{"id":876,"date":"2019-06-27T11:24:23","date_gmt":"2019-06-27T15:24:23","guid":{"rendered":"http:\/\/hydrouncertainty.org\/?p=876"},"modified":"2019-07-20T18:48:16","modified_gmt":"2019-07-20T22:48:16","slug":"latest-publications-on-hydrologic-uncertainty-may-2019","status":"publish","type":"post","link":"http:\/\/hydrouncertainty.org\/2019\/06\/27\/latest-publications-on-hydrologic-uncertainty-may-2019\/","title":{"rendered":"Latest Publications on ‘Hydrologic Uncertainty’ – May 2019"},"content":{"rendered":"\n

Water Resources Research: <\/h2>\n\n\n\n
  1. Prieto, C., Le Vine, N., Kavetski, D., Garc\u00eda, E., & Medina, R. (May 2019). Flow prediction in ungauged catchments using probabilistic random forests regionalization and new statistical adequacy tests. Water Resources Research<\/em>, 55, 4364\u2013 4392. https:\/\/doi.org\/10.1029\/2018WR023254<\/a> <\/li>
  2. Zhao, Q., Cai, X., & Li, Y. (May 2019). Determining inflow forecast horizon for reservoir operation. Water Resources Research<\/em>, 55, 4066\u2013 4081. https:\/\/doi.org\/10.1029\/2019WR025226<\/a> <\/li>
  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<\/em>, 55, 4393\u2013 4409. https:\/\/doi.org\/10.1029\/2018WR023417<\/a> <\/li>
  4. Taner, M. \u00dc., Ray, P., & Brown, C. (May 2019). Incorporating multidimensional probabilistic information into robustness\u2010based water systems planning. Water Resources Research<\/em>, 55, 3659\u2013 3679. https:\/\/doi.org\/10.1029\/2018WR022909<\/a> <\/li>
  5. Mo, S., Zabaras, N., Shi, X., & Wu, J. (May 2019). Deep autoregressive neural networks for high\u2010dimensional inverse problems in groundwater contaminant source identification. Water Resources Research<\/em>, 55, 3856\u2013 3881. https:\/\/doi.org\/10.1029\/2018WR024638<\/a> <\/li>
  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<\/em>, 55, 3941\u2013 3959. https:\/\/doi.org\/10.1029\/2018WR023815<\/a> <\/li>
  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<\/em>, 55, 3997\u2013 4013. https:\/\/doi.org\/10.1029\/2018WR024028<\/a> <\/li>
  8. Meri\u00f6, L.\u2010J., Ala\u2010aho, P., Linjama, J., Hjort, J., Kl\u00f8ve, B., & Marttila, H. (May 2019). Snow to precipitation ratio controls catchment storage and summer flows in boreal headwater catchments. Water Resources Research<\/em>, 55, 4096\u2013 4109. https:\/\/doi.org\/10.1029\/2018WR023031<\/a> <\/li>
  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\u2010discharge patterns. Water Resources Research<\/em>, 55, 4233\u2013 4251. https:\/\/doi.org\/10.1029\/2018WR024257<\/a> <\/li>
  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\u2010depleting countries. Water Resources Research<\/em>, 55, 4266\u2013 4287. https:\/\/doi.org\/10.1029\/2018WR023086<\/a> <\/li>
  11. Schaperow, J. R., Li, D., Margulis, S. A., & Lettenmaier, D. P. (May 2019). A curve\u2010fitting method for estimating bathymetry from water surface height and width. Water Resources Research<\/em>, 55, 4288\u2013 4303. https:\/\/doi.org\/10.1029\/2019WR024938<\/a> <\/li>
  12. Khan, H. F., & Brown, C. M. (May 2019). Effect of hydrogeologic and climatic variability on performance of a groundwater market. Water Resources Research<\/em>, 55, 4304\u2013 4321. https:\/\/doi.org\/10.1029\/2018WR024180<\/a> <\/li>
  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\u20132193. https:\/\/doi.org\/10.1029\/2018WR024249<\/a><\/li>
  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\u20138362. https:\/\/doi.org\/10.1029\/2017WR021895<\/a><\/li><\/ol>\n\n\n\n

    Environmental Modelling and Software:<\/h2>\n\n\n\n
    1.  Jaxa-Rozen, M., Kwakkel,\nJ. H., & Bloemendal, M. (May 2019). A coupled simulation architecture for\nagent-based\/geohydrological modelling with NetLogo and MODFLOW. Environmental\nModelling & Software<\/em>, 115, 19-37. https:\/\/doi.org\/10.1016\/j.envsoft.2019.01.020<\/a>\n<\/li>
    2. H\u00e4rk\u00f6nen, Sanna, Mathias Neumann, Volker Mues, Frank Berninger,\nKarol Bronisz, Giuseppe Cardellini, Gherardo Chirici et al. “A\nclimate-sensitive forest model for assessing impacts of forest management in\nEurope.” Environmental Modelling & Software,<\/em> 115 (May 2019):\n128-143. https:\/\/doi.org\/10.1016\/j.envsoft.2019.02.009<\/a>\n<\/li>
    3. Shelia, V., Hansen, J., Sharda, V., Porter, C., Aggarwal, P.,\nWilkerson, C. J., & Hoogenboom, G. (May 2019). A multi-scale and\nmulti-model gridded framework for forecasting crop production, risk analysis,\nand climate change impact studies. Environmental Modelling & Software<\/em>,\n115, 144-154. https:\/\/doi.org\/10.1016\/j.envsoft.2019.02.006<\/a>\n<\/li>
    4. Sandric, I., Ionita, C., Chitu, Z., Dardala, M., Irimia, R.,\n& Furtuna, F. T. (May 2019). Using CUDA to accelerate uncertainty propagation\nmodelling for landslide susceptibility assessment. Environmental Modelling\n& Software<\/em>, 115, 176-186. https:\/\/doi.org\/10.1016\/j.envsoft.2019.02.016<\/a>\n<\/li><\/ol>\n\n\n\n

      Hydrology and Earth System Sciences:<\/strong><\/h2>\n\n\n\n
      1.  Mustafa, S. M. T., Hasan,\nM. M., Saha, A. K., Rannu, R. P., Uytven, E. V., Willems, P., & Huysmans,\nM. (May 2019). Multi-model approach to quantify groundwater-level prediction\nuncertainty using an ensemble of global climate models and multiple abstraction\nscenarios. Hydrology and Earth System Sciences<\/em>, 23(5), 2279-2303. https:\/\/doi.org\/10.5194\/hess-23-2279-2019<\/a>\n<\/li><\/ol>\n\n\n\n

        Journal of Hydrology:<\/h2>\n\n\n\n
        1. Kanani-Sadat, Y., Arabsheibani, R., Karimipour, F., &\nNasseri, M. (May 2019). A new approach to flood susceptibility assessment in\ndata-scarce and ungauged regions based on GIS-based hybrid multi criteria\ndecision-making method. Journal of\nHydrology<\/em>, 572, 17-31. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.02.034<\/a>\n<\/li>
        2. Bonakdari, H., Zaji, A. H., Binns, A. D., & Gharabaghi, B. (May\n2019). Integrated Markov chains and uncertainty analysis techniques to more\naccurately forecast floods using satellite signals. Journal of Hydrology<\/em>, 572, 75-95. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.02.027<\/a>\n<\/li>
        3. H\u00f6ge, M., Guthke, A., & Nowak, W. (May 2019). The\nhydrologist\u2019s guide to Bayesian model selection, averaging and combination. Journal of Hydrology<\/em>. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.01.072<\/a>\n<\/li>
        4. Li, W., Lin, K., Zhao, T., Lan, T., Chen, X., Du, H., &\nChen, H. (May 2019). Risk assessment and sensitivity analysis of flash floods\nin ungauged basins using coupled hydrologic and hydrodynamic models. Journal of Hydrology<\/em>. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.03.002<\/a>\n<\/li>
        5. Sun, X., Xiang, Y., Shi, Z., Hu, X., & Zhang, H. (May 2019).\nSensitivity of the response of well-aquifer systems to different periodic\nloadings: A comparison of two wells in Huize, China. Journal of Hydrology<\/em>, 572, 121-130. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.02.029<\/a>\n<\/li>
        6. Mohammadi, Z., & Illman, W. A. (May 2019). Detection of\nkarst conduit patterns via hydraulic tomography: A synthetic inverse modeling\nstudy. Journal of Hydrology<\/em>, 572,\n131-147. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.02.044<\/a>\n<\/li>
        7. Lin, C. C., Liou, K. Y., Lee, M., & Chiueh, P. T. (May 2019).\nImpacts of urban water consumption under climate change: An adaptation measure\nof rainwater harvesting system. Journal\nof Hydrology<\/em>, 572, 160-168. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.02.032<\/a>\n<\/li>
        8. Chen, F., Shang, H., Panyushkina, I. P., Meko, D. M., Yu, S.,\nYuan, Y., & Chen, F. (May 2019). Tree-ring reconstruction of Lhasa River\nstreamflow reveals 472 years of hydrologic change on southern Tibetan Plateau. Journal of Hydrology<\/em>. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.02.054<\/a>\n<\/li>
        9. Sharma, S., & Mujumdar, P. P. (May 2019). On the\nrelationship of daily rainfall extremes and local mean temperature. Journal of Hydrology<\/em>, 572, 179-191. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.02.048<\/a>\n<\/li>
        10. Silvestro, F., Rossi, L., Campo, L., Parodi, A., Fiori, E.,\nRudari, R., & Ferraris, L. (May 2019). Impact-based flash-flood forecasting\nsystem: Sensitivity to high resolution numerical weather prediction systems and\nsoil moisture. Journal of Hydrology<\/em>. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.02.055<\/a>\n<\/li>
        11. Rezaie-Balf, M., Kim, S., Fallah, H., & Alaghmand, S.\n(2019). Daily river flow forecasting using ensemble empirical mode\ndecomposition based heuristic regression models: Application on the perennial\nrivers in Iran and South Korea. Journal\nof Hydrology<\/em>. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.03.046<\/a>\n<\/li>
        12. Wang, K., Liu, X., Tian, W., Li, Y., Liang, K., Liu, C., …\n& Yang, X. (2019). Pan coefficient sensitivity to environment variables\nacross China. Journal of Hydrology<\/em>. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.03.039<\/a>\n<\/li>
        13. Brunner, M. I., & Sikorska, A. E. (2019). Dependence of\nflood peaks and volumes in modeled discharge time series: effect of different\nuncertainty sources. Journal of Hydrology<\/em>.\nhttps:\/\/doi.org\/10.1016\/j.jhydrol.2019.03.024<\/a>\n<\/li><\/ol>\n\n\n\n

          Hydrological Sciences Journal:<\/h2>\n\n\n\n
          1. Twenty-three Unsolved Problems in Hydrology (UPH) \u2013 a community\nperspective – https:\/\/www.tandfonline.com\/doi\/full\/10.1080\/02626667.2019.1620507<\/a><\/li>
          2. Uncertainty in stage\u2013discharge rating curves: application to\nAustralian Hydrologic Reference Stations data \u2013 https:\/\/www.tandfonline.com\/doi\/full\/10.1080\/02626667.2019.1577555<\/a><\/li><\/ol>\n\n\n\n

            Earth-Science Reviews<\/h2>\n\n\n\n
            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<\/a><\/li><\/ol>\n","protected":false},"excerpt":{"rendered":"

              Water Resources Research: Prieto, C., Le Vine, N., Kavetski, D., Garc\u00eda, 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\u2013 4392. https:\/\/doi.org\/10.1029\/2018WR023254 Zhao,<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0},"categories":[1,13],"tags":[],"jetpack_featured_media_url":"","_links":{"self":[{"href":"http:\/\/hydrouncertainty.org\/wp-json\/wp\/v2\/posts\/876"}],"collection":[{"href":"http:\/\/hydrouncertainty.org\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/hydrouncertainty.org\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/hydrouncertainty.org\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"http:\/\/hydrouncertainty.org\/wp-json\/wp\/v2\/comments?post=876"}],"version-history":[{"count":4,"href":"http:\/\/hydrouncertainty.org\/wp-json\/wp\/v2\/posts\/876\/revisions"}],"predecessor-version":[{"id":912,"href":"http:\/\/hydrouncertainty.org\/wp-json\/wp\/v2\/posts\/876\/revisions\/912"}],"wp:attachment":[{"href":"http:\/\/hydrouncertainty.org\/wp-json\/wp\/v2\/media?parent=876"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/hydrouncertainty.org\/wp-json\/wp\/v2\/categories?post=876"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/hydrouncertainty.org\/wp-json\/wp\/v2\/tags?post=876"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}