Journal Article

Modeling Compound Hydrologic Disturbances in the Rio Grande Headwaters

Date: 2023/09/12

Author(s): Schneider K.E., Rust A., Hogue T.

Publication: Journal of the American Water Resources Association, v. 60, p. 95-109

URL: https://doi.org/10.1111/1752-1688.13162

DOI: 10.1111/1752-1688.13162

Abstract:

In recent decades, the western United States (U.S.) has experienced increasing magnitudes and frequencies of natural land cover disturbances that impact water budget partitioning. Post-disturbance hydrologic response is often variable at the stream outlet and is difficult to detect and quantify with traditional before–after control–impact studies. This study uses a modified version of the U.S. Geological Survey's Monthly Water Balance Model (MWBM) to simulate and separate the hydrologic response to several forest disturbances, including (1) wildfire, (2) forest conversion (subalpine to mid-elevation forest) and (3) a climate that is hotter and drier than present in the Rio Grande Headwaters (RGH) in Colorado, U.S. (this climate scenario was derived from an ensemble of climate scenarios from the Coupled Model Intercomparison Project phases 3 and 5, which were selected based on water stress potential in the state of Colorado). We leverage historic post-disturbance vegetation data in the RGH to add quantitative vegetation representation to the MWBM, then modeled synthetic future (2021–2050) streamflow scenarios as both single and compound disturbances. Relative to a baseline scenario, modeled scenarios predict several changes to average annual water trends over the final simulation decade (2041–2050); (1) decreases in average annual water yield under a hot and dry climate (−14%), except during the rising limb of annual snowmelt; (2) increases in average annual water yield (+32%) and peak runoff under a fire simulation; and (3) increases in average annual water yield (+24%) along with earlier and higher peak runoff under compound (fire + hot/dry) conditions. These findings show the strengths of hydrologic models in separating compound disturbance signals at the stream outlet and a need for quantitative vegetation representation within models to adequately represent dynamic disturbance conditions.