Quantifying Streamflow Prediction Uncertainty Through Process-Aware Data-Driven Models

IF 3.2 3区 地球科学 Q1 Environmental Science
Abhinanda Roy, K. S. Kasiviswanathan
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Abstract

The hydrological model simulation accompanied with uncertainty quantification helps enhance their overall reliability. Since uncertainty quantification including all the sources (input, model structure and parameter) is challenging, it is often limited to only addressing model parametric uncertainty, neglecting other uncertainty sources. This paper focuses on exploiting the potential of state-of-the-art data-driven models (or DDMs) in quantifying the prediction uncertainty of process-based hydrological models. This is achieved by integrating the robust predictive ability of the DDMs with the process understanding ability of the hydrological models. The Bayesian-based data assimilation (DA) technique is used to quantify uncertainty in process-based hydrological models. This is accomplished by choosing two DDMs, random forest algorithm (RF) and support vector machine (SVM), which are distinctly integrated with two process-based hydrological models: HBV and HyMOD. Particle filter algorithm (PF) is chosen for uncertainty quantification. All these combinations led to four different process-aware DDMs: HBV-PF-RF, HBV-PF-SVM, HyMOD-PF-RF and HyMOD-PF-SVM. The assessment of these models on the Baitarani, Beas and Sunkoshi river basins exemplified an improvement in the accuracy of the daily streamflow simulations with a reduction in the prediction uncertainty across all the models for all the basins. For example, the nash-sutcliffe efficiency improved by 54.69% and 10.61% in calibration and validation of the Baitarani river basin, respectively. Equivalently, average bandwidth improved by 79.37% and 71.59%, respectively. This signified the (a) potential of the DDMs in quantifying and reducing the prediction uncertainty of the hydrological model simulations, (b) transferability of the model with an appreciable performance irrespective of the choice of basins having varying topography and climatology and (c) ability to perform significantly irrespective of different process-based and DDMs being involved, thereby ensuring generalizability. Thus, the framework is expected to assist in effective decision-making, including various environmental management and disaster preparedness.

Abstract Image

通过过程感知的数据驱动模型量化流场预测的不确定性
水文模型模拟与不确定性量化相结合,有助于提高模型的整体可靠性。由于包括所有来源(输入、模型结构和参数)的不确定性量化具有挑战性,因此通常仅限于解决模型参数的不确定性,而忽略了其他不确定性来源。本文的重点是利用最先进的数据驱动模型(或 DDM)在量化基于过程的水文模型预测不确定性方面的潜力。这是通过将数据驱动模型的稳健预测能力与水文模型的过程理解能力相结合来实现的。基于贝叶斯的数据同化(DA)技术用于量化基于过程的水文模型的不确定性。为此,选择了随机森林算法(RF)和支持向量机(SVM)这两种 DDM,并将其与两种基于过程的水文模型结合起来:HBV 和 HyMOD。不确定性量化选择了粒子滤波算法(PF)。所有这些组合产生了四种不同的过程感知 DDM:HBV-PF-RF、HBV-PF-SVM、HyMOD-PF-RF 和 HyMOD-PF-SVM。这些模型在 Baitarani、Beas 和 Sunkoshi 河流域的评估结果表明,所有流域的所有模型都提高了日溪流模拟的准确性,减少了预测的不确定性。例如,在 Baitarani 河流域的校准和验证中,纳什-萨特克利夫效率分别提高了 54.69% 和 10.61%。同样,平均带宽也分别提高了 79.37% 和 71.59%。这表明:(a) 多元数据模型在量化和减少水文模型模拟预测不确定性方面具有潜力;(b) 无论选择不同地形和气候的流域,该模型都具有可移植性和可观的性能;(c) 无论涉及不同的基于过程和多元数据模型,该模型都具有显著的性能,从而确保了通用性。因此,预计该框架将有助于有效决策,包括各种环境管理和备灾。
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来源期刊
Hydrological Processes
Hydrological Processes 环境科学-水资源
CiteScore
6.00
自引率
12.50%
发文量
313
审稿时长
2-4 weeks
期刊介绍: Hydrological Processes is an international journal that publishes original scientific papers advancing understanding of the mechanisms underlying the movement and storage of water in the environment, and the interaction of water with geological, biogeochemical, atmospheric and ecological systems. Not all papers related to water resources are appropriate for submission to this journal; rather we seek papers that clearly articulate the role(s) of hydrological processes.
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