Environmental Modelling & Software最新文献

This study was based on two premises; the ultimate objective of hydrological modelling is a contribution to sustainable water resources management, and the inherent uncertainties in model results should be realistically quantified. The study uses methods of uncertainty analysis that have been previously applied in the southern Africa region which are based on constraining model outputs using the likely ranges of a set of hydrological indices. One objective was to offer suggestions for sound modelling practice and highlight potential problems. The approach is applied to two case studies where there are very limited streamflow observations. The uncertainty ensemble outputs from the hydrological model are input into a water supply allocation model to assess system performance under different abstraction scenarios. The results are compared with the limited available evidence of system performance and the conclusion is that the uncertainty bands are generally acceptable for future decision making.

Fuel moisture content (FMC) is a critical parameter in fire and plume behaviors, showing diurnal and spatial variations influenced by local meteorological conditions, soil characteristics, and fuel properties. In low-intensity fires, small-scale FMC variations intensify, leading to an amplification of their effects on fire physics. In an effort to capture these variations, this paper presents the development of a physics-based model that couples a thermodynamic-based FMC prediction model for dead fuels with the Fire Dynamics Simulator of the National Institute of Standards and Technology. The model accuracy is validated against several existing experimental data, showing improvements over the baseline model which uses the kinetic-based Arrhenius drying approach. A case study of flame propagation in a small fuel bed is also presented, indicating the improved performance of the new model and its novel capabilities in capturing complex processes of fuel drying and moisture flux exchanges between the fuel and ambient atmosphere.

This paper presents improved explicit local time-stepping (LTS) schemes of both first and second order accuracy for storm surge modeling. The two-dimensional shallow water equations are numerically solved on unstructured triangular meshes using finite volume method with Roe’s approximate Riemann solver. The LTS algorithms are designed based on explicit Euler and strong stability preserving Runge–Kutta time integration methods. A single-layer interface prediction–correction scheme is adopted to combine coarse and fine time discretization, further enhancing the stability of the LTS schemes, particularly at higher LTS levels and during long time simulations. An ideal numerical test validates the efficiency of the improved LTS models, revealing their capability to improve computational speed while preserving conservation properties and reducing accuracy loss as LTS levels increase. We further apply the LTS models to cross-scale simulations of storm surges in the Northwest Pacific. Results show that compared to the global time-stepping (GTS) models, the LTS models significantly boost computational speed by up to 37%, all while delivering equally reliable computational outcomes. With expanding high-resolution coastal data and the need for high-resolution modeling, the improved LTS models show great potential for cross-scale storm surge modeling.

Hydrological modeling plays a significant role in addressing global water challenges. Streamlining the implementation of hydrological models enables more people to participate in designing solutions to water issues. This study presents the development and workflow of a QGIS-based tool called MapSWAT, designed to facilitate the application of the Soil and Water Assessment Tool (SWAT+). MapSWAT is an open-source plugin written in Python for collecting and producing ready-to-use SWAT + input maps. It can generate SWAT + input maps for any location globally, including limited data regions, by connecting to the Google Earth Engine. The potential for and feasibility of MapSWAT have been demonstrated through its application in a case study in southeast Spain. The results indicate that MapSWAT is a useful tool for efficiently generating SWAT + input maps that optimizes the hydrological modeling process with SWAT+. The MapSWAT software is freely and publicly available at https://github.com/AdrLBallesteros/MapSWAT.

We investigated improvements to further speed up the multi-threaded scaling of the distributed hydrological model wflow_sbm. To gain insight in the speed improvements for operational applications, we connected the improved code to ECMWF’s Fields Database to allow for on-the-fly pre-processing of the forcing, which accelerated the entire forecasting chain. In the original wflow_sbm implementation, run times increased when more than eight threads were used due to Julia’s native threading overhead. Now, run times are 2 to 11 times faster, depending on the chosen routing scheme, number of threads and catchment size. We show the advantages of the improvements in a test setup where ECMWF forecasts and 35 years of ERA5 reanalysis data were used to force wflow_sbm models at 1x1 km spatial resolution for Europe. The attained speedup allows for using distributed hydrological models in large-scale hydrological forecasting and climate-change applications, which is currently often limited to lumped models.

We present the implementation of a new fully distributed multiclass soil erosion module. The model is based on a 2D finite volume solver (Iber+) for the 2D shallow water equations that computes the overland flow water depths and velocities. From these, the model evaluates the transport of sediment particles due to bed load and suspended load, including rainfall-driven and runoff-driven erosion processes, and using well-established physically-based formulations. The evolution of the mass of sediment particles in the soil layer is computed from a mass conservation equation for each sediment class. The solver is implemented using High Performance Computing techniques that take advantage of the computational capabilities of standard Graphical Processing Units, achieving speed-ups of two orders of magnitude relative to a sequential implementation on the CPU. We show the application and validation of the model at different spatial scales, ranging from laboratory experiments to meso-scale catchments.

The increase in air pollution has posed numerous new challenges for human society, making the exploration of an effective method for predicting air pollutant concentrations highly significant. The current research faces several primary challenges: the neglect of non-Euclidean characteristics of site distribution on data and the strong spatiotemporal dependencies in the dispersion process of pollutants. To address these issues, this paper constructs a spatiotemporal hybrid prediction model – the MGAtt-LSTM method – for predicting PM_2.5 concentrations, which employs the dynamic multi-graph attention module (MGAtt) to tackle spatial dependencies and Long Short-Term Memory networks (LSTM) to address temporal dependencies. Additionally, extensive experiments are conducted by using historical air pollutant monitoring data and meteorological data from the Beijing-Tianjin-Hebei region. The results demonstrate that the proposed MGAtt-LSTM model achieved superior performance in concentration prediction compared to existing benchmark models.

Various approaches are available to assist stakeholders in identifying and resolving ecosystem service tradeoffs. However, existing tools fall short in simulating land use configuration effects on ecosystem services and subsequently making these effects accessible to users with varying levels of expertise. To address this gap, we introduce PLACES, a tool that estimates land use impacts on multiple ecosystem services by incorporating landscape-level processes. Tool results are provided in real-time and visualized to support a dialogue between different stakeholders. This study presents the tool development and application during a mixed stakeholder workshop, after which mental models, questionnaires, and videos were analyzed to evaluate PLACES. The tool increased the participants’ understanding of insights of spatial processes and sparked discussions on the societal goals for sustainable landscapes. For future applications of PLACES, we encourage careful tailoring of the landscape representation and land use impact simulations to match the knowledge of the respective users.

The formation and evolution of coastal dunes result from a complex interplay of eco-morphodynamic processes. State-of-the-art models can simulate aeolian transports and morphological dune evolution under certain conditions. However, a model combining these processes for coastal engineering applications was not yet available. This study aims to develop a predictive tool for dune development to inform coastal management decisions and interventions. The aeolian sediment transport model AeoLiS is extended with functionalities that allow for simulations of coastal landforms. The added functionalities include the effect of topographic steering on wind shear, avalanching of steep slopes and vegetation processes in the form of growth and wind shear reduction. The model is validated by simulating four distinct coastal landforms; barchan-, parabolic-, embryo dunes and blowouts. Simulations, based on real-world conditions, replicate the landform formation, migration rates and seasonal variability.

Extensive data and computational requirements limit the application of existing urban hydrology models at municipal scales. Community-enabled Lifecycle Analysis of Stormwater Infrastructure Costs (CLASIC) is a web-based deployment of the SWMM model with decoupled hydrologic and hydraulic components to enable hydrologic assessment at the municipal and larger scales. This study comprehensively evaluates the performance validity of CLASIC for characterization of hydrologic responses against SWMM and observed data. Furthermore, global sensitivity analysis is used to explore the significance of hydrologic and hydraulic model parameters across spatial and temporal scales. CLASIC reliably represents the urban hydrological processes and accurately quantifies stream discharge at the municipal scale and temporal scales greater than the catchment's time of concentration. Notably, the computational requirements of CLASIC are substantially lower than those of SWMM as the catchment drainage area increases. The application of CLASIC for flood assessment may be conducted with careful examination of the estimated peak discharge at sub-daily timescales.