Environmental Modelling & Software最新文献

This study addresses the challenging task of analysing multifunctional landscapes through an innovative integrated modelling approach. Acknowledging the limitations of disciplinary models in assessing diverse landscape functions, we present a conceptual framework for their integration. Demonstrating the feasibility and effectiveness of this approach in a Netherlands case study, we assess alternative land use changes for drought and carbon sequestration. Results underscore the framework's efficacy in elucidating the intricate relationship between carbon and water across multiple model runs and iterations. Notably, the alternative land use scenario reveals an average increase in soil moisture during dry periods and an increase in soil organic carbon content across four model runs. This softly coupled approach offers valuable insights into environmental modelling, facilitating navigation of complex integration challenges for researchers and practitioners. Furthermore, it enhances modelling transparency by elucidating variable representation and processes, providing a foundation for informed decisions in sustainable landscape management.

Wastewater utilities face competing priorities as they work to protect human health and water quality, and to maintain infrastructure in their communities. Budgetary constraints can be especially pronounced among small to medium-sized utilities. Utilities are increasingly turning to so-called intelligent water approaches as a cost-effective alternative to upgrading aging infrastructure. Intelligent water encompasses automated control and real-time decision support technologies and can be applied at scale to large and small utilities alike accommodating differences in needs, capabilities, and funds. Intelligent water upgrades can be designed to optimize existing conveyance, storage, and treatment during storms to help mitigate flooding and combined sewer overflows. The most promising real-time control algorithms coordinate control of upstream and downstream assets and are designed using urban hydrologic and hydraulic modeling software. The capabilities of legacy software, however, can sometimes inhibit the creation of sophisticated control algorithms. In this paper, we present PySWMM — an open-source Python wrapper developed for the EPA Storm Water Management Model (SWMM). PySWMM enables runtime interactions with the SWMM computational engine to flexibly read, modify system parameters, and control digital infrastructure during a simulation. Crucially, it allows modelers to easily combine SWMM with the rich set of scientific computing, big data, and machine learning modules found in the Python ecosystem. We highlight two real-world intelligent water case studies utilizing PySWMM in the cities of Cincinnati and Columbus, Ohio where it has helped to eliminate tens of millions of gallons of combined sewer overflows annually.

Mixed enterprise farming systems that integrate more than one production system are important in agricultural production world-wide. Understanding and improving them can be made easier by modelling them with software tools. Modelling mixed enterprise farming systems can be a complex task as the interaction between the enterprises will introduce many dependencies. There are many software tools available that can model single enterprise systems, while there are few with the ability to model the biophysical systems in mixed farming. AusFarm has been designed and used to model mixed enterprise farming systems, integrating livestock, pasture, and crop models in one software tool and allowing flexible management of the whole farm. This paper demonstrates some key techniques that have been used for building and simulating mixed enterprise Australian farm systems in AusFarm. Examples of how to structure a cropping system and a livestock system are given. Key livestock and crop management tasks are implemented using flexible management rules.

Predicting soil erosion by water is an essential natural resource management activity. The Water Erosion Prediction Project (WEPP) model is a process-based continuous simulation tool based on erosion mechanics and channel hydraulics. Due to the extensive data requirements, preparing input parameter settings for WEPP can be time-consuming. A Geospatial Interface for WEPP (GeoWEPP) overcame this major disadvantage but had limitations regarding keeping track of operating systems and proprietary software updates. QGeoWEPP is a newly developed open-source QGIS-based GeoWEPP, offering additional novel user-based customizations of model simulations including validation data sets. QGeoWEPP provides a platform for applying WEPP at the hillslope and watershed scales that integrates the whole model application and validation process. QGeoWEPP allows applying WEPP in any study area worldwide with minimum data limitations and more spatial and temporal capabilities in areas such as soil and water conservation, land management, geohazard assessment due to land use and climate change, and many more.

The Full of Economic-Environment Linkages and Integration dX/dt (FeliX) model is a System Dynamics-based Integrated Assessment Model (IAM), explicitly incorporating human behaviors and their dynamic interactions among global systems. This paper presents FeliX 2.0, describing the detailed framework and key interactions among nine integrated modules. FeliX 2.0 refined its original version in population dynamics, food and land use systems, and socioeconomic settings for poverty analysis. Robust calibration is applied to key variables against their historical data since 1950. Future projections of multiple variables up to 2100 demonstrate coherences between FeliX 2.0 and the IAMs used in IPCC assessments. Both outputs (the robust calibration results and future projections) underscore the efficacy of FeliX 2.0 in capturing complex interdependencies within global systems. FeliX 2.0 stands as an informative tool and offers insights into interactions within the human-Earth system and the analysis of complex economic-environmental-social challenges in short- and long-term future.

Global food security is threatened by aquifer depletion in arid and semi-arid regions. Ensuring aquifer sustainability while minimizing farmers' costs requires effective groundwater management policies, which are informed by hydro-economic models. However, the reliability of these models is typically compromised by oversimplifications. This study introduces a hydro-economic modeling framework (ABM-MODFLOW), applied in the U.S. High Plains, overcoming previous models’ shortcomings. It employs an interdisciplinary approach, simulating real-world farming decisions and the resultant impacts on groundwater systems. Model validation indicates satisfactory performance in reproducing historical data and trends, outperforming the standalone groundwater model. Results show that agents with high irrigation capacities and fine soils earn the highest profits, whereas those with low irrigation capacities and coarse soils achieve the lowest profits. This modeling framework provides an advanced approach for simulating interactions between human decisions, crops, and groundwater in heavily stressed aquifers globally, supporting the development of effective groundwater management policies.

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.