Abstract

Seasonal rainfall events reinforce the link between terrestrial and fluvial domains and are crucial for assessing hydrological control over riverine nutrient dynamics and pollutant source behaviors, especially in a semi-arid watershed. Taking the Qingshuihe river basin, a semi-arid mountainous basin in China, as an example, this paper investigated storm effects on riverine nitrogen (N) and phosphorus (P) dynamics (i.e. concentration, load, and composition changes) through continuous sampling of four storm events of the 2019 rainy season, including one small storm, two moderate storms, and a large storm. Pollutant sources and transport pathways were then examined over the storm sequence via hysteresis analysis. The results revealed a strong linkage between N/P dynamics and hydrological processes. Storm runoff caused a 6-fold increase in particulate-P (PP) and a 4-fold increase in ammonia-N (NH4-N) fluxes through four storms (most sensitive nutrients to storms). On average, PP shared 86% of P exports, and nitrate-N (NO3-N) contributed 79% of N exports. PP and NH4-N were delivered primarily from overland sources and transported by surface runoff. Nonetheless, mobilization of channel sediment reserves was also an important way of PP supply during storms. The results suggested groundwater as the principal NO3-N source in the watershed, and subsurface flow was important for NO3-N and total dissolved-P (TDP) delivery during storms. The large storm (>20 mm) often registered the highest N/P load exports. However, there were other influencing factors/processes on stormflow N/P dynamics in the semi-arid watershed, which complicate/override the effects of different storm magnitudes. Total suspended solids (TSS)/PP source availability and inter- and intra-storm export trends influenced P behaviors through storms. Moreover, impacts of mobilization processes on NO3-N behavior appeared over the storm sequence. These findings enhance our understanding of storm events induced N/P exports in water-scarce regions and provide references for water quality predictions and control in flood seasons.

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