Climatic Change

, Volume 141, Issue 2, pp 273–286 | Cite as

Accuracy versus variability of climate projections for flood assessment in central Italy

  • S. CamiciEmail author
  • L. Brocca
  • T. Moramarco


Climatic extremes are changing and decision-makers express a strong need for reliable information on future changes over the coming decades as a basis for adaption strategies. In the hydrological-hydraulic context, to estimate changes on floods, a modeling chain composed by general circulation models (GCMs), bias correction (BC) methods, and hydrological modeling is generally applied. It is well-known that each step of the modeling chain introduces uncertainties, resulting in a reduction of the reliability of future climate projections. The main goal of this study is the assessment of the accuracy and variability (i.e., model accuracy, climate intermodel variability, and natural variability) on climate projections related to the present period. By using six different GCMs and two BC methods, the “climate intermodel variability” is evaluated. “Natural variability” is estimated through random realizations of stochastic weather generators. By comparing observed and simulated extreme discharge values, obtained through a continuous rainfall-runoff model, “model accuracy” is computed. The Tiber River basin in central Italy is used as a case study. Results show that in climate projections, model accuracy and climate intermodel variability components have to be clearly distinguished. For accuracy, the hydrological model is found to be the largest source of error; for variability, natural variability contributes for more than 75% to the total variability while GCM and BC have a much lower influence. Moreover, accuracy and variability components vary significantly, and not consistently, between catchments with different permeability characteristics.


Return Period Climate Change Impact Natural Variability Runoff Coefficient Relative Root Mean Square Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors acknowledge the World Climate Research Programme’s Working Group on Coupled Modeling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table S2 of this paper) for producing and making available their model output. For CMIP, the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. Moreover, the authors are grateful to two anonymous reviewer for their useful comments and suggestions allowing to improve the quality of the paper and to Umbria Region for providing most of the analyzed data.

Supplementary material

10584_2016_1876_MOESM1_ESM.pdf (960 kb)
ESM 1 (PDF 959 kb)


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Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  1. 1.Research Institute for Geo-Hydrological Protection, National Research CouncilPerugiaItaly

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