Hydrological Model Adaptability to Rainfall Inputs of Varied Quality

Author:

Wang Jiao1ORCID,Zhuo Lu12ORCID,Han Dawei1ORCID,Liu Ying1ORCID,Rico‐Ramirez Miguel Angel1ORCID

Affiliation:

1. Department of Civil Engineering University of Bristol Bristol UK

2. School of Earth and Environmental Sciences Cardiff University Cardiff UK

Abstract

AbstractNumerous studies have evaluated the reliability and hydrologic utility of various rainfall data sets through hydrological modeling. However, the calibration of hydrological models compensates for errors in rainfall inputs. The drivers, conditions, and factors affecting the calibration of hydrological models given the accuracy of rainfall inputs are not well understood. Here, we explore hydrological model adaptability to rainfall inputs of varied quality and its potential mechanisms. Twenty‐eight rainfall products from multiple sources are collected for a headwater catchment in the Southern United States. These rainfall data sets include measurements from rain gauges, weather radars, satellites, reanalysis products, and Weather Research and Forecasting model simulations. Such rainfall data sets with varied errors are used to independently calibrate a widely used conceptual Xin'anjiang (XAJ) hydrological model. Results suggest that the hydrological model can often adapt well to two scenarios of inaccurate rainfall inputs producing high‐performance streamflow simulations. This adaptive ability is controlled by an adaptable threshold of the overall bias of the rainfall inputs. Moreover, hydrological model adaptability to rainfall inputs is further influenced by how event‐based rainfall bias shapes the overall rainfall bias, especially from those of heavy rainstorms. The hydrological model can adapt to those rainfall inputs that contain important information content for model calibration. Notably, the adaptability to rainfall inputs of the XAJ model is mainly controlled by a bias reduction through adjustment of evapotranspiration and soil moisture storage, yielding satisfactory effective rainfall. The study quantitatively sheds new light on hydrological model adaptability to rainfall input quality.

Publisher

American Geophysical Union (AGU)

Subject

Water Science and Technology

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