On constructing limits-of-acceptability in watershed hydrology using decision trees
On constructing limits-of-acceptability in watershed hydrology using decision trees
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利用决策树构建流域水文学的可接受限度
DOI：
10.1016/j.advwatres.2023.104486
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发表时间：
2023
影响因子：
4.7
通讯作者：
Merwade, Venkatesh
中科院分区：
文献类型：
--
作者：
Gupta, Abhinav;Govindaraju, Rao S.;Li, Pin-Ching;Merwade, Venkatesh
水文模型存在三种不确定性：测量不确定性、结构不确定性和参数不确定性。例如，在降雨-径流模型中，由于降雨和径流数据的测量误差，存在测量不确定性。由于水文过程的数学表示存在错误，因此存在结构性不确定性。参数不确定性是由于我们无法测量有效的模型参数、可用于校准模型参数的有限数据以及测量和结构不确定性造成的。这些主要是认知性不确定性的存在使得模型推理变得困难。文献中已经提出了可接受限度(LoA)框架，用于拒绝派框架下的模型推理。如果LOA反映了降雨和径流测量误差的影响，它们在模型推断中可能是有用的。在这项研究中，探讨了分位数随机森林(QRF)算法在构建LOA中的有效性。将QRF得到的LOA与额定曲线分析得到的不确定度界限和径流比法得到的LOA进行了比较。额定曲线分析只在径流测量中产生不确定性，径流比法预计将反映降雨量和径流量测量的不确定性。利用QRF得到的LOA被发现包含了由于流量测量误差而引起的不确定范围。QRF法和径流比法得到的LOA结果基本一致。此外，从定性和定量两方面考察了QRF LOA对降雨不确定性影响的反映能力。结果表明，QRF LOAS反映了降雨不确定性的影响：标准差随平均径流的增大而增大，变异系数随平均径流的增大而减小。对QRF方法得到的LOA进行了数学分析，为进一步研究提供了理论依据。
A hydrological model incurs three types of uncertainties: measurement, structural and parametric uncertainty. For instance, in rainfall-runoff models, measurement uncertainty exists due to errors in measurements of rainfall and streamflow data. Structural uncertainty exists due to errors in mathematical representation of hydrological processes. Parametric uncertainty is a consequence of our inability to measure effective model parameters, limited data available to calibrate model parameters, and measurement and structural uncertainties. The existence of these predominantly epistemic uncertainties makes the model inference difficult. Limits-of-acceptability (LOA) framework has been proposed in the literature for model inference under a rejectionist framework. LOAs can be useful in model inference if they reflect the effect of errors in rainfall and streamflow measurements. In this study, the usefulness of quantile random forest (QRF) algorithm has been explored for constructing LOAs. LOAs obtained by QRF were compared to the uncertainty bounds obtained by rating-curve analysis and the LOAs obtained by runoff ratio method. Rating curve analysis yields uncertainty in streamflow measurements only and the runoff ratio method is expected to reflect uncertainty in rainfall and streamflow volume measurements. LOAs obtained by using QRF were found to envelop the uncertainty bounds due to streamflow measurement errors. LOAs obtained by QRF and runoff ratio methods were similar. Further, QRF LOAs were scrutinized in terms of their ability to reflect the effect of rainfall uncertainty, both qualitatively and quantitatively. Results indicate that QRF LOAs reflect the effect of rainfall uncertainty: increase in standard deviation with increase in mean streamflow values and decrease in coefficient of variation with increase in mean streamflow values. A mathematical analysis of the LOAs obtained by the QRF method is presented to provide a theoretical foundation.
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