Nat Hazards (2012) 64:821–838 DOI 10.1007/s11069-012-0273-7 ORIGINAL PAPER
Development of python-FALL3D: a modified procedure for modelling volcanic ash dispersal in the Asia-Pacific region A. N. Bear-Crozier • Nugraha Kartadinata Anjar Heriwaseso • Ole Nielsen
•
Received: 4 March 2012 / Accepted: 20 June 2012 / Published online: 15 July 2012 Ó Crown Copyright 2012
Abstract Volcanic ash is the most widespread of all volcanic hazards and has the potential to affect hundreds of thousands, or even millions, of people in the densely populated islands of Indonesia. There is limited information available for this region on the hazard posed by volcanic ash, particularly from volcanoes that have not erupted in recent times. There is a need for computational models capable of accurately predicting volcanic ash dispersal at ground level when coupled with field observations of historical or ongoing eruptive activity. To maximise the effectiveness of such models, they should be readily accessible, easy to use and well tested. Geoscience Australia in collaboration with the Australia-Indonesia Facility for Disaster Reduction and the Indonesian Centre for Volcanology and Geohazard Mitigation has collaboratively adapted an existing open-source volcanic ash dispersion model for use in Indonesia. The core model is the widely used, open-source volcanic ash dispersion model FALL3D. A Python wrapper (name here python-FALL3D) has been developed, which modifies the modelling procedure of FALL3D in order to simplify its use for those with little or no background in computational modelling. The modified procedure does not alter the core functionality of FALL3D, but simplifies the modelling procedure by streamlining the installation process, automating both the pre-processing of input meteorological datasets and configuring and executing each utility program in a single-step process. An application example was presented using python-FALL3D for an active volcano in West Java, Indonesia. The example showed that communities located on the western side of Gunung Gede are always susceptible to volcanic ash ground loading regardless of the seasonal variations in wind conditions, whereas communities on the eastern side of Gunung Gede have a marked increase in susceptibility to ground loading during rainy season conditions when prevailing winds include a strong easterly component. A. N. Bear-Crozier (&) Geoscience Australia, Canberra, ACT, Australia e-mail:
[email protected] N. Kartadinata A. Heriwaseso Pusat Vulkanologi dan Mitigasi Bencana Geologi, Jl. Diponegoro No. 57, Bandung 40122, Indonesia O. Nielsen Australia-Indonesia Facility for Disaster Reduction, Menara Thamrin Building Suite 1505, Jl. MH. Thamrin Kav. 3, Jakarta 10250, Indonesia
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Fig. 1 Volcano locations (active in the last 10,000 years) and population density data (per km2) in Indonesia (modified after Simpson et al. 2011)
Keywords
Volcanic ash Modelling FALL3D Hazard map Probabilistic
1 Introduction Volcanic ash represents a serious hazard to many towns, cities and even megacities (population in excess of 10 million) in the vicinity of active volcanoes in developing countries like Indonesia. Volcanic hazard maps are essential tools for contingency planning and assist decision-makers when establishing mitigation procedures (Macedonio et al. 2008; Costa et al. 2009). Undertaking volcanic ash fallout hazard assessments is an important scientific, economic and political exercise and of great importance to public safety, especially for communities on the many densely populated islands of Indonesia. One-third of the worlds largest volcanic eruptions that have occurred since 1800 have taken place within the Asia-Pacific. According to Simpson et al. (2011), Indonesia is considered to have the greatest volcanic eruption hazard in the Asia-Pacific region as it features the highest frequency of VEI 4 or larger eruptions (VEI 4: 1 in 14 years; VEI 5: 1 in 99 years; VEI 6: 1 in 679 years) and has experienced a loss of approximately 100,000 lives over the last 200 years directly related to volcanic activity. Indonesia has approximately 127 active volcanoes distributed across the 13,000 islands that make up the archipelago (Fig. 1). Any volcanic hazard assessment undertaken in Indonesia must take into account that the volcanic eruption hazard is not consistent across all of the islands (Simpson et al. 2011). Denser volcano clusters are associated with the islands of Java and Sulawesi and a volcanic hazard assessment must consider both the frequency of volcanic eruptions that have occurred historically and population density of an area in order to fully understand the potential impacts.
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1.1 Pyroclastic particles Explosive eruptions eject large quantities of volcanic blocks, bombs, lapilli, coarse-ash and fine-ash pyroclasts into the atmosphere over relatively long periods of time. Pyroclastic particles can be transported and emplaced close to ground surface through entrainment by pyroclastic density currents (PDCs) or dispersed through the atmosphere as pyroclastic fallout. The largest blocks and bombs typically follow a ballistic trajectory and are deposited close to the eruptive source, whereas lapilli, coarse-ash and fine-ash pyroclasts can remain airborne for hours, days or weeks (Rose and Durant 2011). The rate and distance over which pyroclasts are dispersed are dependant on the effects of gravity, wind advection (wind speed and direction) and atmospheric turbulence. Individual pyroclasts are dispersed depending on their terminal settling velocity where coarser-grained pyroclasts (lapilli) settle closer to the source than finer-grained pyroclasts (coarse and fine ash). Pyroclasts dispersed through the atmosphere, collectively referred to here as volcanic ash fallout, affect vast areas proximally (tens of km), medially (hundreds of km) and distally (thousands of km) from the source vent (Folch and Sulpizio 2010). This has important implications for communities living in the vicinity of active volcanoes. 1.2 Volcanic ash impacts and existing hazard information for Indonesia The potential impacts of volcanic ash fallout are widespread, varying and highly dependant on the scale of the eruption and the distance from source (Blong 1984; Heiken et al. 1992; Spence et al. 2005; Horwell and Baxter 2006; Costa et al. 2009; Durant et al. 2010). These impacts include but are not limited to: (1) damage to human settlements and buildings in the form of roof collapse from ash loading; (2) disruption of transportation systems due to a decrease in or loss of visibility, covering of roads/railways by ash or direct damage to vehicles; (3) partial or total destruction of agricultural crops, damage to forestry, decrease in soil permeability and increased surface run-off promoting flooding; (4) destruction of pastures and health risks for livestock (e.g. fluorosis); (5) disruption of communication systems (e.g. equipment and power lines); (6) temporary shutdown of airports due to degraded engine performance, failure of navigation equipment or loss of visibility; (7) volcanic ash leaching, which can lead to chemical and physical changes in the quality of open water supplies; (8) adverse health effects (e.g. irritation of eyes and skin and potential respiratory symptoms) associated with ash inhalation. These potential impacts stress the socio-economic implications of volcanic ash fallout and highlight the relevance of adequate hazard assessment and risk mitigation (Folch et al. 2008a, b; Costa et al. 2009; Folch and Sulpizio 2010). Volcanic ash hazard maps in Indonesia are constructed historically by investigating the eruptive history of a volcano and the known extent of historical eruptive products and delineating the hazard zone accordingly. Whilst understanding the historical behaviour of an active volcano is important, it cannot provide insights into what may happen if (1) the volcano erupts differently to previous events (i.e. a larger magnitude eruption) or (2) volcanoes that have not erupted in historical times erupt in the future (a potential issue for Indonesia). Forecasting the scale and style of future eruptions is an arduous task and cannot be performed deterministically (Bonadonna 2006). Ideally, a probabilistic approach should be taken whereby an eruptive scenario is defined and then run using a tephra dispersal computational model (Selva et al. 2010). This scenario should be modelled over a period of time sufficiently large as to capture all possible meteorological conditions (Folch and Sulpizio 2010). Ideally, each volcanological parameter should be derived from an
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associated probability density function (PDF) that takes into consideration natural variability and uncertainty. However, in the absence of such information, data from analogue volcanoes should be used. Significant progress has been made in recent decades towards development and application of computational modelling tools and techniques for assessing volcanic ash fallout hazards (Barberi et al. 1990; Hurst 1994; Searcy et al. 1998; Hurst and Turner 1999; Folch and Felpeto 2005; Macedonio et al. 2005; Costa et al. 2006; Barsotti et al. 2008; Folch et al. 2008a, b, 2009, 2012; Macedonio et al. 2008; Costa et al. 2009; Bonadonna and Costa 2012; Folch and Sulpizio 2010). These models when combined with traditional field investigations have the potential to greatly enhance the volcanic hazard information currently available for Indonesia. 1.3 Computational models for volcanic ash hazards Computational models together with field studies and monitoring efforts are essential tools for scientists undertaking volcanic ash hazard assessments. The utility of such models is twofold: (1) when combined with studies of eruptive deposits of previous eruptions, these models serve to quantify relevant parameters of past events by means of solving an inverse problem (i.e. provide a better understanding of eruption column height or wind conditions); (2) these models also serve to envisage the characteristics of a future or ongoing eruption when used in the context of a forward problem assuming a range of input parameters. These models are becoming increasing necessary to quantify volcanic ash hazard scenarios or provide short-term forecasts during emergency situations (Folch et al. 2008a, b). This study focuses on advection-diffusion-sedimentation (ADS) models, used for assessing volcanic ash load at ground level within the proximal setting of an active volcano (i.e. B100–150 km from source). Numerical simulations of volcanic ash fallout generally involve running an eruptive scenario that represents the most likely event (based on historical investigation and/or modern analogues) over a period of time sufficiently large as to capture all possible meteorological conditions (Folch et al. 2008a, b; Folch and Sulpizio 2010). A volcanic ash hazard map is then generated that quantifies the probability of having a certain load of volcanic ash (kg/m2) exceeding a particular threshold value (e.g. the threshold value for roof collapse). This paper will present an assessment of the benefits and limitations associated with the computational model FALL3D (an ADS model) as a useful tool for assessing volcanic ash hazards in Indonesia. Secondly, an open-source python program (python-FALL3D) developed jointly by Geoscience Australia (GA), the Australia-Indonesia Facility for Disaster Reduction (AIFDR) and Badan Geologi (Indonesia’s mandated volcanic risk agency) will be introduced, which modifies the modelling procedure of FALL3D in order to simplify its use for end-users with little or no background in computational modelling. Finally, an application example will be presented using python-FALL3D for an active volcano in West Java, Indonesia. The example will be based on an eruptive scenario within the acceptable range of possible future eruptive events for this volcano, granulometry as determined through field studies and a meteorological dataset that represents the range of possible wind conditions for the region.
2 FALL3D FALL3D is a 3D time-dependant Eulerian model used for simulating the dispersal of volcanic ash including both particle concentration in the atmosphere and particle loading at
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FileInp FALL3D Block
GRANULOMETRY Block
SOURCE Block
SETGRN
METEO DATABASE Block
SETDBS
POSTPROCESS Block
FileDat FileTop
FileGrn
SETSRC
FileDbs
FileSrc
FALL3D (Optional) FileRes
FALL3DPOSTP
FileLog FileGRD
*TD Load maps
TD Thickness maps
TD Concentration maps
* - Time dependent
Fig. 2 Operational procedure for the volcanic ash dispersal model FALL3D
ground level (Folch and Felpeto 2005; Costa et al. 2006; Folch et al. 2008a, b; Macedonio et al. 2008; Folch et al. 2009). FALL3D is capable of modelling a wide variety of grainsizes (lapilli to fine ash), and resulting particle classes are characterised by particle diameter, shape and density. Global-scale (thousands of km resolution) or mesoscale (tens to hundreds of km) meteorological parameters provide the necessary meteorological data input, and FALL3D can account for changes in terrain effects. The code is distributed in serial and parallelised versions that include FALL3D and a series of pre and post-process utility programs (SETGRN, SETSRC, SETDBS and FALLPOSTP (optional); Fig. 2). Key volcanological input parameters include an assessment of the mass eruption rate and the total grainsize distribution, height and shape of the eruption column. These parameters together describe the eruptive source term needed to simulate the dispersal of volcanic ash. The source term can be defined as either a 1-D buoyant plume model (Bursik 2001) or as an empirical relationship (Suzuki 1983). The buoyant plume model will provide a mass eruption rate for a user-defined eruption column height and vertical distribution of mass parameters. The empirical relationship (suzuki) requires a mass eruption rate (MER) and an eruption column height (H) defined using known best-fit relationships of MER versus H (Sparks et al. 1997). The ADS equation is solved on a structured terrainfollowing mesh that couples meteorological data and topography for the modelled domain. FALL3D outputs are time-dependant deposit load, thickness and airborne mass concentration for each particle class (Folch et al. 2012).
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2.1 Benefits FALL3D exhibits numerous features highly favourable for widespread application and use by technical agencies in the Asia-Pacific region mandated with assessing volcanic hazards. Firstly, it is a freely available, open-source software, which is easily accessible to all. The code is modifiable by users and well supported by the developers with regular release of new versions that add to and improve current functionality. The code has been extensively tested for accuracy against known tephra deposits and space-based ash cloud observations for numerous eruptions including the 79AD, 1631 and 1944 eruptions of Vesuvius, Southern Italy (Macedonio et al. 2008); the 1992 eruption of Mt. Spurr, Alaska (Folch et al. 2008a, b); the 2001 eruption of Mt. Etna, Sicily (Costa et al. 2006; Folch et al. 2009; Corradini et al. 2011); the 2008 eruption of Chaiten, Chile (Folch et al. 2008a, b) and the 2010 eruption of Eyjafjallajokull, Iceland (Folch et al. 2012). Most importantly, FALL3D has been used widely for evaluating the proximal and distal hazard associated with volcanic ash for high-risk communities living in the vicinity of active volcanoes. Volcanic ash hazard assessments have been undertaken using FALL3D for communities spatially associated with Campi Flegrei Caldera, Italy (Costa et al. 2009), and Somma-Vesuvius, Italy (Folch and Sulpizio 2010). All the aforementioned factors indicate that FALL3D is suitably well tested and validated for use in the Asia-Pacific region. 2.2 Limitations Limitations associated with FALL3D include the technical nature of the installation procedure, time-consuming pre-processing of input datasets and a generally low level of usability for those with no background in computational modelling or programming. FALL3D is installed and executed from within a UNIX/Linux operating system and requires a number of dependency programs in order to operate and, in most instances, an internet connection to obtain these dependencies. The technical user manual is not targeted towards users with little or no experience with navigating through a Unix/Linux operating system or with command line protocols. Pre-processing of topographic and meteorological data into a FALL3D-friendly format can be time consuming for the user and can result in unnecessary errors that delay the modelling procedure. The modelling procedure itself is divided into a series pre-processing phases where the user runs each utility program (SETGRN, SETDBS and SETSRC; Fig. 2) one at a time in order to generate input data files (FileGrn, FileDbs and FileSrc; Fig. 2) before running the fallout model itself (FALL3D; Fig. 2). This procedure is highly repetitive and can result in typographic errors that delay the procedure. FALL3D results are in NetCDF format (FileRes; Fig. 2). This file contains all time-dependant results pertaining to the modelled run and can be viewed using a NetCDF viewer. However, extracting information from this file for spatial analysis in geographic information software (GIS) is not straightforward. An optional post-processing utility program (FALL3DPOSTP) is available for extracting modelled results and generating files in GRD; however, this format requires proprietary software to view.
3 Development of python-FALL3D The most significant limitation for widespread application of FALL3D in this region is the high level of user expertise needed to execute a scenario and generate a volcanic ash
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hazard map. A Python wrapper was developed jointly by Geoscience Australia (GA), the Australia-Indonesia Facility for Disaster Reduction (AIFDR) and Badan Geologi (BG), which modifies the modelling procedure of FALL3D to simplify its use for those with no background in computational modelling. The development of this wrapper, named here python-FALL3D, was undertaken in line with the needs of government agencies and emergency managers in the Asia-Pacific region. Python-FALL3D introduces a number of enhancements to the modelling procedure for FALL3D that do not change the operation or functionality of the core model but greatly simplify its overall use. The python scripts wrap around the existing model providing a buffer between the user and FALL3D. These enhancements are targeted towards streamlining the installation process, automating and simplifying both the pre-processing of input meteorological datasets and configuring and executing each utility program in a single-step process, reducing the likelihood of errors. Python-FALL3D will produce output files that are geospatially referenced in a range of file formats compatible with geographic information system (GIS) and visualisation software packages (ArcGIS, Google Earth). These formats are directly comparable against other important datasets including population density, exposure of the built environment and crop extents. These hazard maps are intended for use by government agencies (or other users) to assess the risk and potential impact of volcanic ash on communities. The enhancements incorporated into pythonFALL3D are described here in detail. 3.1 Installation of python-FALL3D The python-FALL3D software package is freely available, open-source, and can be downloaded from (http://www.aifdr.org/projects/aim/) using a Unix/Linux operating system such as Ubuntu (http://www.ubuntu.com/). The python-FALL3D package contains the core FALL3D model (http://datasim.ov.ingv.it/Fall3d.html), the python wrapper, a user manual, template python scripts for designing new scenarios and two validated scenarios (1840 eruption of Guntur, Indonesia and 1994 eruption of Tavurvur, Papua New Guinea). These scenarios are provided to familiarise new users with the modelling procedure and include input data, scripts and example output data for comparison. The python-FALL3D user manual is targeted towards users with little or no background in computational modelling. New user’s run the installation script with a single command. Python-FALL3D will automatically download and install all dependency programs required by FALL3D. It will then configure and install all the utility programs and FALL3D before creating a repository for the storage of output data called TEPHRA. The user is guided through a simple step-by-step process for testing whether the installation was successful, setting up a modelling area and running the example scenarios for Indonesia and Papua New Guinea using data and scripts provided. The user manual then details how to use python-FALL3D to prepare input data and execute a new scenario. 3.2 Preparation of meteorological data FALL3D requires time-dependant meteorological data including wind speed, direction and temperature together with topographic data in order to generate a meteorological database of wind conditions used to model the dispersal of volcanic ash during an eruption. The simplest option for achieving this is to input a vertical wind profile, essentially a text file that documents changing wind speed, direction and temperature with altitude (or pressure levels) at a point closest to the source vent which can be used to interpolate meteorological
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conditions across the remaining model domain (the total land area under consideration). Creation of this vertical wind profile is time consuming and highly susceptible to typographic errors; therefore, python-FALL3D automates this step for the user. The NCEPNCAR global mesh, which has been utilised widely by other FALL3D users, was considered to be the most suitable meteorological dataset for use in Indonesia as it provides four- times daily and daily averaged analysis of wind conditions for the Indonesian region and is freely available for the period 1948–present. Python-FALL3D reads in a wind dataset from the four-times daily re-analysis at a point along the NCEP/NCAR global mesh closest to the volcano of interest (http://www. esrl.noaa.gov/psd/data/reanalysis). It extracts the available data on wind speed, direction and temperature with altitude and automatically builds a vertical wind profile for the user for the required vent location, date and time period (i.e. 0–21,600 s). Python-FALL3D can be used on larger domains for considering distal distributions of volcanic ash when complete meteorological fields are used rather than vertical wind profiles. Python-FALL3D is primarily used for proximal applications, and therefore, a vertical wind profile is considered sufficient. Python-FALL3D users have the option to create two kinds of vertical wind profiles depending on the intended application (Fig. 3). The first are ‘multiple’ 6-hourly vertical wind profiles for a given time. These are particularly useful for probabilistic hazard mapping. The second are single ‘merged’ vertical wind profiles that might cover a period of several days. These are useful for deterministic modelling. Vertical wind profiles generated by python-FALL3D are in text file format and readily compatible with FALL3D. 3.3 Preparation of topography data and georeferencing FALL3D reads in a topography file that provides the limiting boundaries and cell size of the model domain. The majority of freely available topographic datasets that cover the Indonesian region are accessible primarily in ascii format. The Shuttle Radar Topography Mission (SRTM) digital elevation dataset sampled at 90 m resolution was considered the most appropriate topographic dataset for use in Indonesia, and users of python-FALL3D are encouraged to download elevation data for the region of interest in ascii format along with an accompanying projection file (text file) that contains the relevant coordinate reference system information (http://glcf.umiacs.umd.edu/data/srtm/index.shtml). PythonFALL3D reads in the SRTM topography data and converts it into a format compatible with FALL3D (Fig. 3). 3.4 Automation of the modelling procedure The user determines the volcanological parameters for the intended scenario by populating a template ‘scenario’ script. This script outlines the temporal parameters (eruption start time, eruption duration, post-eruptive settling duration), the vent location and the vertical extent of the model domain. The user specifies the location (on disk) of the topographic and meteorological datasets prepared earlier in the scenario script such that pythonFALL3D can automate input into each utility program as needed (Fig. 3). Granulometry parameters are specified here (minimum/maximum grainsize, average grainsize, sorting, density and sphericity) as is a determination of the eruptive source conditions (vent height, mass eruption rate and eruption column height) and dispersal characteristic for the fallout of the volcanic ash (velocity, turbulence, diffusion). Finally, the user is asked to specify the output data type, units of measurements and contour intervals where applicable.
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SRTM Digital Elevation Model
DEM
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PRJ
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ACCESS-T
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windprofile extraction script (python-FALL3D)
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(D, P & F)
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FileRes D&F
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FileRes
FileRes
FileRes
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ash thickness (mm/cm/m)
ash load (kg/m2)
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TIFF
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KML
KML
hazard map script (python-FALL3D) hazard map (% probability of load threshold exceednace)
ASC
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QuantumGIS (analysis)
KML
Google Earth (visualisation)
Key: DEM - digital elevation model; PRJ - geographic projection; VP - vertical wind profile; ASC - ascii text file (raster); SHP - shapefile (vector); TIFF - georeferenced image file; KML - Google Earth (vector)
‘D’ deterministic (single wind)
‘P’probabilistic (multiple wind)
‘F’ forecasting
Fig. 3 Operational procedure for python-FALL3D
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Figure 3 outlines the python-FALL3D operational procedure for modifying and simplifying the modelling procedure. The scenario script is used to automate all steps necessary to run the dispersal model FALL3D. Executing the scenario script with a single command will (1) generate the volcanological input file (FileInp; Fig. 3), (2) read in and convert topographic data (FileTop; Fig. 3), (3) read in and convert a vertical wind profile (or multiple vertical wind profiles; FileDat; Fig. 3), (4) automatically run the three utility programs (SetGrn, SetDbs and SetSrc; Fig. 3) and the dispersal model (FALL3D; Fig. 3), (5) automatically run the post-processing utility (POSTP; Fig. 3) and convert the FALL3D native output into a variety of file formats (ASC, SHP, TIFF and KML; Fig. 3) that can be read into open-source GIS packages such as Quantum GIS or Google Earth for visualisation and spatial analysis. 3.5 Hazard map generation Running python-FALL3D for deterministic or forecasting purposes involves running a single scenario and results in the generation of a single set of outputs (Fig. 3). These include time-dependant ash load (kg/m2), time-dependant ash thickness (mm, cm or m) and time-dependant ash concentration in the atmosphere (kg/m3). For probabilistic scenarios, it involves running multiple scenarios where one parameter is changed (i.e. wind conditions) and results in the generation of multiple sets of output data. An additional step is required to merge these scenarios into a single hazard map output. A script for computing the probability of ground ash load exceeding a particular threshold value (hazard map script) is used to derive a ground ash load hazard map (Fig. 3). The user must specify the vent location, a single or list of ash load thresholds (kg/m2), contour intervals (where applicable) and the location of modelled scenarios (input). The user executes the hazard map script and python-FALL3D automates the generation of ground ash load hazard maps. Similar to deterministic and forecasting purposes, the probabilistic maps are generated in several file formats (ASC, SHP, TIFF and KML).
4 Application example: Gunung Gede, West Java, Indonesia 4.1 Eruptive history and existing hazard information Gunung Gede is located in the province of West Java on the island of Java, which is situated along the Quaternary volcanic front of the Sunda Arc (Situmorang and Hadisantono 1992; Handley et al. 2010). The Sunda Arc forms the western delineation of the Indonesian subduction zone driven by the northward subduction of the Indo-Australian plate beneath the Eurasian plate. Gunung Gede is a stratovolcano that forms part of the Gede Volcanic Complex (GVC). The GVC comprises twin stratovolcanoes; Gunung Gede (2,958 m) and Gunung Pangrango (3,019 m; Fig. 4). Gunung Pangrango is an extinct edifice located on the north-eastern rim of a large caldera collapse structure open to the south-west (Situmorang and Hadisantono 1992; Handley et al. 2010). Gunung Gede represents the active centre of volcanism within the complex and can be divided stratigraphically into Old Gede and Young Gede. Old Gede is the largest crater in the complex (1,600 m in diameter). Remnants of the crater rim are exposed at Gunung Gumuruh (50–200 m in height), and majority of the volcanic deposits associated with Old Gede are distributed across the south-eastern slopes. Old Gede is truncated by Young Gede, a 1-km wide, steep-sided crater containing a series of smaller craters (Kawah Sela, Kawah Ratu,
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Fig. 4 Gunung Gede application example; a locality map for Gunung Gede in West Java, Indonesia; b schematic representation of the Gede Volcanic Complex and field localities for grainsize data collection (G1–G14); c quantitative grainsize distributions of pyroclastic fall deposits associated with historical eruptions at Gunung Gede; d granulometry input parameters for python-FALL3D as derived from quantitative grainsize and density analyses. Granulometry parameters (minimum, maximum and average grainsize) were derived through averaging across the sampled locations
Kawah Lanag, Kawah Wadon and Kawah Baru; Handley et al. 2010; Situmorang and Hadisantono 1992). Eruption products associated with these craters are confined to the north-east and south-west. Gunung Gede has experienced approximately 20 short-lived eruptions over the last two centuries, predominantly explosive in nature. The last eruption occurred in 1957 and involved a brief ejection of volcanic ash on the surrounding landscape. 4.2 Definition of the eruptive scenario NCEP/NCAR re-analysis four-times daily meteorological data were extracted for a 3-year period between 1 January 2007 and 31 December 2009 in order to provide a representative suite of seasonal wind conditions for the West Java region. A Suzuki source type was used for an eruption column height of 15 km. The choice of eruption column height was based on historical eruptions within the acceptable range of possible future events at Gede (Table 1). Mass flow rate was computed automatically by the utility program SetSRC based on empirical fits and the eruption column height provided (Mastin et al. 2009). The grainsize distribution was derived from field observations and quantitative grainsize analysis (Fig. 4). It is important to note that all sampled locations were located proximal to
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Table 1 Summary of input parameters used to run python-FALL3D Input parameter
Value
Computational domain
618604E 9149100S (lower left corner)
Cell size
1,342 m
Vertical discretisation
*1 km
Eruption column height
15 km
Mass flow rate
3.45 9 106 kg/s (estimate by FALL3D using empirical relationships)
Total erupted mass
7.4 9 1010 kg
Duration of eruption
6h
Duration of PESa
3h
Initial grainsize
10 classes -4Ø to 4Ø (16–0.0625 mm)
a
post-eruptive settling of particles
the source (within 10–15 km), and it is acknowledged that the resulting total grain size distribution is fine-depleted as a result of this. The fines-depleted nature of the field data was considered acceptable as the objective of this paper is to address proximal hazard assessments using Python-FALL3D. Granulometry parameters (minimum, maximum and average grainsize) were derived through averaging across the sampled locations. For modelling purposes, we grouped the total grainsize distribution into eight classes at 1ø interval and fixed the end members at -4ø (16–32 mm in diameter) and 4ø (31–62.5 lm in diameter; Table 1). The density of the juvenile clasts was also measured. For modelling purposes, a density range between 570 and 1,300 kg/m3 was set for juvenile clasts and a constant value of 2,300 kg/m3 was set for lithic and crystal fragments. An eruption duration of 6 h was selected, allowing an additional 3 h of post-eruptive particle settling (total simulated eruption duration of 9 h; Table 1). The 4,045 representative wind profiles for the region were divided into dry season (April–September) and rainy season (October–March) to assess the impact of seasonal wind conditions on the distribution of volcanic ash ground loading. It is important to note that rainy season wind conditions do not include the effects of rainfall on aggregation of falling ash particles or compaction of the resulting deposit. Only seasonal variations in wind speed, temperature and direction are considered here. The eruptive scenario was run for each wind profile, generating 1,856 dry season outputs and 2,189 rainy season outputs. Outputs were weighted and merged according to the number of instances that ash load overcame a designated threshold value. Ground loading probability maps were generated for ash load thresholds of 1 kg/m2 and 10 kg/m2 for dry and rainy season meteorological conditions (roughly equivalent to 1 mm and 1 cm ash thickness, respectively, given an average density of 2,000 kg/m3). 4.3 Results Ground loading probability maps are presented here for ash load thresholds of 1 and 10 kg/ m2 for dry and rainy season meteorological conditions. These load thresholds represent conservative estimates for crop damage and partial damage to buildings based on the observed impact of volcanic ash during historical eruptions in the Asia-Pacific region and analogous studies worldwide (Blong 2003; Folch and Sulpizio 2010). Each hazard map depicts the percentage probability of exceeding the ash load threshold given the thousands
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Dry season (April - September)
A
Rainy season (October - March) 50 km
0
C
50 km
0
Bogor
Bogor Ciawi
9260000
Ciawi Cisarua
Cisarua Pacet
Pacet
Warungkondang
Cibadak
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Cibadak
Sukabumi
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Sukabumi
1 kg/m2
1 kg/m2
B
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D Bogor
Bogor
Ciawi
Ciawi
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Cisarua
Pacet
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Warungkondang
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Sukabumi
10 kg/m 2 640000
1
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680000
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Probability of exceedance (%)
80
640000
90
100
1
10
680000
20
30
720000
40
50
60
760000
70
80
90
100
Probability of exceedance (%)
Fig. 5 Ground load probability maps showing probability in % that the deposit load exceeds 1 kg/m2 (top) and 10 kg/m2 (bottom) for dry season (left) and rainy season (right) wind conditions
of possible wind fields. Dry and rainy season maps are considered to account for seasonal variations in wind speed, direction and temperature with altitude within the region. The percentage of cases in which volcanic ash deposit load exceeds 1 and 10 kg/m2 (approximately 1 mm and 1 cm, respectively) for dry season versus rainy season meteorological conditions is shown in Fig. 5. Accumulations of 1 kg/m2 (*1 mm equivalent) are distributed up to 95 km from the summit region under dry season conditions, predominantly in a westerly direction. Accumulations of this load value for rainy season conditions occur to a similar extent in the west (up to 85 km); however, in contrast, probability contours are also elongated in an easterly direction up to 75 km from the summit region. Deposits of 10 kg/m2 (*1 cm equivalent) also occur predominantly to the west under dry season conditions (up to 45 km), but are also elongated in both the west and easterly directions under rainy season conditions where accumulations are distributed up to 40 km from the summit region. The averaged accumulation times (in hours) for both threshold values of ground load (1 and 10 kg/m2) and for both dry and rainy season meteorological conditions are presented as isochron contours in Fig. 6. Isochron contours for dry season conditions are slightly skewed towards the west. Rainy season isochron contours exhibit a similar distribution to
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Dry season (April - September)
A
Rainy season (October - March) 50 km
0
C
Bogor
Bogor
9260000
Ciawi
Ciawi Cisarua
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Sukabumi
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D
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1
2
3
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Fig. 6 Isochron maps showing averaged accumulation time (in hours) for the volcanic ash load threshold values 1 kg/m2 (top) and 10 kg/m2 (bottom) for dry season (left) and rainy season (right) conditions
the west, but also feature elongation in an easterly direction. Typical accumulation times for ground load at medial to distal localities were 7–8 h for dry season conditions and less than 7 h for rainy season conditions. The LandScan 2008 Global Population Database (Oak Ridge National Laboratory) was used to determine the population potentially impacted by an eruption at Gunung Gede. This database uses available census data to estimate population per square kilometre. The number of people (km2) likely to be impacted by 1 kg/m2 of ground load during dry season wind conditions during an eruption is shown in Fig. 7. The number of people per probability contour and in total for the remaining scenarios (dry—10 kg/m2; rainy 1 kg/m2 and rainy—10 kg/m2) is reported in Table 2. 4.4 Discussion Accumulation of ground load is heavily influenced by the prevailing wind conditions for the region, and the effects of seasonal variations in wind speed, direction and temperature are reflected in the distribution footprint. During the dry season (April–September), a high proportion of the ash accumulated on the proximal western slopes extending out distally to
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Nat Hazards (2012) 64:821–838 640000
720000
9260000
640000
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50 80 90 30 40 70 60
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Legend 10
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Population density (per km2) 20
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835
50 80 90 30 40 70 60
0 - 10 10 - 100
10
1
100 - 1000
1
1000 - 5000 0
50 km
0
50 km
0
50 km
5000 +
Fig. 7 Population density extractions based on ground load probability maps. The maps indicate the number of people (km2) in each probability contour interval likely to be impacted by 1 kg/m2 of ground load during dry season conditions
the west and southwest. This suggests that damage to crops up to 95 km to the west (1 kg/m2) and cosmetic damage to buildings (10 kg/m2) associated with communities up to 45 km west of Gunung Gede likely occurred during the dry season period. Comparatively little ash was simulated on the eastern slopes, which is likely the result of strong westerly winds at this time of year. The implications are that communities located on the western side of Gunung Gede (including the city of Bogor—population 949,000) are more susceptible to volcanic ash ground loading during dry season metrological conditions than those located on the eastern side. Accumulations of ground load during rainy season meteorological conditions follow a similar pattern of distribution in a westerly direction (up to 85 km) as during the dry season. However, an elongation of the footprint towards the east suggests that communities east of Gunung Gede are also susceptible during the rainy season months. Damage to crops up to 75 km to the east (1 kg/m2) and cosmetic damage to buildings (10 kg/m2) may afflict communities east of Gunung Gede during the rainy season. The implications are that during the rainy season, a higher proportion of people could be potentially impacted by volcanic ash ground loading during an eruption. The averaged accumulation times (how long it took for ash load to exceed the threshold value at a given location) for dry season meteorological conditions (7–8 h) versus rainy season meteorological conditions (less than 7 h) also support the presence of a strong westerly wind during the dry season which distributes ground load further from the vent source. Ground load probability maps were directly compared with population density (per km2) values extracted from the footprint of distribution for both rainy and dry season (1 kg/m2) conditions. The highest concentration of people occurs within the 1.01–10 % probability contour for both load thresholds and both seasonal wind conditions; however, as many as 28,460 people have a 60–70 % chance of experiencing 1 kg/m2 under dry season conditions, and 48,715 have a 40–50 % chance of encountering 1 kg/m2 under rainy season conditions. Overall, comparisons between distribution of ground load and
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Table 2 Population density extractions based on ground load probability maps; number of people (km2) in each probability contour interval likely to be impacted by 1 kg/m2 of ground load during dry season conditions, by 10 kg/m2 during dry season conditions, by 1 kg/m2 during rainy season conditions and by 10 kg/m2 during rainy seasons Probability of exceedance (%)
Approximate no. of people
Probability of exceedance (%)
Approximate no. of people
Gede (dry season: April–September): 1 kg/m2
Gede (dry season: April–September): 10 kg/m2
0–1
n/a
0–1
1.01–10
1,451,000
1.01–10
567,000
10.01–20
301,000
10.01–20
106,000
20.01–30
265,000
20.01–30
39,000
30.01–40
64,000
30.01–40
16,000
40.01–50
32,000
40.01–50
7,000
50.01–60
43,000
50.01–60
2,600
60.01–70
28,000
60.01–70
380
70.01–80
13,000
70.01–80
260
80.01–90
1,800
80.01–90
120
90.01–100
440
90.01–100
60
Total
2,156,240
Total
738,420
n/a
Gede (rainy season: October–March): 1 kg/m2
Gede (rainy season: October–March): 10 kg/m2
0–1
n/a
0–1
1.01–10
2,760,000
1.01–10
645,000
10.01–20
389,000
10.01–20
166,000
20.01–30
367,000
20.01–30
55,000
30.01–40
117,000
30.01–40
14,000
40.01–50
48,000
40.01–50
1,500
50.01–60
20,000
50.01–60
280
60.01–70
18,000
60.01–70
140
70.01–80
7,000
70.01–80
120
80.01–90
690
80.01–90
90
90.01–100
330
90.01–100
60
Total
3,727,020
Total
882,190
n/a
population density suggest a higher proportion of people are more likely to be impacted under rainy season conditions (1 kg/m2—3,731,059; 10 kg/m2—884,417) rather than under dry season conditions (1 kg/m2—2,204,043; 10 kg/m2—739,742).
5 Summary and conclusions Computational models together with field studies and monitoring efforts are essential tools for scientists undertaking volcanic ash hazard assessments. FALL3D exhibits numerous features highly favourable for widespread application and use by technical agencies in the Asia-Pacific region mandated with assessing volcanic hazards. FALL3D is feely available, open-source and modifiable, and it has been extensively tested for accuracy against known tephra deposits and has been used widely for evaluating the proximal and distal hazard
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associated with volcanic ash for high-risk communities living in the vicinity of active volcanoes. However, the technical nature of the installation procedure, the time-consuming pre-processing of input datasets and the generally low level of usability for those with no background in computational modelling are all limiting factors that preclude its widespread use by technical agencies in the Asia-Pacific. Python-FALL3D introduces enhancements that address these limitations, but do not change the operation or functionality of the core model. These enhancements are targeted towards streamlining the installation process, automating and simplifying both the preprocessing of input meteorological datasets and configuring and executing each utility program in a single-step process, reducing the likelihood of errors. An application example was presented using python-FALL3D for an active volcano in West Java, Indonesia. An eruption was simulated at Gunung Gede using eruptive parameters within the acceptable range of possible future events for this volcano, granulometry as determined through field studies and a meteorological dataset that represented the range of possible wind conditions expected during both the dry and the rainy seasons for the region. The application example showed that communities located on the western side of Gunung Gede are always susceptible to volcanic ash ground loading regardless of the season (slightly higher chance of impact during the dry season), whereas communities on the eastern side of Gunung Gede have a marked increase in susceptibility to ground loading during the rainy season when prevailing winds can include a strong easterly component. Acknowledgments This work was supported by the Indonesian Agency for Disaster Management (BNPB) and AusAID through the Australia-Indonesia Facility for Disaster Reduction (AIFDR).
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