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
Latest Publications on ‘Hydrologic Uncertainty’ – May 2019

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