Scope to predict soil properties at within-field scale from small samples using proximally sensed gamma-ray spectrometer and EM induction data

Spatial prediction of soil properties are needed for various purposes, including site-specific management, soil quality assessment, soil mapping, and solute transport modelling, to mention a few. However, the costs associated with soil sampling and laboratory analysis are substantial. One way to improve efficiencies is to combine measurement of soil properties with collection of cheaper-to-measure ancillary data. There are two possible approaches. The first is the formation of classes from ancillary data. A second is the use of a simple predictive linear model of the target soil property on the ancillary variables. Here, results are presented and compared where proximally sensed gamma-ray (gamma-ray) spectrometry and electromagnetic induction (EMI) data are used to predict the variation in topsoil properties (e.g. clay content and pH). In the first instance, the proximal data is numerically clustered using a fuzzy k-means (FKM) clustering algorithm, to identify contiguous classes. The resultant digital soil maps (i.e. k = 2 - 10 classes) are consistent with a soil series map generated using traditional soil profile description, classification and mapping methods at a highly variable site near the township of Shelford, Nottinghamshire UK. In terms of prediction, the calculated expected value of mean squared prediction error (i.e. sigma2p,C) indicated that values of k = 7 and 8 were ideal for predicting clay and pH. Secondly, a linear mixed model (LMM) is fitted in which the proximal data are fixed effects but the residuals are treated as a combination of a spatially correlated random effect and an independent and identically distributed error. In terms of prediction, the expected value of the mean squared prediction error from a regression (sigma2p,R) suggested that the regression models were able to predict clay content, better than FKM clustering. The reverse was true with respect to pH, however. It is concluded that both methods have merit. In the case of the clustering, the approach is able to account for soil properties which have non-linearity with the ancillary data (i.e. pH), whereas the LMM approach is best when there is a strong linear relationship (i.e. clay)

Digital Soil Mapping Approaches for Assisting Site-Specific Soil Management in Sugarcane Growing Areas

The Australian sugarcane industry has developed the “Six Easy Steps” nutrient and ameliorant management guidelines with the aim of optimising productivity and profitability, without adversely influencing the soil condition and causing off-farm effects. This involves knowing the spatial variation of soil properties, such as; cation exchange capacity (CEC), exchangeable calcium (Exch. Ca) and magnesium (Mg) and exchangeable sodium percentage (ESP). One way to generate soil information is to use a digital soil mapping (DSM) approach. Specifically, combine limited soil data with easier to collect ancillary data via mathematical models. This thesis focusses on developing digital soil maps (DSM) in different Australia sugarcane growing districts. Chapter 1 describes the need for DSM while Chapter 2 describes the basic components of DSM, including proximal sources of ancillary data and mathematical models. Moreover, the literature is reviewed to provide demonstrated case studies of DSM of various soil properties (e.g. CEC, Exch. Ca, Exch. Mg and ESP), with gaps identified and research chapters presented to bridge these. In Chapter 3, the application of DSM to predict CEC is explored to assist with the quantification of uncertainty due to ancillary data. In Chapter 4, the aim was to determine optimal components for DSM of topsoil Exch. Ca and Mg. In Chapter 5, the potential of wavelet analysis was explored where there was complex variation in ancillary data relative to topsoil ESP. In Chapter 6, a comparison was made of DSM to account for topsoil (0 – 0.3 m) ESP using mathematical or numerical clustering (FKM) models to create soil classes with a conventional Soil Order map (e.g. soils and land suitability of Burdekin River Irrigation Area). The results showed DSM can be applied to a wide range of soil properties and classes, especially when all the available ancillary data was used in combination. Useful guidelines on operational aspects including transect spacing (7.5 – 30 m) and soil samples for calibration (1 per hectare) were described. Future research should explore other ancillary data sources (e.g. crop yield), mathematical models (e.g. machine learning) and follow up improvement in soil condition as a function of the application of nutrient and ameliorants in accordance with the “Six Easy Steps” guidelines in the various study areas

Ribbed moraines and subglacial geomorphological signatures of interior-sector palaeo-ice sheet dynamics

Transverse, subglacial bedforms (ribbed moraines) occur frequently in southern Keewatin, Nunavut, Canada, where they record a complex glacial history, including shifting centers of ice dispersal and fluctuating basal thermal regimes. Comprehensive mapping and quantitative morphometric analysis of the subglacial bedform archive in this sector reveals that ribbed moraines are spatially clustered by size and assume a broad range of visually distinct forms. Results suggest that end-member morphologies are consistent with a dichotomous polygenetic origin, and that a continuum of forms emerged through subsequent reshaping processes of variable intensity and duration. Translocation of mobile, immobile and quasi-mobile beds throughout the last glacial cycle conditioned the development of a subglacial deforming bed mosaic, and is likely responsible for the patchy zonation of palimpsest and inherited landscape signatures within this former core region of the Laurentide Ice Sheet. Comparison against field evidence collected from central Norway suggests that bedforming processes can be locally mediated by pre-existing topography

Potential and limitations of using soil mapping information to understand landscape hydrology

Abstract. This paper addresses the following points: how can whole soil data from normally available soil mapping databases (both conventional and those integrated by digital soil mapping procedures) be usefully employed in hydrology? Answering this question requires a detailed knowledge of the quality and quantity of information embedded in and behind a soil map. To this end a description of the process of drafting soil maps was prepared (which is included in Appendix A of this paper). Then a detailed screening of content and availability of soil maps and database was performed, with the objective of an analytical evaluation of the potential and the limitations of soil data obtained through soil surveys and soil mapping. Then we reclassified the soil features according to their direct, indirect or low hydrologic relevance. During this phase, we also included information regarding whether this data was obtained by qualitative, semi-quantitative or quantitative methods. The analysis was performed according to two main points of concern: (i) the hydrological interpretation of the soil data and (ii) the quality of the estimate or measurement of the soil feature. The interaction between pedology and hydrology processes representation was developed through the following Italian case studies with different hydropedological inputs: (i) comparative land evaluation models, by means of an exhaustive itinerary from simple to complex modelling applications depending on soil data availability, (ii) mapping of soil hydrological behaviour for irrigation management at the district scale, where the main hydropedological input was the application of calibrated pedo-transfer functions and the Hydrological Function Unit concept, and (iii) flood event simulation in an ungauged basin, with the functional aggregation of different soil units for a simplified soil pattern. In conclusion, we show that special care is required in handling data from soil databases if full potential is to be achieved. Further, all the case studies agree on the appropriate degree of complexity of the soil hydrological model to be applied. We also emphasise that effective interaction between pedology and hydrology to address landscape hydrology requires (i) greater awareness of the hydrological community about the type of soil information behind a soil map or a soil database, (ii) the development of a better quantitative framework by the pedological community for evaluating hydrological features, and (iii) quantitative information on soil spatial variability

Remote Sensing Applications in Coastal Environment

Coastal regions are susceptible to rapid changes, as they constitute the boundary between the land and the sea. The resilience of a particular segment of coast depends on many factors, including climate change, sea-level changes, natural and technological hazards, extraction of natural resources, population growth, and tourism. Recent research highlights the strong capabilities for remote sensing applications to monitor, inventory, and analyze the coastal environment. This book contains 12 high-quality and innovative scientific papers that explore, evaluate, and implement the use of remote sensing sensors within both natural and built coastal environments

Practicable methodologies for delivering comprehensive spatial soils information

This thesis is concerned with practicable methodologies for delivering comprehensive spatial soil information to end-users. There is a need for relevant spatial soil information to complement objective decision-making for addressing current problems associated with soil degradation; for modelling, monitoring and measurement of particular soil services; and for the general management of soil resources. These are real-world situations, which operate at spatial scales ranging from field to global scales. As such, comprehensive spatial soil information is tailored to meet the spatial scale specifications of the end user, and is of a nature that fully characterises the whole-soil profile with associated prediction uncertainties, and where possible, both the predictions and uncertainties have been independently validated. ‘Practicable’ is an idealistic pursuit but nonetheless necessary because of a need to equip land-holders, private-sector and non-governmental stakeholders and, governmental departments including soil mapping agencies with the necessary tools to ensure wide application of the methodologies to match the demand for relevant spatial soil information. Practicable methodologies are general and computationally efficient; can be applied to a wide range of soil attributes; can handle variable qualities of data; and are effective when working with very large datasets. In this thesis, delivering comprehensive spatial soil information relies on coupling legacy soil information (principally site observations made in the field) with Digital Soil Mapping (DSM) which comprises quantitative, state-of-the-art technologies for soil mapping. After the General Introduction, a review of the literature is given in Chapter 1 which describes the research context of the thesis. The review describes soil mapping first from a historical perspective and rudimentary efforts of mapping soils and then tracks the succession of advances that have been made towards the realisation of populated, digital spatial soil information databases where measures of prediction certainties are also expressed. From the findings of the review, in order to deliver comprehensive spatial soil information to end-users, new research was required to investigate: 1) a general method for digital soil mapping the whole-profile (effectively pseudo-3D) distribution of soil properties; 2) a general method for quantifying the total prediction uncertainties of the digital soil maps that describe the whole-profile distribution of soil properties; 3) a method for validating the whole-profile predictions of soil properties and the quantifications of their uncertainties; 4) a systematic framework for scale manipulations or upscaling and downscaling techniques for digital soil mapping as a means of generating soil information products tailored to the needs of soil information users. Chapters 2 to 6 set about investigating how we might go about doing these with a succession of practicable methodologies. Chapter 2 addressed the need for whole-profile mapping of soil property distribution. Equal-area spline depth functions coupled with DSM facilitated continuous mapping the lateral and vertical distribution of soil properties. The spline function is a useful tool for deriving the continuous variation of soil properties from soil profile and core observations and is also suitable to use for a number of different soil properties. Generally, mapping the continuous depth function of soil properties reveals that the accuracy of the models is highest at the soil surface but progressively decreases with increasing soil depth. Chapter 3 complements the investigations made in Chapter 2 where an empirical method of quantifying prediction uncertainties from DSM was devised. This method was applied for quantifying the uncertainties of whole-profile digital soil maps. Prediction uncertainty with the devised empirical method is expressed as a prediction interval of the underlying model errors. The method is practicable in the sense that it accounts for all sources of uncertainty and is computationally efficient. Furthermore the method is amenable in situations where complex spatial soil prediction functions such as regression kriging approaches are used. Proper evaluation of digital soil maps requires testing the predictions and the quantification of the prediction uncertainties. Chapter 4 devised two new criteria in which to properly evaluate digital soil maps when additional soil samples collected by probability sampling are used for validation. The first criterion addresses the accuracy of the predictions in the presence of uncertainties and is the spatial average of the statistical expectation of the Mean Square Error of a simulated random value (MSES). The second criterion addresses the quality of the uncertainties which is estimated as the total proportion of the study area where the (1-α)-prediction interval (PI) covers the true value (APCP). Ideally these criteria will be coupled with conventional measures of map quality so that objective decisions can be made about the reliability and subsequent suitability of a map for a given purpose. It was revealed in Chapter 4, that the quantifications of uncertainty are susceptible to bias as a result of using legacy soil data to construct spatial soil prediction functions. As a consequence, in addition to an increasing uncertainty with soil depth, there is increasing misspecification of the prediction uncertainties. Chapter 2, 3, and 4 thus represent a framework for delivering whole-soil profile predictions of soil properties and their uncertainties, where both have been assessed or validated across mapping domains at a range of spatial scales for addressing field, farm, regional, catchment, national, continental or global soil-related problems. The direction of Chapters 5 and 6 however addresses issues specifically related to tailoring spatial soil information to the scale specifications of the end-user through the use of scale manipulations on existing digital soil maps. What is proposed in Chapter 5 is a scaling framework that takes into account the scaling triplet of digital soil maps—extent, resolution, and support—and recommends pedometric methodologies for scale manipulation based on the scale entities of the source and destination maps. Upscaling and downscaling are descriptors for moving up to coarser or down to finer scales respectively but may be too general for DSM. Subsequently Fine-gridding and coarse-gridding are operations where the grid spacing changes but support remains unchanged. Deconvolution and convolution are operations where the support always changes, which may or may not involve changing the grid spacing. While disseveration and conflation operations occur when the support and grid size are equal and both are then changed equally and simultaneously. There is an increasing richness of data sources describing the physical distribution of the Earth’s resources with improved qualities and resolutions. To take advantage of this, Chapter 6 devises a novel procedure for downscaling, involving disseveration. The method attempts to maintain the mass balance of the fine scaled predictions with the available coarse scaled information, through an iterative algorithm which attempts to reconstruct the variation of a property at a prescribed fine scale through an empirical function using environmental or covariate information. One of the advantages associated with the devised method is that soil property uncertainties at the coarse scale can be incorporated into the downscaling algorithm. Finally Chapter 7 presents a synthesis of the investigations made in Chapters 2 to 6 and summarises the pertinent findings. Directly from the investigations carried out during this project there are opportunities for further work; both in terms of addressing shortcomings that were highlighted but not investigated in the thesis, and more generally for advancing digital soil mapping to an operational status and beyond

Water Resources Management and Modeling

Hydrology is the science that deals with the processes governing the depletion and replenishment of water resources of the earth's land areas. The purpose of this book is to put together recent developments on hydrology and water resources engineering. First section covers surface water modeling and second section deals with groundwater modeling. The aim of this book is to focus attention on the management of surface water and groundwater resources. Meeting the challenges and the impact of climate change on water resources is also discussed in the book. Most chapters give insights into the interpretation of field information, development of models, the use of computational models based on analytical and numerical techniques, assessment of model performance and the use of these models for predictive purposes. It is written for the practicing professionals and students, mathematical modelers, hydrogeologists and water resources specialists