MURAL - Maynooth University Research Archive Library

    Generating probabilistic estimates of hydrological response for Irish catchments using a weather generator and probabilistic climate change scenarios

    Bastola, Satish and Murphy, Conor and Fealy, Rowan (2012) Generating probabilistic estimates of hydrological response for Irish catchments using a weather generator and probabilistic climate change scenarios. Hydrological Processes, 26. pp. 2307-2321. ISSN 0885-6087

    [img] Download (892kB)

    Share your research

    Twitter Facebook LinkedIn GooglePlus Email more...

    Add this article to your Mendeley library


    In accounting for uncertainties in future simulations of hydrological response of a catchment, two approaches have come to the fore: deterministic scenario-based approaches and stochastic probabilistic approaches. As scenario-based approaches result in a wide range of outcomes, the role of probabilistic-based estimates of climate change impacts for policy formulation has been increasingly advocated by researchers and policy makers. This study evaluates the impact of climate change on seasonal river flows by propagating daily climate time series, derived from probabilistic-based climate scenarios using a weather generator (WGEN), through a set of conceptual hydrological models. Probabilistic scenarios are generated using two different techniques. The first technique used probabilistic climate scenarios developed from statistically downscaled scenarios for Ireland, hereafter called SDprob. The second technique used output from 17 global climate models (GCMs), all of which participated in CMIP3, to generate change factors (hereafter called CF). Outputs from both the SDprob and the CF approach were then used in combination with WGEN to generate daily climate scenarios for use in the hydrological models. The range of simulated flow derived with the CF method is in general larger than those estimated with the SDprob method in winter and vice versa because of the strong seasonality in the precipitation signal for the 17 GCMs. Despite this, the simulated probability density function of seasonal mean streamflow estimated with both methods is similar. This indicates the usefulness of the SDprob or probabilistic approach derived from regional scenarios compared with the CF method that relies on sampling a diversity of response from the GCMs. Irrespective of technique used, the probability density functions of seasonal mean flow produced for four selected basins is wide indicating considerable modelling uncertainties. Such a finding has important implications for developing adaptation strategies at the catchment level in Ireland

    Item Type: Article
    Keywords: probabilistic climate change scenario; hydrological models; weather generators; Generalised Likelihood Uncertainty Estimation (GLUE);
    Academic Unit: Faculty of Social Sciences > Geography
    Faculty of Social Sciences > Research Institutes > Irish Climate Analysis and Research Units, ICARUS
    Item ID: 4360
    Depositing User: Rowan Fealy
    Date Deposited: 13 May 2013 13:16
    Journal or Publication Title: Hydrological Processes
    Publisher: Wiley-Blackwell
    Refereed: Yes
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

    Repository Staff Only(login required)

    View Item Item control page


    Downloads per month over past year

    Origin of downloads