Environmental Modelling & Software最新文献

The accuracy of soil erosion models in agroecosystems with heterogeneous field conditions is challenging due to uncertainties from soil water fluxes and crop growth. In this study, we coupled two modeling methods (Freebairn and Rose) to represent soil erosion with a process-based crop and runoff models within the SIMPLACE framework. Their accuracy was compared to a statistical model developed using 16 erosion plots (each of 625 cm²) within the same field. Uncertainty analysis showed that runoff and slope angle were the most critical components for predicting sediment yield in both models, followed by soil erodibility in the Freebairn model and entrainment efficiency in the Rose model. However, due to plot size constraints, slope-length effects were not examined. The Freebairn model had a slightly higher accuracy (RMSE = 0.69 t ha⁻¹ d⁻¹) of sediment yield predictions than the Rose model (RMSE = 0.83 t ha⁻¹ d⁻¹). Both models are effective for predicting soil loss with appropriate parameter values.

Temperature can be a valuable indicator for the identification and quantification of water fluxes in surface and subsurface water bodies. Therefore, the measurement and analysis of vertical temperature profiles is an important part of the characterization of a water body. Besides its easy application and widespread use, the analysis of temperature profiles can be complex due to its nature as a non-stationary measurement in a dynamic system. This work presents a data-based algorithm to process and segment vertical water temperature profiles avoiding subjectivity in the quantitative analysis of these profiles. Special emphasis is given to studying the reproducibility and precision of the method from data acquisition to data processing and analysis. The presented method provides a blueprint for adjacent applications and showcases the explanatory power of vertical profiles of water temperature to study water fluxes.

With the development of geographic simulation methods in recent decades, a great deal of resources have accumulated to support their implementation. These resources can be divided into model resources for analyzing or predicting geographic phenomena or processes, data resources for representing the characteristics of real or simulated environment, and computing resources for supporting simulation tasks. These resources are characterized by geospatial distribution and are difficult to discover and reuse. OpenGMS has carried out a series of fundamental research to sharing and collaborating distributed resources on the web. On this basis, this paper presents the concept of the OpenGMS open portal. The portal adopts FAIR principles and supports resources sharing and reuse to facilitate collaboration and exchange between resource contributors and users. This paper takes applications of the portal in resource sharing and reuse case and online training courses as examples to illustrate how the portal can bridge contributors and users of resources.

This study develops a toolkit for implementing the WA + framework, integrating observational data and global databases to enhance data collection for water accounting assessment. By addressing data gaps, updating processes, and coverage issues through automated systems, it compiles key variables like precipitation, evapotranspiration, and groundwater fluctuations, leveraging GLDAS (Global Land Data Assimilation System), GLEAM (Global Land Evaporation Amsterdam Model), and GRACE (Gravity Recovery and Climate Experiment) datasets. The toolkit, named NWBWAS (National Web Based Water Accounting System), automates water accounting components' calculation, providing comprehensive reports and indicators. Using the Tashk-Bakhtegan watershed as a case study, NWBWAS demonstrated significant advancements, offering a robust, efficient solution for rapid, cost-effective initial water accounting assessments, marking a substantial improvement over previous approaches.

2D hydraulic models are one of the tools to simulate water levels for effective river management. Mesh resolution in 2D models directly impacts the discretization of the bathymetry, the discharge capacity, and consequently, the accuracy of simulated water levels. The objective of this study is to develop a modified mesh setup that corresponds with the cross-sectional flow volume of the measured cross-section but with a low resolution for the entire discharge range. An algorithm is developed to vertically adjust mesh nodes within a limited range to achieve this objective. Subsequently, the D-Flow-FM software is utilized to model four hypothetical 100-kilometer river reaches to evaluate the modified mesh setup. The findings reveal that the water level using the modified low-resolution mesh is up to 90% closer to the high resolution mesh compared to the original low-resolution mesh for all discharges. Additionally, the simulation of the low-resolution mesh runs approximately 12.5 times faster than their high-resolution counterparts.

Currently, the deep geological repository approach for spent nuclear fuel is regarded as the most dependable and secure method for permanently disposing of this kind of waste. Among its key safety components is an engineered barrier made from compacted bentonite, which isolates the encapsulated waste from the surrounding host rock. As a result, understanding how bentonites react to varying compositions of groundwater is crucial. This is where numerical modelling becomes essential.

It is generally approved by the scientific community to idealise bentonite as a material structured under a double porosity system composed of the macro and microstructure. In this context, this paper illustrates the capabilities of a double-porosity reactive transport model for bentonites fully implemented in the multiphysics COMSOL platform. For this purpose, different experimental tests were simulated based on the evaluation of diffusive ion transport, mineral dissolution and cation exchange processes in MX-80 bentonite, obtaining very satisfactory results.

Accurate representation of river channel geometry is important for hydrologic and hydraulic modeling of fluvial systems. Often, channel geometry is estimated using simple rating curves that can be applied across various spatial scales. However, such methods are limited to power law relations that do not employ many potentially relevant catchment and river attributes. This paper introduce a new dataset, IFMHA (Inventory of Field Measurement of Hydraulic Attributes), to enable research studies on channel geometry and streamflow characteristics. IFMHA is derived from the National Water Information System (NWIS) site inventory for surface water field measurements and stream attributes from the National Hydrography Dataset (NHD). IFMHA includes 2,802,532 records from 10,050 sites (NWIS streamgaging stations). The dataset utility is demonstrated here by presenting a series of conceptual models for estimating channel geometry parameters (i.e., channel mean depth, channel maximum depth, wetted perimeter, and roughness) based on the available field attributes within IFMHA. Such a dataset and attributed channel geometry parameters can enhance the performance of operational flood forecasting frameworks (e.g. National Water Model) by providing more accurate initial conditions used in hydrologic and hydraulic routing models.

Wireless sensor networks support decision-making in diverse environmental contexts. Adoption of these networks has increased dramatically due to technological advances that have increased value while lowering cost. However, real-time information only allows for reactive management. As most interventions take time, predictions across these sensor networks enable better planning and decision making. Prediction models across large water level and discharge sensor networks do exist. However, they have limitations in their accessibility, automaticity, and data requirements. We present an open-source method for automatically generating computationally cheap rainfall-runoff models for any depth or discharge sensor given only its measurements and location. We characterize reliability in a real-world case study across 200,000 km${}^2$, evaluate long-term accuracy, and assess sensitivity to measurement noise and errors in catchment delineation. The method’s accuracy, computational efficiency, and automaticity make it a valuable asset to support operational decision making for diverse stakeholders including bridge inspectors and utilities.

Wildfires in western US forests increased over the last two decades, resulting in elevated solid and nutrient loadings to streams, and occasionally threatening drinking water supplies. We demonstrated that a linked LANDIS (LANDscape DIsturbance and Succession)-VELMA (Visualizing Ecosystem Land Management Assessments) modeling approach can simulate wildland fire effects on water quality using the 2002 Colorado Hayman Fire. Utilizing LANDIS-II's forest landscape model to simulate forest composition and VELMA's eco-hydrologic model to simulate pre- and post-fire water quantity and quality, the best calibration performance yielded a Nash-Sutcliffe Efficiency (NSE) of 0.621 during 2000–2006 (most optimal annual - 0.921) in comparison to North American Land Data Assimilation System (NLDAS) runoff. Pre-fire modeled runoff, nitrate, and surface water temperature (SWT) correlated with observations. Simulated post-fire runoff (229%) and SWT (20.6%) were elevated relative to pre-fire, with nitrate concentrations 34 times greater than the aquatic life threshold (0.01 mg N/L).

Ambient air pollution is a pervasive issue with wide-ranging effects on human health, ecosystem vitality, and economic structures. Utilizing data on ambient air pollution concentrations, researchers can perform comprehensive analyses to uncover the multifaceted impacts of air pollution across society. To this end, we introduce Environmental Insights, an open-source Python package designed to democratize access to air pollution concentration data. This tool enables users to easily retrieve historical air pollution data and employ a Machine Learning model for forecasting potential future conditions. Moreover, Environmental Insights includes a suite of tools aimed at facilitating the dissemination of analytical findings and enhancing user engagement through dynamic visualizations. This comprehensive approach ensures that the package caters to the diverse needs of individuals looking to explore and understand air pollution trends and their implications.

Code repository clickable link

Environmental Insights Github Home Page.