The Geology teaching and learning processes should be carried out through the interaction of different learning environments, inside the classroom and outdoors. Practical activities are a methodological strategy widely suggested by the Portuguese Ministry of Education for the Geology teaching whose aim is to promote the development of skills (knowledge, ability and attitudes) denying the idea that the subjects of this science are complex and far from the students’ academic interests and needs. This work presents some formal and non-formal education practical activities (e.g. laboratory and fieldwork, mineral fairs, national contests), developed throughout the last year in Sé School, a small school in the north of Portugal, with students of the different grades (Kindergarten to high school).

An aerosol albedometer was combined with laser-induced incandescence (LII) to achieve simultaneous measurements of aerosol scattering, extinction coefficient, and soot mass concentration. Frequency doubling of a Nd:YAG laser line resulted in a colinear beam of both λ = 532 and 1064 nm. The green beam was used to perform cavity ring-down spectroscopy (CRDS), with simultaneous measurements of scattering coefficient made through use of a reciprocal sphere nephelometer. The 1064 nm beam was selected and directed into a second integrating sphere and used for LII of light-absorbing kerosene lamp soot. Thermal denuder experiments showed the LII signals were not affected by the particle mixing state when laser peak power was 1.5–2.5 MW. The combined measurements of optical properties and soot mass concentration allowed determination of mass absorption cross section (M.A.C., m2/g) with 1 min time resolution when soot concentrations were in the low microgram per cubic meter range. Fresh kerosene nanosphere soot (ns-soot) exhibited a mean M.A.C and standard deviation of 9.3 ± 2.7 m2/g while limited measurements on dry ambient aerosol yielded an average of 8.2 ± 5.9 m2/g when soot was >0.25 μg/m3. The method also detected increases in M.A.C. values associated with enhanced light absorption when polydisperse, laboratory-generated ns-soot particles were embedded within or coated with ammonium nitrate, ammonium sulfate, and glycerol. Glycerol coatings produced the largest fractional increase in M.A.C. (1.41-fold increase), while solid coatings of ammonium sulfate and ammonium nitrate produced increases of 1.10 and 1.06, respectively. Fresh, ns-soot did not exhibit increased M.A.C. at high relative humidity (RH); however, lab-generated soot coated with ammonium nitrate and held at 85% RH exhibited M.A.C. values nearly double the low-humidity case. The hybrid instrument for simultaneously tracking soot mass concentration and aerosol optical properties in real time is a valuable tool for probing enhanced absorption by soot at atmospherically relevant concentrations.

High spatial resolution soil moisture information is important for regional–scale hydrologic, climatic and agricultural applications. However, available point-scale in-situ measurements and coarse-scale (~10s of km) satellite soil moisture retrievals are unable to capture hillslope to sub-catchment level spatial variability of soil moisture as required by many of these applications. Downscaling L-band satellite soil moisture retrievals appears to be a viable technique in estimating near surface (~ top 5 cm) soil moisture at a high spatial resolution. Among different downscaling approaches, thermal data based methods exhibits a good potential over arid and semi-arid regions, i.e. in many parts of Australia. This study investigates three downscaling approaches based on soil thermal inertia to estimate near surface soil moisture at high spatial resolution (1 km) over Krui and Merriwa River catchments in the Upper Hunter region of New South Wales, Australia. These methods are based upon the relationship between the diurnal soil temperature difference (ΔT) and daily mean soil moisture content (μSM). Regression tree models between ΔT and μSM were developed by using in-situ observations (in the first approach) and using land surface model (LSM) based estimates (in the second approach). The relationship between ΔT and μSM was modulated by the vegetation density and the Austral season. In the in-situ data based approach, soil texture was also employed as a modulating factor. These in-situ datasets were obtained from the Scaling and Assimilation of Soil Moisture and Streamflow (SASMAS) network and model-based estimates from the Global Land Data Assimilation System (GLDAS). Moderate Resolution Imaging Spectroradiometer (MODIS) derived Normalized Difference Vegetation Index (NDVI) products were used to define vegetation density. An ensemble machine-learning model was employed in the third approach using ΔT, NDVI and Austral season as predictors and μsm values as responses. Aggregated airborne soil moisture retrievals were used as the coarse resolution soil moisture products. These coarse resolution soil moisture simulations were downscaled to 1 km by employing the above three approaches using MODIS-derived ΔT and NDVI values. The results from the three downscaling methods were compared against the 1 km soil moisture retrievals from the National Airborne Field Experiment 2005 (NAFE’05) over 3 days in November 2005. The results from both in-situ data and GLDAS-based regression tree models show RMSEs of 0.07 cm3/cm3 when compared against the high resolution NAFE’05 airborne soil moisture observations. The GLDAS-based model can be applied over a larger extent, whereas the in-situ data based model is catchment specific. These results were compared with the results from the machine-learnt model. A combination of these methods with additional forcing factors such as topography, meteorology, etc. can be utilized to develop an improved downscaling model. Such a mod

The Earth orientation parameters (EOP), the regular products of IERS Earth Orientation Centre, are computed at daily bases by combination of EOP solutions by different astro-geodetic techniques. At SYRTE we have developed a new strategy of combination using the Global Navigation Satellite Systems (GNSS) and Very Long Baseline Interferometry (VLBI) techniques at normal equation level thanks to the IERS COL-WG [1]. This approach allows to produce the EOP at the daily bases, polar coordinates (x,y) and rates (xr,yr), universal time UT1 and rate LOD, and corrections from IAU200A/2006 precession-nutation mode (dX,dY), simultaneously with station coordinates constituting the terrestrial frame (TRF) and possibly the quasar coordinates constituting the celestial frame (CRF). For studying these EOP solutions continuously with respect to the IERS EOP products, we have developed an operational process which take the recently solution files produced respectively by IGS and IVS international services which are converted at normal equation level. The recorded solutions obtained from GNSS and VLBI combination at weekly bases is recently maintained by SYRTE. The process of this combination are presented and results are analysed.

An intense tropical cyclone, Typhoon Jebi, landed the central part of Japan in September 2018 and caused severe damages due to strong winds. Typhoon Jebi obtained the lifetime minimum central pressure of 915 hPa and took a track very similar to past disastrous typhoons: Typhoon Nancy (1961) and Muroto Typhoon (1934). In Osaka City, the 1st, 2nd, and 3rd highest record of instantaneous wind speeds are 60.0 m/s in September 1934, 50.6 m/s in September 1961, and 47.4 m/s in September 2018, respectively, suggesting that a typhoon has been the most threatening windstorm in the area. Buildings and structures in urban areas are known to affect significantly the magnitude of wind gustiness. Because of the growing urbanization globally, quantification of turbulent winds in densely-built, urban districts is important to understand the underlying risks of wind damages. We investigate the influences of densely built urban environments on the occurrence of wind gusts in urban districts of Osaka and Kyoto City during the landfall of Typhoon Jebi by merging mesoscale meteorological and building-resolving large-eddy simulations (LES). By explicitly representing realistic buildings and structures in LES, this study examines complex/complicated characteristics of winds within the densely built urban environment. With the successful reproduction of the track and intensity of the typhoon in meteorological simulations, the simulated winds at the boundary-layer top of the LES model are used to quantify the wind gusts in the urban district. The maximum wind gust in the analysis area of Osaka is around 60-70 m/s, which is comparable to the wind speed at the height of about 300 m. Such wind gusts are generated by instantaneous downward momentum transfer in areas of a cluster of buildings with variable heights. Instantaneous wind gusts are further examined in terms of building density and are found to be strongest when the building packing density is moderate. The results suggest that the risks of wind damages would be maximized in urban districts where the building height is inhomogeneous and the packing density is moderate.

Protocols for rejuvenation of large to moderately-sized rivers using remote sensing data is well established in different parts of the world. With the advent of commercially viable drones, highresolution imageries in sub-centimetre level can be generated, and this has made the assessment of degradation of small rivers a reality. The Sakri is a small river in the state of Chhattisgarh, central India with a basin area of 410 km , channel width < 30m. Even with an average rainfall of 1232 mm, and a perennial source feeding the river, the river remains dry in most stretches. People from 23 villages and the town of Kawardha are mostly dependent on this river, and therefore, an attempt to rejuvenate this river was taken up using an innovative technology of drone surveys coupled with

In response to a growing number of natural and anthropogenic threats, the long-term sustainability of coastal river deltas and wetlands has come into question worldwide. Tools such as remote sensing and numerical modeling have been implemented in an effort to monitor and predict the hydro-geomorphological evolution of our coasts. Hydrological connectivity is known to play an important role in deltaic evolution by delivering flow, sediment, and nutrients to the interior of deltaic islands/wetlands. However, estimating connectivity typically requires detailed field work or numerical modeling, which is difficult to implement over broad spatial and temporal scales. In the present work, we investigate the potential of using remote sensing to estimate hydrological connectivity in the Wax Lake Delta (WLD) and Atchafalaya Delta region of the Louisiana coast. During a three-hour window, five difference maps of water level in the WLD and surrounding wetlands were collected using UAVSAR L-band radar in repeat-pass interferometric mode. We then modeled the WLD subsection of the domain using a 2D shallow-water hydrodynamic model configured to run on the same discharge, tide, and wind conditions as recorded at nearby monitoring stations during the observational window, with vegetation parameterized as a source of additional drag in the deltaic islands. Modeling allowed us to determine the relative influence of tides, vegetation, and wind on WLD water levels, which could then be extrapolated to infer the behavior throughout the rest of the domain. Over the observational window, UAVSAR measured a cumulative loss of over 22 megatons of water from non-channelized wetlands as tides fell. We find that the model tends to under-predict the observed water level draw-down, as well as the degree of hydrological activity in proximal islands that we observe in the UAVSAR data. Models that neglect the influence of wind underestimate the volume of water leaving the islands by up to two-thirds, suggesting the importance of wind on deltaic hydrodynamics during the observational window. With the information gained from the numerical modeling, as well as the computation of information theory statistics, we extend the WLD results to analyze and quantify the water level behavior in the surrounding wetlands and Atchafalaya delta.

The timely understanding of flood extent is critical information for emergency managers during disaster response. Search and rescue operations require a boundary to reduce fruitless efforts and to prioritize critical or limited resources. However, high-resolution aerial imagery is often unavailable or lacks the necessary geographic extent, making it difficult to obtain real-time information about where flooding is occurring. Volunteered geographic information (VGI) is a subset of crowdsourcing and can be used to disseminate spatially relevant information or request help. In this study, we present a novel approach to map the extent of urban flooding in Harris County, TX during Hurricane Harvey (August 25-31, 2017) and identify where people were most likely to need immediate emergency assistance based on a subset of crowdsourced SOS requests. Using the machine learning software Maximum Entropy (MaxEnt), we predict the spatial extent of flooding based on several physical and socio-economic characteristics. We compare the results against two alternative flood datasets available after Hurricane Harvey (i.e., Copernicus satellite imagery and fluvial flood depths estimated by FEMA), and we validate the performance of the model using a 15% subset of the rescue requests and Houston 311 flood calls. We find that the model predicts a much larger area of flooding than was shown by either Copernicus or FEMA when compared against the locations of rescue requests, and that it performs well using both a subset of rescue requests (AUC 0.917) and 311 calls (AUC 0.929).

The need to better understand climate change has driven model simulations to greater fidelity with improved spatiotemporal resolution (e.g., < 10 km at sub-hourly cadence). For example, the 7 km GEOS-5 Nature Run (G5NR) with 30-minute outputs from 2005-07 at the NASA Center for Climate Simulation (NCCS) is ~4 PB and is not easily portable. The rise of these high-fidelity climate models coincides with the emergence of cloud computing as a viable platform for scientific analytics. NASA has adopted a cloud computing strategy using public providers like Amazon Web Services (AWS). However, it is not cost- or time- effective to move the High- Performance Computing (HPC)-based model computations and data to the cloud. Thus, there is a need for scalable model evaluation compatible with both the cloud and HPC platforms like NCCS. To fill this need we have extended the analytics component of the Apache Science Data Analytics Platform (SDAP) with a streamlined version that specifically targets high-resolution science data products and climate model outputs on a regular coordinate grid. Gridded inputs (as opposed to other data structures like point clouds or swath-based measurements supported by SDAP), enable offsets to particular grid cells to be directly computed, allow for processing on the original NetCDF or HDF granules, do not require a second tiled copy of the data, and accommodate a simpler technology stack since no geospatial database is required for lookups or tile storage. Our core module, Parmap, abstracts the map-reduce model so that users can select from a variety of map computational modes, including Spark, Dask, serverless AWS Lambda, PySparkling, and Python multiprocessing. Example analytics include area-averaged time series and time-averaged, correlation and climatological maps. Benchmarks compare favorably with the full SDAP implementation.

Modeling future glacier changes on regional or global scales is challenging due to a scarcity of data for calibrating model parameters. This makes it difficult to derive accurate model parameters and also to convey the model uncertainty associated with those parameters. Previous global-scale models have typically optimized a single glacier or region specific set of model parameters to project global-scale glacier mass changes. As part of the development of a new open source global-scale glacier evolution model this research implements a Markov Chain Monte Carlo method to address this problem. We use geodetic mass-balance observations to determine probability distributions of a set of model parameters at each glacier. We then form an ensemble of model simulations by sampling parameters from these distributions. Using an ensemble allows us to create a range of mass balance changes for each glacier and quantify the model uncertainty. Applying this calibration technique, we use temperature and precipitation projections of one global climate model to model the mass changes until year 2100 of thousands of glaciers in High Mountain Asia. By using climate projections forced by multiple emission scenarios, we compare the uncertainties in changing climate caused by possible emission scenarios with the uncertainties in the glacier evolution model, and evaluate the results of different climate scenarios on glacier evolution.

With the mass adoption of data analysis in several scientific fields such as climatology, medicine, astronomy and astrophysics, the availability of an appropriate analytics infrastructure has become a necessity increasingly recognized by the scientific community. However, appropriate tools and applications are required to process the large volume of data collected and generated by researchers. One of the biggest challenges lies in the fact that these tools need to be gathered to be applied in specific domains. The area of bioclimatic data is a scientific field that still has much to improve in this matter. It is a field of study that lacks great efforts in the direction to provide methodologies and tools to facilitate the understanding of the complex phenomena involved in the influence that environmental variables have on biodiversity on the planet. Thus, the purpose of this work is to propose a big data analytics architecture that presents an ecosystem that systematizes and facilitates the task of the scientists to deal with the complexity in the bioclimatic data analysis, providing tools for storage, management, analysis using machine learning algorithms and data mining, and visualization tools. The methodological approach of this work was to make a thorough bibliographical study to verify the most used tools and the suitability of each one to the purpose of the work. In addition, the literature provided indications of software ecosystem implementations methodologies that served as a guide in the architecture design. Within the architecture, we attempted to gather a set of bioclimatic data based on a subset of data obtained from the Atmospheric Radiation Measurement (ARM) data repository for climatic data, and the Brazilian Biodiversity Portal for biodiversity data. As a result, we were able to gather a series of tools to access data such as Cassandra, distribution of processing such as Spark, programming interface represented by Jupyter Notebook, system modules for data format conversion, machine learning algorithms libraries and software for data visualization. This research discuss the importance of a domain purpose design of a data analysis architecture for bioclimatic data. We concluded that this type of ecosystem is imperative to facilitate the research process and increase the quality of the results.

With temperatures rising in Arctic regions at double the global average, decomposition rates and subsequent greenhouse gas release into the atmosphere are expected to rise, potentially shifting these regions from carbon sinks to carbon sources. General circulation models predict the relationship between respiration and temperature using exponential functions, showing a temperature-independent rate of increase to decomposition rates with temperature. However, enzymes catalyzing carbon depolymerization reactions have demonstrated departures from this predicted increase between 10 and 15 °C. Organic tundra soils were incubated across this range (4 to 20 °C, 4 °C increments) and harvested consecutively after carbon losses equivalent to those after 2, 4, 8, and 12 days at 20 °C. During each harvest, β-glucosidase (BG) and β-xylosidase (BX) activities were measured at both the incubation temperature and at 20 °C to determine temperature limitations to enzyme activity and production. While enzyme activities in soils incubated at different temperatures diverged significantly when measured at different temperatures, these differences were overcome when measured at 20 °C, suggesting enzyme activities are more limited than enzyme production. Two exceptions to this trend were the first harvest of the 4 and 8 °C incubations, which had significantly higher BG and BX activities when assayed at 20 °C than soils incubated at higher temperatures, indicating larger enzyme pool sizes in soils incubated at lower temperatures. For BG activities, increased enzyme pool size offset low temperature limitations to enzyme activity, resulting in no difference in measured rates when the 4 and 8 °C soils were compared with the 12 and 16 °C soils, respectively. However, in subsequent harvests, BG and BX activities measured at 20 °C declined and activities at 4 and 8 °C were significantly lower than in assays at 12, 16, and 20 °C. Declining activities of these enzymes in the 4 and 8 °C incubations may indicate limited substrate accessibility or nutrient availability at lower temperatures due to changes in the relative rates of other decomposition reactions with temperature. Improving our understanding of the multifaceted impacts of temperature on decomposition reactions will be important to better model and predict future carbon release.

Solar-induced chlorophyll fluorescence (SIF) offers a promising method to remotely monitor photosynthesis at the canopy and regional scale. However, in order to interpret satellite SIF measurements in a biological context, there needs to be a better understanding of the diurnal dynamics of SIF and photosynthesis under variable stress conditions. Photons absorbed by a leaf can enter photochemistry, be actively dissipated via non-photochemical quenching (NPQ), or be re-emitted as chlorophyll fluorescence. Therefore, having better understanding of NPQ should improve interpretation of SIF. At the canopy scale, we captured diurnal dynamics of SIF and GPP. We then coupled leaf-level measurements of chlorophyll fluorescence, NPQ and photosynthesis and plant water status with canopy-level SIF and GPP to explore the mechanisms controlling diurnal dynamics of fluorescence vs. photosynthesis in a rain-fed maize field situated in upstate New York.

Effective, considerate shale play water management support operations and protect the environment. A parameter often overlooked is total dissolved solids (TDS). Knowledge of TDS is important to meet these dual goals. Subsurface TDS typically increases with depth. However, produced-water samples from the Eagle Ford Shale show a strong TDS decrease by a factor of ~10 with increasing well depth (~200,000 ppm at ~2.5 km to 18,000 ppm at ~3.6 km). Water stable isotopes strongly suggest that the low TDS is not due to dilution by meteoric water. Rather, it is attributed to smectite-to-illite conversion, in which the smectite interlayer water is released into the pore space. Depth, temperature, and other related indicators (source for K, excess silica) support such a mechanism. In addition, water-isotope patterns and 87Sr/86Sr ratios suggest a conversion operating in a closed system. Order-of-magnitude calculations show that the 8% of mixed-layer clay present on average in the Lower Eagle Ford Shale is sufficient to dilute brines to observed levels. Stakeholders could then have a more optimistic outlook on water recycling and on using produced water for other uses (irrigation, municipal) because the low salinity is an intrinsic property of the formation rather than due to short-term mixing.

We are investigating terrain deformations of long range and persistence that take place on continental scale over the stable shield of continental South America. The crustal deformation is being investigated by correlating with velocities and directions obtained from time series of approximately 60 GNSS stations deployed in Brazil and neighboring countries that take part of the Continuous network of the Geocentric Reference System for the Americas (SIRGAS-C). We attempt to estimate velocities and mid-plate strain rates using the best GNSS stations located in the stable South America mid-plate. The velocities have been estimated by Least Square Estimation (LSE) using SIRGAS weekly time series with a stochastic model composed of white noise plus flicker or random walk noises. Variance Component Estimation (VCE; Amiri-Simkooei et al., 2007) has been applied to classify the type of noise and compose the time series stochastic model. In addition, the time series breaks and offsets are taken into account in the LSE. The noises were classified as white plus flicker noise in approximately 70% of the horizontal component and most of random walk appears for stations located in the Amazon and Pantanal basins or near the coastal zones. The estimated formal precision reach about 0.10 mm/year with RMS of residual near 1.5 and 4.5 mm respectively for horizontal and vertical velocities. The estimated velocities by LSE were also computed by using the MIDAs code (Blewit et al 2015) and the results show an agreement of the order of 0.30 mm/y. The computed strain rates in the central part of Brazil indicate shortening, consistent with the predominance of reverse faulting mechanisms. When a station pair includes one station near the coast, the linear strain rates indicate extension. However, strain rates do not clearly indicate a preferred principal direction and do not seem compatible with the stress patterns derived from the focal mechanisms. In addition, the rate of seismic moment release indicates strain rates from earthquake occurrence two to three orders of magnitude lower than the observed strain rates from the geodetic network. Assessment of South America linear strain rates computed through estimated GNSS velocities will be discussed.

An improved understanding of the mechanisms and factors affecting glacial flow is crucial to better predict sea level rise. Glacial ice often contains impurities such as the presence of small insoluble particles. Mixtures of ice and dust can be found in many places throughout the world, specifically in areas of high latitude and altitude (Moore, 2014). This study aims to understand the effect of entrained insoluble debris on processes of glacial motion. Glaciers move through a combination of internal ice deformation and basal sliding. Internal ice deformation, the flow of individual ice grains, has been found to be grain-size dependent in both field and laboratory studies (Goldsby and Kohlstedt, 2001). In an attempt to better understand ice grain size, this study considers the effect of debris on grain growth. Samples of pure ice and ice with debris were fabricated with a standard protocol and maintained at -5°C for controlled annealing. Microstructural characterization was preformed using a light microscope to image the samples, and calculating the average grain sizes using a linear-intercept method. The ice with debris was found to have smaller grain sizes, thought to be associated with grain-boundary pinning. Extrapolated values were used with a flow law, projecting that ice with debris will have lower viscosity, thus flow faster. To address basal sliding, the other form of glacial movement, we conducted a second phase of study. Basal sliding, the process of a glacier sliding over the bedrock, is influenced by the presence of meltwater at the base of the glacier (Hoffman et al., 2011). Frictional heating, from ice-on-rock friction, was studied as a factor affecting meltwater production. We conducted a simple 1D computer model using laboratory friction measurements of ice with entrained debris (Zoet et al., 2013). We find that debris content and frictional heating are directly proportional. Trials run at faster glacial velocities also show larger amounts of frictional heating. As frictional heating may increase meltwater, glaciers with debris may slide faster over bedrock. Overall, by better understanding the motion of debris-rich glaciers, we can focus our attention to areas around the world at risk, and better predict/prepare for sea level rise.

Since 2001, the Mars Exploration Program Analysis Group (MEPAG) has maintained a document outlining community consensus priorities for scientific goals, objectives, and investigations for the robotic and human exploration of Mars [1]. This “Goals Document” is a living document that is revised regularly (~every few years) in light of new Mars science results. It is organized into a hierarchy of goals, objectives, and investigations. The four Goals are not prioritized and are organized around major areas of scientific knowledge: “Life”, “Climate”, “Geology”, and “Preparation for Human Exploration”. Don Banfield is the current MEPAG Goals Committee Chair, and he oversees 2-3 representatives per Goal [2]. The most recent round of revisions (2018) was prompted by discussion at the 6th International Mars Polar Science and Exploration Conference (held in 2016 in Reykjavik, Iceland [3]), which pointed out that current high-priority Polar Science and Present-Day Activity questions were not well represented in content or priorities within the 2015 Goals Document. Upon request from the MEPAG Executive and Goals Committees [2], specific areas of disconnect were highlighted by representatives of the Mars Polar Science community; these were evaluated by the Goals Committee who proposed changes at sub-objective and investigation levels within the Climate and Geology Goals. These proposed changes were open for comment by the larger Mars community for 6 weeks, and then finalized. The official MEPAG 2018 Goals Document will be presented at the meeting. Additionally, the presentation will describe plans for the next round of revisions, which are expected to primarily come out of the presentations and discussion at the 9th International Conference on Mars (to be held at Caltech, Pasadena, CA in July 2019 [4]), and which are expected to include reference to returned sample science. The 2019 MEPAG Goals Document will form an important input to the next Planetary Science Decadal Survey [5]. [1] https://mepag.jpl.nasa.gov/reports.cfm?expand=science [2] https://mepag.jpl.nasa.gov/about.cfm [3] https://www.hou.usra.edu/meetings/marspolar2016/ [4] https://www.hou.usra.edu/meetings/ninthmars2019/ [5] NASEM, 2017. CAPS: Getting Ready for the Next Planetary Science Decadal Survey. https://doi.org/10.17226/24843.

Severe forest and peat fires in Indonesia have occurred more frequently in recent years. These fires have been associated with various types of ecosystems and land uses, ranging from the simplest to the most complex ones. However, recent fire control efforts in the country have primarily focused on fire suppression efforts instead of preventive measures. Fire prevention efforts are mostly statements of empty jargon rather than specific actions or policies. Fire suppression activities have been greatly preferred but have proceeded without properly accounting for whether the fires are on mineral soils or occurring within peat soils. Efforts to extinguish fires should be based upon their typology to be effective, especially for peat fires that having the distinct behavior of smoldering within the peat soils. However, field observations have shown that generalized approaches for extinguishing fire are undertaken on bot mineral soils where flaming surface fires predominate and peat soils where smoldering ground fires are common. Peat fires cannot be extinguished using the same suppression measures used for flaming surface fires and will, therefore, not achieve effective results without consideration of their unique burning typology. The severe peat fires in 2015 should be taken into account as a lesson learned and impetus for better peat fire control and prevention in the future. Keywords: Peat fires, fire typology, fire control, fire prevention