Learning Regionalization Using Accurate Spatial Cost Gradients Within a Differentiable High-Resolution Hydrological Model: Application to the French Mediterranean Region

IF 4.6 1区 地球科学 Q2 ENVIRONMENTAL SCIENCES Water Resources Research Pub Date : 2024-11-01 DOI:10.1029/2024wr037544
Ngo Nghi Truyen Huynh, Pierre-André Garambois, François Colleoni, Benjamin Renard, Hélène Roux, Julie Demargne, Maxime Jay-Allemand, Pierre Javelle
{"title":"Learning Regionalization Using Accurate Spatial Cost Gradients Within a Differentiable High-Resolution Hydrological Model: Application to the French Mediterranean Region","authors":"Ngo Nghi Truyen Huynh, Pierre-André Garambois, François Colleoni, Benjamin Renard, Hélène Roux, Julie Demargne, Maxime Jay-Allemand, Pierre Javelle","doi":"10.1029/2024wr037544","DOIUrl":null,"url":null,"abstract":"Estimating spatially distributed hydrological parameters in ungauged catchments poses a challenging regionalization problem and requires imposing spatial constraints given the sparsity of discharge data. A possible approach is to search for a transfer function that quantitatively relates physical descriptors to conceptual model parameters. This paper introduces a Hybrid Data Assimilation and Parameter Regionalization (HDA-PR) approach incorporating learnable regionalization mappings, based on either multi-linear regression or artificial neural networks (ANNs), into a differentiable hydrological model. This approach demonstrates how two differentiable codes can be linked and their gradients chained, enabling the exploitation of heterogeneous data sets across extensive spatio-temporal computational domains within a high-dimensional regionalization context, using accurate adjoint-based gradients. The inverse problem is tackled with a multi-gauge calibration cost function accounting for information from multiple observation sites. HDA-PR was tested on high-resolution, hourly and kilometric regional modeling of 126 flash-flood-prone catchments in the French Mediterranean region. The results highlight a strong regionalization performance of HDA-PR especially in the most challenging upstream-to-downstream extrapolation scenario with ANN, achieving median Nash-Sutcliffe efficiency (NSE) scores from 0.6 to 0.71 for spatial, temporal, spatio-temporal validations, and improving NSE by up to 30% on average compared to the baseline model calibrated with lumped parameters. Multiple evaluation metrics based on flood-oriented hydrological signatures also indicate that the use of an ANN leads to better performances than a multi-linear regression in a validation context. ANN enables to learn a non-linear descriptors-to-parameters mapping which provides better model controllability than a linear mapping for complex calibration cases.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Water Resources Research","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1029/2024wr037544","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

Estimating spatially distributed hydrological parameters in ungauged catchments poses a challenging regionalization problem and requires imposing spatial constraints given the sparsity of discharge data. A possible approach is to search for a transfer function that quantitatively relates physical descriptors to conceptual model parameters. This paper introduces a Hybrid Data Assimilation and Parameter Regionalization (HDA-PR) approach incorporating learnable regionalization mappings, based on either multi-linear regression or artificial neural networks (ANNs), into a differentiable hydrological model. This approach demonstrates how two differentiable codes can be linked and their gradients chained, enabling the exploitation of heterogeneous data sets across extensive spatio-temporal computational domains within a high-dimensional regionalization context, using accurate adjoint-based gradients. The inverse problem is tackled with a multi-gauge calibration cost function accounting for information from multiple observation sites. HDA-PR was tested on high-resolution, hourly and kilometric regional modeling of 126 flash-flood-prone catchments in the French Mediterranean region. The results highlight a strong regionalization performance of HDA-PR especially in the most challenging upstream-to-downstream extrapolation scenario with ANN, achieving median Nash-Sutcliffe efficiency (NSE) scores from 0.6 to 0.71 for spatial, temporal, spatio-temporal validations, and improving NSE by up to 30% on average compared to the baseline model calibrated with lumped parameters. Multiple evaluation metrics based on flood-oriented hydrological signatures also indicate that the use of an ANN leads to better performances than a multi-linear regression in a validation context. ANN enables to learn a non-linear descriptors-to-parameters mapping which provides better model controllability than a linear mapping for complex calibration cases.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
在可微分高分辨率水文模型中使用精确的空间成本梯度学习区域化:法国地中海地区的应用
在未经测量的集水区估算空间分布的水文参数是一个极具挑战性的区域化问题,由于排水数据稀少,需要施加空间限制。一种可行的方法是寻找一种传递函数,将物理描述符与概念模型参数定量联系起来。本文介绍了一种混合数据同化和参数区域化(HDA-PR)方法,将基于多线性回归或人工神经网络(ANN)的可学习区域化映射纳入可微分水文模型。该方法展示了如何将两个可微分代码连接起来并将其梯度链化,从而在高维区域化背景下,利用基于精确邻接的梯度,在广泛的时空计算域内利用异构数据集。反演问题采用多规校准成本函数来解决,该函数考虑了来自多个观测点的信息。HDA-PR 在法国地中海地区 126 个山洪易发流域的高分辨率、小时和千米区域建模中进行了测试。结果表明,HDA-PR 具有很强的区域化性能,尤其是在最具挑战性的上游到下游外推法情景中,在空间、时间和时空验证方面,HDA-PR 获得了从 0.6 到 0.71 的纳什-苏特克利夫效率(NSE)中位数分数,与使用整块参数校准的基线模型相比,NSE 平均提高了 30%。基于以洪水为导向的水文特征的多个评价指标也表明,在验证情况下,使用 ANN 比多线性回归具有更好的性能。方差分析可以学习描述符到参数的非线性映射,在复杂的标定情况下,这种映射比线性映射具有更好的模型可控性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Water Resources Research
Water Resources Research 环境科学-湖沼学
CiteScore
8.80
自引率
13.00%
发文量
599
审稿时长
3.5 months
期刊介绍: Water Resources Research (WRR) is an interdisciplinary journal that focuses on hydrology and water resources. It publishes original research in the natural and social sciences of water. It emphasizes the role of water in the Earth system, including physical, chemical, biological, and ecological processes in water resources research and management, including social, policy, and public health implications. It encompasses observational, experimental, theoretical, analytical, numerical, and data-driven approaches that advance the science of water and its management. Submissions are evaluated for their novelty, accuracy, significance, and broader implications of the findings.
期刊最新文献
Learning Regionalization Using Accurate Spatial Cost Gradients Within a Differentiable High-Resolution Hydrological Model: Application to the French Mediterranean Region Multivariate Evaluation of Flash Drought Across the United States Gas Transfer Across Air-Water Interfaces in Inland Waters: From Micro-Eddies to Super-Statistics Sea-Level Rise, Drinking Water Quality and the Economic Value of Coastal Tourism in North Carolina Enhancing Streamflow Prediction in Ungauged Basins Using a Nonlinear Knowledge-Based Framework and Deep Learning
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1