Long‐Lead Prediction of the 2015 Fire and Haze Episode in Indonesia

Shawki, Dilshad; Field, Robert D.; Tippett, Michael K.; Saharjo, Bambang Hero; Albar, Israr; Atmoko, Dwi; Voulgarakis, Apostolos

doi:10.1002/2017gl073660

Cited by 22 publications

(23 citation statements)

References 49 publications

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“…In view of the lack of data and methods and the need for hydrologic prediction in tropical peatlands, simple lumped models have been widely used that represent the hydraulic state of large peatland areas with a single number representing their wetness or dryness (e.g., Field et al, 2004;Kurnianto et al, 2015;Mezbahuddin et al, 2015;Shawki et al, 2017;Taufik et al, 2017). We call these "scalar" models to distinguish them from models that track the distribution of moisture in catchments.…”

Section: Introductionmentioning

confidence: 99%

Scalar Simulation and Parameterization of Water Table Dynamics in Tropical Peatlands

Cobb

Harvey

2019

Water Resources Research

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Peatlands cover many low‐lying areas in the tropics. Tropical peatlands are intriguing systems because of their tight coupling between hydrology and carbon storage: They accumulate carbon over thousands of years because of waterlogging, and they remain waterlogged after growing into domed shapes because peat restricts drainage. This feedback between waterlogging and landscape morphology generates landforms with special hydrologic properties that enable simplifications of standard watershed models. In natural tropical peatlands, the water table is always near the surface and infiltration is almost immediate. In addition, water table fluctuations relative to the peat surface are remarkably uniform across tropical peatlands because these peatlands acquire shapes with a uniform topographic wetness index. In this paper, we show that because of these distinctive properties, simple hydrologic models that represent the hydraulic state of a catchment by a scalar quantity that describes total water storage are useful and physically meaningful in tropical peatlands. We demonstrate how to efficiently derive hillslope‐scale parameterizations of transmissivity and specific yield as functions of water table height for a tropical peatland from water table, rainfall, and topographic data. Our findings suggest that natural tropical peatland subcatchments could be usefully modeled as single hydrologic response units for river flow and flood forecasting.

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Section: Introductionmentioning

confidence: 99%

Scalar Simulation and Parameterization of Water Table Dynamics in Tropical Peatlands

Cobb

Harvey

2019

Water Resources Research

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“…Seasonal climate forecasts represent an important tool to inform end-users and are increasingly used across a range of application areas (Lemos et al, 2012;Iizumi et al, 2013;Doblas-Reyes et al, 2013;White et al, 2017;Ceglar et al, 2018;Soares et al, 2018). However, even though additional value for fire applications in several regions of the world has been shown (Chen et al, 2011;Fernandes et al, 2011;Gudmundsson et al, 2014;Marcos et al, 2015;Spessa et al, 2015;Shawki et al, 2017;Turco et al, 2018b), seasonal climate forecasts of fire risk have faced numerous challenges to adequately meet end-users' expectations. This is mainly due to limitations in observations, difficulties in disentangling the many determinants of fire and in translating climate-fire predictions into useful information.…”

Section: Introductionmentioning

confidence: 99%

Seasonal prediction of climate-driven fire risk for decision-making and operational applications in a Mediterranean region

Turco

Marcos-Matamoros

Castro

et al. 2019

Science of The Total Environment

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In this paper, we assess and develop a climate service focused on the production of seasonal predictions for summer wildfires in a Mediterranean region through a participatory approach with end-users. We start by building a data-driven model that links a drought indicator (Standardised Precipitation Evapotranspiration Index; SPEI) with a series of burned areas in Catalonia (northeastern Spain). Afterwards, we feed this model with SPEI forecasts obtained through a combination of the antecedent observed conditions and climatology. Finally, we assess the forecasting skill of the system by using cross-validation to evaluate the predictions as if they had been made operationally. Our fire forecasting system reveals an untapped and useful burned area predictive ability. We argue that this source of predictability is mostly attributable to the effect of observed initial conditions on summer drought conditions. This system was conceived with the stakeholders, merging climate-driven predictions with information that is of interests to the users, including the identification of climate variables, thresholds and models. The co-production of this customized system allows fire-risk outlooks to be translated into usable information for fire management. This fire forecasting ability plays a crucial role in developing proactive fire management practices such as long-term fuel assessment and other fire-risk planning, thus minimising the impact of adverse climate conditions on summer burned area. © 2019. Licensed under the Creative Commons [CC-BY-NC-ND https://creativecommons.org/licenses/by-nc-nd/4.0/]. Highlights• A climate service for seasonal predictions for summer wildfires is proposed • Logistic regression models linking drought and fire variables are defined • Persistence-based drought forecasts feed this data-driven fire model• Summer fires can be forecast months in advance with this strategy • The co-production of this system is key to providing usable information

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“…energy and water management, insurance, agriculture 2 , 3 ). However, studies assessing the skill of seasonal climate predictions (as obtained from dynamical climate models) to forecast fire burned areas (BA) are still relatively scarce 4 – 8 and mostly limited to a single season or region. Moreover, most studies that exploit the use of statistical models for forecasting fire activity based on climate information rely on few predictors and have regional focus 9 – 11 .…”

Section: Introductionmentioning

confidence: 99%

Skilful forecasting of global fire activity using seasonal climate predictions

et al. 2018

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Societal exposure to large fires has been increasing in recent years. Estimating the expected fire activity a few months in advance would allow reducing environmental and socio-economic impacts through short-term adaptation and response to climate variability and change. However, seasonal prediction of climate-driven fires is still in its infancy. Here, we discuss a strategy for seasonally forecasting burned area anomalies linking seasonal climate predictions with parsimonious empirical climate–fire models using the standardized precipitation index as the climate predictor for burned area. Assuming near-perfect climate predictions, we obtained skilful predictions of fire activity over a substantial portion of the global burnable area (~60%). Using currently available operational seasonal climate predictions, the skill of fire seasonal forecasts remains high and significant in a large fraction of the burnable area (~40%). These findings reveal an untapped and useful burned area predictive ability using seasonal climate forecasts, which can play a crucial role in fire management strategies and minimise the impact of adverse climate conditions.

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Long‐Lead Prediction of the 2015 Fire and Haze Episode in Indonesia

Cited by 22 publications

References 49 publications

Scalar Simulation and Parameterization of Water Table Dynamics in Tropical Peatlands

Scalar Simulation and Parameterization of Water Table Dynamics in Tropical Peatlands

Seasonal prediction of climate-driven fire risk for decision-making and operational applications in a Mediterranean region

Skilful forecasting of global fire activity using seasonal climate predictions

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