地下水—地表水交互是水文循环中的关键环节，这一过程涉及物质运移与能量传输，直接影响着流域水文循环和水生态环境，因此，研究地下水—地表水交互作用具有重要意义。农业灌溉活动是影响地下水—地表水交互的主要人为干扰之一。农业灌溉活动改变了自然水文循环，对地下水—地表水交互和自然生态环境有着深远影响，因此，亟需研究灌溉对地下水—地表水交互的影响机制。水文集成模型（integrated hydrologic model）是研究地下水与地表水交互的有效手段，有助于加深对地下水—地表水交互作用机制的认识，为流域水资源管理提供依据。因此，本文基于ParFlow-CLM构建了高时空分辨率的黑河流域中游农业区水文集成模型，开展了不同灌溉情景下的地下水—地表水交互数值模拟研究，定量分析了关键水文气象要素的时空分布特征，揭示了农业灌溉活动对地下水—地表水交互的影响机制。同时，采用4种不同的气象驱动数据集开展数值模拟研究，评估了不同气象驱动数据对模拟结果的影响，综合评价了不同数据集在黑河流域中游农业区的适用性。主要研究成果如下：
     （1）基于ParFlow-CLM构建了高时空分辨率（0.005°×0.005°，1 h）的黑河流域中游农业区水文集成模型，并采用观测数据对模拟结果进行了验证。结果显示，模型模拟径流在肃南站表现良好（R-Spearman > 0.8），模型模拟的地下水位埋深与观测数据的概率分布曲线较为一致，地表温度模拟结果与观测数据一致性较高（R-Spearman > 0.6，bias < 3 K）。以上结果均证明，所构建模型可较为准确地模拟黑河流域中游农业区地下水—地表水交互过程。
     （2）揭示了灌溉对黑河流域中游农业区地下水—地表水交互的影响机制。基于构建的模型模拟了四组灌溉情景下的黑河流域中游农业区地下水—地表水交互过程，比较了不同灌溉情景下关键水文气象要素（地表径流、地下水位埋深、地表温度、潜热通量和感热通量）的差异，并分析了地表温度、潜热通量、感热通量变化与地下水位埋深的关系。结果显示，灌溉总体上增加了农业区内的地表径流和夏季平均潜热通量，降低了夏季平均地下水位埋深、地表温度和感热通量。灌溉干渠的设置对径流量的增加有一定的贡献，但灌溉用水对模拟流量的增加起主导作用。夏季平均地表温度的降低幅度可达约 6 ℃，平均感热通量降幅可达约 20 W/m2，平均潜热通量增幅可达约20 W/m2。灌溉水源和干渠设置增加了农田区域内地表温度、潜热通量和感热通量等要素变化的变异性。
     （3）定量评估了不同气象驱动数据集对地下水—地表水交互模拟结果的影响。首先采用四种气象驱动数据集（WRF、ERA5、ERA5-Land和JRA-55）驱动模型模拟，并对模拟结果展开验证，随后对不同气象数据集驱动下模拟的地表径流、地下水位埋深和地表温度等要素进行了对比与分析。结果表明：四种气象数据集驱动下模拟的地表径流、地下水位埋深和地表温度与观测数据较为一致，模型模拟结果具有一定的可信度。对比不同驱动数据模拟的水文气象要素发现，不同气象驱动数据集对不同水文气象要素模拟结果的表现不一。在地表径流模拟中，ERA5-Land与观测吻合最佳，而ERA5的夏季地表温度模拟与观测一致性最高。对于相同要素（如，地下水位埋深和地表温度），不同数据集驱动下模拟结果的空间分布存在一定相似性，同时也具有明显的局部差异。不同气象驱动数据集各有优劣，数据集的选择应综合研究感兴趣的要素及其时空尺度进行考虑。

       Groundwater-surface water interaction (GW-SW) is a crucial component of the hydrological cycle. It involves material transport and energy transfer process, which directly impacts the hydro-ecological environment of watersheds. Therefore, it is of significant importance to investigate GW-SW. Irrigation is one of the main anthropogenic disturbances affecting GW-SW. It alters the natural hydrological cycle, which has profound impacts on GW-SW and natural ecological environments. Therefore, it is urgent to investigate the impact mechanism of irrigation on GW-SW. The integrated hydrologic models serve as a powerful tool for studying GW-SW, helping to better understand the mechanisms of GW-SW and providing a basis for watershed water resources management. In this study, integrated hydrological modeling of the middle Heihe River Basin was performed with ParFlow-CLM. Based on numerical experiments with different scenarios, the spatio-temporal distribution characteristics of hydro-meteorological variables were analyzed and the impact of irrigation on GW-SW was explored. Besides, the simulated hydrological variables driven by four meteorological forcings were compared to evaluate the impact of meteorological forcings on hydrological responses, and to assess the applicability of these datasets in the middle Heihe River Basin. The main results are summarized as follows:
       (1) A high resolution (0.005°×0.005°, 1 hour) integrated hydrologic model for the middle Heihe River Basin was constructed based on ParFlow-CLM. The model was validated using observed data of surface runoff, groundwater table depth and land surface temperature in the study area. The results suggest that the model performs well in simulating streamflow at the Sunan hydrological station (R-Spearman > 0.8). Besides, the simulated groundwater table depth distribution is similar with the observed data. Additionally, the land surface temperature simulations match well with the observed data (R-Spearman > 0.6, mean bias < 3 K). These results indicate that the constructed model can accurately simulate the groundwater-surface water interaction processes in the middle Heihe River Basin.
       (2) The impact mechanism of irrigation on GW-SW in the middle Heihe River Basin was revealed. Based on the constructed hydrological model, numerical experiments, were performed with four irrigation scenarios. The differences in surface runoff, groundwater table depth, land surface temperature, latent heat flux, and sensible heat flux under different irrigation scenarios were compared. Besides, the relationship between groundwater table depth and changes of average land surface temperature, average latent heat flux and average sensible heat flux in summer were analyzed. The results show that the effects of irrigation on these variables are mainly distributed in agricultural areas. Specifically, irrigation generally increases surface runoff and summer average latent heat flux, decreases the summer average groundwater table depth, summer average surface temperature, and summer average sensible heat flux. The irrigation canals contribute to the increase in streamflow, but irrigation water plays a dominant role in increasing simulated streamflow. The decrease in summer average surface temperature can reach approximately -6°C, the summer average sensible heat flux can decrease by approximately -20 W/m2, while the increase in summer average latent heat flux can reach approximately 20 W/m2. Additionally, irrigation water and canals increase the variability of changes in factors such as surface temperature, latent heat flux, and sensible heat flux in the agricultural areas.
       (3) The effects of meteorological forcing datasets on hydrological variables was investigated. Firstly, the model was driven by four meteorological forcing datasets (i.e. WRF, ERA5, ERA5-Land, JRA-55) and the simulated results were validated. Subsequently, the simulated hydrological responses including streamflow, groundwater table depth, and surface temperature driven by different meteorological datasets were compared. The results indicate that the simulated streamflow, groundwater table depth, and land surface temperature are relatively consistent with the observed data. Comparison of hydrological variables driven by different meteorological forcings shows that these datasets have diverse performance in different hydrological variables. ERA5-Land shows the best agreement with observed surface runoff, while the simulated summer surface temperature driven by ERA5 is the most consistent with the observed data. The simulations of the same elements driven by different forcings exhibit similarity in spatial distribution, while also demonstrating notable local variations. Each meteorological forcing dataset has its own strengths and weaknesses, and the selection of datasets should depend on the variables of interest and the spatial and temporal scale.