Wildfires are an integral component of many terrestrial biomes [1
], including Mediterranean ecosystems [2
]. It is estimated that every year, an average ~45,000 wildfires occur in the Mediterranean [3
], generating approximately 85% of the total burnt area (BA) of Europe [4
]. A proportion of these wildfires are associated with an increased risk of direct damages to humans and properties [5
], and adverse primary and secondary environmental impacts (e.g., soil erosion) [6
]. Such catastrophic wildfires have repeatedly affected the Mediterranean countries in recent decades [7
]. For instance, the wildfires that broke out in Portugal in 2003, caused the death of 21 people and induced damages estimated at over 1 billion Euro [8
], while most recently, in 2018, 103 people lost their lives in the deadliest wildfire in the modern history of Greece [9
]. The growing concern about wildfires and their adverse consequences is further supported by the increasing trends in both their frequency of occurrence and spatial extent, as a result of climate change [10
] and other anthropogenic factors, such as the expansion of the wildland-urban interface [11
To contain the socio-economic impacts of wildfires, policymakers, land managers, and operational firefighting agencies must rely on information related to wildfire spread and behavior [12
]. Such information is critically important for wildfire management, particularly with respect to evaluating the most effective tactical wildfire suppression options during the incident support phase [13
]. Numerical models, able to simulate the perimeter growth and behavior of wildfires, are thus considered to be a highly valuable tool.
The numerical modeling of wildfire spread and behavior dates back to the early 1970s when the first models were developed to understand observed incidents, predict wildfire spread, and evaluate the influence of varying environmental conditions on wildfire behavior [14
]. Today, the wide array of available models ranges from advanced 3D computational fluid dynamics and combustion models such as FIRETEC [15
] and WFDS (Wildland-Urban Interface Fire Dynamics Simulator) [16
] to empirical models such as FARSITE [17
] and BehavePlus [18
]. Each model is characterized by certain advantages and disadvantages, all related to computational demands, data requirements, accuracy, robustness, and transferability [19
From an operational point of view, the fundamental requirement is to achieve a balance between the physics and the overall complexity of a wildfire simulator, which ultimately affects its accuracy and computational cost. Purely physical models [15
] are considered to be among the most accurate, operating at very fine spatial resolutions and resolving the combustion processes. However, these models are also the most inappropriate for operational applications, as they demand a large computational effort that limits their implementation to small simulation domains or/and coarse representations of the landscape characteristics [20
]. Even if computational restrictions did not exist, the full exploitation of the capabilities of such models would require the availability of extremely high-resolution input data (e.g., fuel type and condition, topography), which are rarely available for real-time applications. On the other extreme, empirical and semi-empirical models [17
] are the most computationally efficient and have been shown to provide accurate wildfire spread predictions, even outside of the limits of their training datasets [21
]. In between, there exists the approach of coupling a numerical weather prediction (NWP) model with a 2D wildfire spread model. Coupled fire-atmosphere models are considered as an intermediate trade-off, providing the necessary balance between the realism of the represented physical processes and the computational demands for performing real-time predictions [22
While adapted to a real-time response, coupled fire-atmosphere models have been proven to be able to reproduce wildfire spread [22
] and smoke dispersion [31
], and even anticipate several dynamic, transient phenomena, such as convective plumes, fire-induced winds, and horizontal roll vortices [34
]. Nevertheless, such models are still not widely used in wildfire incident management. Instead, the common practice is to use an empirical model driven by external meteorological forcing [13
]. Although this is an efficient approach, it presents several limitations. For instance, the wind field driving wildfire spread is provided by an external meteorological model that typically operates at a spatial resolution that is insufficient for resolving local effects of topography. Finer resolution wind fields could be obtained by employing a diagnostic model based on mass conservation [41
], but this approach would not increase the temporal resolution of the primary meteorological forcing (typically 1 h). Most importantly, the use of the external meteorological forcing does not allow for considering the significant two-way interactions between the fire and the atmosphere (i.e., “the fire creates its own weather”).
In this work, we present the operational implementation of a rapid response fire spread forecasting system. Named IRIS, after a messenger goddess in Greek mythology, the forecasting system was developed to provide operational support to the Hellenic Fire Corps (HFC) and is currently, to the best of our knowledge, the first of its kind in the European Union (EU). The system was developed under the framework of the DISARM (Drought and fIre obServatory and eArly waRning systeM) project which used state-of-the-art observational and modeling techniques for building a common prevention and mitigation framework for the vulnerable region of the southeast Mediterranean [43
]. IRIS is based on the coupled fire-atmosphere WRF-Fire model [44
], properly adapted to account for the pyric environment of Greece [24
]. IRIS was operationally deployed in Greece during the 2019 fire season, providing real-time, on-demand wildfire spread predictions for 17 incidents in total. Satellite-based remote sensing data were employed for assessing the accuracy of the system’s predictions and evaluating its overall performance. Our overarching goal is to demonstrate that IRIS, a coupled fire-atmosphere modeling system, has great potential to effectively support tactical wildfire suppression planning through the provision of added-value forecast guidance. Compared to previous studies that employed coupled fire-atmosphere models, our work differs in the sense that it considers several events for providing a quantitative and qualitative performance evaluation of the model. Therefore, we believe that this study is one of the first, if not the first, to introduce a detailed accuracy assessment of an operational fire spread forecasting system that is based on coupled fire-atmosphere modeling.
4. Discussion and Conclusions
Coupled fire-atmosphere modeling systems are still not widely used in wildfire incident management. Instead, operational agencies and wildfire managers tend to rely on empirical and semi-empirical models that have proven to be able to provide reliable wildfire spread predictions [13
]. However, such models neglect fire-atmosphere feedbacks that can be of great importance for the accurate simulation of wildfire spread. In this work, we aim to demonstrate that coupled fire-atmosphere modeling systems have great potential to be operationally employed in wildfire incident management. For this, we present the operational implementation and evaluation of such a forecasting system, which was specifically developed and used for providing rapid response support to the tactical wildfire suppression activities of the HFC in Greece.
Named IRIS, the presented forecasting system is based on the coupled fire-atmosphere WRF-Fire model [44
]. The latter has been used in several past studies that focused primarily on simulating selected large wildfires [23
]. While providing useful insights into the model performance, previous studies hardly included any quantitative assessment. More importantly, the consideration of single events does not allow for drawing a wider picture in terms of the capacity of the model to support operational activities. Our work differs significantly from such an approach, presenting and evaluating, both quantitatively and qualitatively, wildfire spread predictions that have been produced and used operationally during the 2019 fire season in Greece. To the best of our knowledge, this should be one of the first, if not the first, studies that introduce the performance evaluation of an operational rapid response fire spread forecasting system based on a coupled fire-atmosphere modeling system.
In a previous study, Giannaros et al. [24
] showed that with proper adaptation, IRIS could be exploited for operationally supporting wildfire incident management. The present study confirms this preliminary conclusion. Our results, obtained through a quantitative verification of operational wildfire spread predictions for eight events, clearly highlight that IRIS is able to provide forecast guidance that is meaningful and useful to wildfire incident managers. In particular, the conducted accuracy assessment of the system’s predictions revealed an overall satisfactory performance (Table 2
). Actual and predicted wildfire perimeters were found to be characterized by moderate agreement, with the model overestimating BAs in all examined events (Figure 3
). As previously noted, this tendency of IRIS to overestimate BAs is not that surprising, considering that firefighting activities are neglected. Focusing on the largest event of the 2019 fire season, we also showed that IRIS was able to predict well enough the propagation of the wildfire (Figure 5
Besides the positives of IRIS’s operational performance in 2019, one should also notice and discuss the negatives, in particular with respect to the further development and improvement of the system. Focusing on the largest event of the 2019 fire season, it was found that the consideration of the actual ignition time instead of IRIS’s activation time does improve wildfire spread prediction (Figure 5
and Figure 6
). While this improvement may not always compensate for BA underprediction, it is important to be taken into account. IRIS was implemented operationally for the first time in 2019 and officers of the HFC needed time to familiarize themselves with its use and build experience on when to request its activation. Indeed, the time difference between the system’s activation and the actual ignition time was gradually reduced towards the end of the fire season (Table 1
). Accounting for this, and based on the results of this work, it was decided that for the operational implementation of IRIS in 2020, the system’s activation time will be adjusted by 30 min; i.e., 30 min will be subtracted by the time of IRIS’s activation to better estimate the actual ignition time.
Other sources of uncertainty in IRIS’s predictions are related to the simulation of meteorological conditions and the representation of fuels and fuel moisture. Considering the former, our work has shown that special attention should be paid to properly simulate wind conditions. As for the representation of fuels, the prototype fuel map used by IRIS [24
] is currently being updated and upgraded; new fuel models are being added and the horizontal resolution is being increased, so as to better represent spatial variability. Last but not least, the development of IRIS is continued by examining the inclusion of fire spotting and the online computation of fuel moisture, as well as by increasing the system’s spatial resolution in order to better capture the dynamic fire-atmosphere interactions.
In conclusion, the operational implementation of IRIS during the 2019 fire season in Greece and its subsequent performance evaluation revealed that coupled fire-atmosphere modeling systems do have great potential to support wildfire incident management. Our statement is not only supported by the results of this study, but also by the fact that IRIS’s activations almost doubled in 2020. IRIS was activated 17 times by the HFC in 2019, and 31 times during the ongoing 2020 fire season. As far as we can be aware, IRIS is currently the sole coupled fire-atmosphere modeling system implemented operationally within the European Union, and certainly one of the very few such operational systems implemented worldwide [23