|Leveraging erosion models with established land health assessments to support management decisions
|Year of Publication
|Wheeler, B, Webb, NP, Williams, CJ, Edwards, BL, Faist, AM, Herrick, JE, Kachergis, EJ, Lepak, N, McCord, SE, Newingham, B
|Society for Range Management: Rangelands Without Borders
Land health assessments support management and mitigation of soil erosion that negatively impacts environmental and human health. Land managers can evaluate land health by relating qualitative and measured erosion indicators describing ecosystem attributes (e.g., ecosystem structure) to erosion evidence, such as in the widely applied Interpreting Indicators of Rangeland Health (IIRH) protocol. In contrast, erosion models such as the Aeolian EROsion (AERO) model and Rangeland Hydrology and Erosion Model (RHEM) estimate sediment transport rates and erosion risk using weather inputs, common ecological measurements, and their interactions. Comparison of model estimates to benchmarks can be used to assess site susceptibility to erosion, site stability, and land status. Integrating use of erosion models into land health assessments could promote better understanding of ecosystem function and further inform land management decisions and planning. We identify workflows and establish a conceptual basis for using model-driven (AERO and RHEM) and qualitative and measured (IIRH) erosion indicators together to evaluate land health through: 1) conducting a post hoc review of erosion indicators to determine land health; or 2) incorporating evaluation of modeled erosion indicators into the IIRH assessment process. An example from southern New Mexico clarifies application of the combined approach. We also illustrate through examples the fundamental characteristics of modelled, measured, and observed erosion indicators that users should consider during evaluations. Integration of modeled erosion indicators into IIRH will provide land health assessments that consider erosion evidence, sediment transport, and current and potential erosion risk that better inform landscape condition and management decisions.