Latest Publications on ‘Hydrologic Uncertainty’ – November 2019

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