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The MCX N94x and N54x are based on dual high-performance Arm Cortex-M33 cores running up to 150 MHz, with 2MB of Flash with optional full ECC RAM, a DSP co-processor and an integrated eIQ Neutron NPU. The NPU delivers up to 42x faster machine learning throughput compared to a CPU core alone enabling it to spend less time awake and reducing overall power consumption.
The multicore design delivers improved system performance and reduces consumption by enabling smart, efficient distribution of workloads to the analog and digital peripherals. The devices are supported by the MCUXpresso Developer Experience to optimize, ease and help accelerate embedded system development.
The MCX N94x offers a wider set of analog and motor control peripherals, while the MCX N54x family provides peripherals ranging from high-speed USB with a PHY to secure digital (SD) and smart card interfaces.
Seasonal simulations were carried out to assess the changes in the soil-water system that result from changing the soil types (GSDE vs. STATSGO) and their corresponding hydraulic properties. Wherever GSDE has finer grains than STATSGO (e.g., over the US Great Plains), the soil will retain water more strongly as evidenced by smaller latent heat fluxes and larger sensible heat flux. On the other hand, areas of coarser grains in GSDE (e.g., over central Mexico) exhibit an increase in latent heat fluxes and a corresponding decrease in sensible heat flux. Regions with an increase/decrease in latent heat flux have a corresponding increase/decrease in the 2-m moisture content. Similar relations are obtained between sensible heat flux and 2-m temperature. These changes also affect the atmospheric column, which responds with an increase/decrease of temperature and height of the planetary boundary layer. Changes in the vertical structure induce changes in the vertical instability and winds. Interestingly, the chain of modifications resulting from soil texture changes impact the moisture fluxes, and more generally, the atmospheric water budget.
The question we ask in this study is: how does uncertainty in the soil hydraulic parameters propagate in global ecosystem responses? To achieve this, we deploy a numerical experiment covering many different ecosystems. The terrestrial ecosystem model T&C is used to model energy, water, and carbon dynamics at 80 locations worldwide, spanning all climatological regimes, major biomes and soil types. Soil hydraulic properties at each site were estimated using six widely used PTFs starting from local soil textural information. Uncertainty propagation from soil hydraulic properties to modelled ecosystem dynamics was evaluated for all sites and its dependence on soil textural properties and local topography was quantified.
The ability of correlation functions to describe structure (Karsanina et al., 2015; Karsanina et al., 2018) and provide means to reconstruct the structure based on correlation functions (Gerke and Karsanina, 2015; Karsanina and Gerke, 2018) alone was proposed as means to effectively compress and store structural information (Gerke et al., 2015). This is especially appealing considering the fact that truly multi-scale digital 3D soil structure model for a single genetic horizon even with the resolution not finer than 1 m will contain enormous amount (approx., up to 10^15 voxels or even more) of data. Effective management and pore-scale simulations based on such datasets does not seem feasible at the moment. Another approach would be to retrieve only a relevant part of the dataset and operate on it indirectly, in particular based on correlation functions or stochastic reconstructions. The main aim of this work was to investigate the possibility to compress soil structural data, as resulted from X-ray microtomography data and directional correlation functions computation (Gerke et al., 2014), into a very limited number of parameters, potentially with minimal information content loss. We show that with the help of the proposed technique it is possible to compress a 3D image of 900^3-1300^3 voxels into a set of correlation functions, that with the help of fitting of an analytical function in the form of the superposition of three different basis functions may help to map all these correlation functions in a vector of six parameters. We apply the proposed methodology to 16 different soil 3D images and discuss numerous important implications that can help to achieve the ultimate goal of building 3D multi-scale soil structure model from meter to nm. Such model would help in establishing a fully multi-scale hydrological model operating from first principles as opposed to coarse continuum scale models.
Indirect methods for estimating soil hydraulic properties from particle size distribution have been developed due to the difficulty in accurately determining soil hydraulic properties, and the fact that particle size distribution is one piece of basic soil physical information normally available. The similarity of the functions describing the cumulative distribution of particle size and pore size in the soil has been the basis for relating particle size distribution and the water retention function in the soil. Empirical and semi-physical models have been proposed, but these are based on strong assumptions that are not always valid. For example, soil particles are normally assumed to be spherical, with constant density regardless of their size; and the soil pore space has been described by an assembly of capillary tubes, or the pore space in the soil matrix is assumed to be arranged in a similar way regardless of particle size. However, in a natural soil the geometry of the pores may vary with the size of the particles, leading to a variable relation between particle radius and pore radius.
The current work is based on the hypothesis that the geometry of the pore size and the void ratio depends on the size of the soil particles, and that a physically based model can be generalised to predict the water retention curve from particle size distribution. The rearrangement of the soil particles is considered by introducing a mixing function that modulates the cumulative particle size distribution, while the total porosity is constrained by the saturated water content.
Clay fraction affects soil hydraulic and mechanical properties and dominates specific surface area. Clay fraction is used for soil classification and in pedotransfer functions (PTFs) to estimate soil hydraulic functions from simpler soil properties (texture). Remarkably, despite large variations in composition and properties of clay minerals, PTFs use this attribute in undifferentiated manner, applied similarly to soils in the tropics dominated by Kaolinite and temperate soils with Montmorillonite. The large specific surface area of Montmorillonite compared to Kaolinite reduces both the soil hydraulic conductivity and the residual friction angle. We develop PTFs informed by clay-type via soil specific surface area effects on saturated hydraulic conductivity and residual friction angle. For friction angle, PTFs were fitted to experimental data using information on clay content and clay type. For hydraulic conductivity, analytical models based on surface area and particle size were adapted to capture conductivity data from different climatic regions. Global distributions of clay types are used to map soil specific surface area and related hydro-mechanical properties to improve land-surface models (especially in the tropics) and refine natural hazard risk assessment (landslides and debris flows).
Stony soils are soils that contain a high amount of stones and are widespread all over the world. The effective soil hydraulic properties (SHP), i.e. the water retention curve (WRC) and the hydraulic conductivity curve (HCC) are influenced by the presence of stones in the soil. This influence is normally neglected in vadose zone modeling due to the considerable measurement challenges in stony soils. The available data on the effect of stones on SHP is scarce and there is not a systematic modeling approach to obtain the effective SHP in stony soils. Most of the past studies are limited to the effect of stones on the WRC and saturated hydraulic conductivity and low and medium stone contents (up to 40 % v/v). We investigated the effect of stone content on the effective SHP of stony soils through a series of evaporation experiments. Two soil materials a) sandy loam and b) silt loam as background soils were packed with different volumetric contents (0, 10, 30 and 60 %) of medium stones were in containers with a volume of 5060 cm3. Volumetric stone contents were chosen in a way to present stone-free, moderately stony and highly stony soils. All of the experiments were carried out in two replicate packings with an almost identical bulk density. Packed samples were saturated with water from the bottom and subjected to evaporation in a climate-controlled room. During the evaporation experiments, the pressure head and soil temperature were continuously monitored and the water loss from the soil columns was measured with a balance. The dewpoint method provided additional data on the WRC in the dry soil. The resulting data were evaluated by inverse modeling with the Richards equation to identify effective SHP and to analyze the effect of stone content on the evaporation rate, soil temperature, the effective WRC and the effective HCC. The applied methodology was successful in identifying effective SHP with high precision over the full moisture range. The results reveal a quicker transition from stage I to stage II of evaporation in highly stony soils. Evaporation rate reduces with the increase of the volumetric stone content. The existence of a high amount of stone content shorten stage II of evaporation driven by the vapor diffusion through the restricted soil evaporative surface.
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