Water Resources Research:

  1. Grimaldi, S., Schumann, G. P., Shokri, A., Walker, J. P., & Pauwels, V. R. N. (2019). Challenges, opportunities and pitfalls for global coupled hydrologic‐hydraulic modeling of floods. Water Resources Research. https://doi.org/10.1029/2018WR024289
  2. Leach, J. A., & Moore, R. D. (2019). Empirical stream thermal sensitivities may underestimate stream temperature response to climate warming. Water Resources Research, 55(7), 5453-5467. https://doi.org/10.1029/2018WR024236
  3. Gan, Y., Liang, X. Z., Duan, Q., Chen, F., Li, J., & Zhang, Y. (2019). Assessment and Reduction of the Physical Parameterization Uncertainty for Noah‐MP Land Surface Model. Water Resources Research, 55(7), 5518-5538. https://doi.org/10.1029/2019WR024814
  4. Heudorfer, B., Haaf, E., Stahl, K., & Barthel, R. (2019). Index‐based characterization and quantification of groundwater dynamics. Water Resources Research. https://doi.org/10.1029/2018WR024418
  5. Rizzo, C. B., & de Barros, F. P. (2019). Minimum Hydraulic Resistance Uncertainty and the Development of a Connectivity‐Based Iterative Sampling Strategy. Water Resources Research, 55(7), 5593-5611. https://doi.org/10.1029/2019WR025269
  6. Magnusson, J., Eisner, S., Huang, S., Lussana, C., Mazzotti, G., Essery, R., … & Beldring, S. (2019). Influence of spatial resolution on snow cover dynamics for a coastal and mountainous region at high latitudes (Norway). Water Resources Research, 55(7), 5612-5630. https://doi.org/10.1029/2019WR024925
  7. Ling, F., Boyd, D., Ge, Y., Foody, G. M., Li, X., Wang, L., … & Du, Y. (2019). Measuring River Wetted Width from Remotely Sensed Imagery at the Sub‐pixel Scale with a Deep Convolutional Neural Network. Water Resources Research. https://doi.org/10.1029/2018WR024136
  8. Araya, S. N., & Ghezzehei, T. A. (2019). Using machine learning for prediction of saturated hydraulic conductivity and its sensitivity to soil structural perturbations. Water Resources Research, 55(7), 5715-5737. https://doi.org/10.1029/2018WR024357
  9. Zhong, Z., Sun, A. Y., & Jeong, H. (2019). Predicting co2 plume migration in heterogeneous formations using conditional deep convolutional generative adversarial network. Water Resources Research, 55(7), 5830-5851. https://doi.org/10.1029/2018WR024592
  10. Quinn, J. D., Reed, P. M., Giuliani, M., & Castelletti, A. (2019). What Is Controlling Our Control Rules? Opening the Black Box of Multireservoir Operating Policies Using Time‐Varying Sensitivity Analysis. Water Resources Research, 55(7), 5962-5984. https://doi.org/10.1029/2018WR024177
  11. Mazzoleni, M., Amaranto, A., & Solomatine, D. P. (2019). Integrating qualitative flow observations in a lumped hydrologic routing model. Water Resources Research, 55(7), 6088-6108. https://doi.org/10.1029/2018WR023768
  12. Karst, N., Dralle, D., & Müller, M. F. (2019). On the Effect of Nonlinear Recessions on Low Flow Variability: Diagnostic of an Analytical Model for Annual Flow Duration Curves. Water Resources Research, 55(7), 6125-6137. https://doi.org/10.1029/2019WR024912
  13. Stillinger, T., Roberts, D. A., Collar, N. M., & Dozier, J. (2019). Cloud masking for Landsat 8 and MODIS Terra over snow‐covered terrain: Error analysis and spectral similarity between snow and cloud. Water Resources Research, 55(7), 6169-6184. https://doi.org/10.1029/2019WR024932
  14. Rajagopalan, B., Erkyihun, S. T., Lall, U., Zagona, E., & Nowak, K. (2019). A Nonlinear Dynamical Systems‐Based Modeling Approach for Stochastic Simulation of Streamflow and Understanding Predictability. Water Resources Research, 55(7), 6268-6284. https://doi.org/10.1029/2018WR023650
  15. Lin, P., Pan, M., Beck, H. E., Yang, Y., Yamazaki, D., Frasson, R., … & Gleason, C. J. (2019). Global reconstruction of naturalized river flows at 2.94 million reaches. Water Resources Research. https://doi.org/10.1029/2019WR025287
  16. Ruddell, B. L., Drewry, D. T., & Nearing, G. S. Information Theory for Model Diagnostics: Structural Error is Indicated by Trade‐Off Between Functional and Predictive Performance. Water Resources Research. https://doi.org/10.1029/2018WR023692
  17. Karamouz, M., & Fereshtehpour, M. (2019). Modeling DEM Errors in Coastal Flood Inundation and Damages: A Spatial Nonstationary Approach. Water Resources Research. https://doi.org/10.1029/2018WR024562
  18. Krapu, C., Borsuk, M., & Kumar, M. (2019). Gradient‐based inverse estimation for a rainfall‐runoff model. Water Resources Research. https://doi.org/10.1029/2018WR024461
  19. Forbes, W. L., Mao, J., Ricciuto, D. M., Kao, S. C., Shi, X., Tavakoly, A. A., … & Thornton, P. E. (2019). Streamflow in the Columbia River Basin: Quantifying changes over the period 1951‐2008 and determining the drivers of those changes. Water Resources Research. https://doi.org/10.1029/2018WR024256
  20. Steinschneider, S., Ray, P., Rahat, S. H., & Kucharski, J. (2019). A Weather‐Regime‐Based Stochastic Weather Generator for Climate Vulnerability Assessments of Water Systems in the Western United States. Water Resources Research. https://doi.org/10.1029/2018WR024446
  21. Parente, M. T., Bittner, D., Mattis, S., Chiogna, G., & Wohlmuth, B. (2019). Bayesian calibration and sensitivity analysis for a karst aquifer model using active subspaces. Water Resources Research. https://doi.org/10.1029/2019WR024739
  22. Tso, C. H. M., Kuras, O., & Binley, A. (2019). On the Field Estimation of Moisture Content Using Electrical Geophysics: The Impact of Petrophysical Model Uncertainty. Water Resources Research. https://doi.org/10.1029/2019WR024964
  23. Crosbie, R. S., Doble, R. C., Turnadge, C., & Taylor, A. R. (2019). Constraining the magnitude and uncertainty of specific yield for use in the water table fluctuation method of estimating recharge. Water Resources Research. https://doi.org/10.1029/2019WR025285
  24. Despax, A., Le Coz, J., Hauet, A., Mueller, D. S., Engel, F. L., Blanquart, B., … & Oberg, K. A. Decomposition of Uncertainty Sources in Acoustic Doppler Current Profiler Streamflow Measurements Using Repeated Measures Experiments. Water Resources Research. https://doi.org/10.1029/2019WR025296
  25. Goetz, J., & Brenning, A. (2019). Quantifying uncertainties in snow depth mapping from structure from motion photogrammetry in an alpine area. Water Resources Research. https://doi.org/10.1029/2019WR025251
  26. Ravindranath, A., Devineni, N., Lall, U., Cook, E. R., Pederson, G., Martin, J., & Woodhouse, C. (2019). Streamflow Reconstruction in the Upper Missouri River Basin Using a Novel Bayesian Network Model. Water Resources Research. https://doi.org/10.1029/2019WR024901
  27. Zhao, D., & Wu, S. Projected changes in permafrost active layer thickness over the Qinghai–Tibet Plateau under climate change. Water Resources Research. https://doi.org/10.1029/2019WR024969
  28. Tolley, D., Foglia, L., & Harter, T. Sensitivity Analysis and Calibration of an Integrated Hydrologic Model in an Irrigated Agricultural Basin with a Groundwater‐Dependent Ecosystem. Water Resources Research. https://doi.org/10.1029/2018WR024209
  29. Ciriello, V., Lauriola, I., & Tartakovsky, D. M. (2018). Distribution‐based global sensitivity analysis in hydrology. Water Resources Research. https://doi.org/10.1029/2019WR025844
  30. Wrzesien, M. L., Pavelsky, T. M., Durand, M. T., Dozier, J., & Lundquist, J. D. (2019). Characterizing biases in mountain snow accumulation from global datasets. Water Resources Research. https://doi.org/10.1029/2019WR025350
  31. Hayek, M., Ramarao, B., & Lavenue, M. (2019). An Adjoint Sensitivity Model for Steady‐State Sequentially Coupled Radionuclide Transport in Porous Media. Water Resources Research. https://doi.org/10.1029/2019WR025686

Environmental Modelling and Software:

  1. Wagena, M. B., Bhatt, G., Buell, E., Sommerlot, A. R., Fuka, D. R., & Easton, Z. M. (2019). Quantifying model uncertainty using Bayesian multi-model ensembles. Environmental Modelling & Software, 117, 89-99. https://doi.org/10.1016/j.envsoft.2019.03.013
  2. McKenzie, P. F., Duveneck, M. J., Morreale, L. L., & Thompson, J. R. (2019). Local and global parameter sensitivity within an ecophysiologically based forest landscape model. Environmental Modelling & Software, 117, 1-13. https://doi.org/10.1016/j.envsoft.2019.03.002
  3. Zadeh, F. K., Nossent, J., Woldegiorgis, B. T., Bauwens, W., & van Griensven, A. (2019). Impact of measurement error and limited data frequency on parameter estimation and uncertainty quantification. Environmental Modelling & Software, 118, 35-47. https://doi.org/10.1016/j.envsoft.2019.03.022
  4. Ye, L., Gao, L., Marcos-Martinez, R., Mallants, D., & Bryan, B. A. (2019). Projecting Australia’s forest cover dynamics and exploring influential factors using deep learning. Environmental Modelling & Software, 119, 407-417. https://doi.org/10.1016/j.envsoft.2019.07.013
  5. Guillaume, J. H., Jakeman, J. D., Marsili-Libelli, S., Asher, M., Brunner, P., Croke, B., … & Stigter, J. D. (2019). Introductory overview of identifiability analysis: A guide to evaluating whether you have the right type of data for your modeling purpose. Environmental Modelling & Software. https://doi.org/10.1016/j.envsoft.2019.07.007

Hydrology and Earth System Sciences:

  1. Lane, R. A., Coxon, G., Freer, J. E., Wagener, T., Johnes, P. J., Bloomfield, J. P., Greene, S., Macleod, C. J. A., and Reaney, S. M.: Benchmarking the predictive capability of hydrological models for river flow and flood peak predictions across over 1000 catchments in Great Britain, Hydrol. Earth Syst. Sci., 23, 4011–4032, https://doi.org/10.5194/hess-23-4011-2019 , 2019.
  2. Jennings, K. S. and Molotch, N. P.: The sensitivity of modeled snow accumulation and melt to precipitation phase methods across a climatic gradient, Hydrol. Earth Syst. Sci., 23, 3765–3786, https://doi.org/10.5194/hess-23-3765-2019 , 2019.
  3. Erdal, D. and Cirpka, O. A.: Global sensitivity analysis and adaptive stochastic sampling of a subsurface-flow model using active subspaces, Hydrol. Earth Syst. Sci., 23, 3787–3805, https://doi.org/10.5194/hess-23-3787-2019 , 2019.
  4. Dalledonne, G. L., Kopmann, R., and Brudy-Zippelius, T.: Uncertainty quantification of floodplain friction in hydrodynamic models, Hydrol. Earth Syst. Sci., 23, 3373–3385, https://doi.org/10.5194/hess-23-3373-2019 , 2019.
  5. Brunner, M. I., Bárdossy, A., and Furrer, R.: Technical note: Stochastic simulation of streamflow time series using phase randomization, Hydrol. Earth Syst. Sci., 23, 3175–3187, https://doi.org/10.5194/hess-23-3175-2019 , 2019.
  6. O, S. and Foelsche, U.: Assessment of spatial uncertainty of heavy rainfall at catchment scale using a dense gauge network, Hydrol. Earth Syst. Sci., 23, 2863–2875, https://doi.org/10.5194/hess-23-2863-2019 , 2019.
  7. Yu, D., Yang, J., Shi, L., Zhang, Q., Huang, K., Fang, Y., and Zha, Y.: On the uncertainty of initial condition and initialization approaches in variably saturated flow modeling, Hydrol. Earth Syst. Sci., 23, 2897–2914, https://doi.org/10.5194/hess-23-2897-2019 , 2019.

Journal of Hydrology:

  1.  Achieng, K. O., & Zhu, J. (2019). Application of Bayesian framework for evaluation of streamflow simulations using multiple climate models. Journal of Hydrology, 574, 1110-1128. https://doi.org/10.1016/j.jhydrol.2019.05.018
  2. Zhu, Y., Chen, L., Wei, G., Li, S., & Shen, Z. (2019). Uncertainty assessment in baseflow nonpoint source pollution prediction: The impacts of hydrographic separation methods, data sources and baseflow period assumptions. Journal of Hydrology, 574, 915-925. https://doi.org/10.1016/j.jhydrol.2019.05.010
  3. Farsi, N., & Mahjouri, N. (2019). Evaluating the contribution of the climate change and human activities to runoff change under uncertainty. Journal of hydrology, 574, 872-891. https://doi.org/10.1016/j.jhydrol.2019.04.028
  4. Li, W., Duan, Q., Ye, A., & Miao, C. (2019). An improved meta-Gaussian distribution model for post-processing of precipitation forecasts by censored maximum likelihood estimation. Journal of Hydrology, 574, 801-810. https://doi.org/10.1016/j.jhydrol.2019.04.073
  5. Yan, L., Xiong, L., Ruan, G., Xu, C. Y., Yan, P., & Liu, P. (2019). Reducing uncertainty of design floods of two-component mixture distributions by utilizing flood timescale to classify flood types in seasonally snow covered region. Journal of Hydrology, 574, 588-608. https://doi.org/10.1016/j.jhydrol.2019.04.056
  6. Janetti, E. B., Guadagnini, L., Riva, M., & Guadagnini, A. (2019). Global sensitivity analyses of multiple conceptual models with uncertain parameters driving groundwater flow in a regional-scale sedimentary aquifer. Journal of Hydrology, 574, 544-556. https://doi.org/10.1016/j.jhydrol.2019.04.035
  7. Ehlers, L. B., Sonnenborg, T. O., Heuvelink, G. B. M., He, X., & Refsgaard, J. C. (2019). Joint treatment of point measurement, sampling and neighborhood uncertainty in space-time rainfall mapping. Journal of Hydrology, 574, 148-159. https://doi.org/10.1016/j.jhydrol.2019.03.100
  8. Wang, M., Zhang, M., Shi, H., Huang, X., & Liu, Y. (2019). Uncertainty analysis of a pollutant-hydrograph model in assessing inflow and infiltration of sanitary sewer systems. Journal of Hydrology, 574, 64-74. https://doi.org/10.1016/j.jhydrol.2019.04.011
  9. Rana, S. M., Boccelli, D. L., Scott, D. T., & Hester, E. T. (2019). Parameter Uncertainty with Flow Variation of the One-dimensional Solute Transport Model for Small Streams using Markov chain Monte Carlo. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2019.06.003
  10. Guo, Y., Huang, S., Huang, Q., Wang, H., Wang, L., & Fang, W. (2019). Copulas-based bivariate socioeconomic drought dynamic risk assessment in a changing environment. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2019.06.010
  11. Wani, O., Scheidegger, A., Cecinati, F., Espadas, G., & Rieckermann, J. (2019). Exploring a copula-based alternative to additive error models–for non-negative and autocorrelated time series in hydrology. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2019.06.006
  12. Zhou, L., Meng, Y., & Abbaspour, K. C. (2019). A new framework for multi-site stochastic rainfall generator based on empirical orthogonal function analysis and Hilbert-Huang transform. Journal of Hydrology, 575, 730-742. https://doi.org/10.1016/j.jhydrol.2019.05.047
  13. Varouchakis, E. A., Theodoridou, P. G., & Karatzas, G. P. (2019). Spatiotemporal geostatistical modeling of groundwater levels under a Bayesian framework using means of physical background. Journal of Hydrology, 575, 487-498. https://doi.org/10.1016/j.jhydrol.2019.05.055
  14. Chen, W., Wang, X., Deng, S., Liu, C., Xie, H., & Zhu, Y. (2019). Integrated urban flood vulnerability assessment using local spatial dependence-based probabilistic approach. Journal of Hydrology, 575, 454-469.
  15. Lindenschmidt, K. E., Rokaya, P., Das, A., Li, Z., & Richard, D. (2019). A novel stochastic modelling approach for operational real-time ice-jam flood forecasting. Journal of Hydrology, 575, 381-394. https://doi.org/10.1016/j.jhydrol.2019.05.048
  16. Jaros, A., Rossi, P. M., Ronkanen, A. K., & Kløve, B. (2019). Parameterisation of an integrated groundwater-surface water model for hydrological analysis of boreal aapa mire wetlands. Journal of Hydrology, 575, 175-191. https://doi.org/10.1016/j.jhydrol.2019.04.094
  17. Gupta, A., & Govindaraju, R. S. (2019). Propagation of structural uncertainty in watershed hydrologic models. Journal of Hydrology, 575, 66-81. https://doi.org/10.1016/j.jhydrol.2019.05.026
  18. Culley, S., Bennett, B., Westra, S., & Maier, H. R. (2019). Generating realistic perturbed hydrometeorological time series to inform scenario-neutral climate impact assessments. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2019.06.005
  19. Zhu, J. (2019). Sensitivity of advective contaminant travel time to the soil hydraulic parameters in unsaturated heterogeneous soils. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2019.06.035
  20. Zhu, F., Zhong, P. A., Cao, Q., Chen, J., Sun, Y., & Fu, J. (2019). A stochastic multi-criteria decision making framework for robust water resources management under uncertainty. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2019.06.049
  21. Gangrade, S., Kao, S. C., Dullo, T. T., Kalyanapu, A. J., & Preston, B. L. (2019). Ensemble-based Flood Vulnerability Assessment for Probable Maximum Flood in a Changing Environment. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2019.06.027
  22. Guo, A., Chang, J., Wang, Y., Huang, Q., Guo, Z., & Li, Y. (2019). Uncertainty analysis of water availability assessment through the Budyko framework. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2019.06.033
  23. De Pue, J., Rezaei, M., Van Meirvenne, M., & Cornelis, W. M. (2019). The relevance of measuring saturated hydraulic conductivity: Sensitivity analysis and functional evaluation. Journal of Hydrology, 576, 628-638. https://doi.org/10.1016/j.jhydrol.2019.06.079
  24. Leandro, J., Gander, A., Beg, M. N. A., Bhola, P., Konnerth, I., Willems, W., … & Disse, M. (2019). Forecasting upper and lower uncertainty bands of river flood discharges with high predictive skill. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2019.06.052
  25. Rammay, M. H., Elsheikh, A. H., & Chen, Y. (2019). Quantification of prediction uncertainty using imperfect subsurface models with model error estimation. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2019.02.056
  26. Siade, A. J., Rathi, B., Prommer, H., Welter, D., & Doherty, J. (2019). Using heuristic multi-objective optimization for quantifying predictive uncertainty associated with groundwater flow and reactive transport models. Journal of Hydrology, 577, 123999.
  27. Karanitsch-Ackerl, S., Mayer, K., Gauster, T., Laaha, G., Holawe, F., Wimmer, R., & Grabner, M. (2019). A 400-year reconstruction of spring–summer precipitation and summer low flow from regional tree-ring chronologies in North-Eastern Austria. Journal of Hydrology, 577, 123986. https://doi.org/10.1016/j.jhydrol.2019.123986
  28. Cai, C., Wang, J., & Li, Z. (2019). Assessment and modelling of uncertainty in precipitation forecasts from TIGGE using fuzzy probability and Bayesian theory. Journal of Hydrology, 577, 123995. https://doi.org/10.1016/j.jhydrol.2019.123995
  29. Umer, Y. M., Jetten, V. G., & Ettema, J. (2019). Sensitivity of flood dynamics to different soil information sources in urbanized areas. Journal of hydrology, 577, 123945. https://doi.org/10.1016/j.jhydrol.2019.123945
  30. Hutchins, M. G., & Hitt, O. E. (2019). Sensitivity of river eutrophication to multiple stressors illustrated using graphical summaries of physics-based river water quality model simulations. Journal of Hydrology, 577, 123917. https://doi.org/10.1016/j.jhydrol.2019.123917
  31. Pan, Z., Liu, P., Gao, S., Cheng, L., Chen, J., & Zhang, X. (2019). Reducing the uncertainty of time-varying hydrological model parameters using spatial coherence within a hierarchical Bayesian framework. Journal of Hydrology, 577, 123927. https://doi.org/10.1016/j.jhydrol.2019.123927

Advances in Water Resources:

  1. Ehlers, L. B., Wani, O., Koch, J., Sonnenborg, T. O., & Refsgaard, J. C. (2019). Using a simple post-processor to predict residual uncertainty for multiple hydrological model outputs. Advances in Water Resources, 129, 16-30. https://doi.org/10.1016/j.advwatres.2019.05.003
  2. Nury, A. H., Sharma, A., Marshall, L., & Mehrotra, R. (2019). Characterising Uncertainty in Precipitation Downscaling using a Bayesian Approach. Advances in Water Resources. https://doi.org/10.1016/j.advwatres.2019.05.018
  3. Gómez, J. E., & Torres-Verdín, C. (2019). Permeability sensitivity functions and rapid simulation of multi-point pressure measurements using perturbation theory. Advances in Water Resources, 129, 198-209. https://doi.org/10.1016/j.advwatres.2019.05.015
  4. Hassanzadeh, E., Nazemi, A., Adamowski, J., Nguyen, T. H., & Van-Nguyen, V. T. (2019). Quantile-based downscaling of rainfall extremes: Notes on methodological functionality, associated uncertainty and application in practice. Advances in Water Resources, 131, 103371. https://doi.org/10.1016/j.advwatres.2019.07.001
Latest Publications on ‘Hydrologic Uncertainty’ – October 2019

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