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stagg:: A data pre-processing R package for climate impacts analysis Environ. Model. Softw. (IF 4.8) Pub Date : 2024-09-02 Tyler Liddell, Anna S. Boser, Sara Orofino, Tracey Mangin, Tamma Carleton
The increasing availability of high-resolution climate data has greatly expanded the study of how the climate impacts humans and society. However, the processing of these multi-dimensional datasets poses significant challenges for researchers in this growing field, most of whom are social scientists. This paper introduces stagg, or “space-time aggregator”, a new R package that streamlines three critical
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Review of Jamal Mabrouki, Azrour Maroude, and Azeem Irshad (eds.), Artificial Intelligence Systems in Environmental Engineering.. Environ. Model. Softw. (IF 4.8) Pub Date : 2024-09-02 Fransiskus Serfian Jogo
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PyMTRD: A Python package for calculating the metrics of temporal rainfall distribution Environ. Model. Softw. (IF 4.8) Pub Date : 2024-09-01 Zhengxu Guo, Yang Wang, Caiqin Liu, Wanhong Yang, Junzhi Liu
Temporal rainfall distribution facilitates the understanding of rainfall patterns at various time scales, extreme events, and corresponding water resources implications. Researchers have developed various metrics of temporal rainfall distribution but there exist no easy-to-use software packages for calculating these metrics. To address this gap, we developed the package, which can be conveniently used
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Analysis of the spatial heterogeneity of glacier melting in Tibet Autonomous Region and its influential factors using the K-means and XGBoost-SHAP algorithms Environ. Model. Softw. (IF 4.8) Pub Date : 2024-08-31 Tingting Xu, Aohua Tian, Jay Gao, Haoze Yan, Chang Liu
This study employed machine learning to comprehensively analyze glacier melting in Tibet Autonomous Region (TAR) and its vital influencing factors. Existing machine learning research often lacks detailed explanations, leading to generalized predictions without considering essential driving factors necessary for yielding an insightful understanding of glacier melting dynamics. To overcome these limitations
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XR-based interactive visualization platform for real-time exploring dynamic earth science data Environ. Model. Softw. (IF 4.8) Pub Date : 2024-08-29 Xuelei Zhang, Hu Yang, Chunhua Liu, Qingqing Tong, Aijun Xiu, Lingsheng Kong, Mo Dan, Chao Gao, Meng Gao, Huizheng Che, Xin Wang, Guangjian Wu
The transition from 2D planar displays to immersive holographic 3D environments has brought advancements in visualization technology. However, there remains a lack of effective interactive visualization tools for complex multi-dimensional structured or unstructured datasets in immersive space. To address this gap, we have developed MetIVA, a state-of-the-art multiscale interactive data visualization
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Bayesian Optimization for Anything (BOA): An open-source framework for accessible, user-friendly Bayesian optimization Environ. Model. Softw. (IF 4.8) Pub Date : 2024-08-24 Madeline E. Scyphers, Justine E.C. Missik, Haley Kujawa, Joel A. Paulson, Gil Bohrer
We introduce Bayesian Optimization for Anything (BOA), a high-level Bayesian Optimization (BO) framework and model wrapping toolkit, which presents a novel approach to simplifying BO, with the goal of making it more accessible and user-friendly, particularly for those with limited expertise in the field. BOA addresses common barriers in implementing BO, focusing on ease of use, reducing the need for
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ForestAdvisor: A multi-modal forest decision-making system based on carbon emissions Environ. Model. Softw. (IF 4.8) Pub Date : 2024-08-23 Tong Ji, Yifeng Lin, Yuer Yang
Effectively balancing carbon emission reduction with economic viability through regional forest management is a significant challenge for global ecosystems. This paper introduces an innovative multi-modal forest decision-making system, integrating deep learning and natural language processing technologies, aimed at optimizing forest management strategies. Experimental validation of this system was
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Increasing parameter identifiability through clustered time-varying sensitivity analysis Environ. Model. Softw. (IF 4.8) Pub Date : 2024-08-19 Lu Wang, Yue-Ping Xu, Jiliang Xu, Haiting Gu, Zhixu Bai, Peng Zhou, Hongjie Yu, Yuxue Guo
Hydrological models are becoming progressively complex, leading to unclear internal model behavior, increasing uncertainty, and the risk of equifinality. Accordingly, our study provided a research framework based on global sensitivity analysis, aiming at unraveling the process-level behavior of high-complexity models, teasing out the main information, and ultimately exploiting its usage for model parameterization
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How to assess conditions for the acceptance of climate change adaptation measures by applying implementation probability Bayesian Networks in participatory processes Environ. Model. Softw. (IF 4.8) Pub Date : 2024-08-14 Laura Müller, Max Czymai, Birgit Blättel-Mink, Petra Döll
Climate change adaptation measures are best identified participatorily, yet their implementation poses challenges. While Bayesian Network (BN) modeling has been widely used to assess how adaptation measures mitigate risks, we present how to develop, in a participatory process, an innovative BN type that quantifies the implementation probability of adaptation measures by considering conditions for actors’
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Modernizing the US National Fire Danger Rating System (version 4): Simplified fuel models and improved live and dead fuel moisture calculations Environ. Model. Softw. (IF 4.8) Pub Date : 2024-08-13 W. Matt Jolly, Patrick H. Freeborn, Larry S. Bradshaw, Jon Wallace, Stuart Brittain
The US National Fire Danger Rating System (USNFDRS) supports wildfire management decisions nationwide, but it has not been updated since 1988. Here we implement new fuel moisture models, and we simplify the fuel models while maintaining the overall USNFDRS structure. Modeled and measured live fuel moisture content values were highly correlated ( with defaults and when species and location optimized)
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PyCHAMP: A crop-hydrological-agent modeling platform for groundwater management Environ. Model. Softw. (IF 4.8) Pub Date : 2024-08-13 Chung-Yi Lin, Maria Elena Orduna Alegria, Sameer Dhakal, Sam Zipper, Landon Marston
The Crop-Hydrological-Agent Modeling Platform (PyCHAMP) is a Python-based open-source package designed for modeling agro-hydrological systems. The modular design, incorporating aquifer, crop field, groundwater well, finance, and behavior components, enables users to simulate and analyze the interactions between human and natural systems, considering both environmental and socio-economic factors. This
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Web application of an integrated simulation for aquatic environment assessment in coastal and estuarine areas Environ. Model. Softw. (IF 4.8) Pub Date : 2024-08-12 Yoshitaka Matsuzaki, Tetsunori Inoue, Masaya Kubota, Hiroki Matsumoto, Tomoyuki Sato, Hikari Sakamoto, Daisuke Naito
This paper introduces the web application-type Graphical User Interface that has been developed and also presents an application example. The introduced simulator conducts hydrodynamics and ecosystems in coastal and estuarine areas. It consists of (1) a hydrodynamic model that can simulate the current velocity, water temperature, salinity, and water level; (2) an ecosystem model that can simulate dissolved
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Modelling vegetation dynamics for future climates in Australian catchments: Comparison of a conceptual eco-hydrological modelling approach with a deep learning alternative Environ. Model. Softw. (IF 4.8) Pub Date : 2024-08-12 Hui Zou, Lucy Marshall, Ashish Sharma, Jie Jian, Clare Stephens, Philippa Higgins
Dynamically simulating leaf area index assists in modelling the feedbacks between eco-hydrologic and climatic processes. The particular challenge for Australia is the prevalence of arid and semi-arid ecosystems where water availability plays a crucial role in vegetation productivity. To understand whether existing LAI models can capture plant dynamics under changing climates, we tested two competing
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An explainable MHSA enabled deep architecture with dual-scale convolutions for methane source classification using remote sensing Environ. Model. Softw. (IF 4.8) Pub Date : 2024-08-12 Kamakhya Bansal, Ashish Kumar Tripathi
Methane is the second most abundant greenhouse gas after carbon dioxide. Anthropogenic sources are the dominant emitters of methane. The poor spatial resolution of satellite imagery, high interclass similarity, the multi-scalar nature of features, and the dominance of background limit the performance of the previous approaches. Further, the reliance on high-resolution imagery limits the cost-effective
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Cloud-based system for monitoring event-based hydrological processes based on dense sensor network and NB-IoT connectivity Environ. Model. Softw. (IF 4.8) Pub Date : 2024-08-11 Ernesto Sanz, Jorge Trincado, Jorge Martínez, Jorge Payno, Omer Morante, Andrés F. Almeida-Ñaulay, Antonio Berlanga, José M. Molina, Sergio Zubelzu, Miguel A. Patricio
Hydrologists claim high-quality experimental data are required to improve the understanding of hydrological processes. Though accurate devices for measuring hydrological processes are available, the on-site deployment and operation of effective monitoring networks face many relevant issues caused by the peculiar characteristics of hydrological systems. In this manuscript, we present a self-developed
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Extending our understanding on the retrievals of surface energy fluxes and surface soil moisture from the “triangle” technique Environ. Model. Softw. (IF 4.8) Pub Date : 2024-08-10 George P. Petropoulos
The present study demonstrates the capability of an inversion modelling scheme so-called the “triangle” to retrieve spatiotemporal estimates of surface energy fluxes and soil surface moisture (SSM) at high resolution using ASTER satellite imagery synergistically with SimSphere land biosphere model. In addition, as a further objective of this study is to examine the use of the technique for retrieving
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Development and evaluation of a general approach for predicting pathogen decay in surface waters using hierarchical Bayesian modeling Environ. Model. Softw. (IF 4.8) Pub Date : 2024-08-10 Kara Dean, Jade Mitchell
A general approach for predicting indicator and pathogen decay in surface waters was developed using Bayesian hierarchical modeling, a persistence database, and a two-parameter model form. The resulting hierarchical regression describes general persistence behaviors with target-level intercepts and population-level coefficients. Uncertainty factors calculated with the approach suggest fecal indicator
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An introduction to data-driven modelling of the water-energy-food-ecosystem nexus Environ. Model. Softw. (IF 4.8) Pub Date : 2024-08-10 Elise Jonsson, Andrijana Todorović, Malgorzata Blicharska, Andreina Francisco, Thomas Grabs, Janez Sušnik, Claudia Teutschbein
Attaining resource security in the ater, nergy, ood, and cosystem (WEFE) sectors, the WEFE nexus, is paramount. This necessitates the use of quantitative modelling, which presents many challenges, as this is a complex system acting at the intersection of the physical- and social sciences. However, as WEFE data is becoming more widely available, data-driven methods of modelling this system are becoming
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Pywr-DRB: An open-source Python model for water availability and drought risk assessment in the Delaware River Basin Environ. Model. Softw. (IF 4.8) Pub Date : 2024-08-10 Andrew L. Hamilton, Trevor J. Amestoy, Patrick M. Reed
The Delaware River Basin (DRB) in the Mid-Atlantic region of the United States is an institutionally complex water resources system that provides drinking water for 13.5 million people, plus water for energy, industry, recreation, and ecosystems. This paper introduces Pywr-DRB, an open-source Python model exploring the impacts of reservoir operations, transbasin diversions, and minimum flow targets
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Segmentation of underwater fish in complex aquaculture environments using enhanced Soft Attention Mechanism Environ. Model. Softw. (IF 4.8) Pub Date : 2024-08-05 Dashe Li, Yufang Yang, Siwei Zhao, Jinqiang Ding
Underwater fish segmentation technology serves as a crucial foundation for extracting aquatic biological information. However, due to intricate and fluctuating underwater environments, existing segmentation models fail to precisely focus on key image regions. Based on this, the paper developed an underwater fish segmentation model, Receptive Field Expansion Model(RFEM), by enhancing soft attention
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A modeller’s fingerprint on hydrodynamic decision support modelling Environ. Model. Softw. (IF 4.8) Pub Date : 2024-08-02 J.O.E. Remmers, A.J. Teuling, L.A. Melsen
Model results can have far-reaching societal implications, requiring fit-for-purpose models. However, model output is resulting from a particular path chosen with each modelling decision. We interviewed fourteen modellers in the Dutch water management sector in order to study how decision support hydrodynamic modellers make modelling decisions. An inductive-content analysis was performed. We identified
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A two-way coupled CHANS model for flood emergency management, with a focus on temporary flood defences Environ. Model. Softw. (IF 4.8) Pub Date : 2024-07-26 Haoyang Qin, Qiuhua Liang, Huili Chen, Varuna De Silva
This study presents a novel Coupled Human And Natural Systems (CHANS) modelling framework that integrates a hydrodynamic model with an agent-based model at the memory level within a multi-GPU computing environment. This two-way coupled model captures real-time interactions between human activities and flood dynamics, with a focus on the deployment of temporary flood defences during the 2015 Desmond
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Enhanced water level monitoring for small and complex inland water bodies using multi-satellite remote sensing Environ. Model. Softw. (IF 4.8) Pub Date : 2024-07-26 Kwanghee Han, Seokhyeon Kim, Rajeshwar Mehrotra, Ashish Sharma
Water level monitoring in lakes and reservoirs is essential for effective water resource management, especially in remote areas where traditional ground sensors are costly and difficult to maintain. Remote sensing offers an alternative, but improving the quality, resolution, and accuracy of satellite data remains crucial. This paper introduces MoRLa (Measurement of Reservoir Level from Altimetry),
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Leveraging innovization and transfer learning to optimize best management practices in large-scale watershed management Environ. Model. Softw. (IF 4.8) Pub Date : 2024-07-26 Kalyanmoy Deb, A. Pouyan Nejadhashemi, Gregorio Toscano, Hoda Razavi, Lewis Linker
Recent research in evolutionary multi-objective optimization (EMO) highlights the concept of “Innovization”, which identifies essential patterns in high-quality, non-dominated solutions. This study introduces a novel method to pinpoint influential Best Management Practices (BMPs) in the Chesapeake Bay Watershed, optimizing the trade-off solution process. This approach, though innovative, demands considerable
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Ozone exceedance forecasting with enhanced extreme instance augmentation: A case study in Germany Environ. Model. Softw. (IF 4.8) Pub Date : 2024-07-25 Tuo Deng, Astrid Manders, Arjo Segers, Arnold Willem Heemink, Hai Xiang Lin
Accurately forecasting ozone levels that exceed specific thresholds is pivotal for mitigating adverse effects on both the environment and public health. However, predicting such ozone exceedances remains challenging due to the infrequent occurrence of high-concentration ozone data. This research, leveraging data from 57 German monitoring stations from 1999 to 2018, introduces an Enhanced Extreme Instance
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Enhancing algal bloom forecasting: A novel framework for machine learning performance evaluation during periods of special temporal patterns Environ. Model. Softw. (IF 4.8) Pub Date : 2024-07-25 Wei Xia, Ilija Ilievski, Christine Ann Shoemaker
The evaluation of algal bloom forecasting models typically relies on error metrics that quantify the forecasting performance over the whole test set as a single number. Furthermore, the comparison with simple baseline methods is often omitted. To address this, we introduce a novel framework for Model performance Analysis and Visualization of time series forecasting (MAVts). MAVts incorporates novel
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Digital twin-based virtual modeling of the Poyang Lake wetland landscapes Environ. Model. Softw. (IF 4.8) Pub Date : 2024-07-24 Hao Chen, Xin Xiao, Chao Chen, Min Chen, Chaoyang Li, Kai Lu, Hui Lin, Chaoyang Fang
Virtual wetland landscapes of provide fundamental support for digital twin watershed constructions. However, most digital twin applications in natural environments have focused on static digital scenes and little consideration for wetlands. The Poyang Lake is characterized by seasonal hydrologic changes, with periodic plant community successions, making it necessary to capture dynamic changes in the
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Transfer learning with convolutional neural networks for hydrological streamline delineation Environ. Model. Softw. (IF 4.8) Pub Date : 2024-07-24 Nattapon Jaroenchai, Shaowen Wang, Lawrence V. Stanislawski, Ethan Shavers, Zhe Jiang, Vasit Sagan, E. Lynn Usery
Hydrological streamline delineation is critical for effective environmental management, influencing agriculture sustainability, river dynamics, watershed planning, and more. This study develops a novel approach to combining transfer learning with convolutional neural networks that capitalize on image-based pre-trained models to improve the accuracy and transferability of streamline delineation. We
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A novel multi-model ensemble framework for fluvial flood inundation mapping Environ. Model. Softw. (IF 4.8) Pub Date : 2024-07-22 Nikunj K. Mangukiya, Shashwat Kushwaha, Ashutosh Sharma
Floods pose a significant threat to communities and infrastructure, necessitating timely predictions for effective disaster management. Conventional hydrodynamic models often encounter limitations in data requirements and computational efficiency. To overcome these constraints, we propose a novel multi-model ensemble framework integrating the flood extent and depth models for fluvial flood mapping
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A unified runoff generation scheme for applicability across different hydrometeorological zones Environ. Model. Softw. (IF 4.8) Pub Date : 2024-07-22 Qinuo Zhang, Ke Zhang, Lijun Chao, Xinyu Chen, Nan Wu
Runoff generation in humid and semi-arid regions are usually dominated by saturation-excess mechanism and infiltration-excess mechanism, respectively. However, both mechanisms can co-exist in semi-humid regions. Therefore, we proposed a unified runoff generation scheme to represent the single and mixed runoff generation processes, making it applicable for different hydrometeorological conditions. The
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GeoGOBLIN: A catchment-scale land balance model for assessment of climate mitigation pathways considering environmental trade-offs for multiple impact categories Environ. Model. Softw. (IF 4.8) Pub Date : 2024-07-19 Colm Duffy, Daniel Henn, David Styles, Gregory G. Toth, Remi Prudhomme, Pietro P.M. Iannetta, Ken Byrne
GeoGOBLIN, a novel environmental impact and land balance assessment tool, builds upon the GOBLIN biophysical land use emissions model for Ireland, offering enhanced resolution and system-level detail. It combines remotely sensed data and national agricultural census data to model climate change, air quality, and eutrophication emissions at the catchment level. Integration of the CBM-CFS3 forest carbon
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GIS-based modelling of landscape patterns in mountain areas using climate indices and regression analysis Environ. Model. Softw. (IF 4.8) Pub Date : 2024-07-18 Hristina Prodanova, Stoyan Nedkov, Galin Petrov
The approach of defining landscape patterns based on climate indices is applied in a case study area in North-Central Bulgaria. The results proved the strong interrelation between the climate indices and the elevation, enabling the implementation of a regression model. The results of the regression are used to define threshold values for delineation of all potential contours based on climate indices
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Virtual forests for decision support and stakeholder communication Environ. Model. Softw. (IF 4.8) Pub Date : 2024-07-18 Stefan Holm, Janine Schweier
Challenges in forest management are increasing due to climate change and its associated risks. Considering the needs and demands of various stakeholders leads to more complex decision-making. The increasing amount and quality of available geographic, forest and individual tree data, the combination of this data, and the use of forest growth simulators make it possible to support forest managers in
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A calibration protocol for soil-crop models Environ. Model. Softw. (IF 4.8) Pub Date : 2024-07-17 Daniel Wallach, Samuel Buis, Diana-Maria Seserman, Taru Palosuo, Peter J. Thorburn, Henrike Mielenz, Eric Justes, Kurt-Christian Kersebaum, Benjamin Dumont, Marie Launay, Sabine Julia Seidel
Process-based soil-crop models are widely used in agronomic research. They are major tools for evaluating climate change impact on crop production. Multi-model simulation studies show a wide diversity of results among models, implying that simulation results are very uncertain. A major path to improving simulation results is to propose improved calibration practices that are widely applicable. This
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A machine learning framework for multi-step-ahead prediction of groundwater levels in agricultural regions with high reliance on groundwater irrigation Environ. Model. Softw. (IF 4.8) Pub Date : 2024-07-16 Feilin Zhu, Mingyu Han, Yimeng Sun, Yurou Zeng, Lingqi Zhao, Ou Zhu, Tiantian Hou, Ping-an Zhong
This study presents a machine learning framework for multi-step-ahead prediction of groundwater levels in agricultural regions heavily reliant on groundwater irrigation. The framework utilizes a comprehensive set of predictive factors, including meteorological, hydrological, and human activity data. An optimal combination of input variables and their temporal delays was determined using a novel selection
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Comparison of predictive modeling approaches to estimate soil erosion under spatially heterogeneous field conditions Environ. Model. Softw. (IF 4.8) Pub Date : 2024-07-14 Ahsan Raza, Murilo dos Santos Vianna, Seyed Hamid Ahmadi, Muhammad Habib-ur-Rahman, Thomas Gaiser
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
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Algorithm-based segmentation of temperature-depth profiles: Examples from a mine Environ. Model. Softw. (IF 4.8) Pub Date : 2024-07-14 Thorsten Gökpinar, Thomas Heinze
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
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Accelerated numerical modeling of shallow water flows with MPI, OpenACC, and GPUs Environ. Model. Softw. (IF 4.8) Pub Date : 2024-07-14 Ayhan H. Saleem, Matthew R. Norman
In this paper, a time-explicit Finite-Volume method is adopted to solve the 2-D shallow water equations on an unstructured triangular mesh, using a two-stage Runge-Kutta integrator and a monotone MUSCL model to achieve second-order accuracy in time and space, respectively. A multi-GPU model is presented that uses the Message Passing Interface (MPI) with OpenACC and uses the METIS library to produce
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The portal of OpenGMS: Bridging the contributors and users of geographic simulation resources Environ. Model. Softw. (IF 4.8) Pub Date : 2024-07-11 Kai Xu, Min Chen, Songshan Yue, Fengyuan Zhang, Jin Wang, Yongning Wen, Guonian Lü
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
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Toolkit for assessing water accounting in data-scarce river basins using global databases Environ. Model. Softw. (IF 4.8) Pub Date : 2024-07-10 Sobhan Rostami, Majid Delavar, Shokri Kuchak Vahid, Majid Mirzaei
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
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A large dataset of fluvial hydraulic and geometry attributes derived from USGS field measurement records Environ. Model. Softw. (IF 4.8) Pub Date : 2024-07-10 Seyed Mohammad Hassan Erfani, Mahdi Erfani, Sagy Cohen, Austin R.J. Downey, Erfan Goharian
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
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Increasing the water level accuracy in hydraulic river simulation by adapting mesh level elevation Environ. Model. Softw. (IF 4.8) Pub Date : 2024-07-10 Parisa Khorsandi Kuhanestani, Anouk Bomers, Martijn J. Booij, Jord J. Warmink, Suzanne J.M.H. Hulscher
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
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Qualification of a double porosity reactive transport model for MX-80 bentonite in deep geological repositories for nuclear wastes Environ. Model. Softw. (IF 4.8) Pub Date : 2024-07-10 Virginia Cabrera, Rubén López-Vizcaíno, Ángel Yustres, Vicente Navarro
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
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Automated hydrologic forecasting using open-source sensors: Predicting stream depths across 200,000 km[formula omitted] Environ. Model. Softw. (IF 4.8) Pub Date : 2024-07-08 Travis Adrian Dantzer, Branko Kerkez
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.
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Model linkage to assess forest disturbance impacts on water quality: A wildfire case study using LANDIS(II)-VELMA Environ. Model. Softw. (IF 4.8) Pub Date : 2024-07-03 Kar'retta Venable, John M. Johnston, Stephen D. LeDuc, Lourdes Prieto
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
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A probabilistic approach to training machine learning models using noisy data Environ. Model. Softw. (IF 4.8) Pub Date : 2024-07-02 Ayman H. Alzraiee, Richard G. Niswonger
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
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UIDS: A Matlab-based urban flood model considering rainfall-induced and surcharge-induced inundations Environ. Model. Softw. (IF 4.8) Pub Date : 2024-07-02 Vinh Ngoc Tran, Jongho Kim
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
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Environmental Insights: Democratizing access to ambient air pollution data and predictive analytics with an open-source Python package Environ. Model. Softw. (IF 4.8) Pub Date : 2024-07-02 Liam J. Berrisford, Ronaldo Menezes
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 Environmen, an open-source Python package designed to democratize access
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Finite element software for calculating fluid flow and heat transport for seamounts Environ. Model. Softw. (IF 4.8) Pub Date : 2024-07-02 V.C. Manea, E.G. Sewell, M. Manea, S. Yoshioka, N. Suenaga, E.J. Moreno
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
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Global optimization-based calibration algorithm for a 2D distributed hydrologic-hydrodynamic and water quality model Environ. Model. Softw. (IF 4.8) Pub Date : 2024-07-02 Marcus Nóbrega Gomes Jr., Marcio Hofheinz Giacomoni, Fabricio Alonso Richmond Navarro, Eduardo Mario Mendiondo
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
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Deep learning prediction of rainfall-driven debris flows considering the similar critical thresholds within comparable background conditions Environ. Model. Softw. (IF 4.8) Pub Date : 2024-06-28 Hu Jiang, Qiang Zou, Yunqiang Zhu, Yong Li, Bin Zhou, Wentao Zhou, Shunyu Yao, Xiaoliang Dai, Hongkun Yao, Siyu Chen
Machine learning has been widely applied to predict the spatial or temporal likelihood of debris flows by leveraging its powerful capability to fit nonlinear features and uncover underlying patterns or rules in the complex formation mechanisms of debris flows. However, traditional approaches, including some current machine learning-based prediction models, still have limitations when used for debris
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Long-term drought prediction using deep neural networks based on geospatial weather data Environ. Model. Softw. (IF 4.8) Pub Date : 2024-06-28 Alexander Marusov, Vsevolod Grabar, Yury Maximov, Nazar Sotiriadi, Alexander Bulkin, Alexey Zaytsev
The problem of high-quality drought forecasting up to a year in advance is critical for agriculture planning and insurance. Yet, it is still unsolved with reasonable accuracy due to data complexity and aridity stochasticity. We tackle drought data by introducing an end-to-end approach that adopts a spatio-temporal neural network model with accessible open monthly climate data as the input. Our systematic
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On the global parameterization of a 1DV hydromorphodynamic model of estuaries, the case of the Ems estuary Environ. Model. Softw. (IF 4.8) Pub Date : 2024-06-27 Keivan Kaveh, Andreas Malcherek
Each submodel in a hydro-morphodynamic model has its own local calibration parameters, leading to a high degree of uncertainty in their application. This paper proposes a global parameterization framework of hydro-morphodynamic models, which involves the development and implementation of submodels that share some common calibration parameters. The proposed model reduces the total number of adjustable
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A novel insight on input variable and time lag selection in daily streamflow forecasting using deep learning models Environ. Model. Softw. (IF 4.8) Pub Date : 2024-06-25 Amina Khatun, M.N. Nisha, Siddharth Chatterjee, Venkataramana Sridhar
This study investigates the feasibility of using hybrid models namely Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN)-Gated Recurrent Unit (GRU), for short-to-medium range streamflow forecasting in the Mahanadi River basin in India. The performance of these hybrid models is compared with that of standalone models. It investigates the impact of
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A spatial machine learning model developed from noisy data requires multiscale performance evaluation: Predicting depth to bedrock in the Delaware river basin, USA Environ. Model. Softw. (IF 4.8) Pub Date : 2024-06-21 P. Goodling, K. Belitz, P. Stackelberg, B. Fleming
Spatial machine learning models can be developed from observations with substantial unexplainable variability, sometimes called ‘noise’. Traditional point-scale metrics (e.g., R) alone can be misleading when evaluating these models. We present a multi-scale performance evaluation (MPE) using two additional scales (distributional and geostatistical). We apply the MPE framework to predictions of depth
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Commentary on “Cloud-based urgent computing for forest fire spread prediction” by Fraga et al. Environ. Model. Softw. (IF 4.8) Pub Date : 2024-06-20 Robertas Damaševičius
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Design and implementation of a BigQuery dataset and application programmer interface (API) for the U.S. National Water Model Environ. Model. Softw. (IF 4.8) Pub Date : 2024-06-19 Kel N. Markert, Gui da Silva, Daniel P. Ames, Iman Maghami, Gustavious P. Williams, E. James Nelson, James Halgren, Arpita Patel, Adler Santos, Michael J. Ames
We introduce an open-source web-based Application Programming Interface (API) developed within a representational state transfer (REST) architecture framework that provides access to the operational streamflow forecasts from the U.S. National Water Model (NWM). We built this API within the Google Cloud infrastructure, taking advantage of Google's API Gateway, BigQuery, and the Google Cloud Run architecture
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Bayes_Opt-SWMM: A Gaussian process-based Bayesian optimization tool for real-time flood modeling with SWMM Environ. Model. Softw. (IF 4.8) Pub Date : 2024-06-19 Ahad Hasan Tanim, Corinne Smith-Lewis, Austin R.J. Downey, Jasim Imran, Erfan Goharian
Real-time flood model plays a pivotal role in averting urban flood damage, particularly when there is minimal lead time for preparatory measures. However, urban flood modeling in real-time often contends with inherent uncertainties arising from input data uncertainty and parameter ambiguities. This study introduces a real-time calibration (RTC) tool called , specifically tailored for real-time urban
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Climate change impacts on crop yields: A review of empirical findings, statistical crop models, and machine learning methods Environ. Model. Softw. (IF 4.8) Pub Date : 2024-06-19 Tongxi Hu, Xuesong Zhang, Sami Khanal, Robyn Wilson, Guoyong Leng, Elizabeth M. Toman, Xuhui Wang, Yang Li, Kaiguang Zhao
Understanding crop responses to climate change is crucial for ensuring food security. Here, we reviewed ∼230 statistical crop modeling studies for major crops and summarized recent progress in estimating climate change impacts on crop yields. Evidence was strong that increasing temperatures reduce crop yields. A 1 °C warming decreased the yields by 7.5 ± 5.3% (maize), 6.0 ± 3.3% (wheat), 6.8 ± 5.9%
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Hydrodynamic modeling of floodplains with inundated trees and floating plants Environ. Model. Softw. (IF 4.8) Pub Date : 2024-06-16 Wencai Zhou, John M. Melack, Sally MacIntyre
Hydrodynamic influences of inundated forests and floating plants, common on floodplains, include modifying exchange flows and thermal structure. This study added algorithms to represent these plants in a three-dimensional hydrodynamic model, applied the model to a tropical floodplain, and validated the results with high resolution field measurements. The simulations reproduced temporal and spatial