{"id":966,"date":"2019-10-13T23:45:05","date_gmt":"2019-10-14T03:45:05","guid":{"rendered":"http:\/\/hydrouncertainty.org\/?p=966"},"modified":"2019-10-13T23:45:28","modified_gmt":"2019-10-14T03:45:28","slug":"latest-publications-on-hydrologic-uncertainty-october-2019","status":"publish","type":"post","link":"http:\/\/hydrouncertainty.org\/2019\/10\/13\/latest-publications-on-hydrologic-uncertainty-october-2019\/","title":{"rendered":"Latest Publications on ‘Hydrologic Uncertainty’ – October 2019"},"content":{"rendered":"\n

Water Resources Research: <\/strong><\/h2>\n\n\n\n
  1. Grimaldi,\nS., Schumann, G. P., Shokri, A., Walker, J. P., & Pauwels, V. R. N. (2019).\nChallenges, opportunities and pitfalls for global coupled hydrologic\u2010hydraulic\nmodeling of floods. Water Resources Research<\/em>. https:\/\/doi.org\/10.1029\/2018WR024289<\/a> <\/li>
  2. Leach,\nJ. A., & Moore, R. D. (2019). Empirical stream thermal sensitivities may\nunderestimate stream temperature response to climate warming. Water Resources\nResearch, 55(7), 5453-5467. https:\/\/doi.org\/10.1029\/2018WR024236<\/a> <\/li>
  3. Gan,\nY., Liang, X. Z., Duan, Q., Chen, F., Li, J., & Zhang, Y. (2019).\nAssessment and Reduction of the Physical Parameterization Uncertainty for\nNoah\u2010MP Land Surface Model. Water Resources Research<\/em>, 55<\/em>(7),\n5518-5538. https:\/\/doi.org\/10.1029\/2019WR024814<\/a> <\/li>
  4. Heudorfer,\nB., Haaf, E., Stahl, K., & Barthel, R. (2019). Index\u2010based characterization\nand quantification of groundwater dynamics. Water Resources Research<\/em>. https:\/\/doi.org\/10.1029\/2018WR024418<\/a> <\/li>
  5. Rizzo,\nC. B., & de Barros, F. P. (2019). Minimum Hydraulic Resistance Uncertainty\nand the Development of a Connectivity\u2010Based Iterative Sampling Strategy. Water\nResources Research, 55(7), 5593-5611. https:\/\/doi.org\/10.1029\/2019WR025269<\/a> <\/li>
  6. Magnusson,\nJ., Eisner, S., Huang, S., Lussana, C., Mazzotti, G., Essery, R., … &\nBeldring, S. (2019). Influence of spatial resolution on snow cover dynamics for\na coastal and mountainous region at high latitudes (Norway). Water Resources\nResearch<\/em>, 55<\/em>(7), 5612-5630. https:\/\/doi.org\/10.1029\/2019WR024925<\/a> <\/li>
  7. Ling,\nF., Boyd, D., Ge, Y., Foody, G. M., Li, X., Wang, L., … & Du, Y. (2019).\nMeasuring River Wetted Width from Remotely Sensed Imagery at the Sub\u2010pixel\nScale with a Deep Convolutional Neural Network. Water Resources Research<\/em>.\nhttps:\/\/doi.org\/10.1029\/2018WR024136<\/a> <\/li>
  8. Araya,\nS. N., & Ghezzehei, T. A. (2019). Using machine learning for prediction of\nsaturated hydraulic conductivity and its sensitivity to soil structural\nperturbations. Water Resources Research<\/em>, 55<\/em>(7), 5715-5737. https:\/\/doi.org\/10.1029\/2018WR024357<\/a> <\/li>
  9. Zhong,\nZ., Sun, A. Y., & Jeong, H. (2019). Predicting co2 plume migration in\nheterogeneous formations using conditional deep convolutional generative\nadversarial network. Water Resources Research<\/em>, 55<\/em>(7), 5830-5851.\nhttps:\/\/doi.org\/10.1029\/2018WR024592<\/a> <\/li>
  10. Quinn, J. D., Reed, P. M., Giuliani,\nM., & Castelletti, A. (2019). What Is Controlling Our Control Rules?\nOpening the Black Box of Multireservoir Operating Policies Using Time\u2010Varying\nSensitivity Analysis. Water Resources Research<\/em>, 55<\/em>(7), 5962-5984.\nhttps:\/\/doi.org\/10.1029\/2018WR024177<\/a> <\/li>
  11. Mazzoleni, M., Amaranto, A., &\nSolomatine, D. P. (2019). Integrating qualitative flow observations in a lumped\nhydrologic routing model. Water Resources Research<\/em>, 55<\/em>(7),\n6088-6108. https:\/\/doi.org\/10.1029\/2018WR023768<\/a> <\/li>
  12. Karst, N., Dralle, D., & M\u00fcller,\nM. F. (2019). On the Effect of Nonlinear Recessions on Low Flow Variability:\nDiagnostic of an Analytical Model for Annual Flow Duration Curves. Water\nResources Research<\/em>, 55<\/em>(7), 6125-6137. https:\/\/doi.org\/10.1029\/2019WR024912<\/a> <\/li>
  13. Stillinger, T., Roberts, D. A.,\nCollar, N. M., & Dozier, J. (2019). Cloud masking for Landsat 8 and MODIS\nTerra over snow\u2010covered terrain: Error analysis and spectral similarity between\nsnow and cloud. Water Resources Research<\/em>, 55<\/em>(7), 6169-6184. https:\/\/doi.org\/10.1029\/2019WR024932<\/a> <\/li>
  14. Rajagopalan, B., Erkyihun, S. T.,\nLall, U., Zagona, E., & Nowak, K. (2019). A Nonlinear Dynamical\nSystems\u2010Based Modeling Approach for Stochastic Simulation of Streamflow and\nUnderstanding Predictability. Water Resources Research<\/em>, 55<\/em>(7),\n6268-6284. https:\/\/doi.org\/10.1029\/2018WR023650<\/a> <\/li>
  15. Lin, P., Pan, M., Beck, H. E., Yang,\nY., Yamazaki, D., Frasson, R., … & Gleason, C. J. (2019). Global\nreconstruction of naturalized river flows at 2.94 million reaches. Water\nResources Research<\/em>. https:\/\/doi.org\/10.1029\/2019WR025287<\/a> <\/li>
  16. Ruddell, B. L., Drewry, D. T., &\nNearing, G. S. Information Theory for Model Diagnostics: Structural Error is\nIndicated by Trade\u2010Off Between Functional and Predictive Performance. Water\nResources Research<\/em>. https:\/\/doi.org\/10.1029\/2018WR023692<\/a> <\/li>
  17. Karamouz, M., & Fereshtehpour, M.\n(2019). Modeling DEM Errors in Coastal Flood Inundation and Damages: A Spatial\nNonstationary Approach. Water Resources Research<\/em>. https:\/\/doi.org\/10.1029\/2018WR024562<\/a> <\/li>
  18. Krapu, C., Borsuk, M., & Kumar, M.\n(2019). Gradient\u2010based inverse estimation for a rainfall\u2010runoff model. Water\nResources Research<\/em>. https:\/\/doi.org\/10.1029\/2018WR024461<\/a> <\/li>
  19. Forbes, W. L., Mao, J., Ricciuto, D.\nM., Kao, S. C., Shi, X., Tavakoly, A. A., … & Thornton, P. E. (2019).\nStreamflow in the Columbia River Basin: Quantifying changes over the period\n1951\u20102008 and determining the drivers of those changes. Water Resources\nResearch<\/em>. https:\/\/doi.org\/10.1029\/2018WR024256<\/a> <\/li>
  20. Steinschneider, S., Ray, P., Rahat, S.\nH., & Kucharski, J. (2019). A Weather\u2010Regime\u2010Based Stochastic Weather\nGenerator for Climate Vulnerability Assessments of Water Systems in the Western\nUnited States. Water Resources Research<\/em>. https:\/\/doi.org\/10.1029\/2018WR024446<\/a> <\/li>
  21. Parente, M. T., Bittner, D., Mattis,\nS., Chiogna, G., & Wohlmuth, B. (2019). Bayesian calibration and\nsensitivity analysis for a karst aquifer model using active subspaces. Water\nResources Research<\/em>. https:\/\/doi.org\/10.1029\/2019WR024739<\/a> <\/li>
  22. Tso, C. H. M., Kuras, O., & Binley,\nA. (2019). On the Field Estimation of Moisture Content Using Electrical\nGeophysics: The Impact of Petrophysical Model Uncertainty. Water Resources\nResearch<\/em>. https:\/\/doi.org\/10.1029\/2019WR024964<\/a> <\/li>
  23. Crosbie, R. S., Doble, R. C.,\nTurnadge, C., & Taylor, A. R. (2019). Constraining the magnitude and\nuncertainty of specific yield for use in the water table fluctuation method of\nestimating recharge. Water Resources Research<\/em>. https:\/\/doi.org\/10.1029\/2019WR025285<\/a> <\/li>
  24. Despax, A., Le Coz, J., Hauet, A.,\nMueller, D. S., Engel, F. L., Blanquart, B., … & Oberg, K. A.\nDecomposition of Uncertainty Sources in Acoustic Doppler Current Profiler\nStreamflow Measurements Using Repeated Measures Experiments. Water Resources\nResearch<\/em>. https:\/\/doi.org\/10.1029\/2019WR025296<\/a> <\/li>
  25. Goetz, J., & Brenning, A. (2019).\nQuantifying uncertainties in snow depth mapping from structure from motion\nphotogrammetry in an alpine area. Water Resources Research<\/em>. https:\/\/doi.org\/10.1029\/2019WR025251<\/a> <\/li>
  26. Ravindranath, A., Devineni, N., Lall,\nU., Cook, E. R., Pederson, G., Martin, J., & Woodhouse, C. (2019).\nStreamflow Reconstruction in the Upper Missouri River Basin Using a Novel\nBayesian Network Model. Water Resources Research<\/em>. https:\/\/doi.org\/10.1029\/2019WR024901<\/a> <\/li>
  27. Zhao, D., & Wu, S. Projected\nchanges in permafrost active layer thickness over the Qinghai\u2013Tibet Plateau\nunder climate change. Water Resources Research. https:\/\/doi.org\/10.1029\/2019WR024969<\/a> <\/li>
  28. Tolley, D., Foglia, L., & Harter,\nT. Sensitivity Analysis and Calibration of an Integrated Hydrologic Model in an\nIrrigated Agricultural Basin with a Groundwater\u2010Dependent Ecosystem. Water\nResources Research<\/em>. https:\/\/doi.org\/10.1029\/2018WR024209<\/a> <\/li>
  29. Ciriello, V., Lauriola, I., &\nTartakovsky, D. M. (2018). Distribution\u2010based global sensitivity analysis in\nhydrology. Water Resources Research<\/em>. https:\/\/doi.org\/10.1029\/2019WR025844<\/a> <\/li>
  30. Wrzesien, M. L., Pavelsky, T. M.,\nDurand, M. T., Dozier, J., & Lundquist, J. D. (2019). Characterizing biases\nin mountain snow accumulation from global datasets. Water Resources Research. https:\/\/doi.org\/10.1029\/2019WR025350<\/a> <\/li>
  31. Hayek, M., Ramarao, B., & Lavenue,\nM. (2019). An Adjoint Sensitivity Model for Steady\u2010State Sequentially Coupled\nRadionuclide Transport in Porous Media. Water Resources Research<\/em>.\nhttps:\/\/doi.org\/10.1029\/2019WR025686<\/a> <\/li><\/ol>\n\n\n\n

    Environmental Modelling and Software:<\/strong><\/h2>\n\n\n\n
    1. Wagena, M. B., Bhatt, G., Buell, E.,\nSommerlot, A. R., Fuka, D. R., & Easton, Z. M. (2019). Quantifying model\nuncertainty using Bayesian multi-model ensembles. Environmental Modelling\n& Software<\/em>, 117<\/em>, 89-99. https:\/\/doi.org\/10.1016\/j.envsoft.2019.03.013<\/a> <\/li>
    2. McKenzie, P. F., Duveneck, M. J.,\nMorreale, L. L., & Thompson, J. R. (2019). Local and global parameter\nsensitivity within an ecophysiologically based forest landscape model.\nEnvironmental Modelling & Software, 117, 1-13. https:\/\/doi.org\/10.1016\/j.envsoft.2019.03.002<\/a> <\/li>
    3. Zadeh, F. K., Nossent, J.,\nWoldegiorgis, B. T., Bauwens, W., & van Griensven, A. (2019). Impact of\nmeasurement error and limited data frequency on parameter estimation and\nuncertainty quantification. Environmental Modelling & Software<\/em>, 118<\/em>,\n35-47. https:\/\/doi.org\/10.1016\/j.envsoft.2019.03.022<\/a> <\/li>
    4. Ye, L., Gao, L., Marcos-Martinez, R.,\nMallants, D., & Bryan, B. A. (2019). Projecting Australia’s forest cover\ndynamics and exploring influential factors using deep learning. Environmental\nModelling & Software<\/em>, 119<\/em>, 407-417. https:\/\/doi.org\/10.1016\/j.envsoft.2019.07.013<\/a> <\/li>
    5. Guillaume, J. H., Jakeman, J. D.,\nMarsili-Libelli, S., Asher, M., Brunner, P., Croke, B., … & Stigter, J.\nD. (2019). Introductory overview of identifiability analysis: A guide to\nevaluating whether you have the right type of data for your modeling purpose. Environmental\nModelling & Software<\/em>. https:\/\/doi.org\/10.1016\/j.envsoft.2019.07.007<\/a> <\/li><\/ol>\n\n\n\n

      Hydrology and Earth System Sciences:<\/strong><\/h2>\n\n\n\n
      1. Lane,\nR. A., Coxon, G., Freer, J. E., Wagener, T., Johnes, P. J., Bloomfield, J. P.,\nGreene, S., Macleod, C. J. A., and Reaney, S. M.: Benchmarking the predictive\ncapability of hydrological models for river flow and flood peak predictions\nacross over 1000 catchments in Great Britain, Hydrol. Earth Syst. Sci., 23,\n4011\u20134032, https:\/\/doi.org\/10.5194\/hess-23-4011-2019<\/a> , 2019.<\/li>
      2. Jennings,\nK. S. and Molotch, N. P.: The sensitivity of modeled snow accumulation and melt\nto precipitation phase methods across a climatic gradient, Hydrol. Earth Syst.\nSci., 23, 3765\u20133786, https:\/\/doi.org\/10.5194\/hess-23-3765-2019<\/a> , 2019.<\/li>
      3. Erdal,\nD. and Cirpka, O. A.: Global sensitivity analysis and adaptive stochastic\nsampling of a subsurface-flow model using active subspaces, Hydrol. Earth Syst.\nSci., 23, 3787\u20133805, https:\/\/doi.org\/10.5194\/hess-23-3787-2019<\/a> , 2019.<\/li>
      4. Dalledonne,\nG. L., Kopmann, R., and Brudy-Zippelius, T.: Uncertainty quantification of\nfloodplain friction in hydrodynamic models, Hydrol. Earth Syst. Sci., 23,\n3373\u20133385, https:\/\/doi.org\/10.5194\/hess-23-3373-2019<\/a> , 2019.<\/li>
      5. Brunner,\nM. I., B\u00e1rdossy, A., and Furrer, R.: Technical note: Stochastic simulation of\nstreamflow time series using phase randomization, Hydrol. Earth Syst. Sci., 23,\n3175\u20133187, https:\/\/doi.org\/10.5194\/hess-23-3175-2019<\/a> , 2019.<\/li>
      6. O,\nS. and Foelsche, U.: Assessment of spatial uncertainty of heavy rainfall at\ncatchment scale using a dense gauge network, Hydrol. Earth Syst. Sci., 23,\n2863\u20132875, https:\/\/doi.org\/10.5194\/hess-23-2863-2019<\/a> , 2019.<\/li>
      7. Yu,\nD., Yang, J., Shi, L., Zhang, Q., Huang, K., Fang, Y., and Zha, Y.: On the\nuncertainty of initial condition and initialization approaches in variably\nsaturated flow modeling, Hydrol. Earth Syst. Sci., 23, 2897\u20132914, https:\/\/doi.org\/10.5194\/hess-23-2897-2019<\/a> , 2019.<\/li><\/ol>\n\n\n\n

        Journal of\nHydrology:<\/strong><\/strong><\/h2>\n\n\n\n
        1.  Achieng, K. O., & Zhu, J. (2019).\nApplication of Bayesian framework for evaluation of streamflow simulations\nusing multiple climate models. Journal of Hydrology<\/em>, 574<\/em>,\n1110-1128. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.05.018<\/a> <\/li>
        2. Zhu,\nY., Chen, L., Wei, G., Li, S., & Shen, Z. (2019). Uncertainty assessment in\nbaseflow nonpoint source pollution prediction: The impacts of hydrographic\nseparation methods, data sources and baseflow period assumptions. Journal of\nHydrology<\/em>, 574<\/em>, 915-925. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.05.010<\/a> <\/li>
        3. Farsi,\nN., & Mahjouri, N. (2019). Evaluating the contribution of the climate\nchange and human activities to runoff change under uncertainty. Journal of\nhydrology, 574, 872-891. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.04.028<\/a> <\/li>
        4. Li,\nW., Duan, Q., Ye, A., & Miao, C. (2019). An improved meta-Gaussian\ndistribution model for post-processing of precipitation forecasts by censored\nmaximum likelihood estimation. Journal of Hydrology<\/em>, 574<\/em>,\n801-810. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.04.073<\/a> <\/li>
        5. Yan,\nL., Xiong, L., Ruan, G., Xu, C. Y., Yan, P., & Liu, P. (2019). Reducing\nuncertainty of design floods of two-component mixture distributions by\nutilizing flood timescale to classify flood types in seasonally snow covered\nregion. Journal of Hydrology<\/em>, 574<\/em>, 588-608. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.04.056<\/a> <\/li>
        6. Janetti,\nE. B., Guadagnini, L., Riva, M., & Guadagnini, A. (2019). Global\nsensitivity analyses of multiple conceptual models with uncertain parameters\ndriving groundwater flow in a regional-scale sedimentary aquifer. Journal of\nHydrology, 574, 544-556. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.04.035<\/a> <\/li>
        7. Ehlers,\nL. B., Sonnenborg, T. O., Heuvelink, G. B. M., He, X., & Refsgaard, J. C.\n(2019). Joint treatment of point measurement, sampling and neighborhood\nuncertainty in space-time rainfall mapping. Journal of Hydrology, 574, 148-159.\nhttps:\/\/doi.org\/10.1016\/j.jhydrol.2019.03.100<\/a> <\/li>
        8. Wang,\nM., Zhang, M., Shi, H., Huang, X., & Liu, Y. (2019). Uncertainty analysis\nof a pollutant-hydrograph model in assessing inflow and infiltration of\nsanitary sewer systems. Journal of Hydrology, 574, 64-74. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.04.011<\/a> <\/li>
        9. Rana,\nS. M., Boccelli, D. L., Scott, D. T., & Hester, E. T. (2019). Parameter\nUncertainty with Flow Variation of the One-dimensional Solute Transport Model\nfor Small Streams using Markov chain Monte Carlo. Journal of Hydrology<\/em>. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.06.003<\/a> <\/li>
        10. Guo,\nY., Huang, S., Huang, Q., Wang, H., Wang, L., & Fang, W. (2019).\nCopulas-based bivariate socioeconomic drought dynamic risk assessment in a\nchanging environment. Journal of Hydrology<\/em>. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.06.010<\/a> <\/li>
        11. Wani,\nO., Scheidegger, A., Cecinati, F., Espadas, G., & Rieckermann, J. (2019).\nExploring a copula-based alternative to additive error models\u2013for non-negative\nand autocorrelated time series in hydrology. Journal of Hydrology. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.06.006<\/a> <\/li>
        12. Zhou,\nL., Meng, Y., & Abbaspour, K. C. (2019). A new framework for multi-site\nstochastic rainfall generator based on empirical orthogonal function analysis\nand Hilbert-Huang transform. Journal of Hydrology<\/em>, 575<\/em>, 730-742.\nhttps:\/\/doi.org\/10.1016\/j.jhydrol.2019.05.047<\/a> <\/li>
        13. Varouchakis,\nE. A., Theodoridou, P. G., & Karatzas, G. P. (2019). Spatiotemporal\ngeostatistical modeling of groundwater levels under a Bayesian framework using\nmeans of physical background. Journal of Hydrology<\/em>, 575<\/em>, 487-498.\nhttps:\/\/doi.org\/10.1016\/j.jhydrol.2019.05.055<\/a> <\/li>
        14. Chen,\nW., Wang, X., Deng, S., Liu, C., Xie, H., & Zhu, Y. (2019). Integrated\nurban flood vulnerability assessment using local spatial dependence-based\nprobabilistic approach. Journal of Hydrology<\/em>, 575<\/em>, 454-469.<\/li>
        15. Lindenschmidt,\nK. E., Rokaya, P., Das, A., Li, Z., & Richard, D. (2019). A novel stochastic\nmodelling approach for operational real-time ice-jam flood forecasting. Journal\nof Hydrology<\/em>, 575<\/em>, 381-394. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.05.048<\/a> <\/li>
        16. Jaros,\nA., Rossi, P. M., Ronkanen, A. K., & Kl\u00f8ve, B. (2019). Parameterisation of\nan integrated groundwater-surface water model for hydrological analysis of\nboreal aapa mire wetlands. Journal of Hydrology<\/em>, 575<\/em>, 175-191.\nhttps:\/\/doi.org\/10.1016\/j.jhydrol.2019.04.094<\/a> <\/li>
        17. Gupta,\nA., & Govindaraju, R. S. (2019). Propagation of structural uncertainty in\nwatershed hydrologic models. Journal of Hydrology, 575, 66-81. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.05.026<\/a> <\/li>
        18. Culley,\nS., Bennett, B., Westra, S., & Maier, H. R. (2019). Generating realistic\nperturbed hydrometeorological time series to inform scenario-neutral climate\nimpact assessments. Journal of Hydrology<\/em>. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.06.005<\/a> <\/li>
        19. Zhu,\nJ. (2019). Sensitivity of advective contaminant travel time to the soil\nhydraulic parameters in unsaturated heterogeneous soils. Journal of\nHydrology<\/em>. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.06.035<\/a> <\/li>
        20. Zhu,\nF., Zhong, P. A., Cao, Q., Chen, J., Sun, Y., & Fu, J. (2019). A stochastic\nmulti-criteria decision making framework for robust water resources management\nunder uncertainty. Journal of Hydrology<\/em>. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.06.049<\/a> <\/li>
        21. Gangrade,\nS., Kao, S. C., Dullo, T. T., Kalyanapu, A. J., & Preston, B. L. (2019).\nEnsemble-based Flood Vulnerability Assessment for Probable Maximum Flood in a\nChanging Environment. Journal of Hydrology<\/em>. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.06.027<\/a> <\/li>
        22. Guo,\nA., Chang, J., Wang, Y., Huang, Q., Guo, Z., & Li, Y. (2019). Uncertainty\nanalysis of water availability assessment through the Budyko framework. Journal\nof Hydrology<\/em>. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.06.033<\/a> <\/li>
        23. De\nPue, J., Rezaei, M., Van Meirvenne, M., & Cornelis, W. M. (2019). The\nrelevance of measuring saturated hydraulic conductivity: Sensitivity analysis\nand functional evaluation. Journal of Hydrology<\/em>, 576<\/em>, 628-638.\nhttps:\/\/doi.org\/10.1016\/j.jhydrol.2019.06.079<\/a> <\/li>
        24. Leandro,\nJ., Gander, A., Beg, M. N. A., Bhola, P., Konnerth, I., Willems, W., … &\nDisse, M. (2019). Forecasting upper and lower uncertainty bands of river flood\ndischarges with high predictive skill. Journal of Hydrology<\/em>. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.06.052<\/a> <\/li>
        25. Rammay,\nM. H., Elsheikh, A. H., & Chen, Y. (2019). Quantification of prediction\nuncertainty using imperfect subsurface models with model error estimation. Journal\nof Hydrology<\/em>. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.02.056<\/a> <\/li>
        26. Siade,\nA. J., Rathi, B., Prommer, H., Welter, D., & Doherty, J. (2019). Using\nheuristic multi-objective optimization for quantifying predictive uncertainty\nassociated with groundwater flow and reactive transport models. Journal of\nHydrology<\/em>, 577<\/em>, 123999.<\/li>
        27. Karanitsch-Ackerl,\nS., Mayer, K., Gauster, T., Laaha, G., Holawe, F., Wimmer, R., & Grabner,\nM. (2019). A 400-year reconstruction of spring\u2013summer precipitation and summer\nlow flow from regional tree-ring chronologies in North-Eastern Austria. Journal\nof Hydrology<\/em>, 577<\/em>, 123986. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.123986<\/a><\/li>
        28. Cai,\nC., Wang, J., & Li, Z. (2019). Assessment and modelling of uncertainty in\nprecipitation forecasts from TIGGE using fuzzy probability and Bayesian theory.\nJournal of Hydrology<\/em>, 577<\/em>, 123995. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.123995<\/a><\/li>
        29. Umer,\nY. M., Jetten, V. G., & Ettema, J. (2019). Sensitivity of flood dynamics to\ndifferent soil information sources in urbanized areas. Journal of hydrology<\/em>,\n577<\/em>, 123945. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.123945<\/a><\/li>
        30. Hutchins,\nM. G., & Hitt, O. E. (2019). Sensitivity of river eutrophication to multiple\nstressors illustrated using graphical summaries of physics-based river water\nquality model simulations. Journal of Hydrology, 577, 123917. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.123917<\/a><\/li>
        31. Pan,\nZ., Liu, P., Gao, S., Cheng, L., Chen, J., & Zhang, X. (2019). Reducing the\nuncertainty of time-varying hydrological model parameters using spatial\ncoherence within a hierarchical Bayesian framework. Journal of Hydrology<\/em>,\n577<\/em>, 123927. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.123927<\/a><\/li><\/ol>\n\n\n\n

          Advances in\nWater Resources:<\/strong><\/h2>\n\n\n\n
          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<\/em>, 129<\/em>, 16-30. https:\/\/doi.org\/10.1016\/j.advwatres.2019.05.003<\/a> <\/li>
          2. Nury, A. H., Sharma, A., Marshall, L., & Mehrotra, R. (2019). Characterising Uncertainty in Precipitation Downscaling using a Bayesian Approach. Advances in Water Resources<\/em>. https:\/\/doi.org\/10.1016\/j.advwatres.2019.05.018<\/a> <\/li>
          3. G\u00f3mez, J. E., & Torres-Verd\u00edn, C. (2019). Permeability sensitivity functions and rapid simulation of multi-point pressure measurements using perturbation theory. Advances in Water Resources<\/em>, 129<\/em>, 198-209. https:\/\/doi.org\/10.1016\/j.advwatres.2019.05.015<\/a> <\/li>
          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<\/a> <\/li><\/ol>\n","protected":false},"excerpt":{"rendered":"

            Water Resources Research: Grimaldi, S., Schumann, G. P., Shokri, A., Walker, J. P., & Pauwels, V. R. N. (2019). Challenges, opportunities and pitfalls for global coupled hydrologic\u2010hydraulic modeling of floods. Water Resources Research. https:\/\/doi.org\/10.1029\/2018WR024289 Leach, J. A., & Moore, R.<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0},"categories":[1,13],"tags":[],"jetpack_featured_media_url":"","_links":{"self":[{"href":"http:\/\/hydrouncertainty.org\/wp-json\/wp\/v2\/posts\/966"}],"collection":[{"href":"http:\/\/hydrouncertainty.org\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/hydrouncertainty.org\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/hydrouncertainty.org\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"http:\/\/hydrouncertainty.org\/wp-json\/wp\/v2\/comments?post=966"}],"version-history":[{"count":1,"href":"http:\/\/hydrouncertainty.org\/wp-json\/wp\/v2\/posts\/966\/revisions"}],"predecessor-version":[{"id":967,"href":"http:\/\/hydrouncertainty.org\/wp-json\/wp\/v2\/posts\/966\/revisions\/967"}],"wp:attachment":[{"href":"http:\/\/hydrouncertainty.org\/wp-json\/wp\/v2\/media?parent=966"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/hydrouncertainty.org\/wp-json\/wp\/v2\/categories?post=966"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/hydrouncertainty.org\/wp-json\/wp\/v2\/tags?post=966"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}