2023 |
Laugesen, R., Thyer, M., McInerney, D., & Kavetski, D. (2023). Flexible forecast value metric suitable for a wide range of decisions: application using probabilistic subseasonal streamflow forecasts. Hydrology and Earth System Sciences, 27(4), 873-893. DOI |
2023 |
Renard, B., McInerney, D., Westra, S., Leonard, M., Kavetski, D., Thyer, M., & Vidal, J. P. (2023). Floods and Heavy Precipitation at the Global Scale: 100-Year Analysis and 180-Year Reconstruction. Journal of Geophysical Research: Atmospheres, 128(9), 26 pages. DOI |
2023 |
Dal Molin, M., Kavetski, D., Albert, C., & Fenicia, F. (2023). Exploring Signature-Based Model Calibration for Streamflow Prediction in Ungauged Basins. Water Resources Research, 59(7), 32 pages. DOI |
2022 |
Prieto, C., Le Vine, N., Kavetski, D., Fenicia, F., Scheidegger, A., & Vitolo, C. (2022). An Exploration of Bayesian Identification of Dominant Hydrological Mechanisms in Ungauged Catchments. Water Resources Research, 58(3), 1-28. DOI Scopus4 WoS4 |
2022 |
Partington, D., Thyer, M., Shanafield, M., McInerney, D., Westra, S., Maier, H., . . . Kavetski, D. (2022). Predicting wildfire induced changes to runoff: A review and synthesis of modeling approaches. WIREs Water, 9(5), 1-25. DOI Scopus3 WoS3 |
2022 |
Qin, Y., Kavetski, D., Kuczera, G., McInerney, D., Yang, T., & Guo, Y. (2022). Technical Note: Can Gauss‐Newton algorithms outperform stochastic optimization algorithms when calibrating a highly parameterized hydrological model? A case study using SWAT. Water Resources Research, 58(11), 14 pages. DOI |
2022 |
Mcinerney, D., Thyer, M., Kavetski, D., Laugesen, R., Woldemeskel, F., Tuteja, N., & Kuczera, G. (2022). Seamless streamflow forecasting at daily to monthly scales: MuTHRE lets you have your cake and eat it too. Hydrology and Earth System Sciences, 26(21), 5669-5683. DOI Scopus2 |
2022 |
Renard, B., Thyer, M., McInerney, D., Kavetski, D., Leonard, M., & Westra, S. (2022). A Hidden Climate Indices Modeling Framework for Multi‐Variable Space‐Time Data. Water Resources Research, 58(2), 1-27. DOI Scopus4 WoS5 |
2021 |
Fenicia, F., & Kavetski, D. (2021). Behind every robust result is a robust method: Perspectives from a case study and publication process in hydrological modelling. Hydrological Processes, 35(8), 9 pages. DOI Scopus4 WoS3 |
2021 |
Prieto, C., Kavetski, D., Le Vine, N., Álvarez, C., & Medina, R. (2021). Identification of Dominant Hydrological Mechanisms Using Bayesian Inference, Multiple Statistical Hypothesis Testing, and Flexible Models. Water Resources Research, 57(8), 32 pages. DOI Scopus7 WoS5 |
2021 |
Hunter, J., Thyer, M., McInerney, D., & Kavetski, D. (2021). Achieving high-quality probabilistic predictions from hydrological models calibrated with a wide range of objective functions. Journal of Hydrology, 603, 1-22. DOI Scopus5 WoS4 |
2021 |
McInerney, D., Thyer, M., Kavetski, D., Laugesen, R., Woldemeskel, F., Tuteja, N., & Kuczera, G. (2021). Improving the reliability of sub‐seasonal forecasts of high and low flows by using a flow‐dependent non‐parametric model. Water Resources Research, 57(11), 16 pages. DOI Scopus4 WoS4 |
2021 |
Dal Molin, M., Kavetski, D., & Fenicia, F. (2021). SuperflexPy 1.3.0: an open-source Python framework for building, testing, and improving conceptual hydrological models. GEOSCIENTIFIC MODEL DEVELOPMENT, 14(11), 7047-7072. DOI Scopus5 WoS5 |
2020 |
Lerat, J., Thyer, M., McInerney, D., Kavetski, D., Woldemeskel, F., Pickett-Heaps, C., . . . Feikema, P. (2020). A robust approach for calibrating a daily rainfall-runoff model to monthly streamflow data. Journal of Hydrology, 591, 1-15. DOI Scopus11 WoS10 |
2020 |
McInerney, D., Thyer, M., Kavetski, D., Laugesen, R., Tuteja, N., & Kuczera, G. (2020). Multi‐temporal hydrological residual error modelling for seamless sub‐seasonal streamflow forecasting. Water Resources Research, 56(11), 1-33. DOI Scopus18 WoS19 |
2019 |
Prieto, C., Le Vine, N., Kavetski, D., García, E., & Medina, R. (2019). Flow prediction in ungauged catchments using probabilistic random forests regionalization and new statistical adequacy tests. Water Resources Research, 55(5), 4364-4392. DOI Scopus48 WoS46 |
2019 |
McInerney, D., Kavetski, D., Thyer, M., Lerat, J., & Kuczera, G. (2019). Benefits of explicit treatment of zero flows in probabilistic hydrological modelling of ephemeral catchments. Water Resources Research, 55(12), 11035-11060. DOI Scopus15 WoS15 |
2018 |
Kavetski, D., Fenicia, F., Reichert, P., & Albert, C. (2018). Signature-domain calibration of hydrological models using approximate bayesian computation: theory and comparison to existing applications. Water Resources Research, 54(6), 4059-4083. DOI Scopus30 WoS27 |
2018 |
Fenicia, F., Kavetski, D., Reichert, P., & Albert, C. (2018). Signature-domain calibration of hydrological models using approximate bayesian computation: empirical analysis of fundamental properties. Water Resources Research, 54(6), 3958-3987. DOI Scopus29 WoS27 |
2018 |
Hostache, R., Chini, M., Giustarini, L., Neal, J., Kavetski, D., Wood, M., . . . Matgen, P. (2018). Near-Real-Time Assimilation of SAR-Derived Flood Maps for Improving Flood Forecasts. Water Resources Research, 54(8), 5516-5535. DOI Scopus76 WoS69 |
2018 |
Kavetski, D., Qin, Y., & Kuczera, G. (2018). The Fast and the Robust: Trade-Offs Between Optimization Robustness and Cost in the Calibration of Environmental Models. Water Resources Research, 54(11), 9432-9455. DOI Scopus12 WoS12 |
2018 |
Woldemeskel, F., McInerney, D., Lerat, J., Thyer, M., Kavetski, D., Shin, D., . . . Kuczera, G. (2018). Evaluating post-processing approaches for monthly and seasonal streamflow forecasts. Hydrology and Earth System Sciences, 22(12), 6257-6278. DOI Scopus31 WoS32 |
2018 |
Qin, Y., Kavetski, D., & Kuczera, G. (2018). A robust Gauss-Newton algorithm for the optimization of hydrological models: from standard Gauss-Newton to robust Gauss-Newton. Water Resources Research, 54(11), 9655-9683. DOI Scopus24 WoS25 |
2018 |
Qin, Y., Kavetski, D., & Kuczera, G. (2018). A robust Gauss-Newton algorithm for the optimization of hydrological models: benchmarking against industry-standard algorithms. Water Resources Research, 54(11), 9637-9654. DOI Scopus29 WoS24 |
2018 |
Gibbs, M. S., McInerney, D., Humphrey, G., Thyer, M. A., Maier, H. R., Dandy, G. C., & Kavetski, D. (2018). State updating and calibration period selection to improve dynamic monthly streamflow forecasts for an environmental flow management application. Hydrology and Earth System Sciences, 22(1), 871-887. DOI Scopus30 WoS27 |
2018 |
McInerney, D., Thyer, M. A., Kavetski, D., Githui, F., Thayalakumaran, T., Liu, M., & Kuczera, G. (2018). The importance of spatiotemporal variability in irrigation inputs for hydrological modelling of irrigated catchments. Water Resources Research, 54(9), 6792-6821. DOI Scopus22 WoS20 |
2018 |
McInerney, D., Thyer, M. A., Kavetski, D., Bennett, B., Lerat, J., Gibbs, M., & Kuczera, G. (2018). A simplified approach to produce probabilistic hydrological model predictions. Environmental Modelling & Software, 109, 306-314. DOI Scopus18 WoS20 |
2018 |
Henn, B., Clark, M., Kavetski, D., Newman, A., Hughes, M., McGurk, B., & Lundquist, J. (2018). Spatiotemporal patterns of precipitation inferred from streamflow observations across the Sierra Nevada mountain range. Journal of Hydrology, 556, 993-1012. DOI Scopus33 WoS34 |
2017 |
McInerney, D., Thyer, M., Kavetski, D., Lerat, J., & Kuczera, G. (2017). Improving probabilistic prediction of daily streamflow by identifying Pareto optimal approaches for modeling heteroscedastic residual errors. Water Resources Research, 53(3), 2199-2239. DOI Scopus92 WoS89 |
2017 |
Schaefli, B., & Kavetski, D. (2017). Bayesian spectral likelihood for hydrological parameter inference. Water Resources Research, 53(8), 6857-6884. DOI Scopus7 WoS6 |
2016 |
Qin, Y., Kuczera, G., & Kavetski, D. (2016). Comparison of Newton-type and SCE optimisation algorithms for the calibration of conceptual hydrological models. Australian Journal of Water Resources, 20(2), 169-176. DOI Scopus8 |
2016 |
Henn, B., Clark, M., Kavetski, D., McGurk, B., Painter, T., & Lundquist, J. (2016). Combining snow, streamflow, and precipitation gauge observations to infer basin-mean precipitation. Water Resources Research, 52(11), 8700-8723. DOI Scopus20 WoS20 |
2016 |
Giustarini, L., Hostache, R., Kavetski, D., Chini, M., Corato, G., Schlaffer, S., & Matgen, P. (2016). Probabilistic Flood Mapping Using Synthetic Aperture Radar Data. IEEE Transactions on Geoscience and Remote Sensing, 54(12), 6958-6969. DOI Scopus97 WoS88 |
2016 |
Hill, M., Kavetski, D., Clark, M., Ye, M., Arabi, M., Lu, D., . . . Mehl, S. (2016). Practical use of computationally frugal model analysis methods. Groundwater, 54(2), 159-170. DOI Scopus137 WoS51 Europe PMC1 |
2016 |
Fenicia, F., Kavetski, D., Savenije, H., & Pfister, L. (2016). From spatially variable streamflow to distributed hydrological models: analysis of key modeling decisions. Water Resources Research, 52(2), 954-989. DOI Scopus73 WoS69 |
2015 |
Lockart, N., Kavetski, D., & Franks, S. (2015). A new stochastic model for simulating daily solar radiation from sunshine hours. International Journal of Climatology, 35(6), 1090-1106. DOI Scopus13 WoS13 |
2015 |
Clark, M., Nijssen, B., Lundquist, J., Kavetski, D., Rupp, D., Woods, R., . . . Marks, D. (2015). A unified approach for process-based hydrologic modeling: 2. Model implementation and case studies. Water Resources Research, 51(4), 2515-2542. DOI Scopus134 WoS43 |
2015 |
Clark, M., Nijssen, B., Lundquist, J., Kavetski, D., Rupp, D., Woods, R., . . . Rasmussen, R. (2015). A unified approach for process-based hydrologic modeling: 1. Modeling concept. Water Resources Research, 51(4), 2498-2514. DOI Scopus289 WoS280 |
2015 |
Henn, B., Clark, M., Kavetski, D., & Lundquist, J. (2015). Estimating mountain basin-mean precipitation from streamflow using Bayesian inference. Water Resources Research, 51(10), 8012-8033. DOI Scopus38 WoS41 |
2015 |
Wrede, S., Fenicia, F., Martínez-Carreras, N., Juilleret, J., Hissler, C., Krein, A., . . . Pfister, L. (2015). Towards more systematic perceptual model development: a case study using 3 Luxembourgish catchments. Hydrological Processes, 29(12), 2731-2750. DOI Scopus70 WoS70 |
2014 |
Fenicia, F., Kavetski, D., Savenije, H., Clark, M., Schoups, G., Pfister, L., & Freer, J. (2014). Catchment properties, function, and conceptual model representation: is there a correspondence?. Hydrological Processes, 28(4), 2451-2467. DOI Scopus124 WoS118 |
2014 |
Pagano, T., Wood, A., Ramos, M., Cloke, H., Pappenberger, F., Clark, M., . . . Verkade, J. (2014). Challenges of operational river forecasting. Journal of Hydrometeorology, 15(4), 1692-1707. DOI WoS107 |
2014 |
Westra, S., Thyer, M., Leonard, M., Kavetski, D., & Lambert, M. (2014). A strategy for diagnosing and interpreting hydrological model nonstationarity. Water Resources Research, 50(6), 5090-5113. DOI Scopus127 WoS119 |
2014 |
Evin, G., Thyer, M., Kavetski, D., McInerney, D., & Kuczera, G. (2014). Comparison of joint versus postprocessor approaches for hydrological uncertainty estimation accounting for error autocorrelation and heteroscedasticity. Water Resources Research, 50(3), 2350-2375. DOI Scopus122 WoS114 |
2013 |
Ershadi, A., McCabe, M., Evans, J., Mariethoz, G., & Kavetski, D. (2013). A Bayesian analysis of sensible heat flux estimation: quantifying uncertainty in meteorological forcing to improve model prediction. Water Resources Research, 49(5), 2343-2358. DOI Scopus16 WoS15 |
2013 |
van Esse, W., Perrin, C., Booij, M., Augustijn, D., Fenicia, F., Kavetski, D., & Lobligeois, F. (2013). The influence of conceptual model structure on model performance: A comparative study for 237 French catchments. Hydrology and Earth System Sciences, 17(10), 4227-4239. DOI Scopus78 WoS74 |
2013 |
Hill, M., Faunt, C., Belcher, W., Sweetkind, D., Tiedeman, C., & Kavetski, D. (2013). Knowledge, transparency, and refutability in groundwater models, an example from the Death Valley regional groundwater flow system. Physics and Chemistry of the Earth (Print), 64, 105-116. DOI Scopus11 WoS11 |
2013 |
Evin, G., Kavetski, D., Thyer, M., & Kuczera, G. (2013). Pitfalls and improvements in the joint inference of heteroscedasticity and autocorrelation in hydrological model calibration. Water Resources Research, 49(7), 4518-4524. DOI Scopus87 WoS81 |
2012 |
Clark, M., Kavetski, D., & Fenicia, F. (2012). Reply to comment by K. Beven et al. on "Pursuing the method of multiple working hypotheses for hydrological modelling". Water Resources Research, 48(11), 1-7. DOI Scopus24 WoS26 |
2012 |
Lockhart, N., Kavetski, D., & Franks, S. (2012). On the role of soil moisture in daytime evolution of temperatures. Hydrological Processes, online(26), 1-9. DOI Scopus10 WoS9 |
2012 |
Fenicia, F., Pfister, L., Kavetski, D., Matgen, P., Iffly, J., Hoffmann, L., & Uijlenhoet, R. (2012). Microwave links for rainfall estimation in an urban environment: Insights from an experimental setup in Luxembourg-City. Journal of Hydrology, 464, 69-78. DOI Scopus36 WoS30 |
2011 |
Renard, B., Kavetski, D., Leblois, E., Thyer, M., Kuczera, G., & Franks, S. (2011). Toward a reliable decomposition of predictive uncertainty in hydrological modeling: Characterizing rainfall errors using conditional simulation. Water Resources Research, 47(11), 1-21. DOI Scopus175 WoS162 |
2011 |
McMillan, H., Jackson, B., Clark, M., Kavetski, D., & Woods, R. (2011). Rainfall uncertainty in hydrological modelling: An evaluation of multiplicative error models. Journal of Hydrology, 400(1-2), 83-94. DOI Scopus177 WoS164 |
2011 |
Clark, M., McMillan, H., Collins, D., Kavetski, D., & Woods, R. (2011). Hydrological field data from a modeller's perspective: Part 2: process-based evaluation of model hypotheses. Hydrological Processes, 25(4), 523-543. DOI Scopus96 WoS92 |
2011 |
Clark, M., Hendrikx, J., Slater, A., Kavetski, D., Anderson, B., Cullen, N., . . . Woods, R. (2011). Representing spatial variability of snow water equivalent in hydrologic and land-surface models: a review. Water Resources Research, 47(7), 1-25. DOI Scopus230 WoS332 |
2011 |
Kavetski, D., Fenicia, F., & Clark, M. (2011). Impact of temporal data resolution on parameter inference and model identification in conceptual hydrological modeling: insights from an experimental catchment. Water Resources Research, 47(5), 1-25. DOI Scopus80 WoS100 |
2011 |
Clark, M., Kavetski, D., & Fenicia, F. (2011). Pursuing the method of multiple working hypotheses for hydrological modeling. Water Resources Research, 47(9), 1-16. DOI Scopus387 WoS251 |
2011 |
Kavetski, D., & Fenicia, F. (2011). Elements of a flexible approach for conceptual hydrological modeling: 2. Application and experimental insights. Water Resources Research, 47(11), 1-19. DOI Scopus67 WoS55 |
2011 |
Fenicia, F., Kavetski, D., & Savenije, H. (2011). Elements of a flexible approach for conceptual hydrological modeling: 1. Motivation and theoretical development. Water Resources Research, 47(11), 1-13. DOI Scopus241 WoS241 |
2011 |
Thyer, M., Leonard, M., Kavetski, D., Need, S., & Renard, B. (2011). The open source RFortran library for accessing R from Fortran, with applications in environmental modelling. Environmental Modelling & Software, 26(2), 219-234. DOI Scopus15 WoS13 |
2011 |
Kavetski, D., & Clark, M. (2011). Numerical troubles in conceptual hydrology: approximations, absurdities and impact on hypothesis testing. Hydrological Processes, 25(4), 661-670. DOI Scopus56 WoS53 |
2010 |
Franks, S. W., Kavetski, D., & Lockart, N. (2010). Reply to the comment of Cai et al. on the paper "on the recent warming in the Murray-Darling Basin: Land surface interactions misunderstood" by Lockart et al.. Geophysical Research Letters, 37(10), 1 page. DOI Scopus2 WoS2 |
2010 |
Kuczera, G., Renard, B., Thyer, M., & Kavetski, D. (2010). There are no hydrological monsters, just models and observations with large uncertainties!. Hydrological Sciences Journal-Journal des Sciences Hydrologiques, 55(6), 980-991. DOI Scopus63 WoS61 |
2010 |
Kuczera, G., Kavetski, D., Renard, B., & Thyer, M. (2010). A limited memory acceleration strategy for MCMC sampling in hierarchical Bayesian calibration of hydrological models. Water Resources Research, 46(7), 1-6. DOI Scopus30 WoS32 |
2010 |
Renard, B., Kavetski, D., Kuczera, G., Thyer, M., & Franks, S. (2010). Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors. Water Resources Research, 46(5), 1-22. DOI Scopus582 WoS501 |
2010 |
Kavetski, D., & Clark, M. (2010). Ancient numerical daemons of conceptual hydrological modeling 2. Impact of time stepping schemes on model analysis and prediction. Water Resources Research, 46(10), 1-27. DOI Scopus131 WoS123 |
2010 |
Clark, M., & Kavetski, D. (2010). Ancient numerical daemons of conceptual hydrological modeling: 1. Fidelity and efficiency of time stepping schemes. Water Resources Research, 46(10), 1-23. DOI Scopus123 WoS210 |
2010 |
Fenicia, F., Wrede, S., Kavetski, D., Pfister, L., Hoffmann, L., Savenije, H., & McDonnell, J. (2010). Assessing the impact of mixing assumptions on the estimation of streamwater mean residence time. Hydrological Processes, 24(12), 1730-1741. DOI Scopus72 WoS71 |
2009 |
Nordbotten, J., Kavetski, D., Celia, M., & Bachu, S. (2009). Model for CO₂ leakage including multiple geological layers and multiple leaky wells. Environmental Science & Technology, 43(3), 743-749. DOI Scopus183 WoS168 Europe PMC11 |
2009 |
Lockart, N., Kavetski, D., & Franks, S. (2009). On the recent warming in the Murray-Darling Basin: land surface interactions misunderstood. Geophysical Research Letters, 36(24), 1-6. DOI Scopus31 WoS29 |
2009 |
Renard, B., Kavetski, D., & Kuczera, G. (2009). Comment on "An integrated hydrologic Bayesian multimodel combination framework: confronting input, parameter, and model structural uncertainty in hydrologic prediction" by Newsha K. Ajami et al.. Water Resources Research, 45(3), W03603-1-W03603-10. DOI Scopus17 WoS14 |
2009 |
Thyer, M., Renard, B., Kavetski, D., Kuczera, G., Franks, S., & Srikanthan, S. (2009). Critical evaluation of parameter consistency and predictive uncertainty in hydrological modeling: A case study using Bayesian total error analysis. Water Resources Research, 45(12), 1-22. DOI Scopus297 WoS267 |
2008 |
Viswanathan, H., Pawar, R., Stauffer, P., Kaszuba, J., Carey, W., Olsen, S., . . . Guthrie, G. (2008). Development of a hybrid process and system model for the assessment of wellbore leakage at a geologic CO₂ sequestration site. Environmental Science & Technology, 42(19), 7280-7286. DOI Scopus124 WoS116 Europe PMC7 |
2007 |
Kavetski, D., & Kaszuba, J. (2007). Model smoothing strategies to remove microscale discontinuities and spurious secondary optima in objective functions in hydrological calibration. Water Resources Research, 43(3), 1-9. DOI Scopus83 WoS76 |
2006 |
Kuczera, G., Kavetski, D., Franks, S., & Thyer, M. (2006). Towards a Bayesian total error analysis of conceptual rainfall-runoff models: Characterising model error using storm-dependent parameters. Journal of Hydrology, 331(1-2), 161-177. DOI Scopus277 WoS244 |
2006 |
Kavetski, D., Kuczera, G., & Franks, S. W. (2006). Bayesian analysis of input uncertainty in hydrological modeling: 1. Theory. Water Resources Research, 42(3), 9 pages. DOI Scopus468 WoS521 |
2006 |
Kavetski, D., Kuczera, G., & Franks, S. W. (2006). Bayesian analysis of input uncertainty in hydrological modeling: 2. Application. Water Resources Research, 42(3), 10 pages. DOI Scopus219 WoS53 |
2006 |
Kavetski, D., Kuczera, G., & Franks, S. W. (2006). Calibration of conceptual hydrological models revisited: 1. Overcoming numerical artefacts. Journal of Hydrology, 320(1-2), 173-186. DOI Scopus96 WoS92 |
2006 |
Kavetski, D., Kuczera, G., & Franks, S. W. (2006). Calibration of conceptual hydrological models revisited: 2. Improving optimisation and analysis. Journal of Hydrology, 320(1-2), 187-201. DOI Scopus56 WoS52 |
2004 |
Kavetski, D., Binning, P., & Sloan, S. W. (2004). Truncation error and stability analysis of iterative and non-iterative Thomas-Gladwell methods for first-order non-linear differential equations. International Journal for Numerical Methods in Engineering, 60(12), 2031-2043. DOI Scopus15 WoS13 |
2003 |
Kavetski, D., Kuczera, G., & Franks, S. W. (2003). Semidistributed hydrological modeling: A "saturation path" perspective on TOPMODEL and VIC. Water Resources Research, 39(9), 8 pages. DOI Scopus52 WoS51 |
2002 |
Kavetski, D., Binning, P., & Sloan, S. W. (2002). Noniterative time stepping schemes with adaptive truncation error control for the solution of Richards equation. Water Resources Research, 38(10), 29-1-29-10. DOI Scopus49 WoS49 |
2002 |
Kavetski, D., Binning, P., & Sloan, S. W. (2002). Adaptive backward Euler time stepping with truncation error control for numerical modelling of unsaturated fluid flow. International Journal for Numerical Methods in Engineering, 53(6), 1301-1322. DOI Scopus85 WoS69 |
2001 |
Kavetski, D., Binning, P., & Sloan, S. W. (2001). Adaptive time stepping and error control in a mass conservative numerical solution of the mixed form of Richards equation. Advances in Water Resources, 24(6), 595-605. DOI Scopus99 WoS86 |
- |
Puma, M. J., Celia, M. A., Rodriguez-Iturbe, I., Nordbotten, J. M., Guswa, A. J., & Kavetski, D. (n.d.). Effects of Spatial Heterogeneity in Rainfall and Vegetation Type on Soil Moisture and Evapotranspiration. |
- |
Gibbs, M. S., McInerney, D., Humphrey, G., Thyer, M. A., Maier, H. R., Dandy, G. C., & Kavetski, D. (n.d.). State Updating and Calibration Period Selection to Improve Dynamic Monthly Streamflow Forecasts for a Wetland Management Application. DOI |
- |
Woldemeskel, F., McInerney, D., Lerat, J., Thyer, M., Kavetski, D., Shin, D., . . . Kuczera, G. (n.d.). Evaluating residual error approaches for post-processing monthly and seasonal streamflow forecasts. DOI |
- |
Laugesen, R., Thyer, M., McInerney, D., & Kavetski, D. (n.d.). A flexible approach for evaluating the value of probabilistic forecasts for different decision types and risk averse decision-makers. DOI |
- |
Laugesen, R., Thyer, M., McInerney, D., & Kavetski, D. (n.d.). Supplementary material to "Flexible forecast value metric suitable for a wide range of decisions: application using probabilistic subseasonal streamflow forecasts". DOI |
- |
Laugesen, R., Thyer, M., McInerney, D., & Kavetski, D. (n.d.). Flexible forecast value metric suitable for a wide range of decisions: application using probabilistic subseasonal streamflow forecasts. DOI |
- |
Thyer, M., McInerney, D., Kavetski, D., Laugesen, R., Woldemeskel, F., Tuteja, N., & Kuczera, G. (n.d.). Seamless subseasonal probabilistic streamflow forecasting: MuTHRE lets you have your cake and eat it too. DOI |