Abstract
Springs strongly influence the physical, chemical, and ecological properties of streams, yet their inclusion in national hydrography datasets used for water resource management is limited. We evaluated how springs affect stream permanence by comparing two adjacent catchments within the Dry Creek Experimental Watershed in southwestern Idaho, USA, one spring‐rich (high‐spring catchment) and one spring‐poor (low‐spring catchment), and assessed the accuracy of two datasets that include perenniality (e.g., perennial versus nonperennial) as a stream property. We combined long‐term hydrologic records with seasonal field mapping of active flow networks to quantify differences in hydrologic stability, flow duration, and the scaling of active stream networks. The high‐spring catchment contained five times as many springs as the low‐spring catchment, maintained perennial flow through dry seasons, and exhibited a lower network scaling exponent than the more dynamic low‐spring catchment. Both the National Hydrography Dataset Plus High Resolution and the Probability of Stream Permanence model underpredicted the extent of perennial channels in the spring‐fed catchment, particularly in dry years. These findings indicate that existing models and datasets misrepresent stream permanence in regions where springs sustain perennial flow independent of topography or climate‐driven inputs. Incorporating spring locations into hydrography datasets and stream permanence models should improve the accuracy of mapped stream networks and enhance water resource management, especially under changing climatic conditions that increase stream intermittency. This study compared adjacent spring‐rich and spring‐poor catchments using long‐term hydrologic records and seasonal stream mapping. The spring‐rich catchment maintained perennial flow, exhibited greater hydrologic stability, and supported more extensive perennial networks. National and regional hydrography datasets underpredicted perennial channels sustained by springs. Incorporating spring locations into hydrography datasets and stream permanence models could improve stream network mapping and water resource management under increasing climate‐driven intermittency.