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首页> 外文期刊>Journal of hydrologic engineering >Parameter Uncertainty of a Hydrologic Model Calibrated with Remotely Sensed Evapotranspiration and Soil Moisture
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Parameter Uncertainty of a Hydrologic Model Calibrated with Remotely Sensed Evapotranspiration and Soil Moisture

机译：用远程感测蒸发和土壤水分校准水文模型的参数不确定性

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摘要

Remotely sensed (RS) observations are becoming prevalent for hydrological model calibration in sparsely monitored regions. In this study, the parameter uncertainty associated with a hydrological model calibrated with RS evapotranspiration (ET) and soil moisture (SM) is investigated in detail using a Markov chain Monte Carlo (MCMC) approach. The daily Commonwealth Scientific and Industrial Research Organization (CSIRO) Moderate Resolution Imaging Spectrometer (MODIS) ReScaled potential ET (CMRSET) and SM retrievals from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) are used to calibrate a simplified Australian Water Resource Assessment Landscape (AWRA-L) model at 10 small catchments in Eastern Australia. The study inspects the changes in parameter uncertainty with respect to different RS observations and catchment rainfall conditions and the impact of parameter uncertainty on model predictions. Results suggest that uncertainty in posterior parameter distributions increases from high- to low-rainfall catchments due to the intricate nonlinear relationship between rainfall and runoff in low-yielding catchments. Uncertainty is narrower for ET calibrations than SM calibrations, representing higher uncertainty associated with SM data processing. The study concluded that quantification of parameter uncertainty alone is not enough to provide satisfactory prediction uncertainty.
机译：远程感测（RS）观察对于稀疏监测区域的水文模型校准普遍存在普遍存在。在该研究中，使用Markov链蒙特卡罗（MCMC）方法详细研究了与RS蒸发（ET）和土壤水分（SM）校准的水文模型相关的参数不确定性。每日英联邦科学和工业研究组织（CSIRO）中等分辨率成像光谱仪（MODIS）重新分校潜在的ET（CMRSET）和来自先进微波扫描辐射计地球观测系统（AMSR-E）的SM检索用于校准简化的澳大利亚水资源评估景观（AWRA-L）在澳大利亚东部10个小集水区的模型。该研究检查了不同RS观测结果和集水区降雨条件的参数不确定性的变化以及参数不确定性对模型预测的影响。结果表明，由于低收益集水区中的降雨和径流复杂的非线性关系，后参数分布的不确定性从高度降至低降雨量增加。不确定性对于ET校准比SM校准更窄，代表与SM数据处理相关的更高的不确定性。该研究得出结论，单独的参数不确定性的定量不足以提供令人满意的预测不确定性。

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