Outputs (16)

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. (2024). Unravelling long-term impact of water abstraction and climate change on endorheic lakes: A case study of Shortandy Lake in Central Asia. PLoS ONE, 19(7), Article e0305721. https://doi.org/10.1371/journal.pone.0305721

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.

Towards evaluation criteria in participatory flood risk management (2018)
Journal Article
Maskrey, S. A., Priest, S., & Mount, N. J. (2019). Towards evaluation criteria in participatory flood risk management. Journal of Flood Risk Management, 12(2), Article e12462. https://doi.org/10.1111/jfr3.12462

Flood risk consists of complex and dynamic problems, whose management calls for innovative ways of engaging with a wide range of local stakeholders, many of whom lack the technical expertise to engage with traditional flood risk management practices.... Read More about Towards evaluation criteria in participatory flood risk management.

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., Eisner, S., Gerten, D., Gudmundsson, L., Haddeland, I., Hanasaki, N., Kim, H., Leng, G., Liu, J., Masaki, Y., Oki, T., Pokhrel, Y., Satoh, Y., Schewe, J., & 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). 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.