This Page is updated every month. Please visit the website regularly to see the latest publications about ‘Hydrologic Uncertainty’ every month.

April 2019 version of the latest publications

Water Resources Research:

  1. Abbaszadeh, P., Moradkhani, H., & Daescu, D. N. (February 2019). The Quest for Model Uncertainty Quantification: A Hybrid Ensemble and Variational Data Assimilation Framewor. Water Resources Research, 55. https://doi.org/10.1029/2018WR023629
  2. Barendrecht, M. H., Viglione, A., Kreibich, H., Merz, B., Vorogushyn, S., & Blöschl, G. (January 2019). The value of empirical data for estimating the parameters of a sociohydrological flood risk model. Water Resources Research, 55, 1312– 1336. https://doi.org/10.1029/2018WR024128
  3. Broderick, C., Murphy, C., Wilby, R. L., Matthews, T., Prudhomme, C., & Adamson, M. (January 2019). Using a scenario‐neutral framework to avoid potential maladaptation to future flood risk. Water Resources Research, 55, 1079– 1104. https://doi.org/10.1029/2018WR023623
  4. Camacho Suarez, V. V., Schellart, A. N. A., Brevis, W., & Shucksmith, J. D. (April 2019). Quantifying the impact of uncertainty in the dispersion coefficient on assessment of regulatory compliance. Water Resources Research, 55. https://doi.org/10.1029/2018WR023417
  5. Dai, H., Chen, X., Ye, M., Song, X., Hammond, G., Hu, B., & Zachara, J. M. (March 2019). Using Bayesian Networks for Sensitivity Analysis of Complex Biogeochemical Models. Water Resources Research, 55. https://doi.org/10.1029/2018WR023589
  6. Dobson, B., Wagener, T., & Pianosi, F. (January 2019). How important are model structural and contextual uncertainties when estimating the optimized performance of water resource systems? Water Resources Research, 55. https://doi.org/10.1029/2018WR024249
  7. Ford, T. W., & Quiring, S. M. (February 2019). Comparison of contemporary in situ, model, and satellite remote sensing soil moisture with a focus on drought monitoring. Water Resources Research, 55, 1565– 1582. https://doi.org/10.1029/2018WR024039
  8. Frank, J. M., Massman, W. J., Ewers, B. E., & Williams, D. G. (January 2019). Bayesian analyses of 17 winters of water vapor fluxes show bark beetles reduce sublimation. Water Resources Research, 55, 1598– 1623. https://doi.org/10.1029/2018WR023054
  9. Günther, D., Marke, T., Essery, R., & Strasser, U. (March 2019). Uncertainties in Snowpack Simulations ‐ Assessing the Impact of Model Structure, Parameter Choice and Forcing Data Error on Point‐Scale Energy‐Balance Snow Model Performance. Water Resources Research, 55. https://doi.org/10.1029/2018WR023403
  10. Kavetski, D., Fenicia, F., Reichert, P. and Albert, C. (April 2018). Signature-domain calibration of hydrological models using Approximate Bayesian Computation: Theory and comparison to existing applications, Water Resources Research, 54(6), 4059–4083, https://doi:10.1002/2017WR02052
  11. Mai, J., & Tolson, B. A. (March 2019). Model Variable Augmentation (MVA) for Diagnostic Assessment of Sensitivity Analysis Results. Water Resources Research.
  12. McInerney, D., Thyer, M., Kavetski, D., Lerat, J. and Kuczera, G. (February 2017). Improving probabilistic prediction of daily streamflow by identifying Pareto optimal approaches for modelling heteroscedastic residual errors, Water Resources Research, 53(3), 2199–2239, https://doi:10.1002/2016WR019168
  13. Prieto, C., Le Vine, N., Kavetski, D., Garcia, E., & Medina, R. (March 2019). Flow prediction in ungauged catchments using probabilistic Random Forests regionalization and new statistical adequacy tests. Water Resources Research, 55. https://doi.org/10.1029/2018WR023254
  14. Qin Y, Kavetski D and Kuczera G (August 2018) A robust Gauss‐Newton algorithm for the optimization of hydrological models: Benchmarking against industry‐standard algorithms, Water Resources Research, 54, 9637–9654. https://doi.org/10.1029/2017WR02248.
  15. Sharma, S., Siddique, R., Reed, S., Ahnert, P., & Mejia, A. (January 2019). Hydrological model diversity enhances streamflow forecast skill at short‐ to medium‐range timescales. Water Resources Research, 55, 1510– 1530. https://doi.org/10.1029/2018WR023197
  16. Smyth, E. J., Raleigh, M. S., & Small, E. E. (January 2019). Particle filter data assimilation of monthly snow depth observations improves estimation of snow density and SWE. Water Resources Research, 55, 1296– 1311. https://doi.org/10.1029/2018WR023400
  17. Tasseff, B., Bent, R., & Van Hentenryck, P. (January 2019). Optimization of structural flood mitigation strategies. Water Resources Research, 55, 1490– 1509. https://doi.org/10.1029/2018WR024362
  18. Verdin, A., Rajagopalan, B., Kleiber, W., Podestá, G., & Bert, F. (March 2019). BayGEN: A Bayesian space‐time stochastic weather generator. Water Resources Research, 55. https://doi.org/10.1029/2017WR022473
  19. Yin, J., Zhan, X., Liu, J., & Schull, M. (January 2019). An Inter‐comparison of Noah Model Skills with Benefits of Assimilating SMOPS Blended and Individual Soil Moisture Retrievals. Water Resources Research. https://doi.org/10.1029/2018WR024326
  20. Zwieback, S., Westermann, S., Langer, M., Boike, J., Marsh, P., & Berg, A. (February 2019). Improving permafrost modeling by assimilating remotely sensed soil moisture. Water Resources Research, 55. https://doi.org/10.1029/2018WR023247

Environmental Modelling and Software:

  1. Gatel, L., Lauvernet, C., Carluer, N., Weill, S., Tournebize, J., & Paniconi, C. (March 2019). Global evaluation and sensitivity analysis of a physically based flow and reactive transport model on a laboratory experiment. Environmental Modelling & Software, 113, 73-83. https://doi.org/10.1016/j.envsoft.2018.12.006
  2. Hung, F., & Hobbs, B. F. (March 2019). How can learning-by-doing improve decisions in stormwater management? A Bayesian-based optimization model for planning urban green infrastructure investments. Environmental Modelling & Software, 113, 59-72. https://doi.org/10.1016/j.envsoft.2018.12.005
  3. Krapu, C., & Borsuk, M. (April 2019). Probabilistic programming: A review for environmental modellers. Environmental Modelling & Software. https://doi.org/10.1016/j.envsoft.2019.01.014
  4. McInerney D, Thyer M, Kavetski D, Bennett B, Lerat J, Gibbs M and Kuczera G (November 2018) A simplified approach to produce probabilistic hydrological model predictions, Environmental Modelling and Software, 109, 306-314, https://doi.org/10.1016/j.envsoft.2018.07.001
  5. Razavi, S., & Gupta, H. V. (April 2019). A multi-method Generalized Global Sensitivity Matrix approach to accounting for the dynamical nature of earth and environmental systems models. Environmental Modelling & Software, 114, 1-11. https://doi.org/10.1016/j.envsoft.2018.12.002
  6. Razavi, S., Sheikholeslami, R., Gupta, H. V., & Haghnegahdar, A. (February 2019). VARS-TOOL: A toolbox for comprehensive, efficient, and robust sensitivity and uncertainty analysis. Environmental Modelling & Software, 112, 95-107. https://doi.org/10.1016/j.envsoft.2018.10.005
  7. Saltelli, A., Aleksankina, K., Becker, W., Fennell, P., Ferretti, F., Holst, N., Li, S. and Wu, Q., (April 2019). Why so many published sensitivity analyses are false: A systematic review of sensitivity analysis practices. Environmental Modelling & Software. https://doi.org/10.1016/j.envsoft.2019.01.012
  8. Sheikholeslami, R., Razavi, S., Gupta, H. V., Becker, W., & Haghnegahdar, A. (January 2019). Global sensitivity analysis for high-dimensional problems: How to objectively group factors and measure robustness and convergence while reducing computational cost. Environmental modelling & software, 111, 282-299. https://doi.org/10.1016/j.envsoft.2018.09.002
  9. Stritih, A., Bebi, P., & Grêt-Regamey, A. (January 2019). Quantifying uncertainties in earth observation-based ecosystem service assessments. Environmental Modelling & Software, 111, 300-310. https://doi.org/10.1016/j.envsoft.2018.09.005
  10. Visser, A. G., Beevers, L., & Patidar, S. (April 2019). A coupled modelling framework to assess the hydroecological impact of climate change. Environmental Modelling & Software. https://doi.org/10.1016/j.envsoft.2019.01.004
  11. Warren, R.F., Edwards, N.R., Babonneau, F., Bacon, P.M., Dietrich, J.P., Ford, R.W., Garthwaite, P., Gerten, D., Goswami, S., Haurie, A. and Hiscock, K. (January 2019). Producing policy-relevant science by enhancing robustness and model integration for the assessment of global environmental change. Environmental Modelling & Software, 111, 248-258. https://doi.org/10.1016/j.envsoft.2018.05.010

Hydrology and Earth System Sciences:

  1. Mackay, J. D., Barrand, N. E., Hannah, D. M., Krause, S., Jackson, C. R., Everest, J., Aðalgeirsdóttir, G., and Black, A. R. (April 2019). Future evolution and uncertainty of river flow regime change in a deglaciating river basin, Hydrol. Earth Syst. Sci., 23, 1833-1865, https://doi.org/10.5194/hess-23-1833-2019.
  2. Schürz, C., Hollosi, B., Matulla, C., Pressl, A., Ertl, T., Schulz, K., and Mehdi, B. (2019). A comprehensive sensitivity and uncertainty analysis for discharge and nitrate-nitrogen loads involving multiple discrete model inputs under future changing conditions, Hydrol. Earth Syst. Sci., 23, 1211-1244, https://doi.org/10.5194/hess-23-1211-2019.
  3. Woldemeskel F, McInerney D, Lerat J, Thyer M, Kavetski D, Shin D, Tuteja N and Kuczera G (December 2018) Evaluating residual error approaches for post-processing monthly and seasonal streamflow forecasts, Hydrological and Earth System Sciences, 22, 6257-6278, doi: https://doi.org/10.5194/hess-2018-214

Journal of Hydrology:

  1. Ahmed, K., Shahid, S., Chung, E. S., Wang, X. J., & Harun, S. B. (March 2019). Climate Change Uncertainties in Seasonal Drought Severity-Area-Frequency Curves: Case of Arid Region of Pakistan. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2019.01.019
  2. Ahn, K. H., & Kim, Y. O. (March 2019). Incorporating Climate Model Similarities and Hydrologic Error Models to Quantify Climate Change Impacts on Future Riverine Flood Risk. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2018.12.061
  3. Chen, L., Chen, S., Li, S., & Shen, Z. (March 2019). Temporal and spatial scaling effects of parameter sensitivity in relation to non-point source pollution simulation. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2019.01.045
  4. Gebrehiwot, S. G., Di Baldassarre, G., Bishop, K., Halldin, S., & Breuer, L. (March 2019). Is observation uncertainty masking the signal of land use change impacts on hydrology?. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2018.12.058
  5. Hazra, A., Maggioni, V., Houser, P., Antil, H., & Noonan, M. (March 2019). A Monte Carlo-Based Multi-Objective Optimization Approach to Merge Different Precipitation Estimates for Land Surface Modeling. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2018.12.039
  6. Koohbor, B., Fahs, M., Ataie-Ashtiani, B., Belfort, B., Simmons, C. T., & Younes, A. (April 2019). Uncertainty analysis for seawater intrusion in fractured coastal aquifers: Effects of fracture location, aperture, density and hydrodynamic parameters. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2019.01.052
  7. Li, Z., Jasechko, S., & Si, B. (April 2019). Uncertainties in tritium mass balance models for groundwater recharge estimation. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2019.01.030
  8. Peleg, N., Molnar, P., Burlando, P., & Fatichi, S. (April 2019). Exploring stochastic climate uncertainty in space and time using a gridded hourly weather generator. Journal of Hydrology, 571, 627-641. https://doi.org/10.1016/j.jhydrol.2019.02.010
  9. Pirot, G., Huber, E., Irving, J., & Linde, N. (April 2019). Reduction of conceptual model uncertainty using ground-penetrating radar profiles: Field-demonstration for a braided-river aquifer. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2019.01.047
  10. Sharifi, E., Saghafian, B., & Steinacker, R. (March 2019). Copula-based Stochastic Uncertainty Analysis of Satellite Precipitation Products. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2019.01.035
  11. Soltani, M., Laux, P., Mauder, M., & Kunstmann, H. (April 2019). Inverse distributed modelling of streamflow and turbulent fluxes: A sensitivity and uncertainty analysis coupled with automatic optimization. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2019.02.033
  12. Yang, W., Long, D., & Bai, P. (March 2019). Impacts of future land cover and climate changes on runoff in the mostly afforested river basin in North China. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2018.12.055
  13. Wine, M. L. (2019). Under non-stationarity securitization contributes to uncertainty and Tragedy of the Commons. Journal of Hydrology, 568, 716-721, https://doi.org/10.1016/j.jhydrol.2018.11.044
  14.  Jiang, S.Y., Zhang, Q., Werner, A.D., Wellen, C., Jomaa, S., Zhu, Q.D., Büttner, O., Meon, G. and Rode, M. (2019). Effects of stream nitrate data frequency on watershed model performance and prediction uncertainty. Journal of Hydrology, 569, 22-36, https://doi.org/10.1016/j.jhydrol.2018.11.049.

Geophysical Research Letters:

  1. Chen, H., Morrison, A. K., Dufour, C. O., & Sarmiento, J. L. (March 2019). Deciphering patterns and drivers of heat and carbon storage in the Southern Ocean. Geophysical Research Letters, 46. https://doi.org/10.1029/2018GL080961
  2. Wise, E. K., & Dannenberg, M. P. (March 2019). Climate factors leading to asymmetric extreme capture in the tree‐ring record. Geophysical Research Letters, 46. https://doi.org/10.1029/2019GL082295

Ecological Engineering:

  1. Kiesel, J., Gericke, A., Rathjens, H., Wetzig, A., Kakouei, K., Jähnig, S. C., & Fohrer, N. (2019). Climate change impacts on ecologically relevant hydrological indicators in three catchments in three European ecoregions. Ecological Engineering, 127, 404-416 https://doi.org/10.1016/j.ecoleng.2018.12.019

Science of the Total Environment:

  1. Fortesa, J., García-Comendador, J., Calsamiglia, A., López-Tarazón, J. A., Latron, J., Alorda, B., & Estrany, J. (2019). Comparison of stage/discharge rating curves derived from different recording systems: Consequences for streamflow data and water management in a Mediterranean island. Science of The Total Environment, 665, 968-981, https://doi.org/10.1016/j.scitotenv.2019.02.158

Book chapters:

  1. Kavetski D (2018) Parameter estimation and predictive uncertainty quantification in hydrological modelling, Book Chapter 25-1 in Duan Q et al (eds) Handbook of Hydrometeorological Ensemble Forecasting, Springer-Verlag, doi: 10.1007/978-3-642-40457-3_25-1.
Latest publications on ‘Hydrologic Uncertainty’

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