Real-time simulation of flood inundation helps to mitigate the catastrophic effects on human lives by facilitating emergency evacuations. Traditional two-dimensional (2D) physics-based hydrodynamic models, though accurate, require significant computational time, thereby rendering them unsuitable for such real-time applications. To address this limitation, we developed Random Forest (RF) models as surrogate

Excessive dependence on and unreasonable use of pesticides in actual crop growth will lead to excessive pesticide residues and exacerbate environmental pollution. Therefore, the prediction of pesticide residues in crops is particularly important. In order to ensure the accuracy and generalization of the pesticide residue prediction model, a ZHPO-LightXBoost pesticide residue prediction model based

Convergent Cross Mapping (CCM) is a powerful tool for analyzing causality in complex dynamic systems. However, standard CCM and Geographical CCM (GCCM) focus exclusively on temporal or spatial attributes, failing to integrate both dimensions. This study introduces a spatial-temporal CCM that quantifies the state of convergence to enable batched analyses of large-scale spatial datasets. The proposed

Distributed hydrologic models (DHM) are essential tools for understanding how and where water moves through a landscape. However, DHMs can be time-consuming and challenging to setup, limiting their application. Here, we present pytRIBS, a tool that addresses these challenges for the TIN-based Real-time Integrated Basin Simulator (tRIBS). pytRIBS is an open-source Python package with an object-oriented

The global warming problem has seriously threatened the sustainable development of human society. In order to effectively control carbon emissions, this study integrates economic, social, energy, and environment (ESEE) factors to develop a comprehensive, multi-dimensional carbon emissions prediction (CEP) index system, crucial for analyzing the determinants of carbon emissions and forecasting future

The variations in pollutant concentrations during tunnel construction, in urban atmosphere, and along pedestrian paths exhibit distinct periodicity. Accurate prediction of pollutant concentrations is crucial for improving the quality of the construction and living environments. Using tunnel construction scenarios as a case, this study proposes a Convolutional Neural Network and Bidirectional Long Short-Term

Accurate prediction of head-fire rate of spread is essential to fire management decisions during wildfires, however, evaluation of existing models is limited. Acquisition of reliable rate of spread observations for model evaluation is a key challenge, since wildfires are typically rare and difficult to monitor. We applied recent advances in satellite active fire remote sensing to generate a novel set

We introduce a significantly enhanced version of AquaNutriOpt, now equipped with advanced mathematical optimization capabilities absent in its initial release (Khanal et al., 2024). AquaNutriOpt II is a user-friendly, free, open-source Python tool designed to address the complex challenge of optimizing nutrient management for controlling harmful algal blooms. In this latest version, users gain the

State-of-the-art General Circulation Models (GCMs) typically operate at a coarse spatial resolution, requiring a refinement to assess regional climate changes and their impacts. This weakness is mainly known for representing regional-scale topography and meteorological processes, particularly those responsible for extreme events. Dynamical downscaling methods are computationally demanding. In contrast

Channel sinuosity is ubiquitous along river networks, producing complex patterns that encapsulate and influence morphodynamic processes and ecosystem services. Accurately characterizing these patterns is challenging with traditional curvature-based algorithms. Here, we present WigglyRivers, a Python package that builds on existing wavelet-based methods to create an unsupervised meander identification

Lack of historical data is a major bottleneck for hydrologists to proceed with reliable climate change studies. This work proposes Gaussian Process-Integrated Neural Network (GAUSNET) technique for streamflow hindcasting by considering significant hydrological variables and Global climatic oscillations (GCO) identified by Variance Inflation Factor as system inputs. Dynamic Time Warping based Interpolation

This paper addresses the potential for hydroecological models of everywhere to be used, in conjunction with interaction with local stakeholders, as a way of learning about places as well as being used as predictive tools. The importance of facilitating stakeholder involvement in defining assumptions and uncertainties, and in model evaluation is stressed. The potential for using data science and real-time

We present a novel optimization strategy for the numerical simulation of lava flows that automatically find the best combination of input parameters to fit observed flows considering their uncertainties. The approach is based on the Metropolis algorithm, a Monte Carlo Markov Chain (MCMC) method that performs a sequence of simulations aiming to refine the sampling of unknown parameters to determine

How can models do well for the common good? Narratives and counter-narratives are possible to answer the question. The latter are taken here to argue that models may misuse their epistemic authority for reasons of occasion, opportunity and interest. A solution calls for the involvement of more disciplines and actors, but these are not by themselves sufficient due to the present governance, practice

An extension of the BLUECAT approach and software for uncertainty assessment of environmental predictions is presented, allowing the application to multimodel outputs. BLUECAT operates by transforming a point prediction provided by deterministic models to a corresponding stochastic formulation, thereby allowing the estimation of a bias corrected expected value along with confidence limits. In this

The Soil and Water Assessment Tool plus (SWAT+) model is widely used to analyze water dynamics in hydrological processes. It improves upon the earlier SWAT version by incorporating decision tables that allow for the specification of different land use management activities and scenarios. However, accurate watershed representation requires proper calibration and validation. Among the available open-source

One of the profound effects produced by climate change is shifting the seasons in terms of both duration and start/end dates. It is important for sustainable management to detect and predict any such seasonal changes as they may trigger earlier-than-usual timing of plant phenology, animal migration, and other ecological, environmental, economic, and social implications. In this study, we are using

Dust storms, common meteorological hazard in arid and semi-arid regions, have significant environmental and societal impacts. Rapid and accurate detecting dust storms is critical for early warning systems. Over the past few decades, dust storm detection primarily relied on satellite remote sensing techniques using multi-channel imagery, but these methods have limitations in temporal resolution. With

Accurate and rapid prediction of wildfire trends is crucial for effective management and mitigation. However, the stochastic nature of fire propagation poses significant challenges in developing reliable simulators. In this paper, we introduce PyTorchFire, an open-access, PyTorch-based software that leverages GPU acceleration. With our redesigned differentiable wildfire Cellular Automata (CA) model

Scientific and management challenges in the water domain require synthesis of diverse data. Many analysis tasks are difficult because datasets are large and complex, standard formats are not always agreed upon or mapped to efficient data structures, scientists may lack training for tackling large and complex datasets, and it can be difficult to share and reproduce data science workflows. Overcoming

Performing efficient and fine-grained viewshed analysis in 3D complex urban models, particularly when handling large-scale datasets, presents a significant challenge in Geographic Information Systems (GIS). Existing methods are primarily designed for 2.5D raster models and struggle to effectively manage large-scale data. Furthermore, the commonly utilized approaches for 3D models need large display

This paper seeks to answer the question “can Large Language Models (LLMs) effectively reason about adverse weather conditions?”. To address this question, we utilized multiple LLMs to harness the US National Weather Service (NWS) flood report data spanning from June 2005 to September 2024. Bidirectional and Auto-Regressive Transformer (BART), Bidirectional Encoder Representations from Transformers

Ozone pollution threatens ecosystems and human health, necessitating accurate forecasting for better management and policy implementation. To address this, we developed O3ConvNet, a convolution-based dynamic spatiotemporal deep learning model. It incorporates ModernTCN, a multivariate time series feature module, and a spatial message passing module using a dynamic adjacency matrix with geographic and

This study applies global sensitivity analysis (GSA) to the Iowa Food-Water-Energy system, focusing on nitrogen export into the Mississippi River. A binning method combined with simulation decomposition (SimDec) quantifies and visualizes the influence of crucial aggregate input variables — manure nitrogen (MN), commercial nitrogen (CN), grain nitrogen (GN), and fixation nitrogen (FN) — on nitrogen

Dissolved oxygen (DO) is a critical parameter for monitoring water quality. However, most existing deep learning models have overlooked the physical relationship between DO and other parameters during simulation, leading to simulated values that deviate from the actual physical laws. Moreover, the inherent opacity of deep learning models restricts their applicability. Here, we propose the prior knowledge-constrained

A novel algorithm for generating multi-component, high-resolution emission inventories for air quality models (AQMs) was developed, enhancing the 0.01° spatial allocation scheme for points, lines, and surfaces based on land use, population density, and road networks. It incorporated localized chemical species allocation based on recent Volatile Organic Compounds (VOCs) and Particulate Matter (PM) composition

The number and devastating impacts of natural disasters have grown significantly worldwide, and floods are one of the most dangerous and frequent natural disasters. Recent studies emphasize the importance of public awareness in disaster preparedness and response activities. FloodGame is designed as a web-based interactive serious game geared towards educating K-12 and college students and raising public

This paper introduces a new high temporal resolution suspended sediment routing model that integrates fine sediment deposition and re-entrainment processes of individual size fractions with suspended sediment transport throughout the river network. This multi-size fraction model provides unique insights into the effects of sediment size classes on key sediment attributes, including suspended sediment

Air pollution problem seriously affects the ecological environment and human health. More accurate predictions over a longer time span would enhance the effectiveness of early warning and prevention measures. Although existing methods have made progress in short sequence prediction, the predictions on long sequences remain challenges due to information loss. In this paper, we propose a spatial–temporal

PlotToSat offers a practical and time efficient way to the challenge of extracting time-series from multiple Earth Observation (EO) datasets at numerous plots spread across a landscape. This opens up new opportunities to understand and model various ecosystems. Regarding forest ecology, plot networks play a vital role in monitoring and understanding the dynamics of forest ecosystems. These networks

The aim of this work is to find an effective combination of modelling based on the boosting technique and Shapley value computation with the practise of evaluating an undirected graph model. To this end, we created an XGBoost-SHAP regression model in which the target variable is the cyanobacteria concentration and the model variables consist of 20 environmental factors. Two partial correlation-based

Sigmoid growth models are often used to study population dynamics. The size of a population at equilibrium commonly depends explicitly on the availability of resources, such as an energy or nutrient source, which is not explicit in standard sigmoid growth models. A simple generalised extension of sigmoid growth models is introduced that can explicitly account for this resource-dependence, demonstrated

In deep learning (DL)-based regionalized streamflow modeling, basin similarity has demonstrated to be effective for sharing hydrological information. However, the differences in the use of hydrological information by DL due to different basin similarity strategies remain underexplored. For this, we cluster and regionalize 222 Australian basins based on hydrology, climate, landscape, and position characteristics

Spectral libraries link surface reflectance characteristics to thematic cover type interpretation. Despite their potential, spectral libraries are rarely used beyond their original application or analysis. In this paper we introduce the concept of a Generic Urban Spectral Library (GUSL). A GUSL is a thoroughly labelled collection of multi-site, -sensor and -temporal spectral libraries that supports

Aerial and ground-based survey routinely employs technology such as digital photogrammetry, Light Detecting and Ranging (LiDAR) and Terrestrial Laser Scanning (TLS). These systems produce huge data sets with varying accuracy and reliability. At present there are no guidelines for the grid size dimension needed to accurately and reliably represent common features such as rills, gullies and contour drains

The Community Land Model Urban (CLMU) is a process-based numerical urban climate model that simulates the interactions between the atmosphere and urban surfaces, serving as a powerful tool for the convergence of urban and climate science research. However, CLMU presents significant challenges due to the complexities of model installation, environment and case configuration, and generating model inputs

Citizen science has become key to biodiversity monitoring but critically depends on accurate quality control that is scalable and tailored to the focal region. We developed FlorID, a free-to-use identification service for all native and many non-native plants of Switzerland. FlorID can identify >3000 species, using vision transformers trained on 1.5M photos, and ecological predictions from multilayer

Bushfires are one of the major natural disasters that cause huge losses to livelihoods and the environment. Understanding and analysing the severity of bushfires is crucial for effective management and mitigation strategies, helping to prevent the extensive damage and loss caused by these natural disasters. This study presents an in-depth analysis of bushfire severity in Australia over the last twelve

This paper presents a high-resolution 2D eco-hydraulics model accelerated by GPU technology, specifically designed for a spawning ground of Gymnocypris eckloni located downstream of the B hydropower station in the Upper Yellow River. The model evaluates the quality of the spawning habitat from April to June during a typical year. The calculation efficiency is improved by 12.1 times eco-hydraulics on

Drought is a hazard that causes great economic, ecological, and human loss. With an ever-growing risk of climate change, their frequency and magnitude are expected to increase. While there are many indices and metrics available for the analysis of droughts, assessing their impacts represents one of the best ways to understand their magnitude and extent. However, there are no systematic records outlining

porousRTFoam v1.0 is a software developed to solve pore-scale hydro-bio-geochemical processes in porous media. It is developed based on OpenFOAM® by using the micro-continuum approach, which is adopted to solve a system of equations, including the Darcy-Brinkman-Stokes equation, the advection-diffusion equation with geochemical source terms, as well as biomass evolution with Monod kinetics for biofilm

Parametric geographic scene modeling serves as the primary method for achieving large-scale rapid spatial visualization. However, balancing modeling efficiency and specificity of geographic entities poses significant challenges due to the complexity and diversity of real-world geographic environments. This study proposes a novel 3D geographic scene modeling approach that integrates knowledge graphs

Digital agriculture has transformed the landscape of agricultural technology innovation and has led to increased attention towards managing innovation in this domain. This study seeks to provide a comprehensive understanding of digital agriculture innovation management by proposing a new retrieval strategy and constructing a dataset of 1878 research papers from the WoS-SSCI core collection spanning

Reaction Path Modelling was used to calculate the fluxes in terms of solutes and CO2 consumption during the water-rock interaction process at the basin-scale, considering the current and future climate scenarios (temperature and atmospheric CO2 concentration) and two types of solid reagent (Silicate Solid Reagent-SSR and Carbonate-Silicate Reagent C-SSR). Two modelling were performed considering solid

The impacts of climate change are increasingly evident through the rise in severe droughts globally. These events result in intensified socio-economic and environmental effects. Proactive drought management requires effective forecasting and an improved understanding of the underlying hydro-climatic variables. The present study focuses on developing a national-scale drought forecasting model tailored

Passive Sonic Monitoring (PSM) refers to the analysis of patterns and structures shaped by sound, offering a complementary approach to traditional landscape analysis methods, such as satellite imagery. In particular, satellite-based methods alone may overlook specific dynamics of the organism at multiple taxonomic levels and local abiotic interactions. This paper introduces a novel unsupervised methodology

The response mechanism of lake flow under the influence of a complex wind field is a cross-disciplinary hotspot in the fields of hydrology and modern sedimentation research. Qinghai Lake, influenced by multiple wind fields, exhibits complex internal hydrodynamics with largely unknown controlling factors. Current studies predominantly focus on the impact of climate and prevailing northwesterly winds

Multi-source precipitation products (MSPs) are critical for hydrologic modeling, but their spatial and temporal heterogeneity and uncertainty present challenges to simulation accuracy that need to be addressed urgently. This study assessed the impact of different precipitation data sources on hydrologic modeling in an arid basin. There were seven precipitation products and meteorological station interpolated

Forests have many functions, including habitat provision, timber production, and carbon sequestration, but they are under increasing pressure. Coupled forest - soil models are therefore of great importance for a better understanding of forest development, and biogeochemical cycles in a changing world. This study provides a decision support framework for researchers to select appropriate models to study

Accurate inflow forecasts are crucial for managing water resources, particularly in regions experiencing both floods and droughts. This study proposes a combined optimizer (CO) that combines adaptive moment and vision correction algorithms to improve the shortcomings of deep learning optimizers, thereby enhancing deep learning accuracy. CO improves the shortcomings of deep learning optimizers, such

Machine learning-based evapotranspiration (ET) models capture complex nonlinear relationships but struggle with global extrapolation due to unbalanced data distribution, limiting accurate ET assessments crucial for understanding water and energy cycles. This study used Domain-Adversarial Neural Networks (DANN) to improve the geographical adaptability of ET models by mitigating site-level distributional

Reductions of nitrogen, phosphorus, and sediment loads have been the focus of watershed restoration in many regions for improving water quality, including the Chesapeake Bay. Watershed models and riverine monitoring data can provide important information on the progress of load reductions but do not always generate consistent interpretations. A new framework for integrated visualization and analysis

Ground-level ozone (O3) is a significant public health concern. We developed maps of monthly average 1-h maximum O3 concentrations in New South Wales, Australia (2005–2018), a region with sparse monitoring. For the first time Bayesian Maximum Entropy (BME) blending was used within a Deep Ensemble Machine Learning (DEML) framework for air pollution predictions. The DEML combined geographical predictors

A coupled hydrodynamic-water quality model was employed to investigate water quality improvement in Waihai of Lake Dianchi under different water diversion scenarios, including different volumes, inflow/outflow locations, and seasonal allocations. The accuracy of coupled model was reasonably validated against observed data on water level and temperature, total phosphorus (TP), total nitrogen (TN), dissolved

Accurate and synoptic estimation of Evapotranspiration (ET) is crucial for water management. A Google Earth Engine workflow is implemented to estimate daily ET at 30m. The algorithm uses Landsat and ERA5-Land datasets and includes the Two Source Energy Balance (TSEB) model, an Artificial Neural Network for Leaf Area Index, and a gap-filling approach based on crop coefficient. The outputs were evaluated

Accurate streamflow prediction is crucial for effective water resources management and flood risk assessment. The Tagus Headwaters River Basin (THRB), a semi-arid watershed, serves over 10 million residents in Peninsular Spain and diverts water to the Segura River Basin. As the THRB nears its water allocation limits, precise streamflow simulations are essential for sustainable management. This study

We estimate long-term groundwater storage loss in California's Central Valley (CV) using a novel data imputation method that combines in situ data with Earth Observations to generate temporally and spatially interpolated groundwater elevations. We combine these data with storage coefficient maps to produce time series of groundwater volume changes which compare well with previously published groundwater

With the development of the ocean economy, accurate forecasting using ocean models has become increasingly important. Existing parallel versions of the Princeton Ocean Model (POM) often feature complex code and limited portability. To address these issues and meet the computational demands of high-resolution ocean models while reducing program runtime, we developed an OpenACC-based parallel version

Modeling groundwater flow using physically based models requires knowing the geometry of the karst conduit network. Often, this geometry is not accessible and unknown. It is therefore crucial to be able to model it. This paper presents pyKasso, an open-source Python package that generates those geometry based on a pseudo-genetic approach. The model accounts for multiple data sources: a 3D geologic

Subsurface hydraulic properties, critical in the development of groundwater models, are often inferred from aquifer tests and complemented by geologic information. In alluvial aquifers in particular, well and boring logs can provide a three-dimensional distribution of the presence of coarse-grained and fine-grained sediment (texture) as an important mapping of heterogeneity often correlated with hydraulic