• Simple and easy to use interactive webapp for single site analysis
  • Provides high quality probabilistic predictions that are robust to a wide range of common objective functions
  • Incorporates the latest research advances in residual error model selection to handle common features of predictive errors, (see 'Help/About')
  • Evaluates predictive performance using a range of commonly used metrics and diagnostics
  • Users simply upload a time series of predictions and observations (see 'Getting Started')
  • Users can easily download time series of probabilistic predictions, probability limits and summary metrics
  • Command-line functionality in R package provides opportunity for automated analysis of a large number of sites


  • Probabilistic predictions provide realistic estimates of water resource system risks - without uncertainty, risks are under-estimated providing a false sense of security
  • Incorporating the uncertainty enables decision-makers with different attitudes to risk aversion to act differently if aware of the uncertainty
  • Encourages the modeller to think about the modelling processes and the quality of information used to inform decisions
  • Decision-makers and the public have the 'right to know' all limitations of a design/analysis in order to make up their own minds and lobby for their individual causes
Inspired by Pappenberger & Beven 2006 and Vogel & Farmer 2016 .

Development Team

Current lead developer

Dr David McInerney , Senior Research Associate, School of Architecture and Civil Engineering, University of Adelaide (Email)


Jason Hunter , PhD Candidate, School of Architecture and Civil Engineering, University of Adelaide (Email) Dr Mark Thyer , Associate Professor in Water Resources Engineering, School of Architecture and Civil Engineering, University of Adelaide (Email) Prof. Dmitri Kavetski , Professor in Environmental Modelling, School of Architecture and Civil Engineering, University of Adelaide (Email) Dr Bree Bennett , Senior Lecturer, School of Architecture and Civil Engineering, University of Adelaide (Email)

1. Prepare and upload data

  • Users need to calibrate their own hydrological model to streamflow 'observations' and use the model to generate streamflow 'predictions' for their catchment of interest
  • Prepare a data file (csv format) of the streamflow 'predictions' and 'observations' for upload to the webapp
  • See 'Simulation | Input Data' for details and an example input file

2. Evaluate and enhance predictive data

Introduction to evaluating probabilistic predictions: What makes a good probabilistic prediction? Introduction to residual diagnostics to evaluate error model assumptions Enhancing predictive performance Practical guidance on representing uncertainty in hydrological predictions

3. Output

  • Replicates: CSV data file of multiple individual time series (replicates) of probabilistic streamflow predictions generated by the residual error model - useful to be used as input into other models
  • Probability Limits: CSV data file of the probability limits (5%, 95%) of the probabilistic predictions - useful for plotting purposes
  • Summary: PDF file that summarises the analysis, includes input data details, summary metrics and diagnostics, and probabilistic time series

Input Data

Residual Model Parameters

Data integrity checks

Performance metric plot type
Performance benchmarking

Metric Summary

Residual plot type

Output Data

Note: Interface must be opened in browser for downloads to be available - see option 'Open in Browser' at the top of the interface window.


Please report any bugs, issues or feature requests to https://github.com/AdWater/ProbApp/issues

Licenses, Warranty and Disclaimer

This open source project is provided under the GPLv3 license. Please see link for terms covering warranty, disclaimers, liability and use of this software.


Residual error model development

Hunter, J., Thyer, M., McInerney, D. & Kavetski, D. 2020. Achieving high-quality probabilistic predictions from hydrological models calibrated with a wide range of objective functions. Journal of Hydrology , vol 603, DOI: https://doi.org/10.1016/j.jhydrol.2021.126578 . McInerney, D., Kavetski, D., Thyer, M., Lerat, J. & Kuczera, G. 2019, Benefits of explicit treatment of zero flows in probabilistic hydrological modeling of ephemeral catchments. Water Resources Research , vol. 55, no. 12, pp 11035-11060, DOI: 10.1029/2018WR024148 . McInerney, D., Thyer, M., Kavetski, D., Bennett, B. Gibbs, M. & Kuczera, G. 2018. A simplified approach to produce probabilistic hydrological model predictions. Environmental Modelling and Software , DOI: 10.1016/j.envsoft.2018.07.001 . 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 , vol. 53 no. 3, pp. 2199-2239, DOI: 10.1002/2016WR019168. 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 , vol. 50 no. 3, pp. 2350-2375,DOI: 10.1002/2013WR014185. 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 , vol. 49, no. 7, pp. 4518-4524, DOI: 10.1002/wrcr.20284

Applications in streamflow forecasting

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, Hydrol. Earth Syst. Sci. , 26, 5669–5683, DOI: https://doi.org/10.5194/hess-26-5669-2022 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 nonparametric model. Water Resources Research , 57, e2020WR029317, DOI: https://doi.org/10.1029/2020WR029317 McInerney, D., Thyer, M., Kavetski, D., Laugesen, R., Tuteja, N. & Kuczera, G. 2020. Multi-temporal hydrological residual error modeling for seamless subseasonal streamflow forecasting. Water Resources Research , vol. 56, no. 11, pp. 2019WR026979, DOI: 10.1029/2019WR026979 Woldemeskel, F., McInerney D., Lerat J., Thyer M., Kavetski D., Shin D., Tuteja N. & Kuczera G. 2018. Evaluating post-processing approaches for monthly and seasonal streamflow forecasts. Hydrology and Earth System Sciences , vol. 22, no. 12, pp. 6257-6278, DOI: 10.5194/hess-22-6257-2018