Article
Computer Science, Interdisciplinary Applications
Tessa Maurer, Francesco Avanzi, Carlos A. Oroza, Steven D. Glaser, Martha Conklin, Roger C. Bales
Summary: The Gaussian Mixture Models (GMMs) provide a more robust, objective, and repeatable method for spatial distribution of hydrologic models, better representing the distribution of watershed features relevant to the hydrologic cycle. It demonstrates superior or equivalent performance compared to traditional distribution models while significantly reducing time costs.
ENVIRONMENTAL MODELLING & SOFTWARE
(2021)
Article
Engineering, Civil
A. E. Brookfield, H. Ajami, R. W. H. Carroll, C. Tague, P. L. Sullivan, L. E. Condon
Summary: Over the past decades, hydrologic models have evolved from independent models to integrated models that can capture the entire terrestrial hydrologic cycle. These models have expanded to include various biogeochemical processes, sedimentation and erosion, human activities, and atmospheric processes. The development of these models has been driven by advancements in computing power, data availability, and understanding of the processes. Challenges include selecting appropriate models among many options and obtaining necessary data for parameterization and calibration. However, advancements in technology and data assimilation provide new ways to address these challenges. Furthermore, computational platforms and machine learning techniques support the use of larger and more complex models. It is important to develop accessible models that meet the needs of all users, not just researchers. Diverse modeling platforms should consider user needs, data availability, and computational resources.
JOURNAL OF HYDROLOGY
(2023)
Article
Engineering, Civil
Anoop Valiya Veettil, Ashok K. Mishra, Timothy R. Green
Summary: This study provides an overview of water security assessment by focusing on various water security indicators and the concept of water footprint. The application of physically-based hydrological models can offer valuable insights into the impact of climate and anthropogenic activities on water security at different scales.
JOURNAL OF HYDROLOGY
(2022)
Article
Computer Science, Interdisciplinary Applications
Adnan Rajib, I. Luk Kim, Mehmet B. Ercan, Venkatesh Merwade, Lan Zhao, Carol Song, Kuan-Hung Lin
Summary: This paper introduces an approach to improve the efficiency of automatic calibration in hydrologic models through the expansion and enhancement of the web-based modeling platform SWATShare. Three implementation case studies are conducted to validate the effectiveness of SWATShare autocalibration. The results show that SWATShare autocalibration produces comparable streamflow hydrograph and parameter values to commonly used offline calibration methods, with the added advantage of producing more physically relevant parameter values in some instances. The design presented in this paper can serve as an Open Science blueprint for similar developments in other hydrologic models and Earth system sciences.
ENVIRONMENTAL MODELLING & SOFTWARE
(2022)
Article
Meteorology & Atmospheric Sciences
Michael J. F. Vieira, Tricia A. Stadnyk
Summary: This study utilizes global climate models to assess future hydrologic droughts on a global scale. The findings show that a significant portion of the analyzed regions are at risk of unprecedented drought severity and duration, while northern latitudes may experience increased runoff and less severe droughts. However, predictions for other regions are either uncertain or unreliable due to conflicting signal-to-noise ratios and ensemble agreement.
NPJ CLIMATE AND ATMOSPHERIC SCIENCE
(2023)
Article
Meteorology & Atmospheric Sciences
Jesse Norris, Alex Hall, Chad W. Thackeray, Di Chen, Gavin D. Madakumbura
Summary: This study demonstrates the correlation between the strength of hydrologic sensitivity (HS) under El Nino-Southern Oscillation (ENSO) and HS in the context of climate change. The findings suggest that central Pacific ENSO events are a better predictor of HS under future warming. GCMs with greater HS exhibit a weakening of the atmospheric circulation and expansion under ENSO.
JOURNAL OF CLIMATE
(2022)
Article
Environmental Sciences
Y. Xiao, L. Fang, K. W. Hipel
Summary: Two basin-wide hydrologic-economic optimization models were presented to estimate water conservation potential while maintaining the same economic output level, considering different interpretations of water consumption. The results show that substantial water can be conserved without sacrificing overall economic output, with irrigation users contributing the most to conservation and being compensated by benefits from municipal and industrial users. Considering interactions among users is crucial, as independent actions by industrial users limit overall water conservation. The implications of the study help policy makers understand current water usage and aid in making informed decisions for water demand management.
JOURNAL OF ENVIRONMENTAL INFORMATICS
(2021)
Article
Environmental Sciences
Grey R. Evenson, Margaret Kalcic, Yu-Chen Wang, Dale Robertson, Donald Scavia, Jay Martin, Noel Aloysius, Anna Apostel, Chelsie Boles, Michael Brooker, Remegio Confesor, Awoke Teshager Dagnew, Tian Guo, Jeffrey Kast, Haley Kujawa, Rebecca Logsdon Muenich, Asmita Murumkar, Todd Redder
Summary: Using an ensemble of models to simulate critical source areas (CSAs) at the watershed scale, the study found that CSA locations are highly uncertain and may vary substantially across models, with only a subset of CSAs being consistently identified by different models.
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2021)
Article
Computer Science, Interdisciplinary Applications
Ethan T. Coon, Pin Shuai
Summary: Integrated, distributed hydrologic models utilize computational power and data accessibility to improve predictive understanding of the water cycle, but are rarely used due to difficulties in integrating models and data. This research presents Watershed Workflow version 1.2, a library that automates and enables complex workflows for defining inputs to high resolution, integrated, distributed hydrologic models, along with accompanying tools.
ENVIRONMENTAL MODELLING & SOFTWARE
(2022)
Article
Environmental Sciences
Yifu Gao, Abdullah Sahin, Jasper A. Vrugt
Summary: Variance-based analysis is used to quantify the sensitivity of the output y to the input variables x. This paper focuses on the sensitivity analysis of correlated input variables using high-dimensional model representation (HDMR) to separate the structural and correlative contributions. The results show that HDMR and HDMRext successfully analyze the structural and correlative contributions of the model output and provide an optimal experimental design for parameter correlation.
WATER RESOURCES RESEARCH
(2023)
Article
Environmental Sciences
W. J. M. Knoben, M. P. Clark, J. Bales, A. Bennett, S. Gharari, C. B. Marsh, B. Nijssen, A. Pietroniro, R. J. Spiteri, G. Tang, D. G. Tarboton, A. W. Wood
Summary: Despite the challenges of reproducibility in hydrologic and water resources research, open sharing of code can lead to efficiency gains for the modeling community. This study presents a model configuration workflow that separates data preprocessing from model-specific requirements, allowing for full reproducibility of model instantiations.
WATER RESOURCES RESEARCH
(2022)
Article
Engineering, Civil
Tomasz Janus, James Tomlinson, Daniela Anghileri, Justin Sheffield, Stefan Kollet, Julien J. Harou
Summary: This study investigates the impact of hydrologic-land feedbacks and a hydrologic-water management linkage on optimized land cover arrangements within a multiobjective land cover design framework. It integrates a spatially-distributed and physically-based hydrologic model with a network-based multi-sector water resources management model. Results show that trade-offs between water, food, energy, and environment objectives depend on land cover composition and spatial arrangement. The study demonstrates the added benefits of coupling distributed hydrologic models with water management simulation for multisector multicriteria land cover planning.
JOURNAL OF HYDROLOGY
(2023)
Article
Geosciences, Multidisciplinary
Thomas Lees, Marcus Buechel, Bailey Anderson, Louise Slater, Steven Reece, Gemma Coxon, Simon J. Dadson
Summary: LSTM models were applied to hydrological simulation in 669 catchments in Great Britain, showing superior performance compared to benchmark conceptual models. The models exhibited the largest performance improvements in the north-east of Scotland and south-east of England.
HYDROLOGY AND EARTH SYSTEM SCIENCES
(2021)
Article
Environmental Sciences
Andrew Bennett, Bart Nijssen
Summary: By embedding DL methods into PBHM models, it is possible to improve the accuracy of modeling hydrologic processes and achieve better predictive results.
WATER RESOURCES RESEARCH
(2021)
Article
Computer Science, Interdisciplinary Applications
Marjan Asgari, Wanhong Yang, John Lindsay, Hui Shao, Yongbo Liu, Rodrigo De Queiroga Miranda, Maryam Mehri Dehnavi
Summary: A research gap in calibrating distributed watershed hydrologic models is addressed by proposing a fault-tolerant and portable parallel calibration approach. The approach utilizes multiple perturbation factors and parallel dynamic searching strategies to achieve a balance between exploration and exploitation. Using Chapel programming language, the approach achieves super-linear speedup and high parallel efficiency, while maintaining low communication overhead and benefiting from knowledge-sharing in the convergence behavior of the parallel DDS algorithm.
ENVIRONMENTAL MODELLING & SOFTWARE
(2023)
Editorial Material
Meteorology & Atmospheric Sciences
Efi Foufoula-Georgiou, Clement Guilloteau, Phu Nguyen, Amir Aghakouchak, Kuo-Lin Hsu, Antonio Busalacchi, F. Joseph Turk, Christa Peters-Lidard, Taikan Oki, Qingyun Duan, Witold Krajewski, Remko Uijlenhoet, Ana Barros, Pierre Kirstetter, William Logan, Terri Hogue, Hoshin Gupta, Vincenzo Levizzani
BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY
(2020)
Article
Engineering, Civil
Yuan-Heng Wang, Chia-Chu Chu, Gene Jiing-Yun You, Hoshin V. Gupta, Peng-Hao Chiu
JOURNAL OF HYDROLOGIC ENGINEERING
(2020)
Article
Computer Science, Interdisciplinary Applications
Saman Razavi, Anthony Jakeman, Andrea Saltelli, Clementine Prieur, Bertrand Iooss, Emanuele Borgonovo, Elmar Plischke, Samuele Lo Piano, Takuya Iwanaga, William Becker, Stefano Tarantola, Joseph H. A. Guillaume, John Jakeman, Hoshin Gupta, Nicola Melillo, Giovanni Rabitti, Vincent Chabridon, Qingyun Duan, Xifu Sun, Stefan Smith, Razi Sheikholeslami, Nasim Hosseini, Masoud Asadzadeh, Arnald Puy, Sergei Kucherenko, Holger R. Maier
Summary: Sensitivity analysis is becoming an essential part of mathematical modeling, with untapped potential benefits for both mechanistic and data-driven modeling as well as decision making. This perspective paper revisits the current status of SA and outlines research challenges in various areas, emphasizing the need for structuring and standardizing SA as a discipline, tapping into its potential for systems modeling, addressing computational burdens, progressing SA in the context of machine learning, clarifying its relationship with uncertainty quantification, and evolving its use in decision making. An outlook for the future of SA is provided to better serve science and society.
ENVIRONMENTAL MODELLING & SOFTWARE
(2021)
Article
Computer Science, Interdisciplinary Applications
Tirthankar Roy, Hoshin Gupta
Summary: Model-based simulations often use prediction interval estimates, which may underestimate the width of the intervals; adjusting the interval width can lead to better estimation of prediction intervals; the method is applicable to different probability density functions and particularly useful when large samples are not available.
ENVIRONMENTAL MODELLING & SOFTWARE
(2021)
Article
Water Resources
Tao Liu, Luke A. McGuire, Haiyan Wei, Francis K. Rengers, Hoshin Gupta, Lin Ji, David C. Goodrich
Summary: This study uses a hydrological model to investigate the changes in hydraulic parameters following a wildfire in the Arroyo Seco watershed in California. Results show that saturated hydraulic conductivity is lowest in the first year post-fire and increases at an average rate of about 4.2 mm/h/year during the first 5 years of recovery. Channel hydraulic roughness was lowest in the first year post-fire, but doubled after one year of recovery, with changes related to grain roughness and vegetation in channels.
HYDROLOGICAL PROCESSES
(2021)
Editorial Material
Environmental Sciences
Grey S. Nearing, Frederik Kratzert, Alden Keefe Sampson, Craig S. Pelissier, Daniel Klotz, Jonathan M. Frame, Cristina Prieto, Hoshin V. Gupta
Summary: This paper is derived from a keynote talk given at Google's 2020 Flood Forecasting Meets Machine Learning Workshop. Recent experiments applying deep learning to rainfall-runoff simulation show that there is more information in large-scale hydrological data sets than previously thought. The paper calls for the hydrology community to focus on developing a quantitative understanding of the value of hydrological process understanding in a modeling discipline increasingly dominated by machine learning.
WATER RESOURCES RESEARCH
(2021)
Article
Environmental Sciences
Shervan Gharari, Hoshin V. Gupta, Martyn P. Clark, Markus Hrachowitz, Fabrizio Fenicia, Patrick Matgen, Hubert H. G. Savenije
Summary: This study introduces a systematic approach that treats models as hierarchical assemblages of hypotheses to investigate the impact of model development decisions on model fidelity. Each model development step progressively changes uncertainty regarding the input-state-output behavior of the system.
WATER RESOURCES RESEARCH
(2021)
Article
Physics, Multidisciplinary
Hoshin Gupta, Mohammad Reza Ehsani, Tirthankar Roy, Maria A. Sans-Fuentes, Uwe Ehret, Ali Behrangi
Summary: A simple Quantile Spacing method is developed for accurate probabilistic estimation of one-dimensional entropy, which requires no hyper-parameter tuning and is applicable to various shapes of probability density functions.
Article
Engineering, Civil
Menberu B. Meles, Dave C. Goodrich, Hoshin Gupta, I. Shea Burns, Carl L. Unkrich, Saman Razavi, D. Phillip Guertin
Summary: The study assessed the behavior and predictive uncertainty of the KINEROS2 model in semi-arid subwatersheds in Arizona, with results showing that uncertainties in flow responses are mainly due to parameters related to hydraulic conductivity, Manning's coefficient, soil capillary coefficient, and cohesion in sediment and flow. The level of influence of K2 parameters depends on the type of model response surface, rainfall, and watershed size.
JOURNAL OF HYDROLOGY
(2021)
Article
Meteorology & Atmospheric Sciences
Timothy M. Lahmers, Pieter Hazenberg, Hoshin Gupta, Christopher Castro, David Gochis, Aubrey Dugger, David Yates, Laura Read, Logan Karsten, Yuan-Heng Wang
Summary: The NOAA National Water Model (NWM), which provides operational hydrological guidance throughout the contiguous United States, was recently modified for semiarid domains to improve predictive skill. Model performance in semiarid environments is limited by current settings and biases of the calibration scheme, but an alternative calibration scheme with a new objective function can ameliorate model biases in some semiarid environments.
JOURNAL OF HYDROMETEOROLOGY
(2021)
Article
Environmental Sciences
Mohammad A. Moghaddam, Paul A. T. Ferre, Mohammad Reza Ehsani, Jeffrey Klakovich, Hoshin Vijay Gupta
Summary: The study confirms that energy dissipation weighting is the most accurate approach for determining the effective hydraulic conductivity of a binary K grid. The deep learning algorithm UNET can infer K-eff with extremely high accuracy, but it may be less accurate for cases with highly localized flow control structures. The UNET architecture can learn to infer energy dissipation weighting even without direct training, but the representation of weights may not be immediately interpretable by a human user.
Article
Environmental Sciences
Yanhong Dou, Lei Ye, Hoshin Gupta, Hairong Zhang, Ali Behrangi, Huicheng Zhou
Summary: The proposed CRC-M method performs well in accurate and stable flood forecasting by utilizing the range of runoff volumes computed using multiple near-real-time SPPs for constrained runoff correction. Key factors for good performance include relatively reliable SPPs and wider constraint ranges, as indicated by experiments.
WATER RESOURCES RESEARCH
(2021)
Article
Environmental Sciences
Tadesse Alemayehu, Hoshin Gupta, Ann van Griensven, Willy Bauwens
Summary: Spatially distributed hydrologic models are useful for understanding water balance dynamics, but their calibration and evaluation are challenging in poorly gauged basins. This study demonstrates that the judicious use of available information can help facilitate meaningful calibration and evaluation of hydrologic models to support decision making in poorly gauged river basins.
Review
Environmental Sciences
Daniel Partington, Mark Thyer, Margaret Shanafield, David McInerney, Seth Westra, Holger Maier, Craig Simmons, Barry Croke, Anthony John Jakeman, Hoshin Gupta, Dmitri Kavetski
Summary: Wildfires cause diverse hydrological changes that affect water quantity and quality. It is necessary to assess the implications of increasing wildfire frequency and severity on hydrological response. Physically based models are likely to become more important due to their ability to simulate simultaneous changes to multiple processes, but they require more data and have lower computational speed. Combining physically based models with computationally faster conceptual and reduced-order models can lead to advances in predicting hydrological impacts from wildfires.
WILEY INTERDISCIPLINARY REVIEWS-WATER
(2022)
Article
Geochemistry & Geophysics
Mohammad Reza Ehsani, Ariyan Zarei, Hoshin Vijai Gupta, Kobus Barnard, Eric Lyons, Ali Behrangi
Summary: This study develops two nowcasting models using deep neural network structures to address the challenge of precipitation nowcasting. Training and testing with global precipitation measurement data show that these models outperform other machine learning methods and meteorological benchmark models.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)