This dissertation is focused on a study of selected environmental effects influencing terrestrial gravity observations. Global geopotential models were used to compute quantities of the disturbing gravity field and its variations during time period. Furthermore, analyses of an atmospherical and hydrological effects on the gravity observations were provided. The computation was realized at the station Pecný, however the concept was proved and compared with data from station Strasbourg. Consequently, the results were discussed with respect to quasigeoid modelling established from gravity and astrogeodetic measurements. The result and recommendations for future research are exposed.

MSCAGR / Department of Soil Science / Soil hydraulic and hydraulic-related physical properties are key to soil productivity and these properties are widely studied. Nevertheless, their spatial variability is least understood. Two sites were selected for this study (University of Venda Experimental farm and Roodeplaat, Agricultural Research Council farm). The objectives of this study were to determine the spatial variability of soil water content, water infiltration and hydraulic conductivity on selected soils. Field measurements were done on a 20 m × 20 m. Soil hydraulic and hydraulic-related physical properties were studied at two depths, 0 – 0.2 m top soil and 0.2 – 0.4 m sub soil. The field was irrigated to saturation and let to drain freely for two days. The soil was quickly secured in water cans to avoid further loss of water by evaporation and taken to the laboratory for analysis. Data was analysed using ordinary kriging method in ArcMap® software version 10.4 to generate spatial variability maps and semi variograms. The University of Venda Experimental farm had lesser spatial variability with coefficient of variation ranging from 9.6 to 33.4%. The spatial variability of soil was very low confirmed by contour maps depicting slightly homogeneity. Whereas, the soil hydro-physical properties displayed greater spatial variability at Roodeplaat, Agricultural Research Council Experimental farm. The empirical variograms of spherical model fits were also assuming weak spatial dependence with a curve variogram. The coefficient of variation ranged from 10.5 to 51.9%. Therefore, the greater variability at Roodeplaat, Agricultural Research Council Experimental farm indicated that coarse soil texture under conventional tillage has a greater influence on the spatial variability of the soil hydro-physical properties. / NRF

- Einleitung: Projektvorstellung 3D – Saat - Material und Methode zur Einflussermittlung von Bodenparametern auf die Ergebnisse der Feuchtemessung - Präsentation und Diskussion der Ergebnisse - Zusammenfassung und Ausblick

Increasing population and water conflicts are making water management one of the most important issues of the present world. It has become absolutely necessary to find ways to manage water more efficiently. Technological advancement has introduced various techniques for data acquisition and analysis, and these tools can be used to address some of the critical issues that challenge water resource management. This research used learning machine techniques and information acquired through remote sensing, to solve problems related to soil moisture estimation and crop identification on large spatial scales. In this dissertation, solutions were proposed in three problem areas that can be important in the decision making process related to water management in irrigated systems. A data assimilation technique was used to build a learning machine model that generated soil moisture estimates commensurate with the scale of the data. The research was taken further by developing a multivariate machine learning algorithm to predict root zone soil moisture both in space and time. Further, a model was developed for supervised classification of multi-spectral reflectance data using a multi-class machine learning algorithm. The procedure was designed for classifying crops but the model is data dependent and can be used with other datasets and hence can be applied to other landcover classification problems. The dissertation compared the performance of relevance vector and the support vector machines in estimating soil moisture. A multivariate relevance vector machine algorithm was tested in the spatio-temporal prediction of soil moisture, and the multi-class relevance vector machine model was used for classifying different crop types. It was concluded that the classification scheme may uncover important data patterns contributing greatly to knowledge bases, and to scientific and medical research. The results for the soil moisture models would give a rough idea to farmers/irrigators about the moisture status of their fields and also about the productivity. The models are part of the framework which is devised in an attempt to provide tools to support irrigation system operational decisions. This information could help in the overall improvement of agricultural water management practices for large irrigation systems. Conclusions were reached based on the performance of these machines in estimating soil moisture using remotely sensed data, forecasting spatial and temporal variation of soil moisture and data classification. These solutions provide a new perspective to problem–solving techniques by introducing new methods that have never been previously attempted.

Global Sensitivity Analysis (GSA) approach helps to identify the effectiveness of model parameters or inputs and thus provides essential information about the model performance. The effects of 14 parameters and one input (forcing data) of the Sacramento Soil Moisture Accounting (SAC-SMA) model are analyzed by using two GSA methods: Sobol' and Fourier Amplitude Sensitivity Test (FAST). The simulations are carried out over five sub-basins within the Columbia River Basin (CRB) for three different periods: one-year, four-year, and seven-year. The main parameter sensitivities (first-order) and the interactions sensitivities (second-order) are evaluated in this study. Our results show that some hydrological processes are highly affected by the simulation length. In other words, some parameters reveal importance during the short period simulation (e.g. one-year) while other parameters are effective in the long period simulations (e.g. four-year and seven-year). Moreover, the reliability of the sensitivity analysis results is compared based on 1) the agreement between the two sensitivity analysis methods (Sobol' and FAST) in terms of highlighting the same parameters or input as the most influential parameters or input and 2) how the methods are cohered in ranking these sensitive parameters under the same conditions (sub-basins and simulation length). The results show that the coherence between the Sobol' and FAST sensitivity analysis methods. Additionally, it is found that FAST method is sufficient to evaluate the main effects of the model parameters and inputs. This study confirms that the Sobol' and FAST methods are reliable GSA methods that can be applied in different scientific applications. Finally, as a future work, we suggest to study the uncertainty associated with the sensitivity analysis approach regarding the reliability of evaluating different sensitivity analysis methods.

L'humidité du sol est une variable climatique essentielle dont le suivi à l'échelle globale est possible grâce à des instruments micro-ondes à bord des satellites. La précision de ces estimations est validée par comparaison directe aux mesures au sol. Tandis que les estimations satellitaires ont des résolutions allant de 30 à 100 km, les capteurs in situ sont généralement représentatifs d'une zone de quelques centimètres (résolution ponctuelle). Cette différence entre l'échelle spatiale des estimations satellitaires et in situ impacte le processus de validation et les statistiques obtenues à un niveau qui n'est pas connu actuellement. Cette thèse vise à améliorer la connaissance de l'impact du changement d'échelle spatiale, ainsi qu'à fournir des méthodes d'évaluation de celle-ci applicables à toute zone de validation. Pour ce faire, la relation entre les échelles spatiales et temporelles a été étudiée. Des séries modélisées et mesurées sur des régions différentes du globe ont été décomposées en échelles de temps allant de 0,5 et 128 jours, en utilisant des transformées en ondelettes. La représentativité spatiale des mesures à résolution ponctuelle a ensuite été évaluée, par échelle de temps, avec 4 approches différentes : l'analyse de la stabilité temporelle, la triple colocation, le pourcentage de zones corrélées (CArea) et une nouvelle approche utilisant des corrélations basées sur des ondelettes (WCor). De plus, l'incertitude d'échantillonnage a été évaluée séparément avec des approches bootstrap et des simulations de Monte Carlo de séries à résolution ponctuelle. À l'issue de ces expériences, il y a été constaté que la moyenne des valeurs de représentativité spatiale obtenues tend à augmenter avec l'échelle de temps, mais aussi leur dispersion. Cela implique que certaines stations ont de vastes zones de représentativité à des échelles saisonnières, tandis que d'autres ne l'ont pas. Aux échelles sous-hebdomadaires, toutes les stations présentaient de très petites zones de représentativité. Enfin, l'impact de l'incertitude d'échantillonnage s'est avéré assez important dans les métriques de validation satellitaire. / Soil moisture is an essential climate variable that is globally monitored with the help of satellite borne microwave instruments. The accuracy of satellite soil moisture estimations is assessed by direct comparison to in situ measurements. While satellite estimates have a resolution ranging between 30 and 100 km, in situ sensors typically measure over an area of a few centimetres (point resolution). This spatial scale mismatch between satellite and in situ soil moisture estimates impairs the validation process and the respective summary statistics to an extent that is not currently known. This thesis aims at improving the knowledge of the spatial scale mismatch, as well as providing methods for its assessment applicable to any validation area. To this end, the connection between the SM spatial and time scales was investigated. Modelled and measured soil moisture series at different regions of the globe were decomposed into time scales ranging from 0.5 to 128 days, using wavelet transforms. The spatial representativeness of the point measurements was then assessed, on a per time scale basis, with 4 different approaches: temporal stability analysis, triple collocation, the percentage of correlated areas (CArea) and a new approach that uses wavelet- based correlations (WCor). Moreover, one of the components of the mismatch, the sampling uncertainty, has been assessed separately with bootstrap and Monte Carlo simulations of point-support series. It was found that the average of the spatial representativeness values tends to increase with the time scales but so does their dispersion. This implies that some stations had large representativeness areas at seasonal scales, while others do not. At sub-weekly scales, all stations exhibited very small representativeness areas. Finally, the sampling uncertainty has been observed to have a considerable impact on satellite validation statistics.

Urban pluvial floods occur during extreme rain events and both occurrence and magnitude of these floods are expected to increase. Preserving or constructing green areas in urban areas has been shown to mitigate and control these floods. The common way to evaluate flood risks is to set up a rainfall-runoff model, but these studies are often case related and only investigate the soil characteristics for that specific case. Multiple studies have also stated that the difference between major and minor flooding effects is connected to the antecedent soil moisture content. This thesis attempts to investigate how different soil characteristics influencing infiltration affect the hydraulic response in two Swedish urban catchments and if antecedent soil moisture is a critical factor. To evaluate the hydraulic response, a two-dimensional surface runoff model of two different urban catchments was forced with a hyetograph of a CDS-rain with a return period of 100 years. The simulations were conducted with three different soil types for all urban green areas: clay, sandy loam and sand, and three different antecedent soil moisture contents for clay and sandy loam. Flood extent and discharge from catchment area was evaluated, as was flood depth and overland flow in 16 chosen evaluation points. The results showed that with decreasing infiltration rate of a soil and with increasing antecedent soil moisture content, the severity of the flood and discharge at the catchment outlet was increased. It was also concluded that soil type affects flood extent, flood depth, overland flow and discharge from catchment to a greater extent than antecedent soil moisture.

No description available.

The potential for high spatio-temporal resolution microwave measurements has urged the adoption of the signals of opportunity (SoOp) passive radar technique for use in remote sensing. Recent trends in particular target highly complex remote sensing problems such as root-zone soil moisture and snow water equivalent. This dissertation explores the continued open-sourcing of the SoOp coherent bistatic scattering model (SCoBi) and its use in soil moisture sensing applications. Starting from ground-based applications, the feasibility of root-zone soil moisture remote sensing is assessed using available SoOp resources below L-band. A modularized, spaceborne model is then developed to simulate land-surface scattering and delay-Doppler maps over the available spectrum of SoOp resources. The simulation tools are intended to provide insights for future spaceborne modeling pursuits.

This thesis includes two studies. The first study examined changes in soil characteristics as a result of prolonged conifer dominance in successional aspen-conifer forests. Changing disturbance patterns in aspen-conifer forests appear to be altering successional dynamics that favors conifer expansion in aspen forests. The primary objective of this paper was to identify how increasing conifer dominance that develops in later successional stages alters forest soil characteristics. Soil measurements were collected along a stand composition gradient: aspen dominated, aspen-conifer mix, conifer dominated and open meadow, which includes the range of conditions that exists through the stages of secondary succession in aspen-conifer forests. Soil chemistry, moisture content, respiration, and temperature were measured. There was a consistent trend in which aspen stands demonstrated higher mean soil nutrient concentrations than adjacent meadows, mixed or conifer stands. Soil moisture was significantly higher in aspen stands and meadows in early summer. Soil respiration was significantly higher in aspen stands than conifer stands or meadows throughout the summer. The results indicate that soil resource availability and respiration peak within aspen dominated stands that are present during early succession and then decrease as conifer abundance increases along our stand composition gradient, representative of stand characteristics present in mid to late successional stages. Emerging evidence from other studies suggest that these observed changes in soil characteristics with increasing conifer dominance may have negative feedbacks on aspen growth and vigor. The second study examined the facilitation effect between aspen and subalpine fir establishment. In subalpine forests, conifer species are often found intermixed with broadleaf species. However, few if any studies have explored the existence and influence of facilitation between broadleaf tree species and conifers. We have observed the general establishment of subalpine fir seedlings at the base of aspen trees in a subalpine forest, indicating that a facilitative relationship may exist. To explore the potential facilitative relationship during secondary succession in subalpine forests, subalpine fir seeds were planted across a stand composition gradient (aspen dominated → mixed → conifer dominated stands) at six study sites in the Fishlake National Forest. Seeds were placed during the fall of 2010, at distances of 0 cm and 25 cm in each cardinal direction at the base of mature aspen and subalpine fir trees in each of the three stand types. Seeds were also planted within stand interspaces and in adjacent meadows. Seedling emergence was recorded at the beginning of the summer of 2011 and seedling mortality was recorded in October 2011. Soil moisture content was measured at the position that seeds were placed during the summers of 2009 and 2011. Aspen dominated stands had subalpine fir germination that was on average 11 times greater than mixed or conifer dominated stands. Germination was 2.3 fold greater at the base of aspen trees than fir trees and two fold greater at the base of aspen trees than interspaces. Seedling mortality was lower in aspen stands but was not significantly influenced by position relative to mature trees. Soil moisture was highest in aspen dominated stands, with better soil moisture conditions at the base of aspen trees and in interspaces compared to the base of fir trees. Few if any studies regarding conifer facilitation have provided evidence for facilitation at the germination life stage, rather they focus on seedling survival. However, our study illustrates a strong facilitative interaction in which both aspen dominated stands and aspen trees increase the likelihood of subalpine fir seedling establishment by drastically increasing rates of subalpine fir germination. Because of aspen's primary role in initiating secondary succession through post-disturbance sucker regeneration, and the subsequent dependence of conifers on aspen for establishment, aspen mortality via competition with conifers under longer fire cycles, droughts, or intensive ungulate browsing may result in a loss of aspen-conifer forest communities in some locales.