Building cross-scale predictability of land-to-aquatic nitrogen loads in agriculture-dominated watersheds

Issue

Modeling assessment is becoming increasingly important to developing quantitative insights into the processes governing nutrient exports from land to water bodies, and to making predictions about future conditions. However, the efficacy and attribution capacity (i.e., “get right answer for right reasons”) of the existing water quality models is uncertain owing to limited model testing against long-term observations of nutrient concentration, flow and loading within a hydrologic hierarchy system.

Objective

Researchers propose to develop a data-model integration framework to identify and quantify the issues leading to model incapability in accurately estimating N delivery across scales.

Approach

In this project, researchers will test and improve a process-based hydro-ecological model to simulate in-stream N transport and decay processes and benchmark the model’s performance using long-term water quality monitoring data at the delivery scale, along ditches and stream channels downstream of the outlets of subsurface drainage networks, and at the scale of a United State Geological Survey Hydrologic Unit Code 12 (HUC 12) watershed (~5000 ha). This work will be conducted in the HUC 12 Walnut Creek watershed located south of Ames, Iowa. The Walnut Creek watershed has been subject to extensive monitoring of nutrient loads and flows over decades at various scales, at multiple locations along the mainstem of Walnut Creek and some of its tributaries. In this project, researchers will employ a portion of the historical flow and nutrient concentration time-series monitoring data to improve and calibrate the model and validate it with the remainder. Then, the improved model will be applied in the entire watershed to quantify the relative contributions of in-field and edge-of-field management practices, and in-stream processes on nitrate-N reductions observed at the watershed outlet under current climate conditions. Finally, researchers will identify “hot-spot” areas within the watershed that are prone to losing excessive quantities of N through hydrological flushing.