Numerical algorithms for solving shallow water hydro-sediment-morphodynamic equations

Purpose - The purpose of this paper is to present a fully conservative numerical algorithm for solving the coupled shallow water hydro-sediment-morphodynamic equations governing fluvial processes, and also to clarify the performance of a conventional algorithm, which redistributes the variable water-sediment mixture density to the source terms of the governing equations and accordingly the hyperbolic operator is rendered similar to that of the conventional shallow water equations for clear water flows. Design/methodology/approach - The coupled shallow water hydro-sediment-morphodynamic equations governing fluvial processes are arranged in full conservation form, and solved by a well-balanced weighted surface depth-gradient method along with a slope-limited centred scheme. The present algorithm is verified for a spectrum of test cases, which involve complex flows with shock waves and sediment transport processes with contact discontinuities over irregular topographies. The computational results of the conventional algorithm are compared with those of the present algorithm and evaluated by available referenced data. Findings - The fully conservative numerical algorithm performs satisfactorily over the spectrum of test cases, and the conventional algorithm is confirmed to work similarly well. Originality/value - A fully conservative numerical algorithm, without redistributing the water-sediment mixture density, is proposed for solving the coupled shallow water hydro-sediment-morphodynamic equations. It is clarified that the conventional algorithm, involving redistribution of the water-sediment mixture density, performs similarly well. Both algorithms are equally applicable to problems encountered in computational river modelling

A Quasi-Single-Phase Model for Debris Flows Incorporating Non-Newtonian Fluid Behavior

Debris-flow modeling is a great challenge due to its complex physical mechanism that remains poorly understood. The present research incorporates the effect of rheological features of the non-Newtonian fluid into the complete quasi-single-phase mixture model, which explicitly accommodates the interactions between flow, non-uniform sediment transport, and bed evolution. The effect of rheological features is estimated by Hersch–Bulkley–Papanastasiou model that can be simplified to Bingham or Newtonian models with specific coefficients. The governing equations are solved by a fully conservative numerical algorithm, using an explicit finite volume discretization with well-balanced slope-limited centered scheme combined with an implicit discretization method. One set of large-scaled U.S. Geological Survey debris-flow experiments is applied to investigate the influence of the non-Newtonian fluid on debris-flow modeling. It is found that the present model closed by Hersch–Bulkley–Papanastasiou model performs better than the models without considering effect of rheological features, which may facilitate the development of quasi-single-phase mixture modeling for debris flows

A Quasi-Single-Phase Model for Debris Flows Incorporating Non-Newtonian Fluid Behavior

Debris-flow modeling is a great challenge due to its complex physical mechanism that remains poorly understood. The present research incorporates the effect of rheological features of the non-Newtonian fluid into the complete quasi-single-phase mixture model, which explicitly accommodates the interactions between flow, non-uniform sediment transport, and bed evolution. The effect of rheological features is estimated by Hersch–Bulkley–Papanastasiou model that can be simplified to Bingham or Newtonian models with specific coefficients. The governing equations are solved by a fully conservative numerical algorithm, using an explicit finite volume discretization with well-balanced slope-limited centered scheme combined with an implicit discretization method. One set of large-scaled U.S. Geological Survey debris-flow experiments is applied to investigate the influence of the non-Newtonian fluid on debris-flow modeling. It is found that the present model closed by Hersch–Bulkley–Papanastasiou model performs better than the models without considering effect of rheological features, which may facilitate the development of quasi-single-phase mixture modeling for debris flows

A Hybrid Causal Structure Learning Algorithm for Mixed-Type Data

Inferring the causal structure of a set of random variables is a crucial problem in many disciplines of science. Over the past two decades, various approaches have been pro- posed for causal discovery from observational data. How- ever, most of the existing methods are designed for either purely discrete or continuous data, which limit their practical usage. In this paper, we target the problem of causal structure learning from observational mixed-type data. Although there are a few methods that are able to handle mixed-type data, they suffer from restrictions, such as linear assumption and poor scalability. To overcome these weaknesses, we formulate the causal mechanisms via mixed structure equation model and prove its identifiability under mild conditions. A novel locally consistent score, named CVMIC, is proposed for causal directed acyclic graph (DAG) structure learning. Moreover, we propose an efficient conditional independence test, named MRCIT, for mixed-type data, which is used in causal skeleton learning and final pruning to further improve the computational efficiency and precision of our model. Experimental results on both synthetic and real-world data demonstrate that our proposed hybrid model outperforms the other state-of-the-art methods. Our source code is available at https://github.com/DAMO-DI-ML/AAAI2022-HCM

Difference and Potential of the Upward and Downward Sun-Induced Chlorophyll Fluorescence on Detecting Leaf Nitrogen Concentration in Wheat

Precise detection of leaf nitrogen concentration (LNC) is helpful for nutrient diagnosis and fertilization guidance in farm crops. Numerous researchers have estimated LNC with techniques based on reflectance spectra or active chlorophyll fluorescence, which have limitations of low accuracy or small scale in the field. Given the correlation between chlorophyll and nitrogen contents, the response of sun-induced chlorophyll fluorescence (SIF) to chlorophyll (Chl) content reported in a few papers suggests the feasibility of quantifying LNC using SIF. Few studies have investigated the difference and power of the upward and downward SIF components on monitoring LNC in winter wheat. We conducted two field experiments to evaluate the capacity of SIF to monitor the LNC of winter wheat during the entire growth season and compare the differences of the upward and downward SIF for LNC detection. A FluoWat leaf clip coupled with a ASD spectrometer was used to measure the upward and downward SIF under sunlight. It was found that three (↓FY687, ↑FY687/↑FY739, and ↓FY687/↓FY739) out of the six SIF yield (FY) indices examined were significantly correlated to the LNC (R2 = 0.6, 0.51, 0.75, respectively). The downward SIF yield indices exhibited better performance than the upward FY indices in monitoring the LNC with the ↓FY687/↓FY739 being the best FY index. Moreover, the LNC models based on the three SIF yield indices are insensitive to the chlorophyll content and the leaf mass per area (LMA). These findings suggest the downward SIF should not be neglected for monitoring crop LNC at the leaf scale, although it is more difficult to measure with current instruments. The downward SIF could play an increasingly important role in understanding of the SIF emission for LNC detection at different scales. These results could provide a solid foundation for elucidating the mechanism of SIF for LNC estimation at the canopy scale

Data_Sheet_1_Biochar-mediated changes in the microbial communities of rhizosphere soil alter the architecture of maize roots.PDF

Aeolian sandy soil is a key resource for supporting food production on a global scale; however, the growth of crops in Aeolian sandy soil is often impaired due to its poor physical properties and lack of nutrients and organic matter. Biochar can be used to enhance the properties of Aeolian sandy soil and create an environment more suitable for crop growth, but the long-term effects of biochar on Aeolian sandy soil and microbial communities need to be clarified. Here, a field experiment was conducted in which biochar was applied to a maize (Zea mays L.) field in a single application at different rates: CK, 0 Mg ha−1; C1, 15.75 Mg ha−1; C2, 31.50 Mg ha−1; C3, 63.00 Mg ha−1; and C4, 126.00 Mg ha−1. After 7 years of continuous maize cropping, verify the relationship between root architecture and soil microbial communities under biochar application using a root scanner and 16S/ITS rRNA gene sequencing. The application of biochar promoted the growth of maize. Specifically, total root length, total root surface area, total root volume, and root biomass were 13.99–17.85, 2.52–4.69, 23.61–44.41, and 50.61–77.80% higher in treatments in which biochar was applied (C2, C3, and C4 treatments) compared with the control treatment, respectively. Biochar application increased the diversity of bacterial communities, the ACE index, and Chao 1 index of C1, C2, C3, and C4 treatments increased by 5.83–8.96 and 5.52–8.53%, respectively, compared with the control treatment, and significantly changed the structure of the of bacterial communities in rhizosphere soil. However, there was no significant change in the fungal community. The growth of maize roots was more influenced by rhizosphere bacteria and less by fungal community. A microbial co-occurrence network revealed strong associations among rhizosphere microorganisms. The core taxa (Module hubs taxa) of the bulk soil microbial co-occurrence network were closely related to the total length and total surface area of maize roots, and the core taxa (Connectors taxa) of the rhizosphere soil were closely related to total root length. Overall, our findings indicate that the application of biochar promotes the growth of maize roots in aeolian sandy soil through its effects on bacterial communities in rhizosphere soil.</p