Skip to main content
  • Loading metrics

Advances on water quality modeling in burned areas: A review


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.

1. Introduction

Forest catchments are the main providers of important ecosystem services, such as clean water supply and its regulation [1, 2]. Despite their importance, these ecosystems are threatened by wildfires [3, 4], which can severely affect aquatic environments located downstream of the burned area [5, 6] and compromise the security of water supply [7].

Wildfires are known to increase surface runoff and sediment transport in recently burned areas [8, 9] (Fig 1). The combustion of the vegetation and litter layer leaves the soil highly vulnerable to rainfall. This, combined with fire-induced changes on the topsoil such as the occurrence of soil water repellency and aggregate stability reduction, increases surface runoff and the associated sediment transport [811] (Fig 2). The type of sediments arriving to aquatic ecosystems is rather diverse, depending not only on the ash layer deposited on the soil surface, but also on the degree of connectivity between the burned area and the downstream waterbody. In the aquatic environment, this mixture of topsoil and ash has been reported to increase sediment and nutrient concentrations [1215], but can also be an important source of water contamination due to the associated transport of hazardous substances such as metals and polycyclic aromatic hydrocarbons (PAHs) [12, 1618].

Fig 1. Schematic representation of wildfire impacts on the waterbodies as function of burned severities.

Fig 2. Water quality during a rain event at the outlet of a recently burned area.

Serra de Cima flume, Earth and Surface Processes Team (ESPT).

The magnitude of fire impacts on aquatic ecosystems depends on burn severity, post-fire rainfall regime, and pre-fire land cover [11, 1922]. The history of disturbances of the affected area is also relevant [23], as this is known to influence post-fire recovery [24]. Burn severity has been recognized as a predictor of wildfire impacts [20, 25], being singled out as an important factor to be included in post-fire hydrological and soil erosion modelling [26, 27] (Fig 1). Moreover, when simulating the post-fire period, the recovery of vegetation and soil properties presents different dynamics between species and soil types [28], thus increasing the complexity of predictions.

Hydrological models can be useful tools for anticipating the off-site impacts of wildfires, as they allow testing various scenarios and can be applied to a wide range of catchments and time scales. Depending on data availability and quality, hydrological models can be used to understand how specific processes (water availability and quality) change with different post-fire land management options [29]. However, such models have been criticized for their oversimplification of reality (empirical models), or their high input demand (process-based models). Within the post-fire context, empirical models such as the Universal Soil Loss Equation (USLE) and its revised version (RUSLE) have often been used for soil erosion predictions, while more complex models have been generally used to predict post-fire runoff and erosion [3032], as well as the effects of post-fire erosion mitigation treatments at slope and catchment scale [32, 33]. However, only a limited number of models have been used to predict the impacts of fires on water quality [3439]. It should also be highlighted that only a few models were designed for post-fire conditions [26]. This means that post-fire hydrological modelling often requires the adaptation of models originally designed for other purposes, such as simulating the impacts of agriculture on water resources. Despite these limitations, several researchers have been able to successfully improve existing model applications. Nevertheless, there is still a major knowledge gap that limits the ability of resource managers to plan post-fire emergency measures that effectively protect water bodies, and minimize the risks for environmental and human health.

To address such research gap, a critical review was performed on post-fire model adaptations, focusing mainly on modelling studies that aim to predict the impacts of wildfires on water quality. The most frequently targeted parameters have been investigated and the most common processes analyzed. In addition, we have also evaluated if model adaptations consider the heterogeneity of impacts in burned areas, such as distinct burn severities and ecosystem recovery dynamics, and whether there is an attempt to address the legacy effects of wildfires. This study also attempts to identify current research shortcomings and provide recommendations for future research. A literature search was conducted using the Scopus database on 27th October 2021, for articles written in English using the following keywords: model, fire, and water quality. The search returned 42 publications, divided into 56 cases, depending on the number of models applied (S1 Text).

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 [4245]. 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, 4951] 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 [3436, 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.

3. Current model adaptations to post-fire conditions

3.1 Fire-induced changes on hydrological processes

The way to integrate post-fire conditions into models depends on their complexity, and the scope of the simulation (Fig 3). Land cover change is a widely applied adaptation (Table 1), changing the pre-fire vegetation to vegetation types with less coverage [36] or with bare soil [56]. Sometimes this adaptation is the only applicable change, especially in the case of empirical models, too simplistic to incorporate more detailed variations [36, 53].

Fig 3. Scheme of integrated modelling framework for post-fire conditions.

Table 1. Number and percentage of cases that address each research question.

With the increasing complexity of models, it is possible to consider additional impacts on hydrological processes and soil properties [26]. The increase in surface runoff after wildfires is attributed not only to the loss of interception by canopies, but also to the reduced infiltration rates [57] (Fig 1). To achieve such reduction, modelers generally decrease the saturated (ks) or the effective (ke) hydraulic conductivity coefficients. WEPP-based models like ERMiT [58] and the disturbed WEPP model [59] use tabulated values dependent on soil type and burn severity, with a proportional inverse relationship between ks and ke, burn severity. Generally, research studies that apply a reduction in the infiltration capacity vary between three methodologies: definition of such values during the calibration procedure [52, 60, 61], measuring such parameters in field experiments [62], and relying on values obtained from the literature [42].

The curve number (CN) is another parameter often adapted to simulate the increase in runoff under post-fire conditions. The addition of 5, 10, and 15 to the initial value for low, moderate, and high severity fires, respectively, is widely found in the literature [34, 41, 63, 64]. To accommodate the increase of post-fire runoff peaks and the faster rising limb of such peaks, researchers often reduce Manning’s n coefficient. Like the adaptation of ks, Manning’s n reduction also differs between publications. While most works apply values typical of bare soil [60, 61, 65], some publications consider the reduction of pre-fire values with a percentage [52, 66].

Soil erosion due to rainfall or increased surface flow in burned areas is mainly triggered by the reduced water retention capacity of the soil due to the presence of a water repellent front [10]. Parameters that control soil erodibility are the most modified to account for the increase in sediment loss, with some researchers applying a constant increase with increasing burn severity [34, 46, 51] (Fig 3). The alternation of the K factor of the USLE equation performed when adapting empirical models was also used to account for changes in infiltration, causing criticisms regarding the reliability of these simulations [26]. To take into account the impacts of land cover change and surface roughness, the crop factor (C) of the USLE equation has been extensively modified. Its increase is often simulated by reducing the coefficient for the roughness of an untilled surface (Ru) present in the SC component [49, 51, 67, 68].

3.2 Integrating burn severity in modelling predictions

Burn severity has become widely recognized as a key parameter for modelling post-fire impacts, given its crucial role in post-fire vegetation recovery. Consequently, its inclusion in hydrological models is considered fundamental for accurately predict post-fire hydrological response [11, 26, 27]. The burn severity can be determined in situ using the available descriptors [8, 69, 70], remote sensing indices such as Normalized Burn Ratio (NBR, 62), or by a combination of both (Fig 3). In the absence of field assessment, NBR is considered the best available method for catchment-scale assessments [47, 63, 7173].

Until now, the burn severity component has only been included in the WEPP and ERMIT models. In general, modelling requires authors to use alternative ways to include this component, either by defining land cover attributes considering burn severity for catchment-scale model applications [34, 74], or by multiplying a fire factor into their predictions to take into account such differences for plot [49] and regional predictions [47]. Regardless of those difficulties, burn severity was included in 56% of the studies addressing post-fire impacts on water quantity and quality (Table 1), revealing an increasing concern of the scientific community to address this issue.

3.3 Ecosystem recovery

The impacts of fires on vegetation and soil properties decline through time, as well as the effects on waterbodies. Post-fire vegetation recovery depends both on the fire severity and the type of vegetation affected. Areas covered by shrubs and herbs tend to regenerate faster than forests, covering large parts of the affected area only a few months after fire [75]. Forests, especially the ones burned at higher burn severities, take longer to recover and, depending on their species composition, can resprout or germinate from the surviving seed banks [76, 77]. A widely used methodology is to consider fixed time-steps to simulate the recovery of the vegetation and soil properties. Rulli and Rosso [61], for instance, assumed a two-year period independent of vegetation type, while Papathanasiou et al. [64] defined recovery periods of 2 to 4 years depending on burn severity. In a more detailed approach, Vieira et al. [50] considered seasonal changes in ground cover. The ERMiT [58] and the disturbed WEPP model [59], which both use the WEPP technology, simulate recovery as a process with a one-year timestep, the former gradually changing the input parameters related to soil [78], and the letter adding the vegetation regrowth [51]. It must be emphasized that changing the vegetation variables alone, overlooks the possible recovery of soil properties and may cause erroneous estimates of post-fire impacts. The same is true when only fire impacts on soil properties are considered, ignoring the possible changes linked to interception as well as evaporation and, thereby, possibly overestimating, for example, effective rainfall amounts and intensities, and soil moisture contents.

3.4 Long-term effects

Wildfire impacts can last several years, depending on various variables such as burn severity, post-fire climate, vegetation recovery, sediment availability, and catchment morphology [11, 21]. However, it is not possible to provide an accurate estimate of the recovery time because of the marked differences between burnt areas and vegetation types [21]. Nonetheless, Bladon et al. (2014) [79] and Stone et al. (2011) [80] observed that post-fire sediments can settle in channel networks for long periods, often being remobilized and transported further downstream during high-intensity hydrological events, leading to a gradual release of elements bonded to the sediments, like phosphorus and metals. The resulting increases in the window of disturbance can go up to 8 years in terms of hillslope post-fire soil erosion, and produce a legacy effect of 10 to >100 years in terms of water quality downstream of the burned area. Nonetheless, most of the studies modelled post-fire impacts on suspended solids only for the first post-fire year (71%). Most long-term analysis have simulated the first few years after the fire [30, 3436, 43, 51, 64, 78, 81], and only a few studies have simulated the impacts for periods over a decade [41, 61, 82, 83].

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:

  1. processes—the inclusion of ash mobilization in modelling predictions is currently limited;
  2. 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;
  3. time scale–most studies only address immediate and not mid- to long-term post-fire impacts;
  4. fire impacts on vegetation and soil and their subsequent recovery with time-since-fire are represented coarsely;
  5. 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.

Supporting information


  1. 1. Mingfang Z, Xiaohua W. Deforestation, forestation, and water supply. Science (80-). 2021 Mar 5;371(6533):990–1. Available from:
  2. 2. Reid W, Mooney H, Cropper A, Capistrano D, Carpenter S, Chopra K, et al. Millenium Ecosystem Assessment Synthesis Report. 2005.
  3. 3. Robinne FN, Hallema DW, Bladon KD, Flannigan MD, Boisramé G, Bréthaut CM, et al. Scientists’ warning on extreme wildfire risks to water supply. Hydrol Process. 2021 May 1;35(5):e14086. Available from: pmid:34248273
  4. 4. FAO. Global Forest Fire Assessment 1990–2000. Forest Resources Assessment Programme Working Paper 55. Rome; 2001.
  5. 5. Blandon K. Rethinking wildfires and forest watersheds. Science (80-). 2018 Mar 2;359(6379):1001–2. Available from:
  6. 6. Moody JA, Martin DA. Wildfire impacts on reservoir sedimentation in the western United States. In: Proceedings of the Ninth International Symposium on River Sedimentation [Internet]. Citeseer; 2004. p. 1095–102.
  7. 7. Emelko MB, Silins U, Bladon KD, Stone M. Implications of land disturbance on drinking water treatability in a changing climate: Demonstrating the need for “source water supply and protection” strategies. Water Res. 2011;45(2):461–72. Available from: pmid:20951401
  8. 8. Shakesby RA, Doerr SH. Wildfire as a hydrological and geomorphological agent. Earth-Science Rev. 2006;74(3):269–307. Available from:
  9. 9. Shakesby RA. Post-wildfire soil erosion in the Mediterranean: Review and future research directions. Earth-Science Rev. 2011;105(3):71–100. Available from:
  10. 10. Certini G. Effects of fire on properties of forest soils: a review. Oecologia. 2005;143(1):1–10. Available from: pmid:15688212
  11. 11. Moody JA, Shakesby RA, Robichaud PR, Cannon SH, Martin DA. Current research issues related to post-wildfire runoff and erosion processes. Earth-Science Rev. 2013;122:10–37. Available from:
  12. 12. Smith HG, Sheridan GJ, Lane PNJ, Nyman P, Haydon S. Wildfire effects on water quality in forest catchments: A review with implications for water supply. J Hydrol. 2011;396(1):170–92. Available from:
  13. 13. Serpa D, Ferreira R V, Machado AI, Cerqueira MA, Keizer JJ. Mid-term post-fire losses of nitrogen and phosphorus by overland flow in two contrasting eucalypt stands in north-central Portugal. Sci Total Environ. 2020;705:135843. Available from: pmid:31822414
  14. 14. Bladon KD, Silins U, Wagner MJ, Stone M, Emelko MB, Mendoza CA, et al. Wildfire impacts on nitrogen concentration and production from headwater streams in southern Alberta’s Rocky Mountains. Can J For Res. 2008 Sep;38(9):2359–71. Available from:
  15. 15. Bixby RJ, Cooper SD, Gresswell RE, Brown LE, Dahm CN, Dwire KA. Fire effects on aquatic ecosystems: an assessment of the current state of the science. Freshw Sci. 2015 Nov 18;34(4):1340–50. Available from:
  16. 16. Campos I, Abrantes N, Pereira P, Micaelo AC, Vale C, Keizer JJ. Forest fires as potential triggers for production and mobilization of polycyclic aromatic hydrocarbons to the terrestrial ecosystem. L Degrad Dev. 2019 Dec 1;30(18):2360–70. Available from:
  17. 17. Vila-Escalé M, Vegas-Vilarrúbia T, Prat N. Release of polycyclic aromatic compounds into a Mediterranean creek (Catalonia, NE Spain) after a forest fire. Water Res. 2007 May;41(10):2171–9. Available from: pmid:17397897
  18. 18. Rust AJ, Hogue TS, Saxe S, McCray J. Post-fire water-quality response in the western United States. Int J Wildl Fire. 2018;27(3):203–16. Available from:
  19. 19. Vieira DCS, Fernández C, Vega JA, Keizer JJ. Does soil burn severity affect the post-fire runoff and interrill erosion response? A review based on meta-analysis of field rainfall simulation data. J Hydrol. 2015;523:452–64. Available from:
  20. 20. Keeley JE. Fire intensity, fire severity and burn severity: a brief review and suggested usage. Int J Wildl Fire. 2009;18(1):116–26. Available from:
  21. 21. Wagenbrenner JW, Ebel BA, Bladon KD, Kinoshita AM. Post-wildfire hydrologic recovery in Mediterranean climates: A systematic review and case study to identify current knowledge and opportunities. J Hydrol. 2021;602:126772. Available from:
  22. 22. Murphy SF, Writer JH, McCleskey RB, Martin DA. The role of precipitation type, intensity, and spatial distribution in source water quality after wildfire. Environ Res Lett. 2015;10(8):84007. Available from:
  23. 23. Vieira DCS, Malvar MC, Fernández C, Serpa D, Keizer JJ. Annual runoff and erosion in a recently burn Mediterranean forest–The effects of plowing and time-since-fire. Geomorphology. 2016;270:172–83. Available from:
  24. 24. Vieira DCS, Malvar MC, Martins MAS, Serpa D, Keizer JJ. Key factors controlling the post-fire hydrological and erosive response at micro-plot scale in a recently burned Mediterranean forest. Geomorphology. 2018;319:161–73. Available from:
  25. 25. Fernández C, Vega JA. Modelling the effect of soil burn severity on soil erosion at hillslope scale in the first year following wildfire in NW Spain. Earth Surf Process Landforms. 2016 Jun 15;41(7):928–35. Available from:
  26. 26. Lopes AR, Girona-García A, Corticeiro S, Martins R, Keizer JJ, Vieira DCS. What is wrong with post-fire soil erosion modelling? A meta-analysis on current approaches, research gaps, and future directions. Earth Surf Process Landforms. 2021 Jan 1;46(1):205–19. Available from:
  27. 27. Shakesby RA, Moody JA, Martin DA, Robichaud PR. Synthesising empirical results to improve predictions of post-wildfire runoff and erosion response. Int J Wildl Fire. 2016;25(3):257–61. Available from:
  28. 28. Ireland G, Petropoulos GP. Exploring the relationships between post-fire vegetation regeneration dynamics, topography and burn severity: A case study from the Montane Cordillera Ecozones of Western Canada. Appl Geogr. 2015;56:232–48. Available from:
  29. 29. De Girolamo AM, Cerdan O, Grangeon T, Ricci GF, Vandromme R, Lo Porto A. Modelling effects of forest fire and post-fire management in a catchment prone to erosion: Impacts on sediment yield. CATENA. 2022;212:106080. Available from:
  30. 30. Nunes JP, Naranjo Quintanilla P, Santos JM, Serpa D, Carvalho-Santos C, Rocha J, et al. Afforestation, Subsequent Forest Fires and Provision of Hydrological Services: A Model-Based Analysis for a Mediterranean Mountainous Catchment. L Degrad Dev. 2018 Mar 1;29(3):776–88. Available from:
  31. 31. Thomas G, Rosalie V, Olivier C, Anna Maria DG, Antonio LP. Modelling forest fire and firebreak scenarios in a mediterranean mountainous catchment: Impacts on sediment loads. J Environ Manage. 2021;289:112497. Available from: pmid:33823410
  32. 32. Vieira DCS, Serpa D, Nunes JPC, Prats SA, Neves R, Keizer JJ. Predicting the effectiveness of different mulching techniques in reducing post-fire runoff and erosion at plot scale with the RUSLE, MMF and PESERA models. Environ Res. 2018;165:365–78. Available from: pmid:29803019
  33. 33. Rulli MC, Offeddu L, Santini M. Modeling post-fire water erosion mitigation strategies. Hydrol Earth Syst Sci. 2013 Jun 27;17(6):2323–37. Available from:
  34. 34. Basso M, Vieira DCS, Ramos TB, Mateus M. Assessing the adequacy of SWAT model to simulate postfire effects on the watershed hydrological regime and water quality. L Degrad Dev. 2020 Mar 1;31(5):619–31. Available from:
  35. 35. Feikema PM, Sheridan GJ, Argent RM, Lane PNJ, Grayson RB. Estimating catchment-scale impacts of wildfire on sediment and nutrient loads using the E2 catchment modelling framework. Environ Model Softw. 2011;26(7):913–28. Available from:
  36. 36. Morrison KD, Kolden CA. Modeling the impacts of wildfire on runoff and pollutant transport from coastal watersheds to the nearshore environment. J Environ Manage. 2015;151:113–23. Available from: pmid:25549866
  37. 37. Basso M, Mateus M, Ramos TB, Vieira DCS. Potential Post-Fire Impacts on a Water Supply Reservoir: An Integrated Watershed-Reservoir Approach [Internet]. Vol. 9, Frontiers in Environmental Science. 2021. p. 201. Available from:
  38. 38. Neris J, Santin C, Lew R, Robichaud PR, Elliot WJ, Lewis SA, et al. Designing tools to predict and mitigate impacts on water quality following the Australian 2019/2020 wildfires: Insights from Sydney’s largest water supply catchment. Integr Environ Assess Manag. 2021 Nov 1;17(6):1151–61. Available from: pmid:33751793
  39. 39. Maina FZ, Siirila-Woodburn ER. Watersheds dynamics following wildfires: Nonlinear feedbacks and implications on hydrologic responses. Hydrol Process. 2020 Jan 1;34(1):33–50. Available from:
  40. 40. Chen L, Berli M, Chief K. Examining modeling approaches for the rainfall‐runoff process in wildfire‐affected watersheds: Using San Dimas Experimental Forest. JAWRA J Am Water Resour Assoc. 2013;49(4):851–66. Available from:
  41. 41. Havel A, Tasdighi A, Arabi M. Assessing the hydrologic response to wildfires in mountainous regions. Hydrol Earth Syst Sci. 2018 Apr 25;22(4):2527–50. Available from:
  42. 42. Lanini JS, Clark EA, Lettenmaier DP. Effects of fire-precipitation timing and regime on post-fire sediment delivery in Pacific Northwest forests. Geophys Res Lett. 2009 Jan 13;36(1):L01402. Available from:
  43. 43. Pereira MG, Fernandes LS, Carvalho S, Santos RB, Caramelo L, Alencoão A. Modelling the impacts of wildfires on runoff at the river basin ecological scale in a changing Mediterranean environment. Environ Earth Sci. 2016;75(5):392. Available from:
  44. 44. Rodrigues EL, Jacobi CM, Figueira JEC. Wildfires and their impact on the water supply of a large neotropical metropolis: A simulation approach. Sci Total Environ. 2019;651:1261–71. Available from: pmid:30360258
  45. 45. Semenova O, Lebedeva L, Volkova N, Korenev I, Forkel M, Eberle J, et al. Detecting immediate wildfire impact on runoff in a poorly-gauged mountainous permafrost basin. Hydrol Sci J. 2015 Aug 3;60(7–8):1225–41. Available from:
  46. 46. Loiselle D, Du X, Alessi DS, Bladon KD, Faramarzi M. Projecting impacts of wildfire and climate change on streamflow, sediment, and organic carbon yields in a forested watershed. J Hydrol. 2020;590:125403. Available from:
  47. 47. Karamesouti M, Petropoulos GP, Papanikolaou ID, Kairis O, Kosmas K. Erosion rate predictions from PESERA and RUSLE at a Mediterranean site before and after a wildfire: Comparison & implications. Geoderma. 2016;261:44–58. Available from:
  48. 48. Myronidis D, Arabatzis G. Evaluation of Greek Post-Fire Erosion Mitigation Policy through Spatial Analysis. Polish J Environ Stud [Internet]. 2009;18(5).
  49. 49. Fernández C, Vega JA, Vieira DCS. Assessing soil erosion after fire and rehabilitation treatments in NW Spain: Performance of rusle and revised Morgan–Morgan–Finney models. L Degrad Dev. 2010 Jan 1;21(1):58–67. Available from:
  50. 50. Vieira DCS, Prats SA, Nunes JP, Shakesby RA, Coelho COA, Keizer JJ. Modelling runoff and erosion, and their mitigation, in burned Portuguese forest using the revised Morgan–Morgan–Finney model. For Ecol Manage. 2014;314:150–65. Available from:
  51. 51. Larsen IJ, MacDonald LH. Predicting postfire sediment yields at the hillslope scale: Testing RUSLE and Disturbed WEPP. Water Resour Res. 2007 Nov 1;43(11). Available from:
  52. 52. Wu J, Baartman JEM, Nunes JP. Testing the impacts of wildfire on hydrological and sediment response using the OpenLISEM model. Part 2: Analyzing the effects of storm return period and extreme events. CATENA. 2021;207:105620. Available from:
  53. 53. Rengers FK, McGuire LA, Kean JW, Staley DM, Dobre M, Robichaud PR, et al. Movement of Sediment Through a Burned Landscape: Sediment Volume Observations and Model Comparisons in the San Gabriel Mountains, California, USA. J Geophys Res Earth Surf. 2021 Jul 1;126(7):e2020JF006053. Available from:
  54. 54. Ferreira R V, Serpa D, Machado AI, Rodríguez-Blanco ML, Santos LF, Taboada-Castro MT, et al. Short-term nitrogen losses by overland flow in a recently burnt forest area in north-central Portugal: A study at micro-plot scale. Sci Total Environ. 2016;572:1281–8. Available from: pmid:26765507
  55. 55. Ferreira R V, Serpa D, Cerqueira MA, Keizer JJ. Short-time phosphorus losses by overland flow in burnt pine and eucalypt plantations in north-central Portugal: A study at micro-plot scale. Sci Total Environ. 2016;551–552:631–9. Available from: pmid:26897406
  56. 56. Bellot J, Bonet A, Sanchez JR, Chirino E. Likely effects of land use changes on the runoff and aquifer recharge in a semiarid landscape using a hydrological model. Landsc Urban Plan. 2001;55(1):41–53. Available from:
  57. 57. Robichaud PR. Fire effects on infiltration rates after prescribed fire in Northern Rocky Mountain forests, USA. J Hydrol. 2000;231:220–9. Available from:
  58. 58. Robichaud P, Elliot W, Pierson F, Hall D, Moffet C, Ashmun L. Erosion Risk Management Tool (ERMiT) User Manual. 2007.
  59. 59. Elliot WJ, Hall DE, Scheele DL. Disturbed WEPP: WEPP interface for Disturbed Forest and Range Runoff, Erosion and Sediment Delivery. 2002. 2007.
  60. 60. Rulli MC, Rosso R. Modeling catchment erosion after wildfires in the San Gabriel Mountains of southern California. Geophys Res Lett. 2005 Oct 1;32(19). Available from:
  61. 61. Rulli MC, Rosso R. Hydrologic response of upland catchments to wildfires. Adv Water Resour. 2007;30(10):2072–86. Available from:
  62. 62. Langhans C, Smith HG, Chong DMO, Nyman P, Lane PNJ, Sheridan GJ. A model for assessing water quality risk in catchments prone to wildfire. J Hydrol. 2016;534:407–26. Available from:
  63. 63. Argentiero I, Ricci GF, Elia M, D’Este M, Giannico V, Ronco F V, et al. Combining Methods to Estimate Post-Fire Soil Erosion Using Remote Sensing Data [Internet]. Vol. 12, Forests. 2021. Available from:
  64. 64. Papathanasiou C, Makropoulos C, Mimikou M. Hydrological modelling for flood forecasting: Calibrating the post-fire initial conditions. J Hydrol. 2015;529:1838–50. Available from:
  65. 65. Beeson PC, Martens SN, Breshears DD. Simulating overland flow following wildfire: mapping vulnerability to landscape disturbance. Hydrol Process. 2001 Oct 30;15(15):2917–30. Available from:
  66. 66. Wu J, Nunes JP, Baartman JEM, Faúndez Urbina CA. Testing the impacts of wildfire on hydrological and sediment response using the OpenLISEM model. Part 1: Calibration and evaluation for a burned Mediterranean forest catchment. CATENA. 2021;207:105658. Available from:
  67. 67. Fernández C, Vega JA. Evaluation of the rusle and disturbed wepp erosion models for predicting soil loss in the first year after wildfire in NW Spain. Environ Res. 2018;165:279–85. Available from: pmid:29734029
  68. 68. Fernández C, Vega JA. Evaluation of RUSLE and PESERA models for predicting soil erosion losses in the first year after wildfire in NW Spain. Geoderma. 2016;273:64–72. Available from:
  69. 69. Parson A, Robichaud PR, Lewis SA, Napper C, Clark JT. Field guide for mapping post-fire soil burn severity. Gen Tech Rep RMRS-GTR-243 Fort Collins, CO US Dep Agric For Serv Rocky Mt Res Station 49 p [Internet]. 2010;243.
  70. 70. Vega Hidalgo JA, Fontúrbel Lliteras M, Fernández Filgueira C, Arellano Díaz A, Díaz Raviña M, Carballas T, et al. Acciones urgentes contra la erosión en áreas forestales quemadas. Guía para su planificación en Galicia. 2013;
  71. 71. Key CH, Benson NC. Landscape assessment (LA). In: Lutes, Duncan C.; Keane, Robert E.; Caratti, John F.; Key, Carl H.; Benson, Nathan C.; Sutherland Steve; Gangi, Larry J. 2006. FIREMON: Fire effects monitoring and inventory system. Gen. Tech. Rep. RMRS-GTR-164-CD. Vol. 164. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. p. LA-1-55; 2006.
  72. 72. Fox DM, Laaroussi Y, Malkinson LD, Maselli F, Andrieu J, Bottai L, et al. POSTFIRE: A model to map forest fire burn scar and estimate runoff and soil erosion risks. Remote Sens Appl Soc Environ. 2016;4:83–91. Available from:
  73. 73. Psomiadis E, Soulis KX, Efthimiou N. Using SCS-CN and Earth Observation for the Comparative Assessment of the Hydrological Effect of Gradual and Abrupt Spatiotemporal Land Cover Changes [Internet]. Vol. 12, Water. 2020. Available from:
  74. 74. Wu J, Baartman JEM, Nunes JP. Comparing the impacts of wildfire and meteorological variability on hydrological and erosion responses in a Mediterranean catchment. L Degrad Dev. 2021 Jan 30;32(2):640–53. Available from:
  75. 75. Cerdá A, Doerr SH. Influence of vegetation recovery on soil hydrology and erodibility following fire: an 11-year investigation. Int J Wildl Fire. 2005;14(4):423–37. Available from:
  76. 76. Moreira F, Ferreira A, Abrantes N, Catry F, Fernandes P, Roxo L, et al. Occurrence of native and exotic invasive trees in burned pine and eucalypt plantations: Implications for post-fire forest conversion. Ecol Eng. 2013;58:296–302. Available from:
  77. 77. Maia P, Pausas JG, Arcenegui V, Guerrero C, Pérez-Bejarano A, Mataix-Solera J, et al. Wildfire effects on the soil seed bank of a maritime pine stand—The importance of fire severity. Geoderma. 2012;191:80–8. Available from:
  78. 78. Robichaud PR, Elliot WJ, Pierson FB, Hall DE, Moffet CA. Predicting postfire erosion and mitigation effectiveness with a web-based probabilistic erosion model. CATENA. 2007;71(2):229–41. Available from:
  79. 79. Bladon KD, Emelko MB, Silins U, Stone M. Wildfire and the Future of Water Supply. Environ Sci Technol. 2014 Aug 19;48(16):8936–43. Available from: pmid:25007310
  80. 80. Stone M, Emelko MB, Droppo IG, Silins U. Biostabilization and erodibility of cohesive sediment deposits in wildfire-affected streams. Water Res. 2011;45(2):521–34. Available from: pmid:20970822
  81. 81. Murphy BP, Czuba JA, Belmont P. Post-wildfire sediment cascades: A modeling framework linking debris flow generation and network-scale sediment routing. Earth Surf Process Landforms. 2019 Sep 15;44(11):2126–40. Available from:
  82. 82. Srivastava A, Wu JQ, Elliot WJ, Brooks ES, Flanagan DC. A Simulation Study to Estimate Effects of Wildfire and Forest Management on Hydrology and Sediment in a Forested Watershed, Northwestern U.S. Trans ASABE. 2018;61(5):1579–601. Available from:
  83. 83. Lane PNJ, Feikema PM, Sherwin CB, Peel MC, Freebairn AC. Modelling the long term water yield impact of wildfire and other forest disturbance in Eucalypt forests. Environ Model Softw. 2010;25(4):467–78. Available from:
  84. 84. Chafer CJ, Santín C, Doerr SH. Modelling and quantifying the spatial distribution of post-wildfire ash loads. Int J Wildl Fire. 2016;25(2):249–55. Available from:
  85. 85. Rumpel C, Ba A, Darboux F, Chaplot V, Planchon O. Erosion budget and process selectivity of black carbon at meter scale. Geoderma. 2009;154(1):131–7. Available from:
  86. 86. Bodí MB, Martin DA, Balfour VN, Santín C, Doerr SH, Pereira P, et al. Wildland fire ash: Production, composition and eco-hydro-geomorphic effects. Earth-Science Rev. 2014;130:103–27. Available from:
  87. 87. Pastor AV, Nunes JP, Ciampalini R, Koopmans M, Baartman J, Huard F, et al. Projecting Future Impacts of Global Change Including Fires on Soil Erosion to Anticipate Better Land Management in the Forests of NW Portugal. Water. 2019 Dec 11;11(12):2617. Available from:
  88. 88. Parente J, Girona-García A, Lopes AR, Keizer JJ, Vieira DCS. Post-fire Soil Erosion Risk in Portugal–Prediction, Validation and Uncertainties of a Nation-wide MMF Application. Sci Rep. 2021; Available from:
  89. 89. Miller WW, Johnson DE, Loupe TM, Sedinger JS, Carroll EM, Murphy JH, et al. Nutrients flow from runoff at burned forest site in Lake Tahoe Basin. Calif Agric. 2006 Apr 1;60(2):65–71. Available from:
  90. 90. Santos F, Wymore AS, Jackson BK, Sullivan SMP, McDowell WH, Berhe AA. Fire severity, time since fire, and site-level characteristics influence streamwater chemistry at baseflow conditions in catchments of the Sierra Nevada, California, USA. Fire Ecol. 2019;15(1):3. Available from: