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Advances on water quality modeling in burned areas: A review [1]

['Marta Basso', 'Department Of Environment', 'Planning', 'Centre For Environmental', 'Marine Studies', 'Cesam', 'University Of Aveiro', 'Aveiro', 'Dalila Serpa', 'Marcos Mateus']

Date: 2022-08 

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.

Citation: Basso M, Serpa D, Mateus M, Keizer JJ, Vieira DCS (2022) Advances on water quality modeling in burned areas: A review. PLOS Water 1(7): e0000025. https://doi.org/10.1371/journal.pwat.0000025 Editor: Fadji Z. Maina, NASA Goddard Space Flight Center, UNITED STATES Published: July 25, 2022 Copyright: © 2022 Basso et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: Thanks are due for the financial support from FCT – Fundação para a Ciência e a Tecnologia, I.P. and CESAM by FCT/MCTES (UIDP/50017/2020+UIDB/50017/2020+ LA/P/0094/2020), through national funds. This work was supported by the projects ASHMOB (CENTRO-01-0145-FEDER-029351), FEMME (PCIF/MPG/0019/2017), FIRECNUTS (PTDC/AGRCFL/104559/2008), FIREMIX (PTDC/BIA-ECO/29601/2017) project, funded by FEDER, through COMPETE2020 - Programa Operacional Competitividade e Internacionalização, and by national funds, through FCT/MCTES, and WAFLE (PTDC/ASP-SIL/31573/2017) funded by FEDER, through COMPETE2020 - Programa Operacional Competitividade e Internacionalização (POCI), and by national funds (OE), through FCT/MCTES, and by the project EPyRIS (SOE2/P5/E0811), funded by the European Union through the SUDOE INTERREG Program. We also want to acknowledge individual contracts funded by FCT of MB (SFRH/BD/146356/2019), and JJK (IF/01465/2015). DS is funded by national funds (OE), through FCT in the scope of the framework contract foreseen in the numbers 4, 5 and 6 of the article 23, of the Decree-Law 57/2016, of August 29, changed by Law 57/2017, of July 19. MM was funded by FCT/MCTES (PIDDAC) through project LARSyS - FCT Pluriannual funding 2020-2023 (UIDB/EEA/50009/2020). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist.

2. Post-fire water quality processes and parameters Despite the diversity of hydrological and water quality parameters affected by wildfires (Fig 1), the scientific community has focused its modelling applications on a restricted number of processes and parameters. These include runoff and flow, soil erosion and sediments, nutrients and, more recently, ash. 2.1 Runoff/Flow Water quantity parameters, as runoff and flow, are widely addressed in post-fire simulations because they are the trigger for erosion and the associated transport of nutrients and contaminants to downstream waterbodies. Furthermore, the increase in runoff in burned areas itself could lead to flood episodes during extreme rainfall events, making its prediction critical [8]. Runoff and flow are also the most monitored parameters. Compared to water quality parameters, they have extensive databases, which explains why they are frequently addressed in post-fire simulations. Several publications rely on national or regional databases either for model calibration [40, 41] or for analysis of post-fire scenarios [42–45]. Physical models are the most used to simulate runoff and flow, being the Soil and Water Assessment Tool (SWAT) the one that has been more frequently adapted to post-fire conditions at catchment scale [30, 34, 41, 44, 46]. 2.2 Erosion/Sediment yield Soil erosion or sediment export are the most frequently predicted parameters for post-fire conditions across scales. Such focus can be explained by the large number of empirical and semi-empirical models used for the prediction of post-fire soil erosion risk such as USLE, RUSLE, or Morgan-Morgan-Finney (MMF). These models focus on the identification of high-risk areas to prioritize post-fire management actions, being post-fire soil erosion predicted for the entire burned area at slope scale over a region [47, 48] or for individual field assays [24, 49–51] with a seasonal or annual basis. When modelling studies are executed at catchment scale, the research is focused more concerned with post-fire off-site effects. Such studies rely on physically- or process-based models to accommodate increasing complexity such as catchment configuration, burn severity heterogeneity, or proportion of burned and unburned drainage area [30, 34, 41, 52]. These simulations are frequently performed at shorter time-steps such as events or daily basis, addressing the sediments exports impacts on water quality [34], but also the potential of the transport of debris to downstream values at risk infrastructures [53]. 2.3 Nutrients and contaminants Nutrients and contaminants (e.g. metals), in both their dissolved and particulate forms, tend to increase in waterbodies during the first years after fire, thereby posing a risk to the health of the ecosystems [7, 14, 15, 18]. However, modelling studies evaluating the impacts of post-fire mobilization of nutrients and contaminants on water quality are relatively scarce [34–36, 38]. This discrepancy may arise from the flawed assumption that these impacts can be indirectly estimated from sediment data alone, possibly because nutrients and contaminants mobilized by water erosion shortly after fire are of major concern for water managers [13, 54, 55]. However, there is evidence that post-fire nutrients and contaminants (metals, in particular) may also be transported in dissolved forms by subsurface flow (50). Another reason for the lack of post-fire water quality modelling studies can be attributed to the scarcity of data available for model calibration and validation. Such parameters are rarely measured or monitored at too low frequency by typical water quality monitoring protocols, the exception being for water reservoirs for human supply [37]. From the few existing modelling studies involving nutrients, only the work of Basso et al. (2020) [34] was based on a physically-based hydrological model (SWAT). Other approaches relied on process-based or empirical models, such as the E2 catchment model [35] and the empirical models OpenNSPECT [36], respectively. The advantage of physically-based models over other types of modeling approaches relies on their capacity to establish links between nutrient dynamics and changes in hydrological and erosion processes induced by fires. Regarding fire-related contaminants, only one recent modelling study was found [38], using a new WEPP model (WEPPcloud-WATAR-AU, i.e. Water Erosion Prediction Project cloud‐Wildfire Ash Transport And Risk‐Australia), develop for Lake Burragorang, one of Australia’s largest urban supply reservoirs, with the sole purpose of predicting contaminants transport associated to sediment and ash.

4. Research gaps and future research One of the main challenges in predicting post-fire impacts is the fact that most models do not include specific post-fire processes in their structure [26], and their adaptations still lack essential components for an accurate assessment of the post-fire water quality risk. Despite the substantial model adaptations made in the last 20 years, there are still five major research gaps: processes—the inclusion of ash mobilization in modelling predictions is currently limited; data availability–studies often lack detailed data for vegetation and soil properties after the fire, and water quality data for proper model calibration and adaptation; time scale–most studies only address immediate and not mid- to long-term post-fire impacts; fire impacts on vegetation and soil and their subsequent recovery with time-since-fire are represented coarsely; spatial scale—most studies focus on hillslope impacts or impacts at catchment outlets. The difficulties in accounting for ash mobilization in post-fire hydrological models are related to the determination of ash loads, since ash characteristics are not only dependent on the type and amount of pre-fire vegetation and litter cover but also on burn severity [84], topography, and post-fire meteorological conditions. The magnitude of the first post-fire rainfall events is particularly relevant for determining the fate of ash, i.e., the leaching of its constituents into the soil, or its downstream mobilization by water erosion [38]. Another major limitation consists in the exhaustion of the ash layer that cannot be considered in most existing hydrological models since they simulate soil layers with a fixed soil depth. Moreover, these models often consider a very well-defined soil particle size and sediment transport process, which may be incompatible with simple adaptations to account for ash in model predictions. Ash presents different transport physics, such as floatability [85], and also changes the topsoil infiltration capacity [86], whereas the inclusion of an ash layer variable in time and space would require a deep change in most model structures. Besides the already mentioned uncertainties in model parameters and structure [26, 87, 88], data availability is a major limitation for applying hydrological models to post-fire conditions. Typically, field data collection to assess post-fire impacts is focused on surface processes as the main drivers of soil erosion, as well as extreme downstream hydrological responses, overlooking the role of subsurface and groundwater fluxes in catchment-scale hydrological and water-quality responses. There are many difficulties in implementing a monitoring program to evaluate post-fire impacts in detail and, consequently, substantiate model calibration and validation [8, 11]. Such field assessments of post-fire impacts at catchment scale are highly dependent on funding cycles and national priorities, but also require specific technical knowledge and human resources [26]. However, having continuous long-term measurements of discharge, sediments, nutrients, and contaminant exports in an entirely burned catchment and beyond the fire-affected watercourse, is imperative to improve the capacity of models to predict fire impacts on water quality over the short to long terms. Considering the simulation over a long period, models must simulate the recovery of vegetation and soil properties in a joint manner. Failure to take into account this combined effects or the incorrect consideration of the recovery of the environment could lead to an inaccurate assessment of the impacts of fires. Another challenge in understanding the risks posed by wildfires on waterbodies is the lack of tools able to predict post-fire water, sediment, and the associated nutrient and contaminant fluxes and their impacts on downstream aquatic ecosystems. Reservoirs are frequently the endpoint of materials and constituents originating in the watershed. Therefore, a part of the pyrogenic and fire-mobilized contaminants from fires will be transported by surface and subsurface flow or groundwater, eventually accumulating in channels, rivers, and finally reservoirs. Consequently, the development of integrated modelling frameworks linking the on- and off-site impacts of fires is urgent. Such frameworks could combine a watershed model with an aquatic ecosystem model applied to a stream, river, or reservoir. The aquatic models would allow complementing previous predictions with hydrodynamic, geochemical, and biological models, to account, not only for fire-induced changes in water quality, but also in aquatic habitats and biota. So far, Basso et al. [37] combined a watershed with a reservoir model to study the post-fire impacts on drinking water supply. However, this framework presented limitations since the adapted models were not developed for post-fire conditions. Heat-induced changes affecting the state of compounds such as sulphate, nitrate, and ortho-phosphate in the soil are known to facilitate their movement in overland flow, but this type of process is not simulated in the current models [89]. Likewise, the dynamics of the pyrogenic C (PyC) produced by the thermal degradation of biomass are generally simulated as a “standard” organic compound, leading to an approximation of the impacts of this component. Reservoir modelling addressing post-fire impacts brings an additional challenge due to the intrinsic variability of the natural systems. Therefore, additional data is needed for model calibration and adaptation, with adequate temporal and spatial resolutions. Moreover, to ensure that models can provide relevant insights to land and reservoir managers and, hence, can be useful in post-fire mitigation and land management planning, they must necessarily include a wide range of impacts. Models will eventually require significant upgrades, or have to be developed from scratch, as also pointed out by Lopes et al. [26] for post-fire soil erosion, through a conceptual framework where fire and post-fire processes are explicitly addressed. The above-mentioned challenges in post-fire modelling indicate that the ability to produce robust and predictive tools to address the biochemical responses of waterbodies to wildfires remains elusive [90]. This means that if a proper assessment of the impact of fires on stream and reservoir water quality is the goal, then the presence of pyrogenic and fire-mobilized contaminants and their spatial and temporal variability is one of the most significant challenges to modellers.

5. Final considerations The present review analyzed the advances in water quality modeling in burned areas. From the literature reviewed, it was possible to conclude that research studies tend to prioritize the predictions of runoff and erosion and, to a smaller extent, nutrients and contaminants. This is probably because exports of nutrients and contaminants are assumed to be closely linked to sediments exports over short time periods, but also due to model limitations. Empirical models are still commonly used to simulate post-fire conditions, resulting in a simplified simulation of the processes involved. As model complexity increases, more detailed processes such as burn severity patterns and ecosystem recovery can be considered, increasing the number of water quality parameters that can be predicted. However, the amount of data required in more complex models can become a major limitation. The temporal and spatial scale of each study is highly connected to the model structure. Slope scale modelling with empirical models can offer great insights on the source of sediments and the associated nutrients and contaminants, and it also allows to identify areas with high erosion risk that are a priority for the application of emergency stabilization treatments. Nevertheless, such models are generally applied with an annual resolution and do not take into consideration seasonal patterns. Catchment-scale models, on the other hand, can combine slope connectivity, ecosystem recovery, and various land management decisions to assess downstream values-at-risk. The time-step of these models is also much smaller, which has been identified as an advantage to increase our preparedness to tackle future climate extremes. However, and despite the diversity of models available, few studies have simulated hydrological and soil erosion recovery beyond the first post-fire year. So far, the most important model adaptations to post-fire conditions have been established within pre-existing model structures, by changing infiltration, protective cover, soil properties, and by considering burn severity. However, several specific processes such as those linked to the ash layer and its mobilization by wind and water erosion or the recovery of soil properties following strong to extreme soil burnt severity seem to have been poorly addressed so far, so that important adaptations or new models are needed to tackle those limitations. Based on the research, it was found that simulations generally stop at catchment scale, showing a lack of post-fire water quality models applied beyond the watershed scale. To this end, the use of integrated modelling frameworks is essential to assess the direct and indirect impacts of wildfires on downstream waterbodies.

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[1] Url: https://journals.plos.org/water/article?id=10.1371/journal.pwat.0000025

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