Water Resources Research:

  1.  Herman, J. D., Quinn, J. D., Steinschneider, S., Giuliani, M., & Fletcher, S. (2019). Climate adaptation as a control problem: Review and perspectives on dynamic water resources planning under uncertainty. Water Resources Research, e24389. https://doi.org/10.1029/2019WR025502
  2. Nguyen, H., Mehrotra, R., & Sharma, A. (2020). Assessment of Climate Change Impacts on Reservoir Storage Reliability, Resilience, and Vulnerability Using a Multivariate Frequency Bias Correction Approach. Water Resources Research56(2), e2019WR026022. https://doi.org/10.1029/2019WR026022
  3. Dell’Oca, A., Riva, M., & Guadagnini, A. (2020). Global Sensitivity Analysis for Multiple Interpretive Models with Uncertain Parameters. Water Resources Research56(2), e2019WR025754. https://doi.org/10.1029/2019WR025754
  4. Tajiki, M., Schoups, G., Hendricks Franssen, H. J., Najafinejad, A., & Bahremand, A. (2020). Recursive Bayesian estimation of conceptual rainfall‐runoff model errors in real‐time prediction of streamflow. Water Resources Research56(2), e2019WR025237. https://doi.org/10.1029/2019WR025237
  5. Niu, Y., Mostaghimi, P., Shabaninejad, M., Swietojanski, P., & Armstrong, R. T. (2020). Digital rock segmentation for petrophysical analysis with reduced user bias using convolutional neural networks. Water Resources Research56(2), e2019WR026597. https://doi.org/10.1029/2019WR026597
  6. Gelsinari, S., Doble, R., Daly, E., & Pauwels, V. R. (2020). Feasibility of Improving Groundwater Modeling by Assimilating Evapotranspiration Rates. Water Resources Research56(2), e2019WR025983. https://doi.org/10.1029/2019WR025983
  7. Alawadhi, A., & Tartakovsky, D. M. (2020). Bayesian Update and Method of Distributions: Application to Leak Detection in Transmission Mains. Water Resources Research. https://doi.org/10.1029/2019WR025879
  8. Wang, S., Taha, A. F., Sela, L., Giacomoni, M. H., & Gatsis, N. (2020). A New Derivative‐Free Linear Approximation for Solving the Network Water Flow Problem With Convergence Guarantees. Water Resources Research56(3). https://doi.org/10.1029/2019WR025694
  9. Zhang, J., Vrugt, J. A., Shi, X., Lin, G., Wu, L., & Zeng, L. Improving Simulation Efficiency of MCMC for Inverse Modeling of Hydrologic Systems with a Kalman‐Inspired Proposal Distribution. Water Resources Research. https://doi.org/10.1029/2019WR025474
  10. Tran, V. N., Dwelle, M. C., Sargsyan, K., Ivanov, V. Y., & Kim, J. (2020). A novel modeling framework for computationally efficient and accurate real‐time ensemble flood forecasting with uncertainty quantification. Water Resources Research. https://doi.org/10.1029/2019WR025727
  11. Guo, D., Zheng, F., Gupta, H., & Maier, H. R. On the Robustness of Conceptual Rainfall‐Runoff Models to Calibration and Evaluation Dataset Splits Selection: A Large Sample Investigation. Water Resources Research. https://doi.org/10.1029/2019WR026752
  12. Siade, A. J., Cui, T., Karelse, R. N., & Hampton, C. (2020). Reduced‐Dimensional Gaussian Process Machine Learning for Groundwater Allocation Planning using Swarm Theory. Water Resources Research. https://doi.org/10.1029/2019WR026061
  13. Do, N. C., & Razavi, S. Correlation effects? A major but often neglected component in sensitivity and uncertainty analysis. Water Resources Research. https://doi.org/10.1029/2019WR025436
  14. Guillon, H., Byrne, C. F., Lane, B. A., Solis, S. S., & Pasternack, G. B. (2020). Machine Learning Predicts Reach‐Scale Channel Types from Coarse‐Scale Geospatial Data in a Large River Basin. Water Resources Research. https://doi.org/10.1029/2019WR026691
  15. Arshadi, M., De Paolis Kaluza, M. C., Miller, E. L., & Abriola, L. M. (2020). Subsurface Source Zone Characterization and Uncertainty Quantification Using Discriminative Random Fields. Water Resources Research56(3), e2019WR026481. https://doi.org/10.1029/2019WR026481
  16. Bhowmik, R. D., Ng, T. L., & Wang, J. P. (2019). Understanding the impact of observation data uncertainty on probabilistic streamflow forecasts using a dynamic hierarchical model. Water Resources Research. https://doi.org/10.1029/2019WR025463
  17. Spear, R. C., Cheng, Q., & Wu, S. L. (2020). An Example of Augmenting Regional Sensitivity Analysis Using Machine Learning Software. Water Resources Research. https://doi.org/10.1029/2019WR026379

Environmental Modelling and Software:

  1.  Douglas-Smith, D., Iwanaga, T., Croke, B. F., & Jakeman, A. J. (2020). Certain trends in uncertainty and sensitivity analysis: An overview of software tools and techniques. Environmental Modelling and Software124, 104588. https://doi.org/10.1016/j.envsoft.2019.104588
  2. Pianosi, F., Sarrazin, F., & Wagener, T. (2020). How successfully is open-source research software adopted? Results and implications of surveying the users of a sensitivity analysis toolbox. Environmental Modelling & Software124, 104579. https://doi.org/10.1016/j.envsoft.2019.104579
  3. Jang, W. S., Engel, B., & Yeum, C. M. (2020). Integrated environmental modeling for efficient aquifer vulnerability assessment using machine learning. Environmental Modelling & Software124, 104602. https://doi.org/10.1016/j.envsoft.2019.104602
  4. Barton, D. N., Sundt, H., Bustos, A. A., Fjeldstad, H. P., Hedger, R., Forseth, T., … & Madsen, A. L. (2020). Multi-criteria decision analysis in Bayesian networks-Diagnosing ecosystem service trade-offs in a hydropower regulated river. Environmental Modelling & Software124, 104604. https://doi.org/10.1016/j.envsoft.2019.104604
  5. Zhang, Y., Arabi, M., & Paustian, K. (2020). Analysis of parameter uncertainty in model simulations of irrigated and rainfed agroecosystems. Environmental Modelling & Software126, 104642. https://doi.org/10.1016/j.envsoft.2020.104642
  6. Mustafa, S. M. T., Nossent, J., Ghysels, G., & Huysmans, M. (2020). Integrated Bayesian Multi-model approach to quantify input, parameter and conceptual model structure uncertainty in groundwater modeling. Environmental Modelling & Software126, 104654. https://doi.org/10.1016/j.envsoft.2020.104654
  7. Su, Y., Kern, J. D., Denaro, S., Hill, J., Reed, P., Sun, Y., … & Characklis, G. W. (2020). An open source model for quantifying risks in bulk electric power systems from spatially and temporally correlated hydrometeorological processes. Environmental Modelling & Software126, 104667. https://doi.org/10.1016/j.envsoft.2020.104667

Hydrology and Earth System Sciences:

  1. Alam, M. S., Barbour, S. L., and Huang, M.: Characterizing uncertainty in the hydraulic parameters of oil sands mine reclamation covers and its influence on water balance predictions, Hydrol. Earth Syst. Sci., 24, 735–759, https://doi.org/10.5194/hess-24-735-2020, 2020.
  2. Gallart, F., von Freyberg, J., Valiente, M., Kirchner, J. W., Llorens, P., and Latron, J.: Technical note: An improved discharge sensitivity metric for young water fractions, Hydrol. Earth Syst. Sci., 24, 1101–1107, https://doi.org/10.5194/hess-24-1101-2020, 2020.
  3. Jachens, E. R., Rupp, D. E., Roques, C., and Selker, J. S.: Recession analysis revisited: impacts of climate on parameter estimation, Hydrol. Earth Syst. Sci., 24, 1159–1170, https://doi.org/10.5194/hess-24-1159-2020, 2020.

Journal of Hydrology:

  1.  Qi, W., Liu, J., Xia, J., & Chen, D. (2020). Divergent sensitivity of surface water and energy variables to precipitation product uncertainty in the Tibetan Plateau. Journal of Hydrology581, 124338. https://doi.org/10.1016/j.jhydrol.2019.124338
  2. Li, Y., Hernandez, J. H., Aviles, M., Knappett, P. S., Giardino, J. R., Miranda, R., … & Morales, J. (2020). Empirical Bayesian Kriging method to evaluate inter-annual water-table evolution in the Cuenca Alta del Río Laja aquifer, Guanajuato, México. Journal of Hydrology582, 124517. https://doi.org/10.1016/j.jhydrol.2019.124517
  3. Yuan, L., He, W., Degefu, D. M., Liao, Z., Wu, X., An, M., … & Ramsey, T. S. (2020). Transboundary water sharing problem; a theoretical analysis using evolutionary game and system dynamics. Journal of Hydrology582, 124521. https://doi.org/10.1016/j.jhydrol.2019.124521
  4. Haro-Monteagudo, D., Palazón, L., & Beguería, S. (2020). Long-term Sustainability of Large Water Resource Systems under Climate Change: a Cascade Modeling Approach. Journal of Hydrology, 124546. https://doi.org/10.1016/j.jhydrol.2020.124546
  5. Lee, S., Yen, H., Yeo, I. Y., Moglen, G. E., Rabenhorst, M. C., & McCarty, G. W. (2020). Use of multiple modules and Bayesian Model Averaging to assess structural uncertainty of catchment-scale wetland modeling in a Coastal Plain landscape. Journal of Hydrology, 124544. https://doi.org/10.1016/j.jhydrol.2020.124544
  6. Wang, Y., Li, Z., Guo, S., Zhang, F., & Guo, P. (2020). A risk-based fuzzy boundary interval two-stage stochastic water resources management programming approach under uncertainty. Journal of Hydrology, 124553. https://doi.org/10.1016/j.jhydrol.2020.124553
Latest Publications on ‘Hydrologic Uncertainty’ – April 2020

One thought on “Latest Publications on ‘Hydrologic Uncertainty’ – April 2020

  • August 9, 2020 at 6:25 pm

    Das ist wirklich interessant. Sie sind ein sehr erfahrener Blogger. Ich bin Ihrem RSS-Feed beigetreten und freue mich darauf, mehr von Ihrem wunderbaren Beitrag zu erhalten. Außerdem habe ich Ihre Website in meinen sozialen Netzwerken geteilt! Marietta Desmund Dick


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