GroMoPo Metadata for Qaidam Basin FEFLOW model

Traditional numerical models usually use extensive observed hydraulic-head data as calibration targets. However, this calibration process is not applicable in remote areas with limited or no monitoring data. This study presents an approach to calibrate a large-scale groundwater flow model using the monthly Gravity Recovery and Climate Experiment (GRACE) satellite data, which have been available globally on a spatial grid of 1A degrees in the geographic coordinate system since 2002. A groundwater storage anomaly isolated from the terrestrial water storage (TWS) anomaly is converted into hydraulic head at the center of the grid, which is then used as observed data to calibrate a numerical model to estimate aquifer hydraulic conductivity. The aquifer system in the remote and hyperarid Qaidam Basin, China, is used as a case study to demonstrate the applicability of this approach. A groundwater model using FEFLOW is constructed for the Qaidam Basin and the GRACE-derived groundwater storage anomaly over the period 2003-2012 is included to calibrate the model, which is done using an automatic estimation method (PEST). The calibrated model is then run to output hydraulic heads at three sites where long-term hydraulic head data are available. The reasonably good fit between the calculated and observed hydraulic heads, together with the very similar groundwater storage anomalies from the numerical model and GRACE data, demonstrate that this approach is generally applicable in regions of groundwater data scarcity.传统数值模型通常以大量实测水力水头数据作为校准靶标。然而，该校准流程无法适用于监测数据匮乏或无监测数据的偏远地区。本研究提出一种基于月尺度重力恢复与气候实验（Gravity Recovery and Climate Experiment，简称GRACE）卫星数据的大型地下水流模型校准方法——该数据自2002年起以地理坐标系下1°空间格网形式在全球范围内公开。研究人员将从陆地水储量（terrestrial water storage，简称TWS）异常中分离得到的地下水储量异常，转换为格网中心点处的水力水头，以此作为实测数据用于数值模型的校准，进而估算含水层水力传导系数。本研究以中国极端干旱的柴达木盆地为案例，验证该方法的适用性。研究构建了基于FEFLOW的柴达木盆地地下水数值模型，并利用2003-2012年期间由GRACE反演得到的地下水储量异常数据，通过自动参数估算法（PEST）完成模型校准。随后运行校准后的模型，输出三处具备长期水力水头监测数据的站点的水力水头值。计算水头与实测水头之间的拟合效果良好，且数值模型模拟得到的地下水储量异常与GRACE数据结果高度吻合，这表明该方法在地下水数据稀缺区域具备普遍适用性。