{"id":1018,"date":"2020-04-26T18:27:12","date_gmt":"2020-04-26T22:27:12","guid":{"rendered":"http:\/\/hydrouncertainty.org\/?p=1018"},"modified":"2020-04-26T18:27:40","modified_gmt":"2020-04-26T22:27:40","slug":"latest-publications-on-hydrologic-uncertainty-january-2020","status":"publish","type":"post","link":"http:\/\/hydrouncertainty.org\/2020\/04\/26\/latest-publications-on-hydrologic-uncertainty-january-2020\/","title":{"rendered":"Latest Publications on ‘Hydrologic Uncertainty’ – January 2020"},"content":{"rendered":"\n

Water Resources Research: <\/h2>\n\n\n\n
  1. Harken, B., Chang,\nC.\u2010F., Dietrich, P., Kalbacher, T., & Rubin, Y. ( 2019). Hydrogeological\nmodeling and water resources management: Improving the link between data,\nprediction, and decision making. Water Resources Research<\/em>, 55, 10340\u2013\n10357. https:\/\/doi.org\/10.1029\/2019WR025227<\/a> <\/li>
  2. Gebler, S., Kurtz, W.,\nPauwels, V. R. N., Kollet, S. J., Vereecken, H., & Hendricks Franssen,\nH.\u2010J. ( 2019). Assimilation of High\u2010Resolution Soil Moisture Data Into an\nIntegrated Terrestrial Model for a Small\u2010Scale Head\u2010Water Catchment. Water\nResources Research<\/em>, 55, 10358\u2013 10385. https:\/\/doi.org\/10.1029\/2018WR024658<\/a> <\/li>
  3. Mortazavi\u2010Naeini, M.,\nBussi, G., Elliott, J. A., Hall, J. W., & Whitehead, P. G. ( 2019).\nAssessment of risks to public water supply from low flows and harmful water\nquality in a changing climate. Water Resources Research<\/em>, 55, 10386\u2013\n10404. https:\/\/doi.org\/10.1029\/2018WR022865<\/a> <\/li>
  4. Lombardo, F.,\nNapolitano, F., Russo, F., & Koutsoyiannis, D. ( 2019). On the exact\ndistribution of correlated extremes in hydrology. Water Resources Research<\/em>,\n55, 10405\u2013 10423. https:\/\/doi.org\/10.1029\/2019WR025547<\/a> <\/li>
  5. Melsen, L. A., &\nGuse, B.( 2019). Hydrological drought simulations: How climate and model\nstructure control parameter sensitivity. Water Resources Research<\/em>, 55,\n10527\u2013 10547. https:\/\/doi.org\/10.1029\/2019WR025230<\/a> <\/li>
  6. Russo, D. ( 2019).\nStochastic Analysis of the Soil Water Content Standard Deviation\u2010Mean Value\nRelationships: On the Physical Significance of the Critical Mean Soil Water\nContent. Water Resources Research<\/em>, 55, 10588\u2013 10601. https:\/\/doi.org\/10.1029\/2019WR026405<\/a> <\/li>
  7. L\u00fcdtke, S., Schr\u00f6ter,\nK., Steinhausen, M., Weise, L., Figueiredo, R., & Kreibich, H. ( 2019). A\nconsistent approach for probabilistic residential flood loss modeling in\nEurope. Water Resources Research<\/em>, 55, 10616\u2013 10635. https:\/\/doi.org\/10.1029\/2019WR026213<\/a> <\/li>
  8. Yang, X., Jomaa, S.,\n& Rode, M. ( 2019). Sensitivity analysis of fully distributed parameterization\nreveals insights into heterogeneous catchment responses for water quality\nmodeling. Water Resources Research<\/em>, 55, 10935\u2013 10953. https:\/\/doi.org\/10.1029\/2019WR025575<\/a> <\/li>
  9. Alexander, R. B.,\nSchwarz, G. E., & Boyer, E. W. ( 2019). Advances in quantifying streamflow\nvariability across continental scales: 2 improved model regionalization and\nprediction uncertainties using hierarchical bayesian methods. Water\nResources Research<\/em>, 55, 11061\u2013 11087. https:\/\/doi.org\/10.1029\/2019WR025037<\/a> <\/li>
  10. Lv, Z., Pomeroy, J. W., & Fang, X. ( 2019). Evaluation of SNODAS\nsnow water equivalent in western Canada and assimilation into a Cold Region\nHydrological Model. Water Resources Research<\/em>, 55, 11166\u2013 11187. https:\/\/doi.org\/10.1029\/2019WR025333<\/a> <\/li>
  11. Lemoubou, E. L., Tagne Kamdem, H. T., Bogning, J. R., & Zefack\nTonnang, E. H. ( 2019). Thermal, moisture and solute transport responses\neffects on unsaturated soil hydraulic parameters estimation. Water Resources\nResearch<\/em>, 55, 11225\u2013 11249. https:\/\/doi.org\/10.1029\/2019WR025542<\/a> <\/li>
  12. Pestana, S., Chickadel, C. C., Harpold, A., Kostadinov, T. S., Pai, H.,\nTyler, S., et al. ( 2019). Bias correction of airborne thermal infrared\nobservations over forests using melting snow. Water Resources Research<\/em>,\n55, 11331\u2013 11343. https:\/\/doi.org\/10.1029\/2019WR025699<\/a> <\/li>
  13. Kratzert, F., Klotz, D., Herrnegger, M., Sampson, A. K., Hochreiter, S.,\n& Nearing, G. S. ( 2019). Toward improved predictions in ungauged basins:\nExploiting the power of machine learning. Water Resources Research<\/em>, 55,\n11344\u2013 11354. https:\/\/doi.org\/10.1029\/2019WR026065<\/a> <\/li>
  14. Do, H. X., Westra, S., Leonard, M., & Gudmundsson, L. (2020).\nGlobal\u2010scale prediction of flood timing using atmospheric reanalysis. Water\nResources Research<\/em>, 56, e2019WR024945. https:\/\/doi.org\/10.1029\/2019WR024945<\/a> <\/li>
  15. Konapala, G., & Mishra, A. (2020). Quantifying climate and catchment\ncontrol on hydrological drought in the continental United States. Water\nResources Research<\/em>, 56, e2018WR024620. https:\/\/doi.org\/10.1029\/2018WR024620<\/a> <\/li>
  16. Zhang, J., Zheng, Q., Chen, D., Wu, L., & Zeng, L. (2020).\nSurrogate\u2010Based Bayesian Inverse Modeling of the Hydrological System: An\nAdaptive Approach Considering Surrogate Approximation Error. Water Resources\nResearch<\/em>, 56, e2019WR025721. https:\/\/doi.org\/10.1029\/2019WR025721<\/a> <\/li>
  17. Puri, R., & Maas, A. (2020). Evaluating the sensitivity of\nresidential water demand estimation to model specification and instrument\nchoices. Water Resources Research<\/em>, 56, e2019WR026156. https:\/\/doi.org\/10.1029\/2019WR026156<\/a> <\/li>
  18. Gou, J., Miao, C., Duan, Q., Tang, Q., Di, Z., Liao, W., et al. ( 2020).\nSensitivity analysis\u2010based automatic parameter calibration of the VIC model for\nstreamflow simulations over China. Water Resources Research<\/em>, 56,\ne2019WR025968. https:\/\/doi.org\/10.1029\/2019WR025968<\/a> <\/li>
  19. Bremer, L. L., Hamel, P., Ponette\u2010Gonz\u00e1lez, A. G., Pompeu, P. V., Saad,\nS. I., & Brauman, K. A. ( 2020). Who are we measuring and modeling for?\nSupporting multilevel decision\u2010making in watershed management. Water\nResources Research<\/em>, 56, e2019WR026011. https:\/\/doi.org\/10.1029\/2019WR026011<\/a> <\/li>
  20. Demb\u00e9l\u00e9, M., Hrachowitz, M., Savenije, H. H. G., Mari\u00e9thoz, G., &\nSchaefli, B. ( 2020). Improving the predictive skill of a distributed\nhydrological model by calibration on spatial patterns with multiple satellite\ndata sets. Water Resources Research<\/em>, 56, e2019WR026085. https:\/\/doi.org\/10.1029\/2019WR026085<\/a> <\/li><\/ol>\n\n\n\n

    Environmental Modelling and Software:<\/h2>\n\n\n\n
    1. \u00a0Moallemi, 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\u2013natural systems. Environmental Modelling & Software<\/em>, 104551. https:\/\/doi.org\/10.1016\/j.envsoft.2019.104551<\/a> <\/li>
    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<\/a> <\/li>
    3. Sahraei, S., Asadzadeh, M., & Shafii, M. (2019). Toward effective many-objective optimization: Rounded-archiving. Environmental Modelling & Software<\/em>, 122<\/em>, 104535. https:\/\/doi.org\/10.1016\/j.envsoft.2019.104535<\/a> <\/li>
    4. Garcia, D., Arostegui, I., & Prellezo, R. (2019). Robust combination of the Morris and Sobol methods in complex multidimensional models. Environmental Modelling & Software<\/em>, 122<\/em>, 104517. https:\/\/doi.org\/10.1016\/j.envsoft.2019.104517<\/a> <\/li>
    5. Marschmann, G. L., Pagel, H., K\u00fcgler, P., & Streck, T. (2019). Equifinality, sloppiness, and emergent structures of mechanistic soil biogeochemical models. Environmental Modelling & Software<\/em>, 122<\/em>, 104518. https:\/\/doi.org\/10.1016\/j.envsoft.2019.104518<\/a> <\/li>
    6. Willis, T., Wright, N., & Sleigh, A. (2019). Systematic analysis of uncertainty in 2D flood inundation models. Environmental Modelling & Software<\/em>, 122<\/em>, 104520. https:\/\/doi.org\/10.1016\/j.envsoft.2019.104520<\/a> <\/li>
    7. Jato-Espino, D., Sillanp\u00e4\u00e4, 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<\/em>, 122<\/em>, 103960. https:\/\/doi.org\/10.1016\/j.envsoft.2017.05.008<\/a> <\/li><\/ol>\n\n\n\n

      Hydrology and Earth System Sciences:<\/strong><\/h2>\n\n\n\n
      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\u20135068, https:\/\/doi.org\/10.5194\/hess-23-5059-2019, 2019.<\/li><\/ol>\n\n\n\n

        Journal of\nHydrology:<\/strong><\/strong><\/h2>\n\n\n\n
        1. \u00a0Shrestha, 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<\/em>, 580<\/em>, 124264. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.124264<\/a> <\/li>
        2. Giri, S., Lathrop, R. G., & Obropta, C. C. (2020). Climate change vulnerability assessment and adaptation strategies through best management practices. Journal of Hydrology<\/em>, 580<\/em>, 124311. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.124311<\/a> <\/li>
        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\u2013The Naryn River in Central Asia. Journal of Hydrology<\/em>, 580<\/em>, 124349. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.124349<\/a> <\/li>
        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<\/em>, 580<\/em>, 124321. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.124321<\/a> <\/li>
        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<\/em>, 580<\/em>, 124356. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.124356<\/a> <\/li>
        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<\/em>, 579<\/em>, 124091. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.124091<\/a> <\/li>
        7. Yan, X., Dong, W., An, Y., & Lu, W. (2019). A Bayesian-based integrated approach for identifying groundwater contamination sources. Journal of Hydrology<\/em>, 579<\/em>, 124160. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.124160<\/a> <\/li>
        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<\/em>, 579<\/em>, 124158. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.124158<\/a> <\/li>
        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<\/em>, 579<\/em>, 124181. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.124181<\/a> <\/li>
        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<\/em>, 579<\/em>, 124207. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.124207<\/a><\/li><\/ol>\n","protected":false},"excerpt":{"rendered":"

          Water Resources Research: Harken, B., Chang, C.\u2010F., 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\u2013 10357. https:\/\/doi.org\/10.1029\/2019WR025227 Gebler, S.,<\/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\/1018"}],"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=1018"}],"version-history":[{"count":1,"href":"http:\/\/hydrouncertainty.org\/wp-json\/wp\/v2\/posts\/1018\/revisions"}],"predecessor-version":[{"id":1019,"href":"http:\/\/hydrouncertainty.org\/wp-json\/wp\/v2\/posts\/1018\/revisions\/1019"}],"wp:attachment":[{"href":"http:\/\/hydrouncertainty.org\/wp-json\/wp\/v2\/media?parent=1018"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/hydrouncertainty.org\/wp-json\/wp\/v2\/categories?post=1018"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/hydrouncertainty.org\/wp-json\/wp\/v2\/tags?post=1018"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}