Broderick, C, Matthews, TR, Wilby, RL, Bastola, S and Murphy, C (2016) Transferability of hydrological models and ensemble averaging methods between contrasting climatic periods. Water Resources Research, 52 (10). pp. 8343-8373. ISSN 1944-7973

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Abstract

Understanding hydrological model predictive capabilities under contrasting climate conditions enables more robust decision making. Using Differential Split Sample Testing (DSST) we analyse the performance of six hydrological models for 37 Irish catchments under climate conditions unlike those used for model training. Additionally, we consider four ensemble averaging techniques when examining inter-period transferability. DSST is conducted using two/three-year non-continuous blocks of (i) the wettest/driest years on record based on precipitation totals, and (ii) years with a more/less pronounced seasonal precipitation regime. Model transferability between contrasting regimes was found to vary depending on the testing scenario, catchment and evaluation criteria considered. As expected, the ensemble average outperformed most individual ensemble members. However, averaging techniques differed considerably in the number of times they surpassed the best individual model-member. Bayesian Model Averaging (BMA) and the Granger-Ramanathan (GRA) method were found to outperform the simple arithmetic mean (SAM) and Akaike Information Criteria Averaging (AICA). Here, GRA performed better than the best individual model in 51% to 86% of cases (according to the Nash-Sutcliffe criterion). When assessing model predictive skill under climate change conditions we recommend (i) setting up DSST to select the best available analogues of expected annual mean and seasonal climate conditions; (ii) applying multiple performance criteria; (iii) testing transferability using a diverse set of catchments and; (iv) using a multi-model ensemble in conjunction with an appropriate averaging technique. Given the computational efficiency and performance of GRA relative to BMA, the former is recommended as the preferred ensemble averaging technique for climate assessment.

Item Type:	Article
Uncontrolled Keywords:	0905 Civil Engineering, 0907 Environmental Engineering, 1402 Applied Economics
Subjects:	G Geography. Anthropology. Recreation > GE Environmental Sciences
Divisions:	Natural Sciences & Psychology (closed 31 Aug 19)
Publisher:	American Geophysical Union (AGU)
Date Deposited:	25 Oct 2016 09:54
Last Modified:	04 Sep 2021 12:22
DOI or ID number:	10.1002/2016WR018850
URI:	https://researchonline.ljmu.ac.uk/id/eprint/4690

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