Calibrating the data logger…
HBV Input Files and Climate & Catchment Characteristic Tables Abstract The importance of subsurface water dynamics, such as water storage and flow partitioning, is well recognized. Yet, our understanding of their drivers and links to streamflow generation have remained elusive, especially in small headwater streams that are often data-limited but crucial for downstream water quantity and quality. Large-scale analyses have focused on streamflow characteristics across rivers with varying drainage areas, often overlooking the subsurface water dynamics that shape streamflow behavior. Here we ask the question: What are the climate and landscape characteristics that regulate subsurface dynamic storage, flow path partitioning, and dynamics of streamflow generation in headwater streams? To answer this question we used streamflow data and a widely-used hydrological model (HBV) for 15 headwater catchments across the contiguous United States. Results show that climate characteristics such as aridity and precipitation phase (snow or rain) and land attributes such as topography and soil texture are key drivers of streamflow generation dynamics. In particular, steeper slopes generally promoted more streamflow, regardless of aridity. Streams in flat, rainy sites (< 30% precipitation as snow) with finer soils exhibited flashier regimes than those in snowy sites (>30% precipitation as snow) or sites with coarse soils and deeper flow paths. In snowy sites, less weathered, thinner soils promoted shallower flow paths such that discharge was more sensitive to changes in storage, but snow dampened streamflow flashiness overall. Results here indicate that land characteristics such as steepness and soil texture modify subsurface water storage and shallow and deep flow partitioning, ultimately regulating streamflow response to climate forcing. As climate change increases uncertainty in water availability, understanding the interacting climate and landscape features that regulate streamflow will be essential to predict hydrological shifts in headwater catchments and improve water resources management.
Knowledge graph centered on DevonKerins/HydrologicalDynamicsinHeadwaterCatchme with 9 nodes and 33 connections. Top connected: groundwater storage dynamics, precipitation, streamflow, aridity, groundwater.
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