Capturing Forest Disturbance in a Watershed Model in the Rio Grande Headwaters
Abstract
The Rio Grande Headwaters (RGH) is a heavily forested, snow-dominated catchment in Colorado's San Juan Mountains that has recently experienced several forest disturbances. Most notably, the RGH has experienced drought, the 2013 West Fork Complex fire and extensive insect induced forest mortality since the early 2000s. However, in the RGH and beyond, the impact of forest disturbances is often difficult to capture at the stream outlet, especially when disturbances overlap. The RGH serves as a source water supply to numerous water users in Colorado and downstream states, and as watershed disturbances worsen, concern rises over future RGH water supply planning. In this study, we collaborated with local RGH stakeholders to develop and implement climate and forest disturbance scenarios into a modified version of the US Geological Survey's Monthly Water Balance Model (MWBM). The modified model, called MWBM-Leaf Area Index ("MWBM-LAI") incorporates LAI into the model framework to capture dynamic vegetation change. We produced synthetic future (2021-2050) streamflow under both separate and overlapping disturbance scenarios including wildfire, forest conversion (subalpine to mid-elevation forest) and climate change (hotter-and-drier than present). Relative to a baseline scenario, we observed earlier and faster spring runoff for all scenarios, progressive decreases in annual water yield under a hot-and-dry climate scenario, and temporary increases in annual water yield and peak runoff after wildfire. These findings demonstrate 1) the necessity of representing dynamic vegetation change in models used to study post-disturbance hydrology and 2) the strengths of models in separating overlapping disturbance signals in streamflow.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2022
- Bibcode:
- 2022AGUFM.H12C..05S