Please use this identifier to cite or link to this item: http://hdl.handle.net/10348/10929
Title: A new very high-resolution climatological dataset in Portugal: Application to hydrological modeling in a mountainous watershed
Authors: Fonseca, A.R.
Santos, J. A.
Keywords: Distributed hydrologic model
Statistical downscaling
Issue Date: 2019
Abstract: The study of precipitation and temperature variability in Portugal, including their extremes, is often restricted by the lack of high-resolution gridded datasets at daily timescales and available for sufficiently long time periods. They are of quite importance specially when considering hydrology modeling at a local scale. To overcome this limitation, we develop new high-resolution gridded datasets (∼1 km) of daily precipitation (1950–2015) over Portugal. Daily precipitation is downscaled by ordinary kriging from a coarser gridded dataset (∼20 km). Daily temperatures were retrieved from a previous work and extracted for the target watershed in this study, Corgo River (northern Portugal). The aim of the present study was to investigate the potential of the new high-resolution data in improving the performance of a distributed hydrologic model, Hydrological Simulation Program – FORTRAN (HSPF) in simulating flowrates at one target watershed in northern Portugal (Corgo River watershed), thus providing a practical basis for subsequent hydrological analysis. The performances of the HSPF model, driven by either a single weather station or the new gridded datasets are compared. The results clearly hint at an improved model performance when using our dataset (Nash-Sutcliffe coefficient of efficiency at daily timescale: 0.34 for the single-station run and 0.64 for the multi-point run). A good performance was also found in reproducing specific flash flood events. Although the advantage of using these novel climatic datasets for hydrologic modeling in Portugal is demonstrated herein, they can be applied to other areas of research, such as ecology, agriculture and forestry, contributing to more accurate decision support systems to assist decisionmakers and stakeholders.
Peer Reviewed: yes
URI: https://doi.org/10.1016/j.pce.2018.06.001
http://hdl.handle.net/10348/10929
ISSN: 1474-7065
Document Type: Article
Appears in Collections:DFIS - Artigo publicado em Revista Científica Indexada

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