Advances on water quality modeling in burned areas: A review

Abstract

Wildfires are a recurring hazard in forested catchments representing a major threat to water security worldwide. Wildfires impacts on water quality have been thoroughly addressed by the scientific community through field studies, laboratory experiments, and, to a lesser extent, the use of hydrological models. Nonetheless, models are important tools to assess on-site and off-site wildfires impacts and provide the basis for post-fire land management decisions. This study aims to describe the current state of the art of post-fire model adaptation, understanding how wildfires impacts are simulated and the options taken by the modelers in selecting parameters. For this purpose, 42 publications on modeling wildfire impacts on the hydrologic cycle and water quality were retrieved from the SCOPUS database. Most studies simulated post-fire hydrological and erosion response in the first year after the fire, while few assessed nutrients changes and long-term impacts. In addition, most simulations ended at the watershed outlet without considering the fate of pollutants in downstream waterbodies. Ash transport was identified as a major research gap, given the difficulties of its incorporation in the current models’ structure and the high complexity in predicting the heterogeneous ash layer. Including such layer would improve models’ ability to simulate water quality in post-fire conditions, being ash a source of nutrients and contaminants. Model complexity and data limitations influenced the spatial and temporal scale chosen for simulations. Post-fire model adaptations to simulate on-site soil erosion are well established, mainly using empirical equations extensively calibrated in the literature. At the watershed level, however, physical and process-based models are preferred for their ability to simulate more complex burned area characteristics. Future research should focus on the simulation of the ash transport and the development of integrated modelling frameworks, combining watershed and aquatic ecosystem models to link the on and off-site impacts of fires.

A call for strategic water-quality monitoring to advance assessment and prediction of wildfire impacts on water supplies

“…Water providers, reservoir operators, land managers, and emergency response agencies would benefit from improved assessment and prediction of the character, magnitude, and duration of waterquality impacts after wildfire in watersheds across a wide range of ecoregions. A lack of adequate pre-and post-wildfire water-quality data hinders model calibration and adaptation (Basso et al, 2022), assessment of post-wildfire recovery (Hampton et al, 2022), and understanding how wildfires will affect regulatory requirements (Paul et al, 2022). Here we describe a path forward for strategic, consistent post-wildfire water-quality data collection for surface water that, when deployed across a range of ecosystems, will lead to vastly improved assessment and prediction of impacts of wildfire on water supplies.…”

“…In addition, because wildfires can reduce the threshold precipitation intensity at which overland flow occurs, post-wildfire stream discharge and sediment concentrations can be orders of magnitude greater than they would have been for similar storms pre-wildfire (Wondzell and King, 2003;Murphy et al, 2015), which poses challenges to current monitoring capabilities. As a result of these challenges, there are numerous gaps in post-wildfire water-quality data (Yu and Cheng, 2008;Rust et al, 2018;Basso et al, 2022;Hampton et al, 2022;Paul et al, 2022;Robinne et al, 2022;Raoelison et al, 2023), as shown in Figure 1.…”

Wildfires drive multi-year water quality degradation over the western U.S.

“…Data-driven models often have lower data requirements than physical models, offering a unique opportunity to deepen understanding 16 . Studies using these techniques for post-fire water quality analysis 4,[29][30][31][32][33][34][35] have been relatively narrow in scope, for example, neglecting sediment, DOM, and nutrient responses across western U.S. Here, we apply data-driven methods to quantify postfire water quality signals across a wide range of constituents, evaluating a large sample of watersheds over several decades.…”

Fire, fine sediment, and fractional ground cover: Representation of fire in the Cape York � Great Barrier Reef water quality model

“…It is hypothesized that the key model input that represents fire in the GBR water quality models (in lieu of a model specifically adapted for fire) is the remotely sensed seasonal ground cover dataset, used to represent the cover factor (C-factor) in the calculation of hillslope erosion via the Revised Universal Soil Loss Equation (RUSLE). Land cover change is the most widely applied water quality model adaptation to represent fire impacts (Basso et al, 2022).…”