EXPLORATION OF LIGNIN-BASED SUPERABSORBENT POLYMERS (HYDROGELS) FOR SOIL WATER MANAGEMENT AND AS A CARRIER FOR DELIVERING RHIZOBIUM SPP.

Superabsorbent polymers (hydrogels) as soil amendments may improve soil hydraulic properties and act as carrier materials beneficial to soil microorganisms. Researchers have mostly explored synthetic hydrogels which may not be environmentally sustainable. This dissertation focused on the development and application of lignin-based hydrogels as sustainable soil amendments. This dissertation also explores the development of pedotransfer transfer functions (PTFs) for predicting saturated hydraulic conductivity using statistical and machine learning methods with a publicly available large data set. A lignin-based hydrogel was synthesized, and its impact on soil water retention was determined in silt loam and loamy fine sand soils. Hydrogel treatment significantly increased water retention at saturation/near saturation by 0.12 cm3 cm-3 and at field capacity by 0.08 cm3 cm-3 for silt loam soil compared to a control treatment with no added lignin hydrogel. Hydrogel application significantly increased water retention at -3 cm to -15,000 cm soil water pressure head by 0.01 - 0.03 cm3 cm-3 for the loamy fine sand soil. Calculations demonstrated that at a 1% (w/w) concentration or lower, lignin-based hydrogels in silt loam and loamy fine sand soils would not increase plant available soil water storage. The incorporation of lignin-hydrogels significantly decreased saturated hydraulic conductivity. In unsaturated conditions, application of the lignin-based hydrogel at 0.1 and 0.3% (w/w) increased hydraulic conductivity. New pedotransfer functions (PTFs) for predicting saturated hydraulic conductivity were developed using machine learning (ML) and a large public database. Random forest regression and gradient boosted regression both gave the best performances with R2 =0.71 and RMSE = 0.47 cm h-1 on the validation data set. The concentration of lignin-alginate hydrogel added to Rhizobial cell culture did not affect cell survival. All treatments of wet bioencapsulated beads achieved a similar yield of 97%, however, the presence of starch in the lignin-alginate beads increased the survival of Rhizobium cells

Calibration of pesticide leaching models

Complex deterministic models are being used within the context of pesticide registration to assess the potential for crop protection products to impact on the environment. Although calibration is in many ways at the heart of pesticide fate modelling, it has received little attention in the past. Sensitivity analyses were carried out for the four main leaching models used for pesticide registration in Europe (PELMO, PRZM, PESTLA and MACRO) using four different leaching scenarios and two approaches to sensitivity assessment (one-at-a-time and Monte Carlo sensitivity analyses). Also, an inverse modelling approach was used to estimate values for sorption and degradation parameters from leaching data for seven lysimeters using the PESTRAS model. The overall conclusions of the PhD can be summarised as follows: 1. Sensitivity analyses for the four leaching models mainly used for pesticide registration in Europe demonstrated that predictions for pesticide loss are most sensitive to parameters related to sorption and degradation. In a small number of scenarios, hydrological parameters were found to also have a large influence on predictions for pesticide loss. 2. Sensitivity analysis proved to be an effective approach not only for ranking parameters according to their influence on model predictions, but also for investigating model behaviour in a more general context. However, the research questioned the robustness of the Monte Carlo approach to sensitivity analysis as issues of replicability were uncovered. 3. Inverse modelling exercises demonstrated that non-uniqueness is likely to be widespread in the calibration of pesticide leaching models. Correlation between parameters within the modelling, such as that between sorption and degradation parameters when predicting pesticide leaching, may prevent the robust derivation of values through an inverse modelling approach. Depending on the calibration system considered, these parameters may act as fitting variables and integrate inaccuracies, uncertainties and limitations associated with experimental data, modelling and calibration. 4. A special implementation of error surface analysis termed lattice modelling was proposed in the PhD as an efficient technique to i) assess the likely extent of nonuniqueness issues in the calibration of pesticide leaching models; and, ii) replace traditional parameter estimation procedures where non-uniqueness is expected. Care should be exercised when assessing the results obtained by both modelling and inverse modelling studies. Suggestions to improve the reliability in the calibration of pesticide leaching models have been proposed.Ph

Утицај периода калибрације на оцене параметара концептуалних хидролошких модела различитих структура

Conceptual hydrologic models are commonly applied for flow forecasting, estimation ofdesign flows and assessment of climate change impact on water resources. Therefore,reliability of hydrologic simulations obtained by employing these models is crucial.However, these simulations are fraught with uncertainties, which stem, inter alia, fromparameter estimates. The parameter estimates are affected by data errors, objectivefunctions and optimisation algorithm employed for model calibration, but also byproperties of the calibration period. Namely, model calibration over different periods mayresult in quite different parameter estimates because parameter optimality does not holdoutside the calibration period. This temporal variability of optimal parameter estimatesyields deterioration in model performance outside the calibration period. Therefore,variability of optimal parameter estimates is major issue when it comes to application ofhydrologic models, because these models are primarily used for runoff simulationsoutside the calibration period.In this Thesis temporal variability in parameters of the 3DNet-Catch model is analysed.The AMALGAM algorithm, aimed at multi-objective optimisation, is applied for modelcalibration. The model is calibrated in dynamic manner, over all 1- to 25-year longcalibration periods, with one water year prior to every calibration aimed at model warmup.Prior ranges of the parameters and settings for the optimisation algorithms (e.g.population size, mutation probability, etc.) are kept constant through all simulations forgiven catchment. The analysis of temporal variability in model parameters is based on thenon-dominated, or Pareto-optimal sets, which are selected subsequent to the optimisationof the initially sampled population of parameter sets. Impact of combination of objectivefunctions used for model calibration and model structural complexity on temporalvariability in the Pareto-optimal parameters is also examined in this research. To isolatetemporal variability in parameters from anthropogenic effects (e.g. urbanisation or riverengineering works) three catchments that have not undergone human-induced changesare considered in this research: the Kolubara River catchment upstream of the Slovac...Концептуални хидролошки модели су нашли широку примену у израдихидролошких прогноза и предикција, и у анализи утицаја климатских промена наводне ресурсе. Стога је поузданост симулација добијених применом ових моделавеома важна. Међутим, у хидролошким симулацијама постоје неизвесности, којепотичу и од оцена параметара модела. На оцене параметара модела утичу грешке уподацима, избор критеријумских функција и оптимизационог алгоритма, али икарактеристике калибрационог периода. Наиме, калибрација модела токомразличитих периода даће различите оцене параметара, зато што параметри који суоптимални током једног периода не морају бити оптимални изван њега. Последицаваријабилности оптималних параметара у времену је и лошија ефикасност моделатј. мање поуздане симулације ван калибрационог периода. Имајући у виду да сехидролошки модели користе за хидролошке симулације ван калибрационогпериода, за њихову примену кључно је изучавање променљивости оптималнихпараметара модела током времена.У овој дисертацији анализиран је утицај калибрационог периода на оценепараметара хидролошког модела 3DNet-Catch. За калибрацију модела коришћен јесавремени алгоритам за вишекритеријумску оптимизацију AMALGAM, којипредставља комбинацију неколико глобалних оптимизационих алгоритама.Хидролошки модел је калибрисан на свим периодима дужине од једне до 25узастопних хидролошких година, уз једну хидролошку годину намењену„загревању“ модела. Овакав приступ је назван „динамичка“ калибрација модела.Почетни опсези параметара, као и подешавања за оптимизациони алгоритам (нпр.број чланова популације, вероватноћа мутације и др.) исти су за све калибрационепериоде за разматрани слив. Након оптимизације параметара издвојена су тзв.међусобно недоминантна решења (Парето оптимални скупови параметара илискупови са Парето фронта), на основу којих је вршена анализа променљивости..

Bio-inspired optimization in integrated river basin management

Water resources worldwide are facing severe challenges in terms of quality and quantity. It is essential to conserve, manage, and optimize water resources and their quality through integrated water resources management (IWRM). IWRM is an interdisciplinary field that works on multiple levels to maximize the socio-economic and ecological benefits of water resources. Since this is directly influenced by the river’s ecological health, the point of interest should start at the basin-level. The main objective of this study is to evaluate the application of bio-inspired optimization techniques in integrated river basin management (IRBM). This study demonstrates the application of versatile, flexible and yet simple metaheuristic bio-inspired algorithms in IRBM. In a novel approach, bio-inspired optimization algorithms Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) are used to spatially distribute mitigation measures within a basin to reduce long-term annual mean total nitrogen (TN) concentration at the outlet of the basin. The Upper Fuhse river basin developed in the hydrological model, Hydrological Predictions for the Environment (HYPE), is used as a case study. ACO and PSO are coupled with the HYPE model to distribute a set of measures and compute the resulting TN reduction. The algorithms spatially distribute nine crop and subbasin-level mitigation measures under four categories. Both algorithms can successfully yield a discrete combination of measures to reduce long-term annual mean TN concentration. They achieved an 18.65% reduction, and their performance was on par with each other. This study has established the applicability of these bio-inspired optimization algorithms in successfully distributing the TN mitigation measures within the river basin. Stakeholder involvement is a crucial aspect of IRBM. It ensures that researchers and policymakers are aware of the ground reality through large amounts of information collected from the stakeholder. Including stakeholders in policy planning and decision-making legitimizes the decisions and eases their implementation. Therefore, a socio-hydrological framework is developed and tested in the Larqui river basin, Chile, based on a field survey to explore the conditions under which the farmers would implement or extend the width of vegetative filter strips (VFS) to prevent soil erosion. The framework consists of a behavioral, social model (extended Theory of Planned Behavior, TPB) and an agent-based model (developed in NetLogo) coupled with the results from the vegetative filter model (Vegetative Filter Strip Modeling System, VFSMOD-W). The results showed that the ABM corroborates with the survey results and the farmers are willing to extend the width of VFS as long as their utility stays positive. This framework can be used to develop tailor-made policies for river basins based on the conditions of the river basins and the stakeholders' requirements to motivate them to adopt sustainable practices. It is vital to assess whether the proposed management plans achieve the expected results for the river basin and if the stakeholders will accept and implement them. The assessment via simulation tools ensures effective implementation and realization of the target stipulated by the decision-makers. In this regard, this dissertation introduces the application of bio-inspired optimization techniques in the field of IRBM. The successful discrete combinatorial optimization in terms of the spatial distribution of mitigation measures by ACO and PSO and the novel socio-hydrological framework using ABM prove the forte and diverse applicability of bio-inspired optimization algorithms

Modellierung hydrologisch relevanter Strukturen und Prozesse in der initialen Entwicklungsphase eines künstlich geschaffenen Einzugsgebiets

The present dissertation deals with the model-based investigation of hydrologically relevant structures and processes of the initial development phase of the 6 ha large man-made catchment ‘Chicken Creek’ located near Cottbus (Germany). A highly threshold-like relationship between measured rainfall and total runoff was revealed in the catchment. In the first part of the thesis, a new model based on Percolation Theory was developed to simulate the subsurface flow paths and the threshold-like runoff response to rainfall. The sudden establishment of a high hydrologic connectivity between soil elements was shown to be the key factor to explain the nonlinear behaviour of the rainfall-runoff response. The initial phase of catchment development was characterised by the evolution of erosion rills driven by surface runoff. In the second part of the thesis, two models were developed to describe the emergence of the erosion rills. First, we used a self-organised critical network (SOCN) approach with soil erosion and deposition governed by a local critical shear stress. Second, a model based on the Manning equation was developed to compare the results of the SOCN approach with results from a simple, but more physically-based approach. Both models were able to simulate the rill characteristics (rill network length, rill depth) in the right order of magnitude, as well as the position and temporal evolution of the erosion rills. In the third part of the thesis, a new hydrological model was conceptualised to tackle a few drawbacks of present models. By describing water transport in discrete volume units, the new model was able to represent nonlinear runoff processes, water transit times and the emergence of macro-scale patterns in the same model framework. First encouraging model simulations were able to reproduce transit times and the temporal evolution of runoff patterns, and to estimate the effect of the rills on runoff quantities.Diese Dissertation behandelt die modellgestützte Untersuchung hydrologisch relevanter Strukturen und Prozesse in der initialen Entwicklungsphase des 6 ha grossen, künstlich geschaffenen Einzuggebiets ‘Hühnerwasser’ (Cottbus, Deutschland). Im Einzugsgebiet wurde ein stark nichtlinearer Zusammenhang zwischen gemessenem Niederschlag und Gesamtabfluss beobachtet. Im ersten Teil der Arbeit wurde ein neues perkolationstheoretisches Modell entwickelt, um den Gebietsabfluss sowie unterirdische Fliesspfade zu simulieren. Das plötzliche Eintreten einer hohen hydrologischen Konnektivität zwischen Bodenelementen wurde als Schlu¨sselfaktor zur Erklärung des nichtlinearen Abflussverhaltens ermittelt. Die initiale Entwicklungsphase des Einzugsgebiets war gekennzeichnet von Erosionsrillenbildung durch Oberflächenabfluss. Im zweiten Teil der Arbeit wurden zwei Modelle entwickelt, um die Rillenentstehung zu beschreiben. Das erste Modell basiert auf dem Prinzip der selbstorganisierten Kritikalität (SOK). Erosion und Deposition sind dabei abhängig von der lokalen kritischen Scherspannung. Das zweite Modell basiert auf der Manning-Gleichung und wurde entwickelt, um die Resultate des SOK-Modells mit jenen eines einfachen, aber physikalisch basierten Ansatzes zu vergleichen. Beide Modelle waren in der Lage die raum-zeitliche Entwicklung der Rillen und Rilleneigenschaften (Länge, Tiefe) in der richtigen Grössenordnung darzustellen. Im dritten Teil der Arbeit wurde ein neues hydrologisches Modell konzipiert, um Nachteile gegenwärtiger Modelle anzugehen. Die Beschreibung des Wassertransportes durch diskrete Volumeneinheiten erlaubt dabei die Simulation von nichtlinearen Abflussprozessen, Transportzeiten und die Entwicklung von Makroskala-Mustern (z.B. Fliesspfaden) mit ein und derselben Modellstruktur. Erste vielversprechende Simulationen konnten Transportzeiten und die zeitliche Entwicklung von Fliesspfaden sowie die Auswirkung von Rillen auf die Quantität der Abflusskomponenten aufzeigen

A data driven approach for diagnosis and management of yield variability attributed to soil constraints

Australian agriculture does not value data to the level required for true precision management. Consequently, agronomic recommendations are frequently based on limited soil information and do not adequately address the spatial variance of the constraints presented. This leads to lost productivity. Due to the costs of soil analysis, land owners and practitioners are often reluctant to invest in soil sampling exercises as the likely economic gain from this investment has not been adequately investigated. A value proposition is therefore required to realise the agronomic and economic benefits of increased site-specific data collection with the aim of ameliorating soil constraints. This study is principally concerned with identifying this value proposition by investigating the spatially variable nature of soil constraints and their interactions with crop yield at the sub-field scale. Agronomic and economic benefits are quantified against simulated ameliorant recommendations made on the basis of varied sampling approaches. In order to assess the effects of sampling density on agronomic recommendations, a 108 ha site was investigated, where 1200 direct soil measurements were obtained (300 sample locations at 4 depth increments) to form a benchmark dataset for analysis used in this study. Random transect sampling (for field average estimates), zone management, regression kriging (SSPFe) and ordinary kriging approaches were first investigated at various sampling densities (N=10, 20, 50, 100, 150, 200, 250 and 300) to observe the effects of lime and gypsum ameliorant recommendation advice. It was identified that the ordinary kriging method provided the most accurate spatial recommendation advice for gypsum and lime at all depth increments investigated (i.e. 0–10 cm, 10–20 cm, 20–40 cm and 40–60 cm), with the majority of improved accuracy being achieved up to 50 samples (≈0.5 samples/ha). The lack of correlation between the environmental covariates and target soil variables inhibited the ability for regression kriging to outperform ordinary kriging. To extend these findings in an attempt to identify the economically optimal sampling density for the investigation site, a yield prediction model was required to estimate the spatial yield response due to amelioration. Given the complex nonlinear relationships between soil properties and yield, this was achieved by applying four machine learning models (both linear and nonlinear) consisting of a mixed-linear regression, a regression tree (Cubist), an artificial neural network and a support vector machine. These were trained using the 1200 directly measured soil samples, each with 9 soil measurements describing structural features (i.e. soil pH, exchangeable sodium percentage, electrical conductivity, clay, silt, sand, bulk density, potassium, cation exchange capacity) to predict the spatial yield variability at the investigation site with four years of yield data. It was concluded that the Cubist regression tree model produced superior results in terms of improved generalization, whilst achieving an acceptable R2 for training and validation (up to R2 =0.80 for training and R2 =0.78 for validation). The lack of temporal yield information constrained the ability to develop a temporally stable yield prediction model to account for the uncertainties of climate interactions associated with the spatial variability of yield. Accurate predictive performance was achieved for single-season models. Of the spatial prediction methods investigated, random transect sampling and ordinary kriging approaches were adopted to simulate ‘blanket-rate’ (BR) and ‘variable-rate’ (VR) gypsum applications, respectively, for the amelioration of sodicity at the investigated site. For each sampling density, the spatial yield response as a result of a BR and VR application of gypsum was estimated by application of the developed Cubist yield prediction model, calibrated for the investigation site. Accounting for the cost of sampling and financial gains, due to a yield response, the most economically optimum sampling density for the investigation site was 0.2 cores/ha for 0–20 cm treatment and 0.5 cores/ha for 0–60 cm treatment taking a VR approach. Whilst this resulted in an increased soil data investment of $26.4/ha and$136/ha for 0–20 cm and 0–60 cm treatment respectively in comparison to a BR approach, the yield gains due to an improved spatial gypsum application were in excess of 6 t and 26 t per annum. Consequently, the net benefit of increased data investment was estimated to be up to $104,000 after 20 years for 0–60 cm profile treatment. Identifying the influence on qualitative data and management information on soil-yield interaction, a probabilistic approach was investigated to offer an alternative approach where empirical models fail. Using soil compaction as an example, a Bayesian Belief Network was developed to explore the interactions of machine loading, soil wetness and site characteristics with the potential yield declines due to compaction induced by agricultural traffic. The developed tool was subsequently able to broadly describe the agronomic impacts of decisions made in data limiting environments. This body of work presents a combined approach to improving both the diagnosis and management of soil constraints using a data driven approach. Subsequently, a detailed discussion is provided to further this work, and improve upon the results obtained. By continuing this work it is possible to change the industry attitude to data collection and significantly improve the productivity, profitability and soil husbandry of agricultural systems

State Of the Art Report in the fields of numerical analysis and scientific computing. Final version as of 16/02/2020 deliverable D4.1 of the HORIZON 2020 project EURAD.: European Joint Programme on Radioactive Waste Management

Document information Project Acronym EURAD Project Title European Joint Programme on Radioactive Waste Management Project Type European Joint Programme (EJP) EC grant agreement No. 847593 Project starting / end date 1 st June 2019-30 May 2024 Work Package No. 4 Work Package Title Development and Improvement Of NUmerical methods and Tools for modelling coupled processes Work Package Acronym DONUT Deliverable No. 4.