Rapid-response tools and datasets for post-fire remediation: linking remote sensing and process-based hydrological models
M. E. Miller A C , W. J. Elliot B , M. Billmire A , P. R. Robichaud B and K. A. Endsley A
+ Author Affiliations
- Author Affiliations
A Michigan Tech Research Institute, Michigan Technological University, 3600 Green Court, Suite 100, Ann Arbor, MI 48105, USA.
B US Department of Agriculture, Forest Service, Rocky Mountain Research Station, 1221 South Main Street, Moscow, ID 83843, USA.
C Corresponding author. Email: mmaryellen@gmail.com
International Journal of Wildland Fire 25(10) 1061-1073 https://doi.org/10.1071/WF15162
Submitted: 4 September 2015 Accepted: 19 July 2016 Published: 3 October 2016
Abstract
Post-wildfire flooding and erosion can threaten lives, property and natural resources. Increased peak flows and sediment delivery due to the loss of surface vegetation cover and fire-induced changes in soil properties are of great concern to public safety. Burn severity maps derived from remote sensing data reflect fire-induced changes in vegetative cover and soil properties. Slope, soils, land cover and climate are also important factors that require consideration. Many modelling tools and datasets have been developed to assist remediation teams, but process-based and spatially explicit models are currently underutilised compared with simpler, lumped models because they are difficult to set up and require properly formatted spatial inputs. To facilitate the use of models in conjunction with remote sensing observations, we developed an online spatial database that rapidly generates properly formatted modelling datasets modified by user-supplied soil burn severity maps. Although assembling spatial model inputs can be both challenging and time-consuming, the methods we developed to rapidly update these inputs in response to a natural disaster are both simple and repeatable. Automating the creation of model inputs facilitates the wider use of more accurate, process-based models for spatially explicit predictions of post-fire erosion and runoff.
Additional keywords: database, forest fire, forestry, hazards, hydrology.
References
Agnew W, Lahn R, Harding M (1997) Buffalo Creek, Colorado, fire and flood of 1996. Land and Water 41, 27–29. [Verified 21 July 2016]http://www.landandwater.com/features/vol41no1/vol41no1_1.htmlBenavides-Solorio J, MacDonald L (2005) Measurement and prediction of post-fire erosion at the hillslope scale, Colorado Front Range. International Journal of Wildland Fire 14, 457–474.
| Measurement and prediction of post-fire erosion at the hillslope scale, Colorado Front Range.Crossref | GoogleScholarGoogle Scholar |
Benda L, Cundy T (1990) Predicting deposition of debris flows in mountain channels. Canadian Geotechnical Journal 27, 409–417.
| Predicting deposition of debris flows in mountain channels.Crossref | GoogleScholarGoogle Scholar |
Bobbe T, Finco M, Quayle B, Lannom K, Sohlberg R, Parsons A (2001) Field measurements for the training and validation of burn severity maps from spaceborne, remotely sensed imagery. USDI Joint Fire Science Program Final Project Report JFSP RFP, 2. Available at http://www.fs.fed.us/eng/rsac/baer/final_report_01B-2-1-01.pdf [Verified 1 April 2015]
Bonnin G, Todd D, Parzybok T, Lin B, Riley D, Yekta M (2004) Precipitation-frequency atlas of the United States. Atlas 14, Volume 1, Version 3.2’ (US Department of Commerce, National Oceanic and Atmospheric Administration, National Weather Service: Silver Springs, MD).
Brazier R, Beven K, Freer J, Rowan J (2000) Equifinality and uncertainty in physically based soil erosion models: application of the GLUE methodology to WEPP – the Water Erosion Prediction Project – for sites in the UK and USA. Earth Surface Processes and Landforms 25, 825–845.
| Equifinality and uncertainty in physically based soil erosion models: application of the GLUE methodology to WEPP – the Water Erosion Prediction Project – for sites in the UK and USA.Crossref | GoogleScholarGoogle Scholar |
Calkin DE, Thompson MP, Finney MA, Hyde KD (2011) A real-time risk assessment tool supporting wildland fire decision-making. Journal of Forestry 109, 274–280.
Cannon S, Gartner J, Rupert M, Michael J, Rea A, Parrett C (2010) Predicting the probability and volume of post-wildfire debris flows in the intermountain western United States. Geological Society of America Bulletin 122, 127–144.
| Predicting the probability and volume of post-wildfire debris flows in the intermountain western United States.Crossref | GoogleScholarGoogle Scholar |
Certini G (2005) Effects of fire on properties of forest soils: a review. Oecologia 143, 1–10.
| Effects of fire on properties of forest soils: a review.Crossref | GoogleScholarGoogle Scholar | 15688212PubMed |
Daly C, Taylor G, Gibson W (1997) The PRISM approach to mapping precipitation and temperature. In ‘Proceedings of the 10th conference on applied climatology’, 20–24 October 1997, Reno, NV. pp. 10–12. (American Meteorological Society: Boston, MA) Available at ftp://rattus.nacse.org/pub/prism/docs/appclim97-prismapproach-daly.pdf [Verified 14 July 2015].
DeBano L (2000) The role of fire and soil heating on water repellency in wildland environments: a review. Journal of Hydrology 231–232, 195–206.
| The role of fire and soil heating on water repellency in wildland environments: a review.Crossref | GoogleScholarGoogle Scholar |
Dun S, Wu J, Elliot W, Robichaud P, Flanagan D, Frankenberger J, Brown R, Xu A (2009) Adapting the Water Erosion Prediction Project (WEPP) model for forest applications. Journal of Hydrology 366, 46–54.
| Adapting the Water Erosion Prediction Project (WEPP) model for forest applications.Crossref | GoogleScholarGoogle Scholar |
Eidenshink J, Schwind B, Brewer K, Zhu Z, Quayle B, Howard S (2007) Project for monitoring trends in burn severity. Fire Ecology 3, 3–21.
| Project for monitoring trends in burn severity.Crossref | GoogleScholarGoogle Scholar |
Elliot W (2004) WEPP internet interfaces for forest erosion prediction. Journal of the American Water Resources Association 40, 299–309.
| WEPP internet interfaces for forest erosion prediction.Crossref | GoogleScholarGoogle Scholar |
Elliot W (2013) Erosion processes and prediction with WEPP technology in forests in the north-western US. Transactions of the ASABE 56, 563–579.
| Erosion processes and prediction with WEPP technology in forests in the north-western US.Crossref | GoogleScholarGoogle Scholar |
Elliot W, Hall D, Scheele D (1999) Forest Service interfaces for the Water Erosion Prediction Project computer model. Available at http://forest.moscowfsl.wsu.edu/fswepp/ [Verified 9 August 2016]
Elliot W, Miller I, Glaza B (2006) Using WEPP technology to predict erosion and runoff following wildfire. In ‘ASABE annual international meeting, Portland, OR.
Elliot W, Hyde K, MacDonald L, McKean J (2010) Tools for analysis. In ‘Cumulative watershed effects of fuel management in the western United States’. (Ed. WJ Elliot, IS Miller, L Audin) pp. 247–277. USDA Forest Service, Rocky Mountain Research Station, General Technical Report RMRS-GTR-231. Pp. 247–277. (Fort Collins, CO)
Elliot WJ, Miller M, Enstice N (2016) Targeting forest management through fire and erosion modelling. International Journal of Wildland Fire 25, 876–887.
| Targeting forest management through fire and erosion modelling.Crossref | GoogleScholarGoogle Scholar |
Fernández C, Vega JA (2015) Modelling the effect of soil burn severity on soil erosion at hillslope scale in the first year following wildfire in NW Spain. Earth Surface Processes and Landforms 41, 928–935.
| Modelling the effect of soil burn severity on soil erosion at hillslope scale in the first year following wildfire in NW Spain.Crossref | GoogleScholarGoogle Scholar |
Flanagan D, Nearing M (1995) USDA – Water Erosion Prediction Project: hillslope profile and watershed model documentation. USDA-ARS National Soil Erosion Research Laboratory No. 10. (West Lafayette, IN)
Flanagan D, Frankenberger J, Cochrane T, Renschler C, Elliot W (2013) Geospatial application of the Water Erosion Prediction Project (WEPP) model. Transactions of the ASABE 56, 591–601.
| Geospatial application of the Water Erosion Prediction Project (WEPP) model.Crossref | GoogleScholarGoogle Scholar |
Frankenberger JR, Dun S, Flanagan DC, Wu JQ, Elliot WJ (2011) Development of a GIS interface for WEPP model application to Great Lakes forested watersheds. In ‘International symposium on erosion and landscape evolution (ISELE)’, 18–21 September 2011, Anchorage, AK, p. 139. (American Society of Agricultural and Biological Engineers: St Joseph, MI)
Fu X (2004) A physical model of dry ravel movement. MSc thesis, Washington State University, Pullman, WA.
Gallegos A (2014) Silverado fire debris flow hazard assessment report Burn Area Emergency Response (BAER) open file report. Cleveland National Forest Supervisors Office. (Ranch San Bernardo, CA)
Garbrecht J, Martz L (1999) An overview of TOPAZ: an automated digital landscape analysis tool for topographic evaluation, drainage identification, watershed segmentation and subcatchment parameterization. USDA Agricultural Research Service, Publication GRL 99-1, pp. 1–26. (El Reno, OK) Available at http://homepage.usask.ca/~lwm885/topaz/overview.html [Verified 8 August 2016]
Gesch D (2007) The national elevation dataset. In ‘Digital elevation model technologies and applications: the DEM users’ manual.’ (Ed. DF Maune) (American Society for Photogrammetry and Remote Sensing: Bethesda, MD)
Gesch D, Oimoen M, Greenlee S, Nelson C, Steuck M, Tyler D (2002) The National Elevation Dataset. Photogrammetric Engineering and Remote Sensing 68, 5–11.
Hudak A, Morgan P, Bobbitt M, Smith A, Lewis S, Lentile L, Robichaud P, Clark J, McKinley R (2007) The relationship of multispectral satellite imagery to immediate fire effects. Fire Ecology 3, 64–90.
| The relationship of multispectral satellite imagery to immediate fire effects.Crossref | GoogleScholarGoogle Scholar |
Hyde KD, Jencso K, Wilcox AC, Woods S (2015) Influences of vegetation disturbance on hydrogeomorphic response following wildfire. Hydrological Processes 30, 1131–1148.
| Influences of vegetation disturbance on hydrogeomorphic response following wildfire.Crossref | GoogleScholarGoogle Scholar |
Joyce K, Belliss S, Samsonov S, McNeill S, Glassey P (2009) A review of the status of satellite remote sensing and image processing techniques for mapping natural hazards and disasters. Progress in Physical Geography 33, 183–207.
| A review of the status of satellite remote sensing and image processing techniques for mapping natural hazards and disasters.Crossref | GoogleScholarGoogle Scholar |
Key C, Benson N (2006) Landscape assessment (LA). USDA Forest Service, Rocky Mountain Research Station, General Technical Report RMRS-GTR-164-CD. (Fort Collins, CO)
Klein LR, Hendrix WG, Lohr VI, Kaytes JB, Sayler RD, Swanson ME, Elliot WJ, Reganold JP (2015) Linking ecology and aesthetics in sustainable agricultural landscapes: lessons from the Palouse region of Washington, USA. Landscape and Urban Planning 134, 195–209.
| Linking ecology and aesthetics in sustainable agricultural landscapes: lessons from the Palouse region of Washington, USA.Crossref | GoogleScholarGoogle Scholar |
Kolden C, Smith A, Abatzoglou J (2015) Limitations and utilisation of Monitoring Trends in Burn Severity products for assessing wildfire severity in the USA. International Journal of Wildland Fire 24, 1023–1028.
Laflen J, Elliot W, Flanagan D, Meyer C, Nearing M (1997) WEPP – predicting water erosion using a process-based model. Journal of Soil and Water Conservation 52, 96–102.
Land Processes Distributed Active Archive Center (2009) ASTER global digital elevation map. Version 2. (NASA Earth Observing System Data and Information System Land Processes Distributed Active Archive Centers, USGS Earth Resources Observation and Science (EROS) Center: Sioux Falls, SD)
LANDFIRE (2011) LANDFIRE 1.1.0 existing vegetation type and biophysical settings layers. Available at http://landfire.cr.usgs.gov/viewer/ [Verified 1 April 2015].
Larsen I, MacDonald L (2007) Predicting post-fire sediment yields at the hillslope scale: testing RUSLE and Disturbed WEPP. Water Resources Research 43, W11412
| Predicting post-fire sediment yields at the hillslope scale: testing RUSLE and Disturbed WEPP.Crossref | GoogleScholarGoogle Scholar |
Larsen I, MacDonald L, Brown E, Rough D, Welsh M, Pietraszek J, Libohova Z, de Dios Benavides-Solorio J, Schaffrath K (2009) Causes of post-fire runoff and erosion: water repellency, cover, or soil sealing? Soil Science Society of America Journal 73, 1393–1407.
| Causes of post-fire runoff and erosion: water repellency, cover, or soil sealing?Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXos1Ggtr8%3D&md5=a71774f8ae470afcb00e70fcf47f8fb8CAS |
Lathrop R, Aber J, Bognar J (1995) Spatial variability of digital soil maps and its impact on regional ecosystem modeling. Ecological Modelling 82, 1–10.
| Spatial variability of digital soil maps and its impact on regional ecosystem modeling.Crossref | GoogleScholarGoogle Scholar |
Lentile L, Holden Z, Smith A, Falkowski M, Hudak A, Morgan P, Lewis S, Gessler P, Benson N (2006) Remote sensing techniques to assess active fire characteristics and post-fire effects. International Journal of Wildland Fire 15, 319–345.
| Remote sensing techniques to assess active fire characteristics and post-fire effects.Crossref | GoogleScholarGoogle Scholar |
Lewis S, Wu J, Robichaud P (2006) Assessing burn severity and comparing soil water repellency, Hayman Fire, Colorado. Hydrological Processes 20, 1–16.
| Assessing burn severity and comparing soil water repellency, Hayman Fire, Colorado.Crossref | GoogleScholarGoogle Scholar |
Miller D, White R (1998) A conterminous United States multilayer soil characteristics dataset for regional climate and hydrology modeling. Earth Interactions 2, 1–26.
| A conterminous United States multilayer soil characteristics dataset for regional climate and hydrology modeling.Crossref | GoogleScholarGoogle Scholar |
Miller M, Elliot W (2015) Predicting post-fire hillslope erosion for the Butte Fire, CA. Report to the Federal Emergency Management Agency. Michigan Tech Research Institute, Ann Arbor, MI.
Momjian B (2001) ‘PostgreSQL: Introduction and concepts.’ (Addison-Wesley: New York)
Moody J, Martin D (2001) Hydrologic and sedimentologic response of two burned watersheds in Colorado. USDA Geological Survey Water Resources Investigative Report 01–4122 (Denver, CO)
Moody J, Shakesby R, Robichaud P, Cannon S, Martin D (2013) Current research issues related to post-wildfire runoff and erosion processes. Earth-Science Reviews 122, 10–37.
| Current research issues related to post-wildfire runoff and erosion processes.Crossref | GoogleScholarGoogle Scholar |
Moody J, Ebel B, Nyman P, Martin D, Stoof C, McKinley R (2016) Relations between soil hydraulic properties and burn severity. International Journal of Wildland Fire 25, 279–293.
| Relations between soil hydraulic properties and burn severity.Crossref | GoogleScholarGoogle Scholar |
Nachtergaele F, Van Velthuizen H, Verelst L, Batjes N, Dijkshoorn K, Van Engelen V, Fischer G, Jones A, Montanarella L, Petri M (2008) ‘Harmonized world soil database.’ (Food and Agriculture Organization of the United Nations: Laxenburg, Austria)
Neary D, Ryan K, DeBano L (2005) Wildland fire in ecosystems: effects of fire on soils and water. USDA Forest Service, Pacific Northwest Research Station, General Technical Report RMRS-GTR-42-vol. 4. (Ogden, UT)
Obe, RO, Hsu, LS (2015) ‘PostGIS in action.’ (Manning Publications Co.: Greenwich, CT)
Panagos P (2006) The European soil database GeoConnexion 5, 32–33.
Parsons A, Robichaud P, Lewis S, Napper C, Clark J (2010) ‘Field guide for mapping post-fire soil burn severity.’ (USDA Forest Service, Rocky Mountain Research Station: Fort Collins, CO)
PostGIS (2016) PostGIS – Spatial and geographic objects for PostgreSQL. Available at http://postgis.net/ [Verified 25 April 2016]
PostgreSQL (2016) PostgreSQL: The world’s most advanced open source database. Available at http://www.postgresql.org/ [Verified 25 April 2016]
Reany S (2013) Physical causes of rainfall threshold and connectivity for post-wildfire runoff. (Eds JA Moody, DA Martin) In ‘AGU Chapman Conference’, Estes Park, CO, 25–31 August 2013. (American Geophysical Union: Washington, DC)
Reid L (2010) Cumulative effects of fuel treatments on channel erosion and mass wasting. In ‘Cumulative watershed effects of fuel management in the western United States.’ (Eds WJ Elliot, IS Miller, L Audin) (USDA Forest Service, Rocky Mountain Research Station: Fort Collins, CO)
Renschler C (2003) Designing geospatial interfaces to scale process models: the GeoWEPP approach. Hydrological Processes 17, 1005–1017.
| Designing geospatial interfaces to scale process models: the GeoWEPP approach.Crossref | GoogleScholarGoogle Scholar |
Robichaud P (2000) Fire effects on infiltration rates after prescribed fire in northern Rocky Mountain forests, USA. Journal of Hydrology 231–232, 220–229.
| Fire effects on infiltration rates after prescribed fire in northern Rocky Mountain forests, USA.Crossref | GoogleScholarGoogle Scholar |
Robichaud P, Ashmun L (2013) Tools to aid post-wildfire assessment and erosion-mitigation treatment decisions. International Journal of Wildland Fire 22, 95–105.
| Tools to aid post-wildfire assessment and erosion-mitigation treatment decisions.Crossref | GoogleScholarGoogle Scholar |
Robichaud P, Brown R (2000) What happened after the smoke cleared: onsite erosion rates after a wildfire in eastern Oregon. Available at http://forest.moscowfsl.wsu.edu/engr/library/Robichaud/Robichaud1999d/1999d.html [Verified 10 August 2016]
Robichaud P, Elliot W, Pierson F, Hall D, Moffet C (2007a) Predicting post-fire erosion and mitigation effectiveness with a web-based probabilistic erosion model. Catena 71, 229–241.
| Predicting post-fire erosion and mitigation effectiveness with a web-based probabilistic erosion model.Crossref | GoogleScholarGoogle Scholar |
Robichaud P, Lewis S, Laes D, Hudak A, Kokaly R, Zamudio J (2007b) Post-fire soil burn severity mapping with hyperspectral image unmixing. Remote Sensing of Environment 108, 467–480.
| Post-fire soil burn severity mapping with hyperspectral image unmixing.Crossref | GoogleScholarGoogle Scholar |
Robichaud P, Elliot W, Pierson F, Hall D, Moffet C (2009) A probabilistic approach to modeling post-fire erosion after the 2009 Australian bushfires. Available at http://www.nrs.fs.fed.us/pubs/34125 [Verified 10 August 2016]
Rollins M (2009) LANDFIRE: a nationally consistent vegetation, wildland fire, and fuel assessment. International Journal of Wildland Fire 18, 235–249.
| LANDFIRE: a nationally consistent vegetation, wildland fire, and fuel assessment.Crossref | GoogleScholarGoogle Scholar |
RSAC (2011) Burned Area Emergency Response (BAER) imagery support. Available at http://www.fs.fed.us/eng/rsac/baer/ [Verified 1 April 2015].
Santín C, Doerr S, Otero X, Chafer C (2015) Quantity, composition and water contamination potential of ash produced under different wildfire severities. Environmental Research 142, 297–308.
| Quantity, composition and water contamination potential of ash produced under different wildfire severities.Crossref | GoogleScholarGoogle Scholar | 26186138PubMed |
Smith H, Sheridan G, Lane P, Nyman P, Haydon S (2011) Wildfire effects on water quality in forest catchments: a review with implications for water supply. Journal of Hydrology 396, 170–192.
| Wildfire effects on water quality in forest catchments: a review with implications for water supply.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXhsF2htbnK&md5=e4bf7eee756ff5fda0b433a46018c986CAS |
Stoof CR, Vervoort R, Iwema J, Elsen E, Ferreira A, Ritsema C (2012) Hydrological response of a small catchment burned by experimental fire. Hydrology and Earth System Sciences 16, 267–285.
| Hydrological response of a small catchment burned by experimental fire.Crossref | GoogleScholarGoogle Scholar |
Tiedemann A, Conrad C, Dieterich J, Hornbeck J, Megahan W, Viereck L, Wade D (1979) Effects of fire on water: a state-of-knowledge review. USDA Forest Service, General Technical Report WO-10. (Fort Collins, CO)
Tralli D, Blom R, Zlotnicki V, Donnellan A, Evans D (2005) Satellite remote sensing of earthquake, volcano, flood, landslide and coastal inundation hazards. ISPRS Journal of Photogrammetry and Remote Sensing 59, 185–198.
| Satellite remote sensing of earthquake, volcano, flood, landslide and coastal inundation hazards.Crossref | GoogleScholarGoogle Scholar |
USDA National Resource Conservation Service, National Soil Survey Center (1991) State Soil Geographic (STATSGO) data base: data use information. Miscellaneous publication no. 1492. (Revised July 1994) Available at http://water.usgs.gov/GIS/metadata/usgswrd/XML/ussoils.xml [Verified 8 August 2016]
USDA and Forest Service (2004) Forest Service manual 2520, amendment no. 2500–2004–1. (USDA Forest Service: Washington, DC) Available at http://www.fs.fed.us/im/directives/fsm/2500/2520.doc [Verified 14 April 2015].
USDA, Department of the Interior (2009) Monitoring Trends in Burn Severity. MTBS Project Team (Forest Service and US Geological Survey: Salt Lake City, UT) Available at http://www.mtbs.gov/index.html [Verified 1 April 2015].
USDA, Natural Resources Conservation Service (2014) Web soil survey. Available online at http://websoilsurvey.nrcs.usda.gov/ [Verified 21 June 2016]
US Department of the Interior (2006) Interagency Burned Area Rehabilitation guidebook. (Washington, DC) Available at http://www.fws.gov/fire/ifcc/esr/Policy/BAR_Guidebook11-06.pdf [Verified 22 June 2015].
US Department of the Interior (2013) USDA and Interior announce partnership to protect America’s water supply from increased wildfire risk. Available at http://www.usda.gov/wps/portal/usda/usdahome?contentid=2013/07/0147.xml&navid=NEWS_RELEASE&navtype=RT&parentnav=LATEST_RELEASES&edeployment_action=retrievecontent [Verified 21 July 2016]
Wells W (1981) Some effects of brushfires on erosion processes in coastal southern California. In ‘Erosion and sediment transport in Pacific rim steeplands’. (Eds TRH Davies, AJ Pearce) pp. 305–342. (International Association of Hydrological Sciences: Christchurch, New Zealand)
Wu H, Adler RF, Tian Y, Huffman GJ, Li H, Wang J (2014) Real‐time global flood estimation using satellite‐based precipitation and a coupled land surface and routing model. Water Resources Research 50, 2693–2717.
| Real‐time global flood estimation using satellite‐based precipitation and a coupled land surface and routing model.Crossref | GoogleScholarGoogle Scholar |
Zhang J, Wu J, Chang K, Elliot W, Dun S (2009) Effects of DEM source and resolution on WEPP hydrologic and erosion simulation: a case study of two forest watersheds in northern Idaho. Transactions of the ASABE 52, 447–457.
| Effects of DEM source and resolution on WEPP hydrologic and erosion simulation: a case study of two forest watersheds in northern Idaho.Crossref | GoogleScholarGoogle Scholar |
