Article
Environmental Sciences
Shailesh Kumar Singh, George A. Griffiths
Summary: The study addresses the challenging problem of predicting the time for a natural basin's outflow to decline from the average to a low flow value, offering new methods for accurate predictions. The developed models show high accuracy in predicting recession time and can be confidently applied elsewhere, with further testing recommended.
WATER RESOURCES RESEARCH
(2021)
Article
Water Resources
Mustafa Utku Yilmaz, Bihrat Onoz
Summary: This study presents an extension of statistical streamflow estimation to ungauged basins and applies it to two sub-basins of the Euphrates basin in Turkey. Ensemble approaches with a weighting system based on various performance measures are proposed to improve the daily streamflow estimation in ungauged basins.
HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES
(2022)
Article
Engineering, Civil
Javier Senent-Aparicio, Patricia Jimeno-Saez, Raquel Martinez-Espana, Julio Perez-Sanchez
Summary: This study focuses on streamflow prediction in the Mino River basin in northwest Spain. A novel regionalisation approach is developed, which utilizes hydrological similarities between gauged and ungauged subbasins, as well as physiographic and climatic attributes, to predict streamflow. The results demonstrate satisfactory performance in the streamflow prediction, indicating the effectiveness of the regionalisation approach.
WATER RESOURCES MANAGEMENT
(2023)
Article
Environmental Sciences
Senlin Tang, Fubao Sun, Wenbin Liu, Hong Wang, Yao Feng, Ziwei Li
Summary: In this study, it was found that LSTM-corrected GHMs can improve streamflow prediction in ungauged basins. GHM-forced LSTM was also demonstrated to be an effective approach for bridging the gap between traditional LSTM modeling in ungauged basins and autoregressive modeling in data-rich basins. The incorporation of hydrological similarity among catchments further enhanced the migration performance of GHM-forced LSTM in ungauged basins.
WATER RESOURCES RESEARCH
(2023)
Article
Computer Science, Interdisciplinary Applications
Alban de Lavenne, Tom Loree, Herve Squividant, Christophe Cudennec
Summary: This R package gathers methods developed and lessons learnt for estimating discharge of ungauged outlets using a runoff-runoff approach. It utilizes observed discharge from nearby gauged basins and a geomorphology-based deconvolution-convolution modeling approach. The package allows for the estimation and simulation of discharge series in targeted ungauged basins. The methodology has been tested and further evaluation, improvement, and operational applications are encouraged.
ENVIRONMENTAL MODELLING & SOFTWARE
(2023)
Article
Water Resources
Pramod Soni, Shivam Tripathi, Rajesh Srivastava
Summary: The study evaluated five regionalization methods for SWAT in tropical river basins of India and found that regression outperformed ANN in predicting daily and peak discharges. Regression equations developed in the study can be utilized for nearby basins, but distant or diverse basins should be avoided for regionalization.
JOURNAL OF WATER AND CLIMATE CHANGE
(2021)
Article
Environmental Sciences
Abby Eurich, Stephanie K. Kampf, John C. Hammond, Matt Ross, Katie Willi, Anthony G. Vorster, Bryce Pulver
Summary: This publication introduces new regression models for estimating mean annual and mean monthly streamflow in Colorado, which incorporate snow persistence among other variables. The models show excellent performance with high accuracy in streamflow predictions, outperforming current regional regression models.
RIVER RESEARCH AND APPLICATIONS
(2021)
Article
Environmental Sciences
Francisco Jose Matos Nogueira Filho, Francisco de Assis Souza Filho, Victor Costa Porto, Renan Vieira Rocha, Alyson Brayner Sousa Estacio, Eduardo Savio Passos Rodrigues Martins
Summary: This study discusses the application of LSTM as a regional method for rainfall-runoff modeling in adverse conditions. The results show that both LSTM and FFNN perform better than traditional hydrological models in streamflow regionalization, with FFNN being superior. Additionally, neural network methods have the ability to aggregate process understanding from different watersheds.
Article
Ecology
Jeonghyeon Choi, Jeonghoon Lee, Sangdan Kim
Summary: The Long Short-Term Memory (LSTM) network, a deep learning approach, has shown excellent performance in streamflow prediction. This study investigates the applicability of LSTM in ungauged basins without hydrological observations and achieves satisfactory results.
ECOLOGICAL ENGINEERING
(2022)
Article
Computer Science, Interdisciplinary Applications
Abdullahi Uwaisu Muhammad, S. I. Abba
Summary: Floods are significant global hazards that cause loss of lives and properties. Researchers have proposed AI algorithms for flood prediction, but the assumption of similar and sufficient data sets often does not hold true. This paper proposes two hybrid transfer learning models for streamflow forecasting, incorporating GRU and LSTM. The TL+GRU model outperforms the baseline models in most basins, showing the enhanced performance achieved through the integration of neural networks and transfer learning.
EARTH SCIENCE INFORMATICS
(2023)
Article
Green & Sustainable Science & Technology
Lingxue Liu, Li Zhou, Maksym Gusyev, Yufeng Ren
Summary: In this study, a novel bias-correction system equipped with the proposed Piecewise Random Forest (P-RF) model was developed to improve the potential of the global-scale river discharge reanalysis product GloFAS-ERA5 (GloFAS) as a calibration benchmark for building hydrological models in ungauged basins. The system was tested in three ungauged scenarios in China and Japan, and the results showed better performance on the temporal scale, significant impact of sample integrity and adequacy on spatial and spatiotemporal bias-corrections, and a reduction of 25%-50% in statistical metric differences through the bias-correction.
JOURNAL OF CLEANER PRODUCTION
(2023)
Article
Meteorology & Atmospheric Sciences
Yanchen Zheng, Jianzhu Li, Ting Zhang, Youtong Rong, Ping Feng
Summary: This study investigates the potential of using flood scaling properties as constraints in model calibration, with results showing that this method can effectively capture flood peaks with fewer requirements of observed streamflow data, providing a new alternative approach for hydrological model calibration in ungauged watersheds. The proposed strategy enhances the physical connection of flood peak among subbasins and considers watershed actual conditions and climatic characteristics for each flood event, suggesting its adoption in model calibration for both gauged and data-scarce watersheds.
JOURNAL OF HYDROMETEOROLOGY
(2021)
Article
Water Resources
Charalampos Skoulikaris, Michael Piliouras
Summary: This study introduces a methodology for simulating ungauged hydrosystems by calibrating and validating regional rainfall-runoff models using large-scale hydrological models. The research evaluates the accuracy of simulated discharge data and tunes the regional model for optimal performance. Comparisons with a large-scale hydrological model indicate the satisfactory performance of the proposed approach.
HYDROLOGICAL PROCESSES
(2023)
Article
Water Resources
Dessalegn Worku Ayalew, Andrea Petroselli, Davide Luciano De Luca, Salvatore Grimaldi
Summary: This study summarizes the current practice in Ethiopia and explores the possibility of using a simplified continuous modeling approach to estimate design hydrograph characteristics. The study finds that this modeling approach is applicable in Ethiopia and can provide useful outputs for practical hydrological studies.
JOURNAL OF HYDROLOGY-REGIONAL STUDIES
(2022)
Article
Geosciences, Multidisciplinary
Johannes Laimighofer, Michael Melcher, Gregor Laaha
Summary: Accurate prediction of seasonal low flows is crucial for water management tasks. This study proposes an extreme gradient tree boosting model for predicting monthly low flows in ungauged catchments. The model focuses on the lowest values and uses an expectile loss function to enhance prediction accuracy. Tests and evaluations reveal that the model with expectile value 0.5 performs the best.
HYDROLOGY AND EARTH SYSTEM SCIENCES
(2022)
Article
Water Resources
Caitline Barber, Jonathan R. Lamontagne, Richard M. Vogel
HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES
(2020)
Article
Engineering, Civil
William H. Farmer, Jacob H. LaFontaine, Lauren E. Hay
JOURNAL OF HYDROLOGIC ENGINEERING
(2019)
Article
Environmental Sciences
Scott. C. Worland, Scott Steinschneider, William Farmer, William Asquith, Rodney Knight
WATER RESOURCES RESEARCH
(2019)
Article
Water Resources
Jory S. Hecht, Richard M. Vogel
ADVANCES IN WATER RESOURCES
(2020)
Article
Statistics & Probability
Richard M. Vogel
Summary: The sample geometric mean (SGM) is a measure of central tendency with various applications, while the theoretical definition of the population geometric mean (GM) was only introduced recently. The GM can be calculated for many common probability distributions, but lacks a clear physical interpretation. Its estimator SGM exhibits bias and mean square error, depending on sample size, skewness, and kurtosis. Therefore, the justification for using GM in many applications is limited.
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
(2022)
Article
Engineering, Civil
Jory S. Hecht, Richard M. Vogel, Ryan A. McManamay, Charles N. Kroll, J. Michael Reed
JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT
(2020)
Article
Environmental Sciences
Sara B. Levin, William H. Farmer
Article
Environmental Sciences
Mu Xiao, Ming Gao, Richard M. Vogel, Dennis P. Lettenmaier
WATER RESOURCES RESEARCH
(2020)
Article
Water Resources
Richard M. Vogel, Charles N. Kroll
ADVANCES IN WATER RESOURCES
(2020)
Article
Engineering, Civil
Eliot S. Meyer, Daniel P. Sheer, Paul V. Rush, Richard M. Vogel, Hannah E. Billian
JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT
(2020)
Article
Environmental Sciences
Jonathan R. Lamontagne, Caitline A. Barber, Richard M. Vogel
WATER RESOURCES RESEARCH
(2020)
Article
Engineering, Civil
Lei Ye, Xuezhi Gu, Dingbao Wang, Richard M. Vogel
Summary: The study reveals an increasing demand for reliable metrics of relative variability for high frequency streamflow series, leading to the introduction of a new C estimator model based on compound distributions theory, which provides more reliable estimates for the relative variability of high frequency streamflow series. These findings contribute to a better understanding of the variability of streamflow in different watersheds.
JOURNAL OF HYDROLOGY
(2021)
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
Water Resources
Richard M. Vogel, Charles N. Kroll
Summary: Despite the existence of guidelines for flood flow frequency analysis in the U.S. since 1966, there is a lack of uniform national guidelines for hydrologic drought streamflow frequency analysis, leading to challenges in water resources design, planning, and management under low streamflow conditions.
Article
Geosciences, Multidisciplinary
A. Sankarasubramanian, Dingbao Wang, Stacey Archfield, Meredith Reitz, Richard M. Vogel, Amirhossein Mazrooei, Sudarshana Mukhopadhyay
HYDROLOGY AND EARTH SYSTEM SCIENCES
(2020)
Article
Engineering, Civil
Arfan Arshad, Ali Mirchi, Javier Vilcaez, Muhammad Umar Akbar, Kaveh Madani
Summary: High-resolution, continuous groundwater data is crucial for adaptive aquifer management. This study presents a predictive modeling framework that incorporates covariates and existing observations to estimate groundwater level changes. The framework outperforms other methods and provides reliable estimates for unmonitored sites. The study also examines groundwater level changes in different regions and highlights the importance of effective aquifer management.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Lihua Chen, Jie Deng, Wenzhe Yang, Hang Chen
Summary: A new grid-based distributed karst hydrological model (GDKHM) is developed to simulate streamflow in the flood-prone karst area of Southwest China. The results show that the GDKHM performs well in predicting floods and capturing the spatial variability of karst system.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Faruk Gurbuz, Avinash Mudireddy, Ricardo Mantilla, Shaoping Xiao
Summary: Machine learning algorithms have shown better performance in streamflow prediction compared to traditional hydrological models. In this study, researchers proposed a methodology to test and benchmark ML algorithms using artificial data generated by physically-based hydrological models. They found that deep learning algorithms can correctly identify the relationship between streamflow and rainfall in certain conditions, but fail to outperform traditional prediction methods in other scenarios.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Yadong Ji, Jianyu Fu, Bingjun Liu, Zeqin Huang, Xuejin Tan
Summary: This study distinguishes the uncertainty in drought projection into scenario uncertainty, model uncertainty, and internal variability uncertainty. The results show that the estimation of total uncertainty reaches a minimum in the mid-21st century and that model uncertainty is dominant in tropical regions.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Z. R. van Leeuwen, M. J. Klaar, M. W. Smith, L. E. Brown
Summary: This study quantifies the effectiveness of leaky dams in reducing flood peak magnitude using a transfer function noise modelling approach. The results show that leaky dams have a significant but highly variable impact on flood peak magnitude, and managing expectations should consider event size and type.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Zeda Yin, Yasaman Saadati, M. Hadi Amini, Linlong Bian, Beichao Hu
Summary: Combined sewer overflows pose significant threats to public health and the environment, and various strategies have been proposed to mitigate their adverse effects. Smart control strategies have gained traction due to their cost-effectiveness but face challenges in balancing precision and computational efficiency. To address this, we propose exploring machine learning models and the inversion of neural networks for more efficient CSO prediction and optimization.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Qimou Zhang, Jiacong Huang, Jing Zhang, Rui Qian, Zhen Cui, Junfeng Gao
Summary: This study developed a N-cycling model for lowland rural rivers covered by macrophytes and investigated the N imports, exports, and response to sediment dredging. The findings showed a considerable N retention ability in the study river, with significant N imports from connected rivers and surrounding polders. Sediment dredging increased particulate nitrogen resuspension and settling rates, while decreasing ammonia nitrogen release, denitrification, and macrophyte uptake rates.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Xue Li, Yingyin Zhou, Jian Sha, Man Zhang, Zhong-Liang Wang
Summary: High-resolution climate data is crucial for predicting regional climate and water environment changes. In this study, a two-step downscaling method was developed to enhance the spatial resolution of GCM data and improve the accuracy for small basins. The method combined medium-resolution climate data with high-resolution topographic data to capture spatial and temporal details. The downscaled climate data were then used to simulate the impacts of climate change on hydrology and water quality in a small basin. The results demonstrated the effectiveness of the downscaling method for spatially differentiated simulations.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Tongqing Shen, Peng Jiang, Jiahui Zhao, Xuegao Chen, Hui Lin, Bin Yang, Changhai Tan, Ying Zhang, Xinting Fu, Zhongbo Yu
Summary: This study evaluates the long-term interannual dynamics of permafrost distribution and active layer thickness on the Tibetan Plateau, and predicts future degradation trends. The results show that permafrost area has been decreasing and active layer thickness has been increasing, with an accelerated degradation observed in recent decades. This has significant implications for local water cycle processes, water ecology, and water security.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Chi Zhang, Xu Zhang, Qiuhong Tang, Deliang Chen, Jinchuan Huang, Shaohong Wu, Yubo Liu
Summary: Precipitation over the Tibetan Plateau is influenced by systems such as the Asian monsoons, the westerlies, and local circulations. The Indian monsoon, the westerlies, and local circulations are the main systems affecting precipitation over the entire Tibetan Plateau. The East Asian summer monsoon primarily affects the eastern Tibetan Plateau. The Indian monsoon has the greatest influence on precipitation in the southern and central grid cells, while the westerlies have the greatest influence on precipitation in the northern and western grid cells. Local circulations have the strongest influence on the central and eastern grid cells.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Manuel Almeida, Antonio Rodrigues, Pedro Coelho
Summary: This study aimed to improve the accuracy of Total Phosphorus export coefficient models, which are essential for water management. Four different models were applied to 27 agroforestry watersheds in the Mediterranean region. The modeling approach showed significant improvements in predicting the Total Phosphorus diffuse loads.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Yutao Wang, Haojie Yin, Ziyi Wang, Yi Li, Pingping Wang, Longfei Wang
Summary: This study investigated the distribution and transformation of dissolved organic nitrogen (DON) in riverbed sediments impacted by effluent discharge. The authors found that the spectral characteristics of dissolved organic matter (DOM) in surface water and sediment porewater could be used to predict DON variations in riverbed sediments. Random forest and extreme gradient boosting machine learning methods were employed to provide accurate predictions of DON content and properties at different depths. These findings have important implications for wastewater discharge management and river health.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Saba Mirza Alipour, Kolbjorn Engeland, Joao Leal
Summary: This study assesses the uncertainty associated with 100-year flood maps under different scenarios using Monte Carlo simulations. The findings highlight the importance of employing probabilistic approaches for accurate and secure flood maps, with the selection of probability distribution being the primary source of uncertainty in precipitation.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Janine A. de Wit, Marjolein H. J. van Huijgevoort, Jos C. van Dam, Ge A. P. H. van den Eertwegh, Dion van Deijl, Coen J. Ritsema, Ruud P. Bartholomeus
Summary: The study focuses on the hydrological consequences of controlled drainage with subirrigation (CD-SI) on groundwater level, soil moisture content, and soil water potential. The simulations show that CD-SI can improve hydrological conditions for crop growth, but the success depends on subtle differences in geohydrologic characteristics.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Constantin Seidl, Sarah Ann Wheeler, Declan Page
Summary: Water availability and quality issues will become increasingly important in the future due to climate change impacts. Managed Aquifer Recharge (MAR) is an effective water management tool, but often overlooked. This study analyzes global MAR applications and identifies the key factors for success, providing valuable insights for future design and application.
JOURNAL OF HYDROLOGY
(2024)