Transferability of hydrological models and ensemble averaging methods between contrasting climatic periods
2016
Understanding hydrological model predictive capabilities under contrasting climate conditions
enables more robust decision making. Using Differential Split Sample Testing (DSST), we analyze 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
interperiod transferability. DSST is conducted using 2/3 year noncontinuous 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 Averaging (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%–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
multimodel 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.
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