Probability and Consequence of Postfire Erosion for Treatability of Water in an Unfiltered Supply System

Abstract

Large cities around the world source their drinking water from forested catchments, which deliver water that is treatable at minimal cost. These forests are often prone to wildfire, which tend to increase surface runoff, destabilize soils, and trigger increased sediment delivery into water reservoirs (

Modeling Post‐Wildfire Hydrologic Response: Review and Future Directions for Applications of Physically Based Distributed Simulation

“…In certain circumstances, inclusion of specific processes can produce more robust modeling results. For example, if a stream flows into a reservoir after wildfire (e.g., Nyman et al., 2020) or other situation with a backwater then the DW approximation may be more rigorous (Table 2). The level of process representation for infiltration is likely more critical in a situation when IE runoff is important (e.g., peak flows, debris flows, erosion) but less so in a long‐term modeling approach focused on subsurface streamflow mechanisms (Table 2).…”

“…Modeling efforts across the globe have been aimed at assessing, predicting, and understanding wildland fire implications for water availability and water‐related hazards. Multiple modeling approaches have been applied, from simple empirical models for rapid post‐wildfire assessments (e.g., Chen et al., 2013; Foltz et al., 2009; Kinoshita et al., 2014; Moody, 2011) to complex, physically based distributed models used for forensic analysis of high‐impact flash floods (e.g., Canfield et al., 2005; Ebel et al., 2016; Johnson, 2000) and debris flows (e.g., Barnhart et al., 2021), and detailed spatial and temporal predictions related to water quality (e.g., Neris et al., 2021; Nyman et al., 2021). Recent efforts have expanded post‐wildfire streamflow predictions to include machine‐learning approaches (Wilder et al., 2021; Zema et al., 2020).…”

“…Perhaps the greatest utility of physically based distributed models is for problems like post‐wildfire streamflow generation with highly variable distributed responses, where the spatio‐temporal connections across the landscape are important (Cawson et al., 2013; Hallema et al., 2017; C. J. Williams et al., 2016), and predictions of interest are for specific variables at critical locations and times of maximum response/concern. These predictions can then be implemented for hazard/risk assessments and emergency planning (e.g., Kean & Staley, 2021; Nyman et al., 2021).…”

Predicting wildfire induced changes to runoff: A review and synthesis of modeling approaches

“…In this review, we focus on the processes and representation of climate fire, vegetation, and soil dynamics in models, but do not explore the uncertainty of system forcing, for example, rainfall stochasticity. We do acknowledge that uncertainty is highly important, as has been demonstrated in assessing water quality risks from surface runoff after fires (Jones et al, 2014;Langhans et al, 2016;Nyman et al, 2021). We also do not consider the inclusion of processes associated with anthropogenic climate change; rather, we consider the outputs from climate models as strictly forcing the hydrology, vegetation, and fire spread and effects processes.…”

Peculiarities of Hydrodynamics of Small Surface Water Bodies in Zones of Active Technogenesis (on the Example of the Verkhne-Zyryansk Reservoir, Russia)

“…The mechanism of the formation of a large-scale salinity eddy at the confluence of two rivers with different salinities was described in detail in [20]. The influence of the consequences of a fire on the physicochemical properties of water in a reservoir used for water supply was considered in [22]. Here also, the spatial and temporal characteristics of sediments, the hydrodynamics of the reservoir, and the erodibility of the catchment area were investigated.…”