The EGU General Assembly最新文献

EOT20 is the latest in a series of empirical ocean tide (EOT) models derived using residual tidal analysis of multi-mission satellite altimetry at DGFI-TUM. The amplitudes and phases of seventeen tidal constituents are provided on a global 0.125-degree grid based on empirical analysis of eleven satellite altimetry missions. The EOT20 model shows significant improvements compared to the previous iteration of the global model (EOT11a) throughout the ocean, particularly in the coastal and shelf regions, due to the inclusion of more recent satellite altimetry data as well as more missions, the use of the updated FES2014 tidal model as a reference to estimated residual signals, the inclusion of the ALES retracker and improved coastal representation. In the validation of EOT20 using tide gauges and ocean bottom pressure data, these improvements in the model compared to EOT11a are highlighted with the root-square sum (RSS) of the eight major tidal constituents improving by ~3 cm for the entire global ocean with the major improvement in RSS (~3.5 cm) occurring in coastal regions (<1 km to the coast). Compared to the other global ocean tidal models, EOT20 shows a clear improvement of ~0.4 cm in RSS compared to the closest model (FES2014) in the global ocean. Compared to the FES2014 model, the RSS improvement in EOT20 is mainly seen in the coastal region (~0.45 cm) while in the shelf and open ocean regions these two models only vary in terms of RSS by ~0.005 cm. The significant improvement of EOT20, particularly in the coastal region, provides encouragement for the use of the EOT20 model as a tidal correction of satellite altimetry in coastal sea level research. 

The value of the acceleration due to gravity is of interest in a wide range of fields, from geophysics, geodesy, water-floor monitoring, and hazard forecasting to oil, gas and mineral exploration. For this purpose, relative or absolute gravimeters have been developed and used for decades. While absolute gravimeters are mainly used in monitoring stations or as reference, relative gravimeters are those actually used to determine the relative variations of the local gravitational field given their smaller dimension, lighter weight, and better reading resolution, despite the high costs and the difficulty in being used under severe environmental conditions. In the last years, the advent of micro-electromechanical-systems (MEMS), in particular MEMS accelerometers, has opened up the doors to new measuring possibilities at very low-costs. As a consequence, different international research groups focused their efforts to develop relative MEMS gravimeters and showed that this technology might be really useful for monitoring the gravitational field. However, their current production is limited to a few specimens and prototypes that cannot be exploited on a large scale at the present day. For this reason, this work investigates the possibilities and the limits in the use of commercial digital MEMS accelerometers as relative gravimeters. The digital MEMS accelerometers investigated in this work are two commercial low-cost digital MEMS accelerometers (STM, model LSM6DSR, and Sequoia, model GEA). The first is composed of an accelerometer sensor, a charge amplifier, and an analog-to-digital converter and is connected by a serial cable to a separated external microcontroller (ST, model 32F769IDISCOVERY), in which other electronic components are integrated. The second is composed of the sensing element and the analog-to-digital converter. Both are connected to the computer via USB cable. The two devices are included in a thermally insulated case, in which a resistive heater and a resistance thermometer (PT1000), connected in loop, are placed in order to guarantee temperature stability during use. The system, installed on a tilting table to ensure higher accuracy in the evaluation of local g, is calibrated in static conditions by comparison to the absolute gravimeter IMGC-02 at a specific measurement location at INRIM. Calibration is repeated several times over a period of a few weeks in order to evaluate repeatability, reproducibility and stability over time. Despite the promising future prospects of this technology, at present, the levels of precisions are low compared to the ones required by most of geodynamics applications.

Green Infrastructure (GI) offers multiple and integrated benefits to urban areas, including relieving pressure on ‘grey’ infrastructure systems by locally managing surface runoff within cities to reduce the risk of urban flooding. Although the use of GI has been shown to attenuate flooding, monitored and quantifiable data determining the effectiveness of GI is imperative for supporting widespread adoption of GI within cities and to provide an evidence-base to inform the design and maintenance procedures of such systems and ultimately influence key decision makers .

The National Green Infrastructure Facility (NGIF) based in Newcastle-upon-Tyne, UK, is a purpose-built, publicly accessible, ‘living laboratory’ and demonstration site established in 2017, funded by the UK Collaboratorium for Research on Infrastructure and Cities. The NGIF explores how a wide range of green features such as trees, shrubs and soils can help reduce flooding in cities and make them more resilient and sustainable to future changes in climate and urban pressures. The facility hosts a number of novel GI features of varying scale, monitored with dense sensor networks to allow the in-situ measurement of key hydrological, climatic and biophysical variables (e.g. precipitation, temperature, soil moisture, water depth, runoff and outflow rates) which are able to provide quantified evidence of the hydrological performance of sustainable drainage systems (SuDS). Such systems generate detailed insights into how SuDS and nature-based solutions can be used to improve surface water management, optimise geo-energy for building heating/cooling and how systems can be used for urban water treatment.

GI features across the NGIF include an experimental  and fully functional swale, providing protection to the area of Newcastle-upon-Tyne in which the feature is located, 10 lysimeter bioretention cells, a series of rain-garden ‘ensembles’ and a monitored green roof system. All experimental features are subjected to prevalent environmental conditions and act as fully functional GI systems, but conditions can also be augmented and simulated to ensure that the GI features act as semi-controlled experimental systems to determine responses outside of the natural instrumented record. All environmental data is recorded at high temporal (< 5 minutes) and spatial resolution and is publicly accessible in real-time via the NGIF API.

This presentation provides an overview of the NGIF and discusses the current research activities taking place across the site. Data is presented from each of the GI systems to demonstrate and discuss their performance and responses during natural and simulated events, including extremes, and to assess their effectiveness in responding to localised changes in climate. Future research directions and collaborative opportunities are also highlighted.

Olive is a widespread crop within Mediterranean area and Italy is one of the biggest producer of olives and oil in the world. From an environmental point of view, centered on carbon (C) sequestration, managing olive orchards sustainably is an urgent and actual issue.

This trial was done in a 2-ha olive orchard (Olea europaea L., cv. ‘Maiatica’; 70-year-old plants, with a distance of 8 × 8 m and NE orientation) located in Ferrandina (Southern Italy, Basilicata region; N 40°29’; E 16°28’). The soil is a sandy loam (Haplic Calcisol - WRB), with a mean bulk density of 1.30 g cm^–3 and sediment as parental material. The major landform is plain, the slope form is classified as convex-straight and the gradient class as gently sloping (2-5%). Half of the orchard has been managed using sustainable agricultural practices (sustainable management, _Sung) for 20 years (2000-2020). Trees were drip-irrigated from March to October with urban wastewater. A light pruning was carried out every year during winter. The soil was permanently covered by spontaneous self-seeding weeds, mowed twice a year. Cover crop residues and prunings were shredded and left along the row as mulch.

The other half of the orchard was kept as ‘control’ plot. It was rainfed and conducted with a locally conventional management (C_mng), according to the practices usually adopted by farmers. The C_mng was managed by tillage performed 2-3 times per year to control weeds. Intensive pruning was carried out every two years, but pruned residues were removed from the orchard. A mineral fertilization was carried out once per year, during the fruit set and pit hardening phase (early spring).

The average value (n = 5; 0-100 cm soil depth) of baseline soil organic carbon (SOC) stock (related to the C_mng) in the 20-year period was 4.79 t SOC ha^–1, with an average additional SOC storage potential because of the adoption of the S_mng of 0.15 t SOC ha^–1 yr^–1, and a SOC stock after 20 years of S_mng of 7.75 t SOC ha^–1 yr^–1.

In the S_mng system, soil acted as a significant sink for C, especially due to the supplies of the organic resources internal to the system. The S_mng system, made up of mature olive trees, was also able to fix in its aboveground and belowground components, a > 2-times higher total amount of C than the C_mng. Spontaneous vegetation was the most important pool, sequestering about 35% of the total fixed C. Also pruning material had a substantial importance in C fixation. Emissions of CO₂ eq per kg of olives, calculated according to the Life Cycle Assessment (LCA), were 0.08 kg in the S_mng system a

Wastewater networks are mandatory for urbanization. Their management, which includes reparation and expansion operations, requires precise information about their underground components, mainly pipes. For hydraulic  modelling purposes, the characteristics of the nodes and pipes in the model must be fully known via specific, complete and consistent attribute tables. However, due to years of service and interventions by different actors, information about the attributes and characteristics associated with the various objects constituting a network are not  properly  tracked and reported. Therefore, databases related to wastewater networks, when available, still suffer from a large amount of missing data.

A wastewater network constitutes a graph composed of nodes and edges. Nodes represent manholes, equipment, repairs, etc. while edges represent pipes. Each of the nodes and edges has a set of properties in the form of attributes such as diameters of the pipes. In this work, we seek to complete the missing attributes of wastewater networks using machine learning techniques. The main goal is to make use of the graph structures in the learning process, taking into consideration the topology and the relationships between their components (nodes and edges) to predict missing attribute values.

Graph Convolutional Network models (GCN) have gained a lot of attention in recent years and achieved state of the art in many applications such as chemistry. These models are applied directly on graphs to perform diverse machine learning tasks. We present here the use of GCN models such as ChebConv to complete the missing attribute values of two datasets (1239 and 754 elements) extracted from the wastewater networks of  Montpellier and Angers Metropolis in France. To emphasize the importance of the graph structure in the learning process and thus on the quality of the predictions, GCNs' results are benchmarked against non-topological neural networks. The application on diameter value completion, indicates that using the structure of the wastewater network in the learning process has a significant impact on the prediction results especially for minority classes. Indeed, the diameter classes are very heterogeneous in terms of number of elements with a highly majority class and several classes with few elements. Non-topological neural networks always fail to predict these classes and affect the majority class value to every missing diameter, yielding a perfect precision for this class but a null one for all the others. On the contrary, the ChebConv model precision is slightly lower (0.93) for the majority class but much higher (increases from 0.3 to 0.81) for other classes, using only the structure of the graphs. The use of other available information in the learning process may enhance these results.

Fractures can provide principal fluid flow pathways in the Earth’s crust, making them a critical feature influencing subsurface geoenergy applications, such as the storage of anthropogenic waste, emissions or energy. In such scenarios, fluid-conductive fault and fracture networks are synonymous with two-phase flow, due to the injection of an additional fluid (e.g. CO₂) into an already saturated (e.g. brine) system. Predicting and modelling the resulting (partly-)immiscible fluid-fluid interactions, and the nature of fluid flow, on the field-scale, requires an understanding of the constitutive relationships (e.g. relative permeability and capillary pressure) governing fluid flow on the single-fracture scale. In addition to capillary and viscous forces, fracture relative permeability is influenced by aperture heterogeneity, arising from surface roughness. The degree to which surface roughness controls relative permeability behaviour in fractures remains unclear. As all fractures display roughness to various degrees, furthering our understanding of two-phase flow in fractures benefits from a systematic investigation into the impact of roughness on flow properties. To this end, we performed co-injection experiments on two 3D-printed (polymeric resin) fractures with different controlled and quantified surface roughness distributions (Joint Roughness Coefficients of 5 & 7). Brine and decane were simultaneously injected at a series of incrementally decreasing brine fractional flow rates (1, 0.75, 0.5, 0.25, and 0), at low total volumetric flow rates (0.015 mL/min). Steady-state fluid occupancy patterns, preferential flow pathways and overall fluid saturations in each fracture were imaged and compared using an environmental laboratory-based μ-CT scanner with a 5.8 μm voxel size (EMCT; Ghent University Centre for X-ray Computed Tomography). Experimental results highlight the importance of roughness on the relative permeability behaviour of fractures, which is, for example, a principal control on leakage rates from geological stores.