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|Title: ||Transferability of hydrological models and ensemble averaging methods between contrasting climatic periods|
|Authors: ||Broderick, Ciaran|
Matthews, Tom K.R.
Wilby, Robert L.
|Issue Date: ||2016|
|Publisher: ||© American Geophysical Union Publications|
|Citation: ||BRODERICK, C. ... et al, 2016. Transferability of hydrological models and ensemble averaging methods between contrasting climatic periods. Water Resources Research, 52 (10), pp. 8343-8373.|
|Abstract: ||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.|
|Description: ||An edited version of this paper was published by AGU. Copyright 2016 American Geophysical Union. To view the published open abstract, go to http://dx.doi.org/10.1002/2016WR018850|
|Sponsor: ||C.M. and C.B. acknowledge funding provided by the Irish Environmental Protection Agency
under project 2014-CCRP-MS.16.|
|Publisher Link: ||http://dx.doi.org/10.1002/2016WR018850|
|Appears in Collections:||Published Articles (Geography and Environment)|
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