Editorial Material
Environmental Sciences
Christoph Schuerz, Karsten Schulz
Summary: This reply responds to Abbaspour's (2022) discussion by highlighting a fallacy in using best-fit model solutions in hydrologic modeling studies. Stochastic model calibration and the use of R- and P-Factor statistics for model evaluation are proposed, along with suggested threshold values for model acceptance. A minimal working example is provided to demonstrate that the proposed metrics and thresholds accept implausible model ensemble simulations, which would have been rejected using the NSE metric for individual assessment. The caution against relying solely on single performance metrics and globally defined thresholds for model evaluation is emphasized.
SCIENCE OF THE TOTAL ENVIRONMENT
(2022)
Article
Environmental Sciences
Xiaomang Liu, Kun Yang, Vagner G. Ferreira, Peng Bai
Summary: The traditional calibration strategy of hydrologic models based on runoff observations has limitations. This study used remote sensing ET and TWSC products to design calibration schemes, and found that multi-objective calibration using the combination of ET and TWSC products achieved better accuracy in runoff simulation.
WATER RESOURCES RESEARCH
(2022)
Article
Engineering, Multidisciplinary
Dohoon Kim, Muhammad Muzammil Azad, Salman Khalid, Heung Soo Kim
Summary: Excessive medical waste is generated in medical facilities, especially post-Covid. To overcome the challenge of over-designed sterilization-based shredding systems, a data-driven surrogate model framework is proposed for sensitivity analysis and design optimization based on different loading environments and capacity requirements. The proposed approach significantly reduces computational costs and yields promising accuracy for the optimization process.
ALEXANDRIA ENGINEERING JOURNAL
(2023)
Article
Automation & Control Systems
Anuj Pal, Ling Zhu, Yan Wang, Guoming G. Zhu
Summary: This article proposes a methodology to perform engine calibration using surrogate assisted optimization for diesel engines, optimizing engine efficiency and emissions by calibrating three control variables. Kriging surrogate models and a nondominated sorting algorithm are used for optimization, resulting in optimal points close to true Pareto optimal front.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2021)
Article
Computer Science, Interdisciplinary Applications
Tengfei Wang, Weihang Chen, Taifeng Li, David P. Connolly, Qiang Luo, Kaiwen Liu, Wensheng Zhang
Summary: This paper proposes a hybrid modeling framework to incorporate soil property uncertainty into embankment settlement calculations. The framework includes uncertainty modeling, finite element method, surrogate modeling, and probabilistic analysis. It uses a neural network with Monte Carlo dropout to correlate soil properties and predict post-construction settlements. The framework is validated through a case study and a cost-effective improved ground is designed using an exhaustive search approach.
COMPUTERS AND GEOTECHNICS
(2023)
Article
Engineering, Geological
Jan Machacek, Patrick Staubach, Carlos Eduardo Grandas Tavera, Torsten Wichtmann, Hauke Zachert
Summary: This paper presents an approach for the automatic calibration of a hypoplastic constitutive soil model. The software developed in this work simplifies the calibration process and reduces the subjective factor. The performance of the software is demonstrated by comparing with reference parameter sets and finding correlations between the parameters of the hypoplastic model.
Article
Environmental Sciences
Robert Chlumsky, Juliane Mai, James R. Craig, Bryan A. Tolson
Summary: The improvement of hydrological modeling frameworks allows for both model structure and parameters to be automatically calibrated and evaluated. The blended model structure calibration method can identify near-optimal model structures at significantly lower computational cost, as well as help identify dominant processes and model structures in catchments.
WATER RESOURCES RESEARCH
(2021)
Article
Environmental Sciences
M. S. Pleasants, T. J. Kelleners, A. D. Parsekian, K. M. Befus
Summary: Both hydrological and geophysical data are used to calibrate hillslope hydrologic models, but these data often reflect hydrological dynamics at different spatial scales. The study explores differences in hydraulic parameters and hillslope-scale storage and flux dynamics of models calibrated with different data sets. Results show that different calibration data can significantly affect the predictions of hillslope runoff and internal hydrological dynamics.
WATER RESOURCES RESEARCH
(2023)
Review
Computer Science, Interdisciplinary Applications
Marjan Asgari, Wanhong Yang, John Lindsay, Bryan Tolson, Maryam Mehri Dehnavi
Summary: This paper reviews the application of parallel computing in calibrating watershed hydrologic models and summarizes their contributions, knowledge gaps, and future research directions. The studies parallelized models using random-sampling-based algorithms or optimization algorithms and achieved significant speedup gain and efficiency. However, the speedup gain and efficiency decrease as the number of parallel processing units increases, especially after a certain threshold. Various combinations of hydrologic models, optimization algorithms, parallelization strategies, architectures, and communication modes need to be explored to improve speedup gain, efficiency, and solution quality. A standardized set of performance evaluation metrics should be developed to assess parallelization approaches. Interactive multiobjective optimization algorithms and integrated sensitivity analysis and calibration algorithms can also be potential future research areas.
ENVIRONMENTAL MODELLING & SOFTWARE
(2022)
Editorial Material
Environmental Sciences
Martyn P. Clark, Richard M. Vogel, Jonathan R. Lamontagne, Naoki Mizukami, Wouter J. M. Knoben, Guoqiang Tang, Shervan Gharari, Jim E. Freer, Paul H. Whitfield, Kevin R. Shook, Simon Michael Papalexiou
Summary: This commentary critically evaluates the use of popular performance metrics in hydrologic modeling, emphasizing the substantial sampling uncertainty in the NSE and KGE estimators. The importance of quantifying this uncertainty when selecting and comparing models is highlighted to improve the estimation, interpretation, and use of performance metrics in hydrologic modeling.
WATER RESOURCES RESEARCH
(2021)
Article
Engineering, Chemical
Bianca Williams, Selen Cremaschi
Summary: This study evaluates the performance of eight surrogate modeling techniques in surface approximation and surrogate-based optimization, finding that multivariate adaptive regression spline models and Gaussian process regression provide the most accurate predictions for surface approximation. Additionally, random forests, support vector machine regression, and Gaussian process regression models are the most reliable for identifying optimum locations and values in surrogate-based optimization.
CHEMICAL ENGINEERING RESEARCH & DESIGN
(2021)
Article
Geochemistry & Geophysics
Zhong Peng, Bo Yang, Yixian Xu, Feng Wang, Lian Liu, Yi Zhang
Summary: This article proposes an algorithm that uses neural operators to solve the EM data modeling problem in the frequency domain. By introducing an extended Fourier neural operator, the calculation speed is at least 100 times faster than the conventional FDM solver while maintaining good precision. The proposed method has great potential as a general rapid surrogate forward solver in EM data inversion scheme, as demonstrated in the testing of 2D and 3D magnetotelluric data modeling problems.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Computer Science, Interdisciplinary Applications
Ren Kai Tan, Chao Qian, Michael Wang, Wenjing Ye
Summary: The study proposes a solution to reduce the training cost of artificial-neural-network (ANN)-based surrogate models by reducing the number of numerical simulations during training data generation. The solution utilizes a Mapping Network to map a coarse field to a fine field, generating fine-scale training data.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2022)
Article
Mechanics
Jose Eduardo Gubaua, Gabriela Wessling Oening Dicati, Thiago da Silva, Eduardo Marcio de Oliveira Lopes, Jucelio Tomas Pereira, Carlos Alberto Bavastri
Summary: The finite element method is a useful numerical tool for solving engineering problems by simulating the behavior of structures under specific boundary conditions. The finite element method update (FEMU) is a systematic approach that aims to reduce the differences between numerical and experimental dynamic behavior by optimizing the structural or material parameters. This study successfully characterizes the dynamic behaviors of structures using the FEMU methodology.
Article
Engineering, Environmental
Jinsu Kim, Moon-Kyung Cho, Myungwon Jung, Jeeeun Kim, Young-Seek Yoon
Summary: This study investigates the dezincification behavior of a commercial-scale rotary hearth furnace used to recycle byproduct dust. A mathematical model was developed to analyze the temperature and solid weight distribution, and it showed good agreement with operational data. The study identified six factors that affect dezincification ratio and proposed optimal solutions using the mathematical model.
CHEMICAL ENGINEERING JOURNAL
(2023)
Article
Computer Science, Interdisciplinary Applications
Juliane Mai, James R. Craig, Bryan A. Tolson
Summary: This article provides a simple algorithm for randomly sampling a set of weights with their sum constrained to be equal to one. The algorithm has potential applications in calibration, uncertainty analysis, and sensitivity analysis of environmental models. The author demonstrates the efficiency and superiority of the proposed method compared to alternative sampling methods through three example applications.
ENVIRONMENTAL MODELLING & SOFTWARE
(2022)
Article
Environmental Sciences
Hongren Shen, Bryan A. Tolson, Juliane Mai
Summary: This study empirically assesses how different data splitting methods influence post-validation model testing period performance in hydrological modeling. The findings suggest that calibrating to older data and then validating models on newer data produces inferior model testing period performance, while calibrating to the full available data and skipping model validation is the most robust split-sample decision. The experimental findings remain consistent across different factors and strongly support revising the traditional split-sample approach in hydrological modeling.
WATER RESOURCES RESEARCH
(2022)
Article
Multidisciplinary Sciences
Juliane Mai, James R. Craig, Bryan A. Tolson, Richard Arsenault
Summary: The sensitivity of streamflow simulations to different hydrologic processes is analyzed in this study, using a novel analysis method that considers both parametric and structural uncertainties. The results show that quickflow is the most sensitive process for streamflow simulations across North America. Approximations of model process and parameter sensitivities are derived based on physiographic and climatologic data, and detailed spatio-temporal inputs and results are shared through an interactive website.
NATURE COMMUNICATIONS
(2022)
Article
Water Resources
Howard S. Wheater, John W. Pomeroy, Alain Pietroniro, Bruce Davison, Mohamed Elshamy, Fuad Yassin, Prabin Rokaya, Abbas Fayad, Zelalem Tesemma, Daniel Princz, Youssef Loukili, Chris M. DeBeer, Andrew M. Ireson, Saman Razavi, Karl-Erich Lindenschmidt, Amin Elshorbagy, Matthew MacDonald, Mohamed Abdelhamed, Amin Haghnegahdar, Ala Bahrami
Summary: Cold regions play a crucial role in providing water resources globally, but are facing rapid changes due to climate warming. Modelling the hydrology of these regions is challenging due to limited ground-based data and complex hydrological processes, controlled by phase change energetics. Recent developments in modelling technology, such as the MESH scheme in Canada, aim to improve representations of cold region processes and water management, though challenges in predicting accurately remain.
HYDROLOGICAL PROCESSES
(2022)
Article
Water Resources
Saman Razavi, David M. Hannah, Amin Elshorbagy, Sujay Kumar, Lucy Marshall, Dimitri P. Solomatine, Amin Dezfuli, Mojtaba Sadegh, James Famiglietti
Summary: Machine learning applications in Earth and environmental sciences have evolved separately from traditional process-based modeling paradigms. Overcoming cultural barriers and exploring the strengths and weaknesses of both approaches are essential for developing a coevolutionary approach to model building.
HYDROLOGICAL PROCESSES
(2022)
Article
Environmental Sciences
Mohammad Mohammadlou, Abdolreza Bahremand, Daniel Princz, Nicholas Kinar, Amin Haghnegahdar, Saman Razavi
Summary: The Global Environmental Multiscale Model (GEM) is compared with observed data in Iran to evaluate its forecasting accuracy, showing good agreement with daily temperature outputs and slight differences in annual precipitation outputs. The model's elevation also differs from observed data, but this can be corrected using environmental lapse rates.
NATURAL RESOURCE MODELING
(2022)
Article
Environmental Sciences
Kailong Li, Guohe Huang, Shuo Wang, Saman Razavi, Xiaoyue Zhang
Summary: This study proposes a joint probabilistic rainfall-runoff model (JPRR) that effectively simulates high-to-extreme flow and outperforms conventional machine learning models. The study also highlights the importance of copulas with right tail dependence in streamflow simulations, particularly in mountainous basins. Furthermore, the research suggests that flood risks may be underestimated by traditional machine learning models under changing climatic conditions.
WATER RESOURCES RESEARCH
(2022)
Article
Environmental Sciences
Amin Dezfuli, Saman Razavi, Benjamin F. Zaitchik
Summary: This study analyzes the impact of climate change on environmental, agricultural, water conflict, and public health issues in the Middle East. The climate model projections suggest that the region will experience reduced precipitation, increased temperature, and enhanced interannual variability of precipitation, especially in the Tigris-Euphrates headwaters. The findings highlight the risks to the viability of the Southeastern Anatolia Project and downstream water security, as well as the potential for water-related conflicts and migration in the region.
Article
Multidisciplinary Sciences
Heidi Kreibich, Anne F. Van Loon, Kai Schroeter, Philip J. Ward, Maurizio Mazzoleni, Nivedita Sairam, Guta Wakbulcho Abeshu, Svetlana Agafonova, Amir AghaKouchak, Hafzullah Aksoy, Camila Alvarez-Garreton, Blanca Aznar, Laila Balkhi, Marlies H. Barendrecht, Sylvain Biancamaria, Liduin Bos-Burgering, Chris Bradley, Yus Budiyono, Wouter Buytaert, Lucinda Capewell, Hayley Carlson, Yonca Cavus, Anais Couasnon, Gemma Coxon, Ioannis Daliakopoulos, Marleen C. de Ruiter, Claire Delus, Mathilde Erfurt, Giuseppe Esposito, Didier Francois, Frederic Frappart, Jim Freer, Natalia Frolova, Animesh K. Gain, Manolis Grillakis, Jordi Oriol Grima, Diego A. Guzman, Laurie S. Huning, Monica Ionita, Maxim Kharlamov, Dao Nguyen Khoi, Natalie Kieboom, Maria Kireeva, Aristeidis Koutroulis, Waldo Lavado-Casimiro, Hong-Yi Li, Maria Carmen LLasat, David Macdonald, Johanna Mard, Hannah Mathew-Richards, Andrew McKenzie, Alfonso Mejia, Eduardo Mario Mendiondo, Marjolein Mens, Shifteh Mobini, Guilherme Samprogna Mohor, Viorica Nagavciuc, Thanh Ngo-Duc, Thi Thao Nguyen Huynh, Pham Thi Thao Nhi, Olga Petrucci, Hong Quan Nguyen, Pere Quintana-Segui, Saman Razavi, Elena Ridolfi, Jannik Riegel, Md Shibly Sadik, Elisa Savelli, Alexey Sazonov, Sanjib Sharma, Johanna Sorensen, Felipe Augusto Arguello Souza, Kerstin Stahl, Max Steinhausen, Michael Stoelzle, Wiwiana Szalinska, Qiuhong Tang, Fuqiang Tian, Tamara Tokarczyk, Carolina Tovar, Thi Van Thu Tran, Marjolein H. J. Van Huijgevoort, Michelle T. H. van Vliet, Sergiy Vorogushyn, Thorsten Wagener, Yueling Wang, Doris E. Wendt, Elliot Wickham, Long Yang, Mauricio Zambrano-Bigiarini, Gunter Bloschl, Giuliano Di Baldassarre
Summary: Risk management can reduce the impacts of floods and droughts, but faces difficulties in managing unprecedented events of a greater magnitude. Improved risk management and integrated management can help lower the impacts of more hazardous events.
Article
Engineering, Civil
Kailong Li, Guohe Huang, Shuo Wang, Saman Razavi
Summary: Data-driven hydrological modeling has rapidly developed in recent years due to its flexibility in approximating complex relationships between driving forces and hydrological fluxes. This study proposes a baseflow-filtered hydrological inference model to understand hydrological processes in irrigated watersheds. The model separates the streamflow process into two sub-processes and simulates each sub-process using a new interpretable data-driven model. The model outperforms conventional data-driven models and identifies the dominant factors influencing flows in saturated and unsaturated zones. Important predictors include air temperature, long-term and short-term irrigation, and precipitation. The accuracy of the hydrological inference is demonstrated through sensitivity analysis.
JOURNAL OF HYDROLOGY
(2022)
Article
Water Resources
Mahdi Sedighkia, Bithin Datta, Saman Razavi
Summary: Thermal pollution caused by large dams altering the natural temperature of downstream river ecosystems is an important environmental concern. This study introduces a simulation-optimization framework to mitigate thermal pollution downstream from reservoirs, and tests it on a real-world case study. The results demonstrate the need for balancing water temperature regulation and water supply objectives in reservoir operation to protect downstream habitats.
WATER QUALITY RESEARCH JOURNAL
(2022)
Article
Environmental Sciences
Martin Gauch, Frederik Kratzert, Oren Gilon, Hoshin Gupta, Juliane Mai, Grey Nearing, Bryan Tolson, Sepp Hochreiter, Daniel Klotz
Summary: Building accurate rainfall-runoff models is crucial in hydrological science and practice. In this study, expert opinions were compared with quantitative metrics, and it was found that experts generally agreed with the metrics and showed a preference for Machine Learning models over traditional hydrological models. Although there were inconsistencies in expert opinions, where there was agreement, the opinions could be predicted from the quantitative metrics.
WATER RESOURCES RESEARCH
(2023)
Article
Meteorology & Atmospheric Sciences
Mohamed S. Abdelhamed, Mohamed E. Elshamy, Saman Razavi, Howard S. Wheater
Summary: This study applies the MESH-CLASS modeling framework to three regions in the Mackenzie River Basin using various meteorological forcing data sets. The results show complex trade-offs in the selection of forcing data sets and the need for bias correction in current and future scenarios. The study identifies influential model parameters and reveals that permafrost simulation is most sensitive to parameters controlling surface insulation and runoff generation, but many of these parameters are unidentifiable, highlighting the challenges in model parameterization.
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
(2023)
Article
Geosciences, Multidisciplinary
Mohammad Ghoreishi, Amin Elshorbagy, Saman Razavi, Guenter Bloeschl, Murugesu Sivapalan, Ahmed Abdelkader
Summary: This paper examines the conflict-and-cooperation phenomena in the Eastern Nile River basin and proposes a quantitative model to represent the main factors influencing willingness to cooperate at both the national and river basin scales. The findings suggest that political stability and foreign direct investment contribute to the changing cooperation patterns in the basin. However, long-term lack of trust among riparian countries hinders basin-wide cooperation. Although the proposed model has limitations, it provides a quantitative representation of cooperation pathways and can be used to analyze the effects of future management decisions on conflict and cooperation in the basin.
HYDROLOGY AND EARTH SYSTEM SCIENCES
(2023)
Article
Geosciences, Multidisciplinary
Juliane Mai, Hongren Shen, Bryan A. Tolson, Etienne Gaborit, Richard Arsenault, James R. Craig, Vincent Fortin, Lauren M. Fry, Martin Gauch, Daniel Klotz, Frederik Kratzert, Nicole O'Brien, Daniel G. Princz, Sinan Rasiya Koya, Tirthankar Roy, Frank Seglenieks, Narayan K. Shrestha, Andre G. T. Temgoua, Vincent Vionnet, Jonathan W. Waddell
Summary: This study conducted a model intercomparison to compare different model setups in simulating outputs in the Great Lakes region. The results showed that the machine-learning-based model performed the best in simulating streamflow, while the locally calibrated models and regionally calibrated models showed varying performances in different areas. The study also compared additional model outputs, such as evapotranspiration, soil moisture, and snow water equivalent, against gridded reference datasets.
HYDROLOGY AND EARTH SYSTEM SCIENCES
(2022)