Mesoscale mapping of sediment source hotspots for dam sediment management in data-sparse semi-arid catchments

Land degradation and water availability in semi-arid regions are interdependent challenges for management that are influenced by climatic and anthropogenic changes. Erosion and high sediment loads in rivers cause reservoir siltation and decrease storage capacity, which pose risk on water security for citizens, agriculture, and industry. In regions where resources for management are limited, identifying spatial-temporal variability of sediment sources is crucial to decrease siltation. Despite widespread availability of rigorous methods, approaches simplifying spatial and temporal variability of erosion are often inappropriately applied to very data sparse semi-arid regions. In this work, we review existing approaches for mapping erosional hotspots, and provide an example of spatial-temporal mapping approach in two case study regions. The barriers limiting data availability and their effects on erosion mapping methods, their validation, and resulting prioritization of leverage management areas are discussed.BMBF, 02WGR1421A-I, GROW - Verbundprojekt SaWaM: Saisonales Wasserressourcen-Management in Trockenregionen: Praxistransfer regionalisierter globaler Informationen, Teilprojekt 1DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli

Applying Automatic Mapping Processing By GMT to Bathymetric and Geophysical Data: Cascadia Subduction Zone, Pacific Ocean

The Cascadia Trench is stretching along the convergent plate boundaries of Pacific Plate, North America Plate and Juan De Fuca Plate. It is an important geomorphological structural feature in the north-east Pacific Ocean. The aim of the paper is to analyse the geomorphology of the Cascadia Trench west of Vancouver Island (Canada and USA) using the GMT cartographic scripting toolset. The unique geomorphological feature of the Cascadia Trench is that the thick sediment layer completely obscures the subduction zone and abyssal hills. This results in the asymmetric profile in the cross-section of the trench. Bathymetric data were extracted from the GEBCO 2019 dataset (15 arc-second grid), sediment thickness by the GlobSed dataset. Due to the dominance of high sedimentary rate and complexity of the tectonic processes and geologic settings, Cascadia Trench develops very specific asymmetric geomorphic shape comparing to the typical V-form. The results of the geomorphic modelling show that eastern side of the trench has a gentle curvature (slope: 35.12°), partially stepped, due to the tectonic movements and faults. The opposite, oceanward side is almost completely leveled. The trench is narrow with maximal depth at the selected segment -3489 m and for the whole dataset -6201 m. The most repetitive depth is in a range -2500 to -2400 m (267 samples) and -2500 to -2600 m (261 samples). The bottom is mostly flat due to the high sedimentation rates indicating the accumulative leveling processes. Marine free-air gravity anomalies along the Cascadia Subduction Zone are characterized by weakly positive values (20 mGal) increasing rapidly in the zone of the continental slope (>200 mGal), which is associated with a decrease in thickness of the Earth’s crust

Quantitative Predictive Modelling Approaches to Understanding Rheumatoid Arthritis:A Brief Review

Rheumatoid arthritis is a chronic autoimmune disease that is a major public health challenge. The disease is characterised by inflammation of synovial joints and cartilage erosion, which lead to chronic pain, poor life quality and, in some cases, mortality. Understanding the biological mechanisms behind the progression of the disease, as well as developing new methods for quantitative predictions of disease progression in the presence/absence of various therapies is important for the success of therapeutic approaches. The aim of this study is to review various quantitative predictive modelling approaches for understanding rheumatoid arthritis. To this end, we start by briefly discussing the biology of this disease and some current treatment approaches, as well as emphasising some of the open problems in the field. Then, we review various mathematical mechanistic models derived to address some of these open problems. We discuss models that investigate the biological mechanisms behind the progression of the disease, as well as pharmacokinetic and pharmacodynamic models for various drug therapies. Furthermore, we highlight models aimed at optimising the costs of the treatments while taking into consideration the evolution of the disease and potential complications.Publisher PDFPeer reviewe

Sustainable Reservoir Management Approaches under Impacts of Climate Change - A Case Study of Mangla Reservoir, Pakistan

Reservoir sedimentation is a major issue for water resource management around the world. It has serious economic, environmental, and social consequences, such as reduced water storage capacity, increased flooding risk, decreased hydropower generation, and deteriorated water quality. Increased rainfall intensity, higher temperatures, and more extreme weather events due to climate change are expected to exacerbate the problem of reservoir sedimentation. As a result, sedimentation must be managed to ensure the long-term viability of reservoirs and their associated infrastructure. Effective reservoir sedimentation management in the face of climate change necessitates an understanding of the sedimentation process and the factors that influence it, such as land use practices, erosion, and climate. Monitoring and modelling sedimentation rates are also useful tools for forecasting future impacts and making management decisions. The goal of this research is to create long-term reservoir management strategies in the face of climate change by simulating the effects of various reservoir-operating strategies on reservoir sedimentation and sediment delta movement at Mangla Reservoir in Pakistan (the second-largest dam in the country). In order to assess the impact of the Mangla Reservoir's sedimentation and reservoir life, a framework was developed. This framework incorporates both hydrological and morphodynamic models and various soft computing models. In addition to taking climate change uncertainty into consideration, the proposed framework also incorporates sediment source, sediment delivery, and reservoir morphology changes. Furthermore, the purpose of this study is to provide a practical methodology based on the limited data available. In the first phase of this study, it was investigated how to accurately quantify the missing suspended sediment load (SSL) data in rivers by utilizing various techniques, such as sediment rating curves (SRC) and soft computing models (SCMs), including local linear regression (LLR), artificial neural networks (ANN) and wavelet-cum-ANN (WANN). Further, the Gamma and M-test were performed to select the best-input variables and appropriate data length for SCMs development. Based on an evaluation of the outcomes of all leading models for SSL estimation, it can be concluded that SCMs are more effective than SRC approaches. Additionally, the results also indicated that the WANN model was the most accurate model for reconstructing the SSL time series because it is capable of identifying the salient characteristics in a data series. The second phase of this study examined the feasibility of using four satellite precipitation datasets (SPDs) which included GPM, PERSIANN_CDR, CHIRPS, and CMORPH to predict streamflow and sediment loads (SL) within a poorly gauged mountainous catchment, by employing the SWAT hydrological model as well as SWAT coupled soft computing models (SCMs) such as artificial neural networks (SWAT-ANN), random forests (SWAT-RF), and support vector regression (SWAT-SVR). SCMs were developed using the outputs of un-calibrated SWAT hydrological models to improve the predictions. The results indicate that during the entire simulation, the GPM shows the best performance in both schemes, while PERSIAN_CDR and CHIRPS also perform well, whereas CMORPH predicts streamflow for the Upper Jhelum River Basin (UJRB) with relatively poor performance. Among the best GPM-based models, SWAT-RF offered the best performance to simulate the entire streamflow, while SWAT-ANN excelled at simulating the SL. Hence, hydrological coupled SCMs based on SPDs could be an effective technique for simulating streamflow and SL, particularly in complex terrain where gauge network density is low or uneven. The third and last phase of this study investigated the impact of different reservoir operating strategies on Mangla reservoir sedimentation using a 1D sediment transport model. To improve the accuracy of the model, more accurate boundary conditions for flow and sediment load were incorporated into the numerical model (derived from the first and second phases of this study) so that the successive morphodynamic model could precisely predict bed level changes under given climate conditions. Further, in order to assess the long-term effect of a changing climate, a Global Climate Model (GCM) under Representative Concentration Pathways (RCP) scenarios 4.5 and 8.5 for the 21st century is used. The long-term modelling results showed that a gradual increase in the reservoir minimum operating level (MOL) slows down the delta movement rate and the bed level close to the dam. However, it may compromise the downstream irrigation demand during periods of high water demand. The findings may help the reservoir managers to improve the reservoir operation rules and ultimately support the objective of sustainable reservoir use for societal benefit. In summary, this study provides comprehensive insights into reservoir sedimentation phenomena and recommends an operational strategy that is both feasible and sustainable over the long term under the impact of climate change, especially in cases where a lack of data exists. Basically, it is very important to improve the accuracy of sediment load estimates, which are essential in the design and operation of reservoir structures and operating plans in response to incoming sediment loads, ensuring accurate reservoir lifespan predictions. Furthermore, the production of highly accurate streamflow forecasts, particularly when on-site data is limited, is important and can be achieved by the use of satellite-based precipitation data in conjunction with hydrological and soft computing models. Ultimately, the use of soft computing methods produces significantly improved input data for sediment load and discharge, enabling the application of one-dimensional hydro-morphodynamic numerical models to evaluate sediment dynamics and reservoir useful life under the influence of climate change at various operating conditions in a way that is adequate for evaluating sediment dynamics.:Chapter 1: Introduction Chapter 2:Reconstruction of Sediment Load Data in Rivers Chapter 3:Assessment of The Hydrological and Coupled Soft Computing Models, Based on Different Satellite Precipitation Datasets, To Simulate Streamflow and Sediment Load in A Mountainous Catchment Chapter 4:Simulating the Impact of Climate Change with Different Reservoir Operating Strategies on Sedimentation of the Mangla Reservoir, Northern Pakistan Chapter 5:Conclusions and Recommendation

Controlling realism and uncertainty in reservoir models using intelligent sedimentological prior information

Forecasting reservoir production has a large associated uncertainty, since this is the final part of a very complex process, this process is based on sparse and indirect data measurements. One the methodologies used in the oil industry to predict reservoir production is based on the Baye’s theorem. Baye’s theorem applied to reservoir forecasting, samples parameters from a prior understanding of the uncertainty to generate reservoir models and updates this prior information by comparing reservoir production data with model production response. In automatic history matching it is challenging to generate reservoir models that preserve geological realism (obtain reservoir models with geological features that have been seen in nature). One way to control the geological realism in reservoir models is by controlling the realism of the geological prior information. The aim of this thesis is to encapsulate sedimentological information in order to build prior information that can control the geological realism of the history-matched models. This “intelligent” prior information is introduced into the automatic history-matching framework rejecting geologically unrealistic reservoir models. Machine Learning Techniques (MLT) were used to build realistic sedimentological prior information models. Another goal of this thesis was to include geological parameters into the automatic history-match framework that have an impact on reservoir model performance: vertical variation of facies proportions, connectivity of geobodies, and the use of multiple training images as a source of realistic sedimentological prior information. The main outcome of this thesis is that the use of “intelligent” sedimentological prior information guarantees the realism of reservoir models and reduces computing time and uncertainty in reservoir production prediction

The Influence of Sedimentation to The Morfology Change of Serang River Estuary at The National Strategic Area Yogyakarta International Airport (Ksn Yia)

Abstrak. Bandara Internasional Yogyakarta terletak di Kawasan Strategis Nasional, Kabupaten Kulon Progo. Daerah ini secara geologis merupakan daerah dataran rendah yang diapit oleh Sungai Bogowonto dan Sungai Serang yang menyebabkan banjir tahunan pada musim hujan. Sistem pengendalian banjir dikembangkan untuk menjaga kinerja bandara. Penetapan Kawasan Strategis Nasional juga menyebabkan perubahan tata guna lahan di sekitarnya yang dapat mempengaruhi perubahan morfologi sungai. Perubahan morfologi di kedua sungai tersebut telah diidentifikasi berdasarkan pengamatan di lapangan. Berdasarkan pengamatan tersebut dapat diketahui bahwa peningkatan laju sedimentasi merupakan parameter terpenting yang dapat mengubah morfologi kedua sungai tersebut. Pengaruh perubahan morfologi di muara sungai Serang telah dipelajari dengan menggunakan software DELFT3D, sedimentasi di muara sungai Serang telah disimulasikan dengan beberapa skenario antara lain pada saat monsun barat dan monsun timur. Hasil pemodelan menunjukkan bahwa tebal sedimentasi di muara Serang pada kondisi eksisting adalah 3,5 m pada musim barat dengan luas 0,063 ha dan 4,0 m pada musim timur dengan luas 0,437 ha. Kata-kata Kunci: Morfologi, muara, Delft3D Abstract. The Yogyakarta International Airport has located in the National Strategic Area, Kulon Progo regency. This area is geologically a low-lying area flanked by the Bogowonto River and the Serang River which causes annual flooding in the rainy season. A flood control system was developed to maintain airport performance. The determination of the National Strategic Area has also led to changes in the surrounding land use which can affect changes in the morphology of the rivers. The morphological changes in the two rivers have been identified based on field observations. Based on this observation, it can be seen that the increase in sedimentation rate is the most important parameter that can change the morphology of the two rivers. The effect of morphological changes in the Serang river estuary has been studied using DELFT3D software, the sedimentation in the Serang river estuary has been simulated with several scenarios, including during the west monsoon and east monsoon. The modeling results show that the sedimentation thickness in the Serang estuary under existing conditions is 3.5 m in the west season with an area of ​​0.063 ha and 4.0 m in the east monsoon with an area of ​​0.437 ha. Keywords: Morphology, estuary, Delft3

Strange Assemblage

This paper contends that the power of Deleuze & Guattari’s (1988) notion of assemblage as theorised in 1000 Plateaus can be normalised and reductive with reference to its application to any social-cultural context where an open system of dynamic and fluid elements are located. Rather than determining the assemblage in this way, this paper argues for an alternative conception of ‘strange assemblage’ that must be deliberately and consciously created through rigorous and focused intellectual, creative and philosophical work around what makes assemblages singular. The paper will proceed with examples of ‘strange assemblage’ taken from a film by Peter Greenaway (A Zed and 2 Noughts); the film ‘Performance’; educational research with Sudanese families in Australia; the book, Bomb Culture by Jeff Nuttall (1970); and the band Hawkwind. Fittingly, these elements are themselves chosen to demonstrate the concept of ‘strange assemblage’, and how it can be presented. How exactly the elements of a ‘strange assemblage’ come together and work in the world is unknown until they are specifically elaborated and created ‘in the moment’. Such spontaneous methodology reminds us of the 1960s ‘Happenings’, the Situationist International and Dada/Surrealism. The difference that will be opened up by this paper is that all elements of this ‘strange assemblage’ cohere in terms of a rendering of ‘the unacceptable.'

Dataset compilation by GRASS GIS for thematic mapping of Antarctica: Topographic surface, ice thickness, subglacial bed elevation and sediment thickness

This paper presents the GRASS GIS-based thematic mapping of Antarctica using scripting approach and associated datasets on topography and geophysics. The state-of-the art in cartographic development points at two important aspects. The first one comprises shell scripting promoted repeatability of the GIS technique, increased automatization in cartographic workflow, and compatibility of GRASS with Python, PROJ and GDAL libraries which enables advanced geospatial data processing: converting formats, re-projecting and spatial analysis. The second aspect is that data visualization greatly influences geologic research through improving the interpretation between the Antarctic glaciation and surface. This includes the machine learning algorithms of image classification enabling to distinguish between glacier and non-glacier surfaces through automatically partitioning data and analysis of various types of surfaces. Presented detailed maps of Antarctic include visualized datasets from the ETOPO1, GlobSed, EGM96 and Bedmap2 projects. The grids include bed and surface elevation, ETOPO1-based bathymetry and topography, bed, ice and sediment thickness, grounded bed uncertainty, subglacial bed elevation, geoid undulations, ice mask grounded and shelves. Data show the distribution of the present-day glacier, geophysical fields and topographic landforms for analysis of processes and correlations between the geophysical and geological phenomena. Advances in scripting cartography are significant contributions to the geological and glaciological research. Processing high-resolution datasets of Southern Ocean retrieved by remote sensing methods present new steps in automatization of the digital mapping, as presented in this research, and promotes comprehensive monitoring of geological, permafrost and glacial processes in Antarctica. All maps have been plotted using GRASS GIS version 7.8. with technical details of scripts described and interpreted.

Modeling Posidonia oceanica shoot density and rhizome primary production

Posidonia oceanicameadows rank among the most important and most productive ecosystems in the Mediterranean basin, due to their ecological role and to the goods and services they provide. estimations of crucial ecological process such as meadows productivity could play a major role in an environmental management perspective and in the assessment of P. oceanicaecosystem services. In this study, a Machine Learning approach, i.e. Random Forest, was aimed at modeling P. oceanica shoot density and rhizome primary production using as predictive variables only environmental factors retrieved from indirect measurements, such as maps. Our predictive models showed a good level of accuracy in modeling both shoot density and rhizome productivity (R 2 = 0.761 and R 2 = 0.736, respectively). Furthermore, as shoot density is an essential parameter in the estimation of P. oceanica productivity, we proposed a cascaded approach aimed at estimating the latter using predicted values of shoot density rather than observed measurements. In spite of the complexity of the problem, the cascaded Random forest performed quite well (R2 = 0.637). While direct measurements will always play a fundamental role, our estimates could support large scale assessment of the expected condition of P. oceanica meadows, providing valuable information about the way this crucial ecosystem works