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
Latest Publications on ‘Hydrologic Uncertainty’ – January 2020

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