EGU23-16413, updated on 26 Feb 2023
EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Comparing the performance of process-based models for drought simulation in Scotland 

Shaini Naha, Kit Macleod, Zisis Gagkas, and Miriam Glendell
Shaini Naha et al.
  • The James Hutton Institute, Craigiebuckler, Aberdeen AB15 8QH, UK (

Scotland is increasingly vulnerable to periods of dry weather, impacting water users and the natural environment. In 2022, large parts of Scotland have experienced water scarcity, resulting in Scotland Environmental Protection Act (SEPA) suspending water abstractions for abstraction licence holders in some Scottish catchments. To understand and manage these water scarcity events in Scotland, we need to monitor and model the drought processes. This research is a part of a Scottish Government funded project ‘Understanding the vulnerabilities of Scotland’s water resources to drought’ which has been co-constructed with a range of national level stakeholders and aims to understand what the specific impacts of droughts are and what are the vulnerabilities that may apply to Scotland under future change. This includes the understanding of the spatial variability and characteristics of future hydrological drought events and short-term forecasting of drought duration to inform adaptive catchment management, while considering water resources requirements of different user sectors. As a first step towards constructing a national short-term drought forecasting framework, we have reviewed the state-of-the art hydrological modelling approaches currently applied in the UK. Our review suggests a lumped conceptual model, GR6J and a distributed hydrological response unit-based model, HYPE, are the most appropriate hydrological models for both simulating and short-term forecasting of droughts, based on the following criteria: openly available model code, proven ability at simulating and forecasting low flows, and widely used and supported model. In next steps, we will design a common modelling framework for drought simulation and forecasting in Scotland. Using both HYPE and GR6J, we will set up and test both models in a medium size long-term monitoring test catchment in Tarland in northeast Scotland (~70km2) where we have good process understanding and recent hydro climatological datasets. Comparison of the model performances of HYPE and GR6J will guide us to take a decision on which model to move forward with for upscaling in Scotland. Machine learning approaches for low-flow forecasting using long-short-memory networks will also be explored in developing a multi-model drought forecasting ensemble.  

Keywords: Drought, water scarcity, modelling, HYPE, GR6J, forecasting 

How to cite: Naha, S., Macleod, K., Gagkas, Z., and Glendell, M.: Comparing the performance of process-based models for drought simulation in Scotland , EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-16413,, 2023.