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Cycles-L: A coupled, 3-D, land surface, hydrologic, and agroecosystem landscape model
  • Yuning Shi,
  • Felipe Montes,
  • Armen R Kemanian
Yuning Shi
The Pennsylvania State University
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Felipe Montes
Pennsylvania State University
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Armen R Kemanian
Pennsylvania State University

Corresponding Author:kxa15@psu.edu

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Managing landscapes to increase agricultural productivity and environmental stewardship requires spatially distributed models that can integrate data and operate at spatial and temporal scales that are intervention-relevant. This paper presents Cycles-L, a landscape-scale, coupled agroecosystem hydrologic modeling system. Cycles-L couples a 3-D land surface hydrologic model, Flux-PIHM, with a 1-D agroecosystem model, Cycles. Cycles-L takes the landscape and hydrology structure from Flux-PIHM and most agroecosystem processes from Cycles. Consequently, Cycles-L can simulate landscape level processes affected by topography, soil heterogeneity, and management practices, owing to its physically-based hydrologic component and ability to simulate horizontal and vertical transport of mineral nitrogen (N) with water. The model was tested at a 730-ha agricultural experimental watershed within the Mahantango Creek watershed in Pennsylvania. Cycles-L simulated well stream water discharge and N exports (Nash-Sutcliffe coefficient 0.55 and 0.58, respectively), and grain crop yield (root mean square error 1.01 Mg ha−1), despite some uncertainty in the accuracy of survey-based input data. Cycles-L outputs are as good if not better than those obtained with the uncoupled Flux-PIHM (water discharge) and Cycles (crop yield) models. Model predicted spatial patterns of N fluxes clearly show the combined control of crop management and topography. Cycles-L spatial and temporal resolution fills a gap in the availability of analytical models at an operational scale relevant to evaluate costly strategic and tactical interventions in silico, and can become a core component of tools for applications in precision agriculture, precision conservation, and artificial intelligence-based decision support systems.