Calibration of Hydrologic Models Using Multiobjectives and Visualization Techiques

使用多目标和可视化技术校准水文模型

基本信息

  • 批准号:
    9418147
  • 负责人:
  • 金额:
    $ 22.44万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    1995
  • 资助国家:
    美国
  • 起止时间:
    1995-02-15 至 1998-07-31
  • 项目状态:
    已结题

项目摘要

9418147 Sorooshian The reliance on land surface hydrologic models as tools in the study of hydroclimatology is increasing as hydrologists and climatologists examine emerging problems and exploit new data sources. However, hydrologic models can only be as reliable as model assumptions, inputs, and parameter estimates. Field measurements, prior information, and calibration are three techniques used in parameter estimation. While the use of field measurements is gaining in importance, experience has shown that as a practical matter, virtually all models require calibration of at least some parameters. However, extensive experience with model calibration has indicated that it is very often not possible to find a preferred (best) solution. That is, for a given model and calibration data, there are usually sizable regions of the parameter space that appear to give roughly equivalent results. This can gives rise to questions about the reliability and realism of the model, and the confidence in its predictions. The primary objective of this research is to develop techniques for calibrating hydrologic models that improve the prospects for finding preferred solutions. Major objectives are to: 1. Recognize the inherently multi-objective nature of the hydrologic model calibration problem and pose the calibration procedure in the framework of multiple objectives. 2. Explore innovative ways of using multi-criteria, data sub-sets (that emphasize different hydrologic processes or different aspect of model performance), measures of information content and global search algorithms in identifying the non-inferior solution space and preferred solutions. 3. On test cases, determine if a satisfactory and reliable estimate of the non-inferior solution space can be identified, from which a preferred parameter estimate (or set of estimates) can be selected. Develop techniques for estimating confidence intervals on paramete rs and simulated variable, taking into account the non-inferior solution space and data errors. Gain insights into the roles that data error, model error, and parameter interaction play in producing non-inferior solutions. 4. Determine, for test cases, how preferred solutions can be reliably, effectively and efficiently obtained through a systematic computer based exploration of the non-inferior solution space. Implement a systematic workstation based calibration stratigy and compare it with existing single-objective strategies. This research will focus its attention on the emerging generation of land surface hydrologic models being considered as viable land-surface soil-vegetation-atmosphere transfer schemes (SVATS); such models are receiving widespread attention among hydrologists and hydrolmeteorologists but the calibration issues surrounding them have yet to receive systematic attention. It is expected that the proposed techniques will help hydrologists build more reliable and more realistic models. In terms of general scientific knowledge, it is expected that the research will result in insights into problems of hydrologic model identification can calibration, particularly in the area of reliability and uniqueness.
9418147随着水文学家和气候学家审查新出现的问题并开发新的数据来源,对陆地表面水文模型作为水文气候学研究工具的依赖正在增加。然而,水文模型只能与模型假设、输入和参数估计一样可靠。现场测量、先验信息和校准是参数估计中使用的三种技术。虽然现场测量的使用越来越重要,但经验表明,作为一个实际问题,几乎所有的模型都需要至少校准一些参数。然而,在模型校准方面的丰富经验表明,通常不可能找到首选(最佳)解决方案。也就是说,对于给定的模型和校准数据,参数空间中通常有相当大的区域似乎给出了大致相同的结果。这可能会引发人们对该模型的可靠性和现实性以及对其预测的信心的质疑。这项研究的主要目标是开发校准水文模型的技术,以改善找到首选解决方案的前景。主要目的是:1.认识水文模型定标问题本质上的多目标性质,并在多目标框架下提出定标程序。2.探索使用多准则、数据子集(强调不同的水文过程或模型性能的不同方面)、信息量的度量和全局搜索算法来识别非劣解空间和优选解的创新方法。3.在测试用例上,确定是否可以识别非劣解空间的满意和可靠的估计,从该估计中可以选择优选的参数估计(或估计集)。开发了在考虑非劣解空间和数据误差情况下估计参数Rs和模拟变量的可信区间的技术。深入了解数据错误、模型错误和参数交互在生成非劣质解决方案中所起的作用。4.对于测试用例,确定如何通过对非劣解空间的基于计算机的系统探索来可靠、有效和高效地获得优选解。实施基于工作站的系统校准策略,并将其与现有的单目标策略进行比较。这项研究将重点关注被认为是可行的陆地-地表土壤-植被-大气转移方案(SVATS)的新一代陆地表面水文模型;这种模型受到水文学家和水文气象学家的广泛关注,但围绕它们的校准问题尚未得到系统的关注。预计拟议的技术将帮助水文学家建立更可靠、更现实的模型。在一般科学知识方面,预计这项研究将使人们深入了解水文模型识别和定标的问题,特别是在可靠性和唯一性方面。

项目成果

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Soroosh Sorooshian其他文献

Improve streamflow simulations by combining machine learning pre-processing and post-processing
通过结合机器学习预处理和后处理来改进流量模拟
  • DOI:
    10.1016/j.jhydrol.2025.132904
  • 发表时间:
    2025-07-01
  • 期刊:
  • 影响因子:
    6.300
  • 作者:
    Yuhang Zhang;Aizhong Ye;Jinyang Li;Phu Nguyen;Bita Analui;Kuolin Hsu;Soroosh Sorooshian
  • 通讯作者:
    Soroosh Sorooshian
Error Characteristics and Scale Dependence of Current Satellite Precipitation Estimates Products in Hydrological Modeling
水文模拟中现有卫星降水估算产品的误差特征和尺度依赖性
  • DOI:
    10.3390/rs13163061
  • 发表时间:
    2021-08
  • 期刊:
  • 影响因子:
    5
  • 作者:
    Yuhang Zhang;Aizhong Ye;Phu Nguyen;Bita Analui;Soroosh Sorooshian;Kuolin Hsu
  • 通讯作者:
    Kuolin Hsu
Regional and global hydrology and water resources issues: The role of international and national programs
  • DOI:
    10.1007/pl00012589
  • 发表时间:
    2002-12-01
  • 期刊:
  • 影响因子:
    1.800
  • 作者:
    Soroosh Sorooshian;Martha P. L. Whitaker;Terri S. Hogue
  • 通讯作者:
    Terri S. Hogue
Fine-tuning long short-term memory models for seamless transition in hydrological modelling: From pre-training to post-application
用于水文建模中无缝过渡的微调长短期记忆模型:从预训练到应用后
  • DOI:
    10.1016/j.envsoft.2025.106350
  • 发表时间:
    2025-03-01
  • 期刊:
  • 影响因子:
    4.600
  • 作者:
    Xingtian Chen;Yuhang Zhang;Aizhong Ye;Jinyang Li;Kuolin Hsu;Soroosh Sorooshian
  • 通讯作者:
    Soroosh Sorooshian
Regional Hydrological Response to Climate Change. 1997. By J. A. A. Jones, C. Lui, M.-K. Woo & H.-T Kung (eds.)
  • DOI:
    10.1023/a:1008021712023
  • 发表时间:
    1999-02-01
  • 期刊:
  • 影响因子:
    1.300
  • 作者:
    Shayesteh. Mahani;Soroosh Sorooshian
  • 通讯作者:
    Soroosh Sorooshian

Soroosh Sorooshian的其他文献

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{{ truncateString('Soroosh Sorooshian', 18)}}的其他基金

CyberSEES:Type 2: Precipitation Estimation from Multi-Source Information using Advanced Machine Learning
Cyber​​SEES:类型 2:使用高级机器学习从多源信息估算降水量
  • 批准号:
    1331915
  • 财政年份:
    2013
  • 资助金额:
    $ 22.44万
  • 项目类别:
    Standard Grant
The Influence of Rainfall Characteristics, Hydrologic Characteristics and Rainfall Measurement Strategy on the Accuracy of Flash Flood Forecasts
降雨特征、水文特征及测雨策略对山洪预报精度的影响
  • 批准号:
    9307411
  • 财政年份:
    1994
  • 资助金额:
    $ 22.44万
  • 项目类别:
    Continuing Grant
(SGER) A Novel Approach for Calibration of Hydrologic Models Using Multiobjectives and Visualization Techniques
(SGER) 使用多目标和可视化技术校准水文模型的新方法
  • 批准号:
    9415437
  • 财政年份:
    1994
  • 资助金额:
    $ 22.44万
  • 项目类别:
    Standard Grant
U.S.-France Cooperative Research: Integration of Multispectral Data with Hydrologic Models for Transfer of Heat and Moisture in Temperate Regions.
美法合作研究:将多光谱数据与温带地区热量和水分传递的水文模型相结合。
  • 批准号:
    9314872
  • 财政年份:
    1994
  • 资助金额:
    $ 22.44万
  • 项目类别:
    Standard Grant
Proposal for NSF Graduate Research Traineeships Hydrologic Sciences
NSF 水文科学研究生研究实习计划提案
  • 批准号:
    9355029
  • 财政年份:
    1993
  • 资助金额:
    $ 22.44万
  • 项目类别:
    Continuing Grant
A Distributed Physically-Based Modeling Approach to Flash Flood Forecasting in Semi-arid Regions
半干旱地区山洪预报的分布式物理建模方法
  • 批准号:
    8920851
  • 财政年份:
    1990
  • 资助金额:
    $ 22.44万
  • 项目类别:
    Standard Grant
U.S.-Japan Workshop on Collaborative Research Topics on Emerging Hydrologic Hazard And Water Resources Engineering Issues; August 28-29, 1990, Yamanashi, Japan
美日新兴水文灾害和水资源工程问题合作研究主题研讨会;
  • 批准号:
    9015504
  • 财政年份:
    1990
  • 资助金额:
    $ 22.44万
  • 项目类别:
    Standard Grant
Evaluation of the Type and Quantity of Data Appropriate for Model Calibration: Case of Flood Forecasting Models
评估适合模型校准的数据类型和数量:洪水预报模型案例
  • 批准号:
    8610584
  • 财政年份:
    1986
  • 资助金额:
    $ 22.44万
  • 项目类别:
    Standard Grant
U.S.-New Zealand Cooperative Research: Modeling River Flowsfrom Rainfall Measurements (Hydrology)
美国-新西兰合作研究:根据降雨测量模拟河流流量(水文学)
  • 批准号:
    8413539
  • 财政年份:
    1985
  • 资助金额:
    $ 22.44万
  • 项目类别:
    Standard Grant
Group International Travel Support for the IFAC Symposium onWater Resources Systems, Budapest, Hungary, July 2-6, l984
集团国际旅行支持 IFAC 水资源系统研讨会,匈牙利布达佩斯,1984 年 7 月 2-6 日
  • 批准号:
    8401082
  • 财政年份:
    1984
  • 资助金额:
    $ 22.44万
  • 项目类别:
    Standard Grant

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在小型到大型水文模型中适当纳入地下水流特征和过程
  • 批准号:
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    1528298
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    2015
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