Modelling Vulnerability to Severe Weather

We present a spatial analysis of weather related fire brigade operations in Berlin. By comparing operation occurrences to insured losses for a set of severe weather events we demonstrate the representativeness and usefulness of such data in the analysis of weather impacts on local scales. We investigate factors influencing the local rate of operation occurrence. While depending on multiple factors – which are often not available – we focus on publicly available quantities. These include topographic features, land use information based on satellite data and information on urban structure based on data from the 10 open street map project. After identifying suitable predictors such as housing density or local density of the road network we set-up a statistical model to be able to predict local operation densities. Such model can be used to determine potential “hotspots” for weather impacts even in areas or cities where no systematic records are available and can thus serve as a basis for a broad range of tools or applications in emergency management and planning. 15


Introduction
It has been stated within the Sendai Framework for Disaster Risk Reduction 2015-2030 by the United Nations (UNISDR, 2015) that the implementation of effective disaster risk reduction measures should be based on an understanding of disaster risks, including all its dimensions of vulnerability, capacity, exposure of persons and assets, hazard characteristics and the environment.On local and national levels, this requires to systematically evaluate, record, share and publicly account for disaster losses to gain understanding of the impacts in the context of event-specific hazard, exposure and vulnerability information.
While insurance records are a very useful data source and have been used in many analyses of regional weather impacts, their availability is generally limited due to economic interests of insurance providers.Making use of records of local emergency managers (first responders) yields an immense potential as an alternative database for analysing weather impacts, particularly on local scales.While often such records exist, they mostly lack systematic and homogenous data format and quality standards.
Definition of such data standards must be regarded key requisite to be able to scientifically address disaster losses as required within the Sendai Framework.
Relating emergency call data to extreme weather, most studies analyse ambulance callout data or emergency department visits in face of temperature extremes, in particular extreme heat (Bassil et al., (2005); Dolney and Sheridan (2008); Schaffer et al.  2014)).Wargon et al. 2009 have done a review on studies concerned with the modelling and forecasting of emergency department visits.It is found that the number of patient visits at emergency departments or walk-in clinics can be modelled with rather good performance.Mostly based on predictors such as the day of the week or season these models explain between 31% and 75% of patient-volume variability.However including meteorological data apparently failed to improve model performance (Wargon et al. 2009).Findings of more recent studies however do find that weather factors such as temperature and humidity play a role in the demand for ambulance services and demonstrate that including weather forecast data can in fact improve forecasts of daily ambulance demand (Wong and Lai (2014)).
There have been only few studies making use of spatial information of emergency callout data (i.e. the location of an assistance request) in relation to severe weather events.Two studies by Schuster et al. (2005) and Rossi et al. (2013) compared emergency call data with radar reflectivity data for a severe hailstorm event and found a satisfying representation of the hailstorm path in the density of emergency calls on the ground.Other studies tried to utilize similar data, however facing problems concerning the availability of accurate data.Busch (2008) reports of problems that in case of catastrophic events, the archival of fire callout data is often severely limited by fire departments.In particular, this means that spatial information on the individual location of callouts is not archived, hindering spatial analyses for these events.Pardowitz and Göber (2016) have demonstrated -similar to the studies mentioned above-that satisfying correspondence of radar reflectivity for severe thunderstorm events and locations of emergency calls can be found.However, the occurrence of emergency is strongly modulated by other factors such as the density of buildings.This is a confirmation of the common understanding, that the occurrence and the height of impacts is determined by the simultaneous existence of a hazard and vulnerability against this hazard.
Based on a dataset of fire brigade callouts in Berlin for the period of 2002-2012, this study aims at assessing the latter, namely the vulnerability against hydro-meteorological hazards.Within the metropolitan area of Berlin, we aim to identify factors describing the local vulnerability and thus influencing the local risk for weather impacts as given by the fire brigade callouts.
Potential factors include topographic features, land use information based on satellite data and information on urban structure based on data from the open street map project.After identifying suitable predictors such as housing density or local density of the road network we set-up a statistical model to be able to predict local operation densities.Such model can be used to determine potential "hotspots" for weather impacts even in areas or cities where no systematic records are available.
Similar approaches to address the local vulnerability have been developed in flood impact modelling.Apel et al. (2009) and Jongman et al. (2012) describe different modelling approaches to estimate economic damages for flood events (particularly the 2002 flood event in Saxony).Based on data from digital elevation models (DEM), local damages are estimated in dependence of inundation depth.Furthermore, such depth-damage relation can be differentiated -e.g. by considering information on land use-to account for variable vulnerabilities.
The remainder of this paper is structured as follows.Section 2 describes the various datasets that are used to describe impacts as well as potential predictors for vulnerability.Methodological steps and modelling approaches are described in Section 3, while results are shown in Section 4. Finally, Section 5 provides a discussion of results as well as the major conclusions that can be drawn from this study.

Fire brigade operations
A dataset provided by the Berlin fire brigade is analysed, comprising weather related fire brigade operations for the period 2002-2011.The dataset contains location and time of alerts, as well as keywords associated to each operation indicating the type of operation.Keywords indicate "water-related" operations, "tree-related" operations, "traffic obstructions", operations related to "construction elements", operations due to "ice and snow" and few operations associated to other keywords.Total counts of weather related fire brigade operations in the period 2002-2011 accounted to slightly above 10.000 per year.This is about 27% of all operations of the Berlin fire brigade, which -according to the annual reports-accounted to about 37.000 operations per year in the same period.In comparison, fire extinction operations (about 7.500 per year) accounted for about 20% of all operations.Note that ambulance call outs (~245.000per year) and false alarms (~31.000 per year) have been disregarded here.Most weather-related operations are due to water damages (33%), traffic obstruction account for 25% of operations and tree related for about 17% (compare Table 1).Operations related to construction elements accounted for about 14% and Ice and snow related operations for 2%, which naturally occur exclusively in winter.Some other keywords (individually accounting for 1% or less each) have been used which sum up to about 8%.Stratifying by season shows that in total, operations are equally distributed over winter and summer half year.The individual types of operations however partly show distinct differences in summer and winter (compare Table 1).Particularly tree related operations occur mainly in summer (73%) while ice and snow related operations naturally occur in winter exclusively.

Building Loss Data
Insurance data on losses to residential buildings were provided by the German insurance association (Gesamtverband der Deutschen Versicherungswirtschaft e.V., GDV).Berlin-wide damages are available on daily basis for the period 1997-2011, while data on zip code level (190 within Berlin) is available for selected events only.While meteorological station measurements provide pointwise measurements, the dataset comprises area-wide coverage of losses due to windstorm and thunderstorm.For investigations of severe weather events -particularly small-scale events such as thunderstorms-insurance loss data is thus extremely valuable.However, difficulties arise when interpreting the insurance data since the dataset does not allow a direct attribution of losses to their cause (i.e.hail or wind-storm induced).In addition, faulty attribution of individual insurance claims (both temporal and spatial) can cause inaccurate loss figures.E.g., this can be because the exact day of occurrence of a damage is unknown in some cases.In addition, if damage occurs at a house administered by a real estate management, the insurance claim might be attributed according to their administrative centre instead of the actual origin.For a set of events, including 3 convectively driven summer events and 2 winter storm events the number of insurance claims for individual zip code areas are evaluated.Also to address temporal correlations to the occurrence of fire-brigade operations berlin-wide losses (in €) are analysed.According to the insurance loss records (covering storm and hail damages), 8.12 Mio € damages are recorded for Berlin per year.The temporal distribution is rather balanced with 48% of damages occurring in summer and 52% in winter.While most damages in winter are related to intense winter windstorms, summer damages are mostly due to thunderstorms, particularly hailfall (Donat et al., 2011;Gerstengarbe et al., 2013).

Open Street Map (OSM) Data
Data from the open source project OpenStreetMap (OSM, www.openstreetmap.org)are used to derive predictors for local vulnerability.Particularly we analyse georeferenced information on individual buildings (including their location and extent) as well as information on road networks.As a first predictor, the number of houses per grid cell on a regular 1x1km grid is derived.Also, by including information on housing extent, the fraction of the grid cell covered by buildings is calculated.As discussed later, even though these quantities are highly correlated both predictors are valuable to be considered since enabling the distinction between high density city centre with very large buildings in comparison to suburban areas with high numbers of detached houses.As a different quantity, the density of the road networks is considered by calculating the total length of road segments within a 1x1km grid cell (specified as a length per grid cell area, thus km/km²).The OSM dataset contains a classification of the road networks (the major categories being highway, primary, secondary and tertiary road networks), which is why road densities can be assessed individually for these classes.

CORINE land cover data
The CORINE (Coordination of Information on the Environment) land cover (CLC) data set provides European-wide information on land cover and land use, based on a unified classification of the most important types of land usage (CEC, 1994;Bossard et al., 2000, Büttner et al., 2012).More specifically, we used CLC2006, which is based on SPOT-4/5 and IRS P6 LISS III satellite data.Geometric accuracy of satellite images is specified to be smaller than 25m and resulting mapping unit (width) within CLC is specified to be 25 ha (100m).In total, 44 land usage classes are used in CLC2006 being subcategories of the main land usage types "Artificial surfaces", "Agricultural areas", "Forest and semi natural areas", "Wetlands" and "Water bodies".More details on CLC2006 can be found in Büttner et al. (2012).The original data consists of polygon data in form of shape files, which have been processed to calculate land use characteristics on a grid-point basis.For this, the area fractions of all 44 CLC types (adding up to 100%) are calculated on a specified grid.Here we use a regular lonlat grid with a 1x1km resolution.These gridded fields of the area fraction are then used as predictors in the following analyses.

Data from digital elevation model
Data from the digital elevation model dgm200 (GeoBasis-DE / BKG 2016) as well is used to derive orographic height and slope.Original data has a horizontal resolution of 200m and is available for the territory of Germany.Alternatively, GTOPO30 has been used which has a lower horizontal resolution of 30 arc seconds (approximately 1km).However, GTOPO30 is available globally.The data is used to derive orographic height as well as the slope on a regular 1x1km grid for Germany.In case of dgm200 which has a finer resolution compared to the target grid, orographic height is calculated as the average height over all original 200m x 200m grid boxes within a target grid box.In case of GTOPO30 orographic height on the target grid is determined by means of a nearest neighbour remapping.Since differences for the Berlin region were negligible, dgm200 has been used in the following.The slope is calculated according to the algorithm proposed by Horn (1981).The algorithm also assesses the aspect, which -in further studies-might be considered as an additional vulnerability predictor.However, since the area for which the vulnerabilities are analyses is limited to Berlin (featuring no considerable height variations), topographic features play only a minor role and do not play a major role here.However, in future studies including other investigation areas, topographic features may be more important to consider.

Comparison of fire brigade operations and building damage data
To assess in how far representative spatial information on weather impacts on a sub-city scale can be derived from the callout dataset and in how far there is a temporal correspondence between daily damages and callout numbers, a comparison of building loss data and fire brigade callouts is performed.For a set of events, including 3 convectively driven summer events and 2 winter storm events, a qualitative and quantitative comparison is performed between the spatial patterns of building damages and the occurrence of fire brigade operations.This is done by calculating total callout count numbers for zip code areas (190 within Berlin) for each of the 5 events.Besides total callout numbers, counts for callouts related to individual keywords are assessed.Resulting maps are compared and spatial correlations calculated.Daily total callout counts for Berlin are furthermore compared to Berlin-wide damages, which are available on daily level for the period of 2002-2011.Temporal correlations to daily building damages are calculated, again for both total count and counts for callouts related to individual keywords.

Spatial correlation analysis with potential vulnerability predictors
To identify potential vulnerability predictors, a spatial correlation analysis between numerous quantities derived from the different geospatial data sets and gridded operation densities is performed.Variables include gridded densities of man-made structures (buildings, streets), topographic features (height, slope) as well as land use information.The latter are pre-processed such that the area fraction of a specific land use type (as specified in the CORINE data set) within each 1x1km grid box is given.Again, spatial correlations are assessed using either operations of one specific category or operations irrespective of their type.

Multiple linear regression model
Having identified a set of potential vulnerability predictors, a statistical model is set up based on multiple linear regression to analyse the predictability of the spatial distribution of (long-term) operation occurrence rates.Such model could potentially be To provide robust results and prevent overfitting of the data, an appropriate subset of variables need to be chosen from the set of potential predictors.To do so we chose an iterative procedure which -starting from an initial model-stepwise removes or adds predictor variables.Which predictor to add to (or remove from) the list of predictors in the model is decided in each iterative step by maximization of the Akaike information criterion (AIC, compare Akaike, 1985).The basic idea is to assume a certain penalty for each (additional) predictor within the model.This penalty needs to be balanced to the resulting goodness of the model fit (e.g. by means of R²) leading to an optimization problem between the total penalty and fit quality.The algorithm converges if no predictor can be added or removed to further optimise the model in terms of the AIC.To perform this optimization procedure, the weight of the penalty can be varied by means of the parameter k.While k=2 corresponds to the classical AIC criterion, higher k result in an increased penalty for additional predictors.Different choices of k will ultimately lead to different optimized models including more (less) predictor variables if k is lower (higher).

Model validation methodology
To assess the predictive skill of the optimized model a cross validation is set up.For this, the investigation area berlin is divided into four sectors.Then the model -using the set of predictors identified by means of the iterative procedure described aboveis fitted four times, each time using all grid points within three of the four sectors.Each model fit is then used for predicting the operation density for grid points within the fourth sector.In this way predictions are obtained for data that has not been used for model fitting.Calculating the mean squared error of these model predictions in comparison to observed callout density values results in the cross-validation error, which is used as the criterion for predictive model skill.The optimal choice of k in the iterative optimization procedure described above is not known up front.Different choices of k lead to differing number of predictor variables.Thus, to find the optimal model for predictive purposes, we vary k and compare the predictive model skill of the resulting models.The optimal choice is found by maximising the predictive model skill i.e. minimising the cross validation error.

Comparison of fire brigade operations and building damage data
Daily operations counts in the period 2002-2011 for the whole of Berlin are correlated to daily building losses in Berlin.
Correlations are calculated for total operation counts, as well as counts for operations associated with individual alert keywords, additionally stratified by season.Highest correlations are found between tree related operations and building damages, particularly in winter (0.74).In addition, operations associated to the alert keyword "construction element" show rather high correlations to building damages (0.67).In both cases, winter correlations are higher which indicates that a large share of these operations are caused by severe wind gusts, coinciding with roofing damages or other wind-caused building damages.Counts of water damage operations in summer do not show any correlation to building losses, which is due to the fact, that flooding damages are not contained in the insurance data set available.In winter however, considerable correlation is found (0.41).It can be assumed that this correlation is because water related operations in winter often occur in conjunction with large-scale storm events.Correlating tree-related and water-related operations gives further weight to this assumption.While correlation is considerable in winter (0.25), there is low correlation in summer (0.08).Similar results are found correlating operations related to the keyword "construction elements" and water-related operations.Thus operations caused by severe winds (treerelated and construction elements) and water-related operations seem to occur mostly independently in summer, while in winter they seem to coincide more often.However, the low correlation between summer damages and water-related operations is still surprising.The fact that flooding damages to housing are not included in the loss dataset obviously leads to a non-existing correlation, if regarding only effects due to rainfall.Because thunderstorm events are often related to severe precipitation and in some case to hail would suggest a certain correlation between hail-induced building damages and water-related operations in summer.The fact that no correlation is found, might in turn indicate that either hailfall is sufficiently rare to make up for a significant effect or, that hailfall impacts do not play a major role for the occurrence of operations.Spatial patterns of insured losses and operation occurrences were compared for a set of 3 winter storm events (Kyrill, Emma and Xynthia) and 3 convectively driven summer events (Aram and Gunnar and Lothar07).A visual comparison of impacts for the winter storm Kyrill (2007/01/17-2007/01/19) and the thunderstorms related to the frontal passage of Gunnar (2011/06/22) can be found in Figure 1.In general, a rather good agreement in the patterns of the number of operations per zip code area and the number of insurance claims.For Kyrill, both datasets show considerably higher impacts in the south of Berlin, while central and some northern parts of Berlin featured lower impacts.It can be argued that there is an influence of the size of areas that is not homogeneous (and particularly large areas are found in the south, while particularly small areas in central Berlin).However considering relative numbers (normalizing for the zip code area) did not alter the qualitative findings.For the thunderstorms related to the frontal passage of Gunnar, spatial patterns also show considerable agreement.Affected areas are considerably larger when considering fire brigade operations, while building damages are more concentrated on individual zip code areas.
This might be related to hailfall that might have occurred localised leading to localised occurrence of building damages, while precipitation and wind gusts occurred more widespread leading to water-related operations and wind induced treefall in larger areas.A spatial correlation analysis is performed, correlating the number of insurance claims and the number of operations within each zip code area.It has been tested, that using different quantities (e.g.damage ratio and normalized operation densities) does not qualitatively influence the correlations.Also, it needs to be kept in mind that these spatial correlations are evaluated only for individual events, which may thus not be generalized.Resulting spatial correlations for the 6 events are given in Table 2. Most prominently, significant correlations are found for tree-related operations in relation to building damages.This might affirm that tree-related operations mostly represent wind-induced treefall, which relates directly to wind-induced building damages.For some events (Kyrill, Lothar07 and Aram) considerable correlation for water related operations while for the others there is no correlation at all.While no direct water induced damages are included in loss dataset, there might be an indirect relation.Areas of severe precipitation coincide with hailfall which themselves induce damages.For Lothar07 and Aram there are confirmed hail observations in berlin or its surrounding.For Kyrill a study indicates that there has been thunderstorm activity during the frontal passage, which might have been related to hailfall (Fink et al., 2009).The authors also note, that the severe precipitation could have increased damages.This might in turn explain why for Kyrill, Lothar07 and Aram, correlations for tree-related operations and building damages are particularly high.
Thus, it shows that the relationship is far from being an identity between building damages and fire brigade operations.While considerable agreement in spatial patterns can be found, it seems to be an interplay between different meteorological variables (severe gusts, precipitation and hail) leading to the various impacts.Additional factors might distort the relationship between insured damages and operations.These include the fact that in case of major events both insurers as well as emergency services might alter their usual procedural strategies.For instance, insurances relinquish detailed plausibility checks for individual damage reports in case of cumulative loss events.Also, emergency services request the public to handle non-life-threatening damages by themselves in certain situations to relieve workload for first responders.Both might have been the case considering that for the event Kyrill an immensely high insured loss is recorded (particularly in comparison with the other events, nearly factor of 10 in comparison to Lothar07), while the number of fire brigade operations is not that extraordinarily high (comparable to the event Lothar07).

Spatial correlation analysis with potential vulnerability predictors
Patterns of average operation densities per km² per year are calculated on a 1x1km grid (Figure 2).Considering all operations (Figure 2a), distinct spatial variations can be observed.In general, high densities are found in central areas of Berlin, while outskirts feature low densities.However, numerous additional spatial variations can be found, such as particularly low callout densities in less densely (or unsettled) areas such as the "Grunewald" and areas in the south-east of Berlin.But also for central parts of Berlin, distinct local minima in operation densities are found, e.g. for the "Tierpark" or the former airport "Tempelhof".
Considering individual alert keywords shows that patterns of the spatial densities of operations considerably vary.While waterrelated operations show a rather similar spatial pattern compared with all operations, operations related to traffic-obstructions or treefall are distributed rather different.Both are distributed more broadly over the area of Berlin, not featuring the distinct concentration on the centre.Furthermore, for operations related to traffic obstructions a concentration of emergency operations near important junctions can be found (compare Figure 2c).For tree related operations, it seems that maxima of operation occurrence are not found in forest areas themselves but rather at their borders with housing areas (e.g.compare the border areas of the "Grunewald" in Figure 2d).This is not unexpected since major impacts due to treefall is not expected in wooden Examples for the spatial patterns of potential predictors for vulnerability are given in Figure 3.Even though building density (shown in Figure 3a) and building coverage (Figure 3b) are based on the same data (i.e.individual housing information as derived from open street map), different information can be extracted.While building density is calculated as the number of houses per square kilometre, building coverage assesses the area fraction covered by buildings.Hence, building density is particularly high in suburban areas with numerous small houses while building coverage is highest in central areas with concentrated large buildings.Similarly, information on the density of the road network can be derived from Open Street Map (compare Figure 3c).Additional predictor variables from the CORINE land cover dataset are assessed, by calculating the fraction of a grid box that is covered by areas of a specific CORINE land use type (compare Figure 3d).Again, quite different information can be gathered, e.g. when considering the different land use types encoded in CORINE.Finally, with respect to the aim of modelling local vulnerabilities, the characteristics of the local urban structure can be described on the basis of not only one but instead multiple of these predictor variables.
For the predictor variables listed in Table 3 the spatial correlation to the gridded operations densities is calculated.For this, only grid points within Berlin are considered for which data on operations is available.Furthermore, correlation is assessed for individual alert keywords, as well as considering all operations.Resulting correlations are listed in Table 3, with colours indicating positive correlation (in red) and negative correlations (in blue).Several predictor variables stand out in this table, in particular the building coverage and the area fraction of continuous urban fabric, which have high correlations with spatial patterns of operations disregarding their alert keyword.One exception are tree-related operations for which correlation to both building coverage and area fraction of continuous urban fabric are considerably lower.Instead, in this case correlations are rather high for building density and the area fraction of discontinuous urban fabric.It can thus be deduced, that except for treerelated operations the degree of urbanisation (both expressed by the area coverage of housing and indicated by continuous urban fabric areas) plays a crucial role.Both variables can be interpreted as a proxy for the number of "objects" at harm (e.g. the number of basements or drainage systems in case of water related emergencies).For tree-related operations, the picture is quite different however.Operation densities are particularly high in areas of discontinuous urban fabric, and seem to be enhanced in areas of high building densities (i.e. the number of houses per km²).Both indicates, that tree-related operations are more likely in less densely covered urban areas, where assumedly more roadside trees or trees as part of recreational areas can be found in close vicinity to building structures.Considering area fraction of wooded areas (particularly coniferous forests), negative correlations with operations of all alert keywords are found.This can be explained by the fact, that this variable is essentially inverted to areas with a high fraction covered by urban structures.The fact that this also holds for the correlation with tree-related operations again indicates that it is not forest areas that are prone to be impacted but areas in which trees are found in vicinity of man-made structures are particularly vulnerable.Considering the density of the road network it is found that positive correlation to the patterns of each individual alert keyword exist.Particularly this holds for the secondary and tertiary road network.A simple explanation for this is that areas in which a high density of secondary and tertiary roads exist mostly coincide with areas of high building coverage.Additionally, it can be found, that correlations of road density patterns are highest with respect to operations related to traffic obstruction.Obviously, this is due to the fact that traffic obstructions are more likely to occur in areas with a high density of roads.All the above-mentioned findings show, that even though there is no complete correspondence between individual predictors and the occurrence of operations, numerous predictors can be found explaining a considerable share of the spatial variability of weather impacts.This shall be exploited in the following, by building multivariate models to statistically describe the spatial patterns of operation occurrences.

Multi-variate modelling of the occurrence of fire brigade operations
The set of predictors described above is used to set up a multi-variate model to be able to predict the local occurrence rate of operations.As described in Section 3, the iterative procedure consists of the repeated application of a parameter selection algorithm while iteratively increasing the penalty for additional model parameters.The optimal model is then chosen by means of the cross-validation error (to prevent overfitting and estimate the predictive ability of the resulting model).The procedure is applied to fire brigade operations associated to individual alert keywords, as well as all operations together.For the latter, the resulting optimal model includes a set of 12 predictor variables (listed in Table 4), explaining 83% of the variance in the spatial pattern of operations.In accordance to the correlation analysis, the predictor "building coverage" possesses the highest contribution to the explained variance (EV=59%) while for other variables lower contributions are found (e.g."area fraction continuous urban fabric" contributes 11% and "building density" 6%).Of course, there is not a direct correspondence in the contribution to the EV and the correlation as listed in Table 4, since certain predictor variables are strongly correlated.By adding a predictor which is correlated to predictors already in the model, increase in model performance might be small even though the correlation to the predictant is high.The results described above apply to a basic linear model.Alternatively, the predictor selection methodology can be applied while using alternative models, i.e. a log-normal and a Poisson model.Results showed that in general predictive abilities of the statistical models (in terms of the cross validation error) are not increased (not shown).By means of the mean squared cross validation error (msvce), the linear models appears to perform best.However, the linear model suffers from the disadvantage of predicting negative values for the number of emergency calls in some cases, while both log-normal and Poisson model do not.While it can compare those models in terms of their predictive skill, it should be noted however, that as a result of the optimization procedure described above, each of the different models may contain a different set and even different number of predictors.In the following, results are shown using the linear model, performing best in terms of the predictive skill (assessed by means of the mscve).In comparison to the maps shown in Figure 2, Figure 4 shows model predictions for the average number of operations on a 1x1km grid cell.In the case considering all emergency operations (Figure 4a) or only water related (Figure 4b), the model nicely reproduces the concentration of operation occurrences in central parts of Berlin, while especially forest areas such as the Grunewald feature very low occurrence rates.In addition, the amplitude of this variation (ranging from 0 to about 80 operations per km² per year considering all operations) is well captured.Individual hotspots of high operation occurrence rates however are only partly reproduced.Particularly this is the case for a hotspot in the south-east of Berlin centre (compare Figure 2a and 2b, corresponding to northern parts of the district "Neukölln").It can be found that in this area particularly water related operations are very high.It is possible that this is influenced by an extraordinarily high population density in these areas, an information which is only partly (and indirectly) covered by predictor variables such as building coverage.Also, other factors such as housing conditions or very localized troughs (potentially leading to water accumulation in case of severe rain) might of course affect the occurrence of emergency operations.Such information however has not been addressed in this study since not available.In case of traffic-and tree-related operations, predicted patterns (shown in Figure 4 c and d) again reproduce observed patterns rather well, in both cases occurrence rates are not that focussed on central parts of Berlin.Particularly in case of tree related operations, model predictions show a rather homogeneous distribution over large parts of Berlin (Figure 4d), while local maxima in the observed operation density (Figure 2d) are poorly captured.Considering the explained variation (EV) for the different models, it is confirmed that for tree-related operations the predictive ability of the model is worst, with an EV of 53%.In comparison, the model for all operations has an explained variation of 83% (compare Table 4).

Conclusions and Discussion
A comparison of a new data set containing spatial and temporal information on emergency operations of the Berlin fire brigade with damage data has been performed.Spatial patterns can be derived and correspondences amongst both impact data sets can be found.However, a complex interplay of meteorological conditions leads to a variety of weather impacts, making it very hard to directly compare the datasets.Instead, the availability of both datasets might be considered as particularly valuable for the reconstructing the multifaceted impacts of severe weather events.
The relation to predictor variables for the structure of settlement as well as characteristics on land use has been addressed by means of an analysis of spatial correlations.Particularly the information on the local building coverage and shows a rather high influence on the occurrence of operations.Accordingly, areas classified as continuous urban fabric (within the CORINE land cover dataset) exhibit high rates of fire brigade operations.Analysing individual alert keywords, other variables turn out as valuable predictors.E.g. in case of traffic related operation these include the local density of the road network.In case of tree related operations particularly the areas classified as discontinuous urban fabric correlate with high occurrence rates.An interpretation for this is that in these areas a higher number of trees are present in the direct vicinity of man-made structures (e.g.roadside trees or trees in recreational areas).
Multi-variate modelling including an iterative prediction selection algorithm has been conducted, with resulting models being able to predict the local vulnerabilities.Evaluation of models showed moderate model performances for tree-related operation occurrences (explained variance of 53%), while for other types of operationsi.e.water related, traffic related or all operations combined-model results were better (explained variance of 70 -80%).In all cases, spatial patterns of operation occurrences can be reproduced well.Except for tree related operations, the amplitude of variations can also be reproduced.However, individual hotspots with high occurrence rates are only insufficiently predicted indicating that particular information influencing the local vulnerabilities are not included in the predictor variables available in this study.In case of water related operations these might for example include housing conditions.Also, information on local tree stocks, particularly in vicinity of vulnerable structures might be very valuable to better model tree-related operation occurrences.
The model has been developed and tested for the Berlin area, due to the availability of fire brigade operation records for Berlin.
Yet, model predictions can be derived for the whole of Germany.Such model predictions might be particularly valuable for regions with no systematic records on weather impacts.However, such extrapolation might suffer from potentially severe limitations.The occurrence of severe weather conditions are not homogenous over Germany, with storm frequencies being higher in northern regions and thunderstorm frequencies being higher in southern regions.Thus the distribution of hazards causing local impacts can differ considerably, which will certainly affect the occurrence of emergency operations.Such effects are excluded in the presented modelling approach, which assume a homogenous distribution of hazards.For the investigation are of Berlin this is certainly a valid assumption.Extracting model predictions for other urban areas might suffer from an offset in terms of absolute number of operations.Such model predictions can however still be very valuable since they can provide information on spatial variation in operation occurrences on a sub-city scale.Still, future work should include also meteorological/climatological information on different hazards, which will strongly influence local vulnerability and thus predicted weather impacts.
Nat. Hazards Earth Syst.Sci.Discuss., https://doi.org/10.5194/nhess-2017-326Manuscript under review for journal Nat.Hazards Earth Syst.Sci. Discussion started: 12 September 2017 c Author(s) 2017.CC BY 4.0 License.used to identify "hotspots" in the local occurrence of callouts in areas where no explicit data on operations is available and might be highly relevant in terms of long term planning of capacities for an effective emergency management.In the following, three different types of models area addressed.A linear model, a logarithmic variant (assuming a log-normal distribution of the predictant, modelling the logarithm of operation density) and a Poisson model (typically used to model count variables).
Nat. Hazards Earth Syst.Sci.Discuss., https://doi.org/10.5194/nhess-2017-326Manuscript under review for journal Nat.Hazards Earth Syst.Sci. Discussion started: 12 September 2017 c Author(s) 2017.CC BY 4.0 License.areas but particularly in areas where trees are present in the direct vicinity of man-made structures (e.g.roadside trees or trees in recreational areas).This implies that only in very few cases the modelling of vulnerabilities to (meteorological) hazards can be made in a univariate fashion.Instead, combinations of multiple factors will determine local vulnerability and consequently those need to be considered.

Figure 2 :
Figure 2: Mean yearly density of fire brigade operations during 2002-2011 calculated on a 1x1km grid [units: operations per km² per year].Operation recordings are available for Berlin only (boundaries are indicated by black solid lines), i.e. zero values outside of Berlin are due to unavailability of data.Note the different colouring scale due to the fact that the absolute numbers of operations for a certain operation type vary considerably.

Figure 4 :
Figure 4: Modelled mean yearly density of fire brigade operations [units: operations per km² per year].(a) Results are shown for the model including all operations disregarding their type (a), for water related operations (b), for traffic obstructions (c) and for tree related operations (d).