Identification and simulation of extreme precipitation using a computationally inexpensive methodology

Includes abstract.Includes bibliographical references (leaves 164-187).An examination of characteristics extreme precipitation in the greater Cape Town region is undertaken. Thereafter, an investigation into the characteristics of these changes is made using two approaches. The first is an empirical methodology to explore the historical attributes of extreme events and the second a numerical method. These are used to demonstrate an approach to produce high resolution forecasts of extreme precipitation if computational resources are scarce. Initially, changes in the characteristics of extreme precipitation in the greater Cape Town region is documented. Then self organizing maps are used to identify archetypal synoptic circulations that are associated with extreme precipitation over the region. Thereafter, days whose synoptic state matched those of the synoptic archetypes are simulated at a resolution of one kilometer to capture regional topographic modification of extreme precipitation. Following this, the simulated precipitation is validated against observed data and the model performance is assessed. These approaches were tested over Cape Town, South Africa which has complex topography where extreme rainfall is not well predicted. As this methodology is computationally relatively inexpensive, it has applicability to regions of the world where these resources are limited, more especially Africa where the state of climate science is poor. An analysis of historical station data from three locations in the greater Cape Town region showed mixed trends in extreme rainfall where extreme rainfall was taken as that in the 90th percentile. One station, located in the lee of topography, showed a statistically significant increase in the intensity of extreme rainfall and another, at a relatively topography-free location, a significant decrease. The third station showed no significant trend. Decadal changes in monthly precipitation show a shift in the start and end of the extreme rainfall season to starting later in winter and continuing into the early spring. The station with the significant increase in extreme rainfall intensity also showed an increase in 99th percentile rainfall intensity. Synoptic states associated with extreme rainfall in the greater Cape Town region were then examined. These were identified as mid-latitude cyclones with centers at relatively low latitudes. They were characterized by strong pressure gradients at the surface and in the upper air high as well as high regional humidities. Precipitation characteristics of the frontal systems ranged from precipitation that fell over a number of days in relatively low daily amounts to very heavy precipitation that fell in one day. Over the twenty-three year test period examined, there are change

Modelling the long-term morphological evolution of tidal embayments

A numerical model was developed to improve the understanding of the long-term morphological evolution of tidal embayments. Morphological change was simulated as a result of the interactions between hydrodynamics, sediment transport, and the evolving topography. Numerical simulations indicate that these morphodynamic interactions can lead to the initiation of tidal channels and potentially give rise to large scale channel pattern development. The tidal range and the depth of the initially unchannelized tidal basin determined the time scale over which the channel network developed. Channels and intertidal areas rapidly formed when the basin was shallow and the tidal range large. For a large tidal range and a deep tidal basin, the tidal flow imported large volumes of sediment. The large water depths inhibited the formation of channels and the imported sediment formed a flood-tidal delta. The flood-tidal delta grew and became shallower over time until it became incised by channels. Ultimately, a complete channel network developed. Changes in the morphology of a deep basin were slowed down when the tidal range was small and the channel network then remained underdeveloped over long time scales. All the simulated morphologies, with different combinations of the tidal range and depth of the basin, evolved toward a state of less morphodynamic activity and obtained a hypsometry which resembles those of natural systems. Basins with well-developed channel networks were used to explore the response of tidal embayments to sea level rise. During sea level rise, the intertidal geometry adjusted to the changing environmental forcing conditions. Tidal channels became larger and more widely-spaced and expanded landward because of headward erosion. This landward shift of the channel network can be accompanied by a change in the asymmetry between the flood and ebb tidal currents. Sea level rise can even lead to a transition from exporting to importing sediment. These findings indicate that morphodynamic interactions need to be included in the study of sea level rise impacts on tidal systems. The morphodynamic model was extended to account for the interactions between mangroves and physical processes. Mangroves affected hydrodynamics and sediment dynamics in a variety of ways. In turn, hydrodynamic conditions controlled the colonization, growth, and dying of mangroves. Mangroves influenced channel network evolution by enhancing the branching of channels because the extra flow resistance in mangrove forests drove flow concentration and thus sediment erosion in between vegetated areas. On the other hand, mangroves hindered the landward expansion of channels. When the sea level was rising, mangroves increased the ability of areas to maintain an elevation above mid tide. Channel network expansion, induced by the rise in sea level, occurred differently when mangroves were present because they hindered both the branching and headward erosion of the expanding channels

Using a deep-learning approach to infer and forecast the Indonesian Throughflow transport from sea surface height

The Indonesian Throughflow (ITF) connects the tropical Pacific and Indian Oceans and is critical to the regional and global climate systems. Previous research indicates that the Indo-Pacific pressure gradient is a major driver of the ITF, implying the possibility of forecasting ITF transport by the sea surface height (SSH) of the Indo-Pacific Ocean. Here we used a deep-learning approach with the convolutional neural network (CNN) model to reproduce ITF transport. The CNN model was trained with a random selection of the Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations and verified with residual components of the CMIP6 simulations. A test of the training results showed that the CNN model with SSH is able to reproduce approximately 90% of the total variance of ITF transport. The CNN model with CMIP6 was then transformed to the Simple Ocean Data Assimilation (SODA) dataset and this transformed model reproduced approximately 80% of the total variance of ITF transport in the SODA. A time series of ITF transport, verified by Monitoring the ITF (MITF) and International Nusantara Stratification and Transport (INSTANT) measurements of ITF, was then produced by the model using satellite observations from 1993 to 2021. We discovered that the CNN model can make a valid prediction with a lead time of 7 months, implying that the ITF transport can be predicted using the deep-learning approach with SSH data

Real Time Fusion of Radioisotope Direction Estimation and Visual Object Tracking

Research into discovering prohibited nuclear material plays an integral role in providing security from terrorism. Although many diverse methods contribute to defense, there exists a capability gap in localizing moving sources. This thesis introduces a real time radioisotope tracking algorithm assisted by visual object tracking methods to fill the capability gap. The proposed algorithm can estimate carrier likelihood for objects in its field of view, and is designed to assist a pedestrian agent wearing a backpack detector. The complex, crowd-filled, urban environments where this algorithm must function combined with the size and weight limitations of a pedestrian system makes designing a functioning algorithm challenging.The contribution of this thesis is threefold. First, a generalized directional estimator is proposed. Second, two state-of-the-art visual object detection and visual object tracking methods are combined into a single tracking algorithm. Third, those outputs are fused to produce a real time radioisotope tracking algorithm. This algorithm is designed for use with the backpack detector built by the IDEAS for WIND research group. This setup takes advantage of recent advances in detector, camera, and computer technologies to meet the challenging physical limitations.The directional estimator operates via gradient boosting regression to predict radioisotope direction with a variance of 50 degrees when trained on a simple laboratory dataset. Under conditions similar to other state-of-the-art methods, the accuracy is comparable. YOLOv3 and SiamFC are chosen by evaluating advanced visual tracking methods in terms of speed and efficiency across multiple architectures, and in terms of accuracy on datasets like the Visual Object Tracking (VOT) Challenge and Common Objects in Context (COCO). The resultant tracking algorithm operates in real time. The outputs of direction estimation and visual tracking are fused using sequential Bayesian inference to predict carrier likelihood. Using lab trials evaluated by hand on visual and nuclear data, and a synthesized challenge dataset using visual data from the Boston Marathon attack, it can be observed that this prototype system advances the state-of-the-art towards localization of a moving source

Renewable Energy Resource Assessment and Forecasting

In recent years, several projects and studies have been launched towards the development and use of new methodologies, in order to assess, monitor, and support clean forms of energy. Accurate estimation of the available energy potential is of primary importance, but is not always easy to achieve. The present Special Issue on ‘Renewable Energy Resource Assessment and Forecasting’ aims to provide a holistic approach to the above issues, by presenting multidisciplinary methodologies and tools that are able to support research projects and meet today’s technical, socio-economic, and decision-making needs. In particular, research papers, reviews, and case studies on the following subjects are presented: wind, wave and solar energy; biofuels; resource assessment of combined renewable energy forms; numerical models for renewable energy forecasting; integrated forecasted systems; energy for buildings; sustainable development; resource analysis tools and statistical models; extreme value analysis and forecasting for renewable energy resources

Autonomous 3D Urban and Complex Terrain Geometry Generation and Micro-Climate Modelling Using CFD and Deep Learning

Sustainable building design requires a clear understanding and realistic modelling of the complex interaction between climate and built environment to create safe and comfortable outdoor and indoor spaces. This necessitates unprecedented urban climate modelling at high temporal and spatial resolution. The interaction between complex urban geometries and the microclimate is characterized by complex transport mechanisms. The challenge to generate geometric and physics boundary conditions in an automated manner is hindering the progress of computational methods in urban design. Thus, the challenge of modelling realistic and pragmatic numerical urban micro-climate for wind engineering, environmental, and building energy simulation applications should address the complexity of the geometry and the variability of surface types involved in urban exposures. The original contribution to knowledge in this research is the proposed an end-to-end workflow that employs a cutting-edge deep learning model for image segmentation to generate building footprint polygons autonomously and combining those polygons with LiDAR data to generate level of detail three (LOD3) 3D building models to tackle the geometry modelling issue in climate modelling and solar power potential assessment. Urban and topography geometric modelling is a challenging task when undertaking climate model assessment. This paper describes a deep learning technique that is based on U-Net architecture to automate 3D building model generation by combining satellite imagery with LiDAR data. The deep learning model used registered a mean squared error of 0.02. The extracted building polygons were extruded using height information from corresponding LiDAR data. The building roof structures were also modelled from the same point cloud data. The method used has the potential to automate the task of generating urban scale 3D building models and can be used for city-wide applications. The advantage of applying a deep learning model in an image processing task is that it can be applied to a new set of input image data to extract building footprint polygons for autonomous application once it has been trained. In addition, the model can be improved over time with minimum adjustments when an improved quality dataset is available, and the trained parameters can be improved further building on previously learned features. Application examples for pedestrian level wind and solar energy availability assessment as well as modeling wind flow over complex terrain are presented

Fungal infection in plant leaves-A Review

The primary resource of a country is agriculture and crop production. The economic development of the country also resides on the agricultural products which ultimately determines the growth of the citizen. The major crisis in food production is the influence of diseases in plants. This ultimately abolish the economy of the country, as major portion of progress of the nation is dependent on agriculture and its products. The challenges faced by the farmers are the unawareness of the various diseases that affects different parts of the plants. They should be able to identify the early infection caused in plants by different pathogens like bacteria, fungi, virus etc., Main disease-causing agent is found to be the fungus which was the vital factor that produce serious loss in the agriculture. Again, the pesticides and fertilizers used by the agriculturist changes to be hazardous for human beings and wild life species. This problem should be considered as a chief calamity and an alternate measure must be found to support the cultivators. An innovative step adopted by the researchers are prompt detection of the diseases using machine learning and deep learning algorithms. These algorithms use different image processing techniques and computer vision process to classify the disease in plant parts at an earlier stage. This paper provides a detailed review on the fungal infection caused in plant leaves and its identification using deep learning methodology

Soil Erosion

In the first section of this book on soil erosion, an introduction to the soil erosion problem is presented. In the first part of the second section, rainfall erosivity is estimated on the basis of pluviograph records and cumulative rainfall depths by means of empirical equations and machine learning methods. In the second part of the second section, a physically-based, hydrodynamic, finite element model is described for the computation of surface runoff and channel flows. In the first part of the third section, the soil erosion risk is assessed in two different basins. In the second part of the third section, the soil erosion risk management in a basin is evaluated, and the delimitation of the areas requiring priority planning is achieved