In December 2017, when I first attended AGU in New Orleans, I was very eager to learn about the AGU community. In conversation with some new friends,  I  heard that Hydrology Section has technical committees (TCs) with student members. I googled and found out that there is a TC for Hydrological Uncertainty (HU), which has been my own research area. I knew many of committee’s members from their papers and research. I was very excited, and sent an email to Mary Hill and Ming Ye, previous chairs of the HU-TC: “I’m very interested to join the Hydrologic Uncertainty Committee, and I was wondering what the process is for that, and what the expectations are from the (student) committee members.” I was invited to the annual TC meeting chaired by Saman Razavi, and then they encouraged me to apply to join the committee. From this very beginning everyone was encouraging and welcoming. I got to know different members of this committee who are leading researchers in this area. Being included in the TC communications and decision making processes has been a great learning experience: to know what topics and issues are of interest to this community, how an international technical committee is coordinated, how they discuss and make decisions, how the conference sessions are structured, etc.

Working with Saman Razavi and Xingyuan Chen, current chair and co-chair of the TC, has been a delight. They are both very energetic and methodic in their leadership roles. To reach out to a broader audience we have upgraded from the TC’s older blog http://aguhu.blogspot.com/ to a new website http://hydrouncertainty.org/, with regular updates on news and workshops in the area of uncertainty. Particularly, we developed and continuously update a library of publications on uncertainty, led by our other student committee member, Kasra Keshavarz from University of Saskatchewan. This has been a great reference point for those who are interested to dive into the uncertainty literature. Since September 2018, we’re active on Twittersphere https://twitter.com/AGU_HU as well.

I’m very grateful for this opportunity to be a member of HU-TC. I feel that I now have an extended network of mentors that contribute to my professional development, even if they don’t know it themselves. And this all started because I dared to ask. I know from experience that many students and early career scientists (ECS), as much as they are eager to be active, are hesitant or unsure to reach out. They may perceive established scientists or such committees as intimidating, fear rejection or judgement, or feel incompetent. This is even more challenging for those from minority cohorts, e.g. women, people of color, LGBTIQA+, non-native English speakers, etc [see our study on gender (in)equality in the Earth and space sciences as an example https://doi.org/10.1029/2019EA000706]. I certainly acknowledge such fears and feelings, as I personally belong to a few minority groups. Yet I promise we can make it through to the other side of anxiety and fear, where we find excitement, self-confidence, and new colleagues and friends. While many established scientists may come across as too smart, too busy to bother, or even arrogant, they usually have a lot of room at the bottom. They care and like to nurture the next generation. They would see their younger selves in us, when we hit the right buttons.

So, I’d like to encourage my peers, students and ECS, to be proactive about their involvement in professional communities such as AGU’s TCs. This may seem a bit more challenging during a global pandemic with less chances of face-to-face interactions and networking. That said, most technical communities are active and accessible through online platforms e.g. email and Twitter. There is no shame nor harm in approaching (e.g. emailing!) people you don’t know to express your interest in community service, to help and to learn. If they say no, just try other opportunities. Of course, it is important to be professional and strategic about what you say, and how you say it. But the bottom line is that sometimes we — as students and ECS — should create our own opportunities. I’d like to think that a genuine intention, to help the community and learn from its leaders, is easy to read.

About the author

Sina Khatami (@SinaKhatami) is now a Postdoc Researcher at Stockholm University and a committee member of AGU’s Hydrology Section Hydrological Uncertainty Technical Committee since 2018. Correspondence to sina.khatami@natgeo.su.se.

This article was first published in AGU Hydrology Section Newsletter (July 2020). Upon the invitation of Scott Tyler (Hydrology Section President at AGU), student members wrote their TC article for this newsletter.

1.  Herman, J. D., Quinn, J. D., Steinschneider, S., Giuliani, M., & Fletcher, S. (2019). Climate adaptation as a control problem: Review and perspectives on dynamic water resources planning under uncertainty. Water Resources Research, e24389. https://doi.org/10.1029/2019WR025502
2. Nguyen, H., Mehrotra, R., & Sharma, A. (2020). Assessment of Climate Change Impacts on Reservoir Storage Reliability, Resilience, and Vulnerability Using a Multivariate Frequency Bias Correction Approach. Water Resources Research, 56(2), e2019WR026022. https://doi.org/10.1029/2019WR026022
3. Dell’Oca, A., Riva, M., & Guadagnini, A. (2020). Global Sensitivity Analysis for Multiple Interpretive Models with Uncertain Parameters. Water Resources Research, 56(2), e2019WR025754. https://doi.org/10.1029/2019WR025754
4. Tajiki, M., Schoups, G., Hendricks Franssen, H. J., Najafinejad, A., & Bahremand, A. (2020). Recursive Bayesian estimation of conceptual rainfall‐runoff model errors in real‐time prediction of streamflow. Water Resources Research, 56(2), e2019WR025237. https://doi.org/10.1029/2019WR025237
5. Niu, Y., Mostaghimi, P., Shabaninejad, M., Swietojanski, P., & Armstrong, R. T. (2020). Digital rock segmentation for petrophysical analysis with reduced user bias using convolutional neural networks. Water Resources Research, 56(2), e2019WR026597. https://doi.org/10.1029/2019WR026597
6. Gelsinari, S., Doble, R., Daly, E., & Pauwels, V. R. (2020). Feasibility of Improving Groundwater Modeling by Assimilating Evapotranspiration Rates. Water Resources Research, 56(2), e2019WR025983. https://doi.org/10.1029/2019WR025983
7. Alawadhi, A., & Tartakovsky, D. M. (2020). Bayesian Update and Method of Distributions: Application to Leak Detection in Transmission Mains. Water Resources Research. https://doi.org/10.1029/2019WR025879
8. Wang, S., Taha, A. F., Sela, L., Giacomoni, M. H., & Gatsis, N. (2020). A New Derivative‐Free Linear Approximation for Solving the Network Water Flow Problem With Convergence Guarantees. Water Resources Research, 56(3). https://doi.org/10.1029/2019WR025694
9. Zhang, J., Vrugt, J. A., Shi, X., Lin, G., Wu, L., & Zeng, L. Improving Simulation Efficiency of MCMC for Inverse Modeling of Hydrologic Systems with a Kalman‐Inspired Proposal Distribution. Water Resources Research. https://doi.org/10.1029/2019WR025474
10. Tran, V. N., Dwelle, M. C., Sargsyan, K., Ivanov, V. Y., & Kim, J. (2020). A novel modeling framework for computationally efficient and accurate real‐time ensemble flood forecasting with uncertainty quantification. Water Resources Research. https://doi.org/10.1029/2019WR025727
11. Guo, D., Zheng, F., Gupta, H., & Maier, H. R. On the Robustness of Conceptual Rainfall‐Runoff Models to Calibration and Evaluation Dataset Splits Selection: A Large Sample Investigation. Water Resources Research. https://doi.org/10.1029/2019WR026752
12. Siade, A. J., Cui, T., Karelse, R. N., & Hampton, C. (2020). Reduced‐Dimensional Gaussian Process Machine Learning for Groundwater Allocation Planning using Swarm Theory. Water Resources Research. https://doi.org/10.1029/2019WR026061
13. Do, N. C., & Razavi, S. Correlation effects? A major but often neglected component in sensitivity and uncertainty analysis. Water Resources Research. https://doi.org/10.1029/2019WR025436
14. Guillon, H., Byrne, C. F., Lane, B. A., Solis, S. S., & Pasternack, G. B. (2020). Machine Learning Predicts Reach‐Scale Channel Types from Coarse‐Scale Geospatial Data in a Large River Basin. Water Resources Research. https://doi.org/10.1029/2019WR026691
15. Arshadi, M., De Paolis Kaluza, M. C., Miller, E. L., & Abriola, L. M. (2020). Subsurface Source Zone Characterization and Uncertainty Quantification Using Discriminative Random Fields. Water Resources Research, 56(3), e2019WR026481. https://doi.org/10.1029/2019WR026481
16. Bhowmik, R. D., Ng, T. L., & Wang, J. P. (2019). Understanding the impact of observation data uncertainty on probabilistic streamflow forecasts using a dynamic hierarchical model. Water Resources Research. https://doi.org/10.1029/2019WR025463
17. Spear, R. C., Cheng, Q., & Wu, S. L. (2020). An Example of Augmenting Regional Sensitivity Analysis Using Machine Learning Software. Water Resources Research. https://doi.org/10.1029/2019WR026379

Environmental Modelling and Software:

1.  Douglas-Smith, D., Iwanaga, T., Croke, B. F., & Jakeman, A. J. (2020). Certain trends in uncertainty and sensitivity analysis: An overview of software tools and techniques. Environmental Modelling and Software, 124, 104588. https://doi.org/10.1016/j.envsoft.2019.104588
2. Pianosi, F., Sarrazin, F., & Wagener, T. (2020). How successfully is open-source research software adopted? Results and implications of surveying the users of a sensitivity analysis toolbox. Environmental Modelling & Software, 124, 104579. https://doi.org/10.1016/j.envsoft.2019.104579
3. Jang, W. S., Engel, B., & Yeum, C. M. (2020). Integrated environmental modeling for efficient aquifer vulnerability assessment using machine learning. Environmental Modelling & Software, 124, 104602. https://doi.org/10.1016/j.envsoft.2019.104602
4. Barton, D. N., Sundt, H., Bustos, A. A., Fjeldstad, H. P., Hedger, R., Forseth, T., … & Madsen, A. L. (2020). Multi-criteria decision analysis in Bayesian networks-Diagnosing ecosystem service trade-offs in a hydropower regulated river. Environmental Modelling & Software, 124, 104604. https://doi.org/10.1016/j.envsoft.2019.104604
5. Zhang, Y., Arabi, M., & Paustian, K. (2020). Analysis of parameter uncertainty in model simulations of irrigated and rainfed agroecosystems. Environmental Modelling & Software, 126, 104642. https://doi.org/10.1016/j.envsoft.2020.104642
6. Mustafa, S. M. T., Nossent, J., Ghysels, G., & Huysmans, M. (2020). Integrated Bayesian Multi-model approach to quantify input, parameter and conceptual model structure uncertainty in groundwater modeling. Environmental Modelling & Software, 126, 104654. https://doi.org/10.1016/j.envsoft.2020.104654
7. Su, Y., Kern, J. D., Denaro, S., Hill, J., Reed, P., Sun, Y., … & Characklis, G. W. (2020). An open source model for quantifying risks in bulk electric power systems from spatially and temporally correlated hydrometeorological processes. Environmental Modelling & Software, 126, 104667. https://doi.org/10.1016/j.envsoft.2020.104667

Hydrology and Earth System Sciences:

1. Alam, M. S., Barbour, S. L., and Huang, M.: Characterizing uncertainty in the hydraulic parameters of oil sands mine reclamation covers and its influence on water balance predictions, Hydrol. Earth Syst. Sci., 24, 735–759, https://doi.org/10.5194/hess-24-735-2020, 2020.
2. Gallart, F., von Freyberg, J., Valiente, M., Kirchner, J. W., Llorens, P., and Latron, J.: Technical note: An improved discharge sensitivity metric for young water fractions, Hydrol. Earth Syst. Sci., 24, 1101–1107, https://doi.org/10.5194/hess-24-1101-2020, 2020.
3. Jachens, E. R., Rupp, D. E., Roques, C., and Selker, J. S.: Recession analysis revisited: impacts of climate on parameter estimation, Hydrol. Earth Syst. Sci., 24, 1159–1170, https://doi.org/10.5194/hess-24-1159-2020, 2020.

Journal of Hydrology:

1.  Qi, W., Liu, J., Xia, J., & Chen, D. (2020). Divergent sensitivity of surface water and energy variables to precipitation product uncertainty in the Tibetan Plateau. Journal of Hydrology, 581, 124338. https://doi.org/10.1016/j.jhydrol.2019.124338
2. Li, Y., Hernandez, J. H., Aviles, M., Knappett, P. S., Giardino, J. R., Miranda, R., … & Morales, J. (2020). Empirical Bayesian Kriging method to evaluate inter-annual water-table evolution in the Cuenca Alta del Río Laja aquifer, Guanajuato, México. Journal of Hydrology, 582, 124517. https://doi.org/10.1016/j.jhydrol.2019.124517
3. Yuan, L., He, W., Degefu, D. M., Liao, Z., Wu, X., An, M., … & Ramsey, T. S. (2020). Transboundary water sharing problem; a theoretical analysis using evolutionary game and system dynamics. Journal of Hydrology, 582, 124521. https://doi.org/10.1016/j.jhydrol.2019.124521
4. Haro-Monteagudo, D., Palazón, L., & Beguería, S. (2020). Long-term Sustainability of Large Water Resource Systems under Climate Change: a Cascade Modeling Approach. Journal of Hydrology, 124546. https://doi.org/10.1016/j.jhydrol.2020.124546
5. Lee, S., Yen, H., Yeo, I. Y., Moglen, G. E., Rabenhorst, M. C., & McCarty, G. W. (2020). Use of multiple modules and Bayesian Model Averaging to assess structural uncertainty of catchment-scale wetland modeling in a Coastal Plain landscape. Journal of Hydrology, 124544. https://doi.org/10.1016/j.jhydrol.2020.124544
6. Wang, Y., Li, Z., Guo, S., Zhang, F., & Guo, P. (2020). A risk-based fuzzy boundary interval two-stage stochastic water resources management programming approach under uncertainty. Journal of Hydrology, 124553. https://doi.org/10.1016/j.jhydrol.2020.124553

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

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

1. Grimaldi, S., Schumann, G. P., Shokri, A., Walker, J. P., & Pauwels, V. R. N. (2019). Challenges, opportunities and pitfalls for global coupled hydrologic‐hydraulic modeling of floods. Water Resources Research. https://doi.org/10.1029/2018WR024289
2. Leach, J. A., & Moore, R. D. (2019). Empirical stream thermal sensitivities may underestimate stream temperature response to climate warming. Water Resources Research, 55(7), 5453-5467. https://doi.org/10.1029/2018WR024236
3. Gan, Y., Liang, X. Z., Duan, Q., Chen, F., Li, J., & Zhang, Y. (2019). Assessment and Reduction of the Physical Parameterization Uncertainty for Noah‐MP Land Surface Model. Water Resources Research, 55(7), 5518-5538. https://doi.org/10.1029/2019WR024814
4. Heudorfer, B., Haaf, E., Stahl, K., & Barthel, R. (2019). Index‐based characterization and quantification of groundwater dynamics. Water Resources Research. https://doi.org/10.1029/2018WR024418
5. Rizzo, C. B., & de Barros, F. P. (2019). Minimum Hydraulic Resistance Uncertainty and the Development of a Connectivity‐Based Iterative Sampling Strategy. Water Resources Research, 55(7), 5593-5611. https://doi.org/10.1029/2019WR025269
6. Magnusson, J., Eisner, S., Huang, S., Lussana, C., Mazzotti, G., Essery, R., … & Beldring, S. (2019). Influence of spatial resolution on snow cover dynamics for a coastal and mountainous region at high latitudes (Norway). Water Resources Research, 55(7), 5612-5630. https://doi.org/10.1029/2019WR024925
7. Ling, F., Boyd, D., Ge, Y., Foody, G. M., Li, X., Wang, L., … & Du, Y. (2019). Measuring River Wetted Width from Remotely Sensed Imagery at the Sub‐pixel Scale with a Deep Convolutional Neural Network. Water Resources Research. https://doi.org/10.1029/2018WR024136
8. Araya, S. N., & Ghezzehei, T. A. (2019). Using machine learning for prediction of saturated hydraulic conductivity and its sensitivity to soil structural perturbations. Water Resources Research, 55(7), 5715-5737. https://doi.org/10.1029/2018WR024357
9. Zhong, Z., Sun, A. Y., & Jeong, H. (2019). Predicting co2 plume migration in heterogeneous formations using conditional deep convolutional generative adversarial network. Water Resources Research, 55(7), 5830-5851. https://doi.org/10.1029/2018WR024592
10. Quinn, J. D., Reed, P. M., Giuliani, M., & Castelletti, A. (2019). What Is Controlling Our Control Rules? Opening the Black Box of Multireservoir Operating Policies Using Time‐Varying Sensitivity Analysis. Water Resources Research, 55(7), 5962-5984. https://doi.org/10.1029/2018WR024177
11. Mazzoleni, M., Amaranto, A., & Solomatine, D. P. (2019). Integrating qualitative flow observations in a lumped hydrologic routing model. Water Resources Research, 55(7), 6088-6108. https://doi.org/10.1029/2018WR023768
12. Karst, N., Dralle, D., & Müller, M. F. (2019). On the Effect of Nonlinear Recessions on Low Flow Variability: Diagnostic of an Analytical Model for Annual Flow Duration Curves. Water Resources Research, 55(7), 6125-6137. https://doi.org/10.1029/2019WR024912
13. Stillinger, T., Roberts, D. A., Collar, N. M., & Dozier, J. (2019). Cloud masking for Landsat 8 and MODIS Terra over snow‐covered terrain: Error analysis and spectral similarity between snow and cloud. Water Resources Research, 55(7), 6169-6184. https://doi.org/10.1029/2019WR024932
14. Rajagopalan, B., Erkyihun, S. T., Lall, U., Zagona, E., & Nowak, K. (2019). A Nonlinear Dynamical Systems‐Based Modeling Approach for Stochastic Simulation of Streamflow and Understanding Predictability. Water Resources Research, 55(7), 6268-6284. https://doi.org/10.1029/2018WR023650
15. Lin, P., Pan, M., Beck, H. E., Yang, Y., Yamazaki, D., Frasson, R., … & Gleason, C. J. (2019). Global reconstruction of naturalized river flows at 2.94 million reaches. Water Resources Research. https://doi.org/10.1029/2019WR025287
16. Ruddell, B. L., Drewry, D. T., & Nearing, G. S. Information Theory for Model Diagnostics: Structural Error is Indicated by Trade‐Off Between Functional and Predictive Performance. Water Resources Research. https://doi.org/10.1029/2018WR023692
17. Karamouz, M., & Fereshtehpour, M. (2019). Modeling DEM Errors in Coastal Flood Inundation and Damages: A Spatial Nonstationary Approach. Water Resources Research. https://doi.org/10.1029/2018WR024562
18. Krapu, C., Borsuk, M., & Kumar, M. (2019). Gradient‐based inverse estimation for a rainfall‐runoff model. Water Resources Research. https://doi.org/10.1029/2018WR024461
19. Forbes, W. L., Mao, J., Ricciuto, D. M., Kao, S. C., Shi, X., Tavakoly, A. A., … & Thornton, P. E. (2019). Streamflow in the Columbia River Basin: Quantifying changes over the period 1951‐2008 and determining the drivers of those changes. Water Resources Research. https://doi.org/10.1029/2018WR024256
20. Steinschneider, S., Ray, P., Rahat, S. H., & Kucharski, J. (2019). A Weather‐Regime‐Based Stochastic Weather Generator for Climate Vulnerability Assessments of Water Systems in the Western United States. Water Resources Research. https://doi.org/10.1029/2018WR024446
21. Parente, M. T., Bittner, D., Mattis, S., Chiogna, G., & Wohlmuth, B. (2019). Bayesian calibration and sensitivity analysis for a karst aquifer model using active subspaces. Water Resources Research. https://doi.org/10.1029/2019WR024739
22. Tso, C. H. M., Kuras, O., & Binley, A. (2019). On the Field Estimation of Moisture Content Using Electrical Geophysics: The Impact of Petrophysical Model Uncertainty. Water Resources Research. https://doi.org/10.1029/2019WR024964
23. Crosbie, R. S., Doble, R. C., Turnadge, C., & Taylor, A. R. (2019). Constraining the magnitude and uncertainty of specific yield for use in the water table fluctuation method of estimating recharge. Water Resources Research. https://doi.org/10.1029/2019WR025285
24. Despax, A., Le Coz, J., Hauet, A., Mueller, D. S., Engel, F. L., Blanquart, B., … & Oberg, K. A. Decomposition of Uncertainty Sources in Acoustic Doppler Current Profiler Streamflow Measurements Using Repeated Measures Experiments. Water Resources Research. https://doi.org/10.1029/2019WR025296
25. Goetz, J., & Brenning, A. (2019). Quantifying uncertainties in snow depth mapping from structure from motion photogrammetry in an alpine area. Water Resources Research. https://doi.org/10.1029/2019WR025251
26. Ravindranath, A., Devineni, N., Lall, U., Cook, E. R., Pederson, G., Martin, J., & Woodhouse, C. (2019). Streamflow Reconstruction in the Upper Missouri River Basin Using a Novel Bayesian Network Model. Water Resources Research. https://doi.org/10.1029/2019WR024901
27. Zhao, D., & Wu, S. Projected changes in permafrost active layer thickness over the Qinghai–Tibet Plateau under climate change. Water Resources Research. https://doi.org/10.1029/2019WR024969
28. Tolley, D., Foglia, L., & Harter, T. Sensitivity Analysis and Calibration of an Integrated Hydrologic Model in an Irrigated Agricultural Basin with a Groundwater‐Dependent Ecosystem. Water Resources Research. https://doi.org/10.1029/2018WR024209
29. Ciriello, V., Lauriola, I., & Tartakovsky, D. M. (2018). Distribution‐based global sensitivity analysis in hydrology. Water Resources Research. https://doi.org/10.1029/2019WR025844
30. Wrzesien, M. L., Pavelsky, T. M., Durand, M. T., Dozier, J., & Lundquist, J. D. (2019). Characterizing biases in mountain snow accumulation from global datasets. Water Resources Research. https://doi.org/10.1029/2019WR025350
31. 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

Environmental Modelling and Software:

1. Wagena, M. B., Bhatt, G., Buell, E., Sommerlot, A. R., Fuka, D. R., & Easton, Z. M. (2019). Quantifying model uncertainty using Bayesian multi-model ensembles. Environmental Modelling & Software, 117, 89-99. https://doi.org/10.1016/j.envsoft.2019.03.013
2. McKenzie, P. F., Duveneck, M. J., Morreale, L. L., & Thompson, J. R. (2019). Local and global parameter sensitivity within an ecophysiologically based forest landscape model. Environmental Modelling & Software, 117, 1-13. https://doi.org/10.1016/j.envsoft.2019.03.002
3. Zadeh, F. K., Nossent, J., Woldegiorgis, B. T., Bauwens, W., & van Griensven, A. (2019). Impact of measurement error and limited data frequency on parameter estimation and uncertainty quantification. Environmental Modelling & Software, 118, 35-47. https://doi.org/10.1016/j.envsoft.2019.03.022
4. Ye, L., Gao, L., Marcos-Martinez, R., Mallants, D., & Bryan, B. A. (2019). Projecting Australia’s forest cover dynamics and exploring influential factors using deep learning. Environmental Modelling & Software, 119, 407-417. https://doi.org/10.1016/j.envsoft.2019.07.013
5. Guillaume, J. H., Jakeman, J. D., Marsili-Libelli, S., Asher, M., Brunner, P., Croke, B., … & Stigter, J. D. (2019). Introductory overview of identifiability analysis: A guide to evaluating whether you have the right type of data for your modeling purpose. Environmental Modelling & Software. https://doi.org/10.1016/j.envsoft.2019.07.007

Hydrology and Earth System Sciences:

1. Lane, R. A., Coxon, G., Freer, J. E., Wagener, T., Johnes, P. J., Bloomfield, J. P., Greene, S., Macleod, C. J. A., and Reaney, S. M.: Benchmarking the predictive capability of hydrological models for river flow and flood peak predictions across over 1000 catchments in Great Britain, Hydrol. Earth Syst. Sci., 23, 4011–4032, https://doi.org/10.5194/hess-23-4011-2019 , 2019.
2. Jennings, K. S. and Molotch, N. P.: The sensitivity of modeled snow accumulation and melt to precipitation phase methods across a climatic gradient, Hydrol. Earth Syst. Sci., 23, 3765–3786, https://doi.org/10.5194/hess-23-3765-2019 , 2019.
3. Erdal, D. and Cirpka, O. A.: Global sensitivity analysis and adaptive stochastic sampling of a subsurface-flow model using active subspaces, Hydrol. Earth Syst. Sci., 23, 3787–3805, https://doi.org/10.5194/hess-23-3787-2019 , 2019.
4. Dalledonne, G. L., Kopmann, R., and Brudy-Zippelius, T.: Uncertainty quantification of floodplain friction in hydrodynamic models, Hydrol. Earth Syst. Sci., 23, 3373–3385, https://doi.org/10.5194/hess-23-3373-2019 , 2019.
5. Brunner, M. I., Bárdossy, A., and Furrer, R.: Technical note: Stochastic simulation of streamflow time series using phase randomization, Hydrol. Earth Syst. Sci., 23, 3175–3187, https://doi.org/10.5194/hess-23-3175-2019 , 2019.
6. O, S. and Foelsche, U.: Assessment of spatial uncertainty of heavy rainfall at catchment scale using a dense gauge network, Hydrol. Earth Syst. Sci., 23, 2863–2875, https://doi.org/10.5194/hess-23-2863-2019 , 2019.
7. Yu, D., Yang, J., Shi, L., Zhang, Q., Huang, K., Fang, Y., and Zha, Y.: On the uncertainty of initial condition and initialization approaches in variably saturated flow modeling, Hydrol. Earth Syst. Sci., 23, 2897–2914, https://doi.org/10.5194/hess-23-2897-2019 , 2019.

Journal of Hydrology:

1.  Achieng, K. O., & Zhu, J. (2019). Application of Bayesian framework for evaluation of streamflow simulations using multiple climate models. Journal of Hydrology, 574, 1110-1128. https://doi.org/10.1016/j.jhydrol.2019.05.018
2. Zhu, Y., Chen, L., Wei, G., Li, S., & Shen, Z. (2019). Uncertainty assessment in baseflow nonpoint source pollution prediction: The impacts of hydrographic separation methods, data sources and baseflow period assumptions. Journal of Hydrology, 574, 915-925. https://doi.org/10.1016/j.jhydrol.2019.05.010
3. Farsi, N., & Mahjouri, N. (2019). Evaluating the contribution of the climate change and human activities to runoff change under uncertainty. Journal of hydrology, 574, 872-891. https://doi.org/10.1016/j.jhydrol.2019.04.028
4. Li, W., Duan, Q., Ye, A., & Miao, C. (2019). An improved meta-Gaussian distribution model for post-processing of precipitation forecasts by censored maximum likelihood estimation. Journal of Hydrology, 574, 801-810. https://doi.org/10.1016/j.jhydrol.2019.04.073
5. Yan, L., Xiong, L., Ruan, G., Xu, C. Y., Yan, P., & Liu, P. (2019). Reducing uncertainty of design floods of two-component mixture distributions by utilizing flood timescale to classify flood types in seasonally snow covered region. Journal of Hydrology, 574, 588-608. https://doi.org/10.1016/j.jhydrol.2019.04.056
6. Janetti, E. B., Guadagnini, L., Riva, M., & Guadagnini, A. (2019). Global sensitivity analyses of multiple conceptual models with uncertain parameters driving groundwater flow in a regional-scale sedimentary aquifer. Journal of Hydrology, 574, 544-556. https://doi.org/10.1016/j.jhydrol.2019.04.035
7. Ehlers, L. B., Sonnenborg, T. O., Heuvelink, G. B. M., He, X., & Refsgaard, J. C. (2019). Joint treatment of point measurement, sampling and neighborhood uncertainty in space-time rainfall mapping. Journal of Hydrology, 574, 148-159. https://doi.org/10.1016/j.jhydrol.2019.03.100
8. Wang, M., Zhang, M., Shi, H., Huang, X., & Liu, Y. (2019). Uncertainty analysis of a pollutant-hydrograph model in assessing inflow and infiltration of sanitary sewer systems. Journal of Hydrology, 574, 64-74. https://doi.org/10.1016/j.jhydrol.2019.04.011
9. Rana, S. M., Boccelli, D. L., Scott, D. T., & Hester, E. T. (2019). Parameter Uncertainty with Flow Variation of the One-dimensional Solute Transport Model for Small Streams using Markov chain Monte Carlo. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2019.06.003
10. Guo, Y., Huang, S., Huang, Q., Wang, H., Wang, L., & Fang, W. (2019). Copulas-based bivariate socioeconomic drought dynamic risk assessment in a changing environment. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2019.06.010
11. Wani, O., Scheidegger, A., Cecinati, F., Espadas, G., & Rieckermann, J. (2019). Exploring a copula-based alternative to additive error models–for non-negative and autocorrelated time series in hydrology. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2019.06.006
12. Zhou, L., Meng, Y., & Abbaspour, K. C. (2019). A new framework for multi-site stochastic rainfall generator based on empirical orthogonal function analysis and Hilbert-Huang transform. Journal of Hydrology, 575, 730-742. https://doi.org/10.1016/j.jhydrol.2019.05.047
13. Varouchakis, E. A., Theodoridou, P. G., & Karatzas, G. P. (2019). Spatiotemporal geostatistical modeling of groundwater levels under a Bayesian framework using means of physical background. Journal of Hydrology, 575, 487-498. https://doi.org/10.1016/j.jhydrol.2019.05.055
14. Chen, W., Wang, X., Deng, S., Liu, C., Xie, H., & Zhu, Y. (2019). Integrated urban flood vulnerability assessment using local spatial dependence-based probabilistic approach. Journal of Hydrology, 575, 454-469.
15. Lindenschmidt, K. E., Rokaya, P., Das, A., Li, Z., & Richard, D. (2019). A novel stochastic modelling approach for operational real-time ice-jam flood forecasting. Journal of Hydrology, 575, 381-394. https://doi.org/10.1016/j.jhydrol.2019.05.048
16. Jaros, A., Rossi, P. M., Ronkanen, A. K., & Kløve, B. (2019). Parameterisation of an integrated groundwater-surface water model for hydrological analysis of boreal aapa mire wetlands. Journal of Hydrology, 575, 175-191. https://doi.org/10.1016/j.jhydrol.2019.04.094
17. Gupta, A., & Govindaraju, R. S. (2019). Propagation of structural uncertainty in watershed hydrologic models. Journal of Hydrology, 575, 66-81. https://doi.org/10.1016/j.jhydrol.2019.05.026
18. Culley, S., Bennett, B., Westra, S., & Maier, H. R. (2019). Generating realistic perturbed hydrometeorological time series to inform scenario-neutral climate impact assessments. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2019.06.005
19. Zhu, J. (2019). Sensitivity of advective contaminant travel time to the soil hydraulic parameters in unsaturated heterogeneous soils. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2019.06.035
20. Zhu, F., Zhong, P. A., Cao, Q., Chen, J., Sun, Y., & Fu, J. (2019). A stochastic multi-criteria decision making framework for robust water resources management under uncertainty. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2019.06.049
21. Gangrade, S., Kao, S. C., Dullo, T. T., Kalyanapu, A. J., & Preston, B. L. (2019). Ensemble-based Flood Vulnerability Assessment for Probable Maximum Flood in a Changing Environment. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2019.06.027
22. Guo, A., Chang, J., Wang, Y., Huang, Q., Guo, Z., & Li, Y. (2019). Uncertainty analysis of water availability assessment through the Budyko framework. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2019.06.033
23. De Pue, J., Rezaei, M., Van Meirvenne, M., & Cornelis, W. M. (2019). The relevance of measuring saturated hydraulic conductivity: Sensitivity analysis and functional evaluation. Journal of Hydrology, 576, 628-638. https://doi.org/10.1016/j.jhydrol.2019.06.079
24. Leandro, J., Gander, A., Beg, M. N. A., Bhola, P., Konnerth, I., Willems, W., … & Disse, M. (2019). Forecasting upper and lower uncertainty bands of river flood discharges with high predictive skill. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2019.06.052
25. Rammay, M. H., Elsheikh, A. H., & Chen, Y. (2019). Quantification of prediction uncertainty using imperfect subsurface models with model error estimation. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2019.02.056
26. Siade, A. J., Rathi, B., Prommer, H., Welter, D., & Doherty, J. (2019). Using heuristic multi-objective optimization for quantifying predictive uncertainty associated with groundwater flow and reactive transport models. Journal of Hydrology, 577, 123999.
27. Karanitsch-Ackerl, S., Mayer, K., Gauster, T., Laaha, G., Holawe, F., Wimmer, R., & Grabner, M. (2019). A 400-year reconstruction of spring–summer precipitation and summer low flow from regional tree-ring chronologies in North-Eastern Austria. Journal of Hydrology, 577, 123986. https://doi.org/10.1016/j.jhydrol.2019.123986
28. Cai, C., Wang, J., & Li, Z. (2019). Assessment and modelling of uncertainty in precipitation forecasts from TIGGE using fuzzy probability and Bayesian theory. Journal of Hydrology, 577, 123995. https://doi.org/10.1016/j.jhydrol.2019.123995
29. Umer, Y. M., Jetten, V. G., & Ettema, J. (2019). Sensitivity of flood dynamics to different soil information sources in urbanized areas. Journal of hydrology, 577, 123945. https://doi.org/10.1016/j.jhydrol.2019.123945
30. Hutchins, M. G., & Hitt, O. E. (2019). Sensitivity of river eutrophication to multiple stressors illustrated using graphical summaries of physics-based river water quality model simulations. Journal of Hydrology, 577, 123917. https://doi.org/10.1016/j.jhydrol.2019.123917
31. Pan, Z., Liu, P., Gao, S., Cheng, L., Chen, J., & Zhang, X. (2019). Reducing the uncertainty of time-varying hydrological model parameters using spatial coherence within a hierarchical Bayesian framework. Journal of Hydrology, 577, 123927. https://doi.org/10.1016/j.jhydrol.2019.123927

Advances in Water Resources:

1. Ehlers, L. B., Wani, O., Koch, J., Sonnenborg, T. O., & Refsgaard, J. C. (2019). Using a simple post-processor to predict residual uncertainty for multiple hydrological model outputs. Advances in Water Resources, 129, 16-30. https://doi.org/10.1016/j.advwatres.2019.05.003
2. Nury, A. H., Sharma, A., Marshall, L., & Mehrotra, R. (2019). Characterising Uncertainty in Precipitation Downscaling using a Bayesian Approach. Advances in Water Resources. https://doi.org/10.1016/j.advwatres.2019.05.018
3. Gómez, J. E., & Torres-Verdín, C. (2019). Permeability sensitivity functions and rapid simulation of multi-point pressure measurements using perturbation theory. Advances in Water Resources, 129, 198-209. https://doi.org/10.1016/j.advwatres.2019.05.015
4. Hassanzadeh, E., Nazemi, A., Adamowski, J., Nguyen, T. H., & Van-Nguyen, V. T. (2019). Quantile-based downscaling of rainfall extremes: Notes on methodological functionality, associated uncertainty and application in practice. Advances in Water Resources, 131, 103371. https://doi.org/10.1016/j.advwatres.2019.07.001

Martyn is a Professor of Hydrology at the University of Saskatchewan, Associate Director of the University of Saskatchewan’s Centre for Hydrology and the Canmore Coldwater Laboratory, Editor-in-Chief of Water Resources Research, and Fellow of the American Geophysical Union. Martyn’s research focuses in three main areas: (i) the developing and evaluating process-based hydrologic models; (ii) understanding the sensitivity of water resources to climate variability and change; and (iii) developing the next generation streamflow forecasting systems. Martyn has authored or co-authored over 150 journal articles since receiving his PhD in 1998.

I was in Vienna for EGU 2019 that I realized that Martyn Clark (MC) is also coming. I decided to ask him for an interview, and so I sent him an email. As thrilling as the opportunity for me was, I got anxious. I was thinking in my head to be professional, ask him good questions, don’t embarrass myself, not to waste his time, etc. Not to mention that an interview with a smart and intelligent scientist can be quite intimidating as well.  Martyn accepted my interview request cheerfully. As we were chatting over email to set the date and venue to meet, my anxiety morphed into comfort and further excitement. We set the meeting details, and his final email to me was “Cool bananas.. see you soon.”

SK: Can you tell us a little about your background and education?
How far back do you like to go?

SK: As far as you like.

Ok… I was born in a small farming community in New Zealand surrounded by an abundance of cows [Martyn laughs]. When I first went to the university, I decided that I wanted to be a park ranger, because I loved the outdoors. It turned out that it was not for me. I did my undergraduate degree at University of Canterbury, and then my masters there in snow hydrology. That was in the early 1990s. It was fun actually because I was at the ski area after it was closed. So, they gave me the keys to the ski area.

SK: So, you had a private resort to yourself! [I laugh]

It wasn’t really a resort. It was a club area. So, they had two rope tows. We wore a climbing harness with this contraption called a “nut cracker” that you flick on to the rope. And then you lay back and get towed up the mountain. Each of the rope tows is operated by tractor that sits in the shed at the bottom of the hill. So, I operated that and you know… that was fun. It was a wonderful place to study the snow surface energy balance and water movement through snow. Then in 1995 I did my PhD at the University of Colorado in Boulder. That was going from the site scale to the global scale, because my thesis was on the role of snow cover in the climate system. At that time, I was also doing research on hydro-climatic variability in the Western USA.

After I finished my PhD, I stayed at the Cooperative Institute for Research in Environmental Sciences (CIRES) at the University of Colorado, which provides a career track for people doing research. I supported myself with soft money for many years there, mostly working on developing advanced methods for probabilistic streamflow forecasting in snowmelt-dominated river basins. In 2006, I went back to New Zealand to build a flood forecasting system; in 2010 I came back to Boulder and started working at NCAR to build a program on climate and water. In December of 2018 I moved to University of Saskatchewan in Canmore.

SK: What inspired a park ranger enthusiast to become a scientist, particularly a hydrologist?

Well, I haven’t always wanted to be a scientist. My main interest in going to park ranger school was because I love the mountains. I was mostly a mediocre student through most of my high school education. Because I loved going out to the mountains more than studying. Even through my undergraduate, I was kind of an average type of student, because I spent a lot of time in the mountains. At that level I didn’t really adhere well to the structure of the education. I didn’t necessarily fit into the box. I originally gravitated towards park ranger school because I love the outdoors, and studying hydrology was a way to study natural processes. I did my master thesis in field research in the Southern Alps in New Zealand, which are spectacular. So originally, the science was an excuse to be outdoors but as I was going through university, I became more interested in the science as well.

SK: And in this journey of yours, what were the major hurdles along the way, and where did you find inspiration to overcome them, to become who you are now?

I didn’t really experience too many hurdles in the early days. I did my undergraduate degree at the University of Canterbury in New Zealand. I decided I wanted to do a Master’s thesis, and I did my masters at the same place. I really enjoyed the research. I had a good support network. My advisers were very good at that time. Then when I went to apply for PhD programs, I really had no idea what I was doing [we both smile]. So, you know, a lot of it was luck. I looked at two PhD opportunities: one was at the University of Colorado at Boulder and one at the Arizona State University (ASU) in Phoenix. I wrote an application for both of those places. But you know it was a lot of money to apply for those schools. I was a poor student in New Zealand, and I didn’t have a lot of money. University of Colorado told me that they accepted me the day that I was about to post my application to ASU. So, I kept the money and went to Colorado [Martyn laughs]. I had no idea really about the difference between those two universities — but the mountains in Colorado are spectacular.

SK: I was about to say…

MC: It was the right place to land! [he says with a smile]

SK: Your research spans across a wide range of domains of hydrology, hydro-climatology and model development. How did you expand your knowledge and expertise so widely?

In the early days, it was more of a random walk. My interests evolved into different areas and I pursued opportunities where they were. I read a lot. Even when I was doing my master thesis, I read and read and read. So, I was able to get a fairly good understanding of the literature and identify what the major science questions are. Later on in my career, I’ve been much more strategic than tactical as I was in early stages of my career: thinking about what the big problems are that we want to solve, and how we can go for the funding opportunities out there that lead more towards this larger vision… more of a proactive approach, than a reactive one.

SK: Over the past few years, you’ve become the Editor-in-Chief of WRR (which is an enormous commitment), moved from public sector (NCAR) to academia and from Colorado to the Canadian Rockies. Each of these decisions are big enough to be a challenge for a few years. So, first, how’re you holding up [I laugh]? And what motivated such major changes?

Well these were more sequential than simultaneous [we laugh]. So, let’s deal with them sequentially. I was asked to apply for the Editor-in-Chief position for WRR. They had a search committee together and they asked me if I would consider doing it. My initial response was no. Then I thought about it for a while. Two things had happened in that year. First, I was promoted to senior scientist at NCAR, which is the top level there. So, I didn’t have any opportunities for additional promotion. And also, I was elected as Fellow of AGU. So, I thought I have kind of established myself in my career and perhaps now is the time to give back to the community more. And there was this opportunity. I was weighing all of my commitments and then thinking about how I could push the field forward. And I thought, well… what good can I do? I thought if I publish, say, two fewer papers a year and be the WRR Editor-in-Chief instead, I can probably do more good and continue along my current trajectory. I was also keen for a new additional challenge.

NCAR is federally funded research and development centre and received a lot of its funding from government and through NSF (National Science Foundation in the USA). The decision to move to the University of Saskatchewan was in part because I wanted the broader challenges that comes with the university setting. And it was in part because of the funding that they already had in place with the Canadian government as part of the Global Water Futures (https://gwf.usask.ca/) programs. This really provided the opportunity to achieve a lot of my research ambitions that I’ve had for many years.

SK: Have these new roles and changes impacted your research?

Being the WRR Editor-in-Chief gives me a broader perspective. I find it easier to identify what the key science questions are that we need to address as a community, because I see so much [of the current research being done].

SK: How many papers do you read on a daily basis?

MC: It depends how you define read?

SK: Handle let’s say.

Five.

SK: And some of them interest you to look deeper into them?

We are getting close to 2000 submissions a year. All papers come to me, first. Then, I will either ask another editor to handle a paper or I handle it myself. So, every paper I will at least skim through to figure out what the topic is, what the research questions are, what the conclusions are, check the figures to see to what extent they support the research questions and conclusions. But I read in more detail the papers that I handle.

SK: Gaining this bigger picture of the research community is probably influencing your own approach to defining new questions, particularly for your new career line at University of Saskatchewan.

Yes. For my new career at the University of Saskatchewan at Canmore, a wonderful location by the way, we are building up the research program there (https://uofs-comphyd.github.io/). A lot of the research thrusts and the Global Water Futures program are the things that I have been working on over the past twenty years anyway. It is dominated by two main application questions: (1) improving streamflow forecasting methods, and (2) improving assessments of impacts of climate change on water security. Those are the two applied questions that have guided my research on process understanding, model development, strengthening the link between algorithms and theories, etc. It is not as if I’m going to a new research area; I’m going into an area where I have had an extensive presence for a very long time. So, that part of it is not new. But the part that is forcing me to extend myself a little bit is that the funding available is more than an order of magnitude larger than what I ever had before. So, being able to think more strategically, like build up a large cadre of postdocs to answer these questions, or how to orchestrate a large research program — it is really exciting.

View of one of the mountain ranges near Canmore, showing (l-r) Mt Lougheed, the iconic Three Sisters, Ship’s Prow, Mt Lawrence Grassi and Ha Ling peak, where the leading photo of this interview was taken 

SK: Are there any skills you have crossed over from NCAR that you developed uniquely within that work environment, but which will now contribute to your new role in academia?

I think the Global Water Futures program is unusual in the sense that academia does not normally have that large a program. The size of the program is more similar to what you see in a public setting. So, the skills that I had in terms of managing a large team and pushing them forward are easily transferable. The skills that I need to learn is working with students. I haven’t had many interactions with students. I finished my PhD in the mid-1990s and at NCAR and other places that I worked I supervised postdocs, career scientists and other people like that. I have been able to push those people forward but now I am beginning to work with people who are the beginning of their careers. It is something that I don’t have much experience in, and I am really looking forward to it.

SK: Looking back at your research career, what do you think your major breakthroughs are and why?

I think my major breakthrough is quite broad. But I can list some specific papers if you want. Developing a more structured approach to hydrological model development is something that I’ve been working on for many years. The first paper that I really published in that area was my FUSE paper (Clark et al., 2008), working with bucket style models. Then my most recent big modelling paper was my SUMMA paper (Clark et al., 2015a, 2015b) [both are modelling frameworks that allow a user to analyse the impact of individual modelling decision; such as the choice of model structure, the choice of specific flux equations, and the choice of numerical method with which to solve the model equations].

SK: Any interesting or inspiring stories about them that you like to share with younger hydrologists?

I’m not sure if it’s inspiring, but it could be interesting [we both laugh]. I view the SUMMA paper, a two-part paper, as my best paper that I’ve ever written. It’s also the only paper that I ever had rejected.

SK: [I laugh hysterically and ask] on what grounds was it rejected?

On philosophical grounds, not on any of the technical details. The approach that we were proposing challenged some of the reviewers and the reviewers challenged me. But I think that was a good thing. We got a really rigorous review. The reviewers really challenged us to sharpen our message.

SK: And you are happy that you had that challenge, because you think it improved the paper?

Oh, it really improved the paper a lot. I’m really grateful to the reviewers of the paper, to spend the time that they did in order to help us strengthen the paper.

SK: How do you describe your research style? Or, what are the main elements for you when you’re impressed by a piece of research?

For me, personally, I’m really interested in making a step change in our modelling capabilities. So, most of the major papers that I’m proud of have had a gestation period of more than five years. And so, if you look at my publication history — it’s kind of interesting — I had no first-author research publications in the time period of 2011 to 2015, when I was developing SUMMA. And that can be a little bit dangerous [he laughs] for people at earlier stages of their career. I really wanted to make a major contribution in the way that we develop models. I was worried that a lot of our model development was somewhat ad hoc, and we didn’t have the structure that we needed in order to really understand where and what model weaknesses are. I was worried that model evaluation wasn’t done in a controlled way and that we really needed a new framework in order to push forward in those areas.

SK: On that note, would you say that creativity and success are correlated or not necessarily?

I think they are. I think you need to be creative, but you also need to be bold. So, it depends on how you view creativity. You can view creativity as a clever twist on an existing idea.

SK: And what do you exactly mean being bold?

Take the steps that are necessary to advance our capabilities. Don’t settle for incremental advances. Incremental advances are important, but they need to be conducted in the context of achieving a larger scale change.

SK: What would you identify as the main gaps or big picture questions of hydrological sciences for the coming decades that you think early career scientists can pursue?

I think we really need to evolve towards a more interdisciplinary Earth System Science approach to modelling. For many years, hydrology has been rooted somewhat in what was called rainfall-runoff modelling. That term is not really applicable anymore, because we now are modelling a large number of complex interrelated processes in the terrestrial water cycle. So, multi-process modelling in an Earth System modelling context, not just focusing on the short-term fluxes but also the longer-term evolution of our systems. Understanding the evolution of soils in the catchment, understanding the evolution of vegetation in the catchment and understanding how those slowly varying processes feed back on to the higher frequency variability, which has typically been the domain of hydrologists.

SK: And this goes back to the SUMMA paper that you mentioned?

Well that’s just a part of the bigger picture. SUMMA has a more complete representation of the terrestrial hydrological cycle than many hydrological models. But many models already have that level of complexity. SUMMA doesn’t even begin to get into the issues of bio-geochemistry, catchment co-evolution, etc., which are going to be really important. What SUMMA does is provides a structured template for process-based hydrological models which can be extended into the Earth system modelling framework. But it’s nowhere near complete enough of what we need moving toward. So, what I’m talking about is not something that we can do in the next couple of years but something that we need much more concerted effort over the timescales of several decades.

SK: Are there any papers or books that you would like to recommend on this grand idea of expanding the spectrum of processes within current hydrological models towards Earth system modelling?

The first part of the SUMMA paper (Clark et al., 2015a) provides some beginning thoughts in that area but it doesn’t go as far as it needs to. We wrote a paper on improving the representation of hydrological processes on Earth System models (Clark et al., 2015c). That’s really just beginning to scratch the surface as well. I think the paper that everybody should read is the one by Fan et al. (2019) on providing the link between hillslope hydrology and Earth system modelling that provides lots of pointers in that direction. But it’s funny that you ask that. There’s something that I’ve been kind of stewing on for a while, which is to put together a coherent commentary paper that emphasizes the research direction that’s necessary.

SK: What are your main hobbies besides work, especially nowadays that you have a lot on your plate?

Well we love the outdoors! As I mentioned to you at the beginning of the interview, I spent a lot of my childhood in the mountains. We were riding up our bikes up to the Southern Alps in New Zealand and would go backpacking for several days. Actually, in New Zealand, we call it tramping, not backpacking. In New Zealand, and I guess everywhere, there were two ways that you could advance. You could graduate from a tramper either into a hunter or into a climber. I started getting into a lot of climbing and did a lot of rock climbing. That’s been my hobby for many years. That’s kind of decreased over time you know as I’ve got busier and as we now have kids, we’ve been looking for activities that were more suitable for the family. But it’s something that I’m beginning to get back into… [he pauses and then says with a smile] My strength to weight ratio isn’t quite what it used to be [we both laugh], so that’s a little bit more challenging. But we have hired a senior hydrologist to come to Canmore, and he’s made it clear that he expects to be dragging me up mountains and he’s told me in no uncertain terms that I need to get myself in shape before his arrival.

SK: So, how do you manoeuvre between work and life to balance them out?

Sometimes poorly. I’ve found what works for me, but this doesn’t work for everyone. I do a lot of work in the early mornings. So, I would often go to bed quite early and wake up early in the morning. Then a couple of hours of work before breakfast, before the kids get up. When I first started WRR, I was getting up at 4:00 a.m. every morning. I did that for a period of time, and then I found that unsustainable. So, it’s more like 5:30 or 6, and I really begin to make some progress before the day starts. When our kids were young, it was more difficult to balance work and life. Now that they’re getting older, they’ve got their own interests and it’s more acceptable for me to open up the laptop on the couch on the weekend and begin to get some work done.

SK: You’ve pointed out many great things so far, is there any other advice you may have for young hydrologists?

I think I’ve covered a lot of it already. Be bold. Think about how you can really make substantial advances in the research frontier. Be strategic. You need the incremental progress. You need the intermediate scale products as you are conducting your research so that you can feed the beast [he smiles] and work effectively through the career track. But those intermediate scale products need to be conducted within the context of a larger scale vision. So, really think about defining that vision. Talk about that with your colleagues and keep refining that. And having an idea how your career contributions will really begin to make a difference.

Some guidance would be to think about three levels of strategic planning or technical planning in some respects: (1) what do you want to accomplish in your career? In terms of always keeping that and the longest timescale. (2) What’s the thing that you’re going to present at the next conference? Most people are thinking about those two or perhaps not giving as much attention to the vision aspects as they should. But the third that often gets neglected based on my interactions with people is (3) what are you going to do tomorrow, and the coming week? So, basically organizing your activities on the shorter timescale, so that they are feeding the ambitions that you have on the longer timescales, I think is really important.

SK: This might be a somewhat stupid question. Do you have any measures to evaluate a good PhD or postdoc? Like the number of their publications or good publications in a year, etc.

Yeah, this has been my problem. I don’t like the way that people are being judged in academics. There’s a saying that managers know how to count but they don’t know how to read [we both laugh]… In the sense that people are focused too much on outputs, like how many papers you published, than outcomes. I think that things are going to change. I wrote an editorial in WRR on the citation impact of hydrology journals (Clark & Hanson, 2017). There I was talking about the need to shift away from quantitative assessments to more qualitative assessments to really begin to measure how people are making a difference in the community. For me that’s the major thing. So, if we get back to what would help people get a job, I can tell you what I’m looking for. Yeah, you need some papers to get on people’s radar screen. If you have finished your PhD and you don’t have any papers then that’s a red flag. But what you really need, in my mind, is to be known for something. That people look at you and say okay that person has done X, or that person has accomplished Y. So, the number of papers that you’ve written become less important. So, what I’m looking for is what have you done to make a difference in the community. And that’s what a lot of other people are beginning to look for more.

SK: I’m curious to know more about this. How would this qualitative assessment process work, to assess the impact of a person on hydrological sciences or even the broader geosciences?

You should read the Declaration on Research Assessment (DORA) [https://sfdora.org/read/], which I also referred to in our 2017 editorial (Clark and Hanson, 2017). DORA comes up with a set of guidelines for funding agencies, universities, managers, etc. to show how they can move towards research assessment practices that are more fair. It has been picked up by a lot of different institutions and universities. A lot of it is there. It’s more just changing the structure of the research assessment. You know there’s not going to be one size fits all template that people can use but structuring it in a way that emphasizes the contributions rather than the specific papers. It takes more work, but we should value our colleagues and take the time to really make sure people’s efforts are directed in productive ways.

About the author

Sina Khatami (@SinaKhatami) is currently the Secretary of Young Hydrologic Society (YHS) and an Editor of YHS Blogs. He is also a committee member of AGU’s Hydrology Section Hydrological Uncertainty Technical Committee since 2018, and Student Subcommittee (H3S) since 2017. Correspondence to sina.khatami@unimelb.edu.au

References

Clark, M. P., Slater, A. G., Rupp, D. E., Woods, R. A., Vrugt, J. A., Gupta, H. V., … & Hay, L. E. (2008). Framework for Understanding Structural Errors (FUSE): A modular framework to diagnose differences between hydrological models. Water Resources Research, 44(12).

Clark, M. P., Nijssen, B., Lundquist, J. D., Kavetski, D., Rupp, D. E., Woods, R. A., … & Arnold, J. R. (2015a). A unified approach for process‐based hydrologic modeling: 1. Modeling concept. Water Resources Research, 51(4), 2498-2514.

Clark, M. P., Nijssen, B., Lundquist, J. D., Kavetski, D., Rupp, D. E., Woods, R. A., … & Rasmussen, R. M. (2015b). A unified approach for process‐based hydrologic modeling: 2. Model implementation and case studies. Water Resources Research, 51(4), 2515-2542.

Clark, M. P., Fan, Y., Lawrence, D. M., Adam, J. C., Bolster, D., Gochis, D. J., … & Maxwell, R. M. (2015c). Improving the representation of hydrologic processes in Earth System Models. Water Resources Research, 51(8), 5929-5956.

Clark, M. P., & Hanson, R. B. (2017). The citation impact of hydrology journals. Water Resources Research, 53(6), 4533-4541.

Fan, Y., Clark, M., Lawrence, D. M., Swenson, S., Band, L. E., Brantley, S. L., … & Kirchner, J. W. (2019). Hillslope hydrology in global change research and Earth system modeling. Water Resources Research, 55(2), 1737-1772.

1. Tang, Y., Marshall, L., Sharma, A., Ajami, H., & Nott, D. J. (2019). Ecohydrologic error models for improved Bayesian inference in remotely sensed catchments. Water Resources Research. https://doi.org/10.1029/2019WR025055
2. Brunner, M. I., Hingray, B., Zappa, M., & Favre, A. C. Future trends in the interdependence between flood peaks and volumes: hydro‐climatological drivers and uncertainty. Water Resources Research. https://doi.org/10.1029/2019WR024701
3. Lee, T., & Ouarda, T. B. (2019). Multivariate Nonstationary Oscillation Simulation of Climate Indices with Empirical Mode Decomposition. Water Resources Research. https://doi.org/10.1029/2018WR023892
4. Apurv, T., & Cai, X. Evaluation of the stationarity assumption for meteorological drought risk estimation at the multi‐decadal scale in contiguous US. Water Resources Research. https://doi.org/10.1029/2018WR024047

Environmental Modelling and Software:

•  Badham, J., Elsawah, S., Guillaume, J. H., Hamilton, S. H., Hunt, R. J., Jakeman, A. J., … & Gober, P. (2019). Effective modeling for Integrated Water Resource Management: A guide to contextual practices by phases and steps and future opportunities. Environmental Modelling & Software, 116, 40-56. https://doi.org/10.1016/j.envsoft.2019.02.013

Hydrology and Earth System Sciences:

• O, S. and Foelsche, U.: Assessment of spatial uncertainty of heavy rainfall at catchment scale using a dense gauge network, Hydrol. Earth Syst. Sci., 23, 2863-2875, https://doi.org/10.5194/hess-23-2863-2019, 2019.
• Huang, Y., Bárdossy, A., and Zhang, K.: Sensitivity of hydrological models to temporal and spatial resolutions of rainfall data, Hydrol. Earth Syst. Sci., 23, 2647-2663, https://doi.org/10.5194/hess-23-2647-2019, 2019.

Journal of Hydrology:

•  Goodarzi, L., Banihabib, M. E., & Roozbahani, A. (2019). A decision-making model for flood warning system based on ensemble forecasts. Journal of hydrology, 573, 207-219. https://doi.org/10.1016/j.jhydrol.2019.03.040
• Chen, Y., Xu, C. Y., Chen, X., Xu, Y., Yin, Y., Gao, L., & Liu, M. (2019). Uncertainty in simulation of land-use change impacts on catchment runoff with multi-timescales based on the comparison of the HSPF and SWAT models. Journal of Hydrology, 573, 486-500. https://doi.org/10.1016/j.jhydrol.2019.03.091
• Wang, L., Li, X., Ma, C., & Bai, Y. (2019). Improving the prediction accuracy of monthly streamflow using a data-driven model based on a double-processing strategy. Journal of Hydrology, 573, 733-745. https://doi.org/10.1016/j.jhydrol.2019.03.101
• Strange, B. M., Maxwell, J. T., Robeson, S. M., Harley, G. L., Therrell, M. D., & Ficklin, D. L. (2019). Comparing three approaches to reconstructing streamflow using tree rings in the Wabash River basin in the Midwestern, US. Journal of Hydrology, 573, 829-840. https://doi.org/10.1016/j.jhydrol.2019.03.057
• Tsegaw, A. T., Alfredsen, K., Skaugen, T., & Muthanna, T. M. (2019). Predicting hourly flows at ungauged small rural catchments using a parsimonious hydrological model. Journal of Hydrology, 573, 855-871. https://doi.org/10.1016/j.jhydrol.2019.03.090

Hydrological Sciences Journal:

1.  Alipour, M. H., & Kibler, K. M. (2019). Streamflow prediction under extreme data scarcity: a step toward hydrologic process understanding within severely data-limited regions. Hydrological Sciences Journal, https://doi.org/10.1080/02626667.2019.1626991
2. Ahmadi, A., Nasseri, M., & Solomatine, D. P. (2019). Parametric uncertainty assessment of hydrological models: coupling UNEEC-P and a fuzzy general regression neural network. Hydrological Sciences Journal, 1-15, https://doi.org/10.1080/02626667.2019.1610565

Advances in Water Resources:

1. Ceriotti, G., Russian, A., Bolster, D., & Porta, G. (2019). A double-continuum transport model for segregated porous media: Derivation and sensitivity analysis-driven calibration. Advances in Water Resources, 128, 206-217. https://doi.org/10.1016/j.advwatres.2019.04.003
2. Dell’Oca, A., Riva, M., Ackerer, P., & Guadagnini, A. (2019). Solute transport in random composite media with uncertain dispersivities. Advances in Water Resources, 128, 48-58. https://doi.org/10.1016/j.advwatres.2019.04.005

Asst/Prof. Grey Nearing is a hydrologist at the Department of Geological Sciences at The University of Alabama (UA). Prior to joining UA, he has worked as Project Scientist at the National Center for Atmospheric Researchand, and Research Scientist at the NASA Hydrologic Sciences Lab. I’ve enjoyed an ongoing dialectical debate with Grey, intense yet delightful, on the philosophy of science particularly hydrological uncertainty. It’s been a pleasure to interview Grey.

Can you tell us a little about your background and education?
I studied Math in undergraduate because I felt that this would keep my options open in terms of future career paths. I went into the Environmental Sciences mostly because this is where I found a graduate assistantship (through the US Department of Agriculture). I chose my PhD adviser because I enjoyed reading his papers.

Was becoming a scientist your career plan when you were a student? Tell us about the journey how you got here?
I started college to be a software engineer but decided that I didn’t want to spend all my time writing code (I currently spend about a third to half of my time programming). Several of my friends from high school and college that did become software engineers ended up being fairly bored with their careers after a few years. Then again, some of the young software engineers that I work with (e.g., at consulting and tech companies) really love their work and have skills that allow them to accomplish more in the field than any trained Hydrologist that I know.

Your research has a particular focus on Information Theory (IT) and its application in hydrological sciences. How did you become interested in IT, and do you think it’s a worthwhile research focus to pursue in hydrological sciences?
I’m not sure that information theory is a worthwhile research topic. I got into it because I was unsatisfied with the philosophical discussion about uncertainty in Hydrology journals. It didn’t make sense to me to try to quantify what we don’t know (i.e., to quantify uncertainty), and information theory seemed to provide a way to reframe questions about inference under partial information. There are certain boundedness theorems in information theory that don’t exist in probability theory that I think might help provide coherency to questions about imperfect inference; if we learn how to ask scientific questions in the context of information theory rather than probability theory. I’m not certain about this, but I am certain that I don’t like the existing literature on uncertainty quantification.

You’ve moved from academia (University of Arizona) to the public sector (NCAR and NASA) and then moved back to academia again (University of Alabama). How did you find these transitions?
I’m not sure it’s that big of a jump from academia to government research, but there are differences. Projects at government labs tend to be high-profile, and you work on them with large(r) teams. If you want to be part of a big project and don’t mind having to meet deadlines related to code production, then this is a good option. My work as a project scientist at NCAR and NASA was definitely less stressful than academia. But I had less creative freedom.

And what skills do you think you need to develop now for an academic career that you didn’t need to previously?
Time management, perpetual self-motivation, and the ability to handle rejection are the skills that matter most in academia. It’s hard to work so much, and have your grant proposals rejected so often, without losing the sense of excitement that keeps the work interesting. Academia is hyper-competitive, which was exciting at first, but starts to be a little tedious after a few years.

What are your current research projects?
I’m working on the intersection of machine learning and process-modeling. How can we take the best parts of both and combine them?

You’ve been recognized as an AGU’s Outstanding Reviewers of 2017 by the Water Resources Research Editorial. What do you think led to this great recognition, and what is your advice on paper review to young and early career hydrologists?
Some of the advice I sometimes get from senior scientists about paper and proposal reviews is that this should not be a priority for young scientists. But if you don’t do your fair share then you are a burden on the community. My rule is to review at least 3x the number of papers that I submit, otherwise I’m asking others for more review effort than I am willing to give. I spend a lot of time on (most) of my reviews, and I think I am often the third reviewer because I recommend to reject a lot.

What do you account as your major breakthrough so far in your research career as an early career scientist?
I don’t have a major breakthrough. There aren’t many major breakthroughs in Hydrology. I’ve not seen one in the field in several years. My opinion is that Hydrology isn’t a field defined by breakthroughs – it is a field of incremental progress toward relatively well-defined objectives.

What do you account as your major challenges ahead of you in your research career as an early career academic?
Success boils down to funding. That’s really all there is to it. If I’m successful in getting proposals funded, I will have the resources (people) to pursue bigger projects. The other path to success in science is fundamentally new theory development, but I don’t see this as likely in a field as saturated as Hydrology.

How do you describe your research style? Is novelty your main criterion for research?
I am a contrarian, so not only is novelty the main criteria for me in choosing a research direction, but I often choose to work on projects just because they are contrary to what others are working on.

How do you approach creativity in your research? In other words, what people and activities helped you develop the mindset for “outside the box” thinking.
When I’m being creative, I just think a lot. I think about projects when I’m at the gym, walking to class, eating dinner, going to bed, etc. If I think about something long enough, understand it well enough, and keep myself active, I’ll often have an interesting thought. In the ~10 years that I’ve been in the field I’ve gone in and out of phases where I do this vs. where I just work on routine stuff that needs to get done.

Do you think that creativity and success are correlated?
Probably not. The big names in Hydrology got there because they led a big project with societal relevance. My personal assessment is that creativity and success in Hydrology are anti-correlated, if anything. Creativity is rewarded in science when there are new things to discover, but in an incremental, applied field like Hydrology, the qualities that lead to success are the ability to clearly articulate and implement the next incremental step.

What are your main hobbies besides work?
I don’t do anything other than work. Honestly, I don’t think it’s realistic for most people to have a work-life balance as a non-tenured academic. Maybe an exceptional person can pull this off.

How are you balancing your work and life? Any regrets or advice for early career and aspiring hydrologists?
I don’t have any regrets. My job is what I’m interested in and what I love doing. My only advice is that you really have to love doing this work if you go into academia. There are plenty of jobs in industry that pay more and require less time commitment and stress.

What are the grand gaps/questions of hydrological sciences that in your opinion the community, particularly the early career hydrologists, should tackle?
Right now, I think the big challenge that Hydrology might participate in is about understanding the interface between physical science and data-driven discovery. In its current form, this is about how we can merge physics with machine learning, but it’s fundamentally the same problem as understanding the roles of hypotheses/theory vs. statistics in traditional scientific inference. At a philosophical level, merging physics with machine learning is the same problem as uncertainty quantification, which is just the problem of merging probability with physics.

I also still see some potential for finding scale-relevant hydrological laws. I know that this is generally considered to be a failed project, but I think there may have been some opportunities missed in previous efforts to constrain interactions between multi-timescale models using maximum entropy and maximum entropy production. This has been tried for decades (going back to the 1950s), but I’m not sure that it was done well. An example of this is the study by Wang and Bras (2011) on maximum entropy production models of evapotranspiration, which are fantastic.

I really only see two fundamental challenges in the field – scale-relevant theories and uncertainty quantification, in whatever form the latter might take (I would argue that it’s really a problem of inference under partial information). Both of these projects have been around for a long time with some practical advances but no real theoretical advances. If someone could make a real dent against one of these problems, it would be a big deal. Of course, there are a lot of practical challenges in Hydrology – predicting effects of climate change on local hydrological systems, predictive modeling anthropogenic influences on watersheds, closing the water cycle with remote sensing, partitioning evaporation and transpiration over large scales, etc. – but these aren’t what I would call Grand Challenges. The Grand Challenges in the field have not changed since Beven outlined the two I mentioned above in his speech at the Water for the Future conference published in 1987.

What are the biggest challenges and opportunities for hydrologists in the next 10 years? In the next 50 years? Especially the ones that interest you.
To be honest, I think our discipline is at an existential tipping point. We are firmly in the era of Big Data, and machine learning is better, almost across the board, at simulating hydrological systems than our best process-based models that have resulted from the previous decades of incremental development. Soon, I expect that most water managers will buy their water-related information products from companies like IBM and Google; the latter is apparently working on a global streamflow modeling system.

I think the major question facing our community right now is to understand where, when, and under what conditions our traditional physical science adds value in the emerging Age of Artificial Intelligence. Maybe calling this an existential crisis is a little dramatic, but I do think that understanding this cohesion (science + machine learning) will be the challenge that ends up having the largest overall impact on our field over the next 50 years.

About the author
Sina Khatami, a PhD Candidate in hydrology and uncertainty at the University of Melbourne, is currently the Secretary of Young Hydrologic Society (YHS) and an Editor of YHS Blogs. He is also a committee member of AGU’s Hydrology Section Hydrological Uncertainty Technical Committee since 2018, and Student Subcommittee (H3S) since 2017. Correspondence to sina.khatami@unimelb.edu.au.

References
Beven, K. J. (1987) Towards a new paradigm in hydrology. In Water for the future: hydrology in perspective, International Association of Hydrological Sciences, Rome Symposium (eds JC Rodda, NC Matalas), IAHS Publ. No. 164, pp. 393–403. London, UK: IAHS.

Wang, J., and Bras, R. L. ( 2011), A model of evapotranspiration based on the theory of maximum entropy production, Water Resour. Res., 47, W03521, doi:10.1029/2010WR009392.

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

Water Resources Research:

1. Abbaszadeh, P., Moradkhani, H., & Daescu, D. N. (February 2019). The Quest for Model Uncertainty Quantification: A Hybrid Ensemble and Variational Data Assimilation Framewor. Water Resources Research, 55. https://doi.org/10.1029/2018WR023629
2. Barendrecht, M. H., Viglione, A., Kreibich, H., Merz, B., Vorogushyn, S., & Blöschl, G. (January 2019). The value of empirical data for estimating the parameters of a sociohydrological flood risk model. Water Resources Research, 55, 1312– 1336. https://doi.org/10.1029/2018WR024128
3. Broderick, C., Murphy, C., Wilby, R. L., Matthews, T., Prudhomme, C., & Adamson, M. (January 2019). Using a scenario‐neutral framework to avoid potential maladaptation to future flood risk. Water Resources Research, 55, 1079– 1104. https://doi.org/10.1029/2018WR023623
4. Camacho Suarez, V. V., Schellart, A. N. A., Brevis, W., & Shucksmith, J. D. (April 2019). Quantifying the impact of uncertainty in the dispersion coefficient on assessment of regulatory compliance. Water Resources Research, 55. https://doi.org/10.1029/2018WR023417
5. Dai, H., Chen, X., Ye, M., Song, X., Hammond, G., Hu, B., & Zachara, J. M. (March 2019). Using Bayesian Networks for Sensitivity Analysis of Complex Biogeochemical Models. Water Resources Research, 55. https://doi.org/10.1029/2018WR023589
6. Dobson, B., Wagener, T., & Pianosi, F. (January 2019). How important are model structural and contextual uncertainties when estimating the optimized performance of water resource systems? Water Resources Research, 55. https://doi.org/10.1029/2018WR024249
7. Ford, T. W., & Quiring, S. M. (February 2019). Comparison of contemporary in situ, model, and satellite remote sensing soil moisture with a focus on drought monitoring. Water Resources Research, 55, 1565– 1582. https://doi.org/10.1029/2018WR024039
8. Frank, J. M., Massman, W. J., Ewers, B. E., & Williams, D. G. (January 2019). Bayesian analyses of 17 winters of water vapor fluxes show bark beetles reduce sublimation. Water Resources Research, 55, 1598– 1623. https://doi.org/10.1029/2018WR023054
9. Günther, D., Marke, T., Essery, R., & Strasser, U. (March 2019). Uncertainties in Snowpack Simulations ‐ Assessing the Impact of Model Structure, Parameter Choice and Forcing Data Error on Point‐Scale Energy‐Balance Snow Model Performance. Water Resources Research, 55. https://doi.org/10.1029/2018WR023403
10. Kavetski, D., Fenicia, F., Reichert, P. and Albert, C. (April 2018). Signature-domain calibration of hydrological models using Approximate Bayesian Computation: Theory and comparison to existing applications, Water Resources Research, 54(6), 4059–4083, https://doi:10.1002/2017WR02052
11. Mai, J., & Tolson, B. A. (March 2019). Model Variable Augmentation (MVA) for Diagnostic Assessment of Sensitivity Analysis Results. Water Resources Research.
12. McInerney, D., Thyer, M., Kavetski, D., Lerat, J. and Kuczera, G. (February 2017). Improving probabilistic prediction of daily streamflow by identifying Pareto optimal approaches for modelling heteroscedastic residual errors, Water Resources Research, 53(3), 2199–2239, https://doi:10.1002/2016WR019168
13. Prieto, C., Le Vine, N., Kavetski, D., Garcia, E., & Medina, R. (March 2019). Flow prediction in ungauged catchments using probabilistic Random Forests regionalization and new statistical adequacy tests. Water Resources Research, 55. https://doi.org/10.1029/2018WR023254
14. Qin Y, Kavetski D and Kuczera G (August 2018) A robust Gauss‐Newton algorithm for the optimization of hydrological models: Benchmarking against industry‐standard algorithms, Water Resources Research, 54, 9637–9654. https://doi.org/10.1029/2017WR02248.
15. Sharma, S., Siddique, R., Reed, S., Ahnert, P., & Mejia, A. (January 2019). Hydrological model diversity enhances streamflow forecast skill at short‐ to medium‐range timescales. Water Resources Research, 55, 1510– 1530. https://doi.org/10.1029/2018WR023197
16. Smyth, E. J., Raleigh, M. S., & Small, E. E. (January 2019). Particle filter data assimilation of monthly snow depth observations improves estimation of snow density and SWE. Water Resources Research, 55, 1296– 1311. https://doi.org/10.1029/2018WR023400
17. Tasseff, B., Bent, R., & Van Hentenryck, P. (January 2019). Optimization of structural flood mitigation strategies. Water Resources Research, 55, 1490– 1509. https://doi.org/10.1029/2018WR024362
18. Verdin, A., Rajagopalan, B., Kleiber, W., Podestá, G., & Bert, F. (March 2019). BayGEN: A Bayesian space‐time stochastic weather generator. Water Resources Research, 55. https://doi.org/10.1029/2017WR022473
19. Yin, J., Zhan, X., Liu, J., & Schull, M. (January 2019). An Inter‐comparison of Noah Model Skills with Benefits of Assimilating SMOPS Blended and Individual Soil Moisture Retrievals. Water Resources Research. https://doi.org/10.1029/2018WR024326
20. Zwieback, S., Westermann, S., Langer, M., Boike, J., Marsh, P., & Berg, A. (February 2019). Improving permafrost modeling by assimilating remotely sensed soil moisture. Water Resources Research, 55. https://doi.org/10.1029/2018WR023247

Environmental Modelling and Software:

1. Gatel, L., Lauvernet, C., Carluer, N., Weill, S., Tournebize, J., & Paniconi, C. (March 2019). Global evaluation and sensitivity analysis of a physically based flow and reactive transport model on a laboratory experiment. Environmental Modelling & Software, 113, 73-83. https://doi.org/10.1016/j.envsoft.2018.12.006
2. Hung, F., & Hobbs, B. F. (March 2019). How can learning-by-doing improve decisions in stormwater management? A Bayesian-based optimization model for planning urban green infrastructure investments. Environmental Modelling & Software, 113, 59-72. https://doi.org/10.1016/j.envsoft.2018.12.005
3. Krapu, C., & Borsuk, M. (April 2019). Probabilistic programming: A review for environmental modellers. Environmental Modelling & Software. https://doi.org/10.1016/j.envsoft.2019.01.014
4. McInerney D, Thyer M, Kavetski D, Bennett B, Lerat J, Gibbs M and Kuczera G (November 2018) A simplified approach to produce probabilistic hydrological model predictions, Environmental Modelling and Software, 109, 306-314, https://doi.org/10.1016/j.envsoft.2018.07.001
5. Razavi, S., & Gupta, H. V. (April 2019). A multi-method Generalized Global Sensitivity Matrix approach to accounting for the dynamical nature of earth and environmental systems models. Environmental Modelling & Software, 114, 1-11. https://doi.org/10.1016/j.envsoft.2018.12.002
6. Razavi, S., Sheikholeslami, R., Gupta, H. V., & Haghnegahdar, A. (February 2019). VARS-TOOL: A toolbox for comprehensive, efficient, and robust sensitivity and uncertainty analysis. Environmental Modelling & Software, 112, 95-107. https://doi.org/10.1016/j.envsoft.2018.10.005
7. Saltelli, A., Aleksankina, K., Becker, W., Fennell, P., Ferretti, F., Holst, N., Li, S. and Wu, Q., (April 2019). Why so many published sensitivity analyses are false: A systematic review of sensitivity analysis practices. Environmental Modelling & Software. https://doi.org/10.1016/j.envsoft.2019.01.012
8. Sheikholeslami, R., Razavi, S., Gupta, H. V., Becker, W., & Haghnegahdar, A. (January 2019). Global sensitivity analysis for high-dimensional problems: How to objectively group factors and measure robustness and convergence while reducing computational cost. Environmental modelling & software, 111, 282-299. https://doi.org/10.1016/j.envsoft.2018.09.002
9. Stritih, A., Bebi, P., & Grêt-Regamey, A. (January 2019). Quantifying uncertainties in earth observation-based ecosystem service assessments. Environmental Modelling & Software, 111, 300-310. https://doi.org/10.1016/j.envsoft.2018.09.005
10. Visser, A. G., Beevers, L., & Patidar, S. (April 2019). A coupled modelling framework to assess the hydroecological impact of climate change. Environmental Modelling & Software. https://doi.org/10.1016/j.envsoft.2019.01.004
11. Warren, R.F., Edwards, N.R., Babonneau, F., Bacon, P.M., Dietrich, J.P., Ford, R.W., Garthwaite, P., Gerten, D., Goswami, S., Haurie, A. and Hiscock, K. (January 2019). Producing policy-relevant science by enhancing robustness and model integration for the assessment of global environmental change. Environmental Modelling & Software, 111, 248-258. https://doi.org/10.1016/j.envsoft.2018.05.010

Hydrology and Earth System Sciences:

1. Mackay, J. D., Barrand, N. E., Hannah, D. M., Krause, S., Jackson, C. R., Everest, J., Aðalgeirsdóttir, G., and Black, A. R. (April 2019). Future evolution and uncertainty of river flow regime change in a deglaciating river basin, Hydrol. Earth Syst. Sci., 23, 1833-1865, https://doi.org/10.5194/hess-23-1833-2019.
2. Schürz, C., Hollosi, B., Matulla, C., Pressl, A., Ertl, T., Schulz, K., and Mehdi, B. (2019). A comprehensive sensitivity and uncertainty analysis for discharge and nitrate-nitrogen loads involving multiple discrete model inputs under future changing conditions, Hydrol. Earth Syst. Sci., 23, 1211-1244, https://doi.org/10.5194/hess-23-1211-2019.
3. Woldemeskel F, McInerney D, Lerat J, Thyer M, Kavetski D, Shin D, Tuteja N and Kuczera G (December 2018) Evaluating residual error approaches for post-processing monthly and seasonal streamflow forecasts, Hydrological and Earth System Sciences, 22, 6257-6278, doi: https://doi.org/10.5194/hess-2018-214

Journal of Hydrology:

1. Ahmed, K., Shahid, S., Chung, E. S., Wang, X. J., & Harun, S. B. (March 2019). Climate Change Uncertainties in Seasonal Drought Severity-Area-Frequency Curves: Case of Arid Region of Pakistan. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2019.01.019
2. Ahn, K. H., & Kim, Y. O. (March 2019). Incorporating Climate Model Similarities and Hydrologic Error Models to Quantify Climate Change Impacts on Future Riverine Flood Risk. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2018.12.061
3. Chen, L., Chen, S., Li, S., & Shen, Z. (March 2019). Temporal and spatial scaling effects of parameter sensitivity in relation to non-point source pollution simulation. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2019.01.045
4. Gebrehiwot, S. G., Di Baldassarre, G., Bishop, K., Halldin, S., & Breuer, L. (March 2019). Is observation uncertainty masking the signal of land use change impacts on hydrology?. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2018.12.058
5. Hazra, A., Maggioni, V., Houser, P., Antil, H., & Noonan, M. (March 2019). A Monte Carlo-Based Multi-Objective Optimization Approach to Merge Different Precipitation Estimates for Land Surface Modeling. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2018.12.039
6. Koohbor, B., Fahs, M., Ataie-Ashtiani, B., Belfort, B., Simmons, C. T., & Younes, A. (April 2019). Uncertainty analysis for seawater intrusion in fractured coastal aquifers: Effects of fracture location, aperture, density and hydrodynamic parameters. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2019.01.052
7. Li, Z., Jasechko, S., & Si, B. (April 2019). Uncertainties in tritium mass balance models for groundwater recharge estimation. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2019.01.030
8. Peleg, N., Molnar, P., Burlando, P., & Fatichi, S. (April 2019). Exploring stochastic climate uncertainty in space and time using a gridded hourly weather generator. Journal of Hydrology, 571, 627-641. https://doi.org/10.1016/j.jhydrol.2019.02.010
9. Pirot, G., Huber, E., Irving, J., & Linde, N. (April 2019). Reduction of conceptual model uncertainty using ground-penetrating radar profiles: Field-demonstration for a braided-river aquifer. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2019.01.047
10. Sharifi, E., Saghafian, B., & Steinacker, R. (March 2019). Copula-based Stochastic Uncertainty Analysis of Satellite Precipitation Products. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2019.01.035
11. Soltani, M., Laux, P., Mauder, M., & Kunstmann, H. (April 2019). Inverse distributed modelling of streamflow and turbulent fluxes: A sensitivity and uncertainty analysis coupled with automatic optimization. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2019.02.033
12. Yang, W., Long, D., & Bai, P. (March 2019). Impacts of future land cover and climate changes on runoff in the mostly afforested river basin in North China. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2018.12.055
13. Wine, M. L. (2019). Under non-stationarity securitization contributes to uncertainty and Tragedy of the Commons. Journal of Hydrology, 568, 716-721, https://doi.org/10.1016/j.jhydrol.2018.11.044
14.  Jiang, S.Y., Zhang, Q., Werner, A.D., Wellen, C., Jomaa, S., Zhu, Q.D., Büttner, O., Meon, G. and Rode, M. (2019). Effects of stream nitrate data frequency on watershed model performance and prediction uncertainty. Journal of Hydrology, 569, 22-36, https://doi.org/10.1016/j.jhydrol.2018.11.049.

Geophysical Research Letters:

1. Chen, H., Morrison, A. K., Dufour, C. O., & Sarmiento, J. L. (March 2019). Deciphering patterns and drivers of heat and carbon storage in the Southern Ocean. Geophysical Research Letters, 46. https://doi.org/10.1029/2018GL080961
2. Wise, E. K., & Dannenberg, M. P. (March 2019). Climate factors leading to asymmetric extreme capture in the tree‐ring record. Geophysical Research Letters, 46. https://doi.org/10.1029/2019GL082295

Ecological Engineering:

1. Kiesel, J., Gericke, A., Rathjens, H., Wetzig, A., Kakouei, K., Jähnig, S. C., & Fohrer, N. (2019). Climate change impacts on ecologically relevant hydrological indicators in three catchments in three European ecoregions. Ecological Engineering, 127, 404-416 https://doi.org/10.1016/j.ecoleng.2018.12.019

Science of the Total Environment:

1. Fortesa, J., García-Comendador, J., Calsamiglia, A., López-Tarazón, J. A., Latron, J., Alorda, B., & Estrany, J. (2019). Comparison of stage/discharge rating curves derived from different recording systems: Consequences for streamflow data and water management in a Mediterranean island. Science of The Total Environment, 665, 968-981, https://doi.org/10.1016/j.scitotenv.2019.02.158

Book chapters:

1. Kavetski D (2018) Parameter estimation and predictive uncertainty quantification in hydrological modelling, Book Chapter 25-1 in Duan Q et al (eds) Handbook of Hydrometeorological Ensemble Forecasting, Springer-Verlag, doi: 10.1007/978-3-642-40457-3_25-1.