The hillslope and channel dynamics that govern streamflow permanence in headwater systems have important implications for ecosystem functioning and downstream water quality. Recent advancements in process-based, semi-distributed hydrologic models that build upon empirical studies of streamflow permanence in well-monitored headwater catchments show promise for characterizing the dynamics of streamflow permanence in headwater systems. However, few process-based models consider the continuum of hillslope-stream network connectivity as a control on streamflow permanence in headwater systems. The objective of this study was to expand a process-based, catchment-scale hydrologic model to better understand the spatiotemporal dynamics of headwater streamflow permanence and to identify controls of streamflow expansion and contraction in a headwater network. Further, we aimed to develop an approach that enhanced the fidelity of model simulations, yet required little additional data, with the intent that the model might be later transferred to catchments with limited long-term and spatially explicit measurements. This approach facilitated network-scale estimates of the controls of streamflow expansion and contraction, albeit with higher degrees of uncertainty in individual reaches due to data constraints. Our model simulated that streamflow permanence was highly dynamic in first-order reaches with steep slopes and variable contributing areas. The simulated stream network length ranged from nearly 98±2% of the geomorphic channel extent during wet periods to nearly 50±10% during dry periods. The model identified a discharge threshold of approximately 1 mm d-1, above which the rate of streamflow expansion decreases by nearly an order of magnitude, indicating a lack of sensitivity of streamflow expansion to hydrologic forcing during high-flow periods. Overall, we demonstrate that process-based, catchment-scale models offer important insights on the controls of streamflow permanence, despite uncertainties and limitations of the model. We encourage researchers to increase data collection efforts and develop benchmarks to better evaluate such models.

Keywords: expansion and contraction; hydrologic connectivity; modeling; streamflow permanence.

坡地和河道动力学对头水系统径流持久性的影响对于生态系统功能和下游水质具有重要意义。基于经验研究的机制型半分布式水文模型在表征头水系统径流持久性的动态方面显示出潜力，这些经验研究是在监测良好的头水流域中进行的。然而，很少有机制型模型考虑坡地-河网连通性连续体作为控制头水系统径流持久性的因素。本研究的目标是扩展一种基于过程的、流域尺度的水文模型，以更好地理解头水径流持久性的时空动态，并识别影响头水网络中径流转扩和收缩的因素。此外，我们旨在开发一种方法来提高模型模拟的真实性，但只需要少量额外的数据，以便该模型可以后来转移到数据有限的长期且空间明确测量的流域中。这种方法促进了对控制径流转扩和收缩的网络规模估计，尽管由于数据限制，在个别河段上的不确定性更高。我们的模型模拟表明，在坡度陡峭、汇水区面积多变的一级河流中，径流持久性具有高度动态性。在湿润期，模拟的河道长度范围接近98±2%的地形河道延伸范围，而在干旱期则降至约50±10%。该模型识别出一个约为1 mm d-1的流量阈值，在此阈值之上，径流转扩速率几乎减少了一个数量级，表明在高流速期间径流转扩对水文驱动因素缺乏敏感性。总的来说，我们展示了基于过程的流域尺度模型对于控制径流持久性的见解，尽管存在模型的不确定性和局限性。我们鼓励研究人员增加数据收集工作，并制定基准以更好地评估此类模型。

关键词：转扩和收缩；水文学连通性；建模；径流持久性。