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Outputs (14)

Unravelling long-term impact of water abstraction and climate change on endorheic lakes: A case study of Shortandy Lake in Central Asia (2024)
Journal Article
Baigaliyeva, M., Mount, N., Gosling, S. N., & McGowan, S. (in press). Unravelling long-term impact of water abstraction and climate change on endorheic lakes: A case study of Shortandy Lake in Central Asia. PLoS ONE,

Endorheic lakes, lacking river outflows, are highly sensitive to environmental changes and human interventions. Central Asia (CA) has over 6000 lakes that have experienced substantial water level variability in the past century, yet causes of recent... Read More about Unravelling long-term impact of water abstraction and climate change on endorheic lakes: A case study of Shortandy Lake in Central Asia.

Doing flood risk modelling differently: Evaluating the potential for participatory techniques to broaden flood risk management decision‐making (2021)
Journal Article
Maskrey, S. A., Mount, N. J., & Thorne, C. R. (2022). Doing flood risk modelling differently: Evaluating the potential for participatory techniques to broaden flood risk management decision‐making. Journal of Flood Risk Management, 15(1), Article e12757. https://doi.org/10.1111/jfr3.12757

Responsibility for flood risk management (FRM) is increasingly being devolved to a wider set of stakeholders, and effective participation by multiple FRM agencies and communities at risk calls for engagement approaches that supplement and make the... Read More about Doing flood risk modelling differently: Evaluating the potential for participatory techniques to broaden flood risk management decision‐making.

Exploring the value of machine learning for weighted multi-model combination of an ensemble of global hydrological models (2019)
Journal Article
Zaherpour, J., Mount, N., Gosling, S., Dankers, R., Eisner, S., Gerten, D., …Wada, Y. (2019). Exploring the value of machine learning for weighted multi-model combination of an ensemble of global hydrological models. Environmental Modelling and Software, 114, 112-128. https://doi.org/10.1016/j.envsoft.2019.01.003

This study presents a novel application of machine learning to deliver optimised, multi-model combinations (MMCs) of Global Hydrological Model (GHM) simulations. We exemplify the approach using runoff simulations from five GHMs across 40 large global... Read More about Exploring the value of machine learning for weighted multi-model combination of an ensemble of global hydrological models.

Worldwide evaluation of mean and extreme runoff from six global-scale hydrological models that account for human impacts (2018)
Journal Article
Zaherpour, J., Gosling, S. N., Mount, N. J., Müller Schmied, H., Veldkamp, T., Dankers, R., …Wada, Y. (2018). Worldwide evaluation of mean and extreme runoff from six global-scale hydrological models that account for human impacts. Environmental Research Letters, 13(6), https://doi.org/10.1088/1748-9326/aac547

Global-scale hydrological models are routinely used to assess water scarcity, flood hazards and droughts worldwide. Recent efforts to incorporate anthropogenic activities in these models have enabled more realistic comparisons with observations. Here... Read More about Worldwide evaluation of mean and extreme runoff from six global-scale hydrological models that account for human impacts.

Improved validation framework and R-package for artificial neural network models (2017)
Journal Article
Humphrey, G. B., Maier, H. R., Wu, W., Mount, N. J., Dandy, G. C., Abrahart, R., & Dawson, C. (2017). Improved validation framework and R-package for artificial neural network models. Environmental Modelling and Software, 92, https://doi.org/10.1016/j.envsoft.2017.01.023

Validation is a critical component of any modelling process. In artificial neural network (ANN) modelling, validation generally consists of the assessment of model predictive performance on an independent validation set (predictive validity). However... Read More about Improved validation framework and R-package for artificial neural network models.

Erratum to: A comparison of changes in river runoff from multiple global and catchment-scale hydrological models under global warming scenarios of 1 °C, 2 °C and 3 °C (Climatic Change, 10.1007/s10584-016-1773-3) (2016)
Journal Article
Gosling, S. N., Zaherpour, J., Mount, N. J., Hattermann, F. F., Dankers, R., Arheimer, B., …Zhang, X. (2017). Erratum to: A comparison of changes in river runoff from multiple global and catchment-scale hydrological models under global warming scenarios of 1 °C, 2 °C and 3 °C (Climatic Change, 10.1007/s10584-016-1773-3). Climatic Change, 141(3), 597-598. https://doi.org/10.1007/s10584-016-1855-2

In the initial online publication, a middle initial “J.” was added to the name of second author Jamal Zaherpour. This middle initial should not be there. The original publication has now been corrected as well.

On the physical and operational rationality of data-driven models for suspended sediment prediction in rivers (2016)
Book Chapter
Mount, N., Abrahart, R., & Dawson, C. (2016). On the physical and operational rationality of data-driven models for suspended sediment prediction in rivers. River System Analysis and Management (31-46). Verlag: Springer. https://doi.org/10.1007/978-981-10-1472-7_3

Suspended sediment remains an important variable for prediction in river studies. Knowledge of suspended sediment concentration or load at different downstream locations within a channel allows the temporal and spatial patterns of catchment sediment... Read More about On the physical and operational rationality of data-driven models for suspended sediment prediction in rivers.

A comparison of changes in river runoff from multiple global and catchment-scale hydrological models under global warming scenarios of 1°C, 2°C and 3°C (2016)
Journal Article
Gosling, S., Zaherpour, J., Mount, N. J., Hattermann, F., Dankers, R., Arheimer, B., …Zhang, X. (in press). A comparison of changes in river runoff from multiple global and catchment-scale hydrological models under global warming scenarios of 1°C, 2°C and 3°C. Climatic Change, https://doi.org/10.1007/s10584-016-1773-3

We present one of the first climate change impact assessments on river runoff that utilises an ensemble of global hydrological models (Glob-HMs) and an ensemble of catchment-scale hydrological models (Cat-HMs), across multiple catchments: the upper A... Read More about A comparison of changes in river runoff from multiple global and catchment-scale hydrological models under global warming scenarios of 1°C, 2°C and 3°C.

Data-driven modelling approaches for socio-hydrology: Opportunities and challenges within the Panta Rhei Science Plan (2016)
Journal Article
Mount, N. J., Maier, H. R., Toth, E., Elshorbagy, A., Solomatine, D., Chang, F., & Abrahart, R. J. (2016). Data-driven modelling approaches for socio-hydrology: Opportunities and challenges within the Panta Rhei Science Plan. Hydrological Sciences Journal, 61(7), 1192-1208. https://doi.org/10.1080/02626667.2016.1159683

© 2016 IAHS. “Panta Rhei - Everything Flows” is the science plan for the International Association of Hydrological Sciences scientific decade 2013-2023. It is founded on the need for improved understanding of the mutual, two-way interactions occurrin... Read More about Data-driven modelling approaches for socio-hydrology: Opportunities and challenges within the Panta Rhei Science Plan.

Sensitivity analysis for comparison, validation and physical-legitimacy of neural network-based hydrological models (2014)
Journal Article
Dawson, C., Mount, N. J., Abrahart, R., & Louis, J. (2014). Sensitivity analysis for comparison, validation and physical-legitimacy of neural network-based hydrological models. Journal of Hydroinformatics, 16(2), https://doi.org/10.2166/hydro.2013.222

This paper addresses the difficult question of how to perform meaningful comparisons between neural network-based hydrological models and alternative modelling approaches. Standard, goodness-of-fit metric approaches are limited since they only assess... Read More about Sensitivity analysis for comparison, validation and physical-legitimacy of neural network-based hydrological models.

Legitimising data-driven models: exemplification of a newdata-driven mechanistic modelling framework (2013)
Journal Article
Mount, N. J., Dawson, C., & Abrahart, R. (2013). Legitimising data-driven models: exemplification of a newdata-driven mechanistic modelling framework. Hydrology and Earth System Sciences, 17(7), 2827-2843. https://doi.org/10.5194/hess-17-2827-2013

In this paper the difficult problem of how to legitimise data-driven hydrological models is addressed using an example of a simple artificial neural network modelling problem. Many data-driven models in hydrology have been criticised for their black-... Read More about Legitimising data-driven models: exemplification of a newdata-driven mechanistic modelling framework.

Evolutionary, multi-scale analysis of river bank line retreat using continuous wavelet transforms: Jamuna River, Bangladesh (2013)
Journal Article
Mount, N. J., Tate, N. J., Sarker, M. H., & Thorne, C. R. (2013). Evolutionary, multi-scale analysis of river bank line retreat using continuous wavelet transforms: Jamuna River, Bangladesh. Geomorphology, 183, https://doi.org/10.1016/j.geomorph.2012.07.017

In this study continuous wavelet transforms are used to explore spatio-temporal patterns of multi-scale bank line retreat along a 204 km reach of the Jamuna River, Bangladesh. A sequence of eight bank line retreat series, derived from remotely-sense... Read More about Evolutionary, multi-scale analysis of river bank line retreat using continuous wavelet transforms: Jamuna River, Bangladesh.

The need for operational reasoning in data-driven rating curve prediction of suspended sediment (2012)
Journal Article
Mount, N. J., Abrahart, R., Dawson, C., & Ab Ghani, N. (2012). The need for operational reasoning in data-driven rating curve prediction of suspended sediment. Hydrological Processes, 26(26), https://doi.org/10.1002/hyp.8439

The use of data-driven modelling techniques to deliver improved suspended sediment rating curves has received considerable interest in recent years. Studies indicate an increased level of performance over traditional approaches when such techniques a... Read More about The need for operational reasoning in data-driven rating curve prediction of suspended sediment.

Ideal point error for model assessment in data-driven river flow forecasting (2012)
Journal Article
Dawson, C., Mount, N. J., Abrahart, R., & Shamseldin, A. (2012). Ideal point error for model assessment in data-driven river flow forecasting. Hydrology and Earth System Sciences, 16(8), https://doi.org/10.5194/hess-16-3049-2012

When analysing the performance of hydrological models in river forecasting, researchers use a number of diverse statistics. Although some statistics appear to be used more regularly in such analyses than others, there is a distinct lack of consisten... Read More about Ideal point error for model assessment in data-driven river flow forecasting.