Article
Meteorology & Atmospheric Sciences
Jeffrey S. Whitaker, Anna Shlyaeva, Stephen G. Penny
Summary: This study compares two methods for incorporating a time-invariant, high-rank covariance estimate in an ensemble-based data assimilation system: the hybrid-covariance approach and the hybrid-gain approach. The results show that the simpler and less expensive hybrid-gain approach can achieve similar performance if the incremental normal-mode balance constraint applied to the ensemble-part of the hybrid-covariance update is turned off.
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
(2022)
Article
Meteorology & Atmospheric Sciences
Troy Arcomano, Istvan Szunyogh, Alexander Wikner, Jaideep Pathak, Brian R. Hunt, Edward Ott
Summary: This paper describes the implementation of a combined hybrid-parallel prediction approach on a low-resolution atmospheric global circulation model. The hybrid model, which combines a physics-based numerical model with a machine learning component, produces more accurate forecasts for various atmospheric variables compared to the host model. Furthermore, the hybrid model exhibits smaller systematic errors and more realistic temporal variability in simulating the climate.
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
(2022)
Article
Multidisciplinary Sciences
Kevin Raeder, Timothy J. Hoar, Mohamad El Gharamti, Benjamin K. Johnson, Nancy Collins, Jeffrey L. Anderson, Jeff Steward, Mick Coady
Summary: An ensemble Kalman filter reanalysis data set with a global, 80 member ensemble spanning from 2011 to 2019 is archived, providing opportunities for robust statistical analysis and machine learning training.
SCIENTIFIC REPORTS
(2021)
Article
Environmental Sciences
G. Piazzi, G. Thirel, C. Perrin, O. Delaigue
Summary: Skillful streamflow forecasts are crucial for water-related applications, with a growing emphasis on improving initial condition estimates through data assimilation. This study assesses the sensitivity of DA-based IC estimation to various uncertainties and model updates over 232 watersheds in France. The comparison of two ensemble-based techniques shows that accurate routing store estimates benefit the DA-based IC estimation, with the EnKF outperforming the PF in forecasting meteorological uncertainty.
WATER RESOURCES RESEARCH
(2021)
Article
Meteorology & Atmospheric Sciences
Haohao Sun, Lili Lei, Zhengyu Liu, Liang Ning, Zhe-Min Tan
Summary: An analog offline ensemble Kalman filter (AOEnKF) is proposed, which constructs ensemble priors from a control climate simulation for each assimilation time based on an analog criterion using proxy observations. AOEnKF generates smaller posterior errors and requires much less computational cost compared to the online cycling EnKF (CEnKF). It has the advantages of having a more accurate prior ensemble mean and flow-dependent background error covariances compared to the commonly applied offline EnKF (OEnKF).
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
(2022)
Article
Meteorology & Atmospheric Sciences
Lars Nerger
Summary: The study introduces a hybrid filter combining LETKF and NETF with the performance improved by adjusting the hybrid weight. Results show that a hybrid variant applying NETF followed by LETKF yields the best results in complex nonlinear models. Calculating the hybrid weight based on skewness, kurtosis, and effective sample size reduces estimation errors and enhances stability of the hybrid filter.
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY
(2022)
Article
Water Resources
Andrew Pensoneault, Witold F. Krajewski, Nicolas Velasquez, Xueyu Zhu, Ricardo Mantilla
Summary: This paper discusses the application of data assimilation techniques in hydrology, focusing on the potential of EnKF and its extensions in sequential state estimation and Bayesian inverse problems. The authors improve the streamflow in a virtual catchment using the EKI algorithm and demonstrate its favorable performance.
ADVANCES IN WATER RESOURCES
(2023)
Article
Mathematics, Applied
Alexander Wikner, Jaideep Pathak, Brian R. Hunt, Istvan Szunyogh, Michelle Girvan, Edward Ott
Summary: This study discusses the forecasting of chaotic dynamical systems using noisy partial measurements data, with a focus on combining machine learning with knowledge-based models to improve predictions. By assimilating synthetic data and training machine learning models with partial measurements, it shows potential to correct imperfections in knowledge-based models and improve forecasting accuracy.
Article
Computer Science, Interdisciplinary Applications
Ashesh Chattopadhyay, Ebrahim Nabizadeh, Eviatar Bach, Pedram Hassanzadeh
Summary: Data assimilation (DA) is a crucial part of forecasting models, allowing for better estimation of initial conditions in imperfect dynamical systems using observations. Ensemble Kalman filter (EnKF) is a widely-used DA algorithm, but its computational complexity is problematic for large systems. In this study, a hybrid ensemble Kalman filter (H-EnKF) is proposed, utilizing a data-driven surrogate to generate a large ensemble and accurately compute the background error covariance matrix. H-EnKF outperforms EnKF without the need for ad-hoc localization strategies, making it applicable to high-dimensional systems.
JOURNAL OF COMPUTATIONAL PHYSICS
(2023)
Article
Engineering, Civil
Behmard Sabzipour, Richard Arsenault, Magali Troin, Jean-Luc Martel
Summary: Data assimilation is an important step in improving hydrological model predictions. This study aims to identify optimal seasonal parameterizations to reduce uncertainty in initial conditions in a snow-dominated catchment in Canada. Sensitivity analysis shows that forecast performance is sensitive to individual hyperparameters and the choice of state variables.
JOURNAL OF HYDROLOGY
(2023)
Article
Cardiac & Cardiovascular Systems
Dario De Marinis, Dominik Obrist
Summary: The proposed data assimilation methodology aims to enhance the spatial and temporal resolution of voxel-based data obtained from biomedical imaging modalities, specifically focusing on turbulent blood flow assessment in large vessels. The methodology, utilizing a Stochastic Ensemble Kalman Filter approach, combines observed flow fields with numerical simulations to improve the accuracy of flow field predictions. Validation against canonical flows and application to a clinically relevant scenario demonstrate the potential of the method to enhance 4D flow MRI data for future use.
FRONTIERS IN CARDIOVASCULAR MEDICINE
(2021)
Article
Geosciences, Multidisciplinary
Sammy Metref, Emmanuel Cosme, Matthieu Le Lay, Joel Gailhard
Summary: Accurately predicting seasonal streamflow supply is crucial for operating hydroelectric dams and avoiding hydrology-related hazard. However, scarce observation data and oversimplified physics representation may lead to significant forecast errors. This paper aims to improve predictions by assimilating snow observations, which has been rarely studied but has the potential to enhance the forecast accuracy.
HYDROLOGY AND EARTH SYSTEM SCIENCES
(2023)
Article
Engineering, Ocean
Shintaro Gomi, Tsutomu Takagi, Katsuya Suzuki, Rika Shiraki, Ichiya Ogino, Shigeru Asaumi
Summary: A control method for changing the geometry of a fishing net was proposed, utilizing data assimilation to estimate unknown parameters and achieve the intended net geometry. The automatic control system was validated through numerical simulation experiments, demonstrating the successful control of net geometry using the extended Kalman filter.
APPLIED OCEAN RESEARCH
(2021)
Article
Mechanics
Zhiwen Deng, Chuangxin He, Yingzheng Liu
Summary: This paper focuses on the optimal sensor placement strategy based on a deep neural network for turbulent flow recovery within the data assimilation framework of the ensemble Kalman filter. The results demonstrate the effectiveness and robustness of the proposed strategy, showing that RANS models with EnKF augmentation were substantially improved over their original counterparts. The study concludes that the DNN-based OSP with the selection of the five most sensitive sensors can efficiently reduce the number of sensors while achieving similar or better assimilated performance.
Article
Engineering, Civil
Teng Xu, J. Jaime Gomez-Hernandez, Zi Chen, Chunhui Lu
Summary: Understanding a contaminant source is crucial for managing a polluted aquifer, but source information may be unavailable when pollutants are detected. The Ensemble Smoother with Multiple Data Assimilation (ES-MDA) is proposed as a more efficient solution than the restart Ensemble Kalman Filter (r-EnKF), but requires a large number of assimilations to achieve the same level of accuracy.
JOURNAL OF HYDROLOGY
(2021)
Article
Water Resources
C. Wannasin, C. C. Brauer, R. Uijlenhoet, W. J. van Verseveld, A. H. Weerts
Summary: The study focuses on how reservoir operation affects water balance, daily flow regime, and extreme flows in the upper region of the Greater Chao Phraya River (GCPR) basin in Thailand. Results show that reservoir operation led to increased evaporation, altered flow seasonality, and smoothed daily flow regime, while mitigating historical extreme flow incidents. Effective decision making for real-time operation is crucial for water resource management in the region.
JOURNAL OF HYDROLOGY-REGIONAL STUDIES
(2021)
Article
Water Resources
C. Wannasin, C. C. Brauer, R. Uijlenhoet, W. J. van Verseveld, A. H. Weerts
Summary: This study introduces a global-data-driven hydrological model for estimating daily streamflow in the upper Greater Chao Phraya River basin, showing good performance especially for natural catchments. The model provides an opportunity for streamflow estimation in other ungauged or data-scarce basins in Southeast Asia. However, difficulties in reservoir system modeling highlight the need for a better understanding of human intervention on daily streamflow.
JOURNAL OF HYDROLOGY-REGIONAL STUDIES
(2021)
Article
Water Resources
Peter T. La Follette, W. Jesse Hahm, Daniella M. Rempe, William E. Dietrich, Claudia C. Brauer, Albrecht H. Weerts, David N. Dralle
Summary: This study investigates the significance of deeper unsaturated zone water storage, specifically in weathered bedrock vadose zone, for flow generation. The authors find that the majority of plant available water in seasonally dry landscapes is stored in the weathered bedrock vadose zone, which mediates recharge to groundwater systems and supports streamflow. However, there is a lack of observations and explicit representations of these processes in runoff models. The authors develop a simple representation of the weathered bedrock vadose zone and calibrate it using different scenarios, finding that using Pareto optimal parameter sets can accurately simulate dynamics in rock moisture and streamflow. They also find that calibration on streamflow alone is insufficient and that the choice of calibration scenario affects model parameter uncertainty. These findings suggest that incorporating rock moisture data in calibration can improve model accuracy and reduce parameter uncertainty.
HYDROLOGICAL PROCESSES
(2022)
Article
Environmental Sciences
R. O. Imhoff, C. C. Brauer, K. J. van Heeringen, R. Uijlenhoet, A. H. Weerts
Summary: In this study, radar rainfall nowcasting was used to construct discharge forecasts for Dutch catchments. The results showed that both rainfall and discharge forecast errors increase with increasing rainfall intensity and spatial variability. The performance of discharge forecasts depends on the initial conditions, with faster increase in forecast error for shallow groundwater table. Among the tested algorithms, Rainymotion DenseRotation, Pysteps deterministic, and probabilistic methods outperformed the others in discharge forecasting. The study also found that the threshold exceedance forecasts provided advanced warning compared to no rainfall forecasts, with PS-D and PS-P methods producing lower false alarm ratio and inconsistency index values.
WATER RESOURCES RESEARCH
(2022)
Article
Engineering, Civil
Bingxin Bai, Yumin Tan, Gennadii Donchyts, Arjen Haag, Bo Xu, Ge Chen, Albrecht H. Weerts
Summary: This paper proposes a surface water gap-filling method based on Naive Bayes classification. It uses historical data and uncontaminated pixels to fill spatial gaps in surface water images, achieving high-accuracy surface water monitoring and water level measurement.
JOURNAL OF HYDROLOGY
(2023)
Article
Water Resources
Dian Nur Ratri, Albrecht Weerts, Robi Muharsyah, Kirien Whan, Albert Klein Tank, Edvin Aldrian, Mugni Hadi Hariadi
Summary: This study focuses on the skill of streamflow forecasts in the Citarum river basin using the Empirical Quantile Mapping corrected ECMWF SEAS5 model. The findings show that the model provides accurate and practical predictions during the agriculturally important months of July to October in Java. This study contributes new hydrological insights for the region.
JOURNAL OF HYDROLOGY-REGIONAL STUDIES
(2023)
Article
Water Resources
Alberto Assis dos Reis, Albrecht Weerts, Maria-Helena Ramos, Fredrik Wetterhall, Wilson dos Santos Fernandes
Summary: The proposed blending method, which uses the uncertainty of the original datasets to define the weighting factors, was efficient in generating a precipitation product that performs better than each dataset separately when used to force a hydrological model. The use of the double-mass curve correlation to extend the time series of the datasets beyond their common period allowed us to produce long time series of precipitation for South American basins and hydrological applications. The study shows that it is possible to rely on river discharge data and hydrological modeling to select and combine different precipitation products in the region and presents a step-by-step methodology to do so.
JOURNAL OF HYDROLOGY-REGIONAL STUDIES
(2022)
Article
Engineering, Civil
Junfu Gong, Albrecht H. Weerts, Cheng Yao, Zhijia Li, Yingchun Huang, Yuanfang Chen, Yifei Chang, Pengnian Huang
Summary: This study utilizes the EnKF assimilation scheme and employs the maximum a posteriori estimation method (MAP) to estimate error models, aiming to improve the performance of flood simulation in small and medium-sized catchments. Two catchments in China with different characteristics were tested, and the performance differences under two types of rainfall forcing were compared. The results show that MAP is beneficial in specifying error models and providing reliable ensemble spread, effectively ameliorating the degradation of distributed hydrological model performance due to uncalibrated model parameters and/or poor quality of input data.
JOURNAL OF HYDROLOGY
(2023)
Article
Meteorology & Atmospheric Sciences
Ruben O. Imhoff, Lesley De Cruz, Wout Dewettinck, Claudia C. Brauer, Remko Uijlenhoet, Klaas-Jan van Heeringen, Carlos Velasco-Forero, Daniele Nerini, Michiel Van Ginderachter, Albrecht H. Weerts
Summary: Flash flood early warning requires accurate and timely rainfall forecasts. Radar rainfall nowcasting combined with numerical weather prediction (NWP) forecasts can be used to extend the lead time of short-term rainfall forecasts. In this study, an adaptive ensemble blending method was implemented to combine extrapolation nowcasts, NWP forecasts, and noise components with skill-dependent weights. The method was evaluated using heavy rainfall events in Belgian and Dutch catchments and compared with other forecast methods. The results showed that the blending approach performs similarly or better than only nowcasting and adds value compared to NWP for the first hours of the forecast.
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY
(2023)
Article
Engineering, Environmental
S. R. Rusli, V. F. Bense, A. Taufiq, A. H. Weerts
Summary: In this study, the authors used a combination of the Wflow_sbm hydrological model and the MODFLOW groundwater model to assess groundwater storage changes in the Bandung groundwater basin. They compared the calculated soil moisture storage change and groundwater storage change to the water storage change estimated by the GRACE satellite. The results showed a moderate correlation and highlighted the importance of considering local groundwater information in basin-scale assessments.
GROUNDWATER FOR SUSTAINABLE DEVELOPMENT
(2023)
Article
Remote Sensing
Bingxin Bai, Yumin Tan, Kailei Zhou, Gennadii Donchyts, Arjen Haag, Albrecht H. Weerts
Summary: In this study, a method for filling gaps in optical satellite-derived surface water monitoring images is proposed, which effectively utilizes the spatiotemporal characteristics of water to correct and mosaic historical water images, achieving good results.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2022)
Article
Geosciences, Multidisciplinary
Jerom P. M. Aerts, Rolf W. Hut, Nick C. van de Giesen, Niels Drost, Willem J. van Verseveld, Albrecht H. Weerts, Pieter Hazenberg
Summary: This study investigates the spatial scaling in distributed hydrological modelling and evaluates the streamflow estimates at different spatial resolutions. The results show that finer spatial resolution does not necessarily improve the accuracy of streamflow estimates. Although there are statistical differences among the three model instances, the conclusion is inconclusive due to high uncertainties in the sampling. The results also indicate significant differences between model instances, providing research directions for studying the changes in flux and state partitioning in hyper-resolution hydrological modelling.
HYDROLOGY AND EARTH SYSTEM SCIENCES
(2022)
Article
Geosciences, Multidisciplinary
Laurene J. E. Bouaziz, Emma E. Aalbers, Albrecht H. Weerts, Mark Hegnauer, Hendrik Buiteveld, Rita Lammersen, Jasper Stam, Eric Sprokkereef, Hubert H. G. Savenije, Markus Hrachowitz
Summary: This study tests the sensitivity of hydrological model predictions to changes in vegetation parameters reflecting ecosystem adaptation to climate and potential land use changes. By integrating a time-dynamic representation of changing vegetation properties, the study aims to provide more reliable hydrological predictions under change.
HYDROLOGY AND EARTH SYSTEM SCIENCES
(2022)
Article
Water Resources
Frederiek C. Sperna Weiland, Robrecht D. Visser, Peter Greve, Berny Bisselink, Lukas Brunner, Albrecht H. Weerts
Summary: Ensemble projections of future changes in discharge over Europe show large variation, with different weighting approaches influencing the consistency of the projections. Weighting methods favored projections from the same General Circulation Models, but high weights obtained through past good performance can provide deviating projections for the future. In Central Europe, differences between models become more pronounced, highlighting the need for robust weighting methods to increase the reliability of change signals.
FRONTIERS IN WATER
(2021)
Article
Geosciences, Multidisciplinary
Dirk Eilander, Willem van Verseveld, Dai Yamazaki, Albrecht Weerts, Hessel C. Winsemius, Philip J. Ward
Summary: The IHU method efficiently upscales high-resolution flow direction data to the coarser resolutions of distributed hydrological models while preserving the upstream-downstream relationship of river structure, achieving improved accuracy in model simulations.
HYDROLOGY AND EARTH SYSTEM SCIENCES
(2021)
Article
Computer Science, Interdisciplinary Applications
Yapo Abole Serge Innocent Oboue, Yunfeng Chen, Sergey Fomel, Wei Zhong, Yangkang Chen
Summary: Strong noise can disrupt the recorded seismic waves and negatively impact subsequent seismological processes. To improve the signal-to-noise ratio (S/N) of seismological data, we introduce MATamf, an open-source MATLAB code package based on an advanced median filter (AMF) that simultaneously attenuates various types of noise and improves S/N. Experimental results demonstrate the usefulness and advantages of the proposed AMF workflow in enhancing the S/N of a wide range of seismological applications.
COMPUTERS & GEOSCIENCES
(2024)
Article
Computer Science, Interdisciplinary Applications
Upkar Singh, P. N. Vinayachandran, Vijay Natarajan
Summary: The Bay of Bengal maintains its salinity distribution due to the cyclic flow of high salinity water and the mixing with freshwater. This paper introduces an advection-based feature definition and algorithms to track the movement of high salinity water, validated through comparison with observed data.
COMPUTERS & GEOSCIENCES
(2024)
Article
Computer Science, Interdisciplinary Applications
Bijal Chudasama, Nikolas Ovaskainen, Jonne Tamminen, Nicklas Nordback, Jon Engstro, Ismo Aaltonen
Summary: This contribution presents a novel U-Net convolutional neural network (CNN)-based workflow for automated mapping of bedrock fracture traces from aerial photographs acquired by unmanned aerial vehicles (UAV). The workflow includes training a U-Net CNN using a small subset of photographs with manually traced fractures, semantic segmentation of input images, pixel-wise identification of fracture traces, ridge detection algorithm and vectorization. The results show the effectiveness and accuracy of the workflow in automated mapping of bedrock fracture traces.
COMPUTERS & GEOSCIENCES
(2024)
Article
Computer Science, Interdisciplinary Applications
Ruizhen Wang, Siyang Wan, Weitao Chen, Xuwen Qin, Guo Zhang, Lizhe Wang
Summary: This paper proposes a novel framework to generate a finer soil strength map based on RCI, which uses ensemble learning models to obtain USCS soil classification and predict soil moisture, in order to improve the resolution and reliability of existing soil strength maps.
COMPUTERS & GEOSCIENCES
(2024)
Article
Computer Science, Interdisciplinary Applications
Zhanlong Chen, Xiaochuan Ma, Houpu Li, Xuwei Xu, Xiaoyi Han
Summary: Simulated terrains are important for landform and terrain research, disaster prediction, rescue and disaster relief, and national security. This study proposes a deep learning method, IGPN, that integrates global information and pattern features of the local terrain to generate accurate simulated terrains quickly.
COMPUTERS & GEOSCIENCES
(2024)
Article
Computer Science, Interdisciplinary Applications
Daniele Secci, Vanessa A. Godoy, J. Jaime Gomez-Hernandez
Summary: Neural networks excel in various machine learning applications, but lack physical interpretability and constraints, limiting their accuracy and reliability in predicting complex physical systems' behavior. Physics-Informed Neural Networks (PINNs) integrate neural networks with physical laws, providing an effective tool for solving physical problems. This article explores recent developments in PINNs, emphasizing their application in solving unconfined groundwater flow, and discusses challenges and opportunities in this field.
COMPUTERS & GEOSCIENCES
(2024)
Article
Computer Science, Interdisciplinary Applications
Renguang Zuo, Ying Xu
Summary: This study proposes a hybrid deep learning model consisting of a one-dimensional convolutional neural network (1DCNN) and a graph convolutional network (GCN) to extract joint spectrum-spatial features from geochemical survey data for mineral exploration. The physically constrained hybrid model performs better in geochemical anomaly recognition compared to other models.
COMPUTERS & GEOSCIENCES
(2024)
