Environmental Modelling & Software最新文献

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.

Machine learning (ML) models are increasingly popular in environmental and hydrologic modeling, but they typically contain uncertainties resulting from noisy data (erroneous or outlier data). This paper presents a novel probabilistic approach that combines ML and Markov Chain Monte Carlo simulation to (1) detect and underweight likely noisy data, (2) develop an approach capable of detecting noisy data during model deployment, and (3) interpret the reasons why a data point is deemed noisy to help heuristically distinguish between outliers and erroneous data. The new algorithm recognizes that there is no unique way to split the training data into noisy and clean data, and thus produces an ensemble of plausible splits. The algorithm successfully detected noisy data in synthetic benchmark problems with varying complexity and a real-world public supply water withdrawal dataset. The algorithm is generic and flexible, making it suitable for application across a broad range of hydrologic and environmental disciplines.

A large number of bathymetric discontinuities mark the bottom of the oceans. Among these features, seamounts protruding the sedimentary layer can play a major role in establishing a continuous exchange of fluids and heat between the oceanic lithosphere and the ocean. Here we present finite element codes for calculating the flow, temperature and pressure distributions inside seamounts using a general-purpose finite element solver. We solve the coupled equations of continuity, Darcy equation, and energy conservation equation in 2-D. We present a numerical axisymmetrical model tailored to the real geometry of the Grizzly Bare seamount located on the Juan de Fuca plate. The surface heat flow shows a good correlation between our models and in-situ available observations. In this work we provide complete open access to numerical codes which are intended to be simple and easy to adapt for a wide range of seamounts shapes and sizes.

The Urban Inundation-Drainage Simulator (UIDS) is a new coupled model for simulating urban flooding dynamics, developed as an open-source, MATLAB-based platform. It integrates a rainfall-runoff model with a two-dimensional overland flow model (OFM) and a one-dimensional sewer flow model (SFM). Unlike conventional models limited to either rainfall-induced or sewer surcharge-induced flooding, UIDS captures bidirectional surface-underground interactions to simulate both processes simultaneously. The OFM employs an explicit time-stepping scheme and robust wet-dry front treatment, while a weir equation describes roof-to-ground flow exchange for numerical stability. Timing synchronization facilitates continuous OFM-SFM coupling. Benchmarking and case studies of Gangnam flood events demonstrate UIDS's ability to accurately simulate urban flooding, particularly subcritical flows. The open-source nature of UIDS allows user flexibility in accessing and modifying the MATLAB code. Ultimately, UIDS is expected to serve as an accessible and adaptable tool for urban flood modeling and risk assessment.

Hydrodynamic models with rain-on-the-grid capabilities are usually computationally expensive for automatic parameter estimation. In this paper, we present a global optimization-based algorithm to calibrate a fully distributed hydrologic-hydrodynamic and water quality model (HydroPol2D) using observed data (i.e., discharge, or pollutant concentration) as input. The algorithm finds near-optimal set of parameters to explain observed gauged data. This framework, although applied in a poorly-gauged urban catchment, is adapted for catchments with more detailed observations. The results of the automatic calibration indicate NSE $=$ 0.99 for the V-Tilted catchment, RMSE $=$ 830 mg L^-1 for salt concentration pollutograph in a wooden-plane (i.e., 8.3% of the event mean concentration), and NSE $=$ 0.89 in a urban real-world catchment. This paper also explores the issue of equifinality (i.e., multiple parameters giving the same calibration performance) in model calibration indicating the performance variation of calibrating only with an outlet gauge or with multiple gauges within the catchment.