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ABSTRACT The importance of subsurface water dynamics, such as water storage and flow partitioning, is well recognised. Yet, our understanding of their drivers and links to streamflow generation has 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 behaviour. 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.
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This content will become publicly available on July 3, 2026
Estimating Daily Nitrate Loads in Iowa Streams Using a Partial Least Squares Regression Framework
ABSTRACT Agricultural nitrate pollution is a major threat to water quality in Iowa. Iowa uses a majority of its land for row crop agriculture and maintains a large livestock population, which together cause high nitrate loads in streams. High‐frequency stream nitrate data can aid policy decisions for reducing nitrate emissions by identifying streams with high nitrate loads, historical trends of improvement or deterioration in nitrate loads, and land use or practice changes that affect water quality. We developed a time series regression model framework to supplement existing sensor data and predict daily nitrate loads in Iowa streams lacking nitrate monitoring. Using nitrate data from statewide and national resources, this framework was trained and validated using 11 study sites of diverse geography and land use in Iowa. Partial least squares regression (PLSR) was used with geographical predictors, including land use, hydrogeology, and meteorology, to predict streamflow‐nitrate load relationships across the study sites. The developed PLSR model, combined with daily streamflow data, was then used to predict daily nitrate loads with high accuracy over a three‐year study period with a mean Kling–Gupta Efficiency of 0.74. Our framework was then used to estimate mean nitrate concentrations at 34 sites that lack nitrate sensors, demonstrating a low‐cost, facile method for the accurate prediction of daily nitrate loads in Iowa streams.
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- Award ID(s):
- 2133423
- PAR ID:
- 10612561
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- JAWRA Journal of the American Water Resources Association
- Volume:
- 61
- Issue:
- 4
- ISSN:
- 1093-474X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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