This Page is updated every month. Please visit the website regularly to see the latest publications about ‘Hydrologic Uncertainty’.
Water Resources Research:
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Mai, J., & Tolson, B. A. (March 2019). Model Variable Augmentation (MVA) for Diagnostic Assessment of Sensitivity Analysis Results. Water Resources Research.
- 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
- 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
- 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.
- 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
- 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
- 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
- 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
- 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
- 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:
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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:
- 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.
- 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.
- 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:
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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:
- 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
- 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:
- 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:
- 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:
- 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’ – April 2019