This paper deals with the simulation of inundated areas for a region of 84,000 km2 from estimated flood discharges at a resolution of 2 m. We develop a modeling framework that enables efficient ...parallel processing of the project region by splitting it into simulation tiles. For each simulation tile, the framework automatically calculates all input data and boundary conditions required for the hydraulic simulation on‐the‐fly. A novel method is proposed that ensures regionally consistent flood peak probabilities. Instead of simulating individual events, the framework simulates effective hydrographs consistent with the flood quantiles by adjusting streamflow at river nodes. The model accounts for local effects from buildings, culverts, levees, and retention basins. The two‐dimensional full shallow water equations are solved by a second‐order accurate scheme for all river reaches in Austria with catchment sizes over 10 km2, totaling 33,380 km. Using graphics processing units (GPUs), a single NVIDIA Titan RTX simulates a period of 3 days for a tile with 50 million wet cells in less than 3 days. We find good agreement between simulated and measured stage–discharge relationships at gauges. The simulated flood hazard maps also compare well with local high‐quality flood maps, achieving critical success index scores of 0.6–0.79.
Key Points
Second‐order accurate scheme discretizing the full shallow water equations at a resolution of 2 m for a stream network of 33,880 km length
Streamflow adjustment for regionally consistent flood peak probabilities and automatic estimation of boundary conditions for arbitrary domain tiling
Model accuracy comparable to local models (critical success index scores of 0.6–0.79)
The representation of phosphorus (P) cycling in global land models remains quite simplistic, particularly on soil inorganic phosphorus. For example, sorption and desorption remain unresolved and ...their dependence on soil physical and chemical properties is ignored. Empirical parameter values are usually based on expert knowledge or data from few sites with debatable global representativeness in most global land models. To overcome these issues, we compiled from data of inorganic soil P fractions and calculated the fraction of added P remaining in soil solution over time of 147 soil samples to optimize three parameters in a model of soil inorganic P dynamics. The calibrated model performed well (r2 > 0.7 for 122 soil samples). Model parameters vary by several orders of magnitude, and correlate with soil P fractions of different inorganic pools, soil organic carbon and oxalate extractable metal oxide concentrations among the soil samples. The modeled bioavailability of soil P depends on, not only, the desorption rates of labile and sorbed pool, inorganic phosphorus fractions, the slope of P sorbed against solution P concentration, but also on the ability of biological uptake to deplete solution P concentration and the time scale. The model together with the empirical relationships of model parameters on soil properties can be used to quantify bioavailability of soil inorganic P on various timescale especially when coupled within global land models.
Plain Language Summary
Phosphorus (P) is a major nutrient limiting the productivity of many terrestrial ecosystems. About 20%–60% of soil phosphorus is in inorganic form, and most inorganic soil P is sorbed or fixed on soil particles, leaving a small fraction (<1%) in soil solution available for direct uptake by plants. Sorption and desorption control inorganic P in solution and vary significantly with soil properties. However, sorption and desorption are not explicitly represented in most global land models. This study developed and calibrated a model of inorganic P dynamics using the observations from 147 soils worldwide. We found that the parameters in the model can vary by several orders of magnitude, and that a significant proportion of those variations can be explained by soil chemical properties, particularly soil P fractions, oxalate extractable metal oxide and soil organic carbon concentrations. The model and empirical relationships between model parameters and soil properties as developed in this study can be used to improve the representation of P cycle in land models.
Key Points
We developed and calibrated a model of soil inorganic P dynamics using the measured soil Phosphorus (P) fractions and isotopic exchange kinetics of 147 soils
We derived empirical relationships between model parameters and some soil chemical properties
Soil P bioavailability depends on soil P fractions, solution P concentration, desorption rate constants and the time scale
The inventory and variability of oceanic dissolved inorganic carbon (DIC) is driven by the interplay of physical, chemical, and biological processes. Quantifying the spatiotemporal variability of ...these drivers is crucial for a mechanistic understanding of the ocean carbon sink and its future trajectory. Here, we use the Estimating the Circulation and Climate of the Ocean‐Darwin ocean biogeochemistry state estimate to generate a global‐ocean, data‐constrained DIC budget and investigate how spatial and seasonal‐to‐interannual variability in three‐dimensional circulation, air‐sea CO2 flux, and biological processes have modulated the ocean sink for 1995–2018. Our results demonstrate substantial compensation between budget terms, resulting in distinct upper‐ocean carbon regimes. For example, boundary current regions have strong contributions from vertical diffusion while equatorial regions exhibit compensation between upwelling and biological processes. When integrated across the full ocean depth, the 24‐year DIC mass increase of 64 Pg C (2.7 Pg C year−1) primarily tracks the anthropogenic CO2 growth rate, with biological processes providing a small contribution of 2% (1.4 Pg C). In the upper 100 m, which stores roughly 13% (8.1 Pg C) of the global increase, we find that circulation provides the largest DIC gain (6.3 Pg C year−1) and biological processes are the largest loss (8.6 Pg C year−1). Interannual variability is dominated by vertical advection in equatorial regions, with the 1997–1998 El Niño‐Southern Oscillation causing the largest year‐to‐year change in upper‐ocean DIC (2.1 Pg C). Our results provide a novel, data‐constrained framework for an improved mechanistic understanding of natural and anthropogenic perturbations to the ocean sink.
Plain Language Summary
The ocean has absorbed roughly 40% of fossil fuel carbon dioxide (CO2) emissions since the beginning of the industrial era. This so‐called “ocean carbon sink,” which primarily sequesters emissions in the form of dissolved inorganic carbon (DIC), plays a key role in regulating climate and mitigating global warming. However, we still lack a mechanistic understanding of how physical, chemical, and biological processes impact the ocean DIC reservoir in both space and time, and hence how the storage rates of emissions may change in the future. Here we use a global‐ocean biogeochemistry model Estimating the Circulation and Climate of the Ocean‐Darwin, which ingests both physical and biogeochemical observations to improve its accuracy, to map how ocean circulation, air‐sea CO2 exchange, and marine ecosystems have modulated the combined natural and anthropogenic ocean DIC budget for 1995–2018. We find that in the upper ocean, circulation provides the largest supply of DIC while biological processes drive the largest loss. Year‐to‐year changes in the ocean carbon sink are dominated by El Niño‐Southern Oscillation events in the equatorial Pacific Ocean, which then affect DIC globally. In summary, our data‐constrained, global‐ocean DIC budget constitutes a significant step forward toward understanding climate‐related changes to the ocean DIC reservoir.
Key Points
We evaluate the global dissolved inorganic carbon (DIC) budget for 1995–2018 using an ocean biogeochemistry state estimate Estimating the Circulation and Climate of the Ocean‐Darwin
In the upper ocean, circulation provides the largest gain of DIC and biological processes are the dominant loss
Interannual variability is greatest in equatorial regions and is associated with El Niño‐Southern Oscillation
•Both methods are equally effective for developing and analyzing the structural relationship.•CB-SEM demands a lot from the data, whereas PLS-SEM is quite lenient.•For a factor-based model, CB-SEM ...should be used.•For a composite-based model, PLS-SEM should be considered.•CB-SEM and PLSc-SEM methods provide almost similar results.
This study compares the two widely used methods of Structural Equation Modeling (SEM): Covariance based Structural Equation Modeling (CB-SEM) and Partial Least Squares based Structural Equation Modeling (PLS-SEM). The first approach is based on covariance, and the second one is based on variance (partial least squares). It further assesses the difference between PLS and Consistent PLS algorithms. To assess the same, empirical data is used. Four hundred sixty-six respondents from India, Saudi Arabia, South Africa, the USA, and few other countries are considered. The structural model is tested with the help of both approaches. Findings indicate that the item loadings are usually higher in PLS-SEM than CB-SEM. The structural relationship is closer to CB-SEM if a consistent PLS algorithm is undertaken in PLS-SEM. It is also found that average variance extracted (AVE) and composite reliability (CR) values are higher in the PLS-SEM method, indicating better construct reliability and validity. CB-SEM is better in providing model fit indices, whereas PLS-SEM fit indices are still evolving. CB-SEM models are better for factor-based models like ours, whereas composite-based models provide excellent outcomes in PLS-SEM. This study contributes to the existing literature significantly by providing an empirical comparison of all the three methods for predictive research domains. The multi-national context makes the study relevant and replicable universally. We call for researchers to revisit the widely used SEM approaches, especially using appropriate SEM methods for factor-based and composite-based models.
•The Chesapeake Bay is the largest, most productive, and most biologically diverse estuary in the continental United States.•Pressures from human population growth and agricultural intensification ...have led to excessive nutrient and sediment inputs.•The Chesapeake Bay program partnership has been developing and applying a complex modeling system as a planning tool to inform management decisions and Bay restoration efforts.•This paper provides a description of the modeling system along with specific recommendations that emerged from a 2018 workshop designed to inform future model development.
The Chesapeake Bay is the largest, most productive, and most biologically diverse estuary in the continental United States providing crucial habitat and natural resources for culturally and economically important species. Pressures from human population growth and associated development and agricultural intensification have led to excessive nutrient and sediment inputs entering the Bay, negatively affecting the health of the Bay ecosystem and the economic services it provides. The Chesapeake Bay Program (CBP) is a unique program formally created in 1983 as a multi-stakeholder partnership to guide and foster restoration of the Chesapeake Bay and its watershed. Since its inception, the CBP Partnership has been developing, updating, and applying a complex linked modeling system of watershed, airshed, and estuary models as a planning tool to inform strategic management decisions and Bay restoration efforts. This paper provides a description of the 2017 CBP Modeling System and the higher trophic level models developed by the NOAA Chesapeake Bay Office, along with specific recommendations that emerged from a 2018 workshop designed to inform future model development. Recommendations highlight the need for simulation of watershed inputs, conditions, processes, and practices at higher resolution to provide improved information to guide local nutrient and sediment management plans. More explicit and extensive modeling of connectivity between watershed landforms and estuary sub-areas, estuarine hydrodynamics, watershed and estuarine water quality, the estuarine-watershed socioecological system, and living resources will be important to broaden and improve characterization of responses to targeted nutrient and sediment load reductions. Finally, the value and importance of maintaining effective collaborations among jurisdictional managers, scientists, modelers, support staff, and stakeholder communities is emphasized. An open collaborative and transparent process has been a key element of successes to date and is vitally important as the CBP Partnership moves forward with modeling system improvements that help stakeholders evolve new knowledge, improve management strategies, and better communicate outcomes.
Heterogeneous uptake of hypoiodous acid (HOI), the dominant inorganic iodine species in the marine boundary layer (MBL), on sea‐salt aerosol (SSA) to form iodine monobromide and iodine monochloride ...has been adopted in models with assumed efficiency. Recently, field measurements have reported a much faster rate of this recycling process than previously assumed in models. Here, we conduct global model simulations to quantify the range of effects of iodine recycling within the MBL, using Conventional, Updated, and Upper‐limit coefficients. When considering the Updated coefficient, iodine recycling significantly enhances gaseous inorganic iodine abundance (∼40%), increases halogen atom production rates (∼40% in I, >100% in Br, and ∼60% in Cl), and reduces oxidant levels (−7% in O3, −2% in OH, and −4% in HO2) compared to the simulation without the process. We appeal for further direct measurements of iodine species, laboratory experiments on the controlling factors, and multiscale simulations of iodine heterogeneous recycling.
Plain Language Summary
The interaction between ocean and atmosphere affects atmospheric chemistry and the climate system. Due to the technical difficulties in direct measurements in the open ocean and coastal environments, the understanding of the marine atmosphere has been heavily dependent on the utilization of multiscale models with limited observational constraints. The development in instrumentation facilitates the direct observation of previously undetected species and unquantified parameters calling for updates of atmospheric models and revisions of the role of relevant processes. Reactive halogen (chlorine, bromine, and iodine) chemistry plays a vital role in controlling the atmospheric composition and oxidation in the marine environment. In particular, iodine chemistry dominates the halogen effects in the marine boundary layer and hypoiodous acid (HOI) is the most abundant iodine species. Recently, field evidence shows that the heterogeneous recycling of iodine is much faster than previously assumed. Here we update a global model with larger coefficients and revisit the role of the HOI heterogeneous processing in marine atmospheric chemistry. These results indicate that the substantial effect of iodine heterogeneous recycling on iodine partitioning, halogen recycling, and oxidant budget may have been underestimated in previous studies and in current models.
Key Points
Heterogeneous recycling of iodine on sea‐salt aerosol leads to large changes in iodine level and partitioning in the global MBL
Iodine recycling substantially enhances the production rates of halogen atoms and reduces the levels of oxidants
Effect of iodine recycling is very sensitive to the uptake efficiency and its range is explored using recently reported coefficients
The Stanford Geostatistical Modeling Software (SGeMS) is an open-source computer package for solving problems involving spatially related variables. It provides geostatistics practitioners with a ...user-friendly interface, an interactive 3-D visualization, and a wide selection of algorithms. This practical book provides a step-by-step guide to using SGeMS algorithms. It explains the underlying theory, demonstrates their implementation, discusses their potential limitations, and helps the user make an informed decision about the choice of one algorithm over another. Users can complete complex tasks using the embedded scripting language, and new algorithms can be developed and integrated through the SGeMS plug-in mechanism. SGeMS was the first software to provide algorithms for multiple-point statistics, and the book presents a discussion of the corresponding theory and applications. Incorporating the full SGeMS software (now available from www.cambridge.org/9781107403246), this book is a useful user-guide for Earth Science graduates and researchers, as well as practitioners of environmental mining and petroleum engineering.
LiDAR Point Clouds to 3-D Urban Models : A Review Wang, Ruisheng; Peethambaran, Jiju; Chen, Dong
IEEE journal of selected topics in applied earth observations and remote sensing,
02/2018, Letnik:
11, Številka:
2
Journal Article
Recenzirano
Three-dimensional (3-D) urban models are an integral part of numerous applications, such as urban planning and performance simulation, mapping and visualization, emergency response training and ...entertainment, among others. We consolidate various algorithms proposed for reconstructing 3-D models of urban objects from point clouds. Urban models addressed in this review include buildings, vegetation, utilities such as roads or power lines and free-form architectures such as curved buildings or statues, all of which are ubiquitous in a typical urban scenario. While urban modeling, building reconstruction, in particular, clearly demand specific traits in the models, such as regularity, symmetry, and repetition; most of the traditional and state-of-the-art 3-D reconstruction algorithms are designed to address very generic objects of arbitrary shapes and topology. The recent efforts in the urban reconstruction arena, however, strive to accommodate the various pressing needs of urban modeling. Strategically, urban modeling research nowadays focuses on the usage of specialized priors, such as global regularity, Manhattan-geometry or symmetry to aid the reconstruction, or efficient adaptation of existing reconstruction techniques to the urban modeling pipeline. Aimed at an in-depth exploration of further possibilities, we review the existing urban reconstruction algorithms, prevalent in computer graphics, computer vision and photogrammetry disciplines, evaluate their performance in the architectural modeling context, and discuss the adaptability of generic mesh reconstruction techniques to the urban modeling pipeline. In the end, we suggest a few directions of research that may be adopted to close in the technology gaps.
Digital Twins (DTs) are enablers for the fast optimisation processes required in the Industry 4.0 context. Declarative equation-based modelling languages, in turn, enable the creation of large-scale ...simulation-based DTs, as they relieve the analyst from creating the solution code. However, most industrial assets are Cyber-Physical Systems (CPSs), the Cyber part being their digital controls. With the available technology, a precise representation of modulating and logic controls conflicts with DT simulation performance. The result is a barrier to using DTs for system-level optimisation. We analyse the problem, propose a modelling paradigm to solve it and suggest how to integrate that paradigm into equation-based language compilers. We support our proposal by presenting a Modelica/C++ library, that we release as free software, built according to the said paradigm.
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