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Application of Hierarchy Theory to Cross-Scale Hydrologic Modeling of Nutrient Loads
Citation:
Tran, L. T., R. V. O'Neill, E. R. SMITH, R. J. BRUINS, AND C. Harden. Application of Hierarchy Theory to Cross-Scale Hydrologic Modeling of Nutrient Loads. WATER RESOURCES MANAGEMENT. Springer, New York, NY, 1:1-17, (2013).
Impact/Purpose:
Among various national and regional water resource issues, non-point source pollution is 29 arguably one of the most pressing and challenging water quality problems [National Research 30 Council, 2004, 2008, 2011]. As in the case of the Mississippi River basin (MRB), the large 31 majority of nutrient yields across MRB are from non-point sources which are associated with 32 agricultural activities, especially applications of nitrogen-based fertilizers and runoff from 33 concentrated livestock feeding operations [Alexander et al., 2008; Smith et al., 1997]. Hypoxia in 34 the northern Gulf of Mexico (over-enrichment and the creation of a seasonal zone of oxygen-35 deficient waters) is mainly due to high inputs of nutrients from the Atchafalaya and Mississippi 36 Rivers [USEPA, 2008; USGS, 2004]. Unfortunately limited progress to date has been recorded in 37 resolving the hypoxia problem in the Gulf of Mexico as areal extent of the hypoxic zone 38 measured each summer has shown a general trend of increasing [LUMCON, 2010].
Description:
We describe a model called Regional Hydrologic Modeling for Environmental Evaluation 16 (RHyME2) for quantifying annual nutrient loads in stream networks and watersheds. RHyME2 is 17 a cross-scale statistical and process-based water-quality model. The model quantifies the direct 18 impact of flow on annual nutrient load in a non-linear fashion and estimates transport, and 19 transformation of nutrient sources in the terrestrial and aquatic ecosystems to predict nutrient 20 load associated with long-term balanced flow at regional scale in a regional module. Using 21 output from the regional module as background information, RHyME 2 calibrates its sub-regional module with temporal input data of nutrient sources and other physical characteristics (e.g., 23 temperature, precipitation) to predict annual nutrient load. High values of R2 (>0.95) and the 24 Nash–Sutcliffe model efficiency coefficient (>0.85) show that RHyME2 sufficiently captured the 25 magnitude and pattern of the annual nitrogen load at monitoring sites.