations

Heterogeneities in textural properties of surficial formations at field scale result in spatial variations in hydraulic parameters governing unsaturated zone flow. This study aims at quantifying the influence of such variations on solute transport resulting from a localized source of radioactive contaminant at ground level.The study focuses on three hydraulic parameters related to the Mualem-van Genuchten formalism, namely the saturated hydraulic conductivity Ks, the parameter α inversely proportional to the air-entry value, and the parameter n related to the pore-size distribution. Sets of random fields accounting for spatial variability of these parameters are generated using lognormal distributions with different variances and correlation lengths. These random fields are used as inputs to an unsaturated flow and transport model to simulate radionuclide plume migration.Each simulated plume is characterized by its size (plume surface area), position (location of center of mass) and shape (elongation ratio) within the unsaturated zone. By comparison with the homogeneous medium, Ks-, α- and n-random fields generated with the mean variances computed through the analysis of a global soil database respectively result in average in (i) 25 (variable Ks), 20 (variable α) and 65% (variable n) increase in plume size; (ii) 0.8, 1 and 1.8 m horizontal offsets of the plume center; and (iii) 20, 30 and 50% decrease in plume circularity. In addition, changes in the variance values within one order of magnitude appear to have critical consequences only for the n parameter.The issue of spatial variability of hydraulic parameters is thus crucial for characterizing the evolution of pollutant plumes within an unsaturated zone and for developing better remediation strategies for industrial sites.

Anti-symmetric mode excitation and seismic response of base-isolated bridges under asynchronous input motion

This paper investigates the effect of asynchronous earthquake ground motion on the transverse response of base-isolated bridges. In this context, the excitation of anti-symmetric modes of vibration under asynchronous input is examined and is statistically correlated with characteristic engineering demand parameters. Different ground motion scenarios are considered for various combinations of soil class, wave propagation velocity and loss of correlation patterns among different support motions, using a spectral representation method to generate m-variate, fully non-stationary, EC8 spectrum-compatible ground motion vector processes. It is shown that in the idealised case of the wave passage effect only, the detrimental effects of asynchronous excitation are concentrated on the very last piers along the direction of the seismic waves. However, when loss of coherency is also taken into account in a more realistic scenario, the impact of spatial variability is significantly more uniformly distributed. Most importantly, the conditional probability of a detrimental increase in an EDP of interest (i.e., pier base bending moments and deck drift) under multi-support excitation given that an anti-symmetric mode is excited is not only uniform but also considerably high. This is a clear evidence that the local increase of seismic demand in the bridge studied is associated with the excitation of the first anti-symmetric mode of vibration.

Effect of Cone Penetration Conditioning on Random Field Model Parameters and Impact of Spatial Variability on Liquefaction-Induced Differential Settlements

AbstractThe prediction of liquefaction-induced damage to structures can be aided in part by an understanding of the magnitude of inherent spatial variability of the underlying liquefaction-suscepti...

The impact of spatial variables in dust storms on desert cities Study Area / Ramadi City

Our cities is desert cities because of desert climate factors in them , this research deals with role of spatial factors in the dust storms of desert cities . Two factors effected in spatial : Difference of elevation and distance from the center city . We found multi relationships between spatial factors and dust storms in Ramadi city , Which these relationship very important in city design and planning such as in objective , urban and rural environment , environmental planning

The influence of urban environments on our subjective momentary experiences

In today's developed world, the ability of a city to generate good experiences for its residents and visitors is a main aspect of its attractiveness. A good city is considered to be one in which people feel secure, relaxed, and happy. This article explores the factors that influence the subjective momentary experiences of individuals in the city, while focusing on the impact of spatial variables on these experiences; 91 students living in Jerusalem, Israel, were asked to repeatedly self-report four dimensions of episodic experience, namely, sense of security, happiness, annoyance, and sense of comfort. Reports were sent in real time using a smartphone application during an eight-month period. The results, based on over 5000 experience samples, indicate that subjective momentary experiences, particularly sense of comfort and sense of security, are highly influenced by situational variables and environmental characteristics including type of activity and environment, place characteristics, and company. Surprisingly, personality variables which are considered to be a main determinant of wellbeing and general life satisfaction were found to be non-significant in the multilevel models that were implemented. This finding further supports the notion that momentary experiences greatly differ from general evaluations of subjective wellbeing.

Stochastic analysis of the hydraulic conductivity estimated for a heterogeneous aquifer via numerical modelling

Abstract. The paper aims to evaluate the impacts of the average hydraulic conductivity of the heterogeneous aquifer on the estimated hydraulic conductivity using the observations from pumping tests. The results of aquifer tests conducted at a karst aquifer are first introduced. A MODFLOW groundwater flow model was developed to perform numerical pumping tests, and the heterogeneous hydraulic conductivity (K) field was generated using the Monte Carlo method. The K was estimated by the Theis solution for an unconfined aquifer. The effective hydraulic conductivity (Ke) was calculated to represent the hydraulic conductivity of a heterogeneous aquifer. The results of numerical simulations demonstrate that Ke increase with the mean of hydraulic conductivity (EK), and decrease with the coefficient of variation of the hydraulic conductivity (Cv). The impact of spatial variability of K on the estimated Ke at two observation wells with smaller EK is less significant compared to the cases with larger EK.

Impact of spatial variability in undrained shear strength on active lateral force in clay

This study explores the mechanism of the active lateral force (Pa) in clay when there is spatial variability in the undrained shear strength. It is shown that the effect of such spatial variability cannot be fully explained by considering the spatial averaging over a prescribed area or line only; the mechanism to seek the favorable failure path is also important. Ignoring this important mechanism is risky, rendering the Pa estimate smaller than the actual Pa value. The mechanism of seeking the favorable failure path is explored, and a probability distribution model for Pa is proposed to characterize this sophisticated mechanism. Furthermore, a simplified procedure is proposed to simulate the Pa samples without the use of the finite element method or limit equilibrium method.

Impacts of Spatial Variability on Aboveground Biomass Estimation from L-Band Radar in a Temperate Forest

Estimation of forest aboveground biomass (AGB) has become one of the main challenges of remote sensing science for global observation of carbon storage and changes in the past few decades. We examine the impact of plot size at different spatial resolutions, incidence angles, and polarizations on the forest biomass estimation using L-band polarimetric Synthetic Aperture Radar data acquired by NASA’s Unmanned Aerial Vehicle Synthetic Aperture Radar (UAVSAR) airborne system. Field inventory data from 32 1.0 ha plots (AGB < 200 Mg ha−1) in approximately even-aged forests in a temperate to boreal transitional region in the state of Maine were divided into subplots at four different spatial scales (0.0625 ha, 0.25 ha, 0.5 ha, and 1.0 ha) to quantify aboveground biomass variations. The results showed a large variability in aboveground biomass at smaller plot size (0.0625 ha). The variability decreased substantially at larger plot sizes (>0.5 ha), suggesting a stability of field-estimated biomass at scales of about 1.0 ha. UAVSAR backscatter was linked to the field estimates of aboveground biomass to develop parametric equations based on polarized returns to accurately map biomass over the entire radar image. Radar backscatter values at all three polarizations (HH, VV, HV) were positively correlated with field aboveground biomass at all four spatial scales, with the highest correlation at the 1.0 ha scale. Among polarizations, the cross-polarized HV had the highest sensitivity to field estimated aboveground biomass (R2 = 0.68). Algorithms were developed that combined three radar backscatter polarizations (HH, HV, and VV) to estimate aboveground biomass at the four spatial scales. The predicted aboveground biomass from these algorithms resulted in decreasing estimation error as the pixel size increased, with the best results at the 1 ha scale with an R2 of 0.67 (p < 0.0001), and an overall RMSE of 44 Mg·ha−1. For AGB < 150 Mg·ha−1, the error reduced to 23 Mg·ha−1 (±15%), suggesting an improved AGB prediction below the L-band sensitivity range to biomass. Results also showed larger bias in aboveground biomass estimation from radar at smaller scales that improved at larger spatial scales of 1.0 ha with underestimation of −3.62 Mg·ha−1 over the entire biomass range.

Simulation of water quality in a flood detention area using models of different spatial discretization

Detention areas provide a means to lower peak discharges in rivers by temporarily storing excess water. In the case of extreme flood events, the storage effect reduces the risk of dike failures or extensive inundations for downstream reaches and near the site of abstraction. Due to the large amount of organic matter contained in the river water and the inundation of terrestrial vegetation in the detention area, a deterioration of water quality may occur. In particular, decay processes can cause a severe depletion of dissolved oxygen (DO) in the temporary water body. In this paper, we studied the potential of a water quality model to simulate the DO dynamics in a large but shallow detention area to be built at the Elbe River (Germany). Our focus was on examining the impact of spatial discretization on the model’s performance and usability. Therefore, we used a zero-dimensional (0D) and a two-dimensional (2D) modeling approach in parallel. The two approaches solely differ in their spatial discretization, while conversion processes, parameters, and boundary conditions were kept identical. The dynamics of DO simulated by the two models are similar in the initial flooding period but diverge when the system starts to drain. The deviation can be attributed to the different spatial discretization of the two models, leading to different estimates of flow velocities and water depths. Only the 2D model can account for the impact of spatial variability on the evolution of state variables. However, its application requires high efforts for pre- and post-processing and significantly longer computation times. The 2D model is, therefore, not suitable for investigating various flood scenarios or for analyzing the impact of parameter uncertainty. For practical applications, we recommend to firstly set up a fast-running model of reduced spatial discretization, e.g. a 0D model. Using this tool, the reliability of the simulation results should be checked by analyzing the parameter uncertainty of the water quality model. A particular focus may be on those parameters that are spatially variable and, therefore, believed to be better represented in a 2D approach. The benefit from the application of the more costly 2D model should be assessed, based on the analyses carried out with the 0D model. A 2D model appears to be preferable only if the simulated detention area has a complex topography, flow velocities are highly variable in space, and the parameters of the water quality model are well known.

Effect of rainfall spatial variability and sampling on salinity prediction in an estuarine system

Summary Reliable and accurate forecasts of salinity changes are essential for the success of current and future management scenarios aimed at restoring and sustaining natural resources of coastal and estuarine ecosystems. Because of the physical complexity of such ecosystems, information on uncertainty associated with salinity forecasts should be assessed and incorporated into management and restoration decisions. This study focuses on the impact of spatial variability and limited sampling of rainfall on salinity prediction in an estuarine system. The analysis is conducted on the Barataria basin, which is a wetland-dominated estuarine system located directly west of the Mississippi Delta complex on the United States coast of south Louisiana. The basin has been experiencing significant losses of wetland at a rate of nearly 23 km2/year. Radar-rainfall data with high spatial resolution are used to simulate various scenarios of hypothetical rain gauge sampling densities over the basin. A mass-balance hydrologic salinity model is used to assess the effect of reduced rainfall sampling on salinity prediction in the basin. The results indicated that, due to the critical role played by rainfall in determining the overall balance of the basin freshwater budget, a high degree of uncertainty exists in salinity predictions when using typical average rain gauge densities (e.g., 1.3 gauges/1000 km2 in the US). These uncertainties decline sharply as the number of available gauges is increased beyond the typically available density. Uncertainties in salinity predictions in the Barataria basin are larger in inland locations and smaller near the mouth of the basin, where salinity conditions in the coastal waters of the Gulf of Mexico exert a large influence. Rainfall uncertainties also affected parameter estimation during model calibration, where the estimation of some parameters exhibited significant levels of bias and random scatter. The study highlights the necessity of improving rainfall monitoring especially in estuarine systems that are controlled by rainfall as a main source of freshwater and where the management of freshwater supply is a viable option.

The significance of spatial rainfall representation for flood runoff estimation: A numerical evaluation based on the Lee catchment, UK

Summary In the context of flood management, this paper investigates the relationship between spatial rainfall and runoff production, based on 15 years of radar data, 16 raingauges and 12 flow stations from the 1400 km 2 Lee catchment, UK. Event-based, semi-distributed rainfall–runoff modelling is undertaken. Alternative rainfall estimators (radar data and raingauge networks of various density) are considered and their effects on simulated runoff evaluated as a function of rainfall type, catchment type and catchment scale. An index of spatial variability is defined, based on the difference between the reference rainfall (defined by the full raingauge network) and alternative rainfall estimators. A modified Nash–Sutcliffe efficiency criterion measures the performance of the simulated runoff with respect to reference simulated runoff. Results show a complex picture. The dominant effect is the spatial variability of the rainfall. No clear pattern emerges as a function of catchment scale, or response time, except that the impact of spatial variability is damped at the whole catchment scale. The sensitivity to spatial rainfall is enhanced on urbanised catchments.

E-Shopping in the Netherlands: Does Geography Matter?

Why consumers shop via the Internet, is a frequently asked question. As yet, the impact of spatial variables on e-shopping has received little attention. In this paper we report our investigation of the spatial distribution of Internet users and online buyers in the Netherlands for the time period 1996–2001 and the impact of spatial variables (residential environment and shop accessibility) on e-shopping. Two hypotheses are tested empirically. The first is that e-shopping is a predominantly urban phenomenon, because new technology usually starts in centres of innovation (innovation-diffusion hypothesis). The second is that people are more likely to adopt e-shopping when their accessibility to shops is relatively low (efficiency hypothesis). Our findings indicate that Internet use and online buying are still largely urban phenomena in the Netherlands, but that there is a trend towards diffusion to the weakly urbanised and rural areas. Not only the innovation diffusion hypothesis, but also the efficiency hypothesis is confirmed by our findings. People living in a (very) strongly urbanised area have a higher likelihood of buying online, but people with a low shop accessibility buy more often online. The analysis also shows that the support for the two hypotheses depends on the type of product. Airline tickets are still mainly bought in very strongly urbanised areas, whereas compact discs, videos, DVDs, and clothing are bought relatively more often in weakly urbanised areas. In conclusion, geography seems to matter for e-shopping.

Uncertainties in Leaching Risk Assessments Due to Field Averaged Transfer Function Parameters

The transfer function model is widely used to estimate solute transport patterns at the field scale. Leaching risk assessments with the transfer function model may be influenced by spatial variability in the net applied water (NAW) distribution, but few researchers have investigated this possibility. The objective of this study was to evaluate the impact of spatial variability in the NAW distribution on leaching risk assessment and identification of leaching risk categories at the field scale. Bromide concentration profiles, irrigation depths, bulk densities, and soil moisture contents were measured in 40 plots across a 57-ha potato (Solanum tuberosum L.) farm, along with field-scale evapotranspiration estimates, to estimate solute transport parameters. Values for field- and plot-scale means and standard deviations for the NAW distribution were estimated using the stochastic convective lognormal transfer function (CLT) model. The probability of NO 3 - leaching below a depth of 2 m was then estimated using field-averaged versus plot-scale estimates for the mean and standard deviation of the NAW distribution. For 30-cm NAW, NO 3 - leaching risks estimated with the CLT model and plot-scale means and standard deviations were very high in 0.4 ha of the field, high in 1.8 ha, moderate in 8.7 ha, low in 23.0 ha, and none in 23.1 ha. In contrast, when field-scale average estimates of NAW were used, there was a low risk of NO 3 - leaching for the entire field. Thus, when estimating leaching risks using the CLT, information about spatial variability of the NAW distribution is important.