@article {bnh-8100, title = {Continental-scale prediction of live fuel moisture content using soil moisture information}, journal = {Agricultural and Forest Meteorology}, volume = {307}, year = {2021}, month = {06/2021}, abstract = {

Live fuel moisture content (LFMC) is a key factor that determines the flammability of vegetation in ecosystems. Soil moisture (SM) is one of the variables that is known to influence plant water use. The present study analyses the LFMC-SM relationship over Australia using gridded, remote sensing-based LFMC and land surface model-based SM products. A lag-correlation analysis conducted over 60 selected sites shows that the strength of the relationship between LFMC and SM varies from site to site and, in general, is moderately strong (median lag-correlation of ~0.5). However, the strength of the relationship changes with vegetation type and also with soil profile depth. At all the sites, SM is found to be a leading indicator of LFMC. The lag also varies with the location and is found to range from days to months. Based on the location-based correlation analysis, we identify the 0-35 cm SM profile (SM0-35cm) to be the best predictor of LFMC. We developed a simple model to predict daily LFMC, where it is hypothesised that daily variations in LFMC from its annual cycle can be predicted using daily deviations from the annual cycle in SM0-35cm. The annual cycles of LFMC and SM0-35cm are modelled using Fourier cosine series. The averaged (over 60 sites) correlation obtained for the validation period is 0.74 when a time-lag of 14 days is assumed at all locations. When the model is applied nationally at a 5 km grid, the normalised root mean squared error for the validation period is found to be less than 25\% in general. The results from the present study highlight a modelling strategy that can be used to address a critical gap in the forecast of spatially and temporally continuous LFMC at regional scales in advance for operational fire management applications.

}, keywords = {AFMS, Fire, JASMIN, JULES, live fuel moisture, Soil moisture}, doi = {https://doi.org/10.1016/j.agrformet.2021.108503}, url = {https://www.sciencedirect.com/science/article/abs/pii/S0168192321001866}, author = {Vinod Kumar and Imtiaz Dharssi and Marta Yebra and Paul Fox-Hughes} } @article {bnh-7839, title = {Mitigating the effects of severe fire, floods and heatwaves through the improvements of land dryness measures and forecasts - final project report}, number = {646}, year = {2021}, month = {02/2021}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, abstract = {

This Bushfire and Natural Hazards CRC project, titled Mitigating the effects of severe fires, floods and heatwaves through the improvements of land dryness measures and forecasts, was a partnership with the Bureau of Meteorology, and examined the use of detailed land surface models, satellite measurements and ground-based observations for the monitoring and prediction of landscape dryness. This project addresses a fundamental limitation in our ability to prepare for fires, floods and heatwaves and is directly linked to pre-event planning as well as forecasting of events. The research conducted in the present project solely focuses on the application of soil and land dryness/moisture in the context of fire danger and fire management practices. The lack of focus on flood and heatwave is circumstantial. The research priorities were set and driven by the requirements of the project end-users, all of them from various fire management agencies across Australia. Hence, the end-use interest was solely on the application of the research in fire management. Nevertheless, it is worth pointing that there is a substantial amount of research literature which establishes the importance of soil moisture in flood and heatwave prediction and applications.

Currently, landscape dryness for fire management is estimated in Australia using simple empirical models developed in the 1960s. The most prominent of those used in Australia are the Keetch-Byram Drought Index (KBDI) and the Soil Dryness Index (SDI). An initial study performed as part of this project suggested that analyses of soil moisture can be improved by using physics-based land surface models, remote sensing measurements and data assimilation.

JASMIN prototype

To address this, the present project developed a standalone prototype land surface modelling system, called Joint UK Land Environment Simulator based Australian Soil Moisture Information (JASMIN) to produce daily soil moisture analyses at 5km resolution and 4 soil layers. Verification against ground-based soil moisture observations shows that this prototype system is significantly more skilful than both KBDI and SDI.

Though JASMIN can supplement many applications that require accurate soil moisture estimates, the biggest beneficiary of this new system will be the fire agencies. The soil moisture estimate from the new system provides a robust alternative to the methods currently used in fire prediction. This is evident from the verifications performed against in situ measurements. KBDI and SDI show large errors over regions where they are used operationally. KBDI, for example, has a large wet bias over southern regions that could undermine fire danger ratings. The JASMIN system can produce reliable soil moisture information over a wide range of land-use types, which potentially extends its applicability to other fields as well. Also, JASMIN is shown to have good skill for both surface and deep soil horizons.

JASMIN calibration

To promote an effective adoption of JASMIN in current operational practices, calibration methods were applied to the native JASMIN soil moisture datasets. The key aim of these methods was to calibrate JASMIN outputs in units of moisture excess to moisture deficit values that range from 0{\textendash}200, as required by McArthur{\textquoteright}s Forest Fire Danger Index (FFDI; McArthur, 1967). The calibration offers a simple, faster and cost-effective way to make significant upgrades to the existing operational systems used by fire and other environmental agencies.

The calibration methods applied were minimum-maximum matching, mean-variance matching, and cumulative distribution function matching. The selection of these calibration methods was based on the potential end-user requirement, whether that is to simply replace the legacy systems with a new product with high skill (e.g., minimum-maximum method), or to replace the existing system that captures the temporal variations better while preserving the climatology of the older system (e.g., mean-variance and cumulative distribution function matching). The latter could be useful if existing operating systems are already tuned to offset the bias in the current soil moisture deficit methods.

Improving high spatial resolution mapping

This project also aimed to improve applications such as fire danger mapping that may require soil moisture information at higher spatial resolution due to the large spatial variability of soil moisture in the landscape. A common practice to overcome such a problem is to employ downscaling methods to increase the spatial scale of the product. Recent advances in optical remote sensing have allowed researchers to use different remote sensing products that reflect soil moisture variability as ancillary information. A method based on a {\textquotedblleft}universal triangle{\textquotedblright} concept is used in several previous studies, which establishes a relationship between soil moisture, vegetation index, and surface radiant temperature from optical remote sensing. This project applied three downscaling methodologies: two based on regression and one based on a physics-based approach.

Results from the downscaling methodologies indicate that it is feasible to improve the spatial resolution of JASMIN using all three disaggregating algorithms and preserve the general large-scale spatial structure seen in JASMIN soil moisture estimates. However, the seasonal means obtained at 1 km show that each product displays characteristic soil moisture spatial variability at fine scales. Results from the comparison with ground-based soil moisture measurements indicate that there is no significant degradation of the bias in the three methods when moving to higher spatial resolution.

Predicting live fuel moisture content

Prediction of the moisture status in live fuels is an important gap in current fire management practices which, if filled, can potentially be useful for spatial and temporal assessment of landscape dryness. The final objective of the project was thus to explore the relationship between soil moisture and live fuel moisture content (LFMC) using the datasets from JASMIN and Australian Flammability Monitoring System (AFMS), respectively. The analysis carried out indicates that soil moisture is a leading indicator of LFMC. This project developed a simple yet skilful model to predict live fuel moisture content for the whole of Australia.

The key variable is the 0-350 mm layer soil moisture derived from the JASMIN system. The modelling strategy pursued consists of a linear combination of two sub-models: one to capture the annual cycle and one to capture the daily variations. A time function represents the LFMC annual cycle model. The daily deviations in LFMC are captured by using a linear regression model with 14-day lagged daily deviations in soil moisture as the input. The daily changes in soil moisture are computed by deviations from its annual cycle.

When evaluated over 60 sites, the approach returned an average R2 of 0.64 with normalised root mean square error values of \<25\% at all sites. As researchers were employing a gridded soil moisture product, this strategy facilitates the reconstruction of past events, as well as data gap filling. The lag of 14 days implies a lead time of 14 days for predicting the LFMC. This has significant operational implications, as daily variations in LFMC can be predicted using soil moisture information from JASMIN on a national scale.

JASMIN is currently run as a prototype research system, with soil moisture analysis done only near-real-time. However, JASMIN can be extended to produce both real-time analysis and forecasts. The prognostic mode can provide soil moisture forecasts for up to 10 days. This means a maximum lead time of 24 days can be achieved by utilising soil moisture forecasts.

JASMIN utilisation

A key focus of the project from its inception was to create pathways for easier utilisation of the project deliverables. This is reflected in both the scientific and technical approaches adopted in this project. For example, the calibration of JASMIN to KBDI and SDI was done to facilitate the ready utilisation of JASMIN in the existing operational system. A total of 8 calibrated JASMIN soil dryness products were developed and made available through the Bureau of Meteorology{\textquoteright}s THREDDS server. The JASMIN soil moisture in volumetric units at 4 layers are also provided via the THREDDS server for interested parties to evaluate. The volumetric soil moisture fields from the top two JASMIN layers (0-100 mm and 100-350 mm) are available via AFMS as well.

The datasets on both THREDDS and AFMS are updated near-real-time. There is a continuing interest in the end-user community in utilising JASMIN for various fire management applications. In that respect, JASMIN has been assessed in the Western Australian Department of Biodiversity, Conservation and Attractions study on tall wet forest fuel availability. Tasmania Parks and Wildlife has also been using JASMIN as a decision-support tool to restrict the use of open fires in national parks. Also, JASMIN data were updated specifically to assist with Tasmanian decision-making for 2018-19 seasonal bushfire assessment workshop and preseason consultative committee on fire weather.

The JASMIN system can produce reliable soil moisture estimates over a wide range of land-use types and can support many applications that require accurate soil moisture information. However, there is still scope for improvements to the JASMIN system, whether it be the skill or the scale.

An immediate focus could be the use of data assimilation techniques to improve the skill of JASMIN. Data assimilation allows uncertainties in land surface model soil moisture to be offset to some extent by routinely updating the hydrological conditions using the information provided by observations on state variables used by land surface models. The assimilation of satellite observations is shown to improve the model soil moisture state. In that respect, the use of NASA{\textquoteright}s land information system (LIS) is being evaluated at the Bureau. The LIS is a complex framework that uses extensible interfaces to allow the incorporation of new domains, land surface models, land surface parameters, meteorological inputs, data assimilation and optimisation algorithms. The extensible nature of these interfaces, and the component style specifications of the system, allow rapid prototyping and development of new applications. The JASMIN system can be incorporated within LIS to facilitate the assimilation of various observation types. Further, it can be leveraged to run JASMIN with an enhanced spatial resolution, desirably at 1 km.

}, keywords = {Floods, forecasts, heatwaves, land dryness, measures, mitigation, severe fire}, issn = {646}, author = {Vinod Kumar and Imtiaz Dharssi and Paul Fox-Hughes} } @article {bnh-8190, title = {Soil and fuel moisture precursors of fire activity during the 2019-20 fire season, in comparison to previous seasons - Black Summer final report}, number = {686}, year = {2021}, month = {08/2021}, institution = {Bushfire and Natural Hazards CRC}, address = {MELBOURNE}, abstract = {

Australia experienced unprecedented bushfires during the 2019-20 fire season. Millions of hectares were burned, almost 3,000 homes were destroyed, there were 26 fatalities, and communities were exposed to smoke for extended periods.\  Low soil and vegetation moisture content due to antecedent dry conditions were a key driver of fire activity.

This report extends the work in two Bushfire and Natural Hazards CRC projects to examine the 2019-20 fire season:

The national spatial datasets produced by these projects are consistent and regularly updated, separately examining soil and vegetation moisture variability, as both quantities influence fuel availability and therefore fire behaviour.

This project explores the overlaps between these data sets, as well as comparing other available data (including the Australian Landscape Water Balance) to identify atmospheric, soil and fuel moisture characteristics that contributed to fire risk during Black Summer.

Spatially coherent historical and regularly updated soil and fuel moisture datasets are critically important to fire managers so that an accurate, quantifiable assessment of potential fire activity in the landscape can be made, and this can be benchmarked against historical conditions. This information is crucial to making accurate assessments of risk and identifying situations that fall outside of historical records. One of the valuable aspects of JASMIN is that it can also be used in a predictive way and is therefore useful in anticipating soil moisture and therefore fire risk into the future in a quantitative manner on weather (short-term) and seasonal timescales. Research work in the {\textquoteright}Land Dryness{\textquoteright} project also demonstrated the capacity of soil moisture to predict future vegetation moisture content and thus more directly anticipate fire potential.

This project examines the interaction of atmospheric parameters with soil and fuel moisture content over scales of days, weeks to months, and years. Such an integration has not been undertaken previously.\ Consideration of each of these timeframes is important when making accurate assessments of fire risk. The interaction between them is also critical, as a compounding of the individual processes at the different scales occurs and this was seen in the cumulative effects of antecedent dry years, low winter rainfall and heatwaves during the Black Summer.\ 

This project examined six fire events that were nominated for investigation by the relevant jurisdictions. The fires are:

The scope of the project included examining the following:

Key findings of the project include:\ 

The findings confirm impressions of fire practitioners regarding the extremity of conditions antecedent to the 2019-20 fire season. Importantly, the datasets used in the project present a measurable and spatially coherent approach to estimating fire risk from observed and modelled soil and fuel moisture. Operational application of the datasets and approaches used here will assist in producing accurate soil and vegetation moisture forecasts for prediction of fire risk in the future.

Some immediate benefits for fire managers of this work include:

Each of the meteorological and moisture variables investigated in this study contributed useful information to an understanding of increased fire risk at each site during the 2019-20 Australian summer. Fuel moisture content provided perhaps the most immediate indicator of present fire risk. Soil moisture content permitted an assessment of future changes in fuel moisture content. Both of these integrated changes in meteorological parameters. As such, the meteorological parameters (temperature, precipitation, atmospheric moisture represented by vapour pressure) contributed an understanding of why soil and fuel moisture changed in the ways that they did and offered information on how they would change in the future. Monitoring these quantities can help fire and land managers understand how and why fire risk changes across a landscape.\ 

Future work

This project was conducted over a short time period with one objective being to establish a pathway for future research and operational application.

The following opportunities for future research with likely operational benefits have been identified:

In summary, better understanding and spatial mapping of the influence of atmospheric, soil and fuel moisture will assist fire and land managers predict fire risk, which is critical for strategic and tactical planning. Many of the recommendations could be inclusions to the Australian Fire Danger Rating System (AFDRS), and therefore of immediate benefit to fire and land managers following the introduction of the AFDRS to operational use over the next two fire seasons.

}, keywords = {black summer, Fire, fire activity, fuel, moisture, precursors, soil}, issn = {686}, author = {Paul Fox-Hughes and Marta Yebra and Shukhrat Shokirov and Vinod Kumar and Dowdy, Andrew J and Pandora Hope and Mika Peace and Sugata Narsey and Francois Delage and Huqiang Zhang} } @article {bnh-7470, title = {Exploring the soil moisture-live fuel moisture relationship}, number = {624}, year = {2020}, month = {10/2020}, institution = {Bushfire and Natural Hazards CRC}, address = {MELBOURNE}, abstract = {

Live fuel moisture content is a key factor that determines the flammability of vegetation in ecosystems. Prediction of live fuel moisture content is inherently a very difficult problem since it is modulated by the complex physiological, phenological and ecological processes characteristic of the plant species. Soil moisture is one of the key variables that is known to influence plant water use. Recently, a new live fuel moisture content near-real-time product has been developed for Australia using a radiative transfer model inversion technique on the MODerate Resolution Imaging Spectroradiometer reflectance data. This live fuel moisture content product forms the basis of the Australian Flammability Monitoring System. At the same time, an advanced soil moisture analysis system has been developed by the Bureau of Meteorology recently, called the Joint United Kingdom Land Environment Simulator based Soil Moisture Information (JASMIN). JASMIN can estimate soil moisture at 5 km resolution on a daily timestep for the whole of Australia.

The present study brings together the above two products and explores the live fuel moisture content{\textendash}soil moisture relationship on a national scale. This study will report the preliminary work carried out in understanding live fuel moisture content{\textendash}soil moisture relationship and suggests an approach that may be constructive in advancing the ability to predict live fuel moisture content reliably to support fire management. A preliminary analysis is being conducted over 60 selected locations where JASMIN is found to have good skill. These 60 sites together represent a range of land cover types and climate zones typical of the Australian landscape. All the possible soil moisture profiles that can be derived from the four JASMIN soil layers are used for the analysis. Lag-correlation analysis shows that the strength of the relationship between live fuel moisture content and soil moisture varies from site to site and in general, is moderately strong (median lag-correlation of ~0.5). However, the strength of the relationship varies with vegetation type and also with soil profile depth. At all the sites, soil moisture is found to be an (important) leading indicator of live fuel moisture content. The lag also varies with the location and is found to range from days to months. Except for the forested sites, the top two soil layers exhibit a higher correlation with live fuel moisture content compared to the deeper layers. We develop a simple model to predict live fuel moisture content. The model is found to have good skill with an average R2 of 0.64 over the 60 sites. The normalized root mean square error for the model prediction is found to be less than 25\% in general.

}, keywords = {live fuel moisture, Soil moisture}, issn = {624}, author = {Vinod Kumar and Imtiaz Dharssi and Marta Yebra and Paul Fox-Hughes} } @article {bnh-6237, title = {Disaggregation of JASMIN soil moisture product to 1KM resolution}, number = {521}, year = {2019}, month = {12/2019}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, abstract = {

Fire intensity, spread rate, and ignition are very sensitive to fuel dryness which in turn is strongly linked to soil moisture deficit. Though the value of soil moisture deficit in predicting fire danger has been long established, very few fire danger rating systems employ a comprehensive methodology to estimate it. Most such fire danger rating systems use very simple empirical water balance models which are found to have errors. The Bureau of Meteorology has recently developed a prototype, highresolution, land surface modelling based, state-of-the-art soil moisture analysis for Australia. The product, called Joint United Kingdom Land Environment Simulator (JULES) based Australian Soil Moisture Information (JASMIN), has a spatial resolution of 5 km at hourly timesteps. However, applications like fire danger mapping may require soil moisture information at higher spatial resolution due to the large spatial variability of soil moisture in the landscape. We focus on some of the research carried out to downscale the JASMIN product from 5 km to 1 km spatial resolution. We discuss the application of three downscaling algorithms: two regression-based methods and one with a theoretical basis. The three methods applied are based on the well-known surface temperature {\textendash} vegetation index space. We present an overview of the application of each method, along with an evaluation and comparison against each other and against ground-based soil moisture observations. Results from comparison with ground-based soil moisture measurements indicate that there is no significant degradation of the bias in the three methods, when going into higher spatial resolutions. However, the regression methods, in general, fail to capture the observed temporal variability. The theoretical based method, on the other hand, provides a temporal correlation of 0.81 and captures the skill of the parent JASMIN product.

}, keywords = {dryness, Emergency management, JASMIN, resolution, Soil moisture}, issn = {521}, author = {Vinod Kumar and Imtiaz Dharssi and Paul Fox-Hughes} } @article {bnh-5008, title = {Evaluation and calibration of a high-resolution soil moisture product for wildfire prediction and management}, journal = { Agricultural and Forest Meteorology}, volume = {264}, year = {2019}, month = {01/2019}, pages = {12}, chapter = {27}, abstract = {

Soil moisture deficit is a key variable used in operational fire prediction and management applications. In Australia, operational fire management practices use simple, empirical water balances models to estimate soil moisture deficit. The Bureau of Meteorology has recently developed a prototype, high-resolution, land surface modelling based, state-of-the-art soil moisture analyses for Australia. The present study examines this new product for use in operational fire prediction and management practices in Australia. The approach used is twofold. First, the new soil moisture product is evaluated against observations from ground based networks. Among the results, the mean Pearson{\textquoteright}s correlation for surface soil moisture across the three in-situ networks is found to be between 0.78 and 0.85. Secondly, the study evaluate a few different calibration methods to facilitate the ready utilization of the new soil moisture product in the current operational fire prediction framework. The calibration approaches investigated here are: minimum-maximum matching, mean-variance matching and, cumulative distribution function matching. Validation of the calibrated products using extended triple collocation technique shows that the minimum-maximum method has the highest skill. Evaluation of the calibrated products against MODIS fire radiative power data highlights that large fires correspond to a drier soil in minimum-maximum outputs compared to other calibration results and the current operational method.

}, keywords = {Calibration, Fire danger rating, Land surface model, Soil moisture deficit, Triple collocation, Verification}, doi = {10.1016/j.agrformet.2018.09.012}, url = {https://www.sciencedirect.com/science/article/pii/S0168192318303071}, author = {Vinod Kumar and Imtiaz Dharssi} } @conference {bnh-6532, title = {JASMIN: a high-resolution soil moisture analysis system for fire prediction}, booktitle = {AFAC19 powered by INTERSCHUTZ - Bushfire and Natural Hazards CRC Research Forum}, year = {2019}, month = {12/2019}, publisher = {Australian Institute for Disaster Resilience}, organization = {Australian Institute for Disaster Resilience}, address = {Melbourne}, abstract = {

Soil moisture is found to be a key factor that influences fuel moisture content. Consequently, operational forest fire prediction systems typically include soil moisture as one of the inputs for fire behaviour calculations. The soil moisture input to these fire prediction models is usually provided in the form of moisture deficit. There is evidence that the current operational methods used in Australia for fire prediction perform poorly in estimating soil moisture status. A research project was initiated in partnership with the Bushfire and Natural Hazards Cooperative Research Centre to develop an advanced, state-of-the-art soil moisture analysis for Australia. Consequently, a prototype, high-resolution, land surface modelling-based soil moisture analysis called JULES based Soil Moisture Information (JASMIN) has been developed. JASMIN can provide hourly moisture estimates for four soil layers, at a spatial resolution of 5 km.

The present paper will discuss the evaluation of JASMIN carried out against observations from ground-based networks. Among the results, the mean Pearson{\textquoteright}s correlation for surface soil moisture across three in-situ networks is found to be between 0.78 and 0.85. We also focus on the research carried out to downscale the JASMIN product from 5 km to 1 km spatial resolution. The downscaling research is motivated by the desirable impact a higher resolution soil moisture product can provide for fire prediction, considering the high spatial variability in soil moisture and fuel moisture. We discuss the application of three downscaling algorithms: two regressionbased methods and one with a theoretical basis. The three methods applied in the present study are based on the information derived from characterizing a two-dimensional surface temperature/vegetation index scatterplot domain obtained from thermal and optical remote sensing observations. We present an overview of the application of each method, along with an evaluation against ground-based soil moisture observations. Evaluation results indicate that the regression methods, in general, fail to capture the observed temporal variability. The theoretically based method, on the other hand, provides a temporal correlation of 0.81 and captures the skill of the parent JASMIN product.

Download the full non-peer reviewed research proceedings\ from the Bushfire and Natural Hazards CRC Research Forumhere.

}, keywords = {analysis system, fire prediction, Preparedness, risk management, Soil moisture}, url = {https://knowledge.aidr.org.au/resources/australian-journal-of-emergency-management-monograph-series/}, author = {Vinod Kumar and Imtiaz Dharssi and Paul Fox-Hughes} } @article {bnh-5804, title = {Mitigating the effects of severe fires, floods and heatwaves through the improvements of land dryness measures and forecasts - annual report 2018-2019}, number = {507}, year = {2019}, month = {08/2019}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, abstract = {

The Bushfire and Natural Hazards CRC project titled {\textquotedblleft}Mitigating the Effects of Severe Fires, Floods and Heatwaves through the Improvements of Land Dryness Measures and Forecasts{\textquotedblright} examines the use of detailed land surface models,
satellite measurements and ground-based observations for the monitoring and prediction of landscape dryness. This project will address a fundamental limitation in our ability to prepare for fires, floods and heatwaves and is directly
linked to pre-event planning as well as forecasting of events.

Currently landscape dryness is estimated in Australia using simple empirical models developed in the 1960{\textquoteright}s. The most prominent of those used in Australia are the Keetch-Byram Drought Index (KBDI; Keetch \& Byram 1968) and the Soil Dryness Index (SDI; Mount 1972). An initial study performed as part of this project suggest that analyses of soil moisture can be improved by using physically based land surface models, remote sensing measurements and data assimilation. The project has developed a stand-alone prototype land surface modelling system to produce daily soil moisture analyses at 5km resolution and at 4 soil layers. Verification against ground-based soil moisture observations show that this prototype system is significantly more skilful than both KBDI and SDI.

The present report documents the activities undertaken in 2018-2019. A major focus for the year has been on the downscaling of soil moisture from the JASMIN system, enhancing the spatial resolution from 5 km to 1km. Applications like fire danger mapping may require soil moisture information at higher spatial resolution due to the large spatial variability of soil moisture in the landscape. Another major outcome from the project during this year was the publication of a peerreviewed paper in the highly regarded Agriculture and Forest Meteorology journal.

}, keywords = {Emergency management, fires, Floods, Forecasting, heatwaves, land dryness}, issn = {507}, author = {Vinod Kumar and Paul Fox-Hughes and Imtiaz Dharssi} } @article {bnh-5684, title = {Use of remote sensing measurements and data assimilation techniques to improve estimates of landscape dryness}, number = {482}, year = {2019}, month = {07/2019}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, abstract = {

Fire intensity, spread rate and ignition are very sensitive to the fuel dryness which in turn is strongly linked to soil moisture deficit. Though the value of soil moisture deficit in predicting fire danger has been long established, very few fire danger rating systems employ a comprehensive methodology to estimate it. Most fire danger rating systems use very simple empirical water balance models which are found to have errors. Hence they are poor drivers of the sophisticated fire models used operationally to manage and warn for dangerous fire conditions and spread. With advances in the science of measurement, in the form of satellite remote sensing, and in prediction, in the form of physically based land surface models, soil moisture can now be better analysed and predicted. Neither observations nor models give a complete picture of the soil moisture state in isolation, however. Data assimilation combines observational and model information optimally, yielding increasingly consistent and complete estimates of soil moisture. In this paper, we touch on the various operational satellite observations available. We also discuss land surface
data assimilation methods used widely in soil moisture research and operations. This report is prepared for those with very limited technical and scientific background in satellite remote sensing or data assimilation. Hence complex mathematical and physical formulations are carefully omitted. However, the problems discussed here are highly non-trivial and inter-desciplinary, with much progress made in recent decades. Hence some technicalities are unavoidable. Also, the discussion is not intended to be complete. Our intention is to highlight, especially to the emergency management community, soil moisture estimation methods that may not be well known outside the scientific community.

}, keywords = {dryness, fire rate, fire spread, Landscape ecology, Soil moisture deficit}, issn = {482}, author = {Vinod Kumar and Imtiaz Dharssi} } @conference {bnh-4794, title = {Evaluation and calibration of a land surface based soil moisture for fire danger ratings}, booktitle = {AFAC18}, year = {2018}, month = {09/2018}, publisher = {Bushfire and Natural Hazards CRC}, organization = {Bushfire and Natural Hazards CRC}, address = {Perth}, abstract = {

We present the evaluation of high-resolution, JASMIN soil moisture analysis developed for Australia. The prototype JASMIN system has been developed primarily for use in fire and land management. JASMIN produce hourly soil moisture estimates over four soil layers at 5 km horizontal resolution. We evaluate JASMIN against three ground-based networks in Australia. Among the results, the median Pearson{\textquoteright}s correlation obtained for surface soil moisture across the observation networks for JASMIN is between 0.78 and 0.85. JASMIN generally has a better skill than the Keetch-Byram Drought Index and Soil Dryness Index models used operationally in Australia. We also apply and evaluate a few rescaling approaches to the JASMIN soil moisture to facilitate its use in the current operational fire danger rating system. Minimummaximum matching, mean-variance matching, and cumulative distribution function (CDF) matching are the rescaling approaches applied. Validation of the rescaled products is performed using ground-based observations and MODIS fire radiative power data. The rescaling readily enables fire agencies to utilize the JASMIN product in their existing fire prediction models. However, the potential of JASMIN is greatest in the National Fire Danger Rating System (NFDRS), currently being prototyped across Australia. Particularly, the ability of JASMIN to estimate soil moisture at several levels is expected to be advantageous in the NFDRS. For example, the Spinifex fuel model implemented in the current NFDRS prototype uses 0-10cm soil moisture as an input.\  This soil moisture information is available natively in JASMIN.

}, author = {Vinod Kumar and Imtiaz Dharssi and Paul Fox-Hughes} } @article {bnh-3599, title = {Comparison of soil wetness from multiple models over Australia with observations}, journal = {Water Resources Research}, volume = {53}, year = {2017}, month = {01/2017}, pages = {633-646}, chapter = {633}, abstract = {

The McArthur Forest Fire Danger Index used in Australia for operational fire warnings has a component representing fuel availability called the Drought Factor (DF). The DF is partly based on soil moisture deficit, calculated as either the Keetch-Byram Drought Index (KBDI) or Mount{\textquoteright}s Soil Dryness Index (MSDI). The KBDI and MSDI are simplified water balance models driven by observation based daily rainfall and temperature. In this work, gridded KBDI and MSDI analyses are computed at a horizontal resolution of 5 km and are verified against in-situ soil moisture observations. Also verified is another simple model called the Antecedent Precipitation Index (API). Soil moisture analyses from the Australian Community Climate and Earth System Simulator (ACCESS) global Numerical Weather Prediction (NWP) system as well as remotely sensed soil wetness retrievals from the Advanced Scatterometer (ASCAT) are also verified. The verification shows that the NWP soil wetness analyses have greater skill and smaller biases than the KBDI, MSDI and API analyses. This is despite the NWP system having a coarse horizontal resolution and not using observed precipitation. The average temporal correlations (root mean square difference) between cosmic ray soil moisture monitoring facility observations and modeled or remotely sensed soil wetness are 0.82 (0.15 {\textpm}0.02), 0.66 (0.33 {\textpm}0.07), 0.77 (0.20 {\textpm}0.03), 0.74 (0.22 {\textpm}0.03) and 0.83 (0.18 {\textpm}0.04) for NWP, KBDI, MSDI, API and ASCAT. The results from this study suggests that analyses of soil moisture can be greatly improved by using physically based land surface models, remote sensing measurements and data assimilation.

}, doi = {10.1002/2015WR017738}, url = {http://onlinelibrary.wiley.com/doi/10.1002/2015WR017738/abstract}, author = {Vinod Kumar and Imtiaz Dharssi and John Bally and Steinle, P. and D. McJannet and Jeffrey Walker} } @article {bnh-4233, title = {Downscaling of soil dryness estimates: a short review}, number = {353}, year = {2017}, month = {11/2017}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, abstract = {

Accurate and fine-scale landscape dryness estimation is critical for the management and timely warning of disasters like landscape fires, floods, heatwaves, landslips. It has application in environmental management, agriculture and other types of farming like livestock, and silviculture as well. In a fire danger context, the estimated landscape dryness is calculated for assessing the fuel availability. Though new techniques like remote sensing and land surface modelling provide accurate soil moisture information, it is at a relatively coarser scale than that is required for the above mentioned applications. A common practice to overcome such a problem is to employ downscaling methods to increase the spatial scale of the product. The downscaling approach can be broadly subdivided into deterministic and stochastic. The present study provide a brief review on some of these downscaling methods that are used to derive finer scale information from remote sensing or land surface model outputs. We also highlight some of the studies which has used the above methods for soil moisture applications. The discussion presented here is not intended to be complete and reflect authors{\textquoteright} interest. But we still hope that it helps to highlight some of the most commonly used downscaling approaches that are well known to the hydrological community.

}, issn = {353}, author = {Vinod Kumar and Imtiaz Dharssi} } @article {bnh-4219, title = {Evaluation of daily soil moisture deficit used in Australian forest fire danger rating system}, number = {349}, year = {2017}, month = {11/2017}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, abstract = {

The fuel availability estimates in McArthur Forest Fire Danger Index used in Australia for issuing\ operational fire warnings are partly based on soil moisture deficit, calculated as either the Keetch{\textendash}Byram Drought Index (KBDI) or Mount{\textquoteright}s Soil Dryness Index (SDI). These indices are essentially\ simple water balance models designed to estimate soil moisture depletion in the upper soil levels. In\ the present study, daily values of the above two indices are calculated over Australia at 0.05\ resolution from 1974 onwards. A detailed verification of these two models against in-situ soil moisture\ measurements from CosmOz, OzNet and OzFlux networks are performed. The validations are done\ using soil moisture observations that represent both surface and deeper soil moisture. The verifications\ for deeper soil are restricted to OzNet and OzFlux, as only these two networks provide corresponding to\ observations. The verification results show that both KBDI and SDI have relatively low skill in\ estimating shallow layer (0 {\textendash} approx. 30 cm) soil moisture compared to a much deeper soil profile (0 {\textendash} approx. 1m). The modest sensitivity of both KBDI and SDI to weather changes indicate that they are\ not an accurate measure for estimating duff layer soil moisture.

}, issn = {349}, author = {Vinod Kumar and Imtiaz Dharssi} } @article {bnh-6927, title = {JASMIN: A prototype high resolution soil moisture analysis system for Australia}, year = {2017}, month = {10/2017}, institution = {Bureau of Meterology}, address = {Canberra}, abstract = {

Accurate soil dryness information is essential for the calculation of accurate fire danger ratings, fire behavior prediction, flood forecasting and landslip warnings. Soil dryness also strongly influences temperatures and heatwave development by controlling the partitioning of net surface radiation into sensible, latent and ground heat fluxes. Rainfall forecasts are crucial for many applications and many studies suggest that soil dryness can significantly influence rainfall. Currently, soil dryness for fire danger prediction in Australia is estimated using very simple water balance models developed in the 1960s that ignore many important factors such as incident solar radiation, soil types, vegetation height and root depth. This work presents a prototype high resolution soil moisture analysis system based around the Joint UK Land Environment System (JULES) land surface model. This prototype system is called the JULES based Australian Soil Moisture INformation (JASMIN) system. The JASMIN system can include data from many sources; such as surface observations of rainfall, temperature, dew-point temperature, wind speed, surface pressure as well as satellite derived measurements of rainfall, surface soil moisture, downward surface short-wave radiation, skin temperature, leaf area index and tree heights. The JASMIN system estimates soil moisture on four soil layers over the top 3 meters of soil, the surface layer has a thickness of 10 cm. The system takes into account the effect of different vegetation types, root depth, stomatal resistance and spatially varying soil texture. The analysis system has a one hour time-step with daily updating. For the surface soil layer, verification against ground based soil moisture observations from the OzNet, CosmOz and OzFlux networks shows that the JASMIN system is significantly more accurate than other soil moisture analysis system used at the Bureau of Meteorology. For the root-zone, the JASMIN system has similar skill to other commonly used soil moisture analysis systems. The Extended Triple Collocation (ETC) verification method also confirms the high skill of the JASMIN system.

}, keywords = {Fire behaviour, fire danger ratings, flood forecast, soil dryness}, issn = { Bureau Research Report {\textendash} BRR026}, url = {http://www.bom.gov.au/research/publications/researchreports/BRR-026.pdf}, author = {Imtiaz Dharssi and Vinod Kumar} } @article {bnh-4201, title = {Mitigating the effects of severe fires, floods and heatwaves through the improvements of land dryness measures and forecasts: annual project report 2016-17}, number = {317}, year = {2017}, month = {09/2017}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, abstract = {

The Bushfire and Natural Hazards CRC project titled {\textquotedblleft}Mitigating the Effects of Severe Fires, Floods and Heatwaves through the Improvements of Land Dryness Measures and Forecasts{\textquotedblright} examines the use of detailed land surface models, satellite measurements and ground based observations for the monitoring and prediction of landscape dryness. This project will address a fundamental limitation in our ability to prepare for fires, floods and heatwaves and is directly linked to pre-event planning as well as forecasting of events.\ 

Currently landscape dryness is estimated in Australia using simple empirical models developed in the 1960{\textquoteright}s. The most prominent of those used in Australia are the Keetch-Byram Drought Index (KBDI; Keetch \& Byram 1968) and the Soil Dryness Index (SDI; Mount 1972). An initial study performed as part of this project suggest that analyses of soil moisture can be improved by using physically based land surface models, remote sensing measurements and data assimilation. The project has developed a stand alone prototype land surface modelling system to produce daily soil moisture analyses at 5km resolution and at 4 soil layers. Verification against ground based soil moisture observations show that this prototype system is significantly more skilful than both KBDI and SDI.

The present report documents the activities undertaken in 2016-2017. The main focus of the year has been on the calibration of soil moisture from a new high resolution land surface modelling system allowing for easier utilisation within existing operational fire prediction systems. The calibrated outputs will be evaluated against numerous case studies that include past bush fire occurrences and fuel reduction burns conducted by fire agencies. This work is in progress and eight case studies have been identified so far. These case studies were selected with the help of end users. All case studies will be documented and could be used as training documentation by fire agencies.

}, issn = {317}, author = {Vinod Kumar and Imtiaz Dharssi and Alexander Holmes} } @article {bnh-3227, title = {Comparison of remotely sensed and modelled soil moisture data sets across Australia}, journal = {Remote Sensing of Environment}, volume = {186}, year = {2016}, month = {12/2016}, abstract = {

This study compared surface soil moisture from 11 separate remote sensing and modelled products across Australia in a common framework. The comparison was based on a correlation analysis between soil moisture products and\ in situ\ data collated from three separate ground-based networks: OzFlux, OzNet and CosmOz. The correlation analysis was performed using both original data sets and temporal anomalies, and was supported by examination of the time series plots. The interrelationships between the products were also explored using cluster analyses. The products considered in this study include: Soil Moisture Ocean Salinity (SMOS; both Land Parameter Retrieval Model (LPRM) and L-band Microwave Emission of the Biosphere (LMEB) algorithms), Advanced Microwave Scanning Radiometer 2 (AMSR2; both LPRM and Japan Aerospace Exploration Agency (JAXA) algorithms) and Advanced Scatterometer (ASCAT) satellite-based products, and WaterDyn, Australian Water Resource Assessment Landscape (AWRA-L), Antecedent Precipitation Index (API), Keetch-Byram Drought Index (KBDI), Mount{\textquoteright}s Soil Dryness Index (MSDI) and CABLE/BIOS2 model-based products. The comparison of the satellite and model data sets showed variation in their ability to reflect\ in\ situ\ soil moisture conditions across Australia owing to individual product characteristics. The comparison showed the satellite products yielded similar ranges of correlation coefficients, with the possible exception of AMSR2_JAXA. SMOS (both algorithms) achieved slightly better agreement with\ in situ\ measurements than the alternative satellite products overall. Among the models, WaterDyn yielded the highest correlation most consistently across the different locations and climate zones considered. All products displayed a weaker performance in estimating soil moisture anomalies than the original data sets (i.e.\ the absolute values), showing all products to be more effective in detecting interannual and seasonal soil moisture dynamics rather than individual events. Using cluster analysis we found satellite products generally grouped together, whereas models were more similar to other models. SMOS (based on LMEB algorithm and ascending overpass) and ASCAT (descending overpass) were found to be very similar to each other in terms of their temporal soil moisture dynamics, whereas AMSR2 (based on LPRM algorithm and descending overpass) and AMSR2 (based on JAXA algorithm and ascending overpass) were dissimilar. Of the model products, WaterDyn and CABLE were similar to each other, as were the API/AWRA-L and KBDI/MSDI pairs. The clustering suggests systematic commonalities in error structure and duplication of information may exist between products. This evaluation has highlighted relative strengths, weaknesses, and complementarities between products, so the drawbacks of each may be minimised through a more informed assessment of fitness for purpose by end users.

}, doi = {http://dx.doi.org/10.1016/j.rse.2016.09.015}, url = {http://www.sciencedirect.com/science/article/pii/S0034425716303583}, author = {C.M Holgate and R.A.M. De Jeu and Albert van Dijk and Y.Y Liu and Luigi J. Renzullo and Vinod Kumar and Imtiaz Dharssi and R.M. Parinussa and R. Van Der Schalie and A. Gevaert and Jeffrey Walker and D. McJannet and J. Cleverly and V. Haverd and Trudinger, C and Briggs, P} } @conference {bnh-2933, title = {A high-resolution land dryness analysis system for Australia}, booktitle = {AFAC16}, year = {2016}, month = {08/2016}, publisher = {Bushfire and Natural Hazards CRC}, organization = {Bushfire and Natural Hazards CRC}, address = {Brisbane}, abstract = {

Good estimates of landscape dryness underpin fire danger rating, fire behaviour models, flood prediction, and landslip warning. Soil dryness also strongly influences heatwave development by driving the transfer of solar heating from the soil surface into air temperature rise. Currently landscape dryness, for fire danger prediction, is estimated using very crude models developed in the 1960s that do not take into account different soil types, slope, aspect and many other factors. This work presents a high-resolution soil dryness analysis system that includes data from many sources; such as surface observations of rainfall, temperature, dew-point temperature, wind speed, surface pressure, as well as satellite-derived measurements of rainfall, surface soil moisture, downward surface shortwave radiation, skin temperature, leaf area index and tree heights. The analysis system estimates soil dryness on four soil layers over the top three metres of soil, the surface layer has a thickness of 10cm. The system takes into account the effect of different vegetation types, root depth, stomatal resistance and spatially varying soil texture. The analysis system has a one hour time-step with daily updating. Data assimilation methods are used to extract the maximum amount of useful information from the observations and model. The only practical way to observe the land surface on a national scale is through satellite remote sensing. Unfortunately, such satellite data is prone to biases and corruption. Therefore, it is essential to apply quality control and bias correction. In addition, satellite measurements are infrequent with repeat times of about one day and contain gaps. Data assimilation can filter the random errors from the satellite measurements and fill in both the spatial and temporal gaps in the measurements. Verification against ground-based soil moisture observations from the OzNet, CosmOz and OzFlux networks shows that the new system is significantly more accurate than the traditional soil dryness indices.

}, author = {Imtiaz Dharssi and Vinod Kumar} } @article {bnh-3033, title = {Mitigating the effects of severe fires, floods and heatwaves through the improvements of land dryness measures and forecasts: Annual project report 2015-2016}, number = {174}, year = {2016}, month = {09/2016}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, abstract = {

The Australian people, businesses and environment are all vulnerable to wildfires, floods and other natural hazards. Deloitte Access Economics (2016) estimate the 2015 total economic cost of natural disasters in Australia exceeded $9 billion. Some examples of recent extreme events are the 2015 West Australia Esperance bushfire, the 2015 South Australia Pinery bushfire, the 2009 Victoria Black Saturday bushfires, the Millennium drought spanning from 1998 to 2009 and the summer 2010/2011 floods in Eastern Australia. A recent United Kingdom Meteorological Office report concludes that investment in weather services provides around a tenfold return (Gray 2015).

Accurate soil moisture information is critical for the management and warning of fires, floods and landslips and this project addresses fundamental limitations in our ability to prepare for these events. Fire ignition, intensity and spread rate are strongly influenced by soil moisture content. Knowledge of soil moisture is essential for the accurate prediction of wildfire incidence and the occurrence of large destructive fires corresponds to very large soil moisture deficit values. Soil moisture also strongly influences temperatures and heatwave development by controlling the partitioning of net surface radiation into sensible, latent and ground heat fluxes. Rainfall forecasts are crucial for many applications and many studies suggest that soil moisture can significantly influence rainfall.

Currently soil moisture, for fire danger prediction, is estimated using very crude models developed in the 1960s that oversimplify the calculation of evapotranspiration and runoff leading to significant errors. These crude models do not take into account different vegetation properties, soil types, terrain and many other factors. They are poor drivers of the models used by fire agencies, and the Bureau of Meteorology to predict dangerous fire conditions as the science is outdated and has been verified as not effective (Dharssi and Vinodkumar 2015).

Soil moisture can have very high spatial variability due to the very high spatial variability of vegetation properties, soil textures, orography and rainfall. Therefore, it is highly desirable to analyse soil moisture at the highest possible spatial resolution. Many applications, such as fire danger warnings, agriculture and weather forecasting, require soil moisture information at a spatial resolution of 5 km or better. A state of the art, high resolution soil moisture information system is under development with daily updating and a spatial resolution of 5 km. The system provides information on 4 soil layers. The top layer is 10 cm thick and the total thickness of the soil column is 3 m. Verification against ground based soil moisture observations and a case study show that this prototype system is significantly more skillful than the over-simplified models currently used operationally. This new system will significantly improve Australia{\textquoteright}s ability to manage multiple hazard types and create a more resilient community.

}, issn = {174}, author = {Imtiaz Dharssi and Vinod Kumar} } @conference {bnh-1557, title = {Mitigating the Effects of Severe Fires, Floods and Heatwaves Conference Paper 2014}, booktitle = {Bushfire and Natural Hazards CRC and AFAC Wellington Conference 2014}, year = {2015}, abstract = {

Knowledge of landscape dryness is critical for the management and warning of fires, floods, heatwaves and landslips. This project will address fundamental limitations in our ability to prepare for these events. Currently landscape dryness is estimated using simplified soil moisture accounting systems developed in the 1960{\textquoteright}s. Similarly, flood prediction, runoff potential and water catchment/dam management also are not using the best available science and technology.
This paper describes research to be carried out by the Bushfire and Natural Hazards Cooperative Research Centre that will examine the use of detailed land surface models, satellite measurements and ground based observations for the monitoring and prediction of landscape dryness. The new information will be calibrated for use within existing fire and flood forecasting systems.
An inter-comparison will be performed of the traditional Keetch-Byram Drought Index and Soil Dryness Index with weather prediction models, satellite measurements, ground based measurements, and rainfall-runoff models. Soil moisture from weather prediction and reanalysis will be calibrated for the calculation of a high resolution historical dataset of KBDI and SDI. These datasets will be a valuable resource for researchers working on fire climatologies across Australia. The outputs of this project will improve Australia{\textquoteright}s ability to manage multiple hazard types and create a more resilient community, by developing a state of the art, world{\textquoteright}s best practice in soil moisture analysis that underpins flood, fire and heatwave forecasting.
Longer term work will explore the use of multi-model predictions and data assimilation to forecast soil dryness indices for operational application to fire, flood and heat wave hazards. The vegetation and soil parameterisations in current land surface models will be developed to match Australian conditions.

}, author = {Imtiaz Dharssi and Vinod Kumar and Yeo, Claire and John Bally and Jeffrey Kepert} } @article {bnh-2337, title = {Mitigating the effects of severe fires, floods and heatwaves through improvements to land dryness measures and forecasts: Annual project report 2014-2015}, number = {131}, year = {2015}, month = {02/11/2015}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, abstract = {

The Australian people, businesses and environment are all vulnerable to wildfires, floods and other natural hazards. Deloitte Access Economics estimate the 2012 total economic cost of natural disasters in Australia exceeded $6 billion. Some examples of recent extreme events are the Millennium drought spanning from 1998 to 2009, the 2009 Black Saturday bushfires, the 2011 cyclone Yasi and the summer 2010/2011 floods in eastern Australia.

Knowledge of landscape dryness is critical for the management and warning of fires, floods, heatwaves and landslips. This project will address fundamental limitations in our ability to prepare for these events. Currently landscape dryness is estimated using simplified soil moisture accounting systems developed in the 1960{\textquoteright}s. Similarly, flood prediction, runoff potential and water catchment/dam management also are not using the best available science and technology.

The McArthur Forest Fire Danger Index used in Australia for operational fire warnings has a component representing fuel availability called the Drought Factor (DF). The DF is partly based on soil moisture deficit, calculated as either the Keetch-Byram Drought Index (KBDI) or Mount{\textquoteright}s Soil Dryness Index (MSDI). The KBDI and MSDI are simplified water balance models driven by observation based daily rainfall and temperature. The KBDI and MSDI models oversimplify the parameterisations of evapotranspiration and runoff leading to significant errors.

A verification study has performed an inter-comparison of the traditional KBDI and MSDI with weather prediction models, satellite measurements and ground based measurements. The verification shows that soil moisture analyses from weather models have greater skill and smaller biases than the KBDI and MSDI. This is despite the weather prediction models having a coarse horizontal resolution and not using observed rainfall. Verification also shows that the remotely sensed Advanced Scatterometer soil wetness product is of good quality. This study suggests that analyses of soil moisture can be greatly improved by using physically based land surface models, remote sensing measurements and data assimilation.

The outputs of this project will improve Australia{\textquoteright}s ability to manage multiple hazard types and create a more resilient community, by developing a state of the art, world{\textquoteright}s best practice in soil moisture analysis that underpins flood, fire and heatwave forecasting.\ 

}, issn = {131}, author = {Imtiaz Dharssi and Vinod Kumar} } @article {bnh-2400, title = {Mitigating the effects of severe fires, floods and heatwaves through the improvements of land dryness measures and forecasts}, year = {2015}, author = {Imtiaz Dharssi and Vinod Kumar} } @article {bnh-6928, title = {Sources of soil dryness measures and forecasts for fire danger rating }, year = {2015}, month = {12/2015}, institution = {Bureau of Meteorology }, address = {Canberra}, abstract = {

The fuel availability estimates in McArthur Forest Fire Danger Index used in Australia for issuing operational fire warnings is based on soil moisture deficit, calculated as either the Keetch{\textendash}Byram Drought Index (KBDI) or Mount{\textquoteright}s Soil Dryness Index (MSDI). These indices are essentially simplified, empirical water balance models designed to estimate soil moisture depletion in the upper soil levels. These two models over-simplify processes like evapotranspiration and runoff which can lead to large uncertainties in the predicted soil moisture deficit. With advancements in the science of soil moisture measurement and modelling, better products are available for use in fire danger ratings. As such, a detailed review of the established and emerging soil moisture estimation techniques becomes necessary. With this in view, efforts have been made in this paper to discuss various soil moisture estimation methods, their advantages and limitations in a fire danger rating context. The discussion is not intended to be complete and reflect the authors{\textquoteright} interests, but we hope that it helps to highlight the soil moisture data sources that may not be well known outside the hydrological community, especially the people in fire management.

}, keywords = {fire management, Forecasting, forest fire danger index, fuel, soil dryness}, isbn = {978-0-642-70672-0}, issn = {Bureau Research Report - BRR009}, url = {http://www.bom.gov.au/research/publications/researchreports/BRR-009.pdf}, author = {Vinod Kumar and Imtiaz Dharssi} } @conference {bnh-2099, title = {Verification of soil moisture from land surface models and traditional soil dryness indices - non peer reviewed extended abstract}, booktitle = {Adelaide Conference 2015}, year = {2015}, address = {Adelaide, Australia}, abstract = {

Research proceedings from the Bushfire and Natural Hazards CRC \& AFAC Conference in Adelaide, 1-3 September 2015.\ 

}, author = {Vinod Kumar and Imtiaz Dharssi} }