Publications – AGU Hydrological Uncertainty website http://hydrouncertainty.org Mon, 27 Apr 2020 01:42:04 +0000 en-US hourly 1 https://wordpress.org/?v=5.7.10 http://hydrouncertainty.org/wp-content/uploads/2019/01/cropped-AGU100_logo_V-CMYK-32x32.png Publications – AGU Hydrological Uncertainty website http://hydrouncertainty.org 32 32 Latest Publications on ‘Hydrologic Uncertainty’ – April 2020 http://hydrouncertainty.org/2020/04/26/latest-publications-on-hydrologic-uncertainty-april-2020/?utm_source=rss&utm_medium=rss&utm_campaign=latest-publications-on-hydrologic-uncertainty-april-2020 http://hydrouncertainty.org/2020/04/26/latest-publications-on-hydrologic-uncertainty-april-2020/#comments Mon, 27 Apr 2020 01:42:02 +0000 http://hydrouncertainty.org/?p=1020 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 Research, 56(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 Research, 56(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 Research, 56(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 Research, 56(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 Research, 56(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 Research, 56(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 Research, 56(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 Software, 124, 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 & Software, 124, 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 & Software, 124, 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 & Software, 124, 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 & Software, 126, 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 & Software, 126, 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 & Software, 126, 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 Hydrology, 581, 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 Hydrology, 582, 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 Hydrology, 582, 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
]]> http://hydrouncertainty.org/2020/04/26/latest-publications-on-hydrologic-uncertainty-april-2020/feed/ 2 Latest Publications on ‘Hydrologic Uncertainty’ – January 2020 http://hydrouncertainty.org/2020/04/26/latest-publications-on-hydrologic-uncertainty-january-2020/?utm_source=rss&utm_medium=rss&utm_campaign=latest-publications-on-hydrologic-uncertainty-january-2020 http://hydrouncertainty.org/2020/04/26/latest-publications-on-hydrologic-uncertainty-january-2020/#respond Sun, 26 Apr 2020 22:27:12 +0000 http://hydrouncertainty.org/?p=1018 Water Resources Research:
  1. Harken, B., Chang, C.‐F., Dietrich, P., Kalbacher, T., & Rubin, Y. ( 2019). Hydrogeological modeling and water resources management: Improving the link between data, prediction, and decision making. Water Resources Research, 55, 10340– 10357. https://doi.org/10.1029/2019WR025227
  2. Gebler, S., Kurtz, W., Pauwels, V. R. N., Kollet, S. J., Vereecken, H., & Hendricks Franssen, H.‐J. ( 2019). Assimilation of High‐Resolution Soil Moisture Data Into an Integrated Terrestrial Model for a Small‐Scale Head‐Water Catchment. Water Resources Research, 55, 10358– 10385. https://doi.org/10.1029/2018WR024658
  3. Mortazavi‐Naeini, M., Bussi, G., Elliott, J. A., Hall, J. W., & Whitehead, P. G. ( 2019). Assessment of risks to public water supply from low flows and harmful water quality in a changing climate. Water Resources Research, 55, 10386– 10404. https://doi.org/10.1029/2018WR022865
  4. Lombardo, F., Napolitano, F., Russo, F., & Koutsoyiannis, D. ( 2019). On the exact distribution of correlated extremes in hydrology. Water Resources Research, 55, 10405– 10423. https://doi.org/10.1029/2019WR025547
  5. Melsen, L. A., & Guse, B.( 2019). Hydrological drought simulations: How climate and model structure control parameter sensitivity. Water Resources Research, 55, 10527– 10547. https://doi.org/10.1029/2019WR025230
  6. Russo, D. ( 2019). Stochastic Analysis of the Soil Water Content Standard Deviation‐Mean Value Relationships: On the Physical Significance of the Critical Mean Soil Water Content. Water Resources Research, 55, 10588– 10601. https://doi.org/10.1029/2019WR026405
  7. Lüdtke, S., Schröter, K., Steinhausen, M., Weise, L., Figueiredo, R., & Kreibich, H. ( 2019). A consistent approach for probabilistic residential flood loss modeling in Europe. Water Resources Research, 55, 10616– 10635. https://doi.org/10.1029/2019WR026213
  8. Yang, X., Jomaa, S., & Rode, M. ( 2019). Sensitivity analysis of fully distributed parameterization reveals insights into heterogeneous catchment responses for water quality modeling. Water Resources Research, 55, 10935– 10953. https://doi.org/10.1029/2019WR025575
  9. Alexander, R. B., Schwarz, G. E., & Boyer, E. W. ( 2019). Advances in quantifying streamflow variability across continental scales: 2 improved model regionalization and prediction uncertainties using hierarchical bayesian methods. Water Resources Research, 55, 11061– 11087. https://doi.org/10.1029/2019WR025037
  10. Lv, Z., Pomeroy, J. W., & Fang, X. ( 2019). Evaluation of SNODAS snow water equivalent in western Canada and assimilation into a Cold Region Hydrological Model. Water Resources Research, 55, 11166– 11187. https://doi.org/10.1029/2019WR025333
  11. Lemoubou, E. L., Tagne Kamdem, H. T., Bogning, J. R., & Zefack Tonnang, E. H. ( 2019). Thermal, moisture and solute transport responses effects on unsaturated soil hydraulic parameters estimation. Water Resources Research, 55, 11225– 11249. https://doi.org/10.1029/2019WR025542
  12. Pestana, S., Chickadel, C. C., Harpold, A., Kostadinov, T. S., Pai, H., Tyler, S., et al. ( 2019). Bias correction of airborne thermal infrared observations over forests using melting snow. Water Resources Research, 55, 11331– 11343. https://doi.org/10.1029/2019WR025699
  13. Kratzert, F., Klotz, D., Herrnegger, M., Sampson, A. K., Hochreiter, S., & Nearing, G. S. ( 2019). Toward improved predictions in ungauged basins: Exploiting the power of machine learning. Water Resources Research, 55, 11344– 11354. https://doi.org/10.1029/2019WR026065
  14. Do, H. X., Westra, S., Leonard, M., & Gudmundsson, L. (2020). Global‐scale prediction of flood timing using atmospheric reanalysis. Water Resources Research, 56, e2019WR024945. https://doi.org/10.1029/2019WR024945
  15. Konapala, G., & Mishra, A. (2020). Quantifying climate and catchment control on hydrological drought in the continental United States. Water Resources Research, 56, e2018WR024620. https://doi.org/10.1029/2018WR024620
  16. Zhang, J., Zheng, Q., Chen, D., Wu, L., & Zeng, L. (2020). Surrogate‐Based Bayesian Inverse Modeling of the Hydrological System: An Adaptive Approach Considering Surrogate Approximation Error. Water Resources Research, 56, e2019WR025721. https://doi.org/10.1029/2019WR025721
  17. Puri, R., & Maas, A. (2020). Evaluating the sensitivity of residential water demand estimation to model specification and instrument choices. Water Resources Research, 56, e2019WR026156. https://doi.org/10.1029/2019WR026156
  18. Gou, J., Miao, C., Duan, Q., Tang, Q., Di, Z., Liao, W., et al. ( 2020). Sensitivity analysis‐based automatic parameter calibration of the VIC model for streamflow simulations over China. Water Resources Research, 56, e2019WR025968. https://doi.org/10.1029/2019WR025968
  19. Bremer, L. L., Hamel, P., Ponette‐González, A. G., Pompeu, P. V., Saad, S. I., & Brauman, K. A. ( 2020). Who are we measuring and modeling for? Supporting multilevel decision‐making in watershed management. Water Resources Research, 56, e2019WR026011. https://doi.org/10.1029/2019WR026011
  20. Dembélé, M., Hrachowitz, M., Savenije, H. H. G., Mariéthoz, G., & Schaefli, B. ( 2020). Improving the predictive skill of a distributed hydrological model by calibration on spatial patterns with multiple satellite data sets. Water Resources Research, 56, e2019WR026085. https://doi.org/10.1029/2019WR026085

Environmental Modelling and Software:

  1.  Moallemi, E. A., Zare, F., Reed, P. M., Elsawah, S., Ryan, M. J., & Bryan, B. A. (2019). Structuring and evaluating decision support processes to enhance the robustness of complex human–natural systems. Environmental Modelling & Software, 104551. https://doi.org/10.1016/j.envsoft.2019.104551
  2. Choi, S. Y., Seo, I. W., & Kim, Y. O. (2020). Parameter uncertainty estimation of transient storage model using Bayesian inference with formal likelihood based on breakthrough curve segmentation. Environmental Modelling & Software, 123, 104558. https://doi.org/10.1016/j.envsoft.2019.104558
  3. Sahraei, S., Asadzadeh, M., & Shafii, M. (2019). Toward effective many-objective optimization: Rounded-archiving. Environmental Modelling & Software, 122, 104535. https://doi.org/10.1016/j.envsoft.2019.104535
  4. Garcia, D., Arostegui, I., & Prellezo, R. (2019). Robust combination of the Morris and Sobol methods in complex multidimensional models. Environmental Modelling & Software, 122, 104517. https://doi.org/10.1016/j.envsoft.2019.104517
  5. Marschmann, G. L., Pagel, H., Kügler, P., & Streck, T. (2019). Equifinality, sloppiness, and emergent structures of mechanistic soil biogeochemical models. Environmental Modelling & Software, 122, 104518. https://doi.org/10.1016/j.envsoft.2019.104518
  6. Willis, T., Wright, N., & Sleigh, A. (2019). Systematic analysis of uncertainty in 2D flood inundation models. Environmental Modelling & Software, 122, 104520. https://doi.org/10.1016/j.envsoft.2019.104520
  7. Jato-Espino, D., Sillanpää, N., Charlesworth, S. M., & Rodriguez-Hernandez, J. (2019). A simulation-optimization methodology to model urban catchments under non-stationary extreme rainfall events. Environmental Modelling & Software, 122, 103960. https://doi.org/10.1016/j.envsoft.2017.05.008

Hydrology and Earth System Sciences:

  1. Correa, A., Ochoa-Tocachi, D., and Birkel, C.: Technical note: Uncertainty in multi-source partitioning using large tracer data sets, Hydrol. Earth Syst. Sci., 23, 5059–5068, https://doi.org/10.5194/hess-23-5059-2019, 2019.

Journal of Hydrology:

  1.  Shrestha, A., Nair, A. S., & Indu, J. (2020). Role of precipitation forcing on the uncertainty of land surface model simulated soil moisture estimates. Journal of Hydrology, 580, 124264. https://doi.org/10.1016/j.jhydrol.2019.124264
  2. Giri, S., Lathrop, R. G., & Obropta, C. C. (2020). Climate change vulnerability assessment and adaptation strategies through best management practices. Journal of Hydrology, 580, 124311. https://doi.org/10.1016/j.jhydrol.2019.124311
  3. Liu, Y. R., Li, Y. P., Ma, Y., Jia, Q. M., & Su, Y. Y. (2020). Development of a Bayesian-copula-based frequency analysis method for hydrological risk assessment–The Naryn River in Central Asia. Journal of Hydrology, 580, 124349. https://doi.org/10.1016/j.jhydrol.2019.124349
  4. Bugna, G. C., Grace, J. M., & Hsieh, Y. P. (2020). Sensitivity of using stable water isotopic tracers to study the hydrology of isolated wetlands in North Florida. Journal of Hydrology, 580, 124321. https://doi.org/10.1016/j.jhydrol.2019.124321
  5. Das, J., Jha, S., & Goyal, M. K. (2020). Non-stationary and copula-based approach to assess the drought characteristics encompassing climate indices over the Himalayan states in India. Journal of Hydrology, 580, 124356. https://doi.org/10.1016/j.jhydrol.2019.124356
  6. Raei, E., Alizadeh, M. R., Nikoo, M. R., & Adamowski, J. (2019). Multi-objective decision-making for green infrastructure planning (LID-BMPs) in urban storm water management under uncertainty. Journal of Hydrology, 579, 124091. https://doi.org/10.1016/j.jhydrol.2019.124091
  7. Yan, X., Dong, W., An, Y., & Lu, W. (2019). A Bayesian-based integrated approach for identifying groundwater contamination sources. Journal of Hydrology, 579, 124160. https://doi.org/10.1016/j.jhydrol.2019.124160
  8. Hu, J., Chen, S., Behrangi, A., & Yuan, H. (2019). Parametric uncertainty assessment in hydrological modeling using the generalized polynomial chaos expansion. Journal of Hydrology, 579, 124158. https://doi.org/10.1016/j.jhydrol.2019.124158
  9. Mahmoudi, P., Rigi, A., & Kamak, M. M. (2019). Evaluating the sensitivity of precipitation-based drought indices to different lengths of record. Journal of Hydrology, 579, 124181. https://doi.org/10.1016/j.jhydrol.2019.124181
  10. Liu, Y., Qin, H., Zhang, Z., Yao, L., Wang, Y., Li, J., … & Zhou, J. (2019). Deriving reservoir operation rule based on Bayesian deep learning method considering multiple uncertainties. Journal of Hydrology, 579, 124207. https://doi.org/10.1016/j.jhydrol.2019.124207
]]> http://hydrouncertainty.org/2020/04/26/latest-publications-on-hydrologic-uncertainty-january-2020/feed/ 0 Latest Publications on ‘Hydrologic Uncertainty’ – November 2019 http://hydrouncertainty.org/2020/04/26/latest-publications-on-hydrologic-uncertainty-november-2019/?utm_source=rss&utm_medium=rss&utm_campaign=latest-publications-on-hydrologic-uncertainty-november-2019 http://hydrouncertainty.org/2020/04/26/latest-publications-on-hydrologic-uncertainty-november-2019/#respond Sun, 26 Apr 2020 22:23:29 +0000 http://hydrouncertainty.org/?p=1013 Water Resources Research:
  1.  Yang, H. J., Boso, F., Tchelepi, H. A., & Tartakovsky, D. M. (2019). Probabilistic forecast of single‐phase flow in porous media with uncertain properties. Water Resources Research. https://doi.org/10.1029/2019WR026090
  2. Koppa, A., Gebremichael, M., Zambon, R. C., Yeh, W. W. G., & Hopson, T. (2019). Seasonal Hydropower Planning for Data Scarce Regions Using Multi Model Ensemble Forecasts, Remote Sensing Data, and Stochastic Programming. Water Resources Research. https://doi.org/10.1029/2019WR025228
  3. Li, D., Lettenmaier, D. P., Margulis, S. A., & Andreadis, K. (2019). The role of rain‐on‐snow in flooding over the conterminous United States. Water Resources Research. https://doi.org/10.1029/2019WR024950
  4. D’Oria, M., Maranzoni, A., & Mazzoleni, M. (2019). Probabilistic Assessment of Flood Hazard due to Levee Breaches Using Fragility Functions. Water Resources Research. https://doi.org/10.1029/2019WR025369
  5. Ciriello, V., Lauriola, I., & Tartakovsky, D. M. (2018). Distribution‐based global sensitivity analysis in hydrology. Water Resources Research. https://doi.org/10.1029/2019WR025844
  6. Ancey, C., Bardou, E., Funk, M., Huss, M., Werder, M. A., & Trewhela, T. Hydraulic reconstruction of the 1818 Gitro glacial lake outburst flood. Water Resources Research. https://doi.org/10.1029/2019WR025274
  7. 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
  8. Khatami, S., Peel, M. C., Peterson, T. J., & Western, A. W. Flux Mapping: a new approach to evaluating model process representation under uncertainty. Water Resources Research. https://doi.org/10.1029/2018WR023750
  9. Arheimer, B., & Lindström, G. (2019). Detecting changes in river flow caused by wildfires, storms, urbanization, regulation and climate across Sweden. Water Resources Research. https://doi.org/10.1029/2019WR024759
  10. Gold, D. F., Reed, P. M., Trindade, B. C., & Characklis, G. W. (2019). Identifying Actionable Compromises: Navigating Multi‐City Robustness Conflicts to Discover Cooperative Safe Operating Spaces for Regional Water Supply Portfolios. Water Resources Research. https://doi.org/10.1029/2019WR025462

Environmental Modelling and Software:

  1.  Wu, X., Marshall, L., & Sharma, A. (2019). The influence of data transformations in simulating Total Suspended Solids using Bayesian inference. Environmental Modelling & Software, 121, 104493. https://doi.org/10.1016/j.envsoft.2019.104493
  2. Mindham, D. A., & Tych, W. (2019). Dynamic harmonic regression and irregular sampling; avoiding pre-processing and minimising modelling assumptions. Environmental Modelling & Software, 121, 104503. https://doi.org/10.1016/j.envsoft.2019.104503
  3. Panda, S. S., Amatya, D. M., Muwamba, A., & Chescheir, G. (2019). Estimation of evapotranspiration and its parameters for pine, switchgrass, and intercropping with remotely-sensed images based geospatial modeling. Environmental Modelling & Software, 121, 104487. https://doi.org/10.1016/j.envsoft.2019.07.012

Hydrology and Earth System Sciences:

  1. Yearsley, J. R., Sun, N., Baptiste, M., and Nijssen, B.: Assessing the impacts of hydrologic and land use alterations on water temperature in the Farmington River basin in Connecticut, Hydrol. Earth Syst. Sci., 23, 4491–4508, https://doi.org/10.5194/hess-23-4491-2019,  2019.

Journal of Hydrology:

  1. Srivastava, A., Grotjahn, R., Ullrich, P. A., & Risser, M. (2019). A unified approach to evaluating precipitation frequency estimates with uncertainty quantification: Application to Florida and California watersheds. Journal of Hydrology, 578, 124095. https://doi.org/10.1016/j.jhydrol.2019.124095
  2. Ren, K., Huang, S., Huang, Q., Wang, H., Leng, G., & Wu, Y. (2019). Defining the robust operating rule for multi-purpose water reservoirs under deep uncertainties. Journal of Hydrology, 578, 124134. https://doi.org/10.1016/j.jhydrol.2019.124134
  3. Ahmadalipour, A., & Moradkhani, H. (2019). A data-driven analysis of flash flood hazard, fatalities, and damages over the CONUS during 1996–2017. Journal of Hydrology, 578, 124106. https://doi.org/10.1016/j.jhydrol.2019.124106
  4. Lu, H., Kang, Y., Liu, L., & Li, J. (2019). Comprehensive groundwater safety assessment under potential shale gas contamination based on integrated analysis of reliability–resilience–vulnerability and gas migration index. Journal of Hydrology, 578, 124072. https://doi.org/10.1016/j.jhydrol.2019.124072
  5. Liu, Z., & Merwade, V. (2019). Separation and prioritization of uncertainty sources in a raster based flood inundation model using hierarchical Bayesian model averaging. Journal of Hydrology, 578, 124100. https://doi.org/10.1016/j.jhydrol.2019.124100
  6. Liu, J., Zhou, Z., Yan, Z., Gong, J., Jia, Y., Xu, C. Y., & Wang, H. (2019). A new approach to separating the impacts of climate change and multiple human activities on water cycle processes based on a distributed hydrological model. Journal of Hydrology, 578, 124096. https://doi.org/10.1016/j.jhydrol.2019.124096
  7. Kang, X., Shi, X., Revil, A., Cao, Z., Li, L., Lan, T., & Wu, J. (2019). Coupled hydrogeophysical inversion to identify non-Gaussian hydraulic conductivity field by jointly assimilating geochemical and time-lapse geophysical data. Journal of Hydrology, 578, 124092. https://doi.org/10.1016/j.jhydrol.2019.124092
  8. Zhuang, C., Zhou, Z., Illman, W. A., & Wang, J. (2019). Geostatistical inverse modeling for the characterization of aquitard heterogeneity using long-term multi-extensometer data. Journal of Hydrology, 578, 124024. https://doi.org/10.1016/j.jhydrol.2019.124024

Advances in Water Resources:

  1. Lopez-Alvis, J., Hermans, T., & Nguyen, F. (2019). A cross-validation framework to extract data features for reducing structural uncertainty in subsurface heterogeneity. Advances in Water Resources, 133, 103427. https://doi.org/10.1016/j.advwatres.2019.103427
  2. Chembolu, V., Kakati, R., & Dutta, S. (2019). A laboratory study of flow characteristics in natural heterogeneous vegetation patches under submerged conditions. Advances in Water Resources, 133, 103418. https://doi.org/10.1016/j.advwatres.2019.103418
  3. Gokdemir, C., Rubin, Y., Li, X., Li, Y., & Xu, H. (2019). Vulnerability analysis method of vegetation due to groundwater table drawdown induced by tunnel drainage. Advances in Water Resources, 133, 103406. https://doi.org/10.1016/j.advwatres.2019.103406
]]> http://hydrouncertainty.org/2020/04/26/latest-publications-on-hydrologic-uncertainty-november-2019/feed/ 0 Latest Publications on ‘Hydrologic Uncertainty’ – October 2019 http://hydrouncertainty.org/2019/10/13/latest-publications-on-hydrologic-uncertainty-october-2019/?utm_source=rss&utm_medium=rss&utm_campaign=latest-publications-on-hydrologic-uncertainty-october-2019 http://hydrouncertainty.org/2019/10/13/latest-publications-on-hydrologic-uncertainty-october-2019/#respond Mon, 14 Oct 2019 03:45:05 +0000 http://hydrouncertainty.org/?p=966 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
]]> http://hydrouncertainty.org/2019/10/13/latest-publications-on-hydrologic-uncertainty-october-2019/feed/ 0 Latest Publications on ‘Hydrologic Uncertainty’ – June 2019 http://hydrouncertainty.org/2019/07/20/latest-publications-on-hydrologic-uncertainty-june-2019/?utm_source=rss&utm_medium=rss&utm_campaign=latest-publications-on-hydrologic-uncertainty-june-2019 http://hydrouncertainty.org/2019/07/20/latest-publications-on-hydrologic-uncertainty-june-2019/#respond Sat, 20 Jul 2019 22:58:44 +0000 http://hydrouncertainty.org/?p=928 Water Resources Research:
  1. Tang, Y., Marshall, L., Sharma, A., Ajami, H., & Nott, D. J. (2019). Ecohydrologic error models for improved Bayesian inference in remotely sensed catchments. Water Resources Research. https://doi.org/10.1029/2019WR025055
  2. Brunner, M. I., Hingray, B., Zappa, M., & Favre, A. C. Future trends in the interdependence between flood peaks and volumes: hydro‐climatological drivers and uncertainty. Water Resources Research. https://doi.org/10.1029/2019WR024701
  3. Lee, T., & Ouarda, T. B. (2019). Multivariate Nonstationary Oscillation Simulation of Climate Indices with Empirical Mode Decomposition. Water Resources Research. https://doi.org/10.1029/2018WR023892
  4. Apurv, T., & Cai, X. Evaluation of the stationarity assumption for meteorological drought risk estimation at the multi‐decadal scale in contiguous US. Water Resources Research. https://doi.org/10.1029/2018WR024047

Environmental Modelling and Software:

  •  Badham, J., Elsawah, S., Guillaume, J. H., Hamilton, S. H., Hunt, R. J., Jakeman, A. J., … & Gober, P. (2019). Effective modeling for Integrated Water Resource Management: A guide to contextual practices by phases and steps and future opportunities. Environmental Modelling & Software, 116, 40-56. https://doi.org/10.1016/j.envsoft.2019.02.013

Hydrology and Earth System Sciences:

  • 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.
  • Huang, Y., Bárdossy, A., and Zhang, K.: Sensitivity of hydrological models to temporal and spatial resolutions of rainfall data, Hydrol. Earth Syst. Sci., 23, 2647-2663, https://doi.org/10.5194/hess-23-2647-2019, 2019.

Journal of Hydrology:

  •  Goodarzi, L., Banihabib, M. E., & Roozbahani, A. (2019). A decision-making model for flood warning system based on ensemble forecasts. Journal of hydrology, 573, 207-219. https://doi.org/10.1016/j.jhydrol.2019.03.040
  • Chen, Y., Xu, C. Y., Chen, X., Xu, Y., Yin, Y., Gao, L., & Liu, M. (2019). Uncertainty in simulation of land-use change impacts on catchment runoff with multi-timescales based on the comparison of the HSPF and SWAT models. Journal of Hydrology, 573, 486-500. https://doi.org/10.1016/j.jhydrol.2019.03.091
  • Wang, L., Li, X., Ma, C., & Bai, Y. (2019). Improving the prediction accuracy of monthly streamflow using a data-driven model based on a double-processing strategy. Journal of Hydrology, 573, 733-745. https://doi.org/10.1016/j.jhydrol.2019.03.101
  • Strange, B. M., Maxwell, J. T., Robeson, S. M., Harley, G. L., Therrell, M. D., & Ficklin, D. L. (2019). Comparing three approaches to reconstructing streamflow using tree rings in the Wabash River basin in the Midwestern, US. Journal of Hydrology, 573, 829-840. https://doi.org/10.1016/j.jhydrol.2019.03.057
  • Tsegaw, A. T., Alfredsen, K., Skaugen, T., & Muthanna, T. M. (2019). Predicting hourly flows at ungauged small rural catchments using a parsimonious hydrological model. Journal of Hydrology, 573, 855-871. https://doi.org/10.1016/j.jhydrol.2019.03.090

Hydrological Sciences Journal:

  1.  Alipour, M. H., & Kibler, K. M. (2019). Streamflow prediction under extreme data scarcity: a step toward hydrologic process understanding within severely data-limited regions. Hydrological Sciences Journal, https://doi.org/10.1080/02626667.2019.1626991
  2. Ahmadi, A., Nasseri, M., & Solomatine, D. P. (2019). Parametric uncertainty assessment of hydrological models: coupling UNEEC-P and a fuzzy general regression neural network. Hydrological Sciences Journal, 1-15, https://doi.org/10.1080/02626667.2019.1610565

Advances in Water Resources:

  1. Ceriotti, G., Russian, A., Bolster, D., & Porta, G. (2019). A double-continuum transport model for segregated porous media: Derivation and sensitivity analysis-driven calibration. Advances in Water Resources, 128, 206-217. https://doi.org/10.1016/j.advwatres.2019.04.003
  2. Dell’Oca, A., Riva, M., Ackerer, P., & Guadagnini, A. (2019). Solute transport in random composite media with uncertain dispersivities. Advances in Water Resources, 128, 48-58. https://doi.org/10.1016/j.advwatres.2019.04.005
]]> http://hydrouncertainty.org/2019/07/20/latest-publications-on-hydrologic-uncertainty-june-2019/feed/ 0 Latest Publications on ‘Hydrologic Uncertainty’ – May 2019 http://hydrouncertainty.org/2019/06/27/latest-publications-on-hydrologic-uncertainty-may-2019/?utm_source=rss&utm_medium=rss&utm_campaign=latest-publications-on-hydrologic-uncertainty-may-2019 http://hydrouncertainty.org/2019/06/27/latest-publications-on-hydrologic-uncertainty-may-2019/#respond Thu, 27 Jun 2019 15:24:23 +0000 http://hydrouncertainty.org/?p=876 Water Resources Research:
  1. Prieto, C., Le Vine, N., Kavetski, D., García, E., & Medina, R. (May 2019). Flow prediction in ungauged catchments using probabilistic random forests regionalization and new statistical adequacy tests. Water Resources Research, 55, 4364– 4392. https://doi.org/10.1029/2018WR023254
  2. Zhao, Q., Cai, X., & Li, Y. (May 2019). Determining inflow forecast horizon for reservoir operation. Water Resources Research, 55, 4066– 4081. https://doi.org/10.1029/2019WR025226
  3. Camacho Suarez, V. V., Schellart, A. N. A., Brevis, W., & Shucksmith, J. D. (May 2019). Quantifying the impact of uncertainty within the longitudinal dispersion coefficient on concentration dynamics and regulatory compliance in rivers. Water Resources Research, 55, 4393– 4409. https://doi.org/10.1029/2018WR023417
  4. Taner, M. Ü., Ray, P., & Brown, C. (May 2019). Incorporating multidimensional probabilistic information into robustness‐based water systems planning. Water Resources Research, 55, 3659– 3679. https://doi.org/10.1029/2018WR022909
  5. Mo, S., Zabaras, N., Shi, X., & Wu, J. (May 2019). Deep autoregressive neural networks for high‐dimensional inverse problems in groundwater contaminant source identification. Water Resources Research, 55, 3856– 3881. https://doi.org/10.1029/2018WR024638
  6. Ilampooranan, I., Van Meter, K. J., & Basu, N. B. (May 2019). A race against time: Modeling time lags in watershed response. Water Resources Research, 55, 3941– 3959. https://doi.org/10.1029/2018WR023815
  7. Baran, S., Hemri, S., & El Ayari, M. (May 2019). Statistical postprocessing of water level forecasts using Bayesian model averaging with doubly truncated normal components. Water Resources Research, 55, 3997– 4013. https://doi.org/10.1029/2018WR024028
  8. Meriö, L.‐J., Ala‐aho, P., Linjama, J., Hjort, J., Kløve, B., & Marttila, H. (May 2019). Snow to precipitation ratio controls catchment storage and summer flows in boreal headwater catchments. Water Resources Research, 55, 4096– 4109. https://doi.org/10.1029/2018WR023031
  9. Zhi, W., Li, L., Dong, W., Brown, W., Kaye, J., Steefel, C., & Williams, K. H. (May 2019). Distinct source water chemistry shapes contrasting concentration‐discharge patterns. Water Resources Research, 55, 4233– 4251. https://doi.org/10.1029/2018WR024257
  10. Bierkens, M. F. P., Reinhard, S., Bruijn, J. A., Veninga, W., & Wada, Y. (May 2019). The shadow price of irrigation water in major groundwater‐depleting countries. Water Resources Research, 55, 4266– 4287. https://doi.org/10.1029/2018WR023086
  11. Schaperow, J. R., Li, D., Margulis, S. A., & Lettenmaier, D. P. (May 2019). A curve‐fitting method for estimating bathymetry from water surface height and width. Water Resources Research, 55, 4288– 4303. https://doi.org/10.1029/2019WR024938
  12. Khan, H. F., & Brown, C. M. (May 2019). Effect of hydrogeologic and climatic variability on performance of a groundwater market. Water Resources Research, 55, 4304– 4321. https://doi.org/10.1029/2018WR024180
  13. Dobson, B., Wagener, T., & Pianosi, F. (2019). How important are model structural and contextual uncertainties when estimating the optimized performance of water resource systems? Water Resources Research, 55, 2170–2193. https://doi.org/10.1029/2018WR024249
  14. Nijzink, R. C., Almeida, S., Pechlivanidis, I. G., Capell, R., Gustafssons, D., Arheimer, B., et al. (2018). Constraining conceptual hydrological models with multiple information sources. Water Resources Research, 54, 8332–8362. https://doi.org/10.1029/2017WR021895

Environmental Modelling and Software:

  1.  Jaxa-Rozen, M., Kwakkel, J. H., & Bloemendal, M. (May 2019). A coupled simulation architecture for agent-based/geohydrological modelling with NetLogo and MODFLOW. Environmental Modelling & Software, 115, 19-37. https://doi.org/10.1016/j.envsoft.2019.01.020
  2. Härkönen, Sanna, Mathias Neumann, Volker Mues, Frank Berninger, Karol Bronisz, Giuseppe Cardellini, Gherardo Chirici et al. “A climate-sensitive forest model for assessing impacts of forest management in Europe.” Environmental Modelling & Software, 115 (May 2019): 128-143. https://doi.org/10.1016/j.envsoft.2019.02.009
  3. Shelia, V., Hansen, J., Sharda, V., Porter, C., Aggarwal, P., Wilkerson, C. J., & Hoogenboom, G. (May 2019). A multi-scale and multi-model gridded framework for forecasting crop production, risk analysis, and climate change impact studies. Environmental Modelling & Software, 115, 144-154. https://doi.org/10.1016/j.envsoft.2019.02.006
  4. Sandric, I., Ionita, C., Chitu, Z., Dardala, M., Irimia, R., & Furtuna, F. T. (May 2019). Using CUDA to accelerate uncertainty propagation modelling for landslide susceptibility assessment. Environmental Modelling & Software, 115, 176-186. https://doi.org/10.1016/j.envsoft.2019.02.016

Hydrology and Earth System Sciences:

  1.  Mustafa, S. M. T., Hasan, M. M., Saha, A. K., Rannu, R. P., Uytven, E. V., Willems, P., & Huysmans, M. (May 2019). Multi-model approach to quantify groundwater-level prediction uncertainty using an ensemble of global climate models and multiple abstraction scenarios. Hydrology and Earth System Sciences, 23(5), 2279-2303. https://doi.org/10.5194/hess-23-2279-2019

Journal of Hydrology:

  1. Kanani-Sadat, Y., Arabsheibani, R., Karimipour, F., & Nasseri, M. (May 2019). A new approach to flood susceptibility assessment in data-scarce and ungauged regions based on GIS-based hybrid multi criteria decision-making method. Journal of Hydrology, 572, 17-31. https://doi.org/10.1016/j.jhydrol.2019.02.034
  2. Bonakdari, H., Zaji, A. H., Binns, A. D., & Gharabaghi, B. (May 2019). Integrated Markov chains and uncertainty analysis techniques to more accurately forecast floods using satellite signals. Journal of Hydrology, 572, 75-95. https://doi.org/10.1016/j.jhydrol.2019.02.027
  3. Höge, M., Guthke, A., & Nowak, W. (May 2019). The hydrologist’s guide to Bayesian model selection, averaging and combination. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2019.01.072
  4. Li, W., Lin, K., Zhao, T., Lan, T., Chen, X., Du, H., & Chen, H. (May 2019). Risk assessment and sensitivity analysis of flash floods in ungauged basins using coupled hydrologic and hydrodynamic models. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2019.03.002
  5. Sun, X., Xiang, Y., Shi, Z., Hu, X., & Zhang, H. (May 2019). Sensitivity of the response of well-aquifer systems to different periodic loadings: A comparison of two wells in Huize, China. Journal of Hydrology, 572, 121-130. https://doi.org/10.1016/j.jhydrol.2019.02.029
  6. Mohammadi, Z., & Illman, W. A. (May 2019). Detection of karst conduit patterns via hydraulic tomography: A synthetic inverse modeling study. Journal of Hydrology, 572, 131-147. https://doi.org/10.1016/j.jhydrol.2019.02.044
  7. Lin, C. C., Liou, K. Y., Lee, M., & Chiueh, P. T. (May 2019). Impacts of urban water consumption under climate change: An adaptation measure of rainwater harvesting system. Journal of Hydrology, 572, 160-168. https://doi.org/10.1016/j.jhydrol.2019.02.032
  8. Chen, F., Shang, H., Panyushkina, I. P., Meko, D. M., Yu, S., Yuan, Y., & Chen, F. (May 2019). Tree-ring reconstruction of Lhasa River streamflow reveals 472 years of hydrologic change on southern Tibetan Plateau. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2019.02.054
  9. Sharma, S., & Mujumdar, P. P. (May 2019). On the relationship of daily rainfall extremes and local mean temperature. Journal of Hydrology, 572, 179-191. https://doi.org/10.1016/j.jhydrol.2019.02.048
  10. Silvestro, F., Rossi, L., Campo, L., Parodi, A., Fiori, E., Rudari, R., & Ferraris, L. (May 2019). Impact-based flash-flood forecasting system: Sensitivity to high resolution numerical weather prediction systems and soil moisture. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2019.02.055
  11. Rezaie-Balf, M., Kim, S., Fallah, H., & Alaghmand, S. (2019). Daily river flow forecasting using ensemble empirical mode decomposition based heuristic regression models: Application on the perennial rivers in Iran and South Korea. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2019.03.046
  12. Wang, K., Liu, X., Tian, W., Li, Y., Liang, K., Liu, C., … & Yang, X. (2019). Pan coefficient sensitivity to environment variables across China. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2019.03.039
  13. Brunner, M. I., & Sikorska, A. E. (2019). Dependence of flood peaks and volumes in modeled discharge time series: effect of different uncertainty sources. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2019.03.024

Hydrological Sciences Journal:

  1. Twenty-three Unsolved Problems in Hydrology (UPH) – a community perspective – https://www.tandfonline.com/doi/full/10.1080/02626667.2019.1620507
  2. Uncertainty in stage–discharge rating curves: application to Australian Hydrologic Reference Stations data – https://www.tandfonline.com/doi/full/10.1080/02626667.2019.1577555

Earth-Science Reviews

  1. Wagener, T. and Pianosi, F. (2019). What has Global Sensitivity Analysis ever done for us? A systematic review to support scientific advancement and to inform policy-making in earth system modelling. Earth-Science Reviews, 194, 1-18. https://doi.org/10.1016/j.earscirev.2019.04.006
]]> http://hydrouncertainty.org/2019/06/27/latest-publications-on-hydrologic-uncertainty-may-2019/feed/ 0 Latest publications on ‘Hydrologic Uncertainty’ – April 2019 http://hydrouncertainty.org/2019/04/25/latest-publication-about-hydrologic-uncertainty/?utm_source=rss&utm_medium=rss&utm_campaign=latest-publication-about-hydrologic-uncertainty http://hydrouncertainty.org/2019/04/25/latest-publication-about-hydrologic-uncertainty/#respond Thu, 25 Apr 2019 17:19:03 +0000 http://hydrouncertainty.org/?p=810 This Page is updated every month. Please visit the website regularly to see the latest publications about ‘Hydrologic Uncertainty’.

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.
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