Hydrological modelling of the Chaohe Basin in China: Statistical model formulation and Bayesian inference
Hydrological modelling of the Chaohe Basin in China: Statistical model formulation and Bayesian inference
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DOI：
10.1016/j.jhydrol.2007.04.006
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发表时间：
2007-07
影响因子：
6.4
通讯作者：
Jing Yang;P. Reichert;K. Abbaspour;Hong Yang
中科院分区：
文献类型：
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作者：
Jing Yang;P. Reichert;K. Abbaspour;Hong Yang
由于模型输入和响应的测量误差、模型结构的误差以及分布式模型中大量的不可识别参数，水文模型的校准非常困难。在降水季节性变化较大的干旱地区，模拟残差往往表现出较高的异方差性和自相关性，困难甚至会增加。另一方面，在干旱地区，特别是在城市化导致水需求增加的情况下，水文模型对水管理的支持非常重要。为此目的使用和评估模型结果需要仔细校准和不确定性分析。扩展了这一领域的早期工作，我们开发了一种方法来克服（i）通过引入聚集参数和使用贝叶斯推断来克服分布参数的不可识别性问题，（ii）通过将结果和数据的Box-Cox变换与季节相关误差方差相结合来克服误差的异方差性问题，（iii）自相关误差问题，连续时间自回归误差模型的缺失数据和离群值遗漏问题，以及（iv）误差相关性随季节变化的特征相关时间的季节性变化问题。在中国北方的潮河流域的土壤和水评估工具（SWAT）的水文子模型的校准的技术进行了测试。结果表明，这种方法的不确定性分析，特别是在误差模型的统计假设的履行良好的性能。与独立误差模型和仅考虑所建议技术的子集的误差模型的比较清楚地显示了基于上述所有特征（i）-（iv）的方法的优越性。
Calibration of hydrologic models is very difficult because of measurement errors in input and response, errors in model structure, and the large number of non-identifiable parameters of distributed models. The difficulties even increase in arid regions with high seasonal variation of precipitation, where the modelled residuals often exhibit high heteroscedasticity and autocorrelation. On the other hand, support of water management by hydrologic models is important in arid regions, particularly if there is increasing water demand due to urbanization. The use and assessment of model results for this purpose require a careful calibration and uncertainty analysis. Extending earlier work in this field, we developed a procedure to overcome (i) the problem of non-identifiability of distributed parameters by introducing aggregate parameters and using Bayesian inference, (ii) the problem of heteroscedasticity of errors by combining a Box–Cox transformation of results and data with seasonally dependent error variances, (iii) the problems of autocorrelated errors, missing data and outlier omission with a continuous-time autoregressive error model, and (iv) the problem of the seasonal variation of error correlations with seasonally dependent characteristic correlation times. The technique was tested with the calibration of the hydrologic sub-model of the Soil and Water Assessment Tool (SWAT) in the Chaohe Basin in North China. The results demonstrated the good performance of this approach to uncertainty analysis, particularly with respect to the fulfilment of statistical assumptions of the error model. A comparison with an independent error model and with error models that only considered a subset of the suggested techniques clearly showed the superiority of the approach based on all the features (i)–(iv) mentioned above.