利用分布式时变增益水文模型（DTVGM）的产流机制改进CLM3.5（Community Land Model version 3.5）的水文过程参数化,同时嵌入地下水开采对流域水循环影响的模块,并考虑经济社会用水,建立能够描述流域自然-人文过程的大尺度陆地水循环模型（CLM-DTVGM）。以地下水开采严重的海河流域为研究区,以中科院青藏高原研究所建立的中国区域高时空分辨率地面气象要素数据集作为大气强迫驱动,针对海河流域1980年-2010年的水循环过程进行模拟,分析地下水开采活动对流域水循环过程的影响。结果表明：地下水开采活动引起海河流域地表径流和土壤湿度整体有所减少,蒸散发则呈增加趋势,且各水循环要素空间分异较大。
Parameter identification, model calibration, and uncertainty quantification are important steps in the model-building process, and are necessary for obtaining credible results and valuable information. Sensitivity analysis of hydrological model is a key step in model uncertainty quantification, which can identify the dominant parameters, reduce the model calibration uncertainty, and enhance the model optimization efficiency. There are, however, some shortcomings in classical approaches, including the long duration of time and high computation cost required to quantitatively assess the sensitivity of a multiple-parameter hydrological model. For this reason, a two-step statistical evaluation framework using global techniques is presented. It is based on (1) a screening method (Morris) for qualitative ranking of parameters, and (2) a variance-based method integrated with a meta-model for quantitative sensitivity analysis, i.e., the Sobol method integrated with the response surface model (RSMSobol). First, the Morris screening method was used to qualitatively identify the parameters' sensitivity, and then ten parameters were selected to quantify the sensitivity indices. Subsequently, the RSMSobol method was used to quantify the sensitivity, i.e., the first-order and total sensitivity indices based on the response surface model (RSM) were calculated. The RSMSobol method can not only quantify the sensitivity, but also reduce the computational cost, with good accuracy compared to the classical approaches. This approach will be effective and reliable in the global sensitivity analysis of a complex large-scale distributed hydrological model.
