Article
Environmental Sciences
Camille Garnaud, Vincent Vionnet, Etienne Gaborit, Vincent Fortin, Bernard Bilodeau, Marco Carrera, Dorothy Durnford
Summary: The study presents a new snow analysis method within the Canadian NSRPS system, which improves the quality of snow analysis and has a significant impact on water conservation. The new method enhances river flow simulations and lays the groundwork for producing reliable snow analyses in the future.
Article
Geosciences, Multidisciplinary
Rebecca Mott, Adam Winstral, Bertrand Cluzet, Nora Helbig, Jan Magnusson, Giulia Mazzotti, Louis Queno, Michael Schirmer, Clare Webster, Tobias Jonas
Summary: The Swiss Operational Snow-hydrological (OSHD) model system is developed to provide daily analysis and forecasts on snow cover dynamics throughout Switzerland. It utilizes station data, meteorological forcing data, and reanalysis products to combine snow modeling with advanced data assimilation and meteorological downscaling methods. The system offers models of varying complexity, tailored to specific modeling strategies and applications.
FRONTIERS IN EARTH SCIENCE
(2023)
Article
Environmental Sciences
Jude L. Musuuza, Louise Crochemore, Ilias G. Pechlivanidis
Summary: Earth observations (EOs) are a valuable complement to in situ measurements in hydrology because they provide information in locations where direct measurements are unavailable or prohibitively expensive to make. Recent advances have enabled the assimilation of data sets of different physical variables into hydrological models to better estimate states and fluxes.
WATER RESOURCES RESEARCH
(2023)
Article
Engineering, Civil
Nicola Di Marco, Diego Avesani, Maurizio Righetti, Mattia Zaramella, Bruno Majone, Marco Borga
Summary: This study introduces a general multi-objective parameter estimation framework to reduce predictive streamflow uncertainty in snow-dominated catchments using MODIS-based snow cover maps, with positive feedback between streamflow and snow cover area likelihoods observed in the results. The potential of this approach is confirmed by operational quality data from two mountainous basins in the eastern Italian Alps.
JOURNAL OF HYDROLOGY
(2021)
Article
Engineering, Civil
Patrice Dion, Jean-Luc Martel, Richard Arsenault
Summary: This study proposes a methodology based on a multi-hydrological model approach, addressing the biases and under-dispersion issues in ensemble streamflow predictions. By assimilating data and post-processing individual ESPs, the methodology successfully improves reliability in short-term hydrological forecasts through a multi-model approach.
JOURNAL OF HYDROLOGY
(2021)
Article
Engineering, Civil
Rajesh Maddu, Indranil Pradhan, Ebrahim Ahmadisharaf, Shailesh Kumar Singh, Rehana Shaik
Summary: This study explores the relevance of large-scale climate phenomenon indices in improving short-term reservoir inflow prediction. A framework combining machine learning algorithms and climate variables is developed, and an ensemble model is created using a weighted voting method. The model consistently outperforms standalone algorithms in predicting high and low flows in two different reservoirs.
JOURNAL OF HYDROLOGY
(2022)
Article
Environmental Sciences
Timothy M. Lahmers, Sujay Kumar, Daniel Rosen, Aubrey Dugger, David J. Gochis, Joseph A. Santanello, Chandana Gangodagamage, Rocky Dunlap
Summary: The NASA LIS/WRF-Hydro system, combining the capabilities of the NASA Land Information System (LIS) and WRF-Hydro model, is used to analyze the Tuolumne River basin in California. Assimilation of NASA Airborne Snow Observatory (ASO) snow water equivalent (SWE) estimates was found to reduce snow and streamflow biases, and improve streamflow skill scores in wet and dry years.
WATER RESOURCES RESEARCH
(2022)
Article
Environmental Sciences
Shichao Xu, Yangbo Chen, Lixue Xing, Chuan Li
Summary: For reservoir basins, the complex conditions make inflow flood forecasting difficult and traditional models cannot meet required accuracy values. This study uses a physically based distributed hydrological model to design a high-precision inflow flood forecast scheme for the Baipenzhu Reservoir in Guangdong Province. The Liuxihe model shows strong applicability and accuracy in simulating the inflow flood process, with different DEM data sources impacting the model structure but still providing accurate results. The use of the Particle swarm optimization algorithm helps reduce model forecast uncertainty, leading to Grade A forecast schemes for real-time flood forecasting.
Article
Engineering, Civil
J. W. Pomeroy, T. Brown, X. Fang, K. R. Shook, D. Pradhananga, R. Armstrong, P. Harder, C. Marsh, D. Costa, S. A. Krogh, C. Aubry-Wake, H. Annand, P. Lawford, Z. He, M. Kompanizare, J. I. Lopez Moreno
Summary: CRHM is a flexible hydrological modeling platform that simulates hydrological processes and basin response in cold regions. It is suitable for various research and applications, including model validation, prediction, land use change and water quality analysis.
JOURNAL OF HYDROLOGY
(2022)
Review
Green & Sustainable Science & Technology
Sarmad Dashti Latif, Ali Najah Ahmed
Summary: This review paper explores the use of deep learning and machine learning algorithms for reservoir inflow prediction in hydrological forecasting. It analyzes the application of AI models in various hydrology sectors and focuses on the two primary categories of deep learning and machine learning. The study examines the long short-term memory deep learning method and three traditional machine learning algorithms, and provides a summary of the findings, benefits, and drawbacks discovered through literature reviews.
ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY
(2023)
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
Engineering, Civil
Zachary C. Herbert, Zeeshan Asghar, Carlos A. Oroza
Summary: The study introduces a new multi-step forecasting approach that significantly improves the long-term accuracy of reservoir inflow predictions by training an Encoder-Decoder algorithm with historical data. The optimal deep learning algorithm outperforms traditional statistical methods and the baseline model in long-term water supply forecasts.
JOURNAL OF HYDROLOGY
(2021)
Article
Engineering, Civil
Rodolfo Alvarado-Montero, Gokcen Uysal, Antonio-Juan Collados-Lara, A. Arda Sorman, David Pulido-Velazquez, Aynur Sensoy
Summary: This study compares the application of Ensemble Kalman Filter (EnKF) and Moving Horizon Estimation (MHE) for data assimilation in mountainous basins. The results show that MHE outperforms EnKF in streamflow and snow state predictions.
JOURNAL OF HYDROLOGY
(2022)
Article
Engineering, Civil
Di Liu, Ashok K. Mishra, Zhongbo Yu, Haishen Lu, Yajie Li
Summary: Groundwater is a crucial resource for various sectors, but its depletion is accelerating due to increased water demand, reduced rainfall, and rising temperatures. Therefore, developing predictive tools for water resource management is essential in addressing drought events.
JOURNAL OF HYDROLOGY
(2021)
Article
Environmental Sciences
Khaled Mohammed, Robert Leconte, Melanie Trudel
Summary: Soil moisture modeling is important for various applications, and assimilating soil moisture observations can improve the model performance. This study examines the impact of spatial and temporal data gaps on soil moisture modeling and streamflow modeling. The results indicate that the absence of root-zone soil moisture estimates from satellite data has the greatest impact on modeling performance. Temporal and horizontal spatial gaps in satellite data also have an impact, but to a lesser extent. Real-data experiments using the SMAP product improve soil moisture modeling in the upper soil layers, but not as much in the bottom soil layer. Assimilating observations also improves streamflow modeling in synthetic experiments, but not in real-data experiments.
Article
Computer Science, Interdisciplinary Applications
Yuqing Chang, Rolf J. Lorentzen, Geir Naevdal, Tao Feng
COMPUTATIONAL GEOSCIENCES
(2020)
Article
Computer Science, Interdisciplinary Applications
Rolf J. Lorentzen, Tuhin Bhakta, Dario Grana, Xiaodong Luo, Randi Valestrand, Geir Naevdal
COMPUTATIONAL GEOSCIENCES
(2020)
Article
Engineering, Civil
Xue Yang, Jan Magnusson, Shaochun Huang, Stein Beldring, Chong-Yu Xu
JOURNAL OF HYDROLOGY
(2020)
Article
Geochemistry & Geophysics
Kjersti Solberg Eikrem, Geir Naevdal, Morten Jakobsen
Summary: This work uses the Lippmann-Schwinger equation to model seismic waves in strongly scattering acoustic media, improving efficiency by developing new preconditioners based on randomized matrix approximations and hierarchical matrices. Experimental results demonstrate the excellent performance of the method on two 2-D models.
GEOPHYSICAL JOURNAL INTERNATIONAL
(2021)
Article
Environmental Sciences
Bikas Chandra Bhattarai, Olga Silantyeva, Aynom T. Teweldebrhan, Sigbjorn Helset, Ola Skavhaug, John F. Burkhart
Article
Geochemistry & Geophysics
Kui Xiang, Kjersti Solberg Eikrem, Morten Jakobsen, Geir Naevdal
Summary: In this study, a convergent scattering series solution for the frequency-domain wave equation in acoustic media with variable density and velocity has been derived. Through the homotopy analysis method, an iterative solution for the vectorial Lippmann-Schwinger equation has been obtained, achieving convergence even in strongly scattering media. The computational cost of the developed algorithm scales as N2 and involves a convergence control operator selected using hierarchical matrices.
GEOPHYSICAL PROSPECTING
(2022)
Article
Energy & Fuels
Dean S. Oliver, Kristian Fossum, Tuhin Bhakta, Ivar Sando, Geir Naevdal, Rolf Johan Lorentzen
Summary: Reservoir simulation models require a large number of parameters to predict future reservoir behavior while being constrained by various factors to reduce uncertainty. The use of seismic surveys to observe changes in reservoir properties between wells offers potential in reducing prediction uncertainty, but faces challenges in practical applications.
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
(2021)
Article
Biophysics
Geir Naevdal, Einar K. Rofstad, Kjetil Soreide, Steinar Evje
Summary: Pancreatic cancer has a propensity for early metastasis, even for small early stage tumors. A computer model simulating tumor progression suggests high interstitial fluid pressure (IFP) as a possible driver for metastasis, shedding light on the clinical aggressiveness of pancreatic cancer.
JOURNAL OF BIOMECHANICS
(2022)
Article
Geochemistry & Geophysics
Kui Xiang, Morten Jakobsen, Kjersti Solberg Eikrem, Geir Naevdal
Summary: The distorted Born iterative method reduces a nonlinear inverse scattering problem to (ill-posed) linear inverse scattering problems that can be solved using a regularized least-squares formulation. It has been applied to electromagnetic and acoustic problems in three dimensions and to seismic problems for moderately large two-dimensional models.
GEOPHYSICAL PROSPECTING
(2023)
Article
Engineering, Mechanical
Steinar Evje, Hans Joakim Skadsem, Geir Naevdal
Summary: Conservation laws of the generic form c(t)+f(c)(x)=0 are important in engineering, but identifying unknown flux functions from observation data is challenging. This study explores a Bayesian method combined with iterative ensemble Kalman filtering to learn unknown nonlinear flux functions. Experimental results demonstrate the method's strong ability to identify unknown flux functions.
NONLINEAR DYNAMICS
(2023)
Article
Geosciences, Multidisciplinary
Rebecca Mott, Adam Winstral, Bertrand Cluzet, Nora Helbig, Jan Magnusson, Giulia Mazzotti, Louis Queno, Michael Schirmer, Clare Webster, Tobias Jonas
Summary: The Swiss Operational Snow-hydrological (OSHD) model system is developed to provide daily analysis and forecasts on snow cover dynamics throughout Switzerland. It utilizes station data, meteorological forcing data, and reanalysis products to combine snow modeling with advanced data assimilation and meteorological downscaling methods. The system offers models of varying complexity, tailored to specific modeling strategies and applications.
FRONTIERS IN EARTH SCIENCE
(2023)
Article
Geography, Physical
Nora Helbig, Michael Schirmer, Jan Magnusson, Flavia Mader, Alec van Herwijnen, Louis Queno, Yves Buhler, Jeff S. Deems, Simon Gascoin
Summary: The study introduces a seasonal fSCA algorithm to track snow depth and snow water equivalent, using a scale-independent parameterization method to describe the spatial variability of snow cover in mountainous terrain. Through simulations and evaluations on different data sets, it is found that the seasonal fSCA algorithm can better represent seasonal trends.
Article
Geosciences, Multidisciplinary
John F. Burkhart, Felix N. Matt, Sigbjorn Helset, Yisak Sultan Abdella, Ola Skavhaug, Olga Silantyeva
Summary: Shyft is a hydrologic modeling software designed for streamflow forecasting in hydropower production environments and research. It enables rapid development and implementation of distributed hydrologic modeling with multiple configurations, while maintaining high computational performance and open-source availability for effective cooperation.
GEOSCIENTIFIC MODEL DEVELOPMENT
(2021)
Proceedings Paper
Engineering, Electrical & Electronic
Ulas Taskin, Kjersti Solberg Eikrem, Geir Naevdal, Morten Jakobsen, Dirk J. Verschuur, Koen W. A. van Dongen
PROCEEDINGS OF THE 2020 IEEE INTERNATIONAL ULTRASONICS SYMPOSIUM (IUS)
(2020)