Spatial modelling and prediction on river networks: up model, down model or hybrid?

Preservation of rivers and water resources is crucial in most environmental policies and many efforts are made to assess water quality. Environmental monitoring of large river networks are based on measurement stations. Compared to the total length of river networks, their number is often limited and there is a need to extend environmental variables that are measured locally to the whole river network. The objective of this paper is to propose several relevant geostatistical models for river modeling. These models use river distance and are based on two contrasting assumptions about dependency along a river network. Inference using maximum likelihood, model selection criterion and prediction by kriging are then developed. We illustrate our approach on two variables that differ by their distributional and spatial characteristics: summer water temperature and nitrate concentration. The data come from 141 to 187 monitoring stations in a network on a large river located in the Northeast of France that is more than 5000 km long and includes Meuse and Moselle basins. We first evaluated different spatial models and then gave prediction maps and error variance maps for the whole stream network.

Spatial methods for plot-based sampling of wildlife populations

Classical sampling methods can be used to estimate the mean of a finite or infinite population. Block kriging also estimates the mean, but of an infinite population in a continuous spatial domain. In this paper, I consider a finite population version of block kriging (FPBK) for plot-based sampling. The data are assumed to come from a spatial stochastic process. Minimizing mean-squared-prediction errors yields best linear unbiased predictions that are a finite population version of block kriging. FPBK has versions comparable to simple random sampling and stratified sampling, and includes the general linear model. This method has been tested for several years for moose surveys in Alaska, and an example is given where results are compared to stratified random sampling. In general, assuming a spatial model gives three main advantages over classical sampling: (1) FPBK is usually more precise than simple or stratified random sampling, (2) FPBK allows small area estimation, and (3) FPBK allows nonrandom sampling designs.

Spatial statistical models that use flow and stream distance

We develop spatial statistical models for stream networks that can estimate relationships between a response variable and other covariates, make predictions at unsampled locations, and predict an average or total for a stream or a stream segment. There have been very few attempts to develop valid spatial covariance models that incorporate flow, stream distance, or both. The application of typical spatial autocovariance functions based on Euclidean distance, such as the spherical covariance model, are not valid when using stream distance. In this paper we develop a large class of valid models that incorporate flow and stream distance by using spatial moving averages. These methods integrate a moving average function, or kernel, against a white noise process. By running the moving average function upstream from a location, we develop models that use flow, and by construction they are valid models based on stream distance. We show that with proper weighting, many of the usual spatial models based on Euclidean distance have a counterpart for stream networks. Using sulfate concentrations from an example data set, the Maryland Biological Stream Survey (MBSS), we show that models using flow may be more appropriate than models that only use stream distance. For the MBSS data set, we use restricted maximum likelihood to fit a valid covariance matrix that uses flow and stream distance, and then we use this covariance matrix to estimate fixed effects and make kriging and block kriging predictions.

Aplicação da teoria da confiabilidade em estacas escavadas de grande diâmetro para obtenção das superfícies resistentes de projeto

In this work, the author looks forward to develop a new method capable of incorporate the concepts of the Reliability Theory and Ruin Probability in Deep Foundations, in order to do a better quantification of the uncertainties, which is intrinsic in all geotechnical projects, meanly because we don't know all the properties of the materials that we work with. Using the methodologies of Decourt Quaresma and David Cabral, resistance surfaces have been developed utilizing the data achieved from the Standard Penetration Tests performed in the field of study, in conjecture with the loads defined in the executive project of the piles. The construction of resistance surfaces shows to be a very useful tool for decision making, no matter in which phase it is current on, projecting or execution. The surfaces were developed by Kriging (using the software Surfer® 12), making it easier to visualize the geotechnical profile of the field of study. Comparing the results, the conclusion was that a high safety factor doesn't mean higher security. It is fundamental to consider the loads and resistance of the piles in the whole field, carefully choosing the project methodology responsible to define the diameter and length of the piles

Geoelectrical mapping of contamination in the cemeteries: the case study in Piracicaba, Sao Paulo/Brazil

This paper presents the results of electrical resistivity methods in the area delineation that was potentially contaminated by liquefaction products, which are also called putrefactive liquids in Vila Rezende municipal cemetery, Piracicaba, So Paulo, Brazil. The results indicate a depth of water table between 3.1 and 5.1 m, with two groundwater direction flows, one to the SW and another to the SE. Due to the contamination plumes, which have the same groundwater direction flow, as well the conductive anomalies observed in the geoelectric sections, the contamination suspicions in the area were confirmed. The probable plume to the SE extends beyond the limits of the cemetery. The location of the conductive anomalies and the probable contamination plumes showed that the contamination is linked with the depth of the water table and the burial time. Mapping using the geostatistical method of ordinary kriging applied to the work drew structural characteristics of the regional phenomenon and spatial behavior of the electrical resistivity data, resulting in continued surfaces. Thus, this method has proved to be an important tool for mapping contamination plumes in cemeteries.

Mapping an uncertainty zone between interpolated types of a categorical variable

Categorical data cannot be interpolated directly because they are outcomes of discrete random variables. Thus, types of categorical variables are transformed into indicator functions that can be handled by interpolation methods. Interpolated indicator values are then backtransformed to the original types of categorical variables. However, aspects such as variability and uncertainty of interpolated values of categorical data have never been considered. In this paper we show that the interpolation variance can be used to map an uncertainty zone around boundaries between types of categorical variables. Moreover, it is shown that the interpolation variance is a component of the total variance of the categorical variables, as measured by the coefficient of unalikeability. (C) 2011 Elsevier Ltd. All rights reserved.

SPATIAL VARIABILITY OF THE RAINFALL EROSIVE POTENTIAL IN THE STATE OF MATO GROSSO DO SUL, BRAZIL

Information about rainfall erosivity is important during soil and water conservation planning. Thus, the spatial variability of rainfall erosivity of the state Mato Grosso do Sul was analyzed using ordinary kriging interpolation. For this, three pluviograph stations were used to obtain the regression equations between the erosivity index and the rainfall coefficient EI30. The equations obtained were applied to 109 pluviometric stations, resulting in EI30 values. These values were analyzed from geostatistical technique, which can be divided into: descriptive statistics, adjust to semivariogram, cross-validation process and implementation of ordinary kriging to generate the erosivity map. Highest erosivity values were found in central and northeast regions of the State, while the lowest values were observed in the southern region. In addition, high annual precipitation values not necessarily produce higher erosivity values.

Estimativa de densidade amostral para elaboração de mapas de produtividade

Yield mapping represents the spatial variability concerning the features of a productive area and allows intervening on the next year production, for example, on a site-specific input application. The trial aimed at verifying the influence of a sampling density and the type of interpolator on yield mapping precision to be produced by a manual sampling of grains. This solution is usually adopted when a combine with yield monitor can not be used. An yield map was developed using data obtained from a combine equipped with yield monitor during corn harvesting. From this map, 84 sample grids were established and through three interpolators: inverse of square distance, inverse of distance and ordinary kriging, 252 yield maps were created. Then they were compared with the original one using the coefficient of relative deviation (CRD) and the kappa index. The loss regarding yield mapping information increased as the sampling density decreased. Besides, it was also dependent on the interpolation method used. A multiple regression model was adjusted to the variable CRD, according to the following variables: spatial variability index and sampling density. This model aimed at aiding the farmer to define the sampling density, thus, allowing to obtain the manual yield mapping, during eventual problems in the yield monitor.

Employing Distances in Design-based Spatial Estimation

In the last couple of decades we assisted to a reappraisal of spatial design-based techniques. Usually the spatial information regarding the spatial location of the individuals of a population has been used to develop efficient sampling designs. This thesis aims at offering a new technique for both inference on individual values and global population values able to employ the spatial information available before sampling at estimation level by rewriting a deterministic interpolator under a design-based framework. The achieved point estimator of the individual values is treated both in the case of finite spatial populations and continuous spatial domains, while the theory on the estimator of the population global value covers the finite population case only. A fairly broad simulation study compares the results of the point estimator with the simple random sampling without replacement estimator in predictive form and the kriging, which is the benchmark technique for inference on spatial data. The Monte Carlo experiment is carried out on populations generated according to different superpopulation methods in order to manage different aspects of the spatial structure. The simulation outcomes point out that the proposed point estimator has almost the same behaviour as the kriging predictor regardless of the parameters adopted for generating the populations, especially for low sampling fractions. Moreover, the use of the spatial information improves substantially design-based spatial inference on individual values.

Tecniche regionali di stima della portata di progetto: Impatto della correlazione spaziale tra le serie di osservazioni

La stima degli indici idrometrici in bacini non strumentati rappresenta un problema che la ricerca internazionale ha affrontato attraverso il cosiddetto PUB (Predictions on Ungauged Basins – IAHS, 2002-2013). Attraverso l’analisi di un’area di studio che comprende 61 bacini del Sud-Est americano, si descrivono e applicano due tecniche di stima molto diverse fra loro: il metodo regressivo ai Minimi Quadrati Generalizzati (GLS) e il Topological kriging (TK). Il primo considera una serie di fattori geomorfoclimatici relativi ai bacini oggetto di studio, e ne estrae i pesi per un modello di regressione lineare dei quantili; il secondo è un metodo di tipo geostatistico che considera il quantile come una variabile regionalizzata su supporto areale (l’area del bacino), tenendo conto della dislocazione geografica e l’eventuale struttura annidata dei bacini d’interesse. L’applicazione di questi due metodi ha riguardato una serie di quantili empirici associati ai tempi di ritorno di 10, 50, 100 e 500 anni, con lo scopo di valutare le prestazioni di un loro possibile accoppiamento, attraverso l’interpolazione via TK dei residui GLS in cross-validazione jack-knife e con differenti vicinaggi. La procedura risulta essere performante, con un indice di efficienza di Nash-Sutcliffe pari a 0,9 per tempi di ritorno bassi ma stazionario su 0,8 per gli altri valori, con un trend peggiorativo all’aumentare di TR e prestazioni pressoché invariate al variare del vicinaggio. L’applicazione ha mostrato che i risultati possono migliorare le prestazioni del metodo GLS ed essere paragonabili ai risultati del TK puro, confermando l’affidabilità del metodo geostatistico ad applicazioni idrologiche.

Stima della variabilità intra-annuale del regime dei deflussi idrici superficiali in bacini non strumentati

La curva di durata di lungo periodo (POR) è uno strumento grafico molto efficace che mette in evidenza la relazione fra intensità e frequenza delle portate osservate in un determinato intervallo temporale. Essa è ricavata dall'idrogramma dei deflussi, ma presenta il problema della perdita di informazioni relative alla variabilità annuale e stagionali delle portate. Per tal motivo si è reso necessario l'utilizzo di due nuove interpretazioni delle curve di durate: le curve di durata annuali (alle quali può essere associato il concetto di percentile e quindi di probabilità di superamento di un particolare valore di portata) e le curve a base stagionale. La costruzione di tali curve, come nel caso delle POR complessive, è ostacolata dall'insufficienza di dati di portata, per cui sono utilizzate, a tale scopo, procedure di stima basate sulla regionalizzazione dei deflussi. Tra di esse è stato analizzata la tecnica geostatistica del Top-kriging applicata all'indice TND che sintetizza l'intera curva (Pugliese et al., 2014) nella ricostruzione, in cross-validazione, delle curve di durata annuali e stagionali di 182 bacini della regione sud-orientale degli Stati Uniti.

Rappresentazione della caratteristiche spaziali di eventi temporaleschi di elevata intensità attraverso l'uso combinato di misure da pluviometro e da radar

Il presente lavoro di Tesi si inserisce nell’ambito della previsione e caratterizzazione spaziale di fenomeni convettivi, tipicamente intensi e a carattere locale, come i sistemi temporaleschi organizzati che spesso sono i protagonisti di eventi calamitosi importanti. Lo studio è stato condotto in modo tale da poter apportare un contributo ai sistemi previsionali, i quali attualmente non consentono una valutazione accurata ed una caratterizzazione spaziale attendibile di detti fenomeni temporaleschi (Elisabetta Trovatore, Ecoscienza, numero 4, 2012). Lo scopo è stato quello di verificare l’esistenza caratteristiche spaziali comuni a questa tipologia di eventi di precipitazione mediante un confronto tra la curva di riduzione della precipitazione media all’area, ottenuta dalle mappe di precipitazione cumulata oraria desunte da radar meteorologici e da mappe corrispondenti ricavate a partire dai dati pluviometrici osservati al suolo servendosi di tre modelli di interpolazione spaziale: Kriging ordinario (con variogramma desunto da dati ai pluviometri e da dati al radar), Inverso delle Distanze Pesate (Inverse Distance Weighted, IDW) e Poligoni di Voronoi.Le conclusioni del lavoro di Tesi hanno evidenziato che: - la curva di riduzione della precipitazione valutata da dati radar viene in generale meglio approssimata da due metodi: il Kriging ordinario, che utilizza come modello di variogramma teorico quello dedotto da misure ai pluviometri, e le distanze inverse pesate (IDW, Inverse Distance Weighted); - paragonando le curve di riduzione della precipitazione media all’area dedotte da radar si è potuta notare l’esistenza di quattro curve, di cui tre relative ad eventi che hanno registrato valori elevati di intensità di precipitazione (superiori ai 140 mm/h), che presentano un comportamento analogo, mentre nella porzione superiore del grafico sono presenti due curve che non seguono tale andamento e che sono relative ad eventi convettivi meno intensi; - non esiste una distanza alla quale tutti i variogrammi, desunti da dati radar, raggiungono la stazionarietà ma si è visto come questa, per i sei casi di studio, vari tra 10000 e 25000 metri.

Tecnologie per lo studio delle acque sotterranee in relazione al progetto di riqualificazione della SP46 Rho-Monza

Il presente lavoro di tesi è stato svolto a seguito delle indagini geognostiche previste per un progetto ingegneristico volto alla riqualificazione con caratteristiche autostradali della SP46 Rho-Monza e del preesistente sistema autostradale A8/A52, la cui area interessata dai lavori è ubicata nella parte Nord del comune di Milano. Lo studio è stato finalizzato alla valutazione, attraverso metodologie e tecnologie specifiche, delle caratteristiche idrodinamiche delle acque sotterranee presenti nella zona oggetto dei lavori. A seguito di misure sul livello piezometrico della falda, compiute dopo la realizzazione di 8 piezometri, è stata realizzata (con l’ausilio del software Surfer 8.0® – Golden Software Inc.) una mappa relativa all’andamento delle isopieze e dei gradienti di flusso, attraverso interpolazione spaziale con metodo Kriging delle misure. La ricostruzione dell’assetto della falda così ottenuto ha permesso di fornire utili indicazioni riguardo le successive scelte progettuali.

The Importance of Scale for Spatial-confounding Bias and Precision of Spatial Regression Estimators

Increasingly, regression models are used when residuals are spatially correlated. Prominent examples include studies in environmental epidemiology to understand the chronic health effects of pollutants. I consider the effects of residual spatial structure on the bias and precision of regression coefficients, developing a simple framework in which to understand the key issues and derive informative analytic results. When the spatial residual is induced by an unmeasured confounder, regression models with spatial random effects and closely-related models such as kriging and penalized splines are biased, even when the residual variance components are known. Analytic and simulation results show how the bias depends on the spatial scales of the covariate and the residual; bias is reduced only when there is variation in the covariate at a scale smaller than the scale of the unmeasured confounding. I also discuss how the scales of the residual and the covariate affect efficiency and uncertainty estimation when the residuals can be considered independent of the covariate. In an application on the association between black carbon particulate matter air pollution and birth weight, controlling for large-scale spatial variation appears to reduce bias from unmeasured confounders, while increasing uncertainty in the estimated pollution effect.

Practical Large-Scale Spatio-Temporal Modeling of Particulate Matter Concentrations

The last two decades have seen intense scientific and regulatory interest in the health effects of particulate matter (PM). Influential epidemiological studies that characterize chronic exposure of individuals rely on monitoring data that are sparse in space and time, so they often assign the same exposure to participants in large geographic areas and across time. We estimate monthly PM during 1988-2002 in a large spatial domain for use in studying health effects in the Nurses' Health Study. We develop a conceptually simple spatio-temporal model that uses a rich set of covariates. The model is used to estimate concentrations of PM10 for the full time period and PM2.5 for a subset of the period. For the earlier part of the period, 1988-1998, few PM2.5 monitors were operating, so we develop a simple extension to the model that represents PM2.5 conditionally on PM10 model predictions. In the epidemiological analysis, model predictions of PM10 are more strongly associated with health effects than when using simpler approaches to estimate exposure. Our modeling approach supports the application in estimating both fine-scale and large-scale spatial heterogeneity and capturing space-time interaction through the use of monthly-varying spatial surfaces. At the same time, the model is computationally feasible, implementable with standard software, and readily understandable to the scientific audience. Despite simplifying assumptions, the model has good predictive performance and uncertainty characterization.