流域
环境科学
水文学(农业)
溪流
比例(比率)
滞后
空间生态学
土壤科学
大气科学
地质学
生态学
地理
生物
地图学
计算机网络
岩土工程
计算机科学
作者
Andréas Musolff,Qing Zhan,Rémi Dupas,Camille Minaudo,Jan H. Fleckenstein,Michael Rode,Joni Dehaspe,Karsten Rinke
摘要
Abstract The analysis of concentration‐discharge (C‐Q) relationships from low‐frequency observations is commonly used to assess solute sources, mobilization, and reactive transport processes at the catchment scale. High‐frequency concentration measurements are increasingly available and offer additional insights into event‐scale export dynamics. However, only few studies have integrated inter‐annual and event‐scale C‐Q relationships. Here, we analyze high‐frequency measurements of specific conductance (EC), nitrate (NO 3 ‐N) concentrations and spectral absorbance at 254 nm (SAC 254 , as a proxy for dissolved organic carbon) over a two year period for four neighboring catchments in Germany ranging from more pristine forested to agriculturally managed settings. We apply an integrated method that adds a hysteresis term to the established power law C‐Q model so that concentration intercept, C‐Q slope and hysteresis can be characterized simultaneously. We found that inter‐event variability in C‐Q hysteresis and slope were most pronounced for SAC 254 in all catchments and for NO 3 ‐N in forested catchments. SAC 254 and NO 3 ‐N event responses in the smallest forested catchment were closely coupled and explainable by antecedent conditions that hint to a common near‐stream source. In contrast, the event‐scale C‐Q patterns of EC in all catchments and of NO 3 ‐N in the agricultural catchment without buffer zones around streams were less variable and similar to the inter‐annual C‐Q relationship indicating a homogeneity of mobilization processes over time. Event‐scale C‐Q analysis thus added key insights into catchment functioning whenever the inter‐annual C‐Q relationship contrasted with event‐scale responses. Analyzing long‐term and event‐scale behavior in one coherent framework helps to disentangle these scattered C‐Q patterns.
科研通智能强力驱动
Strongly Powered by AbleSci AI