Regionalization of hydrological models for flow estimation in ungauged catchments in Ireland

Abstract

Study Region: The study area consists of 44 catchments across Ireland. Study Focus: We regionalize two hydrological models (GR4J and GR6J) to produce continuous discharge simulations and compare performance in simulating high, median and low flow conditions with other established approaches to prediction in ungauged basins and a simple benchmark of using the median parameter set across all catchments. These include K-nearest neighbor (KNN) and statistical methods for predicting flow quantiles using catchment characteristics. Different objective functions were selected for different parts of flow regime and the success of different methods for regionalizing hydrological model parameters; including multiple linear regression (MLR), non-linear regression (NL) and random forests (RF) were evaluated. New Hydrological insights for the Region: All regionalization approaches perform well for average flow conditions. The GR4J model regionalized using RF performs best for simulating high flows, though all regionalized models underestimate the median annual flood. GR6J regionalized using RF performs best for low flows. While KNN and statistical approaches that directly leverage physical catchment descriptors provide comparable median performances across catchments, the spread in relative error across our sample is reduced using regionalized hydrological models. Our results highlight that the choice of hydrological model, objective functions for optimization and approach to linking model parameters and physical catchment descriptors significantly influence the success of regionalization for low and high flows.

Keywords

• Regionalization
• Hydrologic models
• High flows
• Low flows
• Machine learning