Dryland salinity is increasing in the upper catchments of central and northern New South Wales, Australia. Consequently, salts may be exported downstream, which could adversely affect cotton irrigated-farming systems. In order to assess the potential threat of salinity a simple salt balance model based on progressively saline water (i.e., ECiw 0.4, 1.5, 4.0 and 9.0 dS/m) was used to simulate the potential impact of salinisation due to the farming systems. The study was carried out in the lower Namoi valley of northern New South Wales, Australia. A comparison has been made of the various non-linear techniques (indicator kriging, multiple indicator kriging and disjunctive kriging) to determine an optimal simulation method for the risk assessment. The simulation results indicate that potential salinisation due to application of the water currently used for irrigation (ECiw) is minimal and may not pose any problems to sustainability of irrigated agriculture. The same results were obtained by simulation based on irrigation using slightly more saline water (ECiw 1.4 dS/m). However, simulations based on irrigation using water of even lower quality (ECiw of 4 and 9.0 dS/m), shows potential high salinisation, which will require management inputs for sustainable cropping systems, especially legumes and wheat, which are used extensively in rotation with cotton. Disjunctive kriging was the best simulation method, as it produced fewer misclassifications in comparison with multiple-indicator kriging and indicator kriging. This study thus demonstrates that we can predict the salinity risk due to application of irrigation water of lower quality than that of the current water used. In addition, the results suggest here problems of excessive deep drainage and inefficient use of water might be a problem. The second part of this thesis deals with soil information required at the field scale for management practices particularly in areas where deep drainage is large. Unfortunately, traditional methods of soil inventory at the field level involve the design and adoption of sampling regimes and laboratory analysis that are time-consuming and costly. Because of this more often than not only limited data are collected. In areas where soil salinity is prevalent, detailed quantitative information for determining its cause is required to prescribe management solutions. This part deals with the description of a Mobile Electromagnetic Sensing System (MESS) and its application in an irrigated-cotton field suspected of exhibiting soil salinity. The field is within the study area of part one of this thesis-located about 2 km south west of Wee Waa. The EM38 and EM31 (ECa) data provide information, which was used in deciding where soil sample sites could be located in the field. The ECa data measured by the EM38 instrument was highly correlated with the effective cation exchange capacity. This relationship can be explained by soil mineralogy. Using different soil chemical properties (i.e. ESP and Ca/Mg ratio) a detailed transect study was undertaken to measure soil salinity adjoining the water storage. It is concluded that the most appropriate management option to remediation of the problem would be to excavate the soil directly beneath the storage floor where leakage is suspected. It is recommended that the dam not be enlarged from its current size owing to the unfavourable soil mineralogy (i.e. kaolin/illite) located in the area where it is located.

Dryland salinity is increasing in the upper catchments of central and northern New South Wales, Australia. Consequently, salts may be exported downstream, which could adversely affect cotton irrigated-farming systems. In order to assess the potential threat of salinity a simple salt balance model based on progressively saline water (i.e., ECiw 0.4, 1.5, 4.0 and 9.0 dS/m) was used to simulate the potential impact of salinisation due to the farming systems. The study was carried out in the lower Namoi valley of northern New South Wales, Australia. A comparison has been made of the various non-linear techniques (indicator kriging, multiple indicator kriging and disjunctive kriging) to determine an optimal simulation method for the risk assessment. The simulation results indicate that potential salinisation due to application of the water currently used for irrigation (ECiw) is minimal and may not pose any problems to sustainability of irrigated agriculture. The same results were obtained by simulation based on irrigation using slightly more saline water (ECiw 1.4 dS/m). However, simulations based on irrigation using water of even lower quality (ECiw of 4 and 9.0 dS/m), shows potential high salinisation, which will require management inputs for sustainable cropping systems, especially legumes and wheat, which are used extensively in rotation with cotton. Disjunctive kriging was the best simulation method, as it produced fewer misclassifications in comparison with multiple-indicator kriging and indicator kriging. This study thus demonstrates that we can predict the salinity risk due to application of irrigation water of lower quality than that of the current water used. In addition, the results suggest here problems of excessive deep drainage and inefficient use of water might be a problem. The second part of this thesis deals with soil information required at the field scale for management practices particularly in areas where deep drainage is large. Unfortunately, traditional methods of soil inventory at the field level involve the design and adoption of sampling regimes and laboratory analysis that are time-consuming and costly. Because of this more often than not only limited data are collected. In areas where soil salinity is prevalent, detailed quantitative information for determining its cause is required to prescribe management solutions. This part deals with the description of a Mobile Electromagnetic Sensing System (MESS) and its application in an irrigated-cotton field suspected of exhibiting soil salinity. The field is within the study area of part one of this thesis-located about 2 km south west of Wee Waa. The EM38 and EM31 (ECa) data provide information, which was used in deciding where soil sample sites could be located in the field. The ECa data measured by the EM38 instrument was highly correlated with the effective cation exchange capacity. This relationship can be explained by soil mineralogy. Using different soil chemical properties (i.e. ESP and Ca/Mg ratio) a detailed transect study was undertaken to measure soil salinity adjoining the water storage. It is concluded that the most appropriate management option to remediation of the problem would be to excavate the soil directly beneath the storage floor where leakage is suspected. It is recommended that the dam not be enlarged from its current size owing to the unfavourable soil mineralogy (i.e. kaolin/illite) located in the area where it is located.

To adapt to climate change which results in increasing flood frequency and intensity, the European Community has proposed Flood Directive 2007/60/EC. It requires member states to conduct risk assessments of all river basins and coastal areas and to establish Flood Risk Management Plans focused on prevention, protection and preparedness by 2015. Sustainable Flood Retention Basins (SFRB) that impound water are a new concept that arose in 2006. They can have a pre-defined or potential role in flood defense and were supposed to facilitate the implementation of the Flood Directive. Early and preliminary studies of SFRB were derived from case studies in Southern Baden, Germany. In Scotland, there are a relatively high number of SFRB which could contribute to flood management control. This research aimed to produce a guidance manual for the rapid survey of SFRB and to propose a series of frameworks for comprehensive analysis and assessment of SFRB. Precisely 372 SFRB in central Scotland and 202 SFRB in Southern Baden were investigated and characterized by 43 holistic variables. Based on this practical experience, a detailed guidance manual was created, guiding users to conduct a SFRB survey in a standardized and straightforward way. To explore the hidden data structure of data arising from the SFRB survey, various widely used machine learning algorithms and geo-statistical techniques were applied. For instance, cluster analysis showed intrinsic groupings of SFRB data, assisting with SFRB categorization. Principal Component Analysis (PCA) was applied to reduce the dimensions of SFRB data from the original 43 to 23, simplifying the SFRB system. Self-organizing Maps (SOM) visualized the relationships among variables and predicted certain variables as well as the types of SFRB by using the highly related variables. Three feature-selection techniques (Information Gain, Mutual Information and Relief) and four benchmark classifiers (Support Vector Machine, K-Nearest Neighbours, C4.5 Decision Tree and Naive Bayes) were used to select and verify the optimal subset of variables, respectively. Findings indicated that only nine important variables were required to accurately classify SFRB. Three popular multi-label classifiers (Multi-Label Support Vector Machine (MLSVM), Multi-Label K-Nearest Neighbour (MLKNN) and Back- Propagation for Multi-Label Learning (BP-MLL)) were applied to classify SFRB with multiple types. Experiments demonstrated that the classification framework achieved promising results and outperformed traditional single-label classifiers. Ordinary Kriging was used to estimate the spatial properties of the flood-related variables across the research area, while Disjunctive kriging was used to assess the probability of these individual variables exceeding specific management thresholds. The results provided decision makers with an effective tool for spatial planning of flood risk management. To assess dam failure hazards and risks of SFRB, a rapid screening tool was proposed based on expert judgement. It demonstrated that the levels of Dam Failure Hazard and Dam Failure Risk varied for different SFRB types and in different regions of central Scotland. In all, this thesis provided a guidance manual for rapid survey of SFRB and presented various effective, efficient and comprehensive frameworks for SFRB analysis and assessment, helping to promote the understanding and management of SFRB and thus to contribute to Flood Risk Management Plans in the context of the Flood Directive.

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Aquesta tesi estudia com estimar la distribució de les variables regionalitzades l'espai mostral i l'escala de les quals admeten una estructura d'espai Euclidià. Apliquem el principi del treball en coordenades: triem una base ortonormal, fem estadística sobre les coordenades de les dades, i apliquem els output a la base per tal de recuperar un resultat en el mateix espai original. Aplicant-ho a les variables regionalitzades, obtenim una aproximació única consistent, que generalitza les conegudes propietats de les tècniques de kriging a diversos espais mostrals: dades reals, positives o composicionals (vectors de components positives amb suma constant) són tractades com casos particulars. D'aquesta manera, es generalitza la geostadística lineal, i s'ofereix solucions a coneguts problemes de la no-lineal, tot adaptant la mesura i els criteris de representativitat (i.e., mitjanes) a les dades tractades. L'estimador per a dades positives coincideix amb una mitjana geomètrica ponderada, equivalent a l'estimació de la mediana, sense cap dels problemes del clàssic kriging lognormal. El cas composicional ofereix solucions equivalents, però a més permet estimar vectors de probabilitat multinomial. Amb una aproximació bayesiana preliminar, el kriging de composicions esdevé també una alternativa consistent al kriging indicador. Aquesta tècnica s'empra per estimar funcions de probabilitat de variables qualsevol, malgrat que sovint ofereix estimacions negatives, cosa que s'evita amb l'alternativa proposada. La utilitat d'aquest conjunt de tècniques es comprova estudiant la contaminació per amoníac a una estació de control automàtic de la qualitat de l'aigua de la conca de la Tordera, i es conclou que només fent servir les tècniques proposades hom pot detectar en quins instants l'amoni es transforma en amoníac en una concentració superior a la legalment permesa. / This Thesis presents an estimation procedure for the distribution of regionalized variables with sample space and scale admitting an Euclidean structure. We apply the principle of working on coordinates: choose an orthonormal basis; do statistics on the coordinates of your observations on that basis; and, by applying the output to the basis, you will recover a result within the original space. Applying this procedure to regionalized variables, we obtain a unified, consistent method, with the same properties of classical linear kriging techniques, but valid for several sample spaces: real data, positive data and compositions (vectors of positive components summing up to a constant) are regarded as particular cases. In this way we generalize the linear kriging techniques, and offer a solution to several well-known problems of the non-linear ones, by adapting the measure of the space and the averaging criterion (the way means are computed) to the data. The obtained estimator for positive variables is a weighted geometric mean, equivalent to estimate the median, which has none of the drawback of classical lognormal kriging. For compositional data, equivalent results are obtained, but which also serve to treat multinomial probability vectors. By combining this with a preliminary Bayesian estimation, our kriging for compositions become also a valid alternative to indicator kriging, without its order-relation problems (i.e. the rather-usual negative estimates of some probabilities). These techniques are validated by studying the ammonia pollution hazard in an automatic water quality control station placed in a small Mediterranean river. Only the proposed techniques allow us to assess when the secondary pollution by ammonia exceeds the existing legal threshold.