Article
Environmental Sciences
Alvaro Ossandon, Balaji Rajagopalan, William Kleiber
Summary: We develop a Bayesian hierarchical modeling framework for flood risk attributes using generalized extreme value and Poisson distributions, with non-stationary parameters, and Gaussian copulas for capturing spatial dependence. The best covariates are selected using the WAIC. The framework enables the forecast of flood risk attributes at multiple gauges with useful long lead skill.
WATER RESOURCES RESEARCH
(2023)
Article
Geosciences, Multidisciplinary
Leah A. Jackson-Blake, Francois Clayer, Elvira de Eyto, Andrew S. French, Maria Dolores Frias, Daniel Mercado-Bettin, Tadhg Moore, Laura Puertolas, Russell Poole, Karsten Rinke, Muhammed Shikhani, Leon van der Linden, Rafael Marce
Summary: This study examines the value of seasonal forecasting for decision-making in extratropical regions and highlights the need to reduce forecast uncertainty and develop practical experience before incorporating forecasts into operational decision-making.
HYDROLOGY AND EARTH SYSTEM SCIENCES
(2022)
Article
Environmental Sciences
Pedram Darbandsari, Paulin Coulibaly
Summary: This study evaluates the impact of different hydrologic models on the performance of the hydrologic uncertainty processor (HUP) and proposes a multimodel Bayesian postprocessor (HUP-BMA). Results demonstrate the superiority of HUP-BMA in quantifying hydrologic uncertainty and forecasting compared to traditional HUP and BMA methods.
WATER RESOURCES RESEARCH
(2021)
Article
Economics
Zijian Zeng, Meng Li
Summary: A Bayesian median autoregressive model was developed for time series forecasting, utilizing time-varying quantile regression at the median and a Laplace error instead of Gaussian error. Model parameters were estimated using Markov chain Monte Carlo, with Bayesian model averaging and model selection used to address model uncertainty. The methods showed favorable predictive performance in real data applications.
INTERNATIONAL JOURNAL OF FORECASTING
(2021)
Article
Meteorology & Atmospheric Sciences
Zohreh Javanshiri, Maede Fathi, Seyedeh Atefeh Mohammadi
Summary: This study explores the use of ensemble forecasting in probabilistic weather forecasting, emphasizing the importance of statistical post-processing to improve forecast quality. Results show that BMA and EMOS-CSG techniques are successful in enhancing WRF ensemble forecasts, with BMA being more accurate, skillful, and reliable, while EMOS-CSG method exhibits better resolution in predicting high-precipitation events.
METEOROLOGICAL APPLICATIONS
(2021)
Article
Environmental Sciences
Alvaro Ossandon, Balaji Rajagopalan, Upmanu Lall, J. S. Nanditha, Vimal Mishra
Summary: The novel BHNM model leverages the spatial dependence induced by river network topology and hydrometeorological variables to improve ensemble forecasts of daily streamflow, demonstrating high skill in predicting monsoon period streamflow in Central India. Incorporating upstream flow information and precipitation as covariates allows for modeling spatial correlation of flows with parsimony. The validation results show that the BHNM model outperforms a null-model of generalized linear regression, highlighting its reliability and skillfulness in streamflow predictions.
WATER RESOURCES RESEARCH
(2021)
Article
Engineering, Multidisciplinary
Zhangkang Shu, Jianyun Zhang, Lin Wang, Junliang Jin, Ningbo Cui, Guoqing Wang, Zhouliang Sun, Yanli Liu, Zhenxin Bao, Cuishan Liu
Summary: In this study, a general ensemble framework based on Bayesian model averaging was developed to evaluate the impact of multi-source uncertainties in complex forecast systems. The study found that the input uncertainty of numerical weather prediction was more significant than the uncertainty of hydrological models, and the structural uncertainty of hydrological models was more prominent than parameter uncertainty. Considering all three uncertainty sources together resulted in more realistic streamflow representation. This framework improves the understanding and reliability of complex meteorological and hydrological forecasting systems.
Article
Energy & Fuels
Yi Wang, Leandro Von Krannichfeldt, Thierry Zufferey, Jean-Francois Toubeau
Summary: This paper proposes an ensemble approach for short-term nodal voltage forecasting in the presence of distributed energy resources, covering both deterministic and probabilistic forecasting. By selecting relevant features through a joint model- and data-driven method, training individual forecasting models, and combining them using weighted averaging and quantile regression averaging, the effectiveness and superiority of the proposed method is verified through case studies on a real-world distribution grid.
Article
Engineering, Civil
Mohammad Akbarian, Bahram Saghafian, Saeed Golian
Summary: This study evaluates 1 to 3-month runoff forecasts in 30 basins in Iran using the Copernicus Climate Change Service (C3S) data store. The results show that C3S runoff ensembles have the highest impact on forecast accuracy, followed by precipitation and temperature. The ANN, XGBoost, and RF models performed the best, while the MLR and SVR models performed the worst.
JOURNAL OF HYDROLOGY
(2023)
Article
Multidisciplinary Sciences
Eva Steirou, Lars Gerlitz, Xun Sun, Heiko Apel, Ankit Agarwal, Sonja Totz, Bruno Merz
Summary: This study investigates the impact of catchment or climate state on the distribution of maximum seasonal streamflow. By fitting the Generalized Extreme Value distribution to extreme seasonal streamflow data from 600 stations across Europe, the researchers found that there is potential for seasonal forecasting of flood probabilities. The potential varies depending on the season and region, with season-ahead catchment wetness showing the highest potential.
SCIENTIFIC REPORTS
(2022)
Article
Geosciences, Multidisciplinary
Silvia Terzago, Giulio Bongiovanni, Jost Von Hardenberg
Summary: Climate warming in mountain regions is causing glacier and snow cover reduction, leading to changes in meltwater runoff and water availability. Effective adaptation strategies involve seasonal forecasts to optimize snow and water resources. A prototype system was developed to generate seasonal forecasts of snow depth, meeting the needs of water management, hydropower production, and ski tourism. The system was based on bias-corrected and downscaled seasonal forecasts, showing good skill in predicting snow depth deviations from normal conditions.
HYDROLOGY AND EARTH SYSTEM SCIENCES
(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
Engineering, Civil
Xin Liu, Liping Zhang, Dunxian She, Jie Chen, Jun Xia, Xinchi Chen, Tongtiegang Zhao
Summary: This paper develops an integrated postprocessing framework for hydrometeorological ensemble forecasts, which aims to address the data correction issue for hydrological models. By using a reasonable design of canonical events and employing postprocessed ensemble precipitation forecasts, the performance of hydrological forecasts can be improved in terms of lead times and accuracy. The Bayesian model averaging (BMA) scheme further enhances the forecast effect by generating more skillful and reliable probabilistic hydrological forecasts.
JOURNAL OF HYDROLOGY
(2022)
Article
Operations Research & Management Science
Andrea Bucci, Lidan He, Zhi Liu
Summary: The application of artificial neural networks to finance has gained attention as a forecasting tool but may lead to poor predictions when dealing with a large number of predictors. This paper addresses the issue by employing dimensionality reduction methods and combining them with artificial neural networks. The findings show that the reduced models outperform the compared models without regularization in terms of predictive accuracy for stock asset price volatility.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Engineering, Civil
Mahrokh Moknatian, Rajith Mukundan
Summary: This study conducted uncertainty analysis on SWAT-HS model using multiple objective functions and found that using multiple objective functions can better capture prediction uncertainty. The Bayesian Model Averaging method was applied to quantify overall prediction uncertainty, and it was observed that uncertainty intervals estimated using multiple objective functions were wider than those using a single objective function. The study demonstrates that using multiple objective functions is an effective option in streamflow modeling and uncertainty analysis.
JOURNAL OF HYDROLOGY
(2023)
Article
Meteorology & Atmospheric Sciences
Yawen Shao, Quan J. Wang, Andrew Schepen, Dongryeol Ryu
Summary: This study discusses how to better incorporate climate trends into seasonal climate forecasts to improve their accuracy and reliability.
INTERNATIONAL JOURNAL OF CLIMATOLOGY
(2021)
Article
Engineering, Civil
Shuai Xie, Wenyan Wu, Sebastian Mooser, Q. J. Wang, Rory Nathan, Yuefei Huang
Summary: Flood inundation models are important in flood management, with artificial neural network models performing better in data-rich regions. The hybrid modeling approach significantly improves model performance in data-sparse regions.
JOURNAL OF HYDROLOGY
(2021)
Article
Meteorology & Atmospheric Sciences
Pengcheng Zhao, Quan J. Wang, Wenyan Wu, Qichun Yang
Summary: Deterministic numerical weather prediction models and ensemble numerical weather prediction models are both used worldwide to assist weather forecasting. Ensemble forecasts are found to outperform deterministic forecasts in terms of correlation, accuracy, and reliability when comparing their performance in forecasting daily precipitation in Australia over a 3-year period, despite their coarser resolution. Post-processing greatly improves the forecasts from both models, narrowing the performance gap between them.
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY
(2021)
Article
Engineering, Environmental
Shipeng Chu, Tuqiao Zhang, Tingchao Yu, Quan J. Wang, Yu Shao
Summary: Hydraulic models are powerful for simulating water distribution systems, but measurement uncertainty needs to be considered. A variational Bayesian approach can be used to estimate noise covariance and nodal water demands in real-time, effectively avoiding model overfitting. The approach is effective in determining model structural errors caused by topological structure parameterization.
Article
Environmental Sciences
Glen R. Walker, Avril C. Home, Quan J. Wang, Rob Rendell
Summary: This study evaluates the impact of improving irrigation efficiency (IE) projects on return flows using a water balance model and finds that the reductions in return flows are estimated to be less than 20% of the total proposed IE savings. The lower estimate is mainly due to different assumptions being used on groundwater connectivity between irrigation areas and major streams.
Article
Meteorology & Atmospheric Sciences
Yawen Shao, Quan J. Wang, Andrew Schepen, Dongryeol Ryu
Summary: Skillful subseasonal forecasts are crucial for early warnings of extreme weather events, but global climate models often fail to reproduce observed temperature trends. By adapting a trend-aware forecast postprocessing method developed for seasonal forecasts, this study improves the calibration and correction of trend in subseasonal forecasts. This method enhances forecast reliability and accuracy by embedding long-term climate trends, even with shorter hindcast periods.
INTERNATIONAL JOURNAL OF CLIMATOLOGY
(2022)
Article
Environmental Sciences
Danlu Guo, Arash Parehkar, Dongryeol Ryu, Quan J. Wang, Andrew W. Western
Summary: Missing data and low data quality are common issues in field observations of evapotranspiration. This study developed and evaluated three parsimonious gap-filling models for infilling sub-daily data. The MaxCor model performed the best and its suitability in different practical situations was discussed.
Article
Agronomy
Danlu Guo, Quan J. Wang, Dongryeol Ryu, Qichun Yang, Peter Moller, Andrew W. Western
Summary: This study aims to develop an uncertainty-based analysis framework for evaluating irrigation scheduling decisions under uncertainty, with a focus on the uncertainty arising from short-term rainfall forecasts. A crop model was used to simulate root-zone soil water content, and different irrigation scheduling decisions were evaluated using ensemble short-term rainfall forecasts. Risk quantification of over- and under-irrigation was conducted to inform the timing of the next irrigation event.
IRRIGATION SCIENCE
(2023)
Article
Engineering, Civil
Qichun Yang, Quan J. Wang, Kirsti Hakala
Summary: This study proposes a calibration strategy for short-term reference crop evapotranspiration (ETo) forecasts based on ETo anomalies and climatological mean, improving forecast quality and showing significant improvements at longer lead times. The effectiveness and robustness of the strategy are validated through calibrations across different spatial scales.
JOURNAL OF HYDROLOGY
(2022)
Article
Environmental Sciences
Niels Fraehr, Quan J. Wang, Wenyan Wu, Rory Nathan
Summary: This study proposes a hybrid surrogate model to simulate the dynamic evolution of flood extent. The model consists of a low-resolution hydrodynamic model and a Sparse Gaussian Process model. The low-fidelity modeling results are corrected using the Sparse GP model to improve accuracy. The dimensionality of the data is reduced using Empirical Orthogonal Functions analysis. The model is validated and found to be effective and computationally efficient.
WATER RESOURCES RESEARCH
(2022)
Article
Meteorology & Atmospheric Sciences
Wentao Li, Qingyun Duan, Quan J. Wang, Sainan Huang, Shiyuan Liu
Summary: This study evaluated short-term reforecast products in mainland China during summer and found that the forecasts from ECMWF outperformed GEFSv12 in terms of accuracy and discrimination in most regions. However, when combining the two forecasts using Bayesian model averaging, the results showed a combination of advantages from both forecasts.
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES
(2022)
Article
Environmental Sciences
Yuerong Zhou, Wenyan Wu, Rory Nathan, Q. J. Wang
Summary: In this study, a new approach is proposed to simulate the temporal and spatial variation of flood inundation for a floodplain with complex flow paths. The combination of a 1D convolutional neural network model and a U-Net method achieves accurate water depth simulation and flood surface reconstruction.
WATER RESOURCES RESEARCH
(2022)
Article
Environmental Sciences
Niels Fraehr, Quan J. J. Wang, Wenyan Wu, Rory Nathan
Summary: To address the issue of high computational cost in running high-resolution hydrodynamic models, the LSG model was introduced, which uses a combination of low-fidelity simulations, spatial analysis, and Gaussian process learning. However, the LSG model has only been tested on hydrodynamic models with structured grids and information on flood extent alone is often insufficient for accurate flood risk assessments.
WATER RESOURCES RESEARCH
(2023)
Article
Water Resources
Nibedita Samal, R. Ashwin, Qichun Yang, Ankit Singh, Sanjeev Kumar Jha, Q. J. Wang
Summary: In this study, deterministic precipitation forecasts from ECMWF models were post-processed using the SCC model, resulting in improved skill in generating probabilistic ensemble forecasts.
INTERNATIONAL JOURNAL OF RIVER BASIN MANAGEMENT
(2023)
Article
Engineering, Civil
Tristan D. J. Graham, Quan J. J. Wang, Yating Tang, Andrew Western, Wenyan Wu, Guy Ortlipp, Mark Bailey, Senlin Zhou, Kirsti Hakala, Qichun Yang
Summary: Water agencies allocate water based on agreed entitlement systems, often using historical climatology and a limited selection of climatic scenarios to issue seasonal water allocation outlooks. However, these outlooks have large uncertainties and lead to inefficient water use. This study investigates the use of ensemble seasonal inflow forecasts to improve the production of water allocation outlooks, resulting in outlooks that are closer to actual determinations and with reduced uncertainty. The integration of streamflow forecasts can lead to more efficient water use and water market participation.
JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT
(2023)