Evaluation of Gridded Meteorological Datasets for Hydrological Modeling

Author:

Raimonet Melanie1,Oudin Ludovic1,Thieu Vincent1,Silvestre Marie2,Vautard Robert3,Rabouille Christophe3,Le Moigne Patrick4

Affiliation:

1. Sorbonne Universités, UPMC, Univ. Paris 06, CNRS, EPHE, IPSL, UMR 7619 Metis, Paris, France

2. Sorbonne Universités, UPMC, Univ. Paris 06, CNRS, FR3020 FIRE, Paris, France

3. Laboratoire des Sciences du Climat et de l’Environnement, UMR CEA-CNRS-UVSQ 8212 et IPSL, Gif sur Yvette, France

4. CNRM UMR 3589, CNRS/Météo-France, Toulouse, France

Abstract

Abstract The number and refinement of gridded meteorological datasets are on the rise at the global and regional scales. Although these datasets are now commonly used for hydrological modeling, the representation of precipitation amount and timing is crucial to correctly model streamflow. The Génie Rural à 4 paramètres journalier (GR4J) conceptual hydrological model combined with the CEMANEIGE snow routine was calibrated using four temperature and precipitation datasets [Système d’analyse fournissant des renseignements atmosphériques à la neige (SAFRAN), Mesoscale Analysis (MESAN), E-OBS, and Water and Global Change (WATCH) Forcing Data ERA-Interim (WFDEI)] on 931 French gauged catchments ranging in size from 10 to 10 000 km2. The efficiency of the calibrated hydrological model in simulating streamflow was higher for the models calibrated on high-resolution meteorological datasets (SAFRAN, MESAN) compared to coarse-resolution datasets (E-OBS, WFDEI), as well as for reanalysis (SAFRAN, MESAN, WFDEI) compared to datasets based on interpolation only (E-OBS). The systematic decrease in efficiency associated with precipitation bias or temporality highlights that the use of a hydrological model calibrated on meteorological datasets can assess these datasets, most particularly precipitation. It appears essential that datasets account for high-resolution topography to accurately represent elevation gradients and assimilate dense ground-based observation networks. This is particularly emphasized for hydrological applications in mountainous areas and areas subject to finescale events. For hydrological applications on nonmountainous regions, not subject to finescale events, both regional and global datasets give satisfactory results. It is crucial to continue improving precipitation datasets, especially in mountainous areas, and to assess their sensitivity to eventual corrupted observations. These datasets are essential to correct the bias of climate model outputs and to investigate the impact of climate change on hydrological regimes.

Funder

Labex L-IPSL

EC2CO

Publisher

American Meteorological Society

Subject

Atmospheric Science

Reference42 articles.

1. Evaluation of dynamically downscaled reanalysis precipitation data for hydrological application;Bastola;Hydrol. Processes,2014

2. Evaluation of global precipitation in reanalyses;Bosilovich;J. Appl. Meteor. Climatol.,2008

3. Hydrological validation of statistical downscaling methods applied to climate model projections;Bourqui;IAHS Publ.,2011

4. Impact of improved meteorological forcing, profile of soil hydraulic conductivity and data assimilation on an operational hydrological ensemble forecast system over France;Coustau;J. Hydrol.,2015

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3